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Reliable knowledge of the near-future climate change signal of extremes is important for adaptation and mitigation strategies. Especially compound extremes, like heat and drought occurring simultaneously, may have a greater impact on society than their univariate counterparts and have recently become an active field of study. In this paper, we use a 12-member ensemble of high-resolution (7 km) regional climate simulations with the regional climate model COSMO-CLM over central Europe to analyze the climate change signal and its uncertainty for compound heat and drought extremes in summer by two different measures: one describing absolute (i.e., number of exceedances of absolute thresholds like hot days), the other relative (i.e., number of exceedances of time series intrinsic thresholds) compound extreme events. Changes are assessed between a reference period (1971–2000) and a projection period (2021–2050). Our findings show an increase in the number of absolute compound events for the whole investigation area. The change signal of relative extremes is more region-dependent, but there is a strong signal change in the southern and eastern parts of Germany and the neighboring countries. Especially the Czech Republic shows strong change in absolute and relative extreme events. [ABSTRACT FROM AUTHOR]

Given the increasingly severe challenges of global climate change and sustainable development, accurate prediction of daily carbon dioxide (CO 2) emissions has become crucial. However, current research on real-time daily forecasts remains relatively scarce, often limited to annual predictions and point estimates. This paper proposes an innovative integrated model, based on quantile regression and Attention-Bidirectional Long Short-Term Memory (BILSTM), specifically designed for probabilistic prediction of daily carbon emissions. Testing the model on daily carbon emission data from major carbon-emitting countries including China, the United States, India, Russia, and Italy has demonstrated its superior performance across various metrics, including mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), and normalized average width of prediction intervals (PINAW), compared to other benchmark models. This achievement not only highlights the model's effectiveness in predicting daily carbon emissions but also provides policymakers with a powerful tool for real-time monitoring and assessment of carbon emissions. Furthermore , the application of probabilistic forecasting methods enables decision-makers to gain a more comprehensive understanding of the potential range of future carbon emissions, providing strong support for the formulation of targeted policies. [ABSTRACT FROM AUTHOR]

Accurately estimating actual evapotranspiration (ET) across global land surface is one of the key challenges in terrestrial hydrological cycles and energy flux balance studies. Gridded ET products have the potential for application in ungauged basins, but their uncertainties are possibly large and it remains unclear which one is best for a given basin. The water balance (WB) method provides a direct estimate of basin scale ET, but it cannot be used in ungauged basins where streamflow data are unavailable. Here, we first assess the performance of ET from 10 global ET products against WB ET estimates in 43 large river basins. The paper then uses three indirect evaluation methods [Three Cornered Hat (TCH), Arithmetic Average (AA), and Bayesian Three Cornered Hat] to identify the optimal ET products without the need of prior information, and to generate fusion products combining the ET from multiple products. Using the evaluation results derived from the WB method as the reference, the results show that the three methods have great success in identifying poorer products, suggesting that they are useful in filtering poor ET products in applications. However, the ability of such methods in identifying better ET products degrades slightly. The AA fusion product, which combines ET outputs from multiple products, is marginally better than the best single ET product in many of the 43 basins. Because of its simplicity, it could be used to reduce the uncertainty in ET estimates from multiple products for ungauged basins and regions. Key Points: A comprehensive study on reducing the uncertainty in evapotranspiration (ET) estimates from 10 global ET products for application in ungauged river basinsThe indirect evaluation methods without using prior information, particularly Arithmetic Average (AA) and Bayesian Three Cornered Hat can successfully identify the poor ET productsThe AA fusion product, which combines ET outputs from multiple products, is generally marginally better than the best single ET product [ABSTRACT FROM AUTHOR]

ABSTRACT The biases in the Global Circulation Models ( GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree ( RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970-1999. [ABSTRACT FROM AUTHOR]

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971-2000) and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating annual and decadal temperature in the Mekong River Basin from 1950 to 2005. By use of Bayesian multi-model averaging method, the future projection of temperature variation under different scenarios are also analyzed. The results show, the performances of climate model are more accurate in space than time, the model can catch the warming characteristics in the Mekong river Basin, but the accuracy of simulation is not good enough. Bayesian multi-model averaging method can improve the annual and decadal temperature simulation when compared to a single result. The projected temperature in Mekong River will increase by 0.88 °C/100 year, 2.15 °C/100 year and 4.96 °C/100 year for the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, over the twenty-first century. The findings will be beneficial for local people and policy-maker to formulate regional strategies against the potential menaces of warming scenarios. [ABSTRACT FROM AUTHOR]

