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Reliable projection of evapotranspiration (ET) is important for planning sustainable water management for the agriculture field in the context of climate change. A global dataset of monthly climate variables was generated to estimate potential ET (PET) using 14 General Circulation Models (GCMs) for four main shared socioeconomic pathways (SSPs). The generated dataset has a spatial resolution of 0.5° × 0.5° and a period ranging from 1950 to 2100 and can estimate historical and future PET using the Penman-Monteith method. Furthermore, this dataset can be applied to various PET estimation methods based on climate variables. This paper presents that the dataset generated to estimate future PET could reflect the greenhouse gas concentration level of the SSP scenarios in latitude bands. Therefore, this dataset can provide vital information for users to select appropriate GCMs for estimating reasonable PETs and help determine bias correction methods to reduce between observation and model based on the scale of climate variables in each GCM. [ABSTRACT FROM AUTHOR]

Atmospheric radiation is one of the most important atmospheric physics, and its expensive computation cost severely restricts the numerical simulation of atmospheric general circulation models. Therefore, it is necessary to study an efficient radiation parameterization scheme. Due to the powerful computing power of GPU, more and more numerical models are being transplanted to GPU. The CUDA C version (CC-RRTMG_SW) of the rapid radiative transfer model for general circulation models (RRTMG) shortwave radiation scheme (RRTMG_SW) has successfully run on GPU, but its computing efficiency is not yet very high, and the performance potential of GPU computing needs to be realized further. This paper is dedicated to optimizing CC-RRTMG_SW and exploring its maximum computing performance on GPU. First, a three-dimensional acceleration algorithm for CC-RRTMG_SW is proposed. Then, some optimization methods, such as decoupling data dependency, optimizing memory access, and I/O optimization, are studied. Finally, the optimized version of CC-RRTMG_SW is developed, namely CC-RRTMG_SW++. The experimental results demonstrate that the proposed acceleration algorithm and performance optimization methods are effective. CC-RRTMG_SW++ achieved good acceleration effects on different GPU architectures, such as NVIDIA Tesla K20, K40, and V100. Compared to RRTMG_SW running on a single Intel Xeon E5-2680 v2 CPU core, CC-RRTMG_SW++ obtained a speedup of 99.09 × on a single V100 GPU without I/O transfer. Compared to CC-RRTMG_SW, the computing efficiency of CC-RRTMG_SW++ increased by 174.46%. [ABSTRACT FROM AUTHOR]

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

The ocean general circulation model (OGCM) is an essential tool for researching oceanography and atmospheric science. The LASG/IAP climate system ocean model version 3 (LICOM3) is a parallel version of the OGCM. Our goal is to implement and optimize a GPU version of LICOM3 based on compute unified device architecture (CUDA) called LICOM3-CUDA. Considering the characteristics of LICOM3 and CUDA, we design and implement some pivotal optimization methods, including redesigning the numerical algorithms of complicated functions, decoupling data dependency, avoiding memory write conflicts, and optimizing communication. In this paper, we selected two experiments, including 1 ∘ (small-scale) and 0.1 ∘ (large-scale) resolutions to evaluate the performance of LICOM3-CUDA. Under the experimental environment of two Intel Xeon Gold 6148 CPUs and four NVIDIA Quadro GV100s, the LICOM3-CUDA (1 ∘ ) achieves a simulation speed of 114.3 simulation-year-per-day (SYPD). Compare with the performance of LICOM3, the LICOM3-CUDA can run much faster with 6.5 times, and the compute-intensive module achieves over 70 × speedup. In addition, the energy consumption for the simulation year is reduced by 41.3%. As for high-resolution and large-scale simulation, the number of GPUs increased from 96 to 1536 as well as the LICOM3-CUDA (0.1 ∘ ) time consumption decreased from 3261 to 720 seconds with approximately 4.5 × of speedup. [ABSTRACT FROM AUTHOR]

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Accurate climate projections help policymakers mitigate the negative effects of climatic changes and prioritize environmental issues based on scientific evidences. These projections rely heavily on the outputs of GCMs (General Circulation Models), but the large number of GCMs and their different outputs in each region confuses researchers in their selection. In this paper, we analyzed the performance of a CMIP6 (Climate Model Intercomparison Project Phase 6) multi-model ensemble for Pr (precipitation) data over NE (Northern Europe). First of all, we evaluated the overall performance of 12 CMIP6 models from GCMs in 30 years of 1985–2014. Furthermore, future projections were analyzed between 2071 and 2100 using SSP1-2.6 and SSP5-8.5 (Shared Socioeconomic Pathways). Then, simulations were statistically improved using an ensemble method to correct the systematic error of the CMIP6 models and then the capacity of postprocessed data to reproduce historical trends of climate events was investigated. Finally, the possible spatio-temporal changes of future Pr data were explored in Tana River Basin. The results of this study show that different CMIP6 models do not have the same accuracy in estimating Pr in the study area. However, the ensemble method can be effective in increasing the accuracy of the projections. The results of this study projected a change in the monthly Pr data over Tana River Basin by 2.46% and 2.06% from 2071 to 2100 compared to the historical period, based on SSP1-2.6 and SSP5-8.5, respectively. [ABSTRACT FROM AUTHOR]

To adapt to Earth's rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040–2059, compared to the historical period 1990–2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day. [ABSTRACT FROM AUTHOR]

Uncertainty sources in climate change impact studies can be addressed in two distinct phases, climate modelling and impact assessment. Generally, uncertainty from the climate modelling phase is the larger contributor and the impact phase uncertainty is sometimes marginalized. This paper aims to develop integrated uncertainty analysis approaches that deal with uncertainties at the impact level with all sources included. The existing uncertainty analysis method of Reliability Ensemble Averaging (REA) is modified for impact studies by bracketing the hydrological model parameter uncertainty. A novel probabilistic approach referred to as Bootstrapped Ensemble Uncertainty Modelling (BEUM) is also proposed for uncertainty analysis at the impact phase. Modified REA and BEUM are applied to streamflow projections of the Hulu Langat basin with uncertainties stemming from emission scenarios, general circulation models (GCM), downscaling method, and a hydrological model parametrization for a comprehensive integrated uncertainty assessment. The modified REA increased the reliability measure of each streamflow scenario and improved the uncertainty coverage percentage when compared with the original REA. The REA eliminated the projections of the less reliable GCMs, and therefore reduced uncertainty bands compared to the BEUM. The BEUM can create a large ensemble of streamflow projections through nonparametric bootstrapping of the limited impact scenarios and define the streamflow distribution for every quantile. Therefore, BEUM is able to encompass larger uncertainty intervals and explain the behavior of ensemble members through probabilistic distributions that can assist in decision making for climate change mitigation and adaptation planning. [ABSTRACT FROM AUTHOR]

