Regional Climate Research Articles

Projected change in the rainfall behaviour over Odisha

ABSTRACT Climate change has impacted the rainfall characteristics and the extremes are on the rise across the world. These changes threaten agriculture, water resources, and disaster management. Odisha, an eastern Indian state with an agrarian economy, heavily relies on monsoon rainfall. The study analyses the projected change in the rainfall characteristics across Odisha at different specific warming targets of the Paris Climate Agreement using high-resolution regional climate models. The study adopts a bi-fold approach; first, it employs a robust method to select the best model experiments; afterwards, the model ensemble is used to examine the projected rainfall characteristics. The results indicate a 4–16% increase in projected rainfall over Odisha, with an extended rainy season. The projected number of consecutive wet days, moderate and extreme rainfall, is expected to rise under the global warming scenario. The prolonged rainy season with heavy rainfall can result in disasters like post-monsoon floods, while higher rainfall variability will increase the risk of floods and droughts across Odisha, threatening agriculture. The results will help pinpoint regions most vulnerable to climate change. The study also suggested measures to assist governments and planners in developing short-term and long-term strategies for adaptation and mitigation to lessen climate change impacts.

Temporal accumulation and lag effects of precipitation on carbon fluxes in terrestrial ecosystems across semi-arid regions in China

Precipitation (PRE) plays a vital role in hydrological processes, ecological vegetation, and land-atmosphere interactions in semi-arid regions. Previous research has mainly focused on the impact of PRE on large-scale regional climate change and ecological evolution. However, there have been few studies on the long-term effects of PRE on carbon fluxes in these regions, especially the time-accumulation and -lag effects. Here, we employed observational data from the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) and integrated multiple data sources, including remote sensing and carbon flux simulation data, to quantitatively assess the lagged response of carbon fluxes to PRE and elucidate the underlying mechanisms from multiple perspectives. Characterization of PRE, soil water content (SWC) and carbon fluxes at SACOL qualitatively reveals the existence of a time-delayed response of carbon fluxes to PRE, both on monthly and finer daily temporal scales. The average lagged response of net ecosystem exchange (NEE) and gross primary productivity (GPP) to accumulated PRE (APRE) is approximately 42 days. When considering time-accumulation and -lag effects, the combined direct and indirect effects of APRE on NEE and GPP increase by 0.37 and 0.58, respectively. Notably, preceding APRE primarily exerts a direct effect on current carbon fluxes, whereas the impact of SWC at a depth of 0.1 m is primarily mediated through the memory effect of preceding APRE, resulting in an indirect effect on carbon fluxes. These findings emphasize the importance of preceding APRE. Significantly, our subsequent study indicates that the delay in NEE and GPP responses to APRE also extends to approximately 40 to 50 days at the regional scale. Our findings emphasize the significant time effects of APRE on carbon fluxes, and considering these effects will contribute to a better understanding of the interplay between PRE and vegetation over semi-arid regions in China.

Urbanization Effect on Regional Thermal Environment and Its Mechanisms in Arid Zone Cities: A Case Study of Urumqi

