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Ken Mitchell Jesse Meng, Rongqian Yang, Helin Wei, George Gayno

Ken Mitchell Jesse Meng, Rongqian Yang, Helin Wei, George Gayno. NCEP Environmental Modeling Center (EMC). The Land Model and Land Assimilation of the CFS Reanalysis and Reforecast (CFSRR). Assistance from other EMC members: Suru Saha, Shrinivas Moorthi, Cathy Thiaw.

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Ken Mitchell Jesse Meng, Rongqian Yang, Helin Wei, George Gayno

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  1. Ken Mitchell Jesse Meng, Rongqian Yang, Helin Wei, George Gayno NCEP Environmental Modeling Center (EMC) The Land Model and Land Assimilation of the CFS Reanalysis and Reforecast (CFSRR) Assistance from other EMC members: Suru Saha, Shrinivas Moorthi, Cathy Thiaw • Land Assimilation Collaborators: • NASA GSFC Hydrological Sciences Branch CFSRR Advisory Board Meeting 07-08 November 2007

  2. ONE DAY OF REANALYSIS: Note daily GLDAS (spans prior 24-hrs) 12Z GSI 18Z GSI 0Z GSI 6Z GSI 0Z GLDAS 12Z GODAS 18Z GODAS 0Z GODAS 6Z GODAS 9-hr coupled T382L64 forecast guess(GFS + MOM4 + Noah) 1 Jan 0Z 2 Jan 0 Z 3 Jan 0Z 4 Jan 0Z 5 Jan 0Z 2-day T382L64 coupled forecast ( GFS + MOM4 + Noah )

  3. Outline • Next-generation CFS • Analysis & physics upgrades: Atmos, Ocean, Land, Sea-Ice • History, summary, and assessment of Noah LSM • Noah LSM features compared to forerunner OSU LSM • Noah LSM Impact in coupled GFS and Regional Reanalysis • Impact in N. American Regional Reanalysis (NARR) • Impact in medium-range Ops GFS upgrades of May 2005 • GLDAS: Global Land Data Assimilation System • Configuration and results from lower-resolution 27-year execution • CFS Reforecast Experiments: Land Component tests • Land Models: Two models (Noah LSM, OSU LSM) • Land initial states: Two sources (Global Reanal 2, GLDAS) • Conclusions and Pending Issues

  4. New CFS implementation • Analysis Systems : Operational DAS: Atmospheric (GSI) Ocean (GODAS) and Land (GLDAS) • 2. Atmospheric Model : Operational GFS • 3. Land Model New Noah Land Model • 4. Ocean Model : New MOM4 Ocean Model • New SEA ICE Model

  5. EMC Land Surface Partnerships: GCIP/GAPP/CPPA (NOAA/CPO) and JCSDA NCEP/EMC NWS/OHD NESDIS Dan Tarpley John Schaake Ken Mitchell Michael Ek Dag Lohmann Victor Koren Bruce Ramsay AFWA NCAR John Eylander Fei Chen Mukul Tewari Univ. Maryland NASA/GSFC Hugo Berbery Princeton Univ. Rachel Pinker Christa Peters-Lidard Brian Cosgrove Eric Wood Justin Sheffield Univ. Washington Univ. Oklahoma Rutgers Univ. Dennis Lettenmaier Laura Bowling Ken Crawford Jeff Basara Alan Robock Lifeng Luo NOAA/ARL Tilden Meyers Jon Pliem Univ. Arizona NOAA/FSL James Shuttleworth Hoshin Gupta Stan Benjamin Tanya Smirnova GLDAS COLA/GMU Paul Houser Paul Dirmeyer

  6. History of the Noah LSM • Oregon State University: 1980’s • OSU/CAPS LSM was forerunner of Noah LSM • Initial development was funded by Air Force • Transitioned to Air Force in late 1980’s • Implemented in Air Force GLDAS (known as AGRMET) • Transitioned to NCEP Ops mesoscale Eta model in 1996 • Coined “NOAH” LSM after NCEP, OSU, Air Force and OHD upgrades • Transitioned to NCAR in late 1990’s • Implemented in NCAR Community MM5 mesoscale model (F. Chen) • Applied in NCEP N. American Regional Reanalysis (NARR) • 1979 to present • Implemented with NCEP Ops WRF meso model in Jun 06 • Implemented in NCEP Ops medium-range GFS in May 2005 • GFS: Global Forecast System

