Orals: Fri, 2 May | Room 0.49/50
The goal of the Coupled Model Intercomparison Project (CMIP) is to better understand past, present, and future changes in the Earth system in a multi-model context. In an effort to increase the project’s scientific and societal relevance, improve accessibility, and widen participation, the CMIP Panel advocated for establishing a number of Task Teams aimed at supporting the design, scope, and definition of the next phase of CMIP, as well as the evolution of infrastructure for and operationalization of CMIP activities.
An important prerequisite for providing credible information about the Earth system from models is to understand their capabilities and limitations. Thus, systematic and comprehensive assessment of models in comparison with best-available observational and reanalysis data is essential. For CMIP7 new model evaluation challenges stemming from higher resolution, enhanced complexity, and machine learning components need to be rigorously addressed. The Climate Model Benchmarking Task Team aims to provide a vision and concrete guidance for establishing a systematic, open, and rapid performance evaluation of the expected large number of models participating in CMIP7, including a variety of performance metrics and informative diagnostics. The Task Team designed a plan for a community Rapid Evaluation Framework (REF) that would leverage and integrate existing open source community model evaluation tools for benchmarking the performance of CMIP simulations contributed by participating modeling centers.
Based on community input, an initial set of metrics and diagnostics were identified to be applied to Intergovernmental Panel on Climate Change (IPCC) Seventh Assessment Report (AR7) Fast Track simulations. With co-sponsorship from the US Department of Energy (DOE) and the European Space Agency (ESA), the development of the REF was launched in November 2024. The REF delivery team will integrate evaluation tools and a workflow system that will be deployed at two or more primary Earth System Grid Federation (ESGF) node sites to provide automated production of diagnostic information for CMIP model developers, data users, and stakeholders. The REF is expected to evolve to provide additional metrics and diagnostics and to use more data products in the future through the guidance of a community panel or consortium that will be formed in the coming year.
How to cite: Hoffman, F., Hassler, B., Lewis, J., Andela, B., Collier, N., Lee, J., Ordonez, A., Turner, B., Ullrich, P., and Xu, M. and the CMIP Climate Model Benchmarking Task Team: The CMIP Rapid Evaluation Framework (REF) for automated and systematic benchmarking of coupled models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14773, https://doi.org/10.5194/egusphere-egu25-14773, 2025.
The diversity of the Earth System Model (ESM) ecosystem, with output data varying in format, grids and metadata, pose significant challenges for effective intercomparison and evaluation. Additionally, the comparison with observational dataset and the rapid development of Artificial Intelligence techniques to progress ESM representations may introduce additional complexity, with the need of a fast integration of them in a uniform and standardized evaluation pipeline.
AQUA, an Application for QUality Assessment, is a python package developed in the context of Destination Earth Climate Digital Twin, a major initiative by the European Commission. AQUA is designed to overcome these challenges by standardizing variables and metadata across different models and observations. Additionally, it provides a simple and unified interface to open and process different data formats and support different APIs (netCDF, GRIB, FDB, Zarr, Parquet) to access data. It leverages the power of Dask, Xarray and Intake libraries, for a lazy data access to all the supported formats.
AQUA offers the possibility to add any dataset in a catalog, a series of yaml files describing the path or the API needed to open data. The code can accommodate a hierarchy of needs, from simple access to complex data structure, going through extensive usage of the processing capabilities such as metadata conversion, data regrid and integrated area weighting as an extension of Xarray, or eventually by exploiting the integrated suite of diagnostics for model evaluation.
Additionally, the code provides diagnostics that can exploit lazy access to high-resolution data, a suite for uncertainty quantification of the diagnostics output and provides the backend capabilities for the development of project dashboards.
Finally, the code supports the possibility to develop other diagnostics and to have them integrated in the model evaluation along with the others.
How to cite: Nurisso, M., von Hardenberg, J., Caprioli, S., Ghosh, S., Rajput, M. M., Nazarova, N., Cadau, M., and Davini, P.: Advancing Earth System Model Evaluation with AQUA: A Modular Framework for Quality Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9002, https://doi.org/10.5194/egusphere-egu25-9002, 2025.
The sixth assessment report (AR6) issued by the Intergovernmental Panel on Climate Change (IPCC) projects that 1 in 50 years, heat waves become about 8 times more frequent, and 1 in 10 years, extreme precipitation events become twice as frequent in a 1.5-degree warmer climate compared to the pre-industrial period. We need to prepare and adapt to those changes in our global climate. The current best way to do this is to use Earth System Models (ESMs) to project the next 50 to 100 years of our climate, employing Greenhouse-gas (GHG) emission and anthropogenic aerosol-based scenarios. The most prominent initiative dedicated to this aim is the Coupled Model Intercomparison Project (CMIP). Reproducibility is important in this collaborative effort, tracing back simulations to specific configurations, model versions, and compilation flags to reproduce the same simulation in the same environment again, achieving identical results. Equally important is replicability, achieving “identical” results when performing the same experiment configuration using different clusters, computing environments, or compilers. Achieving replicable results is much more difficult, and in practice, bit-to-bit replicability can almost never be achieved. However, results can be replicable in the sense that the model's climate in one computing environment is statistically indistinguishable compared to results from simulations performed in another environment. Due to the large number of simulations conducted in CMIP, the simulations are usually distributed on different clusters.
How do we ensure the model’s climate is replicable? It has been demonstrated that the non-linearity of the models leads to significantly different trajectories, even for a different set of compiler flags. If replicability does not hold, differences between contrasting projection scenarios performed on different clusters cannot be interpreted exclusively in terms of the changes in external forcing. Built on state-of-the-art replicability verification methods, we developed a methodology to evaluate the statistical power and sensitivity of current replicability methods and made improvements based on our results. One of our findings is that the power of the current methods is poor when the effective differences are subtle. For instance, the standard threshold of 80% of the statistical power of a prominent method (Massonnet et al., 2020, https://doi.org/10.5194/gmd-13-1165-2020) is only met if the ensemble means of the evaluation metric are more than two standard deviations apart. However, we observed differences in biomass burning emission (BMB) forcing in the CESM2 Large Ensemble Community Project (LENS2), which changes the model’s climate, only show effective differences of about 0.5 standard deviations.
Our new methodology provides (1) new metrics capable of resolving the differences in the data better, meaning increasing the effect size, and (2) new statistical tests that trigger smaller effect sizes. We will present our new methodology evaluating the LENS2 ensemble, based on the period affected by the different BMB forcing, and analyzing different perturbation schemes of the control ensemble initialized in 1850. Additionally, we will present a recent study that we performed on IFS-NEMO simulations, using a double, single, and mixed precision NEMO implementation.
How to cite: Keller, K., Alerany, M., and Acosta, M.: Earth system model replicability - Statistical validation of a model's climate under a change of computing environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4259, https://doi.org/10.5194/egusphere-egu25-4259, 2025.
In the tropics, the upper troposphere warms faster than the lower troposphere and the surface in response to increased greenhouse gas concentrations. This differential warming is a robust feature in climate models. High-quality observational datasets confirm the stronger warming at higher altitudes, but limitations in the observing systems have made it difficult to accurately characterize the vertical structure of observed temperature trends in the tropics. Additionally, the low vertical resolution of most satellite-based data records with a global coverage further complicate the comparison of climate models and observations.
