Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large-scale circulation

<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 &#8211; 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>

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

Monthly Weather Review ◽

10.1175/mwr-d-18-0362.1 ◽

2019 ◽

Vol 147 (4) ◽

pp. 1415-1428 ◽

Cited By ~ 7

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.

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

Journal of Climate ◽

10.1175/jcli-d-17-0404.1 ◽

2018 ◽

Vol 31 (6) ◽

pp. 2115-2131 ◽

Cited By ~ 13

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.

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

Journal of Climate ◽

10.1175/jcli-d-15-0910.1 ◽

2017 ◽

Vol 30 (4) ◽

pp. 1307-1326 ◽

Cited By ~ 16

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.

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Large-scale atmospheric patterns associated with mesoscale features leading to extreme precipitation events in Northwestern Italy

Advances in Water Resources ◽

10.1016/j.advwatres.2004.10.017 ◽

2005 ◽

Vol 28 (6) ◽

pp. 601-614 ◽

Cited By ~ 40

Author(s):

Roberto Rudari ◽

Dara Entekhabi ◽

Giorgio Roth

Keyword(s):

Extreme Precipitation ◽

Large Scale ◽

Extreme Precipitation Events ◽

Precipitation Events

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Climatology of Daily Precipitation and Extreme Precipitation Events in the Northeast United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0147.1 ◽

2015 ◽

Vol 16 (6) ◽

pp. 2537-2557 ◽

Cited By ~ 46

Author(s):

Laurie Agel ◽

Mathew Barlow ◽

Jian-Hua Qian ◽

Frank Colby ◽

Ellen Douglas ◽

...

Keyword(s):

Extreme Precipitation ◽

Seasonal Cycle ◽

Large Scale ◽

Daily Precipitation ◽

Warm Season ◽

Extreme Precipitation Events ◽

Tail Distribution ◽

Hourly Data ◽

Early Fall ◽

Precipitation Events

Abstract This study examines U.S. Northeast daily precipitation and extreme precipitation characteristics for the 1979–2008 period, focusing on daily station data. Seasonal and spatial distribution, time scale, and relation to large-scale factors are examined. Both parametric and nonparametric extreme definitions are considered, and the top 1% of wet days is chosen as a balance between sample size and emphasis on tail distribution. The seasonal cycle of daily precipitation exhibits two distinct subregions: inland stations characterized by frequent precipitation that peaks in summer and coastal stations characterized by less frequent but more intense precipitation that peaks in late spring as well as early fall. For both subregions, the frequency of extreme precipitation is greatest in the warm season, while the intensity of extreme precipitation shows no distinct seasonal cycle. The majority of Northeast precipitation occurs as isolated 1-day events, while most extreme precipitation occurs on a single day embedded in 2–5-day precipitation events. On these extreme days, examination of hourly data shows that 3 h or less account for approximately 50% of daily accumulation. Northeast station precipitation extremes are not particularly spatially cohesive: over 50% of extreme events occur at single stations only, and 90% occur at only 1–3 stations concurrently. The majority of extreme days (75%–100%) are related to extratropical storms, except during September, when more than 50% of extremes are related to tropical storms. Storm tracks on extreme days are farther southwest and more clustered than for all storm-related precipitation days.

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A Comparison of CMIP3 Simulations of Precipitation over North America with Observations: Daily Statistics and Circulation Features Accompanying Extreme Events

Journal of Climate ◽

10.1175/jcli-d-12-00374.1 ◽

2013 ◽

Vol 26 (10) ◽

pp. 3209-3230 ◽

Cited By ~ 26

Author(s):

Anthony M. DeAngelis ◽

Anthony J. Broccoli ◽

Steven G. Decker

Keyword(s):

North America ◽

Extreme Events ◽

Extreme Precipitation ◽

Large Scale ◽

Climate Model ◽

Heavy Precipitation ◽

Convective Precipitation ◽

Southern Mexico ◽

Extreme Precipitation Events ◽

Precipitation Events

Abstract Climate model simulations of daily precipitation statistics from the third phase of the Coupled Model Intercomparison Project (CMIP3) were evaluated against precipitation observations from North America over the period 1979–99. The evaluation revealed that the models underestimate the intensity of heavy and extreme precipitation along the Pacific coast, southeastern United States, and southern Mexico, and these biases are robust among the models. The models also overestimate the intensity of light precipitation events over much of North America, resulting in fairly realistic mean precipitation in many places. In contrast, heavy precipitation is simulated realistically over northern and eastern Canada, as is the seasonal cycle of heavy precipitation over a majority of North America. An evaluation of the simulated atmospheric dynamics and thermodynamics associated with extreme precipitation events was also conducted using the North American Regional Reanalysis (NARR). The models were found to capture the large-scale physical mechanisms that generate extreme precipitation realistically, although they tend to overestimate the strength of the associated atmospheric circulation features. This suggests that climate model deficiencies such as insufficient spatial resolution, inadequate representation of convective precipitation, and overly smoothed topography may be more important for biases in simulated heavy precipitation than errors in the large-scale circulation during extreme events.

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Sensitivity of Heavy Precipitation Forecasts to Small Modifications of Large-Scale Weather Patterns for the Elbe River

Journal of Hydrometeorology ◽

10.1175/2010jhm1186.1 ◽

2010 ◽

Vol 11 (3) ◽

pp. 770-780 ◽

Cited By ~ 10

Author(s):

Ingo Schlüter ◽

Gerd Schädler

Keyword(s):

Extreme Precipitation ◽

Large Scale ◽

Spatiotemporal Variability ◽

Small Scale ◽

Synoptic Situation ◽

Flood Events ◽

Weather Patterns ◽

Extreme Precipitation Events ◽

The Impact ◽

Precipitation Events

Abstract Extreme flood events are caused by long-lasting and/or intensive precipitation. The detailed knowledge of the distribution, intensity, and spatiotemporal variability of precipitation is, therefore, a prerequisite for hydrological flood modeling and flood risk management. For hydrological modeling, temporal and spatial high-resolution precipitation data can be provided by meteorological models. This study deals with the question of how small changes in the synoptic situation affect the characteristics of extreme forecasts. For that purpose, two historic extreme precipitation events were hindcasted using the Consortium for Small Scale Modeling (COSMO) model of the German Weather Service (DWD) with different grid resolutions (28, 7, and 2.8 km), where the domains with finer resolutions were nested into the ones with coarser resolution. The results show that the model is capable of simulating such extreme precipitation events in a satisfactory way. To assess the impact of small changes in the synoptic situations on extreme precipitation events, the large-scale atmospheric fields were shifted to north, south, east, and west with respect to the orography by about 28 and 56 km, respectively, in one series of runs while in another series, the relative humidity and temperature were increased to modify the amount of precipitable water. Both series were performed for the Elbe flood events in August 2002 and January 2003, corresponding to two very different synoptic situations. The results show that the modeled precipitation can be quite sensitive to small changes of the synoptic situation with changes in the order of 20% for the maximum daily precipitation and that the types of synoptic situations play an important role. While van Bebber weather conditions, of Mediterranean origin, were quite sensitive to modifications, more homogeneous weather patterns were less sensitive.

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