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

===== 2.3.1.4.5 Stratospheric polar vortex and sudden warming events =====

<div id="h4-22-siblings" class="h4-siblings"></div>

The AR5 assessed changes in the polar vortices and reported a ''likely'' decrease in the lower-stratospheric geopotential heights over Antarctica in spring and summer at least since 1979.

Multiple definitions for the polar vortex strength and sudden stratospheric warming (SSW) events have been proposed and compared ( [[#Butler--2015|Butler et al., 2015]] ; [[#Palmeiro--2015|Palmeiro et al., 2015]] ; [[#Waugh--2017|Waugh et al., 2017]] ; [[#Butler--2018|Butler and Gerber, 2018]] ), and new techniques identifying daily vortex patterns and SSWs have been developed (D.M. [[#Mitchell--2013|]] [[#Mitchell--2013|Mitchell et al., 2013]] ; [[#Kretschmer--2018|Kretschmer et al., 2018]] ). Errors in reanalysis stratospheric winds were assessed and discrepancies in stratospheric atmospheric circulation and temperatures between reanalyses, satellites and radiosondes have been reported (D.M. [[#Mitchell--2013|]] [[#Mitchell--2013|Mitchell et al., 2013]] ; [[#Duruisseau--2017|Duruisseau et al., 2017]] ).

The northern stratospheric polar vortex has varied intra-seasonally and with altitude during recent decades. Multiple reanalysis and radiosonde datasets show that the midwinter lower stratospheric geopotential height (150 hPa) over the polar region north of 60°N has increased significantly since the early 1980s ( [[#Bohlinger--2014|Bohlinger et al., 2014]] ; [[#Garfinkel--2017|Garfinkel et al., 2017]] ). This signal extends to the middle and upper stratosphere. In January-February zonal winds north of 60°N at 10 hPa have been weakening ( [[#Kim--2014|Kim et al., 2014]] ; [[#Kretschmer--2018|Kretschmer et al., 2018]] ). Daily atmospheric circulation patterns over the northern polar stratosphere exhibit a decreasing frequency of strong vortex events and commensurate increase in more-persistent weak events, which largely explains the observed significant weakening of the vortex during 1979–2015 ( [[#Kretschmer--2018|Kretschmer et al., 2018]] ). The northern polar vortex has weakened in early winter but strengthened during late winter ( [[#Bohlinger--2014|Bohlinger et al., 2014]] ; [[#Garfinkel--2015a|Garfinkel et al., 2015a]] , 2017; [[#Ivy--2016|Ivy et al., 2016]] ; [[#Seviour--2017|Seviour, 2017]] ; [[#Kretschmer--2018|Kretschmer et al., 2018]] ). In the middle and upper stratosphere, a strengthening trend of the northern polar vortex during DJF has occurred since 1998, contrasting the weakening trend beforehand (D. [[#Hu--2018|]] [[#Hu--2018|Hu et al., 2018]] ). The position of the polar vortex also has long-term variations, exhibiting a persistent shift toward Northern Siberia and away from North America in February over the period 1979–2015 ( [[#Zhang--2016|Zhang et al., 2016]] ; J. [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|]] [[#Zhang--2018|Zhang et al., 2018]] ). Multiple measures show similar location changes ( [[#Seviour--2017|Seviour, 2017]] ).

Sudden stratospheric warming (SSW), a phenomenon of rapid stratospheric air temperature increases (sometimes by more than 50°C in 1–2 days), is tightly associated with the reversal of upper stratospheric zonal winds, and a resulting collapse or substantial weakening of the stratospheric polar vortex ( [[#Butler--2015|Butler et al., 2015]] ; [[#Butler--2018|Butler and Gerber, 2018]] ) and on average occurs approximately 6 times per decade in the NH winter ( [[#Charlton--2007|Charlton et al., 2007]] ; [[#Butler--2015|Butler et al., 2015]] ). The SSW record from all modern reanalyses is very consistent. There is a higher occurrence of major midwinter SSWs in the 1980s and 2000s with no SSW events during 1990–1997 ( [[#Reichler--2012|Reichler et al., 2012]] ; [[#Butler--2015|Butler et al., 2015]] ). An assessment of multi-decadal variability and change in SSW events is sensitive to both chosen metric and methods ( [[#Palmeiro--2015|Palmeiro et al., 2015]] ). Due to the lack of assimilation of upper air data, the centennial-scale reanalyses do not capture SSW events, even for the most recent decades ( [[#Butler--2015|Butler et al., 2015]] , 2017) and hence cannot inform on earlier behaviour. There has been considerably less study of trends in the SH stratosphere polar vortex strength despite the interest in the ozone hole and the potential impact of the SH stratosphere polar vortex strength on it. The occurrence of SSW events in the SH is not as frequent as in the NH, with only 3 documented events in the last 40 years ( [[#Shen--2020|Shen et al., 2020]] ).

