Total Precipitation Research Articles

Hydro-meteorological dynamics of rainfall erosivity risk in the Amazon River Delta-Estuary

ABSTRACT The Amazon River Delta-Estuary region presents a scenario of high annual precipitation totals, governed by large- and meso-scale hydrometeorological events. Intense precipitation can lead to significant erosive susceptibility in the region, depending on hydrometeorological influences and different types of land use and cover. This study aimed to assess rainfall erosivity risk in a time series spanning from 1985 to 2022, with the hypothesis that hydrometeorological events, as well as land use and cover, influence this factor. The dynamics of erosivity were evaluated from the perspective of the influence of hydrometeorological events and land use and cover types. Rainfall erosivity (R) ranged from 10078,7 to 13975,16 MJ·mm−1·ha−1·h−1·year−1, classified as very high erosivity in all years of the study period. The erosive potential showed climatic variability according to the positive and negative phases of the Atlantic Meridional Mode (AMM), and the warm (El Niño) and cold (La Niña) phases of the El Niño-Southern Oscillation (ENSO). The R-factor is positively related to natural non-forest formation, agriculture, non-vegetated area, and wetland ecosystems; being influenced by hydrometeorological events and spatially by different types of land use and cover, confirming the hypothesis raised.

Basin-informed flood frequency analysis using deep learning exhibits consistent projected regional patterns over CONUS

Climate change poses a significant threat to flood-prone areas by altering precipitation patterns and the water cycle. Here, we analyzed the impact of climate change on future flood trends. We trained a Long Short-Term Memory (LSTM) model to estimate long term discharge at 638 river sites over contiguous United States (CONUS) based on inputs from the gridMET meteorological datasets, and downscaled and bias-corrected Coupled Model Intercomparison Project 5 (CMIP5) projections. Our results indicate that the LSTM model can replicate observed discharge with reliable accuracy. The projected changes in flood magnitude for the 10-year and 100-year return periods reveal consistent geographical patterns robust across climate models, with increasing trends of approximately + 10 to + 40% in the East and West coastal regions and decreasing trends of about − 10 to − 30% in the Southwestern areas. The regions exhibiting an increasing flood trend are likely driven by an increase in total seasonal extreme precipitation and changes in the timing and amount of peak flow. In contrast, the decreasing flood trends result from a significant reduction in snowpack. To support adaptation planning, we developed an interactive map providing the historical and projected flood changes for 10- and 100-year floods across the 638 selected basins over CONUS.

Evaluating the ability of gridded climate datasets to capture temperature and precipitation trends and extremes

Data informs policy and drives decisions. With an increasing number of regulations requiring entities to use local climate data in their planning, it’s more important than ever to understand the strengths and limitations of data we use. While they have been shown to capture long-term statistics on global or regional levels, the ability of gridded climate datasets to capture trends and extreme events is not common knowledge. Four widely used gridded datasets, ERA5, ERA5-Land, MERRA-2, and PRISM, were assessed for their ability to capture extreme heat, extreme cold, and heavy precipitation events, as well as trends in annual maximum and minimum temperatures and total precipitation, over the contiguous US (CONUS). Spatial patterns are evident in each dataset, with the largest differences between observations and the gridded data across the western US for temperature and along the Gulf Coast for heavy precipitation. In general, gridded datasets captured extreme heat better than extreme cold or heavy precipitation, and trends in annual maximum temperature better than trends in annual minimum temperatures and annual total precipitation. All dataset capture extreme heat days comparably, but PRISM generally performed best for extreme cold and the bias-adjusted MERRA-2 dataset generally performed best for heavy precipitation days.

