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

==== 2.3.1.4 Atmospheric Circulation ====

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This section focuses on large-scale changes in a subset of components of the atmospheric circulation (Cross-Chapter Box 2.2). [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] assesses large-scale as well as regional aspects of circulation components and their impact on the hydrological cycle, while ( [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] assesses the association of circulation changes and variability with extreme events.

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<span id="the-hadley-and-walker-circulations"></span>
===== 2.3.1.4.1 The Hadley and Walker circulations =====

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The AR5 reported ''low confidence'' in trends in the strength of the Hadley circulation (HC) and the Walker circulation (WC) due to uncertainties in available reanalysis datasets and the large interannual-to-decadal variability of associated circulation patterns. However, AR5 indicated a ''likely'' widening of the tropical belt since the 1970s, albeit with large uncertainty in the magnitude of this change. There was ''high confidence'' that the post-1990s strengthening of the Pacific WC reversed its weakening observed from the mid-19th century to the 1990s.

Paleo reconstructions of rainfall and trade winds extending over the last 100 kyr show an intensification of the NH HC concurrently with a weakening of the SH HC and a southward shift of the inter tropical convergence zone (ITCZ) during Heinrich stadials ( [[#Deplazes--2013|Deplazes et al., 2013]] ; [[#McGee--2018|McGee et al., 2018]] ; [[#Stríkis--2018|Stríkis et al., 2018]] ; [[#Wendt--2019|Wendt et al., 2019]] ). An intensification of the HC associated with conditions similar to La Niña (northward migrations of both the ITCZ and the SH westerlies) was found in reconstructions for the MH ( [[#McGee--2014|McGee et al., 2014]] ; [[#Mollier-Vogel--2019|Mollier-Vogel et al., 2019]] ). Changes in insolation from the mid to late Holocene favoured a southward migration in the position of the ITCZ and the descending branch of the HC in the NH, approaching its current width and position ( [[#Wirth--2013|Wirth et al., 2013]] ; [[#Thatcher--2020|Thatcher et al., 2020]] ). Tree ring chronologies from the NH mid-latitudes over the last 800 years show that the northern edge of the HC tended to migrate southward during positive phases of ENSO and PDV, with northward shifts during negative phases ( [[#Alfaro-Sánchez--2018|Alfaro-Sánchez et al., 2018]] ). Between 1400 and 1850 CE the HC over both hemispheres and the ITCZ were displaced southward, consistent with occurrence of drought conditions in several NH regions ( [[#Wirth--2013|Wirth et al., 2013]] ; [[#Burn--2014|Burn and Palmer, 2014]] ; [[#Lechleitner--2017|Lechleitner et al., 2017]] ; [[#Alfaro-Sánchez--2018|Alfaro-Sánchez et al., 2018]] ; [[#Flores-Aqueveque--2020|Flores-Aqueveque et al., 2020]] ). Moreover, several proxy records showed not only inter-hemispheric shifts in the ITCZ but a contraction of the tropical belt during 1400–1850 CE, which followed an expansion during 950–1250 CE ( [[#Denniston--2016|Denniston et al., 2016]] ; [[#Griffiths--2016|Griffiths et al., 2016]] ).

From centennial-scale reanalyses, [[#Liu--2012|Liu et al. (2012)]] and [[#D’Agostino--2017|D’Agostino and Lionello (2017)]] found divergent results on HC extent over the last 150 years, although with unanimity upon an intensification of the SH HC. A substantial discrepancy between HC characteristics in centennial-scale reanalyses and in ERA-Interim ( [[#D’Agostino--2017|D’Agostino and Lionello, 2017]] ) since 1979 yields significant questions regarding their ability to capture changes in HC behaviour. Taken together with the existence of apparent non-climatic artefacts in the datasets ( [[#Nguyen--2015|Nguyen et al., 2015]] ), this implies ''low confidence'' in changes in the extent and intensity of HC derived from centennial-scale reanalyses. However, using multiple observational datasets and centennial-scale reanalyses, [[#Bronnimann--2015|Bronnimann et al. (2015)]] identified a southward shift in the NH HC edge from 1945 to 1980 of about 0.25° latitude per decade, consistent with observed changes in global land monsoon precipitation ( [[#2.3.1.4.2|Section 2.3.1.4.2]] ).

