Simulating transient ice-ocean Ekman transport in the Regional Arctic System Model and Community Earth System Model

Roberts, Andrew; Craig, Anthony P; Maslowski, Wieslaw; Osiński, Robert; DuVivier, Alice K.; Hughes, Mimi; Nijssen, Bart; Cassano, John J.; Brunke, Michael A.

doi:10.3189/2015aog69a760

Cited by 43 publications

(59 citation statements)

References 63 publications

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“…3) The Los Alamos Sea Ice model (CICE; Hunke et al 2013) is widely used in regional and global climate simulations. Roberts et al (2015) provide a description of the application of CICE, version 5, within RASM. 4) The streamflow routing model used is an adapted version of a linear routing model frequently used to route streamflows from VIC output (RVIC; Lohmann et al 1996Lohmann et al , 1998.…”

Section: Model Description a Rasmmentioning

confidence: 99%

“…2) The Parallel Ocean Program model (POP; Smith and others 2010) is a general circulation ocean model. Roberts et al (2015) provide a description of the application of POP, version 2, within RASM. 3) The Los Alamos Sea Ice model (CICE; Hunke et al 2013) is widely used in regional and global climate simulations.…”

Section: Model Description a Rasmmentioning

confidence: 99%

“…The ocean and sea ice models are configured on a 1 /128 rotated stereographic grid. Each model exchanges fluxes with the coupler every 20 minutes (Roberts et al 2015).…”

Section: Model Description a Rasmmentioning

confidence: 99%

“…RASM version 1.0 uses the Community Earth System Model (CESM) coupling infrastructure (CPL7; Craig et al 2012;Roberts et al 2015) and is composed of five component models:…”

Section: Model Description a Rasmmentioning

confidence: 99%

“…The Regional Arctic System Model (RASM) is a fully coupled regional Earth system model (Roberts et al 2015) applied over the pan-Arctic domain (Fig. 1a).…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Model Description a Rasmmentioning

confidence: 99%

“…The ocean and sea ice models are configured on a 1 /128 rotated stereographic grid. Each model exchanges fluxes with the coupler every 20 minutes (Roberts et al 2015).…”

Section: Model Description a Rasmmentioning

confidence: 99%

“…RASM version 1.0 uses the Community Earth System Model (CESM) coupling infrastructure (CPL7; Craig et al 2012;Roberts et al 2015) and is composed of five component models:…”

Section: Model Description a Rasmmentioning

confidence: 99%

“…The Regional Arctic System Model (RASM) is a fully coupled regional Earth system model (Roberts et al 2015) applied over the pan-Arctic domain (Fig. 1a).…”

Section: Introductionmentioning

confidence: 99%

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Land Surface Climate in the Regional Arctic System Model

et al. 2016

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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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Modeling the Arctic freshwater system and its integration in the global system: Lessons learned and future challenges

et al. 2016

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Numerous components of the Arctic freshwater system (atmosphere, ocean, cryosphere, and terrestrial hydrology) have experienced large changes over the past few decades, and these changes are projected to amplify further in the future. Observations are particularly sparse, in both time and space, in the polar regions. Hence, modeling systems have been widely used and are a powerful tool to gain understanding on the functioning of the Arctic freshwater system and its integration within the global Earth system and climate. Here we present a review of modeling studies addressing some aspect of the Arctic freshwater system. Through illustrative examples, we point out the value of using a hierarchy of models with increasing complexity and component interactions, in order to dismantle the important processes at play for the variability and changes of the different components of the Arctic freshwater system and the interplay between them. We discuss past and projected changes for the Arctic freshwater system and explore the sources of uncertainty associated with these model results. We further elaborate on some missing processes that should be included in future generations of Earth system models and highlight the importance of better quantification and understanding of natural variability, among other factors, for improved predictions of Arctic freshwater system change.

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Simulating transient ice-ocean Ekman transport in the Regional Arctic System Model and Community Earth System Model

Cited by 43 publications

References 63 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

Modeling the Arctic freshwater system and its integration in the global system: Lessons learned and future challenges

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