1. Earth System Reconstructions and Digital Twins
- Integrating paleogeographic data and advanced modeling (e.g., machine learning) to reveal past environmental changes and major Earth system transitions.
- Building digital twins of the planet by fusing diverse datasets and numerical models, emphasizing open, community-driven approaches.
2. Anthropogenic and Natural Forcing for CMIP6, CMIP7, and beyond
- Developing and evaluating historical and future time series of climate drivers (e.g., greenhouse gases, aerosols, land-use changes).
- Investigating how changes in forcing propagate through the climate system, using both observational data and idealized or multi-model experiments (CMIP6, CMIP7, etc.).
3. Model Disparities and Uncertainty
- Identifying the causes of divergent outcomes within CMIP ensembles, including internal variability, parameterization, external forcings, and ESM architectures.
- Employing reduced-complexity models and emulators to capture underexplored regions of uncertainty and guide more robust climate projections.
4. Critical Model Development and Impact Research
- Refining ESMs to reduce uncertainties and improve model performance, with emphasis on interdisciplinary approaches.
- Addressing regional-scale challenges in using CMIP outputs for impact studies, ensuring that policymakers and non-experts can effectively interpret climate projections.
We encourage submissions that bridge these topics, highlight open research and interdisciplinary collaboration, and showcase the work of early career researchers.
Orals: Thu, 1 May | Room -2.41/42
Climate forcings are the input drivers to coupled climate models (AGOCMs) and earth system models (ESMs). They are routinely used as part of the coupled model intercomparison project (CMIP). Here we present the historical greenhouse gas forcing used in the seventh phase of CMIP (CMIP7) and compare it to its predecessors from CMIP6 and CMIP5. We show that revised methods and input data have had little effect on historical estimates of greenhouse gas forcing, but that greenhouse gas forcing has continued to increase since 2015 (the end of the CMIP6 historical experiment), even if forcing from some specific gases has decreased. Beyond the greenhouse gas forcings, there are a number of other forcings involved in CMIP. Following on from our involvement in the CMIP Forcings Task Team, we present an outline for moving towards sustained, roughly annual, releases of these forcings and discuss the challenges for realising this possibility.
How to cite: Nicholls, Z., Pflüger, M., and Meinshausen, M.: CMIP7 historical greenhouse gas forcing and steps towards sustained releases, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5765, https://doi.org/10.5194/egusphere-egu25-5765, 2025.
Stratospheric aerosols, most of which originate from explosive volcanic sulfur emissions into the stratosphere, are a key natural driver of climate variability. They are thus one of the forcings provided by the Coupled Model Intercomparison Project (CMIP) Climate Forcings Task Team for the CMIP7 Fast Track, a set of climate model experiments designed to deliver the Intergovernmental Panel on Climate Change (IPCC) 7th assessment cycle. In this work, we document the final version of the stratospheric aerosol forcing datasets delivered to modelling groups for CMIP7 Fast Track. Our datasets cover the 1750-2023 period to meet to the need of modelling groups who might run extended historical simulations starting in 1750 instead of 1850. We produced one volcanic stratospheric sulfur emission dataset catering for the needs of models which have a prognostic interactive stratospheric aerosol scheme, as well as a stratospheric sulfate aerosol optical property dataset required by models that cannot interactively simulate stratospheric sufate aerosols. For the satellite era (from 1979 onwards), sulfur emissions and sufate aerosol optical properties are based on the MSVOLSO2L4 and GloSSAC datasets, respectively. For the pre-satellite era (1750-1978), the emission dataset is based on ice-core datasets complemented by the geological record for small-moderate magnitude eruptions not captured in ice-core records. Although inferring emissions of these eruptions from the geological record is highly uncertain, our approach minimizes an important bias in the pre-satellite era forcing, both in terms of mean and variability. The pre-satellite aerosol optical property dataset is directly derived from emissions using an updated version of EVA_H, a reduced-complexity volcanic aerosol model. This ensures methodological consistency between our emission and optical property datasets, and maximizes consistency with methodologies used in the paleoclimate (PMIP) and volcanic forcing (VolMIP) model intercomparison projects in CMIP6. We will present extensive comparison between our CMIP7 Fast Track dataset and the CMIP6 dataset. Last, we will discuss the main challenges to improve stratospheric aerosol datasets in the future and to move to high frequency (yearly or less) extension and update instead of an ad-hoc production for each CMIP phase.
How to cite: Aubry, T., Toohey, M., Schmidt, A., Kovilakam, M., Sigl, M., Khanal, S., Chim, M. M., Johnson, B., Carn, S., Verkerk, M., Nicholls, Z., and Smith, I.: Historical stratospheric aerosol optical properties and volcanic sulfur emissions for CMIP7 Fast Track, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3831, https://doi.org/10.5194/egusphere-egu25-3831, 2025.
We present the CMIP7 solar forcing dataset and its validation both for the effects of solar irradiance and particle forcing. In particular we present the results from first simulation runs that use the new CMIP7 as well as the previous CMIP6 dataset for the period 2002-2012, covering two solar maxima and a deep solar minimum. Specifically, we present simulation runs carried out with the chemistry-climate models WACCM, SOCOL, EMAC, ICON and KASIMA to determine the response to the solar SSI and particle forcing. The performance of the CMIP7 recommendations with respect to atmospheric radiative heating and composition will be evaluated both compared to the CMIP6 recommendations, and to satellite observations of atmospheric trace gases. The different responses and their implications will be discussed.
How to cite: Haberreiter, M. and the CMIP7 Solar Forcing Validation Team: CMIP7 solar forcing validation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20295, https://doi.org/10.5194/egusphere-egu25-20295, 2025.
