CL4.3

In recent years, unprecedented temperature and precipitation extremes have been observed across the world. With further global warming, climate models project extreme events to get even more intense and likely break observational records by large margins. It is challenging to estimate how extreme climate events could get and to quantify the contribution of physical drivers in the future or even in the present climate? To address these questions, we introduce the ensemble boosting method, a model-based method that generates large samples of re-initialized extreme events in climate simulations. In doing so, the method provides physically consistent storylines of climate extremes that can be used to analyse the driving factors and estimate the very high return levels for the event type beyond observational records. We apply ensemble boosting to heat waves in the millennial pre-industrial control run, made with CESM1 and to heavy precipitation in the large ensemble near future simulations, carried out with CESM2. We find that individual members of the boosted ensembles can substantially exceed the most extreme heat and precipitation events over Europe and North America in the respective climatology. Furthermore, we show that estimated upper bounds of heat correspond to the statistical estimates by the generalized extreme value (GEV) distribution and regression models. Therefore, the framework of ensemble boosting might ultimately contribute to adaption and the stress testing of ecosystems or socioeconomic systems, increasing the resilience to extreme climate stressors.

How to cite: Gessner, C., Fischer, E. M., Beyerle, U., and Knutti, R.: Quantifying and understanding very rare climate extremes using ensemble boosting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4292, https://doi.org/10.5194/egusphere-egu22-4292, 2022.

Research on extreme weather typically investigated the physical and dynamical processes involved in the formation of specific meteorological events that occur on time scales of hours to a several days (e.g., heavy precipitation events, windstorms, heat waves). Such events can be extremely hazardous, but for certain socioeconomic sectors the seasonal aggregation of weather is particularly harmful. These sectors include, for instance, agriculture, forestry, energy, and reinsurance. This presentation introduces the concept of “extreme seasons” as an important and not yet thoroughly investigated research field at the interface of weather and climate science. Extreme seasons are defined as seasons during which a particular meteorological or impact-related parameter (or a combination thereof) strongly deviates from climatology. An important conclusion of the presentation will be that large ensemble climate simulations (here using an extended CESM1-LENS data set with 6-hourly output of 3D fields), with about 1000 simulated years per climate period, are an essential resource enabling novel quantitative insight into the processes leading to and characteristics of extreme seasons. The presentation provides examples for the identification of extreme seasons and emphasizes the importance of studying their substructure, including the occurrence of specific weather systems. A first approach to systematically study extreme seasons is to consider the top 10 seasons (for a given metric) in the large ensemble at every grid point, e.g., the 10 wettest winters or hottest summers, or the 10 summers with the largest vapour pressure deficit (as an example for a more impact-related metric). Alternatively, one can look at anomalies in a multi-dimensional parameter phase space, identifying extreme seasons that result from a highly unusual combination of, e.g., surface temperature, precipitation, and surface energy balance. Or, using a pragmatic method based on fitting a statistical model to seasonal mean values at each grid point, spatially coherent extreme season objects can be identified that exceed a local return period threshold of, e.g., 40 years. The same statistical approach can be applied to ERA5 reanalyses to compare characteristics of extreme season objects (e.g., their size and intensity) in climate models with observation-based data. With this approach we can meaningfully estimate how often, e.g., an observed extreme winter like the cold North American 2013/14 winter is expected anywhere in midlatitude regions. The last part of the presentation addresses the substructure and weather system characteristics of extreme seasons. Illustrative results are shown that address the questions: (i) Where are extremely hot summers the result of the warmest days being anomalously hot vs. the coldest days being anomalously mild? (ii) Where are wettest seasons the result of more frequent wet days vs. more intense precipitation on wet days? and (iii) How does the frequency of weather systems and their precipitation efficiency change during the wettest seasons? The answers to these questions reveal interesting and large regional differences.

How to cite: Wernli, H., Beyerle, U., Boettcher, M., Fischer, E., Flaounas, E., Frei, C., Hartmuth, K., Hermann, M., Knutti, R., Lehner, F., Papritz, L., Röthlisberger, M., Sprenger, M., and Zschenderlein, P.: Processes leading to extreme seasons – research at the weather-climate interface based on reanalyses and large ensemble climate simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8735, https://doi.org/10.5194/egusphere-egu22-8735, 2022.

Heat waves are among the most hazardous climate extremes in Europe, commonly affecting large regions for a considerable amount of time. Especially in the recent past heat waves account for substantial economic, social and ecologic impacts and loss. Projections suggest that their number, duration and intensity increase under changing climate conditions, stressing the importance of quantifying their characteristics. Yet, apart from the analysis of single historical events, little research is dedicated to the general propagation of heat waves in space and time. 

