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1.
Knowledge of the likely future wind, wave and surge climate in Liverpool Bay is of importance for coastal flood defence management. We examine a 140-year time series (1960–2100) of wind and wave model projections at the WaveNet buoy location in Liverpool Bay and also of surge model projection at two ports in Liverpool Bay, namely Liverpool and Heysham. To this end we use model projections from the UK Climate Projections 09 (UKCP09) programme. We use a medium emissions scenario ensemble from the HadCM3 climate model sensitivity tests. A continental shelf model (CS3) with ~12 km resolution was used to separately simulate the waves and the surge. The models are forced by hourly wind and pressure data from the Met Office (Hadley Centre) regional climate model (RCM). Swell wave boundary conditions are generated over the full Atlantic using global climate model (GCM) winds. Analysis of significant changes in the statistics over time shows that there is little change in extreme wave and surge conditions in Liverpool Bay. Although there is a slight increase in the severity of the most extreme events, the frequency of extreme wind and wave events is slightly reduced, while the frequency of extreme surge events slightly increases over the 140-year period. From the model projections, we find that the trends in the local wind are directly reflected in the wave field within Liverpool Bay. The trends in the skew surge projections deviate slightly from those in the wind patterns.  相似文献   

2.
We present future fire danger scenarios for the countries bordering the Mediterranean areas of Europe and north Africa building on a multi-model ensemble of state-of-the-art regional climate projections from the EU-funded project ENSEMBLES. Fire danger is estimated using the Canadian Forest Fire Weather Index (FWI) System and a related set of indices. To overcome some of the limitations of ENSEMBLES data for their application on the FWI System—recently highlighted in a previous study by Herrera et al. (Clim Chang 118:827–840, 2013)—we used an optimal proxy variable combination. A robust assessment of future fire danger projections is undertaken by disentangling the climate change signal from the uncertainty derived from the multi-model ensemble, unveiling a positive signal of fire danger potential over large areas of the Mediterranean. The increase in the fire danger signal is accentuated towards the latest part of the transient period, thus pointing to an elevated fire potential in the region with time. The fire-climate links under present and future conditions are further discussed building upon observed climate data and burned area records along a representative climatic gradient within the study region.  相似文献   

3.
Due to inherent limitations in climate models, their output is biased in relation to observed climate and as such does not provide reliable climate projections. In this study, nine methods used to account for biases in daily precipitation are tested. First, cross-validation tests were made using a set of ENSEMBLES regional model simulations to gain insights in the potential performance of the methods in the future climate. The results show that quantile mapping type methods, being able to modify the shape of the precipitation distribution, often outperform other types of methods. Yet, as the performance depends on time of the year, location and part of the distribution considered, it is not possible to distinguish one universally best performing method. In addition, the improvement relative to the projections that would have been obtained assuming unchanged climate is relatively modest, particularly in the early twentyfirst century conditions. Further tests with different method combinations show that the projections could be potentially improved by using several well performing methods in parallel. In the second part of the study, contributions of method and model differences to the overall variation of precipitation projections are assessed. It is shown that although intermodel differences play an important role, uncertainties related to intermethod differences are substantial, particularly in the tails of the distribution. This suggests that method uncertainty should be taken into account when constructing daily precipitation projections, possibly by using several methods in parallel.  相似文献   

4.
Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.  相似文献   

5.
Because of model biases, projections of future climate need to combine model simulations of recent and future climate with information on observed climate. Here, 10 methods for projecting the distribution of daily mean temperatures are compared, using six regional climate change simulations for Europe. Cross validation between the models is used to assess the potential performance of the methods in projecting future climate. Delta change and bias correction type methods show similar cross-validation performance, with methods based on the quantile mapping approach doing best in both groups due to their apparent ability to reduce the errors in the projected time mean temperature change. However, as no single method performs best under all circumstances, the optimal approach might be to use several well-behaving methods in parallel. When applying the various methods to real-world temperature projection for the late 21st century, the largest intermethod differences are found in the tails of the temperature distribution. Although the intermethod variation of the projections is generally smaller than their intermodel variation, it is not negligible. Therefore, it should be preferably included in uncertainty analysis of temperature projections, particularly in applications where the extremes of the distribution are important.  相似文献   

