Jump to content
Main menu
Main menu
move to sidebar
hide
Navigation
Main page
Recent changes
Random page
Help about MediaWiki
Special pages
ClimateKG
Search
Search
English
Appearance
Create account
Log in
Personal tools
Create account
Log in
Pages for logged out editors
learn more
Contributions
Talk
Editing
IPCC:AR6/WGI/Chapter-2
(section)
IPCC
Discussion
English
Read
Edit source
View history
Tools
Tools
move to sidebar
hide
Actions
Read
Edit source
View history
General
What links here
Related changes
Page information
In other projects
Appearance
move to sidebar
hide
Warning:
You are not logged in. Your IP address will be publicly visible if you make any edits. If you
log in
or
create an account
, your edits will be attributed to your username, along with other benefits.
Anti-spam check. Do
not
fill this in!
====== Combined data products ====== At the time of AR5 a limitation of conventional datasets was the lack of coverage, especially in high latitudes, which although recognized as an issue ( [[#Simmons--2010|Simmons et al., 2010]] ) had not been addressed in most products. Interpolation involves the statistical imputation of values across regions with limited data and can add both systematic and random uncertainties ( [[#Lenssen--2019|Lenssen et al., 2019]] ). [[#Cowtan--2014|Cowtan and Way (2014)]] applied a kriging-based method to extend existing datasets to polar regions, while [[#Kadow--2020|Kadow et al. (2020)]] used an artificial intelligence-based method, and [[#Vaccaro--2021|Vaccaro et al. (2021)]] used gaussian random Markov fields, for the same purpose, although only [[#Kadow--2020|Kadow et al. (2020)]] uses the most recent generation of datasets as its base. The Berkeley Earth merged product ( [[#Rohde--2020|Rohde and Hausfather, 2020]] ), HadCRUT5 ( [[#Morice--2021|Morice et al., 2021]] ) and NOAA GlobalTemp-Interim ( [[#Vose--2021|Vose et al., 2021]] ) all include interpolation over reasonable distances across data sparse regions which results in quasi-global estimates from the late 1950s when continuous Antarctic observations commenced. Interpolated datasets with substantial coverage of high latitudes show generally stronger warming of GMST than those with limited data in polar regions ( [[#Vose--2021|Vose et al., 2021]] ), and their strong warming at high northern latitudes is consistent with independent estimates from reanalyses ( [[#Simmons--2017|Simmons et al., 2017]] ; [[#Lenssen--2019|Lenssen et al., 2019]] ) and satellites ( [[#Cowtan--2014|Cowtan and Way, 2014]] ). Given the spatial scales of surface temperature variations and the verification of the methods, it is ''extremely likely'' that interpolation results in a less-biased estimate of the actual global temperature change than ignoring regions with limited or no data. In total there are five conventional datasets which meet spatial coverage requirements and draw from the most recent generation of SST analyses, four of which have sufficient data in the 1850β1900 period to allow an assessment of changes from that baseline (Table 2.3). A fifth dataset is added to the assessment for changes over land areas. Datasets share SST and LSAT data products and in several cases differ solely in the post-processing interpolation applied meaning that there are far fewer methodological degrees of freedom than implied by a straight count of the number of available estimates. <div id="_idContainer033" class="Basic-Text-Frame"></div> Table 