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Background An increase in fire weather is expected in a warming climate, but its translation to fire activity (fire numbers and sizes) remains largely unknown. Additionally, disentangling the extent to which geographic and seasonal extensions as well as intensification contribute to future fire activity remain largely unknown. Aims We aimed to assess the impact of future climate change on fire activity in southeastern France and estimate changes in spatial and seasonal distributions. Methods We projected future fire activities using a Bayesian modelling framework combined with ensemble climate simulations. Changes in numbers of escaped fires (>1 ha), large fires (>100 ha) and burned area were studied for different emission scenarios or degrees of global warming. Key results Fire activity could increase by up to +180% for +4°C of global warming, with large expansions of fire-prone regions and long seasonal lengthenings. Overall, changes will be dominated by intensification within the historical fire niche, representing two-thirds of additional future fire activity, half of this occurring during the high fire season. Conclusions This study confirms that major changes in fire niches would be expected in Euro-Mediterranean regions. Implications Long-term strategic policies for adapting prevention and suppression resources and ecosystems are needed to account for such changes.

Vapor Pressure Deficit (VPD, atmospheric drought) and soil water potential (Ψsoil, soil drought) have both been reported to affect terrestrial plant water stress, plant functions (growth, stomatal conductance, transpiration) and vulnerability to ecosystem disturbances (mortality or vulnerability to wildfires). Which of atmospheric drought or soil drought has the greatest influence on these responses is yet an unresolved question. Using a state-of-the-art soil-plant-atmosphere hydraulic model, we conducted an in-silico experiment where VPD and Ψsoil were manipulated one at a time to quantify the relative importance of atmospheric vs soil drought on most critical plant functions. The model simulates the combined effects of soil drought and atmospheric drought on plant water potential (ΨPlant), a physiologically meaningful metric of plant water status driving plant turgor, stomatal conductance, hydraulic conductance or water content, and thus mortality and fire risks. Contrary to expectations, we showed that VPD had a weaker effect than Ψsoil on tree water stress and forest disturbances risk (i.e leaf moisture content). While physiological responses associated with low water stress such as stomatal closure or turgor loss could be driven by both VPD or soil drought, consequences of extreme water stress such as hydraulic failure, leaf desiccation and vulnerability to wildfires were almost exclusively driven by low Ψsoil. Our results therefore suggest that most plant functions are affected by VPD through its cumulative effect on Ψsoil via increased plant transpiration, rather than through a direct instantaneous effect on plant water potential. We argue that plant hydraulics provide a strong foundation for predicting tree and terrestrial ecosystem responses to climate changes and propose a list of explanations and testable hypotheses to reconcile plant hydraulic theory and observations of soil and atmospheric drought effects on plant functions.

Southern Europe is a highly fire-prone region where extreme fires have often disastrous consequences on both structures and people. Human activities and fire weather conditions favouring ignitions and propagation have always been the drivers of such fires but anthropogenic climate change alongside the extension of wildland-urban interface (WUI) that concentrates both assets and fire ignitions have the compounding effect of exacerbating fire risk. WUI are currently not adequately prepared to sustain events whose frequency and intensity are foreseen to increase in the future as shown during the extreme fires that occurred recently in Euro-Mediterranean countries. This work presents the context of WUI fires in the Euro-Mediterranean region, their driving forces and their impacts on society, with insights from three recent catastrophic fires that drew much attention. In this context, we propose a conceptual framework for understanding the WUI issue assessing the implications for fire risk and providing some guidance to mitigate this risk, updated management strategies as well as comments about gaps in our current knowledge and how we might address this problem in the future. A successful approach to reduce fire risk in the future will require building resilient landscapes and communities better prepared to face these extreme fire events in which WUI population, forest managers, land planners, civil protection, and policy-makers need to work together to improve the safety and resilience of these fire-prone areas.

We investigate the global distribution of hourly precipitation and its connections with the El Niño–Southern Oscillation (ENSO) using both satellite precipitation estimates and the global sub-daily rainfall gauge dataset. Despite limited moisture availability over continental surfaces, we find that the highest mean and extreme hourly precipitation intensity (HPI) values are mainly located over continents rather than over oceans, a feature that is not evident in daily or coarser resolution data. After decomposing the total precipitation into the product of the number of wet hours (NWH) and HPI, we find that ENSO modulates total precipitation mainly through the NWH, while its effects on HPI are more limited. The contrasting responses to ENSO in NWH and HPI is particularly apparent at the rising branches of the Pacific and Atlantic Walker Circulations, and is also notable over land-based gauges in Australia, Malaysia, the USA, Japan and Europe across the whole distribution of hourly precipitation (i.e. extreme, moderate and light precipitation). These results provide new insights into the global precipitation distribution and its response to ENSO forcing.

