Regional Climate Model Research Articles

Land use modulates resistance of grasslands against future climate and inter-annual climate variability in a large field experiment.

Climate and land-use change are key drivers of global change. Full-factorial field experiments in which both drivers are manipulated are essential to understand and predict their potentially interactive effects on the structure and functioning of grassland ecosystems. Here, we present 8 years of data on grassland dynamics from the Global Change Experimental Facility in Central Germany. On large experimental plots, temperature and seasonal patterns of precipitation are manipulated by superimposing regional climate model projections onto background climate variability. Climate manipulation is factorially crossed with agricultural land-use scenarios, including intensively used meadows and extensively used (i.e., low-intensity) meadows and pastures. Inter-annual variation of background climate during our study years was high, including three of the driest years on record for our region. The effects of this temporal variability far exceeded the effects of the experimentally imposed climate change on plant species diversity and productivity, especially in the intensively used grasslands sown with only a few grass cultivars. These changes in productivity and diversity in response to alterations in climate were due to immigrant species replacing the target forage cultivars. This shift from forage cultivars to immigrant species may impose additional economic costs in terms of a decreasing forage value and the need for more frequent management measures. In contrast, the extensively used grasslands showed weaker responses to both experimentally manipulated future climate and inter-annual climate variability, suggesting that these diverse grasslands are more resistant to climate change than intensively used, species-poor grasslands. We therefore conclude that a lower management intensity of agricultural grasslands, associated with a higher plant diversity, can stabilize primary productivity under climate change.

Robust changes to the wettest and driest days of the year are hidden within annual rainfall projections: a New Zealand case study

Understanding how the statistical properties of daily rainfall will respond to a warming climate requires ensembles of climate model data which are much larger than those typically available from existing centennial-scale modelling experiments. While such centennial-scale experiments are very useful to explore scenario uncertainty in twenty-first century climate, ensemble size constraints often result in regional climate change assessments restricting their focus to annual- or season-mean rainfall projections without providing robust information about changes to the most extreme events. Here, we make use of multi-thousand member ensembles of regional climate model output from the Weather@Home project to explicitly resolve how the wettest and driest days of the year over New Zealand will respond to simulations of a 3 °C world, relative to simulations of the climate of the recent past (2006–15). Using a novel framework to disentangle changes during the wettest and driest days of the year, we show that many regions which show negligible change in annual mean rainfall are in fact experiencing significant changes in the amount of rain falling during both the wettest and driest spells. Exploring these changes through the lens of drought risk, we find many agricultural regions in New Zealand will face significant changes in the frequency of low-rainfall extremes in a warmer world.

Substantial contribution of slush to meltwater area across Antarctic ice shelves

Surface melting occurs across many of Antarctica’s ice shelves, mainly during the austral summer. The onset, duration, area and fate of surface melting varies spatially and temporally, and the resultant surface meltwater is stored as ponded water (lakes) or as slush (saturated firn or snow), with implications for ice-shelf hydrofracture, firn air content reduction, surface energy balance and thermal evolution. This study applies a machine-learning method to the entire Landsat 8 image catalogue to derive monthly records of slush and ponded water area across 57 ice shelves between 2013 and 2021. We find that slush and ponded water occupy roughly equal areas of Antarctica’s ice shelves in January, with inter-regional variations in partitioning. This suggests that studies that neglect slush may substantially underestimate the area of ice shelves covered by surface meltwater. Furthermore, we found that adjusting the surface albedo in a regional climate model to account for the lower albedo of surface meltwater resulted in 2.8 times greater snowmelt across five representative ice shelves. This extra melt is currently unaccounted for in regional climate models, which may lead to underestimates in projections of ice-sheet melting and ice-shelf stability.

