A novel method to identify subseasonal clustering episodes of extreme precipitation events and their contribution to large accumulations

2021 ◽  
Author(s):  
Jérôme Kopp ◽  
Pauline Rivoire ◽  
S. Mubashshir Ali ◽  
Yannick Barton ◽  
Olivia Martius
Keyword(s):  
Time Scales ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Time Window ◽  
Heat Waves ◽  
Reanalysis Data ◽  
Data Set ◽  
Temporal Clustering ◽  
Extreme Precipitation Events ◽  
Precipitation Events

<p>Temporal clustering of extreme precipitation events on subseasonal time scales is a type of compound event, which can cause large precipitation accumulations and lead to floods. We present a novel count-based procedure to identify subseasonal clustering of extreme precipitation events. Furthermore, we introduce two metrics to characterise the frequency of subseasonal clustering episodes and their relevance for large precipitation accumulations. The advantage of this approach is that it does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this methodology to the ERA5 reanalysis data set, we identify regions where subseasonal clustering of annual high precipitation percentiles occurs frequently and contributes substantially to large precipitation accumulations. Those regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southeast of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the subseasonal time window (here 2 – 4 weeks). The procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (e.g. for drought years). <span>For a complementary study on the subseasonal clustering of European extreme precipitation events and its relationship to large-scale atmospheric drivers, please refer to Barton et al.</span></p>

scholarly journals A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods

2021 ◽  
Author(s):  
Jérôme Kopp ◽  
Pauline Rivoire ◽  
S. Mubashshir Ali ◽  
Yannick Barton ◽  
Olivia Martius
Keyword(s):  
Time Scales ◽  
Extreme Precipitation ◽  
Time Window ◽  
Heat Waves ◽  
Reanalysis Data ◽  
Data Set ◽  
The North ◽  
Extreme Precipitation Events ◽  
Compound Event ◽  
Precipitation Events

Abstract. Temporal (serial) clustering of extreme precipitation events on sub-seasonal time scales is a type of compound event. It can cause large precipitation accumulations and lead to floods. We present a novel, count-based procedure to identify episodes of sub-seasonal clustering of extreme precipitation. We introduce two metrics to characterise the frequency of sub-seasonal clustering episodes and their relevance for large precipitation accumulations. The procedure does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this procedure to daily precipitation from the ERA5 reanalysis data set, we identify regions where sub-seasonal clustering occurs frequently and contributes substantially to large precipitation accumulations. The regions are the east and northeast of the Asian continent (north of Yellow Sea, in the Chinese provinces of Hebei, Jilin and Liaoning; North and South Korea; Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the sub-seasonal time window (here 2–4 weeks). This procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different time scales (sub-seasonal to decadal). The code is available at the listed GitHub repository.

scholarly journals A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods

2021 ◽  
Vol 25 (9) ◽  
pp. 5153-5174 ◽  
Author(s):  
Jérôme Kopp ◽  
Pauline Rivoire ◽  
S. Mubashshir Ali ◽  
Yannick Barton ◽  
Olivia Martius
Keyword(s):  
Extreme Precipitation ◽  
Time Window ◽  
Heat Waves ◽  
Reanalysis Data ◽  
Data Set ◽  
The North ◽  
Extreme Precipitation Events ◽  
Compound Event ◽  
Central Canada ◽  
Precipitation Events

Abstract. Temporal (serial) clustering of extreme precipitation events on sub-seasonal timescales is a type of compound event. It can cause large precipitation accumulations and lead to floods. We present a novel, count-based procedure to identify episodes of sub-seasonal clustering of extreme precipitation. We introduce two metrics to characterise the prevalence of sub-seasonal clustering episodes and their contribution to large precipitation accumulations. The procedure does not require the investigated variable (here precipitation) to satisfy any specific statistical properties. Applying this procedure to daily precipitation from the ERA5 reanalysis data set, we identify regions where sub-seasonal clustering occurs frequently and contributes substantially to large precipitation accumulations. The regions are the east and northeast of the Asian continent (northeast of China, North and South Korea, Siberia and east of Mongolia), central Canada and south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and the north of Argentina and south of Bolivia. Our method is robust with respect to the parameters used to define the extreme events (the percentile threshold and the run length) and the length of the sub-seasonal time window (here 2–4 weeks). This procedure could also be used to identify temporal clustering of other variables (e.g. heat waves) and can be applied on different timescales (sub-seasonal to decadal). The code is available at the listed GitHub repository.

