Sea Surface Research Articles

Generation Mechanisms of SST Anomalies Associated with the Canonical El Niño Focusing on Vertical Mixing

Abstract The mean vertical advection of anomalous vertical temperature gradient is considered the dominant generation mechanism of positive sea surface temperature (SST) anomalies associated with the canonical El Niño. However, most past studies had a residual term in their heat budget analysis and/or did not quantify the role of vertical mixing even though active vertical turbulent mixing in the upper ocean is observed in the eastern equatorial Pacific. To quantitatively assess the importance of vertical mixing, a mixed layer heat budget analysis is performed using a hindcast simulation forced by daily mean atmospheric reanalysis data. It is found that when the mixed layer depth is defined as the depth at which potential density increases by 0.125 kg m−3 from the sea surface, the development of positive SST anomalies is predominantly governed by reductions in the cooling by vertical mixing, and their magnitude is much larger than those by vertical advection. The anomalous warming by vertical mixing may be partly explained by an anomalous deepening of the thermocline that leads to a decrease in the vertical temperature gradient, giving rise to suppression of the climatological cooling by vertical mixing. Also, an anomalously thick mixed layer reduces sensitivity to cooling by the mean vertical mixing and contributes to the anomalous SST warming. On the other hand, the dominant negative feedbacks are attributed to both anomalous surface heat loss and anomalous deepening of the mixed layer that weakens warming by the mean surface heat flux.

Exploring the Factors Controlling the Annual Range of Amazon Precipitation

Abstract The annual range (AR) of precipitation in the Amazon River basin has increased steadily since 1979. This increase may have resulted from natural variability and/or anthropogenic forcing, such as local land-use changes and global warming, which has yet to be explored. In this study, climate model experiments using the Community Earth System Model, version 2 (CESM2), were conducted to examine the relative contributions of sea surface temperatures (SSTs) variability and anthropogenic forcings to the AR changes in the Amazon rainfall. With CESM2, we design several factorial simulations, instead of actual model projection. We found that the North Atlantic SSTs fluctuation dominantly decreases the precipitation AR trend over the Amazon by −85%. In contrast, other factors, including deforestation and carbon dioxide, contributed to the trend changes, ranging from 25% to 35%. The dynamic component, specifically the tendency of vertical motion, made negative contributions, along with the vertical profiles of moist static energy (MSE) tendency. Seasonal-dependent changes in atmospheric stability could be associated with variations in precipitation. It is concluded that surface ocean warming associated with the North Atlantic natural variability and global warming is the key factor in the increased precipitation AR over the Amazon from 1979 to 2014. The continuous local land-use changes may potentially influence the precipitation AR in the future. Significance Statement The annual range (AR) in precipitation, the difference between wet- and dry-season precipitation, has increased from 1979 to 2014 in the Amazon. This increase may have resulted from global warming, deforestation, and sea surface temperature variability in North Atlantic and Pacific. To explore the role of each of these factors in altering the Amazon precipitation AR, five experiments were designed in the climate model (CESM). Among these experiment results, the effect of North Atlantic SSTs was the strongest. In the future, deforestation, global warming, and different ocean temperature states in the North Atlantic and Pacific may become increasingly influential on the changes in precipitation. Further investigation is needed to ascertain how the AR of precipitation in the Amazon will change.

Strengthened Combined Impact of the Pacific and Atlantic Meridional Modes on Eastern North Pacific Tropical Cyclones since the 1990s

