Lagged teleconnections of climate variables identified via complex rotated Maximum Covariance Analysis

AbstractA proper description of ocean-atmosphere interactions is key for a correct understanding of climate evolution. The interplay among the different variables acting over the climate is complex, often leading to correlations across long spatial distances (teleconnections). In some occasions, those teleconnections occur with quite significant temporal shifts that are fundamental for the understanding of the underlying phenomena but which are poorly captured by standard methods. Applying orthogonal decomposition such as Maximum Covariance Analysis (MCA) to geophysical data sets allows to extract common dominant patterns between two different variables, but generally suffers from (i) the non-physical orthogonal constraint as well as (ii) the consideration of simple correlations, whereby temporally offset signals are not detected. Here we propose an extension, complex rotated MCA, to address both limitations. We transform our signals using the Hilbert transform and perform the orthogonal decomposition in complex space, allowing us to correctly correlate out-of-phase signals. Subsequent Varimax rotation removes the orthogonal constraints, leading to more physically meaningful modes of geophysical variability. As an example of application, we have employed this method on sea surface temperature and continental precipitation; our method successfully captures the temporal and spatial interactions between these two variables, namely for (i) the seasonal cycle, (ii) canonical ENSO, (iii) the global warming trend, (iv) the Pacific Decadal Oscillation, (v) ENSO Modoki and finally (vi) the Atlantic Meridional Mode. The complex rotated modes of MCA provide information on the regional amplitude, and under certain conditions, the regional time lag between changes on ocean temperature and land precipitation.

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Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis

Atmospheric Measurement Techniques ◽

10.5194/amt-7-2531-2014 ◽

2014 ◽

Vol 7 (8) ◽

pp. 2531-2549 ◽

Cited By ~ 12

Author(s):

J. Li ◽

B. E. Carlson ◽

A. A. Lacis

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Satellite Data ◽

Covariance Analysis ◽

Data Sets ◽

Data Set ◽

Maximum Covariance Analysis ◽

Source Regions ◽

Cross Covariance ◽

Aerosol Source

Abstract. In this paper, we introduce the usage of a newly developed spectral decomposition technique – combined maximum covariance analysis (CMCA) – in the spatiotemporal comparison of four satellite data sets and ground-based observations of aerosol optical depth (AOD). This technique is based on commonly used principal component analysis (PCA) and maximum covariance analysis (MCA). By decomposing the cross-covariance matrix between the joint satellite data field and Aerosol Robotic Network (AERONET) station data, both parallel comparison across different satellite data sets and the evaluation of the satellite data against the AERONET measurements are simultaneously realized. We show that this new method not only confirms the seasonal and interannual variability of aerosol optical depth, aerosol-source regions and events represented by different satellite data sets, but also identifies the strengths and weaknesses of each data set in capturing the variability associated with sources, events or aerosol types. Furthermore, by examining the spread of the spatial modes of different satellite fields, regions with the largest uncertainties in aerosol observation are identified. We also present two regional case studies that respectively demonstrate the capability of the CMCA technique in assessing the representation of an extreme event in different data sets, and in evaluating the performance of different data sets on seasonal and interannual timescales. Global results indicate that different data sets agree qualitatively for major aerosol-source regions. Discrepancies are mostly found over the Sahel, India, eastern and southeastern Asia. Results for eastern Europe suggest that the intense wildfire event in Russia during summer 2010 was less well-represented by SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and OMI (Ozone Monitoring Instrument), which might be due to misclassification of smoke plumes as clouds. Analysis for the Indian subcontinent shows that here SeaWiFS agrees best with AERONET in terms of seasonality for both the Gangetic Basin and southern India, while on interannual timescales it has the poorest agreement.

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Estimating the Observed Atmospheric Response to SST Anomalies: Maximum Covariance Analysis, Generalized Equilibrium Feedback Assessment, and Maximum Response Estimation

Journal of Climate ◽

10.1175/2010jcli3696.1 ◽

2011 ◽

Vol 24 (10) ◽

pp. 2523-2539 ◽

Cited By ~ 19

Author(s):

Claude Frankignoul ◽

Nadine Chouaib ◽

Zhengyu Liu

Keyword(s):

