From c1cd587fe8a9e97f7322588276cecb96e3c5e63a Mon Sep 17 00:00:00 2001
From: Victoria McDonald <victoria.mcdonald@jpl.nasa.gov>
Date: Tue, 11 Jul 2023 22:04:19 -0700
Subject: [PATCH] Add AWS Lambda tutorial for ESIP summer meeting

---
 notebooks/aws_lambda_sst/.dockerignore        |    1 +
 notebooks/aws_lambda_sst/.gitignore           |    5 +
 notebooks/aws_lambda_sst/Dockerfile           |   18 +
 notebooks/aws_lambda_sst/LICENSE              |  201 ++
 notebooks/aws_lambda_sst/README.md            |   45 +
 ...podaac-lambda-invoke-sst-global-mean.ipynb |  815 ++++++
 notebooks/aws_lambda_sst/requirements.txt     |   30 +
 .../sst-global-mean-exploratory.ipynb         | 2302 +++++++++++++++++
 notebooks/aws_lambda_sst/sst.py               |  176 ++
 .../terraform/.terraform.lock.hcl             |   26 +
 notebooks/aws_lambda_sst/terraform/main.tf    |   36 +
 .../aws_lambda_sst/terraform/sst-lambda.tf    |   54 +
 .../aws_lambda_sst/terraform/terraform.tfvars |    6 +
 .../aws_lambda_sst/terraform/variables.tf     |   46 +
 14 files changed, 3761 insertions(+)
 create mode 100644 notebooks/aws_lambda_sst/.dockerignore
 create mode 100644 notebooks/aws_lambda_sst/.gitignore
 create mode 100644 notebooks/aws_lambda_sst/Dockerfile
 create mode 100644 notebooks/aws_lambda_sst/LICENSE
 create mode 100644 notebooks/aws_lambda_sst/README.md
 create mode 100644 notebooks/aws_lambda_sst/podaac-lambda-invoke-sst-global-mean.ipynb
 create mode 100644 notebooks/aws_lambda_sst/requirements.txt
 create mode 100644 notebooks/aws_lambda_sst/sst-global-mean-exploratory.ipynb
 create mode 100644 notebooks/aws_lambda_sst/sst.py
 create mode 100644 notebooks/aws_lambda_sst/terraform/.terraform.lock.hcl
 create mode 100644 notebooks/aws_lambda_sst/terraform/main.tf
 create mode 100644 notebooks/aws_lambda_sst/terraform/sst-lambda.tf
 create mode 100644 notebooks/aws_lambda_sst/terraform/terraform.tfvars
 create mode 100644 notebooks/aws_lambda_sst/terraform/variables.tf

diff --git a/notebooks/aws_lambda_sst/.dockerignore b/notebooks/aws_lambda_sst/.dockerignore
new file mode 100644
index 00000000..56fe0d68
--- /dev/null
+++ b/notebooks/aws_lambda_sst/.dockerignore
@@ -0,0 +1 @@
+terraform
\ No newline at end of file
diff --git a/notebooks/aws_lambda_sst/.gitignore b/notebooks/aws_lambda_sst/.gitignore
new file mode 100644
index 00000000..3e44bff0
--- /dev/null
+++ b/notebooks/aws_lambda_sst/.gitignore
@@ -0,0 +1,5 @@
+__pycache__
+
+terraform/.terraform/
+terraform.tfstate*
+terraform/tfplan
\ No newline at end of file
diff --git a/notebooks/aws_lambda_sst/Dockerfile b/notebooks/aws_lambda_sst/Dockerfile
new file mode 100644
index 00000000..28f038c0
--- /dev/null
+++ b/notebooks/aws_lambda_sst/Dockerfile
@@ -0,0 +1,18 @@
+# Stage 0 - Create from Python 3.10-alpine3.15 image
+FROM amazon/aws-lambda-python:3.9
+RUN yum update -y && yum install -y tcsh
+
+# Stage 1 - Install dependencies
+# FROM stage0 as stage1
+COPY requirements.txt .
+RUN  pip3 install -r requirements.txt --target "${LAMBDA_TASK_ROOT}"
+
+# Stage 2 - Copy Generate code
+# FROM stage1 as stage2
+COPY . ${LAMBDA_TASK_ROOT}
+
+# Stage 3 - Execute code
+# FROM stage2 as stage3
+LABEL version="0.1" \
+    description="Containerized Lambda: SST"
+CMD [ "sst.lambda_handler" ]
\ No newline at end of file
diff --git a/notebooks/aws_lambda_sst/LICENSE b/notebooks/aws_lambda_sst/LICENSE
new file mode 100644
index 00000000..261eeb9e
--- /dev/null
+++ b/notebooks/aws_lambda_sst/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/notebooks/aws_lambda_sst/README.md b/notebooks/aws_lambda_sst/README.md
new file mode 100644
index 00000000..bb59e529
--- /dev/null
+++ b/notebooks/aws_lambda_sst/README.md
@@ -0,0 +1,45 @@
+# AWS Lambda example for SST calculations
+
+This program uses AWS Lambda to calculate a global mean on the MUR 25km dataset.
+
+## Files
+
+sst.py - the code to be run by AWS Lambda. This gets packaged in a Docker container to be deployed to AWS.
+
+sst-global-mean-exploratory.ipynb - a notebook that explores the MUR25 dataset and runs through the global mean calculation offline. Can be run locally outside of AWS to trial run the code deployed to Lambda in sst.py
+
+podaac-lambda-invoke-sst-global-mean.ipynb - the main notebook to invoke the Lambda code. Finds the files in Earthdata cloud, invokes Lambda on each, and plots the results as a timeseries.
+
+Dockerfile - the instructions for Docker to build the container image with to deploy to AWS Lambda
+
+requirements.txt - the required python packages to include in the Dockerfile. These may be different than the packages required to run the notebooks
+
+terraform - terraform deploys AWS infrastructure. This folder contains the terraform configuration files
+    > terraform.tfvars
+    > main.tf
+    > sst-lambda.tf
+    > variables.tf
+
+## AWS Infrastructure
+
+This program includes the following AWS services:
+
+- Lambda function to execute science code, deployed via Docker container.
+- AWS IAM role for Lambda function to execute as
+- AWS Parameter Store to manage Earthdata login credentials
+- S3 bucket to store the output of the Lambda function
+
+## Deploy AWS Resources with Terraform
+
+Deploys AWS infrastructure and stores state in an S3 backend.
+
+To deploy:
+
+1. Edit `terraform.tfvars` for environment to deploy to.
+2. Initialize terraform: `terraform init`
+3. Plan terraform modifications: `terraform plan -out=tfplan`
+4. Apply terraform modifications: `terraform apply tfplan`
+
+## Run the notebook to invoke the Lambda function
+
+Run aws_lambda_sst/podaac-lambda-invoke-sst-global-mean.ipynb.