We have analyzed the vertical structure of tropical temperature trends using satellite-based Radio Occultation (RO) data from EUMETSAT’s RO Meteorology Satellite Application Facility (ROM SAF) for the period from 2002 to 2024. These data are compared with the CMIP6 ensemble of about 250 members from more than 30 models using combined historical and scenario runs under SSP2-4.5. The RO data provides a stable climate reference, combining global coverage with high vertical resolution, and has only recently reached the length necessary for trend analysis. Our comparison confirms the warming biases in the CMIP6 models previously reported in the literature – the models warm faster than the observations in the upper troposphere. In contrast, they cool faster in the stratosphere than the RO data. Furthermore, we demonstrate that there are substantial differences in the vertical trend structure between the CMIP6 models and the RO data: the models show peak trends in the middle to upper troposphere around 250-300 hPa, while the RO data have maximum trends in the lower stratosphere, around 50-100 hPa. In this presentation, we describe the characteristics of the RO climate data records and discuss the significance of the RO-CMIP6 differences, considering the uncertainties of the observations and the spread amongst the CMIP6 models.
How to cite: Nielsen, V. W., Christiansen, B., Yang, S., Gleisner, H., and Lauritsen, K. B.: Vertical profiles of tropical temperature trends: comparing satellite-based radio occultation data with CMIP6 climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10697, https://doi.org/10.5194/egusphere-egu25-10697, 2025.
Simulation of the carbon cycle in climate models is important due to its impact on climate change, but many weaknesses in its reproduction were found in previous models. Improvements in the representation of the land carbon cycle in Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) include the interactive treatment of both the carbon and nitrogen cycles, improved photosynthesis, and soil hydrology. To assess the impact of these model developments on aspects of the global carbon cycle, the Earth System Model Evaluation Tool (ESMValTool) is expanded to compare CO2-concentration- and CO2-emission-driven historical simulations from CMIP5 and CMIP6 to observational data sets. A particular focus is on the differences in models with and without an interactive terrestrial nitrogen cycle. Overestimations of photosynthesis (gross primary productivity (GPP)) in CMIP5 were largely resolved in CMIP6 for participating models with an interactive nitrogen cycle but remain for models without one. This points to the importance of including nutrient limitation in models. Simulating the leaf area index (LAI) remains challenging, with a large model spread in both CMIP5 and CMIP6. The global mean land carbon uptake (net biome productivity (NBP)) is well reproduced in the CMIP5 and CMIP6 multi-model means. This is the result of an underestimation of NBP in the Northern Hemisphere, compensated by an overestimation in the Southern Hemisphere and the tropics. Models from modeling groups participating in both CMIP phases generally perform similarly or better in their CMIP6 version compared to their CMIP5 version. Emission-driven simulations perform just as well as the concentration-driven models, despite the added process realism. Due to this, we recommend that ESMs in future Coupled Model Intercomparison Project (CMIP) phases perform emission-driven simulations as the standard so that climate–carbon cycle feedbacks are fully active. The inclusion of the nitrogen limitation led to a large improvement in photosynthesis compared to models not including this process, suggesting the need to view the nitrogen cycle as a necessary part of all future carbon cycle models. Overall, a slight improvement in the simulation of land carbon cycle parameters is found in CMIP6 compared to CMIP5, but with many biases remaining, further improvements of models in particular for LAI and NBP is required. Due to the inclusion of the study in ESMValTool, the analysis can easily be repeated on the upcoming CMIP7 models to evaluate the progress from CMIP6.
How to cite: Gier, B. K., Schlund, M., Friedlingstein, P., Jones, C. D., Jones, C., Zaehle, S., and Eyring, V.: Representation of the terrestrial carbon cycle in CMIP6, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10017, https://doi.org/10.5194/egusphere-egu25-10017, 2025.
Accurate projections of regional climate change are crucial for understanding future risks and formulating effective adaptation strategies. Switzerland's complex topography and diverse climate pose challenges to obtaining reliable modeling of its future climate. This study evaluates the EURO-CORDEX regional climate models (RCMs) to identify a subset of models best suited for temperature and precipitation projections in Switzerland. The evaluation focuses on their ability to capture trends, climatological means, and variability, while ensuring the models do not produce unrealistic or physically implausible results. Using both qualitative and quantitative methods, we address the challenges of model sub-selection within the context of Switzerland's complex terrain.
We evaluate model performance using observational datasets, including high-resolution gridded climatologies and in situ measurements. Statistical metrics such as bias, root mean square error (RMSE), and correlation coefficients are used to assess the agreement between model outputs and observations. Additionally, we evaluate projected future changes by examining their consistency with established physical principles and observed climate trends.
Our analysis demonstrates that the selected subset of models captures a large fraction of the uncertainty in projections from the full ensemble while aligning more closely with observed trends. Furthermore, using a transient time-resampling approach, we show that this subset provides robust information on climate change at different Global Warming Levels. This method can be readily applied to other regions and helps mitigate biases in warming projections caused by time-invariant aerosol forcing.
How to cite: Jónsdóttir, L. S., Merrifield Könz, A., Fischer, E., and Knutti, R.: Evaluation of EURO-CORDEX Regional Climate Models: Model Subselection for Switzerland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17809, https://doi.org/10.5194/egusphere-egu25-17809, 2025.
Despite tremendous efforts made to improve model performance over the past several decades, climate models exhibit systematic errors or biases in the simulation of many aspects and regions of the climate system. These systematic biases indicate the misrepresentations of physical processes in the models, which can be amplified by feedbacks among different processes and/or climate components. The magnitude of the model biases is often similar to the magnitude of the climate change that have been observed in the past several decades in some regions and for some parameters. This gives rise to large uncertainties in the climate predictions and projections. Furthermore, using climate models with such biases for assessing future climate change implies an assumption that these biases are stationary over time. However, the assumption may not be justified due to the internal variability and the evolving background state of the climate system.
In this study, we investigate model biases of key climate variables with the aim of understanding their links with biases of the same or other variables at remote locations. We analyze the multi-model multi-member ensemble (MME) of CMIP6 historical runs. We first focus on the model bias of the Atlantic meridional overturning circulation (AMOC) and its links with remote oceanic biases (e.g., sea surface temperature, sea surface salinity), North Atlantic deep water formation, etc.) and sea ice extent, as well as the atmospheric biases. We assess to what extent these linkages may affect the AMOC change. We further explore the representations of the circulation modes (e.g., North Atlantic Oscillation) in the CMIP6 MME relative to the observations, with emphasis on understanding how the internal variability influences the representation of the circulation modes and their relationship with other climate variables, and how these relationships in turn impact the climate predictability.
How to cite: Yang, S., Christiansen, B., Guo, C., Mahmood, R., and Tian, T.: The systematic biases of CMIP6 climate models: remote connections and impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13494, https://doi.org/10.5194/egusphere-egu25-13494, 2025.
The global cimate models (GCMs) included in the CMIP6 compilation provide critical insights into global warming but often exhibit biases at local and regional scales. While bias-correction and downscaling are common, existing methods rarely incorporate multi-model ensembles or extend their applicability to a global field. We present a downscaled and bias-corrected CMIP6 synthesis that spans the full range of CMIP6 projections to enhance the accuracy of future climate projections, facilitate adaptation efforts, and increase climate resilience.
