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This study considers the large uncertainty in projected changes in local precipitation. It aims to map, and begin to understand, the relative roles of uncertain modelling and natural variability, using 20-year mean data from four perturbed physics or multi-model ensembles. The largest-280-member-ensemble illustrates a rich pattern in the varying contribution of modelling uncertainty, with similar features found using a CMIP3 ensemble (despite its limited sample size, which restricts it value in this context). The contribution of modelling uncertainty to the total uncertainty in local precipitation change is found to be highest in the deep tropics, particularly over South America, Africa, the east and central Pacific, and the Atlantic. In the moist maritime tropics, the highly uncertain modelling of sea-surface temperature changes is transmitted to a large uncertain modelling of local rainfall changes. Over tropical land and summer mid-latitude continents (and to a lesser extent, the tropical oceans), uncertain modelling of atmospheric processes, land surface processes and the terrestrial carbon cycle all appear to play an additional substantial role in driving the uncertainty of local rainfall changes. In polar regions, inter-model variability of anomalous sea ice drives an uncertain precipitation response, particularly in winter. In all these regions, there is therefore the potential to reduce the uncertainty of local precipitation changes through targeted model improvements and observational constraints. In contrast, over much of the arid subtropical and mid-latitude oceans, over Australia, and over the Sahara in winter, internal atmospheric variability dominates the uncertainty in projected precipitation changes. Here, model improvements and observational constraints will have little impact on the uncertainty of time means shorter than at least 20 years. Last, a supplementary application of the metric developed here is that it can be interpreted as a measure of the agreement amongst models of their projected local precipitation change. Results differ from, but are complementary to, those of the more usual approach. [ABSTRACT FROM AUTHOR]

We assess the ability of individual models (single-model ensembles) and the multi-model ensemble (MME) in the European Union-funded ENSEMBLES project to simulate the intraseasonal oscillations (ISOs; specifically in 10-20-day and 30-50-day frequency bands) of the Indian summer monsoon rainfall (ISMR) over the Western Ghats (WG) and the Bay of Bengal (BoB), respectively. This assessment is made on the basis of the dynamical linkages identified from the analysis of observations in a companion study to this work. In general, all models show reasonable skill in simulating the active and break cycles of the 30-50-day ISOs over the Indian summer monsoon region. This skill is closely associated with the proper reproduction of both the northward propagation of the intertropical convergence zone (ITCZ) and the variations of monsoon circulation in this band. However, the models do not manage to correctly simulate the eastward propagation of the 30-50-day ISOs in the western/central tropical Pacific and the eastward extension of the ITCZ in a northwest to southeast tilt. This limitation is closely associated with a limited capacity of models to accurately reproduce the magnitudes of intraseasonal anomalies of both the ITCZ in the Asian tropical summer monsoon regions and trade winds in the tropical Pacific. Poor reproduction of the activity of the western Pacific subtropical high on intraseasonal time scales also amplify this limitation. Conversely, the models make good reproduction of the WG 10-20-day ISOs. This success is closely related to good performance of the models in the representation of the northward propagation of the ITCZ, which is partially promoted by local air-sea interactions in the Indian Ocean in this higher-frequency band. Although the feature of westward propagation is generally represented in the simulated BoB 10-20-day ISOs, the air-sea interactions in the Indian Ocean are spuriously active in the models. This leads to active WG rainfall, which is not present in the observed BoB 10-20-day ISOs. Further analysis indicates that the intraseasonal variability of the ISMR is generally underrepresented in the simulations. Skill of the MME in seasonal ISMR forecasting is strongly dependent on individual model performance. Therefore, in order to improve the model skill with respect to seasonal ISMR forecasting, we suggest it is necessary to better represent the robust dynamical links between the ISOs and the relevant circulation variations, as well as the proportion of intraseasonal variability in the individual models. [ABSTRACT FROM AUTHOR]

The performance of several state-of-the-art climate model ensembles, including two multi-model ensembles (MMEs) and four structurally different (perturbed parameter) single model ensembles (SMEs), are investigated for the first time using the rank histogram approach. In this method, the reliability of a model ensemble is evaluated from the point of view of whether the observations can be regarded as being sampled from the ensemble. Our analysis reveals that, in the MMEs, the climate variables we investigated are broadly reliable on the global scale, with a tendency towards overdispersion. On the other hand, in the SMEs, the reliability differs depending on the ensemble and variable field considered. In general, the mean state and historical trend of surface air temperature, and mean state of precipitation are reliable in the SMEs. However, variables such as sea level pressure or top-of-atmosphere clear-sky shortwave radiation do not cover a sufficiently wide range in some. It is not possible to assess whether this is a fundamental feature of SMEs generated with particular model, or a consequence of the algorithm used to select and perturb the values of the parameters. As under-dispersion is a potentially more serious issue when using ensembles to make projections, we recommend the application of rank histograms to assess reliability when designing and running perturbed physics SMEs. [ABSTRACT FROM AUTHOR]

