Addendum I: Paleoclimate and the Solar-Insolation/Tidal-Resonance Climate Model

Recent papers provide detailed analyses of more than 40 high-resolution time series culled from the extensive paleoclimate literature that appear to define cyclical elements of the Solar-Insolation/Tidal-Resonance Climate Model. This model was earlier referred to as the Milankovitch/Pettersson Climatic Theory. This paper provides comparable analyses of an additional 20 or so, evidently supportive, climate and volcanic time series. The tree-ring, historical, pollen, cultural, time-frequency, and hydrologic records range in length from 400 to 90,000 years and spatially from Alaska to Tierra del Fuego.

Investigating changes in global tropical cyclone storm frequency and intensity

Understanding fluctuations in tropical cyclone activity along United States shores and abroad becomes increasingly important as coastal managers and planners seek to save lives, mitigate damage, and plan for resilience in the face of changing storminess and sea-level rise. Tropical cyclone activity has long been of concern to coastal areas as they bring strong winds, heavy rains, and high seas. Given projections of a warming climate, current estimates suggest that not only will tropical cyclones increase in frequency, but also in intensity (maximum sustained winds and minimum central pressures). An understanding of what has happened historically is an important step in identifying potential future changes in tropical cyclone frequency and intensity. The ability to detect such changes depends on a consistent and reliable global tropical cyclone dataset. Until recently no central repository for historical tropical cyclone data existed. To fill this need, the International Best Track Archive for Climate Stewardship (IBTrACS) dataset was developed to collect all known global historical tropical cyclone data into a single source for dissemination. With this dataset, a global examination of changes in tropical cyclone frequency and intensity can be performed. Caveats apply to any historical tropical cyclone analysis however, as the data contributed to the IBTrACS archive from various tropical cyclone warning centers is still replete with biases that may stem from operational changes, inhomogeneous monitoring programs, and time discontinuities. A detailed discussion of the difficulties in detecting trends using tropical cyclone data can be found in Landsea et al. 2006. The following sections use the IBTrACS dataset to show the global spatial variability of tropical cyclone frequency and intensity. Analyses will show where the strongest storms typically occur, the regions with the highest number of tropical cyclones per decade, and the locations of highest average maximum wind speeds. (PDF contains 3 pages)

Maximum likelihood estimation of mortality and growth with individual variability from multiple length-frequency data

We consider estimation of mortality rates and growth parameters from length-frequency data of a fish stock and derive the underlying length distribution of the population and the catch when there is individual variability in the von Bertalanffy growth parameter L∞. The model is flexible enough to accommodate 1) any recruitment pattern as a function of both time and length, 2) length-specific selectivity, and 3) varying fishing effort over time. The maximum likelihood method gives consistent estimates, provided the underlying distribution for individual variation in growth is correctly specified. Simulation results indicate that our method is reasonably robust to violations in the assumptions. The method is applied to tiger prawn data (Penaeus semisulcatus) to obtain estimates of natural and fishing mortality.

Assessing the precision of frequency distributions estimated from trawl-survey samples

In trawl surveys a cluster of fish are caught at each station, and fish caught together tend to have more similar characteristics, such as length, age, stomach contents etc., than those in the entire population. When this is the case, the effective sample size for estimates of the frequency distribution of a population characteristic can, therefore, be much smaller than the number of fish sampled during a survey. As examples, it is shown that the effective sample size for estimates of length-frequency distributions generated by trawl surveys conducted in the Barents Sea, off Namibia, and off South Africa is on average approximately one fish per tow. Thus many more fish than necessary are measured at each station (location). One way to increase the effective sample size for these surveys and, hence, increase the precision of the length-frequency estimates, is to reduce tow duration and use the time saved to collect samples at more stations.

Strong, low frequency (decadal) environmental fluctuations during the 20th century in the North Pacific Ocean, on the Washington coast, and in Puget Sound

At decadal period (10-20 years), dynamic linkage was evident between atmospheric low pressure systems over the North Pacific Ocean and circulation in a Pacific Northwest fjord (Puget Sound). As the Aleutian low pressure center shifts, storms arriving from the North Pacific Ocean deposit varying amounts of precipitation in the mountains draining into the estuarine system; in turn, the fluctuating addition of fresh water changes the density distribution near the fjord basin entrance sill, thereby constraining the fjord's vertical velocity structure. This linkage was examined using time series of 21 environmental parameters from 1899 to 1987. Covariation in the time series was evident because of the strong decadal cycles compared with long-term averages, interannual variability, and seasonal cycles.

