Abstract
The importance of land surface and vegetation characteristics for climate has long been hypothesisized and is reflected by increasingly sophisticated land surface schemes used in climate models. However, accurate parameterisation of land surface processes is still hampered by the complexity of the processes, and by data availability at the global scale required for general circulation models. It is, therefore, desirable to utilise additional data sources for land surface models, of which satellite data appear to be the most promising in terms of availability and spatial and temporal coverage. Here, monthly satellite-derived fields of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are assimilated into a land surface and vegetation model, the Biosphere Energy-Transfer Hydrology scheme (Bethy). Assimilation offers the advantage that uncertainties of both the satellite-derived fAPAR and model parameters can be accounted for. Since fAPAR can also be predicted by the model, this information is not discarded as in other approaches where satellite data are used as forcing. During assimilation, a number of model parameters are adjusted until a cost function reaches its minimum. This cost function is defined by the squared deviation between monthly model-simulated and satellite-derived fAPAR as well as between initial and adjusted model parameters, both normalised by their assumed error variances. One of the adjusted parameters, the maximum plant-available soil moisture, is used in a subsequent sensitivity study with the Echam-4 climate model. The results show that changes in this parameter as a result of satellite data assimilation can lead to significant changes in simulated soil moisture and 2m air temperature over large parts of the tropics, where soil water storage is usually underestimated in climate and vegetation models. A comparison of Bethy simulations with soil water measurements from Amazonia supports this finding, and also shows that using fAPAR as forcing would have lead to inconsistencies between the carbon balance, predicting a strong decrease in fAPAR at negative carbon gains, and the value of fAPAR prescribed from the satellite data. The study aims at demonstrating the potential of assimilating satellite data into land surface models, as well as the significance of vegetation for the land surface climate. It is further intended to indicate a methodology for the assimilation of satellite data into general circulation models that include an interactive, i.e. climate-responsive, vegetation component.
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Anderson, E., J. Pailleux, J.-N. Thépaut, J.R. Eyre, A.P. McNally, G.A. Kelly, and P. Courtier, Use of cloud-cleared radiances in three/four-dimensional variational data assimilation, Ouaterly Journal of the Royal Meteorological Society, 120, 627–653, 1994.
Asrar, G. M. R.B., and B.J. Choudhury, Spatial heterogeneity in vegetation canopies and remote sensing of absorbed photosynthetically active radiation: a modeling study, RemoteSensing of Environment, 41, 627–653, 1994.
Beerling, D.J., and W.P. Quick, A new technique for estimating rates of carboxylation and electron transport in leaves of C3 plants for use in dynamic global vegetation models, Global Change Biology, 1, 289–294, 1995.
Berthelot, B., G. Dedieu, F. Cabot, and S. Adam, Estimation of surface reflectance and vegetation index using NOAA/AVHRR: Methods and results at global scale, in 6 th international symposium on physical measurements and signatures in remote sensing, ISPRS, Val d’Isère, France, 1994.
Blyth, K., The use of microwave remote sensing to improve spatial parameterization of hydrological models, Journal of Hydrology, 152, 103–129, 1993.
Box, E.O., Macroclimate and plant forms: An introduction to predictive modeling inphytogeography, 174 pp., Junk, Den Haag, 1981.
Brutsaert, W., Evaporation into the atmosphere, 299 pp., Reidel, Dordrecht, The Netherlands, 1982.
Budyko, M.I., Heat balance of the Earth’s surface, 255 pp., 1956.
Budyko, M.I., Climate and Life, 508 pp., Academic Press, New York, 1974.
Budyko, M.I., and A.A. Sokolov, Water Balance of the Earth, 663 pp., UNESCO Press, 1978.
Canadell, J., R.B. Jackson, J.R. Ehleringer, H.A. Mooney, D.E. Sala, and E.D. Schulze, Maximum rooting depth of vegetation types at the global scale, Oecologia, 108, 583–595, 1996.
Charney, J.G., W.J. Quirk, S.-H. Chow, and J. Kornfield, A comparative study of the effects of albedo change on drought in semi-arid regions, Journal of Atmospheric Sciences, 34, 1366–1385, 1977.
Charney, J.G., P.H. Stone, and W.J. Quirk, Drought in the Sahara: A biogeophysical feedback mechanism, Science, 187, 434–435, 1975.
