Search

Atmospheric rivers (ARs) are closely associated with extreme precipitation and hydrological events in East Asia. Predicting the impacts of climate change on ARs is crucial for preventing the damage caused by extreme precipitation and ensuring the effective operation of water management facilities. We aimed to conduct future projections (2080–2099) of annual and seasonal changes based on the assessment of East Asian AR and AR-related precipitation, using the Coupled Model Intercomparison Project Phase 6 (CMIP6) Multi-model ensemble (MME). The annual average integrated vapor transport (IVT) in East Asia in 2080–2099 will increase by approximately 32.5% compared to 1995–2014. Meanwhile, the annual average AR frequency (FAR) will increase by approximately 111%. Examination of the water vapor and moist wind components of the IVT revealed that the future increase in the IVT was primarily from increases in water vapor. The increase in IVT is largely responsible for the increase in AR frequency. Changes in AR following global warming have also affected precipitation, increasing the total precipitation for East Asia. An examination of the changes in AR characteristics shows that the frequency of intense AR events will also increase owing to global warming. Increases in the frequency of strong AR events during the East Asian summer monsoon season are projected to occur. Projections regarding the frequency and intensity of AR events vary substantially by region, such as Korean Peninsula, Southern China and Western Japan. [ABSTRACT FROM AUTHOR]

ABSTRACT In the context of climate change, the widespread practice of directly employing Multi‐Model Ensembles (MMEs) for projecting future climate extremes, without prior evaluation of MME performance in historical periods, remains underexplored. This research addresses this gap through a comprehensive analysis of ensemble means derived from CMIP6‐based models, including both simple and weighted averages of precipitation (SEMP and WEMP) and temperature (SEMT and WEMT) time series, as well as simple (SEME) and weighted (WEME) averages of extremes based on model‐by‐model analysis. The study evaluates the efficacy of MMEs in capturing mean annual values of ETCCDI indices over India for the period 1951–2014, utilising the IMD gridded data set as a reference. The results reveal that SEME and WEME consistently align closely with IMD data across various precipitation indices. At the same time, SEMP and WEMP consistently display underestimation biases ranging from 20% to 80% across all precipitation indices, except for CWD, where there is an overestimation bias. Moreover, SEMP and WEMP consistently underestimate CDD and overestimate CWD, indicating a systematic bias in these ensemble means, while WEME and SEME demonstrate satisfactory performance. SEMT and WEMT exhibit notable underestimation in temperature indices. In summary, adopting SEME and SEMT leads to a more robust assessment of precipitation and temperature extremes, respectively. These findings highlight the limitations of traditional MME methodologies in reproducing observed extreme precipitation events across various climatic zones in India, offering essential insights for refining climate models and improving the reliability of climate projections specific to the Indian subcontinent. [ABSTRACT FROM AUTHOR]

To help with preparedness efforts of Canadian public health and safety systems for adaptation to climate change, the humidity index (humidex) and three threshold‐based humidex indices (annual number of days with humidex greater than 30, 35 and 40) were computed for a multi‐model ensemble of climate change projections, over Canada. The ensemble consists of one run from each 19 Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models and offers historical simulations starting in 1950 and future projections out to 2100 following Shared Socioeconomic Pathways (SSPs): SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5. Each ensemble member was bias‐adjusted and statistically downscaled using the Multivariate bias correction—N‐dimensional probability density function transform (MBCn) with hourly data from ERA5‐Land as the target dataset and following a method proposed by Diaconescu et al. (2023; International Journal of Climatology, 43, 837) to calculate humidex from daily climate model outputs. This paper details the steps for data production including evaluation of the target historical gridded data and selection of downscaling method and presents some of the resulting humidex projections at the end of the century. [ABSTRACT FROM AUTHOR]

One of the most challenging issues in water resource management is the impact of climate change on the water supply. It is necessary to examine the effects of climate change on Afghanistan, which is vital to Iran's water resources. To investigate the temperature and precipitation and their variability in Afghanistan and the two basins of Helmand and Hariroud, MSWX data was used from 1981 to 2020. To project the future climate, the output of five CMIP6 models was used for the near future (2026-2050). To reduce the uncertainty of individual models, a multi-model ensemble was generated. The area-averaged precipitation trend showed that the precipitation in Afghanistan, Helmand, and Hariroud basins has decreased by 11.2, 12.2, and 12.3 mm/decade, respectively. The area-averaged temperature has increased by 0.43, 0.45, and 0.57 oC/decade over Afghanistan, Helmand, and Hariroud basins, respectively. The results showed that the temperature in all three investigated regions will have a positive anomaly in the near future under SP2-4.5 and SSP5-8.5. On the other hand, the precipitation anomaly will be negative under SSP2-4.5 in the northern regions of Afghanistan and the two studied basins. The entire area of Afghanistan and two basins will experience a negative anomaly of precipitation under SSP5-8.5.

Selecting appropriate Global Climate Models (GCMs) presents a significant challenge for accurate climate projections. To address this, a novel framework based on information theory based minimum redundancy and maximum relevancy (MRMR) method identifies top-performing GCMs across the entire study region using multicriteria decision analysis methodology. A subset of the ten best-performing models out of twenty-two GCMs is chosen for multi-model ensemble analysis. Five MME methods are selected to assess the ensemble performance of the ten selected GCMs, categorized into simple, regression-based, geometric-based, and machine learning ensembles. This study evaluates the effectiveness of the MME method based on a comprehensive index called the extended distance between indices of simulation and observation. An Adaptive Multimodel Standardized Drought Index (AMSDI) has been developed based on the optimal MME method. For the application of the framework and the proposed index, historical precipitation data from 1950 to 2014 were utilized from 28 grid points in the Punjab province of Pakistan as the reference dataset. Additionally, simulations from 22 models of the Coupled Model Intercomparison Project phase 6, both past and future, were employed for the estimation procedure. In AMSDI indicator, we used improved multimodel ensemble of precipitation for future drought characterization under various future scenarios. Outcome associated with this research show that AMSDI effectively have ability to effectively identifiy extreme drought events for all three future scenarios. In conclusion, the AMSDI method is shown to be effective and flexible, improving accuracy in monitoring droughts. [ABSTRACT FROM AUTHOR]

In this study, drought projections in Türkiye's Mediterranean region under future climate scenarios (Shared Socioeconomic Pathways SSP2-4.5 and SSP5-8.5) were investigated using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). A multi-model ensemble (MME) was created with the General Circulation Models (GCMs) that best predicted observed precipitation and temperature values using Artificial Neural Network (ANN)-based statistical downscaling. The MME outputs assessed changes in drought indices for 2021–2060 and 2061–2100, compared to the baseline period of 1979–2020. Results indicate that future drought indices calculated by SPEI are more severe than those using SPI, highlighting the significance of potential evapotranspiration (PET) in drought monitoring. There is a notable increase in PET, especially in coastal regions, projected to rise to 120 mm/year by the century's end under SSP5-8.5. Due to significant temperature increases, the expected rise in drought severity and frequency is more pronounced in mountainous areas. [ABSTRACT FROM AUTHOR]

Accurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China. [ABSTRACT FROM AUTHOR]

