Simulated Precipitation Research Articles

Effects of urban areas on the diurnal cycle of temperature and precipitation in a global climate simulation

AbstractAnalyses of global climate model results for urban impacts on temperature and precipitation are rare. Previous analyses of the global Conformal Cubic Atmospheric Model simulation results for 1985–2010 have revealed urban effects on minimum and maximum temperatures. Using the same dataset to derive time‐zone‐corrected three‐hourly local time period (LTP) data, averaged diurnal cycles of temperature and precipitation were calculated for grid cells with greater than 10% urban fraction (urban grid cells) globally. Three latitudinal bands were assessed: northern extratropics (NET, 274 urban grid cells), southern extratropics (SET, 39 urban grid cells), and the Tropics (26 urban grid cells). The largest statistically significant urban influences on temperature are consistently found at night, in agreement with many previous studies on urban heat islands. Signs of urban cooling were found for a few hours, from 09:00 to 15:00 LTP, most often in summer. Influences of urban areas on precipitation varied, with small increases and decreases in all latitudinal bands and seasons. For NET, increases were generally found. In the Tropics, increases were found from 21:00 to 09:00 LTP for all seasons except DJF, with decreases for all seasons from 15:00 to 18:00 LTP. In SET, all seasons had increases for 21:00 to 00:00 LTP and decreases for 15:00 to 18:00 LTP. DJF had decreases for all LTPs except 21:00 to 00:00 LTP and SON had increases for all times except 15:00 to 18:00 LTP. Differences in rainfall in the region surrounding the urban areas were broadly similar to local changes in NET. For the Tropics and SET, regional decreases were found for DJF and JJA, with a more varied pattern for other months. Regional effects appeared to be more restricted to near‐urban areas in the Tropics than in NET. The results indicate some influence of nearby urban areas on regional temperature and precipitation.

Cloud droplet size distributions governed by condensation, collision–coalescence, and sedimentation. Part 1: Monte Carlo simulations and scaling relations for growth in a convection–cloud chamber

Abstract Atmospheric clouds are crucial to weather and climate, and the rate at which droplets collide and coalesce to form precipitation is one of the fundamental controlling processes. A convection-cloud chamber allows the interactions between aerosols and cloud droplets produced by condensation to be investigated within a turbulent environment. Studying the full range of microphysical conditions in atmospheric clouds is not possible, however, unless conditions for droplet growth by collision and coalescence are also achieved. In this study, we explore the conditions favorable to collision-coalescence growth in convection-cloud chambers, extending previous work on steady-state droplet size distributions due to condensation only to obtain analytic expressions for number concentration and supersaturation as a function of small-droplet injection rate, and for collision-coalescence rates. We derive several scaling laws and demonstrate consistency between these theoretical results and Monte-Carlo simulations of growth and precipitation within a convection-cloud chamber. Specifically, the coalescence rate is shown to scale as the square of the liquid water content, which in turn can be modulated in a convection-cloud chamber by varying the small-droplet injection rate as well as the applied temperature difference.

Reducing the Uncertainty in the Tropical Precipitation through a Multi‐Criteria Decision‐Making Approach

ABSTRACTThe inherent model uncertainty in precipitation projections is found to be more dominant over tropical regions thereby reducing the reliability of using them in climate change impact assessment studies. To address such issues, a subset of well performing global climate models (GCMs) can provide narrow range of possible future outcomes, which can be helpful in formulating mitigation and adaptation strategies that are more targeted and efficient. In this study, climate models are selected based on their performance in simulating relative humidity and vertical velocity since these variables play an important role in precipitation simulation and significantly contribute toward the intermodel spread. The models are evaluated by using various statistical performance measures and ranked using multi‐criteria decision‐making approaches. Finally, based on Jenks natural breaks optimization algorithm, subset of GCMs consisting of ACCESS1.0, ACCESS1.3 and INM‐CM4 models, are considered as the best possible subset for precipitation simulation over tropical land regions. Two observational precipitation datasets are further considered to investigate the effectiveness of the proposed framework. The proposed methodology is validated to be effective in identifying the best climate models since the resulting subset is capable of both capturing observed precipitation and minimizing the uncertainty in future projections. Hence, this methodology can be utilized further for performance evaluation of GCMs focusing different geography and climatic drivers.

