Journal Issue: Cambridge Journal of Climate Research - Volume 2, Issue 2
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Volume
2
Number
2
Issue Date
2025-12
Journal Title
Journal ISSN
3050-2020
Journal Volume
Articles
Foreword
(2025-12) Edmonds, Marie
President's Letter
(2025-12) Todt, Niklas
What Drives General Circulation Model Biases in Precipitation Extremes? A Generalised Pareto Distribution (GPD)-Based Diagnostic of CMIP6 Tail Behaviour
(2025-12) Aladalli, Apoorva
This study uses a point-scale Peaks-Over-Threshold (POT) Generalised Pareto Distribution (GPD) framework to evaluate extreme precipitation simulated by 32 CMIP6 models from 2015 to 2024. Daily rainfall exceeding high thresholds is fitted to the GPD using three estimators: Maximum Likelihood Estimation, L-moments, and Bayesian inference. Return levels for 2, 5, 10, 20, 50, and 100 year events are computed. Two climatically distinct sites are analysed, one in tropical South India and one in mid-latitude Northern Italy, with observational benchmarks from IMD4 and E-OBS datasets. Results show systematic model biases. CMIP6 models generally overestimate extreme rainfall in India and underestimate it in Italy. Biases are closely linked to convection parameterisation schemes. Zhang-McFarlane models simulate Indian monsoon extremes within about 30 percent accuracy but underpredict Italian 100-year rainfall by 50 to 65 percent. Gregory-Rowntree scheme models show mixed results. Models with simple bulk convection schemes tend to have the largest errors, often exceeding 100 percent in both regions. Notably, the finely tuned Gregory-Rowntree model KACE-1-0-G performed well in both domains, highlighting the importance of targeted calibration regardless of scheme type. Variability in threshold selection, driven by convection scheme differences and drizzle bias, affects return level estimates more than GPD shape or scale parameter variations. Differences across estimation methods are minor compared to inter-model spread, with Bayesian inference showing the most stable results. Increasing model resolution alone does not guarantee improved simulation of extreme precipitation. Overall, findings underscore that model physics, particularly convection parameterisation, is the primary determinant of extreme precipitation biases, suggesting that advancements in physical processes offer greater potential for reducing biases than resolution enhancements alone.
Delineating the Skyscape: Mapping Sustainable Aviation Policy Levers for Climate Risk
(2025-12) Song, Qi
The aviation sector faces a growing risk of failing to meet international climate targets, as current mitigation efforts fall short of the scale and pace required. Closing this gap demands the aligned mobilisation of technical, societal, and operational solutions across emerging transition pathways, underscoring the need for coherent and timely policy intervention. This viewpoint article provides a stocktake of sustainable aviation policy levers across five key impact domains identified in recent academic literature and research reports: system efficiency improvements, sustainable aviation fuels, alternative aircraft design, air travel demand management, and the mitigation of non-carbon dioxide climate effects. Together, these domains shape the sector’s transition towards a climate-neutral future, offering an integrated perspective on how strategic and well-sequenced policy design can accelerate change. The article outlines key policy mechanisms for addressing aviation-related climate risk and introduces a heuristic framework for rethinking system-level governance. It highlights the need for the effective and coordinated deployment of regulatory, economic, voluntary, and capacity-building instruments across multiple stakeholder groups in the sector. Collectively, the design of policy strategy, the choice of instruments, and the styles of policy processes warrant greater attention from this cross-cutting scoping viewpoint.