Designing Effective Low-Impact Developments for a Changing Climate: A HYDRUS-Based Vadose Zone Modeling Approach

Low-Impact Developments (LIDs), like green roofs and bioretention cells, are vital for managing stormwater and reducing pollution. Amidst climate change, assessing both current and future LID systems is crucial. This study utilizes variably saturated flow modeling with the HYDRUS software (version 4.17) to analyze ten locations in Ontario, Canada, focusing on Toronto. Historical and projected climate data are used in flow modeling to assess long-term impacts. Future predicted storms, representing extreme precipitation events, derived from a regional climate model, were also used in the flow modeling. This enabled a comprehensive evaluation of LID performance under an evolving climate. A robust methodology is developed to analyze LID designs, exploring parameters like water inflow volumes, peak intensity, time delays, runoff dynamics, and ponding patterns. The findings indicate potential declines in LID performance attributed to rising water volumes, resulting in notable changes in infiltration for green roofs (100%) and bioretention facilities (50%) compared to historical conditions. Future climate predicted storms indicate reduced peak reductions and shorter time delays for green roofs, posing risks of flooding and erosion. Anticipated extreme precipitation is projected to increase ponding depths in bioretention facilities, resulting in untreated stormwater overflow and prolonged ponding times exceeding baseline conditions by up to 13 h at numerous Ontario locations.

Incorporation of RCM-simulated spatial details into climate change projections derived from global climate models

Regional climate models (RCMs) exhibit greater potential than global models (GCMs) in capturing geographical details of climate change arising from orography and land–water distribution, but dynamical downscalings are only available for a limited number of GCMs. The full GCM ensembles are much more representative. Furthermore, the current EURO-CORDEX RCM runs most likely underestimate future warming. Thus, neither GCMs nor RCMs as such constitute an ideal tool for preparing reliable spatially detailed climate projections. This study introduces an easy-to-use GCM-RCM hybrid method that takes advantage of the best properties of both model categories. The large-scale response is adopted from GCM simulations, but the pattern is enriched with RCM-simulated details. For temperature projections, the procedure resembles the conventional pattern-scaling technique. However, the spatial averages of temperature change used for scaling are calculated over an area surrounding each grid point, either by giving an equal weight to the entire area or by taking into account the land–sea distribution. For precipitation, a linearised version of the method has been formulated. The method is demonstrated by integrating spatial details from 12 EURO-CORDEX RCM simulations with the CMIP6 multi-GCM mean projection. The resulting temperature responses include RCM-generated spatial details of up to ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 1 ∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ$$\\end{document}C while effectively correcting the general tendency of RCMs to underestimate warming in Europe. For precipitation, geographical details originating from the different CORDEX runs tend to diverge, resulting in a low signal-to-noise ratio. This probably reflects the substantial impact of internal variability on small-scale changes in precipitation.

Enhancing Atmospheric Monitoring Capabilities: A Comparison of Low- and High-Cost GNSS Networks for Tropospheric Estimations

Global Navigation Satellite System (GNSS) signals experience delays when passing through the atmosphere due to the presence of free electrons in the ionosphere and air density in the non-ionized part of the atmosphere, known as the troposphere. The Precise Point Positioning (PPP) technique demonstrates highly accurate positioning along with Zenith Tropospheric Delay (ZTD) estimation. ZTD estimation is valuable for various applications including climate modelling and determining atmospheric water vapor. Current GNSS network resolutions are not completely sufficient for the scale of a few kilometres that regional climate and weather models are increasingly adopting. The Centipede-RTK network is a low-cost option for increasing the spatial resolution of tropospheric monitoring. This study is motivated by the question of whether low-cost GNSS networks can provide a viable alternative without compromising data quality or precision. This study compares the performance of the low-cost Centipede-RTK network in calculating the Zenith Tropospheric Delay (ZTD) to that of the existing EUREF Permanent Network (EPN), using two alternative software packages, RTKLIB demo5 version and CSRS-PPP version 3, to ensure robustness and software independence in the findings. This investigation indicated that the ZTD estimations from both networks are almost identical when processed by the CSRS-PPP software, with the highest mean difference being less than 3.5 cm, confirming that networks such as Centipede-RTK could be a reliable option for dense precise atmospheric monitoring. Furthermore, this study revealed that the Centipede-RTK network, when processed using CSRS-PPP, provides ZTD estimations that are very similar and consistent with the EUREF ZTD product values. These findings suggest that low-cost GNSS networks like Centipede-RTK are viable for enhancing network density, thus improving the spatial resolution of tropospheric monitoring and potentially enriching climate modelling and weather prediction capabilities, paving the way for broader application and research in GNSS meteorology.

