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1.
We estimated how the possible changes in wind climate and state of the forest due to climate change may affect the probability of exceeding critical wind speeds expected to cause wind damage within a forest management unit located in Southern Sweden. The topography of the management unit was relatively gentle and the forests were dominated by Norway spruce (Picea abies (L.) Karst.). We incorporated a model relating the site index (SI) to the site productivity into the forest projection model FTM. Using estimated changes in the net primary production (NPP) due to climate change and assuming a relative change in NPP equal to a relative change in the site productivity, we simulated possible future states of the forest under gradual adjustment of SI in response to climate change. We estimated changes in NPP by combining the boreal-adapted BIOMASS model with four regional climate change scenarios calculated using the RCAO model for the period 2071–2100 and two control period scenarios for the period 1961–1990. The modified WINDA model was used to calculate the probability of wind damage for individual forest stands in simulated future states of the forest. The climate change scenarios used represent non-extreme projections on a 100-year time scale in terms of global mean warming. A 15–40% increase in NPP was estimated to result from climate change until the period 2071–2100. Increasing sensitivity of the forest to wind was indicated when the management rules of today were applied. A greater proportion of the calculated change in probability of wind damage was due to changes in wind climate than to changes in the sensitivity of the forest to wind. While regional climate scenarios based on the HadAM3H general circulation model (GCM) indicated no change (SRES A2 emission scenario) or a slightly reduced (SRES B2 emission scenario) probability of wind damage, scenarios based on the ECHAM4/OPYC3 GCM indicated increased probability of wind damage. The assessment should, however, be reviewed as the simulation of forest growth under climate change as well as climate change scenarios are refined.  相似文献   

2.
The MIT 2D climate model is used to make probabilistic projections for changes in global mean surface temperature and for thermosteric sea level rise under a variety of forcing scenarios. The uncertainties in climate sensitivity and rate of heat uptake by the deep ocean are quantified by using the probability distributions derived from observed twentieth century temperature changes. The impact on climate change projections of using the smallest and largest estimates of twentieth century deep ocean warming is explored. The impact is large in the case of global mean thermosteric sea level rise. In the MIT reference (“business as usual”) scenario the median rise by 2100 is 27 and 43 cm in the respective cases. The impact on increases in global mean surface air temperature is more modest, 4.9 and 3.9 C in the two respective cases, because of the correlation between climate sensitivity and ocean heat uptake required by twentieth century surface and upper air temperature changes. The results are also compared with the projections made by the IPCC AR4’s multi-model ensemble for several of the SRES scenarios. The multi-model projections are more consistent with the MIT projections based on the largest estimate of ocean warming. However, the range for the rate of heat uptake by the ocean suggested by the lowest estimate of ocean warming is more consistent with the range suggested by the twentieth century changes in surface and upper air temperatures, combined with the expert prior for climate sensitivity.  相似文献   

3.
A structurally highly simplified, globally integrated coupled climate-economic costs model SIAM (Structural Integrated Assessment Model) is used to compute optimal paths of global CO2 emissions that minimize the net sum of climate damage and mitigation costs. The model is used to study the sensitivity of the computed optimal emission paths with respect to various critical input assumptions. The climate module is represented by a linearized impulse-response model calibrated against a coupled ocean-atmosphere general circulation climate model and a three-dimensional global carbon-cycle model. The cost terms are represented by strongly simplified expressions designed for maximal transparency with respect to sensitive input assumptions. These include the discount rates for mitigation and damage costs, the inertia of the socio-economic system, and the dependence of climate damages on the change in temperature and the rate of change of temperature. Different assumptions regarding these parameters are believed to be the cause of the marked divergences of existing cost-benefit analyses based on more sophisticated economic models. The long memory of the climate system implies that very long time horizons of several hundred years need to be considered to optimize CO2 emissions on time scales relevant for a policy of sustainable development. Cost-benefit analyses over shorter time scales of a century or two can lead to dangerous underestimates of the long term climatic impact of increasing greenhouse-gas emissions. To avert a major long term global warming, CO2 emissions need to be reduced ultimately to very low levels. However, the draw-down can be realized as a gradual transition process over many decades and even centuries. This should nevertheless not be interpreted as providing a time cushion for inaction: the transition becomes more costly the longer the necessary mitigation policies are delayed. However, the long time horizon provides adequate flexibility for later adjustments. Short term energy conservation alone is insufficient and can be viewed only as a useful measure in support of the necessary long term transition to carbon-free energy technologies. For standard climate damage cost expressions, optimal emission paths limiting long term global warming to acceptable sustainable development levels are recovered only if climate damage costs are not significantly discounted. Discounting of climate damages at normal economic rates yields emission paths that are only weakly reduced relative to business as usual scenarios, resulting in high global warming levels that are incompatible with the generally accepted requirements of sustainable development. The solutions are nevertheless logically consistent with the underlying discounting assumption, namely that the occurrence of global warming damages in the distant future as a result of present human activities is of negligible concern today. It follows that a commitment to long term sustainable development, if it in fact exists, should be expressed by an intertemporal relation for the value of the earth's future climate which does not degrade significantly over the time horizon relevant for climate change. Since the future climate is a common assett whose value cannot be determined on the market, the appropriate discount rate for future climate damages should be determined by an assessment of the public willingness to pay today for the mitigation of future climate change. To translate our general conclusions into quantitative cost estimates required by decision makers, the present exploratory study needs to be extended using more detailed disaggregated climate damage and mitigation cost estimates and more realistic socio-economic models, including multi-actor interactions, inherent variability, the role of uncertainty and adaptive control strategies.  相似文献   

