Search Results

You are looking at 51 - 60 of 61 items for

  • Author or Editor: Michael Ghil x
  • Refine by Access: All Content x
Clear All Modify Search
Chaojiao Sun, Zheng Hao, Michael Ghil, and J. David Neelin

Abstract

The assimilation problem for the coupled ocean–atmosphere system in the tropical Pacific is investigated using an advanced sequential estimator, the extended Kalman filter (EKF). The intermediate coupled model used in this study consists of an upper-ocean model and a steady-state atmospheric response to it. Model errors arise from the uncertainty in atmospheric wind stress. Data assimilation is applied in this idealized context to produce a time-continuous, dynamically consistent description of the model's El Niño–Southern Oscillation, based on incomplete and inaccurate observations. This study has two parts: Part I (the present paper) deals with state estimation for the coupled system, assuming that model parameters are correct, while Part II will deal with simultaneous state and parameter estimation.

The dynamical structure of forecast errors is estimated sequentially using a linearized Kalman filter and compared with that of an uncoupled ocean model. The coupling produces large changes in the structure of the error-correlation field. For example, error correlations with opposite signs in the western and eastern part of the model basin are caused by wind stress feedbacks.

The full EKF method is used to assimilate various model-generated synthetic oceanic datasets into the coupled model in an identical-twin framework. The assimilated datasets include the sea surface temperature and a combination of wave velocities and thermocline depth anomaly. With the EKF, the model's forecast-assimilation cycle is able to estimate correctly the phase and amplitude of the basic ENSO oscillation while using very few observations. This includes a set of observations that only cover a single meridional section of the ocean, preferably in the eastern basin.

Full access
Brian H. Kahn, Annmarie Eldering, Michael Ghil, Simona Bordoni, and Shepard A. Clough

Abstract

A set of simulated high-resolution infrared (IR) emission spectra of synthetic cirrus clouds is used to perform a sensitivity analysis of top-of-atmosphere (TOA) radiance to cloud parameters. Principal component analysis (PCA) is applied to assess the variability of radiance across the spectrum with respect to microphysical and bulk cloud quantities. These quantities include particle shape, effective radius (r eff), ice water path (IWP), cloud height Z cld and thickness ΔZ cld, and vertical profiles of temperature T(z) and water vapor mixing ratio w(z). It is shown that IWP variations in simulated cloud cover dominate TOA radiance variability. Cloud height and thickness, as well as T(z) variations, also contribute to considerable TOA radiance variability.

The empirical orthogonal functions (EOFs) of radiance variability show both similarities and differences in spectral shape and magnitude of variability when one physical quantity or another is being modified. In certain cases, it is possible to identify the EOF that represents variability with respect to one or more physical quantities. In other instances, similar EOFs result from different sets of physical quantities, emphasizing the need for multiple, independent data sources to retrieve cloud parameters. When analyzing a set of simulated spectra that include joint variations of IWP, r eff, and w(z) across a realistic range of values, the first two EOFs capture approximately 92%–97% and 2%–6% of the total variance, respectively; they reflect the combined effect of IWP and r eff. The third EOF accounts for only 1%–2% of the variance and resembles the EOF from analysis of spectra where only w(z) changes. Sensitivity with respect to particle size increases significantly for r eff several tens of microns or less. For small-particle r eff, the sensitivity with respect to the joint variation of IWP, r eff, and w(z) is well approximated by the sum of the sensitivities with respect to variations in each of three quantities separately.

Full access
Kyung-Il Chang, Michael Ghil, Kayo Ide, and Chung-Chieng Aaron Lai

Abstract

Multiple equilibria as well as periodic and aperiodic solution regimes are obtained in a barotropic model of the midlatitude ocean’s double-gyre circulation. The model circulation is driven by a steady zonal wind profile that is symmetric with respect to the square basin’s zonal axis of north–south symmetry, and dissipated by lateral friction.

