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Objectives

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Objectives

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  1. The Impact of Coupled versus Observed SST on Summer Season Predictions over America with the NCEP CFS Land Upgrades Rongqian Yang, Michael Ek, Jesse Meng and Ken Mitchell NCEP Environmental Modeling Center NCEP-COLA CTB Joint Seminar Series 24 March 2010

  2. Objectives Examine the impact of land upgrades in the next-generation Climate Forecast System (CFS) on summer season predictions. Any meaningful improvements? B. Examine the relative contribution from land anomaly forcing to summer seasonal predictability ( vs.SST anomaly forcing). Motivation SST anomalies : the foremost source of seasonal predictability in coupled global models. Land surface anomalies:the second most important source of seasonal predictability (e.g. anomalies of soil moisture, snowpack, vegetation cover).

  3. Outline • Overview of Currently Operational CFS Skill • Next-generation CFS Upgrades (with land) • CFS Experiments Part A: Coupled SST (CMIP) runs and results Part B: Observed SST (AMIP) runs and comparison with part A Part C: Ocean-land-atmosphere interaction comparison between A & B • Conclusions

  4. Overview ofCurrently Operational CFS Seasonal Prediction Skill (next 3 frames)

  5. 1-Month Lead (valid summer) 6-Month Lead (valid winter) Ops CFS SeasonalSSTForecast Skill:Correlation of CFS SST forecast with observed SST: 1982-2003For April initial conditions: 15-member ensemble mean high correlation skill in tropical Pacific

  6. Ops CFS Summer SeasonPrecipitationForecast Skill:Correlation of CFS precip forecast with observed precip: 1982-2003For April initial conditions: 15-member ensemble mean Short-lead summer forecast correlation skill is low across bulk of CONUS (lower than longer-lead winter forecast)

  7. Ops CFS Summer SeasonTemperatureForecast Skill:Correlation of CFS 2m-T forecast with observed 2m-T: 1982-2003For April initial conditions: 15-member ensemble mean 1-Month Lead (valid summer) 6-Month Lead (valid winter) Short-lead summer forecast correlation skill is low across bulk of CONUS

  8. Next-generation CFS Upgrades • Upgrades to CFS physics • Atmosphere (with co2 trend added),ocean, land model,sea-ice • New 4DDA analysis systems (CFSR) • Atmosphere, ocean,land • Double the CFS resolution • T126 / L64(versus T62 / L28) More details about the CFS see Saha et al., The NCEP Climate Forecast System, 2006, J. Clim, 19(15), 3483-3517 The CFS experiments presented below incorporate the CFS upgrades(land related upgrades highlighted above in red)

  9. Land Model UpgradeNoah LSM (new) versus OSU LSM (old): • Noah LSM • 4 soil layers (10, 30, 60, 100 cm) • Frozen soil physics included • Surface fluxes weighted by snow cover fraction • Improved seasonal cycle of vegetation cover • Spatially varying root depth • Runoff and infiltration account for sub-grid variability in precipitation & soil moisture • Improved soil & snow thermal conductivity • Higher canopy resistance • Other • OSU LSM • 2 soil layers (10, 190 cm) • No frozen soil physics • 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 More details see: Ek et al., 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108(D22). Noah LSM replaced OSU LSM in operational NCEP medium-range Global Forecast System (GFS) in late May 2005

  10. Land Data Assimilation System UpgradeGLDAS/Noah & Global Reanalysis 2 (GR2/OSU): • GLDAS:an uncoupledGlobal Land Data Assimilation System driven by observed precipitation analyses (CPC CMAP analyses) • Executed using same grid, land mask, terrain field and four-layerNoah LSMas in experimental CFS forecasts • Non-precipitation land forcing is from 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 theOSU LSMwith two soil layers • nudges soil moisture based on differences between model and CPC CMAP precipitation

  11. 90-day ending 01 May 99 Precip Anomaly & climatology 01 May 1999 Soil Moisture Anomaly disagree. Noah/GLDAS Closer to Observed Precip Anomaly over western CONUS Time series of Total SM (mm) over Illinois (81-04) 01 May 1999 Different anomaly (% volume) 01 May Similar climatology (% volume) Noah/GLDAS has higher SM Annual Cycle (mm) Soil Moisture Difference between GLDAS and GR2 over CONUS(land anomaly forcing comparison) GLDAS GR2

