Future Precipitation Changes Research Articles

Precipitation Drives Soil Protist Diversity and Community Structure in Dry Grasslands.

Protists are essential components of soil microbial communities, mediating nutrient cycling and ecosystem functions in terrestrial ecosystems. However, their distribution patterns and driving factors, particularly, the relative importance of climate, plant and soil factors, remain largely unknown. This limits our understanding of soil protist roles in ecosystem functions and their responses to climate change. This is particularly a concern in dryland ecosystems where soil microbiomes are more important for ecosystem functions because plant diversity and growth are heavily constrained by environmental stresses. Here, we explored protist diversity and their driving factors in grassland soils on the Tibetan Plateau, which is a typical dryland region with yearly low temperatures. Soil protist diversity significantly decreased along the gradient of meadow, steppe, and desert. Soil protist diversity positively correlated with precipitation, plant biomass and soil nutrients, but these correlations were changed by grazing. Structural equation and random forest models demonstrated that precipitation dominated soil protist diversity directly and indirectly by influencing plant and soil factors. Soil protist community structure gradually shifted along meadow, steppe and desert, and was driven more by precipitation than by plant and soil factors. Soil protist community compositions were dominated by Cercozoa, Ciliophora and Chlorophyta. In particular, Ciliophora increased but Chlorophyta decreased in relative abundance along the gradient of meadow, steppe and desert. These results demonstrate that precipitation plays more important roles in driving soil protist diversity and community structure than plant and soil factors, suggesting that future precipitation change profoundly alters soil protist community and functions in dry grasslands.

Changes in convective and stratiform precipitation over the Tibetan Plateau projected by global and regional climate models

AbstractPrecipitation is a crucial influencing factor in the global hydrological cycle and terrestrial ecosystems. Previous studies have mostly focused on the historical precipitation variations over the Tibetan Plateau (TP). However, little attention has been given to the differences between convective and stratiform precipitation under future climate change scenarios. In order to acquire the high‐resolution precipitation information over the TP, the dynamical downscaling simulation was conducted utilizing a regional climate model (Weather Research and Forecasting [WRF] model) forced with a global climate model (Community Climate System Model [CCSM]). In this study, we focus on projections of future changes in convective and stratiform precipitation over the TP. The downscaling results during a 26‐year period 1980–2005 are compared for dry season, wet season and annual mean precipitation against the gridded observation dataset. Then, the changes in the wet season convective and stratiform precipitation with warming between CCSM and WRF‐CCSM were compared in the future period 2070–2099 under two scenarios (RCP4.5 and RCP8.5). Compared with the coarse‐resolution forcing, the historical precipitation of dry season, wet season and annual mean over the TP were found to better reproduce in WRF‐CCSM simulation. WRF‐CCSM projects an opposite spatial pattern of total precipitation change between the northern and southern TP in the future under different scenarios, which were attributed to the decreased stratiform precipitation in the southern TP and the increased convective precipitation in the northern TP, contrasting with the uniform increase of total precipitation in its forcing. The surface air temperature projected in WRF‐CCSM is larger increase than in the CCSM forcing. Compared to the CCSM forcing, WRF‐CCSM projects more pronounced increase in convective precipitation in response to rapid warming, and increasingly dominates changes of total precipitation with temperature exceeds the Clausius–Clapeyron rate over the northern TP.

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.

Effects of Precipitation Variation on Annual and Winter Soil Respiration in a Semiarid Mountain Shrubland in Northern China

