Ensemble Of General Circulation Models Research Articles

Pseudo-climate modelling study on projected changes in extreme extratropical cyclones, storm waves and surges under CMIP5 multi-model ensemble: Baltic Sea perspective

In order to estimate the possible parameters of future extreme extratropical cyclones (ETCs), a pseudo-climate modelling study of three historical storms originating from the Atlantic Ocean and one from the Black Sea area was performed using multi-model approach considering IPCC emission scenarios RCP4.5 and RCP8.5 for the twenty-first century. Applying Weather Research and Forecasting atmosphere model (WRF), Finite Volume Community Ocean model (FVCOM-SWAVE) and the Simulating WAves Nearshore (SWAN) model, the changes in initial conditions in atmospheric air temperature, sea surface temperature and relative humidity were considered on the basis of 14 CMIP5 general circulation models ensemble. According to the future scenario results, no notable changes are expected in minimum atmospheric pressure within the ETCs of the future; however, the low pressure area was slightly larger and the strong wind zone was extending further south with greater peak wind speeds in the future (year 2081–2100) simulations. This, in turn, yielded a small surge height increase at Parnu under RCP4.5 scenario; however, under RCP8.5 scenario the surge increase was up to 22–59 cm. Westerly approaching ETCs will bring more precipitation to the Baltic Sea area in the (warmer) future. In case of a southerly cyclone, the results were more mixed. An insignificant increase in wave heights during extreme storm conditions occurred. Although RCP8.5 future scenario is usually considered as unrealistic, the results of this study still suggest that the extreme ETCs may become more dangerous in the future, although probably not as certainly as tropical cyclones.

The Optimal Multimodel Ensemble of Bias-Corrected CMIP5 Climate Models over China

AbstractA multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multimodel ensemble approach has not considered different uncertainties across GCMs, which can be evaluated from the comparisons of simulations against observations. This study developed a comprehensive index to generate an optimal ensemble for two main climate fields (precipitation and temperature) for the studies of hydrological impacts of climate change over China. The index is established on the skill score of each bias-corrected model and different multimodel combinations using the outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results show that the optimal ensemble of the nine selected models accurately captures the characteristics of spatial–temporal variabilities of precipitation and temperature over China. We discussed the uncertainty of subset ensembles of ranking models and optimal ensemble based on historical performance. We found that the optimal subset ensemble of nine models has relative smaller uncertainties compared with other subsets. Our proposed framework to postprocess the multimodel ensemble data has a wide range of applications for climate change assessment and impact studies.

Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria

Selection of a suitable general circulation model (GCM) ensemble is crucial for effective water resource management and reliable climate studies in developing countries with constraint in human and computational resources. A careful selection of a GCM subset by excluding those with limited similarity to the observed climate from the existing pool of GCMs developed by different modeling centers at various resolutions can ease the task and minimize uncertainties. In this study, a feature selection method known as symmetrical uncertainty (SU) was employed to assess the performance of 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM outputs under Representative Concentration Pathway (RCP) 4.5 and 8.5. The selection was made according to their capability to simulate observed daily precipitation (prcp), maximum and minimum temperature (Tmax and Tmin) over the historical period 1980–2005 in the Niger Delta region, which is highly vulnerable to extreme climate events. The ensemble of the four top-ranked GCMs, namely ACCESS1.3, MIROC-ESM, MIROC-ESM-CHM, and NorESM1-M, were selected for the spatio-temporal projection of prcp, Tmax, and Tmin over the study area. Results from the chosen ensemble predicted an increase in the mean annual prcp between the range of 0.26% to 3.57% under RCP4.5, and 0.7% to 4.94% under RCP 8.5 by the end of the century when compared to the base period. The study also revealed an increase in Tmax in the range of 0 to 0.4 °C under RCP4.5 and 1.25–1.79 °C under RCP8.5 during the periods 2070–2099. Tmin also revealed a significant increase of 0 to 0.52 °C under RCP4.5 and between 1.38–2.02 °C under RCP8.5, which shows that extreme events might threaten the Niger Delta due to climate change. Water resource managers in the region can use these findings for effective water resource planning, management, and adaptation measures.

