Precipitation Frequency Research Articles

ROS-mediated waterlogging memory, induced by priming, mitigates photosynthesis inhibition in tomato under waterlogging stress.

With global climate change, the frequency and intensity of waterlogging events are increasing due to frequent and heavy precipitation. Little is known however about the response of plants to repeated waterlogging stress events. The aim is to clarify physiological regulation mechanisms of tomato plants under repeated waterlogging stress, and whether Trichoderma harzianum can alleviate waterlogging injury. We identified two genotypes of tomato, 'MIX-002' and 'LA4440', as waterlogging tolerant and sensitive genotypes, respectively, based on plant biomass accumulation. The two tomato genotypes were subjected to a waterlogging priming treatment for 2 days (excess water for 1cm above substrate surface) followed by a recovery stage for 2 days, and then a second waterlogging stress for 5 days (excess water for 1cm above substrate surface) followed by a second recovery stage for 3 days. Leaf physiological, plant growth parameters, and the expression of five key genes were investigated. We found that the two genotypes responded differently to waterlogging priming and stress in terms of photosynthesis, reactive oxygen species (ROS), and osmotic regulatory mechanisms. Waterlogging stress significantly increased H2O2 content of 'MIX-002', while that of 'LA4440' had no significant change. Under waterlogging stress, photosynthesis of the two genotypes treated with waterlogging priming returned to the control level. However, Trichoderma harzianum treatment during the second recovery stage did not show positive mitigative effects. The plants of 'LA4440' with priming showed lower peroxidase (POD) activity and proline content but higher H2O2 content than that without priming under waterlogging stress. Under waterlogging stress with priming as compared to without priming, SODCC2 was downregulated in two tomatoes, and AGR2 and X92888 were upregulated in 'MIX-002' but downregulated in 'LA4440'. Overall, the two tomato genotypes exhibited distinct photosynthetic, ROS and osmotic regulatory mechanisms responding to the waterlogging stress. Waterlogging priming can induce stress memory by adjusting stomatal conductance, sustaining ROS homeostasis, regulating osmotic regulatory substances and key gene expressions mediated by H2O2, and thus alleviate the damage on tomato photosynthesis when waterlogging reoccurred.

The deterioration and restoration of dried soil layers: New evidence from a precipitation manipulation experiment in an artificial forest

Ongoing climate change alters the amount and frequency of precipitation, deteriorating the water status of soils and the ecosystem services that they provide. The decline of soil water formed the dried soil layers (DSLs), which is particularly relevant for dryland ecosystems like the forests planted on the Chinese Loess Plateau (CLP), because there is an increased water demand for vegetation transpiration. To assess the impacts of precipitation changes on DSLs, we conducted a large throughfall manipulation experiment comprising seven precipitation gradients, ranging from 30%, 50%, and 65% precipitation interception treatments (PIT) to the ambient condition and then to 30%, 50% and 65% precipitation accession treatments (PAT), under a Robinia pseudoacacia planting forest on the CLP. We measured changes in soil water content (SWC) and examined the development of DSLs for over a year. We found that: (1) mean SWC in the 0–2100 cm soil profile and in DSLs decreased by 0.44%–0.77% and 0.13%–0.36% under the PIT, respectively, while increasing by 0.13%–0.36% and 0.16%–0.92% under the PAT, respectively; (2) DSLs were observed c. 100 cm and c. 100–180 cm below the soil surface under the PIT and PAT, respectively; (3) DSLs varied seasonally, with the largest reduction occurring during the transition period between the dry and rainy seasons, while the reduction developed more rapidly under the PAT than the PIT; (4) the incremental rate of SWC in DSLs increased linearly (p < 0.05) with increasing precipitation, while the increase was faster when precipitation was above (PAT) than below the ambient conditions (PIT), indicating greater precipitation sensitivity of DSLs under enriched than limited water supply. These changes in DSLs provide experimental evidence for the effects of climate change on soil water, and may have strong effect on the sustainable development management of drylands.

