analysis of sea ice fraction in the community earth climate model justin perket aoss 586
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Analysis of Sea Ice Fraction in the Community Earth Climate Model
Justin PerketAOSS 586
Motivation
Background in physics & chemistry Want to focus research on improving
microphysics modelling Goal to gain great understanding of how to
handle model/data output
Sea Ice and Observational Data
Dagmar Budikova, Journal of Global & Planetary Change. 68,3. 2009, pg. 149-163.( http://www.sciencedirect.com/science/article/pii/S0921818109000654 )
Understanding the interaction between sea ice conditions and climate requires accurate records of field conditions throughout Arctic with variation documented over multiple decades and seasons.
Reliable records are essential in driving and evaluating the results from atmospheric and coupled General Circulation Models and conducting observational studies.
Satellite technology has enabled conditions to be monitored continuously for since 1978.
Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) until 1987
Special Sensor Microwave Imager (SSM/I)
Before the records of sea ice conditions consist primarily of aircraft, ship, and coastal observations
scattered locations, irregular time intervals
Sea Ice and Observational Data
Dagmar Budikova, Journal of Global & Planetary Change. 68,3. 2009, pg. 149-163.( http://www.sciencedirect.com/science/article/pii/S0921818109000654 )
Significant differences among the various sea ice datasets have been documented posing multitude of challenges for effective comparisons of results from studies that utilize them
Continuous records often derived from diverse sources and from different algorithms
Singarayer et al. (2005) compares records of sea ice cover simulated from 3 monthly sea ice climatologies:
NASA's satellite data algorithm
Bootstrap derived PMR datasets,
National Ice Center records
Found the uncertainty in the estimation of sea ice cover to be a combination of random and systematic errors, both temporarily and spatially dependent.
Sea Ice and GCMs
General Circulation Models can predict impact on climate from variations in polar sea ice
Realistic simulations under various polar sea ice conditions require fully coupled ocean–atmosphere models
Captures complex interactions between the atmosphere, oceans,and sea ice conditions.
Simultaneous evolution of atmospheric and oceanic conditions
Challenge of isolating the precise contribution of ocean and atmosphere to the sea/atmosphere interaction.
Determining the nature and strength of the ocean's back interaction on the atmosphere remains a challenge
Example: response to a sea surface temperature or sea ice anomaly can provide a significant signal at the 500 hPa level
But signal usually much smaller than natural variability,making detection difficult in GCM integrations.
Sea Ice and CCSM
Sea ice component in CCSM configuration is the Climatological Data Ice Model
interacts with the CCSM Coupler, but is not an active model Rather, it takes ice fraction data from input data,
infers an ice extent, and sends this data to coupler Ignores any forcing data received from the coupler Useful for seeing how an active atmosphere
component behaves when coupled to climatological ice extent.
http://www.cesm.ucar.edu/models/ccsm3.0/dice6/
Sea Ice Data Used
Ice Frac from CAM2, 1° resolution 20th Century Ensemble Member #6 (MOAR) 1850-2005, monthly averages 1981-2005 with observations
Sanity Check: Global Annual Surf. Temp. Comparison
Source: Goddard Institute for Space Studies Surface Temperature Analysis: http://data.giss.nasa.gov/gistemp/
Checked differences with T-test (α=0.05)
Ave. Ice Fraction
Ice Extent
Ice Extent Diagnostic
http://www.cgd.ucar.edu/cms/rneale/tools/amwg_diagnostics.html
Ice Fraction Diagnostic
Comparison with Historical DataWalsh and Chapman Northern Hemisphere Sea Ice
Data Set http://www.cgd.ucar.edu/cas/guide/Data/walsh.html
Northern Hemisphere, 1870-2008, annual & seasonal averages
Sources of Data:1. Danish Meteorlogical Institute
2. Japan Meteorological Agency
3. Naval Oceanographic Office (NAVOCEANO)
4. Kelly ice extent grids (based upon Danish Ice Charts)
5. Walsh and Johnson/Navy-NOAA Joint Ice Center
6. Navy-NOAA Joint Ice Center Climatology
7. Temporal extension of Kelly data (see note below)
8. Nimbus-7 SMMR Arctic Sea Ice Concentrations or DMSP SSM/I Sea Ice Concentrations using the NASA Team Algorithm
http://arctic.atmos.uiuc.edu/cryosphere/IMAGES/seaice.area.arctic.png
Comparison with Historical Data
Problem: CCSM data independent of Walsh & Chapman data
Dummy Test: Autocorrelation of North Ice Extent
Coherence between North & South Ice Extents
%% Lag Corr.-North & South Ice Frac.clear X; clear b; clear lags; clear xc; b=length(Iexts)-600;for n = 1:b x= Iextn(n:n+600); y =Iexts(n:n+600); [xc,lags] = xcorr(x,y,24,'coeff'); X(:,n) = xc;end[c,h] = contourf(1:b,lags,X); colorbar;xlabel('Month'); ylabel('Lags');
Ice Fraction and Surface Temp.
