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Evaluating the variability and budgets of global water cycle components. V. Sridhar 1 , G. Goteti 2 , J. Sheffield 2 , J. Adam 1 D.P. Lettenmaier 1 , E.F. Wood 2 and C. Birkett 3 1 Department of Civil and Environmental Engineering Box 352700, University of Washington, Seattle, WA 98195 - PowerPoint PPT Presentation
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Evaluating the variability and budgets of global water cycle
components
Evaluating the variability and budgets of global water cycle
componentsV. Sridhar1, G. Goteti2, J. Sheffield2, J. Adam1 D.P. Lettenmaier1, E.F. Wood2 and C. Birkett3
1Department of Civil and Environmental Engineering Box 352700, University of Washington, Seattle, WA 981952 Princeton University, Princeton, NJ3 NASA/GSFC, Greenbelt, MD
V. Sridhar1, G. Goteti2, J. Sheffield2, J. Adam1 D.P. Lettenmaier1, E.F. Wood2 and C. Birkett3
1Department of Civil and Environmental Engineering Box 352700, University of Washington, Seattle, WA 981952 Princeton University, Princeton, NJ3 NASA/GSFC, Greenbelt, MD
Global Water Cycle-IntroductionGlobal Water Cycle-Introduction• Thermohaline circulation of the world ocean is
due to the flux of continental freshwater.• The surface water balance eqn.over land is
dS/dt = P-E-Q
Where P – Precipitation; E-Evapotranspiration; and Q is streamflow
S is the sum of dominant terms ( soil moisture, snow water storage and lakes, wetlands and impoundments)
• Thermohaline circulation of the world ocean is due to the flux of continental freshwater.
• The surface water balance eqn.over land is
dS/dt = P-E-Q
Where P – Precipitation; E-Evapotranspiration; and Q is streamflow
S is the sum of dominant terms ( soil moisture, snow water storage and lakes, wetlands and impoundments)
Global Water Cycle-Introduction(contd.)
Global Water Cycle-Introduction(contd.)
• The surface energy budget eqn. is
Rn = LH+SH+GH
Where Rn-Net radiation; LH-latent heat flux; SH-ground heat flux and GH- Ground heat flux
• Changes in the water cycle due to natural variability and anthropogenic causes are linked as evaporation is common in both water and energy balance equations.
• Therefore, understanding each term and its variability becomes important to get the budgets to balance.
• The surface energy budget eqn. is
Rn = LH+SH+GH
Where Rn-Net radiation; LH-latent heat flux; SH-ground heat flux and GH- Ground heat flux
• Changes in the water cycle due to natural variability and anthropogenic causes are linked as evaporation is common in both water and energy balance equations.
• Therefore, understanding each term and its variability becomes important to get the budgets to balance.
Models considered in this studyModels considered in this study• Parallel Climate Model (PCM):
– It is a coupled climate model that executes on the Cray T3E computer
– The components are interfaced by a flux coupler that passes the energy, moisture, and momentum fluxes between components.– Under numerous forcing scenarios model runs have been made by NCAR and simulations have reprocessed by the PCMDI. Historic run B06.27 is used here.
• Variable Infiltration Capacity (VIC)
• Parallel Climate Model (PCM): – It is a coupled climate model that executes on the Cray T3E computer
– The components are interfaced by a flux coupler that passes the energy, moisture, and momentum fluxes between components.– Under numerous forcing scenarios model runs have been made by NCAR and simulations have reprocessed by the PCMDI. Historic run B06.27 is used here.
• Variable Infiltration Capacity (VIC)
Atmospheric/landsurface componentAtmospheric/landsurface component
Sea ice componentSea ice componentOcean componentOcean component
Hydrology Model -VICHydrology
Model -VIC
• Multiple vegetation classes in each cell and are specified by their leaf area index, root distribution and canopy resistances
• Sub-grid elevation band definition (for snow)
• Snow pack accumulation and ablation simulated by a 2-layer energybalance model with canopy effects
• 3 soil layers used• Explicit 2-layer parameterization
for ground heat flux• Energy and water budget
closure at each time step• Subgrid infiltration/runoff
variability• Non-linear baseflow generation
• Multiple vegetation classes in each cell and are specified by their leaf area index, root distribution and canopy resistances
• Sub-grid elevation band definition (for snow)
• Snow pack accumulation and ablation simulated by a 2-layer energybalance model with canopy effects
• 3 soil layers used• Explicit 2-layer parameterization
for ground heat flux• Energy and water budget
closure at each time step• Subgrid infiltration/runoff
variability• Non-linear baseflow generation
Seasonal and Interannual variability
Seasonal and Interannual variability
• Quantification of variability in water budget components* MSV---the mean of the monthly range
(maximum minus minimum) in the 21-year global simulations
* MIAV---the mean absolute difference in annual totals for each of the variables
• Quantification of variability in water budget components* MSV---the mean of the monthly range
(maximum minus minimum) in the 21-year global simulations
* MIAV---the mean absolute difference in annual totals for each of the variables
MSV-PreciptationMSV-Preciptation
• Both PCM and VIC model, show a predominent seasonal change in precipitation along the equatorial low (where precipitation is abundant in all seasons)
• Subtropical high (North and southern Hemisphere) exhibit relatively moderate change (dry in all seasons).
