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This study was undertaken to evaluate how future uncertain climate-related hydrological responses and low and highflow frequencies affect local hydrology up to the 2100, depending on different Global Circulation Models (GCM) and different Concentration Scenarios (CS). To this end, daily total precipitation and daily mean temperature data were used for RCP4.5 and RCP8.5 CSs by employing GCMs (GFDL-ESM2M, HadGEM2-ES and MPI-ESM-MR). In the study, the years 1971–2000 were chosen as the baseline period. Future streamflow predictions were modeled by machine learning. The models were calibrated with baseline period data and future streamflow values were predicted by using the best Streamflow Prediction Model during the 2055s (2041–2070) and 2085s (2071–2100) periods. Flow Duration Curves from the streamflow predictions were obtained and by using them, discharges corresponding to 95% probability of exceedance for lowflow and 5% probability of exceedance for highflow were calculated. According to the findings, it is predicted that there will be decrease of approximately 21% in lowflow discharges and approximately 30% in highflow discharges. This prediction has suggested water resources managers to implement mitigation measures to climate change in Eastern Black Sea Basin and most probably in Türkiye, especially in the context of continuity of aquatic life and sustainable water supply. It is expected that the paper will take a very important place in estimating and evaluating the expected changes in basin hydrology in the coming years, especially in the highflows and lowflows, which are the key concepts in hydroelectric power generation and in flood management. • Development of streamflow prediction model for climatic projection data. • Streamflow estimation using limited input data in small catchment. • Obtaining far future streamflow for water resources pre-planning. • Discussion of the impact of climate change on daily streamflow extremes. [ABSTRACT FROM AUTHOR]

Abstract Conductor galloping is a common phenomenon during the operation of the transmission line, which can easily result in electrical and mechanical accidents such as tower collapses and interphase flashover. And aerodynamic parameters are key factors to study galloping. In this paper, the aerodynamic parameters of crescent-shaped and D-shaped conductors, which are two typical iced conductors, are studied by wind tunnel test. The instability analysis is carried out according to Den Hartog instability mechanism and the linear galloping model. The results indicate that wind velocity, ice thickness, conductor type, bundle spacing and splitting number all have a great influence on aerodynamic parameters and conductor galloping. Then the galloping situation of a 500 kV quad bundle conductor are simulated according to the parameters obtained from wind tunnel test. The calculating results correspond with the actual situation. The results obtained in this paper are important to predict the galloping amplitudes under different weather conditions and investigate anti-galloping technology of iced quad bundle conductor. Highlights • Aerodynamic coefficients under different Reynolds numbers, ice thicknesses, etc. are obtained. • Effects of Reynolds number on galloping are analyzed according to Den Hartog mechanism and the general linear model. • Effects of ice thickness, conductor type, bundle spacing and bundle number are studied by Den Hartog instability mechanism. • A multi-span 4DOF (4°-of-freedom) nonlinear dynamic model for galloping is developed to simulate the galloping situation. [ABSTRACT FROM AUTHOR]

Assessing the impacts of Land Use (LU) and climate change on future streamflow projections is necessary for efficient management of water resources. However, model projections are burdened with significant uncertainty arising from various sources. Most of the previous studies have considered climate models and scenarios as major sources of uncertainty, but uncertainties introduced by land use change and hydrologic model assumptions are rarely investigated. In this paper an attempt is made to segregate the contribution from (i) general circulation models (GCMs), (ii) emission scenarios, (iii) land use scenarios, (iv) stationarity assumption of the hydrologic model, and (v) internal variability of the processes, to overall uncertainty in streamflow projections using analysis of variance (ANOVA) approach. Generally, most of the impact assessment studies are carried out with unchanging hydrologic model parameters in future. It is, however, necessary to address the nonstationarity in model parameters with changing land use and climate. In this paper, a regression based methodology is presented to obtain the hydrologic model parameters with changing land use and climate scenarios in future. The Upper Ganga Basin (UGB) in India is used as a case study to demonstrate the methodology. The semi-distributed Variable Infiltration Capacity (VIC) model is set-up over the basin, under nonstationary conditions. Results indicate that model parameters vary with time, thereby invalidating the often-used assumption of model stationarity. The streamflow in UGB under the nonstationary model condition is found to reduce in future. The flows are also found to be sensitive to changes in land use. Segregation results suggest that model stationarity assumption and GCMs along with their interactions with emission scenarios, act as dominant sources of uncertainty. This paper provides a generalized framework for hydrologists to examine stationarity assumption of models before considering them for future streamflow projections and segregate the contribution of various sources to the uncertainty. [ABSTRACT FROM AUTHOR]

The spectral characteristics of paleoclimate observations spanning the last millennium suggest the presence of significant low-frequency (multi-decadal to centennial scale) variability in the climate system. Since this low-frequency climate variability is critical for climate predictions on societally-relevant scales, it is essential to establish whether General Circulation models (GCMs) are able to simulate it faithfully. Recent studies find large discrepancies between models and paleoclimate data at low frequencies, prompting concerns surrounding the ability of GCMs to predict long-term, high-magnitude variability under greenhouse forcing ( Laepple and Huybers, 2014a , 2014b ). However, efforts to ground climate model simulations directly in paleoclimate observations are impeded by fundamental differences between models and the proxy data: proxy systems often record a multivariate and/or nonlinear response to climate, precluding a direct comparison to GCM output. In this paper we bridge this gap via a forward proxy modeling approach, coupled to an isotope-enabled GCM. This allows us to disentangle the various contributions to signals embedded in ice cores, speleothem calcite, coral aragonite, tree-ring width, and tree-ring cellulose. The paper addresses the following questions: (1) do forward-modeled “pseudoproxies” exhibit variability comparable to proxy data? (2) if not, which processes alter the shape of the spectrum of simulated climate variability, and are these processes broadly distinguishable from climate? We apply our method to representative case studies, and broaden these insights with an analysis of the PAGES2k database ( PAGES2K Consortium, 2013 ). We find that current proxy system models (PSMs) can help resolve model-data discrepancies on interannual to decadal timescales, but cannot account for the mismatch in variance on multi-decadal to centennial timescales. We conclude that, specific to this set of PSMs and isotope-enabled model, the paleoclimate record may exhibit larger low-frequency variability than GCMs currently simulate, indicative of incomplete physics and/or forcings. [ABSTRACT FROM AUTHOR]

• Vertical grids impact the modeling of baroclinic modes and water mass distribution. • 32-layer HYCOM is adequate to represent first 5 baroclinic modes in mid-latitudes. • A simulation with 16 layers exhibits no dense overflow water and a shallower AMOC. • Simulations with 24 to 96 layers exhibit overall similar North Atlantic circulation. In contrast to the large volume of studies on the impact of horizontal resolution in oceanic general circulation models (OGCMs), the impact of vertical resolution has been largely overlooked and there is no consensus on how one should construct the vertical grid to represent the vertical structure of the baroclinic modes as well as the distribution of distinct water masses throughout the global ocean. In this paper, we document the importance of vertical resolution in the representations of vertical modes and water masses in the North Atlantic and show i) that vertical resolution is unlikely to undermine the resolution capability of the horizontal grid in representing the vertical modes and a 32-layer isopycnal configuration is adequate to represent the first five baroclinic modes in mid-latitudes and ii) that vertical resolution should focus on representing water masses. A coarse vertical resolution (16-layer) simulation exhibits virtually no transport in the dense overflow water which leads to a weaker and significantly shallower Atlantic meridional overturning circulation (AMOC) despite resolving the first baroclinic mode throughout the domain, whereas there are overall very small differences in the subtropical and subpolar North Atlantic circulation in the simulations with finer vertical resolution (24 to 96 layers). We argue that accurately representing the water masses is more important than representing the baroclinic modes for an OGCM in modeling the low-frequency large-scale circulation. [ABSTRACT FROM AUTHOR]

