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Evaluation of Atmosphere-Ocean-Sea Ice-Ocean
Interface Processes in the Regional Arctic
System Model Version 1.0 (RASM1.0)
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MICHAEL A. BRUNKE1,*, NICHOLAS DAWSON1, XUBIN ZENG1, AND COAUTHORS
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Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona
To be submitted to Journal of Climate
*Corresponding author address: Michael A. Brunke, Department of Hydrology and
Atmospheric Sciences, The University of Arizona, P.O. Box 210081, Tucson, AZ 85721-0081.
E-mail: [email protected]
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Abstract
The Regional Arctic System Model version 1.0 (RASM1.0) has been developed to
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provide high-resolution simulations of the Arctic atmosphere-ocean-sea ice-land system. A
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major baseline for the performance of RASM is its comparison with reanalysis (that provides the
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lateral boundary condition for RASM) and with the coarser-resolution Earth system models. We
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provide such a baseline here regarding interface processes by comparing RASM with the
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Community Earth System Model (CESM) and the spread in three recent reanalyses. Evaluations
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of surface and 2-m air temperature, surface radiative and turbulent fluxes, and precipitation in the
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various models and reanalyses are performed, first, regionally using global datasets and, then,
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locally with surface observations made at land flux towers, during northern high-latitude ship
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cruises over oceans, and during the Surface Heat Budget of the Arctic (SHEBA) experiment over
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sea ice. We also use upscaled snow depth averaged over three 2°  2° boxes to evaluate snow in
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RASM. These evaluations reveal that precipitation is better simulated in RASM than in CESM,
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since CESM produces an erroneous annual cycle with maximum precipitation in spring rather
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than summer as observed. The sea ice interface is well simulated with surface fluxes and
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radiation generally falling within observational uncertainty throughout the SHEBA year. Snow
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depth is better simulated over a flatter landscape than more mountainous terrain. RASM shows
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signs of a problematic representation of clouds in the region which are manifested as biases in
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surface radiation and temperature. These problems will be addressed in the development of
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version 2.
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1. Introduction
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The late 20th and early 21st centuries have been marked by dramatic changes in the
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northern high latitudes. Most notably are the declines in sea ice cover (Serreze et al. 2007)
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which has accelerated in recent years (e.g., Comiso et al. 2008). Sea ice thickness has also been
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decreasing (Johanssen et al. 2004; Serreze et al. 2007). The loss of sea ice reduces the surface
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albedo, initiating a feedback in which the surface is warmed by an increase in absorbed radiation.
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This further enhances sea ice melt (Hartmann 1994), causing warming to be highest in the
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Arctic, a process which has been termed Arctic amplification (Holland and Bitz 2003; Johanssen
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et al. 2004; Serreze and Francis 2006; Serreze et al. 2009). Further enhancement is realized with
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increased water vapor from more evaporation from the added open water (Screen and Simmonds
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2010). Other parts of the Arctic cryosphere are also in decline, such as large declines in snow
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cover which adds to the amplification (Serreze et al. 2009) and decreasing Arctic glacier mass
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(Serreze et al. 2000). Furthermore, ecological changes associated with increased plant growth, a
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shrubbier tundra, and a northward advance of the tree line have been noted (Serreze et al. 2000).
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Arctic amplification is consistent with what climate models simulate for climate change due to
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anthropogenic greenhouse gas emissions (Holland and Bitz 2003; Serreze et al. 2007).
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Even though global climate models (GCMs) and Earth system models (ESMs) capture
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the larger temperature trends in the Arctic, they are problematic in capturing other climatic
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trends in the region (Serreze and Francis 2006). In particular, they have difficulty in capturing
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observed sea ice trends (Holland and Bitz 2003; Stroeve et al. 2007; Zhang 2010) largely due to
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errors in simulated atmospheric circulation (Maslowski et al. 2012). Such errors include those in
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capturing the correct phase of the Arctic Oscillation and North Atlantic Oscillation (Moritz et al.
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2002).
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Thus, the development of an Arctic regional system model has been proposed (Roberts et
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al. 2010) in order to better resolve important small-scale processes [such as ice-ocean inertial
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oscillations (Roberts et al. 2015)]. Such a regional coupled model would incorporate high-
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resolution atmospheric, ocean, sea ice, and land surface models. Further additions of models for
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mountain glaciers, ice sheets, dynamic vegetation, and ice-ocean biogeochemistry would move
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such a model towards a true regional system model (Maslowski et al. 2012). Such a model
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would be more advanced than the simpler atmospheric or atmosphere-ocean-sea ice coupled
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models previously developed for the Arctic region (e.g., Dorn et al. 2007; Döscher et al. 2010)
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Here, we evaluate just such an Arctic regional system model called the Regional Arctic
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System Model (RASM). RASM incorporates high-resolution versions of the Weather Research
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and Forecasting model (WRF) as the atmospheric model, the Variable Infiltration Capacity
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(VIC) land surface, the Parallel Ocean Program (POP) ocean model, and the Los Alamos
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Community Ice Model (CICE) to represent sea ice. The latter two are also used in the global
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Community Earth System Model (CESM), and the development of RASM has made important
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contributions to the development of the latest version of CICE to be used in CESM2 (Roberts et
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al. 2016). Along with the use of CESM’s ocean and sea ice models, coupling between the
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various components is performed by the CESM coupler, CPL7 (Craig et al. 2012,
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http://www.cesm.ucar.edu/models/ccsm4.0/cpl7/) modified to perform polar simulations
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(Roberts et al. 2016).
