Global Climate Model Projections Research Articles

Modelling Floodplain Vegetation Response to Climate Change, Using the Soil and Water Assessment Tool (SWAT) Model Simulated LAI, Applying Different GCM’s Future Climate Data and MODIS LAI Data

Scientists widely agree that anthropogenically driven climate change significantly impacts vegetation growth, particularly in floodplain areas, by altering river flow and flood regimes. This impact will accelerate in the future, according to climate change projections. For example, in Australia, climate change has been attributed to a decrease in winter precipitation in the range of 56% to 72.9% and an increase in summer from 11% to 27%, according to different climate scenarios. This research attempts to understand vegetation responses to climate change variability at the floodplain level. Further, this study is an effort to enlighten our understanding of temporal climate change impacts under different climate scenarios. To achieve these aims, a semi-distributed hydrological model was applied at a sub-catchment level to simulate the Leaf Area Index (LAI). The model was simulated against future time series of climate data according to Global Climate Model (GCM) projections. The time series data underwent a non-parametric Mann–Kendall test to detect trends and assess the magnitude of change. To quantify the model’s performance, calibration and validation were conducted against the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. The calibration and validation results show Nash–Sutcliffe efficiency (NSE) values of 0.85 and 0.78, respectively, suggesting the model’s performance is very good. The modeling results reveal that the rainfall pattern fluctuates under climate projections within the study site, in which vegetation tends to be more vibrant during the warmer seasons. Moreover, the modeling results highlighted increases in the average projected future winter temperatures, which can help vegetation growth during winter. The results of this study may be employed for sustainable floodplain management, restoration, land-use planning, and policymaking, and help floodplain communities better prepare for and respond to changing flood patterns and related challenges under a future changing climate.

Evaluation of the Impact of Climate Change on the Water Balance of the Mixteco River Basin with the SWAT Model

Assessing the impact of climate change is essential for developing water resource management plans, especially in areas facing severe issues regarding ecosystem service degradation. This study assessed the effects of climate change on the hydrological balance using the SWAT (Soil and Water Assessment Tool) hydrological model in the Mixteco River Basin (MRB), Oaxaca, Mexico. Temperature and precipitation were predicted with the projections of global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6); the bias was corrected using CMhyd software, and then the best performing GCM was selected for use in the SWAT model. According to the GCM MPI-ESM1-2-LR, precipitation might decrease by between 83.71 mm and 225.83 mm, while temperature might increase by between 2.57 °C and 4.77 °C, causing a greater atmospheric evaporation demand that might modify the hydrological balance of the MRB. Water yield might decrease by 47.40% and 61.01% under the climate scenarios SP245 and SSP585, respectively. Therefore, adaptation and mitigation measures are needed to offset the adverse impact of climate change in the MRB.

Multi-Variable Hard Physical Constraints for Climate Model Downscaling

Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change. However, they often operate at a coarse spatial resolution that limits their accuracy in reproducing local-scale phenomena. Statistical downscaling methods leveraging deep learning offer a solution to this problem by approximating local-scale climate fields from coarse variables, thus enabling regional GCM projections. Typically, climate fields of different variables of interest are downscaled independently, resulting in violations of fundamental physical properties across interconnected variables. This study investigates the scope of this problem and, through an application on temperature, lays the foundation for a framework introducing multi-variable hard constraints that guarantees physical relationships between groups of downscaled climate variables.

Projections of severe weather and the impacts on transmission line towers in Santa Catarina, Brazil, under future scenarios of global climate change

Transmission line towers are highly exposed to weather hazards. In southern Brazil, several damages to transmission lines have been reported due to severe weather events. Climate change projections indicate that the frequency and magnitude of extreme events may increase, making planning and adaptation of the transmission system exposure even more critical. In this work, we propose a severe weather index (SWI) as a proxy for detecting severe storms that can potentially result in extreme weather events. The SWI combines thresholds of instability indices based on observed severe events around the transmission tower-line systems. These indices were calculated using the downscaling by the Eta model at 20 km resolution of three global climate model projections and at 5 km of one global climate model for the historical period and the near future. The results show that the SWI captures the severe storms observed in the region. All downscaling projections agree with the increase of extreme weather events in the near future and the expansion of vulnerable areas. The constructed index can be employed to assess the risks and to plan the power transmission system better.