4.
This paper presents the overview of the Shared Socioeconomic Pathways (SSPs) and their energy, land use, and emissions implications. The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature. A multi-model approach was used for the elaboration of the energy, land-use and the emissions trajectories of SSP-based scenarios. The baseline scenarios lead to global energy consumption of 400–1200 EJ in 2100, and feature vastly different land-use dynamics, ranging from a possible reduction in cropland area up to a massive expansion by more than 700 million hectares by 2100. The associated annual CO2 emissions of the baseline scenarios range from about 25 GtCO2 to more than 120 GtCO2 per year by 2100. With respect to mitigation, we find that associated costs strongly depend on three factors: (1) the policy assumptions, (2) the socio-economic narrative, and (3) the stringency of the target. The carbon price for reaching the target of 2.6 W/m2 that is consistent with a temperature change limit of 2 °C, differs in our analysis thus by about a factor of three across the SSP marker scenarios. Moreover, many models could not reach this target from the SSPs with high mitigation challenges. While the SSPs were designed to represent different mitigation and adaptation challenges, the resulting narratives and quantifications span a wide range of different futures broadly representative of the current literature. This allows their subsequent use and development in new assessments and research projects. Critical next steps for the community scenario process will, among others, involve regional and sectoral extensions, further elaboration of the adaptation and impacts dimension, as well as employing the SSP scenarios with the new generation of earth system models as part of the 6th climate model intercomparison project (CMIP6).  相似文献   

5.
Probabilistic climate change projections using neural networks   总被引：5，自引：0，他引：5  
Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.  相似文献   

6.
Global GDP projections for the 21st century are needed for the exploration of long-term global environmental problems, in particular climate change. Greenhouse gas emissions as well as climate change mitigation and adaption capacities strongly depend on growth of per capita income. However, long-term economic projections are highly uncertain. This paper provides five new long-term economic scenarios as part of the newly developed shared socio-economic pathways (SSPs) which represent a set of widely diverging narratives. A method of GDP scenario building is presented that is based on assumptions about technological progress, and human and physical capital formation as major drivers of long-term GDP per capita growth. The impact of these drivers differs significantly between different shared socio-economic pathways and is traced back to the underlying narratives and the associated population and education scenarios. In a highly fragmented world, technological and knowledge spillovers are low. Hence, the growth impact of technological progress and human capital is comparatively low, and per capita income diverges between world regions. These factors play a much larger role in globalization scenarios, leading to higher economic growth and stronger convergence between world regions. At the global average, per capita GDP is projected to grow annually in a range between 1.0% (SSP3) and 2.8% (SSP5) from 2010 to 2100. While this covers a large portion of variety in future global economic growth projections, plausible lower and higher growth projections may still be conceivable. The GDP projections are put into the context of historic patterns of economic growth (stylized facts), and their sensitivity to key assumptions is explored.  相似文献   