Mesoscale Convective Systems Tracking Method Intercomparison (MCSMIP): Application to DYAMOND Global km‐Scale Simulations

AbstractGlobal kilometer‐scale models represent the future of Earth system modeling, enabling explicit simulation of organized convective storms and their associated extreme weather. Here, we comprehensively evaluate tropical mesoscale convective system (MCS) characteristics in the DYAMOND (DYnamics of the atmospheric general circulation modeled on non‐hydrostatic domains) simulations for both summer and winter phases. Using 10 different feature trackers applied to simulations and satellite observations, we assess MCS frequency, precipitation, and other key characteristics. Substantial differences (a factor of 2–3) arise among trackers in observed MCS frequency and their precipitation contribution, but model‐observation differences in MCS statistics are more consistent across trackers. DYAMOND models are generally skillful in simulating tropical mean MCS frequency, with multi‐model mean biases ranging from −2%–8% over land and −8%–8% over ocean (summer vs. winter). However, most DYAMOND models underestimate MCS precipitation amount (23%) and their contribution to total precipitation (17%). Biases in precipitation contributions are generally smaller over land (13%) than over ocean (21%), with moderate inter‐model variability. While models better simulate MCS diurnal cycles and cloud shield characteristics, they overestimate MCS precipitation intensity and underestimate stratiform rain contributions (up to a factor of 2), particularly over land, albeit observational uncertainties exist. Additionally, models exhibit a wide range of precipitable water in the tropics compared to reanalysis and satellite observations, with many models showing exaggerated sensitivity of MCS precipitation intensity to precipitable water. The MCS metrics developed here provide process‐oriented diagnostics to guide future model development.

Patterns of Snow Drought Under Climate Change: From Dry to Warm Dominance

AbstractGlobal warming may trigger more frequent snow droughts (SD). SD can result from low total precipitation (dry‐SD), high temperatures leading to less solid precipitation (warm‐SD), or a combination of both (dry‐warm compound SD). Those three SD types threaten ecosystems differently. Nevertheless, the regions dominated by SD types, the transition patterns, and future risks under climate change remain unclear. We investigated the dominance of the three SD types and their transition patterns across historical and future periods. By 2100, compared to 1981, the results project global increases in SD frequency by more than 3‐fold and 4‐fold under SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Moreover, the share of warm SD is increasing and projected to account for 65% of the three SD types by 2050. Compared to historical period, the probabilities of dry‐warm compound SD and warm SD in the future period is expected to increase by 3.7 and 6.6 times, respectively.

Thermodynamic and Dynamic Changes in the Japan Sea Polar Air Mass Convergence Zone and Its Associated Heavy Snowfall Under Warmer Climate

AbstractThe Japan Sea polar air mass convergence zone (JPCZ) has the potential to cause heavy snowfall events several times per year in the west side of the Japanese archipelago. The associated severe snowfall can cause adverse effects on the social, economic, and ecological well‐being in the affected areas. Therefore, understanding the changes of JPCZ and its associated snowfall under warmer climate conditions is of high importance. In the present study, pseudo global warming (PGW) experiments, considering the 2050s under SSP5‐8.5 scenario, were carried out to evaluate these thermodynamic and dynamic changes. The amount of PGW experiment‐simulated snowfall in 13 historical cases during 2015–2024 were in good agreement with ground observations. Warmer climate conditions can potentially reduce the amount of heavy snowfall as statistically supported by the t test, though increase the rainfall mainly due to the shift in hydrometeors in the convective zone. On the other hand, total precipitation does not significantly change, likely due to almost no changes in the sum of latent and sensible heat fluxes. Further, dynamic changes are not significant as the position of the convergence zone seems not to be different between PGW and historical climate, though local shifts in the convergence zone were simulated probably due to the changes in the geopotential fields and perturbed wind velocity in the lateral boundary. Our study shows that thermodynamic changes of the JPCZ can be significant, and it has the potential to change the ratio of snowfall within the total precipitation from 57.7% under historical condition to 34.7% under warmer climates.

