Representative Concentration Pathways Research Articles

Representative concentration pathways (RCPs) used as a tool to evaluate climate change impact on maize crop production in the Woybo catchment of Ethiopia

Representative concentration pathways (RCPs) used as a tool to evaluate climate change impact on maize crop production in the Woybo catchment of Ethiopia

Predicted range expansion of Prostephanus truncatus (Coleoptera: Bostrichidae) under projected climate change scenarios.

The larger grain borer (Prostephanus truncatus [Horn] [Coleoptera: Bostrichidae]) is a wood-boring insect native to Central America and adapted to stored maize and cassava. It was accidentally introduced to Tanzania and became a pest across central Africa. Unlike many grain pests, P. truncatus populations can establish and move within forests. Consequently, novel infestations can occur without human influence. The objectives of our study were to (i) develop an updated current suitability projection for P. truncatus, (ii) assess its potential future distribution under different climate change scenarios, and (iii) identify climate variables that best inform the model. We used WALLACE and MaxEnt to predict potential global distribution by incorporating bioclimatic variables and occurrence records. Future models were projected for 2050 and 2070 with Representative Concentration Pathways (RCPs) 2.6 (low change) and 8.5 (high change). Distribution was most limited by high precipitation and cold temperatures. Globally, highly suitable areas (> 75%) primarily occurred along coastal and equatorial regions with novel areas in northern South America, India, southeastern Asia, Indonesia, and the Philippines, totaling 7% under current conditions. Highly suitable areas at RCPs 2.6 and 8.5 are estimated to increase to 12% and 15%, respectively, by 2050 and increase to 19% in 2070 under RCP 8.5. Centroids of highly suitable areas show distribution centers moving more inshore and away from the equator. Notably, the result is a range expansion, not a shift. Results can be used to decrease biosecurity risks through more spatially explicit and timely surveillance programs for targeting the exclusion of this pest.

Effects of forest management intensity and climate change severity on volume growth, timber yield, carbon stocks, and the amount of deadwood in Scots pine, Norway spruce and silver birch stands in boreal conditions

We studied how management intensity and climate severity affect volume growth, timber yield, carbon stocks, and the amount of deadwood in Scots pine (Pinus sylvestris (L.), Norway spruce (Picea abies (L.) Karst.) and silver birch (Betula pendula Roth.) dominated stands in the Republic of Karelia and Arkhangelsk region of northwest Russia. Using the forest ecosystem model (SIMA) under different climates (current and representative concentration pathway scenarios, RCP4.5 and RCP8.5), no-thinning, low, medium, and high intensity thinning rotational forestry regimes were simulated. Under RCPs, the volume growth and timber yield (5-53%), carbon stocks (1-22%), and deadwood amounts (11-75%) increased for all Scots pine and silver birch stands. The use of low intensity management increased volume growth and carbon stocks (3-16%) and deadwood amount (up to 60%) under RCPs, but not timber yield (±3%) in these stands. For Norway spruce stands, the volume growth (5-26%), timber yield (23-75%), and carbon stocks (5-15%) decreased under RCP8.5, but deadwood amount increased (up to 142%). Intensive management increased volume growth (4-19%), timber yield (4-63%), carbon stocks (up to 14%) and deadwood amounts (up to 49%). Our results highlight that effects of climate severity and management intensity are site and species-specific for Eurasian’s boreal forests.

Future Projections of Precipitation Extremes for Greece Based on an Ensemble of High-Resolution Regional Climate Model Simulations

Future Projections of Precipitation Extremes for Greece Based on an Ensemble of High-Resolution Regional Climate Model Simulations

Assessment of Suitable Habitat of the Demoiselle Crane (Anthropoides virgo) in the Wake of Climate Change: A Study of Its Wintering Refugees in Pakistan

