CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

Abstract

Many weather forecast and climate models simulate warm surface air temperature (T2m) biases over midlatitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multimodel intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T2m bias using a short‐term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement Southern Great Plains sites. The present study examines the contributions of surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central United States and Southern Great Plains. Nevertheless, biases in the net shortwave and downward longwave fluxes as well as surface evaporative fraction (EF) are contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias.

CAUSES: Diagnosis of the Summertime Warm Bias in CMIP5 Climate Models at the ARM Southern Great Plains Site

“…The surface energy budget analysis shows that both surface solar radiation and evaporative fraction errors are contributors to the warm bias. These findings based on long‐term climate simulations are similar to those discovered from short‐term hindcast experiments (Ma et al, ; Van Weverberg et al, ), although short‐term hindcasts in general have smaller warm bias which is associated with smaller errors in radiation and evaporative fraction compared to climate simulations. The relative magnitude of biases between hindcast and climate simulations is consistent with an interaction between large‐scale circulation and physical processes that amplifies the warm bias at the surface in climate integrations.…”

An Overview of the Atmospheric Component of the Energy Exascale Earth System Model

“…EAM temperatures are much warmer over the NH continents in winter poleward of 40°N due at least in part to the tuning choices used in the Bergeron process, although the amplitude of the temperature bias is about the same magnitude. Temperature biases in the same regions in NH summer are much reduced compared to CAM5 and many other climate models (Ma, Klein et al, 2018). EAM is also generally warmer over sea ice and ice sheets, particularly in winter, and this is due to the substantial changes in the surface energy budget due to differences in clouds and the surface energy budget discussed below.…”

“…Since brute force strategies using many multiyear simulations were not feasible at high resolution, other strategies were employed. Xie et al () and H. Ma, Klein, et al () showed that model biases related to fast physical processes in short‐term hindcasts resemble those from long‐term climate simulations, so two frameworks using short simulations were used as an integral part of model development. A hindcasting methodology identified with Transpose‐AMIP (Atmospheric Model Intercomparison Project) and Cloud‐Associated Parameterizations Testbed (Phillips et al, ; Williams et al, ) was used to assess candidate physical parameterizations and tune the computationally expensive high‐resolution EAM configurations, following protocols described in previous studies (Boyle & Klein, ; Liu et al, ; Lin et al, ; Qian et al, ; H. Ma, Klein, et al, ; van Weverberg et al, ; Xie et al, ).…”

“…Xie et al () and H. Ma, Klein, et al () showed that model biases related to fast physical processes in short‐term hindcasts resemble those from long‐term climate simulations, so two frameworks using short simulations were used as an integral part of model development. A hindcasting methodology identified with Transpose‐AMIP (Atmospheric Model Intercomparison Project) and Cloud‐Associated Parameterizations Testbed (Phillips et al, ; Williams et al, ) was used to assess candidate physical parameterizations and tune the computationally expensive high‐resolution EAM configurations, following protocols described in previous studies (Boyle & Klein, ; Liu et al, ; Lin et al, ; Qian et al, ; H. Ma, Klein, et al, ; van Weverberg et al, ; Xie et al, ). Short, few‐day simulations are also capable of revealing parametric sensitivities, and Wan et al () and Qian et al () developed a protocol for using perturbed parameters ensembles of 3‐day simulations to reveal model sensitivities to parameter changes that provide insight similar to that found in longer, multiyear simulations with a substantial saving in computing resources and dramatic reduction of simulation turnaround time.…”

Introduction to CAUSES: Description of Weather and Climate Models and Their Near‐Surface Temperature Errors in 5 day Hindcasts Near the Southern Great Plains

“…This suggests that whatever combination of physical processes it is that is leading to the diurnal cycle of the T2M error having the shape and phase that it does at SGP, that behavior is likely to also be happening over a much wider area in many of the models. As a result, detailed evaluations of cloud-and-radiation and land surface issues at SGP, such as those presented by Van Weverberg et al (2018) and Ma et al (2018), respectively, have the potential to lead to significant reductions in the magnitude and extent of the warm biases which have been presented here.…”