Articles | Volume 11, issue 11
https://doi.org/10.5194/gmd-11-4451-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-4451-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 Nov 2018
Development and technical paper |
| 05 Nov 2018
| 05 Nov 2018
Implementing spatially explicit wind-driven seed and pollen dispersal in the individual-based larch simulation model: LAVESI-WIND 1.0
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und
Meeresforschung, Research Unit Potsdam, Telegrafenberg A43, 14473 Potsdam,
Germany
Alexander Gerdes
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und
Meeresforschung, Research Unit Potsdam, Telegrafenberg A43, 14473 Potsdam,
Germany
Institute of Physics and Astronomy, University of Potsdam, 14476
Potsdam, Germany
Nadja J. Kath
Institute of Biology and Biochemistry, University of Potsdam, 14476
Potsdam, Germany
Ulrike Herzschuh
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und
Meeresforschung, Research Unit Potsdam, Telegrafenberg A43, 14473 Potsdam,
Germany
Institute of Biology and Biochemistry, University of Potsdam, 14476
Potsdam, Germany
Institute of Earth and Environmental Science, University of Potsdam,
14476 Potsdam, Germany
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Ulrike Herzschuh, Thomas Böhmer, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Chenzhi Li, Xianyong Cao, Odile Peyron, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
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Ulrike Herzschuh, Thomas Böhmer, Chenzhi Li, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Xianyong Cao, Nancy H. Bigelow, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Odile Peyron, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
Earth Syst. Sci. Data, 15, 2235–2258, https://doi.org/10.5194/essd-15-2235-2023, https://doi.org/10.5194/essd-15-2235-2023, 2023
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Furong Li, Marie-José Gaillard, Xianyong Cao, Ulrike Herzschuh, Shinya Sugita, Jian Ni, Yan Zhao, Chengbang An, Xiaozhong Huang, Yu Li, Hongyan Liu, Aizhi Sun, and Yifeng Yao
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Timon Miesner, Ulrike Herzschuh, Luidmila A. Pestryakova, Mareike Wieczorek, Evgenii S. Zakharov, Alexei I. Kolmogorov, Paraskovya V. Davydova, and Stefan Kruse
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Ulrike Herzschuh, Chenzhi Li, Thomas Böhmer, Alexander K. Postl, Birgit Heim, Andrei A. Andreev, Xianyong Cao, Mareike Wieczorek, and Jian Ni
Earth Syst. Sci. Data, 14, 3213–3227, https://doi.org/10.5194/essd-14-3213-2022, https://doi.org/10.5194/essd-14-3213-2022, 2022
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Ramesh Glückler, Rongwei Geng, Lennart Grimm, Izabella Baisheva, Ulrike Herzschuh, Kathleen R. Stoof-Leichsenring, Stefan Kruse, Andrei Andreev, Luidmila Pestryakova, and Elisabeth Dietze
EGUsphere, https://doi.org/10.5194/egusphere-2022-395, https://doi.org/10.5194/egusphere-2022-395, 2022
Preprint archived
Short summary
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Despite rapidly intensifying wildfire seasons in Siberian boreal forests, little is known about long-term relationships between changes in vegetation and shifts in wildfire activity. Using lake sediment proxies, we reconstruct such environmental changes over the past 10,800 years in Central Yakutia. We find that a more open forest may facilitate increased amounts of vegetation burning. The present-day dense larch forest might yet be mediating the current climate-driven wildfire intensification.
Chenzhi Li, Alexander K. Postl, Thomas Böhmer, Xianyong Cao, Andrew M. Dolman, and Ulrike Herzschuh
Earth Syst. Sci. Data, 14, 1331–1343, https://doi.org/10.5194/essd-14-1331-2022, https://doi.org/10.5194/essd-14-1331-2022, 2022
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Here we present a global chronology framework of 2831 palynological records, including globally harmonized chronologies covering up to 273 000 years. A comparison with the original chronologies reveals a major improvement according to our assessment. Our chronology framework and revised chronologies will interest a broad geoscientific community, as it provides the opportunity to make use in synthesis studies of, for example, pollen-based vegetation and climate change.
