Protocol

The GFMIP protocol is presented in this preprint. Preliminary spatial feedbacks are available at this GitHub page.

Boundary conditions

There are four types of simulations, with the following boundary conditions. For more details on the simulations to be run with these boundary conditions, see the protocol. Note that the only difference between GFMIP simulations in the sea surface temperature; sea ice is to be kept at the climatology of 1971-2020 from AMIP, which we provide here. All files are on a 1ºx1º grid.

• control (Google drive link) - contains two variables, sst and sic, each lasting twelve months, being climatologies of years 1971-2020 from AMIP. Loop these for 21 years for the control simulations.
• patches (Google drive link) - contains 436 variables, four for each patch: +2K and -2K perturbations, both summed with the control (i.e., the ones to use as boundary conditions) and just as anomalies (for reference).
• historical (Google drive link) - contains three variables, sst (taken from AMIP), sst anomaly (for reference), and sic (which is a loop of the control sic)
• abrupt4x (Google drive link) - contains three variables, sst (taken as average anomalies from 17 CMIP6 models' first 150 years of their abrupt4x simulations), sst anomaly (for reference), and sic (which is a loop of the control sic)

Publications

Background

The use of patches of sea surface warming/cooling perturbations to estimate the linear response of the atmosphere to the sea surface temperature field was first explored in a dynamical context by Barsugli and Sardeshmukh 2002 and Barsugli et al. 2006 and then applied to climate feedbacks by Zhou et al. 2017 and Dong et al. 2019.

• Barsugli and Sardeshmukh (2002), Global Atmospheric Sensitivity to Tropical SST Anomalies throughout the Indo-Pacific Basin, J. Clim.
• Barsugli, Shin, and Sardeshmukh (2006), Sensitivity of global warming to the pattern of tropical ocean warming, Clim. Dyn.
• Zhou, Zelinka, and Klein (2017), Analyzing the dependence of global cloud feedback on the spatial pattern of sea surface temperature change with a Green's function approach, JAMES
• Dong, Proistosescu, Armour, and Battisti (2019), Attributing Historical and Future Evolution of Radiative Feedbacks to Regional Warming Patterns using a Green's Function Approach: The Preeminence of the Western Pacific, J. Clim.
• Williams, Jeevanjee, and Bloch-Johnson (2023), Circus Tents, Convective Thresholds, and the Non-Linear Climate Response to Tropical SSTs GRL
• Zhang, Zhao, and Tan (2023), Using a Green's Function Approach to Diagnose the Pattern Effect in GFDL AM4 and CM4, J. Clim.
• Alessi and Rugenstein (2023), Surface temperature pattern scenarios suggest higher warming rates than current projections, GRL

Contact

The organizers can be reached at gfmip.organizers@gmail.com.