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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. [ABSTRACT FROM AUTHOR]

Climate change projections are commonly based on multi-model ensembles of climate simulations. In this paper we consider the choice of bias models in Bayesian multimodel predictions. Buser et al. (Clim Res 44(2-3):227-241, 2010a) introduced a hybrid bias model which combines commonly used constant bias and constant relation bias assumptions. The hybrid model includes a weighting parameter which balances these bias models. In this study, we use a cross-validation approach to study which bias model or bias parameter leads to, in a specific sense, optimal climate change projections. The analysis is carried out for summer and winter season means of 2 m-temperatures spatially averaged over the IPCC SREX regions, using 19 model runs from the CMIP5 data set. The cross-validation approach is applied to calculate optimal bias parameters (in the specific sense) for projecting the temperature change from the control period (1961-2005) to the scenario period (2046-2090). The results are compared to the results of the Buser et al. (Clim Res 44(2-3):227-241, 2010a) method which includes the bias parameter as one of the unknown parameters to be estimated from the data. [ABSTRACT FROM AUTHOR]

How climate change will unfold in the years to come is a central topic in today's environmental debate, in particular at the regional level. While projections using large ensembles of global climate models consistently indicate a future decrease in summer precipitation over southern Europe and an increase over northern Europe, individual models substantially modulate these distinct signals of change in precipitation. So far model improvements and higher resolution from regional downscaling have not been seen as able to resolve these disagreements. In this paper we assess whether 2 decades of investments in large ensembles of downscaling experiments with regional climate model simulations for Europe have contributed to a more robust model assessment of the future climate at a range of geographical scales. We study climate change projections of European seasonal temperature and precipitation using an ensemble-suite comprised by all readily available pan-European regional model projections for the twenty-first-century, representing increasing model resolution from ~ 50 to ~ 12 km grid distance, as well as lateral boundary and sea surface temperature conditions from a variety of global model simulations. Employing a simple scaling with global mean temperature change we identify emerging robust signals of future seasonal temperature and precipitation changes also found to resemble current observed trends, where these are judged to be statistically significant. [ABSTRACT FROM AUTHOR]

Eastern China is regularly exposed to extreme precipitation with significant socio-economical consequences. Following an observational analysis in a first part of this study, here the ability of the Coupled Model Intercomparison Project Phase 6 models to reproduce the main modes of interannual variability of 5-day summer extreme precipitation over eastern China is evaluated, using an empirical orthogonal teleconnection (EOT) method. These models capture the main patterns and magnitudes of the different EOT patterns, although the North China mode is less well represented. Models also reproduce the dynamical features associated with each mode. There is no systematic improvement in the ability of models to simulate either the pattern or the 5-day intensity when using higher resolution models compared to coarser resolution ones. Instead, multi-member or multi-model ensembles lead to results closer to observations. Using a low mitigation projection pathway (SSP-370), it is shown that the risk of the most extreme 5-day precipitation events by about 40%, 80% and more than 150% for global-mean warming levels, relative to 1850–1900, of + 1.5, + 2 and + 3 ∘ C respectively. This increase is found to be more significant for 5-days events than for seasonal scale precipitation, consistent with previous studies. [ABSTRACT FROM AUTHOR]

Studies that evaluate climate change projections over the whole of South America (SA) and including different seasons and models are scarce. In this context, the objective of this work is to assess climate projections for SA through the use of climatic indices, considering the entire continent, distinct seasons, and ensembles of models. Projections performed with the Eta regional climate model and its driving global climate models (GCMs) are analysed. From these projections, 19 climate indices based on daily precipitation and maximum and minimum temperature are computed. The results focus on two ensembles (GCMs and Eta), time slices (1980–2005 and 2050–2080), and scenarios (RCP4.5 and RCP8.5). In the validation of the present climate, it is shown that Eta adds value to GCMs. Future projections indicate, for both austral summer (DJF) and winter (JJA), an increase in the frequency and intensity of extreme events of daily rainfall over southeastern and extreme north of SA. Over the Amazon, during DJF, there is a statistically significant increase in the number of consecutive dry days and a decrease in the consecutive wet days. For northeastern Brazil, these features are more intense in JJA. The frequency of cold (warm) nights and days is projected to decrease (increase) over the whole continent and seasons. The climate change signal for the 19 climate indices is more intense under RCP8.5, and the regions more vulnerable to climate change are the Amazon, northeastern Brazil, and southeastern SA. Considering Brazil, the projections of precipitation and air temperature are also shown by biomes. [ABSTRACT FROM AUTHOR]