diff --git a/notebooks/aws_lambda_sst/podaac-lambda-invoke-sst-global-mean.ipynb b/notebooks/aws_lambda_sst/podaac-lambda-invoke-sst-global-mean.ipynb
new file mode 100644
index 00000000..4d2407fc
--- /dev/null
+++ b/notebooks/aws_lambda_sst/podaac-lambda-invoke-sst-global-mean.ipynb
@@ -0,0 +1,815 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scale Scientific Analysis in the Cloud with AWS Lambda\n",
+    "\n",
+    "### ***IMPORTANT (July 2023):**  This tutorial is in development and is still undergoing testing. Running code in AWS Lambda will incur charges in your AWS account. Cost scoping and benchmarking is not yet complete. Please proceed with caution.\n",
+    "\n",
+    "This tutorial demonstrates how to plot a timeseries of global mean sea surface temperature values using AWS Lambda to perform the global mean computation. We use the MUR 25km dataset. \n",
+    "\n",
+    "This is one example of how to take advantage of AWS Cloud Computing capabilities for scientific research. Note that using AWS Compute services will incur costs that will be charged to your AWS account. As we complete testinng we will include estimates of the compute cost associated with this tutorial. Note that apexpanding the analysis to a longer time period or different dataset will affect the compute costs charged to your AWS account. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisite Steps: Set up AWS infrastructure\n",
+    "\n",
+    "This tutorial takes advantage of numerous AWS Services including Lambda, Parameter Store, Elastic Compute Cloud (EC2), Elastic Container Registry (ECR), and Simple Storage Service (S3). Full documentation for setting up these services to run this tutorial is still being developed and will be linked here when complete. \n",
+    "\n",
+    "#### Deploy Lambda function using Terraform\n",
+    "\n",
+    "AWS Lambda is compute service that runs code in response to events. The Lambda code is packaged in a Docker image, and we use Terraform to handle setting up the AWS services including the Lambda function.\n",
+    "\n",
+    "The /terraform/ directory contains the terraform configuration files. Edit terraform.tfvars with the names you want to give the AWS Elastic Container Registry (ecr_repo), the AWS Lambda role (lambda_role), the prefix to use for the Earthdata login parameters, and the AWS profile name you use for your account (optional). For example:\n",
+    "\n",
+    "    ecr_repo    = \"podaac-sst\"\n",
+    "    lambda_role = \"podaac-sst-lambda-role\"\n",
+    "    prefix      = \"podaac\"\n",
+    "    profile     = \"saml-pub\"\n",
+    "\n",
+    "Run the following command to initialize the terraform configuration files:\n",
+    "    \n",
+    "    terraform init\n",
+    "\n",
+    "\n",
+    "Run terraform plan to check infrastructure state:\n",
+    "    \n",
+    "    terraform plan -out=tfplan\n",
+    "\n",
+    "If there are no modifications to the infrastructure required and everything looks correct, apply the plan:\n",
+    "    \n",
+    "    terraform apply tfplan\n",
+    "\n",
+    "#### Set up Earthdata credentials in AWS Parameter Store\n",
+    "\n",
+    "In this tutorial the Lambda function reads data files from the Earthdata S3 bucket directly. To avoid hard-coding Earthdata credentials or packaging a .netrc file in the Docker image that deploys the Lambda code, we use the AWS parameter store to set the Earthdata credentials. This means that the same Lambda code can run without modification in any user environment and will assume the correct EDL that is set in the AWS Parameter Store. \n",
+    "\n",
+    "#### Set up S3 bucket to hold granules and results\n",
+    "\n",
+    "The Lambda function writes the results of the calculation back to a NetCDF file. In this scenario, there will be one results file generated for each granule processed. The results files are saved to an S3 bucket, where they can persist or be downloaded for further analysis & plotting. You need to create the S3 bucket that the Lambda function will use to save the results files.\n",
+    "\n",
+    "#### Test Lambda function\n",
+    "\n",
+    "#### Connect to EC2 instance to run this notebook\n",
+    "\n",
+    "This notebook cannot be run on a local computer, as it heavily depends on direct in-cloud access. To run this notebook in AWS, connect to an EC2 instance running in the us-west-2 region, [following the instructions in this tutorial](https://podaac.github.io/tutorials/external/July_2022_Earthdata_Webinar.html). Once you have connected to the EC2 instance, you can clone this repository into that environment, install the required packages, and run this notebook. \n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1: Log in to Earthdata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the [earthaccess](https://nsidc.github.io/earthaccess/)  Python library to handle Earthdata authentication for the initial query to find the granules of interest. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import earthaccess\n",
+    "import json\n",
+    "import boto3\n",
+    "import s3fs\n",
+    "import xarray as xr\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EARTHDATA_USERNAME and EARTHDATA_PASSWORD are not set in the current environment, try setting them or use a different strategy (netrc, interactive)\n",
+      "You're now authenticated with NASA Earthdata Login\n",
+      "Using token with expiration date: 06/16/2023\n",
+      "Using .netrc file for EDL\n"
+     ]
+    }
+   ],
+   "source": [
+    "auth = earthaccess.login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Granules found: 365\n"
+     ]
+    }
+   ],
+   "source": [
+    "granules = earthaccess.search_data(\n",
+    "    short_name='MUR25-JPL-L4-GLOB-v04.2',\n",
+    "    cloud_hosted=True,\n",
+    "    temporal=(\"2022-01-01\", \"2023-01-01\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "granule_paths = [g.data_links(access='direct')[0] for g in granules]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "s3://podaac-ops-cumulus-protected/MUR25-JPL-L4-GLOB-v04.2/20220101090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc\n"
+     ]
+    }
+   ],
+   "source": [
+    "for path in granule_paths:\n",
+    "    print(path)\n",
+    "    break"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Invoke the Lambda function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up a boto3 session to connect to your AWS instance and invoke the Lambda function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "session = boto3.Session(profile_name='saml-pub')\n",
+    "\n",
+    "lambda_client = session.client('lambda', region_name='us-west-2')\n",
+    "\n",
+    "s3_results_bucket = \"podaac-sst\"\n",
+    "\n",
+    "for granule in granule_paths:\n",
+    "    lambda_payload = {\"input_granule_s3path\": granule, \"output_granule_s3bucket\": s3_results_bucket, \"prefix\":\"podaac\"}\n",
+    "\n",
+    "    lambda_client.invoke(\n",
+    "        FunctionName=\"podaac-sst\",\n",
+    "        InvocationType=\"Event\",\n",
+    "        Payload=json.dumps(lambda_payload)\n",
+    "    )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Plot results as timeseries"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Open the resulting global mean files in xarray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up the connection to the S3 bucket holding the results\n",
+    "s3_results = s3fs.S3FileSystem(\n",
+    "    anon=False,\n",
+    "    profile='saml-pub'\n",
+    ")\n",
+    "\n",
+    "s3_files = s3_results.glob(\"s3://\" + s3_results_bucket + \"/MUR25/*\")\n",
+    "\n",
+    "\n",
+    "# iterate through s3 files to create a fileset\n",
+    "fileset = [s3_results.open(file) for file in s3_files]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# open all files as an xarray dataset\n",
+    "data = xr.open_mfdataset(fileset, combine='by_coords', engine='scipy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:       (time: 365)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 2022-01-01T09:00:00 ... 2023-01-01T09...\n",
+       "Data variables:\n",
+       "    analysed_sst  (time) float64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c49af75f-68af-435c-a55a-5f12620e514c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c49af75f-68af-435c-a55a-5f12620e514c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 365</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-035b8745-af1c-40f5-8524-c8dc298c0c9f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-035b8745-af1c-40f5-8524-c8dc298c0c9f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-01-01T09:00:00 ... 2023-01-...</div><input id='attrs-2fc0b36a-2b7a-48a0-8c99-5a9e018808dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2fc0b36a-2b7a-48a0-8c99-5a9e018808dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faa2b8f5-0e09-42f9-aaaf-8d1a75333930' class='xr-var-data-in' type='checkbox'><label for='data-faa2b8f5-0e09-42f9-aaaf-8d1a75333930' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-01-01T09:00:00.000000000&#x27;, &#x27;2022-01-02T09:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-01-03T09:00:00.000000000&#x27;, ..., &#x27;2022-12-30T09:00:00.000000000&#x27;,\n",
+       "       &#x27;2022-12-31T09:00:00.000000000&#x27;, &#x27;2023-01-01T09:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eaaab9af-f1ec-40d1-96e8-1a098e1b4378' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eaaab9af-f1ec-40d1-96e8-1a098e1b4378' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>analysed_sst</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-68520525-6838-4a63-a641-9529f811cba2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68520525-6838-4a63-a641-9529f811cba2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54a1042f-d0b3-4802-9e1c-05b1aafbe9ae' class='xr-var-data-in' type='checkbox'><label for='data-54a1042f-d0b3-4802-9e1c-05b1aafbe9ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Area-weighted global mean sea surface temperature calculated using AWS Lambda</dd><dt><span>units :</span></dt><dd>celcius</dd><dt><span>date_created :</span></dt><dd>Jun-06-2023</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 2.85 kiB </td>\n",
+       "                        <td> 8 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (365,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 365 chunks in 731 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
+       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
