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Design patterns provide a systematic way to convey solutions to recurring modeling challenges. This paper introduces design patterns for hybrid modeling, an approach that combines modeling based on first principles with data-driven modeling techniques. While both approaches have complementary advantages there are often multiple ways to combine them into a hybrid model, and the appropriate solution will depend on the problem at hand. In this paper, we provide four base patterns that can serve as blueprints for combining data-driven components with domain knowledge into a hybrid approach. In addition, we also present two composition patterns that govern the combination of the base patterns into more complex hybrid models. Each design pattern is illustrated by typical use cases from application areas such as climate modeling, engineering, and physics. [ABSTRACT FROM AUTHOR]

In this paper, we present results of refining the characteristics of the eclipsing pre-cataclysmic variable SDSS J162256.6+473051 based on a model analysis of its optical radiation. We used the spectra of the system obtained with the 6-m BTA telescope of SAO RAS and the data from its multi-band photometric observations with the 1.5-m Russian-Turkish telescope RTT150. Calculations of the optical radiation of SDSS J162256.6+473051 were carried out using the method of irradiated atmosphere models. When analyzing the spectra of the system at the minimum brightness, we found the following atmospheric parameter estimates of the sdB sub-dwarf: K, , [He/H] . The light curves of SDSS J162256.6+473051 were modeled and a complete set of fundamental parameters of the system was obtained. Their satisfactory agreement with the literature data allows us to conclude that the characteristics of similar systems with weak reflection effects can be analyzed together, even when they are determined using different methods of radiation modeling. [ABSTRACT FROM AUTHOR]

Research on climate anxiety is rapidly growing, with ongoing exploration of population prevalence, contributing factors, and mitigation strategies that transform anxiety into helpful action. What remains unclear is whether and how to delineate climate anxiety from mental ill health. A limited conceptualization of climate anxiety restricts efforts to identify and support those adversely affected. This paper draws on psychological and existential theories to propose a theoretical model of climate anxiety and coping, extending previous conceptualizations. The model theorizes that climate change evokes an existential conflict that manifests affectively as climate anxiety (and other emotional experiences), wherein cognitive and behavioral coping processes are activated. These processes fall on a continuum of adaptivity, depending on functional impact. Responses might range from meaningful engagement with activities that address climate change to maladaptive strategies that negatively impact personal, social, and occupational functioning. Applications of this model in research and practice are proposed. [ABSTRACT FROM AUTHOR]

A study of climate change scenarios is presented in this paper by projecting the model of recorded precipitation/temperature data into three future periods by statistical downscaling methods through LARS-WG with data from 7 synoptic stations. The study area largely spans the closed basin of Lake Urmia with overlaps to some of the surrounding basins. The model is at two stages (the downscaling stage comprises the following: (i) it uses large-scale GCM models to provide climate variables (predictors), and (ii) the outcomes are downscaled to the local climatic variables for correlating with the observed time series (e.g. rainfall) for the period of T0, 1961–2001—40 years; the projecting stage comprises the derived precipitation/temperature values at T0 to T1: 2011–2030; T2: 2046–2065; and T3: 2080–2099 periods at synoptic stations using three standard scenarios of A1B, A2, and B1). The outputs are used to map the climate zones, which show the following: (i) climates at T1 are still similar to T0, and if there is any declining trend in precipitation during both periods, they are small to the extent that the shrinkage of Lake Urmia is unlikely to be driven by climate change; (ii) changes are likely at T2 and T3 periods including climatic regimes may turn peakier at the northern and drier at central regions; and (iii) precipitation is likely to decrease in some of the zones at T2 and T3 periods. This underpins the need for more responsive policymaking, and should this not be realised in the near future, the prospect seems bleak in terms of serious damages to Lake Urmia, depleting aquifers and subsequent immigration from the region, but can be averted by responsive planning. [ABSTRACT FROM AUTHOR]

Aimed at image distortion caused in process of defogging images and interfered by bright objects, a single adaptive image defogging method based on sky segmentation is proposed in this paper. Firstly, the combination of the dark channels and bright channels is used to estimate the atmospheric light. And then the sky region and the accurate atmospheric light are obtained by the information entropy and the dark channel prior. Next, the initial transmittance is estimated by the dark channel prior, and then the fast guiding filter is utilized to estimate the transmittance accurately. Further, an adaptive weight factor and an error amplification compensation factor of bright objects are introduced to optimize the transmittance mapping constraint and to correct the sky region and the non-sky region, respectively. Finally, bright adjustments are performed on the fog-free image obtained from the atmospheric scattering model by a nonlinear mapping method, which causes a clearer and more natural defogging image to be got. The experimental results show that adaptive single image defogging algorithms proposed in this paper has the higher value of peak signal-to-noise ratio, the higher value of structural similarity index measure, the higher value of naturalness image quality evaluator, the higher value of fog aware density evaluator, and the less time consumed, which illustrates that the adaptive single image defogging algorithm proposed in this paper has a better defogging effect on haze images with sky areas and bright objects. [ABSTRACT FROM AUTHOR]

Scientists have proposed many "Beyond-GDP" indicators to replace the Gross Domestic Product (GDP) in order to quantify genuine societal development. While GDP is regularly projected, research on future trajectories of Beyond-GDP indicators is lacking, failing to meet policymakers' needs. Focusing on the Human Development Index (HDI), this paper attempts to calculate one of the first global well-being projections for 161 countries by 2100 using the shared socioeconomic pathways (SSPs), the socioeconomic inputs underlying global climate change scenarios. The results indicate a potential global well-being improvement from medium to very high level, depending on the SSPs, with most countries reaching high human development under SSPs 1&5. While serving as an initial step in well-being projection, the results highlight a crucial gap in existing climate change models which are used by the IPCC—they inadequately account for the feedback effects of climate change on well-being. This oversight results in counterintuitive or potentially misleading well-being projections. Therefore, we propose steps to improve this situation. By synthesizing climate change feedback effects on HDI determinants, this assessment delves into their implications for well-being and further underscores the necessity for interdisciplinary collaborations among well-being researchers, climate scientists and policy modelers to achieve sound integral well-being projections. [ABSTRACT FROM AUTHOR]

