Representative Concentration Path Research Articles

Marginal land in China suitable for bioenergy crops under diverse socioeconomic and climate scenarios from 2020 to 2100

ABSTRACT Maximizing the development of renewable energy plays a critical role in mitigating the climate crisis. Marginal land provides space for the development of biomass energy; however, it remains unclear how the amount and spatial distribution of marginal land that is suitable for energy crop development will change in the future. Here, we project energy marginal land changes in China following the shared socioeconomic pathway (SSP) and/or representative concentration path (RCP). We provide datasets of marginal land, agriculturally suitable land, and potentially suitable for energy crops under historical scenarios and six future scenarios (i.e. SSP1–1.9, SSP1–2.6, SSP4–3.4, SSP2–4.5, SSP4–6.0, and SSP3–7.0) for the period 2020–2100, with a spatial resolution of 5 km. Under the six scenarios, from 2020–2100, the area of suitable marginal land ranged from 1.90–16.28 (Jatropha curcas L.) to 37.37–73.97 (Panicum virgatum L.) (×104 km2), depending on the choice of energy crops and climate scenario. Based on the growing suitability of eight important bioenergy crops—Ricinus communis L., Saccharum officinarum L., Pistacia chinensis Bunge, Panicum virgatum L., Jatropha curcas L., Miscanthus giganteus J., Manihot esculenta Crantz, and Sorghum bicolor Moench—our dataset can be used to identify suitable locations for specific energy crops. This new synthetic dataset could support the development of multiscenario-based solutions related to carbon neutrality, ecosystem services, and energy transition.

Effects of land use and land cover change under shared socioeconomic pathways on future climate in the Yellow River basin, China

The Yellow River basin of China has experienced significant land use and land cover change (LUCC) due to excessive exploitation of nature resources, ecological degradation, and rapid urbanization, which plays an important role in the regional climate. It is crucial to explore the climate patterns of the basin under different future development scenarios to mitigate climate issues and achieve “carbon peaking and carbon neutrality goals”. Here we utilized land use and land cover (LULC) data and projections of future climate under two shared socioeconomic path – representative concentration path (SSP245 and SSP585) scenarios. Using the Weather Research and Forecasting (WRF) model, we simulated four future spatial variation patterns of temperature and precipitation in the basin. Results indicated that under the SSP245 and SSP585 scenarios, the temperature is projected to increase by 0.18 °C and 0.46 °C, respectively, while precipitation is expected to rise by 32.21 mm and 134.24 mm, respectively. The impact of LUCC was found to be relatively minor and mainly concentrated in the middle reaches of the basin. It resulted in a slight increase in temperature in both scenarios and an increase in precipitation in SSP245, but a decrease in precipitation in SSP585. Changes in farmland and urban area exhibited a certain warming effect in both scenarios, with urban areas having a greater influence, leading to a temperature increase by 0.25 °C and 0.28 °C, respectively. Forest, grassland, and bare areas had a lesser impact on temperature and showed different trends under two scenarios. Regarding precipitation, forests and urban areas had a greater influence in both SSP245 and SSP585 scenarios. This study identified the significant role of LUCC under different development scenarios in shaping future temperature and precipitation changes, providing valuable insights for effectively addressing climate issues in the Yellow River basin. It also highlights the need for clear policy recommendations and identifies institutions or agencies responsible for implementing such recommendations.