The southwest monsoon onset over Kerala (MOK) is very crucial for an Agricultural country like India hence its prediction in the extended range time scale (about 3 weeks in advance) is very useful for the Kharif season in India. As the declaration of MOK involves subjective interpretation of the forecasters, an objective method of prediction of MOK based on dynamical models could successfully avoid bogus onsets. Two objective prediction methods for MOK are developed based on the real-time extended range forecast (ERF) of rainfall measured over Kerala coast 080-120N, 740-780E and the strength and depth of the westerly wind over the Arabian Sea (050-120N, 550-750E) for the period from 2003 to 2022 are used in the first method. In addition to these 3 indices, meridional pressure gradient along the west coast of India is used in the second method with 4 indices. The MOK date is defined objectively based on these variables exceeding the thresholds in both the methods. The results indicate that the MOK forecast with four indices performed well compared to that with three indices during the whole period from 2003 to 2022 with the mean deviation days of MOK found to be 0.75 and 3.05 days respectively. Overall, the dynamically defined onset date over Kerala based on the real-time ERF indicated useful skill at least about 2–3 weeks in advance. It is also demonstrated that the district-level Multi-Model Ensemble forecast in the medium range (5 days in advance) based on 5 global models can add value to the categorical ERF of MOK for the exact forecast date of MOK.Article Highlights: The objective method for prediction of monsoon onset date over India in extended range time scale is presented. The extended range prediction of onset based on dynamical model shows useful skill up to 2 to 3 weeks. Combining the extended range forecast of onset with medium range is helpful for predicting the exact onset date. [ABSTRACT FROM AUTHOR]

Based on the historical (period of 1990–2014) spatial and temporal ranking, a future projection of four extreme precipitation indices over Iran is conducted. A multi-model ensemble approach and a rank-based weighting method with ten models from the CMIP6 dataset are used for this projection. The weight of each model is calculated based on its historical simulation skill, and weighted models are employed for future projections across three periods (2026–2050, 2051–2075, and 2076–2100), under four Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results show that an increase in total extreme precipitation (R95p) and the absolute intensity of extreme precipitation (AEPI) in Iran is almost certain in all periods, under all scenarios. The maximum increase of the R95p index is 10%, and the probability of its increase in all periods and scenarios (except for SSP1-2.6 scenario in the 2076–2100 period) exceeds 50%. This probability of increase is particularly high in the first period, ranging from 70 to 90%. In all periods and scenarios, the median of the number of days with extreme precipitation (R95d) is close to zero or negative. This index exhibits a decrease compared to the historical period, with a probability of over 60%, except for the 2026–2050 period under SSP1-2.6 and SSP5-8.5 scenarios. Furthermore, the probability of an increase in the AEPI compared to the historical period is more than 75%. This study finds no significant increase or decrease in the fraction of total rainfall from events exceeding the extreme precipitation threshold (R95pT). [ABSTRACT FROM AUTHOR]

Climate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid‐based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid‐based RF classification model, employed to distinguish between rainy and non‐rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non‐extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN‐RF), was validated using precipitation data from the Divandareh‐Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning‐based downscaling model (EN‐Single‐RF). Comparative analysis against a commonly used single machine learning‐based downscaling model (EN‐Single‐RF) revealed the superior performance of the EN‐RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN‐SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN‐RF to other methods (EN‐Single‐RF, EN‐SVM, and QM) in terms of various accuracy metrics (Kling‐Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio‐temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns. [ABSTRACT FROM AUTHOR]

With global climate warming, extreme climate events are becoming more frequent, posing a great threat to crop production. In this study, twelve extreme climate indices (ECIs) were defined to characterize climate events prone to occurring during key phenological stages of wheat. Additionally, eighteen Global Climate Models (GCMs) from the Coupled Model Inter-comparison Project phase 6 (CMIP6) were selected to analyze the spatial–temporal characteristics and trends of these ECIs under four emission scenarios of the future Shared Socioeconomic Pathway (SSP). The Delta Change Method (DCM) was used to correct the bias of GCM data, and the arithmetic mean and Independence Weighted Mean (IWM) were used to aggregate the results of different GCMs to improve the projection accuracy of ECIs. Overall, the IWM ensemble results can better reproduce historical changes of ECIs than multi-model arithmetic mean and any individual GCM. The results indicated that the ECIs across wheat growing area in China were significant spatial heterogeneity during the historical period from 1981 to 2010. Under future climate scenarios, the frequency of extreme high temperature events would significantly increase in most regions, and the intensity will increase by 0.13–0.99 ° and 0.44–2.41 ℃ during 2031–2060 and 2071–2100. However, the stress of extreme low temperature events during wheat growth periods would decrease. Although the changes of extreme precipitation events under different climate scenarios were not significant, these showed considerably spatial differences across wheat growing area. In order to maintain high and stable yield of wheat, it is important to take measures to mitigate the effects of future extreme climate events on wheat production. [ABSTRACT FROM AUTHOR]

This paper studies the past and future annual droughts in the five agro-ecological zones (AEZs) of Cameroon. Station data and model outputs from the Coupled Model Intercomparison Project Phase 5 were used to compute areal datasets for each AEZ. Six statistical metrics and compromise programming method were used to evaluate and rank the models, respectively. The top three models were used to build multi-model ensemble (MME) and deduce bias-corrected MME data. They were then used to compute the Standardized Precipitation Index (SPI) used as drought indicator. As a result, the performance of the models depends on the AEZ and decreases with the increase in drought intensity. The 1980s was the most marked by severe-extreme droughts and a significant increase in drought intensity was observed in the entire domain during the past period, the years 1974, 1985 and 1988 showing the highest drought intensities. The MME tends to overestimate and underestimate the frequencies and the magnitude of these events, respectively. Bias-corrected MME data improve the results in most cases. As for the period 2071–2099, all the AEZs are likely to experience severe-extreme droughts which are expected to be more frequent before 2083 in the North (AEZs 1 and 2) and after this year in the South (AEZs 3, 4 and 5). It is also expected a slight increasing trend of the mean spatial SPIs showing a slight decrease in drought intensity. The RCPs 8.5 and 2.6 project the lowest and the highest decrease in drought intensity, respectively, while the RCP4.5 shows an average decrease. This study highlights future periods and areas at potential risk of severe-extreme droughts and can guide decision-makers in mitigation and adaptation measures. [ABSTRACT FROM AUTHOR]