Abstract There has been increased focus in recent years on the impact of the Pacific meridional mode (PMM) and the Atlantic meridional mode (AMM) on weather and climate events. This study shows an increased synergistic impact of both the PMM and AMM on eastern North Pacific (ENP) extended boreal summer (June–November) tropical cyclone frequency (TCF) since the 1990s. This increase in the combined impact of both the PMM and AMM on ENP TCF is mainly due to a stronger modulation of the AMM on TCF since the early 1990s and of a stronger modulation of the PMM on TCF since the late 1990s. A budget analysis of the genesis potential index highlights the important contribution of changes in vertical wind shear to the recent strengthened AMM–TCF relationship, while potential intensity and vertical wind shear are the two most important drivers of the recent increase in the PMM–TCF relationship. This intensified association is largely explained by changes in the mean state of sea surface temperatures in the tropical Atlantic associated with the Atlantic multidecadal oscillation (AMO) and trade wind magnitude in the subtropical Pacific Ocean associated with the Pacific decadal oscillation (PDO). This study highlights an asymmetric effect of the AMO and PDO on these two meridional modes and ENP TC genesis frequency and provides a better understanding of ENP TC activity on interannual-to-decadal time scales.

Propagation and Maintenance of the Quasi-Biweekly Oscillation over the Western North Pacific in Boreal Winter

Abstract This study investigates the propagation and maintenance mechanisms of the dominant intraseasonal oscillation over the western North Pacific in boreal winter, the quasi-biweekly oscillation (QBWO). The wintertime QBWO over the western North Pacific is characterized by the westward-northwestward movement from the tropical western Pacific to the western North Pacific and resembles the n = 1 equatorial Rossby wave. Its westward migration is primarily driven by the seasonal-mean zonal winds that advect vorticity anomalies in the lower–middle troposphere and moisture anomalies in the lower troposphere. Its northward movement is preconditioned by the vorticity dynamics of the beta effect, the low-level vertical moisture variation, and the local air–sea interaction. The latter involves the atmospheric forcing on the underlying ocean by changing the surface heat flux fluctuations and the sea surface temperature feedback on the low-level atmospheric instability. Its maintenance is primarily through atmospheric external energy sources from diabatic heating, which first generates eddy available potential energy and then converts it to eddy kinetic energy. Significance Statement Atmospheric quasi-biweekly oscillation (QBWO) is an important climate phenomenon over the western North Pacific in boreal winter. It can trigger significant influences both in the tropical and extratropical regions. Given the importance, it is necessary to investigate the propagation and maintenance mechanisms of the QBWO over the western North Pacific in boreal winter. The QBWO usually propagates northwestward from the tropical western Pacific to the western North Pacific. The westward propagation is driven by the seasonal mean zonal winds that advect vorticity anomalies in the lower–middle troposphere and moisture anomalies in the lower troposphere. The northward propagation is caused jointly by several processes, including the atmospheric forcing on the ocean via the surface heat flux fluctuations, the feedback of ocean on the low-level atmospheric instability, the vorticity dynamics of the beta effect, and the vertical variation of low-level moisture. In addition, converting from eddy available potential energy to eddy kinetic energy is essential in maintaining the QBWO’s development. The disclosure of these crucial processes deepens the dynamics of the QBWO over the western North Pacific in boreal winter.

What if the upwelling weakens? Effects of rising temperature and nutrient depletion on coastal assemblages.

Surface temperature of the oceans has increased globally over the past decades. In coastal areas influenced by eastern boundary upwelling systems (EBUS), winds push seawater offshore and deep, cold and nutrient-rich seawater rise towards the surface, partially buffering global warming. On the North coast of Portugal, the NW Iberian upwelling system allows extensive kelp forests to thrive in these "boreal-like" conditions, fostering highly diverse and productive communities. However, the warming of the upper layer of the ocean may weaken this upwelling, leading to higher sea surface temperature and lower nutrient input in the coastal areas. The effects of these changes on the structure and function of coastal ecosystems remain unexplored. The present study aimed to examine the combined effects of elevated temperature and nutrient depletion on semi-naturally structured assemblages. The eco-physiological responses investigated included growth, chlorophyll fluorescence and metabolic rates at the levels of individual species and whole assemblages. Our findings showed interactive effects of the combination of elevated temperature with nutrient depletion on the large canopy-forming species (i.e., kelp). As main contributor to community response, those effects drove the whole assemblage responses to significant losses in productivity levels. We also found an additive effect of elevated temperature and reduced nutrients on sub-canopy species (i.e., Chondrus crispus), while turfs were only affected by temperature. Our results suggest that under weakening upwelling scenarios, the ability of the macroalgal assemblages to maintain high productivity rates could be seriously affected and predict a shift in community composition with the loss of marine forests.

