Sea Surface Temperature Features Research Articles

Decoding low-frequency climate variations: A case study on ENSO and ocean surface warming

Current perspectives on lower frequency variations and secular warming have predominantly been shaped by traditional anomalies that assume an annual cycle (AC) with a time-invariant amplitude. However, this anomaly framework falls short in capturing the complexity of multiple periodic modes with intricate waveforms and time dependent amplitude—the traits, in general, shared by externally forced responses of complex dynamical systems. By allowing interannual amplitude modulation of ACs, we show that the monotonic amplitude increases of the first AC of Sea Surface Temperature (SST) are manifested as the basin-wide secular warming of the ocean surface. Notably, the first SST-AC exhibits significant interannual variances and the largest linear warming rates in the Pacific Warm Pool. While the linear warming pattern mirrors that of a long-term mean SST, it depicts an entirely different warming pattern on the surface of the tropical Pacific Ocean compared to those reported so far. Moreover, all interannual warm (El Niño) and cold (La Niña) events in tropical Pacific regions are abnormal interannual modulations in the third and fourth ACs of SST, respectively. Specifically, a strong El Niño event occurs when a positive amplitude modulation leads to the overlap of two consecutive positive phases of the third AC of SST. Conversely, the absence of such overlaps during negative amplitude modulations significantly contributes to the positive skewness of SST anomalies. No systematic decadal changes in the zonal propagation characteristics of SST in the eastern Pacific (EP) and central Pacific (CP) regions were detected. These findings underscore that the secular warming and low-frequency events in EP and CP are intrinsic to three distinct ACs.

Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment

We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data on sea surface temperature (SST) to construct a deep convolutional embedded clustering (DCEC) model and extract the monthly SST features (FM) based on an optimized number of clusters determined by the Davies–Bouldi index (DBI). We use the extracted FM to construct a series of Generalized Additive Models (GAM) to forecast the catch per unit effort (CPUE) of jumbo flying squid within a spatial resolution of 0.5° × 0.5°. Our results demonstrate the following findings: (1) The SST feature clusters obtained through the DCEC model could capture the SST monthly variations; (2) The GAM models with FM outperform the models with the traditional monthly average SST in terms of predictive accuracy; (3) Using both FM and average SST together can further improve model performance. This study demonstrates the effectiveness of the DCEC combined with DBI in extracting marine environmental features and highlights the ocean environment feature extraction method to enhance the precision and reliability of fishery forecasting models.

Tropical cyclone Saudel impact on ocean dynamic over the South China Sea

Tropical cyclones (TC) are atmospheric phenomena categorized as extreme weather that is perilous and destructive. The parameters used include sea surface temperature (SST), wind speed (WS), and air pressure (AP), which are factors in the formation of TC. The emergence of TC occurred in October 2020, when it grew in Philippine waters and died out in Vietnamese waters. The characteristics of the waters of the South China Sea (SCS) in particular and the Pacific Ocean in general make them one of the waters with the highest storm potential in the world; therefore, this research was conducted. The purpose of this research is to understand the characteristics of Saudel Cyclone (SC) based on the parameters SST, WS, and AP, which are obtained from the ECMWF website, to be able to describe the process of growth until the extinction of SC. Data from the ECMWF website has limitations because it is model data (re-analysis). The results of this study can determine the process of starting the extinction of SC as well as the characteristics of SST, AP, and WS in the conditions of starting SC, during the peak of SC, and starting the extinction of SC.

A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km2 is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported.

On the importance of the atmospheric coupling to the small-scale ocean in the modulation of latent heat flux