Annual maxima (AM) series of precipitation from 15 simulations of the North American Regional Climate Change Assessment Program (NARCCAP) have been analysed for gridpoints covering Canada and the northern part of United States. The NARCCAP Regional Climate Models' simulations have been classified into the following three groups based on the driving data used at the RCMs boundaries: (1) NCEP (6 simulations); (2) GCM-historical (5 simulations); and (3) GCM-future (4 simulations). Historical simulations are representative of the 1968-2000 period while future simulations cover the 2041-2070 period. A reference common grid has been defined to ease the comparison. Multi-model average intensities of AM precipitation of 6-, 12-, 24-, 72-, and 120-h for 2-, 5-, 10-, and 20-year return periods have been estimated for each simulation group. Comparison of results from NCEP and GCM-historical groups shows good overall agreement in terms of spatial distribution of AM intensities. Comparison of GCM-future and GCM-historical groups clearly shows widespread increases with median relative changes across all gridpoints ranging from 12 to 18% depending on durations and return periods. Fourteen Canadian climatic regions have been used to define regional projections and average regional changes in intense precipitation have been estimated for each duration and return period. Uncertainties on these regional values, resulting from inter-model variability, were also estimated. Results suggest that inland regions (e.g. Ontario and more specifically Southern Ontario, the Prairies, Southern Quebec) will experience the largest relative increases in AM intensities while coastal regions (e.g. Atlantic Provinces and the West Coast) will experience the smallest ones. These projections are most valuable inputs for the assessment of future impact of climate change on water infrastructures and the development of more efficient adaptation strategies. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Abstract: More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting. [Copyright &y& Elsevier]

Future change in precipitation intensity of East Asian summer monsoon is investigated using the present-day climate simulations (1991-2000) and the future climate simulations (2091-2100, A1B emission scenario) by the Couple Model Intercomparison Project 3 (CMIP3) models. Target period is the month from June to July which is the main part of the rainy season over Japan and Korea. In the present-day climate simulations, we have quantitatively evaluated model's reproducibility of precipitation climatology and Simple Daily precipitation Intensity Index (SDII), using skill S proposed by Taylor (J Geophys Res 106:7183-7192, ). Models with higher reproducibility of precipitation climatology tend to show higher reproducibility of precipitation intensity. In the future climate simulations, simple Multi-Model Ensemble (MME) average using all models show the increase of precipitation intensity over almost all regions of East Asia. Introduction of S as weighting factor does not make large difference in the geographical distribution of precipitation intensity change. In case of MME using five best models, the geographical pattern of precipitation intensity change is qualitatively similar to that using all models, but the local magnitude of changes are much affected by the best model. In order to interpret future change in precipitation climatology and SDII, we have calculated change in vertically integrated horizontal transport of moisture. Changes in precipitation climatology and SDII can be interpreted as the moisture convergence change associated with change in horizontal transport of moisture. Large moisture convergence was found due to either intensification or weakening of subtropical high depending on models. [ABSTRACT FROM AUTHOR]

The leading mode of southern hemisphere (SH) climatic variability, the southern annular mode (SAM), has recently seen a shift towards its positive phase due to stratospheric ozone depletion and increasing greenhouse gas (GHG) concentrations. Here we examine how sensitive the SAM (defined as the leading empirical orthogonal function of SH sea level pressure anomalies) is to future GHG concentrations. We determine its likely evolution for three intergovernmental panel on climate change (IPCC) special report on emission scenarios (SRES) for austral summer and winter, using a multi-model ensemble of IPCC fourth assessment report models which resolve stratospheric ozone recovery. During the period of summer ozone recovery (2000-2050), the SAM index exhibits weakly negative, statistically insignificant trends due to stratospheric ozone recovery which offsets the positive forcing imposed by increasing GHG concentrations. Thereafter, positive SAM index trends occur with magnitudes that show sensitivity to the SRES scenario utilised, and thus future GHG emissions. Trends are determined to be strongest for SRES A2, followed by A1B and B1, respectively. The winter SAM maintains a similar dependency upon GHG as summer, but over the entire twenty-first century and to a greater extent. We also examine the influence of ozone recovery by comparing results to models that exclude stratospheric ozone recovery. Projections are shown to be statistically different from the aforementioned results, highlighting the importance of ozone recovery in governing SAM-evolution. We therefore demonstrate that the future SAM will depend both upon GHG emissions and stratospheric ozone recovery. [ABSTRACT FROM AUTHOR]

Abstract: Projections of changes in climate extreme are important in assessing the potential impacts of climate change on social and natural systems. This article presents future projections of climate extremes in the Tibetan Plateau constructed from ensembles of coupled general circulation models (CGCMs) contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4), under a range of emission scenarios. The extremes of temperature and precipitation are described by seven indices, namely, the frost day (FD), percentage of nights when Tmin >90th percentile (TN90), consecutive dry days (CDD), annual count of days when precipitation >=10mm (R10), maximum 5-day precipitation total (R5D), simple daily intensity index (SDII), and annual total precipitation when precipitation >95th percentile (R95T). Results indicate that frost days decrease over the Tibetan Plateau in the 21st century. More frequent warm nights are also projected in the plateau. The increases of these temperature extremes under A2 and A1B scenarios are more pronounced than under B1. Heavy precipitation events for single days and pentads are projected to increase in their intensity over most parts of Tibetan Plateau. CDD, R10, R5D, R95T and SDII collectively suggest more extreme precipitation in the region (2011–2020). In addition, impacts of climate extremes changes on local water resources and fragile ecosystem are discussed as an extension of this article. The findings will be beneficial to project regional responses in this unique region to global climate change, and then to formulate regional strategies against the potential menaces of climate change. [Copyright &y& Elsevier]

