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Heat_Budget_Plot_Spatial_NA2panel.m
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Heat_Budget_Plot_Spatial_NA2panel.m
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% This script makes plots of the spatial structure of the
% diathermal fluxes in the MOM simulations.
close all;
clear all;
base = '/srv/ccrc/data03/z3500785/mom/mat_data/';
% $$$ base = 'archive/mat_data/';
RUNS = { ...
% MOM01-SIS:
% $$$ {'MOM01',[4567]}, ...
% $$$ % MOM025-SIS:
% $$$ {'MOM025_kb3seg',[101120]}, ...
% $$$ {'MOM025',[15:19]}, ...
% $$$ {'MOM025_kb1em5',[95:99]}, ...
% $$$ {'MOM025_kb1em6',[30]}, ...
% $$$ % ACCESS-OM2 Gadi runs:
% $$$ % 1-degree
% $$$ {'ACCESS-OM2_1deg_jra55_ryf',[31]}, ...
% $$$ {'ACCESS-OM2_1deg_jra55_ryf_sgl',[31]}, ...
% $$$ {'ACCESS-OM2_1deg_jra55_ryf_gfdl50',[31]}, ...
% $$$ {'ACCESS-OM2_1deg_jra55_ryf_kds75',[3135]}, ...
% $$$ {'ACCESS-OM2_1deg_jra55_ryf_kds100',[3135]}, ...
% $$$ {'ACCESS-OM2_1deg_jra55_ryf_kds135',[3135]}, ...
% $$$ % 1/4-degree
% $$$ {'ACCESS-OM2_025deg_jra55_ryf',[7680]}, ...
{'ACCESS-OM2_025deg_jra55_ryf_norediGM',[7680]}, ...
% $$$ {'ACCESS-OM2_025deg_jra55_ryf_noGM',[7680]}, ...
% $$$ {'ACCESS-OM2_025deg_jra55_ryf_rediGM_kb1em5',[7781]}, ...
% $$$ {'ACCESS-OM2_025deg_jra55_ryf_rediGM_kbvar',[7781]}, ...
% $$$ {'ACCESS-OM2_025deg_jra55_ryf_kds75',[7680]}, ...
% $$$ {'ACCESS-OM2_025deg_jra55_ryf8485_gmredi',[73]}, ...
% $$$ {'ACCESS-OM2_025deg_jra55_ryf',[80]}, ...
% $$$ {'ACCESS-OM2_025deg_jra55_ryf',[300]}, ...
% $$$ {'ACCESS-OM2_01deg_jra55_ryf',[636643]}, ...
% $$$ {'ACCESS-OM2_1deg_jra55_rdf',[51:55]}, ...
% $$$ {'ACCESS-OM2_1deg_jra55_rdf_pert',[51:55]}, ...
% $$$ {'ACCESS-OM2_01deg_jra55_ryf_k_smag_iso3',[640643]}, ...
};
cols = {'b','r','k','m','g'};
rr = 1;
% $$$ figure;
set(gcf,'Position',[158 40 1641 963]);%get(0,'ScreenSize'));
set(gcf,'Position',[87 52 848 937]); % North Atlantic Two panel.
% $$$ for rr = 1:length(RUNS);
outputs = RUNS{rr}{2};
model = RUNS{rr}{1};
% $$$ clearvars -except base RUNS rr outputs model;
load([base model sprintf('_output%03d_BaseVars.mat',outputs(1))]);
if (~exist('ndays'))
ndays = diff(time_snap);
ndays = ndays(1:12);
end
if (ndays(end) <= 0); ndays(end) = 365-ndays(end);end;
region = 'Global';
nyrs = tL/12;
if (round(nyrs)~=nyrs)
anavg = 1;
nyrs = tL;
months = {[1:1]};
else
anavg = 0;
months = {[1:12]};
end
yrs = 1:nyrs;
ycur = 1;
%%% Spatial Structure:
% $$$ VAR = 'FlI';
TYPE = 'VertInt';
Tls = 15;
% $$$ Tls = [22.5 15 5];
% $$$ % $$$ VAR = 'EKE';
% $$$ % $$$ TYPE = 'variances';
% $$$ % $$$ Tl = 5;
% $$$ % $$$ Tls = [15 5];%22.5;
% $$$ % $$$ Tls = [22.5 15 5];%22.5;
% $$$ % $$$ VAR = 'Tdzsq';
% $$$ % $$$ VAR = 'WMTI';
% $$$ % $$$ TYPE = 'WMT';
% $$$ % $$$ Tl = 19.75;
% $$$ % $$$ labels = {'(a) MOM025-kb0','(b) MOM025-kb5','(f) $5^\circ$C'};%10$^\circ$C'};%$15^\circ$C'};
% labels = {'(a) $22.5^\circ$','(c) $15^\circ$C','(e) $5^\circ$C'};%10$^\circ$C'};%$15^\circ$C'};
labels = {'(a) $1/4^\circ$','(b) $2.5^\circ$ Remap'};%$22.5^\circ$','(c) $15^\circ$C','(e) $5^\circ$C'};%10$^\circ$C'};%$15^\circ$C'};
% $$$ % $$$ labels = {'(a) Numerical Mixing','(b) Vertical Mixing','(f) $5^\circ$C'};%10$^\circ$C'};%$15^\circ$C'};
% $$$ % $$$ labels = {'(a) ACCESS-OM2-025-RG $15^\circ$C','(b) ACCESS-OM2-025-RG $5^\circ$C', ...
% $$$ % $$$ '(c) ACCESS-OM2-025 $15^\circ$C','(d) ACCESS-OM2-025 $5^\circ$C', ...