• Both PCM and VIC model, show a predominent seasonal change in precipitation along the equatorial low (where precipitation is abundant in all seasons)
• Subtropical high (North and southern Hemisphere) exhibit relatively moderate change (dry in all seasons).
VIC PCM
MIAV-PrecipitationMIAV-Precipitation
• MIAV is quite distinct in the equatorial low (over Amazon basin) and mid-latitudes (of USA) and in South Asia.
• Subpolar low regions (Alaska, Canada) shows some variability (where precipitation is abundant)
• A high variability in the “source” term is expected to have strong impact on other water budget components.
• MIAV is quite distinct in the equatorial low (over Amazon basin) and mid-latitudes (of USA) and in South Asia.
• Subpolar low regions (Alaska, Canada) shows some variability (where precipitation is abundant)
• A high variability in the “source” term is expected to have strong impact on other water budget components.
VIC PCM
MSV-EvaporationMSV-Evaporation
• MSV in Evaporation is quite high both in equatorial and subtropical high regions.
• The only region that showed less changes are sub-Sahara Africa and Australian desert.
• Evaporation variability is partly driven by variabilities in precipitation.
• MSV in Evaporation is quite high both in equatorial and subtropical high regions.
• The only region that showed less changes are sub-Sahara Africa and Australian desert.
• Evaporation variability is partly driven by variabilities in precipitation.
VIC PCM
MIAV-EvaporationMIAV-Evaporation
• MIAV is relatively less from VIC simulations across the continents, except Australia.
• PCM displays higher variability over much of Africa, Australasia and S. America.
• MIAV is relatively less from VIC simulations across the continents, except Australia.
• PCM displays higher variability over much of Africa, Australasia and S. America.
VIC PCM
MSV-Change in SWQMSV-Change in SWQ
• Greater change in snow water equivalent is obvious over high latitudes.
• Obviously absense of snow in the lower regions and thereby no variabilities, except over Himalayas.
• Greater change in snow water equivalent is obvious over high latitudes.
• Obviously absense of snow in the lower regions and thereby no variabilities, except over Himalayas.
VIC PCM
•Higher MSV in precipitation (~270 mm) results in high variability in runoff (~50 mm) over S. America
•Europe and S. America exhibit high variability (~80 mm) in evaporation that is equal in magnitude.
•Out of Asia, Europe and N. America—more varibility in snow water equivalent is in N.America.
•MSV in soil moisture is about 50-60 mm across all continents
•Higher MSV in precipitation (~270 mm) results in high variability in runoff (~50 mm) over S. America
•Europe and S. America exhibit high variability (~80 mm) in evaporation that is equal in magnitude.
•Out of Asia, Europe and N. America—more varibility in snow water equivalent is in N.America.
•MSV in soil moisture is about 50-60 mm across all continents
•The magnitudes of MIAV is relatively less than those of MSVs for all variables.
•MIAV in precipitation is the highest for S.America followed by Australia and Europe and Asia show the least.
•Australia hasthe highest MIAV in evaporation
•Runoff variability is quite significant for S. America and Oceania that reflects the variability in precipitation as well.
•Variability in snow water equivalent is the highest for N. America followed by Europe.
•Australia has the highest variability in soil moisture and average is about 50 mm, equal in magnitude as that MSV.
•The magnitudes of MIAV is relatively less than those of MSVs for all variables.
•MIAV in precipitation is the highest for S.America followed by Australia and Europe and Asia show the least.
•Australia hasthe highest MIAV in evaporation
•Runoff variability is quite significant for S. America and Oceania that reflects the variability in precipitation as well.
•Variability in snow water equivalent is the highest for N. America followed by Europe.
•Australia has the highest variability in soil moisture and average is about 50 mm, equal in magnitude as that MSV.
Lakes, Wetlands and ImpoundmentsLakes, Wetlands and Impoundments• Lakes and wetlands are good
indicators of climate change. They play a major part in global water budget computations.