Dynamical prediction of monsoon rainfall has been an important topic and a long-standing issue in both research and operational community. This paper provides a comprehensive evaluation of the subseasonal-to-seasonal (S2S) prediction skill of the East Asian summer monsoon (EASM) rainfall using the hindcast record from the Beijing Climate Center Climate System Model, BCC CSM1.1m, during the period 1983–2019. The model exhibits reasonable skills for predicting the EASM rainfall at all lead times with the skill dropping dramatically from the shortest lead time of about 2 weeks (LM0) to 1-month lead (LM1), and then fluctuating remarkably throughout 2-month to 12-month lead times. Over the EASM domain, the rapid decline of the S2S rainfall prediction skill from LM0 to LM1 is mainly caused by the inferior skills over Central China in July and over Northeast China in August. Composite analysis based on hindcast records suggest that these inferior skills are directly tied to the model's difficulties in capturing above-normal precipitation over eastern Central China and Northeast China in the respective months, which are further shown to be associated with anomalous weakening and meridional movement of the Northwestern Pacific subtropical high and the activity of large-scale teleconnection pattern hard to be predicted over northeastern Asia in summer, respectively. These findings inform the intrinsic limits of the S2S predictability of the EASM rainfall by a dynamical model, and strongly suggest that the level of confidence placed upon S2S forecasts should be stratified by large-scale circulation anomalies known to significantly affect the prediction skill, e.g., the subtropical high and high-latitude teleconnection patterns for summer monsoon rainfall prediction in this region. [ABSTRACT FROM AUTHOR]

The paper discusses modeling the dynamics of nickel concentration in soils, water, and the bottom sediments of lakes caused by atmospheric emissions from the Pechenganickel plant, Kola Peninsula, throughout its whole operation period. The applied technology of balanced identification makes it possible to use a mathematical description of heterogeneous geochemical processes in ecosystems to combine heterogeneous experimental data and build up a computer model with an optimal balance of its complexity and fitting quality of the data. The model is used to analyze the spatial and temporal variability of natural objects in the zone of distribution of atmospheric pollution (nickel) from the Pechenganickel plant. The paper presents and discusses results of this study, including estimates of the retrospective state of the simulated objects (before the start of the intense studies) and a forecast of their dynamics until 2030. According to the model calculations, the intensity of Ni accumulation in the soil and bottom sediments was 2.35 and 4.48 mg/(m2 year) during the maximum deposition periods (1980–2005), whereas the model predicts a decrease in the intensity of Ni accumulation in the bottom sediments (0.23 mg/(m2 year)) and slow Ni leaching from the soil (0.19 mg/(m2 year)) after the shutdown of the plant. [ABSTRACT FROM AUTHOR]

While political debates on climate change become increasingly heated, our houses and city infrastructure continue to suffer from an increasing trend of damages due to adverse atmospheric events, from heavier-than-usual rainfalls to heat waves, droughts, and floods. Adapting our homes and critical infrastructure to sustain the effects of climate dynamics requires novel data-driven interdisciplinary approaches for efficient risk mitigation. We develop a new systematic framework based on the machinery of statistical and machine learning tools to evaluate water-related home insurance risks and quantify uncertainty due to varying climate model projections. Furthermore, we introduce the concept of data depth to the analysis of weather and climate ensembles, which remains a novel territory for statistical depth methodology as well as the field of environmental risk and ensemble forecasting in general. We illustrate the new data-driven methodology for risk analysis in application to rainfall-related home insurance in the Canadian Prairies over 2002–2011. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

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We thank the authors for this interesting paper that highlights important ideas and concepts for the future of climate model ensembles and their storage, as well as future uses of stochastic emulators. Stochastic emulators are particularly relevant because of the statistical nature of climate model ensembles, as discussed in previous work of the authors (Castruccio et al. in J Clim 32:8511–8522, 2019; Hu and Castruccio in J Clim 34:8409–8418, 2021). We thank the authors for sharing of some of their data with us in order to illustrate this discussion. In the following, in Sect. 1 we discuss alternative techniques currently used and studied, namely lossy compression and ideas emerging from the climate modeling community, that could feed the discussion on ensemble and storage. In that section, we also present numerical results of compression performed on the data shared by the authors. In Sect. 2, we discuss the current statistical model proposed by the authors and its context. We discuss other potential uses of stochastic emulators in climate and Earth modeling. [ABSTRACT FROM AUTHOR]

The majority of existing physical model-based dehazing algorithms have problems, including color distortion and halo effects, when restoring hazy outdoor scenes that often contain large areas of the sky. This paper proposes a single image dehazing algorithm based on sky segmentation and an optimal transmission map to improve the quality of dehazed images containing sky regions. The proposed algorithm acquires the sky region of a hazy image by using the mean shift technique and prior information of sky color rules and estimates the atmospheric light by introducing adaptive threshold constraints based on the sky region. Next, a hazy image feature-based objective function is designed, and a transmission map is accurately estimated by introducing gradient domain-guided filtering. On this basis, images are restored with the atmospheric scattering model, and the final dehazed images are obtained through tone adjustment. The experimental results demonstrate that the proposed algorithm is robust and can effectively eliminate haze and enrich the edge details of images. Compared to other algorithms, the saturated pixel ratio of the present algorithm is approximately zero, indicating the preferable color saturation of the restored images. [ABSTRACT FROM AUTHOR]

Discoveries made during the 18 May 1980 eruption of Mount St. Helens advanced our understanding of tephra transport and deposition in fundamental ways. The eruption enabled detailed, quantitative observations of downwind cloud movement and particle sedimentation, along with the dynamics of co-pyroclastic-density current (PDC) clouds lofted from ground-hugging currents. The deposit was mapped and sampled over more than 150,000 km2 within days of the event and remains among the most thoroughly documented tephra deposits in the world. Abundant observations were made possible by the large size of the eruption, its occurrence in good weather during daylight hours, cloud movement over a large, populated continent, and the availability of images from recently deployed satellites. These observations underpinned new, quantitative models for the rise and growth of volcanic plumes, the importance of umbrella clouds in dispersing ash, and the roles of particle aggregation and gravitational instabilities in removing ash from the atmosphere. Exceptional detail in the eruption chronology and deposit characterization helped identify the eruptive phases contributing to deposition in different sectors of the distal deposit. The eruption was the first to significantly impact civil aviation, leading to the earliest documented case of in-flight engine damage. Continued eruptive activity in 1980 also motivated pioneering use of meteorological models to forecast ash-cloud movement. In this paper, we consider the most important discoveries and how they changed the science of tephra transport. [ABSTRACT FROM AUTHOR]

