Sea Surface Temperature Fields Research Articles

Objective delineation of persistent SST fronts based on global satellite observations

A novel method is introduced to objectively and automatically identify persistent thermal fronts across the ocean from 60° N to 60° S: FROnt Delineation in the Ocean (FRODO), an algorithm developed to delineate sea surface temperature (SST) fronts, was applied to global satellite observations. By relying on long-term averaged frontal probability fields of SST, persistent SST fronts were located globally in an automated manner. The algorithm was applied to AVHRR Pathfinder and MODIS observations, spanning the period from 1982 through 2011, and resulted in new insights regarding frontal zones: new fronts were detected, the location of some fronts identified in the existing literature has been updated, the accuracy of the algorithm was tested through comparison with visible color and SST data, and the variability in frontal location over climatological seasons was explored. While significant spatial variability prevents the algorithm from accurately tracking some fronts, the results were found to agree very well with observations in the case of fronts associated with ocean bathymetry. The output of the algorithm consists of five digital global sets of persistent fronts: one based on the 30 year time series and four based on the climatological seasons derived from the same time series, which will facilitate the study of the links between SST fronts and other oceanic processes.

Sea Surface Temperature Analysis for Fengyun-3C Data Using Oriented Elliptic Correlation Scales.

Sea surface temperature (SST) is critical for global climate change analysis and research. In this study, we used visible and infrared scanning radiometer (VIRR) sea surface temperature (SST) data from the Fengyun-3C (FY-3C) satellite for SST analysis, and applied the Kalman filtering methods with oriented elliptic correlation scales to construct SST fields. Firstly, the model for the oriented elliptic correlation scale was established for SST analysis. Secondly, observation errors from each type of SST data source were estimated using the optimal matched datasets, and background field errors were calculated using the model of oriented elliptic correlation scale. Finally, the blended SST analysis product was obtained using the Kalman filtering method, then the SST fields using the optimum interpolation (OI) method were chosen for comparison to validate results. The quality analysis for 2016 revealed that the Kalman analysis with a root-mean-square error (RMSE) of 0.3243 °C had better performance than did the OI analysis with a RMSE of 0.3911 °C, which was closer to the OISST product RMSE of 0.2897 °C. The results demonstrated that the Kalman filtering method with dynamic observation error and background error estimation was significantly superior to the OI method in SST analysis for FY-3C SST data.

The Increasing Frequency of Tropical Cyclones in the Northeastern Atlantic Sector

North Atlantic Tropical Cyclones (TCs) are major atmospheric hazards that can cause large disruptions to coastal and near-coastal societies. Although most studies focus on those areas with highest impact (e.g., Caribbean Islands, the Gulf and western coast of United States), there is an increasing interest in characterizing changes in occurrence and impacts in areas usually less affected by TCs, particularly in the framework of a changing climate. Here we provide a long-term context evaluating changes in the frequency of TC in the Northeast Atlantic (NEA) basin during the 1978–2019 period. In the last decades, scattered information has shown an impact both from TCs and Post-Tropical Cyclones (PTC) in the NEA. We compute several complementary linear trends and show a significant (p ≤ 0.1) increase in the number of stronger storms in the entire North Atlantic basin, and the amount of TCs and PTCs that reach the NEA, in agreement with previous works. A highly significant relation (p ≤ 0.05) is found between the Atlantic Multidecadal Oscillation (AMO) index and TC activity in both the entire North Atlantic and the NEA basin. Sea surface temperature anomaly maps are produced to better encapsulate the annual variability without the multidecadal oscillation effects and, important cold (warm) pools in cyclogenesis and development zones are found in years with low (high) TC activity. It is also found that the sea surface temperature field plays a minor role in the guiding of storms into the NEA sector. Long-term trends as well as high/low seasonal activity analysis suggest that atmospheric circulation (vertical wind shear, lapse rate, mean sea level pressure and upper-level steering) is more relevant than sea surface temperature in the NEA region.

