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- ItemModeling forest plantations for carbon uptake with the LPJmL dynamic global vegetation model(Göttingen : Copernicus Publ., 2019) Braakhekke, Maarten C.; Doelman, Jonathan C.; Baas, Peter; Müller, Christoph; Schaphoff, Sibyll; Stehfest, Elke; van Vuuren, Detlef P.We present an extension of the dynamic global vegetation model, Lund-Potsdam-Jena Managed Land (LPJmL), to simulate planted forests intended for carbon (C) sequestration. We implemented three functional types to simulate plantation trees in temperate, tropical, and boreal climates. The parameters of these functional types were optimized to fit target growth curves (TGCs). These curves represent the evolution of stemwood C over time in typical productive plantations and were derived by combining field observations and LPJmL estimates for equivalent natural forests. While the calibrated model underestimates stemwood C growth rates compared to the TGCs, it represents substantial improvement over using natural forests to represent afforestation. Based on a simulation experiment in which we compared global natural forest versus global forest plantation, we found that forest plantations allow for much larger C uptake rates on the timescale of 100 years, with a maximum difference of a factor of 1.9, around 54 years. In subsequent simulations for an ambitious but realistic scenario in which 650Mha (14% of global managed land, 4.5% of global land surface) are converted to forest over 85 years, we found that natural forests take up 37PgC versus 48PgC for forest plantations. Comparing these results to estimations of C sequestration required to achieve the 2°C climate target, we conclude that afforestation can offer a substantial contribution to climate mitigation. Full evaluation of afforestation as a climate change mitigation strategy requires an integrated assessment which considers all relevant aspects, including costs, biodiversity, and trade-offs with other land-use types. Our extended version of LPJmL can contribute to such an assessment by providing improved estimates of C uptake rates by forest plantations. © 2019 American Institute of Physics Inc.. All rights reserved.
- ItemThe effect of univariate bias adjustment on multivariate hazard estimates(Göttingen : Copernicus Publ., 2019) Zscheischler, Jakob; Fischer, Erich M.; Lange, StefanBias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model outputs and observations for many climate-related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias-adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty in bias-adjusted hazard indicators due to internal variability and show how imperfect bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modeled climate impacts are associated with uncertainties related to the choice of bias adjustment. We conclude that in cases where impacts depend on multiple dependent climate variables these uncertainties can be reduced using statistical bias-adjustment approaches that correct the variables' multivariate dependence structure. © 2019 Copernicus GmbH. All rights reserved.
- ItemA multi-model analysis of teleconnected crop yield variability in a range of cropping systems(Göttingen : Copernicus Publ., 2020) Heino, Matias; Guillaume, Joseph H.A.; Müller, Christoph; Iizumi, Toshichika; Kummu, MattiClimate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño-Southern Oscillation (ENSO), which has been found to impact crop yields on all continents that produce crops, while two other climate oscillations - the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) - have been shown to especially impact crop production in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD, and NAO on the growing conditions of maize, rice, soybean, and wheat at the global scale by utilising crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that, while accounting for their potential co-variation, climate oscillations are correlated with simulated crop yield variability to a wide extent (half of all maize and wheat harvested areas for ENSO) and in several important crop-producing areas, e.g. in North America (ENSO, wheat), Australia (IOD and ENSO, wheat), and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed and fully fertilised scenarios, while the sensitivity tends to be lower if crops were to be fully irrigated. Since the development of ENSO, IOD, and NAO can potentially be forecasted well in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate-related shocks. © 2020 American Institute of Physics Inc.. All rights reserved.
- ItemDescription and evaluation of the process-based forest model 4C v2.2 at four European forest sites(Katlenburg-Lindau : Copernicus, 2020) Lasch-Born, Petra; Suckow, Felicitas; Reyer, Christopher P. O.; Gutsch, Martin; Kollas, Chris; Badeck, Franz-Werner; Bugmann, Harald K. M.; Grote, Rüdiger; Fürstenau, Cornelia; Lindner, Marcus; Schaber, JörgThe process-based model 4C (FORESEE) has been developed over the past 20 years to study climate impacts on forests and is now freely available as an open-source tool. The objective of this paper is to provide a comprehensive description of this 4C version (v2.2) for scientific users of the model and to present an evaluation of 4C at four different forest sites across Europe. The evaluation focuses on forest growth as well as carbon (net ecosystem exchange, gross primary production), water (actual evapotranspiration, soil water content), and heat fluxes (soil temperature) using data from the PROFOUND database. We applied different evaluation metrics and compared the daily, monthly, and annual variability of observed and simulated values. The ability to reproduce forest growth (stem diameter and biomass) differs from site to site and is best for a pine stand in Germany (Peitz, model efficiency ME=0.98). 4C is able to reproduce soil temperature at different depths in Sorø and Hyytiälä with good accuracy (for all soil depths ME > 0.8). The dynamics in simulating carbon and water fluxes are well captured on daily and monthly timescales (0.51 < ME < 0.983) but less so on an annual timescale (ME < 0). This model–data mismatch is possibly due to the accumulation of errors because of processes that are missing or represented in a very general way in 4C but not with enough specific detail to cover strong, site-specific dependencies such as ground vegetation growth. These processes need to be further elaborated to improve the projections of climate change on forests. We conclude that, despite shortcomings, 4C is widely applicable, reliable, and therefore ready to be released to the scientific community to use and further develop the model.