In response to global climate change, future precipitation changes are expected to profoundly influence soil respiration in arid and semiarid areas. However, few studies focus on CO2 emissions from soils undergoing precipitation changes in semiarid mountain shrublands in winter. A precipitation-manipulation experiment with three levels of precipitation (30% decreased precipitation (DP), ambient precipitation (AP), and 30% increased precipitation (IP)) was performed to examine the effects of variable precipitation on soil respiration (SR) and wintertime contributions to annual SR emissions in Vitex negundo var. heterophylla shrub ecosystems located on the Middle Taihang Mountain in Hebei Province, northern China. The results showed that the average annual SR rates and winter SR rates ranged from 1.37 to 1.67 μmol m−2 s−1 and 0.42 to 0.59 μmol m−2 s−1 among the different precipitation treatments. The model based on soil moisture better represented the soil-respiration rates, suggesting that the variable precipitation extended the water’s limitation of the soil’s CO2 emissions. The cumulative annual soil CO2 emissions were 523, 578, and 634 g C m−2 in response to the DP, AP, and IP treatments, respectively. The ratio of the soil CO2 emissions in winter to the annual CO2 emissions varied from 7.6 to 8.8% in response to the different precipitation treatments. Therefore, ignoring the soil CO2 emissions in winter leads to the underestimation of the carbon losses in semiarid shrublands. Our results highlight that variable precipitation significantly influences soil-respiration rates, and soil CO2 emissions in winter must not be ignored when predicting the future feedback between SR and climate change in semiarid regions.

Projection of Future Meteorological Droughts in Lake Urmia Basin, Iran

Future changes (2015–2100) in precipitation and meteorological droughts in Lake Urmia Basin were investigated using an average mean ensemble of eight general circulation models (GCMs) with high-resolution datasets in socioeconomic pathway scenarios (SSPs) from the Coupled Model Intercomparison Project (CMIP6). In order to project the drought, the standardized precipitation index (SPI) was calculated. Overall, the results revealed that precipitation in Lake Urmia Basin will decrease by 3.21% and 7.18% in the SSP1-2.6 and SSP5-8.5 scenarios, respectively. The results based on 6-month-timescale SPI indices projected more “Extremely dry” events in SSP5-8.5 scenarios. The frequency of “Extremely dry” months in SSP5-8.5 compared to SSP1-2.6 is expected to increase by 14, 7, 14, 10, 5, 14, and 7 months for the Mahabad, Maragheh, Saqez, Sarab, Tabriz, Takab, and Urmia stations, respectively. In contrast, the frequency of “Extremely wet” months will decline for all stations in Lake Urmia Basin. The results of this study provide useful insight for considering drought prevention measures to be implemented in advance for Lake Urmia Basin, which is currently experiencing various environmental issues.

Photosynthetic and Antioxidant Responses of Gymnocarpos przewalskii to Simulated Rainfall Changes

Gymnocarpos przewalskii is a rare Tertiary relict species, mainly distributed in desert areas of northwestern China. Changes in rainfall have a significant impact on the physiological characteristics of desert plants. In the present study, the effects of five simulated rainfall levels on the gas exchange parameters, chlorophyll fluorescence characteristics, and antioxidant system of G. przewalskii were studied. The results show that with increased rainfall the net photosynthetic rate (Pn) and transpiration rate increase significantly. The reduction in Pn is caused by stomatal and non-stomatal limitations under decreased rainfall. Decreased rainfall markedly improves the instantaneous water-use efficiency of leaves. With increased rainfall, the maximum photosynthetic rate, apparent quantum efficiency, and light utilization range significantly increase. Under reduced rainfall, the chlorophyll content, maximum photochemical efficiency of PSII, and steady-state optical quantum efficiency decrease and photoinhibition is caused in the PSII system. A rainfall reduction of 30% leads to massive production of superoxide anions and hydrogen peroxide, causing obvious peroxidation damage. Meanwhile, superoxide dismutase, peroxidase, and catalase in the leaves are significantly enhanced to remove excess reactive oxygen species and alleviate the injury to photosynthetic apparatus. Our study reveals the effect of rainfall changes on the photosynthetic characteristics and antioxidant system of G. przewalskii, and can improve understanding of the adaptive strategies of desert plants under future precipitation changes in northwestern China.

Influence of anthropogenic and natural forcings on future changes in precipitation projected by the CMIP6–DAMIP models

AbstractWarming has already changed the patterns of precipitation and increased the risk of extreme climate events. An assessment of the influences from anthropogenic and natural forcings on future changes in precipitation is therefore essential to define appropriate mitigation and adaptation policies. We assess the effects of natural (NAT) and anthropogenic forcings (greenhouse gases [GHG] and anthropogenic aerosols [AA]) on precipitation over land areas simulated under the Coupled Model Intercomparison Project Phase 6 (CMIP6) SSP2‐4.5 scenario. Our results indicate that changes in precipitation due to GHG forcing, AA forcing and all external forcings (ALL) are projected to increase by about 3.0%–24.8%, 1.1%–6.9% and 4.2%–31.2%, respectively, across most land areas in the twenty‐first century, especially over the mid‐ and high latitudes of the Northern Hemisphere and central Africa, with an increase in the mean and flattening of the probability distribution functions (PDFs). By contrast, the NAT‐induced changes are relatively small, with a slight increase in the mean of the PDFs. The frequencies of both future extreme precipitation and dry events are projected to increase by 11%–101% and 3%–72%, respectively, over many continental regions under the influence of external forcings, especially under GHG forcing. Fingerprinting detection shows that the signals of GHG (except for Australia), AA and ALL forcing are detectable at the likely level (signal‐to‐noise ratio >0.95; 66% confidence) over most land areas. The GHG signals are stronger and detected earlier (after 2010s, likely level) than the AA signals (after 2060s, likely level).

Selecting regional climate models based on their skill could give more credible precipitation projections over the complex Southeast Asia region

This study focuses on future seasonal changes in daily precipitation using Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiments-Southeast Asia ensemble (CORDEX-SEA). Projections using this RCM ensemble generally show a larger inter-model spread in winter than in summer, with higher significance and model agreement in summer over most land areas. We evaluate how well the RCMs simulate climatological precipitation using two skill metrics. To extract reliable projections, two sub-ensembles of ‘better’ and ‘worse’ performing models are selected and their respective projections compared. We find projected intensification of summer precipitation over northern SEA, which is robust across RCMs. On the contrary, in the southern part of SEA, the ‘worse’ ensemble projects a significant and widespread decrease in summer rainfall intensity whereas a slight intensification is projected by the ‘better’ ensemble. Further exploration of inter-model differences in future changes reveals that these are mainly explained by changes in moisture supply from large-scale sources (i.e., moisture convergence) with enhanced effects from local sources (i.e., evapotranspiration). The ‘worse’ models project greater changes in atmospheric circulation compared with the ‘better’ models, which can explain part of the uncertainty in projections for daily precipitation over the CORDEX-SEA domain. Hence, our findings might help assess more reliable projections over the SEA region by selecting models based on a two-step model evaluation: the ability of models to simulate historical daily precipitation and their performance in reproducing key physical processes of the regional climate.

Analyzing and Modeling the Spatial-Temporal Changes and the Impact of GLOTI Index on Precipitation in the Marmara Region of Türkiye

Precipitation is a particularly important part of the Earth’s hydrological cycle and, therefore, is a necessary variable for maintaining natural balance. This study investigated past, present, and future changes in precipitation in the Marmara region, and examined the effects of global warming on this variable. The study period was from 1960 to 2020, and the climate data of 15 synoptic stations in the Marmara region were used for this purpose. To achieve the objectives of the study, linear and 6th order polynomial regression, ombrothermic and hythergraph diagrams, geostatistical models, Mann-Kendall test, Pearson correlation, standard Z-scores, and multi-layer perceptron artificial neural network models (MLP-ANN) were used to model and predict precipitation. The results of the linear regression analysis showed that of the 15 stations, 6 stations had an increasing trend, 6 stations had a trendless pattern, and 3 stations had a decreasing trend. In terms of periodic analysis, the main downward trend started in 1964 and continued until 1992, while the main periodic upward trend started in 1992 and continued until 2016. The synoptic stations in the Marmara region showed a lack of precipitation over six to seven months of the year, and the precipitation changes in the region were stronger than the temperature changes. In addition, the highest precipitation was observed on the southeast coast of the Black Sea, and the lowest precipitation was observed in the eastern parts of the region. Moreover, except for the Bilecik and Kocaeli stations, the changes in the long-term trend of precipitation at the other stations were significant. Among the 15 stations, only the Kocaeli and Sarıyer stations showed a positive correlation with global temperature during the annual period. In addition, the developed ANN model was accurate in simulating and predicting precipitation and showed an upward trend over the next seven years.

Projection of disaster-causing risk of extreme precipitation in the Yangtze River Basin based on CMIP6

Abstract Global warming will significantly affect the frequency and intensity of extreme precipitation and further affect the spatio-temporal pattern of disaster-causing risk of extreme precipitation. This study analyzes the spatio-temporal trends of extreme precipitation and projects its disaster-causing risk under different climate scenarios in the Yangtze River Basin from 2021 to 2100. The results indicate that the extreme precipitation in the Yangtze River Basin shows an increasing trend in the future. Annual precipitation (PRCPTOT) increases by 33.05–42.56% under five scenarios compared with the historical period. The future change in heavy precipitation (R95p) also shows a significant increase, but heavy rain days (R50) and 5-day maximum precipitation (RX5day) decrease. The disaster-causing risk of extreme precipitation in the Yangtze River Basin is mainly Levels III and IV, accounting for 57.23–65.99% of the total basin area. The area with Level V is mainly distributed in the Poyang Lake Basin and the lower main stream of the Yangtze River. Moreover, the changes in disaster-causing risk of extreme precipitation are mainly manifested in the decrease of areas with low risk (Levels I and II) and the increase of areas with medium risk (Levels III and IV) in different periods.

The intensification of short-duration rainfall extremes due to climate change – Need for a frequent update of intensity–duration–frequency curves

Numerous studies have investigated future changes in extreme precipitation by employing climate models. However, trends in extreme short-duration (20–180 min) rainfall intensities, that is, design storm rainfall intensities used for stormwater infrastructure design, have received considerably less attention despite their importance. This study quantified the effect of climate change on design storm intensity based on observations. Trends were investigated for diverse short-term rainfall durations, ranging from 20 to 180 min, at 11 meteorological stations in Northern Europe, Estonia. The Mann-Kendall test and Sen’s slope estimator were used to detect trends and calculate the trend magnitudes by analyzing a 70 year period of high-resolution temporal observed rainfall data. The Sen’s slope indicated a positive magnitude of slopes for all the durations and stations investigated, and two stations in Northeast Estonia showed significantly increasing trends (p < 0.05). Unsurprisingly, the spatial distribution of trends revealed notable variations from station to station, as extreme rainfall events are heavily localized due to cloudburst. Despite this variety, it can be generalized that the annual maximum rainfall intensities in Estonia have increased at an average rate of 4 % per decade due to changing climate conditions, invariant of the rainfall duration analyzed. The use of decades enables to easily keep the intensity–duration–frequency curves updated, and it is more practical and relatable for engineers as well. The findings of this study provide critical knowledge for urban water management decision-makers, indicating the need to frequently update urban design criteria to design climate resilient urban infrastructure.

Study on the Impact of Future Climate Change on Extreme Meteorological and Hydrological Elements in the Upper Reaches of the Minjiang River

Global warming increases global average precipitation and evaporation, causing extreme climate and hydrological events to occur frequently. Future changes in temperature, precipitation, and runoff from 2021 to 2050 in the upper reaches of the Minjiang River were analyzed using a distributed hydrological model, the SWAT (Soil and Water Assessment Tool), under a future climate scenario. Simultaneously, future variation characteristics of extreme climate hydrological elements in the upper reaches of the Minjiang River were analyzed using extreme climate and runoff indicators. The research shows that the frequency and intensity of the extreme temperature warming index will increase, while those of the extreme temperature cooling index will increase and then weaken in the upper reaches of the Minjiang River under a future climate scenario. The duration of precipitation, the intensity of continuous heavy precipitation, and the frequency of heavy precipitation will increase, whereas the intensity of short-term heavy precipitation and the frequency of heavy precipitation will decrease. However, spatial distribution of flood in the upper reaches is different, and thus flood risk in the upstream source area will still tend to increase. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River.

Modelling the impact of climate change and tillage practices on soil CO2 emissions from dry farmland in the Loess Plateau of China

Conservation tillage to agricultural fields has been widely used in the Loess Plateau of China. However, uncertainties remain regarding the long-term effects of this approach on soil carbon dioxide (CO2) emissions under future climate scenarios. We used the pre-validated DeNitrification DeComposition (DNDC) model to investigate the effects of no-tillage and straw mulch implementation on soil CO2 emissions from dry farmland in the Loess Plateau of China under different future climate scenarios and their dynamic changes. Based on the baseline period (1971–2019) and climate data (2022–2070) under RCP4.5 and RCP8.5 scenarios for two climate models, soil CO2 emissions from dry farmland were simulated under four tillage practices (CT: conventional tillage, CTS: conventional tillage with straw mulch, NT: no-tillage, NTS: no-tillage with straw mulch). The results showed that future climate change would affect soil CO2 emissions of each treatment. Compared with the baseline period, the annual mean CO2 emissions of four treatments increased under the RCP4.5 and RCP8.4 scenarios for the two climate models, with increases ranging from 5.81% to 56.73% under the RCP4.5 scenario and 24.48% to 68.75% under the RCP8.5 scenario. In addition, compared with CT and NT treatments, the CTS and NTS treatments with straw mulch had higher annual mean CO2 emissions under both climate scenarios, while the NT treatment had the lowest. Further analysis showed that the changes in soil CO2 emissions of each treatment under different future climate scenarios were related to the constant changes in annual mean temperature, annual precipitation, and atmospheric CO2 concentration. Simulation of the long-term effects on soil CO2 emissions under different tillage practices based on future climate scenarios led to the conclusion that conservation tillage practices, together with future changes in temperature, precipitation, and atmospheric CO2 concentrations, affect soil CO2 emissions, while no-tillage has potential to mitigate soil CO2 emissions from dry farmland in the Loess Plateau of China under future climate change.

Evaluation and Prediction of Water Yield Services in Shaanxi Province, China

The water yield module of the InVEST model was used to estimate the water yield and its temporal and spatial variation characteristics in Shaanxi Province from 2000 to 2020. Moreover, the influences of future precipitation changes and land use changes on water yield in Shaanxi Province were discussed in the 2030s and 2050s. The results showed that: (1) from 2000 to 2020, the multi-year average water yields in northern Shaanxi, Guanzhong and southern Shaanxi were 33.23 × 108 m3, 73.75 × 108 m3, and 280.63 × 108 m3, respectively; (2) the spatial pattern of water yield depth displayed a characteristic of gradually increasing from north to south; (3) under the precipitation change scenario, the water yield under different emission scenarios wa s in the order of RCP (Representative Concentration Pathways) 8.5 &gt; RCP2.6 &gt; RCP4.5; under the land use change scenario, the water yield depth of Shaanxi Province as a whole and in the three regions in the 2030s and 2050s showed a decline. The research results can provide scientific support for water ecological security, water resources, and regional high-quality sustainable development in Shaanxi Province.

Assessment of total and extreme precipitation over central Asia via statistical downscaling: Added value and multi-model ensemble projection

Central Asia (CA) is highly sensitive and vulnerable to changes in precipitation due to global warming, so the projection of precipitation extremes is essential for local climate risk assessment. However, global and regional climate models often fail to reproduce the observed daily precipitation distribution and hence extremes, especially in areas with complex terrain. In this study, we proposed a statistical downscaling (SD) model based on quantile delta mapping to assess and project eight precipitation indices at 73 meteorological stations across CA driven by ERA5 reanalysis data and simulations of 10 global climate models (GCMs) for present and future (2081–2100) periods under two shared socioeconomic pathways (SSP245 and SSP585). The reanalysis data and raw GCM outputs clearly underestimate mean precipitation intensity (SDII) and maximum 1-day precipitation (RX1DAY) and overestimate the number of wet days (R1MM) and maximum consecutive wet days (CWD) at stations across CA. However, the SD model effectively reduces the biases and RMSEs of the modeled precipitation indices compared to the observations. Also it effectively adjusts the distributional biases in the downscaled daily precipitation and indices at the stations across CA. In addition, it is skilled in capturing the spatial patterns of the observed precipitation indices. Obviously, SDII and RX1DAY are improved by the SD model, especially in the southeastern mountainous area. Under the intermediate scenario (SSP245), our SD multi-model ensemble projections project significant and robust increases in SDII and total extreme precipitation (R95PTOT) of 0.5 mm d−1 and 19.7 mm, respectively, over CA at the end of the 21st century (2081–2100) compared to the present values (1995–2014). More pronounced increases in indices R95PTOT, SDII, number of very wet days (R10MM), and RX1DAY are projected under the higher emission scenario (SSP585), particularly in the mountainous southeastern region. The SD model suggested that SDII and RX1DAY will likely rise more rapidly than those projected by previous model simulations over CA during the period 2081–2100. The SD projection of the possible future changes in precipitation and extremes improves the knowledge base for local risk management and climate change adaptation in CA.

Revealing historical observations and future projections of precipitation over Northwest China based on dynamic downscaled CMIP6 simulations

The warming climate driven by global change has great potential in altering regional and global hydrologic cycles, thus leading to considerable changes in spatial variability and temporal pattern of precipitation. Northwest China (NW) has witnessed a significant wetting trend over the past decades, while the persistence of this wetting trend and potential changes in precipitation under future climate impacts remains elusive. In this study, long-term meteorological observations were used to probe historical variations of precipitation from 1951 to 2020, and the WRF model was employed as a regional climate model to examine future precipitation patterns over NW. Two 9-year downscaled WRF simulations were conducted comprising of historical (WRF-HIST; 2012–2020) and future climate change scenarios (WRF-SSP585; 2047–2055) using bias-corrected global climate model outputs from Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with ground observations, the WRF model exhibited strong capability in capturing the spatial pattern and temporal variations of precipitation across the NW. Intense precipitation was mainly found in stations located at northern NW and southeastern NW. Summertime precipitation substantially contributed to annual precipitation over the study region. Future precipitation projections suggest significant decreases of precipitation across the southern and eastern NW, with a stronger reduction magnitude in summer. Further, extreme precipitation events were projected to decrease in spring and summer, suggesting that the NW may become drier and the wetting trend may shift to another pattern in the 2050s under the SSP585 climate scenario. Overall, this study reveals historical and future potential changes in precipitation over NW through a high-resolution, dynamically downscaled dataset from WRF modeling, which in turn will help inform regional mitigation and adaption on potential impacts of future climate change on NW.

Evaluating future changes in the South Indian Ocean Convergence Zone projected by CMIP5 models and associated uncertainty

Southern Africa relies heavily on precipitation for agricultural purposes; therefore, spatial and temporal changes in precipitation are crucial to identify and understand. The South Indian Ocean Convergence Zone (SIOCZ), a large-scale, austral summer rainfall feature extending across southern Africa into the southwest Indian Ocean, is evaluated in future projections. Using a best-fit algorithm, future projections of the SIOCZ are determined, which indicate a northward shift of approximately 120 km in CMIP5 models under RCP8.5. A dipole pattern of precipitation wetting/drying is evident, where wetting occurs to the north of the climatological axis of maximum rainfall, implying a northward shift of the Inter Tropical Convergence Zone, consistent with the SIOCZ shift. Common drivers responsible for model changes include enhanced warming in the northern Indian Ocean in line with the ‘warmest-get-wetter’ sea surface temperature hypothesis, which impacts circulation by transporting moisture away from the SIOCZ towards the equator. Most CMIP5 models exhibit drying trends over the SIOCZ region, with mechanisms driving uncertainty related to diverse warming trends across models. Empirical orthogonal function patterns of future precipitation changes across CMIP5 models exhibit a pattern much like the SIOCZ, which is related to inter-model changes in future temperature changes. Reductions in model spread are established in SIOCZ projections, whereby model processes of change exhibit agreement, despite differing initial SIOCZ conditions. Therefore, model process convergence and coherence are established with respect to projected changes in the SIOCZ, irrespective of initial climatology biases. Understanding future changes in this feature will help inform decision-making for water and agriculture adaptation planning in southern Africa.

