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- ItemPotential climate change impacts on the water balance of subcatchments of the River Spree, Germany(München : European Geopyhsical Union, 2012) Pohle, I.; Koch, H.; Grünewald, U.Lusatia is considered one of the driest regions of Germany. The climatic water balance is negative even under current climate conditions. Due to global climate change, increased temperatures and a shift of precipitation from summer to winter are expected. Therefore, it is of major interest whether the excess water in winter can be stored and to which extent it is used up on increasing evapotranspiration. Thus, this study focuses on estimating potential climate change impacts on the water balance of two subcatchments of the River Spree using the Soil and Water Integrated Model (SWIM). Climate input was taken from 100 realisations each of two scenarios of the STatistical Analogue Resampling scheme STAR assuming a further temperature increase of 0 K (scenario A) and 2 K by the year 2055 (scenario B) respectively. Resulting from increased temperatures and a shift in precipitation from summer to winter actual evapotranspiration is supposed to increase in winter and early spring, but to decrease in later spring and early summer. This is less pronounced for scenario A than for scenario B. Consequently, also the decrease in discharge and groundwater recharge in late spring is lower for scenario A than for scenario B. The highest differences of runoff generation and groundwater recharge between the two scenarios but also the highest ranges within the scenarios occur in summer and early autumn. It is planned to estimate potential climate change for the catchments of Spree, Schwarze Elster and Lusatian Neisse.
- ItemEstimating carbon emissions from African wildfires(München : European Geopyhsical Union, 2009) Lehsten, V.; Tansey, K.; Balzter, H.; Thonicke, K.; Spessa, A.; Weber, U.; Smith, B.; Arneth, A.We developed a technique for studying seasonal and interannual variation in pyrogenic carbon emissions from Africa using a modelling approach that scales burned area estimates from L3JRC, a map recently generated from remote sensing of burn scars instead of active fires. Carbon fluxes were calculated by the novel fire model SPITFIRE embedded within the dynamic vegetation model framework LPJ-GUESS, using daily climate input. For the time period from 2001 to 2005 an average area of 195.5±24×104 km2 was burned annually, releasing an average of 723±70 Tg C to the atmosphere; these estimates for the biomass burned are within the range of previously published estimates. Despite the fact that the majority of wildfires are ignited by humans, strong relationships between climatic conditions (particularly precipitation), net primary productivity and overall biomass burnt emerged. Our investigation of the relationships between burnt area and carbon emissions and their potential drivers available litter and precipitation revealed uni-modal responses to annual precipitation, with a maximum around 1000 mm for burned area and emissions, or 1200 mm for litter availability. Similar response patterns identified in savannahs worldwide point to precipitation as a chief determinant for short-term variation in fire regime. A considerable variability that cannot be explained by fire-precipitation relationships alone indicates the existence of additional factors that must be taken into account.
- 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.
- ItemClimate impacts on human livelihoods: Where uncertainty matters in projections of water availability(München : European Geopyhsical Union, 2014) Lissner, T.K.; Reusser, D.E.; Schewe, J.; Lakes, T.; Kropp, J.P.Climate change will have adverse impacts on many different sectors of society, with manifold consequences for human livelihoods and well-being. However, a systematic method to quantify human well-being and livelihoods across sectors is so far unavailable, making it difficult to determine the extent of such impacts. Climate impact analyses are often limited to individual sectors (e.g. food or water) and employ sector-specific target measures, while systematic linkages to general livelihood conditions remain unexplored. Further, recent multi-model assessments have shown that uncertainties in projections of climate impacts deriving from climate and impact models, as well as greenhouse gas scenarios, are substantial, posing an additional challenge in linking climate impacts with livelihood conditions. This article first presents a methodology to consistently measure what is referred to here as AHEAD (Adequate Human livelihood conditions for wEll-being And Development). Based on a trans-disciplinary sample of concepts addressing human well-being and livelihoods, the approach measures the adequacy of conditions of 16 elements. We implement the method at global scale, using results from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to show how changes in water availability affect the fulfilment of AHEAD at national resolution. In addition, AHEAD allows for the uncertainty of climate and impact model projections to be identified and differentiated. We show how the approach can help to put the substantial inter-model spread into the context of country-specific livelihood conditions by differentiating where the uncertainty about water scarcity is relevant with regard to livelihood conditions – and where it is not. The results indicate that livelihood conditions are compromised by water scarcity in 34 countries. However, more often, AHEAD fulfilment is limited through other elements. The analysis shows that the water-specific uncertainty ranges of the model output are outside relevant thresholds for AHEAD for 65 out of 111 countries, and therefore do not contribute to the overall uncertainty about climate change impacts on livelihoods. In 46 of the countries in the analysis, water-specific uncertainty is relevant to AHEAD. The AHEAD method presented here, together with first results, forms an important step towards making scientific results more applicable for policy decisions.
- 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.
- ItemThe impact of climate change and variability on the generation of electrical power(Stuttgart : Gebrueder Borntraeger Verlagsbuchhandlung, 2015) Koch, H.; Vögele, S.; Hattermann, F.F.; Huang, S.
- ItemForests under climate change: Potential risks and opportunities(Stuttgart : Gebrueder Borntraeger Verlagsbuchhandlung, 2015) Lasch-Born, P.; Suckow, F.; Gutsch, M.; Reyer, C.; Hauf, Y.; Murawski, A.; Pilz, T.
- ItemThe Vulnerability, Impacts, Adaptation and Climate Services Advisory Board (VIACS AB v1.0) contribution to CMIP6(München : European Geopyhsical Union, 2016) Ruane, Alex C.; Teichmann, Claas; Arnell, Nigel W.; Carter, Timothy R.; Ebi, Kristie L.; Frieler, Katja; Goodess, Clare M.; Hewitson, Bruce; Horton, Radley; Kovats, R. Sari; Lotze, Heike K.; Mearns, Linda O.; Navarra, Antonio; Ojima, Dennis S.; Riahi, Keywan; Rosenzweig, Cynthia; Themessl, Matthias; Vincent, KatharineThis paper describes the motivation for the creation of the Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), its initial activities, and its plans to serve as a bridge between climate change applications experts and climate modelers. The climate change application community comprises researchers and other specialists who use climate information (alongside socioeconomic and other environmental information) to analyze vulnerability, impacts, and adaptation of natural systems and society in relation to past, ongoing, and projected future climate change. Much of this activity is directed toward the co-development of information needed by decision-makers for managing projected risks. CMIP6 provides a unique opportunity to facilitate a two-way dialog between climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services to solicit community feedback that increases the applications relevance of the CMIP6-Endorsed Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments of the greatest interest to the climate model applications community, indicating the applicability and societal relevance of climate model simulation outputs. The VIACS Advisory Board also recommended an impacts version of Obs4MIPs and indicated user needs for the gridding and processing of model output.
- 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.
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