This paper describes an approach that gives an estimate of the reconstructive skill of a proxy-based palaeoclimatological reconstruction. Uncertainties in proxy data are likely to be reflected in variations in reconstructive skill of the proxy-based climatic reconstructions. The method is based on making an ensemble of reconstructions, providing a probability distribution of each reconstruction estimate. The relative breadth of the distribution for a particular reconstructed value should give an indication of the reconstructive skill. The ensemble reconstruction approach draws on the ensemble prediction system as used for operational weather forecasting. The ensemble reconstruction approach is tested using a recent multiproxy reconstruction of the winter North Atlantic Oscillation (NAO) index. It is shown that the ensemble approach provides a representation of the degree of certainty associated with the reconstructed NAO-index values. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) have been widely used to predict species ranges and their future distribution under climate change scenarios, mostly using only climatic variables. An important factor that is usually neglected, is the habitat of the species that are being modelled. Even when included, it is often considered a fixed factor, but in reality, it is also subjected to changes. In this study, we assessed if this omission can lead to different projected distributions of the species. For this purpose, we applied an ensemble of SDMs, and we projected the distribution of rare bryophyte species in Scotland in the 2050s. Bryophytes are generally very climate-reliant and lend themselves to bioclimatic studies, and we selected species different grades of affinity with blanket bogs, which are threatened by climate change. Blanket bog extension was included in the model as an explanatory variable, and the models were run for three 2050s scenarios: once with the current blanket bog distribution and twice using the blanket bog distribution derived from two bioclimatic models (Lindsay modified and Blanket Bog Tree model), under the same climate change projections. The results showed some differences in the predicted future distribution of those species with a strong relationship with blanket bogs, when habitat changes were accounted for. For example, Sphagnum majus, the species with the highest affinity with blanket bog in our study, was not predicted to change its future distribution when blanket bog is held constant at the current level, but was predicted to lose up to 60% of its current suitable area when the projected loss of blanket bog is included. Our results suggest that adding future habitat changes could improve the reliability of SDMs in the first steps of planning for conservation and restoration. [ABSTRACT FROM AUTHOR]

Seasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting. [ABSTRACT FROM AUTHOR]

An emergent consequence of climate change is its potential impacts on transatlantic flight routes and durations, with studies suggesting that future optimal paths will likely lead to decreased (increased) eastbound (westbound) journey times. However, these earlier works all rely on a class of flight planning algorithms (based on the so‐called 'shooting method') that are fundamentally different to those typically used by the aviation industry (based on the so‐called 'network method'). To help resolve any ambiguities associated with these differences and better align both the academic and operational communities, we have therefore conducted an evaluation of methods of estimating time‐optimal flight routes to relate the result obtained from the shooting method with the result obtained from the network method, using identical data from an ensemble of eight different climate models included within the CMIP5 project. Our findings suggest that, contrary to the existing literature, journey times will likely be shorter, irrespective of direction of travel and season, although the magnitude of this difference is negligible given the typical duration of transatlantic flights. Trajectory prediction (TP) results are largely dependent on the characteristics of the ensemble, their method of generation, the projected climate scenario and the temporal periods considered. Importantly, we conclude the choice of TP models is not a crucial factor in assessing the impact of climate change on flight durations—as long as this sample size (i.e., number of trajectories used in deriving statistics) is large. [ABSTRACT FROM AUTHOR]

Herpetospermum darjeelingense (C.B.Clarke) H. Schaef. & S.S. Renner is a rare cucurbit found in Darjeeling, Himalaya. It is known for its use as food and medicine with possible pharmaceutical applications. Here we assess the current and future habitat suitability of H. darjeelingense in the study area using MaxEnt modeling. In order to obtain accurate results for future models, the ensemble method was used. The current suitable habitat covers only 13% of the study area, while the future models for 2050 and 2070 show zero habitat suitability for the species. This strongly indicates a possible local extinction of the species indicating a need for rapid and decisive conservation efforts. [ABSTRACT FROM AUTHOR]

