Climate Downscaling Research Articles

Evaluation of precipitation extremes over the Tibetan plateau using the NASA global daily downscaled datasets NEX-GDDP-CMIP6

The utility of statistically downscaled data is in its provision of detailed, high-resolution insights, surpassing global models, which is essential for precise assessments of climate impact. This study delves into the changes in extreme precipitation across the Tibetan Plateau (TP) and its five subregions, thereby deepening our understanding of climate impacts in the region. Employing NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6), which are bias-corrected and statistically downscaled, in conjunction with three gauge-based datasets (CN05.1, CMFD, GMFD) spanning from 1979 to 2014, this study conducts an extensive analysis using eight extreme precipitation indices in the TP. The findings reveal significant spatial disparities in extreme precipitation, with the southern subregion experiencing a significant increase while the two northern subregions show statistically insignificant changes. NEX-GDDP-CMIP6 performs best in the eastern subregion but fails to capture extreme precipitation characteristics in the southern and western subregions. Averaging over the whole period of 1979–2014 (climatology), the NEX-GDDP-CMIP6 dataset performs better on the intensity and persistence indices than on the absolute and relative threshold indices. NEX-GDDP-CMIP6's performance on climate trends is significantly below that of climatology. The NEX-GDDP-CMIP6 dataset has limitations over the TP because of its reliance on the underperforming GMFD reference data. This study provides a comprehensive understanding of the variability of extreme precipitation and disparities among different subregions of the TP, laying a robust scientific groundwork for climate impact studies and future projection in this region.

Potential impacts of 1.5 °C and 2 °C global warming levels on drought modes over Eastern Africa

This study examines the impacts of 1.5 °C and 2.0 °C global warming levels (GWLs) on the characteristics of four major drought modes over Eastern Africa in the future under two climate forcing scenarios (RCP4.5 and RCP8.5). The droughts were quantified using two drought indices: the standardized precipitation evapotranspiration index (SPEI) and the standardized precipitation index (SPI) at 12-month scale. Four major drought modes were identified with the principal component analysis (PCA). Multi-model simulation datasets from the Coordinated Regional Climate Downscaling Experiment (CORDEX) were analysed for the study. The skill of the models to reproduce the spatial distribution and frequency of past drought modes over Eastern Africa was examined by comparing the simulated results with the Climate Research Unit (CRU) observation. The models give realistic simulations of the historical drought modes over the region. The correlation between the simulated and observed spatial pattern of the drought modes is high (r ≥ 0.7). Over the hotspot of the drought modes, the observed drought frequency is within the simulated values, and the simulations agree with the observation that the frequency of SPI-12 droughts is less than that of SPEI-12 droughts. For both RCP4.5 and RCP8.5 scenarios, the simulation ensemble projects no changes in the spatial structure of the drought modes but suggests an increase in SPEI-12 drought intensity and frequency over the hotspots of the drought modes. The magnitude of the increase, which varies over the drought mode hotspots, is generally higher at 2 °C than at 1.5 °C global warming levels. More than 75% of the simulations agree on these projections. The projections also show that the increase in drought intensity and frequency is more from increased potential evapotranspiration than from reduced precipitation. Hence, the study suggests that to reduce impacts of global warming on future drought, the adaptation activities should focus on reducing evaporative loss surface water.

Investigation of Added Value in Regional Climate Models for East Asian Storm Track Analysis

AbstractStudies have shown that regional climate models (RCMs) can simulate local climates at a higher resolution for specific regions compared to global climate models (GCMs), making dynamic downscaling using RCMs a more effective approach. Therefore, RCMs have become valuable tools for evaluating the potential impacts of climate change on specific regions and for informing local adaptation strategies. To fully understand the added value (AV) of RCMs, it is essential to understand how the characteristics differ between land and ocean. The complex topography of East Asia, including land and sea, makes it a suitable region for evaluating the AV of RCMs. In this study, we compared two regional simulations that integrated the same RCMs but employed different GCMs from the Coordinated Regional Climate Downscaling Experiment for their ability to simulate storm tracks in East Asia. The results of the RCMs for the period from 1982 to 2005 were compared with their host Coupled Model Intercomparison Project GCM projections and high‐resolution reanalysis. In mountainous regions, the AV of the RCMs weakened the bias of the GCM and improved its agreement with the reanalysis due to the dynamical process near the high‐resolution topography. In plains and coastal areas, owing to the increase in horizontal resolution and clearly determined coastline in RCMs, small‐scale phenomena are well represented, and the storm track of RCMs shows similar values to that of the GCM in maritime regions. This study demonstrates the value of RCMs for improving the accuracy of climate projections in East Asia, informing adaptation strategies, and enhancing climate research.

