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LandFlux assesment

LandFlux assesment. C. Jimenez , S. Seneviratne, B. Mueller, M. McCabe, W. Rossow. and many other contributing with datasets and particpating in the analyses. <carlos.jimenez@obspm.fr>. LandFlux WS, Vienna, 04/2011. Introduction. Objectives: to develop the needed capabilities to produce

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LandFlux assesment

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  1. LandFlux assesment C. Jimenez, S. Seneviratne, B. Mueller, M. McCabe, W. Rossow and many other contributing with datasets and particpating in the analyses <carlos.jimenez@obspm.fr> LandFlux WS, Vienna, 04/2011

  2. Introduction Objectives: • to develop the needed capabilities to produce • a global, multi-decadal surface turbulent flux data product. • - consistent with other GRP products • - at compatible space/time scales • LandFlux-EVAL • Activity within LandFlux attempting to: • identify the regions / regimes with large differences between the existing land surface heat flux estimates. • understand the origin of the discrepancies, providing a forum for discussions about the improvement of the products. • promoting analysis that can help selecting a specific methodology and a choice of drivers for the LandFlux product. 1.Introduction 2. Evaluations • 3. Outlook [http://www.iac.ethz.ch/groups/seneviratne/research/LandFlux-EVAL] Agenda: 1st workshop in Toulouse, May 2007. 2nd workshop in Melbourne, Aug 2009. 3rd workshop in Vienna, April 2011. <carlos.jimenez@obspm.fr> 2

  3. Introduction • Reminders: • (1) possibilities for global estimation of land surface heat fluxes (Qle, Qh) [e.g. monthly latent fluxes August 93] (a) using observations to force ‘complex’ land surface model (such as GSWP-2) e.g., Dirmeyer, BAMS, 2006 1.Introduction 2. Evaluations • 3. Outlook (b) assimilating observations into a coupled land-atmosphere model in NWP framework e.g., Kalnay, BAMS, 1996 (c) using observations to infer the properties of the atmosphere and surface needed to derive the fluxes by dedicated physical / empirical formulations e.g., Fisher, RSE, 2007 <carlos.jimenez@obspm.fr> 3

  4. Introduction • Reminders: • (1) possibilities for global estimation of land surface heat fluxes (Qle, Qh) [e.g. monthly latent fluxes August 93] (a) using observations to force ‘complex’ land surface model (such as GSWP-2) e.g., Dirmeyer, BAMS, 2006 1.Introduction 2. Evaluations • 3. Outlook (b) assimilating observations into a coupled land-atmosphere model in NWP framework e.g., Kalnay, BAMS, 1996 (c) using observations to infer the properties of the atmosphere and surface needed to derive the fluxes by dedicated physical / empirical formulations satellite-based diagnostics e.g., Fisher, RSE, 2007 <carlos.jimenez@obspm.fr> 4

  5. Reminders: • (2) different to some of the other GEWEX products, Qle and Qh do not have a unique signature that can be remotely detected, so satellite observations need to be combined to infer them. Introduction Source of water Soil Vegetation 1.Introduction 2. Evaluations • 3. Outlook Source of energy MODEL Qle SATELLITE OBSERVATIONS Short/Long-wave radiation Sink for vapour Atmosphere uncertainty in data drivers & model limitations contribute to the final uncertainty <carlos.jimenez@obspm.fr> 5

  6. 1st LandFlux assesments • The number of global datasets of evapotranspiration continues to grow, important to keep up LandFlux-EVAL as a framework to independently evaluate products and monitor progress. 1.Introduction 2. Evaluations • 3. Outlook Publications: • Mueller et al. (2011), Evaluation of observation-based evapotranspiration datasets and IPCC AR4 simulations, 38, GRL. • Jimenez et al. (2011), Global inter-comparison of 12 land surface flux estimates, JGR, 116. • JImenez et al., A comparison of satellite-based radiation driven land surface heat fluxes, in preparation. <carlos.jimenez@obspm.fr>6

  7. Comparisons [1] • Comparing ET [monthly means / 1989-1995] 1.Introduction 2. Evaluations • 3. Outlook [Mueller et al. (2011), Evaluation of observation-based evapotranspiration datasets and IPCC AR4 simulations, 38, GRL] <carlos.jimenez@obspm.fr>7

  8. [Ref = Mean( , , )] Comparisons [1] • Multi-year globalETmean for each group • Mean global land ET values for each dataset with mean and standard deviation for each category (numbers). <carlos.jimenez@obspm.fr>8

  9. [Ref = Mean( , , )] Comparisons [1] • Multi-year globalETmean for each group <carlos.jimenez@obspm.fr>9