Climate change is expected to exacerbate the hydrological cycle globally and have a significant impact on water resources. The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report states that observed and projected increases in both temperature and precipitation variability are the main reasons for projected climate change impacts on natural water resources. The examination of meteorological variables of the region, especially when agriculture is rainfall dependent, is very essential to formulate feasible adaptation strategies. As a result, using CORDEX-SA (Coordinated Regional Downscaling Experiment-South Asia) rainfall data (2021 to 2050), trend analysis was used to examine variations in rainfall data in the Kokkarne catchment of the Seetha river basin. Regression analysis was used to identify the season-wise rainfall trend. Annual, Summer, Monsoon, and Winter rainfall have depicted increasing trends with a rate of 2.46, 1.21, 2.77, and 0.009 mm per year respectively. The post-monsoon rainfall has projected a declining trend with a rate of -1.54 mm per year. Hence it is recommended that the designed strategies in the agricultural sector have to take the increasing, decreasing, and erratic nature of the trend of rainfall into consideration. Further considering the use of a Multi-Model Ensemble (MME) is reducing the SD and CV of rainfall data by 862 mm and 48.5% respectively. 87% of annual rainfall is contributed by monsoon season only with a Standard deviation of 424.4 mm and CV of 12%. [ABSTRACT FROM AUTHOR]

A multiparameter ensemble generated by four configurations of the regional model RegCM4 using different cumulus parameterizations and driven by the HadGEM2‐ES global model is used to project changes in the Caribbean wind field, with the focus on the Caribbean Low‐Level Jet (CLLJ). Two scenarios are considered, the RCP4.5 and RCP8.5. The CLLJ shows a strengthening (weakening) during the summer (winter) months compared to present day conditions in both a near future (2020–2049) and far future (2070–2099) time slice, with an eastward and northward expansion (contraction) in the core region. The warmer conditions produce an increase in specific humidity and an intensified moisture flux from the lower to the middle atmosphere in correspondence of the jet intensification. This occurs as a result of an expansion (contraction) of the North Atlantic Subtropical High of about +2 hPa (−2 hPa) into the Caribbean. All the changes are statistically significant at the 95% confidence level with a consensus across all the ensemble members. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are efficient tools for modeling species geographic distribution under climate change scenarios. Due to differences among predictions of these models, their results are combined using consensus methods to form an ensemble model. This paper provides an optimal combination of the common SDMs according to accuracy and correlation to model the climatic suitability of Quercus brantii in the west of Iran and projects it into the years 2050 and 2070. This is done using 1000 samples of the species presence and absence, 4 bioclimatic variables related to temperature and precipitation, and 10 modeling algorithms. An ensemble combination of Global Climate Models (GCMs) and 4 optimistic and pessimistic greenhouse-gas emissions scenarios were utilized to identify the climatically suitable areas in the years 2050 and 2070. These models were combined using three common statistics, including mean, median, and weighted mean. The predictive accuracies of the single-models and the consensus methods were assessed using the area under the curve (AUC) metric that validates the acceptable performance of the 9 out of the 10 models studied. Applying the genetic algorithm, the best combination of the models was selected including 4 algorithms with accuracy and correlation equal 0.95 and 0.30 respectively. The results show that the Random Forest (RF) model causes less error in the ensemble model and also compensates other models' errors more. Projections into the years 2050 and 2070 showed that in both time periods and under all scenarios, changes will occur in the spatial distribution of this species, and the most severe one would be a 55.6% loss under the most pessimistic scenario in 2070. • Modeling climate change effects on Zagros forests by species distribution models. • Using the genetic algorithm to find the optimum combination of models. • Ensemble modeling approach provides more accurate predictions. • Suitable climatic habitats of the species will decrease over time. [ABSTRACT FROM AUTHOR]