Environmental variability and larval supply to wild and cultured shellfish populations

Coastal upwelling ecosystems support some of the most productive fisheries of the planet together with a large shellfish aquaculture sector that depends on oceanographic processes to deliver planktonic larvae to replenish and feed the farmed stock. Coastal shellfish aquaculture operations in Chile and Perú have experienced large interannual fluctuations in larval supply over the past decade, yet the drivers of such variability remain unidentified. We focused on the effects of environmental variability on larval supply of the farmed Peruvian bay scallop Argopecten purpuratus in a bay in northern Chile (Tongoy Bay, 30∘S) that accounts for over 90% of countrywide landings. We examined the hypothesis that the environmental processes governing larval supply were shared with wild benthic invertebrates with planktonic larval development and compared time series of larval abundance for the scallop with larval supply rates to benthic populations of two well-studied wild intertidal species: the Chthamalid barnacle Jehlius cirratus and the purple mussel Perumytilus purpuratus. To this end, we examined the cross-correlation of larval supply to environmental variability using MODIS satellite fields of sea surface temperature (SST) chlorophyll-a concentration (chl-a) and fluorescence line height (nFLH), together with three climate indices relevant for the south east Pacific sector: the Southern Oscillation index (SOI), the Pacific Decadal Oscillation (PDO) and the Antarctic Oscillation Index (AAO). Our results showed that over the five-year study period (2009–2013), patterns of larval supply to the scallop population were related to inter-annual variability in the environmental processes as captured by their Empirical Orthogonal Functions (EOFs), likely to adult condition before spawning. Surprisingly, larval supply for none of the wild species showed a clear association to the EOFs. In contrast, scallops and wild species showed significant association to lower frequency climate variability as captured by the SOI and the PDO, but not the AAO. Results suggest that larval supply patterns to Tongoy Bay may be modulated by regional patterns of climatic variability, particularly of tropical origin. Thus, changes in coastal oceanography associated with ongoing changes in global climate could have strong and lasting effects on the supply of seedstock for wild and cultivated species across this eastern boundary coastal system and argue for the establishment of long-term ocean observing and early warning systems along the region.

Artificial Intelligence powered forecast of oceanic mesoscale phenomena: A typhoon cold wake case occurring in Northwest Pacific Ocean

The phenomenon of typhoon cold wake has always been a key research object in the field of ocean–atmosphere interaction. Different from the physical equation-based numerical model which has long been dependent on, inspired by largely available forecasting and reanalyzed data and the advance of artificial intelligence technology, we build RC_ViT deep learning model for forecasting the typhoon cold wake. The total RMSE of grid points at each moment was between 0.5 and 0.9 °C, indicating that the model was accurate to forecast the sea surface temperature after the pass of typhoon. During the testing period of 11 years (2010–2020), using the deep learning method, our artificial intelligence model accurately and efficiently forecasts the sea surface temperature field. The cold wake of typhoon Megi (2010) was simulated by a Regional Ocean Model System (ROMS) and the artificial intelligence model separately. The results show that before the typhoon passing, the SST forecasted by the artificial intelligence model is more accurate, and after the typhoon passes, the ROMS forecast result has a smaller RMSE value. The reason may be that the intensity of typhoon Megi was too strong, and the artificial intelligence model training samples were few, resulting in artificial intelligence model forecast accuracy lower than traditional numerical models after the Megi passing. This study demonstrates the strong potential of the data-driven artificial intelligence model as an alternative to traditional numerical models for forecasting oceanic phenomena.

The impact of ocean data assimilation on the simulation of mesoscale eddies at São Paulo plateau (Brazil) using the regional ocean modeling system

