4.6 Article

Evolving CO2 Rather Than SST Leads to a Factor of Ten Decrease in GCM Convergence Time

Journal

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2021MS002505

Keywords

planetary atmospheres; climate dynamics; global climate models

Funding

  1. NASA Astrobiology Program grant [80NSSC18K0829]
  2. European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program [786427]
  3. U.S. Department of Energy's Atmospheric System Research, an Office of Science, Office of Biological and Environmental Research program
  4. DOE [DE-AC02-05CH11231]
  5. National Natural Science Foundation of China (NSFC) [42075046]

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The inverse climate modeling (InvCM) method fixes global mean sea surface temperature (SST) and evolves CO2 mixing ratio to equilibrate climate, producing same climate as normal simulations but converging faster. It can be used for higher resolution simulations, broader parameter space exploration, finding unstable and hidden climate states.
The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO2 mixing ratio to equilibrate climate, has been implemented in a cloud-resolving model. In this article, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global-mean SST. We find that InvCM produces the same climate as normal slab-ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO2 for SSTs ranging from 290 to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

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