Drift Correction and Sub‐Ensemble Predictive Skill Evaluation of the Decadal Prediction Large Ensemble With Application to Regional Studies

AbstractA large ensemble of experiments is required to reveal the predictable climate signal masked by the background noise in the decadal climate prediction (DCP). This is one of the main obstacles which complicates the generation of high‐resolution decadal climate information at regional scale, given the computing cost of the task. In this study, a set of representative sub‐ensembles of three members (ENS3) from the Decadal Prediction Large Ensemble has been selected to produce dynamically downscaled DCPs in future studies, minimizing the amount of computing resources required to conduct the regionalization while reducing as much as possible the loss of predictive skill with respect to the full ensemble (ENS40). The procedure to follow comprises two steps: first, an analysis to choose the most appropriate method of drift correction to remove the model drift; second, the selection of three members to build ENS3 and the evaluation of its performance and the impact of ensemble size on sub‐ensemble performance. The study has been focused on sea surface temperature (SST), near‐surface temperature anomaly and sea level pressure over some Coordinated Regional Climate Downscaling Experiment regions: Europe, South America and North America. The initial condition‐based approach has been shown to be the most suitable method in the three domains. Although there is an inevitable loss of predictive skill when reducing the ensemble size, ENS3 has shown to be a relatively good alternative to ENS10 and ENS40 when facing computing constraints and the analysis is focused on SST.

Future changes in mean and extreme precipitation over Peninsular Malaysia using CORDEX-SEA 5 km simulations

The Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia further downscaled three of its 25 km products, i.e. EC-Earth, HadGEM2-ES and MPI-ESM-MR forced regional climate simulations to a higher resolution of 5 km. A newer RegCM4.7 model was used for this exercise. For the simulations over Peninsular Malaysia, analysis shows that these simulations have much smaller precipitation biases. They can correctly predict the annual precipitation cycle over the east coast of Peninsular Malaysia compared to the 25 km simulations. The ensemble of these 5 km simulations indicates that Peninsular Malaysia is expected to experience a decrease in its seasonal mean precipitation, regardless of the RCP 4.5 or RCP 8.5 scenarios. Aside from the reduction of seasonal mean precipitation, consecutive dry and wet days are expected to increase and decrease, respectively. This indicates that Peninsular Malaysia will experience a long dry spell in the future. At the same time, it is suggested that Peninsular Malaysia will have fewer days with very heavy precipitation. Overall, the findings from this study suggest that the 5 km downscaled climate simulations improve significantly over 25 km and that the Peninsular Malaysia region can expect a drier future climate and extremes.

Meteorological and Hydrological Drought Risks under Future Climate and Land-Use-Change Scenarios in the Yellow River Basin

The primary innovation of this study lies in the development of an integrated modeling framework that combines downscaled climate projections, land-use-change simulations, and copula-based risk analysis. This framework allows for the assessment of localized sub-seasonal and seasonal drought hazards under future scenarios. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent the future climate for 2025–2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future land-use-change distributions. The climate–land use–drought modeling nexus enables the generation of refined spatio-temporal projections of meteorological and hydrological drought risks in the Yellow River Basin (YRB) in the future period of 2025–2060. The results highlight the increased vulnerability of the upper YRB to sub-seasonal meteorological droughts, as well as the heightened sub-seasonal hydrological drought risks in the Loess Plateau. Furthermore, downstream areas experience escalated seasonal hydrological drought exposure due to urbanization. By providing actionable insights into localized future drought patterns, this integrated assessment approach advances preparedness and climate adaptation strategies. The findings of the study enhance our understanding of potential changes in this integral system under the combined pressures of global climate change and land use shifts.

Changes in the Seasonal Cycle of Heatwaves, Dry and Wet Spells over West Africa Using CORDEX Simulations