This work presents simulations of ocean circulation in the southwestern South Atlantic Ocean with emphasis on the São Paulo Plateau region, a unique physiographic feature at the foot of the continental slope offshore southeast Brazil, where the flow is dominated by ocean eddies and dipoles. Surface dynamics south of 23°S presents a heterogeneous circulation pattern intimately related to the presence of mesoscale features, such as meanders of the Brazil Current along the continental slope, and eddies that interacts with deep ocean features. The reproduction of these eddies at the correct time and spatial structure are quite challenging in the context of a non-assimilative model, considering the limitations imposed by the model’s numerical schemes, physical parameterizations, boundary conditions and surface forcing. In this sense, the Regional Ocean Modeling System (ROMS) 4-dimensional Variational Data Assimilation (4D-Var) module was used to increase model skill and minimize deviations from oceanic observations. Weekly observations of Sea Surface Height (SSH), daily Sea Surface Temperature (SST) fields and in situ vertical profiles of temperature and salinity from multiple sources were used in consecutive assimilation cycles to produce an accurate representation of ocean features and their variability. Both the assimilative and free run were evaluated against observations, including in situ current measurements. Results showed significant improvements of the assimilation procedure on both assimilated and non-assimilated variables, with RMSE reductions of 27% and 47% for SST and SSH, respectively, and 51% for current speed measurements during a dipole event in the São Paulo Plateau. The assimilative run was able to reproduce a remarkable event of mesoscale eddies interaction (dipole) and consequent current intensification, leading to important improvements on model skill. The correct use of observational information of open ocean surface features through data assimilation is a key factor for a proper simulation of such eddy dipole events.

Can the Structure Similarity of Training Patches Affect the Sea Surface Temperature Deep Learning Super-Resolution?

Meso- and fine-scale sea surface temperature (SST) is an essential parameter in oceanographic research. Remote sensing is an efficient way to acquire global SST. However, single infrared-based and microwave-based satellite-derived SST cannot obtain complete coverage and high-resolution SST simultaneously. Deep learning super-resolution (SR) techniques have exhibited the ability to enhance spatial resolution, offering the potential to reconstruct the details of SST fields. Current SR research focuses mainly on improving the structure of the SR model instead of training dataset selection. Different from generating the low-resolution images by downscaling the corresponding high-resolution images, the high- and low-resolution SST are derived from different sensors. Hence, the structure similarity of training patches may affect the SR model training and, consequently, the SST reconstruction. In this study, we first discuss the influence of training dataset selection on SST SR performance, showing that the training dataset determined by the structure similarity index (SSIM) of 0.6 can result in higher reconstruction accuracy and better image quality. In addition, in the practical stage, the spatial similarity between the low-resolution input and the objective high-resolution output is a key factor for SST SR. Moreover, the training dataset obtained from the actual AMSR2 and MODIS SST images is more suitable for SST SR because of the skin and sub-skin temperature difference. Finally, the SST reconstruction accuracies obtained from different SR models are relatively consistent, yet the differences in reconstructed image quality are rather significant.

Improving WRF‐Hydro runoff simulations of heavy floods through the sea surface temperature fields with higher spatio‐temporal resolution

AbstractThis study investigates the impact of the spatio‐temporal accuracy of four different sea surface temperature (SST) datasets on the accuracy of the Weather Research and Forecasting (WRF)‐Hydro system to simulate hydrological response during two catastrophic flood events over the Eastern Black Sea (EBS) and the Mediterranean (MED) regions of Turkey. Three time‐variant and high spatial resolution external SST products (GHRSST, Medspiration and NCEP‐SST) and one coarse‐resolution and time‐invariant SST product (ERA5‐ and GFS‐SST for EBS and MED regions, respectively) already embedded in the initial and the boundary conditions datasets of WRF model are used in deriving near‐surface atmospheric variables through WRF. After the proper event‐based calibration is performed to the WRF‐Hydro system using hourly and daily streamflow data in both regions, uncoupled model simulations for independent SST events are conducted to assess the impact of SST‐triggered precipitation on simulated extreme runoff. Some localized and temporal differences in the occurrence of the flood events with respect to observations depending on the SST representation are noticeable. SST products represented with higher cross‐correlations (GHRSST and Medspiration) revealed significant improvement in flood hydrographs for both regions. The GHRSST dataset shows a substantial improvement in NSE (~70%), RMSE reduction up to 20%, and an increase in correlation from 0.3 to 0.8 with respect to the invariable SST (ERA5) in simulated runoffs over the EBS region. The use of both GHRSST and Medspiration SST data characterized with high spatio‐temporal correlation resulted in runoff simulations exactly matching the observed runoff peak of 300 m3/s by reducing the overestimation seen in invariable SST (GFS) in the MED region. Improved precipitation simulation skills of the WRF model with the detailed SST representation show that the hydrographs of GHRSST and Medspiration simulations show better performance compared to the simulated hydrographs by observed precipitation.

