Search Results (39)

Typhoon Yagi (2024) was a rapidly moving storm that lasted for eight days and made landfall in three locations, producing heavy rainfall over Hainan and Vietnam. This study aims to investigate the dynamical processes contributing to the heavy rainfall, concentrating on enthalpy flux (EF) and moisture flux (MF). The results indicate that both EF and MF increased significantly during the typhoon’s intensification stage and were high at the time of landfall. Before landfalling at Hainan, latent heat flux (LHF) reached 600 W/m², while sensible heat flux (SHF) was recorded as 80 W/m². Landfall at Hainan resulted in a decrease in LHF and SHF. LHF and SHF subsequently increased to 700 W/m² and 100 W/m², respectively, as noted prior to the landfall in Vietnam. The increased LHF led to higher evaporation, which subsequently elevated moisture flux (MF) following the landfall in Vietnam, while the region’s topography further intensified the rainfall. The mean daily rainfall observed over Philippines is 75 mm on 2 September (landfall and passing through), 100 mm over Hainan (landfall and passing through) on 6 September, and 95 mm at over Vietnam on 7 September (landfall and after), respectively. Heavy rainfall was observed over the land while the typhoon was passing and during the landfall. This research reveals that Typhoon Yagi’s intensity was maintained by a well-organized and extensive circulation system, supported by favorable weather conditions, including high sea surface temperatures (SST) exceeding 30.5 °C, substantial low-level moisture convergence, and elevated EF during the landfall in Vietnam. Full article

Puerto Rico has experienced recent increases in annual, seasonal and daily temperatures that have been associated with climate change. More recently, the island has been experiencing an increase in the frequency of extremely warm days that are causing significant environmental and socio-economic impacts. This study focuses on examining how annual, seasonal and daily temperatures have changed over recent decades in 12 historical sites spread across the island for the 1970–2024 period and how it relates to climate change. The Mann–Kendall tests for trends were employed for the annual and seasonal series to identify areas of the island where warming has been found to be statistically significant. The 90th, 95th, and 99th percentiles of daily temperature series were also analyzed. This study found that Puerto Rico has experienced significant warming from 1970 to 2024, with the most consistent increases in minimum temperatures, especially during the summer and nighttime hours. The frequency of extreme heat events has increased across nearly all stations in different areas of the island. Stepwise regression models identified surface air temperature (SAT), sea surface temperature (SST), and total precipitable water (TPW) as the most influential regional climate predictors driving mean temperature trends and the occurrence of extreme heat events. Full article

This study introduces two distinct post-processing strategies to address systematic biases in sea surface temperature (SST) numerical forecasts, thereby enhancing SST predictive accuracy. The first strategy implements a spatiotemporal four-dimensional multi-grid analysis (4D-MGA) scheme within a three-dimensional variational (3D-Var) data assimilation framework. The second strategy establishes a hybrid deep learning architecture integrating empirical orthogonal function (EOF) analysis, empirical mode decomposition (EMD), and a backpropagation (BP) neural network (designated as EE–BP). The 4D-MGA strategy dynamically corrects systematic biases through a temporally coherent extrapolation of analysis increments, leveraging its inherent capability to characterize intrinsic temporal correlations in model error evolution. In contrast, the EE–BP strategy develops a bias correction model by learning the systematic biases of the SST numerical forecasts. Utilizing a satellite fusion SST dataset, this study conducted bias correction experiments that specifically addressed the daily SST numerical forecasts with 7-day lead times in the Kuroshio region south of Japan during 2017, systematically quantifying the respective error reduction potentials of both strategies. Quantitative verification reveals that EE–BP delivers enhanced predictive skill across all forecast horizons, achieving 18.1–22.7% root–mean–square error reduction compared to 1.2–9.1% attained by 4D-MGA. This demonstrates deep learning’s unique advantage in capturing nonlinear bias evolution patterns. Full article