Heat waves are rare in their occurrence and limited observational data provide little means for robust analyses and the understanding of dynamical spatio-temporal patterns. Therefore, we seek to increase the number of analyzable events by using a single-model initial condition large ensemble of a regional climate model (Canadian Regional Climate Model Version 5, CRCM5-LE). This provides 50 model members of comparable climate statistics to robustly assess various spatial patterns and pathways of European heat waves in a data set of high spatial resolution. 

Using the CRCM5-LE allows us to explore a novel data-driven approach to infer cause-and-effect relationships, in this case the spatio-temporal propagation of spatially distributed phenomena. Our aim is to investigate specifically the transitions and inter-dependencies among heat wave core regions in Europe to better understand their evolution during the recent past.

We define heat waves as a minimum of three consecutive hot days with temperatures above the 95^th JJA (1981-2010) percentile. If a reasonable fraction of the domain land area exhibits a hot day, this time step is used for clustering in order to derive core regions. Each core region is represented by a spatially aggregated time series of the cluster footprint. The approach further includes the derivation of directed links between these core regions using causal discovery and the analysis of associated atmospheric conditions.

Results indicate that directed links among core regions of heat wave occurrence over Europe reproduce parts of observed movements. This helps to group and characterize heat waves according to, e.g. seasonality. Examples of these heat wave cluster transitions show an associated shift of high pressure patterns, suggesting that the approach allows capturing the spatial dislocation of heat wave centers. 

How to cite: Böhnisch, A., Felsche, E., and Ludwig, R.: Detecting the spatio-temporal propagation of heat waves in a regional single-model large ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5090, https://doi.org/10.5194/egusphere-egu22-5090, 2022.

The frequency of precipitation extremes is set to change in response to a warming climate. Thereby, the change in precipitation extreme event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions from either of them (mean or variability) to the change in precipitation extremes are, is largely unknown. This is however relevant for a better understanding of how and why climate extremes change. The mechanisms behind a change in either the mean or the variability can thereby be very different.

For this study, two sets of forcing experiments from the regional CRCM5 initial-condition large ensemble are used. A set of 50 members with historical and RCP8.5 forcing as well as a 35-member (700 year) ensemble of pre-industrial natural forcing. The concept of the probability risk ratio is used to partition the change in extreme event occurrence into contributions from a change in mean climate or a change in variability.

The results show that the contributions from a change in variability are in parts equally important to changes in the mean, and can even exceed them. The level of contributions shows high spatial variation which underlines the importance of regional processes for changes in extremes. Further, the results reveal a smaller influence of the level of warming and level of extremeness on the individual contributions then the seasonality or temporal aggregation (3h, 24h, 72h). These results highlight the need for a better understanding of changes in climate variability to better understand the mechanisms behind changes in climate extremes.

How to cite: Wood, R. R.: Role of mean and variability change for changes in European seasonal extreme precipitation events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7697, https://doi.org/10.5194/egusphere-egu22-7697, 2022.

Climate models are the available tools to assess risks of extreme precipitation events due to climate change. Models simulating historical climate successfully are often reliable to simulate future climate. Here, we assess the performance of CMIP6 models in reproducing the observed annual maxima of daily precipitation (AMP) beyond the commonly used methods. This assessment takes three scales: (1) univariate comparison based on L-moments and relative difference measures; (2) bivariate comparison using Kernel densities of mean and L-variation, and of L-skewness and L-kurtosis, and (3) comparison of the entire distribution function using the Generalized Extreme Value () distribution coupled with a novel application of the Anderson-Darling Goodness-of-fit test. The results depict that 70% of simulations have mean and variation of AMP with a percentage difference within 10 from the observations. Also, the statistical shape properties, defining the frequency and magnitude of AMP, of simulations match well with observations. However, biases are observed in the mean and variation bivariate properties. Several models perform well with the HadGEM3-GC31-MM model performing well in all three scales when compared to the ground-based Global Precipitation Climatology (GPCC) data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi-arid regions.

How to cite: Abdelmoaty, H., Papalexiou, S. M., Rajulapati, C. R., and AghaKouchak, A.: A global investigation of CMIP6 simulated extreme precipitation beyond biases in means, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1376, https://doi.org/10.5194/egusphere-egu22-1376, 2022.

Climate sensitivity refers to the amount of global surface warming that will occur in response to a doubling of atmospheric CO₂ concentrations when compared to pre-industrial levels. Understanding climate sensitivity and reducing uncertainty in the estimation of climate sensitivity are therefore critical to reducing spread in projected climate change under given scenarios. The aim of this study is to estimate real-world Equilibrium Climate Sensitivity (ECS) by exploiting relationships found between observable parameters and the magnitude of climate change. We develop an emergent constraint based on surface temperature variability, which we test using preindustrial control and historical simulations from CMIP5 and CMIP6 models. We estimate the relationship between model-to-model differences (M2MDs) in ECS and M2MDs in global, tropical and tropical Pacific temperature variability, using the various measures of variability on interannual through to multidecadal timescales. We find higher correlations between MDMDs in ECS and M2MDs in the standard deviation of temperature variability in the tropics, which peaks at the decadal timescale, with larger spread in CMIP6 models. These results are then optimally combined to constrain observed temperature decadal variability and provide a distribution of real-world ECS. 