Current Forest–Savanna Transition in Northern South America Departs from Typical Climatic Thresholds

The forest–savanna transition is the most widespread ecotone in the tropics, with important ecological, climatic, and biogeochemical implications at local to global scales. However, the factors and mechanisms that control this transition vary among continents and regions. Here, we analyzed which factors best explain the transition in northern South America (Llanos ecoregion and northwestern Amazon), where common thresholds on typical environmental factors (for example, mean annual precipitation (MAP), wet season precipitation) fail to predict it. For instance, savannas in the Llanos occur at MAP levels (> 1500 mm) which are typical of forests in other tropical regions. We examined the transition’s climate features, soils, and disturbance (fire frequency) spaces using remotely sensed data. We used logistic generalized linear models to assess the effect of seasonal (season length) and intra-seasonal (daily precipitation frequency and intensity) precipitation metrics during the dry season, soil silt content, and fire frequency, on the transition using canopy cover, tree cover, and the maximum Plant Area Volume Density as vegetation structure descriptor variables. Fire frequency and precipitation frequency were the most important variables explaining the transition. Although most fires occur in savannas, we found that a significant percentage of savanna pixels (46%) had no fires. This study indicates that the transition should be characterized regionally in response to biogeographic differences (for example, climatic space) among regions and continents. Our results highlight the importance of fire frequency and intra-seasonal precipitation in determining the transition in northern South America. Furthermore, future studies should consider regional differences in the climatic space of forest and savanna to improve projections of global change impacts on these highly diverse ecosystems.

Differences in Global Precipitation Regimes between Land and Ocean Areas Based on the GPM IMERG Product

Climate change research has received increasing attention from both researchers and the public, and the analysis of precipitation is one of the most important topics in this field. As a supplement to gauge observations, satellite-derived precipitation data present advantages, as they have high spatiotemporal resolution and good continuity. The Global Precipitation Measurement (GPM) mission is the newest generation of precipitation measurement products designed to conduct quasi-global satellite observations. This study used the latest Integrated Multi-satellitE Retrievals for GPM data collected between 2001 and 2020 to analyze changes in precipitation amount, frequency, and intensity on global land and ocean surfaces. The results showed that precipitation intensity over the ocean was generally higher than on land, and the two were more similar at the hourly scale than at the daily scale, as shown by the JS divergence statistics: 0.0323 and 0.0461, respectively. This may be due to the thermodynamic differences between land and the ocean, which can accumulate over a longer time scale. The average number of annual precipitation hours and days increased by 50 h and 5 days during 2011–2020 compared with the previous decade. The absence of obvious changes in annual precipitation amounts led to a decrease in annual precipitation intensity and the weakening of extreme precipitation on land. The analysis of precipitation regimes in nine mainland regions of the globe showed a significant increasing trend for both hourly and daily precipitation in North Asia, while insignificant changes or even decreasing trends were detected in the other regions. The results of this study elucidated the variations in precipitation between land and ocean areas and can contribute to the understanding of global precipitation.

Intermittent Precipitation-Dependent Interactions, Encompassing Allee Effect, May Yield Vegetation Patterns in a Transitional Parameter Range.

We construct a spatial model that incorporates Allee-type and competition interactions for vegetation as an evolving random field of biomass density. The cumulative effect of close-range precipitation-dependent interactions is controlled by a parameter defining precipitation frequency. We identify a narrow parameter range in which the behavior of the system changes from survival of vegetation to extinction, via a transitional aggregation pattern. The aggregation pattern is tied to the initial configuration and appears to arise differently from Turing's diffusion and differential flow patterns of other models. There is close agreement of our critical transition parameter range with that of the corresponding evolving random mean-field model.

Earth-system-model evaluation of cloud and precipitation occurrence for supercooled and warm clouds over the Southern Ocean's Macquarie Island

