Search Results (207)

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

Intensifying heatwaves threaten the reliability of electric distribution systems, yet the quantitative relationship between heatwave characteristics and observed power outage behavior remains poorly understood at multi-year, statewide scales. This study develops an event-based, spatiotemporal framework to quantify heatwave-induced outage risk across 254 Texas counties from 2014–2021 by integrating county-level EAGLE-I outage records with reanalysis-derived heat index measurements. An adaptive percentile-based threshold identifies 3048 heatwave events; logistic regression quantifies the probabilistic relationship between heat intensity and major-outage occurrence under three severity definitions. Across 3048 identified heatwave events, 51% involved at least one outage, a rate significantly above the non-heatwave warm-season baseline and revealing widespread heat-related reliability challenges. Outage severity and duration exhibit heavy-tailed distributions, with a small number of extreme events disproportionately affecting customers. Logistic regression models under three severity definitions (P90, P95, and ≥500 customers) demonstrate that heat intensity is a statistically robust probabilistic predictor of major outages, with each +1 °F increase in mean event heat index raising the odds by approximately 43–52%. The predicted probability of a P90-severity major outage approximately doubles across the interquartile range of event heat intensity (~7% to ~14%), providing actionable guidance for utility pre-staging decisions during forecast heatwave episodes. These findings offer a scalable methodology for climate-related reliability assessment, supporting grid hardening, resource planning, and public health preparedness. Full article

Nova Scotia is positioned to become the first Canadian province to develop offshore wind energy. Recently, Nova Scotia announced four Wind Energy Areas (WEAs) selected for bidding following extensive review of ecological and land-use considerations. In selecting these areas, the effect of climate change and extreme winds was neglected. This study looks to assess the impact of climate change, extreme winds, and tropical cyclones on turbine siting across the Scotian Shelf with a focus on the four WEAs. Analysis of historical wind climate using ERA5 reanalysis data and return period methods reveals that extreme winds intensify with distance from shore, with the highest values concentrated near Sable Island and outer shelf regions. Fifty-year return wind speeds across the WEAs range from approximately 40.7 to 45.4 m/s, resulting in IEC Class II designation for Sable Island Bank and Class III for the remaining sites. Projections derived from CMIP6 climate models indicate that future mean wind speed changes are modest across all emission scenarios, always within 4% of the historical baseline. Critically, these projected changes do not alter the IEC turbine class designations for any WEA, suggesting that classifications based on historical data remain valid under the range of climate futures considered. Three recommendations are made to strengthen future assessments: expanding the buoy observation network on the Scotian Shelf; investigating the influence of climate indicators such as sea surface temperatures on extreme winds and tropical cyclone activity; and conducting targeted measurement campaigns within the WEAs to support site-specific analysis and developer confidence. Full article

Precipitation is among the most uncertain and poorly predicted weather products in earth system science. Local convective precipitation is particularly sensitive to strong land–atmosphere coupling. Two indices derived from atmospheric thermodynamic vertical profiles, convective triggering potential (CTP), a measure of the temperature lapse rate between approximately 1 and 3 km above the ground surface, and low-level humidity (HI_low), have become preferred measures of land–atmospheric coupling strength. To complement previous studies that primarily relied on limited station observations or regional analyses, this study provides a 20-year assessment of the CTP-HI_low framework for a wide area of the Continental United States (CONUS) using integrated satellite observations, reanalysis products, and surface datasets. The study further identifies important regional limitations in the framework’s predictive skill and demonstrates the influence of mid-level vertical wind shear on precipitation occurrence during both wet and dry soil advantage conditions. These findings provide new insight into why the framework performs inconsistently across different climate regions and suggest pathways for improving land–atmosphere coupling-based precipitation prediction. The objective is to determine the atmospheric and land-surface factors that control the regional performance of the CTP-HI_low framework and to identify how additional datasets that include more atmospheric variables can improve precipitation prediction skill. Full article