How the solar wind influences the magnetospheres of the outer planets is a fundamentally important question, but is difficult to answer in the absence of consistent, simultaneous monitoring of the upstream solar wind and the large‐scale dynamics internal to the magnetosphere. To compensate for the relative lack of in‐situ solar wind data, propagation models are often used to estimate the ambient solar wind conditions at the outer planets for comparison to remote observations or in‐situ measurements. This introduces another complication: the propagation of near‐Earth solar wind measurements introduces difficult‐to‐assess uncertainties. Here, we present the Multi‐Model Ensemble System for the outer Heliosphere (MMESH) to begin to address these issues, along with the resultant multi‐model ensemble (MME) of the solar wind conditions near Jupiter. MMESH accepts as input any number of solar wind models together with contemporaneous in‐situ spacecraft data. From these, the system characterizes typical uncertainties in model timing, quantifies how these uncertainties vary under different conditions, attempts to correct for systematic biases in the input model timing, and composes a MME with uncertainties from the results. For the Juno‐era (04/07/2016–04/07/2023) MME hindcast for Jupiter presented here, three solar wind propagation models were compared to in‐situ measurements from the near‐Jupiter spacecraft Ulysses and Juno spanning diverse geometries and phases of the solar cycle across >14,000 hr of data covering 2.5 decades. The MME gives the most‐probable near‐Jupiter solar wind conditions for times within the tested epoch, outperforming the input models and returning quantified estimates of uncertainty. Plain Language Summary: The sun interacts with all the planets in the solar system through the solar wind, a stream of charged particles which blow outwards from the sun in all directions, carrying the interplanetary magnetic field with them. Both the magnetic field and particles interact with planetary magnetic fields with dramatic effects, including the aurora–which shine not only on the Earth, but on gas giants of the outer solar system, like Jupiter, too. Characterizing the relationship between the solar wind and planetary magnetic fields is easiest with direct spacecraft measurements of both. Spacecraft between the Earth and Sun measure the solar wind, providing valuable context for understanding its interaction with the Earth. Unfortunately, there are no such permanent spacecraft near the other planets. Instead, models can be used to estimate the solar wind at these planets; however, these models can have significant, difficult‐to‐characterize uncertainties. Here we present the Multi‐Model Ensemble System for the outer Heliosphere (MMESH), a framework designed to measure these uncertainties and attempt to correct for them by comparing multiple solar wind models to spacecraft measurements over a long time span. The final result here is an improved solar wind model, with estimated uncertainties, for Jupiter. Key Points: The performance of several existing solar wind propagation models at the orbit of Jupiter is measured for multiple spacecraft epochsA flexible system is developed to generate an ensemble of multiple propagation models so as to best leverage each input model's strengthsOver the epoch tested, the multi‐model ensemble outperforms individual input models by 7%–110% in forecasting the solar wind flow speed [ABSTRACT FROM AUTHOR]

Accurate prediction of future surface wind speed (SWS) changes is the basis of scientific planning for wind turbines. Most studies have projected SWS changes in the 21st century over China on the basis of the multi-model ensemble (MME) of the 6th Coupled Model Intercomparison Project (CMIP6). However, the simulation capability for SWS varies greatly in CMIP6 multi-models, so the MME results still have large uncertainties. In this study, we used the reliability ensemble averaging (REA) method to assign each model different weights according to their performances in simulating historical SWS changes and project the SWS under different shared socioeconomic pathways (SSPs) in 2015–2099. The results indicate that REA considerably improves the SWS simulation capacity of CMIP6, eliminating the overestimation of SWS by the MME and increasing the simulation capacity of spatial distribution. The spatial correlations with observations increased from 0.56 for the MME to 0.85 for REA. Generally, REA could eliminate the overestimation of the SWS by 33% in 2015–2099. Except for southeastern China, the SWS generally decreases over China in the near term (2020–2049) and later term (2070–2099), particularly under high-emission scenarios. The SWS reduction projected by REA is twice as high as that by the MME in the near term, reaching −4% to −3%. REA predicts a larger area of increased SWS in the later term, which expands from southeastern China to eastern China. This study helps to reduce the projected SWS uncertainties.

This works proposes a probabilistic framework for rainy season onset forecasts over Greater Horn of Africa derived from bias-corrected, long range, multi-model ensemble precipitation forecasts. A careful analysis of the contribution of the different forecast systems to the overall multi-model skill shows that the improvement over the best performing individual model can largely be explained by the increased ensemble size. An alternative way of increasing ensemble size by blending a single model ensemble with climatology is explored and demonstrated to yield better probabilistic forecasts than the multi-model ensemble. Both reliability and skill of the probabilistic forecasts are better for OND onset than for MAM and JJAS onset where forecasts are found to be late biased and have only minimal skill relative to climatology. The insights gained in this study will help enhance operational subseasonal-to-seasonal forecasting in the GHA region. [ABSTRACT FROM AUTHOR]

Seasonal forecasts are commonly issued in the form of anomalies, which are departures from the average over a specified multiyear reference period (climatology). The model climatology is estimated as the average of the retrospective forecasts over the hindcast period. However, different operational centers that provide seasonal ensemble predictions use different hindcast periods based on their model climatology. Additionally, the hindcast periods of recently developed and upgraded newer models have shifted in the recent years. In this paper, we discuss the recent challenges faced by APCC multi‐model ensemble (MME) operations, especially changes in the hindcast period for individual models. Based on the results of various experiments for MME prediction, we propose changing the hindcast period, which is the most appropriate solution for APCC operation. This makes the newly developed models join the MME and increases the total number of participating models, which facilitates the skill improvement of the MME prediction. Plain Language Summary: In seasonal forecasting, it is well known that the MME, which combines different single‐model predictions from various operational and research centers, is a more effective way to improve forecast skill. Since 2005, the APCC has provided the MME seasonal forecasts, and the models participating in the APCC MME operations have been continuously changing. In particular, as the hindcast periods of newly developed models shift to the latest, they cannot participate in operational MME forecasts because of climatological discrepancies. However, over time, as the number of new models expected to provide skillful forecasts gradually increases, the APCC faces the challenge of continuously reducing the number of participating models or changing the hindcast period to more recent years. Considering various aspects such as the number of participating models, skills, and climatology period, we selected the most appropriate method for APCC operation. Thus, the MME prediction skill has improved over most of the globe and seasons because of the increase in the number of participating models, particularly the inclusion of newer models. Key Points: APCC, which combines all the information from different ensemble prediction systems, recently faced challenges in hindcast period issuesThe proposed solution leads to an increase in the number of models contributing to MME prediction, particularly recently developed modelsIt shows improved skills for both temperature and precipitation predictions over most of the globe and seasons [ABSTRACT FROM AUTHOR]

The increase in extreme precipitation with global warming (GW) and associated uncertainties are major challenges for climate adaptation. To project future extreme precipitation on different time and intensity scales (return periods [RPs] from 1 to 100 a and durations from 1 h to 3 days), we use a novel convection‐permitting (CP), multi‐global climate model ensemble of COSMO‐CLM regional simulations with a transient projection time (1971–2100) over Germany. We find an added value of the CP scale (2.8 km) with respect to the representation of hourly extreme precipitation intensities compared to the coarser scale with parametrized deep convection (7 km). In general, the return levels (RLs) calculated from the CP simulations are in better agreement with those of the conventional observation‐based risk products for the region for short event durations than for longer durations, where an overestimation by the simulation‐based results was found. A maximum climate change signal of 6–8.5% increase per degree of GW is projected within the CP ensemble, with the largest changes expected for short durations and long RPs. Analysis of the uncertainty in the climate change signal shows a substantial residual standard deviation of a linear approximation, highlighting the need for transient data sets instead of time‐slice experiments to increase confidence in the estimates. Furthermore, the ensemble spread is found to be smallest for intensities of short duration, where changes are expected to be based mainly on thermodynamic contributions. The ensemble spread is larger for long, multi‐day durations, where a stronger dependence on the dynamical component is ascribed. In addition, an increase in spatial variance of the RLs with GW implies a more variable future climate and points to an increasing importance of accounting for uncertainties. [ABSTRACT FROM AUTHOR]

