Machine Learning based Parameter Sensitivity of Regional Climate Models
  -- A Case Study of the WRF Model for Heat Extremes over Southeast Australia
P. Jyoteeshkumar Reddy Sandeep Chinta Richard Matear John Taylor Harish Baki Marcus Thatcher Jatin Kala Jason Sharples
Heatwaves and bushfires cause substantial impacts on society and ecosystems
across the globe. Accurate information of heat extremes is needed to support
the development of actionable mitigation and adaptation strategies. Regional
climate models are commonly used to better understand the dynamics of these
events. These models have very large input parameter sets, and the parameters
within the physics schemes substantially influence the model's performance.
However, parameter sensitivity analysis (SA) of regional models for heat
extremes is largely unexplored. Here, we focus on the southeast Australian
region, one of the global hotspots of heat extremes. In southeast Australia
Weather Research and Forecasting (WRF) model is the widely used regional model
to simulate extreme weather events across the region. Hence in this study, we
focus on the sensitivity of WRF model parameters to surface meteorological
variables such as temperature, relative humidity, and wind speed during two
extreme heat events over southeast Australia. Due to the presence of multiple
parameters and their complex relationship with output variables, a machine
learning (ML) surrogate-based global sensitivity analysis method is considered
for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity
of 24 adjustable parameters in seven different physics schemes of the WRF
model. Results show that out of these 24, only three parameters, namely the
scattering tuning parameter, multiplier of saturated soil water content, and
profile shape exponent in the momentum diffusivity coefficient, are important
for the considered meteorological variables. These SA results are consistent
for the two different extreme heat events. Further, we investigated the
physical significance of sensitive parameters. This study's results will help
in further optimising WRF parameters to improve model simulation.