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Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many statistical downscaling models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD model. Thus, it is desirable to compare the performance of SD models to ensure their adaptability in future climate studies. In this study, a statistical downscaling model (SDSM) or multi-linear regression and the Least Square Support Vector Machine (LS-SVM) were used to do downscaling and compare the results with those obtained from general circulation model (GCM) for identifying the best SD model for the Indira Sagar Canal Command area located in Madhya Pradesh, India. The GCM, Hadley Centre Coupled Model version 3 (HadCM3), was utilized to extract and downscale precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) for 1961–2001 and then for 2001–2099. Before future projections, both SD models were initially calibrated (1961–1990) and validated (1991–2001) to evaluate their performance for precipitation and temperature variables at all gauge stations, namely Barwani, East Nimar, and West Nimar. Results showed that the precipitation trend was under-predicted owing to large errors in downscaling, while temperature was over-predicted by SD models.

We describe the method of history matching, a method currently used to help quantify parametric uncertainty in climate models, and argue for its use in identifying and removing structural biases in climate models at the model development stage. We illustrate the method using an investigation of the potential to improve upon known ocean circulation biases in a coupled non-flux-adjusted climate model (the third Hadley Centre Climate Model; HadCM3). In particular, we use history matching to investigate whether or not the behaviour of the Antarctic Circumpolar Current (ACC), which is known to be too strong in HadCM3, represents a structural bias that could be corrected using the model parameters. We find that it is possible to improve the ACC strength using the parameters and observe that doing this leads to more realistic representations of the sub-polar and sub-tropical gyres, sea surface salinities (both globally and in the North Atlantic), sea surface temperatures in the sinking regions in the North Atlantic and in the Southern Ocean, North Atlantic Deep Water flows, global precipitation, wind fields and sea level pressure. We then use history matching to locate a region of parameter space predicted not to contain structural biases for ACC and SSTs that is around 1 % of the original parameter space. We explore qualitative features of this space and show that certain key ocean and atmosphere parameters must be tuned carefully together in order to locate climates that satisfy our chosen metrics. Our study shows that attempts to tune climate model parameters that vary only a handful of parameters relevant to a given process at a time will not be as successful or as efficient as history matching.

 The alteration of global climate regimes due to anthropic action and excessive emission of greenhouse gases has been widely researched because it alters the patterns of climatological normals, generating changes in temperatures and precipitation worldwide. This study aimed to analyze the spatial and temporal variability of precipitation in the Paraitinga and Paraibuna watersheds that together form the Paraibuna Dam, the main one of the Paraiba do Sul river watershed. This dam supplies the São Paulo Metropolitan Region by transporting water to the Cantareira System, the Rio de Janeiro Metropolitan Region by transporting water to the Guandu watershed, and the Paraiba Valley Metropolitan Region, one of the most industrialized in Brazil. To investigate future precipitation trends, past and future climate simulations were used from the HadCM3/Eta model using the SRES (Special Report Emission Scenarios) A1B, and precipitation analysis using Quantis techniques to determine extreme rainfall and drought periods. The results point to an increase in precipitation averages in the region, followed by a greater intensity of extreme rainfall, which may lead to a higher occurrence of natural disasters such as landslides.

This study focused on the Upper Manyame sub-catchment which covers an area of approximately 3 786 km2 and forms part of the Manyame catchment, one of the seven catchments of Zimbabwe. Manyame catchment has its source in Marondera town and drains into the Zambezi River downstream of the Kariba Dam and upstream of the Cahora Bassa Dam, in the northern part of the country. This study assessed potential climate change impacts on the streamflow and reservoir inflows in the Upper Manyame sub-catchment. Hydrologic simulations for future climate (2030s and 2060s) were carried out using statistically downscaled bias-corrected variables from the HadCM3 (HadCM3A2a and HadCM3B2a scenarios) and CanESM2 (RCP2.6 and RCP8.5) global circulation models. The HEC-HMS hydrological model was set up for two gauged micro-catchments and eight ungauged tributary micro-catchments. Model calibration for gauged micro-catchments of Upper Manyame over the period from 2000-2010 revealed satisfactory model performance of 4.3% (RVE) and 0.1 (bias) for Mukuvisi micro-catchment and 9.5% (RVE) and 0.15 (bias) for Marimba micro-catchment. Model simulations resulted in a projected decrease in streamflow by 7.4-26.4% for HadCM3. For CanESM2, simulations resulted in a projected decrease in streamflow by 2.5-34.7%. Reservoir inflows into Lake Chivero and Lake Manyame, the main water supply sources for Harare, will decrease by 10.5-18% for HadCM3 and by 8-33.6% for CanESM2.