The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982-2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models. [ABSTRACT FROM AUTHOR]

A pattern projection downscaling method is employed to predict monthly station precipitation. The predictand is the monthly precipitation at 1 station in China, 60 stations in Korea, and 8 stations in Thailand. The predictors are multiple variables from the output of operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction is made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of the model downscaled precipitation forecasts using the best predictors and is referred to as DMME. It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse resolution predictions of general circulation models. The correlation coefficient between the prediction of DMME and the observation in Beijing of China reaches 0.71; the skill is improved to 0.75 for Korea and 0.61 for Thailand. The improvement of the prediction skills for the first two cases is attributed to three steps: coupled pattern selection, optimal predictor selection, and multi-model downscaled precipitation ensemble. For Thailand, we use the single-predictor prediction, which results in a lower prediction skill than the other two cases. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected well, can be used to make skillful predictions of local precipitation by means of appropriate statistical downscaling. [ABSTRACT FROM AUTHOR]

The skill of probability density function (PDF) prediction of summer rainfall over East China using optimal ensemble schemes is evaluated based on the precipitation data from five coupled atmosphere-ocean general circulation models that participate in the ENSEMBLES project. The optimal ensemble scheme in each region is the scheme with the highest skill among the four commonly-used ones: the equally-weighted ensemble (EE), EE for calibrated model-simulations (Cali-EE), the ensemble scheme based on multiple linear regression analysis (MLR), and the Bayesian ensemble scheme (Bayes). The results show that the optimal ensemble scheme is the Bayes in the southern part of East China; the Cali-EE in the Yangtze River valley, the Yangtze-Huaihe River basin, and the central part of northern China; and the MLR in the eastern part of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform the climatology prediction, indicating that current commonly-used multi-model ensemble schemes are able to produce skillful PDF prediction of summer rainfall over East China, even though more information for other model variables is not derived. [ABSTRACT FROM AUTHOR]

A statistical downscaling model, based on the outputs of general circulation models (GCMs) as predictors, was proposed to simulate the daily precipitations in the Shih-Men reservoir catchment in Taiwan. The structure of the proposed downscaling model is composed of two parts: classification and regression. Predictors of classification and regression models were selected from the large-scale weather variables in the National Centers for Environmental Prediction (NECP) reanalysis data based on statistical tests. Discriminant analysis and support vector regression (SVR) were applied to build the classification and regression models. The outputs of five atmosphere-ocean GCMs, which are reported to have properly considered tropical cyclone information and East Asian Monsoon modelling, were used for projecting future precipitations. Data from four grids covering Taiwan were used for developing the downscaling model. The potential of the downscaling models in simulating local precipitations was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily precipitations from large-scale weather variables. Projected local precipitations under two emission scenarios show that the precipitations in the study area tend to decrease. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Long-lead prediction of waxing and waning of the Western North Pacific (WNP)-East Asian (EA) summer monsoon (WNP-EASM) precipitation is a major challenge in seasonal time-scale climate prediction. In this study, deficiencies and potential for predicting the WNP-EASM precipitation and circulation one or two seasons ahead were examined using retrospective forecast data for the 26-year period of 1981-2006 from two operational couple models which are the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the Bureau of Meteorology Research Center (BMRC) Predictive Ocean-Atmosphere Model for Australia (POAMA). While both coupled models have difficulty in predicting summer mean precipitation anomalies over the region of interest, even for a 0-month lead forecast, they are capable of predicting zonal wind anomalies at 850 hPa several months ahead and, consequently, satisfactorily predict summer monsoon circulation indices for the EA region (EASMI) and for the WNP region (WNPSMI). It should be noted that the two models' multi-model ensemble (MME) reaches 0.40 of the correlation skill for the EASMI with a January initial condition and 0.75 for the WNPSMI with a February initial condition. Further analysis indicates that prediction reliability of the EASMI is related not only to the preceding El Niño and Southern Oscillation (ENSO) but also to simultaneous local SST variability. On other hand, better prediction of the WNPSMI is accompanied by a more realistic simulation of lead-lag relationship between the index and ENSO. It should also be noted that current coupled models have difficulty in capturing the interannual variability component of the WNP-EASM system which is not correlated with typical ENSO variability. To improve the long-lead seasonal prediction of the WNP-EASM precipitation, a statistical postprocessing was developed based on the multiple linear regression method. The method utilizes the MME prediction of the EASMI and WNPSMI as predictors. It is shown that the statistical postprocessing is able to improve forecast skill for the summer mean precipitation over most of the WNP-EASM region at all forecast leads. It is noteworthy that the MME prediction, after applying statistical postprocessing, shows the best anomaly pattern correlation skill for the EASM precipitation at a 4-month lead (February initial condition) and for the WNPSM precipitation at a 5-month lead (January initial condition), indicating its potential for improving long-lead prediction of the monsoon precipitation. [ABSTRACT FROM AUTHOR]

