Investigating the synchrony and interdependency of heavy rainfall occurrences across and between different tropical regions offers a new perspective on the underlying physical mechanisms. In this context, studies utilizing functional network representations have recently contributed to significant advances in the understanding and prediction of extreme weather events. Here, we systematically contrast previous results on spatiotemporal extreme precipitation patterns in three key monsoon regions (India, South America and East Asia) based on the concept of event synchronization (ES) with corresponding patterns obtained using the closely related event coincidence analysis (ECA) approach. Our findings demonstrate that an additional window size parameter of ECA not involved in ES allows for a more detailed analysis of the formation and propagation processes associated with heavy precipitation events. While the resulting network connectivity patterns based on both approaches closely resemble each other for the case of the South American monsoon system and the Indian summer monsoon, there exist subtle differences that carry climatologically relevant information. We further exploit the advanced potentials provided by ECA for studying in greater detail the spatial organization of East Asian summer monsoon (EASM) related heavy precipitation across the relevant season in a time-dependent fashion. Our results show that the formation of the Baiu front as a main feature of the EASM is accompanied by a double-band of synchronous heavy rainfall with two spatially dislocated centers north and south of the front. Although these bands are closely related to low- and high-level winds which are commonly assumed to be independent of each other, it is rather their mutual interconnectivity that changes during the different phases of the EASM season in a characteristic way. The thus obtained insights could provide relevant information for improving existing forecasting strategies for monsoon onset and strength.

References:

Odenweller, R.V. Donner: Disentangling synchrony from serial dependency in paired-event time series. Physical Review E, 101(5), 052213 (2020)

Wolf, J. Bauer, N. Boers, R.V. Donner: Event synchrony measures for functional climate network analysis: A case study on South American rainfall dynamics. Chaos, 30(3), 033102 (2020)

Wolf, U. Öztürk, K. Cheung, R.V. Donner: Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season. Earth System Dynamics Discussions. doi:10.5194/esd-2020-69 (2020)

Wolf, R.V. Donner: Spatial organization of connectivity in functional climate networks describing event synchrony of heavy precipitation. European Physical Journal Special Topics (in review)

How to cite: Donner, R. and Wolf, F.: Event synchrony based complex network analysis of heavy precipitation in different monsoon regions revealing dynamical patterns of extreme event formation and propagation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13164, https://doi.org/10.5194/egusphere-egu21-13164, 2021.

Under the rapid and amplified warming of the Arctic, changes in the occurrence of Arctic weather and climate extremes are evident which have substantial cryospheric and biophysical impacts like floods, droughts, coastal erosion or wildfires. Furthermore, these changes in weather and climate extremes have the potential to further amplify Arctic warming. 
Here we study extreme cyclone events in the Arctic, which often occur during winter and are associated with extreme warming events that are caused by cyclone-related heat and moisture transport into the Arctic. In that way Arctic extreme cyclones have the potential to retard sea-ice growth in autumn and winter or to initiate an earlier melt-season onset. 
To get a better understanding of these extreme cyclones and their occurrences in the Arctic, it is important to reveal the related atmospheric teleconnection patterns and understand their underlying mechanisms. In this study, the methodology of complex networks is used to identify teleconnections associated with extreme cyclones events (ECE) over Spitzbergen. We have chosen Spitzbergen, representative for the Arctic North Atlantic region which is a hot spot of Arctic climate change showing also significant recent changes in the occurrence of extreme cyclone events. 
Complex climate networks have been successfully applied in the analysis of climate teleconnections during the last decade. To analyze time series of unevenly distributed extreme events, event synchronization (ES) networks are appropriate. Using this framework, we analyze the spatial patterns of significant synchronization between extreme cyclone events over the Spitzbergen area and extreme events in sea-level pressure (SLP) in the rest of the Northern hemisphere for the extended winter season from November to March. Based on the SLP fields from the newest atmospheric reanalysis ERA5, we constructed the ES networks over the time period 1979-2019.
The spatial features of the complex network topology like Eigenvector centrality, betweenness centrality and network divergence are determined and their general relation to storm tracks, jet streams and waveguides position is discussed. Link bundles in the maps of statistically significant links of ECEs over Spitzbergen with the rest of the Northern Hemisphere have revealed two classes of teleconnections: Class 1 comprises links from various regions of the Northern hemisphere to Spitzbergen, class 2 comprises links from Spitzbergen to various regions of the Northern hemisphere. For each class three specific teleconnections have been determined. By means of composite analysis, the corresponding atmospheric conditions are characterized.
As representative of class 1, the teleconnection between extreme events in SLP over the subtropical West Pacific and delayed ECEs at Spitzbergen is investigated. The corresponding lead-lag analysis of atmospheric fields of SLP, geopotential height fields and meridional wind fields suggests that the class 1 teleconnections are caused by tropical forcing of poleward emanating Rossby wave trains. As representative of class 2, the teleconnection between ECEs at Spitzbergen and delayed extreme events in SLP over Northwest Russia is analyzed. The corresponding lead-lag analysis of atmospheric fields of SLP and geopotential height fields from the troposphere to the stratosphere suggests that the class 2 teleconnections are caused by troposphere-stratosphere coupling processes.

