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

=== 10.5.1 Sources of Regional Climate Information ===

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Regional climate information may be constructed from a diverse range of sources, each depending on different assumptions and affected by different methodological limitations (Sections 10.2, 10.3 and 10.4). The construction of information may lead to products for direct adoption by users, or intermediate products for further analysis by users and climate services agencies in collaboration with climate scientists. Widely used sources include:

* Extrapolation of observed historical trends into the future (e.g., [[#Livezey--2007|Livezey et al., 2007]] ; [[#Laaha--2016|Laaha et al., 2016]] ). Given that internal variability can affect regional trends significantly on decadal to multi-decadal time scales ( [[#10.4|Section 10.4]] ), this approach could be potentially misleading without other supporting evidence ( [[#Westra--2010|Westra et al., 2010]] ), or finding congruence with other changes (e.g., [[#Langodan--2020|Langodan et al., 2020]] ).
* The output from global models ( [[#10.3.1|Section 10.3.1]] ), including high-resolution GCMs and ESMs, for which performance has been assessed and documented ( [[#10.3.3|Section 10.3.3]] ). Model data can be used in its raw form or may be bias adjusted ( [[#10.3.1|Section 10.3.1]] and Cross-Chapter Box 10.2) or weighted ( [[#10.3.4|Section 10.3.4]] and Box 4.1).
* The output from dynamically ( [[#10.3.1.2|Section 10.3.1.2]] ) or statistically ( [[#10.3.1.3|Section 10.3.1.3]] ) downscaled global model simulations for which performance has been assessed and documented as trustworthy ( [[#10.3.3|Section 10.3.3]] ). Model data can be used in its raw form or may be bias adjusted, in the case of regional climate models (RCMs, [[#10.3.1|Section 10.3.1]] ).
* Process understanding about climate and the drivers of regional climate variability and change, grounded in theory about dynamics, thermodynamics and other physics of the climate system as a basis for process-based evaluation. For instance, teleconnections are useful to understand the links between large and regional scales at both near and long-term depending on the application. (Sections 10.1.3, 10.3.3, 10.4.1, 10.4.3 and Annex IV).
* Idealized scenarios of possible future climates as narratives to explore the implications and consequences of such scenarios in the presence of uncertainty ( [[#Jack--2021|Jack et al., 2021]] ). This approach has been used to explore the response to geoengineering ( [[#Cao--2016a|Cao et al., 2016a]] ), as well as alternative scenarios where model projections are highly uncertain ( [[#Brown--2016|Brown et al., 2016]] ; [[#Jack--2021|Jack et al., 2021]] ).
* Information directly from research reported in the peer-reviewed scientific literature (e.g., [[#Sanderson--2017|Sanderson et al., 2017]] ) or related research reports such as communications to the UN Framework Convention on Climate Change (UNFCCC) about national adaptation.
* Engaging with climate scientists and local communities who may provide indigenous information ( [[#Rosenzweig--2013|Rosenzweig and Neofotis, 2013]] ; [[#Makondo--2018|Makondo and Thomas, 2018]] ).
* Relevant information may also be drawn from paleoclimate studies (e.g., [[#McGregor--2018|McGregor, 2018]] ; Armstrong et al., 2020; [[#Kiem--2020|Kiem et al., 2020]] ) to support and contextualize other sources about more recent and projected changes.

Different sources of information may be more appropriate for some purposes than others, as they may provide information better aligned to the spatial and temporal scales of interest, in different formats, and tailored to different types of application. In some cases, a purpose may be best served using several types of information together. For example, when model data is the primary source, it can be advantageous to employ data from multiple models or even from a range of different experiment types ( [[#10.3.2|Section 10.3.2]] ) supported by assessing how the models reflect changes in driving processes. In this manner a purpose may be best served by seeking the congruence of several types of information together, though one needs to recognize how well the attributes of each source align with the specific need for information. Depending on resources, one may even design model experiments specifically for a given use, such as constructing physical climate storylines of individual events ( [[#10.3.2|Section 10.3.2]] and Box 10.2). Such analyses may be complemented by event attribution studies ( [[IPCC:Wg1:Chapter:Chapter-11#11.1.4|Section 11.1.4]] ).

Users of climate information may face the so-called practitioner’s dilemma: a plethora of different and potentially contrasting sources (Figure 10.16) may be available without a comprehensive and user-relevant evaluation, and these datasets may also lack a transparent and easily understandable explanation of underlying assumptions, strengths and limitations ( [[#Barsugli--2013|Barsugli et al., 2013]] ; [[#Hewitson--2017|Hewitson et al., 2017]] ). Often, the choice of information source is therefore not determined by what is most relevant and informative for the question at hand, but rather by practical constraints such as accessibility and ease of use and may be limited to the availability of just one source in extreme cases ( [[#Rössler--2019a|Rössler et al., 2019a]] ).

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