General circulation models (GCM) show projections of climate variables that when downscaled can be applied to analyse future behaviour in different areas or places. Using them is possible not just to obtain expected values of climate variables but also to calculate their distributions and use those values to assess the effects of climate change at a local level. However, these calculations depend on the GCM selected. In this paper, daily maximum near-surface air temperatures from 21 climate models under representative concentration pathway (RCP) scenarios RCP 4.5 and RCP 8.5 and historic daily maximum temperatures (1990–2019) from nine cities in southern Spain are used with two objectives: first, to investigate past behaviour broken down into a deterministic part and a stochastic part; second, to compare historical data (2006–2019) with the information extracted from the 21 GCMs based on calculating goodness of fit in the period for both deterministic and stochastic parts. The methodology proposed may be useful in selecting a model or a range of models for use in a specific study. The results show positive historical and future trends in maximum daily temperature for these cities. The GCMs with the best fit for each city in this specific case are also presented. [ABSTRACT FROM AUTHOR]

The prediction of internal solitary waves (ISWs) propagation is a difficult problem in the field of oceanography due to the complexity of its generative mechanism and the lack of in-situ data. In this paper, the simulation data is used to establish a prediction model of ISWs propagation in the southern Andaman Sea by deep learning. The global tidal model TPXO7.2 is used to obtain the semi-diurnal tidal velocity data at the generation source of ISWs. The generation and propagation characteristics of ISWs are obtained by the Massachusetts Institute of Technology General Circulation Model (MITgcm). The generative location, amplitude, and propagation characteristics of the ISWs are calculated based on MITgcm. The database is set up by the extracted data from MITgcm. Then, the prediction model of ISWs propagation is established using long and short-term memory (LSTM) networks. Finally, the accuracy of the model is verified by the ISWs Moderate Resolution Imaging Spectroradiometer (MODIS) images of the southern Andaman Sea and in-situ data. The results demonstrate that, compared with the MODIS images, the average relative error of time predicted by the LSTM model is 17.81%, and the average absolute error is 0.95 h. The average relative error of longitude predicted by the LSTM model is 0.10%, and the average absolute error is 0.10°. The average of the amplitudes predicted by the LSTM model is 56.99 m. The in-situ data of the amplitude of ISWs is 60 m in the previous research. The two amplitudes are relatively close. [ABSTRACT FROM AUTHOR]

Observations of vibrationally excited hydroxyl (OH*) emissions are widely used to obtain information on atmospheric dynamics and composition. In this paper, several analytical approximations are presented for characteristics of the hydroxyl layer in the Martian atmosphere, such as OH* concentrations at the maximum and the height of the maximum. Relationships are also given for estimating the influence of various factors on the OH* layer in nighttime conditions. These characteristics are determined by the ambient temperature and the concentration of atomic oxygen, including their vertical gradients. The obtained relationships are applied to the results of numerical modeling using the global atmospheric circulation model to predict the seasonal behavior of the hydroxyl layer on Mars. Based on the modeling data, the annual and intra-annual variations in the concentration of excited hydroxyl and the height of the OH* layer on Mars show both similarities to and considerable differences from those on Earth. The concentration and the height of the maximum in the equatorial, northern, and southern middle latitudes vary with the season, with the maximum concentrations and the lowest height being recorded in the first half of the year. Model calculations confirmed the presence of a peak in the OH* concentration in polar latitudes in winter at a height of approximately 50 km with volume emission densities of 2.1, 1.4, and 0.6 × 104 photons cm–3 s–1 for the transitions of vibrational levels 1–0, 2–1, and 2– 0, respectively. The resulting relationships can be used to analyze measurements and interpret their variations. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

Solar Irradiance depicts the light energy produced by the Sun that hits the Earth. This energy is important for renewable energy generation and is intrinsically fluctuating. Forecasting solar irradiance is crucial for efficient solar energy generation and management. Work in the literature focused on the short-term prediction of solar irradiance, using meteorological data to forecast the irradiance for the next hours, days, or weeks. Facing climate change and the continuous increase in greenhouse gas emissions, particularly from the use of fossil fuels, the reliance on renewable energy sources, such as solar energy, is expanding. Consequently, governments and practitioners are calling for efficient long-term energy generation plans, which could enable 100% renewable-based electricity systems to match energy demand. In this paper, we aim to perform the long-term prediction of daily solar irradiance, by leveraging the downscaled climate simulations of Global Circulation Models (GCMs). We propose a novel Bayesian deep learning framework, named DeepSI (denoting Deep Solar Irradiance), that employs bidirectional long short-term memory autoencoders, prefixed to a transformer, with an uncertainty quantification component based on the Monte Carlo dropout sampling technique. We use DeepSI to predict daily solar irradiance for three different locations within the United States. These locations include the Solar Star power station in California, Medford in New Jersey, and Farmers Branch in Texas. Experimental results showcase the suitability of DeepSI for predicting daily solar irradiance from the simulated climate data, its superiority over related machine learning methods, and its ability to reproduce the daily variability. We further use DeepSI with future climate simulations to produce long-term projections of daily solar irradiance, up to year 2099. [ABSTRACT FROM AUTHOR]

Accurate streamflow estimation is vital for effective water resources management, including flood mitigation, drought warning, and reservoir operation. This paper aims to evaluate four machine learning (ML) algorithms, namely, Long Short-Term Memory (LSTM), Regression Tree, AdaBoost, and Gradient Boosting algorithms, to predict the futuristic streamflow of the Swat River basin. Ten General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSPs) 245 and 585 were used for futuristic streamflow assessment. The ML models were developed using maximum temperature, minimum temperature, and precipitation as the input variables while streamflow as the target variable. The performance of ML models was assessed via statistical performance indicators, namely the coefficient of determination (R2), mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), Nash Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). The AdaBoost exhibits exceptional performance (R2: 0.99 during training, 0.86 during testing). The futuristic streamflow projection shows an increase in mean annual streamflow between 2050 and 2080 s from 3.26 to 7.52% for SSP245 and 3.77–13.55% for SSP585. ML models, notably adaboost, provide a reliable method for projecting streamflow, will assist in hazard and water management in the area. [ABSTRACT FROM AUTHOR]

The well-posedness of the dynamic framework in earth-system model (ESM for short) is a common issue in earth sciences and mathematics. In this paper, the authors first introduce the research history and fundamental roles of the well-posedness of the dynamic framework in the ESM, emphasizing the three core components of ESM, i.e., the atmospheric general circulation model (AGCM for short), land-surface model (LSM for short) and oceanic general circulation model (OGCM for short) and their couplings. Then, some research advances made by their own research group are outlined. Finally, future research prospects are discussed. [ABSTRACT FROM AUTHOR]

There has been a growing interest in model-wise uncertainty decomposition, which quantifies contribution of individual models such as emission scenarios, global circulation models, bias correction techniques and hydrological models, to the total uncertainty of a hydrological projection. However, little research has been conducted for model-wise uncertainty decomposition in spite of its usefulness. In this paper, we propose a novel method for decomposing the total uncertainties into model-wise uncertainties. The proposed model-wise uncertainty decomposition method can be applied with general uncertainty measures, which include mean absolute deviation and variance measures. Moreover, the proposed method provides an intuitive interpretation of the quantified model-wise uncertainties. The results of analyzing real data by the proposed method are presented. [ABSTRACT FROM AUTHOR]

