Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

Abstract

Soil respiration (Rs), the soil‐to‐atmosphere CO2 flux produced by microbes and plant roots, is a critical but uncertain component of the global carbon cycle. Our current understanding of the variability and dynamics is limited by the coarse spatial resolution of existing estimates. We predicted annual Rs and associated uncertainty across the world at 1‐km resolution using a quantile regression forest algorithm trained with observations from the global Soil Respiration Database spanning from 1961 to 2011. This model yielded a global annual Rs estimate of 87.9 Pg C/year with an associated global uncertainty of 18.6 (mean absolute error) and 40.4 (root mean square error) Pg C/year. The estimated annual heterotrophic respiration (Rh), derived from empirical relationships with Rs, was 49.7 Pg C/year over the same period. Predicted Rs rates and associated uncertainty varied widely across vegetation types, with the greatest predicted rates of Rs in evergreen broadleaf forests (accounting for 20.9% of global Rs). The greatest prediction uncertainties were in northern latitudes and arid to semiarid ecosystems, suggesting that these areas should be targeted in future measurement campaigns. This study provides predictions of Rs (and associated prediction uncertainty) at unprecedentedly high spatial resolution across the globe that could help constrain local‐to‐global process‐based models. Furthermore, it provides insights into the large variability of Rs and Rh across vegetation classes and identifies regions and vegetation types with poor model performance that should be prioritized for future data collection.

Definitions and methods to estimate regional land carbon fluxes for the second phase of the REgional Carbon Cycle Assessment and Processes Project (RECCAP-2)

“…and ≈500 for heterotrophic respiration many peer-reviewed literature in the SRDB 4.0 database (Bond-Lamberty, 2018) allows regional and global up-scaling of this flux for averages over a given period (Hashimoto et al, 2015;Konings et al, 2019;Warner et al, 2019) or with annual variations (Yao et al 2020) that can be used for RECCAP2.…”

A restructured and updated global soil respiration database (SRDB-V5)

“…The updated SRDB-V5 provides opportunities for constraining global RS estimates in the future. Currently, estimated global RS ranged from 68-101 Pg C yr -1 , with many uncertainties associated with measurements and propagation of errors evident when upscaling site-specific RS measurements to regional and global scales (Bond-Lamberty and Thomson, 2010b;Jian et al, 2018aJian et al, , 2018bRaich et al, 2002;Raich and Potter, 1995;Raich and Schlesinger, 1992;Warner et al, 2019). For example, RS has been usually measured during daylight hours,…”

“…et al (2018b) found that how RS responds to temperature is significantly different among climate regions, and therefore climate-specific models may be more appropriate than a global single model to estimate global RS. Alternatively, machine learning approaches that account for non-linearity and multiple potential combinations of environmental factors have been used to estimate global RS (Warner et al, 2019). SRDB-V5 also significantly increased the RS sample size, and analyses could be conducted to test whether the increasing sample size of RS helps 315 reduce uncertainty when upscaling from site to global scale RS.…”

“…Due to a lack of data, far fewer studies have analyzed RH and RA and estimated global RH and RA in the past decades. According to our knowledge, there are only five global RH (or RA) estimates based on the very limited extant data (n < 500) (Hashimoto et al, 2015;Konings et al, 2019;Tang et al, n.d.;Warner et al, 2019;Yao et al, n.d.). In the "srdb-…”

Evaluating two soil carbon models within a global land surface model using surface and spaceborne observations of atmospheric CO&lt;sub&gt;2&lt;/sub&gt; mole fractions

“…For soil respiration, including both autotrophic respiration by roots and heterotrophic respiration, some global datasets have been developed (Bond-Lamberty and Thomson, 2010;Hashimoto et al, 2015). Recently efforts to estimate heterotrophic respiration have also been made using machine learning techniques, Tang et al (2019) and Warner et al (2019) being the first attempts. The separation of heterotrophic respiration from the autotrophic respiration remains still a challenge and in these two studies it has been done at yearly scales.…”