Assessing cryosphere-biosphere linkages in boreal forests with Earth Observation and modelling
Bibliographic record
Abstract
Climate change induced increases in surface temperatures in the northern high-latitudes have consequences for cryosphere conditions in the boreal zone (snow cover, soil freeze-thaw and permafrost). Cryosphere changes will in turn influence the biosphere e. g. through changes in the carbon uptake and release by vegetation. However, the current knowledge about these interactions is insufficient for assessing the carbon balance accurately and uncertainties remain in model predictions of how the carbon cycle will respond to the changing climate.In this work, we assessed the suitability of satellite-based and in situ soil freeze and thaw observations to inform on the start and end of the carbon uptake period in boreal forest and modelling to investigate the relationship between freeze-thaw dynamics and the carbon uptake and release by boreal forest ecosystems. Eddy covariance measurements from six coniferous forest sites in Finland and Canada were used to determine the start and end dates of the carbon uptake period. Satellite-based soil freeze and thaw dates, determined from the ESA SMOS Level 3 Soil Freeze and Thaw product (Rautiainen et al. 2016) for the period 2010 to 2020, agreed well in timing with site level observations and significant relationships with start and end dates of the carbon uptake period were found. This suggests that SMOS soil thaw and freeze dates could be used in the estimation of the length of the carbon uptake period in boreal coniferous forests although the relationship weakens for the warmer southern boreal site (Hyytiälä, Finland).For the modelling, the terrestrial biosphere model QUINCY (QUantifying Interactions between Nutrient Cycles and the climate) (Thum et al. 2019) will be applied at three coniferous forest sites, stretching from the southern to the northern boreal zone. QUINCY has a multi-layer snow scheme (Lacroix et al. 2022) and fully coupled carbon, water, energy, and nitrogen cycles. First simulations were carried out for a Scots pine forest at Sodankylä (Finland). At the Sodankylä site, gross primary production (GPP) started when soil thaw was detected from in situ and satellite observations. The increase of total ecosystem respiration (TER) lagged behind GPP in spring and occurred when snow had melted. QUINCY captured the seasonal cycle of GPP well, however, simulated TER showed biases in spring that were related to snow melt dynamics. Simulations showed snow depth was too low and melting was too early which in turn led to increase in simulated TER too early in the year. The QUINCY modelling will be extended to sites Hyytiälä (Finland) and the Saskatchewan, Old Jack Pine forest (Canada). In further work, we plan to combine satellite information on snow melt with soil thaw and freeze to provide proxy indicators on the carbon uptake and release period that could be utilized in model evaluation. ReferencesThum, T., et al., 2019. Geosci. Model Dev. 12, 4781-4802.Rautiainen, K., et al., 2016. Remote Sensing of Environment, SMOS special issue 180, 346-360.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".