  12. Question? How the land upgrades in thenew CFS system perform? Any improvements?

  13. CFS Land Experiments: Part A • CMIP Runs (Coupled SST) • Noah/GLDAS • vs • OSU/GR2 • Highly Controlled • 25-year summer reforecasts (80-04) • 10 member (ICs from 00Z of Apr 19-23, Apr 29-May 03) • Same atmospheric ICs, physics, and Oceanic initial states • Same resolution (T126/L64)

  14. Verification Methods and Data Main Skill Measures: Anomaly Correlation (AC) Anomaly Cross Correlation Area-averaged AC scores (AAC) Percentage Count of Positive AC (PAC) Focusing on CONUSsummer season (June-July-August) • Precip: 1/8th NLDAS (Mitchell et al.,2004, JGR 109 ) and CMAP • SST:1X1 degree daily OI SST (Reynolds et al,2002, J. Clim,15) with CMIP • T2m: monthly T126 (Fan and H. van den Dool, 2008, JGR 113) • Atmospheric data: 2.5x2.5 degree NCEP/DOE GR2

  15. JJA CFS AC skill: SST(25 yrs ensemble mean) The two configurations yield similar SST correlation patterns CFS Noah/GR2 CFS Noah/GLDAS Slightly better with Noah/GLDAS over higher latitudes 0.6 0.1 0.9 0.1 0.6 0.9 Similar SST Performance over Nino 3.4 CFS OSU/GR2

  16. Time series of predicted JJA Nino 3.4SSTAnomaly over the 25 yrs (defined as 5˚S-5˚N, 120˚W-170˚W) 2.0 (defined as 5˚S-5˚N, 120˚W-170˚W) Predicted JJA Niño 3.4 anomaly and Obs 0.5 0 -0.5 1999 -2.0 Similar performance over most years Good Agreement with Observations over most years & So the difference in CFS performance (next) is likely due to land upgrades

  17. JJA CFS AC Skill: precipitation (25 yrs ensemble mean) 0.6 Better Performance over Pacific Northwest and northern Great Plains - 0.6 Positive AC points (%) are 64.9 (Noah/GLDAS & 58.4(OSU/GR2) 0.6 Better performance over Central great plains and the Gulf States - 0.6

  18. CONUS-average Anomaly Correlation: CFS JJA ensemble mean precipitation forecasts from the 25-year 10-member reforecasts of the two CFS configurations Improvement with Noah/GLDAS, but the score is still low

  19. Anomaly Correlation: CFS JJA ensemble mean temperature forecastsfrom the 25-year reforecasts of 2 T126 CFS tests (10 members each from same late April and early May initial times) better performance with OSU/GR2 from Mid-west to The Great Plains

  20. CONUS-average Anomaly Correlation: CFS JJA ensemble mean temperature forecasts from the 25-year 10-member reforecasts of the two CFS configurations

  21. Further Analysis  To examine the CFS performance with different ENSO signals Partition into ENSO Neutral & Non-neutral samples using MJJ Nino3.4 SST anomaly of 0.7C as a threshold magnitude. 15 neutral summers:80,81,84,85,86,89,90,94,95,96,98,00,01,03,04 10 non-neutral summers:82,83,87,88,91,92,93,97,99,02 (red: warm, blue: cold)

  22. 10 non-neutral ENSO years: JJA precipitation AC Score Much better performance than 25-yr avgs Due to Strong SST signals Extremely similar geographical patterns from west of the Mississippi River to the Pacific states

  23. 15 neutral ENSO years: JJA precipitation AC Score Worse performance than 25-yr avgs As expected Weak SST impact Similar to 25-yr avgs The large differences are mostly over the western CONUS

  24. Non-Neutral years 0.18 0.10 0 CONUS-average JJAprecipitationAC score Significance test (T-statistic) shows differences are not significant at 90% confidence level. Neutral years Significance test (T-statistic) shows differences are significantat 90% confidence level. 0.04 0.00 -0.15

  25. 10 non-neutral ENSO years: JJA temperature AC score Due to Strong SST signals Similar geographical patterns With both CFS Slightly better with OSU/GR2

  26. 15 neutral ENSO years: JJA temperature AC score Degraded Performance with both cases Slightly better over with Noah/GLDAS Over the Rocky Mountain states and the N. Pacific states Disagree over the southern Great Plains

  27. CONUS-average JJAtemperatureAC score Non-Neutral years 0.5 0.4 Significance test (T-statistic) shows differences are not significant at 90% confidence level. 0.2 0 Neutral years 0.2 0.14 0

  28. How are the AMIP runs compared to the CMIP runs? To answer: CFS Land Experiments: Part B Replace the coupled SST in CFS Experiments Part A with observed SST (Extremely ControlledAMIP runs) Question? Is the skill gain really from the land upgrades Noah/GLDAS or better SST?

