Editing IPCC:AR6/WGI/Chapter-1 (section)

== 1.5 Major Developments and Their Implications ==

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This section presents a selection of key developments since AR5 of the capabilities underlying the lines of evidence used in the present report: observational data and observing systems ( [[#1.5.1|Section 1.5.1]] ); new developments in reanalyses ( [[#1.5.2|Section 1.5.2]] ); climate models ( [[#1.5.3|Section 1.5.3]] ); and modelling techniques, comparisons and performance assessments ( [[#1.5.4|Section 1.5.4]] ). For brevity, we focus on the developments that are of particular importance to the conclusions drawn in later chapters, though we also provide an assessment of potential losses of climate observational capacity.

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=== 1.5.1 Observational Data and Observing Systems ===

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Progress in climate science relies on the quality and quantity of observations from a range of platforms: surface-based instrumental measurements, aircraft, radiosondes and other upper-atmospheric observations, satellite-based retrievals, ocean observations, and paleoclimatic records. An historical perspective to these types of observations is presented in [[#1.3.1|Section 1.3.1]] .

Observed large-scale climatic changes assessed in Chapter 2, attribution of these changes in Chapter 3, and regional observations of specific physical or biogeochemical processes presented in other Chapters, are supported by improvements in observational capacity since AR5. Attribution assessments can be made at a higher likelihood level than in AR5, due in part to the availability of longer observational datasets (Chapter 3). Updated assessments are made based on new and improved datasets, for example of global temperature change (Cross-Chapter Box 2.3) or regional climate information (Section 10.2). Of particular relevance to the AR6 assessment are the Essential Climate Variables (ECVs; [[#Hollmann--2013|Hollmann et al., 2013]] ; [[#Bojinski--2014|Bojinski et al., 2014]] ), and Essential Ocean Variables (EOVs; [[#Lindstrom--2012|Lindstrom et al., 2012]] ), compiled by the Global Climate Observing System (GCOS; [[#WMO--2016|WMO, 2016]] ), and the Global Ocean Observing System (GOOS), respectively. These variables include physical, chemical and biological variables or groups of linked variables, and underpin ‘headline indicators’ (a selected set of essential parameters representing the state of the climate system) for climate monitoring ( [[#Trewin--2021|Trewin et al., 2021]] ).

We highlight below the key advances in observational capacity since AR5, including major expansions of existing observational platforms as well as new and/or emerging observational platforms that play a key role in AR6. We then discuss potential near-term losses in key observational networks due to climate change or other adverse human-caused influence.

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==== 1.5.1.1 Major Expansions of Observational Capacity ====

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===== 1.5.1.1.1 Atmosphere, land and hydrological cycle =====

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Satellites provide observations of a large number of key atmospheric and land-surface variables, ensuringsustained observations over wide areas. Since AR5, such observations have expanded to include satellite retrievals of atmospheric CO <sub>2</sub> via the NASA Orbiting Carbon Observatory satellites (OCO-2 and OCO-3; [[#Eldering--2017|Eldering et al., 2017]] ), following on from similar efforts employing the Greenhouse Gases Observing Satellite (GOSat; [[#Yokota--2009|Yokota et al., 2009]] ; [[#Inoue--2016|Inoue et al., 2016]] ). By combining remote sensing and in situ measurements, knowledge of fluxes between the atmosphere and land surface has improved ( [[#Rebmann--2018|Rebmann et al., 2018]] ). FLUXNET ( https://fluxnet.org/ ) has been providing eddy covariance measurements of carbon, water, and energy fluxes between the land and the atmosphere, with some of the stations operating for over 20 years ( [[#Pastorello--2017|Pastorello et al., 2017]] ), while the Baseline Surface Radiation Network (BSRN) has been maintaining high-quality radiation observations since the 1990s ( [[#Ohmura--1998|Ohmura et al., 1998]] ; [[#Driemel--2018|Driemel et al., 2018]] ).

Observations of the composition of the atmosphere have been further improved through expansions of existing surface observation networks ( [[#Bodeker--2016|Bodeker et al., 2016]] ; [[#De%20Mazière--2018|De Mazière et al., 2018]] ) and through in situ measurements such as aircraft campaigns (Sections 2.2, 5.2 and Section 6.2). Examples of expanded networks include the Aerosols, Clouds and Trace Gases Research Infrastructure (ACTRIS; [[#Pandolfi--2018|Pandolfi et al., 2018]] ), which focuses on short-lived climate forcers, and the Integrated Carbon Observation System (ICOS), which allows scientists to study and monitor the global carbon cycle and GHG emissions ( [[#Colomb--2018|Colomb et al., 2018]] ). Examples of recent aircraft observations include the Atmospheric Tomography Mission (ATom), which has flown repeatedly along the north–south axis of both the Pacific and Atlantic oceans, and the continuation of the In-service Aircraft for a Global Observing System (IAGOS) effort, which measures atmospheric composition from commercial aircraft ( [[#Petzold--2015|Petzold et al., 2015]] ).

Two distinctly different but important remote-sensing systems can provide information about temperature and humidity since the early 2000s. Global navigation satellite systems (e.g., GPS), radio occultation and limb soundings provide information, although only data for the upper troposphere and lower stratosphere are suitable to support climate change assessments ( [[#Angerer--2017|Angerer et al., 2017]] ; [[#Scherllin-Pirscher--2017|Scherllin-Pirscher et al., 2017]] ; [[#Gleisner--2020|Gleisner et al., 2020]] ; [[#Steiner--2020|Steiner et al., 2020]] ). These measurements complement those from the Atmospheric Infrared Sounder (AIRS; [[#Chahine--2006|Chahine et al., 2006]] ). AIRS has limitations in cloudy conditions, although these limitations have been partly solved using new methods of analysis ( [[#Blackwell--2014|Blackwell and Milstein, 2014]] ; [[#Susskind--2014|Susskind et al., 2014]] ). These new data sources now have sufficiently long records to strengthen the analysis of atmospheric warming in [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.2|Section 2.3.1.2]] ).

Assessments of the hydrological cycle in Chapters 2 and 8 are supported by longer time series and new developments. Examples are new satellites ( [[#McCabe--2017|McCabe et al., 2017]] ) and measurements of water vapour using commercial laser absorption spectrometers and water vapour isotopic composition ( [[#Steen-Larsen--2015|Steen-Larsen et al., 2015]] ; [[#Zannoni--2019|Zannoni et al., 2019]] ). Data products of higher quality have been developed since AR5, such as the multi-source weighted ensemble precipitation ( [[#Beck--2017|Beck et al., 2017]] ) and multi-satellite terrestrial evaporation products ( [[#Fisher--2017|Fisher et al., 2017]] ). Longer series are available for satellite-derived global inundation data ( [[#Prigent--2020|Prigent et al., 2020]] ). Observations of soil moisture are now available via the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) satellite retrievals, filling critical gaps in the observation of hydrological trends and variability over land ( [[#Dorigo--2017|Dorigo et al., 2017]] ). Similarly, the Gravity Recovery and Climate Experiment GRACE and GRACE-FO satellites ( [[#Tapley--2019|Tapley et al., 2019]] ) have provided key constraints on groundwater variability and trends around the world ( [[#Frappart--2018|Frappart and Ramillien, 2018]] ). The combination of new observations with other sources of information has led to updated estimates of heat storage in inland waters ( [[#Vanderkelen--2020|Vanderkelen et al., 2020]] ), contributing to revised estimates of heat storage on the continents (Section 7.2.2.3; [[#von%20Schuckmann--2020|von Schuckmann et al., 2020]] ).

The ongoing collection of information about the atmosphere as it evolves is supplemented by the reconstruction and digitization of data about past conditions. Programmes aimed at recovering information from sources such as handwritten weather journals and ships’ logs continue to make progress, and are steadily improving spatial coverage and extending our knowledge backward in time. For example, [[#Brönnimann--2019a|Brönnimann et al. (2019a)]] has recently identified several thousand sources of climate data for land areas in the pre-1890 period, with many from the 18th century. The vast majority of these data are not yet contained in international digital data archives, and substantial quantities of undigitized ships’ weather log data exist for the same period ( [[#Kaspar--2015|Kaspar et al., 2015]] ). Since AR5 there has been a growth of ‘citizen science’ activities, making use of volunteers to rapidly transcribe substantial quantities of weather observations. Examples of projects include: [http://oldWeather.org oldWeather.org] and [http://SouthernWeatherDiscovery.org SouthernWeatherDiscovery.org] (both of which used ship-based logbook sources); the DRAW project (Data Rescue: Archival and Weather, which recovered land-based station data from Canada); [http://WeatherRescue.org WeatherRescue.org] (land-based data from Europe); [http://JungleWeather.org JungleWeather.org] (data from the Congo); and the Climate History Australia project (data from Australia; e.g., [[#Park--2018|Park et al., 2018]] ; [[#Hawkins--2019|Hawkins et al., 2019]] ). Undergraduate students have also been recruited to successfully digitize rainfall data in Ireland ( [[#Ryan--2018|Ryan et al., 2018]] ). Such observations are an invaluable source of weather and climate information for the early historical period that continues to expand the digital archives (e.g., [[#Freeman--2017|Freeman et al., 2017]] ) which underpin observational datasets used across several Chapters.

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===== 1.5.1.1.2 Ocea ''n'' =====

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Observations of the ocean have expanded significantly since AR5, with expanded global coverage of in situ ocean temperature and salinity observations, in situ ocean biogeochemistry observations, and satellite retrievals of a variety of EOVs. Many recent advances are extensively documented in a compilation by [[#Lee--2019|Lee et al. (2019)]] . Below we discuss those most relevant for the current assessment.

Argo is a global network of nearly 4000 autonomous profiling floats ( [[#Roemmich--2019|Roemmich et al., 2019]] ), delivering detailed constraints on the horizontal and vertical structure of temperature and salinity across the global ocean. Argo has greatly expanded since AR5, including biogeochemistry and measurements deeper than 2000 m ( [[#Jayne--2017|Jayne et al., 2017]] ), and the longer time series enable more rigorous climate assessments of direct relevance to estimates of ocean heat content (Sections 2.3.3.1 and 7.2.2.2). Argo profiles are complemented by animal-borne sensors in several key areas, such as the seasonally ice-covered sectors of the Southern Ocean ( [[#Harcourt--2019|Harcourt et al., 2019]] ).

Most basin-scale arrays of moored ocean instruments have expanded since AR5, providing decades-long records of the ocean and atmosphere properties relevant for climate, such as the El Niño–Southern Oscillation ( [[#Chen--2018|Chen et al., 2018]] ), deep convection ( [[#de%20Jong--2018|de Jong et al., 2018]] ) or transports through straits ( [[#Woodgate--2018|Woodgate, 2018]] ). Key basin-scale arrays include transport-measuring arrays in the Atlantic Ocean, continuing ( [[#McCarthy--2020|McCarthy et al., 2020]] ) or newly added since AR5 ( [[#Lozier--2019|Lozier et al., 2019]] ), supporting the assessment of regional ocean circulation (Section 9.2.3). Tropical ocean moorings in the Pacific, Indian and Atlantic oceans include new sites, improved capability for real-time transmission, and new oxygen and CO <sub>2</sub> sensors ( [[#Bourlès--2019|Bourlès et al., 2019]] ; [[#Hermes--2019|Hermes et al., 2019]] ; [[#Smith--2019|]] [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ).

A decade of observations of sea-surface salinity is now available via the SMOS and SMAP satellite retrievals, providing continuous and global monitoring of surface salinity in the open ocean and coastal areas for the first time (Section 9.2.2.2; [[#Vinogradova--2019|Vinogradova et al., 2019]] ; [[#Reul--2020|Reul et al., 2020]] ).

The global network of tide gauges, complemented by a growing number of satellite-based altimetry datasets, allows for more robust estimates of global and regional sea level rise (Sections 2.3.3.3 and 9.6.1.3). Incorporating vertical land motion derived from the Global Positioning System (GPS), the comparison with tide gauges has allowed the correction of a drift in satellite altimetry series over the period 1993–1999 ( [[#Watson--2015|Watson et al., 2015]] ; [[#Chen--2017|Chen et al., 2017]] ), thus improving our knowledge of the recent acceleration of sea level rise (Chapter 2, [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ). These datasets, combined with Argo and observations of the cryosphere, allow a consistent closure of the global mean sea level budget (Cross-Chapter Box 9.1; [[#WCRP%20Global%20Sea%20Level%20Budget%20Group--2018|WCRP Global Sea Level Budget Group, 2018]] ).

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===== ''1.5.1.1.3 Cryosphere'' =====

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For the cryosphere, there has been much recent progress in synthesizing global datasets covering larger areas and longer time periods from multi-platform observations. For glaciers, the Global Terrestrial Network for Glaciers, which combines data on glacier fluctuations, mass balance and elevation change with glacier outlines and ice thickness, has expanded and provided input for assessing global glacier evolution and its role in sea level rise (Sections 2.3.2.3 and 9.5.1; [[#Zemp--2019|Zemp et al., 2019]] ). New data sources include archived and declassified aerial photographs and satellite missions, and high-resolution (10 m or less) digital elevation models ( [[#Porter--2018|Porter et al., 2018]] ; [[#Braun--2019|Braun et al., 2019]] ).

Improvements have also been made in the monitoring of permafrost. The Global Terrestrial Network for Permafrost (GTN-P; [[#Biskaborn--2015|Biskaborn et al., 2015]] ) provides long-term records of permafrost temperature and active layer thickness at key sites to assess their changes over time. Substantial improvements to our assessments of large-scale snow changes come from intercomparison and blending of several datasets, for snow water equivalent ( [[#Mortimer--2020|Mortimer et al., 2020]] ) and snow cover extent ( [[#Mudryk--2020|Mudryk et al., 2020]] ), and from bias corrections of combined datasets using in situ data (Sections 2.3.2.5 and 9.5.2; [[#Pulliainen--2020|Pulliainen et al., 2020]] ).