Sensitivity of joint atmospheric-terrestrial water balance simulations to soil representation: Convection-permitting coupled WRF-Hydro simulations for southern Africa

Regional weather and climate models play a crucial role in understanding and representing the regional water cycle, yet the accuracy of soil data significantly affects their reliability. In this study, we employ the fully coupled Weather Research and Forecasting Hydrological Modeling system (WRF-Hydro) to assess how soil hydrophysical properties influence regional land-atmosphere coupling and the water cycle over the southern Africa region. We utilize four widely-used global soil datasets, including default soil data for model from the Food and Agriculture Organization, and alternative datasets from the Harmonized World Soil Database, Global Soil Dataset for Earth System Model, and global gridded soil information system SoilGrids. By conducting convection-permitting coupled WRF-Hydro simulations with the Noah-MP land surface model using each of the aforementioned soil datasets, our benchmark analysis reveals substantial differences in soil hydrophysical properties and their significant impact on the simulated regional water cycle during the austral summer. Alterations in soil datasets lead to both spatial and temporal variations in surface water and energy fluxes, which in turn profoundly influence the atmospheric thermodynamic structure. Reduced soil water-holding capacity leads to subsequent reduction in soil moisture and latent heat, resulting in significant decreases in convective available potential energy and convective inhibition, signaling potential effects on precipitation distributions. In arid interior regions of southern Africa, shifts towards drier and warmer surface conditions due to soil data discrepancies are found to enhance atmospheric moisture convergence, suggesting a possible localized negative feedback of soil moisture on precipitation. Overall, the results for southern Africa indicate that soil data discrepancies exert more pronounced impact on terrestrial fields in dry subregions and on atmospheric fields in temperate subregions, highlighting the broad uncertainties in the regional water cycle reproduced within the model.

Spatial variability in the seasonal precipitation lapse rates in complex topographical regions – application in France

Abstract. Seasonal precipitation estimation in ungauged mountainous areas is essential for understanding and modeling a physical variable of interest in many environmental applications (hydrology, ecology, and cryospheric studies). Precipitation lapse rates (PLRs), defined as the increasing or decreasing rate of precipitation amounts with the elevation, play a decisive role in high-altitude precipitation estimation. However, the documentation of PLR in mountainous regions remains weak even though their utilization in environmental applications is frequent. This article intends to assess the spatial variability and the spatial-scale dependence of seasonal PLRs in a varied and complex topographical region. At the regional scale (10 000 km2), seven different precipitation products are compared in their ability to reproduce the altitude dependence of the annual/seasonal precipitation of 1836 stations located in France. The convection-permitting regional climate model (CP-RCM) AROME is the best in this regard, despite severe precipitation overestimation in high altitudes. The fine resolution of AROME allows for a precise assessment of the influence of altitude on winter and summer precipitation on 23 massifs at the sub-regional scale (∼ 1000 km2) and 2748 small catchments (∼ 100 km2) through linear regressions. With AROME, PLRs are often higher in winter at the catchment scale. The variability in the PLR is higher in high-altitude regions such as the French Alps, with higher PLRs at the border than inside the massifs. This study emphasizes the interest of conducting a PLR investigation at a fine scale to reduce spatial heterogeneity in the seasonal precipitation–altitude relationships.