This paper assesses the long-term combined effects of land use (LU) and climate change on river hydrology and water scarcity of two rivers of the Western Ghats of India. The historical LU changes were studied for four decades (1988–2016) using the maximum likelihood algorithm and the long-term LU (2016–2075) was estimated using the Dyna-CLUE prediction model. Five General Circulation Models (GCMs) were utilized to assess the effects of climate change (CC) and the Soil and Water Assessment Tool (SWAT) model was used for hydrological modeling of the two river catchments. To characterize granular effects of LU and CC on regional hydrology, a scenario approach was adopted and three scenarios depicting near-future (2006–2040), mid-future (2041–2070), and far-future (2071–2100) based on climate were established. The present rate of LU change indicated a reduction in forest cover by 20% and an increase in urbanized areas by 9.5% between 1988 and 2016. It was estimated that forest cover in the catchments may be expected to halve compared to the present-day LU (55% in 2016 to 23% in 2075), along with large-scale conversion to agricultural lands (13.5% in 2016 to 49.5% in 2075). As a result of changes to LU and forecasted climate, it was found that rivers in the Western Ghats of India might face scarcity of fresh water in the next two decades until the year 2040. However, because of large-scale LU conversion toward the year 2050, streamflow in rivers might increase as high as 70.94% at certain times of the year. Although an increase in streamflow is perceived favorable, the streamflow changes during summer and winter may be expected to affect the cropping calendar and crop yield. The changes to streamflow were also linked to a 4.2% increase in ecologically sensitive wetlands of the Aghanashini river catchment. [ABSTRACT FROM AUTHOR]

Weather generators (WGs) are one of the major downscaling tools for assessing regional climate change impacts. However, some deficiencies in the performance of WGs have limited their usage. This paper presents a method for correcting the low-frequency variability (LFV) of precipitation in the improved weather generator (IWG) model. The method is based on bias correction in the monthly precipitation distribution of the generated daily series. The performance of the modified model was tested directly by comparing the statistics of generated and observed weather data for 14 stations, and also indirectly by comparing the characteristics of simulated stream-flows of a basin from the simulation run based on generated and observed weather data. The results showed that the method not only corrected the LFV of precipitation but also improved the reproduction of many other statistics. The provided IWG2 model can serve as a useful tool for the downscaling of general circulation model (GCM) scenarios to assess regional climate change impacts, especially hydrological effects. [ABSTRACT FROM AUTHOR]

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets. [ABSTRACT FROM AUTHOR]

Over time scales between 10 days and 10–20 years—the macroweather regime—atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the (fractional) energy balance equation, which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. They are obtained when the order of the relaxation equation is fractional rather than integer and they are solved as past value problems rather than initial value problems. StocSIPS was first developed for monthly and seasonal forecast of globally averaged temperature. In this paper, we extend it to the prediction of the spatially resolved temperature field by treating each grid point as an independent time series. Compared to traditional global circulation models (GCMs), StocSIPS has the advantage of forcing predictions to converge to the real-world climate. It extracts the internal variability (weather noise) directly from past data and does not suffer from model drift. Here we apply StocSIPS to obtain monthly and seasonal predictions of the surface temperature and show some preliminary comparison with multi-model ensemble (MME) GCM results. For 1 month lead time, our simple stochastic model shows similar—but somewhat higher—values of the skill scores than the much more complex deterministic models. [ABSTRACT FROM AUTHOR]

Dynamics of the stratosphere and ozone layer are among important sources of atmospheric circulation predictability at subseasonal-to-seasonal time scales. The simulation of the stratospheric dynamics with the SL-AV atmospheric general circulation model for the SLAV072L96 seasonal weather prediction configuration is analyzed. The configuration is currently under preoperational testing at the Hydrometcenter of Russia. The model simulates both winter and summer averaged distributions of zonal wind and temperature close to the reanalysis data. The quasi-biennial oscillation of equatorial zonal wind is simulated with a realistic period and amplitude. There is a significant reduction of errors as compared with the previous stratosphere-resolving SL-AV model configuration. It is shown that the stratospheric process simulation enhancement is largely due to the reduction of systematic errors in the simulation of troposphere dynamics. The work on the inclusion of the CHARM photochemical model in the SL-AV model is described. The results of first experiments with the coupled model are given. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the level of description of the main physical processes in the Earth's atmosphere in modern models of the general circulation of the Earth's atmosphere and gives a brief overview of the modern models used by the main forecasting centers. The promising directions of the development of models of the general circulation of the Earth's atmosphere are discussed. [ABSTRACT FROM AUTHOR]

For ocean afforestation (lower illustration), a proportion of the nutrient inventory, which controls the magnitude of all carbon pools and fluxes, are diverted to the corresponding seaweed carbon pools and fluxes. Also, it assumes linearity between the baseline and the ocean afforestation scenario although the implementation of ocean afforestation may have synergistic/antagonistic effects on phytoplankton carbon sequestration. This statement is remarkable because Wang et al. swiftly convert ocean afforestation into a sub-category of ocean nutrient fertilisation in which carbon sequestration is enabled by the supply of additional nutrients and then supposed to be boosted by seaweeds. [Extracted from the article]

Bimodality in precipitation frequency distributions is often evident in atmospheric models, but rarely in observations. This study i) proposes a metric to objectively quantify the bimodality in precipitation distributions, ii) evaluates model simulations contributed to the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5), phase 6 (CMIP6), and the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) project by comparing them to satellite-based and reanalysis precipitation products, and iii) investigates possible origins of bimodal precipitation distributions. Our results reveal that about 83% (20 out of 24) of CMIP5 and 70% (21 out of 30) of CMIP6 models used in this study exhibit bimodal distributions. The few DYAMOND models that use a deep convective parameterization also show bimodal distributions, while most DYAMOND models do not. Predictably, the bimodality originates from the separation of precipitation process between resolved grid-scale and parameterized subgrid-scale. However, in a larger number of models bimodality arises from the parameterized subgrid-scale convective precipitation alone. [ABSTRACT FROM AUTHOR]

This study evaluates hydrology and hydrological extremes under future climate change scenarios. The climate change scenarios were developed from multiple Global Circulation Models (GCMs), Representative Concentration Pathway (RCP) scenarios, and statistical downscaling techniques. To ensure hydrological model robustness, the Soil Water Assessment Tool (SWAT) was calibrated and validated using the Differential Split Sample Test (DSST) approach. The model was also calibrated and validated at the multi-gauges of the watershed. Future climate change scenarios revealed a reduction in precipitation (in the order of −9.1% to 4.9%) and a consistent increase in maximum temperature (0.34°C to 4.10°C) and minimum temperature (−0.15 °C to 3.7°C) in different climate model simulations. The climate change scenarios triggered a reduction of surface runoff and streamflow and a moderate increase in evapotranspiration. Future climate change scenarios projected a decrease in high flow (Q5) and low flow (Q95). A higher reduction of Q5 and annual minimum flow is also simulated in future climate scenarios, whereas an increase in annual maximum flow is simulated in climate change scenarios developed from the RCP8.5 emission scenario. The study suggests optimal water management structures which can reduce the effect of change in high and low flows. [ABSTRACT FROM AUTHOR]