Urumqi is located in the arid region of northwestern China, known for being one of the most delicate ecological environments and an area susceptible to climate change. The urbanization of Urumqi has progressed rapidly, yet there is a lack of research on the urbanization effect (UE) in Urumqi in terms of the regional climate. This study investigates the UE of Urumqi (urban built-up area) on the regional thermal environment and its mechanisms for the first time, based on the WRF (Weather Research and Forecasting) model (combined with the Urban Canopy Model, UCM) simulation data of 10 consecutive years (2012–2021). The results show that the UE on surface temperature (Ts) and air temperature at 2 m (T2m) is strong (weak) during the night (daytime) in all seasons, and the UE on these is largest (smallest) in spring (winter). In addition, the maximum UE on both Ts and T2m is present over southern Urumqi in winter, whereas the maximum UE is identified over the northern Urumqi in other seasons. The maximum UE on Ts occurred in northwestern Urumqi at 18 LST (Local Standard Time, i.e., UTC+6) in autumn (reaching 5.2 °C), and the maximum UE on T2m occurred in northern Urumqi at 4 LST in summer (reaching 2.6 °C). Urbanization showed a weak cooling effect during daytime in summer and winter, reflecting the unique characteristics of the UE in arid regions, which are different from those in humid regions. The maximum cooling of Ts occurred in northern Urumqi at 11 LST in summer (reaching −0.4 °C), while that of T2m occurred at 10 LST in northern and northwestern Urumqi in winter (reaching −0.25 °C), and the cooling effect lasted for a longer period of time in summer than in winter. The UE of Urumqi causes the increase of Ts mainly through the influence of net short-wave radiation and geothermal flux and causes the increase of T2m through the influence of sensible heat flux and net long-wave radiation. The UE on the land surface energy balance in Urumqi can be used to explain the seasonal variation and spatial differences of the UEs on the regional thermal environment and the underlying mechanism.

Cave monitoring in the Peruvian Andes reveals monsoon climate preserved in speleothem calcite

Speleothem paleoclimate records from the Peruvian Andes have been interpreted to reflect the strength of the South American monsoon. While these interpretations have been verified through comparison with other regional and global climate records, the mechanics of the cave environment that facilitate the preservation of this signal with such consistency remain unstudied. Here, we present four years of environmental data from Huagapo and Pacupahuain cave, and one year from Antipayarguna cave. The data reveal that the cave environment is very stable with little to no change in temperature and 100% relative humidity year-round. This stability in cave air is juxtaposed with the monsoonal drip water pulse that increases drip rates over 40 times on average across all seven monitored drip sites. Compared to the amount-weighted precipitation average δ18Oprecip value, the cave drip water δ18ODW values are evaporatively 18O enriched during infiltration through the soil/epikarst. As the monsoonal precipitation pulse fades and drip rates decrease, changes in the drip water chemistry (trace elements Mg/Ca and Sr/Ca, dissolved inorganic carbon δ13CDW, and δ18ODW values) indicate that prior calcite precipitation (PCP) drives the trace element and δ13CDW variability. The δ13Cc and δ18Oc values of farmed slide calcite are highly variable. However, high drip rate and lower cave air pCO2 during the monsoon combine to increase calcite precipitation rates. This causes speleothem records from these caves to be weighted toward annual monsoon conditions. Calcite isotope values from actively growing stalagmite tops support this finding. These results suggest that speleothems from these caves are sensitive to changes in monsoon precipitation amount, because it determines the duration of the monsoon drip water pulse, and therein, the extent of dry season PCP. Further, these data indicate that heterogeneity in the dolomitic limestone massif causes offsets between the carbon isotopes and trace metal concentrations between the caves, highlighting the need to normalize these datasets when chronology-stacking these proxies.

Accurate prediction of electric vehicles mileage for proactive battery management in varied conditions using multi-criteria approach

This research addresses several critical challenges in managing battery aging within Electric Vehicles (EVs). Key challenges include minimizing capacity degradation while maximizing vehicle performance and accurately predicting State of Charge (SoC) under diverse operational conditions. Additionally, integrating factors such as vehicle and battery age, driving cycles, environmental conditions, and regional climate variations posed significant hurdles in achieving comprehensive battery management. To tackle these challenges, the research presents a novel framework integrating an advanced Multihead cross attention-based optimizer with a bidirectional long short-term memory network, further enhanced by the coati optimization algorithm. This innovative approach aims to improve prediction accuracy for critical battery performance metrics, such as SoC and battery lifetime. Implemented and rigorously evaluated using Python, the framework demonstrates a substantial 20% increase in battery performance through detailed SoC and temperature analysis, coupled with an impressive 40% enhancement in battery lifetime. These results highlight the ability to overcome previous challenges and provide robust solutions for effective battery management in real-world EV applications.