In this study, ocean and atmosphere satellite observations, an atmospheric reanalysis and a set of regional numerical simulations of the lower atmosphere are used to assess the coupling between the sea-surface temperature (SST) and the marine atmospheric boundary layer (MABL) as well as the latent heat flux (LHF) sensitivity to SST in the north-west tropical Atlantic Ocean. The results suggest that the SST-MABL coupling depends on the spatial scale of interest. At scales larger than the ocean mesoscale (larger than 150 km), negative correlations are observed between near-surface wind speed (U10m) and SST and positive correlations between near-surface specific humidity (q2m) and SST. However, when smaller scales (1 – 150 km, i.e., encompassing the ocean mesoscale and a portion of the submesoscale) are considered, U10m-SST correlate inversely and the q2m-SST relation significantly differs from what is expected using the Clausius-Clapeyron equation. This is interpreted in terms of an active ocean modifying the near-surface atmospheric state, driving convection, mixing and entrainment of air from the free troposphere into the MABL. The estimated values of the ocean-atmosphere coupling at the ocean small-scale are then used to develop a linear and SST-based downscaling method aiming to include and further investigate the impact of these fine-scale SST features into an available low-resolution latent heat flux (LHF) data set. The results show that they induce a significant increase of LHF (30% to 40% per °C of SST). We identify two mechanisms causing such a large increase of LHF: (1) the thermodynamic contribution that only includes the increase in LHF with larger SSTs associated with the Clausius-Clapeyron dependence of saturating water vapor pressure on SST and (2) the dynamical contribution related to the change in vertical stratification of the MABL as a consequence of SST anomalies. Using different downscaling setups, we conclude that largest contribution comes from the dynamic mode (28% against 5% for the thermodynamic mode). To validate our approach and results, we have implemented a set of high-resolution WRF numerical simulations forced by high-resolution satellite SST that we have analyzed in terms of LHF using the same algorithm. The LHF estimate biases are reduced by a factor of 2 when the downscaling is applied, providing confidence in our results.

Manifestation of the Early 20th Century Warming in the East-European Plain: Atmospheric Circulation Anomalies and Its Connection to the North Atlantic SST and Sea Ice Variability

A study of the climatic characteristics and annual runoff of the Volga and Severnaya Dvina rivers demonstrates that, on the East European Plain (EEP), Early Twentieth Century Warming (ETCW) manifested in a multiyear drought between 1934 and 1940; this drought has no analogues in this region in terms of intensity and duration according to Palmer’s classification, and caused extreme hydrological events. The circulation conditions during this event were characterized by an extensive anticyclone over Eastern Europe, combined with a cyclonic anomaly in the circumpolar region. An analysis of the spatial features of sea surface temperature (SST) anomalies indicate that the surface air temperature (SAT) anomalies in July on the EEP during ETCW were related not only to the North Atlantic (NA) warming and positive AMO phase, but also to a certain spatial pattern of SST anomalies characteristic of the 1920–1950 period. The difference between the SST anomalies of the opposite sign in the different NA zones, used as the indicator of the obtained spatial pattern, shows the quite close relations between the July SAT anomalies on the EEP and the atmospheric circulation patterns responsible for them. The positive phase of the Atlantic Multidecadal Oscillation (AMO) and the expansion of the subtropical high-pressure belt to the north and to the east can be considered as global-scale drivers of this phenomenon. The AMO also impacts the sea ice cover in the Barents–Kara Sea region, which, in turn, could have led to specific atmospheric circulation patterns and contributed to droughts on the EEP in the 1930s.

Sea Surface Temperature Gradients Estimation Using Top-of-Atmosphere Observations from the ESA Earth Explorer 10 Harmony Mission: Preliminary Studies

The Harmony satellite mission was recently approved as the next European Space Agency (ESA) Earth Explorer 10. The mission science objectives cover several applications related to solid earth, the cryosphere, upper-ocean dynamics and air–sea interactions. The mission consists of a constellation of two satellites, flying with the Copernicus Sentinel 1 (C or D) spacecraft, each hosting a C-band receive-only radar and a thermal infrared (TIR) payload. From an ocean dynamics/air–sea interaction perspective, the mission will provide the unique opportunity to observe simultaneously the signature of submesoscale upper-ocean processes via synthetic aperture radar and TIR imagery. The TIR imager is based on microbolometer technology and its acquisitions will rely on four channels: three narrow-band channels yielding observations at a ≃1 km spatial sampling distance (SSD) and a panchromatic (PAN, 8–12 μm) channel characterized by a ≃300 m SSD. Our study investigates the potential of Harmony in retrieving spatial features related to sea surface temperature (SST) gradients from the high-resolution PAN channel, relying on top-of-atmosphere (TOA) observations. Compared to a standard SST gradient retrieval, our approach does not require atmospheric correction, thus avoiding uncertainties due to inter-channel co-registration and radiometric consistency, with the possibility of exploiting the higher resolution of the PAN channel. The investigations were carried out simulating the future Harmony TOA radiances (TARs), as well as relying on existing state-of-the-art level 1 satellite products. Our approach enables the correct description of SST features at the sea surface avoiding the generation of spurious features due to atmospheric correction and/or instrumental issues. In addition, analyses based on existing satellite products suggest that the clear-sky TOA observations, in a typical mid-latitude scene, allow the reconstruction of up to 85% of the gradient magnitudes found at the sea-surface level. The methodology is less efficient in tropical areas, suffering from smoothing effects due to the high concentrations of water vapor.