Anticipating future fire activity at global and regional scales is critical in a changing climate. Indeed, fire seasons are expected to lengthen and fire prone areas are expected to extend, but the magnitude, location and timing of such increases remain uncertain. Moreover, an intensification is expected during the core of the fire season of already fire-prone regions. However, quantifying seasonal and spatial impacts of climate change on fire activity is challenging. Here, we projected future fire activities in Southern France using the Firelihood model. This Bayesian probabilistic model operates on a daily basis in 8-km pixels, allowing to analyze both seasonal and spatial distributions of fire activities in a framework integrating stochasticity. Projections were computed for 13 GCM-RCM couples under two RCP scenarios (4.5 and 8.5), assuming that the only factor of change in future fire activity was the daily fire weather. The fire season was defined as the period with fire-activity level higher than the level of the 15th of July of the present period. The fire prone region corresponded to locations with fire-activity levels higher than the 2nd level of a 5-level fire-activity scale derived from numbers of fires larger than 1ha, 100ha (N1ha and N100ha) and burnt areas (BA). Simulations under RCP8.5 show that large increases in fire activity should be expected from the mid-century and that the rate of increase should then accelerate, leading to up to three-fold increases for number of fires larger than 100ha by the end of the century. In particular, all metrics except N1ha increased faster than the mean FWI and even the mean DSR. Such increases were partly caused by a massive seasonal lengthening from 45-50 days to up to 125 days, equally distributed between spring and autumn. However, the intensification during the present fire season was found to contribute slightly more to the overall increase than the lengthening itself. For example, for N100ha, the intensification would represent a 280 % increase in fire activity with respect to the present seasonal reference, whereas the lengthening outside of the present season would represent +230%. The fire prone area would increase by 168%, shifting from 22 to 56% of region total area. However, the intensification inside the already fire-prone region was found to contribute more to the increase than the spatial extension. For example, for N100ha, the intensification would represent a 190% increase with respect to the present fire-prone regional reference, whereas the extension outside of this area would represent +110%. These drastic increases provide a good indication of the potential lengthening of the fire season, spatial extension and intensification of future fire activities under RCP 8.5, all three being importantly concerned, but dominated by intensification. Extending and lengthening suppression policies may allow to mitigate projected increases, but the intensification of fire activity during the core of the fire season overwhelm current fire suppression capacities.

Wildland fire effects are strongly associated with fire regime characteristics. Here, we developed the first European pyrogeography based on different fire regime components to better understand fire regimes across the continent. We identified four large-scale pyroregions: a non-fire-prone (NFP) pyroregion featuring nominal fire activity across central and northern Europe; a cool-season fire (CSF) pyroregion scattered throughout Europe; a fire-prone (FP) pyroregion extending mostly across southern Europe; and a highly fire-prone (HFP) pyroregion spanning across northern Portugal, Sicily, and western Balkans. Land cover analysis indicates that pyroregions were first shaped by vegetation and then by anthropogenic factors. On interannual timescales the spatial extent of pyroregions was found to vary, with NFP showing more stability. Interannual correlations between climate and burned area, fire frequency, and the length of fire period exhibited distinct patterns, strengthening in fire-prone pyroregions (FP and HFP) and weakening in NFP and CSF. Proportion of cool-season fires and large fires were related to fuel accumulation in fire-prone pyroregions. Overall, our findings indicate that such a pyrogeography should allow a more accurate estimate of the effects of climate on fire regimes while providing an appropriate framework to better understand fire in Europe This work was funded by project MED-Star, supported by the European Union under the Operational Program Italy/France Maritime (project number CUP E88H19000120007). Marcos Rodrigues has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003890, FirEUrisk – DEVELOPING A HOLISTIC, RISK-WISE STRATEGY FOR EUROPEAN WILDFIRE MANAGEMENT.

International audience; Global warming is expected to increase droughts and heatwaves, and consequently fire danger in southern Europe in the forthcoming decades. However, an assessment of the uncertainties associated with this general trend at regional scales, relevant to decision-making, is still missing. This study aims at assessing potential climate change impacts on fire danger over France through the projection of the widely used Fire Weather Index (FWI) and at quantifying the different sources of climate-driven uncertainty associated with these projections. We used daily climate experiments covering the 1995-2098 period under two scenarios (RCP4.5 and RCP8.5) provided by the EURO-CORDEX initiative. Our results show an overall increase in FWI throughout the century, with the largest absolute increases in the Mediterranean area. Model uncertainty was very high in western France, previously identified as a potential fire-prone region under future climate. In contrast, large increases in FWI in the Mediterranean area showed low uncertainty across models. Besides, analyzing the natural variability of FWI revealed that extreme years under present-day climate could become much more frequent by the end of the century. The FWI is projected to emerge from the background of natural variability by mid-twenty-first century with a summer elevated fire danger three times more likely when summer temperature anomaly exceeds + 2 degrees C.