This study analyzes the potential response of the seasonal cycle of heatwaves (HWDI) and dry (CDD) and wet (CWD) spell indices over West Africa for the near- (2031–2060) and the far-future periods (2071–2100) under RCP4.5 and RCP8.5 scenarios using Coordinated Regional Climate Downscaling Experiment (CORDEX) simulations. Despite the fact that some relative biases (an underestimation of 30% for CDD, an overestimation of about 60% for CWD, and an overestimation of about 50% for HWDI) exist, during the historical period (1976–2005) in general, the CORDEX simulations and their ensemble mean outperform the seasonal variability in the above-mentioned indices over three defined subregions of West Africa (i.e., the Gulf of Guinea and Western and Eastern Sahel). They show high correlation coefficients (0.9 in general) and less RMSE. They project an increase (about 10 and 20 days) in heatwave days for both the near- and far-future periods over the whole West African region under both RCP scenarios. In addition, projections indicate that the Sahel regions will experience a decrease (about 5 days) in wet spell days from March to November, while in the Gulf of Guinea, a decrease (about 3 days) is projected throughout the year, except in the CCCLM simulation, which indicates an increase (about 5 days) during the retreat phase of the monsoon (October to December). Our results also highlight an increase (about 80%) in dry spells over the Sahel regions that are more pronounced during the March–November period, while over the Gulf of Guinea, an increase (about 40%) is projected over the entire year. Moreover, the months of increasing dry spells and decreasing wet spells coincide, suggesting that countries in these regions could be simultaneously exposed to dry seasons associated with a high risk of drought and heatwaves under future climate conditions.

Frequency dominates intensity of future heat waves over India

Future changes in heat wave characteristics over India have been analyzed using Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SA) regional climate model simulations for mid-term (2041-2060) and long-term (2081-2099) future under the representative concentration pathway (RCP) 4.5 and RCP 8.5 emission scenarios, respectively. SMHI_CSIRO-MK3.6 was found to be the best model in simulating heat wave trend over India for historical period. Future projections show a four-to-seven-fold increase in heat wave frequency for mid-term and long-term future under RCP 4.5 scenario, and five-to-ten-fold increase under RCP 8.5 scenario with increase in frequency dominating intensity in both the scenarios. Northwestern, Central, and South-central India emerged as future heat wave hotspots with largest increase in the south-central region. This high-resolution regional future projection of heat wave occurrence will serve as a baseline for developing transformational heat-resilient policies and adaptation measures to reduce potential impact on human health, agriculture, and infrastructure.

Climate Variability and Glacier Evolution at Selected Sites Across the World: Past Trends and Future Projections

AbstractThe availability of freshwater from glaciers and snowmelt is of vital importance for people and ecosystems in the context of global climate change. Here, we focus on 25 glaciers located in different climates and latitudes and investigate their recent (1958–2020) and future projected trends (2020–2050 and 2070–2100) in monthly precipitation (Pr), maximum and minimum temperatures, ice mass loss, and their relationships with cloud properties. The study sites are located in Temperate Europe (France), the Inner (Ecuador, Venezuela, and Colombia) and Outer Tropics (Bolivia and Peru), Central America (Mexico), tropical Southeast Asia (Indonesia), Equatorial Africa (Uganda), and the Southern dry and Patagonian Andes (Chile and Argentina). The climate analyses are based on TerraClimate data (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces) and 28 CORDEX (Coordinated Regional Climate Downscaling Experiment) climate simulations. Our findings reveal that, extrapolating current glacier volume change trends, almost half of the studied glaciers are likely to vanish (95%–100% volume loss) by 2050, with widespread warming and drying trends since 1958. A shift toward wetter conditions at Pico Humboldt (Venezuela) and Martial Este (Argentina) identifiable in the CORDEX simulation will very likely not have a limiting impact on glacier mass loss owing to increasing temperatures, which will raise the elevation of the rain/snow limit. Our results provide useful new information to better understand glacier‐climate relationships and future scenarios dominated by ice mass loss trends across the globe. These findings suggest serious consequences for future water availability, which exacerbate the vulnerability of local populations and ecosystems.

Future photovoltaic solar power resources in Zambia: a CORDEX-CORE multi-model synthesis

The exploration of renewable energy such as wind and solar radiation has the potential of reducing reliance on fossil fuels, thus cutting emissions of carbon dioxide, particulate matter, and several other greenhouse gasses. However, recent findings indicate that wind speed across Zambia is very slow, it is increasing but remains unlikely to support large commercial wind farms. In this study, we explore the future impacts of climate change on solar photovoltaic resources. To do this, we examine the new high-resolution (25 km) Coordinated Regional Climate Downscaling Experiment—CORDEX-CORE simulations for the African domain, using two different emission scenarios until 2100. At an annual scale, results indicate a weak but steady decrease in PVRes of around 0.02 W/m2 per annum under RCP2.6 and about 0.005 W/m2 per annum under RCP8.5. Results further show that at an average of ~ 237 ± 3.3 W/m2 and 212 ± 2.5 W/m2, respectively, RCP2.6 comes along with 12 ± 3% more PVRes than RCP8.5. Thus RCP2.6, a greener and climate-friendly pathway, points towards a higher renewable energy potential across Zambia compared to the business-as-usual pathway.

