Using the long-term ARM Tropical Western Pacific datasets as a tropical testbed for climate models

Description

Climate models have large biases in predicting clouds and their radiative effects over the tropics. The long-term ARM datasets collected at the three Tropical Western Pacific (TWP) sites provide an excellent resource for evaluating climate models using both statistical and process-oriented approaches. In this study, we use the ARM datasets to evaluate the ability of the Community Atmosphere Model version 5 (CAM5) to simulate the various types of clouds, their seasonal and diurnal variations, and their impact on surface radiation around the TWP sites. We conducted a series of CAM5 simulations at various horizontal grid sizes (around 2°, 1°, 0.5°, and 0.25°). The modeled meteorological fields were nudged to an atmospheric reanalysis to minimize the impact of known CAM5 biases in meteorology (e.g., winds, temperature, etc.) on clouds. Model biases in the seasonal cycle of cloudiness are only weakly dependent on model resolution. By decomposing the modeled and observed clouds into convective versus stratiform and liquid versus ice clouds, we found that biases in the total cloud fraction appear mostly in stratiform ice clouds. Model biases in the diurnal cycle of clouds and surface radiative fluxes vary by site. For example, at Manus and Nauru a positive bias in cloud cover occurs year round during the daytime, but for the Darwin site more noteworthy negative biases tend to occur at night. A secondary goal of our work is to demonstrate how we can best use the long-term ARM datasets as a tropical testbed to identify the dominant sources of model biases and uncertainties. This testbed approach can be easily adapted for the evaluation of the DOE Accelerated Climate Model for Energy (ACME) model simulations.