Modelling snow ice and superimposed ice on landfast sea ice in Kongsfjorden, Svalbard

Wang, Caixin; Cheng, Bin; Wang, Keguang; Gerland, Sebastian; Pavlova, Olga

doi:10.3402/polar.v34.20828

Cited by 33 publications

(31 citation statements)

References 63 publications

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“…The cumulative SWE TP is based on total precipitation assuming precipitation falls as snow when T2M is < 0 • C. The cumulative snowfall (SF) is calculated in the same period as for the cumulative TP. The accumulated SWE measured by the buoy is estimated using a climatological monthly mean snow density based on Warren et al (1999). (Maykut, 1986).…”

Section: Era5 and Era-i Reanalysis Datamentioning

confidence: 99%

“…Here we compare the precipitation from ERA-I and ERA5 with snow depth measurements from the buoys (Table 1). For this comparison, snow depth from the buoys is converted to snow water equivalent (SWE) using a climatological monthly mean snow density of 220-380 kg m −3 (Warren et al, 1999). Caution must be taken here, as the buoys reflect point observations, while the reanalyses provide a grid cell average.…”

Section: Comparison Of Precipitation and Snowfall From Era5 And Era-imentioning

confidence: 99%

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Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution

et al. 2019

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Abstract. Rapid changes are occurring in the Arctic, including a reduction in sea ice thickness and coverage and a shift towards younger and thinner sea ice. Snow and sea ice models are often used to study these ongoing changes in the Arctic, and are typically forced by atmospheric reanalyses in absence of observations. ERA5 is a new global reanalysis that will replace the widely used ERA-Interim (ERA-I). In this study, we compare the 2 m air temperature (T2M), snowfall (SF) and total precipitation (TP) from ERA-I and ERA5, and evaluate these products using buoy observations from Arctic sea ice for the years 2010 to 2016. We further assess how biases in reanalyses can influence the snow and sea ice evolution in the Arctic, when used to force a thermodynamic sea ice model. We find that ERA5 is generally warmer than ERA-I in winter and spring (0–1.2 ∘C), but colder than ERA-I in summer and autumn (0–0.6 ∘C) over Arctic sea ice. Both reanalyses have a warm bias over Arctic sea ice relative to buoy observations. The warm bias is smaller in the warm season, and larger in the cold season, especially when the T2M is below −25 ∘C in the Atlantic and Pacific sectors. Interestingly, the warm bias for ERA-I and new ERA5 is on average 3.4 and 5.4 ∘C (daily mean), respectively, when T2M is lower than −25 ∘C. The TP and SF along the buoy trajectories and over Arctic sea ice are consistently higher in ERA5 than in ERA-I. Over Arctic sea ice, the TP in ERA5 is typically less than 10 mm snow water equivalent (SWE) greater than in ERA-I in any of the seasons, while the SF in ERA5 can be 50 mm SWE higher than in ERA-I in a season. The largest increase in annual TP (40–100 mm) and SF (100–200 mm) in ERA5 occurs in the Atlantic sector. The SF to TP ratio is larger in ERA5 than in ERA-I, on average 0.6 for ERA-I and 0.8 for ERA5 along the buoy trajectories. Thus, the substantial anomalous Arctic rainfall in ERA-I is reduced in ERA5, especially in summer and autumn. Simulations with a 1-D thermodynamic sea ice model demonstrate that the warm bias in ERA5 acts to reduce thermodynamic ice growth. The higher precipitation and snowfall in ERA5 results in a thicker snowpack that allows less heat loss to the atmosphere. Thus, the larger winter warm bias and higher precipitation in ERA5, compared with ERA-I, result in thinner ice thickness at the end of the growth season when using ERA5; however the effect is small during the freezing period.

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Section: Era5 and Era-i Reanalysis Datamentioning

confidence: 99%

Section: Comparison Of Precipitation and Snowfall From Era5 And Era-imentioning

confidence: 99%

Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution

et al. 2019

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“…studies to accurately resolve the evolution of snow/ice thickness and temperature profile. The sensitivity of the model to a variety of external weather forcing factors, snow and ice physical parameterization schemes, and numerical resolutions has been investigated extensively against observations (Cheng, Zhang, et al, 2008;Cheng et al, 2013;Wang et al, 2015). Model parameterizations are given in Table S1 in the supporting information (Briegleb et al, 2004;Ebert & Curry, 1993;Grenfell & Maykut, 1977;Huwald et al, 2005;Maykut & Untersteiner, 1971;Perovich, 1996;Pringle et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…By using three different reanalyses products, we can assess the sensitivity of the model to the choice of forcing data sets that are used. Regarding the model's vertical resolution, we use 20 layers in the ice and 10 layers in the snow, which is considered optimal to capture internal thermodynamic processes, such as heat conduction, temperature regimes, and snow to ice transformation (Cheng, Zhang, et al, 2008;Cheng et al, 2013;Wang et al, 2015). Although the spatial variability of snow and ice cannot be solved by a 1-D model, snow and ice mass balance and interaction are well represented by HIGHTSI.…”

