## Briffa 2013 Satellite Temperature Download

Posted by Jeff Id on January 25, 2014

To look at the local temp data in comparison to the Briffa 2013 polar.mxd data, I downloaded both the RSS and UAH gridded lower troposphere satellite data. Unlike ground temperature data, the lower troposphere data is a layer of air which extends miles above the surface of the earth (Blue curve in Figure 1).

UAH and RSS regional trends are shown in Figures 2 and 3.

From past posts here, we have discussed that UAH estimates the polar regions whereas RSS leaves them out. For fun, I plotted the difference of the two series globally and placed a green marker over the Yamal area where the data for Briffa 2013 was taken.

Data from that single gridcell for each series is sown in Figures 5 and 6.

The two series are mostly comprised of the same data. UAH made the decision to change to station keeping satellites in recent years which eliminates some of the corrections necessary for satellites with orbits that decay over years. Still the series are very highly correlated with an r^2 of 0.93. Next, I will get the ground data series for the region and then we can see how well things match up with the treerings.

I almost forgot: R sourcecode is below and should be turnkey.

plotTrend = function (x, st = 1978, en = c(2013, 12), y.pos = NA, x.pos = NA, main.t = "Untitled") { ### Get trend trend = lm(window(x, st, en) ~ I(time(window(x, st, en)))) ### Initialize variables N = length(window(x, st, en)) I = seq(1:N) / frequency(x) rmI = ssq(I - mean(I)) ### Calculate sum of squared errors SSE = ssq(trend[[2]]) / (N - 2) ### Calculate OLS standard errors seOLS = sqrt(SSE / rmI) * 10 ### Calculate two-tailed 95% CI ci95 = seOLS * 1.964 ### Get lag-1 ACF resids = residuals(trend) r1 = acf(resids, lag.max=1, plot=FALSE)$acf[[2]] ### Calculate CIs with Quenouille adjustment for autocorrelation Q = (1 - r1) / (1 + r1) ciQ = ci95 / sqrt(Q) ### Plot data plot(window(x, st, en), main=main.t, ylab="Anomaly Deg C/Dec") grid() ### Add trendline and x-axis abline(h = 0, col = 2) abline(trend, col = 4, lwd = 2) ### Annotate text text(x = ifelse(is.na(x.pos), st, x.pos), y = ifelse(is.na(y.pos), max(x, na.rm = TRUE) - 0.2 * max(x, na.rm = TRUE), y.pos), paste("Slope: ", signif(trend$coef[2] * 10, 3), "+/-", signif(ciQ, 3), "Deg C/Decade\n(corrected for AR(1) serial correlation)\nLag-1 value:", signif(r1, 3)), cex = 0.8, col = 4, pos = 4) ### Return r = list(trend$coef[2] * 10, ciQ) } cal.Isig=function(dat=dat, st=0,en=2020) { ### Get trend fm=lm(window(dat, st, en)~I(time(window(dat, st, en)))) ### Initialize variables N=length(window(dat, st, en)) I=seq(1:N)/frequency(dat) rmI=ssq(I-mean(I)) ### Calculate sum of squared errors SSE=ssq(fm[[2]])/(N-2) ### Calculate OLS standard errors seOLS=sqrt(SSE/rmI) ### Calculate two-tailed 95% CI ci95=seOLS*1.964 ### Get lag-1 ACF resids=residuals(lm(window(dat, st, en)~seq(1:N))) r1=acf(resids, lag.max=1, plot=FALSE)$acf[[2]] ### Calculate CIs with Quenouille (Santer) adjustment for autocorrelation Q=(1-r1)/(1+r1) ciQ=ci95/sqrt(Q) return(c(fm$coef[2],ciQ)) } ssq=function(x) {sum(x^2)} plt.avg=function(dat, st=1957, en=c(2006, 12), y.pos=NA, x.pos=NA, main.t="Untitled") { ### Get