The value of gravity-based estimates of changes in ice-sheet mass has increased, as the time series from the GRACE and GRACE-FO satellites – homogenized and absolutely calibrated – is close to 20 years in length. The European Space Agency’s (ESA’s) Cryosat-2 radar altimetry satellite mission has continued to provide measurements of the changes in the thickness of sea ice and the elevation of the Greenland and Antarctic ice sheets ( [[#Tilling--2018|Tilling et al., 2018]] ). Other missions include NASA’s Operation IceBridge, collecting airborne remote-sensing measurements to bridge the gap between ICESat (Ice, Cloud and land Elevation Satellite) and the upcoming ICESat-2 laser altimetry missions. Longer time series from multiple missions have led to considerable advances in understanding the origin of inconsistencies between the mass balances of different glaciers and reducing uncertainties in estimates of changes in the Greenland and Antarctic ice sheets ( [[#Bamber--2018|Bamber et al., 2018]] ; [[#Shepherd--2018|A. Shepherd et al., 2018]] ; [[#Shepherd--2020|Shepherd et al., 2020]] ). Last, the first observed climatology of snowfall over Antarctica was obtained using the cloud/precipitation radar onboard NASA’s CloudSat ( [[#Palerme--2014|Palerme et al., 2014]] ).

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===== ''1.5.1.1.4 Biosphere'' =====

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Satellite observations have recently expanded to include data on the fluorescence of land plants as a measure of photosynthetic activity via the Global Ozone Monitoring Experiment (GOME; [[#Guanter--2014|Guanter et al., 2014]] ; [[#Yang--2015|Yang et al., 2015]] ) and OCO-2 satellites ( [[#Sun--2017|Sun et al., 2017]] ). Climate data records of leaf area index (LAI), characterizing the area of green leaves per unit of ground area, and the fraction of absorbed photosynthetically active radiation (FAPAR) – an important indicator of photosynthetic activity and plant health ( [[#Gobron--2009|Gobron et al., 2009]] ) – are now available for over 30 years ( [[#Claverie--2016|Claverie et al., 2016]] ). In addition, key indicators such as fire disturbances/burned areas are now retrieved via satellite ( [[#Chuvieco--2019|Chuvieco et al., 2019]] ). In the US, the National Ecological Observational Network (NEON) provides continental-scale observations relevant to the assessment of changes in aquatic and terrestrial ecosystems via a wide variety of ground-based, airborne, and satellite platforms ( [[#Keller--2008|Keller et al., 2008]] ). All these long-term records reveal range shifts in ecosystems ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.4|Section 2.3.4]] ).

The ability to estimate changes in global land biomass has improved due to the use of different microwave satellite data ( [[#Liu--2015|Liu et al., 2015]] ) and in situ forest census data and co-located lidar, combined with the Moderate Resolution Imaging Spectroradiometer (MODIS; [[#Baccini--2017|Baccini et al., 2017]] ). This has allowed for improved quantification of land temperature ( [[#Duan--2019|Duan et al., 2019]] ), carbon stocks and human-induced changes due to deforestation (Chapter 2, [[IPCC:Wg1:Chapter:Chapter-2#2.2.7|Section 2.2.7]] ). Time series of Normalized Difference Vegetation Index (NDVI) from MODIS and other remote-sensing platforms is widely applied to assess the effects of climate change on vegetation in drought-sensitive regions ( [[#Atampugre--2019|Atampugre et al., 2019]] ). New satellite imaging capabilities for meteorological observations, such as the advanced multispectral imager aboard Himawari-8 ( [[#Bessho--2016|Bessho et al., 2016]] ), also allow for improved monitoring of challenging quantities such as seasonal changes of vegetation in cloudy regions ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.4.3|Section 2.3.4.3]] ; [[#Miura--2019|Miura et al., 2019]] ).

In the ocean, efforts are underway to coordinate observations of biologically relevant EOVs around the globe ( [[#Muller-Karger--2018|Muller-Karger et al., 2018]] ; [[#Canonico--2019|Canonico et al., 2019]] ) and to integrate observations across disciplines (e.g., the Global Ocean Acidification Observing Network, GOA-ON; [[#Tilbrook--2019|Tilbrook et al., 2019]] ). A large number of coordinated field campaigns during the 2015/2016 El Niño event enabled the collection of short-lived biological phenomena such as coral bleaching and mortality caused by a months-long ocean heatwave ( [[#Hughes--2018|Hughes et al., 2018]] ); beyond this event, coordinated observations of coral reef systems are increasing in number and quality ( [[#Obura--2019|Obura et al., 2019]] ). Overall, globally coordinated efforts focused on individual components of the biosphere (e.g., the Global Alliance of Continuous Plankton Recorder Surveys, GACS; [[#Batten--2019|Batten et al., 2019]] ) contribute to improved knowledge of the ways in which marine ecosystems are changing ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.4.2|Section 2.3.4.2]] ).

Given widespread evidence for decreases in global biodiversity in recent decades – and that these decreases are related to climate change and other forms of human disturbance ( [[#IPBES--2019|IPBES, 2019]] ) – a new international effort to identify a set of Essential Biodiversity Variables (EBVs) is underway ( [[#Pereira--2013|Pereira et al., 2013]] ; [[#Navarro--2017|Navarro et al., 2017]] ).

In summary, the observational coverage of ongoing changes to the climate system is improved at the time of AR6, relative to what was available for AR5 ( ''hi'' ''gh confidence'' ).

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===== ''1.5.1.1.5 Paleoclimate'' =====

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Major paleoreconstruction efforts completed since AR5 include a variety of large-scale, multi-proxy temperature datasets and associated reconstructions spanning the last 2000 years ( [[#PAGES%202k%20Consortium--2017|PAGES 2k Consortium, 2017]] , 2019; [[#Neukom--2019|Neukom et al., 2019]] ), the Holocene ( [[#Kaufman--2020|Kaufman et al., 2020]] ), the Last Glacial Maximum ( [[#Cleator--2020|Cleator et al., 2020]] ; [[#Tierney--2020b|Tierney et al., 2020b]] ), the mid-Pliocene Warm Period ( [[#McClymont--2020|McClymont et al., 2020]] ), and the Early Eocene Climatic Optimum ( [[#Hollis--2019|Hollis et al., 2019]] ). Newly compiled borehole data ( [[#Cuesta-Valero--2019|Cuesta-Valero et al., 2019]] ), as well as advances in statistical applications to tree ring data, result in more robust reconstructions of key indices such as Northern Hemisphere temperature over the last millennium (e.g., [[#Wilson--2016|Wilson et al., 2016]] ; [[#Anchukaitis--2017|Anchukaitis et al., 2017]] ). Such reconstructions provide a new context for recent warming trends (Chapter 2) and serve to constrain the response of the climate system to natural and anthropogenic forcing (Chapters 3 and 7).

Ongoing efforts have expanded the number of large-scale, tree ring-based drought reconstructions that span the last centuries to millennium at annual resolution (Chapter 8; [[#Cook--2015|Cook et al., 2015]] ; [[#Stahle--2016|Stahle et al., 2016]] ; [[#Aguilera-Betti--2017|Aguilera-Betti et al., 2017]] ; [[#Morales--2020|Morales et al., 2020]] ). Likewise, stalagmite records of oxygen isotopes have increased in number, resolution and geographic distribution since AR5, providing insights into regional-to-global-scale hydrological change over the last centuries to millions of years (Chapter 8; [[#Cheng--2016|Cheng et al., 2016]] ; [[#Denniston--2016|Denniston et al., 2016]] ; [[#Comas-Bru--2019|Comas-Bru and Harrison, 2019]] ). A new global compilation of water isotope-based paleoclimate records spanning the last 2000 years (PAGES Iso2K) lays the groundwork for quantitative multi-proxy reconstructions of regional- to global-scale hydrological and temperature trends and extremes ( [[#Konecky--2020|Konecky et al., 2020]] ).

Recent advances in the reconstruction of climate extremes – aside from temperature and drought – include expanded datasets of past El Niño–Southern Oscillation extremes ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.2|Section 2.4.2]] ; e.g., [[#Barrett--2018|Barrett et al., 2018]] ; [[#Freund--2019|Freund et al., 2019]] ; [[#Grothe--2020|Grothe et al., 2020]] ) and other modes of variability ( [[#Hernández--2020|Hernández et al., 2020]] ), hurricane activity (e.g., [[#Burn--2015|Burn and Palmer, 2015]] ; [[#Donnelly--2015|Donnelly et al., 2015]] ), jet stream variability ( [[#Trouet--2018|Trouet et al., 2018]] ) and wildfires (e.g., [[#Taylor--2016|Taylor et al., 2016]] ).

New datasets as well as recent data compilations and syntheses of sea level over the last millennia ( [[#Kopp--2016|Kopp et al., 2016]] ; [[#Kemp--2018|Kemp et al., 2018]] ), the last 20 kyr ( [[#Khan--2019|Khan et al., 2019]] ), the last interglacial period ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] : [[#Dutton--2015|Dutton et al., 2015]] ), and the Pliocene (Cross-Chapter Box 2.4; [[#Dumitru--2019|Dumitru et al., 2019]] ; [[#Grant--2019|Grant et al., 2019]] ) help constrain sea level variability and its relationship to global and regional temperature variability, and to estimates of contributions to sea level change from different sources on centennial to millennial time scales (Section 9.6.2).

Reconstructions of paleo ocean pH ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.5|Section 2.3.3.5]] ) have increased in number and accuracy, providing new constraints on ocean pH across the last centuries (e.g., [[#Wu--2018|Wu et al., 2018]] ), the last glacial cycles (e.g., [[#Moy--2019|Moy et al., 2019]] ), and the last several million years (e.g., [[#Anagnostou--2020|Anagnostou et al., 2020]] ). Such reconstructions inform processes and act as benchmarks for Earth system models of the global carbon cycle over the recent geologic past (Section 5.3.1), including previous high-CO <sub>2</sub> warm intervals such as the Pliocene (Cross-Chapter Box 2.4). Particularly relevant to such investigations are reconstructions of atmospheric CO <sub>2</sub> ( [[#Honisch--2012|Honisch et al., 2012]] ; [[#Foster--2017|Foster et al., 2017]] ) that span the past millions to tens of millions of years.

Constraints on the timing and rates of past climate changes have improved since AR5. Analytical methods have increased the precision and reduced sample-size requirements for key radiometric dating techniques, including radiocarbon ( [[#Gottschalk--2018|Gottschalk et al., 2018]] ; [[#Lougheed--2018|Lougheed et al., 2018]] ) and uranium–thorium dating ( [[#Cheng--2013|Cheng et al., 2013]] ). More accurate ages of many paleoclimate records are also facilitated by recent improvements in the radiocarbon calibration datasets (IntCal20, [[#Reimer--2020|Reimer et al., 2020]] ). A recent compilation of global cosmogenic nuclide-based exposure dates ( [[#Balco--2020b|Balco, 2020b]] ) allows for a more rigorous assessment of the evolution of glacial landforms since the Last Glacial Maximum ( [[#Balco--2020a|Balco, 2020a]] ).

Advances in paleoclimate data assimilation (Section 10.2.3.2) leverage the expanded set of paleoclimate observations to create physically consistent gridded fields of climate variables for data-rich intervals of interest (e.g., over the last millennium, ( [[#Hakim--2016|Hakim et al., 2016]] ) or last glacial period ( [[#Cleator--2020|Cleator et al., 2020]] ; [[#Tierney--2020b|Tierney et al., 2020b]] )). Such efforts mirror advances in our understanding of the relationship between proxy records and climate variables of interest, as formalized in so-called proxy system models (e.g., [[#Tolwinski-Ward--2011|Tolwinski-Ward et al., 2011]] ; [[#Dee--2015|Dee et al., 2015]] ; [[#Dolman--2018|Dolman and Laepple, 2018]] ).

Overall, the number, temporal resolution and chronological accuracy of paleoclimate reconstructions have increased since AR5, leading to improved understanding of climate system processes (or Earth system processes) ( ''hi'' ''gh confidence'' ).

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==== 1.5.1.2 Threats to Observational Capacity or Continuity ====

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The lockdowns and societal outcomes arising from the COVID-19 pandemic pose a new threat to observing systems. For example, WMO and UNESCO-IOC (Intergovernmental Oceanographic Commission) published a summary of the changes to Earth system observations during COVID-19 ( [[#WMO--2020b|WMO, 2020b]] ). Fewer aircraft flights (down 75–90% in May 2020, depending on region) and ship transits (down 20% in May 2020) mean that onboard observations from those networks have reduced in number and frequency ( [[#James--2020|James et al., 2020]] ; [[#Ingleby--2021|Ingleby et al., 2021]] ). Europe has deployed more radiosonde soundings to account for the reduction in data from air traffic. Fewer ocean observing buoys were deployed during 2020, and reductions have been particularly prevalent in the tropics and Southern Hemisphere. The full consequences of the pandemic, and responses to it, will come to light over time. Estimates of the effect of the reduction in aircraft data assimilation on weather forecasting skill are small ( [[#James--2020|James et al., 2020]] ; [[#Ingleby--2021|Ingleby et al., 2021]] ), potentially alleviating concerns about veracity of future atmospheric reanalyses of the COVID-19 pandemic period.

Surface-based networks have reduced in their coverage or range of variables measured due to COVID-19 and other factors. Over land, several factors, including the ongoing transition from manual to automatic observations of weather, have reduced the spatial coverage of certain measurement types, including rainfall intensity, radiosonde launches and pan evaporation, posing unique risks to datasets used for climate assessment ( [[#WMO--2017|WMO, 2017]] ; [[#Lin--2019|Lin and Huybers, 2019]] ). Ship-based measurements, which are important for ocean climate and reanalyses through time ( [[#Smith--2019|]] [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ), have been in decline due to the number of ships contributing observations. There has also been a decline in the number of variables recorded by ships, but an increase in the quality and time-resolution of others (e.g., sea level pressure, [[#Kent--2019|Kent et al., 2019]] ).

