Dynamics of Atmospheres and Oceans最新文献

A comparative study has been conducted between genetic algorithm (GA) and deep learning models to predict swell wave heights in the Bay of Bengal (BOB) region. To simulate the required parameter SWAN (Simulating Waves Nearshore) model is integrated with daily 25 km wind from 2009 to 2018 for July and December separately representing the southwest and northeast monsoons respectively. For the BOB region empirical orthogonal function (EOF) analysis is applied on the swell parameter to study the spatial and temporal patterns. GA is applied on the principal component of swell wave heights to generate a forecast explicit equation and thus a basin scale EOF-GA model is established. Next a grid (20⁰ N, 90⁰E) is chosen in the head bay region and the outcomes of the standalone GA model and the deep learning models are compared to predict the time series data of swell wave heights (SWS). It is observed that the performances of the deep learning model is better during the calm conditions in December than the rough seas in July. Another grid (15⁰ N, 82⁰E) is chosen along the east coast through which the severe cyclonic storm PHETHAI (13–18 December 2018) passed and the model accuracies are tested. The EOF-GA model serves as an effective computationally cheap basin scale forecast model. Thus, both the genetic algorithm and deep learning models can be developed and utilized for normal and extreme wave prediction having wide application in the ocean engineering domains.

In this study, the anomalies of dynamical tropopause associated with blocking events in wintertime of the period 1959–2020 are analyzed using the JRA-55 re-analysis data with focus on the Southwest Asia. The identification and analysis of blocking properties is based on a wave breaking index. To this end, at first, the periods of occurrence of blocking are identified, and then the anomalies of the tropopause in the upstream and downstream of the relevant blocking locations in the two sectors of West Asia (Aral) and Euro–Atlantic are investigated. The analysis is carried out for the whole blocking events irrespective of their strength and blocking events with the large blocking index. Results show that the general characteristics obtained for blocking, such as location and frequency of occurrence, are in agreement with most previous studies. In addition, with the occurrence of blocking in the above-mentioned sectors, the characteristics of tropopause in the geographical area of blocking occurrence generally undergo well-defined changes. However, in the downstream of the respective atmospheric blockings, corresponding to the Southwest Asia, the changes in the characteristics of tropopause are relatively small. Although the changes are small compared to those in blocking event area itself, but they are expected to have important implications for the evolution of synoptic systems. For both the two sectors, results for the population of events with large blocking index indicate a significant eastward displacement of the location of blocking relative to that of the whole population of blocking events.

In the context of climate change and human activities, the global sea level is facing a rising trend, which poses serious challenges to the ecological environment of coastal areas. In this study, we selected the monthly mean sea level (MSL) time series of 9 stations in the coastal areas of China as the research object. First, we analyzed the spatiotemporal distribution characteristics of the monthly MSL in the coastal areas of China. Secondly, we analyzed the ability of ensemble empirical mode decomposition (EEMD) to decompose the monthly MSL series. Finally, we choose three machine learning models, namely Back Propagation (BP), K-Nearest Neighbor (KNN), and Long Short-Term Memory (LSTM) neural network models to compare model prediction effect between single machine learning models with machine learning models combined with EEMD. The results show that except for the YANTAI (YT) station, which showed an insignificant downward trend, the monthly MSL of other stations showed an upward trend, indicating that the coastal areas of China are facing the risk of sea level rise. EEMD can effectively reduce the complexity of the original monthly MSL time series, and different intrinsic mode functions (IMFs) reflect changes in monthly MSL at different frequencies. Comparing the single machine learning model and the machine learning model combined with EEMD, it is found that the simulation effect of the machine learning model combined with EEMD is better than that of the single model. The model with the best prediction effect on monthly MSL in the coastal areas of China is LSTM-EEMD, followed by KNN-EEMD. This study provides an important reference for systematically understanding sea level changes and selecting an appropriate monthly MSL prediction model in the coastal areas of China.

The Hatteras coastal ocean is centrally located along the east coast of the 48 contiguous United States, offshore of Cape Hatteras in a complex land/ocean/atmosphere region where major ocean currents of differing temperatures and salinities meet and interact, where the atmosphere fluctuates on a wide range of time scales, and where atmosphere-ocean interactions vary both spatially and temporally. The Gulf Stream current typically leaves its contact with the continental margin here. Continental shelf currents from the north and from the south converge here, resulting in a net shelf-to-ocean transport of shelf waters that carry important water properties and constituents. The two major drivers of these shelf currents and exchanges are the atmosphere and the oceanic Gulf Stream. Atmospheric driving of the Hatteras coastal ocean is through surface wind stress and heat flux across the air-sea interface. The complexity and importance of this region motivated the NSF-sponsored PEACH research program during 2017–2018 (PEACH: Processes driving Exchange At Cape Hatteras). In this paper, we utilize the substantial number of observations available during PEACH to describe the atmospheric forcing of the ocean then. Atmospheric conditions are described in terms of two seasons: the warm season (May to mid-September), with predominantly mild northeastward winds punctuated by occasional tropical cyclones (TCs); and the cool season (mid-September through April), with a nearly continuous, northeastward progression of energetic extratropical cyclones (ETCs) through the region. Cool season ETCs force the region with strong wind stress and ocean-to-atmosphere heat flux episodes, each with a time-scale of several days. Wind stress fluctuation magnitudes typically exceed mean stress magnitudes in each season by a factor of 3–5. These stresses account for just over 40% of the total current variability in the region, showing the wind to be a major driver of the ocean here. Atmosphere-ocean heat flux is typically into the ocean throughout the warm season (~100 W m^-2); it is essentially always out of the ocean during the cool season (~500 W m^-2 or more). New results herein include: southward intraseasonal oscillations of the jet stream’s position drove the strongest ETCs (including one “bomb” cyclone); and during the 41 years leading up to and including PEACH, the season-averaged number and strength of atmospheric cyclones passing over the Hatteras coastal ocean have shown little long-term change. Looking ahead, the NSF Pioneer Array is scheduled to be relocated to the northern portion of the Hatteras coastal ocean in 2024, and the NASA SWOT satellite has begun its ocean topography mission, which has a ground-track cross-over here.

The tidal behavior in the Colombia basin is described based on the analysis of eighteen tide gauge time series, fourteen in the Colombian coasts and four placed in neighboring countries. Tidal constituents are published for the first time at nine of these stations. Harmonic analysis shows that the main constituents in the Caribbean correspond to three diurnals (K₁, O₁, P₁); three semidiurnals (M₂, N₂, S₂), and one long period harmonic (M_f), showing amplitudes and phase lag that correspond to previous tidal reports in the basin. In Turbo, due to the shallow and extensive continental shelf in the Urabá gulf, M₂ is amplified, and shallow water harmonics appear. The amplitude and phase of each observed constituent are compared with global tide models FES2014, TPXO9 and DTU10, showing good agreement. The most significant differences occur with semidiurnal harmonics at stations close to the amphidromic point in the eastern Caribbean. In M_f, considerable interannual variations are found, supporting the need of over one year of sea level data to assess this constituent in the Colombia basin accurately. The radiational component of S₂ is assessed using barometric pressure in thirteen stations, confirming its importance when compared to the gravitational contribution to the observed sea level harmonic. A trend in the atmospheric S₂ is found in Cartagena, which supports those trends in sea-level S_2, previously reported in the Caribbean Sea, are caused by variations in the radiational forcing.