Extreme precipitation events in the southeastern United States pose significant challenges for numerical weather prediction because subgrid-scale processes strongly influence rainfall intensity, spatial distribution, and timing. This study assesses the sensitivity of Weather Research and Forecasting (WRF) Model v4.7 precipitation simulations during an intense rainfall episode over Alabama from 27 April to 3 May 2014. Stage IV observations indicated localized precipitation totals exceeding 200 mm, with a domain-mean accumulation of 42.48 mm. A 12-member physics ensemble was created by pairing two double-moment microphysics schemes, WDM6 and Morrison, with six cumulus parameterization schemes: Grell–Devenyi (GD), Grell–Freitas (GF), Betts–Miller–Janjic (BMJ), Kain–Fritsch (KF), KF-CuP, and Tiedtke. Model outputs were regridded to the 4 km Stage IV analysis grid and evaluated using conventional error statistics alongside spatial verification metrics, such as the Fractions Skill Score (FSS), spatial correlation, and temporal diagnostics. This multifaceted evaluation mitigated sensitivity to minor spatial displacement errors, enabling a more nuanced assessment of forecast performance. In addition, two simulations were conducted without cumulus parameterization as a bounding check on this modeling choice at the 4 km, gray-zone grid spacing. Within this experimental design, the choice of cumulus parameterization was the largest source of spread in precipitation forecast skill, strongly affecting storm-total rainfall, spatial distribution, and peak precipitation timing. Configurations using the KF family of schemes typically provided the most accurate representation of organized heavy rainfall characteristics, whereas the BMJ scheme notably underestimated both precipitation intensity and areal coverage. Although the effects of microphysics were secondary, distinct patterns emerged: the Morrison scheme generally improved volumetric precipitation accuracy, whereas WDM6 concentrated extreme rainfall into more localized cores when paired with aggressively triggered cumulus schemes. Spatial verification techniques uncovered performance differences that traditional grid-point statistics did not fully capture, allowing us to identify the most effective physics configuration among those tested. However, overall forecast skill remained limited; none of the configurations met the commonly accepted useful-skill threshold (FSS ≥ 0.5) for the ≥200 mm precipitation category at any neighborhood scale analyzed. This underscores the ongoing challenges in accurately representing the most intense rainfall cores. Because this analysis is based on a single extreme event, caution is warranted when extrapolating these findings. Further evaluations across a wider array of storm environments are essential to substantiate the identified model sensitivity.
Estimating long-term changes in seasonal snow cover and the conditions of the snow cover period is a key task in climate research. The study analyzes changes in snow cover extent and winter climatic parameters in the south of the Central Russian Upland of the East European Plain in the first decades of the 21st century. Assessments were conducted using MODIS snow products and meteorological observation data. In the period 2000–2025, a trend of decreasing average area of snow cover in winter was established. In 2020–2025, the average snow cover in November–March was 18% less than in the same period in 2000–2005. A decrease in snow cover extent occurred at the beginning and end of the winter period. Time series of snow cover area showed a negative trend for all months of the winter period except January. The snow cover area in 2020–2025 compared with 2000–2005 showed a considerable decrease in November, December, and March. The trend towards a decrease in snow cover extent occurred simultaneously with a trend towards warmer winters. The average temperature of the winter period showed a statistically significant positive trend. At the same time, the average temperature showed no considerable changes during the middle of the winter period. The average November–March temperature in 2020–2025 was 1.5 °C higher than in 2000–2005. Among the individual months of the winter period, the average temperature showed an increase in November, December, and March. At the same time, winter precipitation did not change significantly.