Implications of crop yield distributions for multiperil crop insurance rating in Ghana: a lasso model application

PurposeThe objective of this paper is to examine crop yield predictions and their implications on MPCI in Ghana. Farmers in developing countries struggle with their ability to deal with agricultural risks. Providing aid for farmers and their households remains instrumental in combatting poverty in Africa. Several studies have shown that correctly understanding and implementing risk management strategies will help in the poverty alleviation agenda.Design/methodology/approachThis study examines the importance of crop yield distributions in Ghana and its implication on multiperil crop insurance (MPCI) rating using the Lasso regression model. A Bonferroni test was employed to test the independence of crop yields across the regions while the Kruskal-Wallis H test was conducted to examine statistical differences in mean yields of crops across the ten regions. The Bayesian information criteria and k-fold cross-validation methods are used to select an appropriate Lasso regression model for the prediction of crop yields. The study focuses on the variability of the threshold yields across regions based on the chosen model.FindingsIt is revealed that threshold yields differ significantly across the regions in the country. This implies that the payment of claims will not be evenly distributed across the regions, and hence regional disparities need to be considered when pricing MPCI products. In other words, policymakers may choose to assign respective weights across regions based on their threshold yields.Research limitations/implicationsThe primary limitation is the unavailability of regional climate data which could have helped in a better explanation of the variation across the regions.Originality/valueThis is the first study to examine the implications of regional crop yield variations on multiperil crop insurance rating in Ghana.

Environmental Aspects and Impacts of Porto Seco: the Case of the Largest Dry Port in Latin America, Located in Foz do Iguaçu (Brazil)

Objective: This work aims to present and describe the process of identifying and classifying environmental aspects in a Brazilian dry port. Theoretical Framework: The Climate Change Adaptation Strategies aim to reduce greenhouse gas (GHG) emissions, increase the share of renewable energy in total energy production and increase the energy efficiency. Unfortunately, road-based dry ports increase heavy truck congestion, energy consumption and, inherently, GHG emissions. Method: The general approach includes a literature review on the importance of managing environmental aspects and impacts in dry ports, followed by the description of the holistic procedure that involves the inventory of port activities linked to environmental aspects, the Leopold matrix and specialized knowledge about the local context (e.g., compliance with law, regional climate change adaptation strategy, stakeholder concerns) to perform the classification. Results and Discussion: The results showed that atmospheric emissions, noise and resource consumption are among the most significant environmental aspects of dry port. The absence of a railway that enables a multimodal transport system and the fact that the port is located in the city center constitute a disadvantage that worsens road congestion and air emissions, since the train would reduce GHG emissions. Research Implications: The work contributes to the planning and management of the activities of Foz do Iguaçu Dry Port considering environmental aspects and impacts, direct and indirect, from a holistic perspective. Originality/Value: The work presents a ranking of the environmental aspects and impacts of dry port to help defining priority actions in light of climate change strategies.

Increasing frequency and precipitation intensity of convective storms in the Peruvian Central Andes: Projections from convection‐permitting regional climate simulations

Increasing frequency and precipitation intensity of convective storms in the Peruvian Central Andes: Projections from convection‐permitting regional climate simulations

Running to warmer-drier springs in the Greater Mekong Subregion as climate warms

Using the multivariate EOF analysis performed on the observation datasets, this study reveals that the Greater Mekong Subregion (GMS) has been toward warm-dry springs over most regions especially after the 2000s, accompanied by increased surface temperature and decreased precipitation anomalies. Such climate condition provides favourable environment for compound heat and drought events, raising questions about how the spring climate in GMS will change as climate warms. Based on a multi-model weighting scheme among CMIP6 models, we project the regional climate changes in GMS in a warmer world under the SSP5–8.5 scenario. Results show that the southern GMS including Vietnam, Laos, Thailand, and Cambodia will become warmer-drier throughout the 21st century in response to global warming. In contrast, the climate condition over the northern GMS including Myanmar and Yunnan province of Southwest China is expected to experience a transition from dry to wet springs in a warmer world, resulting in a tendency toward the warm-wet springs particularly in the long-term future. This knowledge indicates the spatial non-uniformity of regional climate under global warming and will aid in advance to plan for water management and agricultural adaptation.

