Nonlinear time series models for the North Atlantic Oscillation

2020 ◽  
Author(s):  
Abdel Hannachi ◽  
Thomas Önskog ◽  
Christian Franzke
Keyword(s):  
Time Series ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Nonlinear Models ◽  
Weather Conditions ◽  
Dominant Mode ◽  
Statistical Properties ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

<p>The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. This is the result of complex and nonlinear interactions between many spatio-temporal scales. Here, the authors study a number of linear and nonlinear models for a station-based time series of the daily winter NAO index. It is found that nonlinear autoregressive models including both short and long lags perform excellently in reproducing the characteristic statistical properties of the NAO, such as skewness and fat tails of the distribution and the different time scales of the two phases. As a spinoff of the modelling procedure, we are able to deduce that the interannual dependence of the NAO mostly affects the positive phase and that timescales of one to three weeks are more dominant for the negative phase. The statistical properties of the model makes it useful for the generation of realistic climate noise.</p>

scholarly journals Nonlinear time series models for the North Atlantic Oscillation

2020 ◽  
Vol 6 (2) ◽  
pp. 141-157
Author(s):  
Thomas Önskog ◽  
Christian L. E. Franzke ◽  
Abdel Hannachi
Keyword(s):  
Time Series ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Nonlinear Models ◽  
Weather Conditions ◽  
Dominant Mode ◽  
Statistical Properties ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. This is the result of complex and nonlinear interactions between many spatio-temporal scales. Here, the authors study a number of linear and nonlinear models for a station-based time series of the daily winter NAO index. It is found that nonlinear autoregressive models, including both short and long lags, perform excellently in reproducing the characteristic statistical properties of the NAO, such as skewness and fat tails of the distribution, and the different timescales of the two phases. As a spin-off of the modelling procedure, we can deduce that the interannual dependence of the NAO mostly affects the positive phase, and that timescales of 1 to 3 weeks are more dominant for the negative phase. Furthermore, the statistical properties of the model make it useful for the generation of realistic climate noise.

scholarly journals Predictability and Non-Gaussian Characteristics of the North Atlantic Oscillation

2018 ◽  
Vol 31 (2) ◽  
pp. 537-554 ◽  
Author(s):  
Thomas Önskog ◽  
Christian L. E. Franzke ◽  
Abdel Hannachi
Keyword(s):  
Time Series ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Gaussian Noise ◽  
Weather Conditions ◽  
Dominant Mode ◽  
The North ◽  
The North Atlantic ◽  
Non Gaussian ◽  
The North Atlantic Oscillation

The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. It is the result of complex and nonlinear interactions between many spatiotemporal scales. Here, the authors study the statistical properties of two time series of the daily NAO index. Previous NAO modeling attempts only considered Gaussian noise, which can be inconsistent with the system complexity. Here, it is found that an autoregressive model with non-Gaussian noise provides a better fit to the time series. This result holds also when considering time series for the four seasons separately. The usefulness of the proposed model is evaluated by means of an investigation of its forecast skill.

scholarly journals Coupled decadal variability of the North Atlantic Oscillation, regional rainfall and karst spring discharges in the Campania region (southern Italy)

