Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation
2019
Abstract. Understanding the unfolding challenges of climate change relies on climate
models, many of which have large summer warm and dry biases over Northern
Hemisphere continental midlatitudes. This work, with the example of the
model used in the updated version of the weather@home distributed climate
model framework, shows the potential for improving climate model simulations
through a multiphased parameter refinement approach, particularly over
the northwestern United States (NWUS). Each phase consists of (1) creating a
perturbed parameter ensemble with the coupled global–regional atmospheric
model, (2) building statistical emulators that estimate climate metrics as
functions of parameter values, (3) and using the emulators to further refine
the parameter space. The refinement process includes sensitivity analyses to
identify the most influential parameters for various model output metrics;
results are then used to cull parameters with little influence. Three phases
of this iterative process are carried out before the results are considered
to be satisfactory; that is, a handful of parameter sets are identified that
meet acceptable bias reduction criteria. Results not only indicate that
74 % of the NWUS regional warm biases can be reduced by refining global
atmospheric parameters that control convection and hydrometeor transport,
as well as land surface parameters that affect plant photosynthesis, transpiration,
and evaporation, but also suggest that this iterative approach to perturbed
parameters has an important role to play in the evolution of physical
parameterizations.
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