scholarly journals Extreme wet seasons – their definition and relationship with synoptic scale weather systems

2020 ◽  
Author(s):  
Emmanouil Flaounas ◽  
Matthias Röthlisberger ◽  
Maxi Boettcher ◽  
Michael Sprenger ◽  
Heini Wernli

Abstract. An extreme aggregation of precipitation on the seasonal timescale, leading to a so-called extreme wet season, can have substantial environmental and socio-economic impacts. In contrast to extreme precipitation events on hourly to daily timescales, which are typically caused by single weather systems, an extreme wet season may be attributed to a combination of different and/or recurring weather systems. In fact, extreme wet seasons may be formed by almost continuously occurring moderate events, or by more frequent and/or more intense short-duration extreme events, or by a combination of these scenarios. This study aims at identifying and statistically characterizing extreme wet seasons around the globe, and elucidating their relationship with specific weather systems. To define extreme wet seasons, we used 40 years (1979–2018) of ERA-Interim reanalyses. Primary extreme seasons were defined independently at every grid point as the consecutive 90-day period with the highest accumulated precipitation. Secondary extreme seasons were also considered, if accumulated precipitation amounts to at least 90 % of the precipitation in the primary season at the same grid point. A high number of secondary extreme seasons was found for instance in the extratropical storm tracks, suggesting that these regions are less likely to experience an exceptional amount of precipitation in a particular 90-day period. In most continental regions, the extreme seasons occur during the warm months of the year, especially in the mid-latitudes. Nevertheless, colder periods might be also relevant to extreme seasons within the same continent, especially in coastal areas. All identified extreme seasons were statistically characterised in terms of anomalies compared to the climatology of the number of wet days and daily extreme events. Results show that daily extremes are decisive for the occurrence of extreme wet seasons in regions of frequent precipitation, e.g. in the tropics. In contrast, e.g., in arid regions where wet days are scarce, extreme seasons may occur only due to anomalously high numbers of wet days. In the subtropics and more precisely within the transitional zones between arid areas and regions of frequent precipitation, both an anomalously high occurrence of daily extremes and wet days are related to the formation of extreme wet seasons. The spatial extent of regions affected by the same extreme wet season is variable and can reach continental scales, although the vast majority of extreme seasons is limited to scales of the order of 20 × 105 km2. Finally, the relationship of extreme seasons to synoptic-scale weather systems was investigated on the basis of four objectively identified weather systems that are known to be associated with intense precipitation: cyclones, warm conveyor belts, tropical moisture exports and breaking Rossby waves. A grid-to-grid association of these weather systems to daily precipitation allows quantifying their role for extreme wet seasons. In particular, cyclones and warm conveyor belts contribute strongly to extreme wet seasons in most regions of the globe. But interlatitudinal influences are also shown to be important: tropical moisture exports, i.e., the poleward transport of tropical moisture, can contribute to extreme wet seasons in the mid-latitudes, while breaking Rossby waves, i.e., the equatorward intrusion of stratospheric air, may decisively contribute to the formation of extreme wet seasons in the tropics. Four illustrative examples provide insight into the synergetic effects of the four identified weather systems on the formation of extreme wet seasons in the Arctic, the mid-latitudes, Australia, and the tropics.

2021 ◽  
Vol 2 (1) ◽  
pp. 71-88
Author(s):  
Emmanouil Flaounas ◽  
Matthias Röthlisberger ◽  
Maxi Boettcher ◽  
Michael Sprenger ◽  
Heini Wernli