Groundwater plays an important part in protecting the ecological values of the earth's systems. Assessment of groundwater (GW) recharge due to change in land use/land cover (LULC) and climate variables is essential for integrated water management and adaptation. This study is an attempt to check the combined influence of change in LULC and climate variables on GW recharge in Kallada river basin, Kerala, India. The LULC map is predicted for the year 2030 based on LULC maps during the years 1985, 1995, 2005 and 2015 using Markov chain and multilayer perceptron model. The major LULC category in the study area is plantation with an area of 797.8 km2 (49.6% in 2015) and dense forest with an area of 366.5 km2 (22.8% in 2015). Considering the period 1985–2005, a decrease of 7.43% dense forest and an increase of 2.55% built area were observed. The predicted LULC shows that there is a reduction in plantation (3.77%) and dense forest (1.28%) and increase in built-up land (4.12%) by 2030 as compared to the year 2015. The CMIP5 General Circulation Model is used for future projections of climate variables, namely daily precipitation, maximum temperature and minimum temperature under RCP 4.5 and RCP 8.5 scenarios. Multilayer perceptron neural network model was used for statistical downscaling of GCM projections. The SWAT model was used to estimate the groundwater recharge for the year 2030 using the predicted LULC map and hydro-meteorological variables. The results illustrate a significant drop in GW recharge in Kallada river basin by the year 2030. Average GW recharge in the Vettikavala and kottarakkara, which is in the western region of the basin, is reduced to 4.6% and 9.3% under RCP 4.5 and RCP 8.5 scenarios, compared to the average GW recharge in 2005, which was 24.8% of rainfall. The decline in the groundwater recharge in the western regions may be due the increase in built-up area and decrease in plantation. The proposed model results provide a reliable insight for water resources planners in conducting future research on groundwater resources. [ABSTRACT FROM AUTHOR]

The INMIO-CICE hydrodynamic model of the Caspian Sea was used to calculate equilibrium river runoff and evaporation from the sea surface over a wide range of sea levels (from –85 to +50 m a.s.l.) for different climatic conditions: the Last Glacial Maximum (about 21 ka BP) and the pre-industrial climate (~1850). Data from the INMCM4.8 climate model were used as boundary conditions. It was found out that a river runoff of about 400 km3/yr was required in the Last Glacial Maximum to maintain the sea level at 35–50 m a.s.l. corresponding to the maximum values of the Khvalynian transgression. In the Last Glacial Maximum, evaporation from the sea surface decreased by 105–175 mm (12–22%), and a precipitation layer, according to the INMCM4.8 model, decreased by 50–70 mm (15–30%). These conditions caused the equilibrium runoff to decrease by about 10–20% compared to the pre-industrial conditions. Smaller absolute and relative changes corresponded to lower sea levels. The maximum evaporation decrease occurred at 5 m a.s.l. [ABSTRACT FROM AUTHOR]

Emerging large ensemble climate datasets produced by multiple general circulation models and their downscaling products challenge the limits of hydrodynamic models because of the immense data size. To overcome this new challenge and estimate the discharge quantiles corresponding to different return periods at all river sections in an entire region, this study proposes an event-based regional approach that uses a nationwide distributed rainfall–runoff model as well as large climate projection ensembles. This approach addresses the high computational burden associated with continuous simulations and solves the problem of conventional event-based simulations serving only a single outlet of a basin. For our analysis, we extracted 372 annual maximum 48 h rainfall events that cover the entirety of Shikoku Island and its eight major river basins. Peak discharges were estimated using a 150 m resolution rainfall–runoff–inundation model. These discharges were then screened using either the peak-over-threshold (POT) method or block maxima (BM) method, and frequency curves were subsequently constructed and evaluated. The primary reason for the necessity of POT or BM was to avoid interference from extraneous low discharges. The POT-based frequency curves showed good accuracy when using peak discharges in the range of the top 10–50%, and the results remain stable within this threshold range. The BM method, employing block sizes of 2–5 years, can generate relatively accurate frequency curves, but the choice of block size introduces significant variations in results among certain basins. Generally, the accuracy of results based on the POT method surpasses that of the BM method. Considering the accuracy, computational cost, and result stability, the POT method is preferred. The error introduced by the regional approach was acceptable with more than half of the relative root-mean-square errors remaining within 10% and basically all of the results are within 20%. The results of the regional approach exhibited good accuracy across climate scenarios and provided consistent information regarding future flood quantiles. This study serves as the foundation for high-resolution future flood risk assessment. [ABSTRACT FROM AUTHOR]

Eucalyptus globulus is a species endemic to southeastern Australia. It has naturalized non-native ranges in other parts of Australia, Europe, Africa, and the western United States. This study is the first of its kind in Ethiopia to model and map the spatiotemporal distribution of the species using species distribution models (SDMs). A total of 874 occurrence records were used from the online Global Biodiversity Information Facility (GBIF) database and field observation. Three environmental variables, including terrain, climate, and soil were used to predict the species' distribution. The terrain, climate, and soil raster grids were resampled to a 200-meter resolution. The Global Circulation Model (GCM) HadGEM3-GC3.1 was used to extract future climate data. This GCM has a good match between the atmospheric and oceanic components showing little drift in its surface climate. Besides, it has the best coverage of Africa. Three climate change scenarios (SSPs 1-2.6, SSPs 2-4.5, and SSPs 5-8.5) were used for predicting suitable habitat of the species. The jackknife test was chosen to assess the importance of each environmental predictor variable. The model's performance was evaluated using the Area under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The model had excellent predictive performance with an average AUC of 0.94. Altitude, rooting conditions, slope, dry-month precipitation, and temperature seasonality are the most important environmental factors in shaping E. globulus distribution. Ethiopian highlands are predicted to be more suitable to the species, but the increase in temperature seasonality may reduce suitable habitat under the high-forcing climate change scenario. Climate change is expected to create more suitable habitats for eucalyptus in the future which may encourage plantations in potential distribution areas. Consequently, ensuring long-term forest health necessitates robust management systems prioritizing native trees and responsible grower or farmer practices. [ABSTRACT FROM AUTHOR]

Use cases for climate forecasts over the Mediterranean were demonstrated during MEDSCOPE, and skilful forecasts depend on the ability of dynamical, empirical and hybrid systems to represent climate drivers, climate variability patterns and teleconnections that are relevant at these time scales. The overarching goal of ERA4CS-MEDSCOPE project (www.medscope-project.eu) co-funded by JPI-Climate and the European Union Horizon 2020 program from 2017 to 2021 was to enhance the uptake of climate predictions at the seasonal-to-decadal timescale over the Mediterranean region. [Extracted from the article]

