Using regime analysis to identify the contribution of clouds to surface temperature errors in weather and climate models. (1st September 2015)

• Record Type: Journal Article
• Authors: Van Weverberg, Kwinten; Morcrette, Cyril J.; Ma, Hsi‐Yen; Klein, Stephen A.; Petch, Jon C.
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 141:Number 693(2015:Oct.)
• Page Start: 3190
• Page End: 3206
• Publication Date: 2015-09-01
• Subjects: surface temperature bias -- GCM -- clouds -- ARM -- Unified Model -- CAM; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.2603
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract : Many global circulation models (GCMs) exhibit a persistent bias in the 2 m temperature over the midlatitude continents, present in short‐range forecasts as well as long‐term climate simulations. A number of hypotheses have been proposed, revolving around deficiencies in the soil–vegetation–atmosphere energy exchange, poorly resolved low‐level boundary‐layer clouds or misrepresentations of deep‐convective storms. A common approach to evaluating model biases focuses on the model‐mean state. However, this makes difficult an unambiguous interpretation of the origins of a bias, given that biases are the result of the superposition of impacts of clouds and land‐surface deficiencies over multiple time steps. This article presents a new methodology to objectively detect the role of clouds in the creation of a surface warm bias. A unique feature of this study is its focus on temperature‐error growth at the time‐step level. It is shown that compositing the temperature‐error growth by the coinciding bias in total downwelling radiation provides unambiguous evidence for the role that clouds play in the creation of the surface warm bias during certain portions of the day. Furthermore, the application of an objective cloud‐regime classification allows for the detection of the specific cloud regimes that matter most for the creation of the bias. We applied this method to two state‐of‐the‐art GCMs that exhibit a distinct warm bias over the Southern Great Plains of the USA. Our analysis highlights that, in one GCM, biases in deep‐convective and low‐level clouds contribute most to the temperature‐error growth in the afternoon and evening respectively. In the second GCM, deep clouds persist too long in the evening, leading to a growth of the temperature bias. The reduction of the temperature bias in both models in the morning and the growth of the bias in the second GCM in the afternoon could not be assigned to a cloud issue, but are more likely caused by a land‐surface deficiency.