A Comprehensive Evaluation of Mean and Extreme Climate for the Conformal Cubic Atmospheric Model (CCAM)
Authors: Schroeter Benjamin J. E. , Ng Benjamin , Takbash Alicia , Rafter Tony , Thatcher Marcus 
Published: September 2024

Abstract
This study evaluates the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling fifth major global reanalysis produced by European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA5) reanalysis data from 1985 to 2014, following a 5-yr spinup period. It focuses on daily maximum and minimum temperatures and daily precipitation, comparing CCAM to ERA5 and the Australian Gridded Climate Data (AGCD). The CCAM effectively reduces warm biases in daily minimum temperatures but struggles with cold biases in daily maximum temperatures, particularly in northern Australia during the wet season, possibly due to high-level cloud overestimation. Precipitation tends to be overestimated, especially in extreme rainfall events, though offset by an underestimation of low rainfall. The study showcases improvements in the annual minimum of daily minimum temperatures across most of Australia, while identifying challenges in forecasting cooler extreme temperatures. It adds value to annual maximum daily maximum temperatures in southern Australia but less so in the north. The analysis of the 5% annual exceedance probability (AEP5%) yields mixed results influenced by location and potential ocean temperature changes. Some coastal areas exhibit lost value, possibly linked to ocean temperature shifts. Furthermore, CCAM’s representation of maximum annual daily and 5-day rainfall reveals lost value, particularly in eastern Australia due to an overestimate of extreme rainfall. Despite the challenges of comparing a dynamical downscaling model like CCAM to ERA5, this study highlights its benefits in reducing biases, especially in temperature representation. Given the larger biases in phase 6 of Coupled Model Intercomparison Project (CMIP6) global climate models, CCAM appears suitable for dynamic downscaling in climate projections, emphasizing the need for ongoing model enhancements, including addressing biases related to ephemeral water bodies and extreme rainfall.

Significance Statement
This study critically assesses the performance of the Conformal Cubic Atmospheric Model (CCAM) in dynamically downscaling ERA5 reanalysis data from 1985 to 2014, offering valuable insights into climate modeling. Focusing on temperature and precipitation, CCAM proves effective in mitigating warm biases in daily minimum temperatures but encounters challenges with cold biases in daily maximum temperatures, particularly in northern Australia. The analysis reveals the overestimation of precipitation, especially in extreme events, yet identifies improvements in annual minimum daily minimum temperatures across Australia. The study underscores CCAM’s potential in reducing biases compared to CMIP6 global climate models, making it a promising tool for dynamic downscaling in climate projections. It emphasizes the necessity for ongoing model enhancements, particularly addressing biases related to ephemeral water bodies and extreme rainfall.