General circulation models (GCM) can reasonably reproduce the global mean temperature trend during the historical period. In this study, we examine the performance of CMIP6 models in reproducing the changes in land-ocean temperature contrast between 1979-2014 during the comprehensive satellite observation era. The observed land-ocean warming contrast, defined as the land warming trend divided by the ocean warming trend, is completely outside the model spread of the historical scenario, indicating that the models severely underestimate land temperature increase relative to global mean temperature warming. Even when sea surface temperatures are prescribed to observations in AMIP experiments, the land warming trend remains outside the model spread, particularly between 15S and 15N. This was shown to be because GCM overestimates the increase in specific humidity on tropical land and underestimates the drying trend on tropical land. Since future projections over land have a significant impact on human activity, improving the representation of tropical land surface processes in GCMs is essential.
How to cite: Toda, M., Kang, S., and Shaw, T.: Climate Models Underestimate Satellite Era Land-ocean Warming Contrast in the Tropics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11291, https://doi.org/10.5194/egusphere-egu25-11291, 2025.
The projected changes in the hydrological cycle under global warming remain highly uncertain across current climate models. Here, we demonstrate that the observational past warming trend can be utilized to effectively constrain future projections in mean and extreme precipitation on both global and regional scales. The physical basis for such constraints relies on the relatively constant climate sensitivity in individual models and the reasonable consistency of regional hydrological sensitivity among the models, which is dominated and regulated by the increases in atmospheric moisture. For the high-emission scenario, on the global average, the projected changes in mean precipitation are lowered from 6.9% to 5.2% and those in extreme precipitation from 24.5% to 18.1%, with the inter-model variances reduced by 31.0% and 22.7%, respectively. Moreover, the constraint can be applied to regions in middle-to-high latitudes, particularly over land. These constraints result in spatially resolved corrections that deviate substantially and inhomogeneously from the global mean corrections. This study provides regionally constrained hydrological responses over the globe, with direct implications for climate adaptation in specific areas.
How to cite: Dai, P., Nie, J., Yu, Y., and Wu, R.: Constraints on regional projections of mean and extreme precipitation under warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14119, https://doi.org/10.5194/egusphere-egu25-14119, 2025.
The CMIP ensembles represent a partial exploration of our uncertainty in the future physical climate under various scenarios for future greenhouse gas emissions. They are thus valuable tools for exploring the potential consequences of climate change for society. One aspect of this is the impact on global and national economies. In the economics literature this is often addressed through a “damage function” which relates economic damages to national, regional or global changes in temperature.
Here we will present an assessment of the economic damages implied by the CMIP6 ensemble for various nations/regions, different Shared Socio-Economic pathways, and, crucially, a variety of different damage functions. A number of important factors will be highlighted including:
- The uncertainty in economic damages which arises from the chaotic nature of the climate system, characterised by those CMIP6 models with relatively large initial condition ensembles.
- The relative consequences for economic assessments of uncertainty in the damage function, the choice of CMIP6 model (model uncertainty), chaotic uncertainty (initial condition uncertainty), and scenario uncertainty.
- How these factors vary by country and region.
CMIP6 only represents a limited exploration of uncertainty in the physical climate response and there is also considerable uncertainty in the damage functions beyond that currently explored in the literature. These represent deep uncertainty. We will present plans for future work to embed more thorough explorations of epistemic uncertainty into future analyses, including the consequences of crossing tipping points. These considerations are valuable when considering the design and implementation of the CMIP7 project. What would be the most useful design characteristics if the target were economic assessments? We will address this question in terms of both the size of initial condition sub-ensembles, the diversity of models included, and the value of a mixture of higher and lower resolution model implementations.
How to cite: Rosser, J. and Stainforth, D.: Economic consequences of CMIP diversity and targets for CMIP7, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12178, https://doi.org/10.5194/egusphere-egu25-12178, 2025.
Earth system reconstructions are key to understanding past climate dynamics, environmental variability, and biogeochemical cycles, revealing how Earth responds to natural and human influences. These reconstructions require integrating geological, geophysical, and geochemical data with advanced computational models.
Recent advancements in Large Language Models (LLMs) enhance the processing of complex datasets, improving the accuracy and predictive power of Earth system reconstructions. To leverage this, we develope GeoGPT, a domain-specific LLM for geosciences, trained on open-source data. This open-source, non-profit project encourages broad collaboration among experts in broad branches of the geoscience and AI. GeoGPT helps advance Earth system reconstructions, offering deeper insights into Earth's past and future, and guiding responses to environmental challenges.
By harnessing the combined strengths of Geoscientists, AI experts, and the broader research community, GeoGPT aspires to unlock new avenues of exploration, accelerate breakthrough discoveries.
How to cite: Chen, H.: GeoGPT and Its Potential Applications for Earth system Reconstructions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7628, https://doi.org/10.5194/egusphere-egu25-7628, 2025.
Paleomagnetism provides the main quantitative tool for reconstructing Earth’s paleogeography. Apparent polar wander paths (APWPs), derived from paleomagnetic data, trace the motion of tectonic plates relative to the Earth’s rotation axis through geological time, providing a paleogeographic framework for studying the evolution of Earth’s interior, surface, and atmosphere. Traditionally, APWPs are calculated from study-mean paleomagnetic poles that are assigned equal weight, regardless of the number of paleomagnetic sites used to compute it and the uncertainties in the position or age of the pole. Here, we introduce the next generation of APWPs that are calculated from site-level paleomagnetic data instead of from study-mean poles. This alternative approach assigns larger weight to larger data sets and allows the incorporation of spatial and temporal uncertainties. We demonstrate the advantages of this new method with recently published APWPs based on compiled (Gallo et al., 2023) and simulated site-level data (Vaes et al., 2023). We show how the latter, a global APWP for the last 320 Ma, provides more reliable estimates of the apparent polar wander rate of all major tectonic plates, and discuss its implications for the rate and magnitude of true polar wander since 320 Ma. In addition, we introduce APWP-online.org: an online, open-source environment that provides user-friendly tools to compute site-level APWPs and to use them to quantify relative paleomagnetic displacements. We showcase how these tools are currently used to compute site-level APWPs, e.g., for the North China block and Tibetan terranes. Finally, we provide future directions for the construction of APWPs and highlight opportunities for improving their quality and resolution.
How to cite: Vaes, B. and van Hinsbergen, D.: Reconstructing paleogeography using site-level apparent polar wander paths, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16299, https://doi.org/10.5194/egusphere-egu25-16299, 2025.
Active margin topography has profoundly shaped Earth’s climate, biodiversity, and natural resource distribution over geological time. However, reconstructing paleotopography in these regions remains challenging due to the sparse and uneven distribution of proxies like stable isotope paleoaltimetry, palynology, paleobotany, and thermochronology. These traditional methods often leave large spatial and temporal gaps, with uncertainties in paleoelevation estimates reaching up to 2,000–3,000 m. To address these challenges, we introduce an innovative workflow utilizing artificial intelligence to reconstruct paleotopography at active margins since the Devonian. Using Explainable Boosting Machines (EBMs), we identify key factors such as plate kinematics, mantle dynamics, and climate that govern active margin topography. Insights from the EBM analysis guided the development of a Random Forest (RF)-based regressor which was then used to predict paleotopography through time. Our RF model achieved a mean error of 554 m when validated against present-day ETOPO elevation data. Our model highlights time-evolving subduction flux, trench migration rates, and upper mantle temperature as the primary controls on active margin topography. To validate our approach, we compare our reconstructions with existing paleotopographic models and geological proxies in two regions: the Cenozoic Andes and Mesozoic-Cenozoic Eastern China. For the Andes, our model closely matches the existing reconstructions, highlighting a ~4,000 km rapid rise of the Altiplano since the late Oligocene, driven by an increase in subduction flux (from 0.03 km³/yr to 0.10 km³/yr) and a transition in trench migration from retreating (2 cm/yr) to stationary, likely due to slab anchoring. In Eastern China, our model predicts sustained high topography (>2,500 m) during much of the Cretaceous, attributed to high subduction flux (>0.12 km³/yr) from the Pacific Plate and an advancing trench. A subsequent shift to trench retreat (-2 cm/yr) in the Late Cretaceous–Early Cenozoic led to back-arc extension and a decline in elevation to ~1000 m. Our study offers a transformative approach to bridging gaps in paleotopographic constraints, improving our understanding of the interplay between surface and interior processes. By providing a robust framework for reconstructing past landscapes, our model has significant implications for studying ecosystems, biodiversity evolution, and the metallogenesis of convergent margins.
How to cite: Singh, S. P., Seton, M., Zahirovic, S., and Wright, N. M.: Reconstructing the Topographic Evolution of Active Margins Since the Devonian Using Artificial Intelligence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9233, https://doi.org/10.5194/egusphere-egu25-9233, 2025.
One of the major challenges faced by the geotectonic community is how to determine the paleolongitude of continents and tectonic plates as we try to reconstruct Earth’s tectonic history back in time, because classic paleomagnetic record is only sensitive to paleolatitude. Torsvik et al. (2014) previously used mantle structure as a reference frame for palaeolongitude constraints back in Earth history, assuming that the two equatorial and antipodal large low shear velocity provinces (LLSVPs) observed in present-day Earth’s lower mantle are fixed and stable ancient structures unrelated to plate tectonic history and subduction geometry. However, such an assumption is inconsistent with true polar wander (TPW) record (Li et al., 2004, 2023), the cyclic occurrence of global mantle plume activity coupled with the supercontinent cycle (Li et al., 2008; and Zhong, 2009), and geodynamic modelling results (Zhong et al., 2007; Zhang et al., 2010; Flament et al., 2017).
In a recent paper of Li et al. (2023), we utilized palaeomagnetically interpreted TPW record, particularly inertia interchange true polar wander (IITPW) events, and global mantle plume record, to develop a dynamic global mantle reference frame that not only provides a first-order mantle dynamic evolution for the past 2 billion years, but also for the first time provides a way to trace the longitudinal change of continents and tectonic plates back in time. In particular, through the recognition of newly-defined type-1 and type-2 IITPW events coupled with plume record checking, we are now able to hypothesis that: (1) in periods with type-1 IITPW, the concerned supercontinent had developed its own degree-2 mantle structure (e.g., the antipodal LLSVPs divided by concurrent circum-supercontinent subduction girdle); (2) in periods with type-2 IITPW, a young supercontinent or multiple plates during the assembly of that supercontinent were moving over a legacy degree-2 mantle structure of the immediate ancestor supercontinent prior to the maturity of its own mantle structure. In our model, Nuna (lifespan 1600–1300 Ma) assembled at about the same longitude as the latest supercontinent Pangaea (lifespan 320–170 Ma), with an equatorial degree-2 mantle structure starting to exist as early as ca. 1700 Ma. Rodinia (lifespan 900–720 Ma) formed through introversion assembly over the legacy Nuna subduction girdle either ca. 90◦ to the west or to the east before the subduction girdle surrounding it generated its own degree-2 mantle structure by ca. 780 Ma (but not before 800 Ma). Pangea assembled over the subduction girdle of legacy Rodinian degree-2 mantle structure, with its own degree-2 mantle structure (the one we still observe today) formed no much earlier than 270 Ma.
References
Flament, N., Williams, S., Müller, R.D. et al., 2017. Nat. Commun. 8, 14164.
Li, Z.-X., Liu, Y. and Ernst, R., 2023. Earth-Sci. Rev. 238, 104336.
Torsvik, T.H., van der Voo, R., Doubrovine, P.V. et al., 2014. Proc. Natl. Acad. Sci. 111 (24), 8735–8740.
Zhang, N., Zhong, S., Leng, W., Li, Z.-X., 2010. J. Geophys. Res. Solid Earth 115(B6), B06401.
How to cite: Li, Z.-X.: Absolute longitudinal constraints for palaeogeographic reconstruction based on a dynamic mantle reference frame, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15897, https://doi.org/10.5194/egusphere-egu25-15897, 2025.
Posters on site: Thu, 1 May, 10:45–12:30 | Hall X5
The provision of solar forcing datasets for CMIP7 includes a dataset with scenarios from the present to 2300. This dataset contains daily values of the same variables as in the historical solar forcing for CMIP7, namely: solar spectral irradiance, medium energy electrons, solar energetic protons and galactic cosmic rays. In contrast to CMIP6, which had only two scenarios, for CMIP7 we will provide a large ensemble of scenarios to avoid selection bias.
Let us stress that we are providing scenarios, not forecasts: the reconstructions vary randomly in time, but their statistical and spectral properties are fully consistent with historical variations, providing realistic surrogates for solar forcing.
In this presentation we explain how historical observations are used to build these surrogate reconstructions. This process involves several steps, starting with the 14C reconstructions of past solar activity. These will be described in detail, together with the first version of the dataset.
How to cite: Dudok de Wit, T., Funke, B., Haberreiter, M., Marsh, D., Ermolli, I., Kinnison, D., Nesse, H., Seppälä, A., Sinnhuber, M., Usoskin, I., Asikainen, T., Bender, S., Chatzistergos, T., Coddington, O., Koldoboskiy, S., Lean, J., van de Kamp, M., and Verronen, P.: Solar forcing for CMIP7: making of future scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17597, https://doi.org/10.5194/egusphere-egu25-17597, 2025.
Back in 2017, solar forcing recommendations for the 6th round of the Coupled Model Intercomparison Project (CMIP) were provided which covered, for the first time, all relevant solar irradiance and energetic particle contributions. Since then, this dataset has been extensively used in climate model experiments and has been tested in various intercomparison studies. Further, new datasets have been come available. An International Space Sciene Institute (ISSI) Working Group has been established to review these recent achievements in order to define the strategy for building a revised solar forcing dataset for the 7th round of CMIP. After receiving community feedback on this strategy, a historical solar forcing dataset for CMIP7 has been recently constructed. Major changes with respect to CMIP6 include the adoption of the new Total and Spectral Solar Irradiance Sensor (TSIS-1) solar reference spectrum for solar spectral irradiance and an improved description of top-of-the-atmosphere energetic electron fluxes, as well as their reconstruction back to 1850 by means of geomagnetic proxy data. Solar irradiance varaibility in the reference forcing dataset is based on historical reconstructions generated with the new empirical NASA NOAA LASP (NNL) Solar Spectral Irradiance Version 1 model, NNLSSI1. In adition, an alternative solar irradiance dataset, based on SATIRE, is provided for sensitivity experiments. In this talk we will discuss the applied modifications with respect to CMIP6 and their implication for climate modeling. Ongoing activities on solar forcing uncertainty quantification and the construction of future solar forcing scenarios will also be summarized.
How to cite: Funke, B., Dudok de Wit, T., Haberreiter, M., Marsh, D., Ermolli, I., Kinnison, D., Nesse, H., Seppälä, A., Sinnhuber, M., Usoskin, I., Asikainen, T., Bender, S., Chatzistergos, T., Coddington, O., Koldoboskiy, S., Lean, J., van de Kamp, M., and Verronen, P.: Solar forcing for CMIP7, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15908, https://doi.org/10.5194/egusphere-egu25-15908, 2025.
Paleoclimate information has played a key role in demonstrating how the Earth System responds to a variety of external forcings and how the earth’s climate is tightly related to atmospheric greenhouse gas concentrations. Although no strict analogue of possible future climate states exists, testing our understanding of the earth system, as embedded in earth system models, for conditions widely different from the historical period, is made possible by the existence of paleoclimate and paleoenvironmental reconstructions. Since its start in 1995, PMIP, the Paleoclimate Modelling Intercomparison Project (https://pmip.lsce.ipsl.fr/), has fostered and coordinated model-model and model-data comparisons for key periods: the mid-Holocene, ~6000 years ago, the Last Glacial Maximum (LGM), 21,000 years ago, the last two millennia, the last interglacial, the mid-Pliocene warm period (MPWP) were the key periods for PMIP4-CMIP6, with specific targets for each period. For instance, the enhanced monsoons and response of the northern high latitudes for the mid Holocene, the fate of Arctic sea ice and climate of the last interglacial, large spatial gradients and equilibrium climate sensitivity for the LGM and MPWP. In addition, each of these periods stood as reference for further PMIP experiments aimed to better understand the response of the climate system to external forcings.
For the next CMIP phase, PMIP continues to contribute studies on the responses to external forcings. This poster will present the targets for the FastTrack last interglacial experiment (abrupt-127k) as well as future opportunities related to other periods (e. g. Kageyama et al., 2024). We look forward to discuss with the CMIP and PMIP communities to plan further cross-cutting work and analyses.
Acknowledgements and cited reference.
We are acknowledging the help of the PMIP community in building PMIP over the years.
Kageyama M, et al., (2024) Lessons from paleoclimates for recent and future climate change: opportunities and insights. Front. Clim. 6:1511997. doi: 10.3389/fclim.2024.1511997
How to cite: Kageyama, M., Brierley, C., and Peterschmitt, J.-Y. and the the PMIP community: Earth system responses to external forcings : opportunities from paleoclimate studies and the Paleoclimate Modelling Intercomparison Project (PMIP) for CMIP, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18925, https://doi.org/10.5194/egusphere-egu25-18925, 2025.
Earth system modelling is an important instrument to investigate climate change in an integrated way, taking into account the interactions between the different compartments of the Earth system. It is also an important tool to perform climate projections for different climate scenarios in order to take appropriate mitigation and adaptation measures. Such climate simulations are coordinated internationally as part of the World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project Phase 7 (CMIP7).
Climate forcings are key for defining the main drivers of climate change in climate simulations. A very important aspect of the CMIP7 intercomparison is that all participating models were run under similar experimental conditions. In particular, in using the same climate forcings in the different models.
The Alfred Wegener Institute (AWI) will participate in the CMIP7 project with two state-of-the-art Earth system models AWI-CM3 and AWI-ESM3. This is being done as part of the German contribution to the Coupled Model Intercomparison Project (CAP7). The AWI contribution to CAP7 includes the adaptation of the AWI-CM3 model to be able to use different forcing data (such as greenhouse gases, solar forcing, O3, and aerosol forcing) to fulfil the requirements of CMIP7. One task is therefore the implementation of the climate forcing dataset for anthropogenic aerosols MACv2-SP (currently available for CMIP6plus [Fiedler and Sudarchikova, 2024]) provided for CMIP7. For this purpose, an aerosol interface will be implemented in the AWI-CM3 climate model to read and process the aerosol forcing data provided by the MACv2-SP dataset.
In this presentation we will discuss the implementation of the MACv2-SP data into the AWI-CM3 climate model and present first results of the responses to these forcings.
How to cite: Wieters, N., Streffing, J., Goessling, H., and Jung, T.: Preparing AWI-CM3 for CMIP7: Implementing anthropogenic aerosol forcing (MACv2-SP), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10752, https://doi.org/10.5194/egusphere-egu25-10752, 2025.
A common set of simulation is important for intercomparison between different models. The ‘entry-card’ to participate in CMIP is to perform the baseline DECK simulations (1850-control, 1pctCO2, abrupt-4xCO2, historical-amip). However, performing the control and historical simulations from an 1850 baseline is prohibitively expensive for high-resolution, fully-coupled, general-circulation-models (GCMs). Therefore, HighResMIP chose to use a shorter experimental protocol based on 1950 conditions alongside a shorter spin-up length and simplified aerosols. Because of this difference in protocol, it is not clear exactly how the HighResMIP simulations relate to the other CMIP simulations. In this study we analyse the control and historical simulations of the HadGEM3-GC3.1 model, which performed control and historical simulations based on both 1950 and 1850 baselines. Our results show that the absolute temperature is sensitive to the different experimental protocol, but the anomalies are much more comparable. This opens an interesting discussion on whether climate change should be discussed in terms of absolute values or anomalies. The difference in the absolute value (and mean state) is largely due to the different aerosol scheme used in CMIP and HighResMIP for this particular model. The second phase of HighResMIP no longer require models to use Easy Aerosol, so modelling centres should use the same aerosol scheme if they would like their HighResMIP simulations to be comparable to CMIP simulations.
How to cite: Lai, M. and Roberts, M.: 1950-control vs 1850-control: How do HighResMIP simulations relate to CMIP simulations?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12926, https://doi.org/10.5194/egusphere-egu25-12926, 2025.
The global sea surface warming pattern emerged since the early 1980s is characterized by a boomerang-shape warming in the western Pacific, the basin-wide warming in the Indian Ocean north of 30°S, and a triple-stripe warming in the North Atlantic. This pattern can be obtained with or without El Niño/La Niña signals, indicating the independence of El Niño/La Niña, and is the leading EOF with the El Niño/La Niña signals removed. A negative phase of this pattern started emerging in the early 1980s, switched to positive phase in the 1990s, and has been becoming more prominent for the past few years.
CMIP models have been found to have difficulty simulating observed global sea surface temperature (SST) trend, especially the cooling trend in the tropical eastern Pacific. However, the cooling trend in the tropical eastern Pacific in the past four decades is statistically insignificant in our trend analysis adopting a more stringent signal detection method (namely, the False Discovery Rate, FDR). By applying the same trend detection and EOF approach to the simulated SST in the historical simulations of forty CMIP6 models by removing El Niño/La Niña signals, we detected in the ensemble mean SST a trend pattern closely resembling the observed, which also changes from negative to positive phases in the late 1990s and continues becoming more positive into 2014. Whereas each model has slightly different performance in simulating this trend pattern, the ensemble time series of corresponding trend pattern in each model correctly reflects the emergence and enhancement of the warming pattern in the past four decades. However, this model ability seems to be masked by the large fluctuations of El Niño/La Niña, an intrinsic climate mode contributing large internal variability to the global domain, and its temporal fluctuations cannot be synchronized in the coupled models in the historical experiments, which are strongly driven by continuously increasing radiative effect of greenhouse gases concentration. On the other hand, The models seem to be capable of simulating the emergence of the global ocean warming pattern in response to the prescribed increasing greenhouse gas concentration.
How to cite: Hsu, H.-H. and Chen, Y.-L.: CMIP6 Models Properly Simulate the Emergence of Global Ocean Warming Pattern, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14140, https://doi.org/10.5194/egusphere-egu25-14140, 2025.
Reliable climate projections are crucial for informed decision-making in water resource planning and management. However, selecting suitable Global Climate Models (GCMs) remains challenging due to inherent uncertainties and computational constraints. This study introduces a novel hybrid approach for GCM selection, focusing on models that exhibit consistency in projecting future climate changes and skill in representing current climate conditions, including average climate, seasonal patterns, and climatic variations. GCM performance in simulating these critical properties was evaluated for rainfall, maximum temperature, and minimum temperature using the Kling-Gupta Efficiency (KGE) metric, resulting in a structured 3×3 performance matrix for each GCM. The matrix distances, quantifying the disparities between each GCM's performance matrix and the ideal reference matrix, were used to represent overall model performance. GCMs were then ranked based on these differences using the Jenks natural breaks classification method to identify the top-performing models for ensemble construction. The proposed method was tested by selecting GCMs for Nigeria from 19 CMIP6 GCMs. Results indicate that 15 GCMs consistently projected future climate within a 95% confidence interval. Further evaluation reveals that ACCESS.ESM1.5, BCC.CSM2.MR, CMCC.ESM2, and MRI.ESM2.0 are the most suitable for simulating Nigeria's climate. The multi-model ensemble means of the selected GCMs projected a notable increase in rainfall by 10 to 40% over most of the country and maximum and minimum temperatures by 1.0 to 3.5°C and 0.5 to 4.0°C, respectively. The proposed approach offers an effective tool for GCM selection to enhance climate projection reliability.
