BACI: 2015-2019


WP4 New downstream data products

Lead: MPG
Main Contact: Martin Jung, WP4 Leader

The overall aim of this work package is to derive novel synergistic products of essential ecosystem variables (EEVs) and ecosystem functional properties by integrating ground measurements and EO with machine learning methods. More specifically, our objectives are:

  • Upscaling the diurnal cycle of carbon and energy fluxes from half-hourly FLUXNET data
  • Mapping key ecosystem functional properties related to e.g. radiation use efficiency and water use efficiency of gross primary production
  • Generating a spatially explicit reconstruction of tree-ring variability for Europe
  • Producing spatially and temporally explicit patterns of ecosystem scale plant traits
  • Providing an objective functional classification of the biosphere

Latent heat map (left): Eddy covariance towers can monitor carbon, water, and energy fluxes on the long-term and provide one measurement each half hour. Using modern machine learning methods and a wide array of additionally data, we can now estimate these fluxes quite accurately across large areas. Here, we show fluxes estimate over Europe for the time 17:30 19th July 2001

Essential Ecosystem Variables (EEVs): Fundamental interactions among ECVs and EBVs govern the states, processes and functions of ecosystems.

Image Licence: CC BY 4.0

Papers that were supported by this WP

1Alexander, M. R., Rollinson, C. R., Babst, F., Trouet, V., Moore, D. J. P. (2018). Relative influences of multiple sources of uncertainty on cumulative and incremental tree-ring-derived aboveground biomass estimates. Trees, 32(1), 265-276. doi:10.1007/s00468-017-1629-0.
Postprint available
2Babst, F., Bodesheim, P., Charney, N., Friend, A. D., Girardin, M. P., Klesse, S., Moore, D. J., Seftigen, K., Björklund, J., Bouriaud, O., Dawson, A., DeRose, R. J., Dietze, M. C., Eckes, A. H., Enquist, B., Frank, D. C., Mahecha, M. D., Poulter, B., Record, S., Trouet, V., Turton, R. H., Zhang, Z., Evans, M. E. (2018). When tree rings go global: Challenges and opportunities for retro- and prospective insight. Quaternary Science Reviews, 197, 1-20. doi:10.1016/j.quascirev.2018.07.009.
3Babst, F., Bouriaud, O., Poulter, B., Trouet, V., Girardin, M. P., Frank, D. C. (2019). Twentieth century redistribution in climatic drivers of global tree growth. Science Advances, 5(1): eaat4313. doi:10.1126/sciadv.aat4313.
4Babst, F., Poulter, B., Bodesheim, P., Mahecha, M. D., Frank, D. C. (2017). Improved tree-ring archives will support earth-system science. Nature Ecology & Evolution, 1: 0008. doi:10.1038/s41559-016-0008.
Postprint available
5Belmecheri, S., Babst, F., Hudson, A. R., Betancourt, J., Trouet, V. (2017). Northern hemisphere jet stream position indices as diagnostic tools for climate and ecosystem dynamics. Earth Interactions, 21(8), 1-23. doi:10.1175/EI-D-16-0023.1.
6Bodesheim, P., Jung, M., Gans, F., Mahecha, M. D., Reichstein, M. (2018). Upscaled diurnal cycles of land-atmosphere fluxes: a new global half-hourly data product. Earth System Science Data, 10, 1327-1365. doi:10.5194/essd-10-1327-2018.
7Evans, M. E. K., Falk, D. A., Arizpe, A., Swetnam, T. L., Babst, F., Holsinger, K. E. (2017). Fusing tree-ring and forest inventory data to infer influences on tree growth. Ecosphere, 8(7): e01889. doi:10.1002/ecs2.1889.
8Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S., Ahlström, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein, P., Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B., Raduly, B., Rödenbeck, C., Tramontana, G., Viovy, N., Wang, Y.-P., Weber, U., Zaehle, S., Zeng, N. (2017). Compensatory water effects link yearly global land CO2 sink changes to temperature. Nature, 541(7638), 516-520. doi:10.1038/nature20780.
Postprint available
9Klesse, S., Babst, F., Lienert, S., Spahni, R., Joos, F., Bouriaud, O., Carrer, M., Filippo, A. D., Poulter, B., Trotsiuk, V., Wilson, R., Frank, D. C. (2018). A combined tree ring and vegetation model assessment of european forest growth sensitivity to interannual climate variability. Global Biogeochemical Cycles, 32(8), 1226-1240. doi:10.1029/2017GB005856.
10Koirala, S., Jung, M., Reichstein, M., de Graaf, I. E. M., Camps-Valls, G., Ichii, K., Papale, D., Raduly, B., Schwalm, C. R., Tramontana, G., Carvalhais, N. (2017). Global distribution of groundwater-vegetation spatial covariation. Geophysical Research Letters, 44(9), 4134-4142. doi:10.1002/2017GL072885.
Posprint available
11Lu, X., Liang, E., Wang, Y., Babst, F., Leavitt, S. W., Camarero, J. J. (2019). Past the climate optimum: Recruitment is declining at the world’s highest juniper shrublines on the Tibetan Plateau. Ecology, 100(2): e02557. doi:10.1002/ecy.2557.
