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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Synthesis of ARM User Facility Surface Rainfall Datasets to Construct a Best Estimate Value Added Product (PrecipBE)

Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h −1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

Silber, Israel [Pacific Northwest National Laborat↗
DOE OSTI2024

Characteristics of Precipitation and Mesoscale Convective Systems Over the Peruvian Central Andes in Multi 5‐Year Convection‐Permitting Simulations

Abstract Using the Weather Research and Forecasting model with two planetary boundary layer schemes, ACM2 and MYNN, convection‐permitting model (CPM) regional climate simulations were conducted for a 6‐year period, including a one‐year spin‐up period, at a 15‐km grid spacing covering entire South America and a nested convection‐permitting 3‐km grid spacing covering the Peruvian central Andes region. These two CPM simulations along with a 4‐km simulation covering South America produced by National Center for Atmospheric Research (NCAR), three gridded precipitation products, and rain gauge data in Peru and Brazil, are used to document the characteristics of precipitation and mesoscale convective systems (MCSs) in the Peruvian central Andes region. Results show that all km‐scale simulations generally capture the spatiotemporal patterns of precipitation and MCSs at both seasonal and diurnal scales, although biases exist in aspects such as precipitation intensity and MCS frequency, size, propagation speed, and associated precipitation intensity. The 3‐km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4‐km simulation demonstrate superior performance in the western Amazon Basin, based on the comparison to the gridded precipitation products and gauge data. Dynamic factors, primarily low‐level jet and terrain‐induced uplift, are the key drivers for precipitation and MCS genesis along the east slope of the Andes, while thermodynamic factors control the precipitation and MCS activity in the western Amazon Basin and over elevated mountainous regions. The study suggests model improvements and better model configurations for future regional climate projections.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Back-to-back high category atmospheric river landfalls occur more often on the west coast of the United States

Abstract The catastrophic December 2022-January 2023 nine atmospheric rivers in California underscore the urgent need to better understand such high-risk weather extremes. Here we applied a machine learning clustering tool to understand the activity of atmospheric river clusters. Reanalysis results show that clusters with high density, that is the time fraction under atmospheric river conditions within a cluster, exhibit more frequent high-category atmospheric rivers, alongside an increased likelihood for extreme precipitation and severe land surface responses. The key circulation patterns of atmospheric river clusters are primarily attributed to subseasonal variability. Furthermore, the occurrence and density of atmospheric river clusters are modulated by the daily variability of the geopotential height field. Climate model projections suggest that atmospheric river clusters with higher density and higher categories will be more frequent as warming level increases. Our findings emphasize the important role of atmospheric river clusters in the development of climate adaptation and resilience strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Improving Short Term Predictability of Hydrologic Models with Deep Learning

Focal Area: Focal Area 2. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Science Challenge: A major challenge exists in the lack of reliable predictive modeling of water-cycle extremes using macro-scale hydrologic models that are driven by atmospheric climate data. This causes a critical knowledge gap in understanding the magnitudes, probabilities, and severity of droughts, rainfall, and flooding. Improved understanding of these extreme events requires more accurate modeling at high spatial and temporal resolutions. This shortcoming is especially apparent when complex couplings between atmospheric quantities and engineered systems emerge, such as during extreme precipitation or drought events and at intersections of atmospheric-land-fluvial-ocean systems. Added complexity around representing operations of engineered infrastructure leads to a limited capability to analyze the temporal and spatial implications of compounding extreme events across inland and coastal regions. This results in profound challenges for water and power systems operators, agriculture production, and regional infrastructure planning. The science challenge can be summarized as How can we use AI/ML to improve short term predictability of extreme water-cycle events and associated risk, especially compounding events and extreme rainfall patterns that result in flooding, to inland and coastal communities under different climate scenarios?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multipoint Observations of Quasiperiodic Emission Intensification and Effects on Energetic Electron Precipitation