% $$$ % $$$ '(e) ACCESS-OM2-025-R $15^\circ$C','(f) ACCESS-OM2-025-R $5^\circ$C'};
% $$$ labels = {'(a) ACCESS-OM2-01 $\mathcal{I}$ $15^\circ$C','(b) ACCESS-OM2-01-hvisc minus ACCESS-OM2-01 $\mathcal{I}$ $15^\circ$C'};
% $$$ for iii=1:length(Tls)
iii = 1;
Tl = Tls(iii);
iii = 1;
% $$$ name = [base model sprintf('_output%03d',outputs(1)) '_' TYPE '_T' strrep(num2str(Tl),'.','p') 'C.mat']
% $$$ eval(['load(name,''' VAR ''');']);
% $$$ eval([VAR '(isnan(' VAR ')) = 0.0;']);
% $$$ if (length(outputs)==1)
% $$$ if (anavg)
% $$$ eval([VAR ' = mean(' VAR '(:,:,yrs),3);']);
% $$$ else
% $$$ eval([VAR ' = reshape(' VAR ',[length(' VAR '(:,1,1)) length(' VAR '(1,:,1)) 12 nyrs]);']);
% $$$ eval([VAR ' = mean(' VAR '(:,:,:,yrs),4);']);
% $$$ end
% $$$ eval([VAR '(' VAR '==0) = NaN;']);
% $$$ else
% $$$ eval([VAR 'a = ' VAR ';']);
% $$$ for i=2:length(outputs)
% $$$ name = [base model sprintf('_output%03d',outputs(i)) '_' TYPE '_T' strrep(num2str(Tl),'.','p') 'C.mat']
% $$$ eval(['load(name,''' VAR ''');']);
% $$$ eval([VAR '(isnan(' VAR ')) = 0.0;']);
% $$$ eval([VAR 'a = ' VAR 'a + ' VAR ';']);
% $$$ end
% $$$ eval([VAR ' = ' VAR 'a/length(outputs);']);
% $$$ eval([VAR '(' VAR '==0) = NaN;']);
% $$$ end
% $$$ eval(['FlM = ' VAR ';']);
% $$$
% $$$ if (rr == 1)
% $$$ FlMB = FlM;
% $$$ else
% $$$ FlM = FlM-FlMB;
% $$$ end
% $$$ % Ratio of FlM and FlI (with averaged output:
name = [base model sprintf('_output%03d',outputs(1)) '_' TYPE '_T' strrep(num2str(Tl),'.','p') 'C.mat']
load(name,'FlI','FlM');
% $$$ FlM(isnan(FlM)) = 0;
% $$$ FlI(isnan(FlI)) = 0;
% $$$ nans = FlM==0.0 | FlI==0.0;
% $$$ % $$$ FlM = log10(abs(FlI)./abs(FlM));
% $$$ FlM = abs(FlI)./(abs(FlI)+abs(FlM));
% $$$ FlM(nans) = NaN;
FlM = FlI;
FlM(FlM==0) = NaN;
% $$$ FlMs = FlM;
% $$$ FlMs = FlMs+FlM;
% $$$ % CHECK spatial structure sums to total:
% $$$ % $$$ Tls = [0:2.5:27.5];
% $$$ Tls = [0 5 10 15 20 22.5 25 27.5];
% $$$ SUM = zeros(size(Tls));
% $$$ for ii = 1:length(Tls)
% $$$
% $$$ Tl = Tls(ii)
% $$$ name = [base model sprintf('_output%03d',outputs(1)) '_' TYPE '_T' strrep(num2str(Tl),'.','p') 'C.mat']
% $$$ eval(['load(name,''' VAR ''');']);
% $$$ eval([VAR '(isnan(' VAR ')) = 0.0;']);
% $$$ % $$$ if (length(outputs)==1)
% $$$ % $$$ eval([VAR ' = reshape(' VAR ',[length(' VAR '(:,1,1)) length(' VAR '(1,:,1)) 12 nyrs]);']);
% $$$ % $$$ eval([VAR ' = mean(' VAR '(:,:,:,yrs),4);']);
% $$$ % $$$ else
% $$$ % $$$ eval([VAR 'a = ' VAR ';']);
% $$$ % $$$ for i=2:length(outputs)
% $$$ % $$$ name = [base model sprintf('_output%03d',outputs(i)) '_' TYPE '_T' strrep(num2str(Tl),'.','p') 'C.mat']
% $$$ % $$$ eval(['load(name,''' VAR ''');']);
% $$$ % $$$ eval([VAR '(isnan(' VAR ')) = 0.0;']);
% $$$ % $$$ eval([VAR 'a = ' VAR 'a + ' VAR ';']);
% $$$ % $$$ end
% $$$ % $$$ eval([VAR ' = ' VAR 'a/length(outputs);']);
% $$$ % $$$ eval([VAR '(' VAR '==0) = NaN;']);
% $$$ % $$$ end
% $$$ eval(['FlM = ' VAR ';']);
% $$$ tmp = FlM;
% $$$ tmp(isnan(tmp)) = 0.0;
% $$$ % $$$ Z = monmean(tmp(:,:,months),3,ndays(months));
% $$$ % $$$ Z(Z == 0) = NaN;
% $$$ Z = mean(tmp,3);
% $$$ SUM(ii) = nansum(nansum(area.*Z));
% $$$ end
% $$$ plot(Tls,SUM/1e15,'X','color',cols{rr},'MarkerSize',12,'LineWidth',2);
% $$$ end
% $$$
% $$$ %plot(Tls,SUM/1e15,'Xb','MarkerSize',12,'LineWidth',2);
% $$$
% $$$ %%% Regional time series
% $$$ % $$$
% $$$ %[mask_t,~] = Heat_Budget_Mask('Pacific','','','',base,'MOM025');
% $$$ mask_t = zeros(size(lon));
% $$$ months = [1:12];
% $$$ %Region choice:
% $$$ regions = { ...
% $$$ {'Global',lat==lat,'k'}, ...
% $$$ {'Equatorial',abs(lat)<=10,'k'}, ...
% $$$ {'Eastern Pacific',lat > -10 & lat < 10 & lon > -160 & lon<-70,[0.4667 0.6745 0.1882]}, ...
% $$$ {'Kuroshio', lat < 50 & lat > 10 & lon > -260 & lon < -140,'r'}, ...
% $$$ {'Gulf Stream', lat < 50 & lat > 10 & lon > -100 & lon < 0 & ~mask_t,'b'}, ...
% $$$ {'Indian', lat < 25 & lat > -55 & (lon > 20 | lon < -260),[0.4941 0.1843 0.5569]}, ...
% $$$ {'South Pacific/Atlantic', lat < -10 & lat > -55 & lon > -260 & lon < 20,'g'}, ...
% $$$ };
% $$$ % $$$ {'Eastern Pacific',lat > -20 & lat < 20 & lon > -150 & lon<-60 & mask_t,[0.4667 0.6745 0.1882]}, ...
% $$$ % $$$ {'WWV',abs(lat)<=5 & lon>
% $$$ % $$$ {'NH',lat>10,'k'}, ...
% $$$ % $$$ {'SH',lat<-10,'k'}, ...
% $$$ % $$$ {'Outside Equatorial',lat<-10 | lat > 10,'k'}, ...
% $$$ % $$$ {'Nino 3',lat > -5 & lat < 5 & lon > -150 & lon < -90,'m'}, ...
% $$$ % $$$ {'Eastern Pacific',lat > -10 & lat < 10 & lon > -160 & mask_t,'c'}, ...
% $$$ % $$$ {'Equatorial Atlantic',lat > -10 & lat < 10 & lon > -65 & lon < 20,[0 0.5 0]}, ...
% $$$ % $$$ {'Western Pacific',lat > -10 & lat < 10 & lon > -260 & lon < -160,[0.5 0.5 0.5]}, ...
% $$$ % $$$ {'Gulf Stream', lat < 45 & lat > 10 & lon > -100 & lon < -40 & ~mask_t,'b'}, ...
% $$$
% $$$ Field = zeros(12,length(regions));
% $$$ AREA = zeros(12,length(regions));
% $$$ for i=1:12
% $$$ Mtmp = FlM(:,:,i);
% $$$ Atmp = area;
% $$$ Atmp(isnan(Mtmp)) = NaN;
% $$$
% $$$ for ii=1:length(regions)
% $$$ Field(i,ii) = nansum(nansum(area(regions{ii}{2}).*Mtmp(regions{ii}{2}),1),2);
% $$$ AREA(i,ii) = nansum(nansum(Atmp(regions{ii}{2}),1),2);
% $$$ end
% $$$ end
% $$$
% $$$ %Display output in terminal for table:
% $$$ str = {['Area fractions model ' model ' Temp ' num2str(Tl)] ;
% $$$ ' '};
% $$$ for ii = 1:length(regions)
% $$$ str{ii+2} = sprintf([regions{ii}{1} ' area = %3.2f'], ...
