CMIP at EGU24
14 April, 2024 @ 8:00 am – 19 April, 2024 @ 5:00 pm Central European Time
The EGU General Assembly 2024 (EGU24) brings together geoscientists from all over the world to one meeting covering all disciplines of the Earth, planetary, and space sciences. The EGU aims to provide a forum where scientists, especially early career researchers, can present their work and discuss their ideas with experts in all fields of geoscience.
Abstract submission is now open for EGU24. The abstract submission deadline is Wednesday, 10 January 2024, 13:00 CET. There is a €50 abstract processing charged due for each submitted abstract. Please note that you must be an EGU24 or an EGU lifetime member to submit an abstract as the first author.
To submit an abstract, browse through the session programme (see details about our CMIP sessions below) and select the most relevant session for you. You can not submit the same abstract to more than one session. Authors are allowed as first author to submit either one regular abstract plus one abstract solicited by a convener, or two solicited abstracts. A second regular abstract can be submitted to sessions led by the Education and Outreach Sessions (EOS) programme group (the maximum number of abstracts, including solicited abstracts, remains two).
More details on abstract submission can be found on the EGU24 website.
A number of our task teams have organised sessions at EGU24.
Addressing and Understanding Uncertainties in CMIP: Key Insights and Future Directions
Lina Teckentrup, Yiwen Li, Camilla Mathison, Julia Mindlin, Alexander J. Winkler
The Coupled Model Intercomparison Project (CMIP) is instrumental in advancing our understanding of the Earth’s climate system and its future projections. However, Earth system models (ESM) exhibit disparities in critical aspects, particularly in their responses to anthropogenic forcings and the intricate coupling of physical and biogeochemical systems. Given that the Earth system science community, and notably the IPCC, relies on CMIP outputs to inform policy and mitigation strategies, it becomes imperative to address these inherent uncertainties through a multidisciplinary approach that unites atmospheric, oceanic, and terrestrial modeling analyses. In this session, we invite studies that investigate uncertainties and model disagreements across all facets associated with CMIP projections. These may include contributions that relate to:
- Identification of processes and key entities with significant disparities across CMIP models: Quantifying sources of uncertainty across CMIP models, which may include i) internal variability, ii) process representations/model parameterization, iii) ESM architecture, and iv) external forcing.
- Critical scientific priorities for future CMIP/Earth system model development: Recognizing and comprehending uncertainties and their underlying mechanisms are essential for guiding future model development and refining climate projections. We encourage contributions that address pivotal questions crucial for enhancing model performance and reducing uncertainties across disciplines in upcoming CMIP iterations.
- Opportunities, challenges, and constraints in using CMIP output for impact research: Uncertainties are amplified at regional scales; nevertheless, CMIP model projections are extensively utilized for impact studies by researchers unfamiliar with these sources of uncertainty and structural limitations of CMIP projections. We invite contributions that focus on innovative approaches employing CMIP output to tackle these challenges in impact studies.
In summary, this session aims to cultivate a collaborative environment where climate scientists and modellers across disciplines can engage in constructive dialogues and collaboratively chart a course towards tackling CMIP output to effectively meet the pressing challenges posed by climate change.
Climate forcing: quantifying the roles and responses of anthropogenic and natural climate drivers
Climate change is the result of perturbations to atmospheric composition or land use affecting the surface albedo, amongst other external natural of anthropogenic forcings. These “climate forcing” agents cause an energy imbalance at the top of the atmosphere, driving a warming Earth. This session invites research contributions assessing the climate responses to forcing and uncertainties in the evolution of different forcing agents. We are especially interested in time-dependent physical and biogeochemical responses to climate forcing, based on the coupled model intercomparison project phase six (CMIP6) or previous CMIP phases. Contributions on all aspects of climate-forcing research are welcome. These may include, but are not limited to, the development of historical and future forcing, studies that use idealised, single- or multi-Earth System Model approaches, observational methods to evaluate climate responses, as well as works accounting for multiple climate system realms, i.e., the ocean, atmosphere, cryosphere, land surface/subsurface, and biosphere, their linkages, and feedbacks in the system. This session is convened by the WCRP CMIP Forcing Task Team which is working to prepare next-generation climate-forcing datasets for CMIP6Plus and CMIP7.
Benchmarking and Evaluation of Earth System Models
Earth System Models (ESMs) have evolved considerably in complexity, capability and scale as evidenced in projects such as the Coupled Model Intercomparison Project Phase 6 and the forthcoming CMIP7 project. There is therefore a need to credibly assess such developments and capabilities for effective research on climate variability and change. The CMIP7 Model Benchmarking Task Team was established by the World Climate Research Programme to advance the full integration of routine benchmarking and evaluation into the CMIP workflow. As part of this initiative, we invite studies that explore novel approaches for benchmarking and evaluation of ESMs including cross-domain and process -based evaluation, observational uncertainties, science and performance metrics and benchmarks. The development of tools and methods including Machine Learning and Artificial Intelligence approaches are also welcomed.