Skill and uncertainty in surface wind fields from general circulation models: Intercomparison of bias between AGCM, AOGCM and ESM global simulations. (19th November 2019)

• Record Type: Journal Article
• Authors: Morim, Joao; Hemer, Mark; Andutta, Fernando; Shimura, Tomoya; Cartwright, Nick
• Is Part Of: International journal of climatology. Volume 40:Number 5(2020)
• Page Start: 2659
• Page End: 2673
• Publication Date: 2019-11-19
• Subjects: atmosphere–ocean coupling -- carbon‐cycle dynamics -- GCM -- surface wind fields -- uncertainty; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.6357
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract: Understanding the reliability of global climate models (GCMs) to reproduce the historical surface wind fields is integral part of building robust projections of surface wind‐climate, and other wind‐dependent geophysical climatic variables. Understanding the skill of atmosphere‐only models (AGCM), coupled atmosphere–ocean models (AOGCM) and fully coupled earth system models (ESM) is likewise paramount to assess any systematic model improvements. In this paper, we systematically assess whether surface wind fields obtained from 28 CMIP5 GCMs can represent large‐scale spatial patterns and temporal variability of historical surface winds. We show that inter‐model uncertainty is typically 2–4 times larger than the uncertainty associated with GCM internal variability, although the latter can be significant within specific regions. We also find that CMIP5 models are typically capable of reliably reproducing large‐scale spatial patterns of historical near‐surface winds, but considerable uncertainty lies within the CMIP5 ensemble with strong latitudinal dependence. CMIP5 models show limitations in their ability to reliably represent inter‐annual and inter‐seasonal variability particularly within tropical‐cyclone‐affected regions. In further analysis, we quantify and intercompare historical wind bias from different types of models with different dynamical cores, based on multiple CMIP5 diagnostic experiments. We find that bias in surface wind fields are largely intrinsic to the atmospheric components of the models, and that the inclusion of carbon‐cycle dynamics has insignificant effect on simulated surface winds (at decadal time‐scales). Inconsistencies between AGCM and AOGCM simulations are largely driven by errors in sea surface temperatures (SST); though such differences are not statistically significant relative to the inter‐model uncertainty within the CMIP5 ensemble. These results show that the dominant source of bias in simulated wind fields lies in the underlying physics of the atmospheric component of the models. Abstract : Schematic diagram of the coupling between the different model components of a generic GCM. The components and processes included within the GCM (grey and white backgrounds outlined by dashed boxes) are shown, with each model constituent component having a specific colour. The connecting dashed arrows (coloured) indicate exchanges that couple individual model components, and black arrows indicate the two‐way feedback between model components and the climate system. The anthropogenic and natural forcing of each CMIP5 experiment (coloured boxes and respective dashed arrows) is shown along with the number and name of the experiment. The flow of carbon flux within the coupled system domain is also shown as indicated by the dashed arrows. The description of the GCMs used and their components are described in Section 2.