Displays

Cross-timescale interference involves linear and non-linear interactions between climate modes acting at multiple timescales (Muñoz et al., 2015, 2016, 2017; Robertson et al., 2015; Moron et al., 2015), and that are related to windows of opportunity for enhanced predictive skill (Mariotti et al., 2020), with relevant societal impacts (e.g., Doss-Gollin et al., 2018; Anderson et al., 2020). Using a simple mathematical model, reanalysis data and gridded observations, here we analyze plausible mechanisms for cross-timescale interference, describing conditions for coupling of oscillating modes and its impact on extreme rainfall occurrence and predictive skill. Concrete examples for Northeast North America and southern South America are discussed, as well as implications for climate model diagnostics.

How to cite: Muñoz, Á. G.: Cross-timescale interference and predictability of extremes: a chimera?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21105, https://doi.org/10.5194/egusphere-egu2020-21105, 2020.

The Kuroshio Extension (KE) is the inertial meandering jet formed by the convergence of the Kuroshio and Oyashio currents in the Northern Pacific. It is widely mentioned in the literature that the KE variability is bimodal on the decadal time scale. The nature of this low frequency variability (LFV) is still under debate; some authors suggest that internal oceanic mechanisms play a fundamental role in the phenomenon but there is also evidence from the observations that the KE LFV is connected with changes in broader patterns of variability such as the Pacific Decadal Oscillation.

We first inspect the interplay between the ocean and the atmosphere in the KE by taking advantage of the OCCIPUT 1/4° model dataset: it consists in an ensemble of 50 global ocean–sea-ice hindcasts performed over the period 1960–2015 (hereafter OCCITENS), and in a one-member 330-yr climatological simulation (hereafter OCCICLIM). In this context, OCCITENS simulates both the intrinsic and forced variability and allows for their separation via ensemble statistics, while OCCICLIM simulates the "pure" intrinsic variability of the jet. We then explore some features of the KE dynamical system attractor in the quasi-autonomous (OCCICLIM) and nonautonomous (OCCITENS) regimes: we thus assess the KE predictability in the OCCIPUT dataset in order to better understand the ocean-atmosphere interactions and the source of the associated predictability.

Our analyses show that both oceanic and atmospheric drivers control the KE LFV variability. In this framework, the results suggest that the jet oscillates between the two intrinsic oceanic modes with transitions triggered by the atmosphere.

How to cite: Fedele, G., Penduff, T., Pierini, S., Alvarez-Castro, M. C., Bellucci, A., and Masina, S.: Interannual-to-decadal variability of the Kuroshio extension: Analyzing an ensemble of global hindcasts from a Dynamical System viewpoint., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3062, https://doi.org/10.5194/egusphere-egu2020-3062, 2020.

We study how mesoscale air-sea interactions over the North Atlantic can influence weather extremes, e.g. heavy precipitation and wind storms, and the overall atmospheric circulation both locally and downstream in the midlatitudes. We use a global coupled climate model with a high-resolution North Atlantic grid (dx ~ 8 km) and an atmosphere model resolution of either 125 km or 25 km. The high-resolution North Atlantic grid allows the model to resolve the current systems and SST fronts associated with e.g. the Gulf Stream and North Atlantic Current. As air-sea fluxes of momentum, heat and freshwater are calculated on the atmosphere grid, spatial variations in fluxes associated with sharp SST fronts are much better represented when using the high-resolution atmosphere then when using the low-resolution model. 

Preliminary results show that coupling to the high-resolution (dx ~ 25 km) rather than low-resolution (dx ~ 125 km) atmosphere model increases the intensity and variance of surface heat and freshwater fluxes over eddy-rich regions such as the Gulf Stream. As a result, the high-resolution model simulates more intense heavy precipitation events over most of the North Atlantic Ocean. We also show that more frequent coupling between the atmosphere and ocean components increases the intensity of the air-sea fluxes, in particular wind stress, which has a large impact on the ocean. More intense air-sea fluxes can provide more energy for cyclogenesis and we will discuss how the oceanic mesoscale, in particular in the eddy-rich regions, can alter the storm tracks and jet stream to influence extreme weather and the climate over Europe.

The coupled model comprises NEMO 3.6/LIM2 ocean and OpenIFS 40r1 atmosphere, and works by allowing the global OpenIFS model to send and receive fields from both a global coarse-resolution ocean grid and a refined grid over the North Atlantic grid via the OASIS3-MCT4 coupler. The ability to run these simulations is a very recent development and we will give a brief overview of the coupled modelling system and benefits of using regional grid refinement in coupled models.

How to cite: Kjellsson, J., Park, W., Martin, T., Maisonnave, E., and Latif, M.: Greatness from small beginnings: Impact of oceanic mesoscale on weather extremes and large-scale atmospheric circulation in midlatitudes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17899, https://doi.org/10.5194/egusphere-egu2020-17899, 2020.