Data are available in PROMÉTHÉE2: Forest fires database for Mediterranean area in France, and can be extracted at https://www.promethee.com/en/incendies using the following selection criteria: fire type, Forêt; date range, 1 January 1995 to 31 December 2018.; International audience; Modeling wildfire activity is crucial for informing science-based risk management and understanding the spatiotemporal dynamics of fire-prone ecosystems worldwide. Models help disentangle the relative influences of different factors, understand wildfire predictability, and provide insights into specific events. Here, we develop Firelihood, a two-component, Bayesian, hierarchically structured, probabilistic model of daily fire activity, which is modeled as the outcome of a marked point process: individual fires are the points (occurrence component), and fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the stochastic partial differential equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and generalized Pareto distributions, adjusted with INLA. The Fire Weather Index (FWI) and forest area are the main explanatory variables. Temporal and spatial residuals are included to improve the consistency of the relationship between weather and fire occurrence. The posterior distribution of the Bayesian model provided 1,000 replications of fire activity that were compared with observations at various temporal and spatial scales in Mediterranean France. The number of fires larger than 1 ha across the region was coarsely reproduced at the daily scale, and was more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10-100 ha) was predicted as well, but the accuracy degraded with size, as the model uncertainty increased with event rareness. Local predictions of fire numbers or burned areas also required a longer aggregation period to maintain model accuracy. The estimation of fires larger than 1 ha was also consistent with observations during the extreme fire season of the 2003 unprecedented heat wave, but the model systematically underrepresented large fires and burned areas, which suggests that the FWI does not consistently rate the actual danger of large fire occurrence during heat waves. Firelihood enabled a novel analysis of the stochasticity underlying fire hazard, and offers a variety of applications, including fire hazard predictions for management and projections in the context of climate change.

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’.

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’.

Short-duration (1–3 h) rainfall extremes can cause serious damage to societies through rapidly developing (flash) flooding and are determined by complex, multifaceted processes that are altering as Earth’s climate warms. In this Review, we examine evidence from observational, theoretical and modelling studies for the intensification of these rainfall extremes, the drivers and the impact on flash flooding. Both short-duration and long-duration (>1 day) rainfall extremes are intensifying with warming at a rate consistent with the increase in atmospheric moisture (~7% K−1), while in some regions, increases in short-duration extreme rainfall intensities are stronger than expected from moisture increases alone. These stronger local increases are related to feedbacks in convective clouds, but their exact role is uncertain because of the very small scales involved. Future extreme rainfall intensification is also modulated by changes to temperature stratification and large-scale atmospheric circulation. The latter remains a major source of uncertainty. Intensification of short-duration extremes has likely increased the incidence of flash flooding at local scales, and this can further compound with an increase in storm spatial footprint to considerably increase total event rainfall. These findings call for urgent climate change adaptation measures to manage increasing flood risks. Short-duration rainfall extremes are determined by complex processes that are affected by the warming climate. This Review assesses the evidence for the intensification of short-duration rainfall extremes, the associated drivers and the implications for flood risks.

[Departement_IRSTEA]Territoires [ADD1_IRSTEA]Adaptation des territoires au changement global; International audience; Large wildfires across parts of France can cause devastating damage which puts lives, infrastructure, and the natural ecosystem at risk. In the climate change context, it is essential to better understand how these large wildfires relate to weather and climate and how they might change in a warmer world. Such projections rely on the development of a robust modelling framework linking large wildfires to present-day atmospheric variability. Drawing from a MODIS product and a gridded meteorological dataset, we derived a suite of biophysical and fire danger indices and developed generalized linear models simulating the probability of large wildfires ( > 100 ha) at 8 km spatial and daily temporal resolutions across the entire country over the last two decades. The models were able to reproduce large-wildfire activity across a range of spatial and temporal scales. Different sensitivities to weather and climate were detected across different environmental regions. Long-term drought was found to be a significant predictor of large wildfires in flammability-limited systems such as the Alpine and south-western regions. In the Mediterranean, large wildfires were found to be associated with both short-term fire weather conditions and longer-term soil moisture deficits, collectively facilitating the occurrence of large wildfires. Simulated probabilities on days with large wildfires were on average 2-3 times higher than normal with respect to the mean seasonal cycle, highlighting the key role of atmospheric variability in wildfire spread. The model has wide applications, including improving our understanding of the drivers of large wildfires over the historical period and providing a basis on which to estimate future changes to large wildfires from climate scenarios.

