Atmospheric and Oceanic Physics
New submissions
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New submissions for Fri, 3 May 24
- [1] arXiv:2405.00763 [pdf, ps, other]
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Title: Wave-induced biases in ADCP measurements from quasi Lagrangian platformsComments: 33 pages, 14 figuresSubjects: Atmospheric and Oceanic Physics (physics.ao-ph); Instrumentation and Detectors (physics.ins-det)
Compact autonomous marine vehicles, both surface and submersible, are now commonly used to conduct observations of ocean velocities using Acoustic Doppler Current Profilers (ADCPs). However, in the inevitable presence of surface waves, ADCP measurements conducted by these platforms are susceptible to biases stemming from wave-coherent orbital motion and platform tilting. In typical ocean conditions, the magnitude of the bias can reach tens of centimeters per second. This paper presents analytical derivation of the depth-dependent bias formulas for a variety of scenarios, encompassing surface and subsurface platforms, upward- and downward-looking ADCPs, free-drifting and self-propelled vehicles. The bias is shown to be a function of the wave field properties, platform response dynamics, and the ADCP configuration (particularly, orientation and beam angle). In all cases, the wave-induced biases show parametric scaling similar to that of the Stokes drift, albeit with a number of critical nuances. Analytical derivations are validated with a semi-analytical model, which can also be used to estimate the biases for more complex measurement configurations. Further analysis reveals unexpected fundamental differences between the upward- and downward-looking ADCP configurations, offering insights for experimental design aimed at minimizing and mitigating wave-induced biases in autonomous oceanographic observations.
- [2] arXiv:2405.01287 [pdf, ps, other]
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Title: Strength and Sensitivity of Land-Atmosphere InteractionAuthors: Jun YinSubjects: Atmospheric and Oceanic Physics (physics.ao-ph); Geophysics (physics.geo-ph)
The land-atmosphere coupling strength has been defined as the percentage of precipitation variability explained by the variation of soil moisture in the Global Land-Atmosphere Coupling Experiment (GLACE). While it is useful to identify global hotspots of land-atmosphere interaction, this coupling strength is different from coupling sensitivity, which directly quantifies how precipitation generation responds to the perturbation of soil moisture and is essential for our understanding of the global water cycle. To disentangle these two quantities, here we theoretically explore the relationships among coupling strength, sensitivity, and soil moisture variances. We use climate model outputs to show that the largest soil moisture variances are located in the transitional climate zones and the variations of soil moisture largely account for the geographical patterns of coupling hotspots. The coupling sensitivity is not necessarily low in non-hotspot regions, which could impose great impacts on the development of extreme climate events. We therefore call for more research attention on coupling sensitivity to improve our understanding of the climate system.
Cross-lists for Fri, 3 May 24
- [3] arXiv:2405.00879 (cross-list from cs.LG) [pdf, other]
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Title: Machine Learning Techniques for Data Reduction of Climate ApplicationsComments: 7 pages. arXiv admin note: text overlap with arXiv:2404.18063Subjects: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the integrity of QoI. For many spatiotemporal applications, these QoI are binary in nature and represent presence or absence of a physical phenomenon. We present a pipelined compression approach that first uses neural-network-based techniques to derive regions where QoI are highly likely to be present. Then, we employ a Guaranteed Autoencoder (GAE) to compress data with differential error bounds. GAE uses QoI information to apply low-error compression to only these regions. This results in overall high compression ratios while still achieving downstream goals of simulation or data collections. Experimental results are presented for climate data generated from the E3SM Simulation model for downstream quantities such as tropical cyclone and atmospheric river detection and tracking. These results show that our approach is superior to comparable methods in the literature.
- [4] arXiv:2405.00931 (cross-list from astro-ph.EP) [pdf, other]
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Title: Milanković Forcing in Deep TimeComments: Final revised version. In press, Paleoceanography and PaleoclimatologySubjects: Earth and Planetary Astrophysics (astro-ph.EP); Atmospheric and Oceanic Physics (physics.ao-ph); Geophysics (physics.geo-ph)
Astronomical (or Milankovi\'c) forcing of the Earth system is key to understanding rhythmic climate change on time scales >~ 10 kyr. Paleoceanographic and paleoclimatological applications concerned with past astronomical forcing rely on astronomical calculations (solutions), which represent the backbone of cyclostratigraphy and astrochronology. Here we present state-of-the-art astronomical solutions over the past 3.5 Gyr. Our goal is to provide tuning targets and templates for interpreting deep-time cyclostratigraphic records and designing external forcing functions in climate models. Our approach yields internally consistent orbital and precession-tilt solutions, including fundamental solar system frequencies, orbital eccentricity and inclination, lunar distance, luni-solar precession rate, Earth's obliquity, and climatic precession. Contrary to expectations, we find that the long eccentricity cycle (previously assumed stable and labeled ''metronome'', recent period ~405 kyr), can become unstable on long time scales. Our results reveal episodes during which the long eccentricity cycle is very weak or absent and Earth's orbital eccentricity and climate-forcing spectrum are unrecognizable compared to the recent past. For the ratio of eccentricity-to-inclination amplitude modulation (frequently observable in paleorecords) we find a wide distribution around the recent 2:1 ratio, i.e., the system is not restricted to a 2:1 or 1:1 resonance state. Our computations show that Earth's obliquity was lower and its amplitude (variation around the mean) significantly reduced in the past. We therefore predict weaker climate forcing at obliquity frequencies in deep time and a trend toward reduced obliquity power with age in stratigraphic records. For deep-time stratigraphic and modeling applications, the orbital parameters of our 3.5-Gyr integrations are made available at 400-year resolution.
Replacements for Fri, 3 May 24
- [5] arXiv:2210.11529 (replaced) [pdf, other]
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Title: Identifying Lightning Processes in ERA5 Soundings with Deep LearningSubjects: Atmospheric and Oceanic Physics (physics.ao-ph)
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