Since AR5 several studies based upon a range of metrics and different reanalyses products have suggested that the annual mean HC extent has shifted poleward at an approximate rate of 0.1°–0.5° latitude per decade over the last about 40 years ( [[#Allen--2017|Allen and Kovilakam, 2017]] ; [[#Davis--2017|Davis and Birner, 2017]] ; [[#Grise--2018|Grise et al., 2018]] ; [[#Staten--2018|Staten et al., 2018]] , 2020; [[#Studholme--2018|Studholme and Gulev, 2018]] ; [[#Grise--2020|Grise and Davis, 2020]] ). The observed widening of the annual mean HC, revealed by a variety of metrics, is primarily due to poleward shift of the Northern Hemisphere HC. There have been stronger upward trends in the NH extent of HC after 1992 (Figure 2.17a). The estimated magnitude of the recent changes based on modern-era reanalyses is not as large as that in AR5, due to apparent biases in older-generation reanalyses ( [[#Grise--2019|Grise et al., 2019]] ). Moreover, large interannual variability leads to uncertainties in estimates of long-term changes ( [[#Nguyen--2013|Nguyen et al., 2013]] ; [[#Garfinkel--2015b|Garfinkel et al., 2015b]] ; [[#Seviour--2018|Seviour et al., 2018]] ; [[#Staten--2018|Staten et al., 2018]] ), particularly for the NH given its zonal asymmetries ( [[#Staten--2020|Staten et al., 2020]] ; [[#Wang--2020|Wang et al., 2020]] ). These large-scale features of the HC based on reanalyses agree with estimates revealed from the Integrated Global Radiosonde Archive (IGRA) during 1979–2012 ( [[#Lucas--2015|Lucas and Nguyen, 2015]] ; [[#Mathew--2016|Mathew et al., 2016]] ). Recent trends based on reanalyses indicate a larger seasonal widening in the HC for summer and autumn in each hemisphere, although the magnitude of changes in HC extent is strongly dependent on dataset and metrics used ( [[#Grise--2018|Grise et al., 2018]] ; Y. [[#Hu--2018|]] [[#Hu--2018|Hu et al., 2018]] ; [[#Staten--2018|Staten et al., 2018]] ). The shifts in the HC position were accompanied by a narrowing ITCZ over the Atlantic and Pacific basins, with no significant change in its location and increases in the precipitation intensity ( [[#Byrne--2018|Byrne et al., 2018]] ).

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[[File:edccb51f7c800aa7409e4500c5eec32e IPCC_AR6_WGI_Figure_2_17.png]]

'''Figure''' '''2.17 |''' '''Time series of the annual mean Northern Hemisphere (NH, top curves) and Southern Hemisphere (SH, bottom curves) Hadley cell extent (a) and Hadley cell intensity (b) since 1979.''' Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

Trends in the HC intensity since 1979 differ between reanalyses, although there is a tendency toward HC intensification (Figure 2.17b; [[#Nguyen--2013|Nguyen et al., 2013]] ; [[#Chen--2014|Chen et al., 2014]] ; [[#D’Agostino--2017|D’Agostino and Lionello, 2017]] ; R. [[#Huang--2019|Huang et al., 2019]] ), which is more marked in the NH than the SH ( [[#Studholme--2018|Studholme and Gulev, 2018]] ). However, the ability of reanalyses to represent the HC strength has been questioned due to inaccurate representation of latent heating distribution, which is directly related to tropical convection and influences the HC dynamics ( [[#Chemke--2019|Chemke and Polvani, 2019]] ; [[#Mathew--2019|Mathew and Kumar, 2019]] ).

Paleo evidence during the LGM indicates a weaker WC over the Indian Ocean ( [[#DiNezio--2018|DiNezio et al., 2018]] ; [[#Windler--2019|Windler et al., 2019]] ) with a stronger Pacific WC ( [[#DiNezio--2013|DiNezio and Tierney, 2013]] ). During the Holocene, a transition from a strong WC located more westward during the Early-to-Mid Holocene towards a weak and eastward shifted WC during the late Holocene was inferred from proxy records from the Pacific Warm Pool and South East Asia ( [[#Barr--2019|Barr et al., 2019]] ; [[#Dang--2020|Dang et al., 2020]] ; [[#Griffiths--2020|Griffiths et al., 2020]] ), in concurrence with changes in ENSO activity ( [[#2.4.2|Section 2.4.2]] ). Reconstructions for the CE showed weakened WC during 1000–1250 and since 1850, with an intensified circulation during 1500–1850 CE ( [[#Xu--2016|Xu et al., 2016]] ; [[#Deng--2017|Deng et al., 2017]] ).