Elucidating the boundary layer turbulence dissipation rate using high-resolution measurements from a radar wind profiler network over the Tibetan Plateau

Abstract. The planetary boundary layer (PBL) over the Tibetan Plateau (TP) exerts a significant influence on regional and global climate, while its vertical structures of turbulence and evolution features remain poorly understood, largely due to the scarcity of observations. This study examines the vertical profile of and daytime variation in the turbulence dissipation rate (ε) in the PBL and free troposphere over the TP using the high-resolution (6 min and 120 m) measurements from a radar wind profiler (RWP) network, combined with hourly data from ERA5 during the period from 1 September 2022 to 31 October 2023. Observational analyses show that the magnitude of ε below 3 km under all-sky conditions exhibits a large spatial discrepancy over the six RWP stations over the TP. Particularly, the values of ε at Minfeng and Jiuquan over the northern TP and at Dingri (alternately Tingri) over the southern TP are roughly an order of magnitude greater than those at Lijiang, Ganzi (alternately Garzê), and Hongyuan over the eastern TP. This could be partially attributed to the difference in land cover across the six RWP stations. In terms of the diurnal variation, ε rapidly intensifies from 09:00 local standard time (LST) to 14:00 LST and then gradually levels off in the late afternoon. Under clear-sky conditions, both ε and the planetary boundary layer height (zi) are greater compared with cloudy-sky conditions, which could be due to the cooling effect of clouds, which reduces the solar irradiation reaching the surface. In the lower PBL (0.3 ≤ z/zi ≤ 0.5), where z is the height above ground level, the dominant influential factor in the development of turbulence is the surface–air temperature difference (Ts−Ta). By comparison, in the upper PBL (0.6 ≤ z/zi ≤ 1.0), both Ts−Ta and vertical wind shear (VWS) affect the development of turbulence. Above the PBL (1.0 < z/zi ≤ 2.0), the shear production resulting from VWS dominates the variation in turbulence. Under cloudy-sky conditions, the reduced Ts−Ta and weakened surface sensible heat flux tend to inhibit the turbulent motion in the PBL. On the other hand, the strong VWS induced by clouds enhances the turbulence above the PBL. The findings obtained here underscore the importance of the RWP network in revealing the fine-scale structures of the PBL over the TP and gaining new insight into the PBL evolution.

Probability of Firn Aquifer Presence in Antarctica by Combining Remote Sensing and Regional Climate Model Data

AbstractDespite in‐situ observations of perennial firn aquifers (PFAs) at specific locations of the Antarctic ice sheet, a comprehensive continent‐wide mapping of PFA distribution is currently lacking. We present an estimate of their distribution across Antarctica in the form of a probability assessment using a Monte Carlo technique. Our approach involves a novel methodology that combines observations from Sentinel‐1 and Advanced SCATterometer (ASCAT) with output from a regional climate model. To evaluate our method, we conduct an extensive comparison with Operation Ice Bridge observations from the Greenland Ice Sheet. Application to Antarctica reveals high PFA probabilities in the Antarctic Peninsula (AP), particularly along its northern, northwestern, and western coastlines, as well as on the Wilkins, Müller, and George VI ice shelves. Outside the AP, PFA probability is low, except for some locations with marginally higher probabilities, such as on the Abbot, Totten, and Shackleton ice shelves.

