This book analyses China’s maritime strategy for the 21st century, integrating strategic planning, policy thinking and strategic prediction.
It explains the construction and application of China’s ...military, political, economic and diplomatic means for building maritime power and predicts the future of China’s maritime power by 2049, as well as development trends in global maritime politics. It explores both the strengths and the limitations of President Xi’s ‘Maritime Dream’ and provides a candid assessment of the likely future balance at sea between China and the United States. This volume explains and discusses China’s claims and intentions in the East and South China Seas, and makes some recommendations for China’s future policy that will lessen the chance of conflict with the United States and its closer neighbors.
This book will be of much interest to students of maritime strategy, naval studies, Chinese politics and International Relations in general.
Coastal Sea level rise around the China Seas Qu, Ying; Jevrejeva, Svetlana; Jackson, Luke P. ...
Global and planetary change,
January 2019, 2019-01-00, Letnik:
172
Journal Article
Recenzirano
Odprti dostop
We analyze the sea level rise along the Bohai Sea, the Yellow Sea, the East China Sea, and the South China Sea (the “China Seas”) coastline using 25 tide gauge records beginning with Macau in 1925, ...but with most starting during the 1950s and 60s. The main problem in estimating sea level rise for the period is the lack of vertical land movement (VLM) data for the tide gauge stations. We estimated VLM using satellite altimetry covering the 18 stations with records spanning 1993–2016. The results show that many tide gauge stations, typically in cities, have undergone significant subsidence due to groundwater extraction. After removing the VLM from tide gauge records, the 1993–2016 sea level rise rate is 3.2 ± 1.1 mm/yr, and 2.9 ± 0.8 mm/yr over the longer 1980–2016 period. We estimate the steric sea level contribution to be up to 0.9 ± 0.3 mm/yr, and contributions from ice mass loss from glaciers and ice sheets of up to 1.1 ± 0.1 mm/yr over the last 60 years. Contributions from VLM range between −4.5 ± 1.0 mm/yr and 1.4 ± 1.3 mm/yr across the stations. Projections of coastal sea level probability distributions under future climate scenarios show that the steric factor is the main contributor under both the RCP 4.5 and High-end RCP 8.5 scenarios except in the upper tails under High-end RCP 8.5 when the Antarctic ice sheet makes the greatest contribution. By 2100 we expect median coastal sea level rises at the stations of 48–61 cm under RCP 4.5, and 84–99 cm under High-end RCP 8.5 scenario.
•The rate of sea level rise around the China Seas is 3.2 ± 1.1 mm/yr since 1993; 2.9 ± 0.8 mm/yr over 1980-2016 period.•Vertical land movement ranges between −4.5 ± 1.0 mm/yr and 1.4 ± 1.3 mm/yr across 25 stations.•Steric contribution is up to 0.9 ± 0.3 mm/yr, ice mass loss contribution is up to 1.1 ± 0.1 mm/yr over the last 60 years.•By 2100 median coastal sea level rise with range of 48-61 cm at the tide gauge locations under RCP 4.5 scenario.•And ranging 84-99 cm under High-end RCP8.5 scenario.
Abstract
Based on statistical analyses of gridded data over the past four decades (1979–2020), we examined the teleconnection between the variability of the Antarctic sea ice and the leading modes of ...subtropical sea surface temperature (SST) variability known as the Indian Ocean subtropical dipole (IOSD), the South Atlantic subtropical dipole (SASD) and the South Pacific subtropical dipole (SPSD). We show that the pattern and strength of the teleconnection differ with the SST modes. For each mode, while the regional distributions of significant Antarctic sea ice anomalies are broadly similar throughout the year, the areal extent and the magnitudes of the anomalies display a strong seasonality. Larger areas and magnitudes of significant sea ice anomalies occur in austral winter for the SASD, autumn and spring for the SPSD and non‐summer seasons for the IOSD. We demonstrate that the spatial and seasonal variations of the sea ice anomalies associated with each of the three subtropical SST variability modes are largely consistent with the patterns of the anomalous temperature advection and sea ice transport by the anomalous atmospheric circulations induced by planetary wavetrains that are triggered by anomalous convective activities over different regions of subtropical oceans. These relationships between subtropical SST modes and Antarctic sea ice may serve as a valuable reference for predicting seasonal to interannual scale variations of Antarctic sea ice concentrations across each austral season.
