DOI: 10.5061/dryad.zpc866tj4
title: Data from: A framework for minimizing remote effects of regional climate interventions: Cooling the Great Barrier Reef without teleconnections

description: Climate interventions like Marine Cloud Brightening (MCB) have gained
 attention for their potential to protect vulnerable marine ecosystems from
 the worst impacts of climate change. Early modeling studies raised
 concerns about potential harmful global side effects stemming from
 regional interventions. Here we propose a modeling framework to evaluate
 these risks based on using maximal deployment scenarios in a global
 climate model to identify potential pathways of concern, combined with
 more realistic large intervention levels. We demonstrate this framework by
 modeling a cooling intervention over the Great Barrier Reef using the
 Community Earth System Model (CESM2). We identify potential impacts on
 tropical convection that could produce remote impacts, and show that
 limiting intervention duration to deployment in the key season largely
 eliminates these risks. Overall we illustrate that the local ecological
 goals can be achieved at a level of cooling well below what poses a risk
 of significant remote effects.
description: The simulations for this study were conducted using the Community
 Earth System Model (CESM2, Danabasoglu et al. 2020; Computational and
 Information Systems Laboratory, 2023)) running in a “slab ocean”
 configuration. In this mode a simplified ocean simulates the mixed layer
 of the ocean and its interactions with the atmosphere without simulating
 full ocean circulation. The exchange of heat between the mixed layer and
 the deeper ocean is approximated with a prescribed monthly Q-flux that is
 derived from a fully coupled run of the model. All simulations are carried
 out under preindustrial conditions, with a 50-year spin up period to
 ensure the model reaches a stable equilibrium before perturbations are
 applied.  Cooling interventions are implemented using
 an additional Q-flux term to specify a forcing at the surface of the
 ocean. The advantage of this approach is that it allows direct control of
 the location and amount of forcing applied, and can be used to represent
 cooling that is achieved through cloud brightening, surface albedo
 enhancement, deep ocean pumping, or any combination of intervention
 technologies. The limitations of this approach are that it does not
 include any dynamic adjustments or feedbacks that are specific to an
 albedo modification or cloud intervention. The simplified ocean also
 neglects the advection of cooled surface waters due to ocean circulation.
 This modeling configuration is not aimed at evaluating the efficacy of
 particular intervention technology at producing cooling, but whether a
 given level of regional cooling would produce significant impacts outside
 the region of intervention, in line with step 1 of our framework. Despite
 these limitations, the SST variability in the Coral Sea region in the
 slab-ocean simulation is quite similar to both a fully-coupled
 pre-industrial run of CESM2 and with ERA5 reanalysis (Figure
 S1). For each level of forcing, three separate
 simulations are branched from the control run at five year intervals and
 run for 13 years. The first year of each branch is discarded as the system
 is adjusting, producing 36 years of data for each scenario. Using several
 shorter runs rather than a single long run allows us additional statistics
 to examine impacts that emerge in the first decade of deployment, when
 increased scrutiny will be placed on both the effectiveness and any
 unintended side effects of an intervention. Spacing the branches by five
 years allows more sampling across conditions of natural
 variability.
description: # Data from: A framework for minimizing remote effects of regional climate
 interventions: Cooling the Great Barrier Reef without teleconnections
 [https://doi.org/10.5061/dryad.zpc866tj4](https://doi.org/10.5061/dryad.zpc866tj4) ## Description of the data and file structure ### File naming scheme The naming scheme for all model output netcdf files is as follows: `derecho-som-[experiment]-[branch_year]-[variable]-[timescale].nc` * *experiment* * ‘control-qdpnoise[n]’ to indicate the 3 control branches that each have a different random noise pattern added * ‘anom-eez-[forcing]’ to indicate a [forcing] W/m2 of cooling applied year-round within the domain bounded by the exclusive economic zone * ‘anom-eez-[forcing]DJF’ to indicate a [forcing] W/m2 of cooling applied only during DJF within the domain bounded by the exclusive economic zone * *branch_year* * Indicates which year the simulation was branched from the spinup simulation * *variable* * CESM2 variable names. Included variables are: CLDTOT, FLUT, OMEGA, PRECC, PRECT, PS, SST, T, TREFHT, TS, U, V, Z3 * *timescale* * Either ‘monthly’ or ‘quarterly-JFM’ indicating the timescale that the data is averaged on. Quarterly data is defined as JFM, AMJ, JAS, NOD. ### Files and variables Model output is grouped into the following zip files. Each zip file contains files for all variables at three simulation branches for one level of forcing. #### File: cool-20wm2-eez-quarterly.zip **Description:** NetCDF files following the above naming scheme for year-round cooling of 20 W/m2 over coral sea eez domain. Timescale: Quarterly averaged into JFM, AMJ, JAS, NOD Forcing: 20 W/m2 year-round Simulation branches: b50, b55, b60 Variables: CLDTOT, FLUT, OMEGA, PRECC, PRECT, PS, SST, T, TREFHT, TS, U, V, Z3 #### File: cool-40wm2-eez-quarterly.zip **Description:**  NetCDF files following the above naming scheme for year-round cooling of 40 W/m2 over coral sea eez domain. Timescale: Quarterly averaged into JFM, AMJ, JAS, NOD Forcing: 40 W/m2 year-round Simulation branches: b50, b55, b60 Variables: CLDTOT, FLUT, OMEGA, PRECC, PRECT, PS, SST, T, TREFHT, TS, U, V, Z3 #### File: cool-60wm2-eez-quarterly.zip **Description:**  NetCDF files following the above naming scheme for year-round cooling of 60 W/m2 over coral sea eez domain. Forcing: 60 W/m2 year-round Simulation branches: b50, b55, b60 Variables: CLDTOT, FLUT, OMEGA, PRECC, PRECT, PS, SST, T, TREFHT, TS, U, V, Z3 #### File: cool-40wm2-DJF-eez-quarterly.zip **Description:** NetCDF files following the above naming scheme for DJF cooling of 40 W/m2 over coral sea eez domain. Forcing: 40 W/m2 only in DJF Timescale: Quarterly averaged into JFM, AMJ, JAS, NOD Simulation branches: b50, b55, b60 Variables: CLDTOT, FLUT, OMEGA, PRECC, PRECT, PS, SST, T, TREFHT, TS, U, V, Z3 #### File: SST-monthly.zip **Description:** NetCDF files following the above naming scheme for all cooling scenarios (year-round and DJF) Timescale: Monthly average Forcing: 20/40/60 W/m2 year-round and 40 W/m2 seasonal DJF Simulation branches: b50, b55, b60 Variables: SST #### File: control-branches-quarterly.zip **Description:** NetCDF files following the above naming scheme for all control runs. Timescale: Quarterly average Forcing: None Simulation branches: b50, b55, b60 Variables: CLDTOT, FLUT, OMEGA, PRECC, PRECT, PS, SST, T, TREFHT, TS, U, V, Z3 #### File: SOM\_forcing\_files.zip **Description:** NetCDF files used to set the SOM forcing within CESM2, following the following naming scheme: pop_frc.b.e21.B1850.f09_g17.CMIP6-piControl.001_branch2.012120_anom_eez_[forcing].nc Forcing: 20/40/60 W/m2 year-round and 40 W/m2 seasonal DJF ## Software * paper_figures.ipynb 381.25 KB * Python notebook containing the code to generate the figures in the manuscript from the included model output files * supplementary_figs.ipynb 5.15 MB * Python notebook containing the code to generate the figures in the supplement from the included model output files