The 2018 summer was unusually hot in large areas of the Northern Hemisphere and simultaneous heat waves on three continents led to major impacts to agriculture and society. The event was driven by the anomalous atmospheric circulation pattern during that summer and it was only possible in a climate with global warming. There are indications that in a future, warmer climate similar events might occur regularly, affecting major ‘breadbasket’ regions of the Northern Hemisphere.

This study aims to understand the role of climate change for driving the intensity of the 2018 summer and to explore the sensitivity to changing warming levels. Model simulations are performed using the Community Earth System Model to investigate storylines for the extreme 2018 summer given the observed atmospheric large-scale circulation but different levels of background global warming: no human imprint, the 2018 conditions, and different mean global warming levels (1.5°C, 2°C, 3°C, and 4°C). The storylines explore the consequences of the event in an alternative warmer or colder world and thus help to increase our understanding of the drivers involved. The results reveal a strong contribution by the present-day level of global warming and provide an outlook to similar events in a possible future climate.

How to cite: Wehrli, K., Hauser, M., and Seneviratne, S. I.: Storylines of the 2018 Northern Hemisphere heat wave at pre-industrial and higher global warming levels, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13802, https://doi.org/10.5194/egusphere-egu2020-13802, 2020.

Large-scale circulations (LSCs) explain a significant part of Alpine precipitations. Characterizing circulations triggering heavy precipitation is usually done using weather-type classifications. A different characterization is implemented here, based on analogy using the atmospheric descriptors proposed in Blanchet et al 2018, 2019. These descriptors are both related to the dynamics of LSC and to their relative position in the atmospheric space. This work is applied to the Isère river catchment for the 1950-2011 period, considering a 3-day time step. The 500 hPa and 1000 hPa geopotential heights covering part of the western Europe are used separately to represent LSC. Two analogy criteria are investigated for constructing the atmospheric descriptors, namely TWS and RMSE.

Our results reveal that LSCs triggering heavy precipitation amounts correspond to strong geostrophic wind with quasi constant direction during the three days, corresponding to blocking situations in altitude. Moreover, those patterns of circulation are among the least singulars, and they show the highest degree of clustering in the atmospheric space. We interpret the latest results by the fact that heavy precipitation LSCs feature twin circulation patterns. In addition, the 500 hPa geopotential height appears to discriminate better heavy precipitation situations than the 1000 hPa one. Finally, our work points out the benefit of a combined use of TWS and RMSE. TWS gives information about the direction of geostrophic wind, while RMSE -combined with TWS- informs about its strength.

References:

Blanchet, J., Stalla, S., and Creutin, J.-D. (2018). Analogy of multi-day sequences of atmospheric circulation favoring large rainfall accumulation over the French Alps. Atmospheric Science Letters.

Blanchet, J., Creutin, J-D. (2019). Modelling rainfall accumulations over several days in the French Alps using low-dimensional atmospheric predictors based on analogy. Journal of Applied Meteorology and Climatology.

How to cite: Blanc, A., Blanchet, J., and Creutin, J.-D.: Characterizing large-scale circulation triggering heavy precipitation amounts over the northern French Alps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6687, https://doi.org/10.5194/egusphere-egu2020-6687, 2020.

Global warming is projected to intensify during the 21st century. This warming will be more readily perceived by human populations if it occurs rapidly and if it induces a thermal heat stress on the human body. Yet, only few studies investigate how climate change could be felt by future populations. Here we assess this possible perceived evolution between 1959 and 2100 only combining thermodynamic and statistical indicators. We analyse extremes of temperature (T₉₉) and simplified Wet-Bulb Globe Temperature (WBGT₉₉), a common heat stress index assessing the combined effect of elevated temperature and humidity on the human body. For each year of the period, we define the speed of change as a difference between two successive 20-year periods (i.e. with a moving baseline), and assess how these running changes emerge from each last 20-y inter-annual variability.

According to a subset of 12 CMIP5 Earth System Models and the RCP8.5 scenario, the change of T₉₉ and WBGT₉₉ will be twice as fast in the future compared to the current speed of change in the mid-latitudes, and by up to four times faster tropical regions such as Amazonia. Warming accelerations are thus similar for both T₉₉ and WBGT₉₉. However, in tropical regions by 2080, the speed is projected to be 2.3 times larger than the recent inter-annual variability for WBGT₉₉, and only 1.5 to 1.8 times larger for T₉₉. Currently, speeds of change are only 0.2 to 0.8 times as large as the recent year-to-year variability for both metrics. We also show that 36% of the total world population will experience an emergent WBGT₉₉ intensification in 2080, but only 15% of the population for T₉₉. According to future projections, the accelerated warming of future heat extremes will be more felt by populations than current changes, and this perceived change will be more severe for WBGT₉₉ than for T₉₉, particularly in the tropics.

