The Effect of Alternative Representations of Lake Temperatures and Ice on WRF Regional Climate Simulations Megan Mallard 1, Chris Nolte 1, Russ Bullock 1, Tanya Otte 1, Jerry Herwehe 1, Kiran Alapaty 1, and Jonathan Gula 2 1 US EPA National Exposure Research Laboratory, RTP, NC 2 University of California, Los Angeles 1. Motivation: Lake Surface Properties Downscaled from a GCM Larger inland lakes exert significant influence over regional climate, modifying overlying air masses including air pollutants through fluxes of sensible and latent heat, enhancing precipitation (lake-effect snow) and damping extremes in temperature. Regional climate models (RCMs) must have an adequate representation of lake surface temperature (LST) and ice coverage in order to accurately simulate future changes in precipitation and temperature extremes. Interaction with the atmosphere is dependent on the timing and extent of ice cover, both of which are affected by climate change (e.g., Gula et al. 2012). Within the Weather Research and Forecasting (WRF) model, sea surface temperatures (SSTs) and ice coverage are prescribed inputs and are not prognostic variables. 3. Experimental Design 4a. Lake Surface Temperatures (LSTs) 4b. Ice Coverage 5. Summary 6. Future Work References Appel, K. W., R. C. Gilliam, N. Davis, A. Zubrow, and S. C. Howard, 2011: Overview of the atmospheric model evaluation tool (AMET) v1.1 for evaluating meteorological and air quality models. Env. Modelling & Software, 26, Gula, J. and W. R. Peltier, 2012: Dynamical downscaling over the Great Lakes basin in North America using the WRF regional climate model: The impact of the Great Lakes system on regional greenhouse warming. J. Climate, 25, Kourzeneva, E., 2009: Global dataset for the parameterization of lakes in numerical weather prediction and climate modeling. ALADIN Newsletter, 37, July-December, 2009, F. Boutier and C. Fischer, Eds., Meteo-France, Toulouse, France, Mironov, D., 2008: Parameterization of lakes in numerical weather prediction. Description of a lake model. COSMO Technical Report, No. 11, Deutscher Wetterdienst, Offenbach am Main, Germany, 41 pp. Otte, T. L., C. G. Nolte, M. J. Otte, and J. H. Bowden 2012: Does nudging squelch the extremes in regional climate modeling? J. Climate, 25, Freshwater Lake (FLake) model 2-layer column model; only required inputs are depth of lake and initial LST. Accounts for wind-driven & convective mixing, as well as solar heating of the water column. Computationally efficient, but is reliant on empirical relationships, some of which break down for large, deep lakes. Driving fields needed from meteorological data are: 2-m temperature & specific humidity,10-m winds, SW and LW radiation at surface. Gula & Peltier (2012) ran WRF simulations driven by “offline” FLake. Driving meteorological data was provided to FLake by coarser dataset being downscaled. In this study, dynamically coupled WRF-FLake is used. WRF passes variables to FLake at every timestep for each lake point and WRF is updated with FLake-generated lake surface temperature and ice thickness values. Ice concentration set to 100% where thickness > 0. Offline FLake is run for a 10-year lake spin-up period and provides initial conditions for the dynamically coupled model. Lake depths needed for use of coupled WRF-FLake, taken from Global Lakes Dataset (Kourzeneva 2009) The interpolation of coarse water temperatures and ice cover for inland lakes from R2, here used as a proxy for a GCM, is found to result in cool biases in LSTs and 2-m temperatures, as well as unrealistic ice coverage and skin temperature gradients. The use of a coupled lake model, WRF-FLake, produces realistic formation of ice cover, improved 2-m temperatures, and more accurate LSTs in smaller and shallower lakes. All runs are prone to producing too much monthly precipitation, but strong warming in FLake-simulated LSTs leads to a more dramatic wet bias in some months. Daily-average lake surface temperatures (K) from all 3 runs, spatially averaged over Lakes Superior (top) & Erie (bottom). As found in similar RCM studies using FLake, it performs best for small, shallow lakes (like Erie). In large, deep lakes (like Superior), FLake warms too early & strongly in spring, compared with AVHRR data. Overall, LSTs simulated by FLake exaggerate the annual cycle with warm (cool) biases in summer (winter). Cool biases are maximized at the edges of lakes where thicker layers of ice are present. When ice coverage is enhanced in 2007, cool biases spike downward during winter. LSTs interpolated from GCM proxy in CTLR2 are consistently too cool in both lakes throughout both years. When downscaling future global climate model (GCM) projections, water surface temperatures & ice cover are taken from datasets with very coarse landsea masks. Interpolation