The ocean–atmosphere turbulent heat exchange plays a critical role in the energy and moisture budgets of the Tropical Atlantic Ocean (TAO) and in weather and climate forecasts. However, its estimation strongly depends on the choice of bulk parameterization, as direct in situ measurements are sparse. This study evaluates sensible (Hs) and latent (Hl) heat fluxes derived from three bulk parameterization schemes used operationally in models at the Brazilian Center for Weather Forecast and Climate Studies (CPTEC) of the National Institute for Space Research (INPE), Brazil: the Brazilian Atmospheric Model (BAM), the Modular Ocean Model version 6 (MOM6), and the Weather Research and Forecasting (WRF) model. Using daily in situ observations from seven Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) buoys across the TAO during 1997–2023, we computed monthly mean fluxes and compared them against the Coupled Ocean–atmosphere Response Experiment (COARE) algorithm version 3.0b (COARE 3.0b) reference. COARE version 3.6 (COARE 3.6) and European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis 5th generation (ERA5) data were included as additional benchmarks. All offline schemes were forced with identical buoy data, isolating differences in internal physical assumptions. Hl is approximately one order of magnitude larger than Hs across all sites, and inter-scheme differences are substantially larger for Hl (±50 W∙m⁻²) than for Hs (±5 W∙m⁻²). All schemes reproduce the seasonal cycle linked to the Intertropical Convergence Zone (ITCZ) migration and trade-wind variability, with correlations generally exceeding 0.8 (p < 0.001) for most buoys. However, systematic magnitude biases remain. The Coordinated Ocean Research Experiments (CORE) bulk formulation implemented in MOM6 (MOM6-CORE) shows high temporal correlation (often r ≈ 1.0) but a persistent negative bias for both Hs and Hl (e.g., B1 Hl bias = −24.0 W∙m⁻²), indicating weaker turbulent exchange relative to COARE 3.0b. BAM overestimates Hs (by 1–3 W∙m⁻²) and underestimates Hl at most northern and southern sites, while the parametrization of the Yonsei University (YSU) implemented in the WRF model (WRF-YSU) amplifies Hs variability intermittently, particularly at the equator (B4). As expected, COARE 3.6 remains the closest to the reference (differences < 1 W∙m⁻² for Hs and <7 W∙m⁻² for Hl; r ≈ 0.99). ERA5 captures temporal variability well (r ≈ 0.7–0.9) but systematically overestimates Hl (positive bias up to +47.6 W∙m⁻² at B7), implying stronger evaporative cooling. Buoy-specific regimes modulate skill. The choice of bulk formulation thus remains a first-order source of uncertainty in turbulent heat flux estimates over the TAO, with direct implications for mixed-layer heat budgets, SST evolution, and coupled ocean–atmosphere variability. MOM6-CORE provides the most consistent performance relative to the COARE reference and emerges as the most robust option for operational applications at CPTEC/INPE. The findings also provide guidance for improving the representation of ocean–atmosphere turbulent exchanges in MONAN (Model for Ocean-Land-Atmosphere Prediction), the new Brazilian Earth System Model under development for weather and climate prediction. Full article

The Indonesian Throughflow (ITF) is a critical conduit connecting the tropical western Pacific Ocean and the Indian Ocean, constituting an essential component of the global ocean circulation and exerting a significant influence on its large-scale balance. Under the backdrop of global warming, both the magnitude of ITF transport and its relationships with El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) are expected to undergo substantial changes. Using the SODA3.15.2 reanalysis as an observational benchmark, this study evaluates the ability of 14 CMIP6 models to simulate ITF volume transport. Following a systematic performance assessment, four poorly performing models were excluded, and the remaining 10-model ensemble was employed to construct a multi-model ensemble mean (MME). The MME is then employed to investigate the long-term trends in ITF transport during the historical period (1850–2014) and under two future emissions scenarios, SSP2-4.5 and SSP5-8.5 (2015–2100). During the historical period, ITF transport exhibits a transition from a weak strengthening to a weak weakening trend around 1934–1935, detected by both the sliding t-test and the Pettitt test, with relatively modest overall change. Under SSP2-4.5 and SSP5-8.5 scenarios, ITF transport weakens at rates of 0.318 Sv decade⁻¹ and 0.466 Sv decade⁻¹, respectively, with projected declines of approximately 3 Sv (27%) and 4 Sv (36%) by 2100. Reductions during boreal winter and spring exceed those in summer, indicating a pronounced seasonal asymmetry in the ITF response to future warming. The interannual variability of ITF is predominantly driven by ENSO, while the IOD also exerts an independent yet weaker modulating influence. Full article

