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Climate change is expected to increase the frequency and severity of climate extremes, which will negatively impact crop production. As one of the main food and feed crops, maize is also vulnerable to extreme climate events. In order to accurately and comprehensively assess the future climate risk to maize, it is urgent to project and evaluate the stress of extreme climate related maize production under future climate scenarios. In this study, we comprehensively evaluated the spatio-temporal changes in the frequency and intensity of six extreme climate indices (ECIs) across China's maize harvest region by using a multi-model ensemble method, and examined the capability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to capture these variations. We found that the Independence Weight Mean (IWM) ensemble results calculated by multiple Global Climate Models (GCMs) with bias correction could better reproduce each ECI. The results indicated that heat stress for maize showed consistent increase trends under four future climate scenarios in the 21st century. The intensity and frequency of the three extreme temperature indices in 2080s were significantly higher than these in 2040s, and in the high emission scenario were significantly higher than these in the low emission scenario. The three extreme precipitation indices changed slightly in the future, but the spatial changes were more significant. Therefore, with the uncertainty of climate change and the differences of climate characteristics in different regions, the optimization of specific management measures should be considered in combination with the specific conditions of future local climate change. [ABSTRACT FROM AUTHOR]

Abstract Understanding and anticipating the impacts of climate change on hydrological processes is crucial for sustainable water resource management. This study investigates the projected alterations in streamflow within the Tamor River Basin, Nepal, under changing climatic conditions, utilizing the soil and water assessment tool (SWAT). Future climatic variables, including precipitation, maximum, and minimum temperature, were assessed for the near (2022–2047), mid (2048–2073), and far future (2074–2100) periods under two shared socioeconomic pathways (SSPs): SSP245 and SSP585. Bias-corrected outputs from coupled model intercomparison project, phase 6 (CMIP6) models were integrated into the SWAT model to simulate the basin's hydrological response. Results indicate that, under the SSP245 scenario, annual average maximum and minimum temperatures are expected to rise by ~ 0.046 °C and 0.050 °C, respectively, with a 12.70% increase in precipitation. Similarly, the SSP585 scenario predicts temperature increases of 0.063 °C and 0.085 °C, alongside an 11.90% rise in precipitation. These climatic changes are projected to result in a significant increase in streamflow, with an estimated rise up to 20% by the end of the twenty-first century. The findings of this research provide valuable insights for policymakers and stakeholders, facilitating informed decision-making for the sustainable management of water resources in the face of climate change.

More than half of South Korea's land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past decade, more than half of these types of fires occurred during the spring season. Although human activities are the primary cause of forest fires, the fact that they are concentrated in the spring underscores the strong association between forest fires and meteorological factors. When meteorological conditions favor the occurrence of forest fires, certain triggering factors can lead to their ignition more easily. The purpose of this study is to analyze the meteorological factors influencing forest fires and to develop a machine learning-based prediction model for forest fire occurrence, focusing on meteorological data. The study focuses on four regions within Gangwon province in South Korea, which have experienced substantial damage from forest fires. To construct the model, historical meteorological data were collected, surrogate variables were calculated, and a variable selection process was applied to identify relevant meteorological factors. Five machine learning models were then used to predict forest fire occurrence and ensemble techniques were employed to enhance the model's performance. The performance of the developed forest fire prediction model was evaluated using evaluation metrics. The results indicate that the ensemble model outperformed the individual models, with a higher F1-score and a notable reduction in false positives compared to the individual models. This suggests that the model developed in this study, when combined with meteorological forecast data, can potentially predict forest fire occurrence and provide insights into the expected severity of fires. This information could support decision-making for forest fire management, aiding in the development of more effective fire response plans. [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]

Climate change is expected to increase the frequency and severity of climate extremes, which will negatively impact crop production. As one of the main food and feed crops, maize is also vulnerable to extreme climate events. In order to accurately and comprehensively assess the future climate risk to maize, it is urgent to project and evaluate the stress of extreme climate related maize production under future climate scenarios. In this study, we comprehensively evaluated the spatio-temporal changes in the frequency and intensity of six extreme climate indices (ECIs) across China’s maize harvest region by using a multi-model ensemble method, and examined the capability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) to capture these variations. We found that the Independence Weight Mean (IWM) ensemble results calculated by multiple Global Climate Models (GCMs) with bias correction could better reproduce each ECI. The results indicated that heat stress for maize showed consistent increase trends under four future climate scenarios in the 21st century. The intensity and frequency of the three extreme temperature indices in 2080s were significantly higher than these in 2040s, and in the high emission scenario were significantly higher than these in the low emission scenario. The three extreme precipitation indices changed slightly in the future, but the spatial changes were more significant. Therefore, with the uncertainty of climate change and the differences of climate characteristics in different regions, the optimization of specific management measures should be considered in combination with the specific conditions of future local climate change.

