10:00 〜 10:15
[ACG35-05] Soil respiration in the global carbon cycle: variability and knowledge gaps
キーワード：データ駆動型、土壌CO2、土壌呼吸、空間分布、時間変化
The release of carbon dioxide from the soil to the atmosphere (soil respiration) is the second largest terrestrial carbon flux after photosynthesis. Accurately quantifying global soil respiration is essential for understanding the global carbon cycle and predicting the future climate more accurately; however, the convergence of data-driven soil respiration estimates is unclear. We collated all historical data-driven estimates of global soil respiration to analyze the convergence and uncertainty in the estimates.
We identified 23 studies on global estimates of total soil respiration. After collation and screening, we obtained 11 spatial estimates of global soil respiration. The total global soil respiration ranged from less than 70 Pg C yr−1 to more than 100 Pg C yr−1 (range: 68–101 Pg C yr−1, excluding screened estimates). Despite the development of a global dataset and advanced scaling techniques (e.g., machine learning) over the last two decades, we found that inter-model variability has increased. While soil respiration is generally high in warm and humid regions and low in dry and/or cold regions, the maps demonstrate varying magnitudes and spatial patterns of soil respiration. Some regions had higher variability than others; these regions included the North African dry area, deserted areas in the central Eurasian continent, and the islands in Southeast Asia. All but one estimate showed an increasing trend over time, but the slope of the linear trend differed between estimates.
Future efforts to better constrain global soil respiration estimates can be categorized into four fundamental pillars: data-driven modeling, observation data, mechanisms, and mutual, multiple constraints. Reducing inter-model variability is not an easy task; however, when the puzzle pieces of the carbon cycle fit together perfectly, climate change prediction will be more reliable. Refining estimates of critical components, such as soil respiration, is a step towards ensuring that all pieces of the global carbon cycle fit together.
Hashimoto S., K. Nishina, A. Ito (2023) Divergent data-driven estimates of global soil respiration. Communications Earth & Environment https://doi.org/10.1038/s43247-023-01136-2
We identified 23 studies on global estimates of total soil respiration. After collation and screening, we obtained 11 spatial estimates of global soil respiration. The total global soil respiration ranged from less than 70 Pg C yr−1 to more than 100 Pg C yr−1 (range: 68–101 Pg C yr−1, excluding screened estimates). Despite the development of a global dataset and advanced scaling techniques (e.g., machine learning) over the last two decades, we found that inter-model variability has increased. While soil respiration is generally high in warm and humid regions and low in dry and/or cold regions, the maps demonstrate varying magnitudes and spatial patterns of soil respiration. Some regions had higher variability than others; these regions included the North African dry area, deserted areas in the central Eurasian continent, and the islands in Southeast Asia. All but one estimate showed an increasing trend over time, but the slope of the linear trend differed between estimates.
Future efforts to better constrain global soil respiration estimates can be categorized into four fundamental pillars: data-driven modeling, observation data, mechanisms, and mutual, multiple constraints. Reducing inter-model variability is not an easy task; however, when the puzzle pieces of the carbon cycle fit together perfectly, climate change prediction will be more reliable. Refining estimates of critical components, such as soil respiration, is a step towards ensuring that all pieces of the global carbon cycle fit together.
Hashimoto S., K. Nishina, A. Ito (2023) Divergent data-driven estimates of global soil respiration. Communications Earth & Environment https://doi.org/10.1038/s43247-023-01136-2