Being able to reconstruct past climate evolution is a necessary step for enhancing our capacity to look into the future and, therefore, extensive improvements of state-of-the-art Earth System Models (ESMs) are needed. So far, ESMs are mainly calibrated and validated with respect to the instrumental records of the last ~170 years of relatively stable climate, while the Earth’s longer-term history is characterised by an interplay of gradual climate change, variability and critical transitions between competing states, with profound impacts on climate subsystems, ecosystems, and civilisations.
Understanding the leading dynamical processes and feedbacks and in particular improving our ability to model and anticipate critical transitions in the climate and ecosystems is key to project future climate change on spatio-temporal scales relevant for societies, ecosystems and the planet.
We invite contributions that
- use knowledge of past climates to advance our understanding of climate variability, abrupt changes and climate response to greenhouse gas changes and other forcing on spatio-temporal scales relevant for societies, ecosystems and the planet as a whole;
- make use of information from paleoenvironmental proxy data, from past civilisations, from ESMs, and from rigorous theoretical approaches - individually or combined;
- explore modern approaches to incorporate palaeoclimate information into the development processes of ESMs of varying complexity;
Orals: Mon, 4 May, 16:15–18:00 | Room -2.31
Past2Future (P2F) aims to develop, expand, and leverage the wealth of paleoclimate data to significantly improve existing Earth System Models and deepen our understanding of Earth’s climate response to various types of forcing, with a focus on abrupt climate transitions and tipping points. To achieve this, our work focuses on the compilation, integration, and re-evaluation of past sea surface temperature (SST) data with the aim of defining the states and variability of the ocean temperatures across four pivotal climate intervals: the Mid-Holocene (6.5-5.5 ka), Last Glacial Maximum (23-19 ka), Eemian (130-116 ka), and the mid-Pliocene Warm Period (3.3-3 Ma).
To date, we have identified all published global SST records spanning the Last Glacial Maximum and the Mid-Holocene, reconstructed using both geochemical techniques and faunal assemblages. For the Last Glacial Maximum, we identified 1,426 geochemical and faunal proxy records from over 1,100 cores. For the Mid-Holocene we identified 1,014 geochemical and faunal proxy records from 790 cores. Subsequently, we assessed the suitability of these records for climate model evaluation and tuning by considering: i) the robustness of each record’s age model; ii) the SST reconstruction methodology and associated uncertainties; and iii) site location and representativeness. Consequently, we have prioritised marine sediment records that feature robust age models, high-resolution SST records, low calibration uncertainties, derived from sites minimally influenced by additional climatic or environmental factors (e.g. upwelling), and, where possible, supported by alternative multi-proxy SST reconstructions. To address remaining spatial and temporal data gaps, we will generate new SST records using alkenone (UK’₃₇) and glycerol dialkyl glycerol tetraether (TEX₈₆) proxies, generating datasets that support climate models. The resulting curated and expanded SST datasets will provide a robust benchmark for climate model evaluation and tuning, ultimately contributing to more robust and accurate simulations of past climate states and more reliable projections of future climate change.
How to cite: Cutmore, A., Sliwinska, K., and McClymont, E.: Past to Future: Defining the states and variability of the ocean, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2331, https://doi.org/10.5194/egusphere-egu26-2331, 2026.
Instrumental observations only capture a short interval of the climate history of the Earth, and are insufficient to fully constrain low-frequency variability, internal dynamics, and the response of the climate system to changing background states. Paleoclimate archives, by contrast, document a wide range of past climate changes, yet translating this information into Earth System Models (ESMs) to enhance their performance and future projections remains a major challenge. Paleoclimate Data Assimilation (PDA) provides a promising pathway to bridge this gap by combining proxy records and ESMs within a physically consistent framework. Here we present a paleo reanalysis based on an adaptation of the Norwegian Climate Prediction Model (NorCPM), in which an ensemble Kalman filter is used to assimilate hundreds of annually resolved proxy records (including coral, tree-ring, and ice-core records) back to 1600 CE. Unlike many existing paleo reanalyses for past centuries, which primarily constrain the atmospheric state and only indirectly represent ocean variability, our approach explicitly accounts for ocean dynamics. By nudging three dimensional atmospheric wind fields derived from the paleo atmospheric reanalysis, we generate a dynamically consistent coupled reanalysis, by simulating the response of the ocean to large-scale wind variability, as well as their associated impacts through thermodynamical feedbacks. The climate reanalysis represents both forced and internal variability over the last four centuries and shows good agreement with independent instrumental observations. By construction, this approach yields a dynamically coherent, multivariate reconstruction that goes beyond traditional proxy reconstructions and enables direct investigation of climate dynamics and feedback. Here, we focus on methodological aspects and perspectives of PDA, highlighting how paleo reanalyses can (i) constrain modes of low-frequency variability and their stability across different climate states, and (ii) evaluate and refine the calibration of the ESMs beyond the instrumental period. Such approaches are essential for improving confidence in future climate projections, particularly with respect to long-timescale variability, feedback, and the potential for abrupt transitions in the Earth system.
