Transcript Document 7274536

The Land Model and Land Assimilation of the
CFS Reanalysis and Reforecast (CFSRR)
Ken Mitchell
Jesse Meng, Rongqian Yang, Helin Wei, George Gayno
NCEP Environmental Modeling Center (EMC)
Assistance from other EMC members:
Suru Saha, Shrinivas Moorthi, Cathy Thiaw
Land Assimilation Collaborators:
NASA GSFC Hydrological Sciences Branch
CFSRR Advisory Board Meeting
07-08 November 2007
ONE DAY OF REANALYSIS: Note daily GLDAS (spans prior 24-hrs)
12Z GSI
18Z GSI
0Z GSI
6Z GSI
0Z GODAS
6Z GODAS
0Z GLDAS
12Z GODAS
18Z GODAS
9-hr coupled T382L64 forecast guess (GFS + MOM4 + Noah)
1 Jan 0Z
2 Jan 0 Z
3 Jan 0Z
4 Jan 0Z
5 Jan 0Z
2-day T382L64 coupled forecast ( GFS + MOM4 + Noah )
Outline
• Next-generation CFS
– Analysis & physics upgrades: Atmos, Ocean, Land, Sea-Ice
• History, summary, and assessment of Noah LSM
– Noah LSM features compared to forerunner OSU LSM
• Noah LSM Impact in coupled GFS and Regional Reanalysis
– Impact in N. American Regional Reanalysis (NARR)
– Impact in medium-range Ops GFS upgrades of May 2005
• GLDAS: Global Land Data Assimilation System
– Configuration and results from lower-resolution 27-year execution
• CFS Reforecast Experiments: Land Component tests
– Land Models: Two models (Noah LSM, OSU LSM)
– Land initial states: Two sources (Global Reanal 2, GLDAS)
• Conclusions and Pending Issues
New CFS implementation
1. Analysis Systems :
Operational DAS:
Atmospheric (GSI)
Ocean (GODAS) and
Land (GLDAS)
2. Atmospheric Model :
Operational GFS
3. Land Model
New Noah Land Model
4. Ocean Model :
New MOM4 Ocean Model
New SEA ICE Model
EMC Land Surface Partnerships:
GCIP/GAPP/CPPA (NOAA/CPO) and JCSDA
NCEP/EMC
Ken Mitchell
Michael Ek
Dag Lohmann
NASA/GSFC
Christa Peters-Lidard
Brian Cosgrove
GLDAS
Univ. Maryland
Hugo Berbery
Rachel Pinker
COLA/GMU
Paul Houser
Paul Dirmeyer
NCAR
Fei Chen
Mukul Tewari
NWS/OHD
John Schaake
Victor Koren
Princeton Univ.
Eric Wood
Justin Sheffield
Univ. Oklahoma
Ken Crawford
Jeff Basara
NOAA/ARL
Tilden Meyers
Jon Pliem
AFWA
John Eylander
NESDIS
Dan Tarpley
Bruce Ramsay
Univ. Washington
Dennis Lettenmaier
Laura Bowling
Rutgers Univ.
Alan Robock
Lifeng Luo
Univ. Arizona
James Shuttleworth
Hoshin Gupta
NOAA/FSL
Stan Benjamin
Tanya Smirnova
History of the Noah LSM
• Oregon State University: 1980’s
– OSU/CAPS LSM was forerunner of Noah LSM
– Initial development was funded by Air Force
• Transitioned to Air Force in late 1980’s
– Implemented in Air Force GLDAS (known as AGRMET)
• Transitioned to NCEP Ops mesoscale Eta model in 1996
– Coined “NOAH” LSM after NCEP, OSU, Air Force and OHD upgrades
• Transitioned to NCAR in late 1990’s
– Implemented in NCAR Community MM5 mesoscale model (F. Chen)
• Applied in NCEP N. American Regional Reanalysis (NARR)
– 1979 to present
• Implemented with NCEP Ops WRF meso model in Jun 06
• Implemented in NCEP Ops medium-range GFS in May 2005
– GFS: Global Forecast System
GFS and CFS: Land Model Upgrade
Noah LSM (new) versus OSU LSM (old):
• Noah LSM (vegetation, snow, ice)
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• OSU LSM
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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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
Noah LSM replaced OSU LSM in operational NCEP medium-range
Global Forecast System (GFS) in late May 2005
Noah LSM Testing Sequence
• Uncoupled testing
– 1-d column model
– 3-d NLDAS and GLDAS
• National and Global Land Data Assimilation Systems
• Coupled testing (then Ops Implementations)
– ETA and WRF mesoscale model (NAM)
– N. American Regional Reanalysis (NARR)
– Global Forecast System (GFS)
– Coupled Forecast System (CFS)
NLDAS surface energy
Fluxes across ARMCART sites of Oklahoma.
