The SUMMA-SUNDIALS Earth System Model
Bibliographic record
Abstract
The next generation of Earth System models promisesunprecedented predictive power through the application of improvedphysical representations, data collection, and high-performancecomputing. A key component to the accuracy, efficiency, and robustnessof the Earth System simulations is the time integration ofdifferential equations describing the physical processes. Manyexisting Earth System models are simulated using low-order,constant-stepsize time-integration methods with no error control,opening them up to being inaccurate, inefficient, or require aninfeasible amount of manual tweaking when run over multipleheterogeneous domains or scales. We have implemented the variable-stepize, variable-order differentialequation solver SUNDIALS as the time integrator within the Structurefor Unifying Multiple Modelling Alternatives (SUMMA) modelframework. The model equations in SUMMA were modified and augmented toexpress conservation of mass and enthalpy. Water and energy balanceerrors were tracked and kept below a strict tolerance. The resultingSUMMA-SUNDIALS software was successfully run in a fully automatedfashion to simulate hydrological processes on the North Americancontinent, sub-divided into over 500,000 catchments. We compared the performance of SUMMA-SUNDIALS with a version (calledSUMMA-BE) that used the backward Euler method with a fixed stepsize asthe time-integration method. We find that SUMMA-BE required two ordersof magnitude more CPU time to produce solutions of comparable accuracyto SUMMA-SUNDIALS. Solutions obtained with SUMMA-BE in a similar orshorter amount of CPU time than SUMMA-SUNDIALS often contained largediscrepancies. We conclude that sufficient accuracy, efficiency, and robustness ofnext-generation Earth System model simulations can realistically onlybe obtained through the use of adaptive solvers. Furthermore, wesuggest simulations produced with low-order, constant-stepsizesolvers deserve more scrutiny in terms of their accuracy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".