Nov 7, 2007Shrinivas Moorthi1 Atmospheric Model for the Climate Forecast System Reanalysis and Retrospective Forecasts Shrinivas Moorthi

Nov 7, 2007 Shrinivas Moorthi 1

Atmospheric Model for the Climate Forecast System Reanalysis and

Retrospective Forecasts

Shrinivas Moorthi


Thanks to

Glenn White who prepared several slides in this presentation

and

YuTai Hou who prepared the slides related to radiation parameterization


GFS AM

• Latest version of Global Forecast System (GFS) Atmospheric Model (AM) is being considered for CFSRR.

• GFS AM - developed by the staff of Global Climate and Weather Modeling Branch of EMC.

• The first reanalysis (NCEP/NCAR – R1) was based on the operational GFS AM of January 1995.

• GFS AM has undergone major revisions since the first reanalysis.


CDAS (R1) GFS AM (OPR)

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


GFS AM improvements through reanalysis

• Some specific problems found in NCEP/NCAR reanalysis, addressed in later AM changes

• -- valley snow• -- wrong snow cover• -- wrong ocean albedo• -- SH paobs mislocated• -- ”pathological” problems in stratosphere

• New reanalysis will find problems in GFS that will be addressed and produce improved GFS, improved future reanalysis and improved future CFS

• We’ll keep doing it until we get it right(Glenn White)


Comparison between AMs in R1, CFS (opr) and GFS (opr)R1 (T62L28) OPR CFS AM (T126L64) OPR GFS AM (T382L64)

SAS Convection SAS Convection with momentum mixing

SAS Convection with momentum mixing

Tiedtke Shallow convection Tiedtke Shallow convection Tiedtke Shallow convection

Seasonal/zonal mean Ozone Prognostic Ozone Prognostic Ozone

OSU LSM (2 layers) OSU LSM (2 layers) Noah LSM (4 layers) and sea ice model

Diagnostic clouds Prognostic cloud condensate Prognostic cloud condensate

Boundary layer Nonlocal Boundary layer Non local boundary layer

Graviry wave drag Gravity wave drag GWD with Mountain Blocking

GFDL IR radiation

Random overlap

GFDL IR radiation

Random overlap

RRTM IR radiation

Max/random overlap

NCEP SW -93 radiation

(Chou ) Random overlap

NCEP SW -95 radiation (Chou) Random overlap

NCEP SW radiation (Chou)

Random overlap

Vertical diffusion Vertical diffusion Vertical diffusion with reduced background diffusion

2nd order horz diffusion 2nd order horz diffusion 6th order horz diffusion

Virtual Temperature Virtual Temperature Virtual Temperature


Operational CFS GFDL-LW Radiationvs. RRTM-LW Radiation

GFDL RRTM

Description: - 15 bands 16 bands- trans table look-up 140 cor-k terms- O3,H2O,CO2 O3,H2O,CO2,O2,CH4

CO, 4 CFCs

Advantages/ - comp efficient more comp efficientDisadvantages: - no aerosols effect aerosol effect capable

- fixed CO2 only varying CO2 capable- fixed sfc emis varying emis capable- random cld ovlp random or max-ran- larger errors, especially improved accuracy at upper stratosphere, at upper stratosphere- simple cloud optical prop advanced cloud optical

property property


Clear sky LW cooling comparison for tropical,mid-latitude and subarctic winter profiles


Cloudy sky LW cooling comparison for tropical,mid-latitude and subarctic winter profiles


The current operational GFS AM has Realistic moisture prediction with better depiction of no-rain areas Prognostic Ozone

Prognostic cloud condensate Cloud cover only where cloud condensate > 0 Momentum mixing in deep convection Fast and accurate AER RRTM for IR radiation Mountain blocking parameterization Noah land model Sea-ice model Improved treatment of snow, ice, orography Better hurricane track prediction ESMF based modern computer algorithms


Options in GFS AM being considered for next operational model

• 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 (Until now IR is called every 3 hours)

• Use of historical and spatially varying CO2 and volcanic aerosols


Why Enthalpy as a prognostic variable?

Collaboration between Space Environmental 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 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 has the form

Tv 1 Rv /Rd 1 q T

with ideal-gas law in the form

p RdTv

dTv

dt

Tv

p

dp

dtQ

where

Rd

CP

Rd

CPd CPv CPd q

d

1 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

h

p

dp

dtQ

RTp

The thermodynamic equation, derived from internal energy equation is (Akmaev, 2006 – Space Environmental Center)

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


NCEP Operational SW Radiationvs. New RRTM SW Radiation

NCEP RRTM

Description: - 8 uv+vis, 1-nir 5 uv+vis, 9-nir bnds- 38 k-dis terms 112 cor-k terms- O3,H2O,CO2,O2 O3,H2O,CO2,O2,CH4

Advantages: - Comp. Efficient Accu. (use ARM’s data) clr-sky - 10-30 w/m2

reduction cld-sky - adv. scheme

Disadvantages: - large errors Comp. slow, 4 times clear-sky - und est slower than opr sw cloudy-sky - over est