  7. Noah LSM (vegetation, snow, ice) 4 soil layers (10, 30, 60, 100 cm) Frozen soil physics included Add glacial ice treatment Two snowpack states (SWE, density) Surface fluxes weighted by snow cover fraction Improved seasonal cycle of vegetation Spatially varying root depth Runoff and infiltration account for sub-grid variability in precipitation & soil moisture Improved thermal conduction in soil/snow Higher canopy resistance Improved evaporation treatment over bare soil and snowpack OSU LSM 2 soil layers (10, 190 cm) No frozen soil physics Only one snowpack state (SWE) Surface fluxes not weighted by snow fraction Vegetation fraction never less than 50 percent Spatially constant root depth Runoff & infiltration do not account for subgrid variability of precipitation & soil moisture Poor soil and snow thermal conductivity, especially for thin snowpack GFS and CFS: Land Model UpgradeNoah LSM (new) versus OSU LSM (old): Noah LSM replaced OSU LSM in operational NCEP medium-range Global Forecast System (GFS) in late May 2005

  8. Noah LSM Testing Sequence • Uncoupled testing • 1-d column model • 3-d NLDAS and GLDAS • National and Global Land Data Assimilation Systems • Coupled testing (then Ops Implementations) • ETA and WRF mesoscale model (NAM) • N. American Regional Reanalysis (NARR) • Global Forecast System (GFS) • Coupled Forecast System (CFS)

  9. NLDAS surface energy Fluxes across ARM- CART sites of Oklahoma. Multi-station average ff model and obs Jan 98 – Sep 99 Three land models shown: Blue -- Noah Green -- VIC Red -- Mosaic Noah performs well, arguably the best.

  10. 1993 (the summer flood) 1988 (the summer drought) Average during 16-31 July (at 21 GMT) Noah LSM in N. American Regional Reanalysis: NARR Soil moisture availability (percent of saturation) Top 1-meter of soil column The hallmark assimilation of high-resolution hourly precipitation analyses in the NARR is not feasible in the Global Reanalysis: owing to lack of timely Global precip analysis of sufficient quality and retrospective availability.

  11. GFS Implementation of Noah LSM31 May 2005 • NCEP TPB: • http://www.emc.ncep.noaa.gov/gc_wmb/Documentation/TPBoct05/T382.TPB.FINAL.htm • Increase in horizontal resolution • Noah LSM replaces OSU LSM • New sea-ice treatment • Enhanced mountain blocking • Modified vertical diffusion • Analysis upgrades • Additional satellite radiance data • Enhanced quality control • Improved surface emissivity calculations over snow

  12. Annual mean biases in surface energy fluxes: In five operational GCMs during 2003-2004 w.r.t. nine flux-station sites distributed world-wide from K. Yang et al. CEOP Study (2007, J. Meteor. Soc. Japan) lE – Latent Heat Flux H – Sensible Heat Flux Rn – Net Radiation µ – Global mean Mean Bias Error (MBE) Pre-May 2005 NCEP GFS had large positive bias in surface latent heat flux and corresponding large negative bias in surface sensible heat flux. Also large positive bias in precipitation in humid regions (not shown).

  13. Mean GFS surface latent heat flux: 09-25 May 2005: Upgrade to Noah LSM significantly reduced the GFS surface latent heat flux (especially in non-arid regions) Pre-May 05 GFS: with OSU LSM Post-May 05 GFS: with new Noah LSM

  14. Global Land Data Assimilation System (GLDAS):with Noah LSM(Next 7 Frames) • Motivation for GLDAS • high precip bias over tropical land mass in coupled GDAS • GLDAS Configuration for T382 CFSRR • T126 uncoupled (about 1-deg resolution) • Precip forcing: CPC global 5-day CMAP precip anal • Only over Tropical Latitudes (otherwise model precipitation) • Non-Precip forcing: T62 NCEP/DOE Global Reanal 2 (GR2) • GLDAS Results from low-res multi-decade test • Period: 1979-2006 • “Cold Start”: 5-year spin up with 1979 forcing • Compared with Global Reanalysis 2