Spatial patterns of near-surface wind speeds and resulting loss potentials associated with severe winter storms were investigated in multi-model simulations with regional climate models (RCMs), driven by ERA40 re-analyses. The benefits and limitations of dynamical downscaling for windstorm loss calculations were explored, including a quantification of the performance of the multi-model ensemble as a whole and the systematic investigation of the influence of model selection on the ensemble results. A comparison of the wind fields in the different models revealed both systematic biases in individual RCMs and model-specific anomalies over mountainous regions. Further, a storm loss model was applied to the RCM wind fields, and the calculated losses were validated against observed annual insurance loss data available for Germany. Generally, a distinct advantage from dynamical downscaling was obvious. However, all RCMs failed in realistically simulating 1 specific major event. If this particular event was excluded from the considerations, almost all simulations revealed high correlations (above 0.8) with observed losses, comparable to losses calculated directly from the large-scale reanalysis wind field. For the best performing models, considerably higher loss correlations up to 0.95 were obtained, suggesting that the high-resolution RCMs exceeded the value of assimilation in the driving data for the area considered. Combining calculated losses from the individual RCMs into a multi-model ensemble, the performance of the ensemble mean was as good as the performance of the best single model. Examining all possible sub-ensembles, we found that generally a higher minimum performance was obtained with a larger number of ensemble members, whereas the maximum performance was hardly affected by the ensemble size. [ABSTRACT FROM AUTHOR]

Daily output from the PRUDENCE ensemble of regional climate simulations for the end of the twentieth and twenty-first centuries over Europe is used to show that the increasing intensity of the most damaging summer heat waves over Central Europe is mostly due to higher base summer temperatures. In this context, base temperature is defined as the mean of the seasonal cycle component for those calendar days when regional heat waves occur and is close, albeit not identical, to the mean temperature for July-August. Although 36-47% of future Central Europe July and August days at the end of the twenty-first century are projected to be extreme according to the present day climatology, specific changes in deseasonalized heat wave anomalies are projected to be relatively small. Instead, changes in summer base temperatures appear much larger, clearly identifiable and of the same order of magnitude as changes in the whole magnitude of heat waves. Our results bear important consequences for the predictability of central European heat wave intensity under global warming conditions. [ABSTRACT FROM AUTHOR]

Performance of seven fully coupled models in simulating Indian summer monsoon climatology as well as the inter-annual variability was assessed using multi member 1 month lead hindcasts made by several European climate groups as part of the program called Development of a European multi-model ensemble system for seasonal-to-inter-annual prediction (DEMETER). Dependency of the model simulated Indian summer monsoon rainfall and global sea surface temperatures on model formulation and initial conditions have been studied in detail using the nine ensemble member simulations of the seven different coupled ocean-atmosphere models participated in the DEMETER program. It was found that the skills of the monsoon predictions in these hindcasts are generally positive though they are very modest. Model simulations of India summer monsoon rainfall for the earlier period (1959-1979) are closer to the 'perfect model' (attainable) score but, large differences are observed between 'actual' skill and 'perfect model' skill in the recent period (1980-2001). Spread among the ensemble members are found to be large in simulations of India summer monsoon rainfall (ISMR) and Indian ocean dipole mode (IODM), indicating strong dependency of model simulated Indian summer monsoon on initial conditions. Multi-model ensemble performs better than the individual models in simulating ENSO indices, but does not perform better than the individual models in simulating ISMR and IODM. Decreased skill of multi-model ensemble over the region indicates amplification of errors due to existence of similar errors in the individual models. It appears that large biases in predicted SSTs over Indian Ocean region and the not so perfect ENSO-monsoon (IODM-monsoon) tele-connections are some of the possible reasons for such lower than expected skills in the recent period. The low skill of multi-model ensemble, large spread among the ensemble members of individual models and the not so perfect monsoon tele-connection with global SSTs points towards the importance of improving individual models for better simulation of the Indian monsoon. [ABSTRACT FROM AUTHOR]

Abstract: Several techniques have been developed over the last decade for the ensemble treatment of atmospheric dispersion model predictions. Among them two have received most of the attention, the multi-model and the ensemble prediction system (EPS) modeling. The multi-model approach relies on model simulations produced by different atmospheric dispersion models using meteorological data from potentially different weather prediction systems. The EPS-based ensemble is generated by running a single atmospheric dispersion model with the ensemble weather prediction members. In the paper we compare both approaches with the help of statistical indicators, using the simulations performed for the ETEX-1 tracer experiment. Both ensembles are also evaluated against measurement data. Among the most relevant results is that the multi-model median and the mean of EPS-based ensemble produced the best results, hence we consider a combination of multi-model and EPS-based approaches as an interesting suggestion for further research. [ABSTRACT FROM AUTHOR]

Given observed initial conditions, how well do coupled atmosphere–ocean models predict precipitation climatology with 1-month lead forecast? And how do the models’ biases in climatology in turn affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981–2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models’ ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian–Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated. [ABSTRACT FROM AUTHOR]

A range of possible changes in the frequency and characteristics of European wind storms under future climate conditions was investigated on the basis of a multi-model ensemble of 9 coupled global climate model (GCM) simulations for the 20th and 21st centuries following the IPCC SRES A1B scenario. A multi-model approach allowed an estimation of the (un)certainties of the climate change signals. General changes in large-scale atmospheric flow were analysed, the occurrence of wind storms was quantified, and atmospheric features associated with wind storm events were considered. Identified storm days were investigated according to atmospheric circulation, associated pressure patterns, cyclone tracks and wind speed patterns. Validation against reanalysis data revealed that the GCMs are in general capable of realistically reproducing characteristics of European circulation weather types (CWTs) and wind storms. Results are given with respect to frequency of occurrence, storm-associated flow conditions, cyclone tracks and specific wind speed patterns. Under anthropogenic climate change conditions (SRES AIB scenario), increased frequency of westerly flow during winter is detected over the central European investigation area. In the ensemble mean, the number of detected wind storm days increases between 19 and 33 % for 2 different measures of storminess, only 1 GCM revealed less storm days. The increased number of storm days detected in most models is disproportionately high compared to the related CWT changes. The mean intensity of cyclones associated with storm days in the ensemble mean increases by about 10 (± 10)% in the Eastern Atlantic, near the British Isles and in the North Sea. Accordingly, wind speeds associated with storm events increase significantly by about 5 (±5)% over large parts of central Europe, mainly on days with westerly flow. The basic conclusions of this work remain valid if different ensemble contructions are considered, leaving out an outlier model or including multiple runs of one particular model. [ABSTRACT FROM AUTHOR]

