Using a Freshwater Lake Model Coupled with WRF for Dynamical Downscaling Applications Megan Mallard 1, Chris Nolte 1, Russ Bullock 1, Tanya Spero 1 and Jonathan Gula 2 1 Atmospheric Modeling and Analysis Division, U.S. Environmental Protection Agency 2 University of California, Los Angeles

Climate Change and the Great Lakes 1 Lakes important to regional climates because they provide fluxes of heat and moisture to overlying air masses. Great Lakes lake surface temperatures (LSTs) are projected to warm more strongly than inland temperatures (Austin & Colman 2007). Decreasing ice coverage linked with amplification of warming signal and extension of lake-effect snow season (Gula & Peltier 2012). Difficult to project regional affects of climate change on surface temperatures & precipitation without realistic treatment of future changes in ice & LSTs. 1

– Goal: Evaluation of dynamical downscaling methods, using a global reanalysis, for the purpose of downscaling future GCM projections. NCEP-DOE AMIP-II Reanalysis (R2), ∆x = ⁰ at equator Spurious and unrealistic lower boundary conditions over lakes, w.r.t. LSTs & ice, with default interpolation methods in WRF. Skin temperatures & ice interpolated from R2 to 12- km domain Valid 9 Jan 2007 Large ∆T in LST & lake-land temps Lakes Superior & Michigan freeze over abruptly. No ice elsewhere. Superior Huron Michigan Erie Ontario 2

3 pts in R2 data used to set LSTs in Superior, Michigan, Huron & most of Erie. In Lake Ontario, east end of Erie, and several lakes in southeast, closest oceanic water temperature used. Deep lakes abruptly freeze over because single R2 point represents large sections of lake. R2 landmask over 12-km domain 3 12-km landmask

WRF Configuration WRF-ARW with spectral nudging (towards R2) Initial & lateral boundary conditions from 36-km runs All runs are initialized at 1 Nov 2005 and run until 1 Dec 2007 with 30 day spin-up 34 vertical levels, model top at 50 hPa Physics –WSM6 microphysics –RRTMG longwave and shortwave radiation –Noah land-surface model –YSU planetary boundary layer scheme –Grell G3 convective scheme 4

Simulations CTLR2: Control run where R2 interpolation is used. CTLObs: “Benchmark” control run where higher- resolution obs are used in lakes only. –0.25 ⁰ SST dataset produced from AVHRR by the Group for High-Resolution Sea Surface Temperature (GHRSST) –National Ice Center (NIC) 2.5-km Great Lakes Ice Analysis charts WRF-FLake (WF): WRF coupled with the Freshwater Lake (FLake) model. 5

6 Freshwater Lake (FLake) model 6 WRF 2-m Temperature & Humidity 10-m Wind Speed Downward SW & LW radiation at sfc Lake depth FLake Lake Surface Temperature Ice thickness (H) Mironov, COSMO Tech Report Mixed layer Thermocline 1D column model highly reliant on empirical relationships. 2 layer model. Also simulates profile of temperature in ice layer. WRF-FLake is computationally efficient, easy to use, portable to future WRF versions. Little information needed about future lakes. Z ↑ Temperature → If H > 0, ice = 100%

Daily LSTs CLTR2 shows consistent cool bias. WF exaggerates annual cycle w/ early spring warming & warm bias in summer. Worst performance for large, deep lakes. Best for small, shallow lakes. –Found in prior FLake studies (Martynov et al. ‘10; Samuelsson et al. ‘10). 7 Problem: GHRSST has fixed minimum value, even at ice points. Solution: Use GHRSST for analysis of open water pts only & MODIS-derived temperatures over ice.

LST Error: Open Water 8 CTLR2 points are consistently too cool. WF bias maximized (minimized) in summer (winter) months. Use of WF reduces MAE in 4 of 5 Great Lakes. 8 Mean Absolute Error (MAE) CTLR2WFWF-CTL All Great Lakes Lake Superior Lake Erie MAE (left) and bias (right) in daily-average LSTs [K] for CTLR2 & WF, taken against GHRSST.