How to cite: Boschat, G., Power, S., and Colman, R.: Can interannual to decadal variability help increase the accuracy of climate sensitivity estimates?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11097, https://doi.org/10.5194/egusphere-egu22-11097, 2022.

Despite the ongoing global warming, Eurasian winter surface air temperature (SAT) has been decreasing in recent decades. This study investigates the nature of Eurasian winter cooling and its reproductivity in the Community Earth System Model Large Ensemble simulation (CESM-LE). It is found that Eurasian winter cooling and the related atmospheric circulation change are not captured by the model ensemble mean. When 40 ensemble members are divided into two groups, ensembles with Eurasian cooling tend to show a positive sea surface temperature (SST) trend over the western Pacific warm pool, whereas the other group has the opposite SST trend. The causal relationship between tropical SST warming and Eurasian winter cooling is further tested by conducting a series of linear baroclinic model experiments. These experiments reveal that the warm pool warming and the resultant convection can effectively excite the Rossby wave train that resembles atmospheric circulation change shown in the Eurasian cooling ensembles. Specifically, a cyclonic circulation forms over the Aleutian region through the teleconnection and it is followed by an anticyclonic circulation over Siberia resulting from mass redistribution. This result indicates that Eurasian winter cooling in CESM-LE is possibly determined by the internal variability of tropical SST. It also suggests that the recent Eurasian winter cooling has been likely influenced by tropical climate variability.

How to cite: Jun, Y.-J., Son, S.-W., and Kim, H.: A potential driver of Eurasian winter cooling in CESM large ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10844, https://doi.org/10.5194/egusphere-egu22-10844, 2022.

Air pollutants accumulate in the near-surface atmosphere when atmospheric scavenging, horizontal dispersion, and vertical escape are reduced. This is often termed "air stagnation". Recent studies have investigated the influence that climate change could exert on the frequency of stagnation in different regions of the globe throughout the 21st century. Although they provide a probabilistic view based on multi-model means, there are still large discrepancies among climate model projections. Storylines of atmospheric circulation change, or physically self-consistent narratives of plausible future events, have recently been proposed as a non-probabilistic means to represent uncertainties in climate change projections. This work applies the storyline approach to 21st century projections of summer air stagnation over Europe and the United States. For that purpose, we use a CMIP6 ensemble to generate stagnation storylines based on the forced response of three remote drivers of the Northern Hemisphere mid-latitude atmospheric circulation: North Atlantic warming, North Pacific warming, and tropical versus Arctic warming.

Under a high radiative forcing scenario (SSP5-8.5), strong tropical warming relative to Arctic warming is associated with a strengthening and poleward shift of the upper westerlies, which in turn would lead to decreases in stagnation over the northern regions of North America and Europe, as well as increases in some southern regions, as compared to the multi-model mean. On the other hand, North Pacific warming tends to increase the frequency of stagnation over some regions of the U.S. by enhancing the frequency of stagnant winds, while reduced North Atlantic warming does the same over Europe by promoting the frequency of dry days.

Given the response of stagnation to these remote drivers, their evolution in future projections will substantially determine the magnitude of the stagnation increases. Our results show differences of up to 2%/K (~2 stagnant days in summer per degree of global warming) among the storylines for some regions. We will discuss the combination of remote driver responses leading to the highest uncertainties in future air stagnation separately for Europe and the U.S.

How to cite: Garrido-Pérez, J. M., Ordóñez, C., Barriopedro, D., García-Herrera, R., Schnell, J. L., and Horton, D. E.: A storyline view of the projected role of remote drivers on summer air stagnation in Europe and the United States, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2451, https://doi.org/10.5194/egusphere-egu22-2451, 2022.

The Mediterranean Basin, including the Mediterranean Sea and the surrounding countries, is referred to as a hotspot in terms of climate change, primarily because of a basin-wide drying trend projected for its future. The Mediterranean Sea plays an important role in the climate of the basin through air-sea interactions, and it is, therefore, important to understand how it is coupled with global as well as regional atmosphere. Coarse resolution fully coupled Earth System Models (ESM) show inaccurate results in terms of sea surface temperature (SST) and precipitation over the Mediterranean Sea and Europe. Better representation of the Mediterranean Sea SST (MedSST) by ESMs is a critical issue for the Euro-Mediterranean climate.