Projections of future climate change cannot rely on a single model. It has become common to rely on multiple simulations generated by Multi-Model Ensembles (MMEs), especially to quantify the uncertainty about what would constitute an adequate model structure. But, as Parker points out (2018), one of the remaining philosophically interesting questions is: "How can ensemble studies be designed so that they probe uncertainty in desired ways?" This paper offers two interpretations of what General Circulation Models (GCMs) are and how MMEs made of GCMs should be designed. In the first interpretation, models are combinations of modules and parameterisations; an MME is obtained by "plugging and playing" with interchangeable modules and parameterisations. In the second interpretation, models are aggregations of expert judgements that result from a history of epistemic decisions made by scientists about the choice of representations; an MME is a sampling of expert judgements from modelling teams. We argue that, while the two interpretations involve distinct domains from philosophy of science and social epistemology, they both could be used in a complementary manner in order to explore ways of designing better MMEs. • Ensembles of climate models could be better designed to quantify uncertainty. • Models can be interpreted as combinations of modules and parameterisations. • Models can also be interpreted as aggregations of expert judgements. • The two interpretations involve philosophy of science or social epistemology. • They are complementary in order to explore ways of designing better ensembles. [ABSTRACT FROM AUTHOR]

We explore the effects of seasonal variability for the climate of Earth-like planets as determined by the two parameters polar obliquity and orbital eccentricity using a general circulation model of intermediate complexity. In the first part of the paper we examine the consequences of different values of obliquity and eccentricity for the spatio-temporal patterns of radiation and surface temperatures as well as for the main characteristics of the atmospheric circulation. In the second part of the paper we analyse the associated implications for the habitability of planets close to the outer edge of the habitable zone (HZ). The second part focuses in particular on the multistability property of climate, i.e. the parallel existence of both an ice-free and an ice-covered climate state. Our results show that seasonal variability affects both the existence of and transitions between the two climate states. Moreover, our experiments reveal that planets with Earth-like atmospheres and high seasonal variability can have ice-free areas at much larger distance from the host star than planets without seasonal variability, which leads to a substantial expansion of the outer edge of the HZ. Sensitivity experiments exploring the role of azimuthal obliquity and surface heat capacity test the robustness of our results. On circular orbits, our findings obtained with a general circulation model agree well with previous studies based on one dimensional energy balance models, whereas significant differences are found on eccentric orbits. [ABSTRACT FROM AUTHOR]

Summary The effects of climate change on water resources have been studied extensively throughout the world through the use of hydrologic models coupled with General Circulation Model (GCM) output or climate sensitivity scenarios. This paper examines the effects of hydrologic model parameterization uncertainty or equifinality, where multiple unique hydrologic model parameter sets can result in adequate calibration metrics, on hydrologic projections from downscaled GCMs for three snowmelt-dependent watersheds (upper reaches of the Clearwater, Gunnison, and Sacramento River watersheds) in the western United States. The hydrologic model used in this study is the Soil and Water Assessment Tool (SWAT) and is calibrated for discharge at the watershed outlet in each watershed. Despite achieving similar calibration metrics, a majority of hydrologic projections of average annual streamflow during the 2080s were statistically different, with differences in magnitude and direction (increase or decrease) compared to historical annual streamflows. At the average monthly time-scale, a majority of the hydrologic projections varied in peak streamflow timing, peak streamflow magnitude, summer streamflows, as well as overall increases or decreases compared to the historical monthly streamflows. Snowmelt projections from the SWAT model also widely varied, both in depth and snowmelt peak timing, for all watersheds. Since a large portion of the runoff-producing regions in the western United States is snowmelt-dependent, this has large implications for the prediction of the amount and timing of streamflow in the coming century. This paper shows that hydrologic model parameterizations that give similar adequate calibration metrics can lead to statistically significant differences in hydrologic projections under climate change. Therefore, researchers and water resource managers should account for this uncertainty by assembling ensemble projections from both multiple parameter sets and GCMs. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) have been developed and extensively applied for dynamically downscaling coarse resolution information from different sources, such as general circulation models (GCMs) and reanalyses, for different purposes including past climate simulations and future climate projection. Thus far, the nature, the methods, and a number of crucial issues concerning the use of dynamic downscaling are still not well understood. The most important issue is whether, and if so, under what conditions dynamic downscaling is really capable of adding more information at different scales compared to the GCM or reanalysis that imposes lateral boundary conditions (LBCs) to the RCMs. There are controversies regarding the downscaling ability. In this review paper we present several factors that have consistently demonstrated strong impact on dynamic downscaling ability in intraseasonal and seasonal simulations/predictions and future projection. Those factors include setting of the RCM experiment (e.g. imposed LBC quality, domain size and position, LBC coupling, and horizontal resolution); as well as physical processes, mainly convective schemes and vegetation and soil processes that include initializations, vegetation specifications, and planetary boundary layer and surface coupling. These studies indicate that RCMs have downscaling ability in some aspects but only under certain conditions. Any significant weaknesses in one of these aspects would cause an RCM to lose its dynamic downscaling ability. This paper also briefly presents challenges faced in current RCM dynamic downscaling and future prospective, which cover the application of coupled ocean-atmosphere RCMs, ensemble applications, and future projections. [ABSTRACT FROM AUTHOR]

• An accurate SWAT model is constructed by considering the influences of various human activities. • The influence of GCM uncertainty on climate change prediction is reduced through multi-model integration and bias correction. • Combined with SDSM model and SWAT model, it is concluded that the future temperature, precipitation and runoff of Yongding watershed will increase. Climate change has affected hydrological processes at global scale, and redistributed water resources in space and time. Anthropogenic activities even exacerbated the water resources problems. In this paper, Statistical DownScaling Model (SDSM) was used to downscale the predicted precipitation and temperature in the mountainous area of Yongding watershed under 4 SSP-RCP climate scenarios in 4 General Circulation Models (GCMs). Bayesian model averaging (BMA) method was employed for multi-model integration to reduce the impact of GCM uncertainty on climate change prediction. Then, the multi-model ensemble data were corrected by the Quantile mapping (QM) method. Soil and Water Assessment Tool (SWAT) was established in the study area considering the intensive human activities, including the water use in irrigation areas and regulation of the 8 large and medium-sized reservoirs. The SWAT model was applied to simulate the runoff response under the future climate change. The results showed that the maximum temperature (T max), minimum temperature (T min), precipitation (P) and runoff all show an upward trend under the four future climate scenarios. SSP585 scenario always showed the highest increase in temperature and precipitation. The simulated future annual runoff increases significantly compared with the reference period, indicating that the water shortage of the watershed may be alleviated in the future. The research results can provide a scientific basis for the decision-making of water resources management and protection in the watershed. [ABSTRACT FROM AUTHOR]

Changes in winter low flow (WLF) in cold-region rivers affect engineering design, water quality, and river ecosystem health. This paper aims to quantify to what extent WLF will change under climate warming and to propose a practical forecasting model for WLF predictions. The new concept of cumulative watershed temperature (CWT) is used as a convenient lumped surrogate for complicated hydrological processes that influence WLF. Statistical analyses of century-long flow data from the Fraser River in British Columbia, Canada show a strong dependence of WLF on CWT. In winter season, time series of CWT and WLF are shown to each divide into a falling and a rising limb at the data point of their lowest values, being −358 °C and 827 m3/s (historical averages), respectively. The falling and rising limbs describe the cumulative freezing and thawing effects of air temperatures fluctuating around the freezing point on WLF changes. The correlation between CWT and WLF (time series) data points on the rising limbs forms the forecasting model, expressing WLF as a function of CWT in terms of their z-scores. The model takes input of Global Circulation Model (GCM) projections of temperature increases by the end of 21st century under different Representative Concentration Pathways (RCPs), and predicts changes of Fraser River WLF from the historical average. RCP4.5 causes a 22.6% increase (ensemble average from six GCMs). RCP8.5 increases WLF by 20% (for some future years) to 44% (for others). The methods reported in this paper can be adapted to other cold regions. • Climate warming causes future winter low flow of the Fraser River in B.C. to increase. • Cumulative watershed air temperature controls the magnitude of winter low flow. • Design low flows from traditional methods need corrections due to climate warming. [ABSTRACT FROM AUTHOR]

Abstract: Modeling of global climate change is moving from global circulation model (GCM)-type projections with coupled biogeochemical models to projections of ecological responses, including food web and upper trophic levels. Marine and coastal ecosystems are highly susceptible to the impacts of global climate change and also produce significant ecosystem services. The effects of global climate change on coastal and marine ecosystems involve a much wider array of effects than the usual temperature, sea level rise, and precipitation. This paper is an overview for a collection of 12 papers that examined various aspects of global climate change on marine ecosystems and comprise this special issue. We summarized the major features of the models and analyses in the papers to determine general patterns. A wide range of ecosystems were simulated using a diverse set of modeling approaches. Models were either 3-dimensional or used a few spatial boxes, and responses to global climate change were mostly expressed as changes from a baseline condition. Three issues were identified from the across-model comparison: (a) lack of standardization of climate change scenarios, (b) the prevalence of site-specific and even unique models for upper trophic levels, and (c) emphasis on hypothesis evaluation versus forecasting. We discuss why these issues are important as global climate change assessment continues to progress up the food chain, and, when possible, offer some initial steps for going forward. [Copyright &y& Elsevier]

