The field of forecasting oceanic variables has traditionally relied on numerical models, which effectively consider the ocean's dynamic evolution and are of physical importance. However, to make the ...models more realistic, complicated processes need to be considered, which is difficult because their calculations are complex. In fact, information on the internal dynamic mechanisms and external driving forces of the ocean are already embedded in the time series of observations. Therefore, we can determine the patterns of ocean variations through data mining of these series to achieve forecasting. Furthermore, to predict variations in ocean processes more realistically, interactions between variables and spatial correlations should be effectively considered. Thus, inspired by available remote sensing data and advancements in deep learning technologies, we develop a hybrid model based on a statistical method and a deep learning model to predict multiple sea surface variables. A case study in the South China Sea shows that this model is highly promising for short‐term daily forecasts of the sea surface height anomaly (SSHA) and sea surface temperature (SST). When the forecast time is 10 days, the root mean square errors of this model forecasts for SSHA and SST are approximately 0.0276 m and 0.46°C, respectively, which are much smaller than those of persistence, climatology and linear regression predictions. The anomaly correlation coefficients for SSHA and SST are approximately 0.864 and 0.633, respectively. The model performs satisfactorily under both normal and typhoon weather conditions.
Plain Language Summary
Deep learning techniques of neural networks are widely used for the forecasting of ocean variables due to their good prediction performance. However, most of the models are based on a single point and a single variable, without considering the interactions between different variables, so they lack physical significance to some extent. In this study, a prediction model for multiple oceanic variables combining multivariate empirical orthogonal function (MEOF) analysis and a Conv1D‐LSTM neural network is established in the South China Sea, which can effectively solve these problems. The MEOF analysis in this study has four main functions: (a) establishing the spatial correlation between different discrete points; (b) considering the correlation between different variables; (c) reducing calculations; and (d) decorrelation. To serve as the basis, the principal component series are used to train and verify the Conv1D‐LSTM model. This model performs satisfactorily under both normal and extreme conditions, and it is expected to provide a reference for further research on deep learning‐based methods in the field of marine prediction.
Key Points
A hybrid prediction model combining a statistical method and a deep learning model is proposed for forecasting sea surface multivariate
Multivariate empirical orthogonal analysis is used to consider the correlations among different variables and reduce the computation
This model performs satisfactorily under both normal and extreme conditions
The low‐amplitude, large‐scale, interannual, and longer‐term sea level changes are linked to the variations of ocean heat and freshwater content and strongly controlled by ocean dynamics. Near the ...coast, especially in low‐lying and flood‐vulnerable regions, these changes can provide background conditions favorable for the occurrence of extreme sea levels that represent a threat for coastal communities and ecosystems. In this study, we identify a tripole mode of the ocean gyre‐scale sea surface height variability in the North Atlantic and show that this mode is responsible for most of the interannual‐to‐decadal sea surface height changes along the southeast coast of the United States, including the Gulf of Mexico. We also show that these changes are largely driven by the large‐scale heat divergence related to the Atlantic Meridional Overturning Circulation and linked to the low‐frequency North Atlantic Oscillation.
Plain Language Summary
The global mean sea level rise caused by ocean warming and terrestrial glacier melting is one of the most alarming aspects of climate change. However, ocean and atmosphere dynamics make sea level change spatially and temporally nonuniform. In fact, the ocean exhibits certain patterns of sea level change with alternating signs over different time periods. These patterns provide background conditions, on top of which shorter‐period and often stronger weather‐driven sea level fluctuations are superimposed. In order to improve our capacity to predict regional sea level variability, it is important to identify these patterns and to explore the mechanisms responsible for their evolution. In this study, we identify such a pattern in the North Atlantic Ocean and show that it is largely responsible for year‐to‐year changes of coastal sea level south of Cape Hatteras and in the Gulf of Mexico. These coastal regions of the United States are particularly vulnerable to extreme weather conditions, such as tropical storms and hurricanes, that can cause catastrophic flooding. We show that the temporal evolution of the identified pattern is due to the basin‐scale ocean heat content changes in the North Atlantic, driven by changes in the large‐scale ocean and atmosphere circulations.