Turbulence is one of the major weather hazards to aviation. Studies have shown that clear‐air turbulence may well occur more frequently with future climate change. Currently the two World Area Forecast Centres use deterministic models to generate forecasts of turbulence. It has been shown that the use of multi‐model ensembles can lead to more skilful turbulence forecasts. It has also been shown that the combination of turbulence diagnostics can also produce more skilful forecasts using deterministic models. This study puts the two approaches together to expand the range of diagnostics to include predictors of both convective and mountain wave turbulence, in addition to clear‐air turbulence, using two ensemble model systems. Results from a 12 month global trial from September 2016 to August 2017 show the increased skill and economic value of including a wider range of diagnostics in a multi‐diagnostic multi‐model ensemble. [ABSTRACT FROM AUTHOR]

Climate change is known as one of the key challenges of the 21st century for sustainable agricultural development over Southwest Asia. However, not much is known about the way that climate changes might affect precipitation, temperature, and climate zones as their consequence over Southwest Asia. Here we have analysed probable changes in modified Thornthwaite climate zones by using monthly temperature and precipitation data from 320 meteorological stations during the historical period of 1971–2015, as well as the future projections of 17 GCMs (General Circulation Models) outputs under the RCP4.5 and RCP8.5 scenarios in two future periods, 2041–2070 and 2071–2100. The CMIP5 climate projections were bias corrected using the correction factors derived in the historical period and applied to simulate future climatic conditions over Southwest Asia. Results derived from multi model ensemble projections of precipitation and temperature indicated that the regional mean annual temperature by the future period of 2071–2100 is expected to increase by 2.4 and 3.8°C, under RCP4.5 and RCP8.5, respectively. Also, except for the southern and eastern parts of the Arabian Peninsula, by 2071–2100, the mean annual precipitation would significantly decrease by on average 6.2 and 7.5%, under RCP4.5 and RCP8.5, respectively. Computing climate types based on averages from three climatic periods (1971–2015, 2041–2070, and 2071–2100) showed that Southwest Asia tends to become warmer and dryer. For example, by the end of the 21st century, the percentage of the area occupied by the torrid‐arid climate type (which already covers 13.8% of the total area) would be 29.3 and 36.4% under RCP4.5 and RCP8.5, respectively. These changes are important because they would definitely have profound ecological, hydrologic, and social consequences and may affect the basic components of sustainable agricultural systems like crop and livestock productions. [ABSTRACT FROM AUTHOR]

This study analyzes the uncertainty of seasonal (winter and summer) precipitation extremes as simulated by a recent version of the Canadian Regional Climate Model (CRCM) using 16 simulations (1961-1990), considering four sources of uncertainty from: (a) the domain size, (b) the driving Atmosphere-Ocean Global Climate Models (AOGCM), (c) the ensemble member for a given AOGCM and (d) the internal variability of the CRCM. These 16 simulations are driven by 2 AOGCMs (i.e. CGCM3, members 4 and 5, and ECHAM5, members 1 and 2), and one set of re-analysis products (i.e. ERA40), using two domain sizes (AMNO, covering all North America and QC, a smaller domain centred over the Province of Québec). In addition to the mean seasonal precipitation, three seasonal indices are used to characterize different types of variability and extremes of precipitation: the number of wet days, the maximum number of consecutive dry days, and the 95th percentile of daily precipitation. Results show that largest source of uncertainty in summer comes from the AOGCM selection and the choice of domain size, followed by the choice of the member for a given AOGCM. In winter, the choice of the member becomes more important than the choice of the domain size. Simulated variance sensitivity is greater in winter than in summer, highlighting the importance of the large-scale circulation from the boundary conditions. The study confirms a higher uncertainty in the simulated heavy rainfall than the one in the mean precipitation, with some regions along the Great Lakes-St-Lawrence Valley exhibiting a systematic higher uncertainty value. [ABSTRACT FROM AUTHOR]