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.

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]

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 study investigates the effects of incremental global warming, specifically the transition from 1.5 °C to 2.0 °C, on drought conditions in the Iberian Peninsula (IP). Our findings confirm a substantial increase in the frequency and intensity of droughts in the IP due to anthropogenic climate change. We highlight the importance of temperature in drought representation and underscore the urgent need to limit global warming below 1.5 °C, in line with international climate policies. The analysis reveals that the exacerbation of drought conditions is more pronounced under higher emission scenarios, particularly RCP8.5, emphasizing the critical role of emission reduction in climate change mitigation. Furthermore, a substantial increase in affected land area and population exposure to drought is observed, especially under the higher-emission scenario. Climate change emerges as the primary factor contributing to increased drought exposure, with emission reduction efforts offering potential mitigation. To overcome limitations associated with model uncertainties, a multi-model multi-variable ensemble approach was employed to enhance the regional specificity of the findings. This provides valuable insights for local climate adaptation and mitigation strategies. Results suggest that mitigating anthropogenic warming by 0.5 °C to achieve the 1.5 °C warmer climate rather than 2.0 °C may provide benefits for future drought risks and impacts in the IP and underscore the urgency of implementing stringent climate policies. By offering a comprehensive assessment of drought conditions and population exposure, this study informs decision-making and climate resilience strategies, emphasizing the need for immediate action to mitigate adverse impacts on ecosystems and human populations.

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]

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]

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]

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.

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]

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]

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]

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]

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]

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]

Plant disease is a significant health concern among all living creatures. Early diagnosis can help farmers take necessary steps to cure the disease and accelerate the production rate efficiently. Our research has been conducted with five most common rice leaf diseases, such as bacterial leaf blight, brown spot, leaf blast,leaf scald, and narrow brown spot, including healthy class, and two categories of betel leaf, such as healthy and unhealthy class. A robust new deep ensemble model, based on InceptionResNetV2, EfficientNetV2L,and Xception, has been proposed, known as PlantDet, in this research. PlantDet solves not only underfitting problems but also leverage nourished performances simultaneously for scarce dataset of the sparse number of different background image dataset. PlantDet integrates efficient data augmentation, preprocessing, Global Average Pooling layer, Dropout mechanism, L2 regularizers, PReLU activation function, Batch Normalization layers, and more Dense layers that make the model more robust compared to all existing models and help to handle underfitting and overfitting problems while maintaining high performance. PlantDet exceeds the previous state-of-art model for the Rice Leaf dataset with an Accuracy of 98.53%, a Precision of 98.50%, a Recall of 98.35%, a F1 of 98.42% and a Specificity of 99.71%. In addition, for the Betel Leaf dataset, PlantDet also surpassed all existing base models, including several robust ensemble models. Finally, Grad-CAM and Score-CAM have been accomplished with the Xception method to explain the model performances particularly to elaborate how the Deep Learning (DL) models works for this complex dataset. Score-CAM slightly outperformed Grad-CAM++ in terms of localizing the predicted area.

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.