Local and Remote Effects of the Sub‐Grid Turbulent Orographic Form Drag on the Summer Monsoon Precipitation Over Eastern China

AbstractThe sub‐grid turbulent orographic form drag (STOFD) significantly affects the regional circulation and precipitation. This study explores the local and remote effects of the STOFD on the summer monsoon precipitation across Eastern China using the Regional Climate Model Version 4 adopting a STOFD scheme. Results indicate that the local and remote effects of the STOFD primarily influence the improvement of summer precipitation simulation in the Southeastern and Northern China, respectively. The local effects of the STOFD can lead to 37.1% and 10.7% reduction of the absolute error and the root mean square error (RMSE) of simulated summer precipitation in the Southeastern China with complex sub‐grid terrains. The remote effects of the STOFD within the Indochina Peninsula and Yunnan‐Guizhou Plateau result in the absolute error and RMSE of simulated summer precipitation in the Northern China with mild sub‐grid terrain decreased by 90.1% and 32.9%, respectively. Moreover, the remote effects of the STOFD within the Tianshan Mountains and Tibetan Plateau can clearly improve the simulated precipitation in both the Southeastern and Northern China. The disturbances generated by the local effects of the STOFD are more locally concentrated than those produced by the STOFD remote effects, leading to a more significant improvement of precipitation simulation in the Southeastern China. While the disturbances resulted from the remote effects of the STOFD affect the summer precipitation in both the Southeastern and Northern China obviously. This study highlights the significance of the remote effects of the STOFD in improving the summer precipitation simulation in Eastern China.

Kilometer-Scale Precipitation Forecasting Utilizing Convolutional Neural Networks: A Case Study of Jiangsu’s Coastal Regions

High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, are examined in this study for their downscaling capabilities in precipitation simulation. During a precipitation event on 23 June 2022, in Jiangsu Province, China, distinct rain belts emerged in both southern and northern Jiangsu, precisely captured by a numerical model (the Weather Research and Forecasting, WRF) with a 3 km spatial resolution. Specifically, precipitation was prevalent in northern Jiangsu from 00:00 to 11:00 Beijing Time (BJT), transitioning to southern Jiangsu from 12:00 to 23:00 BJT on the same day. Upon dynamic downscaling, the model reproduced precipitation in these periods with an average error of 12.35 mm at 3 km and 12.48 mm at 1 km spatial resolutions. Employing CNN technology for statistical downscaling to a 1 km spatial resolution, samples from the initial period were utilized for training, while those from the subsequent period served for validation. Following dynamic downscaling, CNNs with four, five, six, and seven layers exhibited average errors of 8.86 mm, 8.93 mm, 9.71 mm, and 9.70 mm, respectively, accompanied by correlation coefficients of 0.550, 0.570, 0.574, and 0.578, respectively. This analysis indicates that for this precipitation event, a shallower CNN depth yields a lower average error and correlation coefficient, whereas a deeper architecture enhances the correlation coefficient. By employing deep network architectures, CNNs are capable of capturing nonlinear patterns and subtle local features from complex meteorological data, thereby providing more accurate predictions during the downscaling process. Leveraging faster computation and reduced storage requirements, machine learning has demonstrated immense potential in high-resolution forecasting research. There is significant scope for advancing technologies that integrate numerical models with machine learning to achieve higher-resolution numerical forecasts.

