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

==== 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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