Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation

The global potential distribution of biomes (natural vegetation) was modelled using 8,959 training points from the BIOME 6000 dataset and a stack of 72 environmental covariates representing terrain and the current climatic conditions based on historical long term averages (1979-2013). An ensemble machine learning model based on stacked regularization was used, with multinomial logistic regression as the meta-learner and spatial blocking (100 km) to deal with spatial autocorrelation of the training points. Results of spatial cross-validation for the BIOME 6000 classes show an overall accuracy of 0.67 and R²_logloss of 0.61, with "tropical evergreen broadleaf forest" being the class with highest gain in predictive performances (R²_logloss = 0.74) and "prostrate dwarf shrub tundra" the class with the lowest (R²_logloss = -0.09) compared to the baseline. Temperature-related covariates were the most important predictors, with the mean diurnal range (BIO2) being shared by all the base-learners (i.e.,random forest, gradient boosted trees and generalized linear models). The model was next used to predict the distribution of future biomes for the periods 2040-2060 and 2061-2080 under three climate change scenarios (RCP 2.6, 4.5 and 8.5). Comparisons of predictions for the three epochs (present, 2040-2060 and 2061-2080) show that increasing aridity and higher temperatures will likely result in significant shifts in natural vegetation in the tropical area (shifts from tropical forests to savannas up to 1.7 ×10⁵ km² by 2080) and around the Arctic Circle (shifts from tundra to boreal forests up to 2.4 ×10⁵ km² by 2080). Projected global maps at 1 km spatial resolution are provided as probability and hard classes maps for BIOME 6000 classes and as hard classes maps for the IUCN classes (six aggregated classes). Uncertainty maps (prediction error) are also provided and should be used for careful interpretation of the future projections.

Effect of climate change on reservoir water balance and irrigation water demand: a case of Gidabo irrigation project, Rift Valley Basin, Ethiopia

The consequences of climate change on agriculture water demand are among the current and prospective challenges. The amount of water needed by crops is significantly affected by the regional climate. The influence of climate change on irrigation water demand and reservoir water balance components were examined. The results of seven regional climate models were compared, and the top-performing model was chosen for the study area. After model calibration and validation, the HEC-HMS model was used to forecast future water availability in the reservoir. The results show that under the RCP 4.5 and RCP 8.5 emission scenarios, the reservoir's water availability in the 2050s will decline by approximately 7% and 9%, respectively. The CROPWAT results showed that the required irrigation water might rise by 26 to 39% in the future. However, the water supply for irrigation may be drastically reduced due to the drop in reservoir water storage. As a result, the irrigation command area could drop up to 21% (2878.4 ha) to 33% (4502 ha) in future climatic conditions. Therefore, we recommend alternative watershed management techniques and climate change adaptation measures to endure upcoming water shortages in the area.

Future climate change could reduce irrigated and rainfed wheat water footprint in arid environments

The concept of water footprint (WF) has been used to manage freshwater resources for the past two decades and is considered as indicator of the sustainability of agricultural systems. Accordingly, the current study aimed to quantify WF and its components in the future climate for rainfed and irrigated wheat agro-ecosystems in 17 provinces of Iran located in arid or semi-arid environments. The provinces were divided into five climate classes. The simulations were conducted under current (1980-2010) and future climate (2040-2070) using the Agricultural Production Systems sIMulator (APSIM) crop model, following the Agricultural Model Intercomparison and Improvement Project (AgMIP) protocol. Baseline simulations indicated that the total WF, averaged across all climate classes, was 1148 m³ t^-1 for irrigated and 1155 m³ t^-1 for rainfed wheat. WF was projected to decline in the future compared to baseline in both irrigated and rainfed systems mostly because of increases in yield of +9% in rainfed systems and 3.5% in irrigated systems, and decreases in water consumption by -5.4% and -10.1%, respectively. However, the share of gray water footprint (WF_gray) was projected to increase in the near future for both rainfed (+5.4%) and irrigated (+6.9%) systems. These findings suggest that cleaner and more sustainable production (i.e. obtaining grain yield under optimal water and nitrogen consumption) could be achieved in irrigated and rainfed wheat ago-ecosystems if optimal N fertilizer management is adopted. Additionally, rainfed cultivation can be further expanded in some areas which is expected to result in a substantial reduction in blue water (i.e. less irrigation), especially in sub-humid and semi-arid cool areas.

