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

==== 10.5.3.1 Information Construction ====

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Data, either from observations or models, is in general not inherently information, but may contain relevant information if interpreted appropriately ( [[#Hewitson--2017|Hewitson et al., 2017]] ). The same applies to other sources of climate information. Relevance is controlled by the given user context ( [[#10.5.2.1|Section 10.5.2.1]] ) and relates to the required temporal and spatial scales ( [[#10.5.2.3|Section 10.5.2.3]] ), the characteristics of required variables (often referred to as indicators), and the meteorological and climatic phenomena driving these variables ( [[#10.1.3|Section 10.1.3]] ). For example, if climate information for driving impact models is sought (e.g., [[#McSweeney--2015|McSweeney et al., 2015]] ), the impact modelling analysis in the target region is the specific user context.

Climate risk assessment considers all plausible outcomes ( [[#Weaver--2017|Weaver et al., 2017]] ; [[#Marchau--2019|Marchau et al., 2019]] ; [[#Sutton--2019|Sutton, 2019]] ). Thus, a key element of information construction is the exploration and reconciliation of different sources of information ( [[#Barsugli--2013|Barsugli et al., 2013]] ; [[#Hewitson--2014b|Hewitson et al., 2014b]] ; [[#Maraun--2018b|Maraun and Widmann, 2018b]] ) and involves mainly two issues: first, assessing the fitness of different sources in the given context and thereby potentially omitting (or down-weighting) selected sources (Sections 10.3.3), and, second, integrating different sources into a broader picture within a context (Sections 10.3.4).

A non-comprehensive selection of approaches that may contribute to the construction of information includes:

* Overall assessment and intercomparison of different sources of information, including hierarchies of models and identification of potentially conflicting results (Figure 10.16), where observational availability plays a critical role ( [[#10.2.3|Section 10.2.3]] ).
* Assessing the emergence of forced trends from internal variability ( [[#10.4.3|Section 10.4.3]] ), and testing whether differences in simulations can be explained by internal variability, ideally using initial-condition large ensembles (Sections 10.3.4.3 and 10.4.3).
* Assessing the interdependence of chosen models to identify the amount of independent information ( [[#10.3.4.4|Section 10.3.4.4]] ).
* Process-based evaluation with focus on those processes that are relevant for the specific application (Sections 10.3.3.4–10.3.3.10).
* Weighting or sub-selecting ensembles based on a priori knowledge or the outcome of a process-based evaluation, while sampling as much uncertainty as possible ( [[#10.3.4.4|Section 10.3.4.4]] ).
* Tracing back differences in projections to the representation of fundamental processes, for example, by using physical climate storylines (Sections 10.3.4.2 and Box 10.2) or sensitivity simulations ( [[#10.3.2.3|Section 10.3.2.3]] ).
* Producing physical-climate storylines (Box 10.2) to explore uncertainties not sampled by available model ensembles ( [[#Shepherd--2018|Shepherd et al., 2018]] ), for example in pseudo-global warming experiments ( [[#10.3.2.2|Section 10.3.2.2]] ), or to simulate events that have never happened before but are nevertheless plausible ( [[#Lin--2016|Lin and Emanuel, 2016]] ).
* Attributing observed changes to different external forcings and internal drivers ( [[#10.4.1|Section 10.4.1]] ).
* Comparing observed trends with past simulated trends in order to constrain projections with, for instance, the Allen–Stott–Kettleborough method ( [[#Allen--2000|Allen et al., 2000]] ; [[#Stott--2002|Stott and Kettleborough, 2002]] ; [[#Stott--2013|Stott et al., 2013]] ) to explain drivers of past observed trends ( [[#10.4.2|Section 10.4.2]] ) for understanding future trends.
* Integrating present-day performance via emergent constraints to reduce projection uncertainty ( [[#10.3.2|Section 10.3.2]] ).
* Complementing the observational and model-based sources with expert judgement (e.g., integrating knowledge from theory or experience that is available from experts or the literature; [[#10.5.1|Section 10.5.1]] ).

These approaches often can be used in combination to increase confidence in conclusions drawn ( [[#Hewitson--2017|Hewitson et al., 2017]] ).

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