2.3 | '''Principal characteristics of GMST in situ data products considered in AR6 WGI, highlighting interdependencies in underlying land and SST products and whether inclusion criteria are met.''' {| class="wikitable" |- | '''Dataset''' | '''Period of Record''' | '''Land Component''' | '''SST Component''' | '''Ensemble Uncertainties?''' | '''Meets all Inclusion Criteria?''' | '''Principal Reference''' |- | '''HadCRUT5''' | 1850β2020 | CRUTEM5 | HadSST4 | Yes | Yes | [[#Morice--2021|Morice et al. (2021)]] |- | '''NOAA GlobalTemp β Interim''' | 1850β2020 | GHCNv4 | ERSSTv5 | Yes, on earlier version | Yes | [[#Vose--2021|Vose et al. (2021)]] |- | '''Berkeley Earth''' | 1850β2020 | Berkeley | HadSST4 | No | Yes | [[#Rohde--2020|Rohde and Hausfather (2020)]] |- | '''Kadow et al.''' | 1850β2020 | CRUTEM5 | HadSST4 | No | Yes | [[#Kadow--2020|Kadow et al. (2020)]] |- | '''China β MST''' | 1856β2020 | CLSAT | ERSSTv5 | No | Land only | [[#Sun--2021|Sun et al. (2021)]] |- | '''GISTEMP''' | 1880β2020 | GHCNv4 | ERSSTv5 | Yes | Post-1880 only | [[#Lenssen--2019|Lenssen et al. (2019)]] |- | '''Cowtan and Way''' | 1850β2020 | CRUTEM4 | HadSST3 | Yes | No | [[#Cowtan--2014|Cowtan and Way (2014)]] |- | '''Vaccaro et al.''' | 1850β2020 | CRUTEM4 | HadSST3 | No | No | [[#Vaccaro--2021|Vaccaro et al. (2021)]] |} Estimates of GMST have also benefitted from improved estimation of parametric uncertainties. New versions of three long-standing products from NASA GISTEMP v4 ( [[#Lenssen--2019|Lenssen et al., 2019]] ), NOAA GlobalTempv5 ( [[#Huang--2019b|]] [[#Huang--2019|B. Huang et al., 2019]] b ) and HadCRUT5 ( [[#Morice--2021|Morice et al., 2021]] ) are all now available as ensemble estimates. These ensembles each account for a variety of systematic and random uncertainty effects in slightly different ways, giving broadly similar results, which are incorporated into the present assessment, with the total uncertainty generally declining up until the mid-20th century as data coverage improves. Another significant development has been the incorporation of reanalysis products ( [[IPCC:Wg1:Chapter:Chapter-1#1.5.2|Section 1.5.2]] ) into operational monitoring of GSAT. It was reported in AR5 that various reanalyses were broadly consistent with conventional surface datasets in the representation of trends since the mid-20th century. Since that time, [[#Simmons--2017|Simmons et al. (2017)]] found that the ERA-Interim ( [[#Dee--2011|Dee et al., 2011]] ) and JRA-55 ( [[#Kobayashi--2015|Kobayashi et al., 2015]] ) reanalyses continued to be consistent, over the last 20 years, with those surface datasets which fully represented the polar regions. GSAT trends from ERA5 reanalysis ( [[#Hersbach--2020|Hersbach et al., 2020]] ) are also broadly consistent with GMST trends from conventional surface datasets. However, the MERRA-2 reanalysis ( [[#Gelaro--2017|Gelaro et al., 2017]] ) GSAT spuriously cooled sharply relative to ERA-Interim and JRA-55 in about 2007 ( [[#Funk--2019|Funk et al., 2019]] ). Since the early 2000s, analyses of surface temperature, from which near-surface temperature may be derived, have also been available from various satellites ( [[#Famiglietti--2018|Famiglietti et al., 2018]] ; [[#Prakash--2018|Prakash et al., 2018]] ; [[#Susskind--2019|Susskind et al., 2019]] ), which have the potential to improve assessments of temperature changes over data-sparse regions. Most land areas in the extratropical Northern Hemisphere (NH) have warmed faster than the GMST average over both the 1900β2020 and 1980β2020 periods (Figure 2.11b), although at more regional scales, particularly in data sparse regions, considerable uncertainty is introduced by sometimes large differences in trends between different LSAT datasets ( [[#Rao--2018|Rao et al., 2018]] ). Temperatures averaged over land areas globally have warmed by 1.59 [1.34 to 1.83] Β°C from 1850β1900 to 2011β2020, substantially higher than the SST warming of 0.88 [0.68 to 1.01] Β°C. The four conventional surface temperature products which meet all criteria to be included in the final assessment (Table 2.4) agree that each of the last four decades has consecutively been the warmest globally since the beginning of their respective records (Figure 2.11c and Table 2.4). Each of the six years 2015 to 2020 has ''very likely'' been at least 0.9Β°C warmer than the 1850β1900 average. <div id="_idContainer034" class="Basic-Text-Frame"></div> '''Table 2.4''' '''|''' '''Observed increase (Β°C) in GMST and underlying LSAT and SST estimates in various datasets.''' Numbers in square brackets indicate 5β95% confidence ranges. Trend values are calculated with ordinary least squares following [[#Santer--2008|Santer et al. (2008)]] and expressed as a total change over the stated period. Datasets considered in this table are those with data for at least 90% of global grid points in each year from 1960 onwards. GMST and SST are shown only for data sets which use air temperature (as opposed to climatological SST values) over sea ice. Changes from an 1850β1900 baseline are calculated only for those datasets which have data in at least 80% of years over 1850β1900. GMST values for each year are calculated as the mean of hemispheric means for the NH and SH, while LSAT and SST values are calculated from hemispheric means weighted according to the proportion of land (ocean) in the two hemispheres. This may vary from the methods used by individual data set providers in their own reporting. Products which meet all criteria to be included in the final assessment and contribute to the average are shown in italics. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1). {| class="wikitable" |- | Diagnostic/ Dataset | | '''1850β1900 to 1995β2014''' (Β°C) | '''1850β1900 to 2001β2020''' (Β°C) | '''1850β1900 to 2011β2020''' (Β°C) | '''Trend''' '''1880β2020''' (Β°C) | '''Trend''' '''1960β2020''' (Β°C) | '''Trend''' '''1980β2020''' (Β°C) |- | rowspan="3"| '''HadCRUT5''' | GMST | ''0.87'' ''[0.81 to 0.94]'' | ''1.01'' ''[0.94 to 1.09]'' | ''1.12'' ''[1.06 to 1.18]'' | ''1.10'' ''[0.89 to 1.32]'' | ''1.04'' ''[0.93 to 1.14]'' | ''0.76'' ''[0.65 to 0.87]'' |- | LSAT | ''1.23'' ''[1.06 to 1.38]'' | ''1.44'' ''[1.26 to 1.59]'' | ''1.55'' ''[1.39 to 1.70]'' | ''1.43'' ''[1.16 to 1.70]'' | ''1.50'' ''[1.33 to 1.67]'' | ''1.20'' ''[1.04 to 1.36]'' |- | SST | ''0.73'' ''[0.69 to 0.78]'' | ''0.85'' ''[0.81 to 0.90]'' | ''0.94'' ''[0.90 to 0.99]'' | ''1.03'' ''[0.80 to 1.25]'' | ''0.90'' ''[0.80 to 0.99]'' | ''0.62'' ''[0.51 to 0.72]'' |- | rowspan="3"| '''NOAA GlobalTemp β Interim''' | GMST | ''0.76'' | ''0.91'' | ''1.02'' | ''1.06'' ''[0.80 to 1.32]'' | ''1.01'' ''[0.90 to 1.11]'' | ''0.75'' ''[0.63 to 0.87]'' |- | LSAT | ''1.34'' | ''1.55'' | ''1.69'' | ''1.58'' ''[1.32 to 1.84]'' | ''1.54'' ''[1.40 to 1.68]'' | ''1.19'' ''[1.04 to 1.35]'' |- | SST | ''0.53'' | ''0.65'' | ''0.75'' | ''0.85'' ''[0.59 to 1.12]'' | ''0.79'' ''[0.69 