2012 ◽  
Vol 16 (5) ◽  
pp. 1389-1399 ◽  
Author(s):  
P. De Vita ◽  
V. Allocca ◽  
F. Manna ◽  
S. Fabbrocino
Keyword(s):  
Time Series ◽  
North Atlantic Oscillation ◽  
Air Temperature ◽  
North Atlantic ◽  
Karst Spring ◽  
The North ◽  
Nao Index ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. Thus far, studies on climate change have focused mainly on the variability of the atmospheric and surface components of the hydrologic cycle, investigating the impact of this variability on the environment, especially with respect to the risks of desertification, droughts and floods. Conversely, the impacts of climate change on the recharge of aquifers and on the variability of groundwater flow have been less investigated, especially in Mediterranean karst areas whose water supply systems depend heavily upon groundwater exploitation. In this paper, long-term climatic variability and its influence on groundwater recharge were analysed by examining decadal patterns of precipitation, air temperature and spring discharges in the Campania region (southern Italy), coupled with the North Atlantic Oscillation (NAO). The time series of precipitation and air temperature were gathered over 90 yr, from 1921 to 2010, using 18 rain gauges and 9 air temperature stations with the most continuous functioning. The time series of the winter NAO index and of the discharges of 3 karst springs, selected from those feeding the major aqueducts systems, were collected for the same period. Regional normalised indexes of the precipitation, air temperature and karst spring discharges were calculated, and different methods were applied to analyse the related time series, including long-term trend analysis using smoothing numerical techniques, cross-correlation and Fourier analysis. The investigation of the normalised indexes highlighted the existence of long-term complex periodicities, from 2 to more than 30 yr, with differences in average values of up to approximately ±30% for precipitation and karst spring discharges, which were both strongly correlated with the winter NAO index. Although the effects of the North Atlantic Oscillation (NAO) had already been demonstrated in the long-term precipitation and streamflow patterns of different European countries and Mediterranean areas, the results of this study allow for the establishment of a link between a large-scale atmospheric cycle and the groundwater recharge of carbonate karst aquifers. Consequently, the winter NAO index could also be considered as a proxy to forecast the decadal variability of groundwater flow in Mediterranean karst areas.

scholarly journals Impact of the North Atlantic Oscillation on Southern Europe Water Distribution: Insights from Geodetic Data

2015 ◽  
Vol 19 (10) ◽  
pp. 1-16 ◽  
Author(s):  
Pierre Valty ◽  
Olivier de Viron ◽  
Isabelle Panet ◽  
Xavier Collilieux
Keyword(s):  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Water Distribution ◽  
Principal Component ◽  
Dominant Mode ◽  
The Black Sea ◽  
Southern Europe ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract From space gravity and station position data over southern Europe from 2002 to 2010, this study investigates the interannual mass redistributions using principal component analysis. The dominant mode, which appears both in gravity and positioning, results from the North Atlantic Oscillation (NAO). This analysis allows us to isolate and characterize the NAO impact on the mass distribution, which appears centered over the Black Sea and its two main catchment basins, the Danube and Dnieper.

scholarly journals Reconstruction of the Gulf Stream from 1940 to the Present and Correlation with the North Atlantic Oscillation

2017 ◽  
Vol 47 (11) ◽  
pp. 2741-2754 ◽  
Author(s):  
Sylvain Watelet ◽  
Jean-Marie Beckers ◽  
Alexander Barth
Keyword(s):  
Time Series ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Gulf Stream ◽  
World Ocean ◽  
In Situ Data ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

AbstractIn this study, the Gulf Stream (GS)’s response to the North Atlantic Oscillation (NAO) is investigated by generating an observation-based reconstruction of the GS path between 70° and 50°W since 1940. Using in situ data from the World Ocean Database (WOD), SeaDataNet, International Council for the Exploration of the Sea (ICES), Hydrobase3, and Argo floats, a harmonized database of more than 40 million entries is created. A variational inverse method implemented in the software Data Interpolating Variational Analysis (DIVA) allows the production of time series of monthly analyses of temperature and salinity over the North Atlantic (NA). These time series are used to derive two GS indices: the GS north wall (GSNW) index for position and the GS delta (GSD) index as a proxy of its transport. This study finds a significant correlation (0.37) between the GSNW and the NAO at a lag of 1 year (NAO preceding GS) since 1940 and significant correlations (0.50 and 0.43) between the GSD and the NAO at lags of 0 and 2 years between 1960 and 2014. The authors suggest this 2-yr lag is due to Rossby waves, generated by NAO variability, that propagate westward from the center of the NA. This is the first reconstruction of GS indices over a 75-yr period based on an objective method using the largest in situ dataset so far.