In the 1960s, theoretical biologist Richard Levins criticised modellers in his own discipline of population biology for pursuing the "brute force" strategy of building hyper-realistic models. Instead of exclusively chasing complexity, Levins advocated for the use of multiple different kinds of complementary models, including much simpler ones. In this paper, I argue that the epistemic challenges Levins attributed to the brute force strategy still apply to state-of-the-art climate models today: they have big appetites for unattainable data, they are limited by computational tractability, and they are incomprehensible to the human modeller. Along the lines Levins described, this uncertainty generates a trade-off between realistic, precise models with predictive power and simple, highly idealised models that facilitate understanding. In addition to building ensembles of highly complex dynamical models, climate modellers can address model uncertainty by comparing models of different types, such as dynamical and data-driven models, and by systematically comparing models at different levels of what climate modellers call the model hierarchy. Despite its age, Levins' paper remains incredibly insightful and should be considered an important entry into the philosophy of computational modelling. [ABSTRACT FROM AUTHOR]

The National Center for Atmospheric Research released a global atmosphere model named Community Atmosphere Model version 5.0 (CAM5), which aimed to provide a global climate simulation for meteorological research. Among them, the cloud microphysics scheme is extremely time-consuming, so developing efficient parallel algorithms faces large-scale and chronic simulation challenges. Due to the wide application of GPU in the fields of science and engineering and the NVIDIA's mature and stable CUDA platform, we ported the code to GPU to accelerate computing. In this paper, by analyzing the parallelism of CAM5 cloud microphysical schemes (CAM5 CMS) in different dimensions, corresponding GPU-based one-dimensional (1D) and two-dimensional (2D) parallel acceleration algorithms are proposed. Among them, the 2D parallel algorithm exploits finer-grained parallelism. In addition, we present a data transfer optimization method between the CPU and GPU to further improve the overall performance. Finally, GPU version of the CAM5 CMS (GPU-CMS) was implemented. The GPU-CMS can obtain a speedup of 141.69 × on a single NVIDIA A100 GPU with I/O transfer. In the case without I/O transfer, compared to the baseline performance on a single Intel Xeon E5-2680 CPU core, the 2D acceleration algorithm obtained a speedup of 48.75 × , 280.11 × , and 507.18 × on a single NVIDIA K20, P100, and A100 GPU, respectively. [ABSTRACT FROM AUTHOR]

This paper investigates the process behind climate model biases, especially in rainfall, during the southwest (June to August; JJA) and northeast monsoon (December to February; DJF) seasons over the Philippines using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA). Biases in the surface and near-surface climate are assessed over land in eastern (EastPH) and western (WestPH) Philippines using observation and reanalysis datasets. Results show that models with well-represented sea surface temperature (SST) provided better added values after downscaling. Although the CMIP5-driven models tend to overestimate both surface energy and water partitioning, simulations with well-represented SST have better surface energy partitions. In contrast to the reference simulation driven by ERA-Interim reanalysis, the CMIP5-driven simulations tend to underestimate (overestimate) moisture flux convergence during the DJF (JJA) season, which might have contributed to the overall underestimation (overestimation) of rainfall in this season. Further investigation along a vertical transect shows the differences in governing mechanisms for each season. During DJF (JJA) season, the simulation with underestimated (overestimated) SST, near-surface air temperature, and mixing ratio results in underestimated (overestimated) rainfall over EastPH (WestPH). In addition, the CMIP5-driven models were found to overestimate low-level winds, which may have induced the deep convection regardless of season, suggesting the role of low-level winds in the convection scheme used in RegCM4 aside from topography and land cover. [ABSTRACT FROM AUTHOR]

This paper introduces a fast urban microscale meteorological model with a horizontal resolution of O(10) m, named URBAN (Urban Rapid & Building-Aware Neighborhood), which is capable of rapid assessment of meteorological fields over key urban areas, including wind speed, air temperature, humidity and thermal comfort index, with the execution time less than 10 minutes consuming 1 CPU core. URBAN uses a fast wind diagnostic method to construct three-dimensional (3-D) wind fields surrounding complex building clusters with their geometry resolved explicitly To enhance the accuracy of wind reconstruction and the continuity of the initial wind field around irregular buildings, we propose a new parameterization method based on stream functions, which can accurately characterize the influences of complex urban building clusters on the three-dimensional wind field The model can provide various results for the meteorological service of large outdoor activities, including conventional meteorological elements (wind, temperature, humidity, radiation, etc.) and the Universal Thermal Comfort Index, which is derived from the relationship between physiological processes and environmental meteorological conditions. In this paper, URBAN is applied to develop an automatic analysis and forecast system of microscale meteorological elements over the central Beijing region in summer during a large outdoor event. By comparing with the half-hourly observations from three auto weather stations (AWSs) in the region, the root-mean-square errors (RMSEs) of the modeled 10-meter-height wind speed, 2-meter-height air temperature and humidity are 0.98 m s−1,1.37 °C and 7.37%, respectively. [ABSTRACT FROM AUTHOR]

Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rainfall in Iran's Sefidrood basin. A radial basis function neural network (RBFNN) and a multilayer perceptron (MLP) network were used to predict monthly rainfall. The models were trained using the naked mole rat (NMR) algorithm, firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO) algorithm. Large lagged climate indices, as well as three hybrid models, i.e., inclusive multiple model (IMM)-MLP, IMM-RBFNN, and the simple average method (SAM), were then employed to predict rainfall. This paper aims to predict rainfall using large climate indices, ensemble models, and optimized artificial neural network models. Also, the paper considers the uncertainty resources in the modeling process. The inputs were selected using a new input selection method, namely a hybrid gamma test (GT). The GT was integrated with the NMR algorithm to create a new test for determining the best input scenario. Therefore, the main innovations of this study were the introduction of the new ensemble and the new hybrid GT, as well as the new MLP and RBFNN models. The introduced ensemble models of the current study are not only useful for rainfall prediction but also can be used to predict other metrological parameters. The uncertainty of the model parameters and input data were also analysed. It was found that the IMM-MLP model reduced the root mean square error (RMSE) of the IMM-RBFNN, SAM, MLP-NMR, RBFNN-NMR, MLP-FFA, RBFNN-FFA, MLP-PSO, RBFNN-PSO, MLP-GA, and RBFNN-GA, MLP, and RBFNN models by 12%, 25%, 31%, 55%, 60%, 62%, 66%, 69%, 70%, 71%, 72%, and 72%, respectively. The IMMs, such as the IMM-MLP, IMM-RBFNN, and SAM, outperformed standalone models. The uncertainty bound of the multiple inclusive models was narrower than that of the standalone MLP and RBFNN models. The MLP-NMR model decreased the RMSE of the RBFNN-NMR, RBFNN-FFA, RBFNN-PSO, and RBFNN models by 15%, 26%, 37%, 42%, and 45%, respectively. The proposed ensemble models were robust tools for combining standalone models to predict hydrological variables. [ABSTRACT FROM AUTHOR]

