Browsing by Author "Frieler, Katja"
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- ItemAssessing inter-sectoral climate change risks: The role of ISIMIP(Bristol : IOP Publishing, 2017) Rosenzweig, Cynthia; Arnell, Nigel W.; Ebi, Kristie L.; Lotze-Campen, Hermann; Raes, Frank; Rapley, Chris; Smith, Mark Stafford; Cramer, Wolfgang; Frieler, Katja; Reyer, Christopher P.O.; Schewe, Jacob; van Vuuren, Detlef; Warszawski, LilaThe aims of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) are to provide a framework for the intercomparison of global and regional-scale risk models within and across multiple sectors and to enable coordinated multi-sectoral assessments of different risks and their aggregated effects. The overarching goal is to use the knowledge gained to support adaptation and mitigation decisions that require regional or global perspectives within the context of facilitating transformations to enable sustainable development, despite inevitable climate shifts and disruptions. ISIMIP uses community-agreed sets of scenarios with standardized climate variables and socio-economic projections as inputs for projecting future risks and associated uncertainties, within and across sectors. The results are consistent multi-model assessments of sectoral risks and opportunities that enable studies that integrate across sectors, providing support for implementation of the Paris Agreement under the United Nations Framework Convention on Climate Change.
- ItemAssessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)(München : European Geopyhsical Union, 2017) Frieler, Katja; Lange, Stefan; Piontek, Franziska; Reyer, Christopher P.O.; Schewe, Jacob; Warszawski, Lila; Zhao, Fang; Chini, Louise; Denvil, Sebastien; Emanuel, Kerry; Geiger, Tobias; Halladay, Kate; Hurtt, George; Mengel, Matthias; Murakami, Daisuke; Ostberg, Sebastian; Popp, Alexander; Riva, Riccardo; Stevanovic, Miodrag; Suzuki, Tatsuo; Volkholz, Jan; Burke, Eleanor; Ciais, Philippe; Ebi, Kristie; Eddy, Tyler D.; Elliott, Joshua; Galbraith, Eric; Gosling, Simon N.; Hattermann, Fred; Hickler, Thomas; Hinkel, Jochen; Hof, Christian; Huber, Veronika; Jägermeyr, Jonas; Krysanova, Valentina; Marcé, Rafael; Müller Schmied, Hannes; Mouratiadou, Ioanna; Pierson, Don; Tittensor, Derek P.; Vautard, Robert; van Vliet, Michelle; Biber, Matthias F.; Betts, Richard A.; Bodirsky, Benjamin Leon; Deryng, Delphine; Frolking, Steve; Jones, Chris D.; Lotze, Heike K.; Lotze-Campen, Hermann; Sahajpal, Ritvik; Thonicke, Kirsten; Tian, Hanqin; Yamagata, YoshikiIn Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a "special report in 2018 on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways". In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5°C, including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity).
- ItemATTRICI v1.1 – counterfactual climate for impact attribution(Katlenburg-Lindau : Copernicus, 2021) Mengel, Matthias; Treu, Simon; Lange, Stefan; Frieler, KatjaAttribution in its general definition aims to quantify drivers of change in a system. According to IPCC Working Group II (WGII) a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system's behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause” (IPCC, 2014). Impact attribution following this definition remains a challenge because the counterfactual baseline, which characterizes the system behavior in the hypothetical absence of climate change, cannot be observed. Process-based and empirical impact models can fill this gap as they allow us to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution setup of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.
- ItemBenchmarking 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, FangThe 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.
- ItemBetter insurance could effectively mitigate the increase in economic growth losses from U.S. hurricanes under global warming(Washington, DC [u.a.] : Assoc., 2023) Otto, Christian; Kuhla, Kilian; Geiger, Tobias; Schewe, Jacob; Frieler, KatjaGlobal warming is likely to increase the proportion of intense hurricanes in the North Atlantic. Here, we analyze how this may affect economic growth. To this end, we introduce an event-based macroeconomic growth model that temporally resolves how growth depends on the heterogeneity of hurricane shocks. For the United States, we find that economic growth losses scale superlinearly with shock heterogeneity. We explain this by a disproportional increase of indirect losses with the magnitude of direct damage, which can lead to an incomplete recovery of the economy between consecutive intense landfall events. On the basis of two different methods to estimate the future frequency increase of intense hurricanes, we project annual growth losses to increase between 10 and 146% in a 2°C world compared to the period 1980–2014. Our modeling suggests that higher insurance coverage can compensate for this climate change–induced increase in growth losses.