Orinoco revisited: Comprehensive analysis of the Orinoco River basin present and future hydroclimate

The Orinoco River basin, ranked as South America’s third-largest catchment, is pivotal in contributing to the Atlantic Ocean’s water volume. This study provides a comprehensive update on the basin’s surface water balance, examining trends using gridded precipitation and total evaporation datasets. We also explore projected changes in precipitation until the end of the 21st century, focusing on the RCP8.5 climate change scenario. To achieve this, we utilize data from regional climate models designed by the CORDEX-CORE experiment, selecting an ensemble that excels in performance across South America and Central America. We estimate the accuracy of reference datasets in capturing water balance dynamics. We identify increasing trends in precipitation and total evaporation across most of the basin, enhancing our understanding of its long-term hydrological balance. Notably, the Andean and Guianese sectors of the basin contribute equally to half of the mean surface runoff, although the latter encompasses only 30% of the total area. This underscores the key role of the Guianese shield sub-basins. In regional climate modeling, despite some underestimation, the model runs for the CORDEX South America domain simulate effectively the precipitation across the basin. Regarding climate scenarios, our analysis using the RCP8.5 scenario projects an average annual precipitation reduction of around 45% for the entire basin. These findings emphasize the urgency of adopting measures to mitigate potential adverse effects on the Orinoco River basin’s hydrological sustainability in response to evolving climate patterns.

A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Peak Flow Timing

AbstractGlobal climate models (GCMs) are unable to produce detailed runoff conditions at the basin scale. Assumptions are commonly made that dynamical downscaling can resolve this issue. However, given the large magnitude of the biases in downscaled GCMs, it is unclear whether such projections are credible. Here, we use an ensemble of dynamically downscaled GCMs to evaluate this question in the Sierra‐Cascade mountain range of the western US. Future projections across this region are characterized by earlier seasonal shifts in peak flow, but with substantial inter‐model uncertainty (−25 ± 34.75 days, 95% confidence interval (CI)). We apply the emergent constraint (EC) method for the first time to dynamically downscaled projections, leading to a 39% (−28.25 ± 20.75 days, 95% CI) uncertainty reduction in future peak flow timing. While the constrained results can differ from bias corrected projections, the EC is based on GCM biases in historical peak flow timing and has a strong physical underpinning.

Projected energy and hydrological budgets over the Indus River Basin under a CORDEX-SA regional climate model framework

The Indus River is one of the largest transboundary rivers in Asia originating from Tibet autonomous region of China. This supplies water to the great plains of Punjab, significantly influencing agricultural productivity and shaping the socioeconomic conditions of the region's inhabitants. The present study investigates the potential impact of climate change on the water resources of the Indus River Basin (IRB). The study utilized the output from a regional climate model, REMO, to examine the projected change in energy and hydrological budget at 1.5, 2 specific warming levels and at the end of the century over IRB under three different representative concentration pathways (RCPs). The projected rise in the temperature from the historical period to the end of the century (both summer and winter) can be attributed to the increase in net downward energy flux compared to net upward energy flux. In the mid-century, the downward longwave radiation flux (sensible and latent heat flux) plays a dominant role in determining the surface energy balance during winter (summer). While, the combined impact of the energy components becomes more dominant at the end of the century for both seasons. Rainfall is projected to increase in the mountains of the upper Indus basin and gradually decline on the plains of the lower Indus basin till the end of the century. The projected decline in water storage over the plains of IRB can be attributed to the combination of declining rainfall and increasing evaporation. This will exert significant pressure on agriculture, industry and more importantly on the potable water supply. The IRB is found to be highly susceptible to climate extremes such as floods and drought. Climate change information is crucial for policy planners, governance, decision-makers, management authorities, etc. as it enables the implementation of better management practices, Additionally, it provides both short-term and long-term goals for a mitigation-based approach to reduce the impact of climate change.