Our new work synthesizes a bias-corrected and downscaled surface temperature product derived from 45 GCMs and 374 runs across five shared socioeconomic pathways. We employ a robust bias-correction framework that compares historical model runs against reanalysis data (ERA5: 1940-present) and observation-based data (Berkeley Earth: 1850-present). Each grid cell is decomposed into three components: (1) long-term trends, (2) annual seasonality, and (3) short-term weather variability. Trends and seasonality are calculated with a LOESS fit with Gaussian weighting. Residual daily variability is represented using evolving probability distributions with explicitly modeled extremes through generalized Pareto formalism to enable accurate estimation of rare events. The resulting fields are then statistically downscaled to 0.25° x 0.25° latitude-logitude resolution using predictive regressions derived from high resolution historical observations. Further, each component is bias corrected and scaled to match the mean and trends observed in the historical period.
This analysis results in bias corrected and downscaled verions of each of the input GCMs. These bias corrections, based on constraints over the historical period, significantly reduce the spread in model projections of the future, and by extension the implied uncertainty in long-term warming scenarios.
The corrected models are then further synthesized into a unified dataset, with model selection and weighting guided by historical accuracy. This provides easy access to local changes, consistently representing both past temperature changes and expected future changes. Data is provided both for long-term trends as well as daily extremes with measures of uncertainty guided by the remaining variations across models. Further, this approach makes it easy to calculate changes in temperature derived variables, such as cooling-degree days or heat wave indices.
The Berkeley Earth climate model synthesis will deliver detailed probabilities of extreme climate events for each shared socioeconomic pathway. The initial focus is on surface temperature changes, with an additional synthesis of precipitation changes planned for the next phase of this work. This product aims to support future academic research, inform policy and adaptation strategies, and provide actionable climate risk insights for asset management and decision-making.
How to cite: Rohde, R. and Rand, D.: Berkeley Earth Climate Model Synthesis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13866, https://doi.org/10.5194/egusphere-egu25-13866, 2025.
To improve the computational efficiency and scientific productivity, efforts have been made to transition the coupling infrastructure of the current E3SM model from the Model Coupling Toolkit (MCT) to a topology-aware library, the Mesh Oriented datABase (MOAB). Unlike MCT, MOAB provides a comprehensive description of the topology for each submodel in E3SM and exposes interfaces to seamlessly query and serialize field data directly on mesh entities. With complete mesh representations, the new E3SM coupler can now compute and apply optimal decompositions, generate traditional conservative (first and high-order) mapping weights using TempestRemap and apply the weights to compute field projections that are essential in coupled simulations. MOAB's purely local memory model without the use of O(p) data structures, provides good scaling and its C++ code base provides multiple avenues to exploit fine-grained parallelism with GPUs. Simultaneously, E3SM is adding new coupling pathways for dynamic ice sheets and surface wave models. We will present the new infrastructure and its performance in comparison to MCT for representative cases.
How to cite: Jacob, R., Grindeanu, I., Mahadevan, V., and Wu, D.: New Coupling Capabilities in the Energy Exascale Earth System Model (E3SM), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14629, https://doi.org/10.5194/egusphere-egu25-14629, 2025.
Earth system modeling is vital for understanding the Earth's complex processes and predicting climate change impacts. These models require high degrees of freedom to accurately represent interactions and feedbacks among the atmosphere, oceans, and biosphere. This complexity is essential for generating reliable climate projections, informing policy, and developing adaptation strategies.
In this context, the recent development of the AWI-CM3, a coupled atmosphere-ocean general circulation model (AOGCM), marks a significant advancement in climate modeling, addressing the need for improved accuracy and resolution. The AWI-CM3 outperforms CMIP6 standards at low resolution (~100 km) and demonstrates impressive capabilities for high-resolution simulations, with global coupled models evaluated at resolutions of 31 km and 9 km, and a 4.5 km resolution model currently running.
Our ongoing efforts further reflect this commitment to complexity and precision. Here, we present results from the recent upgrade to OpenIFS 48r1, integrating cutting-edge features from NWP/ECMWF to improve core atmospheric processes. This upgrade strengthens the model’s capacity to simulate weather extremes, atmospheric dynamics, and precipitation patterns with greater fidelity. Additionally, we evaluate the first major step in the evolution from AWI-CM3 to AWI-ESM3: enhancing the model through dynamic vegetation coupling with LPJ-Guess 4.1.2. This integration allows for more realistic simulations of vegetation feedbacks, and land-atmosphere interactions, which are essential for accurately capturing long-term climate trends and ecosystem changes.
Looking ahead, the incorporation of ice sheet dynamics using the Parallel Ice Sheet Model (PISM) will extend the capabilities of our Earth System Model, AWI-ESM3, enabling more comprehensive climate projections. Parallel work is ongoing to couple the ocean biogeochemistry model FESOM2/RECOM with the atmospheric carbon cycle, providing a more holistic representation of the carbon-climate feedbacks. Additionally, integrating CMIP7 forcing datasets will further align the model with the latest climate modeling standards, reinforcing its role in advancing both regional and global climate assessments.
How to cite: Streffing, J., Hajdu, L. H., Miller, P., Nieradzik, L., Lindahl, M., le Sager, P., Fladrich, U., Lohmann, G., and Jung, T.: AWI-CM3 to AWI-ESM3: Expanding Degrees of Freedom in Kilometer-Scale Climate Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16600, https://doi.org/10.5194/egusphere-egu25-16600, 2025.
We present an evaluation of ocean biogeochemistry simulations in a newly developed Earth System model configuration: ICON XPP. ICON XPP is an outcome of a national effort to develop an Earth system model configuration within the ICON architecture, serving as a baseline for next-generation climate predictions and projections. This model will underpin the German contribution to CMIP7.
The ocean biogeochemistry in ICON XPP is represented by the HAMburg Ocean Carbon Cycle model (HAMOCC; Ilyina et al. 2013), with novel developments over its predecessor in the MPI-ESM CMIP6 version. HAMOCC has already been implemented in the previous configurations of the ICON-based models (Jungclaus et al. 2022; Hohenegger et al. 2023). Recent key advancements include integrating a prognostic calculation for marine aggregate sinking speeds (Maerz et al. 2020), providing an improved distribution of particulate organic carbon fluxes critical to the biological pump. Additionally, the model incorporates an enhanced nitrogen cycle, enabling a more comprehensive representation of nutrient dynamics and N2O fluxes.
Here we discuss the HAMOCC model performance within the higher-resolution ICON XPP configuration, capturing finer-scale oceanographic processes. The framework features horizontal grid spacing of approximately 80km with 130 vertical levels in the atmosphere, and 20km with 72 vertical levels for the ocean, running in concentration-driven mode. We detail the ocean biogeochemistry model tuning steps and show that the simulated present-day distributions of biogeochemical fields compare well with observations, demonstrating the robustness of the ICON XPP configuration for next-generation Earth system modeling.
How to cite: Chegini, F., Wu, J., and Ilyina, T.: Advancing Ocean Biogeochemistry in the ICON-XPP Earth System Model for CMIP7 Contribution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13903, https://doi.org/10.5194/egusphere-egu25-13903, 2025.
