An Observation-Based Dataset of Global Sub-Daily Precipitation Indices (GSDR-I).

Precipitation indices based on daily gauge observations are well established, openly available and widely used to detect and understand climate change. However, in many areas of climate science and risk management, it has become increasingly important to understand precipitation characteristics, variability and extremes at shorter (sub-daily) durations. Yet, no unified dataset of sub-daily indices has previously been available, due in large part to the lesser availability of suitable observations. Following extensive efforts in data collection and quality control, this study presents a new global dataset of sub-daily precipitation indices calculated from a unique database of 18,591 gauge time series. Developed together with prospective users, the indices describe sub-daily precipitation variability and extremes in terms of intensity, duration and frequency properties. The indices are published for each gauge where possible, alongside a gridded data product based on all gauges. The dataset will be useful in many fields concerned with variability and extremes in the climate system, as well as in climate model evaluation and management of floods and other risks.

Towards Quantifying the Uncertainty in Estimating Observed Scaling Rates.

Short-duration precipitation extremes (PE) increase at a rate of around 7%/K explained by the Clausius-Clapeyron relationship. Previous studies show uncertainty in the extreme precipitation-temperature relationship (scaling) due to various thermodynamic/dynamic factors. Here, we show that uncertainty may arise from the choice of data and methods. Using hourly precipitation (PPT) and daily dewpoint temperature (DPT) across 2,905 locations over the United States, we found higher scaling for quality-controlled data, all locations showing positive (median 6.2%/K) scaling, as compared to raw data showing positive (median 5.3%/K) scaling over 97.5% of locations. We found higher scaling for higher measurement precision of PPT (0.25 mm: median 7.8%/K; 2.54 mm: median 6.6%/K). The method that removes seasonality in PPT and DPT gives higher (with seasonality: median 6.2%/K; without seasonality: median 7.2%/K) scaling. Our results demonstrate the importance of quality-controlled, high-precision observations and robust methods in estimating accurate scaling for a better understanding of PE change with warming.

Sub-hourly resolution quality control of rain-gauge data significantly improves regional sub-daily return level estimates.

This research demonstrates how the use of high-resolution rain-gauge data for quality control (QC) significantly changes extreme rainfall estimates, with implications in scientific, meteorological and engineering applications. Current open QC algorithms only consider data at hourly or daily accumulations. Here we present the first open QC algorithm utilising sub-hourly rain-gauge data from official networks at a national, multi-decade scale. We use data from 1,301 rain-gauges in Great Britain (GB) to develop a threshold-based methodology for sub-hourly QC that can be used to complement existing, freely available hourly QC methods by developing an algorithm for sub-hourly QC that uses monthly thresholds for 1 hr, 15 min and 1 min rainfall totals. We then evaluated the effect of combining these QC procedures on rainfall distributions using graphical and statistical methods, with an emphasis on extreme value analysis. We demonstrate that the additional information in sub-hourly rainfall allows our new QC to remove spuriously large values undetected by existing methods which generate errors in extreme rainfall estimates. This results in statistically significant differences between extreme rainfall estimates for 15 min and 1 hr accumulations, with smaller differences found for 6 and 24 hr totals. We also find that extremes in the distributions of 15 min and 1 hr rainfall accumulations tend to grow more rapidly with return period than for longer accumulation periods. We observe similarities between the shape parameter populations for 15 min and 1 hr rainfall accumulations, suggesting that hourly records may be used to improve shape parameter estimates for extreme sub-hourly rainfall in GB. Sub-hourly QC moderates unrealistically large return level estimates for short-duration rainfall, with beneficial impacts on data required for the design of urban drainage infrastructure and the validation of high-resolution climate models.

Carbon emission savings and short-term health care impacts from telemedicine: An evaluation in epilepsy.

