High‐Resolution CCAM Simulations Over New Zealand and the South Pacific for the Detection and Attribution of Weather Extremes

Gibson, Peter B.; Stone, Dáithí A.; Thatcher, Marcus; Broadbent, Ashley M.; Dean, S. M.; Rosier, Suzanne M.; Stuart, Stephen; Sood, Abha

doi:10.1029/2023jd038530

Cited by 7 publications

(4 citation statements)

References 107 publications

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“…However, most research has used CCAM as an RCM to downscale atmospheric data with coarser resolutions over a particular region based on global simulations with spatially varying resolutions. Recently, Gibson et al (2023) evaluated CCAM as an AGCM in simulating New Zealand weather and climate. Their results showed that CCAM performed particularly well at simulating the variability and extremes of temperature and precipitation over New Zealand.…”

Section: Hosted Filementioning

confidence: 99%

Sensitivity of Australian rainfall to driving SST datasets in a variable-resolution global atmospheric model

Liu,

Alexander,

Evans

et al. 2024

Preprint

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In this study, we employ the Conformal Cubic Atmospheric Model (CCAM), a variable-resolution global atmospheric model, driven by two distinct sea surface temperature (SST) datasets: the 0.25° Optimum Interpolation Sea Surface Temperature (CCAM_OISST) version 2.1 and the 2° Extended Reconstruction SSTs Version 5 (CCAM_ERSST5). Model performance is assessed using a benchmarking framework, revealing good agreement between both simulations and the climatological rainfall spatial pattern, seasonality, and annual trends obtained from the Australian Gridded Climate Data (AGCD). Notably, wet biases are identified in both simulations, with CCAM_OISST displaying a more pronounced bias. Furthermore, we have examined CCAM’s ability to capture El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) correlations with rainfall during Austral spring (SON) utilizing a novel hit rate metric. Results indicate that only CCAM_OISST successfully replicates observed SON ENSO- and IOD-rainfall correlations, achieving hit rates of 86.6% and 87.5%, respectively, compared to 52.7% and 41.8% for CCAM_ERSST5. Large SST differences are found surrounding the Australian coastline between OISST and ERSST5 (termed the “Coastal Effect”). Differences can be induced by the spatial interpolation error due to the discrepancy between model and driving SST. An additional CCAM experiment, employing OISST with SST masked by ERSST5 in 5° proximity to the Australian continent, underscores the “Coastal Effect” has a significant impact on IOD-Australian rainfall simulations. In contrast, its influence on ENSO-Australian rainfall is limited. Therefore, simulations of IOD-Australian rainfall teleconnection are sensitive to local SST representation along coastlines, probably dependent on the spatial resolution of driving SST.

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Section: Hosted Filementioning

confidence: 99%

Sensitivity of Australian rainfall to driving SST datasets in a variable-resolution global atmospheric model

Liu,

Alexander,

Evans

et al. 2024

Preprint

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“…Our RCM emulator was trained using predictor and target variables from the Conformal Cubic Atmospheric Model (CCAM), a global non-hydrostatic atmospheric model with a variable-resolution cubic grid (Chapman et al, 2023;Gibson et al, 2023;McGregor & Dix, 2008;Thatcher & McGregor, 2009). In contrast to commonly used RCMs like the Weather Research and Forecasting Model (WRF), which rely on lateral boundary conditions from reanalysis or CMIP6 GCMs, CCAM is run as a global variable-resolution model (McGregor & Dix, 2008).…”

Section: Regional Climate Model Configurationmentioning

confidence: 99%

“…CCAM is run globally with spectral nudging to input fields from GCM atmospheric variables. A detailed evaluation of CCAM is presented in Gibson et al (2023) for this region, which used a very similar version of CCAM (i.e. model grid and physics configuration).…”

Section: Regional Climate Model Configurationmentioning

confidence: 99%

“…New Zealand is also exposed to weather phenomena such as tropical cyclones, atmospheric rivers, and large-scale climate drivers such as the El Niño-Southern Oscillation (ENSO), and the Southern Annular Mode (SAM) (Refs). While physical processes governing New Zealand's regional climate are generally well captured by physics-based RCMs (Ackerley et al, 2012;Gibson et al, 2023), a key challenge is ensuring that RCM emulators can also learn these processes (Rampal et al, 2024).…”