Changes in Sea Surface Temperature and Sea Ice Concentration in the Arctic Ocean over the Past Two Decades

With global warming, the decrease in sea ice creates favorable conditions for Arctic activities. Sea surface temperature (SST) is not only an important driven factor of sea ice concentration (SIC) changes but also an important medium of the ocean–atmosphere interaction. However, the response of sea surface temperature to Arctic sea ice varies in different sea areas. Using the optimal interpolated SST data from the National Centers for Environmental Information (NCEI) and SIC data from the University of Bremen, the temporal and spatial characteristics of SST and SIC in the Arctic above 60°N and their relationship are studied, and the melting and freezing time of sea ice are calculated, which is particularly important for the prediction of Arctic shipping and sea ice. The results show that (1) the highest and lowest monthly mean Arctic SST occur in August and March, respectively, while those of SIC are in March and September. The maximum trends of SST and SIC changes are in autumn, which are +0.01 °C/year and −0.45%/year, respectively. (2) There is a significant negative correlation between the Arctic SST and SIC with a correlation coefficient of −0.82. (3) The sea ice break-up occurs on Day of the Year (DoY) 143 and freeze-up occurs on DoY 296 in the Arctic. The melting and freezing processes lasted for 27 days and 14 days, respectively. (4) The Kara Sea showed the strongest trend of sea ice melting at −1.22 d/year, followed by the Laptev Sea at −1.17 d/year. The delay trend of sea ice freezing was the most significant in the Kara Sea +1.75 d/year, followed by the Laptev Sea +1.70 d/year. In the Arctic, the trend toward earlier melting of sea ice is smaller than the trend toward later freezing.

Comprehensive Analysis of Ocean Current and Sea Surface Temperature Trend under Global Warming Hiatus of Kuroshio Extent Delineated Using a Combination of Spatial Domain Filters

The effect of climate prevails on a diverse time scale from days to seasons and decades. Between 1993 and 2013, global warming appeared to have paused even though there was an increase in atmospheric greenhouse gases. The variations in oceanographic variables, like current speed and sea surface temperature (SST), under the influence of the global warming hiatus (1993–2013), have drawn the attention of the global research community. However, the magnitude of ocean current and SST characteristics oscillates and varies with their geographic locations. Consequently, investigating the spatio-temporal changing aspects of oceanographic parameters in the backdrop of climate change is essential, specifically in coastal regions along Kuroshio current (KC), where fisheries are predominant. This study analyzes the trend of ocean current and SST induced mainly during the global warming hiatus, before and till the recent time based on the daily ocean current data from 1993 to 2020 and SST between 1982 and 2020. The Kuroshio extent is delineated from its surrounding water masses using an aggregation of raster classification, stretching, equalization, and spatial filters such as edge detection, convolution, and Laplacian. Finally, on the extracted Kuroshio extent, analyses such as time series decomposition (additive) and statistical trend computation methods (Yue and Wang trend test and Theil–Sen’s slope estimator) were applied to dissect and investigate the situations. An interesting downward trend is observed in the KC between the East coast of Taiwan and Tokara Strait (Tau = −0.05, S = −2430, Sen’s slope = −5.19 × 10−5, and Z = −2.61), whereas an upward trend from Tokara Strait to Nagoya (Tau = 0.89, S = 4344, Sen’s slope = 8.4 × 10−5, and Z = 2.56). In contrast, a consistent increasing SST in trend is visualized in the southern and mid-KC sections but with varying magnitude.