This study evaluates the potential impact of the Copernicus Imaging Microwave Radiometer (CIMR) mission on the sea surface temperature (SST) products of the Mediterranean Sea. Currently, infrared (IR) radiometers provide accurate, high-resolution SST measurements, but they are limited by their inability to see through clouds. Passive microwave (PMW) radiometers, on the other hand, offer monitoring capabilities in almost all weather conditions but typically at lower spatial resolutions. The CIMR mission represents a notable advance in microwave remote sensing of SSTs, as it will ensure a ≤15 km spatial resolution in the recovered SST field. Using an observing system simulation experiment (OSSE), this study evaluates the effect of inserting synthetic CIMR observations into the Copernicus Mediterranean SST analysis system, which is based on an optimal interpolation (OI) algorithm. The OSSE was conducted using data for the year 2017, including daily SST and salinity outputs from a Mediterranean Sea model, hourly precipitation rates from the IMERG, and wind and cloud cover data from ERA5. The results suggest that the improved spatial resolution and accuracy of the CIMR could potentially improve SST retrievals in the Mediterranean Sea, offering better insights for climate and environmental monitoring in semi-closed basins. Including CIMR data in the OI algorithm reduced the mean error and root mean square error (RMSE) of the SST analysis, especially under conditions of low IR coverage. The greatest improvements were found to occur in July, corresponding to coastal upwelling and Atlantic inflow into the Alboran Sea. Improvements ranged from 16% to 29%, with an overall improvement of 26% for the full year of 2017. In conclusion, this preliminary study indicates that Copernicus Mediterranean Sea HR SST products could benefit from the inclusion of the CIMR in the current IR sensor constellation. Full article

We present a modified version of a coupled circulation–wave modelling system for the northwest Atlantic (CWMS-NWA) by including additional physics associated with wave–current interactions. The latest modifications include a parameterization of Langmuir turbulence and surface flux of turbulent kinetic energy from wave breaking in vertical mixing. The performance of the modified version of CWMS-NWA during Hurricane Arthur in 2014 is assessed using in situ measurements and satellite data. Several error statistics are used to evaluate the model performance, including correlation (R), root mean square error (RMSE), normalized model variance of model errors (${\gamma }^2$) and relative bias (RB). It is found that the simulated surface waves (R ≈ 94.0%, RMSE ≈ 27.5 cm, ${\gamma }^2\approx$ 0.16) and surface elevations (R ≈ 97.3%, RMSE ≈ 24.0 cm, ${\gamma }^2\approx$ 0.07) are in a good agreement with observations. The large-scale circulation, hydrography and associated storm-induced changes in the upper ocean during Arthur are reproduced satisfactorily by the modified version of CWMS-NWA. Relative to satellite observations of the daily averaged sea surface temperature (SST), the model reproduces large-scale features as demonstrated by the error metrics: R ≈ 97.8%, RMSE ≈ 1.6 °C and RB ≈ 8.6 $\times {10}^{-3}$°C. Full article

As a contribution to the Integrated Marine Observing System (IMOS), the Bureau of Meteorology introduces new reprocessed Himawari-8 satellite-derived Sea Surface Temperature (SST) products. The Radiative Transfer Model and a Bayesian cloud clearing method is used to retrieve SSTs every 10 min from the geostationary satellite Himawari-8. An empirical Sensor Specific Error Statistics (SSES) model, introduced herein, is applied to calculate bias and standard deviation for the retrieved SSTs. The SST retrieval and compositing method, along with validation results, are discussed. The monthly statistics for comparisons of Himawari-8 Level 2 Product (L2P) skin SST against in situ SST quality monitoring (iQuam) in situ SST datasets, adjusted for thermal stratification, showed a mean bias of −0.2/−0.1 K and a standard deviation of 0.4–0.7 K for daytime/night-time after bias correction, where satellite zenith angles were less than 60° and the quality level was greater than 2. For ease of use, these native resolution SST data have been composited using a method introduced herein that retains retrieved measurements, to hourly, 4-hourly and daily SST products, and projected onto the rectangular IMOS 0.02 degree grid. On average, 4-hourly products cover ≈$10\%$ more of the IMOS domain, while one-night composites cover ≈$25\%$ more of the IMOS domain than a typical 1 h composite. All available Himawari-8 data have been reprocessed for the September 2015–December 2022 period. The 10 min temporal resolution of the newly developed Himawari-8 SST data enables a daily composite with enhanced spatial coverage, effectively filling in SST gaps caused by transient clouds occlusion. Anticipated benefits of the new Himawari-8 products include enhanced data quality for applications like IMOS OceanCurrent and investigations into marine thermal stress, marine heatwaves, and ocean upwelling in near-coastal regions. Full article