Future changes in crop evapotranspiration (ETc) are of interest to water management stakeholders. However, long-term projections are complex and merit further investigation due to uncertainties in climate data, differential responses of crops to climate and elevated atmospheric CO2, and adaptive agricultural management. We conducted factor-control simulation experiments using the process-based CropSyst model and investigated the contribution of each of these factors. Five major irrigated crops in the Columbia Basin Project area of the USA Pacific Northwest were selected as a case study and fifteen general circulation models (GCM) under two representative concentration pathways (RCP) were used as the climate forcing. Results indicated a wide range in ETc change, depending on the time frame, crop type, planting dates, and CO2 assumptions. Under the 2090s RCP8.5 scenario, ETc changes were crop-specific: +14.3% (alfalfa), +8.1% (potato), −5.1% (dry bean), −8.1% (corn), and −12.5% (spring wheat). Future elevated CO2 concentrations decreased ETc for all crops while earlier planting increased ETc for all crops except spring wheat. Changes in reference ET (ETo) only partially explains changes in ETc because crop responses are an important modulating factor; therefore, caution must be exercised in interpreting ETo changes as a proxy for ETc changes. [ABSTRACT FROM AUTHOR]

ABSTRACT We consider the impact of climate change on the wind energy resource of Ireland using an ensemble of regional climate model (RCM) simulations. The RCM used in this work is the Consortium for Small-scale Modelling-climate limited-area modelling (COSMO-CLM) model. The COSMO-CLM model was evaluated by performing simulations of the past Irish climate, driven by European Centre for Medium-Range Weather Forecasts ERA-40 data, and comparing the output with observations. For the investigation of the influence of the future climate under different climate scenarios, the Max Planck Institute's global climate model, ECHAM5, was used to drive the COSMO-CLM model. Simulations are run for a control period 1961-2000 and future period 2021-2060. To add to the number of ensemble members, the control and future simulations were driven by different realizations of the ECHAM5 data. The future climate was simulated using the Intergovernmental Panel on Climate Change emission scenarios, A1B and B1. The research was undertaken to consolidate, and as a continuation of, similar research using the Rossby Centre's RCA3 RCM to investigate the effects of climate change on the future wind energy resource of Ireland. The COSMO-CLM projections outlined in this study agree with the RCA3 projections, with both showing substantial increases in 60 m wind speed over Ireland during winter and decreases during summer. The projected changes of both studies were found to be statistically significant over most of Ireland. The agreement of the COSMO-CLM and RCA3 simulation results increases our confidence in the robustness of the projections. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Changes over the twentieth century in seasonal mean potential predictability (PP) of global precipitation, 200 hPa height and land surface temperature are examined by using 100-member ensemble. The ensemble simulations have been conducted by using an intermediate complexity atmospheric general circulation model of the International Center for Theoretical Physics, Italy. Using the Hadley Centre sea surface temperature (SST) dataset on a 1° grid, two 31 year periods of 1920-1950 and 1970-2000 are separated to distinguish the periods of low and high SST variability, respectively. The standard deviation values averaged for the ('Niño-3.4'; 5°S-5°N, 170°W-120°W) region are 0.71 and 1.15 °C, for the periods of low and high SST variability, respectively, with a percentage change of 62 % during December-January-February (DJF). The leading eigenvector and the associated principal component time series, also indicate that the amplitude of SST variations have positive trend since 1920s to recent years, particularly over the El Niño Southern Oscillation (ENSO) region. Our hypothesis states that the increase in SST variability has increased the PP for precipitation, 200 hPa height and land surface temperature during the DJF. The analysis of signal and noise shows that the signal-to-noise (S/N) ratio is much increased over most of the globe, particularly over the tropics and subtropics for DJF precipitation. This occurs because of a larger increase in the signal and at the same time a reduction in the noise, over most of the tropical areas. For 200 hPa height, the S/N ratio over the Pacific North American (PNA) region is increasing more than that for the other extratropical regions, because of a larger percentage increase in the signal and only a small increase in noise. It is also found that the increase in seasonal mean transient signal over the PNA region is 50 %, while increase in the noise is only 12 %, during the high SST variability period, which indicates that the increase in signal is more than the noise. For DJF land surface temperature, the perfect model notion is utilized to confirm the changes in PP during the low and high SST variability periods. The correlation between the perfect model and the other members clearly reveal that the seasonal mean PP changed. In particular, the PP for the 31 years period of 1970-2000 is higher than that for the 31 years period of 1920-1950. The land surface temperature PP is increased in northern and southern Africa, central Europe, southern South America, eastern United States and over Canada. The increase of the signal and hence the seasonal mean PP is coincides with an increase in tropical Pacific SST variability, particularly in the ENSO region. [ABSTRACT FROM AUTHOR]

