Browsing by Author "Steinkamp, Jörg"

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    Benchmarking carbon fluxes of the ISIMIP2a biome models
    (Bristol : IOP Publishing, 2017) Chang, Jinfeng; Ciais, Philippe; Wang, Xuhui; Piao, Shilong; Asrar, Ghassem; Betts, Richard; Chevallier, Frédéric; Dury, Marie; François, Louis; Frieler, Katja; Ros, Anselmo García Cantú; Henrot, Alexandra-Jane; Hickler, Thomas; Ito, Akihiko; Morfopoulos, Catherine; Munhoven, Guy; Nishina, Kazuya; Ostberg, Sebastian; Pan, Shufen; Peng, Shushi; Rafique, Rashid; Reyer, Christopher; Rödenbeck, Christian; Schaphoff, Sibyll; Steinkamp, Jörg; Tian, Hanqin; Viovy, Nicolas; Yang, Jia; Zeng, Ning; Zhao, Fang
    The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). We evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions (E LUC) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al (2015), and 2) the land CO2 fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F Jena and F CAMS respectively. The model ensemble-mean NBP (that includes seven models with land-use change) is higher than but within the uncertainty of R + L, while the simulated positive NBP trend over the last 30 yr is lower than that from R + L and from the two inversion systems. ISIMIP2a biome models well capture the interannual variation of global net terrestrial ecosystem carbon fluxes. Tropical NBP represents 31 ± 17% of global total NBP during the past decades, and the year-to-year variation of tropical NBP contributes most of the interannual variation of global NBP. According to the models, increasing Net Primary Productivity (NPP) was the main cause for the generally increasing NBP. Significant global NBP anomalies from the long-term mean between the two phases of El Niño Southern Oscillation (ENSO) events are simulated by all models (p < 0.05), which is consistent with the R + L estimate (p = 0.06), also mainly attributed to NPP anomalies, rather than to changes in heterotrophic respiration (Rh). The global NPP and NBP anomalies during ENSO events are dominated by their anomalies in tropical regions impacted by tropical climate variability. Multiple regressions between R + L, F Jena and F CAMS interannual variations and tropical climate variations reveal a significant negative response of global net terrestrial ecosystem carbon fluxes to tropical mean annual temperature variation, and a non-significant response to tropical annual precipitation variation. According to the models, tropical precipitation is a more important driver, suggesting that some models do not capture the roles of precipitation and temperature changes adequately.
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    Evaluating changes of biomass in global vegetation models: the role of turnover fluctuations and ENSO events
    (Bristol : IOP Publ., 2018) García Cantú, Anselmo; Frieler, Katja; Reyer, Christopher P O; Ciais, Philippe; Chang, Jinfeng; Ito, Akihiko; Nishina, Kazuya; François, Louis; Henrot, Alexandra-Jane; Hickler, Thomas; Steinkamp, Jörg; Rafique, Rashid; Zhao, Fang; Ostberg, Sebastian; Schaphoff, Sibyll; Tian, Hanqin; Pan, Shufen; Yang, Jia; Morfopoulos, Catherine; Betts, Richard
    This paper evaluates the ability of eight global vegetation models to reproduce recent trends and inter-annual variability of biomass in natural terrestrial ecosystems. For the purpose of this evaluation, the simulated trajectories of biomass are expressed in terms of the relative rate of change in biomass (RRB), defined as the deviation of the actual rate of biomass turnover from its equilibrium counterpart. Cumulative changes in RRB explain long-term changes in biomass pools. RRB simulated by the global vegetation models is compared with its observational equivalent, derived from vegetation optical depth reconstructions of above-ground biomass (AGB) over the period 1993–2010. According to the RRB analysis, the rate of global biomass growth described by the ensemble of simulations substantially exceeds the observation. The observed fluctuations of global RRB are significantly correlated with El Niño Southern Oscillation events (ENSO), but only some of the simulations reproduce this correlation. However, the ENSO sensitivity of RRB in the tropics is not significant in the observation, while it is in some of the simulations. This mismatch points to an important limitation of the observed AGB reconstruction to capture biomass variations in tropical forests. Important discrepancies in RRB were also identified at the regional scale, in the tropical forests of Amazonia and Central Africa, as well as in the boreal forests of north-western America, western and central Siberia. In each of these regions, the RRBs derived from the simulations were analyzed in connection with underlying differences in net primary productivity and biomass turnover rate ̶as a basis for exploring in how far differences in simulated changes in biomass are attributed to the response of the carbon uptake to CO2 increments, as well as to the model representation of factors affecting the rates of mortality and turnover of foliage and roots. Overall, our findings stress the usefulness of using RRB to evaluate complex vegetation models and highlight the importance of conducting further evaluations of both the actual rate of biomass turnover and its equilibrium counterpart, with special focus on their background values and sources of variation. In turn, this task would require the availability of more accurate multi-year observational data of biomass and net primary productivity for natural ecosystems, as well as detailed and updated information on land-cover classification.