The two Van Allen Probes simultaneously recorded a coherently modulated quasiperiodic (QP) emission that persisted for 3 h. The magnetic field pulsation at the locations of the two satellites showed a substantial difference, and their frequencies were close to but did not exactly match the repetition frequency of QP emissions for most of the time, suggesting that those coherent QP emissions probably originated from a common source, which then propagated over a broad area in the magnetosphere. The QP emissions were amplified by local anisotropic electron distributions, and their large-scale amplitudes were modulated by the plasma density. A novel observation of this event is that chorus waves at frequencies above QP emissions exhibit a strong correlation with QP emissions. Those chorus waves intensified when the QP emissions reach their peak frequency. This indicates that embryonic QP emissions may be critical for its own intensification as well as chorus waves under certain circumstances. A Low-Earth-Orbit POES satellite observed enhanced energetic electron precipitation in conjunction with the Van Allen Probes, providing direct evidence that QP emissions precipitate energetic electrons into the atmosphere. Finally, this scenario is quantitatively confirmed by our quasilinear diffusion simulation results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Understanding the Spatial Organization of Simultaneous Heavy Precipitation Events Over the Conterminous United States

We introduce the idea of simultaneous heavy precipitation events (SHPEs) to understand whether extreme precipitation has a spatial organization manifested as specified tracks or contiguous fields with inherent scaling relationships. For this purpose, we created a database of SHPEs using ground-based precipitation observations recorded by the daily Global Historical Climatology Network across the conterminous United States during 1900–2014. SHPEs are examined for their seasonality, spatial manifestation, orientation, and areal extent. We quantified the spatial distribution of the centroids and principal axes of SHPEs and their quasi-elliptical manifestations, azimuthal orientations, and areal extents on the ground. Four seasons, December-January-February (DJF), March-April-May (MAM),June-July-August (JJA), and September-October-November (SON) are considered to examine the spatial patterns and associated large-scale atmospheric circulations. Results indicate that there are 54, 58, 103, and204 SHPEs in DJF, MAM, JJA, and SON seasons, and their longest stretches range on average between 650and 1,600, between 850 and 1,500, between 950 and 1,550, and between 750 and 1,450 km, respectively. SHPEs in the DJF, MAM, and JJA seasons occur mostly over the Pacific coast and central and midwestern United States, respectively. The atmospheric circulation patterns and mechanisms of precipitable water vapor and moisture transport in the atmosphere are also discussed in relation to these SHPEs. Power laws explain SHPEs' underlying area scaling behavior in all the four seasons, with stronger evidence in DJF and MAM. A seasonal spatial risk model is developed to predict the likelihood of SHPEs. Quantifying the characteristics of SHPEs and modeling their footprints can improve the projections of flood risk and understanding of damages to interconnected infrastructure systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Clusters of Regional Precipitation Seasonality Change in the Community Earth System Model, Version 2

Abstract The likely changes to precipitation seasonality with warming are both impactful and not well understood. This work aims to describe areas that experience similar changes to seasonal precipitation irrespective of the original underlying precipitation seasonality. We train a self-organizing map on the difference between the seasonal cycle of precipitation in the past and in a high-warming future climate as represented by the Community Earth System Model, version 2, to create regions with similar changes in precipitation seasonality. This method is applied separately over land and ocean surfaces because of the differing processes leading to precipitation over each. This method indicates that future changes in seasonal precipitation are most varied in the tropics because of a southward shift in the intertropical convergence zone. The seasonal shifts found over midlatitude oceans indicate a poleward shift in atmospheric river activity. We find a correspondence between certain land-based precipitation changes and Köppen climate classification. The seasonality of large-scale and convective precipitation is examined for each region. The relationship between the seasonal changes to precipitation and associated atmospheric processes is discussed. These processes include atmospheric rivers, the intertropical convergence zone, tropical cyclones, and monsoons.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Detection of visible-wavelength aurora on Mars

Mars hosts various auroral processes despite the planet’s tenuous atmosphere and lack of a global magnetic field. To date, all aurora observations have been at ultraviolet wavelengths from orbit. We describe the discovery of green visible-wavelength aurora, originating from the atomic oxygen line at 557.7 nanometers, detected with the SuperCam and Mastcam-Z instruments on the Mars 2020 Perseverance rover. Near–real-time simulations of a Mars-directed coronal mass ejection (CME) provided sufficient lead-time to schedule an observation with the rover. The emission was observed 3 days after the CME eruption, suggesting that the aurora was induced by particles accelerated by the moving shock front. To our knowledge, detection of aurora from a planetary surface other than Earth has never been reported, nor has visible aurora been observed at Mars. This detection demonstrates that auroral forecasting at Mars is possible, and that during events with higher particle precipitation, or under less dusty atmospheric conditions, aurorae will be visible to future astronauts.

