Presentation on theme: "THE NCEP CLIMATE FORECAST SYSTEM VERSION 2 SURANJANA SAHA THE ENVIRONMENTAL MODELING CENTER NCEP/NWS/NOAA 1 NEMS/GFS Modeling Summer School 2013 Presentation."— Presentation transcript:
THE NCEP CLIMATE FORECAST SYSTEM VERSION 2 SURANJANA SAHA THE ENVIRONMENTAL MODELING CENTER NCEP/NWS/NOAA 1 NEMS/GFS Modeling Summer School 2013 Presentation on Climate 8:30-9:00am, Wednesday July 31, 2013
CFSv2 consists of the following components: 1.Analysis Systems :Atmospheric (CDAS)-GSI Ocean-Ice (GODAS) and Land (GLDAS) 2. Atmospheric Model :Operational CFS Noah Land Model 3. Ocean Model :MOM4 Ocean Model Sea Ice Model
The models used by the CFSv2 are: 1.Atmosphere at horizontal resolution of spectral T126 (~100 km) and vertical resolution of 64 sigma-pressure hybrid levels 2.Interactive ocean with 40 levels in the vertical, to a depth of 4737 m, and horizontal resolution of 0.25 degree at the tropics, tapering to a global resolution of 0.5 degree northwards and southwards of 10N and 10S respectively 3.Interactive 3 layer sea-ice model 4.Interactive land model with 4 soil levels
The NCEP Climate Forecast System Version 2 (CFSv2) was implemented into operations on March 30, A new Reanalysis of the atmosphere, ocean, seaice and land was made over a 32-year period ( ) to provide consistent initial conditions for: A complete Reforecast of the new CFSv2 over the 29-year period ( ), in order to provide stable calibration and skill estimates of the new system, for operational seasonal prediction at NCEP
What is a Reanalysis ? analysis made after the fact (not ongoing in real time) with an unchanging model to generate the model guess (MG) with an unchanging data assimilation method (DA) no data cut-off windows and therefore more quality controlled observations (usually after a lot of data mining) 5
Motivation to make a Reanalysis ? To create a homogeneous and consistent climate record Examples: R1/CDAS1: NCEP/NCAR Reanalysis (1948-present) Kalnay et al., Kistler et al R2/CDAS2 : NCEP/DOE Reanalysis (1979-present) Kanamitsu et al ERA40, ERA-Interim, MERRA, JRA25, NARR, etc…. To create a large set of initial states for Reforecasts (hindcasts, retrospective forecasts..) to calibrate real time extended range predictions (error bias correction). 6
12Z GSI18Z GSI0Z GSI 9-hr coupled T382L64 forecast guess (GFS + MOM4 + Noah) 12Z GODAS 0Z GLDAS 6Z GSI ONE DAY OF ANALYSIS 18Z GODAS0Z GODAS6Z GODAS 7
CFSR T382 horizontal resolution (~38 Km) Sigma-pressure hybrid vertical coordinate with 64 levels with top pressure ~0.266 hPa Simplified Arakawa-Schubert convection with momentum mixing Tiedtke (1983) shallow convection modified to have zero diffusion above the low level inversions Prognostic ozone with climatological production and destruction terms computed from 2D chemistry models Prognostic cloud condensate from which cloud cover is diagnosed Orographic gravity wave drag based on Kim and Arakawa(1995) approach and sub-grid scale mountain blocking following Lott and Miller (1997) Courtesy: Shrinivas Moorthi 8
CFSR (contd) AER RRTM IR radiation with maximum/random cloud overlap and observed global mean CO2 AER RRTM SW radiation with maximum/random overlap and observed global mean CO2, aerosols including volcanic origin plus rare gases. Non-local vertical diffusion in the PBL with local-K in the free atmosphere with exponentially decaying background diffusion coefficient Eighth order horizontal diffusion Specific enthalpy as a prognostic variable. More accurate thermodynamic equation. Noah 4 layer land surface model Coupled to GFDL MOM4 and a 3 layer sea-ice model Courtesy: Shrinivas Moorthi 9
CFSR data dump volumes, , in GB/month Courtesy: Jack Woollen 10
Another innovative feature of the CFSR GSI is the use of the historical concentrations of carbon dioxide when the historical TOVS instruments were retrofit into the CRTM. Satellite Platform Mission Mean (ppmv)b TIROS-N NOAA NOAA NOAA NOAA NOAA NOAA NOAA GEOS GEOS GEOS NOAA-14 to NOAA IASI METOP-A NOAA Courtesy: 11
Courtesy: Huug van den Dool The linear trends are 0.66, 1.02 and 0.94K per 31 years for R1, CFSR and GHCN_CAMS respectively. (Keep in mind that straight lines may not be perfectly portraying climate change trends). 12