Evaluating the effects of climate change on precipitation and temperature for Iran using RCP scenarios

AbstractClimate change has caused many changes in hydrologic processes and climatic conditions globally, while extreme events are likely to occur more frequently at a global scale with continued warming. Given the importance of general circulation models (GCMs) as an essential tool for climate studies at global/regional scales, together with the wide range of GCMs available, selecting appropriate models is of great importance. In this study, six synoptic weather stations were selected as representative of different climatic zones over Iran. Utilizing monthly data for 20 years (1981–2000), the outputs of 25 GCMs for surface air temperature (SAT) and precipitation were evaluated for the historical period. The root-mean-square error and skill score were chosen to evaluate the performance of GCMs in capturing observed seasonal climate. Finally, the outputs of selected GCMs for the three Representative Concentration Pathways emission scenarios (RCPs), namely RCP2.6, RCP4.5, and RCP8.5, were downscaled using the change factor method for each station for the period 2046–2065. Results indicate that SAT in all months is likely to increase for each region, while for precipitation, large uncertainties emerge, despite the selection of climate models that best capture the observed seasonal cycle. These results highlight the importance of selecting a representative ensemble of GCMs for assessing future hydro-climatic changes for Iran.

Selection of Suitable Precipitation CMIP-5 Sets of GCMs for Iraq Using a Symmetrical Uncertainty Filter

Prediction of future water resources in Iraq for the period 2020 to 2100 based on different scenarios of climatic change models by harnessing many calibrated Arc-SWAT models needs to select future precipitation data from suitable sources. Therefore, the selection of the appropriate source of the possible future precipitation time series data sets was studied by examining 20 models of the fifth phase of the Coupled Model Inter-comparison Project (CMIP-5.0) for General Circulation Models (GCMs). The Symmetrical Uncertainty (SU) approach was used to determine the performance of the 20 CMIP-5.0 sets of GCMs against a monthly scale of historical precipitation datasets at each one of 35 rain gauge stations spread throughout Iraq, and an appropriate ensemble of GCMs selected. The examined models were ranked as 1st, 2nd, 3rd, etc. based on the SU values at each station and the final ranking of the models was carried out using a multi-criteria decision-making (MCDM) method. The results showed that the HadGEM2-AO and HadGEM2-ES were the best (1st ranked) models at 31 stations, while the MIROC-5 and CSIRO-Mk-3.6 were the best models at Zakho and Duhok stations, respectively, and the BCC.CSM1.1.m and FIO.ESM were the best models at Samarra and Hilla stations, respectively. There was a variation in simulation preference regionally between the two models HadGEM2-AO and HadGEM2-ES in the second ranking except at Tel-Afer and Najaf stations. No predominant models were found in other ranks throughout Iraq. The results of the final ranking of these 20 CMIP-5 sets by the MCDM method thus showed that there are only four suitable GCMs, HadGEM2-AO, HadGEM2-ES, CSIRO.Mk3.6, and MIROC5 for data projections studying scenarios involving future water resources in Iraq.

Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics

Abstract. The climate modelling community has trialled a large number of metrics for evaluating the temporal performance of general circulation models (GCMs), while very little attention has been given to the assessment of their spatial performance, which is equally important. This study evaluated the performance of 36 Coupled Model Intercomparison Project 5 (CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan using state-of-the-art spatial metrics, SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The multi-model ensemble (MME) precipitation and maximum and minimum temperature data were generated through the intelligent merging of simulated precipitation and maximum and minimum temperature of selected GCMs employing random forest (RF) regression and simple mean (SM) techniques. The results indicated some differences in the ranks of GCMs for different spatial metrics. The overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the best GCMs in simulating the spatial patterns of mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan. MME precipitation and maximum and minimum temperature generated based on the best-performing GCMs showed more similarities with observed precipitation and maximum and minimum temperature compared to precipitation and maximum and minimum temperature simulated by individual GCMs. The MMEs developed using RF displayed better performance than the MMEs based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation and maximum and minimum temperature. The approach proposed in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.