OBJECTIVE: Health systems make a sizeable contribution to national emissions of greenhouse gases that contribute to global climate change. The UK National Health Service is committed to being a net zero emitter by 2040, and a potential contribution to this target could come from reductions in patient travel. Achieving this will require actions at many levels. We sought to determine potential savings and risks over the short term from telemedicine through virtual clinics. METHODS: During the severe acute respiratory syndrome coronavirus 2 (SARS-2-CoV) pandemic, scheduled face-to-face epilepsy clinics at a specialist site were replaced by remote teleclinics. We used a standard methodology applying conversion factors to calculate emissions based on the total saved travel distance. A further conversion factor was used to derive emissions associated with electricity consumption to deliver remote clinics from which net savings could be calculated. Patients' records and clinicians were interrogated to identify any adverse clinical outcomes. RESULTS: We found that enforced telemedicine delivery for over 1200 patients resulted in the saving of ~224 000 km of travel with likely avoided emissions in the range of 35 000-40 000 kg carbon dioxide equivalent (CO2 e) over a six and half month period. Emissions arising directly from remote delivery were calculated to be <200 kg CO2 e (~0.5% of those for travel), representing a significant net reduction of greenhouse gas emissions. Only one direct adverse outcome was identified, with some additional benefits identified anecdotally. SIGNIFICANCE: The use of telemedicine can make a contribution toward reduced emissions in the health care sector and, in the delivery of specialized epilepsy services, had minimal adverse clinical outcomes over the short term. However, these outcomes will likely vary with clinic locations, medical specialties and conditions.

Incorporating climate change in flood estimation guidance.

Research into potential implications of climate change on flood hazard has made significant progress over the past decade, yet efforts to translate this research into practical guidance for flood estimation remain in their infancy. In this commentary, we address the question: how best can practical flood guidance be modified to incorporate the additional uncertainty due to climate change? We begin by summarizing the physical causes of changes in flooding and then discuss common methods of design flood estimation in the context of uncertainty. We find that although climate science operates across aleatory, epistemic and deep uncertainty, engineering practitioners generally only address aleatory uncertainty associated with natural variability through standards-based approaches. A review of existing literature and flood guidance reveals that although research efforts in hydrology do not always reflect the methods used in flood estimation, significant progress has been made with many jurisdictions around the world now incorporating climate change in their flood guidance. We conclude that the deep uncertainty that climate change brings signals a need to shift towards more flexible design and planning approaches, and future research effort should focus on providing information that supports the range of flood estimation methods used in practice. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes.

A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration. Evidence suggests that the intensity of long-duration (1 day+) heavy precipitation increases with climate warming close to the Clausius-Clapeyron (CC) rate (6-7% K-1), although large-scale circulation changes affect this response regionally. However, rare events can scale at higher rates, and localized heavy short-duration (hourly and sub-hourly) intensities can respond more strongly (e.g. 2 × CC instead of CC). Day-to-day scaling of short-duration intensities supports a higher scaling, with mechanisms proposed for this related to local-scale dynamics of convective storms, but its relevance to climate change is not clear. Uncertainty in changes to precipitation extremes remains and is influenced by many factors, including large-scale circulation, convective storm dynamics andstratification. Despite this, recent research has increased confidence in both the detectability and understanding of changes in various aspects of intense short-duration rainfall. To make further progress, the international coordination of datasets, model experiments and evaluations will be required, with consistent and standardized comparison methods and metrics, and recommendations are made for these frameworks. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Scaling and responses of extreme hourly precipitation in three climate experiments with a convection-permitting model.