Certain satellite frequencies are used to detect meteorological features that are vital to climate change monitoring. These can be disturbed by certain radio communications ( [[#Anterrieu--2016|Anterrieu et al., 2016]] ), although scientists work to remove noise from the signal ( [[#Oliva--2016|Oliva et al., 2016]] ). For example, water vapour in the atmosphere naturally produces a weak signal at 23.8 gigahertz (GHz), which is within the range of frequencies of the 5G cellular communications network ( [[#Liu--2021|Liu et al., 2021]] ). Concern has been raised about potential leakage from 5G network transmissions into the operating frequencies of passive sensors on existing weather satellites, which could adversely influence their ability to remotely observe water vapour in the atmosphere ( [[#Yousefvand--2020|Yousefvand et al., 2020]] ).

Threats to observational capacity also include the loss of natural climate archives that are disappearing as a direct consequence of warming temperatures. Ice-core records from vulnerable alpine glaciers in the tropics ( [[#Permana--2019|Permana et al., 2019]] ) and the mid-latitudes ( [[#Gabrielli--2016|Gabrielli et al., 2016]] ; [[#Winski--2018|Winski et al., 2018]] ; [[#Moreno--2021|Moreno et al., 2021]] ) document more frequent melt layers in recent decades, with glacial retreat occurring at a rate and geographic scale that is unusual in the Holocene ( [[#Solomina--2015|Solomina et al., 2015]] ). The scope and severity of coral bleaching and mortality events have increased in recent decades ( [[#Hughes--2018|Hughes et al., 2018]] ), with profound implications for the recovery of coral climate archives from new and existing sites. An observed increase in the mortality of larger, long-lived trees over the last century is attributed to a combination of warming, land-use change, and disturbance (e.g., [[#McDowell--2020|McDowell et al., 2020]] ). The ongoing loss of these natural, high-resolution climate archives endanger an end in their coverage over recent decades, given that many of the longest monthly- to annually-resolved paleoclimate records were collected in the 1960s to 1990s (e.g., the PAGES2K database as represented in [[#PAGES%202k%20Consortium--2017|PAGES 2k Consortium, 2017]] ). This gap presents a barrier to the calibration of existing decades-to-centuries-long records needed to constrain past temperature and hydrology trends and extremes.

Historical archives of weather and climate observations contained in ships’ logs, weather diaries, observatory logbooks and other sources of documentary data also risk being lost, for example to natural disasters or accidental destruction. These archives include measurements of temperature (air and sea surface), rainfall, surface pressure, wind strength and direction, sunshine amount, and many other variables back into the 19th century. While internationally coordinated data-rescue efforts are focused on recovering documentary sources of past weather and climate data (e.g., [[#Allan--2011|Allan et al., 2011]] ), no such coordinated efforts exist for vulnerable paleoclimate archives. Furthermore, oral traditions about local and regional weather and climate from indigenous peoples represent valuable sources of information, especially when used in combination with instrumental climate data ( [[#Makondo--2018|Makondo and Thomas, 2018]] ), but are in danger of being lost as indigenous knowledge-holders pass away.

In summary, while the quantity, quality and diversity of climate system observations have grown since AR5, the loss or potential loss of several critical components of the observational network is also evident ( ''hi'' ''gh confidence'' ).

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<span id="new-developments-in-reanalyses"></span>
=== 1.5.2 New Developments in Reanalyses ===

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Reanalyses are usually the output of a model (e.g., a numerical weather prediction model) constrained by observations using data assimilation techniques, but the term has also been used to describe observation-based datasets produced using simpler statistical methods and models (Annex I: Observational Products). This section focuses on the model-based methods and their recent developments.

Reanalyses complement datasets of observations in describing changes through the historical record and are sometimes considered as ‘maps without gaps’ because they provide gridded output in space and time, often global, with physical consistency across variables on sub-daily time scales, and information about sparsely observed variables (such as evaporation; [[#Hersbach--2020|Hersbach et al., 2020]] ). They can be globally complete, or regionally focussed and constrained by boundary conditions from a global reanalysis (Section 10.2.1.2). They can also provide feedback about the quality of the observations assimilated, including estimates of biases and critical gaps for some observing systems.

Many early reanalyses are described in Box 2.3 of [[#Hartmann--2013|Hartmann et al. (2013)]] . These were often limited by the underlying model, the data assimilation schemes and observational issues ( [[#Thorne--2010|Thorne and Vose, 2010]] ; [[#Zhou--2018|Zhou et al., 2018]] ). Observational issues include the lack of underlying observations in some regions, changes in the observational systems over time (e.g., spatial coverage, introduction of satellite data), and time-dependent errors in the underlying observations or in the boundary conditions, which may lead to stepwise biases in time. The assimilation of sparse or inconsistent observations can introduce mass or energy imbalances ( [[#Valdivieso--2017|Valdivieso et al., 2017]] ; [[#Trenberth--2019|Trenberth et al., 2019]] ). Further limitations and some efforts to reduce the implications of these observational issues are detailed below.

The methods used in the development of reanalyses have progressed since AR5 and, in some cases, this has important implications for the information they provide on how the climate is changing. [[IPCC:Wg1:Chapter:Annex-i|Annex I]] includes a list of reanalysis datasets used in AR6. Recent major developments in reanalyses include the assimilation of a wider range of observations, higher spatial and temporal resolution, extensions further back in time, and greater efforts to minimize the influence of a temporally varying observational network.

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<span id="atmospheric-reanalyses"></span>
==== 1.5.2.1 Atmospheric Reanalyses ====

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Extensive improvements have been made in global atmospheric reanalyses since AR5. The growing demand for high-resolution data has led to the development of higher-resolution atmospheric reanalyses, such as the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; [[#Gelaro--2017|Gelaro et al., 2017]] ) and ERA5 ( [[#Hersbach--2020|Hersbach et al., 2020]] ). There is a focus on ERA5 here because it has been assessed as of high enough quality to present temperature trends alongside more traditional observational datasets ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1|Section 2.3.1.1]] ) and is also used in the Interactive Atlas.

Atmospheric reanalyses that were assessed in AR5 are still being used in the literature, and results from ERA-Interim (about 80 km resolution, production stopped in August 2019; [[#Dee--2011|Dee et al., 2011]] ), the Japanese 55-year Reanalysis (JRA-55; [[#Ebita--2011|Ebita et al., 2011]] ; [[#Kobayashi--2015|Kobayashi et al., 2015]] ; [[#Harada--2016|Harada et al., 2016]] ) and Climate Forecast System Reanalysis (CFSR; [[#Saha--2010|Saha et al., 2010]] ) are assessed in AR6. Some studies still also use the NCEP/NCAR reanalysis, particularly because it extends back to 1948 and is updated in near-real time ( [[#Kistler--2001|Kistler et al., 2001]] ). Older reanalyses have a number of limitations, which have to be accounted for when assessing the results of any study that uses them.

ERA5 provides hourly atmospheric fields at about 31 km resolution on 137 levels in the vertical, as well as land-surface variables and ocean waves. It is available from 1979 onwards and is updated in near-real time, with plans to extend back to 1950. A 10-member ensemble is also available at coarser resolution, allowing uncertainty estimates to be provided (e.g., [[IPCC:Wg1:Chapter:Chapter-2#2.3|Section 2.3]] ). MERRA-2 includes many updates from the earlier version, including the assimilation of aerosol observations, several improvements to the representation of the stratosphere, including ozone, and improved representations of cryospheric processes. All of these improvements increase the usefulness of these reanalyses (Section 7.3; [[#Hoffmann--2019|Hoffmann et al., 2019]] ).

Models of atmospheric composition and emissions sources and sinks allow the forecast and reanalysis of constituents such as O <sub>3</sub> , carbon monoxide (CO), nitrogen oxides (NOx) and aerosols. The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis shows improvement against earlier atmospheric composition reanalyses, giving greater confidence for its use to study trends and evaluate models (Section 7.3; e.g., [[#Inness--2019|Inness et al., 2019]] ).

The intercomparison of reanalyses with each other, or with earlier versions, is often done for particular variables or aspects of the simulation. ERA5 is assessed as the most reliable reanalysis for climate trend assessment ( [[IPCC:Wg1:Chapter:Chapter-2#2.3|Section 2.3]] ). Compared to ERA-Interim, the ERA5 forecast model and assimilation system, as well as the availability of improved reprocessing of observations, resulted in relatively smaller errors when compared to observations, including a better representation of global energy budgets, radiative forcing from volcanic eruptions (e.g., Mt. Pinatubo: [[#Allan--2020|Allan et al., 2020]] ), the partitioning of surface energy ( [[#Martens--2020|Martens et al., 2020]] ), and wind ( [[#Kaiser-Weiss--2015|Kaiser-Weiss et al., 2015]] , 2019; [[#Borsche--2016|Borsche et al., 2016]] ; [[#Scherrer--2020|Scherrer, 2020]] ). In ERA5, higher resolution means a better representation of Lagrangian motion convective updrafts, gravity waves, tropical cyclones, and other meso- to synoptic-scale features of the atmosphere ( [[#Hoffmann--2019|Hoffmann et al., 2019]] ; [[#Martens--2020|Martens et al., 2020]] ). Low-frequency variability is found to be generally well represented and, from 10 hPa downwards, patterns of anomalies in temperature match those from the ERA-Interim, MERRA-2 and JRA-55 reanalyses. Inhomogeneities in the water cycle have also been reduced ( [[#Hersbach--2020|Hersbach et al., 2020]] ).

Precipitation is not usually assimilated in reanalyses and, depending on the region, reanalysis precipitation can differ from observations by more than the observational error ( [[#Zhou--2017|Zhou and Wang, 2017]] ; [[#Sun--2018|Sun et al., 2018]] ; [[#Alexander--2020|Alexander et al., 2020]] ; [[#Bador--2020|Bador et al., 2020]] ), although these studies did not include ERA5. Assimilation of radiance observations from microwave imagers which, over ice-free ocean surfaces, improve the analysis of lower-tropospheric humidity, cloud liquid water and ocean-surface wind speed have resulted in improved precipitation outputs in ERA5 ( [[#Hersbach--2020|Hersbach et al., 2020]] ). Global averages of other fields, particularly temperature, from ERA-Interim and JRA-55 reanalyses continue to be consistent over the last 20 years with surface observational data sets that include the polar regions ( [[#Simmons--2015|Simmons and Poli, 2015]] ), although biases in precipitation and radiation can influence temperatures regionally ( [[#Zhou--2018|Zhou et al., 2018]] ). The global average surface temperature from MERRA-2 is far cooler in recent years than temperatures derived from ERA-Interim and JRA-55, which may be due to the assimilation of aerosols and their interactions ( [[IPCC:Wg1:Chapter:Chapter-2#2.3|Section 2.3]] ).

A number of regional atmospheric reanalyses (Section 10.2.1.2) have been developed, such as COSMO-REA ( [[#Wahl--2017|Wahl et al., 2017]] ), and the Australian Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA; [[#Su--2019|Su et al., 2019]] ). Regional reanalyses can add value to global reanalyses due to the lower computational requirements, and can allow multiple numerical weather prediction models to be tested (e.g., [[#Kaiser-Weiss--2019|Kaiser-Weiss et al., 2019]] ). There is some evidence that these higher-resolution reanalyses better capture precipitation variability than global lower-resolution reanalyses ( [[#Jermey--2016|Jermey and Renshaw, 2016]] ; [[#Cui--2017|Cui et al., 2017]] ). They are further assessed in Section 10.2.1.2 and used in the Interactive Atlas.

In summary, the improvements in atmospheric reanalyses, and the greater number of years since the routine ingestion of satellite data began, relative to AR5, mean that there is increased confidence in using atmospheric reanalysis products alongside more standard observation-based datasets in AR6 ( ''hi'' ''gh confidence'' ).

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<span id="sparse-input-reanalyses-of-the-instrumental-era"></span>
==== 1.5.2.2 Sparse Input Reanalyses of the Instrumental Era ====

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Although reanalyses such as ERA5 take advantage of new observational datasets and present a great improvement in atmospheric reanalyses, the issues introduced by the evolving observational network remain. Sparse input reanalyses, where only a limited set of reliable and long-observed records are assimilated, address these issues, with the limitation of fewer observational constraints. These efforts are sometimes called centennial-scale reanalyses. One example is the atmospheric 20th century Reanalysis ( [[#Compo--2011|Compo et al., 2011]] ; [[#Slivinski--2021|Slivinski et al., 2021]] ) which assimilates only surface and sea-level pressure observations, and is constrained by time-varying observed changes in atmospheric constituents, prescribed sea surface temperatures and sea ice concentration, creating a reconstruction of the weather over the whole globe every three hours for the period 1806–2015. The ERA-20C atmospheric reanalysis (covering 1900–2010; [[#Poli--2016|Poli et al., 2016]] ) also assimilates marine wind observations, and CERA-20C is a centennial-scale reanalysis that assimilates both atmospheric and oceanic observations for the 1901–2010 period ( [[#Laloyaux--2018|Laloyaux et al., 2018]] ). These centennial-scale reanalyses are often run as ensembles that provide an estimate of the uncertainty in the simulated variables over space and time. [[#Slivinski--2021|Slivinski et al. (2021)]] conclude that the uncertainties in surface circulation fields in version 3 of the 20th century Reanalysis are reliable and that there is also skill in its tropospheric reconstruction over the 20th century. Long-term changes in other variables, such as precipitation, also agree well with direct observation-based datasets (Sections 2.3.1.3 and 8.3.2.8).

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<span id="ocean-reanalyses"></span>
==== 1.5.2.3 Ocean Reanalyses ====

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Since AR5, ocean reanalyses have improved due to: increased model resolution ( [[#Zuo--2017|Zuo et al., 2017]] ; [[#Lellouche--2018|Lellouche et al., 2018]] ; [[#Heimbach--2019|Heimbach et al., 2019]] ); improved physics ( [[#Storto--2019|Storto et al., 2019]] ); improvements in the atmospheric forcing from atmospheric reanalyses (see [[#1.5.2.1.3|Section 1.5.2.1.3]] ); and improvements in the data quantity and quality available for assimilation (e.g., [[#Lellouche--2018|Lellouche et al., 2018]] ; [[#Heimbach--2019|Heimbach et al., 2019]] ), particularly due to Argo observations (Annex I; [[#Zuo--2019|Zuo et al., 2019]] ).