Selection of general circulation models for the projections of spatio-temporal changes in temperature of Borneo Island based on CMIP5

A study has been conducted to evaluate the changes in temperature of Borneo Island for different representative concentration pathway (RCP) scenarios through statistical downscaling of an ensemble of general circulation models (GCMs) selected using entropy-based methods. A combination of past-performance and envelope approaches was used for the selection of GCM ensemble from a pool of 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs. Ranking of the GCMs was done separately at each grid point by using three entropy-based feature selection methods. Climate Research Unit (CRU) gridded temperature data was used as dependent variable and the simulated temperature of different GCMs for the period 1961–2005 was used as independent variable for the selection of GCMs. The scores obtained by the GCMs at different CRU grids were aggregated using compromise programming method and then ranked for entire Borneo Island using group decision-making method. Linear scaling method was applied to remove the biases in GCMs, followed by random forest (RF)–based regression method to generate multi-model ensemble projections. The results revealed that FIO-ESM, MRI-CGCM3, GFDL-CM3, and IPSL-CM5A-MR are the most suitable GCMs for the projection of temperature in the study area. The projection of temperature using the selected GCMs indicated increase in temperature in the Borneo Island, particularly in the southwest regions. The minimum temperature was projected to increase more (3.3 to 4.7 °C) compared with maximum temperature (3.0 to 4.6 °C) and thus, the diurnal temperature range (DTR) was projected to decrease gradually until the end of the twenty-first century. Increase in average and maximum temperatures was projected more during the southwest (SW) monsoon compared with the northeast (NE) monsoon. The study indicated that large increase in temperature and decrease in DTR would have significant impact on ecology and bio-environment of the island which have one of the dense ecological diversities in the world.

Biased estimates of the isotope ratios of steady‐state evaporation from the assumption of equilibrium between vapour and precipitation

AbstractStable water isotope ratios are measured as a tracer of environmental processes in materials such as leaves, soils, and lakes. Water in these archives may experience evaporation, which increases the abundance of heavy isotopologues proportionally to the gradients in humidity and isotope ratio between the evaporating water and the surrounding atmosphere. The isotope ratio of the atmosphere has been difficult to measure until recently, and measurements remain scarce. As a result, several assumptions have been adopted to estimate isotope ratios of atmospheric water vapour. Perhaps the most commonly employed assumption in terrestrial environments is that water vapour is in isotopic equilibrium with precipitation. We evaluate this assumption using an eight‐member ensemble of general circulation model (GCM) simulations that include explicit calculation of isotope ratios in precipitation and vapour. We find that across the model ensemble, water vapour is typically less depleted in heavy isotopologues than expected if it were in equilibrium with annual precipitation. Atmospheric vapour likely possesses higher‐than‐expected isotope ratios because precipitation isotope ratios are determined by atmospheric conditions that favour condensation, which do not reflect atmospheric mixing and advection processes outside of precipitation events. The effect of this deviation on theoretical estimates of isotope ratios of evaporating waters scales with relative humidity. As a result, the equilibrium assumption gives relatively accurate estimates of the isotope ratios of evaporating waters in low latitudes but performs increasingly poorly at increasing latitudes. Future studies of evaporative water pools should include measurements of atmospheric isotope ratios or constrain potential bias with isotope‐enabled GCM simulations.

Intensification of hot Eurasian summers by climate change and land\u2013atmosphere interactions

Persistent abnormal hot weather can cause considerable damage to human society and natural environments. In northern Eurasia, the recent change in summer surface air temperature exhibits a heterogeneous pattern with accelerated warming around the Eastern European Plain and Central Siberia, forming a wave train-like structure. However, the key factors that determine the magnitude and spatial distribution of this summer temperature trend remain unclear. Here, a huge ensemble of general circulation model (GCM) simulations show that the recent summer temperature trend has been intensified by two factors: steady warming induced by external forcing and inhomogeneous warming induced by internal atmosphere–land interactions that amplify quasi-stationary waves. The latter is sensitive to both snow cover and soil moisture anomalies in the spring, suggesting the potential of land surface monitoring for better seasonal prediction of summer temperatures. Dramatic changes in the circumpolar environment, characterised by Eurasian snow variation and Arctic Ocean warming, collectively affect summertime climate via memory effects of the land surface.