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This model is described in more detail in Section 2a along with the simulations
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performed using it that are evaluated here. The evaluation presented here provides a baseline for
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the performance of these simulations by comparing with an ESM, reanalyses, and observational
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data. The ESM used here, CESM1, is also described in Section 2a, and the reanalyses used are
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described in Section 2b. The observational data, both globally gridded data and surface
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observations are described in Sections 2c and d, respectively. The evaluation is given in Section
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3. Finally, conclusions are given in Section 4.
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2. Model simulations and evaluation datasets
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a.
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Model simulations
RASM is run over a pan-Arctic domain that encompasses all of the Arctic Ocean the
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surrounding drainage basins. RASM includes version 3.2 of the Advanced Research WRF
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(Skamarock et al. 2008) modified for use in the Arctic (Cassano et al. 2011). Spectral nudging is
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applied above ~540 hPa at a horizontal scale of ~3400 km to prevent circulation biases that
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appeared in uncoupled WRF run on the RASM domain (Cassano et al. 2011). In addition, the
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boundary layer, surface layer, and radiation parameterizations have been adapted to facilitate the
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model’s coupling with other models. DuVivier et al. (2015) describe the version of WRF used in
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RASM in more detail.
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Version 4.04 of VIC (Liang et al. 1996, 1994) used in RASM is modified for coupling to
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the other components and to include a broadband snow albedo that depends on vegetation cover
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(Barlage et al. 2005). Other modifications include an increase in the bare surface albedo to
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simulate bare ice at very high latitudes, and a decrease in surface emissivity to 0.97 to be
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consistent with the other components. Hamman et al. (2016a) describe this version of VIC in
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more detail. RASM also includes an option to route streamflow from the land to the river outlets
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into the ocean. This routing model, RVIC, is described in more detail in Hamman et al. (2016b).
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WRF and VIC both share the same 50 km polar stereographic grid.
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RASM uses version 2 of POP (Smith et al. 1992; Dukowicz 1994; Smith et al. 2010)
modified for a regional closed boundary domain. CICE was substantially improved upon from
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the version used in CESM1 in the development of RASM. As it is used in RASM currently,
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CICE now includes explicit melt ponds (Hunke et al. 2013), mushy-layer thermodynamics
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(Turner et al. 2013), and anisotropic sea ice mechanics (Tsamados et al. 2013). Further details
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on the development of this version of CICE can be found in Roberts et al. (2015). POP and CICE
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are both run on an inner domain defined in Roberts et al. (2015) on a 1/12 (~9 km) rotated
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sphere grid with an extended ocean domain utilizing climatological sea surface temperatures
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(SSTs) to provide surface fluxes for WRF.
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Two RASM simulations are evaluated here: RASM1.0.0 and RASM1.0.1. Both
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simulations were run fully coupled with RVIC. The European Centre for Medium-Range
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Forecasts (ECMWF) interim reanalysis (ERA-Interim; Dee et al. 2011) was used as boundary
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conditions and to nudge the upper atmosphere of the model. We mainly focus on RASM1.0.0, as
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the land climatology of this simulation was extensively evaluated in Hamman et al. (2016).
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RASM1.0.1 is a sensitivity test containing physics changes to the atmospheric and sea ice
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components (see Section 3e for more detail).
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In order to assess the improvement of the Arctic regional climate simulation of RASM to
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a global model, we select the CESM, the Earth system model upon which RASM is developed
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from. Output from the CESM large ensemble (LE) of 30 members (Kay et al. 2015) is mostly
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used here, since many of the quantities investigated here are provided for monthly, daily, and 6-
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hourly means. CESM is also included in CMIP5, and there are four ensemble members provided
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for that purpose. However, only monthly means are provided for most of the quantities looked at
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here. The ensemble mean monthly climatologies from CESM-LE are almost the same as those
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from the CESM-CMIP5 as can be seen for surface temperature in Fig. S1. Thus, from
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henceforth, we will refer to CESM-LE as CESM1.
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b.
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Reanalyses
In order to gauge how well the model simulations are, we compare them to the spread in
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the latest generation of reanalyses: the Modern Era Retrospective Analysis for Research and
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Applications (MERRA, Rienecker et al. 2011), ERA-Interim (Dee et al. 2011), and the Coupled
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Forecasting System Reanalysis (CFSR, Saha et al. 2010). The last two can be used as boundary
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conditions and to nudge the upper atmosphere of RASM.
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MERRA data used here include the surface turbulent flux, surface radiation and single-
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level atmospheric bulk variables data collections given at the model’s native horizontal
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resolution of 1/2° latitude  2/3° longitude. Hourly, monthly means, and monthly mean diurnal
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cycles are used here.
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Monthly mean ERA-Interim data and three-hourly means derived from the 6-hourly
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surface analyses and 12-hourly forecasts are used here. These were at the model horizontal
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resolution of ~0.703  0.702. The 3-hourly monthly mean diurnal cycles on a uniform
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horizontal grid of 0.75  0.75 are also used.
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CFSR’s monthly mean (derived from the 0-5 h forecasts) and hourly time-series products
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are utilized here. These data are at the model resolution of ~0.31  0.31.
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c.
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Global evaluation datasets
The simulated monthly means are first evaluated using several global monthly mean
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gridded datasets. Monthly mean 2-m air temperature over land is compared to the dataset
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generated by Wang and Zeng (2013, hereafter WZ13). WZ13 includes adjusted 2-m air
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temperature from four reanalyses: MERRA, ERA-Interim, the ECMWF 40-year reanalysis, and
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the NCEP/NCAR reanalysis. The reanalysis monthly mean maximum and minimum air
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temperature biases are corrected according to the Climate Research Unit (CRU) surface
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temperature data. In this study, we utilize only the adjusted air temperatures from the two newer
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reanalyses (MERRA and ERA-Interim), taking the average of the two.