6.
Regional climate projections using climate models commonly use an “all-model” ensemble based on data sets such as the Intergovernmental Panel on Climate Change’s (IPCC) 4th Assessment (AR4). Some regional assessments have omitted models based on specific criteria. We use a criteria based on the capacity of climate models to simulate the observed probability density function calculated using daily data, model-by-model and region-by-region for each of the AR4 models over Australia. We demonstrate that by omitting those climate models with relatively weak skill in simulating the observed probability density functions of maximum and minimum temperature and precipitation, different regional projections are obtained. Differences include: larger increases in the mean maximum and mean minimum temperatures, but smaller increases in the annual maximum and minimum temperatures. There is little impact on mean precipitation but the better models simulate a larger increase in the annual rainfall event combined with a larger decrease in the number of rain days. The weaker models bias the amount of mean warming towards lower increases, bias annual maximum temperatures to excessive warming and bias precipitation such that the amount of the annual rainfall event is under-estimated. We suggest that omitting weak models from regional scale estimates of future climate change helps clarify the nature and scale of the projected impacts of global warming.  相似文献   

7.
We investigate how weather affects the UK’s electricity network, by examining past data of weather-related faults on the transmission and distribution networks. By formalising the current relationship between weather-related faults and weather, we use climate projections from a regional climate model (RCM) to quantitatively assess how the frequency of these faults may change in the future. This study found that the incidences of both lightning and solar heat faults are projected to increase in the future. There is evidence that the conditions that cause flooding faults may increase in the future, but a reduction cannot be ruled out. Due to the uncertainty associated with future wind projections, there is no clear signal associated with the future frequency of wind and gale faults, however snow, sleet and blizzard faults are projected to decrease due to a reduction in the number of snow days.  相似文献   

8.
The effect of climate change on wildfires constitutes a serious concern in fire-prone regions with complex fire behavior such as the Mediterranean. The coarse resolution of future climate projections produced by General Circulation Models (GCMs) prevents their direct use in local climate change studies. Statistical downscaling techniques bridge this gap using empirical models that link the synoptic-scale variables from GCMs to the local variables of interest (using e.g. data from meteorological stations). In this paper, we investigate the application of statistical downscaling methods in the context of wildfire research, focusing in the Canadian Fire Weather Index (FWI), one of the most popular fire danger indices. We target on the Iberian Peninsula and Greece and use historical observations of the FWI meteorological drivers (temperature, humidity, wind and precipitation) in several local stations. In particular, we analyze the performance of the analog method, which is a convenient first choice for this problem since it guarantees physical and spatial consistency of the downscaled variables, regardless of their different statistical properties. First we validate the method in perfect model conditions using ERA-Interim reanalysis data. Overall, not all variables are downscaled with the same accuracy, with the poorest results (with spatially averaged daily correlations below 0.5) obtained for wind, followed by precipitation. Consequently, those FWI components mostly relying on those parameters exhibit the poorest results. However, those deficiencies are compensated in the resulting FWI values due to the overall high performance of temperature and relative humidity. Then, we check the suitability of the method to downscale control projections (20C3M scenario) from a single GCM (the ECHAM5 model) and compute the downscaled future fire danger projections for the transient A1B scenario. In order to detect problems due to non-stationarities related to climate change, we compare the results with those obtained with a Regional Climate Model (RCM) driven by the same GCM. Although both statistical and dynamical projections exhibit a similar pattern of risk increment in the first half of the 21st century, they diverge during the second half of the century. As a conclusion, we advocate caution in the use of projections for this last period, regardless of the regionalization technique applied.  相似文献   

9.
Tourism destinations often require information about climate to assess their climate potential. This can be performed in terms of mean conditions of relevant climatological parameters. For a user-friendly analysis and visualization of climate data relevant for tourism application in Luxembourg, information is prepared based on the facets of climate in tourism. Information on thermal comfort/stress conditions as well as aesthetical and physical parameters is considered. In the present study, relevant and sensible factors were identified and presented. Therefore, physiologically equivalent temperature, precipitation patterns and the Climate-Tourism/Transfer-Information-Scheme are applied. In addition, extreme events relevant for heat stress are analysed based on existing data sets (i.e. heat waves of 2010). Expected climatic conditions for the future are investigated using the projections of two different regional climate models. The results concerning climate change conditions reveal increasing heat stress and sultriness but decreasing cold stress. This information is the basis for an adequate assessment to provide relevant information for different environmental planning issues as well as for the growing tourism sector of Luxembourg.  相似文献   

