Sea Surface Temperature Research Articles

The Causal Relation within Air–Sea Interaction as Inferred from Observations

Abstract The intricate cause–effect relations in air–sea interaction are investigated using a quantitative causal inference formalism. The formalism is first validated with a classical stochastic coupled model and then applied to the observational time series of sea surface temperature (SST) and air–sea turbulent surface heat flux (SHF). We identify an overall asymmetry of causality between the two variables, namely, the causality from SHF to SST is significantly larger than that from SST to SHF over most of the global oceans. Geographically, the coupling is strongest in the tropics and gets weaker substantially in the extratropics. In the midlatitude ocean, SST makes higher contributions to the SHF variability in frontal regions. We further show that the identified causality is space and time scale dependent. The dominance of SHF driving SST occurs at basin scales, whereas the dominance of SST driving SHF mostly occurs at scales smaller than 10°. The causalities in both directions get larger with increasing time scale and are less asymmetric at longer time scales. The seasonality of the causality is also discussed here.

Revisiting the “East African Paradox”: CMIP6 Models Also Struggle to Reproduce Strong Observed Long Rain Drying Trends

Abstract Societies in much of the Horn of Africa are affected by variability in two distinct rainy seasons: the March–May (MAM) “long” rains and the October–December (OND) “short” rains. A recent five-season, La Niña–forced drought has renewed concerns about possible anthropogenic drying trends in the long rains, which had partially recovered after a multidecadal drying trend in the 1980s through the 2000s. Despite observed drying, previous generations of global climate models (GCMs) have consistently projected long-term wetting due to increased greenhouse gas concentrations, an East African “paradox” which complicates the interpretation of East African rainfall projections. We investigate the paradox in new phase 6 of the Coupled Model Intercomparison Project (CMIP6) and seasonal forecast models, leveraging an improved observational record and large ensembles to better differentiate internal and forced trends. We find observed drying trends are at the limits of the GCM spread during the peak paradox period, though the recent recovery is comfortably within the model spread. We find that the apparent paradox is largely removed by prescribing sea surface temperatures (SSTs) and is likely caused by the GCM difficulties in simulating observed tropical Pacific SST trends in recent decades. In line with arguments that these SST trends are at least partially forced anthropogenically, we recommend users of future rainfall projections in East Africa consider the possibility of long-term MAM drying despite GCM wetting and call for future model simulations that better sample the expected spread of SSTs. Significance Statement Societies in the Horn of Africa depend on the March–May “long” rains and the September–December “short” rains to support agricultural and pastoral practices on rain-fed lands. Recent major droughts have raised worries about possible anthropogenically forced drying trends in the long rains through global climate models (GCMs) project wetting. This East African “paradox” complicates long-term climate adaptation planning. We find the paradox continues in the newest generation of GCMs and seasonal forecast models; though a recent recovery in rainfall is well-captured, the strongest observed drying trends are rare in simulations. The paradox likely arises from known GCM biases in the Pacific Ocean interacting with natural variability. We recommend that researchers and policymakers consider possible long-term drying despite GCM projections of wetting.

Atmospheric Variability Drives Anomalies in the Bering Sea Air–Sea Heat Exchange

Abstract High latitudes, including the Bering Sea, are experiencing unprecedented rates of change. Long-term Bering Sea warming trends have been identified, and marine heatwaves (MHWs), event-scale elevated sea surface temperature (SST) extremes, have also increased in frequency and longevity in recent years. Recent work has shown that variability in air–sea coupling plays a dominant role in driving Bering Sea upper-ocean thermal variability and that surface forcing has driven an increase in the occurrence of positive ocean temperature anomalies since 2010. In this work, we characterize the drivers of the anomalous surface air–sea heat fluxes in the Bering Sea over the period 2010–22 using ERA5 fields. We show that the surface turbulent heat flux dominates the net surface heat flux variability from September to April and is primarily a result of near-surface air temperature and specific humidity anomalies. The airmass anomalies that account for the majority of the turbulent heat flux variability are a function of wind direction, with southerly (northerly) wind advecting anomalously warm (cool), moist (dry) air over the Bering Sea, resulting in positive (negative) surface turbulent flux anomalies. During the remaining months of the year, anomalies in the surface radiative fluxes account for the majority of the net surface heat flux variability and are a result of anomalous cloud coverage, anomalous lower-tropospheric virtual temperature, and sea ice coverage variability. Our results indicate that atmospheric variability drives much of the Bering Sea upper-ocean temperature variability through the mediation of the surface heat fluxes during the analysis period. Significance Statement A long-term ocean warming trend and a recent increase in marine heatwaves in the Bering Sea have been identified. Previous work showed that anomalies in the exchange of heat between the ocean and the atmosphere were the primary driver of Bering Sea temperature variability, but the processes responsible for the heat exchange anomalies were unknown. In this work, we show that the atmosphere is the primary driver of anomalies in the Bering Sea air–sea heat exchange and therefore plays an important role in altering the thermal state of the Bering Sea. Our results highlight the importance of understanding more about how the ocean and the atmosphere interact at high latitudes and how this relationship will be affected by future climate change.