scholarly journals Clustering of Regional-Scale Extreme Precipitation Events in Southern Switzerland

2016 ◽  
Vol 144 (1) ◽  
pp. 347-369 ◽  
Author(s):  
Yannick Barton ◽  
Paraskevi Giannakaki ◽  
Harald von Waldow ◽  
Clément Chevalier ◽  
Stephan Pfahl ◽  
...  
Keyword(s):  
Time Scales ◽  
Extreme Precipitation ◽  
Wave Breaking ◽  
Large Scale ◽  
Statistical Significance ◽  
Regional Scale ◽  
Breaking Waves ◽  
Temporal Clustering ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract Temporal clustering of extreme precipitation events on subseasonal time scales is of crucial importance for the formation of large-scale flood events. Here, the temporal clustering of regional-scale extreme precipitation events in southern Switzerland is studied. These precipitation events are relevant for the flooding of lakes in southern Switzerland and northern Italy. This research determines whether temporal clustering is present and then identifies the dynamics that are responsible for the clustering. An observation-based gridded precipitation dataset of Swiss daily rainfall sums and ECMWF reanalysis datasets are used. Also used is a modified version of Ripley’s K function, which determines the average number of extreme events in a time period, to characterize temporal clustering on subseasonal time scales and to determine the statistical significance of the clustering. Significant clustering of regional-scale precipitation extremes is found on subseasonal time scales during the fall season. Four high-impact clustering episodes are then selected and the dynamics responsible for the clustering are examined. During the four clustering episodes, all heavy precipitation events were associated with an upper-level breaking Rossby wave over western Europe and in most cases strong diabatic processes upstream over the Atlantic played a role in the amplification of these breaking waves. Atmospheric blocking downstream over eastern Europe supported this wave breaking during two of the clustering episodes. During one of the clustering periods, several extratropical transitions of tropical cyclones in the Atlantic contributed to the formation of high-amplitude ridges over the Atlantic basin and downstream wave breaking. During another event, blocking over Alaska assisted the phase locking of the Rossby waves downstream over the Atlantic.

scholarly journals North American extreme precipitation events and related large-scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

2019 ◽  
Vol 53 (11) ◽  
pp. 6835-6875 ◽  
Author(s):  
Mathew Barlow ◽  
William J. Gutowski ◽  
John R. Gyakum ◽  
Richard W. Katz ◽  
Young-Kwon Lim ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Climate Models ◽  
Early Stage ◽  
Reanalysis Data ◽  
Model Representation ◽  
Dynamics Modeling ◽  
Considerable Uncertainty ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

On the subseasonal clustering of European extreme precipitation events and its relationship to large-scale atmospheric drivers

2021 ◽  
Author(s):  
Yannick Barton ◽  
Pauline Rivoire ◽  
Jérôme Kopp ◽  
S. Mubashshir Ali ◽  
Olivia Martius
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Extreme Rainfall ◽  
The Arctic ◽  
Extreme Rainfall Events ◽  
Baseline Rate ◽  
Temporal Clustering ◽  
Extreme Precipitation Events ◽  
Western Spain ◽  
Precipitation Events

