Richard Kleeman - Academia.edu (original) (raw)

Uploads

Papers by Richard Kleeman

Research paper thumbnail of The Relationship between Oscillating Subtropical Wind Stress and Equatorial Temperature*

Journal of Physical Oceanography, May 1, 2002

Research paper thumbnail of A Simple Model of the Atmospheric Response to ENSO Sea Surface Temperature Anomalies

Journal of the Atmospheric Sciences, 1991

Research paper thumbnail of Predictability and Stochastic Modeling of Geophysical Flows

AGU Spring Meeting Abstracts, May 1, 2001

Abstract Modeling the turbulent dynamical systems underlying climate is a significant challenge. ... more Abstract Modeling the turbulent dynamical systems underlying climate is a significant challenge. Recent results from our group in this area include: 1) The development of rigorous methods for reducing turbulent geophysical flows to stochastically forced low order dynamical systems. 2) The application of information theoretical ideas to such systems in order to develop a deeper understanding of the nature of their predictability. 3) The application of statistical mechanical frameworks to these systems in order to clarify their ...

Research paper thumbnail of Information flow in ensemble weather predictions

Bulletin of the American Meteorological Society, Apr 1, 2007

Research paper thumbnail of Limits to statistical atmospheric predictability

AGUFM, Dec 1, 2005

ABSTRACT Recently the author and coworkers have developed a set of tools from information theory ... more ABSTRACT Recently the author and coworkers have developed a set of tools from information theory for rigorously examining ensemble or statistical predictability. These have been applied to the mid-latitude athmospheric system and reveal that there is a finite limit to statistical predictabilty of order one month. Beyond this limit initial conditions have no influence at all on ensemble variables. Here we extend these results to the more general global case. In addition we examine the factors responsible for the variation in predictability with initial conditions. In general certain predictions have more utility than others and this can be due to either the flow instability present in the initial conditions or else the amplitude of deviations from the mean. We determine which is more important for weather predictions.

Research paper thumbnail of Statistical predictability in the atmosphere and other dynamical systems

Physica D: Nonlinear Phenomena, Jun 1, 2007

Research paper thumbnail of A rigorous formalism of information transfer between dynamical system components. II. Continuous flow

Physica D: Nonlinear Phenomena, Mar 1, 2007

Research paper thumbnail of A rigorous formalism of information transfer between dynamical system components. I. Discrete mapping

Physica D: Nonlinear Phenomena, Jul 1, 2007

Research paper thumbnail of Comparison of Information-Based Measures of Forecast Uncertainty in Ensemble ENSO Prediction

Journal of Climate, Jan 15, 2008

Research paper thumbnail of The Differences between the Optimal Perturbations of Coupled Models of ENSO

Research paper thumbnail of Measuring the potential utility of seasonal climate predictions

Geophysical Research Letters, Nov 1, 2004

Research paper thumbnail of Predictability in a Model of Geophysical Turbulence

Journal of the Atmospheric Sciences, Aug 1, 2005

Research paper thumbnail of The dynamics of error growth and predictability in a coupled model of ENSO

Quarterly Journal of the Royal Meteorological Society, Jul 1, 1996

Research paper thumbnail of Reliability of ENSO Dynamical Predictions

Journal of the Atmospheric Sciences, 2005

In this study, ensemble predictions were constructed using two realistic ENSO prediction models a... more In this study, ensemble predictions were constructed using two realistic ENSO prediction models and stochastic optimals. By applying a recently developed theoretical framework, the authors have explored several important issues relating to ENSO predictability including reliability measures of ENSO dynamical predictions and the dominant precursors that control reliability. It was found that prediction utility (R), defined by relative entropy, is a useful measure for the reliability of ENSO dynamical predictions, such that the larger the value of R, the more reliable the prediction. The prediction utility R consists of two components, a dispersion component (DC) associated with the ensemble spread and a signal component (SC) determined by the predictive mean signals. Results show that the prediction utility R is dominated by SC. Using a linear stochastic dynamical system, SC was examined further and found to be intrinsically related to the leading eigenmode amplitude of the initial conditions. This finding was validated by actual model prediction results and is also consistent with other recent work. The relationship between R and SC has particular practical significance for ENSO predictability studies, since it provides an inexpensive and robust method for exploring forecast uncertainties without the need for costly ensemble runs.