Stefan Kruse, Simone M. Stuenzi, Julia Boike, Moritz Langer, Josias Gloy, and Ulrike Herzschuh
Geosci. Model Dev., 15, 2395–2422, https://doi.org/10.5194/gmd-15-2395-2022, https://doi.org/10.5194/gmd-15-2395-2022, 2022
Short summary
Short summary
We coupled established models for boreal forest (LAVESI) and permafrost dynamics (CryoGrid) in Siberia to investigate interactions of the diverse vegetation layer with permafrost soils. Our tests showed improved active layer depth estimations and newly included species growth according to their species-specific limits. We conclude that the new model system can be applied to simulate boreal forest dynamics and transitions under global warming and disturbances, expanding our knowledge.
Anne Dallmeyer, Martin Claussen, Stephan J. Lorenz, Michael Sigl, Matthew Toohey, and Ulrike Herzschuh
Clim. Past, 17, 2481–2513, https://doi.org/10.5194/cp-17-2481-2021, https://doi.org/10.5194/cp-17-2481-2021, 2021
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Using the comprehensive Earth system model, MPI-ESM1.2, we explore the global Holocene vegetation changes and interpret them in terms of the Holocene climate change. The model results reveal that most of the Holocene vegetation transitions seen outside the high northern latitudes can be attributed to modifications in the intensity of the global summer monsoons.
Stuart A. Vyse, Ulrike Herzschuh, Gregor Pfalz, Lyudmila A. Pestryakova, Bernhard Diekmann, Norbert Nowaczyk, and Boris K. Biskaborn
Biogeosciences, 18, 4791–4816, https://doi.org/10.5194/bg-18-4791-2021, https://doi.org/10.5194/bg-18-4791-2021, 2021
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Lakes act as important stores of organic carbon and inorganic sediment material. This study provides a first investigation into carbon and sediment accumulation and storage within an Arctic glacial lake from Far East Russia. It shows that major shifts are related to palaeoclimate variation that affects the development of the lake and its surrounding catchment. Spatial differences to other lake systems from other regions may reflect variability in processes controlled by latitude and altitude.
Ramesh Glückler, Ulrike Herzschuh, Stefan Kruse, Andrei Andreev, Stuart Andrew Vyse, Bettina Winkler, Boris K. Biskaborn, Luidmila Pestryakova, and Elisabeth Dietze
Biogeosciences, 18, 4185–4209, https://doi.org/10.5194/bg-18-4185-2021, https://doi.org/10.5194/bg-18-4185-2021, 2021
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Data about past fire activity are very sparse in Siberia. This study presents a first high-resolution record of charcoal particles from lake sediments in boreal eastern Siberia. It indicates that current levels of charcoal accumulation are not unprecedented. While a recent increase in reconstructed fire frequency coincides with rising temperatures and increasing human activity, vegetation composition does not seem to be a major driver behind changes in the fire regime in the past two millennia.
Lydia Stolpmann, Caroline Coch, Anne Morgenstern, Julia Boike, Michael Fritz, Ulrike Herzschuh, Kathleen Stoof-Leichsenring, Yury Dvornikov, Birgit Heim, Josefine Lenz, Amy Larsen, Katey Walter Anthony, Benjamin Jones, Karen Frey, and Guido Grosse
Biogeosciences, 18, 3917–3936, https://doi.org/10.5194/bg-18-3917-2021, https://doi.org/10.5194/bg-18-3917-2021, 2021
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Our new database summarizes DOC concentrations of 2167 water samples from 1833 lakes in permafrost regions across the Arctic to provide insights into linkages between DOC and environment. We found increasing lake DOC concentration with decreasing permafrost extent and higher DOC concentrations in boreal permafrost sites compared to tundra sites. Our study shows that DOC concentration depends on the environmental properties of a lake, especially permafrost extent, ecoregion, and vegetation.
Iuliia Shevtsova, Ulrike Herzschuh, Birgit Heim, Luise Schulte, Simone Stünzi, Luidmila A. Pestryakova, Evgeniy S. Zakharov, and Stefan Kruse
Biogeosciences, 18, 3343–3366, https://doi.org/10.5194/bg-18-3343-2021, https://doi.org/10.5194/bg-18-3343-2021, 2021
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In the light of climate changes in subarctic regions, notable general increase in above-ground biomass for the past 15 years (2000 to 2017) was estimated along a tundra–taiga gradient of central Chukotka (Russian Far East). The greatest increase occurred in the northern taiga in the areas of larch closed-canopy forest expansion with Cajander larch as a main contributor. For the estimations, we used field data (taxa-separated plant biomass, 2018) and upscaled it based on Landsat satellite data.
