Wofsy, StevenManninen, Ethan Daniel2026-06-0920262026-05-122026Manninen, Ethan Daniel. 2026. On the Capacity of Remote Sensing to Constrain Greenhouse Gas Surface Fluxes from Methane Plumes and the Terrestrial Biosphere Diurnal Cycle. Doctoral Dissertation, Harvard University Graduate School of Arts and Sciences.32701444https://dash.harvard.edu/handle/1/42740507Constraining surface fluxes of greenhouse gasses is a grand challenge for atmospheric chemists. Here we focus on remote sensing of two greenhouse gas fluxes: methane emissions from oil and gas operations, and carbon dioxide fluxes due to the terrestrial biosphere. For each flux, we ask the question: what remote sensing program is needed to constrain different possible flux distributions? We begin with modeling the characteristics of imaging spectrometers needed to detect methane plumes. Next, we consider the temporal resolution needed to capture short duration, high consequence methane emission events. Finally, we evaluate whether it is possible capture daily terrestrial biosphere carbon dioxide drawdown with a single observation per day, and attempt to constrain transpiration using remotely sensed water isotopologues. Strategies for mitigating methane emissions rely on understanding the underlying drivers of methane losses to the atmosphere. Observations of methane plumes emerging from point sources, combined with correct statistical interpretation, can provide key information. In this work, we examine a critical parameter, the probability of detection of a plume. For a given observing system, probability of detection is affected by the properties of the sensor, plume detection algorithm, observing conditions, and emission rate of the source. We parameterize relevant aspects of remotely sensed scenes containing plumes using a nondimensional observability parameter that predicts probability of detection. Our probability of detection model is trained using simulated plumes to capture natural variability in different meteorological conditions, and validated with data from controlled release experiments. We model probability of detection for two airborne imaging spectrometer systems, MethaneAIR and Insight M LeakSurveyor, and one high resolution satellite system, MethaneSAT. Monte Carlo simulations of emissions distributions implied by data from the extensive 2023 MAIRX campaign of MethaneAIR demonstrate the importance of an accurate probability of detection model, due to the heavy tailed emission distribution found in most oil and gas basins. Remote detection of methane plumes informs efforts to mitigate emissions from oil and gas operations. We present first of their kind plume observations from an airborne imaging spectrometer campaign with hour median temporal resolution. To understand the effect of temporal features of plumes, we develop a statistical framework based on a Poisson process. We estimate the prevalence of rare events accounting for temporal features, and demonstrate that steady state accounting of plume emissions do not converge to the truth. We document a diurnal cycle in plume activity in the Permian basin, which implies a >30% bias in emissions estimates that assume time-invariant plume populations. Our findings update the interpretations of recent plume datasets by other aircraft (Global Airborne Observer Permian Basin) and satellites (GHGSat, Global). The significance of the diurnal cycle and the impact of rare, high emission rate events lead us to conclude that multiple remote sensing observations spanning the daytime are needed to constrain CH4 plume emissions in dynamic oil and gas basins like the Permian. The diurnal cycles of the biosphere are critical to the study of global carbon budgets and greenhouse gas monintoring. We estimate diurnal drawdown of carbon dioxide (CO2) by the biosphere from measuerements by continuous daytime column averaged CO2 concentrations. Comparison of top down estimates with bottom up flux inventories from the Vegetation Photosynthesis Respiration Model and CarbonTracker agree in the multiyear mean. However, the bottom up estimates do not recreate top down interannual variability. We also test whether continent spanning observations of XCO2 taken at a single time of day can recover diurnal CO2 drawdown. Recovery of a representative diurnal drawdown rate requires both morning and afternoon observations. We also attempt to recover regional transpiration use efficiency from Total Carbon Column Observing Network (TCCON) measured column concentrations of water and CO2, with promising results. Finally, we investigate diurnal trends in water isotopologues recovered by TCCON. These data record a signal, but we are not able to definitively differentiate between possible underlying mechanisms without additional data.application/pdfenEnvironmental engineeringThesis or Dissertation2026-06-090009-0009-5793-3585