High‐Resolution CCAM Simulations Over New Zealand and the South Pacific for the Detection and Attribution of Weather Extremes
Peter B. Gibson, Dáithí A. Stone, Marcus Thatcher, Ashley M. Broadbent, S. M. Dean, Suzanne M. Rosier, Stephen Stuart, Abha Sood
Abstract
Abstract Detection and attribution experiments are designed for the causal diagnosis of features in the climate system, including trends in mean climate and extreme events. While several detection and attribution data sets now exist, the coarse resolution of the climate models used (∼100‐km) often hinders their application to topographically complex regions like Aotearoa New Zealand and small island nations. The coarse atmospheric resolution may also be detrimental for simulating certain features of the atmospheric circulation, including the jets, blocking and cyclones. To address this, here we introduce a new set of climate model runs consisting of high‐resolution atmospheric simulations from the Conformal Cubic Atmospheric Model (CCAM) non‐hydrostatic global model. The variable‐resolution grid employed by CCAM enables targeted high‐resolution simulations over New Zealand (12‐km) and intermediate resolution over the wider South Pacific region (12–35‐km). Simulations from the historical experiment (years 1982–2021), consisting of ten initial condition ensemble members, are presented and evaluated here. The evaluation focuses on the representation of the large‐scale atmospheric circulation over the Southern Hemisphere including the jet streams, storm tracks, cyclones, blocking and teleconnections, as well as more localized temperature and precipitation variability and extremes specifically over New Zealand. While certain biases are highlighted and discussed for the large‐scale atmospheric circulation, CCAM is found to perform especially well for various precipitation and temperature‐based extreme indices at smaller scales across New Zealand, generally outperforming state‐of‐the‐art reanalysis and coarser resolution global atmospheric models. These results support further application of the CCAM ensemble for studying weather and climate extremes in attribution studies.