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Mean annual maximum accumulated precipitation from cyclone clustering and associated scaling. Multimodel mean of the (a) mean annual maximum accumulated precipitation and the (b) associated scaling shown on the right-hand side of equation (1), for the period 1975–2004. Multimodel mean of (c) the number of cyclones within the cluster and (d) mean-precipitation per cyclone within the clusters leading to annual maxima of accumulated precipitation from cyclone clustering.
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Wintertime extreme precipitation from cyclone clusters, i.e. consecutive cyclones moving across the same region, can lead to flooding and devastating socio-economic impacts in Europe. Previous studies have suggested that the future direction of the changes in these events are uncertain across climate models. By employing an impact-based metric of a...
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Daily Peczely circulation types are investigated over 140 years (1881–2020). After presenting monthly mean frequencies and durations of the 13 circulation types, two further questions are investigated: (i) How do the circulation types influence local weather extremes?; (ii) Are there significant trends in the frequency of the original and the group...
Citations
... Such a move explicitly distinguishes between thermodynamic and dynamical aspects of changes in extremes, which is a device that has been widely used in the literature and reflects the different levels of uncertainty in the two aspects (Shepherd 2016, Pfahl et al 2017. It can be done statistically for classes of events (Cattiaux et al 2010, Horton et al 2015, in which case dynamical storylines of future risk can be constructed (Bevacqua et al 2020). It can also be done for singular events (Reed et al 2020, van Garderen et al 2021, where the attribution question can be reframed as 'What would a similar extreme event have looked like in the past?' . ...
Attribution — the explanation of an observed change in terms of multiple causal factors — is the cornerstone of climate-change science. For anthropogenic climate change (ACC), the central causal factor is evidently ACC itself, and one of the primary tools used to reveal ACC is aggregation, or grouping together, of data, e.g. global mean surface temperature. Whilst this approach has served climate-change science well, the landscape is changing rapidly. First, there is an increasing focus on regional or local aspects of climate change, and on singular or unprecedented events, which require varying degrees of disaggregation. Relatedly, climate change is increasingly apparent in observations at the local scale, which is challenging the primacy of climate model simulations. Finally, the explosion of climate data is leading to more phenomena-laden methodologies such as machine learning. All this demands a re-think of how attribution is performed and causal explanations are constructed. Here we use Lloyd’s ‘Logic of Research Questions’ framework to show how the way in which the attribution question is framed can strongly constrain its possible and responsive answers. To address the Research Question “What was the effect of ACC on X?” (RQ1), scientists generally consider the question “What were the causal factors leading to X, and was ACC among them?”. If the causal factors include only external forcing and internal variability (RQ2), then answering RQ2 also answers RQ1. However, this unconditional attribution is not always possible. In such cases, allowing the causal factors to include elements of the climate system itself (RQ3) — the conditional, storyline approach — is shown to allow for a wider range of possible and responsive answers than RQ2, including that of singular causation. This flexibility is important when uncertainties are high. As a result, the conditional RQ3 mitigates against the sort of epistemic injustice that can arise from the unconditional RQ2.
... Quantifying and understanding uncertainties in climate projections and identifying the potentially wide range of plausible climate futures is highly relevant for policymakers 25,57,58 . It is important to distinguish between uncertainty arising from internal climate variability, which is irreducible because of the inherently chaotic nature of the climate system, and uncertainty from structural differences amongst climate models, which could be reduced through, for instance, model improvements or emergent-constraints 59 . ...
Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events — such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events — require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks.
... However, the change of EC total precipitation with warming, indicating the spatial and temporal integral of precipitation intensity, varies greatly among studies (Yettella and Kay 2016;Zappa et al. 2014). EC total precipitation is primarily influenced by EC number, life duration, and precipitation intensity (Bevacqua et al. 2020;Catto et al. 2019;Zappa et al. 2015). EC precipitation was found to be positively correlated with EC number (Basu et al. 2018;Hawcroft et al. 2018). ...