High-frequency precipitation variance is calculated in 12 different free-running (non-data-assimilative) coupled high resolution atmosphere–ocean model simulations, an assimilative coupled atmosphere–ocean weather forecast model, and an assimilative reanalysis. The results are compared with results from satellite estimates of precipitation and rain gauge observations. An analysis of irregular sub-daily fluctuations, which was applied by Covey et al. (Geophys Res Lett 45:12514–12522, 2018. https://doi.org/10.1029/2018GL078926) to satellite products and low-resolution climate models, is applied here to rain gauges and higher-resolution models. In contrast to lower-resolution climate simulations, which Covey et al. (2018) found to be lacking with respect to variance in irregular sub-daily fluctuations, the highest-resolution simulations examined here display an irregular sub-daily fluctuation variance that lies closer to that found in satellite products. Most of the simulations used here cannot be analyzed via the Covey et al. (2018) technique, because they do not output precipitation at sub-daily intervals. Thus the remainder of the paper focuses on frequency power spectral density of precipitation and on cumulative distribution functions over time scales (2–100 days) that are still relatively "high-frequency" in the context of climate modeling. Refined atmospheric or oceanic model grid spacing is generally found to increase high-frequency precipitation variance in simulations, approaching the values derived from observations. Mesoscale-eddy-rich ocean simulations significantly increase precipitation variance only when the atmosphere grid spacing is sufficiently fine (< 0.5°). Despite the improvements noted above, all of the simulations examined here suffer from the "drizzle effect", in which precipitation is not temporally intermittent to the extent found in observations. [ABSTRACT FROM AUTHOR]

Rainfall prediction, based on meteorological data and models, forecasts the possible rainfall conditions for a period in the future. It is one of the important issues in meteorology and hydrology, and holds significant scientific and social value for enhancing human society's adaptive capacity, reducing the risk of natural disasters, promoting sustainable development, and protecting the environment. This study proposes a rainfall prediction model based on CEEMDAN-VMD-BiLSTM, which couples CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise), VMD (Variational Mode Decomposition), and BiLSTM (Bidirectional Long Short-Term Memory). The model first employs CEEMDAN and VMD, two decomposition algorithms, for a secondary decomposition of the original data, followed by prediction using the BiLSTM network. The study integrates the characteristics of CEEMDAN, which include adaptability, completeness, denoising capability, and high precision, the characteristic of VMD in extracting trend information, and the ability of the BiLSTM model to better capture contextual information in sequence data and solve long-term dependency issues, thereby increasing the accuracy of rainfall prediction. The research selected Zhongwei City in the Ningxia Hui Autonomous Region as the study object and used 20 years of monthly rainfall data from 2001 to 2020 as the research data. The model was compared with standalone BiLSTM models, CEEMDAN-BiLSTM coupled models, and VMD-BiLSTM coupled models. The model was validated using four indicators: RMSE, MARE, MAE, and NSE. The results showed that the maximum relative error of the CEEMDAN-VMD-BiLSTM neural network rainfall prediction coupled model was 7.22%, and the minimum relative error was -7.03%. The prediction qualification rate was 100%. The overall NSE value of the model ranged from 0.63 to 0.97, with most values between 0.86 and 0.97. The excellent rate was about 84.6%, and the good and above rate was 92.3%. In the rainfall prediction for Zhongwei City, the prediction accuracy of this coupled model was better than the other three models. In summary, the CEEMDAN-VMD-BiLSTM rainfall prediction model proposed in this paper combines the advantages of various methods and has shown good predictive effects in experiments, providing an effective prediction method for rainfall. [ABSTRACT FROM AUTHOR]

Wildfire is one of the primary natural disturbance agents in the forests of China. The forecast of fire danger is critically important to assist stakeholders to avoid and mitigate wildfire-induced hazards and losses to both human society and natural ecosystems. Currently, fire danger rating methods often focus on fire weather classification based on fixed thresholds, which has shortcomings in generalizability and robustness. Based on historical fire occurrence data and meteorological data of Northeastern China from 2004 to 2015, we proposed a forest fire danger rating classification and forecasting model by combining the advantages of the Canadian Fire Weather Index (FWI) system and two machine learning models such as the Long Short-Term Memory (LSTM) network and Random Forest (RF) model. The method is divided into two stages. The first stage is the LSTM-based FWI system indexes prediction. In the first stage, the future FWI system indexes are obtained through the LSTM-based prediction model, and the RMSE and MAE of the prediction results are calculated to verify the prediction performance of the model. The second stage is random forest-based fire danger rating prediction method. In the second stage, we use the random forest method to get the fire danger occurrence probability and present the fire danger rating classification scheme. Then we verify the reliability of the fire danger rating classification scheme by using the forest fire danger data in Qipan Mountain. Our method predicts two randomly selected future intervals, and the prediction accuracy is 87.5%. The experimental results show that our machine learning-based forest fire danger rating classification method can provide a new idea for forest fire danger warnings. [ABSTRACT FROM AUTHOR]

The paper discusses issues related to the main features of subseasonal variability in the tropics, as well as the influence of the Madden–Julian oscillation (MJO) on atmospheric processes in the Northern Hemisphere mid-latitudes. To identify weather patterns in the tropics, the RMM (Real-time Multivariate) index proposed by M. Wheeler and H. Hendon is used. Teleconnection indices are used to quantify atmospheric processes in the Northern Hemisphere mid-latitudes. The analysis of variance leads to the conclusion that there are teleconnections between the weather patterns in the tropics and the mid-latitudes at time intervals of about 5–7 days, which are most clearly pronounced in the Pacific–North American region, as well as in Asia. The MJO effect on the circulation in the Euro-Atlantic sector is quite complex and requires consideration of additional factors, in particular, the circulation in the stratosphere and El Niño events. Using the INM-CM5 climate model (Russia), the potential of predicting the MJO over a three-week time interval is demonstrated. The results are expected to be used in the operational practice of the North Eurasia Climate Center. [ABSTRACT FROM AUTHOR]

An Emergent Constraint (EC) is a physically-explainable relationship between model simulations of a past climate variable (predictor) and projections of a future climate variable (predictand). If a significant correlation exists between the predictand and the predictor, observations of the latter can be used to constrain model projections of the former and to narrow their uncertainties. In the present study, the EC technique has been applied to the analysis of precipitation, one of the variables most affected by model uncertainties and still insufficiently analysed in the context of ECs, particularly for the recent CMIP6 model ensemble. The main challenge in determining an EC is establishing if the relationship found is physically meaningful and robust to the composition of the model ensemble. Four precipitation ECs already documented in the literature and so far tested only with CMIP3/CMIP5, three of them involving the analysis of extreme precipitation, have been reconsidered in this paper. Their existence and robustness are evaluated using different subsets of CMIP5 and CMIP6 models, verifying if the EC is still present in the most recent ensemble and assessing its sensitivity to the detailed ensemble composition. Most ECs considered do not pass this test: we found one EC not to be robust in both CMIP5 and CMIP6, other two exist and are robust in CMIP5 but not in CMIP6, and only one is verified and is robust in both model ensembles. [ABSTRACT FROM AUTHOR]

Climate model, a complex numerical representation of the global climate system, has been developed to simulate current climate and used to project future climatic conditions. Simulated climatic variables from climate models often exhibit significant deviations from observations. In climate projections, different approaches were introduced to deal with systematic deviations and random model errors. This paper demonstrates the intercomparison of four bias correction approaches (linear scaling, delta change correction, distribution mapping, and variance scaling) underlying the assumptions of stationary output from climate models. Mean monthly temperatures derived from five global climate models were corrected by four bias correction approaches for five states of southern India. The suitability of correction approaches depends upon the climate models and regional framework. The applied approaches improve the mean values and other statistical properties. The results show that all four bias techniques significantly improved the simulated data, but distribution mapping and variance scaling were more effective in removing systematic model biases. [ABSTRACT FROM AUTHOR]

Gridded observational datasets are often used for the evaluation of regional climate model (RCM) simulations. However, the uncertainty of observations affects the evaluation. This work introduces a novel method to quantify the uncertainties in the observational datasets and how these uncertainties affect the evaluation of RCM simulations. Besides precipitation and temperature, our method uses geographic variables (e.g. elevation, variability of elevation, effect of station), which are considered as uncertainty sources. To assess these uncertainties, a complex analysis based on various statistical tools, e.g. correlation analysis and permutation test, was carried out. Furthermore, we used a special metric, the reduction of error (RE) to identify where the RCM shows improvement compared to the lateral boundary conditions (LBCs). We focused on the Carpathian region, because of its unique orographic and climatic conditions. The method is applied to two observational datasets (CarpatClim and E-OBS) and to RegCM simulations for 2010, the wettest year in this region since 1901. The results show that CarpatClim is wetter than E-OBS, while temperature is similar over the lowland; however, E-OBS is significantly warmer than CarpatClim over the mountains. By the RE metric, RegCM has improvement against the LBCs over mountains for temperature and areas with dense station network for precipitation. Nevertheless, there are significant differences in the results depending on which observational dataset was used concerning precipitation. The evaluation method can be applied to other datasets, different time periods and areas. It is also suitable to find dataset errors, which is also exemplified in this paper. [ABSTRACT FROM AUTHOR]

This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using "convection-permitting" models to produce reliable climate change information over the Fenno-Scandinavian region. [ABSTRACT FROM AUTHOR]

Precipitation in Europe shows natural fluctuations that differ considerably between seasons and geographical regions. A number of studies have linked local or seasonal rainfall variability to various long-distance air pressure differences in north–south or west–east direction. This paper presents the first continent-wide analysis of European rainfall variability on a month-by-month and country-by-country basis. We calculated Pearson r values for unsmoothed monthly rainfall data of 39 European countries over the period 1950–2019 with five potential climatic drivers, namely the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the North Sea Caspian Pattern (NCP), and two indices of Mediterranean Oscillation (MOI2, WeMOI). For a variety of countries and months we found substantial and statistically significant r values of up to r = 0.7 and more. The dynamic temporal-spatial evolution of the Pearson correlations was mapped out across the continent, tracking the gradual or abrupt expansion, displacement and subsequent waning of the various effects over the course of the year. The correlation analysis was complemented by best subset multiple regression, controlling for intercorrelation of the potential drivers. Our results may help to improve short- to midterm rainfall prognoses in Europe and provide important calibration data for the further refinement of climate models. [ABSTRACT FROM AUTHOR]

Severe pollution environment over the Israel Electric (IECo) Power Grid is created by synergy between Saharan dust and sea-salt aerosols (SSA) originated from the Mediterranean Sea. In the presence of high air humidity, the resultant pollution stress remains one of the major factors affecting operational reliability of ceramic insulators in the IECo Network. In respect with the severe polluted conditions, the outdoor insulation coordination requires detailed statistical studies and refined geographic mapping of the natural pollution deposit. This paper presents numeric maps of Sea-Salt Sedimentation Rate (SSSR) that was developed for the Eastern Mediterranean region including Israel. The paper demonstrates an adopted meteorological model, calculating algorithm and spatial–temporal characteristics of sea-salt aerosols within the considered domain. The acquired SSSR-Maps have been verified against the Equivalent Salt Deposit Density (ESDD) field-data and available ground-level measurements of SSA-concentrations. Good agreement was obtained between SSA model predictions and ground-based measurements on a daily and monthly basis. A case-study of a joint analysis of desertdust and sea-salt deposition along the selected overhead line was presented. The effect of the sea-salt deposit on the pollution flashover risk of ceramic insulators has been demonstrated. It is showed that the predicted ESDD-values might quite commensurate those ESDD-portions absorbed by desert dust. Hence, the contribution of sea-salt deposit cannot be neglected when evaluating pollution flashover risk for local outdoor insulators, especially in coastal areas. The study outcomes have demonstrated that the proposed SSA-modeling approach has sufficient meaningfulness to be implemented in practical tasks of outdoor insulation coordination and insulators dimensioning. [ABSTRACT FROM AUTHOR]

