General Circulation Model Data Research Articles

Optimizing urban water sustainability: Integrating deep learning, genetic algorithm, and CMIP6 GCM for groundwater potential zone prediction within a social-ecological-technological framework

This study presents a novel approach to groundwater resource assessment and drought vulnerability, achieved through integrating cutting-edge deep-learning algorithms into a comprehensive framework. In regions susceptible to drought, ensuring groundwater availability is of paramount importance. Addressing this critical need, our research employs an ensemble of advanced algorithms, including long short-term memory, convolutional neural network, deep neural network, and recurrent neural network. These algorithms are further enhanced through optimization using a genetic algorithm to map groundwater potential zones (GWPZ). Leveraging model validation based on the area under the receiver operating characteristic curve (AUCROC), the long short-term memory-genetic algorithm model emerges as the superior algorithm, boasting the highest values of AUCROC: 0.995 for training and 0.996 for testing). Utilizing this optimized model, this study developed the GWPZ map, subsequently overlaying it with established drought maps developed by the Bangladesh Agricultural Research Council across distinct periods—Pre-Kharif, Kharif, and Rabi. The derived baseline GWPZ spatial distribution revealed five categories—very low (34.99%), low (27.67%), moderate (13.26%), high (11.71%), and very high (12.37%)—and their intersection with drought-prone regions, indicative of probabilities of drought occurrence ranging from very severe to low (15.14% to 24.69%). Moreover, employing the best-predicted model, this study projected future GWPZ for 2050 and 2100 using the coupled model intercomparison project 6 general circulation model data under the ambit of three distinct shared socioeconomic pathways (SSPs): SSP1-2.6, SSP2-4.5, and SSP5-8.5. Our findings suggested a contraction in the groundwater potential area by the end of the twenty-first century (2100). This pioneering integration sheds light on the intricate relationship between groundwater availability and drought susceptibility, furnishing invaluable insights for formulating targeted water resource management strategies. By amalgamating advanced computational techniques with geospatial analyses, this research contributes to a more comprehensive grasp of water resource dynamics within the context of escalating climate challenges. Consequently, it offers a foundation for informed decision-making and implementing sustainable water management practices in regions grappling with the dual challenges of groundwater scarcity and recurrent droughts.

Tropical Cyclone Changes in Convection‐Permitting Regional Climate Projections: A Study Over the Shanghai Region

AbstractChanges in tropical cyclones due to greenhouse‐gas forcing in the Shanghai area have been studied in a double‐nesting regional model experiment using the Met Office convection‐permitting model HadREM3‐RA1T at 4 km resolution and the regional model HadREM3‐GA7.05 at 12 km for the intermediate nest. Boundary conditions for the experiment have been constructed from HadGEM2‐ES, a General Circulation Model (GCM) from the 5th Coupled Model Intercomparison Project (CMIP5), directly using high‐frequency data for the atmosphere (6‐hourly) and the ocean (daily), for the historical period (1981–2000) and under the Representative Concentration Pathway 8.5 (2080–2099). These choices identify one of the warmest climate scenarios available from CMIP5. Given the direct use of GCM data for the baseline, large scale conditions relevant for tropical cyclones have been analyzed, demonstrating a realistic representation of environmental conditions off the coast of eastern China. GCM large scale changes show a reduction in wind shear in addition to the expected strong increase in sea‐surface temperature. Tropical cyclones from the 4 km historical simulation have a negative bias in intensity, not exceeding Category 4, and a wet bias in the rainfall associated with these cyclones. However, there is a clear improvement in cyclone intensity and rainfall at 4 km in comparison with the 12 km simulation. Climate change responses in the 4 km simulation include an extension of the tropical cyclone season, and strong increases in frequency of the most intense cyclones (approximately by a factor of 10) and associated rainfall. These are consistent with the results from the 12 km simulation.