It is widely recognized that future rainfall extremes will intensify. This expectation is tied to the Clausius-Clapeyron (CC) relation, stating that the maximum water vapour content in the atmosphere increases by 6-7% per degree warming. Scaling rates for the dependency of hourly precipitation extremes on near-surface (dew point) temperature derived from day-to-day variability have been found to exceed this relation (super-CC). However, both the applicability of this approach in a long-term climate change context, and the physical realism of super-CC rates have been questioned. Here, we analyse three different climate change experiments with a convection-permitting model over Western Europe: simple uniform-warming, 11-year pseudo-global warming and 11-year global climate model driven. The uniform-warming experiment results in consistent increases to the intensity of hourly rainfall extremes of approximately 11% per degree for moderate to high extremes. The other two, more realistic, experiments show smaller increases-usually at or below the CC rate-for moderate extremes, mostly resulting from significant decreases to rainfall occurrence. However, changes to the most extreme events are broadly consistent with 1.5-2 times the CC rate (10-14% per degree), as predicted from the present-day scaling rate for the highest percentiles. This result has important implications for climate adaptation. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Storm types in India: linking rainfall duration, spatial extent and intensity.

We examine wet events (WEs) defined from an hourly rainfall dataset based on 64 gauged observations across India (1969-2016). More than 90% of the WEs (accounting for nearly 60% of total rainfall) are found to last less than or equal to 5 h. WEs are then clustered into six canonical local-scale storm profiles (CanWE). The most frequent canonical type (CanWE#1 and #2) are associated with very short and nominal rainfall. The remaining canonical WEs can be grouped into two broad families: (i) CanWE#3 and #5 with short (usually less than or equal to 3-4 h), but very intense rainfall strongly phase-locked onto the diurnal cycle (initiation peaks in mid-afternoon) and probably related to isolated thunderstorms or small mesoscale convective clusters (MCS), and (ii) CanWE#4 and #6 with longer and lighter rainfall in mean (but not necessarily for their maximum) and more independent of the diurnal cycle, thus probably related to larger MCSs or tropical lows. The spatial extent of the total rainfall received during each CanWE, as shown by IMERG gridded rainfall, is indeed smaller for CanWE#3 and #5 than for CanWE#4 and especially #6. Most of the annual maximum 1 hour rainfalls occur during CanWE#5. Long-term trend analysis of the June-September canonical WEs across boreal monsoonal India reveals an increase in the relative frequency of the convective storm types CanWE#3 and #5 in recent years, as expected from global warming and thermodynamic considerations. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Climate change and epilepsy: Insights from clinical and basic science studies.

Climate change is with us. As professionals who place value on evidence-based practice, climate change is something we cannot ignore. The current pandemic of the novel coronavirus, SARS-CoV-2, has demonstrated how global crises can arise suddenly and have a significant impact on public health. Global warming, a chronic process punctuated by acute episodes of extreme weather events, is an insidious global health crisis needing at least as much attention. Many neurological diseases are complex chronic conditions influenced at many levels by changes in the environment. This review aimed to collate and evaluate reports from clinical and basic science about the relationship between climate change and epilepsy. The keywords climate change, seasonal variation, temperature, humidity, thermoregulation, biorhythm, gene, circadian rhythm, heat, and weather were used to search the published evidence. A number of climatic variables are associated with increased seizure frequency in people with epilepsy. Climate change-induced increase in seizure precipitants such as fevers, stress, and sleep deprivation (e.g. as a result of more frequent extreme weather events) or vector-borne infections may trigger or exacerbate seizures, lead to deterioration of seizure control, and affect neurological, cerebrovascular, or cardiovascular comorbidities and risk of sudden unexpected death in epilepsy. Risks are likely to be modified by many factors, ranging from individual genetic variation and temperature-dependent channel function, to housing quality and global supply chains. According to the results of the limited number of experimental studies with animal models of seizures or epilepsy, different seizure types appear to have distinct susceptibility to seasonal influences. Increased body temperature, whether in the context of fever or not, has a critical role in seizure threshold and seizure-related brain damage. Links between climate change and epilepsy are likely to be multifactorial, complex, and often indirect, which makes predictions difficult. We need more data on possible climate-driven altered risks for seizures, epilepsy, and epileptogenesis, to identify underlying mechanisms at systems, cellular, and molecular levels for better understanding of the impact of climate change on epilepsy. Further focussed data would help us to develop evidence for mitigation methods to do more to protect people with epilepsy from the effects of climate change.

PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979-2018.

We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979-2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R2) values ranged from 0.76 to 0.80 for the daily P occurrence indices, and from 0.44 to 0.84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0.1 mm 3 h-1 threshold, P was estimated to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively. The highest P intensities were found over parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and in the intertropical convergence zone. The PPDIST dataset is available via www.gloh2o.org/ppdist .

Europe-wide precipitation projections at convection permitting scale with the Unified Model.

For the first time, we analyze 2.2 km UK Met Office Unified Model convection-permitting model (CPM) projections for a pan-European domain. These new simulations represent a major increase in domain size, allowing us to examine the benefits of CPMs across a range of European climates. We find a change to the seasonality of extreme precipitation with warming. In particular, there is a relatively muted response for summer, which contrasts with much larger increases in autumn and winter. This flattens the hourly extreme precipitation seasonal cycle across Northern Europe which has a summer peak in the present climate. Over the Western Mediterranean, where autumn is the main extreme precipitation season, there is a regional increase in hourly extreme precipitation frequency, but local changes for lower precipitation thresholds are often insignificant. For mean precipitation, decreases are projected across Europe in summer, smaller decreases in autumn, and increases in winter; comparable changes are seen in the driving general circulation model (GCM) simulations. The winter mean increase is accompanied by a large decrease of winter mean snowfall. Comparing the driving GCM projections with the CPM ones, the CPMs show a robust enhanced intensification of precipitation extremes at the convection-permitting scale compared to coarser resolution climate model projections across various European regions for summer and autumn.

Advances in understanding large-scale responses of the water cycle to climate change.

Globally, thermodynamics explains an increase in atmospheric water vapor with warming of around 7%/°C near to the surface. In contrast, global precipitation and evaporation are constrained by the Earth's energy balance to increase at ∼2-3%/°C. However, this rate of increase is suppressed by rapid atmospheric adjustments in response to greenhouse gases and absorbing aerosols that directly alter the atmospheric energy budget. Rapid adjustments to forcings, cooling effects from scattering aerosol, and observational uncertainty can explain why observed global precipitation responses are currently difficult to detect but are expected to emerge and accelerate as warming increases and aerosol forcing diminishes. Precipitation increases with warming are expected to be smaller over land than ocean due to limitations on moisture convergence, exacerbated by feedbacks and affected by rapid adjustments. Thermodynamic increases in atmospheric moisture fluxes amplify wet and dry events, driving an intensification of precipitation extremes. The rate of intensification can deviate from a simple thermodynamic response due to in-storm and larger-scale feedback processes, while changes in large-scale dynamics and catchment characteristics further modulate the frequency of flooding in response to precipitation increases. Changes in atmospheric circulation in response to radiative forcing and evolving surface temperature patterns are capable of dominating water cycle changes in some regions. Moreover, the direct impact of human activities on the water cycle through water abstraction, irrigation, and land use change is already a significant component of regional water cycle change and is expected to further increase in importance as water demand grows with global population.

UKGrsHP: a UK high-resolution gauge-radar-satellite merged hourly precipitation analysis dataset.

There is an urgent need for high-quality and high-spatial-resolution hourly precipitation products around the globe, including the UK. Although hourly precipitation products exist for the UK, these either contain large errors, or are insufficient in spatial resolution. An efficient way to solve this is to develop a merged precipitation product that combines the information and benefits from multiple data sources, improving both the spatial resolution and accuracy of hourly precipitation estimates over the UK. In this study, we develop a UK high-resolution gauge-radar-satellite merged hourly precipitation analysis: the UKGrsHP. It covers the UK from 12.5° W to 3.5° E, 49° N-60° N, with a spatial resolution of 0.01° × 0.01° in latitude/longitude (equivalent to 1 km resolution in the mid-latitudes). An optimal interpolation (OI)-based multi-source merging scheme with compound strategy is developed and tested for producing the UKGrsHP. Three input data sources are used: gauge analysis data interpolated from 1903 quality-controlled hourly observations, the UK Nimrod radar precipitation analysis and the GSMaP global satellite precipitation analysis. Using independent tests against ~ 220 independent gauge observations on 1 year's experimental UKGrsHP, covering the period from January to December 2014, we find that the final merged data performs better than three individual precipitation analyses used as inputs. A full version of the UKGrsHP starting in April 2004 is now under production, which will have wide applications in climate services and scientific research across multiple disciplines.

