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Non-stationary methods of flood frequency analysis are widespread in research but rarely implemented by practitioners. One reason may be that research papers on non-stationary statistical models tend to focus on model fitting rather than extracting the sort of results needed by designers and decision makers. It can be difficult to extract useful results from non-stationary models that include stochastic covariates for which the value in any future year is unknown. We explore the motivation for including such covariates, whether on their own or in addition to a covariate based on time. We set out a method for expressing the results of non-stationary models as an integrated flow estimate, which removes the dependence on the covariates. This can be defined either for a particular year or over a longer period of time. The methods are illustrated by application to a set of 375 river gauges across England and Wales. We find annual rainfall to be a useful covariate at many gauges, sometimes in conjunction with a time-based covariate. For estimating flood frequency in future conditions, we advocate exploring hybrid approaches that combine the best attributes of non-stationary statistical models and simulation models that can represent changes in climate and river catchments. [ABSTRACT FROM AUTHOR]

Building performance simulation serves the derivation of the relevant building performance indicators (e.g., energy use, indoor-environmental conditions) given the assumptions of certain model input parameters (i.e., description of the building, boundary conditions, occupants' presence and behaviour). Simulation can be employed for multiple purposes, including but not limited to building design support, building systems configuration, and code compliance demonstration. It has been suggested that the level of detail and resolution of simulation models must match their deployment purpose. However, there is arguably a lack of definitive guidelines for the purpose-dependent selection of appropriate simulation models. To address this challenge, the present contribution suggests that the attributes of a simulation model in general, and the type of the adopted occupant model in particular, must correspond to the specifics of the building performance indicator under investigation. To make progress in this area, a typological classification of building performance indicators is proposed along three salient dimensions, namely the indicators' topical domain (e.g., energy use, thermal comfort, noise control), their spatial attributes, and their temporal attributes. Following a detailed analysis, the paper presents a high-level approach to derive the basic requirements concerning occupant models as a categorical function of the simulation purpose. [ABSTRACT FROM AUTHOR]

This paper assesses the reliability of regional climate downscaling products from the Coordinated Regional Downscaling Experiment-Southeast Asia (CORDEX-SEA) in reproducing the present-day wind at the East Coast of Peninsular Malaysia (ECPM). A total of five CORDEX-SEA products, namely CNRM-CM5(RegCM4), CSIRO-Mk3-6-0(RegCM4), EC-EARTH(RegCM4), HadGEM2-ES(RegCM4) and MPI-ESM-MR(RegCM4), with a resolution of 25 km, were compared to reference data from the European Centre Medium-Range Weather Forecasts (ECMWF) fifth generation atmospheric reanalysis ERA5 spanning 1980–2005. This study focused on the period of the southwest monsoon, specifically the months of June, July and August (JJA), as wind during this season is known to play an important role in generating upwelling at the ECPM. The results obtained show that only three of the CORDEX-SEA products, CSIRO-Mk3-6-0(RegCM4), HadGEM2-ES(RegCM4) and MPI-ESM-MR(RegCM4), were able to reproduce the wind in terms of wind speed, wind stress curl and wind pattern as effectively as ERA5. An ensemble was made from these three CORDEX-SEA products, and it was found that the ensemble was able to reproduce a value of the Upwelling Index (UI) that was similar to the reference data, albeit with some bias. Hence, only the three stated CORDEX-SEA products and their ensemble are suitable to be used for future climate change studies within the region. [ABSTRACT FROM AUTHOR]

The objective of this paper is to assess climate change impacts on spatiotemporal changes in annual and seasonal runoff and its components in the basins of two large European rivers, the Vistula and the Odra, for future horizons. This study makes use of the Soil and Water Assessment Tool (SWAT) model, set up at high resolution, and driven by a multi-model ensemble (MME) of nine bias-corrected EURO-CORDEX simulations under two representative concentration pathways (RCPs), 4.5 and 8.5. This paper presents a wealth of illustrative material referring to the annual and seasonal runoff (R) in the reference period as well as projections for the future (MME mean change), with explicit illustration of the multi-model spread based on the agreement between models and statistical significance of change according to each model. Annual R increases are dominating, regardless of RCP and future horizon. The magnitude of the MME mean of spatially averaged increase varies between 15.8% (RCP 4.5, near future) and 41.6% (RCP 8.5, far future). The seasonal patterns show the highest increase in winter and the lowest in spring, whereas the spatial patterns show the highest increase in the inner, lowland part, and the lowest in the southern mountainous part of the basin. [ABSTRACT FROM AUTHOR]

Design flood estimation (DFE) is essential in the planning and design of hydraulic structures. In South Africa, outdated methods are widely applied for DFE. In this paper the potential of a continuous simulation modelling (CSM) approach to DFE in South Africa, using the daily time-step ACRU agrohydrological model, is investigated. The paper focuses on the links and similarities between the SCS-SA and ACRU models and the subsequent preliminary investigations that were undertaken to account for and incorporate the land cover classes, including land management practices and hydrological condition, of the SCS-SA model into the ACRU CSM approach. The approach to this study was to investigate how design volumes simulated by the SCS-SA model for various land management practices or conditions could be simulated by the ACRU model. Since peak discharge estimation in both models is directly dependent on simulated volumes, this preliminary study focused only on design runoff volumes, with subsequent investigations on peak discharge required in future research. In the absence of observed data, design runoff volumes and changes in design runoff volumes, as simulated by the SCS-SA model, were used as a substitute for observed data, i.e., as a reference, to achieve similar design runoff volumes and changes in design volumes in the ACRU model. This was achieved by adjusting relevant input parameters in the ACRU model to represent the change in management practice or hydrological condition, as represented in the SCS-SA model. Following a sensitivity analysis of relevant ACRU parameters, calibration of 2 selected parameters against SCS-SA CN values for selected land cover classes was performed. A strong linear relationship (R2 = 0.94) between these ACRU parameters and SCS-SA CNs for selected land cover classes was found and consequently specific rules and equations were developed to represent SCS-SA land cover classes in ACRU. Recommendations are made to further validate and verify the approach and to further the development of a CSM system for DFE in South Africa. [ABSTRACT FROM AUTHOR]

The paper presents an energy simulation-based study to investigate the impacts of climate change on energy consumptions of an office building located in five different cities, United States. Annual energy consumption of a medium-size office building was predicted by building energy simulation software—EnergyPlus using TMY3 weather data and future climate data. In this study, we have used two sets of further climate data with different climate models including (1) Hadley Centre Coupled Model, version 3 (HadCM3), and (2) NCAR Community Earth System Model version 1 (CESM1) with the Community Atmosphere Model version 5 (CAM5). A morphing method was applied to downscale the monthly weather forecast to hourly forecast for use in building energy simulation for the two General Circulation Models (GCMs): HadCM3 and CESM1. Using the generated future weather files, HVAC operation related mitigation measures including adjustment of thermostat setpoints, reduced HVAC operation hours, reduced VAV box minimum flow setting, and mixed-mode ventilation were simulated to compare with ASHRAE Standard 90.1 baseline simulated using TMY3 weather data. Simulation results on predicted whole building, heating and cooling energy consumptions were examined. The paper highlighted the importance of efficiently operating mechanical systems in buildings to ensure a more sustainable future. [ABSTRACT FROM AUTHOR]

