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.

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 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.

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.