In this study, we conduct three simulations using the fully-coupled Community Earth System Model (CESM): i) a historical control simulation integrated for the 1850-2014 period subject to anthropogenic forcings; ii) a Mediterranean Pacemaker-I (MedP-I) experiment where MedSST is nudged to the monthly Extended Reconstructed SST (ERSST) starting from 1880; and iii) a Mediterranean Pacemaker-II (MedP-II) experiment where the MedSST is nudged to the Optimum Interpolation SST (OISST)  starting from 1980. In both pacemaker experiments, in comparison with the control simulation, nudging of the MedSST affects the poleward energy flux transported by the atmospheric latent and dry heat, and changes the total meridional energy flux by more than ±0.1 PW over lower latitudes. Similarly, net radiation flux at the surface is changed by about ±2 W/m2 over the Mediterranean Basin. The fidelity of the nudging method was investigated by comparing solutions from MedP-I and MedP-II with respective fields from the control simulation and those from observations, i.e., World Ocean Atlas, Hadley Centre Sea Ice and SST, Climate Prediction Center, and European Observations for the 1981 - 2010 period. The control simulation shows higher surface temperatures than observations and overestimates the total precipitation over Euro-Mediterranean and Turkey. In contrast, both MedP-I and MedP-II show improvements in reproducing total precipitation over the Euro-Mediterranean region, Turkey, and at the entrance of the Gibraltar Strait. While MedP-I has improvements over the northeast Europe and the southern Mediterranean Basin regarding the surface temperatures, MedP-II has some improvements over Turkey and at the coastal areas of the Mediterranean Sea. MedP-II has more improvements for the SST and sea surface salinity (SSS) values over the Mediterranean Sea and the Black Sea compared to MedP-I. Additionally, MedP-II has a better representation of the North Atlantic SSS bias compared to the control simulation, while both MedP-I and MedP-II have some SST improvements for different areas over the North Atlantic. Core climate indices defined by the European Climate Assessment and Dataset project are calculated using simulated daily parameters and results are compared with the Global Land Data Assimilation System dataset. Accordingly, MedP-II is found to have improvements over more areas, especially for the indices calculated by using daily precipitation. Overall, we conclude that Mediterranean Sea Pacemaker simulations improve our understanding of how the Mediterranean Sea impacts the surface temperature and precipitation over the Euro-Mediterranean.

How to cite: Toker, E., Ilicak, M., Danabasoglu, G., and Sen, O. L.: The Impacts of SST-Nudging on Performance of Community Earth System Model (CESM) in Representing the Euro-Mediterranean Climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12502, https://doi.org/10.5194/egusphere-egu22-12502, 2022.

Ensembles of regional climate projections provide information about the range of possible scenarios of future climate change at the local scale, with more detail and better representation of fine-scale processes than can be provided by lower-resolution global circulation models (GCMs). The CORDEX ensembles are multi-model ensembles, with each member obtained by using a GCM to drive a higher-resolution regional climate model (RCM). Due to resource limitations however, users of regional climate information typically do not want to use an entire ensemble and must select a sample of its members for their purposes. To preserve as much information as possible, such a sample should be chosen to be representative of the variation within the ensemble.

Analysis of variance (ANOVA) has often been used to characterise ensemble variation by apportioning the total variation to differences between the GCMs or between the RCMs (Yip et al., 2011; Déqué et al., 2012), and to produce maps of the geographical regions where variance between the runs is ascribed to one or other model component (Christensen and Kjellström, 2020). However, traditional ANOVA methods require a balanced ensemble in which all possible GCM-RCM pairs are available. The analysis of unbalanced ensembles therefore typically proceeds either by discarding surplus runs or imputing missing ones, or by using computationally intensive Bayesian methods to account for the lack of balance.

We here propose two enhancements to the existing techniques for analysis of ensemble variation. The first is a modification of the standard ANOVA approach, based on the underlying statistical model, that can be applied directly to unbalanced ensembles: the modification is computationally cheap and hence suitable for routine application, and provides ranges of variation that are potentially attributable to the different sources.

The second enhancement adds further detail to the partitioning of variation, using an eigenanalysis that characterises the principal spatial modes of variation within an ensemble. As well as identifying the dominant spatial patterns of variation associated with the GCMs and RCMs, the analysis characterises the contribution from each model, for example by identifying models with different treatments of orography, rain shadows, or urban heat island effects. As well as informing the selection of subsets of ensemble members, this enhancement offers the possibility of emulating missing ensemble members where the GCM-RCM matrix is only partially filled. The method is applied to the EuroCORDEX ensemble with a focus on the UK.