Watering the trees for the forest: Drought alleviation in oaks and pines by ancestral ditches

Traditional ditches (“acequias” in Spanish) derive meltwater and infiltrate groundwater providing ecological services downstream in the semi-arid Sierra Nevada range (SE Spain). Therefore, they may act as a nature-based solution by alleviating drought stress in trees growing near ditches by enhancing growth and reducing their intrinsic water-use efficiency (iWUE). Such a mitigation role of acequias is critical given that some oak (Quercus pyrenaica) and pine (Pinus sylvestris) stands reach their xeric distribution limits in Europe. We compared tree-ring width data and wood δ13C, a proxy of iWUE, in oak and pine stands located near or far (control) from ditches with different infiltration capacity in two watersheds. We assessed how trees responded to climate data, drought stress, and vegetation greenness through correlations and resilience indices. Oak trees located near ditches grew more and responded less to precipitation, soil moisture, a drought index, and greenness than control trees. In pines, we did not find this pattern, and ditch trees grew more than control trees only during an extremely dry year (1995). Climate-growth correlations suggested a longer growing season in ditch pines. Growth of ditch oaks from the “Acequia Nueva” (AN), with high infiltration capacity, responded more to autumn soil moisture and showed the lowest δ13C. Growth was enhanced by cool-wet spring conditions in pines and also by warm-wet conditions in the prior winter in the case of oaks. Control trees showed lower resistance to drought. Control trees presented higher wood δ13C values except for old oaks from the “Acequia Grande” (AG) site which may show long-term acclimation. Traditional ditches alleviate drought stress in oak and pine stands subjected to regional xeric climate conditions.

Elevation-dependent biases of raw and bias-adjusted EURO-CORDEX regional climate models in the European Alps

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of different observational datasets on the evaluation, including E-OBS, APGD, and high-resolution national datasets. Furthermore, we assessed the bi-variate dependency of temperature and precipitation biases, their temporal evolution, and the impact of different bias adjustment methods and bias adjustment reference datasets. Biases in seasonal temperature, seasonal precipitation, and wet-day frequency were found to increase with elevation. Differences in temporal trends between RCMs and observations caused a temporal dependency of biases, which could be removed by detrending both observations and RCMs. The choice of the reference observation datasets used for bias adjustment turned out to be more relevant than the choice of the bias adjustment method itself. Consequently, climate change assessments in mountain regions need to pay particular attention to the choice of observational dataset and, furthermore, to the elevation dependence of biases and the increasing observational uncertainty with elevation in order to provide robust information on future climate.

Unraveling climate trends in the mediterranean: a hybrid machine learning and statistical approach

This study presents a comprehensive spatiotemporal analysis of sea surface temperatures (SST) and surface air temperatures (TAS) across 15 Mediterranean coastal stations, leveraging centennial-scale data to analyze regional climate dynamics. The modeling framework integrates three sequential phases: data preprocessing, statistical analysis, and advanced machine learning techniques, creating a robust analytical pipeline. The data preprocessing phase harmonizes diverse datasets, addresses missing values, and applies transformations to ensure analytical consistency. The statistical modeling employs the Pettitt test for change point detection and linear trend analysis to unveil underlying patterns. The machine learning phase utilizes K-means clustering for climate regime classification and implements tailored Convolutional Neural Networks (CNNs) for cluster-specific future climate anomaly projections. Results unveil a marked anthropogenic climate signal, with contemporary observations consistently surpassing historical baselines. Breakpoint analyses and linear trend assessments reveal heterogeneous climatic shifts, with pronounced warming in the northern Mediterranean. Notably, Nice and Ajaccio exhibit the highest SST increases (0.0119 and 0.0113 °C/decade, respectively), contrasting with more modest trends in Alexandria (0.0052 °C/decade) and Antalya (0.0047 °C/decade) in the eastern Mediterranean. The application of clustering and CNN projections provides granular insights into differential warming trajectories. By 2050, cooler northwestern Mediterranean zones are projected to experience dramatic SST anomalies of approximately 3 °C above the average, with corresponding TAS increases of 2.5 °C. In contrast, warmer eastern and southern regions display more subdued warming patterns, with projected SST and TAS increases of 1.5–2.5 °C by mid-century. This research’s importance is highlighted by its potential to inform tailored adaptation strategies and contribute to the theoretical understanding of climate dynamics, advancing climate modeling and analysis efforts.