The first Ocean Reanalyses Intercomparison project (ORA-IP; [[#Balmaseda--2015|Balmaseda et al., 2015]] ) focussed on the uncertainty in key climate indicators, such as ocean heat content ( [[#Palmer--2017|Palmer et al., 2017]] ), thermosteric sea level ( [[#Storto--2017|Storto et al., 2017]] , 2019), salinity ( [[#Shi--2017|Shi et al., 2017]] ), sea ice extent ( [[#Chevallier--2017|Chevallier et al., 2017]] ), and the AMOC ( [[#Karspeck--2017|Karspeck et al., 2017]] ). Reanalysis uncertainties occur in areas of inhomogeneous or sparse observational data sampling, such as for the deep ocean, the Southern Ocean, and western boundary currents ( [[#Lellouche--2018|Lellouche et al., 2018]] ; [[#Storto--2019|Storto et al., 2019]] ). Intercomparisons have also been dedicated to specific variables such as mixed-layer depths ( [[#Toyoda--2017|Toyoda et al., 2017]] ), eddy kinetic energy, globally ( [[#Masina--2017|Masina et al., 2017]] ) and in the polar regions ( [[#Uotila--2019|Uotila et al., 2019]] ). [[#Karspeck--2017|Karspeck et al. (2017)]] found disagreement in the AMOC variability and strength in reanalyses over observation-sparse periods, whereas [[#Jackson--2019|Jackson et al. (2019)]] reported a lower spread in AMOC strength across an ensemble of ocean reanalyses of the recent period (1993–2010), linked to improved observation availability for assimilation. Reanalyses also have a larger spread of ocean heat uptake than data-only products and can produce spurious overestimates of heat uptake ( [[#Palmer--2017|Palmer et al., 2017]] ), which is important in the context of estimating climate sensitivity ( [[#Storto--2019|Storto et al., 2019]] ). The ensemble approach for ocean reanalyses provides another avenue for estimating uncertainties across ocean reanalyses ( [[#Storto--2019|Storto et al., 2019]] ).

While there are still limitations in their representation of oceanic features, ocean reanalyses add value to products based only on observation, and are used to inform assessments in AR6 (Chapters 2, 3, 7 and 9). Reanalyses of the atmosphere or ocean alone may not account for important atmosphere–ocean coupling, motivating the development of coupled reanalyses ( [[#Laloyaux--2018|Laloyaux et al., 2018]] ; [[#Schepers--2018|Schepers et al., 2018]] ; [[#Penny--2019|Penny et al., 2019]] ), but these are not assessed in AR6.

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<span id="reanalyses-of-the-pre-instrumental-era"></span>
==== 1.5.2.4 Reanalyses of the Pre-Instrumental Era ====

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Longer reanalyses that extend further back in time than the beginning of the instrumental record are being developed. They include the complete integration of paleoclimate archives and newly available early instrumental data into extended reanalysis datasets. Such integration leverages ongoing development of climate models that can simulate paleoclimate records in their units of analysis (i.e., oxygen isotope composition, tree ring width, etc.), in many cases using physical climate variables as input for so-called proxy system models ( [[#Evans--2013|Evans et al., 2013]] ; [[#Dee--2015|Dee et al., 2015]] ). Ensemble Kalman filter data assimilation approaches allow for combining paleoclimate data and climate model data to generate annually resolved fields (Last Millenium Reanalysis, [[#Hakim--2016|Hakim et al., 2016]] ; [[#Tardif--2019|Tardif et al., 2019]] ) or even monthly fields ( [[#Franke--2017|Franke et al., 2017]] ). This allows for a greater understanding of decadal variability ( [[#Parsons--2019|Parsons and Hakim, 2019]] ) and greater certainty around the full range of the frequency and severity of climate extremes. This, in turn, allows for better-defined detection of change. It also helps to identify the links between biogeochemical cycles, ecosystem structure and ecosystem functioning, and to provide initial conditions for further model experiments or downscaling (Chapter 2).

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<span id="applications-of-reanalyses"></span>
==== 1.5.2.5 Applications of Reanalyses ====

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The developments in reanalyses described above mean that they are now used across a range of applications. In AR6, reanalyses provide information for fields and in regions where observations are limited. There is growing confidence that modern reanalyses can provide another line of evidence in describing recent temperature trends (Tables 2.4 and 2.5). As their spatial resolution increases, the exploration of fine-scale extremes in both space and time becomes possible (e.g., wind; [[#Kaiser-Weiss--2015|Kaiser-Weiss et al., 2015]] ). Longer reanalyses can be used to describe the change in the climate over the last 100 to 1000 years. Reanalyses have been used to help post-process climate model output, and drive impact models; however, they are often bias adjusted first (Cross-Chapter Box 10.2; e.g., [[#Weedon--2014|Weedon et al., 2014]] ). Copernicus Climate Change Service (C3S) provides a bias-adjusted dataset for global land areas based on ERA5 called WFDE5 ( [[#Cucchi--2020|Cucchi et al., 2020]] ) which, combined with ERA5 information over the ocean (W5E5; [[#Lange--2019|Lange, 2019]] ), is used as the AR6 Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] reference for the bias adjustment of model output.

The growing interest in longer-term climate forecasts (from seasonal to multi-year and decadal) means that reanalyses are now more routinely being used to develop the initial state for these forecasts, such as for the Decadal Climate Prediction Project (DCPP; [[#Boer--2016|Boer et al., 2016]] ). Ocean reanalyses are now being used routinely in the context of climate monitoring, (e.g., the Copernicus Marine Environment Monitoring Service Ocean State Report; [[#von%20Schuckmann--2019|von Schuckmann et al., 2019]] ).

In summary, reanalyses have improved since AR5 and can increasingly be used as a line of evidence in assessments of the state and evolution of the climate system ( ''high confidence'' ). Reanalyses provide consistency across multiple physical quantities, and information about variables and locations that are not directly observed. Since AR5, new reanalyses have been developed with various combinations of increased resolution, extended records, more consistent data assimilation, estimation of uncertainty arising from the range of initial conditions, and an improved representation of the atmosphere or ocean system. While noting their remaining limitations, this Report uses the most recent generation of reanalysis products alongside more standard observation-based datasets.

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<span id="climate-models"></span>
=== 1.5.3 Climate Models ===

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A wide range of numerical models is widely used in climate science to study the climate system and its behaviour across multiple temporal and spatial scales. These models are the main tools available to look ahead into possible climate futures under a range of scenarios ( [[#1.6|Section 1.6]] ). Global Earth system models (ESMs) are the most complex models that contribute to AR6. At the core of each ESM is a GCM (general circulation model) representing the dynamics of the atmosphere and ocean. ESMs are complemented by regional models (Section 10.3.1) and by a hierarchy of models of lower complexity. This section summarizes major developments in these different types of models since AR5. Past IPCC reports have made use of multi-model ensembles generated through various phases of the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project (CMIP). Analysis of the latest CMIP Phase 6 (CMIP6; [[#Eyring--2016|Eyring et al., 2016]] ) simulations constitute a key line of evidence supporting this Assessment Report ( [[#1.5.4|Section 1.5.4]] ). The key characteristics of models participating in CMIP6 are listed in Annex II: Models.

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<span id="earth-system-models"></span>
==== 1.5.3.1 Earth System Models ====

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Earth system models are mathematical formulations of the natural laws that govern the evolution of climate-relevant systems: atmosphere, ocean, cryosphere, land, and biosphere, as well as the carbon cycle ( [[#Flato--2011|Flato, 2011]] ). They build on the fundamental laws of physics (e.g., Navier–Stokes or Clausius–Clapeyron equations) or empirical relationships established from observations and, when possible, they are constrained by fundamental conservation laws (e.g., mass and energy). The evolution of climate-relevant variables is computed numerically using high-performance computers ( [[#André--2014|André et al., 2014]] ; [[#Balaji--2017|Balaji et al., 2017]] ), on three-dimensional discrete grids ( [[#Staniforth--2012|Staniforth and Thuburn, 2012]] ). The spatial (and temporal) resolution of these grids in both the horizontal and vertical directions determines which processes need to be parameterized or whether they can be explicitly resolved. Developments since AR5 in model resolution, parameterizations and modelling of the land and ocean biosphere and of biogeochemical cycles are discussed below.

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<span id="model-grids-and-resolution"></span>
===== ''1.5.3.1.1 Model grids'' ''and resolution'' =====

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The horizontal resolution and the number of vertical levels in ESMs is generally higher in CMIP6 than in CMIP5 (Figure 1.19). Global models with finer horizontal grids better represent many aspects of the circulation of the atmosphere ( [[#Gao--2020|Gao et al., 2020]] ; [[#Schiemann--2020|Schiemann et al., 2020]] ) and ocean ( [[#Bishop--2016|Bishop et al., 2016]] ; [[#Storkey--2018|Storkey et al., 2018]] ), bringing improvements in the simulation of the global hydrological cycle ( [[#Roberts--2018|Roberts et al., 2018]] ). CMIP6 includes a dedicated effort (HighResMIP, [[#Haarsma--2016|Haarsma et al., 2016]] ) to explore the effect of higher horizontal resolution, such as ~50 km, ~25 km and even ~10 km ( [[#1.5.4.2|Section 1.5.4.2]] and Annex II, Table AII.6). Improvements are documented in the highest-resolution coupled models used for HighResMip ( [[#Hewitt--2017|]] [[#Hewitt--2017|Hewitt et al., 2017]] ; [[#Roberts--2019|Roberts et al., 2019]] ). Flexible grids allowing spatially variable resolution in the atmosphere ( [[#McGregor--2015|McGregor, 2015]] ; [[#Giorgetta--2018|Giorgetta et al., 2018]] ) and in the ocean ( [[#Wang--2014|Wang et al., 2014]] ; [[#Petersen--2019|Petersen et al., 2019]] ) are more widely used than at the time of the AR5.

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[[File:336d8e067ca4415dae38e7aaf9eb07bf IPCC_AR6_WGI_Figure_1_19.png]]

'''Figure 1.19 |''' '''Resolution of the atmospheric and oceanic components of global climate models participating in CMIP5, CMIP6 and HighResMIP:''' '''(a, b)''' horizontal resolution (km), and '''(c, d)''' number of vertical levels. Darker-colour circles indicate high-top models (in which the top of the atmosphere is above 50 km). The crosses are the median values. These models are documented in Annex II. Note that duplicated models in a modelling group are counted as one entry when their horizontal and vertical resolutions are the same. For HighResMIP, one atmosphere–ocean coupled model with the highest resolution from each modelling group is used. The horizontal resolution (rounded to 10 km) is the square root of the surface area of the Earth divided by the number of grid points, or the area of the ocean surface divided by the number of surface ocean grid points, for the atmosphere and ocean, respectively.

The number of vertical levels in the atmosphere of global models has increased (Figure 1.19), partly to enable simulations to include higher levels in the atmosphere and better represent stratospheric processes ( [[#Charlton-Perez--2013|Charlton-Perez et al., 2013]] ; [[#Kawatani--2019|Kawatani et al., 2019]] ). Half the modelling groups now use ‘high-top’ models with a top level above the stratopause (a pressure of about 1 hPa). The number of vertical levels in the ocean models has also increased in order to achieve finer resolution over the water column and especially in the upper mixed layer and to better resolve the diurnal cycle ( [[IPCC:Wg1:Chapter:Chapter-3#3.5|Section 3.5]] and Annex II; [[#Bernie--2008|Bernie et al., 2008]] ).

Despite the documented progress of higher resolution, the model evaluation carried out in subsequent chapters shows that improvements between CMIP5 and CMIP6 remain modest at the global scale ( [[IPCC:Wg1:Chapter:Chapter-3#3.8.2|Section 3.8.2]] ; [[#Bock--2020|Bock et al., 2020]] ). Lower resolution alone does not explain all model biases, for example, a low blocking frequency ( [[#Davini--2020|Davini and D’Andrea, 2020]] ) or a wrong shape of the Intertropical Convergence Zone ( [[#Tian--2020|Tian and Dong, 2020]] ). Model performance depends on model formulation and parameterizations as much as on resolution (Chapters 3, 8 and 10).

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<span id="representation-of-physical-and-chemical-processes-in-esms"></span>
===== 1.5.3.1.2 Representation of physical and chemical processes in ESMs =====

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Atmospheric models include representations of physical processes such as clouds, turbulence, convection and gravity waves that are not fully represented by grid-scale dynamics. The CMIP6 models have undergone updates in some of their parameterization schemes compared to their CMIP5 counterparts, with the aim of better representing the physics and bringing the climatology of the models closer to newly available observational datasets. Most notable developments are to schemes involving radiative transfer, cloud microphysics, and aerosols, in particular a more explicit representation of the aerosol indirect effects through aerosol-induced modification of cloud properties. Broadly, aerosol–cloud microphysics has been a key topic for the aerosol and chemistry modelling communities since AR5, leading to improved understanding of the climate influence of short-lived climate forcers, but they remain the single largest source of spread in ESM calculations of climate sensitivity ( [[#Meehl--2020|Meehl et al., 2020]] ), with numerous parameterization schemes in use (Section 6.4; [[#Gettelman--2016|Gettelman and Sherwood, 2016]] ; [[#Zhao--2018|Zhao et al., 2018]] ; [[#Gettelman--2019|Gettelman et al., 2019]] ). The treatment of droplet size and mixed-phase clouds (liquid and ice) was found to lead to changes in the climate sensitivity (Glossary) of some models between AR5 and AR6 (Section 7.4; [[#Bodas-Salcedo--2019|Bodas-Salcedo et al., 2019]] ; [[#Gettelman--2019|Gettelman et al., 2019]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ).

