Japan Geoscience Union Meeting 2025

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-CG Complex & General

[A-CG42] Projection and detection of global environmental change

Thu. May 29, 2025 3:30 PM - 5:00 PM 103 (International Conference Hall, Makuhari Messe)

convener:Michio Kawamiya(Japan Agency for Marine-Earth Science and Technology), Kaoru Tachiiri(Japan Agency for Marine-Earth Science and Technology), Hiroaki Tatebe(Japan Agency for Marine-Earth Science and Technology), V Ramaswamy(NOAA GFDL), Chairperson:Hiroaki Tatebe(Japan Agency for Marine-Earth Science and Technology)

3:30 PM - 3:45 PM

[ACG42-07] Efficient spin-up of Earth System Models using sequence acceleration

*Samar Khatiwala1, Shogo Urakawa2, Tomohiro Hajima3 (1.Waseda University, 2.Meteorological Research Institute, 3.Japan Agency for Marine-Earth Science and Technology)

The ocean and land carbon cycles plays a critical role in the climate system and are key components of the Earth System Models (ESMs) used to project future changes in the environment. However, their slow adjustment time also hinders effective use of ESMs because of the enormous computational resources required to integrate them to a pre-industrial quasi-equilibrium, a prerequisite for performing any simulations with these models. A newly developed solution to this so-called "spin-up" problem [Khatiwala, Sci. Adv. (2024)] promises to greatly accelerate the equilibration of state-of-the-art marine and land biogeochemical models typical of those embedded in ESMs by over an order of magnitude. Based on a "sequence acceleration" method, the new technique can be applied in a "black box" fashion to any existing model. In this presentation we describe our experience implementing and applying this scheme to the ocean biogeochemical components of two leading ESMs from the Meteorological Research Institute (MRI) and the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). Preliminary results show significant speed-up to equilibrium, which would enable for the first time a quantification of major parametric uncertainties in these models, lead to more accurate estimates of metrics such as climate sensitivity, and allow increased model resolution beyond what is currently feasible. These results are especially timely for the next phase of the Coupled Model Intercomparison Project (CMIP7).