%/nas/edl/readme.m mfig; p=[1.0267 3.6669 6.4404 3.6536]; set(gcf, 'paperposition',p); % /dods/matlib/rnt/util/lprf.m %================================================================ %% Compute model canonical ENSO and CPI from PC1 and 2 of tropical SSTa %================================================================ pac.xlim=[-266 -64]; pac.ylim=[-15 15]; trange=[datenum(1950, 1,15) datenum(2008, 12, 15)]; tmp=rnc_Extract_NOAA_SST(pac.xlim,pac.ylim,trange,0); tmp = rnc_forcdStats(tmp,'SST', [1950 2008]); %[eof,pc,vexp]=rnt_doEof(tmp.ano, tmp.mask); n.time=tmp.datenum; %n.enso=-nn(pc(:,1)); %n.cpi=nn(pc(:,2)); n.sst=tmp; [year,month]=dates_datenum(n.time); n.month=month; n.year=year; in=find(n.month == 2); %[eof,pc,vexp]=rnt_doEof(tmp.ano(:,:,in), tmp.mask); %n.ensod=-nn(pc(:,1)); %n.cpid=nn(pc(:,2)); n.yeard=n.year(in); n.timed=n.time(in); ba=load('/nas/edl/Bruce/INDICES.mat'); ba.time=n.time(2:end-1); ba.year=n.year(2:end-1); ba.month=n.month(2:end-1); ba.enso=ba.nina3_mon_smooth; ba.cpi=ba.modoki_mon_smooth; clf; plot(ba.time, ba.enso, 'color','r','linewidth',2); datetick; set(gca,'xlim',[ba.time(1) ba.time(end)]); set(gca,'ylim',[-3 3.5]); clf; plot(ba.time, ba.cpi, 'color','b','linewidth',2); datetick; set(gca,'xlim',[ba.time(1) ba.time(end)]); set(gca,'ylim',[-3 3.5]); o=rnc_corr2(sstn.time, sstn.ano, agcm.times, agcm.npos,30); E2=ConvertXYT_into_ZT( o.r1(i,j) , sstn.mask(i,j)); E=ConvertXYT_into_ZT( sstn.ano(i,j,:) , sstn.mask(i,j)); y = E'*E2; y=nn(y); en=rout_enso; red_signi(sstn.time, y, ba.time, ba.cpi,30,1500); %================================================================ %% NPO classical computation %================================================================ lat_range=[20 70]; w_type=1; runmean=3; atm=Compute_NPO_AL(lat_range, w_type, runmean,'ncep'); %================================================================ %% Load NCEP SLPa and NOAA SSTa %================================================================ pac.xlim=[-326 -64.0553]; pac.ylim=[-50 65]; p=pac;trange=[datenum(1950, 1,15) datenum(2008, 12, 15)]; % SLPa slpn=rnc_Extract_NCEP_SLP(p.xlim,p.ylim,trange,0); slpn = rnc_forcdStats(slpn,'SLP', [1950 2008]); slpn.time=slpn.datenum; [year,month]=dates_datenum(slpn.time); slpn.month=month; slpn.year=year; slpn.ano3=rnt_filter3D(slpn.ano, slpn.mask, 3, 'blackman'); slpn.ano3(isnan(slpn.ano3))=0; % SSTA sstn=rnc_Extract_NOAA_SST(p.xlim,p.ylim,trange,0); sstn = rnc_forcdStats(sstn,'SST', [1950 2008]); sstn.time=sstn.datenum; [year,month]=dates_datenum(sstn.time); sstn.month=month; sstn.year=year; sstn.ano3=rnt_filter3D(sstn.ano, sstn.mask, 3, 