Morphometry of small recent impact craters on Mars: size and terrain dependence, short-term modification

Most recent studies of crater morphometry on Mars have addressed large craters (D\u3e5 km) using elevation models derived from laser altimetry. In the present work, we examine a global population of small (25 m ≤D≤ 5 km), relatively well-preserved simple impact craters using HiRISE stereo-derived elevation models. We find that scaling laws from prior studies of large simple craters generally overestimate the depth and volume at small diameters. We show that crater rim curvature exhibits a strong diameter dependence that is well-described by scaling laws for Ddiameter, upper rim slopes begin to exceed typical repose angles and crater rims sharpen significantly. This transition is likely the result of gravity-driven collapse of the upper cavity walls during crater formation or short-term modification. In addition, we identify a tendency for small craters (Dm) to be more conical than large craters, and we show that the average cavity cross-section is well-described by a power law with exponent ~1.75 (neither conical nor paraboloidal). We also conduct a statistical comparison of crater subpopulations to illuminate trends with increasing modification and target strength. These results have important implications for describing the “initial condition” of simple crater shape as a function of diameter and geological setting, and for understanding how impact craters are modified on the martian surface over time

A geostatistical framework for quantifying the imprint of mesoscale atmospheric transport on satellite trace gas retrievals

National Aeronautics and Space Administration's Orbiting Carbon Observatory‐2 (OCO‐2) satellite provides observations of total column‐averaged CO2 mole fractions (X_(CO₂)) at high spatial resolution that may enable novel constraints on surface‐atmosphere carbon fluxes. Atmospheric inverse modeling provides an approach to optimize surface fluxes at regional scales, but the accuracy of the fluxes from inversion frameworks depends on key inputs, including spatially and temporally dense CO₂ observations and reliable representations of atmospheric transport. Since X_(CO₂) observations are sensitive to both synoptic and mesoscale variations within the free troposphere, horizontal atmospheric transport imparts substantial variations in these data and must be either resolved explicitly by the atmospheric transport model or accounted for within the error covariance budget provided to inverse frameworks. Here, we used geostatistical techniques to quantify the imprint of atmospheric transport in along‐track OCO‐2 soundings. We compare high‐pass‐filtered (<250 km, spatial scales that primarily isolate mesoscale or finer‐scale variations) along‐track spatial variability in X_(CO₂) and X_(H₂O) from OCO‐2 tracks to temporal synoptic and mesoscale variability from ground‐based X_(CO₂) and X_(H₂O) observed by nearby Total Carbon Column Observing Network sites. Mesoscale atmospheric transport is found to be the primary driver of along‐track, high‐frequency variability for OCO‐2 X_(H₂O). For X_(CO₂), both mesoscale transport variability and spatially coherent bias associated with other elements of the OCO‐2 retrieval state vector are important drivers of the along‐track variance budget

A Geostatistical Framework for Quantifying the Imprint of Mesoscale Atmospheric Transport on Satellite Trace Gas Retrievals

