Independent Component Analysis of Climate Data: A New Look at EOF Rotation

Hannachi, Abdel; Unkel, Steffen; Trendafilov, Nickolay T.; Jolliffe, Ian T.

doi:10.1175/2008jcli2571.1

Cited by 62 publications

(50 citation statements)

References 42 publications

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“…Dobricic et al (2016) performed an ICA of atmospheric reanalysis data finding a link between the increasing trend of near-surface temperature in the Arctic during winter (DJF) and three atmospheric patterns. Hannachi et al (2009) first proposed applying the ICA instead of the commonly used EOF in climate studies, showing that ICA may be explained by a rotation of EOFs. The major difference between the two estimates is that ICA does not assume the Gaussian distributions of event probabilities.…”

Section: Maximum Likelihood Estimate (Mle) Of Atmospheric Pollutantsmentioning

confidence: 99%

Impacts of large-scale atmospheric circulation changes in winter on black carbon transport and deposition to the Arctic

et al. 2017

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Abstract. Winter warming and sea-ice retreat observed in the Arctic in the last decades may be related to changes of largescale atmospheric circulation pattern, which may impact the transport of black carbon (BC) to the Arctic and its deposition on the sea ice, with possible feedbacks on the regional and global climate forcing. In this study we developed and applied a statistical algorithm, based on the maximum likelihood estimate approach, to determine how the changes of three large-scale weather patterns associated with increasing temperatures in winter and sea-ice retreat in the Arctic impact the transport of BC to the Arctic and its deposition. We found that two atmospheric patterns together determine a decreasing winter deposition trend of BC between 1980 and 2015 in the eastern Arctic while they increase BC deposition in the western Arctic. The increasing BC trend is mainly due to a pattern characterized by a high-pressure anomaly near Scandinavia favouring the transport in the lower troposphere of BC from Europe and North Atlantic directly into to the Arctic. Another pattern with a high-pressure anomaly over the Arctic and low-pressure anomaly over the North Atlantic Ocean has a smaller impact on BC deposition but determines an increasing BC atmospheric load over the entire Arctic Ocean with increasing BC concentrations in the upper troposphere. The results show that changes in atmospheric circulation due to polar atmospheric warming and reduced winter sea ice significantly impacted BC transport and deposition. The anthropogenic emission reductions applied in the last decades were, therefore, crucial to counterbalance the most likely trend of increasing BC pollution in the Arctic.

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Section: Maximum Likelihood Estimate (Mle) Of Atmospheric Pollutantsmentioning

confidence: 99%

Impacts of large-scale atmospheric circulation changes in winter on black carbon transport and deposition to the Arctic

et al. 2017

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“…Although the monthly NDVI anomaly can indicate deviation from the long term cycle, we cannot reliably identify the continuous change trend from a single anomaly image. This paper uses an empirical orthogonal function (EOF) analysis to extract monthly NDVI change tendencies from the monthly time-series NDVI anomalies, because EOF offers a clear advantage in analyzing spatial-temporal changes [51]. EOF analysis uses properties of matrix algebra to decompose a dataset into spatially orthogonal Eigen-functions and associated temporal coefficients, and is among the most widely used methods in meteorological science [52,53].…”

Section: Extracting the Spatial-temporal Change By Using An Empiricalmentioning

confidence: 99%

Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation Index

Deng

Liang

et al. 2010

Remote Sensing

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Abstract:The pattern of vegetation change in response to global change still remains a controversial issue. A Normalized Difference Vegetation Index (NDVI) dataset compiled by the Global Inventory Modeling and Mapping Studies (GIMMS) was used for analysis. For the period 1982-2006, GIMMS-NDVI analysis indicated that monthly NDVI changes show homogenous trends in middle and high latitude areas in the northern hemisphere and within, or near, the Tropic of Cancer and Capricorn; with obvious spatio-temporal heterogeneity on a global scale over the past two decades. The former areas featured increasing vegetation activity during growth seasons, and the latter areas experienced an even greater amplitude in places where precipitation is adequate. The discussion suggests that one should be cautious of using the NDVI time-series to analyze local vegetation dynamics because of its coarse resolution and uncertainties.

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“…More recently, the higher-order statistical technique of independent component analysis (ICA, Cardoso and Souloumiac 1993;Hyvärinen 1999a, classified here as (b)) has been introduced in order to decompose these data into statistically independent components (e.g., Aires et al 2002;Westra et al 2007;Hannachi et al 2009;Frappart et al 2010Frappart et al , 2011. Forootan andKusche (2012, 2013) argue that different physical processes generate statistically independent source signals that are superimposed in geophysical time series; thus, application of ICA likely helps separating (extracting) their contribution from the total signal.…”

Section: Introductionmentioning

confidence: 99%

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

et al. 2017

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In recent decades, decomposition techniques have enabled increasingly more applications for dimension reduction, as well as extraction of additional information from geophysical time series. Traditionally, the principal component analysis (PCA)/empirical orthogonal function (EOF) method and more recently the independent component analysis (ICA) have been applied to extract, statistical orthogonal (uncorrelated), and independent modes that represent the maximum variance of time series, respectively. PCA and ICA can be classified as stationary signal decomposition techniques since they are based on decomposing the autocovariance matrix and diagonalizing higher (than two) order statistical tensors from centered time series, respectively. However, the stationarity assumption in these techniques is not justified for many geophysical and climate variables even after removing cyclic components, e.g., the commonly removed dominant seasonal cycles. In this paper, we present a novel decomposition method, the complex independent component analysis (CICA), which can be applied to extract non-stationary (changing in space and time) patterns from geophysical time series. Here, CICA is derived as an extension of realvalued ICA, where (a) we first define a new complex dataset that contains the observed time series in its real part, and their Hilbert transformed series as its imaginary part, (b) an

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Independent Component Analysis of Climate Data: A New Look at EOF Rotation

Cited by 62 publications

References 42 publications

Impacts of large-scale atmospheric circulation changes in winter on black carbon transport and deposition to the Arctic

Impacts of large-scale atmospheric circulation changes in winter on black carbon transport and deposition to the Arctic

Investigation on the Patterns of Global Vegetation Change Using a Satellite-Sensed Vegetation Index

Developing a Complex Independent Component Analysis (CICA) Technique to Extract Non-stationary Patterns from Geophysical Time Series

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