How to cite: Handorf, D., Sahin, O., Rinke, A., and Kurths, J.: A complex network analysis of extreme cyclones in the Arctic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3984, https://doi.org/10.5194/egusphere-egu21-3984, 2021.

Local properties of chaotic systems can be summarized by dynamical indicators, that describe the recurrences of all states in phase space. Faranda et al. (2017) defined such indicators with the local dimension (d, approximating the local number of degrees of freedom of the system) and the inverse of persistence (θ, approximating the time it takes to leave a local state). It has been conjectured that such indicators give access to the local predictability of systems. The aim of this study is to evaluate how the predictability of climate variables such as temperature and precipitation is related to dynamical properties of the atmospheric flow.

The predictability of a chaotic system can be evaluated through ensembles of simulations, with probability scores (e.g. Continuous Rank Probability Score, CRPS). In this work, we consider ensembles of climate forecasts with a stochastic weather generator (SWG) based on analogs of atmospheric circulation (Yiou and Déandréis, 2019). We are interested in relating predictability scores of European temperatures and precipitation, obtained with this SWG, and the local dynamical properties of the synoptic atmospheric circulation, obtained from the NCEP reanalysis. We show experimentally that the CRPS of local climate variables can be predicted from large-scale (d, \ θ) values of geopotential height fields, for time leads of 5 to 10 days. A practical application is that the predictability of local variables (in Europe) can be anticipated from large-scale dynamical quantities, which can help to dimension the size of ensemble forecasts.

References

Faranda, D., Messori, G., Yiou, P., 2017. Dynamical proxies of North Atlantic predictability and extremes. Sci. Rep. 7, 41278. https://doi.org/10.1038/srep41278

Caby, T. Extreme Value Theory for dynamical systems, with applications in climate and neuroscience. Mathematics [math]. Université de Toulon Sud; Universita dell’Insubria, 2019. English.tel-02473235v1

Yiou, P., Déandréis, C., 2019. Stochastic ensemble climate forecast with an analogue model. Geosci. Model Dev. 12, 723–734. https://doi.org/10.5194/gmd-12-723-2019

Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844.

How to cite: Krouma, M., Yiou, P., Faranda, D., Thao, S., and Déandréis, C.: Quantification of the relation between dynamical properties of meteorological variables and their predictability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9869, https://doi.org/10.5194/egusphere-egu21-9869, 2021.

One of the big challenges today is to appropriately describe heat waves, which are relevant due to their impact on human society. Common characteristics in mid-latitudes involve meanders of the westerly flow and concomitant large anticyclonic anomalies of the geopotential field. These anomalies form the so-called teleconnection patterns, and thus it is natural to ask how robust such structures are in various models and how much data we require to make statistically significant inferences. In addition, it is natural to ask what are the precursor phenomena that would improve forecasting capabilities of the heat waves. In particular, what kind of long term effect does the soil moisture have and how it compares to the respective quantitative contribution to the predictability of the teleconnection patterns.