Chase, T.N., R.A. Pielke, T.G.F. Kittel, R.R. Nemani, and S.W. Running, Simulated impacts of historical land cover changes on global climate in northern winter, Climate Dynamics, 16, 93–105, 2000.
Christensen, J.H., B. Machenhauer, R.G. Jones, C. Schär, P.M. Ruti, M. Castro, and G. Visconti, Validation of present-day regional climate simulations over Europe — LAM simulations with observed boundary conditions, Climate Dynamics, 13, 489–506, 1997.
Claussen, M., U. Lohmann, E. Roeckner, and U. Schulzweida, A global data set of land-surface parameters, Max-Planck-Institut für Meteorologie, Hamburg, Germany, 1994.
Claussen, M., U. Lohmann, E. Roeckner, and U. Schulzweida, The greening of the Sahara during the mid-holocene — results of an interactive atmosphere-biome model, GlobalEcology and Biogeography Letters, 6, 369–377, 1997.
Collatz, G.J., M. Ribas-Carbo, and J.A. Berry, Coupled photosynthesis-stomatal conductance model for leaves of C4 plants, Australian Journal of Plant Physiology, 19, 519–538, 1992.
Dickinson, R.E., A. Henderson-Sellers, P.J. Kennedy, and M.F. Wilson, Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model, National Center for Atmospheric Research, Boulder, CO, 1986.
Farquhar, G.D., S. von Caemmerer, and J.A. Berry, A biochemical model of photosynthesis in leaves of C3 species, Planta, 149, 78–90, 1980.
Federer, C.A., Transpirational supply and demand: plant, soil, and atmospheric effects evaluated by simulation, Water Resources Research, 18,355–362, 1982.
Fiasse, S., and M.M. Verstraete, Monitoring the environment with vegetation indices: comparison of NDVI and GEMI using AVHRR data over Africa, in Vegetation, Modellingand Climate Change Effects, edited by F. Veroustraete, and R. Ceulemans, pp. 107–135, Academic Publishing, The Hague, The Netherlands, 1994.
Foley, J.A., I.C. Prentice, N. Ramankutty, S. Levis, D. Pollard, S. Sitch, and A. Haxeltine, An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics, Global Biogeochemical Cycles, 10 (4), 603–628, 1996.
Ganopolski, A., C. Kubatzki, M. Claussen, V. Brovkin , and V. Petoukhov, The influence of vegetation-atmosphere-ocean interaction on climate during the mid-holocene, Science, 280, 1916–1919, 1998.
Garratt, J.R., Sensitivity of climate simulations to land-surface and atmospheric boundary-layer treatments — a review, Journal of Climate, 6, 419–449, 1993.
Geiger, R., The Climate near the Ground, Harvard University Press, Cambridge MA, 1965.
Geiger, R., R.H. Aron, and P. Todhunter, The Climate near the Ground, 528 pp., Vieweg-Verlag, Braunschweig, Germany, 1995.
Geng, S., F. Penning de Vries, and I. Supit, A simple method for generating daily rainfall data, Agricultural and Forestry Meteorology, 36, 363–376, 1986.
Gobron, N., B. Pinty, M.M. Verstraete, and Y. Govaerts, A semi-discrete model for the scattering of light by vegetation, Journal of Geophysical Research, 102, 9431–9446, 1997.
Goel, N.S., and W. Qin, Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: A computer simulation, Remote Sens. Rev., 10, 309–347, 1994.
Grace, J., J. Lloyd, J. McIntyre, A. Miranda, P. Meir, H. Miranda, C. Nobre, J. Moncrieff, J. Massheder, Y. Mahli, I. Wright, and J. Gash, Carbon dioxide uptake by an undisturbed tropical rain forest in South-West Amazonia, Science, 270, 778–780, 1995.
Gutman, G., Numerical experimants on land surface alterations with a zonal model allosin for interaction between geobotanic state and climate, Journal of Atmosperic Sciences, 41, 2679–2685, 1984.
Gutman, G., D. Tarpley, A. Ignatov, and S. Olson, The enhanced NOAA global land dataset from the Advanced Very High Resolution Radiometer, Bulletin of the AmericanMeteorological Society, 76, 1141–1156, 1995.
