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The randomness of sea conditions and loading sequences are among the key contributing factors that affect our ability to accurately predict the fatigue crack growth in ships. Climate change may alter the long-term characteristics of these factors along the service life of existing or newly constructed ships. This paper presents a framework for quantifying the impact of climate change on crack propagation in ship hulls. A probabilistic fatigue crack propagation approach is developed to account for uncertainties associated with material properties and loading conditions; specifically, those affected by climate change. Global Climate Models (GCMs) are used to quantify the long-term effects of climate change on the sea conditions and the resulting ship loading time histories. The proposed approach is applied to a tanker ship operating within predefined routes in the Atlantic Ocean. The results show that the effect of climate change on the crack propagation depends on the navigation route. [ABSTRACT FROM AUTHOR]

Cities have long been subject to urban containment policies against urban sprawl. Climate change concerns have recently added to the imperative to densify urban space. Urban compaction is often pursued through the creation of 'exemplar' urban developments that superficially implement 'best practice' ideas from elsewhere. In this paper, we abandon the notion of 'best practice' in favour of context-sensitive 'good practices'. Taking London's King's Cross redevelopment as a case study, this paper draws on qualitative methods to examine the contribution of context and path-dependency, as a product of local and non-local forces, to the emergence of King´s Cross as 'good practice'. [ABSTRACT FROM AUTHOR]

In this paper, a novel single image dehazing method based on pyramid multi-scale transposed convolutional network (MST-Net) is proposed. Conventional haze removal algorithms based on the atmospheric scattering model may lead to some problems such as incomplete dehazing and colour distortion due to the inaccurate parameter approximations or indirect image optimization and reconstruction. Therefore, we design a real end-to-end image dehazing network to directly learn the mapping relationship between hazy images and the corresponding clear images. In this network, the cascaded feature extraction blocks extract the diversified feature information of the input images by multi-channel concatenation structure, which can effectively fuse the local features of the first convolution layer into the semantic features of subsequent layers in the block. In order to reconstruct high-quality dehazed images relieving the colour distortion, we design a multi-scale transposed convolution block to gradually expand the resolution of the obtained feature maps, and introduce skip connections from the feature extraction module to supplement the detailed information of the feature map pyramid. Extensive experimental results demonstrate that the proposed method in this paper can remove the haze completely and achieve superior performance in subjective and objective evaluation over the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Though all the aspects concerning hydrology, geology, topography and operational needs are taken into accounted while designing a spillway, eventually some spillways of existing dams need upgradation. Upgradation for the purpose of increasing the storage of the reservoir involves raising of the reservoir level above the normal operating level and this would result in additional submergence of land on the upstream. Upgradation in the gated spillways, necessitating the provision of piers, the full reservoir level (FRL), is required to be raised. The New Umtru Project is one of these projects. It is proposed to be upgraded by providing gates to the existing overflow spillway to utilize the flows optimally. Hydraulic model studies of the proposed modifications were conducted on 1:30 scale 2D sectional model at Central Water and Power Research Station (CWPRS) for evolving the optimum design of spillway and energy dissipator. It was observed from the studies that the original design of spillway and energy dissipator was required to be revised. Hence, improvement in the performance of the spillway after these modifications and other recommendations from the model studies are described in the paper. The present paper also describes the numerical simulation for upgraded spillway using computational fluid dynamics (CFD). [ABSTRACT FROM AUTHOR]

This paper proposes a conceptual framework for climate policy engagement that is focused on building the social conditions conducive to support for climate policy. Despite the ongoing scientific and social cases being made for climate action, social divisions remain in some contexts such as Australia and the United States. The success of climate policies depend in part on the general social support base for that solution, with public polarisation acting as a barrier. Dominant communication models have tended to rely on one-way strategically focused communication to exert influence with the aim of shifting one's attitudes. However, there is a need to extend such approaches to go beyond strategic messaging and instead establish the social conditions necessary for policy support. Drawing on diverse literature and framing theory, a new engagement model for climate policy is proposed which centres around a goal of relationship-building between actors to facilitate productive discussion on climate policy. [ABSTRACT FROM AUTHOR]

An intelligent single radar image de-raining method based on unsupervised self-attention generative adversarial networks is proposed to improve the accuracy of wave height parameter inversion results. The method builds a trainable end-to-end de-raining model with an unsupervised cycle-consistent adversarial network as an AI framework, which does not require pairs of rain-contaminated and corresponding ground-truth rain-free images for training. The model is trained by feeding rain-contaminated and clean radar images in an unpaired manner, and the atmospheric scattering model parameters are not required as a prior condition. Additionally, a self-attention mechanism is introduced into the model, allowing it to focus on rain clutter when processing radar images. This combines global and local rain clutter context information to output more accurate and clear de-raining radar images. The proposed method is validated by applying it to actual field test data, which shows that compared with the wave height derived from the original rain-contaminated data, the root-mean-square error is reduced by 0.11 m and the correlation coefficient of the wave height is increased by 14% using the de-raining method. These results demonstrate that the method effectively reduces the impact of rain on the accuracy of wave height parameter estimation from marine X-band radar images. [ABSTRACT FROM AUTHOR]

