Climate‐driven uncertainties in modeling terrestrial gross primary production: a site level to global‐scale analysis

Abstract

We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis-driven GPP bias was significantly positive with respect to the observed meteorology-driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m(-2) yr(-1) across sites (ca. 15% of site level GPP). At the northern mid-/high-latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20-30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short-wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site-level reanalysis-driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman et al., ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate-driven uncertainties in carbon, energy, and water fluxes using a single modeling framework.

Estimates of Biomass Yield for Perennial Bioenergy Grasses in the USA

“…The model calculates carbon, nitrogen, energy, and water fluxes at 0.5×0.5°spatial resolution and at multiple temporal resolutions ranging from half-hour to yearly time steps. The details about the model structure, parameterization, and performance have been introduced in previous studies [21][22][23][24][25]. In the following, we provide the details of the processes added to the model, which are specific for this study.…”

“…While ISAM methodologies to model carbon assimilation, water and energy fluxes, and carbon and nitrogen dynamics for various plant functional types have been described elsewhere [21][22][23][24][25], this study extends ISAM model by accounting additional dynamic structural properties of vegetation, which are specific to the perennial bioenergy grasses. These include the following: (1) a specific phenology development scheme and its variation with latitude, which is controlled by thermal, photoperiod, and extreme meteorological conditions (e.g., frost and drought); (2) a dynamic carbon allocation process to allocate assimilated carbon among root, rhizome, leaf, and stem based on resource availability (e.g., light, water, and nutrient); (3) parameterization of N resorption rate; (4) parameterization of leaf area index (LAI) growth process, which is sensitive to photoperiod.…”

Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought

“…Gross primary production (GPP) from photosynthesis has been well understood at leaf and canopy levels; however, ecosystem level estimation of GPP has not yet been well investigated (Asaf et al, 2013;Barman, Jain, & Liang, 2014). Since the 1990s, the eddy covariance method has been used as an important tool to measure heat, water, and CO 2 exchanges as well as trace green-house gases (Baldocchi, 2014).…”

“…To better understand the global carbon-cycle feedback to climate change, it is critical to estimate GPP variability due to climate variation (e.g., drought), as it dominates the global GPP anomalies (Barman et al, 2014;Zscheischler et al, 2014). Previous studies have shown that EVI-based VPM, TG, GR, and VI models perform well in forest, grassland and cropland ecosystems under non-drought condition (Gitelson et al, 2006;Kalfas, Xiao, Vanegas, Verma, & Suyker, 2011;Sims et al, 2008;Wu, Gonsamo, Zhang, & Chen, 2014;Wu, Munger, Niu, & Kuang, 2010;Wu et al, 2011;Xiao et al, 2005).…”

Scaling the ISAM Land Surface Model through Parallelization of Inter-component Data Transfer

“…ISAM has two main components: (1) a biogeophysical module representing sunlit-shaded photosynthesis schemes [7], energy, and soil/snow hydrology [8], [9], [7] and (2) a biogeochemical module, where assimilated carbon is allocated to vegetation, litter, and soil carbon pools [5], [7], [10]. The carbon cycle and nitrogen cycle are coupled together.…”