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Andrew T. Wittenberg

Abstract

Surface wind stresses are fundamental to understanding El Niño, yet most observational stress products are too short to permit multidecadal ENSO studies. Two exceptions are the Florida State University subjective analysis (FSU1) and the NCEP–NCAR reanalysis (NCEP1), which are widely used in climate research. Here, the focus is on the aspects of the stress most relevant to ENSO—namely, the climatological background, anomaly spectrum, response to SST changes, subannual “noise” forcing, and seasonal phase locking—and how these differ between FSU1 and NCEP1 over the tropical Pacific for 1961–99.

The NCEP1 stress climatology is distinguished from FSU1 by weaker equatorial easterlies, stronger off-equatorial cyclonic curl, stronger southerlies along the Peruvian coast, and weaker convergence zones with weaker seasonality. Compared to FSU1, the NCEP1 zonal stress anomalies (τx) are weaker, less noisy, and show less persistent westerly peaks during El Niño events. NCEP1 also shows a more stationary spectrum that more closely resembles that of equatorial east Pacific SST anomalies. After the 1970s, the equatorial trade winds and stress variability shift east and strengthen in FSU1, while the opposite occurs in NCEP1. Both products show increased mean convergence in the equatorial far west Pacific in recent decades, with weaker mean easterlies near the date line, an increased stress response to SST anomalies, and stronger interannual and subannual τx in the central equatorial Pacific (Niño-4; 5°N–5°S, 160°E–150°W). The variance of Niño-4 τx is highly seasonal in both datasets, with an interannual peak in October–November and a subannual peak in November–February; yet apart from interannual Niño-4 τx after 1980, stress anomalies are not well correlated between the products. Newer and more reliable stress estimates generally fall between NCEP1 and FSU1, with most closer to FSU1.

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Kit-Yan Choi, Gabriel A. Vecchi, and Andrew T. Wittenberg

Abstract

The observed equatorial Pacific zonal wind response during El Niño tends to be stronger than during La Niña. Most global coupled climate models in phase 5 of CMIP (CMIP5) exhibit such nonlinearity, although weaker than observed. The wind response nonlinearity can be reproduced by driving a linear shallow water atmospheric model with a model’s or the observed precipitation anomalies, which can be decomposed into two main components: the zonal and meridional redistribution of the climatological precipitation. Both redistributions contribute comparably to the total rainfall anomalies, whereas the zonal redistribution plays the dominant role in the zonal wind response. The meridional redistribution component plays an indirect role in the nonlinear wind response by limiting the zonal redistribution during La Niña and thus enhancing the nonlinearity in the wind response significantly. During La Niña, the poleward movement of the ITCZ/SPCZ reduces the equatorial zonal-mean precipitation available for the zonal redistribution and its resulting zonal wind response. Conversely, during El Niño, the equatorward movement of the ITCZ and SPCZ do not limit the zonal redistribution of precipitation. The linear equatorial zonal wind response to ENSO is found to have a significant linear correlation with the equatorial central Pacific climatological precipitation and SST among the CMIP5 models. However, no linear correlation is found between the nonlinear equatorial zonal wind response and the climatological precipitation.

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Jonghun Kam, Thomas R. Knutson, Fanrong Zeng, and Andrew T. Wittenberg
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Thomas R. Knutson, Jonghun Kam, Fanrong Zeng, and Andrew T. Wittenberg
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Chen Chen, Mark A. Cane, Andrew T. Wittenberg, and Dake Chen

Abstract

Focusing on ENSO seasonal phase locking, diversity in peak location, and propagation direction, as well as the El Niño–La Niña asymmetry in amplitude, duration, and transition, a set of empirical probabilistic diagnostics (EPD) is introduced to investigate how the ENSO behaviors reflected in SST may change in a warming climate.

EPD is first applied to estimate the natural variation of ENSO behaviors. In the observations El Niños and La Niñas mainly propagate westward and peak in boreal winter. El Niños occur more at the eastern Pacific whereas La Niñas prefer the central Pacific. In a preindustrial control simulation of the GFDL CM2.1 model, the El Niño–La Niña asymmetry is substantial. La Niña characteristics generally agree with observations but El Niño’s do not, typically propagating eastward and showing no obvious seasonal phase locking. So an alternative approach is using a stochastically forced simulation of a nonlinear data-driven model, which exhibits reasonably realistic ENSO behaviors and natural variation ranges.

EPD is then applied to assess the potential changes of ENSO behaviors in the twenty-first century using CMIP5 models. Other than the increasing SST climatology, projected changes in many aspects of ENSO reflected in SST anomalies are heavily model dependent and generally within the range of natural variation. Shifts favoring eastward-propagating El Niño and La Niña are the most robust. Given various model biases for the twentieth century and lack of sufficient model agreements for the twenty-first-century projection, whether the projected changes for ENSO behaviors would actually take place remains largely uncertain.

