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This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. The uncertainty in maize‐yield simulations might arise mostly from global climate models (GCMs), followed by crop models and representative concentration pathways (RCPs), the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. [ABSTRACT FROM AUTHOR]

We assess the impact of three bias correction approaches on present day means and extremes, and climate change signal, for six climate variables (precipitation, minimum and maximum temperature, radiation, vapour pressure and mean sea level pressure) from dynamically downscaled climate simulations over Queensland, Australia. Results show that all bias‐correction methods are effective at removing systematic model biases, however the results are variable and season‐dependent. Importantly, our results are based on fully independent cross‐validation, an advantage over similar studies. Linear scaling preserves the climate change signals for temperature, while quantile mapping and the distribution‐based transfer function modify the climate change signal and patterns of change. The Perkins score for all the values above the 95th percentile and below the 5th percentile was used to evaluate how well the climate model matches the observational data. Bias correction improved Perkins score for extremes for some variables and seasons. We rank the bias‐correction methods based on the Kling–Gupta efficiency (KGE) score calculated during the validation period. We find that linear scaling and empirical quantile mapping are the best approaches for Queensland for mean climatology. On average, bias correction led to an improvement in the KGE score of 23% annually. However, in terms of extreme values, quantile mapping and statistical distribution‐based transfer function approaches perform best, and linear scaling tends to perform worst. Our results show that, except linear scaling, all approaches impact the climate change signal. [ABSTRACT FROM AUTHOR]

An emergent consequence of climate change is its potential impacts on transatlantic flight routes and durations, with studies suggesting that future optimal paths will likely lead to decreased (increased) eastbound (westbound) journey times. However, these earlier works all rely on a class of flight planning algorithms (based on the so‐called 'shooting method') that are fundamentally different to those typically used by the aviation industry (based on the so‐called 'network method'). To help resolve any ambiguities associated with these differences and better align both the academic and operational communities, we have therefore conducted an evaluation of methods of estimating time‐optimal flight routes to relate the result obtained from the shooting method with the result obtained from the network method, using identical data from an ensemble of eight different climate models included within the CMIP5 project. Our findings suggest that, contrary to the existing literature, journey times will likely be shorter, irrespective of direction of travel and season, although the magnitude of this difference is negligible given the typical duration of transatlantic flights. Trajectory prediction (TP) results are largely dependent on the characteristics of the ensemble, their method of generation, the projected climate scenario and the temporal periods considered. Importantly, we conclude the choice of TP models is not a crucial factor in assessing the impact of climate change on flight durations—as long as this sample size (i.e., number of trajectories used in deriving statistics) is large. [ABSTRACT FROM AUTHOR]

The active phase of the East Asian summer monsoon (EASM) in Taiwan during May and June, known as Meiyu, produces substantial precipitation for water uses in all sectors of society. Following a companion study that analysed the historical increase in the Meiyu precipitation, the present study conducted model evaluation and diagnosis based on the EASM lifecycle over Taiwan. Higher and lower skill groups were identified from 17 Couple Model Intercomparison Project Phase 5 (CMIP5) models, with five models in each group. Despite the difference in model performance, both groups projected a substantial increase in the Meiyu precipitation over Taiwan. In the higher skill group, weak circulation changes and reduced low‐level convergence point to a synoptically unfavourable condition for precipitation. In the lower skill group, intensified low‐level southwesterly winds associated with a deepened upper level trough enhance moisture pooling. Thus, the projected increase in Meiyu precipitation will likely occur through the combined effects of (1) the extension of a strengthened North Pacific anticyclone enhancing southwesterlies; and (2) more systematically, the Clausius–Clapeyron relationship that increases precipitation intensity in a warmer climate. The overall increase in the Meiyu precipitation projected by climate models of variable performance supports the observed tendency toward more intense rainfall in Taiwan and puts its early June 2017 extreme precipitation events into perspective. [ABSTRACT FROM AUTHOR]

Groundwater recharge depends generally on precipitation. In this study, a geographical information system (GIS) procedure was applied to assess the climate change effect on groundwater recharge in the Grand Est region of France. The analysis comprises high‐resolution climate models that reflect the long‐term climatological regime. The hydrological properties include aquifers, land‐cover and terrain morphology data, which were used to develop the potential infiltration map of the study area. Two parameters, the De Martonne aridity index and effective precipitation, were combined into a 5 × 5 matrix to assess the climate change effect on groundwater recharge during the past (1990s), present (2020s) and future (2050s) periods. The present and future intense aridization and the depletion of the effective precipitation (< 650 mm) reveal the negative effects of climate change on aquifers' recharge in the Grand Est region. The areas with high and very high climate effects will increase in the 2020s and 2050s. These areas extend mainly in the western, north‐central and northeastern parts of the region occupying the Rhine, Aube and Marne valleys. The medium effect could be found in the central, southern and northwestern parts, while the low impact on groundwater recharge was verified in the northwestern and southeastern parts of the region. The area with low effects of climate change extends mostly in the Vosges and Ardennes mountains. These findings contribute to the long‐term hydrogeological studies in the Grand Est region. [ABSTRACT FROM AUTHOR]