Deep Learning‐Based Precipitation Simulation for Tropical Cyclones, Mesoscale Convective Systems, and Atmospheric Rivers in East Asia

AbstractDifferent types of weather events, including tropical cyclones (TCs), mesoscale convective systems (MCSs), and atmospheric rivers (ARs), significantly impact precipitation patterns in East Asia. This study pioneers the application of deep learning (DL) methods, including convolutional neural network, U‐Net, and Attention U‐Net models, to simulate precipitation associated with these weather events. The spatial permutation method is also used to identify key meteorological variables for accurately generating precipitation in DL models. The DL models trained on all timeslots consistently surpass the performance of state‐of‐the‐art numerical simulations, although their efficacy slightly diminishes during extreme weather events. This outperformance is attributed to the appropriate emphasis on key variables that capture precipitation processes, such as low‐level moisture and mid‐level pressure fields. However, new DL models trained separately for TCs, MCSs, and ARs using clipped precipitation as the output does not exceed the performance of the previous DL models. Among all input features, moisture variables contribute the most to precipitation at low intensity, while the importance of other variables increases for more intense precipitation, although some discrepancies vary across models and event types. The spatial results further reveal the detailed locations of variables that are essential for accurately simulating precipitation related to weather events, such as areas of high specific humidity and strong winds. DL models could also acquire useful information from region remote to the events to improve the simulation. Overall, DL models serve as promising tools for simulating and enhancing our understanding of precipitation patterns associated with various weather events in East Asia.

Comparison of the Morrison and WDM6 Microphysics Schemes in the WRF Model for a Convective Precipitation Event in Guangdong, China, Through the Analysis of Polarimetric Radar Data

Numerical weather prediction (NWP) models are indispensable for studying severe convective weather events. Research demonstrates that the outcomes of convective precipitation simulations are profoundly influenced by the choice between single or double-moment schemes for ice precipitation particles and the categorization of rimed ice. The advancement of dual-polarization radar has enriched the comparative validation of these simulations. This study simulated a convective event in Guangdong, China, from May 7 to 8, 2017, employing two bulk microphysical schemes (Morrison and WDM6) in the WRF v4.2 model. Each scheme was divided into two versions: one representing rimed ice particles as graupel (Mor_G, WDM6_G) and the other as hail (Mor_H, WDM6_H). The simulation results indicated negligible differences between the rimed ice set as graupel or hail particles, for both schemes. However, the Morrison schemes (Mor_G, Mor_H) depicted a more accurate raindrop size distribution below the 0 °C height level. A further analysis suggested that disparities between the Morrison and WDM6 schemes could be attributed to the intercept parameter (N0) setting for snow and graupel/hail in WDM6 scheme. The prescribed snow and graupel/hail N0 of WDM6 scheme might influence the melting processes, leading to a higher number concentration but a reduced mass-weighted diameter of raindrops. Reducing the intercept parameter for snow and graupel/hail in the WDM6 scheme could potentially enhance the simulation of convective precipitation. Conversely, the increase in N0 might deteriorate the precipitation simulation performance of the WDM6_G scheme, whereas the WDM6_H scheme exhibits minimal sensitivity to such changes.

Evaluation and projection of changes in temperature and precipitation over Northwest China based on CMIP6 models

AbstractNorthwest China is much more sensitive to climate warming, and the climate has varied rapidly from warm and drought to warm and humid conditions. In addition, due to the complex terrain of Northwest China, the methods and parameterization schemes of different CMIP6 models, these models are mostly applied to arid areas in Northwest China or Central Asia, lacking climate data for plateau areas and eastern Lanzhou, specifically in filtering CMIP6 models and evaluating applicable models. In this paper, 34 CMIP6 climate models are used to evaluate and forecast future trends in Northwest China under the SSP126, SSP245 and SSP585 scenarios in the short, medium and long term. CMIP6 models of temperature and precipitation are identified by applying the interannual variability skill score (IVS) between CN05.1 datasets and historical CMIP6 models, which are suitable for Northwest China. Then, we assess the characteristics, warming and wetting deviations, and uncertainties in the prediction of climatic change according to CMIP6 models over Northwest China. The results show that CMIP6 models in precipitation and temperature applicable to Northwest China are AWI‐CM‐1‐1‐MR, BCC‐CSM2‐MR, FGOALS‐g3, INM‐CM4‐8, INM‐CM5‐0 and MRI‐ESM2‐0. The multi‐model ensemble mean (MMEM) has better capability than individual CMIP6 models in precipitation and temperature prediction. Spatiotemporal climatic change over Northwest China shows overall warming and wetting trends. The IVS provides the ability to estimate CMIP6 model simulation performance both temporally and spatially. The temperature simulation is quite good in the Tarim Basin and Hexi Corridor region, and the precipitation simulation is quite good in the plateau region, Altai Mountains, Tianshan Mountains and Hexi Corridor region. Cold and wet deviations occur in Northwest China due to the topography and few stations, which are common reasons. The main sources of uncertainties in temperature prediction during this century are model uncertainty (before the 2090s) and scenario variability (after the 2090s), and model uncertainty in precipitation for CMIP6 becomes the main source of uncertainty.