Investigation of Added Value in Regional Climate Models for East Asian Storm Track Analysis

AbstractStudies have shown that regional climate models (RCMs) can simulate local climates at a higher resolution for specific regions compared to global climate models (GCMs), making dynamic downscaling using RCMs a more effective approach. Therefore, RCMs have become valuable tools for evaluating the potential impacts of climate change on specific regions and for informing local adaptation strategies. To fully understand the added value (AV) of RCMs, it is essential to understand how the characteristics differ between land and ocean. The complex topography of East Asia, including land and sea, makes it a suitable region for evaluating the AV of RCMs. In this study, we compared two regional simulations that integrated the same RCMs but employed different GCMs from the Coordinated Regional Climate Downscaling Experiment for their ability to simulate storm tracks in East Asia. The results of the RCMs for the period from 1982 to 2005 were compared with their host Coupled Model Intercomparison Project GCM projections and high‐resolution reanalysis. In mountainous regions, the AV of the RCMs weakened the bias of the GCM and improved its agreement with the reanalysis due to the dynamical process near the high‐resolution topography. In plains and coastal areas, owing to the increase in horizontal resolution and clearly determined coastline in RCMs, small‐scale phenomena are well represented, and the storm track of RCMs shows similar values to that of the GCM in maritime regions. This study demonstrates the value of RCMs for improving the accuracy of climate projections in East Asia, informing adaptation strategies, and enhancing climate research.

Three methods of characterizing climate-induced changes in extreme rainfall: a comparison study

Abstract (1) Three practical and easily implementable methods are provided to estimate percent increases in extreme rainfall due to climate change for the period 2020–2090 using Global Climate Model (GCM) output. (2) Methods are designed to bracket the expected range of extreme rainfall intensification for 1–24-h events with return intervals of 1 year to 100 years. (3) One method is based on the 20 largest wet days produced by an ensemble of GCMs, and the other two use GCM projections of temperature and Clausius–Clapeyron assumptions. (4) The results of the case study for the Philadelphia area show that, by the end-of-century, extreme rain event volumes might increase from a low of 18% to a high of 61%, depending on the duration and return interval under consideration. (5) Methods have been benchmarked against existing, publicly available projected rainfall intensities to show the methods that provide an accurate range of extreme rainfall intensification due to climate change.

Crop production and water quality under 1.5 °C and 2 °C warming: Plant responses and management options in the mid-Atlantic region

The 2015 “Paris Agreement” aims to limit the global average temperature rise to significantly less than 2 °C, preferably within 1.5 °C above pre-industrial levels. A multitude of studies have focused on evaluating how different sectors respond to such levels of warming. Nonetheless, most of these studies fail to provide a clear roadmap to mitigate these impacts. A case in point is the anticipated decline in corn and soybean yields and increased phosphorus (P) and nitrogen (N) discharge into water bodies, a trend linked to past agricultural practices and climate change. In this research, we employ a novel assessment of how existing management practices under 1.5 °C and 2 °C global warming (GW) scenarios can affect nutrient availability in time and space as well as crop yield in a typical agricultural watershed in the Mid-Atlantic Region, specifically the Upper Maurice River Watershed (UMRW) in New Jersey.Using the Soil and Water Assessment Tool (SWAT) with multiple Global Climate Model (GCM) projections, we found that compared to 1.5 °C, a 2 °C GW scenario would exacerbate runoff, leading to amplified nutrient leaching. These losses decrease nutrient availability during the crop growing season. Moreover, a mismatch between the timing of fertilizer application and crop nutrient absorption caused nutrient-related stress. This nutrient and anticipated temperature stress resulted in a more significant decrease in crop yields under the 2 °C GW scenario than the 1.5 °C scenario. We have designed a management scenario to reduce future nutrient losses while increasing crop yields. The strategy involves altering the timing of planting/harvesting and the fertilizer application rate in response to a warming climate. This approach is projected to increase corn and soybean yields by +39 % (+21 %) and +2 % (+17 %), respectively, under the 1.5 °C (2.0 °C) GW scenario for the RCP-4.5 pathway. Simultaneously, it is expected to decrease the N and P loads at 1.5 °C (2.0 °C) GW. Comparable projections are also observed under the RCP-8.5 pathway.

Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods

Apprehending the hydrological and nutrient variations in rapidly urbanizing watersheds under changing environments is crucial for pollution control and water resource management. However, existing studies have primarily focused on hydrological processes, neglecting water quality aspects, and comprehensive assessment of future runoff and nutrient loads in these watersheds during China's Dual Carbon periods is limited. This study firstly bridges these gaps by constructing multi-scenario with different levels of “Urban Development - Ecological Conservation” and utilizing latest bias-corrected General Circulation Models or Global Climate Models (GCMs) projections to evaluate future runoff and nutrient loads in the Shenzhen River. The calibrated and validated models display satisfactory performance in simulating runoff, nutrient loads, and land use types. The bias-corrected GCMs projections exhibit enhanced accuracy for temperature variables, particularly during the wet season. Implementing effective ecological protection measures is paramount in mitigating water quantity fluctuations and controlling total nitrogen pollution, which is closely associated with urban development and human activities. Conversely, total phosphorus loads demonstrate greater simulation uncertainty, particularly during the dry season of the Carbon Neutrality period, requiring further exploration. Compared to the baseline period, runoff changes minimally, with notable seasonal variations. The findings highlight the escalating uncertainty in load predictions as time progresses. Additionally, addressing uncertainties in precipitation projections driven by GCMs is imperative, given their substantial influence on runoff and nutrient load simulations, particularly during challenging dry seasons. While further research is needed to reduce simulation uncertainty, our study provides valuable insights into nitrogen-phosphorus pollution control and sustainable water resource management in rapidly urbanizing watersheds, especially during the near-term period.

Analysis of future meteorological, hydrological, and agricultural drought characterization under climate change in Kessie watershed, Ethiopia

The objective of this study is to analyze future drought characteristics in meteorological, hydrology, and agricultural droughts under the impact of changing climate in the Kessie watershed, upper Blue Nile Basin, using three drought indices; Reconnaissance Drought Index (RDI), Streamflow Drought Index (SDI) and Agricultural Standardized Precipitation Index (aSPI), respectively. The study used baseline data (1985–2014) and future (2041–2100) downscaled from Coupled Model Intercomparison Project 6 (CMIP-6) based on the three Global Climate Models (GCMs) projections under two scenarios of the Shared Socioeconomic Pathways (SSP) (SSP2-4.5, SSP5-8.5) with well-calibrated Soil and Water Assessment Tool (SWAT) model to simulate future streamflow for two future time horizons 2050s (2041–2070) and 2080s (2071–2100). Based on the yearly time scale, our results indicate that droughts of a high magnitude and rising frequency would affect most of the research area. These droughts will be either meteorological (RDI), agricultural (aSPI), or hydrological (SDI). Short-term agricultural and hydrological droughts are also anticipated to occur more frequently. The projected increases in frequency and trend of agricultural and hydrological droughts in this area are greater due to the anticipated drop in annual rainfall and the larger increase in mean annual temperature in the middle of Kessie. Furthermore, compared to hydrological and agricultural droughts, meteorological drought is less vulnerable to climate change; but, as the accumulation period lengthens, a stronger association develops between hydrological and agricultural droughts. These findings may be useful for water resources management and future planning for mitigation and adaptation to the climate change impact in the study area.

