Presentation on theme: "THE ENVIRONMENTAL MODELING CENTER"— Presentation transcript:
1 THE ENVIRONMENTAL MODELING CENTER THE NCEP CLIMATE FORECAST SYSTEM Version 2 Implementation Date: 18 Jan 2011THE ENVIRONMENTAL MODELING CENTERNCEP/NWS/NOAA
2 Suranjana Saha, Shrinivas Moorthi , Hua-Lu Pan, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, Robert Kistler, John Woollen, David Behringer, Haixia Liu, Diane Stokes, Robert Grumbine, George Gayno, Jun Wang, Yu-Tai Hou, Hui-ya Chuang, Hann-Ming H. Juang, Joe Sela, Mark Iredell,, Russ Treadon, Daryl Kleist, Paul Van Delst, Dennis Keyser, John Derber, Michael Ek, Jesse Meng, Helin Wei, Rongqian Yang, Stephen Lord, Huug van den Dool, Arun Kumar, Wanqiu Wang, Craig Long, Muthuvel Chelliah, Yan Xue, Boyin Huang, Jae-Kyung Schemm , Wesley Ebisuzaki, Roger Lin, Pingping Xie, Mingyue Chen, Shuntai Zhou, Wayne Higgins, Cheng-Zhi Zou, Quanhua Liu, Yong Chen, Yong Han, Lidia Cucurull , Richard W. Reynolds, Glenn Rutledge, and Mitch Goldberg, 2010: The NCEP Climate Forecast System Reanalysis. BAMS, 90,Saha et al, 2011: The NCEP Climate Forecast System Version 2, to be submitted for publication to the Journal of Climate.Website :Thanks to Suru Saha, Huug van den Dool, Steve Lord, Bob Kistler, John Ward for slides
3 Operational CFS Status (CFS V.1) Implemented in 2004 (V.1)GFS GFDL MOM3Direct atmosphere-ocean coupling once a dayDaily four 9-month forecastsInitial StateOld Reanalysis (R2)CalibrationHindcast 1981 to 2008 (15 members each month)Product heavily used by CPCCPC blends all forecasts based on the skill estimatesMajor forecast improvements have resultedCFS converted climate folks who did not believe dynamic models could make skillful seasonal forecastsSkill in predicting ENSO indices (seems every model can predictstrong events six months in advance)Skill in predicting US temperature and precipitation low and lowerCFSv1 and v2 effort led by Suru Saha and Hua-lu Pan; many others participated
4 CFS Reanalysis & Reforecast (CFSRR V.2) GoalsImprove 3-6 week forecasts and seasonal prediction skillUse latest atmosphere, ocean, land surface models and data assimilationInclude historical CO2 changes, solar cycle and volcanic ash (CWL)EMC-CPC collaborationEMC – system design and executionCPC – diagnostic monitoring and evaluationMany problems discovered and fixed due to close working relationshipCFSRR v.3—2017???
5 Coupled Reanalysis of the atmosphere, ocean, sea ice and land over to provide consistent initial conditions for:A complete Reforecast of the new CFS over for stable calibration and skill estimates of the new system, for operational seasonal prediction
6 improved atmosphere with Gridded Statistical Interpolation Scheme (GSI) and improved physics and dynamics of operational Global Forecast System (GFS)Reanalysis T382, 64 levelsCDAS T574, 64 levelsForecasts T126, 64 levelsimproved ocean and ice with Global Ocean Data Assimilation System, (GODAS) and GFDL MOM4 Ocean Model with interactive Sea Ice Model40 vertical levels, to 4737 m, 0.25o at the tropics, tapering to a global resolution of 0.5o polewards of 10N and 10Simproved land with Global Land Data Assimilation System, (GLDAS) and Noah Land model
7 UPGRADES TO THE ATMOSPHERIC MODEL Hybrid vertical coordinate (sigma-pressure)Noah Land Model : 4 soil levels. Improved treatment of snow/frozen soilSea Ice Model : Fractional ice cover and depth allowedSub grid scale mountain blockingReduced vertical diffusionESMF (3.0)EnthalpyAER RRTM Longwave radiationAER RRTM Shortwave RadiationNew Aerosol TreatmentInclusion of historical CO2, solar cycle and volcanic aerosols
8 Testing with CMIP Runs (variable CO2) OBS is CPC Analysis (Fan and van den Dool, 2008)CTRL is CMIP run with 1988 CO2 settings (no variations in CO2)CO2run is the ensemble mean of 3 NCEP CFS runs in CMIP mode--realistic CO2 and aerosols in both troposphere and stratosphereProcessing: 25-month running mean applied to the time series of anomalies (deviations from their own climatologies)
9 There are three main differences with the earlier two NCEP Global Reanalysis efforts: Atmosphere at T382L64 rather than T62L28The guess forecast from a coupled atmosphere – ocean – sea ice - land systemRadiance measurements from the historical satellites instead of NESDIS temperature retrievalsA coupled Reanalysis will hopefully address important issues, such as the correlations between sea surface temperatures and precipitation in the global tropics.
