Where in the world are we confident in terrestrial carbon balance?
Bibliographic record
Abstract
Terrestrial ecosystems play a major role in the global carbon (C) cycle. However, our ability to quantify where in the world is a net C source or sink, and to what extent this is changing continues to be a critical challenge. Terrestrial ecosystems are responsible for the largest C fluxes in the world dwarfing anthropogenic emissions from fossil fuels. These processes are sensitive to climatic and anthropogenic disturbances on varied scales in time and space. This complex interconnection of internal ecosystem processes and external exchanges, mediated by ecosystem properties, challenges both observation and process-based modelling efforts to understand and quantify ecosystem C exchanges.The expansion of satellite-based Earth Observation (EO) has provided unprecedented information at global scales on the state and evolution of terrestrial ecosystems. Increasingly, these data are provided with more robust estimates of their uncertainties and their variation in space and time. Process-models of terrestrial ecosystems have advanced with our growing ecological understanding derived from in-situ information. However, while there is great potential for EO to contribute to model calibration and validation, helping diagnose ecological function and improve model predictive skill, at present the connections between EO and process models are weakly developed.Bayesian model-data fusion (data assimilation) approaches offer a powerful opportunity to integrate EO and process-models by informing the model parameter calibration with a diverse, location-specific array of complementary ecologically relevant observations, fully propagating their uncertainties. In this study, will use the state-of-the-art CARDAMOM Bayesian calibration framework to retrieve parameters for a process-based model of the terrestrial ecosystem (DALEC).We will present a global (0.5 x 0.5 degree) analysis of the global carbon and water cycles for a 21-year period (2003-2023). CARDAMOM is applied uniquely at each 0.5 degree pixel, retrieving uncertainty bounded estimates of DALEC parameters as a function of information available for that location. From these ‘local’ parameters we estimate the state and dynamics of terrestrial ecosystems with fully realised uncertainties in space and time.Our analysis will identify where in the world we have confidence in source / sink dynamics and diagnose environmental relationships driving current trajectories, including the large growth in atmospheric CO2 concentrations in 2023. Our preliminary analyses suggest this increase is driven by elevated fire activity, particularly in south west Amazon and Canadian boreal forests, and broad enhancement of heterotrophic respiration driven by warming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".