  10. Comparisons [1] • Cluster analysis of multi-year mean ET 1989-1995 (global) 1.Introduction 2. Evaluations • 3. Outlook Euclidean distance between datasets on each land grid cell. Datasets in the same branch of the cluster tree share similar global patterns. • Stronger cluster by GSWP simulations, with diagnostics and reanalyses separating into 2 main branches, suggesting distinct spatial patterns. <carlos.jimenez@obspm.fr>10

  11. Comparisons [2] • Comparing Qle/Qh/Rn [monthly means / 1993-1995] • selected sample of global land surface monthly mean Qle (latent), Qh (sensible), and Rn (net radiation) 1.Introduction 2. Evaluations • 3. Outlook [Jimenez et al. (2011), Global inter-comparison of 12 land surface flux estimates, JGR, 116] <carlos.jimenez@obspm.fr>11

  12. Comparisons [2] • Examples of monthly mean Qle e.g. 08/1994 1.Introduction 2. Evaluations • 3. Outlook absolute values differences with all-product average <carlos.jimenez@obspm.fr>12

  13. Comparisons [2] • Examples of monthly mean Qh e.g. 08/1994 1.Introduction 2. Evaluations • 3. Outlook absolute values differences with all-product average absolute values differences with all-product average <carlos.jimenez@obspm.fr>13

  14. Comparisons [2] • Examples of monthly mean Rn e.g. 08/1994 1.Introduction 2. Evaluations • 3. Outlook absolute values differences with all-product average absolute values differences with all-product average <carlos.jimenez@obspm.fr> 14

  15. Comparisons [2] • 1994 global annual means 1.Introduction 2. Evaluations • 3. Outlook Rn (W/m2) • Qle spread of ~ 15 W/m2 for an ensemble average of ~ 45 W/m2, larger for Qh. • Rn spread of ~25 W/m2 for an ensemble average of ~ 85 W/m2. • large EF (Qle/Rn) differences, suggesting a very different way to partition the fluxes. <carlos.jimenez@obspm.fr> 15

  16. Summarising [1,2] • The products all captured the seasonality of the heat fluxes as well as the expected spatial distributions (major climatic regimes and geographical features). • The products correlate well with each other in general, helped by the fact that some of the products use the same forcing data.    • Large differences were also observed, with large evaporative fraction differences suggesting a quite different partitioning of the radiative fluxes. • The correlations are considerably lower when the seasonal component is removed from the fluxes (seasonal variability largely responsible for the high correlations). + 1.Introduction 2. Evaluations • 3. Outlook - • improvements and new products continued to be published. • new inter-comparison exercise with satellite-based products • [insights for LandFlux production] <carlos.jimenez@obspm.fr> 16

  17. Comparisons [3] • Comparing Qle [monthly means / 2003-2004] 1.Introduction 2. Evaluations • 3. Outlook JImenez et al., A comparison of satellite-based radiation driven land surface heat fluxes, in preparation] <carlos.jimenez@obspm.fr>17

  18. Comparisons [3] Rn-driven by: ISCCP-FD SRB 3.0 Rn: O > 150 > o > 75 o W/m2 G-global 2004 <RnISCCP - RnSRB> • Annual averaged Qle as function of Rn 1.Introduction 2. Evaluations • 3. Outlook • Global Qle averages in a range of ~15 W/m2 <carlos.jimenez@obspm.fr>18

  19. Comparisons [3] • Monthly basin-averaged Qle spatial distributions [August 2004] 1.Introduction 2. Evaluations • 3. Outlook • Even when (in principle) radiation biases are not present (e.g. common ISCCP-FD Rn) the spatial distributions for some basins may largely disagree. <carlos.jimenez@obspm.fr>19

  20. Comparisons [3] • Comparison with EC [FluxNet] Qle [2003-2004] 1.Introduction 2. Evaluations • 3. Outlook • Exercise not very conclusive due to the large mismatch between tower and satellite footprint and the fact that some products are calibrated with EC measurements. <carlos.jimenez@obspm.fr>20

  21. Comparisons [3] • Significant differences in the satellite-based products even when forced with close radiative fluxes and close algorithms (PM,PT). • Some thoughts: - Satellite-based heat flux products ready to benchmark model estimates? Yes …. their simpler formulations have fewer degrees of freedom and potential sources of error propagation (compared with more complex models), providing a more direct link between the observations and the final estimates (being closer to a retrieval algorithm rather than to a ecosystem model). • But ….need to reduce/attribute uncertainty in their estimates to build confidence on the inferred values. • ETH group trying to do an statistical wrap up with their collected • datasets for a LandFlux-EVAL benchmarking database 1.Introduction 2. Evaluations • 3. Outlook <carlos.jimenez@obspm.fr>21

