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Analysing monthly and seasonal variability of MHWs

In this template, we provide an example containing simple analysis of monthly and seasonal variability of MHWs

Loading data

Firstly, let’s load NOAA OI SST data (Reynolds et al., 2007). Due to the limitation of file size in Github, these data are stored in folder data for one year per file. We need to reconstruct these data into one combined dataset.

sst_full=NaN(32,32,datenum(2016,12,31)-datenum(1982,1,1)+1);
for i=1982:2016;
    file_here=['sst_' num2str(i)];
    load(file_here);
    eval(['data_here=sst_' num2str(i) ‘;’])
    sst_full(:,:,(datenum(i,1,1):datenum(i,12,31))-datenum(1982,1,1)+1)=data_here;
end

The sst_full contains SST in [147-155E, 45-37S] in resolution of 0.25 from 1982 to 2016.

size(sst_full); %size of data
load('lon_and_lat');
datenum(2016,12,31)-datenum(1982,1,1)+1 % The temporal length is 35 years.
load(‘sst_loc’);% also load lon location and lat location

Detecting MHWs

Here we detect MHWs during 1993-2016 based on climatologies during 1993-2005.

[MHW,mclim,m90,mhw_ts]=detect_full(sst_full,datenum(1982,1,1):datenum(2016,12,31),datenum(1982,1,1),datenum(2005,12,31),datenum(1993,1,1),datenum(2016,12,31)); %take about 30 seconds.

Generating monthly and seasonal MHW metrics

Here we calculate monthly and seasonal MHW metrics, including the average number of MHW days and average MHW intensity.

Firstly, generating date vectors

date_used=datevec(datenum(1993,1,1):datenum(2016,12,31));

The matrix date_used is a date-vector variable, where each row corresponds to the date (year-month-day-hour) of a particular time point during 1993-2016.

We also need the land index, indicating the location of lands where no MHW found.

land_index=isnan(nanmean(mhw_ts,3));

Then we calculate the monthly average of MHW days and MHW intensity based on data during 1993-2016. This could be easily achieved by using a loop from 1-12.

mhwday_month=NaN(size(mhw_ts,1),size(mhw_ts,2),12); % lon-lat-month
mhwint_month=NaN(size(mhw_ts,1),size(mhw_ts,2),12); % lon-lat-month
for i=1:12
    index_used=date_used(:,2)==i;
    mhwday_month(:,:,i)=nansum(~isnan(mhw_ts(:,:,index_used)),3)./(2016-1993+1);
    mhwint_month(:,:,i)=nanmean(mhw_ts(:,:,index_used),3);
end
mhwday_month(repmat(land_index,1,1,12))=nan;

mhwday_month is the average number of MHW days in each month during 1993-2016, and mhwint_month is the average intensity of MHW days in each month during 1993-2016.

Using a comparable approach, it is possible to estimate the seasonal average of both the number of MHW days and the intensity of MHW, with the exception that the seasons need to be clearly defined. Here we define austral seasons as SPR-SON SUM-DJF AUT-MAM WIN-JJA.

seas=[9 10 11;...
    12 1 2;...
    3 4 5;...
    6 7 8];
mhwday_seas=NaN(size(mhw_ts,1),size(mhw_ts,2),4); % lon-lat-seasons
mhwint_seas=NaN(size(mhw_ts,1),size(mhw_ts,2),4); % lon-lat-seasons
for i=1:4
    index_used=ismember(date_used(:,2),seas(i,:));
    mhwday_seas(:,:,i)=nansum(~isnan(mhw_ts(:,:,index_used)),3)./(3*(2016-1993+1));
    mhwint_seas(:,:,i)=nanmean(mhw_ts(:,:,index_used),3);
end
mhwday_seas(repmat(land_index,1,1,4))=nan;

mhwday_seas (days/month) is the average number of MHW days in each season during 1993-2016, and mhwint_seas (^{o}C) is the average intensity of MHW days in each season during 1993-2016.

Simple visualization

Here we visualize the seasonal variability of MHW metrics.

m_proj('miller','lon',[nanmin(lon_used(:)) nanmax(lon_used(:))],'lat',[nanmin(lat_used(:)) nanmax(lat_used(:))]);
name_used={'SPR','SUM','AUT','WIN'};
for i=1:4
    subplot(2,4,i);
    m_pcolor(lon_used,lat_used,(mhwday_seas(:,:,i))');
    shading interp
    m_coast('patch',[0.7 0.7 0.7]);
    m_grid;
    caxis([0 10]);
    colormap('jet');
    s=colorbar('location','southoutside');
    title(['MHW days/month-' name_used{i}]);
end

for i=1:4
    subplot(2,4,i+4);
    m_pcolor(lon_used,lat_used,(mhwint_seas(:,:,i))');
    shading interp
    m_coast('patch',[0.7 0.7 0.7]);
    m_grid;
    caxis([0 3]);
    colormap('jet');
    s=colorbar('location','southoutside');
    title(['MHW days/month-' name_used{i}]);
end

Image text

The first row of the figure corresponds to the average number of MHW days in each season, and the second row is the average intensity of MHW days in each seasons.