Large-scale dynamics have greater role than thermodynamics in driving precipitation extremes over India.

The changing characteristics of precipitation extremes under global warming have recently received tremendous attention, yet the mechanisms are still insufficiently understood. The present study attempts to understand these processes over India by separating the 'dynamic' and 'thermodynamic' components of precipitation extremes using a suite of observed and reanalysis datasets. The former is mainly due to changes in atmospheric motion, while the latter is driven mainly by the changes associated with atmospheric moisture content. Limited studies have attributed dynamic and thermodynamic contributions to precipitation extremes, and their primary focus has been on the horizontal atmospheric motion component of the water budget. Our study, on the other hand, implements the decomposition of vertical atmospheric motion, based on the framework proposed by Oueslati et al. (Sci Rep 9: 2859, 2019), which has often been overlooked, especially for India. With the focus on two major and recent extreme events in the Kerala and Uttarakhand regions of India, we show that the vertical atmospheric motion has a more significant contribution to the events than the horizontal atmospheric motion. Further, decomposition of the vertical atmospheric motion shows that the dynamic component overwhelms the thermodynamic component's contribution to these extreme events, which is found to be negligible. Using a threshold method to define extreme rainfall, we further extended our work to all India, and the results were consistent with those of the two considered events. Finally, we evaluate the contributions from the recently made available CMIP6 climate models, and the results are interestingly in alignment with the observations. The outcomes of this study will play a critical role in the proper prediction of rainfall extremes, whose value to climate adaptation can hardly be overemphasised.

Climate change and epilepsy: Time to take action.

Climate change is the biggest challenge facing humanity today. The associated global warming and humidification, increases in the severity and frequency of extreme climate events, extension of the ranges of vector-borne diseases, and the consequent social and economic stresses and disruption will have major negative consequences on many aspects of health care. People whose resilience to change is already impaired may suffer disproportionately from these environmental changes, which are of unprecedented reach and magnitude. There has been little connection made so far between climate change and epilepsy. We briefly review the history of climate change science and the subsequent response of the global scientific community. We consider how climate change effects might in general affect health and disease. We consider some of the underlying complex interactions that, for example, favor the spread of vector-borne diseases and how climate models operate and may help plan for global and local changes. We then speculate specifically on how these generic ideas may apply specifically to epilepsy. We consider these impacts at levels from molecular to the epidemiological. Data are sparse, and there is undoubtedly a need for more information to enable better estimation of possible effects of climate change on care in epilepsy. We also consider how the professional activities of those involved in epilepsy health care might contribute to global carbon emissions, for example, through flying for conference attendance. Healthcare organizations across the world are already considering, and responding to, many of these issues. We argue for more research in this area, but also for action today. Actions today are likely to generate cobenefits for health care, including care in epilepsy, resulting from efforts to decarbonize, mitigate effects of climate change that has already happened, and plan for adaptation to climate change.

Synoptic-Scale Precursors of Extreme U.K. Summer 3-Hourly Rainfall.