In summary, it is ''likely'' that the northern lower stratospheric polar vortex has weakened since the 1980s in midwinter, and its location has shifted more frequently toward the Eurasian continent. The short record and substantial decadal variability yields ''low confidence'' in any trends in the occurrence of SSW events in the NH winter and such events in the SH are rare.

<div id="cross-chapter-box-2.3" class="h2-container box-container"></div>

'''Cross-Chapter Box 2.3 | New Estimates of Global Warming to Date, and Key Implications'''

<div id="h2-15-siblings" class="h2-siblings"></div>

'''Contributing Authors:''' Peter W. Thorne (Ireland/United Kingdom), Blair Trewin (Australia), Richard P. Allan (United Kingdom), Richard Betts (United Kingdom), Lea Beusch (Switzerland), Chris Fairall (United States of America), Piers Forster (United Kingdom), Baylor Fox-Kemper (United States of America), Jan S. Fuglestvedt (Norway), John C. Fyfe (Canada), Nathan P. Gillett (Canada), Ed Hawkins (United Kingdom), Christopher Jones (United Kingdom), Elizabeth Kent (United Kingdom), Svitlana Krakovska (Ukraine), Elmar Kriegler (Germany), Jochem Marotzke (Germany), H. Damon Matthews (Canada), Thorsten Mauritsen (Germany/Denmark), Anna Pirani (Italy), Joeri Rogelj (United Kingdom, Austria/Belgium), Steven K. Rose (United States of America), Bjørn H. Samset (Norway), Sonia I. Seneviratne (Switzerland), Claudia Tebaldi (United States of America), Andrew Turner (United Kingdom), Russell S. Vose (United States of America), Rachel Warren (United Kingdom)

This Cross-Chapter Box presents the AR6 WGI assessment of observed global warming and describes improvements and updates since AR5 and subsequent Special Reports. The revised estimates result from: the availability of new and revised observational datasets; the occurrence of recent record warm years; and the evaluation of the two primary metrics used to estimate global warming in past IPCC reports: ‘Global mean surface temperature’ (GMST) and ‘Global surface air temperature’ (GSAT). Implications for threshold crossing times, remaining carbon budgets and impacts assessments across AR6 WGs are discussed.

Cross-Chapter Box 2.3

'''Dataset innovations'''

Since AR5, all major datasets used for assessing observed temperature change based upon GMST have been updated and improved ( [[#2.3.1.1.3|Section 2.3.1.1.3]] ). A number of new products have also become available, including new datasets (e.g., Berkeley Earth, [[#Rohde--2020|Rohde and Hausfather, 2020]] ) and new interpolations based on existing datasets (e.g., [[#Cowtan--2014|Cowtan and Way, 2014]] and [[#Kadow--2020|Kadow et al., 2020]] ). These various estimates are not fully independent.

Improvements in global temperature datasets since AR5 have addressed two major systematic issues. First, new SST datasets ( [[#Huang--2017|Huang et al., 2017]] ; [[#Kennedy--2019|Kennedy et al., 2019]] ) address deficiencies previously identified in AR5 relating to the shift from predominantly ship-based to buoy-based measurements; these improvements result in larger warming trends, particularly in recent decades. Second, all datasets now employ interpolation to improve spatial coverage. This is particularly important in the Arctic, which has warmed faster than the rest of the globe in recent decades ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 5.9.2.2); under-sampling of the Arctic leads to a cool bias in recent decades ( [[#Simmons--2017|Simmons et al., 2017]] ; [[#Benestad--2019|Benestad et al., 2019]] ). Some datasets are now spatially complete ( [[#Cowtan--2014|Cowtan and Way, 2014]] ; [[#Kadow--2020|Kadow et al., 2020]] ) while others have expanded spatial coverage ( [[#Lenssen--2019|Lenssen et al., 2019]] ; [[#Rohde--2020|Rohde and Hausfather, 2020]] ; [[#Morice--2021|Morice et al., 2021]] ; [[#Vose--2021|Vose et al., 2021]] ). Several interpolation methods have been benchmarked against test cases (e.g., [[#Lenssen--2019|Lenssen et al., 2019]] ), and comparisons with reanalyses further confirm the value of such interpolation ( [[#Simmons--2017|Simmons et al., 2017]] ). It is ''extremely likely'' that interpolation produces an improved estimate of the changes in GMST compared to ignoring data-void regions.

