Empirical Orthogonal Functions: The Medium is the Message

Monahan, Adam H.; Fyfe, John C.; Ambaum, Maarten H. P.; Stephenson, David B.; North, Gerald R.

doi:10.1175/2009jcli3062.1

Cited by 254 publications

(173 citation statements)

References 44 publications

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“…EOF analyses ultimately provide a statistical, rather than physical, description of the variability of a system and, as noted by numerous authors (e.g., Dommenget and Latif 2002;Monahan et al 2009), cannot be assumed a priori to represent a physical mode of the system under consideration. The results obtained in the analysis presented here can, however, also be obtained by complementary methods: correlations and wavelet coherence analyses of the SIA of the MIZ support the notion that the individual poles of the quadrupole are, overall, uncorrelated.…”

Section: Discussionmentioning

confidence: 99%

The Arctic Winter Sea Ice Quadrupole Revisited

Houssais

Herbaut

2017

J. Climate

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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.

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Section: Discussionmentioning

confidence: 99%

The Arctic Winter Sea Ice Quadrupole Revisited

Houssais

Herbaut

2017

J. Climate

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“…To calculate the EOFs (see e.g., Bjørnsson and Venegas, 1997;Hannachi et al, 2007;Monahan et al, 2009) the anomalies for each historical ensemble member are detrended by calculating deviations from a 5 yr moving average, and a common seasonal cycle for the simulated 1976-2005 period estimated by subtracting separate averages for each month (3 × 30 values are averaged per month). The EOFs, thus, represent spatial structures of the 500 hPa geopotential height fields associated with non-seasonal variations up to a few years, similar to the analysis of Corti et al (1999) which was further extended by Molteni et al (2006), based on NCEP re-analysis data (Kalnay et al, 1996).…”

Section: Nh Eof-analysismentioning

confidence: 99%

The Norwegian Earth System Model, NorESM1-M – Part 2: Climate response and scenario projections

et al. 2013

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Abstract. NorESM is a generic name of the Norwegian earth system model. The first version is named NorESM1, and has been applied with medium spatial resolution to provide results for CMIP5 (http://cmip-pcmdi.llnl.gov/cmip5/index. html) without (NorESM1-M) and with (NorESM1-ME) interactive carbon-cycling. Together with the accompanying paper by Bentsen et al. (2012), this paper documents that the core version NorESM1-M is a valuable global climate model for research and for providing complementary results to the evaluation of possible anthropogenic climate change. NorESM1-M is based on the model CCSM4 operated at NCAR, but the ocean model is replaced by a modified version of MICOM and the atmospheric model is extended with online calculations of aerosols, their direct effect and their indirect effect on warm clouds. Model validation is presented in the companion paper (Bentsen et al., 2012). NorESM1-M is estimated to have equilibrium climate sensitivity of ca. 2.9 K and a transient climate response of ca. 1.4 K. This sensitivity is in the lower range amongst the models contributing to CMIP5. Cloud feedbacks dampen the response, and a strong AMOC reduces the heat fraction available for increasing near-surface temperatures, for evaporation and for melting ice. The future projections based on RCP scenarios yield a global surface air temperature increase of almost one standard deviation lower than a 15-model average. Summer sea-ice is projected to decrease considerably by 2100 and disappear completely for RCP8.5. The AMOC is projected to decrease by 12 %, 15-17 %, and 32 % for the RCP2.6, 4.5, 6.0, and 8.5, respectively. Precipitation is projected to increase in the tropics, decrease in the subtropics and in southern parts of the northern extra-tropics during summer, and otherwise increase in most of the extra-tropics. Changes in the atmospheric water cycle indicate that precipitation events over continents will become more intense and dry spells more frequent. Extra-tropical storminess in the Northern Hemisphere is projected to shift northwards. There are indications of more frequent occurrence of spring and summer blocking in the Euro-Atlantic sector, while the amplitude of ENSO events weakens although they tend to appear more frequently. These indications are uncertain because of biases in the model's representation of present-day conditions. Positive phase PNA and negative phase NAO both appear less frequently under the RCP8.5 scenario, but also this result is considered uncertain. Single-forcing experiments indicate that aerosols and greenhouse gases produce similar geographical patterns of response for near-surface temperature and precipitation. These patterns tend to have opposite signs, although with important exceptions for precipitation at low latitudes. The asymmetric aerosol effects between the two hemispheres lead to a southward displacement of ITCZ. Both forcing agents, thus, tend to reduce Northern Hemispheric subtropical precipitation.

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“…One of the methods most used in weather and climate is empirical orthogonal function (EOF) analysis (Obukov 1947;Lorenz 1956), also known as principal component (PC) analysis. The EOF method seeks to decompose a spacetime dataset into orthogonal EOF patterns and associated uncorrelated time series or PCs by maximizing the explained variance (Jolliffe 2002;Hannachi et al 2007;Monahan et al 2009). Other closely related methods have also been used in atmospheric science (see, e.g., Jolliffe 2002;Hannachi et al 2007;Wilks 2006).…”

Section: Introductionmentioning

confidence: 99%

Archetypal Analysis: Mining Weather and Climate Extremes

Hannachi

Trendafilov

2017

J. Climate

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Conventional analysis methods in weather and climate science (e.g., EOF analysis) exhibit a number of drawbacks including scaling and mixing. These methods focus mostly on the bulk of the probability distribution of the system in state space and overlook its tail. This paper explores a different method, the archetypal analysis (AA), which focuses precisely on the extremes. AA seeks to approximate the convex hull of the data in state space by finding ''corners'' that represent ''pure'' types or archetypes through computing mixture weight matrices. The method is quite new in climate science, although it has been around for about two decades in pattern recognition. It encompasses, in particular, the virtues of EOFs and clustering. The method is presented along with a new manifold-based optimization algorithm that optimizes for the weights simultaneously, unlike the conventional multistep algorithm based on the alternating constrained least squares. The paper discusses the numerical solution and then applies it to the monthly sea surface temperature (SST) from HadISST and to the Asian summer monsoon (ASM) using sea level pressure (SLP) from ERA-40 over the Asian monsoon region. The application to SST reveals, in particular, three archetypes, namely, El Niño, La Niña, and a third pattern representing the western boundary currents. The latter archetype shows a particular trend in the last few decades. The application to the ASM SLP anomalies yields archetypes that are consistent with the ASM regimes found in the literature. Merits and weaknesses of the method along with possible future development are also discussed.

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Empirical Orthogonal Functions: The Medium is the Message

Cited by 254 publications

References 44 publications

The Arctic Winter Sea Ice Quadrupole Revisited

The Arctic Winter Sea Ice Quadrupole Revisited

The Norwegian Earth System Model, NorESM1-M – Part 2: Climate response and scenario projections

Archetypal Analysis: Mining Weather and Climate Extremes

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