Heatwave attribution based on reliable operational weather forecasts
Nature Communications Nature Research (part of Springer Nature)
Abstract:
The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2-50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10-50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.
Probabilistic thunderstorm forecasts using statistical post-processing: Comparison of logistic regression and quantile regression forests and an investigation of physical predictors
Technical report published by KNMI and University of Utrecht
Abstract:
Probabilities of thunderstorm occurrence and conditional probabilities of lightning intensity over The Netherlands are forecast using statistical post-processing with predictors derived from the operational non-hydrostatic numerical weather prediction model Harmonie, at lead times up to 45 hours. Quantile regression forests (QRF) is compared with logistic regression (LR) for thunderstorm occurrence forecasts and with extended LR for lightning intensity forecasts. Using different sets of predictors that these statistical methods may select, it is demonstrated that pre-selection of predictors based on physical understanding and simultaneously exploiting QRF as machine learning tool can help improving statistical post-processing models. QRF is demonstrated to be beneficial for the predictions, with more skillful forecasts than LR for thunderstorm occurrence. Lightning intensity predictions are influenced by inhomogeneity of lightning detection datasets; despite inhomogeneity, skillful predictions can be made with both extended LR and QRF. The regional maximum of Modified Jefferson index and most unstable CAPE are found as best thunderstorm occurrence predictors and the regional minimum of Bradbury index and maximum of K-index emerge as best for lightning intensity. Neither most unstable CAPE nor microphysical predictors (graupel, snow) are essential for thunderstorm occurrence prediction.