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MHW Category Analysis

In this template, we provide an example about some initial analysis of MHW category.

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.

tic
[MHW,mclim,m90,mhw_ts,category_ts]=detect(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.
toc

The last column in the table MHW is just the category of the corresponding MHW event.

>> MHW(1:5,end)

ans =

  5×1 table

    category
    ________

       1    
       1    
       1    
       1    
       1

Here we can see the first 5 MHW events are all assigned to category 1, which is 'moderate' MHW based on Hobday's definition (Hobday et al., 2018).

The output category_ts is the time series representation of MHW category.

>> category_ts(1,1,datenum(1993,4,20)-datenum(1993,1,1)+1)

ans =

     1

Here we can see there is a category-1 MHW at Apr/20/1993 in x1 y1 location.

Frequency of MHWs for each category

Here we do some initial analysis to get frequency of MHWs for each category.

MHW_loc=unique(MHW{:,8:9},'rows');
freq_category=NaN(32,32,4); %lon - lat - category
for x=1:size(MHW_loc,1)
    ts_here=squeeze(category_ts(MHW_loc(x,1),MHW_loc(x,2),:));
    for c=1:4
        bw=bwlabel(ts_here==c);
        % calculating annual frequency
        freq_category(MHW_loc(x,1),MHW_loc(x,2),c)=nanmax(bw)./(2016-1993+1);
    end
end

Mean annual MHW days for each category

Here we do some initial analysis to get mean annual MHW days for each category.

MHW_loc=unique(MHW{:,8:9},'rows');
mhwday_category=NaN(32,32,4); %lon - lat - category
ocean_index=~isnan(nanmean(category_ts,3));
for c=1:4
    % calculating annual MHW days
    mhwday_here=nansum(category_ts==c,3);
    mhwday_here(~ocean_index)=nan;
    mhwday_category(:,:,c)=mhwday_here./(2016-1993+1);
end

Visualization

Running this part requires cmocean toolbox (Thyng et al., 2016)

load('lon_and_lat');
m_proj('miller','lon',[nanmin(lon_used) nanmax(lon_used)],'lat',[nanmin(lat_used) nanmax(lat_used)]);
color_name={'deep','algae','matter','amp'};
title_name={'Moderate','Strong','Severe','Extreme'};
for i=1:4
    subplot(2,4,i)
    m_pcolor(lon_used,lat_used,(freq_category(:,:,i))');
    shading interp
    cmocean(color_name{i});
    m_coast('patch',[0.7 0.7 0.7],'linewidth',2);
    m_grid('fontname','consolas','fontsize',8);
    s=colorbar('fontsize',8,'fontname','consolas');
    s.Label.String='/year';
    title(['MHW frequency - ' title_name{i}],'fontname','consolas','fontsize',12);
    
    subplot(2,4,i+4)
    m_pcolor(lon_used,lat_used,(mhwday_category(:,:,i))');
    shading interp
    cmocean(color_name{i});
    m_coast('patch',[0.7 0.7 0.7],'linewidth',2);
    m_grid('fontname','consolas','fontsize',8);
    s=colorbar('fontsize',8,'fontname','consolas');
    s.Label.String='days/year';
    title(['MHW days - ' title_name{i}],'fontname','consolas','fontsize',12);
end

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