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]

Southeast Europe is considered one of the most vulnerable regions in the context of climate change, and projected future summer warming is expected to exceed global rates significantly. Despite the importance of this problem, there have been few studies that utilized Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Circulation Models (GCMs) and the multi-model ensemble approach to examine extreme heat events on a regional scale. Additionally, the NEX-GDDP dataset, successfully applied in other parts of the world to assess extreme heat, has not yet been utilized for Southeast Europe. This study aims to fill that gap, presenting the time evolution and spatial distribution of extreme heat events in Southeast Europe for the historical period 1950–2005 and for the expected future climate up to the end of the 21st century based on the NEX-GDDP dataset. In order to leverage the strengths of the multi-model ensemble approach, a set of purpose-tailored indicators, such as the annual number of hot days, the maximum number of consecutive hot days, and hot spell duration at different thresholds, is computed by the daily maximum temperature data from all datasets, produced by NEX-GDDP (21 for the historical period and 42 for the future period). The E-OBS dataset is used as a reference for evaluating the NEX-GDDP's capability to simulate the features of the observed historical extreme heat events. The results show that the multi-model ensemble can satisfactorily capture the occurrence of extreme heat events in the historical period, and therefore it is reasonable to assume that the NEX-GDDP dataset has the potential to reproduce such extremes in the projected future. The study provides clear evidence that the persistence and spatial extent of extreme heat will increase significantly. Some indicators that were not relevant for the historical period due to the high-temperature threshold will become helpful in assessing extreme heat in Southeast Europe in the latter part of the century. Thus, under the RCP8.5 scenario, the area-averaged duration of hot spells at 32 °C and 34 °C will increase from near zero in 1976–2005 to 60 and 45 days, respectively, by the end of the century. The indicators used in the study may be helpful for decision-makers to implement climate change mitigation strategies and actions adequately. The findings are consistent with general tendencies in maximum temperatures considered in our previous works but also with the outcomes of recent studies dedicated to the future climate of the region. [ABSTRACT FROM AUTHOR]

Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global Circulation Models (GCMs) are evaluated against corresponding indices with observations from the Global Climate Center Precipitation Dataset (GPCC V2020) over five sub-regions across Arid Central Asia (ACA), using the Taylor diagram, interannual variability skill score (IVS) and comprehensive rating index (MR). Moreover, we compare four multi-model ensemble approaches: arithmetic average multi-model ensemble (AMME), median multi-model ensemble (MME), pattern performance-based multi-model ensemble (MM-PERF) and independence weighted mean (IWM). The results show that CMIP6 models have a certain ability to simulate the spatial distribution of extreme precipitation in ACA and the best ability to simulate simple daily intensity (SDII), but it is difficult to capture the spatial bias of consecutive wet days (CWD). Almost all models represent different degrees of wet bias in the southern Xinjiang (SX). Most GCMs are generally able to capture extreme precipitation trends, but to reproduce the performance of interannual variability for heavy precipitation days (R10mm), SDII and CWD need to be improved. The four multi-model ensemble methods can reduce the internal system bias and variability within individual models and outperform individual models in capturing the spatial and temporal variability of extreme precipitation. However, significant uncertainties remain in the simulation of extreme precipitation indices in SX and Tianshan Mountain (TM). Comparatively, IWM simulations of extreme precipitation in the ACA and its sub-regions are more reliable. The results of this study can provide a reference for the application of GCMs in ACA and sub-regions and can also reduce the uncertainty and increase the reliability of future climate change projections through the optimal multi-model ensemble method. [ABSTRACT FROM AUTHOR]

As climate change continues to impact the planet, the importance of forests is becoming increasingly emphasized. The International Co-operative Program on the Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests) has been monitoring and assessing forests in 40 countries since 1985. In Republic of Korea, the first Forest Health Management (FHM) survey was a nationwide sample point assessment conducted between 2011 and 2015. However, there are limitations in representing the health of forests that occupy 63.7% of Korea's land area due to the nature of sample point surveys, which survey a relatively small area. Accordingly, a species richness map was created to promote species diversity in forest health evaluations in Republic of Korea. The map was created using data from the first FHM survey, which examined 28 factors with 12 survey indicators in four categories: tree health, vegetation health, soil health, and atmospheric health. We conducted an ensemble modeling of species distribution for woody plant species that are major habitats in Republic of Korea. To select the species, we used the first FHM survey data and chose those with more than 100 sample points, resulting in a total of 11 species. We then created the species richness map of Republic of Korea by overlaying their distributions. To verify the accuracy of the derived map, an independent verification was conducted using statistical verification and external data from the National Natural Environment Survey. To support forest management that accounts for climate change adaptation, the derived species richness map was validated based on the vegetation climate distribution map of the Korean Peninsula, which was published by the Korea National Arboretum. The map confirmed that species richness is highest around the boundary of the deciduous forest in the central temperate zone and lowest around the evergreen and deciduous mixed forest in the southern temperate zone. By establishing this map, it was possible to confirm the spatial distribution of species by addressing the limitations of direct surveys, which are unable to represent all forests. However, it is important to note that not all factors of the first FHM survey were considered during the spatialization process, and the target area only includes Republic of Korea. Thus, further research is necessary to expand the target area and include additional items. [ABSTRACT FROM AUTHOR]