Spatio-Temporal Patterns and Drivers of the Urban Heat Island Effect in Arid and Semi-Arid Regions of Northern China

Investigating the urban heat island (UHI) effect and its driving factors is crucial for supporting future climate mitigation actions and human adaptation strategies. Due to the unique climatic characteristics and vulnerable ecological environment of arid and semi-arid regions, it is valuable to detect the UHI effect in cities in these regions, which have not been fully explored yet. Utilizing moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data from 2010 to 2020, this study quantified the summer, winter, and annual diurnal mean surface urban heat island intensity (SUHII) of 30 cities in the arid and semi-arid regions of northern China and comprehensively investigated the spatio-temporal patterns and drivers of UHI. The results showed that the annual mean daytime SUHII had a significant decreasing trend, and the nighttime SUHII had an increasing trend for these cities between 2010 and 2020. The nighttime SUHII was stronger than the daytime SUHII, and some cities exhibited surface urban cool island (SUCI) phenomena during daytime, especially in winter. It was also found that cities at higher latitudes experienced higher daytime SUHII throughout the year, and that it was more pronounced in winter. The driving factor analysis revealed that daytime SUHII was primarily influenced by the urban area size (UAS), total precipitation (TP), and the differences in white sky albedo (ΔWSA), enhanced vegetation index (ΔEVI), and normalized difference moisture index (ΔNDMI) between urban and suburban areas. Nighttime SUHII was mainly correlated with ΔWSA, ΔEVI, ΔNDMI, and the differences in elevation (ΔDEM) between urban and suburban areas. This indicated that the background climate was a potential driver for the spatial pattern of UHI in this region. As for the nightlight difference between urban and sub-urban areas (ΔNTL), no correlation was observed with neither daytime SUHII nor nighttime SUHII. These findings are promising in providing theoretical support and scientific guidance for formulating sustainable development strategies and mitigating the UHI effects of cities in the arid and semi-arid regions.

Legacy and currently-used pesticides in sedimentary archives: Anthropogenic footprint in the pearl river estuary.

Legacy and currently-used pesticides in sedimentary archives: Anthropogenic footprint in the pearl river estuary.

Trends of Extreme Precipitation Events in Serbia Under the Global Warming

This paper examines extreme precipitation events (EXPEs) and their trends based on daily precipitation values observed at 14 stations in Serbia for the period 1961–2020. The following EXPEs were investigated: RR10mm (heavy precipitation days), RR20mm (very heavy precipitation days), Rx1day (highest 1-day precipitation amount), Rx3day (highest 3-day precipitation amount), Rx5day (highest 5-day precipitation amount), R95p (very wet days) and R99p (extremely wet days). A positive trend for all EXPEs was dominant in Serbia from 1961 to 2020. All annual Rx1day time series show a positive trend, which is significant at 12 out of 14 stations. The highest values of all EXPEs were observed in 2014, when the annual precipitation totals were the highest at almost all stations in Serbia. To examine the potential influence of global warming, the mean values of the EXPEs were calculated for two periods: 1961–1990 and 1991–2020. In the second period, higher values were determined for all EXPEs than in the first period. The large-scale variability modes, such as the North Atlantic Oscillation (NAO), the East Atlantic Oscillation (EA), and the East Atlantic–West Russia (EAWR) pattern, were correlated with the EXPEs. A negative correlation was found between the EXPEs and the NAO and the EAWR, and a positive correlation between the EXPEs and the EA pattern. For future research, the contribution of high-resolution data will be examined.

Evaluation of climate indices related to water resources in Iran over the past 3 decades