12Marchand, W., Girardin, M. P., Gauthier, S., Hartmann, H., Bouriaud, O., Babst, F., Bergeron, Y. (2018). Untangling methodological and scale considerations in growth and productivity trend estimates of Canada's forests. Environmental Research Letters, 13: 093001. doi:10.1088/1748-9326/aad82a.
13Montane, F., Fox, A. M., Arellano, A. F., MacBean, N., Alexander, M. R., Dye, A., Bishop, D. A., Trouet, V., Babst, F., Hessl, A. E., Pederson, N., Blanken, P. D., Bohrer, G., Gough, C. M., Litvak, M. E., Novick, K. A., Phillips, R. P., Wood, J. D., Moore, D. J. P. (2017). Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests. Geoscientific Model Development, 10(9), 3499-3517. doi:10.5194/gmd-10-3499-2017.
14Musavi, T., Migliavacca, M., van de Weg, M. J., Kattge, J., Wohlfahrt, G., van Bodegom, P., Reichstein, M., Bahn, M., Carrara, A., Domingues, T., Gavazzi, M., Gianelle, D., Gimeno, C., Granier, A., Gruening, C., Havránková, K., Herbst, M., Hrynkiw, C., Kalhori, A., Kaminski, T., Klumpp, K., Kolari, P., Longdoz, B., Minerbi, S., Montagnani, L., Moors, E., Oechel, W., Reich, P., Rohatyn, S., Rossi, A., Rotenberg, E., Varlagin, A., Wilkinson, M., Wirth, C., Mahecha, M. D. (2016). Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits. Ecology and Evolution, 6(20), 7352-7366. doi:10.1002/ece3.2479.
15Papale, D., Black, T. A., Carvalhais, N., Cescatti, A., Chen, J., Jung, M., Kiely, G., Lasslop, G., Mahecha, M. D., Margolis, H., Merbold, L., Montagnani, L., Moors, E., Olesen, J. E., Reichstein, M., Tramontana, G., van Gorsel, E., Wohlfahrt, G., Ráduly, B. (2015). Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks. Journal of Geophysical Research: Biogeosciences, 120(10), 1941-1957. doi:10.1002/2015JG002997.
Postprint available
16Pappas, C., Mahecha, M. D., Frank, D. C., Babst, F., Koutsoyiannis, D. (2017). Ecosystem functioning is enveloped by hydrometeorological variability. Nature Ecology & Evolution, 1(9), 1263-1270. doi:10.1038/s41559-017-0277-5.
Postprint available
17Reyers, B., Stafford-Smith, M., Erb, K.-H., Scholes, R. J., Selomane, O. (2017). Essential variables help to focus sustainable development goals monitoring. Current Opinion in Environmental Sustainability, 26-27, 97-105. doi:10.1016/j.cosust.2017.05.003.
18Seftigen, K., Frank, D. C., Björklund, J., Babst, F., Poulter, B. (2018). The climatic drivers of normalized difference vegetation index and tree-ring-based estimates of forest productivity are spatially coherent but temporally decoupled in Northern Hemispheric forests. Global Ecology and Biogeography, 27(11), 1352-1365. doi:10.1111/geb.12802.
19Sun, Y., Frankenberg, C., Wood, J. D., Schimel, D. S., Jung, M., Guanter, L., Drewry, D. T., Verma, M., Porcar-Castell, A., Griffis, T. J., Gu, L., Magney, T. S., Köhler, P., Evans, B., Yuen, K. (2017). OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science, 358(6360): eaam5747. doi:10.1126/science.aam5747.
Postprint available
20Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., Papale, D. (2016). Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms. Biogeosciences, 13(14), 4291-4313. doi:10.5194/bg-13-4291-2016.
21Trouet, V., Babst, F., Meko, M. (2018). Recent enhanced high-summer North Atlantic Jet variability emerges from three-century context. Nature Communications, 9: 180. doi:10.1038/s41467-017-02699-3.
22Wu, X., Liu, H., Li, X., Ciais, P., Babst, F., Guo, W., Zhang, C., Magliulo, V., Pavelka, M., Liu, S., Huang, Y., Wang, P., Shi, C., Ma, Y. (2018). Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Global Change Biology, 24(1), 504-516. doi:10.1111/gcb.13920.
23Zhang, Z., Babst, F., Bellassen, V., Frank, D., Launois, T., Tan, K., Ciais, P., Poulter, B. (2018). Converging climate sensitivities of European forests between observed radial tree growth and vegetation models. Ecosystems, 21(3), 410-425. doi:10.1007/s10021-017-0157-5.
Postprint available
24Zscheischler, J., Mahecha, M. D., Avitabile, V., Calle, L., Carvalhais, N., Ciais, P., Gans, F., Gruber, N., Hartmann, J., Herold, M., Ichii, K., Jung, M., Landschützer, P., Laruelle, G. G., Lauerwald, R., Papale, D., Peylin, P., Poulter, B., Ray, D., Regnier, P., Rödenbeck, C., Roman-Cuesta, R. M., Schwalm, C., Tramontana, G., Tyukavina, A. T., Valentini, R., van der Werf, G., West, T. O., Wolf, J. E., Reichstein, M. (2017). Reviews and syntheses: An empirical spatiotemporal description of the global surface–atmosphere carbon fluxes: opportunities and data limitations. Biogeosciences, 14(15), 3685-3703. doi:10.5194/bg-14-3685-2017.