Regionalized dynamic climate series for ecological climate impact research in modern controlled environment facilities

Modern controlled environment facilities (CEFs) enable the simulation of dynamic microclimates in controlled ecological experiments through their technical ability to precisely control multiple environmental parameters. However, few CEF studies exploit the technical possibilities of their facilities, as climate change treatments are frequently applied by static manipulation of an inadequate number of climate change drivers, ignoring intra-annual variability and covariation of multiple meteorological variables. We present a method for generating regionalized climate series in high temporal resolution that was developed to force the TUMmesa Model EcoSystem Analyzer with dynamic climate simulations. The climate series represent annual cycles for a reference period (1987-2016) and the climate change scenarios RCP2.6 and RCP8.5 (2071-2100) regionalized for a climate station situated in a forested region of the German Spessart mountains. Based on the EURO-CORDEX and ReKliEs-DE model ensembles, typical annual courses of daily resolved climatologies for the reference period and the RCP scenarios were calculated from multimodel means of temperature (ta), relative humidity (rh), global radiation (Rg), air pressure (P), and ground-level ozone and complemented by CO2. To account for intra-annual variation and the covariability of multiple climate variables, daily values were substituted by hourly resolved data resampled from the historical record. The resulting present climate Test Reference Year (TRY) well represented a possible annual cycle within the reference period, and expected shifts in future mean values (e.g., higher ta) were reproduced within the RCP TRYs. The TRYs were executed in eight climate chambers of the TUMmesa facility and-accounting for the technical boundaries of the facility-reproduced with high precision. Especially, as an alternative to CEF simulations that reproduce mere day/night cycles and static manipulations of climate change drivers, the method presented here proved well suited for simulating regionalized and highly dynamic annual cycles for ecological CEF studies.

Predicting how climate change threatens the prey base of Arctic marine predators

Arctic sea ice loss has direct consequences for predators. Climate-driven distribution shifts of native and invasive prey species may exacerbate these consequences. We assessed potential changes by modelling the prey base of a widely distributed Arctic predator (ringed seal; Pusa hispida) in a sentinel area for change (Hudson Bay) under high- and low-greenhouse gas emission scenarios from 1950 to 2100. All changes were relatively negligible under the low-emission scenario, but under the high-emission scenario, we projected a 50% decline in the abundance of the well-distributed, ice-adapted and energy-rich Arctic cod (Boreogadus saida) and an increase in the abundance of smaller temperate-associated fish in southern and coastal areas. Furthermore, our model predicted that all fish species declined in mean body size, but a 29% increase in total prey biomass. Declines in energy-rich prey and restrictions in their spatial range are likely to have cascading effects on Arctic predators.

Assessment of Future Water Yield and Water Purification Services in Data Scarce Region of Northwest China

Water shortage and pollution have become prominent in the arid regions of northwest China, seriously affecting human survival and sustainable development. The Bosten Lake basin has been considered as an example of an arid region in northwest China, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model has been used to quantitatively evaluate the future water yield and water purification services for four representative concentration pathways (RCP) scenarios. The results show that for the four RCP scenarios, the annual average precipitation in 2020–2050 decreases compared to that in 1985–2015; the area of cultivated land and unused land decreases, and the area of other land-use types increases from 2015 to 2050. The water yield service reduces, while the water purification service increases from 2015 to 2050 in the Bosten Lake basin. In 2050, the water yield and water purification services are the best for the RCP6.0 scenario, and are the worse for the RCP4.5 scenario and RCP8.5 scenario, respectively. The distribution of the water yield and water purification services show a gradual decline from northwest to southeast.