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Surface temperatures over ocean is evaluated using version 3 of the Hadley Centre sea
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surface temperature (HadSST3) (Kennedy et al. 2011a,b). SST from this product is provided on
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a 5  5 horizontal grid for monthly means. The actual monthly mean SSTs are backed out of
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the anomalies by adding the climatological mean SSTs.
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To understand the biases in 2-m air temperature or surface temperature, we evaluate the
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surface energy balance in the models. Surface radiation is evaluated using satellite
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measurements from the Clouds and the Earth’s Radiant Energy System (CERES) satellite.
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CERES’s level 3B Energy Balanced and Filled (EBAF)-Surface (Li et al. 1993, Li and Kratz
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1997, Gupta et al. 1997) provides surface radiative fluxes on a 1  1 global horizontal grid.
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Finally, we use NCEP’s Climate Prediction Center Merged Analysis of Precipitation
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(CMAP) to evaluate precipitation. This was preferred over the similar Global Precipitation
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Climatology Project dataset, as it has been noted to be more problematic than reanalyses in the
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Arctic (Serreze et al. 2005). Monthly mean values on a 2.5  2.5 are derived from merging
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gauge observations, estimates from several satellites, and forecasts from the NCEP-NCAR
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reanalysis (Xie and Arkin 1997).
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d.
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Local observations
We use point observations to further evaluate RASM, because these data are provided at
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sub-daily timescales. Over land, we use tower observations from FLUXNET (Baldocchi et al.
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2001). FLUXNET is a global network of more than 100 locations where fluxes of CO2, water,
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and energy are measured at various heights. In this study, we use observations of 2-m air
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temperature, sensible heat flux, latent heat flux, downward shortwave radiation, net total
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radiation, 10-m wind speed, and precipitation rate from 26 high-latitude sites across North
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America and Europe listed in Table S1. These locations were chosen, because they have at least
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three years of data during the RASM evaluation period of 1990-2014.
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RASM snow depth over land is evaluated with upscaled in situ observations using the
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methodology of Dawson et al. (2016). With our focus on the Central Arctic, two 2o  2o boxes
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(Fig. S2) were selected for representing relatively flat land (ALASKA MID) and relatively
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mountainous land (ALASKA SOUTH). In addition, we consider another mountainous 2o  2o
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box containing the Adirondacks of New York State. Each of these boxes includes at least four
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observations per day. The daily average of all RASM grid cells within each box are compared to
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the area averages of the upscaled data.
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Over sea ice, we use meteorological and flux observations from the Surface Heat Budget
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of the Arctic (SHEBA, Uttal et al. 2002; Persson et al. 2002). These include measurements made
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at the 20-m tower at the main camp and from four portable automated mesonet (PAM, Militzer et
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al. 1995) stations surrounding the main camp. On the tower, measurements were made at several
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levels. Here, we use the sensible heat fluxes derived from fast measurements of temperature and
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wind made by sonic thermometers and anemometers and latent heat fluxes derived from
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measurements from a fast hygrometer at 8.1 m. Upward and downward shortwave and longwave
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radiation was measured by pyranometers and pyrgeometers on nearby masts at 1.5-2 m height.
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Surface temperature was measured nearby by a downward pointing radiation thermometer. At
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the PAM stations, we use latent and sensible heat fluxes, surface radiation, and surface and air
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temperature made using similar measurements. Further discussion of these instruments and their
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uncertainties is made by Brunke et al. (2006) and Persson et al. (2002).
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Over ocean, we use flux and meteorological observations made aboard ships in three field
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campaigns that fall within the RASM domain, the Fronts and Atlantic Strom Track Experiment
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(FASTEX) and Couplage avec l’Atmosphère en Conditions Hivernales (CATCH) in the North
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Atlantic and the National Oceanic and Atmospheric Administration’s 1999 cruise to serve its
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moorings in the North Pacific (Moorings ’99). The covariance latent and sensible heat fluxes
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from the U.S. cruises (FASTEX and Moorings ‘99), while intertial-dissipation fluxes were only
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available for CATCH. Flow distortion, ship motions, and environmental conditions were
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accounted for as in Brunke et al. (2003). More details on the observations made during these
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cruises can be found in Brunke et al. (2003).
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3. Results
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Domain-wide and regional comparisons
We first evaluate RASM1.0.0 across the pan-Arctic domain. Figure 1 shows the biases in
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land 2-m air temperature in RASM1.0.0 as compared to WZ13 in January and July. For
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reference, the mean values in WZ13 for these two months are shown in Fig. S3. We pick these
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months to represent snow-free and snow-covered periods over most of the domain. July biases
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are small, comparable to that of reanalyses. However, January biases are larger with RASM1.0.0
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being colder than WZ13 throughout the Arctic lowlands. The cold SATs result from a reduction
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of downward longwave (LW) radiation at the surface as is seen in Fig. S4c over the Arctic Ocean
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and surrounding land in January. The strongest underestimates are over the Barents Sea just
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north of the NRU region demarcated by the dark blue box (60-75N, 30-90E) in Fig. 1a.
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Cassano et al. (2016, submitted) suggested another possibility. As there would be increased
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downward LW radiation at the surface in the presence of cloud cover, this winter cold bias could
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also be due to too little cloud in RASM1.0.0. In summer, downward LW radiation is slightly
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overestimated over the Arctic Ocean (Fig. S5c), corresponding to strongly underestimated
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downward SW radiation at the surface. This is suggestive of too much modeled cloud in this
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region.
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This is further illustrated in Fig. 2 which shows the whole annual cycle averaged for all
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of the land in the NRU region. This region has some of the strongest cold biases in January (Fig.