% $$$ monmean(AREA(:,ii),1,ndays(months))/monmean(AREA(:,1),1,ndays(months)));
% $$$ end
% $$$ str
% $$$
% $$$ str = {['Annual totals model ' model ' Temp ' num2str(Tl)] ;
% $$$ ' '};
% $$$ for ii = 1:length(regions)
% $$$ str{ii+2} = [regions{ii}{1} sprintf(' = %3.2fPW (%3.0f)', ...
% $$$ monmean(Field(:,ii),1,ndays(months))/1e15,monmean(Field(:,ii),1,ndays(months))/monmean(Field(:,1),1,ndays(months))*100)];
% $$$ end
% $$$ str
% $$$
% $$$ str = {['SC range model ' model ' Temp ' num2str(Tl)] ;
% $$$ ' '};
% $$$ for ii = 1:length(regions)
% $$$ str{ii+2} = [regions{ii}{1} sprintf(' = %3.2fPW (%3.0f)', ...
% $$$ (max(Field(:,ii))-min(Field(:,ii)))/1e15,(max(Field(:,ii))-min(Field(:,ii)))/(max(Field(:,1))-min(Field(:,1)))*100)];
% $$$ end
% $$$ str
% $$$
% $$$ % $$$ mn1 = 4;mn2 = 7;
% $$$ % $$$ str = {['SC range Apr-Jul model ' model ' Temp ' num2str(Tl)] ;
% $$$ % $$$ ' '};
% $$$ mn1 = 5;mn2 = 8;
% $$$ str = {['SC range May-Aug model ' model ' Temp ' num2str(Tl)] ;
% $$$ ' '};
% $$$ for ii = 1:length(regions)
% $$$ str{ii+2} = [regions{ii}{1} sprintf(' = %3.5fPW (%3.0f)', ...
% $$$ (Field(mn1,ii)-Field(mn2,ii))/1e15,(Field(mn1,ii)-Field(mn2,ii))/(Field(mn1,1)-Field(mn2,1))*100)];
% $$$ end
% $$$ str
% $$$
% $$$
% $$$ % The 1% (within Nino 3) area count of the annual mean:
% $$$ inds = lat > -5 & lat < 5 & lon > -150 & lon < -90;
% $$$ M1p = zeros(length(find(inds)),12);
% $$$ A1p = area(inds);
% $$$ AREAtotal = zeros(12,1);
% $$$ for i=1:12
% $$$ Mtmp = FlM(:,:,i);
% $$$ M1p(:,i) = A1p.*Mtmp(inds);
% $$$ Atmp = area;
% $$$ Atmp(isnan(Mtmp)) = NaN;
% $$$ AREAtotal(i) = nansum(nansum(Atmp,1),2);
% $$$ end
% $$$ M1pA = monmean(M1p,2,ndays(months));
% $$$ [M1pA,I] = sort(M1pA);
% $$$ M1p = M1p(I,:);
% $$$ Atotal = monmean(AREAtotal,1,ndays(months));
% $$$ [tmp ind] = min(abs(cumsum(A1p(I))/Atotal - 0.005));
% $$$ %plot(cumsum(A1p(I))/Atotal*100,cumsum(M1pA)/monmean(MAll,2,ndays(months))*100)
% $$$ M1pA = sum(M1pA(1:ind));
% $$$ M1pSCR = (sum(M1p(1:ind,mn1))-sum(M1p(1:ind,mn2)));
% $$$ MAll = Field(:,1)';
% $$$
% $$$ str = {['1% area within Nino 3 model ' model ' Temp ' num2str(Tl)] ; ...
% $$$ sprintf(' 1p Annual-Mean = %3.2fPW (%3.0f)',M1pA/1e15,M1pA/monmean(MAll,2,ndays(months))*100); ...
% $$$ sprintf(' 1p SC Apr-Jul = %3.2fPW (%3.0f)',M1pSCR/1e15,M1pSCR/((MAll(mn1))-(MAll(mn2)))*100)}
% $$$
% $$$ % $$$ % Average fluxes, isotherm separationl, diffusivity:
% $$$ % $$$ load([base 'MOM025_output002_Ziso_T23C.mat']);
% $$$ % $$$ topiso = ziso;
% $$$ % $$$ load([base 'MOM025_output002_Ziso_T22C.mat']);
% $$$ % $$$ botiso = ziso;
% $$$ % $$$ AvgFlux = MAll./AREAtotal;
% $$$ % $$$ NaNs = isnan(topiso-botiso);
% $$$ % $$$ AvgDZ = zeros(12,1);
% $$$ % $$$ for ii=1:12
% $$$ % $$$ AvgDZ(ii) = nansum(nansum(((topiso(:,:,ii)-botiso(:,:,ii)).*area),1),2)./ ...
% $$$ % $$$ nansum(area(~isnan((topiso(:,:,ii)-botiso(:,:,ii)))));
% $$$ % $$$ end
% $$$ % $$$ AvgDiff = monmean(AvgFlux/rho0/Cp.*AvgDZ',2,ndays(months))
% $$$ % $$$ AvgFlux = monmean(AvgFlux,2,ndays(months))
% $$$ % $$$ AvgDZ = monmean(AvgDZ',2,ndays(months))
% $$$ % $$$ sprintf('Average flux across the isotherm = %3.1f Wm-2',AvgFlux)
% $$$ % $$$ sprintf('Average diffusivity = %3.5f m2s-1',AvgDiff)
% $$$ % $$$
% $$$ figure;
% $$$ set(gcf,'Position',get(0,'ScreenSize'));
% $$$ set(gcf,'defaultlinelinewidth',2);
% $$$
% $$$ legstr = {};
% $$$ for ii=1:length(regions)
% $$$ plot(1:12,Field(:,ii)/1e15,'-','color',regions{ii}{3},'LineWidth',2);
% $$$ hold on;
% $$$ legstr{ii} = regions{ii}{1};
% $$$ end
% $$$ % $$$ plot(1:12,MEEP/1e15,'--r','LineWidth',4);
% $$$ % $$$ hold on;
% $$$ % $$$ plot(1:12,MEq/1e15,'--k','LineWidth',4);
% $$$ % $$$ plot(1:12,MN/1e15,':b','LineWidth',4);
% $$$ % $$$ plot(1:12,MS/1e15,':','color',[0 0.5 0],'LineWidth',4);
% $$$ % $$$ plot(1:12,(MN+MS)/1e15,':k','LineWidth',4);
% $$$ % $$$ plot(1:12,MAll/1e15,'-k','LineWidth',4);
% $$$ xlabel('Month');
% $$$ ylabel(['PW']);
% $$$ % $$$ title(['Vertical mixing heat flux through $' num2str(Tl) '^\circ$C isotherm']);
% $$$ title(['Implicit mixing heat flux through $' num2str(Tl) '^\circ$C isotherm']);
% $$$
% $$$ leg = legend(legstr);
% $$$ % $$$ 'Eastern Equatorial Pacific', ...
% $$$ % $$$ 'Equatorial',['Northern Hemisphere $>' num2str(LATsplit) '^\circ$N'], ...
% $$$ % $$$ ['Southern Hemisphere $<' num2str(LATsplit) '^\circ$S'], ...
% $$$ % $$$ ['Outside Equatorial $>\pm' num2str(LATsplit) '^\circ$'], ...