Modelling wildfire activity is crucial for informing science-based risk management and understanding fire-prone ecosystem functioning worldwide. Models also help to disentangle the relative roles of different factors, to understand wildfire predictability or to provide insights into specific events.Here, we develop a two-component Bayesian hierarchically-structured probabilistic model of daily fire activity, which are modelled as the outcome of a marked point process in which individual fires are the points (occurrence component) and the fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the Stochastic Partial Differential Equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and Generalized-Pareto distributions, also adjusted with INLA. The Fire Weather Index (FWI) and Forest Area are the main explanatory variables. Seasonal and spatial residuals as well as a post-2003 effect are included to improve the consistency of the relationship between climate and fire occurrence, in accordance with parsimonious criteria.A set of 1000 simulations of the posterior model of fire activity is evaluated at various temporal and spatial scales in Mediterranean France. The number of escaped fires (≥1ha) across the region can be coarsely reproduced at the daily scale, and is more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10 to 100 ha) can be predicted as well, but the accuracy decays with size, as the model uncertainty increases with event rareness. Local predictions of fire numbers or burnt areas likewise require a longer aggregation period to maintain model accuracy.Regarding the year 2003 -which was characterized by an extreme burnt area in France associated with a heat wave-, the estimation of the number of escaped fires was consistent with observations, but the model systematically underrepresents larger fires and burnt areas, which suggests that the FWI does not consistently rate the danger of large fire occurrence during heat waves.Our study sheds new light on the stochastic processes underlying fire hazard, and is promising for predicting and projecting future fire hazard in the context of climate change.

Present-day precipitation–temperature scaling relations indicate that hourly precipitation extremes may have a response to warming exceeding the Clausius–Clapeyron (CC) relation; for the Netherlands the dependency on surface dewpoint temperature follows 2 times the CC relation (2CC). The authors’ hypothesis—as supported by a simple physical argument presented here—is that this 2CC behavior arises from the physics of convective clouds. To further investigate this, the large-scale atmospheric conditions accompanying summertime afternoon precipitation events are analyzed using surface observations combined with a regional reanalysis. Events are precipitation measurements clustered in time and space. The hourly peak intensities of these events again reveal a 2CC scaling with the surface dewpoint temperature. The temperature excess of moist updrafts initialized at the surface and the maximum cloud depth are clear functions of surface dewpoint, confirming the key role of surface humidity on convective activity. Almost no differences in relative humidity and the dry temperature lapse rate were found across the dewpoint temperature range, supporting the theory that 2CC scaling is mainly due to the response of convection to increases in near-surface humidity, while other atmospheric conditions remain similar. Additionally, hourly precipitation extremes are on average accompanied by substantial large-scale upward motions and therefore large-scale moisture convergence, which appears to accelerate with surface dewpoint. Consequently, most hourly extremes occur in precipitation events with considerable spatial extent. Importantly, this event size appears to increase rapidly at the highest dewpoint temperature range, suggesting potentially strong impacts of climatic warming.

While summer rain storms are very intermittent, chaotic and influenced by multiple atmospheric drivers, some statistics of observed short duration precipitation actually display surprisingly simple, regular behaviour. As an example, 10-min rainfall extremes derived from Dutch climate data show a dependency of 13% per degree over an almost 20-degree dew point temperature range. Similar behaviour has also been found in hourly precipitation observations. Each degree of warming reflects 6-7% more moisture in the air, following from the well-known Clausius-Clapeyron (CC) relation which is the cornerstone to understand and quantify the influence of climate change on precipitation extremes. According to the above finding, however, precipitation intensities may be increasing with temperature at a rate twice the commonly expected CC rate. In this presentation we will use output from a number of 10-year simulations for present-day and future climate with the convection permitting model HCLIM-AROME to investigate how hourly extremes respond to warming in both a pseudo global warming (PGW) and a GCM driven setup. In particular, we use the scaling diagram -- different percentiles of the rainfall distribution, usually the 90, and 99th conditioned on the occurrence of rain, as a function of dew point temperature -- as a analysis environment. Focus will be on how the scaling diagram is affected by climate change, and what information can be derived from these changes in scaling. While changes in the scaling diagram between present-day and future climate are in general consistent with a CC prediction, evidence of super CC behaviour, between 10 and 14 % per degree dew point, is also present. The same applies to changes in the most extreme events from the simulations, which show super CC behaviour in both PGW and GCM driven setups when scaled with the appropriate dew point temperature change.

[Departement_IRSTEA]Territoires [ADD1_IRSTEA]Adaptation des territoires au changement global; International audience; In recent years, numerous studies have investigated the relationship of extreme precipitation intensities with temperature derived from a binning method using present-day weather data. The aim of this approach is that these dependencies, or so-called apparent scaling rates, provide insights on how precipitation extremes could evolve in the future climate. In a recent paper, Bao et al. showed that there is a large discrepancy between the present-day apparent scaling rates