Model sensitivity in predicting extreme precipitation events in urban areas: A case study over Beijing

Understanding and forecasting the spatial and temporal distributions of extreme precipitation over urban areas is crucial for effective planning and mitigation efforts. However, this task remains challenging as accurate forecasting depends on properly representing urban surfacees and their interactions with the planetary boundary layer (PBL). We examined the hindcast of an extreme precipitation event over Beijing on 21–22 July 2012. The primary focus was assessing its sensitivity to two widely used PBL parameterizations (MYJ and YSU), two urban parameterizations (SLUCM and BEP_BEM), and two different land-use and land-cover (LULC) datasets.Sensitivity experiments were initialized at different times to explore the model dependence on initial conditions. The analyses were conducted over three selected regions: the entire model domain covering the Beijing metropolitan area, an upwind region of Beijing, and the entire urban area of Beijing. The results show that the MYJ PBL scheme performs better than the YSU PBL scheme in capturing near-surface air temperature as well as the location and timing of the heaviest precipitation. The variability in simulated precipitation among the chosen PBL schemes is lower compared to that among different time of initializations. The LULC impacted the spatial distribution of precipitation but its effect on the amount of precipitation was minimal. Overall, using a combination of the MYJ PBL scheme, SLUCM urban parameterization, and locally-enhanced Beijing LULC, and initializing the model simulations at 0000 UTC July 20, 2012, demonstrated superior performance in capturing precipitation levels, despite some spatial discrepancies in the precipitation distribution. The performance of BEP_BEM urban parameterization is similar to SLUCM across various factors such as average rain rate, maximum rain rate, and rain volume. These findings offer valuable insights towards better simulations of extreme precipitation and flooding in rapidly urbanizing areas such as Beijing.

Thermodynamic simulation of stepwise precipitation of NH4VO3 and NaHCO3 from carbonating Na3VO4 solution

Thermodynamic simulation of stepwise precipitation of NH4VO3 and NaHCO3 from carbonating Na3VO4 solution

Considerations in designing climate change assessments for complex, non-linear hydrological systems

Bias correction of climate model simulations is vital to allow climate change impacts to be assessed for water resources systems. However, there has been limited research to date on the implications of system non-linearity on bias correction approaches. Here we bias correct regional climate model simulations of precipitation and evapotranspiration and use the output to force hydrological and water balance models of five small, interconnected lakes located south west of Sydney. We show that substantial, non-linear storage within the lakes amplifies biases that are not evident when the climate forcing or even the hydrological model simulations are evaluated using daily distributions of the climate variables and streamflow.The non-linearity in the stage-storage relationships of the lakes means that each lake responds differently to the same climate forcings. For example, ensemble mean projections for one lake suggest increases in water level across the full distribution of lake levels, whilst other lakes are projected to have decreasing water levels up to the median of the distribution, but increases during wetter conditions. These differences are explained by the varying influence of potential evapotranspiration increases depending on the surface area of the lakes at different depths. Using bottom up climate change assessments, we further explore these non-linear responses of the lakes to different climate forcings. We show that bottom up climate change assessments can provide information on the relative role of potential evapotranspiration changes compared to precipitation changes, providing more guidance to ecosystem managers than just using bias corrected climate model simulations alone. The paper discusses opportunities for future work to improve representation of climate attributes important for storage dominated water resource and natural ecosystems