North Atlantic ◽

Time Lag ◽

Synthetic Data ◽

Covariance Analysis ◽

Maximum Response ◽

Atmospheric Response ◽

Multivariate Statistical ◽

Maximum Covariance Analysis ◽

Generalized Equilibrium ◽

Sst Anomalies

Abstract Three multivariate statistical methods to estimate the influence of SST or boundary forcing on the atmosphere are discussed. Lagged maximum covariance analysis (MCA) maximizes the covariance between the atmosphere and prior SST, thus favoring large responses and dominant SST patterns. However, it does not take into account the possible SST evolution during the time lag. To correctly represent the relation between forcing and response, a new SST correction is introduced. The singular value decomposition (SVD) of generalized equilibrium feedback assessment (GEFA–SVD) identifies in a truncated SST space the optimal SST patterns for forcing the atmosphere, independently of the SST amplitude; hence it may not detect a large response. A new method based on GEFA, named maximum response estimation (MRE), is devised to estimate the largest boundary-forced atmospheric signal. The methods are compared using synthetic data with known properties and observed North Atlantic monthly anomaly data. The synthetic data shows that the MCA is generally robust and essentially unbiased. GEFA–SVD is less robust and sensitive to the truncation. MRE is less sensitive to truncation and nearly as robust as MCA, providing the closest approximation to the largest true response to the sample SST. To analyze the observations, a 2-month delay in the atmospheric response is assumed based on recent studies. The delay strongly affects GEFA–SVD and MRE, and it is key to obtaining consistent results between MCA and MRE. The MCA and MRE confirm that the dominant atmospheric signal is the NAO-like response to North Atlantic horseshoe SST anomalies. When the atmosphere is considered in early winter, the response is strongest and MCA most powerful. With all months of the year, MRE provides the most significant results. GEFA–SVD yields SST patterns and NAO-like atmospheric responses that depend on lag and truncation, thus lacking robustness. When SST leads by 1 month, a significant mode is found by the three methods, but it primarily reflects, or is strongly affected by, atmosphere persistence.

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Forcing of Tropical SST Anomalies by Wintertime AO-like Variability

Journal of Climate ◽

10.1175/2009jcli2749.1 ◽

2010 ◽

Vol 23 (10) ◽

pp. 2465-2472 ◽

Cited By ~ 6

Author(s):

Qigang Wu

Keyword(s):

Large Scale ◽

Southern Oscillation ◽

Time Lag ◽

Strong Impact ◽

Tropical Ocean ◽

Trade Winds ◽

Maximum Covariance Analysis ◽

Enso Modoki ◽

Tropical Sst Anomalies ◽

Sst Anomalies

Abstract A lagged maximum covariance analysis (MCA) is utilized to investigate large-scale patterns of covariability between sea surface temperature (SST) in the global tropics and 500-mb geopotential height (Z500) in the extratropics at monthly to interannual time scales distinct from the conventional El Niño–Southern Oscillation (ENSO) signal during the Northern Hemisphere (NH) winter. The first MCA mode indicates a strong impact of tropical SST anomalies associated with ENSO on the extratropical atmosphere. The second MCA mode corresponds with coupling between Arctic Oscillation (AO)-like atmospheric variations and tropical SST anomalies. An AO-like MCA mode appears to depict an atmosphere-to-ocean forcing, in which the tropical ocean responds to the higher extratropical AO-like atmospheric anomalies with an intraseasonal time lag. In winter, AO-like atmospheric variability is associated with the northern tropical Atlantic mode and the tropical Pacific ENSO Modoki mode through enhanced or weakened trade winds. The above forced SST anomalies by the AO-like variability may play a role in the subsequent evolution of the conventional ENSO phenomena.

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A 2011-2018 Fukushima Perspective on North Pacific Mode Water Pathways

10.5194/egusphere-egu21-10397 ◽

2021 ◽

Author(s):

Alison Macdonald ◽

Sachiko Yoshida ◽

Irina Rypina

Keyword(s):

North Pacific ◽

Half Life ◽

Nuclear Power ◽

North Pacific Ocean ◽

Data Sets ◽

Mode Water ◽

Eastern Basin ◽

Mode Waters ◽

The North ◽

The Pacific

This investigation uses the tracer information provided by the 2011 direct ocean release of radio-isotopes, (137Cs, ~30-year half-life and 134Cs, ~2-year half-life) from the Fukushima Dai-ichi nuclear power plant (FDNPP) together with hydrographic profiles to better understand the origins and pathways of mode waters in the North Pacific Ocean. While using information provided by radionuclide observations taken from across the basin, the main focus is on the eastern basin and results from analyses of two data sets 2015 (GO-SHIP) and 2018 (GEOTRACES) along the 152&#176;W meridian. The study looks at how mode waters formed in the spring of 2011 have spread and mixed, and how they have not. Our radiocesium isotope samples tell a story of a surprisingly confined pathway for these waters and suggest that circulation to the north into the subpolar gyre occurs more quickly than circulation to the south into the subtropical gyre. They indicate that in spite of crossing 6000 km in their journey across the Pacific, the densest 2011 mode waters stayed together spreading by only a few hundred kilometers in the north/south direction, remained subsurface (below ~200 m) for most of the trip, and only saw the atmosphere again as they followed shoaling density surfaces into the boundary of the Alaska Gyre. The more recent data are sparse and do not allow direct measurement of the FDNPP specific 134Cs, however they do provide some information on mode water evolution in the eastern North Pacific seven years after the accident.&#160;