Bat mortality in wind farms of southern Europe: temporal patterns and implications in the current context of climate change

The expansion of renewable energy production, especially wind power, is one of the cornerstones of our strategy for mitigating climate change. However, there is growing concern about the impacts of this energy source on biodiversity. In particular, very little is known about the impact on groups of fauna such as bats, which are especially sensitive to environmental changes. We investigated the temporal patterns of bat fatalities in wind farms in the province of Cádiz, in the south of the Iberian Peninsula. An eleven-year data set (2009–2019) from a surveillance program of bird and bat mortality in wind farms was analysed. A total of 2,858 fatalities concerning 10 bat genera were reported, although more than 90% of the affected animals were individuals of the genera Pipistrellus, Eptesicus and Nyctalus. Bat mortality occurred mainly during the summer and autumn, especially in August. However, species such as the genus Pipistrellus, present collisions throughout the year, including all winter months in the case of the genus Pipistrellus. The probability of mortality was positively correlated with the maximum daily temperature. According to the model prediction, the probability of fatality begins to increase slightly from 20 °C and then rises sharply when the temperature exceeds 30 °C, that can be interpreted as a consequence of increasing bat activity at local scale. According to the regional projections of global climate models, an increase in maximum temperatures and the arrival of milder winters may lead to an increase in the annual mortality of bats in wind farms in the coming decades.

Modeling climate change impact on dryland wheat production for increased crop yield in the Free State, South Africa, using GCM projections and the DSSAT model

Introduction: The impact of climate change on food production in South Africa is likely to increase due to low rainfall and frequent droughts, resulting in food insecurity in the future. The use of well-calibrated and validated crop models with climate change data is important for assessing climate change impacts and developing adaptation strategies. In this study, the decision support system for agrotechnology transfer (DSSAT) crop model was used to predict yield using observed and projected climate data.Materials and Methods: Climate, soil, and crop management data were collected from wheat-growing study sites in Bethlehem, South Africa. The DSSAT wheat model (CROPSIM-CERES) used was already calibrated, and validated by Serage et al. (Evaluating Climate Change Adaptation Strategies for Disaster Risk Management: Case Study for Bethlehem Wheat Farmers, South Africa, 2017) using three wheat cultivar coefficients obtained from the cultivar adaptation experiment by the ARC-Small Grain Institute. The model was run with historical climate data for the eastern Free State (Bethlehem) from 1999 to 2018 as the baseline period. To determine the effects of climate change, the crop model simulation for wheat was run with future projections from four Global Climate Models (GCM): BCC-CSM1_1, GFDL-ESM2G, ENSEMBLE, and MIROC from 2020 to 2077.Results: The average wheat yield for the historic climate data was 1145.2 kg/ha and was slightly lower than the highest average yield of 1215.9 kg/ha from GCM ENSEMBLE during Representative concentration pathways (RCP) 2.6, while the lowest yield of 29.8 kg/ha was produced during RCP 8.5 (GCM GFDL-ESM2G). Model GFDL-ESM2G produced low yields (29.8–47.74 kg/ha) during RCP 8.5 and RCP 6.0, respectively. The yield range for GCM BCC-CSM1_1 was 770.2 kg/ha during RCP 2.6 to 921.68 kg/ha during RCP 4.5 and 547.84 kg/ha during RCP 8.5 to 700.22 kg/ha during RCP 2.6 for GCM MIROC.Conclusion: This study showed a declining trend in yield for future climate projections from RCP2.6 to RCP8.5, indicating that the possible impacts of higher temperatures and reduced rainfall in the projected future climate will slightly decrease wheat production in the eastern Free State. Adaptation measures to mitigate the potential impact of climate change could include possible changes in planting dates and cultivars. Using a crop model to simulate the response of crops to variations in weather conditions can be useful to generate advisories for farmers to prevent low yield.

