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So-called hot droughts are seen as one of the very foreseeable extremes amid the faster-than-expected pace of global warming. In particular, the western part of North America has been pummeled by severe droughts due to a lack of precipitation as well as record-breaking hot temperatures. This study assesses the joint return period of drought severity and duration using the COordinated Regional Climate Downscaling EXperiment-COmmon Regional Experiment (CORDEX-CORE) simulations over the California domain. Six dynamically downscaled simulations with 25 km resolution are analyzed for the historical (1956–2005) and future (2050–2099) periods, whose warming sensitivities differ based on three global climate models (GCMs) driving two regional climate models (RCMs). Our focus is on estimating the joint probabilities of the drought duration and severity constructed from multiple drought indices such as the Palmer Drought Severity Index (PDSI) (with original temperature (PDSI) and its detrended counterpart (PDSI_detrended)) and Standardized Precipitation Index (SPI). Under the RCP8.5 scenario, an unprecedented level of droughts in terms of both duration and severity is likely to emerge only when the drought is characterized by PDSI. A comparison of PDSI and PDSI_detrended explicitly reveals that the occurrence of severe and prolonged droughts is mainly attributed to the warming trend of temperature. Both PDSI_detrended and SPI, which do not incorporate the warming effect explicitly, barely differentiate the joint distributions from the historical and future simulations. Unlike the PDSI based on water budget, the SPI, based on solely accumulated precipitation, shows a loosely coupled joint structure between the severity and duration of droughts, with marked differences in their marginal distributions.

This dataset contains the codes for machine learning and Shapley value analysis over global land and two single spots (Amazona and Southern New South Wales). We have customized the Shapley Value visualization tools based on the original package, so please update the _beeswarm.py and _scatter.py codes in the package when running our codes. (2024-03-14)

This dataset contains the calculation codes of FFDI and FWI. (2024)

Accelerated global warming is expected to increase the severity and frequency of concurrent extremes, which can have negative impacts that often extend beyond those of a single event. Southeastern China has witnessed an increased occurrence of concurrent hot and dry extremes in recent decades, and the anticipated amplification of such events is likely to aggravate economic damages and endanger human welfare. Although the significant impacts of these concurrent extremes have spurred strong demand for reliable future projections at local levels, most studies have primarily focused on univariate analysis of single extremes using coarse-grid global climate model (GCM) projections. Such an approach could potentially overlook the region-specific climate impacts of global warming. In this regard, the present study will integrate convection-permitting (CP) regional climate modeling and multivariate statistical analysis to assess the future changes in concurrent hot and dry extremes. The Weather Research and Forecasting (WRF) model will be used to downscale the bias-corrected CMIP6 GCM projections under the SSP5-8.5 scenario at the CP scales (4 km) over southeastern China. The focus is given to investigating the process-based added value of CP projections when assessing future changes in concurrent hot and dry extremes over the densely populated China regions. The high-resolution simulation is expected to greatly advance the understanding of compound climate extremes and provide quantified insights about prospective climate risks in China.

The fifth version of the regional climate model (RegCM5) has recently been released, incorporating updates in several model components such as the dynamic core and physical parameterizations. Traditionally, sensitivity tests based on random selection have been employed to identify the optimal sets from various combinations of model dynamics and physics. However, this approach is largely limited by computing power, often failing to explore the complete range of possible combinations necessary for an accurate representation of the regional climate. To overcome these limitations, advanced optimization techniques have emerged to efficiently explore the complete range of possible combinations, without relying solely on random-based sensitivity tests. In this study, we employ a micro-genetic algorithm (micro-GA) for combinatorial optimization of the dynamic cores, cumulus parameterizations, and microphysics parameterizations in RegCM5. The model domains consist of one 20km mother domain covering the majority of East Asia, and two 2.5km nested domains covering the Yangtze River Delta (YRD) and Pearl River Delta (PRD), two densely populated regions in China. The focus is on conducting comparative assessments of simulated precipitation and temperature patterns in Southeastern China based on a series of experiments using the coupled RegCM5-micro-GA interface. The findings from this study will provide valuable insights to facilitate the wider use of RegCM5 by customizing its performance over the target regions.