How to cite: Brouillet, A. and Joussaume, S.: More perceived but not faster evolution of heat stress than temperature extremes in the future, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8990, https://doi.org/10.5194/egusphere-egu2020-8990, 2020.

The central United States experienced the hottest summers of the twentieth century in 1934 and 1936, with over 40 heat wave days and maximum temperatures surpassing 44°C at some locations like Kansas and Oklahoma. In fact, as of 2019, the summer of 1936 is still the hottest on record. The heat waves coincided with the decade-long Dust Bowl drought, that caused wide-spread crop failures, dust storms that penetrated to New York and considerable out-migration. In a very-large ensemble regional modelling framework, we show that greenhouse gas increases slightly enhanced the frequency and duration of the Dust Bowl heat waves, and would strongly enhance similar heat waves in the present day under current, further elevated greenhouse gas levels. Specifically, present-day atmospheric greenhouse gas forcing would reduce the return period of a rare (less than once in a century) heat wave summer as observed in 1936 to about 1-in 40-years, with further contribution by sea surface warming. Here, we show that a key driver of this elevated heat wave risk is the reduction in evaporative cooling and increase in sensible heating during dry springs and summers.  Hence, we conclude that a warmer world is creating the potential for future extreme heat in moisture-limited regions, with potentially very damaging impacts.

How to cite: Undorf, S., Cowan, T., Hegerl, G., Harrington, L., and Otto, F.: The influence of greenhouse gases on the 1930s Dust Bowl heat waves across central United States, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6177, https://doi.org/10.5194/egusphere-egu2020-6177, 2020.

Extreme climate events are invariably highly nonlinear, complex events, resulting from the confluence of multiple causal factors, and often quite singular. In any complex system there is a tension between analysis methods that respect the singularity of the extreme events at the price of statistical repeatability, and those that emphasize statistical repeatability at the price of nonlinearity and complexity; this dichotomy is found across all areas of science. In the climate context, the ‘storyline’ approach has emerged in recent years as a way of following the first of these two pathways. I will discuss how the storyline approach can be cast within the mathematical framework of causal networks, which provides a way to bridge between the storyline and probabilistic approaches. This also provides a way to interpret data in an appropriately conditional manner, thereby aiding model-measurement comparison.

How to cite: Shepherd, T.: Storyline approach to extreme event characterization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4896, https://doi.org/10.5194/egusphere-egu2020-4896, 2020.

In the last decades, extremely hot summers (hereafter extreme summers) have challenged societies worldwide through their adverse ecological, economic and public health effects. In this study, extreme summers are identified at all grid points in the Northern Hemisphere in the upper tail of the July–August (JJA) seasonal mean 2-meter temperature (T2m) distribution, separately in ERA-Interim reanalyses and in 700 simulated years with the Community Earth System Model (CESM) large ensemble for present-day climate conditions. A novel approach is introduced to characterize the substructure of extreme summers, i.e., to elucidate whether an extreme summer is mainly the result of the warmest days being anomalously hot, or of the coldest days being anomalously mild, or of a general shift towards warmer temperatures on all days of the season. Such a statistical characterization can be obtained from considering so-called rank day anomalies for each extreme summer, that is, by sorting the 92 daily mean T2m values of an extreme summer and by calculating, for every rank, the deviation from the climatological mean rank value of T2m.  

Applying this method in the entire Northern Hemisphere reveals spatially strongly varying extreme summer substructures, which agree remarkably well in the reanalysis and climate model data sets. For example, in eastern India the hottest 30 days of an extreme summer contribute more than 70% to the total extreme summer T2m anomaly, while the colder days are close to climatology. In the high Arctic, however, extreme summers occur when the coldest 30 days are substantially warmer than climatology. Furthermore, in roughly half of the Northern Hemisphere land area, the coldest third of summer days contribute more to extreme summers than the hottest third, which highlights that milder than normal coldest summer days are a key ingredient of many extreme summers. In certain regions, e.g., over western Europe and western Russia, the substructure of different extreme summers shows large variability and no common characteristic substructure emerges. Furthermore, we show that the typical extreme summer substructure in a certain region is directly related to the region’s overall T2m rank day variability pattern. This indicates that in regions where the warmest summer days vary particularly strongly from one year to the other, these warmest days are also particularly anomalous in extreme summers (and analogously for regions where variability is largest for the coldest days). Finally, for three selected regions, thermodynamic and dynamical causes of extreme summer substructures are briefly discussed, indicating that, for instance, the onset of monsoons, physical boundaries like the sea ice edge, or the frequency of occurrence of Rossby wave breaking, strongly determine the substructure of extreme summers in certain regions.

How to cite: Röthlisberger, M., Sprenger, M., Flaounas, E., Beyerle, U., and Wernli, H.: The substructure of extremely hot summers in the Northern Hemisphere, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4441, https://doi.org/10.5194/egusphere-egu2020-4441, 2020.