How to cite: Dalaiden, Q., Counillon, F., Svendsen, L., Bethke, I., and Keenlyside, N.: Paleoclimate Data Assimilation: unlocking past climate dynamics to better constrain the future, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13882, https://doi.org/10.5194/egusphere-egu26-13882, 2026.
The magnitude and spatial patterns of future changes in temperature variability remain debated. Supplementing direct observations with reconstructions of past climate has shown that CMIP-style simulations lack regional variability on decadal and longer timescales, a shortcoming that likely includes future projections. Here, we assess the range of future climate variability based on the differences between reconstructions and simulations of temperature variability during the Quaternary. The assessment uses a multi-proxy database of surface temperatures as well as long-term transient simulations of the past and possible future climates with an Earth System Model. Comparing simulations with reconstructions, we establish a relationship between warming level and local to global temperature variability for annual to millennial timescales. The identified model-reconstruction mismatch provides the basis for rescaling simulations and thus constraining future climate variability. For this, we decompose variability into its long- and short-term components. We then artificially enhance the long-term variability that is underestimated in simulations to reconstruct a possible, more realistic corresponding temperature field. Taking the uncertainty in reconstructions into account results in a wider range of possible scenarios for future climate variability given this past evidence. Our results have implications for climate indices and temperature extremes on short timescales in future scenarios, informing mitigation and adaptation efforts.
How to cite: Ziegler, E., Kapsch, M.-L., Mikolajewicz, U., and Rehfeld, K.: Constraints on future multidecadal temperature variability from climate models, reconstructions and observations of the past two million years, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13210, https://doi.org/10.5194/egusphere-egu26-13210, 2026.
Diversity in the responses of species to environmental variability is fundamental to building ecosystem resilience. In behavioral ecology, risk refers to variance in the outcomes of behaviors with near-term (i.e., fitness-related) consequences, and humans are especially skilled at finding innovative ways to minimize subsistence risk. Formal models for risk-sensitive decision making can reveal how particular combinations of subsistence activities minimize variance arising from climatic and environmental conditions. However, an analytical framework for assessing the extent to which the diversity of these activities promotes the capacity for human social-ecological systems (SESs) to absorb disturbances and reorganize and renew themselves is yet to be developed, and hence we are unable to reliably address resilience in ancient SESs. Here, we adapt a population dynamics model of multiple interacting and mutualistic species to simulate the impacts of external (i.e., climate) variability on equilibria. In this approach we treat three alternative, yet complementary subsistence strategies (i.e., cultivation, pastoralism, and hunting) as interacting species in a heterogeneous environment. We parameterize interaction effects between the three “species” based on payoff matrices that define the relative benefits of one strategy over another to subsistence farming economies. Different combinations of subsistence activities are expected to arise as payoff matrices are subject to variable climatic and environmental constraints on productivity. We use downscaled TRACE21K-II output variables to simulate interannual variation in returns from each strategy. The simulation produces a time series of idealized proportional contributions of each strategy to overall subsistence. We then test the model predictions against macrobotanical and faunal remains recovered from lakeside settlements (i.e., pile dwellings) in the Northern Alpine Foreland spanning to the Neolithic (6.2-4.3 kya cal. BP).