Climate change is among the most pressing challenges for human advancement in the twenty-first century. Climate change is having a significant impact on the agricultural sector. The main objective of this study is to evaluate the food security status and sustainability of crop production under different climate change scenarios in the arid and semi-arid zones of Iran. To this end, the Statistical Down-Scaling Model (SDSM) and the outputs of the General Circulation Model (GCM) are employed to project future climate parameters under three climate scenarios. The study then employs the Just and Pope approach by using a panel dataset to evaluate the impact of climate change on crop production. Also, the multi-criteria decision-making (MCDM) technique is used to assess the sustainability of crop production from an economic, social, and environmental perspective in the context of climate change and baseline conditions. The results of the study show that precipitation has a significant favourable effect on crop yield in both zones. Maximum temperature is positively and significantly related to crop yield in the semi-arid area, while the relationship is negative in the arid region. Future projections reveal that under different climate scenarios, the production of irrigated wheat, dryland wheat, irrigated barley, dryland barley, and potato crops will change by a maximum of approximately 7.57%, 35.70%, 62.86%, 15.90%, and 26.88% in the semi-arid zone and − 24.07%, 29.73%, − 31.33%, − 4.80%, and 25.80% in the arid zone, respectively. The findings imply that climate change will decrease the food security index for all crops in the arid zone, while improving the situation for crops in the semi-arid region. The results also indicate that future climate change can have a significant adverse effect on economic water productivity, economic benefits, and the sustainability of strategic crop production in the arid zone compared to the semi-arid zone. Given that a large part of Iran is covered by an arid climate, designing coherent adaptation actions and mitigation policies need to be prioritized to tackle the negative impact of climate change on food production systems. [ABSTRACT FROM AUTHOR]

The main objective of this research is to evaluate the effects of climate change first on precipitation and temperature, and then on the runoff characteristics of two tropical watersheds located in Senegal and Guinea-Bissau. To achieve this, eighteen General Circulation Models (GCMs) were selected to measure various climate change scenarios under the Shared Socioeconomic Pathways (SSP) SSP1-2.6 and SSP5-8.5, using the reference period of 1985–2014. The GR2M hydrological model was employed to replicate past monthly surface runoff patterns for the Casamance and Kayanga-Géva watersheds. After calibrating and validating the GR2M model, the researchers simulated the predictable effect of climate change on the flow for the near future (2021–2040), medium future (2041–2060), and distant future (2081–2100) for each watershed, using the GCM multi-model ensemble mean. The quantile method was used to correct bias in temperature and precipitation data. The results of bias correction give a correlation coefficient greater than 0.9% for temperatures and 0,6% precipitation between the outputs of the multi-model ensemble and observations used. The results indicate also that all watersheds are expected to experience drier conditions in the near-future, mid-future, and far-future periods under both the SSP1-2.6 and SSP5-8.5 scenarios. Furthermore, the predictable temperature trends consistently show a warmer situation with growing radiative making in the future times. However, the primary factor influencing changes in flow for all watersheds is the projected precipitation changes. The anticipated drier conditions in the near-future, mid-future, and far-future horizons under both scenarios would lead to significantly reduced runoff volumes at the beginning and middle of the rainy season. Consequently, the projected seasonal changes in river flow for all catchments (e.g., under SSP5-8.5 scenario, a decline of -34.47%, -56.01%, and -68.01% was noted, respectively, for the horizons 2050, 2070, and 2090 for the Casamance basin) could lead to new frequent occurrences of drought and water scarcity associated with past hydrological regimes. These scenarios enhance the necessity of improving water management, water prizing, and water recycling policies, to ensure water supply and to reduce tensions among regions and countries. [ABSTRACT FROM AUTHOR]

Understanding the physical mechanisms of the Madden-Julian Oscillation (MJO) and its evolution is a major concern within the climate community. Its main importance relies on its ability to act as a source of predictability within the intra-seasonal time-scale in tropical and extratropical regions, therefore filling the gap between weather and climate forecasts. However, most atmospheric general circulation models fail to correctly represent MJO's evolution, and their prediction skills are still far from MJO's theoretical predictability. In this work we infer low dimensional models of the MJO from data by applying a recently developed machine learning technique, the Sparse Identification of Non-linear Dynamics (SINDy). We use the daily-mean outgoing longwave radiation MJO index (OMI) as input data to infer bi-dimensional climatological models of the MJO, and analyse the inferred models during El Niño and La Niña years. This approach allows us to diagnose the MJO's behaviour in OMI's phase space. Our results show that MJO can be most frequently represented by a harmonic oscillator, which represents the MJO's eastward propagation and characteristic period. Upon this basic oscillatory behaviour, we find that small non-linear corrections play a fundamental role in representing MJO's non-uniform speed of propagation, explaining its acceleration over the Pacific Ocean region. Particularly, we find that MJO's evolution is most frequently non-linear [linear] during El Niño [La Niña] years. Overall, our work shows that SINDy can robustly model MJO's evolution as a linear oscillator with small non-linear corrections, contributing to understand the MJO's dynamics and dependency on El Niño-Southern Oscillation. [ABSTRACT FROM AUTHOR]

The first rainy season (FRS) in South China from late-spring to early-summer represents the beginning of the summer monsoon rainy season over eastern China. This study investigates the interannual relationship between the South Pacific meridional sea surface temperature (SST) dipole (SPMD) and the concurrent FRS precipitation anomalies over South China during April–June (AMJ) without El Niño/Southern Oscillation (ENSO) impact. During AMJ, after removing the concurrent ENSO signal through the linear regression, the first empirical orthogonal function (EOF) mode of SST anomalies in the South Pacific exhibits a meridional dipole with warming in the tropical southeastern Pacific and cooling in mid-latitudes of the South Pacific. The first EOF mode is referred to as the SPMD pattern. The off-equatorial enhanced diabatic heating associated with the northeastern pole of the SPMD triggers low-level cross-equatorial northerlies in the tropical north central Pacific (TNCP) through the Matsuno–Gill type response. The anomalous northerly and its associated TNCP SST cooling lead to a Northwest Pacific anomalous anticyclone (NWP-AAC) via the wind-evaporation-SST feedback. The atmospheric general circulation model sensitivity experiments further suggest that the SPMD can induce the NWP-AAC directly or collaboratively with the TNCP SST cooling. Additionally, anomalous moisture can be transported from the tropical western Pacific into South China, giving rise to enhancement of the FRS precipitation. The Liang–Kleeman information flow for causality analysis and ensemble mean of 30-member atmospheric model simulation results further suggest a robust physical pathway that the SPMD is related to the FRS precipitation over South China when ENSO absents. Our results may deepen the understanding of interannual rainfall variability in South China in non-ENSO years. [ABSTRACT FROM AUTHOR]

Glyptostrobus pensilis (Cupressaceae) is the only surviving species of the genus Glyptostrobus. Although the species is widely cultivated throughout China, in the wild it only occurs as small stands in southeastern China, central Laos, and southern Vietnam. However, its low genetic variability, lack of recruitment, and the progressive destruction of its habitat caused by humans, have meant that the populations are showing a clear declining trend and the species as a whole is threatened. Ecological niche modeling is used here to study the present potential distribution, as well as in the future (2061–2080) using several global circulation models under two of the shared socioeconomic pathways (SSP 126 and SSP 585) that are being used to produce the IPCC Sixth Assessment Report. The current potential area of G. pensilis is of 1,452,481 km2 with the areas of greatest probability of presence located in southeastern China. This potential area is reduced for the future according to most models, with greater losses for the SSP 585 scenario. Between 6.9 and 31.3% of all wild populations of G. pensilis would be outside potential areas (including the Lao populations, which harbor the highest levels of genetic variability). Conservation measures include the expansion of the current network of protected areas (since over 90% of wild populations do not occur within them), the development of propagation techniques, and the carrying out of translocation activities that should require international collaboration among the countries in which the species is found. With the current knowledge, we have reassessed the threat status of the species under the IUCN criteria, downgrading it from CR to EN both for China and at global level. [ABSTRACT FROM AUTHOR]