7.
In this article, we evaluate and compare results from three integrated assessment models (GCAM, IMAGE, and ReMIND/MAgPIE) regarding the drivers and impacts of bioenergy production on the global land system. The considered model frameworks employ linked energy, economy, climate and land use modules. By the help of these linkages the direct competition of bioenergy with other energy technology options for greenhouse gas (GHG) mitigation, based on economic costs and GHG emissions from bioenergy production, has been taken into account. Our results indicate that dedicated bioenergy crops and biomass residues form a potentially important and cost-effective input into the energy system. At the same time, however, the results differ strongly in terms of deployment rates, feedstock composition and land-use and greenhouse gas implications. The current paper adds to earlier work by specific looking into model differences with respect to the land-use component that could contribute to the noted differences in results, including land cover allocation, land use constraints, energy crop yields, and non-bioenergy land mitigation options modeled. In scenarios without climate change mitigation, bioenergy cropland represents 10–18 % of total cropland by 2100 across the different models, and boosts cropland expansion at the expense of carbon richer ecosystems. Therefore, associated emissions from land-use change and agricultural intensification as a result of bio-energy use range from 14 and 113 Gt CO2-eq cumulatively through 2100. Under climate policy, bioenergy cropland increases to 24–36 % of total cropland by 2100.  相似文献   

8.
The RCP greenhouse gas concentrations and their extensions from 1765 to 2300   总被引：16，自引：2，他引：14  
We present the greenhouse gas concentrations for the Representative Concentration Pathways (RCPs) and their extensions beyond 2100, the Extended Concentration Pathways (ECPs). These projections include all major anthropogenic greenhouse gases and are a result of a multi-year effort to produce new scenarios for climate change research. We combine a suite of atmospheric concentration observations and emissions estimates for greenhouse gases (GHGs) through the historical period (1750?C2005) with harmonized emissions projected by four different Integrated Assessment Models for 2005?C2100. As concentrations are somewhat dependent on the future climate itself (due to climate feedbacks in the carbon and other gas cycles), we emulate median response characteristics of models assessed in the IPCC Fourth Assessment Report using the reduced-complexity carbon cycle climate model MAGICC6. Projected ??best-estimate?? global-mean surface temperature increases (using inter alia a climate sensitivity of 3°C) range from 1.5°C by 2100 for the lowest of the four RCPs, called both RCP3-PD and RCP2.6, to 4.5°C for the highest one, RCP8.5, relative to pre-industrial levels. Beyond 2100, we present the ECPs that are simple extensions of the RCPs, based on the assumption of either smoothly stabilizing concentrations or constant emissions: For example, the lower RCP2.6 pathway represents a strong mitigation scenario and is extended by assuming constant emissions after 2100 (including net negative CO2 emissions), leading to CO2 concentrations returning to 360 ppm by 2300. We also present the GHG concentrations for one supplementary extension, which illustrates the stringent emissions implications of attempting to go back to ECP4.5 concentration levels by 2250 after emissions during the 21st century followed the higher RCP6 scenario. Corresponding radiative forcing values are presented for the RCP and ECPs.  相似文献   

9.
Projections of greenhouse gas (GHG) emissions are critical to enable a better understanding and anticipation of future climate change under different socio-economic conditions and mitigation strategies. The climate projections and scenarios assessed by the Intergovernmental Panel on Climate Change, following the Shared Socioeconomic Pathway (SSP)-Representative Concentration Pathway (RCP) framework, have provided a rich understanding of the constraints and opportunities for policy action. However, the current emissions scenarios lack an explicit treatment of urban emissions within the global context. Given the pace and scale of urbanization, with global urban populations expected to increase from about 4.4 billion today to about 7 billion by 2050, there is an urgent need to fill this knowledge gap. Here, we estimate the share of global GHG emissions driven by urban areas from 1990 to 2100 based on the SSP-RCP framework. The urban consumption-based GHG emissions are presented in five regional aggregates and based on a combination of the urban population share, 2015 urban per capita CO2eq carbon footprint, SSP-based national CO2eq emissions, and recent analysis of urban per capita CO2eq trends. We find that urban areas account for the majority of global GHG emissions in 2015 (61.8%). Moreover, the urban share of global GHG emissions progressively increases into the future, exceeding 80% in some scenarios by the end of the century. The combined urban areas in Asia and Developing Pacific, and Developed Countries account for 65.0% to 73.3% of cumulative urban consumption-based emissions between 2020 and 2100 across the scenarios. Given these dominant roles, we describe the implications for potential urban mitigation in each of the scenario narratives in order to meet the goal of climate neutrality within this century.  相似文献   

