Temperature and precipitation change in Malawi: Evaluation of CORDEX-Africa climate simulations for climate change impact assessments and adaptation planning
Bibliographic record
Abstract
Malawi is highlighted as one of the most vulnerable countries in the world to the effects of climate change. The large uncertainty around future climate change in the region remains a barrier to adaptation planning. Despite this high potential vulnerability, relatively little research has gone into determining how well available models represent this country's climate. This work therefore evaluates the ability of existing General Circulation Models (GCMs) and Regional Climate Models (RCMs) to hindcast climatic variables in Malawi at a resolution appropriate for climate change impact assessment and adaptation planning. We focus on monthly precipitation rate, and mean, maximum and minimum surface air temperature. This assessment compares available observed datasets against the outputs of six ERA-interim driven RCMs and 21 GCM-driven RCMs from the Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative, and the 11 GCMs which form their boundary conditions. It was found that the performance of the RCMs is highly influenced by their boundary conditions. None of the individual or ensemble RCMs or GCMs assessed in this paper correlate well with the observed datasets for any of the assessed climatic variables. While, they do simulate the trending change in temperature variables well, the simulated outputs for precipitation are highly divergent. Based on these findings we suggest that either the ensemble RCMs or ensemble GCMs would be suitable for understanding projected temperature trends, with the RCMs providing better spatial resolution. However, none of the assessed models provide certainty over future precipitation trends in Malawi. As such we suggest that impact assessments and adaptation plans in Malawi will need to be designed and tested against a range of future precipitation scenarios. To improve modelling for Malawi it is recommended that regional climate models be improved for higher spatial resolution and inclusion of the impacts from large water bodies, including Lake Malawi.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".