Measuring the Effects of Disturbance & Climate on the CO2 & Energy Exchange of Ponderosa Pine Forests in the Pacific Northwest: Integration of Eddy Flux, Plant and Soil Measurements

The goal is to quantify and understand the influence of climate and disturbance on ecosystem processes and thus net carbon uptake by forests. The objective is to combine tower and ground-based observations to quantify the effects of disturbance on processes controlling carbon storage and CO{sub 2} and energy exchange in varying climatic conditions. Specific objectives are: (1) Investigate the effects of logging and fire on carbon storage and carbon dioxide and energy exchange in chronosequences of ponderosa pine, using consistent methodology; (2) Determine key environmental factors controlling carbon storage and carbon dioxide and energy exchange in these forests through a combination of measurements and process modeling; and (3) Assess spatial variation of the concentrations and transport in complex terrain. The eddy covariance method is used for measurements of CO2, water vapor, and energy exchanges in a chronosequence of ponderosa pine forests (burned in 2002 wildfire, 10 year-old stand, 90 year-old mature stand). The mature stand has been an AmeriFlux site since 2000 (following previous flux sites in young and old stands initiated in 1996). In addition to the eddy covariance measurements, a large suite of biological processes and ecosystem properties are determined for the purpose of developing independent forest carbon budgets and NEP estimates; these include photosynthesis, stand respiration, soil CO{sub 2} fluxes, annual litterfall, foliar chemistry, and bole increment, and soil organic matter among other parameters. The measurements are being integrated and evaluated with two ecosystem models (BIOME-BGC and SPA). Such analyses are needed to assess regional terrestrial ecosystem carbon budgets. The results will contribute scientific understanding of carbon processes, and will provide comprehensive data sets for forest managers and those preparing national carbon inventories to use in assessments of carbon sequestration in relation to interannual climate variation and disturbance. Frameworks and methodologies developed by the PI will contribute to AmeriFlux Network facility functions for data acquisition, exchange and modeling of results in a broad spectrum of carbon cycle research

An Improved Analysis of Forest Carbon Dynamics using Data Assimilation

There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and only exceeded 0.5gC m-2 day-1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis - which might be provided indirectly by remotely sensed data - improves the analysis of NEE

Sensitivity of a subregional scale atmospheric inverse CO 2 modeling framework to boundary conditions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95175/1/jgrd16614.pd

Atmospheric inverse modeling to constrain regional‐scale CO 2 budgets at high spatial and temporal resolution

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95480/1/jgrd15697.pd

Seasonal variability of forest sensitivity to heat and drought stresses: A synthesis based on carbon fluxes from North American forest ecosystems.

Climate extremes such as heat waves and droughts are projected to occur more frequently with increasing temperature and an intensified hydrological cycle. It is important to understand and quantify how forest carbon fluxes respond to heat and drought stress. In this study, we developed a series of daily indices of sensitivity to heat and drought stress as indicated by air temperature (Ta ) and evaporative fraction (EF). Using normalized daily carbon fluxes from the FLUXNET Network for 34 forest sites in North America, the seasonal pattern of sensitivities of net ecosystem productivity (NEP), gross ecosystem productivity (GEP) and ecosystem respiration (RE) in response to Ta and EF anomalies were compared for different forest types. The results showed that warm temperatures in spring had a positive effect on NEP in conifer forests but a negative impact in deciduous forests. GEP in conifer forests increased with higher temperature anomalies in spring but decreased in summer. The drought-induced decrease in NEP, which mostly occurred in the deciduous forests, was mostly driven by the reduction in GEP. In conifer forests, drought had a similar dampening effect on both GEP and RE, therefore leading to a neutral NEP response. The NEP sensitivity to Ta anomalies increased with increasing mean annual temperature. Drier sites were less sensitive to drought stress in summer. Natural forests with older stand age tended to be more resilient to the climate stresses compared to managed younger forests. The results of the Classification and Regression Tree analysis showed that seasons and ecosystem productivity were the most powerful variables in explaining the variation of forest sensitivity to heat and drought stress. Our results implied that the magnitude and direction of carbon flux changes in response to climate extremes are highly dependent on the seasonal dynamics of forests and the timing of the climate extremes

Empirical assessment of uncertainties of meteorological parameters and turbulent fluxes in the AmeriFlux network

Terrestrial ecosystem-atmosphere exchange of carbon, water vapor, and energy has been measured for over a decade at many sites globally. To minimize measurement and analysis errors, quality assurance data have been collected over short periods along-side tower instruments at AmeriFlux research sites. Theoretical and empirical error and uncertainty values have been reported for various aspects of the eddy covariance technique but until recently it has not been possible to constrain network level variation based on direct comparison of side-by-side measurements. Paired observations, although rare in practice, offer a possibility to obtain real-world error estimates for flux observations and corresponding uncertainties. In this study, we report the relative instrumental errors from the AmeriFlux quality assurance and quality control (QA/QC) site intercomparisons of 84 site visits (2002–2012). Relative errors, including random and systematic instrumental errors, are presented for meteorological and radiation variables, gas concentrations, and the turbulent fluxes. The lowest relative errors (<2%) were found for the meteorological parameters, while the largest relative errors were found for latent heat and CO₂ fluxes. The mean relative instrumental error for CO₂ flux averaged −8.2% (underestimation by the tower instruments). Sensible and latent heat fluxes exhibited mean errors of −1.7% and −5.2%, respectively. Deviation around the mean was also largest for the turbulent fluxes, approaching 20%. Because the data collected during QA/QC site visits are used to identify and correct errors, our results represent a conservative estimate of instrumental errors in the AmeriFlux database. Overall, the presented results confirm the high quality of the network data and underline its status as a valuable data source for the research community.Keywords: uncertainties, AmeriFlux, random error, instrumental error, Fluxnet, eddy covarianceKeywords: uncertainties, AmeriFlux, random error, instrumental error, Fluxnet, eddy covarianc

