modeling two-way land/atmosphere/ocean interactions yongkang xue 1, f. de sales, z. zhang, y. wang,...
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Modeling Two-Way Land/Atmosphere/Ocean Interactions
Yongkang Xue1, F. De Sales, Z. Zhang, Y. Wang, Y. Liu, H.-Y. Ma, L. Marx, M. Ek, R. Yang, J.-W. Lee
1. University of California, Los Angels
NOAA Climate Test Bed SeminarSeptember 3, 2015
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PI: Yongkang Xue; CO-PI: W. Parton, S. Shen, T. Gillespie, F. De Sale, Y. Gu
Surface-Induced Forcing and Decadal Variability and Change of East Asian Climate, Hydrology and Agriculture
This research is supported by U.S. NSF EASM-3 Project
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ATMOSPHERE
Courtesy of Prof Y. Xue
LAND
OCEAN-ATMOSPHERE INTERACTIONS
Source: Woods Hole Oceanographic Institution
OCEANprecipitation, clouds, temperature
Traditionally, L/A interaction and O/A interaction studies separately.
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ATMOSPHERE
Courtesy of Prof Y. Xue
LAND
OCEAN-ATMOSPHERE INTERACTIONS
Source: Woods Hole Oceanographic Institution
OCEANprecipitation, clouds, temperature
?
Traditionally, L/A interaction and O/A interaction studies separately.
?
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Vegetation Biogeophysical Process Effect on JJA precipitation
Soil Moisture JJA coupling strength
Koster et al., 2004, Science
Xue et al., 2010, J. Climate
Effect of L/A Interaction is of the 1st order process in Climate system
VBP: vegetation biogeophysical processes
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Terrestrial ecosystems
Distribution Structure
Bio
phys
ical
fee
dbac
k
Radiation balance
Momentum transfer
Heat transfer
Water cycle
PhotosynthesisRespiration
2nd generation model
Temperature, humidity, precipitation, downward SW and LW radiation, Greenhouse Gas (CO2) and atmospheric circulation
The Climatic System
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Terrestrial ecosystems
Distribution Structure
Bio
phys
ical
fee
dbac
k
Radiation balance
Momentum transfer
Heat transfer
Water cycle
3nd generation model
Temperature, humidity, precipitation, downward SW and LW radiation, Greenhouse Gas (CO2) and atmospheric circulation
The Climatic System
PhotosynthesisRespirationDecompositionNutrient cyclesBiomass burning
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Annual T change due to 2 x air CO2With no photosynthesis effects
Betts, Cox, Lee, Woodward, 1997, NatureSellers et al., 1996, Science
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Annual T change due to 2 x air CO2With no photosynthesis effects
Additional T change due to 2 x air CO2 plus photosynthesis effects
Betts, Cox, Lee, Woodward, 1997, NatureSellers et al., 1996, Science
Additional 0.2K warming
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Additional T change due to 2 x air CO2 plus photosynthesis effects
Additional T change due to 2 x air CO2 plus photosynthesis effects & LAI change
ONLY LAI change under 2xCO2
+7.2%
Betts, Cox, Lee, Woodward, 1997, Nature
Leaf Area Index (LAI): the area of leaf surface per unit area of ground
Additional 0.2K warming
Additional 0.1K Cooling
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Anav, et al., 20131986-2005 Mean Annual LAI
1986
-200
5 L
AI T
rend
1986
-200
5 L
AI S
tand
ard
devi
ation
Coupled Model Intercomparison Project Phase 5 (CMIP5) Simulations
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Anav, et al., 20131986-2005 Mean Annual LAI
1986
-200
5 L
AI T
rend
1986
-200
5 L
AI S
tand
ard
devi
ation
Coupled Model Intercomparison Project Phase 5 (CMIP5) Simulations
Similar results shown in off line DVG intercomparison ( Murray-Tortarolo, Anav et al., 2013)
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Anav, et al., 20131986-2005 Mean Annual LAI
1986
-200
5 L
AI T
rend
1986
-200
5 L
AI S
tand
ard
devi
ation
Coupled Model Intercomparison Project Phase 5 (CMIP5) Simulations
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1986-2005 Mean Annual Precipitation
1986
-200
5 P
reci
pita
tion
Tren
d
Coupled Model Intercomparison Project Phase 5 (CMIP5) Simulations
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Merray-Tortarolo, Anav, et al., 2013
Offline Model Intercomparison Project Simulations
Observation
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(1). models based on observed vegetation perform better than dynamic models. (2). models that include a wider range of PFTs are more similar to the satellite observations. However, using many PFTs leads to an increased uncertainty due to their parameterizations.
