scholarly journals Falling Trend of Western Disturbances in Future Climate Simulations

2019 ◽  
Vol 32 (16) ◽  
pp. 5037-5051 ◽  
Author(s):  
Kieran M. R. Hunt ◽  
Andrew G. Turner ◽  
Len C. Shaffrey

Abstract Western disturbances (WDs) are synoptic-scale cyclonic weather systems advected over Pakistan and northern India by the subtropical westerly jet stream. There, they are responsible for most of the winter precipitation, which is crucial for agriculture of the rabi crop as well for as more extreme precipitation events, which can lead to local flooding and avalanches. Despite their importance, there has not yet been an attempt to objectively determine the fate of WDs in future climate GCMs. Here, a tracking algorithm is used to build up a catalog of WDs in both CMIP5 historical and representative concentration pathway (RCP) experiments of the future. It is shown that in business-as-usual (RCP8.5) future climate simulations, WD frequency falls by around 15% by the end of the twenty-first century, with the largest relative changes coming in pre- and postmonsoon months. Meanwhile, mean WD intensity will decrease, with central vorticity expected to become less cyclonic by about 12% over the same period. Changes in WD frequency are attributed to the projected widening and weakening of the winter subtropical jet as well as decreasing meridional wind shear and midtropospheric baroclinic vorticity tendency, which also explain the changes in intensity. The impact of these changes on regional precipitation is explored. The decline in WD frequency and intensity will cause a decrease in mean winter rainfall over Pakistan and northern India amounting to about 15% of the mean—subject to the ability of the models to represent the responsible processes. The effect on extreme precipitation events, however, remains unclear.

2018 ◽  
Vol 31 (6) ◽  
pp. 2115-2131 ◽  
Author(s):  
Steven C. Chan ◽  
Elizabeth J. Kendon ◽  
Nigel Roberts ◽  
Stephen Blenkinsop ◽  
Hayley J. Fowler

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.


2008 ◽  
Vol 21 (1) ◽  
pp. 22-39 ◽  
Author(s):  
Siegfried D. Schubert ◽  
Yehui Chang ◽  
Max J. Suarez ◽  
Philip J. Pegion

Abstract In this study the authors examine the impact of El Niño–Southern Oscillation (ENSO) on precipitation events over the continental United States using 49 winters (1949/50–1997/98) of daily precipitation observations and NCEP–NCAR reanalyses. The results are compared with those from an ensemble of nine atmospheric general circulation model (AGCM) simulations forced with observed SST for the same time period. Empirical orthogonal functions (EOFs) of the daily precipitation fields together with compositing techniques are used to identify and characterize the weather systems that dominate the winter precipitation variability. The time series of the principal components (PCs) associated with the leading EOFs are analyzed using generalized extreme value (GEV) distributions to quantify the impact of ENSO on the intensity of extreme precipitation events. The six leading EOFs of the observations are associated with major winter storm systems and account for more than 50% of the daily precipitation variability along the West Coast and over much of the eastern part of the country. Two of the leading EOFs (designated GC for Gulf Coast and EC for East Coast) together represent cyclones that develop in the Gulf of Mexico and occasionally move and/or redevelop along the East Coast producing large amounts of precipitation over much of the southern and eastern United States. Three of the leading EOFs represent storms that hit different sections of the West Coast (designated SW for Southwest coast, WC for the central West Coast, and NW for northwest coast), while another represents storms that affect the Midwest (designated by MW). The winter maxima of several of the leading PCs are significantly impacted by ENSO such that extreme GC, EC, and SW storms that occur on average only once every 20 years (20-yr storms) would occur on average in half that time under sustained El Niño conditions. In contrast, under La Niña conditions, 20-yr GC and EC storms would occur on average about once in 30 years, while there is little impact of La Niña on the intensity of the SW storms. The leading EOFs from the model simulations and their connections to ENSO are for the most part quite realistic. The model, in particular, does very well in simulating the impact of ENSO on the intensity of EC and GC storms. The main model discrepancies are the lack of SW storms and an overall underestimate of the daily precipitation variance.


2021 ◽  
Author(s):  
Trine Jahr Hegdahl ◽  
Kolbjørn Engeland ◽  
Malte Müller ◽  
Jana Sillman

<p>Atmospheric rivers (AR) are responsible for the most extreme precipitation events causing devastating landslides and floods in western Norway. In this study an event-based storyline approach is used to compare the flood impact of extreme AR events in a warmer climate to those of the current climate.  The four most extreme precipitation events were selected from 30 years of present and future climate simulations from the high-resolution global climate model, the EC-Earth model. For each of the four events, EC-Earth was rerun creating 10 perturbed realizations. A regional convective permitting weather prediction model, AROME-MetCoOp, was used to further downscale the events, and thereafter the operational Norwegian flood-forecasting model was used to estimate the flood levels for 37 catchments in western Norway. The magnitude and the spatial impact were analyzed, and different hydrological initial conditions, which affect the total flooding, were analyzed.</p><p>The results show that more catchments were affected with larger floods in the future climate events compared to the current climate events. In addition, the combination of multiple realizations of meteorological forcing and different hydrological initial conditions, for example soil saturation and snow storage, were important for the estimation of the maximum flood level. The meteorological forcing had the highest overall effect on flood magnitude; however, varying and depending on event and catchment. Finally, operational flood warning levels were used to visualize the difference between future and current climate flood events. Applying a setup similar to the one used operationally and relating the future events to known current events associated with ARs, enables a common reference and ease communication with end-users and decision makers.</p>