The inevitable impacts of climate change have reverberated across ecosystems and caused substantial global biodiversity loss. Climate-induced habitat loss has contributed to range shifts at both species and community levels. Given the importance of identifying suitable habitats for at-risk species, it is imperative to assess potential current and future distributions, and to understand influential environmental factors. Like many species, the Demoiselle crane is not immune to climatic pressures. Khyber Pakhtunkhwa and Balochistan provinces in Pakistan are known wintering grounds for this species. Given that Pakistan is among the top five countries facing devastating effects of climate change, this study sought to conduct species distribution modeling under climate change using data collected during 4 years of field surveys. We developed a Maximum Entropy distribution model to predict the current and projected future distribution of the species across the study area. Future habitat projections for 2050 and 2070 were carried out using two representative concentration pathways (RCP 4.5 and RCP 8.5) under three global circulation models, including HADGEM2-AO, BCC-CSM1-1, and CCSM4. The most influential factors shaping Demoiselle Crane habitat suitability included the temperature seasonality, annual mean temperature, terrain ruggedness index, and human population density, all of which contributed significantly to the suitability (81.3%). The model identified 35% of the study area as moderately suitable (134,068 km2) and highly suitable (27,911 km2) habitat for the species under current climatic conditions. Under changing climate scenarios, our model predicted a major loss of the species’ current suitable habitat, with shrinkage and shift towards western–central areas along the Pakistan–Afghanistan boarder. The RCP 8.5, which is the extreme climate change scenario, portrays particularly severe consequences, with habitat losses reaching 65% in 2050 and 85% in 2070. This comprehensive study provides useful insights into the Demoiselle Crane habitat’s current and future dynamics in Pakistan.

Real-time emulation of future global warming reveals realistic impacts on the phenological response and quality deterioration in rice

Decreased production of crops due to climate change has been predicted scientifically. While climate-resilient crops are necessary to ensure food security and support sustainable agriculture, predicting crop growth under future global warming is challenging. Therefore, we aimed to assess the impact of realistic global warming conditions on rice cultivation. We developed a crop evaluation platform, the agro-environment (AE) emulator, which generates diverse environments by implementing the complexity of natural environmental fluctuations in customized, fully artificial lighting growth chambers. We confirmed that the environmental responsiveness of rice obtained in the fluctuation of artificial environments is similar to those exhibited in natural environments by validating our AE emulator using publicly available meteorological data from multiple years at the same location and multiple locations in the same year. Based on the representative concentration pathway, real-time emulation of severe global warming unveiled dramatic advances in the rice life cycle, accompanied by a 35% decrease in grain yield and an 85% increase in quality deterioration, which is higher than the recently reported projections. The transcriptome dynamism showed that increasing temperature and CO2 concentrations synergistically changed the expression of various genes and strengthened the induction of flowering, heat stress adaptation, and CO2 response genes. The predicted severe global warming greatly alters rice environmental adaptability and negatively impacts rice production. Our findings offer innovative applications of artificial environments and insights for enhancing varietal potential and cultivation methods in the future.

Historical and Future global warming under different RCP emission scenarios

Understanding the relationship between carbon emissions and climate change has become increasingly urgent amidst the universal endorsement of sustainable development and the establishment of net-zero carbon emission target (The Paris Agreement). In this study, model projections based on Coupled Model Intercomparison Project Phase 5 (CMIP5) are used to examine future climate change under different representative concentration pathway (RCP) emission scenarios till 2300. Besides analysis of future global average temperature change under the emission scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP 8.5, historical data is first collected during the past three decades (1990-2021). The results show that the simulated global warming ranges from 0.9 to 1.3 between 1990 and 2021, consistent with the observations (0.93). Carbon emissions in China arose mostly from manufacturing and construction, electricity and heat and transport. Emissions from electricity, heat and transport increase by a high ratio of 7.7 and 3.5 times due to industry development. Under different RCP scenarios, the global surface temperature anomaly will increase by 0.2-1.0, 1.2-2.0, 3.5-4.3 and 3.0-8.5 , respectively. The surface temperature anomaly values under RCP8.5 are much higher than those under other emission scenarios. Only in the RCP2.6 scenario (sustainability), the surface warming in all simulations stays below a 2 threshold at the end of the 21st century. To attain net zero emissions, it is highly demanded to develop strict emission policies, chart a course toward decarbonization, and institute effective measures aimed at realizing net zero emissions within the short to medium term. Concurrently striving for economic objectives, managing the equilibrium between economic growth and environmental sustainability emerges as a critical imperative.

An assessment of the distributional impacts of autonomous adaptation to climate change from European agriculture

Farmers facing a durable change in climate conditions may autonomously adapt through the intensive margin, the extensive margin, or through the adoption of new practices. Based on a coupling between a microeconomic model of European agriculture (AROPAj) and a crop model (STICS), this article investigates the potential distributional impacts of farm-level autonomous adaptation to climate change within the European Union (EU-27). Considering the representative concentration pathway (RCP) 4.5 of the second report on emission scenario of the fifth assessment report (SRES AR5), we implement two levels of autonomous adaptation for farmers, and three time horizons. The results indicate that ceteris paribus, climate change may lead in terms of social welfare to a slightly worse situation in the middle term and a slightly better situation in the long term with respect to the present. However, the ranking of agents in the distribution is importantly impacted. Our Shapley inequality decomposition shows that income inequality is largely explained by the region and type of farming. Climate change barely affects the marginal contribution of these two characteristics to overall income inequality.