How to cite: Kumar, S., Choudhary, M. K., and Thomas, T.: Comparative Analysis of GCM Selection Approaches for Climate Change Impact Assessment in India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1118, https://doi.org/10.5194/egusphere-egu25-1118, 2025.
With the major challenges posed by climate change and significant shifts in Earth systems, the need for high-precision and diverse climate predictions has grown. These predictions aim to explore a variety of scenarios, such as the Shared Socioeconomic Pathways (SSPs). Advances in computational power have enabled the development of sophisticated coupled physical-biogeochemical-ecological models of marine systems. However, these models remain computationally intensive and energy-demanding, raising questions about the appropriate level of complexity relative to the availability of independent data for accurate calibration, and calls for simplification to reduce execution time. Here, we aim to simplify the Eco3M-MED model, which is a complex biogeochemical model representing the low trophic levels (up to mesozooplankton) in the ocean through 37 state variables, and which is intended to be run at the scale of the Mediterranean basin.
Common simplification methods include conservation analysis, lumping, time exploration, and sensitivity analysis. Since most of these simplification methods reduce or even penalize the ability to interpret model results, or require complex implementation, we have chosen a simple, classic method, based on the local sensitivity analysis (One-Factor-At-A-Time, OFAT) method that does not impair this ability. This work's originality lies in the approach adopted to obtain different declinations of the reduced model. This approach indeed benefits from an original strategy for parametrizing the Eco3M-MED model, initiated several years ago and recently implemented in practice. This strategy consists of the construction of a set of consistent parameters, resulting in the establishment of relations between the so-called core parameters and dependent parameters. Core parameters are perturbed based on the level of knowledge of each parameter. The main objective of this study is to apply this novel approach to identify the biogeochemical processes that can be removed with minimal impact on model performance, thereby enabling model simplification and reducing computational costs. We also apply the principle that a single simplified model is not necessarily the best solution, and aim instead to derive a family of simplified models associated with different usage objectives, ensuring that the simplified model reproduces certain quantities well in particular. The criteria used to derive a simplified model from the sensitivity analysis are also subject to analysis to identify their influence on the degree of simplification. Finally, the computational efficiency and accuracy of simplified models were compared with the full model to determine optimal simplification for specific applications. Future research will focus on performing global sensitivity analysis on high-impact core parameters to assess uncertainties in both the full and simplified models.
How to cite: Zhang, Y., Baklouti, M., and Brasseur, P.: Sensitivity-driven simplification of complex ecosystem models: Integrating mechanistic insights for cost reduction and predictive accuracy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6300, https://doi.org/10.5194/egusphere-egu25-6300, 2025.
3D model is a 3D digital representation of objective things, which has been widely applied in fields like urban construction, disaster prevention and mitigation, medical research, biological science, industrial manufacturing, agricultural production, etc. As a special 3D model, 3D geological model possesses the characteristics of 3D model and plays a fundamental role in geological survey, mineral exploitation, underground engineering and smart city construction.With the development of intelligent sensing technology and 3D geological modeling technology, the scale of 3D geological model data increases exponentially. Meanwhile, with the pace of large-scale underground engineering and smart city continuing to increase, 3D geological model with fine large scenes is being eagerly required. The rapid growth of data and the refinement of large application scenes bring new challenges to the real-time dynamic visualization of 3D geological models. These challenges are mainly reflected in the new technical problems related to 3D geological model rendering.This study focuses on 3D geological model rendering and puts forward the corresponding solutions. The validity of the technology has been proved by the simulation test of cluster cloud environment consisting of 5 computers. The technique has been applied in the construction of 3D geological information and visualization system in transparent Xiong’an.Firstly, the data organization mode of two common structures of 3D geological model (3D geological structure model and 3D geological high-precision grid model) is analyzed, and a distributed storage strategy of 3D geological model based on MongoDB is proposed. Aiming at the characteristics of multi-layer data in z-direction of 3D geological structure model, an octree index mechanism is proposed to improve the efficiency of data scheduling according to the z-direction spatial information and layer information. The rendering optimization of a single node 3D geological model is studied. The rendering in the cloud environment still needs the cooperation of each sub-node. Therefore, the overall rendering efficiency in the cloud environment can be improved by adopting efficient rendering optimization strategies for the 3D geological model of each node and selecting an effective node scheduling strategies. Single-node 3D geological model rendering is mainly performed by transferring data from memory to GPU. The communication between memory and GPU is a bottleneck, which will affect the overall rendering efficiency. Through the strategies of visibility elimination, LOD establishment, data merging and instance rendering optimization, this thesis effectively reduces the number of drawing calls and communication times. How to optimize and improve the overall performance of 3D geological model rendering in cloud environment from a global perspective is studied, and a multi-level distributed SCMP framework is proposed, which integrates the advantages of cluster, GPU, distributed storage, etc., to maximize the distributed computing ability of existing machines and improve the rendering efficiency in cloud environment. From the experimental data, the node invocation optimization strategy with “GPU+CPU” can ensure that the frame rate of the four rendering nodes and the end-user scene in the cloud environment is stable at about 35 frames per second, and can achieve satisfactory cluster load balancing effect.
How to cite: Song, Y., Gao, Z., Song, G., and Li, J.: Research on Key Technologies of 3D Geological Model Rendering in Cloud Environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4053, https://doi.org/10.5194/egusphere-egu25-4053, 2025.
As a data-intensive science, earth system science has been focusing on the research of living environment and constituent element’s characteristics, including its forming time, location and evolution. With big data and AI boosting, there are more opportunities and challenges for geological research transformation. And it is more likely to improve geological survey and geological research by means of information method such as AI algorithm, methods, tools, software, and etc.. As for the storage, distribution and application of different format and discipline data, the key is to set up a series of rules and tools to realize the data services’ flexible using in security. It adopts hybrid data management framework to build up an unified index to support the spatial geological data finding. The matching platform is also developed to realize the geological achievements distribution. Moreover, it can significantly benefit faster and more efficient research. In all, the technique has been applied successful on Chinese geological survey information platform with great reuse adaptability on other platforms.