A novel dynamic modeling methodology for meteorological sensitive load forecasting of smart grids is proposed by using Fisher information theory and online support vector regression (OSVR) technology to improve load prediction accuracy in this paper. According to the changes in the operating state of the smart grids and streaming data characteristics, the OSVR model is employed to implement accurate online training algorithms to avoid retraining the entire training data whenever a sample is added to or removed from the training set. On the other hand, the paper originally utilizes Fisher Information theory to address the introduction of weather factors into the meteorological sensitive load prediction model and feature selection for it. We also present a practical and concise implementation of the proposed methodology. We demonstrate the application of the proposed methodology in the meteorological sensitive load prediction for the local utilities in different periods and compared it with the traditional method. The results indicate that the forecast model constructed by the proposed methodology can obtain the superior prediction performance among the conventional SVR models. [ABSTRACT FROM AUTHOR]

This paper is about spatial–temporal variability of atmospheric temperature across Taiwan, an island with diverse local emission sources partly because of its Asian cultural characteristics. To develop a new land use regression (LUR) model for this study, we used the temperature data collected by the Taiwan Central Weather Bureau from 2000 and 2016, while using the data from 2017 as the external data verification to assess model reliability. Because incense and joss money burning is a cultural-specific emission source in Asia, we further included location of temples, cemeteries, and crematoriums as potential predictors. The overall model performance and tenfold cross-validated are R2 of 0.88 and R2 of 0.87, respectively, which presents a high level of prediction performance. Moreover, we used our LUR model to estimate urban heat islands intensity (UHII) for six metropolises in Taiwan and found Taichung City has the highest UHII value (4.60 °C) among them. These results provide important insights in expanding the remote sensing application on spatial–temporal variation of atmospheric temperature and its further application on UHI effects. [ABSTRACT FROM AUTHOR]

Negative emissions techniques (NETs) promise to capture greenhouse gases from the atmosphere and sequester them. Since decarbonisation efforts have been slow, and the climate crisis is intensifying, it is increasingly likely that removing greenhouse gases from the atmosphere will be necessary to meet internationally-agreed targets. Yet there are fears that pursuing NETs might undermine other mitigation efforts, primarily the reduction (rather than removal) of greenhouse gas emissions. This paper discusses the risk of this phenomenon, named 'mitigation deterrence'. Some of us have previously argued that a cultural political economy framework is needed for analysing NETs. Such a framework explains how promises of future NETs deployment, understood as defensive spatio-temporal fixes, are depoliticised and help defend an existing neoliberal political regime, and its inadequate climate policy. Thus they risk deterring necessary emissions reductions. Here we build on that framework, arguing that to understand such risks, we need to understand them as the result of historically situated, evolving, lived practices. We identify key contributing practices, focussing in particular but not exclusively on climate modelling, and discuss how they have been reproduced and co-evolved, here likened to having dug a hole for ourselves as a society. We argue that understanding and reducing deterrence risks requires phronetic knowledge practices, involving not just disembodied, dispassionate technoeconomic knowledge-making, but also strategic attention to political and normative issues, as well as emotional labour. Reflecting on life in the hole hurts. [ABSTRACT FROM AUTHOR]

With the advancement of technology in both combat and defensive situations, the requirement of the use of autonomous vehicles which carry payload is increasing. For the different types of targets, missiles are used. The missile guidance system calculates the miss distance and the trajectory dynamics through automated system. Using image processing, multiple types of target can be identifies. The use of image processing in the guiding system to differentiate between the objects may provide the contemporary error correction in the flight path. In this paper, multiple approaches are used for target identification, tracking and control of trajectory dynamics. The approach identifies two targets, a massive missile truck and an airplane. The algorithm successfully identifies target despite colour variances and almost equivalent structural arrangements. Further trajectory estimation of target is done through constant velocity and constant turn models. When utilising motion estimates and detection, real-world success depends on the ability to recognise visual targets. The Type 1 system's main objective is flight path management using the yaw, pitch, and roll axes. A common atmospheric model and a set of equations are used by the missile autopilot system to describe the aircraft. In MATLAB Simulink, a simulated missile attack travelling at 328 m/s is modelled and simulated. In the model, the missile and its intended target came together at 3.5 s. [ABSTRACT FROM AUTHOR]

The accurate identification of extreme weather events (EWEs), particularly cyclones, has become increasingly crucial due to the intensifying impacts of climate change. In the Indian subcontinent, the frequency and severity of cyclones have demonstrably risen, highlighting the need for reliable detection methods to minimize casualties and economic losses. However, the inherent limitations of low-resolution data pose significant challenges to traditional detection methods. Deep learning models offer a promising solution, enabling the precise identification of cyclone boundaries crucial for assessing regional impacts using global climate models data. By leveraging the power of deep learning, we can significantly enhance our capabilities for cyclone detection and contribute to improved risk mitigation strategies in the vulnerable Indian subcontinent. Therefore, this paper introduces an edge-enhanced super-resolution GAN (EESRGAN) leveraging an end-to-end detector network. The proposed approach comprised of a generator network equipped by residual-in-residual dense block (RRDB) and discriminator containing Faster RCNN detector. The precise patterns of cyclone had been effectively extracted to help boundary detection. Extensive experiments have been conducted on Community Atmospheric Model (CAM5.1) data taken into account only seven variables. Four matrices including precision, recall, intersection over union, and mean average precision have been considered to assess the proposed approach. The results have been found very effective while achieving accuracy up to 86.3% and average precision (AP) of 88.63%. Moreover, the proposed method demonstrates its superiority while compared with benchmarks object detectors methods. Thus, the proposed method can be employed in the area of extreme climate detection and could enrich the climate research domain. [ABSTRACT FROM AUTHOR]

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates. [ABSTRACT FROM AUTHOR]