The representation of ocean and cryosphere processes has also evolved significantly since CMIP5. The explicit representation of ocean eddies, due to increased grid resolution (typically, from 1° to ¼°), is a major advance in a number of CMIP6 ocean model components ( [[#Hewitt--2017|]] [[#Hewitt--2017|Hewitt et al., 2017]] ). Advances in sea ice models have been made, for example through correcting known shortcomings in CMIP5 simulations, in particular the persistent underestimation of the rapid decline in summer Arctic sea ice extent ( [[#Rosenblum--2016|Rosenblum and Eisenman, 2016]] , 2017; [[#Turner--2017|Turner and Comiso, 2017]] ; [[#Notz--2018|Notz and Stroeve, 2018]] ). The development of glacier and ice-sheet models has been motivated and guided by an improved understanding of key physical processes, including grounding line dynamics, stratigraphy and microstructure evolution, sub-shelf melting, and glacier and ice-shelf calving, among others ( [[#Faria--2014|Faria et al., 2014]] , 2018; [[#Hanna--2020|Hanna et al., 2020]] ). The resolution of ice-sheet models has continuously increased, including the use of nested grids, sub-grid interpolation schemes, and adaptive mesh approaches ( [[#Cornford--2016|Cornford et al., 2016]] ), mainly for a more accurate representation of grounding-line migration and data assimilation ( [[#Pattyn--2018|Pattyn, 2018]] ). Ice-sheet models are increasingly interactively coupled with global and regional climate models, accounting for the height–mass-balance feedback ( [[#Vizcaino--2015|Vizcaino et al., 2015]] ; [[#Le%20clec’h--2019|Le clec’h et al., 2019]] ), and enabling a better representation of ice-ocean processes, in particular for the Antarctic Ice Sheet ( [[#Asay-Davis--2017|Asay-Davis et al., 2017]] ).

Sealevel rise is caused by multiple processes acting on multiple time scales: ocean warming, glaciers and ice-sheet melting, change in water storage on land, and glacial isostatic adjustment (Box 9.1) but no single model can represent all these processes (Section 9.6). In this Report, the contributions are computed separately (Figure 9.28) and merged into a common probabilistic framework and updated from AR5 (Section 9.6; [[#Church--2013|Church et al., 2013]] ; [[#Kopp--2014|Kopp et al., 2014]] ).

Another notable development since AR5 is the inclusion of stochastic parameterizations of sub-grid processes in some comprehensive climate models ( [[#Sanchez--2016|Sanchez et al., 2016]] ). Here, the deterministic differential equations that govern the dynamical evolution of the model are complemented by knowledge of the stochastic variability in unresolved processes. While not yet widely implemented, the approach has been shown to improve the forecasting skill of weather models, to reduce systematic biases in global models ( [[#Berner--2017|Berner et al., 2017]] ; [[#Palmer--2019|Palmer, 2019]] ) and to influence simulated climate sensitivity ( [[#Strommen--2019|Strommen et al., 2019]] ).

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<span id="representation-of-biogeochemistry-including-the-carbon-cycle"></span>
===== 1.5.3.1.3 Representation of biogeochemistry, including the carbon cycle =====

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Since AR5, more sophisticated land-use and land-cover change representations in ESMs have been developed to simulate the effects of land management on surface fluxes of carbon, water and energy ( [[#Lawrence--2016|Lawrence et al., 2016]] ), although the integration of many processes (e.g., wetland drainage, fire as a management tool) remains a challenge ( [[#Pongratz--2018|Pongratz et al., 2018]] ). The importance of nitrogen availability to limit the terrestrial carbon sequestration has been recognized (Section 5.4; [[#Zaehle--2014|Zaehle et al., 2014]] ) and so an increasing number of models now include a prognostic representation of the terrestrial nitrogen cycle and its coupling to the land carbon cycle ( [[#Jones--2016|Jones et al., 2016]] ; [[#Arora--2020|Arora et al., 2020]] ), leading to a reduction in uncertainty for carbon budgets (Section 5.1; [[#Jones--2020|Jones and Friedlingstein, 2020]] ). As was the case in CMIP5 ( [[#Ciais--2013|Ciais et al., 2013]] ), the land surface processes represented vary across CMIP6 models, with at least some key processes (fire, permafrost carbon, microbes, nutrients, vegetation dynamics, plant demography) absent from any particular ESM land model (Table 5.4). Ocean biogeochemical models have evolved to enhance the consistency of the exchanges between ocean, atmosphere and land, through riverine input and dust deposition ( [[#Stock--2014|Stock et al., 2014]] ; [[#Aumont--2015|Aumont et al., 2015]] ). Other developments include flexible plankton stoichiometric ratios ( [[#Galbraith--2015|Galbraith and Martiny, 2015]] ), improvements in the representation of nitrogen fixation ( [[#Paulsen--2017|Paulsen et al., 2017]] ), and the limitation of plankton growth by iron ( [[#Aumont--2015|Aumont et al., 2015]] ). Due to the long time scale of biogeochemical processes, how the models are initialized (spun up) strategies has been shown to affect their performance in AR5 ( [[#Séférian--2016|Séférian et al., 2016]] ).

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==== 1.5.3.2 Model Tuning and Adjustment ====

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When developing climate models, choices have to be made in a number of areas. Besides model formulation and resolution, parameterizations of unresolved processes also involve many choices as, for each of these, several parameters can be set. The acceptable range for these parameters is set by mathematical consistency (e.g., convergence of a numerical scheme), physical considerations (e.g., energy conservation), observations, or a combination of factors. Model developers choose a set of parameters that both falls within this range and mimics observations of individual processes or their statistics.

An initial set of such choices is usually made by (often extensive) groups of modellers working on individual components of the Earth system (e.g., ocean, atmosphere, land or sea ice). As components are assembled to build an ESM, the choices are refined so that the simulated climate best represents a number of pre-defined climate variables, or ‘tuning targets’. When these are met the model is released for use in intercomparisons such as CMIP. Tuning targets can be one of three types: mean climate; regional phenomena and features; or historical trends ( [[#Hourdin--2017|Hourdin et al., 2017]] ). One example of such a goal is that when the simulated climate system receives energy from the sun in accordance with what we observe today, the resulting mean equilibrium temperature should also be close to observations. Whether tuning should be performed to facilitate accurate simulation of long-term trends such as changes in global mean temperature over the historical era, or rather be performed for each process independently such that all collective behaviour is emergent, is an open question ( [[#Schmidt--2017|Schmidt et al., 2017]] ; [[#Burrows--2018|Burrows et al., 2018]] ).

Each modelling group has its own strategy and, after AR5, a survey was conducted to understand the tuning approach used in 23 CMIP5 modelling centres. The results are discussed in [[#Hourdin--2017|Hourdin et al. (2017)]] , which stresses that the behaviour of ESMs depends on the tuning strategy. An important recommendation is that the calibration steps that lead to particular model tuning should be carefully documented. In CMIP6 each modelling group now describes the three levels of tuning, both for the complete ESM and for the individual components (available at https://explore.es-doc.org and in the published model descriptions, Annex II: Models). The most important global tuning target for CMIP6 models is the net top-of-the-atmosphere (TOA) heat flux and its radiative components. Other global targets include: the decomposition of the energy fluxes at TOA into a clear sky component and a component due to the radiative effect of clouds, global mean air and ocean temperature, sea ice extent, sea ice volume, glacial mass balance, and the global root mean square error of precipitation. The TOA heat flux balance is achieved using a diversity of approaches, usually unique to each modelling group. Adjustments are made for parameters associated with uncertain or poorly constrained processes ( [[#Schmidt--2017|Schmidt et al., 2017]] ), for example the aerosol indirect effects, adjustments to ocean albedo, marine dimethyl sulfide (DMS) parameterization, or cloud properties ( [[#Mauritsen--2020|Mauritsen and Roeckner, 2020]] ).

Regional tuning targets include: the AMOC, the Southern Ocean circulation, and temperature profiles in ocean basins ( [[#Golaz--2019|Golaz et al., 2019]] ; [[#Sellar--2019|Sellar et al., 2019]] ); regional land properties and precipitations ( [[#Mauritsen--2019|Mauritsen et al., 2019]] ; [[#Yukimoto--2019|Yukimoto et al., 2019]] ) ; latitudinal distribution of radiation ( [[#Boucher--2020|Boucher et al., 2020]] ); spatial contrasts in TOA radiative fluxes or surface fluxes; and stationary waves in the Northern Hemisphere ( [[#Schmidt--2017|Schmidt et al., 2017]] ; [[#Yukimoto--2019|Yukimoto et al., 2019]] ).

Even with some core commonalities of approaches to model tuning, practices can differ, such as the use of initial drift from initialized forecasts, the explicit use of the transient observed record for the historical period, or the use of the present-day radiative imbalance at the TOA as a tuning target rather than an equilibrated pre-industrial balance. The majority of CMIP6 modelling groups report that they do not tune their model for the observed trends during the historical period (23 out of 29 groups), nor for ECS (25 out of 29). ECS and TCR are thus emergent properties for a large majority of models. The effect of tuning on model skill and ensemble spread in CMIP6 is further discussed in [[IPCC:Wg1:Chapter:Chapter-3#3.3|Section 3.3]] .

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<span id="from-global-to-regional-models"></span>
==== 1.5.3.3 From Global to Regional Models ====

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The need for accurate climate information at the regional scale is increasing (Section 10.1). High-resolution global climate models, such as those taking part in HighResMIP, provide more detailed information at the regional scale ( [[#Roberts--2018|Roberts et al., 2018]] ). However, due to the large computational resources required by these models, only a limited number of simulations per model are available. In addition to CMIP global models, regional information can be derived using regional climate models (RCMs) and downscaling techniques, presented in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-10 Chapter 10] and the Atlas. RCMs are dynamical models, similar to GCMs, that simulate a limited region and are forced with boudary conditions from a global simulation, often correcting for biases (Section 10.3, Cross-Chapter Box 10.2 and Annex II). This approach allows the use of a higher resolution within the chosen domain, and thus better represents important drivers of regional climate such as mountain ranges, land management and urban effects. RCMs resolving atmospheric convection explicitly are now included in intercomparisons ( [[#Coppola--2020|Coppola et al., 2020]] ) and are used in Chapters 10, 11 and 12. Other approaches, such as statistical downscaling, are also used to generate regional climate projections (Section 10.3; [[#Maraun--2018|Maraun and Widmann, 2018]] ).

The number of climate centres or consortia that carry out global climate simulations and projections has grown from 11 in the first CMIP to 19 in CMIP5 and 28 for CMIP6 ( [[#1.5.4.2|Section 1.5.4.2]] and Annex II). Regional climate models participating in the Coordinated Regional Downscaling Experiment (CORDEX) are more diverse than the global ESMs ( [[#1.5.4.3|Section 1.5.4.3]] and Annex II) and engage an even wider international community (Figure 1.20).

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[[File:a514699cf882e9bac13c3ca48d0a4efa IPCC_AR6_WGI_Figure_1_20.png]]

'''Figure 1.20 |''' '''World map showing the increased diversity of modelling centres contributing to CMIP and CORDEX.''' Climate models are often developed by international consortia. One such consortium, EC-Earth, is shown as an example under the label '''8 EU Cities''' (involving SMHI, Sweden; KNMI, The Netherlands; DMI, Denmark; AEMET, Spain; Met Éireann, Ireland; CNR‐ISAC, Italy; Instituto de Meteorologia, Portugal; and FMI, Finland). There are too many such collaborations to display all of them on this map. More complete information about institutions contributing to CORDEX and CMIP6 is found in Annex II.

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<span id="models-of-lower-complexity"></span>
==== 1.5.3.4 Models of Lower Complexity ====

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'''Earth system models of intermediate complexity''' (EMICs) complement the model hierarchy and fill the gap between conceptual, simple climate models and complex GCMs or ESMs ( [[#Claussen--2002|Claussen et al., 2002]] ). EMICs are simplified; they include processes in a more parameterized, rather than explicitly calculated, form and generally have lower spatial resolution compared to the complex ESMs. As a result, EMICs require much less computational resource and can be integrated for many thousands of years without supercomputers ( [[#Hajima--2014|Hajima et al., 2014]] ). The range of EMICs used in climate change research is highly heterogeneous, ranging from zonally averaged or mixed-layer ocean models coupled to statistical-dynamical models of the atmosphere, to low-resolution three-dimensional ocean models coupled to simplified dynamical models of the atmosphere. An increasing number of EMICs include interactive representations of the global carbon cycle, with varying levels of complexity and numbers of processes considered ( [[#Plattner--2008|Plattner et al., 2008]] ; [[#Zickfeld--2013|Zickfeld et al., 2013]] ; [[#MacDougall--2020|MacDougall et al., 2020]] ). Given the heterogeneity of the EMIC community, modellers tend to focus on specific research questions and develop individual models accordingly. As for any type of models assessed in this Report, the set of EMICs undergoes thorough evaluation and fit-for-purpose testing before being applied to address specific climate aspects.

EMICs have been used extensively in past IPCC reports, providing long-term integrations on paleoclimate and future time scales, including stabilization pathways and a range of commitment scenarios, with perturbed physics ensembles and sensitivity studies, or with simulations targeting the uncertainty in global climate–carbon cycle systems (e.g., [[#Meehl--2007b|Meehl et al., 2007b]] ; [[#Collins--2013|Collins et al., 2013]] ). More recently, a number of studies have pointed to the possibility of systematically different climate responses to external forcings in EMICs and complex ESMs ( [[#Frölicher--2015|Frölicher and Paynter, 2015]] ; [[#Pfister--2017|Pfister and Stocker, 2017]] , 2018) that need to be considered in the context of this report. For example, [[#Frölicher--2015|Frölicher and Paynter (2015)]] showed that EMICs have a higher simulated realized warming fraction (i.e., the TCR/ECS ratio) than CMIP5 ESMs and speculated that this may bias the temperature response to zero carbon emissions. But, in a recent comprehensive multi-model analysis of the zero CO <sub>2</sub> emissions commitment, [[#MacDougall--2020|MacDougall et al. (2020)]] did not find any significant differences between EMICs and ESMs in committed temperatures 90 years after halting emissions. While some EMICs contribute to parts of the CMIP6-endorsed MIPs, a coordinated EMICs modelling effort similar to those carried out for AR4 ( [[#Plattner--2008|Plattner et al., 2008]] ) and AR5 ( [[#Eby--2013|Eby et al., 2013]] ; [[#Zickfeld--2013|Zickfeld et al., 2013]] ) is not in place for IPCC AR6; however, EMICs are assessed in a number of chapters. For example, Chapters 4 and 5 use EMICs in the assessment of long-term climate change beyond 2100 (Section 5.5); zero-emissions commitments, overshoot and recovery ( [[IPCC:Wg1:Chapter:Chapter-4#4.7|Section 4.7]] ); consequences of CO <sub>2</sub> removal (CDR) on the climate system and the carbon cycle (Sections 4.6 and 5.6); and long-term carbon cycle–climate feedbacks (Section 5.4).

