Coding_Application_1 Coding Application #1 Computer Analysis of Global Climate Model Output, XXXXXXXXXX Overview Global climate change due to anthropogenic emissions of greenhouse gases has many...

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Coding_Application_1 Coding Application #1 Computer Analysis of Global Climate Model Output, 2006-2100 Overview Global climate change due to anthropogenic emissions of greenhouse gases has many different facets. Among the most important of these is changes in precipitation, which may impact freshwater availability, the occurrence of hydrological extremes such as floods and droughts, and ocean salinity and circulation. In this problem, you will write computer code in Matlab to analyze precipitation changes simulated by global climate models over the period 2006-2100. Climate Model Output We will be working with monthly, gridded climate model output (simulated precipitation) from the following 20 global climate models that were included in the fifth Coupled Model Intercomparison Project (CMIP5): ACCESS1-0, BNU-ESM, CanESM2, CCSM4, CESM1- CAM5, CMCC-CESM, CNRM-CM5, CSIRO-Mk3-6-0, EC-EARTH, FGOALS-g2, FIO-ESM, GFDL-CM3, GISS-E2-R, HadGEM2-AO, inmcm4, IPSL-CM5A-LR, MIROC5, MPI-ESM-LR, MRI-CGCM3 and NorESM1-M. The output from each model has been archived in NetCDF format, and can be accessed by Matlab by specifying the appropriate OPeNDAP URL. For example, for the first model (ACCESS1-0), the OPeNDAP URL is: http://strega.ldeo.columbia.edu:81/CMIP5/.monthly/.byScenario/.rcp85/.atmos/.mon/.pr/.ACCES S1-0/.r1i1p1/.pr/dods. The URLs for the remaining models are identical except for the model name. Code assignment Write a Matlab program to: 1. Calculate and plot the global annual mean precipitation time series for 2006-2100 for each of the 20 models and for the multimodel mean. All time series should be included on the same plot. Use different line colors/styles to distinguish between models, and include a legend. The precipitation should be plotted in units of mm/day. 2. Calculate and plot a geographic map of the multimodel mean precipitation change between 2006-2025 and 2081-2100 (i.e., time average precipitation in the latter period minus time average precipitation in the former period). The map should include a colorbar. The precipitation change should be plotted in units of mm/day. 3. On the same geographic map created in Step 2, find a way to show the locations (i.e., grid points) where at least 90% of the individual models agree on the sign of the precipitation change. 4. Create a new NetCDF file and write to this file the necessary data to reproduce your plot from Steps 2-3. These data include the grid information (i.e., latitudes and longitudes), the multimodel mean precipitation change (Step 2), and the measure of model agreement (Step 3). The latter could be represented as a matrix of zeros and ones, for example. Your new NetCDF file should include appropriate variable attributes (e.g., description and units). Pointers 1. The model output you’ll be reading in is monthly precipitation as a function of latitude and longitude. The output for all models begins in January 2006, but the end of the time series can be different for different models. This should be accounted for in your Matlab code so that you are only reading in the output for the period 2006-2100 (inclusive). The simulated precipitation is provided in units of kg m-2 s-1. You can multiply this by 86,400 (number of seconds per day) to convert to units of mm/day. 2. To complete Step 1, you’ll need to spatially and temporally average the model output in order to go from gridded (i.e., function of latitude and longitude) monthly data to global annual means. The spatial averaging should be done using area weighting. A simple and standard way to do this is to weight by the cosine of latitude. Using this approach in Matlab, the global annual mean precipitation in a given year would be: pr_global = sum(pr.*cosd(lats))/sum(cosd(lats)), where pr is the average precipitation at each latitude (or zonal-mean precipitation), and lats are the latitudes in degrees. (Note that pr and lats must have the same dimensions.) 3. Model output is provided on different grids (i.e., at different latitudes and longitudes) for different models. To complete Steps 2-4, the output from each individual model needs to be regridded to a common grid. You can do this using the function regrid_cmip5.m, which can be downloaded from Courseworks. 4. Groups of 2–3 are allowed to work together, but every student must hand in the complete assignment. Grading You should turn in (1) a time series plot of the global annual mean precipitation for 2006-2100 (Step 1), (2) a geographic plot of the multimodel mean precipitation change overlaid with the measure of model agreement (Steps 2-3), (3) your new NetCDF file (Step 4), and (4) all Matlab routines. The assignment will be graded as follows: • 20%: Time series plot of global annual mean precipitation • 20%: Geographic map of precipitation change • 20%: New NetCDF file • 20%: Matlab code works with only minor adjustments • 10%: Well-structured code (e.g., appropriate use of functions) • 10%: Good commenting and documentation practices If students work together on the assignment, the code must include comments at the top that describe the group and the contributions of each member.
Answered Same DayNov 13, 2021

