analysis of multi-year global evapotranspiration datasets and ipcc … · 2011-12-13 ·...

Introduction

Preliminary results

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IACETH LandFlux-EVAL, 24 June 2010 B. Mueller

Analysis of multi-year global evapotranspiration datasets

and IPCC AR4 model simulations:Results from the

LandFlux-EVAL project

Brigitte Mueller

S. I. Seneviratne, C. Jimenez, T. Corti, W. Dorigo, M. Hirschi, G. Balsamo, P. Ciais, P. Dirmeyer, J. B. Fisher, M. Jung, C. D. Kummerow, F. Maignan, M. F. McCabe, R. Reichle, M. Reichstein, M. Rodell, W. B.

Rossow, J. Sheffield, A. J. Teuling, K. Wang, and E. F. Wood

HESSS2 International ConferenceLandFlux-EVAL session

ETH Zürich

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ET in the climate system (1)

● Land-atmosphere feedbacks (link to precipitation, temperature)

● Impact of climate change on hydrological cycle

● Representation of global water cycle in IPCC models?

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● Few datasets

– In-situ measurements scarce

– Satellite retrievals and models: Uncertainty? Validation?

– Reanalyses not considered reliable (not well constrained)

● ET has received little attention so far

ET in the climate system (2)

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Dataset groups

● 'Diagnostic' datasets

● Land-Surface Models (LSMs)

● Reanalyses

● Global Climate Model (GCM) simulations assessed in the IPCC 4th Assessment Report (IPCC AR4), 20th century conditions

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'Diagnostic' Datasets

Name Provider/Reference Information

UCB Fisher et al. (2008) Priestley-Taylor, ISLSCP-II (SRB, CRU, AVHRR)

MAUNI Wang and Liang (2008) Empirical, calibrated with Ameriflux, ISLSCP-II (SRB, CRU, AVHRR)

PRUNI Sheffield et al. (2010) Penman-Montheith ET, ISCCP, AVHRR

MPI-BGC Jung et al. (2009) Empirical, global upscaling of FLUXNET data, CRU, GPCC, AVHRR

AWB ETH Mueller et al. (2010) Atmospheric water balance (GPCP, ERA-Interim)

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Land-Surface ModelsGS-COLA(-SSiB) GS-CLMTOPGS-NOAH GS-HY-SSIBGS-NSIPP GS-LADGS-VISA GS-MOSAICGS-ISBA GS-MOSES2GS-BUCKET GS-SIBUCGS-SWAP

GSWP(COLA)

13 GSWP LSM simulations, forced with ISLSCP-II and/or reanalysis data

GL-NOAHGL-CLMGL-MOSAIC

GLDAS LSM simulations

EI-ORCHCRU-ORCH

ORCHIDEE(LSCE)

ERA-Interim forcingCRU-NCEP forcing

VIC VIC(U.Arizona)

model/observation forcing

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ReanalysesERA-INT ECMWF (ERA-Interim)

MERRA NASA/GSFC

M-LAND MERRA-LAND from NASA/GSFC

NCEP NOAA/OAR/ESRL PSD

JRA-25 JMA

ECHAM, IPSL, NCAR, MRI, MIROCMED, GFDL, INMCM, HADGEM, HADCM, GISS, CCCMA

IPCC AR4 simulations 20c3m

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Annual means in ET 89-95Means Std. deviations Rel. std. deviations

Diagn.

LSMs

Rean.

IPCC

0.2 0.6 1.0 1.4 1.8 2.2 2.6 3.0 3.4 3.8 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0 4 8 12 16 20 24 28 32 36 40mm/d mm/d

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Difference IPCC-Reference dataMeans Std. deviations Rel. std. deviations

0.9 0.7 0.5 0.3 0.1 0.1 0.3 0.5 0.7 0.9 30 20 10 0 10 20 300.4 0.2 0.0 0.2 0.4mm/d mm/d %

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Global analyses

● Similar spatial patterns

● LSMs and IPCC models lower ET in Amazon region

● Low standard deviations in LSMs in many regions

● IPCC models high standard deviation in India and Australia

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Analysis on river basin scale

Mississippi

Amazon

Central EUbasins

Volga

Nile

Changjiang

MurrayDarling

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Annual means river basins: 89-95

Amazon Mississippi Central European basins

Volga Changjiang Nile MurrayDarling

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Δ E

T [m

m/d

]

UCB MAUNI PRUNI MPI AWB GS-COLAS GS-NOAH GS-NSIPP GS-VISA GS-ISBA GS-BUCKGS-CLMTOP GS-HYSSIB GS-LAD GS-MOSAIC GS-MOSES2 GS-SIBUC GS-SWAP GL-NOAH GL-CLM GL-MOSAIC EI-ORCHCRU-ORCH VIC ERA-INT MERRA M-LAND NCEP JRA25 ECHAM5 INMCM IPSL HADGEMNCAR HADCM MRI GISS MIROC-MED CCCMA GFDL P-R

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River basin analyses

● Spread between single datasets comparable in all 4 dataset groups

● Large spread in Murray-Darling and Nile basins in IPCC models

● Diagnostic mean close to P-R

● Below average ET in LSMs and IPCC models in Amazon region

● Overestimation of ET in reanalyses and IPCC models in Changjiang basin

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Cluster analysis:Annual means

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Similarities between datasets

● IPCC models and LSMs build clusters

● Diagnostic datasets and reanalyses more distinct from each other

● Strong influence of forcing data

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Spatial uncertainties:Triple collocation method

● Collocation of 3 datasets

● Each dataset corresponds to observations of a linear systematic deviation from the reality plus a random error, i.e.