How to cite: Triozzi, N. P., Ashwin, P., Bradshaw, C., del Amo Blanco, I., Abdelkader Di Carlo, I., Hoebe, P. W., Peeters, H., Poska, A., Kolář, J., Jacomet, S., Schibler, J., and Heitz, C.: Assessing Resilience Capacities and Vulnerability in Agropastoral Societies using an adapted Lotka-Volterra modelling framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21842, https://doi.org/10.5194/egusphere-egu26-21842, 2026.
Climate models project a future weakening of the Walker circulation in the tropical Pacific in response to anthropogenic forcing, while a cooling of the eastern equatorial Pacific and a strengthening of the Walker circulation has been observed in the past decades. This discrepancy may arise from models biases in the representation of Pacific dynamics, from a transient response of the ocean atmosphere system, or from unforced decadal climate variability. Here, we propose to use paleoclimate reconstruction of ENSO variance in the mid Holocene to evaluate the skills of CMIP5 and CMIP6 models and constrain climate change projections. The 20 model ensemble shows a slight mean reduction in ENSO variance that underestimates the 50-80% reduction in reconstructions, while exhibiting a large diversity of responses ranging from a 60% decrease to a 40% increase. We show that models that best represent mid Holocene ENSO changes display a weaker modern cold tongue bias, stronger mid Holocene cooling, and a more realistic representation of the ENSO seasonality and wind response to SST. Those models also yield a stronger eastern Pacific warming and zonal gradient reduction by the end of the 21st century in global warming scenarios (SSP585 and rcp85). Although mid-Holocene climate change is driven by orbital forcing rather than GHG, the robustness of this constraint is supported by the fact that ENSO integrates large scale ocean atmosphere feedbacks, which are key to the future response of the Pacific ocean.
How to cite: David, H., Carré, M., Khodri, M., Colas, F., Vialard, J., and Braconnot, P.: Mid-Holocene ENSO constraints point to future weakening of Walker Circulation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18085, https://doi.org/10.5194/egusphere-egu26-18085, 2026.
The risk of crossing critical thresholds in the Earth system is continuously increasing due to anthropogenic climate change, potentially leading to accelerated responses. As one of the major tipping elements, the Atlantic Meridional Overturning Circulation (AMOC) can exhibit abrupt, nonlinear shifts between distinct regimes. Evidence of such tipping behavior is found in paleo-climate records, most prominently as Dansgaard-Oeschger (DO) events. Using the Bern3D fast Earth System Model, we investigated DO events driven by AMOC variability under Marine Isotope Stage 3 (MIS3) conditions. The model exhibits unforced, self-sustained oscillations resembling DO events within a narrow parameter space defined by CO2 concentration, wind stress forcing, and diapycnal diffusivity. We systematically explored this parameter space and its boundaries. Beyond these parameter space boundaries, the AMOC either remains in a weak regime or undergoes an abrupt transition to a stronger state. Within the parameter space, oscillations are stable, with the periodicity being strongly controlled by CO2. The mechanism underlying DO-like oscillations is primarily oceanic and involves heat accumulation and sea ice changes in the eastern North Atlantic. Sea ice acts as an insulating barrier, allowing subsurface heat to build up until it is rapidly redistributed through the water column, melts the sea ice, is released and triggers deep convection, producing an abrupt strengthening of the AMOC. Freshwater input from sea ice melt, in turn, weakens the circulation. These results indicate that abrupt shifts in the AMOC are an inherent feature of the climate system, although the implications for the AMOC’s future evolution remain unclear due to the vastly different boundary conditions.
How to cite: de Huu, A., Pöppelmeier, F., Testorf, P., and Stocker, T.: Variability across parameter space and mechanisms of DO-like oscillations in a fast Earth System Model , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3889, https://doi.org/10.5194/egusphere-egu26-3889, 2026.