Shallow and deep learning of extreme rainfall events from convective atmospheres

Abstract. Our subject is a new catalogue of radar-based heavy rainfall events (CatRaRE) over Germany and how it relates to the concurrent atmospheric circulation. We classify daily ERA5 fields of convective indices according to CatRaRE, using an array of 13 statistical methods, consisting of 4 conventional (“shallow”) and 9 more recent deep machine learning (DL) algorithms; the classifiers are then applied to corresponding fields of simulated present and future atmospheres from the Coordinated Regional Climate Downscaling Experiment (CORDEX) project. The inherent uncertainty of the DL results from the stochastic nature of their optimization is addressed by employing an ensemble approach using 20 runs for each network. The shallow random forest method performs best with an equitable threat score (ETS) around 0.52, followed by the DL networks ALL-CNN and ResNet with an ETS near 0.48. Their success can be understood as a result of conceptual simplicity and parametric parsimony, which obviously best fits the relatively simple classification task. It is found that, on summer days, CatRaRE-convective atmospheres over Germany occur with a probability of about 0.5. This probability is projected to increase, regardless of method, both in ERA5-reanalyzed and CORDEX-simulated atmospheres: for the historical period we find a centennial increase of about 0.2 and for the future period one of slightly below 0.1.

Regional Climate Simulation Ensembles within CORDEX-EA Framework over the Loess Plateau: Evaluation and Future Projections

As a semi-arid to semi-humid transitional zone, the Loess Plateau is sensitive to climate change due to its fragile ecological environment and geographic features. This study assesses the performance of six historical experiments from the Coordinated Regional Climate Downscaling Experiment (CORDEX) in this region during 1980–2005. In addition, projected future changes in surface air temperature and precipitation are investigated under the representative concentration pathways (RCP) 2.6 and 8.5 during three periods in the 21st century: the early future (2011–2040), middle future (2041–2070), and late future (2071–2099). Results show that experiments reasonably reproduce the spatial pattern of 2m temperature and precipitation for all seasons, yet with a slight warm bias and prominent wet bias. In the future, the area-averaged magnitude of change will be 1.1 °C, 1.4 °C, and 1.4 °C under RCP2.6 and 1.3 °C, 2.7 °C, and 4.5 °C under RCP8.5 for the early, middle, and late periods, respectively. The warming effect is greater in elevated areas. Precipitation change in future periods is more complex, with both increasing and decreasing trends, depending on the season, location, and scenario. The results are expected to provide regional climate information for decision makers and benefit applications such as agriculture, ecological environment protection, and water resource management.

Climate of Syria Based on Cordex Simulations: Present and Future

AbstractRegional climate models are widely used to assess current and future impacts of climate change. In this study, we evaluate the performance of regional climate models from the Coordinated Regional Climate Downscaling Experiment programme integrated over the following three CORDEX domains: AFR, MNA and WAS. Four meteorological variables (temperature, precipitation, solar radiation and cloud cover) were evaluated over Syria at a grid spacing of 0.44°. The performance of five models in simulating the present climate characteristics (1989–2008) is evaluated with respect to the observations: CRU, ERA5 reanalysis and SARA and CLARA satellite data. We find that the mini-ensemble captures well the general spatial patterns and annual cycles of the selected variables. Anotheraim of this study was to assess the expected change of the mentioned four climate variables over Syria under the moderate emission scenario (RCP4.5) and the high emission scenario (RCP8.5) in the near future (2031–2050) and in the far future (2080–2099) with respect to the present climate (1989–2008). The simulations show a decreasing trend in cloud cover (between 6% and 10%) and precipitation (up to 9%) by mid and late century, regardless of the forcing scenarios. The simulations show a pronounced warming over Syria, which is expected to reach 6 °C by the end of the twenty-first century following the high greenhouse gas concentration scenario (RCP8.5). Furthermore, such an increase, combined with a decrease in precipitation, will shift Syria’s climate towards a more arid one.