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1a). The cold biases in RASM1.0.0 is clearly evident in this region in winter and fall, while the
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model 2-m air temperature bias is near-zero from March-July. This is compared to the biases in
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CESM1. The CESM1 ensemble average is similar to RASM1.0.0 with a slightly lower cold bias
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in winter and near-zero biases into summer. RASM1.0.0’s biases are generally within the
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CESM1 intraensemble variability (1 standard deviation) as indicated by the dotted green lines.
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Downward shortwave (SW) radiation in winter is minimal (Fig. 2b). Downward longwave (LW)
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radiation is much more substantial and important to the surface energy balance that contributes to
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the near-surface temperature biases (Fig. 2c). It is ~30 W m-2 lower in RASM1.0.0 than from
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CERES, while CESM1’s ensemble average is much closer to the reference. Despite this,
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RASM1.0.0 winter values are just barely outside of the CESM1 intraensemble variability in
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winter and within it the rest of the year.
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Some parts of the domain which correspond to areas of higher terrain are quite
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substantially warmer in January in RASM1.0.0 (Fig. 1a). These warm biases are also made by
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the reanalyses. In fact, CFSR has worse biases in these regions than RASM1.0.0 and ERA-
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Interim. This is further illustrated by the mean annual cycle shown in Fig. S6 for the land in the
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region NSIB demarcated by the purple box (60-75N, 30-90E) in Fig. 1a. This region
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encompasses the January warm biases in the mountainous terrain of this part of Siberia, and,
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thus, the regional mean RASM1.0.0 2-m air temperatures are biased slightly higher than WZ13.
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The reanalyses are biased even higher than RASM1.0.0. Despite this warm bias, RASM1.0.0
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downward radiation biases are similar here to that in NRU (Figs. 2b,c). In addition, the latent
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and sensible heat fluxes are similar here to those in NRU (not shown). In these regions, the
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reference dataset, WZ13, is likely biased itself due to the use of the CRU data to correct the
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reanalysis air temperatures. CRU is likely biased toward colder temperatures in the winter in
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these mountainous regions because of the dominance of observations in the valleys which tend to
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be colder than higher elevations in winter (Hamman et al. 2016). Another problematic area for
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the CRU data is Greenland due to the lack of observations which biases the WZ13 data high
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(WZ13). This results in the strong cold biases in RASM1.0.0 and the reanalyses in January over
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southern Greenland.
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RASM1.0.0 SSTs are compared to HadSST in Figs. 3a,b. There are large differences in
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SST in the marginal ice zone due to differences in sea ice extent, particularly in January.
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However, SSTs are biased cold in the open oceans of the North Pacific and Atlantic with larger
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biases (in excess of -4C) in July than in January. These biases are much colder than those from
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reanalyses like ERA-Interim which is generally near-zero (Figs. 3c,d). These biases are one of
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the motivations for the sensitivity test of RASM1.0.1 that will be discussed later. We see in Fig.
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S5 that in these regions, downward shortwave radiation is substantially underestimated,
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especially in July (Figs. S5c,d), whereas downward longwave radiation is only slightly
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overestimated (Figs. S4c,d).
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This is further illustrated in Fig. 4 with the regional mean annual cycle for the North
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Pacific region defined in blue in Fig. S2. RASM1.0.0 SST is cooler than the HadSST throughout
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the year and lower than the reanalyses which are spread tightly around the global dataset. SSTs
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in CESM1 are also underestimated throughout the year but not as much as in RASM1.0.0. The
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downward shortwave radiation in RASM1.0.0 is similarly underestimated throughout the year,
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whereas CESM1’s straddles CERES and the reanalyses. Downward longwave radiation in
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RASM1.0.0 is very similar to CERES early in the year up until July and is slightly overestimated
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during the latter part of the year. Still, the RASM1.0.0 values are within the CESM1 ensemble
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variability throughout the year. It also lies at the top of the reanalysis spread up until August and
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above it later.
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On the other hand, precipitation is well simulated in RASM1.0.0. The biases in its
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simulated precipitation relative to CMAP are compared to those of ERA-Interim and CESM1 in
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Fig. 5. RASM1.0.0 precipitation biases are very similar to those in ERA-Interim as well as other
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reanalyses in both January and July (Figs. 5a,b,e,f). However, CESM1 precipitation is generally
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much higher over much of the Arctic (Figs. 5c,d). This is further elucidated by the mean annual
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cycle averaged over the Ob River basin indicated by the brown region in the top panel of Fig. 6.
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RASM1.0.0’s precipitation rate is comparable to CMAP and is within the spread in the
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reanalyses from January to May. On the other hand, CESM1’s ensemble mean is much higher
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than CMAP’s and erroneously has its maximum precipitation in April instead of July. CESM1’s
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lowest ensemble variability (-1) is only barely within the highest of the reanalyses from August
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to November.
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b.
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Comparison to land surface observations
To substantiate the above comparisons of RASM1.0.0 with global reference data, we
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further compare the modeled interface conditions to point observations made at the FLUXNET
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towers. Being point measurements, they are not necessarily representative of a model grid cell,
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but they are still of use to model evaluation. In Northern Manitoba is a cluster of eight
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FLUXNET towers (CA-Man and CA-NS1 through 7) that happen to span a RASM1.0.0 grid
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cell. Figure 7 shows the mean annual cycle from the eight towers. Also shown is the mean
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annual cycle from the long-term site at CA-Man. CA-Man’s 2-m air temperatures and net
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radiation are very similar to the tower mean throughout the year, while latent and sensible heat
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fluxes may be substantially different from the mean.
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The range in tower observations can be used to evaluate the RASM1.0.0 simulation. If
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the simulated value falls outside of this range, then the simulation is likely to be problematic.
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This occurs for 2-m temperature and net radiation from late autumn into winter in which the
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model is lower than the site minimum. On the other hand, downward shortwave radiation is
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above the site maximum from mid-summer to the end of the year. Model latent heat flux is as
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well for the spring and especially for summer.