Irrigation is a common anthropogenic activity that changes land use and land cover, which leads to considerable alteration in land-atmosphere interaction and hydrological cycle. As one of the most intensively irrigated regions in the world, agriculture in the North China Plain (NCP) heavily depends on groundwater due to its deficit precipitation and scarce water. Irrigation is already claimed to have a notable impact on the regional climate in the NCP. Moreover, groundwater-dependent irrigation can reduce water storage, but it can also be affected by groundwater exploitation because the declining soil water may exaggerate the irrigation, forming a positive feedback loop. The groundwater depletion is clearly identified from the observation and satellite data, but it is not well implemented with irrigation processes in the -climate models. For example, the current Noah-MP in Weather Research and Forecasting (WRF) model cannot represent the potential feedback loop between groundwater storage and irrigation usage, since the groundwater module is not directly connected with the irrigation scheme. Also, the crop module is not compatible with the dynamic vegetation scheme for non-crop plants. Thus, the current land scheme is inapplicable to a large-extent agricultural area that contains both cropland and non-cropland, such as the NCP. This study aims to simulate groundwater-fed irrigation in the NCP and its surrounding area in eastern China by integrating the existing vegetation, crop, irrigation, and groundwater modules in Noah-MP. The modified model should be able to (1) generate the seasonal dynamic patterns of crop growth under different soil conditions, (2) calculate the irrigation amount based on varied soil moisture and crop growth, and (3) simulate the soil-groundwater interaction considering the irrigation-induced extraction. The results will – help us better comprehend the interactions between the atmosphere, land surface, and subsurface that can modulate climate

Available data includes: PDSI_[hist/rcp85]_[GCM/RCM/OBS] SPI_[hist/rcp85]_[GCM/RCM/OBS] PDSI_[hist/rcp85]_[RCM only]_detrended (2023-11-24)

Severe groundwater depletion due to intensive irrigation has begun to emerge and has been clearly detected by satellite-based estimates in major agricultural areas. The effects of groundwater overexploitation are not restricted locally, but an incursion of dormant groundwater into the active hydrological cycle can critically affect the hydro-climatology of remote places and the global water budget, which eventually leads to terrestrial contributions to rising sea levels. This study explores how much groundwater depletion as a result of intensive irrigation will contribute to accelerating global sea level rise. For this, the long-term simulations with/without groundwater pumping are performed using the Community Earth System Model Version 2 (CESM2). The groundwater-fed irrigation is activated in the global and entire China scale to discern different responses to the locations of the irrigation forcing. In particular, the quantification of the contributions of groundwater pumping to sea level changes will be focused, which will strengthen our understanding of the nonlinear climate processes that occur when the atmosphere, land surface, and subsurface are perturbed by groundwater pumping. [Acknowledgements] This research was supported by project GRF16309719, which was funded by the Research Grants Council (RGC) of Hong Kong.

Statistical bias correction (BC) is a widely used tool topost-process climate model biases in heat-stress impact studies, which areoften based on the indices calculated from multiple dependent variables.This study compares four BC methods (three univariate and one multivariate)with two correction strategies (direct and indirect) for adjusting twoheat-stress indices with different dependencies on temperature and relativehumidity using multiple regional climate model simulations over SouthKorea. It would be helpful for reducing the ambiguity involved in thepractical application of BC for climate modeling and end-user communities.Our results demonstrate that the multivariate approach can improve thecorrected inter-variable dependence, which benefits the indirect correctionof heat-stress indices depending on the adjustment of individual components,especially those indices relying equally on multiple drivers. On the otherhand, the direct correction of multivariate indices using the quantile deltamapping univariate approach can also produce a comparable performance in thecorrected heat-stress indices. However, our results also indicate thatattention should be paid to the non-stationarity of bias brought by climatesensitivity in the modeled data, which may affect the bias-corrected resultsunsystematically. Careful interpretation of the correction process isrequired for an accurate heat-stress impact assessment.

This study evaluates the performance of the non-hydrostatic RegCM4 model (RegCM4-NH) incorporating the building energy model (BEM) in simulating urban climate over the Pearl River Delta (PRD) and Yangtze River Delta (YRD) regions. Specifically, it focuses on examining how the inclusion of BEM can ameliorate the severe warm bias in urban grids observed in high-resolution RegCM4 simulations by improving the surface energy balance. The prognostic calculation of interior building temperature and the consideration of ventilation and heat transfer between the building and the environment can collectively lead to a better estimate of anthropogenic heat flux (AHF). The effect of BEM does not appear to be uniform, showing spatial and temporal variations. While the magnitude of AHF reduction is roughly proportional to the urban density, the maximum reduction occurs at nighttime, which results in an asymmetric response to the minimum and maximum temperatures. Although the degree of lowering temperature is not sufficient to remove the systematic warm bias, adding physical realism to the model is helpful to make further improvements. The performance evaluation of RegCM4-NH with BEM targeted in vast urban agglomerations for the first time will be a valuable reference to facilitate the wider use of RegCM4-NH in the study of urban surface impacts.

