Search Results (167)

Understanding land surface fluxes is essential for sustaining dryland ecosystem functioning and services. However, the scarcity of in situ measurements poses a significant challenge to dryland monitoring. Satellite optical and thermal remote sensing data can provide the instantaneous estimates of land surface fluxes, such as surface temperature (LST), net radiation (Rn), sensible heat flux (H), evapotranspiration (latent heat flux, LE), and gross primary productivity (GPP). However, satellite-based estimates are often limited by sensor revisit frequencies and cloud-cover conditions. To facilitate temporally continuous estimation, process-based land surface models are often used to integrate sparse remote sensing observations and meteorological inputs, thereby generating continuous estimates of energy, water, and carbon fluxes. However, the impact of satellite thermal data accuracy and temporal resolutions on simulating land surface fluxes is under-explored, particularly in dryland ecosystems. Therefore, this study assessed the accuracy of Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data in a dryland tussock grassland ecosystem in southern Spain. We also assessed the incorporation and various temporal frequencies of thermal data into process-based modelling for simulating land surface fluxes. The model simulations were validated against in situ measurements from eddy covariance towers. Results show that MODIS LST has a high correlation but large bias with in situ measurements (R² = 0.81, RMSE = 4.34 °C). After a linear correction of MODIS LST with in situ measurements, we found that the adjusted MODIS LST can effectively improve the half-hourly simulation of LST, Rn, H, LE, SWC, and GPP with relative RMSEs of 7.84, 5.67, 7.81, 11.32, 6.59, and 13.09%, respectively. Such performance is close to the flux simulations driven by in situ LST. We also found that by adjusting the revisit frequency of the satellite sensor to 8 days, the model performance of simulating surface fluxes did not change significantly. This study provides insights into how satellite thermal remote sensing can be integrated with the process-based model to understand dryland ecosystem functioning, which is critical for ecological management and climate adaptation strategies. Full article

Potential evaporation (PE) from saturated bare surfaces is the basis for estimating actual evaporation (E_s) in agricultural and related disciplines. Most models estimate PE using meteorological data. Thus, the dependence of soil temperature (T) on PE is often simplified in applications. To address this gap, we conducted an in situ lysimeter experiment in the Guanzhong Basin, China, continuously measuring PE, T, and soil heat flux (G) at high temporal resolution over three fully saturated sandy soils. Results show that annual PE over fine sand was 7.1% and 11.0% higher than that of coarse sand and gravel. The observed PE differences across textures can be quantitatively explained using the surface energy balance equation and a radiatively coupled Penman-Monteith equation, accounting for the dependence of T on net radiation (R_n) and G. In contrast, PE estimates diverged from observations when R_n and G were assumed to be independent of T. We further evaluated the influence of T and other influencing variables on PE. The random forest model identified that near-surface heat storage variations (∆S) contribute most significantly to PE estimation (relative importance = 0.37), followed by surface temperature (0.24) and sensible heat flux (0.23). These findings highlight the critical role of near-surface temperature in PE estimation. Full article

This study aimed to evaluate the performance of the SiSPAT model in simulating surface energy balance components and soil hydrothermal dynamics under temperate oceanic climate conditions, focusing on sparsely vegetated bare soils commonly found in transitional agroecosystems. The model was validated using high-resolution field data from the United Kingdom, including measurements of net radiation, soil heat flux, latent and sensible heat fluxes, and soil temperature and moisture at multiple depths. Results indicated that SiSPAT effectively reproduced the magnitude and diurnal variations in net radiation, soil heat flux, and subsurface thermal and moisture conditions, with overall agreement exceeding 90% in most cases. Minor underestimations (~10%) were observed for midday latent and sensible heat fluxes, while slight overestimations occurred in topsoil moisture during dry periods—remaining within acceptable simulation limits. These outcomes demonstrate the model’s capability to simulate land–atmosphere interactions under variable surface conditions and moderate humidity. The novelty of this study lies in extending the application of SiSPAT to temperate oceanic regions with partially vegetated soils—an underrepresented context—emphasizing its potential as a decision support tool for sustainable soil management, irrigation planning, and climate-resilient land use strategies in temperate regions with climatic and soil conditions similar to those represented in this study. Full article