References

Christensen, O. and Kjellström, E. (2020). Partitioning uncertainty components of mean climate and climate change in a large ensemble of European regional climate model projections. Climate Dynamics, 54:4293–4308.
Déqué, M., Somot, S., Sanchez-Gomez, E. et al. (2012). The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability. Climate Dynamics, 38:951–964 (2012).
Yip, S., Ferro, C. A. T., Stephenson, D. B., and Hawkins, E. (2011). A simple, coherent framework for partitioning uncertainty in climate predictions. Journal of Climate, 24(17):4634–4643.

How to cite: Barnes, C., Chandler, R., Brierley, C., and Alegre, R.: Identifying patterns of spatial variability within the EuroCORDEX ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7861, https://doi.org/10.5194/egusphere-egu22-7861, 2022.

Recently SMHI has completed and published 50-member ensembles for each of the Tier-1 and Tier-2 future scenarios of ScenarioMIP, using the EC-Earth3 model (SMHI-LENS, Wyser et al. 2021). Monthly and daily output from these simulations are freely available on the ESGF and can serve as a base for assessing the uncertainty of climate projections in a single model, changes in the likelihood, magnitude and duration of extremes, changes in the probability for passing tipping points, or changes in the frequency of occurrence of compound events. To our knowledge SMHI-LENS is the only single-model large ensemble that includes all ScenarioMIP scenarios.

As an application of SMHI-LENS we present results from an evaluation of changes in large-scale circulation types (CTs) over the Scandinavian domain between the present climate and two future periods in the different scenarios. For the classification in 10 CTs we are using the Simulated Annealing and Diversified Randomization (SANDRA) method applied to daily sea level pressure fields where the spatial means have been removed (Hansen and Belusic 2021). Most of the 10 CTs occur predominantly in a specific season and can hence be referred to as summer or winter CTs. We find that the frequency of the CTs does not change significantly towards the middle of the 21st century, but that most significant CT frequency changes happen towards the end of the century during summer. The magnitude of the frequency changes is found to be proportional to the warming in the different scenarios. Our results further suggest that the distinction between summer and winter season in terms of CTs becomes more pronounced in the future climate.

Each CT has its specific effect on other variables such as temperature and precipitation, meaning that a specific CT can, for example, be associated with lower-than-normal temperatures or less-than-normal precipitation. In our study, we also investigate how this effect changes in the different future scenarios. For both temperature and precipitation, the spatial extent of the effect change is considerably larger at the end of the century compared to the change at the mid-century, but the average magnitude of the change is similar in both periods. For temperature, the effect change is strongest in the winter half-year for almost all of the 10 CTs.

Ref: Hansen, F. and D. Belušić. "Tailoring circulation type classification outcomes." International Journal of Climatology (2021).

How to cite: Wyser, K., Hansen, F., Belusic, D., and Koenigk, T.: Future changes in circulation types in the SMHI Large Ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5131, https://doi.org/10.5194/egusphere-egu22-5131, 2022.

Changes in precipitation due to climate change are having and will continue to have substantial societal impact. Although physical process understanding allows insights into some of the model-projected changes, we face many challenges when turning to observations in order to detect these changes, such as large internal variability and limited observational coverage both in time and space.

Here, we aim to address these challenges with a tool from statistical learning, by implementing a regularized linear model to (1) reconstruct historical seasonal full (land+ocean) zonal mean precipitation starting in 1950 and (2) detect anthropogenically forced changes in zonal mean precipitation. The linear model is trained using a climate model large-ensemble archive with its coverage reduced to match gridded station observations on land only. Once trained, the linear model can reconstruct the full zonal mean precipitation from the partial coverage given by observations. The reconstructions (1) are compared against independent satellite observations and other sources of historical precipitation reconstructions. Our approach is successful at recovering a large part of the variability in zonal precipitation. In the Northern hemisphere extra-tropics, with relatively high station coverage, the reconstructions achieve an agreement of R=0.8 (Pearson correlation) or higher with independent satellite precipitation. But correlation values decrease considerably in the Southern hemisphere and parts of the tropics. Next, we estimate trends in the forced response (2) in seasonal zonal-mean precipitation, many of which lie outside the likely range in a preindustrial climate. The detected trends are, in line with the projection of climate models forced with historical greenhouse gas and aerosol emissions but are sensitive to the underlying observational data set.

Our results show that for large scale metrics such as seasonal zonal mean precipitation our reconstruction method can facilitate new insights for the detection and attribution of changes in the hydrological cycle. 

How to cite: Egli, M., Sippel, S., Pendergrass, A., de Vries, I., and Knutti, R.: Reconstructing zonal precipitation from sparse historical observations using climate model information and statistical learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4619, https://doi.org/10.5194/egusphere-egu22-4619, 2022.

