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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 preparedby members of GCWMB branch and climate and land modeling teams.
Disk management at the NCEP Super computers for developers
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The disk partitioning for development use at NCEP:
/u/home - small individual user space meant for keeping small files such .bashrc, .profile, and some small scripts etc
/global/save directory where source code, scripts and any important and hard to replace data. This disk is backed up daily.
/global/noscrub - Bigger chunk of disk where files from forecasts can be saved for some time until archived – not backed up
/ptmp - large temporary space where one can run model and the disk is scrubbed often based on use
/stmp - large disk space which can also be used for running jobs, but scrubbed daily.
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Seasonal to Interannual Prediction at NCEP(CFS-v1) Operational August 2004 – March 2011
ClimateForecastSystem(CFS)
Ocean ModelMOMv3
quasi-global1ox1o (1/3o in tropics)
40 levels
Atmospheric ModelGFS (2003)
T62 (~200 km)64 sigma levels
GODAS (2003)3DVAR
XBTTAOTritonPirataArgo
Salinity (syn.)TOPEX/Jason-1
Reanalysis-23DVAR
T62L28 (1995 GFS)
OIv2 SSTLevitus 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 lagupdated routinely
DailyCoupling
“Weather& Climate”
Model
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1. 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
2. Coupled data assimilation– Reduces “coupling shock”– Improves spin up character of the forecasts
3. Consistent analysis-reanalysis and forecast-reforecast for – Improved calibration and skill estimates
4. Provide basis for a future coupled A-O-L-S forecast system running operationally at NCEP (1 day to 1 year)
CFS-v2 Highlights
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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
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CFSRR
GODAS3DVAR
Ocean ModelMOMv4
fully global1/2ox1/2o (1/4o in tropics)
40 levels
Atmospheric ModelGFS (2008)*
T382 64 levels
Land Model Ice ModelGLDASLIS
GDASGSI
6hr
24hr
6hr
Ice Ext6hr
Climate Forecast System V2
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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
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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
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Why Enthalpy as a prognostic variable?
Collaboration between Space Weather Prediction Center and EMC to develop whole atmosphere model (0-600km) coupled to global ionosphere plasmasphere model
- to help predict potential communication and electrical grid disruption due to solar flares
More accurate thermodynamic equation is essential since top/sfc ~ 10-13
Variation of specific heats in space and time needs to be
accounted for
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The thermodynamic equation used in the operational GFS AM (sigma/p hybrid) has the form
Tv 1 Rv /Rd 1 q T
with ideal-gas law in the form
pRdTv
dTvdt
Tvp
dp
dtQ
where
RdCP
Rd
CPd CPv CPd q
d1 CPv /CPd 1 q
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.
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The ideal-gas law is
Qdt
dp
dt
TdC p
and defining enthalpy h as TCh p
the thermodynamic energy equation can be re-written as
dh
dt
hp
dp
dtQ
RTp
The thermodynamic equation, derived from internal energy equation is (Akmaev, 2006 – SWPC)
which has the same form as operational one
Qdt
dp
p
T
dt
dT vv
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However, here R and Cp are determined by their specific mixing ratios
Ntrac
iiid
Ntrac
iiii qRRqqRR
11
)1(
Ntrac
iiipdp
Ntrac
iiipp qCCqqCC
i11
)1(
Currently, GFS AM has three tracers – specific humidity, ozone and cloud water. Ignoring cloud water,We use : dry air sp. Hum ozone
Ri 287.05 461.50 173.2247Cpi 1004.6 1846.0 820.2391
Henry Juang of EMC implemented Enthalpy in the GFS AM
AM configuration for CDAS(operational climate data assimilation system)
• Operational CDAS associated with CFSV2 was implemented on March 30, 2011
• The vertical coordinate was changed from generalized coordinate of CFSR to sigma-pressure hybrid coordinate of operational GFS.
• The vertical advection of tracers is based on the TVD scheme
• 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:
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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
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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.
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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 (pdryini2=0)• Snow analysis
– Using T574 compatible high-resolution snow analysis 04/21/23 Shrinivas Moorthi
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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
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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.
IMS snow cover AFWA snow depthGDAS-CMAP precip Gauge locations
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Land Information System
Noah LSM CFSRland analysis
Soil MoistureSoil Temperature
Snow
Land SurfaceCharacteristicsTopography Land Cover
Soil
Non-precipMeteorological
Forcing
PrecipitationForcing
Land VariablesSoil Moisture
Soil TemperatureSnow
CFSR surface file
gdas1.t00z.sfcanl
Christa Peters-Lidard et al., NASA/GSFC/HSB
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CFSv1 (T62L64) CFSv2 (T126L64)
OSU LSM (2 layers) Noah LSM (4 layers) and sea ice model
LAND SURFACE 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
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CFSR Soil Moisture Climatology
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CFSR Soil Moisture Climatology
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• Operational in 2011• 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 to the higher resolution MOMv4 and be available for comparison with the coupled version– Updated with new techniques and observations
GODAS in the CFSRR
D. Behringer
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MOM4p0d
Version The ocean is modeled with GFDL’s 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.
