EPA Science Inventory

"The Effect of Alternative Representations of Lake Temperatures and Ice on WRF Regional Climate Simulations"


Mallard, M., Chris Nolte, R. Bullock, T. Otte, J. Herwehe, Kiran Alapaty, AND J. Gula. "The Effect of Alternative Representations of Lake Temperatures and Ice on WRF Regional Climate Simulations". Presented at 12th Annual CMAS Conference, Chapel Hill, NC, October 28 - 30, 2013.


Lakes can play a significant role in regional climate, modulating inland extremes in temperature and enhancing precipitation. Representing these effects becomes more important as regional climate modeling (RCM) efforts focus on simulating smaller scales. When using the Weather Research and Forecasting (WRF) model to downscale future global climate model (GCM) projections into RCM simulations, model users typically must rely on the GCM to represent temperatures at all water points. However, GCMs have insufficient resolution to adequately represent even large inland lakes, such as the Great Lakes. Some interpolation methods, such as setting lake surface temperatures (LSTs) equal to the nearest water point, can result in inland lake temperatures being set from sea surface temperatures (SSTs) that are hundreds of km away. In other cases, a single point is tasked with representing multiple large, heterogeneous lakes. Similar consequences can result from interpolating ice from GCMs to inland lake points, resulting in lakes as large as Lake Superior freezing completely in the space of a single timestep. The use of a computationally-efficient inland lake model can improve RCM simulations where the input data is too coarse to adequately represent inland lake temperatures and ice (Gula and Peltier 2012). This study examines three scenarios under which ice and LSTs can be set within the WRF model when applied as an RCM to produce 2-year simulations at 12 km grid spacing. 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. This first control run (CTL-R2) represents the usual performance when the GCM is tasked with representing ice and LSTs. The second control run (CTL-Ob) is driven with high-resolution observations of ice from the National Ice Center and lake surface temperatures from the Advanced Very High Resolution Radiometer (AVHRR) dataset. This run is a “best case scenario”, where available products that are appropriate for use with a 12-km grid are utilized. However, such an option is not actually available when producing future simulations. Therefore, CTL-Ob is a benchmark for the performance of the WRF model when LSTs and ice are well-represented, but does not provide guidance on choosing a preferred RCM setup for future simulations. The final run utilizes a version of WRF that is dynamically coupled with the Freshwater Lake (FLake) model, providing simulated LSTs and ice concentrations. 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. Evaluation of these 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.


The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.



Record Details:

Start Date: 07/10/2014
Completion Date: 07/10/2014
Record Last Revised: 07/15/2014
Record Created: 07/10/2014
Record Released: 07/10/2014
OMB Category: Other
Record ID: 280782