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 *[email protected]
25
Embed
Polar WRF Forecasts on the Arctic System Reanalysis Domain: Atmospheric Hydrologic Cycle Workshop on Polar Simulations with the Weather Research and Forecasting.
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
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 GroupByrd Polar Research CenterThe Ohio State University
(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
Sensitivity simulations for July 2007
Arctic River Basins
• 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
ObYenisei
LenaMackenzie
Precipitation: Arctic River Basins
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)
8 18
5 13
+80.9 mm (15%)
+204.4 mm (57.5%)
+93.4 mm (24.2% -29.5 mm (-0.6%)
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
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
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
Radiation Sites
Longwave and Shortwave Radiation Biases
July 2007
Longwave W m--2 Shortwave W m-2
NAME OBS BIAS RMSD CORR NUM OBS BIAS RMSD CORR NUM
Expands previous studies with two additional sites (Abisko and Sodankylä)
Compared with middle months of 4 seasons with July shown hereNegative Longwave Radiation Biases…most significant at 99%.Positive Shortwave Radiation Biases… most significant at 99%.Poor longwave correlations/ Good shortwave correlations.
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)
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”
Shortwave and Longwave BiasesRevisited…ASR style.
• LESS Negative Longwave Radiation Bias: Many still significant• LESS Positive Shortwave Radiation: Fewer significant
– 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