The 29 circulation types by Hess & Brezowsky, called “Großwetterlagen”, are one of the most established classification schemes of the large-scale atmospheric circulation patterns influencing Europe. They are widely used in order to assess linkages between atmospheric forcing and surface conditions e.g. extreme events like floods or heat waves. Because of the connection between driving circulation type and extreme event, it is of high interest to understand future changes in the occurrence of circulation types in the context of climate change. Even though the “Großwetterlagen” have been commonly used in conjunction with historic data, only very few studies examine future trends in the frequency distribution of these circulation types using climate models. Among the potential limitations for the application of “Großwetterlagen” to climate models are the lack of an open-source classification method and the high range of internal variability. Due to the dynamic nature of the large-scale atmospheric circulation in the mid-latitudes, it is highly relevant to consider the range of internal variability when studying future changes in circulation patterns and to separate the climate change signal from noise.

We have therefore developed an open-source, automated method for the classification of the “Großwetterlagen” using deep learning and we apply this method to the SMHI-LENS, an initial-condition single-model large ensemble of the CMIP6 generation with 50 members on a daily resolution. A convolutional neural network has been trained to classify the circulation patterns using the atmospheric variables sea level pressure and geopotential height at 500 hPa at 5° resolution. The convolutional neural network is trained for this supervised classification task with a long-term historic record of the “Großwetterlagen”, which covers the 20^th century. It is derived from a subjective catalog of the German Weather Service with daily class affiliations and atmospheric variables from ECMWFs’ reanalysis dataset of the 20th century, ERA-20C.

We present the challenges of the deep learning based classification of subjectively defined circulation types and quantify the uncertainty range intrinsic to deep neural networks using deep ensembles. We furthermore demonstrate the benefits of this automated classification of “Großwetterlagen” with respect to the application to large datasets of climate model ensembles. Our results show the ensemble-averaged future trends in the occurrence of “Großwetterlagen” and the range of internal variability, including the signal-to-noise ratio, for the CMIP6 SMHI-LENS under the SSP37.0 scenario.

How to cite: Mittermeier, M., Weigert, M., Küchenhoff, H., and Ludwig, R.: Classification of atmospheric circulation types over Europe in a CMIP6 Large Ensemble using Deep Learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10421, https://doi.org/10.5194/egusphere-egu22-10421, 2022.

Rising CO₂ concentrations due to anthropogenic carbon emissions and the resulting warming raise expectations of an increase in biospheric activity in temperature-limited ecosystems. Early satellite observations since the 1980s confirm this expectation, revealing so-called "greening" trends of the high northern vegetation. However, since the early 2000s, these observational records show these greening trends have stagnated in high-latitude Eurasia (HLE), with many regions even reversing to browning trends. We propose here that decadal variations of the North Atlantic ocean could have contributed to these HLE browning trends. 

Our analysis shows that roughly 80% of HLE area has become drier in the last two decades compared to the previous decades. It is mainly in these drying regions that the vegetation exhibits browning trends. Satellite observations of vegetation and the ERA5 reanalysis show HLE browning to be concomitant with a stagnation of North Atlantic sea surface temperature (SST). North Atlantic SST was previously shown to potentially influence remote climate by modulating a circumglobal atmospheric Rossby wave train. Indeed, we find a precipitation decrease over Eurasia to potentially originate from this North Atlantic teleconnection, linking SST stagnation to the observed browning trend.

Next, we turn to fully-coupled Earth system models to assess the plausibility of the proposed cause-and-effect chain. We employ a pattern matching algorithm to select realizations with similar-to-observed North Atlantic SST variations from three large ensembles (MPI-GE, IPSL-LE, and CanESM5). These ensembles enable a clean separation of the unforced signal (internal variability) from the forced vegetation response (CO₂ forcing). Our results show that realizations that closely resemble the observed North Atlantic spatio-temporal SST pattern also simulate the respective wave-train and associated precipitation patterns over Eurasia that cause HLE vegetation to change. Thus, the models confirm that unforced decadal variations of HLE vegetation can be modulated by North Atlantic SST via changes in precipitation patterns. In addition, model simulations suggest that the relative decrease in vegetation greenness is accompanied by a reduction in land carbon uptake, such that changes in North Atlantic SST ultimately affect the global carbon balance.

This study therefore demonstrates that the recently observed trend in HLE browning may well be due to an unforced signal originating from the North Atlantic. This implies that even decades-long trends in biospheric variables can emerge from natural climate variability and thus could be incorrectly attributed to an external forcing. This has major implications for the understanding of biospheric dynamics, including carbon uptake and release processes.

How to cite: Winkler, A. J. and Borchert, L. F.: The influence of the North Atlantic on vegetation greening patterns in the northern high latitudes, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2669, https://doi.org/10.5194/egusphere-egu22-2669, 2022.