Mankind is currently facing a crucial challenge in terms of climate change which may result in severe drought in many areas across the world. The present study analysed the joint return periods of meteorological drought events for historical and future projections across the Vidarbha region of India. To describe the drought occurrences in the area, the study used the standardized precipitation evapotranspiration index. The properties of each drought event, viz., duration, severity, and peak, were identified to model the multivariate drought risks. Several copula families were evaluated using statistical tests for joint dependency modelling of drought properties. The obtained joint distribution and selected univariate marginals are further used for estimating trivariate return periods for the historical and future climate change scenarios. The drought properties exhibited good interdependency justifying the use of a trivariate framework. The observed historical data of rainfall, maximum and minimum temperature were collected from the India Meteorological Department. The latest future projections of climate variables from coupled model intercomparison project phase 6 (CMIP6) datasets were acquired from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) for six general circulation models. The copula-based trivariate frequency analysis was performed to compare the return periods associated with 'moderate' and 'above moderate drought' (i.e., combination of severe and extreme drought) events for the reference period (1981–2020) and future periods (2021–2100) under two SSP scenarios, SSP2-4.5 and SSP5-8.5. The 'AND' condition return period showed high drought risk during historical period covering a larger area. The findings of the study suggested that for 'AND' condition the trivariate frequency of above moderate droughts would increase in the future across Vidarbha region. The analysis showed that during near future more area will be under the risk of above moderate drought conditions compared to the far future, and the high-risk zones will be more under SSP5-8.5 than SSP2-4.5 scenario. The frequency of moderate drought episodes will be higher than the above moderate drought events, but the percentage area in the high-risk zone will be lower. The 'OR' condition return period projected alleviation of drought risk in the future, while the far future projected more area under drought risk compared to near future for the SSP scenarios. [ABSTRACT FROM AUTHOR]

Most state-of-the-art general circulation models cannot well simulate the diurnal cycle of precipitation, especially the nocturnal rainfall peak over land. This study assesses the diurnal cycle of precipitation simulated using the Global-to-Regional Integrated forecast SysTem (GRIST) in its numerical weather prediction (NWP) configuration at resolutions typical of current global climate models. In the refinement region, the variable-resolution model well distinguishes the distinct features of diurnal cycle. No apparent artificial features are observed in the transition zone of the variable-resolution mesh. The model also exhibits a similar diurnal cycle pattern to the observation in the coarse-resolution region. We further investigate the model behaviors of dynamics–physics interaction by analyzing hourly dynamical and thermodynamical diagnostics. Composite analysis based on rainfall peak time is applied to examine the model capability in distinguishing different precipitation processes of daytime and nighttime peaks. Over East Asia, the model well reproduces both the nocturnal-to-early-morning and the afternoon rainfall peaks. The model simulates the dominant contribution of large-scale upward moisture advection to the formation of stratiform-like rainfall peaks, and produces daytime surface-heating induced rainfall. Refinement of the resolution substantially increases the composited nighttime precipitation intensity but has little impact on the composite percentage. The model captures the realistic dynamical and thermodynamical conditions for the occurrence of nocturnal rainfall. These results demonstrate that the variable-resolution model is able to reproduce the diurnal cycle of climatological summer rainfall through realistic precipitation processes. [ABSTRACT FROM AUTHOR]

Due to the considerable biases in general circulation models (GCMs) simulation, bias correction methods are required and widely applied to reduce the model biases for impact studies. This study evaluated the performance of two bias correction methods, quantile delta mapping (QDM) and scaled distribution mapping (SDM), for generating high-resolution daily maximum temperature (Tmax) and minimum temperature (Tmin) projections for Canada using the latest GCMs from the Coupled Model Intercomparison Project phase 6 (CMIP6). CMIP6 GCMs show overall consistency with observations before and after bias correction, with better performance on Tmax compared to Tmin. QDM shows better performance relative to observations while SDM shows superior skill in preserving the raw climate signals. QDM and SDM methods are effective in reducing the biases of Tmax and Tmin for all GCMs. Both methods show similar skills in reproducing monthly probability distribution and capturing seasonal spatial patterns over Canada. Multi-model ensemble means have good performance in simulating the monthly mean of Tmax and Tmin but poor performance for high and low quantiles as well as standard deviation. QDM and SDM corrected ensemble means have the best performance. This study presents a comprehensive assessment of bias correction methods applications for individual CMIP6 GCMs and their multi-model ensemble means for high-resolution daily temperature predictions for Canada, providing a reference significance for bias correction studies as well as technical support for further impact assessment and adaptation planning around the world. [ABSTRACT FROM AUTHOR]

Previous studies suggest that the winter surface freshening in the southeastern Arabian Sea (SEAS) contributes to the development of very high Sea Surface Temperatures (SST) thereby influencing the following summer monsoon onset. Here, we use forced and coupled simulations with a regional ocean general circulation model to explore the SEAS Sea Surface Salinity (SSS) variability mechanisms and impact on the monsoon. Both configurations capture the main SEAS oceanographic features, and confirm that the winter SSS decrease results from horizontal advection of Bay of Bengal freshwater by the cyclonic circulation around India during fall. A coupled model sensitivity experiment where salinity has no effect on mixing indicates that the salinity stratification reduces the SEAS mixed layer cooling by vertical processes by 3 °C seasonally. Salinity however enhances mixed layer cooling by a similar amount through concentrating negative winter surface heat fluxes into a thinner mixed layer, resulting in no climatological impact on SST and summer monsoon rainfall. The Indian Ocean Dipole (IOD) is the main driver of the winter SEAS SSS interannual variability (r ~ 0.8). Salty anomalies generated in the western Bay of Bengal during fall by positive IOD events are indeed transported by the cyclonic climatological coastal circulation, reaching the SEAS in winter. By this time, warm IOD-induced SST anomalies in the SEAS are already decaying, and the SEAS SSS anomalies hence do not contribute to their development. Overall, our model results suggest a weak climatological and interannual impact of the SEAS winter freshening on local SST and following monsoon onset. [ABSTRACT FROM AUTHOR]