Granger Causality of Coupled Climate Processes: Ocean Feedback on the North Atlantic Oscillation

2006 ◽  
Vol 19 (7) ◽  
pp. 1182-1194 ◽  
Author(s):  
Timothy J. Mosedale ◽  
David B. Stephenson ◽  
Matthew Collins ◽  
Terence C. Mills
Keyword(s):  
Time Series ◽  
North Atlantic Oscillation ◽  
Granger Causality ◽  
North Atlantic ◽  
General Circulation ◽  
Careful Examination ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract This study uses a Granger causality time series modeling approach to quantitatively diagnose the feedback of daily sea surface temperatures (SSTs) on daily values of the North Atlantic Oscillation (NAO) as simulated by a realistic coupled general circulation model (GCM). Bivariate vector autoregressive time series models are carefully fitted to daily wintertime SST and NAO time series produced by a 50-yr simulation of the Third Hadley Centre Coupled Ocean–Atmosphere GCM (HadCM3). The approach demonstrates that there is a small yet statistically significant feedback of SSTs on the NAO. The SST tripole index is found to provide additional predictive information for the NAO than that available by using only past values of NAO—the SST tripole is Granger causal for the NAO. Careful examination of local SSTs reveals that much of this effect is due to the effect of SSTs in the region of the Gulf Steam, especially south of Cape Hatteras. The effect of SSTs on NAO is responsible for the slower-than-exponential decay in lag-autocorrelations of NAO notable at lags longer than 10 days. The persistence induced in daily NAO by SSTs causes long-term means of NAO to have more variance than expected from averaging NAO noise if there is no feedback of the ocean on the atmosphere. There are greater long-term trends in NAO than can be expected from aggregating just short-term atmospheric noise, and NAO is potentially predictable provided that future SSTs are known. For example, there is about 10%–30% more variance in seasonal wintertime means of NAO and almost 70% more variance in annual means of NAO due to SST effects than one would expect if NAO were a purely atmospheric process.

scholarly journals The Arctic Winter Sea Ice Quadrupole Revisited

2017 ◽  
Vol 30 (9) ◽  
pp. 3157-3167 ◽  
Author(s):  
S. Close ◽  
M.-N. Houssais ◽  
C. Herbaut
Keyword(s):  
Sea Ice ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Large Scale ◽  
Dominant Mode ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

The dominant mode of Arctic sea ice variability in winter is often maintained to be represented by a quadrupole structure, comprising poles of one sign in the Okhotsk, Greenland, and Barents Seas and of opposing sign in the Labrador and Bering Seas, forced by the North Atlantic Oscillation. This study revisits this large-scale winter mode of sea ice variability using microwave satellite and reanalysis data. It is found that the quadrupole structure does not describe a significant covariance relationship among all four component poles. The first empirical orthogonal mode, explaining covariability in the sea ice of the Barents, Greenland, and Okhotsk Seas, is linked to the Siberian high, while the North Atlantic Oscillation only exhibits a significant relationship with the Labrador Sea ice, which varies independently as the second mode. The principal components are characterized by a strong low-frequency signal; because the satellite record is still short, these results suggest that statistical analyses should be applied cautiously.

scholarly journals Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0

2020 ◽  
Vol 13 (2) ◽  
pp. 841-858 ◽  
Author(s):  
Simon Michel ◽  
Didier Swingedouw ◽  
Marie Chavent ◽  
Pablo Ortega ◽  
Juliette Mignot ◽  
...  
Keyword(s):  
Random Forest ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Principal Component Regression ◽  
Principal Component ◽  
Statistical Properties ◽  
Proxy Records ◽  
The North ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. Modes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statistical learning methods applied to proxy records is useful for improving our understanding of their behaviour. For doing so, several statistical methods exist, among which principal component regression is one of the most widely used in paleoclimatology. Here, we provide the software ClimIndRec to the climate community; it is based on four regression methods (principal component regression, PCR; partial least squares, PLS; elastic net, Enet; random forest, RF) and cross-validation (CV) algorithms, and enables the systematic reconstruction of a given climate index. A prerequisite is that there are proxy records in the database that overlap in time with its observed variations. The relative efficiency of the methods can vary, according to the statistical properties of the mode and the proxy records used. Here, we assess the sensitivity to the reconstruction technique. ClimIndRec is modular as it allows different inputs like the proxy database or the regression method. As an example, it is here applied to the reconstruction of the North Atlantic Oscillation by using the PAGES 2k database. In order to identify the most reliable reconstruction among those given by the different methods, we use the modularity of ClimIndRec to investigate the sensitivity of the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the targeted reconstruction period. We obtain the best reconstruction of the North Atlantic Oscillation (NAO) using the random forest approach. It shows significant correlation with former reconstructions, but exhibits higher validation scores.

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