Key Points

• Our study optimises WRF model parameters for Southeast Australia heat extremes, enhancing the accuracy of the model simulation. 

• G-BO method finds optimal parameter ranges, substantially improving the simulation of temperature and humidity. 

• Results suggest updating WRF model's default settings for better extreme heat event simulations.

Abstract 

In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia’s extreme heat events. Using Gaussian process regression-based Bayesian Optimisation (G-BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimised parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions. 

Plain Language Summary 

This study aims to enhance the accuracy of a numerical weather model called the Weather Research and Forecasting (WRF) model, especially for simulating extreme heat events in Southeast Australia. Typically, the accuracy of such models depends on specific settings, which are often set to default values. Our research used a method known as Gaussian process regression-based Bayesian Optimisation (G-BO) to determine the best range of values for these settings. We found that the default settings were not optimal. By applying G-BO, we identified more effective values that substantially improved the model’s simulations of temperature and humidity during heat extremes. This improvement was consistent even when tested on an independent extreme heat event. These advancements are vital for more accurate weather forecasting, which is essential for emergency services, electricity management, and agriculture planning during extreme heat conditions.

Synopsis: A clear signal of human influence on upper tropospheric ozone trends is identifiable with high statistical confidence in a 17-year satellite dataset.

Abstract: Tropospheric ozone (O₃) is a strong greenhouse gas, particularly in the upper troposphere (UT). Limited observations point to a continuous increase in UT O₃ in recent decades, but the attribution of UT O₃ changes is complicated by large internal climate variability.

We show that the anthropogenic signal (“fingerprint”) in the patterns of UT O₃ increases is distinguishable from the background noise of internal variability. The time-invariant fingerprint of human-caused UT O₃ changes is derived from a 16-member initial-condition ensemble performed with a chemistry-climate model (CESM2-WACCM6). The fingerprint is largest between 30°S and 40°N, especially near 30°N. In contrast, the noise pattern in UT O₃ is mainly associated with the El Niño–Southern Oscillation (ENSO). The UT O₃ fingerprint pattern can be discerned with high confidence within only 13 years of the 2005 start of the OMI/MLS satellite record. Unlike the UT O₃ fingerprint, the lower tropospheric (LT) O₃ fingerprint varies significantly over time and space in response to large-scale changes in anthropogenic precursor emissions, with the highest signal-to-noise ratios near 40°N in Asia and Europe.

Our analysis reveals a significant human effect on Earth’s atmospheric chemistry in the UT and indicates promise for identifying fingerprints of specific sources of ozone precursors.