10.
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   

11.
This integrated study examines the implications of changes in crop water demand and water availability for the reliability of irrigation, taking into account changes in competing municipal and industrial demands, and explores the effectiveness of adaptation options in maintaining reliability. It reports on methods of linking climate change scenarios with hydrologic, agricultural, and planning models to study water availability for agriculture under changing climate conditions, to estimate changes in ecosystem services, and to evaluate adaptation strategies for the water resources and agriculture sectors. The models are applied to major agricultural regions in Argentina, Brazil, China, Hungary, Romania, and the US, using projections of climate change, agricultural production, population, technology, and GDP growth.For most of the relatively water-rich areas studied, there appears to be sufficient water for agriculture given the climate change scenarios tested. Northeastern China suffers from the greatest lack of water availability for agriculture and ecosystem services both in the present and in the climate change projections. Projected runoff in the Danube Basin does not change substantially, although climate change causes shifts in environmental stresses within the region. Northern Argentina's occasional problems in water supply for agriculture under the current climate may be exacerbated and may require investments to relieve future tributary stress. In Southeastern Brazil, future water supply for agriculture appears to be plentiful. Water supply in most of the US Cornbelt is projected to increase in most climate change scenarios, but there is concern for tractability in the spring and water-logging in the summer.Adaptation tests imply that only the Brazil case study area can readily accommodate an expansion of irrigated land under climate change, while the other three areas would suffer decreases in system reliability if irrigation areas were to be expanded. Cultivars are available for agricultural adaptation to the projected changes, but their demand for water may be higher than currently adapted varieties. Thus, even in these relatively water-rich areas, changes in water demand due to climate change effects on agriculture and increased demand from urban growth will require timely improvements in crop cultivars, irrigation and drainage technology, and water management.  相似文献   

12.
Shoreline evolution under climate change wave scenarios   总被引：1，自引：1，他引：0  
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?C1990) and a future time-slice (2071?C2100) 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.  相似文献   

13.
Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.  相似文献   

14.
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   

15.
Spatially resolved climate reconstructions are commonly derived from long instrumental series and proxy data via linear regression based approaches that use the main modes of the climate system. Such reconstructions have been shown to underestimate climate variability and are based upon the assumption that the main modes of climate variability are stationary back in time. Climate models simulate physically consistent climate fields but cannot be taken to represent the real past climate trajectory because of their necessarily simplified scope and chaotic internal variability. Here, we present sensitivity tests of, and a 200-year temperature reconstruction from, the PSR (Proxy Surrogate Reconstruction) method. This method simultaneously capitalizes on the individual strengths of instrumental/proxy data based reconstructions and model simulations by selecting the model states (analogs) that are most similar with proxy/instrumental data available at specific places and specific moments of time. Sensitivity experiments reveal an optimal PSR configuration and indicate that 6,500 simulation years of existing climate models provide a sufficient pool of possible analogs to skillfully reconstruct monthly European temperature fields during the past 200?years. Reconstruction verification based upon only seven instrumental stations indicates potential for extensions back in time using sparse proxy data. Additionally the PSR method allows evaluation of single time series, in this case the homogeneity of instrumental series, by identifying inconsistencies with the reconstructed climate field. We present an updated European temperature reconstruction including newly homogenized instrumental records performed with the computationally efficient PSR method that proves to capture the total variance of the target.  相似文献   

16.
Little research has been done on projecting long-term conflict risks. Such projections are currently neither included in the development of socioeconomic scenarios or climate change impact assessments nor part of global agenda-setting policy processes. In contrast, in other fields of inquiry, long-term projections and scenario studies are established and relevant for both strategical agenda-setting and applied policies. Although making projections of armed conflict risk in response to climate change is surrounded by uncertainty, there are good reasons to further develop such scenario-based projections. In this perspective article we discuss why quantifying implications of climate change for future armed conflict risk is inherently uncertain, but necessary for shaping sustainable future policy agendas. We argue that both quantitative and qualitative projections can have a purpose in future climate change impact assessments and put out the challenges this poses for future research.  相似文献   