The Arctic is one of the most important regions in the world’s oceans for understanding the impacts of a changing climate. Yet, it is also difficult to measure because of extreme weather and ice conditions. In this work, we directly compare four datasets from the Group for High-Resolution Sea Surface Temperature (GHRSST) with a NASA Saildrone deployment along the Alaskan Coast and the Bering Sea and Bering Strait. The four datasets used are the Remote Sensing Systems Microwave Infrared Optimally Interpolated (MWIR) product, the Canadian Meteorological Center (CMC) product, the Daily Optimally Interpolated Product (DOISST), and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product. Spatial sea surface temperature (SST) gradients were derived for both the Saildrone deployment and GHRSST products, with the GHRSST products collocated with the Saildrone deployment. Overall, statistics indicate that the OSTIA product had a correlation of 0.79 and a root mean square difference of 0.11 °C/km when compared with Saildrone. CMC had the highest correlation of 0.81. Scatter plots indicate that OSTIA had the slope closest to one, thus best reproducing the magnitudes of the Saildrone gradients. Differences increased at latitudes > 65°N where sea ice would have a greater impact. A trend analysis was then performed on the gradient fields. Overall, positive trends in gradients occurred in areas along the coastal regions. A negative trend occurred at approximately 60°N. A major finding of this study is that future work needs to revolve around the impact of changing ice conditions on SST gradients. Another major finding is that a northward shift in the southern ice edge occurred after 2010 with a maxima at approximately 2019. This indicates that the shift of the southern ice edge is not gradual but has dramatically increased over the last decade. Future work needs to revolve around examining the possible causes for this northward shift. Full article

In recent decades, climate change has led to ocean warming, causing more frequent extreme events such as marine heatwaves (MHWs), which have been understudied in the Caribbean Sea. This study addresses this gap using 30 years of daily sea surface temperature (SST) data, complemented by projections for the 21st century from nineteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. In the 1983–2012 period, significant trends were observed in the spatially averaged MHWs frequency (1.32 annual events per decade and node) and mean duration (1.47 ± 0.29 days per decade) but not in mean intensity. In addition, MHWs show large monthly variations in these metrics, modulated by interannual and seasonal changes. MHWs seasonality is different in the three used metrics, being more intense and frequent in warm and rainy months (intensity between 1.01 to 1.11 °C, duration 6.79 to 7.13 days) and longer lasting in late boreal winter (intensity between 0.82 to 1.00 °C, duration 7.50 to 8.31 days). The MHWs behavior from two extreme months show that these events can occur in both small and large areas in the Caribbean. Overall, models tend to underestimate the annually averaged MHWs frequency and intensity, while they overestimate duration when compared to observations. MHWs projections are more extreme under SSP585, as they are sensible to the radiational scenario. However, an increase in MHWs intensity and duration (events lasting as much as 154 days by 2100) is expected, driving a decrease in frequency (–37.39 events per decade under SSP585 by 2100). These projections imply that MHWs conditions at the beginning of the century will be nearly permanent in the Caribbean’s future. Nonetheless, caution is advised in interpreting these projections due to differences between models’ simulations and observed data. While advancements in oceanic models within CMIP6 demonstrate progress compared to previous CMIP initiatives, challenges persist in accurately simulating extreme events such as marine heatwaves. Full article