Gutman, G., D. Tarpley, A. Ignatov, and S. Olson, The relative merit of cloud/clear identification in the NOAA/NASA Pathfinder AVHRR 10-day composites, InternationalJournal of Remote Sensing, 17, 3295–3304, 1996.
Haxeltine, A., and I.C. Prentice, BIOME3: an equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types, Global Biogeochemical Cycles, 10 (4), 693–709, 1996.
Henderson-Sellers, A., K. McGuffie, and A.J. Pitman, The Project for intercomparison of land-surface parameterization schemes (PILPS): 1992 to 1995, Climate Dynamics, 12, 849–859, 1996.
Holben, B.N., Characteristics of maximum-value composite images from temporal AVHRR data, IntemationalJournal of Remote Sensing, 7, 1417–1434, 1986.
Holdridge, L.R., Determination of world formations from simple climatic data, Science, 105, 193–215, 1947.
Jarvis, P.G., and K.G. McNaughton, Stomatal control of transpiration: scaling up from leaf to region, Advances in Ecological Research, 15, 1–49, 1986.
Jones, H.G., Plants and Microclimate, 323 pp., Cambridge University Press, Cambridge, U.K., 1983.
Kelliher, F.M., R. Leuning, and E.-D. Schulze, Evaporation and canopy characteristics of coniferous forests and grasslands., Oecologia, 95, 152–163, 1993.
Kleidon, A., and M. Heimann, Optimised rooting depth and its impacts on the simulated climate of an atmospheric general circulation model, Geophysical Research Letters, 25, 345–348, 1998.
Knorr, W., Satellitengestützte Fernerkundung und Modellierung des globalen CO2-Austauschs der Landvegetation: Eine Synthese, Max-Planck-Institut für Meteorologie, Hamburg, Germany, 1997 (available in English through http://www.bgc-jena.mpg.de/∼wolfgang.knorr).
Knorr, W., Annual and interannual CO2 exchanges of the terrestrial biosphere: process-based simulations and uncertainties, Global Ecology and Biogeography, 9, 225–252, 2000.
Knorr, W., N. Gobron, P. Martin, B. Pinty, M.M. Verstraete, and G. Dedieu, Constraining a climate driven vegetation model with satellite data, in International Colloquium onPhotosynthesis and Remote Sensing, edited by G. Guyot, pp. 269–279, Earseel, Montpellier, France, 1995.
Le Dimet, F.X., and O. Talagrand, Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects, Tellus A, 38, 91–110, 1986.
Lean, J., and D.A. Warrilow, Simulation of the regional climatic impact of Amazon deforestation, Nature, 342, 411–413, 1989.
Leemans, R., and W. Cramer, The IIASA climate database for mean monthly values of temperature, precipitation and cloudiness on a terrestrial grid, Institute of Applied Systems Analysis, Laxenburg, Austria, 1991.
Legates, D.R., and C.J. Willmott, Mean seasonal and spatial variability in global surface air temperature, Theoretical and Applied Climatology, 41, 11–21, 1990a.
Legates, D.R., and C.J. Willmott, Mean seasonal and spatial variability in gauge-corrected, global precipitation, IntemationalJournal of Climatology, 10, 111–127, 1990b.
Leprieur, C., M.M. Verstraete, and B. Pinty, Evaluation of the performance of various vegetation indices to retrieve vegetation cover from AVHRR data, Remote Sens. Rev., 10, 265–284, 1994.
Los, S.O., C.O. Justice, and C.J. Tucker, A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data, International Journal of RemoteSensing, 15, 3493–3518, 1994.
Manabe, S., Climate and the ocean circulation. I. The atmospheric circulation and the hydrology of the earth’s surface, Monthly Weather Review, 97, 739–774, 1969.
McNider, R.T., A.J. Song, D.M. Casey, P.J. Wetzel, W.L. Crosson, and R.M. Rabin, Towards a dynamic-thermodynamic assimilation of satellite surface temperature in numerical atmospheric models, Monthly Weather Review, 122, 2784–2803, 1994.
Meyer, D., M.M. Verstraete, and B. Pinty, The effect of surface anisotropy and viewing geometry on the estimation of NDVI from AVHRR, Remote Sensing Reviews, 12, 3–27, 1995.