methods in WRF result in 1)SSTs taken from the Atlantic Ocean are used to set LSTs in easternmost Great Lakes, Erie and Ontario 2)Unrealistically sharp temperature gradients 3)Sporadic, widespread ice coverage of large, deep lakes. Simulations using 108- & 36-km grid spacing previously performed over the continental U.S. from by downscaling Reanalysis 2 (R2) data using WRF (V3.2.1), as described in Otte et al. (2012)  x  R2 data used as a proxy for coarse GCM input to test WRF as an RCM. Current goal: Further downscaling retrospectively to 12 km grid spacing 36-km runs used as input to drive 12-km simulations. WRF (V3.4.1) with 34 layers, model top at 50 hPa. The model physics options include: WSM6 microphysics RRTMG longwave and shortwave radiation Noah land-surface model YSU planetary boundary layer scheme Grell G3 convective scheme Spectral nudging towards R2 Simulations from 1 Nov to 1 Dec. 2007, covering relatively low and high years for Great Lakes ice cover & allowing a 30 day spin-up. Simulations compared here are: CTLObs: Benchmark run with 0.25 ⁰ LSTs taken from the Advanced Very High Resolution Radiometer (AVHRR) dataset produced by the Group for High-Resolution SST (GHRSST) & fractional lake ice concentrations from 2.5 km National Ice Center's (NIC) Great Lakes Ice Analysis charts. Ocean temperatures & seaice are set from R2. This setup is not possible for future downscaling applications, but is a benchmark for how well WRF can perform with finer- scale lake data. CLTR2: LSTs and ice taken from R2 data, as shown above. WRF-FLake: LSTs and ice simulated by FLake. Daily-average ice concentrations (%) from 2006 (left) & 2007 (right) for Lakes Superior (top) & Erie (bottom). Here, the NIC ice observations have been converted from a fractional dataset to binary using either the > 50% or > 0 thresholds, in order to be consistent with FLake’s treatment of ice. 4c. 2-m Temperatures 4d. Monthly Total Precipitation All 3 runs perform poorly at simulating monthly total precipitation over the Great Lakes basin. Even the CTLOb run, where WRF is driven with higher-resolution LSTs & ice, has a pronounced wet bias. Warm biases in WRF-FLake provide surface heating for additional convection & precipitation. Even in months where basin-averages are similar, the locations of maxima in the lee of the lakes differ. MAE (K) averaged during summer 2006 for the CTLR2 (left) & WRF- FLake (right) simulations. Seasonally-averaged temperature biases (K) from all 3 runs, spatially averaged in the Great Lakes basin (pictured below). Bias is computed against hourly observations from the NOAA Meteorological Assimilation Data Ingest System (MADIS). The Atmospheric Model Evaluation Tool (AMET) is used to pair up these point observations with the nearest model grid point (Appel et al. 2011). 2-m temperature bias and mean absolute error (MAE) are smallest for WRF-FLake & largest for CTLR2, if averaged over the entire 2-year simulation. Summertime biases are reduced for WRF- FLake, even relative to CTLOb. As WRF- FLake LSTs are warmer than observed during this period for some lakes, this suggests compensating errors in the coupled model run at locations surrounding large, deep lakes. Alternatively, CTLR2’s cool LSTs reinforce WRF’s propensity for producing 2-m temperatures that are too cold. 2-m temperatures are most dramatically improved in areas adjacent to the lakes in all seasons. Overall, inclusion of FLake model improves WRF’s simulation of 2-m temperatures. CTLR2 WRF-FLake Mironov, COSMO Tech Report Mixed layer Thermocline Illustration of the mixed layer and thermocline composing FLake’s 1 st layer, plotted as temperature ( θ) vs. depth (h). Beneath is a layer of thermally-active sediment. A parameterization of the temperature profile in ice is activated when ice forms. Superior Huron Michigan Erie Ontario Bias (K) in WRF-FLake averaged over winter WRF-FLake correctly captures the increase in ice coverage from 2006 to Peak ice coverage is overestimated in Lake Superior, but underestimated in Erie. Overall, WRF-FLake performs well at simulating ice coverage, especially compared with the lack of ice cover in CTLR2. Monthly total precipitation averaged over Great Lakes basin from all 3 simulations, plotted with the Univ. of Delaware 0.5 ⁰ data (interpolated to 12 km). Further comparison of simulated snow and rainfall with other observational or analyzed datasets. Comparison of open-water temperatures between WRF-FLake & CTLOb to assess whether ice parameterization is responsible for large error in WRF-FLake wintertime LSTs ⁰ landmask 12 km domain landmask Interpolated Ice Cover Lake Superior LST Interpolated Skin Temperature Lake Erie LST Lake Superior 2006 Ice Lake Erie 2006 IceLake Erie 2007 Ice Lake Superior 2007 Ice MAE & bias (K) averaged over the 2-year simulations.