  15. Precipitation JJA 2007 Greatest GDAS high precip bias over land appears over tropical land mass: Next Frame (e.g. central Africa, northern S. America, India and Southeast Asia)

  16. GDAS-minus-OBS: Jun-Jul-Aug 2007 Precipitation Total From land perspective: Largest positive bias over tropical latitudes. GR1-minus-Obs GR2-minus-Obs

  17. Motivation for Using CMAP Precipitation in Tropical Latitudes in GLDAS: GDAS shows high bias in tropical precipitation compared to CMAP analysis 10 July – 09 Aug 2007 Example: in tropical Africa CMAP Precip Analysis: 10Jul07– 09Aug07 GDAS Precip Field: 10Jul07– 09Aug07

  18. Two-Year (Oct 05 – Sep 07) Soil Moisture Time Series at Four global locations for 10-40 cm layer in Noah LSM of Ops GDAS(does not utilize CMAP precipitation forcing) North Central USA Equatorial Africa Three of four locations look reasonable, except tropical Africa is spinning up to very moist state Central Amazon Southeast Asia

  19. GLDAS uses computational infrastructure ofNASA/GSFC/HSB Land Information System (LIS)

  20. CFSRR GLDAS Configuration • Uncoupled execution of NASA LIS computational infrastructure • Same Noah LSM source code as in coupled GDAS • same four soil layers (10, 30, 60, 100 cm) • same parameter values • Same computational grid (T382 Gaussian) as in coupled GDAS • same terrain height, same land mask • same land surface characteristics (soils, vegetation, etc) • Applies GDAS atmospheric forcing • hourly from previous 24-hours of coupled GDAS • except precipitation forcing (see next line) • Precipitation forcing is from CMAP precip analysis over tropical lands • temporally disaggregate the 5-day CMAP precipitation with GDAS 6-hrly precip • linearly blend GDAS and CMAP precip forcing between 30-40 deg latitude • Reach-back every 5 days to apply latest 5-day CMAP anal • then reprocess last 6-7 days to maintain continuous cycling from CMAP-driven land states • Same realtime and retrospective configuration • Once daily update of coupled GDAS soil moisture states from GLDAS

  21. ONE DAY OF REANALYSIS: Note daily GLDAS (spans prior 24-hrs) 12Z GSI 18Z GSI 0Z GSI 6Z GSI 0Z GLDAS 12Z GODAS 18Z GODAS 0Z GODAS 6Z GODAS 9-hr coupled T382L64 forecast guess(GFS + MOM4 + Noah) 1 Jan 0Z 2 Jan 0 Z 3 Jan 0Z 4 Jan 0Z 5 Jan 0Z 2-day T382L64 coupled forecast ( GFS + MOM4 + Noah )

  22. GLDAS versus Global Reanalysis 2 (GR2):Land Treatment • GLDAS: an uncoupled land simulation system driven by CMAP observed precipitation over tropics • Executed using same grid, land mask, terrain field and Noah LSM as GFS in experimental CFS • Non-precipitation land forcing is from Global Reanal 2 (GR2) • Executed retrospectively from 1979-2006 (after spin-up) • GR2: a coupled atmosphere/land assimilation system wherein land component is driven by model predicted precipitation • applies the OSU LSM • nudges soil moisture based on differences between model and CPC CMAP precipitation

  23. GLDAS/Noah (top row) versus GR2/OSU (bottom row)2-meter soil moisture (% volume): GLDAS/Noah values are higher Climatology (left column) is from 25-year period of ~1981-2005)May 1stClimatology01 May 1999 Instantaneous Anomaly GLDAS/Noah GLDAS/Noah GR2/OSU GR2/OSU

  24. GLDAS/Noah (top ) versus GR2/OSU (bottom)2-meter soil moisture (% volume) May 1stClimatology 01 May 1999Anomaly Observed 90-day Precipitation Anomaly (mm) valid 30 April 99 GLDAS/Noah GLDAS/Noah GR2/OSU GR2/OSU Left column: GLDAS/Noah soil moisture climo is generally higher then GR2/OSU Middle column: GLDAS/Noah soil moisture anomaly pattern agrees better than that of GR2/OSU with observed precipitation anomaly (right column: top)