How to cite: cui, N. and gao, Z.: Research and application of key technologies of geological data platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5383, https://doi.org/10.5194/egusphere-egu25-5383, 2025.
GeoGPT is a non-profit domain-specific Large Language Model for geosciences, trained based on open-source data. It provides an effective solution to the challenges of managing large data volumes, complex formats, and low efficiency in the utilization of books and papers in the field of paleontology. Its powerful data extraction capabilities will significantly enhance the efficiency of extracting, analyzing, and building databases for data of various formats, sizes, and origins. This enables scientists to construct online fossil datasets and empowers paleontologists to develop innovative tools such as paleontological classification assistants. Not only does this accelerate scientific research progress, but it also makes the acquisition and application of paleontological data, such as invertebrate fossils, more convenient, ultimately driving comprehensive progress in the field of paleontology.
How to cite: Xiang, Z.: GeoGPT: Transforming Paleontology with AI-Powered Data Extraction and Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14239, https://doi.org/10.5194/egusphere-egu25-14239, 2025.
The integration of big data, cloud models, and extensive knowledge to drive new knowledge discovery through data is a new paradigm for research in the field of Earth sciences. Although the advancement of big data technologies and infrastructures has simplified data acquisition, deep-time geoscience still faces challenges such as fragmented data, difficulties in visualization, and insufficient computing power. To assist the broad community of geoscientists, we propose the "Deep Platform," a one-stop online research platform that utilizes cloud computing and advanced technologies. The platform provides open access to deep-time geoscientific data, knowledge, models, and computing power. It is designed to promote collaborative innovation and discovery among global geoscientists. The "Deep Platform" represents a significant advancement in geoscientific exploration, fostering global collaboration and advancing a data-driven research paradigm within the framework of open science.
How to cite: Hu, L.: DEEP Platform: Empowering Global Geoscientists in Data-Driven Research Era, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21952, https://doi.org/10.5194/egusphere-egu25-21952, 2025.
The past topographic evolution of the Tibetan-Himalayan orogen holds the key to understanding interactions between Earth, Climate and Life processes since deep times. This has been hindered so far by the lack of accurate paleogeographic reconstructions of the orogen through time based on well-dated reliable proxies of past elevations. In the sedimentary archives of the basins formed in the orogen during the collision, recovered fossil content including pollen, fish and mammals yielded first order estimates on elevations based on environmental conditions of nearest living relatives while leaf physiognomies provided more direct constraints. Stable isotope composition from ancient meteoric waters preserved in pedogenic carbonates and biomarkers have been recovered and interpreted in terms of paleoelevations assuming past meteoric lapse rates. Outside of the basins in the high massifs, synkinematic hydrous silicates preserving ancient rainfalls have been used for paleoaltimetry purpose, notably in the Himalayas. Despite these significant efforts, the new paleoelevation datasets have led more to controversy than consensus. Fierce debates currently involve several international groups. Widely different topographic growth scenarios have been proposed with end-members ranging from a high Plateau prior to the onset of the India-Asia collision (“Proto-Tibetan Plateau”), to a much more recent - mostly Miocene - uplift and the preservation of broad low elevation valleys late until the Neogene.
As part of the starting TIBETOP project (funded by the french ANR) we propose here a state-of-the-art review of paleoelevation proxies across the Tibetan-Himalayan orogen, ranging from surface records in sedimentary basins to deeper crustal rocks now exhumed in the relief and mountain belts bordering these basins. We present a compilation and reappraisal of the existing regional paleoelevation data including revised provenance, stratigraphy dating, and stable isotope data in basin records as well as structural context, exhumation and fluid-rock deformation interactions at different interfaces of the continental crust. The TIBETOP project thus aims to produce a set of interactive paleogeographic reconstructions through time with associated datasets constraining the Himalayan-Tibetan orogen since the India-Asia collision. These will be improved through the project to include new data and updated paleogeographic reconstructions made available to modelers of climatic, biotic and surface processes to enable testing the above cited fundamental hypotheses on the role of mountain and plateau building on Earth System processes over geologic time.
How to cite: Dupont-Nivet, G., Fidalgo, J.-C., Moreau, K., Rivera, L., Feng, Z., Fang, X., Lavé, J., and Licht, A.: Tibetan Plateau paleogeographic reconstructions during the India-Asia collision: from paleoelevation proxies to geodynamic models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21621, https://doi.org/10.5194/egusphere-egu25-21621, 2025.
A Digital Twin (DT) is a data-driven model of a physical entity with two-information flows that enables the direct interaction between both. DTs of the natural environment are typically constructed by fusing multi-modal measurements of some physical phenomena using Artificial Intelligence (AI) methods. The physical entity interacts with the DT through natural changes whilst the DT interacts with the physical entity through automated changes in sensing systems and through decision-making processes. Large-scale DTs of the Earth system are currently in development through initiatives such as Destination Earth (DestinE) whilst small-scale DTs for local monitoring are in development for numerous applications such as hazard warning, agriculture and eco-hydrology. Currently these systems are being developed independently yet combining them offers opportunities for calibrating large-scale DTs and improving the resolution of large-scale DTs by replicating the dynamics of smaller systems using AI methods. In this contribution, we develop a new concept through which to link small- and large-scale DTs in order to automate an agile sensing system that can respond to natural environmental variability and directly measure changes of interest. Large-scale DTs are built primarily through Earth Observation (EO) data and describe regional to global scale changes in the Earth system whilst small-scale DTs simulate local variability using in situ sensors such as Terrestrial Laser Scanners (TLS). Linking the two means the large-scale DT can inform small-scale DTs by adapting their measurements (e.g. spatial and temporal resolution, focus area of interest, specific physical measurements) in response to regional changes in, for example, weather patterns. We focus on the following components: 1) using the small-scale DT to downscale the large-scale DT and ‘zoom’ into areas of interest; 2) using both the small- and large-scale DT to automatically detect changes in the environment and acquire new measurements without human intervention; and 3) using the small-scale DTs to calibrate large-scale DTs. With the increasing development of digital twin technology in the environmental sciences, our new concept will enable better integration of DTs and improve monitoring performance, which can improve decision-making.
