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Global multimodel wave climate projections are obtained at 1.0° × 1.0° scale from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) global circulation model (GCM) realizations. A semi-supervised weather-typing approach based on a characterization of the ocean wave generation areas and the historical wave information from the recent GOW2 database are used to train the statistical model. This framework is also applied to obtain high resolution projections of coastal wave climate and coastal impacts as port operability and coastal flooding. Regional projections are estimated using the collection of weather types at spacing of 1.0°. This assumption is feasible because the predictor is defined based on the wave generation area and the classification is guided by the local wave climate. The assessment of future changes in coastal impacts is based on direct downscaling of indicators defined by empirical formulations (total water level for coastal flooding and number of hours per year with overtopping for port operability). Global multimodel projections of the significant wave height and peak period are consistent with changes obtained in previous studies. Statistical confidence of expected changes is obtained due to the large number of GCMs to construct the ensemble. The proposed methodology is proved to be flexible to project wave climate at different spatial scales. Regional changes of additional variables as wave direction or other statistics can be estimated from the future empirical distribution with extreme values restricted to high percentiles (i.e., 95th, 99th percentiles). The statistical framework can also be applied to evaluate regional coastal impacts integrating changes in storminess and sea level rise.

Given the ecological and economic importance of eastern boundary upwelling systems like the California Current System (CCS), their evolution under climate change is of considerable interest for resource management. However, the spatial resolution of global earth system models (ESMs) is typically too coarse to properly resolve coastal winds and upwelling dynamics that are key to structuring these ecosystems. Here we use a high-resolution (0.1°) regional ocean circulation model coupled with a biogeochemical model to dynamically downscale ESMs and produce climate projections for the CCS under the high emission scenario, Representative Concentration Pathway 8.5. To capture model uncertainty in the projections, we downscale three ESMs: GFDL-ESM2M, HadGEM2-ES, and IPSL-CM5A-MR, which span the CMIP5 range for future changes in both the mean and variance of physical and biogeochemical CCS properties. The forcing of the regional ocean model is constructed with a “time-varying delta” method, which removes the mean bias of the ESM forcing and resolves the full transient ocean response from 1980 to 2100. We found that all models agree in the direction of the future change in offshore waters: an intensification of upwelling favorable winds in the northern CCS, an overall surface warming, and an enrichment of nitrate and corresponding decrease in dissolved oxygen below the surface mixed layer. However, differences in projections of these properties arise in the coastal region, producing different responses of the future biogeochemical variables. Two of the models display an increase of surface chlorophyll in the northern CCS, consistent with a combination of higher nitrate content in source waters and an intensification of upwelling favorable winds. All three models display a decrease of chlorophyll in the southern CCS, which appears to be driven by decreased upwelling favorable winds and enhanced stratification, and, for the HadGEM2-ES forced run, decreased nitrate content in upwelling source waters in nearshore regions. While trends in the downscaled models reflect those in the ESMs that force them, the ESM and downscaled solutions differ more for biogeochemical than for physical variables.

The Iberian viticultural regions are convened according to the Denomination of Origin (DO) and present different climates, soils, topography and management practices. All these elements influence the vegetative growth of different varieties throughout the peninsula, and are tied to grape quality and wine type. In the current study, an integrated analysis of climate, soil, topography and vegetative growth was performed for the Iberian DO regions, using state-of-the-art datasets. For climatic assessment, a categorized index, accounting for phenological/thermal development, water availability and grape ripening conditions was computed. Soil textural classes were established to distinguish soil types. Elevation and aspect (orientation) were also taken into account, as the leading topographic elements. A spectral vegetation index was used to assess grapevine vegetative growth and an integrated analysis of all variables was performed. The results showed that the integrated climate-soil-topography influence on vine performance is evident. Most Iberian vineyards are grown in temperate dry climates with loamy soils, presenting low vegetative growth. Vineyards in temperate humid conditions tend to show higher vegetative growth. Conversely, in cooler/warmer climates, lower vigour vineyards prevail and other factors, such as soil type and precipitation acquire more important roles in driving vigour. Vines in prevailing loamy soils are grown over a wide climatic diversity, suggesting that precipitation is the primary factor influencing vigour. The present assessment of terroir characteristics allows direct comparison among wine regions and may have great value to viticulturists, particularly under a changing climate.

The predictive performance of a random forest ensemble is highly associated with the strength of individual trees and their diversity. Ensemble of a small number of accurate and diverse trees, if prediction accuracy is not compromised, will also reduce computational burden. We investigate the idea of integrating trees that are accurate and diverse. For this purpose, we utilize out-of-bag observations as a validation sample from the training bootstrap samples, to choose the best trees based on their individual performance and then assess these trees for diversity using the Brier score on an independent validation sample. Starting from the first best tree, a tree is selected for the final ensemble if its addition to the forest reduces error of the trees that have already been added. Our approach does not use an implicit dimension reduction for each tree as random project ensemble classification. A total of 35 bench mark problems on classification and regression are used to assess the performance of the proposed method and compare it with random forest, random projection ensemble, node harvest, support vector machine, kNN and classification and regression tree. We compute unexplained variances or classification error rates for all the methods on the corresponding data sets. Our experiments reveal that the size of the ensemble is reduced significantly and better results are obtained in most of the cases. Results of a simulation study are also given where four tree style scenarios are considered to generate data sets with several structures.