In 2013/14 eastern South America experienced one of its worst droughts, leading to water shortages in São Paulo, the world’s fourth most populated city. This event was also responsible for a dengue fever outbreak that tripled the usual number of fatalities and reduced Brazilian coffee production leading to a global shortages and worldwide price increases. The drought was associated with an anomalous anticyclonic circulation off southeast South America that prevented synoptic systems reaching the region while inhibiting the development of the South Atlantic Convergence Zone and its associated rainfall. A concomitant and unprecedented marine heatwave also developed in the southwest Atlantic. Here we show from observations that such droughts and adjacent marine heatwaves have a common remote cause. Atmospheric blocking triggered by tropical convection in the Indian and Pacific oceans can cause persistent anticyclonic circulation that not only leads to severe drought but also generates marine heatwaves in the adjacent ocean. We show that increased shortwave radiation due to reduced cloud cover and reduced ocean heat loss from weaker winds are the main contributors to the establishment of marine heatwaves in the region. The proposed mechanism, which involves droughts, extreme air temperature over land and atmospheric blocking explains approximately 60% of the marine heatwave events in the western South Atlantic. We also identified an increase in frequency, duration, intensity and extension of marine heatwave events over the satellite period 1982–2016. Moreover, surface primary production was reduced during these events with implications for regional fisheries.

How to cite: Rodrigues, R., Taschetto, A., Sen Gupta, A., and Foltz, G.: From severe droughts in South America to marine heatwaves in the South Atlantic, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5162, https://doi.org/10.5194/egusphere-egu2020-5162, 2020.

A physical climate storyline approach is applied to an autumn flood event caused by an atmospheric river in the West Coast of Norway. The aim is to demonstrate the value and challenges of higher spatial and temporal resolution in simulating impacts. The modelling chain used is the same as the one used operationally, to issue flood warnings for example. Its output is therefore familiar to many users, which we expect will facilitate stakeholder engagement. Two different versions of a hydrological model are run to show that on the one hand, the higher spatial resolution between the global and regional model is necessary to realistically simulate the high spatial variability of precipitation in such a mountainous region. On the other hand we also show that the intensity of the peak streamflow is only captured realistically with hourly data. The higher resolution regional atmospheric model is able to simulate the fact that with the passage of an atmospheric river, some valleys receive high amounts of precipitation and others not. However, the coarser resolution global model shows uniform precipitation in the whole region. Translating the event into the future leads to similar results: while in some catchments, a future flood might be 50% larger than a present one, in others no event occurs because the atmospheric river does not hit that catchment.

How to cite: Schaller, N., Sillmann, J., Müller, M., Haarsma, R., Hazeleger, W., Jahr Hegdahl, T., Kelder, T., van den Oord, G., Weerts, A., and Whan, K.: The role of spatial and temporal model resolution in a flood event storyline approach in Western Norway, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8636, https://doi.org/10.5194/egusphere-egu2020-8636, 2020.

The mathematical formulation of causality in measurable terms of predictability was given by the father of cybernetics N. Wiener [1] and formulated for time series by C.W.J. Granger [2]. The Granger causality is based on the evaluation of predictability in bivariate autoregressive models. This concept has been generalized for nonlinear systems using methods rooted in information theory [3,4]. The information-theoretic approach, defining causality as information transfer, has been successful in many applications and generalized to multivariate data and causal networks [e.g., 5]. This approach, rooted in the information theory of Shannon, usually ignores two important properties of complex systems, such as the Earth climate: the systems evolve on multiple time scales and their variables have heavy-tailed probability distributions. While the multiscale character of complex dynamics, such as air temperature variability, can be studied within the Shannonian framework [6, 7], the entropy concepts of Rényi and Tsallis have been proposed to cope with variables with heavy-tailed probability distributions. We will discuss how such non-Shannonian entropy concepts can be applied in inference of causality in systems with heavy-tailed probability distributions and extreme events, using examples from the climate system.

This study was supported by the Czech Science Foundation, project GA19-16066S.

 [1] N. Wiener, in: E. F. Beckenbach (Editor), Modern Mathematics for Engineers (McGraw-Hill, New York, 1956)

[2] C.W.J. Granger, Econometrica 37 (1969) 424

[3] K. Hlaváčková-Schindler et al., Phys. Rep. 441 (2007)  1

[4] M. Paluš, M. Vejmelka, Phys. Rev. E 75 (2007) 056211

[5] J. Runge et al., Nature Communications 6 (2015) 8502

[6] M. Paluš, Phys. Rev. Lett. 112 (2014) 078702

 [7] N. Jajcay, J. Hlinka, S. Kravtsov, A. A. Tsonis, M. Paluš, Geophys. Res. Lett. 43(2) (2016) 902–909

How to cite: Palus, M.: Causality and information transfer in systems with extreme events, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12772, https://doi.org/10.5194/egusphere-egu2020-12772, 2020.