YuTai Hou of EMC implemented RRTM in the GFS AM


Clear sky SW heating comparison for tropical,mid-latitude and subarctic winter profiles


Cloudy sky SW heating comparison for tropical,mid-latitude and subarctic winter profiles


Coupling of GFS to MOM3 (MOM4)

In the operational CFS, AM and OM are coupled daily with AM and OM running sequentially

In the new CFS, the coupling is MPI-level (developed by Dmitry Shenin) – 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

Coupler details for MOM4 will be presented later in this meeting


RRTM run shows reduced SST warm bias

SST predicted in 50 year coupled simulation (winter)

CTB sponsoredExperiment run byS. Saha and Y. Hou


SST predicted in 50 year coupled simulation (summer)

CTB sponsoredExperiment run byS. Saha and Y. Hou


Jack Woolen and others have spent years improving the data base of conventional observations--much more complete than before--errors better understood

Great deal of experience now with satellite bias corrections

Experienced with changes in observations in last 10 yearsKnowledge is being applied to new reanalysis

GFS produces much more skilled forecasts than CDAS--GFS has proven track record in forecasting hurricane tracks and in seasonal forecasts as CFS, indicating thatGFS produces much more realistic tropical atmosphere than CDAS in both analyses and forecasts


(unusually good month for GFS vs. ECMWF)

GFS has useful skill 1.5 days longer than CDAS

(Fang-Lin Yang)

September 2007

No. Hemisphere500 hPa heightAnomaly correlation


October 2007

GFS has useful skill 1 day longer than CDAS


September 2007

Southern Hemisphere

GFS has useful skill more than 1 day longer than CDAS


OPI CDAS1 CDAS2 GDAS

Global 2.62 2.97 3.42 3.23

Land 2.11 2.73 2.83 2.72

Ocean 2.84 3.07 3.67 3.45

Precipitation JJA 2007



GDAS has most similar pattern to independent estimate

CDAS 1 and 2 have too much rain over southeast US



CFS Reanalysis and Reforecast Scripts

9 (or 48) hr Coupled ModelForecast (first guess)

New GFS + MOM4 with Sea IceMPI-level Coupling

fcst.sh

Prep stepHurricane relocation

Data preparationprep.sh

GLDASGlobal Land Data Assimi-

lationlanl.sh

GDASGlobal Atmospheric Data Assimilation

GSI anal.sh

GODASGlobal Ocean Data

Assimilationoanl.sh

Run RetrospectiveForecast

fcst.sh

AM and OM Post

post.sh

Start hereCopy IC files

copy.sh

Time 00Z ?

Verifyvrfy.sh

Archivedata

arch.sh

RetrospectiveForecast?

CFSRRwebsite



CDAS1 CDAS2 GDAS K&T Range SRB

sh 24 16-27

lh 85 97 87 78 78-90

dsw 199 180 200 198 185

usw 42 24 25 30 24

nsw 157 156 175 168 142-174 161

dlw 346 350 345 324 348

ulw 405 406 408 390 396

nlw -59 -56 -62 66 40-72 48

netrad 98 100 112 102 99-119 113

nhf -4 -5 9

P 2.97 3.42 3.23 2.69 2.69-3.1

E

JJA07 Annual mean climatology


CDAS1 had wrong ocean albedo, reflected too much short wave

CDAS2 too low sensible heat flux

GDAS too much downward short wave, more net heat flux into ocean than CDAS1 or CDAS2



GDAS has pattern most like Air Force estimate, but has too little stratus clouds in eastern ocean too far displaced from coast







GDAS has less evporation, more sensible heat flux overContinents than CDAS1 or 2

COADS estimate based on little data in Southern HemisphereLatent heat estimate smaller than any of reanalyses—may reflectToo weak COADS (COADS fluxes tend to give net heat flux into Ocean) or too strong hyrdological cycle in reanalyses








GDAS has most reasonable pattern of surface short wave radiationBut has too much in tropics

CDAS1 has too high ocean surface albedo





GDAS has more net heat flux into ocean than otherReanalyses

COADS estimate substantially tuned to achieve balance


COADS climatological estimateReanalyses one season


CFS Reanalysis and Reforecast Scripts

9 (or 48) hr Coupled ModelForecast (first guess)

New GFS + MOM4 with Sea IceMPI-level Coupling

fcst.sh

Prep stepHurricane relocation

Data preparationprep.sh

GLDASGlobal Land Data Assimi-

lationlanl.sh

GDASGlobal Atmospheric Data Assimilation

GSI anal.sh

GODASGlobal Ocean Data

Assimilationoanl.sh

Run RetrospectiveForecast

fcst.sh

AM and OM Post

post.sh

Start hereCopy IC files

copy.sh

Time 00Z ?

Verifyvrfy.sh

Archivedata

arch.sh

RetrospectiveForecast?

CFSRRwebsite

Documents

Nov 7, 2007Shrinivas Moorthi1 Atmospheric Model for the Climate Forecast System Reanalysis and Retrospective Forecasts Shrinivas Moorthi