Frost events can cause the deterioration of a wide range of heritage materials, including stone, brick and earth. In a warming world, the frequency and location of frost events is likely to change, affecting the conservation strategies required at heritage sites. We use a multi-model ensemble approach to investigate three types of frost events in East Asia: freeze–thaw cycles; deep frost days and wet frosts. The study uses nine CMIP6 models for the period 1850 to 2100, with future projections run under the SPS585 scenario. Additional analysis is undertaken for five specific 2° ✕ 2° areas located across East Asia. The three frost event parameters are spatially and temporally distinct. A decrease in all three frost parameters is found in Japan, South Korea and East China, with some areas projected to have no frost events by the end of the twenty-first century. However, Northwest China is distinctive as wet frosts are projected to increase over the twenty-first century, while on the Tibetan plateau of Southwest China, freeze–thaw cycles are projected to increase. This suggests that except in some localised regions, heritage managers can focus on risks other than frost weathering in developing plans to address climate change. [ABSTRACT FROM AUTHOR]

Information about wind variations and future wind conditions is essential for a monsoon domain such as the Northwest Pacific (NWP) region. This study utilizes 10 Generalized Circulation Models (GCM) from CMIP6 to evaluate near-future wind changes in the NWP under various climate warming scenarios. Evaluation against the ERA5 reanalysis dataset for the historical period 1985–2014 reveals a relatively small error with an average of no more than 1 m/s, particularly in the East Asian Marginal Seas (EAMS). Future projections (2026–2050) indicate intensified winds, with a 5–8% increase in the summer season in the EAMS, such as the Yellow Sea, East Sea, and East China Sea, while slight decreases are observed in the winter period. Climate mode influences show that winter El Niño tends to decrease wind speeds in the southern study domain, while intensifying winds are observed in the northern part, particularly under SSP5-8.5. Conversely, summer El Niño induces higher positive anomalous wind speeds in the EAMS, observed in SSP2-4.5. These conditions are likely linked to El Niño-induced SST anomalies. For the application of CMIP6 surface winds, the findings are essential for further investigations focusing on the oceanic consequences of anticipated wind changes such as the ocean wave climate, which can be studied through model simulations. [ABSTRACT FROM AUTHOR]

Ovarian cancer is one of the leading causes of death worldwide among the female population. Early diagnosis is crucial for patient treatment. In this work, our main objective is to accurately detect and classify ovarian cancer. To achieve this, two datasets are considered: CT scan images of patients with cancer and those without, and biomarker (clinical parameters) data from all patients. We propose an ensemble deep neural network model and an ensemble machine learning model for the automatic binary classification of ovarian CT scan images and biomarker data. The proposed model incorporates four convolutional neural network models: VGG16, ResNet 152, Inception V3, and DenseNet 101, with transformers applied for feature extraction. These extracted features are fed into our proposed ensemble multi-layer perceptron model for classification. Preprocessing and CNN tuning techniques such as hyperparameter optimization, data augmentation, and fine-tuning are utilized during model training. Our ensemble model outperforms single classifiers and machine learning algorithms, achieving a mean accuracy of 98.96%, a precision of 97.44%, and an F1-score of 98.7%. We compared these results with those obtained using features extracted by the UNet model, followed by classification with our ensemble model. The transformer demonstrated superior performance in feature extraction over the UNet, with a mean Dice score and mean Jaccard score of 0.98 and 0.97, respectively, and standard deviations of 0.04 and 0.06 for benign tumors and 0.99 and 0.98 with standard deviations of 0.01 for malignant tumors. For the biomarker data, the combination of five machine learning models—KNN, logistic regression, SVM, decision tree, and random forest—resulted in an improved accuracy of 92.8% compared to single classifiers. [ABSTRACT FROM AUTHOR]

This study estimated global climate change signals at different latitudes for four main Shared Socioeconomic Pathways (SSPs). Five evaluation metrics were integrated using the Technique for Order of Preference by Similarity to Ideal Solution to quantify the historical reproducibility of 25 CMIP6 General Circulation Models (GCMs) with Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature as the reference. The most suitable GCMs for simulating climate over different latitudes, selected based on evaluation metrics, were used to prepare a multimodel ensemble and project the future annual and seasonal precipitation and temperature in the near (2031–2065) and far future (2066–2100). The results showed that GCMs estimated the historical mean temperature efficiently but underestimated the monthly precipitation compared to the reference data. The changes in precipitation and temperature at mid‐latitudes (N45.5°–60°) showed the highest variability for all scenarios. The maximum increases in both climate variables for SSP5‐8.5 were 80.5% and 4.8% at N45.5°–60°, respectively. In contrast, the temperature and precipitation at S30.5°–45° revealed a decreasing pattern. Mid‐latitude winter (S30.5°–45°) would be drier in the future than in the base period (1980–2014). This study showed that precipitation variability and the mean temperature in the northern hemisphere would be larger for SSPs with higher radiative forcing. Therefore, the results of this study help improve knowledge of global future climate change by latitudes. Plain Language Summary: Climate change is already contributing to various unpredictable phenomena in many fields. A well‐known organization that periodically evaluates the impacts of climate change and actionable response strategies, the IPCC assessment report states that climate change is already directly impacting ecosystems, water cycles, and human activities. Therefore, sufficient exploration of the future climate change is vital for systematically developing a plan for climate change mitigation and adaptation, and the Shared Socioeconomic Pathway scenario contains various factors such as social, economic, and physics, making it reasonable for projecting the future climate. This study evaluated the historical monthly temperature and precipitation reproducibility of CMIP6 General Circulation Model (GCM) using various metrics. Based on this, multi‐model ensemble was built using Technique for Order of Preference by Similarity to Ideal Solution, a multi‐criteria decision‐making technique, for a reasonable future climate assessment. The results of this study showed that the monthly precipitation of CMIP6 GCM over the historical period is overestimated than the reference data, but the monthly temperature performance is stable. For projected future climate, high latitudes in the northern hemisphere are most vulnerable to changes in temperature and precipitation, and the southern hemisphere captured robust dryness for the future. Key Points: General Circulation Models' performances are different by each latitude and their simulations were overestimated for rainfall and well‐estimated for temperatureThe region in N75‐N90 would be most vulnerable to climate change in the future, and the area in S30‐S60 would be drier in the futureVariability of the northern hemisphere would increase more for high emission scenarios but seasonal trends are more chaotic than in the past [ABSTRACT FROM AUTHOR]

Water temperature forecasting in lakes and reservoirs is a valuable tool to manage crucial freshwater resources in a changing and more variable climate, but previous efforts have yet to identify an optimal modeling approach. Here, we demonstrate the first multi‐model ensemble (MME) reservoir water temperature forecast, a forecasting method that combines individual model strengths in a single forecasting framework. We developed two MMEs: a three‐model process‐based MME and a five‐model MME that includes process‐based and empirical models to forecast water temperature profiles at a temperate drinking water reservoir. We found that the five‐model MME improved forecast performance by 8%–30% relative to individual models and the process‐based MME, as quantified using an aggregated probabilistic skill score. This increase in performance was due to large improvements in forecast bias in the five‐model MME, despite increases in forecast uncertainty. High correlation among the process‐based models resulted in little improvement in forecast performance in the process‐based MME relative to the individual process‐based models. The utility of MMEs is highlighted by two results: (a) no individual model performed best at every depth and horizon (days in the future), and (b) MMEs avoided poor performances by rarely producing the worst forecast for any single forecasted period (<6% of the worst ranked forecasts over time). This work presents an example of how existing models can be combined to improve water temperature forecasting in lakes and reservoirs and discusses the value of utilizing MMEs, rather than individual models, in operational forecasts. Key Points: Aggregated lake temperature forecast skill was higher for multi‐model ensemble (MME) forecasts than individual model forecastsIncluding baseline empirical models (day‐of‐year, persistence) with process models improved MME forecast performanceMME forecasts improved forecast skill by "hedging," as no individual model performed best at all horizons or depths [ABSTRACT FROM AUTHOR]