  29. CMIP and AMIP Precip AC SkillAvgd over 25 yrs AAC CMIP Noah AMIP Noah C-Noah A-Noah C-OSU A-OSU CMIP OSU AMIP OSU PAC AMIP loses skill in these regions with both CFS Noah performs better in CMIP

  30. T2m AC skill averagd over the 25 yrs AAC CMIP Noah AMIP Noah PAC AMIP OSU CMIP OSU Noah performs worse with AMIP, no big difference with OSU

  31. Precip AC skill averaged over the 10 non-neutral yrs AAC CMIP Noah AMIP Noah CMIP OSU AMIP OSU PAC AMIP runs are better than CMIP with both LSMs during non-neutral yrs, butnot statistically significant (90%)

  32. Precip AC skill averaged over the 15 neutral yrs AAC Noah is better than OSU, statistically significant (90%)in CMIP CMIP Noah AMIP Noah AMIP OSU CMIP OSU PAC Noah is better than OSU, the difference is statistically significant (90%)in AMIP

  33. T2m AC skill averaged over the 10 non-neutral yrs AAC CMIP Noah AMIP Noah CMIP OSU PAC AMIP OSU No big difference with either LSM in both CMIP/AMIP modes – no clear advantage -- Mixed in both AAC and PAC

  34. T2m AC skill avgd over the 15 neutral yrs AAC CMIP Noah AMIP Noah CMIP OSU AMIP OSU PAC Both LSMs perform slightly better with AMIP; All differences in T2m are not statistically significantat 90% confidence level

  35. Wet Southwest U.S. Monsoon Case Predicted Precipitation Anomaly (in mm) 1999: Predicted JJA Obs CMIP Noah/GLDAS performs better over these areas than AMIP Noah/GLDAS AMIP Noah/GLDAS CMIP Noah/GLDAS -50 50 No big difference over Southwest With Noah/GLDAS CMIP OSU/GR2 AMIP OSU/GR2 Bad performance over Southwest With OSU/GR2

  36. Question? Why the AMIP runs are not as good as CMIP runs? Perfect SST means the upper limit of predictability or only true with perfect atmosphere and land surface? Ocean-Land-Atmosphere interaction comparison Represented by SST, 500 GPH, and Soil Moisture

  37. SST- 500GPHAnomaly Cross Correlation GR2 CMIP Noah AMIP Noah CMIP OSU AMIP OSU All have good agreements with observations, but weaker in CMIP and stronger in AMIP

  38. 500 GPH AC skill Averaged over 25 yrs AMIP Noah CMIP Noah CMIP OSU AMIP OSU CMIP : Noah better over CONUS; AMIPworse than CMIP over bulk of CONUS (Noah better over ocean)

  39. 588 588 588 588 588 Comparison of Predicted 500GPH JJA Climatology with GR2 GR2 CMIP Noah AMIP Noah CMIP OSU AMIP OSU The JJA500GPH Climatology is too low compared to GR2 in AMIP where CMIP shows better agreement with Obs

  40. JJA Soil Moisture and 500 GPH Anomaly Cross Correlation Negatively Correlated in both GLDAS and GR2

  41. Predicted JJA Soil Moisture and 500 GPH Cross Correlation CMIP Noah AMIP Noah CMIP OSU AMIP OSU Predict wrong signs with AMIP runs

  42. Conclusions • The land upgrades in CFS do improve summer season precipitation prediction over CONUS, especially during ENSO-neutral years in both modes when the land anomaly forcings and land-atmospheric interactions contribute more to seasonal predictability. • AMIP runs are usually assumed to be the upper limit of potential predictive skill, but they ignore feedbacks from the atmosphere, and lead to degraded large-scale atmospheric circulation performance which contributes to the skill loss over CONUS (on average).