The downward shortwave radiation (DSR) is a key input parameter for land surface models and climate models. Based on the daily averaged Global Land Surface Satellite downward shortwave radiation (GLASS-DSR) dataset over the Yunnan-Kweichow Plateau (YKP) from 1984 to 2018, this paper analyzes variation trend and breakpoints of DSR. The results show that: annual averaged DSR decreases at a decreasing rate of −1.84 W·m−2·decade−1 over the YKP from 1984 to 2018; the overall distribution of interannual averaged DSR shows higher in the mid-west, and gradually decreasing from west to northeast over the YKP; the estimated averaged DSR is larger in spring than in summer due to the influence of the monsoon; monthly averaged DSR reaches its maximum in May and its minimum in December; breakpoints are found in the seasonal and trend components of daily averaged DSR. Eleven driving factors are examined for their effects on DSR variation, including annual average temperature, precipitation, 10 m wind speed, aerosol optical thickness (AOT), total cloud cover, elevation, slope, aspect, longitude, latitude, and climate zones. According to the findings, AOT predominates in the spatio-temporal distribution of DSR over the YKP. This study will contribute to studies related to climate change and highland radiation. [ABSTRACT FROM AUTHOR]

The hazy weather is currently one of the most common atmospheric environments. It is an urgent problem to realize clear imaging in hazy environment. Orthogonal polarization dehazing is one of the classical dehazing techniques attracting a widespread attention from research workers. However, while using this method, the extinction ratio (ER) parameter of the polarizer was neglected leading to poor dehazing. Therefore, we have introduced the ER of polarizers into the orthogonal polarization dehazing technique in this paper. In the process of sky region division, this study proposes to use HSV, RGB and Lab colour channels to create a comprehensive mask to select the sky region. And the morphological corrosion operation is utilized to increase the region connectivity for better sky region division. Based on subjective visualization, it can be found that the dehazing effect is significantly enhanced with the introduction of ER. The dehazing effect was evaluated based on four parameters of contrast (C0), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and information entropy (En). It is further demonstrated the necessity of introducing ER. The experimental results show that the ER can make the dehazing effect of the orthogonal polarization dehazing technique improved, which is valuable for the engineering application of image processing in hazy environment. The experimental results proved to be encouraging. The main highlights of this work are the following three points: The NDC parameter of the polarizer is a crucial factor that many researchers tend to overlook when utilizing polarizers for detection in the polarization dehazing process. This study aims to address this oversight by incorporating polarizers for detection and introducing the NDC parameter to rectify images at varying polarization angles. Consequently, the polarization dehazing operation yields superior results compared to those achieved without NDC polarization dehazing technology. The separation of the sky area from the non-sky area is achieved through the utilization of a multi-channel mask. This model employs multi-channel processing, specifically the HSV colour channel, RGB colour channel, and Lab colour channel. By generating an appropriate mask to delineate the sky area and the non-sky area, merging the three masks into a comprehensive mask, and subsequently applying morphological etching on the comprehensive mask to enhance the connectivity of the sky area, it is observed that the effectiveness of separating the sky area from the non-sky area is significantly improved. The amalgamation of image restoration and image enhancement is a critical process. Once the polarization dehazing operation is accomplished, we proceed with an image enhancement operation on the restored image. To regulate the filtering level, we employ the standard deviation of the Gaussian function. Furthermore, we utilize the Laplacian operator to intensify the edges of the current channel. By adjusting the parameters, we can control the intensity and scope of the enhancement effect, thereby achieving edge enhancement and augmenting the details of the image. [ABSTRACT FROM AUTHOR]

The quantification of aerosol direct radiative effects (ADREs) under clear sky conditions is essential for understanding the influence of aerosols on climate. This paper introduces a novel approach, the non-uniform five-dimensional lookup table (NU-5D-LUT) method, for calculating the ADREs under clear sky conditions. The NU-5D-LUT method considers key parameters such as aerosol optical thickness, single scattering albedo, asymmetry factor, surface albedo, and solar zenith angle. Validation against the SBDART model shows strong agreement, with correlation coefficients of 0.97 and 0.99 for TOA and BOA ADREs, respectively. Global comparisons demonstrate consistent ADRE distributions, with relative errors below 5.8% for TOA ADRE and 4.4% for BOA ADRE. The NU-5D-LUT method significantly reduces computation time, offering a practical solution for large-scale simulations and climate model evaluations in aerosol research. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) offers immense potential for uncertainty identification, improving the initial estimates for hydrological and atmospheric modelling. This paper reviews the studies in hydrological DA using Kalman filters. Recent applications of Kalman filters in hydrological and atmospheric DA are summarized. Existing challenges for DA studies are briefly described. In addition, three case study examples are presented highlighting the effects of: (a) soil moisture DA in the Noah land surface model; (b) variational assimilation for improving precipitation forecasts in the WRF (Weather Research Forecast) model; and (c) simulating AMSR-2 (Advanced Microwave Scanning Radiometer-2) radiances towards DA. Although there are many unresolved issues in DA that warrant further research, it has immense potential for predicting variables at a better lead time for hydrometeorology. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect is one of the typical characteristics of urban climate, which makes a lot of harmful gases accumulate and cannot be diffused. The UHI effect will cause air pollution, affecting the health and living conditions of humans. In this paper, a new method for calculating UHI intensity (UHII) by using the Global Navigation Satellite Systems (GNSS) derived zenith tropospheric delay (GNSS-ZTD) dataset and atmospheric empirical model was proposed and tested by using UHII observations from ground meteorological sensors for monitoring UHII in the Hong Kong city. The method requires accurate GNSS-ZTD datasets, priori atmospheric parameters (i.e. ground pressure and water vapour partial pressure) and the coefficient of the refractive function as inputs. The GNSS-ZTD datasets were obtained by using observations from the Hong Kong GNSS continuously operating reference stations network, and priori atmospheric parameters were obtained from a prior atmospheric model based on ERA5 (fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting) dataset and the coefficient of the refractive function can be obtained from the historical radiosondes data. The ground refractivity, pressure and water vapour partial pressure can be estimated by using the above-mentioned datasets, then the time series of UHII can be obtained by using a refractivity model proposed by Smith and Weintraub (SW-UHII), or Thayer (Thayer-UHII model). The results showed that the accuracy of the SW-UHII is better than the accuracy of the Thayer-UHII, and the root mean square (RMS) of the SW-UHII is 14% lower than the RMS of the Thayer-UHII. The mean bias, RMS and standard deviation of the SW-UHII compared with meteorological data derived UHII over GNSS stations are 1.82°C, 1.9°C and 1.41°C in all seasons, respectively, and the largest RMS is in winter (day of year 1–61 and 331–365). All the above results indicated that the GNSS technique has a certain prospect for monitoring UHII. [ABSTRACT FROM AUTHOR]