'blackman'); sstn.ano3(isnan(sstn.ano3))=0; % Compute NPO southern pole NCEP n.npo.time=slpn.time; n.npo.slpi=-sq(mean(mean(slpn.ano(66:71 ,19:21,:),1),2)); n.npo.slpi=-sq(mean(mean(slpn.ano3(66:71 ,19:21,:),1),2)); clf; plot(atm.time, -atm.npo, 'color','k', 'linewidth',1.3); hold on plot(n.npo.time, n.npo.slpi, 'color','r', 'linewidth',1.3); hold on; datetick set(gca,'xlim', [n.npo.time(1) n.npo.time(end)]); grid on; lpr fig1a.eps clf; s=AR1_model(n.npo.time, n.npo.slpi,1/10); [npgo_time, npgo]=rout_npgo; plot(npgo_time, -npgo, 'color','b', 'linewidth',1.3); hold on plot(s.time, s.sig, 'color','k', 'linewidth',1.3); hold on; datetick set(gca,'xlim', [n.npo.time(1) n.npo.time(end)]); grid on; lpr fig1b.eps for i=2:12 i s=AR1_model(n.npo.time, n.npo.slpi,1/i); red_signi(s.time, s.sig,npgo_time, -npgo,30,100); pause end red_signi(n.npo.time, n.npo.slpi,atm.time, -atm.npo,30,1500); n.npo.slpi=-atm.npo; in=find(sstn.month == 2); in2=find(n.month ==2); ba.NPO_sst=rnc_corr2(sstn.year(in), sstn.ano3(:,:,in), n.year(in2), n.npo.slpi(in2), 1); ba.NPO_slp=rnc_corr2(slpn.year(in), slpn.ano3(:,:,in), n.year(in2), n.npo.slpi(in2), 1); ba.NPO_sst1=rnc_corr2(sstn.year(in)-1, sstn.ano3(:,:,in), n.year(in2), n.npo.slpi(in2), 1); ba.NPO_slp1=rnc_corr2(slpn.year(in)-1, slpn.ano3(:,:,in), n.year(in2), n.npo.slpi(in2), 1); clf;rnc_map(ba.NPO_slp.corr, slpn); caxis(ax); gradsmap4; colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]); lpr fig1c.eps clf;rnc_map(ba.NPO_sst.corr, sstn); caxis(ax); gradsmap4; colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]); lpr fig1d.eps figure(1); ax=[-.8 .8]; clf;rnc_map(ba.NPO_slp.corr, slpn); caxis(ax); gradsmap4; colorbar 'h'; cc=[ .35 .45 .55 ]; [c,h] =contour(slpn.lon, slpn.lat, ba.NPO_slp1.corr, -cc, 'LineColor', [.7 .7 .7] ,'LineWidth',2); clabel(c,h, 'color', 'k'); set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]); lpr ENSO_slp.eps clf;rnc_map(ba.NPO_slp1.corr, slpn); caxis(ax); gradsmap4;colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]); lpr ENSO_slp1.eps ax=[-.8 .8]; clf;rnc_map(ba.NPO_sst.corr, sstn); caxis(ax); gradsmap4;colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]); lpr ENSO_sst.eps clf;rnc_map(ba.NPO_sst1.corr, sstn); caxis(ax); gradsmap4;colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]);lpr ENSO_sst1.eps %================================================================ %CPI and Nino3 seasonal cycle %================================================================ for i=1:12 in2=find(ba.month==i); in=find(sstn.month==i); ooe(i)=rnc_corr2(sstn.year(in), sstn.ano3(:,:,in), ba.year(in2),ba.enso(in2), 