National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2 (OCO-2) satellite provides observations of total column-averaged CO2 mole fractions (XCO2) at high spatial resolution that may enable novel constraints on surface-atmosphere carbon fluxes. Atmospheric inverse modeling provides an approach to optimize surface fluxes at regional scales, but the accuracy of the fluxes from inversion frameworks depends on key inputs, including spatially and temporally dense CO2 observations and reliable representations of atmospheric transport. Since XCO2 observations are sensitive to both synoptic and mesoscale variations within the free troposphere, horizontal atmospheric transport imparts substantial variations in these data and must be either resolved explicitly by the atmospheric transport model or accounted for within the error covariance budget provided to inverse frameworks. Here, we used geostatistical techniques to quantify the imprint of atmospheric transport in along-track OCO-2 soundings. We compare high-pass-filtered (<250 km, spatial scales that primarily isolate mesoscale or finer-scale variations) along-track spatial variability in XCO2 and XH2O from OCO-2 tracks to temporal synoptic and mesoscale variability from ground-based XCO2 and XH2O observed by nearby Total Carbon Column Observing Network sites. Mesoscale atmospheric transport is found to be the primary driver of along-track, high-frequency variability for OCO-2 XH2O. For XCO2, both mesoscale transport variability and spatially coherent bias associated with other elements of the OCO-2 retrieval state vector are important drivers of the along-track variance budget.Plain Language SummaryNumerous efforts have been made to quantify sources and sinks of atmospheric CO2 at regional spatial scales. A common approach to infer these sources and sinks requires accurate representation of variability of CO2 observations attributed to transport by weather systems. While numerical weather prediction models have a fairly reasonable representation of larger-scale weather systems, such as frontal systems, representation of smaller-scale features (<250 km), is less reliable. In this study, we find that the variability of total column-averaged CO2 observations attributed to these fine-scale weather systems accounts for up to half of the variability attributed to local sources and sinks. Here, we provide a framework for quantifying the drivers of spatial variability of atmospheric trace gases rather than simply relying on numerical weather prediction models. We use this framework to quantify potential sources of errors in measurements of total column-averaged CO2 and water vapor from National Aeronautics and Space Administration’s Orbiting Carbon Observatory-2 satellite.Key PointsWe developed a framework to relate high-frequency spatial variations to transport-induced temporal fluctuations in atmospheric tracersWe use geostatistical analysis to quantify the variance budget for XCO2 and XH2O retrieved from NASA’s OCO-2 satelliteAccounting for random errors, systematic errors, and real geophysical coherence in remotely sensed trace gas observations may yield improved flux constraintsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151988/1/jgrd55658.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151988/2/jgrd55658_am.pd

A geostatistical framework for quantifying the imprint of mesoscale atmospheric transport on satellite trace gas retrievals

National Aeronautics and Space Administration's Orbiting Carbon Observatory‐2 (OCO‐2) satellite provides observations of total column‐averaged CO2 mole fractions (X_(CO₂)) at high spatial resolution that may enable novel constraints on surface‐atmosphere carbon fluxes. Atmospheric inverse modeling provides an approach to optimize surface fluxes at regional scales, but the accuracy of the fluxes from inversion frameworks depends on key inputs, including spatially and temporally dense CO₂ observations and reliable representations of atmospheric transport. Since X_(CO₂) observations are sensitive to both synoptic and mesoscale variations within the free troposphere, horizontal atmospheric transport imparts substantial variations in these data and must be either resolved explicitly by the atmospheric transport model or accounted for within the error covariance budget provided to inverse frameworks. Here, we used geostatistical techniques to quantify the imprint of atmospheric transport in along‐track OCO‐2 soundings. We compare high‐pass‐filtered (<250 km, spatial scales that primarily isolate mesoscale or finer‐scale variations) along‐track spatial variability in X_(CO₂) and X_(H₂O) from OCO‐2 tracks to temporal synoptic and mesoscale variability from ground‐based X_(CO₂) and X_(H₂O) observed by nearby Total Carbon Column Observing Network sites. Mesoscale atmospheric transport is found to be the primary driver of along‐track, high‐frequency variability for OCO‐2 X_(H₂O). For X_(CO₂), both mesoscale transport variability and spatially coherent bias associated with other elements of the OCO‐2 retrieval state vector are important drivers of the along‐track variance budget

Questions and clarity: insights from applying computational methods to paleoclimate archives

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Marine Geology & Geophysics at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution May 2022.It is a scientifically accepted fact that the Earth’s climate is presently undergoing significant changes with the potential for immense negative impacts on human society. As evidence of these impacts become clear and common, it becomes ever more important to constrain the nature, magnitude, and speed of changes to Earth systems. A fundamentally important tool to this understanding is the Earth’s past, recorded in the geologic record. There, lie examples of climate change under various forcings: important data for understanding the fundamental dynamics of climate change on our planet. However, when a climate signal is written in the geologic record, it is coded into the language of proxies and distorted by time. This thesis endeavors to decode that record using a variety of computational methods on a number of challenging proxies, to draw more information from the climate past than has previously been possible. First, machine learning and computer vision are used to decipher the primary, centimeter-scale textures of carbonate deposits in Searles Valley and Mono Lake, California. This work is able to connect facies in the tufa at Searles, grown during the Last Glacial Period, and those forming presently at Mono Lake. Next, the tracks of icebergs purged during Heinrich Events are simulated using the MIT General Circulation Model. This work, running multiple experiments exploring different aspects internal and external to the icebergs, reveals wind and sediment partitioning as centrally important to the spatial extent of Heinrich Layers. Each of these works considers a traditional geologic archive – a carbonate facies, a marine sediment layer – and uses computational methods to approach that archive from a different perspective. By applying these new methods, more information can be gleaned from the geologic record, building a richer narrative of the Earth’s climate history. The final chapter of this thesis discusses effective teaching and strategies for building communities to support teaching practice in Earth Science departments.This thesis work was funded by the MIT EAPS Rasmussen and Whiteman Fellowships, NSF Project Number NSF-EAR-1903544, and the WHOI Academic Programs Office

