Introduction to Climate forecast System Version 2 (CFSV2)

1

• Introduction to Climate forecast System Version 2
  (CFSV2) AM, OM, LM, Sea-ice GODAS and GLDAS

Shrinivas Moorthi
Acknowledgement Many of the slides presented
here are prepared by members of GCWMB branch and
climate and land modeling teams.
2
Overview

• CFS-v1 description and status
• CFS-v2 CFS Reanalysis and Reforecast
  (CFSRR)
• Atmosphere
• Ocean
• Land
• Sea ice
• Future development (CFS-v3)
• Coupled A-O-L-S system
• Long term Reanalysis strategy

3
Seasonal to Interannual Prediction at
NCEP (CFS-v1) Operational August 2004 March
2011
Ocean Model MOMv3 quasi-global 1ox1o (1/3o in
tropics) 40 levels
Climate Forecast System (CFS)
Atmospheric Model GFS (2003) T62 (200 km) 64
sigma levels
Weather Climate Model
Daily Coupling
GODAS (2003) 3DVAR XBT TAO Triton Pirata Argo Sali
nity (syn.) TOPEX/Jason-1
Reanalysis-2 3DVAR T62L28 (1995 GFS) OIv2
SST Levitus SSS clim.
Ocean reanalysis (1980-present) provides initial
conditions for retrospective CFS forecasts used
for calibration and research
Stand-alone version with a 14-day lag updated
routinely
4
CFS-v2 Highlights

• High resolution data assimilation
• Produces better initial conditions for
  operational hindcasts and forecasts (e.g. MJO)
• Enables new products for the monthly forecast
  system
• Enables additional hindcast research
• Coupled data assimilation
• Reduces coupling shock
• Improves spin up character of the forecasts
• Consistent analysis-reanalysis and
  forecast-reforecast for
• Improved calibration and skill estimates
• Provide basis for a future coupled A-O-L-S
  forecast system running operationally at NCEP (1
  day to 1 year)

5
CFSRR Components

• Reanalysis
• 31-year period (1979-2010 and continued in NCEP
  ops)
• Atmosphere
• Ocean
• Land
• Seaice
• Coupled system (A-O-L-S) provides background for
  analysis
• Produces consistent initial conditions for
  climate and weather forecasts
• Reforecast
• 29-year period (1982-2010 and continued in NCEP
  ops )
• Provides stable calibration and skill estimates
  for new operational seasonal system
• Includes upgrades for A-O-L-S developed since CFS
  originally implemented in 2004
• Upgrades developed and tested for both climate
  and weather prediction
• Unified weather-climate strategy (1 day to 1
  year)

6
CFSRR
Climate Forecast System V2
7
CDAS (R1) CFS V2 AM
Vertical coordinate Sigma Sigma/pressure
Spectral resolution T62 T382
Horizontal resolution 210 km 35 km
Vertical layers 28 64
Top level pressure 3 hPa 0.266 hPa
Layers above 100 hPa 7 24
Layers below 850 hPa 6 13
Lowest layer thickness 40 m 20 m
Analysis scheme SSI GSI
Satellite data NESDIS temperature retrievals Radiances
8
AM in CFSR

• Enthalpy (CpT) as a prognostic variable in place
  of Tv
• AER RRTM shortwave radiation with maximum-random
  cloud overlap
• IR and Solar radiation called every hour
• Use of historical and spatially varying CO2 and
  volcanic aerosols

9
Why Enthalpy as a prognostic variable?

• Collaboration between Space Weather Prediction
  Center and EMC to develop whole atmosphere
  model (0-600km) to be coupled to global
  ionosphere plasmasphere model
• More accurate thermodynamic equation is
  essential since rtop/rsfc 10-13
• Variation of specific heats in space and time
  needs to be
• accounted for

10
The thermodynamic equation used in the
operational GFS AM has the form
where
with ideal-gas law in the form
Here Rd and Rv are gas constants for dry air
and water vapor and Cpd, Cpv are specific heats
at constant pressure for dry air and water vapor.