Simone Maria Stuenzi, Julia Boike, William Cable, Ulrike Herzschuh, Stefan Kruse, Luidmila A. Pestryakova, Thomas Schneider von Deimling, Sebastian Westermann, Evgenii S. Zakharov, and Moritz Langer
Biogeosciences, 18, 343–365, https://doi.org/10.5194/bg-18-343-2021, https://doi.org/10.5194/bg-18-343-2021, 2021
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Boreal forests in eastern Siberia are an essential component of global climate patterns. We use a physically based model and field measurements to study the interactions between forests, permanently frozen ground and the atmosphere. We find that forests exert a strong control on the thermal state of permafrost through changing snow cover dynamics and altering the surface energy balance, through absorbing most of the incoming solar radiation and suppressing below-canopy turbulent fluxes.
Mareike Wieczorek and Ulrike Herzschuh
Earth Syst. Sci. Data, 12, 3515–3528, https://doi.org/10.5194/essd-12-3515-2020, https://doi.org/10.5194/essd-12-3515-2020, 2020
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Relative pollen productivity (RPP) estimates are used to estimate vegetation cover from pollen records. This study provides (i) a compilation of northern hemispheric RPP studies, allowing researchers to identify suitable sets for their study region and to identify data gaps for future research, and (ii) taxonomically harmonized, unified RPP sets for China, Europe, North America, and the whole Northern Hemisphere, generated from the available studies.
Basil A. S. Davis, Manuel Chevalier, Philipp Sommer, Vachel A. Carter, Walter Finsinger, Achille Mauri, Leanne N. Phelps, Marco Zanon, Roman Abegglen, Christine M. Åkesson, Francisca Alba-Sánchez, R. Scott Anderson, Tatiana G. Antipina, Juliana R. Atanassova, Ruth Beer, Nina I. Belyanina, Tatiana A. Blyakharchuk, Olga K. Borisova, Elissaveta Bozilova, Galina Bukreeva, M. Jane Bunting, Eleonora Clò, Daniele Colombaroli, Nathalie Combourieu-Nebout, Stéphanie Desprat, Federico Di Rita, Morteza Djamali, Kevin J. Edwards, Patricia L. Fall, Angelica Feurdean, William Fletcher, Assunta Florenzano, Giulia Furlanetto, Emna Gaceur, Arsenii T. Galimov, Mariusz Gałka, Iria García-Moreiras, Thomas Giesecke, Roxana Grindean, Maria A. Guido, Irina G. Gvozdeva, Ulrike Herzschuh, Kari L. Hjelle, Sergey Ivanov, Susanne Jahns, Vlasta Jankovska, Gonzalo Jiménez-Moreno, Monika Karpińska-Kołaczek, Ikuko Kitaba, Piotr Kołaczek, Elena G. Lapteva, Małgorzata Latałowa, Vincent Lebreton, Suzanne Leroy, Michelle Leydet, Darya A. Lopatina, José Antonio López-Sáez, André F. Lotter, Donatella Magri, Elena Marinova, Isabelle Matthias, Anastasia Mavridou, Anna Maria Mercuri, Jose Manuel Mesa-Fernández, Yuri A. Mikishin, Krystyna Milecka, Carlo Montanari, César Morales-Molino, Almut Mrotzek, Castor Muñoz Sobrino, Olga D. Naidina, Takeshi Nakagawa, Anne Birgitte Nielsen, Elena Y. Novenko, Sampson Panajiotidis, Nata K. Panova, Maria Papadopoulou, Heather S. Pardoe, Anna Pędziszewska, Tatiana I. Petrenko, María J. Ramos-Román, Cesare Ravazzi, Manfred Rösch, Natalia Ryabogina, Silvia Sabariego Ruiz, J. Sakari Salonen, Tatyana V. Sapelko, James E. Schofield, Heikki Seppä, Lyudmila Shumilovskikh, Normunds Stivrins, Philipp Stojakowits, Helena Svobodova Svitavska, Joanna Święta-Musznicka, Ioan Tantau, Willy Tinner, Kazimierz Tobolski, Spassimir Tonkov, Margarita Tsakiridou, Verushka Valsecchi, Oksana G. Zanina, and Marcelina Zimny
Earth Syst. Sci. Data, 12, 2423–2445, https://doi.org/10.5194/essd-12-2423-2020, https://doi.org/10.5194/essd-12-2423-2020, 2020
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The Eurasian Modern Pollen Database (EMPD) contains pollen counts and associated metadata for 8134 modern pollen samples from across the Eurasian region. The EMPD is part of, and complementary to, the European Pollen Database (EPD) which contains data on fossil pollen found in Late Quaternary sedimentary archives. The purpose of the EMPD is to provide calibration datasets and other data to support palaeoecological research on past climates and vegetation cover over the Quaternary period.
