Representing High-Latitude Deep Carbon in the Pre-Industrial State of the ORCHIDEE-MICT Land Surface Model (R8704)
crossref（2025）
Abstract
Abstract. Field measurements, after extrapolation, suggest that deep Yedoma deposits (formed during the Pleistocene) and peatlands (formed during the Holocene) account for over 700 Pg C of soil carbon storage. Incorporating this old, deep, cold carbon into land surface models (LSMs) is crucial for accurately quantifying soil carbon responses to future warming. However, it remains underrepresented or absent in current LSMs, which typically include a passive soil carbon pool to represent all 'old carbon' and lack the vertical accumulation processes that deposited deep carbon in the soil layers of peat and Yedoma. In this study, we propose a new, more realistic protocol for simulating deep and cold carbon accumulation in the high latitudes, using the ORCHIDEE-MICT model. This is achieved by 1) integrating deep carbon from Yedoma deposits whose formation is calculated using Last Glacial Maximum climate conditions, and 2) prescribing the inception time and location of northern peatlands during the Holocene using spatially explicit data on peat age. Our results show an additional 157 Pg C in present-day Yedoma deposits, as well as a shallower peat carbon depth (by 1–5 m) and 35 Pg C (43 %) less passive soil carbon in northern peatlands, compared to the old protocol that ignored Yedoma deposits and applied a uniform, long-duration (13,500 years) peat carbon accumulation across all peatlands. As a result, the total organic carbon stock across the Northern Hemisphere (> 30° N) simulated by the new protocol is 2,028 Pg C, which is 226 Pg C higher than the previous estimate. Despite the significant challenge in simulating deep carbon with ORCHIDEE-MICT, the improvements in the representation of carbon accumulation from this study provide a model version to predict deep carbon evolution during the last glacial-deglacial transition and its response to future warming. The methodology implemented for deep carbon initialization in permafrost and cold regions in ORCHIDEE-MICT is also applicable to other LSMs.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper