“The diurnal cycle is a fundamental feature of Earth’s climate. Because of its short time scales and close coupling to surface and atmospheric processes, the simulation of the diurnal cycle provides an ideal test bed for evaluating many aspects of model physics. Despite recent improvements in model resolution and parameterizations, the diurnal amplitude and phase in surface temperature, cloudiness, convection, precipitation and other fields still differ considerably from observations in many climate models. These diurnal biases reflect deficiencies in various physical processes simulated by the models. While there exist many observational datasets with sub-daily resolution, most of them cannot be readily used to evaluate models, and current model evaluation packages often contain very limited data for evaluating the diurnal cycle. Based on our previous work on studying the diurnal cycle and its simulation in models, here we propose to a) develop a new set of diurnal metrics and link them to specific underlying processes for evaluating model physics, and b) apply the diurnal metrics to diagnose and identify deficiencies in the GFDL and other CMIP5 models.
Specifically, we propose to 1) compile a new dataset with high temporal-resolution (hourly to 6-hourly) from surface and satellite observations, field experiments, research sites, and atmospheric reanalyses for studying the diurnal cycle and evaluating models; 2) apply the new dataset to quantify the diurnal cycle and study its underlying processes in various fields over the globe, including surface daily maximum (Tmax) and minimum (Tmin) temperatures, precipitation frequency, intensity and amount, cloud cover, humidity and others; 3) design a new set of effective diurnal metrics and link them to specific physical processes based on analyses of observational data; and 4) apply these diurnal metrics and associated linkages to physical processes to diagnose deficiencies in GFDL and other CMIP5 models by analyzing sub-daily output from these models.
The new diurnal data set and diurnal metrics developed in this project will greatly enhance current model evaluation packages. Our second task will improve our understanding of the diurnal cycle and its underlying physical processes. This understanding is necessary for developing constructive diurnal metrics for evaluating physical processes in models, while tasks 3 and 4 will directly help improve models, especially the GFDL model.
A unique feature of this proposal is that it utilizes the expertise of the PI and others on this proposal in studying the diurnal cycle to identify specific physical processes underlying each of the major diurnal variations (e.g., in Tmax and Tmin or the low-level jet over the central U.S.), so that a modeler can use this information to examine specific areas in his/her model when a diurnal bias is found. Another strength is that it includes two leading modelers from GFDL who have a strong desire to improve the simulation of the diurnal cycle in GFDL’s new models. This collaboration will lead to real model improvements.
Relevance: This proposal is for MAPP Competition – Process-oriented evaluation of climate and Earth system models and derived projections (Area A, Type 2), which emphasizes projects to “”develop and apply process-oriented metrics to evaluate simulated climate phenomena with strong theoretical and observational bases””. The diurnal cycle is a well-studied, fundamental feature of Earth’s climate. The focus of our diurnal metrics on the sub-daily processes and our emphasis on linking diurnal biases to underlying physical processes make our metrics truly process-oriented. We will also apply the new diurnal metrics to diagnose the simulation of the diurnal cycle in the GDFL and other models. Thus, this proposal is directly responsive to the MAPP competition. Improving climate models and our understanding of the diurnal cycle is also an important step to achieve NOAA’s long-term climate goal to improve scientific understanding of the changing climate system and its impacts.”