Global Circulation Model Output Research Articles

Unveiling Marine Heatwave Dynamics in the Persian /Arabian Gulf and the Gulf of Oman: A Spatio-Temporal Analysis and Future Projections

Amid escalating global marine heat waves, the Arabian Gulf is critical due to its shallow depths, high temperatures, and vulnerability to climate change impacts. This study provides a detailed spatio-temporal analysis of marine heatwave events from 1982 to 2022 across the Arabian Gulf and the adjacent Gulf of Oman. The study delineates regions of heightened vulnerability within these water bodies by comprehensively analyzing seasonal and annual variabilities and trend assessments. Moreover, an exploration of prevailing surface circulation patterns, underpinned by an extensive study of global circulation model outputs, elucidates the oceanographic mechanisms contributing to temperature dynamics. Identifying 25 significant heat wave events, with a focused examination of the six most protracted episodes, is noteworthy among the findings. Strikingly, the analysis reveals that the Gulf of Oman surpasses the Arabian Gulf in heatwave intensity. Looking forward, the investigation extends to future surface water temperature projections up to the close of the current century. The collective results underscore the region's acute susceptibility to the climate change perturbations of climate change, emphasizing the urgency of targeted interventions to mitigate these effects and address concurrent local stressors.

Wet bulb globe temperature from climate model outputs: a method for projecting hourly site-specific values and trends.

Increasing temperature will impact future outdoor worker safety but quantifying this impact to develop local adaptations is challenging. Wet bulb globe temperature (WBGT) is the preferred thermal index for regulating outdoor activities in occupational health, athletic, and military settings, but global circulation models (GCMs) have coarse spatiotemporal resolution and do not always provide outputs required to project the full diurnal range of WBGT. This article presents a novel method to project WBGT at local spatial and hourly temporal resolutions without many assumptions inherent in previous research. We calculate sub-daily future WBGT from GCM output and then estimate hourly WBGT based on a site-specific, historical diurnal cycles. We test this method against observations at U.S. Army installations and find results match closely. We then project hourly WBGT at these locations from January 1, 2025, to December 31, 2100, to quantify trends and estimate future periods exceeding outdoor activity modification thresholds. We find regional patterns affecting WBGT, suggesting accurately projecting WBGT demands a localized approach. Results show increased frequency of hours at high WBGT and, using U.S. military heat thresholds, we estimate impacts to future outdoor labor. By mid-century, some locations are projected to average 20 or more days each summer when outdoor labor will be significantly impacted. The method's fine spatiotemporal resolution enables detailed analysis of WBGT projections, making it useful applied at specific locations of interest.

High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method

High-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized the bias correction constructed analogues with quantile mapping reordering (BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using a perfect sibling approach and detailed results are presented by taking two contrasting (the worst and best performing models) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs.

Resilience Appraisal of Water Resources System under Climate Change Influence Using a Probabilistic-Nonstationary Approach

The planning and management of water resources are being impacted by climate change, and are in need of comprehensive adaptation strategies to respond to future projections. The goal of this study is to support those strategies with a new decision-making paradigm that employs a probabilistic-nonstationary hydroclimatic scenario to examine the long-term system resilience for multiple dam objectives. The modified approach to examine resilience was applied, and uses a bottom-up approach with a modified resilience concept to achieve the long-term operation targets. The approach integrates Global Circulation Models (GCMs) with a statistical weather generator (SWG) to produce a range of future scenarios. Then, the system response is evaluated against those scenarios. The study utilizes a pre-developed SWG to synthesize different trajectories by altering three weather variables: the precipitation amount, temperature mean, and wind-speed magnitude. The proposed has four staged phases: (1) identification of the future climate exposure using different GCMs; (2) future water supply estimation for scenarios using hydrological models; (3) future water demand estimation for scenarios of all system stakeholders; and (4) evaluation of system performance resilience for the dam operational purposes. The Diyala River Basin in Iraq was selected as a case study, to apply the suggested paradigm. The analysis of the GCM outputs revealed that the rainfall mean varies between −37% and +31%; temperature mean varies between +0.4 °C and 5.1 °C; and the mean wind speed varies between −22% and 11%. Based on these ranges, the future climate trajectories were simulated. According to the examination of the system’s response to those weather changes, the precipitation is the most effective parameter, followed by the temperature change, and lastly the wind speed. Furthermore, the findings show that the existing system operating rules are reliable in terms of flood protection but vulnerable in terms of drought management. The analysis of system resilience to manage the drought was found to be 0.74 for the future trajectories, while it was 0.91 for flood protection. This indicates that project managers should prioritize the drought and water scarcity management, due to climate change impact and upstream country development. The study also shows that the suggested resilience paradigm is capable of measuring the negative effects of climate change and able to provide long-term adaptation guidance for water resources management.

