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

2. 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 1.Introduction 2. Evaluations 3. Outlook [http://www.iac.ethz.ch/groups/seneviratne/research/LandFlux-EVAL] LandFlux-EVAL Activity within LandFlux attempting to: (1)identify the regions / regimes with large differences between the existing land surface heat flux estimates. (2)understand the origin of the discrepancies, providing a forum for discussions about the improvement of the products. (3)promoting analysis that can help selecting a specific methodology and a choice of drivers for the LandFlux product. Agenda: 1st workshop in Toulouse, May 2007. 2nd workshop in Melbourne, Aug 2009. 3rd workshop in Vienna, April 2011.

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

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

5. 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 Sink for vapour MODEL Source of water Source of energy Short/Long-wave radiation Atmosphere Soil Vegetation SATELLITE OBSERVATIONS Qle uncertainty in data drivers & model limitations contribute to the final uncertainty 1.Introduction 2. Evaluations 3. Outlook

6. 1 st LandFlux assesments 6 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. 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. 1.Introduction 2. Evaluations 3. Outlook

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

8. 8 Multi-year global ET mean for each group [Ref = Mean(,, )] Comparisons [1] Mean global land ET values for each dataset with mean and standard deviation for each category (numbers).

9. 9 Multi-year global ET mean for each group [Ref = Mean(,, )] Comparisons [1]

10. 10 Cluster analysis of multi-year mean ET 1989-1995 (global) 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. Comparisons [1] 1.Introduction 2. Evaluations 3. Outlook

11. selected sample of global land surface monthly mean Q le (latent), Q h (sensible), and R n (net radiation) 11 [Jimenez et al. (2011), Global inter-comparison of 12 land surface flux estimates, JGR, 116] Comparing Qle/Qh/Rn [monthly means / 1993-1995] Comparisons [2] 1.Introduction 2. Evaluations 3. Outlook

12. 12 e.g. 08/1994 absolute valuesdifferences with all-product average Examples of monthly mean Qle Comparisons [2] 1.Introduction 2. Evaluations 3. Outlook

13. e.g. 08/1994 absolute valuesdifferences with all-product average absolute valuesdifferences with all-product average Examples of monthly mean Qh 13 Comparisons [2] 1.Introduction 2. Evaluations 3. Outlook

14. 14 Examples of monthly mean Rn absolute valuesdifferences with all-product average e.g. 08/1994 absolute valuesdifferences with all-product average Comparisons [2] 1.Introduction 2. Evaluations 3. Outlook

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

16. 16 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). + - improvements and new products continued to be published. new inter-comparison exercise with satellite-based products [insights for LandFlux production] Summarising [1,2] 1.Introduction 2. Evaluations 3. Outlook

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

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

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

20. 20 Comparison with EC [FluxNet] Qle [2003-2004] 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. Comparisons [3] 1.Introduction 2. Evaluations 3. Outlook

21. 21 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 Comparisons [3] 1.Introduction 2. Evaluations 3. Outlook

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

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

24. 24 [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] Estimating drivers uncertainty e.g. characterising the surface [land cover] GLC2000 MODISLC1 GLOBCOVER ECOCLIMAP Outlook 1.Introduction 2. Evaluations 3. Outlook

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

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

27. 27 Estimating uncertainty from choice of model [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] Outlook [AIRS, AVHRR, CERES, MODIS, AVHRR satellite forcings] 1.Introduction 2. Evaluations 3. Outlook

28. 28 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. Summary 1.Introduction 2. Evaluations 3. Outlook

29. 29 BACKUP SLIDES

30. Introduction 30 [http://www.iac.ethz.ch/groups/seneviratne/research/LandFlux-EVAL/Workshop_Vienna_April2011]

31. 31

32. Motivation 32 http://www.iac.ethz.ch/groups/seneviratne/research/LandFlux-EVAL/Workshop_Vienna_April2011

33. 33 Forcings 2. Sat-product comparison

34. 34 Forcings 2. Sat-product comparison

35. 35 Forcings 2. Sat-product comparison

36. 36 Forcings 2. Sat-product comparison

37. 37 Forcings 2. Sat-product comparison

38. 38 Forcings 2. Sat-product comparison

39. 39 Forcings 2. Sat-product comparison

40. 40 Forcings 2. Sat-product comparison

41. 41 Forcings 2. Sat-product comparison

42. 42 Forcings 2. Sat-product comparison