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Award Abstract #1243175

Collaborative Research: EaSM 2: Stochastic Simulation and Decadal Prediction of Large-Scale Climate

Division Of Ocean Sciences
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Initial Amendment Date: February 11, 2013
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Latest Amendment Date: February 11, 2013
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Award Number: 1243175
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Award Instrument: Standard Grant
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Program Manager: Eric C. Itsweire
OCE Division Of Ocean Sciences
GEO Directorate For Geosciences
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Start Date: February 15, 2013
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End Date: January 31, 2017 (Estimated)
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Awarded Amount to Date: $398,570.00
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Investigator(s): Dmitri Kondrashov dkondras@atmos.ucla.edu (Principal Investigator)
Michael Ghil (Co-Principal Investigator)
Mickael Chekroun (Co-Principal Investigator)
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Sponsor: University of California-Los Angeles
11000 Kinross Avenue, Suite 211
LOS ANGELES, CA 90095-2000 (310)794-0102
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NSF Program(s): CR, Earth System Models
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Program Reference Code(s): 8012
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Program Element Code(s): 8012


Under this project, large-scale, low-frequency modes (LFMs) will be studied in observations and simulations of state-of-the-art general circulation models (GCMs) of Earth's climate. This project will build on previous NSF- and DOE-funded work on LFMs arising from the ocean's wind-driven and overturning circulations in the Atlantic and Pacific sectors, as well as from their interactions with the atmosphere and tropical climate variability. This project will focus on: (a) improving current understanding of LFMs and interactions between them; (b) developing, revising and testing statistical methods for probabilistic decadal prediction based on these modes; and (c) using these decadal predictions in conjunction with observations and GCM simulations to gain insight into the dynamical causes of climate change and climate variability at decadal and longer time scales. This project will continue the identification of decadal modes in the Atlantic and Pacific, from both observations and the simulations from the two most recent international climate intercomparison modeling projects, using advanced, data-adaptive spectral methods. These methods will include multi-channel singular spectrum analysis (MSSA), as well as harmonic Koopman analysis (HKA), along with stochastic modeling based on empirical model reduction (EMR). The investigators will further apply novel methods for the study of synchronized chaotic oscillators developed at UCLA and UWM, to gain additional understanding of decadal-scale teleconnections within the Pacific and between the Atlantic and the Pacific, as well as their relationships with the El Nino-Southern Oscillation (ENSO). Using the LFMs studied in the first part of the project, the investigators will examine their decadal predictability and assess the skill of retrospective decadal forecasts made with our new empirical forecast models based on EMR, MSSA and HKA. These empirical forecasts will be compared against the initialized climate predictions being made as part of the IPCC's 5th Assessment Report. This comparison will help quantify potential skill due to intrinsic decadal modes and their interactions with ENSO, as well as with climate change. The combination of these activities will lead to advanced stochastic simulation tools for creating benchmark scenarios of future climate, based on a combination of observed data and the most robust and predictable elements of near-term climate change and climate variability, out to about 2050.

The intellectual merit of this project is in developing versatile statistical tools and methodologies for climate prediction and the validation of dynamical climate models. The project will also advance our understanding of global coupling between prominent large-scale low-frequency modes. An expected outcome of the project is improved estimates of reliability of climate projections, by developing and testing novel metrics - associated with coupling and synchronization between multiple LFMs across the globe - for validating climate model predictions.

The project's broader impacts lie in addressing a problem of utmost societal importance, i.e., understanding climate variability and change. The project's investigators are committed to making their stochastic simulations easily available to the climate community. This work will foster scientific partnerships between UCLA and UWM, and further the development of graduate curricula in climate dynamics at both institutions. The principal investigators will broadly disseminate the results by means of a dedicated web site, refereed publications, seminars and presentations at national and international meetings.


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D. Kondrashov, M. Chekroun, A.W. Robertson, M. Ghil.. "Low-order stochastic model and ?past-noise forecasting? of the Madden-Julian oscillation," Geophsy. Resl. Letters, v.40, 2013. 

D. Kondrashov, M. Chekroun, M. Ghil. "Data-driven non-Markovian closure models,," Physica D., 2014. 

D. Mukhin, D. Kondrashov, E. Loskutov, A. Gavrilov, A. Feigin and M. Ghil. "Predicting critical transitions in ENSO models II: Spatially dependent models," Journal of Climate, 2014. 

A.Bousquet, M.D. Chekroun, Y. Hong, R. Temam, and J. Tribbia. "Mathematics of Climate and Weather Forecasting," Numerical simulations of the humid atmosphere above a mountain, v.1, 2015, p. 96. 

D. Kondrashov and P. Berloff. "Stochastic Modeling of Decadal Variability in Ocean Gyres," Geophysical Research Letters, v.42, 2015, p. 1543?1553. 

Groth, A., and M. Ghil. "Monte Carlo SSA revisited: Detecting oscillator clusters in multivariate data sets," J. of Climate, v.28, 2015, p. 7873?7893. 

Hannart, A., J. Pearl, F. Otto, P. Naveau, and M. Ghil. "Causal counterfactual theory for the attribution of weather and climate-related events," Bull. Amer. Meteor. Soc, 2015. 

M.D. Chekroun, E. Park, and R. Temam. "The Stampacchia Maximum Principle for Stochastic Partial Differential Equations and Applications," Journal of Differential Equations, v.260, 2015. 

Rombouts, J., and M. Ghil. "Oscillations in a simple climate-vegetation model," Nonlin. Processes Geophys., v.22, 2015, p. 275. 

Vannitsem, S., J. Demaeyer, L. D. Cruz, and M. Ghil. "Low-frequency variability and heat transport in a low-order nonlinear coupled ocean-atmosphere model," Physica D, v.309, 2015. 


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