MUST: A Multi-source Spatio-Temporal data fusion Model for short-term sea surface temperature prediction

Sea surface temperature (SST) plays an important role in various oceanic applications, including climate prediction, ocean environment monitoring and marine disaster warning. Although many approaches have been developed for predicting SST, most of them conduct the prediction only based on historical SST data. However, SST is essentially affected by many external factors, e.g., the short-wave radiation from the sun and the long-wave radiation from the atmosphere and ground, which are ignored by existing approaches. In this work, we proposed a Multi-source Spatio-Temporal data fusion model (MUST) to fuse multi-source data, including SST data and external factors, to improve the accuracy of short-term SST prediction. Concretely, MUST first introduces Bicubic Convolutional Interpolation (BCI) to address the issue of inconsistent spatial resolutions of multi-source data, then employs the Spatio-Temporal Dilated ConvLSTM (ST-DC) to learn the spatio-temporal features of SST and external factors, and finally fuses the learned features from multi-source data to predict SST with a Cross Data Fusion (CDF) component. As validated on two real datasets, MUST achieves much better performance than existing SST prediction approaches.

The Role of Seasonality and the ENSO Mode in Central and East Pacific ENSO Growth and Evolution

Abstract A cyclostationary linear inverse model (CSLIM) is used to investigate the seasonal growth of tropical Pacific Ocean El Niño–Southern Oscillation (ENSO) events with canonical, central Pacific (CP), or eastern Pacific (EP) sea surface temperature (SST) characteristics. Analysis shows that all types of ENSO events experience maximum growth toward final states occurring in November and December. ENSO events with EP characteristics also experience growth into May and June, but CP events do not. A single dominant “ENSO mode,” growing from an equatorial heat content anomaly into a characteristic ENSO-type SST pattern in about 9 months (consistent with the delayed/recharge oscillator model of ENSO), is essential for the predictable development of all ENSO events. Notably, its seasonality is responsible for the late-calendar-year maximum in ENSO amplification. However, this ENSO mode alone does not capture the observed growth and evolution of diverse ENSO events, which additionally involve the seasonal evolution of other nonorthogonal Floquet modes. EP event growth occurs when the ENSO mode is initially “covered up” in combination with other Floquet modes. The ENSO mode’s slow seasonal evolution allows it to emerge while the other modes rapidly evolve and/or decay, leading to strongly amplifying and more predictable EP events. CP events develop when the initial state has a substantial contribution from Floquet modes with meridional mode–like SST structures. Thus, while nearly all ENSO events involve the seasonally varying ENSO-mode dynamics, the diversity and predictability of ENSO events cannot be understood without identifying contributions from the remaining Floquet modes. Significance Statement The purpose of this study is to identify structures that lead to seasonal growth of diverse types of El Niño–Southern Oscillation (ENSO) events. An important contribution from this study is that it uses an observationally constrained, empirically derived seasonal model. We find that processes affecting the evolution of diverse ENSO events are strongly seasonally dependent. ENSO events with eastern equatorial Pacific sea surface temperature (SST) characteristics are closely related to a single “ENSO mode” that resembles theoretical models of ENSO variability. ENSO events that have central equatorial Pacific SST characteristics include contributions from additional “meridional mode” structures that evolve via different physical processes. These findings are an important step in evaluating the seasonal predictability of ENSO diversity.

Retrieving Remotely Sensed Data Using Plank Algorithm in the Black Sea and Surrounding Seas (2008-2018)

The Black Sea is the world’s largest inner sea, its only connections with other seas being the Kerch Strait, which leads to the Azov Sea, and the Istanbul Strait, which connects it with the Mediterranean Sea. The main aim of this study processes to image analysis and applied algorithm, to retrieve remote SST data from satellite, to assess the estimated SSTs, to understand the characteristics of SST by presenting in the Black Sea and surrounding seas, to discuss the SSTs changes according to climate change impacts. In this study, Landsat 7 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) Level-1 images of TIR bands are used to predict Sea Surface Temperature (SST) changes using the Plank algorithm in the Black Sea and surrounding seas between 2008 and 2018. The SST values were taken from 55 different stations chosen from the study area. The SST result presented the central months of the four hydrological seasons. The increasing temperature data were supported by the literature studies that alien invasive species entered the Black Sea. The climatic fluctuations obtained as a result of this study will increase the invasion level of non-native species and their invasion will continue into the Black Sea and surrounding seas.