Abstract: This paper provides a detailed characterization of the observed daily rainfall series available for the Mekong, Chi, and Mun River Basins in the context of climate change; and describes the linkage between climate simulations given by Global Circulation Models (GCMs) and the local rainfall characteristics using the popular Statistical Downscaling Model (SDSM). Observed daily rainfall records at 11 stations in the study area for the 1961–2007 period were considered. Results of characterizing the available rainfall data for the 1961–1990 and 1991–2007 periods show different trends of rainfall characteristics for different locations in the study area. However, a consistent increase in the annual maximum number of consecutive dry days (CDD) was observed in the Chi catchment area, the eastern part of the Mun watershed, and the western portion of the Mekong River Basin. In addition, decrease in the annual maximum daily rainfall (AMDR) was found in most locations of the study area, except for the central part of the Chi and Mun River Basins. Moreover, it has been shown in this paper that the SDSM could adequately describe the basic statistical and physical characteristics of the observed rainfall processes for the calibration (1961–1975) and validation (1976–1990) periods. This statistical downscaling method was then used to project future rainfall characteristics for the 1961–2099 period using the climate simulations given by the UK HadCM3 (HadCM3) model under A2 and B2 scenarios (HadCM3A2 and HadCM3B2), and by the Canadian GCM3 (CGCM3) model under A2 and A1B scenarios (CGCM3A2 and CGCM3A1B). In general, the projected trends of rainfall characteristics by both HadCM3 and CGCM3 were found to be consistent with the observed historical trends. However, there was a large difference in the projection results given by these two models. This would indicate the presence of high uncertainty in climate simulations provided by different GCMs. In addition, the climate change impacts on the flood and drought problems in the study area were shown using the CDD and AMDR indices of 100-year return period. [Copyright &y& Elsevier]

Abstract: In most cases climate change projections from General Circulation Models (GCM) and Regional Climate Models (RCM) cannot be directly applied to climate change impact studies, and downscaling is therefore needed. A large number of statistical downscaling methods exist but no clear recommendations exist of which methods are more appropriate, depending on the application. This paper compares five statistical downscaling methods based on a common change factor methodology using results from four different RCMs driven by different GCMs. Precipitation time series for a future scenario are generated for a location north of Copenhagen for the period 2071–2100 under climate change projections by the scenario A1B. Special focus is given to the changes of extreme events since downscaling methods mainly differ in the way extreme events are generated. There is a significant uncertainty in the downscaled projected changes of the mean, standard deviation, skewness and probability of dry days. Large uncertainties are also observed in the downscaled changes in extreme event statistics. However, three of the four RCMs analysed show an increase in the extreme precipitation events in the future. The uncertainties are partly due to the variability of the RCM projections and partly due to the variability of the statistical downscaling methods. The paper highlights the importance of acknowledging the limitations and advantages of different statistical downscaling methods as well as the uncertainties in downscaling climate change projections for use in hydrological models. [Copyright &y& Elsevier]

Abstract: Cities are becoming increasingly vulnerable to flooding because of rapid urbanization, installation of complex infrastructure, and changes in the precipitation patterns caused by anthropogenic climate change. The present paper provides a critical review of the current state-of-the-art methods for assessing the impacts of climate change on precipitation at the urban catchment scale. Downscaling of results from global circulation models or regional climate models to urban catchment scales are needed because these models are not able to describe accurately the rainfall process at suitable high temporal and spatial resolution for urban drainage studies. The downscaled rainfall results are however highly uncertain, depending on the models and downscaling methods considered. This uncertainty becomes more challenging for rainfall extremes since the properties of these extremes do not automatically reflect those of average precipitation. In this paper, following an overview of some recent advances in the development of innovative methods for assessing the impacts of climate change on urban rainfall extremes as well as on urban hydrology and hydraulics, several existing difficulties and remaining challenges in dealing with this assessment are discussed and further research needs are described. [Copyright &y& Elsevier]

The paper aims to achieve the modelling of climate change effects on heating and cooling in the building sector, through the use of the available Intergovernmental Panel on Climate Change forecasted data. Data from several different climate models will be fused with regards to mean air temperature, wind speed and horizontal solar radiation. Several climatic models data were analysed ranging from January 2006 to December 2100. Rather than considering each model in isolation, we propose a data fusion approach for providing a robust combined model for morphing an existing weather data file. The final aim is simulating future energy use for heating and cooling of a reference building as a consequence of the expected climate changes. We compare results, in terms of robustness to overfitting, for two different fusion methodologies, based on the comparison between errors on punctual historical data or prediction models that can be obtained by each climate simulator and by the actual ERA-INTERIM data set. Finally, we map the new aggregated data into a prediction trace of heating and cooling energy requirements. The expected energy demand is in the range of the one provided by single climate models, with a variability that reaches up to the 10% of the overall energy requirements. The approach proposed is an advancement as it allows to achieve better fits with existing re-analysis data if compared to specific global circulation models output data. Thus a more reliable estimation of energy use for heating and cooling can be achieved. [ABSTRACT FROM AUTHOR]

• The Med-ROMS model successfully simulated general circulation pattern in the Eastern Mediterranean Sea. • The changes associated with the 1997–1998 North Ionian reversal have been computed. • The RMSE difference between the Med-ROMS SSH and AVISO satellite SSH was about 0.0812 m. • We have observed high positive significant correlation between NAO indices 12 months moving average and Med-ROMS-SSHa -PC1. This paper deals with three hindcasting simulations of circulation and sea level elevation, using Med-ROMS (Mediterranean- Regional Ocean Model System, with high horizontal -resolution (8.6 km). Each simulation has started at the end of previous 100-years spin-up and control run. The model surface boundary conditions, from the corrected ERA-40 re-analyses (Pettenuzzo et al., 2010) and three momentum flux ensemble members are based on a surface wind Bayesian Hierarchical Models (BHM) - Mediterranean Forecasting System (MFS-Wind-BHM) described in Berliner and Kim (2007) and Milliff et al. (2011), cover the period January 1958 to December 2001. The results have been validated by an Empirical Orthogonal Function (EOF) analysis of surface sea height anomalies for satellite and Med-ROMS Hindcasts. The correlation coefficients between Med-ROMS (Principal Component) PC1 (Sea Surface Height anomaly) SSHa hindcasts 1, 2, 3 and AVISO satellite are 0.985, 0.97 and 0.98 respectively, all values are significant at 95% confidence limit. We have defined a Bimodal Oscillating System mechanism responsible for decadal reversals of the Ionian basin-wide circulation (BiOS) (Gacic et al., 2011, 2014) as a pattern of Eastern Mediterranean climate change. The ROMS model successfully simulated the observed general circulation pattern in the Eastern Mediterranean. Over the period from July 1997 till December 2001, the Med-ROMS has successfully simulated the north Ionian inversion; SSHa same as Satellite SSH with an averaged RMSE between them of 0.0792 m. The spatial correlation maps between wind stress curl and Med-ROMS-SSHa/EOF1-SSHa are made and found to be significantly correlated at a 95% confidence limit. The highest significant correlation coefficient for Med-ROMS-SSHa was approximately 0.8 and located off the western Turkish coast inside the Aegean Sea. This strong significant positive correlation reflects the cyclonic motion of Rhodes Gyre. A high significant correlation coefficient for Med-ROMS-EOF1-SSHa, of around 0.51, was found along the western coast of Greece in the northeast Ionian Sea. For the first time, we could correlate the North Ionian Sea inversion to wind stress curl, which was significantly correlated with the EOF1 Med-ROMS SSHa averaged hindcasts during the inversion period. In this transient period, we noticed a significant correlation coefficient, at a 95% confidence limit, between NAO indices moving on average over 12 months and Med-ROMS-SSHa -PC1 over the Eastern Mediterranean. [ABSTRACT FROM AUTHOR]