SST-Precipitation Relationship in CFSR Precipitation-SST lag correlation in tropical Western Pacific simultaneous positive correlation in R1 and R2 Response of Prec. To SST increase : warming too quick in R1 and R2 Courtesy: Jiande Wang 13
5-day T126L64 forecast anomaly correlations Courtesy: Bob Kistler 14
THE OCEAN, SEA ICE AND COUPLER 15
The global number of temperature observations assimilated per month by the ocean component of the CFSR as a function of depth for the years Courtesy: Dave Behringer 16
Courtesy: Dave Behringer The global distribution of all temperature profiles assimilated by the ocean component of the CFSR for the year The distribution is dominated by XBT profiles collected along shipping routes. 17
The global distribution of all temperature profiles assimilated by the ocean component of the CFSR for the year The Argo array (blue) provides a nearly uniform global distribution of temperature profiles Courtesy: Dave Behringer 18
165E 170W 110W DJF JJA The Diurnal Cycle of SST in CFSR The diurnal cycle of SST in the TAO data (black line) and CFSR (blue line) in the Equatorial Pacific for DJF (top three panels) and JJA (bottom three panels). T 165E170W110WDJF JJA Courtesy: Sudhir Nadiga 19
Courtesy: Sudhir Nadiga Zonal and meridional surface velocities for CFSR (top left and top right) and differences between CFSR and drifters from the Surface Velocity Program of TOGA (bottom panels). 20
Courtesy: Sudhir Nadiga The first two EOFs of the SSH variability for the CFSR (left) and for TOPEX satellite altimeter data (right) for the period: The time series amplitude factors are plotted in the bottom panel. 21
Monthly mean sea ice concentration for the Arctic from CFSR (6-hr forecasts) Courtesy: Xingren Wu 22
Monthly mean Sea ice extent (10 6 km 2 ) for the Arctic (top) and Antarctic (bottom) from CFSR (6-hr forecasts). 5-year running mean is added to detect long term trends. Courtesy: Xingren Wu 23
CFS grid architecture in the coupler. ATM is MOM4 atmospheric model (dummy for CFS), SBL is the surface boundary layer where the exchange grid is located, LAND is MOM4 land model (dummy for CFS), ICE is MOM4 sea ice model and OCN is MOM4 ocean model. Courtesy: Jun Wang 24
Data Flow Fast loop: if Δa= Δc= Δi, coupled at every time step Slow loop: Δo ΔoΔo GFSCoupler Sea-ice Ocean ATM (dummy) ΔcΔc ΔaΔa ΔiΔi LAND (dummy) Courtesy: Jun Wang Fast loop: can be coupled at every time step Slow loop: a. passing variables accumulated in fast loop b. can be coupled at each ocean time step 25
Passing variables Atmosphere to sea-ice: - downward short- and long-wave radiations, - tbot, qbot, ubot, vbot, pbot, zbot, - snowfall, psurf, coszen Atmosphere to ocean: - net downward short- and long-radiations, - sensible and latent heat fluxes, - wind stresses and precipitation Sea-ice/ocean to atmosphere – surface temperature, – sea-ice fraction and thickness, and snow depth Courtesy: Jun Wang 26
THE SURFACE 27
2-meter volumetric soil moisture climatology of CFSR for May averaged over Courtesy: Jesse Meng 28
2-meter volumetric soil moisture climatology of CFSR for Nov averaged over Courtesy: Jesse Meng 29
Global Soil Moisture Fields in the NCEP CFSR Soil MoistureAnomaly R(GR2,OBS)=0.48 R(NARR,OBS)=0.67 R(CFSR,OBS)=0.61 [%] [mm] CONUS Illinois GR2 NARR CFSR OBS The CFSR soil moisture climatology is consistent with GR2 and NARR on regional scale. The anomaly agrees with the Illinois observations, correlation coefficient = Courtesy: Jesse Meng 30
Global average of monthly-mean Precipitation (a), Evaporation (b) and E-P (c). Courtesy: Wanqiu Wang 31
Monthly mean hourly surface pressure with the daily mean subtracted for the month of March 1998 Courtesy: Huug van den Dool 32
Fig. 3 Correlation of intraseasonal precipitation with CMORPH. (a) R1, (b) R2, and (c) CFSR. Contours are shaded starting at 0.3 with 0.1 interval. Courtesy: Jiande Wang et al 33
Fig. 4. (a) Standard deviation of intraseasonal rainfall anomalies from CMORPH. (b) differences in standard deviation of intraseasonal rainfall anomalies between R1 and CMORPH. (c) As in (b) except for R2. (d) As in (b) except for CFSR. Contours are shaded at an interval of 2 mm/day in (a) and 1 mm/day in (b), (c) and (d) with values between -1 and 1 plotted as white. Courtesy: Jiande Wang et al 34
