The Role of Internal Variability in Twenty‐First‐Century Projections of the Seasonal Cycle of Northern Hemisphere Surface Temperature
Bibliographic record
Abstract
Abstract The seasonal cycle is fundamental to the Earth's climate system, accounting for the vast majority of temperature variance. Understanding how the seasonal cycle will change in the future, and by when, is a key question with important implications. Here a 40‐member initial condition climate model ensemble is used to investigate the influence of internal variability on the detection of changes in the amplitude and timing of the seasonal cycle of surface temperature over Northern Hemisphere land in response to increasing greenhouse gases. Internal variability renders the detection of these changes challenging; even by the mid‐twenty‐first century, small ensembles will be insufficient to separate the forced signals from internal variability over many continental regions in the Northern Hemisphere. Despite this, projected changes over Europe, North Africa, and Siberia are large and easily detectable, even in a single member. Specifically, amplitude increases over Europe and North Africa while it decreases over Siberia. On the other hand, the timing of the seasonal cycle is delayed over all three regions. It is found that these changes are remarkably robust across model ensembles from the Coupled Model Intercomparison Project phase 5 archive. To understand the mechanisms underlying these robust changes, a simple energy balance model is used to partition changes into contributions arising from changes in the physical parameters that control the seasonal cycle. It is found that future changes in the seasonal cycle over the three regions are most strongly controlled by changes in surface longwave and turbulent heat fluxes.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".