• Measurements of levels of lakes and wetlands are difficult and observations are sparsely available.
• Remote sensing of lake and wetland levels becomes crucial.
• A few major African lakes and wetlands data from TOPEX/POSEIDON satellite was made available to us by Charon Birkett, GSFC/NASA
• They constitute 8.4 % of total lakes and 5% of the total land area.
• Lakes and wetlands are good indicators of climate change. They play a major part in global water budget computations.
• Measurements of levels of lakes and wetlands are difficult and observations are sparsely available.
• Remote sensing of lake and wetland levels becomes crucial.
• A few major African lakes and wetlands data from TOPEX/POSEIDON satellite was made available to us by Charon Birkett, GSFC/NASA
• They constitute 8.4 % of total lakes and 5% of the total land area.
Lake Level ChangesLake Level Changes
Lake Area(km2) Lake Area(km2)Nyasa 6400 Turkana 6750Tana 3600 Victoria 68800Tanganyika 32000 Sudd Marshes ~10000
Lake Area(km2) Lake Area(km2)Nyasa 6400 Turkana 6750Tana 3600 Victoria 68800Tanganyika 32000 Sudd Marshes ~10000
Mean seasonal change in lake level is about 275 mm (~14 mm for 5% of the total land area)
Mean interannual change is about 2.3 mm.
Exclusion of change in lake storage in the water budget equations is therefore expected to cause closure problems in surface water budget.
Mean seasonal change in lake level is about 275 mm (~14 mm for 5% of the total land area)
Mean interannual change is about 2.3 mm.
Exclusion of change in lake storage in the water budget equations is therefore expected to cause closure problems in surface water budget.
ConclusionConclusion
• VIC showed a relatively low variability, but mostly in line with PCMs variability spatially.
• Variabilities are high in S. America, Australia and Africa
• Soil moisture did not include change in storage in wetlands, lakes and impoundments and that is expected to cause potential closure problems in surface water balance computations.
• VIC showed a relatively low variability, but mostly in line with PCMs variability spatially.
• Variabilities are high in S. America, Australia and Africa
• Soil moisture did not include change in storage in wetlands, lakes and impoundments and that is expected to cause potential closure problems in surface water balance computations.
Land vs Atmospheric Water Budget
• Atmosphere and land water budgets linked by P and E
• Land atmosphere feedbacks:
• climate variations, precipitation recycling, vegetation dynamics, …
• Objectives of this study:
• determine annual/seasonal atmospheric and land water budgets
• NCEP/NCAR Reanalysis
• VIC land surface simulations
• determine where the 2 budgets differ
• evaluate the NCEP/NCAR Reanalysis atmospheric moisture
Water Budget Equations
P E
D
RLand
Atmosphere
DPEdt
dW
H
0
, dzqD VQQ
DPE
REPdt
dS
RPE 0 RD
On mean annual scales:
dt
dW
dt
dS
Data
Princeton University
NCEP/NCAR Reanalysis
• 50+ years, 1948-present
• Global coverage
• T62 spectral resolution
• Variables:
• Assimilation of observations (surface, radiosonde, aircraft)
• Model derived variables, e.g. precipitation, evaporation, runoff
• Nudging unclosed water budget
VIC Land Surface Dataset
• 50+ years, 1948-1998
• Global extra-polar land coverage
• 2 degree
• Forced with:
• NCEP/NCAR Reanalysis near surface meteorology
• NCEP/NCAR Reanalysis with near surface meteorology and corrected precipitation
• Closed water budget
Precipitation (mm) Evaporation (mm)
Atmospheric Budget: ReanalysisAtmospheric Budget: ReanalysisAnnual 1950-1996 mean (mm)Annual 1950-1996 mean (mm)
E-P (mm)Divergence (mm)
NCEP Evaporation (mm)
NCEP Runoff (mm)
Land Budget: ReanalysisLand Budget: Reanalysis
NCEP E-P (mm)
Annual 1950-1996 mean (mm)Annual 1950-1996 mean (mm)
NCEP Precipitation (mm)
Land Budget: VIC forced by ReanalysisLand Budget: VIC forced by Reanalysis
VIC E-P (mm)
Annual 1950-1996 mean (mm)Annual 1950-1996 mean (mm)
VIC Runoff (mm)
NCEP Precipitation (mm) VIC Evaporation (mm)