1); i end for i=1:12 clf; rnc_map(ooe(i).r1, sstn); caxis([-2 2]); gradsmap4; colorbar off set(gca, 'xlim', [-260 -65], 'ylim', [-12 12]); title(i); ff=['enso',num2str(i),'.eps']; lprf(ff); end % CPI JFM / SLP JFM in=find(sstn.month == 2); in2=find(ba.month ==2); ba.CPI_sst=rnc_corr2(sstn.year(in), sstn.ano3(:,:,in), ba.year(in2), ba.cpi(in2), 1); ba.CPI_slp=rnc_corr2(slpn.year(in), slpn.ano3(:,:,in), ba.year(in2), ba.cpi(in2), 1); ba.CPI_sst1=rnc_corr2(sstn.year(in)+1, sstn.ano3(:,:,in), ba.year(in2), ba.cpi(in2), 1); ba.CPI_slp1=rnc_corr2(slpn.year(in)+1, slpn.ano3(:,:,in), ba.year(in2), ba.cpi(in2), 1); in=find(sstn.month == 2); in2=find(ba.month ==2); ba.ENSO_sst=rnc_corr2(sstn.year(in), sstn.ano3(:,:,in), ba.year(in2), ba.enso(in2), 1); ba.ENSO_slp=rnc_corr2(slpn.year(in), slpn.ano3(:,:,in), ba.year(in2), ba.enso(in2), 1); ba.ENSO_sst1=rnc_corr2(sstn.year(in)+1, sstn.ano3(:,:,in), ba.year(in2), ba.enso(in2), 1); ba.ENSO_slp1=rnc_corr2(slpn.year(in)+1, slpn.ano3(:,:,in), ba.year(in2), ba.enso(in2), 1); figure(1); ax=[-.8 .8]; clf;rnc_map(ba.ENSO_slp.corr, slpn); caxis(ax); gradsmap4; colorbar 'h'; cc=[ .35 .45 .55 ]; [c,h] =contour(slpn.lon, slpn.lat, ba.ENSO_slp1.corr, -cc, 'LineColor', [.7 .7 .7] ,'LineWidth',2); clabel(c,h, 'color', 'k'); set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]); lpr ENSO_slp.eps clf;rnc_map(ba.ENSO_slp1.corr, slpn); caxis(ax); gradsmap4;colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]); lpr ENSO_slp1.eps clf;rnc_map(ba.CPI_slp.corr, slpn); caxis(ax); gradsmap4;colorbar 'h'; cc=[. .35 .45 .55 ]; [c,h] =contour(slpn.lon, slpn.lat, ba.CPI_slp1.corr, -cc, 'LineColor', [.7 .7 .7] ,'LineWidth',2); clabel(c,h, 'color', 'k'); set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]); lpr CPI_slp.eps clf;rnc_map(ba.CPI_slp1.corr, slpn); caxis(ax); gradsmap4;colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]); lpr CPI_slp1.eps ax=[-.8 .8]; clf;rnc_map(ba.ENSO_sst.corr, sstn); caxis(ax); gradsmap4;colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]); lpr ENSO_sst.eps clf;rnc_map(ba.ENSO_sst1.corr, sstn); caxis(ax); gradsmap4;colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]);lpr ENSO_sst1.eps clf;rnc_map(ba.CPI_sst.corr, sstn); caxis(ax); gradsmap4;colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]);lpr CPI_sst.eps clf;rnc_map(ba.CPI_sst1.corr, sstn); caxis(ax); gradsmap4;colorbar 'h'; set(gca, 'xlim', [-250 -65], 'ylim', [-30 68]);lpr CPI_sst1.eps % ENSO and CPI patterns n.patt1=rnc_corr2(sstn.time, sstn.ano, n.time, n.enso, 30); n.patt2=rnc_corr2(sstn.time, sstn.ano, n.time, n.cpi, 30); % North Pacific