Polar WRF Forecasts on the Arctic System Reanalysis Domain: Atmospheric Hydrologic Cycle

1. Polar WRF Forecasts on the Arctic System Reanalysis Domain: Atmospheric Hydrologic Cycle Workshop on Polar Simulations with the Weather Research and Forecasting (WRF) Model Aaron B. Wilson* , David H. Bromwich, and Keith M. Hines Polar Meteorology Group Byrd Polar Research Center The Ohio State University *wilson.1010@buckeyemail.osu.edu

2. Outline • Motivation • Model Configuration and Domain • Precipitation • Annual, Seasonal, Monthly • Major Arctic River Basins • Clouds • Shortwave and Longwave Radiation • Summary and Conclusions

3. Motivation • The Arctic System Reanalysis • Expansion of Polar WRF Development • Optimizing performance over the Arctic w/o penalty to other areas • Polar Frontier Project and outreach applications • Evaluation of Polar WRF short-term weather forecasts of the atmospheric hydrologic cycle as a compliment to the surface/upper air analysis • Wilson et al. 2011: JGR doi:10.1029/2010JD015013

4. Model Configuration • Polar WRF version 3.1.1 with 39 levels in the vertical • Top set at 10 hPa and lowest level centered at 8 m AGL • Physics • WRF Single moment 6-Class* • Grell-Devenyi 3-D Ensemble • RRTM Longwave* • Goddard Shortwave* • MYJ PBL • Noah Land Surface Model with Eta Similarity • Lower Boundary Conditions • SST: NCEP 0.5º RTG_SST Analysis • Sea Ice: Fractional Sea Ice • Bootstrap SSM/I 25 km from NSIDC • Seasonal transition of sea ice albedo • Fixed albedo winter and spring 0.82 • June: Linear decrease in albedo 0.5 by the end of the month representing a mix of bare ice and melt ponds • July: As ponds deepen and become less reflective albedo increases to 0.65 (representing bare ice only) • August 15: 21 day linear increase of albedo to 0.82

5. Model Domain and Input Data • Precipitation Data: GHCN, AHCCD, ERA-Interim • Clouds: NCDC, CloudSat/Calipso, MODIS • Radiation: BSRN, ARM, SPRS, FMI • 2-Way Nested Domain • Outer: 180km • Includes most of the NH • Inner: 60km • Includes major river basins flowing through the Arctic • Broad Scale Evaluation • Tractable • NCEP/NCAR 1 x 1 Final Analysis (6-hr) • Forecast mode: 48-hr simulations with hours 24-45 retained for analysis • 240 second time step with 3-hr output • Defined Polar and Mid-Latitude Sub Domains 60º N

6. Annual Precipitation: Spatial Comparison • Spatially consistent with ERA-Interim Reanalysis • Highest Precipitation totals located throughout the mid-latitudes and sub-polar storm track regions • Dry throughout the Canadian Archipelago • Slightly higher totals in Pacific NW N. America • Both PWRF and ERA-Interim are higher than GPCP here • Polar WRF shows great detail in higher terrain Polar WRF ERA-Interim GPCP

7. Annual Precipitation • Mid-Latitudes (305): +37.3 mm (+4.6%) • 62% within ±50% (35% within 25%) • NA, Europe, Asian regions: similar results • Polar (78): -58.8 mm (-9.4%) • 69% within ±50% (44% within 25%) • Few stations within 5% • NOT spatially homogenous • Horizontal resolution: a difficult obstacle in grid-point analysis • Large +/- biases in areas of complex terrain • Smooth effects of small scale circulations in area of complex terrain • The fjords of Norway • Canadian Archipelago • Dry throughout the entire year • Mean equatorward water-vapor transport (Serreze et al. 1995) • Winds are inconsistent (southerly)

8. Monthly Precipitation • Mid-Latitudes warm/cool season discrepancy • Cool months: Negative biases when precipitation synoptically driven • Large (+) Biases in Spring and Summer (Jun: 35.2%, Jul: 16.2%, Aug: 15.2%) • Warm months tied to convection • Polar • Negative throughout the year (-5% to -20%) • Positive bias only in July due to convection near stations in the southern part of boundary

9. Evaporation • Annual mean 2 m dew point temperature biases in the mid-latitudes led to an investigation of evaporation • ERA-Interim 2 m dew point biases are smaller compared to observations than Polar WRF • Total evaporation on land shows Polar WRF overpredicts evaporation for July compared to ERA-Interim especially for mid-latitudes • Some regions of the Arctic underpredicted and may help explain negative precipitation biases Polar WRF ERA-Interim

10. Convective Precipitation Sensitivity simulations for July 2007 • 3 Sensitivity Simulations (WRF6C, Morrison, Kain-Fritsch) • Little change in the overall total and convective precipitation (WRF6C, Morrison, Kain-Fritsch) • Grid-nudging of specific humidity towards a drier state in the lower atmosphere • yields negative precipitation bias (25% decrease) and ~1/2 convection • Other areas to investigate include soil moisture and interaction with PBL scheme