<p>Extreme precipitation events that occur in close succession can have important societal and economic repercussions. Few studies have investigated the link between large-scale atmospheric drivers and temporal clustering of extreme precipitation events on a subseasonal scale, i.e. 20-day time scale. Here we use 40 years of reanalysis data (ERA-5) to investigate the link between possibly influential atmospheric variables and the temporal clustering of catchment-averaged extreme rainfall events in Europe. We define extreme events as exceedances above the 99th percentile and runs of consecutive days are declustered. We then explicitly model the seasonal rate of extreme occurrences using penalized cubic splines. The smoothed seasonal rate of extremes is then used to (i) infer the significance of subseasonal clustering and (ii) serves as the baseline rate for the subsequent modelling step. We use a Poisson generalized linear model with the baseline rate set as an offset to model the relationship between the temporal clustering and predictor variables. These variables are the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), atmospheric blocks, and a measure of the recurrence of synoptic-scale Rossby wave packets (RRWPs).</p><p>Initial results from four carefully selected catchments reveal the following patterns: for south-western Spain, the NAO, and AO indices tend to be notably lower on significantly clustered extreme rainfall days, whereas for northern Scotland the opposite effect is observed. Also, for south-western Spain, the Greenland atmospheric blocking frequency is significantly enhanced on clustering days. Last, on clustering days in north-western France, Scandinavian blocks are significantly more frequent.</p><p>For a complementary study on a methodology to identify subseasonal clustering episodes of extreme precipitation events and their contribution to large accumulations please refer to Kopp et al.</p>

scholarly journals Large-Scale Flow Patterns Associated with Extreme Precipitation and Atmospheric Rivers over Norway

2019 ◽  
Vol 147 (4) ◽  
pp. 1415-1428 ◽  
Author(s):  
Imme Benedict ◽  
Karianne Ødemark ◽  
Thomas Nipen ◽  
Richard Moore
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Moisture Flux ◽  
Cold Season ◽  
Fuzzy Cluster ◽  
The South ◽  
Atmospheric Rivers ◽  
The North ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract A climatology of extreme cold season precipitation events in Norway from 1979 to 2014 is presented, based on the 99th percentile of the 24-h accumulated precipitation. Three regions, termed north, west, and south are identified, each exhibiting a unique seasonal distribution. There is a proclivity for events to occur during the positive phase of the NAO. The result is statistically significant at the 95th percentile for the north and west regions. An overarching hypothesis of this work is that anomalous moisture flux, or so-called atmospheric rivers (ARs), are integral to extreme precipitation events during the Norwegian cold season. An objective analysis of the integrated vapor transport illustrates that more than 85% of the events are associated with ARs. An empirical orthogonal function and fuzzy cluster technique is used to identify the large-scale weather patterns conducive to the moisture flux and extreme precipitation. Five days before the event and for each of the three regions, two patterns are found. The first represents an intense, southward-shifted jet with a southwest–northeast orientation. The second identifies a weak, northward-shifted, zonal jet. As the event approaches, regional differences become more apparent. The distinctive flow pattern conducive to orographically enhanced precipitation emerges in the two clusters for each region. For the north and west regions, this entails primarily zonal flow impinging upon the south–north-orientated topography, the difference being the latitude of the strong flow. In contrast, the south region exhibits a significant southerly component to the flow.

scholarly journals Large-Scale Predictors for Extreme Hourly Precipitation Events in Convection-Permitting Climate Simulations

2018 ◽  
Vol 31 (6) ◽  
pp. 2115-2131 ◽  
Author(s):  
Steven C. Chan ◽  
Elizabeth J. Kendon ◽  
Nigel Roberts ◽  
Stephen Blenkinsop ◽  
Hayley J. Fowler
Keyword(s):  
Extreme Precipitation ◽  
Land Surface ◽  
Large Scale ◽  
Future Climate ◽  
Cumulus Parameterization ◽  
Hourly Precipitation ◽  
Vertical Stability ◽  
Extreme Precipitation Events ◽  
Climate Simulations ◽  
Precipitation Events