Abstract. An extreme aggregation of precipitation on the seasonal timescale, leading to a so-called extreme wet season, can have substantial environmental and socio-economic impacts. This study has a twofold aim: first to identify and statistically characterize extreme wet seasons around the globe and second to elucidate their relationship with specific weather systems. Extreme wet seasons are defined independently at every grid point of ERA-Interim reanalyses as the consecutive 90 d period with the highest accumulated precipitation in the 40-year period of 1979–2018. In most continental regions, the extreme seasons occur during the warm months of the year, especially in the midlatitudes. Nevertheless, colder periods might be also relevant, especially in coastal areas. All identified extreme seasons are statistically characterized in terms of climatological anomalies of the number of wet days and of daily extreme events. Results show that daily extremes are decisive for the occurrence of extreme wet seasons in regions of frequent precipitation, e.g., in the tropics. This is in contrast to arid regions where wet seasons may occur only due to anomalously frequent wet days. In the subtropics and more precisely within the transitional zones between arid areas and regions of frequent precipitation, both an anomalously high occurrence of daily extremes and of wet days are related to the formation of extreme wet seasons. A novel method is introduced to define the spatial extent of regions affected by a particular extreme wet season and to relate extreme seasons to four objectively identified synoptic-scale weather systems, which are known to be associated with intense precipitation: cyclones, warm conveyor belts, tropical moisture exports and breaking Rossby waves. Cyclones and warm conveyor belts contribute particularly strongly to extreme wet seasons in most regions of the globe. But interlatitudinal influences are also shown to be important: tropical moisture exports, i.e., the poleward transport of tropical moisture, can contribute to extreme wet seasons in the midlatitudes, while breaking Rossby waves, i.e., the equatorward intrusion of stratospheric air, may decisively contribute to the formation of extreme wet seasons in the tropics. Three illustrative examples provide insight into the synergetic effects of the four identified weather systems on the formation of extreme wet seasons in the midlatitudes, the Arctic and the (sub)tropics.


2021 ◽  
Author(s):  
Marte Gé Hofsteenge ◽  
Rune Grand Graversen ◽  
Johanne Hope Rydsaa ◽  
Zoé Rey

Abstract The Arctic sea-ice extent has strongly declined over recent decades. A large inter-annual variability is superimposed on this negative trend. Previous studies have emphasised a significant warming effect associated with latent energy transport into the Arctic region, in particular due to an enhanced greenhouse effect associated with the convergence of the humidity transport over the Arctic. The atmospheric energy transport into the Arctic is mostly accomplished by waves such as Rossby waves and cyclones. Here we present a systematic study of the effect on Arctic sea ice of these atmospheric wave types. Through a regression analysis we investigate the coupling between transport anomalies of both latent and dry-static energy and sea-ice anomalies. From the state-of-the-art ERA5 reanalysis product the latent and dry-static transport over the Arctic boundary (70 ºN) is calculated. The transport is then split into transport by planetary and synoptic-scale waves using a Fourier decomposition. The results show that latent energy transport as compared to that of dry-static shows a much stronger potential to decrease sea ice concentration. However, taking into account that the variability of dry-static transport is of an order of magnitude larger than latent, the actual impact on the sea ice appears similar for the two components. In addition, the energy transport by planetary waves causes a strong decline of the sea ice concentration whereas the transport by synoptic-scale waves shows only little effect on the sea ice. The study emphasises the importance of the large-scale waves on the sea ice variability.


2017 ◽  
Vol 98 (8) ◽  
pp. 1739-1748 ◽  
Author(s):  
Michael Sprenger ◽  
Georgios Fragkoulidis ◽  
Hanin Binder ◽  
Mischa Croci-Maspoli ◽  
Pascal Graf ◽  
...  

Abstract This paper introduces a newly compiled set of feature-based climatologies identified from ERA-Interim (1979–2014). Two categories of flow features are considered: (i) Eulerian climatologies of jet streams, tropopause folds, surface fronts, cyclones and anticyclones, blocks, and potential vorticity streamers and cutoffs and (ii) Lagrangian climatologies, based on a large ensemble of air parcel trajectories, of stratosphere–troposphere exchange, warm conveyor belts, and tropical moisture exports. Monthly means of these feature climatologies are openly available at the ETH Zürich web page (http://eraiclim.ethz.ch) and are annually updated. Datasets at higher resolution can be obtained from the authors on request. These feature climatologies allow studying the frequency, variability, and trend of atmospheric phenomena and their interrelationships across temporal scales. To illustrate the potential of this dataset, boreal winter climatologies of selected features are presented and, as a first application, the very unusual Northern Hemispheric winter of 2009/10 is identified as the season when most of the considered features show maximum deviations from climatology. The second application considers dry winters in the western United States and reveals fairly localized anomalies in the eastern North Pacific of enhanced blocking and surface anticyclones and reduced cyclones.