10 Hourly coupling between atmosphere and ocean 12Z GSI18Z GSI0Z GSI9-hr coupled T382L64 forecast guess (GFS + MOM4 + Noah)Hourly coupling between atmosphere and ocean12Z GODAS0Z GLDAS6Z GSI18Z GODAS0Z GODAS6Z GODASONE DAY OF REAL TIME ANALYSIS
11 CFSR has less bias than R1, relative to GHCN_CAMS 0.94K/31years1.02K/31years0.66K/31yearsI can talk about this one display for half an hourNWS/CPCH. VandendoolCFSR has less bias than R1, relative to GHCN_CAMSUpward trend in CFSR larger than in R1, more like GHCN_CAMS
13 R. KistlerCFSRR—2008 frozen system—T382, 64 layers, radiancesGFS—operational model over the years
14 AMSU introducedFig. 21. Global average of monthly mean (a) precipitation, (b) evaporation, and (c) evaporation minus precipitation. Averages over ocean (red), land (blue), and ocean plus land (black) are plotted. (Units: mm day–1)
15 Fig. 23. The fit of 6-h forecasts of instantaneous surface pressure against irregularly distributed observations. Shown are annually compiled fit-to-obs data for 1979–2009 (hPa). SH ocean (blue) and NH land (red) are shown.
16 Fig. 24. Global mean temperature anomalies from 1000 to 1 hPa from Jan 1979 through May (Units: K)Run in six streams
17 Fig. 27. Temporal lag correlation coefficient between precipitation and SST in the tropical western Pacific (averaged over 10°S–10°N, 130°–150°E) in R1 (red), R2 (brown), CFSR (green), and observation (black). GPCP daily precipitation and Reynolds ¼° daily SST are used as observational data. Negative (positive) lag in days on the x axis indicates the SST leads (lags) the precipitation. Data for the boreal winter (Nov–Apr) over the period 1979–2008 are bandpass filtered for 20–100 days after removing the climatological mean.
18 Hindcast Configuration for next CFS 9-month hindcasts from every 5th day from all 4 cycles fromrequired to calibrate operational CPC longer-term seasonal predictions (ENSO, etc)A single 1 season (105-day) hindcast, from every 0 UTC cycle between these five daysrequired to calibrate operational CPC first season predictions for hydrological forecasts (precip, evaporation, runoff, streamflow, etc)Three 45-day (1-month) hindcast runs from every 6, 12 and 18 UTC cycles.required for operational CPC week3-week6 predictions of tropical circulations (MJO, PNA, etc)40,880 hindcasts made (the equivalent of running the CFS for over 14,000 yrs !!)Jan 19 month run1 season run45 day runJan 2Jan 3Jan 4Jan 5Jan 6
19 Definitions and DataAC of ensemble average monthly meansGHCN-CAMS (validation for Tmp2m)CMAP (validation for Prate)OIv2 (validation for SST)(27 years)All starting months (minus Sep and Oct)Common 2.5 degree gridv1 (15 members), v2 (24/28 members)Two climos used for all variables within tropics30S-30N: andElsewhere:
20 Anomaly Correlation: All Leads (1-8), All Months (10) THE BOTTOM LINE FOR CPCAnomaly Correlation: All Leads (1-8), All Months (10)Green is goodModelUS TUS PNino34SSTPrateGlobal(50N-50S)CFSv216.39.577.254.542.2CFSv110.371.852.837.7
21 Anomaly Correlation for other Regions (collaboration with EUROSIP and India)All Leads (1-8), All Months (10)Green is goodModelUS TEurope TIndia TUS PEurope PIndia PCFSv216.316.448.19.56.018.9CFSv19.62.410.34.518.0
22 Anomaly correlationSkill=25.8Skill=15.9CFSv1CFSv2Huug van den Dool2-meter Temps:Increase in global skill for CFSv2 over land areas at all leads, probably due to CO2 changes.
23 Precipitation anomaly correlation Skill=14.9Skill=13.3CFSv2CFSv1Huug van den DoolPrecipitation anomaly correlationAll months all leads
24 Skill=38.5Skill=32.4CFSv2CFSv1Huug van den DoolSea Surface Temps:For Nov ICs, more skill at the longer leads for CFSv2 (no spring barrier ?)Interesting skill in CFSv2 in high latitude NW Atlantic. The sea-ice model???
25 Forecast Skill of WH-MJO index Courtesy Qin Zhang – NCEP/CPC
26 Operational Configuration for next CFS 0 UTC6 UTC18 UTC12 UTC9 month run (4)1 season run (3)45 day run (9)
27 NOAA/NCDC actively involved in archival of the CFSRR Access via the NOAA Operational Model Archive and Distribution System (NOMADS).
28 Status and plans Assimilation will evolve with GFS and GSI CFS Reanalysis and Re-Forecasting completeImplementation scheduled 18 January 2011Forecast component frozen (T126L64)Assimilation will evolve with GFS and GSICFS Data Assimilation fully coupled version of T574 GFS/GSIGSI upgrade scheduled early MarchCDAS is being tested with the new GSIR2 , GODAS discontinued Jul.1, 2011Rerunning CFSR at T126 L64 in single stream from 1979R1 (NCEP/NCAR) will continue until CFSR finishes 2010Same frozen system will be run from 1948 until 1980