Analysis of multi-year global evapotranspiration datasets and IPCC AR4 model simulations: Results from the LandFlux-EVAL project
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1 ETH Zürich Analysis of multi-year global evapotranspiration datasets and 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 Conference LandFlux-EVAL session
2 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 models?
3 ET in the climate system (2) Few datasets In-situ measurements scarce Satellite retrievals and models: Uncertainty? Validation? alyses not considered reliable (not well constrained) ET has received little attention so far
4 Dataset groups 'ostic' datasets Land-Surface Models () alyses Global Climate Model (GCM) simulations assessed in the 4th Assessment Report ( AR4), 20th century conditions
5 'ostic' Datasets Name Provider/Reference Information Fisher et al. (2008) Priestley-Taylor, ISLSCP-II (SRB, CRU, AVHRR) Wang and Liang (2008) Empirical, calibrated with Ameriflux, ISLSCP-II (SRB, CRU, AVHRR) 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) UCB MAUNI PRUNI
6 Land-Surface Models GS-COLA(-SSiB) GS-CLMTOP GS-NOAH GS-HY-SSIB GS-NSIPP GS-LAD GS-VISA GS-MOSAIC GS-ISBA GS-MOSES2 GS-BUCKET GS-SIBUC GS-SWAP GSWP (COLA) 13 GSWP LSM simulations, forced with ISLSCP-II and/or reanalysis data GL-NOAH GL-CLM GL-MOSAIC GLDAS LSM simulations EI-ORCH CRU-ORCH ORCHIDEE (LSCE) ERA-Interim forcing CRU-NCEP forcing VIC VIC (U.Arizona) model/observation forcing
7 alyses ERA-INT ECMWF (ERA-Interim) MERRA NASA/GSFC M-LAND MERRA-LAND from NASA/GSFC NCEP NOAA/OAR/ESRL PSD JRA-25 JMA AR4 simulations 20c3m ECHAM, IPSL, NCAR, MRI, MIROC MED, GFDL, INMCM, HADGEM, HADCM, GISS, CCCMA
8 Annual means in ET Means Std. deviations Rel. std. deviations.. mm/d mm/d
9 Difference -Reference data Means Std. deviations Rel. std. deviations mm/d mm/d %
10 Global analyses Similar spatial patterns and models lower ET in Amazon region Low standard deviations in in many regions models high standard deviation in India and Australia
11 Analysis on river basin scale Central EU basins Mississippi Volga Changjiang Amazon Nile Murray Darling
12 Annual means river basins: Δ ET [mm/d] Amazon UCB GS-CLMTOP CRU-ORCH NCAR MAUNI GS-HYSSIB VIC HADCM Mississippi PRUNI GS-LAD ERA-INT MRI Central Euro pean basins MPI GS-MOSAIC MERRA GISS AWB GS-MOSES2 M-LAND MIROC-MED Volga GS-COLAS GS-SIBUC NCEP CCCMA Changjiang GS-NOAH GS-SWAP JRA25 GFDL GS-NSIPP GL-NOAH ECHAM5 P-R Nile GS-VISA GL-CLM INMCM Murray Darling GS-ISBA GL-MOSAIC IPSL GS-BUCK EI-ORCH HADGEM
13 River basin analyses Spread between single datasets comparable in all 4 dataset groups Large spread in Murray-Darling and Nile basins in models ostic mean close to P-R Below average ET in and models in Amazon region Overestimation of ET in reanalyses and models in Changjiang basin
14 Cluster analysis: Annual means
15 Similarities between datasets models and build clusters ostic datasets and reanalyses more distinct from each other Strong influence of forcing data
16 Spatial uncertainties: Triple collocation method Each dataset corresponds to observations of a linear systematic deviation from the reality plus a random error, i.e. = r = r = r Collocation of 3 datasets P P M M G G P M G P M G 1. Solve for α,β 2. Calculate errors 3. Iterate (1) until errors do not change with further iteration Error in each dataset For random errors + for different error magnitudes Application of method?