Overall, dataset innovations and the availability of new datasets have led to an assessment of increased GMST change relative to the directly equivalent estimates reported in AR5 (Cross-Chapter Box 2.3, Table 1 and Figure 1).

'''Effects of warming since AR5 and choice of metrics of global mean temperature change'''

Each of the six years from 2015 to 2020 has ''likely'' been warmer than any prior year in the instrumental record. GMST for the decade 2011–2020 has been 0.19 [0.16 to 0.22] °C warmer than 2003–2012, the most recent decade used in AR5 (Cross-Chapter Box 2.3, Figure 1). A linear trend has become a poorer representation of observed change over time since most of the sustained warming has occurred after the 1970s (Cross-Chapter Box 2.3, Figure 1) and all values since 2012 are at least 0.2°C above a linear trendline for 1850–2020. For this reason, the primary method used to assess observed warming in this report is the change in temperature from 1850–1900 to the most recent decade (2011–2020) or the recent past (1995–2014), replacing the trend-based methods used in AR5 and earlier assessments. The effect of this change from trend-based to change-based metrics is currently relatively minor at –0.03°C (&lt;5%) for the most recent decade, but this may not remain the case in future ( ''high confidence'' ).

<div id="_idContainer032" class="Basic-Text-Frame"></div>

[[File:0eb5647470256cf45e0ca85e9ee7fe91 IPCC_AR6_WGI_CCBox_2_3_Figure_1.png]]

'''Cross-chapter Box 2.3, Figure 1'''

'''|'''

'''Changes in assessed historical surface temperature changes since AR5. (a)''' Summary of the impact of various steps from AR5 assessment warming-to-date number for 1880–2012 using a linear trend fit to the AR6 assessment based upon the difference between 1850–1900 and 2011–2020. Whiskers provide 90% ( ''very likely'' ) ranges. AR6 assessment in addition denotes additional warming since the period around 1750 (Cross-Chapter Box 1.2). '''(b)''' Time series of the average of assessed AR5 series (orange, faint prior to 1880 when only HadCRUT4 was available) and AR6 assessed series (blue) and their differences (offset) including an illustration of the two trend fitting metrics used in AR5 and AR6. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

'''Observed changes in global mean temperature since the pre-industrial era'''

AR5 used 1850–1900 as an approximate pre-industrial baseline for global temperature change, whilst using an earlier pre-industrial baseline of 1750 for radiative forcings. Cross-Chapter Box 1.2 assesses that there was an observed GMST change from the period around 1750 to 1850–1900 of around 0.1°C ( ''likely'' range –0.1 to +0.3°C, ''medium confidence'' ). This additional global temperature change before 1850–1900 is not included when making AR6 assessments on global warming to date, global temperature threshold crossing times, or remaining carbon budgets to ensure consistency with previous ARs.

'''Addressing the non-equivalence of GMST and GSAT'''

GMST is a combination of land surface air temperatures (LSAT) and SSTs, whereas GSAT is a combination of LSAT and marine air temperatures (MATs). Although GMST and GSAT are closely related, the two measures are physically distinct. The implications have become more apparent since AR5 ( [[#Merchant--2013|Merchant et al., 2013]] ; [[#Cowtan--2015|Cowtan et al., 2015]] ; [[#Simmons--2017|Simmons et al., 2017]] ; [[#IPCC--2018|IPCC, 2018]] (SR1.5); [[#Richardson--2018|Richardson et al., 2018]] ), and it has been shown ( [[#Rubino--2020|Rubino et al., 2020]] ) that MAT and SST can show distinct multi-decadal-scale trends and patterns of interannual variability. Although SR1.5 used GMST for observational-based and GSAT for model-based headline warming statements, they noted the importance of the difference for their assessment (SR1.5 [[IPCC:Wg1:Chapter:Chapter-1#1.2.1.1|Section 1.2.1.1]] ). The SR1.5 used information from CMIP5 models to estimate a GSAT equivalent from observation-based GMST for certain applications such as remaining carbon budgets. The following subsections assess available lines of evidence related to the equivalence between GMST and GSAT.