Research paper thumbnail of Skill assessment for ENSO using ensemble prediction

Quarterly Journal of the Royal Meteorological Society, 1998

ABSTRACT

Research paper thumbnail of A new intermediate coupled model for El Niño simulation and prediction

Geophysical Research Letters, 2003

Research paper thumbnail of Information Transfer between Dynamical System Components

Physical Review Letters, Dec 8, 2005

Research paper thumbnail of Greenhouse Warming, Decadal Variability, or El Niño? An Attempt to Understand the Anomalous 1990s

Journal of Climate, Sep 1, 1997

Research paper thumbnail of A hybrid coupled tropical atmosphere ocean models: Sensitivies and hindcast skill

Research paper thumbnail of Modulation of ENSO variability on decadel and longer timescales

Research paper thumbnail of The Relationship between Oscillating Subtropical Wind Stress and Equatorial Temperature*

Journal of Physical Oceanography, May 1, 2002

Research paper thumbnail of A Simple Model of the Atmospheric Response to ENSO Sea Surface Temperature Anomalies

Journal of the Atmospheric Sciences, 1991

Research paper thumbnail of Predictability and Stochastic Modeling of Geophysical Flows

AGU Spring Meeting Abstracts, May 1, 2001

Abstract Modeling the turbulent dynamical systems underlying climate is a significant challenge. ... more Abstract Modeling the turbulent dynamical systems underlying climate is a significant challenge. Recent results from our group in this area include: 1) The development of rigorous methods for reducing turbulent geophysical flows to stochastically forced low order dynamical systems. 2) The application of information theoretical ideas to such systems in order to develop a deeper understanding of the nature of their predictability. 3) The application of statistical mechanical frameworks to these systems in order to clarify their ...

Research paper thumbnail of Information flow in ensemble weather predictions

Bulletin of the American Meteorological Society, Apr 1, 2007

Research paper thumbnail of Limits to statistical atmospheric predictability

AGUFM, Dec 1, 2005

ABSTRACT Recently the author and coworkers have developed a set of tools from information theory ... more ABSTRACT Recently the author and coworkers have developed a set of tools from information theory for rigorously examining ensemble or statistical predictability. These have been applied to the mid-latitude athmospheric system and reveal that there is a finite limit to statistical predictabilty of order one month. Beyond this limit initial conditions have no influence at all on ensemble variables. Here we extend these results to the more general global case. In addition we examine the factors responsible for the variation in predictability with initial conditions. In general certain predictions have more utility than others and this can be due to either the flow instability present in the initial conditions or else the amplitude of deviations from the mean. We determine which is more important for weather predictions.

Research paper thumbnail of Statistical predictability in the atmosphere and other dynamical systems

Physica D: Nonlinear Phenomena, Jun 1, 2007

Research paper thumbnail of A rigorous formalism of information transfer between dynamical system components. II. Continuous flow

Physica D: Nonlinear Phenomena, Mar 1, 2007

Research paper thumbnail of A rigorous formalism of information transfer between dynamical system components. I. Discrete mapping

Physica D: Nonlinear Phenomena, Jul 1, 2007

Research paper thumbnail of Comparison of Information-Based Measures of Forecast Uncertainty in Ensemble ENSO Prediction

Journal of Climate, Jan 15, 2008

Research paper thumbnail of The Differences between the Optimal Perturbations of Coupled Models of ENSO

Research paper thumbnail of Measuring the potential utility of seasonal climate predictions

Geophysical Research Letters, Nov 1, 2004

Research paper thumbnail of Predictability in a Model of Geophysical Turbulence

Journal of the Atmospheric Sciences, Aug 1, 2005

Research paper thumbnail of The dynamics of error growth and predictability in a coupled model of ENSO

Quarterly Journal of the Royal Meteorological Society, Jul 1, 1996

Research paper thumbnail of Reliability of ENSO Dynamical Predictions

Journal of the Atmospheric Sciences, 2005

In this study, ensemble predictions were constructed using two realistic ENSO prediction models a... more In this study, ensemble predictions were constructed using two realistic ENSO prediction models and stochastic optimals. By applying a recently developed theoretical framework, the authors have explored several important issues relating to ENSO predictability including reliability measures of ENSO dynamical predictions and the dominant precursors that control reliability. It was found that prediction utility (R), defined by relative entropy, is a useful measure for the reliability of ENSO dynamical predictions, such that the larger the value of R, the more reliable the prediction. The prediction utility R consists of two components, a dispersion component (DC) associated with the ensemble spread and a signal component (SC) determined by the predictive mean signals. Results show that the prediction utility R is dominated by SC. Using a linear stochastic dynamical system, SC was examined further and found to be intrinsically related to the leading eigenmode amplitude of the initial conditions. This finding was validated by actual model prediction results and is also consistent with other recent work. The relationship between R and SC has particular practical significance for ENSO predictability studies, since it provides an inexpensive and robust method for exploring forecast uncertainties without the need for costly ensemble runs.

Research paper thumbnail of Skill assessment for ENSO using ensemble prediction

Quarterly Journal of the Royal Meteorological Society, 1998

ABSTRACT

Research paper thumbnail of A new intermediate coupled model for El Niño simulation and prediction

Geophysical Research Letters, 2003

Research paper thumbnail of Information Transfer between Dynamical System Components

Physical Review Letters, Dec 8, 2005

Research paper thumbnail of Greenhouse Warming, Decadal Variability, or El Niño? An Attempt to Understand the Anomalous 1990s

Journal of Climate, Sep 1, 1997

Research paper thumbnail of A hybrid coupled tropical atmosphere ocean models: Sensitivies and hindcast skill

Research paper thumbnail of Modulation of ENSO variability on decadel and longer timescales