Land Budget: VIC forced by corrected NCEP precipitation
Land Budget: VIC forced by corrected NCEP precipitation
VIC E-P (mm)
Annual 1950-1996 mean (mm)Annual 1950-1996 mean (mm)
VIC Runoff (mm)
NCEP Corrected Precipitation (mm) VIC Evaporation (mm)
Reanalysis Budget at Seasonal ScalesD – (E-P) (mm)
DJF
JJA
(D – (E-P))/D (%)
DJF
JJA
Seasonal Mean 1950-1996
Reanalysis Budget at Annual Scales
D – (E-P) (mm) (D – (E-P))/D (%)
Annual Mean 1950-1996
Africa
AsiaEuropeN. America
OceaniaS. America
Annual Land-Atmosphere BudgetAnnual Land-Atmosphere BudgetLand: NCEP/NCAR Reanalysis, Atmosphere: NCEP/NCAR ReanalysisLand: NCEP/NCAR Reanalysis, Atmosphere: NCEP/NCAR Reanalysis
D P E -R E-P
Annual Land-Atmosphere BudgetAnnual Land-Atmosphere BudgetLand: VIC with NCEP meteorology, Atmosphere: NCEP/NCAR ReanalysisLand: VIC with NCEP meteorology, Atmosphere: NCEP/NCAR Reanalysis
D P E -R E-P
Africa
AsiaEuropeN. America
OceaniaS. America
Annual Land-Atmosphere BudgetAnnual Land-Atmosphere BudgetLand: VIC with NCEP corrected meteorology, Atmosphere: NCEP/NCAR ReanalysisLand: VIC with NCEP corrected meteorology, Atmosphere: NCEP/NCAR Reanalysis
D P E -R E-P
Africa
AsiaEuropeN. America
OceaniaS. America
Examples of Budget Discrepancies
Niger, NE Africa Amazon, S. America Ganges, S. Asia
Mississippi, N. America Murray, Australia Lena, Russia
D P E -R E-P
Worth of Reanalysis Data
DJF
JJA
NCEP – NVAP (mm) (NCEP – NVAP)/NVAP (%)
DJF
JJA
Worth of Reanalysis Data
Africa DJF
Africa JJA
Asia DJF
Asia JJA Europe JJA
Europe DJF
Average Seasonal Vertically Integrated Moisture Content (mm)
Worth of Reanalysis Data
Oceania DJF
Oceania JJA
N. America DJF
N. America JJA
S. America DJF
S. America JJA
Average Seasonal Vertically Integrated Moisture Content (mm)
ConclusionsConclusions
• Analyzed land and atmospheric water budgets for 1950-1996 using
• NCEP/NCAR Reanalysis and
• VIC forced with reanalysis
• Known non-closure in reanalysis water budget shown
• Generally higher variability in budget in S. America and Africa
• D generally not consistent with E-P on annual scales especially in southern hemisphere
• Reanalysis atmospheric moisture compares well with NVAP in Europe and N. America, but bias and scatter in southern hemisphere and Asia
• Analyzed land and atmospheric water budgets for 1950-1996 using
• NCEP/NCAR Reanalysis and
• VIC forced with reanalysis
• Known non-closure in reanalysis water budget shown
• Generally higher variability in budget in S. America and Africa
• D generally not consistent with E-P on annual scales especially in southern hemisphere
• Reanalysis atmospheric moisture compares well with NVAP in Europe and N. America, but bias and scatter in southern hemisphere and Asia
Sources of Error
• Use of average monthly q and (u,w) to calculate D
• Comparison with NVAP
• Monthly average values
• Horizontal resolution (sharp gradients, steep topography)
• Vertical resolution – pressure coordinates used (not model coordinates)
Observations used in Reanalysis DataRadiosonde Observations Jan 1991
Aircraft Observations Jan 1991
Surface Observations Jan 1991
CPC NCEP/NCAR Reanalysis Project web pagehttp://wesley.wwb.noaa.gov/reanalysis.html
0
20
40
60
80
100
120
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180
200
Africa Asia Europe N. America Oceania S. America
D
P
E
R
E-P
-1000
-500
0
500
1000
1500
2000
Africa Asia Europe N. America Oceania S. America
D
P
E
R
E-P
Annual StatisticsAnnual Mean 1950-1996 (mm)
Annual StdDev 1950-1996 (mm)
0
20
40
60
80
100
120
140
160
180
200
Africa Asia Europe N. America Oceania S. America
D
P
E
R
E-P
-1000
-500
0
500
1000
1500
2000
Africa Asia Europe N. America Oceania S. America
D
P
E
R
E-P
-1000
-500
0
500
1000
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2000
Africa Asia Europe N. America Oceania S. America
D
P
E
R
E-P
0
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Africa Asia Europe N. America Oceania S. America
D
P
E
R
E-P
NCEP
NCEP VICncep
VICncep
VICncep corrected
VICncep corrected