Climate conditions and emissions are among the primary influences on seasonal variations in air quality. Consequently, skillful climate forecasts can greatly enhance the predictability of air quality seasonal forecasts. In this study, we propose a dynamical-statistical method for seasonal forecasting of particulate matter (PM _10 ) concentrations in South Korea in winter using climate forecasts from the Asian Pacific Climate Center (APCC) multi-model ensemble (MME). We identified potential climate predictors that potentially affect the wintertime air quality variability in South Korea in the global domain. From these potential climate predictors, those that can be forecasted skillfully by APCC MME were utilized to establish a multiple-linear regression model to predict the winter PM _10 concentration in South Korea. As a result of evaluating the forecast skill through retrospective forecasts for the past 25 winters (1995/96-2019/20), this model showed statistically significant forecast skill at a lead time of a month to a season. The skill of PM _10 forecast from the MME was overall better than that from a single model. We also found that it is possible to improve forecast skills through optimal MME combinations.

While multi-model ensembles (MMEs) of seasonal climate models (SCMs) have been used for crop yield forecasting, there has not been a systematic attempt to select the most skillful SCMs to optimize the performance of a MME and improve in-season yield forecasts. Here, we propose a statistical model to forecast regional and national wheat yield variability from 1993–2016 over the main wheat production area in Argentina. Monthly mean temperature and precipitation from the four months (August–November) before harvest were used as features. The model was validated for end-of-season estimation in December using reanalysis data (ERA) from the European Centre for Medium-Range Weather Forecasts (ECMWF) as well as for in-season forecasts from June to November using a MME of three SCMs from 10 SCMs analyzed. A benchmark model for end-of-season yield estimation using ERA data achieved a R ^2 of 0.33, a root-mean-square error (RMSE) of 9.8% and a receiver operating characteristic (ROC) score of 0.8 on national level. On regional level, the model demonstrated the best estimation accuracy in the northern sub-humid Pampas with a R ^2 of 0.5, a RMSE of 12.6% and a ROC score of 0.9. Across all months of initialization, SCMs from the National Centers for Environmental Prediction, the National Center for Atmospheric Research and the Geophysical Fluid Dynamics Laboratory had the highest mean absolute error of forecasted features compared to ERA data. The most skillful in-season wheat yield forecasts were possible with a 3-member-MME, combining data from the SCMs of the ECMWF, the National Aeronautics and Space Administration and the French national meteorological service. This MME forecasted wheat yield on national level at the beginning of November, one month before harvest, with a R ^2 of 0.32, a RMSE of 9.9% and a ROC score of 0.7. This approach can be applied to other crops and regions.

This study introduces an ensemble prediction methodology employing multiple machine learning algorithms for forecasting the frequency of typhoons (TYFs) over the western North Pacific (WNP) during June‒November. Potential predictors were initially identified based on the relationships between the year-by-year variation (DY) of the TYFs and preseason (March–May) environmental factors. These predictors were subsequently further refined, resulting in the selection of eight key predictors. Prediction models were constructed using twenty machine learning algorithms, utilizing data from 1965 to 2010. These trained models were then applied to perform hindcasts of TYFs from 2011 to 2023. The forecasted DY was added to the observed TYF of the preceding year to obtain the current year’s TYF. The results indicate that the TYFs predicted by the multi-model ensemble (MME) closely align with the observation during the hindcast period. Compared to individual models, the MME improves the prediction skill for the DY by at least 5.56% and up to 56.92%. Furthermore, the mean bias of the MME for TYF is notably smaller than that of the ECMWF’s most recent seasonal forecasting system (SEAS5) in the years of 2017‒2023. The superior performance of the ensemble prediction approach was also validated through leave-one-out cross-validation. This research underscores the potential of ensemble prediction approach utilizing multiple machine learning algorithms to improve the forecasting skill of TYF over the WNP.