Climatological features of observed annual maximum hourly precipitation have not been documented systematically compared to those on daily timescales due to observational limitations. Drawing from a quality-controlled database of hourly records sampling different climatic regions including the United States, Australia, the British Isles, Japan, India and peninsular Malaysia over the 1950–2016 period, we examined climatological features of annual maximum precipitation (AMP) across timescales ranging from 1-hr (AMP1−hr) to 24-hr (AMP24−hr). Our analysis reveals strong relations between the magnitude of AMP and the climatological average annual precipitation (AAP), with geographic variations in the magnitude of AMP24−hr across topographic gradients not evident in AMP1−hr. Most AMP1−hr are found to be embedded within short-duration storms (>70% of AMP1−hr are embedded within 1–5 h storms), especially in regions with low AAP and in the tropical zone. Likewise, most AMP24−hr are found to be the accumulation of a very limited number of wet hours in the 24-h period (>80% of AMP24−hr are due to storms lasting

The skill of two statistical downscaled seasonal temperature and precipitation forecasts from the North American Multimodel Ensemble (NMME) was evaluated across the western United States at spatial scales relevant to local decision-making. Both statistical downscaling approaches, spatial disaggregation (SD) and bias correction spatial disaggregation (BCSD), exhibited similar correlative skill measures; however, the BCSD method showed superior tercile-based skill measures since it corrects for variance deflation in NMME ensemble averages. Geographic and seasonal variations in downscaled forecast skill revealed patterns across the complex topography of the western United States not evident using coarse-scale skill assessments, particularly in regions subject to inversions and variability in orographic precipitation ratios. Similarly, differences in the skill of cool-season temperature and precipitation forecasts issued when the fall El Niño–Southern Oscillation (ENSO) signal was strong versus ENSO-neutral years were evident across topographic gradients in the northwestern United States.

Although it has been documented that daily precipitation extremes are increasing worldwide, faster increases may be expected for subdaily extremes. Here after a careful quality control procedure, we compared trends in hourly and daily precipitation extremes using a large network of stations across the United States (U.S.) within the 1950–2011 period. A greater number of significant increasing trends in annual and seasonal maximum precipitation were detected from daily extremes, with the primary exception of wintertime. Our results also show that the mean percentage change in annual maximum daily precipitation across the U.S. per global warming degree is ~6.9% °C−1 (in agreement with the Clausius-Clapeyron rate) while lower sensitivities were observed for hourly extremes, suggesting that changes in the magnitude of subdaily extremes in response to global warming emerge more slowly than those for daily extremes in the climate record.

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’.

[Departement_IRSTEA]Territoires [ADD1_IRSTEA]Adaptation des territoires au changement global; International audience; Changes in global fire activity are influenced by a multitude of factors including land-cover change, policies, and climatic conditions. This study uses 17 climate models to evaluate when changes in fire weather, as realized through the Fire Weather Index, emerge from the expected range of internal variability due to anthropogenic climate change using the time of emergence framework. Anthropogenic increases in extreme Fire Weather Index days emerge for 22% of burnable land area globally by 2019, including much of the Mediterranean and the Amazon. By the midtwenty-first century, emergence among the different Fire Weather Index metrics occurs for 33-62% of burnable lands. Emergence of heightened fire weather becomes more widespread as a function of global temperature change. At 2 °C above pre-industrial levels, the area of emergence is half that for 3 °C. These results highlight increases in fire weather conditions with human-caused climate change and incentivize local adaptation efforts to limit detrimental fire impacts.

[Departement_IRSTEA]Territoires [ADD1_IRSTEA]Dynamique et fonctionnement des écosystèmes [Departement_IRSTEA]Territoires [ADD1_IRSTEA]Dynamique et fonctionnement des écosystèmes; International audience; Six weather types (WTs) are computed for tropical Australia during the wet season (November-March 1979-2015) using cluster analysis of 6-hourly low-level winds at 850 hPa. The WTs may be interpreted as a varying combination of at least five distinct phenomena operating at different time scales: the diurnal cycle, fast and recurrent atmospheric phenomena such as transient low pressure, the intraseasonal Madden-Julian oscillation, the annual cycle, and interannual variations mostly associated with El Niño-Southern Oscillation. The WTs are also strongly phase-locked onto the break/active phases of the monsoon; two WTs characterize mostly the trade-wind regime prevalent either at the start and the end of the monsoon or during its breaks, while three monsoonal WTs occur mostly during its core and active phases. The WT influence is strongest for the frequency of wet spells, while the influence on intensity varies according to the temporal aggregation of the rainfall. At hourly time scale, the climatological mean wet intensity tends to be near-constant in space and not systematically larger for the monsoonal WTs compared to other WTs. Nevertheless, one transitional WT, most prevalent around late November and characterized by weak synoptic forcings and overall drier conditions than the monsoonal WTs, is associated with an increased number of high hourly rainfall intensities for some stations, including for the interior of the Cape York Peninsula. When the temporal aggregation exceeds 6-12 h, the mean intensity tends to be larger for some of the monsoonal WTs, in association with more frequent and also slightly longer wet spells.