Midlatitude extreme precipitation events are caused by well-understood meteorological drivers, such as vertical instability and low pressure systems. In principle, dynamical weather and climate models behave in the same way, although perhaps with the sensitivities to the drivers varying between models. Unlike parameterized convection models (PCMs), convection-permitting models (CPMs) are able to realistically capture subdaily extreme precipitation. CPMs are computationally expensive; being able to diagnose the occurrence of subdaily extreme precipitation from large-scale drivers, with sufficient skill, would allow effective targeting of CPM downscaling simulations. Here the regression relationships are quantified between the occurrence of extreme hourly precipitation events and vertical stability and circulation predictors in southern United Kingdom 1.5-km CPM and 12-km PCM present- and future-climate simulations. Overall, the large-scale predictors demonstrate skill in predicting the occurrence of extreme hourly events in both the 1.5- and 12-km simulations. For the present-climate simulations, extreme occurrences in the 12-km model are less sensitive to vertical stability than in the 1.5-km model, consistent with understanding the limitations of cumulus parameterization. In the future-climate simulations, the regression relationship is more similar between the two models, which may be understood from changes to the large-scale circulation patterns and land surface climate. Overall, regression analysis offers a promising avenue for targeting CPM simulations. The authors also outline which events would be missed by adopting such a targeted approach.

scholarly journals A Dynamical and Statistical Characterization of U.S. Extreme Precipitation Events and Their Associated Large-Scale Meteorological Patterns

2017 ◽  
Vol 30 (4) ◽  
pp. 1307-1326 ◽  
Author(s):  
Siyu Zhao ◽  
Yi Deng ◽  
Robert X. Black
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Warm Season ◽  
Base Function ◽  
Regional Patterns ◽  
Statistical Characterization ◽  
Climate System Model ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract Regional patterns of extreme precipitation events occurring over the continental United States are identified via hierarchical cluster analysis of observed daily precipitation for the period 1950–2005. Six canonical extreme precipitation patterns (EPPs) are isolated for the boreal warm season and five for the cool season. The large-scale meteorological pattern (LMP) inducing each EPP is identified and used to create a “base function” for evaluating a climate model’s potential for accurately representing the different patterns of precipitation extremes. A parallel analysis of the Community Climate System Model, version 4 (CCSM4), reveals that the CCSM4 successfully captures the main U.S. EPPs for both the warm and cool seasons, albeit with varying degrees of accuracy. The model’s skill in simulating each EPP tends to be positively correlated with its capability in representing the associated LMP. Model bias in the occurrence frequency of a governing LMP is directly related to the frequency bias in the corresponding EPP. In addition, however, discrepancies are found between the CCSM4’s representation of LMPs and EPPs over regions such as the western United States and Midwest, where topographic precipitation influences and organized convection are prominent, respectively. In these cases, the model representation of finer-scale physical processes appears to be at least equally important compared to the LMPs in driving the occurrence of extreme precipitation.

Predictability of precipitation extremes over the Mediterranean

2020 ◽  
Author(s):  
Ignazio Giuntoli ◽  
Federico Fabiano ◽  
Susanna Corti
Keyword(s):  
Extreme Precipitation ◽  
Winter Season ◽  
Seasonal Prediction ◽  
Reanalysis Data ◽  
Economic Activities ◽  
Weather Regimes ◽  
Extreme Precipitation Events ◽  
The Mediterranean ◽  
Prediction Systems ◽  
Precipitation Events

<p>Intense precipitations events are associated with impacts like damages to infrastructures, economic activities, agricultural crops, power production and society in general. The ability to predict extreme precipitation events months in advance is therefore of great value in densely populated areas like the Mediterranean and may be achieved using seasonal prediction systems like the Copernicus Climate Change Services (C3S) suite of models. Using weather regimes (WRs) from 500 hPa geopotential heights over the Mediterranean the two main objectives of this study are: first to identify how these regimes are linked to extreme precipitation events over the region using reanalysis data; and second to assess the ability of the C3S models in reproducing/predicting these extreme events. We identify four weather regimes for the winter season (DJF) describing the atmospheric circulation in the Mediterranean using the 1993-2016 period as reference, i.e. maximum availability of C3S hindcasts. We thus provide an assessment of the models’s ability in predicting extreme precipitation over the Mediterranean having quantified how daily precipitation anomalies are associated to each WR.</p>

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