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Laurie Trenary and Timothy DelSole

Abstract

This paper investigates the predictive relation between the Atlantic multidecadal oscillation (AMO) and Atlantic meridional overturning circulation across different climate models. Three overturning patterns that are significantly coupled to the AMO on interannual time scales across all climate models are identified using a statistical optimization technique. Including these structures in an autoregressive model extends AMO predictability by 2–9 years, relative to an autoregressive model without these structures.

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Laurie L. Trenary and Weiqing Han

Abstract

The relative importance of local versus remote forcing on intraseasonal-to-interannual sea level and thermocline variability of the tropical south Indian Ocean (SIO) is systematically examined by performing a suite of controlled experiments using an ocean general circulation model and a linear ocean model. Particular emphasis is placed on the thermocline ridge of the Indian Ocean (TRIO; 5°–12°S, 50°–80°E). On interannual and seasonal time scales, sea level and thermocline variability within the TRIO region is primarily forced by winds over the Indian Ocean. Interannual variability is largely caused by westward propagating Rossby waves forced by Ekman pumping velocities east of the region. Seasonally, thermocline variability over the TRIO region is induced by a combination of local Ekman pumping and Rossby waves generated by winds from the east. Adjustment of the tropical SIO at both time scales generally follows linear theory and is captured by the first two baroclinic modes. Remote forcing from the Pacific via the oceanic bridge has significant influence on seasonal and interannual thermocline variability in the east basin of the SIO and weak impact on the TRIO region. On intraseasonal time scales, strong sea level and thermocline variability is found in the southeast tropical Indian Ocean, and it primarily arises from oceanic instabilities. In the TRIO region, intraseasonal sea level is relatively weak and results from Indian Ocean wind forcing. Forcing over the Pacific is the major cause for interannual variability of the Indonesian Throughflow (ITF) transport, whereas forcing over the Indian Ocean plays a larger role in determining seasonal and intraseasonal ITF variability.

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Keri Kodama, Natalie J. Burls, and Laurie Trenary

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Wind power, defined as the energy received by the ocean from wind, has been identified as a potentially viable precursor of ENSO. The correlation between tropical Pacific wind power anomalies and eastern equatorial Pacific sea surface temperature anomalies can be enhanced over a range of lead times by applying an empirical adjusted framework that accounts for both the underlying climatological state upon which a wind power perturbation acts and the directionality of wind anomalies. Linear regression is used to assess the seasonal prediction skill of adjusted wind power in comparison to unadjusted, as well as the conventional ENSO predictors wind stress and warm water volume. The forecast skill of each regression model is evaluated in a 1800-yr preindustrial climate simulation (CESM-LENS), as well as 23 years of observations. The simulation results show that each predictor’s effectiveness varies considerably with the sample record, providing a measure of the uncertainty involved in evaluating prediction skill based on the short observational record. The influence of climatological biases is however a demonstrable concern for results from the simulated climate system. Despite the short record, the observational analysis indicates that adjusted wind power skill is comparable to the conventional dynamical predictors and notably is significantly more predictable than unadjusted wind power when initialized in the summer. Moreover, the adjusted framework results in a reduction of error when evaluating wind power associated with wind bursts, reinforcing previous findings that the adjusted framework is particularly useful for capturing the ENSO response to westerly wind bursts.

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Laurie Trenary, Timothy DelSole, Brian Doty, and Michael K. Tippett
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Laurie Trenary, Timothy DelSole, Michael K. Tippett, and Brian Doty
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Timothy DelSole, Laurie Trenary, Michael K. Tippett, and Kathleen Pegion

Abstract

This paper demonstrates that an operational forecast model can skillfully predict week-3–4 averages of temperature and precipitation over the contiguous United States. This skill is demonstrated at the gridpoint level (about 1° × 1°) by decomposing temperature and precipitation anomalies in terms of an orthogonal set of patterns that can be ordered by a measure of length scale and then showing that many of the resulting components are predictable and can be predicted in observations with statistically significant skill. The statistical significance of predictability and skill are assessed using a permutation test that accounts for serial correlation. Skill is detected based on correlation measures but not based on mean square error measures, indicating that an amplitude correction is necessary for skill. The statistical characteristics of predictability are further clarified by finding linear combinations of components that maximize predictability. The forecast model analyzed here is version 2 of the Climate Forecast System (CFSv2), and the variables considered are temperature and precipitation over the contiguous United States during January and July. A 4-day lagged ensemble, comprising 16 ensemble members, is used. The most predictable components of winter temperature and precipitation are related to ENSO, and other predictable components of winter precipitation are shown to be related to the Madden–Julian oscillation. These results establish a scientific basis for making week-3–4 weather and climate predictions.

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Michael K. Tippett, Laurie Trenary, Timothy DelSole, Kathleen Pegion, and Michelle L. L’Heureux

Abstract

Forecast climatologies are used to remove systematic errors from forecasts and to express forecasts as departures from normal. Forecast climatologies are computed from hindcasts by various averaging, smoothing, and interpolation procedures. Here the Climate Forecast System, version 2 (CFSv2), monthly forecast climatology provided by the NCEP Environmental Modeling Center (EMC) is shown to be biased in the sense of systematically differing from the hindcasts that are used to compute it. These biases, which are unexpected, are primarily due to fitting harmonics to hindcast data that have been organized in a particular format, which on careful inspection is seen to introduce discontinuities. Biases in the monthly near-surface temperature forecast climatology reach 2°C over North America for March targets and start times at the end of January. Biases in the monthly Niño-3.4 forecast climatology are also largest for start times near calendar-month boundaries. A further undesirable consequence of this fitting procedure is that the EMC forecast climatology varies discontinuously with lead time for fixed target month. Two alternative methods for computing the forecast climatology are proposed and illustrated. The proposed methods more accurately fit the hindcast data and provide a clearer representation of the CFSv2 model climate drift toward lower Niño-3.4 values for starts in March and April and toward higher Niño-3.4 values for starts in June, July, and August.

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Rodrigo J. Bombardi, Laurie Trenary, Kathy Pegion, Benjamin Cash, Timothy DelSole, and James L. Kinter III

Abstract

The seasonal predictability of austral summer rainfall is evaluated in a set of retrospective forecasts (hindcasts) performed as part of the Minerva and Metis projects. Both projects use the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) coupled to the Nucleus for European Modelling of the Ocean (NEMO). The Minerva runs consist of three sets of hindcasts where the spatial resolution of the model’s atmospheric component is progressively increased while keeping the spatial resolution of its oceanic component constant. In the Metis runs, the spatial resolution of both the atmospheric and oceanic components are progressively increased. We find that raw model predictions show seasonal forecast skill for rainfall over northern and southeastern South America. However, predictability is difficult to detect on a local basis, but it can be detected on a large-scale pattern basis. In addition, increasing horizontal resolution does not lead to improvements in the forecast skill of rainfall over South America. A predictable component analysis shows that only the first predictable component of austral summer precipitation has forecast skill, and the source of forecast skill is El Niño–Southern Oscillation. Seasonal prediction of precipitation remains a challenge for state-of-the-art climate models. Positive benefits of increasing model resolution might be more evident in other atmospheric fields (i.e., temperature or geopotential height) and/or temporal scales (i.e., subseasonal temporal scales).

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