Simple climate models (SCMs) can emulate many of the trends and processes found in ESMs allowing for much faster runs, but among other trade-offs, they can be limited by what is already available in ESMs. In the case of natural methane emissions, there remains a high degree of uncertainty even among larger scale models over the next century and beyond, particularly in regards to wetlands. Many of the models which do exist show a strong linear relationship between global temperatures and methane emissions from wetlands. The simple climate model MAGICC, meanwhile, had previously not included a dynamic natural methane response. A linear model of wetlands methane as a function of global temperature was calibrated to output data from existing models and incorporated into MAGICC. This new version of MAGICC draws from a distribution of parameters in line with the wide spread of estimates available from existing models and informed by the available observational data. The C1, C2, C3, and C4 scenarios from AR6 were run through the new model, all experiencing a wider spread of projected temperatures. While many uncertainties remain in this simplified approach, this raises concerns about 2°C targets in these scenarios.
How to cite: Sloughter, T., Nicholls, Z., Tang, G., and Rogelj, J.: Improving Carbon Cycle Feedbacks in Simple Climate Models: Adding Wetland Methane to MAGICC, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14075, https://doi.org/10.5194/egusphere-egu25-14075, 2025.
Carbon allocation is a critical process that helps to optimize plant growth and significantly impacts ecosystem structure and function, with immediate implications for the global carbon cycle. Since leaves are the primary organs regulating the exchange of CO₂, energy, and water between terrestrial ecosystems and the atmosphere, accurate simulation of leaf carbon allocation is important. However, most land surface models (LSMs) lack detailed consideration of the leaf economics spectrum, which is represented by the coordination of leaf mass per area (LMA) and leaf longevity (LL) and how their relationship varies with the growth environment. Instead, LSMs commonly predict the proportion of biomass allocated to leaves either directly from the environment based on resource limitation theories, or from their functional relationships with other organs. A new theory based on eco-evolutionary optimality principles successfully predicts changes in LMA and LL with the environment by maximizing the average net carbon gain over the leaf life cycle. In addition, a prognostic, globally applicable Leaf Area Index (LAI) model has been developed recently, using climate data alone to capture LAI dynamics across biomes on the principle that the annual cycle of leaf display is closely related to the cycle of potential primary production by those leaves. Here, combining these two theoretical developments, we provide a universal expression for the proportion of biomass allocated to leaves. We successfully predict foliar carbon allocation as measured on a site basis and capture the plasticity of foliar carbon allocation with environmental change. The global average fraction of biomass production allocated to leaves is estimated as 0.31. The model also accounts for the different regulatory mechanisms of leaf carbon allocation by deciduous and evergreen plants.
How to cite: Zhou, B. and Prentice, C.: How much carbon is allocated to leaves?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3675, https://doi.org/10.5194/egusphere-egu25-3675, 2025.
Earth System Models (ESMs) are the key tool for studying the climate under changing conditions. Over recent decades, it has been established to not only rely on projections of single models, but to combine various ESMs in multi-model ensembles (MMEs) to improve robustness and quantify the uncertainty of the projections. The data access for MME studies has been fundamentally facilitated by the World Climate Research Programme’s Coupled Model Intercomparison Project (CMIP) - a collaborative effort bringing together ESMs from modelling communities all over the world. Despite the CMIP standardisation processes, addressing specific research questions using MMEs requires unique ensemble design, analysis, and interpretation choices. Based on our collective expertise of the Fresh Eyes on CMIP initiative, we have identified common issues and questions encountered while working with climate MMEs. In this project we aim to provide a comprehensive literature review giving an overview over the considerations that have to be taken into account for these decisions. In detail, we provide statistics tracing the development of the field throughout the last decades, we outline guidelines synthesising existing studies regarding model evaluation, model dependence, weighting methods and uncertainties. We summarize a collection of tools and other useful resources for MME studies, we furthermore review common questions and strategies, and finally, we outline emerging trends, such as the integration of machine learning techniques, single model initial-condition large ensembles (SMILES), and computational resource considerations.
How to cite: Katzenberger, A., Črnivec, N., Puthukulangara, P., Galytska, E., Gemmell, K., Leclerc, C., Perez-Carrasquilla, J. S., Roy, I., Varuolo-Clarke, A., and Tošić, M.: Guidelines for Working with Multi-Model Ensembles in CMIP, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16927, https://doi.org/10.5194/egusphere-egu25-16927, 2025.
Ensembles of output from Earth System Models (ESMs) are available in databases such as CMIP6 that can help us learn about the climate. Most work until today has focused on temperature and precipitation for the historical period and future projections. However, a wealth of other information is available, including for different time slices in the past, such as the Last Glacial Maximum or the mid Holocene, and for different physical variables like 3D ocean temperatures and sea-ice extent. Here, we would like to show results from our efforts to build a framework for making probabilistic estimates of the climate state. Our inferences are based on a variety of ESM-data, comprising various time periods and climate variables, and the application of model-weights following recent approaches that take into account model skill and the inter-dependency among models within multi-model ensembles like CMIP6. In this way, we aim to be able to combine multiple possible evaluations to arrive at a final weighting for a given model ensemble. We present the framework – implemented as a Julia package to facilitate data selection and further analysis – and its capabilities, and then analyze how the weighting based on different time periods influences our estimates of the climate state for a given time slice, as well as for future projections.
How to cite: Grusdt, B., Perrette, M., and Robinson, A.: Forming a robust estimate of the climate state through a model ensemble weighting approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18434, https://doi.org/10.5194/egusphere-egu25-18434, 2025.
In many applications, it is desirable to aggregate climate model projections by combining multiple models into a single projection that aims to leverage their collective strengths, often resulting in improved performance compared to individual models. While climate models exhibit varying levels of global average bias, their local performance often displays significantly larger biases—sometimes by an order of magnitude—with each model showcasing distinct strengths and weaknesses in different regions. Aggregating models without accounting for these spatial differences can degrade the quality of projections by diluting strong regional signals from high-performing models. While many approaches ranging in complexity have been developed, including the commonly used Multi-Model Mean (MMM) and weighted MMM, these methods typically apply a global weighting to the models, overlooking the fact that certain models may excel only in specific regions.
To date, the Graph Cut optimization method (Thao et al., 2022) stands out as one of the few techniques effectively leveraging the local capabilities of different models across multi-decadal periods to produce global projections. This method involves selecting the best performing model for each grid point while also ensuring the spatial consistency of the resulting fields. Despite its promising results, which surpass those of other ensemble combination techniques, it is restricted to optimizing for a single variable. This limitation causes inconsistent model selection across variables in multivariate scenarios. This leads to a loss of the multivariate relationships captured in the models. Furthermore, this technique was limited to multi-decadal averages, and is thus unable to capture the distributional characteristics of climate variables, including extreme and compound events.
Here, we present significant enhancements to the Graph Cut optimization method, enabling the combination of distributions of daily values. This approach preserves multivariate relationships, better capturing the complete span of climate dynamics. By employing the Hellinger distance to assess model performance, we can identify, at each grid point, the model that most accurately represents the multivariate distribution of target variables (e.g., temperature, pressure, and precipitation), minimizing the emergence of unrealistic discontinuities in the combined fields.
To demonstrate the use of our method, we combine 22 models from CMIP6 using three variables: temperature, precipitation, and sea level pressure, achieving better reproduction of ERA5 reanalysis compared to the Multi-Model Mean (MMM). Additionally, a perfect model experiment was conducted to evaluate the robustness and stability of the methodology under high climate change scenarios, such as SSP8.5, and over extended timescales reaching the end of the century. These results highlight the method's ability to maintain reliable performance and spatial consistency in challenging future conditions.