As a major anthropogenic activity of changing land use and land cover, irrigation is considered to have a notable impact on the interaction between land and the atmosphere, and further alter the hydroclimate. Due to the rapid expansion of irrigated farmland, the North China Plain (NCP) has already become one of the most intensified agricultural regions in the world. The massive application of irrigation fulfills the explosive agricultural demands, but it also rises public concerns about the potential climate changes that it may bring to the NCP and its surrounding area. The scarce water resources in the NCP not only increase the climate sensitivities to hydrological variations, but also enhance the dependence on groundwater irrigation, which may aggravate the interference on the local climate. This study aims to investigate the regional irrigation impact on the NCP and its surrounding area, especially how precipitation reacts during the irrigated season. We conduct 20-year comparable experiments with and without irrigation using two state-of-art regional climate models (RCMs), WRF and RegCM4. According to our results, the most intensive irrigation happens in late spring or early summer, since precipitation supply is trivial while the water demand is expansive. The induced irrigation water facilitates evaporation and decreases the surface temperature. In May, the surface cooling produces anticyclonic wind differences in the low and midlevel atmosphere, which inhibits convection and stabilizes the atmosphere. However, the cooling stabilization is overwhelmed by the moistening effect over the NCP region. Convective available potential energy (CAPE) escalates greater than convective inhibition (CIN) and the increase in latent heat substantially lifts the total moisture static energy (MSE), which frequently triggers convective rainfall and in turn brings significant increases in the NCP precipitation in May. This study proposes a moistening-dominant irrigation impact in

Anthropogenic land-use change, irrigation, is considered to strongly modulate the hydroclimate at the regional scale by directly triggering evaporative cooling as the preliminary local effect. However, subsequent interactions with the background climate are highly nonlinear, which introduces diverse and unexpected consequences. The North China Plain (NCP) is one of the regions where irrigation has expanded most rapidly since the 20th century. The scarce rainfall in this region makes it necessary for irrigation to supplement the level of soil water for agricultural production. In this study, we quantify the effect of irrigation on the regional climate in China. Two regional climate models, WRF and RegCM, are used to mimic the large-scale practice of irrigation on the NCP. The results of our experiments show consistent cooling and moistening effects centered over the NCP across all experiments. Although the moisture budget and wind field pattern demonstrate that the vertical downdraft and low-level divergence could inhibit rainfall, the humidification dominates the climatic response in the dry April-May-June and increases the amount of precipitation significantly and consistently in the NCP region and the surrounding area in northern China. The enhanced CAPE increase sharply on some ‘calm days’ when the vertical moisture advection is small, especially during afternoon, triggering frequent light rains convectively by destabilizing the atmosphere. The consistent response to irrigation in two different models that employ structurally different land surface schemes could enhance the robustness of the physical mechanism behind the precipitation increase in the heavily irrigated region of NCP.

Abstract When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA Soil Moisture Active Passive satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985–2005 and 2040–2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts in 2015 and 2019. Based on previous studies, such reductions may be expected to accelerate peat carbon emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands.

While the popularity and feasibility of solar power have increased toward achieving a low-carbon and climate-resilient society, it is uncertain how changes in climate attributes will affect the future potential of solar power output. This study presents a comparative assessment of future changes in solar power in terms of the technology for harnessing energy from insolation (PVP vs CSP), climate projections (CMIP5 vs CMIP6), and emission scenarios. Both CMIP5 and CMIP6 multi-model projections capture the major characteristics of the global distribution seen in PVP and CSP calculated using the reanalysis data during the historical period. However, despite the general similarity to CMIP5-based results, CMIP6 models slightly outperform their CMIP5 counterparts regarding quantitative metrics and enhance the robustness of the future change signal estimated by the statistical significance and inter-model consistency. The future changes in PVP and CSP patterns are sensitive to the emission scenarios that can control the degree of warming. Under the fossil-fueled development scenarios, the greater increase in temperatures may lead to a high vulnerability of the solar power supply by reducing the output of both PVP and CSP. This study is timely and relevant to emphasizing the benefits of climate change mitigation, which can support the sustainable development of solar energy.

