Periodic Reporting for period 2 - OptimESM (Optimal High Resolution Earth System Models for Exploring Future Climate Changes)
Reporting period: 2024-04-01 to 2025-06-30
The global climate is rapidly changing, with a global mean temperature that has almost reached 1.5 degree warming compared to pre-industrial values. The world is now close to warming levels where the risk of abrupt changes and tipping points in key Earth system phenomena such as ice sheets, ocean circulation, marine and terrestrial ecosystems is rapidly increasing. Earth system models (ESMs) are the best tool to improve the understanding of such abrupt changes and their consequences for climate, ecosystems and society, but many relevant processes are still not adequately represented in ESMs.
The main goal of OptimESM is therefore to enhance Earth system models (ESMs) by combining higher spatial resolution with a better representation of key physical and biogeochemical processes. New policy-relevant emission and land use scenarios, including some that realise the Paris Agreement and others that temporarily or permanently overshoot the Paris targets, will be developed and realised with the ESMs. The results will be used to deliver policy-relevant knowledge around the consequences of reaching or exceeding different levels of global warming, including the risk of rapid changes in key Earth system phenomena and their regional impacts.
The main goal of OptimESM is therefore to enhance Earth system models (ESMs) by combining higher spatial resolution with a better representation of key physical and biogeochemical processes. New policy-relevant emission and land use scenarios, including some that realise the Paris Agreement and others that temporarily or permanently overshoot the Paris targets, will be developed and realised with the ESMs. The results will be used to deliver policy-relevant knowledge around the consequences of reaching or exceeding different levels of global warming, including the risk of rapid changes in key Earth system phenomena and their regional impacts.
OptimESM has delivered developments in three interlinked areas, (i) Earth system models (ESMs), (ii) new emission scenarios and (iii) machine learning (ML)-based methods for regional downscaling, as well as performed sets of ESM simulations and analysis of these simulations and of CMIP6 data.
First, we have finalised our so-called “post-CMIP6” ESMs, starting from the versions of our four project ESMs that were available at the start of the project. We have used these new “post-CMIP6” ESMs to perform sets of CO2-emission-driven pre-industrial control, historical and idealised simulations, including ramp-up simulations to different global warming levels, zero emission simulations at these global warming levels and ramp-down simulations back to lower warming levels. The post-CMIP6 ESMs have been evaluated for the historical period using observations.
First results from our simulations indicate the importance of temperature overshoot on the regional climate and provide first new knowledge on occurrence of various abrupt changes at different global warming levels. We have also developed a catalogue of abrupt changes and state transitions using CMIP6 projections and tested early warning signals for tipping points.
The post-CMIP6 ESM versions have then been used as a starting point to further improve the representation of e.g. ice sheets, permafrost, fires, soil and vegetation. In addition, we have worked on hybrid resolution methods, where the physical components of a model run at higher spatial resolution than other ESM components, and tested methods to accelerate spin-up and calibration of the ESMs to reduce the computational needs.
The integrated assessment model REMIND-MAgPIE has been applied to develop new land and emission scenarios, including scenarios that reach the warming targets of the Paris agreement and some that overshoot them. They have been extended to 2300 and these extended scenarios have been run with a Simple Climate Model.
In order to provide more detailed information at the regional level, different ML-based tools have been developed to refine the regional representation of the ESM simulations. These ML-methods have been tested on first ESM simulations, and the results have been compared against data from dynamical high resolution regional climate models.
First, we have finalised our so-called “post-CMIP6” ESMs, starting from the versions of our four project ESMs that were available at the start of the project. We have used these new “post-CMIP6” ESMs to perform sets of CO2-emission-driven pre-industrial control, historical and idealised simulations, including ramp-up simulations to different global warming levels, zero emission simulations at these global warming levels and ramp-down simulations back to lower warming levels. The post-CMIP6 ESMs have been evaluated for the historical period using observations.
First results from our simulations indicate the importance of temperature overshoot on the regional climate and provide first new knowledge on occurrence of various abrupt changes at different global warming levels. We have also developed a catalogue of abrupt changes and state transitions using CMIP6 projections and tested early warning signals for tipping points.
