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Surface Energy Fluxes Over Arctic Sea Ice

Prof Ian Brooks (SEE), Dr Ryan Neely (SEE)

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The Arctic is warming 2-3 times faster than the global average. The most obvious manifestation of this warming is the dramatic reduction in the area of sea ice at the end of the summer melt. The dominant control on the formation and melt of sea ice is the surface energy budget – the amount of heat entering or leaving the surface. There are two major components of the surface energy: solar and infra red radiation, and the turbulent exchange of heat between the surface and overlying atmosphere.

Climate models have generally failed to reproduce the recent dramatic decline in minimum ice extent (figure 1). This results in part from poor representation of the ice itself within the models and in part from biases in the modelled surface energy budget. Both problems ultimately stem from inadequate understanding of the physical processes involved, and the interactions and feedbacks between them. This in turn results from sparse observational data sets with which to study them. This project will study the turbulent processes controlling heat exchange between the surface and atmosphere using the most wide-ranging set of turbulence measurements ever made over the Arctic Ocean.

Figure 1. The recent decline in Arctic sea ice minimum extent from satellite measurements and climate models. Note the models fail to reproduce the dramatic decline observed in the latter half of the observational record.

The measurements to be used are drawn from two research cruises on the icebreaker Oden: a 12-week cruise made in July-October 2014, which worked around the marginal ice zone along the Siberian Shelf between Norway and Alaska; and a 6-week cruise in August-September 2016, which worked in the central Arctic Ocean in more consolidated sea ice (figure 2).

Surface turbulent exchanges depend upon the wind speed, the roughness of the surface, the thermal stability of the near-surface air, and the gradient of the quantity being exchanged – in the case of the heat flux, the gradient in temperature between the surface and the measurement height. Over sea ice, the surface temperature may be spatially heterogeneous, with the ice at a different temperature from the exposed water in open leads; these surfaces also have different roughness. The surface fluxes then depend also on the fractions and spatial distributions of ice and water at the surface.

Direct measurements of the turbulent heat flux were made throughout both cruises. These constitute one of the largest such data sets ever collected, and certainly spanning a wider range of surface conditions than any other data set. Digital images of the surface were also obtained at 1 minute intervals throughout the two cruises, from 3 different cameras. During AO2016 thermal images were also obtained from an infra red camera. These images will be processed and analysed using machine learning techniques to classify the surface along the ship track, in order to evaluate the measured heat flux and the thermal turbulent exchange coefficient as a function of ice/water fraction, and thermal contrasts between ice and water surfaces.

Figure 2. Cruise tracks for icebreaker Oden during the 2014 ACSE/SWERUS-C3 (blue) and 2016 AO2016 (red) cruises. The pale blue line shows the ice edge on August 31, 2016.

The radiative flux contributions to the surface energy budget are controlled primarily by cloud cover. Cloud reduces the amount of solar radiation reaching the surface, but also reduces surface energy losses by infra red radiation. The net impact of low cloud over sea ice is usually to warm the surface compared to cloud free conditions, but the precise energy budget is very sensitive to cloud properties. Cloud properties are in turn controlled in part by turbulent processes – the mixing of water vapour and aerosol upwards from the surface, and down across cloud top from the free troposphere. Turbulent mixing can be driven both from the surface, and also by infra red cooling at cloud top.

Turbulent mixing throughout the lower atmosphere and in cloud will be determined using measurements from a Doppler lidar (backscattered laser light) (Achtert et al. 2015) and Doppler cloud radar (O’Connor et al. 2010; Shupe et al. 2012). In combination with other instruments, these also allow detailed retrievals of cloud properties (Illingworth et al. 2007). This will allow the interactions between cloud properties, surface fluxes, and the turbulent structure of the Arctic boundary layer to be studied, and improved parameterizations developed for use in numerical models.

This analysis will contribute to ongoing work to refine the turbulent flux parameterizations in the Met Office operational forecast and climate models, and to improve the model’s representation of boundary layer structure and cloud over Arctic sea ice.

The project will involve direct collaborations with the Oden cruise project partners at Stockholm University and the NOAA Earth System Research Laboratory in Boulder, Colorado. Comparison of the observational results with outputs from the Met Office Unified Model will be undertaken in collaboration with colleagues at the University of East Anglia and at the Met Office.


A good undergraduate background in any physical science, computing or mathematics.

Training & Research Environment

The student will have access to a wide range of training provided through the DTP and other short courses available through the university. There are about 50 PhD students across the Institute for Climate and Atmospheric Science (ICAS) covering climate, dynamics, impacts, with extensive programmes in observations, modelling and lab studies, providing a vibrant research environment.


Achtert, P., I. M. Brooks, B. J. Brooks, B. I. Moat, J. Prytherch, P. O. G. Persson, and M. Tjernström. 2015: Measurement of wind profiles over the Arctic Ocean from ship-borne Doppler lidar. Atmos. Meas. Tech. 8, 4993-5007, doi: 10.5194/amt-8-4993-2015

Illingworth, A.J., Hogan, R.J., O'connor, E.J., Bouniol, D. and et al. (2007), Cloudnet. Bulletin of the American Meteorological Society, 88(6), p.883. doi:10.1175/BAMS-88-6-883.

O’Connor, E. J., A. J. Illingworth, I. M. Brooks, C. D. Westbrook, and R. J. Hogan, F. Davis, B. J. Brooks, 2010: A method for estimating the turbulent kinetic energy dissipation rate from a vertically pointing Doppler lidar, and independent evaluation from balloon-borne in situ measurements, J. Atmos. Oceanic. Tech. 27, 1652-1664. doi: 10.1175/2010JTECHA1455.1.

Shupe, M. D., I. M. Brooks, G. Canut. 2012: Evaluation of turbulent dissipation rate retrievals from Doppler cloud radar, Atmos. Meas. Tech. 5, 1375–1385, doi:10.5194/amt-5-1375-2012

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