- ItemDiverging importance of drought stress for maize and winter wheat in Europe([London] : Nature Publishing Group UK, 2018) Webber, Heidi; Ewert, Frank; Olesen, Jørgen E.; Müller, Christoph; Fronzek, Stefan; Ruane, Alex C.; Bourgault, Maryse; Martre, Pierre; Ababaei, Behnam; Bindi, Marco; Ferrise, Roberto; Finger, Robert; Fodor, Nándor; Gabaldón-Leal, Clara; Gaiser, Thomas; Jabloun, Mohamed; Kersebaum, Kurt-Christian; Lizaso, Jon I.; Lorite, Ignacio J.; Manceau, Loic; Moriondo, Marco; Nendel, Claas; Rodríguez, Alfredo; Ruiz-Ramos, Margarita; Semenov, Mikhail A.; Siebert, Stefan; Stella, Tommaso; Stratonovitch, Pierre; Trombi, Giacomo; Wallach, DanielUnderstanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.
- ItemIncremental improvements of 2030 targets insufficient to achieve the Paris Agreement goals(Göttingen : Copernicus Publ., 2020) Geiges, Andreas; Nauels, Alexander; Yanguas Parra, Paola; Andrijevic, Marina; Hare, William; Pfleiderer, Peter; Schaeffer, Michiel; Schleussner, Carl-FriedrichCurrent global mitigation ambition up to 2030 under the Paris Agreement, reflected in the National Determined Contributions (NDCs), is insufficient to achieve the agreement's 1.5 °C long-term temperature limit. As governments are preparing new and updated NDCs for 2020, the question as to how much collective improvement is achieved is a pivotal one for the credibility of the international climate regime. The recent Special Report on Global Warming of 1.5 °C by the Intergovernmental Panel on Climate Change has assessed a wide range of scenarios that achieve the 1.5 °C limit. Those pathways are characterised by a substantial increase in near-term action and total greenhouse gas (GHG) emission levels about 50 % lower than what is implied by current NDCs. Here we assess the outcomes of different scenarios of NDC updating that fall short of achieving this 1.5 °C benchmark. We find that incremental improvements in reduction targets, even if achieved globally, are insufficient to align collective ambition with the goals of the Paris Agreement. We provide estimates for global mean temperature increase by 2100 for different incremental NDC update scenarios and illustrate climate impacts under those median scenarios for extreme temperature, long-term sea-level rise and economic damages for the most vulnerable countries. Under the assumption of maintaining ambition as reflected in current NDCs up to 2100 and beyond, we project a reduction in the gross domestic product (GDP) in tropical countries of around 60 % compared to a no-climate-change scenario and median long-term sea-level rise of close to 2 m in 2300. About half of these impacts can be avoided by limiting warming to 1.5 °C or below. Scenarios of more incremental NDC improvements do not lead to comparable reductions in climate impacts. An increase in aggregated NDC ambition of big emitters by 33 % in 2030 does not reduce presented climate impacts by more than about half compared to limiting warming to 1.5 °C. Our results underscore that a transformational increase in 2030 ambition is required to achieve the goals of the Paris Agreement and avoid the worst impacts of climate change. © 2020 SPIE. All rights reserved.
- ItemProjecting 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, KatjaThe 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.
- ItemThe GGCMI Phase 2 emulators: Global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)(Katlenburg-Lindau : Copernicus, 2020) Franke, James A.; Müller, Christoph; Elliott, Joshua; Ruane, Alex C.; Jägermeyr, Jonas; Snyder, Abigail; Dury, Marie; Falloon, Pete D.; Folberth, Christian; François, Louis; Hank, Tobias; Izaurralde, R. Cesar; Jacquemin, Ingrid; Jones, Curtis; Li, Michelle; Liu, Wenfeng; Olin, Stefan; Phillips, Meridel; Pugh, Thomas A. M.; Reddy, Ashwan; Williams, Karina; Wang, Ziwei; Zabel, Florian; Moyer, Elisabeth J.Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: Atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: That growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts. © 2020 EDP Sciences. All rights reserved.
