From measurement to model domain: data ensembles of surface heat flux for improving climate model parameterizations

It is of great interest to the climate research community to have high temporally and spatially resolved surface energy flux measurements to evaluate climate model performance. Surface latent and sensible heat flux are the primary land-atmosphere energy transfer mechanisms, but the physical processes governing these exchange rates are highly complex. As a result, there is a great amount of uncertainty in the way in which surface energy and mass transport are parameterized in climate models. Availability of data sets that describe the spatial and temporal behavior of heat fluxes will help in developing better parameterizations and reduce this uncertainty.

Comparison between models and observations is often achieved by averaging measured quantities over time and space to match the spatial and temporal resolution of a gridded climate model without any regard for the statistical errors in doing so. To address this issue and to avoid averaging out key properties of heat flux measurements, we have developed spatio-temporal statistical methods to interpolate 6 years of 30-minute data gathered at 22 energy balance Bowen ratio (EBBR) and eddy correlation (ECOR) sites located in a 300x300km area of the U.S. Southern Great Plains into a 1km gridded surface. As land type proved to be integral in understanding the behavior of the fluxes, satellite information on land surface properties was incorporated into the statistical model. By conducting multiple runs of the spatio-temporal model we obtain data ensembles of the interpolated surface with which we can characterize uncertainty in our method of going from the observational to model domain.

We present the methodology developed for interpolating the observational data and creating the data ensembles. How the ensemble of gridded fluxes can be used to validate climate models will also be discussed.