Summary: The future management of hydrological systems must be informed by climate change projections at relevant time horizons and at appropriate spatial scales. Furthermore, the robustness of such management decisions is dependent on both the uncertainty inherent in future climate change scenarios and the natural climate system. Addressing these needs, we present a new transient rainfall simulation methodology which combines dynamical and statistical downscaling techniques to produce transient (i.e. temporally non-stationary) climate change scenarios. This is used to generate a transient multi-model ensemble of simulated point-scale rainfall time series for 1997–2085 for the polluted Brévilles spring in Northern France. The recovery of this previously potable source may be affected by climatic changes and variability over the next few decades. The provision of locally-relevant transient climate change scenarios for use as input to hydrological models of both water quality and quantity will ultimately provide a valuable resource for planning and decision making. Observed rainfall from 1988–2006 was characterised in terms of a set of statistics for each calendar month: the daily mean, variance, probability dry, lag-1 autocorrelation and skew, and the monthly variance. The Neyman–Scott Rectangular Pulses (NSRP) stochastic rainfall model was fitted to these observed statistics and correctly simulated both monthly statistics and extreme rainfall properties. Multiplicative change factors which quantify the change in each statistic between the periods 1961–1990 and 2071-2100 were estimated for each month and for each of 13 Regional Climate Models (RCMs) from the PRUDENCE ensemble. To produce transient climate change scenarios, pattern scaling factors were estimated and interpolated from four time-slice integrations of two General Circulation Models which condition the RCMs, ECHAM4/OPYC and HadCM3. Applying both factors to the observed statistics provided projected transient rainfall statistics (PTRS) to which piece-wise smoothly varying transient rainfall model parameterizations were fitted. These fits provided good representations of the PTRS for each RCM. An ensemble of 100 continuous daily rainfall time series, with steadily varying stochastic properties which model these projections of transient climate change, was then simulated using a new transient NSRP simulation methodology for each RCM. Together the ensembles form a 1300 member transient multi-model ensemble of rainfall time series. The simulated transient ensemble properties were investigated, identifying RCMs giving rise to unusual behaviour. For the Brévilles, annual rainfall is projected to decrease until 2085 but the change is highly sensitive to General Circulation Model forcing; ECHAM4-driven RCMs project larger annual decreases than HadCM3/HadAM3H/P driven RCMs. All RCMs project an increase in winter rainfall and a larger summer decrease. An increase of ∼10% in the 10-year return period annual maximum rainfall is projected by 2085, however both strong increasing trends and a slight decreasing trend are found for individual RCMs. Compared with transient RCMs, the new methodology provides a number of advantages: reduced biases, point scale scenarios relevant for local-scale impact studies, improved representation of natural variability and improved representation of extremes. [Copyright &y& Elsevier]

One of the main concerns in regional climate modeling is to which extent limited-area regional climate models (RCM) reproduce the large-scale atmospheric conditions of their driving general circulation model (GCM). In this work we investigate the ability of a multi-model ensemble of regional climate simulations to reproduce the large-scale weather regimes of the driving conditions. The ensemble consists of a set of 13 RCMs on a European domain, driven at their lateral boundaries by the ERA40 reanalysis for the time period 1961–2000. Two sets of experiments have been completed with horizontal resolutions of 50 and 25 km, respectively. The spectral nudging technique has been applied to one of the models within the ensemble. The RCMs reproduce the weather regimes behavior in terms of composite pattern, mean frequency of occurrence and persistence reasonably well. The models also simulate well the long-term trends and the inter-annual variability of the frequency of occurrence. However, there is a non-negligible spread among the models which is stronger in summer than in winter. This spread is due to two reasons: (1) we are dealing with different models and (2) each RCM produces an internal variability. As far as the day-to-day weather regime history is concerned, the ensemble shows large discrepancies. At daily time scale, the model spread has also a seasonal dependence, being stronger in summer than in winter. Results also show that the spectral nudging technique improves the model performance in reproducing the large-scale of the driving field. In addition, the impact of increasing the number of grid points has been addressed by comparing the 25 and 50 km experiments. We show that the horizontal resolution does not affect significantly the model performance for large-scale circulation. [ABSTRACT FROM AUTHOR]