The recognition by 19th Century science that glaciers not only move but were, at various times in the past, big enough to submerge continents gave rise to Ice Age Theory and revolutionised our understanding of the Earth system, demonstrating that climate can – and does – change. In the intervening years, however, glacial geology and the physical record of cryospheric growth and decay have been largely relegated to playing a supporting role to higher-resolution, better-dated palaeoclimate proxies that today dominate conversation around ice age cycles, the causes and impacts of abrupt climate change, and the nature of climate tipping points. For instance, ice cores revealed the dramatic shifts in atmospheric conditions during Dansgaard-Oeschger and Heinrich Stadial events, while marine geochemistry tells us of the ocean’s role as a dynamic CO2 reservoir and global heat capacitor. Two key concepts to have emerged from palaeoclimatology include (1) that of North Atlantic ‘stadial’ events as periods of intense regional cooling, typically with fast onset and rapid termination, and (2) the existence of a bipolar seesaw, in which cooling (warming) in one hemisphere drives relative warming (cooling) in the other. Both are deeply rooted in modern conceptual models of abrupt climate change and incorporated in numerical model projections of future climate. Here, we draw from recent refinements in cosmogenic nuclide geochronology and a growing database of well-dated Late Pleistocene moraine records to explore how well these two seminal concepts stand up to scrutiny from a reinvigorated glacial perspective. Exploiting the sensitive yet innately straightforward relationship between melt season (summer) temperature and glacier mass balance, this emerging glacial record paints a fascinating picture of ‘stadial’ climate that contrasts with the traditional view of these severe perturbations as year-round cold anomalies driven by AMOC. We highlight strong similarities between our North Atlantic glacier records and those from other regions globally and propose that anomalous thermal seasonality in the North Atlantic is a regional by-product of overall global warming. The ramifications of this hypothesis extend far beyond the North Atlantic: the interhemispheric bi-polar seesaw hypothesis rests on the coincidence of apparent Northern Hemisphere stadial cooling and Southern Hemisphere warming. Should the pattern of globally uniform glacier (and thus climate) behaviour invoked here prove an accurate representation of atmospheric conditions during abrupt climate shifts, the physical basis for a bipolar seesaw mechanism is undermined.
How to cite: Bromley, G., Hall, B., and Putnam, A.: Exploring new glacial perspectives on tenets of abrupt climate change: North Atlantic stadials and the bi-polar seesaw, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18226, https://doi.org/10.5194/egusphere-egu26-18226, 2026.
The Last Interglacial (LIG; 129–116 thousand years ago) experienced global mean temperatures approximately 1–2 °C above pre-industrial levels, comparable to present-day conditions and those projected for the near future. During the LIG, high and mid-latitudes were substantially warmer, Arctic sea ice was reduced, both the Greenland and Antarctic ice sheets were smaller than today, and global mean sea level was at least 5 m higher than present. Unlike modern warming, which is primarily driven by increased greenhouse gas concentrations, LIG climate anomalies were mainly caused by higher eccentricity and a precession putting NH summer closer to perihelion, with Northern Hemisphere summer insolation exceeding pre-industrial values by more than 70 W m⁻².
Many climate models struggle to reproduce the magnitude of LIG warming and the seasonally ice-free Arctic suggested by proxy evidence. Here, we present results from an abrupt-127 ka experiment following the CMIP7 Fast Track protocol, performed with ICON-XPP v1.0 (07/2024) [1], extended to allow for orbital parameter variations based on Kepler’s approximation. In this simulation, orbital parameters and greenhouse gas concentrations are set to LIG values, while all other boundary conditions (solar constant, prescribed ice sheets, prescribed vegetation, and aerosols) are kept at pre-industrial levels.
Compared to the pre-industrial control simulation, the abrupt-127 ka experiment shows top-of-atmosphere (TOA) radiation anomalies consistent with previously published LIG simulations [2] including an Arctic summer TOA increase of 50–75 W m⁻². However, for ICON-XPP v1.0 the simulated annual global mean temperature decreases by 0.3 K when only orbital parameters are changed, and by 0.47 K when LIG greenhouse gas concentrations are applied in addition. This contradicts proxy reconstructions indicating a global mean temperature increase of approximately 0.5–1.5 K during the LIG.
Exploring Arctic seasonality, we find a summer warming of 4 K in July and a winter cooling of 3 K in January, resulting in an overall Arctic cooling in the annual mean relative to pre-industrial conditions. Arctic sea ice shows little reduction in summer but increases more substantially in winter, leading to an overall annual expansion of sea ice compared to pre-industrial levels. We attribute the simulated cooling and disagreement with proxy evidence to insufficient Arctic amplification in the ICON-XPP version used, likely caused by a weak sea-ice feedback and the lack of interactive vegetation changes. We compare these results to first results obtained with the CMIP7 release of ICON-XPP (2025.10-1) and sensitivity experiments exploring the impact of prescribed vegetation changes and the inclusion of dynamic vegetation. Our findings have major implications for future simulations with ICON-XPP, as the LIG represents a climate state comparable to present-day and future warmth.
