Preprints
https://doi.org/10.5194/gmd-2024-41
https://doi.org/10.5194/gmd-2024-41
Submitted as: model evaluation paper
 | 
15 Apr 2024
Submitted as: model evaluation paper |  | 15 Apr 2024
Status: this preprint is currently under review for the journal GMD.

Evaluation of CORDEX ERA5-forced ‘NARCliM2.0’ regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2

Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew Riley

Abstract. Understanding regional climate model (RCM) capabilities to simulate current climate informs model development and climate change assessments. This is the first evaluation of the NARCliM2.0 ensemble of Weather Forecasting and Research RCMs driven by ECMWF Reanalysis v5 (ERA5) reanalyses over Australia at 20 km resolution contributing to CORDEX-CMIP6 Australasia, and south-eastern Australia at convection-permitting resolution (4 km). RCM performance in simulating mean and extreme maximum, minimum temperature and precipitation is evaluated against observations at annual, seasonal, and daily timescales, and compared to corresponding performances of previous-generation CORDEX-CMIP5 Australasia ERA-Interim-driven RCMs. ERA5-RCMs substantially reduce cold biases for mean and extreme maximum temperature versus ERA-Interim-RCMs, with small mean absolute biases (0.54 K; 0.81 K, respectively), but produce no improvements for minimum temperature. ERA5-RCM precipitation simulations show lower bias magnitudes versus ERA-Interim-RCMs, though dry biases remain over monsoonal northern Australia and extreme precipitation simulation improvements are principally evident at convection-permitting 4 km resolution. Although ERA5 reanalysis data confer improvements over ERA-Interim, only improvements in precipitation simulation by ERA5-RCMs are attributable to the ERA5 driving data, with RCM improvements for maximum temperature more attributable to model design choices, suggesting improved driving data do not guarantee all RCM performance improvements, with potential implications for CMIP6-forced dynamical downscaling. This evaluation shows that NARCliM2.0 ERA5-RCMs provide valuable reference simulations for upcoming CMIP6-forced downscaling over CORDEX-Australasia and are informative datasets for climate impact studies. Using a subset of these RCMs for simulating CMIP6-forced climate projections over CORDEX-Australasia and/or at convection-permitting scales could yield tangible benefits in simulating regional climate.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew Riley

Status: open (until 20 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2024-41', Juan Antonio Añel, 12 May 2024 reply
    • CC1: 'Reply on CEC1', Jatin Kala, 15 May 2024 reply
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew Riley
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew Riley

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Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.