Climate change has significantly altered water resources globally. This study examines the effects of climate change on Iran’s water resources using twelve climate indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) from 1992 to 2022. Daily climate data were obtained from the Global Surface Summary of the Day (GSOD) database, which includes quality-controlled data from Iran’s Meteorological Organization. The GSOD data undergo extensive automated quality control processes to eliminate random errors and ensure data reliability. Long-term trends were analyzed using linear regression, and spatial patterns were identified using K-means and hierarchical clustering methods. Analysis of 29 provincial capitals revealed significant trends in precipitation and temperature patterns. Temperature indices showed widespread increases, with 75.86% of cities experiencing significant rises in Mean daily mean temperature (TMm) (p < 0.05, up to 0.16 °C/year) and 96.55% showing increases in Mean daily maximum temperature (TXm). Heavy precipitation indices showed significant decreases (p < 0.05): 11.11% of cities for Annual Total Precipitation from Very Wet Days (R95pTOT) and 22.22% for Extremely Wet Days (R99pTOT). Annual Total Wet-Day Precipitation (PRCPTOT) showed spatial variation, with Rasht showing a significant increase (19.07 mm/year, p < 0.05), while Bojnourd exhibited a decrease (− 4.28 mm/year, p > 0.05). The Consecutive Dry Days (CDD) index showed a significant increase in 14.81% of the cities, with Khorramabad experiencing the highest increase of 4.4 days/year (p < 0.05). Fraction of days with above average temperature (TXgt50p) increased significantly in over 70% of the cities. Study limitations include incomplete data for Karaj and Qom, and missing precipitation records for Kermanshah and Sari. These findings reflect significant impact of climate change on Iran’s climate patterns, indicating increased water scarcity. Effective water resource management and climate adaptation are crucial.

Changes in extreme precipitation patterns over the Greater Antilles and teleconnection with large-scale sea surface temperature

Abstract. This study examines changes in extreme precipitation over the Greater Antilles and their correlation with large-scale sea surface temperature (SST) for the period 1985 to 2015. The data used for this study were derived from two satellite products, Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) version 2.1, with resolutions of 5 and 25 km, respectively. Then, changes in the characteristics of six extreme precipitation indices defined by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI) are analyzed, and Spearman's correlation coefficient is used and evaluated by t test to investigate the influence of a few large-scale SST indices: (i) Caribbean Sea Surface Temperature (SST-CAR), (ii) Tropical South Atlantic (TSA), (iii) Southern Oscillation Index (SOI), and (iv) North Atlantic Oscillation (NAO). The results show that, at the regional scale, +NAO contributes significantly to a decrease in heavy precipitation (R95p), daily precipitation intensity (SDII), and total precipitation (PRCPTOT), whereas +TSA is associated with a significant increase in daily precipitation intensity (SDII). On an island scale, in Puerto Rico and southern Cuba, the positive phases of +TSA, +SOI, and +SST-CAR are associated with an increase in daily precipitation intensity (SDII) and heavy precipitation (R95p). However, in Jamaica and northern Haiti, the positive phases of +SST-CAR and +TSA are also associated with increased indices (SDII, R95p). In addition, the SST warming of the Caribbean Sea and the positive phase of the Southern Oscillation (+SOI) are associated with a significant increase in the number of rainy days (RR1) and the maximum duration of consecutive wet days (CWD) over the Dominican Republic and in southern Haiti.

Selection of the best-performing climate reanalysis model for the Republic of Sakha (Yakutia) based on mean annual precipitation

Regional scenario-based projections require a comprehensive understanding of baseline climatic conditions, particularly the spatial distribution of total mean annual precipitation within the region. Precipitation data from 40 meteorological stations across the Republic of Sakha (Yakutia) for the period from 1961 to 2020 were used to evaluate the performance of modern reanalyses: CRU TS, ERA5-Land, GPCC, NCEP-NCAR, PREC/L, and JRA55. The performance of each model was assessed by comparing the observed mean annual precipitation to the reanalysis field values in pixels corresponding to the locations of the observation points. The statistical assessment employed Lin’s coefficient of concordance, Wilmott’s index of agreement, Kendall’s tau, and the root mean square error. Interpolation-based models (CRU TS, GPCC, PREC/L) demonstrated a superior ability to reproduce observed total precipitation, whereas modeling-based reanalyses tended to overestimate it by more than 100 mm/year, or by 30% to 50%. The GPCC reanalysis exhibited the best performance when compared to observations; however, it appeared to be significantly overfitted, as evidenced by a substantial negative spatial correlation between total precipitation coverages for the periods from 1961 to 1990 and 1991 to 2020. Consequently, the interpolation uncertainty associated with overfitting precludes the use of GPCC data as a reliable benchmark. Ultimately, the CRU TS 4 reanalysis was determined to be optimal as a baseline for total precipitation coverage. According to CRU TS 4 data, the mean annual precipitation across the Republic of Sakha (Yakutia) was 285 ± 81 mm for the period from 1961 to 1990 and 293 ± 92 mm for 1991 to 2020, indicating an insignificant change of 8 ± 18 mm. Thus, between the two climatic periods, the annual precipitation in the Republic of Sakha (Yakutia) increased by 8 ± 18 mm, a change that is not statistically significant.