Model-based climate change adaptational potential and productivity of some cowpea genotypes and its sensitivity to bias adjustment

Grain legumes are essential for the protein supply to an ever-growing population in Africa. However, little is known about the adaptational potential and thus resilience to abiotic stress of major grain legumes under future climatic change for the evaluation of climate change impact and adaptation. This study assessed the adaptation potential of some cowpea genotypes to future climate change in the moist (Kumasi—Ghana) and dry savanna (Ouagadougou—Burkina Faso) biomes of West Africa based on a validated process-based SIMPLACE model using the output of four global circulation models (GCMs) for two shared socioeconomic pathways (SSPs, i.e., ssp126 and 585). In addition, it assesses the sensitivity of the cowpea model to bias corrections of the GCM outputs. In comparison of future socioeconomic pathways with historic time series, the use of bias-corrected climate model output slightly increased the rate of the phenological development of the genotypes in the future period except in Ouagadougou, in the ssp585 scenario. Without bias correction, this increase of the rate of phenological development in the future scenarios was less pronounced. With bias correction, the total aboveground biomass and yield of all genotypes were reduced in both SSPs. The change in the average water stress and phosphorous stress were genotype specific. Despite a general yield decline in both SSPs, the genotypes Asontem and GH6060 exhibited the adaptational potential to future climate change in the moist and dry savanna biomes. This is by a higher accumulation of total aboveground biomass, higher yield, and tolerance to high temperature as well as high water use and photosynthetic efficiency due to higher atmospheric carbon dioxide concentrations, despite faster phenological development.

STATISTICAL DOWNSCALING MODEL WITH PRINCIPAL COMPONENT REGRESSION AND LATENT ROOT REGRESSION TO FORECAST RAINFALL IN PANGKEP REGENCY

Climate information, especially rainfall, is needed by various sectors in Indonesia, including the marine and fisheries sectors. Estimating high-resolution climate models continues to develop by involving global-scale climate variables, one of which is the global circulation model (GCM) output precipitation. Statistical downscaling (SD) relates global scale climate variables to local scales. Principal component regression (PCR) and latent root regression (LRR) techniques are statistical methods used in the SD model to overcome the high correlation between GCM data grids. PCR focuses on the variability in the predictor variables, while the LRR focuses on the variability between the response variables and predictors. This method was applied to Pangkep Regency rainfall data as a local scale response variable and GCM precipitation as a predictor variable (January 1999 to December 2020). This study aimed to obtain the number of principal component (PC) in the SD model and the forecast value of the 2020 rainfall data. In addition, the dummy variable resulting from K-means was used as a predictor variable in PCR and LRR. The result is that using the first 11-15 PC has a cumulative diversity proportion of 98%. Furthermore, by using the data for the 1999-2019 period, adding a dummy variable to the PCR can increase the accuracy of the model (the coefficient of determination is 92.27%-92.43%). However, LRR with and without dummy variables produces relatively the same model accuracy. In general, the LRR model is better at explaining the diversity of the Pangkep District rainfall data than the PCR model. The prediction of rainfall for the 2020 period at LRR with 13 PC is an accurate prediction based on the highest correlation value (0.97) and the lowest root mean square error prediction (75.17).

Adapting rainfall bias-corrections to improve hydrological simulations generated from climate model forcings