Characteristics of antimony mineralization in the Yangla polymetallic deposit, northwestern Yunnan, SW China: Insights from calcite Sm-Nd dating and C-O-Sr isotopes

Calcite is the main gangue mineral in antimony (Sb) deposits, and its compositions can reflect the physicochemical conditions of Sb mineralization. The Yangla is the largest Sb deposit (10 kt Sb @ 14.87 %) in the Jinshajiang suture zone (SW China), and the lode-type Sb orebodies are stratabound or developed along NE-trending fracture zones in marble. To constrain the time of Sb mineralization and establish any genetic link with the local magmatism and wallrocks, we performed calcite Sm-Nd dating and bulk C-O and in-situ Sr isotope analyses. The results show that the Sb mineralization (∼155 Ma) was considerably younger than the Cu-Pb-Zn mineralization (∼230 Ma), skarn alteration (∼234 Ma), and granitoid emplacement (∼230 Ma) at Yangla, but much older than the local W mineralization (∼30 Ma). The initial 87Sr/86Sr ratio of calcite (0.71972–0.72208) is much higher than that of the Triassic granodiorite (0.71149– 0.71990) and Carboniferous basalt (0.70562–0.70995), suggesting mixed source of calcite from the ore fluids and Devonian wallrocks. The ore-related calcite has δ13CPDB (−4.53 to − 2.33 ‰) and δ18OSMOW (+14.98 to + 16.30 ‰) values that fall between the granite and marine carbonate isotopic fields. This suggests that the ore-forming fluid may be related to the low-temperature alteration of granites and marine carbonate dissolution. Simulated precipitation temperature calculation for the ore-related calcite yielded 200–150 °C, and the calcite C-O isotopes suggest that fluid mixing, fluid-rock interactions, and CO2 degassing may have precipitated the stibnite in the fracture zones under low-temperature conditions. Our new geochemical results and published data suggest that the Yangla polymetallic mineralization was multiphase, comprising the Indosinian Cu-Pb-Zn (∼230 Ma), Yanshanian Sb (∼155 Ma), and Himalayan W-Sb (∼30 Ma) metallogenic events.

Temporal changes in precipitation and correlation with large climate indicators in the Hengshao Drought Corridor, China

Understanding the lagged correlations between regional precipitation and large scale climate indicators (LCI) is crucial for water management and drought risk assessment. Copula functions are commonly used to quantify the relationship between precipitation and these factors. However, existing studies often neglect monsoon inland areas and lack quantification of the lagged correlations of precipitation at the monthly scale. To address these limitations, this study constructed a 3-dimensional vine copula-based model to assess the impact of large scale climatic change on regional precipitation. The analysis focused on the Hengshao Drought Corridor (HDC), the driest region in Hunan Province, China. The results indicate that precipitation in this region is influenced primarily by the Pacific Ocean’s circulation pattern, particularly the Niño1 + 2 and Niño3 indices, with a 2 ∼ 3 month lag. The R-Vine Copula provided the best fit when identifying lagged correlations, but due to its fitting center, it did not perform as well as the C-Vine Copula in generating stochastic simulated precipitation. The study revealed that the HDC receives more precipitation when the Niño1 + 2 and Niño3 are asynchronous, but less precipitation when they are synchronous, making the area more susceptible to drought events. These findings have implications for the HDC and other monsoon inland regions worldwide, providing insights to cope with climate extremes and develop appropriate strategies.

Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (SS316L) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M23C6, γ’, and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate for periods of up to 3000 h. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25 % volume fraction and mechanical properties within 6 % relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ∼1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that microstructural evolution under variable conditions can be predicted using a statistics-based model informed by a practicably obtainable dataset. The potential applications of this type of modeling framework are discussed.