''Physical understanding''

A well-understood physical constraint on the vertical gradient between the air and sea surface temperature is that it is approximately proportional to the turbulent sensible heat flux in the atmospheric surface layer ( [[#Chor--2020|Chor et al., 2020]] ). Similarly, the latent heat flux scales with the vertical humidity gradient and, in the global mean and in most oceanic regions, the latent heat flux is substantially larger than the sensible heat flux (Sections 7.2.1 and 9.2.1.3). If GSAT were to warm faster than GMST, the sensible surface heat flux would respond so as to reduce this difference. However, it is the sum of the sensible, latent, and radiative heat fluxes that controls GMST, so the sensible heat flux effect cannot be considered in isolation. Attempts to further constrain the combination of fluxes (e.g., [[#Lorenz--2010|Lorenz et al., 2010]] ; [[#Siler--2019|Siler et al., 2019]] ) rely on parameterizations or output from Earth system models (ESMs) or reanalyses and so are not considered independent. Apart from the above global considerations, regional and seasonal effects such as changes to the frequency and intensity of storms, sea state, cloudiness, sea ice cover, vegetation and land use may all affect the GSAT to GMST difference, either directly or by altering the relationships between gradients and energy fluxes. These changing energy flux relationships are monitored through observing the stratification of the upper ocean (Section 9.2.1.3) and the response of upper ocean processes (Cross-Chapter Box 5.3) in ESMs and reanalyses, but such monitoring tasks rival the observational challenge of directly observing SSTs and 2 m air temperature under a wide range of conditions. In summary, because of the lack of physical constraints and the complexity of processes driving changes in the GSAT to GMST temperature differences, there is no simple explanation based on physical grounds alone for how this difference responds to climate change.

''Direct observational evidence''

There is currently no regularly updated, entirely observation-based dataset for GSAT. The best available observations of near-surface air temperature over ocean are datasets of night-time marine air temperature (NMAT; e.g., [[#Cornes--2020|Cornes et al., 2020]] ; [[#Junod--2020|Junod and Christy, 2020]] ), though spatial coverage is less extensive than for SST. Night-time measurements are used to avoid potential biases from daytime heating of ship superstructures. [[#Kennedy--2019|Kennedy et al. (2019)]] show little difference between HadNMAT2 and HadSST4 between 1920 and 1990, but a warming of SST relative to NMAT manifesting as a step change of 0.05°C–0.10°C in the early 1990s, which may reflect an actual change, the impact of increasingly divergent spatial coverage between SST and MAT measurements, or unresolved structural uncertainties in one or both datasets. This leads to NMAT warming around 10% more slowly than SST over the last century. In contrast, [[#Junod--2020|Junod and Christy (2020)]] find NMAT trends which are 8–17% larger than those for SST in the ERSSTv4 and HadISST datasets for the period 1900 to 2010, but 11–15% smaller than the SST trends for the same datasets from 1979 to 2010. However, ERSSTv4 uses NMAT data as a basis for homogeneity adjustment so is not fully independent. [[#Kent--2021|Kent and Kennedy (2021)]] note sensitivity to methodological choices in comparisons but find that NMAT is warming more slowly than SST products over most periods considered. [[#Rubino--2020|Rubino et al. (2020)]] exploit tropical Pacific moored buoy arrays, available since the early 1980s, and find differences in NMAT and SST anomalies, which are sensitive to the choice of period and show spatio-temporal ENSO-related (Annex IV) signals in the differences.

Overall, with ''medium evidence'' and ''low agreement'' , available observational products suggest that NMAT is warming less than SST by up to 15%. Given that these ocean observations cover roughly two thirds of the globe, this implies that GMST is warming up to at most 10% faster than GSAT. Substantial uncertainty remains and the effect is highly sensitive to the choice of both time period and choice of NMAT and SST observational products to compare. Observed NMAT warming faster than observed SST cannot be precluded.