17.
It is well accepted within the scientific community that a large ensemble of different projections is required to achieve robust climate change information for a specific region. For this purpose we have compiled a state-of-the-art multi-model multi-scenario ensemble of global and regional precipitation projections. This ensemble combines several global projections from the CMIP3 and CMIP5 databases, along with some recently downscaled regional CORDEX-Africa projections. Altogether daily precipitation data from 77 different climate change projections is analysed; separated into 31 projections for a high and 46 for a low emission scenario. We find a robust indication that, independent of the underlying emission scenario, annual total precipitation amounts over the central African region are not likely to change severely in the future. However some robust changes in precipitation characteristics, like the intensification of heavy rainfall events as well as an increase in the number of dry spells during the rainy season are projected for the future. Further analysis shows that over some regions the results of the climate change assessment clearly depend on the size of the analyzed ensemble. This indicates the need of a “large-enough” ensemble of independent climate projections to allow for a reliable climate change assessment.  相似文献   

18.
Historical increases in agricultural production were achieved predominantly by large increases in agricultural productivity. Intensification of crop and livestock production also plays a key role in future projections of agricultural land use. Here, we assess and discuss projections of crop yields by global agricultural land-use and integrated assessment models. To evaluate these crop yield projections, we compare them to empirical data on attainable yields by employing a linear and plateauing continuation of observed attainable yield trends. While keeping in mind the uncertainties of attainable yields projections and not considering future climate change impacts, we find that, on average for all cereals on the global level, global projected yields by 2050 remain below the attainable yields. This is also true for future pathways with high technological progress and mitigation efforts, indicating that projected yield increases are not overly optimistic, even under systemic transformations. On a regional scale, we find that for developing regions, specifically for sub-Saharan Africa, projected yields stay well below attainable yields, indicating that the large yield gaps which could be closed through improved crop management, may also persist in the future. In OECD countries, in contrast, current yields are already close to attainable yields, and the projections approach or, for some models, even exceed attainable yields by 2050. This observation parallels research suggesting that future progress in attainable yields in developed regions will mainly have to be achieved through new crop varieties or genetic improvements. The models included in this study vary widely in their implementation of yield progress, which are often split into endogenous (crop management) improvements and exogenous (technological) trends. More detail and transparency are needed in these important elements of global yields and land use projections, and this paper discusses possibilities of better aligning agronomic understanding of yield gaps and yield potentials with modelling approaches.  相似文献   

19.
Interest in the impacts of climate change is ever increasing. This is particularly true of the water sector where understanding potential changes in the occurrence of both floods and droughts is important for strategic planning. Climate variability has been shown to have a significant impact on UK climate and accounting for this in future climate change projections is essential to fully anticipate potential future impacts. In this paper a new resampling methodology is developed which includes the variability of both baseline and future precipitation. The resampling methodology is applied to 13 CMIP3 climate models for the 2080s, resulting in an ensemble of monthly precipitation change factors. The change factors are applied to the Eden catchment in eastern Scotland with analysis undertaken for the sensitivity of future river flows to the changes in precipitation. Climate variability is shown to influence the magnitude and direction of change of both precipitation and in turn river flow, which are not apparent without the use of the resampling methodology. The transformation of precipitation changes to river flow changes display a degree of non-linearity due to the catchment’s role in buffering the response. The resampling methodology developed in this paper provides a new technique for creating climate change scenarios which incorporate the important issue of climate variability.  相似文献   

20.
There has been substantial analysis of the possible impact of climate change on water supply, especially with respect to runoff and river flows. Less attention has been given to urban water use. Little is known of the suitability of various water use forecasting models for predicting climate impacts or of the best procedures for assessing this issue. This paper will: (1) demonstrate the feasibility of a scenario approach to describing possible changes in climate, (2) evaluate the IWR-MAIN model as a source of plausible water use forecasts given uncertain future climate, (3) test the effectiveness of conservation and pricing interventions in reversing the postulated effects of climate change, and (4) assess the significance of climate change for future urban water management. Other possible responses to climate change, such as supply augmentation, are not explicitly considered. Using data for the Washington (DC) metropolitan area, the study reveals problems with IWR-MAIN version 5.1 when used for this purpose, but results in a reasonable assessment of the possible water use consequences of climate change. Variation in future water use due to climate uncertainty was found to be moderate compared to other uncertain influences, and well within reach of feasible policy interventions.  相似文献   

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