Meeting Paris agreement objectives will temper seabird winter distribution shifts in the North Atlantic Ocean

We explored the implications of reaching the Paris Agreement Objective of limiting global warming to <2°C for the future winter distribution of the North Atlantic seabird community. We predicted and quantified current and future winter habitats of five North Atlantic Ocean seabird species (Alle alle, Fratercula arctica, Uria aalge, Uria lomvia and Rissa tridactyla) using tracking data for ~1500 individuals through resource selection functions based on mechanistic modeling of seabird energy requirements, and a dynamic bioclimate envelope model of seabird prey. Future winter distributions were predicted to shift with climate change, especially when global warming exceed 2°C under a "no mitigation" scenario, modifying seabird wintering hotspots in the North Atlantic Ocean. Our findings suggest that meeting Paris agreement objectives will limit changes in seabird selected habitat location and size in the North Atlantic Ocean during the 21st century. We thereby provide key information for the design of adaptive marine-protected areas in a changing ocean.

An observation-based scaling model for climate sensitivity estimates and global projections to 2100

We directly exploit the stochasticity of the internal variability, and the linearity of the forced response to make global temperature projections based on historical data and a Green's function, or Climate Response Function (CRF). To make the problem tractable, we take advantage of the temporal scaling symmetry to define a scaling CRF characterized by the scaling exponent H, which controls the long-range memory of the climate, i.e. how fast the system tends toward a steady-state, and an inner scale τ ≈ 2   years below which the higher-frequency response is smoothed out. An aerosol scaling factor and a non-linear volcanic damping exponent were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference which allows us to analytically calculate the transient climate response and the equilibrium climate sensitivity as: 1 . 7 - 0.2 + 0.3   K and 2 . 4 - 0.6 + 1.3   K respectively (likely range). Projections to 2100 according to the RCP 2.6, 4.5 and 8.5 scenarios yield warmings with respect to 1880-1910 of: 1 . 5 - 0.2 + 0.4 K , 2 . 3 - 0.5 + 0.7   K and 4 . 2 - 0.9 + 1.3   K. These projection estimates are lower than the ones based on a Coupled Model Intercomparison Project phase 5 multi-model ensemble; more importantly, their uncertainties are smaller and only depend on historical temperature and forcing series. The key uncertainty is due to aerosol forcings; we find a modern (2005) forcing value of [ - 1.0 , - 0.3 ] Wm - 2 (90 % confidence interval) with median at - 0.7 Wm - 2 . Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to RCP 2.6 for which the probability to remain under 1.5 K is 48 %. RCP 4.5 and RCP 8.5-like futures overshoot with very high probability.

Global Socioeconomic Risk of Precipitation Extremes Under Climate Change

Precipitation extremes are among the most serious consequences of climate change around the world. The observed and projected frequency and intensity of extreme precipitation in some regions will greatly influence the social economy. The frequency of extreme precipitation and the population and economic exposure were quantified for a base period (1986-2005) and future periods (2016-2035 and 2046-2065) based on bias corrected projections of daily precipitation from five global climatic models forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in the shared socioeconomic pathways (SSPs). The RCP8.5-SSP3 scenario produces the highest global population exposure for 2046-2065, with nearly 30% of the global population (2.97 × 109 persons) exposed to precipitation extremes >10 days/a. The RCP2.6-SSP1 scenario produces the highest global GDP exposure for 2046-2065, with a 5.56-fold increase relative to the base period, of up to (2.29 ± 0.20) × 1015 purchasing power parity $-days. Socioeconomic effects are the primary contributor to the exposure changes at the global and continental scales. Population and GDP effects account for 64-77% and 78-91% of the total exposure change, respectively. The inequality of exposure indicates that more attention should be given to Asia and Africa due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries, that is, Luxembourg, Belgium, and the Netherlands, should also commit to effective adaptation measures.

Health impacts and cost-benefit analyses of surface O3 and PM2.5 over the U.S. under future climate and emission scenarios