Extratropical cyclone (EC) is the main source of precipitation at midlatitudes, but its precipitation characteristics change with warming still remains uncertain. Here, using 17 CMIP6 models, ECs in DJF in the Southern Hemisphere are tracked and recorded with concomitant sea level pressure and surface temperatures. EC total precipitation change with warming is decomposed into the contribution from EC number, life duration and precipitation intensity. It is found that decreasing EC total precipitation is strongly related to decreased EC number, with life duration contributing about 1/6 that of EC number change. Increasing EC precipitation intensity offsets the decrease due to EC number. To better quantify EC precipitation intensity change with warming, we employ temperature experienced by ECs instead of regional averaged temperature. A higher precipitation increasing rate per degree of warming (6.05%/K) than previous studies was noted because ECs tend to shift poleward with warming. Furthermore, the noted rate is mainly related to the increase of near-surface temperature (62%), followed by increased EC intensity (23%).
... The second problematic assumption is the relationship between mesoscale circulation and point precipitation as simulated by the Bartlett-Lewis models. Both relationships could change in the future, and evidence is indeed mounting that precipitation dynamics will change in north-western Europe, the area under study here (Bevacqua et al., 2021;Bevacqua, Zappa, & Shepherd, 2020;Kahraman, Kendon, Chan, & Fowler, 2021). Thus, change factor methods might become less applicable and new ways to apply the Bartlett-Lewis models under climate change conditions have to be explored, such as the weather generator studied here. ...
The assessment of future extremes is hindered by the lack of long time series. Weather generators can alleviate this problem, but easily become more complex when generating multiple variables. In this study, a weather generator combining Bartlett-Lewis models and vine copulas is presented. The combination of these models allows for the stochastic and physically coherent generation of longer time series with statistics similar to those of the time series used as input. This model chain has already been assessed on the basis of historical observations, but never on the basis of future simulations. However, the model chain could have practical value for extending climate simulations, which should be investigated. Combining recent versions of the Bartlett-Lewis model (for the generation of precipitation) and vine copulas (for the generation of temperature and evaporation), the model was applied for two time series of historical observations and one time series simulated by the RCA4 RCM for the years 2071-2100. For the future simulations, the weather generator performed comparably as for the historical observations for the statistical moments and the correlation. The results for the extremes were more ambiguous, but still provided valuable information. The adequate performance for the statistical moments and the correlation, combined with the continuous development of both Bartlett-Lewis models and vine copulas, indicates that the weather generator might be of use for the characterization of extreme events under climate change.
... Cyclones are the part of the feedback mechanisms by which large-scale anomalies of ocean-atmosphere interaction impact regions distant from the North Atlantic, as well as the Black Sea-Mediterranean region (Voskresenskaya and Polonskii 1995;Spanos et al. 2003; Bartholy et al. 2009;Maslova et al. 2010;Nojarov 2013;Krichak et al. 2014;Galabov and Chervenkov 2019;Caian et al. 2021). Cyclones lead to meteorological, hydrological, and geomorphological anomalies (Lionello et al. 2006;Dayan et al. 2015;Bevacqua et al. 2020;Owen et al. 2021), resulting in losses of economic, socio-cultural values, and human lives. Thus, cyclone regimes determine regional climatic regimes and environmental anomalies. ...
To study regimes of winter cyclones in the North Atlantic, empirical orthogonal function decomposition was applied separately to the frequency, depth, and area of cyclones obtained using 6-hourly NCEP/NCAR reanalysis data in 1952–2017 and the developed methodology. The first modes represented the opposite changes of cyclone anomalies in the northern and southern/central North Atlantic. The second modes were characterized by the greatest regional anomalies between the phases over Europe, off its coast, and over the Mediterranean. The greatest changes of anomalies for the third modes were in temperate latitudes, both over the ocean and Europe. Linear trends were significant only for the first modes of cyclone parameters. The largest part of variability (74–90% of dispersion) of all cyclone modes corresponded to the periods up to 15 years and was used for spectral analysis, which identified significant spectral peaks: 2.5–3, 4.5, 6, and 8.5 years. These periods coincide with spectral peaks of the main interannual climate signals. Regression analysis allowed to identify the sets of teleconnection patterns responsible jointly for 60–85% of dispersion of the first cyclone modes. The North Atlantic Oscillation and Arctic Oscillation were the main patterns for the first modes of the cyclone parameters. For the second and third frequency modes, the East Atlantic (EA) pattern and a combination of the East Atlantic/West Russia (EA/WR) and Scandinavia patterns played the major role, respectively. As for the third depth and area modes, the association with the EA and EA/WR patterns was shown, respectively.
