Editing IPCC:AR6/WGI/Chapter-2 (section)

===== 2.3.1.1.3 Temperatures during the instrumental period – surface =====

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The AR5 concluded that it was certain that GMST had increased since the late 19th century. Total warming in GMST was assessed as 0.85 [0.65 to 1.06] °C over 1880–2012, while the change from 1850–1900 to 2003–2012 was assessed at 0.78 [0.72 to 0.85] °C, and from 1850–1900 to 1986–2005 at 0.61 [0.55 to 0.67] °C. The SR1.5 reported warming of GMST from 1850–1900 to 2006–2015 of 0.87°C, with an 1880–2012 trend of 0.86°C and an 1880–2015 trend of 0.92°C. The SRCCL concluded that since the pre-industrial period, surface air temperature over land areas has risen nearly twice as much as the global mean surface temperature ( ''high confidence'' ).

Since AR5, there have been substantial improvements in the availability of instrumental archive data both over the ocean and on land. A new version of the International Comprehensive Ocean-Atmosphere Dataset (ICOADS Release 3.0, [[#Freeman--2017|Freeman et al., 2017]] ) comprises over 450 million in situ marine reports and incorporates newly digitized data, increasing coverage in data sparse regions and times (e.g., polar oceans and World War I). The International Surface Temperature Initiative released a much improved collection of fundamental land surface air temperature records ( [[#Rennie--2014|Rennie et al., 2014]] ) comprising more than 35,000 station records. These advances, both of which have substantially improved spatial coverage, have reduced uncertainties in assessments of both land and marine data.

<span id="marine-domain"></span>
====== Marine domain ======

For SST analyses, three products – HadSST4 (1850–present, [[#Kennedy--2019|Kennedy et al., 2019]] ), ERSSTv5 (1850–present, [[#Huang--2017|Huang et al., 2017]] ) and COBE SST2 (1880–present, ( [[#Hirahara--2014|Hirahara et al., 2014]] ) – now have bias adjustments applied throughout the record. The new SST datasets account for two major issues previously identified in AR5: that globally averaged buoy SSTs are about 0.12°C cooler than ship-based SSTs ( [[#Kennedy--2011|Kennedy et al., 2011]] ; [[#Huang--2015|Huang et al., 2015]] ), and that SSTs from ship engine room intakes may have biases for individual ships depending upon the sensor set-up ( [[#Kent--2006|Kent and Kaplan, 2006]] ) but have an overall warm bias when globally aggregated ( [[#Kennedy--2019|Kennedy et al., 2019]] ). The first issue primarily affects data since 1990, when buoys began to increasingly contribute to the observation network ( [[#Woodruff--2011|Woodruff et al., 2011]] ), and the second issue has its largest effect from the 1940s to the 1970s. From the standpoint of uncertainty, ERSSTv4 (W. [[#Liu--2015|]] [[#Liu--2015|Liu et al., 2015]] ; [[#Huang--2016|Huang et al., 2016]] ) and subsequent versions ( [[#Huang--2017|Huang et al., 2017]] ), and HadSST4 have estimates presented as ensembles that sample parametric uncertainty. Comparisons between these independently-derived analyses and the assessed uncertainties ( [[#Kennedy--2014|Kennedy, 2014]] ; [[#Kent--2017|Kent et al., 2017]] ) show unambiguously that global mean SST increased since the start of the 20th century, a conclusion that is insensitive to the method used to treat gaps in data coverage ( [[#Kennedy--2014|Kennedy, 2014]] ).