Approaches to attribution of extreme temperature and precipitation events using multi-model and single-member ensembles of general circulation models

Abstract. Extreme temperature and precipitation events occurring in Australia in recent decades have caused significant socio-economic and environmental impacts, and thus determining the factors contributing to these extremes is an active area of research. Many recently occurring record-breaking temperature and rainfall events have now been examined from an extreme event attribution (EEA) perspective. This paper describes a set of studies that have examined the causes of extreme climate events using various general circulation models (GCMs), presenting a comprehensive methodology for GCM-based attribution of extremes of temperature and precipitation observed on large spatial and temporal scales in Australia. First, we review how Coupled Model Intercomparison Project Phase 5 (CMIP5) models have been used to examine the changing odds of observed extremes. Second, we review how a large perturbed initial condition ensemble of a single climate model (CESM) has been used to quantitatively examine the changing characteristics of Australian heat extremes. For each approach, methodological details and applications are provided and limitations highlighted. The conclusions of this methodological review discuss the limitations and uncertainties associated with this approach and identify key unexplored applications of GCM-based attribution of extremes. Ideally, this information will be useful for the application of the described extreme event attribution approaches elsewhere.

Projected Changes in United States Regional Extreme Heat Days Derived From Bivariate Quantile Mapping of CMIP5 Simulations

AbstractProjections of multivariate climate extremes require methodological approaches that can maintain relationships among variables. Here we apply a piecewise multivariate quantile mapping approach to temperature and humidity projections from a subset of models from the Fifth Coupled Model Intercomparison Project and analyze the resulting climatology of extreme heat days (EHDs) with explicit consideration of the prevailing humidity. The piecewise multivariate bias correction method shows good fidelity in reproducing the frequency of different types of extreme heat events in the historical period. Projections for U.S. regions and individual cities for both the middle and end of the century are characterized by increases in the frequency of EHDs, and especially those characterized by high humidity. For many regions and individual cities, there is no overlap between the frequency of high‐humidity EHDs in general circulation model ensembles from the historical and future periods, indicating that increases in extreme heat are robust. Analysis of 500‐mb height, sea‐level pressure, and low‐level circulation composites for historical and future periods indicates that the Fifth Coupled Model Intercomparison Project models reproduce basic large‐scale circulation features associated with EHDs in U.S. regions and that future changes in extreme heat are related primarily to large‐scale warming rather than enhancement of regional circulation anomalies.

Time of Steady Climate Change.

Under an emission scenario where atmospheric greenhouse gas concentrations are stabilized, previous work suggests that on centennial time scales the rate of global temperature increases would steady at significantly lower rates than those of the 21st century. As climate change is not globally uniform, regional differences in achieving this steady rate of warming can be expected. Here, we define a “Time of Steady Change” (TSC) as the time of reaching this steady rate of warming, and we present a method for estimating TSC with the use of General Circulation Model experiments run under greenhouse gas stabilization scenarios. We find that TSC occurs latest in low latitudes and in the Arctic, despite these areas steadying at very different absolute warming rates. These broad patterns are robust across multiple General Circulation Model ensembles and alternative definitions of TSC. These results indicate large regional differences in the trajectory of climate change in coming centuries.

Fidelity assessment of general circulation model simulated precipitation and temperature over Pakistan using a feature selection method

General Circulation Models (GCMs) provide vital information on the likely future climate, much needed for the effective planning and management of water resources. The performance assessment of GCMs has received significant attention in recent years for reliable estimation of future climate. Even though many approaches have been trialled in the ranking of GCMs for the selection of an appropriate ensemble, there is still a need to explore the potential of the state-of-the-art ranking approaches for a more dependable selection of GCMs suitable for a given task, over a region of interest. The present study assessed the potential of a state-of-the-art feature selection method known as Symmetrical Uncertainty (SU) in ranking 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs based on their ability to simulate monthly precipitation and the monthly average of daily maximum and minimum temperature for annual, monsoon and winter seasons. The performance of GCMs was assessed using gridded climate data obtained from Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU) and Princeton Global Meteorological Forcing dataset (Prin) over the period 1961–2005 considering Pakistan as the study area. The ranks obtained with SU were compared with those obtained using two well-established ranking approaches; (1) Compromise Programming (CP) and (2) Wavelet-based Skill Score (WSS). According to the results of this study, for the simulation of all the three climate variables in all seasons; CESM1-CAM5, HadGEM2-AO, NorESM1-M and HadGEM2-ES were identified as the best GCMs by SU, whereas CESM1-CAM5, HadGEM2-AO, NorESM1-M and GFDL-CM3 were identified as the best GCMs by CP, and CCSM4, CESM1-CAM5, GFDL-ESM2G and HadGEM2-ES by WSS. The comparison of ranks of GCMs obtained using the same ranking approach but based on different gridded data products showed more or less similar ranks for a given GCM. However, the differences were noticeable when the ranking was conducted with the same gridded data but employing different ranking approaches. The approach presented in this study can be extended to any number of GCMs and can be applied over any region, for the identification of the best performing ensemble of GCMs for a set of climate variables.