The Three-River Source Region (TRSR) of the Tibetan Plateau (TP) is a critical headwater area with complex alpine terrain and significant climate variability. Accurately simulating 2 m air temperature (T₂) in this region remains challenging for models such as the Weather Research and Forecasting (WRF) model. This study integrated remote sensing data into the WRF model to improve T₂ simulations over the TRSR. Two simulations were conducted for 2020: a control simulation with default static vegetation parameters (EXP_control) and a sensitivity simulation with realistic vegetation and associated surface albedo of 2020 from the Global Land Surface Satellite (GLASS) datasets (EXP_glass). Results showed that incorporating the GLASS-derived datasets significantly alleviated the cold bias in simulated T₂ during winter and spring, while maintaining comparable performance in summer and autumn. The EXP_glass run achieved better agreement with observations (R = 0.98, p < 0.01) and reduced root-mean-square error (RMSE) by 36.4% compared to EXP_control. Energy balance analysis indicated that the GLASS-derived datasets enhanced solar energy absorption and increased net radiation. Consequently, EXP_glass produced greater turbulent heat fluxes and warmer surface skin temperature (TSK) and T₂. This study underscores the importance of accurate land surface characterization and highlights the utility of remote sensing data for improving regional climate model performance in high-altitude regions. Full article

Regional rapid forecasting of vertical ocean temperature profiles is increasingly important for marine aquaculture, as these profiles directly affect habitat management and the physiological responses of farmed species. However, observational temperature profile data with sufficient temporal resolution are often unavailable, limiting their use in regional rapid forecasting. In addition, traditional numerical ocean models suffer from intensive computational demands and limited operational flexibility, making them unsuitable for regional rapid forecasting applications. To address this gap, we propose PICA-Net (Physics-Inspired CNN–Attention–BiLSTM Network), a hybrid deep learning model that coordinates large-scale satellite observations with local-scale, continuous in situ data to enhance predictive fidelity. The model also incorporates weak physical constraints during training that enforce temporal–spatial diffusion consistency, mixed-layer homogeneity, and surface heat flux consistency, enhancing physical consistency and interpretability. The model uses hourly historical inputs to predict temperature profiles at 6 h intervals over a period of 24 h, incorporating features such as sea surface temperature, sea surface height anomalies, wind fields, salinity, ocean currents, and net heat flux. Experimental results demonstrate that PICA-Net outperforms baseline models in terms of accuracy and generalization. Additionally, its lightweight design enables real-time deployment on edge devices, offering a viable solution for localized, on-site forecasting in smart aquaculture. Full article

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and $r^2$ metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments. Full article

The long-term trend of coastal upwelling under global warming has been a research focus in recent years. Based on datasets including sea surface temperature (SST), sea surface wind, air–sea heat fluxes, ocean currents, and sea level pressure, this study explores the long-term trend and underlying mechanisms of the Oman coastal upwelling intensity in summer during 1993–2018. The results indicate a persistent decrease in SST within the Oman upwelling region during this period, suggesting an intensification trend of Oman upwelling. This trend is primarily driven by the strengthened positive wind stress curl (WSC), while the enhanced net shortwave radiation flux at the sea surface partially suppresses the SST cooling induced by the strengthened positive WSC, and the effect of horizontal oceanic heat transport is weak. Further analysis revealed that the increasing trend in the positive WSC results from the nonuniform responses of sea level pressure and the associated surface winds to global warming. There is an increasing trend in sea level pressure over the western Arabian Sea, coupled with decreasing atmospheric pressure over the Arabian Peninsula and the Somali Peninsula. This enhances the atmospheric pressure gradient between land and sea, and consequently strengthens the alongshore winds off the Oman coast. However, in the coastal region, wind changes are less pronounced, resulting in an insignificant trend in the alongshore component of surface wind. Consequently, it results in the increasing positive WSC over the Oman upwelling region, and sustains the intensification trend of Oman coastal upwelling. Full article

This study explores the relationship between land use and land cover (LULC) changes and a significant cyclogenesis event that occurred in Alabama’s Black Belt region from 6 to 7 October 2021. Utilizing the Weather Research and Forecasting (WRF) model, two scenarios were analyzed: the WRF Control Run, which maintained unchanged LULC, and the WRF Sensitivity Experiment, which converted 56.5% of forested areas into cropland to assess the impact on storm dynamics. Quantitative comparisons of predicted rainfall from both simulations were conducted against observed data. The control run demonstrated a Root Mean Square Error (RMSE) of 1.64, indicating accurate rainfall predictions. In contrast, the modified scenario yielded an RMSE of 2.01, suggesting lower reliability. The Mean Bias (MB) values were 1.32 for the control run and 1.58 for the modified scenario, revealing notable discrepancies in accuracy. The coefficient of determination (R²) was 0.247 for the control run and 0.270 for the modified scenario. The Nash–Sutcliffe Efficiency (NSE) value was 0.1567 for the control run but dropped to −0.2257 following LULC modifications. Sensitivity analyses revealed a 60% increase in heat flux and a 36% rise in precipitation, underscoring the significant impact of LULC on meteorological outcomes. While this study concentrated on the Black Belt region, the methodologies employed could apply to various other areas, though caution is advised when generalizing these results to different climates and socio-economic contexts. Further research is necessary to enhance the model’s applicability across diverse environments. Full article

