NP2.2

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.

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.

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.

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.

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.