The regional climate change index (RCCI) is employed to investigate hot-spots under 21st century global warming over East Asia. The RCCI is calculated on a 1-degree resolution grid from the ensemble of CMIP3 simulations for the B1, A1B, and A2 IPCC emission scenarios. The RCCI over East Asia exhibits marked sub-regional variability. Five sub-regional hot-spots are identified over the area of investigation: three in the northern regions (Northeast China, Mongolia, and Northwest China), one in eastern China, and one over the Tibetan Plateau. Contributions from different factors to the RCCI are discussed for the sub-regions. Analysis of the temporal evolution of the hot-spots throughout the 21st century shows different speeds of response time to global warming for the different sub-regions. Hot-spots firstly emerge in Northwest China and Mongolia. The Northeast China hot-spot becomes evident by the mid of the 21st century and it is the most prominent by the end of the century. While hot-spots are generally evident in all the 5 sub-regions for the A1B and A2 scenarios, only the Tibetan Plateau and Northwest China hot-spots emerge in the B1 scenario, which has the lowest greenhouse gas (GHG) concentrations. Our analysis indicates that subregional hot-spots show a rather complex spatial and temporal dependency on the GHG concentration and on the different factors contributing to the RCCI. [ABSTRACT FROM AUTHOR]

Statistical downscaling of 14 coupled atmosphere-ocean general circulation models (AOGCM) is presented to assess potential changes of the 10 m wind speeds in France. First, a statistical downscaling method is introduced to estimate daily mean 10 m wind speed at specific sites using general circulation model output. Daily 850 hPa wind field has been selected as the large scale circulation predictor. The method is based on a classification of the daily wind fields into a few synoptic weather types and multiple linear regressions. Years are divided into an extended winter season from October to March and an extended summer season from April to September, and the procedure is conducted separately for each season. ERA40 reanalysis and observed station data have been used to build and validate the downscaling algorithm over France for the period 1974–2002. The method is then applied to 14 AOGCMs of the coupled model intercomparison project phase 3 (CMIP3) multi-model dataset. Three time periods are focused on: a historical period (1971–2000) from the climate of the twentieth century experiment and two climate projection periods (2046–2065 and 2081–2100) from the IPCC SRES A1B experiment. Evolution of the 10 m wind speed in France and associated uncertainties are discussed. Significant changes are depicted, in particular a decrease of the wind speed in the Mediterranean area. Sources of those changes are investigated by quantifying the effects of changes in the weather type occurrences, and modifications of the distribution of the days within the weather types. [ABSTRACT FROM AUTHOR]

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we investigate the results of multi-model simulations performed on the ETEX-2 experiment by the ENSEMBLE modelling community. New sets of results were created by taking different percentiles of the distribution of the models'' predicted values. Both single models and the new constructed sets of results have been compared with the observed data. While a similar comparison for the ETEX-1 case indicated the median model was superior to any single model, for the ETEX-2 case, the situation is more difficult due to complex meteorological conditions, and no absolute and clear conclusions can be obtained. However, for emergency response purposes the median model still can be considered the most viable option also for complicated meteorological situation such is the ETEX-2 experiment. [Copyright &y& Elsevier]

Abstract: Predictive models that are composed of a number of combined models, although ubiquitous in climate prediction have not yet become popular in many other areas of environmental modelling, despite growing evidence that they are superior to single-model methods in many ways. These combined-model methodologies are termed ensembles and modules, and this paper reviews their concepts, advantages and how to create them. Additionally, they will be discussed in terms of the critically important bias/variance trade-off. Moreover, ensembles and modules will be discussed with reference to historical and current research papers within environmental modelling. [Copyright &y& Elsevier]

Heat stress in combination with drought has become the biggest concern and threat for maize yield production, especially in arid and hot regions. Accordingly, different optimal solutions should be considered in order to maintain maize production and reduce the risk of heat stress under the changing climate. In the current study, the risk of heat stress across Iranian maize agro-ecosystems was analyzed in terms of both intensity and frequency. The study areas comprised 16 provinces and 24 locations classified into five climate categories: arid and hot, arid and temperate, semi-arid and hot, semi-arid and temperate, and semi-arid and cold. The impact of heat stress on maize under a future climate was based on a 5-multi-model ensemble under two optimistic and pessimistic emission scenarios (RCP4.5 and RCP8.5, respectively) for 2040-2070 using the APSIM crop model. Simulation results illustrated that in the period of 2040-2070, intensity and the frequency of heat stress events increased by 2.37 °C and 79.7%, respectively, during maize flowering time compared to the baseline. The risk of heat stress would be almost 100% in hot regions in the future climate under current management practices, mostly because of the increasing high-risk window for heat stress which will result in a yield reduction of 0.83 t ha -1 . However, under optimal management practices,farmers will economically obtain acceptable yields (6.6 t ha -1 ). The results also indicated that the high-risk windows in the future will be lengthening from 12 to 33 days in different climate types. Rising temperatures in cold regions as a result of global warming would provide better climate situations for maize growth, so that under optimistic emission scenarios and optimal management practices, farmers will be able to boost grain yield up to 9.2 t ha -1 . Overall, it is concluded that farmers in hot and temperate regions need to be persuaded to choose optimal sowing dates and new maize cultivars which are well adapted to each climate to reduce heat stress risk and to shift maize production to cold regions., (© 2022. The Author(s) under exclusive licence to International Society of Biometeorology.)

