Spatial and seasonal variations of black carbon over the Arctic in a regional climate model

Abstract. The performance of the Rossby Centre regional climate model RCA4 is investigated for the Arctic CORDEX (COordinated Regional climate Downscaling EXperiment) region, with an emphasis on its suitability to be coupled to a regional ocean and sea ice model. Large biases in mean sea level pressure (MSLP) are identified, with pronounced too-high pressure centred over the North Pole in summer of over 5 hPa, and too-low pressure in winter of a similar magnitude. These lead to biases in the surface winds, which will potentially lead to strong sea ice biases in a future coupled system. The large-scale circulation is believed to be the major reason for the biases, and an implementation of spectral nudging is applied to remedy the problems by constraining the large-scale components of the driving fields within the interior domain. It is found that the spectral nudging generally corrects for the MSLP and wind biases, while not significantly affecting other variables, such as surface radiative components, two-metre temperature and precipitation.

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Effects of sea ice change on the Arctic climate: insights from experiments with a polar atmospheric regional climate model

Journal of Water and Climate Change ◽

10.2166/wcc.2021.206 ◽

2021 ◽

Author(s):

Xiying Liu ◽

Chenchen Lu

Keyword(s):

Sea Ice ◽

Boundary Conditions ◽

Regional Climate Model ◽

Air Temperature ◽

Climate Model ◽

Regional Climate ◽

Lower Boundary ◽

Arctic Climate ◽

The Arctic ◽

Sea Ice Concentration

Abstract To get insights into the effects of sea ice change on the Arctic climate, a polar atmospheric regional climate model was used to perform two groups of numerical experiments with prescribed sea ice cover of typical mild and severe sea ice. In experiments within the same group, the lateral boundary conditions and initial values were kept the same. The prescribed sea ice concentration (SIC) and other fields for the lower boundary conditions were changed every six hours. 10-year integration was completed, and monthly mean results were saved for analysis in each experiment. It is shown that the changes in annual mean surface air temperature have close connections with that in SIC, and the maximum change of temperature surpasses 15 K. The effects of SIC changes on 850 hPa air temperature is also evident, with more significant changes in the group with reduced sea ice. The higher the height, the weaker the response in air temperature to SIC change. The annual mean SIC change creates the pattern of differences in annual mean sea level pressure. The degree of significance in pressure change is modulated by atmospheric stratification stability. In response to reduction/increase of sea ice, the intensity of polar vortex weakens/strengthens.

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The influence of ocean heat transport in the Barents Sea on the regional sea ice and the atmospheric static stability

Journal Ice and Snow ◽

10.15356/2076-6734-2019-4-417 ◽

2019 ◽

Vol 59 (4) ◽

pp. 529-538

Author(s):

M. G. Akperov ◽

V. A. Semenov ◽

I. I. Mokhov ◽

M. A. Dembitskaya ◽

D. D. Bokuchava ◽

...

Keyword(s):

Sea Ice ◽

Regional Climate Model ◽

Climate Model ◽

Barents Sea ◽

Regional Climate ◽

Static Stability ◽

The Arctic ◽

Oceanic Water ◽

Ensemble Simulations ◽

The Barents Sea

The influence of the oceanic heat inflow into the Barents Sea on the sea ice concentration and atmospheric characteristics, including the atmospheric static stability during winter months, is investigated on the basis of the results of ensemble simulations with the regional climate model HIRHAM/NAOSIM for the Arctic. The static stability of the atmosphere is the important indicator of the spatial and temporal variability of polar mesocyclones in the Arctic region. The results of the HIRHAM/NAOSIM regional climate model ensemble simulations (RCM) for the period from 1979 to 2016 were used for the analysis. The initial and lateral boundary conditions for RCM in the atmosphere were set in accordance with the ERA-Interim reanalysis data. An analysis of 10 ensemble simulations with identical boundary conditions and the same radiation forcing for the Arctic was performed. Various realizations of ensemble simulations with RCM were obtained by changing the initial conditions for integrating the oceanic block of the model. Different realizations of ensemble simulations with RCM are obtained by changing the initial conditions of the model oceanic block integration. The composites method was used for the analysis, i.e. the difference between the mean values for years with the maximum and minimum inflow of oceanic water into the Barents Sea. The statistical significance of the results (at a significance level of p < 0.05) was estimated using Student's t-test. In general, the regional climate model reproduces the seasonal changes in the inflow of the oceanic water and heat into the Barents Sea reasonably well. There is a strong relationship between the changes in the oceanic water and ocean heat inflow, sea ice concentration, and surface air temperature in the Barents Sea. Herewith, the increase in the oceanic water inflow into the Barents Sea in winter leads to a decrease in static stability, which contributes to changes in regional cyclonic activity. The decrease of the static stability is most pronounced in the southern part of the Barents Sea and also to the west of Svalbard.

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Variability of Extreme Temperature in the Arctic - Observation and RCM

The Open Atmospheric Science Journal ◽

10.2174/1874282301004010126 ◽

2010 ◽

Vol 4 (1) ◽

pp. 126-136 ◽

Cited By ~ 6

Author(s):

Heidrun Matthes ◽

Annette Rinke ◽

Klaus Dethloff

Keyword(s):

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

The Arctic ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Growing Degree Days ◽

Degree Days ◽

Warm Spell ◽

Era40 Data

This paper discusses results of a simulation with the regional climate model HIRHAM for 1958-2001, driven by the ECMWF reanalysis (ERA40) data over the Arctic domain. The aim is to analyze the ability of the model to capture certain features of climate extremes derived from daily mean, maximum and minimum temperatures. For this purpose, a range of climate indices (frost days, cold and warm spell days, growing degree days and growing season length) was calculated from the model output as well as from ERA40 data and region-specific station data for Eastern and Western Russian Arctic for comparison. It is demonstrated that the model captures the main features in the spatial distribution and temporal development of most indices well. Though systematic deviations in the seasonal means occur in various indices (frost days, growing degree days), variability and trends are well reproduced. Seasonal mean patterns in frost days are reproduced best, though the model persistently calculates too many frost days. Seasonal means of cold and warm spell days are reproduced without systematic biases, though deviations occur in summer for cold spells and in spring and summer for warm spells due to an early spring warming in the regional climate model and a low variability of the daily maximum temperature over sea ice.

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