As the intensity of the wind forcing increases, an antisymmetric double-gyre flow evolves through a pitchfork bifurcation into a pair of steady mirror-symmetric solutions in which either the subtropical or the subpolar gyre dominates. In either one of the two asymmetric solutions, a pair of intense recirculation vortices forms close to and on either side of the point where the two western boundary currents merge to form the eastward jet. To the east of this dipole, a spatially damped stationary wave arises, and an increase in the steady forcing amplifies the meander immediately to the east of the recirculating vortices. During this process, the transport of the weaker gyre remains nearly constant while the transport of the stronger gyre increases.

For even stronger forcing, the two steady solution branches undergo Hopf bifurcation, and each asymmetric solution gives rise to an oscillatory mode, whose subannual period is of 3.5–6 months. These two modes are also mirror-symmetric in space. The time-average difference in transport between the stronger and the weaker gyre is reduced as the forcing increases further, while the weaker gyre tends to oscillate with larger amplitude than the stronger gyre. Once the average strength of the weaker gyre on each branch equals the stronger gyre’s, the solution becomes aperiodic. The transition of aperiodic flow occurs through a global bifurcation that involves a homoclinic orbit. The subannual oscillations persist and stay fairly regular in the aperiodic solution regime, but they alternate now with a new and highly energetic, interannual oscillation. The physical causes of these two oscillations—as well as of a third, 19-day oscillation—are discussed. During episodes of the high-amplitude, interannual oscillation, the solution exhibits phases of either the subtropical or subpolar gyre being dominant. Even lower-frequency, interdecadal variability arises due to an irregular alternation between subannual and interannual modes of oscillation.

Full access
John D. Farrara, Michael Ghil, Carlos R. Mechoso, and Kingtse C. Mo

Abstract

We present a new empirical orthogonal function (EOF) analysis of winter 500 mb geopotential height anomalies in the Southern Hemisphere. An earlier EOF analysis by two of the present authors prefiltered the anomalies to exclude wavenumbers 5 and higher; we do not. The different preprocessing of data affects the results. All three distinct planetary flow regimes identified in the winter circulation of the Southern Hemisphere by a pattern correlation method are captured by the new set of EOFs; only two of those regimes were captured by the earlier set. The new results, therefore, lend further support to the idea that EOFs point to distinct planetary

Full access
Dmitry Mukhin, Dmitri Kondrashov, Evgeny Loskutov, Andrey Gavrilov, Alexander Feigin, and Michael Ghil

Abstract

The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin–Neelin–Ghil (JNG) hybrid seasonally forced coupled ocean–atmosphere model of El Niño–Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction methodology is applied. Critical transitions in the hybrid periodically forced coupled model are successfully predicted on time scales that are substantially longer than the duration of the learning sample.

Full access
Suzana J. Camargo, Andrew W. Robertson, Scott J. Gaffney, Padhraic Smyth, and Michael Ghil

Abstract

A new probabilistic clustering method, based on a regression mixture model, is used to describe tropical cyclone (TC) propagation in the western North Pacific (WNP). Seven clusters were obtained and described in Part I of this two-part study. In Part II, the present paper, the large-scale patterns of atmospheric circulation and sea surface temperature associated with each of the clusters are investigated, as well as associations with the phase of the El Niño–Southern Oscillation (ENSO). Composite wind field maps over the WNP provide a physically consistent picture of each TC type, and of its seasonality. Anomalous vorticity and outgoing longwave radiation indicate changes in the monsoon trough associated with different types of TC genesis and trajectory. The steering winds at 500 hPa are more zonal in the straight-moving clusters, with larger meridional components in the recurving ones. Higher values of vertical wind shear in the midlatitudes also accompany the straight-moving tracks, compared to the recurving ones.

The influence of ENSO on TC activity over the WNP is clearly discerned in specific clusters. Two of the seven clusters are typical of El Niño events; their genesis locations are shifted southeastward and they are more intense. The largest cluster is recurving, located northwestward, and occurs more often during La Niña events. Two types of recurving and one of straight-moving tracks occur preferentially when the Madden–Julian oscillation is active over the WNP region.