Abstract. Over the remote Southern Ocean (SO), cloud feedbacks contribute substantially to Earth system model (ESM) radiative biases. The evolution of low Southern Ocean clouds (cloud-top heights &lt; ∼ 3 km) is strongly modulated by precipitation and/or evaporation, which act as the primary sink of cloud condensate. Constraining precipitation processes in ESMs requires robust observations suitable for process-level evaluations. A year-long subset (April 2016–March 2017) of ground-based profiling instrumentation deployed during the Macquarie Island Cloud and Radiation Experiment (MICRE) field campaign (54.5∘ S, 158.9∘ E) combines a 95 GHz (W-band) Doppler cloud radar, two lidar ceilometers, and balloon-borne soundings to quantify the occurrence frequency of precipitation from the liquid-phase cloud base. Liquid-based clouds at Macquarie Island precipitate ∼ 70 % of the time, with deeper and colder clouds precipitating more frequently and at a higher intensity compared to thinner and warmer clouds. Supercooled cloud layers precipitate more readily than layers with cloud-top temperatures &gt; 0 ∘C, regardless of the geometric thickness of the layer, and also evaporate more frequently. We further demonstrate an approach to employ these observational constraints for evaluation of a 9-year GISS-ModelE3 ESM simulation. Model output is processed through the Earth Model Column Collaboratory (EMC2) radar and lidar instrument simulator with the same instrument specifications as those deployed during MICRE, therefore accounting for instrument sensitivities and ensuring a coherent comparison. Relative to MICRE observations, the ESM produces a smaller cloud occurrence frequency, smaller precipitation occurrence frequency, and greater sub-cloud evaporation. The lower precipitation occurrence frequency by the ESM relative to MICRE contrasts with numerous studies that suggest a ubiquitous bias by ESMs to precipitate too frequently over the SO when compared with satellite-based observations, likely owing to sensitivity limitations of spaceborne instrumentation and different sampling methodologies for ground- versus space-based observations. Despite these deficiencies, the ESM reproduces the observed tendency for deeper and colder clouds to precipitate more frequently and at a higher intensity. The ESM also reproduces specific cloud regimes, including near-surface clouds that account for ∼ 25 % of liquid-based clouds during MICRE and optically thin, non-precipitating clouds that account for ∼ 27 % of clouds with bases higher than 250 m. We suggest that the demonstrated framework, which merges observations with appropriately constrained model output, is a valuable approach to evaluate processes responsible for cloud radiative feedbacks in ESMs.

How Do the Start Date, End Date, and Frequency of Precipitation Change across China under Warming?

Variations in precipitation have a great influence on human society and the natural environment. Existing studies have provided substantial information regarding variations in the magnitude, frequency, and intensity of precipitation. However, little is known about how the start and end dates of precipitation change, which could offer crucial insights for related studies in agriculture, hydrology, and other related disciplines. Here, we present an analysis of variations in the start date, end date, and frequency of different precipitation intensities, using a widely used gauge-satellite-reanalysis-based merging product, during the latest period, 1980–2022, across China. The results show that the spatial–temporal variations in the start date, end date, and frequency of different precipitation intensities were complex among regions. For example, in northeast and northwest China, light precipitation (LP) started earlier and increased in frequency during the study period. In the Tibetan Plateau, precipitation at different intensities levels started earlier, heavy precipitation (HP) and violent precipitation ended earlier, and the frequency of LP and moderate precipitation increased significantly. The start date of HP shifted earlier in Southeast China (−0.28 days/year). Our findings could be helpful in providing a comprehensive understanding of precipitation changes under global warming and highlight the need to pay close attention to these precipitation changes in the future.

Diurnal Variation Characteristics of Summer Precipitation and Related Statistical Analysis in the Ili Region, Xinjiang, Northwest China

The diurnal variation characteristics and basic statistical features of summer precipitation (from June to August) in the Ili region from 2015 to 2019 were investigated based on 4 km resolution Weather Research and Forecasting model simulation data from Nanjing University (WRF_NJU). The results show that the overall diurnal variation characteristics of precipitation (DVCP) reflected by the WRF_NJU data were consistent with respect to the observations and reanalysis data. The total precipitation pattern exhibited high (low) values on the east (west), with higher (lower) values over the mountainous (valley) area. Hourly precipitation amount (PA), precipitation frequency (PF), and precipitation intensity (PI) show similar diurnal variation characteristics, with peaks occurring at around 1700 LST in the mountainous area and around 2000 LST in valleys. Furthermore, moderate to intense precipitation contributes up to 87.88% of the total precipitation. The peaks in the mountainous area occur earlier than the valleys, while the peaks in western part of the valleys occur earlier than the eastern part. The PA peaks over the valleys and slopes occurred from the evening to early morning and from the afternoon to evening, respectively. In addition, the rotated empirical orthogonal function (REOF) analysis implied that the DVCP exhibits distinct differences between mountainous and valleys, and peak precipitation occurs during the evening in basin– and wedge–shaped areas, while the mountain peaks and foothill regions exhibit semi–diurnal variation characteristics. Among several basic meteorological factors, the vertical velocity (VV) and water vapor mixing ratio (WVMR) provided major contributions to the DVCP in both areas with high and low coefficients of variation, and the WVMR (VV) probably played a more significant role in mountainous (valleys) areas.