The Strengthened Linkage between ENSO and the Eurasian Pattern since the Late 1980s

Abstract The Eurasian (EU) teleconnection pattern is an important atmospheric intrinsic mode over the extratropical Northern Hemisphere, and it has strong influence on the weather and climate over Eurasia and remote regions. Investigating factors for the EU variability is crucial and has important implications for regional climate prediction. This study reveals a remarkable influence of El Niño–Southern Oscillation (ENSO) on the EU in early winter and particularly a notable enhancement of this influence since the late 1980s. The results indicate that ENSO can lead to the vertical motion and atmospheric heating anomalies over the tropical Indian Ocean, triggering an atmospheric wave train propagating along the subtropical westerly jet and inducing an EU-like pattern over Eurasia in early winter. The interdecadal enhancement of the ENSO impact on the EU after the late 1980s may be attributable to the warming of the background mean sea surface temperature (SST) in the tropical Indian Ocean. In particular, ENSO can lead to stronger atmospheric heating and circulation anomalies over the tropical Indian Ocean via a strong air–sea interaction due to a warmer background mean SST after the late 1980s. A warmer background mean SST in the tropical Indian Ocean accelerates the subtropical westerly jet due to the thermal-wind balance, which is more favorable for the propagation of the atmospheric wave train forced by the atmospheric heating anomalies in the tropical Indian Ocean. The observed mechanisms for the enhanced impact of ENSO on the EU are also validated by numerical experiments. Significance Statement The Eurasian (EU) teleconnection pattern is an important atmospheric internal variability with significant influences on the Eurasian climate. The present study finds a remarkable relationship between the EU and ENSO in early winter, and this connection is significantly enhanced in recent decades. Thus, ENSO may serve as an important precursor signal for predicting the EU. Observational analysis and numerical experiments show that the warmer tropical Indian Ocean (TIO) plays a key role in this enhanced relationship. The warmer TIO may enhance the local air–sea interaction and accelerate the subtropical jet. The ENSO-induced anomalous disturbance over the TIO becomes stronger and can more easily propagate along the subtropical jet, exerting a strong influence on the EU especially after the late 1980s.

Delayed Impact of Biomass Burning in the Indochinese Peninsula on the Bay of Bengal Monsoon

Abstract The Indochinese Peninsula experiences a dry season with extensive biomass burning peaking from March to April. As the monsoon arrives, rainfall significantly removes aerosols through deposition, ending the emission season. However, the biomass burning aerosols exert an influence on the atmospheric circulation prior to the monsoon onset. This study employed statistical methods and a regional atmosphere–chemistry model [WRF Model coupled with chemistry (WRF-Chem)] to investigate the delayed impact of biomass burning from the Indochinese Peninsula on the monsoon onset. The results indicate that the cold sea surface temperature anomaly caused by the aerosols during the emission season can be stored in the ocean and inhibit convective activities over the adjacent sea regions in the postemission season, suppressing the southwestward cross-equatorial flow over the southern Bay of Bengal. This suppression delays the westward extension and northward shift of the upper-level South Asian high pressure system, along with its divergence and subsidence effects, thereby postponing the breakdown and retreat of the subtropical high-pressure belt. Simultaneously, the cold sea temperature also suppresses the development of a warm pool in the southeastern Bay of Bengal, which is associated with the generation of a monsoon onset vortex. Consequently, the onset of the Bay of Bengal monsoon is delayed. Due to the decreasing delayed effects of aerosols over time and the counteractive warming from the accelerated abnormal anticyclonic circulation in the upper-level Bay of Bengal, which results in accelerated disappearance of the cold signal in sea surface, the delayed influence of aerosols diminishes gradually after the onset of the Bay of Bengal monsoon until it disappears. Significance Statement This study explores the correlation between spring biomass burning in the Indochinese Peninsula and the onset of the Bay of Bengal and South China Sea monsoons. The research reveals that biomass burning during the emission season might postpone the initiation of the Bay of Bengal monsoon, while it has minimal influence on the South China Sea monsoon. By employing sensitivity experiments using models, the study elucidates the underlying mechanisms responsible for these observed effects.