Statistical Downscaling (SD) is a model that uses satellite data from General Circulation Models (GCM), which in climatology are very useful in predicting climate for the next few decades. GCM data is generally ill-conditioned, which is high dimension and multicollinearity, so a special technique is needed to handle this poorly conditioned. One of the variable selection techniques and handling of multicollinearity which is currently highly developed is regularization techniques including Adaptive Lasso, where selective parameters are adaptive, which can differ for each regression coefficient. Until now, predictions of extreme rainfall in Indonesia have not used Adaptive Lasso in SD modeling. This paper aims to predict the amount of rainfall (in millimeters) at moderate extreme (quantile 0.75) and high extreme rainfall (quantile 0.9 da 0.95) and handling poorly conditioned GCM data with Adaptive Lasso techniques and building predictive models of local rainfall by utilizing GCM data using the Bayes quantile regression model. Response in the form of monthly rainfall at Indramayu district, West Java Indonesia, and 49 explanatory variables in the form of GCM precipitation data in the period January 1981 - December 2013, which handled multicollinearity and variable selection using the Adaptive Lasso. The results are very satisfying with correlation between predicted and real data is above 0.91 for and the RMSEP is less than 50. [ABSTRACT FROM AUTHOR]

Abstract The magnetic field induced in the Earth's ocean by the large-scale global circulation consists of the toroidal and poloidal modes. Lateral variations of the ocean electrical conductivity allow for the energy exchange between both regimes. In this paper, we predict that the eastward component of the toroidal magnetic field in the area of the Antarctic Circumpolar Current can reach amplitudes of 15 nT at the depth of about 1800 m. Moreover, even though the toroidal field is invisible on the ocean surface, it can significantly influence the observable poloidal field, both in terms of its amplitude, and seasonal variations. Highlights • The ocean-induced magnetic field has a large toroidal component. • Its magnitude can be an order of magnitude larger than the poloidal field. • It is driven by the vertical stratification of horizontal ocean flows. • The toroidal field influences the observable poloidal field on the surface. [ABSTRACT FROM AUTHOR]

Highlights • First attempt to apply concept of corporate memory to study strategic planning. • Original empirical study on reclamation for a small open economy. • Original neo-institutional economic inquiry of reclamation. Abstract Informed by property rights economics, this paper explores some unique advantages of reclamation, as a form of expansionist dynamic zoning, compared to densification of existing built up urban land and conversion of rural and peri-urban land. One major argument is that modern reclamation package, in light of global circulation of capital, can be one comprehensive project that avoids old mistakes and tries out new ideas without upsetting pre-existing development or interests. It then explains, with the help of statistics collected from a historical study of land reclamation of Hong Kong, that the future urban development in this open economy will be arrested if she gives up reclamation. The loss of corporate memory, using civil service staff movements based on the Staff List, seldom used in Hong Kong studies on town planning, as a proxy, in strategic planning is advanced as a factor behind the current situation. [ABSTRACT FROM AUTHOR]

Highlights • High resolution catchment scale rainfall predictions were generated from 8 GCMs. • Multiple GCMs is preferable over a best model to account for the future variability. • Simulated climate change has limited impact on pollutant treatment performance. • Larger WSUD systems are recommended to account for the future variability. • Bigger rainwater tank is suggested for same harvesting reliability in the future. Abstract Water Sensitive Urban Design (WSUD) stormwater systems, also known as Low Impact Development (LID) systems or Nature Based Solutions (NBS), are currently implemented based on the underlying assumption of statistical stationarity of rainfall, which threatens to become outdated under climatic uncertainty. This paper applies a new downscaling method to examine the implications of climate change on future rainfall and evaluate the reliability of WSUD stormwater infrastructure in pollution reduction, flow frequency mitigation and reliability as an alternative water supply. A variety of future atmospheric scenarios are considered as part of this comprehensive assessment by analysing an ensemble of eight different downscaled General Circulation Models (GCMs). High resolution catchment-scale rainfall projections for Melbourne, Australia were generated using a scheme called High-resolution Downscaling of Rainfall Using STEPS (HiDRUS) at a fine 1 km and 6-min scale for more precise analysis with uncertainty estimates. Statistical analyses show that, in general, the climate models predict a drier future with fewer rainfall events and longer dry periods when comparing the simulated near future (2040–2049) periods against the base-line period (1995–2004). The difference simulated between historical and future rainfall projections show minimum difference of WSUD performance in pollution removal and flow frequency reduction, with slightly lower harvesting reliability (<3%) observed under future climate; high variabilities, however, were observed across GCM simulations, indicating big uncertainties of system reliability under various conditions, e.g. design wetland sizes may vary from 2.5% to 4.0% of the impervious catchment area according to different future projects across GCMs. Larger WSUD systems are recommended to ensure reliable performance of pollution removal, as well as harvesting reliability under simulated future conditions. The significance of considering an ensemble of different GCMs as opposed to many scenarios generated by a single 'best' climate model was also demonstrated for the robust estimation of uncertainty in future WSUD reliability. This work highlights important considerations for the future design, management and quantitative evaluation of WSUD reliability. [ABSTRACT FROM AUTHOR]

Abstract: Grid computing is nowadays an established technology in fields such as High Energy Physics and Biomedicine, offering an alternative to traditional HPC for several problems; however, it is still an emerging discipline for the climate community and only a few climate applications have been adapted to the Grid to solve particular problems. In this paper we present an up-to-date description of the advantages and limitations of the Grid for climate applications (in particular global circulation models), analyzing the requirements and the new challenges posed to the Grid. In particular, we focus on production-like problems such as sensitivity analysis or ensemble prediction, where a single model is run several times with different parameters, forcing and/or initial conditions. As an illustrative example, we consider the Community Atmospheric Model (CAM) and analyze the advantages and shortcomings of the Grid to perform a sensitivity study of precipitation with SST perturbations in El Niño area, reporting the results obtained with traditional (local cluster) and Grid infrastructures. We conclude that new specific middleware (execution workflow managers) is needed to meet the particular requirements of climate applications (long simulations, checkpointing, etc.). This requires the side-by-side collaboration of IT and climate groups to deploy fully ported applications, such as the CAM for Grid (CAM4G) introduced in this paper. [Copyright &y& Elsevier]

Abstract: The Pliocene Epoch, 5.33–2.58million years ago (Ma), was a generally warmer and wetter interval with atmospheric CO2-concentrations at or slightly above modern levels. This paper provides an overview of Pliocene vegetation, sea surface temperatures and climate modelling outcomes. Most prominent changes in Pliocene biome distribution compared to today include a northwards shift of temperate and boreal vegetation zones in response to a warmer and wetter climate as well as an expansion of tropical savannas and forests at the expense of deserts. Faunal analysis techniques and modelling experiments using the Hadley Centre climate model identified significantly higher Pliocene sea surface temperatures at mid and high latitudes of the northern hemisphere with cooling or unchanged sea surface temperatures at low latitudes. Global mean annual surface temperatures (MAT) are estimated to have been 2 to 3°C higher during the Piacenzian (3.6–2.58Ma) than today with a reduced equator to pole gradient. The marine realm during the Pliocene was characterised by a reconfiguration of ocean gateways, particularly the narrowing of the Indonesian Seaway and closure of the Central American Isthmus, which produced essentially a modern pattern of ocean circulation. In the Southern Ocean a warm early Pliocene gave way to late Piacenzian cooling. Proxy data indicate a reduced east to west sea surface temperature gradient in the tropical Pacific during the Pliocene warmth. The Pliocene is one of the most intensively studied geological intervals of the pre-Quaternary. No other warm period in the geological past yields such a unique combination of near modern atmospheric CO2-concentrations, palaeogeography and palaeobiology. However, this paper also identifies data gaps and shortcomings in the reconstruction of Pliocene environments using proxy data and climate models on which future research should focus. [Copyright &y& Elsevier]

Climate change presents major uncertainties in future safe water access especially in rural communities that are already faced with water insecurity. For Uganda, rainwater harvesting has become a major adaptation strategy to ensure the attainment of safe water access for all. However, climate change effects on rainwater harvesting potential remains largely unexplored. Therefore, this paper assesses climate change effects on water savings and water security from rainwater harvesting systems for Kabarole district, Uganda. The top performing Global circulation models (GCMs), used in this study included; MIROC5, BCC-CSM-1-M, CNRM-CM5, ACCESS1-0, HADGEM2-ES and HADGEM2-CC using projections for 2025–2055 (2040s) and 2060–2090 (2070s) periods. Seasonal analysis used the 4 seasons for Uganda, namely; the 2 dry seasons of December, January, February (DJF) and June, July, August (JJA) and the 2 rainy seasons of September, October, November (SON) and March, April, May (MAM). Although there are conflicting model predictions, generally, the models agree that water savings and security will reduce in DJF and MAM and increase in JJA and SON seasons. MAM rain season will be most affected with water savings and security projected to reduce by more than 50% in the 2070s period. Therefore, households should harness the increased water savings in JJA and SON to cater for the predicted reduction in water savings in MAM and DJF seasons especially for the 2070s period. A larger tank of 5 m 3 along with at least 50 m 2 roof size combined with a low cost household water treatment is thus recommended. [ABSTRACT FROM AUTHOR]