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

Impact of Building Energy Mitigation Measures on Future Climate

As cities are increasing technological efficacy on greenhouse gas (GH) emission reduction efforts, the surrounding urban ecosystems and natural resources may be affected by these measures. In this research, climate indicators such as heat index, extreme heat events, intensified urban heat islands (UHIs), and sea breeze are projected for the middle and end of the 21st century to understand the climate change signal on these variables with and without building energy mitigation measures. Cities amplify extreme heat and UHI impacts by concentrating large populations and critical infrastructure in relatively small areas. Here, we evaluate the combined climate and building energy mitigation impacts on localized climate metrics throughout the 21st century across extreme emission scenarios (RCP8.5) for the tropical coastal city of San Juan. The analysis of statistically downscaled global circulation model outputs shows underestimation for uncorrected summer daily maximum temperatures, leading to lower extreme heat intensity and duration projections from the present time which are corrected using bias-corrected techniques. High-resolution dynamic downscaling simulations reveal a strong dependency of changes in extreme heat events in urban settings, however, the intensities shift to lower-level grasslands and croplands with energy mitigation measures (combination of white roof, tilted photovoltaic roof, and efficient heating ventilation and air conditioning systems). The building energy mitigation measures have the potential of reducing the UHI intensities to 1 °C and 0.5 °C for the 2050 and 2100 climate periods, respectively.

Assessing the Projected Changes in European Air Stagnation due to Climate Change

Abstract Air pollution is a major environmental threat to human health. Pollutants can reach extreme levels in the lower atmosphere when weather conditions permit. As pollutant concentrations depend on scales and processes that are not fully represented in current global circulation models (GCMs), and it is often too computationally expensive to run models with atmospheric chemistry and aerosol processes, air stagnation is often used as a proxy for pollution events with particular success in Europe. However, the variables required to identify air stagnation can have biases in GCM output, which adds uncertainty to projected trends in air stagnation. Here, the representation of air stagnation in GCMs is assessed for Europe in the historical period and in end-of-century projections based on a high-emission scenario using three methods for identifying air stagnation. The monthly frequency of stagnation during summer and autumn is projected to increase with climate change when stagnation is identified by a well-established index. However, this increase is not present when air-stagnation frequency is estimated using a statistical model based on the synoptic- to large-scale atmospheric circulation. This implies that the projected increases in air stagnation are not driven by an increase in frequency or severity of large-scale circulation events that are conducive to stagnation. Indeed, projected changes to the atmospheric circulation in GCMs, in particular a reduction in atmospheric block frequency, would suggest a reduction in future air stagnation. Additional analyses indicate that the projected increases in stagnation frequency follow the trend toward more frequent dry days, which is apparently unrelated to the large-scale drivers of air stagnation.

Daedalus MASE (mission assessment through simulation exercise): A toolset for analysis of in situ missions and for processing global circulation model outputs in the lower thermosphere-ionosphere

Daedalus MASE (Mission Assessment through Simulation Exercise) is an open-source package of scientific analysis tools aimed at research in the Lower Thermosphere-Ionosphere (LTI). It was created with the purpose to assess the performance and demonstrate closure of the mission objectives of Daedalus, a mission concept targeting to perform in-situ measurements in the LTI. However, through its successful usage as a mission-simulator toolset, Daedalus MASE has evolved to encompass numerous capabilities related to LTI science and modeling. Inputs are geophysical observables in the LTI, which can be obtained either through in-situ measurements from spacecraft and rockets, or through Global Circulation Models (GCM). These include ion, neutral and electron densities, ion and neutral composition, ion, electron and neutral temperatures, ion drifts, neutral winds, electric field, and magnetic field. In the examples presented, these geophysical observables are obtained through NCAR’s Thermosphere-Ionosphere-Electrodynamics General Circulation Model. Capabilities of Daedalus MASE include: 1) Calculations of products that are derived from the above geophysical observables, such as Joule heating, energy transfer rates between species, electrical currents, electrical conductivity, ion-neutral collision frequencies between all combinations of species, as well as height-integrations of derived products. 2) Calculation and cross-comparison of collision frequencies and estimates of the effect of using different models of collision frequencies into derived products. 3) Calculation of the uncertainties of derived products based on the uncertainties of the geophysical observables, due to instrument errors or to uncertainties in measurement techniques. 4) Routines for the along-orbit interpolation within gridded datasets of GCMs. 5) Routines for the calculation of the global coverage of an in situ mission in regions of interest and for various conditions of solar and geomagnetic activity. 6) Calculations of the statistical significance of obtaining the primary and derived products throughout an in situ mission’s lifetime. 7) Routines for the visualization of 3D datasets of GCMs and of measurements along orbit. Daedalus MASE code is accompanied by a set of Jupyter Notebooks, incorporating all required theory, references, codes and plotting in a user-friendly environment. Daedalus MASE is developed and maintained at the Department for Electrical and Computer Engineering of the Democritus University of Thrace, with key contributions from several partner institutions.