- ItemChanges in crop yields and their variability at different levels of global warming(München : European Geopyhsical Union, 2018) Ostberg, Sebastian; Schewe, Jacob; Childers, Katelin; Frieler, KatjaAn assessment of climate change impacts at different levels of global warming is crucial to inform the policy discussion about mitigation targets, as well as for the economic evaluation of climate change impacts. Integrated assessment models often use global mean temperature change (ΔGMT) as a sole measure of climate change and, therefore, need to describe impacts as a function of ΔGMT. There is already a well-established framework for the scalability of regional temperature and precipitation changes with ΔGMT. It is less clear to what extent more complex biological or physiological impacts such as crop yield changes can also be described in terms of ΔGMT, even though such impacts may often be more directly relevant for human livelihoods than changes in the physical climate. Here we show that crop yield projections can indeed be described in terms of ΔGMT to a large extent, allowing for a fast estimation of crop yield changes for emissions scenarios not originally covered by climate and crop model projections. We use an ensemble of global gridded crop model simulations for the four major staple crops to show that the scenario dependence is a minor component of the overall variance of projected yield changes at different levels of ΔGMT. In contrast, the variance is dominated by the spread across crop models. Varying CO2 concentrations are shown to explain only a minor component of crop yield variability at different levels of global warming. In addition, we find that the variability in crop yields is expected to increase with increasing warming in many world regions. We provide, for each crop model, geographical patterns of mean yield changes that allow for a simplified description of yield changes under arbitrary pathways of global mean temperature and CO2 changes, without the need for additional climate and crop model simulations.
- ItemClimate change and international migration: Exploring the macroeconomic channel(San Francisco, California, US : PLOS, 2022) Rikani, Albano; Frieler, Katja; Schewe, JacobInternational migration patterns, at the global level, can to a large extent be explained through economic factors in origin and destination countries. On the other hand, it has been shown that global climate change is likely to affect economic development over the coming decades. Here, we demonstrate how these future climate impacts on national income levels could alter the global migration landscape. Using an empirically calibrated global migration model, we investigate two separate mechanisms. The first is through destination-country income, which has been shown consistently to have a positive effect on immigration. As countries' income levels relative to each other are projected to change in the future both due to different rates of economic growth and due to different levels of climate change impacts, the relative distribution of immigration across destination countries also changes as a result, all else being equal. Second, emigration rates have been found to have a complex, inverted U-shaped dependence on origin-country income. Given the available migration flow data, it is unclear whether this dependence-found in spatio-temporal panel data-also pertains to changes in a given migration flow over time. If it does, then climate change will additionally affect migration patterns through origin countries' emigration rates, as the relative and absolute positions of countries on the migration "hump" change. We illustrate these different possibilities, and the corresponding effects of 3°C global warming (above pre-industrial) on global migration patterns, using climate model projections and two different methods for estimating climate change effects on macroeconomic development.
- ItemClimate signals in river flood damages emerge under sound regional disaggregation([London] : Nature Publishing Group UK, 2021) Sauer, Inga J.; Reese, Ronja; Otto, Christian; Geiger, Tobias; Willner, Sven N.; Guillod, Benoit P.; Bresch, David N.; Frieler, KatjaClimate change affects precipitation patterns. Here, we investigate whether its signals are already detectable in reported river flood damages. We develop an empirical model to reconstruct observed damages and quantify the contributions of climate and socio-economic drivers to observed trends. We show that, on the level of nine world regions, trends in damages are dominated by increasing exposure and modulated by changes in vulnerability, while climate-induced trends are comparably small and mostly statistically insignificant, with the exception of South & Sub-Saharan Africa and Eastern Asia. However, when disaggregating the world regions into subregions based on river-basins with homogenous historical discharge trends, climate contributions to damages become statistically significant globally, in Asia and Latin America. In most regions, we find monotonous climate-induced damage trends but more years of observations would be needed to distinguish between the impacts of anthropogenic climate forcing and multidecadal oscillations.