Comparisons between Mean and Turbulent Parameters of Aircraft-Based and Ship-Based Measurements in the Marine Atmospheric Boundary Layer

The Structure des Echanges Mer-Atmosphère, Propriétés Océaniques/ Recherche Expérimentale (SEMAPHORE) experiment was conducted over the oceanic Azores current located in the Azores Basin. The evolution of the marine atmospheric boundary layer (MABL) was studied based on the evaluation of mean and turbulent data using in situ measurements by a ship and two aircrafts. The sea surface temperature (SST) field was characterized by a gradient of approximately 1 °C/100 km. The SST measured by aircraft decreased at a ratio of 0.25 °C/100 m of altitude due to the divergence of the infrared radiation flux from the surface. With the exception of temperature, the mean parameters measured by the two aircrafts were in good agreement with each other. The sensible heat flux was more dispersed than the latent heat flux according to the comparisons between aircraft and aircraft, and aircraft and ship. This study demonstrates the feasibility of using two aircraft to describe the MABL and surface flux with confidence.

Reinitializing Sea Surface Temperature in the Ensemble Intermediate Coupled Model for Improved Forecasts

The Ensemble Intermediate Coupled Model (EICM) is a model used for studying the El Niño-Southern Oscillation (ENSO) phenomenon in the Pacific Ocean, which is anomalies in the Sea Surface Temperature (SST) are observed. This research aims to implement Cressman to improve SST forecasts. The simulation considers two cases in this work: the control case and the Cressman initialized case. These cases are simulations using different inputs where the two inputs differ in terms of their resolution and data source. The Cressman method is used to initialize the model with an analysis product based on satellite data and in situ data such as ships, buoys, and Argo floats, with a resolution of 0.25 × 0.25 degrees. The results of this inclusion are the Cressman Initialized Ensemble Intermediate Coupled Model (CIEICM). Forecasting of the sea surface temperature anomalies was conducted using both the EICM and the CIEICM. The results show that the calculation of SST field from the CIEICM was more accurate than that from the EICM. The forecast using the CIEICM initialization with the higher-resolution satellite-based analysis at a 6-month lead time improved the root mean square deviation to 0.794 from 0.808 and the correlation coefficient to 0.630 from 0.611, compared the control model that was directly initialized with the low-resolution in-situ-based analysis.

Biogeochemical modelling of a tropical coastal area undergoing seasonal upwelling and impacted by untreated submarine outfall

A coupled 3D hydrodynamic-ecological model was applied to the Santa Marta Coastal Area (SMCA, Colombian Caribbean) to provide insights into the role of external stressors (e.g. wastewater outfall and upwelling) on the water quality and benthic – pelagic coupling. The model was calibrated and validated based on benthic metabolic measurements, satellite–derived chlorophyll-a (Chl-a) and sea surface temperature (SST) maps, field and literature water quality data.The model was able to reproduce the complex dynamics and fast transitions of temperature, nutrients, and phytoplankton, including the stratification and mixing periods during the non-upwelling (NUPW) and upwelling (UPW) seasons. Wide and fast changes in the temperatures and the highly flushed environment prevented excess phytoplankton growth and nutrient accumulation in the benthic and pelagic compartments. The model proved to be a reliable research tool to analyze the interactive effects of upwelling and untreated wastewater on the functioning of a tropical bay.