If left unmodeled, the delay suffered by electromagnetic waves while crossing the neutral atmosphere negatively affects Global Navigation Satellite System positioning. The modeling of the delay has been carried out by means of empirical models formulated based on climatological information or using information extracted from numerical weather prediction (NWP) models. This paper explores the potential use of meteorological information of several types that will become available with the increasing number of sensors (e.g. a cell phone, or the thermometer of a nearby smart home) in cyberspace. How can we make use of these potentially huge data-sets, which may help to provide the best possible representation of the neutral atmosphere at any given time, as readily and as accurately as possible? This situation falls in the realm of Big Data. A few potential scenarios, a sequential improvement of Marini mapping function coefficients, a self-feeding NWP, and near real-time empirical model updates, are discussed in this paper. The pros and cons of each approach are discussed in comparison with what is done today. Experiments indicate that they have potential for a positive contribution. [ABSTRACT FROM AUTHOR]

Climate change is increasingly influencing tourism policy and practice and there is a growing need to assess climate risk for destinations and the potential implications for global tourism demand patterns. Climate-dependent tourism markets, such as beach tourism, are particularly sensitive to changes in climate, and understanding the future redistribution of tourism climate resources remains a gap in many world leading tourism regions. This paper presents the first climate change assessment of tourism climate resources in China. The Holiday Climate Index:beach (HCI:beach) and Holiday Climate Index:urban (HCI:urban) are calculated for 775 climate stations across China for the 1981–2010 baseline and mid and late-twenty-first century using projections from six CMIP5 Global Climate Models under low and high emission futures. The projected geographic and seasonal redistribution of tourism climate resources are advantageous for many climate-limited destinations but pose high heat risks for some major city destinations. The differential results for the HCI:beach and HCI:urban reinforce the importance of utilising market-specific indices to assess future climate risk. The results provide new decision-relevant climate information for tourism managers and destination planners throughout China. [ABSTRACT FROM AUTHOR]

We prove the existence of global solutions and of a global attractor for a regularized viscous 3D tropical climate model. The regularization is carried out by means of the Helmholtz filter, in the spirit of what is done for some families of LES-turbulence models, thus obtaining (as α-model) the 3D Bardina tropical climate model for which we consider the associated dynamics. [ABSTRACT FROM AUTHOR]

Atmospheric correction eliminates corruption in reflectance captured by satellite images due to atmospheric elements like gases, aerosols, and water vapours. Existing physics-based approaches employ radiative transfer models constructed using lookup tables computed for different atmospheric parameters. However, these approaches are computationally expensive and rely on estimates of parameters that are difficult to sense accurately. This paper proposes a deep learning model as an alternative to physics-based approaches. We present an end-to-end deep neural network trained on seasonally and spatially rich Landsat 8 satellite images without explicit atmospheric parameterization along with our analysis and its validation. We validate the model's effectiveness vis-a-vis Landsat 8's Land Surface Reflectance Code – LaSRC results in $RMSE \sim 0.042$ R M S E ∼ 0.042 , $SSIM \sim 0.97$ S S I M ∼ 0.97 , and correlation coefficient $r \sim 0.99$ r ∼ 0.99. For ground measurements by RadCalNet, the proposed model has an $RMSE \sim 0.053$ R M S E ∼ 0.053 , $SSIM \sim 0.90$ S S I M ∼ 0.90 , and $r \sim 0.88$ r ∼ 0.88. The results show that the model accurately predicts surface reflectance and correlates highly with reference data. [ABSTRACT FROM AUTHOR]

Rainfall is considered a major input in designing stormwater management measures, especially for any low-impact development (LID) control design. With the impact of climate change, rainfall frequency and its patterns are changing continuously. Quantification of these changes and their impact on the performance of LID design becomes crucial. This paper presents a methodology to quantify the change in rainfall patterns using the Coupled Model Intercomparison Project 5 (CMIP5) climate model and to select the most feasible LID for a catchment with haphazard development. Interconnected decentralization-based LID controls are evaluated with the objective of emulating a pre-urbanized scenario. The overall analyses indicated that green roof (GR) followed by infiltration trenches (IT), rooftop disconnection (RTD), and permeable pavement (PP) showed better performance. Furthermore, a combination of IT, PP, and RTD accomplishes better efficiency for extreme rainfall events. Implementation of the most feasible combination will provide the additional benefit of water recycle and reuse. [ABSTRACT FROM AUTHOR]

This paper explores how scientific uncertainty was addressed in the Australian Government's decision-making process leading to the establishment of the 'sustainable diversion limit' (SDL) for the Murray-Darling Basin Plan in 2012. Our research draws on a systematic document analysis to generate an argument map representing the reasoning presented in government reports and legislation, as well as interviews with current and former public servants and researchers who have knowledge of the decision-making process. Looking towards the review of the Basin Plan, this analysis provides additional evidence for a number of areas to be discussed to advance the management of uncertainty in the associated decision-making. First, there is a need to address the challenges to adaptive management, as adaptive management delegates much of the uncertainty to future changes in policy as improved knowledge is gained. Second, there is a need to generate and use new knowledge for the review since the legislative mandate to use the 'best available science' enabled the use of pre-existing models and historical climate data. Third, there is a need for dialogue on handling of trade-offs between objectives given that uncertainty creates space for political pressures from interest groups. Fourth, there should be wider transparency about uncertainty in decision-making processes. [ABSTRACT FROM AUTHOR]