10.
Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km2) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected. The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore, a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation, temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale.  相似文献   

11.
There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.  相似文献   

12.
The probability of wind damage in forestry under a changed wind climate   总被引：1，自引：0，他引：1  
We (1) estimated how the possible changes in wind climate due to climatic change may affect the probability of exceeding critical wind speeds (CWS) expected to cause significant wind damage within a forest management unit located in southern Sweden, (2) analysed how the probability of exceeding an approximate CWS as observed in the management unit would change in different regions in Sweden if expecting a similar kind of forested area to occur in different geographical locations. The topography of the management unit was relatively gentle and the forests were dominated by Norway spruce (Picea abies (L.) Karst.). Seven regions across Sweden were selected for comparison of possible future probability of damaging wind speed. The model-system WINDA was modified and used for calculations of the probability of wind damage together with regionally downscaled climate change scenario (CCS) data. In total, two climate scenarios downscaled using the RCAO model for the control period 1961–1990 and four for the period 2071–2100 were used. The CCSs represent fairly central projections on a 100-year time scale in terms of global mean warming. Although there is ambiguity between different CCSs, the results indicated that the present pattern of more windy conditions in southern than in northern Sweden will remain. For most sites the probability of exceeding the CWS from westerly to south-westerly directions was indicated to remain comparatively high and the probability of damaging wind from south-westerly to south-easterly directions was indicated to increase in many places. For southernmost Sweden increasing probability of exceeding the CWS from the north-westerly to south-easterly wind directions were indicated for all but one CCS. The results were discussed with respect to spatial planning in forestry under a changing wind climate.  相似文献   

13.
This paper uses the EMF27 scenarios to explore the role of renewable energy (RE) in climate change mitigation. Currently RE supplies almost 20 % of global electricity demand. Almost all EMF27 mitigation scenarios show a strong increase in renewable power production, with a substantial ramp-up of wind and solar power deployment. In many scenarios, renewables are the most important long-term mitigation option for power supply. Wind energy is competitive even without climate policy, whereas the prospects of solar photovoltaics (PV) are highly contingent on the ambitiousness of climate policy. Bioenergy is an important and versatile energy carrier; however—with the exception of low temperature heat—there is less scope for renewables other than biomass for non-electric energy supply. Despite the important role of wind and solar power in climate change mitigation scenarios with full technology availability, limiting their deployment has a relatively small effect on mitigation costs, if nuclear and carbon capture and storage (CCS)—which can serve as substitutes in low-carbon power supply—are available. Limited bioenergy availability in combination with limited wind and solar power by contrast, results in a more substantial increase in mitigation costs. While a number of robust insights emerge, the results on renewable energy deployment levels vary considerably across the models. An in-depth analysis of a subset of EMF27 reveals substantial differences in modeling approaches and parameter assumptions. To a certain degree, differences in model results can be attributed to different assumptions about technology costs, resource potentials and systems integration.  相似文献   

14.
This study presents a comprehensive assessment of the possible regional climate change over India by using Providing REgional Climates for Impacts Studies (PRECIS), a regional climate model (RCM) developed by Met Office Hadley Centre in the United Kingdom. The lateral boundary data for the simulations were taken from a sub-set of six members sampled from the Hadley Centre’s 17- member Quantified Uncertainty in Model Projections (QUMP) perturbed physics ensemble. The model was run with 25 km × 25 km resolution from the global climate model (GCM) - HadCM3Q at the emission rate of special report on emission scenarios (SRES) A1B scenarios. Based on the model performance, six member ensembles running over a period of 1970-2100 in each experiment were utilized to predict possible range of variations in the future projections for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095) with respect to the baseline period (1975-2005). The analyses concentrated on maximum temperature, minimum temperature and rainfall over the region. For the whole India, the projections of maximum temperature from all the six models showed an increase within the range 2.5°C to 4.4°C by end of the century with respect to the present day climate simulations. The annual rainfall projections from all the six models indicated a general increase in rainfall being within the range 15-24%. Mann-Kendall trend test was run on time series data of temperatures and rainfall for the whole India and the results from some of the ensemble members indicated significant increasing trends. Such high resolution climate change information may be useful for the researchers to study the future impacts of climate change in terms of extreme events like floods and droughts and formulate various adaptation strategies for the society to cope with future climate change.  相似文献   