The growing interest in Subseasonal to Seasonal (S2S) prediction data across different industries underscores its potential use in comprehending weather patterns, extreme conditions, and important sectors such as agriculture and energy management. However, concerns about its accuracy have been raised. Furthermore, enhancing the precision of rainfall predictions remains challenging in S2S forecasts. This study enhanced the sub-seasonal to seasonal (S2S) prediction skills for precipitation amount and occurrence over the East Asian region by employing deep learning-based post-processing techniques. We utilized a modified U-Net architecture that wraps all its convolutional layers with TimeDistributed layers as a deep learning model. For the training datasets, the precipitation prediction data of six S2S climate models and their multi-model ensemble (MME) were constructed, and the daily precipitation occurrence was obtained from the three thresholds values, 0 % of the daily precipitation for no-rain events, 67 % for heavy-rain. Based on the precipitation amount prediction skills of the six climate models, deep learning-based post-processing outperformed post-processing using multiple linear regression (MLR) in the lead times of weeks 2–4. The prediction accuracy of precipitation occurrence with MLR-based post-processing did not significantly improve, whereas deep learning-based post-processing enhanced the prediction accuracy in the total lead times, demonstrating superiority over MLR. We enhanced the prediction accuracy in forecasting the amount and occurrence of precipitation in individual climate models using deep learning-based post-processing.

Streamflow forecasting plays a crucial role in the operational planning of hydro-dominant power systems, providing valuable insights into future water inflows to reservoirs and hydropower plants. It relies on complex mathematical models, which, despite their sophistication, face various uncertainties affecting their performance. These uncertainties can significantly influence both short-term and long-term operational planning in hydropower systems. To mitigate these effects, this study introduces a novel Bayesian model averaging (BMA) framework to improve the accuracy of streamflow forecasts in real hydro-dominant power systems. Designed to serve as an operational tool, the proposed framework incorporates predictive uncertainty into the forecasting process, enhancing the robustness and reliability of predictions. BMA statistically combines multiple models based on their posterior probability distributions, producing forecasts from the weighted averages of predictions. This approach updates weights periodically using recent historical data of forecasted and measured streamflows. Tested on inflows to 139 reservoirs and hydropower plants in Brazil, the proposed BMA framework proved to be more skillful than individual models, showing improvements in forecasting accuracy, especially in the South and Southeast regions of Brazil. This method offers a more reliable tool for streamflow prediction, enhancing decision making in hydropower system operations. [ABSTRACT FROM AUTHOR]

Extreme temperature events have increased in Iran in recent decades. In this research, we evaluate the performance of CMIP6-DCPP individual models and a multi-model ensemble (MME) from CMIP6-DCPP against observational data in Iran. We used the delta change factor (DCF) and the independence weighted mean (IWM) methods to correct the bias of individual models and generate the MME. Eighteen temperature indices recommended by ETCCDI were used to predict extreme temperatures in the near term by comparing the hindcast (1981–2019) and forecast (2021–2028) periods. The result shows that DCPP-MME performed well in simulating temperature over most parts of Iran. Positive anomalies in mean, minimum, and maximum temperatures are obvious in Iran in the next decade, which is amplified by the elevation. As a key result, the minimum temperature will increase at a higher rate than the maximum temperature, which will make a negative diurnal temperature range anomaly in most regions in the near-term period. The frequency and intensity of warm (cold) extremes would increase (decrease) in the upcoming years. Therefore, in mountainous and high-latitude regions of Iran, the coldest days and nights are getting warmer compared to the hottest days and nights. In addition, ice days and frost days decrease considerably by almost 15 and 11 days in the north of Iran, respectively. Tropical nights and summer days will increase in all regions, with their maxima in central and eastern arid regions. Also, warm spell duration with an increase of 14 days is very noticeable in the forecast period. [ABSTRACT FROM AUTHOR]

The impact of elevated air temperature and heat stress on human health is a global concern. It not only affects our well-being directly, but also reduces our physical work capacity, leading to negative effects on society and economic productivity. Climate change has already affected the climate in Luxembourg and, based on the results of regional climate models, extreme heat events will become more frequent and intense in the future. To assess historical conditions, the micro-scaleRayManPro 3.1 model was used to simulate the thermal stress levels for different genders and age classes based on hourly input data spanning the last two decades. For the assessment of future conditions, with a special emphasis on heat waves, a multi-model ensemble of regional climate models for different emission scenarios taken from the Coordinated Regional Climate Downscaling Experiment (CORDEX) was used. For both, the past and future conditions in Luxemburg, an increase in the heat stress levels was observed. Small differences for different age groups and genders became obvious. In addition to the increase in the absolute number of heat waves, an intensification of higher temperatures and longer durations were also detected. Although some indications of the adaptation to rising air temperatures can be observed for high-income countries, our results underscore the likelihood of escalating heat-related adverse effects on human health and economic productivity unless more investments are made in research and risk management strategies. [ABSTRACT FROM AUTHOR]

This study details an integrated framework for assessing the water supply reliability of a multi-purpose reservoir under different climate change scenarios, with the case of the Akmese Project in northwest Turkey. In this assessment, the precipitation and temperature simulations of 24 Global Circulation Models (GCMs) from the Couple Model Intercomparison Project phase 6 (CMIP6) are analyzed using two statistical bias correction methods, namely, linear scaling and distribution mapping, to produce the best-performing multi-model ensemble predictions under two different Shared Socio-economic Pathway (SSP) scenarios (SSP245 and SSP585). The future inflow rates of the Akmese reservoir are simulated using the Soil and Water Assessment Tool (SWAT) model. The CROPWAT model is utilized to estimate crop water and crop irrigation requirements under the projected climate conditions. The effects of changing climate on the lake evaporation rates are also taken into consideration in analyzing the future reservoir water availability for domestic usages, irrigation demands, and downstream environmental flow requirements. The 25-year monthly reservoir operations are conducted with the changing inputs of the projected inflows, lake evaporation rates, and irrigation requirements for the historical period of 1990–2014 and near-, mid-, and long-future periods of 2025–2049, 2050–2074, and 2075–2099, respectively. The results indicate that the projected changes in the hydro-climatic conditions of the Akmese Basin will adversely impact the reservoir water availability. Under the high-forcing scenario SSP585, 9.26 and 22.11% of the total water demand, and 20.17 and 38.89% of the total irrigation requirement cannot be supplied, in turn, in the mid- and long-future periods. [ABSTRACT FROM AUTHOR]

Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and adaptability. Recognizing the limitations of single-model forecasting, this study introduces an innovative approach known as the Improved K-Nearest Neighbor Multi-Model Ensemble (IKNN-MME) method to enhance the runoff prediction. IKNN-MME dynamically adjusts model weights based on the similarity of historical data, acknowledging the influence of different training data features on localized predictions. By combining an enhanced K-Nearest Neighbor (KNN) algorithm with adaptive weighting, it offers a more powerful and flexible ensemble. This study evaluates the performance of the IKNN-MME method across four basins in the United States and compares it to other multi-model ensemble methods and benchmark models. The results underscore its outstanding performance and adaptability, offering a promising avenue for improving runoff forecasting. [ABSTRACT FROM AUTHOR]