In order to answer these questions we perform various types of regression on a climate model. We construct the composite maps of the geopotential height at 500 hPa and estimate return times of heatwaves of different severity. Of particular interest to us is a committor function, which is essentially a probability a heat wave occurs given the current state of the system. Committor functions can be efficiently computed using the analogue method, which involves learning a Markov chain that produces synthetic trajectories from the real trajectories. Alternatively they can be estimated using machine learning approach. Finally we compare the composite maps in real dynamics to the ones generated by the Markov chain and observe how well the rare events are sampled, for instance to allow extending the return time plots.

How to cite: Miloshevich, G., Lucente, D., Herbert, C., and Bouchet, F.: Drivers of midlatitude extreme heat waves revealed by analogues and machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15642, https://doi.org/10.5194/egusphere-egu21-15642, 2021.

Although the lifecycle of hurricanes is well understood, it is a struggle to represent their dynamics in numerical models, under both present and future climates. We consider the atmospheric circulation as a chaotic dynamical system, and show that the formation of a hurricane corresponds to a reduction of the phase space of the atmospheric dynamics to a low-dimensional state. This behavior is typical of Bose-Einstein condensates. These are states of the matter where all particles have the same dynamical properties. For hurricanes, this corresponds to a "rotational mode" around the eye of the cyclone, with all air parcels effectively behaving as spins oriented in a single direction. This finding paves the way for new parametrisations when simulating hurricanes in numerical climate models.

How to cite: Faranda, D., Messori, G., Yiou, P., Thao, S., Pons, F., and Dubrulle, B.: Hurricane dynamics and rapid intensification via dynamical systems indicators, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2592, https://doi.org/10.5194/egusphere-egu21-2592, 2021.

The solar wind is characterized by a multiscale dynamics showing features of chaos, turbulence, intermittency, and recurring large-scale patterns, pointing towards the existence of an underlying attractor. However, magnetic field and plasma parameters usually show different scaling regimes with different physical and dynamical properties. Here by using a recent and novel approach developed in the framework of dynamical systems  we investigate the multiscale instantaneous properties of solar wind magnetic field phase space by means of the evaluation of instantaneous dimension and stability. We show the existence of a break in the average attractor dimension occurring at the observed scaling break between the inertial and the dissipative regimes. We further show that sometimes the dynamics is higher dimensional (d>3) suggesting that the phase space is larger than that described by the system variables and invoking for an external forcing mechanism, together with the existence of at least one unstable fixed point that cannot be definitely associated with noise. Instantaneous properties of the attractor therefore provide an efficient way of evaluating dynamical properties and building up improved cascade models.

How to cite: Caby, T., Alberti, T., Faranda, D., Donner, R. V., Consolini, G., Vaienti, S., and Carbone, V.: Instantaneous multiscale properties of solar wind dynamical regimes and their attractors, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8342, https://doi.org/10.5194/egusphere-egu21-8342, 2021.

We analyze recent trends in extreme precipitation in the Southwestern Alps and link these trends to changes in the atmospheric influences triggering extremes. We consider a high-resolution precipitation dataset of 1x1 km2 for the period 1958-2017. A robust method of trend estimation is considered, based on nonstationary extreme value distribution and a homogeneous neighborhood approach. The results show contrasting extreme precipitation trends depending on the season. Excluding autumn, the significant trends are mostly negative in the Mediterranean area, while the French Alps show more contrasted trends, in particular in winter with significant increasing extremes in the Western and Southern French Alps and decreasing extremes in the Northern French Alps and Swiss Valais. In autumn, most of Southern France shows significant increasing trends, with up to 100% increase in the 20-year return level between 1958 and 2017, while the Northern French Alps show decreasing extremes.
By comparing these trends to changes in the occurrence of the dominant weather patterns triggering the extremes, we show that part of the significant changes in extremes can be explained by changes in the dominant influences, particularly in the Mediterranean influenced region. We also show that part of the trends in extremes are explained by a shift in the seasonality of maxima. 