A methodology is described for probabilistic predictions of future climate. This is based on a set of ensemble simulations of equilibrium and time-dependent changes, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean-atmosphere global climate model. These (ongoing) experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century climate at large regional scales. A further ensemble of regional climate simulations at 25 km resolution is being produced for Europe, allowing the specification of probabilistic predictions at spatial scales required for studies of climate impacts. The ensemble simulations are processed using a set of statistical procedures, the centrepiece of which is a Bayesian statistical framework designed for use with complex but imperfect models. This supports the generation of probabilities constrained by a wide range of observational metrics, and also by expert-specified prior distributions defining the model parameter space. The Bayesian framework also accounts for additional uncertainty introduced by structural modelling errors, which are estimated using our ensembles to predict the results of alternative climate models containing different structural assumptions. This facilitates the generation of probabilistic predictions combining information from perturbed physics and multi-model ensemble simulations. The methodology makes extensive use of emulation and scaling techniques trained on climate model results. These are used to sample the equilibrium response to doubled carbon dioxide at any required point in the parameter space of surface and atmospheric processes, to sample time-dependent changes by combining this information with ensembles sampling uncertainties in the transient response of a wider set of earth system processes, and to sample changes at local scales. The methodology is necessarily dependent on a number of expert choices, which are highlighted throughout the paper.

The climate changes affect the hydrological cycle and the flow systems of waterways, which in turn alter the volume of water available resources locally, regionally, and globally at various levels. This study aims to evaluate the impact of the climatic changes on the availability of surface water resources in the basins of the Tigris, Great Zab, and Khazir Rivers in Iraq by applying the general circulation model HadCM3 under two climate change scenarios (SRES-A2 and SRES-B2). According to the analysis findings, the annual rainfall will be reduced in the future under scenario (A2, B2-2030) by 2.67% and continue reducing under scenario (A2, B2-2060) by 5.35%. The decrease in rainfall will reduce surface water runoff within the drainage basin by 2.64% under scenario (A2, B2-2030) and 5.45% under scenario (A2, B2-2060). The obtained results reveal that the decrease in rainfall was countered by an increase in temperature, as well as by an increase in evapotranspiration. The average correlation coefficient of rainfall with temperature and evapotranspiration is − 0.93, whereas the rate of correlation coefficient with relative humidity and surface water runoff is 0.96. All scenarios and predictions indicate that the study area will be on the verge of a drought period in the not-too-distant future, so the study recommends urgently the ministry of water resources in Iraq to manage properly the waters of the three rivers by building dams on their channels and storing water in the wet seasons for the benefit of the dry seasons.

Ethiopia economy depends strongly on Coffea arabica production. Coffee, like many other crops, is sensitive to climate change and recent studies have suggested that future changes in climate will have a negative impact on its yield and quality. An urgent development and application of strategies against negative impacts of climate change on coffee production is important. Agroforestry-based system is one of the strategies that may ensure sustainable coffee production amidst likelihood future impacts of climate change. This system involves the combination of trees in buffer extremes thereby modifying microclimate conditions. This paper assessed coffee production under: (1) coffee monoculture and (2) coffee grown using agroforestry system, under: (a) current climate and (b) two different future climate change scenarios. The study focused on two representative coffee growing regions of Ethiopia under different soil, climate and elevation conditions. A process-based growth model (yield-SAFE) was used to simulate coffee production for a time horizon of 40 years. Climate change scenarios considered were: representative concentration pathways (RCP) 4.5 and 8.5. The results revealed that in monoculture systems, the current coffee yields are between 1200 and 1250 kg ha−1 year−1, with expected decrease between 4–38 and 20–60% in scenarios RCP 4.5 and 8.5, respectively. However, in agroforestry systems, the current yields are between 1600 and 2200 kg ha−1 year−1, the decrease was lower, ranging between 4–13 and 16–25% in RCP 4.5 and 8.5 scenarios, respectively. From the results, it can be concluded that coffee production under agroforestry systems has a higher level of resilience when facing future climate change and reinforce the idea of using this type of management in the near future for adapting climate change negative impacts on coffee production.

