Atmospheric CO₂ concentrations have increased from around 280 parts per million (ppm) in 1800 to over 416 ppm in 2020. This is a direct result of increasing anthropogenic emissions of CO₂ since the industrial era. Nearly half of the emitted anthropogenic CO₂ is taken up by the ocean and terrestrial ecosystems, while the remaining half remains in the atmosphere, where it is a heat-trapping greenhouse gas. The growth of atmospheric CO₂ varies from year to year with inhomogeneous spatial distribution depending on the CO₂ uptake by the ocean and land. The CO₂ uptake by the natural sinks and atmospheric growth are affected by the climate variations and the long-term changes; in turn, the variations of the carbon cycle also modulate global climate change. The state-of-the-art large ensemble simulations based on Earth System Models (ESMs) prescribe the concentration of atmospheric CO₂, but the missing interactive response of atmospheric CO₂ changes to the CO₂ fluxes into the ocean and the land hinders the investigation of the variability in atmospheric CO₂. Furthermore, such simulations will be insufficient to represent the changes in the efficiency of the land and ocean carbon sinks once emissions start to decline. Based on the low-resolution version of the Max Planck Earth System Model v1.2 (MPI-ESM-1.2-LR), we have done a novel set of 30-member ensemble simulations driven by anthropogenic CO₂ emissions. In such simulations, atmospheric CO₂ concentrations are computed prognostically, modulated by the strength of CO₂ fluxes to the land and the ocean. While general trends in atmospheric CO₂ concentrations for different Shared Socioeconomic Pathways (SSP) are well known, trends in its global dispersion and variations within the seasons of each year have not been investigated in ESMs with an interactive carbon cycle. In this project, we use MPI-ESM-1.2-LR large ensemble simulations under four SSP scenarios, i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5, together with historical runs to analyze changes of atmospheric CO₂ concentrations. We focus on seasonal variability and spatial distribution of atmospheric CO₂ changes in the presence of internal climate variability. We address two questions: first, what is the temporal evolution of atmospheric CO₂ in regard to its seasonal variability by the end of the century following different emission pathways; and second, how does atmospheric CO₂ evolve spatially (horizontally across the globe and vertically into the stratosphere) in the historical period and future projections until 2100? This study aims to refine our understanding of the spatial and temporal variations of CO₂ in support of activities to monitor and verify decarbonization measures.

How to cite: Johnson, K., Li, H., and Ilyina, T.: Variability of atmospheric CO2 in Earth System model large-ensemble simulations with an interactive carbon cycle, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7740, https://doi.org/10.5194/egusphere-egu23-7740, 2023.

Heatwaves and dry spells are major climate hazards that severely impact human health, economy, agriculture, and natural ecosystems. Compound hot and dry summers have become more frequent and intense in recent years in Europe. What remains unclear is, however, to which extent the observed trend can be explained by climate change or as a feature of internal climate variability. In this study, we assess the frequency and intensity of compound hot and dry events in Europe by analyzing recent historical events from reanalysis data 1960-2022 and comparing it to i) a counterfactual reference (corresponding to pre-industrial climate conditions), and ii) model data derived from a Single Model Initial-condition Large Ensemble (SMILE).

We use data from the fifth generation of the European Reanalysis (ERA5) to assess the current frequency of the compound hot and dry summers like 2003, 2015, 2018, and 2022 and analyze the intensity of the events. We use the data from the 50-member SMILE Canadian Regional Climate Model Large Ensemble (CRCM5-LE) and calculate the probability of event occurrence for those events in Europe’s current climate. Employing the ensemble allows us to assess the influence of internal climate variability vs. climate change for those events. Additionally, we use pre-industrial conditions (pi-control runs) simulated with CRCM5 to compare the probability of recent hot and dry compound events to a counterfactual world without climate change. 

Our analysis shows that climate change increases the frequency and intensity of compound hot and dry events. We see a substantial increase in occurrence probabilities compared to a pre-industrial world and draw to emerging hotspots of new compound extremes in several European regions. We illustrate the added value of using pi-control runs in a regional SMILE as a novel approach for impact quantification. It provides the means to understand better the already prominent role of climate change on the occurrence, frequency, and intensity of extreme events in a world of still limited warming.

How to cite: Felsche, E., Böhnisch, A., and Ludwig, R.: Assessing the occurrence of compound hot and dry events from pre-industrial conditions to present-day extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9611, https://doi.org/10.5194/egusphere-egu23-9611, 2023.

Projecting how temperature variability is likely to change in the future is important for understanding future extreme events. This comes from the fact that such extremes can change due to both changes in the mean climate and its variability. The recent IPCC report found large regions of low model agreement in the change of temperature variability in both December, January, February (DJF) and June, July, August (JJA) when considering 7 Single Model Initial-Condition Large Ensembles (SMILEs). In this study we use the framework described by Suarez-Gutierrez et al, (2021) to constrain future projections of temperature variability by selecting the SMILEs that best represent observed variability. We use 11 SMILEs with CMIP5 and CMIP6 forcing and consider 9 ocean regions and 24 land regions. We then assess, for both DJF and JJA, whether temperature variability projections are constrained by selecting for models capture observed variability in individual regions and seasons. We consider projected changes at various warming levels to account for differences in warming between models and the use of different future scenarios across CMIP5 and 6. We identify MPI-GE and CESM2 as the SMILEs that capture observed variability sufficiently. across most regions (29 & 30 out of 33 in DJF and 28 and 26 in JJA respectively). Whether temperature variability projections are constrained depends on both season and region. For example, in DJF over South East Asia the constraint does not change the already large spread of projections. Conversely, over the Amazon the constraint tells us temperature variability will increase in DJF whereas the entire model archive does not agree on the sign of the change. This method can be used to better constrain our uncertainty in temperature variability projections by selecting SMILEs that best represent observed variability.

