High-resolution Climate Simulations Research Articles

Projected changes in heatwaves over Central and South America using high-resolution regional climate simulations

Heatwaves (HWs) pose a severe threat to human and ecological systems. Here we assess the projected changes in heatwaves over Latin America using bias corrected high-resolution regional climate simulations under two Representative Concentration Pathway scenarios (RCPs). Heatwaves are projected to be more frequent, long-lasting, and intense in the mid-century under both RCP2.6 and RCP8.5 scenarios, with severe increases under the RCP8.5 scenario. Even under the low emissions scenario of RCP2.6, the frequency of heatwaves doubles over most of the region. A three- to tenfold rise in population exposure to heatwave days is projected over Central and South America, with climate change playing a dominant role in driving these changes. Results show that following the low emission pathway would reduce 57% and 50% of heatwave exposure for Central and South American regions respectively, highlighting the need to control anthropogenic emissions and implement sustainable practices.

A multi-scenario ensemble approach incorporating stepwise cluster analysis to reduce uncertainty in large-scale watershed precipitation projections: a case study of Pearl River Basin, South China.

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds.

Characterizing the uncertainty of CMORPH products for estimating orographic precipitation over Northern California

Satellite-based precipitation products (SPPs), such as the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH), have greatly extended our ability to monitor global precipitation. However, their performance over complex terrain remains highly uncertain. To improve accuracy, CPC has recently upgraded its operational SPP to the second generation CMORPH–CMORPH2. In addition to such efforts, the reliability of SPPs can be further enhanced by developing robust uncertainty estimates. This study employs a censored, shifted gamma distribution (CSGD)-based error modeling framework to develop error models for both CMORPH2 and its predecessor, CMORPH1, at a 6-hourly scale over Northern California—a typical complex terrain region that challenges most SPPs. Using the Stage IV reference precipitation, the relative improvements of CMORPH2 over CMORPH1 are quantified by comparing their CSGD-derived error statistics. The comparison shows that CMORPH2 outperforms CMORPH1 by reducing the overall bias and detection errors, and by better capturing the orographic gradients of precipitation over the Coast Ranges and Sierra Nevada. Validation results show that the trained error models can appropriately represent the bias and random errors; however, the models for both CMORPH2 and CMORPH1 show reduced skills in the high-altitude Sierra Nevada. With high-resolution regional climate simulations, the uncertainty estimates are further improved by incorporating them as covariates. The simulated precipitation shows substantial improvement of the uncertainty estimates over most areas, including the challenging high-altitude Sierra Nevada. Following precipitation, the simulated integrated water vapor transport (IVT) and convective available potential energy (CAPE) also offer modest improvements, mainly along the coast. Independent verification further demonstrates the robustness of the uncertainty estimates during heavy precipitation events. As NOAA is currently reprocessing CMORPH2 to produce an updated global precipitation record from 1991 onward, this error modeling framework holds potential for broader applications in quantifying the uncertainty of CMORPH2 for estimating orographic precipitation over extended periods and locations.

Midlatitude mesoscale thermal Air-sea interaction enhanced by greenhouse warming

The influence of greenhouse warming on mesoscale air-sea interactions, crucial for modulating ocean circulation and climate variability, remains largely unexplored due to the limited resolution of current climate models. Additionally, there is a lack of theoretical frameworks for assessing changes in mesoscale coupling due to warming. Here, we address these gaps by analyzing eddy-resolving high-resolution climate simulations and observations, focusing on the mesoscale thermal interaction dominated by mesoscale sea surface temperature (SST) and latent heat flux (LHF) coupling in winter. Our findings reveal a consistent increase in mesoscale SST-LHF coupling in the major western boundary current regions under warming, characterized by a heightened nonlinearity between warm and cold eddies and a more pronounced enhancement in the northern hemisphere. To understand the dynamics, we develop a theoretical framework that links mesoscale thermal coupling changes to large-scale factors, which indicates that the projected changes are collectively determined by historical background wind, SST, and the rate of SST warming. Among these factors, the large-scale SST and its warming rate are the primary drivers of hemispheric asymmetry in mesoscale coupling intensification. This study introduces a simplified approach for assessing the projected mesoscale thermal coupling changes in a warming world.

