Contributing Authors: J. Box (USA), D. Bromwich (USA), R. Brown (Canada), J.G. Cogley (Canada), J. Comiso (USA), M. Dyurgerov (Sweden, USA), B. Fitzharris (New Zealand), O. Frauenfeld (USA, Austria), H. Fricker (USA), G. H. Gudmundsson (UK, Iceland), C. Haas (Germany), J.O. Hagen (Norway), C. Harris (UK), L. Hinzman (USA), R. Hock (Sweden), M. Hoelzle (Switzerland), P. Huybrechts (Belgium), K. Isaksen (Norway), P. Jansson (Sweden), A. Jenkins (UK), Ian Joughin (USA), C. Kottmeier (Germany), R. Kwok (USA), S. Laxon (UK), S. Liu (China), D. MacAyeal (USA), H. Melling (Canada), A. Ohmura (Switzerland), A. Payne (UK), T. Prowse (Canada), B.H. Raup (USA), C. Raymond (USA), E. Rignot (USA), I. Rigor (USA), D. Robinson (USA), D. Rothrock (USA), S.C. Scherrer (Switzerland), S. Smith (Canada), O. Solomina (Russian Federation), D. Vaughan (UK), J. Walsh (USA), A. Worby (Australia), T. Yamada (Japan), L. Zhao (China)

Observations: Changes in Snow, Ice and Frozen Ground

A coarse resolution coupled ocean–atmosphere simulation in which surface albedo feedback is suppressed by prescribing surface albedo, is compared to one where snow and sea ice anomalies are allowed to affect surface albedo. Canonical CO2-doubling experiments were performed with both models to assess the impact of this feedback on equilibrium response to external forcing. It accounts for about half the high-latitude response to the forcing. Both models were also run for 1000 yr without forcing to assess the impact of surface albedo feedback on internal variability. Surprisingly little internal variability can be attributed to this feedback, except in the Northern Hemisphere continents during spring and in the sea ice zone of the Southern Hemisphere year-round. At these locations and during these seasons, it accounts for, at most, 20% of the variability. The main reason for this relatively weak signal is that horizontal damping processes dilute the impact of surface albedo feedback. When snow albed...

/pdf/the-role-of-surface-albedo-feedback-in-climate-2rl2kryuh4.pdf

The role of surface albedo feedback in climate

[1] The first three-dimensional global model in which time-dependent spectral albedos and emissivities over snow and sea ice are predicted with a radiative transfer solution, rather than prescribed, is applied to study the climate response of fossil fuel plus biofuel black carbon plus organic matter (ff+bf BC+OM) when BC absorption in snow and sea ice is accounted for. The model treats the cycling of size-resolved BC+OM between emission and removal by dry deposition and precipitation from first principles. Particles produce and enter size-resolved clouds and precipitation by nucleation scavenging and aerosol-hydrometeor coagulation. Removal brings BC to the surface, where internally and externally mixed BC in snow and sea ice affects albedo and emissivity through radiative transfer. Climate response simulations were run with a ff+bf BC+OC emission inventory lower than that used in a previous study. The 10-year, globally averaged ff+bf BC+OM near-surface temperature response due to all feedbacks was about +0.27 K (+0.32 in the last 3 years), close to those from the previous study (5-year average of +0.3 K and fifth-year warming of +0.35 K) and its modeled range (+0.15 to +0.5 K) because warming due to soot absorption in snow and sea ice here (10-year average of +0.06 K with a modeled range of +0.03 to +0.11 K) offset reduced warming due to lower emission. BC was calculated to reduce snow and sea ice albedo by ∼0.4% in the global average and 1% in the Northern Hemisphere. The globally averaged modeled BC concentration in snow and sea ice was ∼5 ng/g; that in rainfall was ∼22 ng/g. About 98% of BC removal from the atmosphere was due to precipitation; the rest was due to dry deposition. The results here support previous findings that controlling ff+bf BC+OM and CO2 emission may slow global warming.

https://web.stanford.edu/group/efmh/jacobson/Articles/VIII/SootAlb.pdf

Climate response of fossil fuel and biofuel soot, accounting for soot's feedback to snow and sea ice albedo and emissivity

[1] Ship-based observations are used to describe regional and seasonal changes in the thickness distribution and characteristics of sea ice and snow cover thickness around Antarctica. The data set comprises 23,373 observations collected over more than 2 decades of activity and has been compiled as part of the Scientific Committee on Antarctic Research (SCAR) Antarctic Sea Ice Processes and Climate (ASPeCt) program. The results show the seasonal progression of the ice thickness distribution for six regions around the continent together with statistics on the mean thickness, surface ridging, snow cover, and local variability for each region and season. A simple ridge model is used to calculate the total ice thickness from the observations of level ice and surface topography, to provide a best estimate of the total ice mass, including the ridged component. The long-term mean and standard deviation of total sea ice thickness (including ridges) is reported as 0.87 ± 0.91 m, which is 40% greater than the mean level ice thickness of 0.62 m. Analysis of the structure function along north/south and east/west transects revealed lag distances over which sea ice thickness decorrelates to be of the order of 100–300 km, which we use as a basis for presenting near-continuous maps of sea ice and snow cover thickness plotted on a 2.5 � 5.0 grid.

/pdf/thickness-distribution-of-antarctic-sea-ice-qa3azdaitd.pdf

Thickness distribution of Antarctic sea ice

A summary of the theoretical basis and initial performance of the algorithms that are used to derive sea ice concentration, ice temperature, and snow depth on sea ice from newly acquired Earth Observing System-Aqua/Advanced Microwave Scanning Radiometer-EOS (AMSR-E) radiances is presented. The algorithms have been developed and tested using historical satellite passive microwave data and are expected to provide more accurate products, since they are designed to take advantage of the wider range of frequencies and higher spatial resolution of the AMSR-E microwave instrument. Validation programs involving coordinated satellite, aircraft, and surface measurements to determine the accuracies of these sea ice products and to improve further our capability to monitor global sea ice are currently underway.

Sea ice concentration, ice temperature, and snow depth using AMSR-E data

Snow on Antarctic sea ice plays a complex and highly variable role in air-sea-ice interaction processes and the global climate system. This paper presents snow data collected during the past ten years, and reviews major findings. These include: differences in regional and seasonal snow properties and thicknesses; the unique consequences of snow on Antarctic pack ice relative to the Arctic (e.g. the importance of flooding and snow-ice formation); the potential impact if global change increases snowfall; lower observed values of snow thermal conductivity than those used in models; periodic large-scale melt in winter; and the contrast in summer melt in the Antarctic and Arctic. The new findings have significant implications for modelling and remote-sensing studies. Different snow properties from Arctic conditions are recommended for use in Antarctic models; similar differences could affect the interpretation of remote-sensing data over sea ice.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2000RG000085

Christian Hass

Papers

Snow on Antarctic sea ice