Skill and uncertainty in surface wind fields from general circulation models: Intercomparison of bias between AGCM, AOGCM and ESM global simulations

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.

Effects of Internal Climate Variability on Historical Ocean Wave Height Trend Assessment

“…Despite being a unique database for ocean wave climate research, it only simulates one realization given the same climatological forcing. In terms of the driving wind fields, Morim et al (2020) found that the underlying physics of the atmospheric component of climate models is the dominant source of bias in simulated wind fields, and that inter-model uncertainty is typically 2-4 times larger than the uncertainty associated with internal variability. However, they used a relatively small sample (3-10 model realizations).…”

Climate change/variability and hydrological modelling studies in Zimbabwe: a review of progress and knowledge gaps

“…To this end, the IPCC and other climate scientists have progressed to implement and develop the Coupled Model Intercomparison Project (CMIP) with the latest being CMIP6 [ 160 ]. In CMIP6, various ensembles of GCMs have been run collectively and results compared in an attempt to understand how the global climate will respond to future scenarios of increasing/decreasing anthropogenic radiative forcing relative to present‐day climate conditions [ 157 , 161 , 162 ]. For example, Andrews et al [ 163 ] recently ran simulations using the HadGEM3-GC3.1 for CMIP6, testing climatic responses to historical forcings such as solar irradiance, ozone concentrations, greenhouse gases, land‐use changes, and aerosols compared results to observational data.…”

Climate change impacts on tropical cyclones of the Arabian Sea: Projections and uncertainty investigations

“…Previous studies have evaluated GCMs performance by various approaches, such as employing a grid of measured data (e.g., Zha et al ., 2020) or reanalysis datasets (e.g., Morim et al ., 2020). In the latter approach, the performance of the reanalysis datasets should be first assessed (Morim et al ., 2018).…”

“…Previous studies have evaluated GCMs performance by various approaches, such as employing a grid of measured data (e.g., Zha et al, 2020) or reanalysis datasets (e.g., Morim et al, 2020). In the latter approach, the performance of the reanalysis datasets should be first assessed F I G U R E 3 Accumulated monthly frequency of TCs over the AS within 1891-2020 (adopted from IMD, 2019) [Colour figure can be viewed at wileyonlinelibrary.com] (Morim et al, 2018).…”