Reference carbon cycle dataset for typical Chinese forests via colocated observations and data assimilation

Area of Science:

• Ecology
• Environmental Science
• Forestry

Background:

• Chinese forests represent diverse Northern Hemisphere ecosystems.
• Forests play a critical role as carbon sinks in the global carbon cycle.
• Long-term carbon dynamics data are essential for evaluating forest carbon sequestration.

Purpose of the Study:

• To generate a comprehensive, decadal reference dataset of carbon cycle dynamics in 10 typical Chinese forests.
• To create an assimilated dataset of key carbon cycle parameters and continuous sequestration functions using model-data fusion.
• To provide a benchmark for carbon cycle research and model development under global climate change.

Main Methods:

• Systematic monitoring of soil-biology-atmosphere-water cycles by the Chinese Ecosystem Research Network since 2000.
• Quality control and compilation of decadal data on biomass, leaf area index, litterfall, soil organic carbon, and meteorological factors.
• Model-data fusion techniques to assimilate discrete carbon cycle elements into continuous functions for carbon allocation, turnover, and storage.

Main Results:

• A novel, quality-controlled reference dataset of decadal carbon cycle dynamics for 10 typical Chinese forests.
• An assimilated dataset providing time-continuous functions for key carbon cycle parameters.
• Quantification of soil, vegetation, and ecosystem carbon storage dynamics.

Conclusions:

• The generated dataset serves as a crucial benchmark for validating and improving carbon cycle models.
• This research enhances our understanding of carbon sequestration in Northern Hemisphere forests.
• The findings are vital for assessing forest ecosystem responses to global climate change.