17 Triple collocation:. ET
18 Summary Range large in some regions, i.e. tropics, rainforest Range similar in all dataset groups but mostly smallest in Large scale patterns consistent Importance of forcing data
19 Open questions Inter-dependence of datasets? Approach to evaluate products - standard?
20 Thank you!
21 Supplementary
22 Triple collocation: DS-groups ostic datasets /alyses simulations
23 Annual means in ET Means Std. deviations Rel. std. deviations Refe rence
24 Seasonal means: Dec-Jan-Feb Means Std. deviations Rel. std. deviations Refe rence
25 Seasonal means: Mar-Apr-May Means Std. deviations Rel. std. deviations Refe rence
26 Seasonal means: Jun-Jul-Aug Means Std. deviations Rel. std. deviations Refe rence
27 Seasonal means: Sep-Oct-Nov Means Std. deviations Rel. std. deviations Refe rence
28 Seasonal means: DJF Δ ET [mm/d] Amazon UCB GS-CLMTOP ORCH-CRU NCAR Mississippi MAUNI GS-HYSSIB VIC HADCM PRUNI GS-LAD ERA-INT MRI Central Euro pean basins MPI GS-MOSAIC MERRA GISS AWB GS-MOSES2 M-LAND MIROC-MED Volga GS-COLAS GS-SIBUC NCEP CCCMA Changjiang GS-NOAH GS-SWAP JRA25 GFDL GS-NSIPP GL-NOAH ECHAM5 P-R Nile GS-VISA GL-CLM INMCM Murray Darling GS-ISBA GL-MOSAIC IPSL GS-BUCK ORCH-I HADGEM
29 Seasonal means: MAM Δ ET [mm/d] Amazon UCB GS-CLMTOP ORCH-CRU NCAR Mississippi MAUNI GS-HYSSIB VIC HADCM PRUNI GS-LAD ERA-INT MRI Central Euro pean basins MPI GS-MOSAIC MERRA GISS AWB GS-MOSES2 M-LAND MIROC-MED Volga GS-COLAS GS-SIBUC NCEP CCCMA Changjiang GS-NOAH GS-SWAP JRA25 GFDL GS-NSIPP GL-NOAH ECHAM5 P-R Nile GS-VISA GL-CLM INMCM Murray Darling GS-ISBA GL-MOSAIC IPSL GS-BUCK ORCH-I HADGEM
30 Seasonal means: JJA Δ ET [mm/d] Amazon UCB GS-CLMTOP ORCH-CRU NCAR Mississippi MAUNI GS-HYSSIB VIC HADCM PRUNI GS-LAD ERA-INT MRI Central Euro pean basins MPI GS-MOSAIC MERRA GISS AWB GS-MOSES2 M-LAND MIROC-MED Volga GS-COLAS GS-SIBUC NCEP CCCMA Changjiang GS-NOAH GS-SWAP JRA25 GFDL GS-NSIPP GL-NOAH ECHAM5 P-R Nile GS-VISA GL-CLM INMCM Murray Darling GS-ISBA GL-MOSAIC IPSL GS-BUCK ORCH-I HADGEM
31 Seasonal mean: SON Δ ET [mm/d] Amazon UCB GS-CLMTOP ORCH-CRU NCAR Mississippi MAUNI GS-HYSSIB VIC HADCM PRUNI GS-LAD ERA-INT MRI Central Euro pean basins MPI GS-MOSAIC MERRA GISS AWB GS-MOSES2 M-LAND MIROC-MED Volga GS-COLAS GS-SIBUC NCEP CCCMA Changjiang Nile GS-NOAH GS-SWAP JRA25 GFDL GS-VISA GL-CLM INMCM GS-NSIPP GL-NOAH ECHAM5 P-R Murray Darling GS-ISBA GL-MOSAIC IPSL GS-BUCK ORCH-I HADGEM
32 Cluster analysis: Dec-Jan-Feb
33 Cluster analysis: Mar-Apr-May
34 Cluster analysis: Jun-Jul-Aug
35 Cluster analysis: Sep-Oct-Nov
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