The development and application of chemistry transport models has a long tradition.Within the Netherlands the LOTOS–EUROS model has been developed by a consortium of institutes, after combining its independently developed predecessors in 2005. Recently, version 2.0 of the model was released as an open-source version. This paper presents the curriculum vitae of the model system, describing the model’s history, model philosophy, basic features and a validation with EMEP stations for the new benchmark year 2012, and presents cases with the model’s most recent and key developments. By setting the model developments in context and providing an outlook for directions for further development, the paper goes beyond the common model description. With an origin in ozone and sulfur modelling for the models LOTOS and EUROS, the application areas were gradually extended with persistent organic pollutants, reactive nitrogen, and primary and secondary particulate matter. After the combination of the models to LOTOS–EUROS in 2005, the model was further developed to include new source parametrizations (e.g. road resuspension, desert dust, wildfires), applied for operational smog forecasts in the Netherlands and Europe, and has been used for emission scenarios, source apportionment, and long-term hindcast and climate change scenarios. LOTOS–EUROS has been a front-runner in data assimilation of ground-based and satellite observations and has participated in many model intercomparison studies. The model is no longer confined to applications over Europe but is also applied to other regions of the world, e.g. China. The increasing interaction with emission experts has also contributed to the improvement of the model’s performance. The philosophy for model development has always been to use knowledge that is state of the art and proven, to keep a good balance in the level of detail of process description and accuracy of input and output, and to keep a good record on the effect of model changes using benchmarking and validation. The performance of v2.0 with respect to EMEP observations is good, with spatial correlations around 0.8 or higher for concentrations and wet deposition. Temporal correlations are around 0.5 or higher. Recent innovative applications include source apportionment and data assimilation, particle number modelling, and energy transition scenarios including corresponding land use changes as well as Saharan dust forecasting. Future developments would enable more flexibility with respect to model horizontal and vertical resolution and further detailing of model input data. This includes the use of different sources of land use characterization (roughness length and vegetation), detailing of emissions in space and time, and efficient coupling to meteorology from different meteorological models. [ABSTRACT FROM AUTHOR]

• The performances of single and double diode equivalent circuit models are discussed. • A new approach that combines both the single and double diode models is proposed. • The proposed hybrid approach is tested under climate variation of real field data. Various works investigated different photovoltaic (PV) cell equivalent-circuit models and several techniques were proposed to extract their unknown parameters. The present paper analyzes the current/voltage (I-V) characteristics for Si-crystalline PV modules under non-standard conditions of irradiance and temperature, by using single-diode and double-diode models. The Chaibi and Ishaque methods are employed to determine the parameters for each equivalent-circuit model. Then, the I-V curves provided by the manufacturers and the calculated I-V characteristics are compared at different levels of irradiance and temperature. The comparison suggests prioritizing one of the two equivalent-circuit models according to the prevailing meteorological inputs. As such, a hybrid approach is proposed in order to select the most appropriate model depending on the relevant climatic conditions. The presented approach accuracy is evaluated using real weather data of two PV plants located in two different climatic zones (Mediterranean and Semi-Continental). Results show that the double-diode model is more reliable for low-irradiance levels; however, the single-diode model performs well with low-temperature fluctuations. An error reduction of 53.93% and 21.04% can be reached for the cloudy weather and for the sunny days, respectively. Accordingly, this approach can be easily implemented as a computing tool to achieve more accurate prediction in the PV systems simulations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Bias adjustment improves accuracy and consistency of simulated hydrological signatures. • Not a single bias adjustment method enhances performance in all analyzed signatures. • Univariate distribution scaling (DS) performs well, especially in rainfall-driven catchments. • Multivariate methods perform better for low-flow signatures in snowmelt-driven catchments. Hydrological climate-change-impact studies depend on climatic variables simulated by climate models. Due to parametrization and numerous simplifications, however, climate-model outputs come with systematic biases compared to the observations. In the past decade, several methods of different complexity and dimensionality for adjustment of such biases were introduced, but their benefits for impact studies and accurate streamflow projections are still debated. In this paper, we evaluated the ability of two state-of-the-art, advanced multivariate bias-adjustment methods to accurately reproduce 16 hydrological signatures, and compared their performance against two parsimonious univariate bias-adjustment methods based on a multi-criteria performance evaluation. The results indicated that all bias-adjustment methods considerably reduced biases and increased the consistency of simulated hydrological signatures. The added value of multivariate methods in maintaining dependence structures between precipitation and temperature was not systematically reflected in the resulting hydrological signatures, as they were generally outperformed by univariate methods. The benefits of multivariate methods only emerged for low-flow signatures in snowmelt-driven catchments. Based on these findings, we identified the most suitable bias-adjustment methods for water-resources management in Nordic regions under a changing climate, and provided practical guidelines for the selection of bias-adjustment methods given specific research targets and hydroclimatic regimes. [ABSTRACT FROM AUTHOR]

Increasing water demand coupled with limited water resources has given rise to the need for sustainable water resources development in the Nile River Basin. Managing the basin’s water resources is complex because of its transboundary nature, exacerbated by environmental degradation and strong climate variability. The Nile basin water resources have been extensively studied during the last 125 years for planning and management purposes, in particular with regard to the use of blue water in the downstream part of the basin, though recently some studies have also focused on the upper parts. These studies show that there is no convergence of development plans emerging among the Nile riparian countries. This paper reviews river basin water resource models as applied in the Nile River Basin, distinguishing between simulation, optimization and combined simulation and optimization models. The paper concludes by identifying knowledge gaps to guide future research on water resources planning and management in the Nile. [ABSTRACT FROM PUBLISHER]

This paper presents results of a study of the impact of future climate change scenarios as developed by the Intergovernmental Panel on Climate Change and implemented in weather files for specific future time slices (2020, 2050 and 2080) for the three climatic regions of Greece on the design of the external envelope of a hotel building in Greece. The impact of climate change on the hotel is assessed via hourly simulations of a calibrated model developed using the software TRNSYS. Additionally, the paper aims to identify optimal refurbishment strategies, given the constraints of the existing case-study building when transposed to the three different climatic zones in Greece. Two modes of the hotel building were studied: ‘all year’ and ‘seasonally’ operated. It was found that different external envelope energy-efficient strategies can be applied depending on the climatic zone and whether the hotel is all-year or seasonally operated. [ABSTRACT FROM AUTHOR]

