Number Of Climate Change Scenarios Research Articles

Is Past Variability a Suitable Proxy for Future Change? A Virtual Catchment Experiment

AbstractTo estimate the robustness of hydrologic models under projected future climate change, researchers test transferability between climatically contrasting observed periods. This approach can only assess the performance changes induced by altered precipitation and related environmental dynamics (e.g., greening under wet conditions), since the instrumental record does not contain temperatures or carbon dioxide levels that are similar to future climate change projections. Additionally, there is an inherent assumption that long‐term persistence of changes in precipitation will not further impact catchment response. In this study, we undertake a series of virtual catchment experiments using an ecohydrologic model that simulates dynamic vegetation growth, nutrient cycling, and subsurface hydrology. These experiments explore a number of climate change scenarios. We compare simulations based on persistent altered climate states against simulations designed to represent historical periods with the same precipitation but limited time for ecohydrologic adaptation. We find that persistence of precipitation changes as well as increased temperature and elevated carbon dioxide levels can all substantially impact streamflow under drier future conditions. For wetter future scenarios, simulated differences in the flow regime were smaller, but there was still notable divergence in modeled low flows and other hydrologic variables. The results suggest that historical periods with equivalent precipitation statistics cannot necessarily be used as proxies for future climate change when examining catchment runoff response and/or model performance. The current literature likely underestimates the potential for nonstationarity in hydrologic assessments, especially for drier future scenarios.

Impact of Spatial Aggregation Level of Climate Indicators on a National-Level Selection for Representative Climate Change Scenarios

For sustainable management of water resources, adaptive decisions should be determined considering future climate change. Since decision makers have difficulty in formulating a decision when they should consider a large number of climate change scenarios, selecting a subset of Global Circulation Models (GCM) outputs for climate change impact studies is required. In this study, the Katsavounidis-Kuo-Zhang (KKZ) algorithm was used for representative climate change scenarios selection and a comprehensive analysis has been done through a national-level case study of South Korea. The KKZ algorithm was applied to select a subset of GCMs for each subbasin in South Korea. To evaluate impacts of spatial aggregation level of climate data sets on preserving inter-model variability of hydrologic variables, three different scales (national level, river region level, subbasin level) were tested. It was found that only five GCMs selected by KKZ algorithm can explain almost of whole inter-model variability driven by all the 27 GCMs under Representative Concentration Pathways (RCP) 4.5 and 8.5. Furthermore, a single set of representative GCMs selected for national level was able to explain inter-model variability on almost the whole subbasins. In case of low flow variable, however, use of finer scale of climate data sets was recommended.

An Objective Approach to Select Climate Scenarios when Projecting Species Distribution under Climate Change.

An impressive number of new climate change scenarios have recently become available to assess the ecological impacts of climate change. Among these impacts, shifts in species range analyzed with species distribution models are the most widely studied. Whereas it is widely recognized that the uncertainty in future climatic conditions must be taken into account in impact studies, many assessments of species range shifts still rely on just a few climate change scenarios, often selected arbitrarily. We describe a method to select objectively a subset of climate change scenarios among a large ensemble of available ones. Our k-means clustering approach reduces the number of climate change scenarios needed to project species distributions, while retaining the coverage of uncertainty in future climate conditions. We first show, for three biologically-relevant climatic variables, that a reduced number of six climate change scenarios generates average climatic conditions very close to those obtained from a set of 27 scenarios available before reduction. A case study on potential gains and losses of habitat by three northeastern American tree species shows that potential future species distributions projected from the selected six climate change scenarios are very similar to those obtained from the full set of 27, although with some spatial discrepancies at the edges of species distributions. In contrast, projections based on just a few climate models vary strongly according to the initial choice of climate models. We give clear guidance on how to reduce the number of climate change scenarios while retaining the central tendencies and coverage of uncertainty in future climatic conditions. This should be particularly useful during future climate change impact studies as more than twice as many climate models were reported in the fifth assessment report of IPCC compared to the previous one.

Climate change impacts on transport on the Rhine and Danube: A multimodal approach

This paper measures the potential effects of low water levels on the Rhine and Danube navigation in the context of weather variability and a number of climate change scenarios. A long-term multimodal network transport analysis over the period 2005–2050 is presented; it analyzes the impact of changes on the water depth conditions on transport costs and the modal splits between three competing modes. The results indicate that the impact of climate change until 2050 should be limited.