ABSTRACT Through the use of the climatic classification of Köppen-Trewartha (K-T), the ability to reproduce the current climate of Europe has been shown for an ensemble of 15 regional climate model simulations nested in six global climate models. Depending on the simulation, between 55.4 and 81.3% of the grid points are in agreement with observations regarding the location of climate types in current climate simulations (1971-2000). In this respect, the result of the ensemble of 15 simulations is better than that of any individual model, with 83.5% of the grid points in agreement with observations. K-T classification has also been used to analyse the projected climate change over the 21st century under the SRES-A1B emissions scenario. It was found that 22.3% of the grid points in the domain change their climate by the period 2021-2050 compared to current climate and 48.1% change by 2061-2090. The climate shifts affecting the biggest extensions are projected in Central Europe and Fennoscandia, but other smaller areas suffer more intense changes which potentially are more dangerous to vegetation and ecosystems. Generally, these changes occur at a sustained rate throughout the century, reaching speeds of up to 90 × 103 km2 decade−1 in the retreat or expansion of some climates. [ABSTRACT FROM AUTHOR]

Future climate change projections are constructed from simulated numerical output from a small set of global climate models-samples of opportunity known as multi-model ensembles. Climate models do not produce probabilities, nor are they perfect representations of the real climate, and there are complex inter-relationships due to shared model features. This creates interesting statistical challenges for making inference about the real climate. These issues were the focus of discussions at an Isaac Newton Institute workshop on probabilistic prediction of climate change held at the University of Exeter on 20-23 September 2010. This article presents a summary of the issues discussed between the statisticians, mathematicians, and climate scientists present at the workshop. In addition, we also report the discussion that took place on how to define the concept of climate. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021-2050 and the 1961-1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM × GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021-2050 response which shows a similar pattern to the one obtained for 2071-2100 in PRUDENCE. The uncertainty about precipitation prevents any quantitative assessment on the response at grid point level for the 2021-2050 period. One can however see, as in PRUDENCE, a positive response in winter (more rain in the scenario than in the reference) in northern Europe and a negative summer response in southern Europe. [ABSTRACT FROM AUTHOR]

The Climate Forecast System Reanalysis (CFSR, Saha et al. in Bull Am Meteor Soc 91:1015-1057, ) is the latest global reanalysis from the National Centers of Environmental Prediction (NCEP). In this study, we compare the CFSR tropospheric analyses to two ensembles of analyses. The first ensemble consists of 12 h analyses from various operational analyses for the year 2007. This ensemble shows how well the CFSR analyses can capture the daily variability. The second ensemble consists of monthly means from the available reanalyses from the years 1979 to 2009 which is used to examine the trends. With the 2007 ensemble, we find that the CFSR captures the daily variability in 2007 better than the older reanalyses and is comparable to the operational analyses. With the ensemble of monthly means, the CFSR is often the outlier. The CFSR shows a strong warming trend in the tropics which is not seen in the observations or other reanalyses. [ABSTRACT FROM AUTHOR]

Intrinsic variability (IV) in regional climate models (RCMs) is often assumed to be small because at climatological timescales, the model solutions tend to be dominated by the model's lateral boundary conditions. Recent studies have indicated that this IV may actually be large in certain instances for some variables. Direct interpretation of anomalies from RCM sensitivity studies relies on the assumption that differences between model simulations are entirely due to a physical forcing. However, if IV is as large or larger than the physical signal, then this assumption is violated. Using a 20 member ensemble of RCM simulations, we verify that IV of precipitation within a RCM can be large enough to violate the sensitivity study assumption, and we show that generating ensembles of simulations can help reduce the level of IV. We also present two indicators that can rule out the influence of IV when it is ambiguous whether anomalies within a sensitivity study are due to the sensitivity perturbation or whether they are due to IV. [ABSTRACT FROM AUTHOR]

