Articles | Volume 17, issue 12
https://doi.org/10.5194/tc-17-5061-2023
https://doi.org/10.5194/tc-17-5061-2023
Research article
 | 
30 Nov 2023
Research article |  | 30 Nov 2023

A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR

Marco Tedesco, Paolo Colosio, Xavier Fettweis, and Guido Cervone

Model code and software

Greenland Climate Network (GC-Net) Data K. Steffen et al. Sampson, K., Starkweather, S., Steffen, S., Stroeve, J., Walter, https://doi.org/10.16904/envidat.1

Historical surface mass balance measurements from the ice-sheet ablation area and local glaciers H. Machguth https://doi.org/10.22008/FK2/5VNBQA

A computationally efficient statistically downscaled 100\,m resolution Greenland product from the regional climate model MAR: accompanying dataset M. Tedesco, G. Cervone, P. Colosio, and X. Fettweis https://doi.org/10.5281/zenodo.7803611

Download
Short summary
We developed a technique to improve the outputs of a model that calculates the gain and loss of Greenland and consequently its contribution to sea level rise. Our technique generates “sharper” images of the maps generated by the model to better understand and quantify where losses occur. This has implications for improving models, understanding what drives the contributions of Greenland to sea level rise, and more.