Atmospheric Model for the Climate Forecast System Reanalysis and Retrospective Forecasts

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• Atmospheric Model for the Climate Forecast System
  Reanalysis and Retrospective Forecasts

Shrinivas Moorthi
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Thanks to

• Glenn White who prepared several slides in this
  presentation
• and
• YuTai Hou who prepared the slides related to
  radiation parameterization

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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.

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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
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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
• Well keep doing it until we get it right
• (Glenn White)

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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
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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 efficient
• Disadvantages - 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

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

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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 rtop/rsfc 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 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.

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

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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
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NCEP Operational SW Radiationvs. New RRTM SW
Radiation

• NCEP RRTM
• Description - 8 uvvis, 1-nir 5
  uvvis, 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 ARMs 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

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

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SST predicted in 50 year coupled simulation
(winter)
RRTM run shows reduced SST warm bias
CTB sponsored Experiment run by S. Saha and Y. Hou
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SST predicted in 50 year coupled simulation
(summer)
CTB sponsored Experiment run by S. Saha and Y. Hou
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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
years Knowledge 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 that GFS produces
much more realistic tropical atmosphere than CDAS
in both analyses and forecasts
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(Fang-Lin Yang)
September 2007
No. Hemisphere 500 hPa height Anomaly correlation
(unusually good month for GFS vs. ECMWF) GFS has
useful skill 1.5 days longer than CDAS
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October 2007
GFS has useful skill 1 day longer than CDAS
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September 2007
Southern Hemisphere
GFS has useful skill more than 1 day longer than
CDAS
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Precipitation JJA 2007
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
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GDAS has most similar pattern to independent
estimate CDAS 1 and 2 have too much rain over
southeast US
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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
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JJA07
Annual mean climatology
CDAS1 CDAS2 GDAS KT 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
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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
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GDAS has pattern most like Air Force estimate,
but has too little stratus clouds in eastern
ocean too far displaced from coast
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GDAS has less evporation, more sensible heat flux
over Continents than CDAS1 or 2 COADS estimate
based on little data in Southern
Hemisphere Latent heat estimate smaller than any
of reanalysesmay reflect Too weak COADS (COADS
fluxes tend to give net heat flux into Ocean) or
too strong hyrdological cycle in reanalyses
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GDAS has most reasonable pattern of surface short
wave radiation But has too much in tropics CDAS1
has too high ocean surface albedo
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GDAS has more net heat flux into ocean than
other Reanalyses COADS estimate substantially
tuned to achieve balance
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COADS climatological estimate Reanalyses one
season
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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