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    Photosynthetic productivity and its efficiencies in ISIMIP2a biome models: Benchmarking for impact assessment studies
    (Bristol : IOP Publishing, 2017) Ito, Akihiko; Nishina, Kazuya; Reyer, Christopher P.O.; François, Louis; Henrot, Alexandra-Jane; Munhoven, Guy; Jacquemin, Ingrid; Tian, Hanqin; Yang, Jia; Pan, Shufen; Morfopoulos, Catherine; Betts, Richard; Hickler, Thomas; Steinkamp, Jörg; Ostberg, Sebastian; Schaphoff, Sibyll; Ciais, Philippe; Chang, Jinfeng; Rafique, Rashid; Zeng, Ning; Zhao, Fang
    Simulating vegetation photosynthetic productivity (or gross primary production, GPP) is a critical feature of the biome models used for impact assessments of climate change. We conducted a benchmarking of global GPP simulated by eight biome models participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a) with four meteorological forcing datasets (30 simulations), using independent GPP estimates and recent satellite data of solar-induced chlorophyll fluorescence as a proxy of GPP. The simulated global terrestrial GPP ranged from 98 to 141 Pg C yr−1 (1981–2000 mean); considerable inter-model and inter-data differences were found. Major features of spatial distribution and seasonal change of GPP were captured by each model, showing good agreement with the benchmarking data. All simulations showed incremental trends of annual GPP, seasonal-cycle amplitude, radiation-use efficiency, and water-use efficiency, mainly caused by the CO2 fertilization effect. The incremental slopes were higher than those obtained by remote sensing studies, but comparable with those by recent atmospheric observation. Apparent differences were found in the relationship between GPP and incoming solar radiation, for which forcing data differed considerably. The simulated GPP trends co-varied with a vegetation structural parameter, leaf area index, at model-dependent strengths, implying the importance of constraining canopy properties. In terms of extreme events, GPP anomalies associated with a historical El Niño event and large volcanic eruption were not consistently simulated in the model experiments due to deficiencies in both forcing data and parameterized environmental responsiveness. Although the benchmarking demonstrated the overall advancement of contemporary biome models, further refinements are required, for example, for solar radiation data and vegetation canopy schemes.
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    The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests
    (Katlenburg-Lindau : Copernics Publications, 2020) Reyer, Christopher P.O.; Silveyra Gonzalez, Ramiro; Dolos, Klara; Hartig, Florian; Hauf, Ylva; Noack, Matthias; Lasch-Born, Petra; Rötzer, Thomas; Pretzsch, Hans; Meesenburg, Henning; Fleck, Stefan; Wagner, Markus; Bolte, Andreas; Sanders, Tanja G.M.; Kolari, Pasi; Mäkelä, Annikki; Vesala, Timo; Mammarella, Ivan; Pumpanen, Jukka; Collalti, Alessio; Trotta, Carlo; Matteucci, Giorgio; D'Andrea, Ettore; Foltýnová, Lenka; Krejza, Jan; Ibrom, Andreas; Pilegaard, Kim; Loustau, Denis; Bonnefond, Jean-Marc; Berbigier, Paul; Picart, Delphine; Lafont, Sébastien; Dietze, Michael; Cameron, David; Vieno, Massimo; Tian, Hanqin; Palacios-Orueta, Alicia; Cicuendez, Victor; Recuero, Laura; Wiese, Klaus; Büchner, Matthias; Lange, Stefan; Volkholz, Jan; Kim, Hyungjun; Horemans, Joanna A.; Bohn, Friedrich; Steinkamp, Jörg; Chikalanov, Alexander; Weedon, Graham P.; Sheffield, Justin; Babst, Flurin; Vega del Valle, Iliusi; Suckow, Felicitas; Martel, Simon; Mahnken, Mats; Gutsch, Martin; Frieler, Katja
    Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a “SQLite” relational database or “ASCII” flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R-project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.
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    Projecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales
    (Hoboken, NJ : Wiley-Blackwell, 2020) Lange, Stefan; Volkholz, Jan; Geiger, Tobias; Zhao, Fang; Vega, Iliusi; Veldkamp, Ted; Reyer, Christopher P.O.; Warszawski, Lila; Huber, Veronika; Jägermeyr, Jonas; Schewe, Jacob; Bresch, David N.; Büchner, Matthias; Chang, Jinfeng; Ciais, Philippe; Dury, Marie; Emanuel, Kerry; Folberth, Christian; Gerten, Dieter; Gosling, Simon N.; Grillakis, Manolis; Hanasaki, Naota; Henrot, Alexandra-Jane; Hickler, Thomas; Honda, Yasushi; Ito, Akihiko; Khabarov, Nikolay; Koutroulis, Aristeidis; Liu, Wenfeng; Müller, Christoph; Nishina, Kazuya; Ostberg, Sebastian; Müller Schmied, Hannes; Seneviratne, Sonia I.; Stacke, Tobias; Steinkamp, Jörg; Thiery, Wim; Wada, Yoshihide; Willner, Sven; Yang, Hong; Yoshikawa, Minoru; Yue, Chao; Frieler, Katja
    The extent and impact of climate-related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter-Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross-category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia. ©2020. The Authors.