[1] Müller et al.: The ICON-based Earth System Model for Climate Predictions and Projections (ICON XPP v1.0), EGUsphere, https://doi.org/10.5194/egusphere-2025-2473, 2025.
[2] Otto-Bliesner et al.: Large-scale features of Last Interglacial climate: results from evaluating the lig127k simulations for the Coupled Model Intercomparison Project (CMIP6)–Paleoclimate Modeling Intercomparison Project (PMIP4), Clim. Past, https://doi.org/10.5194/p-17-63-2021, 2021.
How to cite: Rehfeld, K., Brugger, J., Houston, T., Racky, M., Lorenz, S., Wagner, S., and Köhler, M.: Simulations of the Last Interglacial with ICON-XPP indicate the relevance of correct sea-ice and vegetation feedback for Northern Hemisphere warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9552, https://doi.org/10.5194/egusphere-egu26-9552, 2026.
Rising atmospheric carbon dioxide (CO2) concentrations alter the vegetation composition indirectly through climate change and directly through plant physiological modifications. Both responses modulate climate through changed energy, moisture, and carbon fluxes between land and atmosphere. This makes accurate estimates of the responses important for future vegetation and climate projections. Yet, large inter-model differences regarding the magnitude of the direct response persist, leading to uncertain future projections. Here, we quantify the impact of CO2 changes on the vegetation and climate in three Earth system models (ESMs) of varying complexity. The direct and indirect responses are separated using factorization experiments and statistical emulators. While most previous studies focus on either low or high CO2 concentrations, we cover a large range from 150ppm to 1200ppm.
We find that plant function type (PFT) specific responses often follow a logarithmic shape except when threshold crossings create breakpoints. However, the grid box mean responses can differ from PFT-specific responses, indicating substantial modulations of the shape and amplitude by competition between PFTs. While competition amplifies the response for some variables, it dampens the response for others. For example, changes of biophysical properties like leaf area index and canopy height are amplified by competition, contributing to stronger plant-physiological impacts on some components of the terrestrial hydrological cycle than the radiative effect of rising CO2 concentrations. The simulated long-term vegetation impacts can currently not be evaluated against present-day observations or manipulation experiments. Instead, we compare the model results with global compilations of paleobotanical data. Preliminary results for the Last Glacial Maximum indicate a model-dependent overestimation of the plant-physiological response. Future research aims at leveraging these comparisons to calibrate the modeled direct response of vegetation to CO2, which would provide constraints for the long-term impacts of future emission scenarios on natural ecosystems.
How to cite: Weitzel, N., Valdes, P. J., Jones, C. D., and Dallmeyer, A.: A multi-model assessment of the plant-physiological response to high and low carbon dioxide concentrations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12969, https://doi.org/10.5194/egusphere-egu26-12969, 2026.
Posters on site: Mon, 4 May, 14:00–15:45 | Hall X5
The Greenland Ice Sheet (GrIS) plays a key role in the global climate system by acting as an interglacial refrigerator closely coupled to North Atlantic ocean circulation. Global warming is presently forcing the GrIS to lose mass with at least 27 cm of sea level rise committed regardless of future climate pathways. Greenland’s total ice mass corresponds to ~7 m of sea level, and recent paleo-data indicates that at least 1.4 m of ice loss occurred during Marine Oxygen Isotope stage 11 around 420.000 years ago. Thus, it is crucial to inform Earth System Models on past GrIS dynamics, in particular when and how fast major reductions in ice volume occurred. Part of the task for P2F WP9 is to apply information from deep ocean drilling records to identify the response of the Greenland Ice Sheet to warm climate extremes. With focus on the Pleistocene “super-interglacials” and abrupt transitions, this presentation compares various proxy-data from deep drilling sites west (IODP400 Baffin Bay) and south (IODP303 Labrador Sea) of Greenland which are influenced by the warm North Atlantic surface waters.