Response of Larix sibirica Radial Growth to Climate Change in Kanas, Northern Xinjiang, China

Understanding how forest ecosystems respond to climate variability is critical for predicting the impacts of climate change on semi-arid and temperate regions. This study examines the climatic drivers of radial growth in Larix sibirica Ledeb in the Kanas Lake region, northern Xinjiang, China, to explore how climate change may alter forest growth patterns. Using tree-ring chronologies, we examine the relationships between temperature, precipitation, and drought conditions, as well as the influence of large-scale atmospheric circulation patterns on growth. Results indicate that high summer temperatures negatively affect tree growth, while adequate precipitation plays a crucial role in mitigating water stress, especially during key growth periods. Positive correlations with the Palmer Drought Severity Index further underscore the importance of long-term moisture availability. Moreover, the study highlights the role of the El Niño-Southern Oscillation in influencing moisture transport, with significant correlations between sea surface temperatures in the Niño 4 region and tree-ring growth. Future growth simulations under two climate scenarios suggest that moderate warming (SSP 2–4.5) may enhance growth, while more extreme warming (SSP 5–8.5) introduces greater uncertainty and potential growth instability. These findings provide critical guidance for forest management strategies in the face of climate change.

Mapping Potential Fishing Zones for Skipjack Tuna (Katsuwonus pelamis) Using Satellite Imagery

Fisheries are an important economic sector for many countries, including Indonesia. Bone Bay has high potential for skipjack (Katsuwonus pelamis) fishing. This study aims to map potential skipjack fishing zones in Bone Bay and analyze the relationship between sea surface temperature, salinity, and chlorophyll-a concentration with the catch of these fish. Using quantitative methods and probability sampling techniques, primary data were obtained through direct observation of fishing activities using the pole and line method, while secondary data were obtained from satellite images downloaded from the Oceancolor website. The results showed that the potential zones for skipjack fishing are located at coordinates 3o0’0”LS-4o30’0”LS and 120o30’0”BT-121o0’0” BT. The findings are expected to help stakeholders make strategic decisions for more effective and susstainable fisheries management.

Climate, crops, and postharvest conflict

AbstractI present new evidence of the effects of climate shocks on conflict. Focusing on political violence in Africa, I find that El Niño Southern Oscillation shocks during the crop‐growing season affect harvest‐related conflict in croplands exposed to this climate phenomenon. Specifically, a 1°C warming of sea surface temperature in the tropical Pacific Ocean, a proxy for a moderate‐strength El Niño event, reduces political violence in exposed locations with crop agriculture, relative to other areas, by approximately 3%, during the early postharvest season. This effect attenuates toward zero as the crop year progresses. This effect can reach as much as 15% after a strong El Niño event, such as that of 1997 or 2015, in highly exposed croplands, such as parts of Southern Africa and the Sahel. Conversely, a La Niña event, which is a counterpart of an El Niño event, has the opposite effect and thus increases conflict in the exposed croplands during the early postharvest season. Because these events can be predicted several months in advance, the findings of this research can contribute to creating a platform for early warnings about transitory spatiotemporal shifts in political violence in predominantly agrarian societies.