We explore to what extent data-driven prediction models have skills in forecasting daily sea-surface temperature (SST), which are comparable to or perform better than current physics-based operational systems over long-range forecast horizons. Three hybrid deep learning-based models are developed within the South China Sea (SCS) basin by integrating deep neural networks (back propagation, long short-term memory, and gated recurrent unit) with traditional empirical orthogonal function analysis and empirical mode decomposition. Utilizing a 40-year (1982–2021) satellite-based daily SST time series on a 0.25° grid, we train these models on the first 32 years (1982–2013) of detrended SST anomaly (SSTA) data. Their predictive accuracies are then validated using data from 2014 and tested over the subsequent seven years (2015–2021). The models’ forecast skills are assessed using spatial anomaly correlation coefficient (ACC) and root-mean-square error (RMSE), with ACC proving to be a stricter metric. A forecast skill horizon, defined as the lead time before ACC drops below 0.6, is determined to be 50 days. The models are equally capable of achieving a basin-wide average ACC of ~0.62 and an RMSE of ~0.48 °C at this horizon, indicating a 36% improvement in RMSE over climatology. This implies that on average the forecast skill horizon for these models is beyond the available forecast length. Analysis of one model, the BP neural network, reveals a variable forecast skill horizon (5 to 50 days) for each individual day, showing that it can adapt to different time scales. This adaptability seems to be influenced by a number of mechanisms arising from the evident regional and global atmosphere–ocean coupling variations on time scales ranging from intraseasonal to decadal in the SSTA of the SCS basin. Full article

The far Eastern Tropical Pacific (EPAC) and Western Colombia are one of the rainiest places on Earth, and the Choco low-level jet (ChocoJet) is one of the processes that influence the formation of copious precipitation and convection organization in this region. This study investigates the projected changes in precipitation in this region using historical and future simulations based on model output from two models contributing to the Coupled Model Intercomparison Project phase 6 (CMIP6). In close agreement with observations, models simulate that ChocoJet intensity is directly proportional to precipitation in the region. This relationship is also found far inland in Central America, the northwestern part of South America Pacific Coast, and the intermountain valleys of the Colombian Andes. Late 21st century simulations show a southward migration in mean and regional daily precipitation consistent with a decreased ChocoJet intensity. The weaker ChocoJet is related to a projected increase in EPAC tropical sea surface temperatures (SSTs) and an increased frequency and intensity of the warm phase of the Niño 1+2 SST interannual variability. Full article

Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology. Full article

In this study, we investigated the variations in the intensity of the tropical cyclones (TCs) formed in the North Atlantic basin from 1982 to 2021, based on the outputs from the Hurricane Maximum Potential Intensity (HuMPI) model. To feed HuMPI, we computed the annual Sea Surface Temperature (SST) as the SST average from 1 June to 30 November using the Daily Optimum Interpolation SST database. The information for all major hurricanes (MHs, category 3+ on the Saffir–Simpson wind scale) was obtained from the HURDAT2 dataset. While the trend (p < 0.05) in the mean maximum potential intensity (MPI) was approximately 1.14 m/s per decade for the maximum sustained wind speed and −1.57 hPa/decade for the minimum central pressure, the MH intensity did not exhibit any statistically significant trend. The behaviour of the MPI could be explained by the increase (p < 0.05) of the SST at a rate of 0.20 °C/decade. In addition, the increase of the TC intensity in the last 20 seasons (2002–2021) in relation to the period 1982–2001 was quite similar for MHs and MPI, being an increase of 3.89% and 3.20% for the mean maximum wind speed, respectively. Meanwhile, the minimum central pressure decreased by approximately 0.36% in both cases. This latter result is promising for investigating the changes in TC intensity resulting from global warming based on the HuMPI model. Full article

We found that significant errors occurred when diurnal data instead of diurnal–nocturnal data were used to calculate the daily sea-air CO₂ flux (F). As the errors were mainly associated with the partial pressure of CO₂ in seawater (pCO_2w) and the sea surface temperature (SST) in the control experiment, pCO_2w and SST equations were established, which are called the nocturnal effect of the CO₂ flux. The root-mean-square error between the real daily CO₂ flux (F_real) and the daily CO₂ flux corrected for the nocturnal effect (F_com) was 11.93 mmol m⁻² d⁻¹, which was significantly lower than that between the F_real value and the diurnal CO₂ flux (F_day) (46.32 mmol m⁻² d⁻¹). Thus, the errors associated with using diurnal data to calculate the CO₂ flux can be reduced by accounting for the nocturnal effect. The mean global daily CO₂ flux estimated based on the nocturnal effect and the sub-regional pCO_2w algorithm (cor_F_com) was −6.86 mol m⁻² y⁻¹ (September 2020–August 2021), which was greater by 0.75 mol m⁻² y⁻¹ than that based solely on the sub-regional pCO_2w algorithm (day_F_com = −7.61 mol m⁻² y⁻¹). That is, compared with cor_F_com, the global day_F_com value overestimated the CO₂ sink of the global ocean by 10.89%. Full article