The length-biased Birnbaum-Saunders distribution is both useful and practical for environmental sciences. In this paper, we initially derive some new properties for the length-biased Birnbaum-Saunders distribution, showing that one of its parameters is the mode and that it is bimodal. We then introduce a new regression model based on this distribution. We implement the maximum likelihood method for parameter estimation, approach interval estimation and consider three types of residuals. An elaborate Monte Carlo study is carried out for evaluating the performance of the likelihood-based estimates, the confidence intervals and the empirical distribution of the residuals. Finally, we illustrate the proposed regression model with the use of a real meteorological data set. [ABSTRACT FROM AUTHOR]

India is crucial to the future of the global energy system, and hence to efforts to mitigate climate change. However, projecting India's energy future is challenging because of a number of structural uncertainties related to its still emergent development process – there are multiple 'future Indias'. This is particularly so given India's unusual historical development pattern and economic structure. This paper reviews more than 40 energy system scenarios for India to 2050 including scenarios of different policy stringency. This analysis suggests that the task of drawing policy insights is hampered by widely divergent, often non-transparent and insufficiently discussed, assumptions about GDP and its structural attributes as implied by sectoral energy consumption, industrial intensity of GDP, and sectoral demand patterns. It is these assumptions that crucially drive the divergence in scenario results, even more than assumptions about energy and climate policy per se. While divergent results are important for exploring the range of possible future pathways, policy insights can only be generated if scenario producers adequately convey a sense of causality, probability and desirability underpinning their scenarios. This requires linking more detailed socio-economic scenario storylines with the input assumptions and output results of energy system scenarios. To improve modelling for policy insight, the paper proposes that more attention be devoted to developing overarching socio-economic development scenarios, linking these to sector dynamics, and unpacking and interpreting model results. Key policy insights India's development pathway is characterized by a relatively low level of industrialization, precocious growth of services, and low and slow urbanization compared to similar countries. The unusual features of India's development pathway have a material impact on its energy system. There is a wide range of uncertainty pertaining both to India's future rate of GDP growth, but also to the persistence or reversal of the aforementioned structural characteristics. Existing modelling studies do not adequately detail and contextualize assumptions about India's macro-scale development pathway, and its links to micro-scale patterns of sectoral energy demand and supply, leading to a wide divergence of results and uncertainty over how to interpret them. Addressing these lacunae requires enhanced transparency and discussion of overarching socio-economic development scenarios for development, linking these to sector dynamics, and unpacking and interpreting model results. [ABSTRACT FROM AUTHOR]

Selection of suitable Global Climate Models (GCMs) for average temperature (Tavg) simulation is assessed in this paper for Telangana region on the southern part of India covering 14 grid points. Five statistical performance metrics: Skill Score (SS), Correlation Coefficient (CC), Nash Sutcliffe Efficiency (NSE), Normalised Root Mean Square Deviation (NRMSD), and Absolute Normalised Mean Biased Deviation (ANMBD) are considered in this study to evaluate GCMs using historical observations. Three cases (i.e., case 1: CC-SS-NRMSD; case 2: CC-SS-ANMBD; case 3: CC-NSE-SS-NRMSD-ANMBD) were considered to evaluate the suitable models and selection of multi model ensemble for Telangana region which can be further used in climate impact assessment studies. Weights are determined for each performance metric using Entropy technique and sensitivity analysis. Compromise Programming (CP) is applied to rank GCMs based on the distance measure technique. Group decision making is employed to conclude collective ranking patterns of GCMs. The results of the study suggest that MIROC5, CNRM-CM5, Access 1.0 and BCC-CSM1.1(m) are suitable models for prediction of Tavg for the Telangana region under consideration. [ABSTRACT FROM AUTHOR]

Mechanistic-Empirical (ME) pavement design approaches consider seasonal moisture variations in the subgrade and unbound aggregates base (UAB) layers as an important design parameter. Understanding such moisture variation is essential for modelling the pavement section and predicting its performance. The Enhanced Integrated Climate Models (EICM) are often used to predict the moisture contents in the UAB and subgrade layers for different seasons so that the resilient moduli of the layers can be adjusted. This paper provides an in-depth analysis of the moisture effects on the moduli of a UAB material with four different gradations. Commonly used laboratory and field-testing methods were conducted to measure the moduli of the specimens prepared at different moisture contents and fine contents from the same UAB source. The UAB moduli decreased with the increase in the moisture content. However, the patterns in the reduction in modulus depended on the laboratory or field test method used. Although the EICM module of the AASHTOWare Pavement ME Design software can predict the laboratory resilient moduli at moisture contents greater than the optimum moisture content, it was not possible to measure the moduli at those elevated moisture contents for the selected UAB. [ABSTRACT FROM AUTHOR]

Although the atmospheric compensation models developed to date have been generally robust and effective for InSAR, how to choose the right atmospheric correction method for ground-based InSAR is worth studying. This paper uses different methods for atmospheric phase screen (APS) compensation based on studies of ground-based radar (GBR) to zero-baseline acquisitions over the Woda landslide in Sichuan, China. This landslide has a steep topography, thick fog, turbulent rivers and strong rains. Data were acquired at the Ku-band using a GAMMA Portable Radar Interferometer II (GPRI-II) in multiple campaigns (4 July 2019–13 July 2019). In 2019, the Miyun open-pit mine was also studied; the maximum deformation reached 4.03 mm/hour. The collected data were processed within one unit based on the small baseline set (SBAS) approach. The experimental results show the following (1) For long-distance monitoring with complex atmospheric disturbances, range- and/or height-dependent models fail. (2) The iterative decomposition (ITD) method can effectively address complex atmospheric disturbances. (3) If the threshold of wavelength is set to be large, the ITD method becomes a stratified model. [ABSTRACT FROM AUTHOR]