Probabilistic scenario-based assessment of national food security risks with application to Egypt and Ethiopia

Abstract This study presents a novel approach to assessing food security risks at the national level, employing a probabilistic scenario-based framework that integrates both Shared Socio-economic Pathways and Representative Concentration Pathways. This innovative method allows each scenario, encompassing socio-economic, and climate factors, to be treated as a model capable of generating diverse trajectories. This approach offers a more dynamic understanding of food security risks under varying future conditions. The paper details the methodologies employed, showcasing their applicability through a focused analysis of food security challenges in Egypt and Ethiopia, and underscores the importance of considering a spectrum of socio-economic and climatic factors in national food security assessments.

Use of modelling tools to assess climate change impacts on smallholder oil seed yields in South Africa.

Oil seed crops are the second most important field crops after cereals in the agricultural economy globally. The use and demand for oilseed crops such as groundnut, soybean and sunflower have grown significantly, but climate change is expected to alter the agroecological conditions required for oilseed crop production. This study aims to present an approach that utilizes decision-making tools to assess the potential climate change impacts on groundnut, soybean and sunflower yields and the greenhouse gas emissions from the management of the crops. The Decision Support Tool for Agrotechnology Transfer (DSSAT v4.7), a dynamic crop model and the Cool Farm Tool, a GHG calculator, was used to simulate yields and estimate GHG emissions from these crops, respectively. Four representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5), three nitrogen (0, 75, and 150 kg/ha) and phosphorous (0, 30 and 60 P kg/ha) fertilizer rates at three sites in Limpopo, South Africa (Ofcolaco, Syferkuil and Punda Maria) were used in field trials for calibrating the models. The highest yield was achieved by sunflower across all crops, years and sites. Soybean yield is projected to decrease across all sites and scenarios by 2030 and 2050, except at Ofcolaco, where yield increases of at least 15.6% is projected under the RCP 4.5 scenario. Positive climate change impacts are predicted for groundnut at Ofcolaco and Syferkuil by 2030 and 2050, while negative impacts with losses of up to 50% are projected under RCP8.5 by 2050 at Punda Maria. Sunflower yield is projected to decrease across all sites and scenarios by 2030 and 2050. A comparison of the climate change impacts across sites shows that groundnut yield is projected to increase under climate change while notable yield losses are projected for sunflower and soybean. GHG emissions from the management of each crop showed that sunflower and groundnut production had the highest and lowest emissions across all sites respectively. With positive climate change impacts, a reduction of GHG emissions per ton per hectare was projected for groundnuts at Ofcolaco and Syferkuil and for sunflower in Ofcolaco in the future. However, the carbon footprint from groundnut is expected to increase by 40 to 107% in Punda Maria for the period up to 2030 and between 70-250% for 2050, with sunflower following a similar trend. We conclude that climate change will potentially reduce yield for oilseed crops while management will increase emissions. Therefore, in designing adaptation measures, there is a need to consider emission effects to gain a holistic understanding of how both climate change impacts on crops and mitigation efforts could be targeted.

Vegetation redistribution is predicted to intensify soil organic carbon loss under future climate changes on the Tibetan Plateau

Vegetation redistribution may bring unexpected climate-soil carbon cycling in terrestrial biomes. However, whether and how vegetation redistribution alters the soil carbon pool under climate change is still poorly understood on the Tibetan Plateau. Here, we applied the G-Range model to simulate the cover of herbs, shrubs and trees, net primary productivity (NPP) and soil organic carbon density (SOCD) at the depth of 60 cm on Tibetan Plateau for the individual years 2020 and 2060, using climate projection for Representative Concentration Pathways (RCP) 4.5 and RCP8.5 scenarios with the RegCM4.6 model system. Vegetation redistribution was defined as the transitions in bare ground, herbs, shrubs and trees between 2020 and 2060, with approximately 57.9 % (RCP4.5) and 59 % (RCP8.5) of the area will redistribute vegetation over the whole Tibetan Plateau. The vegetation cover will increase by about 2.4 % (RCP4.5) and 1.9 % (RCP8.5), while the NPP and SOCD will decrease by about −14.3 g C m−2 yr−1 and −907 g C m−2 (RCP4.5), and −1.8 g C m−2 yr−1and −920 g C m−2 (RCP8.5). Shrubs and trees will expand in the east, and herbs will expand in the northwest part of the Plateau. These areas are projected to be hotspots with greater SOCD reduction in response to future climate change, and will include lower net plant carbon input due to the negative NPP. Our study indicates that the SOC pool will become a carbon source under increased air temperature and rainfall on the Tibetan Plateau by 2060, especially for the area with vegetation redistribution. These results revealed the potential risk of vegetation redistribution under climate change in alpine ecosystems, indicating the policymakers need to pay attention on the vegetation redistribution to mitigate the soil carbon emission and achieve the goal of carbon neutrality on the Tibetan Plateau.