Identifikasi dan Pemetaan Konsentrasi Salinitas dan Suhu Permukaan Laut di Pesisir Kabupaten Probolinggo dengan Data Citra Satelit Landsat-8

The coastal area of ​​Probolinggo City is flowed by six rivers, namely the Kedunggaleng River, Umbul River, Banger River, Legundi River, Kasbah River and Pancur River. With an average flow length reaching 3.80 km. The river flows throughout the year from the south to the north according to the slope of the coastal area of ​​Probolinggo City. These activities can result in interactions between fresh water and sea water. This interaction influences the distribution pattern of salinity and sea surface temperature in the Probolinggo Coastal waters. Salinity and sea surface temperature are one of the biggest factors causing corrosion in the sea which can damage building structures around the sea. Based on this background, research is needed to monitor the distribution of salinity and sea surface temperature values ​​for 2020 to 2024 on the Probolinggo Coast using Landsat-8 satellite imagery. In this research, direct sample collection and measurement were used, for measuring salinity values ​​a Refractometer was used for sea surface temperature using an Infrared Thermometer. After data collection, regression and ANOVA tests were carried out on the data with the aim of finding out whether there were differences in in-situ data with sensor data from Landsat-8 satellite imagery. The results of the algorithm modeling were obtained, the algorithm model for processing image salinity was the Band_3 wavelength with the Logarithmic equation with y = 10.534ln(x) + 58.369 and the degree of determination value R^2 = 0.2499. Meanwhile, for sea surface temperature, the Exponential equation is y = 25.637e^3.1286x and the degree of determination R^2 = 0.0847. The analysis results from the ANOVA test for the H1 salinity value are accepted, meaning there is a significant difference between the in situ salinity value and the Landsat-8 satellite image salinity value from 2020 to 2024. Meanwhile, for the H1 sea surface temperature, it is accepted, meaning there is a significant difference between in situ SST and Satellite image SST from 2020 to 2024.

A revised ocean mixed layer model for better simulating the diurnal variation in ocean skin temperature

Abstract. Sea surface temperature (SST) is a crucial parameter in climate, weather, and ocean sciences due to its decisive role in ocean–atmosphere interactions. Identifying errors in the prognostic scheme used by the current European Centre for Medium-range Weather Forecasts (ECMWF) model for predicting the diurnal variation in ocean skin temperature led to a revisit and revision of the ocean mixed layer (OML) model. Validation of the revised model was conducted by comparing simulated temperatures at the sub-skin level and 1 m depth with observations from shipborne infrared measurements and buoys. These comparisons revealed a strong correlation, with an absolute mean deviation of less than 0.1 K and a standard deviation under 0.5 K, which are found to be comparable to errors in satellite observations of SST. Given that these results are derived from the same model simulations, the error statistics for the simulated skin temperature and its diurnal variation should have the same degree of accuracy. Furthermore, the simulation results closely align with anticipated solar radiation distributions, whereas ERA5 ocean skin temperature shows a significant lack of alignment with solar radiation. Consequently, the revised OML model shows promising potential for improving the simulation of diurnal SST variations in weather and climate models.

Ocean warming drives immediate mass loss from calving glaciers in the high Arctic

Glaciers in the Arctic have lost considerable mass during the last two decades. About a third of the glaciers by area drains into the ocean, yet the mechanisms and drivers governing mass loss at glacier calving fronts are poorly constrained in part due to few long-term glacier-ocean observations. Here, we combine a detailed satellite-based record of calving front ablation for Austfonna, the largest ice cap on Svalbard, with in-situ ocean records from an offshore mooring and modelled freshwater runoff for the period 2018-2022. We show that submarine melting and calving occur almost exclusively in autumn for all types of outlet glaciers, even for the surging and fast-flowing glacier Storisstraumen. Ocean temperature controls the observed frontal ablation, whereas subglacial runoff of surface meltwater appears to have little direct impact on the total ablation. The seasonal warming of the offshore waters varies both in magnitude, depth and timing, suggesting a complex interplay between inflowing Atlantic-influenced water at depth and seasonally warmed surface water in the Barents Sea. The immediate response of frontal ablation to seasonal ocean warming suggests that marine-terminating glaciers in high Arctic regions exposed to Atlantification are prone to rapid changes that should be accounted for in future glacier projections.

Multi-proxy reconstructions of paleotemperature in the southern South China Sea since the last deglaciation

The accuracy of paleothermometers is a prerequisite for understanding the past sea surface temperature (SST) changes in the tropical seas. Here, we analyzed the SST estimates reconstructed by four lipid proxies with common linear and newly advanced models in parallel in a sediment core collected from the southern South China Sea (SCS). After excluding the impact of terrestrial input, all of the four proxies-inferred SSTs displayed a gradually warming pattern since 18.3 ka. Our long-chain alkenones-derived annual SST at seawater depth of 0–30 m (SST 0–30 m) record closely matched the regional synthetic SST record from the entire southern SCS, corresponding to high-latitude climate events during the deglaciation. The temperatures reconstructed by long-chain diols (LCDs) showed an upper limit of 27 °C, and we thus proposed that they reflected the optimal survival temperature for organisms producing LCDs when SST was higher than 27 °C. Isoprenoid and hydroxy glycerol dialkyl glycerol tetraethers (iGDGTs and OH-GDGTs)-derived temperatures likely reflected the subsurface temperature (subT) at seawater depth of 30–125 m and SST towards the warm season in the tropical sea, respectively.