The different phases of the Pacific Decadal Oscillation (PDO) are a primary source of internal decadal climate variability which have distinct impacts on global climate and human society. However, obtaining a reliable prediction of the PDO phase transition is still challenging. Here, we employed the new technique of climate network analysis to uncover early warning signals prior to a PDO phase transition. An examination of cooperative behaviors in the PDO region revealed an enhanced signal that propagated from the western Pacific, passed through the Kuroshio extension (KE) and the subtropical oceanic frontal (STF) regions, and finally reached the northwest coast of the Americas. This signal captured all six of the PDO phase transitions from the 1890s to 2000s, with a warning time of 6.5±2.3 years in advance. It also underpinned the possible PDO phase transition at years around 2015, which may be triggered by the strong El Niño in 2014-2016.

How to cite: Lu, Z., Yuan, N., Ma, Z., Yang, Q., and Kurths, J.: Early warning of the Pacific Decadal Oscillation phase transition using complex network analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13047, https://doi.org/10.5194/egusphere-egu2020-13047, 2020.

Traditional extreme value analysis based on the generalised ex-
treme value (GEV) or generalised Pareto distribution (GPD) suffers
from two drawbacks: (i) Both methods are wasteful of data as only
block maxima or exceedances over a high threshold are taken into ac-
count and the bulk of the data is disregarded. (ii) Moreover, in the
GPD approach, there is no systematic way to determine the threshold
parameter. Here, all the data are fitted simultaneously using a gener-
alised exponential family model for the bulk and a GPD model for the
tail. At the threshold, the two distributions are linked together with
appropriate matching conditions. The model parameters are estimated
from the likelihood function of all the data. Also the threshold param-
eter can be determined via maximum likelihood in an outer loop. The
method is exemplified on wind speed data from an atmospheric model.

How to cite: Kwasniok, F.: Robust extreme value analysis: the bulk matching method , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22377, https://doi.org/10.5194/egusphere-egu2020-22377, 2020.

Atmospheric jet streams are typically separated into primarily "eddy-driven", or "polar-front" jets and primarily "thermally-driven", or "subtropical" jets. Some regions also display “merged” jets, resulting from the (quasi) co-location of the regions of eddy generation with the subtropical jet. The different location and driving mechanisms of the two jet structures, plus the intermediate “merged” jet, issue from very different underlying mechanisms, and result in very different jet characteristics. Here, we link our understanding of the dynamical jet maintenance mechanisms, mostly issuing from conceptual or idealised models, to the phenomena observed in reanalysis data. We specifically focus on developing a unitary analysis framework, grounded in dynamical systems theory, which may be applied to both the model and reanalysis data and allow for direct intercomparison. Our results provide a proof-of-concept for using dynamical systems indicators to diagnose jet regimes in a versatile, conceptually intuitive and computationally efficient fashion.

How to cite: Harnik, N., Messori, G., Madonna, E., Lachmy, O., and Farranda, D.: A Dynamical Systems Characterisation of Atmospheric Jet Regimes in a Simple Model and Reanalysis Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1723, https://doi.org/10.5194/egusphere-egu2020-1723, 2020.

The Mediterranean (MED) basin is a hot-spot for climate change impacts. We present recently developed techniques derived from Dynamical System Theory to investigate long-term changes in compound hot-wet extremes over the MED. We use three reanalysis products, spanning a 40-year period from 1979 to 2018: ERA5, ERA-Interim and ERA5 10-member ensemble. From these datasets, we extract daily maximum temperature (degC) and total precipitation (mm), which we then use in the dynamical systems analysis.

Results show that the strength of the dynamical coupling between hot and wet extremes increased significantly at both annual and summer (June-August) timescales during the reanalysis period. This means that, regardless of changes in the occurrence of individual hot or wet extremes, joint occurrences may be becoming more frequent.

Compound hot-wet extremes mostly occur during July and August, and correspond to a low-pressure core over the Aegean Sea and the eastern MED. The increasing trends in compound extremes may be associated with surface MED warming. Such enhanced warming can therefore drive compound hot-wet extremes especially during the summer, when localised convection or mesoscale systems such as medicanes are responsible for extreme precipitation events.

How to cite: De Luca, P., Messori, G., Faranda, D., and Coumou, D.: Increasing Strength of Compound Hot-Wet Dynamical Extremes Over the Mediterranean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5626, https://doi.org/10.5194/egusphere-egu2020-5626, 2020.

