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The slowdown of the near-surface wind speed (SWS) has been extensively reported in China, but future projections of the SWS are rare. In this study, the wind speeds of Coupled Model Intercomparison Project phase 5 (CMIP5) datasets were compared with observations over Eastern China, and the possible influences of increasing CO2 emissions on the changes in SWS were investigated. The results show that although the CMIP5 models reproduced the spatial pattern of SWSs, they underestimated the long-term reduction of the SWSs during the historical period from 1979 to 2005. Compared to the traditional arithmetic mean ensemble method (AMEM), the relative error in the weighted mean ensemble method (WMEM) decreased by 8.5%, and the root-mean square error decreased by 0.14 m s−1. Compared to the WMEM, a smaller error was obtained for the results of the statistical downscaling model (SDM), which was established based on the principal component analysis and the stepwise regression equation and used the ensemble meteorological variables as predictor. Based on the SDM, CO2 emission increases could induce the decreases of SWSs in the future, with the significantly decreasing trends of − 0.007 and − 0.002 m s−1 decade−1 under the RCP8.5 and RCP4.5 emission scenarios, respectively. The probability of annual mean SWSs exceeding 2.37 m s−1 decreased by 12.1% under RCP8.5 relative to RCP4.5. Furthermore, the annual mean SWSs could show a weak strengthening over the next 20 years. [ABSTRACT FROM AUTHOR]

Increasing spatial resolution and increasing ensemble size both tend to improve the skill of ensemble forecasts. Due to computational constraints, a balance needs to be found in operational NWP. Here, we examine a scenario where ensembles are formed by pooling k lower‐resolution and m higher‐resolution members such that the overall computational cost is equal to the constraint. The approach is applied to medium‐range weather forecasts with ECMWF's Integrated Forecasting System using horizontal resolutions of 18, 29 and 45 km and ensemble sizes ranging from 8 to 254 members. The methodology is similar to the multi‐level Monte‐Carlo approach but does not use stochastic perturbations that are shared between members at different levels. Probabilistic skill is quantified for 850 hPa temperature verified against analyses and 2 m temperature verified against station observations. Generally, dual‐resolution ensembles with similar numbers of lower and higher‐resolution members provide the optimal configuration for 2 m temperature prediction. In contrast, single‐resolution ensembles appear to be the most skilful for 850 hPa temperature. The dual‐resolution ensembles are a special kind of multi‐model ensemble. An analytic model describing the skill of such a multi‐model ensemble is developed and its parameters are estimated from the actual verification statistics. The model is capable of describing the general differences in behaviour between 2 m temperature and 850 hPa temperature. [ABSTRACT FROM AUTHOR]

This paper examines the forecasting skill of eight Global Climate Models from the North-American Multi-Model Ensemble project (CCSM3, CCSM4, CanCM3, CanCM4, GFDL2.1, FLORb01, GEOS5, and CFSv2) over seven major regions of the continental United States. The skill of the monthly forecasts is quantified using the mean square error skill score. This score is decomposed to assess the accuracy of the forecast in the absence of biases (potential skill) and in the presence of conditional (slope reliability) and unconditional (standardized mean error) biases. We summarize the forecasting skill of each model according to the initialization month of the forecast and lead time, and test the models' ability to predict extended periods of extreme climate conducive to eight 'billion-dollar' historical flood and drought events. Results indicate that the most skillful predictions occur at the shortest lead times and decline rapidly thereafter. Spatially, potential skill varies little, while actual model skill scores exhibit strong spatial and seasonal patterns primarily due to the unconditional biases in the models. The conditional biases vary little by model, lead time, month, or region. Overall, we find that the skill of the ensemble mean is equal to or greater than that of any of the individual models. At the seasonal scale, the drought events are better forecast than the flood events, and are predicted equally well in terms of high temperature and low precipitation. Overall, our findings provide a systematic diagnosis of the strengths and weaknesses of the eight models over a wide range of temporal and spatial scales. [ABSTRACT FROM AUTHOR]

The Bayesian weighted averaging (BWA) method is commonly used to integrate over multi-model ensembles of climate series. This method relies on two criteria to assign weights to individual outputs: model skill in reproducing historical observations, and inter-model agreement in simulating future period. Observations are generally thought to be relevant for correcting biases in model outputs in the BWA framework. However, they concurrently may introduce unpredictable impacts in the context of the downscaling process, in particular, when model output on precipitation is of interest. Specifically, the posterior distribution may excessively depend on few 'outlier models' being close to the observation, when all other models fail to capture observation of the historical period—a common situation for precipitation metrics. Another issue emerges for climates with very dry months: the inclusion of observation in BWA may result in a significant spread of the posterior distribution into the negative region. To address these problems, a modified version of the BWA method that removes observations in the initial phase of downscaling (computation of Factors of Change) and adds them in the estimation of posterior distributions is explored in this work. Comparisons of simulation results for the locations of Miami (FL), Fresno (CA), and Flint (MI) between the modified BWA and the traditional BWA demonstrate consistent outcomes with regards to the effect of observation in the Bayesian framework. Further, the modified BWA approach generally reduces uncertainty, as compared to 'simple averaging' in the Bayesian context, which assigns equal weights to all model outputs. [ABSTRACT FROM AUTHOR]

In this study, probabilistic future changes in sea surface temperature (SST) over East Asian marginal seas between historical (1971–2000) and late twenty-first century (2061–2100) periods are calculated by using both unweighted and weighted averaging methods. Unlike most previous studies, the present study considers uncertainty caused by internal variability and model error, which could reduce the credible intervals. Here, marginal seas are divided into three regions of Yellow Sea, South Sea, and East/Japan Sea, and the projections are computed separately for January–February–March (JFM), April–May–June, July–August–September (JAS), and October–November–December seasons. Our results show that the SSTs for the three regions are projected to increase by about 1–3 K and 2–6 K under the representative concentration pathway (RCP) 4.5 and the RCP8.5 scenarios, respectively, in terms of the 90% credible intervals. The future SST change over the Yellow and the East/Japan seas is larger than that over the South Sea, which is similar to recent observed trends. SSTs are expected to increase more in JAS than in JFM for all three regions. Before making the projections, the method is tested in a suite of one-at-a-time cross-validation experiments. The method well-calibrated results as measured by the 90% posterior credible intervals. [ABSTRACT FROM AUTHOR]