LST Error: Ice 9 WF bias tends to be warm (cool) in early (late) winter. Error at ice points not evaluated for CLTR2 because few days have any ice at all. 9 MAE WF All Great Lakes3.22 Lake Superior3.63 Lake Erie2.44 Simulation-average MAE (left, K) & bias (right) in daily-average LSTs for CTLR2 & WF, taken against MODIS temperatures.

Lake Ice Ice cover in WRF-FLake is significantly more realistic than in CTLR2, where Superior & Michigan freeze almost completely for only 3 days in a span 2 years. WRF-FLake simulates increase in ice from 2006 to CTLR2 & WF compared with the NIC data converted from fractional to binary either using fraction > 0% → 100% threshold (black solid) or > 50% → 100% (dashed) 10

Lake Ice Over predicts ice coverage in Superior, under predicts in Erie. Overall spatial extent of ice cover in WRF-FLake well simulated. Average winter ice cover from fractional NIC obs (top) & WRF-FLake (bottom) 11

2-m Temperature Error taken against NOAA Meteorological Assimilation Data Ingest System (MADIS) observations. Use of WF results in reduced MAE in 2-m temperatures. –Lowers bias by 0.4 K, relative to CTLR2. MAE (K) averaged during summer, 2006 CTLR2 12 CTLOb CTLR2 WF

2-m Temperature Error taken against NOAA Meteorological Assimilation Data Ingest System (MADIS) observations. Use of WF results in reduced MAE in 2-m temperatures. –Lowers bias by 0.4 K, relative to CTLR2. MAE (K) averaged during summer, WRF-FLake CTLOb CTLR2 WF

Precipitation All runs have too much precipitation, even CTLOb. CTLR2 has lowest wet bias, despite poor representation of lakes. Compensating error? Monthly precipitation [mm/day] for all runs plotted with Univ. of Delaware observed precip (dashed). 14 Season-averaged precipitation differences from 2006 & 2007, WF-CTLR2

Seasonal changes 15 2-m temperature (solid, w/ 10-pt smoother) & LST (dashed) averaged in Great Lakes basin. Gray background indicates the climatological lake effect precipitation season. Summer: Cool LSTs typically stabilize airmass. CTLR2 has largest lake- atmosphere contrast in temperatures. Fall Start of lake unstable season, warmer LSTs expected to promote convection. CTLR2 has little contrast. Reduction of wet bias in CTLR2 caused by cooler LSTs providing additional stabilizing influence. CTLOb CTLR2 WF D F A J A O D F A J A O D

Conclusions Use of WRF-FLake results in significant improvement of lake ice & LSTs, relative to interpolation from a coarse proxy-GCM (R2). This improvement in the representation of the Great Lakes results in: –Reduction in 2-m temperature errors –Increase in existing wet bias in monthly precipitation. Control run with erroneously cool LSTs suppresses lake effect precipitation and has lowest wet bias. 16

Select References Mallard, M. S., C. G. Nolte, O. R. Bullock, T. Otte, J. Gula, 2014: Using a coupled lake model with WRF for dynamical downscaling. J. Geophys. Res., In preparation. Gula, J. and W.R. Peltier, 2012: Dynamical Downscaling over the Great Lakes basin of North America using the WRF regional climate model: The impact of the Great Lakes system on regional greenhouse warming. J. Climate. 17

Extra Q & A slides 18

Prognostic variables include mixed layer depth, top & bottom temperatures of each layer, & shape functions for profiles in between. Bulk energy budget for each layer Processes simulated include –Volumetric radiative heating –Convective mixing –Mechanical mixing No 2D processes simulated (e.g., advection, currents) Ice & snow layers have linear temperature profile Accounts for molecular conduction of heat from air or water, & radiative heating FLake parameterization Mixed layer Thermocline Thermally-active sediment Ice Snow