... Here we expand on the existing literature and evaluate the value added by these models in the context of temporal clustering and the occurrence of heavy precipitation events driven by the largescale climate. There is a growing literature pointing to the occurrence of extreme events in clusters, including storms, floods, and heavy precipitation (e.g., Bevacqua et al., 2020;Dacre and Pinto, 2020;Mailier et al., 2006;Mallakpour et al., 2017;Pinto et al., 2013;Villarini et al., 2013;Vitolo et al., 2009), with significant implications for the ecosystem and economy. For instance, Mumby et al. (2011) suggested that the clustering of hurricanes has smaller impacts on coral reefs' health than if they occur randomly. ...
Global climate models (GCMs) have been used extensively as a tool to investigate future changes in heavy precipitation. However, their spatial resolution is generally too coarse for their application for decision making and for impact studies at a more local scale. To mitigate these issues, dynamical downscaling of heavy precipitation using regional climate models (RCMs) can provide information at a spatial resolution that makes their outputs locally useful. Although many studies highlighted the improvements by RCMs in reproducing the precipitation processes, much less is known about their capability in reproducing the temporal clustering of heavy precipitation. Here we use Cox regression to investigate temporal clustering in heavy precipitation driven by four dominant large-scale climate modes (Arctic Oscillation, North Atlantic Oscillation, East-Atlantic Pattern, and Scandinavia Pattern) over Europe. Results are based on high-resolution RCMs (i.e., 0.11° and 0.44°) part of the Coordinated Downscaling Experiment-European Domain (EURO-CORDEX) and use observed daily precipitation from the E-OBS as reference data. In addition to analyses at the continental scale, we consider the added value of these simulations at a regional scale. We find that RCMs are skillful at capturing the observed temporal clustering of heavy precipitation events across Europe. Moreover, the 0.11°-RCMs did not perform better than their 0.44°-version; these findings can potentially suggest that the current configuration of these models has reached an upper limit of performance in reproducing the temporal clustering, and finer resolution and modeling may be necessary to increase the realism in reproducing the processes at play.
... However, there are other types of clusters and several methods to define them Dacre and Pinto (2020). Here we choose a method similar to Bevacqua et al. (2020). A grid point is defined as clustered if at least 3 cyclones pass this grid point within a radius R, as defined above (here using the grid point latitude to calculate f), with a maximum elapsed time of 2.5 days between each other. ...
Extreme precipitation events in Norway in all seasons are often linked to atmospheric rivers (AR). We show that during the period 1979–2018 78.5% of the daily extreme precipitation events in Southwestern Norway are linked to ARs, this percentage decreasing to 59% in the more northern coastal regions and ∼40% in the inland regions. The association of extreme precipitation with AR occurs most often in fall for the coastal areas and in summer inland. All Norwegian regions experience stronger winds and 1–2 °C increase of the temperature at 850 hPa during AR events compared to the climatology, the extreme precipitation largely contributing to the wet climatology (only considering rainy days) in Norway but also in Denmark and Sweden when the rest of Europe is dry. A cyclone is found nearby the AR landfall point in 70% of the cases. When the cyclone is located over the British Isles, as it is typically the case when ARs reach Southeastern Norway, it is associated with cyclonic Rossby wave breaking whereas when the ARs reach more northern regions, anticyclonic wave breaking occurs over Northern Europe. Cyclone-centered composites show that the mean sea level pressure is not significantly different between the eight Norwegian regions, that baroclinic interaction can still take place although the cyclone is close to its decay phase and that the maximum precipitation occurs ahead of the AR. Lagrangian air parcel tracking shows that moisture uptake mainly occurs over the North Atlantic for the coastal regions with an additional source over Europe for the more eastern and inland regions.
... The present methodology can be used for assessing the sources of bias in other types of compound events caused by other sets 395 of dependent drivers, such as compound drought and heat (Zscheischler and Seneviratne, 2017) and compound coastal flooding (Bevacqua et al., 2020b). Other types of compound events, e.g., temporal clustering of storms (Bevacqua et al. 2020c;Priestley et al., 2017) and simultaneous extreme events in distant regions (Kornhuber et al., 2020), can also lead to large impacts and are therefore relevant for the impact community. A compound event-oriented evaluation of impacts similar to that proposed here, i.e. disentangling the biases in the individual physical drivers, could be adopted in future studies to aid present 400 and future impact assessments. ...