Considering instrumental records, there is considerable interdecadal variability in the strength of the WC, resulting in time-period dependent magnitude and even sign of trends ( [[#Carilli--2015|Carilli et al., 2015]] ; [[#Bordbar--2017|Bordbar et al., 2017]] ; [[#Hou--2018|Hou et al., 2018]] ), with some studies reporting weakening over the 20th century (e.g., [[#Power--2011|Power and Kociuba, 2011]] ; [[#Liu--2019|Liu et al., 2019]] ), while others reported strengthening (Z. [[#Li--2020|]] [[#Li--2020|Li et al., 2020]] ), particularly over the last 30–40 years (e.g., [[#Hu--2013|Hu et al., 2013]] ; [[#L’Heureux--2013|L’Heureux et al., 2013]] ; [[#Yim--2017|Yim et al., 2017]] ). Based on estimation of changes in mid-tropospheric velocity from changes in observed cloud cover, [[#Bellomo--2015|Bellomo and Clement (2015)]] suggest a weakening and eastward shift of the WC over 1920–2010, however the robustness of this signal is questionable due to high uncertainty in the ship-reported cloud data used before 1954. Using centennial-scale 20CR reanalysis [[#Tseng--2019|Tseng et al. (2019)]] showed that the vertical westerly wind shear over the western Pacific does not indicate any long-term change during 1900–1980, but shows a marked increase since the 1980s that is not present in ERA-Interim and JRA-55, again calling into question the ability of centennial-scale reanalyses to capture tropical circulation changes. Recent strengthening together with a westward shift of the WC ( [[#Bayr--2014|Bayr et al., 2014]] ; [[#Ma--2016|Ma and Zhou, 2016]] ) was identified across several reanalysis products and observational datasets, and using different metrics for quantifying WC. Nevertheless, satellite observations of precipitation and analyses of upper tropospheric humidity suggest substantially weaker strengthening of the WC than implied by reanalyses ( [[#Chung--2019|Chung et al., 2019]] ). This recent strengthening in the WC is associated with enhanced precipitation in the tropical western Pacific, anomalous westerlies in the upper troposphere, strengthened downwelling in the central and eastern tropical Pacific, and anomalous surface easterlies in the western and central tropical Pacific ( [[#Dong--2013|Dong and Lu, 2013]] ; [[#McGregor--2014|McGregor et al., 2014]] ; [[#Choi--2016|Choi et al., 2016]] ). Positive trends in sea level pressure over the eastern Pacific and concurrent negative trends over the Indonesian region result in a pattern implying a shift towards a La Niña-like WC regime, with strengthening of the Pacific Trade Winds mainly over 1979–2012 ( [[#L’Heureux--2013|L’Heureux et al., 2013]] ; [[#England--2014|England et al., 2014]] ; [[#Sohn--2016|Sohn et al., 2016]] ; [[#Zhao--2019|Zhao and Allen, 2019]] ). Seasonal assessment of the WC showed significant changes in the vertical westerly wind shear over the Pacific during the austral summer and autumn implying a strengthening ( [[#Clem--2017|Clem et al., 2017]] ).

In summary, there has been a ''likely'' widening of the Hadley circulation since the 1980s, mostly due to its extension in the NH, although there is only ''medium confidence'' in the extent of the changes. This has been accompanied by a strengthening of the Hadley circulation, particularly in the NH ( ''medium confidence'' ). There is ''low confidence'' in the estimation of long-term trends in the strength of the Walker circulation, which are time period dependent and subject to dataset uncertainties. Trends since 1980 are better characterized and consistent with a ''very likely'' strengthening that resembles a La Niña-like Walker circulation and a westward shift of the Walker circulation, although with ''medium confidence'' in the magnitude of the changes, arising from the differences between satellite observations and reanalysis products.

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<span id="global-monsoon-gm-changes"></span>

===== 2.3.1.4.2 Global monsoon (GM) changes =====

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The AR5 reported a weakening of the global monsoon (GM) circulation as well as a decrease of global land monsoon rainfall over the second half of the 20th century. Nevertheless, there was ''low confidence'' in the observed circulation trends due to uncertainties in reanalysis products and in the definition of the monsoon area. From a paleo perspective, AR5 only assessed regional monsoon changes.