This study assesses the future heat-related mortality risk under varying levels of warming specified by the RCP2.6 and RCP8.5 scenarios using dynamically downscaled ensemble projections across six different domains. The excess mortality risk due to heat is estimated by the empirical relationship between daily maximum temperature (Tmax) and mortality. The changes in heat-related mortality based on three empirical formulas derived from different countries' data are compared to examine the sensitivity of change patterns to the empirical formula. The ensemble projections reveal a drastic increase in heat-related mortality risk under the RCP8.5 scenario. However, a significant reduction is expected by limiting greenhouse gas emissions to the RCP2.6 level. While mitigation's possible benefit is clearly exemplified by comparing the mortality risk derived from RCP2.6 and RCP8.5 projections, this study also provides valuable insights into regional hotspots by comparing the results from multi-domains. Regardless of the emission scenario (RCP2.6 vs. RCP8.5) and empirical formulas that represent the relationship between temperature and mortality, the most vulnerable regions to heat-related mortality risk are identified in the low-latitude near the equator where the adaptation capacities to avoid serious consequences are found to be poor. The higher risk of heat-related mortality in the future is largely attributable to a significant increase in frequency exceeding the optimum temperature where the mortality risk is minimum during the historical period.

How the value of higher-resolution climate variables dynamically downscaled can affect the hydrological impact assessment has been a long standing issue. This study investigates the potential benefit of high-resolution climate data locally tailored over South Korea in terms of the reduction of uncertainties in hydrological projections. For this purpose, a large ensemble consisting of three Global Climate Model (GCM) projections and their dynamical downscaling products in different resolutions (i.e., 20 and 5 km), and four bias correction (BC) methods is fed into a semi-distributed hydrological model (HM) customized over Korean river basins. The in-depth comparison among the 45-members hydrological simulations proves the benefit in using high-resolution Regional Climate Model (RCM) for the runoff projections. While this study acknowledges the necessity of BC to remove the systematic bias in climate simulations, it is found that the high-resolution dynamical downscaling can significantly narrow the spread brought with different BC methods, thus reducing the uncertainty in the projected hydrological change. The projected runoff changes for both the mean of wet season and the high flows indicate that there will be an intensified runoff, especially for the extremes, over South Korea under the warming. Altogether, this study provides a valuable exploration of uncertainty reduction in hydrological projections from the perspective of resolution effect of dynamical downscaling, which is meaningful for hydroclimate studies and climate change impact assessment.

An increase in heat-related mortality risk has emerged to accompany the ravages of climate change, but its unambiguous assessment remains an onerous task, owing to the non-linear associations between the severity of hot temperatures and human body response. The present study assesses the future heat-related mortality risk under different levels of warming (1.5 degrees C vs. 2 degrees C) using the multi-models' large ensemble simulations. In order to augment the robustness of the patterns for future changes in heat-related mortality risk, multiple indices representing the excess mortality risk solely attributed to higher temperature are estimated from different meteorological variables (maximum temperature, maximum wet-bulb temperature and mean temperature). The ensemble projections reveal a worldwide surge in heat-related mortality risk, albeit with a regionally diverse pattern. Although comparisons of the different indices show some quantitative differences, they provide remarkably consistent regional hotspots, thus amplifying the possible benefit of a mitigation equivalent to 0.5 degrees C less warming in the equatorial region. In addition to the severity of hot temperatures, the demographic changes evolving along the different shared socio-economic pathways also determine the exposure to heat-related mortality risk. Based on multiple indices and large ensemble simulations, this study contributes to the identification of regional hotspots in terms of the exposure of (the elderly) population to heat-related mortality risk, underscoring the necessity of regionally-tailored adaptation strategies.

Near-surface temperature and relative humidity data over South Korea land (125.25-130.75E, 33.25-38.5N) in June-July-August, 1979-2014. The data is extracted from the CORDEX-East Asia domain climate projections. It is the downscaling product of UKESM1-0-LL (historical, r1i1p1,CMIP6) using CCLM, GRIMs, RegCM, WRF and HG3RA, respectively.

As one of the major anthropogenic ways of changing land-use, irrigation is considered to strongly modulates regional hydroclimate. Irrigation directly triggers surface colling and rising evapotranspiration, which has been considered as the preliminary local effect. But the subsequent interactions with the background climate are highly nonlinear, introducing diverse and unexpected consequences. North China Plain (NCP) is one of the regions with the most rapidly expanded irrigation since the twentieth century. The scarce rainfall in northern China gives rise to the necessity for irrigation to supplement the soil water. In this study, we quantify the effect of irrigation on the regional climate in China. We adopt two state-to-art regional climate models, WRF4.3 and RegCM4.7, to mimic the large-scale irrigation practiced in NCP. Several sensitivity tests with different irrigation forcings are also conducted to verify the relationship between irrigation and regional climate. Our experiments conclude a consistent cooling repression and moistening effect centered at NCP all year round from all experiments. Although the moisture budget and wind field pattern visualize the vertical downdraft and low-level divergence which could inhibit rainfall, the humidifying particularly dominant the climate responses in the dry May and increases precipitation significantly and consistently in northern China including NCP. The lifting MSE and CAPE imply that additional water frequently triggers light rains convectively by destabilizing the atmosphere. Our relative niche results may build a foundation for future analysis of less model-dependent and more systematic irrigation impacts that incorporate more anthropogenic activities such as groundwater depletion.

