Abstract
A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.
Original language | English |
---|---|
Article number | e2020RG000715 |
Number of pages | 39 |
Journal | Reviews of Geophysics |
Volume | 59 |
Issue number | 3 |
DOIs | |
Publication status | Published - Sept 2021 |
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In: Reviews of Geophysics, Vol. 59, No. 3, e2020RG000715, 09.2021.
Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Reanalysis in earth system science
T2 - toward terrestrial ecosystem reanalysis
AU - Baatz, R.
AU - Hendricks Franssen, H. J.
AU - Euskirchen, E.
AU - Sihi, D.
AU - Dietze, M.
AU - Ciavatta, S.
AU - Fennel, K.
AU - Beck, H.
AU - De Lannoy, G.
AU - Pauwels, V. R.N.
AU - Raiho, A.
AU - Montzka, C.
AU - Williams, M.
AU - Mishra, U.
AU - Poppe, C.
AU - Zacharias, S.
AU - Lausch, A.
AU - Samaniego, L.
AU - Van Looy, K.
AU - Bogena, H.
AU - Adamescu, M.
AU - Mirtl, M.
AU - Fox, A.
AU - Goergen, K.
AU - Naz, B. S.
AU - Zeng, Y.
AU - Vereecken, H.
N1 - Funding Information: R. Baatz, C. Poppe, and M. Mirtl acknowledge funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871128 (eLTER PLUS). H. J. Hendricks Franssen, C. Montzka, and H. Vereecken acknowledge support from the Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy, EXC-2070?390732324?PhenoRob. E. Euskirchen acknowledges the Biological and Environmental Research (BER) program within the U.S. Department of Energy (DOE) as part of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project as well as DOE grant No. DE-SC0012704. D. Sihi acknowledges support from Emory University's Halle Institute for Global Research and the Halle Foundation Collaborative Research Grant. MCD was supported by NSF 1638577, NSF 1458021, and NASA 16-CMS16-0007. S. Ciavatta was supported by the Natural Environment Research Council (NERC) NCEO and by the NERC CAMPUS project, as well as by the SEAMLESS project, which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101004032. K. Fennel acknowledges support from the NSERC Discovery program, the Ocean Frontier Institute, and MEOPAR. G. De Lannoy was supported by the Research Foundation Flanders (FWO) G0A7320N and the ESA CCN1 CCI?+?Soil Moisture Scientific Evolution project. U, Mishra was supported through a grant from U.S. Department of Energy under Sandia National Laboratories contract DE-NA-0003525. L. Samaniego acknowledges the Advanced Earth System Modeling Capacity project funded by the Helmholtz Association. S Zacharias and H. Beck acknowledge the support of the TERENO (Terrestrial Environmental Observatories) funded by the Helmholtz Association. A. Fox acknowledges funding from NASA Terrestrial Ecosystems Grant 80NSSC19M0103. Open access funding enabled and organized by Projekt DEAL. Funding Information: R. Baatz, C. Poppe, and M. Mirtl acknowledge funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871128 (eLTER PLUS). H. J. Hendricks Franssen, C. Montzka, and H. Vereecken acknowledge support from the Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy, EXC‐2070–390732324–PhenoRob. E. Euskirchen acknowledges the Biological and Environmental Research (BER) program within the U.S. Department of Energy (DOE) as part of the Next‐Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project as well as DOE grant No. DE‐SC0012704. D. Sihi acknowledges support from Emory University's Halle Institute for Global Research and the Halle Foundation Collaborative Research Grant. MCD was supported by NSF 1638577, NSF 1458021, and NASA 16‐CMS16‐0007. S. Ciavatta was supported by the Natural Environment Research Council (NERC) NCEO and by the NERC CAMPUS project, as well as by the SEAMLESS project, which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101004032. K. Fennel acknowledges support from the NSERC Discovery program, the Ocean Frontier Institute, and MEOPAR. G. De Lannoy was supported by the Research Foundation Flanders (FWO) G0A7320N and the ESA CCN1 CCI + Soil Moisture Scientific Evolution project. U, Mishra was supported through a grant from U.S. Department of Energy under Sandia National Laboratories contract DE‐NA‐0003525. L. Samaniego acknowledges the Advanced Earth System Modeling Capacity project funded by the Helmholtz Association. S Zacharias and H. Beck acknowledge the support of the TERENO (Terrestrial Environmental Observatories) funded by the Helmholtz Association. A. Fox acknowledges funding from NASA Terrestrial Ecosystems Grant 80NSSC19M0103. Publisher Copyright: © 2021. The Authors. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/9
Y1 - 2021/9
N2 - A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.
AB - A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.
UR - http://www.scopus.com/inward/record.url?scp=85115813860&partnerID=8YFLogxK
U2 - 10.1029/2020RG000715
DO - 10.1029/2020RG000715
M3 - Article
AN - SCOPUS:85115813860
SN - 8755-1209
VL - 59
JO - Reviews of Geophysics
JF - Reviews of Geophysics
IS - 3
M1 - e2020RG000715
ER -