''CMIP model-based evidence''

CMIP historical simulations and projections agree that GSAT increases faster than GMST, the reverse of what is indicated by many marine observations. Several studies approximate the approach used to derive GMST from observations by blending SST over open ocean and SAT over land and sea ice from model output ( [[#Cowtan--2015|Cowtan et al., 2015]] ; [[#Richardson--2018|Richardson et al., 2018]] ; [[#Beusch--2020|Beusch et al., 2020]] ; [[#Gillett--2021|Gillett et al., 2021]] ). Cowtan et al. found that trends in GSAT are of the order of 9% larger than for GMST in CMIP5, based on data from 1850–2100 (historical + RCP8.5), if anomalies are blended and sea ice is allowed to vary over time ( [[#Cowtan--2015|Cowtan et al., 2015]] ). Broadly consistent numbers are found for both CMIP5 and CMIP6, across a range of SSP and RCP scenarios and time periods ( [[#Richardson--2018|Richardson et al., 2018]] ; [[#Beusch--2020|Beusch et al., 2020]] ; [[#Gillett--2021|Gillett et al., 2021]] ). Blending monthly anomalies and allowing sea ice to vary, the change in GSAT for 2010–2019 relative to 1850–1900 is 2–8% larger than spatially-complete GMST in CMIP6 historical and SSP2-4.5 simulations ( [[#Gillett--2021|Gillett et al., 2021]] ), and 6–12% larger in CMIP5 historical and RCP2.6 and 8.5 simulations for 2007–2016 relative to 1861–1880 ( [[#Richardson--2018|Richardson et al., 2018]] ). However, a true like-for-like comparison to observational products is challenging because methodological choices have a large impact on the relationship between modelled GMST and GSAT and none of these studies fully reproduces the methods used to derive estimates of GMST in recent observational datasets, which use various ways to infill areas lacking in situ observations ( [[#Jones--2020|Jones, 2020]] ).

Marine boundary layer behaviour and parameterizations in all CMIP models are based upon Monin-Obukhov similarity theory (e.g., [[#Businger--1971|Businger et al., 1971]] ), which informs assumptions around gradients in the near-surface boundary layer dependent upon temperature, wind speed and humidity. This leaves open the possibility of a common model bias, while [[#Druzhinin--2019|Druzhinin et al. (2019)]] also point to departures of temperature profiles from theoretical predictions under certain conditions. There remain inadequacies in understanding and modelling of key processes ( [[#Edwards--2020|Edwards et al., 2020]] ), and biases in the representation of the absolute SST-MAT difference have been identified in climate models and reanalyses ( [[#Găinuşă-Bogdan--2015|Găinuşă-Bogdan et al., 2015]] ; [[#Zhou--2020|Zhou et al., 2020]] ).

''Reanalysis-based evidence''

[[#Simmons--2017|Simmons et al. (2017)]] found that in JRA-55 and ERA-Interim (following an adjustment to account for an apparent discontinuity), GSAT increased 2–4% faster than GMST over the period 1979–2016. In atmospheric reanalyses, SST is given as a lower boundary condition from an observed globally interpolated product (such as HadISST; [[#Rayner--2003|Rayner et al., 2003]] ) whereas the air temperature is reliant upon model parameterizations and assimilated observations that do not include MAT observations ( [[#Simmons--2017|Simmons et al., 2017]] ), thereby limiting their capability to constrain differences in GMST and GSAT trends. Furthermore, it is unclear what the lack of dynamic coupling at the ocean-atmosphere interface might imply for the representativeness of reanalysis-based estimates.

''Representation of surface temperatures in sea ice regions''

There is a significant issue in areas where sea ice melts or grows, where the quantity used in observational-based GMST estimates switches between air temperature and sea surface temperature. This primarily affects analyses combining SAT anomalies over land and ice with SST anomalies over ocean. In areas where sea ice has recently melted, the climatological value changes from an air-temperature based estimate to an SST estimate based upon the freezing point of seawater (–1.8°C). This switch in climatology to, in general, a warmer climatology, leads to a bias towards reduced warming in anomalies compared with analyses based on absolute temperatures. [[#Richardson--2018|Richardson et al. (2018)]] found this underestimation to amount to approximately 3% of observed warming in historical model simulations. Given the projected future sea ice losses, the effect will grow in future ( ''low confidence'' ), with potential effects of the order of 0.1°C in the second half of the 21st century under high warming scenarios, although with some uncertainty arising from the large spread of sea ice loss in model projections ( [[#Tokarska--2019|Tokarska et al., 2019]] ).