11
The ideal-gas law is
The thermodynamic equation, derived from internal
energy equation is (Akmaev, 2006 SWPC)
and defining enthalpy h as
the thermodynamic energy equation can be
re-written as
which has the same form as operational one

12
However, here R and Cp are determined by their
specific mixing ratios
Currently, GFS AM has three tracers specific
humidity, ozone and cloud water. Ignoring cloud
water, We use dry air sp. Hum
ozone
Ri 287.05 461.50 173.2247 Cpi
1004.6 1846.0 820.2391
Henry Juang of EMC implemented Enthalpy in the
GFS AM
13
AM configuration For CDAS

• For CDAS vertical coordinate was changed from
  generalized coordinate of CFSR to sigma-pressure
  hybrid coordinate of operational GFS.
• The vertical advection of tracers based on the
  TVD scheme is used
• Latest version of operational GSI is also used
• Convective gravity wave drag and the changes
  related to marine stratus are retained
• Other changes made following the current
  operational GFS are

14
AM configuration For CDAS

• Resolution and ESMF
• Eulerian T574L64 for fcst (0-9hr)
• ESMF 3.1.0rp2
• Radiation and cloud
• RRTM2 for Short Wave Radiation
• RRTM1 Long Wave Radiation with hourly computation
  
• Stratospheric aerosol SW and LW and tropospheric
  aerosol LW
• Changing aerosol SW single scattering albedo from
  0.90 in the operation to 0.99
• Changing SW aerosol asymmetry factor. Using new
  aerosol climatology.
• Maximum/random cloud overlap
• Time and spatially varying CO2
• Yang et al. (2008) scheme to treat the dependence
  of direct-beam surface albedo on solar zenith
  angle over snow-free land surface

15
AM Configuration for CDAS

• Gravity-Wave Drag Parameterization
• Modified GWD routine to automatically scale
  mountain block and GWD stress with resolution.
• Compared to the T382L64 GFS, the T574L64 GFS uses
  four times stronger mountain block and one half
  the strength of GWD.
• Removal of negative water vapor
• Using a positive-definite tracer transport scheme
  in the vertical to replace the operational
  central-differencing scheme to eliminate
  computationally-induced negative tracers.
• Changing GSI factqmin and factqmax parameters to
  reduce negative water vapor and supersaturation
  points from analysis step.
• Modifying cloud physics to limit the borrowing of
  water vapor that is used to fill negative cloud
  water to the maximum amount of available water
  vapor so as to prevent the model from producing
  negative water vapor.
• Changing the minimum value of specific humidity
  in radiation in radiation calculation from 1.0e-5
  in the operation to 1.0e-7 kg/kg.

16
AM configuration For CDAS

• Hurricane relocation
• Running hurricane relocation at the 1760x880
  forecast grid instead of the 1152x576 analysis
  grid
• Posting GDAS pgb files first on Guassian grid
  (1760x880), then convert to 0.5-deg for hurricane
  relocation.
• Post processing and Utility
• Posting GFS forecast master pgb files on 0.5 deg,
  then copygb to 1-deg for postprocessing and
  archive.
• Using a 20-bit and faster copygb instead of the
  operational 16-bit copygb
• Using a new chgres which has double precision and
  has a fix in dry air mass (pdryini20)
• Snow analysis
• Using T574 compatible high-resolution snow
  analysis

17
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, current operations)
• CO2 run is the ensemble mean of 3 NCEP CFS runs
  in CMIP mode
• realistic CO2 and aerosols in both troposphere
  and stratosphere
• Processing 25-month running mean applied to the
  time series of anomalies (deviations from their
  own climatologies)