CEOF PCs load /drive/edl/NEPD/process_data/SalinityMODE/ENSO_NPGO/NPacific_CEOF_PCs.mat npceof npceof.slp1=rnc_corr2(slpn.time, slpn.ano, npceof.time, npceof.p1, 30); npceof.slp2=rnc_corr2(slpn.time, slpn.ano, npceof.time, npceof.p2, 30); npceof.sst1=rnc_corr2(sstn.time, sstn.ano, npceof.time, npceof.p1, 30); npceof.sst2=rnc_corr2(sstn.time, sstn.ano, npceof.time, npceof.p2, 30); % Compute lead correlation FEB vs DEC in=find(slpn.month == 2); in2=find(sstn.month==12); n.slpa12_enso=rnc_corr2(slpn.year(in), slpn.ano(:,:,in), n.year(in2), n.enso(in2), 1); n.slpa12_cpi=rnc_corr2(slpn.year(in), slpn.ano(:,:,in), n.year(in2), n.cpi(in2), 1); n.slpa12d_enso=rnc_corr2(slpn.year(in), slpn.ano(:,:,in), n.yeard, n.ensod, 1); n.slpa12d_cpi=rnc_corr2(slpn.year(in), slpn.ano(:,:,in), n.yeard, n.cpid, 1); cc=[0.35:0.05:0.6]; cc=[0.3 .35 .4 .45 .5]; clf; rnc_map(n.npo.patt.corr, slpn); caxis([-1 1]); gradsmap4; colorbar 'h'; [c,h] =contour(slpn.lon, slpn.lat, n.slpa12d_enso.corr, -cc, 'w'); clabel(c,h, 'color', 'w'); lpr slp_prec_enso.eps cc=[0.35:0.05:0.6]; ccp=[0.3:0.1:1]; tmp=n.slpa12_enso.corr; %tmp( tmp > -0.35)=0; clf; rnc_map(tmp, slpn); caxis([-.6 .6]); gradsmap4; colorbar 'h'; [c,h] =contour(slpn.lon, slpn.lat, n.npo.patt.corr , -ccp, 'k'); clabel(c,h); lpr slp_prec_enso.eps cc=[0.25:0.05:0.6]; cc=[0.3 .35 .38]; clf; rnc_map(n.npo.patt.corr, slpn); caxis([-1 1]); gradsmap4; colorbar 'h'; [c,h] =contour(slpn.lon, slpn.lat, n.slpa12_cpi.corr, -cc, 'w'); clabel(c,h,'color','w'); lpr slp_prec_cpi.eps cc=[0.35:0.05:0.6] clf; rnc_map(npceof.slp2.corr, slpn); caxis([-1 1]); gradsmap4; colorbar 'h'; [c,h] =contour(slpn.lon, slpn.lat, n.slpa12_enso.corr, -cc, 'k'); clabel(c,h); lpr slp_prec_enso.eps cc=[0.25:0.05:0.6] clf; rnc_map(npceof.slp2.corr, slpn); caxis([-1 1]); gradsmap4; colorbar 'h'; [c,h] =contour(slpn.lon, slpn.lat, n.slpa12_cpi.corr, -cc, 'k'); clabel(c,h); lpr slp_prec_cpi.eps cc=[0.35:0.05:0.6] clf; rnc_map(n.patt1.r1, sstn); caxis([-.6 .6]); gradsmap4; colorbar 'h'; [c,h] =contour(slpn.lon, slpn.lat, n.slpa12_enso.corr, -cc, 'k'); clabel(c,h); lpr slp_prec_enso.eps cc=[0.25:0.05:0.6] clf; rnc_map(n.patt2.r1, sstn); caxis([-.6 .6]); gradsmap4; colorbar 'h'; [c,h] =contour(slpn.lon, slpn.lat, n.slpa12_cpi.corr, -cc, 'k'); clabel(c,h); lpr slp_prec_cpi.eps mfig; p=[1.0267 3.6669 6.4404 3.6536]; set(gcf, 'paperposition',p); clf; rnc_map(n.patt1.corr, sstn); caxis([-.7 .7]); gradsmap4; colorbar 'h'; lpr enso_corr.eps clf; rnc_map(n.patt2.corr, sstn); caxis([-.7 .7]); gradsmap4; colorbar 'h'; lpr cpi_corr.eps % Compute PDO, NPGO patterns pdo=rout_pdo; pdo.time=pdo.datenum; [tmp1 