+       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
+       "  <line x1=\"24\" y1=\"0\" x2=\"24\" y2=\"25\" />\n",
+       "  <line x1=\"31\" y1=\"0\" x2=\"31\" y2=\"25\" />\n",
+       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
+       "  <line x1=\"50\" y1=\"0\" x2=\"50\" y2=\"25\" />\n",
+       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
+       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
+       "  <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"25\" />\n",
+       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"81\" y1=\"0\" x2=\"81\" y2=\"25\" />\n",
+       "  <line x1=\"88\" y1=\"0\" x2=\"88\" y2=\"25\" />\n",
+       "  <line x1=\"94\" y1=\"0\" x2=\"94\" y2=\"25\" />\n",
+       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"25\" />\n",
+       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
+       "  <line x1=\"113\" y1=\"0\" x2=\"113\" y2=\"25\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >365</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d2113994-bea0-4e8a-9441-d01e332a0126' class='xr-section-summary-in' type='checkbox'  ><label for='section-d2113994-bea0-4e8a-9441-d01e332a0126' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2fe5ebbc-3b4d-45d4-8b0d-56c94b7d8bc7' class='xr-index-data-in' type='checkbox'/><label for='index-2fe5ebbc-3b4d-45d4-8b0d-56c94b7d8bc7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-01-01 09:00:00&#x27;, &#x27;2022-01-02 09:00:00&#x27;,\n",
+       "               &#x27;2022-01-03 09:00:00&#x27;, &#x27;2022-01-04 09:00:00&#x27;,\n",
+       "               &#x27;2022-01-05 09:00:00&#x27;, &#x27;2022-01-06 09:00:00&#x27;,\n",
+       "               &#x27;2022-01-07 09:00:00&#x27;, &#x27;2022-01-08 09:00:00&#x27;,\n",
+       "               &#x27;2022-01-09 09:00:00&#x27;, &#x27;2022-01-10 09:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-12-23 09:00:00&#x27;, &#x27;2022-12-24 09:00:00&#x27;,\n",
+       "               &#x27;2022-12-25 09:00:00&#x27;, &#x27;2022-12-26 09:00:00&#x27;,\n",
+       "               &#x27;2022-12-27 09:00:00&#x27;, &#x27;2022-12-28 09:00:00&#x27;,\n",
+       "               &#x27;2022-12-29 09:00:00&#x27;, &#x27;2022-12-30 09:00:00&#x27;,\n",
+       "               &#x27;2022-12-31 09:00:00&#x27;, &#x27;2023-01-01 09:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=365, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a156f8a2-6a69-48ac-bfbb-a6ddb194a98f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a156f8a2-6a69-48ac-bfbb-a6ddb194a98f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:       (time: 365)\n",
+       "Coordinates:\n",
+       "  * time          (time) datetime64[ns] 2022-01-01T09:00:00 ... 2023-01-01T09...\n",
+       "Data variables:\n",
+       "    analysed_sst  (time) float64 dask.array<chunksize=(1,), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the data using matplotlib:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Date')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1440x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mpl.rcParams.update({'font.size': 22})\n",
+    "\n",
+    "# set up the figure\n",
+    "fig = plt.Figure(figsize=(20,5))\n",
+    "\n",
+    "# plot the data\n",
+    "plt.plot(data.time, data.analysed_sst, linewidth='3')\n",
+    "plt.title('Global Mean Sea Surface Temperature' + '\\n' + '2022, MUR25')\n",
+    "\n",
+    "plt.ylabel('degrees C')\n",
+    "plt.xlabel('Date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "32a40736d00c188d2effbb1afc97ba7e733733270c902b4998c089acce9c970f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/aws_lambda_sst/requirements.txt b/notebooks/aws_lambda_sst/requirements.txt
new file mode 100644
index 00000000..eddc7dc9
--- /dev/null
+++ b/notebooks/aws_lambda_sst/requirements.txt
@@ -0,0 +1,30 @@
+aiobotocore==2.4.2
+aiohttp==3.8.4
+aioitertools==0.11.0
+aiosignal==1.3.1
+async-timeout==4.0.2
+attrs==22.2.0
+botocore==1.27.59
+certifi==2022.12.7
+charset-normalizer==3.1.0
+frozenlist==1.3.3
+fsspec==2023.3.0
+h5netcdf==1.1.0
+h5py==3.8.0
+idna==3.4
+jmespath==1.0.1
+multidict==6.0.4
+numpy==1.24.2
+packaging==23.0
+pandas==1.5.3
+python-dateutil==2.8.2
+pytz==2022.7.1
+requests==2.28.2
+s3fs==2023.3.0
+scipy==1.10.1
+six==1.16.0
+typing_extensions==4.5.0
+urllib3==1.26.14
+wrapt==1.15.0
+xarray==2023.2.0
+yarl==1.8.2
diff --git a/notebooks/aws_lambda_sst/sst-global-mean-exploratory.ipynb b/notebooks/aws_lambda_sst/sst-global-mean-exploratory.ipynb
new file mode 100644
index 00000000..74b08327
--- /dev/null
+++ b/notebooks/aws_lambda_sst/sst-global-mean-exploratory.ipynb
@@ -0,0 +1,2302 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate MUR25 Sea Surface Temperature Global Mean\n",
+    "\n",
+    "This notebook demonstrates how we calculate the area-weighted global mean sea surface temperature (SST) from the MUR25 L4 dataset. \n",
+    "\n",
+    "You can run this on a local computer by downloading the data using the following command in your terminal:\n",
+    "    \n",
+    "    podaac-data-downloader -c MUR25-JPL-L4-GLOB-v04.2 -d ./data/MUR25-JPL-L4-GLOB-v04.2 --start-date 2022-12-01T00:00:00Z --end-date 2022-12-31T23:00:00Z"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load required libraries and prepare data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "from datetime import date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:           (time: 1, lat: 720, lon: 1440)\n",
+       "Coordinates:\n",
+       "  * time              (time) datetime64[ns] 2022-12-01T09:00:00\n",
+       "  * lat               (lat) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
+       "  * lon               (lon) float32 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n",
+       "Data variables:\n",
+       "    analysed_sst      (time, lat, lon) float32 nan nan nan ... 271.4 271.4 271.4\n",
+       "    analysis_error    (time, lat, lon) float32 nan nan nan ... 0.34 0.34 0.34\n",
+       "    mask              (time, lat, lon) float32 2.0 2.0 2.0 2.0 ... 9.0 9.0 9.0\n",
+       "    sea_ice_fraction  (time, lat, lon) float32 nan nan nan ... 0.97 0.97 0.97\n",
+       "    sst_anomaly       (time, lat, lon) float32 nan nan nan nan ... 0.0 0.0 0.0\n",
+       "Attributes: (12/54)\n",
+       "    Conventions:                CF-1.7, ACDD-1.3\n",
+       "    title:                      Daily 0.25-degree MUR SST, Final product\n",
+       "    summary:                    A low-resolution version of the MUR SST analy...\n",
+       "    keywords:                   Oceans &gt; Ocean Temperature &gt; Sea Surface Temp...\n",
+       "    keywords_vocabulary:        NASA Global Change Master Directory (GCMD) Sc...\n",
+       "    standard_name_vocabulary:   NetCDF Climate and Forecast (CF) Metadata Con...\n",
+       "    ...                         ...\n",
+       "    publisher_name:             GHRSST Project Office\n",
+       "    publisher_url:              https://www.ghrsst.org\n",
+       "    publisher_email:            gpc@ghrsst.org\n",
+       "    file_quality_level:         3\n",
+       "    metadata_link:              http://podaac.jpl.nasa.gov/ws/metadata/datase...\n",
+       "    acknowledgment:             Please acknowledge the use of these data with...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-79cadad9-692f-40c4-b560-c1cde3f64210' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-79cadad9-692f-40c4-b560-c1cde3f64210' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-11c70ade-f8d7-40b3-9603-496661449d8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11c70ade-f8d7-40b3-9603-496661449d8b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-12-01T09:00:00</div><input id='attrs-8b79c0f3-c590-4413-8bf0-2532c35590d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b79c0f3-c590-4413-8bf0-2532c35590d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-693768d1-c9c8-4418-9343-13c462b53105' class='xr-var-data-in' type='checkbox'><label for='data-693768d1-c9c8-4418-9343-13c462b53105' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Nominal time of analyzed fields</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-12-01T09:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-40a4aa9d-77bf-4ec8-86ae-c774d9f2c9c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40a4aa9d-77bf-4ec8-86ae-c774d9f2c9c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1067424-09e4-4672-9679-d0489ba0d486' class='xr-var-data-in' type='checkbox'><label for='data-f1067424-09e4-4672-9679-d0489ba0d486' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-48817494-6f7d-48ab-9525-fbcd349091c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48817494-6f7d-48ab-9525-fbcd349091c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d3b9c89-bcad-42bb-b48e-7865079c7430' class='xr-var-data-in' type='checkbox'><label for='data-1d3b9c89-bcad-42bb-b48e-7865079c7430' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-195c204d-68bb-4f7a-874a-0473bc693804' class='xr-section-summary-in' type='checkbox'  checked><label for='section-195c204d-68bb-4f7a-874a-0473bc693804' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>analysed_sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... 271.4 271.4 271.4</div><input id='attrs-7fd7a753-a137-4cf4-89b6-a3e6c9bfdcc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fd7a753-a137-4cf4-89b6-a3e6c9bfdcc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fb2fb71-4b72-4276-ae31-d9e976a748cd' class='xr-var-data-in' type='checkbox'><label for='data-9fb2fb71-4b72-4276-ae31-d9e976a748cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>analysed sea surface temperature</dd><dt><span>standard_name :</span></dt><dd>sea_surface_foundation_temperature</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-32767</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>comment :</span></dt><dd>&quot;Final&quot; version using Multi-Resolution Variational Analysis (MRVA) method for interpolation</dd><dt><span>source :</span></dt><dd>MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd></dl></div><div class='xr-var-data'><pre>array([[[   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
+       "        ...,\n",
+       "        [271.35, 271.35, 271.35, ..., 271.35, 271.35, 271.35],\n",
+       "        [271.35, 271.35, 271.35, ..., 271.35, 271.35, 271.35],\n",