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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).
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GODAS in CFSv2
GODAS3DVAR
Ocean ModelMOMv4
fully global1/2ox1/2o (1/4o in tropics)
40 levels
Atmospheric ModelGFS (2008)
T126 64 levels
Land Model Ice Mdl SISLDAS
GDASGSI
6hr
24hr
6hr
Ice Ext6hr
Climate Forecast System
coupled inmemory
each 30 min
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MOMv4 Global Tripolar Grid
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)
2 Arctic polesreside in landmass
Higher resolution in equatorial zone
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Tripolar grid (Murray, 1996)
2 Arctic polesreside in landmass
Arctic grid matches sphericalcoordinategrid at 65oN
After Griffies, 2007
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The observing system
XBT
TAO
TP/J-1
Argo
1980 1990 2000
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The changing number of temperatureobservations as a function of time and depth
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Sample annual distributions of T(z) as used by GODAS
XBT-green TAO-red Argo-blue
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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 (>3200 floats, 120K profiles)•Moorings span the Pacific (TAO/TRITON), the Atlantic (PIRATA) and Indian Oceans (RAMA). (>100, ~36K profiles)•Fewer XBTs than in earlier decades (~30K profiles)
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International Argo deployments in 2000
GODAS assimilates all Argo and proto-Argo profiles.
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International Argo deployments as of October 31, 2007
GODAS assimilates all Argo and proto-Argo profiles.
Full Deployment
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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 usedifferent models: MOMv3 vs. MOMv4
Two use the same model: MOMv4, butuse different forcing: R2 vs. CFSR
Two use the same model: MOMv4 andforcing: CFSR, but are uncoupled vs. coupled
The solutions are most alike where there aredata and differ most in the absence of data.
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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 usedifferent models: MOMv3 vs. MOMv4
Two use the same model: MOMv4, butuse different forcing: R2 vs. CFSR
Two use the same model: MOMv4 andforcing: 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.
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Commonality among versions of GODAS during El Nino - La Nina shift of ‘97-’98•Altimetry withheld from these runs
Two forced by NCEP-DOE R2, but usedifferent models: MOMv3 vs. MOMv4
Two use the same model: MOMv4, butuse different forcing: R2 vs. CFSR
Two use the same model: MOMv4 andforcing: CFSR, but are uncoupled vs. coupled
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 uncoupledruns is present in the CFSR and TOPEX, buttoo weak to be seen at 10cm interval.
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MOMv4 based GODAS 1/2o resolutionGlobal
MOMv3 based GODAS 1o resolutionQuasi-global
AOML surface drifter based SST climatology Independent data(Lumpkin et al.)
GODAS compared with surface drifter derived SST
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GODAS compared with independent surface drifter velocities
MOMv4 based GODAS 1/2o resolutionGlobal
MOMv3 based GODAS 1o resolutionQuasi-global
AOML surface drifter based climatology IndependentLumpkin et al.
GODAS has eastward flow on and north of equator in Indian Ocean
Drifters show stronger flow in westernboundary and Southern Ocean
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.
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GODAS compared with tide gauges and TOPEX/Jason-1
For these experiments tide gauges and TOPEX/Jason-1 are independent
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AssimilatingArgo Salinity
GODAS
GODAS-A/S
Salinity variability due to correlation with temperature.
Salinity variability introduced by observations.
Equatorial salinity section in the Pacific (vertical bars show positions of time-series below).
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Assimilating Argo Salinity
ADCP GODAS GODAS-A/S
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.
Comparison with independent ADCP currents.
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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 NCEP’s 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).
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SEA ICE Model in CFSV2SEA ICE Model in CFSV2
Xingren WuEMC/NCEP and IMSG
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Arctic sea ice hits record low
in 2007
NSIDC
9/16/2007
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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
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Sea IceSea ice is a thin skin of frozen water covering the polar oceans. It is a highly variable feature of the earth’s surface.
Nilas & LeadsFirst-Year Ice
Multi-Year Ice
Melt Pond Snow-Ice Rafting
Pancake IceGreece Ice
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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
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Issues related to sea ice forecast
Data assimilation
Initial conditions
Sea ice models and coupling
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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
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Sea ice model and coupling issues
Ice thermodynamics Ice dynamics Ice model coupling to the atmosphere Ice model coupling to the ocean
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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
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Ice Model: Thermodynamics
Based on the principle of the conservation of energy, determine:
• Ice formation• Ice growth• Ice melting• Ice temperature structure
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Ice Model: Dynamics
Based on the principle of the conservation of momentum, determine:
• Ice motions• Ice deformation• Leads (open water)
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• 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
Ice Model: Dynamics (Cont.)
