Understanding the bias in surface latent and sensible heat fluxes in contemporary AGCMs over tropical oceans
- PDF / 6,877,821 Bytes
- 22 Pages / 595.276 x 790.866 pts Page_size
- 72 Downloads / 141 Views
Understanding the bias in surface latent and sensible heat fluxes in contemporary AGCMs over tropical oceans Xin Zhou1 · Pallav Ray1 · Bradford S. Barrett2 · Pang‑Chi Hsu3 Received: 6 April 2020 / Accepted: 18 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The performance of 20 models participating in the atmospheric model intercomparison project (AMIP) is evaluated concerning surface latent (QLH) and sensible (QSH) heat flux over the tropical oceans (30°S–30°N). Biases were calculated by comparing model fluxes to observations from moored buoys and the objectively analyzed air–sea fluxes (OAFlux) database. All 20 AMIP models overestimate QLH with an ensemble mean bias of 20 W m−2, and 18 of the 20 models overestimate QSH with an ensemble mean bias of 5 W m−2 when compared to OAFlux, implying a systematic positive bias over the tropical oceans. A comparison with buoy observations also showed similar biases. To obtain insights into the causes behind model bias, we quantified the contribution from near-surface winds, specific humidity, and temperatures. It is found that near-surface humidity contributes more to the bias in QLH than wind speed, while air temperature contributes more to bias in QSH than wind speed. On the other hand, the root mean squared error (RMSE) in QLH has contributions from both near-surface humidity and wind. The contribution from humidity to the mean bias in QLH is 13 W m−2, with RMSE of 15 W m−2, suggesting a systematic overestimation of sea-air humidity difference in models. The model ensemble, in general, simulates QLH and QSH better than individual models. Models with higher horizontal and vertical resolutions perform better than coarse resolution models. Keywords AMIP · Surface heat flux · Latent heat flux · Sensible heat flux
1 Introduction A vital component of the earth’s surface energy budget is the surface heat flux. Surface heat flux allows the exchange of mass and energy between the ocean, land, and the atmosphere and thereby influences oceanic and atmospheric circulations (e.g., Trenberth et al. 2001; Fasullo and Trenberth 2008; Andersson et al. 2010; Bentamy et al. 2013; Brownlee et al. 2017; Valdivieso et al. 2017). To correctly simulate weather and climate, general circulation models (GCMs) must be able to capture the mean and variability of surface heat flux. A GCM’s ability to simulate the surface heat flux * Pallav Ray [email protected] 1
Meteorology, Florida Institute of Technology, Melbourne, FL 32904, USA
2
US Naval Academy, Annapolis, MD, USA
3
International Laboratory on Climate and Environment Change and Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
feeds directly into its simulation of convection, and convection is one of the critical factors in determining global and regional climate variability and its impact on society (Tost et al. 2006; Guilyardi et al. 2009; Ray et al. 2012; McNeely et al. 2012). For example, the common pr
Data Loading...