Multi-station average
ff model and obs
Jan 98 – Sep 99
Three land models shown:
Blue
-- Noah
Green -- VIC
Red
-- Mosaic
Noah performs well,
arguably the best.
Noah LSM in N. American Regional Reanalysis: NARR
Soil moisture availability (percent of saturation)
Top 1-meter of soil column
1993 (the summer flood)
1988 (the summer drought)
Average during 16-31 July (at 21 GMT)
The hallmark assimilation of high-resolution hourly precipitation analyses
in the NARR is not feasible in the Global Reanalysis: owing to lack of timely
Global precip analysis of sufficient quality and retrospective availability.
GFS Implementation of Noah LSM
31 May 2005
• NCEP TPB:
– http://www.emc.ncep.noaa.gov/gc_wmb/Documentation/
TPBoct05/T382.TPB.FINAL.htm
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Increase in horizontal resolution
Noah LSM replaces OSU LSM
New sea-ice treatment
Enhanced mountain blocking
Modified vertical diffusion
Analysis upgrades
– Additional satellite radiance data
– Enhanced quality control
– Improved surface emissivity calculations over snow
Annual mean biases in surface energy fluxes:
In five operational GCMs during 2003-2004
w.r.t. nine flux-station sites distributed world-wide
from K. Yang et al. CEOP Study (2007, J. Meteor. Soc. Japan)
2.0
50
(a1) MBE for meteorological variables
1.5
40
1.0
30
)
0.5
0
MBE
MBE (W m
-2
20
10
(a1) MBE for surface energy fluxes
H – Sensible Heat Flux
Rn – Net Radiation
0.0
-0.5
-10
Rn
-1.0
(µ=68)
-20
-1.5
-30
lE – Latent Heat Flux
H
(µ=18.5)
NCEP
JMA
UKMO
ECPC
lE
(µ=45)
µ – Global mean
BMRC
-2.0
Mean Bias Error (MBE)
Pre-May 2005 NCEP GFS had large positive bias in surface latent heat flux
and corresponding large negative bias in surface sensible heat flux.
Also large positive bias in precipitation in humid regions (not shown).
Mean GFS surface latent heat flux: 09-25 May 2005:
Upgrade to Noah LSM significantly reduced the GFS surface latent heat flux
(especially in non-arid regions)
Pre-May 05 GFS: with OSU LSM
Post-May 05 GFS: with new Noah LSM
Global Land Data Assimilation System (GLDAS):
with Noah LSM
(Next 7 Frames)
• Motivation for GLDAS
– high precip bias over tropical land mass in coupled GDAS
• GLDAS Configuration for T382 CFSRR
– T126 uncoupled (about 1-deg resolution)
– Precip forcing: CPC global 5-day CMAP precip anal
• Only over Tropical Latitudes (otherwise model precipitation)
– Non-Precip forcing: T62 NCEP/DOE Global Reanal 2 (GR2)
• GLDAS Results from low-res multi-decade test
– Period: 1979-2006
– “Cold Start”: 5-year spin up with 1979 forcing
– Compared with Global Reanalysis 2
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
Greatest GDAS high precip bias over land appears over tropical land mass: Next Frame
(e.g. central Africa, northern S. America, India and Southeast Asia)
GDAS-minus-OBS:
Jun-Jul-Aug 2007
Precipitation Total
GR2-minus-Obs
From land perspective:
Largest positive bias
over tropical latitudes.