● For random errors + for different error magnitudes

● Application of method?

P=

P

Pr

P

M=

M

Mr

M

G=

G

Gr

G

Error in each dataset

1. Solve for α,β2. Calculate errors3. Iterate (1) until errors do not change with further iteration

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Triple collocation: Diagn. ET

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Summary

● Large scale patterns consistent

● Range large in some regions, i.e. tropics, rainforest

● Range similar in all dataset groups

but mostly smallest in LSMs

● Importance of forcing data

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Open questions

● Inter-dependence of datasets?

● Approach to evaluate products - standard?

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Thank you!

www.iac.ethz.ch/url/research/LandFlux-EVAL

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Supplementary

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Triple collocation: DS-groupsDiagnostic datasets

LSMs/Reanalyses

IPCC simulations

Introduction

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Annual means in ET 89-95Means Std. deviations Rel. std. deviations

Diagn.

LSMs

Rean.

IPCC

0.2 0.6 1.0 1.4 1.8 2.2 2.6 3.0 3.4 3.8 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0 4 8 12 16 20 24 28 32 36 40

0.9 0.7 0.5 0.3 0.1 0.1 0.3 0.5 0.7 0.9 30 20 10 0 10 20 300.4 0.2 0.0 0.2 0.4

IPCC Reference

Introduction

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Seasonal means: Dec-Jan-FebMeans Std. deviations Rel. std. deviations

Diagn.

LSMs

Rean.

IPCC

0.2 0.6 1.0 1.4 1.8 2.2 2.6 3.0 3.4 3.8 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0 4 8 12 16 20 24 28 32 36 40

0.9 0.7 0.5 0.3 0.1 0.1 0.3 0.5 0.7 0.9 30 20 10 0 10 20 300.4 0.2 0.0 0.2 0.4

IPCC Reference

Introduction

Preliminary results

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Seasonal means: Mar-Apr-MayMeans Std. deviations Rel. std. deviations

Diagn.

LSMs

Rean.

IPCC

0.2 0.6 1.0 1.4 1.8 2.2 2.6 3.0 3.4 3.8 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0 4 8 12 16 20 24 28 32 36 40

0.9 0.7 0.5 0.3 0.1 0.1 0.3 0.5 0.7 0.9 30 20 10 0 10 20 300.4 0.2 0.0 0.2 0.4

IPCC Reference

Introduction

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Seasonal means: Jun-Jul-AugMeans Std. deviations Rel. std. deviations

Diagn.

LSMs

Rean.

IPCC

0.2 0.6 1.0 1.4 1.8 2.2 2.6 3.0 3.4 3.8 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0 4 8 12 16 20 24 28 32 36 40

0.9 0.7 0.5 0.3 0.1 0.1 0.3 0.5 0.7 0.9 30 20 10 0 10 20 300.4 0.2 0.0 0.2 0.4

IPCC Reference

Introduction

Preliminary results

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Seasonal means: Sep-Oct-NovMeans Std. deviations Rel. std. deviations

Diagn.

LSMs

Rean.

IPCC

0.2 0.6 1.0 1.4 1.8 2.2 2.6 3.0 3.4 3.8 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0 4 8 12 16 20 24 28 32 36 40

0.9 0.7 0.5 0.3 0.1 0.1 0.3 0.5 0.7 0.9 30 20 10 0 10 20 300.4 0.2 0.0 0.2 0.4

IPCC Reference

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Seasonal means: DJF

UCB MAUNI PRUNI MPI AWB GS-COLAS GS-NOAH GS-NSIPP GS-VISA GS-ISBA GS-BUCKGS-CLMTOP GS-HYSSIB GS-LAD GS-MOSAIC GS-MOSES2 GS-SIBUC GS-SWAP GL-NOAH GL-CLM GL-MOSAIC ORCH-IORCH-CRU VIC ERA-INT MERRA M-LAND NCEP JRA25 ECHAM5 INMCM IPSL HADGEMNCAR HADCM MRI GISS MIROC-MED CCCMA GFDL P-R

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Δ E

T [m

m/d

]



Introduction

Preliminary results

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Seasonal means: MAM


Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Δ E

T [m

m/d

]



Introduction

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Seasonal means: JJA


Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Δ E

T [m

m/d

]



Introduction

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Seasonal mean: SON


Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

Diagn LSMs

Rean IPCC

-2.75

-2.25

-1.75

-1.25

-0.75

-0.25

0.25

0.75

1.25

1.75

Δ E

T [m

m/d

]



Introduction

Preliminary results

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Cluster analysis: Dec-Jan-Feb

Introduction

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Cluster analysis: Mar-Apr-May

Introduction

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Cluster analysis: Jun-Jul-Aug

Introduction

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Cluster analysis: Sep-Oct-Nov

analysis of multi-year global evapotranspiration datasets and ipcc … · 2011-12-13 ·...

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