Optimum climate conditions for grapevine growth are limited geographically and may be further challenged by a changing climate. Due to the importance of the winemaking sector in Europe, the assessment of future scenarios for European viticulture is of foremost relevance. A 16-member ensemble of model transient experiments (generated by the ENSEMBLES project) under a greenhouse gas emission scenario and for two future periods (2011–2040 and 2041–2070) is used in assessing climate change projections for six viticultural zoning indices. After model data calibration/validation using an observational gridded daily dataset, changes in their ensemble means and inter-annual variability are discussed, also taking into account the model uncertainties. Over southern Europe, the projected warming combined with severe dryness in the growing season is expected to have detrimental impacts on the grapevine development and wine quality, requiring measures to cope with heat and water stress. Furthermore, the expected warming and the maintenance of moderately wet growing seasons over most of the central European winemaking regions may require a selection of new grapevine varieties, as well as an enhancement of pest/disease control. New winemaking regions may arise over northern Europe and high altitude areas, when considering climatic factors only. An enhanced inter-annual variability is also projected over most of Europe. All these future changes pose new challenges for the European winemaking sector.

The availability of a variety of Global Climate Models (GCMs) has increased the importance of the selection of suitable GCMs for impact assessment studies. In this study, we have used Bayesian Model Averaging (BMA) for GCM(s) selection and ensemble climate projection from the output of thirteen CMIP5 GCMs for the Upper Indus Basin (UIB), Pakistan. The results show that the ranking of the top best models among thirteen GCMs is not uniform regarding maximum, minimum temperature, and precipitation. However, some models showed the best performance for all three variables. The selected GCMs were used to produce ensemble projections via BMA for maximum, minimum temperature and precipitation under RCP4.5 and RCP8.5 scenarios for the duration of 2011–2040. The ensemble projections show a higher correlation with observed data than individual GCM’s output, and the BMA’s prediction well captured the trend of observed data. Furthermore, the 90% prediction intervals of BMA’s output closely captured the extreme values of observed data. The projected results of both RCPs were compared with the climatology of baseline duration (1981–2010) and it was noted that RCP8.5 show more changes in future temperature and precipitation compared to RCP4.5. For maximum temperature, there is more variation in monthly climatology for the duration of 2011–2040 in the first half of the year; however, under the RCP8.5, higher variation was noted during the winter season. A decrease in precipitation is projected during the months of January and August under the RCP4.5 while under RCP8.5, decrease in precipitation was noted during the months of March, May, July, August, September, and October; however, the changes (decrease/increase) are higher than under the RCP4.5.

Anticipated changes in climate by the end of this century are likely to modify suspended‐sediment yields (Sy) in diverse ways. Past work has shown how hydrological non‐stationarity may alter water discharges and hence Sy, but less attention has been given to the impact of likely future changes in upland sediment‐detachment rates on downstream Sy. In certain environments, climatically driven changes in vegetation cover on upland hillslopes may more than counteract the effects of changing runoff on Sy. Changes in precipitation, temperature, and vegetation may, therefore, interact in nonlinear ways to produce unexpected changes. In this work, we simulated future changes to background Sy (i.e., changes unrelated to land‐use changes and dams) with climatological and vegetative data output from an ensemble of CMIP6 Earth System Model (ESM) simulations. Depending on the future scenario, the cumulative annual sediment flux of 780 globally distributed rivers increases by between 2.3% and 8.4%. Significant deviations from historical Sy are projected at high latitudes in response to each forcing variable, while low‐latitude responses are regionally varied. In regions where ensemble members agree on future changes in forcing variables, large Sy changes are forecast with high confidence (e.g., >200% Sy increase for several northeastern U.S. rivers at the 95% level). In contrast, ensemble variability in vegetation projections results in considerable uncertainty in the projected Sy of rivers in other regions. Further improvements to the vegetation components of ESMs will help to reduce regional uncertainties in projected changes to Sy. Plain Language Summary Rivers move sediment eroded from across the landscape downstream through mountains, valleys, and deltas, carrying small‐enough grains in suspension over long distances from source to sink. The amount of suspended sediment a river transports varies according to flow competence and upstream basin characteristics, but anthropogenic climate change could alter baseline sediment mobilization rates in complex ways that cascade through river networks and disturb both natural and human systems. In this study, we combined a multi‐scenario ensemble of earth system models with a global suspended‐sediment‐flux model calibrated to natural conditions in order to understand how future changes in temperature, precipitation, and vegetation will translate into adjustments to the sediment delivered to large river deltas. We find that across future scenarios, cumulative sediment transport will increase by 2%–8% by the year 2100, but many regions (especially at high latitudes) may see declines locally due to greater vegetative cover that reduces the erosive potential of rainfall. Altogether, our results indicate that rivers in the near future may experience large changes in sediment loading, and improvements to dynamic vegetation models will improve confidence in projections of suspended‐sediment fluxes. Key Points We provide spatially distributed ensemble projections of global suspended‐sediment‐yield responses to anthropogenic climatic changes by 2100 We project a median increase of 2.3%–8.4% in the cumulative suspended‐sediment flux of 780 globally distributed rivers Shifts in vegetation, temperature, and rainfall drive significant regional and latitudinal changes in hillslope sediment‐detachment rates

