Tackling model biases in GFDL climate and prediction models
Baoqiang
Xiang
NOAA/GFDL, UCAR
Oral
(Virtual Talk)
Understanding the sources of model deficiencies is essential for further improvement for both the climate models and prediction
models. However, this is of challenge. In this talk, I will discuss how we evaluate model deficiencies together with the basic
processes about how we deal with model biases. Several more detailed examples at different timescales will be presented,
including the diurnal cycle of land precipitation, tropical cyclones, MJO, and atmospheric/oceanic mean states. I will also share
some preliminary results on the impacts of model biases on S2S predictions.
models. However, this is of challenge. In this talk, I will discuss how we evaluate model deficiencies together with the basic
processes about how we deal with model biases. Several more detailed examples at different timescales will be presented,
including the diurnal cycle of land precipitation, tropical cyclones, MJO, and atmospheric/oceanic mean states. I will also share
some preliminary results on the impacts of model biases on S2S predictions.
Presentation file
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