+       "        [271.35, 271.35, 271.35, ..., 271.35, 271.35, 271.35]]],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analysis_error</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.34 0.34 0.34</div><input id='attrs-d793db0f-967b-446a-b90f-9f6af5363bfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d793db0f-967b-446a-b90f-9f6af5363bfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65f4bec1-91ba-4c4e-a76f-02d0cee99758' class='xr-var-data-in' type='checkbox'><label for='data-65f4bec1-91ba-4c4e-a76f-02d0cee99758' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>estimated error standard deviation of analysed_sst</dd><dt><span>coverage_content_type :</span></dt><dd>qualityInformation</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>comment :</span></dt><dd>uncertainty in &quot;analysed_sst&quot;</dd></dl></div><div class='xr-var-data'><pre>array([[[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
+       "        ...,\n",
+       "        [0.34, 0.34, 0.34, ..., 0.34, 0.34, 0.34],\n",
+       "        [0.34, 0.34, 0.34, ..., 0.34, 0.34, 0.34],\n",
+       "        [0.34, 0.34, 0.34, ..., 0.34, 0.34, 0.34]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.0 2.0 2.0 2.0 ... 9.0 9.0 9.0 9.0</div><input id='attrs-597cc018-3a22-4b1e-a701-4939826da5fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-597cc018-3a22-4b1e-a701-4939826da5fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96faeddd-f8da-4571-af82-44643fe8cdde' class='xr-var-data-in' type='checkbox'><label for='data-96faeddd-f8da-4571-af82-44643fe8cdde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea/land field composite mask</dd><dt><span>coverage_content_type :</span></dt><dd>referenceInformation</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>31</dd><dt><span>flag_masks :</span></dt><dd>[ 1  2  4  8 16]</dd><dt><span>flag_meanings :</span></dt><dd>water land optional_lake_surface sea_ice optional_river_surface</dd><dt><span>comment :</span></dt><dd>flag interpretation as integer values:  1=water, 2=land, 5=lake, 9=water with ice in the grid, 13=lake with ice in the grid, 17=river</dd><dt><span>source :</span></dt><dd>GMT &quot;grdlandmask&quot;, ice flag from sea_ice_fraction data</dd></dl></div><div class='xr-var-data'><pre>array([[[2., 2., 2., ..., 2., 2., 2.],\n",
+       "        [2., 2., 2., ..., 2., 2., 2.],\n",
+       "        [2., 2., 2., ..., 2., 2., 2.],\n",
+       "        ...,\n",
+       "        [9., 9., 9., ..., 9., 9., 9.],\n",
+       "        [9., 9., 9., ..., 9., 9., 9.],\n",
+       "        [9., 9., 9., ..., 9., 9., 9.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_fraction</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.97 0.97 0.97</div><input id='attrs-2cf8af21-a85b-4410-833b-7d3bda65895a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cf8af21-a85b-4410-833b-7d3bda65895a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95bf3a8b-e072-4fcb-9017-739f12e2d89d' class='xr-var-data-in' type='checkbox'><label for='data-95bf3a8b-e072-4fcb-9017-739f12e2d89d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea ice area fraction</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>100</dd><dt><span>source :</span></dt><dd>EUMETSAT OSI-SAF, copyright EUMETSAT</dd><dt><span>comment :</span></dt><dd>ice fraction is a dimensionless quantity between 0 and 1; it has been interpolated by a nearest neighbor approach.</dd></dl></div><div class='xr-var-data'><pre>array([[[       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        [       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        [       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        ...,\n",
+       "        [0.96      , 0.96      , 0.96      , ..., 0.96      ,\n",
+       "         0.96      , 0.96      ],\n",
+       "        [0.96      , 0.96      , 0.96      , ..., 0.96999997,\n",
+       "         0.96999997, 0.96999997],\n",
+       "        [0.96      , 0.96      , 0.96      , ..., 0.96999997,\n",
+       "         0.96999997, 0.96999997]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst_anomaly</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.0 0.0 0.0 0.0</div><input id='attrs-ff5e8f10-89f1-490b-ad11-267141e65596' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff5e8f10-89f1-490b-ad11-267141e65596' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d09c3521-1a68-4e60-bae6-5484f76e3948' class='xr-var-data-in' type='checkbox'><label for='data-d09c3521-1a68-4e60-bae6-5484f76e3948' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>SST anomaly from a seasonal SST climatology based on the MUR data over 2003-2014 period</dd><dt><span>coverage_content_type :</span></dt><dd>auxiliaryInformation</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-32767</dd><dt><span>valid_max :</span></dt><dd>32767</dd><dt><span>comment :</span></dt><dd>anomaly reference to the day-of-year average between 2003 and 2014</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
+       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-246153db-7095-4cdd-b8d6-376bdf2d7821' class='xr-section-summary-in' type='checkbox'  ><label for='section-246153db-7095-4cdd-b8d6-376bdf2d7821' class='xr-section-summary' >Attributes: <span>(54)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7, ACDD-1.3</dd><dt><span>title :</span></dt><dd>Daily 0.25-degree MUR SST, Final product</dd><dt><span>summary :</span></dt><dd>A low-resolution version of the MUR SST analysis, a merged, multi-sensor L4 Foundation SST analysis product from JPL.</dd><dt><span>keywords :</span></dt><dd>Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>history :</span></dt><dd>created at nominal 4-day latency; replaced nrt (1-day latency) version.</dd><dt><span>source :</span></dt><dd>MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF</dd><dt><span>platform :</span></dt><dd>Terra, Aqua, GCOM-W, MetOp-B, Buoys/Ships</dd><dt><span>instrument :</span></dt><dd>MODIS, AMSR2, AVHRR, in-situ</dd><dt><span>sensor :</span></dt><dd>MODIS, AMSR2, AVHRR, in-situ</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>cdm_data_type :</span></dt><dd>grid</dd><dt><span>product_version :</span></dt><dd>04.2</dd><dt><span>references :</span></dt><dd>Chin et al. (2017) &quot;Remote Sensing of Environment&quot;, volulme 200, pages 154-169.  http://dx.doi.org/10.1016/j.rse.2017.07.029</dd><dt><span>creator_name :</span></dt><dd>JPL MUR SST project</dd><dt><span>creator_email :</span></dt><dd>ghrsst@podaac.jpl.nasa.gov</dd><dt><span>creator_url :</span></dt><dd>http://mur.jpl.nasa.gov</dd><dt><span>creator_institution :</span></dt><dd>Jet Propulsion Laboratory</dd><dt><span>institution :</span></dt><dd>Jet Propulsion Laboratory</dd><dt><span>project :</span></dt><dd>NASA MEaSUREs and COVERAGE</dd><dt><span>program :</span></dt><dd>NASA Earth Science Data and Information System (ESDIS)</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.0</dd><dt><span>northernmost_latitude :</span></dt><dd>90.0</dd><dt><span>westernmost_longitude :</span></dt><dd>-180.0</dd><dt><span>easternmost_longitude :</span></dt><dd>180.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees north</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>0.25</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees east</dd><dt><span>geospatial_lon_resolution :</span></dt><dd>0.25</dd><dt><span>date_created :</span></dt><dd>2022-12-10</dd><dt><span>start_time :</span></dt><dd>20221201T090000Z</dd><dt><span>stop_time :</span></dt><dd>20221201T090000Z</dd><dt><span>time_coverage_start :</span></dt><dd>20221130T210000Z</dd><dt><span>time_coverage_end :</span></dt><dd>20221201T210000Z</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>license :</span></dt><dd>These data are available free of charge under data policy of JPL PO.DAAC.</dd><dt><span>id :</span></dt><dd>MUR25-JPL-L4-GLOB-v04.2</dd><dt><span>uuid :</span></dt><dd>27665bc0-d5fc-11e1-9b23-0800200c9a66</dd><dt><span>comment :</span></dt><dd>MUR = &quot;Multi-scale Ultra-high Resolution&quot;</dd><dt><span>naming_authority :</span></dt><dd>org.ghrsst</dd><dt><span>gds_version_id :</span></dt><dd>2.0</dd><dt><span>netcdf_version_id :</span></dt><dd>04.2</dd><dt><span>spatial_resolution :</span></dt><dd>0.25 degrees</dd><dt><span>publisher_name :</span></dt><dd>GHRSST Project Office</dd><dt><span>publisher_url :</span></dt><dd>https://www.ghrsst.org</dd><dt><span>publisher_email :</span></dt><dd>gpc@ghrsst.org</dd><dt><span>file_quality_level :</span></dt><dd>3</dd><dt><span>metadata_link :</span></dt><dd>http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&amp;shortName=MUR25-JPL-L4-GLOB-v04.2</dd><dt><span>acknowledgment :</span></dt><dd>Please acknowledge the use of these data with the following statement:  These data were provided by JPL under support by NASA MEaSUREs and COVERAGE programs.</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:           (time: 1, lat: 720, lon: 1440)\n",
+       "Coordinates:\n",
+       "  * time              (time) datetime64[ns] 2022-12-01T09:00:00\n",
+       "  * lat               (lat) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88\n",
+       "  * lon               (lon) float32 -179.9 -179.6 -179.4 ... 179.4 179.6 179.9\n",
+       "Data variables:\n",
+       "    analysed_sst      (time, lat, lon) float32 nan nan nan ... 271.4 271.4 271.4\n",
+       "    analysis_error    (time, lat, lon) float32 nan nan nan ... 0.34 0.34 0.34\n",
+       "    mask              (time, lat, lon) float32 2.0 2.0 2.0 2.0 ... 9.0 9.0 9.0\n",
+       "    sea_ice_fraction  (time, lat, lon) float32 nan nan nan ... 0.97 0.97 0.97\n",
+       "    sst_anomaly       (time, lat, lon) float32 nan nan nan nan ... 0.0 0.0 0.0\n",
+       "Attributes: (12/54)\n",
+       "    Conventions:                CF-1.7, ACDD-1.3\n",
+       "    title:                      Daily 0.25-degree MUR SST, Final product\n",
+       "    summary:                    A low-resolution version of the MUR SST analy...\n",
+       "    keywords:                   Oceans > Ocean Temperature > Sea Surface Temp...\n",
+       "    keywords_vocabulary:        NASA Global Change Master Directory (GCMD) Sc...\n",
+       "    standard_name_vocabulary:   NetCDF Climate and Forecast (CF) Metadata Con...\n",
+       "    ...                         ...\n",
+       "    publisher_name:             GHRSST Project Office\n",
+       "    publisher_url:              https://www.ghrsst.org\n",
+       "    publisher_email:            gpc@ghrsst.org\n",
+       "    file_quality_level:         3\n",
+       "    metadata_link:              http://podaac.jpl.nasa.gov/ws/metadata/datase...\n",
+       "    acknowledgment:             Please acknowledge the use of these data with..."
