The effect of driving climate data on the simulated terrestrial carbon pools and fluxes over North America
Bibliographic record
Abstract
Abstract Dynamic vegetation models provide the ability to simulate terrestrial carbon pools and fluxes and a useful tool to study how these are affected by climate variability and climate change. At the continental scale, the spatial distribution of climate, in particular temperature and precipitation, strongly determines surface vegetation characteristics. Model validation exercises typically consist of driving a model with observation‐based climate data and then comparing simulated quantities with their observation‐based counterparts. However, observation‐based datasets themselves may not necessarily be consistent with each other. Here, we compare simulated terrestrial carbon pools and fluxes over North America with observation‐based estimates. Simulations are performed using the dynamic vegetation model Canadian Terrestrial Ecosystem Model (CTEM) coupled to the Canadian Land Surface Scheme (CLASS) when driven with two reanalysis‐based climate datasets. The driving ECMWF reanalysis data (ERA40) and NCEP/NCAR reanalysis I data (NCEP) show differences when compared to each other, as well as when compared to the observation‐based climate research unit (CRU) data. Most simulated carbon pools and fluxes show important differences, particularly over eastern North America, primarily due to differences in precipitation and temperature in the two reanalysis. However, despite very different gross fluxes, the model yields fairly similar estimates of the net atmosphere‐land CO2 flux when driven with the two forcing datasets. The ERA40 driven simulation produces terrestrial pools and fluxes that compare better with observation‐based estimates. These simulations do not take into account land use change or nitrogen deposition, both of which have been shown to enhance the land carbon sink over North America. The simulated sink of 0.5 Pg C year−1 during the 1980s and 1990s is therefore lower than inversion‐based estimates. The analysis of spatial distribution of trends in simulated carbon pools and fluxes shows that the simulated carbon sink is driven primarily by NPP enhancements over eastern United States. © 2013 The Authors and Her Majesty the Queen in Right of Canada. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".