Section: Regional Climate Model Configurationmentioning

confidence: 99%

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A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation

Rampal,

Gibson,

Sherwood

et al. 2024

Preprint

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Anticipating climate impacts and risks in present or future climates requires predicting the statistics of high-impact weather events at fine-scales. Direct numerical simulations of fine-scale weather are computationally too expensive for many uses. While regression-based (deep-learning or statistical) downscaling of low-resolution climate simulations is several orders of magnitude faster than direct numerical simulations, it suffers from several limitations. These limitations include the tendency to regress to the mean, which produces excessively smooth predictions and underestimates the magnitude of extreme events. Additionally, they also fail to preserve statistical measures that are key for climate research. We use a conditional GAN (c-GAN) architecture to downscale daily precipitation as a Regional Climate Model (RCM) emulator. The c-GAN generates plausible residuals on top of the predictable expectation state produced by a regression-based DL algorithm. The skill of c-GANs is highly sensitive to a hyperparameter known as the weight of the adversarial loss (λadv), and the value of λadv required for accurate results varies with season and performance metric, casting doubt on the robustness of c-GANs as usually implemented. But, by applying a simple intensity constraint to the loss function, it is possible to obtain robust performance results across λadv spanning two orders of magnitude. C-GANs are considerably more skillful in capturing climatological statistics including the distribution and spatial characteristics of extreme events. We expect c-GANs with this modification to be readily transferable to other problems and time periods, making them a useful weather generator for representing extreme event statistics in present and future climates.

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A new, high‐resolution atmospheric dataset for southern New Zealand, 2005–2020

Kropač,

Mölg,

Cullen

2024

Geoscience Data Journal

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The regional climate of New Zealand's South Island is shaped by the interaction of the Southern Hemisphere westerlies with the complex orography of the Southern Alps. Due to its isolated geographical setting in the south‐west Pacific, the influence of the surrounding oceans on the atmospheric circulation is strong. Therefore, variations in sea surface temperature (SST) impact various spatial and temporal scales and are statistically detectable down to temperature anomalies and glacier mass changes in the high mountains of the Southern Alps. To enable future studies on the processes that govern the link between large‐scale SST and local‐scale high‐mountain climate, we utilized dynamical downscaling with the Weather Research and Forecasting (WRF) model to produce a regional atmospheric modelling dataset for the South Island of New Zealand over a 16‐year period between 2005 and 2020. The 2 km horizontal resolution ensures realistic representation of high‐mountain topography and glaciers, as well as explicit simulation of convection. The dataset is extensively evaluated against observations, including weather station and satellite data, on both regional (in the inner domain) and local (on Brewster Glacier in the Southern Alps) scales. Variability in both atmospheric water content and near‐surface meteorological conditions is well captured, with minor seasonal and spatial biases. The local high‐mountain climate at Brewster Glacier, where land use and topographic model settings have been optimized, yields remarkable accuracy on both monthly and daily time scales. The data provide a valuable resource to researchers from various disciplines studying the local and regional impacts of climate variability on society, economies and ecosystems in New Zealand. The model output from the highest resolution model domain is available for download in daily temporal resolution from a public repository at the German Climate Computation Center (DKRZ) in Hamburg, Germany (Kropač et al., 2023; 16‐year WRF simulation for the Southern Alps of New Zealand, World Data Center for Climate (WDCC) at DKRZ [data set]. https://doi.org/10.26050/WDCC/NZ‐PROXY_16yrWRF).

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High‐Resolution CCAM Simulations Over New Zealand and the South Pacific for the Detection and Attribution of Weather Extremes

Cited by 7 publications

References 107 publications

Sensitivity of Australian rainfall to driving SST datasets in a variable-resolution global atmospheric model

Sensitivity of Australian rainfall to driving SST datasets in a variable-resolution global atmospheric model

A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation

A new, high‐resolution atmospheric dataset for southern New Zealand, 2005–2020

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