Simulating the features of the African Easterly Waves (AEWs), such as their westward propagation off the east Atlantic coast, is challenging for coarse-resolution climate models. In this study, we use High-Resolution Atmospheric Model (HiRAM) to simulate AEWs and analyze their future projections by the end of the twenty first century. The simulations are performed globally at a horizontal resolution of ∼ 25 km. The model uses shallow convective parameterization for moist convection and stratiform cloudiness. Future projections are conducted using representative concentration pathway 8.5. The AEWs are separated with respect to their periods as 3–5- and 6–9-day period AEWs, and bandpass filtering is used to filter the waves from the mean flow. HiRAM simulates structure and propagation of the waves well; however, it tends to overestimate the associated precipitation. In the future, the AEW precipitation and intensity of the circulation will considerably increase. The northward extent of the AEW track also shows a significant increase in the future. Enhanced baroclinic overturning and eddy available potential energy generated due to diabatic heating is also observed in the future. [ABSTRACT FROM AUTHOR]

The three-dimensional MITgcm (MIT general circulation model) was applied to simulate wind-induced baroclinic oscillations in Cayuga Lake, to obtain an understanding of the internal seiche/surge dynamics and associated mixing in long narrow lakes. The MITgcm has not been rigorously validated for closed basins against field observations. Thus, qualitative and quantitative methods were used to validate the model and study the sensitivity to different model parameters against observed temperature data. The linear equation of state (EoS) yielded poor results, in comparison to the polynomial EoS formulations where the density gradient was large. The vertical density stratification was strongly sensitive to the background vertical viscosity and diffusivity (when > 10–5 m2s−1), because the prescribed background values control mixing in the KPP scheme, except on the surface and bottom boundary layers. After calibration, the model correctly simulated the vertical stratification, upwelling, basin-scale seiche (with a horizontal mode-one period T1 = 80 h) and surge formation with a basin-wide root-mean-square-error 1.9 °C. Flow visualization indicated that internal surges evolved due to (i) a wind-induced locally downwelled thermocline (wind duration < T1/4), (ii) a basin-scale wind-induced upwelled thermocline (wind duration > T1/4) and (iii) internal hydraulic jumps. Article Highlights: The MITgcm was extensively validated against field data in simulation of Cayuga Lake A polynomial equation-of-state and space–time variable turbulence closure scheme were applied Three distinct processes were found to generate internal surges [ABSTRACT FROM AUTHOR]

The Pacific Walker circulation (PWC) is one of the major atmospheric circulations that plays an essential role in ocean-atmosphere interactions and global climate. The response of the PWC to greenhouse warming remains a mystery and model results are inconsistent. Based on multimodel simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6), this study explores the intermodel uncertainty of the change in the PWC under global warming. The combination of the El Niño-like warming pattern and the interbasin warming contrast between the Indian and Pacific Oceans strengthens (weakens) the west (east) branch of the Pacific trade winds, resulting in a structural shifting of the PWC. By conducting a set of Atmosphere General Circulation Model (AGCM) experiments, we demonstrate that the western Pacific warming plays a critical role in driving the PWC shift. An intensified western Pacific warming counteracts the effect of Indian Ocean warming on the PWC, leading to a uniformly weakened PWC in the tropical Pacific due to the SST gradient. In contrast, a decreased warming in the western Pacific strengthens the west branch of the PWC, shifting the turning longitude of zonal wind changes eastward. Our finding highlights that the relative warming pattern in the Indian and Pacific Oceans is coupled with the PWC change in a warmer climate. [ABSTRACT FROM AUTHOR]

Climate change is imminent and threatens the largest watershed in the world, the Amazon basin. As general circulation models may fail to represent cloud-scale phenomena, precipitation in a changing climate under global warming is still a factor of great uncertainty, especially in Tropical regions. In this study, we used long-term high-resolution simulations from a global cloud-resolving model under the scope of the Coupled Model Intercomparison Project (CMIP6) to verify the climate change impacts on the mesoscale convective systems (MCSs) over the Amazon basin. We generated a complete spatial, temporal, and statistical characterization of the MCSs for the past (1950–1960), present (2000–2010), and near-future (2040–2050). We found that MCSs are a critical mechanism for precipitation, especially in austral winter. The simulations are consistent with the observed precipitation and MCSs patterns over the Amazon basin, indicating that MCSs are less frequent compared to the past and are expected to continuously decline in the near-future. Most decreases are projected from September to December, while an increase between June to August, mainly in the southern portion of the Amazon basin. In addition, the investigation presented here shows the great potential of using a global cloud-resolving model under the CMIP6 scope to investigate mesoscale systems in a warming climate. [ABSTRACT FROM AUTHOR]

The societies of the Greater Horn of Africa (GHA) are vulnerable to variability in two distinct rainy seasons, the March–May 'long' rains and the October–December 'short' rains. Recent trends in both rainy seasons, possibly related to patterns of low-frequency variability, have increased interest in future climate projections from General Circulation Models (GCMs). However, previous generations of GCMs historically have poorly simulated the regional hydroclimate. This study conducts a process-based evaluation of simulations of the long and short rains in CMIP6, the latest generation of GCMs. Key biases in CMIP5 remain or are worsened, including long rains that are too short and weak and short rains that are too long and strong. Model biases are driven by a complex set of related oceanic and atmospheric factors, including simulations of the Walker Circulation. Biased wet short rains in models are connected with Indian Ocean zonal sea surface temperature (SST) gradients that are too warm in the west and convection that is too deep. Models connect equatorial African winds with the strength of the short rains, though in observations a robust connection is primarily found in the long rains. Model mean state biases in the timing of the western Indian Ocean SST seasonal cycle are associated with certain rainfall timing biases, though both biases may be due to a common source. Simulations driven by historical SSTs (AMIP runs) often have larger biases than fully coupled runs. A path towards using biases to better understand uncertainty in projections of GHA rainfall is suggested. [ABSTRACT FROM AUTHOR]

Groundwater is one of the most important sources of water supply in arid and semi-arid regions, but its availability with sustainable quantity and quality is threatened by various factors, among which climate and land-use change have the most important role. Therefore, it is essential to achieve reliable methods for predicting changes in aquifer storage to plan for the sustainable use of groundwater resources. This study aimed to estimate the potential impact of climate change and land use on the groundwater level of Hashtgerd plain for sustainable use of groundwater resources. In this regard, groundwater supply and demand for 2020 as the base year was simulated to forecast the trends until 2050 by considering climate and land-use changes. Five General Circulation Models under four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6, and RCP8.5) were used to project future rainfall and temperature. The LARS-WG model was used to downscale the climatic data. Classification of land-use was performed using Landsat satellite images of 1990, 2005, and 2020 in ENVI 5.3 software. Then, by these maps, the Markov chain method implemented in TerrSet software was used to model land-use change for 2050. The indexes used to evaluate the model were the overall accuracy of prediction and the kappa coefficient. The overall accuracy of 94.34% and kappa coefficient of 0.92 were in the calibration stage and 86.34 and 0.82 in the validation stage, respectively. Finally, the effect of climate and land-use change on the decrease of groundwater level was simulated using the MODFLOW model for the period 2020–2050. RMSE and MAE values of steady-state calibration were 0.91 and 0.75, respectively. These values were estimated to be 0.97 and 0.8 in the calibration step for the unsteady-state and 1.01 and 0.91, respectively, in the validation step. The results of climate change showed a decrease in the average annual precipitation and an increase in the average annual temperature. The annual temperature increased by 1.4 °C and 3.2 °C for RCP 4.5 and 8.5 respectively, by 2050. The annual precipitation decreased by 2.45% and 4.47% for both scenarios. The results of land-use change show an increase of residential, barren, and agricultural lands by 105, 41, and 8%, respectively, and a decrease of 94% in pastures lands. The future land-use change reduced aquifer reserve by up to 41%. The combined impacts of climate and land-use change in the most critical state reduced aquifer reserve by 61%. [ABSTRACT FROM AUTHOR]

