A Lagrangian Model for the Physical and Microwave Properties of Winter Snow on Arctic Sea Ice

who cares about snow on sea ice?

Snow depth on sea ice is a critical climate variable but its distribution is relatively unknown on a Pan-Arctic scale. Thicker snow inhibits ice growth in winter by acting as a warming blanket, but in summer it forms a highly reflective shield, protecting the ice below from the sun’s rays. This reflective blanket also controls the amount of light that reaches primary produces in and under the ice.

As well as having competing physical effects, uncertainties surrounding snow on sea ice limit our ability to estimate sea ice thickness from space. Snow weighs down floating sea ice, ‘hiding’ its thickness below the waterline. It also affects the ability of radar altimeters like CryoSat-2 to ‘see’ the height of the ice surface. What are the spatial and temporal distribution of these effects? We don’t exactly know…

The ratio of its scientific importance to our scientific knowledge is so high that the state of snow on sea ice was identified as one of the “Key Knowledge Gaps and Uncertainties” in a recent IPCC report on the oceans and the cryosphere.

how it’s been done in the past

Winter in the Arctic Ocean is cold, and snow on sea ice is pretty cold too. But the ice underneath is in direct thermal contact with the ocean. This means the ice is warmer than the snow.

This relative warmth means the snow emits much more thermal radiation that the snow. Being comparatively dull, the snow it acts like a ‘filter’ for the radiation of the ice. Snow depths on sea ice have historically been inferred by quantifying the amount of filtration occurring.

One technique to estimate the filtration is the ‘Gradient Ratio’: first measure a frequency of radiation that’s relatively unaffected by snow - 19 GHz. Compare this to a frequency that’s heavily filtered by snow - 37 GHz. By comparing the 37 GHz radiation to the 19 GHz baseline, we can estimate the filtering effect of the snow and thus its depth.

But the depth of snow isn’t the only thing affecting the Gradient Ratio (GR). The geometry and physical makeup of the underlying ice is also a controlling factor, in addition to the morphology of the snow itself.

modelling the snowpack stratigraphy

These processes have historically been modelled with a variety of one dimensional models. In this project we use a newly released sea-ice variant of the popular SNOWPACK model (Wever et al., 2020; GMD). But how can we upscale this 1D model to a Pan-Arctic scale?

These ‘virtual ice floes’ are advected around the Arctic using ice motion vectors from Tschudi et al. (2019). As these floes are pushed around the Arctic, they develop complex layered structure with depth hoar, wind slab and ice lenses. This technique is not new, it has been used by Merkouriadi et al. (2019) using the HIGH-TSI 1D model, and by Liston et al. (in revision) using SnowModel.

The novel aspect to this work is the subsequent modelling of each virtual floe’s microwave properties.

Modelling the snowpack microwave properties

Having modelled the snowpack stratigraphy in detail, we are in a position to model its microwave emissions. To do this we run the Snow Microwave Radiative Transfer emission model (SMRT; Picard et al., 2018). This model is capable of simulating microwave emissions and backscatter at a given frequency and incidence angle.

This allows us to model the ‘Gradient Ratio’, and compare it to the observed gradient ratio. This will allow us to explore where the method over- and under-estimates the true snow depth. Furthermore the method may allow us to correct for biases induced by, say, warm air intrusions that introduce icy layers.

To obtain sufficiently continuous coverage to generate a gridded product of properties such as gradient ratio, snow surface temperature and snow-ice interface temperature, tens of thousands of floes must be modelled and analysed.

As such the model can be configured to run in two ways: the first involves the serial processing of SNOWPACK and SMRT - this is good for investigating individual floes as above. The second involves the simulation of all floes over a year with SNOWPACK, and the subsequent ability to process all the snow timeseries with SMRT in parallel on a high performance computer.

Even with parallel processing, running SMRT on thousands of floes in computationally intensive, which requires extensive use of high performance computing.

Making My Own Tracks

I recently began making my own Lagrangian ice-parcel tracks rather than using those of collaborators. This has allowed me to look much more closely at how well my parcel trajectories reflect reality by comparison with the International Arctic Buoy Program. So far the results of my algorithm are looking very positive!

some radar slides here