scholarly journals Extreme precipitation events in the Mediterranean: Spatiotemporal characteristics and connection to large‐scale atmospheric flow patterns

2020 ◽  
Author(s):  
Nikolaos Mastrantonas ◽  
Pedro Herrera‐Lormendez ◽  
Linus Magnusson ◽  
Florian Pappenberger ◽  
Jörg Matschullat
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Flow Patterns ◽  
Atmospheric Flow ◽  
Spatiotemporal Characteristics ◽  
Extreme Precipitation Events ◽  
The Mediterranean ◽  
Precipitation Events

Predictability of large-scale atmospheric flow patterns connected to extreme precipitation events in the Mediterranean

2021 ◽  
Author(s):  
Nikolaos Mastrantonas ◽  
Linus Magnusson ◽  
Florian Pappenberger ◽  
Jörg Matschullat
Keyword(s):  
Extreme Precipitation ◽  
Mediterranean Region ◽  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Flow Patterns ◽  
Atmospheric Flow ◽  
Extreme Precipitation Events ◽  
The Mediterranean ◽  
Precipitation Events

<p>The Mediterranean region frequently experiences extreme precipitation events with devastating consequences for the affected societies, economies, and environment. Being able to provide reliable and skillful predictions of such events is crucial for mitigating their adverse impacts and related risks. One important part of the risk mitigation chain is the sub-seasonal predictability of such extremes, with information provided at such timescales supporting a range of actions, as for example warn decision-makers, and preposition materials and equipment.</p><p>This work focuses on the predictability of large-scale atmospheric flow patterns connected to extreme precipitation events in the Mediterranean. Previous research has identified strong connections between localized extremes and large-scale patterns. This is promising to provide useful information at sub-seasonal timescales. For such lead times, the Numerical Weather Prediction models are more skillful in predicting large-scale patterns than localized extremes. Here, we analyze the usefulness of these connections at sub-seasonal timescales by using the ECMWF extended-range forecasts. We aim at quantifying related benefits for the different areas in the Mediterranean region and providing insights that are of interest to the operational community.</p><p>Initial results suggest that the ECMWF forecasts provide skillful information in the predictability of large-scale patterns up to about 15 days lead time.</p><p> </p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.3687c29b370068376801161/sdaolpUECMynit/12UGE&app=m&a=0&c=49e65b5908090e0787f0f7f4f8930219&ct=x&pn=gnp.elif&d=1" alt=""></p><p>Large-scale patterns over the Mediterranean based on anomalies of sea level pressure (color shades) and geopotential at 500 hPa (contours) (Figure adapted from Mastrantonas et al, 2020)</p>

Extreme precipitation events in the Mediterranean region: their characteristics and connection to large-scale atmospheric patterns

2020 ◽  
Author(s):  
Nikolaos Mastrantonas ◽  
Linus Magnusson ◽  
Florian Pappenberger ◽  
Jörg Matschullat
Keyword(s):  
Natural Hazards ◽  
Extreme Events ◽  
Extreme Precipitation ◽  
Mediterranean Region ◽  
Large Scale ◽  
Early Warning Systems ◽  
Spatiotemporal Variability ◽  
Extreme Precipitation Events ◽  
The Mediterranean ◽  
Precipitation Events

<p>The Mediterranean region is an area with half a billion population, about 10 percent contribution to the world’s GDP, and locations of global natural, historical and cultural significance. In this context, natural hazards in the area have the potential for severe negative impacts on society, economy, and environment. </p><p>Some of the most frequent and devastating natural hazards that affect the Mediterranean relate to extreme precipitation events causing flash floods and landslides. Thus, given their adverse consequences, it is of immense importance to better understand their statistical characteristics and connection to large-scale atmospheric patterns. Such advances can substantially support the accurate and early identification of these extreme events, improve early warning systems, and contribute to mitigating related risks. </p><p>This work focuses on the characteristics and spatiotemporal variability of extreme precipitation events of large spatial coverage across the Mediterranean region. The study uses the ERA5 dataset, the latest, state of the art, reanalysis dataset from Copernicus/ECMWF. Initially, exploratory analysis is performed to assess the different characteristics at various subdomains within the study area. Furthermore, composite analysis is used to understand the connection of extreme events with large-scale atmospheric patterns. Finally, the Empirical Orthogonal Function (EOF) analysis is implemented to quantify the importance of weather regimes with respect to the frequency of extreme precipitation events. </p><p>Preliminary results indicate that there is a spatial division in the occurrence of identified events. Winter and autumn are the seasons of the highest frequency of extreme precipitation for the east and west Mediterranean respectively. Troughs and cut-off lows in the lower and middle-level troposphere have a strong association with such extreme events, and the effect is modulated by other parameters, such as local orography. Results of this work are in accordance with previous studies in the region and provide information that can be utilized by future research for improving the predictability of such events in the medium- and extended-range forecasts. </p><p>This work is part of the Climate Advanced Forecasting of sub-seasonal Extremes (CAFE) project. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.</p>

scholarly journals Spatiotemporal Variability in Extreme Precipitation in China from Observations and Projections

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1089 ◽  
Author(s):  
Yifeng Peng ◽  
Xiang Zhao ◽  
Donghai Wu ◽  
Bijian Tang ◽  
Peipei Xu ◽  
...  
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Spatiotemporal Variability ◽  
Tibet Plateau ◽  
Cmip5 Models ◽  
Ecological Processes ◽  
The Past ◽  
Spatiotemporal Characteristics ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Extreme precipitation events, which have intensified with global warming over the past several decades, will become more intense in the future according to model projections. Although many studies have been performed, the occurrence patterns for extreme precipitation events in past and future periods in China remain unresolved. Additionally, few studies have explained how extreme precipitation events developed over the past 58 years and how they will evolve in the next 90 years as global warming becomes much more serious. In this paper, we evaluated the spatiotemporal characteristics of extreme precipitation events using indices for the frequency, quantity, intensity, and proportion of extreme precipitation, which were proposed by the World Meteorological Organization. We simultaneously analyzed the spatiotemporal characteristics of extreme precipitation in China from 2011 to 2100 using data obtained from the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Despite the fixed threshold, 95th percentile precipitation values were also used as the extreme precipitation threshold to reduce the influence of various rainfall events caused by different geographic locations; then, eight extreme precipitation indices (EPIs) were calculated to evaluate extreme precipitation in China. We found that the spatial characteristics of the eight EPIs exhibited downward trends from south to north. In the periods 1960–2017 and 2011–2100, trends in the EPIs were positive, but there were differences between different regions. In the past 58 years, the extreme precipitation increased in the northwest, southeast, and the Tibet Plateau of China, while decreased in northern China. Almost all the trends of EPIs are positive in the next two periods (2011–2055 and 2056–2100) except for some EPIs, such as intensity of extreme precipitation, which decrease in southeastern China in the second period (2056–2100). This study suggests that the frequency of extreme precipitation events in China will progressively increase, which implies that a substantial burden will be placed on social economies and terrestrial ecological processes.

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 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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