THE NCEP CLIMATE FORECAST SYSTEM VERSION 2

1. THE NCEP CLIMATE FORECAST SYSTEM VERSION 2 SURANJANA SAHA THE ENVIRONMENTAL MODELING CENTER NCEP/NWS/NOAA NEMS/GFS Modeling Summer School 2013 Presentation on Climate 8:30-9:00am, Wednesday July 31, 2013

2. CFSv2 consists of the following components: • 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

3. The models used by the CFSv2 are: • Atmosphere at horizontal resolution of spectral T126 (~100 km) and vertical resolution of 64 sigma-pressure hybrid levels • 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 • Interactive 3 layer sea-ice model • Interactive land model with 4 soil levels

4. The NCEP Climate Forecast System Version 2 (CFSv2) was implemented into operations on March 30, 2011. A new Reanalysis of the atmosphere, ocean, seaice and land was made over a 32-year period (1979-2010) to provide consistent initial conditions for: A complete Reforecast of the new CFSv2 over the 29-year period (1982-2010), in order to provide stable calibration and skill estimates of the new system, for operational seasonal prediction at NCEP

5. 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)

6. 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).

7. ONE DAY OF ANALYSIS 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)

8. Courtesy: Shrinivas Moorthi

9. Courtesy: Shrinivas Moorthi

10. CFSR data dump volumes, 1978-2009, in GB/month Courtesy: Jack Woollen

11. 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. Courtesy: http://gaw.kishou.go.jp

12. 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). Courtesy: Huug van den Dool

13. Response of Prec. To SST increase : warming too quick in R1 and R2 SST-Precipitation Relationship in CFSR Precipitation-SST lag correlation in tropical Western Pacific simultaneous positive correlation in R1 and R2 Courtesy: Jiande Wang

14. 5-day T126L64 forecast anomaly correlations Courtesy: Bob Kistler

15. THE OCEAN, SEA ICE AND COUPLER

16. The global number of temperature observations assimilated per month by the ocean component of the CFSR as a function of depth for the years 1980-2009. Courtesy: Dave Behringer

17. The global distribution of all temperature profiles assimilated by the ocean component of the CFSR for the year 1985. The distribution is dominated by XBT profiles collected along shipping routes. Courtesy: Dave Behringer

18. The global distribution of all temperature profiles assimilated by the ocean component of the CFSR for the year 2008. The Argo array (blue) provides a nearly uniform global distribution of temperature profiles Courtesy: Dave Behringer

19. 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 165E 165E 110W 170W 110W DJF 170W DJF JJA JJA Courtesy: Sudhir Nadiga

20. 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). Courtesy: Sudhir Nadiga

21. The first two EOFs of the SSH variability for the CFSR (left) and for TOPEX satellite altimeter data (right) for the period: 1993-2008. The time series amplitude factors are plotted in the bottom panel. Courtesy: Sudhir Nadiga

22. Monthly mean sea ice concentration for the Arctic from CFSR (6-hr forecasts) Courtesy: Xingren Wu

23. Monthly mean Sea ice extent (106 km2) 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

24. 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

25. ATM (dummy) LAND (dummy) GFS Coupler Sea-ice Δc Δi Δa Ocean Δo Data Flow Fast loop: if Δa= Δc= Δi, coupled at every time step Slow loop: Δo 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 Courtesy: Jun Wang

26. 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

27. THE SURFACE

28. 2-meter volumetric soil moisture climatology of CFSR for May averaged over 1980-2008. Courtesy: Jesse Meng

29. 2-meter volumetric soil moisture climatology of CFSR for Nov averaged over 1980-2008. Courtesy: Jesse Meng

30. Soil Moisture Anomaly [%] [%] CONUS GR2 NARR CFSR OBS [mm] [mm] Illinois R(GR2,OBS)=0.48 R(NARR,OBS)=0.67 R(CFSR,OBS)=0.61 The CFSR soil moisture climatology is consistent with GR2 and NARR on regional scale. The anomaly agrees with the Illinois observations, correlation coefficient = 0.61. Global Soil Moisture Fields in the NCEP CFSR Courtesy: Jesse Meng

31. Global average of monthly-mean Precipitation (a), Evaporation (b) and E-P (c). Courtesy: Wanqiu Wang

32. Monthly mean hourly surface pressure with the daily mean subtracted for the month of March 1998 Courtesy: Huug van den Dool

33. 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

34. 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

35. 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 1015-1057. doi: 10.1175/2010BAMS3001.1

36. AND NOW…….. THE SECOND ‘R’ IN

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 CO2 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 (Eket 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 (Sahaet 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: • 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. • 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.

41. Jan 2 0 6 12 18 Jan 1 0 6 12 18 Jan 3 0 6 12 18 Jan 4 0 6 12 18 Jan 5 0 6 12 18 Jan 6 0 6 12 18 1 season run 45 day run 9 month run Hindcast Configuration for CFSv2 • 9-month hindcasts were initiated from every 5th day and run from all 4 cycles of that day, beginning from Jan 1 of each year, over a 29 year period from 1982-2010 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 1999-2010. 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 1999-2010. This is required for the operational CPC week3-week6 predictions of tropical circulations (MJO, PNA, etc)

42. 0 UTC 6 UTC 12 UTC 18 UTC 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. 1 season run (3) 45 day run (9) 9 month run (4)

43. 9-MONTH HINDCASTS 28 years: 1982-2009; 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.

44. Definitions and Data AC of ensemble averaged monthly means GHCN-CAMS (validation for Tmp2m) CMAP (validation for Prate) OIv2 (validation for SST) 1982-2009 (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: 1982-1998 and 1999-2009 Elsewhere: 1982-2009

45. More skill globally for CFSv2

46. More skill in the western Pacific for CFSv2

47. More skill west of the dateline and over the Atlantic for CFSv2

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 1982-2009. 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 1982-2009. Below each map is the map integrated BSS value.