Assessing Climate-Change-Driven Impacts on Water Scarcity: A Case Study of Low-Flow Dynamics in the Lower Kalu River Basin, Sri Lanka

The adverse impacts of climate change are becoming more frequent and severe worldwide, and Sri Lanka has been identified as one of the most severely affected countries. Hence, it is vital to understand the plausible climate-change-driven impacts on water resources to ensure water security and socio-economic well-being. This study presents novel assessments on low-flow dynamics along the lower Kalu River Basin, Sri Lanka, and water availability during the dry spells of the 2030–2060 period. Bias-corrected daily precipitation projections of a high resolution (25 km × 25 km) NCC-NORESM1-M regional climate model is used here to force a calibrated HEC-HMS hydrological model to project catchment discharge during the future period considered under the two end-member Representative Concentration Pathways (i.e., RCP 2.6 and RCP 8.5). Our results show that the study area (i.e., Kuda Ganga sub-basin) may become warmer (in non-monsoonal periods) and wetter (in monsoon season) under both scenarios during the near future (2030–2040) when compared to the baseline period (1976–2005) considered. Consequently, the streamflow may reduce, making it the decade with the largest water deficit within the time horizon. The subsequent deficit volume assessment for the 2031–2040 period shows a probable water shortage (~5 million m3) under the RCP 2.6 scenario, which may last for ~47 days with an average daily intensity of 105,000 m3. Our results highlight the need of incorporating climate-change-driven impacts in water resources management plans to ensure water security.

Streamflow Variation under Climate Conditions Based on a Soil and Water Assessment Tool Model: A Case Study of the Bailong River Basin

We coupled the global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Future Land Use Simulation model (FLUS) to evaluate land use change in the Bailong River Basin (BRB) under three shared socioeconomic pathway and representative concentration pathway scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5). Additionally, we used calibrated soil and water assessment tools (SWATs) to evaluate the streamflow in the BRB from 2008 to 2100 under the combined influence of climate and land use changes. The results indicate that (1) under the SSP126-EP scenario, forests have been well preserved, and there has been an increase in the combined area of forests and water bodies. The SSP245-ND scenario has a similar reduction pattern in agricultural land as SSP126-EP, with relatively good grassland preservation and a moderate expansion rate in built-up land. In contrast, the SSP585-EG scenario features a rapid expansion of built-up land, converting a significant amount of farmland and grassland into built-up land. (2) From 2021 to 2100, the annual average flow increases under all three scenarios, and the streamflow change is most significant under SSP5–8.5. (3) Compared to the baseline period, the monthly runoff increases, with the most significant increase occurring during the summer months (June to August). This study offers a thorough assessment of potential future changes in streamflow. Its findings are expected to be applied in the future to improve the management of water resources at a local level.

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

Simulation and prediction of precipitation and temperature under RCP scenarios

ABSTRACT Climate change has an immense impact on the environment, ecology, agriculture, and economy. As the most influential climate prediction platform in the world, the Intergovernmental Panel on Climate Change provides important data on predicted future climate change trends. Based on 10 models under the RCP8.5 and RCP4.5 emission scenarios, in this study, we obtained 0.5 × 0.5° data through down-scaling data processing and predicted future rainfall and temperature changes in the Beijing–Tianjin–Hebei region of China. Combined with historical observed data of 198 meteorological stations in the region, the relationships between the predicted values of the model and the measured values were analyzed using the Taylor diagram method. The results show that (1) the future precipitation capacity assessed by the global climate system is generally higher than observed data; (2) for assessment of future temperatures, RCP8.5 shows a larger increase than RCP4.5; (3) for the Beijing–Tianjin–Hebei region, our results show that the MIROC-ESM-CHE model is more consistent with the evaluation of future precipitation capacity, the HadGEM2-ES model is more consistent with the future RCP4.5 scenario temperature evaluation, and the MIROC-ESM-CHEM model is more consistent with the future RCP8.5 scenario temperature evaluation.