The impact of volcanic forcing on tropical precipitation is investigated in a new set of sensitivity experiments within the Max Planck Institute Grand Ensemble framework. Five ensembles are created, each containing 100 realizations for an idealized “Pinatubo-like” equatorial volcanic eruption with emissions covering a range of 2.5 - 40 Tg sulfur (S). The ensembles provide an excellent database to disentangle the influence of volcanic forcing on monsoons and tropical hydroclimate over the wide spectrum of the climate's internal variability. Monsoons are generally weaker for two years after volcanic eruptions and their weakening is a function of emissions. However, only a stronger than Pinatubo-like eruption (> 10 Tg S) leads to significant and substantial monsoon changes, and some regions (such as North and South Africa, South America and South Asia) are much more sensitive to this kind of forcing than the others. The decreased monsoon precipitation is strongly tied to the weakening of the regional tropical overturning. The reduced atmospheric net energy input at the ITCZ due to the volcanic eruption and, under negligible changes in the gross moist stability, requires a slowdown of the circulation as a consequence of less moist static energy exported away from the ascent.

How to cite: D'Agostino, R. and Timmreck, C.: Sensitivity of regional monsoons to idealised equatorial volcanic eruption of different sulfur emission strengths, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11547, https://doi.org/10.5194/egusphere-egu22-11547, 2022.

Species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to climate change, reflecting an underlying hypothesis that species will move to cooler locations.  However, there is significant variability in observed species range shifts and differences in exposure to climate change may explain some of the variability amongst species.  But this requires identifying regions that have experienced detectable changes in those aspects of the climate system that species are sensitive to. 

To better understand species exposure to climate change, we estimate the time of emergence of climate change for 19 biologically relevant climate variables using observations and initial condition large ensembles from five different climate models.   The time of emergence (ToE) is calculated using Signal/Noise (S/N) thresholds.  The S/N threshold applied in this study is >=2, but this threshold can be easily modified to represent species that are more or less sensitive to climate change.  Preliminary findings from the initial condition large ensembles indicates the strongest emergence for the temperature metrics within the tropical oceanic regions in the absence of upwelling. The earliest emergence over the oceans is found within the western warm pool of the Pacific.  Notable places that haven’t emerged for the temperature metrics include both the North Atlantic and Pacific.  The ToE of a climate change signal for the temperature metrics over land is spatially complex, which may partially explain the complex observed range shifts for terrestrial species.  For instance, multiple initial condition large ensembles indicate a signal has emerge in the most recent decades only for the western and northeastern parts United States.

How to cite: Bowden, J., Suarez-Gutierrez, L., Terando, A. J., Rubenstein, M., Carter, S., Weiskopf, S., and Nguyen, H. T.: Exploring the impact of climate change for biological climate variables using observations and multi-model initial condition large ensembles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13097, https://doi.org/10.5194/egusphere-egu22-13097, 2022.

Mass loss of glaciers and ice caps has been one of the major contributors to sea-level rise over the past century. Glaciers respond slowly to a changing climate. Therefore, glacier evolution over the past century is partly a result of prior changes in the climate, resulting both from internal variability in the climate system and changes in external forcings. Here we present a simulation of global glacier evolution over the period 850-2000 CE and assess the influence that different climate forcings have on the glacier mass balance. The glacier evolution simulation thus serves as a base for the mass balance attribution experiment.

The Open Global Glacier Model (OGGM) was used to simulate glacier geometry and mass balance evolution of land-terminating glaciers. The dynamic simulations were forced with the full length of the Last Millennium Reanalysis (LMR), a climate timeseries covering the period 0-2000 CE, using the first part for spin-up only. The initialization of the glacier states in 850 CE was done with a calibration procedure, making use of glaciers with a relatively short memory for initializing those with a longer one.

To assess the influence of different climate forcings (volcanic, greenhouse gases (GHG), orbital, land cover and land use, solar and anthropogenic ozone and aerosols) on glacier mass balance, simulations of the Community Earth System Model Last Millennium Ensemble (CESM-LME) are being used. The CESM-LME fully forced, single forced and 850 CE control simulations are used to force OGGM in climatic mass balance simulations. In those simulations the glacier geometries are prescribed with those from the LMR forced dynamic simulation, in order to avoid biases in the attribution caused by deviating glacier evolutions under the different forcings.

Results show that the changes in the GHG forcing have little influence on the SMB from 850 to ~1850 CE. After that the influence becomes increasingly more negative. All other forcings that have been assessed here have positive contribution to glacier mass balance over the last millennium. Although the influence of land use and land cover change has not received a lot of attention before in this context, it has a substantial influence on global glacier mass in our simulations. However, the influence of the forcings differs strongly between regions.

How to cite: Vlug, A., Marzeion, B., Prange, M., and Maussion, F.: Global glacier evolution over the last millennium and the influence of climate forcings on the mass balance, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11935, https://doi.org/10.5194/egusphere-egu22-11935, 2022.