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Another measure of how well RASM1.0.0 is simulating the mean annual cycle in these
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quantities is to compare it with the spread in the reanalyses. The reanalyses fall within the
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observational spread for 2-m air temperature, but not necessarily for radiation or sensible and
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latent heat flux. Thus, the RASM1.0.0 autumn and winter cold biases are also below the
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reanalysis spread, but simulated net and downward shortwave radiation is within the reanalysis
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spread even in winter. Model sensible heat flux is also generally within the reanalysis spread,
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while latent heat flux is above even the reanalysis spread during the summer maximum.
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However, the reanalysis spread is above the observational spread in autumn, whereas
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RASM1.0.0 compares well with the observations.
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To evaluate how well RASM1.0.0 is performing across the domain, we look at the other
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single FLUXNET towers (Fig. S7). The model winter cold bias is evident at all locations,
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especially across Eurasia. In the summer, 2-m air temperature is well simulated across Canada
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and at the lower latitude stations. However, simulated 2-m air temperatures at the coastal sites
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and US-Ivo are biased high from late winter into summer, generally outside of the reanalysis
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spread (Fig. S7a).
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The cold biases are generally associated with negative net radiation biases, and warm
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biases are generally associated with positive net radiation biases (Figs. S4b and S5b). In winter,
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the net radiation biases are generally due to downward longwave biases, while downward
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shortwave biases largely contribute to net radiation biases in summer (Fig. S8). On the other
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hand, the biases in sensible and latent heat fluxes tend to compensate each other (not shown).
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This suggests that these biases are not contributing significantly to the near-surface temperature
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biases (Jousse et al. 2016).
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To further elucidate this, we look at the mean annual cycle at three of these locations.
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These specific sites all include measurements of downward longwave and shortwave radiation
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and are representative of the various different annual cycle in SAT biases seen in Fig. S7. At
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US-Ivo in Alaska, simulated downward longwave radiation is underestimated in winter and
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autumn (Fig. 8a). On the other hand, simulated downward longwave radiation is underestimated
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throughout the year at FI-Hyy (Fig. 8b). Along the Siberian coast at RU-Che, RASM1.0.0
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downward longwave radiation is underestimated only in winter and autumn like at US-Ivo.
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Downward shortwave radiation in RASM1.0.0 is underestimated in summer (Fig. 8d), whereas it
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is comparable to observations at FI-Hyy (Fig. 8e). At RU-Che, simulated downward shortwave
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radiation is very similar to observed except in summer when it is overestimated (Fig. 8f). Net
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radiation biases in RASM1.0.0 are largely due to downward longwave radiation errors in winter
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and are due to or are enhanced by downward shortwave radiation biases (Figs. 8g-i).
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Sensible and latent heat flux biases compensate each other at FI-Hyy (Figs. 8k,n). When
sensible heat fluxes are underestimated, latent heat fluxes are overestimated and vice versa. At
15
355
US-Ivo, the latent heat flux biases correspond closely to the net radiation biases (Fig. 8m),
356
whereas sensible heat flux is much higher in summer and lower in winter and autumn (Fig. 8j).
357
At RU-Che, RASM1.0.0 latent and sensible heat fluxes are both overestimated in summer (Figs.
358
8l,o).
359
Unlike at CA-Man, RASM1.0.0 2-m air temperatures at US-Ivo are higher than observed
360
during the first half of the year and lower than observed during the latter half (Fig. 8a). This
361
seems to be due to a shift in the mean annual cycle in which the peak temperature comes a month
362
earlier (June rather than July). The reanalyses follow the observations rather closely. At FI-Hyy,
363
modeled 2-m air temperature is underestimated throughout the year compared to both
364
observations and reanalyses (Fig. 8b).
365
To further understand the monthly means, we analyze the monthly mean diurnal cycles.
366
For example in July, the 2-m air temperature is very similar to observed during the daytime at
367
CA-Man, while being a little underestimated at night below even the reanalysis spread which
368
better fits the observations (Fig. 9a). At US-Ivo, the very good performance of RASM1.0.0 is
369
due to a compensation between overestimation at night and underestimation during the daytime
370
(Fig. 9b). At FI-Hyy where RASM1.0.0 underestimated 2-m air temperatures in July, the model
371
temperatures are lower than observed throughout the day (Fig. 9c). Finally at RU-Che, model 2-
372
m air temperature biases are near-zero during the first half of the day but are underestimated
373
during the latter half of the day (Fig. 9d).
374
This is true of the surface turbulent and radiative fluxes. The low biases in the July mean
375
in RASM1.0.0 coincide with a balance in over- and underestimations throughout the day,
376
whereas high over- or underestimated monthly mean are due to those occurring throughout the
16
377
day. Also, the turbulent and radiative fluxes from RASM1.0.0 and the reanalyses tend to be out
378
of phase with the observations.
379
Snow is a very important component of the Arctic system. Newly fallen snow has a
380
much higher albedo than bare ground or vegetation. Additionally, snow insulates the ground
381
from the cold air above, preventing LH and SH loss and keeping soil temperatures up. We
382
compare RASM1.0.0 snow depth to the upscaled in situ observations in Fig. 10. Observed snow
383
depth is higher in the mountainous ALASKA SOUTH region than in the flatter ALASKA MID
384
region with maximum snow depths of ~1200 mm and ~650 mm in these regions, respectively.