Effects of Climate Change on Rice Yield in Northern Areas of Iran: Humidity as a Large Variability of Climate

Climate has a significant effect on social and economic activities, and currently is a major problem, especially in agricultural yields. This study used two types of climatic and agricultural data. To simulate the climate for the next 30 years (2021-2050) from daily temperature and precipitation data for the base period 1986-2015, Reanalysis Atmospheric Data (NCEP) as observational predictors data and CanESM2 Atmospheric General Circulation Model data with two scenarios RCP 2.6 and RCP 8.5 were used as large-scale predictors. The data is related to the Rasht Rice Research Center field experiments. The results abstained from simulations showed that in future climate conditions, the average temperature would be 0.7 to 0.9°C, and precipitation would be 20 to 70 mm in the study area based on both emission scenarios compared to the base period (1986-2015) increases. The effect of climate change on the rice yield on the planting date of June 5, especially in the eastern parts of the region, is unfavourable in the future. At the regional level, in all planting dates, the length of the rice growth period in the future period (2021-2050) will decrease by 2 to 4 days compared to the base period. The planting date treatment of 5 May with a density level of 50 plants per square meter, a nitrogen fertiliser level of 195 kg per hectare with an intermittent irrigation regime (8-day cycle) is the most suitable adaptation strategy to reduce the negative effects of climate change and increase rice yield in the entire surface of the coastal area in the Caspian Sea.

Uncertainty Quantification of Deep Learning–Based Statistical Downscaling of Climatic Parameters

Abstract This study investigated the uncertainty involved in statistical downscaling of hydroclimatic time series obtained by artificial neural networks (ANNs). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) general circulation model (GCM) Canadian Earth System Model, version 5 (CanESM5), was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANNs, feed forward and long short-term memory (LSTM), were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from the Tabriz and Rasht stations in Iran were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel–driven development scenario shared socioeconomic pathway (SSP) 5-8.5. Projections were compared with the Canadian Regional Climate Model 4 (Can-RCM4) projections via the same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the feedforward neural network (FFNN)-based predictions, with an average of 55% higher Nash–Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience a warmer climate with an increase in average temperature of 2° and 5°C for near and far futures, respectively, and a drier climate with a 20% decrease in precipitation until 2100. Future projections for the Rasht station, however, suggested a more uniform climate with less seasonal variability. Average precipitation will increase by up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1°C until the near future and then be a constant average temperature until the far future. Significance Statement The downscaling of hydroclimatic parameters is subject to uncertainty. The best way is to provide an area with the highest contingency of them as a prediction interval. The reduction width of such an interval leads to increased confidence in explaining and predicting these processes. We proposed and applied a deep learning–based machine learning method for both point prediction and prediction interval estimation of temperature and precipitation parameters for the future over two different climatic regions. The results show the superiority of such a machine learning–based prediction interval estimation for quantification of the downscaling uncertainty.

Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data

Abstract. The alteration in river flow patterns, particularly those that originate in the Himalaya, has been caused by the increased temperature and rainfall variability brought on by climate change. Due to the impending intensification of extreme climate events, as predicted by the Intergovernmental Panel on Climate Change (IPCC) in its Sixth Assessment Report, it is more essential than ever to predict changes in streamflow for future periods. Despite the fact that some research has utilised machine-learning- and deep-learning-based models to predict streamflow patterns in response to climate change, very few studies have been undertaken for a mountainous catchment, with the number of studies for the western Himalaya being minimal. This study investigates the capability of five different machine learning (ML) models and one deep learning (DL) model, namely the Gaussian linear regression model (GLM), Gaussian generalised additive model (GAM), multivariate adaptive regression splines (MARSs), artificial neural network (ANN), random forest (RF), and 1D convolutional neural network (1D-CNN), in streamflow prediction over the Sutlej River basin in the western Himalaya during the periods 2041–2070 (2050s) and 2071–2100 (2080s). Bias-corrected data downscaled at a grid resolution of 0.25∘ × 0.25∘ from six general circulation models (GCMs) of the Coupled Model Intercomparison Project Phase 6 GCM framework under two greenhouse gas (GHG) trajectories (SSP245 and SSP585) were used for this purpose. Four different rainfall scenarios (R0, R1, R2, and R3) were applied to the models trained with daily data (1979–2009) at Kasol (the outlet of the basin) in order to better understand how catchment size and the geo-hydromorphological aspects of the basin affect runoff. The predictive power of each model was assessed using six statistical measures, i.e. the coefficient of determination (R2), the ratio of the root mean square error to the standard deviation of the measured data (RSR), the mean absolute error (MAE), the Kling–Gupta efficiency (KGE), the Nash–Sutcliffe efficiency (NSE), and the percent bias (PBIAS). The RF model with rainfall scenario R3, which outperformed other models during the training (R2 = 0.90; RSR = 0.32; KGE = 0.87; NSE = 0.87; PBIAS = 0.03) and testing (R2 = 0.78; RSR = 0.47; KGE = 0.82; NSE = 0.71; PBIAS = −0.31) period, therefore was chosen to simulate streamflow in the Sutlej River in the 2050s and 2080s under the SSP245 and SSP585 scenarios. Bias correction was further applied to the projected daily streamflow in order to generate a reliable times series of the discharge. The mean ensemble of the model results shows that the mean annual streamflow of the Sutlej River is expected to rise between 2050s and 2080s by 0.79 % to 1.43 % for SSP585 and by 0.87 % to 1.10 % for SSP245. In addition, streamflow will increase during the monsoon (9.70 % to 11.41 % and 11.64 % to 12.70 %) in the 2050s and 2080s under both emission scenarios, but it will decrease during the pre-monsoon (−10.36 % to −6.12 % and −10.0 % to −9.13 %), post-monsoon (−1.23 % to −0.22 % and −5.59 % to −2.83 %), and during the winter (−21.87 % to −21.52 % and −21.87 % to −21.11 %). This variability in streamflow is highly correlated with the pattern of precipitation and temperature predicted by CMIP6 GCMs for future emission scenarios and with physical processes operating within the catchment. Predicted declines in the Sutlej River streamflow over the pre-monsoon (April to June) and winter (December to March) seasons might have a significant impact on agriculture downstream of the river, which is already having problems due to water restrictions at this time of year. The present study will therefore assist in strategy planning to ensure the sustainable use of water resources downstream by acquiring knowledge of the nature and causes of unpredictable streamflow patterns.

Modeling general circulation model bias via a combination of localized regression and quantile mapping methods

Abstract. General circulation model (GCM) outputs are a primary source of information for climate change impact assessments. However, raw GCM data rarely are used directly for regional-scale impact assessments as they frequently contain systematic error or bias. In this article, we propose a novel extension to standard quantile mapping that allows for a continuous seasonal change in bias magnitude using localized regression. Our primary goal is to examine the efficacy of this tool in the context of larger statistical downscaling efforts on the tropical island of Puerto Rico, where localized downscaling can be particularly challenging. Along the way, we utilize a multivariate infilling algorithm to estimate missing data within an incomplete climate data network spanning Puerto Rico. Next, we apply a combination of multivariate downscaling methods to generate in situ climate projections at 23 locations across Puerto Rico from three general circulation models in two carbon emission scenarios: RCP4.5 and RCP8.5. Finally, our bias-correction methods are applied to these downscaled GCM climate projections. These bias-correction methods allow GCM bias to vary as a function of a user-defined season (here, Julian day). Bias is estimated using a continuous curve rather than a moving window or monthly breaks. Results from the selected ensemble agree that Puerto Rico will continue to warm through the coming century. Under the RCP4.5 forcing scenario, our methods indicate that the dry season will have increased rainfall, while the early and late rainfall seasons will likely have a decline in total rainfall. Our methods applied to the RCP8.5 forcing scenario favor a wetter climate for Puerto Rico, driven by an increase in the frequency of high-magnitude rainfall events during Puerto Rico's early rainfall season (April to July) as well as its late rainfall season (August to November).