Complementary use of multi-model climate ensemble and Bayesian model averaging for projecting river hydrology in the Himalaya.

Considering the sensitivity and importance of water resources in the Himalayan uplands, this study intended to assess the hydrological responses to climate change in the Jhelum basin. Representative concentration pathway (RCP)-based projections from six dynamically downscaled global circulation models (GCMs) were bias-corrected for developing the climatic projections over the twenty-first century. The uncertainty associated with GCM outputs was addressed by using multi-model ensemble projections developed through Bayesian model averaging (BMA) technique. The assessment reveals that compared to the baseline (1980-2010) values, the annual mean maximum temperature in the basin will rise by 0.41-2.31°C and 0.63-4.82°C, and the mean minimum temperature will increase by 1.39-2.37°C and 2.14-4.34°C under RCP4.5 and RCP8.5, respectively. While precipitation is expected to decrease by 7.2-4.57% and 4.75-2.47% under RCP4.5 and RCP8.5, correspondingly. BMA ensemble projections were coupled with the Soil and Water Assessment Tool(SWAT) to simulate the future hydrological scenarios of the drainage basin. With the changing climate, the discharge of rivers in the Jhelum basin is expected to witness reductions by about 23-37% for RCP4.5 and 19-46% for RCP8.5. Moreover, the water yield of the basin may also exhibit decreases of 17-25% for RCP4.5 and 18-42% for RCP8.5. The projected scenarios are likely to cause water stress, affect the availability of water for diverse uses, and trigger transboundary water-sharing-related conflicts. The impact of climate change on discharge demands early attention for the formulation of mitigation and adaptive measures at the regional level and beyond.

Integrated modeling to assess the impact of climate change on the groundwater and surface water in the South Aral Sea area

The recession of the South Aral Sea over the last few decades has become a great environmental challenge in Central Asia. Due to declining inflow and irrigation exhaustion, the Amu Darya River vanishes before reaching the South Aral Sea. Therefore, groundwater (GW) has become the vital water source for the South Aral Sea surface water and the local ecosystem. To develop feasible adaptation strategies for water resource management, it is necessary to quantify the GW flux into the South Aral Sea under ongoing climate change. Based on numerical models (MODFLOW and SWAT) and with the support of observed data, remote sensing data, reanalysis data, and the output of Global Circulation Models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6), the hydraulic connection between a shallow aquifer and the lake was investigated and projected for future change. The GW model was calibrated by one in-situ GW well and six Gravity Recovery and Climate Experiment (GRACE) derived GW heads. The root mean square error (RMSE) of the GW flow field simulation (MODFLOW) was lower than 0.21 m from 2005 to 2016. Under three shared socioeconomic pathways (sustainable pathway, medium pathway, and fossil-fueled development), the GCMs indicate that the evapotranspiration (ET) experiences the greatest change, and will potentially rise to 602.6 mm/yr by 2100 in fossil-fueled development. While the precipitation and the runoff of the Amu Darya River will both rise slightly, and with the range of 146.4–163.3 mm/yr and 107.8–118.4 m3/s from 2021 to 2100 separately, and the increase is mainly during winter. With climate change signals forcing on the historical hydrometeorological data, the simulation result revealed that the GW contributes to more than 78 % of the total influx of water into the lake during 2021–2100, and the predicted GW flux into the lake will slightly rise over time. However, the ET will also increase due to rising temperatures and may gradually outpace the recharge growth. The simulation results indicate that the South Aral Sea may maintain a surface area of 4.47×103km2 in fossil-fueled development, which means it will continue to shrink if there is no human intervention. Therefore, a set of sustainable water resource management methods is urgently needed to preserve the ecological environment of the South Aral Sea and prevent ecological disasters.