2021 ◽  
Author(s):  
Dominik Büeler ◽  
Jan Wandel ◽  
Julian F. Quinting ◽  
Christian M. Grams

<div><span>Sub-seasonal numerical weather forecasts (10 – 60 days) primarily aim to predict the evolution of the large-scale circulation and its associated surface weather on continent- and multi-daily scales. In the extratropics, this atmospheric variability is depicted best by so-called weather regimes. Here, we assess the ability of sub-seasonal reforecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) to predict 7 year-round weather regimes in the Atlantic-European region. We first investigate how well the forecasts reproduce frequency, length, and transitions of the weather regime life cycles. We then show that the average forecast skill horizon varies by several days for different weather regimes, seasons, and initial planetary-scale flow states. In a final part, we provide first insight into how synoptic-scale processes, more specifically warm conveyor belts, and their inherent intrinsic predictability limit might affect this flow-dependent sub-seasonal weather regime forecast skill.</span></div>


2021 ◽  
Vol 5 (3) ◽  
pp. 481-497
Author(s):  
Mansour Almazroui ◽  
Fahad Saeed ◽  
Sajjad Saeed ◽  
Muhammad Ismail ◽  
Muhammad Azhar Ehsan ◽  
...  

AbstractThis paper presents projected changes in extreme temperature and precipitation events by using Coupled Model Intercomparison Project phase 6 (CMIP6) data for mid-century (2036–2065) and end-century (2070–2099) periods with respect to the reference period (1985–2014). Four indices namely, Annual maximum of maximum temperature (TXx), Extreme heat wave days frequency (HWFI), Annual maximum consecutive 5-day precipitation (RX5day), and Consecutive Dry Days (CDD) were investigated under four socioeconomic scenarios (SSP1-2.6; SSP2-4.5; SSP3-7.0; SSP5-8.5) over the entire globe and its 26 Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) regions. The projections show an increase in intensity and frequency of hot temperature and precipitation extremes over land. The intensity of the hottest days (as measured by TXx) is projected to increase more in extratropical regions than in the tropics, while the frequency of extremely hot days (as measured by HWFI) is projected to increase more in the tropics. Drought frequency (as measured by CDD) is projected to increase more over Brazil, the Mediterranean, South Africa, and Australia. Meanwhile, the Asian monsoon regions (i.e., South Asia, East Asia, and Southeast Asia) become more prone to extreme flash flooding events later in the twenty-first century as shown by the higher RX5day index projections. The projected changes in extremes reveal large spatial variability within each SREX region. The spatial variability of the studied extreme events increases with increasing greenhouse gas concentration (GHG) and is higher at the end of the twenty-first century. The projected change in the extremes and the pattern of their spatial variability is minimum under the low-emission scenario SSP1-2.6. Our results indicate that an increased concentration of GHG leads to substantial increases in the extremes and their intensities. Hence, limiting CO2 emissions could substantially limit the risks associated with increases in extreme events in the twenty-first century.


2012 ◽  
Vol 12 (4) ◽  
pp. 1785-1810 ◽  
Author(s):  
Y. Qian ◽  
C. N. Long ◽  
H. Wang ◽  
J. M. Comstock ◽  
S. A. McFarlane ◽  
...  

