Parameterizing the Variance of Temperature Fluctuations Over Heterogeneous Landscapes for Surface Boundary Conditions in Atmospheric Models

Abstract:

The implementation of higher-order turbulence closure schemes in Earth system models (e.g., the Cloud Layers Unified by Binormals; CLUBB) aims to improve the modeling of convection and radiative transfer in numerical weather prediction and climate models. However, the added value of these schemes is constrained by the specification of boundary conditions on higher-order statistics. At the land surface, many of the higher order turbulence statistics that are required as boundary conditions are parameterized using formulations more appropriate for stationary and planar-homogeneous flow in the absence of subsidence. A case in point is the variance of the potential temperature fluctuations. Because of the additive nature of variances arising from non-uniformity in surface heating, current parameterizations are not readily generalizable. This presentation aims to address this weakness by leveraging the Ameriflux and National Ecological Observation Network (NEON) networks of eddy covariance towers to assemble a new parameterization for the potential temperature variance.

The turbulence fluctuations of temperature from 49 Ameriflux and 47 NEON sites are processed and quality controlled, removing points occurring at night, while precipitation is falling, with poor energy balance closure, and with sub-zero temperatures. A Random forest model is then fit between observed temperature variance values and site characteristics derived from remote sensing data. Initial results suggest the new parameterization is a significant improvement from the original parameterization with a preliminary normalized root mean squared error of 18.7% compared to 44.5% for the original model. The model predictors that play the largest role include measured sensible heat flux, friction velocity, and the remotely sensed vegetative characteristics. This model is then further simplified using multilinear regression for implementation within Earth system models by revising similarity constants to accommodate site characteristics. The successful improvement of the temperature variance parameterization implies high potential for similar, new, empirically derived parameterizations for the surface boundaries for other higher order turbulent statistics (e.g. temperature skewness) in atmospheric turbulence models.