trend fm=lm(window(dat, st, en)~I(time(window(dat, st, en)))) ### Initialize variables N=length(window(dat, st, en)) I=seq(1:N)/frequency(dat) rmI=ssq(I-mean(I)) ### Calculate sum of squared errors SSE=ssq(fm[[2]])/(N-2) ### Calculate OLS standard errors seOLS=sqrt(SSE/rmI)*10 ### Calculate two-tailed 95% CI ci95=seOLS*1.964 ### Get lag-1 ACF resids=residuals(lm(window(dat, st, en)~seq(1:N))) r1=acf(resids, lag.max=1, plot=FALSE)$acf[[2]] ### Calculate CIs with Quenouille (Santer) adjustment for autocorrelation Q=(1-r1)/(1+r1) ciQ=ci95/sqrt(Q) ### Plot data plot(window(dat, st, en), main=main.t, ylab="Anomaly (Deg C)") ### Add trendline and x-axis abline(h=0, col=2); abline(fm, col=4, lwd=2) ### Annotate text text(x=ifelse(is.na(x.pos), st, x.pos), y=ifelse(is.na(y.pos), max(dat,na.rm=TRUE)-0.2*max(dat,na.rm=TRUE), y.pos), paste("Slope: ", round(fm$coef[2]*10, 4), "+/-", round(ciQ, 4), "Deg C/Decade\n(corrected for AR(1) serial correlation)\nLag-1 value:", round(r1, 3)), cex=.8, col=4, pos=4) } library(mapproj) plt.map=function(dat, coord=sat.coord,maxdat=10,mindat=-10,tit="None") { #pick map colors col=colorRampPalette(c("darkblue","white","darkred")) mapcolors <- col(51)#51 colors ### regress data so that colors are set to zero anomaly equals middle colors=dat*51/(maxdat-mindat)+26 mask=colors>51 colors[mask]=51 mask=colors<1 colors[mask]=1 ### Set up Antarctic projection and lat/lon lines par(mar=c(3, 2, 2, 2)) map("world", proj="mollweide", ylim=c(-90, 90),wrap=TRUE) title(tit) #map("world", proj="gilbert", ylim=c(-90, 90),wrap=TRUE) ### Plot points xy=mapproject(coord[, 1], coord[, 2]) points(xy$x, xy$y, pch=15, cex=1, col=mapcolors[colors]) ### Overlay map map("world", proj="", ylim=c(-90, 90),wrap=TRUE, fill=FALSE, add=TRUE) legend("right",legend=c(seq(mindat,maxdat,length.out=11)),fill=mapcolors[seq(1,51,length.out=11)]) } ###################################### # Setup lat/lon coordinates for UAH and RSS data setupcoord=function() { lo=seq(-178.75,178.75,2.5) lt=seq(-88.75,88.75,2.5) lon=rep(lo,72) lat=rep(lt,144) dim(lat)=c(72,144) lat=t(lat) crd=c(lon,lat) dim(crd)=c(72*144,2) crd } ######################################### # gridded gloabl UAH downloader uahts=function() { uahgts=ts(array(NA,dim=c((2014-1978)*12,144*72)),start=1978,deltat=1/12) for(jj in 1978:2013) { loc=paste("http://vortex.nsstc.uah.edu/data/msu/t2lt/tltmonamg.",jj,"_5.6",sep="") UAHG=read.delim(loc, header = FALSE, sep = "") UAHG=as.numeric(unlist(t(as.vector(UAHG)))) montleng=144*72+16 mont=length(UAHG)/montleng for(i in 0: (mont-1)) { strt=montleng*i yr = UAHG[strt+2] mon= UAHG[strt+3] ind=(yr-1978)*12+mon strt=strt+16 uahgts[ind,]=UAHG[strt:(strt+144*72-1)] print(jj) } } uahgts=uahgts/100 uahgts } ################################################### #ftp://ftp.ssmi.com/msu/data/uah_compatible_format/channel_tlt_tb_anom_1978.v03_3.txt rssts=function() { rssgts=ts(array(NA,dim=c((2014-1978)*12,144*72)),start=1978,deltat=1/12) wd=c(5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5) for(jj in 1978:2013) { loc=paste("ftp://ftp.ssmi.com/msu/data/uah_compatible_format/channel_tlt_tb_anom_",jj,".v03_3.txt",sep="") RSSG=read.fwf(loc, header = FALSE, widths=wd) RSSG=as.numeric(unlist(t(as.vector(RSSG)))) montleng=144*72+16 mont=length(RSSG)/montleng for(i in 0: (mont-1)) { strt=montleng*i yr = RSSG[strt+5] mon= RSSG[strt+8] if(!is.na(RSSG[strt+7])){mon=mon+10} ind=(yr-1978)*12+mon strt=strt+16 rssgts[ind,]=RSSG[strt:(strt+144*72-1)] print(jj) } } mask=rssgts==-9999 rssgts[mask]=NA rssgts=rssgts/100 rssgts } ############################################### # download and plot uahgts=uahts() ##download UAH rssgts=rssts() ##download RSS crd=setupcoord() plt.map(uahgts[360,],crd,maxdat=5,mindat=-5,tit="UAH Temp Anomaly Nov 07") plt.map(rssgts[360,],crd,maxdat=5,mindat=-5,tit="RSS Temp Anomaly Nov 07") slpuah=rep(NA,10368) slprss=slpuah for(i in 1:10368) { if(sum(!is.na(rssgts[,i]))>1) { slprss[i]=coef(lsfit(time(rssgts),rssgts[,i]))[2] } } for(i in 1:10368) { if(sum(!is.na(uahgts[,i]))>1) { slpuah[i]=coef(lsfit(time(uahgts),uahgts[,i]))[2] } } plt.map(slpuah*10,crd,maxdat=1,mindat=-1,tit="UAH Deg C/Decade") plt.map(slprss*10,crd,maxdat=1,mindat=-1,tit="RSS Deg C/Decade") plt.map((slpuah-slprss)*10,crd,maxdat=1,mindat=-1,tit="UAH - RSS Deg C/Decade") #plot yamal # (66.52N,65.38E, 250m.a.s.l.). xy=mapproject(66.52, 65.38) points(xy$x, xy$y, pch=16, cex=2, col="green") #find yamal in matrix ##a=which(crd[,1]==66.25) ##b=which(crd[,2]==66.25) ## crd 9027 === yamalia rssyamal=rssgts[,9027] uahyamal=uahgts[,9027] plotTrend(rssyamal,main.t="RSS Yamal Temperature Anomaly Trend") plotTrend(uahyamal,main.t="UAH Yamal Temperature Anomaly Trend") plotTrend(window(recon,start=1978)) lines(ff(rssyamal),col="red") lines(ff(uahyamal),col="green") plot(rssyamal,uahyamal,main="co",type="l") cor(rssyamal,uahyamal,use="pairwise.complete.obs")

## timetochooseagain said

The above chart for weighting profiles appears to label as “tropospheric temperatures” the MT weighting function. The LT weighting function has more weight lower in the atmosphere and is (theoretically at least) more tightly coupled to surface temperature. This chart:

From Remote Sensing Systems is a bit more comprehensive. I’ve found it useful for a lot of purposes.

At any rate. I have been working for a while now on an “empirical amplification factor” technique to estimate potential bias in surface temperature data which is based on regression of detrended anomalies. the suggested amplification coefficient is then used to convert satellite anomalies into estimated surface temperature anomalies. It would be interesting to see if, in this particular region, there is an amplification factor and if so, what that might imply about the possibility of trend bias.

As an example, over the continental US, the surprising result was: the surface varies more than the atmosphere, but basically in sync with it, and after account for this, the trends were almost exactly the same. This was surprising to me because this was the *opposite* of the result I got with global data: the atmosphere varies more than the surface, and once this is accounted for, surface temperature increases appear exaggerated-at least if we take the satellite data seriously.

Here is the post where I explain this:

http://devoidofnulls.wordpress.com/2013/11/25/temperature-data-internally-consistent-beliefs-and-data-bias/