Scientific and human errors in a snow model intercomparison

Cecile B. Menard; Richard Essery; Gerhard Krinner; Gabriele Arduini; Paul Bartlett; Aaron Boone; Claire Brutel-Vuilmet; Eleanor Burke; Matthias Cuntz; Yongjiu Dai Bertrand Decharme; Emanuel Dutra; Xing Fang; Charles Fierz; Yeugeniy Gusev; Stefan Hagemann; Vanessa Haverd; Hyungjun Kim; Matthieu Lafaysse; Thomas Marke; Olga Nasonova; Tomoko Nitta; Masashi Niwano; John Pomeroy; Gerd Schädler; Vladimir Semenov; Tatiana Smirnova; Ulrich Strasser; Sean Swenson; Dmitry Turkov; Nander Wever; Hua Yuan

Bulletin of the American Meteorological Society 1-46
September 9, 2020
DOI: https://doi.org/10.1175/BAMS-D-19-0329.1

Abstract
Twenty-seven models participated in the Earth System Model – Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modelling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables, and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modelling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parametrizations are problematic and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behaviour and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.

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Simulation of Preferential Flow in Snow With a 2‐D Non‐Equilibrium Richards Model and Evaluation Against Laboratory Data

Nicolas R. Leroux, Christopher B. Marsh, John W. Pomeroy
Published August 10, 2020
Water Resources Research, Volume 56. Issue 9, Pages 1-11
DOI: https://doi.org/10.1029/2020WR027466

Abstract

Recent studies of water flow through dry porous media have shown progress in simulating preferential flow propagation. However, current methods applied to snowpacks have neglected the dynamic nature of the capillary pressure, such as conditions for capillary pressure overshoot, resulting in a rather limited representation of the water flow patterns through snowpacks observed in laboratory and field experiments. Indeed, previous snowmelt models using a water entry pressure to simulate preferential flow paths do not work for natural snowpack conditions where snow densities are less than 380 kg m−3. Because preferential flow in snowpacks greatly alters the flow velocity and the timing of delivery of meltwater to the base of a snowpack early in the melt season, a better understanding of this process would aid hydrological predictions. This study presents a 2‐D water flow through snow model that solves the non‐equilibrium Richards equation. This model, coupled with random perturbations of snow properties, can represent realistic preferential flow patterns. Using 1‐D laboratory data, two model parameters were linked to snow properties and model boundary conditions. Parameterizations of these model parameters were evaluated against 2‐D snowpack observations from a laboratory experiment, and the resulting model sensitivity to varying inputs and boundary conditions was calculated. The model advances both the physical understanding of and ability to simulate water flow through snowpacks and can be used in the future to parameterize 1‐D snowmelt models to incorporate flow variations due to preferential flow path formation.

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Nikolas O. Aksamit and John W. Pomeroy
Published: September 1, 2020
The Cryosphere, volume14, issue 9, pages 2795–2807
DOI: https://doi.org/10.5194/tc-14-2795-2020

Abstract:

Blowing snow transport has considerable impact on the hydrological cycle in alpine regions both through the redistribution of the seasonal snowpack and through sublimation back into the atmosphere. Alpine energy and mass balances are typically modeled with time-averaged approximations of sensible and latent heat fluxes. This oversimplifies nonstationary turbulent mixing in complex terrain and may overlook important exchange processes for hydrometeorological prediction. To determine if specific turbulent motions are responsible for warm- and dry-air advection during blowing snow events, quadrant analysis and variable interval time averaging was used to investigate turbulent time series from the Fortress Mountain Snow Laboratory alpine study site in the Canadian Rockies, Alberta, Canada, during the winter of 2015–2016. By analyzing wind velocity and sonic temperature time series with concurrent blowing snow, such turbulent motions were found to supply substantial sensible heat to near-surface wind flows. These motions were responsible for temperature fluctuations of up to 1 ∘C, a considerable change for energy balance estimation. A simple scaling relationship was derived that related the frequency of dominant downdraft and updraft events to their duration and local variance. This allows for the first parameterization of entrained or advected energy for time-averaged representations of blowing snow sublimation and suggests that advection can strongly reduce thermodynamic feedbacks between blowing snow sublimation and the near-surface atmosphere. The downdraft and updraft scaling relationship described herein provides a significant step towards a more physically based blowing snow sublimation model with more realistic mixing of atmospheric heat. Additionally, calculations of return frequencies and event durations provide a field-measurement context for recent findings of nonstationarity impacts on sublimation rates.

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