How to cite: Blanchet, J., Blanc, A., and Creutin, J.-D.: Explaining recent trends in extreme precipitation in the Southwestern Alps by changes in atmospheric influences , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14291, https://doi.org/10.5194/egusphere-egu21-14291, 2021.

Traditional extreme value analysis based on the generalised extreme value (GEV) or the 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 used and the bulk of the data is disregarded, resulting in a large uncertainty in the tail inference. (ii) In the peak-over-threshold approach the choice of the threshold is often difficult in practice as there are no really objective underlying criteria.
Here, two approaches based on maximum likelihood estimation are introduced which simultaneously model the whole distribution range and thus constrain the tail inference by information from the bulk data. Firstly, the bulk matching method models the bulk of the distribution with a flexible exponential family model and the tail with a GPD. The two distributions are linked together at the threshold with appropriate matching conditions. The threshold can be estimated in an outer loop also based on the likelihood function. Secondly, in the extended generalised Pareto distribution (EGPD) model for non-negative variables the whole distribution is modelled with a GPD overlaid with a transition probability density which is again represented by an exponential family. Appropriate conditions ensure that the model is in accordance with extreme value theory both for the lower and upper tail of the distribution. The methods are successfully exemplified on simulated data as well as wind speed and precipitation data.

How to cite: Kwasniok, F.: Robust extreme value analysis by semiparametric modelling of the entire distribution range, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15271, https://doi.org/10.5194/egusphere-egu21-15271, 2021.

Extreme analysis, via e.g., GEV, was developed to deal with univariate time series, and is very difficult to extend beyond that dimension. Here we explore a different method, the archetypal analysis, which focuses on multivariate extremes. The method seeks to approximate the convex hull in high-dimensional state space, by identifying corners representing "pure" types, i.e. archetypes. The method, encompasses, in particular, the virtues of EOFs and clustering. The method is presented with a new manifold-based optimization algorithm, and applied to a number of atmospheric fields, including SST and SLP gridded data. The application to SST, in particular, reveals important features related to SST extremes. The strengths and weaknesses of the method and possible future perspectives will be discussed.

How to cite: Hannachi, A. and Trendafilov, N.:          Towards Analysing multivariate weather/climate extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3288, https://doi.org/10.5194/egusphere-egu21-3288, 2021.

In the beginning of this century, impacts studies due to climate change were carried out directly with the outputs of the general circulation models of the Atmosphere and the Ocean (AOGCM). However, these models had very low resolutions in the order of several degrees and the climate of some areas, such as monsoon regions, was poorly reproduced. These two disadvantages make it difficult to study the evolution of extremes. Recently, more impact studies are using outputs from multiple AOGCM models that are downscaled and unbiased. The ISIMIP consortium (https://www.isimip.org/) participates in the dissemination of this practice by proposing several AOGCM models with a resolution of 0.5° X 0.5°.

In our study, a high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed  (Noël et al. accepted). This dataset is obtained by using the quantile – quantile method Cumulative Distribution Function transform (CDFt) (Vrac et al. 2012, 2016,, developed over  10 years to bias correct or downscale climate model output, and ERA5 land data as a reference . T

We propose in this communication to present the climate variability by the end of the century in terms of extreme climate indicators such as heat waves or heavy rainfall at the local/grid point level (e.g. city level). Particular attention will be paid to the magnitude of the changes as well as the associated uncertainty.

References

Vrac, M., Drobinski, P., Merlo, A., Herrmann, M., Lavaysse, C., Li, L., & Somot, S. (2012). Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment.Nat. Hazards Earth Syst. Sci., 12, 2769–2784.

Vrac, M., Noël, T., & Vautard, R. (2016). Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter. Journal of Geophysical Research: Atmospheres, 121(10), 5237-5258.