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = xr.load_dataset('~/data/MUR25-JPL-L4-GLOB-v04.2/20221201090000-JPL-L4_GHRSST-SSTfnd-MUR25-GLOB-v02.0-fv04.2.nc')\n",
+    "ds\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;analysed_sst&#x27; (time: 1, lat: 720, lon: 1440)&gt;\n",
+       "array([[[       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        [       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        [       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        ...,\n",
+       "        [-1.7999878, -1.7999878, -1.7999878, ..., -1.7999878,\n",
+       "         -1.7999878, -1.7999878],\n",
+       "        [-1.7999878, -1.7999878, -1.7999878, ..., -1.7999878,\n",
+       "         -1.7999878, -1.7999878],\n",
+       "        [-1.7999878, -1.7999878, -1.7999878, ..., -1.7999878,\n",
+       "         -1.7999878, -1.7999878]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2022-12-01T09:00:00\n",
+       "  * lat      (lat) float32 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
+       "  * lon      (lon) float32 -179.9 -179.6 -179.4 -179.1 ... 179.4 179.6 179.9</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'analysed_sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>lat</span>: 720</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9767177d-e651-45b8-b793-030c7eb02ecf' class='xr-array-in' type='checkbox' checked><label for='section-9767177d-e651-45b8-b793-030c7eb02ecf' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8</span></div><div class='xr-array-data'><pre>array([[[       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        [       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        [       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        ...,\n",
+       "        [-1.7999878, -1.7999878, -1.7999878, ..., -1.7999878,\n",
+       "         -1.7999878, -1.7999878],\n",
+       "        [-1.7999878, -1.7999878, -1.7999878, ..., -1.7999878,\n",
+       "         -1.7999878, -1.7999878],\n",
+       "        [-1.7999878, -1.7999878, -1.7999878, ..., -1.7999878,\n",
+       "         -1.7999878, -1.7999878]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-9009bea7-526b-4a12-8cec-ec6081168f5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9009bea7-526b-4a12-8cec-ec6081168f5c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-12-01T09:00:00</div><input id='attrs-6f8a4ba7-7a2a-42b1-944f-fcf62e79a803' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f8a4ba7-7a2a-42b1-944f-fcf62e79a803' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd86ff1b-1d23-482c-9a18-5ade32c13cda' class='xr-var-data-in' type='checkbox'><label for='data-dd86ff1b-1d23-482c-9a18-5ade32c13cda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>reference time of sst field</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Nominal time of analyzed fields</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-12-01T09:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-487d3c6c-eb93-4ae4-8aaf-7423ea3327ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-487d3c6c-eb93-4ae4-8aaf-7423ea3327ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d8fea9f-75c8-4702-a014-928bc89c3d81' class='xr-var-data-in' type='checkbox'><label for='data-7d8fea9f-75c8-4702-a014-928bc89c3d81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-179.9 -179.6 ... 179.6 179.9</div><input id='attrs-77d4d066-c948-4aab-a9ab-0df0a361d5fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77d4d066-c948-4aab-a9ab-0df0a361d5fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da1b7310-c189-49fb-a59f-9978e73bc3c1' class='xr-var-data-in' type='checkbox'><label for='data-da1b7310-c189-49fb-a59f-9978e73bc3c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0cd72877-dd5d-4d28-a259-f8a45a30585c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0cd72877-dd5d-4d28-a259-f8a45a30585c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'analysed_sst' (time: 1, lat: 720, lon: 1440)>\n",
+       "array([[[       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        [       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        [       nan,        nan,        nan, ...,        nan,\n",
+       "                nan,        nan],\n",
+       "        ...,\n",
+       "        [-1.7999878, -1.7999878, -1.7999878, ..., -1.7999878,\n",
+       "         -1.7999878, -1.7999878],\n",
+       "        [-1.7999878, -1.7999878, -1.7999878, ..., -1.7999878,\n",
+       "         -1.7999878, -1.7999878],\n",
+       "        [-1.7999878, -1.7999878, -1.7999878, ..., -1.7999878,\n",
+       "         -1.7999878, -1.7999878]]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2022-12-01T09:00:00\n",
+       "  * lat      (lat) float32 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
+       "  * lon      (lon) float32 -179.9 -179.6 -179.4 -179.1 ... 179.4 179.6 179.9"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# select sst variable\n",
+    "sst = ds.analysed_sst\n",
+    "\n",
+    "# convert to degrees Celcius\n",
+    "sst = sst - 273.15\n",
+    "sst"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the data\n",
+    "p = sst.plot(subplot_kws=dict(transform=ccrs.PlateCarree()))\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the area-weighted global mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lat&#x27; (lat: 720)&gt;\n",
+       "array([0.00218172, 0.00654498, 0.01090811, 0.01527103, 0.01963366,\n",
+       "       0.02399603, 0.02835783, 0.03271909, 0.03707973, 0.04143966,\n",
+       "       0.04579892, 0.05015719, 0.0545145 , 0.05887078, 0.06322594,\n",
+       "       0.06758001, 0.07193268, 0.07628398, 0.08063382, 0.08498225,\n",
+       "       0.08932894, 0.09367393, 0.09801713, 0.10235848, 0.10669798,\n",
+       "       0.11103535, 0.11537059, 0.11970364, 0.12403442, 0.12836295,\n",
+       "       0.13268891, 0.13701235, 0.14133318, 0.14565133, 0.1499668 ,\n",
+       "       0.15427932, 0.1585889 , 0.16289546, 0.16719891, 0.1714993 ,\n",
+       "       0.1757963 , 0.18008997, 0.1843802 , 0.18866692, 0.19295016,\n",
+       "       0.19722962, 0.20150532, 0.20577718, 0.21004525, 0.2143092 ,\n",
+       "       0.21856907, 0.22282477, 0.22707623, 0.2313235 , 0.23556623,\n",
+       "       0.23980448, 0.24403816, 0.24826722, 0.25249165, 0.25671116,\n",
+       "       0.26092577, 0.26513544, 0.26934004, 0.27353963, 0.2777339 ,\n",
+       "       0.28192288, 0.2861065 , 0.29028466, 0.2944574 , 0.29862443,\n",
+       "       0.30278578, 0.30694136, 0.3110911 , 0.31523505, 0.31937286,\n",
+       "       0.3235046 , 0.32763016, 0.33174962, 0.33586264, 0.33996928,\n",
+       "       0.34406942, 0.34816304, 0.35225013, 0.3563304 , 0.36040387,\n",
+       "       0.3644705 , 0.36853018, 0.37258297, 0.37662855, 0.38066694,\n",
+       "       0.38469812, 0.38872194, 0.3927385 , 0.39674744, 0.40074885,\n",
+       "       0.40474263, 0.4087287 , 0.4127071 , 0.4166775 , 0.42064002,\n",
+       "...\n",
+       "       0.40074885, 0.39674744, 0.3927385 , 0.38872194, 0.38469812,\n",
+       "       0.38066694, 0.37662855, 0.37258297, 0.36853018, 0.3644705 ,\n",
+       "       0.36040387, 0.3563304 , 0.35225013, 0.34816304, 0.34406942,\n",
+       "       0.33996928, 0.33586264, 0.33174962, 0.32763016, 0.3235046 ,\n",
+       "       0.31937286, 0.31523505, 0.3110911 , 0.30694136, 0.30278578,\n",
+       "       0.29862443, 0.2944574 , 0.29028466, 0.2861065 , 0.28192288,\n",
+       "       0.2777339 , 0.27353963, 0.26934004, 0.26513544, 0.26092577,\n",
+       "       0.25671116, 0.25249165, 0.24826722, 0.24403816, 0.23980448,\n",
+       "       0.23556623, 0.2313235 , 0.22707623, 0.22282477, 0.21856907,\n",
+       "       0.2143092 , 0.21004525, 0.20577718, 0.20150532, 0.19722962,\n",
+       "       0.19295016, 0.18866692, 0.1843802 , 0.18008997, 0.1757963 ,\n",
+       "       0.1714993 , 0.16719891, 0.16289546, 0.1585889 , 0.15427932,\n",
+       "       0.1499668 , 0.14565133, 0.14133318, 0.13701235, 0.13268891,\n",
+       "       0.12836295, 0.12403442, 0.11970364, 0.11537059, 0.11103535,\n",
+       "       0.10669798, 0.10235848, 0.09801713, 0.09367393, 0.08932894,\n",
+       "       0.08498225, 0.08063382, 0.07628398, 0.07193268, 0.06758001,\n",
+       "       0.06322594, 0.05887078, 0.0545145 , 0.05015719, 0.04579892,\n",
+       "       0.04143966, 0.03707973, 0.03271909, 0.02835783, 0.02399603,\n",
+       "       0.01963366, 0.01527103, 0.01090811, 0.00654498, 0.00218172],\n",
+       "      dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
+       "Attributes:\n",
+       "    long_name:              latitude\n",
+       "    standard_name:          latitude\n",