High-resolution urban climate simulations for heat and health applications in Philadelphia

Stakeholders need high-resolution urban climate information for city planning and adaptation to climate risks. Climate models have too coarse a spatial resolution to properly represent cities at the relevant scale, and downscaled products often fail to account for urban effects. We propose here a methodological framework for producing high-resolution urban databases that are used to drive the SURFEX-TEB land surface and urban canopy models. A historical simulation is carried out over the period 1991–2020, based on a reanalysis of the city of Philadelphia (Pennsylvania, USA). The simulation is compared with observations outside and inside the city, as well as with a field campaign. The results show good agreement between the model and observations, with average summer biases of only −1 °C and + 0.8 °C for daily minimum and maximum temperatures outside the city, and almost none inside. The simulation is used to calculate the maximum daily heat index (HIX) and to study emergency heat alerts. The HIX is slightly overestimated and, consequently, the model simulates too many heat events if not bias corrected. Overall, HIX conditions at Philadelphia International Airport are found to be suitable proxies for city-wide summer conditions, and therefore are appropriate to use for emergency heat declarations.

A more quiescent deep ocean under global warming

The ocean is a magnificent reservoir of kinetic energy possessed by currents at diverse spatio-temporal scales. These currents transport heat and material, regulating the regional and global climate. It is generally thought that large-scale ocean circulations should become more energetic under global warming, especially in the ocean’s upper layer. However, using high-resolution global climate simulations, here we demonstrate that the total ocean kinetic energy is projected to be significantly reduced in a warming climate, despite overall acceleration of large-scale ocean circulations in the upper layer. This reduction is primarily attributed to weakened ocean mesoscale eddies in the deep ocean. Enhanced vertical stratification under global warming reduces the available potential energy stored in large-scale ocean circulations, diminishing its conversion into eddy kinetic energy. Our findings reveal a more quiescent deep ocean under global warming and suggest a crucial role of mesoscale eddies in determining the anthropogenic change of total ocean kinetic energy.

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.

Dynamical Downscaling of Daily Extreme Temperatures over China Using PRECIS Model

As global warming intensifies and the frequency of extreme weather events rises, posing a major threat to the world’s economy and sustainable development, accurate forecasting of future extreme events is of great significance to mankind’s response to extreme weather events and to the sustainable development of society. Global Climate Models (GCMs) have limitations in their applicability at regional scales due to their coarse resolution. Utilizing dynamical downscaling methods based on regional climate models (RCMs) is an essential approach to obtaining high-resolution climate simulation information in future. This study represents an attempt to extend the use of the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model by employing the BCC-CSM2-MR model from the Beijing Climate Center to drive it, conducting downscaling experiments over China at a spatial resolution of 0.22° (25 km). The simulation and prediction of daily maximum and minimum temperatures across the China region are conducted, marking a significant effort to expand the usage of PRECIS with data from alternative GCMs. The results indicate that PRECIS performs well in simulating the daily maximum and minimum temperatures over the China region, accurately capturing their spatial distribution and demonstrating notable simulation capabilities for both cold and warm regions. In the annual cycle, the simulation performance of PRECIS is superior to its driving GCM, particularly during cold months (i.e., December and from January to May). Regarding future changes, the daily extreme temperatures in most regions are projected to increase gradually over time. In the early 21st century, the warming magnitude is approximately 1.5 °C, reaching around 3 °C by the end of the century, with even higher warming magnitudes exceeding 4.5 °C under the SSP585 scenario. Northern regions will experience greater warming magnitudes than southern regions, suggesting faster increases in extreme temperatures in higher latitudes. This paper provides forecasts of extreme temperatures in China, which will be useful for studying extreme events and for the government to make decisions in response to extreme events.

Projection of Mortality Burden Attributable to Nonoptimum Temperature with High Spatial Resolution in China.