Spatio-temporal compounding of connected extreme events: Projection and hotspot identification

In general, the impact of two different connected extreme events is noticed on the same duration and spatial area. However, the connected extreme events can have footprint over different temporal and spatial scales. Thus, this article analyses the connected extreme events over India using the spatio-temporal compounding technique to understand the impact at different temporal and spatial scales. This approach is applied to analyse the historical and future connected extreme events. In the present study, coincident heat waves and droughts (Event C1), coincident heat waves and extreme precipitation (Event C2) are considered as connected extreme events. The future events are investigated using the suitable global climate models (GCMs) projections under three climate change scenarios (Shared Socioeconomic Pathways (SSP) 2–4.5, SSP3-7.0, and SSP5-8.5). The suitable GCMs are identified with the help of compromise programming. Subsequently, the hotspot regions are identified applying the Regional Climate Change Index (RCCI) method. The outcomes from the study suggest that with increasing temporal compounding, the mean duration of extreme events also increases. Highest increase in mean duration is observed for Event C1 over PI (Peninsular India), WCI (West Central India), and some parts of CNI (Central Northeast India) regions. The regions with high magnitude of duration have low magnitude of occurrence. The duration of Event C1 is likely to increase with respect to climate change scenarios and temporal compounding, especially in the PI region and some parts of WCI. However, there is insignificant change in the duration of Event C2. The PI region identified as the most vulnerable region followed by WCI and HR regions. The highest percentage of area under the emerging hotspot category is noticed under SSP5-8.5 climate change scenario.

Evidence of a link between Amazon fires and lightning

The number of fires in the Amazon region increased dramatically at the beginning of 2022 compared to 2021. Considering that this period is also characterized by a large incidence of cloud-to-ground (CG) lightning in this region, we investigated if these phenomena may be related to each other. Two regions of Brazil were considered in the analysis: Mato Grosso state, on the border of the Amazon region where most of the deforestation occurs, and Amazonas state, in the interior of the Amazon region. In the first four months of 2022, 26,554 fires and approximately 3 million CG lightning flashes in Mato Grosso and 2468 fires and about 3.4 million CG lightning flashes in Amazonas were recorded. By comparison, in 2021, 16,844 fires and approximately 2.7 million CG lightning flashes in Mato Grosso and 1258 fires and about 3.2 million CG lightning flashes in Amazonas were recorded. In this study, we examined whether these phenomena were linked on a 2 to 9-day timescale, utilizing wavelet coherence analysis of both fire and lightning data. Our results indicate that these phenomena are linked (p < 0.05) for several weeks in both regions in both years. Additionally, correlations for 2022 were notably higher (above 0.90 versus 0.80) when the incidence of fires and lightning was higher, lending support to the notion of a relationship between these two events. Considering the periodicity characteristics of our results and that most fires in these regions are human-induced, we conclude that anthropogenic fires may affect lightning activity through aerosol-cloud particle interactions, with additional and not previously documented implications for climate change. Assuming the same is happening in other regions of the planet, we may face a new aspect not considered in the present global climate model projections.

Predicted changes in future precipitation and air temperature across Bangladesh using CMIP6 GCMs

