Comparing Polar Lows in Atmospheric Reanalyses: Arctic System Reanalysis versus ERA-Interim

Smirnova, Julia; Golubkin, Pavel

doi:10.1175/mwr-d-16-0333.1

Cited by 30 publications

(33 citation statements)

References 24 publications

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“…Recent results from the analysis of cyclone activity in the Arctic using high‐resolution ASR have shown an advantage of this reanalysis relative to global reanalyses (Smirnova & Golubkin, ; Tilinina et al, ). ASR is not employed to the same degree as the four more widely used reanalyses in this study due to its short time period (2000–2012).…”

Section: Resultsmentioning

confidence: 99%

Cyclone Activity in the Arctic From an Ensemble of Regional Climate Models (Arctic CORDEX)

et al. 2018

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The ability of state‐of‐the‐art regional climate models to simulate cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic‐CORDEX initiative. Some models employ large‐scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble are compared with the results forced by four reanalyses (ERA‐Interim, National Centers for Environmental Prediction‐Climate Forecast System Reanalysis, National Aeronautics and Space Administration‐Modern‐Era Retrospective analysis for Research and Applications Version 2, and Japan Meteorological Agency‐Japanese 55‐year reanalysis) in winter and summer for 1981–2010 period. In addition, we compare cyclone statistics between ERA‐Interim and the Arctic System Reanalysis reanalyses for 2000–2010. Biases in cyclone frequency, intensity, and size over the Arctic are also quantified. Variations in cyclone frequency across the models are partly attributed to the differences in cyclone frequency over land. The variations across the models are largest for small and shallow cyclones for both seasons. A connection between biases in the zonal wind at 200 hPa and cyclone characteristics is found for both seasons. Most models underestimate zonal wind speed in both seasons, which likely leads to underestimation of cyclone mean depth and deep cyclone frequency in the Arctic. In general, the regional climate models are able to represent the spatial distribution of cyclone characteristics in the Arctic but models that employ large‐scale spectral nudging show a better agreement with ERA‐Interim reanalysis than the rest of the models. Trends also exhibit the benefits of nudging. Models with spectral nudging are able to reproduce the cyclone trends, whereas most of the nonnudged models fail to do so. However, the cyclone characteristics and trends are sensitive to the choice of nudged variables.

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Section: Resultsmentioning

confidence: 99%

Cyclone Activity in the Arctic From an Ensemble of Regional Climate Models (Arctic CORDEX)

et al. 2018

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“…In the higher‐resolution ECMWF operational analysis, Zappa et al s*() detected 70% (23 out of 34) of the events. Investigations by Smirnova and Golubkin () estimate that ERA‐I represents 48% (22 out of 46) of the PLs during the cold seasons 2000/2001–2003/2004 from the STARS database, but only 26% (41 out of 158) from the Smirnova database. Further, they show that the recently developed high‐resolution Arctic System Reanalysis (ASR) version 1 (Bromwich et al , ) represents 89% (41 out of 46) of the PLs from the STARS dataset and 66% (104 out of 158) from the Smirnova dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Further, they show that the recently developed high‐resolution Arctic System Reanalysis (ASR) version 1 (Bromwich et al , ) represents 89% (41 out of 46) of the PLs from the STARS dataset and 66% (104 out of 158) from the Smirnova dataset. The improvement is explained by the improved representation of mesoscale systems in this high‐resolution reanalysis (Smirnova and Golubkin, ). The conclusion from this comparison of different reanalysis products indicates a considerable improvement of ASR over ERA‐I in terms of PL representation; the ECMWF operational analysis used by Zappa et al s*() and ERA‐I downscaled with a 12 km resolution mesoscale model as performed by Laffineur et al s*() are still missing a higher proportion of STARS PLs than ASR.…”

Section: Introductionmentioning

confidence: 99%

“…(Zappa et al , )), making a strict application of the wind criteria problematic. In ASR the near‐surface wind were observed to be more realistic (Smirnova and Golubkin, ).…”

Section: Introductionmentioning

confidence: 99%

“…(Papritz et al , )). Commonly, a threshold of SST – T 500 > 43 K is used, although Terpstra et al s*() and Smirnova and Golubkin () argue that this threshold excludes a considerable number of PL cases. Bracegirdle and Gray () investigated different temperature parameters, and found the difference between the wet‐bulb potential temperature at 700 hPa and the SST to be the most effective of their considered parameters to separate PLs from other cyclones.…”

Section: Introductionmentioning

confidence: 99%

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An objective global climatology of polar lows based on reanalysis data

Stoll

Graversen

Noer

et al. 2018

Quart J Royal Meteoro Soc

120

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Impacts of initial conditions and model configuration on simulations of polar lows near Svalbard using Polar WRF with 3DVAR

Jishan

Bromwich

Xiao

et al. 2021

Quart J Royal Meteoro Soc

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The impacts of initial conditions and model configuration on the simulations of polar lows (PLs) near Svalbard, the global “hotspot” for PL activity, were investigated using the Polar Weather Research and Forecasting (Polar WRF or PWRF) model and WRF three‐dimensional data assimilation (3DVAR). The well‐documented PL case presented by Sergeev et al., in 2017 from March 26, 2013 featuring in situ aircraft observations is revisited using PWRF and 3DVAR to identify the most suitable model configuration to simulate PLs. The simulation results using initial conditions from PWRF and 3DVAR cycling runs were compared with those of cold‐start experiments using reanalysis as the initial condition. For cycling runs, the impacts of initial conditions from synoptic observations together with satellite radiances were superior to the assimilation of synoptic data alone. Several additional experiments were undertaken to further refine the PWRF configuration by considering the horizontal resolution, analysis nudging, digital filter initialization, and two boundary‐layer and four microphysics schemes. Based on the chosen configuration of Polar WRF and 3DVAR from the case‐study, the monthly reanalysis mode with 3 hr time window cycling data assimilation and short‐term forecasts using PWRF alone were performed for March 2013 and the PLs that formed over the Nordic Seas were evaluated. Manual identification and verification of PLs were performed using thermal infrared satellite imagery and 10 m scatterometer winds. Overall, nine PLs were identified in addition to the case presented by Sergeev et al., in 2017. Both the case‐study and monthly simulation results show that the high‐resolution initial condition is the most important factor, but a reasonable forecast model configuration can help to improve the simulation performance. Satellite radiance data are very important for producing the high‐resolution initial conditions for Polar WRF to simulate PLs successfully; the best performance was obtained by assimilating both synoptic data and satellite radiances.

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Comparing Polar Lows in Atmospheric Reanalyses: Arctic System Reanalysis versus ERA-Interim

Cited by 30 publications

References 24 publications

Cyclone Activity in the Arctic From an Ensemble of Regional Climate Models (Arctic CORDEX)

Cyclone Activity in the Arctic From an Ensemble of Regional Climate Models (Arctic CORDEX)

An objective global climatology of polar lows based on reanalysis data

Impacts of initial conditions and model configuration on simulations of polar lows near Svalbard using Polar WRF with 3DVAR

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