We propose a novel approach to the study of compound extremes, grounded in dynamical systems theory. Specifically, we present the co-recurrence ratio (α), which elucidates the dependence structure between maps by quantifying their joint recurrences. This approach is applied to daily climate extremes, derived from the ERA-Interim reanalysis over the 1979-2018 period. The analysis focuses on concurrent (i.e. same-day) wet (total precipitation) and windy (10m wind gusts) extremes in Europe and concurrent cold (2m temperature) extremes in Eastern North America and wet extremes in Europe. Results for wet and windy extremes in Europe, which we use as a test-bed for our methodology, show that α peaks during boreal winter. High αvalues correspond to wet and windy extremes in north-western Europe, and to large-scale conditions resembling the positive phase of the North Atlantic Oscillation (NAO). This confirms earlier findings which link the positive NAO to a heightened frequency of extra-tropical cyclones impacting north-western Europe, resulting in frequent wet and windy extremes. For the Eastern North America-Europe case, α extremes once again reflect concurrent climate extremes -- in this case cold extremes over North America and wet extremes over Europe. Our analysis provides detailed spatial information on regional hotspots for these compound extreme occurrences, and encapsulates information on their spatial footprint which is typically not included in a conventional co-occurrence analysis. We conclude that α successfully characterises compound extremes by reflecting the evolution of the associated meteorological maps. This approach is entirely general, and may be applied to different types of compound extremes and geographical regions.

How to cite: Pons, F., De Luca, P., Messori, G., and Faranda, D.: Dynamical Systems Theory Sheds New Light on Compound Climate Extremes in Europe and Eastern North America, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7579, https://doi.org/10.5194/egusphere-egu2020-7579, 2020.

Climate models aim to project future changes in important drivers of climate including atmosphere, oceans and ice, and their interactions. A comprehensive evaluation of climate models thus requires evaluation methods, or performance measures, that are flexible, specific and can address also extreme events. Climate models have traditionally been assessed by comparing summary statistics or point estimates that derive from the simulated model output to corresponding observed quantities using e.g. RMSE. However, it has been argued persuasively that probability distributions of model output need to be compared to the corresponding empirical distributions of observations or observation-based data products. Observation-based gridded datasets for climate extremes, despite having limitations, are particularly useful and necessary to assess model performance with respect to extremes.  We discuss proper performance measures for comparing distributions of model output against corresponding distributions from data products that are flexible and robust enough to handle the particular aspects of extremes such as limited data availability. The new measures are applied to evaluate CMIP5 and CMIP6 projections of extreme temperature indices over Europe and North-America against the HadEX2 data set as well as the ERA5 and ERA-Interim reanalyses. Several models perform well to the extent that when compared to the HadEX2 data product, these models' performance is competitive with the performance of the reanalysis. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis. 

How to cite: Thorarinsdottir, T., Sillmann, J., and Haugen, M.: Evaluation of CMIP6 simulations of temperature extremes using proper evaluation methods, observations and reanalyses, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5279, https://doi.org/10.5194/egusphere-egu2020-5279, 2020.

We study the properties of recurrence of a smooth observable computed along a trajectory of a chaotic system near a particular value of interest .  Using the framework of Extreme Value Theory, we are able to derive a limit law which is a Gumbel  distribution whose parameters relate to the dimensions of the image measure. We show that this approach allows to have access to the fine structure of the attractor, by using directly observational data. In particular, we are able to compute local dimensions associated to the underlying attractor whenever the dimensionality of the observable is larger than the dimension of the attractor. 

How to cite: caby, T., Faranda, D., Vaienti, S., and Yiou, P.: Extreme Value Theory for Observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5795, https://doi.org/10.5194/egusphere-egu2020-5795, 2020.

Under global warming, changes in extreme temperatures will manifest in more complex ways in locations where temperature distribution tails deviate from Gaussian. For example, uniform warming applied to a temperature distribution with a shorter-than-Gaussian warm tail would lead to greater exceedances in warm-side temperature extremes compared with a Gaussian distribution. Confidence in projections of future temperature extremes and associated impacts under global warming therefore relies on the ability of global climate models (GCMs) to realistically simulate observed temperature distribution tail behavior. This presentation examines the ability of the latest state-of-the-art ensemble of GCMs from the Coupled Model Intercomparison Project phase six (CMIP6) to capture historical global surface temperature distribution tail shape in hemispheric winter and summer seasons. Comparisons between the multi-model ensemble mean and a reanalysis product reveal strong agreement on coherent spatial patterns of longer- and shorter-than-Gaussian tails for the cold and warm sides of the temperature distribution, suggesting that CMIP6 is broadly capturing tail behavior for plausible physical and dynamical reasons. Most individual GCMs are also reasonably skilled at capturing historical tail shape on a global scale, but a division of the domain into sub-regions reveals considerable model and spatial variability. To explore potential mechanisms driving these differences, a back trajectory analysis examining patterns in the origin of air masses on days experiencing extreme temperatures is also discussed.

How to cite: Catalano, A., Loikith, P., and Neelin, J. D.: Evaluating CMIP6 Model Fidelity at Simulating Non-Gaussian Temperature Distribution Tails, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6113, https://doi.org/10.5194/egusphere-egu2020-6113, 2020.