3D Radiative Transfer for Exoplanet Atmospheres. gCMCRT: A GPU-accelerated MCRT Code

Radiative transfer (RT) is a key component for investigating atmospheres of planetary bodies. With the 3D nature of exoplanet atmospheres being important in giving rise to their observable properties, accurate and fast 3D methods are required to be developed to meet future multidimensional and temporal data sets. We develop an open-source GPU RT code, gCMCRT, a Monte Carlo RT forward model for general use in planetary atmosphere RT problems. We aim to automate the post-processing pipeline, starting from direct global circulation model (GCM) output to synthetic spectra. We develop albedo, emission, and transmission spectra modes for 3D and 1D input structures. We include capability to use correlated-k and high-resolution opacity tables, the latter of which can be Doppler-shifted inside the model. We post-process results from several GCM groups, including ExoRad, SPARC/MITgcm THOR, UK Met Office UM, Exo-FMS, and the Rauscher model. Users can therefore take advantage of desktop and HPC GPU computing solutions. gCMCRT is well suited for post-processing large GCM model grids produced by members of the community and for high-resolution 3D investigations.

Modelling the impact of climate change on the flow of the Eerste River in South Africa

This research investigated the impact of climate change on the Eerste River, which is the primary source of water for the Stellenbosch Municipality and the agricultural sector in the surrounding areas of Stellenbosch Town, South Africa. Climate data projected by 11 Global Circulation Models (GCMs) of the Coupled Model Intercomparison Project - Phase 5 enforced by Representative Concentration Pathways 4.5 and 8.5, was statistically downscaled to reflect the “near” (2022–2057) and “far” (2058–2093) future periods, respectively. The ensemble mean of these GCMs outputs for the near and far future periods was compared to that of the present-day period which was accepted as the period between 1983 and 2018. This was done to determine the climate change signal, which was then transferred onto the observed precipitation and evaporation data of the present-day period. The climate change induced data, as well as land use and water demand analysed data, were then used as input to the Pitman Model, which is the conceptual semi-distributed hydrological model, for the simulation of the impact of climate change on the flow of the Eerste River. The results showed that climate change is expected to increase evaporation between 6% and 15% and, at the same time, causing rainfall to decrease between 2% and 8% in the future periods. This future climate is anticipated to cause a reduction in available water of between 8% and 18%, potentially triggering an increase in irrigation demand of between 12% and 29% in the future periods with a possible failure to meet the municipal water abstractions expected in the far-future period. Therefore, this research suggests increasing the capacity of existing farm dams and the promotion of water demand management practices to curb the potential impact of climate change on the available flow of the Eerste River.

Predicting long-term monthly electricity demand under future climatic and socioeconomic changes using data-driven methods: A case study of Hong Kong

Data-driven methods, such as artificial neural networks (ANNs), support vector regression (SVM), Gaussian process regression (GPR), multiple linear regression (MLR), decision trees (DTs), and gradient boosting decision trees (GBDTs), are the most popular and advanced methods for energy demand prediction. However, these methods have not been cross compared to analyze their performances for long-term energy demand predictions. Therefore, this paper aims to identify the best method among these data-driven methods for quantifying the impacts of climatic and socioeconomic changes on future long-term monthly electricity demand in Hong Kong. First, historical 40-year climatic, socioeconomic, and electricity consumption data are used to train and validate these models. Second, different representation concentration pathway (RCP) scenarios and three percentiles of 24 global circulation model outputs are adopted as future climatic changes, while five shared socioeconomic pathways are considered for future socioeconomic uncertainties. The results show that the GBDT method provides the best accuracy, generalization ability, and time-series stability, while ANN method exhibits the lowest accuracy and lower generalization ability. The monthly electricity demands in Hong Kong under the RCP8.5–2090 s scenario are predicted to increase by up to 89.40 % and 54.34 % in the residential and commercial sectors, respectively, when compared with 2018 levels.

Projection of future extreme precipitation: a robust assessment of downscaled daily precipitation

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.

A new approach to statistical downscaling using Tweedie compound Poisson gamma response and lasso regularization

Statistical Downscaling (SDS) is a technique in climatology to analyze the functional relationship of global scale GCM (Global Circulation Model) output data as predictor variables and local scale rainfall data as a response variable. Rainfall contains continuous and discrete components. The continuous component is related to the intensity of rainfall more than zero which can be assumed to be Gamma distribution while the discrete component is related to the occurrence of rain including zero which can be assumed to be Poisson distribution. A combination of both distributions is called Tweedie compound poisson gamma (TCPG). SDS modeling with TCPG response can be used to predict the occurrence of rain and also the rainfall intensity simultaneously. GCM output data generally contain multicollinearity problems which can be overcome by Lasso regularization. This study discusses SDS modeling which assumes TCPG distributed response and uses Lasso to predict some characteristics of rainfall such as the average number of daily rainfall events per month (, shape parameter , the average intensity of daily rainfall per month , probability of no rain event per month , the number of no rain per month . Based on the smallest RMSEP and the high correlation of the actual and predicted data, the TCPG model with Lasso regulation is more reliable and needs to be considered for modeling rainfall than TCPG-generalized linear models and TCPG-principal component analysis.