Diurnal cycle of precipitation and its possible causes: A case study in Shandong Province, China

AbstractAccurate information on diurnal precipitation variations is essential for understanding the mechanism of precipitation formation, assessing the regional climate characteristics, and improving the simulation performance of climate models. Taking Shandong Province, East China, as a case study, 123 stations with hourly observational precipitation data were divided into three categories by the harmonic analysis on diurnal variation of precipitation, and the diurnal variations in precipitation amount, frequency and intensity were further analysed to derive the precipitation partition. The results showed that: (1) the peak precipitation frequency at different types of stations occurred steadily around 0400–0600 LST, whereas the peak precipitation intensity existed at different moments such as 0200 and 1700, and precipitation amount mostly showed a double‐peak pattern in the early morning (0400–0600 LST) and late afternoon (1600–1800 LST); (2) early morning precipitation was mainly driven by persistent precipitation, which contributed between 65.2% and 84.3%, while precipitation peaks in the late afternoon with a significant increase in the contribution of short duration precipitation; and (3) the influence of mountain–plain circulation and the increasing effect of topographic uplift on precipitation may be the main reasons for the formation of early morning precipitation peaks. The strength of sea–land winds and local convection was also key factors affecting the diurnal variation of precipitation.

Vegetation Dynamics and Its Response to Extreme Climate on the Inner Mongolian Plateau during 1982–2020

The impact of extreme climate change on terrestrial ecosystems continues to intensify. This study was conducted to understand extreme climate–vegetation interactions under exacerbated frequency, severity, and duration of extreme climatic events. The Inner Mongolian Plateau (IMP) was selected due to its sensitive natural location, which is particularly vulnerable to climate change. Based on the Normalized Difference Vegetation Index (NDVI) and daily meteorological station data from 1982 to 2020, changes in the patterns of vegetation and extreme climate in the three ecological zones (forest, steppe, and desert steppe) of the IMP were identified. Furthermore, the effects of extreme climate on vegetation were quantified using correlation analysis and a geographical detector. The results showed that the annual NDVI of 95.1%, 50.6%, and 19.5% of the area increased significantly in the forest, steppe, and desert steppe, respectively. The Tx90p (warm days) and Tn90p (warm nights) increased significantly at the rate of 0.21 and 0.235 day·yr−1, respectively, while the Tx10p (cold days) and Tn10p (cold nights) showed a significantly decreasing trend at the rate of −0.105 and −0.117 day·yr−1. An extreme warming phenomenon was observed in all extreme temperature indices on the IMP. The results of both the correlation analysis and factor detector indicated that extreme temperature intensity and frequency greatly affected forest vegetation. In contrast, extreme precipitation intensity and frequency were relatively more important to the vegetation of the desert steppe. The lag in NDVI response to extreme temperature intensity was not less than three months in the IMP; however, extreme precipitation intensity exhibited a two-month time lag in the NDVI. This study can improve our understanding of extreme climate–vegetation interactions, provide theoretical support for disaster mitigation, and aid in understanding the ecological environment of the IMP.

Seasonal prediction of summer extreme precipitation frequencies over Southwest China based on machine learning

It is challenging to predict extreme precipitation over regions with complex terrain, such as Southwest China (SWC). This study develops a statistical seasonal prediction scheme for summer extreme precipitation frequencies (EPFs) over SWC. In this scheme, the year-to-year increment method is first applied to the SWC summer EPFs and boundary forcings in preceding seasons. The first six leading modes of SWC summer EPFs are extracted by empirical orthogonal function (EOF) decomposition. The relationships between the six modes and preceding boundary forcings are investigated. According to these relationships, physics-based predictors for the principal components (PCs) of the modes are selected. The first two modes are taken as examples to analyze the mechanisms by which the predictors influence SWC summer EPFs. Two machine learning (ML) models, random forest and extreme gradient boosting, are trained using the data during 1980–2011. The ML models that perform best for each PC on the training data are used to be independently tested on data during 2012–2021. The predicted EPF patterns can be reconstructed by predicted PCs and observed EOFs. Prediction results indicate that the optimal ML models can well capture the variations in the six modes and spatial patterns of SWC EPFs and outperform the traditional stepwise regression models. The prediction skills for SWC summer EPF anomaly patterns show year-to-year variations which depend on the prediction results of the PCs. This scheme may help the seasonal predictions of SWC summer EPFs.