Changes in Precipitation Patterns in Poland Derived From Projected Downscaled Future Climate Data From CMIP5 and CMIP6

ABSTRACTClimate change is affecting the intensity and frequency of precipitation. The main objective was to assess future changes in precipitation patterns in Poland. Ensembles of daily precipitation projections for 70 stations from CMIP5 and CMIP6 under RCP(SSP)4.5, RCP(SSP)8.5 pathways were statistically downscaled using the Quantile Perturbation Method (QPM), covering the reference period 1961–1990 and future period 2071–2100. We assessed annual and seasonal (winter, summer) changes in 12 extreme Precipitation Indices (PIs), their distributions across Poland, and shifts in design annual maximum (AM) precipitation intensities. Statistical measures included distribution fitting, Intensity‐Duration‐Frequency curves, and return periods. The projected changes (CMIP6‐8.5) in summer include: increase in the length of consecutive dry days (5%, on average), number of heavy precipitation days (4%) and 1‐, 3‐, 5‐day maximum intensity (8%, 6%, 5%), and decrease in the number of wet days (6%) and length of consecutive wet days (6%). In winter, projections show an increase in the number of heavy precipitation days (30%), 1‐,3‐, 5‐day maximum intensity (15%, 13%, 12%), and total precipitation (11%). The changes vary across Poland, with a more intense increase in the number of heavy precipitation days in the north‐west (summer) and in the 1‐day maximum intensity in the south (winter), higher precipitation totals in the south, southeast and coastal areas (winter), and a decrease in total precipitation in the south and east (summer). Uncertainty is large for the number of heavy precipitation days and maximum intensities, while it is low for total precipitation and the number of wet and dry days. Future return periods of extreme events are projected to shorten. A 100‐year 1‐day AM intensity can become a ‐year intensity. The results can be applied in flood and drought management plans, helping to adapt to future changes in precipitation patterns.

Relationships Between Temperature and Precipitation Conditions and Tree- Ring Growth in Küre Mountains National Park in the Context of Climate Change

Aim of study: The aim of this study was to determine the effect of temperature and precipitation on annual ring growth at the upper and lower growth limits of Scots pine, fir and black pine in the Küre Mountains National Park. Area of study: Küre Mountains National Park located in the Western Black Sea part of the Black Sea Region, is located between the borders of Pınarbaşı, Azdavay, Cide and Şenpazar districts of Kastamonu province and the central and Ulus, districts of Bartın province. This park is one of the 9 Hotspots in Türkiye. Material and method: For dendrochronological analyses, samples were taken from the lower and upper growth limits of Scots pine, fir and black pine using increment borers. Measurements of the samples taken were made using the LINTAB-TSAP measurement system with a sensitivity of 0.01 mm. In the analysis of climate-tree ring relations, climate data with a resolution of 0.5° x 0.5° grid, taken from the website of the World Meteorological Organization Regional Climate Center, were used. Response Functions were calculated to reveal the statistical relationships between climate data and tree ring growth. Main results: According to our results, tree ring growth on trees in the research area is positively affected by the increases in monthly total precipitation in June and July for all site all chronologies and negatively affected by November of the previous year and January, February, September and October of the current year. Research highlights: In this study, a positive relationship was found between precipitation and tree-ring growth in March-August. Temperature tree-ring relationships were positive in spring in all sites.

