Retrievals of Arctic Sea‐Ice Volume and Its Trend Significantly Affected by Interannual Snow Variability

Bunzel, Felix; Notz, Dirk; Pedersen, Leif Toudal

doi:10.1029/2018gl078867

Cited by 33 publications

(35 citation statements)

References 22 publications

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“…The strongest correlations between March ice thickness and the previous year's fall transition metrics are found between freeze onset and March ice thickness, with statistically significant correlation coefficients ranging between -0.54 and -0.92 in the models (Table 6). Because of sea ice thickness uncertainties discussed earlier (Bunzel et al, 2018), we are unable to confidently evaluate https://doi.org/10.5194/tc-2020-81 Preprint. Discussion started: 3 April 2020 c Author(s) 2020.…”

Section: Ec-earth3 Ipsl-cm6a-lr Mri-esm2-mentioning

confidence: 99%

“…Ice area and seasonal ice transition dates are practical for assessing sea ice in a pan-Arctic sense, as they are reliably available for both models and observations. Discussion of the sea ice thickness here is limited to model projections, since observations of Arctic sea ice thickness are temporally limited and contain large uncertainties (Bunzel et al, 2018).…”

Section: Seasonal Transitions Affect Sea Ice Area and Thickness Year-mentioning

confidence: 99%

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Seasonal transition dates can reveal biases in Arctic sea ice simulations

Smith¹,

Jahn²,

Wang³

2020

Preprint

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Abstract. Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, break-up, opening, freeze onset, freeze-up and closing. By evaluating a range of seasonal sea ice metrics, CMIP6 sea ice simulations can be evaluated in more detail than by using traditional metrics alone, such as sea ice area. We show that models capture the observed asymmetry in seasonal sea ice transitions, with spring ice loss taking about 1.5–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions of melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability. Through climate model evaluation in the context of both observations and internal variability, we show that biases in seasonal transition dates can compensate for other unrealistic aspects of simulated sea ice. In some models, this leads to September sea ice areas in agreement with observations for the wrong reasons.

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Section: Ec-earth3 Ipsl-cm6a-lr Mri-esm2-mentioning

confidence: 99%

Section: Seasonal Transitions Affect Sea Ice Area and Thickness Year-mentioning

confidence: 99%

Seasonal transition dates can reveal biases in Arctic sea ice simulations

Smith¹,

Jahn²,

Wang³

2020

Preprint

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“…Thus, accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is needed for diagnosing changes in surface heat and freshwater budget of the coupled atmosphere-sea ice-ocean system [14]; understanding rapid changes of Arctic environments, particularly rapid decline of Arctic sea ice [15]; reducing the uncertainty of the retrieval of sea ice thickness from satellite altimetry [16]; evaluating snow depth simulated by numerical weather prediction models and climate system models to improve the forecast of sea ice and climate.…”

Section: Introductionmentioning

confidence: 99%

“…This also contributes to a serious source of uncertainty for the retrieval of sea ice thickness and volume or their trend analysis using satellite altimetry [24]. Bunzel et al [16] found that the snow depth climatology from different sources could lead to significant differences in the retrieved Arctic sea ice thickness and volume. Moreover, it is not clear to what extent variability (i.e., trend) in the retrieved Arctic sea ice thickness and volume are influenced by the inability of the snow depth climatology to properly represent its variability.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

et al. 2019

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The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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“…In practice, both retrievals of concentration and thickness are uncertain. The deduced volume estimates are thus even more uncertain(51,52). This uncertainty, combined with the presence of substantial interannual variability, complicates the evaluation of climate models(53,54).…”

mentioning

confidence: 99%

Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction

Massonnet

2019

Curr Clim Change Rep

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The Arctic and Antarctic are among the regions most exposed to climate change, but ironically, they are also the ones for which the least observations are available. Climate models have been instrumental in completing the big picture. It is generally accepted that observations feed the development of climate models: parameterizations are designed based on empirically observed relationships, climate model predictions are initialized using observational products, and numerical simulations are evaluated given matching observational datasets. Recent research suggests that the opposite also holds: climate models can feed the development of polar observational networks by indicating the type, location, frequency, and timing of measurements that would be most useful for answering a specific scientific question. Here, we review the foundations of this emerging notion with five cases borrowed from the field of polar prediction with a focus on sea ice (sub-seasonal to centennial time scales). We suggest that climate models, besides their usual purposes, can be used to objectively prioritize future observational needsif, of course, the limitations of the realism of these models have been recognized. This idea, which has been already extensively exploited in the context of Numerical Weather Prediction, reinforces the notion that observations and models are two sides of the same coin rather than distinct conceptual entities.

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Retrievals of Arctic Sea‐Ice Volume and Its Trend Significantly Affected by Interannual Snow Variability

Cited by 33 publications

References 22 publications

Seasonal transition dates can reveal biases in Arctic sea ice simulations

Seasonal transition dates can reveal biases in Arctic sea ice simulations

Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction

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