New research based on high-resolution proxies reinforces previous findings on the influence of orbital cycles on GM variability on millennial time scales. The intensity of the monsoon systems is generally out of phase between hemispheres, being associated with the precession cycle (about 21–23 kyr) ( [[#An--2015|An et al., 2015]] ; P.X. [[#Wang--2017|Wang et al., 2017]] ; [[#Seth--2019|Seth et al., 2019]] ), with intensified NH monsoon systems during precession minima ( [[#Toucanne--2015|Toucanne et al., 2015]] ; [[#Wagner--2019|Wagner et al., 2019]] ). The eccentricity forcing (about 100 kyr cycle) shows stronger GM during interglacial periods (P.X. [[#Wang--2014|]] [[#Wang--2014|Wang et al., 2014]] , 2017; [[#An--2015|An et al., 2015]] ; [[#Mohtadi--2016|Mohtadi et al., 2016]] ). Changes in obliquity (about 41 kyr cycle) modify the strength of monsoon systems, with increased summer monsoon rainfall when obliquity is maximal (Y. [[#Liu--2015|]] [[#Liu--2015|Liu et al., 2015]] b; [[#Mohtadi--2016|Mohtadi et al., 2016]] ). Millennial scale variability in GM during the LDT was also linked to the occurrences of Heinrich stadials, resulting in weakened NH monsoons and intensified SH monsoons ( [[#An--2015|An et al., 2015]] ; P.X. [[#Wang--2017|Wang et al., 2017]] ; [[#Margari--2020|Margari et al., 2020]] ).

An intensification of the NH monsoons in the early to mid-Holocene with increased precipitation and regional expansions of rainfall areas identified through a variety of proxy records is shown by [[#Biasutti--2018|Biasutti et al. (2018)]] and P.X. [[#Wang--2017|Wang et al. (2017)]] . The response for the SH monsoons during this period indicates a weakening in both summer and winter precipitation (P.X. [[#Wang--2014|]] [[#Wang--2014|Wang et al., 2014]] , 2017; [[#Sachs--2018|Sachs et al., 2018]] ). A decline in GM precipitation and a retraction of the northern fringes of monsoon areas was inferred from the mid-Holocene onwards, with some regions experiencing wetter conditions during the mid to late Holocene compared with present and a strengthening of the SH monsoons (P.X. [[#Wang--2014|]] [[#Wang--2014|Wang et al., 2014]] , 2017; [[#Sachs--2018|Sachs et al., 2018]] ). For the CE, GM reconstructions exhibit inter-hemispheric contrast during the period 950–1250 CE, with intensified NH monsoons and weakened SH monsoons, and the opposite pattern during 1400–1850 CE (P.X. [[#Wang--2014|]] [[#Wang--2014|Wang et al., 2014]] ; [[#An--2015|An et al., 2015]] ).

Direct observations highlight that the GM land precipitation, particularly over the NH, experienced a slight increase from 1900 through the early 1950s, followed by an overall decrease from the 1950s to the 1980s, and then an increase to present ( [[#Kitoh--2013|Kitoh et al., 2013]] ; [[#Wang--2018|]] [[#Wang--2018|B. Wang et al., 2018]] , 2021; X. [[#Huang--2019b|]] [[#Huang--2019|Huang et al., 2019]] b ). This highlights the existence of multi-decadal variations in the NH monsoon circulation patterns and precipitation intensity ( [[#Wang--2013|Wang et al., 2013]] ; P.X. [[#Wang--2014|]] [[#Wang--2014|Wang et al., 2014]] , 2017; [[#Monerie--2019|Monerie et al., 2019]] ). An overall increase in monsoon precipitation during extended boreal summer (JJAS) over the NH since 1979 is revealed by GPCP ( [[#Deng--2018|Deng et al., 2018]] ; [[#Han--2019|Han et al., 2019]] ) and CMAP for 1980–2010 ( [[#Jiang--2016|Jiang et al., 2016]] ). SH summer monsoon behaviour is dominated by strong interannual variability and large regional differences ( [[#Kitoh--2013|Kitoh et al., 2013]] ; [[#Lin--2014|Lin et al., 2014]] ; [[#Jiang--2016|Jiang et al., 2016]] ; [[#Kamae--2017|Kamae et al., 2017]] ; [[#Deng--2018|Deng et al., 2018]] ; [[#Han--2019|Han et al., 2019]] ), with no significant trends reported by GPCP and CMAP ( [[#Deng--2018|Deng et al., 2018]] ). Uncertainty predominantly arises from the observed increase in tropical precipitation seasonality ( [[#Feng--2013|Feng et al., 2013]] ) and the estimation of GM precipitation over the ocean areas, leading to a large apparent spread across datasets ( [[#Kitoh--2013|Kitoh et al., 2013]] ; [[#Kamae--2017|Kamae et al., 2017]] ).