Irrigation Water Management in a Water-Scarce Environment in the Context of Climate Change

Climate change has a considerable impact on irrigated agriculture, which is vital for food and fiber production. In this study, in the context of climate change, simulation model CROPWAT 8 was employed to compute the reference evapotranspiration, and net irrigation water requirement for wheat, barley, maize, sugar beet, potato, tomato, and date palm. In addition, the WaterGEMS model was utilized to design a new sprinkler irrigation system to run long-term simulations of hydraulic behavior within pressurized pipe networks to irrigate 43 acres for two arid sites (Siwa Oasis and West Elminya fields) inside the 1.5-million-acre groundwater irrigation project in the Egyptian western desert. Five scenarios for climate change were employed in the current (1991–2023), representative concentration path (RCP) 8.5 greenhouse gas emission scenarios for the 2040s, 2060s, 2080s, and 2100s. Mean ETo values for the current scenario show 4.56 and 5.7 mm for the Siwa Oasis and West Elminya fields, respectively. The climate changes cause an increase of the reference evapotranspiration by 4.6, 5.9, 9.4, and 12.7% for RCP: 8.5 greenhouse gas emissions for the 2040s, 2060s, 2080s, and 2100s scenarios, respectively, for the Siwa Oasis field. On the other hand, an increased ratio for the reference evapotranspiration by 4.2, 5.4, 8.6, and 11.6% was observed for the scenarios in the West Elminya field, respectively. The designed sprinkler system indicated a capacity of 111.4 m3 h−1 and 167 m3 h−1 for Siwa and West Elminya fields, respectively. The study suggests using crop patterns for wheat, barley, potato, and sugar beet to save irrigation water.Graphical abstract

Conjunto de modelos para análise estatística de tendências de longo prazo de dados de precipitação e temperatura observados em grade na Bacia do Rio São Francisco, Brasil

ABSTRACT For effective management practices and decision-making, the uncertainties associated with regional climate models (RCMs) and their scenarios need to be assessed in the context of climate change. This study analyzes long-term trends in precipitation and temperature data sets (maximum and minimum values) from the NASA, Earth Exchange Global Daily Downscaled Prediction (NEX-GDDP), under the São Francisco River Basin Representative Concentration Path (RCP) 4.5 and 8.5, using the REA (Reliable Ensemble Average) method. In each grid, the built multi-model was bias-corrected using the CMhyd software for annual, dry, wet, and pre-season periods – for historical (1961-2005) and future (2006-2100) periods. The multi-model and four different methods, namely: The Mann-Kendall, Mann-Kendall pre-brightening test, bias-corrected pre-brightening, and Spearman correlation, were used to detect trends in precipitation, and minimum and maximum temperature. In the analysis of precipitation and temperature metrics, the results for the NRMSD showed that, in general, the CSIRO model presented more satisfactory results in all physiographic regions. Person's correlation coefficient showed a better adjustment of precipitation for the MIROC5, EC.EARTH and NORESMI1 models, in areas of sub-medium and upper São Francisco. For the minimum temperature, the CSIRO and NORESMI1 models showed the best fit, in general. At maximum temperature, the EC.EARTH and CSIRO models showed more satisfactory results. With regard to trend analysis, the results indicated an increasing trend in mean annual temperature and precipitation across the basin. When analyzed by subregion, the results show an increasing trend in monthly average minimum and maximum temperatures in the middle and lower SFRB, while average monthly rainfall increases during the rainy season and preseason in Upper São Francisco. The results of this research can be used by government entities, such as Civil Defense, to subsidize decision-making that requires actions/measures to relocate people/communities to less risky locations to minimize risk or vulnerability situations for the population living nearby to the river.

A climate-spatial matrix growth model for major tree species in Lesser Khingan Mountains and responses of forest dynamics change to different representative concentration path scenarios