Future amazon basin wetland hydrology under projected climate change

Climate change over the Amazon basin has the potential to cause major hydrological and ecological impacts over the region’s extensive wetlands. To investigate this the Joint UK Land Environment Simulator (JULES) land surface model is first extended to include riverine inundation. Potential impacts of future climate change on Amazon basin wetlands are then evaluated by driving this updated JULES model with modelled meteorology projections from six different climate simulations reaching approximately 4°C global warming at the end of the 21st Century. The projected changes in inundation extent and seasonality are assessed over four major wetland regions. The simulations project, on average, a significant decrease in total Amazon basin inundated area of 11% (range: -36% to +9%) by the 2090s. This considerable spread is primarily driven by disparity in simulated precipitation changes, ultimately driven by sea surface temperature differences. The wetter contemporary climate simulations simulate the greatest drying by the end of this Century, resulting in the largest wetland area reductions. The largest qualitative disagreement is over the western Iquitos wetland, with inundated area changes ranging from a very large reduction of -53% to an increase of 12%. A new wetland classification scheme is developed to summarise the projected changes in wetland seasonality. The largest drops in simulated wetland season length occur over the Central/East Manaus and West Iquitos wetland regions, with reductions of up to 10 and 8 months respectively. Such significant changes in future inundation are likely to have a major impact on regional wetland hydrology and their ecosystems.

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.

Hydrological Evaluation of CRA40 and ERA5 Reanalysis Precipitation Products over Ganjiang River Basin in Humid Southeastern China

This study evaluates two reanalysis precipitation products (CRA40 and ERA5) over the Ganjiang River Basin with precipitation data from 37 ground rainfall gauges and surface-observed stream flow data from January 1998 to December 2008. Direct comparison with rain gauge observations shows that both CRA40 and ERA5 can capture the spatial and temporal characteristics of precipitation at the basin scale of the Ganjiang River and reflect most of the precipitation events, but there are pronounced differences in the quality of precipitation between them. ERA5 performs better on the daily scale, capturing precipitation changes more accurately over short periods of time, while CRA40 performs better on the monthly scale, providing more stable and long-term precipitation trends. The results of stream flow simulations using two reanalysis precipitation products driving the VIC hydrological model show that (1) CRA40 outperforms ERA5 with a better Nash–Sutcliffe Efficiency (NSE, 0.65 and 0.6) and higher CC (0.96 and 0.91) in daily and monthly scale stream flow simulations, and ERA5 has a good CC (0.86 and 0.93, respectively), but its NSE is poor (0.29 and 0.30, respectively); (2) both CRA40 and ERA5 generally overestimate basin stream flows, especially during the flood season (April–September), with ERA5’s overestimation being more pronounced. This study is expected to provide a basis for the selection of reliable reanalysis products for Ganjiang River Basin precipitation and hydrological simulation.

Modeling Across Scales of Heavy Precipitation With a Global Variable‐Resolution Model: A Case Study of a Catastrophic Event in China

AbstractAn unprecedented heavy rainfall event in China (“21.7” extreme rainfall event) was simulated using the global variable‐resolution model (MPAS‐Atmosphere) across the scales (4, 8, 16 and 50 km). Although almost all experiments at different resolutions reproduce the spatiotemporal characteristics of precipitation, the simulated precipitation intensity from high to low is 16, 8, 50, and 4 km, with the 16 km simulation being closest to the observations. Precipitation magnitude is prominently influenced by the difference in simulated large‐scale circulation across a range of grid spacings. Further analysis revealed that the differences in latent heating across scales affect the geopotential height and wind field by altering temperature. The latent heating in 4 km simulation is the minimum while the 16 km simulation is maximum. More latent heating release leads to the low‐level pressure depression, amplifies the water vapor flux convergence, produces stronger upward motion and more clouds, and ultimately results in stronger precipitation. The sensitivity experiments for turning off latent heating tendencies during the event showed that the latent heat release has positive feedback on the “21.7” heavy rainfall event. This study highlights the importance of scale‐awareness of latent heat at different resolutions and suggests that the difference in simulated latent heat release during the event is the main reason for simulated different atmospheric circulation and precipitation across scales.