In summary, observed trends during the last century indicate that the GM precipitation decline reported in AR5 has reversed since the 1980s, with a ''likely'' increase mainly due to a significant positive trend in the NH summer monsoon precipitation ( ''medium confidence'' ). However, GM precipitation has exhibited large multi-decadal variability over the last century, creating ''low confidence'' in the existence of centennial-length trends in the instrumental record. Proxy reconstructions show a ''likely'' NH monsoons weakening since the mid-Holocene, with opposite behaviour for the SH monsoons.

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<span id="extratropical-jets-storm-tracks-and-blocking"></span>

===== 2.3.1.4.3 Extratropical jets, storm tracks, and blocking =====

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The AR5 reported a ''likely'' poleward shift of storm tracks and jet streams since the 1970s from different datasets, variables and approaches. These trends were consistent with the HC widening and the poleward shifting of the circulation features since the 1970s. There was ''low confidence'' in any large-scale change in blocking.

Proxy records consistent with modelling results imply a southward shift of the storm tracks over the North Atlantic during the LGM ( [[#Raible--2021|Raible et al., 2021]] ). A variety of proxies are available for the changes in the position of the extratropical jets/westerlies during the Holocene. Recent syntheses of moisture-sensitive proxy records indicate drier-than-present conditions over mid-latitudes of western North America ( [[#Hermann--2018|Hermann et al., 2018]] ; [[#Liefert--2020|Liefert and Shuman, 2020]] ) during the MH, which together with a weakened Aleutian Low ( [[#Bailey--2018|Bailey et al., 2018]] ) implies that the winter North Pacific jetstream was shifted northward. A synthesis of lines of evidence from the SH indicates that the westerly winds were stronger over 14–5 ka, followed by regional asymmetry after 5 ka ( [[#Fletcher--2012|Fletcher and Moreno, 2012]] ). There is no consensus on the shifts of the SH westerlies with some studies implying poleward migrations ( [[#Lamy--2010|Lamy et al., 2010]] ; [[#Voigt--2015|Voigt et al., 2015]] ; [[#Turney--2017|Turney et al., 2017]] ; [[#Anderson--2018|Anderson et al., 2018]] ) and others suggesting an equatorward shift ( [[#Kaplan--2016|Kaplan et al., 2016]] ) in the MH.

During 950–1400 CE, hydroclimate indicators suggest a northward shift of Pacific storm tracks over North America ( [[#McCabe-Glynn--2013|McCabe-Glynn et al., 2013]] ; [[#Steinman--2014|Steinman et al., 2014]] ) which was comparable in magnitude to that over 1979–2015 (J. [[#Wang--2017a|]] [[#Wang--2017|Wang et al., 2017]] a ). Storm tracks over the North Atlantic-European sector shifted northward as indicated by multi-proxy indicators over the North Atlantic ( [[#Wirth--2013|Wirth et al., 2013]] ; [[#Orme--2017|Orme et al., 2017]] ) and Mediterranean ( [[#Roberts--2012|Roberts et al., 2012]] ). Reconstructed westerly winds in the SH suggest a poleward shift ( [[#Lamy--2010|Lamy et al., 2010]] ; [[#Schimpf--2011|Schimpf et al., 2011]] ; [[#Goodwin--2014|Goodwin et al., 2014]] ; [[#Koffman--2014|Koffman et al., 2014]] ; [[#Moreno--2018|Moreno et al., 2018]] ), with latitudinal change comparable to that during recent decades ( [[#Swart--2012|Swart and Fyfe, 2012]] ; [[#Manney--2018|Manney and Hegglin, 2018]] ).

Multiple reanalyses show that since 1979 the subtropical jet wind speeds have generally increased in winter and decreased in summer in both hemispheres, but the trends are regionally dependent ( [[#Pena-Ortiz--2013|Pena-Ortiz et al., 2013]] ; [[#Manney--2018|Manney and Hegglin, 2018]] ; S.H. [[#Lee--2019|]] [[#Lee--2019|Lee et al., 2019]] ). Over NH mid-latitudes, the summer zonal wind speeds have weakened in the mid-troposphere ( [[#Francis--2012|Francis and Vavrus, 2012]] ; [[#Coumou--2014|Coumou et al., 2014]] , 2015; [[#Haimberger--2017|Haimberger and Mayer, 2017]] ). Meanwhile there are indications of enhanced jetstream meandering in boreal autumn at the hemispheric scale ( [[#Francis--2015|Francis and Vavrus, 2015]] ; [[#Di%20Capua--2016|Di Capua and Coumou, 2016]] ), whereas the regional arrangement of meandering depends on the background atmospheric state ( [[#Cohen--2020|Cohen et al., 2020]] ). These meandering trends, however, are sensitive to the metrics used ( [[#Screen--2013|Screen and Simmonds, 2013]] ; [[#Hassanzadeh--2014|Hassanzadeh et al., 2014]] ; [[#Cattiaux--2016|Cattiaux et al., 2016]] ; [[#Vavrus--2018|Vavrus, 2018]] ). Hypothesized links to Arctic warming are assessed in Cross-Chapter Box 10.1.

