Research Interests

My primary research objective is to improve our understanding of the carbon cycle through inverse modeling.
Specifically, I'm interested in quantifying greenhouse gas fluxes and understanding the physical processes driving them. To reach this end, I use atmospheric observations from satellites, aircraft, and surface networks and interpret them in the context of atmospheric models (e.g., chemical transport models and particle dispersion models). Please see below for details on current and past research projects.

Current Research Areas: Past Research Projects:

Current Research Projects

Sources and sinks of atmospheric methane (back to top)

Methane is the second most important anthropogenic greenhouse gas after CO2. As a short-lived climate forcing agent (lifetime ~10 years), it provides a lever for slowing near-term climate change. Major anthropogenic sources of methane include oil/gas exploration and use, livestock, landfills, coal mining, and rice cultivation. Wetlands are the dominant natural source. The magnitude and spatial distribution of methane sources is highly uncertain and difficult to constrain.


Fig. Simulated methane concentrations using emissions constrained by satellite observations.

Objectives:

  • Validate methane observations from orbital platforms using surface, aircraft, and column observations
  • Test the utility of space-borne observations (such as GOSAT, TROPOMI, GeoCARB, and MethaneSAT) for constraining methane emissions
  • Improve our understanding of processes governing spatial patterns and decadal trends of atmospheric methane

Collaborators:

Ron Cohen (UC Berkeley), Inez Fung (UC Berkeley), Daniel Jacob (Harvard), Christian Frankenberg (Caltech/JPL), & Paul Wennberg (Caltech)

References:

  • Turner et al. (2018), Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales, Atmos. Chem. Phys. | PDF
  • Turner et al. (2017), Ambiguity in the causes for decadal trends in atmospheric methane and hydroxyl, Proc. Natl. Acad. Sci. | PDF
  • Turner et al. (2016), A large increase in U.S. methane emissions over the past decade inferred from satellite data and surface observations, Geophys. Res. Lett. | PDF
  • Turner et al. (2015), Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data, Atmos. Chem. Phys. | PDF

Support:

Miller Fellowship, a DOE ERCAP grant, DOE Computational Science Graduate Fellowship, and a NASA Carbon Monitoring System grant.


Chemical feedbacks, variability, and predictability (back to top)

The hydroxyl radical (OH) is the primary oxidant for a number of non-CO2 greenhouse gases and CFCs. It also regulates the production of tropospheric ozone, a leading pollutant. As such, changes in tropospheric OH could have large implications for both future climate and air quality. However we currently lack a predictive understanding of OH on decadal-to-centennial timescales, evidenced by the disagreement between global models in their simulation of OH.


Fig. OH concentrations in a 6000 year equilibrium simulation with a coupled chemistry-climate model.

Objectives:

  • Investigate the chemical feedback mechanisms and their timescales in both simple and complex models
  • Identify aspects of the chemical system that may be more predictable
  • Quantify the importance of the natural feedbacks and oscillations for predictability of greenhouse gas burdens

Collaborators:

Inez Fung (UC Berkeley), Ron Cohen (UC Berkeley), Vaishali Naik (NOAA/GFDL), & Christian Frankenberg (Caltech/JPL)

Support:

Miller Fellowship and a DOE ERCAP grant.


Urban carbon dioxide (back to top)

Carbon dixoide (CO2) is an atmospheric trace gas and the largest anthropogenic radiative forcer. CO2 levels have increased from 280 ppm in pre-industrial times to greater than 400 ppm in the present, largely due to changes in fossil fuel emissions, and can be measured via ground stations, aircraft, and satellites. The paradigm in ground-based trace gas measurements has been to employ a sparse network of high-precision instruments that can be used to measure atmospheric concentrations. These concentrations are then used to estimate emission fluxes, validate numerical models, and quantify changes in physical processes. However, the BEACO2N project (http://beacon.berkeley.edu/Overview.aspx) aims to provide a better understanding of the emissions and physical processes governing CO2 by deploying a high density of moderate-precision instruments.


Fig. We constructed a custom, hourly, 1-km CO2 emission inventory for the Bay Area.

Objectives:

  • Determine the optimal CO2 measurement network for California's Bay Area
  • Use the BEACO2N observations to estimate hourly atmospheric fluxes of CO2 at 1 km spatial resolution
  • Use these hourly fluxes to improve our understanding of the underlying physical processes

Collaborators:

Ron Cohen (UC Berkeley), Rob Harley (UC Berkeley), & Brian McDonald (NOAA)

References:

  • Turner et al. (2016), Network design for quantifying urban CO2 emissions: Assessing trade-offs between precision and network density, Atmos. Chem. Phys. | PDF
  • Shusterman et al. (2016), The BErkeley Atmospheric CO2 Observation Network: initial evaluation, Atmos. Chem. Phys. | PDF

Support:

Miller Fellowship, DOE Computational Science Graduate Fellowship, and a DOE ERCAP grant.


Designing multi-scale state vectors (back to top)

Inverse models quantify the state variables driving the evolution of a physical system by using observations of that system. This requires a physical model that relates a set of input variables (state vector) to a set of output variables (observation vector). A critical step in solving the inverse problem is determining the amount of information contained in the observations and choosing the state vector accordingly. This is a non-trivial problem when using a large ensemble of observations with large errors.


Fig. Illustration of using a Gaussian mixture model and radial basis functions for defining the state vector.

Objectives:

  • Explore different methods of constructing the state vector in an inverse model
  • Quantify the scale dependence of the different error components in the inverse solution
  • Develop a method for evaluating the different methods of constructing a state vector

Collaborators:

Ron Cohen (UC Berkeley) & Daniel Jacob (Harvard)

References:

  • Turner and Jacob (2015), Balancing aggregation and smoothing errors in inverse models, Atmos. Chem. Phys. | PDF

Support:

Miller Fellowship, DOE Computational Science Graduate Fellowship, and a NASA Carbon Monitoring System grant.



© Alexander J. Turner - 2018