% $$$ % $$$ ['Total']);
% $$$ set(leg,'Position',[0.1481 0.2656 0.2314 0.2340]);
% $$$ ylim([-0.8 0]);
% $$$ xlim([1 12]);
% $$$ set(gca,'xticklabel',{'Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'});
% $$$ set(gca,'xtick',[1:12]);
% $$$ set(gca,'Position',[0.1300 0.2451 0.5949 0.6799]);
% $$$ set(gca,'FontSize',25);
% $$$ grid on;
% $$$ % $$$
% $$$ % $$$ axes('Position',[0.74 0.2451 0.13 0.6799]);
% $$$ % $$$ plot([0 1],[min(MAll/1e15) min(MAll/1e15)],'-k','linewidth',2);
% $$$ % $$$ hold on;
% $$$ % $$$ plot([0 1],[max(MAll/1e15) max(MAll/1e15)],'-k','linewidth',2);
% $$$ % $$$ text(0.5,mean([min(MAll/1e15) max(MAll/1e15)]),sprintf('%3.2f ',max(MAll/1e15)-min(MAll/1e15)),'VerticalAlignment','middle','HorizontalAlignment','Center','Rotation',270,'FontSize',25);
% $$$ % $$$ plot([1 2],[min(MEq/1e15) min(MEq/1e15)],'--k','linewidth',2);
% $$$ % $$$ plot([1 2],[max(MEq/1e15) max(MEq/1e15)],'--k','linewidth',2);
% $$$ % $$$ text(1.5,mean([min(MEq/1e15) max(MEq/1e15)]),sprintf('%3.2f ',max(MEq/1e15)-min(MEq/1e15)),'VerticalAlignment','middle','HorizontalAlignment','Center','Rotation',270,'FontSize',25);
% $$$ % $$$ plot([2 3],[min(MEEP/1e15) min(MEEP/1e15)],'--r','linewidth',2);
% $$$ % $$$ plot([2 3],[max(MEEP/1e15) max(MEEP/1e15)],'--r','linewidth',2);
% $$$ % $$$ text(2.5,mean([min(MEEP/1e15) max(MEEP/1e15)]),sprintf('%3.2f ',max(MEEP/1e15)-min(MEEP/1e15)),'VerticalAlignment','middle','HorizontalAlignment','Center','Rotation',270,'color','r','FontSize',25);
% $$$ % $$$ plot([3 4],[min(MN/1e15) min(MN/1e15)],':b','linewidth',2);
% $$$ % $$$ plot([3 4],[max(MN/1e15) max(MN/1e15)],':b','linewidth',2);
% $$$ % $$$ text(3.5,mean([min(MN/1e15) max(MN/1e15)]),sprintf('%3.2f ',max(MN/1e15)-min(MN/1e15)),'VerticalAlignment','middle','HorizontalAlignment','Center','Rotation',270,'color','b','FontSize',25);
% $$$ % $$$ plot([4 5],[min(MS/1e15) min(MS/1e15)],':','linewidth',2,'color',[0 ...
% $$$ % $$$ 0.5 0]);
% $$$ % $$$ plot([4 5],[max(MS/1e15) max(MS/1e15)],':','linewidth',2,'color',[0 ...
% $$$ % $$$ 0.5 0]);
% $$$ % $$$ text(4.5,mean([min(MS/1e15) max(MS/1e15)]),sprintf('%3.2f ',max(MS/1e15)- min(MS/1e15)),'VerticalAlignment','middle','HorizontalAlignment','Center','Rotation',270,'color',[0 0.5 0],'FontSize',25);
% $$$ % $$$ plot([5 6],[min((MN+MS)/1e15) min((MN+MS)/1e15)],':k','linewidth',2);
% $$$ % $$$ plot([5 6],[max((MN+MS)/1e15) max((MN+MS)/1e15)],':k','linewidth',2);
% $$$ % $$$ text(5.5,mean([min((MN+MS)/1e15) max((MN+MS)/1e15)]),sprintf('%3.2f ',max((MN+MS)/1e15)-min((MN+MS)/1e15)),'VerticalAlignment','middle','HorizontalAlignment','Center','Rotation',270,'FontSize',25);
% $$$ % $$$ xlim([0 6]);
% $$$ % $$$ ylim([-0.8 0]);
% $$$ % $$$ set(gca,'xtick',[]);
% $$$ % $$$ set(gca,'ytick',[]);
% $$$ % $$$ box off;
% $$$ % $$$ grid off;
% $$$ % $$$ axis off;
% $$$ % $$$ title('Seasonal Range','FontSize',25);
%%% Plot spatial pattern:
% $$$ FlM = ncread('/srv/ccrc/data03/z3500785/mom/ncdata/out111-121_temp_numdiff_heat_on_nrho.ncra.nc','temp_numdiff_heat_on_nrho');
% $$$ [tmp ind] = min(abs(Te-14));
% $$$ FlM = FlM(:,:,ind);
try
obj = matfile([base model sprintf('_output%03d_SurfaceVars.mat',outputs(1))]);
LAND = obj.SST(:,:,1);
catch
LAND = zeros(size(FlM(:,:,1)));
end
%If MOM01, fix NaN's in grid:
if (strfind(model,'01'))
lon = repmat(lon(:,500),[1 yL]);
latv = nanmean(lat,1);
lat = repmat(latv,[xL 1]);
end
[xL,yL] = size(lon);
xvec = 1:1:xL;
yvec = 1:1:yL;
% $$$ xvec = 720:1:1060; %-100 -> -30
% $$$ yvec = 540:1:730; % +15 -> +50
xvec = 720:1:1320; %-100 -> +50
yvec = 540:1:940; % +15 -> +75
% $$$ xvec = 880:1:1280; %-60 -> +40
% $$$ yvec = 266:1:396; % -50 -> -25
txtmonth = {'Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'};
% $$$ months = {[1:12], ...
% $$$ [3], ...
% $$$ [7], ...
% $$$ [11]};
% $$$ labels = {'(a) Annual', ...
% $$$ '(b) March', ...
% $$$ '(c) July', ...
% $$$ '(d) November'};
% $$$ months = {[1:12]};
% $$$ labels = {'(b) $\overline{u''u''}+\overline{v''v''}$'};%10$^\circ$C'};%$15^\circ$C'};
% $$$ labels = {'(d) $|\Delta_x T|^2+|\Delta_y T|^2$'};%\overline{u''u''}+\overline{v''v''}$'};%10$^\circ$C'};%$15^\circ$C'};
% $$$ labels = {'(f) $|\Delta_z T|^2$'};%\overline{u''u''}+\overline{v''v''}$'};%10$^\circ$C'};%$15^\circ$C'};
% $$$ %Colormap and continents:
sp = 5;
clim = [-100 0];
sp = 5;
clim = [-100 100];
% $$$ sp = 0.25;
% $$$ clim = [-2 2];
% $$$ sp = 0.05;
% $$$ clim = [0 1];
% $$$ sp = 1;
% $$$ clim = [-30 30];
% $$$ clim = [-50 50];
% $$$ sp = 2;
cCH = 0; % 0 = symmetric redblue
% 1 = negative definite parula
% 2 = negative parula with +ve's possible
if (cCH==0)
cpts = [-1e10 clim(1):sp:clim(2) 1e10];
npts = length(cpts);
cmap = redblue(npts-3);
for i=1:(npts-3)
if (cmap(i,:) == 1.0)
cmap(i,:) = [0.94 0.94 0.94];
end
end
elseif (cCH>=1)
cpts = [-1e10 clim(1):sp:clim(2) 1e10];
npts = length(cpts);
cmap = parula(npts-3);
cmap(end,:) = [0.97 0.97 0.8];
cmap(end-1,:) = (cmap(end-1,:)+cmap(end,:))/2;
end
if (cCH == 2)
buf = 2;
clim = [clim(1) buf*sp];
cpts = [-1e10 clim(1):sp:clim(2) 1e10];
cmap(end+1,:) = cmap(end,:); % 1st positive bin
cmap(end+buf-1,:) = [1 0.7 0.9]; % last pink bin
for ii = 1:(buf-2)
cmap(end-buf+1+ii,:) = cmap(end,:)*ii/(buf-1) + ...