IntroductionClimate change affects forest distribution, species composition, structure, and yield due to the sensitivity of forests to temperature, precipitation, and CO2. Therefore, for forest management decisions regarding climate change, it is crucial to explore the response of forest growth, mortality, and recruitment to future climate. We aimed to establish tree species’ responses by introducing variables such as climate, stand spatial structure parameters, and diversity indices.MethodsWe produced fixed parameter transition matrix model (FM), climate-sensitive matrix growth model (CM) and climate-spatial matrix growth model (SCM) using data from 786 plots collected during the 7th (2010), and 8th (2015), Chinese National Forest Inventories in Heilongjiang Province, and long-term predictive performance of CM, SCM, and FM were compared using same data. The models were compared using tenfold cross-validation and long-term predictive performance analysis. To predict the response of major tree species in the Lesser Khingan Mountains to three future climate change scenarios (RCP2.6, RCP4.5, RCP8.5).Results and discussionThe cross-validation results show small significant differences among the three models for short-term prediction (5 years), with the FM performing slightly better than the CM and the SCM. In contrast, for long-term projections (85 years), SCM outperformed FM and CM under three different RCPs, and SCM and CM under three representative concentration paths (RCPs), i.e., RCP2.6, RCP4.5, and RCP8.5, suggesting that rather different dynamics are more reliable, since climatic factors are taken into account which may significantly affect forest dynamics, while changes in stand spatial structure also affect the sensitivity of trees to climate, especially in long-term prediction interval, the results of this paper may provide a theoretical basis for optimizing forest management strategies under climate change.

Modeling impacts of climate change on the geographic distribution and abundances of Tamarindus indica in Tigray region, Ethiopia

Tamarindus indica is a multipurpose dry land species in sub-Saharan that is traditionally used to build resilience into the farming system. The species is highly threatened and listed on the IUCN Red List. However, information on how climatic condition locally influences its ecological distribution is limited. This study investigates the current and future suitable habitat for the species in the Tigray region, in northern Ethiopia. A total of 220 species presence points and the number of T. indica within a 50 m × 50 m plot were collected. In addition, 19 bioclimatic variables, 3 topographic variables and soil data were used to model the impact of future climate conditions under two Representative Concentration Path Ways (RCP4.5 and RCP 8.5). MaxEnt-v-3.3.3 k, Diva-GIS-7.5, and GIS10.6 were used to model the current and future distribution. SPSSv-26 was also utilized to analyze the relationship between the species’ abundance and environmental variables. Results showed that the environmental variables determining most for the distribution of T. indica were mean diurnal range (Bio2 (56.9%)); temperature seasonality (Bio4 (10.3%)) and temperature annual range (Bio7 (9.2%)). The model suggested that the current distribution of T. indica covers an area of 9209 km2 (14.04%). This would have increased to 29,363 km2 (44.78%) and 11,046 km2 (16.85%) by 2070 under RCP4.5 and RCP8.5, respectively. Compared to the high-impact areas, new gains of suitable areas (net 25,081 km2) for the future distribution of the species were predicted in 2070-RCP4.5. Altitude, rainfall, temperature, silt contents of soils and soil pH have significant contributions (P-value<0.05) to the abundance of T. indica. However, altitude has a negative relationship with the abundance of T. indica. Additional studies to understand population trends and other threats are recommended.

Ensemble Evaluation and Member Selection of Regional Climate Models for Impact Models Assessment