Statistical Downscaling Menggunakan Pengelompokan Expectation-Maximization pada Data CFSRv2

General Circulation Model (GCM) is a numerical model that produce a number of data from various climate parameters that are used to estimate climate, one of which is precipitation. GCM is a global scale data and has high resolution. So, the GCM cannot consider the local-scale areas with a higher resolution than the GCM. Therefore, to be able to use the GCM for estimating the local rainfall, namely Statistical Downscaling (SD). SD is a technique used to get the relationship between global-scale (GCM) and local-scale data. SD use the GCM which consist of dependent variables that have multicollinearity. So in this research, the principal component regression (PCR) and partial least square regression (PLSR) will be used to reduce the multicollinearity. In addition, to reduce the RMSEP and increase the correlation value, a clustering technique will be applied before modeling, namely Expectation-Maximization (EM) clustering. This research use CFSRv2 data as GCM and local rainfall data at four rainfall stations in West Java (January 2011 to December 2017). Based on this research, PCR is a good modeling than PLSR and EM clustering get the lower RMSEP and higher correlation value than without clustering before modeling. The conclusion is PCR with EM clustering is a good method for estimating local rainfall using the SD technique especially rainfall in West Java and CFSRv2 data.

A two-step downscaling method for high-scale super-resolution of daily temperature - a case study of Wei River Basin, China.

Climate data with high spatial and temporal resolution were of great significance for regional environmental management, such as for early response to possible predicted local climate changes and extreme weather. However, the current downscaling targets for CMIP6 climate simulations were mostly medium-resolution (MR) reanalysis data, which were still coarse for local analysis. A two-step downscaling method was proposed for 100 × resolution enhancements of general circulation model (GCM) daily temperature data in this study. First, the historical GCM outputs were 10 × downscaled to a set of dynamically predictable MR data using a deep convolutional neural network (CNN), which included both encode-decode structure and long-short skip connections. Then, using high-resolution (HR) topographic data and MR climate data as auxiliary data, the GCM data were super-resolved to a series of images with spatial resolution of 1km. A one-step downscaling analysis combined only with HR topographic data was performed as comparison. Seven evaluation metrics were selected to evaluate the prediction accuracy, and the results showed that the overall performance of two-step downscaling method was better than one-step downscaling method. Higher Nash-Sutcliffe efficiency (NSE) and lower mean absolute relative error (MARE) indicated that the two-step method performed better prediction of peak and low values. It was further confirmed by accuracy evaluation on the 10% max and 10% min values of the testing dataset. The introduction of dynamically predictable MR data could provide effective detailed information during the downscaling process and improve the prediction accuracies. Finally, the projected data of four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) during 2015-2050 were downscaled to the study area. The complex temporal and spatial variations indicated that there were great differences in temperature changes in a basin, and differentiated management measures should be proposed in advance.

Projection of precipitation extremes in China's mainland based on the statistical downscaled data from 27 GCMs in CMIP6

Precipitation extremes have large influences on hydrological, ecological and agricultural systems. This study aimed to investigate variations of precipitation extremes in China's mainland over 1961–2100 based on 27 newly released General Circulation Models (GCMs) in Coupled Model Intercomparison Project phase 6 (CMIP6) under four shared socioeconomic pathway scenarios (SSPs). 10 extreme precipitation indices (EPIs) were extracted for quantifying changes of precipitation extremes. A statistical downscaling method was applied to downscale original precipitation data from the GCMs. Taylor diagram and interannual variability skill methods were used to evaluate performance of the downscaled GCM data for simulating the EPIs. The results displayed that: (1) The downscaled 27 GCMs performed well for the wet EPIs. Based on the multi-model ensemble results, more severe and frequent precipitation extremes over 2015–2100 were projected. (2) The largest changes in extreme precipitation intensities over 2015–2100 would occur in central and southeast China. The maximum changes in extreme precipitation frequencies would occur in Tibetan Plateau and central and southern China. (3) Precipitation extremes in Xinjiang and Tibetan Plateau would be more sensitive to climate change than other sub-regions. The future EPI generally have longer than 10-year of primary periods. (4) The GCMs had larger contribution than the SSPs to the uncertainty in projecting the 10 EPIs. (5) CMIP6 models performed better than CMIP5 models in reproducing interannual variations of EPIs. This study supplies useful information to cope with future intensified precipitation extremes in China.

