File:Temp anomalies 2000yrs pages2k-en.svg

Summary[edit]

DescriptionTemp anomalies 2000yrs pages2k-en.svg	English: History of global mean surface temperatures over the Common Era. Data and computation based upon Pages2k (2019), doi:10.1038/s41561-019-0400-0, fig. 1a Deutsch: Temperaturanomalien der letzten 2000 Jahr, basierend auf Daten und Berechnungen von Pages2K (2019), doi:10.1038/s41561-019-0400-0, in Anlehnung an Abb. 1a
Date	21 September 2019
Source	Own work
Author	DeWikiMan, based upong fig. 1a) of Pages2K (2019), doi:10.1038/s41561-019-0400-0
Other versions	File:Temp anomalies 2000yrs pages2k-de.svg (German)
SVG development InfoField	The SVG code is valid.   This diagram was created with R.

Sources:

• PAGES 2k Consortium Consistent multidecadal variability in global temperature reconstructions and simulations over the Common Era. In: Nature Geoscience. July 24th, 2019, doi:10.1038/s41561-019-0400-0.
• Data and R-Code: PAGES2k Global Common Era Mean Surface Temperature Reconstructions., doi:10.6084/m9.figshare.c.4498373.v1, accessed Sept. 20th, 2019. (computation with Figs_gmst.R, code to plot see below.)

The graph shows the reconstructed global mean surface temperature (GMST) for the Common Era (CE). For the reconstruction the Pages2k Consortium used seven reconstruction methods:

• basic composite-plus-scaling (CPS)
• regression based methods:
  • principal component regression (PCR)
  • regularized error in variables (M08)
• linear methods:
  • optimal information extraction (OIE)
  • Bayesian hierarchical model (BHM)
• pairwise comparison (PAI)
• offline data assimilation (DA)

The colored lines show 30yr low-pass-filtered ensemble medians for each reconstruction method. The grey shading are the quantiles of all reconstruction ensemble members from all seven methods. Dotted lines are the 2,5th, 97,5th percentiles.

For a review on some of the reconstruction methods, see

• Jason E. Smerdon, Edward R. Cook, Nathan J. Steiger: „The Historical Development of Large-Scale Paleoclimate Field Reconstructions Over the Common Era.“ Reviews of Geophysics, Sept. 2023, doi:10.1029/2022RG000782.

For each of the methods, a 1000 member ensemble of reconstructions was generated to probabilistically asses uncertainties. All used the same input dataset, taken from the Pages2k proxy database, and same calibration dataset (infilled version of Hadcrut4). For an overview of types and spatio-temporal distribution of proxies, see Chapter 1 of the supplement. and for more details see PAGES2k Consortium: A global multiproxy database for temperature reconstructions of the Common Era. Scientific Data 4, 170088 (2017) (online at nature.com, open access).

Basic results: All methods show

• that GMST was higher during the first millenium than from 1000 to 1900.
• a significant cooling trend until 1850
• rapid industrial-era warming
• 94 % (89%, 84%) of ensemble members show warmest 10yr (30yr, 50yr) period of last ~2000 yrs in second half of 20th century.

The most pronounced difference between the methods is the amplitude of the long-term cooling trend.

The authors compared their reconstructions with ensemble runs of 23 CMIP climate models and found strong agreement.

For more details, please consult the Nature paper, linked above (paywalled), the methods supplement, available at nature.com, and data and results at figshare.com.

svg(paste0("temp_anomalies_2000yrs_pages2k-en.svg"),width=7.087*1.67,height=5.67*1.67*0.5,bg = "white")

# axis
xl<-c(0,2010)
yl<-c(-0.9,0.4)

# margins
par(mai=c(0.9,0.9,0.6,1.3))
plot.quantile.densities(fullens.30,addmedian = F,sepfac.col = 100,maxcol = 0.05,mcol=1,scol=c(0,0,0),xl=xl,yl=yl)

# axis: ticks and labels
mtext(side=4,"Quantile",padj=5)
mtext(side=1,"Year CE",padj=4)
mtext(side=2,"Temperature anomaly wrt. 1961-1990 (°C)",padj=-5)
axis(2,las=2,at=seq(-0.9,0.4,0.1))
axis(1,at=seq(0,2000,200))
axis(1,at=seq(100,1900,200),labels=F)

# plot
for(i in rev(seq_along(experiment.names))){
  lines(medians.30[,i],lwd=3,col=exp.cols[i])
}
lines(instr.new.ama.6190.30,col=1,lwd=3)

# key, title and subtitle
legtext<-c("instrumental data",experiment.names)
legsorder<-c(1,4,7,2,5,8,3,6)
legend("bottomleft",legtext,col=c(1,exp.cols),lwd=3,ncol=3,bty="n")
title(main="Global temperature history over the Common Era", cex.main=2.2)
text(1650,-1.13,"mean surface temp., 30 yrs low-pass-filtered",pos=4,cex=0.75,xpd=TRUE)
text(1650,-1.2,"based upon Pages2k (2019), cf. fig. 1a",pos=4,cex=0.75,xpd=TRUE)

# grid
grid(col = "gray", lty = "dotted",lwd =2, equilogs = TRUE)

# 2.5 and 97.5 percentiles
lines(ts(apply(fullens.30,1,quantile,probs=0.025,na.rm=T)),lty=3)
lines(ts(apply(fullens.30,1,quantile,probs=0.975,na.rm=T)),lty=3)
lines(c(xl[2]+(xl[2]-xl[1])*0.01,xl[2]+(xl[2]-xl[1])*0.01+(xl[2]-xl[1])*0.01),rep((yl[2]-yl[1])/100*2.5+yl[1],2),lty=3,xpd=T)

dev.off()

Licensing[edit]

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