Arctic System Reanalysis - JRA project

3rd WCRP International Reanalysis Conference Tokyo, Japan

Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University, Columbus, Ohio

David H. Bromwich1,2, Keith M. Hines1

and Le-Sheng Bai1

1Polar Meteorology Group, Byrd Polar Research Center2Atmospheric Sciences Program, Dept. of Geography

The Ohio State UniversityColumbus, Ohio

Arctic System Reanalysis*

*Supported by NSF and NOAA



OutlineArctic System Reanalysis: Why, how and who?

Polar WRF Development at Ohio State

Polar WRF vs. AWS and Polar MM5Greenland: Dec. 2002 + June 2001

SHEBA 1997/98

Arctic land in progress

• Atmospheric Data Assimilation at NCAR

• Noah Land Surface Modeling at NCAR

• Summary

Arctic System Reanalysis Arctic System Reanalysis MotivationMotivation1. Rapid climate change is happening in the Arctic, as

illustrated by the all-time minimum of summer sea ice extent in September 2007. A comprehensive picture of the climate interactions is needed.

2. Global reanalyses encounter many problems at high latitudes. The ASR would use the best available depiction of Arctic processes with improved temporal resolution and much higher spatial resolution.

3. The ASR would provide fields for which direct observation aresparse or problematic (precipitation, radiation, cloud, ...) at higher resolution than from existing reanalyses.

4. A system-oriented approach would provide community focus with the atmosphere, land surface and sea ice communities.

5. The ASR would provide a convenient synthesis of Arctic field programs (SHEBA, LAII/ATLAS, ARM, ...).

ASR OutlineA physically-consistent integration of Arctic data,

including enhanced observations of the Sustained Arctic Observing Network (SAON)

Participants:Ohio State University - Byrd Polar Research Center (BPRC)

- and Ohio Supercomputer Center (OSC)National Center Atmospheric Research (NCAR)University of ColoradoUniversity of IllinoisUniversity of Alaska Fairbanks

High resolution in space (~15 km) and time (3 hours)

Begin with years 2000-2010 (EOS coverage)

Supported by NSF as an IPY project

ASR Duty RosterPolar WRF Model Development and Optimized Sea Ice Representation

OSU BPRC Polar Meteorology Group, PI

Mesoscale Atmospheric Data Assimilation

NCAR MMM (D. Barker + Y.-H. Kuo)

Land Surface Treatment and Data Assimilation

NCAR (F. Chen, developer of the Noah LSM)

University of Colorado (M. Serreze)

Data Ingest, Data Monitoring, and Quality Control

University of Illinois (J. Walsh) and U. Colorado

Computing

Ohio Supercomputer Center

Arctic Regions Supercomputer Center?

Reanalysis Distribution to the Community

U. Illinois/NOAA CDC?/NCAR?



ASR High Resolution Domain

Outer Grid: ~45 km resolution

Inner Grid: ~15 km resolution

Vertical Grid: ~60 levels

Inner Grid includes Arctic river basins



Pressure at SHEBA Camp and Barrow January 1998

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PWRF 2.3 SHEBAPWRF 2.3 Barrow, AlaskaSHEBA ObservationsBarrow, Alaska Observations

ASR Numerical Model: Polar WRFWeather Research and Forecasting Model

SHEBA 1997/8 Grid

January 1998 SHEBA Results

Polar Optimization at Ohio State:Fractional sea iceSea ice albedoMorrison microphysics (2-moment)Noah LSM modificationsHeat transfer through snow and ice



2 m Temperature at Swiss Camp and Summit

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Correlation 0.84

Bias -2.3

RMSE 5.6

Polar WRF

Noah + MYJ + WSM5

Correlation 0.80

Bias 3.0

RMSE 6.0

Polar MM5

Correlation 0.87

Bias 2.5

RMSE 3.1

Polar WRF

Correlation 0.85

Bias 1.5

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Summit


Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University, Columbus, Ohio Incident Longwave Radiation at Summit June 2001

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Incident Longwave Radiation at Summit June 2001

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Incident Shortwave Radiation Summit June 2001

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SHEBA Location (from Perovich et al. 2007)

Test Polar WRF for Arctic Ocean/sea ice with selected SHEBA case studies (1997/1998)



Figure 7. Surface pressure (hPa) from observations and Polar WRF at Ice Station SHEBA for January 1998, June 1998, and August 1998

August Surface Pressure at Ice Station SHEBA

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June Surface Pressure at Ice Station SHEBA

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January Surface Pressure at Ice Station SHEBA

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Dale BarkerNCAR MMM



WRF-Var Observations for ASR WRF-Var Observations for ASR In-Situ:

- Surface (SYNOP, METAR, SHIP, BUOY).- Upper air (TEMP, PIBAL, AIREP, ACARS).

