Evaluation of IPCC Models’ Performance in Simulating Late-Twentieth-Century Climatologies and Weather Patterns over North America
Bibliographic record
Abstract
Abstract The authors analyze the performance of 22 Intergovernmental Panel on Climate Change (IPCC) global climate models (GCMs) over all of North America and its western subregion using several different evaluation metrics. They assess the model skill in simulating climatologies of several climate variables and the skill in simulating the daily synoptic patterns. The evaluation is performed by comparing the model output with the North American Regional Reanalysis (NARR) over the period 1980–99. One set of metrics, based on root-mean-square errors and variance ratios, compares modeled versus the NARR mean annual cycle and interannual variability. Based on these measures the three top performing models are the ECHAM5–Max Planck Institute Ocean Model (MPI-OM), the third climate configuration of the Met Office Unified Model (HadCM3), and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model, version 3.1 [CGCM3.1(T47)]. Models that perform well over all North America also perform well over its western subregion. However, the model ranking is sensitive to the choice of climate variable. For another evaluation measure the method of self-organizing maps was applied to classify the characteristic daily patterns of sea level pressure over the region. The evaluation consists of correlating the frequencies of these patterns, as generated in GCMs, with the frequencies in the NARR over the baseline period. Most of the models are successful in simulating the frequencies of daily anomaly patterns from the 20-yr-average daily pattern. However, very few GCMs are able to reproduce the occurrences of characteristic daily weather patterns in the NARR on seasonal basis over the baseline period. In terms of relative performance, the three top performing models are the Meteorological Research Institute (MRI) CGCM2.3.2, ECHAM5–MPI-OM, and the Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)]. The model skill in simulating daily synoptic patterns is not strongly linked to the skill in simulating the climatologies of selected variables. Despite the large scatter of model performance across all the metrics, some models consistently rank high [e.g., ECHAM5–MPI-OM and MIROC3.2(medres)]. Likewise, some models consistently rank low [e.g., the Community Climate System Model, version 3 (CCSM3) and the Goddard Institute for Space Studies Model E-R (GISS-ER)] independently of the evaluation measures, domain size, and climate variable of interest.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".