불확실성을 고려한 기후변화 시나리오의 선정

Going by the research results of the past, of all the uncertainties resulting from the research on climate change, the uncertainty caused by the climate change scenario has the highest degree of uncertainty. Therefore, depending upon what kind of climate change scenario one adopts, the projection of the water resources in the future will differ significantly. As a matter of principle, it is highly recommended to utilize all the GCM scenarios offered by the IPCC. However, this could be considered to be an impractical alternative if a decision has to be made at an action officer's level. Hence, as an alternative, it is deemed necessary to select several scenarios so as to express the possible number of cases to the maximum extent possible. The objective standards in selecting the climate change scenarios have not been properly established and the scenarios have been selected, either at random or subject to the researcher's discretion. In this research, a new scenario selection process, in which it is possible to have the effect of having utilized all the possible scenarios, with using only a few principal scenarios and maintaining some of the uncertainties, has been suggested. In this research, the use of cluster analysis and the selection of a representative scenario in each cluster have efficiently reduced the number of climate change scenarios. In the cluster analysis method, the K-means clustering method, which takes advantage of the statistical features of scenarios has been employed; in the selection of a representative scenario in each cluster, the selection method was analyzed and reviewed and the PDF method was used to select the best scenarios with the closest simulation accuracy and the principal scenarios that is suggested by this research. In the selection of the best scenarios, it has been shown that the GCM scenario which demonstrated high level of simulation accuracy in the past need not necessarily demonstrate the similarly high level of simulation accuracy in the future and various GCM scenarios were selected for the principal scenarios. Secondly, the "Maximum entropy" which can quantify the uncertainties of the climate change scenario has been used to both quantify and compare the uncertainties associated with all the scenarios, best scenarios and the principal scenarios. Comparison has shown that the principal scenarios do maintain and are able to better explain the uncertainties of all the scenarios than the best scenarios. Therefore, through the scenario selection process, it has been proven that the principal scenarios have the effect of having utilized all the scenarios and retaining the uncertainties associated with the climate change to the maximum extent possible, while reducing the number of scenarios at the same time. Lastly, the climate change scenario most suitable for the climate on the Korean peninsula has been suggested. Through the scenario selection process, of all the scenarios found in the 4th IPCC report, principal climate change scenarios, which are suitable for the Korean peninsula and maintain most of the uncertainties, have been suggested. Therefore, it is assessed that the use of the scenario most suitable for the future projection of water resources on the Korean peninsula will be able to provide the projection of the water resources management that maintains more than 70~80% level of uncertainties of all the scenarios.

Shoreline evolution under climate change wave scenarios

This paper investigates changes in shoreline evolution caused by changes in wave climate. In particular, a number of nearshore wave climate scenarios corresponding to a ‘present’ (1961–1990) and a future time-slice (2071–2100) are used to drive a beach evolution model to determine monthly and seasonal statistics. To limit the number of variables, an idealised shoreline segment is adopted. The nearshore wave climate scenarios are generated from wind climate scenarios through point wave hindcast and inshore transformation. The original wind forcing comes from regional climate change model experiments of different resolutions and/or driving global climate models, representing different greenhouse-gas emission scenarios. It corresponds to a location offshore the south central coast of England. Hypothesis tests are applied to map the degree of evidence of future change in wave and shoreline statistics relative to the present. Differential statistics resulting from different global climate models and future emission scenarios are also investigated. Further, simple, fast, and straightforward methods that are capable of accommodating a great number of climate change scenarios with limited data reduction requirements are proposed to tackle the problem under consideration. The results of this study show that there are statistically significant changes in nearshore wave climate conditions and beach alignment between current and future climate scenarios. Changes are most notable during late summer for the medium-high future emission scenario and late winter for the medium-low. Despite frequent disagreement between global climate change models on the statistical significance of a change, all experiments agreed in future seasonal trends. Finally, a point of importance for coastal management, material shoreline changes are generally linked to significant changes in future wave direction rather than wave height.

A modeling approach for investigating climate change impacts on renewable energy utilization

SUMMARY In this study, an integrated community-scale energy model (ICEM) was developed for supporting renewable energy management (REM) systems planning with the consideration of changing climatic conditions. Through quantitatively reflecting interactive relationships among various renewable energy resources under climate change, not only the impacts of climate change on each individual renewable energy but also the combined effects on power-generation sector from renewable energy resources could be incorporated within a general modeling framework. Also, discrete probability levels associated with various climate change impacts on the REM system could be generated. Moreover, the ICEM could facilitate capacity–expansion planning for energy-production facilities within a multi-period and multi-option context in order to reduce energy-shortage risks under a number of climate change scenarios. The generated solutions can be used for examining various decision options that are associated with different probability levels when availabilities of renewable energy resources are affected by the changing climatic conditions. A series of probability levels of hydropower-, wind- and solar-energy availabilities can be integrated into the optimization process. The developed method has been applied to a case of long-term REM planning for three communities. The generated solutions can provide desired energy resource/service allocation and capacity–expansion plans with a minimized system cost, a maximized system reliability and a maximized energy security. Tradeoffs between system costs, renewable energy availabilities and energy-shortage risks can also be tackled with the consideration of climate change, which would have both positive and negative impacts on the system cost, energy supply and greenhouse-gas emission. Copyright © 2011 John Wiley & Sons, Ltd.