Milly, P.C.D., and K.A. Dunne, Sensitivity of the global water cycle to the water-holding capacity of land, Journal of Climate, 7, 506–526, 1994.
Mintz, Y., The sensitivity of numerically simulated climates to land-surface boundary conditions, in The Global Climate, edited by J.T. Houghton, pp. 79–105, Cambridge University Press, Cambridge, U.K., 1984.
Monteith, J.L., Evaporation and environment, Symposium of the Society for ExperimentalBiology, 19, 205–234, 1965.
Millier, M.J., Selected climatic data for a global set of standard stations for vegetationscience, Junk, Den Haag, The Netherlands, 1982.
Nepstad, D.C., C.R. de Carvalho, E.A. Davidson, P.H. Jipp, P.A. Lefebvre, G.H. Negeiros, E.D. da Silva, T.A. Stone, S.E. Trumbore, and S. Vieira, The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures, Nature, 372, 666–669, 1994.
Olson, J.S., J.A. Watts, and L.J. Allison, Carbon in live vegetation of major world ecosystems, Oak Ridge National Laboratory, Oak Ridge, 1983.
Ottle, C., and D. Vijal-Madjar, Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the Hapex-Mobilhy region, Journal ofHydrology, 158, 241–264, 1994.
Patterson, K.A., Global distributions of total and total-available soilwater-holding capacities, M.S. thesis, 119 pp., University of Delaware, Newark DE, 1990.
Pinker, R.T., and I. Laszlo, Global distribution of photosynthetically active radiaton as observed from satellites, Journal Climate, 5, 56–65, 1992.
Pinty, B., and M.M. Verstraete, GEMI: A non-linear index to monitor global vegetation from satellites, Vegetatio, 101, 1335–1372, 1992.
Polcher, J., and K. Laval, The impact of African and Amazonian deforestation on tropical climate, Journal of Hydrology, 155, 389–405, 1994.
Potter, S.C., J.T. Randerson, C.B. Field, P.A. Matson, P.M. Vitousek, H.A. Mooney, and S.A. Klooster, Terrestrial ecosystem production: a process model based on global satellite and surface data, Global Biogeochemical Cycles, 7, 811–841, 1993.
Press, W.H., S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, in Numerical Recipes inFortran, pp. 402–406, Cambridge University Press, Cambridge, U.K., 1992.
Prince, S.D., A model of regional primary productivity for use with coarse resolution satellite data, International Journal of Remote Sensing, 12, 1313–1330, 1991.
Rahman, H., and G. Dedieu, SMAC: A simplified method for the atmospheric correction of satellite measurements in the solar spectrum, International Journal of Remote Sensing, 15, 123–143, 1994.
Ritchie, J.T., Model for predicting evaporation from a row crop with incomplete cover, WaterResourcers Research, 8, 1204–1213, 1972.
Roeckner, E., K. Arpe, L. Bengtsson, M. Christoph, M. Claussen, L. Dümenil, M. Esch, M. Giorgetta, U. Schlese, and U. Schulzweida, The atmospheric general circulation model ECHAM4: Model description and simulation of present-day climate, Max-Planck-Institut für Meteorologie, Hamburg, Germany, 1996.
Rosenberg, N.J., Microclimate: The Biological Environment, 315 pp., Wiley, New York, 1974.
Ruimy, A., G. Dedieu, and B. Saugier, TURC: A diagnostic model of continental gross primary productivity and net primary productivity, Global Biogeochemical Cycles, 10, 269–285, 1996.
Ryan, M.G., Effects of climate change on plant respiration, Ecological Applications, 1, 157–167, 1991.
Schulz, J.-P., Dümenil, L., J. Polcher, C.A. Schlosser, and Y. Xue, Land surface energy and moisture fluxes: Comparing three models, Journal of Applied Meteorology, 37, 288–307, 1998.
Schulze, E.-D., F.M. Kelliher, C. Körner, J. Lloyd, and R. Leuning, Relationships among maximum stomatal conductance, ecosystem surface conductance, carbon assimilation rate, and plant nitrogen nutrition: a global ecology scaling exercise, Ann. Rev. Ecol. Syst., 25, 629–660, 1994.
