Evaluating the variability and budgets of global water cycle components V. Sridhar 1, G. Goteti 2,...

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

Global 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-QWhere P – Precipitation; E-Evapotranspiration; and Q is streamflowS is the sum of dominant terms ( soil moisture, snow water storage and lakes, wetlands and impoundments)

Global Water Cycle-Introduction(contd.)

• The surface energy budget eqn. isRn = 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 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)

Atmospheric/landsurface component

Sea ice componentOcean component

Hydrology 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

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

MSV-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).

VIC PCM

MIAV-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.

VIC PCM

MSV-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.

VIC PCM

MIAV-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.

VIC PCM

MSV-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.

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

•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 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.

Lake Level Changes

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.

Conclusion

• 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

DPEdtdW

H

0

, dzqD VQQ

DPE

REPdtdS

RPE 0RD

On mean annual scales:

dtdW

dtdS

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: ReanalysisAnnual 1950-1996 mean (mm)

E-P (mm)Divergence (mm)

NCEP Evaporation (mm)

NCEP Runoff (mm)

Land Budget: Reanalysis

NCEP E-P (mm)

Annual 1950-1996 mean (mm)

NCEP Precipitation (mm)

Land Budget: VIC forced by Reanalysis

VIC E-P (mm)


VIC Runoff (mm)

NCEP Precipitation (mm) VIC Evaporation (mm)

Land Budget: VIC forced by corrected NCEP precipitation

VIC E-P (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 BudgetLand: NCEP/NCAR Reanalysis, Atmosphere: NCEP/NCAR Reanalysis

D P E -R E-P

Annual Land-Atmosphere BudgetLand: VIC with NCEP meteorology, Atmosphere: NCEP/NCAR Reanalysis

D P E -R E-P

Africa


OceaniaS. America

Annual Land-Atmosphere BudgetLand: VIC with NCEP corrected meteorology, Atmosphere: NCEP/NCAR Reanalysis

D P E -R E-P

Africa


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


Africa DJF

Africa JJA

Asia DJF

Asia JJA Europe JJA

Europe DJF

Average Seasonal Vertically Integrated Moisture Content (mm)


Oceania DJF

Oceania JJA

N. America DJF

N. America JJA

S. America DJF

S. America JJA

Average Seasonal Vertically Integrated Moisture Content (mm)

Conclusions• 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

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200

Africa Asia Europe N. America Oceania S. America

D

P

E

R

E-P

-1000

-500

0

500

1000

1500

2000


DPERE-P

Annual StatisticsAnnual Mean 1950-1996 (mm)

Annual StdDev 1950-1996 (mm)

0

20

40

60

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100

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D

PERE-P

-1000

-500

0

500

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DPER

E-P

-1000

-500

0

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1000

1500

2000


DPER

E-P

0

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D

PERE-P

NCEP

NCEP VICncep

VICncep

VICncep corrected

VICncep corrected

Evaluating the variability and budgets of global water cycle components V. Sridhar 1, G. Goteti 2,...

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