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    Regional contribution to variability and trends of global gross primary productivity
    (Bristol : IOP Publishing, 2017) Chen, Min; Rafique, Rashid; Asrar, Ghassem R.; Bond-Lamberty, Ben; Ciais, Philippe; Zhao, Fang; Reyer, Christopher P.O.; Ostberg, Sebastian; Chang, Jinfeng; Ito, Akihiko; Yang, Jia; Zeng, Ning; Kalnay, Eugenia; West, Tristram; Leng, Guoyong; Francois, Louis; Munhoven, Guy; Henrot, Alexandra; Tian, Hanqin; Pan, Shufen; Nishina, Kazuya; Viovy, Nicolas; Morfopoulos, Catherine; Betts, Richard; Schaphoff, Sibyll; Steinkamp, Jörg
    Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000–2010 total global GPP estimated from the model ensemble to be 117 ± 13 Pg C yr−1 (mean ± 1 standard deviation), which was higher than MODIS (112 Pg C yr−1), and close to the MTE (120 Pg C yr−1). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However, we observed large uncertainties across the eight ISIMIP2a models, which are probably due to the differences in the formulation of underlying photosynthetic processes. The results of this study are useful in understanding the contributions of different regions to global GPP and its spatiotemporal variability, how the model- and observational-based GPP estimates differ from each other in time and space, and the relative strength of the eight models. Our results also highlight the models' ability to capture the seasonality of GPP that are essential for understanding the inter-annual and seasonal variability of GPP as a major component of the carbon cycle.
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    State-of-the-art global models underestimate impacts from climate extremes
    ([London] : Nature Publishing Group UK, 2019) Schewe, Jacob; Gosling, Simon N.; Reyer, Christopher; Zhao, Fang; Ciais, Philippe; Elliott, Joshua; Francois, Louis; Huber, Veronika; Lotze, Heike K.; Seneviratne, Sonia I.; van Vliet, Michelle T. H.; Vautard, Robert; Wada, Yoshihide; Breuer, Lutz; Büchner, Matthias; Carozza, David A.; Chang, Jinfeng; Coll, Marta; Deryng, Delphine; de Wit, Allard; Eddy, Tyler D.; Folberth, Christian; Frieler, Katja; Friend, Andrew D.; Gerten, Dieter; Gudmundsson, Lukas; Hanasaki, Naota; Ito, Akihiko; Khabarov, Nikolay; Kim, Hyungjun; Lawrence, Peter; Morfopoulos, Catherine; Müller, Christoph; Müller Schmied, Hannes; Orth, René; Ostberg, Sebastian; Pokhrel, Yadu; Pugh, Thomas A. M.; Sakurai, Gen; Satoh, Yusuke; Schmid, Erwin; Stacke, Tobias; Steenbeek, Jeroen; Steinkamp, Jörg; Tang, Qiuhong; Tian, Hanqin; Tittensor, Derek P.; Volkholz, Jan; Wang, Xuhui; Warszawski, Lila
    Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.
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    Understanding the uncertainty in global forest carbon turnover
    (Katlenburg-Lindau [u.a.] : Copernicus, 2020) Pugh, Thomas A.M.; Rademacher, Tim; Shafer, Sarah L.; Steinkamp, Jörg; Barichivich, Jonathan; Beckage, Brian; Haverd, Vanessa; Harper, Anna; Heinke, Jens; Nishina, Kazuya; Rammig, Anja; Sato, Hisashi; Arneth, Almut; Hantson, Stijn; Hickler, Thomas; Kautz, Markus; Quesada, Benjamin; Smith, Benjamin; Thonicke, Kirsten
    The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle, with both recent historical baselines and future responses to environmental change poorly constrained by available observations. In the absence of large-scale observations, models used for global assessments tend to fall back on simplified assumptions of the turnover rates of biomass and soil carbon pools. In this study, the biomass carbon turnover times calculated by an ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to assess their current capability to accurately estimate biomass carbon turnover times in forests and how these times are anticipated to change in the future. Modelled baseline 1985-2014 global average forest biomass turnover times vary from 12.2 to 23.5 years between TBMs. TBM differences in phenological processes, which control allocation to, and turnover rate of, leaves and fine roots, are as important as tree mortality with regard to explaining the variation in total turnover among TBMs. The different governing mechanisms exhibited by each TBM result in a wide range of plausible turnover time projections for the end of the century. Based on these simulations, it is not possible to draw robust conclusions regarding likely future changes in turnover time, and thus biomass change, for different regions. Both spatial and temporal uncertainty in turnover time are strongly linked to model assumptions concerning plant functional type distributions and their controls. Thirteen model-based hypotheses of controls on turnover time are identified, along with recommendations for pragmatic steps to test them using existing and novel observations. Efforts to resolve uncertainty in turnover time, and thus its impacts on the future evolution of biomass carbon stocks across the world's forests, will need to address both mortality and establishment components of forest demography, as well as allocation of carbon to woody versus non-woody biomass growth. © 2020 SPIE. All rights reserved.