Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain

Gerber, Franziska; Bešič, Nikola; Sharma, Varun; Mott, Rebecca; Daniels, Megan; Gabella, Marco; Berne, Alexis; Germann, Urs; Lehning, Michael

doi:10.5194/tc-12-3137-2018

Cited by 45 publications

(49 citation statements)

References 66 publications

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“…For precipitation, particularly wet conditions are simulated by the COSMO-CLM over the Tibetan Plateau. This bias seems to be common to several RCMs for areas characterized by complex topography Gao et al, 2015;Feng and Fu, 2006) and is likely related to an overestimation of orographic precipitation enhancement in the models (Gerber et al, 2018) and/or to an incorrect simulation of the planetary boundary layer (Xu et al, 2016). Additionally, in the COSMO-CLM simulations a significant dry bias occurs over arid and desert regions, especially in summer.…”

Section: Discussionmentioning

confidence: 98%

“…The boundaries have a temporal resolution of 6 h and a spectral resolution of T62 (∼ 1.89 • ). NCEP2 data have been selected as boundary data, instead of commonly employed ERA-Interim reanalyses (Dee et al, 2011), since their resolution is closer to the resolution of the three global circulation models (GCMs) that are used for CORDEX-CORE simulations in the CLM community: MPI-ESM (Giorgetta et al, 2013), HadGEM (The HadGEM2 Development Team, 2011) and NorESM Iversen et al, 2013), with a spatial resolution of, respectively, ∼ 210 × 210, ∼ 210 × 140 and ∼ 270×210 km. Thus, using NCEP2 as drivers allows one to reproduce a resolution jump more similar to the one present when using the considered GCMs.…”

Section: Model and Experiments Descriptionmentioning

confidence: 99%

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Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

et al. 2019

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Due to its extension, geography and the presence of several underdeveloped or developing economies, the Central Asia domain of the Coordinated Regional Climate Downscaling Experiment (CORDEX) is one of the most vulnerable regions on Earth to the effects of climate changes. Reliable information on potential future changes with high spatial resolution acquire significant importance for the development of effective adaptation and mitigation strategies for the region. In this context, regional climate models (RCMs) play a fundamental role.In this paper, the results of a set of sensitivity experiments with the regional climate model COSMO-CLM version 5.0, for the Central Asia CORDEX domain, are presented. Starting from a reference model setup, general model performance is evaluated for the present day, testing the effects of singular changes in the model physical configuration and their mutual interaction with the simulation of monthly and seasonal values of three variables that are important for impact studies: near-surface temperature, precipitation and diurnal temperature range. The final goal of this study is twofold: having a general overview of model performance and its uncertainties for the considered region and determining at the same time an optimal model configuration.Results show that the model presents remarkable deficiencies over different areas of the domain. The combined change of the albedo, taking into consideration the ratio of forest fractions, and the soil conductivity, taking into account the ratio of liquid water and ice in the soil, allows one to achieve the best improvements in model performance in terms of cli-matological means. Importantly, the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features, and that could positively affect the repartition of incoming radiation. The analyses also show that improvements in model performance are not achievable for all domain subregions and variables, and they are the result of a compensation effect in the different cases. The proposed better performing configuration in terms of mean climate leads to similar positive improvements when considering different observational data sets and boundary data employed to force the simulations. On the other hand, due to the large uncertainties in the variability estimates from observations, the use of different boundaries and the model internal variability, it has not been possible to rank the different simulations according to their representation of the monthly variability.This work is the first ever sensitivity study of an RCM for the CORDEX Central Asia domain and its results are of fundamental importance for further model development and for future climate projections over the area.

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Section: Discussionmentioning

confidence: 98%

Section: Model and Experiments Descriptionmentioning

confidence: 99%

Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

et al. 2019

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“…Ground-based radar products are valuable data for evaluating and validating the representation of microphysical processes in atmospheric models (e.g., Gerber et al, 2018, Min et al, 2015, Nicholls et al, 2017. In the austral summer 2015-2016, the intensive Antarctic Precipitation, Remote Sensing from Surface and Space (APRES3) campaign of precipitation observation-including the deployment of a polarimetric radar and of a Multi-Angle Snowflake Camera (hereafter MASC)-took place at Dumont d'Urville (DDU) station, Adélie Land, East Antarctica.…”