Western Europe’s extreme July 2019 heatwave in a warmer world

Summertime heatwaves are extreme events with a large societal impact. Intensity, duration and spatial extent, all heatwave properties are projected to increase in a warming world, implying that summers that qualified as extreme in the past will become increasingly normal. In this paper we quantify how the changes play out for the July 2019 European heatwave that shattered temperature records throughout Western Europe. We combine a storyline approach with ensemble Pseudo Global Warming (PGW) and high-resolution dynamical downscaling. The downscaling is done with a regional climate model (RACMO2, 12 km resolution) and a convection-permitting model (HCLIM-AROME, 2.5 km resolution). Under PGW the maximum temperature during the heatwave rises 1.5 to 2.5 times faster than the global mean, implying that even at moderate warming levels the heatwave impact changes are tangible. Moreover, there is no sign that the increase in the maximum temperature levels off at higher warming levels, implying that at +4K above present-day temperatures could reach 50 ∘C. During heatwaves cities become islands of heat where daily maxima and night-time minima are up to 5 ∘C higher than in rural areas as we show in ultra-high resolution HCLIM-AROME simulations at 150 m resolution.

Distinct Central and Eastern Pacific El Niño Influence on Antarctic Surface Mass Balance

AbstractThe El Niño‐Southern Oscillation causes anomalous atmospheric circulation, temperature and precipitation across southern polar latitudes, but the influence of Central and Eastern Pacific El Niño events on Antarctic surface mass balance and snow accumulation has not yet been assessed. Here, we use reanalysis and reanalysis‐forced regional climate model output and find that Central Pacific El Niño results in significantly increased snow accumulation in the western Ross Sea sector and significantly decreased snow accumulation in the Amundsen Sea sector. Eastern Pacific El Niño is associated with similar but weaker patterns, with some regional exceptions. In some areas, like Dronning Maud Land, or the Wilkes Subglacial Basin, the effect of El Niño on snow accumulation changes from increased to reduced accumulation depending on the type of El Niño. Our results show that projecting El Niño types is important for constraining future changes in Antarctic surface mass balance.

Machine learning and CORDEX-Africa regional model for assessing the impact of climate change on the Gilgel Gibe Watershed, Ethiopia

Climate change is one of the most pressing challenges of our time, profoundly impacting global water resources and sustainability. This study aimed to predict the long-term effects of climate change on the Gilgel Gibe watershed by integrating machine learning (ML) methods and climate model scenarios. Utilizing an ensemble mean of four regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa project, we forecast future climatic conditions. Although global and regional climate simulations offer valuable insights, their limitations necessitate alternative approaches, such as ML, for improved accuracy. Employing an ensemble ML model with Random Forest (RF), Extra Tree (ET), and CatBoost (CB) algorithms, we assessed various bias-correction methods using historical data from 1993 to 2009. Our results highlight the effectiveness of distribution mapping (DM) in capturing temperature variability and precipitation patterns, using the power transpiration (PT) method to represent precipitation variability. Projections indicate a decline in future precipitation under the RCP 8.5 (−32.2%) and SSP 4.5 (−88.8%) for 2024–2049, with further decreases expected for 2050–2099. Conversely, temperatures will rise under RCP 4.5 (TMAX 0.67 °C) and RCP 8.5 (TMAX 0.25 °C and TMIN 1.11 °C) in the near term, exacerbated by higher emissions under SSP 4.5 and 8.5. By leveraging an ensemble mean of four observed RCMs in an ML framework, our study successfully reproduced future Coupled Model Intercomparison Project (CMIP5) and (CMIP6) climatic datasets, with the CB model demonstrating superior performance in predicting future precipitation and temperature trends. These findings offer valuable insights for shaping future climate scenarios and informing policy decisions for the Gilgel Gibe Watershed, thereby enhancing water resource management in the basin and its environs.