Multiyear surface wave dataset from the subsurface “DeepLev” eastern Levantine moored station

Abstract. Processed and analyzed sea surface wave characteristics derived from an up-looking acoustic Doppler current profiler (ADCP) for the period 2016–2022 are presented as a dataset available from the public open-access repository of SEA scieNtific Open data Edition (SEANOE) at https://doi.org/10.17882/96904 (Haim et al., 2022). The collected data include full two-dimensional wave fields, along with computed bulk parameters, such as wave heights, periods, and directions of propagation. The ADCP was mounted on the submerged Deep Levantine (DeepLev) mooring station located 50 km off the Israeli coast to the west of Haifa (bottom depth ∼1470 m). It meets the need for accurate and reliable in situ measurements in the eastern Mediterranean Sea as the area significantly lacks wave data compared to other Mediterranean sub-basins. The developed long-term time series of wave parameters contribute to the monitoring and analysis of the region's wave climate and the quality of wind–wave forecasting models.

Seasonal dynamics of global marine heatwaves over the last four decades

Marine heatwaves (MHWs), prolonged periods of abnormally high sea temperature, have greater devastating impacts on marine ecosystem services and socioeconomic systems than gradual long-term ocean warming. Despite growing evidence of increases in MHW frequency, duration, and intensity, their interseasonal variations remain unclear. Using satellite-derived daily sea surface temperature (SST) data from 1982 to 2022, this work reveals a strong seasonality in MHWs. Typically, the highest cumulative intensity, characterizing total impacts on ecosystems, occurs during the local warm seasons in most oceans, leading to a significant interseasonal difference between warm and cold seasons. The interseasonal difference is predominantly driven by air-sea heat flux, rather than oceanic horizontal advection and vertical process. An increase in these interseasonal differences is observed in mid and high latitudes, with a significant increase in the warm season and a weaker trend in the cold season. In the Equatorial Pacific and Western Equatorial Indian Ocean, intense MHWs are primarily exacerbated by the El Niño-Southern Oscillation (ENSO), which also determines interseasonal variations in MHWs. Understanding the seasonality of MHWs can help better formulate corresponding policies to reduce economic and ecological losses caused by these events and can improve the accuracy of future predictions.

An in-depth investigation of global sea surface temperature behavior utilizing chaotic modeling.

Sea surface temperature (SST), with its complex and dynamic behavior, is a major driver of ocean-atmosphere interactions. The purpose of this study is to investigate the behavior of SST and its prediction using a chaotic approach. Average mutual information (AMI) and Cao methods were used to reconstruct the phase space. The Lyapunov exponent and correlation dimension were used to investigate chaos. The Lyapunov exponent index was used to predict SST with a 5-year average prediction horizon using the local prediction method between 2023 and 2027. The results showed a 3-month delay time for the Pacific and Antarctic Oceans, and a 2-month delay time for the Atlantic, Indian, and Arctic Oceans. The optimal embedding dimension for all oceans is between 6 and 7. Our analysis reveals that the dynamics of SST in all oceans exhibit varying degrees of chaos, as indicated by the correlation dimension. The local prediction method achieves relatively accurate short-term SST predictions due to the clustering of SST points around specific attractors in the phase space. However, in the long term, the accuracy of this method decreases as the points in the phase space of SST can spread randomly. The model performance ranking with a Percent Mean Relative Absolute Error shows that the Indian Ocean has the best performance compared to other oceans, while the Atlantic, Pacific, and Antarctic and Arctic Oceans are in the next ranks. This study contributes to understanding the dynamics of SST and has practical value for use in the development of climate models.