In this research, the impact of the human factors and climate change on groundwater level fluctuations affected by uncertainty within 27-year upcoming period (2018–2045) in the Razan Plain is examined. To simulate the aquifer performance, the GMS model is calibrated and verified for two 18-month periods, respectively. To forecast climate variables changes in the future time-frame, six CMIP5 models with three scenarios Rcp 2.6, Rcp 4.5 and Rcp 8.5 are utilized. To study the prediction uncertainty of the climate change models, the method of probabilistic levels of precipitation and temperature changes were used. In this technique, by combining 6 climate change models and 3 mentioned scenarios for each month, 18 prediction values for ∆T and ∆P in upcoming years were approximated. After that, by implementing appropriate distribution for each month, next values of ∆T and ∆P in the probabilistic levels of 50% and 90% are estimated. Finally, in two probabilistic levels of 50% and 90% considering the uncertainty of general circulation models, the climate variables of precipitation and temperature were forecasted. Eventually, based on the probabilistic level technique and using the GMS model, the influence of the human factors and climate change on the groundwater level variations under these scenarios are determined. Results showed that climatic factors have a lesser contribution in reducing the groundwater level in the plain, and the largest contribution is related to human factors and excessive withdrawal from the aquifer. The contribution of climate change in the reduction of the groundwater level in probability scenarios of 0.9 and 0.5 and emission scenarios Rcp8.5, Rcp4.5 and Rcp2.6 is about 40.8, 24.3, 32.3, 27.6 and 22.2 percent respectively. Based on these results, the first priority for aquifer planning and management should be focused on human activities and controlling the amount of withdrawal from the aquifer. These results clearly show that the main cause of creating sinkholes and the sharp reduction of the groundwater level in the region is the excessive extraction of groundwater resources as a result of human activities, including agriculture and industrial demands, and not climate change. [ABSTRACT FROM AUTHOR]

The aim of this study was investigating the effect of drought on groundwater storage and measuring the impact of future drought events under climate change conditions on Najafabad aquifer in Iran. A Support Vector Machine (SVM) model was developed to simulate groundwater storage by using the Particle Swarm Optimization (PSO) to optimize SVM parameters. Various meteorological and hydrological drought indices were considered as model inputs and the best combination of drought indices was selected by the Gamma Test (GT) method. The drought analysis revealed that the Groundwater Resource Index (GRI) and the Reconnaissance Drought Index (RDI) were the most effective indices in modelling the aquifer. To examine future drought events under climate change, the outputs of five ISI-MIP5 (Inter-Sectoral Impact Model Inter-comparison Project) General Circulation Models (GCMs) were derived under two Representative Concentration Pathways (RCPs). The results indicated that the most severe droughts will occur during the years 2035–2039 and 2033–2041 under RCP4.5 and RCP8.5, respectively. Continuing the current aquifer withdrawal policy, the groundwater storage will be affected by future drought events and decrease by 8.8% and 9.5% compared to the base period (1990–2016) under RCP4.5 and RCP8.5, respectively. [ABSTRACT FROM AUTHOR]

Climate change is linked with change in precipitation, evapotranspiration, and other climatological parameters, and therefore the runoff of a river basin will be affected. The Gomati River basin is the largest in Tripura. The increased settlement in the Gomati River basin and climate change may threaten the natural flow patterns that enable its diversity. This study assesses the impact of climate change on total flow from a catchment in northeast India (the Gomati River catchment). For this assessment, the Group Method of Data Handling (GMDH) model was used to simulate the rainfall–runoff relationship in the catchment with respect to the observed data during 2008–2009. The statistically downscaled outputs of the Hadley Centre Global Environment Model version 2 (HadGEM2-ES) general circulation model scenario was used to assess the impacts of climate change on the Gomati River basin. Future projections were developed for the 2030s, 2040s, and 2050s. The results of this study may contribute to the development of adaptive strategies and future policies for the sustainable management of water resources in northeast Tripura. [ABSTRACT FROM AUTHOR]

Freshwater resources supply has always presented challenges and considering a changing climate quantifying the available water in the future is important. In the Caribbean, based on projections it is likely that the island of Trinidad would receive less intense rainfall, experience more dry days, become drier and warmer, and the water resources be decreased. In this study, the impact of a changing climate on the Navet Reservoir in Trinidad was investigated and reservoir volumes were quantified for the period 2011–2099. This period was subdivided into three time periods, 2011–2040, 2041–2070, and 2071–2099, and evaluated under the representative concentration pathways (RCPs): RCPs 2.6, 4.5, 6.0, and 8.5. Utilizing a calibrated/validated Soil Water Assessment Tool (SWAT) model for the Navet Reservoir together with projections from five general circulation models (GCMs), future monthly and seasonal reservoir volumes were estimated. The GCM precipitation and temperature data were bias-corrected using linear scaling and variance scaling methods. It was found that reservoir volumes are likely to be the lowest during the period 2041–2070 at the Navet Reservoir. Additionally, the projected reservoir volumes are reliable, resilient, and not vulnerable. These results may be used by water managers in the adaptation and mitigation of the impacts of a changing climate, thus, building resilience in the water sector. [ABSTRACT FROM AUTHOR]

Landuse landcover (LULC) and climate change have significant hydrological impacts, especially in humid tropical regions. Cautious planning and development based on early preparedness can result in LULC patterns that may improve the well-being of human beings and ecosystem. This demands significant knowledge related to the climatic and hydrological variables. In this study, the impact of future climate and LULC change of a humid tropical catchment Periyar river basin (PRB) in the Western Ghats, India, is investigated using the Soil and Water Assessment Tool (SWAT). The study critically evaluates the individual and combined impacts of LULC and climate change on water balance components including precipitation, surface runoff, evapotranspiration, soil water storage, percolation, base flow, and lateral flow. The future LULC maps are projected to 2030, 2050, 2075, and 2100 using the past data with the help of the Cellular Automata-Markov transition matrix. LULC change indicated an increased urbanization and cropland, whereas a decreased forests and plantations in future. For future climate change impact assessment, ensemble of five global circulation models with RCP 4.5 and RCP 8.5 scenarios is considered. The results suggest climate change impact is dominant over LULC change in the near (2011–2040) and mid (2041–2070) future, whereas vice-versa in the far (2071–2100) future. The quantitative assessment of spatial and temporal variation in hydrological components append insights to the hydrological processes. The results can further add value to studies related to irrigation requirement, groundwater recharge, crop water demand, soil permeability and integrated water resource management. Furthermore, the current modeling framework can be implemented in other humid tropical river basins to understand the hydrological regime changes aiming effective water resources and environmental management. [ABSTRACT FROM AUTHOR]