'''Physical emulators and simple climate models''' make up a broad class of heavily parametrized models designed to reproduce the responses of the more complex, process-based models, and provide rapid translations of emissions, via concentrations and radiative forcing, into probabilistic estimates of changes to the physical climate system. The main application of emulators is to extrapolate insights from ESMs and observational constraints to a larger set of emissions scenarios (Cross-Chapter Box 7.1). The computational efficiency of various emulating approaches opens new analytical possibilities, given that ESMs take a lot of computational resources for each simulation. The applicability and usefulness of emulating approaches are however constrained by their skill in capturing the global mean climate responses simulated by the ESMs (mainly limited to global mean or hemispheric land/ocean temperatures) and by their ability to extrapolate skilfully outside the calibrated range.

The terms ‘emulator’ and ‘simple climate model’ (SCM) are different, although they are sometimes used interchangeably. SCM refers to a broad class of lower-dimensional models of the energy balance, radiative transfer, carbon cycle, or a combination of such physical components. SCMs can also be tuned to reproduce the calculations of climate-mean variables of a given ESM, assuming that their structural flexibility can capture both the parametric and structural uncertainties across process-oriented ESM responses. When run in this setup, they are termed emulators. Simple climate models do not have to be run in ‘emulation’ mode, though, as they can also be used to test consistency across multiple lines of evidence with regard to ranges in ECS, TCR, TCRE and carbon cycle feedbacks (Chapters 5 and 7). Physical emulation can also be performed with very simple parameterizations (‘one-or-few-line climate models’), statistical methods like neural networks, genetic algorithms, or other artificial intelligence approaches, where the emulator behaviour is explicitly tuned to reproduce the response of a given ESM or model ensemble (Chapters 4, 5 and 7).

Current emulators and SCMs include the generic impulse response model outlined in [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] of AR5 (AR5-IR; Supplementary Material 8.SM.11 of [[#Myhre--2013|Myhre et al., 2013]] ), two-layer models ( [[#Held--2010|Held et al., 2010]] ; [[#Rohrschneider--2019|Rohrschneider et al., 2019]] ; [[#Nicholls--2020|Nicholls et al., 2020]] ), and higher-complexity approaches that include upwelling, diffusion and entrainment in the ocean component (e.g., MAGICC Version 5.3 ( [[#Raper--2001|Raper et al., 2001]] ; [[#Wigley--2009|Wigley et al., 2009]] ); Version 6/7 ( [[#Meinshausen--2011a|Meinshausen et al., 2011a]] ); OSCAR ( [[#Gasser--2017|Gasser et al., 2017]] ); CICERO SCM ( [[#Skeie--2017|Skeie et al., 2017]] ); FaIR ( [[#Millar--2017a|Millar et al., 2017a]] ; [[#Smith--2018|Smith et al., 2018]] ); and a range of statistical approaches ( [[#Schwarber--2019|Schwarber et al., 2019]] ; [[#Beusch--2020b|Beusch et al., 2020b]] ). An example of recent use of an emulator approach is an early estimate of the climate implications of the COVID-19 lockdowns (Cross-Chapter Box 6.1; [[#Forster--2020|Forster et al., 2020]] ).

Since AR5, simplified climate models have been developed further, and their use is increasing. Different purposes motivating development include: being as simple as possible for teaching purposes (e.g., a two-layer energy balance model); being as comprehensive as possible to allow for propagation of uncertainties across multiple Earth system domains (MAGICC and others); or focusing on higher-complexity representation of specific domains (e.g., OSCAR). The common theme motivating many models is to improve parameterizations that reflect the latest findings in complex ESM interactions – such as the nitrogen cycle addition to the carbon cycle, or tropospheric and stratospheric ozone exchange – with the aim of emulating their global mean temperature response. Also, within the simple models that have a rudimentary representation of spatial heterogeneity (e.g., four-box simple climate models), the ambition is to represent heterogeneous forcers such as black carbon more adequately ( [[#Stjern--2017|Stjern et al., 2017]] ), provide an appropriate representation of the forcing–feedback framework (e.g., [[#Sherwood--2015|Sherwood et al., 2015]] ), investigate new parameterizations of ocean heat uptake, and implement better representations of volcanic aerosol-induced cooling ( [[#Gregory--2016a|Gregory et al., 2016a]] ).

MAGICC ( [[#Wigley--2009|Wigley et al., 2009]] ; [[#Meinshausen--2011a|Meinshausen et al., 2011a]] ) and FaIR ( [[#Smith--2018|Smith et al., 2018]] ) were used in IPCC SR1.5 ( [[#IPCC--2018|IPCC, 2018]] ) to categorize mitigation pathways into classes of scenarios that peak near 1.5°C, overshoot 1.5°C, or stay below 2°C. The SR1.5 ( [[#Rogelj--2018b|Rogelj et al., 2018b]] ) concluded that there was ''high agreement'' on the relative temperature response of pathways, but ''medium agreement'' on the precise absolute magnitude of warming, introducing a level of imprecision in the attribution of a single pathway to a given category.

In this Report, there are two notable uses of simple climate models. One is the connection between the assessed range of ECS in Chapter 7, and the projections of future global surface air temperature (GSAT) change in Chapter 4, which is done via a two-layer model based on [[#Held--2010|Held et al. (2010)]] . It is also used as input to sea level projections in Chapter 9. The other usage is the transfer of Earth system assessment knowledge to WGIII, via a set of models (MAGICC, FaIR, CICERO-SCM) specifically tuned to represent the WGI assessment. For an overview of the uses, and an assessment of the related Reduced Complexity Model Intercomparison Project (RCMIP), see [[#Nicholls--2020|Nicholls et al. (2020)]] and Cross-Chapter Box 7.1.

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'''Box 1.3 | Emissions Met''' '''rics in AR6 WGI'''

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Emissions metrics compare the radiative forcing, temperature change, or other climate effects arising from emissions of CO <sub>2</sub> against those from emissions of non-CO <sub>2</sub> radiative forcing agents (such as CH <sub>4</sub> or N <sub>2</sub> O). They have been discussed in the IPCC since the First Assessment Report and are used as a means of aggregating emissions and removals of different gases and placing them on a common (‘CO <sub>2</sub> equivalent’, or ‘CO <sub>2</sub> -eq’) scale.

AR5 included a thorough assessment of common pulse emissions metrics, and how these address various indicators of future climate change ( [[#Myhre--2013|Myhre et al., 2013]] ). Most prominently used are the global warming potentials (GWPs), which integrate the calculated radiative forcing contribution following an idealized pulse (or one-time) emission, over a chosen time horizon ( [[#IPCC--1990a|IPCC, 1990a]] ), or the global temperature change potential (GTP), which considers the contribution of emissions to the global-mean temperature at a specific time after emission. Yet another metric is the global precipitation change potential (GPP), used to quantify the precipitation change per unit mass of emission of a given forcing agent ( [[#Shine--2015|Shine et al., 2015]] ).

As an example of usage, the Paris Rulebook [Decision 18/CMA.1, annex, paragraph 37] states that

Each Party shall use the 100-year time-horizon global warming potential (GWP) values from the IPCC Fifth Assessment Report, or 100-year time-horizon GWP values from a subsequent IPCC assessment report as agreed upon by the ‘Conference of the Parties serving as the meeting of the Parties to the Paris Agreement’ (CMA), to report aggregate emissions and removals of GHGs, expressed in CO <sub>2</sub> -eq. Each Party may in addition also use other metrics (e.g., global temperature potential) to report supplemental information on aggregate emissions and removals of GHGs, expressed in CO <sub>2</sub> -eq.

Since AR5, improved knowledge of the radiative properties, lifetimes and other characteristics of emitted species, and the response of the climate system, have led to updates to the numerical values of a range of metrics (Table 7.15). Another key development is a set of metrics that compare a pulse emission of CO <sub>2</sub> (as considered by GWP and GTP) to step-changes of emission rates for short-lived components (i.e., also considering emissions trends). Termed GWP* (which also includes a pulse component) and combined global temperature change potential (CGTP), these metrics allow the construction of a near-linear relationship between global surface temperature change and cumulative CO <sub>2</sub> and CO <sub>2</sub> -eq emissions of both short- and long-lived forcing agents ( [[#Allen--2016|Allen et al., 2016]] ; [[#Cain--2019|Cain et al., 2019]] ; [[#Collins--2020|Collins et al., 2020]] ). For example, the temperature response to a sustained methane reduction has a similar behaviour to the temperature response to a pulse CO <sub>2</sub> removal (or avoided emission).

In this Report, recent scientific developments underlying emissions metrics, as relevant for WGI, are assessed in full in Section 7.6. In particular, see Box 7.3, which discusses the choice of metric for different usages, and Section 7.6.1, which treats the challenge of comparing the climate implication of emissions of short-lived and long-lived compounds. Also, the choice of metric is of key importance when defining and quantifying net zero GHG emissions (Box 1.4 and Section 7.6.2). [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] applies metrics to attribute GSAT change to short-lived climate forcer (SLCF) and long-lived GHG emissions from different sectors and regions (Section 6.6.2).

The metrics assessed in this Report are also used, and separately assessed, by WGIII. See Cross-Chapter Box 2 and Annex B in [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] of the WGIII contribution to AR6.

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=== 1.5.4 Modelling Techniques, Comparisons and Performance Assessments ===

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Numerical models, however complex, cannot be a perfect representation of the real world. Results from climate modelling simulations constitute a key line of evidence for the present Report, which requires considering the limitations of each model simulation. This section presents recent developments in techniques and approaches to robustly extract, quantify and compare results from multiple, independent climate models, and how their performance can be assessed and validated.

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==== 1.5.4.1 Model ‘Fitness-for-Purpose’ ====

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A key issue addressed in this Report is whether climate models are adequate or ‘fit’ for purposes of interest, that is, whether they can be used to successfully answer particular research questions, especially about the causes of recent climate change and the future evolution of climate (e.g., [[#Parker--2009|Parker, 2009]] ; [[#Notz--2015|Notz, 2015]] ; [[#Knutti--2018|Knutti, 2018]] ; [[#Winsberg--2018|Winsberg, 2018]] ). Assessment of a model’s fitness-for-purpose can be informed both by how the model represents relevant physical processes and by relevant performance metrics ( [[#Baumberger--2017|Baumberger et al., 2017]] ; [[#Parker--2020|Parker, 2020]] ). The processes and metrics that are most relevant can vary with the question of interest. For example, a question about changes in deep-ocean circulation compared with a question about changes in regional precipitation ( [[#Notz--2015|Notz, 2015]] ; [[#Gramelsberger--2020|Gramelsberger et al., 2020]] ). New model-evaluation tools ( [[#1.5.4.5|Section 1.5.4.5]] ) and emergent constraint methodologies ( [[#1.5.4.7|Section 1.5.4.7]] ) can also aid the assessment of fitness-for-purpose, especially in conjunction with process understanding ( [[#Klein--2015|Klein and Hall, 2015]] ; [[#Knutti--2018|Knutti, 2018]] ). The broader availability of large model ensembles may allow for novel tests of fitness that better account for natural climate variability ( [[#1.5.4.2|Section 1.5.4.2]] ). Fitness-for-purpose of models used in this Report is discussed in [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg1:Chapter:Chapter-3#3.8.4|Section 3.8.4]] ) for the global scale, in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-10 Chapter 10] (Section 10.3) for regional climate, and in the other chapters for the process level.

Typical strategies for enhancing the fitness-for-purpose of a model include increasing resolution in order to explicitly simulate key processes, improving relevant parameterizations, and careful tuning. Changes to a model that enhance its fitness for one purpose can sometimes decrease its fitness for others, by upsetting a pre-existing balance of approximations. When it is unclear whether a model is fit for a purpose of interest, there is often a closely related purpose for which the evidence of fitness is clearer. For example, it might be unclear whether a model is fit for providing highly accurate projections of precipitation changes in a region, but reasonable to think that the model is fit for providing projections of precipitation changes that cannot yet be ruled out ( [[#Parker--2009|Parker, 2009]] ). Such information about plausible or credible changes can be useful to inform adaptation. Note that challenges associated with assessing models’ fitness-for-purpose need not prevent reaching conclusions with high confidence if there are multiple other lines of evidence supporting those same conclusions.

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==== 1.5.4.2 Ensemble Modelling Techniques ====

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A key approach in climate science is the comparison of results from multiple model simulations with each other and against observations. These simulations have typically been performed by separate models with consistent boundary conditions and prescribed emissions or radiative forcings, as in the Coupled Model Intercomparison Project phases (CMIP, [[#Meehl--2000|Meehl et al., 2000]] , 2007a; [[#Taylor--2012|Taylor et al., 2012]] ; [[#Eyring--2016|Eyring et al., 2016]] ). Such multi-model ensembles (MMEs) have proven highly useful in sampling and quantifying model uncertainty, within and between generations of climate models. They also reduce the influence on projections of the particular sets of parametrizations and physical components simulated by individual models. The primary usage of MMEs is to provide a well-quantified model range, but when used carefully they can also increase confidence in projections ( [[#Knutti--2010|Knutti et al., 2010]] ). Presently, however, many models also share provenance ( [[#Masson--2011|Masson and Knutti, 2011]] ) and may have common biases that should be acknowledged when presenting and building on MME-derived conclusions ( [[#1.5.4.6|Section 1.5.4.6]] ; [[#Boé--2018|Boé, 2018]] ; [[#Abramowitz--2019|Abramowitz et al., 2019]] ).