Science & Technology - Other Topics↗
DOE OSTI2019

Nonlinear Climate Responses to Increasing CO2 and Anthropogenic Aerosols Simulated by CESM1

Abstract Atmospheric CO2 and anthropogenic aerosols (AA) have increased simultaneously. Because of their opposite radiative effects, these increases may offset each other, which may lead to some nonlinear effects. Here the seasonal and regional characteristics of this nonlinear effect from the CO2 and AA forcings are investigated using the fully coupled Community Earth System Model. Results show that nonlinear effects are small in the global mean of the top-of-the-atmosphere radiative fluxes, surface air temperature, and precipitation. However, significant nonlinear effects exist over the Arctic and other extratropical regions during certain seasons. When both forcings are included, Arctic sea ice in September–November decreases less than the linear combination of the responses to the individual forcings due to a higher sea ice sensitivity to the CO2-induced warming than the sensitivity to the AA-induced cooling. This leads to less Arctic warming in the combined-forcing experiment due to reduced energy release from the Arctic Ocean to the atmosphere. Some nonlinear effects on precipitation in June–August are found over East Asia, with the northward-shifted East Asian summer rain belt to oppose the CO2 effect. In December–February, the aerosol loading over Europe in the combined-forcing experiment is higher than that due to the AA forcing, resulting from CO2-induced circulation changes. The changed aerosol loading results in regional thermal responses due to aerosol direct and indirect effects, weakening the combined changes of temperature and circulation. This study highlights the need to consider nonlinear effects from historical CO2 and AA forcings in seasonal and regional climate attribution analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Aerosol Decline Accelerates the Increasing Extreme Precipitation in China

Extreme precipitation is becoming more intense and frequent. The increasing trends in extreme precipitation in China in warm season related to changes in aerosols and greenhouse gases (GHGs) are investigated using observations, reanalysis data and model simulations. A significant accelerating increase in extreme precipitation occurred around 2010, with the trend in accumulated extreme rainfall amount (R95pTOT) increasing from 2.88 mm per decade during 2000–2010 to 22.88 mm per decade during 2010–2023. The sudden acceleration of the increasing extreme precipitation is largely attributed to the reverse in aerosol trends associated with China’s clean air actions, which affects extreme precipitation through perturbing cloud microphysics and atmospheric dynamics, accounting for half of the change in R95pTOT trends. Future aerosol reduction to achieve carbon neutrality is shown to continue to intensify the extreme precipitation, which overweighs the effect induced by GHGs, highlighting the importance of aerosol changes in modulating future climate and weather extremes.

58 GEOSCIENCES↗
DOE OSTI2025

Data and scripts associated with “Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics within Water-Tagging Enabled Hydrologic Models” (v3)

This data package is associated with the publication “Sequential Precipitation Input Tagging (SPIT) to Estimate Water Transit Times and Hydrologic Tracer Dynamics within Water-Tagging Enabled Hydrologic Models” submitted to Journal of Advances in Modeling Earth Systems (Butler et al. 2025). This study developed the Sequential Precipitation Input Tagging (SPIT) framework to tag input precipitation and estimate water transit times and hydrologic tracers. SPIT tags all precipitation events at regular intervals over an extended period (monthly tags over seven years) in a hydrologic model from 2016-2022. SPIT is applied at six National Ecological Observatory Network (NEON) sites across the continental United States to calculate transit time distributions (TTD) and derive from these mean transit times (MTT), fractions of young water (Fyw), and hydrologic tracer concentrations in stream water (δ18O) within a water-tagging enabled version of the Weather Research and Forecast (WT-WRF-Hydro) model with national water model (NWM) configurations. We go on to validate WT-WRF-Hydro estimates against Butler et al. (2023), who analyzed the same NEON sites using stable water isotope data to estimate water transit times. This new tracking method provides a detailed picture of water movement and helps improve predictions about water availability in the future. This data package was originally published in January 2025. It was updated May 2025 (v2; new and modified files) and October 2025 (v3; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. This data package contains the data and scripts used to develop the SPIT framework WT-WRF-Hydro (Water Tagging Weather Research and Forecasting Hydrologic) model and is associated with the following GitHub repository: https://github.com/zbutler33/SPIT-Framework. This data package contains five parent folders: (1) “Manipulated_outputs”, (2) “Metadata”, (3) “Observed”, (4) “Outputs”, and (5) “Scripts”. Each of these parent folders contains additional subfolders and files. Please see the FLMD (“v*_Butler_2024_WT_WRF_Hydro_flmd.csv”) for a list of all the files contained in this data package and descriptions for each. See the data dictionary (“v*_Butler_2024_WT_WRF_Hydro_dd.csv”) for definitions and units of all of the tabular (files ending in “.csv” and ".tsv") column headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Atmospheric Blocking and Other Large-Scale Precursor Patterns of Landfalling Atmospheric Rivers in the North Pacific: A CESM2 Study