The variability of sea surface temperature in the Patagonian Shelf Argentina, from 35 years of satellite information

ABSTRACT The aim of the present work is to analyse the variations of Sea Surface Temperature (SST) and Anomaly of Sea Surface Temperature (ASST) using more than thirty years of satellite data. We studied if these variations have possible ocean-atmosphere connections with the Southern Annular Mode (SAM), the Southern Oscillation Index (SOI), and the Oceanic Niño Index (ONI) in a crucial region for fisheries off the Patagonian Shelf. For the analysis, we used SST data collected from 1985 to 2019 with Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. The two databases were merged to obtain 35 years of continuous SST data. The study area was divided into three zones to present different oceanographic and bathymetric characteristics. These zones are located in the gulf, in the middle of the continental shelf, and at the shelf break. For each of the zones, the monthly SST climatology and the ASST series are shown. To define their periodicity, monthly ASST data were analysed spectrally by Fast Fourier Transform (FFT) and Wavelet Transform. Cross-Wavelet Transform (XWT) was applied to analyse the co-variance of ASST with respect to climate indices. Our results show that the isotherm distribution seems to be separated by the 64º W meridian. To the east of 64º W, it follows an oblique direction from southwest to northeast; to the west of 64º W, a more complex behaviour of the SST field seems to be associated with the coastal dynamics. The ASST series shows that the values of negative anomalies are higher than those of the positive ones. Nevertheless, positive ASSTs are more extreme and persistent in time than the negative ones. The ASST wavelet diagrams denote a common and non-stationary periodicity in the three zones, with statistically significant power spectra in the intra-seasonal (9-month) and inter-annual (24-month and 4-year) time scales. There are clearly common features in the wavelet power of the time series related to ASST and climate forcing. The SAM exhibited the closest relationship with ASST in the three zones. The SAM and ASST indices denote significant power peaks in the 2-year and 4- to 6-year bands.

Influence of Sea Surface Temperature on a Mesoscale Convective System producing Extreme Rainfall over the Yellow Sea

AbstractAn extreme rainfall-producing linear mesoscale convective systems (MCSs) occurred over the Yellow Sea, Korea, on 13 August 2012, producing 430 mm of rainfall in less than 12 h, causing devastating flash floods and landslides. To understand the causative processes underlying this event, we examined the linear MCSs formation and development mechanisms using observations and cloud-resolving models. The organized linear MCSs produced extreme rainfall at Gunsan in a favorable large-scale environment. The synoptic environment showed the stationary surface front elongating from China to Korea; a southwesterly low-level jet transported the warm, moist air from low latitudes towards the front. In the upper-level synoptic environment, the trough and entrance regions of the upper-level jet were north of the heavy rainfall, promoting the development of convection. The extreme rainfall over the Gunsan area resulted from the back-building mode of the MCSs, in which new convective cells continued to pass over the same area while the entire convective system was nearly stationary. The sea surface temperature (SST) during the extreme rainfall events was abnormally 1°C higher than the 30-year climatological mean, and a local warm pool (>28.5°C) existed where the convective cells were continuously initiated. Cloud-resolving models simulated the low-level convergence, and the latent heat flux was large in the local warm SST field. These induced MCSs formation and development, contributing to a significant rainfall increase over the Yellow Sea. The terrain’s influence on the large rainfall amount in the area was also noted.

Persistent upwelling in the Mid-Atlantic Bight detected using gap-filled, high-resolution satellite SST

This study applied a Spike-Filter (SF) method to identify and remove rapidly moving clouds over the coastal ocean in GOES-16 satellite sea surface temperature fields (SST). These images were then gap-filled to capture upwelling in the Mid Atlantic Bight (MAB), a critical process that impacts ecosystems and atmospheric processes in the region and is important to coastal regions around the world. During the 2019 upwelling season, the default Quality Filter (QF) provided to identify and remove clouds from GOES SST consistently removed upwelling pixels, resulting in ~15% MAB coastal SST coverage. The Spike Filter (SF) method increased MAB coastal SST coverage to 30% and maintained overall accuracy. GOES SF DINEOF SST approximately doubled the number of detected upwelling days compared to MUR SST. The longest upwelling event detected in GOES SF DINEOF persisted for over 17 days, which is longer than upwelling events previously observed in the MAB (Glenn et al., 2004).This suggests that the MAB can have persistent, rather than episodic upwelling as previously thought. Clear detection of the timing and duration of upwelling events is important as it provides estimates for ecological and physical responses in the MAB and coastal regions around the world. GOES-16 SST has the potential to improve upwelling detection and should be further studied for application in ocean and atmospheric modeling.