Multiple reanalyses and radiosonde observations show an increasing number of extratropical cyclones over the NH since the 1950s ( [[#Chang--2016|Chang and Yau, 2016]] ; X.L. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ). The positive trends are generally consistent among reanalyses since 1979, though with considerable spread ( [[#Tilinina--2013|Tilinina et al., 2013]] ; X.L. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ). In recent decades the number of deep extratropical cyclones has increased over the SH (Section 8.3.2.8.1 and Figure 8.12; [[#Reboita--2015|Reboita et al., 2015]] ; X.L. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ), while the number of deep cyclones has decreased in the NH in both winter and summer ( [[#Neu--2013|Neu et al., 2013]] ; [[#Coumou--2015|Coumou et al., 2015]] ; [[#Chang--2016|Chang et al., 2016]] ; J. [[#Wang--2017a|]] [[#Wang--2017|Wang et al., 2017]] a ; [[#Gertler--2019|Gertler and O’Gorman, 2019]] ). The regional changes for different intensity extratropical cyclones are assessed in Section 8.3.2.8.1. The assessment of trends is complicated by strong interannual to decadal variability, sensitivity to dataset choice and resolution ( [[#Tilinina--2013|Tilinina et al., 2013]] ; [[#Lucas--2014|Lucas et al., 2014]] ; X.L. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ; [[#Pepler--2018|Pepler et al., 2018]] ; [[#Rohrer--2018|Rohrer et al., 2018]] ) and cyclone identification/tracking methods ( [[#Neu--2013|Neu et al., 2013]] ; [[#Grieger--2018|Grieger et al., 2018]] ). Thus there is overall ''low'' ''confidence'' for recent changes in global extratropical storm tracks.

A consistent poleward shift of the tropospheric extratropical jets since 1979 is reported by multiple reanalyses (Figure 2.18; [[#Davis--2012|Davis and Rosenlof, 2012]] ; [[#Davis--2013|Davis and Birner, 2013]] ; [[#Pena-Ortiz--2013|Pena-Ortiz et al., 2013]] ; [[#Manney--2018|Manney and Hegglin, 2018]] ), and radiosonde winds ( [[#Allen--2012|Allen et al., 2012]] ). This is generally consistent with the previously reported shifts retrieved from satellite temperature observations ( [[#Fu--2011|Fu and Lin, 2011]] ; [[#Davis--2012|Davis and Rosenlof, 2012]] ). After the 1960s the magnitude of meridional shifts in extratropical jets over both the North Atlantic and North Pacific in August is enhanced compared to multi-century variability ( [[#Trouet--2018|Trouet et al., 2018]] ). Despite some regional differences ( [[#Woollings--2014|Woollings et al., 2014]] ; [[#Norris--2016|Norris et al., 2016]] ; J. [[#Wang--2017a|]] [[#Wang--2017|Wang et al., 2017]] a ; [[#Xue--2017|Xue and Zhang, 2017]] ; [[#Ma--2018|Ma and Zhang, 2018]] ; [[#Melamed-Turkish--2018|Melamed-Turkish et al., 2018]] ), overall poleward deflection of storm tracks in boreal winter over both the North Atlantic and the North Pacific was identified during 1979–2010 ( [[#Tilinina--2013|Tilinina et al., 2013]] ). Over the SH extra-tropics there is a similarly robust poleward shift in the polar jet since 1979 ( [[#Pena-Ortiz--2013|Pena-Ortiz et al., 2013]] ; [[#Manney--2018|Manney and Hegglin, 2018]] ; [[#WMO--2018|WMO, 2018]] ), although after 2000 the December–January–February (DJF) tendency to poleward shift of the SH jet stream position ceased ( [[#Banerjee--2020|Banerjee et al., 2020]] ). The general poleward movement in midlatitude jet streams ( [[#Lucas--2014|Lucas et al., 2014]] ) is consistent with the expansion of the tropical circulation ( [[#2.3.1.4.1|Section 2.3.1.4.1]] ). The changes of extratropical jets and westerlies are also related to the annular modes of variability ( [[#2.4|Section 2.4]] and Annex IV).