How to cite: Knutz, P., Monedero-Contreras, R. D., Störling, T., Perez, L. F., Sliwinska, K., Kjær, H. A., Zeppenfeld, C., Sangiorgi, F., and Nelissen, M.: Abrupt Pleistocene transitions in deep ocean drilling records west and south of Greenland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21541, https://doi.org/10.5194/egusphere-egu26-21541, 2026.
Accurately representing abrupt climate transitions such as Dansgaard–Oeschger (DO) events in climate models is essential for understanding past climate dynamics and improving projections of future tipping points. However, these models contain numerous uncertain parameters that are traditionally tuned manually, a process that is not only time-consuming but also subjective and limited in its ability to quantify parameter uncertainty. While systematic calibration approaches can provide rigorous parameter estimation, Bayesian inference methods such as MCMC require many sequential model evaluations, making them computationally prohibitive for complex climate models.
We present a systematic framework for climate model calibration that combines machine learning emulation with Bayesian inference to rigorously estimate model parameters and their uncertainties. Using CLIMBER-X, an Earth system model of intermediate complexity that successfully simulates DO-like oscillations in the Atlantic Meridional Overturning Circulation (AMOC) (Willeit et al., 2024), we develop a proof-of-concept for this calibration approach. We train an emulator that accurately approximates the model's AMOC response for a set of key ocean parameters, enabling efficient model evaluations.
We employ both Markov Chain Monte Carlo (MCMC) sampling and Simulation-Based Inference (SBI, Cranmer et al., 2020) techniques to estimate posterior distributions of these key model parameters. The ML emulator reduces computational cost by several orders of magnitude, making systematic parameter estimation and efficient exploration of the parameter space feasible. Since CLIMBER-X already produces realistic DO-like events, this serves as an ideal test case for validating the calibration framework. This work emphasizes the potential of ML-based emulation to accelerate systematic calibration in paleoclimate modelling.
References:
Willeit, M., Ganopolski, A., Edwards, N. R., and Rahmstorf, S.: Surface buoyancy control of millennial-scale variations in the Atlantic meridional ocean circulation, Clim. Past, 20, 2719–2739, https://doi.org/10.5194/cp-20-2719-2024, 2024.
Cranmer, K., Brehmer, J., and Louppe, G.: The frontier of simulation-based inference, Proc. Natl. Acad. Sci. USA, 117, 30055–30062, https://doi.org/10.1073/pnas.1912789117, 2020.
How to cite: Kowalczyk, K. and Boers, N.: Efficient Bayesian Calibration of Climate Models via Machine Learning Emulation: Application to Dansgaard-Oeschger Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12141, https://doi.org/10.5194/egusphere-egu26-12141, 2026.
Obtaining a statistical description of the extreme events that occur in a system that exhibits tipping behaviour is challenging due to the strong changes that the system undergoes. In this work, we use non-stationary Generalized Extreme Value (GEV) distributions to study the statistics of the extremes while capturing their temporal variability relative to a covariate data series which can be a driver or a response to the tipping. We exemplify this methodology by employing 8000 year long CCSM4 simulations with low concentrations of atmospheric CO₂ that show spontaneous D-O oscillations. This setting allows to study the minimum annual temperatures across the globe as a function of the temporal variability of the strength of the AMOC. The parameters of the distribution convey information about how the nature of the changes observed and its spatial variability, giving an insight on how the strength of the AMOC is related with the magnitude, variability and tails of the distributions. The extrapolation capabilities of this method are discussed compared to other studies and mechanisms of AMOC collapse.
How to cite: del Amo, I. and Ditlevsen, P.: Dansgaard-Oeschger Events as Laboratory for Extremes Variability under AMOC Collapse, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19670, https://doi.org/10.5194/egusphere-egu26-19670, 2026.
Extreme weather represents one of the most significant consequences of a warming climate. Improving constraints on how such events may manifest in the future is therefore a key priority, particularly for hazards that lead to severe societal, ecological, and financial impacts, such as heatwaves, extreme rainfall, droughts and their compounding effects.