to 0.89]'' | ''0.57'' ''[0.44 to 0.70]'' |- | rowspan="3"| '''GISTEMP v4''' | GMST | | ''1.07'' ''[0.80 to 1.34]'' | ''1.05'' ''[0.94 to 1.16]'' | ''0.79'' ''[0.67 to 0.90]'' |- | LSAT | | ''1.48'' ''[1.19 to 1.78]'' | ''1.56'' ''[1.40 to 1.72]'' | ''1.23'' ''[1.07 to 1.39]'' |- | SST | | ''0.91'' ''[0.65 to 1.17]'' | ''0.84'' ''[0.74 to 0.95]'' | ''0.61'' ''[0.49 to 0.72]'' |- | rowspan="3"| '''Berkeley Earth''' | GMST | ''0.89'' | ''1.03'' | ''1.14'' | ''1.17'' ''[0.94 to 1.40]'' | ''1.09'' ''[1.00 to 1.19]'' | ''0.79'' ''[0.68 to 0.90]'' |- | LSAT | ''1.28'' | ''1.49'' | ''1.60'' | ''1.50'' ''[1.25 to 1.76]'' | ''1.51'' ''[1.36 to 1.66]'' | ''1.16'' ''[1.00 to 1.32]'' |- | SST | ''0.73'' | ''0.85'' | ''0.96'' | ''1.04'' ''[0.81 to 1.26]'' | ''0.93'' ''[0.84 to 1.01]'' | ''0.64'' ''[0.54 to 0.74]'' |- | '''China-MST''' | LSAT | ''1.18'' | ''1.38'' | ''1.49'' | ''1.48'' ''[1.21 to 1.75]'' | ''1.48'' ''[1.31 to 1.65]'' | ''1.16'' ''[1.00 to 1.32]'' |- | rowspan="3"| '''Kadow et al.''' | GMST | ''0.86'' | ''1.00'' | ''1.09'' | ''1.15'' ''[0.95 to 1.35]'' | ''1.01'' ''[0.92 to 1.10]'' | ''0.73'' ''[0.63 to 0.82]'' |- | LSAT | ''1.29'' | ''1.49'' | ''1.61'' | ''1.60'' ''[1.37 to 1.82]'' | ''1.46'' ''[1.30 to 1.61]'' | ''1.14'' ''[0.99 to 1.30]'' |- | SST | ''0.69'' | ''0.80'' | ''0.88'' | ''0.97'' ''[0.78 to 1.16]'' | ''0.83'' ''[0.76 to 0.90]'' | ''0.56'' ''[0.48 to 0.65]'' |- | rowspan="3"| '''Cowtan-Way''' | GMST | 0.82 [0.75 to 0.89] | 0.96 [0.89 to 1.03] | 1.04 [0.97 to 1.11] | 1.03 [0.84 to 1.22] | 0.94 [0.82 to 1.07] | 0.77 [0.67 to 0.87] |- | LSAT | 1.23 | 1.43 | 1.54 | 1.42 [1.15 to 1.68] | 1.48 [1.31 to 1.65] | 1.20 [1.04 to 1.36] |- | SST | 0.66 | 0.76 | 0.84 | 0.88 [0.71 to 1.05] | 0.73 [0.61 to 0.84] | 0.61 [0.52 to 0.69] |- | rowspan="3"| '''Vaccaro et al.''' | GMST | 0.76 | 0.89 | 0.97 | 0.99 [0.81 to 1.17] | 0.89 [0.77 to 1.00] | 0.72 [0.63 to 0.81] |- | LSAT | 1.15 | 1.35 | 1.47 | 1.40 [1.13 to 1.67] | 1.47 [1.29 to 1.64] | 1.21 [1.06 to 1.36] |- | SST | 0.60 | 0.70 | 0.77 | 0.82 [0.67 to 0.97] | 0.66 [0.55 to 0.76] | 0.53 [0.44 to 0.61] |- | rowspan="2"| '''ERA5''' | GSAT | | 0.78 [0.64 to 0.92] |- | LSAT | | 1.21 [1.02 to 1.40] |- | Average β GMST | | 0.85 | 0.99 | 1.09 | 1.11 | 1.04 | 0.76 |- | Average β LSAT | | 1.27 | 1.47 | 1.59 | 1.50 | 1.51 | 1.18 |- | Average β SST | | 0.67 | 0.79 | 0.88 | 0.96 | 0.86 | 0.60 |} To conclude, from 1850β1900 to 1995β2014, GMST increased by 0.85 [0.69 to 0.95] Β°C, to the first two decades of the 21st century (2001β2020) by 0.99 [0.84 to 1.10] Β°C, and to the most recent decade (2011β2020) by 1.09 [0.95 to 1.20] Β°C. Each of the last four decades has in turn been warmer than any decade that preceded it since 1850. Temperatures have increased faster over land than over the oceans since 1850β1900, with warming to 2011β2020 of 1.59 [1.34 to 1.83] Β°C versus 0.88 [0.68 to 1.01] Β°C, respectively. <div id="2.3.1.2" class="h3-container"></div> <span id="temperatures-during-the-instrumental-period-free-atmosphere"></span>
Summary:
Please note that all contributions to ClimateKG may be edited, altered, or removed by other contributors. If you do not want your writing to be edited mercilessly, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource (see
ClimateKG:Copyrights
for details).
Do not submit copyrighted work without permission!
Cancel
Editing help
(opens in new window)
Search
Search
Editing
IPCC:AR6/WGI/Chapter-2
(section)
Add languages
Add topic