cmap(end-buf+1,:)*(buf-1-ii)/(buf-1);
end
end
% $$$ cmap = flipud(cmap);
tmp = LAND;
tmp(isnan(LAND)) = clim(1)-sp/2;
tmp(~isnan(LAND)) = NaN;
LAND = tmp;
cmap(2:(end+1),:) = cmap;
cmap(1,:) = [0 0 0];
climn = [clim(1)-sp clim(2)];
% $$$ % $$$ %Mean of all months:
% $$$ % $$$ cla;
% $$$ figure;
% $$$ % $$$ set(gcf,'Position',[3 59 1916 914]);
% $$$ % $$$ set(gcf,'Position',[88 371 1625 603]);
% $$$ set(gcf,'Position',[3 59 1476 921]); % Production NumMix first fig.
% $$$ set(gcf,'Position',get(0,'ScreenSize'));
% $$$ set(gcf,'Position',[3 40 998 963]); % MOM025-kb0 Kback diff figure.
% $$$ set(gcf,'Position',[40 83 990 897]); % North Atlantic zoom.
% $$$ % $$$ set(gcf,'Position',[3 40 956 963]);
set(gcf,'defaulttextfontsize',20);
set(gcf,'defaultaxesfontsize',20);
% $$$ % $$$
% 2x1:
poss = [0.1300 0.54 0.7403 0.4149; ...
0.1300 0.0876 0.7403 0.4149];
% $$$ % 1+3:
% $$$ poss = [0.1300 0.4553 0.7693 0.4697; ...
% $$$ 0.1300 0.1389 0.2343 0.2680; ...
% $$$ 0.3951 0.1389 0.2343 0.2680; ...
% $$$ 0.6681 0.1389 0.2343 0.2680];
% $$$ for i=1:length(months)
% $$$ if (i == 1)
% $$$ subplot(5,3,[1 9]);
% $$$ else
% $$$ subplot(5,3,[10 13]+(i-2));
% $$$ end
i = 1;
subplot(2,1,iii);
% $$$ subplot(3,2,2*(rr-1)+iii);
X = lon(xvec,yvec);
Y = lat(xvec,yvec);
if (length(months{i})>1)
tmp = FlM;
tmp(isnan(tmp)) = 0.0;
Z = monmean(tmp(:,:,months{i}),3,ndays(months{i}));
Z(Z == 0) = NaN;
else
Z = FlM(:,:,months{i});
end
A = area(xvec,yvec);
Z = Z(xvec,yvec);
cfac = 1;
[Xs,Ys,Zs,As] = conservative_coarsen_grid(X,Y,Z,A,cfac);
Zs(Zs<clim(1)) = clim(1);
% $$$ contourf(Xs,Ys,Zs,cpts,'linestyle','none');
pcolPlot(Xs,Ys,Zs);
hold on;
contourf(X,Y,LAND(xvec,yvec),[clim(1)-sp clim(1)],'linestyle','none');
caxis(climn);
% $$$ if (iii==2)
cb = colorbar;
if (strcmp(TYPE,'VertInt'))
ylabel(cb,'$\mathcal{I}$ (Wm$^{-2}$)');
% $$$ ylabel(cb,'$\log_{10}(\mathcal{I}/\mathcal{M})$');
% $$$ ylabel(cb,'$|\mathcal{I}|/(|\mathcal{I}|+|\mathcal{M}|)$');
else
ylabel(cb,'ms$^{-1}$');
ylabel(cb,'m$^2$s$^{-2}$');%$^\circ$C$^{2}$');
end
ylim(cb,clim);
% $$$ end
%;% $$$ end
hold on;
% $$$ % Plot regions:
% $$$ if (i==1)
% $$$ if (exist('regions'))
% $$$ xlims = get(gca,'xlim');
% $$$ for ii=2:length(regions)
% $$$ contour(lon,lat,regions{ii}{2},[0.5 0.5],'--','color',regions{ii}{3},'linewidth',1);
% $$$ % Add text label with total:
% $$$ flon = min(lon(regions{ii}{2}));
% $$$ flat = nanmean(lat(regions{ii}{2}));
% $$$ if (ii==2)
% $$$ flat = 7;
% $$$ elseif (flat>15)
% $$$ flat = 42;
% $$$ end
% $$$ text(flon,flat,sprintf('%3.2fPW (%3.0f%%)',monmean(Field(:,ii),1,ndays(1:12))/1e15,monmean(Field(:,ii),1,ndays(1:12))/monmean(Field(:,1),1,ndays(1:12))*100),'color',regions{ii}{3},'Interpreter','none','BackgroundColor','w','Margin',0.01);
% $$$ end
% $$$ end
% $$$ end
% $$$ if (rr == 3)
xlabel('Longitude ($^\circ$E)');
% $$$ else
% $$$ set(gca,'xticklabel',[]);
% $$$ end
% $$$ if (iii == 1)
ylabel('Latitude ($^\circ$N)');
% $$$ else
% $$$ set(gca,'yticklabel',[]);
% $$$ end
% $$$ set(gca,'xtick',[-270:30:60]);
% $$$ set(gca,'ytick',[-75:15:75]);
% $$$ poss = [0.0886 0.68 0.345 0.27; ...
% $$$ 0.4503 0.68 0.345 0.27; ...
% $$$ 0.0886 0.38 0.345 0.27; ...
% $$$ 0.4503 0.38 0.345 0.27; ...
% $$$ 0.0886 0.08 0.345 0.27; ...