The updated climate models provide projections at a fine scale, allowing us to estimate health risks due to future warming after accounting for spatial heterogeneity. Here, we utilized an ensemble of high-resolution (25 km) climate simulations and nationwide mortality data from 306 Chinese cities to estimate death anomalies attributable to future warming. Historical estimation (1986-2014) reveals that about 15.5% [95% empirical confidence interval (eCI):13.1%, 17.6%] of deaths are attributable to nonoptimal temperature, of which heat and cold corresponded to attributable fractions of 4.1% (eCI:2.4%, 5.5%) and 11.4% (eCI:10.7%, 12.1%), respectively. Under three climate scenarios (SSP126, SSP245, and SSP585), the national average temperature was projected to increase by 1.45, 2.57, and 4.98 °C by the 2090s, respectively. The corresponding mortality fractions attributable to heat would be 6.5% (eCI:5.2%, 7.7%), 7.9% (eCI:6.3%, 9.4%), and 11.4% (eCI:9.2%, 13.3%). More than half of the attributable deaths due to future warming would occur in north China and cardiovascular mortality would increase more drastically than respiratory mortality. Our study shows that the increased heat-attributable mortality burden would outweigh the decreased cold-attributable burden even under a moderate climate change scenario across China. The results are helpful for national or local policymakers to better address the challenges of future warming.

Future Projections of Heat Waves and Associated Mortality Risk in a Coastal Mediterranean City

Climate change has been linked to the escalating frequency, duration, and intensity of heat waves in the Mediterranean region, intensifying health concerns for the general populace. Urban environments face elevated health risks due to concentrated populations and the urban heat island effect, further amplifying nighttime heat conditions. This study aims to project changes in heat wave characteristics and the associated population exposure risk in a large Mediterranean city, Thessaloniki, Greece. High-resolution climate simulations, using the WRF model, were conducted for three 5-year periods (2006–2010, 2046–2050, 2096–2100) under the RCP8.5 emission scenario, covering Thessaloniki with a 2 km grid. By the end of the century, Thessaloniki is projected to experience over 60 annual heat wave days, compared to ~8 in the present climate, while some episodes were found to persist beyond 30 days. The relative risk during heat wave days is expected to rise, which is primarily due to nighttime heat stress. Interestingly, the results indicate that minimum apparent temperature might be a more reliable indicator in predicting heat-related mortality compared to maximum apparent temperature. These findings emphasize the growing importance of informed heat mitigation and adaptation strategies and healthcare preparedness in urban areas facing escalating heat-related health challenges.

Contrasting latitudinal evolution of East Asian monsoonal precipitation during the Last Interglacial (130–120 ka)

Evolution of East Asian monsoonal precipitation across the Last Interglacial (LIG) remains controversial, owing to the discrepancies between various proxies and their low temporal resolution. Through a transient high-resolution global climate simulation covering the interval of 130–120 ka, we illustrate a long-term increasing (decreasing) trend in summer precipitation over south China (northeast Asia) during the LIG (i.e. 130–120 ka). The out-of-phase precipitation evolution across latitudes were coherently regulated by the weakened monsoonal circulation, southward moved western North Pacific high, and southward displaced East Asian westerly jet from the early to late LIG. These atmospheric circulation variations were in turn determined by sea surface temperature anomalies over the Pacific and the propagation of extratropical Rossby waves originating from North Africa. Our results may provide important insights for reconciling discrepancies between precipitation proxies during the LIG and for precipitation behavior in a warmer-than-present world.

Impact of greenhouse warming on mesoscale eddy characteristics in high-resolution climate simulations

Mesoscale eddies are prevalent throughout the global ocean and have significant implications on the exchange of heat, salt, volume, and biogeochemical properties. These small-scale features can potentially influence regional and global climate systems. However, the effects of climate change on ocean eddies remain uncertain due to limited long-term observational data. To address this knowledge gap, our study focuses on examining the impact of greenhouse warming on surface mesoscale eddy characteristics, utilizing a high-resolution climate simulation project. Our model experiments provided valuable insights into the potential effects of greenhouse warming on mesoscale eddies, suggesting that mesoscale eddies will likely become more frequent under greenhouse warming conditions and exhibit larger amplitudes and radii, especially in regions characterized by strong ocean currents such as the Antarctic Circumpolar Current and western boundary currents. However, a distinctive pattern emerged in the Gulf Stream, with increases in eddy occurrence and radius and significant decreases in eddy amplitude. This phenomenon can be attributed to the relationship between eddy lifespans and their properties. Specifically, in the Kuroshio Current, the amplitude of eddies increased due to the increased occurrence of long-lived eddies. In contrast, in the Gulf Stream, the amplitude of eddies decreased significantly due to the decreased occurrence of long-lived eddies. This distinction arises from the fact that long-lived eddies can accumulate more energy than shorter-lived eddies throughout their lifetime. These findings provide valuable insights into the complex dynamics of mesoscale eddies in a warming world.