18
Noah LSM replaces OSU LSM in new CFS

• 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 and moist soils

• 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
• More

Noah LSM replaced OSU LSM in operational NCEP
medium-range Global Forecast System (GFS) in late
May 2005
Some Noah LSM upgrades assessments were result
of collaborations with CPPA PIs
K. Mitchell
19
CFSRR Reanalysis Land Component Global Land
Data Assimilation System (GLDAS)

• Applies same Noah LSM as in new CFS
• Uses same native grid (T382 Gaussian) as CFSRR
  atmospheric analysis
• Applies CFSRR atmospheric analysis forcing
  (except for precip)
• hourly from previous 24-hours of atmospheric
  analysis
• Precipitation forcing is from CPC analyses of
  observed precipitation
• Model precipitation is blended in only at very
  high latitudes
• GLDAS daily update of the CFSRR reanalysis soil
  moisture states
• Reprocesses last 6-7 days to capture and apply
  most recent CPC precipitation analyses
• Realtime GLDAS configuration will match
  reanalysis configuration
• To sustain the relevance of the climatology of
  the retrospective reanalysis
• Applies LIS uses the computational
  infrastructure of the NASA Land Information
  System (LIS), which is highly parallelized

20
LIS Capabilities

• Flexible choice of 7 different land models
• Includes Noah LSM used operationally by NCEP and
  AFWA
• Flexible domain and grid choice
• Global such as NCEP global model Gaussian grid
• Regional including very high resolution (.1-1
  km)
• Data Assimilation
• Based on Kalman Filter approaches
• High performance parallel computing
• Scales efficiently across multiple CPUs
• Interoperable and portable
• Executes on several computational platforms
• NCEP and AFWA computers included
• Being coupled to NWP CRTM radiative transfer
  models
• Coupling to WRF model has been demonstrated
• Coupling to NCEP global GFS model is under
  development
• Coupling to JCSDA CRTM radiative transfer model
  is nearing completion
• Next-gen AFWA AGRMET model will utilize LIS with
  Noah
• NCEPs Global Land Data Assimilation utilizes LIS

K. Mitchell, C. Peters-Lidard
21
Global Land Data Assimilation System (GLDAS)
GLDAS (running Noah LSM under NASA/Land
Information System) forced with CFSv2/GDAS
atmospheric data assimilation output and blended
precipitation in a semi-coupled mode, versus no
GLDAS in CFSv1, where CFSv2/GLDAS ingested into
CFSv2/GDAS once every 24-hours. In
CFSv2/GLDAS, blended precipitation a function of
satellite (CMAP heaviest weight in tropics),
surface gauge (heaviest in middle latitudes) and
GDAS (modeled high latitude), vs use of model
precipitation comparison with CMAP product and
corresponding adjustment to soil moisture in
CFSv1. Snow cycled in CFSv2/GLDAS if model
within 0.5x to 2.0x of the observed value (IMS
snow cover, and AFWA snow depth products), else
adjusted to 0.5 or 2.0 of observed value.
GDAS-CMAP precip
IMS snow cover
AFWA snow depth
Gauge locations
22
Land Information System
22
Christa Peters-Lidard et al., NASA/GSFC/HSB
23
LAND SURFACE MODEL
CFSv1 (T62L64) CFSv2 (T126L64)
OSU LSM (2 layers) Noah LSM (4 layers) and sea ice model

• 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

• 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

24
CFSR Soil Moisture Climatology
24
25
CFSR Soil Moisture Climatology
25
26
GODAS in the CFSRR

• Operational in 2010
• MOMv4 (1/2o x 1/2o, 1/4o in the tropics, 40
  levels)
• Updated 3DVAR assimilation scheme
• Temperature profiles (XBT, Argo, TAO, TRITON,
  PIRATA)
• Synthetic salinity profiles derived from seasonal
  T-S relationship
• TOPEX/Jason-1 Altimetry
• Data window is asymmetrical extending from
  10-days before the analysis date
• Surface temperature relaxation to (or
  assimilation of) Reynolds new daily, 1/4o OIv2
  SST
• Surface salinity relaxation Levitus
  climatological SSS
• Coupled atmosphere-ocean background
• Current stand-alone operational GODAS will be
  upgraded in 2009 to the higher resolution MOMv4
  and be available for comparison with the coupled
  version
• Updated with new techniques and observations