Interhemispheric synchrony of Mid-Late Holocene SST in the Indo-Pacific warm pool linked to upper water column dynamics

The sea surface temperature (SST) of the Indo-Pacific warm pool (IPWP) plays a crucial role in global climate system. Despite its importance, knowledge of its SST changes over century to millennial timescales remains limited and controversial, hindering our comprehensive understanding of climate change mechanisms. Our study bridges this gap with a novel SST reconstruction spanning approximately 7000 years, derived by aggregating 75 coral Sr/Ca-SST records from the South China Sea. These records reveal a notable synchronization in SST variations with foraminiferal reconstructions across the IPWP, reflecting a prevalent pattern of interhemispheric cooling and multi-century variability in the IPWP during the Mid-Late Holocene. Further analysis reveals that these (sub)millennial SST variations are closely linked to subsurface water temperature and ocean heat content (OHC) changes, supporting the theory that the release of deeper OHC acted as a heat source for low-latitude processes during the Holocene.

Comparing the Atmospheric Responses to Reduced Arctic Sea Ice, a Warmer Ocean, and Increased CO2 and Their Contributions to Projected Change at 2°C Global Warming

Abstract We consider the combined and individual influences of Arctic sea ice loss, sea surface temperature (SST) warming, and the direct radiative effect of increased CO2 on the Northern Hemispheric climate. The surface climate (e.g., temperature and precipitation) and atmospheric circulation responses (e.g., sea level pressure and wind) to these drivers are quantified using simulations from the Polar Amplification Model Intercomparison Project (for sea ice loss and SST warming) and the Cloud Feedback Model Intercomparison Project (for increased CO2). We verify the linear additivity of the PAMIP-derived winter responses to sea ice loss and SST change. The responses to SST change are of greater magnitude than that due to sea ice loss or due to CO2 direct radiative forcing in most seasons and regions, excluding the Arctic. Notably, however, sea ice loss is at least as important as SST change for the winter atmospheric circulation response over the North Atlantic and Siberia. The dynamical responses to sea ice loss and SST change oppose each other in many regions in winter, while the responses to SST and CO2 direct radiative forcing are often opposing in summer. Such opposing responses are less evident for the thermodynamical response. The sum of all three responses reproduces well the spatial patterns of change at 2°C global warming in winter and autumn in phase 6 of the Coupled Model Intercomparison Project projections but overestimates their magnitude.

Spatial and Temporal Bayesian Hierarchical Model Over Large Domains With Application to Holocene Sea Surface Temperature Reconstruction in the Equatorial Pacific

AbstractWe present a novel space‐time Bayesian hierarchical model (BHM) to reconstruct annual Sea Surface Temperature (SST) over a large domain based on SST at limited proxy (i.e., sediment core) locations. The model is tested in the equatorial Pacific. The BHM leverages Principal Component Analysis to identify dominant space‐time modes of contemporary variability of the SST field at the proxy locations and employs these modes in a Gaussian process framework to estimate SSTs across the entire domain. The BHM allows us to model the mean field and covariance, varying in space and time in the process layers of the hierarchy. Using the Markov Chain Monte Carlo (MCMC) method and suitable priors on the model parameters, posterior distributions of the model parameters and, consequently, posterior distributions of the SST fields and the attendant uncertainties are obtained for any desired year. The BHM is calibrated and validated in the contemporary period (1854–2014) and subsequently applied to reconstruct SST fields during the Holocene (0–10 ka). Results are consistent with prior inferences of La Niña‐like conditions during the Holocene. This modeling framework opens exciting prospects for modeling and reconstruction of other fields, such as precipitation, drought indices, and vegetation.

