Influence of GIA uncertainty on climate applications from satellite gravimetry
Bibliographic record
Abstract
Global coupled climate models are important for predicting future climate conditions. Due to sometimes large and often systematic model uncertainties, it is crucial to evaluate the outcome of model experiments against independent observations. Changes in the distribution and availability of terrestrial water storage (TWS), which can be measured by the satellite gravimetry missions GRACE and GRACE-FO, represent an important part of the climate system. However, the use of satellite gravity data for the evaluation of coupled climate models has only very recently become feasible. Challenges arise, e.g., from the still rather short time series of satellite data and from signal separation issues related to GRACE/-FO observing all mass change including non-water related variations such as glacial isostatic adjustment. Apart from climate model uncertainties, these challenges might be the reason for a disagreement between the direction of linear water storage trends of models and observations in several regions of the world, one of them located in Eastern Canada. This presentation will highlight the latest results achieved from our ongoing research on climate model evaluation based on the analysis of an ensemble of models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We will focus on long-term wetting and drying conditions in TWS. Using an ensemble of 52 GIA models that differ in the applied ice history, solid Earth rheology, and numerical code, this presentation will discuss how GIA modeling uncertainty does influence (i) the determination of water storage trends from GRACE/FO data, and (ii) the (dis-)agreement between drying/wetting trends in satellite gravimetry and CMIP6 climate models. We will show that the apparent disagreement between observations and models in highly GIA-affected regions in North America crucially depend on the particular model chosen for reducing the GIA effect from the GRACE satellite data.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".