D. Behringer
27
MOM4p0d

• Version
• The ocean is modeled with GFDLs Modular Ocean
  Model Version 4.0d (MOM4p0d)
• The code has been rewritten from earlier versions
  and is now in Fortran 90.
• MOM4p0d supports 2-dimensional domain
  decomposition for improved efficiency in parallel
  environments as compared with earlier versions.
• MOM4p0d supports the Murray (1996) tripolar grid,
  providing an elegant solution to the problems
  associated with the convergence of a spherical
  coordinate grid in the Arctic.
• Domain and Resolution
• The domain is global (the previous version did
  not have an interactive Arctic Ocean).
• The grid is Arakawa B and the resolution is
  1/2ox1/2o (1/4o within 10o of the equator).
• The vertical grid has 40 Z-levels with variable
  resolution (23 levels in the top 230 meters).
• Physics
• There is a fully interactive ice model.
• The equation of state is the McDougall et al.
  (2002) formulation.
• The non-local boundary layer parameterization,
  KPP, of Large et al. (1994) is used.
• Isoneutral lateral diffusion is used (Griffies et
  al., 1998)
• The formulation is Boussinesq and has a free
  surface.

27
28
GODAS 3DVAR

• Version
• The Global Ocean Data Assimilation System (GODAS)
  is now based on MOM4p0d
• As was the case with MOM4p0d, the code has been
  completely rewritten from Fortran 77 to Fortran
  90.
• Domain and Resolution
• The GODAS now has a global domain.
• The resolution has been increased to match the
  MOM4p0d configuration used in the CFSv2
  1/2ox1/2o (1/4o within 10o of the equator) 40
  Z-levels.
• Functionality
• The analysis core of the GODAS (i.e. the 3DVAR)
  may be compiled either as an executable combing
  the analysis with MOM or an as executable
  containing only the analysis. The latter
  formulation is used with the CFSv2 where it reads
  the forecast from a restart file produced by the
  coupled CFSv2, does the analysis, and updates the
  restart file.
• An additional relaxation of surface temperature
  and salinity to observed fields is also under the
  control of the 3DVAR analysis.
• Data
• The data sets that can be assimilated are XBTs,
  tropical moorings (TAO, TRITON, PIRATA, RAMA),
  Argo floats, CTDs), altimetry (JASON-x).

28
29
GODAS in CFSv2
Climate Forecast System
coupled in memory each 30 min
30
MOMv4 Global Tripolar Grid
2 Arctic poles reside in landmass
Higher resolution in equatorial zone
The resolution is 1/2o X 1/2o increasing to 1/2o
X 1/4o within 10o of the equator (resolution
reduced 4X for display)
31
Tripolar grid (Murray, 1996)
2 Arctic poles reside in landmass
Arctic grid matches spherical coordinate grid
at 65oN
After Griffies, 2007
32
The observing system
XBT
TAO
TP/J-1
Argo
33
(No Transcript)
34
The changing number of temperature observations
as a function of time and depth
35
Sample annual distributions of T(z) as used by
GODAS
XBT-green TAO-red Argo-blue
36
The changing distribution of observations

• Mostly XBTs (green) from fisheries, research
  cruises and shipping lines
• Far more in Northern Hemisphere than in Southern
  Hemisphere
• High concentration along coasts
• Only a few tropical moorings (red)
• About 60K profiles in 1985

• Argo float profiles (blue) now provide nearly
  full global coverage
• Far more uniform distribution (gt3200 floats, 120K
  profiles)
• Moorings span the Pacific (TAO/TRITON), the
  Atlantic (PIRATA) and Indian Oceans (RAMA).
  (gt100, 36K profiles)
• Fewer XBTs than in earlier decades (30K profiles)