Future climate projection across Tanzania under CMIP6 with high-resolution regional climate model

Climate change is one of the most pressing challenges faced by developing countries due to their lower adaptive capacity, with far-reaching impacts on agriculture. The mid-century period is widely regarded as a critical moment, during which adaptation is deemed essential to mitigating the associated impacts. This study presents future climate projections across Tanzania using the latest generation of global climate models (CMIP6) combined with a high-resolution regional climate model. The findings indicate that, the trends in temperature and precipitation in Tanzania from 1991 to 2020, minimum temperatures showed the highest variability with a trend of 0.3 °C, indicating significant fluctuations in minimum temperature over the decades. Maximum temperatures also showed high variability with a trend of 0.4 °C. There is a range of variability in precipitation per decade for different regions in Tanzania, with some regions experiencing significant decreases in precipitation of up to − 90.3 mm and − 127.6 mm. However, there were also regions that experienced increases in precipitation, although these increases were generally less than 4.8 mm over the decades. The projections of minimum and maximum temperatures from 2040 to 2071 under the Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP 5–8.5 are projected to increase by 0.14 °C to 0.21 °C per decade, across different regions. The average projected precipitation changes per decade vary across regions. Some regions are projected to experience increases in precipitation. Other regions are projected to show decreases in precipitation within the range of − 0.6 mm to 15.5 mm and − 1.5 mm to 47.4 mm under SSP2–4.5 and SSP5–8.5 respectively. Overall, both scenarios show an increase in projected temperatures and precipitation for most regions in Tanzania, with some areas experiencing more significant increases compared to others. The changes in temperatures and precipitation are expected to have significant impacts on agriculture and water resources in Tanzania.

Projected changes in heat, extreme precipitation, and their spatially compound events over China’s coastal lands and seas through a high-resolution climate models ensemble

China’s coastal lands and seas are highly susceptible to the changing environment due to their dense population and frequent economic activities. These areas experience more significant impacts from climate change-induced extreme events than elsewhere. The most noticeable effects of climate change are extreme high temperatures and extreme precipitation. We employ an ensemble of RCMs (Regional Climate Models) to investigate and project changes in temperature, precipitation, and Compound Heat-Precipitation Extreme events (CHPEs) over selected China’s coastal lands and seas for both historical (1985–2004) and future periods (2080–2099). The multi-model ensemble projects that daily temperature extremes will increase by 2.9 °C to 5.4 °C across China’s coastal lands and seas, with land areas showing a higher temperature increase than marine areas. Extreme precipitation shows a high geographical heterogeneity with a 2.8–3.9 mm d−1 reduction over the 15–25°N marine areas while a 2.2–5.4 mm d−1 increment over the 25°N-35°N land areas. We use the Clausius–Clapeyron relationship to reveal that the peak of daily extreme precipitation will increase by 2–7 mm d−1 and the temperature at which extreme precipitation peaks will increase by 2 °C to 6 °C by warming. The land area of 25–30°N has the highest peak precipitation increase of 9.87 mm d−1 and a peak temperature increase of 6 °C. As precipitation extremes intensify with daily temperature extremes increase, CHPEs are projected to occur more frequently over both land and marine areas. Compared with the historical period, the frequency of CHPEs will increase by 40.9%-161.2% over marine areas, and by 36.2%-163.6% over land areas in the future. The 15–20°N area has the highest frequency increase of CHPE events, and the 25–30°N area has the largest difference in frequency increase under two different scenarios. It indicated that the 25–30°N area will be more easily affected by climate change.

Analysis of high-resolution convection-permitting regional climate simulations with respect to the impact of spatial resolution on simulated wind climate

In this study, climate model simulations are evaluated with regard to the wind energy resource in Germany. Since accurate determination of local wind requires a high resolution, we consider simulations of Convection Permitting Regional Climate Models (CP-RCMs) with 3km resolution. We analysed RCP8.5 scenario simulations carried out i) within the EUCP project using the RCM REMO in convection permitting mode for the Central European domain (CEU-3) and ii) within the CORDEX FPS-Convection project that provides a model ensemble of CP-RCMs for the Pan-Alpine region (ALP-3). The models are forced with RCMs at intermediate resolution which are by itself driven with General Circulation Models (GCMs) from the Coupled Model Intercomparison Project – Phase 5 (CMIP5) for the two time slices 1996-2005 (’historical’) and 2041-2050 (‘near-future’). Wind energy yield is calculated at 100 m height by using 3 MW wind turbine parameters and a height correction method. The results are compared with observations from four different met-masts at different locations. The RCMs at intermediate resolution are closer to observations than the CP-RCM REMO while the ensemble of CORDEX FPS-Convection reveals that the difference between model and observations is highly model dependent. A significant climate trend in the comparison of ’historical’ to ‘near future’ cannot be seen for Germany in both the EUCP and CORDEX FPS-Convection simulations.

