assessment of the polar wrf representation of near-surface...
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Assessment of the Polar WRF representation of near-surface meteorological variables over West
Antarctica
PranabDebAndrewOrr,Sco-Hosking,JohnTurner
The10thAntarc0cMeteorologicalObserva0on,Modeling,andForecas0ngWorkshopSco$PolarResearchIns0tute,Cambridge,UK
17June2015—19June2015
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West Antarctic grant
Setting up a high resolution climate model over West Antarctica.
Produce hindcast over the study domain since 1979 and identify main drivers of the West Antarctic climate.
Future projections for the next century using our model – forcing with CMIP5 products.
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Why West Antarctica?
• West Antarctica is one of the fastest warming regions on Earth - annually averaged near-surface warming rate of ~0.5 °C per decade at Byrd Station (Bromwich et al., 2013) Byrd
• West Antarctica ice sheet contains enough ice to raise the sea level by ~5 m (Turner and Marshall, 2011)
• acceleration and thinning of ice streams/glaciers at the edge of WAIS (Rignot, 2008) is responsible for 10% of the global sea-level rise (Church and White, 2011)
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Challenges in understanding the West Antarctic climate
Long term weather records are limited
due to remote and hostile environment
Coarse resolution and oversimplified model physics:
reanalysis dataset is less reliable in complex coastal
areas (Turner et al., 2009; Orr et al., 2014)
Correct spatial and temporal variability of air temperature and precipitation is still lacking
– important for surface mass balance
Byrd
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Need for “high resolution” climate models
West Antarctica is characterised by the high spatial variability of the complex orography, coastline, and albedo - winds, temperature and precipitation are highly sensitive to these (Orretal.,2005,2014;Valkonenetal.,2010)
In addition, mesocyclones are prominent in the Amundsen and Bellingshausen Seas (Irving et al., 2010) and has strong control over local wind, precipitation and air temperature
High-resolution regional atmospheric model can be used to dynamically downscale the global reanalysis data (Giorgi et al., 1994)
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Model configuration “Standard” atmosphere-only Polar WRF
with one-way nesting 30 vertical levels with model top at 50 hPa
30-day runs for January
& July 2013
45 km 15 km
5 km 5 km
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Model configuration “Standard” atmosphere-only Polar WRF
with one-way nesting 30 vertical levels with model top at 50 hPa
30-day runs for January
& July 2013
45 km
15 km
5 km
5 km
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Kominko Slade - January
• Accurate representation of pressure, wind speed and wind direction
• Amplified diurnal cycle with larger night time bias
Surface pressure
Wind speed
Wind direction
Air temp @2 m
Modelskilloverinlandsta0ons
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Kominko Slade - July
Surface pressure
Wind speed
Wind direction
Air temp @2 m
• Accurate representation of pressure, wind and temperature
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BEAR PENINSULA- January
• Consistent positive bias in surface pressure
• Reasonable representation of wind
• Amplified diurnal cycle with larger night time bias
Air temp @2 m
Wind direction
Wind speed
Surface pressure
Modelskillovercoastalsta0ons
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BEAR PENINSULA- July
• Strong wind events captured effectively during the second half of July
• Overestimation of wind speed between 4th and 9th July
• Cold bias due to bias in wind direction
Air temp @2 m
Wind direction
Wind speed
Surface pressure
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BEAR PENINSULA- July
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ERA interim IR imagery
0000 UTC 05-07-2013
(source:UniversityofWisconsin‐MadisonAntarcIcMeteorologicalResearchCenter)
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ERA interim Standard Polar WRF run
0000 UTC 05-07-2013
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ERA interim Run with spectral nudging
0000 UTC 05-07-2013
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Wind speed (m/s): BP (July)
ERA interim Run E
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BEAR PENINSULA- JULY
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ERA interim Standard Polar WRF run
0000 UTC 22-07-2013
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Labels Descrip0on RunA BasicrunwithERAinterimforcing,USGStopographydataandNoahLSM
RunB RunA+highresoluIonbootstrapseaicedatafromNSIDC+highresoluIonSSTdatafromAVHRR
RunC RunB+Bedmap2topographydata
RunD
RunE
RunC+NoahMPLSM
RunA+spectralnudgingabovePBL
Sensi0vityStudies
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Correlation
Bias
RMSE
JANUARY JULY
Green: Surf. Pressure Red: wind speed Blue: Air temp
BP: EK:
KS: BY:
coastal
inland
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• Surface pressure shows the best skill, followed by air temperature and wind; comparatively better performance seen over ‘homogeneous’ inland stations than ‘complex’ coastal stations
• Cyclones simulated reasonably well, except a few instances when the interaction of coastal topography and cyclone introduces large errors, nudging improves the results
• Realistic sea ice & SST and Bedmap2 has an overall positive impact on model statistics, Noah MP improves temperature simulation in coastal regions
Conclusions
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Conclusions from the sensitivity studies:
• realistic sea ice /SST improves results in winter
• Bedmap2 improves model statistics in all seasons, but the effect on wind is variable
• Noah MP improves air temperature simulation for coastal stations but worsens for inland stations
• Nudging has a significant positive impact on all parameters
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Thankyouforlistening!
Acknowledgements:
Keith Hines Ohio State University
Steve Colwell, Tony Phillips, Daniel Banister British Antarctic Survey
University of Wisconsin-Madison Automatic Weather Station Program
University of Wisconsin-Madison Antarctic Meteorological Research Center