The Arabian Sea is a key region for global marine biogeochemical research, yet the distribution characteristics and influencing factors of dissolved oxygen and chlorophyll a concentration in its central oxygen minimum zone still require further in-depth investigation. Based on survey data and reanalysis data from 2024, this paper analyzes the distribution characteristics and underlying causes of chlorophyll a concentration and dissolved oxygen using empirical orthogonal function (EOF) decomposition of chlorophyll a concentration and dissolved oxygen saturation along the depth direction, combined with the distribution of the barrier layer, Ekman pumping induced by wind fields, and the diagnostic vertical velocity distribution calculated from ADCP-observed flow velocities. Taking approximately 10° N as the boundary, the chlorophyll a concentration in the layer shallower than 35 m exhibits a distribution pattern of high in the northwest and low in the southeast, while the water layer between 45 m and 95 m shows a pattern of low in the northwest and high in the southeast. A thick barrier layer exists in the southeastern region, whereas the barrier layer in the northwestern region is thinner or absent, resulting in lower surface chlorophyll a concentration in the southeast. ADCP observations indicate that horizontal flow velocities are higher in the south, bringing oxygen-rich water from the south, which leads to higher dissolved oxygen saturation in the southern region compared to the northern region in water shallower than 45 m. At the 65 m water layer, the higher chlorophyll a concentration in the south may result in relatively low dissolved oxygen. The hypoxic zone (dissolved oxygen saturation less than 30%) begins to appear at depths below 105 m, with its southern boundary located between 9° N and 11° N, and this boundary gradually shifts northward as depth increases. The diagnostic vertical velocity between 9° N and 11° N is higher than that in other regions, which may hinder the northward movement of oxygen-rich water from the south. In the southern region, influenced by wind stress, the vertical water movement induced by Ekman pumping is relatively significant, which may lead to a slight increase in dissolved oxygen saturation in water layers with a depth below 125 m. Full article

Over the past two decades, extreme climate and weather events have become increasingly frequent in the United States, and the carbon–water cycle of corn ecosystems has shown high sensitivity to climate change. However, traditional simulation methods that rely on coarse-scale reanalysis data are unable to reflect changes in local water and heat conditions accurately. This study combines in situ meteorological observations with remote sensing, using a long short-term memory model to simulate the daily gross primary productivity (GPP) and evapotranspiration (ET) of 684 corn-growing meteorological stations in the United States. In summer, GPP and ET showed a spatial pattern of gradual decrease from the humid eastern region to the arid western region, and the multi-year daily averages at meteorological stations showed a single-peak pattern. The sensitivity of GPP and ET changes is mainly influenced by leaf area index (LAI) and shortwave radiation downward changes, which together explain more than 90% of the main variation in GPP and ET at the meteorological stations. The 2012 drought caused a general decline in GPP and ET, with the peak occurring approximately 15 days earlier than usual. Water use efficiency (GPP/ET) decreased at 85% of the sites (p < 0.05), but photosynthesis per unit leaf area (GPP/LAI) increased at 63% of the sites (p < 0.05). This study demonstrates the importance of meteorological station-scale data for understanding carbon–water flux dynamics in cornfields. Integrating the models developed in this study with medium-to-long-term climate projections will further guide climate-informed agricultural water management and provide reliable accounting and pricing tools for agricultural land carbon markets and carbon trading. Full article

