Coherence among the Northern Hemisphere land, cryosphere, and oceanresponses to natural variability and anthropogenic forcing during thesatellite era
Bibliographic record
Abstract
Abstract. A lack of long-term measurements across Earth's biological and physical systems has made observation-based detection and attribution of climate change impacts to anthropogenic forcing and natural variability difficult. Here we explore coherence among land, cryosphere and ocean responses to recent climate change using 3 decades (1980–2012) of observational satellite and field data throughout the Northern Hemisphere. Our results show coherent interannual variability among snow cover, spring phenology, solar radiation, Scandinavian Pattern, and North Atlantic Oscillation. The interannual variability of the atmospheric peak-to-trough CO2 amplitude is mostly impacted by temperature-mediated effects of El Niño/Southern Oscillation (ENSO) and Pacific/North American Pattern (PNA), whereas CO2 concentration is affected by Polar Pattern control on sea ice extent dynamics. This is assuming the trend in anthropogenic CO2 emission remains constant, or the interannual changes in the trends are negligible. Our analysis suggests that sea ice decline-related CO2 release may outweigh increased CO2 uptake through longer growing seasons and higher temperatures. The direct effects of variation in solar radiation and leading teleconnections, at least in part via their impacts on temperature, dominate the interannual variability of land, cryosphere and ocean indicators. Our results reveal a coherent long-term changes in multiple physical and biological systems that are consistent with anthropogenic forcing of Earth's climate and inconsistent with natural drivers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".