Atmospheric rivers, narrow channels of strong winds carrying abundant moisture and spanning thousands of kilometers, often result in flooding. The broader weather patterns that precede atmospheric river formation can determine which regions the storms will impact, but our understanding of such processes remains insufficient. In this study, atmospheric river events in the North Pacific region are identified, and their precursor weather patterns are examined in observational data sets and within a global climate model. The analysis shows that when a large area of high pressure forms over Alaska and far eastern Siberia, the preferred path of storms crossing the North Pacific is diverted toward the south. The southward-shifted storms, some of which develop into atmospheric rivers, guide moist air toward the U.S. west coast and can produce extreme precipitation. The climate model performs reasonably well at representing atmospheric rivers and their associated precursor weather patterns. These results highlight important connections between atmospheric rivers and the large-scale weather patterns than can precede them by more than a week. Knowledge of such patterns has the potential to improve predictions of damaging wind and extreme precipitation events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Misaligned Wind‐Waves Behind Atmospheric Cold Fronts

Abstract Atmospheric fronts embedded in extratropical cyclones are high‐impact weather phenomena, contributing significantly to mid‐latitude winter precipitation. The three vital characteristics of the atmospheric fronts, high wind speeds, abrupt change in wind direction, and rapid translation, force the induced surface waves to be misaligned with winds exclusively behind the cold fronts. The effects of the misaligned waves under atmospheric cold fronts on air‐sea fluxes remain undocumented. Using the multi‐year in situ near‐surface observations and direct covariance flux measurements from the Pioneer Array off the coast of New England, we find that the majority of the passing cold fronts generate misaligned waves behind the cold front. Once generated, the waves remain misaligned, on average, for about 8 hr. The parameterized effect of misaligned waves in a fully coupled model significantly increases the roughness length (185%), drag coefficient (19%), and air‐sea momentum flux (11%). The increased surface drag reduces the wind speeds in the surface layer. The upward turbulent heat flux is weakly decreased by the misaligned waves because of the decrease in temperature and humidity scaling parameters being greater than the increase in friction velocity. The misaligned wave effect is not accurately represented in a commonly used wave‐based bulk flux algorithm. Yet, considering this effect in the current formulation improves the overall accuracy of parameterized momentum flux estimates. The results imply that better representing a directional wind‐wave coupling in the bulk formula of the numerical models may help improve the air‐sea interaction simulations under the passing atmospheric fronts in the mid‐latitudes.

Oceanography↗
DOE OSTI2023

The Role of Atmospheric Rivers on Groundwater: Lessons Learned From an Extreme Wet Year

Abstract In the coastal regions of the western United States, atmospheric rivers (ARs) are associated with the largest precipitation generating storms and contribute up to half of annual precipitation, but the impact of ARs on the integrated hydrologic cycle, specifically on groundwater storage and hydrodynamics, is largely unknown. To better explore the hydrologic behavior of AR versus non‐AR event precipitation, we present a novel combination of two water tracking methods (one in the atmosphere and one in the subsurface) to explicitly track the full lifecycle of water parcels generated by ARs. Simulations of northern California's Cosumnes River watershed during the record wet 2017 water year are performed via the coupling of a high‐resolution regional climate model and a land surface‐groundwater model accounting for lateral groundwater flow. Despite ARs contributing more precipitation than non‐AR storms, we find less AR water is preferentially stored in aquifers by year end. Fractionally, ARs result in 300% less snow derived groundwater‐recharged compared to non‐AR precipitation. Rain‐on‐snow (RoS) plays an important role in AR‐driven discharge, where over 50% of total discharge from ARs snow is from RoS events. Finally, despite record‐breaking annual precipitation, simulated groundwater depletion occurs by year end due to estimates of groundwater pumping activities. The results from these simulations serve as a partial analogue of future hydrologic conditions where ARs are expected to intensify and provide a greater fraction of annual precipitation due to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Direct online mass spectrometry measurements of ice nucleating particles at a California coastal site

The formation of ice in clouds can strongly impact cloud properties and precipitation processes during storms, including atmospheric rivers. Sea spray aerosol (SSA) particles are relatively inefficient as ice nucleating particles (INPs) compared to mineral dust. However, due to the vast coverage of the Earth’s surface by the oceans, a number of recent studies have focused on identifying sources of marine INPs, particularly in regions lacking a strong influence from dust. This study describes the integration, validation, and application of a system coupling a continuous flow diffusion chamber with a single particle mass spectrometer using a pumped counterflow virtual impactor to remove non-nucleated particles and selectively measure the composition of INPs with a detection efficiency of 3.10x10-4. In-situ measurements of immersion freezing INP composition were made at a coastal site in California using the integrated system. Mineral dust particles were the most abundant ice crystal residual type during the sampling period and found to be ice-active despite having undergone atmospheric processing. SSA were more abundant in ambient measurements but represented only a minor fraction of the ice crystal residual population at -31 ?C. Notably, the SSA particles that activated were enriched with organic-nitrogen species that were likely transferred from the ocean. Calculations of ice nucleation active site densities were within good agreement with previous studies of mineral dust and SSA.

Cornwell, Gavin C.↗
DOE OSTI2019

Structure and Performance of GFDL's CM4.0 Climate Model

We illustrate the Geophysical Fluid Dynamics Laboratory's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the preindustrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top-of-atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high-quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño-Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasiperiodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Amazon and La Plata River Basins as Moisture Sources of South America: Climatology and Intraseasonal Variability

Abstract Land‐atmosphere interactions are critical for precipitation (PPT) over South America where terrestrial evapotranspiration (ET) constitutes a significant fraction of moisture for rainfall over the ecologically and socio‐economically vital Amazon (AMZ) and La Plata (LPB) river basins. We quantify the contribution of ET from AMZ and LPB to PPT over the continent with a focus on the intraseasonal time scale. Using numerical water tracers embedded in the Weather Research and Forecasting model we track the moisture originating from the two basins. Our findings indicate that approximately 40% of annual rainfall over the eastern foothills of the Andes originates as AMZ ET, and nearly 30% of rainfall over northern Argentina originates as LPB ET. Analysis of moisture transport during both phases of the dominant intraseasonal oscillation pattern over South America reveals an intraseasonal “sloshing” of LPB moisture between the South Atlantic convergence zone (SACZ) and southeastern South America (SESA) regions. AMZ and LPB each supply approximately 6% of moisture for SACZ PPT during periods of intraseasonal enhancement (positive anomalies), highlighting the importance of moisture from the Atlantic Ocean. For the SESA region, LPB supplies 26% of the moisture for PPT during periods of intraseasonal enhancement while AMZ supplies 5%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Trends in Warm Season Mesoscale Convective Systems Over Asia in 2001–2020

Mesoscale convective systems (MCSs) frequently occur over Asia during the warm season, often producing intense precipitation with associated socioeconomic impacts. Here we reveal significant trends in MCS occurrence frequency and related precipitation in Asia during the warm season (March–September) in 2001–2020, using a tracking method that combines cloud and precipitation criteria with high-resolution satellite data from the Global Precipitation Measurement mission. To examine whether there are differences between MCSs of different scales, both meso-α scales (MαCSs) and meso-β scales (MβCSs), with horizontal scales of 200–2,000 km and 20–200 km, are tracked. The distribution pattern of frequency and related precipitation of both MαCSs and MβCSs are quite similar and manifest positive trends over East Asia (EA) and Northeast Asia, and negative trend over Southeast Asia (SEA). The MCS precipitation trend contributes significantly to total precipitation trend, with MαCSs contributing the most. Our analysis indicates the trend in lower-tropospheric water vapor flux convergence has a similar spatial pattern to the MCS frequency and related precipitation trend. Based on an atmospheric moisture flux decomposition analysis, the water vapor flux convergence trend can largely be explained by the change in horizontal wind convergence, while the specific humidity trend driven largely by temperature change plays a minor role. In conclusion, the trend in wind convergence in EA and SEA is possibly related to the evident trend in the lower-tropospheric anticyclone over the western North Pacific and SEA, which might be due to the relatively stronger warming in the Indian Ocean during the past two decades.

54 ENVIRONMENTAL SCIENCES↗