In 2016, northern France experienced an unprecedented wheat crop loss. This extreme event was likely due to particular meteorological conditions, i.e.  too few cold days in late autumn and an abnormally high precipitation during the spring season. The cause of this event is not fully understood yet and none of the most used crop forecast models were able to predict the event (Ben-Ari et al, 2018).  

This work is motivated by two main questions: were the 2016 meteorological conditions the most extreme we could imagine under current climate? and what would be the worst case scenario we could expect that could lead to even worst crop loss? To answer these questions, instead of relying on computationally intensive climate model simulations, we use an analogue based importance sampling algorithm that was recently introduced into this field of research (Yiou and Jézéquel, 2019). This algorithm is a modification of a stochastic weather generator (SWG), which gives more weight to trajectories with more extreme meteorological conditions (here temperature and precipitation). This data driven technique constructs artificial weather events by combining daily observations in a dynamically realistic manner and in a relatively fast way.

This is the first application of SWGs to simulate warm winters and wet springs. We show that with some adjustments both (new) weather events can be adequately simulated with SWGs, highlighting the wide applicability of the method. 

While the number of cold days in late autumn 2015 was close to the plausible maximum, our simulations of extreme spring precipitation show that considerably wetter springs than what was observed in 2016 are possible. Although the crop loss of 2016 is not fully understood yet, these results indicate that similar events with higher impacts could be possible.

How to cite: Yiou, P., Pfleiderer, P., Jézéquel, A., Legrand, J., Legrix, N., Markantonis, J., and Vignotto, E.: Simulating compound weather extremes responsible for critical crop failure with stochastic weather generators, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7555, https://doi.org/10.5194/egusphere-egu2020-7555, 2020.

Heatwaves are likely to occur more frequent, longer, and stronger due to the rise in CO2 concentrations. It is related to the change in the mean of a climate distribution, as well as through the change in variance. Mega-heatwaves, in particular, have a crucial impact on human health. Many studies are trying to understand the mechanisms of mega-heatwaves and also their characteristics included amplitude, duration, frequency. In spite of these efforts, researches are limited because of the small number of mega-heatwaves. In order to overcome these limitations, Earth system model should be needed. This study aims to figure out the comprehensive characteristics of mega-heatwaves using Community Earth System Model (CESM). First, the possibility of the occurrence of mega-heatwaves in preindustrial period in Europe was analyzed. Second, the relation between decadal climate variabilities and mega-heatwaves was investigated. In addition, changes in characteristics of mega-heatwaves were compared between preindustrial and present-day simulations.

How to cite: Shin, J. and An, S.-I.: Comparison of mega-heatwaves in preindustrial and present-day simulations over Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8006, https://doi.org/10.5194/egusphere-egu2020-8006, 2020.

On the decadal time scales, while the influence of Pacific Decadal Oscillation (PDO) on total or average precipitation had been extensively studied, works about its influence on precipitation extremes were limited, especially lack of a global picture.  Using two independent methods, nonstationary generalized extreme value (GEV) model which directly incorporates PDO index into its location parameter and moving GEV model which fits the annual extremes with a sliding window of 30 years and regresses the resulted changing location parameter onto the PDO index, we show that precipitation extremes over a large portion of stations are significantly affected by the PDO with stations in the Pacific Rim demonstrating distinct regional patterns. Over eastern China, the famous ‘southern flood and norther drought’ pattern corresponding to a positive PDO phase extends to extreme rainfalls; over Australia, a tri-polar pattern was revealed, in which the extremes over central Australia positively correlate with the PDO index and those over eastern and western Australia show a negative correlation; and the North America also demonstrates a dipole pattern, by which the northwest (southeast) experiences less (more) intense extreme rainfall in a PDO positive phase. Moreover, the western Europe and the large area between the Ural mountain and eastern Europe were discovered to hold a positive correlation with the PDO in their precipitation extremes. A comparative analysis to the local circulation controlling the precipitation extremes under different PDO phases further confirms the discovered relationships above. These findings have important implication for the future projection of extreme precipitation over related regions because the internal climate variability should be appropriately accounted for beyond the effects induced by global warming.

How to cite: Wei, W., Yan, Z., and Li, Z.: Influence of Pacific Decadal Oscillation on global precipitation extremes on decadal time scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8138, https://doi.org/10.5194/egusphere-egu2020-8138, 2020.