Climate change increasingly is affecting every aspect of human life on the earth. Many regional climate models (RCMs) have so far been developed to carefully assess this important phenomenon on specific regions. In this study, ten RCMs captured from the European Coordinated Downscaling Experiment (EURO CORDEX) platform are evaluated on the river Chiese catchment located in the northeast of Italy. The models’ ensembles are assessed in terms of the uncertainty and error calculated through different statistical and error indices. The uncertainties are investigated in terms of signal (increase, decrease, or neutral changes in the variables) and value uncertainties. Together with the spatial analysis of the data over the catchment, the weighted averaged values are used for the models’ evaluations and data projections. Using weighted catchment variables, climate change impacts are assessed on 10 different hydro-climatological variables showing the changes in the temperature, precipitation, rainfall events’ features, and the hydrological variables of the Chiese catchment between historical (1991–2000) and future (2071–2080) decades under RCP (Representative Concentration Path for increasing greenhouse gas emissions) scenario 4.5. The results show that, even though the multi-model ensemble mean (MMEM) could cover the outputs’ uncertainty of the models, it increases the error of the outputs. On the other hand, the RCM with the least error could cause high signal and value uncertainties for the results. Hence, different multi-model subsets of ensembles (MMEM-s) of 10 RCMs are obtained through a proposed algorithm for different impact models’ calculations and projections, making tradeoffs between two important shortcomings of model outputs, which are error and uncertainty. The single model (SM) and multi-model (MM) outputs imply that catchment warming is obvious in all cases and, therefore, evapotranspiration will be intensified in the future where there are about 1.28% and 6% value uncertainties for monthly temperature increase and the decadal relative balance of evapotranspiration, respectively. While rainfall events feature higher intensity and shorter duration in the SM, there are no significant differences for the mentioned features in the MM, showing high signal uncertainties in this regard. The unchanged catchment rainfall events’ depth can be observed in two SM and MM approaches, implying good signal certainty for the depth feature trend; there is still high uncertainty about the depth values. As a result of climate change, the percolation component change is negligible, with low signal and value uncertainties, while decadal evapotranspiration and discharge uncertainties show the same signal and value. While extreme events and their anomalous outcomes direct the uncertainties in rainfall events’ features’ values towards zero, they remain critical for yearly maximum catchment discharge in 2071–2080 as the highest value uncertainty is observed for this variable.

Utilizing the Harvesting of Rainwater to Provide Safe Road Transportation Efficiency and Increase Water Resources in the Context of Climatic Change

This research investigates the effect of heavy rain on highway traffic volume and average speed, and proposes a recharging well harvesting system as an alternative freshwater source in the context of climate change. The Cairo Autostorad highway was taken as a case study. The highway climate data were collected, and traffic was measured using Metrocount equipment during the period from 2008 to 2020. The results show that the studied road is about 12 km long, and about 40 water ponds exist along the route. Each pond has an estimated water volume of 300 m3, and a 30 cm recharging well, with a maximum recharging capacity of 25 m3/h with satisfactory performance, is recommended to be constructed for rainwater harvesting. The recharging wells will clear the ponding volume within 2.5 to 3.5 h after the rainfall has stopped. The design incorporates a 1.2 safety factor against blockage inside the well. In addition, a model was established between the average rainfall depth and the average measured highway speed for the period (2008–2020) during rainy months, indicating an exponential function with a determination factor R2 = 0.7076. The present rainfall (2020) and the representative concentration path (RCP) for 4.5 and 8.5 emissions scenarios were used to simulate the rainfall for future years: the 2040s, 2060s, 2080s, and 2100s. The results show that in the winter season for the current scenario (2020), the average rainfall depth was 45 mm, and the highway speed was 78 km/h. For the RCP 4.5 emission scenarios for the 2040s, 2060s, 2080s, and 2100s, the rainfall depths were 67.8, 126.4, 131.2, and 143.9 mm, and the corresponding reductions in the highway speeds were 23, 34, 35.3, and 36.9%, respectively, compared to the baseline scenario (2020). On the other hand, the RCP 8.5 emission scenarios show a reduction in the highway speed of 23, 34.5, 36.9, and 36.9% for the years 2040, 2060, 2080, and 2100, respectively, due to rainfall depths of 68.7, 128.4, 143.9, and 143.9 mm, respectively. This study helps policymakers to make wise decisions regarding sustainable water resource management and highway traffic problems related to rainwater depths in the context of climate change.

Climate Change Impact on the Offshore Wind Energy Over the North Sea and the Irish Sea