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Regularized spatial maximum covariance analysis

Environmetrics ◽

10.1002/env.2481 ◽

2017 ◽

Vol 29 (2) ◽

pp. e2481 ◽

Cited By ~ 2

Author(s):

Wen-Ting Wang ◽

Hsin-Cheng Huang

Keyword(s):

Covariance Analysis ◽

Maximum Covariance Analysis

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Soil Moisture Variability in India: Relationship of Land Surface–Atmosphere Fields Using Maximum Covariance Analysis

Remote Sensing ◽

10.3390/rs11030335 ◽

2019 ◽

Vol 11 (3) ◽

pp. 335 ◽

Cited By ~ 9

Author(s):

Kishore Pangaluru ◽

Isabella Velicogna ◽

Geruo A ◽

Yara Mohajerani ◽

Enrico Ciracì ◽

...

Keyword(s):

Soil Moisture ◽

Air Temperature ◽

Land Surface ◽

Water Storage ◽

Surface Air Temperature ◽

Covariance Analysis ◽

Positive Trend ◽

Total Water ◽

Maximum Covariance Analysis ◽

Surface Atmosphere

This study investigates the spatial and temporal variability of the soil moisture in India using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) gridded datasets from June 2002 to April 2017. Significant relationships between soil moisture and different land surface–atmosphere fields (Precipitation, surface air temperature, total cloud cover, and total water storage) were studied, using maximum covariance analysis (MCA) to extract dominant interactions that maximize the covariance between two fields. The first leading mode of MCA explained 56%, 87%, 81%, and 79% of the squared covariance function (SCF) between soil moisture with precipitation (PR), surface air temperature (TEM), total cloud count (TCC), and total water storage (TWS), respectively, with correlation coefficients of 0.65, −0.72, 0.71, and 0.62. Furthermore, the covariance analysis of total water storage showed contrasting patterns with soil moisture, especially over northwest, northeast, and west coast regions. In addition, the spatial distribution of seasonal and annual trends of soil moisture in India was estimated using a robust regression technique for the very first time. For most regions in India, significant positive trends were noticed in all seasons. Meanwhile, a small negative trend was observed over southern India. The monthly mean value of AMSR soil moisture trend revealed a significant positive trend, at about 0.0158 cm3/cm3 per decade during the period ranging from 2002 to 2017.

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Observed Atlantic SST Anomaly Impact on the NAO: An Update

Journal of Climate ◽

10.1175/jcli3523.1 ◽

2005 ◽

Vol 18 (19) ◽

pp. 4089-4094 ◽

Cited By ~ 34

Author(s):

Claude Frankignoul ◽

Elodie Kestenare

Keyword(s):

North Atlantic ◽

Covariance Analysis ◽

Equatorial Atlantic ◽

Maximum Covariance Analysis ◽

The North ◽

Enso Teleconnections ◽

Orthogonality Constraint ◽

Sst Anomaly ◽

The North Atlantic ◽

The North Atlantic Oscillation

Abstract The Pan-Atlantic sea surface temperature (SST) anomaly pattern that was found in a previous study to have a significant impact on the North Atlantic Oscillation (NAO) in early winter seemed to reflect the nearly uncorrelated influence of a horseshoe SST anomaly in the North Atlantic and an SST anomaly in the eastern equatorial Atlantic. A lagged rotated maximum covariance analysis of a slightly longer dataset shows that the horseshoe SST anomaly influence is robust, but it deemphasizes the center of action southeast of Newfoundland, Canada. On the other hand, it suggests that the link between equatorial SST and the NAO was artificial and due both to ENSO teleconnections and the orthogonality constraint in the maximum covariance analysis.