In the French Mediterranean, large fires have significant socio-economic and environmental impacts. We used a long-term geo-referenced fire time series (1958–2017) to analyze spatio-temporal variations of large fires (LF; ≥ 100 ha) throughout a fire-prone area of this region. This area was impacted in some locations up to 5 or 6 times by recurrent LF and 21 % of the total area burned by LF occurred on a surface that previously burned in the past. We found distinct patterns between the East and the West of the study area, the former experiencing fewer LF but of a larger extent compared to the latter, with an average time of occurrence between LF exceeding 4000 ha  50 years, respectively. This longitudinal gradient in LF extent contrasts with what was expected from mean fire weather conditions strongly decreasing eastwards but is consistent with larger fuel cover in the East. The temporal variation of LF, featuring a sharp decrease in both frequency and burned area in the early 1990s, highlighted the efficiency of fire suppression and prevention, reinforced at that time. However, the LF outbreak in 2003 due to the exceptional heat wave remains of major concern in the context of climate change.

[Departement_IRSTEA]Territoires [ADD1_IRSTEA]Adaptation des territoires au changement global; International audience; Midlatitude synoptic weather regimes account for a substantial portion of annual precipitation accumulation as well as multi-day precipitation extremes across parts of the United States (US). However, little attention has been devoted to understanding how synoptic-scale patterns contribute to hourly precipitation extremes. A majority of 1-h annual maximum precipitation (AMP) across the western US were found to be linked to two coherent midlatitude synoptic patterns: disturbances propagating along the jet stream, and cutoff upper-level lows. The influence of these two patterns on 1-h AMP varies geographically. Over 95% of 1-h AMP along the western coastal US were coincident with progressive midlatitude waves embedded within the jet stream, while over 30% of 1-h AMP across the interior western US were coincident with cutoff lows. Between 30-60% of 1-h AMP were coincident with the jet stream across the Ohio River Valley and southeastern US, whereas a a majority of 1-h AMP over the rest of central and eastern US were not found to be associated with either midlatitude synoptic features. Composite analyses for 1-h AMP days coincident to cutoff lows and jet stream show that an anomalous moisture flux and upper-level dynamics are responsible for initiating instability and setting up an environment conducive to 1-h AMP events. While hourly precipitation extremes are generally thought to be purely convective in nature, this study shows that large-scale dynamics and baroclinic disturbances may also contribute to precipitation extremes on sub-daily timescales.

Temperature scaling studies suggest that hourly rainfall magnitudes might increase beyond thermodynamic expectations with global warming1–3; that is, above the Clausius–Clapeyron (CC) rate of ~6.5% °C−1. However, there is limited evidence of such increases in long-term observations. Here, we calculate continental-average changes in the magnitude and frequency of extreme hourly and daily rainfall observations from Australia over the years 1990–2013 and 1966–1989. Observed changes are compared with the uncertainty from natural variability and expected changes from CC scaling as a result of global mean surface temperature change. We show that increases in daily rainfall extremes are consistent with CC scaling, but are within the range of natural variability. In contrast, changes in the magnitude of hourly rainfall extremes are close to or exceed double the expected CC scaling, and are above the range of natural variability, exceeding CC × 3 in the tropical region (north of 23° S). These continental-scale changes in extreme rainfall are not explained by changes in the El Nino–Southern Oscillation or changes in the seasonality of extremes. Our results indicate that CC scaling on temperature provides a severe underestimate of observed changes in hourly rainfall extremes in Australia, with implications for assessing the impacts of extreme rainfall. Theory predicts that hourly rainfall extremes may increase with anthropogenic warming. Observations from Australia suggest changes two to three times above the Clausius–Clapeyron rate, above that expected from natural variability.

[Departement_IRSTEA]Territoires [ADD1_IRSTEA]Adaptation des territoires au changement global; National audience; Historical in situ sub-daily rainfall observations are essential for the understanding of short-duration rainfall extremes but records are typically not readily accessible and data are often subject to errors and inhomogeneities. Furthermore, these events are poorly quantified in projections of future climate change making adaptation to the risk of flash flooding problematic. Consequently, knowledge of the processes contributing to intense, short-duration rainfall is less complete compared with those on daily timescales. The INTENSE project is addressing this global challenge by undertaking a data collection initiative that is coupled with advances in high-resolution climate modelling to better understand key processes and likely future change. The project has so far acquired data from over 23 000 rain gauges for its global sub-daily rainfall dataset (GSDR) and has provided evidence of an intensification of hourly extremes over the US. Studies of these observations, combined with model simulations, will continue to advance our understanding of the role of local-scale thermodynamics and large-scale atmospheric circulation in the generation of these events and how these might change in the future.