Answer To: Coding_Application_1 Coding Application #1 Computer Analysis of Global Climate Model Output,...

Kshitij answered on Nov 17 2021
144 Votes
Climate/Climate.doc
1Analyze precipitation changes simulated by global climate models over the period 2006-2100.
Global annual mean precipitation time series for 2006-2100
1
multimodel mean precipitation change between 2006-2025 and 2081-2100
2
create NewCDF File ans
save as multimodel.nc
3
Analyze precipitation changes simulated by global climate models over the period 2006-2100.
% clear screen
clc
clear all
close all
warning off
% delete model for interrupption
delete Multimodel.nc
Global annual mean precipitation time series for 2006-2100
% load the model
list = dir('*.cdf');
% ploting all the model
figure;
set(gcf,'WindowState','maximized')
for q =1:numel(list)
Modellat = ncread(list(q).name,'lat'); % latitude
Modellon = ncread(list(q).name,'lon'); % longtitude
Modelpr = ncread(list(q).name,'pr'); % precipitation
ModelT = ncread(list(q).name,'T'); % time in months
% finding global annual mean
year=1;
for i=1:12:numel(ModelT)
pr_global(year) = mean(mean(mean(Modelpr(:,:,i:i+11))))*86400;
year=year+1;
end
% store the model data to get multimodel mean
store_pr_global(q,:)=imresize(pr_global,[1 95]);
T = 2006:2005+(numel(ModelT)/12);
% give legends to all plot
Legd = erase(list(q).name,'.cdf');
% plot data
plot(T,pr_global,'DisplayName',Legd)
xlabel('Years')
ylabel('Precipitation mm/day')
title('Global Annual Mean Precipitation')
legend show
legend location northwest
hold on
% clear data to avoid interferences
clear Modellat Modellon Modelpr ModelT pr_global T year
end
% plot the multimodel mean
plot(2006:2100,mean(store_pr_global),'Displayname','MultiModel')
hold off
multimodel mean precipitation change between 2006-2025 and 2081-2100
for w=1:numel(list)
Modellat = ncread(list(q).name,'lat'); % latitude
Modellon = ncread(list(q).name,'lon'); % longtitude
Modelpr = ncread(list(q).name,'pr'); % precipitation
ModelT = ncread(list(q).name,'T'); % time in months
% time average precipitation in the latter period minus time average
% precipitation in the former period
Modelpr = mean(Modelpr(:,:,1:228),3)-mean(Modelpr(:,:,900:length(Modelpr)),3);
% Regrid all the model to the common grid
multimodel(:,:,w)=imresize(Modelpr,[180,360]);
end
% Plot the geographic data
figure;
set(gcf,'WindowState','maximized')
mean_multimodel = mean(multimodel,3);
La=-90:1:90;
Lo=0:1:360;
imagesc(Lo,La,mean_multimodel')
xticks(0:30:360)
yticks(-90:30:90)
grid on
box on
xlabel('longitude')
ylabel('latitude')
title('Multimodel mean precipitation change between 2006-2025 and 2081-2100 (mm/day)')
colorbar('southoutside')
create NewCDF File ans save as...
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