The ability of the 5th phase of the Coupled Model Intercomparison Project (CMIP5) to reproduce twentieth-century climate trends over the seven CONUS regions of the National Climate Assessment is evaluated. This evaluation is carried out for summer and winter for three time periods, 1895–1939, 1940–1979, and 1980–2005. The evaluation includes all 206 CMIP5 historical simulations from 48 unique models and their multi-model ensemble (MME), as well as a gridded in situ dataset of surface air temperature and precipitation. Analysis is performed on both individual members and the MME, and considers reproducing the correct sign of the trends by the members as well as reproducing the trend values. While the MME exhibits some trend bias in most cases, it reproduces historical temperature trends with reasonable fidelity for summer for all time periods and all regions, including at the CONUS scale, except the Northern Great Plains from 1895 to 1939 and Southeast during 1980–2005. Likewise, for DJF, the MME reproduces historical temperature trends across all time periods over all regions, including at the CONUS scale, except the Southeast from 1895 to 1939 and the Midwest during 1940–1979. Model skill was highest across all of the seven regions during JJA and DJF for the 1980–2005 period. The quantitatively best result is seen during DJF in the Southwest region with at least 74% of the ensemble members correctly reproducing the observed trend across all of the time periods. No clear trends in MME precipitation were identified at these scales due to high model precipitation variability. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since the first International Cooperative for Aerosol Prediction (ICAP) multi‐model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An update of the current ICAP status is provided, along with an evaluation of the performance of ICAP‐MME over 2012–2017, with a focus on June 2016–May 2017. Evaluated with ground‐based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate‐resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP‐MME AOD consensus remains the overall top‐scoring and most consistent performer among all models in terms of root‐mean‐square error (RMSE), bias and correlation for total, fine‐ and coarse‐mode AODs as well as dust AOD; this is similar to the first ICAP‐MME study. Further, over the years, the performance of ICAP‐MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP‐MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP‐MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012–2017 suggests a general tendency for model improvements in fine‐mode AOD, especially over Asia. No significant improvement in coarse‐mode AOD is found overall for this time period. [ABSTRACT FROM AUTHOR]

An assessment of the forecast quality of 10 m wind speed by deterministic and probabilistic verification measures has been carried out using the original raw and two statistical bias-adjusted forecasts in global coupled seasonal climate prediction systems (ECMWF-S4, METFR-S3, METFR-S4 and METFR-S5) for boreal winter (December–February) season over a 22-year period 1991–2012. We follow the standard leave-one-out cross-validation method throughout the work while evaluating the hindcast skills. To minimize the systematic error and obtain more reliable and accurate predictions, the simple bias correction (SBC) which adjusts the systematic errors of model and calibration (Cal), known as the variance inflation technique, methods as the statistical post-processing techniques have been applied. We have also built a multi-model ensemble (MME) forecast assigning equal weights to datasets of each prediction system to further enhance the predictability of the seasonal forecasts. Two MME have been created, the MME4 with all the four prediction systems and MME2 with two better performing systems. Generally, the ECMWF-S4 shows better performance than other individual prediction systems and the MME predictions indicate consistently higher temporal correlation coefficient (TCC) and fair ranked probability skill score (FRPSS) than the individual models. The spatial distribution of significant skill in MME2 prediction is almost similar to that in MME4 prediction. In the aspect of reliability, it is found that the Cal method has more effective improvement than the SBC method. The MME4_Cal predictions are placed in close proximity to the perfect reliability line for both above and below normal categorical events over globe, as compared to the MME2_Cal predictions, due to the increase in ensemble size. To further compare the forecast performance for seasonal variation of wind speed, we have evaluated the skill of the only raw MME2 predictions for all seasons. As a result, we also find that winter season shows better performance than other seasons. [ABSTRACT FROM AUTHOR]

Future climate is typically projected using multi-model ensembles, but the ensemble mean is unlikely to be optimal if models' skill at reproducing historical climate is not considered. Moreover, individual climate models are not independent. Here, we examine the interplay between the benefits of optimising an ensemble for the performance of its mean and the the effect this has on ensemble spread as an uncertainty estimate. Using future Australian precipitation change as a case study, we perform optimal subset selection based on present-day precipitation, sea surface temperature and/or 500 hPa eastward wind climatologies. We use either one, two, or all three variables as predictors. Out-of-sample projection skill is assessed using a model-as-truth approach (rather than observations). For multiple variables, multi-objective optimisation is used to obtain Pareto-optimal subsets (an ensemble of model subsets), to gauge the uncertainty in optimisation arising from the multiple constraints. We find that the spread of climate model subset averages typically under-represents the true projection uncertainty (overconfidence), but that the situation can be significantly improved using mixture distributions for uncertainty estimation. The single best predictor, present-day precipitation, gives the most accurate results but is still overconfident—a consequence of calibrating too specifically. It is only when all three constraints are used that projection skill is improved and overconfidence is eliminated, but at the cost of a poorer best estimate relative to one predictor. We thus identify an important trade-off between accuracy and precision, depending on the number of predictors, which is likely relevant for any subset selection or weighting strategy. [ABSTRACT FROM AUTHOR]