Section: Introductionmentioning

confidence: 99%

Microphysics of Snowfall Over Coastal East Antarctica Simulated by Polar WRF and Observed by Radar

et al. 2019

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The current assessment of the Antarctic surface mass balance mostly relies on reanalysis products or climate model simulations. However, little is known about the ability of models to reliably represent the microphysical processes governing the precipitation. This study makes use of recent ground‐based precipitation measurements at Dumont d'Urville station in Adélie Land to evaluate the representation of the precipitation microphysics in the Polar version of the Weather Research Forecast (Polar WRF) atmospheric model. During two summertime snowfall events, high‐resolution simulations are compared to measurements from an X‐band polarimetric radar and from a Multi‐Angle Snowflake Camera (MASC). A radar simulator and a “MASC” simulator in Polar WRF make it possible to compare similar observed and simulated variables. Radiosoundings and surface‐meteorological observations were used to assess the representation of the regional dynamics in the model. Five different microphysical parameterizations are tested. The simulated temperature, wind, and humidity fields are in good agreement with the observations. However, the amount of simulated surface precipitation shows large discrepancies with respect to observations, and it strongly differs between the simulations themselves, evidencing the critical role of the microphysics. The inspection of vertical profiles of reflectivity and mixing ratios revealed that the representation of the sublimation process by the low‐level dry katabatic winds strongly influences the actual amount of precipitation at the ground surface. By comparing the simulated radar signal as well as MASC and model particle size distributions, it is also possible to identify the microphysical processes involved and to pinpoint shortcomings within the tested parameterizations.

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“…This is not an easy task, as snow distribution is very complex (Grünewald et al, 2010;Kirchner et al, 2014;Reuter et al, 2016). Since the mountain snow cover is largly shaped by snow transport by wind, adequate modeling can only be achieved through computationally expensive snow drift modeling (Gerber et al, 2018;Mott and Lehning, 2010;Vionnet et al, 2014). While from an operational point of view, high resolution modeling (resolution of several meters) on large domains is presently out of reach, alternative approaches were suggested (e.g.…”

Section: Discussionmentioning

confidence: 99%

Sensitivity of modeled snow stability data to meteorological input uncertainty

Herwijnen¹,

Rotach²,

Schweizer³

2020

Preprint

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Abstract. To perform spatial snow cover simulations for numerical avalanche forecasting, interpolation and downscaling of meteorological data are required, which introduce uncertainties. The repercussions of these uncertainties on modeled snow stability remain mostly unknown. We therefore assessed the contribution of meteorological input uncertainty on modeled snow stability by performing a global sensitivity analysis. We used the numerical snow cover model SNOWPACK to simulate two snow instability metrics, i.e. the skier stability index and the critical crack length, for a field site equipped with an automatic weather station providing the necessary input for the model. Uncertainty ranges for meteorological forcing covered typical differences observed within a distance of 2 km and an elevation change of 200 m. Three different scenarios were investigated to better assess the influence of meteorological forcing on snow stability during a) the weak layer formation period, b) the slab formation period, and c) the weak layer and slab formation period. For each scenario, 14 000 simulations were performed, by introducing quasi-random uncertainties to the meteorological input. Results showed that a weak layer formed in 99.7 % of the simulations, indicating that the weak layer formation was very robust due to the prolonged dry period. For scenario a), modeled grain size of the weak layer was mainly sensitive to precipitation, while the shear strength of the weak layer was sensitive to most input variables, especially air temperature. Once the weak layer existed (case b), precipitation was the most prominent driver for snow stability. The sensitivity analysis highlighted that for all scenarios, the two stability metrics were mostly sensitive precipitation. Precipitation determined the load of the slab, which in turn influenced weak layer properties. For case b) and c), the two stability metrics showed contradicting behaviors. With increasing precipitation, i.e. deep snowpacks, the skier stability index decreased (less stable). In contrast, the critical crack length increased with increasing precipitation. With regard to spatial simulations of snow stability, the high sensitivity on precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns. With regard to spatial simulations of snow stability, the high sensitivity on precipitation suggests that accurate precipitation patterns are necessary to obtain realistic snow stability patterns.

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Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain

Cited by 45 publications

References 66 publications

Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

Sensitivity studies with the regional climate model COSMO-CLM 5.0 over the CORDEX Central Asia Domain

Microphysics of Snowfall Over Coastal East Antarctica Simulated by Polar WRF and Observed by Radar

Sensitivity of modeled snow stability data to meteorological input uncertainty

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