Deep Learning Models to Predict Sea Surface Temperature in Tohoku Region

The prediction of sea surface temperature (SST) is a challenging task, especially for regions with high SST variability. Such predictions are either achieved by physics-based models, which often yield poor predictions and are computationally intensive, or by using data-driven methods, which are skillful and computationally less intensive. However, recent machine learning studies exploring SST prediction have not included the important meteorological parameters governing SST variability. Therefore, in this study, we propose various of deep learning (DL) models trained using past meteorological features to predict day-ahead SST. The proposed models include a deep multilayer perceptron (deep MLP), long short-term memory network (LSTM) and spatial 2-dimensional convolutional neural network (spatial 2D CNN). We explore the potential of the proposed DL models for day-ahead SST prediction across different locations in the Tohoku region (Japan’s east coast), including in situ validation. Evaluation of these DL models’ prediction skills suggests that the spatial 2D CNN’s are highly skillful at coastal locations, whereas at offshore locations, equal prediction skills were noted from the deep MLP and LSTM. We further attempted to improve the spatial 2D CNN by including past SST features, and such improvisation showed very low errors ranging from 0.35°C to 0.75°C and high correlation skill from 0.64 to 0.96. These improved skills were also compared with persistent model (PM) skills using RMSE and Correlation (RC) phase diagram, where we found that improved skills are consistently better than PM skills. In addition, we extracted features from the spatial 2D CNN to understand the reason underlying such improved skills, and we noted that the proposed DL model successfully captured the major meteorological and oceanic features governing SST variability. This led us to conclude that the proposed DL models are capable of producing highly reliable SST predictions, and may be equally applicable to other study regions.

Validation and statistical analysis of the Group for High Resolution Sea Surface Temperature data in the Arabian Gulf

The combined effect of climate change and steadily increasing seawater demand for industrial and domestic purposes in the Arabian Gulf region has a significant impact on the ecosystem in this region. Additionally, this effect may reduce the efficiency and increase the operating costs of industrial facilities that utilize seawater for cooling and other purposes. In this context, it is important to know various statistical characteristics of the sea surface temperature (SST) and their trends, in addition to the mean climatological characteristics. The analysis conducted in this study utilized a 17-year Group for High Resolution Sea Surface Temperature Level 4 dataset of 0.01 × 0.01° spatial resolution. First, the dataset was compared against a 2-year seawater temperature measurements at the ten offshore buoys in the relatively shallow coastal waters of the United Arab Emirates between Ras Ghumais and Dubai, which showed a reasonably good agreement between the two datasets, with the estimated root mean square deviations ranging from 0.5 to 0.9°C. Subsequently, several statistical SST characteristics were calculated. The trend analysis showed not only positive tendencies in the mean SSTs of up to 0.08°C/year in the northern Gulf, but also the trends in the annual percentile exceedances, particularly the 95th percentiles (near-maximum SSTs), which increased by approximately 0.07°C/year in the western United Arab Emirates and eastern Qatar waters. On the contrary, the 5th percentiles (near-minimum SSTs) decreased by up to 0.1°C/year, especially in the waters around Bahrain, Qatar, and the western United Arab Emirates. These results indicate that extreme hot and cold SST events in the Gulf are becoming more frequent and more extreme than before.

VARIABILITY OF SEA SURFACE TEMPERATURE AT FISHERIES MANAGEMENT AREA 715 IN INDONESIA AND ITS RELATION TO THE MONSOON, ENSO AND FISHERY PRODUCTION

Sea surface temperature (SST) is one of the important oceanographic and climatemparameters. Its variability and anomaliesmoften influence the environment and organisms, both in the oceansand on land. This study aims to identifythe variability of SST and help the fisheries community to understand how climate phenomena such as ENSOand monsoonal phases (represented by wind speed) are related to SST and fishery production in Fisheries Management Area (FMA)715.SST was measured at Parimo, which represents conditionsinthe western partof the areainside Tomini Bay,and at Bitung, which represents SST in the open ocean,with a more exposuredgeographical position. SST wasderived from MODIS satellite imagery, downloaded from the ocean color database (https://oceancolor.gsfc.nasa.gov/) with a4 km spatial resolution, from January 2009 to December 2018. Wind speed data, historical El Niño or La Niña events,and fish production data were also used in the study. Pearson’s correlation (Walpole, 1993) was used to test the relationship between SST variability or anomaly and ENSO and monsoons. The results show that the SST characteristics and variability of the Parimo and Bitung watersare very different, although they bothliein the same FMA 715. SST in Parimo waters is warmer,but with lower variability than in Bitung waters. SST in Parimo has a lowcorrelation with ENSO (r=0.06, n=66), low correlation with wind speed (r=-0.29, n=120),with also a lowcorrelation between SST anomaly and ENSO (r=0.05, n=66). SST in Bitung has a higher, but inverse, correlation with ENSO (r=-0.53, n=66), highcorrelation with wind speed (r=-0.60, n=119), with also a high correlation between SST anomaly and ENSO (r=-0.74, n=66). Unlike in other parts of Indonesia, fishery production in Parimo,or the western part inside Tomini Bay,is not affected by ENSO events