• Impact and uncertainty framework for the identification of compound hydro-hazards. • UK example application using EDgE RCP8.5 projections. • NE Scotland and SW UK identified as spatio-temporally compound hotspot regions. • Hydrological models identified as the largest source of variability. • Raises questions regarding spatial variability of hydroclimatological modelling uncertainty. There exists an increasing need to understand the impact of climate change on the hydrological extremes of flood and drought, collectively referred to as 'hydro-hazards'. At present, current methodology are limited in their scope, particularly with respect to inadequate representation of the uncertainty in the hydroclimatological modelling chain. This paper proposes spatially consistent comprehensive impact and uncertainty methodological framework for the identification of compound hydro-hazard hotspots – hotspots of change where concurrent increase in mean annual flood and drought events is projected. We apply a quasi-ergodic analysis of variance (QE-ANOVA) framework, to detail both the magnitude and the sources of uncertainty in the modelling chain for the mean projected mean change signal whilst accounting for non-stationarity. The framework is designed for application across a wide geographical range and is thus readily transferable. We illustrate the ability of the framework through application to 239 UK catchments based on hydroclimatological projections from the EDgE project (5 CMI5-GCMs and 3 HMs, forced under RCP8.5). The results indicate that half of the projected hotspots are temporally concurrent or temporally successive within the year, exacerbating potential impacts on society. The north-east of Scotland and south-west of the UK were identified as spatio-temporally compound hotspot regions and are of particular concern. This intensification of the hydrologic dynamic (timing and seasonality of hydro-hazards) over a limited time frame represents a major challenge for future water management. Hydrological models were identified as the largest source of variability, in some instances exceeding 80% of the total variance. Critically, clear spatial variability in the sources of modelling uncertainty was also observed; highlighting the need to apply a spatially consistent methodology, such as that presented. This application raises important questions regarding the spatial variability of hydroclimatological modelling uncertainty. In terms of water management planning, such findings allow for more focussed studies with a view to improving the projections which inform the adaptation process. [ABSTRACT FROM AUTHOR]

This study assesses future changes in drought characteristics in response to different emission scenarios over Korea based on multiple climate projections and multiple drought indices. To better resolve regional climate details and enhance confidence in future changes, multi-model projections are dynamically downscaled, and their systematic biases are statistically removed. Bias-corrected climate data are directly used to calculate the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), and they are fed into a hydrological model to generate runoff used for the calculation of the standardized runoff index (SRI). The analysis is focused on changes in the frequencies and severities of severe or extreme droughts measured by the SPI, SPEI, and SRI for the Han River and Nakdong River basins. Fine-scale ensemble projections reveal robust changes in temperatures that monotonically respond to emission forcings, whereas precipitation changes show rather inconsistent patterns across models and scenarios. Temperature and precipitation shifts lead to changes in evapotranspiration (ET) and runoff, which modulate the drought characteristics. In general, the SPEI shows the most robust pattern with significant increases in both drought frequency and severity. This result is mainly due to the excessive potential ET that is hypothetically estimated without considering water availability. While the SPI based on only precipitation exhibits behavior different from that of the SPEI, the SRI that considers actual ET produces an intermediate level of changes between the SPI and SPEI. Compared to the large uncertainty of the frequency changes that overwhelm the change signal due to inconsistency across models and indices, the severity of future drought is likely to be exacerbated with enhanced confidence. [ABSTRACT FROM AUTHOR]

Hydrological models are a mathematical representation of heterogeneous and nonlinear hydrological processes. In the past, a great many simple to complex hydrological models have been developed, but none of these models is superior to the others for all types of practical applications. These hydrological model alternatives have different strengths in representing and capturing complex natural hydrological processes. Yet generally a single hydrological model is used in practice, which may represent certain processes of the catchment well and may be less adequate for others. Moreover, the use of a single hydrological model is restrictive, as the conceptual uncertainty associated with the model structure cannot be identified and quantified. To overcome these issues, the multi-model ensemble approach has recently been applied more commonly to take advantage of the diverse skills of different models. In this study, the multi-model ensemble approach to account for model structure uncertainty is employed to improve hydrological model prediction. While a certain hydrological model may represent particular processes or (extreme) events better than another, two distinct models may represent these processes or events with comparable accuracy. If members of a hydrological ensemble model capture the same process and if they are similar in process representation, then these members will not supply any additional information for prediction and therefore will not improve the accuracy. Hence, by identifying similar models, there is potential to increase the reliability of hydrological ensemble predictions and to reduce computing costs without reducing accuracy. In this study a methodology is presented to identify similar models. The methodology is applied and tested for the Tuapiro catchment in New Zealand. A range of verification statistics are computed to ascertain the validity of the approach. Overall, the multi-model ensemble-based hydrological prediction where non-informative members have been removed is shown to not compromise prediction accuracy. For the case study streamflow prediction an increased flatness of the rank histogram, insignificant changes in the continuous rank probability score, and improved accuracy in terms of Nash-Sutcliffe coefficient, Kling-Gupta efficiency and Root Mean Square Error were found, at lower computing costs. [ABSTRACT FROM AUTHOR]

We propose a general concept for the analysis of the results of urban dispersion simulations of high temporal resolution, taking into account multi-model ensembles. We are motivated by theoretical considerations related both to the characteristics of the measurements and to the representation of the multi-model ensemble. Based on typical mathematical notions, we propose and present several indices, and apply them to the results of the UDINEE dispersion-modelling exercise. We demonstrate that the median model is the proper representation of the ensemble results for the presented methodology. [ABSTRACT FROM AUTHOR]