Limited studies attempted to incorporate the dynamic urban growth into the regional modeling system over intensely urbanized city agglomerations in China. The Weather Research and Forecasting (WRF version 4.2) coupled with the single-layer urban canopy model (SLUCM) simulates the long-term climate impacts of LULC change in China's two fast-growing delta regions. The physical parameterizations in the PRD and YRD are first investigated in a high-resolution (4km) downscaled modeling. Then, the sensitivity experiments and new classified urban data are tested with fine-tuned building parameters and anthropogenic heat input in the SLUCM. The second part of the paper focuses on the impact of transient urbanization on the current and future climate. The most significant response to urbanization impacts comes from the SSP5 in the 2030s. The climatic interactions of different LULC in the nested domains are distinct that the PRD has warming and lower relative humidity on a local urban scale. In contrast, the YRD has broader feedback on a regional scale. Also, the urban effect on diurnal temperature varied significantly based on the forms and location of urban areas. The urbanization-induced warming mainly happened at night in the newly developed urban areas in the PRD, while such warming occurs in the late afternoon in the YRD. The local warming trend induced by urban expansion has already caused heat stress in these highly populated regions. Driven by the NorESM RCP8.5 scenario and the corresponding SSP5 urban growth data, the synergic effects of urban development and global warming are further quantified. Global warming is contributing more climatic impacts than LULC change, while the urban landcover can have an amplified effect on the local climate. The PRD and the YRD will experience dangerous heat stress under the synergic effects, with the risks of extreme in YRD developed more.

Crop productivity is largely affected by meteorological conditions, and therefore, accurate weather forecasts are very important in crop yield prediction. Especially, the ability to predict long-term weather conditions for range has a significant influence on the estimation of the yields. In South Korea, short-term detailed forecasts are only available up to 10 days, and the forecast beyond that time period is delivered in the form of brief information about how the temperature and precipitation will be compared to the normal year. However, this weather “outlook” cannot give practical information for farmers nor for decision makers and policy makers. With the increasing needs for the subseasonal-to-seasonal (S2S) and seasonal prediction, many research efforts have been recently made to improve its capability through various methods such as operational models and machine learning techniques. Dynamical downscaling is one of the methods to improve the accuracy of seasonal forecasts by allowing very high-resolution climate simulations with much realistic surface boundary conditions. The added value of dynamical downscaling in the simulation of weather events and climatology has been widely researched and well appreciated, but its added value in terms of the application of the downscaled results has not yet been intensively evaluated. In this regard, in this dissertation, the original GCM data and its dynamically downscaled simulations over South Korea are used to develop rice yield prediction models, and their results are compared. It is shown that the downscaled results improve the performance of the prediction models, which demonstrates that dynamical downscaling has the potential to enhance the value of meteorological information by benefitting the end-users in the agricultural field.

Both greenhouse gas (GHG) emissions and urban expansion are regarded as important contributors to accelerating warming trends. Southeastern China, where Pearl River Delta (PRD) and Yangtze River Delta (YRD) are nestled, is a representative region with mounting concerns related to anthropogenic warming and the rapid pace of urbanization. Since these places have already experienced thermal discomfort at dangerous levels, it is not farfetched to infer that further warming will increase the heat stress close to the lethal level of human adaptability. However, most climate projections are yet to adequately consider future urban growth and their synergistic effects under global warming, leading to a potential underestimation of the risk of heat-related extremes. This study adopts the latest version of the Regional Climate Model (RegCM) for dynamical downscaling to better resolve geographically diverse climate features and then address the aforementioned limitations. Its performance has been improved by optimizing the configurations over target domains and incorporating a new urban module. First, the double-nested modeling system is developed focusing on the PRD and YRD regions, and its capability to capture region-specific climate characteristics is intensively evaluated in order to assess the effects of convection-permitting and non-hydrostatic dynamical core. Then, the land surface parameterization in RegCM is improved by incorporating a building energy model to prognostically account for the interior building temperatures, thereby enhancing the estimation of released anthropogenic heat fluxes into the urban canopy. Finally, the multiple global climate projections forced by both medium as well as high levels emission scenarios (i.e., RCP4.5 and RCP8.5) are dynamically downscaled using RegCM double-nested system with the improved urban parameterization by prescribing future urban expansion generated from different Shared Socio-Economic Pathways (i.e., SSP2 and SSP5). The