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Jonghun Kam, Thomas R. Knutson, Fanrong Zeng, and Andrew T. Wittenberg
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Thomas R. Knutson, Fanrong Zeng, and Andrew T. Wittenberg

Abstract

Regional surface temperature trends from phase 3 of the Coupled Model Intercomparison Project (CMIP3) and CMIP5 twentieth-century runs are compared with observations—at spatial scales ranging from global averages to individual grid points—using simulated intrinsic climate variability from preindustrial control runs to assess whether observed trends are detectable and/or consistent with the models' historical run trends. The CMIP5 models are also used to detect anthropogenic components of the observed trends, by assessing alternative hypotheses based on scenarios driven with either anthropogenic plus natural forcings combined, or with natural forcings only. Modeled variability is assessed via inspection of control run time series, standard deviation maps, spectral analyses, and low-frequency variance consistency tests. The models are found to provide plausible representations of internal climate variability, although there is room for improvement. The influence of observational uncertainty on the trends is assessed and is found to be generally small in comparison with intrinsic climate variability. Observed temperature trends over 1901–2010 are found to contain detectable anthropogenic warming components over a large fraction (about 80%) of the analyzed global area. In about 70% of the analyzed area, the modeled warming is consistent with the observed trends; in about 10% it is significantly greater than simulated. Regions without detectable warming include the high-latitude North Atlantic Ocean, the eastern United States, and parts of the eastern and northern Pacific Ocean. For 1981–2010, the observed warming trends over only about 30% of the globe are found to contain a detectable anthropogenic warming: this includes a number of regions within about 40°–45° of the equator, particularly in the Indian Ocean, western Pacific, South Asia, and tropical Atlantic.

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Hui Ding, Matthew Newman, Michael A. Alexander, and Andrew T. Wittenberg

Abstract

Seasonal forecasts made by coupled atmosphere–ocean general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields a forecast ensemble, without additional model integration. This technique is applied to four of the eight CGCMs comprising the North American Multimodel Ensemble (NMME) by selecting from prior long control runs those model states whose monthly tropical Indo-Pacific SST and SSH anomalies best resemble the observations at initialization time. Hindcasts are then made for leads of 1–12 months during 1982–2015. Deterministic and probabilistic skill measures of these model-analog hindcast ensembles are comparable to those of the initialized NMME hindcast ensembles, for both the individual models and the multimodel ensemble. In the eastern equatorial Pacific, model-analog hindcast skill exceeds that of the NMME. Despite initializing with a relatively large ensemble spread, model-analogs also reproduce each CGCM’s perfect-model skill, consistent with a coarse-grained view of tropical Indo-Pacific predictability. This study suggests that with little additional effort, sufficiently realistic and long CGCM simulations provide the basis for skillful seasonal forecasts of tropical Indo-Pacific SST anomalies, even without sophisticated data assimilation or additional ensemble forecast integrations. The model-analog method could provide a baseline for forecast skill when developing future models and forecast systems.

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Jonghun Kam, Thomas R. Knutson, Fanrong Zeng, and Andrew T. Wittenberg
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Kit-Yan Choi, Gabriel A. Vecchi, and Andrew T. Wittenberg

Abstract

The El Niño–Southern Oscillation (ENSO) exhibits well-known asymmetries: 1) warm events are stronger than cold events, 2) strong warm events are more likely to be followed by cold events than vice versa, and 3) cold events are more persistent than warm events. Coupled GCM simulations, however, continue to underestimate many of these observed features.

To shed light on these asymmetries, the authors begin with a widely used delayed-oscillator conceptual model for ENSO and modify it so that wind stress anomalies depend more strongly on SST anomalies (SSTAs) during warm conditions, as is observed. Then the impact of this nonlinearity on ENSO is explored for three dynamical regimes: self-sustained oscillations, stochastically driven oscillations, and self-sustained oscillations disrupted by stochastic forcings. In all three regimes, the nonlinear air–sea coupling preferentially strengthens the feedbacks (both positive and delayed negative) during the ENSO warm phase—producing El Niños that grow to a larger amplitude and overshoot more rapidly and consistently into the opposite phase, than do the La Niñas. Finally, the modified oscillator is applied to observational records and to control simulations from two global coupled ocean–atmosphere–land–ice models [Geophysical Fluid Dynamics Laboratory Climate Model version 2.1 (GFDL CM2.1) and version 2.5 (GFDL CM2.5)] to elucidate the causes of their differing asymmetries.

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