Single chamber Mg/Ca analyses of Globigerinoides ruber for paleo-proxy calibration using femtosecond LA-ICP-MS

Mg/Ca is an independent proxy in paleoceanography to reconstruct past seawater temperature. Femtosecond Laser Ablation Inductively Coupled Plasma Mass Spectrometry (fs-LA-ICP-MS) was employed to determine the Mg/Ca composition of tests (shells) of the planktic foraminifer species Globigerinoides ruber albus (white chromotype) and G. ruber ruber (red/pink chromotype) sampled alive from the temperate to subtropical eastern North Atlantic with the research sailing yacht Eugen Seibold. Mg/Ca data are compared to (i) the measured in-situ temperature of ambient seawater, (ii) average mixed layer temperature, and (iii) sea surface temperature (SST). The pooled mean chamber Mg/Ca from each plankton tow site exhibits a positive relationship with SST. Two chamber-specific calibrations are derived, which are consistent with previous calibration equations for comparable paleo-archives. The results confirm fs-LA-ICP-MS as reliable method for determining Mg/Ca in G. ruber, and both the penultimate and antepenultimate chambers of adult specimens may provide comprehensible Mg/Ca temperatures of the surface ocean.

Disagreement on the North Atlantic Cold Blob Formation Mechanisms among Climate Models

Abstract Despite global warming, sea surface temperature (SST) in the subpolar North Atlantic has decreased since the 1900s. This local cooling, known as the North Atlantic cold blob (North Atlantic cold blob), signifies a unique role of the subpolar North Atlantic in uptaking heat and hence impacts downstream weather and climate. However, a lack of observational records and its constraints on climate models leave the North Atlantic cold blob formation mechanism inconclusive. Using simulations from the Coupled Model Intercomparison Project Phase 6, we assess the primary processes driving the North Atlantic cold blob within individual models and the consistency of mechanisms across models. We show that 11 out of 32 models, which we call “Cold Bold” models, simulate subpolar North Atlantic cooling over 1900–2014. Further analyzing the heat budget of subpolar North Atlantic SST shows that models have distinct mechanisms of cold blob formation. Whereas four out of the 11 Cold Blob models indicate decreased Oceanic Heat Transport Convergence (OHTC) as the key mechanism, another four models suggest changes in radiative processes making predominant contributions. The contribution of OHTC and radiative processes are comparable in the remaining three models. Such a model spread in the mechanism of cold blob formation may be associated with distinct base-state Atlantic Meridional Overturning Circulation (AMOC) strength, which explains about 39% of the inter-model spread in the contribution of OHTC to the simulated cold blob. Models with a stronger base-state AMOC suggest a greater role of OHTC, whereas those with a weaker base-state AMOC indicate radiative processes are more responsible. This model discrepancy suggests that the cold blob formation mechanism diagnosed from single models should be interpreted with caution.

Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning

Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is essential to develop a connection between surface oceanic parameters and subsurface DO values. Machine learning (ML) methods can effectively grasp the complex relationship between input attributes and target variables, making them a valuable approach for estimating subsurface DO values based on surface oceanic parameters. In this study, the potential of ML methods for subsurface DO retrieval is analyzed. Among the selected ML methods, namely support vector regression (SVR), random forest (RF) regression, and extreme gradient boosting (XGBoosting) regression, the RF method generally demonstrates superior performance. As the depth increases, the accuracy of DO estimates tends to initially decrease, then gradually improve, with the poorest performance occurring at the depth of 600 decibar. The range of determination coefficients (R2) and root mean square error (RMSE) values based on the test dataset at different depths lies between 0.53 and 47.59 umol/kg to 0.99 and 4.01 umol/kg. In addition, compared to sea surface salinity (SSS) and sea surface chlorophyll-a (SCHL), sea surface temperature (SST) plays a more significant role in DO retrieval. Finally, compared to the pelagic interactions scheme for carbon and ecosystem studies (PISCES) model, the RF method achieves higher retrieval accuracies at depths above 700 decibar. In the deep ocean, the primary differences in DO values obtained from the RF method and the PISCES model-based method are noticeable in the vicinity of the equatorial region.