Future predictions of rainfall patterns in water-scarce regions are highly important for effective water resource management. Global circulation models (GCMs) are commonly used to make such predictions, but these models are highly complex and expensive. Furthermore, their results are associated with uncertainties and variations for different GCMs for various greenhouse gas emission scenarios. Data-driven models including artificial neural networks (ANNs) and adaptive neuro fuzzy inference systems (ANFISs) can be used to predict long-term future changes in rainfall and temperature, which is a challenging task and has limitations including the impact of greenhouse gas emission scenarios. Therefore, in this research, results from various GCMs and data-driven models were investigated to study the changes in temperature and rainfall of the Qassim region in Saudi Arabia. Thirty years of monthly climatic data were used for trend analysis using Mann–Kendall test and simulating the changes in temperature and rainfall using three GCMs (namely, HADCM3, INCM3, and MPEH5) for the A1B, A2, and B1 emissions scenarios as well as two data-driven models (ANN: feed-forward-multilayer, perceptron and ANFIS) without the impact of any emissions scenario. The results of the GCM were downscaled for the Qassim region using the Long Ashton Research Station’s Weather Generator 5.5. The coefficient of determination (R2) and Akaike’s information criterion (AIC) were used to compare the performance of the models. Results showed that the ANNs could outperform the ANFIS for predicting long-term future temperature and rainfall with acceptable accuracy. All nine GCM predictions (three models with three emissions scenarios) differed significantly from one another. Overall, the future predictions showed that the temperatures of the Qassim region will increase with a specified pattern from 2011 to 2099, whereas the changes in rainfall will differ over various spans of the future.

Statistical and dynamic downscaling approaches are commonly used to downscale large-scale climatic variables from global circulation (GCM) and regional circulation (RCM) model outputs to local precipitation. The performance of these two approaches may differ from each other for daily precipitation projections when applied in the same region. This is examined in this study based on the estimation of extreme precipitation. Daily precipitation series are generated from GCM HadCM3, CGCM3/T47 and RCM HadCM3 models for both historical hindcasts and future projections in accordance with the period from 1971 to 2070. The Waikato catchment of New Zealand is selected as a case study. Deterministic and probabilistic performances of the GCM and RCM simulations are evaluated using root-mean-square-error (RMSE) coefficient, percent bias (PBIAS) coefficient and equitable threat score (ETS). The value of RMSE, PBIAS and ETS is 2.89, − 2.16, 0.171 and 8.72, − 4.01, 0.442 for mean areal and at-site daily precipitation estimations, respectively. The study results reveal that the use of frequency analysis of partial duration series (FA/PDS) is very effective in evaluating the accuracy of downscaled daily precipitation series. Both the statistical and the dynamic downscaling perform well for simulating daily precipitation at station level for a return period equal to or less than 100 years. However, the latter outperforms the former for daily precipitation simulation at catchment level.

Climate change and human activities have massively impacted the hydrological cycle. Thus, it is of the greatest concern to examine the effect of climate change on water management, especially at the regional level, to understand the possible future shifts in water supply and water-related crises and support regional water management. Fortunately, there is a high degree of ambiguity in determining the effect of climate change on water requirements. In this paper, the statistical downscaling (SDSM) model is applied to simulate the potential impact of climate on crop water requirements (CWR) by downscaling ET 0 in the region of Western Maharashtra, India, for the future periods, viz., the 2030s, 2050s, and 2080s, across three meteorological stations (Pune, Rahuri, and Solapur). Four crops, i.e., cotton, soybean, onion, and sugarcane, were selected during the analysis. The Penman-Monteith equation calculates reference crop evapotranspiration (ET 0 ). Furthermore, in conjunction with the crop coefficient ( K c ) equation, it calculates crop evapotranspiration (ET c )/CWR. The predictor variables were extracted from the National Centre for Environmental Prediction (NCEP) reanalysis dataset for 1961–2000 and the HadCM3 for 1961–2099 under the H3A2 and H3B2 scenarios. The results indicated by SDSM profound good applicability in downscaling due to satisfactory performance during calibration and validation for all three stations. The projected ET 0 indicated an increase in mean annual ET 0 compared to the present condition during the 2030s, 2050s, and 2080s. The ET 0 would increase for all months (in summer, winter, and pre-monsoon seasons) and decrease from June to September (monsoon season). The estimated future CWR shows variation in the range for cotton (− 0.97 to 2.48%), soybean (− 2.09 to 1.63%), onion (0.49 to 4.62%), and sugarcane (0.05 to 2.86%). The significance of this research lies in its contribution to understanding the potential impacts of climate change at a regional level. This study provides valuable insights into the expected changes in water demand for key crops. The research also manifests implementing an identical methodology for downscaling other environmental parameters using a similar approach.