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Robust estimation of instantaneous phase using a time-frequency adaptive filter

Geophysics ◽

10.1190/geo2011-0435.1 ◽

2013 ◽

Vol 78 (1) ◽

pp. O1-O7 ◽

Cited By ~ 5

Author(s):

Wen-kai Lu ◽

Chang-Kai Zhang

Keyword(s):

Adaptive Filter ◽

Signal To Noise Ratio ◽

Amplitude Spectrum ◽

Estimation Method ◽

Frequency Component ◽

Data Sets ◽

Time Frequency ◽

Instantaneous Phase ◽

The Hilbert Transform ◽

Short Time

The instantaneous phase estimated by the Hilbert transform (HT) is susceptible to noise; we propose a robust approach for the estimation of instantaneous phase in noisy situations. The main procedure of the proposed method is applying an adaptive filter in time-frequency domain and calculating the analytic signal. By supposing that one frequency component with higher amplitude has higher signal-to-noise ratio, a zero-phase adaptive filter, which is constructed by using the time-frequency amplitude spectrum, enhances the frequency components with higher amplitudes and suppresses those with lower amplitudes. The estimation of instantaneous frequency, which is defined as the derivative of instantaneous phase, is also improved by the proposed robust instantaneous phase estimation method. Synthetic and field data sets are used to demonstrate the performance of the proposed method for the estimation of instantaneous phase and frequency, compared by the HT and short-time-Fourier-transform methods.

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Maximum Covariance Analysis of Typhoon Surface Wind and Rainfall Relationships in Taiwan

Journal of Applied Meteorology and Climatology ◽

10.1175/2008jamc1963.1 ◽

2009 ◽

Vol 48 (5) ◽

pp. 997-1016 ◽

Cited By ~ 7

Author(s):

Hsiao-Chung Tsai ◽

Tim Hau Lee

Keyword(s):

Surface Wind ◽

Correlation Coefficients ◽

Covariance Analysis ◽

Mountain Range ◽

Mountain Ranges ◽

Maximum Covariance Analysis ◽

Air Temperatures ◽

Multivariate Relationships ◽

River Valleys ◽

Surface Observations

Abstract The multivariate relationships between hourly surface wind and rainfall observations during typhoons affecting Taiwan have been investigated with maximum covariance analysis (MCA). Historical surface observations from 1987 to 2004 are used when typhoon centers were located inside the domain of 19°–28°N, 117°–127°E. The three leading MCA modes explain 70%, 20.6%, and 7.6% of the squared covariance fraction, and the correlation coefficients are 0.59, 0.48, and 0.49, respectively. The wind directions of the three leading positive modes are 1) northwesterly flow perpendicular to the Snow Mountain Range (SMR), 2) southwesterly flow toward the river valleys of the southwestern Central Mountain Range (CMR) and the southern SMR, and 3) easterly flow toward the northeastern SMR and the northern CMR. The rainfall patterns of the three principal modes reveal the contrast between the windward and the leeward sides of the mountain ranges. Based on the MCA singular vectors, historical typhoon surface wind patterns are categorized into major types. The results show that the three major wind types consist of 53% of the data, with 25%, 9%, and 19%, respectively, for these wind types. Furthermore, the analyses of the corresponding surface air temperatures, relative humidities, and air pressures also reveal contrasting patterns between the windward and leeward sides.

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Synoptic-Eddy Feedbacks and Circulation Regime Analysis

Monthly Weather Review ◽

10.1175/2010mwr3333.1 ◽

2010 ◽

Vol 138 (11) ◽

pp. 4026-4034 ◽

Cited By ~ 16

Author(s):

David M. Straus

Keyword(s):

Cluster Analysis ◽

High Frequency ◽

Low Frequency ◽

Boreal Winter ◽

Maximum Covariance Analysis ◽

The Pacific ◽

Synoptic Eddies ◽

Synthetic Datasets ◽

Definition Of ◽

Environmental Prediction

Abstract A method to incorporate synoptic eddies into the diagnosis of circulation regimes using cluster analysis is illustrated using boreal winter reanalyses of the National Centers of Environmental Prediction (hereafter observations) over the Pacific–North American region. The motivation is to include the configuration of the high-frequency (periods less than 10 days) transients as well as the low-frequency (periods greater than 10 days) flow explicitly into the definition of the regimes. Principle component analysis is applied to the low-frequency 200-hPa height field, and also to the low-frequency “envelope” modulations of the rms of high-frequency meridional velocity at 200 hPa. A maximum covariance analysis of the height and envelope fields, carried out using the appropriate principal components, defines three modes as explaining most of the covariance. This defines the minimum dimensionality of the space in which to apply k-means cluster analysis to the covariance coefficients. Clusters found using this method agree with results of the previous work. Significance is assessed by comparing cluster analyses with results from synthetic datasets that have the same spectral amplitudes (but random phases) of seasonal means and, separately, intraseasonal fluctuations as do the original observed time series. This procedure ensures that the synthetic series have similar autocovariance structures to the observations. Building on earlier work, the clusters obtained are newly tested to be highly significant without the need for quasi-stationary prefiltering.

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