37
International Argo deployments in 2000
GODAS assimilates all Argo and proto-Argo
profiles.
38
International Argo deployments as of October 31,
2007
Full Deployment
GODAS assimilates all Argo and proto-Argo
profiles.
39

• Commonality among versions of GODAS
• during El Nino - La Nina shift of 97-98
• All assimilate same data, incl. TAO
• Altimetry withheld from these runs

Two forced by NCEP-DOE R2, but use different
models MOMv3 vs. MOMv4
Two use the same model MOMv4, but use different
forcing R2 vs. CFSR
Two use the same model MOMv4 and forcing CFSR,
but are uncoupled vs. coupled
The solutions are most alike where there are data
and differ most in the absence of data.
40

• Commonality among versions of GODAS
• during El Nino - La Nina shift of 97-98
• Velocity data are not assimilated
• Altimetry withheld from these runs

Two forced by NCEP-DOE R2, but use different
models MOMv3 vs. MOMv4
Two use the same model MOMv4, but use different
forcing R2 vs. CFSR
Two use the same model MOMv4 and forcing CFSR,
but are uncoupled vs. coupled
Solutions show greater differences in currents
than in temperature. Forced MOMv4 solutions are
most similar. Similarity between the MOMv3
analysis and the CFSR is coincidental.
41
Strong similarities among the model runs and the
observations. The CFSR is weakest in the cold
tongue area. The positive signal at 20oN in the
uncoupled runs is present in the CFSR and TOPEX,
but too weak to be seen at 10cm interval.
42
GODAS compared with surface drifter derived SST
MOMv4 based GODAS 1/2o resolution Global
MOMv3 based GODAS 1o resolution Quasi-global
AOML surface drifter based SST climatology
Independent data (Lumpkin et al.)
43
GODAS compared with independent surface drifter
velocities
The agreement is very good given that GODAS does
not directly assimilate velocity observations and
the drifter velocities are derived from the
lagrangian motion of the drifters.
44
GODAS compared with tide gauges and TOPEX/Jason-1
For these experiments tide gauges and
TOPEX/Jason-1 are independent
45
(No Transcript)
46
(No Transcript)
47
(No Transcript)
48
Equatorial salinity section in the Pacific
(vertical bars show positions of time-series
below).
Assimilating Argo Salinity
GODAS
Salinity variability due to correlation with
temperature.
GODAS-A/S
Salinity variability introduced by observations.
49
In the east, assimilating Argo salinity reduces
the bias at the surface and sharpens the profile
below the thermocline at 110oW.
In the west, assimilating Argo salinity corrects
the bias at the surface and the depth of the
undercurrent core and captures the complex
structure at 165oE.
Assimilating Argo Salinity
Comparison with independent ADCP currents.
ADCP GODAS GODAS-A/S
50
INCOIS NCEP Collaboration
Co-Principal Investigators M. Ravichandran
(INCOIS), D.Behringer (NCEP)
The collaboration was established in November of
2009 for the purpose of transferring a copy of
NCEPs Global Ocean Data Assimilation System
(GODAS) to INCOIS. The GODAS will provide INCOIS
with a real-time analysis of the physical state
of the Indian Ocean through the assimilation of
data sets from a variety of platforms (ships,
moorings, autonomous drifting buoys, satellite).
In return, NCEP will benefit from an ongoing
expert evaluation of the GODAS performance in the
Indian Ocean, leading to model and system
improvements.
GODAS code and sample forcing and assimilation
data suitable for testing were transferred to
INCOIS in January, 2010.
INCOIS had GODAS up and running by the end of
March and had finished a long experiment (2003
present) by the end of April.
INCOIS is currently exploring the sensitivity of
the system to the wind forcing (NCEP vs QuikSCAT).
51
SEA ICE Model in CFSV2
Xingren Wu EMC/NCEP and IMSG
52
NSIDC
Arctic sea ice hits record low in 2007
9/16/2007
53
Outline