Past interglacial periods provide physically realised instances of warm-climate states that can be used to contextualise ongoing anthropogenic warming and to inform future changes. Each interglacial is characterised by a distinct combination of orbital forcing and greenhouse gas concentrations, ice-sheet configuration, and background climate. Comparing these periods allows the partial isolation of the roles played by different climate drivers and large-scale circulation patterns in shaping the frequency, intensity, variability and spatial distribution of extreme events.
Here, we compare extreme weather characteristics across four interglacial periods: the mid-Holocene (6 ka), the Last Interglacial (127 ka), Marine Isotope Stage 11 (408 ka), and Marine Isotope Stage 31 (1072 ka), alongside a pre-industrial control. The analysis is based on preliminary equilibrium time-slice simulations conducted using the HadGEM3-GC5.0 coupled climate model, which also enables an initial assessment of model performance across a range of interglacial climates. We demonstrate how distinct warm-climate conditions have affected polar and global extreme events in the past and discuss the mechanisms underpinning these changes and their relevance for future climates.
How to cite: Neild, J., Sime, L., Zhang, X., McLaren, A., Malmierca-Vallet, I., and Diamond, R.: Extremes of the past: what interglacial periods reveal about weather of the future, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17548, https://doi.org/10.5194/egusphere-egu26-17548, 2026.
The Zagros mountains of Iran reside at a climatically sensitive convergence zone between three major atmospheric circulation systems: the mid-latitude Westerlies, the Indian Ocean Monsoon, and the Intertropical Convergence Zone. At a regional scale, variability in these systems has strongly shaped hydroclimate and human–environment interactions through time. More locally, as this mountain range exists adjacent to the Fertile Crescent, their dynamic interplay has implications for the very earliest of human civilizations. Thus, climate and ecological reconstructions help us to shed light on some of the most pressing archaeological questions, but they also help us to understand how humans have adapted to climatic change.
Lake Zeribar provides a well-established palaeoenvironmental archive for the central Zagros Mountains, with previous palynological analyses of lacustrine sediment cores spanning approximately the last 40,000 years BP. Building on this foundational work, we develop a quantitative climate reconstruction by integrating fossil pollen assemblages with a modern calibration framework. A regional climate space is constructed using open-access pollen data from the Eurasian Modern Pollen Database (EMPD2), while associated climate variables are derived from WorldClim. Fossil pollen assemblages from Lake Zeribar are then used to reconstruct mean annual precipitation and temperature, providing new quantitative constraints on past hydroclimate variability in this climatically sensitive region. While acknowledging the limitations inherent to classical pollen-climate transfer functions, this study represents a primary step in a larger climate modelling project (Swiss-French Sinergia MITRA Project) for the eastern Fertile Crescent region. The resulting reconstruction provides a benchmark against which more complex and mechanistic approaches will be evaluated.
How to cite: Djamali, M., Enke, S., Gandouin, E., and Guiot, J.: A continuous quantitative pollen-based climate reconstruction for Lake Zeribar, Southwest Asia since Marine Isotope Stage 3, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19104, https://doi.org/10.5194/egusphere-egu26-19104, 2026.
Tipping points in the climate system mark abrupt, irreversible dynamic transitions, posing difficulties for prediction and risks for mitigation. Developing reliable early-warning indicators is therefore essential. Linear response has long been used for predicting a system's change with respect to external perturbations. The stability of the predictions depend on how far the system from tipping and, hence, it provides a candidate measure to detect tipping events. Here, we propose using the Koopman operator spectrum to quantify linear response stability through the spectral gap, which shrinks near bifurcation. We apply this approach to Veros, an ocean general circulation model, and demonstrate that spectral-gap narrowing precedes critical transitions— providing the basis for Koopman-based response prediction in complex climate models.
How to cite: Santos Gutierrez, M.: Devicing early-warning signals using linear response: A Koopman operator approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18521, https://doi.org/10.5194/egusphere-egu26-18521, 2026.