GR1-minus-Obs
Motivation for Using CMAP Precipitation in Tropical Latitudes in GLDAS:
GDAS shows high bias in tropical precipitation compared to CMAP analysis
10 July – 09 Aug 2007 Example: in tropical Africa
CMAP Precip Analysis:
10Jul07– 09Aug07
GDAS Precip Field:
10Jul07– 09Aug07
Two-Year (Oct 05 – Sep 07) Soil Moisture Time Series at Four
global locations for 10-40 cm layer in Noah LSM of Ops GDAS
(does not utilize CMAP precipitation forcing)
North Central USA
Equatorial Africa
Three of four locations
look reasonable,
except tropical
Africa is spinning
up to very moist state
Central Amazon
Southeast Asia
GLDAS uses computational infrastructure of
NASA/GSFC/HSB Land Information System (LIS)
CFSRR GLDAS Configuration
• Uncoupled execution of NASA LIS computational infrastructure
• Same Noah LSM source code as in coupled GDAS
– same four soil layers (10, 30, 60, 100 cm)
– same parameter values
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Same computational grid (T382 Gaussian) as in coupled GDAS
– same terrain height, same land mask
– same land surface characteristics (soils, vegetation, etc)
• Applies GDAS atmospheric forcing
– hourly from previous 24-hours of coupled GDAS
– except precipitation forcing (see next line)
• Precipitation forcing is from CMAP precip analysis over tropical lands
– temporally disaggregate the 5-day CMAP precipitation with GDAS 6-hrly precip
– linearly blend GDAS and CMAP precip forcing between 30-40 deg latitude
• Reach-back every 5 days to apply latest 5-day CMAP anal
– then reprocess last 6-7 days to maintain continuous cycling from CMAP-driven
land states
• Same realtime and retrospective configuration
• Once daily update of coupled GDAS soil moisture states from GLDAS
ONE DAY OF REANALYSIS: Note daily GLDAS (spans prior 24-hrs)
12Z GSI
18Z GSI
0Z GSI
6Z GSI
0Z GODAS
6Z GODAS
0Z GLDAS
12Z GODAS
18Z GODAS
9-hr coupled T382L64 forecast guess (GFS + MOM4 + Noah)
1 Jan 0Z
2 Jan 0 Z
3 Jan 0Z
4 Jan 0Z
5 Jan 0Z
2-day T382L64 coupled forecast ( GFS + MOM4 + Noah )
GLDAS versus Global Reanalysis 2 (GR2):
Land Treatment
• GLDAS: an uncoupled land simulation system driven
by CMAP observed precipitation over tropics
– Executed using same grid, land mask, terrain field and Noah
LSM as GFS in experimental CFS
– Non-precipitation land forcing is from Global Reanal 2 (GR2)
– Executed retrospectively from 1979-2006 (after spin-up)
• GR2: a coupled atmosphere/land assimilation system
wherein land component is driven by model predicted
precipitation
– applies the OSU LSM
– nudges soil moisture based on differences between model
and CPC CMAP precipitation
GLDAS/Noah (top row) versus GR2/OSU (bottom row)
2-meter soil moisture (% volume): GLDAS/Noah values are higher
Climatology (left column) is from 25-year period of ~1981-2005)
May 1st Climatology
01 May 1999 Instantaneous Anomaly
GLDAS/Noah
GR2/OSU
GLDAS/Noah
GR2/OSU
GLDAS/Noah (top ) versus GR2/OSU (bottom)
2-meter soil moisture (% volume)
May 1st Climatology
GLDAS/Noah
GR2/OSU
01 May 1999 Anomaly
GLDAS/Noah
GR2/OSU
Left column: GLDAS/Noah soil moisture climo is generally higher then GR2/OSU
Middle column: GLDAS/Noah soil moisture anomaly pattern agrees better
than that of GR2/OSU with observed precipitation anomaly (right column: top)
Observed 90-day
Precipitation Anomaly
(mm) valid 30 April 99
Monthly Time Series (1985-2004) of Area-mean
Illinois 2-meter Soil Moisture [mm]:
Observations (black), GLDAS/Noah (purple), GR2/OSU (green)
Total
Climatology
Anomaly
The climatology of GLDAS/Noah soil moisture is higher and closer to the
observed climatology than that of GR2/OSU, while the anomlies of all three show
generally better agreement with each other (though some exceptions)
New T126 CFS Reforecast Tests:
Land Component Impact
-- New Noah LSM versus Old OSU LSM
-- GLDAS/Noah versus Global Reanal-2/OSU
-- SST: high correlation skill in tropical Pacific (not shown)
-- CONUS precipitation (low correlation skill in summer, later
frame)
CFS Land Experiments (4 configurations)
Experiments of new T126 CFS with Noah LSM and OSU LSM
25-year CFS 6-month summer reforecasts (10 member ensembles)
from late-April and early-May initial conditions (00Z) of 1980-2004
Initial Dates of Ten Members: Apr 19-23, Apr 29-30, May 1-3
(GFDL MOM-3 Model is ocean component)
Choice of Land Model
CFS/Noah
CFS/OSU
GR2/OSU
Choice of
Land
Initial
Conditions
GR2/OSU (CONTROL)
GLDAS/Noah
GLDAS/Noah Climo
Note: “GR2” denotes NCEP/DOE Global Reanalysis 2
JJA Precipitation Correlation Skill
CFS/Noah/GR2 case is clearly worst case (least spatial extent of positive correlation).