Noël, T., Loukos, H., Defrance, D., Vrac, M., & Levavasseur, G. (2020). High-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with ERA5 reanalyses for climate change impact assessments. Data in Brief (accepted, https://doi.org/10.31223/X53W3F)

How to cite: Defrance, D., Noël, T., and Loukos, H.: What is the added value of using downscaled CMIP5 data for  the study of climate extremes under  climate change?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12186, https://doi.org/10.5194/egusphere-egu21-12186, 2021.

Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.

Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.

To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.

The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. 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., Haugen, M., Gissibl, N., and Sandstad, M.: Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9722, https://doi.org/10.5194/egusphere-egu21-9722, 2021.

Previous detection and attribution analyses suggest that human-induced increases in greenhouse gases have contributed to observed changes in extreme precipitation. However, all previous detection and attribution studies of observed changes in extreme precipitation i) use station data that have been heavily processed via gridding, transformation, and spatial and temporal averaging or other dimension reduction approaches, as well as using climate models to estimate the responses to external forcing, ii) also use models to estimate the unforced natural variability of extreme precipitation. Both aspects reduce user confidence in detection and attribution results.

We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing and use climate models only to obtain estimates of the space-time pattern of extreme precipitation response to external forcing. We use records of the annual maximum one day (Rx1day) or five consecutive days (Rx5day) precipitation accumulations from 5,081 land-based stations spanning the period 1950-2014 with at least 45 years of coverage, including at least 3 years in the period 2010-2014. Expected responses to external forcings are estimated from ALL and NAT forcings simulations from large ensemble simulations performed with CanESM2 and from a multi-model ensemble that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Changes at these stations are evaluated by fitting non-stationary Generalized Extreme Value (GEV) distributions at individual stations to the logarithms of observed Rx1day and Rx5day values. Non-stationarity in the GEV distribution is permitted by using location parameters that are allowed to be linearly dependent on climate model-simulated responses in ln(Rx1day) and ln(Rx5day) to different forcings. We perform the detection and attribution analysis across different spatial scales, including the global, continental, and regional scales.

The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (western Northern Hemisphere, western Eurasia, and eastern Eurasia), and many smaller IPCC regions, including C. North-America, E. Asia, E.C. Asia, E. Europe, E. North-America, N. Europe, and W. Siberia for Rx1day, and C. North-America, E. Europe, E. North-America, N. Europe, Russian-Arctic, and W. Siberia for Rx5day. Consistency between our study and previous studies substantially increases confidence in detection and attribution findings concerning extreme precipitation. The attributed effects of anthropogenic forcing on extreme precipitation include substantially decreased waiting times between extreme annual maximum events in regions where anthropogenic influence has been detected and intensification of extreme precipitation that, at a global scale is consistent with the Clausius-Clapeyron rate of about 7% per 1°C of warming.  

How to cite: Sun, Q., Zwiers, F., Zhang, X., and Yan, J.: Quantifying the human influence on the intensity of extreme 1- and 5-day precipitation amounts at global, continental, and regional scales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8471, https://doi.org/10.5194/egusphere-egu21-8471, 2021.

This talk will contrast how U.S. decision makers’ impacts-focused perspective on compound extreme events differs from climate science-based perspectives. Examples from around the U.S. will be provided, with an emphasis on cascading impacts that have spanned multiple regions and sectors. The talk will also propose a path forward for synthesizing ‘top-down’ and ‘bottom-up’ approaches to compound extremes, to facilitate adaptation. Time-permitting, preliminary findings from an analysis of sequential humid heat and extreme precipitation over the U.S. may be shown, as a guiding example. The work described reflects a collaboration of scientists funded by NOAA’s Regional Integrated Sciences and Assessments (RISA) program, charged with co-generating ‘useable science’ by working closely with stakeholders.   

How to cite: Horton, R., Keener, V., Kornhuber, K., Lesk, C., and Walsh, J.: Aligning compound extreme events as defined from climate science and sectoral impact perspectives, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15761, https://doi.org/10.5194/egusphere-egu21-15761, 2021.