+       "    coverage_content_type:  coordinate\n",
+       "    axis:                   Y\n",
+       "    units:                  degrees_north\n",
+       "    valid_min:              -90.0\n",
+       "    valid_max:              90.0\n",
+       "    comment:                geolocations inherited from the input data withou...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lat'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b6fc986b-2431-4e0b-a5f0-5b9ed975f5cd' class='xr-array-in' type='checkbox' checked><label for='section-b6fc986b-2431-4e0b-a5f0-5b9ed975f5cd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.002182 0.006545 0.01091 0.01527 ... 0.01091 0.006545 0.002182</span></div><div class='xr-array-data'><pre>array([0.00218172, 0.00654498, 0.01090811, 0.01527103, 0.01963366,\n",
+       "       0.02399603, 0.02835783, 0.03271909, 0.03707973, 0.04143966,\n",
+       "       0.04579892, 0.05015719, 0.0545145 , 0.05887078, 0.06322594,\n",
+       "       0.06758001, 0.07193268, 0.07628398, 0.08063382, 0.08498225,\n",
+       "       0.08932894, 0.09367393, 0.09801713, 0.10235848, 0.10669798,\n",
+       "       0.11103535, 0.11537059, 0.11970364, 0.12403442, 0.12836295,\n",
+       "       0.13268891, 0.13701235, 0.14133318, 0.14565133, 0.1499668 ,\n",
+       "       0.15427932, 0.1585889 , 0.16289546, 0.16719891, 0.1714993 ,\n",
+       "       0.1757963 , 0.18008997, 0.1843802 , 0.18866692, 0.19295016,\n",
+       "       0.19722962, 0.20150532, 0.20577718, 0.21004525, 0.2143092 ,\n",
+       "       0.21856907, 0.22282477, 0.22707623, 0.2313235 , 0.23556623,\n",
+       "       0.23980448, 0.24403816, 0.24826722, 0.25249165, 0.25671116,\n",
+       "       0.26092577, 0.26513544, 0.26934004, 0.27353963, 0.2777339 ,\n",
+       "       0.28192288, 0.2861065 , 0.29028466, 0.2944574 , 0.29862443,\n",
+       "       0.30278578, 0.30694136, 0.3110911 , 0.31523505, 0.31937286,\n",
+       "       0.3235046 , 0.32763016, 0.33174962, 0.33586264, 0.33996928,\n",
+       "       0.34406942, 0.34816304, 0.35225013, 0.3563304 , 0.36040387,\n",
+       "       0.3644705 , 0.36853018, 0.37258297, 0.37662855, 0.38066694,\n",
+       "       0.38469812, 0.38872194, 0.3927385 , 0.39674744, 0.40074885,\n",
+       "       0.40474263, 0.4087287 , 0.4127071 , 0.4166775 , 0.42064002,\n",
+       "...\n",
+       "       0.40074885, 0.39674744, 0.3927385 , 0.38872194, 0.38469812,\n",
+       "       0.38066694, 0.37662855, 0.37258297, 0.36853018, 0.3644705 ,\n",
+       "       0.36040387, 0.3563304 , 0.35225013, 0.34816304, 0.34406942,\n",
+       "       0.33996928, 0.33586264, 0.33174962, 0.32763016, 0.3235046 ,\n",
+       "       0.31937286, 0.31523505, 0.3110911 , 0.30694136, 0.30278578,\n",
+       "       0.29862443, 0.2944574 , 0.29028466, 0.2861065 , 0.28192288,\n",
+       "       0.2777339 , 0.27353963, 0.26934004, 0.26513544, 0.26092577,\n",
+       "       0.25671116, 0.25249165, 0.24826722, 0.24403816, 0.23980448,\n",
+       "       0.23556623, 0.2313235 , 0.22707623, 0.22282477, 0.21856907,\n",
+       "       0.2143092 , 0.21004525, 0.20577718, 0.20150532, 0.19722962,\n",
+       "       0.19295016, 0.18866692, 0.1843802 , 0.18008997, 0.1757963 ,\n",
+       "       0.1714993 , 0.16719891, 0.16289546, 0.1585889 , 0.15427932,\n",
+       "       0.1499668 , 0.14565133, 0.14133318, 0.13701235, 0.13268891,\n",
+       "       0.12836295, 0.12403442, 0.11970364, 0.11537059, 0.11103535,\n",
+       "       0.10669798, 0.10235848, 0.09801713, 0.09367393, 0.08932894,\n",
+       "       0.08498225, 0.08063382, 0.07628398, 0.07193268, 0.06758001,\n",
+       "       0.06322594, 0.05887078, 0.0545145 , 0.05015719, 0.04579892,\n",
+       "       0.04143966, 0.03707973, 0.03271909, 0.02835783, 0.02399603,\n",
+       "       0.01963366, 0.01527103, 0.01090811, 0.00654498, 0.00218172],\n",
+       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-c27408d9-ba05-444a-9a0e-bf50cf4eceb1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c27408d9-ba05-444a-9a0e-bf50cf4eceb1' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.88 -89.62 ... 89.62 89.88</div><input id='attrs-7c4ad244-4219-48fa-a78f-309f0ae57c0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c4ad244-4219-48fa-a78f-309f0ae57c0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1b06554-0860-4eeb-b6e6-c71e924c3f50' class='xr-var-data-in' type='checkbox'><label for='data-b1b06554-0860-4eeb-b6e6-c71e924c3f50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div><div class='xr-var-data'><pre>array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],\n",
+       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-206e1efb-ab25-499a-a676-be982ca6084c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-206e1efb-ab25-499a-a676-be982ca6084c' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>comment :</span></dt><dd>geolocations inherited from the input data without correction</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'lat' (lat: 720)>\n",
+       "array([0.00218172, 0.00654498, 0.01090811, 0.01527103, 0.01963366,\n",
+       "       0.02399603, 0.02835783, 0.03271909, 0.03707973, 0.04143966,\n",
+       "       0.04579892, 0.05015719, 0.0545145 , 0.05887078, 0.06322594,\n",
+       "       0.06758001, 0.07193268, 0.07628398, 0.08063382, 0.08498225,\n",
+       "       0.08932894, 0.09367393, 0.09801713, 0.10235848, 0.10669798,\n",
+       "       0.11103535, 0.11537059, 0.11970364, 0.12403442, 0.12836295,\n",
+       "       0.13268891, 0.13701235, 0.14133318, 0.14565133, 0.1499668 ,\n",
+       "       0.15427932, 0.1585889 , 0.16289546, 0.16719891, 0.1714993 ,\n",
+       "       0.1757963 , 0.18008997, 0.1843802 , 0.18866692, 0.19295016,\n",
+       "       0.19722962, 0.20150532, 0.20577718, 0.21004525, 0.2143092 ,\n",
+       "       0.21856907, 0.22282477, 0.22707623, 0.2313235 , 0.23556623,\n",
+       "       0.23980448, 0.24403816, 0.24826722, 0.25249165, 0.25671116,\n",
+       "       0.26092577, 0.26513544, 0.26934004, 0.27353963, 0.2777339 ,\n",
+       "       0.28192288, 0.2861065 , 0.29028466, 0.2944574 , 0.29862443,\n",
+       "       0.30278578, 0.30694136, 0.3110911 , 0.31523505, 0.31937286,\n",
+       "       0.3235046 , 0.32763016, 0.33174962, 0.33586264, 0.33996928,\n",
+       "       0.34406942, 0.34816304, 0.35225013, 0.3563304 , 0.36040387,\n",
+       "       0.3644705 , 0.36853018, 0.37258297, 0.37662855, 0.38066694,\n",
+       "       0.38469812, 0.38872194, 0.3927385 , 0.39674744, 0.40074885,\n",
+       "       0.40474263, 0.4087287 , 0.4127071 , 0.4166775 , 0.42064002,\n",
+       "...\n",
+       "       0.40074885, 0.39674744, 0.3927385 , 0.38872194, 0.38469812,\n",
+       "       0.38066694, 0.37662855, 0.37258297, 0.36853018, 0.3644705 ,\n",
+       "       0.36040387, 0.3563304 , 0.35225013, 0.34816304, 0.34406942,\n",
+       "       0.33996928, 0.33586264, 0.33174962, 0.32763016, 0.3235046 ,\n",
+       "       0.31937286, 0.31523505, 0.3110911 , 0.30694136, 0.30278578,\n",
+       "       0.29862443, 0.2944574 , 0.29028466, 0.2861065 , 0.28192288,\n",
+       "       0.2777339 , 0.27353963, 0.26934004, 0.26513544, 0.26092577,\n",
+       "       0.25671116, 0.25249165, 0.24826722, 0.24403816, 0.23980448,\n",
+       "       0.23556623, 0.2313235 , 0.22707623, 0.22282477, 0.21856907,\n",
+       "       0.2143092 , 0.21004525, 0.20577718, 0.20150532, 0.19722962,\n",
+       "       0.19295016, 0.18866692, 0.1843802 , 0.18008997, 0.1757963 ,\n",
+       "       0.1714993 , 0.16719891, 0.16289546, 0.1585889 , 0.15427932,\n",
+       "       0.1499668 , 0.14565133, 0.14133318, 0.13701235, 0.13268891,\n",
+       "       0.12836295, 0.12403442, 0.11970364, 0.11537059, 0.11103535,\n",
+       "       0.10669798, 0.10235848, 0.09801713, 0.09367393, 0.08932894,\n",
+       "       0.08498225, 0.08063382, 0.07628398, 0.07193268, 0.06758001,\n",
+       "       0.06322594, 0.05887078, 0.0545145 , 0.05015719, 0.04579892,\n",
+       "       0.04143966, 0.03707973, 0.03271909, 0.02835783, 0.02399603,\n",
+       "       0.01963366, 0.01527103, 0.01090811, 0.00654498, 0.00218172],\n",
+       "      dtype=float32)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88\n",
+       "Attributes:\n",
+       "    long_name:              latitude\n",
+       "    standard_name:          latitude\n",
+       "    coverage_content_type:  coordinate\n",
+       "    axis:                   Y\n",
+       "    units:                  degrees_north\n",
+       "    valid_min:              -90.0\n",
+       "    valid_max:              90.0\n",
+       "    comment:                geolocations inherited from the input data withou..."