Underwater Image Enhancement Algorithm Based on Adversarial Training

Ocean observation is the first step in the development of the ocean, whose abundant resources and strategic significance are attracting increasing attention. Observation methods based on visual sensor networks have received great attention from researchers due to their visualization capability and high information capacity. However, below the sea surface, objective factors such as blurriness, turbulence, and underwater color casting can cause image distortion and affect the acquisition of images. In this paper, the enhancement of underwater images is tackled using an adversarial learning-based approach. First, pre-processing is applied to address the significant color casting in the dataset, thus enhancing feature learning for subsequent style transfer. Then, corresponding improvements are made to a generative adversarial network’s structure and loss functions to better restore the features of the network output. Finally, evaluations and comparisons are performed using underwater image quality assessment metrics and several public datasets. Through multidimensional experiments, the proposed algorithm is shown to exhibit excellent performance in both subjective and objective evaluation metrics compared to state-of-the-art algorithms, as well as in practical visual applications.

Assessment of Accuracy of Moderate-Resolution Imaging Spectroradiometer Sea Surface Temperature at High Latitudes Using Saildrone Data

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high-latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck, whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean difference in SSTskin from the SD-1036 and SD-1037 measurements is ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, −0.234 K for Aqua and −0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin.

Sensitivity of Philippine historically damaging tropical cyclone events to surface and atmospheric temperature forcings

As the climate warms, sea surface temperature (SST) is projected to increase, along with atmospheric variables which may have an impact on tropical cyclone (TC) properties. Climate models have well-known errors in simulating current climate SSTs that will likely affect future TC projections. Therefore, a better understanding of the impact of SST changes will help us identify the largest uncertainty in projecting TC changes. This study employs three different and independent methodologies to investigate the impact of sea surface and atmospheric temperature changes and tropical cyclone (TC) characteristics, focusing on three historically damaging TCs in the Philippines: Typhoons Haiyan, Bopha, and Mangkhut. These methodologies include initially simulations with uniform SST anomalies between −4 to +4°C, then experiments using delta from CMIP6 CESM2 for SST and atmospheric temperature in the far future, and, finally, simulations imposing Radiative-Convective Equilibrium (RCE) conditions. The experiments reveal significant insights into TC dynamics under varying environmental conditions. Changes in SSTs resulted in changes in TC track, intensity, and rainfall. In the positive SST simulations, TCs tended to move northwards and resulted in substantial increases in maximum wind speeds reaching a difference of up to 10, 13, 23 ms−1 for Typhoons Haiyan, Bopha, and Mangkhut, respectively. Analysis of the accumulated rainfall also showed that increased SST results in increased rainfall. Inclusion of atmospheric warming offsets the intensification due to SST change. Moreover, warmer SSTs resulted in slower-moving TCs and increased TC size. Further analyses incorporating atmospheric temperature adjustments derived from CESM2 and RCE simulations offer better insights on TC response. Under near-RCE conditions, TCs exhibit reduced sensitivity to SST changes, with smaller intensity and size modifications simulated when stable relative humidity is imposed. The smaller changes in TC intensity and size observed in these experiments suggest that maintaining atmospheric stability through pre-storm atmospheric adjustments dampens the response of TCs to SST warming.