REFERENCES
Thao, S., Garvik, M., Mariethoz, G. et al. Combining global climate models using graph cuts. Clim Dyn 59, 2345–2361 (2022). https://doi.org/10.1007/s00382-022-06213-4
How to cite: Schmutz, L., Thao, S., Vrac, M., Allard, D., and Mariethoz, G.: Improved climate projections by combining CMIP6 models according to their local multivariate performance, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18233, https://doi.org/10.5194/egusphere-egu25-18233, 2025.
Over the tropics, a robust statistical relationship between radiation and precipitation underscores the convection conversion efficiency associated with the cloud-radiative effect in a certain climate state. In this study, we define an "RP ratio", a metric derived from outgoing longwave radiation and precipitation anomalies, for linearly estimating the radiation-precipitation relation (RP relation). The RP ratio exhibits significant disparities across global climate models (GCMs), with the majority overestimating it relative to the observation.
Since the RP ratio and future extreme precipitation (EP) are found to be highly correlated over the tropics, an emergent constraint (EC) on the hydrological cycle projections is applied based on this correlation. The EC in this study shows its novelty due to the first application of the RP relation on hydrological cycle projections. According to the EC results, we find the fractional increase in the 99.9th percentile of EP by the end of the 21st century is lowered from 28% to 14% (from 16% to 5%), with the uncertainty reducing by 22% (12%), under the high-emission scenario (median-emission scenario). Overall, the GCMs underestimate the future tropical EP while overestimating its fractional increase. The results provide valuable insights for model improvement and better climate adaptation planning.
How to cite: Huang, Y., Yang, Z., and Shi, X.: Tropical Radiation-Precipitation Relationship and Future Extreme Precipitation Constraint, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2725, https://doi.org/10.5194/egusphere-egu25-2725, 2025.
The Western Mediterranean (WMed) has been pointed out as a hotspot region for both warming and drying signals. However, there is still large uncertainty in future projections due to model uncertainty and misrepresentation of specific processes. Thus, the need for a better understanding of the future climate of the WMed becomes evident. Improved climate indicators for decision-making can benefit from a deeper insight on future climate extremes and their related atmospheric circulation, taking into account the spread in model performances over the WMed region.
The present work is based on the analysis of future projections of rainfall and temperature extremes from a set of CMIP6 global climate models (GCMs) during 2070-2100, according to their representation of the dominant synoptic circulation patterns (CPs). CPs are defined using daily mean sea level pressure (SLP) using hierarchical clustering and data reduction through empirical orthogonal functions. The ERA5 reanalysis during 1950-2022 was considered as the reference to evaluate the historical GCMs simulations, constructing the CPs with SLP and analyzing their link to surface variables including precipitation, maximum and minimum temperatures. To assess the future synoptic circulation, the clustering algorithm is replicated and future CPs are compared to the historical ones in terms of frequency, shape and intensity changes in the CPs.
Based on the historical CPs, a model ranking is generated using a combination of spatial and temporal reproduction metrics for the SLP patterns and the associated surface conditions. GCMs manage to reproduce the annual cycle of the CPs frequency, with a dominant summer CP enhancing warm and dry conditions. However, the correct timing of this pattern and the transitional CPs (that is, during the autumn and spring seasons) still need to be more accurate. The analysis of the associated surface patterns shows good model performance, better for temperature than for rainfall, particularly in the transition seasons, for which the GCMs spread in their skill score increases. This process-based evaluation leads to a model ranking that is used to construct multiple model ensembles, considering different weighting strategies based on model performance, spread and independence.
In terms of climate extremes, the uncertainty in future projections of the indices ─including the expected increases in the frequency of warm days and dry spells─ can be reduced by selecting specific subsets of GCMs, according to the process-based ranking. In particular, the warming and drying signals over areas such as the northeastern Iberian Peninsula are clearer in the best-performing GCMs ensemble. This constraining procedure shows more clear results in summer than in winter, when natural variability has a larger role in modulating the WMed changing climate.
These changes in temperature and rainfall extremes were related to the changing frequency of the CPs driving the specific extremes. CPs present some differences in their seasonal distribution for the late 21st century compared to their historical records, while the centroids of the CPs often present changes, evidencing modifications in the intra-pattern variability. Altogether, the projected future extremes can be associated with differences in future climate variability, given a context of global warming.
How to cite: Olmo, M., Cos, P., Campos, D., Muñoz, Á., Soret, A., and Doblas-Reyes, F.: Filtered future projections in the Western Mediterranean: atmospheric circulation patterns and climate extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12909, https://doi.org/10.5194/egusphere-egu25-12909, 2025.
Anthropogenic emissions are changing Earth’s global mean temperature towards levels unseen over the observational period. During the Last Interglacial (LIG) warm period, 129-116 thousand years ago, global mean temperature reached up to 1-2 degrees above preindustrial conditions, forced primarily by changes in Earth’s orbit. Both the Greenland and Antarctic Ice sheet were smaller, with sea level at least 5m, potentially up to 10m, above present levels.
The seasonal distribution of solar insolation during the LIG was characterized by higher eccentricity, obliquity, and precession leading to increased Northern, and decreased Southern Hemisphere summer insolation. Most Earth System Models included in the Palaeoclimate Model Intercomparison Project phases 3 and 4 have shown a temperature anomaly of -0.5 up to around 0.5 degrees, far lower than what has been suggested from reconstructions [Otto-Bliesner et al., 2021].
Here, we show first results from idealized equilibrium simulations with a configuration of ICON for climate prediction and projections [ICON-xpp, Müller et al 2025], varying the planetary orbit using Kepler’s solutions [Roeckner et al 2003]. We investigate the impact of different orbital forcing on global and regional temperature and precipitation mean state and variability, and discuss the impact of the choice of the land model (JSBACH vs. TERRA).
Simulating climate conditions during past warm periods, and evaluating how they compare to palaeoclimate reconstructions, improves our understanding of the Earth System, and can enhance the robustness of future projections. Our initial characterization and evaluation of orbital impacts on climate variability in ICON-xpp is therefore a crucial step towards simulating and then evaluating model performance for longer timescales, or deeper-time periods such as the Eocene, Miocene or Pliocene warmth. Moreover, using higher-resolution ICON paleoclimate simulations could provide a better basis for upcoming model-proxy data comparisons and forward modeling approaches on regional-to-local scales.
References
Otto-Bliesner, Bette L., et al. “The PMIP4 Contribution to CMIP6 - Part 2: Two Interglacials, Scientific Objective and Experimental Design for Holocene and Last Interglacial Simulations.” Geoscientific Model Development 10, no. 11 (2017): 3979–4003. https://doi.org/10.5194/gmd-10-3979-2017.
Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M., Giorgetta, M., et al. (2003).The atmospheric general circulation model ECHAM 5. PART I: Model description. Report / Max-Planck-Institut für Meteorologie, 349.
Müller, W.; Lorenz, S., 2024, "Source code and scripts for publication 'The ICON-based coupled Earth System Model for Climate Predictions and Projections (ICON XPP)'", https://doi.org/10.17617/3.UUIIZ8
How to cite: Rehfeld, K., Brugger, J., Racky, M., Baudouin, J.-P., Jungclaus, J., Kelemen, F. D., Lorenz, S., Wagner, S., and Köhler, M.: First steps towards paleoclimate constraints for climate prediction and projections with the ICON model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13114, https://doi.org/10.5194/egusphere-egu25-13114, 2025.