Climate change uncertainties in seasonal drought severity-area-frequency curves: Case of arid region of Pakistan

The uncertainty assessment of the changes in drought characteristics due to climate change has caught the attention of the scientific community. This study used gauge-based gridded precipitation data obtained from Global Precipitation Climatology Centre (GPCC) to reconstruct historical droughts and downscale future precipitation projected by seven general circulation models (GCMs) of Coupled Model Inter-comparison Project phase 5 (CMIP5) under four Representative Concentration Pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Support vector machine (SVM) and quantile mapping were used for downscaling and GCM bias correction, respectively. The model performances were assessed based on statistical measures. The historical and future projected precipitation data were finally used to characterize the seasonal droughts using Standardized Precipitation Index (SPI) for different crop growing periods. The drought severity-area-frequency (SAF) curves for the historical (1961–2010) and three future periods (2010–2039, 2040–2069, and 2070–2099) were developed. The uncertainty band of future drought SAF curves was estimated using Bayesian bootstrap (BB) at a 95% confidence level. As a result, SVM was successful in downscaling the precipitation of all selected CMIP5 GCMs. The seasonal ensemble of GCMs projected an increase in precipitation ranging from 8% to 41% under all scenarios. The historical SAF curves revealed that for equal drought severity, larger areas are affected by droughts having higher return periods. Future projections of droughts revealed the increase in affected area for lower severity and return period droughts and the decrease for higher severity and return period droughts. The uncertainty bands of drought SAF curves with higher return periods were found much wider compared to those with lower return periods which indicates more uncertainty in the projection of higher severity and return period droughts.

Identifying the Relationship between Assignments of Scenario Weights and their Positions in the Derivation of Reservoir Operating Rules under Climate Change

In order to mitigate the adverse impacts of climate change, adaptive operating rules (AOR) are generally derived using an ensemble of General Circulation Models (GCMs). Up to date, most of related literatures only focus on one fold of the following issues concerning the derivation of AOR using the GCMs ensemble, including: (1) consideration of different scenario weighing methods, or (2) analysis of different positions to locate scenario weights. And less concern is given to the latter compared with the former. However, few studies identify the relationship between (1) and (2) in the derivation of AOR based on the GCMs ensemble. In this study, we attempt to investigate where to use Equal and REA scenario weights in the derivation of reservoir operating rules under climate change. Equal weights (EW) and unequal weights based on the reliability ensemble average (REA) method are used in two positions: (I) the optimization objective of the reservoir operation model, which is to maximize the weighted average hydropower generation for all future scenarios; and (II) the incorporation of GCMs ensemble climate projections into the weighted climate conditions, and then it is input into the reservoir operation model with the objective of maximizing annual hydropower generation. Four AORs, including EW-AOR(I), REA-AOR(I), EW-AOR(II) and REA-AOR(II), are derived, and their optimized parameters are obtained by the simulation-based optimization (SBO) method with the Complex algorithm. The case study in the Jinxi Reservoir in China indicates that REA-AOR(I) outperforms the other three operation schemes, and EW-AOR(II) performs better than REA-AOR(II). Therefore, equal weights are preferably used to incorporate climate conditions, while unequal weights based on REA method can improve the performance of the reservoir operation model. Generally, REA-AOR(I) and EW-AOR(II) are suggested for adaptive reservoir management under climate change.

