scholarly journals Prediction of North Atlantic Oscillation Index Associated with the Sea Level Pressure Using DWT-LSTM and DWT-ConvLSTM Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Bin Mu ◽  
Jing Li ◽  
Shijin Yuan ◽  
Xiaodan Luo
Keyword(s):  
North Atlantic Oscillation ◽  
Sea Level ◽  
North Atlantic ◽  
Short Term Memory ◽  
Global Climate ◽  
Time Series Prediction ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Sea Level Pressure ◽  
Level Pressure

The North Atlantic Oscillation (NAO), which manifests as an irregular atmospheric fluctuation, has a profound effect on the global climate change. The NAO index (NAOI) is the quantitative indicator that can reflect the intensity of the NAO events, and its traditional definition is the normalized sea level pressure (SLP) difference between Azores and Iceland. From the variation tendency of the NAOI, we found that it is difficult to predict the NAO with the characteristics of variability and complexity. As a data-driven approach, the deep neural network presents great potential in learning the mechanisms of climate forecasting. In this paper, we adopt long short-term memory (LSTM) and ConvLSTM to predict the NAO from two aspects, NAOI and SLP, respectively. In previous studies, LSTM has been regarded as a resultful method for time series prediction. ConvLSTM can capture both the temporal and spatial interdependencies of the SLP field; then, the NAOI can be calculated from the SLP output. In order to improve the prediction reliability, we utilize the discrete wavelet transform (DWT) as a preprocessing technique to decompose original data into different frequencies, considering the local time dependency. It can effectively preserve the features of high-frequency data and forecast extreme events more accurately. The proposed DWT-LSTM and DWT-ConvLSTM models are compared against multiple advanced models, such as LSTM, Holt-Winters, support vector regression (SVR), and gated recurrent unit (GRU). The results indicate that both DWT-LSTM and DWT-ConvLSTM perform better, particularly at peak values. As for the 31 NAO events from 2006 to 2015, our models achieve the lowest prediction error and the best stability. Compared with the forecast products of CPC named Global Forecast System (GFS) and the ensemble forecasts (ENSM), our models are much closer to observation in multistep forecasting.

scholarly journals Effect of Synoptic Systems on the Variability of the North Atlantic Oscillation

2005 ◽  
Vol 133 (10) ◽  
pp. 2894-2904 ◽  
Author(s):  
Ulrike Löptien ◽  
Eberhard Ruprecht
Keyword(s):  
North Atlantic Oscillation ◽  
Sea Level ◽  
North Atlantic ◽  
Low Frequency ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
The North ◽  
Low Frequency Variability ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract The North Atlantic Oscillation (NAO) represents the dominant mode of atmospheric variability in the North Atlantic region. In the present study, the role of the synoptic systems (cyclones and anticyclones) in generating the NAO pattern is investigated. To study the intermonthly variations of the NAO, NCEP–NCAR reanalysis data are used, and for the interdecadal variations the results of a 300-yr control integration under present-day conditions of the coupled model ECHAM4/OPYC3 are analyzed. A filtering method is developed for the sea level pressure anomalies. Application of this method to each grid point yields the low-frequency variability in the sea level pressure field that is due to the synoptic systems. The low-frequency variability of the filtered and the original data are in high agreement. This indicates that the low-frequency pressure variability, and with it the variability of the NAO, is essentially caused by the distribution of the synoptic systems. The idea that the distribution of the synoptic systems is the cause of the variation of the NAO is confirmed by high correlation between the latitudinal position of the polar front over the North Atlantic and the NAO index. Since most of the low-frequency variability in sea level pressure can be explained through the distribution of the synoptic systems, the NAO seems to be a reflection of the distribution of the synoptic systems, rather than the source for variations in the cyclone tracks.

scholarly journals Multidecadal Mobility of the North Atlantic Oscillation

2013 ◽  
Vol 26 (8) ◽  
pp. 2453-2466 ◽  
Author(s):  
G. W. K. Moore ◽  
I. A. Renfrew ◽  
R. S. Pickart
Keyword(s):  
North Atlantic Oscillation ◽  
Sea Level ◽  
North Atlantic ◽  
Pressure Field ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Modes Of Variability ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract The North Atlantic Oscillation (NAO) is one of the most important modes of variability in the global climate system and is characterized by a meridional dipole in the sea level pressure field, with centers of action near Iceland and the Azores. It has a profound influence on the weather, climate, ecosystems, and economies of Europe, Greenland, eastern North America, and North Africa. It has been proposed that around 1980, there was an eastward secular shift in the NAO’s northern center of action that impacted sea ice export through Fram Strait. Independently, it has also been suggested that the location of its southern center of action is tied to the phase of the NAO. Both of these attributes of the NAO have been linked to anthropogenic climate change. Here the authors use both the one-point correlation map technique as well as empirical orthogonal function (EOF) analysis to show that the meridional dipole that is often seen in the sea level pressure field over the North Atlantic is not purely the result of the NAO (as traditionally defined) but rather arises through an interplay among the NAO and two other leading modes of variability in the North Atlantic region: the East Atlantic (EA) and the Scandinavian (SCA) patterns. This interplay has resulted in multidecadal mobility in the two centers of action of the meridional dipole since the late nineteenth century. In particular, an eastward movement of the dipole has occurred during the 1930s to 1950s as well as more recently. This mobility is not seen in the leading EOF of the sea level pressure field in the region.