Heike H. Zimmermann, Kathleen R. Stoof-Leichsenring, Stefan Kruse, Juliane Müller, Ruediger Stein, Ralf Tiedemann, and Ulrike Herzschuh
Ocean Sci., 16, 1017–1032, https://doi.org/10.5194/os-16-1017-2020, https://doi.org/10.5194/os-16-1017-2020, 2020
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This study targets high-resolution, diatom-specific sedimentary ancient DNA using a DNA metabarcoding approach. Diatom DNA has been preserved with substantial taxonomic richness in the eastern Fram Strait over the past 30 000 years with taxonomic composition being dominated by cold-water and sea-ice-associated diatoms. Taxonomic reorganisations took place after the Last Glacial Maximum and after the Younger Dryas. Peak proportions of pennate diatoms might indicate past sea-ice presence.
Elisabeth Dietze, Kai Mangelsdorf, Andrei Andreev, Cornelia Karger, Laura T. Schreuder, Ellen C. Hopmans, Oliver Rach, Dirk Sachse, Volker Wennrich, and Ulrike Herzschuh
Clim. Past, 16, 799–818, https://doi.org/10.5194/cp-16-799-2020, https://doi.org/10.5194/cp-16-799-2020, 2020
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Long-term climate change impacts on fire, vegetation and permafrost in the Arctic are uncertain. Here, we show the high potential of organic compounds from low-temperature biomass burning to serve as proxies for surface fires in lake deposits. During warm periods of the last 430 000 years, surface fires are closely linked to the larch taiga forest with its moss–lichen ground vegetation that isolates the permafrost. They have reduced in warm–wet, spruce–dominated and cool–dry steppe environments.
Xianyong Cao, Fang Tian, Andrei Andreev, Patricia M. Anderson, Anatoly V. Lozhkin, Elena Bezrukova, Jian Ni, Natalia Rudaya, Astrid Stobbe, Mareike Wieczorek, and Ulrike Herzschuh
Earth Syst. Sci. Data, 12, 119–135, https://doi.org/10.5194/essd-12-119-2020, https://doi.org/10.5194/essd-12-119-2020, 2020
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Pollen percentages in spectra cannot be utilized to indicate past plant abundance directly because of the different pollen productivities among plants. In this paper, we applied relative pollen productivity estimates (PPEs) to calibrate plant abundances during the last 40 kyr using pollen counts from 203 pollen spectra in northern Asia. Results indicate the vegetation are generally stable during the Holocene and that climate change is the primary factor.
Xianyong Cao, Fang Tian, Furong Li, Marie-José Gaillard, Natalia Rudaya, Qinghai Xu, and Ulrike Herzschuh
Clim. Past, 15, 1503–1536, https://doi.org/10.5194/cp-15-1503-2019, https://doi.org/10.5194/cp-15-1503-2019, 2019
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The high-quality pollen records (collected from lakes and peat bogs) of the last 40 ka cal BP form north Asia are homogenized and the plant abundance signals are calibrated by the modern relative pollen productivity estimates. Calibrated plant abundances for each site are generally consistent with in situ modern vegetation, and vegetation changes within the regions are characterized by minor changes in the abundance of major taxa rather than by invasions of new taxa during the last 40 ka cal BP.
Stefan Kruse, Alexander Gerdes, Nadja J. Kath, Laura S. Epp, Kathleen R. Stoof-Leichsenring, Luidmila A. Pestryakova, and Ulrike Herzschuh
Biogeosciences, 16, 1211–1224, https://doi.org/10.5194/bg-16-1211-2019, https://doi.org/10.5194/bg-16-1211-2019, 2019
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How fast might the arctic treeline in northern central Siberia migrate northwards under current global warming? To answer this, we newly parameterized dispersal processes in the individual-based and spatially explicit model LAVESI-WIND based on parentage analysis. Simulation results show that northernmost open forest stands are migrating at an unexpectedly slow rate into tundra. We conclude that the treeline currently lags behind the strong warming and will remain slow in the upcoming decades.