How to cite: Maher, N., Suarez-Gutierrez, L., and Milinski, S.: Constraining temperature variability projections using SMILEs that best represent observed variability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10531, https://doi.org/10.5194/egusphere-egu23-10531, 2023.

ModE-Sim is a medium size ensemble that can be used to study climate variability of the past 600 years. It was created using the atmospheric general circulation model ECHAM6 in its LR version (T63L47). With 60 ensemble members between 1420 and 1850 and 36 ensemble members from 1850 to 2009 ModE-Sim consists of 31620 simulated years in total. The dataset was designed as an input for a data assimilation procedure that combines historical climate informations with additional constraints from a climate model to produce a novel gridded 3-dimensional dataset of the modern era. Additionally, ModE-Sim on its own is also suitable for many other applications as its various subsets can be used as initial condition ensemble to study climate variability. We show that the ensemble has a realistic response to external forcings and that it is capable of capturing internal variability on monthly to annual time scales. At the example of heat waves we show that ModE-Sim can even be useful to study extreme events.

How to cite: Hand, R., Hövel, L., Samakinwa, E., and Brönnimann, S.: First results from ModE-Sim - A medium size AGCM ensemble to study climate variability during the past 600 years, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14112, https://doi.org/10.5194/egusphere-egu23-14112, 2023.

Köppen-Geiger climate classification is a valuable tool to define climate zones based on the annual cycles of temperature and precipitation. In this study, we use the high-emission scenario global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and phase 5 (CMIP5) along with observations and apply the Köppen-Geiger climate classification. We aim to address the ecological consequences of climate change and compare the two generations of models. Compared to their predecessors, CMIP6 models show slightly improved performance in representing the reference period (1976-2005) observed climate zones. CMIP6 models project a 42-48% change in climate zones by the end of the century, depending on which ensemble subset is used. The projected change rates based on CMIP6 are above the global average for Europe (81-88%) and North America (57-66%). The reductions in the areas of cold and polar climate zones are more pronounced in CMIP6 models compared to CMIP5. Using an ensemble subset of CMIP6 models that are consistent with the latest evidence for equilibrium climate sensitivity limits the changes in climate zones, and their results converge towards the results based on CMIP5. CMIP6 models also project a greater rate of climate zone change throughout the century than CMIP5. The greater change rate observed in CMIP6 is essentially dominated by the stronger projected warming rates of these models, whose plausibility is a matter of concern.

How to cite: Bayar, A. S., Yılmaz, M. T., Yücel, İ., and Dirmeyer, P.: Greater rate of climate zone change in CMIP6 Earth System Models due to stronger warming rates, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16537, https://doi.org/10.5194/egusphere-egu23-16537, 2023.

Large ensemble simulations may be exploited to appreciate plausible extreme climate impacts that we may not yet have seen. Such information can be vital for decision makers to anticipate otherwise unforeseen impacts. Large ensemble simulations can generate larger data samples than the observed record but biases are likely to exist, which may occasionally produce unrealistic extreme events. Interpreting simulated 'unseen' events that are more extreme than those seen in historical records is therefore crucial, but adequate evaluation is complicated by observational uncertainties and natural variability. In this talk, we introduce a three-step procedure to assess the realism of simulated extreme events based on the model properties (step 1), statistical features (step 2), and physical credibility of the extreme events (step 3). We use the global climate model EC-Earth and global hydrological model PCR-GLOBWB to demonstrate these steps for a 2000 year Amazon monthly flood ensemble. The spatial model resolution of 1x1° and daily temporal resolution is coarse, but no reason to dismiss monthly flood simulations over the Amazon a priori. We find that the simulations are statistically inconsistent with the observations, but we cannot determine whether simulations outside observed variability are inconsistent for the right physical reasons. For example, there could be legitimate discrepancies between simulations and observations resulting from infrequent temporal compounding of multiple flood peaks, rarely seen in observations. Physical credibility checks are crucial to assessing their realism and show that the unseen Amazon monthly floods were generated by an unrealistic bias correction of precipitation. Based on this case study, we discuss the takeaway challenges when evaluating extreme climate impacts from large ensemble simulations. Understanding the drivers of simulations outside observed variability helps to gain trust in unseen simulations. Uncovering the characteristics of events in the models may reveal the most important model deficiencies or improve our scientific understanding of unseen events.

How to cite: Kelder, T., Marjoribanks, T., Slater, L., Wanders, N., Wilby, R., and Prudhomme, C.: Interpreting extreme climate impacts from large ensemble simulations — are they unseen or unrealistic?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12849, https://doi.org/10.5194/egusphere-egu23-12849, 2023.