Section: Introductionmentioning

confidence: 99%

Critical Role of Snow on Sea Ice Growth in the Atlantic Sector of the Arctic Ocean

Merkouriadi

Cheng

Graham

et al. 2017

Geophysical Research Letters

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During the Norwegian young sea ICE (N‐ICE2015) campaign in early 2015, a deep snowpack was observed, almost double the climatology for the region north of Svalbard. There were significant amounts of snow‐ice in second‐year ice (SYI), while much less in first‐year ice (FYI). Here we use a 1‐D snow/ice thermodynamic model, forced with reanalyses, to show that snow‐ice contributes to thickness growth of SYI in absence of any bottom growth, due to the thick snow. Growth of FYI is tightly controlled by the timing of growth onset relative to precipitation events. A later growth onset can be favorable for FYI growth due to less snow accumulation, which limits snow‐ice formation. We surmise these findings are related to a phenomenon in the Atlantic sector of the Arctic, where frequent storm events bring heavy precipitation during autumn and winter, in a region with a thinning ice cover.

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“…Validation studies have been carried out to test some of the available sea ice models. Some of these studies focused on large spatial scales, attempting to test how accurately different models predicted sea ice concentration, distribution, and thickness in the Arctic Ocean or Southern Ocean [e.g., Sedlacek et al, 2007;Vancoppenolle et al, 2009;Shu et al, 2015], whereas others tested models against observations at a local scale [e.g., Arrigo et al, 1993;Arrigo and Sullivan, 1994;Huwald et al, 2005;Sedlacek et al, 2007;Vancoppenolle et al, 2007;Jeffery et al, 2011;Pogson et al, 2011;Turner et al, 2013;Jeffery and Hunke, 2014;Wang et al, 2015], with a focus on different aspects of sea ice physics and biology.…”

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confidence: 99%

Sea ice thermohaline dynamics and biogeochemistry in the Arctic Ocean: Empirical and model results

Duarte

Meyer

Olsen

et al. 2017

J. Geophys. Res. Biogeosci.

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Large changes in the sea ice regime of the Arctic Ocean have occurred over the last decades justifying the development of models to forecast sea ice physics and biogeochemistry. The main goal of this study is to evaluate the performance of the Los Alamos Sea Ice Model (CICE) to simulate physical and biogeochemical properties at time scales of a few weeks and to use the model to analyze ice algal bloom dynamics in different types of ice. Ocean and atmospheric forcing data and observations of the evolution of the sea ice properties collected from 18 April to 4 June 2015, during the Norwegian young sea ICE expedition, were used to test the CICE model. Our results show the following: (i) model performance is reasonable for sea ice thickness and bulk salinity; good for vertically resolved temperature, vertically averaged Chl a concentrations, and standing stocks; and poor for vertically resolved Chl a concentrations. (ii) Improving current knowledge about nutrient exchanges, ice algal recruitment, and motion is critical to improve sea ice biogeochemical modeling. (iii) Ice algae may bloom despite some degree of basal melting. (iv) Ice algal motility driven by gradients in limiting factors is a plausible mechanism to explain their vertical distribution. (v) Different ice algal bloom and net primary production (NPP) patterns were identified in the ice types studied, suggesting that ice algal maximal growth rates will increase, while sea ice vertically integrated NPP and biomass will decrease as a result of the predictable increase in the area covered by refrozen leads in the Arctic Ocean.

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Modelling snow ice and superimposed ice on landfast sea ice in Kongsfjorden, Svalbard

Cited by 33 publications

References 63 publications

Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution

Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution

Critical Role of Snow on Sea Ice Growth in the Atlantic Sector of the Arctic Ocean

Sea ice thermohaline dynamics and biogeochemistry in the Arctic Ocean: Empirical and model results

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