'''Cross Chapter Box 2.3, Table'''

'''1 |'''

'''Summary of key observationally based global warming estimates (in °C) to various reference periods in the present report and selected prior reports (AR5 WGI and SR1.5) and their principal applications (see [[IPCC:Wg1:Chapter:Chapter-1#1.4.1|Section 1.4.1]] for further information on reference periods).''' Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).
{| class="wikitable"
|-
| '''Reference Period'''

| '''AR6 GMST''' (° '''C)'''

| '''AR6 GSA''' '''T''' <sup>a</sup> '''(''' ° '''C)'''

| '''AR5 and/or''' SR1.5 '''(''' italics ''') – Only Where Reported''' (° '''C)'''

| '''Principal Use of This Period in this Report and Previous Reports'''

|-
| 1850–1900 to 2011–2020

| 1.09 [0.95 to 1.20]

| 1.09 [0.91 to 1.23]

|
| Warming to present in AR6 WGI

|-
| 1850–1900 to 2010–2019

| 1.06 [0.92 to 1.17]

| 1.06 [0.88 to 1.21]

|
| Attributable warming assessment period in AR6 WGI

|-
| 1850–1900 to 2006–2019

| 1.03 [0.89 to 1.14]

| 1.03 [0.86 to 1.18]

|
| AR6 WGI warming estimate as a line of evidence for energy budget constraints to estimate ECS and TCR

|-
| 1850–1900 to 2006–2015

| 0.94 [0.79 to 1.04]

| 0.94 [0.76 to 1.08]

| ''0.87 [0.75 to 0.99] – GMST''

''0.97 [0.85 to 1.09] – GSA'' ''T'' <sup>b</sup>

| Warming to date in SR1.5

|-
| 1850–1900 to 2003–2012

| 0.90 [0.74 to 1.00]

| 0.90 [0.72 to 1.03]

| 0.78 [0.72 to 0.85]

| Warming to date in AR5 WGI

|-
| 1850–1900 to 2001–2020

| 0.99 [0.84 to 1.10]

| 0.99 [0.81to 1.14]

|
| Warming to first two decades of 21st century

|-
| 1850–1900 to 1995–2014

| 0.85 [0.69 to 0.95]

| 0.85 [0.67 to 0.98]

|
| Warming to recent past in AR6 WGI

|-
| 1850–1900 to 1986–2005

| 0.69 [0.54 to 0.79]

| 0.69 [0.52 to 0.82]

| 0.61 [0.55 to 0.67] <sup>c</sup>

| Warming to recent past in AR5 WGI. This difference is used to report in this box the implications of the AR6 historical global surface temperature assessment in a way that is directly comparable to the AR5 estimate.

|-
| 1850–1900 to 1961–1990

| 0.36 [0.23 to 0.44]

| 0.36 [0.22 to 0.45]

|
| Warming to reference period recommended by WMO for national-level data sets used for climate change assessment (included in the AR6 WGI Atlas)

|-
| 1880–2012 OLS trend

| 0.92 [0.68 to 1.17]

|
| 0.85 [0.65 to 1.06]

| Warming trend to date in AR5 WGI Summary for Policymakers and AR5 Synthesis Report

|}

<sup>a</sup> As the uncertainty in the relationship between GMST and GSAT changes is independent of the uncertainty in the assessed change in GMST, these uncertainties are combined in quadrature.

<sup>b</sup> The SR1.5 derived a GSAT estimate by taking the CMIP5 ensemble mean GSAT change of 0.99°C, sub-sampling to HadCRUTv4.6, noting the offset in trends (0.84°C HadCRUT4 observed GMST vs. 0.86°C modelled GMST) and adjusting by this to arrive at an estimate of 0.97°C change in GSAT. The ''likely'' uncertainty range of ±0.12°C was not further adjusted.

<sup>c</sup> Note that the AR5 approach for the change from 1850–1900 to both 1986–2005 and 2003–2012 was based upon one dataset (HadCRUT4) and its parametric uncertainty estimates are known to underestimate the true uncertainty.