Understanding spatiotemporal variability in precipitation and temperature and their future projections is critical for assessing environmental hazards and planning long-term mitigation and adaptation. In this study, 18 Global Climate Models (GCMs) from the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) were employed to project the mean annual, seasonal, and monthly precipitation, maximum air temperature (Tmax), and minimum air temperature (Tmin) in Bangladesh. The GCM projections were bias-corrected using the Simple Quantile Mapping (SQM) technique. Using the Multi-Model Ensemble (MME) mean of the bias-corrected dataset, the expected changes for the four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were evaluated for the near (2015–2044), mid (2045–2074), and far (2075–2100) futures in comparison to the historical period (1985–2014). In the far future, the anticipated average annual precipitation increased by 9.48%, 13.63%, 21.07%, and 30.90%, while the average Tmax (Tmin) rose by 1.09 (1.17), 1.60 (1.91), 2.12 (2.80), and 2.99 (3.69) °C for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. According to predictions for the SSP5-8.5 scenario in the distant future, there is expected to be a substantial rise in precipitation (41.98%) during the post-monsoon season. In contrast, winter precipitation was predicted to decrease most (11.12%) in the mid-future for SSP3-7.0, while to increase most (15.62%) in the far-future for SSP1-2.6. Tmax (Tmin) was predicted to rise most in the winter and least in the monsoon for all periods and scenarios. Tmin increased more rapidly than Tmax in all seasons for all SSPs. The projected changes could lead to more frequent and severe flooding, landslides, and negative impacts on human health, agriculture, and ecosystems. The study highlights the need for localized and context-specific adaptation strategies as different regions of Bangladesh will be affected differently by these changes.

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa— assessment of the historical simulations

This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange, and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodel spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm, respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodel spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

Inferences of Groundwater Response to Projected Hydroclimatic Changes in North Florida

Discerning anthropogenic stressors on groundwater is critical for climate change adaptation to reduce risks and increase resiliency. Long-term groundwater level trends are forecasted and examined at three sites in North Florida using a large ensemble of Global Climate Model (GCM) projections under low and medium emission scenarios. The forecasts indicate groundwater levels are likely to decline from 2020 to 2099. However, the declines are expected to accelerate after 2040s, reaching critical levels by the end of this century. Pumping impact constitutes 10% to 45% of future declines but is amplified by enhanced drought. Examination of distinct influence of rainfall, evapotranspiration (ET), and groundwater pumping on future trends shows highly divergent groundwater response to projected hydroclimatic changes. The future long-term rainfall trend may lead to rising groundwater levels, which may be overshadowed by heightened ET loss driven by climate change and increased groundwater pumping, causing steep declines. This study also reveals poor performance of predictions driven by GCM projections in replicating the timing of high and low levels at the sites influenced by Florida’s peninsular climate due to the limitation of downscaling and bias-correction to capture oscillations in climate cycles driving hydrologic memory. However, groundwater levels are predicted well by a few GCMs at one site influenced primarily by continental climate. Additionally, a multidecadal harmonic analysis exposes presence of centennial periodicity in groundwater levels, which opens a new perspective in the understanding of climate change impacts on groundwater resources. Further investigation is needed to better understand the effect of centennial cycles on future groundwater levels and how these cycles can be incorporated into the downscaling methods. Hence, GCM-based forecasts are recommended to be cautiously utilized for groundwater resource planning when they significantly depart from historical long-term cyclic patterns.

Use of observed hydroclimatic trends to constrain projections of snowmelt season runoff in the Rio Grande headwaters

AbstractA method is developed for choosing 21st Century streamflow projections among widely varying results from a large ensemble of climate model‐driven simulations. We quantify observed trends in climate–streamflow relationships in the Rio Grande headwaters, which has experienced warming temperature and declining snowpack since the mid‐20th Century. Prominent trends in the snowmelt runoff season are used to assess corresponding statistics in downscaled global climate model projections. We define “Observationally Consistent (OC)” simulations as those that reproduce historical changes to linear statistics of diminished snowpack–streamflow coupling in the headwaters and an associated increase in the contribution of spring season (post‐peak snowpack) precipitation to streamflow. Only a modest fraction of the ensemble of simulations meets these consistency metrics. The subset of OC simulations projects significant decreases in headwaters flow, whereas the simulations that poorly replicate historical trends exhibit a much wider range of projected changes. These results bolster confidence in model‐based projections of declining runoff in the Rio Grande headwaters in the snowmelt runoff season and offer an example of a methodology for evaluating model‐based projections in basins with similar hydroclimates that have experienced pronounced climate changes in the recent historical record.

