Title: Continental United States may lose 1.8 petagrams of soil organic carbon under climate change by 2100

Abstract

Aims: High-resolution information on soils’ vulnerability to climate-induced soil organic carbon (SOC) loss can enable environmental scientists, land managers, and policy makers to develop targeted mitigation strategies. This study aims to estimate baseline and decadal changes in continental US surface SOC stocks under future emission scenarios. Location: Continental United States. Time period: 2014–2100. Methods: We used recent SOC field observations (n = 6,213 sites), environmental factors (n = 32), and an ensemble machine learning (ML) approach to estimate baseline SOC stocks in surface soils across the continental United States at 100-m spatial resolution, and decadal changes under the projected climate scenarios of Coupled Model Intercomparison Project Phase Six (CMIP6) earth system models (ESMs). Results: Baseline SOC projections from ML approaches captured more than 50% of variability in SOC observations, whereas ESMs represented only 6–16% of observed SOC variability. ML estimates showed a mean total loss of 1.8 Pg C from US surface soils under the high-emission scenario by 2100, whereas ESMs showed no significant change in SOC stocks with wide variation among ESMs. Both ML and ESM predictions agree on the direction of SOC change (net emissions or sequestration) across 46–51% of continental US land area. These differences are attributablemore » to the high-resolution site-specific data used in the ML models compared to the relatively coarse grid represented in CMIP6 ESMs. Main conclusions: Our high-resolution estimates of baseline SOC stocks, identification of key environmental controllers, and projection of SOC changes from US land cover types under future climate scenarios suggest the need for high-resolution simulations of SOC in ESMs to represent the heterogeneity of SOC. We found that the SOC change is sensitive to key soil related factors (e.g. soil drainage and soil order) that have not been historically considered as input parameters in ESMs, because currently more than 95% variability in the SOC of CMIP6 ESMs is controlled by net primary productivity, temperature, and precipitation. Using additional environmental factors to estimate the baseline SOC stocks and predict the future trajectory of SOC change can provide more accurate results.« less

Authors:

Gautam, Sagar  ^[1];

• Search DOE PAGES for author "Gautam, Sagar"
• Search DOE PAGES for ORCID "0000-0002-0828-1631"

Mishra, Umakant ^[1]; Scown, Corinne D.  ^[2];

• Search DOE PAGES for author "Scown, Corinne D."
• Search DOE PAGES for ORCID "0000-0003-2078-1126"

Wills, Skye A. ^[3]; Adhikari, Kabindra  ^[4];

• Search DOE PAGES for author "Adhikari, Kabindra"
• Search DOE PAGES for ORCID "0000-0002-1365-7015"

Drewniak, Beth A. ^[5]

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+ Show Author Affiliations

1. Bioscience Division Sandia National Laboratory Livermore California USA, Joint BioEnergy Institute Lawrence Berkeley National Laboratory Emeryville California USA
2. Joint BioEnergy Institute Lawrence Berkeley National Laboratory Emeryville California USA, Energy Analysis &, Environmental Impact Division Lawrence Berkeley National Laboratory Berkeley California USA, Biological Systems and Engineering Division Lawrence Berkeley National Laboratory Berkeley California USA, Energy &, Biosciences Institute University of California Berkeley California USA
3. National Soil Survey Center USDA‐NRCS Lincoln Nebraska USA
4. Grassland, Soil and Water Research Laboratory USDA‐ARS Temple Texas USA
5. Environmental Science Division Argonne National Laboratory Lemont Illinois USA

Publication Date:

Sun Mar 20 00:00:00 EDT 2022

Research Org.:

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:

1855887

Alternate Identifier(s):

OSTI ID: 1866517; OSTI ID: 1882904; OSTI ID: 1916472

Report Number(s):

SAND2022-3171J
Journal ID: ISSN 1466-822X

Grant/Contract Number:  

NA0003525; AC02-05CH11231; AC02-06CH11357

Resource Type:

Published Article

Journal Name:

Global Ecology and Biogeography

Additional Journal Information:

Journal Name: Global Ecology and Biogeography Journal Volume: 31 Journal Issue: 6; Journal ID: ISSN 1466-822X

Publisher:

Wiley-Blackwell

Country of Publication:

United Kingdom

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES; climate; earth system model; environmental factor; future projection; machine learning; soil organic carbon