The evolution of scaling laws in the sea ice floe size distribution

Abstract

The sub‐gridscale floe size and thickness distribution (FSTD) is an emerging climate variable, playing a leading‐order role in the coupling between sea ice, the ocean, and the atmosphere. The FSTD, however, is difficult to measure given the vast range of horizontal scales of individual floes, leading to the common use of power‐law scaling to describe it. The evolution of a coupled mixed‐layer‐FSTD model of a typical marginal ice zone is explicitly simulated here, to develop a deeper understanding of how processes active at the floe scale may or may not lead to scaling laws in the floe size distribution. The time evolution of mean quantities obtained from the FSTD (sea ice concentration, mean thickness, volume) is complex even in simple scenarios, suggesting that these quantities, which affect climate feedbacks, should be carefully calculated in climate models. The emergence of FSTDs with multiple separate power‐law regimes, as seen in observations, is found to be due to the combination of multiple scale‐selective processes. Limitations in assuming a power‐law FSTD are carefully analyzed, applying methods used in observations to FSTD model output. Two important sources of error are identified that may lead to model biases: one when observing an insufficiently small range of floe sizes, and one from the fact that floe‐scale processes often do not produce power‐law behavior. These two sources of error may easily lead to biases in mean quantities derived from the FSTD of greater than 100%, and therefore biases in modeled sea ice evolution.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 01, 2017
Source ID
10.1002/2016jc012573

Entities

People

  • Christopher Horvat
  • Eli Tziperman

Organizations

  • Harvard University
  • National Aeronautics and Space Administration
  • National Science Foundation
  • United States Department of Defense

Tags

Fields of Study

  • Environmental science

Readers

  • Ocean-Atmosphere Mesoscale Modeling, Data Assimilation, and Flux Boundary Layers
  • Polar and Arctic Studies
  • Regression Analysis.