Precipitation patterns and N availability legacy govern microbial response to rewetting in a plant-soil system

Climate change models predict shifts in the frequency and magnitude of rain events (precipitation patterns). We studied how precipitation history shapes microbial community responses to rewetting and how these effects depend on N status. Twelve weeks of contrasting precipitation and N input left a legacy effect by shaping present (DNA-based) and potentially active (rRNA-based) bacterial and fungal communities. This legacy effect determined the microbial response to rewetting, as demonstrated by differences in the post-wet potentially active bacterial and fungal communities as well as the flux of soil-emitted CO2 over a 29-h period. Despite contrasting effects of precipitation and N input history on fungal:bacterial ratio and microbial community composition, the timing of the potentially active bacterial and fungal response to rewetting was not altered. Thus, regardless of precipitation or N input history, potentially active bacteria responded with a small shift in community composition within 1 h of rewetting but did not change further for the remaining 28 h analyzed. The potentially active fungi did not respond to rewetting within 29 h. Even though more extreme fluctuations in soil moisture changed soil microbial community composition, the short-term response of microbial communities to rewetting was conserved. Soil CO2 efflux upon rewetting was higher from systems with a history of frequent precipitation and underline the role of plants in drying-rewetting processes. We suggest that shifts in the fungal:bacterial ratio, as well as N-cycling potentials, may have consequences on food web stability and soil biogeochemical cycling.

Seasonal Differences of Precipitation and Microphysical Characteristics over the Asian Monsoon Region Using Spaceborne Dual-Frequency Precipitation Radar

Abstract This study aimed to reveal the seasonal climatic variations in the microphysical properties of precipitation over the Asian monsoon region. We used the Dual-Frequency Precipitation Radar satellite product aboard the Global Precipitation Measurement Mission Core Observatory for 8 years from 2014 to 2021 to statistically analyze the mass-weighted mean diameter (Dm) and frequency of heavy ice precipitation (graupel and hail). The results showed statistically significant seasonal changes. The microphysical characteristics of large Dm and frequent heavy ice precipitation were observed over the Indian subcontinent and Indochina Peninsula in the premonsoon season and over the western Himalayan region in the mature-monsoon season, which can be related to the intense and deeply developed precipitation systems. The relationship between precipitation rate and Dm was also examined. The results indicated that changes in Dm were not caused only by changes in precipitation rate but were probably induced by changes in precipitation characteristics. In terms of the relationship between the microphysical properties, heavy ice precipitation particles in the upper atmosphere above the melting layer were observed more frequently as Dm near the surface increased. We also studied lower-atmospheric instability by investigating the vertical gradients of the dry and moist static energies. The results indicated that instability properties were different; dry and wet instabilities were dominant in the premonsoon and monsoon seasons, respectively, consistent with the results of the precipitation characteristics. Significance Statement The purpose of this study was to reveal seasonal variations in precipitation microphysical characteristics, such as precipitation particle size and the existence of graupel and hail in the upper atmosphere, by climatological analysis over the Asian monsoon region. In previous studies, microphysical characteristics have mainly been addressed using ground-based observations. However, more sampling is needed to expand our understanding of climatological perspectives; therefore, we used recently available satellite observations. As a result, we found that precipitation particles at the surface were larger, and more graupel and hail existed in clouds in the premonsoon season when less precipitation was observed, compared to the mature-monsoon season when precipitation amount and frequency were abundant.