Studies of the seasonal prediction of heavy late spring rainfall over southeastern China

The late spring rainfall may account for 15% of the annual total rainfall, which is crucial to early planting in southeastern China. A better understanding of the precipitation variations in the late spring and its predictability not only greatly increase our knowledge of related mechanisms, but it also benefits society and the economy. Four models participating in the North American Multi-Model Ensemble (NMME) were selected to study their abilities to forecast the late spring rainfall over southeastern China and the major sources of heavy rainfall from the perspective of the sea surface temperature (SST) field. We found that the models have better abilities to forecast the heavy rainfall over the middle and lower reaches of the Yangtze River region (MLYZR) with only a 1-month lead time, but they failed for a 3-month lead time since the occurrence of the heavy rainfall was inconsistent with the observations. The observations indicate that the warm SST anomalies in the tropical eastern Indian Ocean are vital to the simultaneously heavy rainfall in the MLYZR in May, but an El Niño event is not a necessary condition for determining the heavy rainfall over the MLYZR. The heavy rainfall over the MLYZR in May is always accompanied by warming of the northeastern Indian Ocean and of the northeastern South China Sea (NSCS) from April to May in the models and observations, respectively. In the models, El Niño events may promote the warming processes over the northeastern Indian Ocean, which leads to heavy rainfall in the MLYZR. However, in the real world, El Niño events are not the main reason for the warming of the NSCS, and further research on the causes of this warming is still needed.

Drivers of Atmospheric and Oceanic Surface Temperature Variance: A Frequency Domain Approach

AbstractOcean–atmosphere coupling modifies the variability of Earth’s climate over a wide range of time scales. However, attribution of the processes that generate this variability remains an outstanding problem. In this article, air–sea coupling is investigated in an eddy-resolving, medium-complexity, idealized ocean–atmosphere model. The model is run in three configurations: fully coupled, partially coupled (where the effect of the ocean geostrophic velocity on the sea surface temperature field is minimal), and atmosphere-only. A surface boundary layer temperature variance budget analysis computed in the frequency domain is shown to be a powerful tool for studying air–sea interactions, as it differentiates the relative contributions to the variability in the temperature field from each process across a range of time scales (from daily to multidecadal). This method compares terms in the ocean and atmosphere across the different model configurations to infer the underlying mechanisms driving temperature variability. Horizontal advection plays a dominant role in driving temperature variance in both the ocean and the atmosphere, particularly at time scales shorter than annual. At longer time scales, the temperature variance is dominated by strong coupling between atmosphere and ocean. Furthermore, the Ekman transport contribution to the ocean’s horizontal advection is found to underlie the low-frequency behavior in the atmosphere. The ocean geostrophic eddy field is an important driver of ocean variability across all frequencies and is reflected in the atmospheric variability in the western boundary current separation region at longer time scales.

Simulation of a flash-flood event over the Adriatic Sea with a high-resolution atmosphere\u2013ocean\u2013wave coupled system

On the morning of September 26, 2007, a heavy precipitation event (HPE) affected the Venice lagoon and the neighbouring coastal zone of the Adriatic Sea, with 6-h accumulated rainfall summing up to about 360 mm in the area between the Venetian mainland, Padua and Chioggia. The event was triggered and maintained by the uplift over a convergence line between northeasterly flow from the Alps and southeasterly winds from the Adriatic Sea. Hindcast modelling experiments, using standalone atmospheric models, failed to capture the spatial distribution, maximum intensity and timing of the HPE. Here we analyze the event by means of an atmosphere-wave-ocean coupled numerical approach. The combined use of convection permitting models with grid spacing of 1 km, high-resolution sea surface temperature (SST) fields, and the consistent treatment of marine boundary layer fluxes in all the numerical model components are crucial to provide a realistic simulation of the event. Inaccurate representations of the SST affect the wind magnitude and, through this, the intensity, location and time evolution of the convergence zone, thus affecting the HPE prediction.