Since AR5, an increase in computing power has made it possible to investigate simulated internal variability and to provide robust estimates of forced model responses, using large initial condition ensembles (ICEs), also referred to as single model initial condition large ensembles (SMILEs). Examples using GCMs or ESMs that support assessments in AR6 include the CESM Large Ensemble ( [[#Kay--2015|Kay et al., 2015]] ), the MPI Grand Ensemble ( [[#Maher--2019|Maher et al., 2019]] ), and the CanESM2 large ensembles ( [[#Kirchmeier-Young--2017|Kirchmeier-Young et al., 2017]] ). Such ensembles employ a single GCM or ESM in a fixed configuration, but starting from a variety of different initial states. In some experiments, these initial states only differ slightly. As the climate system is chaotic, such tiny changes in initial conditions lead to different evolutions for the individual realizations of the system as a whole. Other experiments start from a set of well-separated ocean initial conditions to sample the uncertainty in the circulation state of the ocean and its role in longer-time scale variations. These two types of ICEs have been referred to as ‘micro’ and ‘macro’ perturbation ensembles respectively ( [[#Hawkins--2016|Hawkins et al., 2016]] ). In support of this Report, most models contributing to CMIP6 have produced ensembles of multiple realizations of their historical and scenario simulations (Chapters 3 and 4).

Recently, the ICE technique has been extended to atmosphere-only simulations ( [[#Mizuta--2017|Mizuta et al., 2017]] ), single-forcer influences such as volcanic eruptions ( [[#Bethke--2017|Bethke et al., 2017]] ), regional modelling ( [[#Mote--2015|Mote et al., 2015]] ; [[#Fyfe--2017|Fyfe et al., 2017]] ; [[#Schaller--2018|Schaller et al., 2018]] ; [[#Leduc--2019|Leduc et al., 2019]] ), and to attribution of extreme weather events using crowdsourced computing ( [http://climateprediction.net climateprediction.net] ; [[#Massey--2015|Massey et al., 2015]] ).

ICEs can also be used to evaluate climate model parameterizations, if models are initialized appropriately ( [[#Phillips--2004|Phillips et al., 2004]] ; [[#Williams--2013|Williams et al., 2013]] ), mostly within the framework of seamless weather and climate predictions (e.g., [[#Palmer--2008|Palmer et al., 2008]] ; [[#Hurrell--2009|Hurrell et al., 2009]] ; [[#Brown--2012|Brown et al., 2012]] ). Initializing an atmospheric model in hindcast mode and observing the biases as they develop permits testing of the parameterized processes, by starting from a known state rather than one dominated by quasi-random short-term variability ( [[#Williams--2013|Williams et al., 2013]] ; [[#Ma--2014|Ma et al., 2014]] ; [[#Vannière--2014|Vannière et al., 2014]] ). However, single-model initial-conditions ensembles cannot cover the same degrees of freedom as a multi-model ensemble, because model characteristics substantially affect model behaviour ( [[#Flato--2013|Flato et al., 2013]] ).

A third common modelling technique is the perturbed parameter ensemble (PPE; note that the abbreviation also sometimes refers to the sub-category ‘perturbed physics ensemble’). These methods are used to assess uncertainty based on a single model, with individual parameters perturbed to reflect the full range of their uncertainty ( [[#Murphy--2004|Murphy et al., 2004]] ; [[#Knutti--2010|Knutti et al., 2010]] ; [[#Lee--2011|Lee et al., 2011]] ; [[#Shiogama--2014|Shiogama et al., 2014]] ). Statistical methods can then be used to detect which parameters are the main causes of uncertainty across the ensemble. PPEs have been used frequently in simpler models, such as EMICs, and are being applied to more complex models. A caveat of PPEs is that the estimated uncertainty will depend on the specific parameterizations of the underlying model and may well be an underestimation of the ‘true’ uncertainty. It is also challenging to disentangle forced responses from internal variability using a PPE alone.

Together, the three ensemble methods (MMEs, ICEs, PPEs) allow investigation of climate model uncertainty arising from internal variability, initial and internal boundary conditions, model formulations and parameterizations ( [[#Parker--2013|Parker, 2013]] ). Figure 1.21 illustrates the different ensemble types. Recent studies have also started combining multiple ensemble types or using ensembles in combination with statistical analytical techniques. For example, [[#Murphy--2018|Murphy et al. (2018)]] combine MMEs and PPEs to give a fuller assessment of modelling uncertainty. [[#Wagman--2018|Wagman and Jackson (2018)]] use PPEs to evaluate the robustness of MME-based emergent constraints. [[#Sexton--2019|Sexton et al. (2019)]] study the robustness of ICE approaches by identifying parameters and processes responsible for model errors at the two different time scales.

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[[File:94c89175c1897131f36d3a673881b375 IPCC_AR6_WGI_Figure_1_21.png]]

'''Figure 1.21 |''' '''Illustration of common types of model ensemble, simulating the time evolution of a quantity Q (such as global mean surface temperature).''' '''(a)''' Multi-model ensemble, where each model has its own realization of the processes affecting Q, and its own internal variability around the baseline value (dashed line). The multi-model mean (black) is commonly taken as the ensemble average. '''(b)''' Initial condition ensemble, where several realizations from a single model are compared. These differ only by minute (‘micro’) perturbations to the initial conditions of the simulation, such that over time, internal variability will progress differently in each ensemble member. '''(c)''' Perturbed physics ensemble, which also compares realizations from a single model, but where one or more internal parameters that may affect the simulations of Q are systematically changed to allow for a quantification of the impact of those quantities on the model results. Additionally, each parameter set may be taken as the starting point for an initial condition ensemble. In this figure, each set has three ensemble members.

Overall, we assess that increases in computing power and the broader availability of larger and more varied ensembles of model simulations have contributed to better estimations of uncertainty in projections of future change ( ''high confidence'' ). Note, however, that despite their widespread use in climate science today, the cost of the ensemble approach in human and computational resources, and the challenges associated with the interpretation of multi-model ensembles, has been questioned ( [[#Palmer--2019|Palmer and Stevens, 2019]] ; [[#Touzé-Peiffer--2020|Touzé-Peiffer et al., 2020]] ).

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<span id="the-sixth-phase-of-the-coupled-model-intercomparison-project-cmip6"></span>
==== 1.5.4.3 The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) ====

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The Coupled Model Intercomparison Project (CMIP) provides a framework to compare the results of different GCMs or ESMs performing similar experiments. Since its creation in the mid-1990s, it has evolved in different phases, involving all major climate modelling centres in the world (Figure 1.20). The results of these phases have played a key role in previous IPCC reports, and the present Report assesses a range of results from CMIP5 that were not published until after the AR5, as well as the first results of the 6th phase of CMIP (CMIP6; [[#Eyring--2016|Eyring et al., 2016]] ). The CMIP6 experiment design is somewhat different from previous phases. It now consists of a limited set of DECK (Diagnostic, Evaluation and Characterization of Klima) simulations and an historical simulation that must be performed by all participating models, as well as a wide range of CMIP6-Endorsed model intercomparison projects (MIPs) covering specialized topics (Figure 1.22; [[#Eyring--2016|Eyring et al., 2016]] ). Each MIP activity consists of a series of model experiments, documented in the literature (Table 1.3) and in an online database ( [http://es-doc.org es-doc.org] ; Annex II; [[#Pascoe--2020|Pascoe et al., 2020]] ).

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[[File:794fa33a3216f334817859f0cf2ff96b IPCC_AR6_WGI_Figure_1_22.png]]

'''Figure 1.22 |''' '''Structure of CMIP6, the 6th phase of the Coupled Model Intercomparison Project''' . The centre shows the common DECK (Diagnostic, Evaluation and Characterization of Klima) and historical experiments that all participating models must perform. The outer circles show the topics covered by the endorsed (red) and other MIPs (orange). See Table 1.3 for explanation of the MIP acronyms. Figure is adapted from [[#Eyring--2016|Eyring et al. (2016)]] .

'''Table 1.3 | CMIP6-Endorsed MIPs, their key references, and where they are used or referenced throughout this Report.'''

[[File:80356ae2da8a03e6aeb0d297efbb29ab IPCC_AR6_WGI_Chapter_1_Table_1_3.png]]
The CMIP DECK simulations form the basis for a range of assessments and projections in the following chapters. As in CMIP5, they consist of: a ‘pre-industrial’ control simulation (piControl, where ‘pre-industrial’ is taken as fixed 1850 conditions in these experiments); an idealized, abrupt quadrupling of CO <sub>2</sub> concentrations relative to piControl (to estimate equilibrium climate sensitivity); a 1% per year increase in CO <sub>2</sub> concentrations relative to piControl (to estimate the transient climate response); and a transient simulation with prescribed sea-surface temperatures for the period 1979–2014 (termed ‘AMIP’ for historical reasons). In addition, all participating models perform a historical simulation for the period 1850–2014. For the latter, common CMIP6 forcings are prescribed (Cross-Chapter Box 1.4, Table 2). Depending on the model setup, these include emissions and concentrations of short-lived species ( [[#Hoesly--2018|Hoesly et al., 2018]] ; [[#Gidden--2019|Gidden et al., 2019]] ), long-lived GHGs ( [[#Meinshausen--2017|Meinshausen et al., 2017]] ), biomass burning emissions ( [[#van%20Marle--2017|van Marle et al., 2017]] ), global gridded land-use forcing data ( [[#Ma--2020|Ma et al., 2020]] ), solar forcing ( [[#Matthes--2017|Matthes et al., 2017]] ), and stratospheric aerosol data from volcanoes ( [[#Zanchettin--2016|Zanchettin et al., 2016]] ). The methods for generating gridded datasets are described in [[#Feng--2020|Feng et al. (2020)]] . For AMIP simulations, common sea surface temperatures (SSTs) and sea ice concentrations (SICs) are prescribed. For simulations with prescribed aerosol abundances (i.e., not calculated from emissions), optical properties and fractional changes in cloud droplet effective radius are generally prescribed in order to provide a more consistent representation of aerosol forcing relative to earlier CMIP phases ( [[#Fiedler--2017|Fiedler et al., 2017]] ; [[#Stevens--2017|Stevens et al., 2017]] ). For models without ozone chemistry, time-varying gridded ozone concentrations and nitrogen deposition are also provided ( [[#Checa-Garcia--2018|Checa-Garcia et al., 2018]] ).

Beyond the DECK and the historical simulations, the CMIP6-Endorsed MIPs aim to investigate how models respond to specific forcings, their potential systematic biases, their variability, and their responses to detailed future scenarios such as the Shared Socio-economic Pathways (SSPs; [[#1.6|Section 1.6]] ). Table 1.3 lists the 23 CMIP6-Endorsed MIPs and key references. Results from a range of these MIPs, and many others outside of the most recent CMIP6 cycle, will be assessed in the following chapters (also shown in Table 1.3). References to all the CMIP6 datasets used in the report are found in Annex II, Table AII.10.

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==== 1.5.4.4 Coordinated Regional Downscaling Experiment (CORDEX) ====

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The Coordinated Regional Downscaling Experiment (CORDEX; [[#Gutowski%20Jr.--2016|Gutowski Jr. et al., 2016]] ) is an intercomparison project for regional models and statistical downscaling techniques, coordinating simulations on common domains and under common experimental conditions in a similar way to the CMIP effort. Dynamical and statistical downscaling techniques can provide higher-resolution climate information than is available directly from global climate models (Section 10.3). These techniques require evaluation and quantification of their performance before they can be considered appropriate as usable regional climate information or be used in support of climate services. CORDEX simulations have been provided by a range of regional downscaling models for 14 regions, together covering much of the globe (Figure Atlas.7), and they are used extensively in the AR6 WGI [[IPCC:Wg1:Chapter:Atlas|Atlas]] (Atlas.1.4 and Annex II).

In support of AR6, CORDEX has undertaken a new experiment (CORDEX-CORE) in which regional climate models downscale a common set of global model simulations, performed at a coarser resolution, to a spatial resolution spanning from 12–25 km over most of the CORDEX domains (Box Atlas.1). CORDEX-CORE represents an improved level of coordinated intercomparison of downscaling models ( [[#Remedio--2019|Remedio et al., 2019]] ).

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<span id="model-evaluation-tools"></span>
==== 1.5.4.5 Model Evaluation Tools ====

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For the first time in CMIP, a range of comprehensive evaluation tools are now available that can run alongside the commonly used distributed data platform – Earth System Grid Federation (ESGF; see Annex II) – to produce comprehensive results as soon as the model output is published to the CMIP archive.

For instance, the Earth System Model Evaluation Tool (ESMValTool; [[#Eyring--2020|Eyring et al., 2020]] ; [[#Lauer--2020|Lauer et al., 2020]] ; [[#Righi--2020|Righi et al., 2020]] ) is used by a number of chapters. It is an open-source community software tool that includes a large variety of diagnostics and performance metrics relevant for coupled Earth system processes, such as for the mean, variability and trends, and it can also examine emergent constraints ( [[#1.5.4.7|Section 1.5.4.7]] ). ESMValTool also includes routines provided by the WMO Expert Team on Climate Change Detection and Indices for the evaluation of extreme events ( [[#Min--2011|Min et al., 2011]] ; [[#Sillmann--2013|Sillmann et al., 2013]] ) and diagnostics for key processes and variability. Another example of an evaluation tool is the CLIVAR 2020 ENSO metrics package ( [[#Planton--2021|Planton et al., 2021]] ).

These tools are used in several chapters of this report for the creation of the figures that show CMIP results. Together with the Interactive Atlas, they allow for traceability of key results, and an additional level of quality control on whether published figures can be reproduced. It also provides the capability to update published figures with, as much as possible, the same set of models in all figures, and to assess model improvements across different phases of CMIP ( [[IPCC:Wg1:Chapter:Chapter-3#3.8.2|Section 3.8.2]] ).