Year‐round variability in the Ross Gyre (RG), Antarctica, during 2011–2015, is derived using radar altimetry. The RG is characterized by a bounded recirculating component and a westward throughflow ...to the south. Two modes of variability of the sea surface height and ocean surface stress curl are revealed. The first represents a large‐scale sea surface height change forced by the Antarctic Oscillation. The second represents semiannual variability in gyre area and strength, driven by fluctuations in sea level pressure associated with the Amundsen Sea Low. Variability in the throughflow is also linked to the Amundsen Sea Low. An adequate description of the oceanic circulation is achieved only when sea ice drag is accounted for in the ocean surface stress. The drivers of RG variability elucidated here have significant implications for our understanding of the oceanic forcing of Antarctic Ice Sheet melting and for the downstream propagation of its ocean freshening footprint.
Plain Language Summary
The Ross Gyre is one of the main current systems of the Southern Ocean and conveys heat toward the cold continental shelves of the Antarctic Pacific sector, thus impacting the stability of diverse ice shelves. Due to the seasonal sea ice cover, measurements are sparse and little is known about the variability of the gyre's circulation and its driving forces. Here we use satellite radar altimetry to generate new light on the Ross Gyre variability. Two key aspects are identified: (i) large‐scale variability of the sea surface height driven by the zonal winds that flow around Antarctica and (ii) changes in area and strength of the gyre, which are linked to a regional center of low pressure that modulates the local meteorology and sea ice conditions. This same pressure system regulates the strength of the coastal currents, which potentially impacts on the distribution of key oceanic properties toward the Ross Sea. The processes identified in this study have strong implications for our understanding of the oceanic forcing of Antarctic Ice Sheet melting and for the downstream propagation of its ocean freshening footprint.
Key Points
Variability in Ross Gyre circulation is assessed using radar altimetry, including in ice‐covered regions
Ross Gyre area, strength, and throughflow vary semiannually, in response to atmospheric forcing associated with the Amundsen Sea Low
Accounting for sea ice drag is essential to understand the Ross Gyre's response to atmospheric forcing
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
The major cause of sea-level change during ice ages is the exchange of water between ice and ocean and the planet’s dynamic response to the changing surface load. Inversion of ∼1,000 observations for ...the past 35,000 y from localities far from former ice margins has provided new constraints on the fluctuation of ice volume in this interval. Key results are: ( i ) a rapid final fall in global sea level of ∼40 m in <2,000 y at the onset of the glacial maximum ∼30,000 y before present (30 ka BP); ( ii ) a slow fall to −134 m from 29 to 21 ka BP with a maximum grounded ice volume of ∼52 × 10 ⁶ km ³ greater than today; ( iii ) after an initial short duration rapid rise and a short interval of near-constant sea level, the main phase of deglaciation occurred from ∼16.5 ka BP to ∼8.2 ka BP at an average rate of rise of 12 m⋅ka ⁻¹ punctuated by periods of greater, particularly at 14.5–14.0 ka BP at ≥40 mm⋅y ⁻¹ (MWP-1A), and lesser, from 12.5 to 11.5 ka BP (Younger Dryas), rates; ( iv ) no evidence for a global MWP-1B event at ∼11.3 ka BP; and ( v ) a progressive decrease in the rate of rise from 8.2 ka to ∼2.5 ka BP, after which ocean volumes remained nearly constant until the renewed sea-level rise at 100–150 y ago, with no evidence of oscillations exceeding ∼15–20 cm in time intervals ≥200 y from 6 to 0.15 ka BP.