Climate change projections for Algeria: the 2030 water sector development strategy

PurposeThe first aim of this study is to show the impact of global warming on temperature and precipitation changes in Algeria. The second objective is to exhibit the strategy planned by the country to mitigate these effects on water resources in the future.Design/methodology/approachThis research assesses the expected changes in temperature, precipitation and SPEI index, over Algeria (16 weather stations), between two horizons (2030, 2050), by using an ensemble of 16 general circulation models under RCP2.6 and RCP8.5 scenarios.FindingsMore warming and drought will be experienced under RCP8.5 than RCP2.6 scenario. The highest warming is observed at the Southern stations. However, the lowest precipitation is projected in the western stations. The results of SPEI calculation indicate that the severity of drought spread progressively across time and space. The highest values were observed over 2050 with values varied between 0.15 and −2.08 under RCP2.6 scenario, and range from −0.73 to −2.63 under RCP8.5. These results indicate that Algeria is highly vulnerable to the impact of climate change on water resources, which stressed the need to develop a strategy against this situation.Originality/valueThis study is one of the first to simulate the future climate changes over 16 Algerian weather stations by using an average of 16 general circulation models data, under two RCP scenarios. This study shows the 2030 water development strategy to mitigate the effect of drought and water scarcity on different sectors.

Downscaling model for rainfall based on the influence of typhoon under climate change

Abstract The effects of typhoon rainfall cannot be easily investigated to downscale the GCMs (general circulation models) data, because typhoons are short-term highly nonlinear processes. In this study, to explore the impact on the typhoon rainfall for downscaling models, the monthly rainfalls are divided into typhoon rainfall and non-typhoon rainfall. The GA-RBFN downscaling models, integrating genetic algorithm to optimize a radial basis function neural network, were established to enquire into the future rainfall under the effect of typhoons in Taichung and Hualien, Taiwan. The GCM data in this study included MRI-CGCM3 and CSIRO-Mk3.0 from the 5th Assessment Report of the Intergovernmental Panel on Climate Change. The historical monthly rainfall of Taichung and Hualien stations are collected from the Central Weather Bureau, Taiwan, and the best track of typhoons from the Joint Typhoon Warning Center. Principal component analysis (PCA) and the stepwise regression procedure (SRP) were adopted, respectively, to select input variables from the GCM data. The future rainfall trends and uncertainties are evaluated by the best GA-RBFN model, which is selected with the highest performance by applying holistic information criteria. The simulated results show that the model with variables transformed by PCA performs well in forecasting non-typhoon rainfalls, while the model with variables transformed by SRP performs well in forecasting monthly total rainfalls. According to the three classifications of future rainfalls in wet and dry seasons, the mid- and long-term rainfall amount are mainly low to normal for Taichung and normal to high for Hualien. For the long-term rainfall assessment and the probability analysis on exceeding historical rainfalls, the long-term rainfall variability of the two stations is higher than the mid-term rainfall variability. It demonstrates that the future rainfall estimation has high uncertainty under the force of typhoons. Finally, the rainfall variability in the dry period is higher than that in the wet season, and the average probability of the future dry season rainfalls exceeding historical rainfall is high.

Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir

With improvements in data quality and technology, the statistical downscaling data of General Circulation Models (GCMs) for climate change impact assessment have been refined from monthly data to daily data, which has greatly promoted the data application level. However, there are differences between GCM downscaling daily data and rainfall station data. If GCM data are directly used for hydrology and water resources assessment, the differences in total amount and rainfall intensity will be revealed and may affect the estimates of the total amount of water resources and water supply capacity. This research proposes a two-stage bias correction method for GCM data and establishes a mechanism for converting grid data to station data. Five GCMs were selected from 33 GCMs, which were ranked by rainfall simulation performance from a baseline period in Taiwan. The watershed of the Zengwen Reservoir in southern Taiwan was selected as the study area for comparison of the three different bias correction methods. The results reveal that the method with the wet-day threshold optimized by objective function with observation rainfall wet days had the best result. Error was greatly reduced in the hydrology model simulation with two-stage bias correction. The results show that the two-stage bias correction method proposed in this study can be used as an advanced method of data pre-processing in climate change impact assessment, which could improve the quality and broaden the extent of GCM daily data. Additionally, GCM ranking can be used by researchers in climate change assessment to understand the suitability of each GCM in Taiwan.