Data assimilation (DA) aims to optimally combine model forecasts and observations that are both partial and noisy. Multi‐model DA generalizes the variational or Bayesian formulation of the Kalman filter, and we prove that it is also the minimum variance linear unbiased estimator. Here, we formulate and implement a multi‐model ensemble Kalman filter (MM‐EnKF) based on this framework. The MM‐EnKF can combine multiple model ensembles for both DA and forecasting in a flow‐dependent manner; it uses adaptive model error estimation to provide matrix‐valued weights for the separate models and the observations. We apply this methodology to various situations using the Lorenz96 model for illustration purposes. Our numerical experiments include multiple models with parametric error, different resolved scales, and different fidelities. The MM‐EnKF results in significant error reductions compared to the best model, as well as to an unweighted multi‐model ensemble, with respect to both probabilistic and deterministic error metrics. Plain Language Summary: Forecasts that combine multiple imperfect models of a system are used in many fields, including the physical, natural and socio‐economic sciences. In particular, data assimilation (DA), the process by which observations are integrated with model forecasts, is critical in the prediction of chaotic systems. Multi‐model DA (MM‐DA) unifies multi‐model forecast combination and DA into a single process. Here, we significantly improve on previous formulations of MM‐DA by accounting for model error, and formulate a multi‐model ensemble Kalman filter appropriate for high‐dimensional systems. Key Points: Multiple models and observations can be optimally combined for data assimilation (DA) and forecasting using multi‐model DAWe formulate a multi‐model ensemble Kalman filter (MM‐EnKF), which incorporates model error and is appropriate for high‐dimensional modelsUsing numerical experiments, we show that the MM‐EnKF can significantly outperform the best model and an unweighted multi‐model ensemble [ABSTRACT FROM AUTHOR]

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This 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. [ABSTRACT FROM AUTHOR]

Adaptation-relevant predictions of climate change are often derived by combining climate model simulations in a multi-model ensemble. Model evaluation methods used in performance-based ensemble weighting schemes have limitations in the context of high-impact extreme events. We introduce a locally time-invariant method for evaluating climate model simulations with a focus on assessing the simulation of extremes. We explore the behavior of the proposed method in predicting extreme heat days in Nairobi and provide comparative results for eight additional cities.

Verification processes of rainfall-runoff modeling are important to improve the skill of hydrological models to reproduce water cycles in river basins. It is ideal that newly developed models are compared with many benchmarking conventional models in many river basins as part of the verification process. However, this robust verification is timeconsuming if model developers prepare data and models from scratch. Here we present a useful dataset which can accelerate the robust verification of hydrological models. Our newly developed dataset, Multi-model Ensemble for Robust Verification of hydrological modeling in Japan (MERV-Jp), provides runoff simulation by 44 calibrated conceptual hydrological models in 135 Japanese river basins as well as meteorological forcing which is necessary to drive conceptual hydrological models. By comparing simulated runoff with river discharge observations which are not used for the calibration of hydrological models, we find that the best models in the 44 models can reproduce observed river runoff with KGE larger than 0.6 in most of the 135 river basins, so that the runoff simulation of MERV-Jp is reasonably accurate. MERV-Jp is publicly available to support all hydrological model developers to robustly verify their model improvement. [ABSTRACT FROM AUTHOR]

It is a grand challenge to realize robust rainfall-runoff prediction for a changing climate through conceptual hydrological models. Although multi-model ensemble (MME) is considered useful in improving the robustness of hydrological prediction, it has yet to be thoroughly evaluated. We evaluated the robustness of MME by 44 conceptual hydrological models in 582 river basins. We found that MME was more accurate and robust than each individual model alone. Although the performance of MME degrades in the validation period, the extent of degradation is smaller for MME than for individual models, especially when the climatology of river discharge in the validation period is greatly different from that in the calibration period. This implies the robustness of MME to climate change. It was found to be difficult to quantify the robustness of MME when the number of basins and models is small, which implies the importance of the large number of models and watersheds to evaluate the robustness and uncertainty in hydrological prediction. [ABSTRACT FROM AUTHOR]