% $$$ 0.4503 0.08 0.345 0.27];
% $$$ set(gca,'Position',[poss(2*(rr-1)+iii,:)]);
% $$$ ylim([-65 75]);
% $$$ text(-277,70,labels{2*(rr-1)+iii},'BackgroundColor','w','Margin',0.5,'FontSize',20);
% $$$ text(-277,72,labels{2*(rr-1)+iii},'BackgroundColor','w','Margin',0.5,'FontSize',12);
% $$$ ylim([-60 80]);
% $$$ text(-277,70,labels{iii},'BackgroundColor','w','Margin',0.5,'FontSize',20);
% $$$ ylim([-50 70]);
% $$$ set(gca,'FontSize',17);
colormap(cmap);
% $$$ ylim([-45 45]);
% $$$ text(-278,41,labels{2},'BackgroundColor','w','Margin',0.5,'FontSize',15);
set(gca,'xtick',[-270:20:60]);
set(gca,'ytick',[-75:10:75]);
ylim([15 47]);
xlim([-95 -15]);
text(-93,45,labels{iii},'BackgroundColor','w','Margin',0.5,'FontSize',20);
% $$$ text(-93,45,'(a) $14^\circ$C','BackgroundColor','w','Margin',0.5,'FontSize',15);
% $$$ % $$$ % North Atlantic two panel:
if (iii == 1)
set(gca,'Position',[0.1253 0.5318 0.6707 0.4326]);
set(gca,'xticklabel',[]);
xlabel('');
else
set(gca,'Position',[0.1253 0.08 0.6707 0.4326]);
end
end
end
% $$$ %% Variances:
% $$$ Tl = 22.5;
% $$$
% $$$ VARS = {'EKE','wvar','Tdxsq','Tdysq','Tdzsq'};
% $$$ TYPE = 'variances';
% $$$ name = [base model sprintf('_output%03d',outputs(1)) '_' TYPE '_T' strrep(num2str(Tl),'.','p') 'C.mat']
% $$$ for ii=1:length(VARS)
% $$$ VAR = VARS{ii}
% $$$ eval(['load(name,''' VAR ''');']);
% $$$ eval([VAR '(isnan(' VAR ')) = 0.0;']);
% $$$ if (length(outputs)==1)
% $$$ eval([VAR ' = reshape(' VAR ',[length(' VAR '(:,1,1)) length(' VAR '(1,:,1)) 12 nyrs]);']);
% $$$ else
% $$$ eval([VAR 'a = ' VAR ';']);
% $$$ for i=2:length(outputs)
% $$$ name = [base model sprintf('_output%03d',outputs(i)) '_' TYPE '_T' strrep(num2str(Tl),'.','p') 'C.mat']
% $$$ eval(['load(name,''' VAR ''');']);
% $$$ eval([VAR '(isnan(' VAR ')) = 0.0;']);
% $$$ eval([VAR 'a = ' VAR 'a + ' VAR ';']);
% $$$ end
% $$$ eval([VAR ' = ' VAR 'a/length(outputs);']);
% $$$ end
% $$$ eval([VAR ' = monmean(' VAR ',3,ndays);']);
% $$$ eval([VAR '(' VAR '==0) = NaN;']);
% $$$ end
% $$$ Tdhsq = Tdxsq+Tdysq;
% $$$
% $$$ VARS = {'EKE','wvar','Tdhsq','Tdzsq'};
% $$$ names = {'$\overline{u''u''}+\overline{v''v''}$',['$\' ...
% $$$ 'overline{w''w''}$'],'$|\Delta_x T|^2 + |\Delta_y T|^2$','$|\Delta_z T|^2$'};
% $$$ units = {'$m^2s^{-1}$','$m^2s^{-1}$','$^\circ C^2$','$^\circ C^2$'};
% $$$
% $$$ %%% Plot spatial pattern:
% $$$
% $$$ try
% $$$ obj = matfile([base model sprintf('_output%03d_SurfaceVars.mat',outputs(1))]);
% $$$ LAND = obj.SST(:,:,1);
% $$$ catch
% $$$ LAND = zeros(size(FlM(:,:,1)));
% $$$ end
% $$$
% $$$ [xL,yL] = size(lon);
% $$$ xvec = 1:1:xL;
% $$$ yvec = 1:1:yL;
% $$$
% $$$ %Colormaps:
% $$$ clims = {[0 0.08],[0 0.3e-7],[0 4e-9],[0 20]};
% $$$ % $$$ clims = {[0 0.02],[0 0.3e-7],[0 4e-9],[0 20]};
% $$$ nlv = 25;
% $$$
% $$$
% $$$ cmapbase = parula(nlv-3);
% $$$ cmapbase(end,:) = [0.97 0.97 0.8];
% $$$ cmapbase(end-1,:) = (cmapbase(end-1,:)+cmapbase(end,:))/2;
% $$$ cmapbase = flipud(cmapbase);
% $$$ for i=1:4
% $$$ sp = (clims{i}(2)-clims{i}(1))/(nlv-3);
% $$$ cpts{i} = [-1e10 clims{i}(1):sp:clims{i}(2) 1e10];
% $$$ cmap{i} = cmapbase;
% $$$ LANDmask{i} = LAND;
% $$$ LANDmask{i}(isnan(LAND)) = clims{i}(1)-sp/2;
% $$$ LANDmask{i}(~isnan(LAND)) = NaN;
% $$$ cmap{i}(2:(end+1),:) = cmapbase;
% $$$ cmap{i}(1,:) = [0 0 0];
% $$$ climns{i} = [clims{i}(1)-sp clims{i}(2)];
% $$$ end
% $$$
% $$$ %Mean of all months:
% $$$ figure;
% $$$ set(gcf,'Position',[1921 1 1920 1005]);
% $$$ set(gcf,'defaulttextfontsize',15);
% $$$ set(gcf,'defaultaxesfontsize',15);
% $$$
% $$$ poss = [0.1300 0.58 0.3548 0.3692; ...
% $$$ 0.5700 0.58 0.3548 0.3692; ...
% $$$ 0.1300 0.1100 0.3548 0.3692; ...
% $$$ 0.5700 0.1100 0.3548 0.3692];
% $$$
% $$$ for i=1:4
% $$$ subplot(2,2,i);
% $$$ X = lon(xvec,yvec);
% $$$ Y = lat(xvec,yvec);
% $$$ eval(['Z = ' VARS{i} '(xvec,yvec);']);
% $$$ Z(Z<clims{i}(1)) = clims{i}(1);
% $$$ contourf(X,Y,Z,cpts{i},'linestyle','none');
% $$$ hold on;
% $$$ contourf(X,Y,LANDmask{i}(xvec,yvec),climns{i},'linestyle','none');
% $$$ caxis(climns{i});
% $$$ cb = colorbar;
% $$$ ylabel(cb,units{i});
% $$$ ylim(cb,clims{i});
% $$$ if (i>=3)
% $$$ xlabel('Longitude ($^\circ$E)');
% $$$ end
% $$$ if (i==1 | i == 3)
% $$$ ylabel('Latitude ($^\circ$N)');
% $$$ end
% $$$ set(gca,'xtick',[-270:30:60]);
% $$$ set(gca,'ytick',[-75:15:75]);
% $$$ ylim([-60 75]);
% $$$ colormap(gca,cmap{i});
% $$$ title([names{i} ' on ' num2str(Tl) '$^\circ$C isotherm']);
% $$$ set(gca,'Position',poss(i,:));
% $$$ end
% $$$ %%% Plot isotherm spacing:
% $$$ Tlm = 22;
% $$$ Tlp = 23;
% $$$ name = [base model sprintf('_output%03d',outputs(1)) '_Ziso_T' strrep(num2str(Tlp),'.','p') 'C.mat'];
% $$$ load(name);
% $$$ Zp = ziso;
% $$$ name = [base model sprintf('_output%03d',outputs(1)) '_Ziso_T' strrep(num2str(Tlm),'.','p') 'C.mat'];
% $$$ load(name);
% $$$ Zm = ziso;
% $$$
% $$$ try