Assessing Future Impacts of Climate Change on Streamflow within the Alabama River Basin

Global climate change is expected to impact future precipitation and surface temperature trends and could alter local hydrologic systems. This study assessed the likely hydrologic responses and changes in streamflow due to future climate change within the Alabama River Basin (ARB) for the mid-21st century 2045 (“2030–2060”) and end-21st century 2075 (“2060–2090”). Using an integrated modeling approach, General Circulation Model (GCM) datasets; the Centre National de Recherches Météorologiques Climate Model 5 (CNRM-CM5), the Community Earth System Model, version 1–Biogeochemistry (CESM1- BGC.1), and the Hadley Centre Global Environment Model version 2 (HADGEM2-AO.1), under medium Representative Concentration Pathway (RCP) 4.5, and based on World Climate Research Program (WCRP)’s Couple Model Intercomparison Phase 5 (CMIP5), were assimilated into calibrated Soil and Water Assessment Tool (SWAT). Mann–Kendall and Theil Sen’s slope were used to assess the trends and magnitude of variability of the historical climate data used for setting up the model. The model calibration showed goodness of fit with minimum Nash–Sutcliffe Efficiency (NSE) coefficient values of 0.83 and Coefficient of Determination (R2) of 0.88 for the three gages within the ARB. Next, the research assessed changes in streamflow for the years 2045 and 2075 against that of the reference baseline year of 1980. The results indicate situations of likely increase and decrease in mean monthly streamflow discharge and increase in the frequency and variability in peak flows during the periods from the mid to end of the 21st century. Seasonally, monthly streamflow increases between 50% and 250% were found for spring and autumn months with decreases in summer months for 2045. Spring and summer months for 2075 resulted in increased monthly streamflow between 50% and 300%, while autumn and spring months experienced decreased streamflow. While the results are prone to inherent uncertainties in the downscaled GCM data used, the simulated dynamics in streamflow and water availability provide critical information for stakeholders to develop sustainable water management and climate change adaptation options for the ARB.

General Circulation Model Selection Technique for Downscaling: Exemplary Application to East Africa

AbstractDownscaling is widely used in studies of local and/or regional climate as it yields a greater spatial resolution than general circulation models (GCMs) can provide. It utilizes GCM output or reanalysis data, which is transformed using mathematical relationships or used to force the lateral boundaries of a regional climate model. However, there is no set selection technique to determine which GCM realization(s) to employ. Here, a comprehensive yet easily applicable model selection technique for studies requiring GCM data as a constraint was developed. The technique evaluates, with respect to a reanalysis product and/or observational data, the ability of GCM realizations to reconstruct the mean state of the climate and the space‐time climatic anomalies for the atmospheric state variables at three distinct pressure levels. It was applied to the region of East Africa, where GISS‐E2‐H r6i1p3 was found to perform the strongest. The top ranked realizations were found to better capture processes when evaluated for the example of the Indian Ocean Dipole. Furthermore, the surface air temperature and precipitation from three 10‐year regional climate model simulations, one forced by the Modern‐Era Retrospective Analysis for Research and Applications version 2 reanalysis, one forced by the top ranked GCM, and one by the lowest ranked one, were compared to gridded observations. Results show that using a top ranked GCM for the boundary conditions leads to a better dynamical downscaling simulation than a low‐ranked GCM, suggesting the potential of the proposed technique for future downscaling techniques.

Intercomparison of Bias-Correction Data Sources and Their Influence on Watershed-Specific Downscaling Climate Projections

