Published May 12, 2022 | Version v1
Dataset Open

A Semi-Empirical Framework for Ice Sheet Response Analysis under Oceanic Forcing in Antarctica and Greenland

Creators

  • 1. Texas Tech University

Description

Supplementary material for

Luo, X. & Lin, T. (2022). “A Semi-Empirical Framework for Ice Sheet Response Analysis under Oceanic Forcing in Antarctica and Greenland.” Climate Dynamics, in press. https://doi.org/10.1007/s00382-022-06317-x

This repository contains data and scripts that are used to facilitate the analysis of Greenland and Antarctica ice sheet melting induced by ocean warming.

The GCM directory contains ocean potential temperature time series for both Antarctica ice shelves near open sea retrieved from FGOALS-s2 under RCP 6.0 and Greenland store glacier retrieved from MIROC5 under RPC8.5 and HadGEM3-GC31-MM projections under update of RCP8.5 based on SSP5. The raw data are accessed from CMIP5 (https://esgf-node.llnl.gov/search/cmip5/) and CMIP6 (https://esgf-node.llnl.gov/search/cmip6/) projects respectively and the processed data deposited here are stored in Excel format.

The WOD directory contains the NetCDF files of Present-Day ocean conditions including temperature, salinity, depth, and other variables retrieved from World Ocean Database (WOD).  The data are deposited as NetCDF format.

The Analysis directory contains 3 subdirectories that illustrates the analysis workflow for both Greenland and Antarctica.

         The Antarctica subdirectory contains the analysis codes to reproduce Antarctica ice shelf melting response and future projection. Antarctica_analysis.m is the main routine for computation and plotting, and the other files are input files including PISM/PICO simulations outputs and ocean temperature time series retrieved from CMIP5 project that can be found on the previous GCM directory.

         The Greenland subdirectory contains the main routine store_analysis.m for Greenland Store glacier computation and plotting, the response time series (store.mat), perturbed response time series (store_pert.mat), ocean temperature projection (store_temp.mat) generated by MIROC5, and subglacial runoff time series (store_runoff.mat) estimated from annual basin surface runoff using MAR data forced by MIROC5 under RCP8.5.

         The Historical subdirectory contains the data and scripts used for the historical analyses in the main text. AIS_melt_historical.m is the main script of the analysis, along with two other plotting functional scripts including plot_melt_rate.m and plot_ocean_temp.m. The input files include the PISM/PICO simulations outputs, which are the same files in Antarctica subdirectory, and other 2 input files that contain the temperature time series from 6 CMIP5 GCMs.

Notes

We acknowledge the World Climate Research Programme (2011)'s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We thank the community organized SeaRISE project, which makes data available for present-day Antarctica. We thank Dr. Ronja Reese (Potsdam Institute for Climate Impact Research, Germany) for providing modeling advice and an input example of PICO ocean component. We thank Dr. Tom Cowton (University of St Andrews, U.K.) for providing IcePlume codes within MITgcm to facilitate the simulation of Greenland glacier plume melting. We thank the editor and the reviewers for their careful review and insightful comments that have helped us improve this manuscript. This work is supported in part by the Edward E. Whitacre, Jr. College of Engineering at Texas Tech University under research salary and travel funds awarded to the Principal Investigator T.L. – corresponding (second) author. The funds provide research assistantship support, conference participation, and programming bootcamp training for the lead student author X.L., along with the tuition scholarship from Department of Civil, Environmental, and Construction Engineering. Additional awards from the Innovation Hub and the Office of Research & Innovation to the Principal Investigator T.L. are gratefully acknowledged. Analyses presented herein were performed using the Quanah and Red Raider computing clusters at Texas Tech University. We thank the team at the High Performance Computing Center (HPCC) for their generous support. In addition, the equipment support from the Vice President for Research & Innovation for T.L.'s Multi-Hazard Sustainability (HazSus) Research Group is gratefully acknowledged.

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