Future Heavy Rainfall and Flood Risks for Native Americans under Climate and Demographic Changes: A Case Study in Oklahoma

Abstract Climate change has posed inequitable risks to different communities. Among communities of color in the United States, Native Americans stand out because 1) they desire resources to sustain resilient nations and 2) they have developed nature-based solutions through experiences with local climate-related challenges, which can provide deep insight for the whole society. Projection of climate risks for Native Americans is essential to assess future risks and support their climate-ready nations, yet there has been lack of useable information. In this study, we projected three climate hazards—heavy rainfall, 2-yr floods, and flash floods—for tribal nations in Oklahoma. To break down into tribal jurisdictions, we utilize a coupled regional climate model at 4 km and flash-flood forecast model at 1 km. A hazard–exposure–vulnerability risk framework is applied to integrate both climate and demographic changes in a high-emissions scenario. It is found that 1) Indigenous people are the most vulnerable community in Oklahoma; 2) heavy rainfall and 2-yr floods have marked increases in risks at 501.1% and 632.6%, respectively, while flash floods have a moderate increase (296.4%); 3) Native Americans bear 68.0%, 64.3%, and 64.0% higher risks in heavy rainfall, 2-yr flooding, and flash flooding, respectively, than the general population in Oklahoma; 4) in comparing climate and demographic changes, it is seen that population growth leads to greater climate hazard risks than does climate change; and 5) emerging tribal nations are projected to have 10 times as much population, resulting in great exposures to climate extremes. This study can raise awareness of the impact of climate changes and draw attention to address climate injustice issues for minoritized communities. Significance Statement This study examines the impact of climate change on a marginalized community—Native Americans in Oklahoma, home to 39 federally recognized tribal nations. We utilized the high-resolution climate simulation at 4-km resolution and hydrologic simulation at 1-km resolution to aggregate three climate extremes to tribal jurisdictions. We find that climate and demographic changes disproportionately put many Native Americans at risk. The heavy rainfall, 2-yr floods, and flash floods are all projected to have increased risks by 501.1%, 632.6%, and 296.4%, respectively. Those risks are 68.0%, 64.3%, and 64.0% higher than the state average for the general population, respectively. We urge proper attention to tribal nations to address climate injustice issues as a whole with the acknowledgment of their distinct relationships to their homelands as sovereign peoples.

Measuring the impact of regional climate change on heating and cooling demand for the Chilean energy transition

The regional impact of climate change on heating and cooling demand is important to consider when designing optimal long-term energy policies. Several studies have addressed this issue, but either at a very aggregated level or without optimizing the whole energy system. The aims of this paper are to fill this gap in a generic way and to assess the impact of climate change on heating and cooling energy demands for residential and commercial sectors at the regional and nodal levels in the context of Chile’s energy transition. We propose a methodology based on high resolution climate simulations for the Representative Concentration Pathways (RCP) RCP 2.6 and RCP 8.5 scenarios. First, a statistical analysis is performed to estimate the long-term trends of so-called heating and cooling degree-days and their impact on final regional energy demands. Then, demand pathways in the energy transition are assessed using a multi-sectoral energy planning model. Numerical experiments using data from Chile show an overall positive economic impact of climate change (limited to heating and cooling demands) for the energy system, with a significant decrease in heating demand compared to a limited increase in cooling requirements. For the RCP 8.5 scenario, cost reductions reach 2.1% of the total discounted system cost on the 2020-2050 period mainly due to a significant decrease of gas consumption for heating. This research highlights the importance for policymakers to consider climate change in efficient energy policies.