In the latest years the capacity and complexity of climate and environmental modeling has increased considerably. Therefore, tools and criteria for model performance evaluation are needed to ensure that different users can benefit from model selection. Among graphical tools, Taylor's diagram is widely used to provide evaluation and comparison of model performances, with particular emphasis on climate models. Taylor's diagram accounts for different statistical features of model outputs and observations, including correlation, variability and centered root mean square error. Not included is model bias, which is an essential feature for climate model evaluations, and it is usually calculated separately to complement the information embedded in Taylor's diagram. In this paper a new diagram is proposed, referred to as Aras' diagram, which allows for visual assessments of the correspondence between model outputs and reference data in terms of total error, correlation, as well as bias and variability ratios through an easy-to-interpret two-dimensional (2D) plot, allowing for proper weighting of different model features. The strengths of the new diagram are exemplified in a case study of performance evaluation of EURO-CORDEX historical experiment over Southern Italy using E-OBS as reference dataset, for three hydrological variables (i.e. daily precipitation, daily surface minimum temperature, and daily maximum surface temperature), and four popular climate indices (i.e. total annual precipitation, annual maxima of daily precipitation, annual minima of daily minimum temperatures, and annual maxima of daily maximum temperatures). The proposed diagram shows interesting properties, in addition to those already included in Taylor's diagram, which may help promoting climate model evaluations based on their accuracy in reproducing the climatological patterns observed in time and space. [ABSTRACT FROM AUTHOR]

Climate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX simulation models work better for predicting average temperatures in the Campania region (Italy). The method involves two key steps: first, using functional data analysis to process climate variables and select optimal models by a hierarchical clustering procedure; second, validating the chosen models by proposing a new conformal prediction approach to the anomalies associated to each cluster. [ABSTRACT FROM AUTHOR]

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the low Mach and low Froude numbers limit for the compressible Navier–Stokes equations with degenerate, density-dependent, viscosity coefficient, in the strong stratification regime. We consider the case of a general pressure law with singular component close to vacuum, and general ill-prepared initial data. We perform our study in the three-dimensional periodic domain. We rigorously justify the convergence to the generalised anelastic approximation, which is used extensively to model atmospheric flows. [ABSTRACT FROM AUTHOR]

This paper is devoted to the global attractors of the tropical climate model. We first establish the global well-posedness of the system. Then by studying the existence of bounded absorbing sets, the global attractor is constructed. The estimates of the Hausdorff dimension and of the fractal dimension of the global attractor are obtained in the end. [ABSTRACT FROM AUTHOR]

While climate models are an invaluable tool for increasing our understanding and therefore, the predictability of the Earth's system for decades, their increase in complexity and resolution has put a considerable, growing strain on the computational resources of research centers and institutions worldwide. The statistics community has a long history of developing stochastic models as a means to save computational time, but the emergence of storage as an additional cost for climate investigations has prompted a reformulation of the aim of statistical models in model-based environmental science. Can stochastic approximations be useful as a mechanism for saving both computational time and storage? We focus on a collection of simulations from a climate model and propose several statistical models of increasing complexity. By analyzing and discussing the associated costs for each model, we demonstrate how computation and storage are closely intertwined, and how a statistical model of increasing complexity is justified only to the extent that information at a fine spatial and/or temporal scale is sought to be preserved.Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

The traditional regional air pollution characteristics analysis method cannot obtain the best meteorological pollution factors, leading to air pollution characteristics analysis results and actual deviation. Therefore, a numerical simulation method of regional air pollution characteristics based on weather factors and improved Elman neural network algorithm is proposed. The best meteorological factors affecting air quality were obtained and the annual mean concentration of air pollutants was analyzed by Spearman rank correlation coefficient method. According to the structure of Elman neural network, an interval prediction model of atmospheric pollution concentration is designed. Based on this, control equations, turbulence model and boundary conditions are constructed. The characteristics of air pollution in a city are simulated to obtain the maximum contribution of air pollution. The results show that the simulated concentration of PM2.5and PM10 is close to the measured values, the relative error between the simulated values and the measured values is small, and the simulated atmospheric concentration is in good agreement with the measured values. Therefore, the numerical simulation analysis of pollution characteristics presented in this paper is more accurate and reliable. [ABSTRACT FROM AUTHOR]

Future climate change will alter regional hydrometeorological conditions, which poses significant risks to the operation of inter-basin water diversion projects in the future. Most previous studies focused on adaptive operations under climate change. Few studies examined how to best manage risks and quantify the effect of climate change on objective losses. This paper intends to present a systematic approach for runoff prediction, impact analysis on objectives, and risk assessment under climate models. The Xin'AnJiang hydrological model, single-objective and multi-objective reservoir operation models, and risk assessment model of multiple schemes are integrated into this approach. Taking the inter-basin water diversion project from Hanjiang to Weihe River as a case, the study has made three contributions. First, the negative impacts of climate change on water transfer, net power generation, and transformation ratio between them are quantified. Second, the difference in water transfer scale between single-objective and multi-objective caused by climate change is analyzed. Third, to recommend scheduling schemes balancing benefits and risks and coping strategies to help policymakers mitigate the inevitable harmful effect of climate change. Consequently, it is extremely necessary to prepare to deal with the risks of insufficient water transfer and nip them in the bud. [ABSTRACT FROM AUTHOR]

Accurate climate projections help policymakers mitigate the negative effects of climatic changes and prioritize environmental issues based on scientific evidences. These projections rely heavily on the outputs of GCMs (General Circulation Models), but the large number of GCMs and their different outputs in each region confuses researchers in their selection. In this paper, we analyzed the performance of a CMIP6 (Climate Model Intercomparison Project Phase 6) multi-model ensemble for Pr (precipitation) data over NE (Northern Europe). First of all, we evaluated the overall performance of 12 CMIP6 models from GCMs in 30 years of 1985–2014. Furthermore, future projections were analyzed between 2071 and 2100 using SSP1-2.6 and SSP5-8.5 (Shared Socioeconomic Pathways). Then, simulations were statistically improved using an ensemble method to correct the systematic error of the CMIP6 models and then the capacity of postprocessed data to reproduce historical trends of climate events was investigated. Finally, the possible spatio-temporal changes of future Pr data were explored in Tana River Basin. The results of this study show that different CMIP6 models do not have the same accuracy in estimating Pr in the study area. However, the ensemble method can be effective in increasing the accuracy of the projections. The results of this study projected a change in the monthly Pr data over Tana River Basin by 2.46% and 2.06% from 2071 to 2100 compared to the historical period, based on SSP1-2.6 and SSP5-8.5, respectively. [ABSTRACT FROM AUTHOR]