Significance Several areas of earth science require knowledge of the fluctuations in sea level and ice volume through glacial cycles. These include understanding past ice sheets and providing boundary conditions for paleoclimate models, calibrating marine-sediment isotopic records, and providing the background signal for evaluating anthropogenic contributions to sea level. From ∼1,000 observations of sea level, allowing for isostatic and tectonic contributions, we have quantified the rise and fall in global ocean and ice volumes for the past 35,000 years. Of particular note is that during the ∼6,000 y up to the start of the recent rise ∼100−150 y ago, there is no evidence for global oscillations in sea level on time scales exceeding ∼200 y duration or 15−20 cm amplitude.
Increasing tensions in the South China Sea have propelled the dispute to the top of the Asia-Pacific's security agenda. Fuelled by rising nationalism over ownership of disputed atolls, growing ...competition over natural resources, strident assertions of their maritime rights by China and the Southeast Asian claimants, the rapid modernization of regional armed forces and worsening geopolitical rivalries among the Great Powers, the South China Sea will remain an area of diplomatic wrangling and potential conflict for the foreseeable future.
Featuring some of the world's leading experts on Asian security, this volume explores the central drivers of the dispute and examines the positions and policies of the main actors, including China, Taiwan, the Southeast Asian claimants, America and Japan. The South China Sea Dispute: Navigating Diplomatic and Strategic Tensions provides readers with the key to understanding how this most complex and contentious dispute is shaping the regional security environment.
This book covers topics ranging from a detailed error analysis of SSTs to new applications employed, for example, in the study of the El Niño–La Niña Southern Oscillation, lake temperatures, and ...coral bleaching. New techniques for interpolation and algorithm development are presented, including improvements for cloud detection. Analysis of the pixel-to-pixel uncertainties provides insight to applications for high spatial resolutions. New approaches for the estimation and evaluation of SSTs are presented. In addition, an overview of the Climate Change Initiative, with specific applications to SST, is presented. The book provides an excellent overview of the current technology, while also highlighting new technologies and their applications to new missions.
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
Recent accelerated warming over the Arctic coincides with sea ice reduction and shifting patterns of land cover. We use a state‐of‐the‐art regional Earth system model, RCAO‐GUESS, which comprises a ...dynamic vegetation model (LPJ‐GUESS), a regional atmosphere model (RCA), and an ocean sea ice model (RCO), to explore the dynamic coupling between vegetation and sea ice during 1989–2011. Our results show that RCAO‐GUESS captures recent trends in observed sea ice concentration and extent, with the inclusion of vegetation dynamics resulting in larger, more realistic variations in summer and autumn than the model that does not account for vegetation dynamics. Vegetation feedbacks induce concomitant changes in downwelling longwave radiation, near‐surface temperature, mean sea level pressure, and sea ice reductions, suggesting a feedback chain linking vegetation change to sea ice dynamics. This study highlights the importance of including interactive vegetation dynamics in modeling the Arctic climate system, particularly when predicting sea ice dynamics.
Plain Language Summary
Recent accelerated warming over the Arctic is associated with dramatic changes in the physical environment, among which unprecedented sea ice decline has received particular attention. In this study, we use a regional Earth system model accounting for interactive coupling between the atmosphere, land vegetation, and sea ice dynamics to explore their potential links. Our model simulates observed spatiotemporal patterns of sea ice thickness and extent reasonably well. Furthermore, the results show that feedbacks of warming‐driven vegetation changes on the near‐surface radiation balance can cause greater variations in sea ice between seasons, which can contribute to an accelerated trend of sea ice reduction. The changes in mean sea level pressure caused by vegetation changes can alter the transport of energy and warm the land, sea, and sea ice surfaces. Downwelling longwave radiation is the dominant factor contributing to the near‐surface warming and increased sea ice melting. Our study highlights the importance of adopting fully coupled Earth system models that account for interactive effects of vegetation dynamics on the physical climate system, in particular when analyzing the reduction of sea ice in the Arctic.
Key Points
Sea ice concentration and extent are simulated by a fully coupled regional Earth system model, including interactive vegetation dynamics
Interactive vegetation dynamics increase the interannual variations of seasonal sea ice cover
Increased downwelling longwave radiation induced by vegetation feedbacks enhances sea ice melting
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