2D correlation between Ice Fraction & Surf Temp Grids
Normalized
Deasonalized
Coherence of Desaonalized Data
Code Used%% midterm scripts
%% Global Surf Temp.
l=length(TSglob);monind=reshape( (1:l)',12,l/12)'; % ea. col. has indices for a month% subtract monthly variability:for i=1:12 TSmonmean(:,i)=mean(TSglob(monind(:,i))); %mean for each month TSdese(monind(:,i))=TSglob(monind(:,i))-TSmonmean(:,i);end
plot(giss(:,1),giss(:,2),giss(:,1),giss(:,3),time2,TSdese)
%% Global Average Icefracsubplot(2,1,1);plot(time2,Igs*100,1850:2005,Ibs*100);title('Northern Hemisphere Average Ice Fraction'); ylabel('% Ice');subplot(2,1,2); plot(time2,Ign*100,1850:2005,Ibn*100);title('Southern Hemisphere Average Ice Fraction'); ylabel('% Ice');
%% Ice Extent%[Iextn1,Iexts1]=hemisum(ICEFRAC,lat,lon); % With all Icefrac[Iextn,Iexts]=hemisum(ICEFRAC,lat,lon); % With ICEFRAC > 0.15for k=1:(length(Iexts)/12) %get annual means a=12*k-11; b=12*k; IextsY(k) = mean(Iexts(a:b) ); IextnY(k) = mean(Iextn(a:b) );% Iexts1Y(k) = mean(Iexts1(a:b) );% Iextn1Y(k) = mean(Iextn1(a:b) );end
% plot( ... % 1850:2005,IextsY, ... % 1850:2005,IextnY, ...% seaiceextent(1:end-2,1),seaiceextent(1:end-2,2)*10^6, ...% seaiceextent(1:end-2,1),seaiceextent(1:end-2,3)*10^6, ...% seaiceextent(1:end-2,1),seaiceextent(1:end-2,4)*10^6, ...% seaiceextent(1:end-2,1),seaiceextent(1:end-2,5)*10^6, ...% seaiceextent(1:end-2,1),seaiceextent(1:end-2,6)*10^6 ...% )% %1850:2005,Iexts1Y, ...% %1850:2005,Iextn1Y, ...% %)% legend('Ice Extent North','Ice Extent Sorth','annual mean','winter','spring','summer','autumn');
subplot(2,1,1); plot(time2,Iexts*1e-6,1850:2005,IextsY*1e-6); ylabel('millions km^2');title('Northern Hemisphere Ice Extent'); legend('monthly','yearly');subplot(2,1,2); plot(time2,Iextn*1e-6,1850:2005,IextnY*1e-6); title('Sorthern Hemisphere Ice Extent'); legend('monthly','yearly'); ylabel('millions km^2');
%%plot(1850:2005,IextsY,seaiceextent(1:end-2,1),seaiceextent(1:end-2,2)*10^6);h=ttest2(seaiceextent(1:136,2),IextsY(21:156))
%% Ice Frac. Annual Cycle
for i=1:12 Iextsmm(:,i)=mean(Iexts(monind(:,i))); %mean for each month Iextnmm(:,i)=mean(Iextn(monind(:,i))); %mean for each monthendplot(1:12,Iextsmm*1e-6,1:12,Iextnmm*1e-6);title('Annual Cycle Hemispehre Sea Ice Extent'); legend('North','South');
Code Used%% Autocorrelationplot(...xcov(... sin(1:100)+2*sin(1:100)+0.6*randn(1,100) ... ) ... )
%% Cross corr.% [xc,lags]=xcorr(Igs,Ign,120);% plot(xc);
% compute autocorr:r=xcorr(Igs, 240, 'coeff');r=r(26:27); % keep r for 1-2 month time laga=( r(1)+sqrt(r(2)) )/2; % est. the AR-1 coeff.W=240;[pw,f]=pwelch(Igs,48,24,length(Igs)) %,W,[],W/2,[]); % format is pwelch(data, window length,... % noverlap, # points in FFT, [] ) pest = (1-a^2)./(1-2*a*cos(2*f*pi)+a^2); %compute the AR-1 power spectrumDOF= 1.2*length(Igs)/length(f); %degree of freedeom, f_e=1.2;
% alternative:% ACF=autocorr(SOInorm,24);% acoef=aryule(SOInorm,1);
% use f statistic% H_0 = spectral peak > AR-1 spectral val. <- 1-side est., not 2 side.fp=icdf('f', 0.95, floor(DOF), floor(DOF));p95 = pest*fp;figure;plot(f,pw, 'o-', f,pest,'-',f,p95,'r-');fval=pw./pest;pval=1-fcdf(fval,floor(DOF),floor(DOF));
%% Lag Corr.-North & South Ice Frac.clear X; clear b; clear lags; clear xc;%X = zeros(81,2172);b=length(Ibn)-120;for n = 1:b x= Ign(n:n+400); %running window of 20 years y =Igs(n:n+400); [xc,lags] = xcorr(x,y,24,'coeff'); X(:,n) = xc;end[c,h] = contourf(1:b,lags,X); colorbar;xlabel('Year'); ylabel('Lags');figure;plot(time2(1:48),Ign(1:48),time2(1:48),Igs(1:48));plot(time2,Ign,time2,Igs)%%[y,lags]=xcorr(Ign,Igs);R=fliplr(y(1:24));stem(y,lags)