An objective analysis is one of the main components of data assimilation. By combining observations with the output of a predictive model we combine the best features of each source of information: the complete spatial and temporal coverage provided by models, with a close representation of the truth provided by observations. The process of combining observations with a model output is called an analysis. To produce an analysis requires the knowledge of observation and model errors, as well as its spatial correlation. This paper is devoted to the development of methods of estimation of these error variances and the characteristic length-scale of the model error correlation for its operational use in the Canadian objective analysis system. We first argue in favor of using compact support correlation functions, and then introduce three estimation methods: the Hollingsworth–Lönnberg (HL) method in local and global form, the maximum likelihood method (ML), and thediagnostic method. We perform one-dimensional (1D) simulation studies where the error variance and true correlation length are known, and perform an estimation of both error variances and correlation length where both are non-uniform. We show that a local version of the HL method can capture accurately the error variances and correlation length at each observation site, provided that spatial variability is not too strong. However, the operational objective analysis requires only a single and globally valid correlation length. We examine whether any statistics of the local HL correlation lengths could be a useful estimate, or whether other global estimation methods such as by the global HL, ML, orshould be used. We found in both 1D simulation and using real data that the ML method is able to capture physically significant aspects of the correlation length, while most other estimates give unphysical and larger length-scale values. Implications: This paper describes a proposed improvement of the objective analysis of surface pollutants at Environment and Climate Change Canada (formerly known as Environment Canada). Objective analyses are essentially surface maps of air pollutants that are obtained by combining observations with an air quality model output, and are thought to provide a complete and more accurate representation of the air quality. The highlight of this study is an analysis of methods to estimate the model (or background) error correlation length-scale. The error statistics are an important and critical component to the analysis scheme. [ABSTRACT FROM PUBLISHER]

Cutler, E.M.; Albert, M.R., and White, K.D., 2019. A low-cost shoreline dynamic simulation model for proposed beach nourishment and dune construction: Introducing a new feasibility analysis tool. Journal of Coastal Research, 35(4), 907–919. Coconut Creek (Florida), ISSN 0749-0208. Beach nourishment and dune construction are common coastal risk-management strategies for shoreline erosion in the United States. Federal involvement with such projects relies on cost–benefit analyses, which in turn rely on life-cycle models of shoreline processes, such as the Army Corps of Engineers' Beach-fx event-driven physical and economic coastal model. However, use of these models can be computationally and data intensive. Costs associated with conducting a feasibility analysis for beach nourishment and dune construction may leave a community with few resources to explore other risk-management options. This paper, therefore, presents a first-order, screening-level, low-cost dynamic model that delivers results approximately comparable with those from life-cycle models. Applying the model to three locations in Florida shows that simulated nourishment intervals are within 5 years of those predicted by Beach-fx, leading to a similar number of nourishments over a 50-year project life span. A discussion on how one could apply the model to other areas is also included. It is intended that this model could serve as a first-pass screening tool for communities considering beach nourishment and dune construction before they decide to invest in the more thorough, but costly and data-intensive, life-cycle model simulations. [ABSTRACT FROM AUTHOR]

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7-8):2261-2278. 10.1007/s00382-016-3443-6, 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15-32. 10.1002/joc.3402, 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability. [ABSTRACT FROM AUTHOR]

A number of severe flooding events have occurred both in South Africa and internationally in recent years. Furthermore, changes in both the intensity and frequency of extreme rainfall events have been documented, both locally and internationally, associated with climate change. The recent loss of life, destruction of infrastructure, and associated economic losses caused by flooding, compounded by the probability of increased rainfall variability in the future, highlight that design flood estimation (DFE) techniques within South Africa are outdated and in need of revision. A National Flood Studies Programme (NFSP) has recently been initiated to overhaul DFE procedures in South Africa. One of the recommendations in the NFSP is the further development of a continuous simulation modelling (CSM) system for DFE in South Africa. The focus of this paper is a review of CSM techniques for DFE, to guide further development for application in South Africa. An introduction to DFE, and particularly the CSM approach, is presented, followed by a brief overview of DFE techniques used in South Africa, leading into a more detailed summary of CSM for DFE within South Africa to date. This is followed by a review of the development and application of CSM methods for DFE internationally, with a focus on the United Kingdom and Australia, where methods have been developed with the intention of national scale implementation. It is important to highlight that there is a plethora of CSM methods available internationally and this review is not exhaustive; it focuses on and identifies some of the strengths and weaknesses of several popular methods, particularly those intended for national scale application, as the intended outcome from this review is to identify a path towards the development of a usable national scale CSM system for DFE in South Africa. Emphasis on a usable method is important, considering the reality that, despite promising results, numerous benefits, and national scale methods being developed, it appears that the CSM method for DFE is rarely used in practice. [ABSTRACT FROM AUTHOR]

Given the general notion that dynamical downscaling leads to added accuracy in both historical simulations as well as climate change projections, this paper investigates its validity over India using historical data (1975-2005) from the CORDEX models and their driving global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5), and comparing them against observed temperature and rainfall. We find that downscaling invariably leads to an improvement in the spatial pattern of surface air temperature, but compared to the driving GCMs, the errors in magnitude after downscaling are even worse in some cases. In regard to JJAS rainfall simulations, the CMIP5 driving GCMs are found to be superior to their dynamically downscaled counterparts both in terms of spatial patterns as well as magnitude of errors. Both CMIP5 driving GCMs as well as the CORDEX models underestimate rainfall during JJAS; however, negative bias in CORDEX models is worse. Unlike the driving CMIP5 GCMs, their dynamically downscaled counterparts simulate an early onset followed by a slow and late withdrawal of the Indian summer monsoon rainfall. The frequency of occurrence of rainfall intensities is simulated well by both sets of models in the lower intensity regime (0-20 mm/day); however, for higher intensities, the driving CMIP5 GCMs underestimate whereas the CORDEX models overestimate. [ABSTRACT FROM AUTHOR]

This paper compared future runoff projections using a Budyko type equation with respect to projections by a global hydrological model (GHM). The comparison was made for the annual mean runoff projections for a future period (2060-2100) after the Budyko parameter was set based on hydrologic model outputs at a present period (1960-2000). The objective of this study was to investigate the performance of the Budyko equation with respect to the hydrologic model at different climate regions. To address the question this study used the spatial average of runoff for the 35 largest basins in the world. According to the comparison, the projections by the two approaches agreed well (R2 = 0.983), in particular in humid tropic region (R2 = 0.986), but with consistent underestimation of future runoff (Median Error, ME = -0.042) by the Budyko equation. In subarctic region the performance of the Budyko equation was low (R2 = 0.599) due to the overestimation of future runoff (ME = 0.110). The results in the dry and temperate regions also showed some discrepancy (R2 = 0.931 and 0.724) without apparent patterns in the errors. The paper discussed possible reasons for the errors with respect to water and energy seasonality and changes in storage component contributions. [ABSTRACT FROM AUTHOR]

