Editing IPCC:AR6/WGI/Chapter-10 (section)

==== 10.1.2.2 Temporal Scales, Baselines and Dimensions of Integration ====

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The concept of a unified and seamless framework for weather and climate prediction ( [[#Brown--2012|]] [[#Brown--2012|A. Brown et al., 2012]] ; [[#Hoskins--2013|Hoskins, 2013]] ) provides the context for understanding and simulating regional climate across multiple spatial and temporal scales. This concept is embodied in the subseasonal-to-seasonal ( [[#Vitart--2017|Vitart et al., 2017]] ) and the seasonal-to-multi-annual ( [[#Smith--2020|Smith et al., 2020]] ) prediction activities that generate regional climate information across temporal scales. The seamless framework benefits from the convergence of methods traditionally used in weather forecasting and climate projections, in particular the role of the initialization in climate models and the strategies for the evaluation of physical processes relevant at different temporal scales.

The relatively short observational record ( [[#10.2|Section 10.2]] ) is a primary challenge to estimate the forced signal and to isolate low-frequency, multi-decadal and longer-term internal variability ( [[#Frankcombe--2015|Frankcombe et al., 2015]] ; [[#Overland--2016|Overland et al., 2016]] ; [[#Bathiany--2018|Bathiany et al., 2018]] ). Because only one realization of the actual climate exists, it is non-trivial to extract estimates of internal and forced variability from the available data ( [[#Frankcombe--2015|Frankcombe et al., 2015]] ). As an alternative, approaches that use large observational ensembles can be applied ( [[#10.4|Section 10.4]] ; [[#McKinnon--2018|McKinnon and Deser, 2018]] ).

There is a close relationship between spatial and temporal scales (Figure 10.3). For example, an individual convective storm may exhibit scales of variability ranging from metres and seconds to kilometres and hours, while for El Niño–Southern Oscillation (ENSO) the scales of variability are regional to hemispheric in extent and multi-year in length. These scales interact and the interactions are represented in climate models, although the ability of current models to simulate regional phenomena and even large-scale climate drivers still leaves room for improvement ( [[#10.3|Section 10.3]] ) and limits their capability to represent the interactions across spatial and temporal scales.

It is important to note that in this chapter and subsequent regional chapters, including the Interactive Atlas, the baselines and reference periods used for climate change estimates from regional models may vary from those used in Chapters 1 to 9. In these chapters three main time baselines are defined for the past, for example, pre-industrial (before 1750), early industrial (1850–1900) and recent (1995–2014), while the future reference periods are 2021–2040 (near term), 2041–2060 (mid-term) and 2081–2100 (long term) ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.1|Section 1.4.1]] and Cross-Chapter Box 1.2). Regional climate simulations used in the recent literature have been performed with different baselines. The differences are often due to the availability of the boundary conditions from global simulations, leading to periods chosen for those simulations like 1950–2005, in line with the CMIP5 historical simulations followed by projections from 2005 onwards ( [[#Vaittinada%20Ayar--2016|Vaittinada Ayar et al., 2016]] ; [[#Zhang--2017|Zhang et al., 2017]] ; L. [[#Cai--2018|]] [[#Cai--2018|Cai et al., 2018]] ). For simulations that use CMIP3 boundary conditions other periods have been used. As a consequence, these regional simulations mix for the recent period historical simulations with projections. The mismatch needs to be considered when assessing results obtained from both global and regional models in the context of the climate information distillation process, or when linking the regional chapters to the assessments performed in previous chapters. The choice of baseline provides a source of uncertainty for the assessment of climate impacts (e.g., for the response of bird species in Africa; [[#Baker--2016|Baker et al., 2016]] ). Besides, a range of different baselines may need to be considered to satisfy a variety of users, since this choice affects the perceived result ( [[#Dobor--2019|Dobor and Hlásny, 2019]] ). The influence of the different baseline periods can be explored using the Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] where different baselines are available, for example, 1986–2005 (according to AR5), 1995–2014 (this Report), and both 1961–1990 and 1981–2010 (WMO).

One way of overcoming the baseline uncertainty is to define the results for a given model based on specific global mean temperature changes from the pre-industrial period (e.g., [[#Sylla--2018|Sylla et al., 2018]] for West Africa; [[#Kjellström--2018|Kjellström et al., 2018]] for Europe; [[#Taylor--2018|Taylor et al., 2018]] for the Caribbean; [[#Montroull--2018|Montroull et al., 2018]] for South America). The specific global mean temperature is known as global warming level (GWL; Sections 1.6.2 and 10.6.4, and Cross-Chapter Box 11.1). The GWL is a useful dimension of integration because important changes in regional climate, including many types of extremes, scale quasi-linearly with the GWLs, often independently of the underlying emissions scenarios (e.g., [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Beusch--2020|Beusch et al., 2020]] ; [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ), always taking into account caveats described in Cross-Chapter Box 11.1. In addition, GWLs allow a separated analysis of the global and regional climate responses associated with a warming level ( [[#10.6.4|Section 10.6.4]] ; [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). The choice of global temperature goal in the context of the 2015 Paris Agreement means that there is an increasing desire for the regional climate information to be expressed as a function of GWLs.

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