Coupled high-resolution GCM downscaling framework for projecting dynamics and drivers of ecosystem services in Pearl River Basin, China

The projection of future ecosystem service (ES) dynamics under SSP-RCP scenarios would be beneficial for the development of effective climate mitigation strategies for policymakers. However, current projection frameworks generally neglect the downscaling of high-resolution climate and land use data, leading to uncertainties in the simulation of ESs under the SSP-RCP scenarios. To address this issue, this study proposes an integrated analysis framework to downscale climate and land use data. We first identified areas of high risk in terms of inadequate water-related ESs in the Pearl River Basin (PRB) of China. Subsequently, we analyzed key socio-environmental factors driving the dynamics of water-related ESs under future SSP-RCP scenarios. The results indicate that 1) water-related ESs in the PRB are expected to show an overall increasing trend, 2) the sub-watersheds located in the downstream is at a higher risk of experiencing insufficient supply capacity for water-related ESs, and 3) under the coupling effects of climate change, land use change, and socio-economic development, drastic changes in future land use are expected to have a stronger impact on watershed ESs than the climate (i.e., global warming). Our work provides valuable insights for policymakers and managers regarding future water and land resource planning. The proposed framework can be integrated with other hydrological or land surface models to project the spatial–temporal evolution characteristics of future watershed ESs with finer temporal resolution.

Widespread habitat loss and redistribution of marine top predators in a changing ocean.

The Northwest Atlantic Ocean and Gulf of Mexico are among the fastest warming ocean regions, a trend that is expected to continue through this century with far-reaching implications for marine ecosystems. We examine the distribution of 12 highly migratory top predator species using predictive models and project expected habitat changes using downscaled climate models. Our models predict widespread losses of suitable habitat for most species, concurrent with substantial northward displacement of core habitats >500 km. These changes include up to >70% loss of suitable habitat area for some commercially and ecologically important species. We also identify predicted hot spots of multi-species habitat loss focused offshore of the U.S. Southeast and Mid-Atlantic coasts. For several species, the predicted changes are already underway, which are likely to have substantial impacts on the efficacy of static regulatory frameworks used to manage highly migratory species. The ongoing and projected effects of climate change highlight the urgent need to adaptively and proactively manage dynamic marine ecosystems.

Comparison of machine learning statistical downscaling and regional climate models for temperature, precipitation, wind speed, humidity and radiation over Europe under present conditions

AbstractThere are two main approaches to downscale global climate projections: dynamical and statistical downscaling. Both families have been widely evaluated, but intercomparison studies between the two families are scarce, and usually limited to temperature and precipitation. In this work, we present a comparison between a statistical downscaling model (SDM) based on machine learning and six regional climate models (RCMs) from EURO‐CORDEX, for five variables of interest: temperature, precipitation, wind, humidity and solar radiation under present climatic conditions. The study is conducted at a continental scale over Europe, with a spatial resolution of 0.11° and daily data. Both the SDM and the RCMs are driven by the ERA‐Interim reanalysis, and observations are taken from the gridded dataset E‐OBS. Several aspects have been evaluated: daily series, mean values and extremes, spatial patterns and also temporal aspects. Additionally, a multivariable index (fire weather index) derived from the fundamental variables has been included. The SDM has better scores than the RCMs for all the evaluated metrics with only a few exceptions, mainly related to an underestimation of the variance. After bias correction, both the SDM and the six RCMs present similar results, with no significant differences among them. Results presented here, combined with the low computational expense of SDMs and the limited availability of RCMs over some CORDEX domains, should motivate the consideration of statistical downscaling at the same level as RCMs by official providers of regional information, and its inclusion in reference sites. Nonetheless, further analysis on crucial aspects such as the impact on long‐term trends or the sensitivity of different methods to being driven by global climate models, is needed.

Potential consequences for rising temperature trends in the Oti River Basin, West Africa

IntroductionOne of the ways that climate change manifest itself is through temperature changes. Though the Oti River basin has been grappling with drought incidents, there has been little or no emphasis on analyzing temperature fluctuations in the basin. This study aimed to analyze the mean annual and seasonal temperature for the observed (1981–2010) and future periods (2021–2050) over the Oti River basin.MethodsHistorical data were obtained from meteorological stations and the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER). Data quality assessment was conducted, and the NASA POWER temperature was validated against the stations' temperature. Ensemble of eight models acquired from the Coordinated Regional Climate Downscaling Experiment (CORDEX–Africa) under two Representative Concentration Pathways (RCP4.5 and RCP8.5), were used for the future projection. The mean annual and seasonal temperatures were analyzed for 1981–2010 and 2021–2050 (under RCPs 4.5 and 8.5 scenarios). The Modified Mann–Kendall test was used for trend analysis at 5% significant level.Results and discussionIn the near-future, temperature is anticipated to increase at the mean monthly scale in the ranges of +0.88°C in October to +2.65°C in January under the RCP4.5 scenario, while the RCP8.5 predicts increases between +2.71°C in July and +6.48°C in January. The annual mean temperature change for the entire basin is projected at +1.47°C (RCP4.5) and +4.2°C (RCP8.5). For the rainy season period, the RCP4.5 projects annual mean temperature changes in the ranges of −0.72°C and +1.52°C while the RCP8.5 predicts changes between +1.06°C and +4.45°C. Concerning the dry season period, the anticipated changes in the annual mean temperature under the RCP4.5 would range from −0.43°C to +2.78°C whereas that of RCP8.5 would be between +1.97°C and 7.25°C. The Modified Mann–Kendall test revealed significantly increasing trends for temperature projections in the basin under both the RCPs 4.5 and 8.5 in the basin. The study provides significant contribution to the comprehension of temperature patterns in time and space which is necessary for the sustenance of rainfed agriculture and water resources within the basin.