Uncertainty sources in climate change impact studies can be addressed in two distinct phases, climate modelling and impact assessment. Generally, uncertainty from the climate modelling phase is the larger contributor and the impact phase uncertainty is sometimes marginalized. This paper aims to develop integrated uncertainty analysis approaches that deal with uncertainties at the impact level with all sources included. The existing uncertainty analysis method of Reliability Ensemble Averaging (REA) is modified for impact studies by bracketing the hydrological model parameter uncertainty. A novel probabilistic approach referred to as Bootstrapped Ensemble Uncertainty Modelling (BEUM) is also proposed for uncertainty analysis at the impact phase. Modified REA and BEUM are applied to streamflow projections of the Hulu Langat basin with uncertainties stemming from emission scenarios, general circulation models (GCM), downscaling method, and a hydrological model parametrization for a comprehensive integrated uncertainty assessment. The modified REA increased the reliability measure of each streamflow scenario and improved the uncertainty coverage percentage when compared with the original REA. The REA eliminated the projections of the less reliable GCMs, and therefore reduced uncertainty bands compared to the BEUM. The BEUM can create a large ensemble of streamflow projections through nonparametric bootstrapping of the limited impact scenarios and define the streamflow distribution for every quantile. Therefore, BEUM is able to encompass larger uncertainty intervals and explain the behavior of ensemble members through probabilistic distributions that can assist in decision making for climate change mitigation and adaptation planning. [ABSTRACT FROM AUTHOR]

General circulation models (GCM) show projections of climate variables that when downscaled can be applied to analyse future behaviour in different areas or places. Using them is possible not just to obtain expected values of climate variables but also to calculate their distributions and use those values to assess the effects of climate change at a local level. However, these calculations depend on the GCM selected. In this paper, daily maximum near-surface air temperatures from 21 climate models under representative concentration pathway (RCP) scenarios RCP 4.5 and RCP 8.5 and historic daily maximum temperatures (1990–2019) from nine cities in southern Spain are used with two objectives: first, to investigate past behaviour broken down into a deterministic part and a stochastic part; second, to compare historical data (2006–2019) with the information extracted from the 21 GCMs based on calculating goodness of fit in the period for both deterministic and stochastic parts. The methodology proposed may be useful in selecting a model or a range of models for use in a specific study. The results show positive historical and future trends in maximum daily temperature for these cities. The GCMs with the best fit for each city in this specific case are also presented. [ABSTRACT FROM AUTHOR]

Selecting the appropriate climate models and determining their strengths and weaknesses are the key steps in examining climate change impacts on water resources. In this paper, the efficiency of monthly, seasonal, and annual precipitation estimations by 21 Global Climate Models (GCMs) provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP) was evaluated against the Observational Precipitation (OP) of 50 synoptic stations located in eight Precipitation Zones (PZs) of Iran. First, the average of GCMs precipitation for the period 1976–2005 was evaluated vs the OP data at both PZ and national levels. Then the efficiency of the GCMs and their Ensemble Average (EA) in estimating monthly, seasonal, and annual precipitation were determined using NSE, NRMSE, KGE, and PBIAS statistics, and subsequently, the GCMs were ranked according to their statistics. The results show that the time variability of the GCMs' average annual precipitation agrees with that of the OP, but it underestimates the extreme values. The agreement of the models' estimations with observation is generally lowest in summer and the highest in winter and spring. Most of the GCMs show a lower ability in precipitation estimation in dry seasons than in wet seasons. EA of the models for all individual months shows the highest errors in the coastal areas of the Caspian Sea, especially for the spring and winter seasons. The examination of the GCMs' efficiency at the national level shows that the EA and GFDL-ESM2G models have the least errors at monthly and annual time scales, respectively. [ABSTRACT FROM AUTHOR]

A number of recent papers have examined sea level data, both local tide gauge records and regional/global averages, to estimate not only how fast sea level is rising but how the rate has changed over time, i.e. its pattern of acceleration and deceleration. In addition, a number of claims of cyclic/quasi-periodic variations have been proposed. However, many of these papers contain technical problems which call their results into question. In particular, the issue of autocorrelation is often ignored, and even when it is addressed its impact has sometimes been misinterpreted. Autocorrelation does more than just affect the standard errors of regression analysis, it can also make the spectrum of a noise process distinctly 'red' and therefore be highly suggestive of low-frequency periodic or pseudo-periodic behavior when none is present. If any analysis is applied which acts as a band-pass filter, it can further exaggerate the illusion of oscillatory behavior. These issues are highlighted in a small number of recent papers, in order to improve the quality of future work on this subject. [ABSTRACT FROM AUTHOR]

The study of climate change adaptation plans for drainage infrastructure in a small country as that of Singapore, rainfall projections on the time scale of minutes and on the spatial scales of 1 km are deemed appropriate In this paper, we introduce an application of radar-based stochastic downscaling for rainfall projections at high temporal and spatial resolutions. The input for stochastic model is derived from a Regional Climate Model. The sub-hourly extreme rainfall intensity derived from stochastic model outputs was validated against observed rain-gauge data over the historical period. Considering the advantage in computational efficiency of the stochastic downscaling method, thousand scenarios of rainfall projections at very high temporal and spatial resolution were generated. The implication of this approach is that, from these stochastically downscaled time series of rainfall, it is possible to study future sub-hourly extreme rainfall intensities which would be useful to address issue of flash floods/drainage systems. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the two-phase free boundary problem of primitive equations for the coupled atmosphere and ocean in three-dimensional strip with surface tension. By using the p -coordinates and another coordinate transformation to transform formulations of the problem into the fixed time-independent domain, the local-in-time existence of a unique strong solution to the transformed problem is proved. [ABSTRACT FROM AUTHOR]

The high mountain regions of Asia contain more glacial ice than anywhere on the planet outside of the polar regions. Because of the large population living in the Indus watershed region who are reliant on melt from these glaciers for fresh water, understanding the factors that affect glacial melt along with the impacts of climate change on the region is important for managing these natural resources. While there are multiple climate data products (e.g., reanalysis and global climate models) available to study the impact of climate change on this region, each product will have a different amount of skill in projecting a given climate variable, such as precipitation. In this research, we develop a spatially varying mixture model to compare the distribution of precipitation in the High Mountain Asia region as produced by climate models with the corresponding distribution from in situ observations from the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) data product. Parameter estimation is carried out via a computationally efficient Markov chain Monte Carlo algorithm. Each of the estimated climate distributions from each climate data product is then validated against APHRODITE using a spatially varying Kullback–Leibler divergence measure. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]