- ItemCorrigendum: The role of storage dynamics in annual wheat prices (2017 Environ. Res. Lett. 12 054005)(Bristol : IOP Publ., 2018) Schewe, Jacob; Otto, Christian; Frieler, Katja[no abstract available]
- ItemThe critical role of the routing scheme in simulating peak river discharge in global hydrological models(Bristol : IOP Publishing, 2017) Zhao, Fang; Veldkamp, Ted I.E.; Frieler, Katja; Schewe, Jacob; Ostberg, Sebastian; Willner, Sven; Schauberger, Bernhard; Gosling, Simon N.; Müller Schmied, Hannes; Portmann, Felix T.; Leng, Guoyong; Huang, Maoyi; Liu, Xingcai; Tang, Qiuhong; Hanasaki, Naota; Biemans, Hester; Gerten, Dieter; Satoh, Yusuke; Pokhrel, Yadu; Stacke, Tobias; Ciais, Philippe; Chang, Jinfeng; Ducharne, Agnes; Guimberteau, Matthieu; Wada, Yoshihide; Kim, Hyungjun; Yamazaki, DaiGlobal hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.
- ItemDifferential climate impacts for policy-relevant limits to global warming: The case of 1.5 °c and 2 °c(München : European Geopyhsical Union, 2016) Schleussner, Carl-Friedrich; Lissner, Tabea K.; Fischer, Erich M.; Wohland, Jan; Perrette, Mahé; Golly, Antonius; Rogelj, Joeri; Childers, Katelin; Schewe, Jacob; Frieler, Katja; Mengel, Matthias; Hare, William; Schaeffer, MichielRobust appraisals of climate impacts at different levels of global-mean temperature increase are vital to guide assessments of dangerous anthropogenic interference with the climate system. The 2015 Paris Agreement includes a two-headed temperature goal: "holding the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C". Despite the prominence of these two temperature limits, a comprehensive overview of the differences in climate impacts at these levels is still missing. Here we provide an assessment of key impacts of climate change at warming levels of 1.5°C and 2°C, including extreme weather events, water availability, agricultural yields, sea-level rise and risk of coral reef loss. Our results reveal substantial differences in impacts between a 1.5°C and 2°C warming that are highly relevant for the assessment of dangerous anthropogenic interference with the climate system. For heat-related extremes, the additional 0.5°C increase in global-mean temperature marks the difference between events at the upper limit of present-day natural variability and a new climate regime, particularly in tropical regions. Similarly, this warming difference is likely to be decisive for the future of tropical coral reefs. In a scenario with an end-of-century warming of 2°C, virtually all tropical coral reefs are projected to be at risk of severe degradation due to temperature-induced bleaching from 2050 onwards. This fraction is reduced to about 90% in 2050 and projected to decline to 70% by 2100 for a 1.5°C scenario. Analyses of precipitation-related impacts reveal distinct regional differences and hot-spots of change emerge. Regional reduction in median water availability for the Mediterranean is found to nearly double from 9% to 17% between 1.5°C and 2°C, and the projected lengthening of regional dry spells increases from 7 to 11%. Projections for agricultural yields differ between crop types as well as world regions. While some (in particular high-latitude) regions may benefit, tropical regions like West Africa, South-East Asia, as well as Central and northern South America are projected to face substantial local yield reductions, particularly for wheat and maize. Best estimate sea-level rise projections based on two illustrative scenarios indicate a 50cm rise by 2100 relative to year 2000-levels for a 2°C scenario, and about 10 cm lower levels for a 1.5°C scenario. In a 1.5°C scenario, the rate of sea-level rise in 2100 would be reduced by about 30% compared to a 2°C scenario. Our findings highlight the importance of regional differentiation to assess both future climate risks and different vulnerabilities to incremental increases in global-mean temperature. The article provides a consistent and comprehensive assessment of existing projections and a good basis for future work on refining our understanding of the difference between impacts at 1.5°C and 2°C warming.
- ItemThe economically optimal warming limit of the planet(Göttingen : Copernicus Publ., 2019) Ueckerd, Falko; Frieler, Katja; Lange, Stefan; Wenz, Leonie; Luderer, Gunnar; Levermann, AndersBoth climate-change damages and climate-change mitigation will incur economic costs. While the risk of severe damages increases with the level of global warming (Dell et al., 2014; IPCC, 2014b, 2018; Lenton et al., 2008), mitigating costs increase steeply with more stringent warming limits (IPCC, 2014a; Luderer et al., 2013; Rogelj et al., 2015). Here, we show that the global warming limit that minimizes this century's total economic costs of climate change lies between 1.9 and 2°C, if temperature changes continue to impact national economic growth rates as observed in the past and if instantaneous growth effects are neither compensated nor amplified by additional growth effects in the following years. The result is robust across a wide range of normative assumptions on the valuation of future welfare and inequality aversion. We combine estimates of climate-change impacts on economic growth for 186 countries (applying an empirical damage function from Burke et al., 2015) with mitigation costs derived from a state-of-the-art energy-economy-climate model with a wide range of highly resolved mitigation options (Kriegler et al., 2017; Luderer et al., 2013, 2015). Our purely economic assessment, even though it omits non-market damages, provides support for the international Paris Agreement on climate change. The political goal of limiting global warming to "well below 2 degrees" is thus also an economically optimal goal given above assumptions on adaptation and damage persistence. © 2019 Copernicus GmbH. All rights reserved.