The impact of climate change on the environment and human activities is one of the biggest concerns for the international community. Wind energy represents one of the most reliable and promising technology to achieve the target reduction of emissions. Though more intense and uniform resources characterize offshore areas, climate change may alter the environmental conditions and thus Levelized Cost of Energy evaluation. In this study, we analyze the impact of climate change on the offshore wind energy sector over the North Sea and the Irish Sea, where the majority of the European investments are located. To this aim, seven regional climate model simulations from the EURO-Cordex project are first evaluated. The ERA5 reanalysis product is considered the historical reference information after its validation against in-situ records and it is used to analyze the climate simulations by assessing their performance to reproduce weather types. Several statistics are calculated to assess the skill of each model in reproducing past climatology for the reference period (1985-2004). Since no significant differences between simulations are highlighted, an ensemble of all the seven simulations is used to characterize future changes in the offshore climate. Weather types under the representative concentration path scenario RCP8.5 for the future period 2081-2100 are then analyzed to describe the changes in climatological mean and extreme events. Regional climate model simulations are bias-corrected by applying the empirical quantile mapping technique. Then, future changes in six wind energy climate indicators (i.e. mean and extreme wind speed, wind power density, operation hours, gross energy yield, and capacity factor) are estimated for seven operating offshore wind farms. Results indicate a slight decrease in wind energy production, particularly in the northwest of the domain of study, testified by a reduction of all the climate indicators. However, large uncertainties in the projected changes are found at the wind farms located close to the south coast of the North Sea. Extreme wind conditions show a modest rise in the southeastern part of the region, related to an increase of the weather types dominated by cyclonic systems off Scotland shores.

Analyzing climate change impacts on health, energy, water resources, and biodiversity sectors for effective climate change policy in South Korea

This study analyzes how climate change affects the economy, society, and environment in South Korea. Then, the study explores the ways to strengthen capabilities that can alleviate climate change impacts. To find them, the study employs a system dynamics simulation method and builds a model with several sectors including the urban, rural, population, and social-environmental sectors. The study compares the size of climate change damages in rural and urban areas. The results with representative concentration path (RCP) 8.5 show that the size of climate change damage will continue to increase by 2050. The projected damages from the reduced industrial outputs in urban areas will be larger than that in rural areas. The results also show that the service sector will face stronger impacts from climate change than the manufacturing and agricultural sectors. However, the total size of damage in the rural areas will be bigger than that of the urban areas. It is because the size of reduced industrial outputs per capita in the rural areas is twice bigger than that of the urban areas. The climate change damage in the social and environmental sectors (including a loss of biodiversity and an increase in health costs) account for the largest part of the total damage. The study finally provides suggestions and policies that can improve the capabilities to reduce the climate change damages. One of the major suggestions of this study is that the increase in the climate change budget corresponding to the GDP growth can minimize the size of climate change impacts.

Spatial-temporal distribution of climate suitability of winter wheat in North China Plain for current and future climate scenarios

Wheat production in the North China Plain (NCP) is critical to food security in China; however, it is affected by climate change. Understanding the spatiotemporal distribution of climate suitability of wheat in NCP may help to rationally use climatic resources as well as reduce natural disasters. Out of 30 general circulation models in the Coupled Model Intercomparison Project Phase 5, the 5 best GCMs were selected for each meteorological factor to project daily data in 2021–2100 under representative concentration path 4.5 (RCP4.5) and RCP8.5 for 23 national climatic stations in NCP. Based on agricultural climatic suitability theory and the fuzzy mathematics method, the suitability of water, temperature, and solar radiation was analyzed. The results showed that the water suitability increases from 0.30 at present to 0.40 for 2021–2100 under both RCP4.5 and RCP8.5 with the lowest mostly distributed in the northwest. The temperature suitability varies from 0.80 at present to 0.75 for 2021–2100 and generally increases from north to south; however, at stage 2 (from overwintering to greening), it drops from 0.67 to 0.50 under RCP4.5 and 0.20 under RCP8.5 for 2081–2100. The decreases of water suitability at stage 5 (from heading to milking) and temperature suitability at stage 2 show negative effect of climate change to winter wheat. The solar radiation suitability shows an increasing trend under two scenarios in the future. Finally, the integrated climate suitability shows a little increasing trend, indicating climate condition of winter wheat in NCP will be slightly improved.