  22. Comparisons [3] • Some thoughts: • - Satellite-based heat flux products ready for trend analysis? Global land-ET variability according to MPI and independent models. 1.Introduction 2. Evaluations • 3. Outlook [Jung, M. et al. (2010) Recent decline in the global land evapotranspiration trend due to limited moisture supply, 1-4, Nature] <carlos.jimenez@obspm.fr>22

  23. Comparisons [3] Used in Jung (2010) Trend [1998-2005] 1.Introduction 2. Evaluations • 3. Outlook • Looking at trends in collected flux datasets • [B. Mueller (ETH), work in progress] <carlos.jimenez@obspm.fr>23

  24. Outlook • Estimating drivers uncertainty e.g. characterising the surface [land cover] GLC2000 GLOBCOVER 1.Introduction 2. Evaluations • 3. Outlook MODISLC1 ECOCLIMAP [Kaptue, A.T. (2011), Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale, 13, IJAEOG] <carlos.jimenez@obspm.fr>24

  25. Outlook • Estimating drivers uncertainty e.g. characterising the vegetation [NDVI] [1982-1999] 1.Introduction 2. Evaluations • 3. Outlook all AVHRR-based, but with different corrections for sensor and atmospheric effects 4 AVHRR NDVI [Beck, H. (2011), Global evaluation of four AVHRR NDVI data sets: Intercomparison and assessment againstLandsat imagery, 115, RSE] <carlos.jimenez@obspm.fr>25

  26. Outlook • Estimating uncertainty from choice of forcings e.g. driving one model with a matrix of different datasets [JPL/UCB PT model] 1.Introduction 2. Evaluations • 3. Outlook [Badgley, G., J. Fisher, C. Jimenez, K. Tu, R. Vinukollu, On uncertainty in global evapotranspiration estimates from choice of input forcing datasets, in preparation] <carlos.jimenez@obspm.fr>26

  27. Outlook • Estimating uncertainty from choice of model [AIRS, AVHRR, CERES, MODIS, AVHRR satellite forcings] 1.Introduction 2. Evaluations • 3. Outlook [Vinukollu, R.K. (2011), Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches, 115, RSE] <carlos.jimenez@obspm.fr>27

  28. Summary • Conducting first assessments characterising the uncertainty in the existing global estimates of land surface heat fluxes. • Global annual Qle are in a range of ~ 15 W/m2 for an ensemble average of ~ 45 W/m2, a bit larger for Qh, with Rn in a range of ~25 W/m2 for an ensemble average of ~ 85 W/m2. • Progress has been made (a growing number of global satellite-based (diagnostic) estimates), but significant differences can still be observed between the different estimates. • To attribute the flux differences to algorithms parameterizations, or to discrepancies in the observational datasets, a more complete assessment is needed whereby the remote sensing algorithms are run at different time and space scales using the same driving data and model protocols. 1.Introduction 2. Evaluations • 3. Outlook <carlos.jimenez@obspm.fr>28

  29. BACKUP SLIDES <carlos.jimenez@obspm.fr>29

  30. Introduction [http://www.iac.ethz.ch/groups/seneviratne/research/LandFlux-EVAL/Workshop_Vienna_April2011] <carlos.jimenez@obspm.fr>30

  31. <carlos.jimenez@obspm.fr>31

  32. Motivation http://www.iac.ethz.ch/groups/seneviratne/research/LandFlux-EVAL/Workshop_Vienna_April2011 <carlos.jimenez@obspm.fr>32

  33. 2. Sat-product comparison Forcings <carlos.jimenez@obspm.fr> 33

  34. 2. Sat-product comparison Forcings <carlos.jimenez@obspm.fr> 34

  35. 2. Sat-product comparison Forcings <carlos.jimenez@obspm.fr> 35

  36. 2. Sat-product comparison Forcings <carlos.jimenez@obspm.fr> 36

  37. 2. Sat-product comparison Forcings <carlos.jimenez@obspm.fr> 37

  38. 2. Sat-product comparison Forcings <carlos.jimenez@obspm.fr> 38

  39. 2. Sat-product comparison Forcings <carlos.jimenez@obspm.fr> 39

  40. 2. Sat-product comparison Forcings <carlos.jimenez@obspm.fr> 40

  41. 2. Sat-product comparison Forcings <carlos.jimenez@obspm.fr> 41

  42. 2. Sat-product comparison Forcings <carlos.jimenez@obspm.fr> 42

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