Romy Zibulski, Felix Wesener, Heinz Wilkes, Birgit Plessen, Luidmila A. Pestryakova, and Ulrike Herzschuh
Biogeosciences, 14, 1617–1630, https://doi.org/10.5194/bg-14-1617-2017, https://doi.org/10.5194/bg-14-1617-2017, 2017
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We investigated variations of isotopic and biochemical parameters in arctic mosses. We were able to differentiate habitat groups of mosses (classified by moisture gradient) by elemental content and isotopic ratios (δ13C, δ15N). Some species showed intraspecific variability in their isotopic composition along the moisture gradient. Furthermore n-alkanes showed interesting patterns for species identification.
Anne Dallmeyer, Martin Claussen, Jian Ni, Xianyong Cao, Yongbo Wang, Nils Fischer, Madlene Pfeiffer, Liya Jin, Vyacheslav Khon, Sebastian Wagner, Kerstin Haberkorn, and Ulrike Herzschuh
Clim. Past, 13, 107–134, https://doi.org/10.5194/cp-13-107-2017, https://doi.org/10.5194/cp-13-107-2017, 2017
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The vegetation distribution in eastern Asia is supposed to be very sensitive to climate change. Since proxy records are scarce, hitherto a mechanistic understanding of the past spatio-temporal climate–vegetation relationship is lacking. To assess the Holocene vegetation change, we forced the diagnostic biome model BIOME4 with climate anomalies of different transient climate simulations.
Heike Hildegard Zimmermann, Elena Raschke, Laura Saskia Epp, Kathleen Rosmarie Stoof-Leichsenring, Georg Schwamborn, Lutz Schirrmeister, Pier Paul Overduin, and Ulrike Herzschuh
Biogeosciences, 14, 575–596, https://doi.org/10.5194/bg-14-575-2017, https://doi.org/10.5194/bg-14-575-2017, 2017
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Organic matter stored in permafrost will start decomposing due to climate warming. To better understand its composition in ice-rich Yedoma, we analyzed ancient sedimentary DNA, pollen and non-pollen palynomorphs throughout an 18.9 m long permafrost core. The combination of both proxies allow an interpretation both of regional floristic changes and of the local environmental conditions at the time of deposition.
Liv Heinecke, Steffen Mischke, Karsten Adler, Anja Barth, Boris K. Biskaborn, Birgit Plessen, Ingmar Nitze, Gerhard Kuhn, Ilhomjon Rajabov, and Ulrike Herzschuh
Clim. Past Discuss., https://doi.org/10.5194/cp-2016-34, https://doi.org/10.5194/cp-2016-34, 2016
Revised manuscript not accepted
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The climate history of the Pamir Mountains (Tajikistan) during the last ~29 kyr was investigated using sediments from Lake Karakul as environmental archive. The inferred lake level was highest from the Late Glacial to the early Holocene and lake changes were mainly coupled to climate change. We conclude that the joint influence of Westerlies and Indian Monsoon during the early Holocene caused comparatively moist conditions, while dominating Westerlies yielded dry conditions since 6.7 cal kyr BP.
M. Fritz, T. Opel, G. Tanski, U. Herzschuh, H. Meyer, A. Eulenburg, and H. Lantuit
The Cryosphere, 9, 737–752, https://doi.org/10.5194/tc-9-737-2015, https://doi.org/10.5194/tc-9-737-2015, 2015
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Ground ice in permafrost has not, until now, been considered to be a source of dissolved organic carbon (DOC), dissolved inorganic carbon (DIC) and other elements that are important for ecosystems and carbon cycling.
Ice wedges in the Arctic Yedoma region hold 45.2 Tg DOC (Tg = 10^12g), 33.6 Tg DIC and a freshwater reservoir of 4200 km³.
Leaching of terrestrial organic matter is the most relevant process of DOC sequestration into ground ice.