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures. [ABSTRACT FROM AUTHOR]

The development of machine learning (ML) provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, and JMA CPS2 model data, we conducted a multi-model ensemble (MME) prediction experiment using three tree-based ML algorithms: the decision tree (DT), random forest (RF), and adaptive boosting (AB) algorithms. On this basis, we explored the applicability of ML algorithms for ensemble prediction of seasonal precipitation in China, as well as the impact of different hyperparameters on prediction accuracy. Then, MME predictions based on optimal hyperparameters were constructed for different regions of China. The results showed that all three ML algorithms had an optimal maximum depth less than 2, which means that, based on the current amount of data, the three algorithms could only predict positive or negative precipitation anomalies, and extreme precipitation was hard to predict. The importance of each model in the ML-based MME was quantitatively evaluated. The results showed that NCEP CFSv2 and JMA CPS2 had a higher importance in MME for the eastern part of China. Finally, summer precipitation in China was predicted and tested from 2019 to 2021. According to the results, the method provided a more accurate prediction of the main rainband of summer precipitation in China. ML-based MME had a mean ACC of 0.3, an improvement of 0.09 over the weighted average MME of 0.21 for 2019–2021, exhibiting a significant improvement over the other methods. This shows that ML methods have great potential for improving short-term climate prediction. [ABSTRACT FROM AUTHOR]

The climate is changing and its impacts on agriculture are a major concern worldwide. The impact of precipitation will influence crop yield and water management. Estimation of such impacts using inputs from the General Circulation Models (GCMs) for future years will therefore assist managers and policymakers. It is therefore important to evaluate GCMs on a local scale for an impact study. As a result, under the Shared Socioeconomic Pathways (SSPs) future climate scenarios, namely SSP245, SSP370, and SSP585, simulations of mean monthly and daily precipitation across Brunei Darussalam in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) were evaluated. The performance of two multi-model ensemble (MME) methods is compared in this study: the basic Arithmetic Mean (AM) of MME and the statistical downscaling (SD) of MME utilizing multiple linear regression (MLR). All precipitation simulations are bias-corrected using linear scaling (LS), and their performance is validated using statistical metrics such as Root Mean Square Error (RMSE) and coefficient of determination (R2). The adjusted mean monthly precipitation during the validation period (2010–2019) shows an improvement, especially for the SD model with R2 = 0.85, 0.86 and 0.84 for SSP245, SSP370 and SSP585, respectively. Although the two models produced unsatisfying results in producing annual precipitation. Future analysis under the SD model shows that there will be a much lower average monthly trend in comparison with the observed trend. On the other hand, the forecasted monthly precipitation under AM predicted the same rainfall trend as the baseline period in the far future. It is projected that the annual precipitation in the near future will be reduced by at least 27% and 11% under the SD and AM models, respectively. In the long term, less annual precipitation changes for the SD model (17%). While the AM model estimated a decrease in precipitation by at least 14%. [ABSTRACT FROM AUTHOR]

Southwest Asia has different climate types including arid, semiarid, Mediterranean, and temperate regions. Due to the complex interactions among components of the Earth system, forecasting precipitation is a difficult task in such large regions. The aim of this paper is to propose a learning approach, based on artificial neural network (ANN) and random forest (RF) algorithms for post-processing the output of forecasting models, in order to provide a multi-model ensemble forecasting of monthly precipitation in southwest Asia. For this purpose, four forecasting models, including GEM-NEMO, NASA-GEOSS2S, CanCM4i, and COLA-RSMAS-CCSM4, included in the North American multi-model ensemble (NMME) project, are considered for the ensemble algorithms. Since each model has nine different lead times, a total of 108 different ANN and RF models are trained for each month of the year. To train the proposed ANN an RF models, the ERA5 reanalysis dataset is employed. To compare the performance of the proposed algorithms, four performance evaluation criteria are calculated for each model. The results indicate that the performance of the ANN and RF post-processing is better than that of the individual NMME models. Moreover, RF outperformed ANN for all lead times and months of the year. [ABSTRACT FROM AUTHOR]