  25. Monthly Time Series (1985-2004) of Area-mean Illinois 2-meter Soil Moisture [mm]:Observations (black), GLDAS/Noah (purple),GR2/OSU (green) Total Climatology Anomaly The climatology of GLDAS/Noah soil moisture is higher and closer to the observed climatology than that of GR2/OSU, while the anomlies of all three show generally better agreement with each other (though some exceptions)

  26. New T126 CFS Reforecast Tests:Land Component Impact -- New Noah LSM versus Old OSU LSM -- GLDAS/Noah versus Global Reanal-2/OSU -- SST: high correlation skill in tropical Pacific (not shown) -- CONUS precipitation (low correlation skill in summer, later frame)

  27. Choice ofLand Model CFS/Noah CFS/OSU Choice of LandInitial Conditions GR2/OSU GR2/OSU (CONTROL) GLDAS/Noah GLDAS/Noah Climo Note: “GR2” denotes NCEP/DOE Global Reanalysis 2 CFS Land Experiments (4 configurations)Experiments of new T126 CFS with Noah LSM and OSU LSM25-year CFS 6-month summer reforecasts (10 member ensembles)from late-April and early-May initial conditions (00Z) of 1980-2004Initial Dates of Ten Members: Apr 19-23, Apr 29-30, May 1-3(GFDL MOM-3 Model is ocean component)

  28. JJAPrecipitation Correlation Skill CFS/Noah/GR2 case is clearly worst case (least spatial extent of positive correlation). Remaining three cases appear to have similar spatial extent of positive correlation, but distributed differently among sub-regions. Still disappointingly small spatial extent of correlations above 0.5 in all four configurations. From hindcasts for years 1981-2004. Ten-member ensemble mean shown for each panel.

  29. JJAPrecipitation Correlation Skill CFS/Noah/GR2 case is clearly worst case (least spatial extent of positive correlation). Remaining three cases appear to have similar spatial extent of positive correlation, but distributed differently among sub-regions. Still disappointingly small spatial extent of correlations above 0.5 in all four configurations. From hindcasts for years 1981-2004. Ten-member ensemble mean shown for each panel.

  30. JJAT2m Correlation Skill Noah/ GLDAS Noah/ GLDAS Climo OSU/ GR2 Ops CFS All the configurations of New CFS are superior to Ops CFS over CONUS (Most likely owing to inclusion of CO2 trend in New CFS) 10 Members each case (same initial dates)

  31. Conclusions from CFS Land-component Experiments • The relatively low CFS seasonal prediction skill for summer precipitation over CONUS is not materially improved by the tested upgrade in land surface physics and land data assimilation • Lack of positive impact likely due to more dominant influence from SST anomalies and internal chaotic noise in the coupled global model • Corollary: The use of initial soil moisture states with instantaneous soil moisture anomalies did not provide an advantage over the climatological soil moisture states, provided the climatology was a product of the very same land model • Separate study by CPC (Soo-Hyun Yoo, S. Yang, J. Schemm) evaluated these same summer CFS experiments over the Asian-Australian Monsoon, showing modestly positive impact from Noah LSM and GLDAS upgrades presented here. • An upgrade to the land surface model of a GCM can possibly degrade GCM performance if the upgraded land model is not also incorporated into the data assimilation suite that supplies the initial land states • The addition of a CO2 trend to the experimental CFS is likely the major source of the improvement in experimental CFS summer season surface temperature forecasts relative to the currently operational CFS • Future work will carry out this same suite of CFS reforecasts for winter season • One focus will be snow cover prediction (Ops CFS has notable low bias in snow cover)

  32. CFSRR: Land ComponentSummary and Pending Issue • Motivation for GLDAS: • High tropical precipitation bias in GFS/GDAS • GLDAS uses CPC CMAP precip anal to force land over tropical latitudes • T382 GLDAS for CFSRR • Codes and scripts delivered to and executing in CFSRR suite • Low-res 28-yr GLDAS retrospective run done & assessed • CMAP precipitation applied globally to force land surface • Non-precipitation land surface forcing from Global Reanalysis 2 • CFS land-component summer reforecasts run for 25-yrs • Land upgrade not yielding better summer precip fcst skill • Winter reforecast tests are underway • Pending Issue • Length of overlap in four CFSRR production streams • I urge at least12-months overlap (6-months is insufficient)

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