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create the weights\n",
+    "\n",
+    "weights = np.cos(np.deg2rad(sst.lat))\n",
+    "weights\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for lat in sst.lat:\n",
+    "    l = lat.values\n",
+    "    if (l>60) or (l<-60):\n",
+    "        weights.loc[dict(lat=l)] = 0\n",
+    "\n",
+    "#weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;analysed_sst&#x27; ()&gt;\n",
+       "array(20.52885965)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'analysed_sst'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-df2ffa9a-0200-4c80-8e9c-2b31ede0cc94' class='xr-array-in' type='checkbox' checked><label for='section-df2ffa9a-0200-4c80-8e9c-2b31ede0cc94' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>20.53</span></div><div class='xr-array-data'><pre>array(20.52885965)</pre></div></div></li><li class='xr-section-item'><input id='section-ebbadb9f-15b7-4c98-bb69-321b81c62fdb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ebbadb9f-15b7-4c98-bb69-321b81c62fdb' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a0ad358b-d01d-4c71-9ec7-83309621939f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a0ad358b-d01d-4c71-9ec7-83309621939f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'analysed_sst' ()>\n",
+       "array(20.52885965)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# apply weights to data\n",
+    "sst_weighted = sst.weighted(weights)\n",
+    "\n",
+    "# calculate the global mean on the weighted data\n",
+    "sst_global_mean = sst_weighted.mean()\n",
+    "\n",
+    "# display the values\n",
+    "sst_global_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;analysed_sst&#x27; (time: 1)&gt;\n",
+       "array([20.52885965])\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2022-12-01T09:00:00\n",
+       "Attributes:\n",
+       "    description:   Area-weighted global mean sea surface temperature calculat...\n",
+       "    units:         celcius\n",
+       "    date_created:  Jun-09-2023</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'analysed_sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-361fbd67-5578-4be9-b853-ac124451daed' class='xr-array-in' type='checkbox' checked><label for='section-361fbd67-5578-4be9-b853-ac124451daed' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>20.53</span></div><div class='xr-array-data'><pre>array([20.52885965])</pre></div></div></li><li class='xr-section-item'><input id='section-e78849b2-8d71-46b0-95de-3c05f97ec634' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e78849b2-8d71-46b0-95de-3c05f97ec634' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-12-01T09:00:00</div><input id='attrs-fb68001f-5f5b-4fe5-95b8-9ee04191ca2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb68001f-5f5b-4fe5-95b8-9ee04191ca2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-641da5e6-1c05-45ed-91d3-8aa5dd081475' class='xr-var-data-in' type='checkbox'><label for='data-641da5e6-1c05-45ed-91d3-8aa5dd081475' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-12-01T09:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-653fe6d0-892b-4846-9007-71a2d4b43b75' class='xr-section-summary-in' type='checkbox'  checked><label for='section-653fe6d0-892b-4846-9007-71a2d4b43b75' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Area-weighted global mean sea surface temperature calculated using AWS Lambda</dd><dt><span>units :</span></dt><dd>celcius</dd><dt><span>date_created :</span></dt><dd>Jun-09-2023</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'analysed_sst' (time: 1)>\n",
+       "array([20.52885965])\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2022-12-01T09:00:00\n",
+       "Attributes:\n",
+       "    description:   Area-weighted global mean sea surface temperature calculat...\n",
+       "    units:         celcius\n",
+       "    date_created:  Jun-09-2023"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "sst_out = sst_global_mean.expand_dims(time=ds.time)\n",
+    "sst_out = sst_out.assign_attrs({\n",
+    "    \"description\": \"Area-weighted global mean sea surface temperature calculated using AWS Lambda\",\n",
+    "    \"units\": \"celcius\",\n",
+    "    \"date_created\": date.today().strftime(\"%b-%d-%Y\")\n",
+    "})\n",
+    "sst_out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "scratch",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/aws_lambda_sst/sst.py b/notebooks/aws_lambda_sst/sst.py
new file mode 100644
index 00000000..727c062a
--- /dev/null
+++ b/notebooks/aws_lambda_sst/sst.py
@@ -0,0 +1,176 @@
+# Imports
+import requests
+import base64
+from datetime import date
+import s3fs
+import boto3
+import botocore
+import json
+import xarray as xr
+import numpy as np
+
+# Constants
+S3_ENDPOINT_DICT = {
+    'podaac':'https://archive.podaac.earthdata.nasa.gov/s3credentials'
+}
+
+# Handle EDL login & S3 credentials
+def get_creds(s3_endpoint, edl_username, edl_password):
+        """Request and return temporary S3 credentials.
+        
+        Taken from: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME
+        """
+        
+        login = requests.get(
+            s3_endpoint, allow_redirects=False
+        )
+        login.raise_for_status()
+
+        auth = f"{edl_username}:{edl_password}"
+        encoded_auth  = base64.b64encode(auth.encode('ascii'))
+
+        auth_redirect = requests.post(
+            login.headers['location'],
+            data = {"credentials": encoded_auth},
+            headers= { "Origin": s3_endpoint },
+            allow_redirects=False
+        )
+        auth_redirect.raise_for_status()
+        final = requests.get(auth_redirect.headers['location'], allow_redirects=False)
+        results = requests.get(s3_endpoint, cookies={'accessToken': final.cookies['accessToken']})
+        results.raise_for_status()
+        return json.loads(results.content)       
+        
+def get_temp_creds(prefix):
+    # retreive EDL credentials from AWS Parameter Store
+    try:
+        ssm_client = boto3.client('ssm', region_name="us-west-2")
+        edl_username = ssm_client.get_parameter(Name=f"{prefix}-sst-edl-username", WithDecryption=True)["Parameter"]["Value"]
+        edl_password = ssm_client.get_parameter(Name=f"{prefix}-sst-edl-password", WithDecryption=True)["Parameter"]["Value"]
+        print("Retrieved Earthdata login credentials.")
+    except botocore.exceptions.ClientError as error:
+        raise error
+
+    # use EDL creds to get AWS S3 Access Keys & Tokens
+    s3_creds = get_creds(S3_ENDPOINT_DICT[prefix], edl_username, edl_password)
+    print("Retrieved temporary S3 access credentials.")
+    
+    return s3_creds
+
+
+# Science functions & lambda handler
+def sst_global_mean(data_in):
+    """
+    Calculate the area-weighted sea surface temperature (sst) global mean
+
+    Parameters
+    ==========
+    data_in: xarray.Dataset()
+            the input dataset
+
+    var_name: string
+            the variable to calculate the global mean on
+             
+    Return
+    ======
+    data_out: ndarray, xarray
+             the global mean for the provided variable
+    """
+    
+    # select the sst variable and select single time
+    data_var = data_in.analysed_sst.isel(time=0)
+
+    # convert to degrees Celcius
+    data_var = data_var - 273.15
+
+    # create the weights
+    weights = np.cos(np.deg2rad(data_var.lat))
+    for lat in data_var.lat:
+        l = lat.values
+        if (l>60) or (l<-60):
+            weights.loc[dict(lat=l)] = 0
+
+    # apply weights to data
+    data_weighted = data_var.weighted(weights)
+
+    # calculate the global mean on the weighted data
+    global_mean = data_weighted.mean()
+
+    sst_out = global_mean.expand_dims(time=data_in.time)
+    sst_out = sst_out.assign_attrs({
+    "description": "Area-weighted global mean sea surface temperature calculated using AWS Lambda",
+    "units": "celcius",
+    "date_created": date.today().strftime("%b-%d-%Y")
+    })
+
+    return sst_out
+    
+
+def lambda_handler(event, context):
+    """Lambda event handler to orchestrate calculation of global mean."""
+    
+    # --------------------
+    # Unpack event payload
+    # --------------------
+
+    prefix = event["prefix"]
+    #key = event["s3_key"]   # Granule name
+
+    input_granule_path = event["input_granule_s3path"]
+    input_bucket, folder, input_key = input_granule_path.replace("s3://", "").split("/", 2)
+
+    # get the name of the user's output S3 bucket
+    output_s3_bucket = event["output_granule_s3bucket"]
+
+    # ---------------------------------------------
+    # Read data from Earthdata S3 buckets using EDL
+    # ---------------------------------------------
+
+    # Get EDL credentials from AWS Parameter Store
+    temp_creds_req = get_temp_creds(prefix)
+
+    # Set up S3 client for Earthdata buckets using EDL creds
+    s3_client_in = s3fs.S3FileSystem(
+        anon=False, 
+        key=temp_creds_req['accessKeyId'], 
+        secret=temp_creds_req['secretAccessKey'], 
+        token=temp_creds_req['sessionToken']
+    )
+
+    # open the granule as an s3 obj
+    s3_file_obj = s3_client_in.open(input_granule_path, mode='rb')
+
+    # -----------------------------------
+    # Do science calculations on the data
+    # -----------------------------------
+        
+    # open data in xarray
+    ds = xr.open_dataset(s3_file_obj, engine='h5netcdf')
+
+    # process the function
+    ds_results = sst_global_mean(ds)
+    
+    # --------------------------------------------------------------
+    # Write results to the user's own S3 bucket for further analysis
+    # --------------------------------------------------------------
+
+    output_key = input_key[:-3] + '_mean.nc'
+
+    # create the temp path for Lambda to write results to locally
+    tmp_file_path = '/tmp/' + output_key
+        
+    # write the results to a new netcdf file
+    try:
+        ds_results.to_netcdf(tmp_file_path, mode='w')
+
+    except Exception as e:
+         print("Problem writing to tmp: " + e)
+    
+    # Set up S3 client for user output bucket.  