2020 ◽  
Author(s):  
Sunil Subba ◽  
Yaoming Ma ◽  
Weiqiang Ma

<p>In recent days there have been discussions regarding the impact of climate change and its vagaries of the weather, particularly concerning extreme events. Nepal, being a mountainous country, is more susceptible to precipitation extreme events and related hazards, which hinder the socioeconomic<br>development of the nation. In this regard, this study aimed to address this phenomenon for one of the most naturally and socioeconomically important regions of Nepal, namely, Eastern Nepal. The data were collected for the period of 1997 to 2016. The interdecadal comparison for two periods<br>(1997–2006 and 2007–2016) was maintained for the calculation of extreme precipitation indices as per recommended by Expert Team on Climate Change Detection and Indices. Linear trends were calculated by using Mann‐Kendall and Sen's Slope estimator. The average annual precipitation was found to be decreasing at an alarming rate of −20 mm/year in the last two decades' tenure. In case of extreme precipitation events, consecutive dry days, one of the frequency indices, showed a solo increase in its trend (mostly significant). Meanwhile, all the intensity indices of extreme precipitation showed decreasing trends (mostly insignificant). Thus, it can be concluded that Eastern Nepal has witnessed some significant drier days in the last two decades, as the events of heavy, very heavy, extremely heavy precipitation events, and annual wet day precipitation (PRCPTOT) were found to be decreasing. The same phenomena were also seen in the Tropical Rainfall Measuring Mission 3B42 V7 satellite precipitation product for whole Nepal.</p>


2021 ◽  
Author(s):  
Alexandre Tuel ◽  
Bettina Schaefli ◽  
Jakob Zscheischler ◽  
Olivia Martius

Abstract. River discharge is impacted by the sub-seasonal (weekly to monthly) temporal structure of precipitation. One example is the successive occurrence of extreme precipitation events over sub-seasonal timescales, referred to as temporal clustering. Its potential effects on discharge have received little attention. Here, we address this question by analysing discharge observations following extreme precipitation events either clustered in time or occurring in isolation. We rely on two sets of precipitation and discharge data, one centered on Switzerland and the other over Europe. We identify "clustered" extreme precipitation events based on the previous occurrence of another extreme precipitation within a given time window. We find that clustered events are generally followed by a more prolonged discharge response with a larger amplitude. The probability of exceeding the 95th discharge percentile in the five days following an extreme precipitation event is in particular up to twice as high for situations where another extreme precipitation event occurred in the preceding week compared to isolated extreme precipitation events. The influence of temporal clustering decreases as the clustering window increases; beyond 6–8 weeks the difference with non-clustered events is negligible. Catchment area, streamflow regime and precipitation magnitude also modulate the response. The impact of clustering is generally smaller in snow-dominated and large catchments. Additionally, particularly persistent periods of high discharge tend to occur in conjunction with temporal clusters of precipitation extremes.


2010 ◽  
Vol 11 (3) ◽  
pp. 770-780 ◽  
Author(s):  
Ingo Schlüter ◽  
Gerd Schädler

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.


2018 ◽  
Vol 18 (7) ◽  
pp. 2047-2056 ◽  
Author(s):  
Stefan Brönnimann ◽  
Jan Rajczak ◽  
Erich M. Fischer ◽  
Christoph C. Raible ◽  
Marco Rohrer ◽  
...  

Abstract. The intensity of precipitation events is expected to increase in the future. The rate of increase depends on the strength or rarity of the events; very strong and rare events tend to follow the Clausius–Clapeyron relation, whereas weaker events or precipitation averages increase at a smaller rate than expected from the Clausius–Clapeyron relation. An often overlooked aspect is seasonal occurrence of such events, which might change in the future. To address the impact of seasonality, we use a large ensemble of regional and global climate model simulations, comprising tens of thousands of model years of daily temperature and precipitation for the past, present, and future. In order to make the data comparable, they are quantile mapped to observation-based time series representative of the Aare catchment in Switzerland. Model simulations show no increase in annual maximum 1-day precipitation events (Rx1day) over the last 400 years and an increase of 10 %–20 % until the end of the century for a strong (RCP8.5) forcing scenario. This fits with a Clausius–Clapeyron scaling of temperature at the event day, which increases less than annual mean temperature. An important reason for this is a shift in seasonality. Rx1day events become less frequent in late summer and more frequent in early summer and early autumn, when it is cooler. The seasonality shift is shown to be related to summer drying. Models with decreasing annual mean or summer mean precipitation show this behaviour more strongly. The highest Rx1day per decade, in contrast, shows no change in seasonality in the future. This discrepancy implies that decadal-scale extremes are thermodynamically limited; conditions conducive to strong events still occur during the hottest time of the year on a decadal scale. In contrast, Rx1day events are also limited by other factors. Conducive conditions are not reached every summer in the present, and even less so in the future. Results suggest that changes in the seasonal cycle need to be accounted for when preparing for moderately extreme precipitation events and assessing their socio-economic impacts.