South Asian river basins in a 1.5\xa0\xb0C warmer world

In 2015, with the signing of the “Paris Agreement”, 195 countries committed to limiting the increase in global temperature to less than 2 °C with respect to pre-industrial levels and to aim at limiting the increase to 1.5 °C by 2100. The regional ramifications of those thresholds remain however largely unknown and variability in the magnitude of change and the associated impacts are yet to be quantified. We provide a regional quantitative assessment of the impacts of a 1.5 versus a 2 °C global warming for a major global climate change hotspot: the Indus, Ganges, and Brahmaputra river basins (IGB) in South Asia, by analyzing changes in climate change indicators based on 1.5 and 2 °C global warming scenarios. In the analyzed ensemble of general circulation models, a global temperature increase of 1.5 °C implies a temperature increase of 1.4–2.6 (μ = 2.1) °C for the IGB. For the 2.0 °C scenario, the increase would be 2.0–3.4 (μ = 2.7) °C. We show that climate change impacts are more adverse under 2 °C versus 1.5 °C warming and that changes in the indicators’ values are in general linearly correlated to average temperature increase. We also show that for climate projections following Representative Concentration Pathways 4.5 and 8.5, which may be more realistic, the regional temperature increases and changes in climate change indicators are much stronger than for the 1.5 and 2 °C scenarios.

Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.

Evaluating the reliability of stormwater treatment systems under various future climate conditions

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.

Drivers, timing and some impacts of global aridity change

The modeled robustness of the aridity response to climate change has already been well established: global drylands are expanding as increases in potential evapotranspiration outpace precipitation increases. This work considers three questions not previously addressed: (1) in which aridity zones are aridity trends expected to first emerge? (2) To what extent are future populations expected to experience changes in aridity? (3) For which locations and populations is aridity change determined by changes in precipitation versus temperature? Using a multi-model ensemble of general circulation models (GCMs) from the CMIP5 archives, together with population statistics from United Nations databases we find the following: (1) drier regions are projected to dry earlier, more severely and to a greater extent than humid regions. (2) By the end of the century, up to 700 million more people are projected to live in arid regions, with half of humankind living in regions experiencing drying of at least 5% and 3 billion experiencing 25% drying under a high emissions scenario. The largest populations to experience extreme drying are in Africa and Asia. According to GCMs, aridity increases have already begun to emerge in Northern Africa. Populations in sub-Saharan Africa appear to be the most vulnerable considering current water scarcity levels there. And (3) precipitation change is projected to drive the most severe drying in arid regions, contributing to the earliest emerging signals of aridity change and driving the differential drying across aridity zones. A comparison of aridity data derived from observations with model ensemble data suggests that increased aridity is already emerging in the driest regions. In sum, widespread aridity trends across a warming state imply that most of humanity will live in a more arid world.

Performance evaluation of hydrological models using ensemble of General Circulation Models in the northeastern China

The Songhua River Basin (SRB) plays a vital role in supplying water resources to northeast China. This river has a substantial effect on agricultural grain producing region. Therefore, the current study was carried out to evaluate the performances of multiple hydrological models driven by bias corrected precipitation estimates from a group of General Circulation Models (GCMs). The variations in discharge obtained from multiple hydrological models were also analyzed. For this purpose, bias correction methods for GCMs and five hydrological models, lumped rainfall runoff (Nedbor Afstromnings Model (NAM), GRJ4), semi-distributed (the Water Evaluation and Planning tool (WEAP), HBV), and the distributed (Soil Water Assessment Tool (SWAT)) were used in the SRB. Before applying the bias correction method, raw GCMs were also plotted against observational data. The results revealed that none of GCM performed well against observational data. The results also showed that quantile mapping method corrects the biases of GCMs better than daily and monthly scale factors. Distributed and semi-distributed hydrological models performed better than lumped rainfall runoff models in the selected study area of SRB. By forcing hydrological models with bias corrected precipitation data, the differences in the simulated discharge by SWAT, HBV, NAM, GR4J and WEAP were −3%, −2%, −4%, −7 and +3, respectively, when compared with observed discharge. The results also show that the overall performance of SWAT, HBV, NAM, and WEAP was better than GRJ4. The results also suggest that the application of multiple hydrological models is necessary to make improvements in the planning and management of agricultural and water resources in the SRB region.