Merray-Tortarolo, Anav, et al. (2013) found that
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litterdtvdC )1(
),,( soillitter TSMCssRdtsdC
Energy balance:(1-α) SW + LW - εσ T4= LE + SE + GH
Water balance:P=E + Runoff +SM +snow +canopy
Carbon Balance:
HYPOTHESIS: Improper simulations in Energy, Water and Carbon balances cause the deficiencies in producing carbon exchange then interactions between L/A interaction
(SM, Tcanopy)
Plant Functional Type Competition
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Reanalysis and Observational data
Satellite data products
Field data
(10 days)
(1-3hours)
15 min
SSiB4Hour, day, intraseasonal, interannual, decadal
Intraseasonal, interannual, decadal
TRIFFID
15-30days
Intraseasonal, Interannual,Decadal, decadal
intraseasonal, interannual, decadal
intraseasonal, interannual, decadal
RCM
CFS6hours
Intraseasonal, interannual, decadal
Initial Conditions
T, Q, u, SW↓, LW ↓, Pr
Tskin, α,LH, SH, u*
Tc, Td, Sm, Anet, Rdc, Wilt RD,RB,
D0,Z0
Earth System Models
DAYCENT
Tg, Td, Sm
N
N
C
FPFT, Z2,LAI, Stem, Cs
(10 days)
(1 days)
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Reanalysis and Observational data
Satellite data products
Field data
(10 days)
(1-3hours)
15 min
SSiB4Hour, day, intraseasonal, interannual, decadal
Intraseasonal, interannual, decadal
TRIFFID
15-30days
Intraseasonal, Interannual,Decadal, decadal
intraseasonal, interannual, decadal
intraseasonal, interannual, decadal
RCM
CFS6hours
Intraseasonal, interannual, decadal
Initial Conditions
T, Q, u, SW↓, LW ↓, Pr
Tskin, α,LH, SH, u*
Tc, Td, Sm, Anet, Rdc, Wilt RD,RB,
D0,Z0
Earth System Models
DAYCENT
Tg, Td, Sm
N
N
C
FPFT, Z2,LAI, Stem, Cs
(10 days)
(1 days)NOAA Climate Program Office provides the first support
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Fig.4. Comparison of simulated and satellite-derived dominant PFTs . (a) GLC2000; (b) MODIS IGB vegetation types based on the 2001-2010 mean; (c) SSiB4 simulation based on the 1998-2008 mean.
1. Needle Leaf 2. Broad Leaf 3. C3 Grass 4. C4 Plants (Savanna) 5. Shrub 6. Tundra shrubs 7. Bare lands 8. Crop lands 9. Mixed forest10. Ice and Snow
Zhang et al., 2015, JGR
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SSiB4/TRIFFID GIMMS FASIR
Correlations: with GIMMS: 0.59; with FASIR: 0.67
DJF
MAM
JJA
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Simulated & Satellite LAI temporal evolutions from 1948-2008
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Wang, Yu, Pal, Mei, Bonan, Levis, Thornton, 2015, Climate Dynamics
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Broad leaf treesC4 plants
Bare groundShrubs
SSiB4/TRIFFID Simulated PFT Fractions(1996-2006) &GLC2000 Dominant Land Cover Classifications
GLC2000 Land CoversavannaGrassforest
Crops
desert
C3 Grass
C4 Plant
Shrubs
C4 plants
Shrubs
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TRIFFID/SSiB4 (2001-2010)-(1979-1988)
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Summary for Part I
1). Integrated biogeophysical and biogeochemical processing and dynamic vegetation modelling are important for climate variability and change studies at interannual-decadal-century scales.