Future trends in atmospheric circulation patterns over Africa south of the equator

Abstract The impact of climate change on the frequency of occurrence of atmospheric circulation patterns can have a wide range of consequences ranging from weather extremes to the modification of pollutant transport. This study uses 11 CMIP6 global climate models (GCMs) to investigate the impact of future climate change on the frequency of occurrence of atmospheric circulation patterns, in Africa south of the equator. Here it is shown from the historical analysis that there are statistically significant trends in the frequency of occurrence of some of the classified circulation types (CTs) in the study region. Further, under the SSP5-8.5 and SSP2-4.5 emission scenarios, the historical CTs were reproduced, suggesting that future climate change will not constrain the existence of the CTs. However, for future emission scenarios, the ensemble of the GCMs projects notable changes in the spatial structure of the CTs and statistically significant trends in the frequency of occurrence of most of the CTs, towards the end of the 21st century. The intensity of the projected changes in the spatial structure and linear trends in the frequency of occurrence of the CTs are relatively stronger under the higher emission scenario. As regards changes in synoptic circulations in the study region, the ensemble of the GCMs project, (i) a positive trend in the frequency of occurrence of austral summer dominant CTs associated with atmospheric blocking of the Southern Hemisphere mid-latitude cyclones, adjacent to South Africa; (ii) alternating frequent periods of enhanced (suppressed) anticyclonic circulation at the western branch of the Mascarene high possibly due to a more positive phase of the Southern Annular Mode (warmer southwest Indian Ocean); (iii) possible weakening of the Angola low. The aforementioned changes can be expected to have direct impacts on the regional climates in the study region.

East Asian heatwaves driven by Arctic-Siberian warming

This study investigates the contributing factors of East Asian heatwaves (EAHWs) linked to the Arctic-Siberian Plain (ASP) over the past 42 years (1979–2020). EAHWs are mainly affected by two time scales of variabilities: long-term externally forced and interannual variabilities. The externally forced EAHWs are attributed to the increasing global warming trend, while their interannual variability is related to the circumglobal teleconnection (CGT) and the ASP teleconnection patterns. In addition to the CGT, the Rossby wave energy originating from the ASP propagates to East Asia through the upper troposphere, amplifying the EAHWs. The stationary high pressure in the ASP is generated by vorticity advection in the upper troposphere. Enhanced surface radiative heating and evaporation on the ASP surface increase the specific humidity and temperature, amplifying the thermal high pressure via positive water vapor feedback. Thermal high-pressure amplified by land–atmosphere interactions in the ASP during the peak summer season leads to EAHWs by the propagation of stationary Rossby wave energy. The results indicate that our enhanced understanding of the ASP teleconnection can improve forecasting of the EAHWs not only on a sub-seasonal time scale but also in future projections of global climate models.

Streamflow Analysis in Data-Scarce Kabompo River Basin, Southern Africa, for the Potential of Small Hydropower Projects under Changing Climate

In developing countries with data scarcity challenges, an integrated approach is required to enhance the estimation of streamflow variability for the design of water supply systems, hydropower generation, environmental flows, water allocation and pollution studies. The Flow Duration Curve (FDC) was adopted as a tool that is influenced by topography, land use land cover, discharge and climate change. The data from Global Climate Model (GCM) projections, based on Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 climate scenarios, were used as input data for the SWAT model for the simulation of streamflow. The FDCs were then derived from the simulated streamflow. The FDC for RCP 4.5 showed insignificant differences, whilst for RCP 8.5 it showed an increase of 5–10% in FDC from the baseline period, which is likely to increase the hydropower generation potential with some considerable streamflow variability. The integrated approach of utilizing FDC, GIS and SWAT for the estimation of flow variability and hydropower generation potential could be useful in data scarce regions.