Climate parameters can be refined for use in a heritage context to capture climate‐based deterioration processes occurring on buildings and sites. The Scheffer index, which combines temperature and rainfall components, is commonly used as a metric of wood decay risk. Understanding how the index is likely to change is important for developing effective conservation strategies for timber heritage. However, there has been limited research assessing the agreement between climate model outputs for such heritage‐based metrics. This is especially important where capturing projected rainfall is known to be problematic, such as over regions of Africa. We address the following questions: to what extent is there model agreement over continental Africa for (i) the magnitude of the Scheffer index and (ii) the direction of change of future Scheffer indices. A multi‐model ensemble (MME) approach was used utilizing 13 CMIP6 models under the SPS585 scenario. Results showed that rainfall was important in determining the magnitude of the index. In regions where rainfall systems are not well captured by climate models, such as in eastern Africa, there was a large range in projected Scheffer values. To help with the transferability of the index to regions with a tropical climate, we suggest the addition of a Scheffer threshold for a very‐high risk of deterioration. Projections of future change to the index were found to be predominantly driven by changes in temperature, rather than rainfall. While there was considerable disagreement in the simulated magnitude of the Scheffer index, there was good agreement between models over the direction of change across Equatorial Africa, where the Scheffer index is greatest. Such agreement between models suggests that heritage decision makers can utilize the direction of change projected by models when shaping future conservation plans. [ABSTRACT FROM AUTHOR]

Boreal summer precipitation over the Tibetan Plateau (TP) is difficult to predict in current climate models and has become a challenging issue. To address this issue, a new analog-based correction method has been developed. Our analysis reveals a substantial correlation between the prediction errors of TP summer precipitation (TPSP) and previous February anomalies of sea surface temperature (SST) in the key regions of tropical oceans. Consequently, these SST anomalies can be selected as effective predictors for correcting prediction errors. With remote-sensing-based and observational datasets employed as benchmarks, the new method was validated using the rolling-independent validation method for the period 1992–2018. The results clearly demonstrate that the new SST analog-based correction method of dynamical models can evidently improve prediction skills of summer precipitation in most TP regions. In comparison to the original model predictions, the method exhibits higher skills in terms of temporal and spatial skill scores. This study offers a valuable tool for effectively improving the TPSP prediction in dynamical models. [ABSTRACT FROM AUTHOR]

Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate‐risk‐prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi‐model ensemble (CMME) of seven state‐of‐the‐art general circulation models (GCMs) from the North American Multi‐Model Ensemble project. The lead‐1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA‐based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real‐time seasonal rainfall forecasts using this new forecast system. [ABSTRACT FROM AUTHOR]

Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020–2059) and the distant future (2060–2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.

Abstract This study estimated global climate change signals at different latitudes for four main Shared Socioeconomic Pathways (SSPs). Five evaluation metrics were integrated using the Technique for Order of Preference by Similarity to Ideal Solution to quantify the historical reproducibility of 25 CMIP6 General Circulation Models (GCMs) with Global Precipitation Climatology Centre precipitation and Climatic Research Unit temperature as the reference. The most suitable GCMs for simulating climate over different latitudes, selected based on evaluation metrics, were used to prepare a multimodel ensemble and project the future annual and seasonal precipitation and temperature in the near (2031–2065) and far future (2066–2100). The results showed that GCMs estimated the historical mean temperature efficiently but underestimated the monthly precipitation compared to the reference data. The changes in precipitation and temperature at mid‐latitudes (N45.5°–60°) showed the highest variability for all scenarios. The maximum increases in both climate variables for SSP5‐8.5 were 80.5% and 4.8% at N45.5°–60°, respectively. In contrast, the temperature and precipitation at S30.5°–45° revealed a decreasing pattern. Mid‐latitude winter (S30.5°–45°) would be drier in the future than in the base period (1980–2014). This study showed that precipitation variability and the mean temperature in the northern hemisphere would be larger for SSPs with higher radiative forcing. Therefore, the results of this study help improve knowledge of global future climate change by latitudes.

AbstractThis research aims to investigate the future changes in summer monsoon rainfall and temperature in Bangladesh. The study revealed that INM-CM5-0 is the best model for projecting temperature, while BCC-CSM2-MR is the best model for projecting rainfall over Bangladesh. Using data from a large ensemble of 27 models from CMIP6, the study examined the rainfall and temperature change projections of Bangladesh during the twenty first century relative to the reference period (1981–2014) under SSP2–4.5 and SSP5–8.5. Under SSP2-4.5 and SSP5-8.5, the multi-model ensemble monsoon mean rainfall over Bangladesh will fluctuate between 40 and 260 mm and 100 and 900 mm, respectively. In most parts of the country’s north, northeastern, and western regions, the projected changes in spatial patterns of monsoon rainfall indicate an increase in rainfall. The projected temperature indicated that Bangladesh’s northwest and west-central areas could face the most significant rise in temperatures, surpassing 3.8 °C under SSP5-8.5.

Wildfires are disturbances that occur in ecosystems, both naturally and derived from anthropogenic factors, often caused by extreme meteorological conditions, and have recurrently destructive impacts on forests throughout the world. The complex nature of the interactions between wildfires, their dynamics, and human interference from a climate change perspective has motivated a growing number of researchers to address this topic. The fire weather index (FWI) has been extensively used to analyze the link between meteorological fire danger and its local to regional characteristics contributing to the severity of these events, as well as real-time operational monitoring at national and international levels. Recently, a new improved fire danger index that includes the effect of atmospheric instability has been developed, the so-called FWIe. The presence of instability in the atmosphere may be a boost to more energetic wildfires, such as the June 2017 extreme event in central Portugal, making it an important asset in risk monitoring and management. Here, a comprehensive examination of future fire risk on the Iberian Peninsula was performed. Additionally, a comparative analysis between FWI and FWIe was pursued in the context of climate change. We computed both FWI and FWIe using a multi-model ensemble composed of 13 Euro-CORDEX Regional Climate Model (RCM) simulations forced by different global climate models. The historical period (1971–2000) and three projected periods of 30 years (2011–2040, 2041–2070, and 2071–2 100), under three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) were considered. When assessing modelled FWI and FWIe, results show that summer values tend to substantially increase in the future when assuming the historical period as the benchmark, with an expected extension of the danger period to June and, in a lower magnitude, to September. The north-western region of Iberia, including the north of Portugal and the north-western-to-central Spain are the regions with larger increases in danger in the future, which may be critical since these are the regions with more fire-prone vegetation. This work also points to large differences in fire danger projections among scenarios, calling for a distinct set of adaptation needs that should be timely prepared by stakeholders and authorities.

