9/13/2015Shrinivas Moorthi1 Introduction to Climate forecast System Version 2 (CFSV2) – AM, OM, LM, Sea- ice– GODAS and GLDAS Shrinivas Moorthi Acknowledgement;

04/21/23 Shrinivas Moorthi 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 preparedby members of GCWMB branch and climate and land modeling teams.

Disk management at the NCEP Super computers for developers


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.


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


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


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


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


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


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


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


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.


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


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:


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 04/21/23 Shrinivas Moorthi

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.

04/21/23 Shrinivas Moorthi

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 (pdryini2=0)• Snow analysis

– Using T574 compatible high-resolution snow analysis 04/21/23 Shrinivas Moorthi


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


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


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


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


CFSR Soil Moisture Climatology


CFSR Soil Moisture Climatology


• 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


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.


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).


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


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


Tripolar grid (Murray, 1996)

2 Arctic polesreside in landmass

Arctic grid matches sphericalcoordinategrid at 65oN

After Griffies, 2007


The observing system

XBT

TAO

TP/J-1

Argo

1980 1990 2000



The changing number of temperatureobservations as a function of time and depth


Sample annual distributions of T(z) as used by GODAS

XBT-green TAO-red Argo-blue


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)


International Argo deployments in 2000

GODAS assimilates all Argo and proto-Argo profiles.


International Argo deployments as of October 31, 2007

GODAS assimilates all Argo and proto-Argo profiles.

Full Deployment


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.


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




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.


Commonality among versions of GODAS during El Nino - La Nina shift of ‘97-’98•Altimetry withheld from these runs




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.


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


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.


GODAS compared with tide gauges and TOPEX/Jason-1

For these experiments tide gauges and TOPEX/Jason-1 are independent





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).


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.


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).


SEA ICE Model in CFSV2SEA ICE Model in CFSV2

Xingren WuEMC/NCEP and IMSG


Arctic sea ice hits record low

in 2007

NSIDC

9/16/2007


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


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


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


Issues related to sea ice forecast

Data assimilation

Initial conditions

Sea ice models and coupling


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


Sea ice model and coupling issues

Ice thermodynamics Ice dynamics Ice model coupling to the atmosphere Ice model coupling to the ocean


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


Ice Model: Thermodynamics

Based on the principle of the conservation of energy, determine:

• Ice formation• Ice growth• Ice melting• Ice temperature structure


Ice Model: Dynamics

Based on the principle of the conservation of momentum, determine:

• Ice motions• Ice deformation• Leads (open water)


• 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.)


• 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


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.)


• 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


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


This avoids a singularity at the North Pole

Tripolar grid of Murray (1996)over the Arctic for the sea ice model


Sea ice concentration from CFSR for the Arctic


Bias of sea ice concentration from CFSR for the Arctic


Sea ice thickness from CFSR for the Arctic


Sea ice concentration from CFSR for the Antarctic


Bias of sea ice concentration from CFSR for the Antarctic


Sea ice thickness from CFSR for the Antarctic


Sea ice extent from CFSR

for the Arcticin March

AndIn September


Surface air temperature from CFSR andthe difference amongst CFSR, R1, R2 and ERA40


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


The Coupled model: MOM4

• Parallel programming model in MOM4: SPMD

ATM+

LAND+

Sea Ice

MOM4.exe

Ocean


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


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


Exchange grid (x-grid)

ATM

SBL

LND ICE

OCNLND

Courtesy: GFDL


Coupled architecture: parallelismGFS

Coupler redistMOM4

ATM

SBL

LNDICE

OCN

Regrid

Regrid with Mask

Redistribution


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)


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


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

Documents

9/13/2015Shrinivas Moorthi1 Introduction to Climate forecast System Version 2 (CFSV2) – AM, OM, LM, Sea- ice– GODAS and GLDAS Shrinivas Moorthi Acknowledgement;