This paper is devoted to study the time decay of tropical climate model. We first establish the optimal decay rates in L 2 of the weak solutions with large initial data in R n and obtain the existence and uniqueness of global smooth solutions with small initial data. In addition, taking advantage of Fourier splitting method, we get the decay rates in L 2 for higher order derivatives of the smooth solutions with small initial data. [ABSTRACT FROM AUTHOR]

The present paper investigates the impact of the global climate change on typhoon-induced wind risk for residential buildings in Japan. It is based on (1) the output from a climate model for an assumed climate change scenario, (2) probabilistic typhoon hazard modelling, (3) reliability-based fragility modelling and (4) failure cost modelling. The objective of the present paper is to demonstrate the availability and effectiveness of a general methodology for carrying out the impact assessment. It also aims at clarifying missing information required for a more precise and reliable impact assessment. Under the employed climate model, assumed climate scenario and vulnerability model and other assumptions made in the present paper, it is found that the typhoon-induced wind risks for residential buildings in Japan are not likely to change significantly in the future. [ABSTRACT FROM AUTHOR]

Multi-Global Navigation Satellite System (GNSS) integration can effectively improve the satellite geometry strength, and certain effect on the precise point positioning (PPP) accuracy and convergence speed. Taking the system difference into account, this paper deduces the unified GNSS observation model, which is extended to the multi-GNSS PPP functional model. Meanwhile, the stochastic model used in Kalman filter parameter estimation is presented in the paper. Furthermore, to evaluate the performance of the multi-GNSS PPP in Asia-Pacific region, observed data from the International GNSS Service reference stations are analysed using the self-developed software. In detail, the results from Global Positioning System (GPS)-, GLObal NAvigation Satellite System (GLONASS)- and BeiDou Navigation Satellite System (BDS)-only PPP, double combined PPP and GPS/GLONASS/BDS combined PPP under the different observation duration and cut-off elevation angles are analysed. Results demonstrate: (1) compared with the single system PPP, the convergence speed of the multi-GNSS PPP is improved while the accuracy is not significantly improved after processing the 24-h data set; (2) when the observation duration is short, such as 0.5 h, the mean convergence percentage of the BDS combined with GPS and GLONASS PPP increases by an average of 49.6% compared with the single individual systems except for BDS, respectively, under the cut-off angle of 5° and (3) when PPP positioning with high cut-off elevation angles, and at the point of centimetre-level positioning, the GPS/GLONASS/BDS combined PPP has a better performance on the convergence percentage and convergence speed. For example, the percentages of the position biases within 0–5 cm for GPS/GLONASS/BDS are increased by 7.2 and 4.5% in North and East direction compared with GPS/GLONASS under the cut-off angle of 35°, more than any other. And the mean convergence time is only 14.5 min. [ABSTRACT FROM PUBLISHER]

This paper examines the performance of three climate re-analysis datasets in Angola, namely the ERA-40, NCEP-r1 and JRA-55, by comparing annual and seasonal estimates of precipitation, surface air temperature and evaporation with ground observation measurements. The observational dataset describes a region poorly covered by international climate databases and it is believed that most of its data have not been used in the data assimilation procedures of the climate models. This paper therefore provides a fresh look at the performance of these climate re-analysis datasets in a vast area where distance and civil war have hindered ground monitoring efforts. The re-analysis exercises offer better temperature estimates than precipitation estimates. When offered, the evaporation estimates from all three products are very poor. The three products are able to describe the main features of the spatial distribution of average annual precipitation and temperature, but struggle to reproduce the temporal changes of these variables. The results from a set of performance criteria show that the correlation between the observed ground measurements and re-analysis estimates is poor overall and the NSE values indicate that the average measured value at each location is usually a better estimate than the re-analysis estimate.EDITOR A. CastellarinASSOCIATE EDITOR S. Kanae [ABSTRACT FROM AUTHOR]

Mangroves are critical in the ecological, economic and social development of coastal rural and urban communities. However, they are under threat by climate change and anthropogenic activities. The Sunda Banda Seascape (SBS), Indonesia, is among the world’s richest regions of mangrove biomass and biodiversity. To inform current and future management strategies, it is critical to provide estimates of how mangroves will respond to climate change in this region. Therefore, this paper utilized spatial analysis with model-based climatic indicators (temperature and precipitation) and mangrove distribution maps to estimate a benchmark for the mangrove biomass of the SBS in six scenarios, namely the Last Inter-glacial Period, the current scenario (1950–2000) and all four projected Representative Concentration Pathways in 2070 due to climate change. Despite mangroves gaining more biomass with climate change (the increase in CO2concentration), this paper highlighted the great proportion of below-ground biomass in mangrove forests. It also showed that the changes in spatial distribution of mangrove biomass became more variable in the context of climate change. As mangroves have been proposed as an essential component of climate change strategies, this study can serve as a baseline for future studies and resource management strategies. [ABSTRACT FROM PUBLISHER]

