the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Introducing CRYOWRF v1.0: Multiscale atmospheric flow simulations with advanced snow cover modelling
- 1School of Architecture, Civil and Environmental Engineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
- 2WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
- 3Sunwell Sàrl, Lausanne, Switzerland
- 1School of Architecture, Civil and Environmental Engineering, Swiss Federal Institute of Technology, Lausanne, Switzerland
- 2WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
- 3Sunwell Sàrl, Lausanne, Switzerland
Abstract. Accurately simulating snow-cover dynamics and the snow-atmosphere coupling is of major importance for topics as wide-ranging as water resources, natural hazards and climate change impacts with consequences for sea-level rise. We present a new modelling framework for atmospheric flow simulations for cryospheric regions called CRYOWRF. CRYOWRF couples the state-of-the-art and widely used atmospheric model WRF, with the detailed snow-cover model SNOWPACK. CRYOWRF makes it feasible to simulate dynamics of a large number of snow layers governed by grain-scale prognostic variables with online coupling to the atmosphere for multiscale simulations from the synoptic to the turbulent scales. Additionally, a new blowing snow scheme is introduced in CRYOWRF and is discussed in detail. CRYOWRF's technical design goals and model capabilities are described and performance costs are shown to compare favourably with existing land surface schemes. Three case studies showcasing envisaged use-cases for CRYOWRF for polar ice sheets and alpine snowpacks are provided to equip potential users with templates for their research. Finally, the future road-map for CRYOWRF's development and usage is discussed.
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Varun Sharma et al.
Status: final response (author comments only)
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RC1: 'Review of gmd-2021-231', Anonymous Referee #1, 12 Oct 2021
Sharma and colleagues present a novel coupled modelling system called CRYOWRF that consists of three components: the atmospheric model WRF, the snow model SNOWPACK, and a new parameterization for snow drift. Only a handful of previous studies have attempted to improve the representation of cryospheric processes in WRF by integrating new modelling components, and there has been no publicly available implementation of blowing snow to date. The authors present three case study simulations across a wide range of horizontal grid spacings, with the associated namelists and scripts provided as templates to facilitate usage of the model by the scientific community. As such, the manuscript fits the scope of Geoscientific Model Development well, and provides a significant advancement in the field of coupled atmosphere-cryosphere modelling. Overall, the paper is very well written and organized. The methods are generally well explained, although I have highlighted a few aspects that would benefit from additional clarification in the minor comments. The main weakness of the paper is the dearth of model evaluation. There is also an issue in relying on asynchronous coupling (i.e., not calling SNOWPACK every WRF timestep) for computational efficiency if one is interested in investigating feedbacks, as mentioned in the major comments below.
Major comments
- The introduction does not mention previous efforts to improve the representation of cryospheric processes in WRF through integration of new modelling components, including Collier et al. (2013; https://doi.org/10.5194/tc-7-779-2013) and Eidhammer et al. 2021 (https://doi.org/10.5194/hess-25-4275-2021).
- In order to reduce the computational overhead of integrating SNOWPACK, the authors suggest to use, and present case studies that employ, asynchronous coupling between WRF and SNOWPACK through the namelist parameter snpack_dt. From the cryospheric perspective, there is no clearly no need to call the snow model every timestep (i.e., every 5 s in a 1-km grid spacing domain). However, from the atmospheric perspective, the difference in the update frequency of turbulent heat fluxes and surface conditions will introduce numerical artefacts that are unrelated to the improved representation of cryospheric processes. The reliance on asynchronous coupling therefore limits the utility of CRYOWRF as a tool to investigate feedbacks, in particular between “offline” simulations with other LSMs and “online” simulations with SNOWPACK. This limitation should be clarified in the manuscript.
- With the exception of Figures 5 & 6, there is no model evaluation presented, and this task is repeatedly designated as future work. Figures 5 & 6 compare simulated near-surface meteorological variables with station data for the first case study (an analysis that is later stated as “establishing the accuracy of the model” at line 709), however there is no evaluation of surface mass balance, or more importantly, snow drift as simulated by the new parameterization. The manuscript would be strengthened by additional evaluation, even if suitable data are only available on a point scale (e.g., for blowing snow).
- In several places in the manuscript, the authors credit CRYOWRF with capabilities that are actually provided by WRF regardless of LSM choice (e.g., lines 411—412; line 552; lines 713-714; WRF is acknowledged at line 587). Therefore, the manuscript would benefit from more careful language around the value added by CRYOWRF.
- It would be helpful to clarify already in the methods section that SNOWPACK can function as a standalone LSM, and therefore also updates surface conditions and fluxes over non-glacierized grid cells. For readers unfamiliar with SNOWPACK, this capability is unclear until lines 623-626.
