Turner Group

Research Interests

Our research is at the intersection of atmospheric chemistry and the carbon cycle.
Specifically, our work aims to quantify the impacts of variations in atmospheric chemistry on the carbon cycle and, conversely, bring insights from the carbon cycle into atmospheric chemistry. The primary tools that we use and develop in our work are: Bayesian inference, machine learning, and satellite remote sensing. Please see below for details on current research areas.

Current Research Areas:

Current Research Areas

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:

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

Collaborators:

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

References:

  • Turner et al. (2019), Interpreting contemporary trends in atmospheric methane, Proc. Natl. Acad. Sci. | PDF
  • 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.


Chemistry-climate 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)

References:

  • Turner et al. (2018), Modulation of hydroxyl variability by ENSO in the absence of external forcing, Proc. Natl. Acad. Sci. | 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. (2013), Summertime cyclones over the Great Lakes Storm Track from 1860-2100: variability, trends, and association with ozone pollution, Atmos. Chem. Phys. | PDF

Support:

Miller Fellowship and a DOE ERCAP grant.


Urban carbon dioxide (back to top)

Carbon dioxide (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. Satellite measurements related to photosynthetic activity.

Objectives:

  • Determine the optimal CO2 measurement network for California's Bay Area
  • Use Solar-Induced chlorophyll Fluorescence (SIF) measurements from TROPOMI to estimate CO2 uptake from the biosphere
  • Use the BEACO2N observations to estimate hourly atmospheric fluxes of CO2 at 1 km spatial resolution

Collaborators:

Ron Cohen (UC Berkeley), Brain McDonald (NOAA), Christian Frankenberg (Caltech), & Troy Magney (UC Davis)

References:

  • Turner et al. (2021), Extreme events driving year-to-year differences in gross primary productivity across the US, Biogeosci. | PDF
  • Turner et al. (2020), Observed impacts of COVID-19 on urban CO2 emissions, Geophys. Res. Lett. | PDF
  • Turner et al. (2020), A double peak in the seasonality of California's photosynthesis as observed from space, Biogeosci. | PDF
  • Turner et al. (2016), Network design for quantifying urban CO2 emissions: Assessing trade-offs between precision and network density, Atmos. Chem. Phys. | PDF

Support:

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


High-dimensional inverse problems (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 formulating the problem in a tractable manner. This is a non-trivial problem when both the input and output variables are high-dimensional, as is commonly encountered in atmospheric science.


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 information content of dense observing systems
  • Develop novel methods for estimating greenhouse gas fluxes

Collaborators:

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

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. (2016), Network design for quantifying urban CO2 emissions: Assessing trade-offs between precision and network density, Atmos. Chem. Phys. | PDF
  • Turner and Jacob (2015), Balancing aggregation and smoothing errors in inverse models, Atmos. Chem. Phys. | PDF
  • Turner et al. (2012), The spatial extent of source influences on modeled column concentrations of short-lived species, Geophys. Res. Lett. | PDF

Support:

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


© Alexander J. Turner - 2022