Fixing the Light Precipitation Problem in Global Climate Models
Global climate models commonly overestimate the frequency of light precipitation events and underestimate the occurrence of rarer, but intense precipitation events. Observations from the 19-month deployment of the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) to Graciosa Island in the Azores provided needed data to assess and improve systematic model errors in the simulation of cloud and precipitation properties. The synergy and colocation of cloud and radiation observations, together with vertically resolved observations of cloud and drizzle droplets, provide deeper insights into the model errors than can be gained from a satellite perspective alone. U.S. Department of Energy researchers identified three specific model issues that contributed to the error in simulated precipitation at Graciosa: 1) triggering of cloud formation in the boundary layer, 2) rate of conversion from cloud droplets to rain, and 3) evaporation of drizzle. New formulations for each of these processes were developed and implemented in the model. Comparison to ARM observations illustrates that the new process formulations improve the occurrence frequency of overcast low clouds in the model, increase their liquid water path, and reduce the overestimate of precipitation occurrence at cloud base and at the surface. Global simulations with the improved model indicate that the changes reduce the mean absolute error in reflected sunlight over large areas of the globe. These results illustrate how the high-resolution ARM observations of cloud and precipitation processes in important climatic regions provide critical information for improving global climate model simulations.
Ahlgrimm, M., and R. Forbes. 2013. “Improving the Representation of Low Clouds and Drizzle in the ECMWF Model Based on ARM Observations from the Azores,” Monthly Weather Review, DOI:10.1175/MWR-D-13-00153.1.