The “New Arctic” regime represents a prominent climatic feature of the Arctic Ocean under global warming, characterized by persistently low summer sea ice extent, a marked reduction in sea ice thickness, and an expansion of open water areas at high latitudes. As a key indicator of the Arctic sea ice system, the spatiotemporal evolution of sea ice thickness and its underlying driving mechanisms remain incompletely understood. Using reanalysis datasets and remote sensing observations, this study identifies major abrupt shifts in Arctic sea ice thickness under the New Arctic regime, reveals the spatiotemporal distribution characteristics of winter sea ice thickness, and examines the driving factors from both thermodynamic and dynamic perspectives. The results show that the evolution of Arctic sea ice thickness can be divided into three phases: a high-level period during the “Traditional Arctic” (1979–1992), a rapid thinning period during the New Arctic transition (1993–2012), and a low-level stabilization period in the New Arctic regime (2013–2023). The first EOF mode of winter sea ice thickness depicts a spatially consistent thinning pattern across the entire Arctic, with the most significant reduction occurring in the multi-year ice regions north of the Canadian Arctic Archipelago and Greenland. The second EOF mode exhibits an out-of-phase variation between the Atlantic and Pacific sectors of the Arctic, accompanied by a shrinking amplitude and weakened regional oscillations. The coupling between surface air temperature and sea ice thickness displays distinct phase dependence: their negative correlation is strongest during the transition period (r = −0.78, p < 0.001) but becomes statistically insignificant in the New Arctic regime. Sea ice motion speed exhibits an overall accelerating trend, which extends from the marginal seasonal ice zones toward the high-latitude multi-year ice regions, accompanied by a notably enhanced sensitivity of sea ice motion to wind forcing. Sea ice volume flux through the Fram Strait is primarily controlled by ice motion speed, whose contribution to the flux is approximately 2.6 times that of ice thickness. The recovery of ice drift speed offsets the thinning of sea ice cover, leading to a partial rebound in volume flux during the New Arctic steady state. This study identifies the evolutionary patterns and drivers of Arctic sea ice thickness under the New Arctic regime, providing a scientific basis for further understanding the changes in the Arctic climate system and associated air–sea ice interactions. Full article

High-Frequency (HF) radar remote sensing offers a unique capability to detect mesoscale air-sea interactions under strong monsoon conditions. This study leveraged HF radar-derived surface currents, buoy observations, and reanalysis data to systematically investigate the driving mechanism of the sea–land breeze (SLB) on surface currents in the Taiwan Strait during the strong winter monsoon. To address the challenge of extracting weak signals from a dominant background flow, we employed the Separation of the Regional Wind Field (SRWF) method and the complex demodulation spectrum shifting technique. The results demonstrate that HF radar observations confirm the presence of regular SLB activity even under the strong monsoon, with its intensity modulated by the land–sea temperature difference influenced by cloud cover. Spatial correlation analysis reveals that the SLB significantly drives diurnal variations in the surface current, with its impact extending up to 110 km offshore and a maximum amplitude of approximately 2.2 cm/s. Additionally, the analysis reveals that the duration of SLB events critically influences the current response: events lasting 7 days produce a stronger and more spatially coherent correlation with the diurnal currents than shorter 5-day events. Furthermore, harmonic analysis indicates that the SLB’s energy primarily affects the non-tidal residual current, with no significant impact on the principal diurnal tidal constituents ($O_1$, $K_1$). This work not only quantifies the SLB-current coupling during sustained SLB events in a strong monsoon regime but, more importantly, demonstrates the capability of HF radar remote sensing for resolving weak signals in complex, high-energy environments, providing a robust methodological framework and valuable insights for regional marine environmental forecasting. Full article

