Abstract
AbstractApproximately 10 years ago, convection‐permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1–4 km) decadal‐long climate simulations with explicitly resolved deep convection. This explicit representation is expected to reduce climate projection uncertainty related to deep convection parameterizations found in most climate models. A recent surge in CPRCM decadal simulations over larger domains, sometimes covering continents, has led to important insights into CPRCM advantages and limitations. Furthermore, new observational gridded datasets with fine spatial and temporal (~1 km; ~1 h) resolutions have leveraged additional knowledge through evaluations of the added value of CPRCMs. With an improved coordination in the frame of ongoing international initiatives, the production of ensembles of CPRCM simulations is expected to provide more robust climate projections and a better identification of their associated uncertainties. This review paper presents an overview of the methodology to produce CPRCM simulations and the latest research on the related added value in current and future climates. Impact studies that are already taking advantage of these new CPRCM simulations are highlighted. This review paper ends by proposing next steps that could be accomplished to continue exploiting the full potential of CPRCMs.This article is categorized under: Climate Models and Modeling > Earth System Models
Highlights
About 10 years ago, regional climate models (RCMs) reached spatial resolutions comparable to the scale of deep convective processes (Δx < 4 km), at which point parameterizations of deep convection—a well known source of modeling uncertainty (Ban et al, 2014; Foley, 2010; Fosser et al, 2015; Kendon et al, 2012)—can be removed from the models
When it comes to assessing convection-permitting regional climate models (CPRCMs) added-value, the interest goes toward sub-daily data that is available only from a subset of weather stations (Lewis et al, 2019)
CPRCMs emerged as a promising tool to reduce uncertainties of climate change projections, especially those associated with precipitation extremes (Fosser, Kendon, Stephenson, & Tucker, 2020)
Summary
About 10 years ago, regional climate models (RCMs) reached spatial resolutions comparable to the scale of deep convective processes (Δx < 4 km), at which point parameterizations of deep convection—a well known source of modeling uncertainty (Ban et al, 2014; Foley, 2010; Fosser et al, 2015; Kendon et al, 2012)—can be removed from the models. Even though some climate centers have embarked on convectionpermitting global climate models (GCMs) for short periods (Fuhrer et al, 2018; Satoh et al, 2019; Stevens et al, 2019), it is expected that it will take at least a decade, and probably longer, before multi-decadal convectionpermitting GCM climate projections become feasible and widely performed (Gutowski et al, 2020; Schär et al, 2020) For those reasons, CPRCMs using limited area domains become an interesting alternative to perform convectionpermitting climate change projections at a lower computational cost. In contrast to GCMs, CPRCM simulations can efficiently target regions where CPRCM added value is expected or where very high resolution climate forcings are required for impact models (e.g., flash floods and urban climate)
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