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

==== 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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