.png){kind=icon}
.png){kind=icon}
Return
Mapping Global Terrestrial Spatiotemporal Dynamics of Atmospheric CO2 Carbon Isoscapes for Ecological Studies
.png){kind=icon}
PRE
AI
H
X
P
X
D
王
Z
DOI：10.1111/gcb.71002.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The isotopic composition of atmospheric carbon dioxide (δ13Catm) provides insights into the terrestrial carbon cycle. However, long-term global δ13Catm maps with both high spatial resolution and continuous temporal coverage remain scarce. Here, we present a new global terrestrial dataset of monthly δ13Catm isoscapes from 2001 to 2020 at 0.05° spatial resolution, developed by integrating in situ observations with optimized 4D CO2 concentration fields from inversion outputs, reanalysis data, and geographic information using machine learning. Among four tested models, the Gradient Boosting Machine demonstrated the highest predictive performance under random validation (R2 = 0.80, RMSE = 0.12‰) and maintained robust performance across three spatially independent validation frameworks (R2 = 0.56–0.67, RMSE = 0.16‰–0.19‰). Key predictors were air temperature and atmospheric CO2 concentration. The resulting global terrestrial isoscapes reveal strong spatial and temporal heterogeneity. Model predictions closely align with National Oceanic and Atmospheric Administration (NOAA) marine boundary layer (MBL) observations in terms of trend magnitude, seasonal amplitude (< 0.13‰ deviation), and latitude gradient (< 0.2‰). In our study, δ13Catm seasonal amplitude varies from 0.06‰ in Southern Hemisphere mid-latitudes to 0.6‰ in Northern Hemisphere high latitudes, indicating strong hemispheric asymmetry. Moreover, over 99.8% of global terrestrial grid cells show negative trends in all seasons, with a global terrestrial average annual depletion rate of −0.030‰ ± 0.0006‰ year−1. The trend shows stronger depletion during summer and autumn, reaching its peak in August (−0.035‰ ± 0.0012‰ year−1), while spring and winter seasons remain comparatively stable. This study delivers a long-term, high-resolution global terrestrial δ13Catm isoscape dataset, offering a valuable tracer for carbon cycle research and, importantly, robust data support for large-scale investigations of carbon–water coupling in terrestrial ecosystems.
Keywords:
atmospheric CO2
carbon cycle
global terrestrial
isoscape
machine learning
spatiotemporal dynamics
Journal
IF:
12
Papers:
8.9K
Citations:
7.6W
Organization
Cited Papers