Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1

Batrak, Yurii; Kourzeneva, Ekaterina; Homleid, Mariken

doi:10.5194/gmd-11-3347-2018

Cited by 22 publications

(14 citation statements)

References 45 publications

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“…Within SURFEX sea‐ice is modelled by the Simple Ice scheme (SICE; Batrak et al . ). In its standard set‐up, SICE keeps sea‐ice thickness set to a constant value, which may be acceptable in numerical weather prediction mode (with typical forecast ranges from a few days to a week), but it leads to large biases in the energy fluxes over longer time‐scales.…”

Section: Methodsmentioning

confidence: 97%

Polar low variability and future projections for the Nordic and Barents Seas

Landgren

Batrak

Haugen

et al. 2019

Quart J Royal Meteoro Soc

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Polar lows are intense mesoscale cyclones occurring during winter over open sea areas in certain polar sub‐regions. Due to their small size, they are not explicitly represented in present global climate models or Earth system models. In this study 18 members of the CESM Large Ensemble were dynamically downscaled to ∼12 km horizontal mesh width using the quasi‐hydrostatic ALARO model within the HARMONIE script system in climate mode (HCLIM‐ALARO). The domain covers the Nordic and Barents Seas. One historical and two future time‐periods were selected. For validation, the ERA‐Interim reanalysis was also downscaled. A cyclone‐tracking algorithm was used to identify tracks of individual polar lows. Their frequency of occurrence, lifetime, and maximum relative vorticity were estimated. Relative to ERA‐Interim, the historical frequency of occurrence of polar lows was slightly overestimated in the Nordic Seas and underestimated in the Barents Sea, which is likely due to positive biases in sea‐surface temperature and sea‐ice concentration. For future climate projections, the regions of polar low genesis are diagnosed to move northwards in accordance with the sea‐ice retreat. In the Nordic Seas, the number of polar lows decreases at the beginning of the season, while there is an increase in March. In the Barents Sea, a February–April increase in the occurrence of polar lows is seen.

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Section: Methodsmentioning

confidence: 97%

Polar low variability and future projections for the Nordic and Barents Seas

Landgren

Batrak

Haugen

et al. 2019

Quart J Royal Meteoro Soc

Self Cite

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“…In all models sea-ice concentration is updated daily by utilising satellite products. Operational configurations of HARMONIE-AROME do not simulate the sea-ice concentration and sea-ice thickness, but simulate sea-ice surface temperature 26,27 .…”

Section: Methodsmentioning

confidence: 99%

On the warm bias in atmospheric reanalyses induced by the missing snow over Arctic sea-ice

Batrak

Müller

2019

Nat Commun

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Over the past decades, the Arctic has been warming more than any other region in the world with profound socio-economic consequences. One of the key elements for understanding this rapid climate change is the surface energy budget. However, in the Arctic this budget is not consistently described across the various climate models, reanalyses and observation products. Recognising the physical causes of these inconsistencies is highly relevant for improving climate predictions and projections. Here we show that a 5 to 10 °C warm bias of the sea-ice surface temperature in global atmospheric reanalyses and weather forecasts is mainly caused by a missing representation of the snow layer on top of the sea-ice. Due to the low thermal conductivity of snow compared to sea-ice, a thin snow layer reduces the conductive heat flux much more efficiently than sea-ice, and thus insulates the cold atmosphere from the relatively warm ocean.

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“…In uncoupled atmosphere‐only mode (FIX), the sea surface is assumed to be stationary (that is, surface currents are set to zero) and the initial prescribed SST distribution is assumed to remain constant (that is, persisted or fixed) through the duration of a simulation period. This is analogous to the assumptions adopted in the operational regional numerical weather forecast configuration for the UK (UKV: for example, Tang et al , ; Bush et al , 2018) and typically at other international NWP centres (for example, Toll et al , ; Batrak et al , ). To support understanding of the coupled results in this study, a number of simulations with different persisted SST initial conditions are considered, as summarized in Table .…”

Section: Model and Methodsmentioning

confidence: 84%

Impact of high‐resolution ocean–atmosphere coupling on fog formation over the North Sea

Fallmann

Lewis

Sanchez

et al. 2019

Quart J Royal Meteoro Soc

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Sea‐surface temperature (SST) is a key driver for various interactions and feedbacks between components of the Earth System and can control local weather and climate. The formation of marine fog, for example, can be sensitive to small changes in SST at a scale of a few kilometres. As a contribution to understanding processes at the interface between air and sea, this article discusses results from a state‐of‐the‐art fully coupled regional atmosphere–land–ocean–wave prediction system for the UK at km scale. This study focuses on the impact of the changes in surface forcing resulting from coupling SST in the marine boundary layer and formation of summertime coastal fog over the North Sea. A study from July 2013 provided a good case to evaluate the role of SST in fog evolution. The benefit of an evolving SST in the coupled simulation is shown in capturing a warming trend in observed SST over the five‐day case study period, with a root‐mean‐square error (RMSE) against in situ observations of 1.1 K. In contrast, in uncoupled atmosphere‐only simulations, the initial‐condition SST is persisted for the duration of the case, as is more typical in current operational numerical weather prediction (NWP). In the uncoupled simulations, a cold bias develops over the modelling period and the RMSE against observed SST is 2.4 K. The impact of coupling is shown to propagate into the overlying marine boundary layer and therefore affect the formation of coastal fog. Increased heat flux from a relatively warmer sea surface in the coupled simulations led to near‐surface atmospheric instability, hampering stratus lowering and destroying the fog‐promoting inversion layer. This significantly reduced fog fractions in selected regions. The value of model coupling was assessed by comparing coupled and uncoupled simulations initialized at different times ahead of fog development.

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Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1

Cited by 22 publications

References 45 publications

Polar low variability and future projections for the Nordic and Barents Seas

Polar low variability and future projections for the Nordic and Barents Seas

On the warm bias in atmospheric reanalyses induced by the missing snow over Arctic sea-ice

Impact of high‐resolution ocean–atmosphere coupling on fog formation over the North Sea

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