[Display omitted] • China's water footprint (WF) under the SSP-RCP scenario was predicted dynamically. • A new attempt to combine the system dynamic (SD) model with SSP-RCP was made. • The grey WF decreases significantly under all scenarios during the forecast year. • The agricultural WF is the main contributor to China's water footprint. • SSP1-2.6 is the best scenario to alleviate water shortages and water pollution. Under the dual influence of socioeconomic development and climate change, water resources in China are under increasing stress. It is of great significance to comprehensively explore the changing trend of China's water footprint (WF) in the future, clarify the water resource challenges that China will face, and alleviate water shortage and water pollution problems. This paper uses System Dynamics (SD) to build a simulation model of China's WF, calculate China's WF from 2000 to 2019, and for the first time, simulate and optimize China's WF from 2020 to 2050 under the SSP-RCP scenario matrix composed of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The results are as follows. (1) From 2000 to 2019, China's WF increased to 2009 and began to decline. The main contributors are grey WF and agricultural WF. (2) During 2020–2050, China's total WF under the SSP1-2.6 scenario will always be at the lowest; compared with other scenarios, the simulation result of China's total WF under the SSP2-4.5 scenario is the closest to the actual situation in China in 2020, 2021 and 2022. China's total WF under the SSP4-6.0 scenario is close to that under the SSP2-4.5 scenario, but the numerical variation characteristics of the WF composition are different. China's total WF under the SSP3-7.0 and SSP5-8.5 scenarios is high. (3) Based on the changes in WF and water resources supply/demand ratio under different scenarios, the SSP1-2.6 scenario is the best scenario to mitigate a future water shortage and water pollution in China. The research results can help decision-makers formulate relevant management policies and socioeconomic development models for water resource utilization and provide decision support for alleviating water shortages and water pollution in China. [ABSTRACT FROM AUTHOR]

Summary The global water cycle is a fundamental component of our climate and Earth system. Many, if not the majority, of the impacts of climate change are water related. We have an imperfect description and understanding of components of the water cycle. This arises from an incomplete observation of some of the stores and fluxes in the water cycle (in particular: precipitation, evaporation, soil moisture and groundwater), problems with the simulation of precipitation by global climate models and the wide diversity of global hydrological models currently in use. This paper discusses these sources of errors and, in particular, explores the errors and advantages of bias correcting climate model outputs for hydrological models using a single large catchment as an example (the Rhine). One conclusion from this analysis is that bias correction is necessary and has an impact on the mean flows and their seasonal cycle. However choice of hydrological model has an equal, if not larger effect on the quality of the simulation. The paper highlights the importance of improving hydrological models, which run at a continental and global scale, and the importance of quantifying uncertainties in impact studies. [ABSTRACT FROM AUTHOR]

Thermal energy demand in the residential building sector represents a big challenge for Serbia. In order to understand how to reduce this demand, and thereby avoiding GHG gas emissions, a bottom-up simulation model was developed. The model built the business-as-usual and two decarbonization scenarios up to 2030. For each scenario, such results as useful and final energy consumption, associated CO2 emissions, energy costs, investment costs and others were prepared at each level of the building stock segmentation. To develop such a detailed model, the topology of the residential building stock was developed and used as an input. For each individual building type, three retrofit packages of different stringency were analyzed. The paper delivers several important messages for the decarbonization of Serbia. First, it argues that the level of thermal energy services consumed by Serbian households is inadequate to address their needs. Second, the households of Serbia are likely to consume more wood than it was reported by national energy balances. Third, thermal energy efficiency retrofits can significantly reduce household energy demand at the same time as offering higher thermal comfort. However, the required investments are high and therefore benefits beyond energy cost savings should also be considered in order to make the transition to the low energy building stock economically feasible. [ABSTRACT FROM AUTHOR]

An accurate quantification of the stratospheric ozone feedback in climate change simulations requires knowledge of the ozone response to increased greenhouse gases. Here, an analysis is presented of the ozone layer response to an abrupt quadrupling of CO2 concentrations in four chemistry--climate models. The authors show that increased CO2 levels lead to a decrease in ozone concentrations in the tropical lower stratosphere, and an increase over the high latitudes and throughout the upper stratosphere. This pattern is robust across all models examined here, although important intermodel differences in the magnitude of the response are found. As a result of the cancellation between the upper- and lower-stratospheric ozone, the total column ozone response in the tropics is small, and appears to be model dependent.Asubstantial portion of the spread in the tropical column ozone is tied to intermodel spread in upwelling. The high-latitude ozone response is strongly seasonally dependent, and shows increases peaking in late winter and spring of each hemisphere, with prominent longitudinal asymmetries. The range of ozone responses to CO2 reported in this paper has the potential to induce significant radiative and dynamical effects on the simulated climate. Hence, these results highlight the need of using an ozone dataset consistent with CO2 forcing in models involved in climate sensitivity studies. [ABSTRACT FROM AUTHOR]

In this paper, we report the comparison of the experimental results with numerical simulation. The numerical simulation was performed using TRNSYS. Fourteen weeks of data (January 28 to May 7) were collected and compared against numerical simulation results obtained using TRNSYS. For the 14-week period, the results showed that there was close agreement between the experimental measurement and the numerical simulation. The measured average temperature was 8.1 °C compared to the simulated average temperature of 8.6 °C. The measured maximum and minimum temperatures were 21 °C and -7.8 °C, respectively, while the numerical simulation maximum and minimum temperatures were 17.8 °C and -7.5 °C, respectively. For the five-year seasonal simulation, the system became fully charged by June 14. The maximum temperature the sand-bed achieved annually was 24.83 °C, occurring approximately on July 10, with a minimum of 11.1 °C occurring on January 24. The results demonstrate that sand-bed solar thermal storage systems are suitable for climates in regions with long periods of freezing temperatures which can contribute towards the net-zero energy status of a residential home. We reported the first experimental study, to the authors' knowledge, of sand-bed solar thermal storage conducted in a region with an extended freezing period carried out on a home situated in Palmer, Alaska, 61.6°N, 149.1°W [see G. Hailu et al., Energies 10, 1873 (2017)]. [ABSTRACT FROM AUTHOR]

Modelling and simulation are of high importance in agriculture and climate change research. This paper aims to simulate and analyse the sour cherry product lines in Hungary. The model presented below is suitable to analyse the market share, the cost and income relations as well as the relation structure of the sour cherry product lines. The vertical levels of the model (soil and climatic conditions, varieties, size of orchards, product type and trade channels) have individual values added from the aspect of the end product. Theoretically, all elements can be connected to any element of the next level, but there can be “prohibited contacts” because of professional, regulation or production practice reasons. The model makes possible to evaluate, compare and filter the different product lines, analyse the effects of the climate change or the use of different varieties, and thereby provides support for strategic planning and decision making. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a study of the factors that influence array layout on wave energy farms. The WEC considered is a two-body heave converter extracted from Babarit et al. (2012). Simulations were run through a time domain model from de Andres et al. (2013) with irregular waves considering different sea states. Factors analyzed in this paper are array layout, WEC separation, number of WECs and wave directionality. Results show that wave directionality is very important in order to achieve constructive interference. When looking at the number of WECs the conclusion is that as the larger the number of WECs, more interactions are possible and therefore, the higher the interaction factor is. Regarding array layout, triangle and square configurations were found to be similar and the efficiency of each one depends on the most probable peak period. Separating distance was found to be a key factor and was set as the optimal one. Finally, wave climate was classified in different subtypes around the globe. The optimum layout in these sites was assessed. The influence of directionality was studied and the triangular configuration was found to be the most favorable for multidirectional climates while square configurations were most adequate in unidirectional climates. [Copyright &y& Elsevier]