Role of Topographic Forcing in the Maritime Continent Region in Interaction Between the East Asian Summer Monsoon and the Australian Winter Monsoon

AbstractThe topography in the Maritime Continent (MC) has significant impact on climate anomalies in the Asian‐Australian monsoons region. In the present study, the Regional Climate Model Version 4.6 (RegCM4.6) is applied for the simulation of climate over the Asian‐Australian monsoon region during the boreal summer. Results demonstrate that the RegcM4.6 is able to well reproduce precipitation, temperature and low‐level and upper‐level circulation patterns over the Asian‐Australian monsoons region. A sensitivity experiment with zero topographic height in the MC region shows that the intensity of western Pacific subtropical high (WPSH) and Australian cold high both weaken simultaneously, while the cross‐equatorial flows also decline and the East Asian‐Australian monsoons become weaker. Meanwhile, the anomalous cyclonic circulation with significant convergence prevails in lower levels over the western Pacific, leading to more precipitation and higher temperature. In the MC region, there are more precipitation and high temperature in the north while there are less precipitation and low temperature in the south. Temperature increases over a large area from the Yunnan‐Guizhou Plateau to the Loess Plateau but decreases in the southeastern coast of China and eastern India. These results have important implications for better understanding the topographic impact of the MC region on the interaction between the east Asian‐Australian monsoons.

Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm

Evaluating, mapping, and monitoring high-quality Direct Normal Irradiance (DNI) is crucial for the design, financing, and operation of solar power plants, especially those utilizing concentrating technologies. In China, limited number of first-class meteorological stations provide DNI observations. To bridge this gap, this study has reconstructed a comprehensive 41-year (1982–2022) daily DNI dataset for China (CHDNI). This dataset integrated CMA ground-based observations with ERA5 and MERRA-2 reanalysis data, employing a spatial resolution of 10 km. The data was processed using the REST2 radiation transfer model combined with Stacking ensemble machine learning. Validation with ground-based measurements indicated that the Stacking model exhibited high and consistent performance. This was evidenced by its sample-based cross-validation results, showing a correlation coefficient (R) of 0.92, a root-mean-square error (RMSE) of 32.04 W/m2, a mean absolute error (MAE) of 23.68 W/m2, and a global performance indicator of 3.86. Among the four analyzed DNI products (CERES, ERA5, SWGDN, and CHDNI), CHDNI demonstrated the closet alignment with ground observations, evidenced by the least deviation (MAE = 21.67 W/m2 and RMSE = 31.00 W/m2) and the highest R value (0.92). Aerosol optical depth and cloud cover emerged as the primary factors influencing the quality and accuracy of DNI products under clear-sky and total-sky conditions. The spatial distribution of DNI across China is complex, showcasing DNI values ranging from 25.82 W/m2 to 194.22 W/m2, with an average value of 98.08 W/m2 over 41-year. This dataset is a valuable resource for analyzing regional climate change, photovoltaic applications, and solar energy resources assessment.

Modeling climate change projection and its impact on the streamflow in the Yadot watershed, Genale Dawa basin, Ethiopia