D2CL: A Dense Dilated Convolutional LSTM Model for Sea Surface Temperature Prediction

Accurately predicting sea surface temperature (SST) is practically important to many applications such as weather forecasting, ocean environment protection, and marine disaster prevention. The major challenge for predicting SST is to capture both spatial and temporal characteristics of SST, which has not yet been well addressed by existing methods. In this work, we proposed a novel Dense Dilated Convolutional LSTM (D2CL) model to predict SST. D2CL first integrates Dilated Convolutional Network and LSTM to learn spatial and temporal features from SST data simultaneously. Then, it uses multiple feature extractors of different dilated kernel sizes to learn features of varying scales. Finally, it introduces dense connection to reduce feature loss during the training process and achieves SST prediction in an Encoder-Decoder style. We have conducted extensive experiments over two real datasets to validate the effectiveness of D2CL and all the proposed enhancing techniques. As suggested by the experimental results, D2CL outperforms the existing methods and could achieve accurate SST prediction for as long as 7 days in future.

Prediction of Sea Surface Temperature in the China Seas Based on Long Short-Term Memory Neural Networks

Sea surface temperature (SST) in the China Seas has shown an enhanced response in the accelerated global warming period and the hiatus period, causing local climate changes and affecting the health of coastal marine ecological systems. Therefore, SST distribution prediction in this area, especially seasonal and yearly predictions, could provide information to help understand and assess the future consequences of SST changes. The past few years have witnessed the applications and achievements of neural network technology in SST prediction. Due to the diversity of SST features in the China Seas, long-term and high-spatial-resolution prediction remains a crucial challenge. In this study, we adopted long short-term memory (LSTM)-based deep neural networks for 12-month lead time SST prediction from 2015 to 2018 at a 0.05° spatial resolution. Considering the sub-regional differences in the SST features of the study area, we applied self-organizing feature maps (SOM) to classify the SST data first, and then used the classification results as additional inputs for model training and validation. We selected nine models differing in structure and initial parameters for ensemble to overcome the high variance in the output. The statistics of four years’ SST difference between the predicted SST and Operational SST and Ice Analysis (OSTIA) data shows the average root mean square error (RMSE) is 0.5 °C for a one-month lead time and is 0.66 °C for a 12-month lead time. The southeast of the study area shows the highest predictable accuracy, with an RMSE less than 0.4 °C for a 12-month prediction lead time. The results indicate that our model is feasible and provides accurate long-term and high-spatial-resolution SST prediction. The experiments prove that introducing appropriate class labels as auxiliary information can improve the prediction accuracy, and integrating models with different structures and parameters can increase the stability of the prediction results.

Eastern equatorial Pacific SST seasonal cycle in global climate models: from CMIP5 to CMIP6