Precipitation patterns strongly affect vertical migration and methylation of mercury in legacy contaminated sites

Legacy-contaminated sites act as significant sources of mercury (Hg) to their surrounding surface and underground environments. Intensified extreme precipitation is posing great threats to the environment and human health by changing the fate of pollutants, yet little is known about its effect on the vertical migration and methylation of Hg in contaminated sites. Here, we applied a range of simulated extreme precipitation patterns (frequency and intensity) to column leaching assays with soils collected near a contaminated site. We observed that precipitation with high frequency but low intensity resulted in more vertical migration of Hg through the soil profile than that with low frequency but high intensity. The majority (> 90%) of leached Hg was prone to migrate vertically within the top 10 cm of the soil profile. Furthermore, rainfall stimulated microbial Hg methylation, as demonstrated by enhanced production of methylmercury (MeHg) in both simulated and field-contaminated soils. We identified specific microbial taxa including Geobacteraceae, Desulfuromonadaceae, Syntrophaceae, Oscillospiraceae, and Methanomicrobiaceae as key predictors of MeHg production, which differed from those typically observed in overlying water of croplands. Particularly, the relative abundance of these dominant Hg methylators significantly increased during rainfall-induced leaching compared to that of the control, suggesting the crucial yet previously overlooked impacts of increased precipitation events on the process of microbial Hg methylation in industry-contaminated sites. Given the rising incidences of extreme precipitation events worldwide due to climate change, this study highlights the significance of assessing Hg mobility and microbial transformation in legacy contaminated sites.

Effects of simulated precipitation changes on soil respiration:Progress and prospects.

Soil respiration, the main pathway for transferring terrestrial carbon pool to atmospheric carbon pool, is profoundly affected by the intensification in global precipitation variability in the context of climate change. Nowadays, variable controlling methods and field manipulation experiments are two main methods widely used to investigate the effects of simulated precipitation changes on soil respiration. Yet, due to the heterogeneity of soil properties, vegetation types, and the magnitude of precipitation change, there is substantial inconsistency in the conclusions of simulated precipitation change effects on soil respiration. Here, we analyzed data from domestic and foreign literature, and examined the effects of simulated precipitation change on soil respiration. Firstly, we described the response pattern of soil respiration to soil moisture fluctuation and pointed out that the magnitude and direction of the response of soil respiration to simulated precipitation change depended on whether soil moisture was optimally conditioned at different precipitation treatments. Second, we summarized the response patterns of soil respiration to symmetric increase and decrease in precipitation, which mainly included symmetric and asymmetric responses (positive and negative asymmetric). Meanwhile, the adaptation of plants and soil microorganisms to drought stress and soil oxygen limitation, as well as the reduction of organic substrates, were the main mechanisms accounting for the shifts of soil respiration response patterns to simulated precipitation change from symmetric to asymmetric responses. Third, we identified a significant effect of ambient climate on soil respiration in response to precipitation treatments as increasing duration of the experimental treatments. In addition, cumulative or buffering effects of ambient climatic conditions on precipitation treatment could affect the sensitivity of soil respiration along precipitation gradient by altering hydrothermal conditions. Finally, to accurately assess the implications of precipitation changes on soil carbon balance processes, we proposed three aspects of future precipitation effects on soil respiration for attention: 1) focusing on the phenomenon of "threshold effects" in the asymmetric response of soil respiration along precipitation gradients; 2) distinguishing the intrinsic mechanisms of autotrophic and heterotrophic components in soil respiration in response to precipitation changes; and 3) focusing on the impacts of intensified precipitation variability on soil respiration in the context of future climate extremes. In conclusion, with the intensified variability in global precipitation patterns, clarifying the response mechanism of soil respiration to precipitation changes is of great significance for accurately predicting and evaluating the alterations of soil carbon cycle processes and carbon balance in the context of global changes.