Remaining three cases appear to have similar spatial extent of positive correlation, but
distributed differently among sub-regions. Still disappointingly small spatial extent of
correlations above 0.5 in all four configurations.
From hindcasts for years 1981-2004. Ten-member ensemble mean shown for each panel.
JJA Precipitation Correlation Skill
CFS/Noah/GR2 case is clearly worst case (least spatial extent of positive correlation).
Remaining three cases appear to have similar spatial extent of positive correlation, but
distributed differently among sub-regions. Still disappointingly small spatial extent of
correlations above 0.5 in all four configurations.
From hindcasts for years 1981-2004. Ten-member ensemble mean shown for each panel.
JJA T2m Correlation Skill
All the configurations of New CFS are superior to Ops CFS over CONUS
(Most likely owing to inclusion of CO2 trend in New CFS)
Ops
CFS
Noah/
GLDAS
Noah/
GLDAS
Climo
OSU/
GR2
10 Members each case (same initial dates)
Conclusions from CFS Land-component Experiments
•
The relatively low CFS seasonal prediction skill for summer precipitation over
CONUS is not materially improved by the tested upgrade in land surface physics
and land data assimilation
– Lack of positive impact likely due to more dominant influence from SST anomalies and
internal chaotic noise in the coupled global model
– Corollary: The use of initial soil moisture states with instantaneous soil moisture
anomalies did not provide an advantage over the climatological soil moisture states,
provided the climatology was a product of the very same land model
•
Separate study by CPC (Soo-Hyun Yoo, S. Yang, J. Schemm) evaluated these
same summer CFS experiments over the Asian-Australian Monsoon, showing
modestly positive impact from Noah LSM and GLDAS upgrades presented here.
•
An upgrade to the land surface model of a GCM can possibly degrade GCM
performance if the upgraded land model is not also incorporated into the data
assimilation suite that supplies the initial land states
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The addition of a CO2 trend to the experimental CFS is likely the major source of
the improvement in experimental CFS summer season surface temperature
forecasts relative to the currently operational CFS
•
Future work will carry out this same suite of CFS reforecasts for winter season
– One focus will be snow cover prediction (Ops CFS has notable low bias in snow cover)
CFSRR: Land Component
Summary and Pending Issue
• Motivation for GLDAS:
– High tropical precipitation bias in GFS/GDAS
– GLDAS uses CPC CMAP precip anal to force land over tropical latitudes
• T382 GLDAS for CFSRR
– Codes and scripts delivered to and executing in CFSRR suite
• Low-res 28-yr GLDAS retrospective run done & assessed
– CMAP precipitation applied globally to force land surface
– Non-precipitation land surface forcing from Global Reanalysis 2
• CFS land-component summer reforecasts run for 25-yrs
– Land upgrade not yielding better summer precip fcst skill
– Winter reforecast tests are underway
• Pending Issue
– Length of overlap in four CFSRR production streams
– I urge at least12-months overlap (6-months is insufficient)