Climate change impacts on small pelagic fish distribution in Northwest Africa: trends, shifts, and risk for food security

Climate change is recognised to lead to spatial shifts in the distribution of small pelagic fish, likely by altering their environmental optima. Fish supply along the Northwest African coast is significant at both socio-economic and cultural levels. Evaluating the impacts of climatic change on small pelagic fish is a challenge and of serious concern in the context of shared stock management. Evaluating the impact of climate change on the distribution of small pelagic fish, a trend analysis was conducted using data from 2363 trawl samplings and 170,000 km of acoustics sea surveys. Strong warming is reported across the Southern Canary Current Large Marine Ecosystem (CCLME), extending from Morocco to Senegal. Over 34 years, several trends emerged, with the southern CCLME experiencing increases in both wind speed and upwelling intensity, particularly where the coastal upwelling was already the strongest. Despite upwelling-induced cooling mechanisms, sea surface temperature (SST) increased in most areas, indicating the complex interplay of climatic-related stressors in shaping the marine ecosystem. Concomitant northward shifts in the distribution of small pelagic species were attributed to long-term warming trends in SST and a decrease in marine productivity in the south. The abundance of Sardinella aurita, the most abundant species along the coast, has increased in the subtropics and fallen in the intertropical region. Spatial shifts in biomass were observed for other exploited small pelagic species, similar to those recorded for surface isotherms. An intensification in upwelling intensity within the northern and central regions of the system is documented without a change in marine primary productivity. In contrast, upwelling intensity is stable in the southern region, while there is a decline in primary productivity. These environmental differences affected several small pelagic species across national boundaries. This adds a new threat to these recently overexploited fish stocks, making sustainable management more difficult. Such changes must motivate common regional policy considerations for food security and sovereignty in all West African countries sharing the same stocks.

Temperature and nutrients control the presence and distribution of long-chain diols in Swiss lakes

Long-chain diols are biomarkers commonly used in the marine realm to reconstruct several environmental parameters such as sea surface temperature and salinity. However, they are also produced in lacustrine and slow-flowing river environments, a characteristic that has proved to be useful to trace past riverine inputs in coastal sedimentary records. So far, their use in lacustrine settings is sparse as their controls are not well-known. Previous studies in two lakes have shown that long-chain diol distribution is linked to changes in temperature (in a small Spanish alpine lake), but also to water column stratification (in a large deep Swiss lake). To understand the controls on i) the presence of long-chain diols in lakes, and ii) the distribution of long-chain diol isomers, surface sediments from 52 Swiss lakes were studied. Long-chain diols are present in 57% of the lakes, and machine learning (i.e., random forest model) showed that their presence is mainly controlled by mean annual air temperature, sodium and potassium concentrations and area of the lakes. Long-chain diol isomer relative distribution seems to react to temperature, nutrient (here nitrate) and oxygen concentrations in the lakes. This new insight was tested on a short sedimentary core from Lake Zurich, and compared with other biomarker proxies (based on branched and isoprenoid glycerol dialkyl glycerol tetraethers), as well as with historical record of nutrient contents and temperature. Variations in the long-chain diol index (LDI) mirror measured temperature, but also reacted to changes in nutrients and oxygenation in the lake. This study highlights the potential of long-chain diols as a proxy to trace both nutrients and temperature in lakes, potentially on geological timescales.

Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability

AbstractWe use neural networks and large climate model ensembles to explore predictability of internal variability in sea surface temperature (SST) anomalies on interannual (1–3 years) and decadal (1–5 and 3–7 years) timescales. We find that neural networks can skillfully predict SST anomalies at these lead times, especially in the North Atlantic, North Pacific, Tropical Pacific, Tropical Atlantic and Southern Ocean. The spatial patterns of SST predictability vary across the nine climate models studied. The neural networks identify “windows of opportunity” where future SST anomalies can be predicted with more certainty. Neural networks trained on climate models also make skillful SST predictions in reconstructed observations, although the skill varies depending on which climate model the network was trained. Our results highlight that neural networks can identify predictable internal variability within existing climate data sets and show important differences in how well patterns of SST predictability in climate models translate to the real world.