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• 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.
Sea Icein the NCEP Global Forecast System
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3-layer3-layerthermodynamicsthermodynamics
Ice modelIce model
SWHeat Flux
LWHeat Flux
TurbulentHeat Flux
OceanicHeat Flux
Salinity Fresh Water
Atmospheric modelAtmospheric model
Ocean modelOcean model
IceTemperature
SurfaceTemperature
Ice/SnowThickness
IceFraction
SnowRate
IceTemperature
surfaceTemperature
Ice/SnowThickness
IceFraction
Sea Ice in the NCEP GFS (cont.)
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• 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).
Sea Ice in CFSv1
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Sea Ice in CFSV2
• Hunke and Dukowicz (1997) elastic-viscous-plastic (EVP) ice dynamics model
➢ Improved numerical method for Hibler’s viscous-plastic (VP) model
➢ Computionally more efficient than Hibler’s 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
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This avoids a singularity at the North Pole
Tripolar grid of Murray (1996)over the Arctic for the sea ice model
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Sea ice concentration from CFSR for the Arctic
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Bias of sea ice concentration from CFSR for the Arctic
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Sea ice thickness from CFSR for the Arctic
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Sea ice concentration from CFSR for the Antarctic
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Bias of sea ice concentration from CFSR for the Antarctic
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Sea ice thickness from CFSR for the Antarctic
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Sea ice extent from CFSR
for the Arcticin March
AndIn September
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Surface air temperature from CFSR andthe difference amongst CFSR, R1, R2 and ERA40
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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 (MPMD)
Coupling frequency is flexible up to the OM time step
Same AM code can run in coupled or standalone mode
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The Coupled model: MOM4
• Parallel programming model in MOM4: SPMD
ATM+
LAND+
Sea Ice
MOM4.exe
Ocean
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Sea IceTime Step Δi
GFS (LAND)Time Step Δa
OceanTime Step Δo
CouplerTime Step Δc
FluxesTsfcSea-Ice
X-grid
Slow loop: Δo
Fast loop: Δa= Δc= Δi
Atmosphere grid
Sea-ice is one component of the CFSv2
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Parallel programming model:
MPMD (Multiple Program Multiple Data))
GFS
Time Step Δa
Time Step Δa
Time Step Δa
Time Step Δc
Coupler
ICE/OCNTime Step Δo
Time Step Δi
Time Step Δo
Time Step Δi
Time Step Δo
Time Step Δi
GFS-Sea Ice/MOM4 Coupler
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Exchange grid (x-grid)
ATM
SBL
LND ICE
OCNLND
Courtesy: GFDL
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Coupled architecture: parallelismGFS
Coupler redistMOM4
ATM
SBL
LNDICE
OCN
Regrid
Regrid with Mask
Redistribution
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Data FlowFast loop: if Δa= Δc= Δi, coupled at every time step
Slow loop: Δo
Δo
GFS CouplerSea-ice
Ocean
ATM (dummy)
ΔcΔa Δi
LAND (dummy)
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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 precipitationSea ice/ocean to atmosphere
- surface temperature,- sea ice fraction and thickness, and snow depth
Air-Sea Ice-Ocean Interaction
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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
Based on the Theory by Chun and Baik 1998
Convective gravity wave drag
2-Dimensional x-z
Steady-state
Non-rotating
Hydrostatic
Inviscid
Boussinesq
Linearized – Small perturbations
U, N Constant with x and z
o
gb
o
p
0 ;x
h)0(U)0(w
Convective Forcing Orographic Forcing
w
u
U = 15 m/s
N = 0.007 s-1
Zb = 1.5 km
Zt = 11 km
a1 = 10 km
a2 = 5 a1
To = 273 K
Qo = 1 J/kg/s
F = -U M Fz = -Uz M
0dz
dM
F M
H L
F = -U M
F M
F = -U M
Parameterization proposed by Chun and Baik 1998
z
1.....
t
u
x
0
dx'w'ux
1
dx'w'uM o
x
M
2o
3
22
2122
UTN
xHccg
c1 c2
c1 = 1.41
c2 = - 0.38
α = 0.1
Wave breaking
Reduction of
Momentum FluxF M
Lindzen’s Saturation Hypothesis
If
Reduce wave amplitude so that Ri = ¼
Gives new reduced
41
zU
NRi
2
W
2W
W
S
Main Reasons for Wave braking Stress
Reduction Momentum
deposition
1.Critical levels
2.Low wind speeds
3.Low density
Vct
V