Statistical downscaling methods are commonly used to address the scale mismatch between coarse resolution Global Climate Model output and the regional or local scales required for climate change impact assessments. The effectiveness of a downscaling method can be measured against four broad criteria: consistency with the existing baseline data in terms of means, trends and distributional characteristics; consistency with the broader scale climate data used to generate the projections; the degree of transparency and repeatability; and the plausibility of results produced. Many existing downscaling methods fail to fulfil all of these criteria. In this paper we examine a block bootstrap simulation technique combined with a quantile prediction and matching method for simulating future daily climate data. By utilising this method the distributional properties of the projected data will be influenced by the distribution of the observed data, the trends in predictors derived from the Global Climate Models and the relationship of these predictors to the observed data. Using observed data from several climate stations in Vanuatu and Fiji and out-of-sample validation techniques, we show that the method is successful at projecting various climate characteristics including the variability and auto-correlation of daily temperature and rainfall, the correlations between these variables and between spatial locations. This paper also illustrates how this novel method can produce more effective point scale projections and a more credible alternative to other approaches in the Pacific region. [ABSTRACT FROM AUTHOR]

The Intergovernmental Panel on Climate Change (IPCC) estimates that baseline global GHG emissions may increase 25–90% from 2000 to 2030, with carbon dioxide (CO2) emissions growing 40–110% over the same period. On-road vehicles are a major source of CO2emissions in all the developed countries, and in many of the developing countries in the world. Similarly, several criteria air pollutants are associated with transportation, for example, carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM). Therefore, the need to accurately quantify transportation-related emissions from vehicles is essential. The new U.S. Environmental Protection Agency (EPA) mobile source emissions model, MOVES2010a (MOVES), can estimate vehicle emissions on a second-by-second basis, creating the opportunity to combine a microscopic traffic simulation model (such as VISSIM) with MOVES to obtain accurate results. This paper presents an examination of four different approaches to capture the environmental impacts of vehicular operations on a 10-mile stretch of Interstate 4 (I-4), an urban limited-access highway in Orlando, FL. First (at the most basic level), emissions were estimated for the entire 10-mile section “by hand” using one average traffic volume and average speed. Then three advanced levels of detail were studied using VISSIM/MOVES to analyze smaller links: average speeds and volumes (AVG), second-by-second link drive schedules (LDS), and second-by-second operating mode distributions (OPMODE). This paper analyzes how the various approaches affect predicted emissions of CO, NOx, PM2.5, PM10, and CO2. The results demonstrate that obtaining precise and comprehensive operating mode distributions on a second-by-second basis provides more accurate emission estimates. Specifically, emission rates are highly sensitive to stop-and-go traffic and the associated driving cycles of acceleration, deceleration, and idling. Using the AVG or LDS approach may overestimate or underestimate emissions, respectively, compared to an operating mode distribution approach. [ABSTRACT FROM AUTHOR]

In this paper evidence of anthropogenic influence over the warming of the 20th century is presented and the debate regarding the time-series properties of global temperatures is addressed in depth. The 20th century global temperature simulations produced for the Intergovernmental Panel on Climate Change’s Fourth Assessment Report and a set of the radiative forcing series used to drive them are analyzed using modern econometric techniques. Results show that both temperatures and radiative forcing series share similar time-series properties and a common nonlinear secular movement. This long-term co-movement is characterized by the existence of time-ordered breaks in the slope of their trend functions. The evidence presented in this paper suggests that while natural forcing factors may help explain the warming of the first part of the century, anthropogenic forcing has been its main driver since the 1970’s. In terms of Article 2 of the United Nations Framework Convention on Climate Change, significant anthropogenic interference with the climate system has already occurred and the current climate models are capable of accurately simulating the response of the climate system, even if it consists in a rapid or abrupt change, to changes in external forcing factors. This paper presents a new methodological approach for conducting time-series based attribution studies. [ABSTRACT FROM AUTHOR]

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971-2000) and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly. [ABSTRACT FROM AUTHOR]

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution. [ABSTRACT FROM AUTHOR]

There have been tremendous changes in the global land use pattern in the past 50 years, which has directly or indirectly exerted significant influence on the global climate change. Quantitative analysis for the impacts of land use and land cover changes (LUCC) on surface climate is one of the core scientific issues to quantitatively analyze the impacts of LUCC on the climate so as to scientifically understand the influence of human activities on the climate change. This paper comprehensively analyzed the primary scientific issues about the impacts of LUCC on the regional climate and reviewed the progress in relevant researches. Firstly, it introduced the influence mechanism of LUCC on the regional climate and reviewed the progress in the researches on the biogeophysical process and biogeochemical process. Then the model simulation of effects of LUCC on the regional climate was introduced, and the development from the global climate model to the regional climate model and the integration of the improved land surface model and the regional climate model were reviewed in detail. Finally, this paper discussed the application of the regional climate models in the development and management of agricultural land and urban land. [ABSTRACT FROM AUTHOR]

Based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, this paper evaluated the performance of the state-of-the-art climate models in simulating the interannual variability of the western North Pacific subtropical high (WNPSH), East Asian jet (EAJ) and East Asian summer rainfall (EASR), and further projected their potential changes in a future warmer world. The results show that the multimodel ensemble mean (MME) simulation has good ability to model the interannual variability of the WNPSH, EAJ and EASR, although some discrepancy exists among the individual models. The MME simulation can also reasonably capture the observed relationship of the EASR with the WNPSH and the EAJ. Under the RCP4.5 and the RCP8.5 scenarios, the interannual variability in WNPSH, EAJ and EASR is projected by the MME to increase in the 21st century. In addition, the WNPSH and EAJ would still be the dominant systems influencing the East Asian summer precipitation under global warming scenarios. But the linkage of the EASR to the WNPSH may be slightly weaker and that to the EAJ may be slightly stronger in the 21st century as compared to the present. [ABSTRACT FROM AUTHOR]

Global climate model simulations project that the tropical Andes Mountains of South America, which are particularly vulnerable to climate change because of a reliance on snow and glacial melt for freshwater resources, will experience enhanced warming in the near future, with both higher rates of warming at higher elevations within the mountain range itself and localized enhancement of warming exceeding surrounding areas of the globe. Yet recent surface temperature changes in the tropical Andes do not show evidence for either elevation-dependent warming or regional enhancement of warming on average. However, it remains a possibility that the expected warming trends in this region have begun to manifest in other ways (e.g., in the free atmosphere or at intermediate mountain elevations). This paper proposes evidence from several reanalysis products that there has indeed been a regional enhancement of midtropospheric warming around the central Andes over the past few decades that makes this region stand out as a hot spot within the broader pantropics. This trend is generally not reproduced by historical AMIP climate model simulations, which suggests that the mechanisms through which the atmosphere is warming over the central Andes are not adequately captured by climate models. Possible explanations for localized enhancement of warming in this region are considered. On the other hand, reanalysis products do not consistently exhibit enhanced warming at intermediate mountain elevations in the central Andes as evidenced by the generally moderate rates of change in the freezing-level height, except perhaps in the highest-resolution reanalysis product. [ABSTRACT FROM AUTHOR]