HighlightsBias correction from three historical data sources (NCDC, PRISM, and NEXRAD) were assessed.Bias correction removed watershed-average bias in general circulation model (GCM) data for a historical period.Subbasin-specific variability was detected in future projections for each bias-correction data source.Data source had less impact on uncertainty in future projections than GCM or a representative concentration pathway.Uncertainty from bias-correction data sources was higher for precipitation than for temperature.Abstract. Climate projections developed by general circulation models (GCM) are often used in watershed modeling applications to project future hydrologic changes. In many models, the climate projections are downscaled to individual map units represented by grid cells or subbasins. Uncertainty of downscaled climate projections are a product of uncertainties arising mainly from the model itself, from the representative concentration pathway (RCP), and from the downscaling procedure. Other sources of uncertainty may include the historical observations used for GCM bias correction and data aggregation from GCM grids to map (often subbasin) units. This study evaluated effects of three sources of historical data (ground-based weather station network, NCDC, and two gridded datasets, NEXRAD and PRISM) on historical variability, and shifts and uncertainty in precipitation and temperature projections. Climate projections from six GCMs and three RCPs were evaluated in 54 subbasins of the Smoky Hill River watershed in the U.S. Central Great Plains. Bias correction of GCM projections reduced bias of watershed-average annual precipitation in the historical period to near zero, but subbasin-specific variability remained in future projections with little difference among bias-correction data sources. For minimum and maximum temperatures, the GCM ensemble statistics for basin-average and subbasin-specific future projections were similar for all bias-correction data sources. Increase in RCP forcing was found to widen the uncertainty in future projections. Overall, the uncertainty due to data source selection was smaller than the uncertainty due to GCM model and RCP forcing selection. The results demonstrate that statistical downscaling is essential to account for local climate factors within a watershed, and that both weather station-based and gridded bias-correction data sources can be used effectively, but that future climate projections may inherit the historical bias in a selected data source. These inherent uncertainties associated with application of GCMs in hydrological and geospatial modeling should be carefully considered for understanding climate projections when building watershed models and interpreting the results. Keywords: Bias correction, Climate change, Downscaling, GCM, Uncertainty, Watershed.

Generating future weather files under climate change scenarios to support building energy simulation – A machine learning approach

General circulation models (GCM) have been used by researchers to assess the effect of climate change in different fields of study. In the case of building energy performance, GCMs can be used to evaluate future building energy performance through simulations. However, a key issue with the use of GCM data in building energy simulation is the inadequate resolution and bias of the data. Therefore, in order to use this data for simulation purposes and better predict future building performance, further processing is required. The first challenge is that the GCMs are usually biased, which means a considerable deviation can be found when the historical GCM data is compared to station observed weather data. The second challenge is that the GCM data has daily temporal resolution rather than the hourly resolution required in building energy simulation.In order to utilize GCM data to estimate future building performance through simulation, the current study suggests a workflow that can be applied to climate change data. First, a bias-correction technique, known as the quantile-quantile method, is applied to remove the bias in the data in order to adapt GCMs to a specific location. The study then uses a hybrid classification-regression model to downscale the bias-corrected GCM data to generate future weather data at an hourly resolution for building energy simulation. In this case, the hybrid model is structured as a combined model, where a classification model serves as the main model together with an auxiliary regression model for cases when data is beyond the range of observed values. The proposed workflow uses observed weather data to determine similar weather patterns from historical data and use it to generate future weather data, contrary to previous studies, which use artificially generated data. However, in cases where the future GCM data showed temperatures ranging outside of the observed data, the study applied a trained regression model to generate hourly weather data.The proposed workflow enables users to generate future weather files year by year under different climate change scenarios and, consequently, extreme weather characteristics are preserved for extreme or reliability analysis and design optimization.

The use of RUSLE and GCMs to predict potential soil erosion associated with climate change in a monsoon-dominated region of eastern India

Soil is one of the most important natural resources; therefore, there is an urgent need to estimate soil erosion. The subtropical monsoon-dominated region also faces a comparatively greater problem due to heavy rainfall with high intensity in a very short time and the presence of longer dry seasons and shorter wet seasons. The Arkosa watershed faces the problem of extreme land degradation in the form of soil erosion; therefore, the rate of soil erosion needs to be estimated according to appropriate models. GCM (general circulation model) data such as MIROC5 (Model for Interdisciplinary Climate Research) of CMIP5 (Coupled Model Intercomparison Project Phase 5) have been used to project future storm rainfall and soil erosion rates following the revised universal soil loss equation (RUSLE) in various influential time frames. Apart from that, different satellite data and relevant primary field-based data for future prediction were considered. The average annual soil erosion of Arkosa watershed ranges from 6 t/ha/year. The very high (> 6 t/ha/year) and high (5–6 t/ha/year) soil loss areas are found in the southern, south-eastern, and eastern part of the watershed. Apart from this, low (1–2 t/ha/year) and very low (< 1 t/ha/year) soil loss areas are associated with the western, northern, southern, and major portion of the watershed. Extreme precipitation rates with high kinetic energy due to climate change are favorable to soil erosion susceptibility. The results of this research will help to implement management strategies to minimize soil erosion by keeping authorities and researchers at risk for future erosion and vulnerability.