Multi-model ensemble prediction is an effective approach for improving the prediction skill short-term climate prediction and evaluating related uncertainties. Based on a combination of localized operation outputs of Chinese climate models and imported forecast data of some international operational models, the National Climate Center of the China Meteorological Administration has established the China multi-model ensemble prediction system version 1.0 (CMMEv1.0) for monthly-seasonal prediction of primary climate variability modes and climate elements. We verified the real-time forecasts of CMMEv1.0 for the 2018 flood season (June-August) starting from March 2018 and evaluated the 1991–2016 hindcasts of CMMEv1.0. The results show that CMMEv1.0 has a significantly high prediction skill for global sea surface temperature (SST) anomalies, especially for the El Niño-Southern Oscillation (EN-SO) in the tropical central-eastern Pacific. Additionally, its prediction skill for the North Atlantic SST triple (NAST) mode is high, but is relatively low for the Indian Ocean Dipole (IOD) mode. Moreover, CMMEv1.0 has high skills in predicting the western Pacific subtropical high (WPSH) and East Asian summer monsoon (EASM) in the June–July–August (JJA) season. The JJA air temperature in the CMMEv1.0 is predicted with a fairly high skill in most regions of China, while the JJA precipitation exhibits some skills only in northwestern and eastern China. For real-time forecasts in March–August 2018, CMMEv1.0 has accurately predicted the ENSO phase transition from cold to neutral in the tropical central-eastern Pacific and captures evolutions of the NAST and IOD indices in general. The system has also captured the main features of the summer WPSH and EASM indices in 2018, except that the predicted EASM is slightly weaker than the observed. Furthermore, CMMEv1.0 has also successfully predicted warmer air temperatures in northern China and captured the primary rainbelt over northern China, except that it predicted much more precipitation in the middle and lower reaches of the Yangtze River than observation. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Projected warming and drying trends over the Mediterranean region represent a substantial threat for wheat production. The present study assesses winter wheat yield response to potential climate change and estimates the quantitative effectiveness of using early flowering cultivars and early sowing dates as adaptation options for the major wheat production region of Portugal. A crop model (STICS) is used for this purpose, which is calibrated for yield simulations before projecting future yields. Climate projections over 2021–2050 and 2051–2080 under two emission scenarios (RCP4.5 and RCP8.5) are retrieved from bias-adjusted datasets, generated by a ten-member climate model ensemble. Projected intensification of water deficits and more frequent high-temperature events during late spring (April–June), coinciding with the sensitive grain filling stage, primarily result in continuous mean yield losses (relative to 1981–2010) by − 14% (both scenarios) during 2021–2050 and by − 17% (RCP4.5) or − 27% (RCP8.5) during 2051–2080, also accompanied by increased yield variabilities. Of evaluated adaptation options at various levels, using earlier flowering cultivars reveals higher yield gains (26–38%) than that of early sowings (6–10%), which are able to reverse the yield reductions. The adopted early flowering cultivars successfully advance the anthesis onset and grain filling period, which reduces or avoids the risks of exposure to enhanced drought and heat stresses in late spring. In contrast, winter warming during early sowing window could affect vernalization fulfillment by slowing effective chilling accumulation, thus increasing the pre-anthesis growth length with limited effects on advancing reproductive stage. Crop yield projections and explored adaptation options are essential to assess food security prospects (availability and stability) of dry Mediterranean areas, providing crucial insights for appropriate policymaking. [ABSTRACT FROM AUTHOR]

This study examines potential benefits of performance‐based multi‐model ensembles (MMEs) in projecting the impacts of climate change on extreme precipitation indices over East Asia (EA) using the data from 19 GCMs in the coupled model intercomparison project 5 (CMIP5). The Taylor skill score is adopted as the measure of the model skills in simulating the spatial and interannual variability of the selected extreme precipitation indices over four EA regions. The overall rank based on the total skill score (TSC) is used to construct two skill‐based MMEs, MME of high‐skill, MMH (MME of low‐skill, MML) that include the top (bottom) seven models, in addition to the simple ensemble of all 19 GCMs (ENS). Inter‐GCM consistency is measured using the signal‐to‐noise ratio (SNR). In the present‐day period, MMH yields higher skill scores than MML and ENS for almost all extreme precipitation indices as well as regions. Regional variations in biases, inter‐model consistency, and TSC are large. The inter‐model consistency is highest for Northern China and Manchuria and is lowest for Southern China. The most notable differences in the key properties of climate change signals from the three MMEs among the three ensembles are that the climate change signals from MMH and ENS exceed the 90% significance level in much larger areas than those from MML. However, the differences in the climate change signals between MMH and MML are generally below the 90% significance level. The SNR of the projected climate change signals shows that MMH yields more consistent climate change signals than ENS/MML. Both the SNR differences and the area in which statistical significance exceed the 90% level suggest that constructing climate change signals from a group of higher‐skill models may yield more reliable projections than constructing MMEs from the entire models or a group of lower‐skilled models. The domains used in the study (SOUTH: 110°–140°E, 20°–30°N, MIDDLE: 110°–140°E, 30°–40°N), NORTH: 110°–140°E, 40°–50°N, EA: 110°–140°E, 20°–50°N) [ABSTRACT FROM AUTHOR]

As solar energy will be an increasingly important renewable energy source in future years, the study of how climate change affects both its temporal and spatial variability is very relevant. In this paper, we study future changes of the solar radiation resource in the Iberian Peninsula (IP) through a set of simulations from ESCENA project (generation of regionalized scenarios of climate change in Spain with high‐resolution models) until mid‐century. The evaluation of the simulations against observations indicates contrasting biases for the different regional climate models (RCMs) in terms of solar irradiation amount and its inter‐annual variability. We propose a diagnostic for the quality of solar energy resource, in which the gridpoints are classified in four categories depending on the combination of solar irradiation amount and variability. The observed large percentage of points in the optimal category (high irradiation/low variability) in the IP is well‐captured by the RCMs in general terms. The analysis of scenarios indicates a future increase in solar irradiation, although not all scenarios agree in the geographical distribution of this increase. The quality of solar energy resource is projected to increase, mostly due to a decrease in variability. This is an important result, as a more stable inter‐annual resource should decrease the need for backup sources and also reduce inter‐annual electricity price variations. Finally, results from a first approximation to the issue of the ability of solar energy to cover power demand peaks in summer show important differences between regions of the IP. However, the spatially averaged correlation of solar irradiation and summer surface temperatures for the whole IP is rather high, which is a positive result as the strong interconnections of the power grid within the IP could allow a distribution of solar power surpluses in certain regions for such high‐temperature episodes. Due to the importance of solar energy in the future we study future changes of the solar radiation over Iberian Peninsula through the simulations from ESCENA project. To evaluate the quality of solar energy resource, the gridpoints are classified in four categories depending on the combination of solar irradiation amount and variability. The observed large percentage of points in the optimal category (high irradiation/low variability) is captured by the models. The analysis of scenarios indicates a future increase in solar irradiation amount and variability, although not all scenarios agree in this respect. [ABSTRACT FROM AUTHOR]

Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are 91.39% and 0.964 respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software.

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.

This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models’ and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models’ performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models’ overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models.