Projected changes in rainfall amount and distribution in the Democratic Republic of Congo – Evidence from an ensemble of high-resolution climate simulations

Extreme precipitation has been threatening many sectors of human society and is likely to intensify with global warming. In the present study, we have analysed the impact of future climate change on daily mean rainfall and heavy rainfall on seasonal and annual scales in the Democratic Republic of Congo (DRC) by using ten long-term runs of high-resolution regional climate models. We initially assessed the performance of each run and the multi-model ensemble mean in simulating the daily mean rainfall (PRE), the maximum one-day rainfall (Rx1day), the simple rainfall intensity index (SDII), the count of days when rainfall is greater than or equal to 20 mm (RR20mm), the total rainfall when the daily rainfall exceeds the 95th percentile of the wet-day rainfall (R95p) and the maximum number of consecutive days with more than or equal to 1 mm (CWD). Next, we examined the future changes of these indices, focusing on the multi-model ensemble mean and spread under the high emission scenario RCP8.5. The time frames considered are the mid and end of the twenty-first century (2035–2065 and 2070–2100, respectively). The results indicate that the performance of REMO2015-MPI-ESM was close to the multi-model ensemble mean in representing the mean rainfall and most heavy rainfall indices, while CanRCM4-CamESM2 was identified as the worst performing model. The key finding of this study is that the multi-model ensemble mean project no significant change regarding the daily mean rainfall throughout the year and across all seasons by the middle of the 21st century, except of the western region of the country where a decrease is projected. Simultaneously, Rx1day, SDII, RR20mm, and R95p are projected to decrease almost everywhere during all seasons. Moreover, a consistent decrease in the number of wet days is projected. Focusing on the end of the century, the multi-model ensemble mean project an overall decrease in daily mean rainfall, especially in the western region of the DRC, with a more pronounced effect during MAM. The increase in Rx1day, SDII, RR20mm, and R95p, along with a decrease in CWD, is amplified in this period. These findings are useful for predicting the potential threats of precipitation-related diseases and natural hazards, as well as for designing climate-resilient infrastructure and socioeconomic activities in the DRC.

Climate projections of meteotsunami hazards

Global climate models, indispensable for projecting the human-driven climate change, have been improving for decades and are nowadays capable of reproducing multiple processes (e.g., aerosols, sea-ice, carbon cycle) at up to 25 km horizontal resolution. Meteotsunami events – tsunami waves generated by mesoscale atmospheric processes – are properly captured only by sub-kilometre-scale downscaling of these models. However, the computational cost of long-term high-resolution climate simulations providing accurate meteotsunami hazard assessments would be prohibitive. In this article, to overcome this deficiency, we present a new methodology allowing to project sub-kilometre-scale meteotsunami hazards and their climate uncertainties at any location in the world. Practically, the methodology uses (1) synoptic indices to preselect a substantial number of short-term meteotsunami episodes and (2) a suite of atmospheric and oceanic models to downscale them from an ensemble of global models to the sub-kilometre-scale. Such approach, using hundreds of events to build robust statistics, could allow for an objective assessment of the meteotsunami hazards at the climate scale which, on top of sea level rise and storm surge hazards, is crucial for building adaptation plans to protect coastal communities worldwide.

Distributional added value analysis of daily CORDEX-CORE RegCM4-7 historical precipitation simulations over Africa

The present study proposes a Distributional Added Value (DAV) analysis of CORDEX-CORE RegCM4-7 precipitation simulations over Africa, with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) data as reference, which is chosen based on a preliminary assessment with other available long-term satellite precipitation estimates. The results revealed that CORDEX-CORE RegCM4-7 does add value to its driving GCMs distributions. The results show a positive Added Value Coverage (AVC) of 36.45 to 59.9% for annual daily events, 25.69 to 56.97% for seasonal daily events, 40.97 to 51.74% for heavy annual daily events (above the 95th percentile), and 48.8 to 58.89% for very heavy annual daily events (above the 99th percentile) regardless of the driving GCMs at both high-resolution and low-resolution. The results also feature an overall non-significant AVC ranging from 17.21 to 52.99%, thus suggesting that CORDEX-CORE RegCM4-7 mostly does not alter distributional properties from the driving GCMs if no value could be added. Although differences in individual GCM-based RegCM4-7 outputs exist, the RCM's internal physics and sub-grid processes appeared to be more influential on the DAV results due to the similarity in high- and low-resolution results from the downscaled outputs. This hypothesis is supported by additional evidence from the consistency of the historical simulations results with the evaluation runs, and the strong spatial dependence of the results to inherent local forcing and processes governing regions where most of the Added Value (AV) is observed. Overall, the results indicate that, for its first attempt to produce high-resolution climate simulations at nearly 25 km resolution, CORDEX-CORE RegCM4-7 has achieved the promise of an AV, especially for extreme events over Africa, even if further improvements will still be needed.