Cross-Seasonal Effect on Tropical Pacific Precipitation: Implication of South Pacific Quadrupole Simulation in CMIP6

Abstract The tropical Pacific convergence zone plays a crucial role in the global climate system. Previous research studies emphasized the cross-seasonal influence of the South Pacific quadrupole (SPQ) mode on the tropical Pacific climate. This study assesses the relationship between austral summer SPQ and austral winter tropical precipitation in phase 6 of the Coupled Model Intercomparison Project (CMIP6) models. The analysis emphasizes the historical experiments conducted within this time frame, spanning from 1979 to 2014. Our findings reveal that the SPQ is accurately represented in all CMIP6 models, but the connection between SPQ and precipitation is inadequately simulated in most models. To investigate the reasons behind these intermodel differences in reproducing SPQ-related processes, we categorize models into two groups. The comparisons demonstrate that the fidelity of model simulations in replicating the SPQ–tropical precipitation relationship hinges significantly on their capacity to reproduce the positive wind–evaporation–sea surface temperature (WES; SST) feedback over both the southwestern Pacific (25°–10°S; 150°E–160°W) and the southeastern Pacific (30°–10°S; 140°–80°W). This positive WES feedback propagates the SPQ signal into the tropics, intensifying the meridional gradient of SST anomaly in the tropical western-central Pacific, which consequently amplifies convection and rainfall in that area. In the group of models that failed to simulate this relationship accurately, the weakened WES feedback can be traced back to biases in wind speed and its variation. Furthermore, this WES feedback establishes a connection between SPQ and El Niño–Southern Oscillation (ENSO). A better rendition of the SPQ–tropical rainfall connection tends to result in a better simulation of the onset of SPQ-related ENSO events. As a result, this study advances our comprehension of extratropical impacts on the tropics, with the potential to enhance the accuracy of tropical climate simulation and prediction. Significance Statement Tropical rainfall plays an important role in the global climate system. Beyond the well-known influence of El Niño–Southern Oscillation (ENSO) on the tropical rainfall, the sea surface temperature (SST) anomaly in the South Pacific has a cross-seasonal impact on the precipitation over the tropical Pacific via air–sea coupled processes. Such SST anomaly pattern shows a quadrupole structure in the extratropical South Pacific, known as the South Pacific quadrupole (SPQ) mode. However, the relationship between SPQ and tropical precipitation remains poorly simulated in most state-of-the-art climate models. One primary reason for this gap between observed and simulated relationships is the underestimation of wind speed and its variation over the south tropical Pacific in these models. This limitation undermines their ability to accurately represent the air–sea interactions that drive tropical precipitation patterns, leading to inaccuracies in simulations. Our study aims to bridge this knowledge gap by enhancing our understanding of the extratropical effects on the tropical Pacific. By exploring the mechanisms underlying the SPQ–precipitation connection, we expect to improve the simulation and prediction capabilities of tropical climate models, thereby enhancing our ability to forecast and adapt to future climatic changes.

Impact of Arctic Sea ice anomalies on tropical cyclogenesis over the eastern North Pacific: Role of northern Atlantic Sea surface temperature anomalies

The present study identifies a close linkage between spring (MAM) sea ice concentration (SIC) anomalies in the Greenland-Barents (GB) Seas and the tropical cyclone (TC) genesis frequency over the eastern North Pacific (ENP) in the subsequent summer and fall (JJASON) during 1979–2022. An increase in MAM GB SIC anomalies results in a decrease in subsequent JJASON ENP TC genesis frequency. The physical process for the influence of Arctic sea ice anomalies on TC formation is further examined. Detailed dynamical diagnosis reveals that a higher GB SIC during MAM results in an increase in upward shortwave radiation, leading to sea surface temperature (SST) cooling. This SST cooling triggers a teleconnection atmospheric wave train, traversing Eurasia, the northern Pacific and the northern America and reaching the northern Atlantic. The associated anomalous cyclone over mid-latitude northern Atlantic is accompanied by anomalous southwesterly winds over the subtropics, leading to SST warming in the subtropical northern Atlantic through weakening total wind speed and upward surface latent heat flux. SST warming in the subtropical northern Atlantic extends southward into the tropical Atlantic via wind-evaporation-SST feedback during the subsequent summer and autumn, which induces an anomalous zonal-vertical circulation with descending motion over the ENP. This descending motion reduces relative humidity and weakens local convection over the ENP, and thus is unfavorable for TC genesis there. This study suggests that the spring GB SIC could serve as a potential predictor of JJASON ENP TC genesis.