2). Current dynamic vegetation models show significant bias in producing LAI, which have important implication in future climate prediction
3). A water/energy/carbon balance approach and validation as did in PILPS are essential to produce the valuable scientific and societal information for interannual to decadal scales
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ATMOSPHERE
Courtesy of Prof Y. Xue
LAND
OCEAN-ATMOSPHERE INTERACTIONS
Source: Woods Hole Oceanographic Institution
OCEANprecipitation, clouds, temperature
?
Traditionally, L/A interaction and O/A interaction studies separately.
?
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New evidence shows the connection between the tropical Pacific climate and the land surface processes
Ma, Mechoso, Xue, Xiao, Neelin, Ji, (2013) J Climate
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Ma, et al., 2013 J. Climate
UCLA CGCM Without L/O/A Interaction
UCLA CGCM With L/O/A Interaction
NINO3 Spectrum
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Reanalysis and Observational data
Satellite data products
Field data
(10 days)
(1-3hours)
15 min
SSiB4Hour, day, intraseasonal, interannual, decadal
Intraseasonal, interannual, decadal
TRIFFID
15-30days
Intraseasonal, Interannual,Decadal, decadal
intraseasonal, interannual, decadal
intraseasonal, interannual, decadal
RCM
CFS6hours
Intraseasonal, interannual, decadal
Initial Conditions
T, Q, u, SW↓, LW ↓, Pr
Tskin, α,LH, SH, u*
Tc, Td, Sm, Anet, Rdc, Wilt RD,RB,
D0,Z0
Earth System Models
DAYCENT
Tg, Td, Sm
N
N
C
FPFT, Z2,LAI, Stem, Cs
(10 days)
(1 days)
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Adjust the lowest model layer height
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Sea Surface Temperature climatology30-yr DJF average (°C)
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Sea Surface Temperture climatology30-yr JJA average (°C)
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Precipitation climatology30-yr average (mm/day)
Annual mean JJA mean
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Surface temperature climatology30-yr average (C)
Annual mean JJA mean
Bias:0.57RMSE:2.34Scor:0.98
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Sea-level pressure climatology30-yr average (hPa)
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Nino 3.4 index
Wavelet spectrum
year
CFS/SSiB2
Hadsst
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MEAN SOIL WATER POTENTIAL SCHEMES
O
Mean soil water potential affects photosynthesis process then transpiration and carbon flux, and phenological process and vegetation growth.
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MEAN SOIL WATER POTENTIAL SCHEMES
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Sahel precipitation recovery(2000-2008) minus (1980-1988)
JJAS mean (mm/day)
Observation CFS/SSiB2-I CFS/SSiB2-II
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CFS/SSiB2-I
CFS/SSiB2-II
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Summary for Part II
1). A 30-year continuous simulation was initialized from a 10-year spin-up run in which atmospheric aerosol concentrations, solar constant and CO2 emissions were maintained at 1979 levels, while ocean and land were allowed to run freely.
2). Results show the model is able to simulate the mean state of atmosphere and ocean climatology but with substantial biases in some aspects. Positive precipitation biases are observed over the equatorial Atlantic and a split ITCZ pattern is simulated in the Pacific ocean. In terms of surface temperature, positive biases were simulated over semi-arid regions, predominantly in the summer month. 3) The model reproduced well the climatological SST and warm pool but with biases over different regions. The model also reproduced well the SST anomaly variation well expect the southern ocean SST after the 2000.
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Summary (Continue)
4) A very preliminary sensitivity test with two different mean soil water potential scheme revealed that the land surface processes have impact on the atmospheric and ocean’s processes and their decadal variability .
5) With adequate soil water potential parameterizations, the model simulated the decadal variability of precipitation and surface temperature in several areas, notably over parts of Africa and North and South Americas. Decadal variability results are better during the summer season than during the winter. Model captured the increase in summer temperatures over land between 1980s and 2000s in most areas