The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during boreal summer (from May to September) was analyzed using the 12-year reforecasts data from five Sub-seasonal to Seasonal (S2S) models, including the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), the National Centers for Environmental Prediction (NCEP), and the Met Office (UKMO). The result shows that, compared with the individual models, our newly derived median multi-model ensemble (MME) can significantly improve the prediction skill of sub-seasonal precipitation in the MC. Both the Temporal Correlation Coefficient (TCC) skill and the Pattern Correlation Coefficient (PCC) skill reached 0.6 in lead week 1, dropped the following week, did not exceed 0.2 in lead week 3, and then lost their significance. The results show higher prediction skill near the Equator than in the north at 10° N. It is difficult to make effective predictions with the models beyond three weeks. The prediction ability of the median MME improves significantly as the total number of model members increases. The prediction performance of the median MME depends not only on the diversity of models but also on the number of model members. Moreover, the prediction skill is particularly sensitive to the intensity and phase of Boreal Summer Intraseasonal Oscillation 1 (BSISO1) with the highest skills appearing at initial phases 1 and 5.

Traditional approaches in constructing response surface models typically ignore model uncertainty. If the relationship between the input factors and output characteristics of a process is very complex, traditional model building approaches may have limited effectiveness. In this paper, we propose a multi-model ensemble and then implement this ensemble model to optimize the process performance. To form a multi-model ensemble, we need to determine the weights of the different models, that is, values indicating relative importance among the models. To determine the weights, a hybrid weighting method is proposed, in which both global and local weighting methods are taken into account. Based on the hybrid weights of different models, a multi-model ensemble is built and optimized. An example is illustrated to verify the effectiveness of the proposed approach. The results show that the proposed model can achieve more accurate predictive capability and that a better process improvement is reached. [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are useful tools for assessing climate change impacts on temperature and rainfall. Although climate data from various GCMs have been increasingly used in climate change impact studies, GCMs configurations and module characteristics vary from one to another. Therefore, it is crucial to assess different GCMs to confirm the extent to which they can reproduce the observed temperature and rainfall. Rather than assessing the interdependence of each GCM, the purpose of this study is to compare the capacity of four different multi‐model ensemble (MME) methods (random forest [RF], support vector machine [SVM], Bayesian model averaging [BMA] and the arithmetic ensemble mean [EM]) in reproducing observed monthly rainfall and temperature. Of these four methods, the RF and SVM demonstrated a significant improvement over EM and BMA in terms of performance criteria. The relative importance of each GCM based on the RF ensemble in reproducing rainfall and temperature could also be ranked. We compared the GCMs importance and Taylor skill score and found that their correlation was 0.95 for temperature and 0.54 for rainfall. Our results also demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. We conclude that machine learning MME could be efficient and useful with improved accuracy in reproducing historical climate variables. The relative change of RMSE for different size of the CMIP5 subset compared with full number of GCMs using the random forest (RF) ensemble simulations. Our results demonstrated that the number of GCMs ensemble simulations could be reduced from 33 to 25 in RF model while maintaining predictive error less than 2%. Having such a representative subset of simulations could reduce computational costs for climate impact modelling and maintain the quality of ensemble at the same time. [ABSTRACT FROM AUTHOR]

An Expert Seasonal Prediction System for operational Seasonal Outlook (ESPreSSO) is developed based on the APEC Climate Center (APCC) Multi-Model Ensemble (MME) dynamical prediction and expert-guided statistical downscaling techniques. Dynamical models have improved to provide meaningful seasonal prediction, and their prediction skills are further improved by various ensemble and downscaling techniques. However, experienced scientists and forecasters make subjective correction for the operational seasonal outlook due to limited prediction skills and biases of dynamical models. Here, a hybrid seasonal prediction system that grafts experts’ knowledge and understanding onto dynamical MME prediction is developed to guide operational seasonal outlook in Korea. The basis dynamical prediction is based on the APCC MME, which are statistically mapped onto the station-based observations by experienced experts. Their subjective selection undergoes objective screening and quality control to generate final seasonal outlook products after physical ensemble averaging. The prediction system is constructed based on 23-year training period of 1983-2005, and its performance and stability are assessed for the independent 11-year prediction period of 2006-2016. The results show that the ESPreSSO has reliable and stable prediction skill suitable for operational use. [ABSTRACT FROM AUTHOR]

Quantification of the inherent uncertainty in hydrologic forecasting is essential for flood control and water resources management. The existing approaches, such as Bayesian model averaging (BMA), hydrologic uncertainty processor (HUP), copula-BMA (CBMA), aim at developing reliable probabilistic forecasts to characterize the uncertainty induced by model structures. In the probability forecast framework, these approaches either assume the probability density function (PDF) to follow a certain distribution, or are unable to reduce bias effectively for complex hydrological forecasts. To overcome these limitations, a copula Bayesian processor associated with BMA (CBP-BMA) method is proposed with ensemble lumped hydrological models. Comparing with the BMA and CBMA methods, the CBP-BMA method relaxes any assumption on the distribution of conditional PDFs. Several evaluation criteria, such as containing ratio, average bandwidth and average deviation amplitude of probabilistic application, are utilized to evaluate the model performance. The case study results demonstrate that the CBP-BMA method can improve hydrological forecasting precision with higher cover ratios more than 90%, which are increased by 4.4% and 3.2%, 2.2% and 1.7% over those of BMA and CBMA during the calibration and validation periods, respectively. The proposed CBP-BMA method provides an alternative approach for uncertainty estimation of hydrological multi-model forecasts. [ABSTRACT FROM AUTHOR]

A new method was proposed for the probabilistic projection of future climate that introduced quantile mapping to a regression method using a multi-model ensemble (QM RMME). Results of this method were then compared with those of the traditional regression method (RMME). Six stations in Japan where 100 year observation records were available were used to evaluate the performance of the methods. An initial 50-year period (1901-1950) was used to develop the regression models and the final period (1951-2000) was used for evaluation. Results showed that the estimation errors at the 50th and 90th percentile were smaller for QM RMME as compared to RMME at most sites. Conversely, when the model development and evaluation periods were limited to 20 years (1901-1920 and 1951-1970, respectively), the 90th percentile error was larger for QM RMME. This was attributed to quantile mapping resulting in over-fitting of the data during the model development period. Furthermore, the QM RMME error increased when the difference of observations between the model development and verification periods was large. Therefore, results indicated that the RMME method was more stable for relatively short data verification periods. [ABSTRACT FROM AUTHOR]