tmp2]=rout_npgo; npgo.time=tmp1; npgo.index=tmp2; n.pdo=rnc_corr2(sstn.time, sstn.ano, pdo.time, pdo.index, 30); n.npgo=rnc_corr2(sstn.time, sstn.ano, npgo.time, npgo.index, 30); clf; rnc_map(n.pdo.corr, sstn); caxis([-.7 .7]); gradsmap4; colorbar 'h'; lpr pdo_corr.eps clf; rnc_map(-n.npgo.corr, sstn); caxis([-.7 .7]); gradsmap4; colorbar 'h'; lpr npgo_corr.eps %================================================================ %% Load Background data for SPEEDY %================================================================ addpath /sdd/PALEO/speedy2/matlib lat=[ -87.16 -83.47 -79.78 -76.07 -72.36 -68.65 -64.94 ... -61.23 -57.52 -53.81 -50.10 -46.39 -42.68 -38.97 -35.26 -31.54 ... -27.83 -24.12 -20.41 -16.70 -12.99 -9.28 -5.57 -1.86 1.86 ... 5.57 9.28 12.99 16.70 20.41 24.12 27.83 31.54 35.26 ... 38.97 42.68 46.39 50.10 53.81 57.52 61.23 64.94 68.65 ... 72.36 76.07 79.78 83.47 87.16]'; lon=0:3.75:3.75*95; [LON,LAT]=meshgrid(lon,lat); LON=LON'; LAT=LAT'; lon=LON-360; lat=LAT; %================================================================ %% NPO classical computation %================================================================ %lat_range=[20 70]; w_type=1; runmean=3; %atm=Compute_NPO_AL(lat_range, w_type, runmean,'ncep'); %atms=Compute_NPO_AL(lat_range, w_type, runmean,'speedy'); %================================================================ % Load ENSEMBLE SPEEDY AGCM %================================================================ % Select the region of interest pac.xlim=[-326 -64.0553]; pac.ylim=[-50 65]; i=find(lon(:,1) >= pac.xlim(1) & lon(:,1) <= pac.xlim(2)); j=find(lat(1,:) >= pac.ylim(1) & lat(1,:) <= pac.ylim(2)); slpc.lon=lon(i,j); slpc.lat=lat(i,j); slpc.mask=lon(i,j)*0+1; load /nas/edl/SPEEDY/NPO_ensemble/SLPA_ENSEMBLE_2008.mat in=[1:14 16:17 19:26 28:30]; in=1:40; slpc.ano=SLPA(i,j,:,in); % change date for this ensemble clear k; k=0; for yr=1950:2008 for imon=1:12 k=k+1; slpc.month(k)=imon; slpc.year(k)=yr; slpc.datenum(k)=datenum(yr,imon, 15); end end slpc.time=slpc.datenum; %================================================================ %% Compute NPO Southern Pole from SPEDDY ensemble %================================================================ slpi_ens= -sq(mean(slpc.ano(43:45,19,25:end,:),1)); al_ens= -sq(mean(slpc.ano(43:45,23,25:end,:),1)); time_ens=slpc.time(25:end); l=mean(slpi_ens,2); l2=mean(al_ens,2); for ii=40:48 for jj=24:28 al_ens= -sq(mean(slpc.ano(ii,jj,25:end,:),1)); l2=mean(al_ens,2); pdo_hs=AR1_model(time_ens, l2, 1/5); red_signi(pdo_hs.time, pdo_hs.sig, pdo.time, pdo.index, 30, 500); ii jj pause end end mfig; p=[2.0135 1.7468 