Assessment of climate change impact on inflows to Amandara headwork using HEC-HMS and ANNs

AbstractThis research has assessed the impact of climate change on temperature, precipitation, and inflows to the Amandara headwork in Pakistan. Trend Analysis using the Mann–Kendall test and Innovative Trend Analysis has been performed. Rainfall-runoff modeling is executed using the Hydrological Engineering Centre-Hydrological Modeling System (HEC-HMS) and Artificial Neural Networks including Feed Forward Neural Network, Conjugate Gradient, Two-layer Backpropagation Neural Network, and Broyden Fletcher-Goldfarb-Shanno. Mean daily hydro-meteorological data (1992 to 2023) was utilized for this study in which 70% was employed for calibration while the remaining 30% was used for validation of the model. Two GCMs namely CSIROMk3-6–0 and HadGEM2-ES with four Representative Concentration Pathways; RCP 2.6, 4.5, 6.0, and 8.5, were employed for future forecasting of temperature and precipitation. This future predicted data was then used to forecast flows up to 2050 by HEC-HMS. The performance of the models was assessed using correlation coefficient (R), Root Mean Square Error, Mean Bias Error, and Nash Sutcliffe Efficiency. Significant patterns in the runoff and temperature with no trend in precipitation were found. GCMs showed an increase in the range of 3–9 °C in temperature, 300 to 500 mm in precipitation, and 45 to 54% in peak flows.

Characteristics of extreme meteorological droughts over the Brazilian Pantanal throughout the 21st century

The Brazilian Pantanal is a remarkable ecosystem, encompassing vital aspects such as fish, fruit, and grain production, greenhouse gas emission regulation, temperature and precipitation modulation, and groundwater recharge. However, 2019 and 2020 experienced prolonged and severe drought that profoundly impacted the Pantanal region. Although these impacts are still under review, their effects on wildlife, vegetation, and the local population are undeniable. To enhance our understanding of the magnitude and consequences of the 2019/2020 drought, the present study conducted a comprehensive evaluation of drought occurrences in the Pantanal biome. Utilizing two well-established drought indices, the standard precipitation index (SPI) and the standard precipitation evapotranspiration index (SPEI), we analyzed the available observational data provided by the Climate Research Unit (CRU). Additionally, we endeavored to project droughts throughout the 21st century, thereby facilitating the visualization of potential reoccurrences of hazardous events in the Pantanal area. Two regional climate model (RCM) ensembles, the Eta ensemble (E1) and CORDEX ensemble (E2), along with representative concentration pathways (RCP4.5 and RCP8.5 scenarios) were used to project the severity of droughts in the Brazilian Pantanal until 2098/2099. The findings indicate that the hydrological year of 2019/2020 was the driest on record in the Pantanal over the past 80 years. The E1 ensemble did not project such an extreme event, whereas the E2 ensemble forecasted extreme droughts before the 2040s under the RCP4.5 and RCP8.5 scenarios. Using the SPEI, both ensembles showed similar results, with extreme droughts projected toward the end of the 21st century. Notably, the SPI demonstrated effectiveness in detecting short-term droughts, while the SPEI was more efficient for long-term assessments, projecting extreme drought events for the last two decades. The SPEI, employed with the Eta and CORDEX ensembles, can provide valuable insights into drought projections. Moreover, the potential recurrence of extreme droughts, characterized by significant below-average precipitation spanning vast areas throughout the hydrological year, poses a severe threat to the Pantanal biome in this century.

Dynamic quantitative assessment of multiple uncertainty sources in future hydropower generation prediction of cascade reservoirs with hydrological variations