+    s3_out = boto3.client('s3')
+
+    s3_out.upload_file(tmp_file_path, output_s3_bucket, output_key)
+
+    # Close dataset and S3 file objects
+    ds.close()
+
diff --git a/notebooks/aws_lambda_sst/terraform/.terraform.lock.hcl b/notebooks/aws_lambda_sst/terraform/.terraform.lock.hcl
new file mode 100644
index 00000000..494c9d5c
--- /dev/null
+++ b/notebooks/aws_lambda_sst/terraform/.terraform.lock.hcl
@@ -0,0 +1,26 @@
+# This file is maintained automatically by "terraform init".
+# Manual edits may be lost in future updates.
+
+provider "registry.terraform.io/hashicorp/aws" {
+  version     = "4.57.1"
+  constraints = "~> 4.0"
+  hashes = [
+    "h1:lyiJFRB0nKUS/OkS8OSqxAYZuLWVBIPpN67VGoDyYak=",
+    "h1:rqJN5HwMnJtHIvIzublREIxUibBFYIKyeQcgOov4DUQ=",
+    "zh:44200c213ddb138df80d2a5ad86c2ebadbb5fd1d08cd7e4fc56ec6dca927659b",
+    "zh:469e6fe6a9e99e60cb168d32f05e2e9a83cf161f39160d075ff96f7674c510e1",
+    "zh:6110ba2c15a2268652ec9ea3797dd0216de84ece428055c49eaf9caa2be1ed62",
+    "zh:62ed7348acca44f64fc087e879e01cfa4e084c7600cc91e8bb7683f8065a9c79",
+    "zh:7a80e6fa9b35be178bb566093f7984dd6ffb7ad9d40b9dd5d5907f054f0c3e60",
+    "zh:8793043c8575a598c1a7cbefcb65ee1776b0061eba719098e552a3adc88f3090",
+    "zh:9b12af85486a96aedd8d7984b0ff811a4b42e3d88dad1a3fb4c0b580d04fa425",
+    "zh:a777a0082114e273b7b3eb14095a3f6f6e703c1aff61ffb1f0846bb869e6dfc7",
+    "zh:b060c3b2973097f2087a98ac6aad7c9c89fe80f7cf3027019049feafc3f8305b",
+    "zh:e7035e74563f4486848ea1feb60852175353790bc374e0e97e241a88dc0908f7",
+    "zh:eaaa8e9eba09ada41e13116d53d4baece04fead8fcf3eab68cca3a67ed738e18",
+    "zh:ec52d8f95a84fad8fe1aae169c89d0c54d5401f75caae0869ad8182c6b6db65b",
+    "zh:f0e33174025b1b57ecfbdd09f2a59c2559ee94d7681e5ae09079e2822ec54ecf",
+    "zh:f69790a21380e5aab9303a252564737333e1e95b5d25567681630e49b17e3ec7",
+    "zh:ff6053942c40a99904bd407f3c082c1fa8f927ecce0374566eb7e8ee8145e582",
+  ]
+}
diff --git a/notebooks/aws_lambda_sst/terraform/main.tf b/notebooks/aws_lambda_sst/terraform/main.tf
new file mode 100644
index 00000000..8d10c0a5
--- /dev/null
+++ b/notebooks/aws_lambda_sst/terraform/main.tf
@@ -0,0 +1,36 @@
+terraform {
+  required_providers {
+    aws = {
+      source  = "hashicorp/aws"
+      version = "~> 4.0"
+    }
+  }
+}
+
+# Configure the AWS Provider
+provider "aws" {
+  default_tags {
+    tags = local.default_tags
+  }
+  region  = var.aws_region
+  profile = var.profile
+}
+
+# Data sources
+data "aws_caller_identity" "current" {}
+
+data "aws_ecr_repository" "podaac_sst_repo" {
+  name = var.ecr_repo
+}
+
+data "aws_iam_role" "lambda_execution_role" {
+  name = var.lambda_role
+}
+
+# Local variables
+locals {
+  account_id = data.aws_caller_identity.current.account_id
+  default_tags = length(var.default_tags) == 0 ? {
+    application : var.app_name,
+  } : var.default_tags
+}
\ No newline at end of file
diff --git a/notebooks/aws_lambda_sst/terraform/sst-lambda.tf b/notebooks/aws_lambda_sst/terraform/sst-lambda.tf
new file mode 100644
index 00000000..1d2fb018
--- /dev/null
+++ b/notebooks/aws_lambda_sst/terraform/sst-lambda.tf
@@ -0,0 +1,54 @@
+# AWS Lambda function
+resource "aws_lambda_function" "aws_lambda_error_handler" {
+  image_uri     = "${data.aws_ecr_repository.podaac_sst_repo.repository_url}:latest"
+  function_name = "${var.prefix}-sst"
+  role          = data.aws_iam_role.lambda_execution_role.arn
+  package_type  = "Image"
+  memory_size   = 6144
+  timeout       = 900
+}
+
+# SSM Parameter Store EDL Credentials
+resource "aws_ssm_parameter" "aws_ssm_parameter_edl_username" {
+  name        = "${var.prefix}-sst-edl-username"
+  description = "Earthdata Login username"
+  type        = "SecureString"
+  value       = var.edl_username
+}
+
+resource "aws_ssm_parameter" "aws_ssm_parameter_edl_password" {
+  name        = "${var.prefix}-sst-edl-password"
+  description = "Earthdata Login password"
+  type        = "SecureString"
+  value       = var.edl_password
+}
+
+# S3 Bucket to hold results
+resource "aws_s3_bucket" "aws_s3_bucket_sst" {
+  bucket = "${var.prefix}-sst"
+  tags   = { Name = "${var.prefix}-sst" }
+}
+
+resource "aws_s3_bucket_public_access_block" "aws_s3_bucket_sst_public_block" {
+  bucket                  = aws_s3_bucket.aws_s3_bucket_sst.id
+  block_public_acls       = true
+  block_public_policy     = true
+  ignore_public_acls      = true
+  restrict_public_buckets = true
+}
+
+resource "aws_s3_bucket_ownership_controls" "aws_s3_bucket_sst_ownership" {
+  bucket = aws_s3_bucket.aws_s3_bucket_sst.id
+  rule {
+    object_ownership = "BucketOwnerEnforced"
+  }
+}
+
+resource "aws_s3_bucket_server_side_encryption_configuration" "aws_s3_bucket_sst_encryption" {
+  bucket = aws_s3_bucket.aws_s3_bucket_sst.bucket
+  rule {
+    apply_server_side_encryption_by_default {
+      sse_algorithm = "AES256"
+    }
+  }
+}
\ No newline at end of file
diff --git a/notebooks/aws_lambda_sst/terraform/terraform.tfvars b/notebooks/aws_lambda_sst/terraform/terraform.tfvars
new file mode 100644
index 00000000..b3c7fafc
--- /dev/null
+++ b/notebooks/aws_lambda_sst/terraform/terraform.tfvars
@@ -0,0 +1,6 @@
+ecr_repo     = ""
+edl_password = ""
+edl_username = ""
+lambda_role  = ""
+prefix       = ""
+profile      = ""
diff --git a/notebooks/aws_lambda_sst/terraform/variables.tf b/notebooks/aws_lambda_sst/terraform/variables.tf
new file mode 100644
index 00000000..73dbfe0b
--- /dev/null
+++ b/notebooks/aws_lambda_sst/terraform/variables.tf
@@ -0,0 +1,46 @@
+variable "app_name" {
+  type        = string
+  description = "Application name"
+  default     = "SST"
+}
+
+variable "aws_region" {
+  type        = string
+  description = "AWS region to deploy to"
+  default     = "us-west-2"
+}
+
+variable "default_tags" {
+  type    = map(string)
+  default = {}
+}
+
+variable "ecr_repo" {
+  type        = string
+  description = "sst-lambda container image repository name"
+}
+
+variable "edl_password" {
+  type        = string
+  description = "Earthdata Login password"
+}
+
+variable "edl_username" {
+  type        = string
+  description = "Earthdata Login useranme"
+}
+
+variable "lambda_role" {
+  type        = string
+  description = "Name of AWS Lambda IAM role"
+}
+
+variable "prefix" {
+  type        = string
+  description = "Prefix to add to all AWS resources as a unique identifier"
+}
+
+variable "profile" {
+  type        = string
+  description = "Named profile to build infrastructure with"
+}
\ No newline at end of file