J. Strauss, L. Schirrmeister, K. Mangelsdorf, L. Eichhorn, S. Wetterich, and U. Herzschuh
Biogeosciences, 12, 2227–2245, https://doi.org/10.5194/bg-12-2227-2015, https://doi.org/10.5194/bg-12-2227-2015, 2015
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Climatic warming is affecting permafrost, including decomposition of organic matter (OM). However, quantitative data for the quality of OM and its availability for decomposition is limited. We analyzed the quality of OM in late Pleistocene (Yedoma) and Holocene (thermokarst) deposits. A lack of depth trends reveals a constant quality of OM showing that permafrost acts like a freezer, preserving OM quality. This OM will be susceptible to decomposition under climatic warming.
B. Aichner, S. J. Feakins, J. E. Lee, U. Herzschuh, and X. Liu
Clim. Past, 11, 619–633, https://doi.org/10.5194/cp-11-619-2015, https://doi.org/10.5194/cp-11-619-2015, 2015
A. Dallmeyer, M. Claussen, N. Fischer, K. Haberkorn, S. Wagner, M. Pfeiffer, L. Jin, V. Khon, Y. Wang, and U. Herzschuh
Clim. Past, 11, 305–326, https://doi.org/10.5194/cp-11-305-2015, https://doi.org/10.5194/cp-11-305-2015, 2015
A. A. Andreev, P. E. Tarasov, V. Wennrich, E. Raschke, U. Herzschuh, N. R. Nowaczyk, J. Brigham-Grette, and M. Melles
Clim. Past, 10, 1017–1039, https://doi.org/10.5194/cp-10-1017-2014, https://doi.org/10.5194/cp-10-1017-2014, 2014
Y. Wang, U. Herzschuh, L. S. Shumilovskikh, S. Mischke, H. J. B. Birks, J. Wischnewski, J. Böhner, F. Schlütz, F. Lehmkuhl, B. Diekmann, B. Wünnemann, and C. Zhang
Clim. Past, 10, 21–39, https://doi.org/10.5194/cp-10-21-2014, https://doi.org/10.5194/cp-10-21-2014, 2014
R. Zibulski, U. Herzschuh, L. A. Pestryakova, J. Wolter, S. Müller, N. Schilling, S. Wetterich, L. Schirrmeister, and F. Tian
Biogeosciences, 10, 5703–5728, https://doi.org/10.5194/bg-10-5703-2013, https://doi.org/10.5194/bg-10-5703-2013, 2013
Related subject area
Biogeosciences
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
Development and assessment of the physical-biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
Parameterisation toolbox for physical-biogeochemical model compatible with FABM. Case study: the coupled 1D GOTM-ECOSMO E2E for the Sylt-Romo Bight, North Sea
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
Estimation of above- and below-ground ecosystem parameters for the DVM-DOS-TEM v0.7.0 model using MADS v1.7.3: a synthetic case study
H2MV (v1.0): Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
Alquimia v1.0: A generic interface to biogeochemical codes – A tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
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Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
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The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
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Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
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Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
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We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
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Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
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Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
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Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-223, https://doi.org/10.5194/gmd-2024-223, 2024
Revised manuscript accepted for GMD
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Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions have changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
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In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
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Physical–biogeochemical ocean global models is difficult to analyze oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The model is an important tool for studying physical–biogeochemical processes.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
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We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Hoa T. T. Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
EGUsphere, https://doi.org/10.5194/egusphere-2024-2710, https://doi.org/10.5194/egusphere-2024-2710, 2024
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Parameterisation is key in modeling to reproduce observations well but is often done manually. This study presents a Particle Swarm Optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, thus providing different insights into ecosystem dynamics, (2) optimized model complexity.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
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We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
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Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
EGUsphere, https://doi.org/10.5194/egusphere-2024-2044, https://doi.org/10.5194/egusphere-2024-2044, 2024
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We use an innovative approach to study the Earth's water cycle by blending advanced computer learning techniques with a traditional water cycle model. We developed a model that learns from meteorological data, with a special focus on understanding how vegetation influences water movement. Our model closely aligns with real-world observations, yet there are areas that need improvement. This study opens up new possibilities to better understand the water cycle and its interactions with vegetation.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
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This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
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Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
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2013.
Short summary
It is of major interest to estimate feedbacks of arctic ecosystems to global warming in the upcoming decades. However, the speed of this response is driven by the potential of species to migrate and the timing and spatial scale for this is rather uncertain. To close this knowledge gap, we updated a very detailed vegetation model by including seed and pollen dispersal driven by wind speed and direction. The new model can substantially help in unveiling the important drivers of migration dynamics.
It is of major interest to estimate feedbacks of arctic ecosystems to global warming in the...