These new developments are facilitated by the definition of common formats for CMIP model output ( [[#Balaji--2018|Balaji et al., 2018]] ) and the availability of reanalyses and observations in the same format as CMIP output (obs4MIPs; [[#Ferraro--2015|Ferraro et al., 2015]] ). The tools are also used to support routine evaluation at individual model centres and simplify the assessment of improvements in individual models or generations of model ensembles ( [[#Eyring--2019|Eyring et al., 2019]] ). Note, however, that while tools such as ESMValTool can produce an estimate of overall model performance, dedicated model evaluation still needs to be performed when analysing projections for a particular purpose, such as assessing changing hazards in a given region. Such evaluation is discussed in the next section, and in greater detail in later chapters of this Report.

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<span id="evaluation-of-process-based-models-against-observations"></span>
==== 1.5.4.6 Evaluation of Process-Based Models Against Observations ====

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Techniques used for evaluating process-based climate models against observations were assessed in AR5 ( [[#Flato--2013|Flato et al., 2013]] ), and have progressed rapidly since ( [[#Eyring--2019|Eyring et al., 2019]] ). The most widely used technique is to compare climatologies (long-term averages of specific climate variables) or time series of simulated (process-based) model output with observations, considering the observational uncertainty. A further approach is to compare the results of process-based models with those from statistical models. In addition to a comparison of climatological means, trends and variability, AR5 already made use of a large set of performance metrics for a quantitative evaluation of the models.

Since AR5, a range of studies has investigated model agreement with observations well beyond large-scale mean climate properties (e.g., [[#Bellenger--2014|Bellenger et al., 2014]] ; [[#Covey--2016|Covey et al., 2016]] ; [[#Pendergrass--2017|Pendergrass and Deser, 2017]] ; [[#Goelzer--2018|Goelzer et al., 2018]] ; [[#Beusch--2020a|Beusch et al., 2020a]] ), providing information on the performance of recent model simulations across multiple variables and components of the Earth system (e.g., [[#Anav--2013|Anav et al., 2013]] ; [[#Guan--2017|Guan and Waliser, 2017]] ). Based on such studies, this Report assesses model improvements across different CMIP DECK, CMIP6 historical and CMIP6-Endorsed MIP simulations, and of differences in model performance between different classes of models, such as high- versus low-resolution models (see e.g., [[IPCC:Wg1:Chapter:Chapter-3#3.8.2|Section 3.8.2]] ).

In addition, process- or regime-oriented evaluation of models has been expanded since AR5. By focusing on processes, causes of systematic errors in the models can be identified and insights can be gained as to whether a mean state or trend is correctly simulated and for the right reasons. This approach is commonly used for the evaluation of clouds (e.g., [[#Williams--2009|Williams and Webb, 2009]] ; [[#Konsta--2012|Konsta et al., 2012]] ; [[#Bony--2015|Bony et al., 2015]] ; [[#Dal%20Gesso--2015|Dal Gesso et al., 2015]] ; [[#Jin--2017|Jin et al., 2017]] ), dust emissions (e.g., [[#Parajuli--2016|Parajuli et al., 2016]] ; [[#Wu--2016|Wu et al., 2016]] ) as well as aerosol–cloud (e.g., [[#Gryspeerdt--2012|Gryspeerdt and Stier, 2012]] ) and chemistry–climate ( [[#SPARC--2010|SPARC, 2010]] ) interactions. Process-oriented diagnostics have also been used to evaluate specific phenomena such as the El Niño–Southern Oscillation (ENSO; [[#Guilyardi--2016|Guilyardi et al., 2016]] ), the Madden–Julian Oscillation (MJO; [[#Ahn--2017|Ahn et al., 2017]] ; [[#Jiang--2018|Jiang et al., 2018]] ), Southern Ocean clouds ( [[#Hyder--2018|Hyder et al., 2018]] ), monsoons ( [[#Boo--2011|Boo et al., 2011]] ; [[#James--2015|James et al., 2015]] ) and tropical cyclones ( [[#Kim--2018|Kim et al., 2018]] ).

Instrument simulators provide estimates of what a satellite would see if looking down on the model-simulated planet, and improve the direct comparison of modelled variables such as clouds, precipitation and upper tropospheric humidity with observations from satellites (e.g., [[#Kay--2011|Kay et al., 2011]] ; [[#Klein--2013|Klein et al., 2013]] ; [[#Cesana--2016|Cesana and Waliser, 2016]] ; [[#Konsta--2016|Konsta et al., 2016]] ; [[#Jin--2017|Jin et al., 2017]] ; [[#Chepfer--2018|Chepfer et al., 2018]] ; [[#Swales--2018|Swales et al., 2018]] ; [[#Zhang--2018|Zhang et al., 2018]] ). Within the framework of the Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6 ( [[#Webb--2017|Webb et al., 2017]] ), a new version of the Cloud Feedback Model Intercomparison Project Observational Simulator (COSP; [[#Swales--2018|Swales et al., 2018]] ) has been released which makes use of a collection of observation proxies or satellite simulators. Related approaches in this rapidly evolving field include simulators for Arctic Ocean observations ( [[#Burgard--2020|Burgard et al., 2020]] ) and measurements of aerosol observations along aircraft trajectories ( [[#Watson-Parris--2019|Watson-Parris et al., 2019]] ).

In this Report, model evaluation is performed in the individual chapters, rather than in a separate chapter as was the case for AR5. This applies to the model types discussed above, and also to dedicated models of subsystems that are not (or not yet) part of usual climate models, for example, glacier or ice-sheet models (Annex II). Further discussions are found in [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] (attribution), [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] (carbon cycle), [[IPCC:Wg1:Chapter:Chapter-6|Chapter 6]] (short-lived climate forcers), [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] (water cycle), [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (ocean, cryosphere and sea level), [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-10 Chapter 10] (regional scale information) and the [[IPCC:Wg1:Chapter:Atlas|Atlas]] (regional models).

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<span id="emergent-constraints-on-climate-feedbacks-sensitivities-and-projections"></span>
==== 1.5.4.7 Emergent Constraints on Climate Feedbacks, Sensitivities and Projections ====

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An emergent constraint is the relationship between an uncertain aspect of future climate change and an observable feature of the Earth System, evident across an ensemble of models ( [[#Allen--2002|Allen and Ingram, 2002]] ; [[#Mystakidis--2016|Mystakidis et al., 2016]] ; [[#Wenzel--2016|Wenzel et al., 2016]] ; [[#Hall--2019|Hall et al., 2019]] ; [[#Winkler--2019|Winkler et al., 2019]] ). Complex Earth system models (ESMs) simulate variations on time scales from hours to centuries, telling us how aspects of the current climate relate to its sensitivity to anthropogenic forcing. Where an ensemble of different ESMs displays a relationship between a short-term observable variation and a longer-term sensitivity, an observation of the short-term variation in the real world can be converted, via the model-based relationship, into an ‘emergent constraint’ on the sensitivity. This is shown schematically in Figure 1.23 (see Glossary; [[#Eyring--2019|Eyring et al., 2019]] ).

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[[File:19c9c2bf553e21560024961e7c247bd8 IPCC_AR6_WGI_Figure_1_23.png]]

'''Figure 1.23 |''' '''The principle of emergent constraints''' . An ensemble of models (blue dots) defines a relationship between an observable mean, trend or variation in the climate (x-axis) and an uncertain projection, climate sensitivity or feedback (y-axis). An observation of the x-axis variable can then be combined with the model-derived relationship to provide a tighter estimate of the climate projection, sensitivity or feedback on the y-axis. Figure adapted from [[#Eyring--2019|Eyring et al. (2019)]] .

Emergent constraints use the spread in model projections to estimate the sensitivities of the climate system to anthropogenic forcing, providing another type of ensemble-wide information that is not readily available from simulations with one ESM alone. As emergent constraints depend on identifying those observable aspects of the climate system that are most related to climate projections, they also help to focus model evaluation on the most relevant observations ( [[#Hall--2019|Hall et al., 2019]] ). However, there is a chance that indiscriminate data-mining of the multi-dimensional outputs from ESMs could lead to spurious correlations ( [[#Caldwell--2014|Caldwell et al., 2014]] ; [[#Wagman--2018|Wagman and Jackson, 2018]] ) and less-than-robust emergent constraints on future changes ( [[#Bracegirdle--2013|Bracegirdle and Stephenson, 2013]] ). To avoid this, emergent constraints need to be tested ‘out of sample’ on parts of the dataset that were not included in its construction ( [[#Caldwell--2018|Caldwell et al., 2018]] ) and should also always be based on sound physical understanding and mathematical theory ( [[#Hall--2019|Hall et al., 2019]] ). Their conclusions should also be reassessed when a new generation of MMEs becomes available, such as CMIP6. As an example, [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] (Section 7.5.4) discusses and assesses recent studies where equilibrium climate sensitivities (ECS) diagnosed in a multi-model ensemble are compared with the same models’ estimates of an observable quantity, such as post-1970s global warming or tropical sea surface temperatures of past climates like the Last Glacial Maximum or the Pliocene. Assessments of other emergent constraints appear throughout later chapters, such as [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] ( [[IPCC:Wg1:Chapter:Chapter-4#4.2.5|Section 4.2.5]] ), [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] (Section 5.4.6) and [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] (Section 7.5.4).

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==== 1.5.4.8 Weighting Techniques for Model Comparisons ====

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Assessments of climate model ensembles have commonly assumed that each individual model is of equal value (‘model democracy’) and when combining simulations to estimate the mean and variance of quantities of interest, they are typically unweighted ( [[#Haughton--2015|Haughton et al., 2015]] ). This practice has been noted to diminish the influence of models exhibiting a good match with observations ( [[#Tapiador--2020|Tapiador et al., 2020]] ). However, exceptions to this approach exist, notably AR5 projections of sea ice, which only selected a few models which passed a model performance assessment ( [[#Collins--2013|Collins et al., 2013]] ), and more studies on this topic have appeared since AR5 (e.g., [[#Eyring--2019|Eyring et al., 2019]] ). Ensembles are typically sub-selected by removing either poorly performing model simulations ( [[#McSweeney--2015|McSweeney et al., 2015]] ) or model simulations that are perceived to add little additional information, typically where multiple simulations have come from the same model. They may also be weighted based on model performance.

Several recent studies have attempted to quantify the effect of various strategies for selection or weighting of ensemble members based on some set of criteria ( [[#Haughton--2015|Haughton et al., 2015]] ; [[#Olonscheck--2017|Olonscheck and Notz, 2017]] ; [[#Sanderson--2017|Sanderson et al., 2017]] ). Model weighting strategies have been further employed since AR5 to reduce the spread in climate projections for a given scenario by using weights based on one or more model performance metrics ( [[#Wenzel--2016|Wenzel et al., 2016]] ; [[#Knutti--2017|Knutti et al., 2017]] ; [[#Sanderson--2017|Sanderson et al., 2017]] ; [[#Lorenz--2018|Lorenz et al., 2018]] ; [[#Liang--2020|Liang et al., 2020]] ). However, models may share representations of processes, parameterization schemes, or even parts of code, leading to common biases. The models may therefore not be fully independent, calling into question inferences derived from multi-model ensembles ( [[#Abramowitz--2019|Abramowitz et al., 2019]] ). Emergent constraints ( [[#1.5.4.5|Section 1.5.4.5]] ) also represent an implicit weighting technique that explicitly links present performance to future projections ( [[#Bracegirdle--2013|Bracegirdle and Stephenson, 2013]] ).

Concern has been raised about the large extent to which code is shared within the CMIP5 multi-model ensemble ( [[#Sanderson--2015a|Sanderson et al., 2015a]] ). [[#Boé--2018|Boé (2018)]] showed that a clear relationship exists between the number of components shared by climate models and how similar the simulations are. The resulting similarities in behaviour need to be accounted for in the generation of best-estimate multi-model climate projections. This has led to calls to move beyond equally-weighted multi-model means towards weighted means that take into account both model performance and model independence ( [[#Sanderson--2015b|Sanderson et al., 2015b]] , 2017; [[#Knutti--2017|Knutti et al., 2017]] ). Model independence has been defined in terms of performance differences within an ensemble ( [[#Masson--2011|Masson and Knutti, 2011]] ; [[#Knutti--2013|Knutti et al., 2013]] , 2017, [[#Sanderson--2015a|Sanderson et al., 2015a]] , b, 2017; [[#Lorenz--2018|Lorenz et al., 2018]] ). However, this definition is sensitive to the choice of variable, observational dataset, metric, time period, and region, and a performance-ranked ensemble has been shown to sometimes perform worse than a random selection ( [[#Herger--2018a|Herger et al., 2018a]] ). The adequacy of the constraint provided by the data and experimental methods can be tested using a ‘calibration-validation’ style partitioning of observations into two sets ( [[#Bishop--2013|Bishop and Abramowitz, 2013]] ), or a ‘perfect model approach’ where one of the ensemble members is treated as the reference dataset and all model weights are calibrated against it ( [[#Bishop--2013|Bishop and Abramowitz, 2013]] ; [[#Wenzel--2016|Wenzel et al., 2016]] ; [[#Knutti--2017|Knutti et al., 2017]] ; [[#Sanderson--2017|Sanderson et al., 2017]] ; [[#Herger--2018a|Herger et al., 2018a]] , b). [[#Sunyer--2014|Sunyer et al. (2014)]] use a Bayesian framework to account for model dependencies and changes in model biases. [[#Annan--2017|Annan and Hargreaves (2017)]] provides a statistical, quantifiable definition of independence that is independent of performance-based measures.

The AR5 quantified uncertainty in CMIP5 climate projections by selecting one realization per model per scenario, and calculating the 5–95% range of the resulting ensemble (Box 4.1) and the same strategy is generally still used in AR6. Broadly, the following chapters take the CMIP6 5–95% ensemble range as the ''likely'' uncertainty range for projections, <sup>[[#footnote-000|8]]</sup> with no further weighting or consideration of model ancestry and as long as no universal, robust method for weighting a multi-model projection ensemble is available (Box 4.1). A notable exception to this approach is the assessment of future changes in global surface air temperature (GSAT), which also draws on the updated best estimate and range of equilibrium climate sensitivity assessed in Chapter 7. For a thorough description of the model-weighting choices made in this Report, and the assessment of GSAT, see [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] (Box 4.1). Model selection and weighting in downscaling approaches for regional assessment is discussed in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-10 Chapter 10] (Section 10.3.4).

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