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[[File:fd4b5ce5996815b4bafa896df1e3d7fb IPCC_AR6_WGI_Figure_2_18.png]]

'''Figure 2.1''' '''8 |''' '''Trends in ERA5 zonal-mean zonal wind speed.''' Shown are '''(a)''' DJF (December–January–February); '''(b)''' MAM (March–April–May); '''(c)''' JJA (June–July–August); and '''(d)''' SON (September–October–November). Climatological zonal winds during the data period are shown in solid contour lines for westerly winds and in dashed lines for easterly. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after [[#Santer--2008|Santer et al. (2008)]] (‘×’ marks denote non-significant trends). Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

Robust trends in blocking have only been found in certain regions and specific seasons during recent decades. Increases in blocking frequency have occurred over low-latitude regions in the North Atlantic in boreal winter ( [[#Davini--2012|Davini et al., 2012]] ), the South Atlantic in austral summer ( [[#Dennison--2016|Dennison et al., 2016]] ) and the southern Indian Ocean in austral spring ( [[#Schemm--2018|Schemm, 2018]] ). Over the subpolar North Atlantic sustained periods of positive Greenland blocking were identified during 1870–1900 and from the late 1990s to 2015 ( [[#Hanna--2015|Hanna et al., 2015]] ). Further analysis of association of Greenland blocking with the NAM is provided in [[#2.4.1.1|Section 2.4.1.1]] . Meanwhile, a reduced blocking frequency has been found over winter in Siberia ( [[#Davini--2012|Davini et al., 2012]] ) and the south-western Pacific in austral spring ( [[#Schemm--2018|Schemm, 2018]] ). Over eastern European Russia and western Siberia (40°E–100°E) a tendency towards longer blocking events was reported by [[#Luo--2016|Luo et al. (2016)]] for 2000–2013 and by [[#Tyrlis--2020|Tyrlis et al. (2020)]] for 1979–2017. Inter-annual variance in the number of blocking events over the SH ( [[#Oliveira--2014|Oliveira et al., 2014]] ) and North Atlantic ( [[#Kim--2015|Kim and Ha, 2015]] ) has enhanced. Blocking events and their trends are sensitive to choice of datasets, calculation periods and methods ( [[#Cheung--2013|Cheung et al., 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ; [[#Pepler--2018|Pepler et al., 2018]] ; [[#Rohrer--2018|Rohrer et al., 2018]] ; [[#Woollings--2018b|Woollings et al., 2018b]] ; [[#Kononova--2020|Kononova and Lupo, 2020]] ). As a result, hemispheric and global trends in blocking frequency have overall ''low'' ''confidence.''

In summary, the total number of extratropical cyclones has ''likely'' increased since the 1980s in the NH ( ''low confidence'' ), but with fewer deep cyclones particularly in summer. The number of strong extratropical cyclones has ''likely'' increased in the SH ( ''medium confidence'' ). The extratropical jets and cyclone tracks have ''likely'' been shifting poleward in both hemispheres since the 1980s with marked seasonality in trends ( ''medium confidence'' ). There is ''low confidence'' in shifting of extratropical jets in the NH during the mid-Holocene and over 950–1400 CE to latitudes that ''likely'' were similar to those since 1979. There is ''low confidence'' in observed global-scale changes in the occurrence of blocking events.

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<span id="surface-wind-and-sea-level-pressure"></span>

===== 2.3.1.4.4 Surface wind and sea level pressure =====

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The AR5 concluded that surface winds over land had generally weakened. The ''confidence'' for both land and ocean surface wind trends was ''low'' owing to uncertainties in datasets and measures used. Sea level pressure (SLP) was assessed to have ''likely'' decreased from 1979–2012 over the tropical Atlantic and increased over large regions of the Pacific and South Atlantic, but trends were sensitive to the period analysed.

Terrestrial in situ wind datasets have been updated and the quality-control procedures have been improved, with particular attention to homogeneity and to better retaining true extreme values ( [[#Dunn--2012|Dunn et al., 2012]] , 2014, 2016). Global mean land wind speed (excluding Australia) from HadISD for 1979–2018 shows a reduction (stilling) of 0.063 m s <sup>–1</sup> per decade ( [[#Azorin-Molina--2019|Azorin-Molina et al., 2019]] ). Trends are broadly insensitive to the subsets of stations used. Although the meteorological stations are unevenly distributed worldwide and sparse in South America and Africa, the majority exhibit stilling particularly in the NH (Figure 2.19). Regionally, strong decreasing trends are reported in central Asia and North America (–0.106 and –0.084 m s <sup>–1</sup> per decade respectively) during 1979–2018 ( [[#McVicar--2012|McVicar et al., 2012]] ; [[#Vautard--2012|Vautard et al., 2012]] ; J. [[#Wu--2018|]] [[#Wu--2018|Wu et al., 2018]] ; [[#Azorin-Molina--2019|Azorin-Molina et al., 2019]] ). This stilling tendency has reversed after 2010 and the global mean surface winds have strengthened ( [[#Zeng--2019b|Zeng et al., 2019b]] ; [[#Azorin-Molina--2020|Azorin-Molina et al., 2020]] ), although the robustness of this reversal is unclear given the short period and interannual variability ( [[#Kousari--2013|Kousari et al., 2013]] ; [[#Kim--2015|Kim and Paik, 2015]] ; [[#Azorin-Molina--2019|Azorin-Molina et al., 2019]] ).