Health impacts of surface ozone (O₃) and fine particulate matter (PM_2.5) are of major concern worldwide. In this work, the Environmental Benefits Mapping and Analysis Program tool is applied to estimate the health and economic impacts of projected changes in O₃ and PM_2.5 in the U.S. in future (2046-2055) decade relative to current (2001-2010) decade under the Representative Concentration Pathway (RCP) 4.5 and 8.5 climate scenarios. Future annual-mean O₃ reductions under RCP 4.5 prevent ~1,800 all-cause mortality, 761 respiratory hospital admissions (HA), and ~1.2 million school loss days annually, and result in economic benefits of ~16 billion, 29 million, and 132 million U.S. dollars (USD), respectively. By contrast, the projected future annual-mean O₃ increases under RCP8.5 cause ~2,400 mortality, 941 respiratory HA, and ~1.6 million school loss days annually and result in economic disbenefits of ~21 billion, 36 million, and 175 million USD, respectively. Health benefits of reduced O₃ double under RCP4.5 and health dis-benefits of increased O₃ increase by 1.5 times under RCP8.5 in future with 2050 population and baseline incidence rate. Because of the reduction in projected future PM_2.5 over CONUS under both scenarios, the annual avoided all-cause deaths, cardiovascular HA, respiratory HA, and work loss days are ~63,000 and ~83,000, ~5,300 and ~7,000, ~12,000 and ~15,000, and ~7.8 million and ~10 million, respectively, leading to economic benefits of ~560 and ~740 billion, ~240 and ~320 million, ~450 and ~590 million, and ~1,400 and ~1,900 million USD for RCP4.5 and 8.5, respectively. Health benefits of reduced PM_2.5 for future almost double under both scenarios with the largest benefits in urban areas. RCP8.5 projects larger health and economic benefits due to a greater reduction in PM_2.5 but with a warmer atmosphere and higher O₃ pollution than RCP4.5. RCP4.5 leads to multiple-benefit goals including reduced O₃ and PM_2.5, reduced mortality and morbidity, and saved costs. Greater reduction in future PM_2.5 under RCP4.5 should be considered to achieve larger multi-benefits.

Simulating soil organic carbon stock as affected by land cover change and climate change, Hyrcanian forests (northern Iran)

Soil organic carbon (SOC) contains a considerable portion of the world's terrestrial carbon stock, and is affected by changes in land cover and climate. SOC modeling is a useful approach to assess the impact of land use, land use change and climate change on carbon (C) sequestration. This study aimed to: (i) test the performance of RothC model using data measured from different land covers in Hyrcanian forests (northern Iran); and (ii) predict changes in SOC under different climate change scenarios that may occur in the future. The following land covers were considered: Quercus castaneifolia (QC), Acer velutinum (AV), Alnus subcordata (AS), Cupressus sempervirens (CS) plantations and a natural forest (NF). For assessment of future climate change projections the Fifth Assessment IPCC report was used. These projections were generated with nine Global Climate Models (GCMs), for two Representative Concentration Pathways (RCPs) leading to very low and high greenhouse gases concentration levels (RCP 2.6 and RCP 8.5 respectively), and for four 20year-periods up to 2099 (2030s, 2050s, 2070s and 2090s). Simulated values of SOC correlated well with measured data (R²=0.64 to 0.91) indicating a good efficiency of the RothC model. Our results showed an overall decrease in SOC stocks by 2099 under all land covers and climate change scenarios, but the extent of the decrease varied with the climate models, the emissions scenarios, time periods and land covers. Acer velutinum plantation was the most sensitive land cover to future climate change (range of decrease 8.34-21.83tCha^-1). Results suggest that modeling techniques can be effectively applied for evaluating SOC stocks, allowing the identification of current patterns in the soil and the prediction of future conditions.

Trend analysis of watershed-scale precipitation over Northern California by means of dynamically-downscaled CMIP5 future climate projections

The impacts of climate change on watershed-scale precipitation through the 21st century were investigated over eight study watersheds in Northern California based on dynamically downscaled CMIP5 future climate projections from three GCMs (CCSM4, HadGEM2-ES, and MIROC5) under the RCP4.5 and RCP8.5 future climate scenarios. After evaluating the modeling capability of the WRF model, the six future climate projections were dynamically downscaled by means of the WRF model over Northern California at 9km grid resolution and hourly temporal resolution during a 94-year period (2006-2100). The biases in the model simulations were corrected, and basin-average precipitation over the eight study watersheds was calculated from the dynamically downscaled precipitation data. Based on the dynamically downscaled basin-average precipitation, trends in annual depth and annual peaks of basin-average precipitation during the 21st century were analyzed over the eight study watersheds. The analyses in this study indicate that there may be differences between trends of annual depths and annual peaks of watershed-scale precipitation during the 21st century. Furthermore, trends in watershed-scale precipitation under future climate conditions may be different for different watersheds depending on their location and topography even if they are in the same region.