A number of recent studies also corroborate important components of the SST record ( [[#Hausfather--2017|Hausfather et al., 2017]] ; [[#Kent--2017|Kent et al., 2017]] ; [[#Cowtan--2018|Cowtan et al., 2018]] ; [[#Kennedy--2019|Kennedy et al., 2019]] ). In particular, ATSR SST satellite retrievals ( [[#Merchant--2012|Merchant et al., 2012]] ; [[#Berry--2018|Berry et al., 2018]] ), the near-surface records from hydrographical profiles ( [[#Gouretski--2012|Gouretski et al., 2012]] ; [[#Huang--2018|Huang et al., 2018]] ), and coastal observations ( [[#Cowtan--2018|Cowtan et al., 2018]] ) have all been shown to be broadly consistent with the homogenized SST analyses. [[#Hausfather--2017|Hausfather et al. (2017)]] also confirmed the new estimate of the rate of warming seen in ERSSTv4 since the late 1990s through comparison with independent SST data sources such as Argo floats and satellite retrievals. Nevertheless, dataset differences remain in the mid-20th century when there were major, poorly-documented, changes in instrumentation and observational practices ( [[#Kent--2017|Kent et al., 2017]] ), particularly during World War II, when ship observations were limited and disproportionately originated from US naval sources ( [[#Thompson--2008|Thompson et al., 2008]] ). [[#Kennedy--2019|Kennedy et al. (2019)]] also identify differences between the new HadSST4 dataset and other SST datasets in the 1980s and 1990s, indicating that some level of structural uncertainty remains during this period, whilst [[#Chan--2019|Chan et al. (2019)]] and [[#Davis--2019|Davis et al. (2019)]] document residual uncertainties in the early and later 20th century records respectively.

Historically, SST has been used as a basis for global temperature assessment on the premise that the less variable SST data provides a better estimate of marine temperature changes than marine air temperature (MAT) ( [[#Kent--2021|Kent and Kennedy, 2021]] ). However, MAT products are used to adjust SST biases in the NOAA SST product because they are assessed to be more homogeneous ( [[#Huang--2017|Huang et al., 2017]] ). Observational datasets exist for night-marine air temperature (NMAT) (e.g., [[#Cornes--2020|Cornes et al., 2020]] ; [[#Junod--2020|Junod and Christy, 2020]] ; [[#Rayner--2020|Rayner et al., 2020]] ) and there are methods to adjust daytime MATs ( [[#Berry--2004|Berry et al., 2004]] ), but there is to date no regularly updated dataset which combines MAT with temperatures over land. MAT datasets are more sparse in recent decades than SST datasets as marine datasets have become increasingly dependent on drifting buoys ( [[#Centurioni--2019|Centurioni et al., 2019]] ) which generally measure SST but not MAT, and there are almost no recent winter MAT data south of 40°S ( [[#Swart--2019|Swart et al., 2019]] ). However, the situation reverses in the 19th century with a greater prevalence of MAT than SST measurements available in the ICOADS data repository ( [[#Freeman--2017|Freeman et al., 2017]] , 2019; [[#Kent--2021|Kent and Kennedy, 2021]] ).

<span id="land-domain"></span>
====== Land domain ======

The GHCNMv4 dataset ( [[#Menne--2018|Menne et al., 2018]] ) includes many more land stations than GHCNMv3, arising from the databank efforts of [[#Rennie--2014|Rennie et al. (2014)]] , and calculates a 100-member parametric uncertainty ensemble drawing upon the benchmarking analysis of [[#Williams--2012|Williams et al. (2012)]] , as well as accounting for sampling effects. A new version of the CRUTEM dataset (CRUTEMv5, [[#Osborn--2021|Osborn et al., 2021]] ) has increased data completeness and additional quality control measures. A new global land dataset, the China Land Surface Air Temperature (CLSAT) dataset ( [[#Xu--2018|Xu et al., 2018]] ) has higher network density in some regions (particularly Asia) than previously existing datasets. Global trends derived from CLSAT are generally consistent with those derived from other land datasets through 2014 ( [[#Xu--2018|Xu et al., 2018]] ).

The AR5 identified diurnal temperature range (DTR) as a substantial knowledge gap. The most recent analysis of Thorne et al. (2016a, b) compared a broad range of gridded estimates of change in DTR, including a new estimate derived from the ISTI databank release using the pairwise homogenization algorithm used to create GHCNMv4, and estimates derived from [[#Vose--2005|Vose et al. (2005)]] , HadEX2 ( [[#Donat--2013a|Donat et al., 2013a]] ), HadGHCND ( [[#Donat--2013b|Donat et al., 2013b]] ), GHCNDEX ( [[#Donat--2013b|Donat et al., 2013b]] ), Berkeley Earth ( [[#Rohde--2013|Rohde et al., 2013]] ), and CRU TS ( [[#Harris--2014|Harris et al., 2014]] ). The analysis highlighted substantial ambiguity in pre-1950 estimates arising from sparse data availability. After 1950 estimates agreed that DTR had decreased globally with most of that decrease occurring over the period 1960–1980. A subsequent DTR analysis using CLSAT further confirmed this behaviour (X. [[#Sun--2018|]] [[#Sun--2018|Sun et al., 2018]] ).