<div id="_idContainer052" class="Basic-Text-Frame"></div>

[[File:26ea185ca5ced7098b8a78c982776822 IPCC_AR6_WGI_Figure_2_19.png]]

'''Figure 2.19''' '''|''' '''Trends in surface wind speed. (a)''' Station observed winds from the integrated surface database (HadISD v2.0.2.2017f); '''(b)''' Cross-Calibrated Multi-Platform wind product; '''(c)''' ERA5; and '''(d)''' wind speed from the Objectively Analyzed Air-Sea Heat Fluxes dataset, release 3 (OAFLUX, release 3). White areas indicate incomplete or missing data. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after [[#Santer--2008|Santer et al. (2008)]] ; ‘×’ marks denote non-significant trends. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).

Over the ocean, datasets demonstrate considerable disagreement in surface wind speed trends and spatial features ( [[#Kent--2013|Kent et al., 2013]] ). Global ocean surface winds from NOCv2.0 demonstrate upward trends of about 0.11 m s <sup>–1</sup> per decade (1979–2015) with somewhat smaller trends from WASwind for 1979–2011 ( [[#Azorin-Molina--2017|Azorin-Molina et al., 2017]] , 2019). The trends are consistent until 1998, but diverge thereafter. Both ERA5 and JRA-55 reanalyses show consistently increasing global marine wind speeds over 1979–2015, though flattening since 2000, whereas MERRA-2 agrees until 1998, but then exhibits increased variability and an overall decrease in the last two decades ( [[#Azorin-Molina--2019|Azorin-Molina et al., 2019]] ). This agrees with estimates by [[#Sharmar--2021|Sharmar et al. (2021)]] showing upward ocean wind trends from 1979 to 2000 which are consistent in ERA-Interim, ERA5 and MERRA-2, but disagree with CFSR trends for the same period. Over 2000–2019 all reanalyses show diverging tendencies. An updated multiplatform satellite database (comprising data from altimeters, radiometers, and scatterometers) from 1985–2018 shows small increases in mean wind speed over the global ocean, with the largest increase observed in the Southern Ocean ( [[#Young--2019|Young and Ribal, 2019]] ), consistent with signals in ERA-Interim, ERA5 and MERRA-2 ( [[#Sharmar--2021|Sharmar et al., 2021]] ). Overall, most products suggest positive trends over the Southern Ocean, western North Atlantic and the tropical eastern Pacific since the early 1980s.

The modern era reanalyses exhibit SLP increases over the SH subtropics with stronger increases in austral winter over 1979–2018. Over the NH, SLP increased over the mid-latitude Pacific in boreal winter and decreased over the eastern subtropical and mid-latitude North Atlantic in boreal summer. Discrepancies in the low-frequency variations during the first half of the 20th century exist in the centennial-scale reanalysis products ( [[#Befort--2016|Befort et al., 2016]] ). Overall, modern reanalysis datasets support the AR5 conclusion that there is no clear signal for trends in the strength and position of the permanent and quasi-permanent pressure centres of action since the 1950s. Instead, they highlight multi-decadal variations. Large-scale SLP is strongly associated with the changes in modes of variability ( [[#2.4|Section 2.4]] and Annex IV).

In summary, since the 1970s a worldwide weakening of surface wind has ''likely'' occurred over land, particularly marked in the NH, with ''low confidence'' in a recent partial recovery since around 2010. Differences between available wind speed estimates lead to ''low confidence'' in trends over the global ocean as a whole but with most estimates showing strengthening globally over 1980–2000 and over the last four decades in the Southern Ocean, western North Atlantic and the tropical eastern Pacific.

<div id="2.3.1.4.5" class="h4-container"></div>

<span id="stratospheric-polar-vortex-and-sudden-warming-events"></span>

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

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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'''

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'''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'' ).

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[[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'' ).

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