Climate change projections are commonly based on multi-model ensembles of climate simulations. In this paper we consider the choice of bias models in Bayesian multimodel predictions. Buser et al. (Clim Res 44(2-3):227-241, 2010a) introduced a hybrid bias model which combines commonly used constant bias and constant relation bias assumptions. The hybrid model includes a weighting parameter which balances these bias models. In this study, we use a cross-validation approach to study which bias model or bias parameter leads to, in a specific sense, optimal climate change projections. The analysis is carried out for summer and winter season means of 2 m-temperatures spatially averaged over the IPCC SREX regions, using 19 model runs from the CMIP5 data set. The cross-validation approach is applied to calculate optimal bias parameters (in the specific sense) for projecting the temperature change from the control period (1961-2005) to the scenario period (2046-2090). The results are compared to the results of the Buser et al. (Clim Res 44(2-3):227-241, 2010a) method which includes the bias parameter as one of the unknown parameters to be estimated from the data. [ABSTRACT FROM AUTHOR]

Climate observations and model simulations produce vast amounts of data. The unprecedented data volume and the complexity of geospatial statistics and analysis requires efficient analysis of big climate data to investigate global problems such as climate change, natural disasters, diseases, and other environmental issues. This paper introduces a high performance query analytical framework to tackle these challenges by leveraging Hive and cloud computing technologies. With this framework, we propose grid transformation, a new perspective for complex climate analysis that applies a series of atomic transformations to terabytes of climate data using SQL-style query (HiveQL). Specifically, we introduce four types of grid transformations (temporal, spatial, local, and arithmetic) to support a broad range of climate analyses, from the basic spatiotemporal aggregation to more sophisticated anomaly detection. Each query is processed as MapReduce tasks in a highly scalable Hadoop cluster as the parallel processing engine. Big climate data are directly stored and managed in a Hadoop Distributed File System without any data format conversion. A prototype is developed to evaluate the feasibility and performance of the framework. Experimental results show that complex and data-intensive climate analysis can be conducted using intuitive SQL queries with good flexibility and performance. This research provides a building block and practical insights in establishing a cyberinfrastructure that provides a high performance and collaborative environment for data-intensive geospatial applications in climate science. [ABSTRACT FROM AUTHOR]

This paper reports a comprehensive study on the observed and projected spatiotemporal changes in mean and extreme climate over the arid region of northwestern China, based on gridded observation data and CMIP5 simulations under the RCP4.5 and RCP8.5 scenarios. The observational results reveal an increase in annual mean temperature since 1961, largely attributable to the increase in minimum temperature. The annual mean precipitation also exhibits a significant increasing tendency. The precipitation amount in the most recent decade was greater than in any preceding decade since 1961. Seasonally, the greatest increase in temperature and precipitation appears in winter and in summer, respectively. Widespread significant changes in temperature-related extremes are consistent with warming, with decreases in cold extremes and increases in warm extremes. The warming of the coldest night is greater than that of the warmest day, and changes in cold and warm nights are more evident than for cold and warm days. Extreme precipitation and wet days exhibit an increasing trend, and the maximum number of consecutive dry days shows a tendency toward shorter duration. Multi-model ensemble mean projections indicate an overall continual increase in temperature and precipitation during the 21st century. Decreases in cold extremes, increases in warm extremes, intensification of extreme precipitation, increases in wet days, and decreases in consecutive dry days, are expected under both emissions scenarios, with larger changes corresponding to stronger radiative forcing. [ABSTRACT FROM AUTHOR]

This paper describes an investigation of various adaptations of rice based cropping systems to climate variability in India's Telangana State. All adaptations were generated through participatory engagement and were field-tested with local smallholder households before being evaluated through cropping system simulation analysis. This approach contrasts with most research about adaptation of cropping systems to climate variability and climate change that is mostly based on simplifying assumptions about current farmer management practices and where the feasibility of implementing proposed adaptations is rarely tested. In this study, the investigation commenced with discussions about climate related issues in rice based farming systems between researchers, farmers and NGOs in three villages in three Mandals of the state of Telangana. Participatory intervention was used to identify new practices that could provide more adaptive and robust responses to climate variability. Suggested adaptations were implemented in on-farm experimentation. Fields demonstrating these adaptations were monitored and results were discussed with participating farmers at regular ‘Climate Club’ village meetings. Crop and soil data from these fields were used to locally parameterise the cropping systems simulator APSIM. Local adaptations that were trialled in the villages were simulated using local soil and long term historical weather data. In each of the case studies, a number of adaptations that were developed and implemented in the villages were shown through simulation to be successful in terms of agricultural production, stability of yields and resource use efficiency. Of the adaptations investigated, sowing rules to reduce the chance of crop failure due to early dry spells were most readily adopted and are also relatively easy to extend to other villages. Strategic irrigation of rainfed crops such as maize and cotton resulted in significant gains to profitability and stability of these crops but cannot be considered in isolation where access to water is limited. Reduced irrigation of rice resulted in over 60 mm/ha/yr. savings in water and some improvements in gross margins but this adaptation was not popular with farmers due to its burden on labour and added risks associated with unreliable supply of electricity for pumping at critical times. The reduced rice area for strategic irrigation of rainfed crops adaptation resulted in improved gross margins per hectare per year and higher net water productivity. This adaptation is most promising but will require institutional change around water use policy and more equitable allocation of limited water resources within villages. These results led us to the proposition that participatory action research with smallholder farmers, coupled with field-testing and simulation analysis can produce practical and productive adaptations to climate variability. [ABSTRACT FROM AUTHOR]

The paper proceeds with a discussion of the interplay of scale and intensity in determining greenhouse gas emissions. This is followed by the presentation of several macroeconomic scenarios using LowGrow, a simulation model of the Canadian economy. The scenarios considered are ‘business as usual’ which is a projection into the future of past trends, ‘selective growth’ in which differential growth rates are applied to parts of the economy according to their direct and indirect greenhouse gas emissions, and ‘degrowth’ where the average GDP/capita of Canadians is reduced towards a level more consistent with a world economy the size of which respects global environmental limits. The paper ends with a comparison of the scenarios. [Copyright &y& Elsevier]

Abstract: In Part 1 of this paper, a carbonation model was developed and experimentally verified which was able to forecast carbonation depth of a concrete specimen considering varying ambient temperature, humidity and CO2 concentrations. Part 2 of the paper applies the carbonation diffusion/reaction model developed in Part 1 to predict the effects of global climate change on the carbonation of concrete. Climate scenarios were formulated and combined with the model for two major Canadian cities, Toronto and Vancouver. Results show that for undamaged and unstressed concrete, climate change will significantly affect carbonation progress. The model showed that for unloaded, non-pozzolanic concrete, ultimate carbonation depths in Toronto and Vancouver could be up to 45% higher. For in-service structures under load, the rates of deterioration are likely to be even faster. This is a cause for concern, and much further effort must be devoted to fully understand these phenomena. [Copyright &y& Elsevier]