Sellers, P.J., Canopy reflectance, photosynthesis and transpiration, International Journal ofRemote Sensing, 6, 1335–1372, 1985.
Sellers, P.J., Bounoua, L., G.J. Collate, D.A. Randall, D.A. Dazlich, S.O. Los, J.A. Berry, I. Fung, C.J. Tucker, C.B. Field, and T.G. Jensen, Comparison of radiative and physiological effects of doubled atmospheric CO2 on climate, Science, 271, 1402–1406, 1996.
Sellers, P.J., Y. Mintz, Y.C. Sud, and A. Dalcher, A Simple Biosphere Model (SiB) for use within general circulation models, Journal of Atmospheric Science, 43, 505–53, 1986.
Shukla, J., and Y. Mintz, Influence of land-surface evapotranspiration on the earth’s climate, Science, 215, 1498–1501, 1982.
Shukla, J., C. Nobre, and P.J. Sellers, Amazon deforestation and climate change, Science, 247, 1322–1325, 1990.
Smith, W.L., H.M. Woolf, C.M. Hayden, D.Q. Wark, and L.M. McMillin, The TIROS-N Operational Vertical Sounder, Bulletin of the American Meteorological Society, 60, 1177–1187, 1979.
Sud, Y.C., J. Shukla, and Y. Mintz, Influence of land surface roughness on atmospheric circulation and precipitation: A sensitivity study with a general circulation model, Journalof Applied Meteorology, 27, 1036–1054, 1988.
van den Hurk, B.J., W. Bastiaanssen, H. Pelgrum, and E. Meijgaard, A new methodology for assimilation of initial soil moisture fields in weather prediction models using Meteosat and NOAAdata, Journal of Applied Meteorology, 36, 1271–1283, 1997.
Verma, S.B., D.D. Baldocchi, D.E. Anderson, D.R. Matt, and R.J. Clement, Eddy fluxes of CO2, water vapor and sensible heat over a deciduous forest, Boundary Layer Meteorology, 36, 71–91, 1986.
Verstraete, M.M., Land surface processes in climate models: status and prospects, in Climateand Geo-Sciences, edited by A. Berger, S. Schneider, and J.C. Duplessy, pp. 321–340, Kluwer, Dordrecht, The Netherlands, 1989.
Verstraete, M.M., Retrieving canopy properties from remote sensing measurements, in Imaging Spectrometry — a tool for Environmental Observations, edited by J. Hill, and J. Mégier, pp. 109–123, ECSC, EEC, EAEC, Brussels and Luxemburg, 1994.
Verstraete, M.M., and B. Pinty, Designing optimal spectral vegetation indices for remote sensing applications, IEEE Transactions in Geoscience and RemoteSensing, 34, 1254–1265, 1996.
Verstraete, M.M., and B. Pinty, Environmental information extraction from satellite remote sending data, in Inverse Methods in Global Biogeochemical Cycles, edited by P. Kasibhatla et al., pp. 125–137, American Geophysical Union, Washington D.C., 2000.
Verstraete, M.M., B. Pinty, and R.E. Dickinson, A physical model of the bidirectional reflectance of vegetation canopies. 1. Theory, Journal of Geophysical Research, 95, 11775–11765, 1990.
Viterbo, P., and A.C.M. Beljaars, An improved land surface parameterization scheme in the ECMWF model and its validation, Journal of Climate, 8, 2716–2748, 1995.
Weiss, A., and J.A. Norman, Partitioning solar radiation into direct and diffuse, visible and near-infrared components, Agricultural and Forestry Meteorology, 34, 205–213, 1985.
Wilson, M.F., and A. Henderson-Sellers, A global archive of land cover and soils data for use in general circulation models, Journal of Climate, 5, 119–143, 1985.
Zeng, N., Seasonal cycle and interannual variability in the Amazon hydrologic cycle, Journalof Geophysical Research, 104 (D8), 9097–9106, 1999.
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Knorr, W., Schulz, JP. (2001). Using Satellite Data Assimilation to Infer Global Soil Moisture Status and Vegetation Feedback to Climate. In: Beniston, M., Verstraete, M.M. (eds) Remote Sensing and Climate Modeling: Synergies and Limitations. Advances in Global Change Research, vol 7. Springer, Dordrecht. https://doi.org/10.1007/0-306-48149-9_12
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