Marine Vertebrates and Low Frequency Sound: Technical Report for LFA EIS

Over the past 50 years, economic and technological developments have dramatically increased the human contribution to ambient noise in the ocean. The dominant frequencies of most human-made noise in the ocean is in the low-frequency range (defined as sound energy below 1000Hz), and low-frequency sound (LFS) may travel great distances in the ocean due to the unique propagation characteristics of the deep ocean (Munk et al. 1989). For example, in the Northern Hemisphere oceans low-frequency ambient noise levels have increased by as much as 10 dB during the period from 1950 to 1975 (Urick 1986; review by NRC 1994). Shipping is the overwhelmingly dominant source of low-frequency manmade noise in the ocean, but other sources of manmade LFS including sounds from oil and gas industrial development and production activities (seismic exploration, construction work, drilling, production platforms), and scientific research (e.g., acoustic tomography and thermography, underwater communication). The SURTASS LFA system is an additional source of human-produced LFS in the ocean, contributing sound energy in the 100-500 Hz band. When considering a document that addresses the potential effects of a low-frequency sound source on the marine environment, it is important to focus upon those species that are the most likely to be affected. Important criteria are: 1) the physics of sound as it relates to biological organisms; 2) the nature of the exposure (i.e. duration, frequency, and intensity); and 3) the geographic region in which the sound source will be operated (which, when considered with the distribution of the organisms will determine which species will be exposed). The goal in this section of the LFA/EIS is to examine the status, distribution, abundance, reproduction, foraging behavior, vocal behavior, and known impacts of human activity of those species may be impacted by LFA operations. To focus our efforts, we have examined species that may be physically affected and are found in the region where the LFA source will be operated. The large-scale geographic location of species in relation to the sound source can be determined from the distribution of each species. However, the physical ability for the organism to be impacted depends upon the nature of the sound source (i.e. explosive, impulsive, or non-impulsive); and the acoustic properties of the medium (i.e. seawater) and the organism. Non-impulsive sound is comprised of the movement of particles in a medium. Motion is imparted by a vibrating object (diaphragm of a speaker, vocal chords, etc.). Due to the proximity of the particles in the medium, this motion is transmitted from particle to particle in waves away from the sound source. Because the particle motion is along the same axis as the propagating wave, the waves are longitudinal. Particles move away from then back towards the vibrating source, creating areas of compression (high pressure) and areas of rarefaction (low pressure). As the motion is transferred from one particle to the next, the sound propagates away from the sound source. Wavelength is the distance from one pressure peak to the next. Frequency is the number of waves passing per unit time (Hz). Sound velocity (not to be confused with particle velocity) is the impedance is loosely equivalent to the resistance of a medium to the passage of sound waves (technically it is the ratio of acoustic pressure to particle velocity). A high impedance means that acoustic particle velocity is small for a given pressure (low impedance the opposite). When a sound strikes a boundary between media of different impedances, both reflection and refraction, and a transfer of energy can occur. The intensity of the reflection is a function of the intensity of the sound wave and the impedances of the two media. Two key factors in determining the potential for damage due to a sound source are the intensity of the sound wave and the impedance difference between the two media (impedance mis-match). The bodies of the vast majority of organisms in the ocean (particularly phytoplankton and zooplankton) have similar sound impedence values to that of seawater. As a result, the potential for sound damage is low; organisms are effectively transparent to the sound – it passes through them without transferring damage-causing energy. Due to the considerations above, we have undertaken a detailed analysis of species which met the following criteria: 1) Is the species capable of being physically affected by LFS? Are acoustic impedence mis-matches large enough to enable LFS to have a physical affect or allow the species to sense LFS? 2) Does the proposed SURTASS LFA geographical sphere of acoustic influence overlap the distribution of the species? Species that did not meet the above criteria were excluded from consideration. For example, phytoplankton and zooplankton species lack acoustic impedance mis-matches at low frequencies to expect them to be physically affected SURTASS LFA. Vertebrates are the organisms that fit these criteria and we have accordingly focused our analysis of the affected environment on these vertebrate groups in the world’s oceans: fishes, reptiles, seabirds, pinnipeds, cetaceans, pinnipeds, mustelids, sirenians (Table 1).