11. Important for the fresh water supply to the Arctic Ocean Headwaters begin as far south as 45ºN representing a strong link between mid-latitude atmospheric processes and effects in the Arctic. Arctic climate system and global ocean circulation Arctic River Basins Ob Yenisei Lena Mackenzie

12. Precipitation: Arctic River Basins 8 18 +80.9 mm (15%) +204.4 mm (57.5%) 13 5 -29.5 mm (-0.6%) +93.4 mm (24.2% • Convection in spring and summer for the Russian rivers lead to large summer biases • Mackenzie River biases related to smoothed terrain effects • Negative overall precipitation especially in late spring • Southern extent of the region influence by Gulf of Mexico through Great Plains low level jet and cyclogenesis • Single events of > 100 mm are observed (MAGS)

13. Diurnal 2 m Temperature Cycle • Larger model diurnal 2 m temperature range (i.e. warm day, cool night) suggests too little cloud cover / too thin • Affects other state variables… and must be seen in the cloud fractions and radiation

14. Cloud Fraction Biases • Estimated cloud fraction based on cloud liquid water and ice (Fogt and Bromwich 2008) • Converted 3-hr observed NCDC cloud categories to decimal value • January: • Positive biases in western Europe and NA associated with storm tracks • July: • Majority of stations reflect negative CF biases • Many stations have < -25% CF compared to observed

15. Clouds Continued • Model shows (+) CF associated with higher terrain perhaps too strongly • Storm tracks in N. Pacific and N. Atlantic depicted well • North Slope CF reasonably well matched with MODIS and CloudSat/Calipso • No increase in cloudiness adjacent to the coast • Only conservative method yields results that approach observed

16. Radiation Sites

17. Longwave and Shortwave Radiation Biases • Expands previous studies with two additional sites (Abisko and Sodankylä) • Compared with middle months of 4 seasons with July shown here • Negative Longwave Radiation Biases…most significant at 99%. • Positive Shortwave Radiation Biases… most significant at 99%. • Poor longwave correlations/ Good shortwave correlations.

18. July 2007: Mid-Latitudes • Fort Peck, Montana • Grassy flat location in the northern Great Plains • SW radiation often overpredicted by the model • LW radiation generally underpredicted throughout the month • Area shows 3ºC warm biases consistent with too much SW reaching the surface and a lack of radiative clouds for LW • Note the difference on 1st, 7th, 11th, and 25th

19. July 2007: Polar • Atqasuk, Alaska • Flat tundra on the North Slope of Alaska (~70 km away from the coast) • SW radiation also overpredicted by the model • LW radiation greatly underpredicted throughout the month • Note large differences around the 10th and 25th but LW also not as tied to SW

20. July 2007: Polar • Abisko, Sweden • Slightly sloping tundra on the south shore of Lake Abiskojaure with terrain SW increasing rapidly • SW radiation also overpredicted by the model but seems offset slightly by equal/opposite errors • Abisko experiences less cloudy conditions due to down-sloping effect from the higher terrain SW not well represented by model (14th, 17th, and 19th)

21. Cloud Water/ Cloud Ice • Scatter plots of Model LW vs. Observed Longwave for various model cloud species • (a) Cloud water and/or cloud ice available • (b) No Cloud water or ice • (c) Cloud water regardless of cloud ice • (d) Cloud ice only • Mid-latitudes • LW correlations are strong for all 4 cases • When cloud water or ice is available, model biases are negative • “Model Clear Sky”: Correlations increase and model agrees better with observations • Again, when cloud water is present (c) the model performs worse (Cloud ice has a zero effect on switch in RRTM scheme) • Polar Region • Model LW suffers greatly compared to observations • Apparent insensitivity between cloud water/cloud ice conditions and “clear sky”

22. Shortwave and Longwave BiasesRevisited…ASR style. • LESS Negative Longwave Radiation Bias: Many still significant • LESS Positive Shortwave Radiation: Fewer significant differences • Improved Longwave correlations/ Mixed Shortwave correlations.

23. July 2007: Polar

24. Summary and Future Work • Model • Precipitation • Spatially consistent with ERA-Interim Reanalysis and GPCP • Small Annual (+) Biases in Mid-Latitude, Larger Annual (-) Polar Biases • Large (+) spring and summer biases tied to convection including Russian sector rivers • Related to high evaporation and a moist lower boundary layer • Cloud Fraction • Appears too low based on cloud water and cloud ice calculation • Cloud frequency technique compares better to MODIS and CloudSat/Calipso discrepancies still exist • Radiation • Significant (+) SW Down Biases and (-) LW Biases • Despite cloud water in the model, LW biases are still negative • Insensitivity to cloud water/cloud ice in the polar region (Perhaps biggest concern needed to address in the future) • ASR • Precipitation • Grid Nudging specific humidity decreases convection • Better constrained moisture field in the boundary layer should improve performance • ASR Precipitation needs to be analyzed • Radiation • Improvements in biases for ASR in both SW and LW radiation

25. Thank You! NSF IPY Grant ARC-0733023 Columbus Zoo and Aquarium