Converging Findings of Climate Models and Satellite Observations on the Positive Impact of European Forests on Cloud Cover

AbstractAlthough afforestation is a potential strategy to mitigate climate change by sequestering carbon, its potential biophysical effects on climate, such as regulating surface albedo, evapotranspiration, and energy balance, have not been fully incorporated into climate change mitigation strategies. This is partly due to the challenges associated with modeling the complex bidirectional interactions between vegetation and climate. In this study, we assess the impact of afforestation on low cloud cover using a regional climate model (RCM) and Earth observation data, applying a space‐for‐time approach to overcome limitations that may arise from comparing satellite and RCM results, such as different background climate conditions or different extents of land cover change. Our results show a consistent increase in low cloud cover in Europe due to afforestation in both datasets (3.71% and 3.56% on average, respectively), but the magnitude and direction of this effect depend on various factors, including location, seasonality, and forest type. These results suggest that afforestation can have important feedbacks on the climate system, and that its biophysical effects must be considered in climate change mitigation strategies. Furthermore, we emphasize the role of the modeling community in developing accurate and reliable approaches to assess the biophysical effects of land cover change on climate.

Projected effects of climate change in precipitation and streamflows of the Upper Orinoco River Basin from global and regional climate models

ABSTRACT We studied the projections of streamflows under climate change scenarios in the Upper Orinoco River Basin by using precipitation from 15 global and regional climate models for the period 2020–2099. For this purpose, we calibrated and validated a hydrological model with a very good performance. Our results show that both precipitation and streamflow have a significant reduction for the RCP8.5 scenario that ranges from 5 and 7% in relation to the long-term means at the end of the century. The changes in both variables are not significant for the RCP2.6 scenario. Moreover, we compute several indicators of change in the streamflow to quantify alterations in high-, mid-, and low-range flows. Our results suggest that the main alterations for the future runoff would be evidenced in the mid-range and low flows, which could increase around ∼5 and ∼25%, respectively. These alterations could lead to changes in the hydrological, environmental, and ecological balance of the basin. This work provides information regarding the possible effects of climate change on the streamflow of one of the most important river basins of northern South America, which is pivotal for supporting future decision-making on water supply for social, environmental, and productive sectors.

On the impact of climate change on urban microclimate, thermal comfort, and human health: Multiscale numerical simulations

Climate change is expected to aggravate urban microclimatic conditions and have adverse effects on human morbidity and mortality. However, there is limited knowledge on the impact of climate change on urban microclimate and human health for actual urban areas. In this study, we investigate the impact of climate change on urban microclimate and pedestrian thermal comfort within a complex district in Nicosia, Cyprus. Two climatic scenarios are considered: (i) current scenario based on meteorological conditions derived from field measurements performed in 2010, and (ii) future scenario for which meteorological conditions of 2050 are based on downscaled Regional Climate Models (RCMs). URANS CFD simulations are performed for the period of 9–10 July with a 1-h time-step. The results show that by 2050, the maximum air temperature at the pedestrian height of the case study area can increase by 2.3 °C which can increase heat-related mortality by more than 240 %. The UTCI is expected to significantly increase especially in the afternoon hours with up to a 4.3 °C increase at 20:00. The “very strong heat stress” conditions (UTCI: 38–46 °C) are expected to prevail for a longer period during the day, increasing from 3.3 to 6.6 h. In addition, the “extreme heat stress” conditions (UTCI: >46 °C) are expected to apply in parts of the area. This prolonged exposure to heat stress can incite additional health risks.