''Summary of lines of evidence''

GMST and GSAT are physically distinct. There is ''high confidence'' that long-term changes in GMST and GSAT differ by at most 10% in either direction. However, conflicting lines of evidence from models and direct observations combined with limitations in theoretical understanding lead to ''low confidence'' in the sign of any difference in long-term trends. The ''very likely'' range of estimated historical GMST warming is combined with the assessed ± 10% uncertainty in the relationship between GMST and GSAT changes to infer a GSAT equivalent, accounting for any possible real-world physical difference. Improvements in understanding may yield a robust basis to apply a scaling-factor to account for the difference in future assessments.

'''Mapping between AR5 and AR6 Assessments'''

The AR5 assessed estimate for historical warming between 1850–1900 and 1986–2005 is 0.61 [0.55 to 0.67] °C. The equivalent in AR6 is 0.69 [0.54 to 0.79] °C, and the 0.08 [-0.01 to 0.12] °C difference is an estimate of the contribution of changes in observational understanding alone (Cross-Chapter Box 2.3, Table 1). The exact value of this contribution depends upon the metric being compared (GMST/GSAT, the method used to calculate a trend or change between two periods, the exact reference period used), with the best estimates (with the exception of the SR1.5 GSAT estimate) falling between 0.07°C and 0.12°C. The choice of 1850–1900 to 1986–2005 as the basis is due to the widespread use of this period across AR5 and SR1.5 in several contexts. The AR6-assessed GMST warming between 1850–1900 and 2011–2020 is 1.09 [0.95 to 1.20] °C. An AR5-equivalent assessment using this estimated difference in observational understanding is thus 1.01 [0.94 to 1.08] °C. These updates and improvements in observational datasets affect other quantities that derive from the assessment of GSAT warming, including estimates of remaining carbon budgets and estimates of crossing times of 1.5°C and 2°C of global warming (see Cross Chapter Box 2.3, Table 1).

'''Updates to estimated Global Warming Level (GWL) crossing times'''

The updated estimate of historical warming is one contribution to the revised time of projected crossing of the threshold of 1.5°C global warming in comparison with SR1.5, but is not the only reason for this update. The AR6 assessment of future change in GSAT (Table 4.5) results in the following threshold-crossing times, based on 20-year moving averages. The threshold-crossing time is defined as the midpoint of the first 20-year period during which the average GSAT exceeds the threshold. During the near term (2021–2040), a 1.5°C GSAT increase relative to the average over the period 1850–1900 is ''very likely'' to occur in scenario SSP5-8.5, ''likely'' to occur in scenarios SSP2-4.5 and SSP3-7.0, and ''more likely than not'' to occur in scenarios SSP1-1.9 and SSP1-2.6. In all scenarios assessed here except SSP5-8.5, the central estimate of crossing the 1.5°C global warming level lies in the early 2030s. This is in the early part of the ''likely'' range (2030–2052) assessed in SR1.5, which assumed continuation of the then-reported warming rate; this estimated rate has been confirmed in AR6 ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1|Section 3.3.1]] ). Roughly half of this difference arises from the higher diagnosed historical warming in AR6. The other half arises because, for central estimates of climate sensitivity, most scenarios show stronger warming over the near term than was assessed as ‘current’ in SR1.5 ( ''medium confidence'' ). When considering scenarios similar to SSP1-1.9 instead of linear extrapolation, the SR1.5 estimate of when 1.5°C global warming is crossed is close to the central estimate reported here (SR1.5, Table 2.SM.12).

'''Implications for assessment of emissions scenarios and remaining carbon budgets'''

To estimate the global warming implications of emissions scenarios, AR5 and SR1.5 combined estimates of observed GMST changes from 1850–1900 to 1986–2005 (Cross-Chapter Box 2.3, Table 1) with GSAT projections of subsequent warming. AR6 undertakes three changes to this approach. First, the AR6 assessment of improved observational records is used. Second, the recent past baseline period is updated from 1986–2005 to 1995–2014, and, third, historical estimates are expressed in GSAT instead of GMST for consistency of historical estimates with future projections. The updated estimates of warming to date in AR6 lead to higher estimates of future warming, all else being equal. The temperature classification of emissions scenarios in the WGIII report adopts the definition of temperature classes as introduced in SR1.5, and assigns emissions scenarios to these classes based on their AR6 assessed GSAT outcomes (Cross-Chapter Box 7.1; WGIII Annex C.II.2.4).