The Dansgaard–Oeschger events are sudden and irregular warmings of the North Atlantic region that occurred during the last glacial period. A key characteristic of these events is a rapid shift to warmer conditions (interstadial), followed by a slower cooling toward a colder climate (stadial), resulting in a saw-tooth pattern in regional proxy temperature records. These events occurred many times during the last 100,000 years and have been hypothesized to result from various mechanisms, including millennial variability of the ocean circulation and/or nonlinear interactions between ocean circulation and other processes. Our starting point is a non-autonomous, conceptual, but process-based, model of Boers et al. [Proc. Natl. Acad. Sci. 115, E11005–E11014 (2018)] that includes a slowly varying non-autonomous forcing represented by reconstructed global mean temperatures. This model can reproduce Dansgaard–Oeschger events in terms of shape, amplitude, and frequency to a reasonable degree. However, the model of Boers et al. has instantaneous switches between different sea-ice evolution mechanisms on crossing thresholds and, therefore, cannot show early warning signals of the onset or offset of these warming events. We present a regularized version of this model by adding a fast dynamic variable so that the switching occurs smoothly and in finite time. This means the model has the potential to show early warning signals for sudden changes. However, the additional fast timescale means these early warning signals may have short time horizons. Nonetheless, we find some evidence of early warning for the transition between slow and rapid cooling for the model.
How to cite: Hobden, B., Ritchie, P., and Aswhin, P.: Regularization of a conceptual model for Dansgaard–Oeschger events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-380, https://doi.org/10.5194/egusphere-egu26-380, 2026.
Arctic sea ice is a critical component of the climate system, yet its long-term variability and drivers remain poorly understood due to the scarcity of direct paleoclimate records. In this study, we utilize bromine records preserved in four Greenland ice cores—NEEM, DYE-3, EGRIP, and RECAP—to reconstruct changes in Arctic sea ice cover over the past 15,000 years. Bromine in polar snow and ice is primarily derived from "bromine explosions" occurring over seasonal sea ice surfaces. These are autocatalytic photochemical reactions in which sea salt from brine and frost flowers on newly formed sea ice is activated, releasing reactive bromine into the atmosphere. Because these processes are strongly linked to the presence of first-year (seasonal) sea ice, bromine enrichment in ice cores reflects the extent and variability of seasonal sea ice cover. The combined records provide a high-resolution, multi-site perspective on sea ice variability during the late glacial–Holocene transition and throughout the Holocene. By integrating these records, we explore the spatial variability of sea ice changes and highlight the heterogeneous response of the Arctic Ocean to climatic perturbation. These results offer new insights into the mechanisms controlling past sea ice variability, providing important context for evaluating future Arctic change.
How to cite: Dey, R., Segato, D., Spolaor, A., and Kjær, H. A.: Reconstructing 15,000 years of Arctic sea ice dynamics: High-resolution bromine records from the late Glacial to the Holocene, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7352, https://doi.org/10.5194/egusphere-egu26-7352, 2026.
The future of the Amazon basin is of great concern front projected impacts due to climate change. Current estimates of hydrological changes are said to point toward unprecedented stages in many aspects, including floods and droughts. However, hydrological monitoring is often limited in time and can mask long term natural variability, affecting conclusions regarding present and future trends. Therefore, tree-ring time series are a valuable complement to this kind of assessment and can provide insights on the long-term occurrence of small and large floods and droughts. Here, we propose a preliminary reconstruction of annual floods from 1850 to 2016 based on a tree-ring δO18 series for the Paru basin, located in the northeastern Amazon. Isotope data present strong (r between 0.6-0.9 in module) and spatially consistent correlation with annual floods and with basin-aggregated rainfall from 1980-2016 at the Paru and nearby basins. This encouraged us to explore a simple linear regression model by fitting δO18 and annual flood series. The model was fitted using simulated discharge from MGB-SA hydrological model from 1980 to 2016 (and compared with observed record), and resulted in a r2 > 0.5 for more than 200 river reaches near the tree-ring data’s site. Regression models presented a success rate of >70% in classifying small flood years, while presenting >60% and >40% for regular and large floods respectively. This preliminary assessment indicates the potential of hydrological reconstruction of floods based on Paru’s δO18 data, enabling valuable insights on part of the Amazon hydrological variability since mid-19th century. Future perspectives could include hydrological modelling based on rainfall ensembles built from this series for more detailed assessments.
How to cite: Torres Miranda, P., Cauduro Dias de Paiva, R., and Granato-Souza, D.: Potential reconstruction of 19th century flood variability in the northeastern Amazon using tree-ring δO18, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13770, https://doi.org/10.5194/egusphere-egu26-13770, 2026.