The realism of a specific configuration of the WRF Regional Climate Model (RCM) to represent the observed temperature evolution over the Iberian Peninsula (IP) in the 1971–2005 period has been analyzed. The E-OBS observational dataset was used for this purpose. Also, the added value of the WRF simulations with respect to the IPSL Earth System Model (ESM) used to drive the WRF RCM was evaluated. In general, WRF presents lower temperatures than in the observations (negative biases) over the IP. These biases are comparatively larger than those of the driving ESM. Once the biases are corrected, WRF provides an added value in terms of a higher spatial representation. WRF introduces more variability in some regions in comparison to gridded observation. Warming trends according to the observations are also well represented by the RCM. In the second part of this study, the projections of future climate performed with both the ESM and the RCM were evaluated for the RCP4.5 and RCP8.5 scenarios during the 21st century. Although both models simulate temperature increases, the RCM simulates a smaller warming than the ESM after the mid-21st century, except for winter. Using the WRF model, the maximum temperature increase reaches 6 ∘ C and 3 ∘ C for RCP8.5 and RCP4.5 in the south east of the Iberian Peninsula by the end of the 21st century, respectively. [ABSTRACT FROM AUTHOR]

Persistent shifts in mean rainfall have wide-ranging impacts to hydrology and water availability, and a reliable set of climate projections of change to mean rainfall is a useful tool for future planning. Most climate models project a decrease in winter rainfall in southern Australia, however there is a wide model range and there is not yet a robust assessment of underlying physical processes that can inform and constrain projections. Here, a multiple linear regression model between indices of atmospheric circulation and gridded rainfall in observations and in CMIP5 climate models is developed for July, representing the peak of winter. The regression is used as an evaluation tool for models and a basis to select models. Spatial distributions of the coefficients from the regression illustrate the relative important of different circulation features for rainfall across the region, and illustrate where climate models have deficiencies. As an additional check of projections, historical years that are an analogue for the projected future mean state of the atmospheric circulation are identified and the rainfall anomaly during those years is examined. Both approaches broadly agree and support previous work in suggesting a constraint on rainfall change to a decrease only. The regression analysis also suggests that the median projection for southwest Western Australia should be revised lower than the median of all climate models. The results demonstrate value in applying statistical techniques to understand relationships of rainfall to circulation and to refine confidence in regional climate projections. [ABSTRACT FROM AUTHOR]

Climate-change projections for boreal winter precipitation in Tropical America has been addressed by statistical downscaling (SD) using the principal component regression with sea-level pressure (SLP) as the predictor variable. The SD model developed from the reanalysis of SLP and gridded precipitation GPCC data, has been applied to SLP outputs from 20 CGMS of CMIP5, both from the present climate (1971-2000) and for the future (2071-2100) under the RCP2.6, RCP4.5, and RCP8.5 scenarios. The SD model shows a suitable performance over large regions, presenting a strong bias only in small areas characterized by very dry climate conditions or poor data coverage. The difference in percentage between the projected SD precipitation and the simulated SD precipitation for present climate, ranges from moderate to intense changes in rainfall (positive or negative, depending on the region and the SD GCM model considered), as the radiative forcing increases from the RCP2.6 to RCP8.5. The disparity in the GCMs outputs seems to be the major source of uncertainty in the projected changes, while the scenario considered appears less decisive. Mexico and eastern Brazil are the areas showing the most coherent decreases between SD GCMs, while northwestern and southeastern South America show consistently significant increases. This coherence is corroborated by the results of the ensemble mean which projects positive changes from 10°N towards the south, with exceptions such as eastern Brazil, northern Chile and some smaller areas, such as the center of Colombia, while projected negative changes are the majority found in the northernmost part. [ABSTRACT FROM AUTHOR]

