A Better Way to Gain Insights into Climate Model Moist Process Errors
A multi-year short-range hindcast experiment and its experiment design are presented for better evaluation of atmospheric moist processes in climate models from diurnal to interannual time scales to facilitate model development.
This multi-year hindcast experiment provides a new opportunity to address several modeling issues associated with moist processes because the phenomena of interests are either interannual climate variability or only happen a few times in a given year, and thus multi-years hindcasts can be used to robustly quantify the errors associated with these phenomena rather than just mean state evaluation.
Three processes – the diurnal cycle of clouds during different cloud regimes over the Central U.S., precipitation and diabatic heating associated with the Madden-Julian Oscillation (MJO), and the response of precipitation, surface radiative and heat fluxes, as well as zonal wind stress to sea surface temperature anomalies associated with the El Niño-Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from the multi-year hindcast experiment to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under a well-controlled large-scale environment because these phenomena are either interannual climate variability or only happen a few times in a given year (e.g. MJO, or cloud regime types). Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model’s climate error directly comes from fast errors in the parameterizations of moist processes.