Impacts of drought and predicted effects of climate change on fish growth in temperate Australian lakes

AbstractClimate change is expected to negatively impact many freshwater environments due to reductions in stream‐flow and increases in temperature. These conditions, however, can already be found today in areas experiencing significant drought; current observations of species' responses to droughts can be used to make predictions about their future responses to climate change. Using otolith analysis, we recreated golden perch (Macquaria ambigua) growth chronologies from two temperate lake populations in southeastern Australia over a 15‐year period pre‐ and during a supraseasonal drought. We related interannual growth variation to landscape‐scale changes in temperature and hydrological regimes: fish growth declined as water levels in the lakes dropped during the drought, but this effect was offset by increased growth in warmer years. We hypothesize that golden perch are responding to fluctuations in food availability and intraspecific competition related to water level and to an optimization of physiological growth conditions related to increases in growing season length. Based on our analyses, we made predictions of future growth under a number of climate change scenarios that incorporate forecast deviations in stream‐flows and air temperature. Despite climatic models predicting significant declines in future water availability, fish growth may increase due to a disproportionate lengthening of the growing season. As the two lakes are at the limit of the southerly range of golden perch, our results are consistent with previous findings of climate‐change driven latitudinal range shifts in a poleward direction. We discuss assumptions concerning the constancy of ecological interactions into the future that warrant further study. Our research provides a novel application of biochronological analysis that could be used elsewhere to further our knowledge of species responses to changing environments.

The Effect of Climate Change on River Flow and Snow Cover in the NOPEX Area Simulated by a Simple Water Balance Model

Within the next few decades, changes in global temperature and precipitation patterns may appear, especially at high latitudes. A simple monthly water-balance model of the NOPEX basins was developed and used for the purposes of investigating the effects on water availability of changes in climate. Eleven case study catchments were used together with a number of climate change scenarios. The effects of climate change on average annual runoff depended on the ratio of average annual runoff to average annual precipitation, with the greatest sensitivity in the catchments with lowest runoff coefficients. A 20% increase in annual precipitation resulted in an increase in annual runoff ranging from 31% to 51%. The greatest changes in monthly runoff were in winter (from December to March) whereas the smallest changes were found in summer. The time of the highest spring flow changed from April to March. An increase in temperature by 4°C greatly shortened the time of snow cover and the snow accumulation period. The maximum amount of snow during these short winters diminished by 50% for the NOPEX area even with an assumed increase of total precipitation by 20%.

Assessment of the impact of climate change on the growth of Sitka spruce in Scotland

A regression model developed to predict the general yield class (GYC, maximum mean annual volume increment) of Sitka spruce (Piceasitchensis (Bong.) Carrière) from a range of site factors has been used to assess the potential impact of climate warming on the growth of Sitka spruce in Scotland. The model was interfaced with a geographic information system to predict and map the GYC of Sitka spruce across Scotland under a number of climate change scenarios. GYC was predicted to increase by 2.8 m3•ha−1•year−1 per °C rise in temperature when a uniform temperature increase was assumed during the year. A more likely assumption is a greater warming during winter months, and under such a scenario the increase in mean GYC was predicted to be 2.4 m3•ha−1•year−1•°C−1 because of a negative correlation between growth rate and mean winter temperature. When assessed in terms of land area capable of supporting a GYC greater than 18 m3•ha−1•year−1 (a value likely to provide an internal rate of return of approximately 6%), this area was predicted to almost double within the next 20 years with a similar increase occurring during the subsequent 40 years.

The effects of climate changes on aquifer storage and river baseflow

Abstract The effects of changes in climate on aquifer storage and groundwater flow to rivers have been investigated using an idealized representation of the aquifer/river system. The generalized aquifer/river model can incorporate spatial variability in aquifer transmissivity and is applied with parameters characteristic of Chalk and Triassic sandstone aquifers in the United Kingdom, and is also applicable to other aquifers elsewhere. The model is run using historical time series of recharge, estimated from observed rainfall and potential evaporation data, and with climate inputs perturbed according to a number of climate change scenarios. Simulations of baseflow suggest large proportional reductions at low flows from Chalk under high evaporation change scenarios. Simulated baseflow from the slower responding Triassic sandstone aquifer shows more uniform and less severe reductions. The change in hydrological regime is less extreme for the low evaporation change scenario, but remains significant for the Chalk aquifer.