- ItemThe effect of overshooting 1.5 °C global warming on the mass loss of the Greenland ice sheet(Göttingen : Copernicus Publ., 2018) Rückamp, Martin; Falk, Ulrike; Frieler, Katja; Lange, Stefan; Humbert, AngelikaSea-level rise associated with changing climate is expected to pose a major challenge for societies. Based on the efforts of COP21 to limit global warming to 2.0 ∘C or even 1.5 ∘C by the end of the 21st century (Paris Agreement), we simulate the future contribution of the Greenland ice sheet (GrIS) to sea-level change under the low emission Representative Concentration Pathway (RCP) 2.6 scenario. The Ice Sheet System Model (ISSM) with higher-order approximation is used and initialized with a hybrid approach of spin-up and data assimilation. For three general circulation models (GCMs: HadGEM2-ES, IPSL-CM5A-LR, MIROC5) the projections are conducted up to 2300 with forcing fields for surface mass balance (SMB) and ice surface temperature (Ts) computed by the surface energy balance model of intermediate complexity (SEMIC). The projected sea-level rise ranges between 21–38 mm by 2100 and 36–85 mm by 2300. According to the three GCMs used, global warming will exceed 1.5 ∘C early in the 21st century. The RCP2.6 peak and decline scenario is therefore manually adjusted in another set of experiments to suppress the 1.5 ∘C overshooting effect. These scenarios show a sea-level contribution that is on average about 38 % and 31 % less by 2100 and 2300, respectively. For some experiments, the rate of mass loss in the 23rd century does not exclude a stable ice sheet in the future. This is due to a spatially integrated SMB that remains positive and reaches values similar to the present day in the latter half of the simulation period. Although the mean SMB is reduced in the warmer climate, a future steady-state ice sheet with lower surface elevation and hence volume might be possible. Our results indicate that uncertainties in the projections stem from the underlying GCM climate data used to calculate the surface mass balance. However, the RCP2.6 scenario will lead to significant changes in the GrIS, including elevation changes of up to 100 m. The sea-level contribution estimated in this study may serve as a lower bound for the RCP2.6 scenario, as the currently observed sea-level rise is not reached in any of the experiments; this is attributed to processes (e.g. ocean forcing) not yet represented by the model, but proven to play a major role in GrIS mass loss.
- ItemThe effects of climate extremes on global agricultural yields(Bristol : IOP Publ., 2019) Vogel, Elisabeth; Donat, Markus G.; Alexander, Lisa V.; Meinshausen, Malte; Ray, Deepak K.; Karoly, David; Meinshausen, Nicolai; Frieler, KatjaClimate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factors—including mean climate as well as climate extremes—explain 20%–49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%–43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes.
- ItemEvaluating 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, RichardThis 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.
- ItemEvaluation of river flood extent simulated with multiple global hydrological models and climate forcings(Bristol : IOP Publ., 2021-8-13) Mester, Benedikt; Willner, Sven Norman; Frieler, Katja; Schewe, JacobGlobal flood models (GFMs) are increasingly being used to estimate global-scale societal and economic risks of river flooding. Recent validation studies have highlighted substantial differences in performance between GFMs and between validation sites. However, it has not been systematically quantified to what extent the choice of the underlying climate forcing and global hydrological model (GHM) influence flood model performance. Here, we investigate this sensitivity by comparing simulated flood extent to satellite imagery of past flood events, for an ensemble of three climate reanalyses and 11 GHMs. We study eight historical flood events spread over four continents and various climate zones. For most regions, the simulated inundation extent is relatively insensitive to the choice of GHM. For some events, however, individual GHMs lead to much lower agreement with observations than the others, mostly resulting from an overestimation of inundated areas. Two of the climate forcings show very similar results, while with the third, differences between GHMs become more pronounced. We further show that when flood protection standards are accounted for, many models underestimate flood extent, pointing to deficiencies in their flood frequency distribution. Our study guides future applications of these models, and highlights regions and models where targeted improvements might yield the largest performance gains.
