Scale-dependent representation of extreme precipitation processes in regional and CPM scale simulations for the greater Alpine region

2021 ◽  
Author(s):  
Alberto Caldas-Alvarez ◽  
Hendrik Feldmann ◽  
Joaquim G. Pinto
Keyword(s):  
Extreme Precipitation ◽  
Rare Events ◽  
Potential Temperature ◽  
Low Pressure ◽  
Scale Dependency ◽  
Data Set ◽  
Extreme Precipitation Events ◽  
Spatial Coverage ◽  
Precipitation Processes ◽  
Precipitation Events

<p>Extreme precipitation events with return periods above 100-years (Most Extreme Precipitation Events; MEPE) are rare events by definition, as the observational record covers very few of such events. Therefore, our knowledge is insufficient to assess their potential intensities and physical processes on different scales. To fill this gap, large regional climate ensembles, like the one provided by the German Decadal Climate Predictions (MiKlip) project (> 10.000 years), are of great value as they provide a larger sample size of such rare events. The RCM ensemble samples present day climate conditions multiple times (Ehmele et al., 2020) with a resolution of 25 km, and thus it does not resolve the convection permitting scales (CPM).</p><p>In this study, we aim to combine the large RCM ensemble with episodic CPM-scale downscaling simulations to derive a better statistical and process related representation of MEPEs for Central Europe. As a first step, we evaluate two re-analysis driven long-term simulations with COSMO-CLM (CCLM) from MiKlip and CORDEX-FPS Convection with respect to their scale-dependent representation.</p><p>The simulations span the period 1971 to 2016 with the 25 km simulation and are forced by ERA40 until 1979 and by ERA-interim afterwards. The CPM simulation (~3 km) is forced by ERA-40 between 1971 and 1999 and by ERA-interim between 2000 and 2016. We validate the simulations against E-OBS (25 km) and the unique HYdrologische RASterdatensätze (HYRAS) precipitation data set (5 km). The investigation area is the greater Alpine area. We employ a Precipitation Severity Index (PSI) adapted from extreme wind detection (Leckebusch et al., 2008; Pinto et al., 2012) for extreme precipitation cases. The advantage of the PSI is its ability to account for extreme grid point precipitation as well as spatial coverage and event duration. The events are categorized objectively into composite Weather Types (WT) to enable further generalization of the findings.</p><p>The results show a clear overestimation of precipitation for the analysed period and area by the RCMs at both resolutions. However, large differences exist the representation of extreme precipitation. Compared to observations, the 3 km (25km) resolution overestimates (underestimates) precipitation intensity for extreme cases. This agrees with previous literature. Five different WTs are identified for the analysed period, with Autumn-Winter WT being the most common, followed by convective summer WT. The Autumn-Winter WT is characterized by deep, cold, low-pressure areas located over Northern Europe. Summer WT cases are characterized by stable high-pressure situations affected by incurring small low-pressure systems on its western flank (convective-prone situations). Regarding the scale dependency of precipitation processes, the coarse resolution tends to overestimate surface moisture in situations of heavy precipitation, leading to larger latent instability (CAPE) in the 25 km resolution than in its 3 km counterpart. Furthermore, a large-scale dependency is found in summer extreme precipitation cases for two stability-related variables, Equivalent Potential Temperature (θ<sub>e</sub><sup>850</sup>) at 850 hPa and moisture flux at the Lower Free Troposphere (LFT-moisture). In these cases, the overestimation (underestimation) of  and LFT-moisture by either resolution is in line with their precipitation overestimation (underestimation).</p>

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 Influences on Summertime Extreme Precipitation in the Northeastern United States

2016 ◽  
Vol 17 (12) ◽  
pp. 3045-3061 ◽  
Author(s):  
Allison B. Marquardt Collow ◽  
Michael G. Bosilovich ◽  
Randal D. Koster
Keyword(s):  
United States ◽  
Sea Level ◽  
Tropical Cyclones ◽  
Extreme Precipitation ◽  
Potential Temperature ◽  
Northeastern United States ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract Observations indicate that over the last few decades there has been a statistically significant increase in precipitation in the northeastern United States and that this can be attributed to an increase in precipitation associated with extreme precipitation events. Here a state-of-the-art atmospheric reanalysis is used to examine such events in detail. Daily extreme precipitation events defined at the 75th and 95th percentile from gridded gauge observations are identified for a selected region within the Northeast. Atmospheric variables from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), are then composited during these events to illustrate the time evolution of associated synoptic structures, with a focus on vertically integrated water vapor fluxes, sea level pressure, and 500-hPa heights. Anomalies of these fields move into the region from the northwest, with stronger anomalies present in the 95th percentile case. Although previous studies show tropical cyclones are responsible for the most intense extreme precipitation events, only 10% of the events in this study are caused by tropical cyclones. On the other hand, extreme events resulting from cutoff low pressure systems have increased. The time period of the study was divided in half to determine how the mean composite has changed over time. An arc of lower sea level pressure along the East Coast and a change in the vertical profile of equivalent potential temperature suggest a possible increase in the frequency or intensity of synoptic-scale baroclinic disturbances.