385
Maximum snow depth in ALASKA MID occurs in March, whereas it occurs a month earlier in
386
ALASKA SOUTH. RASM1.0.0 snow depth is lower in both regions, but it is able to simulate a
387
snow depth closer to observed in the relatively flat ALASKA MID region than in the
388
mountainous ALASKA SOUTH region (~250 mm and ~600 mm lower, respectively). Snow
389
melt is initiated earlier in RASM1.0.0 than observed by about a ½ month in ALASKA MID and
390
a full month in ALASKA SOUTH. In the Adirondacks, maximum snow depth is much lower
391
and earlier than observed in the mean annual cycle. However, the model is able to capture large
392
snowfall events quite well but melts that snow quickly after (not shown).
393
c.
394
Comparison to SHEBA observations over sea ice
Some of the land biases are derived from similar biases over the neighboring central
395
Arctic. Since there is not much data, globally gridded or observed at the surface, for the central
396
Arctic, we have to rely on surface observations made during the year-long SHEBA field
397
campaign (Fig. 11). Observed LH flux is near-zero in autumn but is a little higher in summer
398
(Fig. 11a), while observed SH flux is near zero throughout the year (Fig. 11b). The observed
399
latent heat flux is less reliable, as the mean is based on only one location (at the central tower).
17
400
Modeled SH flux compares well with observations during autumn and winter being within the
401
observational uncertainty, but is slightly higher than observational uncertainty range from May
402
to July (Fig. 11b). LH flux is also similarly simulated by RASM1.0.0 (Fig. 11a). RASM1.0.0
403
compares better to observations than the reanalyses which are largely outside of the
404
observational uncertainty (Figs. 11a,b).
405
The shortwave and longwave radiation components are also compared. Downward and
406
upward shortwave radiation in RASM1.0.0 is within observational uncertainty from autumn to
407
spring but peaks too low and early. Thus, shortwave radiation is too low in summer (Figs.
408
11c,d). On the other hand, downward longwave radiation in RASM1.0.0 is slightly too low in
409
winter but compares well to observed during summer (Fig. 11e). Interestingly, simulated upward
410
longwave radiation is within observational uncertainty throughout the year (Fig. 11f).
411
Reanalysis downward shortwave and longwave radiation is within observational uncertainty
412
(Figs. 11c,e). On the other hand, reanalysis upward shortwave radiation is too low from spring
413
to summer (Fig. 11d), while upward longwave radiation from the reanalysis can be too high in
414
autumn and winter (Fig. 11f).
415
As would be expected, surface temperature is similarly simulated by RASM1.0.0 as
416
upward longwave radiation (Fig. 12a). Reanalysis surface temperature can also be similarly too
417
high from autumn and winter. Wind speed in RASM1.0.0 is too high from January to June (Fig.
418
12b). This may explain the model overestimate of latent and sensible heat fluxes in summer. On
419
the other hand, reanalysis is higher generally than the observed mean but mostly within
420
observational uncertainty.
18
421
d.
Comparison to ship cruise observations
422
As expected from the regional comparisons made above, RASM1.0.0’s SSTs are colder
423
than the surface observations made during the ship cruises (Table 1). The SST bias for the two
424
Atlantic cruises (CATCH and FASTEX) is smaller than that of Moorings in the Pacific. This, of
425
course, causes the 2-m air temperature and specific humidity to be smaller than observed as well.
426
On the other hand, the reanalyses are minimally biased in these quantities. Wind speed during
427
the Atlantic cruises is underestimated in RASM1.0.0, while it is overestimated during Moorings.
428
These surface conditions result in sensible and latent heat fluxes that are overestimated,
429
especially for sensible heat flux. On the other hand, the reanalyses can be less biased, and
430
CESM1 is slightly higher than observed. The reason for the difference between RASM1.0.0 and
431
CESM1 biases becomes clear when we look at a scatter plot of model fluxes to ship observed
432
fluxes (Fig. S9). Most of the RASM1.0.0 sensible and latent heat fluxes are biased high as
433
indicated by the points above the one-to-one line. On the other hand, CESM1 fluxes are all
434
about the same (~100 and 50 W m-2 for LH and SH fluxes, respectively) so that the lower fluxes
435
are slightly overestimated, while the higher fluxes are greatly underestimated.
436
e.
RASM1.0.1
437
The sensitivity test, RASM1.0.1, includes changes to the atmospheric boundary layer and
438
convection parameterizations: the MYNN (Nakanishi and Niino 2006) boundary layer and Kain-
439
Fritsch (Kain 2004) convection schemes were used instead of the YSU (Hong et al. 2006) and
440
Grell-Dévényi (Grell and Dévényi 2002) schemes used in RASM1.0.0. In addition, the sea ice
441
model includes mushy layer thermodynamics (Turner and Hunke 2015), anisotropic sea ice
442
mechanics (Tsamados et al. 2013), and modifications to the snow albedo. One of the
443
motivations for these changes was the cold SST biases in the sub-Arctic oceans. Figures 13a,b
19
444
present the SST biases from HadSST. The sub-Arctic SSTs are higher in RASM1.0.1 in both
445
January and July with near-zero biases over most of the open oceans in January and some of the
446
oceans in July. However, some of the northernmost oceans (e.g., the Bering and Labrador Seas)
447
now have slightly positive biases > 2 K.
448
While the overall SST biases are improved, the land SATs are much higher in summer
449
with much too high July land SATs compared to WZ13 (Fig. 13d). The SATs in January are not
450
affected (Fig. 13c). This is further illustrated in the mean annual cycle for NRU (Fig. 2a).
451
RASM1.0.1’s biases are due to changes in surface radiation. The higher summer air temperature
452
coincides with much higher downward shortwave radiation than CERES measurements and the
453
reanalyses and even CESM1. This occurs even though downward longwave radiation is similar
454
to RASM1.0.0’s. This can also be seen in the flux tower comparisons in Fig. 8. The increased
455
SAT causes SH and LH fluxes to be higher in summer outside of the reanalysis spread (Fig. 8j-
456
o).