The highest correlative relations for air pollution levels are often with meteorological variables such as temperature and wind speed. Today, sophisticated gridded high-resolution meteorological models are used to produce meteorological fields that drive chemical transport models for air quality management. Errors in specification of the physical atmosphere such as temperature, clouds and winds can affect the air quality predictions. Additionally, the efficiency and efficacy of emission control strategies can be compromised by errors in the meteorological fields. In this paper, the role of meteorology in air quality behavior, primarily from the viewpoint of regional ozone modeling as carried out in the U.S., is reviewed. Particular attention is given to physics and new techniques for improving meteorological model performance. Uncertainties in model turbulent mixing in the nighttime boundary layer, where large model differences exist, are examined. The role of spatial mesoscale features such as topography and land/water systems in models are discussed. The nocturnal low-level jet, a mesoscale temporal and spatial feature, and its impact on air quality are examined. Traditional air quality concerns have focused on synoptic conditions at the center of high-pressure systems. However, high ozone levels have also been associated with stationary fronts. The ability of models to capture mesoscale structure and yet retain synoptic structure and its timing is challenging. Data assimilation and its ability to improve model performance are examined. Particular attention is given to vertical nudging strategies that can affect formation of the nocturnal low-level jets. Finally, clouds can have a major impact on air quality since insolation impacts temperature, biogenic emissions and photolysis rates and extremes in stability. Traditional techniques, which attempt to insert cloud water where there is not dynamical support, can lead to additional errors. New dynamical approaches for improving model cloud performance are discussed. Implications: This article shows that there has been a considerable improvement in meteorological models used for air quality simulations. In particular, improvement in the tools for incorporating both traditional observations and new satellite data for retrospective studies has been beneficial to air quality community. However, while this trend is continuing, many challenges remain. As an example, due to having many options available in configuring a model simulation, there is a need to evaluate and recommend sets of options that provide important performance measures. [ABSTRACT FROM AUTHOR]

Atmospheric models constitute the best tools available for the setting of policy, and may, in some cases, be the only tools that are available. The best examples of their kind bring together all current knowledge of pollutant behavior in the atmosphere, making it possible to unravel the often complex interactions between pollutants and atmospheric dynamics. They also allow the possibility of evaluating hypothetical changes in emissions and other conditions to evaluate potential abatement strategies, or to assess the impact of proposed new emission sources. This paper provides an overview of mathematical atmospheric models and their application to the development of air quality policy. The paper discusses the types of atmospheric models currently in use, categorized by spatial scale, and the requirements for credible modeling. Issues associated with model validity and accuracy are described and case studies are reviewed to illustrate atmospheric model use in policy development and the need for careful analysis in interpreting model predictions. [ABSTRACT FROM AUTHOR]

This paper follows a practice-led methodology to explore how the diverse participants in the artist-led 'Performing the Future' project – including scientists working with climate models and over-60s groups in Liverpool and Cambridge – mediated their sense of risk and uncertainty in response to environmental change. We explore the nature of their participation and the different strategies used by the artists leading the project, describing the opportunities that emerged for more nuanced and complex visions of the realities of anthropogenic environmental change. [ABSTRACT FROM AUTHOR]

The paper discusses modelling uncertainties in climate models and how they can be addressed based on physical principles as well as based on how the models perform in light of empirical data. We argue that the reliability of climate models can be judged by three kinds of standards: striking confirmation, supplementing independent causal arguments, and judging the causal core of models by establishing model robustness. We also use model robustness for delimiting confirmational holism. We survey recent results of climate modelling and identify salient results that fulfil each of the three standards. Our conclusion is that climate models can be considered reliable for some qualitative gross features and some long-term tendencies of the climate system as well as for quantitative aspects of some smaller-scale mechanisms. The adequacy of climate models for other purposes is less convincing. Among the latter are probability estimates, in particular, those concerning rare events. On the whole, climate models suffer from important deficits and are difficult to verify, but are still better confirmed and more reliable than parts of the methodological literature suggest. [ABSTRACT FROM AUTHOR]

State-of-the-art hydrological climate impact assessment involves ensemble approaches to address uncertainties. For precipitation, a wide range of climate model runs is available. However, for particular meteorological variables used for the calculation of potential evapotranspiration (ETo), availability of climate model runs is limited. It is preferred that climate model runs are considered coupled when calculating changes in precipitation and ETo amounts, in order to preserve the internal physical consistency. This results in constraints on the maximum ensemble size. In this paper, we investigate the correlation between climate change signals of precipitation and ETo. It is found that, for two medium-sized catchments in Belgium, uncoupling climate model runs used for calculation of change signals of precipitation and ETo amounts does not result in a significant bias for changes in extreme flow. With these results, future impact studies can be conducted with larger ensemble sizes, resulting in a more complete uncertainty estimation. [ABSTRACT FROM AUTHOR]

The main disadvantage of wind energy is its high spatial and temporal variability. This paper presents a web mapping tool to communicate to private users both the available wind resource and information to evaluate the suitability of several types of turbine for any point on the Iberian Peninsula. This tool performs on-the-fly three-dimensional interpolation of wind data from a 10 km horizontal grid database previously obtained using a regional climate model and generates a PDF report. It integrates several open-source GIS applications to build a coherent platform that performs advanced calculations and provides graphics and reports of high quality. [ABSTRACT FROM AUTHOR]

The accuracy of atmospheric mass density models is critical for OP (orbit prediction) of LEO (low earth orbit) space objects. This paper studies the effects of three methods on the improvement of OP accuracy for LEO objects, where only TLE (two-line element) data are used. Fifty objects at altitudes between 400 and 600 km are chosen for the study, with 20 or 40 being used as calibration objects, and the others as verification objects. The results show that about a 30 percent reduction in OP errors can be achieved for a 10-day prediction time. [ABSTRACT FROM AUTHOR]