Compound hot–dry extremes (CHDEs)—manifested through the concurrent occurrence of extreme daily atmospheric temperatures and precipitation deficits (D-CHDEs: daily maximum temperature Tmax ≥ calendar-day 90th percentile, TX90p, and daily precipitation RR < 1.0 mm), compounded by multi-scalar seasonal evaporative drought (SPEI-3, SPEI-6) and root-zone soil moisture depletion—represent one of the most destructive hydrometeorological hazards facing contemporary European agriculture, freshwater security, and socioeconomic infrastructure. While pan-European climate risk assessments predominantly focus on Western and Mediterranean domains, Southeastern Europe and the Lower Danube Basin (predominantly Romania) have emerged as acute, fast-accelerating climate change hotspots characterized by amplified summer warming, rapid evaporative drying, and heightened vulnerability of rainfed agricultural systems. In this study, we provide a comprehensive empirical and modeled assessment of multi-decadal historical trajectories (1970–2025) and near-future projections (2030–2060) of thermal extremes (ETCCDI: SU30, SU35, TR20, TX90p), multi-scalar agricultural drought (SPEI-3, SPEI-6, CDDs), and compound hot–dry frequencies across five agro-climatic sub-regions of Romania benchmarked against pan-European reference domains (MED, CEU, WEU, and EU-27 average) using high-resolution observational datasets (E-OBS v30.0e extended through 2025 via official C3S monthly updates, ERA5-Land reanalysis) and the EURO-CORDEX multi-model regional climate ensemble (EUR-11) under moderate stabilization (RCP4.5) and high-emission (RCP8.5) representative concentration pathways. Our empirical results demonstrate a profound geographical disparity in warming and extreme accumulation: over the 1970–2025 period, summer maximum temperatures (Tmax JJA) in the Romanian Plain and Southeastern Romania increased at +0.62 °C per decade (p < 0.001), substantially outpacing the EU-27 average (+0.38 °C/decade) and Central Europe (+0.41 °C/decade). Pettitt’s changepoint tests identify an abrupt structural regime shift in 1998/1999, after which extreme heat accumulation accelerated dramatically. Extreme Heat Days (SU35: daily Tmax ≥ 35.0 °C, traditionally termed canicular days) in the Romanian Plain surged six-fold, from 3.2 days/year (1971–1990) to 18.4 days/year (2011–2025), culminating in unprecedented peaks of 27–34 days during recent megadrought years (2007, 2012, 2022, 2024). Concurrently, the seasonal frequency of daily compound hot–dry days increased by +264% in southern agricultural zones. Joint Clayton–Gumbel copula modeling indicates that historical 1-in-35-year compound events have compressed into 1-in-5-year return intervals. Under near-future EURO-CORDEX projections (2030–2060), compound event frequencies are projected to surge by +125% to +150% under RCP4.5 and by +240% to +290% under RCP8.5 across the Romanian lowlands. Empirical crop–climate response functions reveal that severe compound events induce grain maize yield losses of −38% to −52%, far exceeding the EU average (−12% to −22%) due to structural vulnerability and low irrigation coverage (>85% rainfed). These findings underscore the critical imperative for modernized pressurized irrigation networks, agro-genetic adaptation, and targeted policies within the EU Common Agricultural Policy (CAP).
This study presents an analysis of the variability of the Caribbean Low-Level Jet (CLLJ) and its characteristics during El Niño–Southern Oscillation (ENSO) events. The primary objective was to understand the contribution of easterly waves (EWs) and the CLLJ to regional climate variability in the Caribbean, and to evaluate their dynamics and interactions with lower-frequency processes such as El Niño and La Niña (including moderate to intense events and neutral years). A Mann–Whitney U test and a dynamic analysis based on the momentum equation were applied to reanalysis data (75 km resolution) corresponding to summer and autumn months from 1986 to 2017. The CLLJ exhibited distinct patterns at its entrance, core, and exit, particularly during La Niña and El Niño events. Furthermore, the meridional convergence term of the zonal momentum associated with disturbances (e.g., EWs) was positive only during El Niño years, indicating that disturbances influenced wind intensity. During La Niña years, disturbance kinetic energy was concentrated over the Caribbean region, suggesting stronger interaction between the mean flow and these disturbances.
Wildfires are an underexplored component of the Earth system that can alter atmospheric chemistry and climate by emitting greenhouse gases and aerosols. Biomass burning (BB) emissions perturb tropospheric chemistry by releasing ozone precursors and reactive nitrogen, degrading air quality and enhancing regional radiative forcing. This study investigates the effects of interannual variability (IAV) in BB emissions on short-lived tropospheric constituents, namely aerosols, CO, O3 and NO2, through global chemistry transport model (CTM) experiments spanning 1997–2015, a period encompassing strong El Niño–Southern Oscillation (ENSO) events and associated major wildfires. BB emissions were derived from the Global Fire Emissions Database version 5 (GFED5), which better represents the atmospheric impacts of major wildfire events than its predecessors. Using the Tracer Model 5 Massively Parallel (TM5-MP) CTM, we conducted three simulations: a base scenario with interannually varying emissions, a mean scenario retaining the seasonal cycle but removing IAV, and a zero scenario with BB emissions nullified. We find that BB IAV contributes substantially to CO IAV in all regions, whereas O3 is influenced much less, except in regions directly affected by ENSO. BB is associated with aerosol variability over boreal and tropical burning regions and contributes to NO2 variability outside industrialised regions.