Spatioteporal variability of precipitation extremes is increasingly the focus of attention in both the climate and hydrology communites, especailly in the context of global climate change. Indicated by the Clausius-Clapeyron equation under the constant relative humudity assumption, it is expected, from the thermodynamic perspective, that extreme precipitation would increase as globe warms. However, when it comes to the regional response of precipitation to global warming, the resutls could be highly uncertain due to the influences of dynamic factors such as large-scale circlation patterns and local effects. Here, we investigate trends in a set of extreme precipitation indices (EPIs) over the Yangtze River Basin (YRB) during the period of 1960-2019. Also, we explore the possible associations between spatiotemporal variability of the EPIs and global warming, ENSO, and local effects. Our resutls show marked rising trends in frequency and intensity of Yangtze precipitation extremes. Global warming tends to enhance the frequency and intensity of preciptation extremes over the YRB. The La Niña phase of ENSO could lead to an increase of precipitation extremes in the current year, but a decrease of precipitation extremes in the coming year. Local warming mainly exerts a reducing effect on precipitation extremes, which is likely associated with the significant decrease of relative humidity in the YRB. Our findings highlight the need for a systematic approach to investigate changes in precipitation extremes over the YRB.

How to cite: Li, X.: Changes in Yangtze Precipitation Extremes and the association with thermodynamic, dynamic, and local drivers  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-772, https://doi.org/10.5194/egusphere-egu21-772, 2021.

The effects of solar irradiance forcing on weather and climate extremes have received relatively less attention compared to the solar-induced changes in the mean climate. In this respect, here we investigate the possible impact of solar irradiance forcing on the Northern Hemisphere extreme weather and climate variability during summer, from a potential vorticity (PV) perspective. The generation of severe weather events in the extra-tropical regions is often related to intrusions of high PV originating from the polar lower stratosphere. Various two-dimensional PV indices, similar to those characterizing surface temperature and precipitation extremes, are defined to measure the frequency of upper level PV intrusion events. Based on long-term reanalysis data, we show that upper level high PV intrusions over Asia (Europe) are more (less) frequent during high relatively to low solar irradiance summers. Consistent with this PV pattern more (less) frequent surface extreme precipitation events are recorded during high relative to low solar irradiance summers in Asia (Europe). Patterns in the frequency of extreme temperatures are largely opposite to the corresponding extreme precipitation. Furthermore, extreme climate anomaly patterns associated with high solar irradiance forcing are similar to the corresponding patterns associated with strong monsoon circulation over Asia during summer. A preliminary analysis reveals the dominant role of upper level solar related PV anomalies in generation of extreme precipitation in the Asian monsoon region during high solar irradiance summers. A persistent blocking like circulation in the Caspian Sea region during low solar irradiance summers is associated more frequent high PV intrusions and extreme precipitation over Europe. The stability of the solar related extreme precipitation and temperature patterns in the last millennium perspective is also discussed based on proxy data as well as model simulations.

How to cite: Rimbu, N., Ionita, M., and Lohmann, G.: Solar forcing on the Northern Hemisphere weather and climate extremes during summer, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9411, https://doi.org/10.5194/egusphere-egu21-9411, 2021.

Droughts are defined as one of the most devastating natural disasters of modern times and a key challenge faced under climate change. The complexity of interacting physical processes that underlie the shortage of rainfall in climate models hampers accurate representation of present-day droughts, and leads to differences in their responses to increased greenhouse gas (GHG) concentrations in the future. As a result, the confidence in drought projections is currently defined as ‘medium to low' by the Intergovernmental Panel on Climate Change (IPCC), and reducing this uncertainty remains one of the main goals in coming years, with significant benefits for human and natural systems. 

In this study we explore a relationship between biases in simulated present-day values of longest annual drought (LAD) and future projections of LAD in an ensemble of CMIP5 and CMIP6 models. We find that present-day model bias explains almost 95 % of the future uncertainty in LAD by the end of the 21st century, attributed to the well-known precipitation simulation errors: “drier” models with longer annual droughts at present tend to predict larger LAD values worldwide in the future, as well as a stronger response to GHG forcing in LAD, which is significant in more than 40 % of the global land area.