The post-CMIP6 ESM versions have then been used as a starting point to further improve the representation of e.g. ice sheets, permafrost, fires, soil and vegetation. In addition, we have worked on hybrid resolution methods, where the physical components of a model run at higher spatial resolution than other ESM components, and tested methods to accelerate spin-up and calibration of the ESMs to reduce the computational needs.
The integrated assessment model REMIND-MAgPIE has been applied to develop new land and emission scenarios, including scenarios that reach the warming targets of the Paris agreement and some that overshoot them. They have been extended to 2300 and these extended scenarios have been run with a Simple Climate Model.
In order to provide more detailed information at the regional level, different ML-based tools have been developed to refine the regional representation of the ESM simulations. These ML-methods have been tested on first ESM simulations, and the results have been compared against data from dynamical high resolution regional climate models.
OptimESM has made progress beyond the state of the art in 5 areas.
a) Developing high-resolution ESMs
We have developed tools to allow for different resolutions in different ESM components to be able to increase resolution where it provides the largest added value. We have also explored new ML-based methods to accelerate development, calibration and application phases of ESMs.
We have advanced the representation of ice sheets and their interactions with ice shelves and ocean, permafrost, wildfires and land surface processes, and their link to regional climate and carbon cycle.
b) New, policy-relevant emission and land use scenarios
New land use and emission scenarios up to 2100 have been derived and then further extended beyond 2100 to 2300.
These scenarios include an improved representation of the forestry sector and the associated carbon stocks, as well as an option to remove carbon via Ocean Alkalinity Enhancement.
c) Evaluating ESMs and uncertainty in climate projections
We have performed small ensembles of historical simulations with each ESM and evaluated our ESMs against observations. A particular focus is on processes relevant for driving abrupt changes. Where possible, we have compared the variability in ESMs to observed variability. However, it turns out that uncertainty in ocean observations is substantial, which limits our ability to determine the realism of the simulated variability in our models.
d) Potential abrupt climate changes
We have compiled a catalogue of possible tipping points and transitions into new states using the CMIP6 database. Most vulnerable areas are the polar regions, where large sea ice reductions cause state transitions of ocean and surface variables. Several ESMs also show collapses in ocean circulation systems. We have also developed techniques for early warning under rapid climate forcing, and have tested them for sub-polar gyre and Amazon forest collapses in CMIP6 simulations.
e) Regional consequences
New methods for statistical downscaling based on ML-methods have been developed and tested against high-resolution observational data and high-resolution regional model data. We have applied the successful downscaling methods on ESM data for the historical time period, and first tests for future projections are ongoing.
a) Developing high-resolution ESMs
We have developed tools to allow for different resolutions in different ESM components to be able to increase resolution where it provides the largest added value. We have also explored new ML-based methods to accelerate development, calibration and application phases of ESMs.
We have advanced the representation of ice sheets and their interactions with ice shelves and ocean, permafrost, wildfires and land surface processes, and their link to regional climate and carbon cycle.
b) New, policy-relevant emission and land use scenarios
New land use and emission scenarios up to 2100 have been derived and then further extended beyond 2100 to 2300.
These scenarios include an improved representation of the forestry sector and the associated carbon stocks, as well as an option to remove carbon via Ocean Alkalinity Enhancement.
c) Evaluating ESMs and uncertainty in climate projections
We have performed small ensembles of historical simulations with each ESM and evaluated our ESMs against observations. A particular focus is on processes relevant for driving abrupt changes. Where possible, we have compared the variability in ESMs to observed variability. However, it turns out that uncertainty in ocean observations is substantial, which limits our ability to determine the realism of the simulated variability in our models.
d) Potential abrupt climate changes
We have compiled a catalogue of possible tipping points and transitions into new states using the CMIP6 database. Most vulnerable areas are the polar regions, where large sea ice reductions cause state transitions of ocean and surface variables. Several ESMs also show collapses in ocean circulation systems. We have also developed techniques for early warning under rapid climate forcing, and have tested them for sub-polar gyre and Amazon forest collapses in CMIP6 simulations.
e) Regional consequences
New methods for statistical downscaling based on ML-methods have been developed and tested against high-resolution observational data and high-resolution regional model data. We have applied the successful downscaling methods on ESM data for the historical time period, and first tests for future projections are ongoing.