Earth System Models (ESMs) are essential for understanding climate dynamics and informing policy decisions. This presentation focuses on the PCMDI Metrics Package (PMP), an open-source, Python-based framework designed for objective "quick-look" comparisons and benchmarking of ESMs against the latest observational data. The PMP has been instrumental in systematically evaluating thousands of simulations from Coupled Model Intercomparison Projects (CMIPs), with a primary focus on physical climate with atmospheric mean and variability.
As we prepare for the upcoming CMIP7, our ongoing work aims to enhance the PMP's capabilities to support modeling groups throughout their development cycles. The PMP offers a diverse suite of evaluation metrics, including large- to global-scale climatology, annual cycle, and variability characteristics associated with ENSO, MJO, and numerous extra-tropical modes, and also includes key measures of simulated sea-ice and ocean states. Notably, the PMP provides a database of pre-calculated statistics for CMIP6 models, facilitating easier comparisons for modeling centers as they assess their results against established benchmarks.
Current ongoing enhancements include the evaluation of the Quasi-Biennial Oscillation (QBO) and its teleconnections to the MJO, atmospheric blocking, and atmospheric river patterns using Machine Learning algorithms. Additionally, we are implementing planetary-scale assessments through Hadley cell expansion metrics. The PMP is also evolving to accommodate higher-resolution simulations from HighResMIPs, cloud-resolving E3SM experiments, and regionally downscaled products.
This presentation will highlight the importance of routine model evaluation, introduce the latest advancements in the PMP, and discuss opportunities for community engagement and collaboration. We invite feedback and suggestions from the community to further enhance our tools and methodologies.
How to cite: Lee, J., Ordonez, A., Ullrich, P., Gleckler, P., Dong, B., Chang, K., Valkonen, E., Caron, J., Hur, I., and Yoo, C.: Enhancing the PCMDI Metrics Package for Comprehensive Evaluation of Earth System Models in CMIP, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3911, https://doi.org/10.5194/egusphere-egu25-3911, 2025.
In recent years, the accelerated impacts of global climate change on sensitive regions have drawn increasing attention. This study focuses on the Upper Yangtze River Basin, evaluating the performance of CMIP6 models in simulating precipitation and temperature during the historical period (1980–2014), providing different insights for future climate studies.
Seventeen commonly used CMIP6 models were selected and systematically evaluated at both annual and monthly scales for their ability to simulate precipitation and temperature. Performance evaluation employed multiple metrics, including bias, standard deviation, root mean square error (RMSE), and correlation coefficients. The Comprehensive Rating Index (CRI) was introduced to quantify the overall performance of each model. Additionally, F-tests and T-tests were conducted to analyze the statistical significance of differences between model simulations and observational data: F-tests assessed the homogeneity of variances between model outputs and observations, while T-tests evaluated differences in means.
Building on this assessment, a single evaluation metric derived from the historical period (1980–2014) is utilized to compute model rankings and a Composite Rating Index (CRI). Subsequently, a rank-based weighting (RBW) method is applied to assign weights to each model at both annual and monthly scales. This approach considers skill differences among models and provides insights for weighted multi-model ensemble (MME) analysis.
The results indicate that most models tend to underestimate annual mean temperature, with CESM2 performing relatively better than other models (CRI = 0.94), while annual cumulative precipitation is generally slightly overestimated, with FGOALS_g3 showing better performance (CRI = 0.89). On a monthly scale, CESM2 performs better in more months for temperature simulation, and FGOALS_g3 similarly performs better in more months for precipitation simulation. However, differences between monthly and annual performance are observed: certain models, such as IPSL_CM6A_LR and INM_CM5_0, which perform less effectively on an annual scale, exhibit relatively better performance in specific months. These findings highlight the variability in model performance across temporal scales and the importance of assessing models on both annual and monthly basis. Additionally, different models exhibit varying simulation capabilities at low-altitude, mid-altitude, and high-altitude observations. This underscores the heterogeneity in model performance across temporal and spatial scales, emphasizing the necessity of rigorous evaluations at both annual and monthly resolutions, as well as across varying spatial scales.
This study provides a comprehensive analysis of the applicability of CMIP6 models in the Upper Yangtze River Basin using multi-scale and multi-altitude approaches, incorporating CRI and RBW methods. The findings emphasize the importance of multi-metric analyses, significance testing, and weighting approaches in optimizing model selection and advancing the understanding of climate change impacts.
How to cite: Han, X. and Bocchiola, D.: Comprehensive Performance Evaluation of CMIP6 Models in Simulating Precipitation and Temperature: A Multi-Scale and Altitude-Based Analysis in the Upper Yangtze River Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19816, https://doi.org/10.5194/egusphere-egu25-19816, 2025.
Climate model evaluation is an essential part of model development because it offers a thorough understanding of the strengths and limitations of specific model components. One of the challenges climate models face is accurately representing precipitation spatial patterns and intensity. This is mainly due to their relatively coarse resolution and the parameterization of various processes, such as atmospheric convection and cloud microphysics. Additionally, the representation of general atmospheric circulation can be incorrect, leading to issues like the misplacement of the Intertropical Convergence Zone (ITCZ) and storm tracks in higher latitudes. In this study, we evaluate the performance of two next-generation Earth System Models (ESMs) developed through the OptimESM project, focusing on how well these models represent precipitation patterns both in tropical regions and on a global scale. Our analysis includes the historical simulations of several ensemble members of the EC-Earth3-ESM-1 and UKESM1.2 ESMs. We compare these models with their CMIP6 ensemble counterparts to determine whether significant improvements have been made and to identify where these enhancements occur around the globe. By utilizing the zonally averaged mass stream function, we identify the main atmospheric circulation cells and assess how effectively the models represent general atmospheric circulation while interpreting the associated precipitation biases.
How to cite: Karpasitis, A., Hadjinicolaou, P., and Zittis, G.: General atmospheric circulation and precipitation evaluation of next-generation Earth System Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3483, https://doi.org/10.5194/egusphere-egu25-3483, 2025.
High-resolution climate simulations at the km-scale present significant challenges for data processing and analysis due to the massive data flow, memory bottlenecks, and scaling limitations. These demands exceed the capabilities of traditional data processing pipelines, requiring the development of innovative approaches to handle and analyze the data efficiently.
The Climate Adaptation Digital Twin, part of the European Commission’s Destination Earth initiative, addresses these challenges with a unified end-to-end workflow. This workflow integrates the full chain, from global km-scale climate simulations to real-world applications in sectors most affected by climate change (such as energy, hydrometeorology, wildfire management etc.)
Embedded within the Climate-DT framework, AQUA (Application for QUality Assessment) is a Python-based tool designed for the scientific evaluation of these simulations, featuring a core engine optimized for efficient access to high-resolution data and modular diagnostics suite that ensure consistent and scalable analysis. It runs within a containerized environment on high-performance computing (HPC) systems, ensuring portability and compatibility across different machines and computational infrastructures.
AQUA evaluates climate model outputs through key diagnostics (such as model biases, timeseries, top-of-the-atmosphere energy balance, ocean circulation metrics etc.) while also providing innovative diagnostics to analyze tropical rainfall, cyclone structures, and other km-scale processes traditionally challenging to study.