- ItemA protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0(Göttingen : Copernicus GmbH, 2018) Tittensor, D.P.; Eddy, T.D.; Lotze, H.K.; Galbraith, E.D.; Cheung, W.; Barange, M.; Blanchard, J.L.; Bopp, L.; Bryndum-Buchholz, A.; Büchner, M.; Bulman, C.; Carozza, D.A.; Christensen, V.; Coll, M.; Dunne, J.P.; Fernandes, J.A.; Fulton, E.A.; Hobday, A.J.; Huber, V.; Jennings, S.; Jones, M.; Lehodey, P.; Link, J.S.; MacKinson, S.; Maury, O.; Niiranen, S.; Oliveros-Ramos, R.; Roy, T.; Schewe, J.; Shin, Y.-J.; Silva, T.; Stock, C.A.; Steenbeek, J.; Underwood, P.J.; Volkholz, J.; Watson, J.R.; Walker, N.D.Model intercomparison studies in the climate and Earth sciences communities have been crucial to building credibility and coherence for future projections. They have quantified variability among models, spurred model development, contrasted within-and among-model uncertainty, assessed model fits to historical data, and provided ensemble projections of future change under specified scenarios. Given the speed and magnitude of anthropogenic change in the marine environment and the consequent effects on food security, biodiversity, marine industries, and society, the time is ripe for similar comparisons among models of fisheries and marine ecosystems. Here, we describe the Fisheries and Marine Ecosystem Model Intercomparison Project protocol version 1.0 (Fish-MIP v1.0), part of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is a cross-sectoral network of climate impact modellers. Given the complexity of the marine ecosystem, this class of models has substantial heterogeneity of purpose, scope, theoretical underpinning, processes considered, parameterizations, resolution (grain size), and spatial extent. This heterogeneity reflects the lack of a unified understanding of the marine ecosystem and implies that the assemblage of all models is more likely to include a greater number of relevant processes than any single model. The current Fish-MIP protocol is designed to allow these heterogeneous models to be forced with common Earth System Model (ESM) Coupled Model Intercomparison Project Phase 5 (CMIP5) outputs under prescribed scenarios for historic (from the 1950s) and future (to 2100) time periods; it will be adapted to CMIP phase 6 (CMIP6) in future iterations. It also describes a standardized set of outputs for each participating Fish-MIP model to produce. This enables the broad characterization of differences between and uncertainties within models and projections when assessing climate and fisheries impacts on marine ecosystems and the services they provide. The systematic generation, collation, and comparison of results from Fish-MIP will inform an understanding of the range of plausible changes in marine ecosystems and improve our capacity to define and convey the strengths and weaknesses of model-based advice on future states of marine ecosystems and fisheries. Ultimately, Fish-MIP represents a step towards bringing together the marine ecosystem modelling community to produce consistent ensemble medium-and long-term projections of marine ecosystems.
- ItemSource apportionment of the organic aerosol over the Atlantic Ocean from 53° N to 53° S: Significant contributions from marine emissions and long-range transport(Katlenburg-Lindau : EGU, 2018) Huang, Shan; Wu, Zhijun; Poulain, Laurent; van Pinxteren, Manuela; Merkel, Maik; Assmann, Denise; Herrmann, Hartmut; Wiedensohler, AlfredMarine aerosol particles are an important part of the natural aerosol systems and might have a significant impact on the global climate and biological cycle. It is widely accepted that truly pristine marine conditions are difficult to find over the ocean. However, the influence of continental and anthropogenic emissions on the marine boundary layer (MBL) aerosol is still less understood and non-quantitative, causing uncertainties in the estimation of the climate effect of marine aerosols. This study presents a detailed chemical characterization of the MBL aerosol as well as the source apportionment of the organic aerosol (OA) composition. The data set covers the Atlantic Ocean from 53∘ N to 53∘ S, based on four open-ocean cruises in 2011 and 2012. The aerosol particle composition was measured with a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS), which indicated that sub-micrometer aerosol particles over the Atlantic Ocean are mainly composed of sulfates (50 % of the particle mass concentration), organics (21 %) and sea salt (12 %). OA has been apportioned into five factors, including three factors linked to marine sources and two with continental and/or anthropogenic origins. The marine oxygenated OA (MOOA, 16 % of the total OA mass) and marine nitrogen-containing OA (MNOA, 16 %) are identified as marine secondary products with gaseous biogenic precursors dimethyl sulfide (DMS) or amines. Marine hydrocarbon-like OA (MHOA, 19 %) was attributed to the primary emissions from the Atlantic Ocean. The factor for the anthropogenic oxygenated OA (Anth-OOA, 19 %) is related to continental long-range transport. Represented by the combustion oxygenated OA (Comb-OOA), aged combustion emissions from maritime traffic and wild fires in Africa contributed, on average, a large fraction to the total OA mass (30 %). This study provides the important finding that long-range transport was found to contribute averagely 49 % of the submicron OA mass over the Atlantic Ocean. This is almost equal to that from marine sources (51 %). Furthermore, a detailed latitudinal distribution of OA source contributions showed that DMS oxidation contributed markedly to the OA over the South Atlantic during spring, while continental-related long-range transport largely influenced the marine atmosphere near Europe and western and central Africa (15∘ N to 15∘ S). In addition, supported by a solid correlation between marine tracer methanesulfonic acid (MSA) and the DMS-oxidation OA (MOOA, R2>0.85), this study suggests that the DMS-related secondary organic aerosol (SOA) over the Atlantic Ocean could be estimated by MSA and a scaling factor of 1.79, especially in spring.