Projecting species loss and turnover under climate change for 111 Chinese tree species

Climate-change-induced habitat loss will be the largest global threat to biodiversity. Therefore, quantifying regional species loss and turnover is important for alerting scientists and policymakers to potential future risks and supporting the development of proactive strategies to reduce the impact of climate change on biodiversity. Here, we projected species turnover and loss for 111 Chinese tree species under four climate change scenarios (representative concentration path ways 2.6 (RCP2.6), RCP4.5, RCP6.0, and RCP8.5). The results show that many Chinese trees will be seriously threatened and the extent of this threat depends firstly on the migration scenarios and secondly on the climate change scenarios. On average across RCP scenarios, about 57% or 23% of the trees would be vulnerable or threatened under no migration assumption or universal migration by 2070, respectively. Projection of species loss and turnover in grid cells indicates considerable variation across climate change scenarios (22–50% and 40–67%, respectively) and across geographical regions (25.4–58.1% and 40.6–78.8%, respectively). Species from arid regions and monsoon regions will undergo more species loss than alpine regions under climate change. This is the opposite for species turnover. Notably, extinction risks for Chinese trees species may be high; even under the most conservative situation (RCP2.6 and universal migration), 18% trees will still be vulnerable or threatened. We suggest that a further studying trees adaptation strategy to climate change is required to ensure the sustainable development of China's forests.

Drought Trends and the Extreme Drought Frequency and Characteristics under Climate Change Based on SPI and HI in the Upper and Middle Reaches of the Huai River Basin, China

The Huai River Basin (HRB) is an important grain and industrial production area in eastern China with frequent droughts. Under the background of current climate change, the hydrological and meteorological characteristics of the basin may be changed, which may lead to the changes of regional drought characteristics. It should be paid more attention on the drought research under climate change and the difference between different drought indices. Coupled Model Intercomparison Project phase 5 (CMIP5) as an important tool for climate change research has been used in the study and the study chosen three global circulation models (GCMs)—such as CNRM-CM5 (CNR), HadGEM2-ES (Had), and MIROC5 (MIR)—to gather an ensemble model (EnM) for providing the future climate information. The Standardized Precipitation Index (SPI) and Humidity Index (HI) were used to evaluate and compare the drought situations in the past and the future periods with two representative concentration path scenarios (RCP4.5 &amp; RCP8.5). Some sequence statistics methods, such as Mann–Kendall test and run theory, were carried out to analyze the trend and the changes of extreme drought frequency and characteristics values. The research showed that the simulation accuracy of the EnM would better. SPI and HI take different factors into count and thus lead to differences in describing drought trend, extreme drought frequency, and characteristic values, such as drought severity, drought duration, mean drought severity, and max drought intensity. The research showed that both SPI and HI showed the same wetting or drying trend in the same timescales (except winter) as in the historical data. However, the future annual and seasonal drought trend reflected by SPI shows a wetting trend while HI shows a drying trend. Both in the past and the future, extreme drought frequency and characteristic values reflected by HI are higher than SPI. The drought trend is greater, and the extreme drought frequency and characteristics tend to be strengthened under RCP8.5. Low precipitation and high potential evapotranspiration (PET), especially the PET caused by temperature rise, are the main influencing factors of drought in the future. Therefore, the influence of the PET should not be ignored in drought analysis and we should strengthen the comparative study of different drought indices in future drought analysis under climate change.