No recent literature has emerged to alter the AR5 finding that it is ''unlikely'' that any uncorrected effects from urbanization (Box 10.3), or from changes in land use or land cover ( [[#2.2.7|Section 2.2.7]] ), have raised global Land Surface Air Temperature (LSAT) trends by more than 10%, although larger signals have been identified in some specific regions, especially rapidly urbanizing areas such as eastern China (Y. [[#Li--2013|]] [[#Li--2013|Li et al., 2013]] ; [[#Liao--2017|Liao et al., 2017]] ; Z. [[#Shi--2019|]] [[#Shi--2019|Shi et al., 2019]] ). There is also no clear indication that site-specific data homogeneity issues have had any significant impact on global trends since the early 20th century; there is more uncertainty in the 19th century, mainly arising from a lack of standardization of instrument shelters, which has been largely accounted for in data from central Europe ( [[#Jones--2012|Jones et al., 2012]] ), but less so elsewhere.

<span id="combined-data-products"></span>
====== 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 1850–1900 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.

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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'''

| 1850–2020

| CRUTEM5

| HadSST4

| Yes

| Yes

| [[#Morice--2021|Morice et al. (2021)]]

|-
| '''NOAA GlobalTemp – Interim'''

| 1850–2020

| GHCNv4

| ERSSTv5

| Yes, on earlier version

| Yes

| [[#Vose--2021|Vose et al. (2021)]]

|-
| '''Berkeley Earth'''

| 1850–2020

| Berkeley

| HadSST4

| No

| Yes

| [[#Rohde--2020|Rohde and Hausfather (2020)]]

|-
| '''Kadow et al.'''

| 1850–2020

| CRUTEM5

| HadSST4

| No

| Yes

| [[#Kadow--2020|Kadow et al. (2020)]]

|-
| '''China – MST'''

| 1856–2020

| CLSAT

| ERSSTv5

| No

| Land only

| [[#Sun--2021|Sun et al. (2021)]]

|-
| '''GISTEMP'''

| 1880–2020

| GHCNv4

| ERSSTv5

| Yes

| Post-1880 only

| [[#Lenssen--2019|Lenssen et al. (2019)]]

|-
| '''Cowtan and Way'''

| 1850–2020

| CRUTEM4

| HadSST3

| Yes

| No

| [[#Cowtan--2014|Cowtan and Way (2014)]]

|-
| '''Vaccaro et al.'''

| 1850–2020

| 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 1900–2020 and 1980–2020 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] °C from 1850–1900 to 2011–2020, substantially higher than the SST warming of 0.88 [0.68 to 1.01] °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 0.9°C warmer than the 1850–1900 average.

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'''Table 2.4''' '''|''' '''Observed increase (°C) in GMST and underlying LSAT and SST estimates in various datasets.''' Numbers in square brackets indicate 5–95% 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 1850–1900 baseline are calculated only for those datasets which have data in at least 80% of years over 1850–1900. 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

|
| '''1850–1900 to 1995–2014''' (°C)

| '''1850–1900 to 2001–2020''' (°C)

| '''1850–1900 to 2011–2020''' (°C)

| '''Trend'''

'''1880–2020''' (°C)

| '''Trend'''

'''1960–2020''' (°C)

| '''Trend'''

'''1980–2020''' (°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 – 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 – GMST

|
| 0.85

| 0.99

| 1.09

| 1.11

| 1.04

| 0.76

|-
| Average – LSAT

|
| 1.27

| 1.47

| 1.59

| 1.50

| 1.51

| 1.18

|-
| Average – SST

|
| 0.67

| 0.79

| 0.88

| 0.96

| 0.86

| 0.60

|}

To conclude, from 1850–1900 to 1995–2014, GMST increased by 0.85 [0.69 to 0.95] °C, to the first two decades of the 21st century (2001–2020) by 0.99 [0.84 to 1.10] °C, and to the most recent decade (2011–2020) by 1.09 [0.95 to 1.20] °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 1850–1900, with warming to 2011–2020 of 1.59 [1.34 to 1.83] °C versus 0.88 [0.68 to 1.01] °C, respectively.

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<span id="temperatures-during-the-instrumental-period-free-atmosphere"></span>