The past few decades have witnessed a change in precipitation patterns around the world. This change includes an increase in heavy precipitation and a decrease in light and moderate precipitation. Global/regional warming has been attributed as one of the major factors causing the observed change in precipitation patterns. Past studies over the Indian region have not focused much on quantification of the impact of warming on changes in precipitation patterns. The present research quantifies the impact of warming on precipitation patterns over the Indian region. For this purpose, rain gauge‐based high‐resolution gridded precipitation data from the India Meteorological Department (IMD) for 113 years between 1901 and 2013 are used to link the changes in precipitation patterns over India during the South West (SW) monsoon season with warming using a new method that focuses on inter‐annual differences rather than time series. It has been reported that during the SW monsoon period, there is an increase of about 51.85% ± 19% in the top 10% precipitation for each degree (K) increase in temperature. Decreases of about 15–30%/K in low and moderate precipitation have also been reported per 1 K increase in temperature. The impact of warming is more severe over Western and North Eastern India and during July and August. An increase in heavy rainy days of about 46.53% ± 14% per 1 K increase in temperature has been reported over the Indian region. Low and moderate rainy days show a decrease of about 24.67% ± 9.53% for a unit degree increase in temperature. Results reported in this research point out increasing trends in droughts and floods due to increased heavy precipitation and decreased low and moderate precipitation in a warming environment. Study focuses on examination of precipitation patterns in changing climate over India. Results report an increase in heavy precipitation and decrease in low and moderate precipitation. Impacts of ENSO and IOD have been examined separately. Seasonal variation of heavy precipitation anomaly over Indian region (I) along with global temperature anomaly. Smoothing has been done to eliminate the fluctuation. [ABSTRACT FROM AUTHOR]

ABSTRACT The spatial climatic characteristics of the Himalayas are complex and a challenge for regional climate models ( RCMs). There is no doubt that some form of correction before any application of RCM simulations is a must. In recent years, simple bias correction techniques have been overshadowed by more popular and complex bias correction techniques. In this study an attempt is made to compare the performance of a simple and of a comparatively complex correction technique for hydrological analysis at a monthly resolution in the Kaligandaki River Basin of Nepal. The research workflow consists of bias correction of temperature and precipitation using a simple technique (linear scaling) and a comparatively complex one (quantile mapping). The performance at monthly resolution is evaluated against observed meteorological data while a combined evaluation is made via hydrological model response analysis. The wetter and colder RCM estimates were significantly improved after bias correction. The hydrological modelling response also shows the importance of the bias correction of the RCMs. However, no significant difference was observed between the outputs of linear scaling and quantile mapping which exhibited almost identical performances. Hence, this study has a novel conclusion that a simple method, such as linear scaling, is sufficient for hydrological analysis at monthly resolution. [ABSTRACT FROM AUTHOR]

ABSTRACT In recent years (2013-2016), extreme weather events have caused substantial disruption to Great Britain's ( GB's) railway infrastructure. In the coming decades this vulnerability is unlikely to subside as the effects of climate change become more intense. Railway stakeholders in GB are strongly engaged with understanding climate change impacts on the railway system and how the industry could adapt to these impacts. Since 2010, Network Rail and RSSB have supported research into these topics under the Tomorrow's Railway and Climate Change Adaptation programme. Under this programme, an analogue study was performed to determine whether lessons could be learned from other countries' weather management. Two types of analogue were used to identify suitable locations. First, climate data from 20 models of the Coupled Model Intercomparison Project phase 5 ( CMIP5) were used to identify regions with similar present-day climate to that projected for GB in the future. The analogue locations were found to be largely insensitive to the climate indicators and the methods used to compare climate at different locations. Next, railway networks in many countries were studied to find those with similar physical and operational characteristics to the GB network. The regions with both climate and railway analogues are France, the Netherlands, Belgium, Germany and Denmark. As part of a wider aim to support the GB railway network's weather resilience and climate change adaptation ( WR/ CCA) activities, focused stakeholder engagement has been undertaken with representatives of most of these countries' railways. This targeted approach is complementary to a broader collation of existing WR/ CCA measures used globally. [ABSTRACT FROM AUTHOR]