DOI: 10.5061/dryad.298rn73
title: Data from: Global, satellite-driven estimates of heterotrophic respiration

description: While heterotrophic respiration (Rh) makes up about a quarter of gross
 global terrestrial carbon fluxes, it remains among the least observed
 carbon fluxes, particularly outside the mid-latitudes. In situ
 measurements collected in the Soil Respiration Database (SRDB) number only
 a few hundred worldwide. Similarly, only a single data-driven wall-to-wall
 estimate of annual average heterotrophic respiration exists, based on
 bottom-up upscaling of SRDB measurements using an assumed functional form
 to account for climate variability. In this study, we exploit recent
 advances in remote sensing of terrestrial carbon fluxes to estimate global
 variations in heterotrophic respiration in a top-down fashion at monthly
 temporal resolution and 4x5o spatial resolution. We combine net ecosystem
 productivity estimates from atmospheric inversions of the NASA Carbon
 Monitoring System- Flux (CMS-Flux) with an optimally-scaled gross primary
 productivity dataset based on satellite-observed solar-induced
 fluorescence variations to estimate total ecosystem respiration as a
 residual of the terrestrial carbon balance. The ecosystem respiration is
 then separated into autotrophic and heterotrophic components based on a
 spatially-varying carbon use efficiency retrieved in a model-data fusion
 framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting
 dataset is independent of any assumptions about how heterotrophic
 respiration responds to climate or substrate variations. It estimates an
 annual average global average heterotrophic respiration flux of 43.6 ±
 19.3 Pg C/yr. Sensitivity and uncertainty analyses showed that the
 top-down Rh are more sensitive to the choice of input GPP and NEP datasets
 than to the assumption of a static CUE value, with the possible exception
 of the wet tropics. These top-down estimates are compared to bottom-up
 estimates of annual heterotrophic respiration, with new uncertainty
 estimates that partially account for sampling and model errors. Top-down
 heterotrophic respiration estimates are higher than those from bottom-up
 upscaling everywhere except at high latitudes, and are 30% greater overall
 (43.6 Pg C/yr vs. 33.4 Pg C/yr). The uncertainty ranges of both methods
 are comparable, except poleward of 45 degrees North, where bottom-up
 uncertainties are greater. The ratio of top-down heterotrophic to total
 ecosystem respiration varies seasonally by as much as 0.6 depending on
 season and climate, illustrating the importance of studying the drivers of
 autotrophic and heterotrophic respiration separately, and thus the
 importance of data-driven estimates of Rh such as those estimated here.
description: Top-down heterotrophic
 respirationThis file includes
 top-down estimates of heterotrophic resolution at 4 degrees by 5 degrees
 spatial resolution and monthly temporal resolution, covering the period
 January 2010 to December 2012. The data are gridded on the CMS-Flux grid
 (also the 4x5 GEOS-Chem grid). Each dataset is a 3-dimensional matrix with
 dimensions longitude, latitude, and time (in order). Datasets included are
 the derived top-down heterotrophic respiration mean, standard deviation,
 25th percentile of the distribution, and 75th percentile of the
 distribution, as well as the mean and standard deviation of the input
 datasets: net ecosystem productivity, gross primary productivity, and
 vegetation carbon use efficiency.HeteroResp.nc