The use of radiative kernels to diagnose climate feedbacks is a recent development that may be applied to existing climate change simulations. We apply the radiative kernel technique to transient simulations from a multi-thousand member perturbed physics ensemble of coupled atmosphere-ocean general circulation models, comparing distributions of model feedbacks with those taken from the CMIP-3 multi GCM ensemble. Although the range of clear sky longwave feedbacks in the perturbed physics ensemble is similar to that seen in the multi-GCM ensemble, the kernel technique underestimates the net clear-sky feedbacks (or the radiative forcing) in some perturbed models with significantly altered humidity distributions. In addition, the compensating relationship between global mean atmospheric lapse rate feedback and water vapor feedback is found to hold in the perturbed physics ensemble, but large differences in relative humidity distributions in the ensemble prevent the compensation from holding at a regional scale. Both ensembles show a similar range of response of global mean net cloud feedback, but the mean of the perturbed physics ensemble is shifted towards more positive values such that none of the perturbed models exhibit a net negative cloud feedback. The perturbed physics ensemble contains fewer models with strong negative shortwave cloud feedbacks and has stronger compensating positive longwave feedbacks. A principal component analysis used to identify dominant modes of feedback variation reveals that the perturbed physics ensemble produces very different modes of climate response to the multi-model ensemble, suggesting that one may not be used as an analog for the other in estimates of uncertainty in future response. Whereas in the multi-model ensemble, the first order variation in cloud feedbacks shows compensation between longwave and shortwave components, in the perturbed physics ensemble the shortwave feedbacks are uncompensated, possibly explaining the larger range of climate sensitivities observed in the perturbed simulations. Regression analysis suggests that the parameters governing cloud formation, convection strength and ice fall speed are the most significant in altering climate feedbacks. Perturbations of oceanic and sulfur cycle parameters have relatively little effect on the atmospheric feedbacks diagnosed by the kernel technique. [ABSTRACT FROM AUTHOR]

The article analyzes the impact of climate changes on extreme seasonal precipitation in Great Britain. It has been proven that enhanced greenhouse conditions attributed to climatic and hydrologic extremes have largest impact on the biodiversity. Increased precipitation and enhanced variability offer the people a hard time coping with its changing intensity and occurrences. Results of the assessment of global and regional climate model simulations and future projections of extreme precipitation show that the trend is primarily dependent on climate changes.

We discuss equilibrium changes in daily extreme surface air temperature and precipitation events in response to doubled atmospheric CO2, simulated in an ensemble of 53 versions of HadSM3, consisting of the HadAM3 atmospheric general circulation model (GCM) coupled to a mixed layer ocean. By virtue of its size and design, the ensemble, which samples uncertainty arising from the parameterisation of atmospheric physical processes and the effects of natural variability, provides a first opportunity to quantify the robustness of predictions of changes in extremes obtained from GCM simulations. Changes in extremes are quantified by calculating the frequency of exceedance of a fixed threshold in the 2 × CO2 simulation relative to the 1 × CO2 simulation. The ensemble-mean value of this relative frequency provides a best estimate of the expected change while the range of values across the ensemble provides a measure of the associated uncertainty. For example, when the extreme threshold is defined as the 99th percentile of the 1 × CO2 distribution, the global-mean ensemble-mean relative frequency of extremely warm days is found to be 20 in January, and 28 in July, implying that events occurring on one day per hundred under present day conditions would typically occur on 20–30 days per hundred under 2 × CO2 conditons. However the ensemble range in the relative frequency is of similar magnitude to the ensemble-mean value, indicating considerable uncertainty in the magnitude of the increase. The relative frequencies in response to doubled CO2 become smaller as the threshold used to define the extreme event is reduced. For one variable (July maximum daily temperature) we investigate this simulated variation with threshold, showing that it can be quite well reproduced by assuming the response to doubling CO2 to be characterised simply as a uniform shift of a Gaussian distribution. Nevertheless, doubling CO2 does lead to changes in the shape of the daily distributions for both temperature and precipitation, but the effect of these changes on the relative frequency of extreme events is generally larger for precipitation. For example, around one-fifth of the globe exhibits ensemble-mean decreases in time-averaged precipitation accompanied by increases in the frequency of extremely wet days. The ensemble range of changes in precipitation extremes (relative to the ensemble mean of the changes) is typically larger than for temperature extremes, indicating greater uncertainty in the precipitation changes. In the global average, extremely wet days are predicted to become twice as common under 2 × CO2 conditions. We also consider changes in extreme seasons, finding that simulated increases in the frequency of extremely warm or wet seasons under 2 × CO2 are almost everywhere greater than the corresponding increase in daily extremes. The smaller increases in the frequency of daily extremes is explained by the influence of day-to-day weather variability which inflates the variance of daily distributions compared to their seasonal counterparts. [ABSTRACT FROM AUTHOR]