This study promotes the critical use of air pollution modelling results for health and agriculture impacts, with the primary goal of providing more reliable estimates to decision makers. To date, the accuracy of air quality (AQ) models and the effects of model-to-model result variability (which we will refer to as model uncertainty) on impact assessment studies have been often ignored, thus undermining the robustness of the information used in the decision making process and the confidence in the results obtained. A suite of twelve PM 2.5 and ozone concentration fields produced by regional-scale chemistry transport Air Quality (AQ) models during the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) has been used to calculate the impact of air pollution on premature deaths and crop yields. An innovative technique is applied to bias-adjust the models to available observations. The model results for ozone and PM 2.5 are combined in a multi-model (MM) ensemble, which is used to estimate the damage and economic cost to human health and crop yields, as well as the associated uncertainties. The MM ensemble quantifies directly the uncertainty introduced by AQ models into the air pollution impact assessment chain, while the indirect use of experimental information through a bias adjustment, reduces the uncertainty in the ozone and PM 2.5 fields and subsequently the uncertainty of the final impact assessment and cost valuation. The analysis over the European countries analysed in this study shows a mean number of premature deaths due to exposure to PM 2.5 and ozone of approximately 370,000 (inter-quantile range between 260,000 and 415,000) and a relative yield loss of approximately 7% to 9% (depending on the exposure metrics used, for wheat and maize together). Furthermore, the results indicate that a reduction in the uncertainty of the modelled ozone by 61% and by 80% (depending on the aggregation metric used) and by 46% for PM 2.5 , produces a reduction in the uncertainty in premature mortality and crop loss of >60%, and of an equivalent percentage in the final uncertainty of cost valuation, providing decision makers with more accurate estimations for more targeted interventions. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration ( ET 0 ) plays a fundamental role in agronomic, forestry, and water resources management. Estimating and forecasting ET 0 have long been recognized as a major challenge for researchers and practitioners in these communities. This work explored the potential of multiple leading numerical weather predictions (NWPs) for estimating and forecasting summer ET 0 at 101 U.S. Regional Climate Reference Network stations over nine climate regions across the contiguous United States (CONUS). Three leading global NWP model forecasts from THORPEX Interactive Grand Global Ensemble (TIGGE) dataset were used in this study, including the single model ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (EC), the National Centers for Environmental Prediction Global Forecast System (NCEP), and the United Kingdom Meteorological Office forecasts (MO), as well as multi-model ensemble forecasts from the combinations of these NWP models. A regression calibration was employed to bias correct the ET 0 forecasts. Impact of individual forecast variables on ET 0 forecasts were also evaluated. The results showed that the EC forecasts provided the least error and highest skill and reliability, followed by the MO and NCEP forecasts. The multi-model ensembles constructed from the combination of EC and MO forecasts provided slightly better performance than the single model EC forecasts. The regression process greatly improved ET 0 forecast performances, particularly for the regions involving stations near the coast, or with a complex orography. The performance of EC forecasts was only slightly influenced by the size of the ensemble members, particularly at short lead times. Even with less ensemble members, EC still performed better than the other two NWPs. Errors in the radiation forecasts, followed by those in the wind, had the most detrimental effects on the ET 0 forecast performances. [ABSTRACT FROM AUTHOR]

Multi‐model ensemble (MME) methods have been developed to improve upon the simulations of individual general circulation models. Their performances can be evaluated using metrics such as correlation and root‐mean‐square error, between the simulations and observations. However, most metrics change with the length of the calibration period, meaning the skill of MME methods in simulating future climate change is poorly known. In the present work, an order preservation criterion and a boundary preservation criterion are proposed to guarantee the reliability of MME simulations in the future. The order preservation criterion makes every model contribute a positive value to the MME simulations, while the boundary preservation criterion restricts the range of variation in the MME simulations. Four commonly used MME methods are evaluated based on these two criteria. The results show that the multiple linear regression method and singular value decomposition method are unsuitable MME methods in most situations. However, the arithmetic ensemble mean and Bayesian model averaging can be used to combine model simulations. The two criteria proposed in this study provide a simple way to evaluate the validity of MME methods. [ABSTRACT FROM AUTHOR]

Crop growth models have many uncertainties that affect the yield response to climate change. Based on that, the aim of this study was to evaluate the sensitivity of crop models to systematic changes in climate for simulating soybean attainable yield in Southern Brazil. Four crop models were used to simulate yields: AQUACROP, MONICA, DSSAT, and APSIM, as well as their ensemble. The simulations were performed considering changes of air temperature (0, + 1.5, + 3.0, + 4.5, and + 6.0 °C), [CO2] (380, 480, 580, 680, and 780 ppm), rainfall (− 30, − 15, 0, + 15, and + 30%), and solar radiation (− 15, 0, + 15), applied to daily values. The baseline climate was from 1961 to 2014, totalizing 53 crop seasons. The crop models simulated a reduction of attainable yield with temperature increase, reaching 2000 kg ha−1 for the ensemble at + 6 °C, mainly due to shorter crop cycle. For rainfall, the yield had a higher rate of reduction when it was diminished than when rainfall was increased. The crop models increased yield variability when solar radiation was changed from − 15 to + 15%, whereas [CO2] rise resulted in yield gains, following an asymptotic response, with a mean increase of 31% from 380 to 680 ppm. The models used require further attention to improvements in optimal and maximum cardinal temperature for development rate; runoff, water infiltration, deep drainage, and dynamic of root growth; photosynthesis parameters related to soil water availability; and energy balance of soil-plant system to define leaf temperature under elevated CO2. [ABSTRACT FROM AUTHOR]

