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

===== Atlas.2.4.2.1 Quantitative Support for Assessments =====

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Many assessment statements make use of evidence derived from observed changes, model projections, and process-oriented attribution of changes to human interventions. The Interactive Atlas shows a small subset of available observations that document climate change, namely surface air temperature and total precipitation (and thus not including observations of other atmospheric and Earth system components used as part of the evidence base for the report). Only datasets that have (near) global or large regional gridded spatial coverage and go back multiple decades are used. For each variable multiple datasets are included, but some of these have overlapping native ground-station observations and so are not independent ( [[#Atlas.1.4.1|Atlas.1.4.1]] ). The datasets show patterns of substantial spatial and temporal variability, and the empirical evidence of a non-stationary climatology needs to be filtered from this information. Issues with quality, representativity and mutual consistency lead to constraints on their use for attribution of causes of trends (see [[IPCC:Wg1:Chapter:Chapter-10#10.4.1.2|Section 10.4.1.2]] for examples). The practice of attributing trends and extreme events to human causes gives confidence that these trends are expected to continue in the (near) future, provided the human drivers of climate change remain unchanged. However, large internal variability at decadal time scales can be misinterpreted as an anthropogenic influence on the likelihood of extreme events, and in that case extrapolation of trends cannot be expected to be a reliable predictor for the future ( [[#Schiermeier--2018|Schiermeier, 2018]] ).

The Interactive Atlas gives access to a specific set of climate variables from a large number of climate model simulations, particularly the (global) CMIP5, CMIP6 and (regional) CORDEX archives. The global model outputs generally give a relatively coarse picture of climate change, which is an important line of evidence for the detection and attribution of climate change, but is rarely directly applicable for local climate change assessment or support of policy design ( [[#van%20den%20Hurk--2018|van den Hurk et al., 2018]] ). To provide additional detail, downscaling global projections with regional climate models (RCMs) or statistical downscaling can be undertaken but also adds a source of uncertainty as it involves additional modelling ( [[IPCC:Wg1:Chapter:Chapter-10#10.3|Section 10.3]] ).

The information displayed in the Interactive Atlas allows a number of sources of uncertainty to be quantified. ‘Observational uncertainty’ is represented by the use of multiple (albeit often not completely independent) observational datasets. ‘Uncertainty due to internal variability’ cannot be quantified directly since multiple realizations from historic and future projections are not accessible (the Interactive Atlas uses a single realization of each model). The use of a large collection of model systems allows for an elaborate quantification of ‘model uncertainty’. In addition, a comparison of CMIP5 and CMIP6 supports evidence of progress in model quality since AR5, while the evaluation of the added value of RCMs reveals model uncertainty related to spatial resolution ( [[IPCC:Wg1:Chapter:Chapter-10#10.3|Section 10.3]] ). Finally, the assessment of ‘scenario uncertainty’ is supported by the inclusion of multiple emissions scenarios for both CMIP5, CORDEX and CMIP6.

The communication of uncertainty has a profound influence on the perception of information that is exchanged during the communication process. An assessment of uncertainty communication and the barriers to climate information construction is given in [[IPCC:Wg1:Chapter:Chapter-10#10.5.4|Section 10.5.4]] .

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