How to cite: Durrant, A., Harcourt, W. D., Höfle, B., Weiser, H., and Tabernig, R.: Linking small- and large-scale Digital Twins: A concept , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18187, https://doi.org/10.5194/egusphere-egu25-18187, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot 2
EGU25-9058 | Posters virtual | VPS30
Deep-Time Digital Twins: Integrating LithoPlates with the EarthBank PlatformFri, 02 May, 14:00–15:45 (CEST) vPoster spot 2 | vP2.9
The dynamic nature of Earth's lithosphere necessitates comprehensive tools for integrating geological data with plate tectonic frameworks across vast spatiotemporal scales. To address this challenge, EarthBank, in collaboration with the Earthbyte Group and Lithodat, has developed LithoPlates - a cloud-based deep-time reconstruction tool designed to support the visualisation and analysis of geological features within their paleogeographic contexts. LithoPlates leverages Earthbyte’s GPlates Web Service, enabling users to access pyGPlates functionalities and advanced plate tectonic models, offering researchers an intuitive platform for spatiotemporal analyses.
LithoPlates incorporates ten plate tectonic models, including the latest model published in 2024, which extends reconstructions back to 1.8 billion years. These models are seamlessly integrated into EarthBank’s public geochemistry data platform, enabling researchers to explore the tectonic settings and geological histories of their area of interest. By applying age-specific filters, users can visualise data within any chosen reconstruction timeslice within 1Ma steps, facilitating precise spatio-temporal analyses of geological processes such as formation, deformation, and material transport across Earth’s surface.
The platform’s dual capability to analyse data in both present-day and palinspastic geography significantly enhances its utility for geoscientific research. LithoPlates supports the reconstruction of geochronological and thermochronological data, providing a robust framework for investigating the evolution of Earth’s lithosphere. Its integration with EarthBank’s relational database further enables on-the-fly analysis of both data and metadata, offering real-time insights into complex geological systems. Robust export functionalities are also present including an open REST API, enabling users to seamlessly integrate their data and share results for further analysis.
Future advancements for LithoPlates include the integration of additional plate tectonic models, enhanced visualisation tools, and advanced filtering capabilities to refine comparative analyses across multiple reconstruction scenarios. These updates will improve uncertainty quantification, allow for more sophisticated model-data fusion, and facilitate the analysis of geophysical and geochemical datasets within a unified paleogeographic framework.
LithoPlates represents a transformative tool for advancing Earth system reconstructions by addressing key challenges in the integration of geological, geophysical, and environmental data. Its interdisciplinary approach aligns with the broader scientific goal of developing digital twins of our planet, contributing to fields as diverse as resource exploration, paleoclimatology, and environmental risk assessment.
This tool exemplifies the potential of combining advanced modeling techniques with expanding geochemical and geophysical datasets, offering a scalable solution for analyzing the spatiotemporal evolution of Earth’s lithosphere. By providing access to comprehensive plate tectonic models and enabling precise spatiotemporal analyses, LithoPlates paves the way for groundbreaking research in understanding Earth’s dynamic geological history and its implications for modern and future challenges.
How to cite: Kohlmann, F., Noble, W., Qin, X., Higton, J., Beucher, R., Theile, M., McInnes, B., and Mueller, D.: Deep-Time Digital Twins: Integrating LithoPlates with the EarthBank Platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9058, https://doi.org/10.5194/egusphere-egu25-9058, 2025.
EGU25-9648 | Posters virtual | VPS30
Quantitative analysis of the impact of realization selection on future climate change impact assessments using CMIP6 dataFri, 02 May, 14:00–15:45 (CEST) | vP2.10
Future climate projection data are increasingly employed to evaluate the potential impacts of global warming across a wide range of domains, including meteorological variables (e.g., temperature and precipitation), hydrological processes, ecosystems, human health, and societal activities. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides an extensive dataset produced through international collaboration, incorporating multiple General Circulation Models (GCMs), diverse future scenarios, and numerous initial conditions. Despite the comprehensive nature of these datasets, most impact assessments rely on a limited subset of realizations, with no standardized methodology guiding their selection. This lack of consensus introduces potential biases into the outcomes of impact studies. This study quantitatively assesses the influence of realization selection on future climate impact assessments. Monthly precipitation and temperature data from CMIP6 were analyzed for both historical experimental periods and multiple Shared Socioeconomic Pathways (SSP) scenarios. Comparisons were conducted between outcomes obtained using all available realizations for each GCM and those derived from a single realization per GCM. Additionally, combinations of GCMs and realizations commonly used in prior studies were evaluated for their representativeness. The findings reveal that global average monthly precipitation is consistently higher when all realizations are utilized compared to scenarios based on a single realization. The inclusion of all realizations captures a broader range of variability, whereas subsets exhibit narrower variability and more localized trends. These results emphasize the significant impact of realization selection on future climate prediction outcomes. Moreover, an analysis of existing studies indicates that while selected datasets often reflect average trends, their overall representativeness requires further scrutiny. This research highlights the necessity of adopting uncertainty-aware methodologies in climate change studies. The findings offer valuable insights for improving the robustness and reliability of future climate impact assessments, paving the way for more informed decision-making in addressing climate change challenges.
How to cite: Nagata, K.: Quantitative analysis of the impact of realization selection on future climate change impact assessments using CMIP6 data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9648, https://doi.org/10.5194/egusphere-egu25-9648, 2025.
