Search Results

You are looking at 1 - 10 of 27 items for

  • Author or Editor: Xiaosong Yang x
  • All content x
Clear All Modify Search
Xiaosong Yang and Timothy DelSole

Abstract

This paper applies a new field significance test to establish the existence and consistency of ENSO teleconnection patterns across models and observations. An ENSO teleconnection pattern is defined as a field of regression coefficients between an index of the tropical Pacific sea surface temperature and a field of variables such as surface air temperature or precipitation. The test is applied to boreal winter and summer in six continents using observations and hindcasts from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) and the ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) projects. This comparison represents one of the most comprehensive and up-to-date assessments of the extent to which ENSO teleconnection patterns exist and can be reproduced by coupled models.

Statistically significant ENSO teleconnection patterns are detected in both observations and models and in all continents and in both winter and summer seasons, except in two cases: 1) Europe (both seasons and variables), and 2) North America (both variables in boreal summer). Despite many ENSO teleconnection patterns being significant, however, the patterns do not necessarily agree between observations and models. The degree of agreement between models and observations is characterized as “robust,” “moderate,” or “low.” Only Australia and South America are found to have robust agreement between ENSO teleconnection patterns, and then only for limited seasons and variables. Although many of our conclusions regarding teleconnection patterns conform to previous studies, there are exceptions, including the fact that the teleconnection for boreal winter precipitation is generally accepted to exist in Africa but in fact has only low agreement with model simulations, while that in Asia is not widely recognized to exist but is found to be significant and in moderate agreement with model teleconnections.

Full access
Songmiao Fan and Xiaosong Yang

Abstract

The wintertime Arctic temperature (T; surface–400 hPa) decreased from 1979 to 1997 and increased rapidly from 1998 to 2012, in contrast to the global mean surface air temperature. Here aspects of circulation variability that are associated with these temperature changes are examined using the NCEP–NCAR reanalysis and ERA-Interim products. It is found that the Nordic–Siberia seesaw of meridional winds near 70°N is associated with two-thirds of the variance of the Arctic winter mean T, possibly contributing to the cooling and warming trends. It is suggested here that the seesaw accounts for much of the difference in Arctic amplification between observations and climate models. Growth of sea ice in winter is hindered by southerly winds over the Nordic region (0°–60°E). Through modulation of the wind seesaw, the eastern Atlantic (EA) pattern is found to be significantly associated with Arctic and East Asia winter climate variations. In one phase of the EA pattern, a midlatitude North Atlantic ridge anomaly is associated with a poleward shift of the mean storm track, a weakened eddy-driven jet over Eurasia, and above-normal sea level pressure (SLP) over Siberia, most significantly in the region to the northwest of Lake Baikal. The EA pattern is associated with two-thirds of the variance of winter-average SLP over Siberia.

Full access
Timothy DelSole and Xiaosong Yang

Abstract

Regression patterns often are used to diagnose the relation between a field and a climate index, but a significance test for the pattern “as a whole” that accounts for the multiplicity and interdependence of the tests has not been widely available. This paper argues that field significance can be framed as a test of the hypothesis that all regression coefficients vanish in a suitable multivariate regression model. A test for this hypothesis can be derived from the generalized likelihood ratio test. The resulting statistic depends on relevant covariance matrices and accounts for the multiplicity and interdependence of the tests. It also depends only on the canonical correlations between the predictors and predictands, thereby revealing a fundamental connection to canonical correlation analysis. Remarkably, the test statistic is invariant to a reversal of the predictors and predictands, allowing the field significance test to be reduced to a standard univariate hypothesis test. In practice, the test cannot be applied when the number of coefficients exceeds the sample size, reflecting the fact that testing more hypotheses than data is ill conceived. To formulate a proper significance test, the data are represented by a small number of principal components, with the number chosen based on cross-validation experiments. However, instead of selecting the model that minimizes the cross-validated mean square error, a confidence interval for the cross-validated error is estimated and the most parsimonious model whose error is within the confidence interval of the minimum error is chosen. This procedure avoids selecting complex models whose error is close to much simpler models. The procedure is applied to diagnose long-term trends in annual average sea surface temperature and boreal winter 300-hPa zonal wind. In both cases a statistically significant 50-yr trend pattern is extracted. The resulting spatial filter can be used to monitor the evolution of the regression pattern without temporal filtering.

Full access
Xiaosong Yang and Edmund K. M. Chang

Abstract

A new split-jet index is defined in this study, and composites based on this index show that the split-flow regime is characterized by a cold–warm–cold tripolar temperature anomaly in the South Pacific that extends equatorward from the Southern Hemisphere (SH) high latitudes, while nonsplit flow occurs when the phase of the tripolar temperature anomaly is reversed. Analyses of the heat budget reveal that the temperature anomalies associated with the split/nonsplit flow are mainly forced by mean flow advection instead of local diabatic heating or convergence of eddy heat fluxes. Localized Eliassen–Palm (E–P) flux diagnostics suggest that the zonal wind anomalies are maintained by the eddy vorticity flux anomalies.