Key Points
Interannual sea surface height variability in the North Atlantic exhibits a tripole pattern
The sea surface height tripole explains up to 60–80% of interannual sea level variance along the southeast U.S. coast and in the Gulf of Mexico
The tripole is associated with gyre‐scale heat divergence in response to low‐frequency North Atlantic Oscillation
We present a new observation‐based estimate of the global oceanic carbon dioxide (CO2) sink and its temporal variation on a monthly basis from 1998 through 2011 and at a spatial resolution of 1°×1°. ...This sink estimate rests upon a neural network‐based mapping of global surface ocean observations of the partial pressure of CO2 (pCO2) from the Surface Ocean CO2 Atlas database. The resulting pCO2 has small biases when evaluated against independent observations in the different ocean basins, but larger randomly distributed differences exist particularly in high latitudes. The seasonal climatology of our neural network‐based product agrees overall well with the Takahashi et al. (2009) climatology, although our product produces a stronger seasonal cycle at high latitudes. From our global pCO2 product, we compute a mean net global ocean (excluding the Arctic Ocean and coastal regions) CO2 uptake flux of −1.42 ± 0.53 Pg C yr−1, which is in good agreement with ocean inversion‐based estimates. Our data indicate a moderate level of interannual variability in the ocean carbon sink (±0.12 Pg C yr−1, 1σ) from 1998 through 2011, mostly originating from the equatorial Pacific Ocean, and associated with the El Niño–Southern Oscillation. Accounting for steady state riverine and Arctic Ocean carbon fluxes our estimate further implies a mean anthropogenic CO2 uptake of −1.99 ± 0.59 Pg C yr−1 over the analysis period. From this estimate plus the most recent estimates for fossil fuel emissions and atmospheric CO2 accumulation, we infer a mean global land sink of −2.82 ± 0.85 Pg C yr−1 over the 1998 through 2011 period with strong interannual variation.
Key Points
A new method permits us to upscale surface ocean pCO2 observationsWe find moderate variability in the global ocean carbon sinkENSO is the dominant mode driving the sink variability
Earth is heading towards a climate that last existed more than three million years ago (Ma) during the 'mid-Pliocene warm period'
, when atmospheric carbon dioxide concentrations were about 400 parts ...per million, global sea level oscillated in response to orbital forcing
and peak global-mean sea level (GMSL) may have reached about 20 metres above the present-day value
. For sea-level rise of this magnitude, extensive retreat or collapse of the Greenland, West Antarctic and marine-based sectors of the East Antarctic ice sheets is required. Yet the relative amplitude of sea-level variations within glacial-interglacial cycles remains poorly constrained. To address this, we calibrate a theoretical relationship between modern sediment transport by waves and water depth, and then apply the technique to grain size in a continuous 800-metre-thick Pliocene sequence of shallow-marine sediments from Whanganui Basin, New Zealand. Water-depth variations obtained in this way, after corrections for tectonic subsidence, yield cyclic relative sea-level (RSL) variations. Here we show that sea level varied on average by 13 ± 5 metres over glacial-interglacial cycles during the middle-to-late Pliocene (about 3.3-2.5 Ma). The resulting record is independent of the global ice volume proxy
(as derived from the deep-ocean oxygen isotope record) and sea-level cycles are in phase with 20-thousand-year (kyr) periodic changes in insolation over Antarctica, paced by eccentricity-modulated orbital precession
between 3.3 and 2.7 Ma. Thereafter, sea-level fluctuations are paced by the 41-kyr period of cycles in Earth's axial tilt as ice sheets stabilize on Antarctica and intensify in the Northern Hemisphere
. Strictly, we provide the amplitude of RSL change, rather than absolute GMSL change. However, simulations of RSL change based on glacio-isostatic adjustment show that our record approximates eustatic sea level, defined here as GMSL unregistered to the centre of the Earth. Nonetheless, under conservative assumptions, our estimates limit maximum Pliocene sea-level rise to less than 25 metres and provide new constraints on polar ice-volume variability under the climate conditions predicted for this century.