Large-ensemble modelling has become an increasingly popular approach to study the climatic response to external forcing. The idea of a large ensemble is to generate different realizations of a forced climate to explicitly reproduce the system’s internal variability. With these large datasets it is not only possible to quantify and statistically test changes in the mean climate, but also changes in climate variability and subsequent changes in extremes. Typically, the approach to generate a large ensemble set is to force the model with a transient forcing and start the different simulations from slightly different initial conditions. However, this is expensive due to the high computational demand of full-complexity GCMs or ESMs.

Here we propose a large-ensemble design that generates a multitude of years to describe the climate states of interest, while being more economical regarding computational resources: a time-slice Large Ensemble. The core of the concept is to generate multiple time slices rather than long transient simulations. The time slices represent the present-day climate and a future warmer climate. These are segments of, for example, 10-years; too short to show significant climate change. Using stochastic physics, we add a randomizing component to the simulations. This allows us to branch multiple simulations from one set of initial conditions.

We present the advantages and limitations of this design and we quantify the underlying assumptions. Further, we demonstrate examples of analyses from earlier work for which this type of large ensemble is well (or better) suited, in particular for studying future extreme events and finding analogues of observed extreme events. Finally, we present ongoing work on the generation and analysis of a new time-slice large-ensemble dataset with EC-Earth v3. The experimental set-up is to branch off from 16 full historical and SSP2-4.5 simulations to represent the present-day climate and a future +2K climate.

How to cite: Muntjewerf, L., Bintanja, R., Reerink, T., and Van der Wiel, K.: A novel approach to large-ensemble modelling: the time-slice Large Ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10314, https://doi.org/10.5194/egusphere-egu22-10314, 2022.

We introduce a 36 to 40-member ensemble of simulations with the atmospheric general circulation model ECHAM6 that is designed to form the basis for a 3-dimensional climate reconstruction dataset in the PALAEO-RA project. It covers the years 1420 to 2009, the period for which combining natural proxies such as tree rings and archives of society such as documentary data allows to perform global climate reconstructions. However, the information provided by these historical sources is usually sparse in temporal and spatial resolution. Our simulations provide the necessary background for data assimilation and thus complement the historical information by adding physical constraints implemented in the model formulation. Our experimental setup is designed to determine the range of internal climate variability under prescribed forcings. It is oriented on the PMIP4 setup with slight modifications, using realistic ocean boundary conditions (SST and sea ice cover) and radiative forcings while also accounting for uncertainties in these.

Our presentation will give an overview of our experimental setup and show the results of the first applications. We present an evaluation of the ensemble, including measures on how well the ensemble can sample the internal variability of some variables of interest. Beyond this, we hope to stimulate a discussion on possible further applications.

How to cite: Hand, R., Samakinwa, E., Hövel, L., Valler, V., and Brönnimann, S.: ModE-Sim - A new medium-size AGCM ensemble to analyze climate variability in the modern era, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12305, https://doi.org/10.5194/egusphere-egu22-12305, 2022.

Large ensembles of climate-scale model simulations are key tools for assessing climate risks, separating internal variability from external forcing, and interpreting the observational record. Several modelling centres have produced such ensembles over the past years. Here we present early plans for the development of a new Large Ensemble based on the HadGEM3 (Hadley Centre Global Environment Model version 3) climate model. The initial plan envisages a 40-member ensemble spanning 150 years of historical/scenario climate (1950-2100) at a resolution of N216 (about 60 km) in the atmosphere and ¼° in the ocean.

This initiative is part of the recently started UK NERC multi-centre project CANARI (Climate change in the Arctic-North Atlantic Region and Impacts on the UK). CANARI aims to advance understanding of the impacts on the UK arising from climate variability and change in the Arctic-North Atlantic region, with a focus on extreme weather and the potential for rapid, disruptive change. While we aim for the new Large Ensemble to become a resource for a wide range of applications, it will support addressing the CANARI science questions in particular. These questions are concerned with, for example, the (i) projected Arctic change and potential lower-latitude influences through atmospheric or oceanic pathways, (ii) the projected change in the large-scale (North Atlantic) ocean/atmosphere circulation, its drivers, and interaction with weather systems, and (iii) projected impacts on the UK arising from extreme weather (windstorms and flooding, blocking, heatwaves and droughts).

This poster invites discussion with the community on all aspects of the design of the new Large Ensemble, and particularly seeks input regarding

• the choice/number of experiments to follow (from CMIP6 Scenario MIP),
• the initialisation strategy, and the degree to which slow (10 years and longer) variability, particularly in the ocean, should be sampled, and
• the desired output.

How to cite: Schiemann, R., Hatcher, R., Lawrence, B., Lister, G., and Shaffrey, L.: Planning for a Large Ensemble based on the HadGEM3 climate model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7280, https://doi.org/10.5194/egusphere-egu22-7280, 2022.