457
Precipitation in RASM1.0.1 is nearly the same as RASM 1.0.0’s in January (Fig. 13e)
458
and increased in July (Fig. 13f). This is further illustrated in the averages over the Ob River
459
basin where RASM1.0.1 precipitation is almost identical to RASM1.0.0’s in winter but is higher
460
from April to November (Fig. 4b). This seems to suggest that the new boundary layer and
461
convection parameterizations produce more convection over land during summer, leading to
462
stronger precipitation events. This leads to a summer maximum that is slightly higher than the
463
reanalyses, similar to the bottom of the CESM1 ensemble variability. Still, RASM1.0.1’s mean
464
annual cycle is closer to that of CMAP than CESM1’s.
465
466
Like with RASM1.0.0, RASM1.0.1’s biases have a diurnal cycle. For example, its
positive SAT biases in July are highest during the daytime maximum at all four locations in Fig.
20
467
9. At US-Ivo and RU-Che, RASM1.0.1 is too warm throughout the day, whereas at CA-Man
468
and FI-Hyy, it is similar to observations or too cold at night. The same can also be said for LH
469
and SH fluxes and net radiation with the higher monthly mean values being dominated by higher
470
daytime values (Fig. 9e-p).
471
RASM1.0.1 produces a higher snow maximum in ALASKA MID and ALASKA
472
SOUTH, more so in the former than in the latter. This is from the higher precipitation produced
473
by RASM1.0.1 as seen in the accumulated precipitation in Fig. S10. RASM1.0.1 produces more
474
precipitation in ALASKA MID than in ALASKA SOUTH. However, RASM1.0.1 snow melts
475
earlier than in RASM1.0.0. The freezing days in RASM1.0.1 are ~10 days less than in
476
RASM1.0.0 (206 vs. 219 days on average in ALASKA MID and 164 vs. 175 days in ALASKA
477
SOUTH). In the Adirondacks, RASM1.0.1 snow depth is similar to RASM1.0.0 early in the
478
season (up until December) but tends to be a little lower later with melting happening a little too
479
early as in the Alaska regions.
480
With the changes to the sea ice physics, the sea ice interface is slightly better represented
481
in RASM1.0.1. SH and LH fluxes, surface radiation, surface temperature, and wind speed are all
482
closer to observed in RASM1.0.1 than in RASM1.0.0 (Figs. 11 and 12). SW radiation, in
483
particular, is improved in summer with downward radiation remaining barely within
484
observational uncertainty in July (Fig. 11c).
485
4. Conclusions
486
Here, we evaluate the newly developed version 1 of the Regional Arctic System Model
487
(RASM1.0.0), a fully coupled atmosphere-land-ocean-sea ice model for improved high-
488
resolution simulation of climate in the northern high-latitude region. The model is run over a
489
pan-Arctic domain with WRF for the atmosphere, VIC for the land surface, POP for the ocean,
21
490
and CICE for simulating sea ice. The model simulation is evaluated by comparing with a coarser
491
resolution global model (CESM1) and the spread in recent reanalyses. Precipitation is better
492
simulated in RASM1.0.0 than in CESM1. Overall, CESM1 produces too much precipitation
493
throughout the year inside the domain, and its mean annual cycle is completely wrong. CMAP
494
indicates maximum precipitation occurs in summer in the Ob River basin, whereas CESM1
495
produces it in spring. Another improvement over the global model is the representation of the
496
sea ice interface. Surface latent and sensible heat fluxes and radiation are generally simulated
497
within the uncertainty of SHEBA observations. Snow depth is also simulated well on average in
498
the relatively flat ALASKA MID region but is somewhat problematic in mountainous terrain
499
(e.g., ALASKA SOUTH and the Adirondacks).
500
Still, RASM1.0.0 has some substantial surface temperature biases. SSTs are colder over
501
the sub-Arctic oceans throughout the year with a maximum in summer, and SATs are colder over
502
most of the land within the domain in winter. The cold land SATs coincide with too little
503
downward longwave radiation at the surface. Since downward shortwave radiation is close to
504
zero during the polar night, the principal radiative source at the surface is longwave radiation.
505
The underestimation suggests that there is too little cloud in RASM1.0.0. The sub-Arctic cold
506
SST biases coincide with too little downward shortwave radiation reaching the surface. These
507
biases point to problems in simulating clouds in WRF, something that has been noted previously
508
(e.g., Bromwich et al. 2009; Porter et al. 2011; Bromwich et al. 2016). The reduced wintertime
509
downward longwave radiation over land is consistent with too little cloud. On the other hand,
510
the lower downward shortwave radiation producing the SST biases is consistent with too much
511
cloud (Cassano et al. 2016, submitted).
22
512
Because there are a number of parameterization choices in WRF, RASM was re-run with
513
a different set of boundary layer and convective physics in WRF and changes to the sea ice
514
physics in CICE (RASM1.0.1; Cassano et al. 2016, submitted). These changes reduce the sub-
515
Arctic SST biases but cause the SATs to be too warm in summer over the land in the domain.
516
These biases suggest that a further refinement of the representation of clouds is needed in the
517
version of WRF used in RASM. The version used in RASM currently does not account for the
518
radiative impacts of convective clouds. This will change in the next version of RASM with the
519
implementation of a more recent version of WRF that does include this effect. The
520
representation of the sea ice interface was improved with better latent and sensible heat fluxes
521
and surface radiation in RASM1.0.1. The greater precipitation in RASM1.0.1 also allows snow
522
to have higher annual maximums in the two Alaska regions but melts a little earlier than
523
RASM1.0.0 which melts too early already. The earlier melt is caused by a decrease in the
524
freezing degree days by ~10 days in RASM1.0.1.