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

Previous studies on multi-model ensemble forecasting mainly focused on the weight allocation of each model, but did not discuss how to suppress the reduction of ensemble forecasting accuracy when adding poorer models. Based on a variant weight (VW) method and the equal weight (EW) method, this study explored this topic through theoretical and real case analyses. A theoretical proof is made, showing that this VW method can improve the forecasting accuracy of a multi-model ensemble, in the case of either the same models combination or adding an even worse model into the original multi-model ensemble, compared to the EW method. Comparative multi-model ensemble forecasting experiments against a real case between the VW and EW methods show that the forecasting accuracy of a multi-model ensemble applying the VW method is better than that of each individual model (including the model from the European Centre for Medium-Range Weather Forecasts). The 2 m temperature forecasting applying the VW method is superior to that applying the EW method for all the multi-model ensembles. Both theoretical proof and numerical experiments show that an improved forecast, better than a best model, is generally possible. [ABSTRACT FROM AUTHOR]

To improve the seasonal prediction of monthly precipitation in summer over Northeast China (NEC), a hybrid prediction scheme is developed to combine the advantages of statistical method with dynamical prediction information from 4 coupled general climate models (CGCMs). As the operational prediction of summer climate is performed in March or earlier, the information of CGCMs employed in this study is from the hindcast/prediction in February for June and July, and March for August. Predictors comprise observations of the preceding winter sea surface temperature and simultaneous information derived from the CGCMs. Stability and multicollinearity are fully considered in predictor selection to avoid over‐fitting. For a single model, the monthly precipitation reconstructed from the predicted time series and the observed spatial load of the leading Empirical Orthogonal Function (EOF) modes with accumulative explained covariance more than 85%. For the multi‐model ensemble (MME), the predictions with the lowest correlation coefficient were removed until the performance of the MME was optimal for the cross‐validation period. This new ensemble method shows some advantages compared to a traditional MME method. The leave‐one‐out cross‐validation for 1982–2010 and the independent validation for 2011–2016 both indicate an improvement of the new hybrid scheme in seasonal predictions of summer monthly precipitation over NEC. The observed and predicted monthly precipitation are significantly correlated with coefficients of 0.71, 0.49, and 0.73, and their hit rates are 77%, 66%, and 77% for June, July, and August precipitation, respectively for the MME over the period 1982–2016. [ABSTRACT FROM AUTHOR]

Based on the daily 24-168 h ensemble precipitation forecasts over China from 1 May to 31 August in 2016 from the global ensemble models of ECMWF, JMA, NCEP, CMA and UKMO extracted from the TIGGE archives, the frequency matching method is tested to calibrate the precipitation frequency of each ensemble member. Then results of multi-model ensemble forecasts before and after calibration, including Kalman filter(KF), multi-model super-ensemble (SUP) and bias-removed ensemble mean(BREM), are analyzed in order to improve the prediction of precipitation based on numerical weather forecast data. Results show that precipitation forecasts calibrated by the frequency matching method, which uses the moderate precipitation to correct light and heavy precipitation, can effectively improve the problem of the underestimation of heavy precipitation caused by ensemble mean forecast and improve the positive deviation of the ensemble forecasting system, so that the precipitation forecast category is closer to the observation. However, the frequency matching method can barely improve the prediction of precipitation area. Different from frequency matching method, multi-model ensemble forecasts can extract and consider features of each model, therefore the prediction of precipitation area is more accurate than each single model, but the result is not as good as the frequency matching method in terms of the prediction of precipitation category. Among different multi-model methods, because of the updated weights over time, the result of Kalman filter forecast is superior to SUP and BREM in terms of threat scores, root mean square error (RMSE) and anomaly correlation coefficient (ACC). Furthermore, combining advantages of the above two methods, the multi-model ensemble precipitation after calibration based on ensemble members is more effective in the prediction of heavy precipitation category and area, which is closer to the observation. Results improve the threat score (TS) of the precipitation in all forecast lead times, especially in the heavy precipitation with the TS of 24 h forecast reaching 0.26, indicating a lower false alarm rate and missing rate compared with single model. Results also improve ACC and RMSE of the heavy precipitation and this method produces the best results among all the other methods, especially in the coastal areas in the south of China. In terms of the prediction of precipitation area, results effectively optimize the area of heavy and light precipitation, making the multi-model ensemble precipitation after calibration best in predicting heavy precipitation processes.