Climate and climate change can impact present and future energy production and demand. In light of this issue, this paper conducts climatological analyses focused on the following meteorological quantities: shortwave downward irradiance (SDI), precipitation (Pr), relative humidity (RH), air temperature ( T a ), 10 m wind speed ( v 10 ), and diurnal temperature range (DTR) for four locations in Corsica. The climate analyses of these atmospheric variables consist of three parts: (1) analysis of the historical trends; (2) correlation analysis; and (3) analysis of climate projections for the decades to come. It is observed that climate change is causing alterations in the trends of Pr, RH, T a , v 10 , and DTR. The correlation analysis reveals a positive correlation for the T a -SDI and v 10 -Pr pairs (both annually and seasonally), and a negative correlation for T a -RH (annually and in summer). For the other variable pairs, the sign of the correlations varies depending on the time period and site considered. The trends in the projections from the multi-model ensemble simulations are consistent or inconsistent with each other depending on the time period (annual or seasonal) considered. The observed historical trends suggest that medium-term planning of the Corsican electric power system should already consider ongoing climate change. The correlation analysis provides insights into the combined effect of different atmospheric variables on electrical power systems (EPSs). Climate projections suggest studying long-term planning that is a compromise among the different (but equally likely) outputs of different climate models. [ABSTRACT FROM AUTHOR]

This study assesses the effects of climate change on hydropower production in the most threatened highlands region of the Euphrates-Tigris Basin, with the case of the Dipni Project. This evaluation is based on the precipitation and temperature predictions of the multi-model ensembles produced by analysing the simulations of 24 global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Soil and Water Assessment Tool (SWAT) model is utilized to estimate the future inflow rates of the Dipni reservoir under the Shared Socio-economic Pathway (SSP) scenarios of SSP245 and SSP585. The 25-year reservoir operations conducted in the past and three future periods indicate possible decreases of up to 10.1% and 21.5% in the annual energy production under the SSP245 and SSP585 scenarios, respectively. The results show the need to take adaptive measures against the projected impacts of climate change to achieve the targeted return for the coming decades. [ABSTRACT FROM AUTHOR]

In recent years, the importance of climate prediction has increased as a scientific source for understanding climate change and evaluating its consequences in political and economic decisions. Providing predictions with less uncertainty, especially for precipitation and temperature is of considerable importance for policymakers in time periods from several months to several decades. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability and variability. The DCPP consists of three components (A, B, and C). Component A comprises of the production and analysis of an extensive archive of retrospective forecasts. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts, and Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (Boer et al. 2016). The aim of this study is to predict precipitation extremes using the decadal Climate Prediction Project contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 2021 to 2028 over Iran. For this purpose, two types of data including 77 synoptic stations and three DCPP models (BCC-CSM2-MR, MPI-ESM1-2-HR, and MRI-ESM2-0) with a horizontal resolution of 100 km were used. The precipitation output of DCPP models, each with nine variants (27 members) were used for two time periods, including Hindcast (1981-2019) and Forecast (2021-2028). To evaluate DCPP models, we used the Root Mean squared error (RMSE), the Pearson correlation coefficient (PCC), the Mean Bias Error (MBE), the Percent bias (PBIAS), and the Taylor diagram methods. In addition, Direct Model Output (DMO) was corrected by the Delta Change Factor (DCF) method, and the Independent Weighted Mean (IWM) was used to generate a multi-model ensemble from 27 members. In this study, the ETCCDI indices including days with Heavy precipitation (R10mm), days with Very heavy precipitation days (R20mm), Simple daily intensity (SDII), The maximum 1-day precipitation amounts (Rx1day), The maximum 3-day precipitation amounts (Rx3day), The maximum 5-day precipitation amounts (Rx5day) were calculated to analyze precipitation extremes for all regions of Iran. Furthermore, the evaluation of the DCPP models showed that the output of mentioned models is acceptable for all regions of Iran. Also, the performance of CMIP6-DCPP-MME is higher than the individual models. The result of the prediction of precipitation extremes showed that the six studied extreme precipitation indices will increase for the next decade. The Southwest and Northeast are the two hotspots of positive anomaly. In contrast, the southern coast of the Caspian Sea for the R10mm index will experience a negative anomaly for the next decade. The findings show that the southeastern region of Iran, from the eastern borders to the north of the Strait of Hormuz, will be the main area of negative precipition anomalies in the country in the next decade. So that the indices of days with heavy (R10mm) and very heavy (R20mm) precipition will decrease by 2.7 and 0.3 days, and daily precipition intensity (SDII) will decrease by 2.6 mm/day. [ABSTRACT FROM AUTHOR]

The popularity of cutting‐edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top‐performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi‐Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo‐climatic basins. Precipitation is best simulated by EC‐Earth3 and BCC‐CSM2‐MR. Maximum temperature by MPI‐ESM1‐2‐HR, EC‐Earth3‐Veg, INM‐CM5‐0 and MPI‐ESM1‐2‐LR. Minimum temperature by INM‐CM5‐0 and MPI‐ESM1‐2‐LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per‐humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms. [ABSTRACT FROM AUTHOR]

Cold wave (CW) events over India are usually observed during the boreal winter months, November–February. This study proposes an objective criterion using the actual, departure from normal and the percentile values of the daily gridded minimum temperature (Tmin) data for the monitoring of the CW events over the Indian region and also checks its usefulness in a multi-model ensemble extended range prediction system. The large scale features associated with these CW events are also discussed. Utilizing this proposed criterion and considering the number of average CW days/year for the entire study period and recent decades, the CW prone region has been identified. By calculating the standardized area-averaged (over the CW prone region) Tmin anomalies time series, the CW events are identified over the period 1951–2022. Analyzing the temporal variability of these events, it is seen that there is no compromise in the occurrences of the CW events even under the general warming scenarios. It is found that the long CW events (> 7 days) are favoured by the La-Nina condition and short CW events (≤ 7 days) are favoured by the neutral condition in the Pacific. Also, the blocking high to the north-west of Indian longitude with very slow movement of westerly trough to the east is found to be associated with the long CW events, whereas in case of short events the blocking high is not so significant. The multi-model ensemble prediction system is found to be reasonably skilful in predicting the CW events over the CW prone region up to 2–3 weeks in advance with decreasing confidence in longer leads. Based on the forecast verifications it is noticed that this forecasting system has a remarkable strength to provide an overall indication about the forthcoming CW events with sufficient lead time in spite of its uncertainties in space and time. [ABSTRACT FROM AUTHOR]

Information about wind variations and future wind conditions is essential for a monsoon domain such as the Northwest Pacific (NWP) region. This study utilizes 10 Generalized Circulation Models (GCM) from CMIP6 to evaluate near-future wind changes in the NWP under various climate warming scenarios. Evaluation against the ERA5 reanalysis dataset for the historical period 1985–2014 reveals a relatively small error with an average of no more than 1 m/s, particularly in the East Asian Marginal Seas (EAMS). Future projections (2026–2050) indicate intensified winds, with a 5–8% increase in the summer season in the EAMS, such as the Yellow Sea, East Sea, and East China Sea, while slight decreases are observed in the winter period. Climate mode influences show that winter El Niño tends to decrease wind speeds in the southern study domain, while intensifying winds are observed in the northern part, particularly under SSP5-8.5. Conversely, summer El Niño induces higher positive anomalous wind speeds in the EAMS, observed in SSP2-4.5. These conditions are likely linked to El Niño-induced SST anomalies. For the application of CMIP6 surface winds, the findings are essential for further investigations focusing on the oceanic consequences of anticipated wind changes such as the ocean wave climate, which can be studied through model simulations.