Within the Climate-DT workflow, AQUA enables real-time monitoring, systematic comparisons, and quality control of ongoing simulations, building confidence in model results for climate adaptation decisions. The pipeline automatically generates intake catalog entries for new data and creates a low-resolution archive derived from high-resolution data, allowing for more efficient execution of default diagnostics. As each new simulation year is generated, analyses are automatically performed and the results are published on a dedicated website and dashboard, offering visualizations and actionable insights based on real-time data.
How to cite: Caprioli, S., von Hardenberg, J., Nurisso, M., Davini, P., Nazarova, N., Ghosh, S., Cadau, M., Mubarak Rajput, M., Gaya-Àvila, A., and Zimmermann, J.: Model evaluation for km-scale simulations within the Climate Adaptation Digital Twin: the AQUA approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10347, https://doi.org/10.5194/egusphere-egu25-10347, 2025.
The ocean carbon cycle plays a crucial role in the uptake and storage of atmospheric carbon dioxide. The Global Carbon Budget (GCB) provides annual estimates of this oceanic carbon sink, with the latest estimate for 2022 indicating a net uptake of 2.8 ± 0.4 Gt C yr-1 (Friedlingstein et al., 2023). However, the estimates by global ocean biogeochemistry models (GOBMs, 2.5 ± 0.4 Gt C yr-1 ) are substantially lower than the estimates based on upscaled observations (3.1 Gt C yr-1 [2.5, 3.3]) and the range of the sink estimates covered by the GOBMs is substantial (1.1 Gt C yr-1 ). Biases and uncertainties in the GOBM estimates of the ocean carbon sink may be due to imperfections in the representation of physical (e.g., transport, mixing) and biogeochemical processes, as well as in the forcing fields.
To address these uncertainties, we employ the International Ocean Model Benchmarking (IOMB) system to evaluate the performance of GCB models against state-of-the-art observations. IOMB is a Python-based open-source software package that is used to evaluate the performance of Earth System Models and the counterpart to ILAMB that is used to evaluate the dynamic vegetation models for the land sink in the GCB. Using IOMB, we analyze the physical (upper ocean temperature, vertical temperature gradient, mixed layer depth, salinity) and biogeochemical (nutrients, chlorophyll, oxygen, total alkalinity, dissolved inorganic carbon (DIC), anthropogenic DIC) variables of interest. but all the variables in the current version of IOMB evaluate the general performance of the ocean biogeochemistry models, not specifically the ocean carbon uptake.The addition of new targeted metrics such as AMOC, stratification indices, CFCs and Revelle factor will help to reduce the source of uncertainty in these models. IOMB evaluates the model performances using statistical measures such as bias, root mean square error (RMSE), annual cycle phase, spatial distribution, interannual variability and a score will be estimated for each model, providing a benchmark for the current state of GCB models and highlighting the areas for further model development.
The goal is to improve the accuracy and reliability of ocean carbon uptake estimates, which are essential for informing climate policy. Through this process, IOMB will provide valuable feedback to the modeling community, offering guidance on how to improve ocean biogeochemical simulations and better constrain the oceanic carbon sink in the context of global carbon budgets. This is a necessary first step towards weighting GCB models in estimating the ocean carbon sink.
How to cite: Mohanan Geethalekshmi, S., Gürses, Ö., Collier, N., and Hauck, J.: Benchmarking Global Ocean Carbon Cycle models: Uncertainties in anthropogenic CO2 uptake estimation , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11493, https://doi.org/10.5194/egusphere-egu25-11493, 2025.
The Atlantic Ocean plays a crucial role in regulating regional and global climate variability, particularly as the Atlantic meridional overturning circulation (AMOC) is weakening and on course to reach a critical tipping point in a rapidly warming climate. As a key tipping component of the climate system, the AMOC influences other Atlantic climate phenomena, e.g. the Atlantic Multidecadal Oscillation (AMO), and impacts global atmospheric circulation through air-sea interaction. Therefore, it is essential that the global Earth system models correctly capture or represent these interactions. This study aims to assess the impact of Atlantic climate phenomena on atmospheric circulation, based on historical and pre-industrial experiments conducted using the newly developed EC-Earth3-ESM-1 model under the framework of the EU project OptimESM. We focus particularly on evaluating the interaction between AMOC, AMO, and prominent large-scale atmospheric circulation patterns (e.g. the North Atlantic Oscillation (NAO), Hadley, and Walker circulation) using Empirical Orthogonal Function (EOF) and maximum covariance analysis methods. Our first results show that the EC-Earth3-ESM-1 model can reasonably capture the spatiotemporal relationship between the atmospheric circulation modes and AMOC/AMO compared to observation/reanalysis datasets. Additionally, we also investigate potential atmospheric circulation mode changes due to increasing CO2 concentration using idealized experiments. More detailed analyses will be further explored in this study.
How to cite: Wang, S., Wyser, K., Koenigk, T., Karami, M. P., and Navarro Labastida, R. G.: Evaluation of the impact of the Atlantic Ocean on the atmospheric circulation using the EC-Earth3-ESM-1 model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9455, https://doi.org/10.5194/egusphere-egu25-9455, 2025.
Against the backdrop of global climate change, Earth System Models (ESM) are widely used in the projection of future climate change. With the continuous development and evolution of Earth System Models, in addition to the inherent errors of the models themselves, the dynamic changes in future human activities also bring extremely significant uncertainties to the projection of climate change. At a time when the impact of human activities on climate change is continuously intensifying, Integrated Assessment Models are being more and more widely applied in the projection of future human social development.
In view of this, we set about coupling the two types of models, Earth System Models and Integrated Assessment Models, to explore the changing trends of future human society and the climate system. Specifically, first, we use an optimized climate change loss function to make the output results of the Integrated Assessment Model more reliable. As a result, the obtained future global CO2 emission data can also better fit the actual development situation of future human society. Secondly, new carbon dioxide emission data is used to drive the Community Earth System Model (CESM). Finally, the experimental results after coupling the Earth System Model and the Integrated Assessment Model are compared with the CMIP6 simulation experimental results to further explore the future climate change in 12 global regions after comprehensively considering human activities. The research results clearly show that the high-value areas of future climate change losses are concentrated in India and Southeast Asia. Moreover, extreme high-temperature and extreme precipitation events in the regions near the equator will increase significantly in the future.
This research not only helps to conduct scientific and rigorous assessments of regional climate change losses and accurately predict future CO2 emission paths, but also greatly enriches the content related to the impact of human activities in Earth System Models and has extremely important promoting significance for the scientific assessment of future climate change.
How to cite: Zhao, W., Chou, J., and Cui, Z.: Coupling model of human-earth system to explore global climate and carbon emission changes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13706, https://doi.org/10.5194/egusphere-egu25-13706, 2025.
In recent years, water shortages have become increasingly apparent, and it is hoped that by predicting where and to what extent water shortages will occur in the future and reflecting this in water use planning, we can contribute to the sustainable development of society. Future changes in water resources are calculated using an Earth System Model (ESM) and a Land Surface Model (LSM) within the ESM, and economic and social changes are calculated using an Integrated Assessment Model (IAM). We have therefore achieved a realistic water balance model between the environment and the economy/society in land areas by two-way coupling between an ESM/LSM and an IAM. We used ILS [1] as the LSM and GCAM [2] as the IAM. When passing the amount of water resources from ILS to GCAM, we did not pass the raw values but the deviation. The constant values that GCAM originally had were rewritten by the values modified using the deviation. The deviation was calculated by comparing the average values for the past 10 years and the average values for the past 5 years from the time steps when the data exchange occurred, and evaluating the ratio of how much the 5-year average was larger/smaller than the 10-year average. The coupling from GCAM to ILS was achieved by passing the land use changes downscaled using Demeter [3]. When the definition of land use types differed between Demeter and ILS, the type of Demeter’s output was converted to that of the input of ILS by carrying out the method developed in [4]. Using this coupled system, we present the results of annual and seasonal evaluations of water stress from 2020 to 2100. In addition, the assessment results of the population exposed to high water stress are also presented.