Exploring the driving mechanism path of ecosystem service relationships based on the social-ecological system framework

Understanding the driving mechanisms behind ecosystem service (ES) trade-offs and synergies is crucial for sustainable ecosystem management. This study integrates the Social-Ecological System Framework (SESF) with path analysis to explore the relationships among crop production (CP), water retention (WR), and soil conservation (SC) in Shanxi Province, China. We identified six key driving factors—annual mean temperature (Tem), total annual precipitation (Pre), Net Primary Productivity (NPP), per capita GDP (GDP), agricultural, forestry, and water fiscal expenditure (Exp), and urban and rural per capita disposable income (Inc)—to analyze their direct and indirect influences on ES interactions over the years 2000, 2010, and 2020. Results show that CP and WR exhibit a persistent trade-off, while CP and SC maintain a synergistic relationship. WR and SC display a trade-off in static time points but shift towards synergy in long-term changes. Path analysis confirms that natural factors (Tem, Pre, and NPP) dominate short-term ES dynamics, whereas socio-economic variables (GDP, Exp, and Inc) play a greater role in long-term ES changes. Mediation analysis reveals that NPP partially mediates climate effects on ESs, while Inc mediates the influence of GDP on ESs. However, Exp does not significantly affect ESs through Inc, likely due to policy implementation delays and regional economic priorities. Findings suggest that spatially targeted policies should be implemented to optimize ES relationships. Long-term ES governance should integrate ecological restoration strategies with socio-economic incentives to enhance ES sustainability.

Detection of driving factors and critical thresholds for carbon sequestration capacity in urban agglomerations using a combined causal inference and machine learning approach

ABSTRACT The carbon sequestration capacity in urban agglomeration ecosystems is crucial for enhancing scientific understanding of the carbon cycle and promoting sustainable development to mitigate climate change. However, existing studies on driving factors, particularly regarding determining causal mechanisms and critical thresholds for carbon sequestration in urban agglomeration ecosystems remain unclear. To address this knowledge gap, we propose a CMSC framework which integrates causal inference and machine learning methods to reveal the underlying mechanisms and determine the thresholds of drivers affecting carbon sequestration in the Yangtze River Delta urban agglomeration (YRDUA). The underlying driving mechanisms of carbon sequestration were heterogeneous between municipal county and non-municipal county in YRDUA. The thresholds of nighttime light, surface solar radiation downwards, air temperature, total precipitation and population density that impacted carbon sequestration in non-municipal (municipal) counties of YRDUA were 0.04 (0.4) nW·cm−2·sr−1·yr−1, −6.1 × 104 (−5.46 × 104) J·m−2·yr−1, 0.013 (0.017) K·yr−1, 3.64 × 10−5 (2.51 × 10−5) m·yr−1 and −0.04 people·km−2·yr−1, respectively. Furthermore, the long-term (from 2021 to 2100) carbon sequestration dataset with county-level scale in the YRDUA was generated using the causal inference-based machine learning model. In the context of carbon neutrality, we found that the optimal emission scenario for low-carbon sustainable development of YRDUA is SSP3, under which the average carbon sequestration in most counties will exceed 1 × 107 t. Our study provides a constructive basis for a science-based carbon cycle and ecological management in the urban agglomeration of China.

Changes in the Distribution of Precipitation with the Potential to Cause Extreme Events in the State of Rio de Janeiro for a Future Climate Change Scenario

Climate change can alter the frequency and magnitude of extreme precipitation events (EPEs), both in terms of scarcity and excess, impacting society as a whole. The aim of this study was, therefore, to identify changes in the distribution of precipitation with the potential to cause extreme events in the state of Rio de Janeiro (SRJ) for current and future climate change scenarios. Climate change indices were selected that refer to changes in the distribution and magnitude of rainfall events for the state of Rio de Janeiro. The analysis was carried out for the historical period between 2000 and 2020 and for future climate change scenarios between the years 2021 and 2100. The analysis for future climate change scenarios was carried out using data from climate models of the general circulation of the atmosphere (CMIP-6) for future climate change scenarios SSP 4.5 and SSP 8.5. Total annual precipitation in the SRJ by the end of the 21st century will be reduced by between 24% and 47% for the intermediate and pessimistic scenarios, respectively. The projections also indicate an increase in the number of consecutive dry days, which could be greater than 130% in the pessimistic scenario, and a reduction in consecutive wet days. An increase in the number of humid and extremely humid days is also projected for the SRJ, which could increase the EPEs.