• Sea Ice
• Sea Ice in the Weather and Climate System
• Sea Ice in the NCEP Forecast System
• - Analysis/Assimilation
• - Forecast GFS, CFS
• Sea Ice in the CFS Reanalysis

54
Sea Ice
Sea ice is a thin skin of frozen water covering
the polar oceans. It is a highly variable feature
of the earths surface.
Nilas Leads First-Year Ice
Pancake Ice
Multi-Year Ice
Greece Ice
Melt Pond
Snow-Ice
Rafting
55
Sea ice affects climate and weather related
processes

• Sea ice amplifies any change of climate due to
  its positive feedback (coupled climate model
  concern)
• Sea ice is white and reflects solar radiation
  back to space. More sea ice cools the Earth, less
  of it warms the Earth. A cooler Earth means more
  sea ice and vice versa.

• Sea ice restricts the exchange of heat/water
  between the air and ocean (NWP concern)

• Sea ice modifies air/sea momentum transfer, ocean
  fresh water balance and ocean circulation
• The formation of sea ice injects salt into the
  ocean which makes the water heavier and causes it
  to flow downwards to the deep waters and drive a
  massive ocean circulation

56

• Issues related to sea ice forecast
• Data assimilation
• Initial conditions
• Sea ice models and coupling

57

• Data assimilation issues
• Sea ice concentration data are available but
  velocity data lack to real time
• Lack of sea ice and snow thickness data

• Initial condition issues
• Sea ice concentration data are available but
  velocity data lack to real time
• Sea ice and snow thickness data are based on
  model spin-up values or climatology

58

• Sea ice model and coupling issues
• Ice thermodynamics
• Ice dynamics
• Ice model coupling to the atmosphere
• Ice model coupling to the ocean

59
NCEP Sea Ice Analysis Algorithm

• 5 minutes latitude-longitude grid from the
  85GHz SSMI information based on NASA Team
  Algorithm
• Half degrees version of the product is used in
  GFS (as initial condition).

Courtesy Robert Grumbine
60
Ice Model Thermodynamics

• Based on the principle of the conservation of
  energy, determine
• Ice formation
• Ice growth
• Ice melting
• Ice temperature structure

61
Ice Model Dynamics

• Based on the principle of the conservation of
  momentum, determine
• Ice motions
• Ice deformation
• Leads (open water)

62
Ice Model Dynamics (Cont.)

• Five major dynamic forces in the momentum
  equation
• air stress at the top of sea-ice
• water stress below sea-ice
• gravitational stress from the tilt of sea surface
  (dynamic topography)
• coriolis force
• pressure stresses within ice
• Nonlinear viscous-plastic (VP) ice rheology

1. Hibler, W.D.III. 1979. A dynamic thermodynamic
   sea ice model. J. Phys. Oceanogr., 9, 815-846

63
Sea Ice in the NCEP Global Forecast System

• A three-layer thermodynamic sea ice model was
  embedded into GFS (May 2005).
• It predicts sea ice/snow thickness, the surface
  temperature and ice temperature structure.
• In each model grid box, the heat and moisture
  fluxes and albedo are treated separately for ice
  and open water.

64
Sea Ice in the NCEP GFS (cont.)
Atmospheric model
SW Heat Flux
LW Heat Flux
Turbulent Heat Flux
Ice Fraction
Snow Rate
Ice Fraction
Ice/Snow Thickness
Ice/Snow Thickness
3-layer thermodynamics Ice model
Ice Temperature
Ice Temperature
Surface Temperature
surface Temperature
Oceanic Heat Flux
Salinity
Fresh Water
Ocean model
65
Sea Ice in CFSv1

• Sea ice is treated in a simple manner - 3 m depth
  with 100 concentration (i.e. no open water
  within the ice covered area). The surface
  temperature is predicted based on energy balance
  at the ice surface.
• Sea ice climatology is used to update sea-ice
  change in CFS (with 50 cutoff for sea-ice cover).