Drought indices are often used for monitoring interannual variability in macroscale hydrology. However, the diversity of drought indices raises several issues: 1) which indices perform best and where; 2) does the incorporation of potential evapotranspiration (PET) in indices strengthen relationships, and how sensitive is the choice of PET methods to such results; 3) what additional value is added by using higher-spatial-resolution gridded climate layers; and 4) how have observed relationships changed through time. Standardized precipitation index, standardized precipitation evapotranspiration index (SPEI), Palmer drought severity index, and water balance runoff (WBR) model output were correlated to water-year runoff for 21 unregulated drainage basins in the Pacific Northwest of the United States. SPEI and WBR with time scales encompassing the primary precipitation season maximized the explained variance in water-year runoff in most basins. Slightly stronger correlations were found using PET estimates from the Penman–Monteith method over the Thornthwaite method, particularly for time periods that incorporated the spring and summer months in basins that receive appreciable precipitation during the growing season. Indices computed using high-resolution climate surfaces explained over 10% more variability than metrics derived from coarser-resolution datasets. Increased correlation in the latter half of the study period was partially attributable to increased streamflow variability in recent decades as well as to improved climate data quality across the interior mountain watersheds.

Changes in the extent of absolute, all-time, daily temperature records across the contiguous United States were examined using observations and climate model simulations. Observations from station data and reanalysis from 1980 to 2013 show increased extent of absolute highest temperature records and decreased extent of absolute lowest temperature records. Conversely, station data from 1920 to 2013 showed decreased extent of absolute highest records with nearly half of such records occurring in the 1930s during exceptional widespread drought. Simulated changes in the extent of absolute temperature records from climate model experiments were in general agreement with observed changes for recent decades. However, fewer lowest temperature records and highest temperature records were observed since 2000 than simulated by most models. Climate models project a continued increase in the occurrence of highest temperature records and decline in lowest temperature records through the mid-21st century.

A majority of area burned in the Eastern United States (EUS) results from a limited number of exceptionally large wildfires. Relationships between climatic conditions and the occurrence of very large-fires (VLF) in the EUS were examined using composite and climate-niche analyses that consider atmospheric factors across inter-annual, sub-seasonal and synoptic temporal scales. While most large-fires in the EUS coincided with below normal fuel moisture and elevated fire weather, VLF preferentially occurred during a long-term drought accompanied by more acute sub-seasonal drought realized through fuel moisture stress and elevated fire-weather conditions. These results were corroborated across the EUS, with varying influences of drought, fire danger and fire weather discriminating VLF from other large fires across different geographical regions. We also show that the probability of VLF conditioned by fire occurrence increases when long-term drought, depleted fuel moisture and elevated fire weather align. This framework illustrates the compounding role of different timescales in VLF occurrence and serves as a basis for improving VLF predictions with seasonal climate forecasts and climate change scenarios.

Santa Ana winds (SAW) are among the most notorious fire-weather conditions in the United States and are implicated in wildfire and wind hazards in Southern California. This study employs large-scale reanalysis data to diagnose SAW through synoptic-scale dynamic and thermodynamic factors using mean sea level pressure gradient and lower-tropospheric temperature advection, respectively. A two-parameter threshold model of these factors exhibits skill in identifying surface-based characteristics of SAW featuring strong offshore winds and extreme fire weather as viewed through the Fosberg fire weather index across Remote Automated Weather Stations in southwestern California. These results suggest that a strong northeastward gradient in mean sea level pressure aligned with strong cold-air advection in the lower troposphere provide a simple, yet effective, means of diagnosing SAW from synoptic-scale reanalysis. This objective method may be useful for medium- to extended-range forecasting when mesoscale model output may not be available, as well as being readily applied retrospectively to better understand connections between SAW and wildfires in Southern California.

An empirical statistical scheme for predicting September–December fires in New Caledonia in the southwestern Pacific Ocean region using a cross-validated generalized linear model has been developed for the 2000–10 period. The predictor employs July sea surface temperatures (SST) recorded over the Niño-4 box (5°S–5°N, 160°–210°E), which are closely related to austral spring (September–November) rainfall anomalies across New Caledonia. The correlation between the logarithm of observed and simulated total burned areas across New Caledonia is 0.87. A decrease in the local-scale skill (median correlation between the log of observed and simulated total burned areas in a 20-km radius around a rain gauge = 0.46) around the main town (Nouméa) and its suburbs in the southwest of Grande Terre, and also in northern New Caledonia, could be associated either with a weaker climatic forcing from the Niño-4 SST index or a small-scale climatic forcing not linearly related to the El Niño–Southern Oscillation (ENSO) phenomenon. It is more likely that the decrease is tied to the influence of human-driven factors that blur the regional-scale climatic signal mostly associated with central Pacific ENSO events.

