As climate change research becomes increasingly applied, the need for actionable information is growing rapidly. A key aspect of this requirement is the representation of uncertainties. The conventional approach to representing uncertainty in physical aspects of climate change is probabilistic, based on ensembles of climate model simulations. In the face of deep uncertainties, the known limitations of this approach are becoming increasingly apparent. An alternative is thus emerging which may be called a ‘storyline’ approach. We define a storyline as a physically self-consistent unfolding of past events, or of plausible future events or pathways. No a priori probability of the storyline is assessed; emphasis is placed instead on understanding the driving factors involved, and the plausibility of those factors. We introduce a typology of four reasons for using storylines to represent uncertainty in physical aspects of climate change: (i) improving risk awareness by framing risk in an event-oriented rather than a probabilistic manner, which corresponds more directly to how people perceive and respond to risk; (ii) strengthening decision-making by allowing one to work backward from a particular vulnerability or decision point, combining climate change information with other relevant factors to address compound risk and develop appropriate stress tests; (iii) providing a physical basis for partitioning uncertainty, thereby allowing the use of more credible regional models in a conditioned manner and (iv) exploring the boundaries of plausibility, thereby guarding against false precision and surprise. Storylines also offer a powerful way of linking physical with human aspects of climate change.

Abstract. A novel approach to modelling the surface wind field of landfalling tropical cyclones (TCs) is presented. The modelling system simulates the evolution of the low-level wind fields of landfalling TCs, accounting for terrain effects. A two-step process models the gradient-level wind field using a parametric wind field model fitted to TC track data and then brings the winds down to the surface using a numerical boundary layer model. The physical wind response to variable surface drag and terrain height produces substantial local modifications to the smooth wind field provided by the parametric wind profile model. For a set of US historical landfalling TCs the accuracy of the simulated footprints compares favourably with contemporary modelling approaches. The model is applicable from single-event simulation to the generation of global catalogues. One application demonstrated here is the creation of a dataset of 714 global historical TC overland wind footprints. A preliminary analysis of this dataset shows regional variability in the inland wind speed decay rates and evidence of a strong influence of regional orography. This dataset can be used to advance our understanding of overland wind risk in regions of complex terrain and support wind risk assessments in regions of sparse historical data.

Precipitation measurements in the Mekong River Basin (MRB) are full of variability due to this domain's varied weather systems, climate conditions, elevation, and specific land–atmosphere interactions. This study provides an in‐depth evaluation of the differences between four gridded precipitation products [i.e. Asian Precipitation—Highly‐Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Tropical Rainfall Measuring Mission (TRMM), CPC MORPHing technique (CMORPH), and Climatic Research Unit (CRU)] over the Greater Mekong Subregion. Precipitation data from a total of 242 stations in this domain are used to investigate the quality of the gridded products. Our analysis verifies that gauge‐based APHRODITE exhibits the highest correlations with the station data as well as the highest probability of detection of daily precipitation. The false alarm ratio, on the other hand, is slightly in favour of TRMM and CMORPH. Subtracting APHRODITE (as baseline) from TRMM and CMORPH reveals the spatial and frequency distribution of potential biases. The results indicate that TRMM appears to have a wet bias in most areas, while CMORPH shows no similar or consistent bias over APHRODITE. To utilize the higher accuracy of APHRODITE and the finer spatial and temporal footprints of CMORPH, a new restructuring algorithm is introduced in this study. The algorithm is capable of eliminating biases and possible artefacts associated with CMORPH while resolving the resolution discrepancies between the two data sets.

Abstract. A novel approach to modelling the surface wind field of landfalling tropical cyclones (TCs) is presented. The modelling system simulates the evolution of the low-level wind fields of landfalling TCs, accounting for terrain effects. A two-step process models the gradient-level wind field using a parametric wind field model fitted to TC track data, then brings the winds down to the surface using a full numerical boundary layer model. The physical wind response to variable surface drag and terrain height produces substantial local modifications to the smooth wind field provided by the parametric wind profile model. For a set of U.S. historical landfalling TCs the simulated footprints compare favourably with surface station observations. The model is applicable from single event simulation to the generation of global catalogues. One application demonstrated here is the creation of a dataset of 714 global historical TC overland wind footprints. A preliminary analysis of this dataset shows regional variability in the inland wind speed decay rates and evidence of a strong influence of regional orography. This dataset can be used to advance our understanding of overland wind risk in regions of complex terrain and support wind risk assessments in regions of sparse historical data.