Abstract The outline of a sophisticated downscaling simulation model developed by the author and colleagues and its application for projecting the future urban thermal environment during the progress of global warming are introduced in this paper. Examples of inevitable uncertainties in future projections brought by the choices of a greenhouse gas emissions scenario and general circulation model (GCM) data are also shown. Moreover, three examples of the environmental assessment for future urban planning, i.e., (1) disaster mitigation and prevention urban structure scenario, (2) compact city scenario, and (3) city master plan in a developing country, by the developed downscaling simulation model are presented. Downscaling simulation is a very powerful environmental assessment tool and is very useful for urban planning, especially in the inevitable future global warming period. Highlights • A sophisticated downscaling simulation model from a global scale to a building scale is introduced. • Projections of the future urban thermal environment during the progress of global warming are presented. • Environmental assessments for future urban planning by downscaling simulations are introduced. • Downscaling simulation is a very powerful environmental assessment tool and is very useful for urban planning, especially in the inevitable future global warming period. [ABSTRACT FROM AUTHOR]

Abstract: The Global Coupled Ionosphere–Thermosphere-Electrodynamics Model developed at Institute of Geology and Geophysics, Chinese Academy of Sciences (GCITEM-IGGCAS), is introduced in this paper. This new model self-consistently calculates the time-dependent three-dimensional (3-D) structures of the main thermospheric and ionospheric parameters in the height range from 90 to 600km, including neutral number density of major species O2, N2, and O and minor species N(2D), N(4S), NO, He and Ar; ion number densities of O+ ,O2 +, N2 +, NO+, N+ and electron; neutral, electron and ion temperature; and neutral wind vectors. The mid- and low-latitude electric fields can also be self-consistently calculated. GCITEM-IGGCAS is a full 3-D code with 5° latitude by 7.5° longitude cells in a spherical geomagnetic coordinate system, which bases on an altitude grid. We show two simulations in this paper: a March Equinox one and a June Solstice one, and compare their simulation results to MSIS00 and IRI2000 empirical model. GCITEM-IGGCAS can reproduce the main features of the thermosphere and ionosphere in both cases. [Copyright &y& Elsevier]

Abstract: In this paper an ensemble model of cirrus ice crystals is tested against midlatitude in situ estimates of ice water content, volume extinction coefficient and the total solar optical depth. During the Winter of 2005 and Spring 2006 the FAAM (Facility for Airborne Atmospheric Measurements) BAE-146 G-LUXE aircraft flew three flights as part of the CAESAR (Cirrus and Anvils: European Satellite and Airborne Radiation measurements project) campaign of flying in cirrus around the UK. The suite of microphysical instrumentation onboard the aircraft included the PMS 2D-C probe and the Stratton Park Engineering Company (SPEC) cloud particle imager (CPI). The campaign characterized cirrus properties such as ice water content, volume extinction coefficient, ice crystal geometric shape and ice crystal effective dimension. Cirrus cloud temperatures ranged approximately between 215 and 240K. From the CPI instrument 60–80% of the ice crystal habits were estimated to be either indeterminate or ‘irregular’ (though such irregular crystals could be composed of pristine components) of some form with hexagonal columns and hexagonal plates accounting for generally much less than 3% of the ice crystal population. The CPI estimated integrated ice water content ranged between 5±2 and 45±22gm−2, whilst the CPI estimate of the total solar optical depth was found to lie between 0.2±0.1 and 1.0±0.5. The CPI estimate of the mean ice crystal effective dimension was found to range between about 59±9 and 90±75μm. The particle size distribution (PSD) function was estimated using a PSD scheme that requires as input the in situ estimated IWC and measured in-cloud temperature. The CPI estimates of the bulk and microphysical properties of the midlatitude cirrus are used to test whether an ensemble model of cirrus ice crystals together with a PSD scheme can predict CPI in situ estimates to within the experimental uncertainty. This paper demonstrates that the ensemble model coupled with a PSD scheme can predict the ice water content and the integrated ice water content to generally well within the experimental uncertainty if a varying density with respect to size is assumed. The ensemble model together with a PSD scheme is also shown to predict the CPI estimated volume extinction coefficient and the derived total solar optical depth to generally well within the experimental uncertainty. The paper demonstrates that an ensemble model of cirrus combined with a PSD scheme can predict the radiative properties of cirrus without the need to invoke the concept of an ice crystal effective dimension. [Copyright &y& Elsevier]

Summary: Climate change impact studies are often based on coarse scale projections of general circulation models (GCMs) under greenhouse gases emissions scenarios. Outputs from GCMs have then to be downscaled to obtain information relevant to hydrologic studies. This paper investigates the uncertainty in catchment-scale precipitation scenarios due to (1) the emissions scenario, (2) the configuration of the GCM, and (3) the downscaling method. Two emissions scenarios, six global climate models and four downscaling methods are used to build climate change scenarios for three catchment case studies in the UK. Missing combinations are reconstructed by using a variance decomposition algorithm. The highly increasing variance towards the end of the century is shown to be largely due to the spread of results from different GCMs. Multimodel averaged results at the catchment scale generally show an increase in the amplitude of the annual cycle of precipitation, with wetter winters and drier summers. Hydrologists and water resources planners make use of downscaled climate scenarios, often with little regard for the performance of scenario construction methods, for informing decisions on water resources and flood risk management policies and projects. This paper shows that both the downscaling method and the multimodel building scheme applied have a significant impact on the seasonal precipitation regime that may in turn lead to quite different conclusions in impacts assessments. [Copyright &y& Elsevier]

Anthropogenic warming may change precipitation patterns, impacting infrastructure performance and reliability. Future precipitation statistics generated using General Circulation Models (GCM) are, however, often biased and not easily applied to problems such as runoff estimation. Stochastic weather generation is hence used as an alternative to GCMs in hydrology and hydraulic modelling. This paper explores the dependence of fine temporal precipitation characteristics on air pressure and air temperature using historic observations. The goal is to develop, based on the key causes of precipitation, a climatological basis for a stochastic precipitation generator for non-stationary precipitation under climate change conditions. The analysis focuses on precipitation in the urban Northeast United States and utilizes pooled observations from meteorological stations in New York City, Philadelphia, and Boston over 60 years. A negative correlation between hourly Probability of Precipitation (POP) and air pressure is observed. When the historical records are discretized using air Pressure Change Events (PCE), Decreasing Pressure Change Events (DePCEs) had a higher POP and a higher Precipitation Depth (PD) than Increasing Pressure Change Events (InPCEs). Temperature was more strongly associated with PD during DePCEs than InPCEs; this association was more pronounced during high magnitude PCEs and extreme events. The potential for simulating future hourly precipitation by associating historic hourly precipitation patterns with PCE’s and monthly temperature is assessed. [ABSTRACT FROM AUTHOR]

Simulations from climate models require bias correction prior to use in impact assessments or for statistical or dynamic downscaling to finer scales. There are a number of different approaches to bias correction, although most of these focus on a single variable for a particular location. Another limitation is that often corrections are only applied for one time scale of interest, for example daily or monthly aggregated simulations despite evidence of different bias structures existing at different time scales. Recent works have sought to address each of these limitations and have led to the development of the Multivariate Recursive Nesting Bias Correction (MRNBC) and Multivariate Recursive Quantile-matching Nested Bias Correction (MRQNBC) methods. An open-source software toolkit in the R statistical computing environment has been developed to provide access to these methods. Several applications of the software are demonstrated in this paper along with information about the capabilities of the software. [ABSTRACT FROM AUTHOR]

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.’69 to Dec.’94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.’95–Dec.’05) and forecast (Jan.’06–Dec.’35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90–95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season. [ABSTRACT FROM AUTHOR]