Deep convection in polar oceans plays a critical role in the variability of global climate. In this study, we investigate potential impacts of atmosphere–sea ice–ocean interaction on deep convection ...in the Southern Ocean (SO) of a climate system model (CSM) by changing sea ice–ocean stress. Sea ice–ocean stress plays a vital role in the horizontal momentum exchange between sea ice and the ocean, and can be parameterized as a function of the turning angle between sea ice and ocean velocity. Observations have shown that the turning angle is closely linked to the sea-ice intrinsic properties, including speed and roughness, and it varies spatially. However, a fixed turning angle, i.e., zero turning angle, is prescribed in most of the state-of-the-art CSMs. Thus, sensitivities of SO deep convection to zero and non-zero turning angles are discussed in this study. We show that the use of a non-zero turning angle weakens open–ocean deep convection and intensifies continental shelf slope convection. Our analyses reveal that a non-zero turning angle first induces offshore movement of sea ice transporting to the open SO, which leads to sea ice decrease in the SO coastal region and increase in the open SO. In the SO coastal region, the enhanced sea-ice divergence intensifies the formation of denser surface water descending along continental shelf by enhanced salt flux and reduced freshwater flux, combined with enhanced Ekman pumping and weakened stratification, contributing to the occurrence and intensification of continental shelf slope convection. On the other hand, the increased sea ice in the open SO weakens the westerlies, enhances sea-level pressure, and increases freshwater flux, whilst oceanic cyclonic circulation slows down, sea surface temperature and sea surface salinity decrease in the open SO response to the atmospheric changes. Thus, weakened cyclonic circulation, along with enhanced freshwater flux, reduced deep–ocean heat content, and increased stability of sea water, dampens the open–ocean deep convection in the SO, which in turn cools the sea surface temperature, increases sea-level pressure, and finally increases sea-ice concentration, providing a positive feedback. In the CSM, the use of a non-zero turning angle has the capability to reduce the SO warm bias. These results highlight the importance of an accurate representation of sea ice–ocean coupling processes in a CSM.
Piracy and smuggling are as great a problem today as they were several hundreds of years ago. The studies in Elusive Pirates, Pervasive Smugglers, for the first time, carefully describe and ...critically analyze piracy and smuggling in the Greater China Seas region from the sixteenth century to the present. Because piracy and smuggling involve complex historical processes that are still evolving, to fully understand contemporary problems it is important to place them in larger historical and comparative perspectives. The essays in this book add significantly to the scholarship on East and Southeast Asian history, and in particular to the maritime history of the region we call the Greater China Seas. This is the first book to analyze the whole region from Japan to Southeast Asia as a single, integrated historical and geographical area. This book takes a radical departure from the standard terra-centered histories to place the seas at the center rather than at the margins of our inquiries. By focusing on the water we are better able to stitch together the diverse histories of Japan, China, and Southeast Asia. Although often dismissed as historically unimportant, the contributors to this anthology show that in fact pirates and smugglers have played significant roles in the development of the modern world. Elusive Pirates, Pervasive Smugglers should appeal to undergraduate and graduate students in history and Asian studies, as well as to general readers interested in pirates and maritime history.
The causes of sea-level rise since 1900 Frederikse, Thomas; Landerer, Felix; Caron, Lambert ...