Assessment of C-Band Polarimetric Radar for the Detection of Diesel Fuel in Newly Formed Sea Ice

There is a heightened risk of an oil spill occurring in the Arctic, as climate change driven sea ice loss permits an increase in Arctic marine transportation. The ability to detect an oil spill and monitor its progression is key to enacting an effective response. Microwave scatterometer systems may be used detect changes in sea ice thermodynamic and physical properties, so we examined the potential of C-band polarimetric radar for detecting diesel fuel beneath a thin sea ice layer. Sea ice physical properties, including thickness, temperature, and salinity, were measured before and after diesel addition beneath the ice. Time-series polarimetric C-band scatterometer measurements monitored the sea ice evolution and diesel migration to the sea ice surface. We characterized the temporal evolution of the diesel-contaminated seawater and sea ice by monitoring the normalized radar cross section (NRCS) and polarimetric parameters (conformity coefficient (μ), copolarization correlation coefficient (ρco)) at 20° and 25° incidence angles. We delineated three stages, with distinct NRCS and polarimetric results, which could be connected to the thermophysical state and the presence of diesel on the surface. Stage 1 described the initial formation of sea ice, while in Stage 2, we injected 20L of diesel beneath the sea ice. No immediate response was noted in the radar measurements. With the emergence of diesel on the sea ice surface, denoted by Stage 3, the NRCS dropped substantially. The largest response was for VV and HH polarizations at 20° incidence angle. Physical sampling indicated that diesel emerged to the surface of the sea ice and trended towards the tub edge and the polarimetric scatterometer was sensitive to these physical changes. This study contributes to a greater understanding of how C-band frequencies can be used to monitor oil products in the Arctic and act as a baseline for the interpretation of satellite data. Additionally, these findings will assist in the development of standards for oil and diesel fuel detection in the Canadian Arctic in association with the Canadian Standards Association Group.

Asian monsoonal temperature and environmental changes since the penultimate deglaciation as revealed by alkenones and alkanes in sediments from the northern South China Sea

A new alkenone sea surface temperature (SST) record from the South China Sea (SCS) over the last 170 ka has been reported here. Our analysis of magnetic susceptibility and AMS 14C dating on the top portion of the core suggests that this record dates to the Marine Isotope Stage (MIS) 6. As indicated in Termination I and II, SST changes are correlated with clear glacial/interglacial cycles and likely dominated by atmospheric CO2 and Western Pacific Warm Pool temperature changes. But the two Terminations were different in magnitude. In Termination I, the minimum and maximum SSTs are 24.5 °C and 27.5 °C, respectively, showing a smaller rise (3 °C) than in Termination II (24–28.3 °C). The n-alkane indices, such as average chain length (ACL) and carbon preference index (CPI), exhibited high/low values during glacial/interglacial periods, and possible more arid climate during MIS 6 than MIS 2. During Termination II, the maximum surface warming in the SCS is accompanied by a progressive lag of approximately 7000 years in relation to maximum summer insolation until Termination I, when they are nearly synchronous. According to the study, SSTs are continuously changing and are influenced by solar insolation, global ice volume changes, and ocean–atmosphere interactions across hemispheres.

Season‐Dependent Atmosphere‐Ocean Coupled Processes Driving SST Seasonality Changes in a Warmer Climate

AbstractAmplification of sea surface temperature (SST) seasonality in response to global warming is a robust feature in climate model projections but season‐dependent regional disparities in this amplification and the associated mechanisms are not well addressed. Here, by analyzing large ensemble simulations using Community Earth System Model version 2, we investigate detailed spatiotemporal characteristics of the amplification of SST seasonality focusing on the North Pacific and North Atlantic, where robust changes are projected to emerge around 2050 under SSP3‐7.0 scenario. Our results indicate that atmosphere‐ocean coupled processes shape regional changes in SST seasonality differently between warm (MAMJJAS) and cold seasons (ONDJF). During the warm season, the projected warming tendency is mainly due to increased net surface heat flux and weakening of vertical mixing. On the other hand, in the cold season, the projected cooling tendency is driven by strengthened vertical mixing over the North Pacific associated with the northward shift of storm tracks but weakened horizontal advection and mixing due to changes in ocean currents over the North Atlantic.