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A Multiyear gridded data ensemble of surface biogenic carbon fluxes for North America: Evaluation and analysis of results

Document Type

Article

Abstract

Accurate and fine-scale estimates of biogenic carbon fluxes are critical for measuring and monitoring the biosphere's responses and feedback to the climate system. Currently available data products from flux towers and model-intercomparison projects struggle to adequately represent spatiotemporal dynamics of surface biogenic carbon fluxes, and to quantify their uncertainties, which also are crucial to atmospheric inversion systems. To address these gaps, we introduce a new perturbed-parameter model ensemble with the CASA model to estimate surface biogenic carbon fluxes at monthly and 3-hourly scales for North America at ~500-m and 5-km resolutions. We first use the Extended Fourier Amplitude Sensitivity Testing to choose the three most sensitive parameters to be perturbed, maximum light use efficiency (Emax), optimal temperature of photosynthesis (Topt), and temperature response of respiration (Q10). The initial range for each parameter is broadly sampled for the L1 ensemble, but then we pruned Emax with site-level primary productivity to derive an L2 ensemble with narrower uncertainty ranges. Ensembles are strongly correlated with site-level results at both monthly and 3-hourly scales, and the spread across L1/L2 ensemble members encompasses the range of AmeriFlux observations. Monthly variability in the L2 ensemble mean is 85% of the observed variability. The L2 ensemble outperforms diverse data products with the highest Taylor skill scores at diurnal to annual scales. The ensemble's seasonality agrees well with other models for most biome types and in high and middle latitudes, but inconsistencies are found in subtropical and tropical ecoregions and for annual totals over North America.

Publication Title

Journal of Geophysical Research: Biogeosciences

Publication Date

2020

Volume

125

Issue

2

ISSN

2169-8953

DOI

10.1029/2019JG005314

Keywords

biogeochemical modeling, model-data comparison, parameters, sensitivity analysis

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