A new multi-scale modeling technique, SOMAR-LES, is presented in this paper. Localized grid refinement gives SOMAR (the Stratified Ocean Model with Adaptive Resolution) access to small scales of the flow which are normally inaccessible to general circulation models (GCMs). SOMAR-LES drives a LES (Large Eddy Simulation) on SOMAR’s finest grids, forced with large scale forcing from the coarser grids. Three-dimensional simulations of internal tide generation, propagation and scattering are performed to demonstrate this multi-scale modeling technique. In the case of internal tide generation at a two-dimensional bathymetry, SOMAR-LES is able to balance the baroclinic energy budget and accurately model turbulence losses at only 10% of the computational cost required by a non-adaptive solver running at SOMAR-LES’s fine grid resolution. This relative cost is significantly reduced in situations with intermittent turbulence or where the location of the turbulence is not known a priori because SOMAR-LES does not require persistent, global, high resolution. To illustrate this point, we consider a three-dimensional bathymetry with grids adaptively refined along the tidally generated internal waves to capture remote mixing in regions of wave focusing. The computational cost in this case is found to be nearly 25 times smaller than that of a non-adaptive solver at comparable resolution. In the final test case, we consider the scattering of a mode-1 internal wave at an isolated two-dimensional and three-dimensional topography, and we compare the results with Legg (2014) numerical experiments. We find good agreement with theoretical estimates. SOMAR-LES is less dissipative than the closure scheme employed by Legg (2014) near the bathymetry. Depending on the flow configuration and resolution employed, a reduction of more than an order of magnitude in computational costs is expected, relative to traditional existing solvers. [ABSTRACT FROM AUTHOR]

Future groundwater recharge estimations are helpful tools for water resources management. However, there is a need for more information about it in many parts of the world, including the Portoviejo River watershed (PRW) in the West of Ecuador, a semi-arid zone with a water deficit where climate change impacts could increase pressure on groundwater resources. Geographical Information Systems were used in this paper, owing to their simplicity of inputs and accuracy of outputs, to estimate current and future (2021–2070) groundwater recharge in the PRW. The study is divided into two stages: 1) a runoff map based on soil permeability, land use, and terrain slope, and 2) current and future recharge calculations using the Thornthwaite and Mather water balance method. Future precipitation and temperature data from an ensemble of four Global Circulation Models were used as inputs, along with a map of runoff spatial distribution. The representative concentration pathways (RCP) 4.5 and 8.5 were the climate change scenarios used in this study. The results showed that the location of the groundwater recharge zone would be impacted by future precipitation and temperature variability in both scenarios for the next fifty years. For the decade 2060–2070 and the scenario RCP 4.5, the highest recharge rate zones were located in the lowlands of the PRW surrounding Portoviejo city. In scenario RCP 8.5, the highest groundwater recharge areas increase considerably in the entire PRW. Currently, the highest groundwater recharge zones are in the highlands of the PRW. The future groundwater recharge scenarios provide information to decision-makers regarding land use, since the future groundwater recharge zones will be in the expansion areas of Portoviejo city. [Display omitted] • This study has established groundwater recharge for two climate change scenarios. • GIS and R were used for estimating future groundwater recharge zones (GRZ). • A runoff map was built using soil permeability, land cover, and slope terrain. • Future variability in precipitation and temperature would affect GRZ. • Future GRZ will be placed in urban growth zones near to the current Portoviejo city. [ABSTRACT FROM AUTHOR]

• Quantile-quantile rainfall mapping methods are extended to improve streamflow simulations. • Extensions correct rainfall bias at multiple time scales and model rainfall autocorrelation. • Methods reduce bias in rainfall sequencing metrics for downscaled GCM simulations. • Methods reduce bias in streamflow simulations forced by downscaled GCM simulations. Global circulation models (GCMs) provide important insights into future climate change. Bias-correction of downscaled GCM output is integral to any hydrological investigations of climate change due to discrepancies between the statistics of downscaled GCM simulations and observations. Many bias-correction techniques have been developed to support hydrological applications. However, there have been few comparisons of the sensitivity of hydrological simulations to different bias-correction assumptions. This paper investigates the importance of two common assumptions: (i) simultaneously correcting bias at multiple time scales, and (ii) explicitly handling rainfall autocorrelation; in quantile mapping of downscaled GCM rainfall data for hydrological simulation. Four quantile mapping methods are applied to correct bias in dynamically downscaled reanalysis and historical GCM simulations of rainfall for 11 catchments in south-eastern Australia and the performance of bias-corrected rainfall and streamflow simulations is evaluated. All quantile mapping methods investigated can effectively eliminate bias in monthly and annual rainfall totals. Quantile mapping methods that consider differences in the temporal dependence (autocorrelation) structure of downscaled GCM and observed rainfall are most effective in reducing bias in rainfall sequencing statistics, such as probability of consecutive wet or dry days. Streamflow simulations of mean and high streamflow percentiles are underestimated when generated using rainfall that is corrected using quantile mapping methods that do not consider differences in the temporal dependence structure of downscaled GCM and observed rainfall. Future hydrological investigations of climate change should therefore adopt methods that explicitly consider the temporal dependence structures of downscaled GCM and observed rainfall. [ABSTRACT FROM AUTHOR]

Regression problems with many potential predictors cannot be modeled using classical regression. In high dimensional data cases, predictors are much greater than its observations, so thus the penalized regression is better approach than classical regression in selecting predictors in the model. Group LASSO is one of penalized regression method which its predictors forming groups. Groups of predictors can be based on high correlation among predictors in a group, or can be based on its identically in shape, behavior or location. This paper performs rainfall data modeling using Group LASSO, with GLMSELECT procedure in SAS. The groups of predictors formed based on Principal Component Analysis. Rainfall data in Indramayu District, West Java, Indonesia as the response and 49 variables of Global Precipitation Climatology Project (GPCP) as its predictors. We have the best model for rainfall data based on the largest Adjusted R Square, minimum SBC and Root MSE. [ABSTRACT FROM AUTHOR]

In this paper we compare a compilation of multiproxy records spanning the eastern African margin with general circulation model simulations of seasonal precipitation fields for the mid-Holocene and the Last Glacial Maximum (LGM) carried out as part of the third phase of the Paleoclimate Modelling Intercomparison Project (PMIP3). Results show good agreement during the mid-Holocene (the ‘6K experiment’), with palaeodata and model outputs correlating well and indicating that changes in insolation drove a stronger northern African monsoon (north of ∼0–5°S) during the terminal “African Humid Period” and a weaker southeast African monsoon. For the LGM (the ‘21K experiment’), however, significant discrepancies exist both between model simulations, and between existing palaeodata and simulated conditions, both in terms of direction and amplitude of change. None of the PMIP3 simulations reflect the pattern inferred from the palaeodata. Two major discrepancies have been identified to explain this: 1) the limited sensitivity of the southern monsoon domain to the colder temperatures of the Indian Ocean (−2 °C), and 2) the absence of changes in the dynamic of the Indian Ocean Walker circulation over the entire basin, despite the exposure of the Sahul and Sunda shelves that weakened convection over the Indo-Pacific Warm Pool during the LGM. These results indicate that some major features of the atmospheric and oceanic teleconnections between the different monsoon regions require further consideration as models evolve. [ABSTRACT FROM AUTHOR]

The Andaman Sea is one of the sea areas with the most internal solitary waves (ISWs) observed in the world and an ideal experimental site for studying the generation and propagation characteristics of ISWs. In this paper the generation and propagation characteristics of the ISWs in the deep south Andaman Sea are investigated based on the results of Massachusetts Institute of Technology General Circulation Model (MITgcm) numerical models. The reliability of the model is verified. The sea surface current gradient of 3D model is consistent with Terra MODIS and Sentinel-1A SAR remote sensing images. The turbulent kinetic energy dissipation rate of 2D model is also consistent with in-situ mooring measurements in other sea areas. The variation of temperature, energy and current field caused by barotropic tides and oceanic stratification are analyzed and compared. The results show that ISWs cannot be generated directly by the individual diurnal tides in our 2D model, and ISWs are mainly generated by the semidiurnal tides in the deep south Andaman Sea. Ocean stratification affects the waveform, the velocity and the generation position of ISWs. ISWs are easier to form in the hot season because of a larger Ursell number and stronger nonlinearity. • To study the generation and propagation of ISWs in the south Andaman Sea, 2D and 3D MITgcm models are investigated. • Internal solitary waves are generated by the semidiurnal tide in the deep south Andaman Sea. • Seasonal ocean stratification affects the waveform, the velocity and the generation position of internal solitary waves. • ISWs are easier to form in the hot season because of a larger Ursell number and stronger nonlinearity. [ABSTRACT FROM AUTHOR]

• Experiment-simulation coupling makes CFD valid tool in support to Gen-IV reactors design. • HLM experimental campaigns provide datasets suited for CFD modelling validation. • Experimentalists take input from simulations and CFD analysts provide useful information. • The SESAME stand facility from CVR. • Investigation of Gen-IV reactors with CFD is now possible on a much more solid basis. The current paper describes the Computational Fluid-Dynamics (CFD) modelling of Heavy Liquid Metal (HLM) flows in a pool configuration and in particular how this is approached within the Horizon 2020 SESAME project. SESAME's work package structure, based on a systematic approach of redundancy and diversification, is explained along with its motivation. The main achievements obtained and the main lessons learned during the project are illustrated. The paper focuses on the strong coupling between the experimental activities and CFD simulations performed within the SESAME project. Two different HLM fluids are investigated: pure lead and Lead-Bismuth Eutectic. The objective is to make CFD a valid instrument used during the design of safe and innovative Gen-IV nuclear plants. Some effort has also been devoted to Proper Orthogonal Decomposition with Galerkin projection modelling (POD-Galerkin), a reduced order model suited for Uncertainty Quantification that operates by post-processing CFD results. Assessment of Uncertainty highly improves the reliability of CFD simulations. Dedicated experimental campaigns on heavily instrumented facilities have been conceived with the specific objective to build a series of datasets suited for the calibration and validation of the CFD modelling. In pool configuration, the attention is focused on the balance between conductive and convective heat transfer phenomena, on transient test-cases representative of incidental scenarios and on the possible occurrence of solidification phenomena. Four test sections have been selected to generate the datasets: (i) the CIRCE facility from ENEA, (ii) the TALL-3D pool test section from KTH, (iii) the TALL-3D Solidification Test Section (STS) from KTH and (iv) the SESAME Stand facility from CVR. While CIRCE and TALL-3D were existing facilities, the STS and SESAME Stand facility have been conceived, built and operated within the project, heavily relying on the use of CFD support. Care has been taken to ensure that almost all tasks were performed by at least two partners. Specific examples are given on how this strategy has allowed to uncover flaws and overcome pitfalls. Furthermore, an overview of the performed work and the achieved results is presented, as well as remaining or new uncovered issues. Finally, the paper is concluded with a description of one of the main goals of the SESAME project: the construction of the Gen-IV ALFRED CFD model and an investigation of its general circulation. [ABSTRACT FROM AUTHOR]

Summary Bias correction is considered as a critical post-processing step to remove systematic errors and improve the quality of General Circulation Model (GCM) simulations before their use in climate change impact assessment applications. A majority of the bias correction approaches correct for biases either at a single time scale or at multiple pre-specified time scales. An inappropriate or insufficient selection of time scales may lead to improper or sub-optimal bias corrected outputs, especially when persistence attributes across a range of scales are of interest. In this paper, we present a new bias correction approach that works in the frequency space and is independent of specific time scales. The approach is named as frequency-based bias correction (FBC). The usefulness of the approach is demonstrated by applying it to the monthly rainfall simulations of MIROC5 GCM over Australia and comparing the results with two other approaches, namely, empirical quantile mapping and recursive nesting bias correction, in cross validation. The comparison is based on the reproduction of various observed distribution and persistence attributes. Cross-validation results indicate that the proposed approach shows similar performance in terms of reproducing the first- and second-order moments of observed precipitation time series, however, outperforms with regard to persistence attributes. The approach shows high potential for use in downscaling and other climate change impact assessment studies, especially for the planning and design of hydrologic systems that are sensitive to the characterisation of persistence in the hydrologic time series. [ABSTRACT FROM AUTHOR]

General circulation model (GCM) simulations of the mid-Pliocene Warm Period (mPWP, 3.264 to 3.025 Myr ago) do not reproduce the magnitude of Northern Hemisphere high latitude surface air and sea surface temperature (SAT and SST) warming that proxy data indicate. There is also large uncertainty regarding the state of sea ice cover in the mPWP. Evidence for both perennial and seasonal mPWP Arctic sea ice is found through analyses of marine sediments, whilst in a multi-model ensemble of mPWP climate simulations, half of the ensemble simulated ice-free summer Arctic conditions. Given the strong influence that sea ice exerts on high latitude temperatures, an understanding of the nature of mPWP Arctic sea ice would be highly beneficial. Using the HadCM3 GCM, this paper explores the impact of various combinations of potential mPWP orbital forcing, atmospheric CO 2 concentrations and minimum sea ice albedo on sea ice extent and high latitude warming. The focus is on the Northern Hemisphere, due to availability of proxy data, and the large data–model discrepancies in this region. Changes in orbital forcings are demonstrated to be sufficient to alter the Arctic sea ice simulated by HadCM3 from perennial to seasonal. However, this occurs only when atmospheric CO 2 concentrations exceed 300 ppm. Reduction of the minimum sea ice albedo from 0.5 to 0.2 is also sufficient to simulate seasonal sea ice, with any of the combinations of atmospheric CO 2 and orbital forcing. Compared to a mPWP control simulation, monthly mean increases north of 60°N of up to 4.2 °C (SST) and 9.8 °C (SAT) are simulated. With varying CO 2 , orbit and sea ice albedo values we are able to reproduce proxy temperature records that lean towards modest levels of high latitude warming, but other proxy data showing greater warming remain beyond the reach of our model. This highlights the importance of additional proxy records at high latitudes and ongoing efforts to compare proxy signals between sites. [ABSTRACT FROM AUTHOR]

Increase in temperature extremes is one of the main expected impacts of climate change, as well as one of the first signs of its occurrence. Nevertheless, results emerging from General Circulation Models, while sufficient for large scales, are not enough for forecasting local trends and, hence, the IPCC has called for local studies based on on-site data. Indeed, it is expected that climate extremes will be detected much earlier than changes in climate averages. Heat waves are among the most important and least studied climate extremes, however its occurrence has been only barely studied and even its very definition remains controversial. This paper discusses the observed changes in temperature trends and heat waves in Northwestern Mexico, one of the most vulnerable regions of the country. The climate records in two locations of the region are analyzed, including one of the cities with extreme climate in Mexico, Mexicali City in the state of Baja California and the Yaqui River basin at Sonora State using three different methodologies. Results showed clear trends on temperature increase and occurrence of heat waves in both of the study zones using the three methodologies proposed. As result, some policy making suggestion are included in order to increase the adaptability of the studied regions to climate change, particularly related with heat wave occurrence. [ABSTRACT FROM AUTHOR]

The ocean tidal velocity and elevation can be estimated concurrently with the ocean circulation by adding the astronomical tidal forcing, parameterized topographic internal wave drag, and self-attraction and loading to the general circulation physics. However, the accuracy of these tidal estimates does not yet match accuracies in the best data-assimilative barotropic tidal models. This paper investigates the application of an augmented state ensemble Kalman Filter (ASEnKF) to improve the accuracy of M 2 barotropic tides embedded in a 1/12.5° three-dimensional ocean general circulation model. The ASEnKF is an alternative to the techniques typically used with linearized tide-only models; such techniques cannot be applied to the embedded tides in a nonlinear eddying circulation. An extra term, meant to correct for errors in the tide model due to imperfectly known topography and damping terms, is introduced into the tidal forcing. Ensembles of the model are created with stochastically generated forcing correction terms. The discrepancies for each ensemble member with TPXO, an existing data-assimilative tide model, are computed. The ASEnKF method yields an optimal estimate of the model forcing correction terms, that minimizes resultant root mean square (RMS) tidal sea surface elevation error with respect to TPXO, as well as an estimate of the tidal elevation. The deep-water, global area-averaged RMS sea surface elevation error of the principal lunar semidiurnal tide M 2 is reduced from 4.4 cm in a best-case non-assimilative solution to 2.6 cm. The largest elevation errors in both the non-assimilative and ASEnKF solutions are in the North Atlantic, a highly resonant basin. Possible pathways for achieving further reductions in the RMS error are discussed. [ABSTRACT FROM AUTHOR]