Climate models' outputs are affected by biases that need to be detected and adjusted to model climate impacts. Many climate hazards and climate-related impacts are associated with the interaction between multiple drivers, i.e. by compound events. So far climate model biases are typically assessed based on the hazard of interest, and it is unclear how much a potential bias in the dependence of the hazard drivers contributes to the overall bias and how the biases in the drivers interact. Here, based on copula theory, we develop a multivariate bias assessment framework, which allows for disentangling the biases in hazard indicators in terms of the underlying univariate drivers and their statistical dependence. Based on this framework, we dissect biases in fire and heat stress hazards in a suite of global climate models by considering two simplified hazard indicators, the wet-bulb globe temperature (WBGT) and the Chandler Burning Index (CBI). Both indices solely rely on temperature and relative humidity. The spatial pattern of the hazards indicators is well represented by climate models. However, substantial biases exist in the representation of extreme conditions, especially in the CBI (spatial average of absolute bias: 21 °C) due to the biases driven by relative humidity (20 °C). Biases in WBGT (1.1 °C) are small compared to the biases driven by temperature (1.9 °C) and relative humidity (1.4 °C), as the two biases compensate each other. In many regions, also biases related to the statistical dependence (0.85 °C) are important for WBGT, which indicates that well-designed physically-based multivariate bias adjustment should be considered for hazards and impacts that depend on multiple drivers. The proposed compound event-oriented evaluation of climate model biases is easily applicable to other hazard types. Furthermore, it can contribute to improved present and future risk assessments through increasing our understanding of the biases’ sources in the simulation of climate impacts.
Persistence is an important concept in meteorology. It refers to surface weather or the atmospheric circulation either remaining in approximately the same state (quasi-stationarity) or repeatedly occupying the same state (recurrence) over some prolonged period of time. Persistence can be found at many different timescales; however, sub-seasonal to seasonal (S2S) timescales are especially relevant in terms of impacts and atmospheric predictability. For these reasons, S2S persistence has been attracting increasing attention from the scientific community. The dynamics responsible for persistence and their potential evolution under climate change are a notable focus of active research. However, one important challenge facing the community is how to define persistence from both a qualitative and quantitative perspective. Despite a general agreement on the concept, many different definitions and perspectives have been proposed over the years, among which it is not always easy to find one's way. The purpose of this review is to present and discuss existing concepts of weather persistence, associated methodologies and physical interpretations. In particular, we call attention to the fact that persistence can be defined as a global or as a local property of a system, with important implications in terms of methods and impacts. We also highlight the importance of timescale and similarity metric selection and illustrate some of the concepts using the example of summertime atmospheric circulation over western Europe.
The frequency of precipitation extremes is set to change
in response to a warming climate. Thereby, the change in
extreme precipitation event occurrence is influenced by both a shift in the mean and a
change in variability. How large the individual contributions are from either of
them (mean or variability) to the change in precipitation extremes is
largely unknown. This is, however, relevant for a better understanding of how
and why climate extremes change. For this study, two sets of forcing
experiments from the regional CRCM5 initial-condition large ensemble are
used: a set of 50 members with historical and RCP8.5 forcing and a
35-member (700-year) ensemble of pre-industrial natural forcing. The concept
of the probability risk ratio is used to partition the change in extreme-event occurrence into contributions from a change in mean climate or a
change in variability. The results show that the contributions from a change
in variability are in parts equally important to changes in the mean and
can even exceed them. The level of contributions shows high spatial
variation, which underlines the importance of regional processes for changes
in extremes. While over Scandinavia or central Europe the mean influences the
increase in extremes more, the increase is driven by changes in
variability over France, the Iberian Peninsula, and the Mediterranean. For
annual extremes, the differences between the ratios of contribution of mean
and variability are smaller, while on seasonal scales the difference in
contributions becomes larger. In winter (DJF) the mean contributes more to
an increase in extreme events, while in summer (JJA) the change in
variability drives the change in extremes. The level of temporal aggregation
(3, 24, 72 h) has only a small influence on annual and winterly extremes,
while in summer the contribution from variability can increase with longer
durations. The level of extremeness for the event definition generally
increases the role of variability. These results highlight the need for a
better understanding of changes in climate variability to better understand
the mechanisms behind changes in climate extremes.