This study highlights the expected range of projected winter air temperature and precipitation trends over the next 30-50 years due to unpredictable fluctuations of the North Atlantic Oscillation (NAO) superimposed upon forced anthropogenic climate change. The findings are based on a 40-member initial-condition ensemble of simulations covering the period 1920-2100 conducted with the Community Earth System Model version 1 (CESM1) at 1° spatial resolution. The magnitude (and in some regions, even the sign) of the projected temperature and precipitation trends over Europe, Russia and parts of the Middle East vary considerably across the ensemble depending on the evolution of the NAO in each individual member. Thus, internal variability of the NAO imparts substantial uncertainty to future changes in regional climate over the coming decades. To validate the model results, we apply a simple scaling approach that relates the margin-of-error on a trend to the statistics of the interannual variability. In this way, we can obtain the expected range of projected climate trends using the interannual statistics of the observed NAO record in combination with the model's radiatively-forced response (given by the ensemble-mean of the 40 simulations). The results of this observationally-based estimate are similar to those obtained directly from the CESM ensemble, attesting to the fidelity of the model's representation of the NAO and the utility of this approach. Finally, we note that the interannual statistics of the NAO and associated surface climate impacts are subject to uncertainty due to sampling fluctuations, even when based on a century of data. [ABSTRACT FROM AUTHOR]

Uncertainty in the strength of the Atlantic Meridional Overturning Circulation (AMOC) is analyzed in the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) projections for the twenty-first century; and the different sources of uncertainty (scenario, internal and model) are quantified. Although the uncertainty in future projections of the AMOC index at 30°N is larger in CMIP5 than in CMIP3, the signal-to-noise ratio is comparable during the second half of the century and even larger in CMIP5 during the first half. This is due to a stronger AMOC reduction in CMIP5. At lead times longer than a few decades, model uncertainty dominates uncertainty in future projections of AMOC strength in both the CMIP3 and CMIP5 model ensembles. Internal variability significantly contributes only during the first few decades, while scenario uncertainty is relatively small at all lead times. Model uncertainty in future changes in AMOC strength arises mostly from uncertainty in density, as uncertainty arising from wind stress (Ekman transport) is negligible. Finally, the uncertainty in changes in the density originates mostly from the simulation of salinity, rather than temperature. High-latitude freshwater flux and the subpolar gyre projections were also analyzed, because these quantities are thought to play an important role for the future AMOC changes. The freshwater input in high latitudes is projected to increase and the subpolar gyre is projected to weaken. Both the freshening and the gyre weakening likely influence the AMOC by causing anomalous salinity advection into the regions of deep water formation. While the high model uncertainty in both parameters may explain the uncertainty in the AMOC projection, deeper insight into the mechanisms for AMOC is required to reach a more quantitative conclusion. [ABSTRACT FROM AUTHOR]

Climate change is expected to alter the mean and variability of future spring and summer drought and wet conditions during the twenty-first century across North America, as characterized by the Standardized Precipitation Index (SPI). Based on Coupled Model Intercomparison Project phase 5 simulations, statistically significant increases are projected in mean spring SPI over the northern part of the continent, and drier conditions across the southwest. Dry conditions in summer also increase, particularly throughout the central Great Plains. By end of century, greater changes are projected under a higher radiative forcing scenario (RCP 8.5) as compared to moderate (RCP 6.0) and lower (RCP 4.5). Analysis of projected changes standardized to a range of global warming thresholds from +1 to +4 °C reveals a consistent spatial pattern of wetter conditions in the northern and drier conditions in the southwestern part of the continent in spring that intensifies under increased warming, suggesting that the magnitude of projected changes in wetness and drought may scale with global temperature. For many regions, SPI interannual variability is also projected to increase (even for regions that are projected to become drier), indicating that climate may become more extreme under greater warming, with increased frequency of both extreme dry and wet seasons. Quantifying the direction and magnitude of projected future trends from global warming is key to informing strategies to mitigate human influence on climate and help natural and managed resources adapt. [ABSTRACT FROM AUTHOR]

Regional and seasonal temperature and precipitation over land are compared across two generations of global climate model ensembles, specifically, CMIP5 and CMIP3, through historical twentieth century skills and multi-model agreement, and twenty first century projections. A suite of diagnostic and performance metrics, ranging from spatial bias or model-consensus maps and aggregate time series plots, to measures of equivalence between probability density functions and Taylor diagrams, are used for the intercomparisons. Pairwise and multi-model ensemble comparisons were performed for 11 models, which were selected based on data availability and resolutions. Results suggest little change in the central tendency or variability or uncertainty of historical skills or consensus across the two generations of models. However, there are regions and seasons, at different levels of aggregation, where significant changes, performance improvements, and even degradation in skills, are suggested. The insights may provide directions for further improvements in next generations of climate models, and in the meantime, help inform adaptation and policy. [ABSTRACT FROM AUTHOR]