scholarly journals Prediction of synoptic-scale sea level pressure over the Indian monsoon region using deep learning

2021 ◽  
Author(s):  
Aryaman Sinha ◽  
Mayuna Gupta ◽  
K S S Sai Srujan ◽  
Hariprasad Kodamana ◽  
Sandeep Sukumaran
Keyword(s):  
Deep Learning ◽  
Sea Level ◽  
Short Term Memory ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Learning Model ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Deep Learning Model

<div><div><div><p>The synoptic-scale (3 - 7 days) variability is a dominant contributor to the Indian summer monsoon (ISM) seasonal precipitation. An accurate prediction of ISM precipitation by dynamical or statistical models remains a challenge. Here we show that the sea level pressure (SLP) can be used as a proxy to predict the active-break cycle as well as the genesis of low- pressure-systems (LPS), using a deep learning model, namely, convolutional long short-term memory (ConvLSTM) networks. The deep learning model is able to reliably predict the daily SLP anomalies over Central India and the Bay of Bengal at a lead time of 7 days. As the fluctuations in SLP drive the changes in the strength of the atmospheric circulation, the prediction of SLP anomalies is useful in predicting the intensity of ISM. It is demonstrated that the ConvLSTM possesses better prediction skill compared to a conventional numerical weather prediction model, indicating the usefulness of a physics guided deep learning model in medium range weather forecasting.</p></div></div></div>

scholarly journals Decadal Shift of NAO-Linked Interannual Sea Level Variability along the U.S. Northeast Coast

2018 ◽  
Vol 31 (13) ◽  
pp. 4981-4989 ◽  
Author(s):  
Jessica S. Kenigson ◽  
Weiqing Han ◽  
Balaji Rajagopalan ◽  
Yanto ◽  
Mike Jasinski
Keyword(s):  
Sea Level ◽  
North Atlantic ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
The North ◽  
Decadal Shift ◽  
Nao Index ◽  
The North Atlantic ◽  
The U.S ◽  
Northeast Coast

Recent studies have linked interannual sea level variability and extreme events along the U.S. northeast coast (NEC) to the North Atlantic Oscillation (NAO), a natural internal climate mode that prevails in the North Atlantic Ocean. The correlation between the NAO index and coastal sea level north of Cape Hatteras was weak from the 1960s to the mid-1980s, but it has markedly increased since around 1987. The causes for the decadal shift remain unknown. Yet understanding the abrupt change is vital for decadal sea level prediction and is essential for risk management. Here we use a robust method, the Bayesian dynamic linear model (DLM), to explore the nonstationary NAO impact on NEC sea level. The results show that a spatial pattern change of NAO-related winds near the NEC is a major cause of the NAO–sea level relationship shift. A new index using regional sea level pressure is developed that is a significantly better predictor of NEC sea level than is the NAO and is strongly linked to the intensity of westerly winds near the NEC. These results point to the vital importance of monitoring regional changes of wind and sea level pressure patterns, rather than the NAO index alone, to achieve more accurate predictions of sea level change along the NEC.

Global Climate Model Performance over Alaska and Greenland

2008 ◽  
Vol 21 (23) ◽  
pp. 6156-6174 ◽  
Author(s):  
John E. Walsh ◽  
William L. Chapman ◽  
Vladimir Romanovsky ◽  
Jens H. Christensen ◽  
Martin Stendel
Keyword(s):  
Sea Level ◽  
Root Mean Square ◽  
Global Climate ◽  
Systematic Errors ◽  
The Arctic ◽  
Future Climate Change ◽  
Mean Square ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Mean Square Errors

Abstract The performance of a set of 15 global climate models used in the Coupled Model Intercomparison Project is evaluated for Alaska and Greenland, and compared with the performance over broader pan-Arctic and Northern Hemisphere extratropical domains. Root-mean-square errors relative to the 1958–2000 climatology of the 40-yr ECMWF Re-Analysis (ERA-40) are summed over the seasonal cycles of three variables: surface air temperature, precipitation, and sea level pressure. The specific models that perform best over the larger domains tend to be the ones that perform best over Alaska and Greenland. The rankings of the models are largely unchanged when the bias of each model’s climatological annual mean is removed prior to the error calculation for the individual models. The annual mean biases typically account for about half of the models’ root-mean-square errors. However, the root-mean-square errors of the models are generally much larger than the biases of the composite output, indicating that the systematic errors differ considerably among the models. There is a tendency for the models with smaller errors to simulate a larger greenhouse warming over the Arctic, as well as larger increases of Arctic precipitation and decreases of Arctic sea level pressure, when greenhouse gas concentrations are increased. Because several models have substantially smaller systematic errors than the other models, the differences in greenhouse projections imply that the choice of a subset of models may offer a viable approach to narrowing the uncertainty and obtaining more robust estimates of future climate change in regions such as Alaska, Greenland, and the broader Arctic.

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