Remotely sensed retrievals:- Atmospheric Motion Vectors (e.g. MODIS).- Ground-based GPS Total Precipitable Water.- SSM/I oceanic surface wind speed and TPW.- Scatterometer oceanic surface winds.- Wind Profiler.- Radar radial velocities and reflectivities.- Satellite temperature/humidities (e.g. TOVS, AIRS?).- GPS refractivity (e.g. COSMIC).

Radiance Assimilation:- Microwave: AMSU, SSM/I, SSMI/S(?)- Infrared: HIRS, AIRS(?), IASI(?).



WRF-Var Radiance Assimilation StatusWRF-Var Radiance Assimilation StatusBUFR 1b radiance ingest.

RTM interface: RTTOV8_5 or CRTM

NESDIS microwave surface emissivity model

Range of monitoring diagnostics.

Quality Control for HIRS, AMSU, AIRS, SSMI/S.

Bias Correction (Adaptive, Variational in 2008)

Variational observation error tuning

Parallel: MPI

Flexible design to easily add new satellite sensors

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

DMSP(SSMI/S)

Aqua (AMSU, AIRS)

NOAA (HIRS, AMSU)



DATC Antarctic TestbedDATC Antarctic Testbed

Testbed Configuration (from MMM/AMPS):Model: WRF-ARW, WRF-Var (version 2.2).Namelists: 60 km (165x217), 31 levels, 240 s timestep.Period: October 2006.Suite: NoDA, 3D-Var (6-hourly full cycling).

SondeCoverage

COSMIC Coverage

Hui Shao, DATC



Land Component for Arctic System Reanalysis

Land Component for Arctic System Reanalysis

Fei Chen and Michael Barlage Research Applications Laboratory (RAL)

The Institute for Integrative and Multidisciplinary Earth Studies (TIIMES)National Center for Atmospheric Research



High-Resolution Land Data Assimilation System (HRLDAS) for ASR

High-Resolution Land Data Assimilation System (HRLDAS) for ASR

Blending atmospheric and land-surface observations and land surface modelTo provide land state variables for driving the coupled Polar WRF/Noah modeling system– Soil moisture (liquid and solid phase) – Soil temperature– Snow water equivalent and depth– Canopy water content – Vegetation characteristics

To provide long-term evolution of the above variables plus surface hydrological cycle (runoff, evaporation) and energy cycle (surface heat flux, ground heat flux, upward long-wave radiation)



ASR Land Modeling TimelineASR Land Modeling Timeline

2000 2010

HRLDAS and WRF coupled simulations

Blended WRF Input to HRLDAS

1hr 1hr

Blended Hourly Forcing DataWRF: T,q,U,SW,LWCMAP: precipitationGDAS: snow, SW, LWAir Force: snowGLDAS: SW, LW

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3hrHRLDAS communicates to WRF

Improved Land Surface StatesSnowSoil Moisture/TemperatureLand Surface Temperature



WRF domainWRF domain• 600 x 600 cells• 20 km• polar projection

• ref_lat = 90• ref_lon = 0• truelat = 70• stand_lon = -110



Summary of ASR StatusSummary of ASR StatusASR grew out of Antarctic NWP. Development of enhanced components are proceeding, and will soon be merged. Coupled atmosphere-land DA, but not atmosphere-ocean. Arctic ocean DA being done by others that offers the prospect of enhanced ocean conditions (e.g., sea ice thickness).WRF (and Noah LSM) physics are being optimized for polar applications beginning with Greenland and Arctic Ocean domains. Arctic land is next.Atmospheric data assimilation advances at NCAR. Start with 3DVAR, but transition to 4DVAR or EnKF anticipated.HRLDAS will provide high-resolution land surface variables on the same grid as WRF-3DVAR.Timeline: Completion of 2000-2010 by 2011. Second phase is anticipated to cover 1958-present in a climate monitoring capacity with major NOAA participation likely.

Documents

Arctic System Reanalysis - JRA project