This study characterizes the future changes in extreme rainfall and air temperature in the Mono river basin where the main economic activity is weather dependent and local populations are highly vulnerable to natural hazards, including flood inundations. Daily precipitation and temperature from observational datasets and Regional Climate Models (RCMs) output from REMO, RegCM, HadRM3, and RCA were used to analyze climatic variations in space and time, and fit a GEV model to investigate the extreme rainfalls and their return periods. The results indicate that the realism of the simulated climate in this domain is mainly controlled by the choice of the RCMs. These RCMs projected a 1 to 1.5 °C temperature increase by 2050 while the projected trends for cumulated precipitation are null or very moderate and diverge among models. Contrasting results were obtained for the intense rainfall events, with RegCM and HadRM3 pointing to a significant increase in the intensity of extreme rainfall events. The GEV model is well suited for the prediction of heavy rainfall events although there are uncertainties beyond the 90th percentile. The annual maxima of daily precipitation will also increase by 2050 and could be of benefit to the ecosystem services and socioeconomic activities in the Mono river basin but could also be a threat. [ABSTRACT FROM AUTHOR]

An ensemble of regional climate change scenarios for the North Sea is validated and analyzed. Five Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs) using three different Representative Concentration Pathways (RCPs) have been downscaled with the coupled atmosphere–ice–ocean model RCA4-NEMO. Validation of sea surface temperature (SST) against different datasets suggests that the model results are well within the spread of observational datasets. The ensemble mean SST with a bias of less than 1 ∘ C is the solution that fits the observations best and underlines the importance of ensemble modeling. The exchange of momentum, heat, and freshwater between atmosphere and ocean in the regional, coupled model compares well with available datasets. The climatological seasonal cycles of these fluxes are within the 95% confidence limits of the datasets. Towards the end of the 21st century the projected North Sea SST increases by 1.5 ∘ C (RCP 2.6), 2 ∘ C (RCP 4.5), and 4 ∘ C (RCP 8.5), respectively. Under this change the North Sea develops a specific pattern of the climate change signal for the air–sea temperature difference and latent heat flux in the RCP 4.5 and 8.5 scenarios. In the RCP 8.5 scenario the amplitude of the spatial heat flux anomaly increases to 5 W/m 2 at the end of the century. Different hypotheses are discussed that could contribute to the spatially non-uniform change in air–sea interaction. The most likely cause for an increased latent heat loss in the central western North Sea is a drier atmosphere towards the end of the century. Drier air in the lee of the British Isles affects the balance of the surface heat budget of the North Sea. This effect is an example of how regional characteristics modulate global climate change. For climate change projections on regional scales it is important to resolve processes and feedbacks at regional scales. [ABSTRACT FROM AUTHOR]

A Coupled Model Intercomparison Project Phase 5 (CMIP5)-derived single-forcing, single-model, and single-scenario dynamic wind-wave climate ensemble is presented, and its historic period (1979–2005) performance in representing the present wave climate is evaluated. A single global climate model (GCM)-forcing wave climate ensemble was produced with the goal of reducing the inter GCM variability inherent in using a multi-forcing approach for the same wave model. Seven CMIP5 EC-Earth ensemble runs were used to force seven WAM wave model realizations, while future wave climate simulations, not analyzed here, were produced using a high-emission representative concentration pathway 8.5 (RCP8.5) set-up. The wave climate ensemble's historic period was extensively compared against a set of 72 in situ wave-height observations, as well as to ERA-Interim reanalysis and Climate Forecast System Reanalysis (CFSR) hindcast. The agreement between the wave climate ensemble and the in situ measurements and reanalysis of mean and extreme wave heights, mean wave periods, and mean wave directions was good, in line with previous studies or even better in some areas of the global ocean, namely in the extratropical latitudes. These results give a good degree of confidence in the ability of the ensemble to simulate a realistic climate change signal. [ABSTRACT FROM AUTHOR]