Using a freshwater lake model coupled with WRF for dynamical downscaling applications

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Tuesday, 4 February 2014: 2:30 PM
Room C209 (The Georgia World Congress Center )
Megan S. Mallard, EPA, Research Triangle Park, NC; and C. G. Nolte, O. R. Bullock Jr., T. L. Otte, J. A. Herwehe, K. Alapaty, and J. Gula

The ability to represent extremes in temperature and precipitation in regional climates (including those affected by inland lakes) has become an area of focus as regional climate models (RCMs) simulate smaller temporal and spatial scales. When using the Weather Research and Forecasting (WRF) model to downscale future global climate model (GCM) projections, model users typically must rely on the GCM to represent temperatures at all water points. However, GCMs typically have insufficient resolution to adequately represent even large inland lakes, such as the Great Lakes. In some cases, a single GCM point is tasked with representing the lake surface temperature (LST) and ice concentration over multiple large, heterogeneous lakes. This treatment can result in lakes as large as Lake Superior freezing completely in the space of a single timestep. When no water points are close enough to interpolate from, the representation of lakes can be further complicated by the setting of the LSTs from the nearest water point, even if the only available water temperatures are from ocean points hundreds of km away.

The current study examines three different ways in which LSTs and lake ice can be set in the WRF model, where it is applied as an RCM to produce 12-km simulations over the eastern U.S. In order to assess the model's performance, the 1.875⁰ NCEP–DOE Atmospheric Model Intercomparison Project Reanalysis-2 (R2) data is used as a proxy for a typically-coarse GCM, and the downscaled WRF output is compared with other observational or analyzed resources. In the control run referred to as “CTLR2”, LSTs and ice are set from the R2 dataset, where the Great Lakes are collectively represented by only three points. A second control run, CTLOb, is driven with high-resolution observations of ice from the National Ice Center and LSTs from the Advanced Very High Resolution Radiometer (AVHRR) dataset. CTLOb is a benchmark “best case scenario” run that demonstrates WRF's performance when analyzed products that are of an appropriate scale for use within a 12-km simulation are utilized. However, it does not provide guidance for dynamical downscaling, since those observational resources will not be available for future GCM projections. Finally, a version of WRF which is dynamically coupled to the Freshwater Lake (FLake) model is compared with the previously-described control runs. FLake is a 1D column model, consisting of a two-layer parametric representation of a time-varying temperature profile that includes a mixed layer and a thermocline extending down to a layer of thermally-active sediment. WRF-FLake's simulated LSTs and ice concentrations are compared to the NIC and AVHRR observations. Analysis of all three runs will focus on 2-m temperatures and rainfall, assessing what impact the choice of lake representation has on WRF's performance in an RCM setup over a two-year period.