Minor comments
- Please provide references for the statements at lines 16-17 & lines 592-593.
- Lines 316-317: Please provide more information about how the stability correction is handled to avoid runaway cooling in the interactive implementation.
- Line 385: Is it correct that only those three variables – latent & sensible heat fluxes and surface albedo – are updated in WRF? If so, why aren’t other surface boundary conditions updated, like surface temperature and roughness?
- Section 4.1.1: Why was this simulation period selected?
- Section 4.1.4: Could the authors discuss why SNOWPACK improves on the warm bias simulated by the Noah-MP LSM?
- Line 529: Could the authors provide a justification for only calling SNOWPACK every 15 minutes? From the cryospheric perspective, this timestep is nearly 10x larger than a characteristic timescale for heat diffusion assuming a top layer height of 1 cm. From the atmospheric perspective, this is a 180x decrease in the frequency of updating surface fluxes and conditions in the 1-km domain (dt=5s).
- Line 687: Another important caveat would be that there has been no evaluation of the results.
Technical comments
- Line 46: Please rephrase “not to speak about”
- Line 129: “OpenMP”
- Line 351: OOP has not been defined
- Line 391: Please rephrase “performed using Noah-MP along with CRYOWRF” to clarify that separate simulations were performed and compared.
- Line 479: “Sublimation”
- Line 490: “period is between”
- Line 495: DDU has not been defined in the text
- Line 500: “topographic”
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RC2: 'Comment on gmd-2021-231', Anonymous Referee #2, 08 Nov 2021
General Comments:
Blowing snow and the associated sublimation for snow redistribution is an important process to incorporate in polar atmospheric models especially those that can capture fine spatial scales through consideration of nonhydrostatic dynamics, as done here with the WRF model. This represents an advance on existing capabilities with the hydrostatic models RACMO and MAR. One can only appreciate the impacts of blowing snow and the associated sublimation by doing runs with and without the blowing snow processes active, not done here.
This is an interesting manuscript on coupled atmosphere-snow cover modeling and its impacts that deserves to be published after some improvements. The “land surface” model implemented into WRF is SNOWPACK. The blowing snow scheme implemented is similar to Dery and Yau (2002) with differing treatments for terminal fall velocities of snow particles and thresholds for onset of snow transport from the surface.
Specific Comments:
- No mention is made of Polar WRF that has pioneered the use of WRF in the polar regions, adapted and added physics treatments, and provided guidance on the parameterization performance in high latitudes, underlying the Vignon et al. (2020) manuscript that is featured prominently here. A place to start is here: http://polarmet.osu.edu/PWRF/
- The big differences between CROWRF and WRF with NoahMP at South Pole (Fig. 6) are the large warm biases of the latter during the warmer part of the year and the much higher relative humidity values during winter. Any explanations? These biases are much larger than previous implementations of WRF/Polar WRF over the Antarctic: doi: 10.1029/2012JD018139. Moisture content of the air is challenging to measure at the low air temperatures at South Pole in winter. Are you certain that the higher relative humidity values simulated by WRF with NoahMP are not more correct? It is often thought that the air there is close to or exceeds saturation with respect to ice. Are your relative humidity values with respect to ice or liquid water? Surface pressure, 10-m wind speed, and 10-m wind diection are much closer, and consistent with previous Antarctic WRF studies.
- The surface mass balance components shown in Fig. 7 look in error to me. If the mean values listed are averages for all of Antarctica including the ice shelves then precipitation and surface mass balance are only 2/3rds the values given by van Wessem et al. (2018) for long-term averages that should approximate the values here. Does the sublimation refer to total values, i.e., blowing snow plus surface sublimation? Do you really think that large melting and refreezing is widespread over the two large ice shelves (up to 200 kg/m*m/yr)? These are cold even in summer. Melting does occur in summer but is limited on average. https://doi.org/10.1002/2013GL058138
- Incorporate this manuscript into your paper: https://doi.org/10.1029/2020JD033936
- Line 142: Add “atmospheric” before “stability corrections”.
- Line 433: Provide details about the vertical levels used in the model: How many? Vertical distribution? What is the lowest level? What is the highest level?
- Line 479: “Sublimation”.
- Line 549: “as well as an acceleration”. Don’t understand what is being said here.
- Line 576: “replace “detained” by “detailed”.
- Fig. 12 caption: Make clear that the contours are potential temperature.
- AC1: 'Response to the two submitted reviews', Varun Sharma, 10 Jul 2022
Varun Sharma et al.
Varun Sharma et al.
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