Producing reliable hydrologic scenarios from raw climate model outputs without resorting to meteorological observations

Abstract. A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. This alternative approach is designed by combining asynchronous hydroclimatic modelling and quantile perturbation applied to streamflow observations. Calibration is run by forcing hydrologic models with raw climate model outputs using an objective function that excludes the day-to-day temporal correlation between simulated and observed hydrographs. The resulting hydrologic scenarios provide useful and reliable information considering that they (1) preserve trends and physical consistency between simulated climate variables, (2) are implemented from a modelling cascade despite observation scarcity, and (3) support the participation of end-users in producing and interpreting climate change impacts on water resources. The proposed modelling workflow is implemented over four sub-catchments of the Chaudière River, Canada, using nine North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) simulations and a pool of lumped conceptual hydrologic models. Results confirm that the proposed workflow produces equivalent projections of the seasonal mean flows in comparison to a conventional hydroclimatic modelling approach. They also highlight the sensibility of the proposed workflow to strong biases affecting raw climate model outputs, frequently causing outlying projections of the hydrologic regime. Inappropriate forcing climate simulations were however successfully identified (and excluded) using the performance of the simulated hydrologic response as a ranking criterion. Results finally suggest that further works should be conducted to confirm the reliability of the proposed workflow to assess the impact of climate change on high- and low-flow events.

Rapid increase of the nighttime electricity demand in Beijing due to compound heatwaves

Under the carbon-neutral development pathway in China, >96% of the total power generation will be provided by clean energy sources by 2060. However, since solar power generation is unavailable at night, it is crucial to investigate the changes in nighttime electricity demand in response to increasing occurrences of tropical nights. This study reveals that nighttime electricity consumption is more sensitive to temperature changes compared to daytime consumption, which has already been extensively studied. The correlation coefficient between nighttime consumption and daily minimum temperature (Tmin) is found to be 0.7. Furthermore, the elasticity of nighttime electricity consumption increases even more when Tmin exceeds 24 °C. The combined effects of heatwaves occurring during both daytime and nighttime can further increase electricity consumption by 40%. By utilizing a high-resolution hybrid downscaling climate projection dataset, we estimate the electricity consumption at the end of the 21st century. The frequency of compound daytime/nighttime heat extremes is projected to increase to 4.2 times the current level under RCP4.5. Consequently, the frequency of large nighttime electricity consumption over 6 kWh per capita will rise by 109%. Our quantitative analysis sheds light on the necessity of energy storage planning to ensure energy security under climate change.

Spatio‐temporal downscaling emulator for regional climate models

AbstractRegional climate models (RCM) describe the mesoscale global atmospheric and oceanic dynamics and serve as dynamical downscaling models. In other words, RCMs use atmospheric and oceanic climate output from general circulation models (GCM) to develop a higher resolution climate output. They are computationally demanding and, depending on the application, require several orders of magnitude of compute time more than statistical climate downscaling. In this article, we describe how to use a spatio‐temporal statistical model with varying coefficients (VC), as a downscaling emulator for a RCM using VC. In order to estimate the proposed model, two options are compared: INLA, and varycoef. We set up a simulation to compare the performance of both methods for building a statistical downscaling emulator for RCM, and then show that the emulator works properly for NARCCAP data. The results show that the model is able to estimate non‐stationary marginal effects, which means that the downscaling output can vary over space. Furthermore, the model has flexibility to estimate the mean of any variable in space and time, and has good prediction results. INLA was the fastest method for all the cases, and the approximation with best accuracy to estimate the different parameters from the model and the posterior distribution of the response variable.