Governments across Australia have long been investing in revegetation in an effort to restore biodiversity and, more recently, mitigate climate change. However, no readily available methods have been described to assist project leaders identify species and provenance material likely to be sustainable under the changing climatic conditions of coming decades. Focussing particularly on trees, as trees are important for biosequestration as well as for providing habitat for other native species, Paper 1 of this two part series briefly reviews species distribution models and growth simulation models that could provide the scientific underpinning to improve and refine selection processes. While these previous scientific studies provide useful insights into how trees may respond to climate change, it is concluded that a readily accessible and easy-to-use approach is required to consider the potential adaptability of the many trees, shrubs and ground cover species that may be needed for biodiverse plantings. In Part 2 of this paper, the Atlas of Living Australia is used to provide preliminary information to assist species selection by assessing the climatic range of individual species based on their current distributions and, where available, cultivated locations. While using the Atlas can assist current selections, ways are outlined in Part 2 in which more reliable selections for changing climatic conditions could be made, building on the methods described here. [ABSTRACT FROM AUTHOR]

In previous work, the authors demonstrated how data from climate simulations can be utilized to estimate regional wind power densities. In particular, it was shown that the quality of wind power densities, estimated from the UPSCALE global dataset in offshore regions of Mexico, compared well with regional high resolution studies. Additionally, a link between surface temperature and moist air density in the estimates was presented. UPSCALE is an acronym for UK on PRACE (the Partnership for Advanced Computing in Europe)—weather-resolving Simulations of Climate for globAL Environmental risk. The UPSCALE experiment was performed in 2012 by NCAS (National Centre for Atmospheric Science)-Climate, at the University of Reading and the UK Met Office Hadley Centre. The study included a 25.6-year, five-member ensemble simulation of the HadGEM3 global atmosphere, at 25km resolution for present climate conditions. The initial conditions for the ensemble runs were taken from consecutive days of a test configuration. In the present paper, the emphasis is placed on the single climate run for a potential future climate scenario in the UPSCALE experiment dataset, using the Representation Concentrations Pathways (RCP) 8.5 climate change scenario. Firstly, some tests were performed to ensure that the results using only one instantiation of the current climate dataset are as robust as possible within the constraints of the available data. In order to achieve this, an artificial time series over a longer sampling period was created. Then, it was shown that these longer time series provided almost the same results than the short ones, thus leading to the argument that the short time series is sufficient to capture the climate. Finally, with the confidence that one instantiation is sufficient, the future climate dataset was analysed to provide, for the first time, a projection of future changes in wind power resources using the UPSCALE dataset. It is hoped that this, in turn, will provide some guidance for wind power developers and policy makers to prepare and adapt for climate change impacts on wind energy production. Although offshore locations around Mexico were used as a case study, the dataset is global and hence the methodology presented can be readily applied at any desired location. [ABSTRACT FROM AUTHOR]

Sub-daily rainfall extremes are of significant societal interest, with implications for flash flooding and the design of urban stormwater systems. It is increasingly recognised that extreme subdaily rainfall will intensify as a result of global temperature increases, with regional climate models (RCMs) representing one of the principal lines of evidence on the likely magnitude and spatiotemporal characteristics of these changes. To evaluate the ability of RCMs to simulate subdaily extremes, it is common to compare the simulated statistical characteristics of the extreme rainfall events with those from observational records. While such analyses are important, they provide insufficient insight into whether the RCM reproduces the correct underlying physical processes; in other words, whether the model 'gets the right answers for the right reasons'. This paper develops a range of metrics to assess the performance of RCMs in capturing the physical mechanisms that produce extreme rainfall. These metrics include the diurnal and seasonal cycles, relationship between rainfall intensity and temperature, temporal scaling, and the spatial structure of extreme rainfall events. We evaluate a high resolution RCM-the Weather Research Forecasting model-over the Greater Sydney region, using three alternative parametrization schemes. The model shows consistency with the observations for most of the proposed metrics. Where differences exist, these are dependent on both the rainfall duration and model parameterization strategy. The use of physically meaningful performance metrics not only enhances the confidence in model simulations, but also provides better diagnostic power to assist with future model improvement. [ABSTRACT FROM AUTHOR]

This paper presents a software tool that allows simulation of PV pumping systems with sun trackers and, also, consideration of the effect of the level of dirt on the PV generator surface. This effect is important in dry climates such as the ones where this kind of systems is usually installed. The simulation tool has been validated with the experimental data obtained in a testing bench. Finally, the paper analyses the influence of the temporary scale in the simulation results, showing that the 12 values of monthly means of daily global irradiation lead to enough precisions. [ABSTRACT FROM PUBLISHER]

Abstract: The investigation presented in this paper has three main objectives. The first one is to study, through a case study, if it is possible to acceptably predict the energy consumption of a massive residential free-running building, when the exact behavior of the occupants is unknown or actual indoor conditions are not monitored, assuming standard use and occupation schedules, for the dry climate of the Argentinean Northwest. The second objective of the paper is to detect the possible causes of differences between actual and predicted energy consumption through an exhaustive thermal monitoring and occupant''s behavior, in order to obtain an improved model of the building giving more accurate predictions of the energy consumption. The third objective is to analyze the effect on the annual energy consumption of changing the massive envelope by a lightweight one. The comparison of real and simulated consumptions under comfort conditions defined by ASHRAE Standard 55 shows that simulations overestimated the energy consumption for heating and cooling. The main causes were detected from the experimental monitoring, indicating a lower use of the air conditioning equipment than the supposed initially. Simulations were improved to consider actual use and occupation conditions. Finally, an annual simulation of the improved model performed by changing the envelope material to a lightweight one showed that energy consumption for heating was increased, while energy consumption for cooling was decreased. In an annual balance, the massive walls are preferable over lightweight ones in arid sunny climates as in the Argentinean Northwest, giving energy savings of around 25%. [Copyright &y& Elsevier]

Abstract: Climate change is a global problem and across the world the transport sector is finding it difficult to break projected increases in carbon dioxide (CO2) emissions; there are very few contexts where deep reductions in transport CO2 emissions are being made. A number of research studies are now examining the potential for future lower CO2 emissions in the transport sector. This paper develops this work to consider some of the wider sustainability impacts (economic, social and local environmental) as well as the lower CO2 transport impacts of different policy trajectories. Hence the central argument made is for an integrated approach to transport policy making over the longer term – incorporating scenario analysis and multi-criteria assessment (MCA) – to help assess likely progress against a range of objectives. The analysis is based on work carried out in Oxfordshire, UK. Different packages of measures are selected and two scenarios developed which satisfy lower CO2 aspirations, one of which also provides wider positive sustainability impacts. A simulation model has been produced to help explore the strategic policy choices and tensions evident for decision-makers involved in local transport planning. The paper argues for a ‘strategic conversation’ () at the sub-regional and city level, based upon future scenario analysis and MCA, discussing the priorities for intervention. Such an approach will help us examine the scale of change and trade-offs required in moving towards sustainable transport futures. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel and fast sizing method under the constant daily load profile for sizing a stand-alone PV system. The term “efficient sizing” means that the approach did not use simulation but could get the result as good as those employing simulation. So, the sizing method is more efficient than the others. Traditionally, a typical day or a typical year’s solar irradiation profile is employed for the sizing task. However, facing the global warming crisis as well as the fact that no 2years would have the same weather condition for a single site, this approach statistically models the trend of climate change year by year and put it into the sizing formula, so that the results are optimal for the current weather condition and for the future as well. Hence, the suitable size for the PV array and the number of batteries are obtained by purely computation. This is different from the traditional sizing curve method. Although the traditional sizing curve method were satisfactory in the normal cases, they might fail in the extreme climate condition. This paper concludes the behavior of the extreme climate for at least 20years. So, the derived system may have statistical confidence for at least 20years of operation. A new reliability index (Loss of Power Probability) in terms of Extreme Value Theory is introduced. LPP provides upper bound reliability for application and rich information for many extreme events. A technological and economical comparison among the traditional daily energy balance method, sizing curve method and this approach is conducted and shows the usefulness of this approach. [Copyright &y& Elsevier]

