skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Climate Modeling and Causal Identification for Sea Ice Predictability

Abstract

This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments in which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidancemore » in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.« less

Authors:
 [1];  [1];  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC). Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1422909
Report Number(s):
LA-UR-18-21021
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; Earth Sciences; "Sea ice" uncertainty

Citation Formats

Hunke, Elizabeth Clare, Urrego Blanco, Jorge Rolando, and Urban, Nathan Mark. Climate Modeling and Causal Identification for Sea Ice Predictability. United States: N. p., 2018. Web. doi:10.2172/1422909.
Hunke, Elizabeth Clare, Urrego Blanco, Jorge Rolando, & Urban, Nathan Mark. Climate Modeling and Causal Identification for Sea Ice Predictability. United States. doi:10.2172/1422909.
Hunke, Elizabeth Clare, Urrego Blanco, Jorge Rolando, and Urban, Nathan Mark. Mon . "Climate Modeling and Causal Identification for Sea Ice Predictability". United States. doi:10.2172/1422909. https://www.osti.gov/servlets/purl/1422909.
@article{osti_1422909,
title = {Climate Modeling and Causal Identification for Sea Ice Predictability},
author = {Hunke, Elizabeth Clare and Urrego Blanco, Jorge Rolando and Urban, Nathan Mark},
abstractNote = {This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments in which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.},
doi = {10.2172/1422909},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Feb 12 00:00:00 EST 2018},
month = {Mon Feb 12 00:00:00 EST 2018}
}

Technical Report:

Save / Share: