Representative Concentration Pathways Research Articles

Habitat suitability of Opuntia ficus-indica (L.) MILL. (CACTACEAE): a comparative temporal evaluation using diverse bio-climatic earth system models and ensemble machine learning approach.

A comprehensive evaluation of the habitat suitability across the India was conducted for the introduced species Opuntia ficus-indica. This assessment utilized a newly developed model called BioClimInd, takes into account five Earth System Models (ESMs). These ESMs consider two different emission scenarios known as Representative Concentration Pathways (RCP), specifically RCP 4.5 and RCP 8.5. Additionally, the assessment considered two future time frames: 2040-2079 (60) and 2060-2099 (80). Current study provided the threshold limit of different climatic variables in annual, quarter and monthly time slots like temperature annual range (26-30°C), mean temperature of the driest quarter (25-28°C); mean temperature of the coldest month (22-25°C); minimum temperature of coldest month (13-17°C); precipitation of the wettest month (250-500mm); potential evapotranspiration Thronthwaite (1740-1800mm). Predictive climatic habitat suitability posits that the introduction of this exotic species is deemed unsuitable in the Northern as well as the entirety of the cooler eastern areas of the country. The states of Rajasthan and Gujarat exhibit the highest degree of habitat suitability for this particular species. Niche hypervolumes and climatic variables affecting fundamental and realized niches were also assessed. This study proposes using multi-climatic exploration to evaluate habitats for introduced species to reduce modeling uncertainties.

Historical and projected response of Southeast Asian lakes surface water temperature to warming climate

The temperature of surface and epilimnetic waters, closely related to regional air temperatures, responds quickly and directly to climatic changes. As a result, lake surface temperature (LSWT) can be considered an effective indicator of climate change. In this study, we reconstructed and investigated historical and future LSWT across different scenarios for over 80 major lakes in mainland Southeast Asia (SEA), an ecologically diverse region vulnerable to climate impacts.Five different predicting models, incorporating statistical, machine and deep learning approaches, were trained and validated using ERA5 and CHIRPS climatic feature datasets as predictors and 8-day MODIS-derived LSWT from 2000 to 2020 as reference dataset. Best performing model was then applied to predict both historical (1986–2020) and future (2020–2100) LSWT for SEA lakes, utilizing downscaled climatic CORDEX-SEA feature data and multiple Representative Concentration Pathway (RCP). The analysis uncovered historical and future thermal dynamics and long-term trends for both daytime and nighttime LSWT.Among 5 models, XGboost results the most performant (NSE 0.85, RMSE 1.14 °C, MAE 0.69 °C, MBE -0.08 °C) and it has been used for historical reconstruction and future LSWT prediction. The historical analysis revealed a warming trend in SEA lakes, with daytime LSWT increasing at a rate of +0.18 °C/decade and nighttime LSWT at +0.13 °C/decade over the past three decades. These trends appeared of smaller magnitude compared to global estimates of LSWT change rates and less pronounced than concurrent air temperature and LSWT increases in neighbouring regions. Projections under various RCP scenarios indicated continued LSWT warming. Daytime LSWT is projected to increase at varying rates per decade: +0.03 °C under RCP2.6, +0.14 °C under RCP4.5, and +0.29 °C under RCP8.5. Similarly, nighttime LSWT projections under these scenarios are: +0.03 °C, +0.10 °C, and +0.16 °C per decade, respectively. The most optimistic scenario predicted marginal increases of +0.38 °C on average, while the most pessimistic scenario indicated an average LSWT increase of +2.29 °C by end of the century.This study highlights the relevance of LSWT as a climate change indicator in major SEA's freshwater ecosystems. The integration of satellite-derived LSWT, historical and projected climate data into data-driven modelling has enabled new and a more nuanced understanding of LSWT dynamics in relation to climate throughout the entire SEA region.

Quo vadis, smallholder forest landscape? An introduction to the LPB-RAP model.

The impacts of the Anthropocene on climate and biodiversity pose societal and ecological problems that may only be solved by ecosystem restoration. Local to regional actions are required, which need to consider the prevailing present and future conditions of a certain landscape extent. Modeling approaches can be of help to support management efforts and to provide advice to policy making. We present stage one of the LaForeT-PLUC-BE model (Landscape Forestry in the Tropics-PCRaster Land Use Change-Biogeographic & Economic model; in short: LPB) and its thematic expansion module RAP (Restoration Areas Potentials). LPB-RAP is a high-resolution pixel-based scenario tool that relies on a range of explicit land use types (LUTs) to describe various forest types and the environment. It simulates and analyzes future landscape configurations under consideration of climate, population and land use change long-term. Simulated Land Use Land Cover Change (LULCC) builds on dynamic, probabilistic modeling incorporating climatic and anthropogenic determinants as well as restriction parameters to depict a sub-national regional smallholder-dominated forest landscape. The model delivers results for contrasting scenario settings by simulating without and with potential Forest and Landscape Restoration (FLR) measures. FLR potentials are depicted by up to five RAP-LUTs. The model builds on user-defined scenario inputs, such as the Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). Model application is here exemplified for the SSP2-RCP4.5 scenario in the time frame 2018-2100 on the hectare scale in annual resolution using Esmeraldas province, Ecuador, as a case study area. The LPB-RAP model is a novel, heuristic Spatial Decision Support System (SDSS) tool for smallholder-dominated forest landscapes, supporting near-time top-down planning measures with long-term bottom-up modeling. Its application should be followed up by FLR on-site investigations and stakeholder participation across all involved scales.

Modeling future (2021–2050) meteorological drought characteristics using CMIP6 climate scenarios in the Western Cape Province, South Africa

Consistent drought modelling under plausible shared socioeconomic–representative concentration pathways (SSP–RCPs) are crucial for effectively managing future drought risk in agricultural environments. The Western Cape (WC) is one of South Africa’s main agro-based provinces and faces a mounting threat of water insecurity due to recurrent drought. The objective of this study was to predict meteorological drought hazard for 2021–2050 based on three CMIP6 scenarios: SSP5–8.5, SSP2–4.5 and SSP1–2.6. Precipitation simulations generated by the sixth version of Model for Interdisciplinary Research on Climate (MIROC6) under the SSP5–8.5, SSP2–4.5 and SSP1–2.6 scenarios were used from fifteen stations across the six AEZs of the WC province. The Standardised Precipitation Index (SPI) was computed at 12-month timescales. Trend analysis of precipitation datasets and the SPI-values were done at p < 0.05 using the Mann–Kendall (M–K) test. The findings revealed negative precipitation trends of − 7.6 mm/year in Ceres, while positive trends of 0.3 mm/year were observed in Malmesbury. These findings indicate an improvement from − 7.8 and − 6.4 mm/year in the same regions, respectively, compared to historical trends observed between 1980 and 2020. The results suggest that in 2042 and 2044, Bredasdorp will experience − 2 < SPI < − 1.5 under the SSP2–4.5 scenarios, while Matroosberg in 2038 under the SSP5–8.5 will experience SPI > − 2. The findings of this study will assist in the development of proactive planning and implementation of drought mitigation strategies and policies aimed at reducing water insecurity in AEZs.

Projected climate oligotrophication of the Adriatic marine ecosystems

The Adriatic Sea hosts diverse marine ecosystems, characterized by rich biodiversity and unique ecological dynamics. Its intricate coastal habitats and open waters support a range of species and contribute to the region's ecological and economic significance. Unraveling the consequences of the ongoing climate changes on this delicate environment is essential to ensure the future safeguard of this basin. To tackle this problem, we developed a biogeochemical model for the entire basin, with a horizontal resolution of about 2 km and 120 vertical levels, forced by the projections of atmosphere, hydrology and ocean circulation between 1992 and 2050, under emission scenario RCP8.5. The changes projected between 2031–2050 and 1992–2011 were evaluated on ecoregions characterized by different trophic conditions, identified using a k-medoid classification technique. The results point toward a generalized oligotrophication of the basin, especially intense in the northern estuarine areas, driven by a substantial decrease in river discharge projected for the rivers of the Po Plain. This scenario of unproductive and declining resources, together with the ongoing warming, salinization, and acidification of marine waters, cast doubt on the long-term resilience of the Northern Adriatic food web structure, which has evolved to thrive in high trophic conditions. The outcome of this study provides the stakeholders with a tool to understand how potential long-term decreases in the regimes of the Northern Adriatic Rivers could affect the marine ecosystem and its goods and services in the future.

Estimating the Incidence and Economic Cost of Lyme Disease Cases in Canada in the 21st Century with Projected Climate Change.

Lyme disease (LD) is emerging in Canada owing to the range expansion of the tick vector Ixodes scapularis (I. scapularis). Our objective was to estimate future LD incidence in Canada, and economic costs, for the 21st century with projected climate change. Future regions of climatic suitability for I. scapularis were projected from temperature output of the North American Coordinated Regional Climate Downscaling Experiment regional climate model ensemble using greenhouse gas Representative Concentration Pathways (RCPs) 4.5 and 8.5. Once regions became climatically suitable for ticks, an algorithm derived from tick and LD case surveillance data projected subsequent increasing LD incidence. Three scenarios (optimistic, intermediate, and pessimistic) for maximum incidence at endemicity were selected based on LD surveillance, and underreporting estimates, from the United States. Health care and productivity cost estimates of LD cases were obtained from the literature. Projected annual LD cases for Canada ranged from 120,000 to by 2050. Variation in incidence was mostly due to the maximum incidence at endemicity selected, with minor contributions from variations among climate models and RCPs. Projected annual costs were substantial, ranging from to a year by 2050. There was little difference in projected incidence and economic cost between RCPs, and from 2050 to 2100, because projected climate up to 2050 is similar for RCP4.5 and RCP8.5 (mitigation of greenhouse gas emissions captured in RCP4.5 does not impact climate before the 2050s) and by 2050 the most densely populated areas of the study region are projected to be climatically suitable for ticks. Future incidence and economic costs of LD in Canada are likely to be substantial, but uncertainties remain. Because densely populated areas of Canada are projected to become endemic under conservative climate change scenarios, mitigation of greenhouse gas emissions is unlikely to provide substantial health co-benefits for LD. https://doi.org/10.1289/EHP13759.

Climate projections of human thermal comfort for indoor workplaces

Climate models predict meteorological variables for outdoor spaces. Nevertheless, most people work indoors and are affected by heat indoors. We present an approach to transfer climate projections from outdoors to climate projections of indoor air temperature (Ti) and thermal comfort based on a combination of indoor sensors, artificial neural networks (ANNs), and 22 regional climate projections. Human thermal comfort and Ti measured by indoor sensors at 90 different workplaces in the Upper Rhine Valley were used as training data for ANN models predicting indoor conditions as a function of outdoor weather. Workplace-specific climate projections were modeled for the time period 2070–2099 and compared to the historical period 1970–1999 using the same ANNs, but ERA5-Land reanalysis data as input. It is shown that heat stress indoors will increase in intensity, frequency, and duration at almost all investigated workplaces. The rate of increase depends on building and room properties, the workplace purpose, and the representative concentration pathway (RCP2.6, RCP4.5, or RCP8.5). The projected increase of the mean air temperature in the summer (JJA) outdoors, by + 1.6 to + 5.1 K for the different RCPs, is higher than the increase in Ti at all 90 workplaces, which experience on average an increase of + 0.8 to + 2.5 K. The overall frequency of heat stress is higher at most workplaces than outdoors for the historical and the future period. The projected hours of indoor heat stress will increase on average by + 379 h, + 654 h, and + 1209 h under RCP2.6, RCP4.5, and RCP8.5, respectively.

Production vulnerability to wheat blast disease under climate change

Wheat blast is a devastating disease caused by the fungal pathogen Magnaporthe oryzae pathotype Triticum that has spread to both neighbouring and distant countries following its emergence in Brazil in the 1980s. Under climate change conditions, wheat blast is predicted to spread primarily in tropical regions. Here we coupled a wheat crop simulation model with a newly developed wheat blast model, to provide quantitative global estimates of wheat blast vulnerability under current and future climates. Under current climatic conditions, 6.4 million hectares of arable land is potentially vulnerable to wheat blast. A more humid and warmer climate in the future (Representative Concentration Pathway 8.5) is likely to increase the area suitable for wheat blast infection, particularly in the Southern Hemisphere, and reduce global wheat production by 69 million tons per year (13% decrease) by mid-century. Impacts of climate change could be further exacerbated and food security problems increased.

The interacting effect of climate change and herbivory can trigger large-scale transformations of European temperate forests.

In many regions of Europe, large wild herbivores alter forest community composition through their foraging preferences, hinder the forest's natural adaptive responses to climate change, and reduce ecosystem resilience. We investigated a widespread European forest type, a mixed forest dominated by Picea abies, which has recently experienced an unprecedented level of disturbance across the continent. Using the forest landscape model iLand, we investigated the combined effect of climate change and herbivory on forest structure, composition, and carbon and identified conditions leading to ecosystem transitions on a 300-year timescale. Eight climate change scenarios, driven by Representative Concentration Pathways 4.5 and 8.5, combined with three levels of regeneration browsing, were tested. We found that the persistence of the current level of browsing pressure impedes adaptive changes in community composition and sustains the presence of the vulnerable yet less palatable P. abies. These development trajectories were tortuous, characterized by a high disturbance intensity. On the contrary, reduced herbivory initiated a transformation towards the naturally dominant broadleaved species that was associated with an increased forest carbon and a considerably reduced disturbance. The conditions of RCP4.5 combined with high and moderate browsing levels preserved the forest within its reference range of variability, defining the actual boundaries of resilience. The remaining combinations of browsing and climate change led to ecosystem transitions. Under RCP4.5 with browsing effects excluded, the new equilibrium conditions were achieved within 120 years, whereas the stabilization was delayed by 50-100 years under RCP8.5 with higher browsing intensities. We conclude that forests dominated by P. abies are prone to transitions driven by climate change. However, reducing herbivory can set the forest on a stable and predictable trajectory, whereas sustaining the current browsing levels can lead to heightened disturbance activity, extended transition times, and high variability in the target conditions.

Cloud- and ice-albedo feedbacks drive greater Greenland Ice Sheet sensitivity to warming in CMIP6 than in CMIP5

Abstract. The Greenland Ice Sheet (GrIS) has been losing mass since the 1990s as a direct consequence of rising temperatures and has been projected to continue to lose mass at an accelerating pace throughout the 21st century, making it one of the largest contributors to future sea-level rise. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) models produce a greater Arctic amplification signal and therefore also a notably larger mass loss from the GrIS when compared to the older CMIP5 projections, despite similar forcing levels from greenhouse gas emissions. However, it is also argued that the strength of regional factors, such as melt–albedo feedbacks and cloud-related feedbacks, will partly impact future melt and sea-level rise contribution, yet little is known about the role of these regional factors in producing differences in GrIS surface melt projections between CMIP6 and CMIP5. In this study, we use high-resolution (15 km) regional climate model simulations over the GrIS performed using the Modèle Atmosphérique Régional (MAR) to physically downscale six CMIP5 Representative Concentration Pathway (RCP) 8.5 and five CMIP6 Shared Socioeconomic Pathway (SSP) 5-8.5 extreme high-emission-scenario simulations. Here, we show a greater annual mass loss from the GrIS at the end of the 21st century but also for a given temperature increase over the GrIS, when comparing CMIP6 to CMIP5. We find a greater sensitivity of Greenland surface mass loss in CMIP6 centred around summer and autumn, yet the difference in mass loss is the largest during autumn with a reduction of 27.7 ± 9.5 Gt per season for a regional warming of +6.7 ∘C and 24.6 Gt per season more mass loss than in CMIP5 RCP8.5 simulations for the same warming. Assessment of the surface energy budget and cloud-related feedbacks suggests a reduction in high clouds during summer and autumn – despite enhanced cloud optical depth during autumn – to be the main driver of the additional energy reaching the surface, subsequently leading to enhanced surface melt and mass loss in CMIP6 compared to CMIP5. Our analysis highlights that Greenland is losing more mass in CMIP6 due to two factors: (1) a (known) greater sensitivity to greenhouse gas emissions and therefore warmer temperatures and (2) previously unnotified cloud-related surface energy budget changes that enhance the GrIS sensitivity to warming.

Effect of methane mitigation on global temperature under a permafrost feedback

Earth systems may fall into an undesirable system state if 1.5°C of warming is exceeded. Carbon release from substantial permafrost stocks vulnerable to near-term warming represents a positive climate feedback that may increase the risk of 1.5°C warming or greater. Methane (CH4) is a short-lived but powerful greenhouse gas with a global warming potential 28.5 times that of carbon dioxide (CO2) over a 100 year time span. Because permafrost thaw in the coming centuries is partly determined by the warming in the 21st century, rapid reductions in methane emissions early in the 21st century could have far reaching effects. We use a reduced complexity carbon cycle model and a permafrost feedback module to explore the possibility that accelerating reductions in methane emissions could help avoid long-term warming by limiting permafrost melt. We simulate three extended Representative Concentration Pathway (RCP) emission scenarios (RCP 2.6, 4.5, and 6) through the year 2300 and impose methane mitigation strategies where we reduce CH4 emissions by 1%, 5% or 10% annually until the long-term scenario emission level is reached. We find that accelerated rates of methane mitigation do not sufficiently alter the global temperature anomaly to prevent or delay a permafrost feedback, nor do they result in meaningful long term reductions in temperatures. We find that the long-term magnitude of methane mitigation (i.e., long-term emission level) and not the rate of reduction, corresponds to long-term temperature change. Further study of methane-climate dynamics is necessary to fully resolve this question.

Testing CORDEX GCMs for Projecting Rainfall in Amhara, Ethiopia

Five CORDEX Global Circulation Models (GCMs): ICHEC-EC-EARTH, MIROC5, HadGEM2-ES, MPI-ESM-LR, and NorESM1-M were tested and validated for projecting rainfall in the Amhara regional state of Ethiopia. The GCMs were evaluated in terms of their performance during the historical period 1981-2020 and of the near-term, mid-term, and long-term future periods in three Representative Concentration Pathway (RCP) scenarios, RCP2.6, RCP4.5, and RCP8.5, across 71 grid points. Monthly observed rainfall data was used to compare the GCMs' performance and correct their biases using three non-parametric quartile mapping methods: robust empirical quartiles, empirical quartiles, and smoothing splines. The results show that HadGEM2-ES and MPI-ESM-LR had the best performance in the study area. The test and validation results for these two GCMs have come up with r = 0.8, NSE = 0.5-0.6, and RMSE = 64-70 mm/month. As there was a large discrepancy in historical and projected CORDEX rainfall data, bias correction was necessary, and the robust empirical quartiles method was found the best for the Amhara region. Compared to the historical, there will be a decrease in the monthly rainfall amount for the months of March, May, June, July, and October and an increase for the rest in all projected scenarios. The result concluded that using an ensemble of HadGEM2-ES &amp; MPI-ESM-LR GCMs would better simulate the rainfall in the Amhara region

Simulating sub-hourly rainfall data for current and future periods using two statistical disaggregation models: case studies from Germany and South Korea

Abstract. The simulation of fast-reacting hydrological systems often requires sub-hourly precipitation data to develop appropriate climate adaptation strategies and tools, i.e. upgrading drainage systems and reducing flood risks. However, these sub-hourly data are typically not provided by measurements and atmospheric models, and many statistical disaggregation tools are applicable only up to an hourly resolution. Here, two different models for the disaggregation of precipitation data from a daily to sub-hourly scale are presented. The first one is a conditional disaggregation model based on first-order Markov chains and copulas (WayDown) that keeps the input daily precipitation sums consistent within disaggregated time series. The second one is an unconditional rain generation model based on a double Poisson process (LetItRain) that does not reproduce the input daily values but rather generates time series with consistent rainfall statistics. Both approaches aim to reproduce observed precipitation statistics over different timescales. The developed models were validated using 10 min radar data representing 10 climate stations in Germany and South Korea; thus, they cover various climate zones and precipitation systems. Various statistics were compared, including the mean, variance, autocorrelation, transition probabilities, and proportion of wet period. Additionally, extremes were examined, including the frequencies of different thresholds, extreme quantiles, and annual maxima. To account for the model uncertainties, 1000-year-equivalent ensembles were generated by both models for each study site. While both models successfully reproduced the observed statistics, WayDown was better (than LetItRain) at reproducing the ensemble median, showing strength with respect to precisely refining the coarse input data. In contrast, LetItRain produced rainfall with a greater ensemble variability, thereby capturing a variety of scenarios that may happen in reality. Both methods reproduced extremes in a similar manner: overestimation until a certain threshold of rainfall and underestimation thereafter. Finally, the models were applied to climate projection data. The change factors for various statistics and extremes were computed and compared between historical (radar) information and the climate projections at a daily and 10 min scale. Both methods showed similar results for the respective stations and Representative Concentration Pathway (RCP) scenarios. Several consistent trends, jointly confirmed by disaggregated and daily data, were found for the mean, variance, autocorrelation, and proportion of wet periods. Further, they presented similar behaviour with respect to annual maxima for the majority of the stations for both RCP scenarios in comparison to the daily scale (i.e. a similar systematic underestimation).

Predicting malaria risk considering vector control interventions under climate change scenarios

Many studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010–2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min–Max]: 16,785 [9,902–74,382] vs 21,289 [11,796–70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.

Historical and projected future runoff over the Mekong River basin

Abstract. The Mekong River (MR) crosses the borders and connects six countries, including China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. It provides critical water resources and supports natural and agricultural ecosystems, socioeconomic development, and the livelihoods of the people living in this region. Understanding changes in the runoff of this important international river under projected climate change is critical for water resource management and climate change adaptation planning. However, research on long-term runoff dynamics for the MR and the underlying drivers of runoff variability remains scarce. Here, we analyse historical runoff variations from 1971 to 2020 based on runoff gauge data collected from eight hydrological stations along the MR. With these runoff data, we then evaluate the runoff simulation performance of five global hydrological models (GHMs) forced by four global climate models (GCMs) under the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Furthermore, based on the best simulation combination, we quantify the impact of future climate change on river runoff changes in the MR. The result shows that the annual runoff in the MR has not changed significantly in the past 5 decades, while the establishment of dams and reservoirs in the basin visibly affected the annual runoff distribution. The ensemble-averaged result of the Water Global Assessment and Prognosis version 2 (WaterGAP2; i.e. GHM) forced by four GCMs has the best runoff simulation performance. Under Representative Concentration Pathways (RCPs; i.e. RCP2.6, RCP6.0 and RCP8.5), the runoff of the MR is projected to increase significantly (p&lt;0.05); e.g. 3.81 ± 3.47 m3s-1a-1 (9 ± 8 % increase in 100 years) at the upper reach under RCP2.6 and 16.36 ± 12.44 m3s-1a-1 (13 ± 10 % increase in 100 years) at the lower reach under RCP6.0. In particular, under the RCP6.0 scenario, the increase in annual runoff is most pronounced in the middle and lower reaches, due to increased precipitation and snowmelt. Under the RCP8.5 scenario, the runoff distribution in different seasons varies obviously, increasing the risk of flooding in the wet season and drought in the dry season.

Study of Temperature Change in Iraq Between 2000-2020 and Its Effects on Rainfall Levels and Agriculture

This study is based on the understanding of the impact of temperature change on the percentage of rainfall in selected areas of Iraq, the data of Iraqi government institutions were relied upon in conducting the statistical assessment on the rates shown by the annual reports of rainfall rates in a number of Iraqi governorates from Mosul in the north to Basra in the south, the period 2000-2020 was chosen, and the CORDEX downsizing project was used, by developing scenario number RCP8.5, it has been shown by using the necessary calculations that rainfall levels will decrease at significant rates in the future if the temperature rises in the same pattern as the current one, which predicts an uncertain future for the nature of the climate in Iraq, this will have a serious impact on the agriculture, industry and all other sectors, this requires the Iraqi government to provide strategic solutions for the coming period. Highlights : Government Data Assessment: Reliance on Iraqi government institutions' data for statistical assessment. Future Projections: Use of the CORDEX downsizing project to develop scenario RCP8.5. Critical Implications: Calculations reveal significant future decreases in rainfall, posing serious threats to agriculture, industry, and other sectors in Iraq. Keywords : Temperature Change, Rainfall Levels, Iraq, CORDEX Downsizing, RCP8.5.

A case study on the impacts of future climate change on soybean yield and countermeasures in Fujin city of Heilongjiang province, China

Global climate change poses a great impact on crop growth, development and yield. Soybean production in Northeast China, which is one of the traditional dominant soybean production areas in China, is of great significance for developing the domestic soybean industry and reducing dependence on imported soybeans. Therefore, it is crucial to evaluate the impacts of future climate change on soybean yield in Northeast China, and to propose reasonable adaptation measures. In this study, we took Fujin city of Heilongjiang province in Northeast China as an example, and used the CROPGRO-soybean model in DSSAT (Decision Support System for Agrotechnology Transfer) to simulate the impacts of future climate change on soybean yield in the four periods of the 2020s (2021-2030), 2030s (2031-2040), 2040s (2041-2050) and 2050s (2051-2060) under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5), and further determine the best agronomic management practices. The results showed that the calibrated and validated model is suitable for simulating soybean in the study area. By analyzing the meteorological data under future climate scenarios RCP4.5 and RCP8.5 from the PRECIS regional climate model, we found that the average temperature, cumulative precipitation and cumulative solar radiation would mostly increase during the growing season in Fujin city of Heilongjiang province. Combined with the model simulation results, it is shown that under the effect of CO2 fertilization, future climate change will have a positive impact on soybean yield. Compared to the baseline (1986-2005), the soybean yield would increase by 0.6% (7.4%), 3.3% (5.1%), 6.0% (16.8%) and 12.3% (20.6%) in the 2020s, 2030s, 2040s and 2050s under RCP4.5 (RCP8.5).Moreover, the optimal sowing dates and the optimal supplemental irrigation amount under RCP4.5 (RCP8.5) are May 10 (May 5) and 50 mm (40mm), respectively. Under future climate conditions, the agronomic management practices, such as advancing the sowing date and supplementary irrigation in the key stage of soybean growth would increase soybean yield and make soybean growth more adaptable to future climate change.

Global spatial distribution of Prosopis juliflora -one of the world's worst 100 invasive alien species under changing climate using multiple machine learning models.

Climate change is one of the factors contributing to the spread of invasive alien species. As a result, it is critical to investigate potential invasion dynamics on a global scale in the face of climate change. We used updated occurrence data, bioclimatic variables, and Köppen-Geiger climatic zones to better understand the climatic niche dynamics of Prosopis juliflora L. (Fabaceae). In this study, we first compared several algorithms-MaxEnt, generalized linear model (GLM), artificial neural network (ANN), generalized boosted model (GBM), generalized additive model (GAM), and random forest (RF)-to investigate the relationships between species-environment and climate for mesquite. We identified the global climate niche similarity sites (NSSs) using the coalesce approach. This study focused on the current and future climatic suitability of P. juliflora under two global circulation models (GCMs) and two climatic scenarios, i.e., Representative Concentration Pathways (RCPs), 4.5 and 8.5, for 2050 and 2070, respectively. Sensitivity, specificity, true skill statistic (TSS), kappa coefficient, and correlation were used to evaluate model performance. Among the tested models, the machine learning algorithm random forest (RF) demonstrated the highest accuracy. The vast swaths of currently uninvaded land on multiple continents are ideal habitats for invasion. Approximately 9.65% of the area is highly suitable for the establishment of P. juliflora. Consequently, certain regions in the Americas, Africa, Asia, Europe, and Oceania have become particularly vulnerable to invasion. In relation to RCPs, we identified suitable area changes (expansion, loss, and stability). The findings of this study show that NSSs and RCPs increase the risk of invasion in specific parts of the world. Our findings contribute to a cross-border continental conservation effort to combat P. juliflora expansion into new potential invasion areas.

Climate change enhances stability of wheat-flowering-date

The stability of winter wheat-flowering-date is crucial for ensuring consistent and robust crop performance across diverse climatic conditions. However, the impact of climate change on wheat-flowering-dates remains uncertain. This study aims to elucidate the influence of climate change on wheat-flowering-dates, predict how projected future climate conditions will affect flowering date stability, and identify the most stable wheat genotypes in the study region. We applied a multi-locus genotype-based (MLG-based) model for simulating wheat-flowering-dates, which we calibrated and evaluated using observed data from the Northern China winter wheat region (NCWWR). This MLG-based model was employed to project flowering dates under different climate scenarios. The simulated flowering dates were then used to assess the stability of flowering dates under varying allelic combinations in projected climatic conditions. Our MLG-based model effectively simulated flowering dates, with a root mean square error (RMSE) of 2.3 days, explaining approximately 88.5 % of the genotypic variation in flowering dates among 100 wheat genotypes. We found that, in comparison to the baseline climate, wheat-flowering-dates are expected to shift earlier within the target sowing window by approximately 11 and 14 days by 2050 under the Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 climate scenarios, respectively. Furthermore, our analysis revealed that wheat-flowering-date stability is likely to be further strengthened under projected climate scenarios due to early flowering trends. Ultimately, we demonstrate that the combination of Vrn and Ppd genes, rather than individual Vrn or Ppd genes, plays a critical role in wheat-flowering-date stability. Our results suggest that the combination of Ppd-D1a with winter genotypes carrying the vrn-D1 allele significantly contributes to flowering date stability under current and projected climate scenarios. These findings provide valuable insights for wheat breeders and producers under future climatic conditions.

Effects of year‐long exposure to elevated pCO2 on the metabolism of back reef and fore reef communities

AbstractThe implications of ocean acidification are acute for calcifying organisms, notably tropical reef corals, for which accretion generally is depressed and dissolution enhanced at reduced seawater pH. We describe year‐long experiments in which back reef and fore reef (17‐m depth) communities from Moorea, French Polynesia, were incubated outdoors under pCO2 regimes reflecting endpoints of representative concentration pathways (RCPs) expected by the end the century. Incubations were completed in three to four flumes (5.0 × 0.3 m, 500 L) in which seawater was refreshed and circulated at 0.1 m s−1, and the response of the communities was evaluated monthly by measurements of net community calcification (NCC) and net community productivity (NCP). For both communities, NCC (but not NCP) was affected by treatments and time, with NCC declining with increasing pCO2, and for the fore reef, becoming negative (i.e., dissolution was occurring) at the highest pCO2 (1067–1433 μatm, RCP8.5). There was scant evidence of community adjustment to reduce the negative effects of ocean acidification, and inhibition of NCC intensified in the back reef as the abundance of massive Porites spp. declined. These results highlight the risks of dissolution under ocean acidification for coral reefs and suggest these effects will be most acute in fore reef habitats. Without signs of amelioration of the negative effects of ocean acidification during year‐long experiments, it is reasonable to expect that the future of coral reefs in acidic seas can be predicted from their current known susceptibility to ocean acidification.