[1] Nitta, Tomoko, et al. "Development of integrated land simulator." Progress in Earth and Planetary Science7.1 (2020): 1-14.
[2] Calvin, Katherine, et al. "GCAM v5. 1: representing the linkages between energy, water, land, climate, and economic systems." Geoscientific Model Development12.2 (2019): 677-698.
[3] Vernon, Chris R., et al. "Demeter–a land use and land cover change disaggregation model." Journal of Open Research Software 6.PNNL-SA-131044 (2018).
[4] Fushio, Keigo, et al. “Water stress assessment by coupled simulation of Integrated Land Simulator (ILS) and Integrated Assessment Model (IAM).”, SEISAN KENKYU75.2 (2023): 135-140.
How to cite: Fukuda, T., Muto, Y., Kawata, H., Nitta, T., Olson, R., and Yoshimura, K.: Two-way coupling between an LSM and an IAM for assessing the future global water stress, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14085, https://doi.org/10.5194/egusphere-egu25-14085, 2025.
Observational constraints are widely used to reduce uncertainty in multi-model projections and have been proven to be effective. Implementations of constraints vary widely, ranging from using temperature trends over different time periods to incorporate the full evolution of the historical climate time series and even a range of covariates. In this study we consider two Bayesian approaches to developing a constraint on future global warming, using the historical time evolution of global mean surface temperature (GMST) in one case, and historical GMST trends during recent decades in another. We also consider which period in the historical GMST record provides the most effective constraint on future projections. We conduct our studying using large ensemble simulations from climate models with different sensitivities. When using a time series of annual GMST values, we find an effective constraint only becomes possible when data from the recent period of rapid transient climate change are included in the analysis. Furthermore, incorporating the full transition from a quasi-equilibrium pre-industrial state to the recent strong transient response results in a better constrain. Using a simple linear warming trend from recent decades does improve upon unconstrained projections but to a lesser extent than using the full time series for the same period. Accounting for the intercept obtained in linear trend estimation, which provides information about the warming that occurred before the trend estimation period and thus how represents the Earth system transitioned from a quasi-stationary state to its current state of rapid transient response, improves the skill of trend based constraints. Nevertheless, a constraint based on both the trend (the recent rate of warming) and intercept (the accumulated warming prior to the trend period) does not perform as well as a constraint that uses the entire historical GMST record from 1850 to present.
How to cite: Li, T., Zwiers, F., and Zhang, X.: How much of the historical global mean surface temperature record is needed to well constrain projections of future warming?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4023, https://doi.org/10.5194/egusphere-egu25-4023, 2025.
The Korea Institute of Atmospheric Prediction Systems (KIAPS) is committed to enhancing the accuracy and reliability of weather forecasts, especially for periods extending beyond the conventional two-week timeframe. To support this goal, we are developing an innovative and comprehensive model that seamlessly integrates various components of the Earth system, including oceans, sea ice, waves, and river systems. This integrated approach is crucial because, while short-term weather predictions typically focus on atmospheric variables, they often overlook the significant role that rivers play in the global hydrological cycle. However, as the forecasting window extends into extended-medium range, the contribution of rivers to the Earth’s water balance becomes increasingly important. To address this, the project employs a specialized model designed to accurately estimate the volume of freshwater that rivers discharge into the oceans, which in turn influences a range of oceanic and atmospheric processes.
In this study, we have integrated the Catchment-based Macro-scale Floodplain (CaMa-Flood) model with the Korean Integrated Model (KIM) Numerical Weather Prediction (NWP) system. The CaMa-Flood model is specifically designed to simulate river discharge and floodplain dynamics on a global scale, making it a valuable tool for enhancing the realism of hydrological processes in weather prediction models. By coupling CaMa-Flood with the KIM, we aim to improve the representation of riverine processes within the broader context of Earth system modeling.
To evaluate the effectiveness of this integration, we conducted a series of validation analysis. These involved comparing river discharge data generated by the coupled KIM/CaMa-Flood model with observational data from various sources, as well as reanalysis datasets. The objective is to assess the model’s ability to accurately reproduce the spatial distribution and seasonal variability of river discharge across different regions. Additionally, the study explores the impact of freshwater influx from rivers on key oceanographic parameters such as sea surface temperature, salinity, and sea ice concentration, particularly in the Arctic region. A significant focus of the research is the Arctic, where the interactions between river discharge, sea ice, and atmospheric conditions are especially complex and influential on global climate variabilities. We are conducting an in-depth analysis of the relationships among sea ice extent, surface air temperatures, and upper atmospheric dynamics in the Kara-Barents Sea region of the Arctic. The findings from this study are expected to provide valuable insights into the role of riverine processes in Arctic climate dynamics and contribute to the development of more accurate and reliable long-term forecasts.
How to cite: Cho, M.-H.: mpact of River Discharge on Arctic Atmosphere: Coupling a River Routing Model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3933, https://doi.org/10.5194/egusphere-egu25-3933, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 5
EGU25-4069 | ECS | Posters virtual | VPS5
The Eddy-Diffusivity Mass-Flux parameterization: improved representation of convective mixing, global evaluations and implications for Ocean Heat ContentMon, 28 Apr, 14:00–15:45 (CEST) | vP5.6
The Eddy-Diffusivity Mass-Flux (EDMF) parameterization (Giordani et al., 2020) offers a new, coherent way to simultaneously parameterize local (diffusivity) and non-local (convective thermal) vertical mixing. This second component parametrizes sub-grid-scale convective plumes propagating through the water column which, through energy conservation, can propagate counter to the stratification gradient. The EDMF scheme is assessed in a 13-year global ¼° coupled NEMO4.2-SI3 simulation, forced by ERA5 atmospheric reanalysis. Its performance in representing observed ocean temperatures is compared to that of a twin simulation using the commonly applied Enhanced Vertical Diffusivity (EVD) parameterization.
The EDMF simulation shows globally reduced temperature biases relative to in-situ observations (0–700 m) compared to the EVD simulation, with similar RMSD (Root Mean Square Deviation) values between the two. By better representing tropical night-time shallow convection, EDMF reduces the cold bias typically observed in EVD simulations within the tropical ocean. We show that the horizontal scales (convective areas), penetration depths and vertical velocities of the simulated plumes agree with measurements of deep convective plumes in the Labrador Sea, and with diurnal convection in the equatorial Pacific Ocean. Additionally, first estimates of convection's contribution to Ocean Heat Content are proposed.
How to cite: Piton, V., Bourdallé-Badie, R., and Giordani, H.: The Eddy-Diffusivity Mass-Flux parameterization: improved representation of convective mixing, global evaluations and implications for Ocean Heat Content, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4069, https://doi.org/10.5194/egusphere-egu25-4069, 2025.
