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

==== Atlas.1.4.1 Observations ====

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There are various sources of observational information available for global and regional analysis. Observational uncertainty is a key factor when assessing and attributing historical trends, so assessment should build on integrated analyses from different datasets (disparity, inadequacy and contradictions in existing datasets are assessed in [[IPCC:Wg1:Chapter:Chapter-10#10.2|Section 10.2]] ). The Atlas chapter can supplement and complement [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] by providing the opportunity to visualize and expand on its assessment. This includes displaying maps of density of stations’ observations (including those that are used in the different datasets) and assessing observational uncertainty by using multiple datasets.

Two of the most commonly used variables in climate studies are gridded surface air temperature and precipitation. There are many datasets available (Annex I) and [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] provides an assessment of key global datasets, including blended land-air and sea surface temperature datasets to assess global mean surface temperature (GMST). The Atlas separately analyses atmospheric and oceanic variables, and for the former a number of common global datasets supporting the assessment done in other chapters is used, including those selected in Chapter 2, but considering land-only information for the blended products. In particular, for air temperature the Atlas uses CRUTEM5 – the land component of the HadCRUT5 dataset – ( [[#Osborn--2021|Osborn et al., 2021]] ), Berkeley Earth ( [[#Rohde--2020|Rohde and Hausfather, 2020]] ) and the Climatic Research Unit CRU TS4 (version 4.04 used here; [[#Harris--2020|Harris et al., 2020]] ). For precipitation the Atlas includes CRU TS4, the Global Precipitation Climatology Centre (GPCC, v2018 used here; [[#Schneider--2011|Schneider et al., 2011]] ), and Global Precipitation Climatology Project (GPCP; monthly version 2.3 used here; [[#Adler--2018|Adler et al., 2018]] ). Although the ultimate source of these datasets is surface-station reported values (GPCP also includes satellite information), each has access to different numbers of stations and lengths of records and employs different ways of creating the gridded product and ensuring quality control. For oceanic variables, the most widely used sea surface temperature (SST) datasets are HadSST4 ( [[#Kennedy--2019|Kennedy et al., 2019]] ), which is the oceanic component of the HadCRUT5 dataset, ERSST ( [[#Huang--2017|]] [[#Huang--2017|B. Huang et al., 2017]] ), and KaplanSST ( [[#Kaplan--1998|Kaplan et al., 1998]] ).

Figure Atlas.5 shows the spatial coverage of the total number of observation stations for different periods (1901–1910, 1971–1980, and 2001–2010) for two illustrative datasets: the CRU TS4 dataset for precipitation and the SST data in HadSST4. The former illustrates spatially the declining trend of station observation data used in the precipitation datasets for certain regions (South America and Africa) after the 1990s. This demonstrates the regional inhomogeneity and temporal change in station density, which is in part a consequence of many stations not reporting to the WMO networks and their data being held domestically or regionally. During early years (before 1950) a limited number of observations are available. This information is used in the Interactive Atlas to blank out regions not constrained with observations in those datasets providing station density information.

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[[File:ffa10a6a7fa959dcec69ae1d98683dcc IPCC_AR6_WGI_Atlas_Figure_5.png]]

'''Figure Atlas.5''' '''|''' '''Number of stations per 0.5° × 0.5° gridcell reported over the periods of 1901–1910, 1971–1980, and 2001–2010 (rows 1–3), and the global total number of stations reported over the entire globe (bottom row) for precipitation in the CRU TS4 dataset (left) and the HadSST4 dataset (right).''' Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

In addition to surface observations, satellites have been widely used to produce rainfall estimates. The advantage of satellite-based rainfall products is their global coverage including remote areas but there is significant uncertainty in these products over complex terrain ( [[#Rahmawati--2018|Rahmawati and Lubczynski, 2018]] ; [[#Satgé--2019|Satgé et al., 2019]] ). Another recent development has been on gridded datasets for climate extremes based on surface stations, such as HadEX3 ( [[#Dunn--2020|Dunn et al., 2020]] ), as described in [[IPCC:Wg1:Chapter:Chapter-11#11.2.2|Section 11.2.2]] .

There are some studies assessing observational datasets globally ( [[#Beck--2017|Beck et al., 2017]] ; Q. [[#Sun--2018|]] [[#Sun--2018|Sun et al., 2018]] ) and regionally ( [[#Manzanas--2014|Manzanas et al., 2014]] ; [[#Salio--2015|Salio et al., 2015]] ; [[#Prakash--2019|Prakash, 2019]] ), reporting large differences among them and stressing the importance of considering observational uncertainty in regional climate assessment studies. Uncertainty in observations is also a key limitation for the evaluation of climate models, particularly over regions with low station density ( [[#Kalognomou--2013|Kalognomou et al., 2013]] ; [[#Kotlarski--2019|Kotlarski et al., 2019]] ). More detailed information on these issues is provided in [[IPCC:Wg1:Chapter:Chapter-10#10.2|Section 10.2]] .

For regional studies, observational datasets with global coverage are complemented by a range of regional observational analyses and gridded products, such as E-OBS ( [[#Cornes--2018|Cornes et al., 2018]] ) over Europe, Daymet ( [[#Thornton--2016|Thornton et al., 2016]] ) over North America, or APHRODITE ( [[#Yatagai--2012|Yatagai et al., 2012]] ) over Asia. These are highlighted in various other chapters and the Atlas expands on their treatment, complementing discussions on discrepancies/conflicts in observations presented in [[IPCC:Wg1:Chapter:Chapter-10|Chapter 10]] and expanding on and replicating their results for other regions. In particular, the Interactive Atlas includes the global and regional observational products described here to assess observational uncertainty over the different regions analysed.

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