% $$$ obj = matfile([base model sprintf('_output%03d_SurfaceVars.mat',outputs(1))]);
% $$$ LAND = obj.SST(:,:,1);
% $$$ catch
% $$$ LAND = zeros(size(FlM(:,:,1)));
% $$$ end
% $$$
% $$$ months = {[1:12]}; labels = {'Annual'};
% $$$
% $$$ %Colormap and continents:
% $$$ sp = 0.5;
% $$$ clim = [0 50];
% $$$
% $$$ cpts = [-1e10 clim(1):sp:clim(2) 1e10];
% $$$ npts = length(cpts);
% $$$ cmap = parula(npts-3);
% $$$ cmap(end,:) = [0.97 0.97 0.8];
% $$$ cmap(end-1,:) = (cmap(end-1,:)+cmap(end,:))/2;
% $$$
% $$$ tmp = LAND;
% $$$ tmp(isnan(LAND)) = clim(1)-sp/2;
% $$$ tmp(~isnan(LAND)) = NaN;
% $$$ LAND = tmp;
% $$$ cmap(2:(end+1),:) = cmap;
% $$$ cmap(1,:) = [0 0 0];
% $$$
% $$$ climn = [clim(1)-sp clim(2)];
% $$$
% $$$ % $$$ Zp(isnan(Zp)) == 0;
% $$$ Zdel = Zm-Zp;
% $$$ % $$$ figure;
% $$$ Z = nanmonmean(Zdel,3,ndays);
% $$$ Z(Z==0) = NaN;
% $$$ Z(Z<clim(1)) = clim(1);
% $$$ contourf(lon,lat,Z,cpts,'linestyle','none');
% $$$ hold on;
% $$$ contourf(lon,lat,LAND,[clim(1)-sp clim(1)],'linestyle','none');
% $$$ caxis(climn);
% $$$ cb = colorbar;
% $$$ ylabel(cb,'m');
% $$$ ylim(cb,clim);
% $$$ xlabel('Longitude ($^\circ$E)');
% $$$ ylabel('Latitude ($^\circ$N)');
% $$$ set(gca,'xtick',[-270:30:60]);
% $$$ set(gca,'ytick',[-75:15:75]);
% $$$ % $$$ set(gca,'Position',[poss(i,:)]);
% $$$ ylim([-45 45]);
% $$$ set(gca,'FontSize',15);
% $$$ colormap(cmap);
% $$$ title('MOM025 kb3seg $22^\circ$C and $23^\circ$C Isotherm Spacing');
% $$$
% $$$ %%% Plot spatial pattern of net heat flux and SST:
% $$$
% $$$ % Load Variable and calculate mean:
% $$$ load([base model sprintf('_output%03d_SurfaceVars.mat',outputs(1))]);
% $$$ shfluxa = shflux;
% $$$ SSTa = SST;
% $$$ for i=2:length(outputs)
% $$$ load([base model sprintf('_output%03d_SurfaceVars.mat',outputs(i))]);
% $$$ shfluxa = shfluxa+shflux;
% $$$ SSTa = SSTa+SST;
% $$$ end
% $$$ shflux = shfluxa/length(outputs);
% $$$ SST = SSTa/length(outputs);
% $$$
% $$$ % diff two runs:
% $$$ SSTsave = monmean(SST,3,ndays);
% $$$ shfluxsave = monmean(shflux,3,ndays);
% $$$ % $$$ SST = monmean(SST,3,ndays)-SSTsave;
% $$$ % $$$ shflux = monmean(shflux,3,ndays)-shfluxsave;
% $$$ SST = SST-SSTsave;
% $$$ shflux = shflux-shfluxsave;
% $$$
% $$$ % Diff flux components:
% $$$ ffname = '/srv/ccrc/data03/z3500785/mom/ice_month.ncra0.39.diff.nc';
% $$$ LH = -ncread(ffname,'LH');
% $$$ SH = -ncread(ffname,'SH');
% $$$ LW = ncread(ffname,'LW');
% $$$ SW = -ncread(ffname,'SW');
% $$$ xvec = 1:2:xL;
% $$$ yvec = 1:2:yL;
% $$$ subplot(2,2,1);
% $$$ contourf(lon(xvec,yvec),lat(xvec,yvec),LH(xvec,yvec),[-200 -20:1:20 200],'linestyle','none');
% $$$ xlabel('Longitude ($^\circ$E)');
% $$$ ylabel('Latitude ($^\circ$N)');
% $$$ colorbar;
% $$$ caxis([-20 20]);
% $$$ title('Latent Heat Flux Anomaly (Wm$^{-2}$)');
% $$$ set(gca,'color','k');
% $$$ subplot(2,2,2);
% $$$ contourf(lon(xvec,yvec),lat(xvec,yvec),SH(xvec,yvec),[-200 -20:1:20 200],'linestyle','none');
% $$$ xlabel('Longitude ($^\circ$E)');
% $$$ ylabel('Latitude ($^\circ$N)');
% $$$ colorbar;
% $$$ caxis([-20 20]);
% $$$ title('Sensible Heat Flux Anomaly (Wm$^{-2}$)');
% $$$ set(gca,'color','k');
% $$$ subplot(2,2,3);
% $$$ contourf(lon(xvec,yvec),lat(xvec,yvec),LW(xvec,yvec),[-200 -20:1:20 200],'linestyle','none');
% $$$ xlabel('Longitude ($^\circ$E)');
% $$$ ylabel('Latitude ($^\circ$N)');
% $$$ colorbar;
% $$$ caxis([-20 20]);
% $$$ title('Long-wave Heat Flux Anomaly (Wm$^{-2}$)');
% $$$ set(gca,'color','k');
% $$$ subplot(2,2,4);
% $$$ contourf(lon(xvec,yvec),lat(xvec,yvec),LW(xvec,yvec)+LH(xvec,yvec)+SH(xvec,yvec),[-200 -20:1:20 200],'linestyle','none');
% $$$ xlabel('Longitude ($^\circ$E)');
% $$$ ylabel('Latitude ($^\circ$N)');
% $$$ colorbar;
% $$$ caxis([-20 20]);
% $$$ title('Sum (Wm$^{-2}$)');
% $$$ set(gca,'color','k');
% $$$ colormap(redblue)
% $$$
% $$$ %If MOM01, fix NaN's in grid:
% $$$ if (strfind(model,'01'))
% $$$ lon = repmat(lon(:,500),[1 yL]);
% $$$ latv = nanmean(lat,1);
% $$$ lat = repmat(latv,[xL 1]);
% $$$ end
% $$$
% $$$ shflux20p = shflux;
% $$$ shflux20p(SST<20) = 0;
% $$$ shflux1520 = shflux;
% $$$ shflux1520(SST<15 & SST>20) = 0;
% $$$ shflux15m = shflux;
% $$$ shflux15m(SST>15) = 0;
% $$$
% $$$ %Sum of all positives:
% $$$ shfluxA = mean(shflux,3);
% $$$ shfluxA1520 = mean(shflux1520,3);
% $$$ shfluxA20p = mean(shflux20p,3);
% $$$ shfluxA15m = mean(shflux20m,3);
% $$$ shfluxP = nansum(shfluxA(shfluxA>0).*area(shfluxA>0))/1e15
% $$$ shfluxM = nansum(shfluxA(shfluxA<0).*area(shfluxA<0))/1e15
% $$$
% $$$ %Sum of mean flux above mean isotherm:
% $$$ SSTA = mean(SST,3);
% $$$ isot = 21.5;
% $$$ shfluxP = nansum(shfluxA(SSTA>=isot).*area(SSTA>=isot))/1e15
% $$$ shfluxM = nansum(shfluxA(SSTA<isot).*area(SSTA<isot))/1e15
% $$$
% $$$ [xL,yL] = size(lon);
% $$$ xvec = 1:3:xL;
% $$$ yvec = 1:3:yL;
% $$$ txtmonth = {'Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'};
% $$$
% $$$ months = {[1:12], ...
% $$$ [3], ...
% $$$ [7], ...