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

Global Climate Models (GCMs) can provide essential meteorological data as inputs for simulating and assessing the impact of climate change on catchment hydrology. However, downscaling of GCM outputs is often required due to their coarse spatial and temporal resolution. As an effective downscaling method, stochastic weather generators can reproduce daily sequences with statistically similar statistical characteristics. Most weather generators can only simulate single-site meteorological data, which are spatially uncorrelated. Therefore, this study introduces a method for multi-site precipitation downscaling based on a combination of a single-site stochastic weather generator, CLIGEN (CLImate GENerator), and a modified shuffle procedure constrained with multi-model ensemble GCM monthly precipitation outputs. The applicability of the downscaling method is demonstrated in the Huangfuchuan Basin (arid to semi-arid climate) for a historical period (1976–2005) and a projection period (2021–2070, historical, the representative concentration path (RCP) 2.6, RCP4.5, RCP4.8 scenarios) to generate spatially correlated daily precipitation. The results show that the proposed downscaling method can accurately simulate the mean of daily, monthly and annual precipitation and the wet spell lengths, and the inter-station correlation among 10 sites in the basin. In addition, this combination method generated the projected precipitation and showed an increasing trend for future years. These findings could help us better cope with the potential risks of climate change.

Impacts of climate variability and adaptation strategies on crop yields and soil organic carbon in the US Midwest.

Climate change is likely to increase the frequency of drought and more extreme precipitation events. The objectives of this study were i) to assess the impact of extended drought followed by heavy precipitation events on yield and soil organic carbon (SOC) under historical and future climate, and ii) to evaluate the effectiveness of climate adaptation strategies (no-tillage and new cultivars) in mitigating impacts of increased frequencies of extreme events and warming. We used the validated SALUS crop model to simulate long-term maize and wheat yield and SOC changes of maize-soybean-wheat rotation cropping systems in the northern Midwest USA under conventional tillage and no-till for three climate change scenarios (one historical and two projected climates under the Representative Concentration Path (RCP) 4.5 and RCP6) and two precipitation changes (extreme precipitation occurring early or late season). Extended drought events caused additional yield reduction when they occurred later in the season (10–22% for maize and 5–13% for wheat) rather than in early season (5–17% for maize and 2–18% for wheat). We found maize grain yield declined under the projected climates, whereas wheat grain yield increased. No-tillage is able to reduce yield loss compared to conventional tillage and increased SOC levels (1.4–2.0 t/ha under the three climates), but could not reverse the adverse impact of climate change, unless early and new improved maize cultivars are introduced to increase yield and SOC under climate change. This study demonstrated the need to consider extreme weather events, particularly drought and extreme precipitation events, in climate impact assessment on crop yield and adaptation through no-tillage and new genetics reduces yield losses.

Climate Change Scenarios and Implications for Marine System Sustainability

Wang, F.; Tian, Y., and Xiong, L., 2019. Climate change scenarios and implications for marine system sustainability. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 55–59. Coconut Creek (Florida), ISSN 0749-0208.Strengthening climate change scenario research is a prerequisite for reducing the impact of climate change on the marine system. This paper systematically reviews the evolution of climate change scenarios from three aspects: its meaning, main characteristics and application. The results show that: (1) Different climate scenarios have played an important role in previous IPCC assessment reports. Special report on emission scenario (SRES) reduce the difference of climate simulation results among different research teams. Representative concentration path (RCP) scenarios strengthen the reference role of emission scenarios for researchers and decision makers in research and response to climate change. Shared socioeconomic path (SSP) scenarios integrate climate policy and socio-economic scenarios to better meet climate change. Research group needs of response, adaptation and vulnerability; (2) Scenario generation technology has gone through three stages of historical data space-time analogy, serial development and parallel development, the improvement of which shortens the development cycle and enhances the consistency of results; (3) The difference of different scenarios is reflected in the characteristics and applications of scenarios, and links the mapping between SRS, RCP and SSP scenarios.