In this study, we defined diagnostic indices to evaluate the CMIP6 models based on the heatwaves mechanisms of Korea presented in previous studies. Based on this, the simulation performance of the model was quantitatively evaluated using Relative Error (RE), Inter-annual Variability Skill-score (IVS), and Correlation Coefficient (CC). The REs in diagnostic indices are still large, especially in heat wave circulation index (HWCI) and Indian summer monsoon rainfall index (IMRI), which is mainly due to weak convective activity bias over the northwestern Pacific Ocean and the northwestern India. However, the IVSs in diagnostic indices have been improved overall in the CMIP6 compared to the CMIP5, especially in the IMRI. The CC between the daily maximum temperature (TMAX) and the diagnostic factors in the model is very higher in HWCI than other indices, indicating that the convective activity over the northwestern Pacific is very important in heat wave in Korea. As a result, the total ranking of the model performance for heatwaves in Korea suggested that EC-Earth3-Veg, EC-Earth3, and UKESM-1-0-LL ranked high in CMIP6.

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI(KMI2018-03410)

How to cite: Oh, J.-S., Kim, M.-K., Yu, D.-G., and Sang, J.: Model evaluation for Heatwaves over South Korea in CMIP6 models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12919, https://doi.org/10.5194/egusphere-egu2020-12919, 2020.

Windstorms, extreme precipitations and instant floods seems to strike the Mediterranean area with increasing frequency. These events occur simultaneously during intense tropical-like Mediterranean cyclones. These intense Mediterranean cyclones are frequently associated with wind, heavy precipitation and changes in temperature, generating high risk situations such as flash floods and large-scale floods with significant impacts on human life and built environment. Although the dynamics of these phenomena is well understood, little is know about their climatology. It is therefore very difficult to make statements about the frequency of occurrence and its response to climate change. Thus, intense Mediterranean cyclones have many different physical aspects that can not be captured by a simple standard approach. 

The first challenge of this work is to provide an extended catalogue and climatology of these phenomena by reconstructing a database of intense Mediterranean cyclones dating back up to 1969 using the satellite, the literature and reanalyses. Applying a method based on dynamical systems theory we analyse and attribute their future changes under different anthropogenic forcings by using future simulations within CMIP framework. Preliminary results show a decrease of the large-scale circulation patterns favoring intense Mediterranean cyclones in all the seasons except summer.

How to cite: Alvarez-Castro, M. C., Gualdi, S., Yiou, P., Vrac, M., Vautard, R., Cavicchia, L., Gallego, D., Ribera, P., Pena-Ortiz, C., and Faranda, D.: Predictability of large scale drivers leading intense Mediterranean cyclones, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13717, https://doi.org/10.5194/egusphere-egu2020-13717, 2020.

In the past decades, severe so called ‚compound events‘ led to critical high water levels at the coasts of southern Africa and as a consequence to property damage and loss of human life. The co-occurrence of storm surges, wind waves, heavy precipitation and resulting runoff increases the risk of coastal flooding and exacerbates the impacts along the vulnerable southern African coasts (e.g. Couasnon et al. 2019). To mitigate these high-impacts, it is essential to understand the underlying processes and driving factors (Wahl et al. 2015). As compound flooding events at southern African coasts are dominated by wind waves, it is of great importance to investigate the regional wave climate to understand the wave forcing as well as the origin of the wave energy.

Wind waves around southern African coasts are affected by the complex interactions between the Agulhas current and the atmosphere. In the research project CASISAC* we analyse the present evolution of the Agulhas Current system and quantify its impact on the future regional wave climate. Ocean waves contributing to high sea levels can be generated offshore resulting in swell or closer to the coasts by strong onshore winds. To identify responsible atmospheric pressure fields that force high wind wave events we apply a hybrid approach: (1) linking south hemispheric pressure fields with offshore wave data using an artificial neural network and (2) determine the prevailing nearshore wave conditions by regional numerical wave propagation models (SWAN). By validating the modelled nearshore wave data from hindcast runs with wave buoy records, this approach allows us to predict future extreme wind wave events and thus potential flooding. In a next step, extreme value analysis is used to determine future return periods of extreme wave events. These results can contribute to the development of more reliable adaptive protection strategies for southern African coast.

*CASISAC (Changes in the Agulhas System and its Impact on Southern African Coasts: Sea level and coastal extremes) is funded by the German Federal Ministry of Education and Research (BMBF) through the project management of Projektträger Jülich PTJ under the grant number 03F0796C

Couasnon, Eilander, Muis, Veldkamp, Haigh, Wahl, Winsemius, Ward (2019): Measuring compound flood potential from river discharge and storm surge extremes at the global scale and its implications for flood hazard. In: Natural Hazards and Earth System Sciences, Discussion Paper, S. 1–24. DOI: 10.5194/nhess-2019-205, in review.
Wahl, Jain, Bender, Meyers, Luther (2015): Increasing risk of compound flooding from storm surge and rainfall for major US cities. In: Nature Climate Change 5 (12), S. 1093–1097. DOI: 10.1038/nclimate2736.

How to cite: Soltau, F., Hirt, M., Kelln, J., Santamaria-Aguilar, S., Dangendorf, S., and Jensen, J.: Application of an artificial neural network to generate wave projections at southern African coasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19940, https://doi.org/10.5194/egusphere-egu2020-19940, 2020.