Sea surface temperature effects on the modelled track and intensity of tropical cyclone Gonu

ABSTRACT The seven simulations were performed to investigate the role of the sea surface temperature (SST) in numerical prediction of tropical cyclones (TCs). The TC Gonu, formed over the Arabian Sea in 2007, was selected for this study. The first five simulations were performed using WRF model. In the first simulation as control simulation (CTL), the SST derived from NCEP-MMAB was used. In the second simulation, 1°C was added to input SST, and in the third simulation, 1°C was subtracted to input SST. It was found that the deviation between the simulated track of simulation SST +1 and CTL is more significant than that between simulation SST −1 and CTL. For the fourth simulation, a homogeneous SST field over the entire basin was used. For the fifth simulation, SST anomaly was calculated, and its values were added to the entire domain. Removing the temperature gradient caused TC intensity to decrease and deviation of the track to the northeast; the increasing temperature gradient had a lower impact on the TC intensity but with a significant deviation of the track to the north with respect to the CTL simulation. In the sixth simulation to consider cyclone-induced SST cooling, a one-dimensional oceanic mixed layer scheme was applied. Results showed no significant reduction in TC intensity. In the seventh simulation, the COAWST modelling system was used. The simulated SST of the COAWST model was consistent with the satellite observations, which finally led to improve the simulation of track and intensity of TC Gonu.

A climate network perspective on the intertropical convergence zone

Abstract. The intertropical convergence zone (ITCZ) is an important component of the tropical rain belt. Climate models continue to struggle to adequately represent the ITCZ and differ substantially in its simulated response to climate change. Here we employ complex network approaches, which extract spatiotemporal variability patterns from climate data, to better understand differences in the dynamics of the ITCZ in state-of-the-art global circulation models (GCMs). For this purpose, we study simulations with 14 GCMs in an idealized slab-ocean aquaplanet setup from TRACMIP – the Tropical Rain belts with an Annual cycle and a Continent Model Intercomparison Project. We construct network representations based on the spatial correlation patterns of monthly surface temperature anomalies and study the zonal-mean patterns of different topological and spatial network characteristics. Specifically, we cluster the GCMs by means of the distributions of their zonal network measures utilizing hierarchical clustering. We find that in the control simulation, the distributions of the zonal network measures are able to pick up model differences in the tropical sea surface temperature (SST) contrast, the ITCZ position, and the strength of the Southern Hemisphere Hadley cell. Although we do not find evidence for consistent modifications in the network structure tracing the response of the ITCZ to global warming in the considered model ensemble, our analysis demonstrates that coherent variations of the global SST field are linked to ITCZ dynamics. This suggests that climate networks can provide a new perspective on ITCZ dynamics and model differences therein.

Multi-level dissipation element analysis of the surface temperature of the South China Sea

The turbulent motions in ocean interact in a broad continuous range of the spatial scales. To study the quantitative structures at multiple spatial scales in oceanic dynamics, the multi-level dissipation element (ML-DE) analysis is proposed. Considering a sea surface temperature (SST) field θ(x,y,t), from any spatial point, one can inevitably reach a local maximum temperature point and a local minimum one along the ascending and descending directions of the gradient of θ(x,y,t), respectively. A dissipation element (DE) is thus the region consisting of the points whose gradient trajectories share the same extremal point pair. The characteristic parameters are the temperature difference Δθ and the distance ℓ between the maximum and minimum points of each DE. ML-DE, which is an extension of DE analysis to any scale level R, can efficiently capture the geometrical features of the turbulent flow at different scale levels, which improves the understanding of the physics in ocean turbulence. In this work, ML-DE is applied to the SST field obtained for the South China Sea. The statistical results have been compared with those from the conventional analysis, e.g. the Fourier spectrum and structure function. The difference of the dynamic behaviors of SST in the winter and summer time is also addressed.