Physics-based SNOWPACK model improves representation of near-surface Antarctic snow and firn density

• Published in: The Cryosphere
• Main Authors: Keenan, Eric, Wever, Nander, Dattler, Marissa, Lenaerts, Jan T. M., Medley, Brooke, Kuipers Munneke, Peter, Reijmer, Carleen
• Format: Text
• Language: English
• Published: 2021
• Subjects: Antarctic; The Antarctic; Antarc*; Ice Sheet
• Online Access: https://doi.org/10.5194/tc-15-1065-2021; https://tc.copernicus.org/articles/15/1065/2021/

Estimates of snow and firn density are required for satellite-altimetry-based retrievals of ice sheet mass balance that rely on volume-to-mass conversions. Therefore, biases and errors in presently used density models confound assessments of ice sheet mass balance and by extension ice sheet contribution to sea level rise. Despite this importance, most contemporary firn densification models rely on simplified semi-empirical methods, which are partially reflected by significant modeled density errors when compared to observations. In this study, we present a new drifting-snow compaction scheme that we have implemented into SNOWPACK, a physics-based land surface snow model. We show that our new scheme improves existing versions of SNOWPACK by increasing simulated near-surface (defined as the top 10 m ) density to be more in line with observations (near-surface bias reduction from − 44.9 to − 5.4 kg m −3 ). Furthermore, we demonstrate high-quality simulation of near-surface Antarctic snow and firn density at 122 observed density profiles across the Antarctic ice sheet, as indicated by reduced model biases throughout most of the near-surface firn column when compared to two semi-empirical firn densification models (SNOWPACK <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow><mtext>mean bias</mtext><mo>=</mo><mo>-</mo><mn mathvariant="normal">9.7</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="88pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="d4563868c957e632f059fa36e2231f24"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-15-1065-2021-ie00001.svg" width="88pt" height="10pt" src="tc-15-1065-2021-ie00001.png"/></svg:svg> kg m −3 , IMAU-FDM <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7" display="inline" overflow="scroll" dspmath="mathml"><mrow><mtext>mean bias</mtext><mo>=</mo><mo>-</mo><mn mathvariant="normal">32.5</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="94pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="69a418015481211ba8e90f0255d99d5f"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-15-1065-2021-ie00002.svg" width="94pt" height="10pt" src="tc-15-1065-2021-ie00002.png"/></svg:svg> kg m −3 , GSFC-FDM mean bias=15.5 kg m −3 ). Notably, our analysis is restricted to the near surface where firn density is most variable due to accumulation and compaction variability driven by synoptic weather and seasonal climate variability. Additionally, the GSFC-FDM exhibits lower mean density bias from 7–10 m (SNOWPACK <math xmlns="http://www.w3.org/1998/Math/MathML" id="M12" display="inline" overflow="scroll" dspmath="mathml"><mrow><mtext>bias</mtext><mo>=</mo><mo>-</mo><mn mathvariant="normal">22.5</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="e6c05470f8e77a22a94e434af6f2a97a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-15-1065-2021-ie00003.svg" width="64pt" height="10pt" src="tc-15-1065-2021-ie00003.png"/></svg:svg> kg m −3 , GSFC-FDM bias=10.6 kg m −3 ) and throughout the entire near surface at high-accumulation sites (SNOWPACK <math xmlns="http://www.w3.org/1998/Math/MathML" id="M16" display="inline" overflow="scroll" dspmath="mathml"><mrow><mtext>bias</mtext><mo>=</mo><mo>-</mo><mn mathvariant="normal">31.4</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="4ad179be01fb3b38b331d9a69c2e79e6"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-15-1065-2021-ie00004.svg" width="64pt" height="10pt" src="tc-15-1065-2021-ie00004.png"/></svg:svg> kg m −3 , GSFC-FDM <math xmlns="http://www.w3.org/1998/Math/MathML" id="M18" display="inline" overflow="scroll" dspmath="mathml"><mrow><mtext>bias</mtext><mo>=</mo><mo>-</mo><mn mathvariant="normal">4.7</mn></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="58pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="e8f3edc2b3728534494a59661e6445e7"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-15-1065-2021-ie00005.svg" width="58pt" height="10pt" src="tc-15-1065-2021-ie00005.png"/></svg:svg> kg m −3 ). However, we found that the performance of SNOWPACK did not degrade when applied to sites that were not included in the calibration of semi-empirical models. This suggests that SNOWPACK may possibly better represent firn properties in locations without extensive observations and under future climate scenarios, when firn properties are expected to diverge from their present state.