Summary In this paper, an updated global map of the current climate zoning and of its projections, according to the Köppen–Geiger classification, is first provided. The map at high horizontal resolution (0.5° × 0.5°), representative of the current (i.e. 1961–2005) conditions, is based on the Climate Research Unit dataset holding gridded series of historical observed temperature and precipitation, while projected conditions rely on the simulated series, for the same variables, by the General Circulation Model CMCC-CM. Modeled variables were corrected for their bias and then projections of climate zoning were generated for the medium term (2006–2050) and long term (2056–2100) future periods, under RCP 4.5 and RCP 8.5 emission scenarios. Results show that Equatorial and Arid climates will spread at the expenses of Snow and Polar climates, with the Warm Temperate experiencing more moderate increase. Maps of climate zones are valuable for a wide range of studies on climate change and its impacts, especially those regarding the water cycle that is strongly regulated by the combined conditions of precipitation and temperature. As example of large scale hydrological applications, in this work we tested and implemented a spatial statistical procedure, the geographically weighted regression among climate zones’ surface and mean annual discharge (MAD) at hydrographic basin level, to quantify likely changes in MAD for the main world rivers monitored through the Global Runoff Data Center database. The selected river basins are representative of more than half of both global superficial freshwater resources and world’s land area. Globally, a decrease in MAD is projected both in the medium term and long term, while spatial differences highlight how some areas require efforts to avoid consequences of amplified water scarcity, while other areas call for strategies to take the opportunity from the expected increase in water availability. Also the fluctuations of trends between the medium to long term time frames is viewed in order to pay attention to the lifespan of investments and decisions. Despite preliminary and illustrative, this study suggests how large scale valuable information can be extracted even through indicators derived from statistical spatial modeling procedures, without the pretention to replace more sophisticated studies that allow process based reproduction of inter-annual and intra-annual discharge variability, and on which a robust and comprehensive assessment of future water resource reliability should be however also based. [ABSTRACT FROM AUTHOR]

Signage system is important to lead occupants to circulate as well as to evacuate people in emergency. Multiple exit signs leading to different directions at an intersection for general circulation may create confusion in people under emergency, and therefore can jeopardize the situation into a chaotic state, especially in buildings with a complex layout. Many past studies focused on implementing different techniques or designs to improve the existing signage system for better visual and navigating effects, such as the use of laser beams, the combination of audio and video cues, and the selection of color, location, and mounting height. How to better control the signage system under emergency has been overlooked. This paper proposed a BIM-controlled signage system to solve the problem. With the help of heat sensors that detect the fire prone areas in the building, the proposed system identifies the best route and activates the corresponding sign blinkers with high intensity. During this action, exit signs that do not lead to the best route become inactive. This system provides a natural interface for remote operation in facility management to efficiently guide people to the right exit under emergency. A prototype of the signage system is presented and future research directions are discussed. [ABSTRACT FROM AUTHOR]

In this article, we present the latest version of an ensemble forecasting system of the hydrodynamics of the Black Sea, based on the GHER model. The system includes the Weakly Constrained Ensembles algorithm to generate random, but physically balanced perturbations to initialize members of the ensemble. On top of initial conditions, the ensemble accounts also for uncertainty on the atmospheric forcing fields, and on some scalar parameters such as river flows or model diffusion coefficients. The forecasting system also includes the Ocean Assimilation Kit, a sequential data assimilation package implementing the SEEK and Ensemble Kalman filters. A novel aspect of the forecasting system is that not only our best estimate of the future ocean state is provided, but also the associated error estimated from the ensemble of models. The primary goal of this paper is to quantitatively show that the ensemble variability is a good estimation of the model error, regardless of the magnitude of the forecast errors themselves. In order for this estimation to be meaningful, the model itself should also be well validated. Therefore, we describe the model validation against general circulation patterns. Some particular aspects critical for the Black Sea circulation are validated as well: the mixed layer depth and the shelfopen sea exchanges. The model forecasts are also compared with observed sea surface temperature, and errors are compared to those of another operational model as well. [ABSTRACT FROM AUTHOR]

The eddy-mean flow interactions are critical processes in the ocean energy cycle. This study investigated the energetics of eddy-mean flow interactions in the western tropical Pacific Ocean using the multiscale window transform (MWT) and MWT-based canonical transfer theory, based on 16-year numerical outputs from the Ocean General Circulation Model for the Earth Simulator and two mooring measurements. Energy analysis revealed that prominent eddy kinetic energy (EKE) appears in the upper layer of the Sulawesi Sea and the source region of North Equatorial Countercurrent (NECC), and in the subsurface layer along the Philippine coast. The kinetic energy in the upper layer is mainly transferred from the mean flow to the eddy field through barotropic instability. Below the thermocline, the prominent subsurface EKE is not only caused by barotropic conversion, but also by baroclinic one. Subsurface mesoscale eddies east of the Mindanao Island extract energy from mean flow through barotropic instability, and those further north seem to release their kinetic energy to the mean flow of North Equatorial Undercurrent, indicating inverse energy cascades. Our results highlight that the advection effect plays a key role in the horizontal distribution of EKE, which generally shifts the high EKE area a few degrees downstream from the eddy formation region. Particularly, in the source region of NECC, the advection term shifts the EKE center 5° eastward from ∼132°E to ∼137°E. • Energetic eddy-mean flow interactions occur in the Sulawesi Sea, the source region of NECC and east of the Philippines. • High EKE in the upper layer is due to BT instability, while the subsurface high EKE is due to both BT and BC instability. • Advection affects the EKE distribution by shifting the high EKE a few degrees away from the eddy formation region. [ABSTRACT FROM AUTHOR]

Summary Return period and risk of extreme hydrological events are critical considerations in water resources management. The stationarity assumption of extreme events for conducting hydrological frequency analysis to estimate return period and risk is now problematic due to climate change. Two different interpretations of return period, i.e. the expected waiting time (EWT) and expected number of exceedances (ENE), have already been proposed in literature to consider nonstationarity in return period and risk analysis by introducing the time-varying moment method into frequency analysis, under the assumption that the statistical parameters are functions only of time. This paper aimed at improving the characterization of nonstationary return period and risk under the ENE interpretation by employing meteorological covariates in the nonstationary frequency analysis. The advantage of the method is that the downscaled meteorological variables from the General Circulation Models (GCMs) can be used to calculate the nonstationary statistical parameters and exceedance probabilities for future years and thus the corresponding return period and risk. The traditional approach using time as the only covariate under both the EWT and ENE interpretations was also applied for comparison. Both approaches were applied to annual minimum monthly streamflow series of two stations in the Wei River, China, and gave estimates of nonstationary return period and risk that were significantly different from the stationary case. The nonstationary return period and risk under the ENE interpretation using meteorological covariates were found more reasonable and advisable than those of the EWT and ENE cases using time alone as covariate. It is concluded that return period and risk analysis of nonstationary low-flow series can be helpful to water resources management during dry seasons exacerbated by climate change. [ABSTRACT FROM AUTHOR]

The Yellow and East China Seas (YECS) has been known to exhibit interannual variability in both physical and biological aspects, in relation with El Niño–Southern Oscillation (ENSO). This paper investigates the interannual variability of summer sea surface salinity (SSS) in the YECS and its linkage with ENSO, using a global ocean general circulation model (OGCM) with a regional focus on the YECS. The OGCM experiment reveals that the dominant interannual variability of the SSS in the YECS is attributed to a variability of the Changjiang river discharge (CRD). The variability of the CRD is linked to ENSO-related precipitation over the Changjiang river; when El Niño events occur in winter, precipitation over the Changjiang river increases in the rainy season of the following years. The increased precipitation in El Niño years results from enhanced southwesterly moisture flux from the South China Sea into southern China. Our finding suggests that ENSO-related precipitation in southern China contributes to the dominant interannual variability of the summer SSS in the YECS by modulating CRD. [ABSTRACT FROM AUTHOR]

Summary Many hydro-climatological applications require use of General Circulation Models (GCMs) outputs. However, the raw information as available from GCMs often contains significant systematic biases when compared with observations. This necessitates some kind of statistical adjustment to be carried out on the GCM fields before their use in any application. It is common to correct the GCM simulations by removing the systematic biases at the time scale of interest, usually individually for each GCM variable that is needed. The outcome is a set of bias corrected variables that are not assessed for bias in their joint dependence structure, nor biases in all attributes at other time scales (such as daily and monthly and annual) except the one under consideration. In this paper, we present a bias correction approach that simultaneously adjusts for the biases in multiple variables across multiple time scales. The proposed Multivariate Recursive Nesting Bias Correction (MRNBC) approach simultaneously corrects many GCM variables and repeats the procedure across different levels of temporal aggregation to impart observed distributional and persistence properties at multiple time scales. The bias corrected series exhibits improvements across all variables and over all the time scales considered. The use of the approach in hydrology and water resources related downscaling applications is expected to deliver better results. [ABSTRACT FROM AUTHOR]