The NCEP Climate Forecast System Reanalysis 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, Mitch Goldberg Bulletin of the American Meteorological Society Volume 91, Issue 8, pp doi: /2010BAMS
AND NOW…….. THE SECOND ‘R’ IN 36
37 Differences between the model used here and in CFSR are mainly in the physical parameterizations of the atmospheric model and some tuning parameters in the land surface model and are as follows: We use virtual temperature as the prognostic variable, in place of enthalpy that was used in major portions of CFSR. This decision was made with an eye on unifying the GFS (which uses virtual temperature) and CFS, as well as the fact that the operational CDAS with CFSv2 currently uses virtual temperature. We also disabled two simple modifications made in CFSR to improve the prediction of marine stratus (Moorthi et al., 2010, Saha et al., 2010, Sun et al., 2010). This was done because including these changes resulted in excessive low marine clouds, which led to increased cold sea surface temperatures over the equatorial oceans in long integrations of the coupled model.
38 A new parameterization of gravity wave drag induced by cumulus convection based on the approach of Chun and Baik (1998) (Johansson, 2009, personal communication). The occurrence of deep cumulus convection is associated with the generation of vertically propagating gravity waves. While the generated gravity waves usually have eastward or westward propagating components, in our implementation only the component with zero horizontal phase speed is considered. This scheme approximates the impact of stationary gravity waves generated by deep convection. The base stress generated by convection is parameterized as a function of total column convective heating and applied at the cloud top. Above the cloud top the vertically propagating gravity waves are dissipated following the same dissipation algorithm used in the orographic gravity wave formulation.
39 In CFSR, a standard cloud treatment is employed in both the RRTM longwave and shortwave parameterizations, that layers of homogeneous clouds are assumed in fractionally covered model grids. In the new CFS model, an advanced cloud-radiation interaction scheme is applied to the RRTM to address the unresolved variability of layered cloud. In McICA, a random column cloud generator samples the model layered cloud into sub- columns and pairs each column with a pseudo-monochromatic calculation in the radiative transfer model. In calculating cloud optical thickness, all the cloud condensate in a grid box is assumed to be in the cloudy region. So the in-cloud condensate mixing ratio is computed by the ratio of grid mean condensate mixing ratio and cloud fraction when the latter is greater than zero. The CO 2 mixing ratio used in these retrospective forecasts includes a climatological seasonal cycle superimposed on the observed estimate at the initial time.
40 The Noah land surface model (Ek et al., 2003) used in CFSv2 was first implemented in the GFS for operational medium-range weather forecast (Mitchell et al., 2005) and then in the CFSR (Saha et al., 2010). Within CFSv2, Noah is employed in both the coupled land- atmosphere-ocean model to provide land-surface prediction of surface fluxes (surface boundary conditions), and in the Global Land Data Assimilation System (GLDAS) to provide the land surface analysis and evolving land states. While assessing the predicted low-level temperature, and land surface energy and water budgets in the CFSRR reforecast experiments, two changes to CFSv2/Noah were made: 1.To address a low-level warm bias (notable in mid-latitudes), the CFSv2/Noah vegetation parameters and rooting depths were refined to increase evapotranspiration, which, along with a change to the radiation scheme (RRTM in GFS and CFSR, and now McICA in CFSv2), helped to improve the predicted 2-meter air temperature over land. 2.To accommodate a change in soil moisture climatology from GFS to CFSv2, Noah land surface runoff parameters were nominally adjusted to favorably increase the predicted runoff.