The synoptic-scale meteorological conditions leading up to the 30 most extreme subdaily summer rain events for two regions of the United Kingdom (northwest and southeast) were examined for the period 1979-2013. Using a recently available, quality controlled, national hourly rain gauge data set, we were able to identify extreme 3-hr rainfall accumulations that may be indicative of flash flooding. Composites of the state of the atmosphere leading up to these dates were produced to investigate synoptic-scale processes, thus potentially allowing for them to be identified in coarse resolution reanalyses and in climate models. The results show that the two regions have different dominant synoptic-scale conditions leading to extreme 3-hr rainfall, which is thought to be related to the type of rainfall typically experienced in each region. In particular, positive anomalies in mean sea level pressure and the geopotential height at 200 hPa over the United Kingdom are associated with extreme rainfall in the northwest, where the position of the westerly jet is also important. For the southeast, no clear anomalous synoptic-scale conditions could be identified; however, localized moisture sources and unstable air masses were observed in association with extremes. These results indicate the importance of better understanding of both synoptic-scale and thermodynamic drivers of short-duration extreme rainfall, with potential implications in forecasting and flood warning, as well as for understanding the representation of key processes by regional climate models.

Thermodynamic controls of the Western Tibetan Vortex on Tibetan air temperature.

The "Western Tibetan Vortex" (WTV)-also termed the Karakoram Vortex-dominates the middle-to-lower troposphere and the near-surface air temperature variability above the western Tibetan Plateau (TP). Here, we explore the thermodynamic mechanisms through which the WTV modulates air temperature over the western TP by diagnosing the three major terms of the thermodynamic energy equation-adiabatic heating, horizontal temperature advection, and diabatic heating-that maintain the atmospheric thermal balance. We composite these major terms to examine the differences between anti-cyclonic and cyclonic WTV events. Our theoretical approach demonstrates that adiabatic sinking-compression (rising-expansion) provides the overwhelming control on both the middle-to-lower tropospheric and lower stratospheric temperature increases (decreases) under anti-cyclonic (cyclonic) WTV conditions over the western TP high mountain area in all four seasons. This also explains the mechanisms behind the anomalous temperature "dipole" found between the mid-lower troposphere and lower stratosphere when the WTV was initially identified. Spatially, adiabatic heating effects are centred on the central western TP in summer and the south slope centring at 70°-80°E of the TP in other seasons. The other two terms, horizontal temperature advection and diabatic heating, have localized importance over the edges of the western TP. In a case study over the Karakoram area, we further demonstrate that adiabatic heating (rising-expanding-cooling/sinking-compressing-warming) is the dominant thermodynamic process controlling Karakoram air temperatures under WTV variability, except for at the very near surface in autumn and winter. Our analysis methods can be applied to investigate the thermodynamic processes of other atmospheric circulation systems or climate variability modes.

A new precipitation and drought climatology based on weather patterns.

Weather-pattern, or weather-type, classifications are a valuable tool in many applications as they characterize the broad-scale atmospheric circulation over a given region. This study analyses the aspects of regional UK precipitation and meteorological drought climatology with respect to a new set of objectively defined weather patterns. These new patterns are currently being used by the Met Office in several probabilistic forecasting applications driven by ensemble forecasting systems. Weather pattern definitions and daily occurrences are mapped to Lamb weather types (LWTs), and parallels between the two classifications are drawn. Daily precipitation distributions are associated with each weather pattern and LWT. Standardized precipitation index (SPI) and drought severity index (DSI) series are calculated for a range of aggregation periods and seasons. Monthly weather-pattern frequency anomalies are calculated for SPI wet and dry periods and for the 5% most intense DSI-based drought months. The new weather-pattern definitions and daily occurrences largely agree with their respective LWTs, allowing comparison between the two classifications. There is also broad agreement between weather pattern and LWT changes in frequencies. The new data set is shown to be adequate for precipitation-based analyses in the UK, although a smaller set of clustered weather patterns is not. Furthermore, intra-pattern precipitation variability is lower in the new classification compared to the LWTs, which is an advantage in this context. Six of the new weather patterns are associated with drought over the entire UK, with several other patterns linked to regional drought. It is demonstrated that the new data set of weather patterns offers a new opportunity for classification-based analyses in the UK.

Storylines: an alternative approach to representing uncertainty in physical aspects of climate change.

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.