Justin E. Bagley

Modelling C3 photosynthesis from the chloroplast to the ecosystem

Globally, photosynthesis accounts for the largest flux of CO₂ from the atmosphere into ecosystems and is the driving process for terrestrial ecosystem function. The importance of accurate predictions of photosynthesis over a range of plant growth conditions led to the development of a C₃ photosynthesis model by Farquhar, von Caemmerer & Berry that has become increasingly important as society places greater pressures on vegetation. The photosynthesis model has played a major role in defining the path towards scientific understanding of photosynthetic carbon uptake and the role of photosynthesis on regulating the earths climate and biogeochemical systems. In this review, we summarize the photosynthesis model, including its continued development and applications. We also review the implications these developments have on quantifying photosynthesis at a wide range of spatial and temporal scales, and discuss the models role in determining photosynthetic responses to changes in environmental conditions. Finally, the review includes a discussion of the larger-scale modelling and remote-sensing applications that rely on the leaf photosynthesis model and are likely to open new scientific avenues to address the increasing challenges to plant productivity over the next century.

Biophysical impacts of climate-smart agriculture in the Midwest United States

The potential impacts of climate change in the Midwest United States present unprecedented challenges to regional agriculture. In response to these challenges, a variety of climate-smart agricultural methodologies have been proposed to retain or improve crop yields, reduce agricultural greenhouse gas emissions, retain soil quality and increase climate resilience of agricultural systems. One component that is commonly neglected when assessing the environmental impacts of climate-smart agriculture is the biophysical impacts, where changes in ecosystem fluxes and storage of moisture and energy lead to perturbations in local climate and water availability. Using a combination of observational data and an agroecosystem model, a series of climate-smart agricultural scenarios were assessed to determine the biophysical impacts these techniques have in the Midwest United States. The first scenario extended the growing season for existing crops using future temperature and CO2 concentrations. The second scenario examined the biophysical impacts of no-till agriculture and the impacts of annually retaining crop debris. Finally, the third scenario evaluated the potential impacts that the adoption of perennial cultivars had on biophysical quantities. Each of these scenarios was found to have significant biophysical impacts. However, the timing and magnitude of the biophysical impacts differed between scenarios.

The influence of photosynthetic acclimation to rising CO2 and warmer temperatures on leaf and canopy photosynthesis models

There is an increasing necessity to understand how climate change factors, particularly increasing atmospheric concentrations of CO2 ([CO2]) and rising temperature, will influence photosynthetic carbon assimilation (A). Based on theory, an increased [CO2] concomitant with a rise in temperature will increase A in C3 plants beyond that of an increase in [CO2] alone. However, uncertainty surrounding the acclimation response of key photosynthetic parameters to these changes can influence this response. In this work, the acclimation responses of C3 photosynthesis for soybean measured at the SoyFACE Temperature by Free-Air CO2 Enrichment experiment are incorporated in a leaf biochemical and canopy photosynthesis model. The two key parameters used as model inputs, the maximum velocity for carboxylation (Vc,max) and maximum rate of electron transport (Jmax), were measured in a full factorial [CO2] by temperature experiment over two growing seasons and applied in leaf- and canopy-scale models to (1) reassess the theory of combined increases in [CO2] and temperature on A, (2) determine the role of photosynthetic acclimation to increased growth [CO2] and/or temperature in leaf and canopy predictions of A for these treatments, and (3) assess the diurnal and seasonal differences in leaf- and canopy-scale A associated with the imposed treatments. The results demonstrate that the theory behind combined increases in [CO2] and temperature is sound; however, incorporating more recent parameterizations into the photosynthesis model predicts greater increases in A when [CO2] and temperature are increased together. Photosynthetic acclimation is shown to decrease leaf-level A for all treatments; however, in elevated [CO2] the impact of acclimation does not result in any appreciable loss in photosynthetic potential at the canopy scale. In this analysis, neglecting photosynthetic acclimation in heated treatments, with or without concomitant rise in [CO2], leads to modeled overestimates of carbon gain for soybean under future predicted conditions.

Estimating methane emissions in California's urban and rural regions using multitower observations

We present an analysis of methane (CH_4) emissions using atmospheric observations from 13 sites in California during June 2013 to May 2014. A hierarchical Bayesian inversion method is used to estimate CH_4 emissions for spatial regions (0.3° pixels for major regions) by comparing measured CH_4 mixing ratios with transport model (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) predictions based on seasonally varying California-specific CH_4 prior emission models. The transport model is assessed using a combination of meteorological and carbon monoxide (CO) measurements coupled with the gridded California Air Resources Board (CARB) CO emission inventory. The hierarchical Bayesian inversion suggests that state annual anthropogenic CH_4 emissions are 2.42 ± 0.49 Tg CH_4/yr (at 95% confidence), higher (1.2–1.8 times) than the current CARB inventory (1.64 Tg CH_4/yr in 2013). It should be noted that undiagnosed sources of errors or uncaptured errors in the model-measurement mismatch covariance may increase these uncertainty bounds beyond that indicated here. The CH_4 emissions from the Central Valley and urban regions (San Francisco Bay and South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, respectively. This study suggests that the livestock sector is likely the major contributor to the state total CH_4 emissions, in agreement with CARBs inventory. Attribution to source sectors for subregions of California using additional trace gas species would further improve the quantification of Californias CH_4 emissions and mitigation efforts toward the California Global Warming Solutions Act of 2006 (Assembly Bill 32).