Speleothem evidence of solar modulation on the south Asia monsoon intensity

The influence of solar variation on climate has long been debated. Here, we utilize a decadal-resolved speleothem δ18O record from Vietnam, spanning 32.5 to 27.5 kyr BP, as a proxy for regional precipitation levels. Our results show a general coherence between Total Solar Irradiance (TSI) and regional precipitation, supporting a positive climate response consistent with conventional monsoon theory. Spectral analysis on studied datasets reveals an approximately 180-year periodicity coinciding with the de Vries cycle of solar activity. Further comparing our record with 35 other speleothem records, we demonstrate the importance of sufficient age control points in capturing solar-related periodicities. Model simulation shows that TSI could enhance monsoonal circulation and regional precipitation. Also highlighted are the implications of chronology control for detecting climate events in proxy records. The new findings underscore the significance of relatively minor radiative forcing in regional climate dynamics over monsoonal Asia on decadal to centennial timescales.

Insights of aerosol-precipitation nexus in the central Arctic through CMIP6 climate models

The Arctic is experiencing heightened precipitation, affected by aerosols impacting rainfall and snowfall. However, sparse aerosol observations in the central Arctic cryosphere contribute to uncertainties in simulating aerosol-precipitation two-way interaction. This study examines aerosol-precipitation co-variation in various climate models during the Arctic spring and summer seasons from 2003 to 2011, leveraging satellite-based aerosol data and various CMIP6 climate models. Findings reveal significant spatio-temporal biases between models and observations. Snowfall dominance occurs in models where total AOD surpasses the observation by 121% (57–186%, confidence interval), intensifying simulated snowfall by two times compared to rainfall during summer. Consequently, climate models tend to underestimate central Arctic rainfall to the total precipitation ratio, suggesting a positive bias towards snowfall dominance. This highlights the importance of constraining total AOD and associated aerosol schemes in climate models using satellite measurements, which potentially could lead to a substantial reduction in snowfall contribution to the total precipitation ratio in the central Arctic, contrary to current multi-model simulations across various spatiotemporal scales.

Spatial–Temporal Variability of Hourly Precipitation Extremes in Portugal: Two Case Studies in Major Wine‐Growing Regions

ABSTRACTHeavy precipitation events are a natural hazard that have significant socioeconomic implications, especially in sectors like agriculture, namely viticulture. This study is the first to present a detailed climatology of extreme precipitation across mainland Portugal, leveraging hourly data gathered from 71 meteorological stations over 23 years, spanning from 2000 to 2022. To understand spatial variability, we focus on Douro and Alentejo, two major winemaking regions where extreme weather events may have significant adverse consequences. High spatial variability in the β‐index patterns (mean ratio between over‐threshold precipitation and total precipitation) shows that hourly precipitation between 10 and 20 mm h−1 contributes the most to winter and autumn precipitation, whereas events exceeding 20 mm h−1 play a more significant role in summer and autumn. Extreme events occur most frequently between September and December, with a secondary peak observed in April and May, particularly pronounced in Alentejo. The diurnal cycle, which displays a peak in the afternoon, exhibits a close relationship with the occurrence of thunderstorms. A more in‐depth analysis was conducted to examine the dynamic and thermodynamic mechanisms that contributed to two extreme precipitation events caused by thunderstorms in the Douro and Alentejo winemaking regions. Differing synoptic environments led to unstable conditions at the origin of these events. The Douro event was driven by a cut‐off low, whereas the Alentejo event was related to an extratropical cyclone, both in their final stages of development. The regional indices and seasonal patterns identified offer insights into spatial distribution and seasonality, essential for future research and applied climate adaptation strategies.