The sea surface temperature (SST) seasonal cycle in the eastern equatorial Pacific (EEP) plays an important role in the El Nino-Southern Oscillation (ENSO) phenomenon. However, the reasonable simulation of SST seasonal cycle in the EEP is still a challenge for climate models. In this paper, we evaluated the performance of 17 CMIP6 climate models in simulating the seasonal cycle in the EEP and compared them with 43 CMIP5 climate models. In general, only CESM2 and SAM0-UNICON are able to successfully capture the annual mean SST characteristics, and the results showed that CMIP6 models have no fundamental improvement in the model annual mean bias. For the seasonal cycle, 14 out of 17 climate models are able to represent the major characteristics of the observed SST annual evolution. In spring, 12 models capture the 1–2 months leading the eastern equatorial Pacific region 1 (EP1; 5°S–5°N, 110°–85°W) against the eastern equatorial Pacific region 2 (EP2; 5°S–5°N, 140°–110°W). In autumn, only two models, GISS-E2-G and SAM0-UNICON, correctly show that the EP1 and EP2 SSTs vary in phase. For the CMIP6 MME SST simulation in EP1, both the cold bias along the equator in the warm phase and the warm bias in the cold phase lead to a weaker annual SST cycle in the CGCMs, which is similar to the CMIP5 results. However, both the seasonal cold bias and warm bias are considerably decreased for CMIP6, which leads the annual SST cycle to more closely reflect the observation. For the CMIP6 MME SST simulation in EP2, the amplitude is similar to the observed value due to the quasi-constant cold bias throughout the year, although the cold bias is clearly improved after August compared with CMIP5 models. Overall, although SAM0-UNICON successfully captured the seasonal cycle characteristics in the EEP and the improvement from CMIP5 to CMIP6 in simulating EEP SST is clear, the fundamental climate models simulated biases still exist.

Modulation of the Relationship between ENSO and Its Combination Mode by the Atlantic Multidecadal Oscillation

AbstractRecent studies demonstrated the existence of a conspicuous atmospheric combination mode (C-mode) originating from nonlinear interactions between El Niño–Southern Oscillation (ENSO) and the Pacific warm pool annual cycle (AC). Here we find that the C-mode exhibits prominent decadal amplitude variations during the ENSO decaying boreal spring season. It is revealed that the Atlantic multidecadal oscillation (AMO) can largely explain this waxing and waning in amplitude. A robust positive correlation between ENSO and the C-mode is detected during a negative AMO phase but not during a positive phase. Similar results can also be found in the relationship of ENSO with 1) the western North Pacific (WNP) anticyclone and 2) spring precipitation over southern China, both of which are closely associated with the C-mode. We suggest that ENSO property changes due to an AMO modulation play a crucial role in determining these decadal shifts. During a positive AMO phase, ENSO events are distinctly weaker than those in an AMO negative phase. In addition, El Niño events concurrent with a positive AMO phase tend to exhibit a westward-shifted sea surface temperature (SST) anomaly pattern. These SST characteristics during the positive AMO phase are both not conducive to the development of the meridionally asymmetric C-mode atmospheric circulation pattern and thus reduce the ENSO/C-mode correlation on decadal time scales. These observations can be realistically reproduced by a coupled general circulation model (CGCM) experiment in which North Atlantic SSTs are nudged to reproduce a 50-yr sinusoidally varying AMO evolution. Our conclusion carries important implications for understanding seasonally modulated ENSO dynamics and multiscale climate impacts over East Asia.

Climatic Zoning of SST in the East China Sea and Adjacent Seas from 1981 to 2010

In this study, the data of sea surface temperature (SST) and current for 1981 to 2010 from the SODA_2.2.4 were used to analyze the climatic zoning characteristics of SST in the East China Sea and adjacent sea areas, and the relationship between SST and relevant factors, to provide a reference for further research on long-term climate change. The results show that : (1) Under the combined action of sea-land distribution, latitude and ocean current, the study area can be divided into three climatic zones: the continental shelf area of the East China Sea, the Kuroshio area of the East China Sea and the Northwest Pacific Ocean. SST from northwest to southeast presents a low - high - second high distribution pattern. (2) Due to the influence of the change of direct point of the sun, the highest SST in the low and high latitudes is 1 month apart, which is July and August respectively. In general, the isotherm in the study area is dense in February. The average SST in the whole region was the lowest, about 19.9°C. The temperature difference between North and South is large, about 17°C. August isotherms are sparse; the average SST in the whole area is the highest, about 28.5°C; the temperature difference between north and south is small, about 6°C. (3) The annual temperature difference of the surface waters of the whole study area is related to latitude. Compared with the latitude, the annual temperature difference in the continental shelf area is the largest, and the Kuroshio area is the smallest. According to coefficient of variation of monthly SST, the annual variation of SST decreases from northwest to southeast. (4) SST rises fastest in May, June and July; and declines fastest in December and January. High-latitude sea areas and near-shore sea areas have rapider and larger changes in SST, and the Kuroshio affected area has slower and smaller changes.