These diagnostic results are confirmed by numerical experiments conducted using a stationary wave model forced by observed eddy forcings and diabatic heating anomalies. The model results show that the effects of the vorticity flux dominates over those of the heat flux, which tend to dampen the flow anomalies, and that tropical diabatic heating anomalies are not important in maintaining the split-/nonsplit-flow anomalies.

The organization of high-frequency eddies by the low-frequency split/nonsplit jet is also studied. Two sets of experiments using a linear storm-track model initialized with random initial perturbations superposed upon the split- and nonsplit-jet basic state, respectively, have been conducted. Model results show that the storm-track anomalies that are organized by the split/nonsplit jet are consistent with observed storm-track anomalies, thus demonstrating that the low-frequency split/nonsplit jet acts to organize the high-frequency eddies.

The results of this paper directly establish that there is a two-way reinforcement between eddies and mean flow anomalies in the low-frequency variability of the SH winter split jet.

Full access
Xiaosong Yang, Timothy DelSole, and Hua-Lu Pan

Abstract

This paper examines the extent to which an empirical correction method can improve forecasts of the National Centers for Environmental Prediction (NCEP) operational Global Forecast System. The empirical correction is based on adding a forcing term to the prognostic equations equal to the negative of the climatological tendency errors. The tendency errors are estimated by a least squares method using 6-, 12-, 18-, and 24-h forecast errors. Tests on independent verification data show that the empirical correction significantly reduces temperature biases nearly everywhere at all lead times up to at least 5 days but does not significantly reduce biases in forecast winds and humidity. Decomposing mean-square error into bias and random components reveals that the reduction in total mean-square error arises solely from reduction in bias. Interestingly, the empirical correction increases the random error slightly, but this increase is argued to be an artifact of the change in variance in the forecasts. The empirical correction also is found to reduce the bias more than traditional “after the fact” corrections. The latter result might be a consequence of the very different sample sizes available for estimation, but this difference in sample size is unavoidable in operational situations in which limited calibration data are available for a given forecast model.

Full access
Xiaosong Yang and Edmund K. M. Chang

Abstract

The eddy–zonal flow feedback in the Southern Hemisphere (SH) winter and summer is investigated in this study. The persistence time scale of the leading principal components (PCs) of the zonal-mean zonal flow shows substantial seasonal variation. In the SH summer, the persistence time scale of PC1 is significantly longer than that of PC2, while the persistence time scales of the two PCs are quite similar in the SH winter. A storm-track modeling approach is applied to demonstrate that seasonal variations of eddy–zonal flow feedback for PC1 and PC2 account for the seasonal variations of the persistence time scale. The eddy feedback time scale estimated from a storm-track model simulation and a wave-response model diagnostic shows that PC1 in June–August (JJA) and December–February (DJF), and PC2 in JJA, have significant positive eddy–mean flow feedback, while PC2 in DJF has no positive feedback. The consistency between the persistence and eddy feedback time scales for each PC suggests that the positive feedback increases the persistence of the corresponding PC, with stronger (weaker) positive feedback giving rise to a longer (shorter) persistence time scale.

Eliassen–Palm flux diagnostics have been performed to demonstrate the dynamics governing the positive feedback between eddies and anomalous zonal flow. The mechanism of the positive feedback, for PC1 in JJA and DJF and PC2 in JJA, is as follows: an enhanced baroclinic wave source (heat fluxes) at a low level in the region of positive wind anomalies propagates upward and then equatorward from the wave source, thus giving momentum fluxes that reinforce the wind anomalies. The difference of PC2 between DJF and JJA is because of the zonal asymmetry of the climatological flow in JJA. For PC2 in DJF, wind anomalies reinforce the climatological jet, thus increasing the barotropic shear of the jet flow. The “barotropic governor” plays an important role in suppressing eddy generations for PC2 in DJF and thus inhibiting the positive eddy–zonal flow feedback.

Full access
Shan Li, Shaoqing Zhang, Zhengyu Liu, Xiaosong Yang, Anthony Rosati, Jean-Christophe Golaz, and Ming Zhao

Abstract

Uncertainty in cumulus convection parameterization is one of the most important causes of model climate drift through interactions between large-scale background and local convection that use empirically set parameters. Without addressing the large-scale feedback, the calibrated parameter values within a convection scheme are usually not optimal for a climate model. This study first designs a multiple-column atmospheric model that includes large-scale feedbacks for cumulus convection and then explores the role of large-scale feedbacks in cumulus convection parameter estimation using an ensemble filter. The performance of convection parameter estimation with or without the presence of large-scale feedback is examined. It is found that including large-scale feedbacks in cumulus convection parameter estimation can significantly improve the estimation quality. This is because large-scale feedbacks help transform local convection uncertainties into global climate sensitivities, and including these feedbacks enhances the statistical representation of the relationship between parameters and state variables. The results of this study provide insights for further understanding of climate drift induced from imperfect cumulus convection parameterization, which may help improve climate modeling.

