Browsing by Author "Baldocchi, Dennis D."

Now showing 1 - 4 of 4

Albedo estimates for land surface models and support for a new paradigm based on foliage nitrogen concentration
(2010-02) Hollinger, David Y.; Ollinger, S. V.; Richardson, Andrew D.; Meyers, T. P.; Dail, D. B.; Martin, M. E.; Scott, N. A.; Arkebauer, T. J.; Baldocchi, Dennis D.; Clark, K. L.; Curtis, P. S.; Desai, Ankur R.; Dragoni, Danilo; Goulden, Michael L.; Gu, Lianhong; Katul, Gabriel G.; Pallardy, S. G.; Paw U, Kyaw Tha; Schmid, H. P.; Stoy, Paul C.; Suyker, Andrew E.; Verma, Shashi B.
Vegetation albedo is a critical component of the Earth's climate system, yet efforts to evaluate and improve albedo parameterizations in climate models have lagged relative to other aspects of model development. Here, we calculated growing season albedos for deciduous and evergreen forests, crops, and grasslands based on over 40 site‐years of data from the AmeriFlux network and compared them with estimates presently used in the land surface formulations of a variety of climate models. Generally, the albedo estimates used in land surface models agreed well with this data compilation. However, a variety of models using fixed seasonal estimates of albedo overestimated the growing season albedo of northerly evergreen trees. In contrast, climate models that rely on a common two‐stream albedo submodel provided accurate predictions of boreal needle‐leaf evergreen albedo but overestimated grassland albedos. Inverse analysis showed that parameters of the two‐stream model were highly correlated. Consistent with recent observations based on remotely sensed albedo, the AmeriFlux dataset demonstrated a tight linear relationship between canopy albedo and foliage nitrogen concentration (for forest vegetation: albedo=0.01+0.071%N, r2=0.91; forests, grassland, and maize: albedo=0.02+0.067%N, r2=0.80). However, this relationship saturated at the higher nitrogen concentrations displayed by soybean foliage. We developed similar relationships between a foliar parameter used in the two‐stream albedo model and foliage nitrogen concentration. These nitrogen‐based relationships can serve as the basis for a new approach to land surface albedo modeling that simplifies albedo estimation while providing a link to other important ecosystem processes.
Biosphere-atmosphere exchange of CO2 in relation to climate: a cross-biome analysis across multiple time scales
(2009-10) Stoy, Paul C.; Richardson, Andrew D.; Baldocchi, Dennis D.; Katul, Gabriel G.; Stanovick, J.; Mahecha, M. D.; Reichstein, M.; Detto, Matteo; Law, Beverly E.; Wohlfahrt, Georg; Arriga, N.; Campos, J.; McCaughey, J. H.; Montagnani, Leonardo; Paw U, Kyaw Tha; Sevanto, S.; Williams, Mathew
The net ecosystem exchange of CO2 (NEE) varies at time scales from seconds to years and longer via the response of its components, gross ecosystem productivity (GEP) and ecosystem respiration (RE), to physical and biological drivers. Quantifying the relationship between flux and climate at multiple time scales is necessary for a comprehensive understanding of the role of climate in the terrestrial carbon cycle. Orthonormal wavelet transformation (OWT) can quantify the strength of the interactions between gappy eddy covariance flux and micrometeorological measurements at multiple frequencies while expressing time series variance in few energetic wavelet coefficients, offering a low-dimensional view of the response of terrestrial carbon flux to climatic variability. The variability of NEE, GEP and RE, and their co-variability with dominant climatic drivers, are explored with nearly one thousand site-years of data from the FLUXNET global dataset consisting of 253 eddy covariance research sites. The NEE and GEP wavelet spectra were similar among plant functional types (PFT) at weekly and shorter time scales, but significant divergence appeared among PFT at the biweekly and longer time scales, at which NEE and GEP were relatively less variable than climate. The RE spectra rarely differed among PFT across time scales as expected. On average, RE spectra had greater low frequency (monthly to interannual) variability than NEE, GEP and climate. CANOAK ecosystem model simulations demonstrate that "multi-annual" spectral peaks in flux may emerge at low (4+ years) time scales. Biological responses to climate and other internal system dynamics, rather than direct ecosystem response to climate, provide the likely explanation for observed multi-annual variability, but data records must be lengthened and measurements of ecosystem state must be made, and made available, to disentangle the mechanisms responsible for low frequency patterns in ecosystem CO2 exchange.
Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes
(2007-04) Yuan, Wenping; Liu, Shuguang; Zhou, Guangsheng; Zhou, Guoyi; Tieszen, Larry L.; Baldocchi, Dennis D.; Bernhofer, Christian; Gholz, Henry; Goldstein, Allen H.; Goulden, Michael L.; Hollinger, David Y.; Hu, Yueming; Law, Beverly E.; Stoy, Paul C.; Vesala, Timo; Wofsy, Steven C.
The quantitative simulation of gross primary production (GPP) at various spatial and temporal scales has been a major challenge in quantifying the global carbon cycle. We developed a light use efficiency (LUE) daily GPP model from eddy covariance (EC) measurements. The model, called EC-LUE, is driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux (used to calculate moisture stress). The EC-LUE model relies on two assumptions: First, that the fraction of absorbed PAR (fPAR) is a linear function of NDVI; Second, that the realized light use efficiency, calculated from a biome-independent invariant potential LUE, is controlled by air temperature or soil moisture, whichever is most limiting. The EC-LUE model was calibrated and validated using 24,349 daily GPP estimates derived from 28 eddy covariance flux towers from the AmeriFlux and EuroFlux networks, covering a variety of forests, grasslands and savannas. The model explained 85% and 77% of the observed variations of daily GPP for all the calibration and validation sites, respectively. A comparison with GPP calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) indicated that the EC-LUE model predicted GPP that better matched tower data across these sites. The realized LUE was predominantly controlled by moisture conditions throughout the growing season, and controlled by temperature only at the beginning and end of the growing season. The EC-LUE model is an alternative approach that makes it possible to map daily GPP over large areas because (1) the potential LUE is invariant across various land cover types and (2) all driving forces of the model can be derived from remote sensing data or existing climate observation networks.
Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations
(2006-07) Heinsch, Faith A.; Zhao, Maosheng; Running, Steven W.; Kimball, John S.; Nemani, Ramakrishna R.; Davis, Kenneth J.; Cook, Bruce D.; Desai, Ankur R.; Ricciuto, Daniel M.; Law, Beverly E.; Oechel, Walter C.; Kwon, Hyojung; Wofsy, Steven C.; Dunn, Allison L.; Munger, J. William; Baldocchi, Dennis D.; Xu, Liukang; Hollinger, David Y.; Richardson, Andrew D.; Stoy, Paul C.; Siqueira, Mario B. S.; Monson, Russell K.; Burns, Sean P.; Flanagan, Lawrence B.; Bolstad, Paul V.; Luo, Hongyan
The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO2 flux-based estimates across diverse land cover types and climate regimes. We examine the potential error contributions from meteorology, leaf area index (LAI)/fPAR, and land cover. The error between annual GPP computed from NASA's Data Assimilation Office's (DAO) and tower-based meteorology is 28%, indicating that NASA's DAO global meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 62% of MOD15-based estimates of LAI were within the estimates based on field optical measurements, although remaining values overestimated site values. Land cover presented the fewest errors, with most errors within the forest classes, reducing potential error. Tower-based and MODIS estimates of annual GPP compare favorably for most biomes, although MODIS GPP overestimates tower-based calculations by 20%-30%. Seasonally, summer estimates of MODIS GPP are closest to tower data, and spring estimates are the worst, most likely the result of the relatively rapid onset of leaf-out. The results of this study indicate, however, that the current MODIS GPP algorithm shows reasonable spatial patterns and temporal variability across a diverse range of biomes and climate regimes. So, while continued efforts are needed to isolate particular problems in specific biomes, we are optimistic about the general quality of these data, and continuation of the MOD17 GPP product will likely provide a key component of global terrestrial ecosystem analysis, providing continuous weekly measurements of global vegetation production