Bias Correction and Spatiotemporal Scales for Downscaling Future Projections of Northwest Atlantic Circulation and Sea Ice
Bibliographic record
Abstract
In the Northwest Atlantic (NWA), including the Labrador Sea, interactions between the atmosphere, ocean circulation, and sea ice play a critical role in regulating the global climate system. The ocean and climate in this region observe rapid and unprecedented, anthropogenically forced changes to the physical environment and biosphere with downstream effects. Future projections of NWA circulation and sea ice can help address pressing questions about these changes and mitigate their potential impacts on the global carbon cycle, coastal communities, and transportation. However, the spatial resolution of current climate models is often insufficient to accurately represent important features in the NWA, such as the location and strength of the Gulf Stream and Labrador Current and their dynamical interactions. This can lead to biases in the model’s mean state, and a misrepresentation of the temporal and spatial scales of ocean variability, e.g., mesoscale eddies, deep convection. Regional ocean models with grid spacing <10 km, forced by global climate simulations, can be used to improve estimates of historical and future circulation and hydrography. However, given the limited spatial resolution and biases in global climate models, a challenge of downscaling their simulations is the appropriate reconstruction of the forcing fields. Here, we present preliminary results of future projections of NWA circulation and sea ice based on downscaled global climate simulations. These projections are performed using an eddy-resolving, coupled circulation-sea ice model based on the Regional Ocean Modeling System (ROMS) and the Los Alamos Sea Ice Model (CICE). We will focus on the value of correcting biases in the mean and variance of the forcing. We further explore the need of including missing spatial and temporal scales in the atmospheric forcing that are not captured by the global models. Implications for the design of model experiments for future projections will be discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".