Abstract. Cloud Fraction (CF) is the dominant modulator of radiative fluxes. In this study, we evaluate CF simulated in the IPCC AR4 GCMs against ARM long-term ground-based measurements, with a focus on the vertical structure, total amount of cloud and its effect on cloud shortwave transmissivity. Comparisons are performed for three climate regimes as represented by the Department of Energy Atmospheric Radiation Measurement (ARM) sites: Southern Great Plains (SGP), Manus, Papua New Guinea and North Slope of Alaska (NSA). Our intercomparisons of three independent measurements of CF or sky-cover reveal that the relative differences are usually less than 10% (5%) for multi-year monthly (annual) mean values, while daily differences are quite significant. The total sky imager (TSI) produces smaller total cloud fraction (TCF) compared to a radar/lidar dataset for highly cloudy days (CF > 0.8), but produces a larger TCF value than the radar/lidar for less cloudy conditions (CF < 0.3). The compensating errors in lower and higher CF days result in small biases of TCF between the vertically pointing radar/lidar dataset and the hemispheric TSI measurements as multi-year data is averaged. The unique radar/lidar CF measurements enable us to evaluate seasonal variation of cloud vertical structures in the GCMs. Both inter-model deviation and model bias against observation are investigated in this study. Another unique aspect of this study is that we use simultaneous measurements of CF and surface radiative fluxes to diagnose potential discrepancies among the GCMs in representing other cloud optical properties than TCF. The results show that the model-observation and inter-model deviations have similar magnitudes for the TCF and the normalized cloud effect, and these deviations are larger than those in surface downward solar radiation and cloud transmissivity. This implies that other dimensions of cloud in addition to cloud amount, such as cloud optical thickness and/or cloud height, have a similar magnitude of disparity as TCF within the GCMs, and suggests that the better agreement among GCMs in solar radiative fluxes could be a result of compensating effects from errors in cloud vertical structure, overlap assumption, cloud optical depth and/or cloud fraction. The internal variability of CF simulated in ensemble runs with the same model is minimal. Similar deviation patterns between inter-model and model-measurement comparisons suggest that the climate models tend to generate larger biases against observations for those variables with larger inter-model deviation. The GCM performance in simulating the probability distribution, transmissivity and vertical profiles of cloud are comprehensively evaluated over the three ARM sites. The GCMs perform better at SGP than at the other two sites in simulating the seasonal variation and probability distribution of TCF. However, the models remarkably underpredict the TCF at SGP and cloud transmissivity is less susceptible to the change of TCF than observed. In the tropics, most of the GCMs tend to underpredict CF and fail to capture the seasonal variation of CF at middle and low levels. The high-level CF is much larger in the GCMs than the observations and the inter-model variability of CF also reaches a maximum at high levels in the tropics, indicating discrepancies in the representation of ice cloud associated with convection in the models. While the GCMs generally capture the maximum CF in the boundary layer and vertical variability, the inter-model deviation is largest near the surface over the Arctic.


1965 ◽  
Vol 97 (9) ◽  
pp. 897-909 ◽  
Author(s):  
D. A. Chant

AbstractMites of the genus Phytoseius Ribaga largely inhabit plants and are at least partly predacious, feeding on tetranychid, eriophyid, and other mites. They probably also feed on pollen, honeydew, and plant juices, as do other phytoseiids that have been studied (Chant 1959; Dosse 1961; McMurty and Scriven 1964). They are not usually found in soil or humus but occur on many kinds of low growing plants as well as coniferous and deciduous trees. They have been collected on all continents and from the arctic to the tropics.


2007 ◽  
Vol 34 (17) ◽  
Author(s):  
E. Sokolova ◽  
K. Dethloff ◽  
A. Rinke ◽  
A. Benkel

2021 ◽  
pp. 1-45

Abstract This study explores the potential predictability of Southwest US (SWUS) precipitation for the November-March season in a set of numerical experiments performed with the Whole Atmospheric Community Climate Model. In addition to the prescription of observed sea surface temperature and sea ice concentration, observed variability from the MERRA-2 reanalysis is prescribed in the tropics and/or the Arctic through nudging of wind and temperature. These experiments reveal how a perfect prediction of tropical and/or Arctic variability in the model would impact the prediction of seasonal rainfall over the SWUS, at various time scales. Imposing tropical variability improves the representation of the observed North Pacific atmospheric circulation, and the associated SWUS seasonal precipitation. This is also the case at the subseasonal time scale due to the inclusion of the Madden-Julian Oscillation (MJO) in the model. When additional nudging is applied in the Arctic, the model skill improves even further, suggesting that improving seasonal predictions in high latitudes may also benefit prediction of SWUS precipitation. An interesting finding of our study is that subseasonal variability represents a source of noise (i.e., limited predictability) for the seasonal time scale. This is because when prescribed in the model, subseasonal variability, mostly the MJO, weakens the El Niño Southern Oscillation (ENSO) teleconnection with SWUS precipitation. Such knowledge may benefit S2S and seasonal prediction as it shows that depending on the amount of subseasonal activity in the tropics on a given year, better skill may be achieved in predicting subseasonal rather than seasonal rainfall anomalies, and conversely.


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