Streamflow is a very important component of the hydrological cycle, and variation in the streamflow can be an indication of hydrological disaster. Thus, the accurate quantification of streamflow variation is a core concern in water resources engineering. In this study, we evaluated the factors influencing streamflow and decomposed their effects in three large rivers: the Buk Han River (BHR), the Nam Han River (NHR), and the Lower Han River (LHB). The Pettit test was used to investigate breakpoints in conjunction with the climate elasticity approach and decomposition framework to quantify and decompose the effects of climate variability and anthropogenic activity. The abrupt breakpoints in the streamflow and precipitation data were detected in 1997 and 1995. Considering these breakpoints, we divided the time series into two periods: the baseline period and the post-baseline period. Climate elasticity approaches were used to quantify the effects of climate variability and anthropogenic activity during the baseline period, post-baseline period, and future periods (2031–2060 and 2071–2100) under the Representative Concentration Pathways' 4.5 and 8.5 scenarios. The results revealed that climate variability was the leading cause of alteration in the streamflow in the BHR and NHR, accounting for 76.52% to 80.51% of the total change, respectively. Meanwhile, the LHR remained more sensitive to anthropogenic activity, which accounted for 56.42% of the total variation in streamflow. Future climate change also showed an increase in precipitation and temperature in both scenarios, especially during the far-future period (2071–2100). This variation in the climatic factor was shown to affect the future streamflow by 22.14% to 27.32%. These findings can play a very important role in future planning for large river basins, considering the impacts of increasing anthropogenic activity and climate change to reduce the risks of hydrological hazards. [ABSTRACT FROM AUTHOR]

Isoprene is a key biogenic volatile organic compound of vital importance for global climate change and air quality. Previous studies projecting future changes of isoprene emissions showed a wide range of uncertainties due to the discrepancies in emission schemes, climate models, and future scenarios. Here, we use an ensemble of models from the Coupled Model Intercomparison Project version 6 (CMIP6) to explore the spatiotemporal variations of global isoprene emissions at present day and their changes by the end of 21st century. At present day, most models predict similar emission rates of 400 Tg C yr−1 but with large differences in the long-term trends. For models using the scheme of Model of Emissions of Gases and Aerosols from Nature, isoprene emissions show limited changes during historical period but significant enhancements after the year 2000. However, for models using another scheme with strong CO 2 inhibition effects, isoprene emissions show decreasing trends during historical period and moderate increasing trends after the year 2000. By the end of 21st century, the ensemble projection shows increases of 21–57% in isoprene emissions with the largest enhancement for the strongest warming scenario. Attribution shows that temperature is the dominant driver for the increase of isoprene emissions, no matter whether the CO 2 inhibition effects are considered or not. The enhanced isoprene emissions increase the risks of ozone pollution in a warmer climate. • CMIP6 models predict similar present-day isoprene emissions but varied trends. • Isoprene emissions are projected to increase by 21–57% at the end of century. • Temperature instead of CO 2 dominates the enhancement of isoprene emissions. [ABSTRACT FROM AUTHOR]

• We coupled EURO-CORDEX and SWAT to analyze future hydrology in a Mediterranean island. • Future projections show a decrease of mean precipitation with an increase of extreme precipitation. • Climate change impact on hydrologic cycle with a decrease of mean discharge and runoff in. • Investigating climate change impacts can provide fundamental information to decision makers. Climate change effect analysis on the hydrologic cycle is essential for the assessment of water management strategies. Climate models can provide projections of precipitation changes in the future, based on greenhouse gas emissions scenarios. In this study the EURO-CORDEX (European COordinated Regional Downscaling Experiment) climate models were first analyzed for the island of Sardinia, against observed precipitation for the historical period of 1976–2005. A Multi-Model Ensemble (ENS) was built, weighting different models based on their performance against observed precipitations. Future projections (2071–2100) were analyzed using the 8.5 RCP emissions scenario to evaluate changes in precipitations. Climate models were then used as climate forcing for the SWAT model (Soil and Water Assessment Tool), aiming to evaluate the effects of such changes on streamflow and runoff of two small catchments located in the South-West Sardinia. A general decrease of mean precipitation values, up to 25 % at yearly scale in South-West Sardinia, is expected for the future, along with an increase of extreme precipitation events. Especially in the eastern and southern areas, extreme events are projected to increase by 30%. Such changes reflect on the hydrologic cycle with a decrease of mean streamflow (ranging from −18% to −25%) and runoff (ranging from −12% to −18%), except in spring, when runoff is projected to increase by 20–30%. This study contributes to the analysis of climate change impacts in the Mediterranean, which is considered a hotspot of the global warming, enhancing the use of modeling tools to investigate possible future scenarios. [ABSTRACT FROM AUTHOR]

• Bayesian model averaging is superior to simple model averaging. • BMA assigns different weights to individual models of a multi-model ensemble. • A full chain of analysis for crop model ensembles is illustrated. A recent trend in crop modeling has been the use of multi-model ensembles (MMEs) for impact assessment, especially as it relates to climate change. Studies have shown that, compared to individual models, the mean or median of a MME is a better predictor that is more accurate in making predictions and capable of providing model uncertainty information. In previous studies that used MMEs, each individual model was assigned an equal weight by simply averaging the predictions over all the models. Here we adopted a different method of creating MMEs, namely using Bayesian model averaging (BMA), which assigns different weights to individual models according to their performance of reproducing historical data. The main purpose of this study is to illustrate how BMA can be applied to crop models, and to compare its performance with simple model averaging. In addition, we also illustrate a full chain of analysis for crop model ensembles, which includes parameter estimation, ensemble creation and evaluation, uncertainty quantification and evaluation. The approaches were implemented with simulations of rice phenology using a small, three-member model ensemble. The results showed that the ensemble model e-BMA had nearly the same prediction accuracy as the best single model and that it predicts quite a bit better than the simply averaging ensemble named e-equal here. Squared bias was found to be the largest contributor to overall prediction uncertainty, both for the individual models and the ensemble models, and thus should be the first priority for model improvement. The estimated prediction uncertainties were larger than the variance of the observations. In the future, BMA should be tested more widely. [ABSTRACT FROM AUTHOR]