Hindcast Configuration for CFSv2 9-month hindcasts were initiated from every 5 th day and run from all 4 cycles of that day, beginning from Jan 1 of each year, over a 29 year period from This is required to calibrate the operational CPC longer-term seasonal predictions (ENSO, etc) There is also a single 1 season (123-day) hindcast run, initiated from every 0 UTC cycle between these five days, over the 12 year period from This is required to calibrate the operational CPC first season predictions for hydrological forecasts (precip, evaporation, runoff, streamflow, etc) In addition, there are three 45-day (1-month) hindcast runs from every 6, 12 and 18 UTC cycles, over the 12-year period from This is required for the operational CPC week3-week6 predictions of tropical circulations (MJO, PNA, etc) Jan month run 1 season run45 day run Jan Jan Jan Jan Jan
Operational Configuration of CFSv2 There are 4 control runs per day from the 0, 6, 12 and 18 UTC cycles of the CFS real- time data assimilation system, out to 9 months. In addition to the control run of 9 months at the 0 UTC cycle, there are 3 additional runs, out to one season. These 3 runs per cycle are initialized with a simple perturbation method. In addition to the control run of 9 months at the 6, 12 and 18 UTC cycles, there are 3 additional runs, out to 45 days. These 3 runs per cycle are initialized with a simple perturbation method. There are a total of 16 CFS runs every day, of which 4 runs go out to 9 months, 3 runs go out to 1 season and 9 runs go out to 45 days. 0 UTC6 UTC18 UTC12 UTC 9 month run (4) 1 season run (3)45 day run (9) 42
9-MONTH HINDCASTS 28 years: ; all 12 months. CFSv1 : 15 members per month, total of 180 initial states per year CFSv2: 24 members per month (28 for November), total of 292 initial states per year. Sample size: 5040 for CFSv1; 8176 forCFSv2. 43
Definitions and Data AC of ensemble averaged monthly means GHCN-CAMS (validation for Tmp2m) CMAP (validation for Prate) OIv2 (validation for SST) (28 years) Common 2.5 degree grid Variables/areas studied: US T, US P, global and Nino34 SST, global and Nino34 Prate. Two climos used for all variables within tropics 30S-30N: and Elsewhere:
More skill globally for CFSv2 45
More skill in the western Pacific for CFSv2 46
More skill west of the dateline and over the Atlantic for CFSv2 47
48 Evaluation of anomaly correlation as a function of target month (horizontal axis) and forecast lead (vertical axis). On the left is CFSv1, on the right CFSv2. Top row shows monthly 2-meter temperature over NH land Middle row shows monthly precipitation over NH land Bottom row shows the SST in the Nino3.4 area. The scale is the same for all 6 panels.
49 The annual mean systematic error in three parameters (SST, T2m and Prate) at lead 3 evaluated as the difference between the predicted and observed climatology for the full period Column on the left (right) is for CFSv1 (CFSv2). The header in each panel contains the root-mean-square difference, as well as the spatial mean difference. Units are K for SST and T2m and mm/day for prate. Contours and colors as indicated by the bar underneath.
50 The Brier Skill Score (BSS) of prediction of the probability of terciles of monthly T2m at lead 1month. On the left (right) the upper (lower) tercile. Upper row is CFSv1 15 members. Middle row is CFSv2 15 member Lower row is CFSv2 all 24 members. All start months combined. Period is Below each map is the map integrated BSS value.
45-DAY HINDCASTS 11 years: ; all 12 months. CFSv1 : 15 members per month, total of 180 initial states per year CFSv2: every cycle of every day of the year, total of 1460 initial states per year. Sample size: 1980 for CFSv1; for CFSv2. 51
52 The bivariate anomaly correlation (BAC)x100 of CFS in predicting the MJO for period , as expressed by the Wheeler and Hendon (WH) index (two EOFs of combined zonal wind and OLR). On the left is CFSv2 and on the right is CFSv1. Both are subjected to Systematic Error Correction. The black lines indicate the 0.5 level of BAC. Qin Zhang and Huug van den Dool, CPC
Before Model Bias CorrectionAfter Model Bias Correction Qin Zhang and Huug van den Dool, CPC 53
Before Model Bias CorrectionAfter Model Bias Correction Qin Zhang and Huug van den Dool, CPC 54
55 The NCEP Climate Forecast System Version 2 (2013) Suranjana Saha, Shrinivas Moorthi, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, David Behringer, Yu-Tai Hou, Hui-ya Chuang, Mark Iredell, Michael Ek, Jesse Meng, Rongqian Yang, Malaquias Pena Mendez, Huug van den Dool, Qin Zhang, Wanqiu Wang, Mingyue Chen, Emily Becker. (Journal of Climate, under review, revised.)