The Precipitation‐Recycling Process Enhanced Extreme Precipitation in Xinjiang, China

AbstractThe amount, frequency and intensity of extreme precipitation over Xinjiang have increased dramatically under the wetting trend in Northwest China, but long‐term trends in the precipitation‐recycling process remain largely unexplored. Based on dynamic recycling model and MERRA2 reanalysis, we revealed a mean recycling ratio for extreme precipitation in Xinjiang of 42.3% with a growth rate of 2.3% decade−1 during 1982–2019. The increasing trend of extreme precipitation was almost equally attributed to increased recycling precipitation (49%) and external precipitation (51%). The extreme precipitation in Xinjiang exhibited two peak centers, the Tianshan Mountains region (TS) and Kunlun Mountains region (KL), highlighting variations in the water cycle. Specifically, the external cycle predominated the increased extreme precipitation in TS (61%), while the recycling process mainly influenced the increase in KL (67%) due to markedly enhanced evapotranspiration. Moisture source attribution further proved the crucial role of evapotranspiration from Xinjiang and its vicinity in extreme precipitation.

Evaluating the Precipitation Biases over the Western Periphery of the Sichuan Basin with the ECMWF Operational Forecast Model

Abstract Based on the hourly merged precipitation product, the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) in simulating the diurnal variations of precipitation during warm season over the western periphery of the Sichuan basin (SCB) has been evaluated, and the underlying physical causes associated with the wet biases have also been investigated. The results show that the IFS well reproduces the spatial distributions of precipitation amount, frequency, and intensity over the SCB, as well as their diurnal variations, but the simulated precipitation peaks earlier than the observation with notable wet biases over the western periphery of the SCB. In addition, the strong wet biases exhibit notable regional differences over the western periphery of the SCB. The simulated wet biases over the southwestern periphery of the SCB expand westward to higher altitudes along the windward slope, with the maximum wet biases occurring at night. The westward expansion of the simulated stronger upward motions results in a westward shift of precipitation. However, the simulated precipitation over the northwestern periphery of the SCB has small difference in terms of the location; hence, the overestimated precipitation is associated with the stronger atmospheric instability, resulting from the higher potential temperature and the larger specific humidity near the surface. The findings revealed in this study indicate that the ECMWF forecast shows distinct uncertainties over the different complex terrain, and thus offers a promising way forward for improvements of model physical processes.

Diurnal Variation Characteristics of Clouds and Precipitation during the Summer Season in Two Typical Climate Regions of the Tibetan Plateau

Mêdog and Nagqu are two typical climate regions of the Tibetan Plateau, with different atmospheric conditions and local orography. This may lead to different diurnal variation patterns of clouds and precipitation. This paper investigates the diurnal variations of clouds and precipitation in Mêdog and Nagqu, using ground-based measurements from Ka-band cloud radar and a Particle Size and Velocity (PARSIVEL) disdrometer. High frequencies of cloud cover and precipitation occur from 23:00 local solar time (LST) to 05:00 LST in Mêdog, while low frequencies appear from 11:00 LST to 17:00 LST. The occurrence frequencies in Nagqu maintain high values from 13:00 LST to 21:00 LST. In terms of mean rain rate, heavier rainfall appears in the evening and at night in Mêdog, with peaks at 00:00 LST and 18:00 LST, respectively. In Nagqu, the heaviest rainfall occurs at 12:00 LST. In addition, the afternoon convective rainfall in Nagqu is characterized by a much higher concentration of large drops, which can be classified as continental-like. The morning rainfall has the lowest concentration of large drops and can be classified as maritime-like. Finally, the mechanisms of diurnal variations in the two regions are discussed. The diurnal cycle of clouds and precipitation in Mêdog may be associated with the nocturnal convergence of moisture flux and mountain–valley wind circulation. Diurnal variations in Nagqu have a high correlation with the diurnal cycle of solar radiation. The high nocturnal frequency of clouds and precipitation in the two regions at night is closely related to the convergence of moisture flux.

Seventy‐Year Trends in Ship‐Reported Oceanic Precipitation Frequency

AbstractShip present‐weather reports from 1950 to 2019 are used to assess trends in the reporting of precipitation occurrence over the global oceans. Annual reported precipitation frequency shows statistically significant positive trends of up to ∼15% per decade throughout most ocean areas equatorward of 45°. However, latitudes poleward of 45° are dominated by negative trends, some areas of which meet the 95% confidence threshold. Nine smaller regions were subjectively selected for further investigation, revealing that the observed trends, both positive and negative, are often but not always nearly linear, with the amplitude of interannual fluctuations usually being much larger than that expected from random sampling error alone. The annual time series reveal that four comparatively dry areas are associated with the largest overall positive trends, ranging from 8.3% to 12.8% (relative) per decade. Trends were also computed separately for each season, revealing remarkable overall consistency in trends across seasons.

