Publications by Type: Presentation

2017
Kondrashov, Dmitri. 2017. “Data-adaptive Harmonic Decomposition and Real-time Prediction of 2016 September Arctic Sea Ice Extent.” 4th Polar Prediction Workshop, 27-30 March 2017, Bremerhaven, Germany. Workshop Website Abstract

Decline in the Arctic sea ice extent (SIE) has profound socio-economic implications and is a focus of active scientific research. Of particular interest is prediction of SIE on subseasonal time scales, i.e.~from early summer into fall, when sea ice coverage in Arctic reaches its minimum. However, subseasonal forecasting of SIE is very challenging due to the high variability of ocean and atmosphere over Arctic in summer, as well as shortness of observational data and inadequacies of the physics-based models to simulate sea-ice dynamics. The Sea Ice Outlook (SIO) by Sea Ice Prediction Network (SIPN, http://www.arcus.org/sipn) is a collaborative effort to facilitate and improve subseasonal prediction of September SIE by physics-based and data-driven statistical models.

Data-adaptive Harmonic Decomposition (DAH) and Multilayer Stuart-Landau Models (MSLM) techniques [Chekroun and Kondrashov, 2017], have been successfully applied to the nonlinear stochastic modeling, as well as retrospective and real-time forecasting of Multisensor Analyzed Sea Ice Extent (MASIE) dataset in key four Arctic regions. In particular, the real-time DAH-MSLM predictions outperformed most statistical models and physics-based models in 2016 SIO submissions. The key success factors are associated with DAH ability to disentangle complex regional dynamics of MASIE by data-adaptive harmonic spatio-temporal patterns that reduce the data-driven modeling effort to elemental MSLMs stacked per frequency with fixed and small number of model coefficients to estimate.

This is a joint work with Mickael Chekroun (UCLA) and Michael Ghil (UCLA,ENS). 

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Ghil, Michael. 2017. “The Mathematics of Climate Change and of its Impacts.” Workshop on "Mathematical Approaches to Climate Change Impacts - MAC2I" at the Istituto Nazionale di Alta Matematica "Francesco Severi" (INdAM), Italy. Workshop website Abstract

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Ghil, Michael. 2017. “Circulation Regimes for the Hitchhiker Through the Galaxy.” Physics school on Diversity of Planetary Circulation Regimes, in our Solar System and beyond, Les Houches, France, March 2017. Conference website Abstract

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Ghil, Michael. 2017. “The atmosphere and oceans as unsteady flows: Intrinsic variability and time-dependent forcing.” BIRS Workshop 17w5048 - Transport in Unsteady Flows: from Deterministic Structures to Stochastic Models and Back Again. Workshop website Abstract
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2016

Spectral analyses of the North Atlantic temperature field in the Simple Ocean Data Analysis (SODA) reanalysis identify prominent and statistically significant interannual oscillations along the Gulf Stream front and in large regions of the North Atlantic. A 7--8-yr oscillatory mode is characterized by a basin-wide southwest-to-northeast--oriented propagation pattern in the sea-surface temperature (SST) field. This pattern is found to be linked to a seesaw in the meridional-dipole structure of the zonal wind-stress forcing (TAUX). In the subpolar gyre, the SST and TAUX fields of this mode are shown to be in phase opposition, which suggests a cooling effect of the wind stress on the upper ocean layer. Over all, this mode's temperature field is characterized by a strong equivalent-barotropic component, as shown by covariations in SST and sea-surface height (SSH), and by phase-coherent behavior of temperature layers at depth with the SST field. On the other hand, this mode shares many features of the gyre mode and raises the possibilty for the existence of an intrinsic oceanic mode of similar 7--8-yr period in the Gulf Stream region.

PDF North Atlantic SST 7.7-yr mode
Ghil, Michael. 2016. “A Mathematical Theory of Climate Sensitivity: A Tale of Deterministic & Stochastic Dynamical Systems.” 11th AIMS Conf. on Dynamical Systems, Differential Equations & Applications, Honoring Peter Lax’s 90th Birthday, Orlando, FL, July 2016. Abstract

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2015
Groth, Andreas. 2015. “Business cycle analysis and forecasting using advanced spectral methods and data-based low-order models.” 35th International Symposium on Forecasting Riverside, California, June 2015. Abstract

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2014
Groth, Andreas. 2014. “Interannual variability in the North Atlantic SST and wind forcing.” Seminar at International Research Institute for Climate and Society, Columbia. Abstract

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Groth, Andreas. 2014. “Oscillatory behavior and oscillatory modes.” SSA workshop Bournemouth, September 2014. Abstract

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2013
Ghil, Michael. 2013. “Lecture 1: Data Assimilation: How We Got Here and Where To Next?” Workshop on Mathematics of Climate Change, Related Hazards and Risks, CIMAT, Guanajuato, Mexico. Abstract

Lecture 1: Data Assimilation: How We Got Here and Where To Next?
Ghil, Michael. 2013. “Lecture 2: Toward a Mathematical Theory of Climate Sensitivity.” Workshop on Mathematics of Climate Change, Related Hazards and Risks, CIMAT, Guanajuato, Mexico. Abstract

Lecture 2: Toward a Mathematical Theory of Climate Sensitivity
Ghil, Michael. 2013. “Lecture 3 : The Coupled Dynamics of Climate and Economics.” Workshop on Mathematics of Climate Change, Related Hazards and Risks, CIMAT, Guanajuato, Mexico. Abstract

Lecture 3 : The Coupled Dynamics of Climate and Economics
2012

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Ghil, Michael. 2012. “The Complex Physics of Climate Change: Nonlinearity and Stochasticity.” Workshop on Critical Transitions in Complex Systems, Imperial College London, United Kingdom. Conference website Abstract

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Ghil, Michael. 2012. “What is a Tipping Point and Why Do We Care?” EGU 2012. Abstract

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2011
Ghil, Michael. 2011. “Toward a Mathematical Theory of Climate Sensitivity.” International Congress on Industrial and Applied Mathematics (ICIAM), Vancouver. Abstract

Presentation
2007
Ghil, Michael. 2007. “Data Assimilation for the Atmosphere, Ocean, Climate and Space Plasmas: Some Recent Results.” Dept. of Meteorology, University of Reading and the NERC Data Assimilation Research Centre (DARC). Abstract

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