Summary: The overall goal of the present study is to show through a simulation experiment the value of adaptive management under climatic variability and change. The Northern California water resources management system is used as an example of a multi-agency, multi-site, multi-objective water resources management system that supports a trillion dollar economy to demonstrate the utility of the methods and to exemplify the assessment studies. Simulations are performed using an adaptation of the Integrated Forecast and Reservoir Management (INFORM) system of Northern California with 6-hourly 1.4° input from the CCSM3.0 climate model for historical/control (1970–2019) and future periods (2050–2099). The present paper examines the assessment of the hydroclimatological sensitivities and a companion paper () examines the assessment of reservoir management sensitivities. The future period was based on the moderate CCSM3.0 A1B scenario. The INFORM system includes an intermediate complexity regional model for dynamic downscaling of the CCSM3.0 scenario output to produce surface precipitation and temperature fields with a 10×10km2 resolution, and snow–soil-channel modeling of all the watersheds upstream of the major reservoirs in Northern California. The flow results indicate a shift in the monthly average flow volume toward earlier times in the year and higher flow variability for the future period. Higher average temperatures in the future period and, consequently, earlier snow pack melt are mainly responsible for these flow changes. Both daytime and nighttime average monthly temperatures are higher in the simulation of the future period for the entire domain with the northern catchments experiencing higher temperature increases. Simulated monthly average precipitation for the future period is higher in the southern high Sierras and lower in the northern drainage basins than the historical period precipitation. The uncertainty in the assessments for Northern California mentioned may be estimated with additional simulations similar to those shown in this work using high spatial and temporal resolution output from different climate models. [ABSTRACT FROM AUTHOR]

Abstract: The idea of the decision support system (DSS) for farmers remains an enigma. Clever technology to bridge the gap between agricultural science and farming practice still seems appropriate. Many more of the conditions for success appear to exist today than ever before. Yet the DSS has yet to significantly colonise farm decision making practice. This paper comes late in a long program of research conducted to see if, and under what conditions, computer simulation of farming scenarios, on which a DSS generally depends, can be valued by farmers. The research approach used an unconventional prototypic information system (IS), comprising local measurements, models, and facilitated discussions that evolved in an action research program. The aim has been to elucidate the means by which successful simulation-based decision support intervention can take place and why it usually does not. This required a significant expansion of the researchers’ concept of the farm as a system to include the farmer’s internal system of practical knowing and learning. This paper reports on a cognitive framework model with transactions at interfaces with both the production system and the analytical IS. Its coarse structure is the classical perception–action cycle influenced by goals and outcome feedback. In the highly uncertain production environment of Australian dryland farming, personal judgement plays a significant mediating role between perception and action, and theory of a continuum between the judgement modes of intuition and analysis adds to framework structure. Further structure comes from the theoretical distinctions between holistic and arbitrary intuition, and between causal and probabilistic analysis. Analytic interventions influence: (a) awareness of current situation conditions and (b) expectations of future conditions and action outcomes, and these serve as primary cognitive resources for evaluation of possible actions in planning and decision making. A theory that matches our research experience in bridging the gap between analytic intervention and intuitive practice posits that virtual situations simulated with analytic models and outputs represented graphically can facilitate vicarious experiential learning. This dovetails with theory concerning the education of intuition. The paper concludes by applying criteria from the field of cognitive engineering to test whether the framework presents a concept of mind that is workable for informing practical model-based research and development aimed at supporting farmers’ judgments and decisions. [Copyright &y& Elsevier]

This study investigates the impacts of climate change on meteorology and air quality conditions in California by dynamically downscaling Parallel Climate Model (PCM) data to high resolution (4 km) using the Weather Research and Forecast (WRF) model. This paper evaluates the present years'' (2000--06) downscaling results driven by either PCM or National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) reanalysis data. The analyses focused on the air quality--related meteorological variables, such as planetary boundary layer height (PBLH), surface temperature, and wind. The differences of the climatology from the two sets of downscaling simulations and the driving global datasets were compared, which illustrated that most of the biases of the downscaling results were inherited from the driving global climate model (GCM). The downscaling process added mesoscale features but also introduced extra biases into the driving global data. The main source of bias in the PCM data is an imprecise prediction of the location and strength of the Pacific subtropical high (PSH). The analysis implied that using simulation results driven by PCM data as the input for air quality models will likely underestimate air pollution problems in California. Regional-averaged statistics of the downscaling results were estimated for two highly polluted areas, the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV), by comparing to observations. The simulations driven by GFS data overestimated surface temperature and wind speed for most of the year, indicating that WRF has systematic errors in these two regions. The simulation matched the observations better during summer than winter in terms of bias. WRF has difficulty reproducing weak surface wind, which normally happens during stagnation events in these two regions. The shallow summer PBLH in the Central Valley is caused by the dominance of high pressure systems over the valley and the strong valley wind during summer. The change of meteorology and air quality in California due to climate change will be explored in Part II of this study, which compares the future (2047--53) and present (2000--06) simulation results driven by PCM data and is presented in a separate paper. [ABSTRACT FROM AUTHOR]

Abstract: Transport is a major user of carbon-based fuels, and it is increasingly being highlighted as the sector which contributes least to CO2 emission reduction targets. This paper reports on the findings of the VIBAT London study (www.vibat.org) which considers the role of the transport sector in reducing CO2 emissions in London. The analysis develops a transport and carbon simulation model (TC-SIM) for London. Within this, users are able to consider the implementation of a series of potential policy packages—low emission vehicles, alternative fuels, pricing regimes, public transport, walking and cycling, strategic and local urban planning, information and communication technologies, smarter choices, ecological driving and slower speeds, long distance travel substitution, freight transport and international air. They can select variable levels of application to help achieve headline CO2 emission reduction targets. The roles of carbon rationing and oil prices are also considered. TC-SIM can be played in different user modes: as ‘free riders’, ‘techno-optimists’, ‘enviro-optimists’, ‘complacent car addicts’ and other typical travel market segments, including a ‘free role’. Game playing or scenario testing such as this helps to highlight perceived levels of homogeneity of views within certain cohorts, the development of entrenched positions and the likely success in achieving objectives. The paper develops various policy packages, scenarios and pathways aimed at reducing transport CO2 emissions. It argues that strategic CO2 emission reduction targets are very ambitious relative to current progress, and that we need to act more effectively across a wide range of policy mechanisms, with a ‘high intensity application’ of many of the options, to get near to achieving these targets. A critical issue here will be in communicating and gaining greater ‘ownership’ of future lifestyle choices with stakeholders and the public, and participation tools such as TC-SIM could become increasingly important in this area. [Copyright &y& Elsevier]