15.
Climate is one factor that determines the potential range of malaria. As such, climate change may work with or against efforts to bring malaria under control. We developed a model of future climate suitability for stable Plasmodium falciparum malaria transmission in Zimbabwe. Current climate suitability for stable malaria transmission was based on the MARA/ARMA model of climatic constraints on the survival and development of the Anopheles vector and the Plasmodium falciparum malaria parasite. We explored potential future geographic distributions of malaria using 16 projections of climate in 2100. The results suggest that, assuming no future human-imposed constraints on malaria transmission, changes in temperature and precipitation could alter the geographic distribution of malaria in Zimbabwe, with previously unsuitable areas of dense human population becoming suitable for transmission. Among all scenarios, the highlands become more suitable for transmission, while the lowveld and areas with low precipitation show varying degrees of change, depending on climate sensitivity and greenhouse gas emission stabilization scenarios, and depending on the general circulation model used. The methods employed can be used within or across other African countries.  相似文献   

16.
The RCP2.6 emission and concentration pathway is representative of the literature on mitigation scenarios aiming to limit the increase of global mean temperature to 2°C. These scenarios form the low end of the scenario literature in terms of emissions and radiative forcing. They often show negative emissions from energy use in the second half of the 21st century. The RCP2.6 scenario is shown to be technically feasible in the IMAGE integrated assessment modeling framework from a medium emission baseline scenario, assuming full participation of all countries. Cumulative emissions of greenhouse gases from 2010 to 2100 need to be reduced by 70% compared to a baseline scenario, requiring substantial changes in energy use and emissions of non-CO2 gases. These measures (specifically the use of bio-energy and reforestation measures) also have clear consequences for global land use. Based on the RCP2.6 scenario, recommendations for further research on low emission scenarios have been formulated. These include the response of the climate system to a radiative forcing peak, the ability of society to achieve the required emission reduction rates given political and social inertia and the possibilities to further reduce emissions of non-CO2 gases.  相似文献   

17.
Economics of climate change mitigation forest policy scenarios for Ukraine   总被引：1，自引：0，他引：1  
Abstract

This article reveals the contribution of woodland expansion in Ukraine to climate change mitigation policies. The opportunities for climate change mitigation of three policy scenarios: (1) carbon storage in forests, (2) carbon storage and additional wood-for-fuel substitution, and (3) carbon storage with additional sink policy for wood products, are investigated by using a simulation technique, in combination with cost—benefit analysis. The article concludes that the Ukraine's forests and their expansion offer a low-cost opportunity for carbon sequestration. Important factors that influence the results are the discount rate and the time horizon considered in the models. The findings provide evidence that the storage climate change mitigation forest policy scenario is most viable for the country, under the assumptions considered in this research.  相似文献   

18.
Uncertainty Analysis of Climate Change and Policy Response   总被引：1，自引：3，他引：1  
  相似文献   

19.
Biomass is often seen as a key component of future energy systems as it can be used for heat and electricity production, as a transport fuel, and a feedstock for chemicals. Furthermore, it can be used in combination with carbon capture and storage to provide so-called “negative emissions”. At the same time, however, its production will require land, possibly impacting food security, land-based carbon stocks, and other environmental services. Thus, the strategies adopted in the supply, conversion, and use of biomass have a significant impact on its effectiveness as a climate change mitigation measure. We use the IMAGE 3.0 integrated assessment model to project three different global, long term scenarios spanning different socioeconomic futures with varying rates of population growth, economic growth, and technological change, and investigate the role of biomass in meeting strict climate targets. Using these scenarios we highlight different possibilities for biomass supply and demand, and provide insights on the requirements and challenges for the effective use of this resource as a climate change mitigation measure. The results show that in scenarios meeting the 1.5 °C target, biomass could exceed 20% of final energy consumption, or 115–180 EJPrim/yr in 2050. Such a supply of bioenergy can only be achieved without extreme levels land use change if agricultural yields improve significantly and effective land zoning is implemented. Furthermore, the results highlight that strict mitigation targets are contingent on the availability of advanced technologies such as lignocellulosic fuels and carbon capture and storage.  相似文献   

20.
Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data. Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.  相似文献   

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