2018 ◽  
Author(s):  
Stefan Brönnimann ◽  
Jan Rajczak ◽  
Erich Fischer ◽  
Christoph C. Raible ◽  
Marco Rohrer ◽  
...  

Abstract. The intensity of precipitation events is expected to increase in the future. The rate of increase depends on the strength or rarity of the events; very strong and rare events tend to follow the Clausius-Clapeyron relation, whereas weaker events or precipitation averages do not. An often overlooked aspect is seasonal occurrence of such events, which might change in the future. To address the impact of seasonality, we use a large ensemble of regional and global climate model simulations, comprising tens of thousands of model years of daily temperature and precipitation for the past, present and future. In order to make the data comparable, they are quantile-mapped to observation-based time series representative of the Aare catchment in Switzerland. Model simulations show no increase in annual maximum 1-day precipitation events (Rx1day) over the last 400 yrs and an increase of 10–20 % until the end of the century for a strong (RCP8.5) forcing scenario. This fits with a Clausius-Clapeyron scaling of temperature at the event day, which increases less than annual mean temperature. An important reason for this is a shift in seasonality. Rx1day events become less frequent in late summer and more frequent in early summer and early fall, when it is cooler. The seasonality shift is shown to be related to summer drying. Models with decreasing annual mean or summer mean precipitation show this behavior more strongly. The highest Rx1day per decade, in contrast, shows no change in seasonality in the future. This discrepancy implies that decadal-scale extremes are thermodynamically limited; conditions conducive to strong events still occur during hottest time of the year on a decadal scale. In contrast, Rx1day events are also limited by other factors. Conducive conditions are not reached every summer in the present, and even less so in the future. Results suggest that changes in the seasonal cycle need to be accounted for when preparing for moderately extreme precipitation events and assessing their socio-economic impacts.


2021 ◽  
Author(s):  
Rohith Muraleedharan Thundathil ◽  
Thomas Schwitalla ◽  
Andreas Behrendt ◽  
Diego Lange ◽  
Cyrille Flamant ◽  
...  

<p>Probabilistic quantitative precipitation forecasting (PrQPF) is a challenging field of meteorology, which is fundamental for the prediction and quantification of extreme precipitation events. With advanced remote-sensing instruments such as lidar systems, it is possible to acquire the high-resolution temporal and spatial dynamical and thermodynamic data for input to the numerical weather prediction (NWP) models through data assimilation (DA) techniques. During the fall, the Mediterranean region is often stricken by heavy precipitation events (HPEs), resulting in a sudden rise of water levels in the rivers and flash floods. Severe damage to life and property arises during these extreme precipitation events every year. A unique and innovative French initiative project, called the Water Vapor Lidar Network assimilation (WaLiNeAs), will start a measurement campaign in early September 2022, deploying a network of autonomous water vapor lidars from research groups of France, Germany, and Italy across the Western Mediterranean. The project aims to implement an integrated prediction tool to enhance the forecast of HPEs in southern France, primarily demonstrating the benefit of assimilating vertically resolved water vapor data in the new version of the French operational AROME NWP system. The Atmospheric Raman Temperature and Humidity Sounder (ARTHUS, (Lange et al. 2019)), from the University of Hohenheim (UHOH), will operate in synergy with other lidar systems. The data collected from the measurement campaign, water vapor and temperature, will be assimilated in the Weather Research and Forecasting (WRF) model system at the Institute of Physics and Meteorology (IPM), UHOH. A thermodynamic lidar operator developed by some of us (Thundathil et al. 2020) will be used to assimilate lidar temperature and water vapor mixing ratio independently. The operator avoids undesirable cross sensitivities to temperature enabling maximum moisture information of the observation to be propagated into the model. An advanced hybrid three-dimensional Variational - Ensemble Transform Kalman Filter (3DVAR-ETKF) DA system with 50 ensemble members, on a convection-permitting resolution of 1.5 km, will be set up for the research study. For the prediction and quantification of the HPE event, the assimilation will be performed in a rapid update cycle mode every 15 minutes before its occurrence. A prototype of the DA system with ten ensemble members and a one-hour rapid update cycle was already developed at IPM (Thundathil et al., 2021). In this case, the impact from a single ground-based lidar spreads spatially for a radius of 100 km. Apart from the improvement in the analyses, the planetary boundary layer height (PBLH) forecast impact persisted 7 hours into forecast time compared with respect to independent ceilometer observations. The results show a promising initiative for future operational lidar network assimilation. We will present the outline and DA setup of the study, highlighting results from our previous lidar DA research.</p>


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