Future climate projections and impact assessments are critical in evaluating the potential impacts of climate change and climate variability on crop production. Climate change impact assessment in combination with crop, climate models under different climate change scenarios is uncertain and it is challenging to select an appropriate climate scenario. This study quantifies the uncertainty associated with projected climate change impacts on cotton yield in Punjab, Pakistan using 29 general circulation models (GCMs) under high and moderate representative concentration pathway (RCP) scenarios (4.5 and 8.5) at near-term (2010–2039) and mid-century (2040–2069) time spans. Cropping System Model (CSM) CROPGRO-Cotton (DSSAT v 4.6) was calibrated and evaluated with field experiment data collected under arid/semi-arid climatic conditions. Enormous variation was observed in GCMs climatic variables, which were therefore classified into different categories. According to mean ensemble of 29 GCMs, there is a projected increase in seasonal average temperature 1.52 °C and 2.60 °C in RCP 4.5 and 1.57 °C and 3.37 °C in RCP 8.5 scenario as compared to the seasonal baseline (31.48 °C) in near-term (2010–2039) and mid-century (2040–2069), respectively. Maximum consensus by GCMs revealed the increase in temperature of 1.2–1.8 °C and 2.2 to 3.1 °C in RCP 4.5 scenario while 1.4–2.2 °C and 3.0–3.9 °C increase is expected under RCP 8.5 for near term and mid-century time periods, respectively. Similarly, rainfall changes are expected −8% to 15% and −5 to 17% in RCP 4.5 scenario while −8 to 22% and −2 to 20% change is expected under RCP 8.5 scenario in near term and mid-century time periods, respectively. Seed cotton yield (SCY) are projected to decrease by 8% on average by 2039 and 20% by 2069under the RCP 4.5 scenario relative to the baseline (1980–2010). Mean seed cotton yield is projected to decrease by 12% and 30% on average under the RCP 8.5 scenario. Uncertainties were observed in GCMs projections and RCPs due to variations in climatic variables projections. GCMs, GFDL-ESM2M (45% and 35%), GFDL-ESM2G (28% and 43%) and MIROC-ESM (39% and 70%) predicted the higher mean SCY reduction ensemble of cultivars than others under emission scenario of 4.5 in near term and mid-century, respectively. Lower SCY reduction was revealed in CCSM4, HADGEM2-CC, HADGEM2-ES, INMCM4 and CNRM-CM5 due to mild behavior of climatic variables especially temperature under RCP 4.5 in the near-term and mid-century. High reduction in mean SCY (16%–19%) is expected in CMCC-CMS, IPSL-CM5B-LR, GISS-E2-H, GFDL-ESM2M and GFDL-ESM2G under the RCP 8.5 scenario. However, under the same scenario, mean SCY increases by 1% in HADGEM2-ES and by 4% in HADGEM2-CC relative to the baseline yield (4147 kg ha −1 ). GFDL-ESM2M and GFDL-ESM2G are hot and dry while HADGEM2-ES and HADGEM2-CC are hot but wet, resulting in less cotton yield loss. MIROC-ESM and GFDL-ESM2G projected a severe reduction in mean SCY (70% and 69%) due to a steep increase in maximum and minimum temperature (6.97 °C and 4.38 °C, 4.91 °C and 3.70 °C), respectively and sever reduction in rainfall by mid-century and may call worse case scenarios. Climate models like, CCSM4, HadGEM2-CC, HadGEM2-ES, INMCM4, CanESM2, CNRM-CM5, ACCESS1-0, BNU-ESM and MIROC5 are found less uncertain and showed stable behavior. Therefore, these models can be used for climate change impact assessment for other crops in the region. Adaptation management options like five weeks early sowing than current (10-May), increasing nitrogen fertilization (30%), higher planting density (18% for spreading and 30% for erect type cultivars) and 17% enhanced genetic potential of cultivars would compensate the negative impacts of climate change on cotton crop. This study provide valuable understandings and direction for cotton management options under climate change scenarios. This multi-model and multi-scenario analysis provides a first overview of projected changes in temperature and precipitation, cotton yield and potential management options under changing climate scenarios in arid to semi-arid climatic conditions of Punjab-Pakistan. [ABSTRACT FROM AUTHOR]

Local climate change risk assessments (LCCRAs) are best supported by a quantitative integration of physical hazards, exposures and vulnerabilities that includes the characterization of uncertainties. We propose to use Bayesian Networks (BNs) for this task and show how to integrate freely-available output of multiple global hydrological models (GHMs) into BNs, in order to probabilistically assess risks for water supply. Projected relative changes in hydrological variables computed by three GHMs driven by the output of four global climate models were processed using MATLAB, taking into account local information on water availability and use. A roadmap to set up BNs and apply probability distributions of risk levels under historic and future climate and water use was co-developed with experts from the Maghreb (Tunisia, Algeria, Morocco) who positively evaluated the BN application for LCCRAs. We conclude that the presented approach is suitable for application in the many LCCRAs necessary for successful adaptation to climate change world-wide. • Co-developed roadmap supports use of Bayesian Networks in climate change adaptation. • Approach explicitly accounts for uncertainty of system knowledge. • Free global data of climate-driven hazards integrated with local stakeholder knowledge. • Case study aimed at water sector but approach is applicable to many other sectors. [ABSTRACT FROM AUTHOR]

• Lagged compound flood and drought events were characterized at different global warming levels using daily streamflow data obtained from a high-resolution hydrologic model. • Two novel indices proposed to quantify the compound events considering the climate change induced non-stationarity. • Increasing frequency of flood to drought events are projected under global warming. • Abrupt flood-to-drought transition is expected over Northwest North America under a warmer climate. • Seasonality of compound flood and drought events is projected to shift to earlier and later occurrences, respectively. The recent upsurge in the occurrence of hydroclimatic extremes and their temporal swings has led to severe consequences in many regions around the world. In this study, we characterize the swings between flood and drought events and assess their spatiotemporal frequency and magnitude under climate change. Different scenarios for nonstationary flood and drought swings are investigated over three major river basins in northwest North America. In addition, two novel indices are developed to quantify the severity of the compound flood and drought events in a changing climate. The frequency of flood-to-drought events is projected to increase, with occurrences at the 1.5 °C global warming level happening almost twice than that of the base period. Such transitions are expected to occur more frequently if the world progressively warms. Additionally, the transition time of flood-to-drought events is projected to decrease across all three basins in the study area. Even though drought-to-flood events are expected to become less frequent in a changing climate, the southwest Columbia basin stands out as a hotspot for such transitions. Furthermore, floods are expected to occur earlier in the season, while droughts are projected to occur later, highlighting the increasing variability of floods and droughts of compound hydroclimatic events in a warming world. Additionally, there is a possibility of increased severity for such compound events if global warming is not limited. Our findings assert the necessity of integrating mitigation measures targeting lagged compound flood and drought events into disaster risk reduction strategies. [ABSTRACT FROM AUTHOR]