In both AR5 and SR1.5, remaining carbon budgets were expressed as a function of GSAT warming, while also highlighting the implications of using historical warming estimates expressed in GMST. The AR5 reported total carbon budgets for GSAT warming relative to 1861–1880. The AR5 Synthesis Report (SYR) also includes remaining carbon budget estimates based on AR5 WGIII scenario projections that use the method for AR5 scenario projections described above. The SR1.5 integrated several methodological advancements to estimate remaining carbon budgets and reported budgets for additional GSAT warming since the 2006–2015 period, estimating, following the application of an adjustment ( [[#Richardson--2016|Richardson et al., 2016]] , Table 1.1, SR1.5) to GMST, that 0.97°C (± 0.12°C) of GSAT warming occurred historically between 1850–1900 and 2006–2015. The AR6 assessment, above, leads to an estimate of 0.94°C of warming between 1850–1900 and 2006–2015. All other factors considered equal, the AR6 estimate thus implies that 0.03°C more warming is considered for remaining carbon budgets compared to SR1.5. Combining this 0.03°C value with the SR1.5 transient climate response to cumulative emissions of CO <sub>2</sub> (TCRE) translates into remaining carbon budgets about 70 [40–140] GtCO <sub>2</sub> larger compared to SR1.5 on a like-for-like basis. Meanwhile, on the same like-for-like basis, updates to historical observational products would reduce remaining carbon budgets reported in AR5 SYR based on WGIII scenario projections by about 180 [120 to 370] GtCO <sub>2</sub> . Box 5.2 provides a further overview of updates to estimates of the remaining carbon budget since AR5.

'''Implications for assessment of impacts and adaptation'''

The assessment of global warming to date now being larger than previously assessed has no consequence on the assessment of past climate impacts, nor does it generally imply that projected climate impacts are now expected to occur earlier. The implications are mainly that the level of warming associated with a particular impact has been revised. This has very limited practical implications for the assessment of the benefits of limiting global warming to specific levels, as well as for the urgency of adaptation action. For example, impacts that occurred in the period 1986–2005 were previously associated with a GMST increase of 0.61°C relative to 1850–1900, relative to AR5 estimates. These impacts are now instead associated with a GMST increase of 0.69°C, relative to the assessment in this Report. The impacts themselves have not changed. Similarly, the impacts previously associated with a GMST or GSAT increase of 1.5°C will now generally be associated with a slightly different global warming level. This is because projections of future warming and its impacts relative to 1850–1900 are normally made by adding projected warming from a recent past baseline to an estimate of the observed warming from 1850–1900, as in AR5 and SR1.5.

Most of the previously projected impacts and risks associated with global warming of 1.5°C have therefore not changed and are still associated with the same level of future warming (0.89°C) relative to 1986–2005. With this warming now estimated as 0.08°C larger than in AR5, the future impacts previously associated with 1.5°C warming are now associated with 1.58°C warming. Similarly, the impacts now associated with 1.5°C warming would have previously been associated with 1.42°C warming. There are exceptions where impacts studies have used a baseline earlier than 1986–2005 (e.g., [[#King--2017|King et al., 2017]] ), for which the new estimate of the historical warming would mean an earlier occurrence of the projected impacts. However, even in these cases, the ostensible difference in impacts associated with a 0.08°C difference in global mean temperature will be small in comparison with the uncertainties. There are also substantial uncertainties in regional climate changes and the magnitude of climate impact-drivers projected to occur with global warming of 1.5°C ( [[#Betts--2018|Betts et al., 2018]] ; [[#Seneviratne--2018|Seneviratne et al., 2018]] ). Furthermore, the time of reaching global warming of 1.5°C is subject to uncertainties of approximately ±10 years associated with uncertainties in climate sensitivity, and ±3 to 4 years associated with the different SSP forcing scenarios ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.4|Section 4.3.4]] , Table 4.5, and see discussion above).

There is therefore ''high confidence'' that assessment of the magnitude and timing of impacts-related climate quantities at 1.5°C is not substantially affected by the revised estimate of historical global warming. The assessment of the implications of limiting global warming to 1.5°C compared to 2°C will also remain broadly unchanged by the updated estimate of historical warming, as this depends on the relative impacts rather than the absolute impacts at any specific definition of global temperature anomaly ( ''high confidence'' ).

<div id="2.3.2" class="h2-container"></div>

<span id="cryosphere"></span>