The Yellow Sea is an important offshore area in China, and the accurate prediction of its seawater temperature is of great significance for marine environmental monitoring and climate adaptation management. However, existing research on predicting the three-dimensional (3D) temperature field in the Yellow Sea is scarce and insufficiently accurate. This study proposes a TCN-U-Net++ fusion model to reconstruct the Yellow Sea temperature field using remote sensing satellite data and SODA reanalysis data, while considering the influence of a series of factors, including wind (USSW and VSSW), absolute bathymetric data (BAT), sea surface height anomaly (SSHA), latitude (LAT), longitude (LON), solar radiation (SR), surface runoff (SRO), and precipitation (P). The results show that the model can accurately capture the temporal and spatial distribution characteristics of the temperature field in the Yellow Sea. The results indicate that the deviations from SODA are generally within 2 °C, with errors being approximately 45% lower than those of other models, while the prediction errors relative to Argo and voyage observations are mostly within 1 °C, further demonstrating the accuracy and robustness of the proposed model. In addition, the predictions of the Yellow Sea Cold Water Mass (CWM) are highly consistent with SODA in terms of their evolution and key characteristic parameters. Specifically, the maximum deviation in core temperature is only 0.3 °C, and the difference in its spatial extent is less than 1%. The results demonstrate that TCN-U-Net++ effectively enhances the accuracy of 3D sea temperature prediction in the Yellow Sea, providing technical support for temperature monitoring, ecological early warning, and climate change research. Full article

Atmospheric aerosols strongly regulate surface solar irradiance in tropical coastal environments through scattering and absorption. This study examines aerosol–irradiance interactions over Penang, Malaysia, using Aerosol Robotic Network (AERONET) observations of aerosol optical depth (AOD), single scattering albedo (SSA), and extinction Ångström exponent (AE); NASA’s Prediction of Worldwide Energy Resource (POWER) irradiance data; and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis for aerosol compositional context. Bottom-of-atmosphere radiative forcing efficiency (BOA RFE) was quantified for global, direct and diffuse irradiance (GHI, DNI and DHI) under clear- and all-sky conditions during 2014–2018. Results show persistent aerosol-induced attenuation of surface radiation, with GHI and DNI RFE predominantly negative, while DHI RFE remains consistently positive, indicating redistribution of solar energy from direct to diffuse components. Time resolved analysis reveals daily GHI RFE typically ranging from approximately −0.5 to −3.5 W m⁻² per unit AOD, with episodic excursions below −4 W m⁻² per AOD during high-aerosol events, whereas DNI RFE frequently reaches values below −0.8 W m⁻² per AOD, confirming its greater sensitivity to aerosol extinction. In contrast, DHI RFE commonly exceeds +5 W m⁻² per AOD and intermittently surpasses +10 W m⁻² per AOD, reflecting enhanced scattering and multiple-scattering effects. AOD-stratified analysis demonstrates a nonlinear weakening of forcing efficiency with increasing aerosol burden, with mean GHI RFE decreasing from approximately −1.6 to −0.4 W m⁻² per AOD between low- and high-AOD regimes, accompanied by corresponding reductions in DNI (−0.35 to −0.1 W m⁻² per AOD) and DHI (+3.3 to +0.8 W m⁻² per AOD). Overall, aerosol loading is identified as the dominant control on BOA radiative forcing efficiency in this tropical coastal environment, while SSA and AE act as secondary modulators. Full article

