Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95609/1/wrcr10022.pd

Nutrient loading and meteorological conditions explain interannual variability of hypoxia in Chesapeake Bay

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109879/1/lno20145920373.pd

Global monthly averaged CO 2 fluxes recovered using a geostatistical inverse modeling approach: 1. Results using atmospheric measurements

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94622/1/jgrd14633.pd

Assessing biophysical controls on Gulf of Mexico hypoxia through probabilistic modeling

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116353/1/eap2015252492.pd

A U.S. Carbon Cycle Science Plan: First Meeting of the Carbon Cycle Science Working Group; Washington, D. C., 17–18 November 2008

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95400/1/eost16754.pd

Global CO2 Distributions over Land from the Greenhouse Gases Observing Satellite (GOSAT)

January 2009 saw the successful launch of the first space-based mission specifically designed for measuring greenhouse gases, the Japanese Greenhouse gases Observing SATellite (GOSAT). We present global land maps (Level 3 data) of column-averaged CO2 concentrations (X(sub CO2)) derived using observations from the GOSAT ACOS retrieval algorithm, for July through December 2009. The applied geostatistical mapping approach makes it possible to generate maps at high spatial and temporal resolutions that include uncertainty measures and that are derived directly from the Level 2 observations, without invoking an atmospheric transport model or estimates of CO2 uptake and emissions. As such, they are particularly well suited for comparison studies. Results show that the Level 3 maps for July to December 2009 on a lO x 1.250 grid, at six-day resolution capture much of the synoptic scale and regional variability of X(sub CO2), in addition to its overall seasonality. The uncertainty estimates, which reflect local data coverage, X(sub CO2) variability, and retrieval errors, indicate that the Southern latitudes are relatively well-constrained, while the Sahara Desert and the high Northern latitudes are weakly-constrained. A probabilistic comparison to the PCTM/GEOS-5/CASA-GFED model reveals that the most statistically significant discrepancies occur in South America in July and August, and central Asia in September to December. While still preliminary, these results illustrate the usefulness of a high spatiotemporal resolution, data-driven Level 3 data product for direct interpretation and comparison of satellite observations of highly dynamic parameters such as atmospheric CO2

The utility of continuous atmospheric measurements for identifying biospheric CO 2 flux variability

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95026/1/jgrd16859.pd

Detectability of CO2 flux signals by a space‐based lidar mission

Satellite observations of carbon dioxide (CO2) offer novel and distinctive opportunities for improving our quantitative understanding of the carbon cycle. Prospective observations include those from space‐based lidar such as the active sensing of CO2 emissions over nights, days, and seasons (ASCENDS) mission. Here we explore the ability of such a mission to detect regional changes in CO2 fluxes. We investigate these using three prototypical case studies, namely, the thawing of permafrost in the northern high latitudes, the shifting of fossil fuel emissions from Europe to China, and changes in the source/sink characteristics of the Southern Ocean. These three scenarios were used to design signal detection studies to investigate the ability to detect the unfolding of these scenarios compared to a baseline scenario. Results indicate that the ASCENDS mission could detect the types of signals investigated in this study, with the caveat that the study is based on some simplifying assumptions. The permafrost thawing flux perturbation is readily detectable at a high level of significance. The fossil fuel emission detectability is directly related to the strength of the signal and the level of measurement noise. For a nominal (lower) fossil fuel emission signal, only the idealized noise‐free instrument test case produces a clearly detectable signal, while experiments with more realistic noise levels capture the signal only in the higher (exaggerated) signal case. For the Southern Ocean scenario, differences due to the natural variability in the El Niño–Southern Oscillation climatic mode are primarily detectable as a zonal increase.Key PointsDetectability of regional changes in CO2 fluxes by space‐based lidarPermafrost thawing flux perturbation readily detectable by ASCENDS‐like missionSouthern Ocean ENSO‐related flux variability detectable as zonal changePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110893/1/jgrd51945.pd

Detectability of CO2 Flux Signals by a Space-Based Lidar Mission

Satellite observations of carbon dioxide (CO2) offer novel and distinctive opportunities for improving our quantitative understanding of the carbon cycle. Prospective observations include those from space-based lidar such as the Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) mission. Here we explore the ability of such a mission to detect regional changes in CO2 fluxes. We investigate these using three prototypical case studies, namely the thawing of permafrost in the Northern High Latitudes, the shifting of fossil fuel emissions from Europe to China, and changes in the source-sink characteristics of the Southern Ocean. These three scenarios were used to design signal detection studies to investigate the ability to detect the unfolding of these scenarios compared to a baseline scenario. Results indicate that the ASCENDS mission could detect the types of signals investigated in this study, with the caveat that the study is based on some simplifying assumptions. The permafrost thawing flux perturbation is readily detectable at a high level of significance. The fossil fuel emission detectability is directly related to the strength of the signal and the level of measurement noise. For a nominal (lower) fossil fuel emission signal, only the idealized noise-free instrument test case produces a clearly detectable signal, while experiments with more realistic noise levels capture the signal only in the higher (exaggerated) signal case. For the Southern Ocean scenario, differences due to the natural variability in the ENSO climatic mode are primarily detectable as a zonal increase

A geostatistical framework for incorporating transport information in estimating the distribution of a groundwater contaminant plume

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95253/1/wrcr10904.pd