|Title||Precipitation and moisture in four leading CMIP5 models: Biases across large-scale circulation regimes and their attribution to dynamic and thermodynamic factors|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Yang M.M, Zhang GJ, Sun D.Z|
|Journal||Journal of Climate|
|Type of Article||Article|
|Keywords||Atmospheric circulation; convection; double-itcz problem; enso asymmetry; historical simulations; Meteorology & Atmospheric Sciences; north-american climate; oceans; parameterization; satellite-observations; tropical; water-vapor|
As key variables in general circulation models, precipitation and moisture in four leading models from CMIP5 (phase 5 of the Coupled Model Intercomparison Project) are analyzed, with a focus on four tropical oceanic regions. It is found that precipitation in these models is overestimated in most areas. However, moisture bias has large intermodel differences. The model biases in precipitation and moisture are further examined in conjunction with large-scale circulation by regime-sorting analysis. Results show that all models consistently overestimate the frequency of occurrence of strong upward motion regimes and peak descending regimes of 500-hPa vertical velocity JCLI-D-17-0718.1 regime, models produce too much precipitation compared to observation and reanalysis. But for moisture, their biases differ from model to model and also from level to level. Furthermore, error causes are revealed through decomposing contribution biases into dynamic and thermodynamic components. For precipitation, the contribution errors in strong upward motion regimes are attributed to the overly frequent . In the weak upward motion regime, the biases in the dependence of precipitation on probability density function (PDF) make comparable contributions, but often of opposite signs. On the other hand, the biases in column-integrated water vapor contribution are mainly due to errors in the frequency of occurrence of , while thermodynamic components contribute little. These findings suggest that errors in the frequency of occurrence are a significant cause of biases in the precipitation and moisture simulation.