Substituting observational LAD estimates from satellite data into this model-revealed “present–future relationlarship” suggests that the 21st century global mean increase in duration of annual meteorological droughts could be significantly larger than predicted by the CMIP5 and CMIP6 model ensembles. This emergent constraint reduces global mean uncertainty range in future LAD estimates from 45–100 to 75–90 days, a level more typical of the prediction range of “drier” models. The findings reveal world regions where climate change may cause stronger meteorological drought aggravation than expected, and emphasise the importance of reducing model errors, which are presently largely owed to rain biases, to increase confidence in future predictions.

How to cite: Y. Petrova, I., G. Miralles, D., Brient, F., and Donat, M.: Underestimation of the projected 21st century increase in drought duration, according to emergent constraint: consistent result in CMIP5 and CMIP6 models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9895, https://doi.org/10.5194/egusphere-egu21-9895, 2021.

To better assess the future risks associated with Intense Mediterranean Cyclones (IMC) a better understanding of their features, variability, frequency and intensity is required, including a robust detection method. The application of different detection algorithms provides results that are remarkably similar in some aspects but may be very different in others even using the same data. Thus, the selection of a particular method can significantly affect the results. For these reasons it is necessary to use different approaches and datasets to study the sensitivity and robustness of the detection approach. Those approaches often use minima in sea-level pressure (SLP) or extrema in relative vorticity or both to first identify the eye of the cyclone. SLP reflects the atmospheric mass distribution, and is representative of synoptic-scale atmospheric processes. On the other hand, the relative vorticity displays higher variability and is representative of the atmospheric circulation, being able to detect several local extrema (more than one centre), it can reduce uncertainties in the cyclone detection and tracking.

Therefore, within the framework of the EFIMERA project and to detect and track IMC we use a combination of different methods based on previous studies found in the literature. This new list of detected IMC events, together with the observed and well documented ones, are used here to create a new IMC database to be used for the study of their impacts and risk associated.

How to cite: Alvarez-Castro, M. C., Gallego, D., Ribera, P., Peña-Ortiz, C., and Faranda, D.: A new database for Intense Mediterranean Cyclones , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15827, https://doi.org/10.5194/egusphere-egu21-15827, 2021.

Previous studies have recognized the societal relevance of climatic extremes on the seasonal timescale and examined physical processes leading to individual high-impact extreme seasons (e.g., extremely wet or warm seasons). However, these findings have not yet been generalizing beyond individual case studies since at any specific location only very few seasonal events of such rarity occurred in the observational record. In this concept paper, a pragmatic approach to pool seasonal extremes across space is developed and applied to investigate hot summers and cold winters in ERA-Interim and the Community Earth System Model Large Ensemble (CESM-LENS). We identify spatial extreme season objects as contiguous regions of extreme seasonal mean temperatures based on statistical modeling. Regional pooling of extreme season objects in CESM-LENS then yields considerable samples of analogues to even the most extreme ERA-Interim events, which allow for climatological analyses of their statistical and physical characteristics.

This approach offers numerous opportunities for analyzing large samples of extreme seasons across regions, and several such analyses are illustrated exemplarily. We perform a climate model evaluation with regard to extreme season size and intensity measures and estimate how often an extreme winter like the cold North American 2013/14 winter is expected anywhere in mid-latitude regions. Moreover, we present a large set of simulated spatial analogues to this event, which allows to study commonalities and differences of their underlying physical processes. Finally, substantial but spatially varying climatological differences in the size of extreme summer and extreme winter objects are identified.

How to cite: Röthlisberger, M., Hermann, M., Frei, C., Lehner, F., Fischer, E. M., Knutti, R., and Wernli, H.: A new framework for identifying and investigating seasonal climate extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-452, https://doi.org/10.5194/egusphere-egu21-452, 2021.