Hydropower is the dominant renewable energy source, which can significantly reduce carbon dioxide emissions while providing electric energy. It is a crucial way to achieve the dual-carbon goal of carbon peak and carbon neutrality. However, climate change inevitably threatens hydropower, either directly or indirectly. Global climate models (GCMs) and hydrological models (HMs) under different representative concentration pathways (RCPs) have been combined to investigate future hydropower generation responses to climate change. The contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological variations remains unclear. Therefore, to address the above challenge, a three-step framework was proposed in this paper: (1) predicting future inflows to reservoirs using three HMs (HBV, SWAT and XAJ) with different structures driven by six GCMs (BNU-ESM, GFDL-ESM2G, GFDL-ESM2M, CanESM2, MIROC-ESM-CHEM and CSIRO-MK3-6-0) under three RCPs (i.e., RCP2.6, RCP4.5 and RCP8.5); (2) diagnosing future hydrological conditions using the Comprehensive Differential Split-sample Test; (3) predicting future hydropower generation of cascade reservoirs by developing a cascade hydropower generation model and dynamically quantifying the contribution of multiple uncertainty sources to multi-scale hydropower generation uncertainties with hydrological conditions based on the variance decomposition method through subsampling. The results indicate that GCMs, HMs, climate emission scenarios and their interactions significantly impacted the uncertainty of hydropower generation with hydrological variations. In the flood season, the relative contributions of GCMs and HMs were the largest, accounting for about 40 %. The interaction effects of multiple uncertainty sources gradually increased before, after and during the non-flood season, with its relative contribution accounting for about 36 %. These findings can facilitate the active response to the new challenges of strategic hydropower energy development under climate change, reducing the losses caused by climate change.

Object-oriented analysis as a foundation for building climate storylines of compounding short-term drought and crop heat stress

IntroductionCrops are vulnerable to precipitation and heat extremes during late spring through summer.MethodsWe analyzed for a north-central U.S. region short-term drought and agricultural heat stress during April-May-June-July. We used the 4-km Parameter Elevation Regression on Independent Slopes Model (PRISM) for observations, aggregated to a 25-km grid, and two 25-km Regional Climate Model version 4 (RegCM4) simulns used either GFDL- or MPI-GCM boundary conditions. We chose 1981-2000 as our contemporary time period, and 2041-2060 as our scenario time period, which used the Representative Concentration Pathway 8.5 emissions scenario. We used object-oriented analysis to identify events of interest in observations and simulations by identifying objects in a space-time domain that meet specified criteria, such as exceeding a heat-stress temperature threshold. The event diagnosis allowed analysis of compound events, occurring when temperature and drought objects overlap.ResultsIdentified objects yielded events that can undermine agricultural productivity and which are thus relevant to decision makers, making them building blocks for possible climate storylines. The observations and simulations showed similar spatial distributions of event frequencies across the analysis region. However, the simulations attained this distribution by having fewer events that tend to cover larger areas compared to observed events, suggesting that the effective resolution of the simulations was coarser than their 25-km grids. Short-term drought frequency increased and heat-stress frequency decreased in transitioning to the scenario climate. When compounding occurred heat-stress events generally preceded the short-term drought events. The overlapping, compound events tended to be more extreme compared to non-overlapping events of either type.DiscussionThe information yielded projected changes in these agriculturally motivated events. One prominent conditional behavior emerging from the work was that a heat-stress event should be a warning to watch for potential drought, as both could compound each other to more intense levels.

Analyzing energy consumption due to occupant interaction with manual and automatic window blinds in multiple climates

Proper daylight management through automated or manual blind systems can enhance user comfort and energy conservation. Furthermore, the influence of weather conditions, coupled with occupant behavior, significantly impacts energy consumption, rendering weather forecasts invaluable in conducting individual building energy simulations to identify energy-efficient strategies for the future. Also, I wonder if it is possible to move some items from room 308 until you can provide items from other resources. Future weather conditions and their impact on building performance can be evaluated using Representative Concentration Pathways (RCP) scenarios provided by the Intergovernmental Panel on Climate Change (IPCC). This research aimed to compare the energy usage of manual and automated window blinds with internal blinds in mid-size office buildings across four main climate zones (2B Hot-Dry; 2A Hot-Humid; 7 Very Cold; 4A Mixed-Humid) in the USA under current and future climate change scenarios (RCP 4.5, 8.5). Utilizing simulation as the methodology and comparing the energy use resulting from the RCP 4.5 and 8.5 scenarios revealed that the most significant decrease in heating energy usage was happening in the Hot-Humid zone with Automated blinds, which is a more efficient alternative than manual Blinds. In contrast, the Very Cold zone experienced almost no changes in either heating or cooling energy consumption. In Climate Zones 2B Hot-Dry and 7 Very Cold, rising temperatures lead to increased cooling energy requirements and reduced heating demands due to climate changes. Climate Zone 4A Mixed-Humid saw reduced energy consumption for cooling but increased usage for heating, particularly under RCP 8.5. In Zone 2A Hot-Humid, a high demand for heating energy was found.