Urban landscapes can not only create their own weather through the heat island effect, but also can influence the development and track of storms, according to research presented at the American Geophysical Union's Fall 2020 meeting. "Alaska can expect three times as many storms, and those storms will be more intense", said NCAR scientist Andreas Prein, a coauthor of the new papers. Meteorologists generally consider 32°F to be the threshold at which rain changes to snow during a storm, and they use this threshold to help forecast rain and snow accumulations. [Extracted from the article]

Climate model development, testing, and analysis involve running the model extensively to tune the subgrid- scale parameters that provide closure to the system. This process demands substantial time and computational resources even for typical spatial resolutions and becomes in feasibly expensive for high-resolution studies. This paper presents alternative, computationally feasible methods to emulate the simulations within acceptable error bounds. This strategy can be easily implemented to obtain an ensemble of model runs. The paper outlines three approximation strategies: (1) interpolation with Lagrange basis functions, (2) least-squares (LS) approximation, and (3) interpolation with radial basis functions. As a proof of concept, a suite of relevant physical quantities are evaluated at unknown grid points of parameters, space, and time. The values obtained by emulation are compared against the simulated values to check the feasibility of the method. The advantages and shortcomings of the above-mentioned approximation schemes are discussed, including the savings of time and computational resources. [ABSTRACT FROM AUTHOR]

This paper aims to estimate the variation of the combined dependable energy output of the set of major hydropower plants within the Brazilian part of the La Plata Basin due to possible climate changes during the twenty-first century. It uses and compares the predictions of two regional climate models, namely PROMES [Castro, M., Fernández, C., and Gaertner, M.A., 1993. Description of a mesoscale atmospheric numerical model. In: J.I. Díaz and J.L. Lions, eds.Mathematics, climate and environment. Rech. Math. Appl. Ser. Mason, 230–253; Gallardo, A., Galvan, C., and Mermejo, R., 2012.PROMES-MOSLEF: An atmosphere-ocean coupled regional model. Coupling and preliminary results over the Mediterranean basin. 4th HYMEX Workshop 2 2010] and RCA models [Rummukainem, M., 2010. State-of-the-art with regional climate models.WIREs Climate Change, 1, 82–96]. Rainfall and temperature predictions are converted into streamflow at key gauge stations using Variable Infiltration Capacity Model [Liang, X., Lettenmaier, D.P., Wood, E.F., and Burges, E.F., 1994. A simple hydrologically based model of land surface water and energy fluxes for general circulation models.Journal of Geophysical Research, 99, n. D7, 14,451–14,428]. The evaluation of the dependable energy output used the natural energy hydrograph method engineering consultants (Canambra Engineering Consultants, 1969.Power study of South Brazil. 13 v. Appendice XVII Final Report. Rio de Janeiro: Canambra Engineering Consultants), combined with the Monte Carlo simulation of synthetic series of natural energy. The main contribution of this paper is the consolidation of a methodology that provides estimates of the system's dependable energy as a function of the return period for both observed and future predicted streamflows. As a conclusion, a reduction of the dependable energy output of the hydropower plants within the La Plata Basin could be expected during the twenty-first century [ABSTRACT FROM PUBLISHER]

Probabilistic thinking can often be unintuitive. This is the case even for simple problems, let alone the more complex ones arising in climate modelling, where disparate information sources need to be combined. The physical models, the natural variability of systems, the measurement errors and their dependence upon the observational period length should be modelled together in order to understand the intricacies of the underlying processes. We use Bayesian networks (BNs) to connect all the above-mentioned pieces in a climate trend uncertainty quantification framework. Inference in such models allows us to observe some seemingly nonsensical outcomes. We argue that they must be pondered rather than discarded until we understand how they arise. We would like to stress that the main focus of this paper is the use of BNs in complex probabilistic settings rather than the application itself. [ABSTRACT FROM AUTHOR]

Climate change impact on rainfall-induced landslide susceptibility of a certain region is often implied based on expected changes in rainfall patterns and rarely explicitly quantified. This study aims to address this gap by implementing coupled landslide and climate modelling chains to explicitly assess the effects of changing rainfall patterns on rainfall-induced landslide susceptibility. The effects of climate change are integrated into the landslide modelling chain via Intensity-Duration-Frequency (IDF) curves for the present and future climate conditions for a landslide-prone study area located in central Norway. The effects of climate change on landslide susceptibility are examined by using a physical-based landslide prediction model with rainfall events of varying duration and intensity that are simulated based on the climate-dependent IDF curves. The novelty of this study is the proposition of a novel probabilistic framework to assess the climate change impact on landslide susceptibility for rainfall events with a given duration. The proposed framework accounts for both the uncertainties of rainfall events through probabilistic interpretation of IDF curves and the uncertainties in the landslide model with the Monte Carlo method. Compared to results based on only intense rainfall events, the proposed framework leads to a lower increase in the probability of landslide initiation and landslide-susceptible extents due to climate change. [ABSTRACT FROM AUTHOR]

Under climate change, extreme heat events are projected to become more frequent and intense. With people spending approximately 90% for their time indoors and buildings having long lifetimes, it is important that the built environment is resilient to these changes. Current methods to assess building performance in a future climate typically use morphed weather files and annual metrics. We compare 30 metrics and 2 weather data sources to assess and improve the representation of extreme heat events in building simulation. We show that morphing an extreme observed year may not necessarily result in an equally extreme year under the future climate and that current annual metrics do not correlate well with heatwave severity. We suggest that weather data from climate models is more robust in representing future weather for the UK and explore the recent UKCP18 data. We propose novel metrics which are able to capture heatwave severity inside buildings. [ABSTRACT FROM AUTHOR]