Climate change impact on soil salt accumulation in Khon Kaen, Northeast Thailand

In northeast Thailand, 17% of the total agricultural land is classified as salt-affected. In the future, climate change may exacerbate salt-affected soil problems. Therefore, in this study, we conducted a field survey to evaluate seasonal changes in soil electrical conductivity (ECe) in salt-affected paddy areas of Ban Phai District, Khon Kaen Province, northeast Thailand. Fifteen soil samples were collected every 2 weeks from October 2016 to December 2018, and the ECe, soil water content, and soil textures were analyzed. Then, the HYDRUS-1D model was applied to estimate seasonal changes in the salinity level, and the simulated results corresponded well with observed data. Using HYDRUS-1D and the global circulation model (MIROC5) outputs under the Representative Concentration Pathways 8.5 scenario, future ECe was predicted. Under a temperature increase of 2.8°C from 2016 to 2100, annual potential evapotranspiration increased from 1,430 mm (2016–2025) to 1,584 mm (2081–2100). The average ECe in cultivation season increased from 2.63 dS/m (2016–2025) to 3.31 dS/m (2081–2100). As a countermeasure to mitigate soil salt accumulation, a 5 cm reduction in groundwater level offsets the negative impact of climate change, and a 10 cm reduction significantly improves the soil ECe relative to the current soil salinity level.

Change in Rainfall Patterns in the Hilly Region of Uttarakhand due to the Impact of Climate Change

Uttarakhand, a Himalayan state of India, may experience an increase in temperature of 1.4°C to 5.8°C by 2100 due to global warming. The rise in temperature may melt the glaciers of the state and may have some significant impact on the rainfall. In this study, we have quantified the changes in the rainfall of the state. Also, an attempt has been made to evaluate the impact of climate change on rainfall. The future rainfall can be estimated by using a global circulation model (GCM). However, due to the very coarse spatial resolution of the different GCM, we cannot use them directly. For matching this spatial inequality between the GCM output and historical precipitation data, we used the statistical downscaling technique. In the present study, we have examined the suitability of the artificial neural network with principal component analysis for downscaling the rainfall for different hilly districts of the state. We used the GCM model developed by Canadian Earth System Model, and the Indian metrological department gridded rainfall data. We performed the analysis for the different scenarios to visualize the impact of climate change on rainfall trends for all nine hilly districts of Uttarakhand. Results show that there was a clear indication of climate change in upper Himalayan Districts like Pithoragarh, Rudraprayag, and Chamoli, which was observed from the peak of monthly rainfall. The percentage change of monsoon rainfall in the future may go up to 200 % in the case of RCP8.5, and the change maybe around 180% for RCP4. Also, the volume of rainfall may increase in the case of RCP8.5 from July to September as compared to the historical data, i.e., there may be a shifting of monsoon rainfall in the future.

Projections of Wind Gusts for New York City Under a Changing Climate

Abstract To determine potential changes in the frequency and intensity of future storm events due to climate change in New York City (NYC), a statistical downscaling technique is proposed. First, a historical benchmark was determined using weather station data from the John F. Kennedy (JFK) and La Guardia (LGA) airports for the period 1973–2017. This historical information was used to perform the bias-correction exercise of near-future (2011–2050) global circulation model (GCM) output (ORNL RegCM4; RCP 8.5). Results show that NYC is projected to experience higher wind gusts under a warming climate for the period 2017–2050 in comparison with the historical data period, with the most extreme event projected to produce a maximum wind gust of approximately 110 mph, a significant increase over the past maximum of 80 mph. The historical 700-year return period event was estimated at 115 mph, while the overall 700-year event (historical and projected) is estimated at 124 mph. The most extreme cases of maximum daily wind gusts are projected to occur during the winter and early spring seasons. No increase in the number of projected tropical storms was observed, but the intensity of the storms is projected to be higher than during the historical period. These changes in extreme wind events could have serious implications for NYC in terms of urban planning, potential power outages, transportation disruptions, impacts on building structures, and public safety.