Climate change can cause property damage and deaths in cities. City-scale climate projections are essential for making informed decisions towards climate change mitigation and adaptation at city levels. This study aims at developing ensemble projections of temperature extremes at the city-level and quantifying the contributions of various factors to the resulting uncertainty of the ensemble projections. The city of Toronto will be used here as an example to demonstrate the effectiveness of the proposed research framework. In particular, the stepwise cluster analysis (SCA) model will be used to perform climate downscaling to three GCM datasets (GFDL, IPSL, and MPI) under three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) in order to generate city-level climate projections for the city of Toronto. The SCA model is demonstrated to be capable of capturing the inter- and intra-annual variations of the daily maximum, mean, and minimum temperatures in the studied city. The results suggest that mean temperatures in Toronto are projected to increase at the rate of 0.15 and 0.5 °C/decade under RCP4.5 and RCP8.5, respectively, while no significant warming trend is detected for RCP2.6. In terms of temperature extremes, extreme warm events are projected to increase while extreme cold events decrease under all emission scenarios. The decrease in the heating demand is two to four times larger than the increase in the cooling demand, indicating a decrease in the city’s total energy use. The projected warming might be beneficial for the urban growers because of the significant increases in the growing season length and growing degree days; however, the residents of the city of Toronto are likely to experience simultaneous increases in the intensity, duration, and frequency of heatwave events in future summers. Because of the warming, coldwave events in winters are likely to become less frequent and be shorter in duration, but their intensity is expected to increase significantly. Through decomposition of the resulting uncertainty of the ensemble projections, emission scenario is found to be the dominant factor for the uncertainty associated with urban climate projection.

Projections of changes in climate are important in assessing the potential impacts of climate change on natural and social systems. However, current knowledge on assembling different GCMs to estimate future climate change over the Pear River basin is still limited so far. This study examined the capability of BMA and arithmetic mean (AM) method in assembling precipitation and temperature from CMIP5 under RCP2.6, RCP4.5 and RCP8.5 scenarios over the Pearl River basin. Results show that the BMA outperforms the traditional AM method. Precipitation tends to increase over the basin under RCP2.6 and RCP4.5 scenarios, whereas decrease under RCP8.5. The most remarkable increase of precipitation is found in the northern region under RCP2.6 scenario. The linear trend of the monthly mean near-surface air temperature increases with the growing CO 2 concentration. The warming trends in four seasons are distinct. The warming rate is prominent in summer and spring than that in other season, meanwhile it is larger in western region than in other parts of the basin. The findings can provide beneficial reference to water resources and agriculture management strategies, as well as the adaptation and mitigation strategies for floods and droughts under the context of global climate change.

Social scientists have argued that good communication around risks in climate hazards requires information to be presented in a user-relevant way, allowing people to better understand the factors controlling those risks. We present a potentially useful way of doing this by explaining future UK winter precipitation in terms of changes in the frequency, and associated average rainfall, of local pressure patterns that people are familiar with through their use in daily weather forecasts. We apply this approach to a perturbed parameter ensemble (PPE) of coupled HadGEM3-GC3.05 simulations of the RCP8.5 emissions scenario, which formed part of the UK Climate Projections in 2018. The enhanced winter precipitation by 2050–99 is largely due to an increased tendency towards westerly and south-westerly conditions at the expense of northerly/easterly conditions. Daily precipitation is generally more intense, most notably for the south-westerlies. In turn, we show that the changes in the frequency of the pressure patterns are consistent with changes in larger scale drivers of winter circulation and our understanding of how they relate to each other; this should build user confidence in the projections. Across the PPE, these changes in pressure patterns are largely driven by changes in the strength of the stratospheric polar vortex; for most members the vortex strengthens over the twenty-first century, some beyond the CMIP6 range. The PPE only explores a fraction of the CMIP6 range of tropical amplification, another key driver. These two factors explain why the PPE is skewed towards exploring the more westerly side of the CMIP6 range, so that the PPE’s description of UK winter precipitation changes does not provide a full picture.