International audience; Daily rainfall occurrence and amount at 55 stations over New Caledonia (NC, 20A degrees S, 166A degrees E) are examined throughout the calendar year during 1980-2010 using a Hidden Markov Model (HMM). Daily rainfall variability is described in terms of six discrete rainfall states identified by the HMM. Three states are interpreted as trade wind regimes associated with persistent low level anticyclonic conditions and weak to strong easterlies. The most prevalent state (state 1; 36 % of days) is dry everywhere and is characterized by an elongated anticyclone centered around Australia; NC is located on the eastern edge of this anticyclone. This state is predominant from mid-May to mid-December and peaks in September. The second most prevalent state (state 2; 27 % of days) and the last trade regime (state 4; 12 % of days) are most frequent in austral summer. States 2 and 4 are associated with the subtropical anticyclone centered south of NC, close to its climatological location in austral summer, and light (state 2) to moderate (state 4) rainfall mostly along the windward coast. A distinct state (state 3; 11 % of days) is mostly associated with wintertime extratropical eastward traveling troughs between New Caledonia and New Zealand, inducing light rainfall over the SW of the main island of New Caledonia. The two last states 5 and 6 are infrequent (< 15 % of days) but account for almost 70 % of total rainfall across the archipelago. They occur mostly in austral summer with their frequency of occurrence peaking in late February-early March. These states are associated with synoptic wave traveling eastward from eastern Australia leading to a strong influx of moisture from the equatorial latitudes when the associated cyclonic centre is located west of New Caledonia. The wettest state is also strongly modulated by intra-seasonal 15-80 days variability. These events contribute to intermittent southwestward shifts of the South Pacific Convergence Zone from its mean location northeast of New Caledonia. On interannual timescales, the occurrence of the rainfall states is modulated by El Nio Southern Oscillation (ENSO) events. The ENSO impact is strongest from mid-August to March with more (less) frequent trade regimes 1-2 and less (more) frequent unstable trade regime 4 and states 5-6 during central-Pacific warm (cold) ENSO events. Stochastic simulations of daily rainfall occurrence and amount at the 55 stations are generated by using predictors based on Nio 4 sea surface temperature index and a local intra-seasonal (15-80 days) OLR index superimposed on a climatological annual cycle. The cross-validated skill peaks in September-November.

[1] New Caledonia (NC; ∼166°E, 22°S) rainfall anomalies are more sensitive to central Pacific (CP) El Nino and La Nina events than to those exhibiting highest sea surface temperature (SST) anomalies in the eastern Pacific (EP). The linear relationship between NC rainfall anomalies and CP SST indices peaks from September to March (S–M). The seasonal S–M atmospheric anomalies observed in the South West (SW) Pacific during the warm CP events are highly dissimilar to the EP ones, while there are more similarities during the cold events with a higher amplitude during the CP ones. The warm CP events strengthen the southern Hadley cell around NC longitudes, with positive rainfall anomalies in the equatorial Pacific leading to an anomalous release of latent heat in the upper troposphere and an increased subsidence in the SW Pacific. Atmospheric anomalies are strongest in September–November because of a combination of a rather strong zonal SST gradient with the warmest SST in the equatorial Pacific just west of the dateline. The cold CP and EP events are associated with a southwestward shift of the South Pacific Convergence Zone with strongest atmospheric anomalies during the CP events. Squared wavelet coherence between NC rainfall and Nino 4 SST index shows that their negative correlations are mostly carried by two distinct timescales: the classical El Nino–Southern Oscillation (i.e., 3–6 years) variability and a quasi-decadal one (i.e., 10–12 years). The high-frequency (>1/8 cycle per year) correlations peak around Christmas and are quasi-stationary since 1950, whereas the low-frequency ones (

[1] We examined the relationships between fires detected by the Along Track Scanning Radiometer, algorithm 2 (ATSR-2) and the Moderate Resolution Imaging Spectrometer (MODIS) sensors and local-scale atmospheric conditions in New Caledonia (SW Pacific ∼165.5°E, 21.5°S) during the 1996–2008 (ATSR) and 2000–2008 (MODIS) time periods. A total of 3707 (MODIS) hotspots, representing 949 distinct fires, and 83 (ATSR) hotspots were observed during the study period. The annual mean frequency of hotspots peaks around the transition between the dry-cool season and the wet-warm season, that is, in September–December, when dry soils and fuels could be combined with increasing temperatures. The antecedent local-scale maximum temperature and rainfall anomalies recorded at the closest meteorological station were analyzed. While the signal in maximum temperature is weak and not robust among the fire records, the local-scale anomalies of rainfall are always clearly negative for at least 3 months before the fires (i.e., between June and December). The Effective Drought Index (EDI), based on rainfall only, and the Fire Weather Index (FWI), which combines rainfall, temperature, relative humidity, and wind speed, show similar behaviors before fires. This suggests that a simple rainfall index, as well as a more comprehensive one, is able to diagnose fire risks. At interannual time scale, cross-correlation analysis reveals that the seasonal June–August Nino 3.4 sea surface temperature index is strongly correlated (r = 0.78 for a second-order polynomial fit) with the monthly frequency of all MODIS hotspots in September–December, suggesting a strong potential predictability of fire variations in New Caledonia with a lead time of 1–4 months.