Predictions of future meteorological drought hazard (~ 2070) under the representative concentration path (RCP) 4.5 climate change scenarios in Raya, Northern Ethiopia

There are very few scientific studies on the predictions of future drought hazard and its effects on economic, social, and environmental under the existing global climate change scenarios. This study, aims to predict the future meteorological drought hazard (2016–2070) under the representative concentration path 4.5 climate change scenarios in Raya and its environs, Northern Ethiopia. The study also investigates the onset, end set, duration, magnitude, intensity, frequency, severity and spatial coverage of future drought occurrences. Monthly tropical applications of meteorology using satellite data and ground–based observations and daily AgMERRA daily climatic data were used from 19 meteorological stations. A robust interpolation technique called Inverse distance weighted was also applied to assess the spatial–temporal distribution of rainfall. Then, the temporal seasonal rainfall variability was analyzed using the coefficient of variation. A standardized precipitation index at 3 months time scale was applied to measure the precipitation deficit and characterize drought properties. The results reveal an increasing and worsening drought event in the future than the last three decades. Hence, the study area will be stricken for about 30–39 times with mild to extreme droughts. The rate of drought recurrence will also be once in every 1.8–2.3 years. The duration (1–5 years) of drought will be similar in all districts, but the magnitude and intensity will vary due to the level of precipitation deficit. It is, therefore, recommended that policy or decision makers and smallholder farmers should improve the existing water management and agricultural practices to cope up with the climate extremes and boost agricultural productivity.

Projecting the Range Shifts in Climatically Suitable Habitat for Chinese Sea Buckthorn under Climate Change Scenarios

Understanding the impact of climate change on range shifts in climatically suitable habitats of tree species is important for national afforestation planning, which can enhance the adaptation of tree plantation to climate change through movement of tree to follow suitable climatic conditions. Here, we overlap the current and future climate-related ranges of Chinese sea buckthorn (Hippophae rhamnoides subsp. sinensis), an important tree used for afforestation in China, to estimate the range shift in three geographic dimensions (latitude, longitude and elevation) between 2000 and 2070, which are projected by the maximum entropy algorithm (MaxEnt) under current climate conditions and four climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Our results show that the performance of the MaxEnt is highly accurate, with test AUC (area under the receiver operating characteristic curve) value of 0.91, Kappa value of 0.83 and predicted accuracy of 92%. About 10.7% area of land in China is climatically suitable for Chinese sea buckthorn plantation. Low representative concentration paths will have more effect on loss of climatic range and less effect on expansion of climatic range for Chinese sea buckthorn, while the impacts of high representative concentration path is the opposite. The centroids of climatic ranges will shift westward or northwestward at the rate of 10.4–22 km per decade, and the centroids of altitude will shift upward at the rate of 43–128 m per decade. The expansion area of climatically suitable habitat, covering 2.6–5.2 × 105 km2, is expected to be mainly located in parts of Qinghai, Ningxia, Gansu, Sichuan, Liaoning, and Jilin provinces; these areas should be monitored for planting of Chinese sea buckthorn in the future.

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960–2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001–2010) and the predicting period (2011–2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.

Adapting wheat ideotypes for climate change: accounting for uncertainties in CMIP5 climate projections

This study describes integration of climate change projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble with the LARS-WG weather generator, which delivers an attractive option for the downscaling of large-scale climate projections from global climate models (GCMs) to local-scale climate scenarios for impact assess- ments. A subset of 18 GCMs from the CMIP5 ensemble and 2 Representative Concentration Path- ways (RCPs), RCP4.5 and RCP8.5, were integrated with LARS-WG. For computationally de - manding im pact assessments, where it is not practical to explore all possible combinations of GCM ×R CP, a climate sensitivity index could be used to select a subset of GCMs which preserves the range of uncertainty found in CMIP5. This would allow us to quantify uncertainty in predictions of impacts resulting from the CMIP5 ensemble by conducting fewer simulation experiments. In a case study, we describe the use of the Sirius wheat simulation model to design in silico wheat ideotypes that are optimised for future climates in Europe, sampling uncertainty in GCMs, emission scenar- ios, time periods and European locations with contrasting climates. Two contrasting GCMs were selected for the analysis, 'hot' HadGEM2-ES and 'cool' GISS-E2-R-CC. Despite large uncertainty in future climate projections, we were able to identify target traits for wheat improvement which may assist breeding for high-yielding wheat cultivars with increased yield stability.