Earth’s energy imbalance (EEI) is a major indicator of climate change. Its metrics are top of the atmosphere radiation imbalance (EEI TOA) and net internal heat uptake. Both EEI and temperature are expected to respond gradually to forcing on annual timescales. This expectation was tested by analyzing regime changes in the inputs to EEI TOA along with increasing ocean heat content (OHC). Outward longwave radiation (OLR) displayed rapid shifts in three observational and two reanalysis records. The reanalysis records also contained shifts in surface fluxes and temperature. OLR, outward shortwave radiation (OSR) and TOA net radiation (Net) from the CERES Energy Balanced and Filled Ed-4.2.1 (2001–2023) record and from 27 CMIP5 historical and RCP4.5 forced simulations 1861–2100, were also analyzed. All variables from CERES contained shifts but the record was too short to confirm regime changes. Contributions of OLR and OSR to net showed high complementarity over space and time. EEI TOA was −0.47 ± 0.11 W m⁻² in 2001–2011 and −1.09 ± 0.11 W m⁻² in 2012–2023. Reduced OSR due to cloud feedback was a major contributor, coinciding with rapid increases in sea surface temperatures in 2014. Despite widely varying OLR and OSR, 26/27 climate models produced stable regimes for net radiation. EEI TOA was neutral from 1861, shifting downward in the 26 reliable records between 1963 and 1995, with 25 records showing it stabilizing by 2039. To investigate heat uptake, temperature and OHC 1955/57–2023 was analyzed for regime change in the 100 m, 700 m and 2000 m layers. The 100 m layer, about one third of total heat content, was dominated by regimes. Increases became more gradual with depth. Annual changes between the 700 m layer and 1300 m beneath were negatively correlated (−0.67), with delayed oscillations during lag years 2–9. Heat uptake at depth is dynamic. These changes reveal a complex thermodynamic response to gradual forcing. We outline a complex arrangement of naturally evolved heat engines, dominated by a dissipative heat engine nested within a radiative engine. EEI is a property of the dissipative heat engine. This far-from-equilibrium natural engine has evolved to take the path of least resistance while being constrained by its maximum power limit (~2 W m⁻²). It is open to the radiative engine, receiving solar radiation and emitting scattered shortwave and longwave radiation. Steady states maximize entropy within the dissipative engine by regulating spatial patterns in surface variables that influence outgoing OLR and OSR. Regime shifts to warmer climates balance the cost of greater irreversibility with increased energy rate density. The result is the regulation of EEI TOA through a form of thermodynamic metabolism. Full article

The estimation of sensible heat flux (H) in drylands is important because it constitutes a significant portion of the net available surface energy. A model to estimate H half-hourly measurements for bare soils was derived by combining the surface renewal (SR) theory and the Monin–Obukhov similarity theory (MOST), involving the land surface temperature (LST), wind speed, and the air temperature in a period of half an hour, H_SR-LST. The surface roughness lengths for momentum ($z_{om}$) and for heat (z_0h) were estimated at neutral conditions. The dataset included dry climates and different measurement heights (1.5 m up to 20 m). Root mean square error ($\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}$) over the mean actual sensible heat flux estimate (H_EC), E $={\displaystyle \frac{\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}{\overline{\mathrm{H}\mathrm{E}\mathrm{C}}}}100\%$, was considered excellent, good, and moderate for E values of up to 25%, 35%, and 40%, respectively. In stable conditions, H_SR-LST and H_MOST values were comparable and both were unacceptable (E > 40%). However, the RMSE using H_SR-LST ranged between 8 Wm² and 12 Wm² and performed slightly better than H_MOST. In unstable conditions, H_SR-LST was in excellent, good, and moderate agreement in 3, 6, and 5 cases, respectively; H_MOST was good in 3 cases; and the remaining 11 cases were intolerable because they required site-specific calibration. Full article

PM_2.5 plays a significant role in urban climate, especially as urban development accelerates. In this study, surface PM_2.5, skin temperature, surface air temperature, net longwave radiation, net shortwave radiation, sensible heat flux, and latent heat flux were directly analyzed in Nanchang from 2020 to 2022. The results indicate that PM_2.5 in Nanchang is highest during winter and lowest in summer. On an annual scale, surface PM_2.5 reduces skin and surface air temperatures at a rate of 0.75 °C/(μg m⁻³) by decreasing net solar radiation and increasing net longwave radiation at night. Conversely, it increases air temperature by absorbing radiation, leading to a surface inversion. Furthermore, surface PM_2.5 influences surface air and skin temperatures by modulating the latent heat fluxes. Full article

The variation in the background temperature field in aquatic environments plays a crucial role in the detection of thermal signatures of maritime moving targets. To elucidate the influence of various meteorological and hydrological parameters on the background temperature field of water bodies, this study employs the COARE 3.0 model to analyze the relationship between the net heat flux at the air–water interface and the characteristics of the cool skin layer. By examining the diurnal fluctuations of environmental parameters, the diurnal variation patterns of the cool skin layer properties are investigated. A dynamic temperature field testing platform was established in an outdoor pool to measure air–water volume variables and validate the accuracy of the water temperature field calculation model. The findings indicate that the cool skin phenomenon is indeed present in natural aquatic environments. The properties of the cool skin layer are predominantly affected by factors such as wind speed, the specific humidity gradient between the near-surface and high-altitude regions, and the temperature gradient between these regions. The temperature of the cool skin layer is typically a few tenths of K lower than that of the subsurface water, with a thickness generally ranging from 2 to 5 mm. Full article

Soil temperature (ST) plays an important role in the surface heat energy balance, and an accurate description of soil temperatures is critical for numerical weather prediction; however, it is difficult to consistently measure soil temperatures. We developed a U-Net-based deep learning model to derive soil temperatures (designated as ST-U-Net) primarily based on 2 m air temperature (T2) forecasts. The model, the domain of which covers the Mt. Lushan region, was trained and tested by utilizing the high-resolution forecast archive of an operational weather research and forecasting four-dimensional data assimilation (WRF-FDDA) system. The results showed that ST-U-Net can accurately estimate soil temperatures based on T2 inputs, achieving a mean absolute error (MAE) of less than 0.8 K on the testing set of 5055 samples. The performance of ST-U-Net varied diurnally, with smaller errors at night and slightly larger errors in the daytime. Incorporating additional inputs such as land uses, terrain height, radiation flux, surface heat flux, and coded time further reduced the MAE for ST by 26.7%. By developing a boundary-layer physics-guided training strategy, the error was further reduced by 8.8%. Full article

In this study, we investigated the impacts of typhoons on the transformation of anticyclonic eddies (AEs) into subsurface anticyclonic eddies (SAEs) in the northwestern subtropical Pacific Ocean (NWSP) based on an ocean reanalysis product and multiple satellite observations. Results suggest that while the heavy precipitation and strong positive wind stress curl (WSC) induced by the passage of typhoons may be two main driving factors that transformed shallow mixed layer depth (MLD) AEs (i.e., those shallower than 50 m at the eddy core) into SAEs, the latter played a greater role in such transformation. In addition, shallow MLD AEs with a less depressed isopycnal structure near the eddy center before the passage of typhoons were more likely to be transformed into SAEs under the impacts of typhoons. The likely timing of such transformation may be within 9 days after the passage of typhoons. For deep MLD AEs (i.e., those deeper than 80 m at the eddy core), the impacts of typhoons may be much less prominent below the mixed layer. Based on a diagnostic analysis of the vertical potential vorticity (PV) flux at the surface, we examined the mechanism and dynamic processes involved in the transformation of deep MLD AEs into SAEs under the impacts of typhoons. Results show that while typhoons played a positive role in maintaining low PV within deep MLD AEs, which was favorable for further transformation into SAEs, the diabatic process associated with the net air–sea heat flux was the crucial favorable condition for the transformation of deep MLD AEs into SAEs. Full article

Strong salinity stratification induced by large freshwater fluxes in the northern Bay of Bengal (BOB) results in the formation of a quasi-permanent barrier layer (BL) that covers almost the entire BOB and leads to a unique temperature inversion within the thick BL in winter. In the presence of temperature inversions, the entrainment process at the bottom of the mixed layer (ML) induces warming effects in the ML, but little is known about this. In this paper, we quantify the contribution of the entrainment process to the ML temperature (MLT) in the northern BOB during the winter of 2013 using monthly and daily data from the Ocean General Circulation Model for the Earth Simulator version 2 (OFES2). It is found that the warming effect of the daily entrainment heat flux (EHF), which resolved the high-frequency variations, is 4 orders of magnitude larger than the monthly EHF for most of the wintertime. This significantly enhanced warming effect in daily data offsets up to 87% of the surface cooling induced by net heat flux during wintertime. A further analysis reveals that the larger daily EHF warming effect compared to its monthly counterpart is closely related to the deepened ML, the larger temperature difference within the ML and vertical velocity at the bottom of the ML. Full article