4.467 7.4938]; set(gcf, 'paperposition',p); npgo_hs=AR1_model(time_ens, l, 1/10); red_signi(npgo_hs.time, npgo_hs.sig, npgo.time, npgo.index, 30, 1500); npgo_hn=AR1_model(n.npo.time, n.npo.slpi, 1/10); red_signi(npgo_hn.time, npgo_hn.sig, npgo.time, npgo.index, 30, 1500); npo.mean=rnc_corr2(time_ens, mean(slpc.ano(:,:,25:end,:),4) , time_ens, l,30); npo_sst.mean=rnc_corr2(sstn.time, sstn.ano , time_ens, l,30); npo.mean12=rnc_corr2(time_ens, mean(SLPA(:,:,25:end,:),4) , time_ens, lowpassa(l,12),30); npo_sst.mean12=rnc_corr2(sstn.time, sstn.ano , time_ens, lowpassa(l,12),30); clf;rnc_map(npo_sst.mean12.corr, sstc); set(gca, 'xlim', [-270 -60], 'ylim', [-20 20]); caxis([-.5 .5]); colorbar 'h' % compute correlation for each month [yr1,m1]=dates_datenum(sstn.time); [yr2,m2]=dates_datenum(time_ens); for imon=1:12 imon in1=find(m1 == imon); in2=find(m2 == imon); seas(imon)=rnc_corr2(yr1(in1), sstc.ssta(:,:,in1) , yr2(in2), l(in2),1); end % cpw region i=38:48; j=12:15; for imon=1:12 clf;rnc_map(seas(imon).corr, sstc); set(gca, 'xlim', [-270 -60], 'ylim', [-13 13]); caxis([-.7 .7]); colorbar 'h' title (imon); cind(imon)=sq(mean(mean( seas(imon).corr(i,j),1),2)); pause end for imon=1:12 imon1=imon-2; lag=0 if imon1 <= 0 imon1=12+imon1; lag=+1 end in1=find(m1 == imon1); in2=find(m2 == imon); seas_lag2(imon)=rnc_corr2(yr1(in1)+lag, sstc.ssta(:,:,in1) , yr2(in2), l(in2),1); cind_lag2(imon)=sq(mean(mean( seas_lag2(imon).corr(i,j),1),2)); end in1=find(m1 == 12); in2=find(m2 == 2); tmp=rnc_corr2(yr1(in1)+1, sstc.ssta(:,:,in1) , yr2(in2), l(in2),1); slpi.ind=[]; slpi.ssta=zeros(69,31,15876); slpi.ind_time=[]; slpi.sst_time=[]; for i=1:27 i itrange=[1:588]+588*(i-1); slpi.ind = [slpi.ind ; slpi_ens(:,i)]; slpi.ind_time=[slpi.ind_time ; time_ens]; slpi.sst_time=[slpi.sst_time ; sstc.datenum(25:end)]; slpi.ssta(:,:,itrange)=sstc.ssta(:,:,25:end); end [yr1,m1]=dates_datenum(slpi.sst_time); [yr2,m2]=dates_datenum(slpi.ind_time); in1=find(m1 == 12); in2=find(m2 == 2); yr=1:49*27; tmp=rnc_corr2(yr+1, slpi.ssta(:,:,in1) , yr, slpi.ind(in2),1); % cpw region i=38:48; j=12:15; for imon=1:12 imon imon1=imon-2; lag=0; if imon1 <= 0 imon1=12+imon1; lag=+1; end in1=find(m1 == imon1); in2=find(m2 == imon); seas_lag2(imon)=rnc_corr2(yr+lag, slpi.ssta(:,:,in1) , yr, slpi.ind(in2),1); cind_lag2(imon)=sq(mean(mean( seas_lag2(imon).corr(i,j),1),2)); end for ie=1:27 i tic npo.ens(ie)=rnc_corr2(time_ens, SLPA(:,:,25:end,ie), time_ens, slpi_ens(:,ie) ,30); npo_sst.ens(ie)=rnc_corr2(sstc.time, sstc.ssta, time_ens, slpi_ens(:,ie) ,30); ll=lowpassa(slpi_ens(:,ie),12); npo.ens12(ie)=rnc_corr2(time_ens, SLPA(:,:,25:end,ie), time_ens, ll ,30); npo_sst.ens12(ie)=rnc_corr2(sstc.time, sstc.ssta, time_ens, ll ,30); toc end SLP_CORR=zeros(69,31); SLP_R1=SLP_CORR; SST_CORR=SLP_CORR; SST_R1=SLP_CORR; num=27; for ie=1:num SLP_CORR=SLP_CORR + npo.ens(ie).corr/num; SLP_R1=SLP_R1 + npo.ens(ie).r1/num; SST_CORR=SST_CORR + npo_sst.ens(ie).corr/num; SST_R1=SST_R1 + npo_sst.ens(ie).r1/num; end for i=1:27 [c99, c95, c12, signi]=red_signi(time_ens, lowpassa(l,2), n.time, lowpassa(time_ens(:,i),2), 30, 1500); % SLP/SST maps form regression with PC2 (NPGO/NPO) modesd k=0; clear CORR_SLP CORR_SST REGR_SST REGR_SLP for imon=+12:-2:-12 imon tic k=k+1; o=rnc_corr(sstc,'ano', slpc.time-imon/12*365, slpi,30); CORR_SST(:,:,k)=o.corr; REGR_SST(:,:,k)=o.r1; o=rnc_corr(slpc,'ano', slpc.time-imon/12*365, slpi,30); CORR_SLP(:,:,k)=o.corr; REGR_SLP(:,:,k)=o.r1; MON(k)=-imon; YEAR(k)=-imon/12; toc end MON=-12:2:12 for i=1:2:11 clf;rnc_map(CORR_SST(:,:,i), sstc); title(MON(i)); caxis([-.3 .3]); colorbar off str=['psst_',num2str(i),'.eps']; pause(0.2);%lpr(str); pause end MON=-12:2:12 for i=1:13 clf;rnc_map(CORR_SLP(:,:,i), slp); title(MON(i)); caxis([-.5 .5]); colorbar off str=['slp_',num2str(i),'.eps']; pause;lpr(str); end % SLP/SST maps form regression with PC2 (NPGO/NPO) modesd k=0; clear CORR_SST REGR_SST CORR_SSTn REGR_SSTn for imon=+12:-1:-12 imon tic k=k+1; o=rnc_corr(sstp,'ano', atms.time-imon/12*365, -p,30); CORR_SST(:,:,k)=o.corr; REGR_SST(:,:,k)=o.r1; o=rnc_corr(sstn,'ano', atm.time-imon/12*365, atm.npo,30); CORR_SSTn(:,:,k)=o.corr; REGR_SSTn(:,:,k)=o.r1; MON(k)=-imon; YEAR(k)=-imon/12; toc end pac.xlim=[-326 -64.0553]; pac.ylim=[-40 65]; trange=[datenum(1950, 1,1) datenum(2000, 12, 31)]; % Load SLP p=pac; tmp=rnc_Extract_SPEEDY_SLP(p.xlim,p.ylim,trange,0); slp = rnc_forcdStats(tmp,'SLP'); p=sq(slp.ano(44,17,:)); % MOVIE for k=1:25 figure(1); clf rnc_map(CORR_SLP(:,:,k),slp); caxis([-0.4 0.4]); gradsmap4; colorbar 'h'; title ([num2str(YEAR(k)), ' YEAR LAG']); figure(2); clf rnc_map(CORR_SST(:,:,k),sstp); caxis([-0.3 0.3]); colorbar 'h'; title ([num2str(YEAR(k)), ' YEAR LAG']); figure(3); clf rnc_map(REGR_SLP(:,:,k),slp); caxis([-1 1]); gradsmap4; colorbar 'h'; title ([num2str(YEAR(k)), ' YEAR LAG']); figure(4); clf rnc_map(REGR_SST(:,:,k),sstp); caxis([-0.5 0.5]); colorbar 'h'; title ([num2str(YEAR(k)), ' YEAR LAG']); k MON(k) pause end % MOVIE for k=1:25 clf; rnc_map(CORR_SST(:,:,k),sstp); caxis([-0.5 0.5]); colorbar 'h'; title ([num2str(YEAR(k)), ' YEAR LAG']); k MON(k) pause end xlim=[-250 -66]; ylim=[-20 20]; i=find (lon(:,1) > xlim(1) & lon(:,1) < xlim(2)); j=find (lat(1,:) > ylim(1) & lat(1,:) < ylim(2));