Characteristics of subsurface mesoscale eddies in the northwestern tropical Pacific Ocean from an eddy-resolving model

Subsurface eddies (SSEs) are common features of the ocean interior. They are particularly abundant in oceanic basins and the vicinity of major intermediate water outflows. They are responsible for subsurface transport of mass, heat, and salt. Analysis of high-resolution general circulation model data has revealed the existence of subsurface anticyclonic eddies (SSAEs) and subsurface cyclonic eddies (SSCEs) in the northwestern tropical Pacific Ocean. SSEs are abundant east of the Philippines (0°-22°N, 120°E-140°E) and in latitude bands between 9°N-17°N east of 140°E. The composite structure of SSEs was investigated. SSEs had a core at about 400-m water depth and their maximum meridional velocity exceeded 10 cm/s. They exhibited two cores with different salinity polarities in the surface and subsurface. Additionally, spatial distributions of heat transport induced by SSEs in the northwestern tropical Pacific were presented for the first time. A net equatorward heat flux toward a temperature up-gradient was observed. The analysis of eddy-mean flow interactions revealed that the circulation is baroclinically and barotropically unstable at different depths and to differing degrees. The energy conversions suggest that both barotropic and baroclinic instabilities are responsible for SSE generation east of the Philippines, whereas baroclinic instability caused by a horizontal density gradient and vertical eddy heat flux are important between 9°N and 17°N east of 140°E. Meridional movement of the north equatorial current and the north equatorial undercurrent can contribute to SSE generation in our study region.

Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows

Hydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observed flows, and the confidence in the results can be relatively low. The objectives of this paper were to bias correct simulated stream flows from calibrated hydrologic models for two basins in New Jersey, USA, and evaluate model performance in comparison to uncorrected simulations. Then, we used stream flow bias correction and flow duration curves (FDCs) to evaluate and assess simulations driven by statistically downscaled GCMs for the historical period and the future time slices 2041–2070 and 2071–2099. Bias correction of stream flow from simulations increased confidence in the performance of two previously calibrated hydrologic models. Results indicated there was no difference in projected FDCs for uncorrected and bias-corrected flows in one basin, while this was not the case in the second basin. This result provided greater confidence in projected stream flow changes in the former basin and implied more uncertainty in projected stream flows in the latter. Applications in water resources can use the methods described to evaluate the performance of GCM-driven simulations and assess the potential impacts of climate change with an appropriate level of confidence in the model results.

Assessing the impact of climate change over the northwest of Iran: an overview of statistical downscaling methods

Due to the spatial-temporal inadequacy of large-scale general circulation models (GCMs), linking large-scale GCM data with small-scale local climatic data has found great interest. In this paper, in order to downscale minimum and maximum temperatures and precipitation predictands, the performance of three statistical downscaling techniques including Long Ashton Research Station-Weather Generator (LARS-WG), statistical downscaling model (SDSM), and artificial neural network (ANN) was compared based on Intergovernmental Panel on Climate Change-Fifth Assessment Report (IPCC-AR5) in northwest Iran. For this purpose, a nonparametric test named Mann-Whitney test, Spearman correlation coefficient, and the root mean square error (RMSE) were utilized to assess the efficiency of downscaling models. To scrutinize the climate change impacts, periods of 1961–1990 and 1991–2005 were considered as the baseline and verification periods, respectively. The findings revealed the superior performance of the ANN model for minimum and maximum temperatures, while for precipitation predictand, the SDSM represented the best performance among the models. Simulation results for future temperature indicated an ascending trend as 0.1–1.3 °C, 0.3–1.7 °C, and 0.5–2.1 °C for LARS-WG, SDSM, and ANN techniques, respectively. On the other hand, simulation outputs for the precipitation indicated a descending trend of 10–30% in future precipitation of the region according to downscaling models under Representative Concentration Pathway 8.5 (RCP8.5) pessimistic scenario of Hadley Center Coupled Model version 3 (HadCM3) GCM model.