European anchovy's abundance, more affected by climatic conditions than fishing activities in the northwest African waters.

The marine waters off the coast of northwest Africa are known for being highly productive upwelling regions in the Eastern Atlantic Ocean. The present study evaluated the combined effects of climate change and sustainable fishing levels on the long-term sustainability of European anchovy (Engraulis encrasicolus), a target pelagic species found along the West African coastal waters. The present study used survey biomass time series from survey vessels and species catch time series from commercial fisheries operating in the region. Hence, we investigated the relationship between fishing dynamics and environmental factors. The status of anchovy stock was determined by analyzing changes in different initial depletion rates using a state-space Bayesian surplus production model. We used Principal component analysis to identify the essential environmental data set, while Generalized Additive Models (GAM) were employed to determine the influence of the environment on fisheries. The stock assessment model revealed that fishing intensity did not affect the abundance of anchovy in the region even when tested under different biomass depletion scenarios. However, by employing GAMs, the research assessed the impact of environmental variables on fish biomass, indicating that sea surface temperature (SST) significantly influences anchovy abundance in the region. The latitudinal gradient also had a significant influence on this species' abundance. These findings underscore the vulnerability of the small-scale fisheries sector in the tropical upwelling zone to climate change. The study concludes that even in the face of potential negative impacts from environmental factors, optimizing fishing methods can contribute to the preservation of pelagic resources in the region. It emphasizes the importance of managing fishery resources in a sustainable, economically viable, and socially acceptable way.

Data-Driven Models for Significant Wave Height Forecasting: Comparative Analysis of Machine Learning Techniques

Accurate prediction of significant wave height (SWH) is critical for coastal safety, marine operations, and disaster management. Traditional numerical models for wave prediction are computationally intensive and often lack accuracy, prompting a shift towards data-driven methods. This study explores the efficacy of machine learning (ML) models in forecasting SWH in the coastline of North Stradbroke Island, Queensland, Australia. Using a dataset spanning 2010 to 2022, the study employs wave characteristics such as maximum wave height (Hmax), wave periods (Tz, Tp), peak direction, and sea surface temperature (SST) as predictors. Three ML models—Linear Regression, Decision Tree, and Random Forest—were trained and evaluated using performance metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). The Random Forest model demonstrated the best predictive accuracy with the lowest MSE (0.0074) and highest R² (0.958), outperforming both Linear Regression and Decision Tree models. This improvement in prediction accuracy supports the model's application for coastal management, ensuring better forecasting of wave conditions. The proposed approach shows significant advantages, including better handling of non-linearities and reduced computational costs compared to conventional numerical methods, such as SWAN and WAM, making it highly applicable for real-time wave forecasting, particularly for regions with complex coastal dynamics such as North Stradbroke Island.

CMIP6 Models Underestimate Arctic Sea Ice Loss during the Early Twentieth-Century Warming, despite Simulating Large Low-Frequency Sea Ice Variability

Abstract The variability of Arctic sea ice extent (SIE) on interannual and multidecadal time scales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in twentieth-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850–1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE) but exhibit pronounced intermodel spread in multidecadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the subpolar North Atlantic. We find that this is associated with differences in models’ sensitivity to Northern Hemispheric sea surface temperatures (SSTs). Additionally, we show that while CMIP6 models are generally capable of simulating multidecadal changes in Arctic sea ice from the mid-twentieth century to present day, they tend to underestimate the observed sea ice decline during the early twentieth-century warming (ETCW; 1915–45). These results suggest the need for an improved characterization of the sea ice response to multidecadal climate variability in order to address the sources of model bias and reduce the uncertainty in future projections arising from intermodel spread. Significance Statement The credibility of Arctic sea ice predictions depends on whether climate models are capable of reproducing changes in the past climate, including patterns of sea ice variability which can mask or amplify the response to global warming. This study aims to better understand how latest-generation global climate models simulate interannual and multidecadal variability of Arctic sea ice relative to available observations. We find that models differ in their representation of multidecadal sea ice variability, which is overall larger than in observations. Additionally, models underestimate the sea ice decline during the period of observed warming between 1915 and 1945. Our results suggest that, to achieve better predictions of Arctic sea ice, the realism of low-frequency sea ice variability in models should be improved.