Near surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global Climate Models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the wind speed in the Arid Region of Northwest China (ARNC) during 1971–2014. Then, the temporal and spatial variations in the surface wind speed of ARNC in the 21st century were projected under four Shared Socioeconomic Pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SP5-8.5. The results reveal that the preferred-model ensemble (PME) can fairly evaluate the temporal and spatial distribution of surface wind speed with the temporal and spatial correlation coefficients exceeding 0.5 at the significance level of p = 0.05 when compared to the 25 single models and their ensemble mean. After deviation correction, the PME can reproduce the distribution characteristics of high wind speed in the east and low in the west, high in mountainous areas, and low in basins. Unfortunately, no models or model ensemble can accurately reproduce the decreasing magnitude of observed wind speed. In the 21st century, the surface wind speed in the ARNC is projected to increase under SSP1-2.6 scenario but will decrease remarkably under the other three scenarios. Moreover, the higher the emission scenarios, the more significant the surface wind speed decreases. Spatially, the wind speed will increase significantly in the west and southeast of Xinjiang, decrease in the north of Xinjiang and the south of Tarim Basin. What's more, under the four scenarios, the surface wind speed will decrease in spring, summer and autumn, especially in summer, and increase in winter. The wind speed will decrease significantly in the north of Tianshan Mountains in summer, decrease significantly in the north of Xinjiang and the southern edge of Tarim Basin in spring and autumn, and increase in fluctuation with high values in Tianshan Mountains in winter. [ABSTRACT FROM AUTHOR]

Global Climate Models are the main tools for climate projections. Since many models exist, it is common to use Multi-Model Ensembles to reduce biases and assess uncertainties in climate projections. Several approaches have been proposed to combine individual models and extract a robust signal from an ensemble. Among them, the Multi-Model Mean (MMM) is the most commonly used. Based on the assumption that the models are centered around the truth, it consists in averaging the ensemble, with the possibility of using equal weights for all models or to adjust weights to favor some models. In this paper, we propose a new alternative to reconstruct multi-decadal means of climate variables from a Multi-Model Ensemble, where the local performance of the models is taken into account. This is in contrast with MMM where a model has the same weight for all locations. Our approach is based on a computer vision method called graph cuts and consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. The performance of the graph cuts approach is assessed based on two experiments: one where the ERA5 reanalyses are considered as the reference, and another involving a perfect model experiment where each model is in turn considered as the reference. We show that the graph cuts approach generally results in lower biases than other model combination approaches such as MMM, while at the same time preserving a similar level of spatial continuity., Supplementary Information: The online version contains supplementary material available at 10.1007/s00382-022-06213-4., Competing Interests: Conflict of interestThe authors have no conflicts of interest that are relevant to the content of this article., (© The Author(s) 2022.)

It is essential to maintain the health of forests so that they are protected against a diverse range of stressors and show improved resilience. An area-based forest health map is required for efficient forest management on a national scale however, most national forest inventories are based on in-situ observations. This study examined methodologies to establish an area-based map on tree vitality grade using field survey data, particularly that containing information on several trees at one point. The forest health monitoring dataset of the Republic of Korea was used in combination with 37 satellite-based environmental predictors. Four methods were considered: Multinomial logistic regression (MLR), random forest classification (RF), indicator kriging (IK), and multi-model ensemble (MME) approaches using species distribution models. The MLR and RF produced biased results, whereby almost all regions were classified as first grade; the spatialization results of these methods were considered inappropriate for forest management. The maps produced using the IK and MME methods improved the distinctions between the distributions of five grades compared to the previous two methodologies however, the MME method produced better results, reliably reflecting topographical and climatic characteristics. Comparisons with the vegetation condition index and bioclimate vulnerability index also emphasized the usefulness of the MME. This study is particularly relevant to the national forest managers who struggle to find the most effective forest monitoring and management strategies. Suggestions to improve spatialization of field survey data are further discussed. [ABSTRACT FROM AUTHOR]

Age, gender, educational background, and so on are the most basic attributes for identifying and portraying users. It is also possible to conduct in-depth mining analysis and high-level predictions based on such attributes to learn users' preferences and personalities so as to enhance users' online experience and to realize personalized services in real applications. In this paper, we propose using classification algorithms in machine learning to predict users' demographic attributes, such as gender, age, and educational background, based on one month of data collected with the Sogou search engine with the goal of making user portraits. A multi-model approach using the fusion algorithms is adopted and hereby described in the paper. The proposed model is a two-stage structure using one month of data with demographic labels as the training data. The first stage of the structure is based on traditional machine learning models and neural network models, whereas the second one is a combination of the models from the first stage. Experimental results show that our proposed multi-model method can achieve more accurate results than the single-model methods in predicting user attributes. The proposed approach also has stronger generalization ability in predicting users' demographic attributes, making it more adequate to profile users. [ABSTRACT FROM AUTHOR]