66
Sea Ice in CFSV2

• Hunke and Dukowicz (1997) elastic-viscous-plastic
  (EVP) ice dynamics model
• Improved numerical method for Hiblers
  viscous-plastic (VP) model
• Computionally more efficient than Hiblers VP
  model
• Winton (2000) 3-layer thermodynamic model plus
  ice thickness distribution
• 2-layer of sea ice and 1-layer of snow
• Fully implicit time-stepping scheme, allowing
  longer time steps
• 5 categories of sea ice

67
Tripolar grid of Murray (1996) over the Arctic
for the sea ice model
This avoids a singularity at the North Pole
68
Sea ice concentration from CFSR for the Arctic
69
Bias of sea ice concentration from CFSR for the
Arctic
70
Sea ice thickness from CFSR for the Arctic
71
Sea ice concentration from CFSR for the Antarctic
72
Bias of sea ice concentration from CFSR for the
Antarctic
73
Sea ice thickness from CFSR for the Antarctic
74
Sea ice extent from CFSR for the Arctic in
March And In September
75
Surface air temperature from CFSR and the
difference amongst CFSR, R1, R2 and ERA40
76
Coupling AM (GFS) and OM

• In CFSV2 the atmosphere-ocean , the coupling
  at is MPI-level (originlly developed by Dmitry
  Shenin for coupling with MOM3, adapted to CFSV2
  by Jun Wang and Xingren Wu)
• AM, OM and the coupler run simultaneously
  
• Coupling frequency is flexible up to the OM
  time step
• Same AM code can run in coupled or
  standalone mode

77
The Coupled model MOM4

• Parallel programming model in MOM4 SPMD

MOM4.exe
ATM LAND Sea Ice
Ocean
78
Sea-ice is one component of the CFSv2
Fast loop ?a ?c ?i
Slow loop ?o
Fluxes
Tsfc Sea-Ice
X-grid
79
GFS-Sea Ice/MOM4 Coupler
Parallel programming model MPMD (Multiple
Program Multiple Data)
ICE/OCN
GFS
Time Step ?o
Time Step ?a
Time Step ?i
Coupler
Time Step ?o
Time Step ?a
Time Step ?i
Time Step ?c
Time Step ?a
Time Step ?o
Time Step ?i
Courtesy Jun Wang
80
Exchange grid (x-grid)
ATM
SBL
LND
ICE
OCN
LND
Courtesy GFDL
81
Coupled architecture parallelism
GFS
Coupler redist
MOM4
ATM
Regrid
SBL
Regrid with Mask
LND
ICE
Redistribution
OCN
82
Data Flow
Fast loop if ?a ?c ?i, coupled at every time
step Slow loop ?o
ATM (dummy)
LAND (dummy)
GFS
Coupler
Sea-ice
?c
?i
?a
Ocean
?o
83
Air-Sea Ice-Ocean Interaction

• 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

84
Coupler Configuration

• 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

85
CFS Reanalysis and Reforecast Scripts
AM and OM Post post.sh
Start here Copy IC files copy.sh
9 (or 48) hr Coupled Model Forecast (first
guess) New GFS MOM4 with Sea Ice MPI-level
Coupling fcst.sh
Verify vrfy.sh
CFSRR website
Prep step Hurricane relocation Data
preparation prep.sh
GODAS Global Ocean Data Assimilation oanl.sh
Archive data arch.sh
Retrospective Forecast?
Time 00Z ?
GDAS Global Atmospheric Data Assimilation GSI
anal.sh
GLDAS Global Land Data Assimi- lation lanl.sh
Run Retrospective Forecast fcst.sh