The intercomparison of seven chemical mechanisms for their suitability for air quality policy formulation and assessment is described. Box modeling techniques were employed using 44 sets of background environmental conditions covering North America to constrain the chemical development of the longer lived species. The selected mechanisms were modified to enable an unbiased assessment of the adequacy of the parameterizations of photochemical ozone production from volatile organic compound (VOC) oxidation in the presence of NOx. Photochemical ozone production rates responded differently to 30% NOxand VOC reductions with the different mechanisms, despite the striking similarities between the base-case ozone production rates. The 30% reductions in NOxand VOCs also produced changes in OH. The responses in OH to 30% reductions in NOxand VOCs appeared to be more sensitive to mechanism choice, compared with the responses in the photochemical ozone production rates. Although 30% NOxreductions generally led to decreases in OH, 30% reductions in VOCs led to increases in OH, irrespective of mechanism choice and background environmental conditions. The different mechanisms therefore gave different OH responses to NOxand VOC reductions and so would give different responses in terms of changes in the fate and behavior of air toxics, acidification and eutrophication, and fine particle formation compared with others, in response to ozone control strategies. Policymakers need to understand that there are likely to be inherent differences in the responses to ozone control strategies between different mechanisms, depending on background environmental conditions and the extents of NOxand VOC reductions under consideration. Implications: The purpose of this paper is to compare predicted ozone responses to NOxand VOC reductions with seven chemical mechanisms under North American conditions. The good agreement found between the tested mechanisms should provide some support for their application in the air quality models used for policymaking. [ABSTRACT FROM PUBLISHER]

This paper is devoted to study the time decay of tropical climate model. We first establish the optimal decay rates in L 2 of the weak solutions with large initial data in R n and obtain the existence and uniqueness of global smooth solutions with small initial data. In addition, taking advantage of Fourier splitting method, we get the decay rates in L 2 for higher order derivatives of the smooth solutions with small initial data. [ABSTRACT FROM AUTHOR]

The article informs about the approach of using several Regional Climate Models (RCM) and changes in precipitationand runoff under climate models. Topics include serve as a guide for other users of the Soil & Water Assessment Tool (SWAT) model and those who work on conceptual or physical models; and distributed hydrological models such as SWAT requires sufficient information to build the model.

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Guidance on what type of rainfall to use when assessing hydraulic capacity of urban drainage systems under climate change is unclear; focus is mainly on what climate factors to use. Based on a case study in Kalmar, Sweden, this paper compares system performance using two design rainfalls, Block rainfalls and Chicago Design Storm (CDS), and selected observed rainfalls, with two methods of addressing future climate: a constant factor and Delta Change (DC) factors that depend on rainfall intensity. The use of CDS rainfalls presents the maximum hydraulic response, whereas Block rainfalls give lower responses but identify critical durations in the system, which may be useful addressing adaptation actions. Observed rainfalls of target return periods gave similar responses to CDS rainfalls, and can be applied with DC factors to address future changes in both intensity and volume. Differences between the two methods indicate a high dependence related to the maximum factors applied on the rainfalls. [ABSTRACT FROM AUTHOR]

Dealing with the potential consequences of climate change on society requires scenarios that accurately project future climate. Uncertainties about future greenhouse gas emissions, climate sensitivity to radiative forcing, and limits to simulating a complex system constrain this objective. This paper reviews literature outlining the inherent challenges of creating future climate scenarios from general circulation models; it examines methods used to improve their interpretation and use; and it explores approaches taken to recognize and address uncertainty when investigating interactions between climate and society. [ABSTRACT FROM AUTHOR]

In a Climate Change context, it is relevant to assess the potential impacts of variations of climate drivers on sediment yield from basins with high soil exportation rates. This paper develops a methodology, based on the erosion potential method, which was applied to the upper basins of Bermejo and Pilcomayo Rivers (in the Upper Plata Basin), to estimate future trends in sediment yield. Projections for climate drivers (rainfall and temperature) were obtained through four regional climate models, with boundary conditions provided by three global climate models, for the period 2011–2100. Satellite imagery and gridded climate data were used to implement the sediment yield baseline scenario (1961–1990), which was calibrated based on solid discharge data. The model was used to determine projections of sediment yields, as time series of mean annual values for each basin. The analysis was performed on average for three-decadal periods, namely, the scenarios of ‘near future’ (2011–2040), for which current yields are essentially maintained; ‘intermediate future’ (2041–2070), where a reduction trend is detected; and ‘far future’ (2071–2100), with a recovery of yields, which even increase above the baseline scenario rate in the case of the Bermejo Upper Basin. [ABSTRACT FROM PUBLISHER]

This paper presents an analysis of the sediment dynamics that takes place at different scales within the Middle and the Lower Parana River in the La Plata Basin. The aim of this study is to provide a multi-disciplinary and multi-scale approach for the prediction of river future morphology in the context of climate change, the intended use of which is the prognosis of river morphodynamics’ long-term impact on manmade structures and activities over or near the river. The study is based on three levels of mathematical modelling, with the output of wider-scale models providing the input conditions for more specific ones. Climate models give the input ensemble, i.e. future precipitation and temperature over La Plata Basin. The semi-distributed macroscale variable infiltration capacity hydrological model simulates the flow discharge time series that are applied to an own-developed 1D morphodynamic model. The 1D model simulates future rate of sediment transport and corresponding bed-level changes at watershed scale and provides the boundary conditions for a 2D model. Therefore, streamflow divagations at channel scale are simulated by means of the MIKE21C code developed by the Danish Hydraulic Institute. The analysis indicates a rather low sensitivity of the Parana River bed profile, i.e. 1D morphology, to the increase predicted in flow discharge, whereas the streamflow appreciably divagates. In particular, surpassing an upper bound in the most frequent discharge appears effective in driving the actual bifurcated morphology into a meandering-multithread configuration. [ABSTRACT FROM PUBLISHER]