Full access
Suzana J. Camargo, Andrew W. Robertson, Scott J. Gaffney, Padhraic Smyth, and Michael Ghil

Abstract

A new probabilistic clustering technique, based on a regression mixture model, is used to describe tropical cyclone trajectories in the western North Pacific. Each component of the mixture model consists of a quadratic regression curve of cyclone position against time. The best-track 1950–2002 dataset is described by seven distinct clusters. These clusters are then analyzed in terms of genesis location, trajectory, landfall, intensity, and seasonality.

Both genesis location and trajectory play important roles in defining the clusters. Several distinct types of straight-moving, as well as recurving, trajectories are identified, thus enriching this main distinction found in previous studies. Intensity and seasonality of cyclones, though not used by the clustering algorithm, are both highly stratified from cluster to cluster. Three straight-moving trajectory types have very small within-cluster spread, while the recurving types are more diffuse. Tropical cyclone landfalls over East and Southeast Asia are found to be strongly cluster dependent, both in terms of frequency and region of impact.

The relationships of each cluster type with the large-scale circulation, sea surface temperatures, and the phase of the El Niño–Southern Oscillation are studied in a companion paper.

Full access
Chih-Pei Chang, Michael Ghil, Hung-Chi Kuo, Mojib Latif, Chung-Hsiung Sui, and John M. Wallace
Full access
Sidonie Brachet, Francis Codron, Yizhak Feliks, Michael Ghil, Hervé Le Treut, and Eric Simonnet

Abstract

The atmospheric effects of sea surface temperature (SST) anomalies over and near western boundary currents are a matter of renewed interest. The general circulation model (GCM) of the Laboratoire de Météorologie Dynamique (LMD-Z) has a zooming capability that allows a regionally increased resolution. This GCM is used to analyze the impact of a sharp SST front in the North Atlantic Ocean: two simulations are compared, one with climatological SSTs and the other with an enhanced Gulf Stream front. The results corroborate the theory developed previously by the present team to explain the impact of oceanic fronts. In this theory, the vertical velocity at the top of the atmospheric boundary layer has two components: mechanical and thermal. It is the latter that is dominant in the tropics, while in midlatitudes both play a role in determining the wind convergence above the boundary layer. The strengthened SST front does generate the previously predicted stronger ascent above the warmer water south of the front and stronger descent above the colder waters to the north. In the GCM simulations, the ascent over the warm anomalies is deeper and more intense than the descent.

Full access
Eviatar Bach, Safa Mote, V. Krishnamurthy, A. Surjalal Sharma, Michael Ghil, and Eugenia Kalnay

Abstract

Oscillatory modes of the climate system are among its most predictable features, especially at intraseasonal time scales. These oscillations can be predicted well with data-driven methods, often with better skill than dynamical models. However, since the oscillations only represent a portion of the total variance, a method for beneficially combining oscillation forecasts with dynamical forecasts of the full system was not previously known. We introduce Ensemble Oscillation Correction (EnOC), a general method to correct oscillatory modes in ensemble forecasts from dynamical models. We compute the ensemble mean—or the ensemble probability distribution—with only the best ensemble members, as determined by their discrepancy from a data-driven forecast of the oscillatory modes. We also present an alternate method that uses ensemble data assimilation to combine the oscillation forecasts with an ensemble of dynamical forecasts of the system (EnOC-DA). The oscillatory modes are extracted with a time series analysis method called multichannel singular spectrum analysis (M-SSA), and forecast using an analog method. We test these two methods using chaotic toy models with significant oscillatory components and show that they robustly reduce error compared to the uncorrected ensemble. We discuss the applications of this method to improve prediction of monsoons as well as other parts of the climate system. We also discuss possible extensions of the method to other data-driven forecasts, including machine learning.

Open access