ABSTRACT Varied streamflow response to climate between river basins and seasons highlights the importance of further research on different basins and watersheds in different seasons to help plan adaptation options at watershed scale. This study investigated the hydrological impacts of climate change over the Yadot watershed. The multi-model ensemble of three regional climate models (CCLM4.8, RACMO22T, and RCA4) under RCP4.5 and RCP8.5 emission scenarios for 2021–2050 and 2051–2080 periods were used. The SWAT model was used to simulate the streamflow. Climate model projections have indicated that precipitation will slightly increase during both the wet and dry seasons from 0.59 to 2.08% and 0.02–1.59%, respectively. The annual projected precipitation will increase by 0.13–1.66%. The change in the projected maximum and minimum temperatures in both dry and wet seasons increased by a range of 0.61–1.9 °C and 0.65–2.07 °C, respectively. Similarly, the change in the projected minimum temperatures in both dry and wet seasons increased by a range of 1.07–2.01 °C and 0.06–1.66 °C, respectively. The wet and dry season stream flow increased by 6.23–9.36% and 3.16–5.46%, respectively. The findings of this study can help to guide water resources planners and designers in planning and managing water resources effectively for future use.

Intensifying aridity induces tradeoffs among biodiversity and ecosystem services supported by trees

AbstractAimChanges in climate are likely to have major impacts on benefits (i.e., biodiversity and ecosystem services) supported by trees. Here we explore the extent to which trees can support multiple benefits, and the potential tradeoffs among them, under increasing dryness.LocationEastern Australia.Time period2018–2019.Major taxa studiedTrees.MethodsWe evaluated changes in biodiversity and services supported by trees and the nature of potential tradeoffs in response to increasing aridity, our proxy of drying regional climates. We assessed six benefits (biodiversity and five ecosystem services) supported by trees at 126 sites across a gradient from Australia's mesic coast to the arid interior.ResultsThe value of average benefits did not vary with aridity, with winners and losers in biodiversity and ecosystem services as aridity intensified. Tradeoffs between biodiversity and soil stability declined with increasing aridity, but only in mesic environments, whereas tradeoffs between wood production potential and carbon storage intensified under greater aridity levels, but only in mesic environments. Aridity and tree structure were the major regulators of these tradeoffs, particularly under dry environments. Increasing aridity affected tradeoffs directly or indirectly by either suppressing the positive effect of tree height or exacerbating the negative effect of tree canopy size.Main conclusionsOur results indicate that biodiversity and most ecosystem services supported by trees are likely to decline under future climate change scenarios and demonstrate the importance of targeting afforestation programs to specific services in particular climatic areas rather than attempting to improve multiple services.

Relations of the Species Wealth of Adventive and Aboriginal Fractions of Floras with the Characteristics of Climate and Relief in the Middle Volga Region

Relations of the Species Wealth of Adventive and Aboriginal Fractions of Floras with the Characteristics of Climate and Relief in the Middle Volga Region

Linking local climate scenarios to global warming levels: applicability, prospects and uncertainties

Abstract Global warming levels (GWLs) are increasingly becoming a central concept in climate change studies. In recent years, their integrative quality for climate change impact analysis has been demonstrated, and methodological advancements have helped to compensate for some inherent shortfalls of the concept. However, their applicability at the regional level is debatable, and no study to date has examined the possibility of linking local climate scenarios to GWLs. For the case of Austria, we have evaluated the relation between global and local warming patterns, and whether version changes of global climate models could be incorporated into local climate scenarios by means of the GWL concept, without updating the actual data. We applied the time sampling approach, where GWLs are determined as periods when global mean temperature anomalies cross a certain threshold. GWL periods were sampled both from the global models in the background of the local climate scenarios (CMIP5), and from an equivalent ensemble of newer-generation climate models (CMIP6). Uncertainties resulting from sampling GWLs from different global climate model ensembles were examined, and prospects for local climate change impact assessments were discussed. Accounting for updated global climate model versions might be useful when the changes at certain GWLs are related to fixed reference periods, but temperature increments between GWLs remained relatively constant across model versions, even on the local level. The study bridges a significant gap to link regional and local climate projections to GWLs. Climate change impacts assessments that build on those datasets can benefit from the integrative character of GWLs, making studies comparable across multiple disciplines and model versions, and thus fostering a way to communicate local climate change impacts more comprehensible.