Assessing Land Use and Climate Change Impacts on Soil Erosion Caused by Water in China

Soil erosion poses a significant threat to land conservation, freshwater security, and ocean ecology. Climate change, with rainfall as one of its primary drivers, exacerbates this problem. Therefore, reliably predicting future soil erosion rates and taking into account anthropogenic influences are crucial for policymakers and researchers in the earth-system field. To address this challenge, we have developed a novel framework that combines the Bayesian Model Averaging (BMA) method with the Revised Universal Soil Loss Equation (RUSLE) model to estimate erosion rates on a national scale. We used BMA to merge five Regional Climate Models (RCMs), reducing uncertainty in ensemble simulations and improving the plausibility of projected changes in climatic regimes over China under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). The RUSLE model was applied to forecast the effects of climate change and land-use change on water erosion in China, using high-resolution climate simulation and prediction inputs. Our findings revealed that under the RCP4.5 and RCP8.5 scenarios, average annual soil loss will increase by 21.20% and 33.06%, respectively, compared to the baseline period. Our analysis also demonstrated a clear distinction between the effects of climate change and land-use change on water erosion. Climate change leads to an increase in precipitation, which exacerbates water erosion rates, with contributions ranging from 59.99% to 78.21%. Furthermore, an increase in radiative forcing will further amplify the effects of climate change. The transformation of land from one that has not been disturbed by humans to one that has been exposed to some soil and water conservation measures will have a mitigating effect on water erosion, with a contribution of −6.96% to −4.68%. Therefore, implementing effective soil and water conservation measures can somewhat mitigate the severity of ongoing soil loss. Our findings have significant implications for policymakers seeking to develop national strategies for soil conservation and model developers working to reduce uncertainty in erosion predictions.

Future perspectives for wind and solar electricity production under high-resolution climate change scenarios

The impact of climate change on the availability and variability of wind and solar resources for renewable electricity production was assessed under the recent Shared Socioeconomic Pathways scenarios. To do this, the Weather Research and Forecasting model was applied to perform very high-resolution climate simulations (≈1 km2) for one present scenario and two future scenarios (SSP2-4.5 and SSP5-8.5). The modelled meteorological variables were then used to calculate the potential future changes in wind power and solar photovoltaic power for electricity production for each climate change scenario when compared to the present. Results show a strong increase in wind speed during Winter of +45% and a −45% decrease during Summer, with future seasonal production differences for wind power varying ≈100 kWh for both scenarios. For solar radiation, the highest differences are found during the Winter (+30% to +45% increase) with solar photovoltaic generation changes varying from −10 kWh to +20 kWh, depending on the scenario and season. The seasonal changes in wind speed and solar radiation suggest a loss of seasonality, which could be a threat to the stability of future renewable electricity production in the region, making efforts towards decarbonisation more difficult. This study highlights the importance of a comprehensive approach in the assessment of the electricity production system since seasonal variability and intermittency of some renewable energy resources could potentially be covered by others.

Investigating the impact of urban microclimate on building thermal performance: A case study of dense urban areas in Hong Kong

Urban microclimate conditions could be an important factor influencing building thermal performance. However, most studies on urban building energy modeling (UBEM) use the typical meteorological year (TMY), often developed from observations of exposed/ rural sites, as input weather data. This study aims to assess the impacts of urban heat on building thermal performance by coupling a high-resolution urban climate simulation with UBEM. The simulated building thermal performance was compared with and without urban microclimate considerations, using weather data from the TMY, weather observations from an urban oasis, and outputs from the urban climate model. The physical parameters of typical building archetypes used in the UBEM were calibrated against indoor-measured data during summer days. Without considering urban microclimate, there was an underestimation of up to 140% of the average overheating risks of urban buildings. Furthermore, neighboring urban contexts and building ventilation rates considerably affected the thermal performance of individual buildings within high-density urban areas. The study reveals that neglecting the influence of the urban microclimate can result in a notable error in the building thermal performance assessment even at the urban level.