Marine heatwaves (MHWs) have recently been at the forefront of climate research due to their devastating impacts on the marine environment. In this study, we have evaluated the spatiotemporal variability and trends of sea surface temperature (SST) and MHWs in the Black Sea. Furthermore, we investigated the relationship between the El Niño–Southern Oscillation (ENSO) and MHW frequency. This is the first attempt to investigate MHWs and their characteristics in the Black Sea using high-resolution remote-sensing daily satellite SST data (0.05° × 0.05°) from 1982 to 2020. The results showed that the spatial average of the SST warming rate over the entire basin was about 0.65 ± 0.07 °C/decade. Empirical orthogonal function (EOF) analysis revealed that SST in the Black Sea exhibited inter-annual spatiotemporal coherent variability. The maximum spatial SST variability was discovered in the central Black Sea, whereas the lowest variability was in the Batumi and Caucasus anti-cyclonic eddies in the eastern Black Sea. The highest SST temporal variability was found in 1994. More than two-thirds of all MHW events were recorded in the last decade (2010–2020). The highest annual MHW durations were reported in 1994 and 2020. The highest MHW frequency was detected in 2018 (7 waves). Over the whole study period (1982–2020), a statistically significant increase in annual MHW frequency and duration was detected, with trends of 1.4 ± 0.3 waves/decade and 2.8 ± 1.3 days/decade, respectively. A high number of MHW events coincided with El Niño (e.g., 1996, 1999, 2007, 2010, 2018, and 2020). A strong correlation (R = 0.90) was observed between the annual mean SST and the annual MHW frequency, indicating that more MHWs can be expected in the Black Sea, with serious consequences for the marine ecosystem. Full article

Marine heatwaves (MHWs) are long-lasting extreme oceanic warming events that can cause devastating effects on warm-water corals and associated ecosystems. The linear trend and interannual variability of MHWs over the Bay of Bengal (BOB) during 1982–2020 are investigated by a high-resolution daily sea surface temperature (SST) dataset. In regions where warm-water coral reefs are concentrated, annual MHW days and frequency significantly increase during 1982–2020, at rates exceeding that of the global mean. The coldest boreal winter season witnesses significant and steady increase trends in MHW days and frequency. In contrast, the trend is insignificant in the climatological warmest season (March to June) south of 15° N in the BOB, mainly due to large interannual variability. El Niño and Southern Oscillation (ENSO) dominates the interannual variability of BOB MHWs, which are highly consistent with the evolution of the mean SST. The negative phase of North Atlantic Oscillation (NAO) also modulates the occurrences of MHWs, especially over the northeastern BOB. The two climate modes synergistically explain about 50~70% of the interannual variances in the BOB’s MHWs. Correlation analysis reveals that south of 15° N in the BOB, the effect of El Niño on MHWs is evident from the boreal autumn of its developing phase to the boreal summer of its decaying phase, along with limited influence from NAO. However, in the northeast of the BOB, the effect of El Niño merely emerges from April to August of its decaying stage. In comparison, boreal winter-to-spring NAO exerts a strong control over March-to-June MHWs in the northeastern BOB. The results suggest that various climate modes may jointly or separately influence MHWs at certain seasons and locations, which is important for the seasonal prediction of MHWs. Indeed, when combining the Niño3.4 mature winter index and boreal winter-to-spring NAO index to build a regression model, it is more effective in reproducing the BOB’s MHW frequency compared to the Niño3.4 index alone. Full article