Search

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.