% $$$ [11]};
% $$$
% $$$ labels = {'(a) Annual', ...
% $$$ '(b) March', ...
% $$$ '(c) July', ...
% $$$ '(d) November'};
% $$$
% $$$ clim = [-200 200];
% $$$ sp = 20;
% $$$
% $$$ cpts = [-1e10 clim(1):sp:clim(2) 1e10];
% $$$ npts = length(cpts)
% $$$ cmap = redblue(npts-3);
% $$$
% $$$ figure;
% $$$ set(gcf,'Position',[3 59 1916 914]);
% $$$ set(0,'defaulttextfontsize',20);
% $$$ set(0,'defaultaxesfontsize',20);
% $$$ poss = [0.1300 0.415 0.7693 0.56; ...
% $$$ 0.1300 0.1 0.2343 0.2680; ...
% $$$ 0.3951 0.1 0.2343 0.2680; ...
% $$$ 0.6681 0.1 0.2343 0.2680];
% $$$ for i=1:length(months)
% $$$ if (i == 1)
% $$$ subplot(5,3,[1 9]);
% $$$ else
% $$$ subplot(5,3,[10 13]+(i-2));
% $$$ end
% $$$ X = lon(xvec,yvec);
% $$$ Y = lat(xvec,yvec);
% $$$ Z = monmean(shflux(:,:,months{i}),3,ndays(months{i}));
% $$$ Z2 = monmean(SST(:,:,months{i}),3,ndays(months{i}));
% $$$ Z = Z(xvec,yvec);
% $$$ Z2 = Z2(xvec,yvec);
% $$$ Z(Z==0) = NaN;
% $$$
% $$$ % Map projection:
% $$$ % $$$ map = 1;
% $$$ % $$$ ax = worldmap('World');
% $$$ % $$$ setm(ax, 'Origin', [0 260 0])
% $$$ % $$$ contourfm(Y,X,Z,[-1e10 -500:20:500 1e10],'linestyle','none');
% $$$ contourf(X,Y,Z,cpts,'linestyle','none');
% $$$ hold on;
% $$$ % $$$ [c,h] = contourm(Y,X,Z2,[-3:2:21 25:2:35],'-k');
% $$$ [c,h] = contour(X,Y,Z2,[-3:2:35],'-k');
% $$$ clabel(c,h);
% $$$ % $$$ hand = clabelm(c,h);
% $$$ % $$$ set(hand,'FontSize',10,'BackgroundColor','none');
% $$$ if (i == 1)
% $$$ [c,h] = contour(X,Y,Z2,[21.5 21.5],'-k','linewidth',2);
% $$$ clabel(c,h)
% $$$ % $$$ [c,h] = contourm(Y,X,Z2,[23 23],'-k','linewidth',2);
% $$$ % $$$ hand = clabelm(c,h);
% $$$ % $$$ set(hand,'FontSize',10,'BackgroundColor','none');
% $$$ end
% $$$ caxis(clim);
% $$$ if (i==1)
% $$$ cb = colorbar;
% $$$ ylabel(cb,'Wm$^{-2}$');
% $$$ end
% $$$ ylim([-75 75]);
% $$$ if (i>1)
% $$$ xlabel('Longitude ($^\circ$E)');
% $$$ end
% $$$ if (i<=2)
% $$$ ylabel('Latitude ($^\circ$N)');
% $$$ end
% $$$ if (i>1)
% $$$ % $$$ text(-277,53,labels{i},'BackgroundColor','w');
% $$$ text(-277,64,labels{i},'BackgroundColor','w','margin',0.01);
% $$$ set(gca,'xtick',[-240:60:60]);
% $$$ else
% $$$ % $$$ text(-279,55,labels{i},'BackgroundColor','w');
% $$$ text(-279,69,labels{i},'BackgroundColor','w','margin',0.01);
% $$$ set(gca,'xtick',[-270:30:60]);
% $$$ end
% $$$ set(gca,'ytick',[-60:30:60]);
% $$$ set(gca,'Position',[poss(i,:)]);
% $$$ set(gca,'color','k');
% $$$
% $$$ % $$$ setm(gca, 'MlabelParallel', 'south');
% $$$ % $$$ setm(gca, 'MlabelParallel', 'south');
% $$$ % $$$ land = shaperead('landareas.shp', 'UseGeoCoords', true);
% $$$ % $$$ geoshow(land, 'FaceColor', [0 0 0])
% $$$ end
% $$$ colormap(cmap);
% $$$
% $$$
% $$$ %% Plot SST difference and shflux difference plots:
% $$$
% $$$ % $$$ % Load MOM01 data:
% $$$ % $$$ base = '/srv/ccrc/data03/z3500785/MOM_HeatDiag/';
% $$$ % $$$ model = 'MOM01';
% $$$ % $$$ outputs = [111 222];
% $$$ % $$$ load([base model sprintf('_output%03d_BaseVars.mat',outputs(1))]);
% $$$ % $$$ ndays = diff(time_snap);
% $$$ % $$$
% $$$ % $$$ [xL,yL] = size(lon)
% $$$ % $$$ %If MOM01, fix NaN's in grid:
% $$$ % $$$ if (strfind(model,'01'))
% $$$ % $$$ lonv = lon(:,500);
% $$$ % $$$ latv = nanmean(lat,1);
% $$$ % $$$ [tmp ind] = min(abs(latv - 60))
% $$$ % $$$ [lon,lat] = ndgrid(lonv,latv(1:ind));
% $$$ % $$$ end
% $$$ % $$$ MOM01lon = lon;
% $$$ % $$$ MOM01lat = lat;
% $$$ % $$$
% $$$ % $$$ load([base model sprintf('_output%03d_SurfaceVars.mat', ...
% $$$ % $$$ outputs(1))]);
% $$$ % $$$ MOM01shflux = shflux(:,1:ind,:);
% $$$ % $$$ MOM01SST = SST(:,1:ind,:);
% $$$
% $$$ % $$$ base = '/srv/ccrc/data03/z3500785/MOM_HeatDiag_kb3seg/mat_data/';
% $$$ % $$$ model = 'MOM025_kb3seg';
% $$$ % $$$ outputs = [75:79];
% $$$ % $$$
% $$$ % $$$ base = '/srv/ccrc/data03/z3500785/MOM_HeatDiag_kb1em5/mat_data/';
% $$$ % $$$ model = 'MOM025_kb1em5';
% $$$ % $$$ outputs = 94;
% $$$ % $$$
% $$$ % $$$ base = '/srv/ccrc/data03/z3500785/MOM_HeatDiag/mat_data/';
% $$$ % $$$ model = 'MOM025';
% $$$ % $$$ outputs = [8:12];
% $$$ % $$$ outputs = [14]
% $$$ % $$$
% $$$ % $$$ base = '/srv/ccrc/data03/z3500785/MOM_HeatDiag_kb1em6/mat_data/';
% $$$ % $$$ model = 'MOM025_kb1em6';
% $$$ % $$$ outputs = 30;
% $$$
% $$$ % $$$ % Load ACCESS-OM2:
% $$$ % $$$ model = 'ACCESS-OM2_1deg_jra55_ryf8485_kds50_may';
% $$$ % $$$ base = '/srv/ccrc/data03/z3500785/mom/mat_data/';
% $$$ % $$$ outputs = 36;
% $$$
% $$$ load([base model sprintf('_output%03d_BaseVars.mat',outputs(1))]);