#SERIES 1 - CHINA # [58] "Three temperature reconstruction time series are presented. The first, called “H-res”, " # [59] "was derived by averaging only the high-resolution proxy records. Considering the relatively " # [60] "large age uncertainties of the lake-sediment and peat records (±50 years), these records " # [61] "were not included in this composite. The second composite, named “Complete”, was derived " # [62] "directly by averaging all 9 proxy records. The “Weighted” composite reconstruction was " # [63] "formed by combining area weighted regional proxy records. Wang and Gong [2000] used this " # [64] "method to establish an annual mean temperature series of China for the period 1880 to 1998. " # [65] "They divided the Chinese territory into ten regions according to inter-correlations among " # [66] "gridded 1° × 1° latitude × longitude mean temperature records. According to Wang and Gong [2000], " # [67] "the area weights of Eastern China, Dunde ice core, Guliya ice core, southern TP tree-rings " # [68] "and Jinchuan peat are 0.329, 0.198, 0.149, 0.182 and 0.131, respectively. The data of " # [69] "Great Ghost Lake and Jiaming Lake in Taiwan were considered with an area weight of 0.011." #LOAD DATA FROM YANG url<-"c:/climate/data/yang/yangbao.data.txt" yang<-read.table(url,sep="\t",fill=TRUE,header=TRUE) yang<-as.matrix(yang) dimnames(yang)[[2]] # [1] "guliya.year" "guliya.O18.original" "Dulan.year" "Dulan.treering.original" # [5] "Dunde.year" "Dunde.O18.original" "Hongyuan.year" "Hongyuan.peat.STD" # [9] "Jiaming.year" "Jiaming.Lake.STD" "GGL.year" "Great.Ghost.Lake.STD" #[13] "Stibet.year" "south.Tibet..treering" "E.China.year" "East.China.STD" #[17] "Japan.year" "Japan.treering.STD" "Dulan.year.1" "Dulan.treering" #[21] "Dunde.year.1" "Dunde.O18.STD" "Guliya.year" "Guliya.O18.STD" #serise are 1. Guliya; 2. Dunde 3. Dulan. 4. S Tibet tree 5. ECHIN 6. GGL 7. Jiaming 8. Jinchuan 9. Japan #Guliya k<-23 yang0<-NULL yang.column<-c(23,21,19,13,15,11,9,7,17) for (i in 1:9) { k<-yang.column[i] h<-yang[,k:(k+1)] temp<-!is.na(h[,1]) h<-h[temp,] if (h[1,1]>h[nrow(h),1]) h<-h[nrow(h):1,] h<-ts(h[,2],start=h[1,1],end=h[nrow(h),1],frequency=.1) yang0<-ts.union(yang0,h) } dimnames(yang0)[[2]]<-c("guliya","dunde","dulan","stibet","echin","ggl","jiaming","jinchuan","japan") yang<-yang0 #save(yang,file="c:/climate/data/yang/yang.tab") id<-c ("Guliya","Dunde","Dulan juniper","S Tibet tree ring", "E China docs", "Great Ghost Lake", "Jiaming Lake", "Jinchuan peat", "Japan tree ring") weights<- c( 0.149 , 0.198 , 0 , 0.182 , 0.329, 0.011/2, 0.011/2, 0.131, 0) #sum to 1 weights<-as.matrix(weights) series1<-ts (apply(yang0,1,mean,na.rm=T) , start=tsp(yang0)[1], end=tsp(yang0)[2],frequency=tsp(yang0)[3]) plot.ts(series1) ##LOAD WDCP VERSION url<-"ftp://ftp.ngdc.noaa.gov/paleo/contributions_by_author/yang2002/china_temp.txt" h<-readLines(url) g<-read.table(url,skip=74,fill=TRUE) #goes from 0 to 1990 dimnames(g)[[2]]<-c("Year","Complete","H-res","Sample Number","Weighted") #f<-approxfun(g[,1],g[,2]) #h<-ts(f(1:1990),start=1,end=1990) WDCP<-ts(g[,2],start=g[1,1],end=g[nrow(g),1],frequency=0.1) combine<-ts.union(WDCP,series1) ts.plot(combine,col=c("black","red")) #the values are identical at the beginning and end #this is fairly close approximation #closing surge is the two Thompson series: Guliya and Dunde #replace with Thompson versions from Jones fred<-scale(decadal[,"Quelccaya"]) yang0[101:200,1]<-fred fred<-scale(decadal[,"Sajama"]) yang0[101:200,2]<-fred #SHOW EFFECT series2<-ts (apply(yang0,1,mean,na.rm=T) , start=tsp(yang0)[1], end=tsp(yang0)[2],frequency=tsp(yang0)[3]) combine<-ts.union(WDCP,series2) ts.plot(combine,col=c("black","red")) legend(1200,1.5,fill=c("black","red"),legend=c("WDCP","Actual Thompson")) dates<- tsp(yang) dates<-seq(dates[1],dates[2],1/dates[3]) #id<-dimnames(yang)[[2]] N<-5 scale0<-array( c( c(0,4,2,0),rep( c(0,4,0,0),N-2), c(4,4,0,0), c(0,0,2,2),rep( c(0,0,0,2),N-2), c(4,0,0,2)), dim=c(4,2*N)) labels0<-c(rep(FALSE,N-1),TRUE, rep(FALSE,N-1),TRUE) nf<-layout (array(1:(2*N),dim=c(N,2)),heights=c(1.2,rep(1,N-2),1.4)) for (i in 1:(2*N-1)) { par(mar=scale0[,i]) m0<-mean(yang[192:200,i],na.rm=TRUE) plot(dates,yang[,i]-m0,type="h",xlab="",ylab="",xlim=c(0,2000),axes=FALSE) axis(side=1,labels=labels0[i]);axis(side=2); box() text(10,.9*max(yang[,i]-m0,na.rm=TRUE),pos=4,paste(id[i]),font=2) } par(mar=scale0[,10]) m0<-mean(WDCP[192:200],na.rm=TRUE) plot(dates,WDCP-m0,type="h",xlab="",ylab="",xlim=c(0,2000),axes=FALSE,col="red") axis(side=1,labels=labels0[i]);axis(side=2); box() text(10,.9*max(WDCP-m0,na.rm=TRUE),pos=4,paste("Yang Composite"),font=2)