Impacts of second‐generation biofuel feedstock production in the central U.S. on the hydrologic cycle and global warming mitigation potential

Biofuel feedstocks provide a renewable energy source that can reduce fossil fuel emissions, however, if produced on a large scale they can also impact local to regional water and carbon budgets. Simulation results for 2005-2014 from a regional weather model adapted to simulate the growth of two perennial grass biofuel feedstocks suggest that replacing at least half the current annual cropland with these grasses would increase water use efficiency and drive greater rainfall downwind of perturbed grid cells, but increased evapotranspiration (ET) might switch the Mississippi River basin from having a net warm-season surplus of water (precipitation minus ET) to a net deficit. While this scenario reduces land required for biofuel feedstock production relative to current use for maize grain ethanol production, it only offsets approximately one decade of projected anthropogenic warming and increased water vapor results in greater atmospheric heat content.

The influence of land cover on surface energy partitioning and evaporative fraction regimes in the U.S. Southern Great Plains

Author(s): Bagley, JE; Kueppers, LM; Billesbach, DP; Williams, IN; Biraud, SC; Torn, MS | Abstract:

Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions

Atmospheric inverse estimates of gas emissions depend on transport model predictions, hence driving a need to assess uncertainties in the transport model. In this study we assess the uncertainty in WRF-STILT (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) model predictions using a combination of meteorological and carbon monoxide (CO) measurements. WRF configurations were selected to minimize meteorological biases using meteorological measurements of winds and boundary layer depths from surface stations and radar wind profiler sites across California. We compare model predictions with CO measurements from four tower sites in California from June 2013 through May 2014 to assess the seasonal biases and random errors in predicted CO mixing ratios. In general, the seasonal mean biases in boundary layer wind speed (< ~ 0.5 m/s), direction (< ~ 15°), and boundary layer height (< ~ 200 m) were small. However, random errors were large (~1.5–3.0 m/s for wind speed, ~ 40–60° for wind direction, and ~ 300–500 m for boundary layer height). Regression analysis of predicted and measured CO yielded near-unity slopes (i.e., within 1.0 ± 0.20) for the majority of sites and seasons, though a subset of sites and seasons exhibit larger (~30%) uncertainty, particularly when weak winds combined with complex terrain in the South Central Valley of California. Looking across sites and seasons, these results suggest that WRF-STILT simulations are sufficient to estimate emissions of CO to up to 15% on annual time scales across California.

On the Long-Term Hydroclimatic Sustainability of Perennial Bioenergy Crop Expansion over the United States

AbstractLarge-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switchgrass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. The hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States are quantified using the Weather Research and Forecasting Model dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted using seasonally evolving biophysical representation of perennial bioenergy cropping systems within the LSM based on observational data. Deployment is carried out only...

A Realistic Meteorological Assessment of Perennial Biofuel Crop Deployment: a Southern Great Plains Perspective

Utility of perennial bioenergy crops (e.g., switchgrass and miscanthus) offers unique opportunities to transition toward a more sustainable energy pathway due to their reduced carbon footprint, averted competition with food crops, and ability to grow on abandoned and degraded farmlands. Studies that have examined biogeophysical impacts of these crops noted a positive feedback between near‐surface cooling and enhanced evapotranspiration (ET), but also potential unintended consequences of soil moisture and groundwater depletion. To better understand hydrometeorological effects of perennial bioenergy crop expansion, this study conducted high‐resolution (2‐km grid spacing) simulations with a state‐of‐the‐art atmospheric model (Weather Research and Forecasting system) dynamically coupled to a land surface model. We applied the modeling system over the Southern Plains of the United States during a normal precipitation year (2007) and a drought year (2011). By focusing the deployment of bioenergy cropping systems on marginal and abandoned farmland areas (to reduce the potential conflict with food systems), the research presented here is the first realistic examination of hydrometeorological impacts associated with perennial bioenergy crop expansion. Our results illustrate that the deployment of perennial bioenergy crops leads to widespread cooling (1–2 °C) that is largely driven by an enhanced reflection of shortwave radiation and, secondarily, due to an enhanced ET. Bioenergy crop deployment was shown to reduce the impacts of drought through simultaneous moistening and cooling of the near‐surface environment. However, simulated impacts on near‐surface cooling and ET were reduced during the drought relative to a normal precipitation year, revealing differential effects based on background environmental conditions. This study serves as a key step toward the assessment of hydroclimatic sustainability associated with perennial bioenergy crop expansion under diverse hydrometeorological conditions by highlighting the driving mechanisms and processes associated with this energy pathway.

Inverse Estimation of an Annual Cycle of California's Nitrous Oxide Emissions

Author(s): Jeong, S; Newman, S; Zhang, J; Andrews, AE; Bianco, L; Dlugokencky, E; Bagley, J; Cui, X; Priest, C; Campos-Pineda, M; Fischer, ML | Abstract: ©2018. American Geophysical Union. All Rights Reserved. Nitrous oxide (N2O) is a potent long-lived greenhouse gas (GHG) and the strongest current emissions of global anthropogenic stratospheric ozone depletion weighted by its ozone depletion potential. In California, N2O is the third largest contributor to the states anthropogenic GHG emission inventory, though no study has quantified its statewide annual emissions through top-down inverse modeling. Here we present the first annual (2013–2014) statewide top-down estimates of anthropogenic N2O emissions. Utilizing continuous N2O observations from six sites across California in a hierarchical Bayesian inversion, we estimate that annual anthropogenic emissions are 1.5–2.5 times (at 95% confidence) the state inventory (41 Gg N2O in 2014). Without mitigation, this estimate represents 4–7% of total GHG emissions assuming that other reported GHG emissions are reasonably correct. This suggests that control of N2O could be an important component in meeting Californias emission reduction goals of 40% and 80% below 1990 levels of the total GHG emissions (in CO2 equivalent) by 2030 and 2050, respectively. Our seasonality analysis suggests that emissions are similar across seasons within posterior uncertainties. Future work is needed to provide source attribution for subregions and further characterization of seasonal variability.