• Evolution of offshore wind resources in the Mediterranean up to 2100 is investigated. • Three climate change scenarios based on the novel Shared Socioeconomic Pathways. • Overall drop in wind power density (20% in worst scenario) and augmented variability. • Strong increases (up to 80%) and decreases (up to 40%) in seasonal wind power density. • Reducing carbon emissions mitigates decline and lowers variability of wind resources. The evolution of offshore wind resources in the Mediterranean Sea up to 2100 is studied following the latest climate-change scenarios, the Shared Socioeconomic Pathways. Three scenarios are considered: a high emissions scenario, i.e., fossil-fuelled development; a business-as-usual scenario, in which current emission levels are maintained; and a low-emissions scenario. Particularly, the low-emissions scenario is of special interest since it represents the achievement of the targets set by the Paris Agreement and EU's Green Deal, which is a novelty in this type of work. A multi-model ensemble is constructed to reduce individual uncertainties with the Global Climate Models that are found to perform best in the Mediterranean Sea. With the exception of the low-emissions scenario, the results show a generalised decline in mean wind power density and augmented variability by 2100. This decline is more pronounced in the high-emissions scenario, with a widespread drop in mean wind power density of ∼20%, rising to 30% around the Balearic Islands. By contrast, the low-emissions scenario anticipates strong, localised growth in certain areas around the Italian Peninsula and off Lebanon and Israel. As for the seasonal variability, profound changes are predicted depending on the scenario and period considered, with increases of up to 80% and decreases of up to 40% in mean seasonal wind power density. These trends have significant implications for wind energy production and can serve as a background for future downscaling initiatives. On the positive side, the comparison between the three scenarios indicates that reducing carbon emissions in line with international climate accords may indeed help to mitigate the decline in wind resources in the Mediterranean Sea and the increase in their variability. [ABSTRACT FROM AUTHOR]

Short- and long-term climate oscillations impact seascapes, and hence, marine ecosystem structure and dynamics. Here, we explored the spatio-temporal patterns of potential squid habitat in the western and central North Pacific across inter-decadal climate transitions, coincident with periods of persistent warming and cooling. Potential habitat distributions of Ommastrephes bartramii were derived from the outputs of multi-ensemble species distribution models, developed using the most influential environmental factors to squid distribution and occurrence data. Our analyses captured the underlying temporal trends in potential squid habitat in response to environmental changes transpiring at each climatic transition, regulated by phase shifts in Pacific decadal oscillation (PDO) from 1999−2013. The spatial differences in environmental conditions were apparent across transitions and presumably modulate the local changes in suitable squid habitat over time. Specifically, during a cold to warm PDO shift, decreases in the summer potential habitat (mean rate ± standard deviation: −0.04 ± 0.02 habitat suitability index (HSI)/yr) were observed along the southern edge of the subarctic frontal zone (162°E−172°W). Coincidentally, this area also exhibits a warming trend (mean temporal trend: 0.06 ± 0.21 °C/yr), accompanied with the prevalence of cold-core mesoscale eddies, west of the dateline (mean temporal trend in sea surface height: −0.19 ± 1.05 cm/yr). These conditions potentially generate less favorable foraging habitat for squid. However, a warm-to-cold PDO transition underpins a northward shift of suitable habitat and an eastward shift of regions exhibiting the highest rate of potential squid habitat loss (170−160°W; mean temporal trend: −0.05 ± 0.03 HSI/yr). Nonetheless, the emergence of the areas with increasingly suitable habitat regardless of climate transitions suggests the ecological importance of these regions as potential squid habitat hotspots and climatic refugia.

A novel method based on the ensemble empirical mode decomposition (EEMD) method was developed to improve model performance. This method was evaluated by applying it to global surface air temperatures, which were simulated by eight general circulation models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The temperature simulations of the eight models were separated into their different components by EEMD. The model's performance improved after the first high-frequency component was removed from the original simulations by EEMD for each model, on both the global and continental scale. Moreover, EEMD was more effective in improving the model's performance compared to the wavelet transform method. The multi-model ensembles (MMEs) were calculated based on the EEMD-improved model simulations using the Average Ensemble Mean, Multiple Linear Regression, Singular Value Decomposition and Bayesian Model Averaging methods. The results showed that the MME forecasts performed better when the calculations were based on the EEMD-improved simulations as opposed to the original simulations on both the global and continental scale. Therefore, the results of the MME were further improved by using the EEMD-improved model simulations. This new method provides a simple way to improve model performance and can be easily applied to further improve MME simulations.

Future variations in the ocean wave climate caused by global warming could affect various coastal issues. Using a third-generation wave model, this study produced projections of the ocean wave climate for winter around Japan, focusing on the Japan Sea side. Wave simulation forcing (sea surface wind) was generated through five different global warming experiments. More than half the future wave projections showed an increasing tendency of the climatological mean significant wave height during winter. However, the maximum significant wave height did not show any clear tendency in future variation. The top 1% of significant wave heights and mean wave periods showed apparent increases in frequencies of higher/longer waves in three out of the five future projections. Frequency distributions of significant wave height, mean wave period, mean wavelength and wave direction showed various future variations (reduction of small ocean waves, increasing frequency of waves from the west). There are large uncertainties in future variations of wave climate in the Japan Sea, but the high probability of variations in daily wave climate is recognized, based on the future wave projections. Variations in daily wave climate are important because they could affect the topography and environment of the coast through long-term repetitive actions.