MODE Bottom Experiment

Pressure fluctuations on the deep seafloor at frequencies below inertial and tidal have been measured. Between 0.1 and 1 cycle per day the variance is about 2 mb2, spectra diminish with increasing frequency as ω−n, n=1.5 to 2, and a signal-to-instrument noise ratio of 10 dB is achieved. Fluctuations are in phase and highly coherent within the MODE area (\u3e0.95 at 200 km) and even with inferred (atmosphere plus sea level) Bermuda subsurface pressures (0.8 at 700 km). Station differences (to which MODE-sized eddies would make the principal contribution) are relatively small. The large horizontal scale of the recorded bottom pressure fluctuations resembles that of atmospheric pressure, yet the coherence locally between atmospheric and bottom pressure is slight; the recorded fluctuations may be related to a barotropic ocean response to a variable wind stress on the subtropical gyre. Bottom temperature records show “sudden” (1 day) changes of order 30 millidegrees Celcius separated by long intervals (20 days) of uniform temperatures. The changes are much larger than have been observed in the Pacific. They are correlated at horizontal separations of 2 km, but uncorrelated to bottom pressure and to temperatures 1 km above the seafloor

MODE Tides

IGPP and AOML bottom pressure measurements at four MODE stations constitute a unique set of deep-sea tidal measurements (although deployed for other purposes). A response analysis relative to a Bermuda reference has been optimized with regard to the number of complex weights and the makeup of gravitational and radiational inputs. Duplicate instrumentation on EDIE capsule gave 32.067, 2.5° 32.074, 2.6° for M2 amplitude (cm) and Greenwich epoch, thus attesting the reality of measured small station differences (order 1 cm, 1°). M2 tidal currents (calculated from the M2 surface and bottom slopes) have u and v speeds of 0.5 and 0.8 em s−1, respectively, in rough agreement (both amplitude and phase) with preliminary estimates from current measurements. M2 and K1 tides are in accord with sonic existing cotidal and co-range charts. M2 tides are a fraction of equilibrium magnitude, whereas M4, M5 and M6 (typically 0.07, 0.05, O.03 cm) vastly exceed equilibrium values. Presumably these overtides are generated by nonlinear coupling in the world\u27s shallow basins, from where they radiate into the global oceans to attain a level where radiative and dissipative processes are somehow balanced

Interpretation of North Pacific Variability as a Short- and Long-Memory Process*

A major difficulty in investigating the nature of interdecadal variability of climatic time series is their shortness. An approach to this problem is through comparison of models. In this paper we contrast a first order autoregressive (AR(1)) model with a fractionally differenced (FD) model as applied to the winter averaged sea level pressure time series for the Aleutian low (the North Pacific (NP) index), and the Sitka winter air temperature record. Both models fit the same number of parameters. The AR(1) model is a ‘short memory ’ model in that it has a rapidly decaying autocovariance sequence, whereas an FD model exhibits ‘long memory ’ because its autocovariance sequence decays more slowly. Statistical tests cannot distinguish the superiority of one model over the other when fit with 100 NP or 146 Sitka data points. The FD model does equally well for short term prediction and has potentially important implications for long term behavior. In particular, the zero crossings of the FD model tend to be further apart, so they have more of a ‘regime’-like character; a quarter century interval between zero crossings is four times more likely with the FD than the AR(1) model. The long memory parameter δ for the FD model can be used as a characterization of regime-like behavior. The estimated δs for the NP index (spanning 100 years) and the Sitka time series (168 years) are virtually identical, and their size implies moderate long memory behavior. Although the NP index and the Sitka series have broadband low frequency variability and modest long memory behavior, temporal irregularities in their zero crossings are still prevalent. Comparison of the FD and AR(1) models indicates that regime-like behavior cannot be ruled out for North Pacific processes. 2 1

Analysis of Subtidal Coastal Sea Level Fluctuations Using Wavelets

this paper, we demonstrate that wavelet analysis based on the maximal overlap discrete wavelet transform (MODWT) is an e#ective technique for quantifying the nonstationary characteristics of subtidal sea level fluctuations as measured by the tide gauge at Crescent City, California. This site is of particular interest because Crescent City has a well-documented history of tsunami inundation in which the maximum heights depend strongly of the heights of subtidal sea level (Petrauskas and Borgman, 1971; Lander and Lockridge, 1989; Mofjeld et al., 1997a). The ocean near Crescent City is also subject to the highest seasonal wind forcing along the U.S. West Coast (Strub et al., 1987b). The period of interest is 1980--1991, which includes the major 1982--83 El Nino/Southern Oscillation event and several other interannual events. The primary scientific purpose of this paper is to characterize the subtidal sea level fluctuations at Crescent City as they relate to coastal hazards. These characterizations can then be used to improve maps and forecasts of coastal inundation (Mofjeld et al., 1997a, b). The MODWT of a time series leads to two types of analyses. The first is an additive decomposition known as multiresolution analysis, which breaks up the series into a number of "details" and a single "smooth." Each detail is a time series describing variations at a particular time scale, while the smooth describes the low frequency variations. We use the details from the Crescent City series to study its temporal behavior at the synoptic (1--10 days) and intraseasonal scales and use the smooth to study its seasonal and longer scale fluctuations. Stacked plots of the details and smooth provide an e#ective means of exploring the relationships between sea level fluctuations at di#erent t..