scholarly journals The Extreme Precipitation Index (EPI): A Coupled Dynamic–Thermodynamic Metric to Diagnose Midlatitude Floods Associated with Flow Reversal

2019 ◽  
Vol 34 (5) ◽  
pp. 1257-1276 ◽  
Author(s):  
Shawn M. Milrad ◽  
Eyad H. Atallah ◽  
John R. Gyakum ◽  
Rachael N. Isphording ◽  
Jonathon Klepatzki
Keyword(s):  
Extreme Precipitation ◽  
Western Europe ◽  
Potential Temperature ◽  
Precipitation Index ◽  
Flow Reversal ◽  
Precipitation Event ◽  
Day Range ◽  
Extreme Precipitation Events ◽  
Extreme Precipitation Index ◽  
Precipitation Events

Abstract The extreme precipitation index (EPI) is a coupled dynamic–thermodynamic metric that can diagnose extreme precipitation events associated with flow reversal in the mid- to upper troposphere (e.g., Rex and omega blocks, cutoff cyclones, Rossby wave breaks). Recent billion dollar (U.S. dollars) floods across the Northern Hemisphere midlatitudes were associated with flow reversal, as long-duration ascent (dynamics) occurred in the presence of anomalously warm and moist air (thermodynamics). The EPI can detect this potent combination of ingredients and offers advantages over model precipitation forecasts because it relies on mass fields instead of parameterizations. The EPI’s dynamics component incorporates modified versions of two accepted blocking criteria, designed to detect flow reversal during the relatively short duration of extreme precipitation events. The thermodynamic component utilizes standardized anomalies of equivalent potential temperature. Proof-of-concept is demonstrated using four high-impact floods: the 2013 Alberta Flood, Canada’s second costliest natural disaster on record; the 2016 western Europe Flood, which caused the worst flooding in France in a century; the 2000 southern Alpine event responsible for major flooding in Switzerland; and the catastrophic August 2016 Louisiana Flood. EPI frequency maxima are located across the North Atlantic and North Pacific mid- and high latitudes, including near the climatological subtropical jet stream, while secondary maxima are located near the Rockies and Alps. EPI accuracy is briefly assessed using pattern correlation and qualitative associations with an extreme precipitation event climatology. Results show that the EPI may provide potential benefits to flood forecasters, particularly in the 3–10-day range.

scholarly journals Identifying and correcting biases in localized downscaling estimates of daily precipitation return values

2021 ◽  
Vol 169 (3-4) ◽  
Author(s):  
Mark D. Risser ◽  
Daniel R. Feldman ◽  
Michael F. Wehner ◽  
David W. Pierce ◽  
Jeffrey R. Arnold
Keyword(s):  
Observational Data ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Time Of Day ◽  
Economic Damage ◽  
Data Set ◽  
Extreme Precipitation Events ◽  
Daily Precipitation Data ◽  
Precipitation Events ◽  
Made In

AbstractExtreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which downscaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the overall performance of LOCA-derived changes in extreme daily precipitation. We show that the low (negative) bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.

scholarly journals Investigating changes in atmospheric circulation patterns connected to extreme precipitation in Norway

2021 ◽  
Author(s):  
Karianne Ødemark ◽  
Malte Müller ◽  
Ole Einar Tveito ◽  
Cyril Palerme
Keyword(s):  
Atmospheric Circulation ◽  
Extreme Precipitation ◽  
Good Description ◽  
Construction Technique ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Atmospheric Circulation Patterns ◽  
Extreme Precipitation Events ◽  
Circulation Patterns ◽  
Precipitation Events

<p>Extreme precipitation events that lead to excess surface water and flood are becoming an amplifying societal cost as a result of both the increasing precipitation amounts in recent years and urbanization. Knowledge about extreme precipitation events is important for the ability to predict them, but also to know how often they occur with various intensities in order to estimate design values for constructions and critical infrastructure. A good description of extreme precipitaton is a challenge since observation networks are often too sparse to describe the spatial structure of precipitation, and the highest amounts are most likely not captured by a precipitation gauge. For the study of extreme precipitation events by means of statistical analysis, long timesteries are required, which is a major challenge when using conventional or new observational data records.  Here, a data set constructed from the numerical seasonal prediction system at ECMWF, SEAS5, has been applied to evaluate mechanisms controlling extreme precipitation events. The construction technique gives the ability to increase the event sample size compared to conventional data sets. We analyze 3-day  maximum precipitation events in the September-October-November season for an area on the west coast of Norway, an area subject to the largest precipitation amounts in Europe. A principal component analysis of the 500 hPa geopotential anomaly has been performed to identify atmospheric circulation patterns related to the extreme precipitation events. We find that two of the EOFs are related to precipitation with high return values for the selected area. These two EOFs have a significant trend over the data period, but with opposing signs. We also investigate the connection between both sea surface temperature (SST) and sea-ice concentration in the Barents-Kara sea and the occurrence of extreme precipitation.</p>

scholarly journals Extreme precipitation events in NW Greece

2006 ◽  
Vol 7 ◽  
pp. 91-96 ◽  
Author(s):  
E. E. Houssos ◽  
A. Bartzokas
Keyword(s):  
Factor Analysis ◽  
Extreme Precipitation ◽  
Grid Point ◽  
Low Pressure ◽  
Data Matrix ◽  
Precipitation Event ◽  
Central Mediterranean ◽  
Pressure System ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract. In this work, the extreme precipitation events in NW Greece are studied. The data used are daily precipitation totals recorded at the meteorological station of Ioannina University for the period 1970–2002. 156 days with precipitation totals above 35 mm (5% upper limit) are only considered. It is seen that, a minimum frequency of extreme precipitation events appears in the period 1986–1991, which is characterized by a high positive NAO index. For each of the 156 extreme precipitation days, at first, the mean sea level pressure pattern over Europe is constructed by using 273 grid point values. Using Factor Analysis, the dimensionality of the 156×273 data matrix is reduced to 156×5 (84% of the total variance) and then, Cluster Analysis is applied on the results of Factor Analysis. Thus, the 156 cases are grouped objectively to 11 clusters, revealing the main pressure patterns, which favour extreme precipitation in NW Greece. Seven of the patterns are encountered in winter and autumn, while three of them cover a period from autumn to spring and one appears mainly in summer. In all of them the cause of the extreme precipitation event is a low pressure system centred west of Greece or a low pressure trough extended eastwards or southwards up to Greece. In some cases the depression is so strong and extended that it covers the whole Europe and the Mediterranean. In the single summer pattern, rainfall is caused by an extension of the SW Asia thermal low up to the central Mediterranean.

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 Identifying and Correcting Biases in Localized Downscaling Estimates of Daily Precipitation Return Values

2021 ◽  
Author(s):  
Mark Risser ◽  
Daniel Feldman ◽  
Michael Wehner ◽  
David Pierce ◽  
Jeff Arnold
Keyword(s):  
Observational Data ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Time Of Day ◽  
Economic Damage ◽  
Data Set ◽  
Extreme Precipitation Events ◽  
Daily Precipitation Data ◽  
Precipitation Events ◽  
Made In

Abstract Extreme precipitation events are a major cause of economic damage and disruption, and need to be addressed for increasing resilience to a changing climate, particularly at the local scale. Practitioners typically want to understand local changes at spatial scales much smaller than the native resolution of most Global Climate Models, for which down scaling techniques are used to translate planetary-to-regional scale change information to local scales. However, users of statistically downscaled outputs should be aware that how the observational data used to train the statistical models is constructed determines key properties of the downscaled solutions. Specifically for one such downscaling approach, when considering seasonal return values of extreme daily precipitation, we find that the Localized Constructed Analogs (LOCA) method produces a significant low bias in return values due to choices made in building the observational data set used to train LOCA. The LOCA low biases in daily extremes are consistent across event extremity, but do not degrade the over all performance of LOCA-derived changes in extreme daily precipitation. We show that the low bias in daily extremes is a function of a time-of-day adjustment applied to the training data and the manner of gridding daily precipitation data. The effects of these choices are likely to affect other downscaling methods trained with observations made in the same way. The results developed here show that efforts to improve resilience at the local level using extreme precipitation projections can benefit from using products specifically created to properly capture the statistics of extreme daily precipitation events.

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