Decreased precipitation reduced the complexity and stability of bacterial co-occurrence patterns in a semiarid grassland.

Grasslands harbor complex bacterial communities, whose dynamic interactions are considered critical for organic matter and nutrient cycling. However, less is known about how changes in precipitation impact bacterial interactions. We conducted precipitation manipulation experiments in the Eastern Eurasian Steppe in China and constructed co-occurrence networks for bacterial communities. The network topological features of the bacterial communities exhibited considerable differences among increased precipitation, control, and decreased precipitation gradients. The bacterial co-occurrence pattern in the increased precipitation gradient was the most complex and stable, with a large network size, followed by those of the control and decreased precipitation gradients. Soil moisture (SM) was the primary factor influencing the complexity, size, and stability of bacterial networks across different precipitation gradients, followed by total nitrogen (TN), belowground biomass, aboveground biomass, and total carbon (TC). Our results indicate that drought conditions reduce the complexity and stability of the bacterial community, and future changes in precipitation will greatly reshape bacterial interactions in semiarid grasslands. Overall, these findings could enhance our understanding of how microbes respond to changing precipitation patterns by regulating their interactions in water-limited ecosystems and will improve our ability to predict the impacts of precipitation regime change on ecosystem nutrient cycling and feedback between ecosystem processes and global climate change.

Future Changes in Temperature and Precipitation over Northeastern Brazil by CMIP6 Model

Global warming is causing an intensification of extreme climate events with significant changes in frequency, duration, and intensity over many regions. Understanding the current and future influence of this warming in northeastern Brazil (NEB) is important due to the region’s greater vulnerability to natural disasters, as historical records show. In this paper, characteristics of climate change projections (precipitation and air temperature) over NEB are analyzed using 15 models of Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) scenarios. By using the Taylor diagram, we observed that the HadGEM3-GC31-MM model simulates the seasonal behavior of climate variables more efficiently. Projections for NEB indicate an irreversible increase in average air temperature of at least 1 °C throughout the 21st century, with a reduction of up to 30% in annual rainfall, as present in scenarios of regional rivalry (SSP3-7.0) and high emissions (SSP5-8.5). This means that a higher concentration of greenhouse gases (GHG) will increase air temperature, evaporation, and evapotranspiration, reducing rainfall and increasing drought events. The results obtained in this work are essential for the elaboration of effective strategies for adapting to and mitigating climate change for the NEB.

Climate change scenarios, their impacts and implications on Indian cardamom-coffee hot spots; one of the two in the world

In this study, we investigated climatic parameters and predicted future changes in precipitation and atmospheric temperature levels based on RCP4. 5/8.5 scenarios in all cardamom-coffee hot spots of southern India. Our results showed more precipitation levels and patterns occurred in Cardamom hills (Kerala), followed by lower Puleny hills in Tamil Nadu. The least variation in precipitation levels has been noticed for temperate upper Puleny hills and Kodagu hills in Karnataka. RCP4.5/8.5 scenario analysis showed greater variability in precipitation, up to 180% increase and 90% decrease for all hot sports. The scenario analysis also predicted extreme temperature variations ranging from 0.5 to 8.5°C increase for the entire study region. A significant change in coffee yield and quality has been recorded over the last 30 years. Increased yield trends in coffee were noticed for Cardamom hills (CH) and Kodagu hills, but significantly lower coffee production was observed for lower Pulney hills. The mixed response of yield variability in coffee has been primarily attributed to the ongoing changing climatic factors. Ecophysiological studies of coffee, cardamom, and black pepper have proved that coffee would adapt well to future challenging climatic conditions, closely followed by cardamom and black pepper. Since all the coffee-cardamom hot spots in southern India undergoes considerable change in precipitation levels and pattern, necessary precautions, including water and irrigation management strategies, must be given utmost priority to increase the crop yield sustainability of these delicate cardamom-coffee hot spots in India.