Toward biologically meaningful net carbon exchange estimates for tall, dense canopies: Multi-level eddy covariance observations and canopy coupling regimes in a mature Douglas-fir forest in Oregon

We sought to improve net ecosystem exchange (NEE) estimates for a tall, dense, mature Douglas-Fir forest in the Oregon Coast range characterized by weak flows, systematic wind directional shear, and limited turbulent mixing throughout the diurnal period. We used eddy covariance (EC) observations at two levels and concurrent biological measurements of carbon and water fluxes collected over a period of 6 years (2006-2011) to develop and test a conceptual framework to i) reduce uncertainty by retaining more measurements for the computation of annual NEE sums, and ii) produce defendable and biologically meaningful estimates by accounting for the missing sub-canopy respiration. The framework assumes that a) the scalar exchange between vertical layers can be categorized into discrete canopy coupling regimes, and b) advection leads to a systematic loss of scalar from the observational volume that can indirectly be estimated and accounted for as sub-canopy respiration flux when canopy layers are decoupled. Periods with a decoupled sub-canopy layer dominated and occupied 65 and 88% of the day- and nighttime periods, respectively. Annual NEE derived from the new framework was estimated as 480 gC m⁻² yr⁻¹, which was reduced by 620 gC m⁻² yr⁻¹ compared to traditional estimates from single-level EC data filtered using a critical friction velocity. The reduced NEE was due to an enhanced ecosystem respiration (RE), while gross ecosystem productivity remained unchanged. Improved RE estimates agreed well with those from independent estimates based on soil, stem, and foliage respiration within 3%. Risks and limitations of the new framework are discussed. We conclude that concurrent above- and sub-canopy EC observations are essential to measure a meaningful carbon balance in tall, dense forests since they do no lend themselves to traditional, standardized processing. The new framework may help to include more tall and dense forests in global carbon cycle synthesis and modeling efforts.KEYWORDS: Douglas-Fir, U-star correction, Eddy covariance, Net ecosystem exchange, Advection, Ecosystem respiration, Canopy flow, Turbulenc

Atmospheric inverse modeling to constrain regional-scale CO₂ budgets at high spatial and temporal resolution

We present an inverse modeling framework designed to constrain CO2 budgets at regional scales. The approach captures atmospheric transport processes in high spatiotemporal resolution by coupling a mesoscale model with Lagrangian Stochastic backward trajectories. Terrestrial biosphere CO₂ emissions are generated through a simple diagnostic flux model that splits the net ecosystem exchange into its major components of gross primary productivity and autotrophic and heterotrophic respirations. The modeling framework assimilates state-of-the-art data sets for advected background CO₂ and anthropogenic fossil fuel emissions as well as highly resolved remote sensing products. We introduce a Bayesian inversion setup, optimizing a posteriori flux base rates for surface types that are defined through remote sensing information. This strategy significantly reduces the number of parameters to be optimized compared with solving fluxes for each individual grid cell, thus permitting description of the surface in a very high resolution. The model is tested using CO₂ concentrations measured in the fall and winter of 2006 at two AmeriFlux sites in Oregon. Because this database does not cover a full seasonal cycle, we focus on conducting model sensitivity tests rather than producing quantitative CO₂ flux estimates. Sensitivity tests on the influence of spatial and temporal resolution indicate that optimum results can be obtained using 4 h time steps and grid sizes of 6 km or less. Further tests demonstrate the importance of dividing biome types by ecoregions to capture their different biogeochemical responses to external forcings across climatic gradients. Detailed stand age information was shown to have a positive effect on model performance

Sensitivity of a subregional scale atmospheric inverse CO₂ modeling framework to boundary conditions

We present an atmospheric inverse modeling framework to constrain terrestrial biosphere CO₂ exchange processes at subregional scales. The model is operated at very high spatial and temporal resolution, using the state of Oregon in the northwestern United States as the model domain. The modeling framework includes mesoscale atmospheric simulations coupled to Lagrangian transport, a biosphere flux model that considers, e.g., the effects of drought stress and disturbance on photosynthesis and respiration CO₂ fluxes, and a Bayesian optimization approach. This study focuses on the impact of uncertainties in advected background mixing ratios and fossil fuel emissions on simulated flux fields, both taken from external data sets. We found the simulations to be highly sensitive to systematic changes in advected background CO₂, while shifts in fossil fuel emissions played a minor role. Correcting for offsets in the background mixing ratios shifted annual CO₂ budgets by about 47% and improved the correspondence with the output produced by bottom-up modeling frameworks. Inversion results were robust against shifts in fossil fuel emissions, which is likely a consequence of relatively low emission rates in Oregon