Questions and Clarity: Insights from Applying Computational Methods to Paleoclimate Archives

It is a scientifically accepted fact that the Earth’s climate is presently undergoing significant changes with the potential for immense negative impacts on human society. As evidence of these impacts become clear and common, it becomes ever more important to constrain the nature, magnitude, and speed of changes to Earth systems. A fundamentally important tool to this understanding is the Earth’s past, recorded in the geologic record. There, lie examples of climate change under various forcings: important data for understanding the fundamental dynamics of climate change on our planet. However, when a climate signal is written in the geologic record, it is coded into the language of proxies and distorted by time. This thesis endeavors to decode that record using a variety of computational methods on a number of challenging proxies, to draw more information from the climate past than has previously been possible. First, machine learning and computer vision are used to decipher the primary, centimeter-scale textures of carbonate deposits in Searles Valley and Mono Lake, California. This work is able to connect facies in the tufa at Searles, grown during the Last Glacial Period, and those forming presently at Mono Lake. Next, the tracks of icebergs purged during Heinrich Events are simulated using the MIT General Circulation Model. This work, running multiple experiments exploring different aspects internal and external to the icebergs, reveals wind and sediment partitioning as centrally important to the spatial extent of Heinrich Layers. Each of these works considers a traditional geologic archive – a carbonate facies, a marine sediment layer – and uses computational methods to approach that archive from a different perspective. By applying these new methods, more information can be gleaned from the geologic record, building a richer narrative of the Earth’s climate history. The final chapter of this thesis discusses effective teaching and strategies for building communities to support teaching practice in Earth Science departments.Ph.D

Modeling Iceberg Longevity and Distribution During Heinrich Events

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Fendrock, M., Condron, A., & McGee, D. Modeling iceberg longevity and distribution during Heinrich Events. Paleoceanography and Paleoclimatology, 37(6), (2022): e2021PA004347, https://doi.org/10.1029/2021pa004347.During the last glacial period (120–12 ka), the Laurentide ice sheet discharged large numbers of icebergs into the North Atlantic. These icebergs carried sediments that were dropped as the icebergs melted, leaving a record of past iceberg activity on the floor of the subpolar North Atlantic. Periods of significant iceberg discharge and increased ice-rafted debris (IRD) deposition, are known as Heinrich Events. These events coincide with global climate change, and the melt from the icebergs involved is frequently hypothesized to have contributed to these changes in climate by adding a significant volume of cold, fresh water to the North Atlantic. Using an iceberg model coupled with the Massachusetts Institute of Technology Global Circulation Model numerical circulation model, we explore the various factors controlling iceberg drift and rates of melt that influence the spatial patterns of IRD deposition during Heinrich Events. In addition to clarifying the influence of sea surface temperature and wind on the path of an armada of icebergs, we demonstrate that the same volume of ice can produce very different patterns of iceberg drift simply by altering the size of icebergs involved. We note also a significant difference in the seasonal locations of icebergs, influenced primarily by the changing winds, and show that the spatial patterns of IRD for Heinrich Event 1 most closely corresponds to where icebergs are located during the summer months. Consistent with proxy evidence, the ocean must be several degrees colder than temperatures estimated for the Last Glacial Maximum in order for icebergs to travel the distance implied by Heinrich Layers