The accuracy of numerical weather prediction largely depends on the quality of the initial conditions. Global Navigation Satellite System radio occultation (GNSS-RO) observations, with their high vertical resolution, play an important role in reducing initial condition errors. In this study, multiple simulations with different initialization times were conducted during the development of Typhoon BEBINCA using the WRF-GSI assimilation system to evaluate the impact of YunYao GNSS-RO observations on improving extreme weather simulation performance and to investigate the sensitivity of refractivity assimilation to different cloud microphysics parameterization schemes. The results show that assimilating YunYao GNSS-RO data significantly improves the consistency between the model initial fields and observations and enhances the analysis quality in the middle and upper troposphere. Compared with ERA5 reanalysis data, the assimilation experiments better reproduce the spatial and temporal evolution of key atmospheric variables, and the improvements persist from 36 h to 120 h forecast lead time. Statistical results from multiple initializations show that the maximum RMSE reductions exceed 0.2 K for temperature, 0.1 m s⁻¹ for wind speed, and geopotential height shows consistent improvements throughout the entire atmosphere. In addition, the assimilation experiments improve the simulation of Typhoon BEBINCA’s track and intensity. Statistical results from multiple initializations indicate that the 84 h track error is reduced by approximately 30 km on average, and the minimum central pressure bias is also reduced. Sensitivity experiments further show that the WSM6 microphysics scheme performs better in track forecasting, while the Thompson scheme is more suitable for intensity forecasting. Overall, YunYao GNSS-RO assimilation effectively improves typhoon forecast accuracy and demonstrates strong potential for operational applications. Full article

Timely monitoring of tourism demand is essential for destination management, yet official monthly arrival statistics are often released with delays and can be difficult to use for near-real-time decision-making, particularly under structural shocks such as coronavirus disease 2019 (COVID-19). This study develops a fully reproducible, open-data nowcasting pipeline for Ecuador’s international tourist arrivals using a Python workflow. The framework integrates (i) the official monthly arrivals series published by Ecuador’s Ministry of Tourism (MINTUR), (ii) open online attention proxies from Wikipedia pageviews retrieved via the Wikimedia REST application programming interface (API), and (iii) open climate covariates derived from the ERA5-Land land reanalysis. Multiple forecasting models are evaluated under a rolling-origin, one-step-ahead backtest, with a mandatory seasonal naïve benchmark and a regime-aware assessment that separates a stress-test window (2019–2021) from an operational post-COVID window (2022–2025). Forecast accuracy is summarized using root mean squared error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE), and statistical significance of performance differences is assessed using the Diebold–Mariano (DM) test. Results show that a ridge-regularized autoregressive model (ridge_ar) achieves the best overall accuracy, reducing RMSE by approximately 79% relative to the seasonal naïve baseline over the full evaluation window. Windowed results confirm robust performance during the shock period and sustained improvements in the post-2022 operational regime, while the incremental benefit of broader exogenous signals is heterogeneous across windows, underscoring the importance of regularization and regime-aware reporting. The proposed approach provides a transparent, low-cost blueprint for reproducible tourism monitoring that is transferable to other destinations using open data and standard computational tools. Full article

Upper troposphere (UT) humidity records are crucial for climate studies. To maximize temporal representativeness and enhance the lidar signal, pseudo-monthly averaging—limited to nighttime measurement—is applied, yielding water vapor mixing ratio (WVMR) profiles up to 16 km. This study evaluates 11 years (2013–2023) of WVMR profiles from a UV Raman lidar (Li1200) at Réunion Island, comparing them with MLS-Aura satellite retrievals, ERA5 reanalysis data, and GRUAN-processed M10 radiosondes. The results reveal a systematic dry shift in MLS of up to 30% above 12 km, particularly during the wet season. The lidar exhibits a slight downward shift in WVMR, approximately 5% lower than ERA5 throughout the UT, with the largest deviations occurring above 14 km and greater variability during the wet season. Calibration-related challenges during the dry season result in lidar WVMR profiles that are up to 10% drier than ERA5. Additionally, comparisons with GRUAN-processed radiosondes show a substantial dry shift relative to the lidar, exceeding 30% above 12 km. We investigate the effect of GNSS-based lidar calibration by applying an alternative calibration method, which produces higher WVMR values. This reveals a dry shift in ERA5 relative to the lidar, increasing with altitude in the UT up to 25%. These measurements contribute to the global effort to monitor and validate tropical and subtropical upper tropospheric humidity. Full article