Exploration of turbulent heat fluxes and wind stress curl in WRF and ERA‐Interim during wintertime mesoscale wind events around southeastern Greenland

DuVivier, Alice K.; Cassano, John J.

doi:10.1002/2014jd022991

Cited by 9 publications

(6 citation statements)

References 60 publications

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“…DuVivier and Cassano (2015) and Cassano et al (2016, manuscript (Glisan et al 2013;Skamarock et al 2008). 2) The Parallel Ocean Program model (POP; Smith and others 2010) is a general circulation ocean model.…”

Section: Model Description a Rasmmentioning

confidence: 99%

Land Surface Climate in the Regional Arctic System Model

et al. 2016

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The Regional Arctic System Model (RASM) is a fully coupled, regional Earth system model applied over the pan-Arctic domain. This paper discusses the implementation of the Variable Infiltration Capacity land surface model (VIC) in RASM and evaluates the ability of RASM, version 1.0, to capture key features of the land surface climate and hydrologic cycle for the period 1979-2014 in comparison with uncoupled VIC simulations, reanalysis datasets, satellite measurements, and in situ observations. RASM reproduces the dominant features of the land surface climatology in the Arctic, such as the amount and regional distribution of precipitation, the partitioning of precipitation between runoff and evapotranspiration, the effects of snow on the water and energy balance, and the differences in turbulent fluxes between the tundra and taiga biomes. Surface air temperature biases in RASM, compared to reanalysis datasets ERA-Interim and MERRA, are generally less than 28C; however, in the cold seasons there are local biases that exceed 68C. Compared to satellite observations, RASM captures the annual cycle of snow-covered area well, although melt progresses about two weeks faster than observations in the late spring at high latitudes. With respect to derived fluxes, such as latent heat or runoff, RASM is shown to have similar performance statistics as ERA-Interim while differing substantially from MERRA, which consistently overestimates the evaporative flux across the Arctic region.

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Section: Model Description a Rasmmentioning

confidence: 99%

Land Surface Climate in the Regional Arctic System Model

et al. 2016

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“…This difference between extreme values of wind speed and UST 99 in WRF50 and WRF10 is likely due to differences in surface layer stability in the two WRF simulations, which we illustrate in section 5.2. The differences between WRF50 and WRF10 in UST would translate to differences in wind stress curl [ DuVivier and Cassano , ], directly relevant to oceanic deep convection and circulation in the Irminger and Greenland Seas [ Våge et al ., ; Pickart et al ., ], with likely larger values of wind stress curl in WRF10 corresponding to the tighter gradients in the winds (not shown); an in‐depth analysis of impacts on wind stress curl are beyond the scope of the present manuscript.…”

Section: Resolution Sensitivity In Turbulent Fluxesmentioning

confidence: 99%

The climatological distribution of extreme Arctic winds and implications for ocean and sea ice processes

Hughes

Cassano

2015

JGR Atmospheres

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Some of the strongest near-surface winds on Earth form in the Arctic and sub-Arctic due to intense midlatitude cyclones and mesoscale processes, and these strong surface winds have important impacts on ocean and sea ice processes. We examine the climatological distribution of over-ocean, near-surface wind speeds within a Pan-Arctic domain for 18 years (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) in four gridded data sets: the European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-I), the Climate Forecast System Reanalysis, version 2 of the Common Ocean-Ice Reference Experiment data set, and a regional climate simulation generated using the Weather Research and Forecasting (WRF) model run at 50 km (WRF50) horizontal resolution with ERA Interim as lateral boundary conditions. We estimate probability density functions, the annual cycle, and map the 50th and 99th percentile winds. We then perform the same statistical analysis of winds for 2 years when 10 km WRF data are available (June 2005 to May 2007); despite the much shorter time period, the Pan-Arctic statistics are very similar to those from the 18 year analysis. We repeat the wind speed statistical analysis within a subdomain surrounding Greenland and find that WRF10 has consistently larger maximum wind speeds, but this difference only appears at wind speed percentiles higher than 99%. Differences in the 99th percentile wind speeds are spatially heterogeneous. An investigation of surface fluxes within WRF50 and WRF10 reveals unrealistically large sensible heat fluxes along the sea ice edge, and the geographic distribution and magnitude of these fluxes is shown to be sensitive to sea ice representation in WRF.

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“…Extreme winds in the Arctic and around Greenland are important for the local weather and for the global climate. They are crucial for exporting polar freshwater via the East Greenland Current (Spall and Price, ; Haine et al ., ), for forcing the Irminger and Labrador gyre (Spall and Pickart, ), and for air–sea interactions that trigger open‐ocean convection in the Nordic Seas (Pickart et al ., ; Martin and Moore, ; DuVivier and Cassano, ; DuVivier et al ., ). For instance, extreme winds enhance the heat loss from the Irminger Sea and cause deeper mixed layers on very short timescales (order of days) (de Jong and de Steur, ; DuVivier et al ., ).…”

Section: Introductionmentioning

confidence: 99%

A model‐based comparison of extreme winds in the Arctic and around Greenland

Gutjahr

Heinemann

2018

Intl Journal of Climatology

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This paper compares extreme value statistics of daily maximum 10 m wind speed in winter simulated by the regional climate model COSMO‐CLM at a horizontal resolution of 15 km (C15) with the reanalyses ERA‐Interim and Arctic System reanalysis (ASR version 1 and 2) and with a satellite data set (CCMPv2). Our C15 simulation (1979/1980–2015/2016, November–April) is thereby the longest high‐resolution simulation available for the Arctic. The results show that the extreme wind speeds tend to increase over the ocean with increasing the horizontal model resolution. A horizontal resolution of ≤15 km is required to sufficiently capture all extreme wind characteristics, in particular for the tip jets and barrier winds over the Irminger Sea and in the Denmark Strait, and for the low‐level jets in the Nares Strait. Of almost equal importance are physical parameterizations of surface fluxes and of turbulence. Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic.

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Exploration of turbulent heat fluxes and wind stress curl in WRF and ERA‐Interim during wintertime mesoscale wind events around southeastern Greenland

Cited by 9 publications

References 60 publications

Land Surface Climate in the Regional Arctic System Model

Land Surface Climate in the Regional Arctic System Model

The climatological distribution of extreme Arctic winds and implications for ocean and sea ice processes

A model‐based comparison of extreme winds in the Arctic and around Greenland

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