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Jessica S. Kenigson
,
Weiqing Han
,
Balaji Rajagopalan
,
Yanto
, and
Mike Jasinski

Abstract

Recent studies have linked interannual sea level variability and extreme events along the U.S. northeast coast (NEC) to the North Atlantic Oscillation (NAO), a natural internal climate mode that prevails in the North Atlantic Ocean. The correlation between the NAO index and coastal sea level north of Cape Hatteras was weak from the 1960s to the mid-1980s, but it has markedly increased since around 1987. The causes for the decadal shift remain unknown. Yet understanding the abrupt change is vital for decadal sea level prediction and is essential for risk management. Here we use a robust method, the Bayesian dynamic linear model (DLM), to explore the nonstationary NAO impact on NEC sea level. The results show that a spatial pattern change of NAO-related winds near the NEC is a major cause of the NAO–sea level relationship shift. A new index using regional sea level pressure is developed that is a significantly better predictor of NEC sea level than is the NAO and is strongly linked to the intensity of westerly winds near the NEC. These results point to the vital importance of monitoring regional changes of wind and sea level pressure patterns, rather than the NAO index alone, to achieve more accurate predictions of sea level change along the NEC.

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R. A. Antonia
,
S. Rajagopalan
, and
A. J. Chambers

Abstract

Conditional sampling and averaging techniques are used to obtain statistics of convectively-driven quasi-ordered structures at a height of 4 m within the atmospheric surface layer. The fraction of time 'y occupiedby these structures, and their frequency of occurrence I can depend on detection criteria parameters, suchas the threshold and hold time. The effect of these parameters on 'y and f is investigated for two conditionalsampling techniques. Both techniques indicate that y decreases continuously with increasing threshold,whereas there is a region in which I is independent of both parameters. When the parameters are suitablyselected, reasonable agreement for both 'y and f can be obtained between the techniques. This agreementdoes not depend on whether the velocity or the temperature fluctuation is used as the basis of detection forone of the techniques.

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R. A. Antonia
,
A. J. Chambers
,
S. Rajagopalan
,
K. R. Sreenivasan
, and
C. A. Friehe

Abstract

Measurements of turbulent momentum, heat and moisture fluxes have been made in Bass Strait from a stable platform, at a height of approximately 5 m above water. Direct measurements of these fluxes are compared with estimates obtained from spectra of velocity, temperature and humidity fluctuations with the use of the inertial dissipation technique. Directly measured momentum and moisture flux values are in reasonable agreement with inertial dissipation values. The sensible heal flux obtained by the inertial dissipation technique is about twice as large as the directly measured heat flux. The dependence on wind speed of bulk transfer coefficients of momentum, heat and moisture and of variances of velocity and scalar fluctuations is discussed and compared with available data.

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Álvaro Ossandón
,
Nanditha J. S.
,
Pablo A. Mendoza
,
Balaji Rajagopalan
, and
Vimal Mishra

Abstract

Despite the potential and increasing interest in physically based hydrological models for streamflow forecasting applications, they are constrained in terms of agility to generate ensembles. Hence, we develop and test a Bayesian hierarchical model (BHM) to postprocess physically based hydrologic model simulations at multiple sites on a river network, with the aim to generate probabilistic information (i.e., ensembles) and improve raw model skill. We apply our BHM framework to daily summer (July–August) streamflow simulations at five stations located in the Narmada River basin in central India, forcing the Variable Infiltration Capacity (VIC) model with observed rainfall. In this approach, daily observed streamflow at each station is modeled with a conditionally independent probability density function with time varying distribution parameters, which are modeled as a linear function of potential covariates that include VIC outputs and meteorological variables. Using suitable priors on the parameters, posterior parameters and predictive posterior distributions—and thus ensembles—of daily streamflow are obtained. The best BHM model considers a gamma distribution and uses VIC streamflow and a nonlinear covariate formulated as the product of VIC streamflow and 2-day precipitation spatially averaged across the area between the current and upstream station. The second covariate enables correcting the time delay in flow peaks and nonsystematic biases in VIC streamflow. The results show that the BHM postprocessor increases probabilistic skill in 60% compared to raw VIC simulations, providing reliable ensembles for most sites. This modeling approach can be extended to combine forecasts from multiple sources and provide skillful multimodel ensemble forecasts.

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