Prediction of La Niña Events Based on BP Neural Network Model

As an extreme weather phenomenon, La Niña event will cause low water temperature in the equatorial eastern Pacific Ocean and abnormal longitudinal circulation in East Asia, resulting in widespread and continuous abnormal cooling of sea surface temperature in the eastern and central equatorial Pacific Ocean, which will cause frequent precipitation and flooding in the north and high temperature and low rainfall and frequent droughts in the south[1]. Therefore, it is particularly important to assess the damage and propose response strategies[2]. For the main countries involved in the global triple La Niña event, this paper selects four representative countries located in different regions to analyze, through descriptive analysis and graphical visualization, and selects the relevant index data of the last three years: monthly average maximum and minimum temperatures, monthly average precipitation rate, monthly average precipitation days, monthly average wind speed and monthly average water temperature, and finds that New York City and Brazil were affected by the triple La Niña event at the same time and were affected to a higher degree, and the temperature changes in Malasila and Jakara were stable, and the temperature effects brought by the triple La Niña event were smaller[3]. Then, a BP neural network model was established to predict the probability of future La Niña events in each region by modeling pressure, temperature, and salinity data, and it was found that a total of 110 regions would experience La Niña events in the future, with a general probability of 55%-60%..

Atlas of probabilistic extreme precipitation based on the early 21st century records in the United States

Study regionThe Conterminous United States Study focusExtreme precipitation property defined by the National Oceanic and Atmospheric Administration’s Precipitation-Frequency Atlas of the United States (NOAA Atlas 14) is losing its representativeness as the standard measure due to the impacts of climate change. This study aims to spatially and quantitatively assess the changes in extreme precipitation properties by comparing the NOAA Atlas 14 with a new atlas of precipitation frequency estimates (PFE) developed in this study from early 21st-century records New hydrological insights for the regionThe NOAA Atlas 14 has quantitatively diverged from the recent 20-year records due to precipitation non-stationarity across the United States. The NOAA Atlas 14 is characterized by more spatially smoothed PFEs compared to the new PFEs, suggesting that employing the NOAA Atlas 14 could lead to an underestimation of physical flood risk at a local scale. As the “new normal” in the 21st century, the majority of the United States faces three times more occurrences of extreme storms corresponding to a 1-in-100-year return period of the 20th century. The recent 20-year extreme events should be treated as the new normal. Until the NOAA Atlas 14 is updated to account for climate-driven non-stationarity and can be shown to resolve local effects better, it is recommended to employ new values between the upper and middle bounds of PFE that capture the non-stationarity of precipitation for the regions where the underestimation of PFEs is highlighted in this study

Evaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 models

Abstract. As the resolution of global Earth system models increases, regional-scale evaluations are becoming ever more important. This study presents a framework for quantifying precipitation distributions at regional scales and applies it to evaluate Coupled Model Intercomparison Project (CMIP) 5 and 6 models. We employ the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (AR6) climate reference regions over land and propose refinements to the oceanic regions based on the homogeneity of precipitation distribution characteristics. The homogeneous regions are identified as heavy-, moderate-, and light-precipitating areas by K-means clustering of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) version 6 final run product (IMERG) precipitation frequency and amount distributions. With the global domain partitioned into 62 regions, including 46 land and 16 ocean regions, we apply 10 established precipitation distribution metrics. The collection includes metrics focused on the maximum peak, lower 10th percentile, and upper 90th percentile in precipitation amount and frequency distributions; the similarity between observed and modeled frequency distributions; an unevenness measure based on cumulative amount; average total intensity on all days with precipitation; and number of precipitating days each year. We apply our framework to 25 CMIP5 and 41 CMIP6 models, as well as six observation-based products of daily precipitation. Our results indicate that many CMIP5 and 6 models substantially overestimate the observed light-precipitation amount and frequency, as well as the number of precipitating days, especially over midlatitude regions outside of some land regions in the Americas and Eurasia. Improvement from CMIP5 to 6 is shown in some regions, especially in midlatitude regions, but it is not evident globally, and over the tropics most metrics point toward degradation.