- ItemA global historical data set of tropical cyclone exposure (TCE-DAT)(München : European Geopyhsical Union, 2018) Geiger, Tobias; Frieler, Katja; Bresch, David N.Tropical cyclones pose a major risk to societies worldwide, with about 22 million directly affected people and damages of USD 29 billion on average per year over the last 20 years. While data on observed cyclones tracks (location of the center) and wind speeds are publicly available, these data sets do not contain information about the spatial extent of the storm and people or assets exposed. Here, we apply a simplified wind field model to estimate the areas exposed to wind speeds above 34, 64, and 96 knots (kn). Based on available spatially explicit data on population densities and gross domestic product (GDP) we estimate (1) the number of people and (2) the sum of assets exposed to wind speeds above these thresholds accounting for temporal changes in historical distribution of population and assets (TCE-hist) and assuming fixed 2015 patterns (TCE-2015). The associated spatially explicit and aggregated country-event-level exposure data (TCE-DAT) cover the period 1950 to 2015 and are freely available at https://doi.org/10.5880/pik.2017.011 (Geiger at al., 2017c). It is considered key information to (1) assess the contribution of climatological versus socioeconomic drivers of changes in exposure to tropical cyclones, (2) estimate changes in vulnerability from the difference in exposure and reported damages and calibrate associated damage functions, and (3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP, employment, or migration.
- ItemHigh-income does not protect against hurricane losses(Bristol : IOP Publishing, 2016) Geiger, Tobias; Frieler, Katja; Levermann, AndersDamage due to tropical cyclones accounts for more than 50% of all meteorologically-induced economic losses worldwide. Their nominal impact is projected to increase substantially as the exposed population grows, per capita income increases, and anthropogenic climate change manifests. So far, historical losses due to tropical cyclones have been found to increase less than linearly with a nation's affected gross domestic product (GDP). Here we show that for the United States this scaling is caused by a sub-linear increase with affected population while relative losses scale super-linearly with per capita income. The finding is robust across a multitude of empirically derived damage models that link the storm's wind speed, exposed population, and per capita GDP to reported losses. The separation of both socio-economic predictors strongly affects the projection of potential future hurricane losses. Separating the effects of growth in population and per-capita income, per hurricane losses with respect to national GDP are projected to triple by the end of the century under unmitigated climate change, while they are estimated to decrease slightly without the separation.
- ItemA multi-model analysis of risk of ecosystem shifts under climate change(Bristol : IOP Publishing, 2013) Warszawski, Lila; Friend, Andrew; Ostberg, Sebastian; Frieler, Katja; Lucht, Wolfgang; Schaphoff, Sibyll; Beerling, David; Cadule, Patricia; Ciais, Philippe; Clark, Douglas B.; Kahana, Ron; Ito, Akihiko; Keribin, Rozenn; Kleidon, Axel; Lomas, Mark; Nishina, Kazuya; Pavlick, Ryan; Rademacher, Tim Tito; Buechner, Matthias; Piontek, Franziska; Schewe, Jacob; Serdeczny, Olivia; Schellnhuber, Hans JoachimClimate change may pose a high risk of change to Earth's ecosystems: shifting climatic boundaries may induce changes in the biogeochemical functioning and structures of ecosystems that render it difficult for endemic plant and animal species to survive in their current habitats. Here we aggregate changes in the biogeochemical ecosystem state as a proxy for the risk of these shifts at different levels of global warming. Estimates are based on simulations from seven global vegetation models (GVMs) driven by future climate scenarios, allowing for a quantification of the related uncertainties. 5–19% of the naturally vegetated land surface is projected to be at risk of severe ecosystem change at 2 ° C of global warming (ΔGMT) above 1980–2010 levels. However, there is limited agreement across the models about which geographical regions face the highest risk of change. The extent of regions at risk of severe ecosystem change is projected to rise with ΔGMT, approximately doubling between ΔGMT = 2 and 3 ° C, and reaching a median value of 35% of the naturally vegetated land surface for ΔGMT = 4 °C. The regions projected to face the highest risk of severe ecosystem changes above ΔGMT = 4 °C or earlier include the tundra and shrublands of the Tibetan Plateau, grasslands of eastern India, the boreal forests of northern Canada and Russia, the savanna region in the Horn of Africa, and the Amazon rainforest.
- ItemThe 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, KatjaProcess-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.