Study Proposes New Scheme to Characterize Land-Atmosphere Interactions and Improve Climate Models

Bhattacharya, A., Pacific Northwest National Laboratory

General Circulation and Single Column Models/Parameterizations

Cloud Life Cycle

Liu G, Y Liu, and S Endo. 2013. "Evaluation of Surface Flux Parameterizations with Long-Term ARM Observations." Monthly Weather Review, 141(2), 10.1175/mwr-d-12-00095.1.

One of the three ARM precipitation radars at the SGP site.

One of the three ARM precipitation radars at the SGP site.

Measurements acquired over long periods, decades at a stretch, matter quite a bit when it comes to making sense of the Earth’s climate system, not to mention improving the performance of global climate models. A new paper published earlier this year once again demonstrates why this is so.

Based on seven years (2003–2010) of measurements of key climate variables at the Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Southern Great Plains site, scientists at Brookhaven National Laboratory have proposed a new scheme to improve forecasting abilities of several climate and weather models in use in the United States.

Surface fluxes, esoteric as they may sound, are just combinations of factors that control the rate of heat and energy exchange between the Earth’s surface and the lowest part of the atmosphere where our “weather”—such as cloud formation and precipitation—occurs.

Scientists derive surface fluxes from measurements of wind speed, air temperature, humidity, and ground temperature. However, observations are simply not available at every grid point. As a result, surface fluxes even within a relatively small geographical region are not based on actual measurements.

In such cases, scientists have to make do with forecasted sets of values, also known as Surface Flux Parameterization or SFP schemes. These schemes are predicted sets of numbers from algorithms that forecast values of surface fluxes over a wide region from a few measurements. Scientists then use these schemes to run larger and more complex global climate scenarios, which they then evaluate against observations. Alternatively, they run these schemes to evaluate the impact of changing surface fluxes on climate. Clearly, there is a strong need to improve the SFP schemes as much as possible.

The Southern Great Plains, spread over 55,000 square miles, provides a unique opportunity to calibrate SFP schemes to different kinds of observations obtained over a long period.

In the study, Gang Liu and his co-authors report that improving the SFP schemes may require a major overhaul—not just in the way algorithms use observations but also the kind of observations. They found that just using long-term observations to “teach” the algorithms improves the scheme, but only under certain conditions—for example, during thunderstorms. The new and improved framework still requires incorporating other existing observations to perform better under regular and stable conditions.

Nonetheless, the researchers strongly advocate using such observation-driven schemes to improve model performances and forecasts; they argue that even with work to be done, it is indeed a step in the right direction.