Ovarian cancer is one of the leading causes of death worldwide among the female population. Early diagnosis is crucial for patient treatment. In this work, our main objective is to accurately detect and classify ovarian cancer. To achieve this, two datasets are considered: CT scan images of patients with cancer and those without, and biomarker (clinical parameters) data from all patients. We propose an ensemble deep neural network model and an ensemble machine learning model for the automatic binary classification of ovarian CT scan images and biomarker data. The proposed model incorporates four convolutional neural network models: VGG16, ResNet 152, Inception V3, and DenseNet 101, with transformers applied for feature extraction. These extracted features are fed into our proposed ensemble multi-layer perceptron model for classification. Preprocessing and CNN tuning techniques such as hyperparameter optimization, data augmentation, and fine-tuning are utilized during model training. Our ensemble model outperforms single classifiers and machine learning algorithms, achieving a mean accuracy of 98.96%, a precision of 97.44%, and an F1-score of 98.7%. We compared these results with those obtained using features extracted by the UNet model, followed by classification with our ensemble model. The transformer demonstrated superior performance in feature extraction over the UNet, with a mean Dice score and mean Jaccard score of 0.98 and 0.97, respectively, and standard deviations of 0.04 and 0.06 for benign tumors and 0.99 and 0.98 with standard deviations of 0.01 for malignant tumors. For the biomarker data, the combination of five machine learning models—KNN, logistic regression, SVM, decision tree, and random forest—resulted in an improved accuracy of 92.8% compared to single classifiers.

The occurrences of summer hypoxia in coastal California Current can significantly affect the benthic and pelagic habitat and lead to complex ecosystem changes. Model-simulated hypoxia in this region is strongly spatially heterogeneous, and its future changes show uncertainties depending on the model used. Here, we used an ensemble of the new generation Earth system models to examine the present-day and future changes of summer hypoxia in this region. We applied model-specific thresholds combined with empirical bias adjustments of the dissolved oxygen variance to identify hypoxia. We found that, although simulated dissolved oxygen in the subsurface varies across the models both in mean state and variability, after necessary bias adjustments, the ensemble shows reasonable hypoxia frequency compared with a hindcast in terms of spatial distribution and average frequency in the coastal region. The models project increases in hypoxia frequency under warming, which is in agreement with deoxygenation projected consistently across the models for the coastal California Current. This work demonstrated a practical approach of using the multi-model ensemble for regional studies while presenting methodology limitations and gaps in observations and models to improve these limitations.

Regional climate models (RCMs) that produce good outputs in one region or for specific variables may underperform for others. Thereby, assessing the performance of various model simulations and their corresponding mean ensemble is critical in identifying the most suitable models. In this regard, a study was conducted to evaluate the performance of ten RCMs against observations from multiple ground-based stations in the East African Transboundary Omo Gibe River Basin, Ethiopia, during the baseline period of 1986–2005. The study evaluated the models' ability to replicate various aspects of climatic variables and their corresponding statistical indicators. The results confirmed that RCMs have varying abilities to reproduce climatic conditions across the basin. The ensembles and RACMO22T (EC-EARTH) were better at replicating the average annual precipitation distribution. Meanwhile, the CCLM4-8-17 (MPI) together with the ensembles better captured the measured precipitation annually, despite the discrepancies in the actual magnitudes. All RCMs were able to simulate the seasonal precipitation patterns effectively, with RACMO22T (EC-EARTH), CCLM4-8-17 (CNRM), RCA4 (CNRM), CCLM4-8-17 (MPI), and REMO2009 (MPI) models captured superior, excluding the maximum value. Interannual and seasonal rainfall pattern variations were more significant than variations in air temperature. Additionally, a better correlation was observed between actual and simulated precipitation at multiple separate monitoring places. The RCA4 (MPI) and CCLM4-8-17 (MPI) demonstrated reasonable minimum and maximum temperatures. The RCA4 (MIROC5) model was more effective in reproducing extreme precipitation events. However, all RCMs and their ensembles tended to overestimate the return periods of these events. In general, the research highlights the importance of selecting reliable RCMs that better replicate observed climatic settings and employing the ensemble mean of top-performing models following systematic bias adjustment for a specific application.

Projections of coral reefs under climate change have important policy implications, but most analyses have focused on the intensification of climate‐related physical stress rather than explicitly modelling how coral populations respond to stressors. Here, we analyse the future of the Great Barrier Reef (GBR) under multiple, spatially realistic drivers which allows less impacted sites to facilitate recovery. Under a Representative Concentration Pathway (RCP) 2.6 CMIP5 climate ensemble, where warming is capped at ~2°C, GBR mean coral cover declined mid‐century but approached present‐day levels towards 2100. This is considerably more optimistic than most analyses. However, under RCP4.5, mean coral cover declined by >80% by late‐century, and reached near zero under RCP ≥6.0. While these models do not allow for adaptation, they significantly extend past studies by revealing demographic resilience of coral populations to low levels of additional warming, though more pessimistic outcomes might be expected under CMIP6. Substantive coral populations under RCP2.6 would facilitate long‐term genetic adaptation, adding value to ambitious greenhouse emissions mitigation. [ABSTRACT FROM AUTHOR]

Climate change has significantly increased the frequency and intensity of heat stress and has more effects than increasing average temperature. This study has investigated the spatial distribution of the universal thermal climate index (UTCI) during historical and future periods in Iran. The UTCI (°C) refers to "the isothermal air temperature of the reference condition that would elicit the same dynamic response (strain) of the physiological model" (Jendritzky et al., 2012). In this way, the UTCI is an equivalent temperature, similar to PT. The thermal impact of the meteorological conditions is compared to the one of a standardized reference "indoor" environment with RH = 50% (Ta<29°C), WS = 0.5 m s-1, pa = 20 hPa (Ta<29°C), and Tmrt = Ta (Shin et al. 2022). Three variables of daily temperature, relative humidity, and wind speed from two sets of data, including 124 meteorological stations and five models from the Coupled Model Intercomparison Project phase 6 (CMIP6) model, including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1 -2-HR, MRI-ESM2-0, and UKESM1-0-LL were investigated with a horizontal resolution of 0.5o. Then, an ensemble model (CMIP6-MME) was generated from these five models using the independent weighted mean (IWM) method. The performances of individual models and the generated ensemble model were examined by Taylor's diagram. The results showed that the multi-model ensemble has higher performance than individual models for all three variables. The results revealed that the spatial distribution of the seasonal averages of the UTCI index has significant variability in Iran, and the variability of this index is affected by the latitude, complex topography, and distance to water resources in Iran. In general, heat stress will increase significantly in Iran by the end of the century. So, we will witness a significant decrease in areas with no heat stress until the end of this century. On the contrary, strong to very strong heat stress events will increase significantly in the country at the end of the century. While the areas with no thermal stress show a spatial displacement to mountainous regions and higher latitudes. These results show that effective adaptation methods should be taken to adapt to global warming and reduce its consequences to avoid the adverse effect of increasing heat stress events in Iran. The results show the overall increasing trend of Iran's heat stress in the near and far future. The highest increase in heat stress anomalies (13.3 degrees Celsius in winter during the far future period under the SSP5-8.5 scenario) can be found in the northwest and west of the country. The increasing intensity of heat stress in the western and northwestern parts of Iran may be related to elevation-dependent warming (EDW). [ABSTRACT FROM AUTHOR]