Nature (London),
08/2020, Volume:
584, Issue:
7821
Journal Article
Peer reviewed
The rate of global-mean sea-level rise since 1900 has varied over time, but the contributing factors are still poorly understood
. Previous assessments found that the summed contributions of ice-mass ...loss, terrestrial water storage and thermal expansion of the ocean could not be reconciled with observed changes in global-mean sea level, implying that changes in sea level or some contributions to those changes were poorly constrained
. Recent improvements to observational data, our understanding of the main contributing processes to sea-level change and methods for estimating the individual contributions, mean another attempt at reconciliation is warranted. Here we present a probabilistic framework to reconstruct sea level since 1900 using independent observations and their inherent uncertainties. The sum of the contributions to sea-level change from thermal expansion of the ocean, ice-mass loss and changes in terrestrial water storage is consistent with the trends and multidecadal variability in observed sea level on both global and basin scales, which we reconstruct from tide-gauge records. Ice-mass loss-predominantly from glaciers-has caused twice as much sea-level rise since 1900 as has thermal expansion. Mass loss from glaciers and the Greenland Ice Sheet explains the high rates of global sea-level rise during the 1940s, while a sharp increase in water impoundment by artificial reservoirs is the main cause of the lower-than-average rates during the 1970s. The acceleration in sea-level rise since the 1970s is caused by the combination of thermal expansion of the ocean and increased ice-mass loss from Greenland. Our results reconcile the magnitude of observed global-mean sea-level rise since 1900 with estimates based on the underlying processes, implying that no additional processes are required to explain the observed changes in sea level since 1900.
Along with the mean sea level rise due to climate change, the sea level exhibits natural variations at a large number of different time scales. One of the most important is the one linked with the ...seasonal cycle. In the Northern Hemisphere winter, the sea level is as much as 20 cm below its summer values in some locations. It is customary to associate these variations with the seasonal cycle of the sea surface net heat flux which drives an upper-ocean thermal expansion creating a positive steric sea level anomaly. Here, using a novel framework based on steric sea level variance budget applied to observations and to the Estimating the Circulation and Climate of the Ocean state estimate, we demonstrate that the steric sea level seasonal cycle amplitude results from a balance between the seasonal sea surface net heat flux and the oceanic advective processes. Moreover, for up to 50% of the ocean surface, surface heat fluxes act to damp the seasonal steric sea level cycle amplitude, which is instead forced by oceanic advection processes. We also show that eddies play an important role in damping the steric sea level seasonal cycle. Our study contributes to a better understanding of the steric sea level mechanisms which is crucial to ensure accurate and reliable climate projections.
Arctic Sea Ice Reemergence Bushuk, Mitchell; Giannakis, Dimitrios; Majda, Andrew J.
Journal of climate,
07/2015, Volume:
28, Issue:
14
Journal Article
Peer reviewed
Open access
Arctic sea ice reemergence is a phenomenon in which spring sea ice anomalies are positively correlated with fall anomalies, despite a loss of correlation over the intervening summer months. This work ...employs a novel data analysis algorithm for high-dimensional multivariate datasets, coupled nonlinear Laplacian spectral analysis (NLSA), to investigate the regional and temporal aspects of this reemergence phenomenon. Coupled NLSA modes of variability of sea ice concentration (SIC), sea surface temperature (SST), and sea level pressure (SLP) are studied in the Arctic sector of a comprehensive climate model and in observations. It is found that low-dimensional families of NLSA modes are able to efficiently reproduce the prominent lagged correlation features of the raw sea ice data. In both the model and observations, these families provide an SST–sea ice reemergence mechanism, in which melt season (spring) sea ice anomalies are imprinted as SST anomalies and stored over the summer months, allowing for sea ice anomalies of the same sign to reappear in the growth season (fall). The ice anomalies of each family exhibit clear phase relationships between the Barents–Kara Seas, the Labrador Sea, and the Bering Sea, three regions that compose the majority of Arctic sea ice variability. These regional phase relationships in sea ice have a natural explanation via the SLP patterns of each family, which closely resemble the Arctic Oscillation and the Arctic dipole anomaly. These SLP patterns, along with their associated geostrophic winds and surface air temperature advection, provide a large-scale teleconnection between different regions of sea ice variability. Moreover, the SLP patterns suggest another plausible ice reemergence mechanism, via their winter-to-winter regime persistence.
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