Full access
Thomas L. Delworth, Fanrong Zeng, Liping Zhang, Rong Zhang, Gabriel A. Vecchi, and Xiaosong Yang

Abstract

The relationship between the North Atlantic Oscillation (NAO) and Atlantic sea surface temperature (SST) variability is investigated using models and observations. Coupled climate models are used in which the ocean component is either a fully dynamic ocean or a slab ocean with no resolved ocean heat transport. On time scales less than 10 yr, NAO variations drive a tripole pattern of SST anomalies in both observations and models. This SST pattern is a direct response of the ocean mixed layer to turbulent surface heat flux anomalies associated with the NAO. On time scales longer than 10 yr, a similar relationship exists between the NAO and the tripole pattern of SST anomalies in models with a slab ocean. A different relationship exists both for the observations and for models with a dynamic ocean. In these models, a positive (negative) NAO anomaly leads, after a decadal-scale lag, to a monopole pattern of warming (cooling) that resembles the Atlantic multidecadal oscillation (AMO), although with smaller-than-observed amplitudes of tropical SST anomalies. Ocean dynamics are critical to this decadal-scale response in the models. The simulated Atlantic meridional overturning circulation (AMOC) strengthens (weakens) in response to a prolonged positive (negative) phase of the NAO, thereby enhancing (decreasing) poleward heat transport, leading to broad-scale warming (cooling). Additional simulations are used in which heat flux anomalies derived from observed NAO variations from 1901 to 2014 are applied to the ocean component of coupled models. It is shown that ocean dynamics allow models to reproduce important aspects of the observed AMO, mainly in the Subpolar Gyre.

Full access
Ángel G. Muñoz, Xiaosong Yang, Gabriel A. Vecchi, Andrew W. Robertson, and William F. Cooke

Abstract

This study proposes an integrated diagnostic framework based on atmospheric circulation regime spatial patterns and frequencies of occurrence to facilitate the identification of model systematic errors across multiple time scales. To illustrate the approach, three sets of 32-yr-long simulations are analyzed for northeastern North America and for the March–May season using the Geophysical Fluid Dynamics Laboratory’s Low Ocean–Atmosphere Resolution (LOAR) and Forecast-Oriented Low Ocean Resolution (FLOR) coupled models; the main difference between these two models is the horizontal resolution of the atmospheric model used. Regime-dependent biases are explored in the light of different atmospheric horizontal resolutions and under different nudging approaches. It is found that both models exhibit a fair representation of the observed circulation regime spatial patterns and frequencies of occurrence, although some biases are present independently of the horizontal resolution or the nudging approach and are associated with a misrepresentation of troughs centered north of the Great Lakes and deep coastal troughs. Moreover, the intraseasonal occurrence of certain model regimes is delayed with respect to observations. On the other hand, interexperiment differences in the mean frequencies of occurrence of the simulated weather types, and their variability across multiple time scales, tend to be negligible. This result suggests that low-resolution models could be of potential use to diagnose and predict physical variables via their simulated weather type characteristics.

Full access
Liping Zhang, Thomas L. Delworth, William Cooke, Hugues Goosse, Mitchell Bushuk, Yushi Morioka, and Xiaosong Yang

Abstract

Previous studies have shown the existence of internal multidecadal variability in the Southern Ocean using multiple climate models. This variability, associated with deep ocean convection, can have significant climate impacts. In this work, we use sensitivity studies based on Geophysical Fluid Dynamics Laboratory (GFDL) models to investigate the linkage of this internal variability with the background ocean mean state. We find that mean ocean stratification in the subpolar region that is dominated by mean salinity influences whether this variability occurs, as well as its time scale. The weakening of background stratification favors the occurrence of deep convection. For background stratification states in which the low-frequency variability occurs, weaker ocean stratification corresponds to shorter periods of variability and vice versa. The amplitude of convection variability is largely determined by the amount of heat that can accumulate in the subsurface ocean during periods of the oscillation without deep convection. A larger accumulation of heat in the subsurface reservoir corresponds to a larger amplitude of variability. The subsurface heat buildup is a balance between advection that supplies heat to the reservoir and vertical mixing/convection that depletes it. Subsurface heat accumulation can be intensified both by an enhanced horizontal temperature advection by the Weddell Gyre and by an enhanced ocean stratification leading to reduced vertical mixing and surface heat loss. The paleoclimate records over Antarctica indicate that this multidecadal variability has very likely happened in past climates and that the period of this variability may shift with different climate background mean state.

Open access