525
The monthly mean biases can be explained by biases in the diurnal cycle. For example in
526
July, monthly mean biased simulated SATs derive from biased SATs during the day with near-
527
zero biases at night. On the other hand, locations where SAT is well simulated in the mean have
528
equally but oppositely biased SATs from day to night. The increases in SAT experienced by
529
RASM1.0.1 also have a diurnal component with higher warm biases at the daytime maximum.
530
The surface turbulent flux and radiation are also similarly biased diurnally. Therefore, the key to
531
advancing SAT and the surface energy budget would be to improve the representation of the
532
diurnal cycle.
533
23
534
Acknowledgments. This work was funded by the U.S. Department of Energy under Grants DE-
535
SC0006693. MERRA data was provided through the Goddard Earth Sciences Data and
536
Information Services Center (http://disc.sci.gsfc.nasa.gov). Monthly mean and sub-daily ERA-
537
Interim data was downloaded from NCAR’s Research Data Arcive (RDA) web site
538
(http://dss.ucar.edu), while the mean diurnal cycles were provided from the ECMWF web site
539
(http://www.ecmwf.int). CFSR was downloaded from NCEP’s National Operational Model
540
Archive and Distribution System (NOMADS) web site (http://nomads.ncdc.noaa.gov). The
541
ASTER GDEM v2 data used in the upscaling of in situ snow observations is available through
542
the Data Pool at the NASA Land Processes Distributed Active Archive Center (LPDAAC).
543
544
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685
TABLE 1. Mean values in various quantities over the three ship cruises used here.
FASTEX
Observations
RASM1.0.0
Reanalyses
Observations
RASM1.0.0
Reanalyses
Observations
RASM1.0.0
Reanalyses
Observations
RASM1.0.0
Reanalyses
Observations
RASM1.0.0
CESM1
Reanalyses
Observations
RASM1.0.0
CESM1
Reanalyses
CATCH
SST (C)
9.07
10.34
7.23
9.28
8.31-8.51
10.28-11.02
SAT (C)
6.68
7.10
4.20
9.28
5.27-6.75
6.78-7.87
2-m specific humidity (g/kg)
4.99
4.62
4.18
4.49
4.61-5.22
4.52-5.39
Wind speed (m s-1)
11.03
10.62
8.50
7.27
10.90-12.28
9.52-10.56
SH flux (W m-2)
44.28
84.16
73.21
85.56
54.57
51.56
31.17-43.63
63.30-76.04
LH flux (W m-2)
98.22
120.83
109.38
130.13
120.48
114.87
98.88-110.25
126.90-152.04
686
687
31
Moorings
14.54
12.24
14.36-14.59
14.54
9.78
13.55-13.74
9.26
6.87
8.53-9.15
4.43
8.87
4.54-5.49
1.89
30.51
42.58
5.33-9.33
35.03
71.02
87.80
38.17-49.47
688
689
FIG. 1. The bias in 2-m surface air temperature (K) in (a,b) RASM1.0.0, (c,d) ERA-Interim, and
690
(e,f) CFSR from that of the Wang and Zeng (2013) dataset in January (top) and July (bottom).
691
32
692
693
FIG. 2. Regional mean of (a) 2-m air temperatures (SAT) and (b) surface downward shortwave
694
(SW) radiation and (c) longwave (LW) radiation for the NRU region defined in the blue box
695
defined in Fig. 1a. Means are given for global datasets [Wang and Zeng (2013) SAT and
696
CERES radiation], RASM1.0.0, RASM1.0.1, and CESM1 along with the range in the three
697
reanalyses (MERRA, ERA-Interim, and CFSR) indicated by the gray shading.
698
33
699
700
FIG. 3. The bias in sea surface temperature (K) in (a,b) RASM1.0.0 and (c,d) ERA-Interim from
701
that of HadSST in January (top) and July (bottom).
34
702
703
704
FIG. 4. Same as Fig. 2 except for the North Pacific (PAC) region defined in Fig. S2.
35
705
706
FIG. 5. The bias in precipitation rate (mm day-1) in (a,b) RASM1.0.0, (c,d) CESM1, and (e,f)
707
ERA-Interim from that of CMAP in January (top) and July (bottom).
708
36
709
710
FIG. 6. (top) The Ob River basin region. (bottom) Regional mean precipitation from CMAP,
711
RASM1.0.0, RASM1.0.1, and CESM1 along with the reanalysis spread.
712
37
713
714
FIG. 7. Mean annual cycle in (a) SAT, (b) net radiation, (c) downward shortwave (SW)
715
radiation, (d) latent heat (LH) flux, and (e) sensible heat (SH) flux from land flux tower
716
observations from the northern Manitoba cluster, RASM1.0.0, and reanalyses. The cluster mean
717
and uncertainty (1 standard deviation) is given as the solid and dotted lines, respectively, while
718
that of the individual tower CA-Man is given as the triangles. The spread in the reanalyses is
719
indicated by the gray shading.
720
38
721
722
FIG. 8. Mean annual cycles in (a-c) downward longwave (LW) and (d-f) shortwave (SW)
723
radiation, (g-i) net radiation, (j-l) sensible heat (SH) flux, (m-o) latent heat (LH) flux, and (p-r) 2-
724
m surface air temperature (SAT) from flux tower observations (black line), RASM1.0.0 (blue
725
line), RASM1.0.1 (purple line), and reanalyses (spread shown as gray shading) at US-Ivo (left
726
column), FI-Hyy (center column), and RU-Che (right column).
39
727
728
729
FIG. 9. Mean diurnal cycles in (a-d) 2-m surface air temperature (SAT), (e-h) sensible heat (SH)
730
and (i-l) latent heat (LH) flux, and (m-p) net radiation from flux tower observations (black line),
731
RASM1.0.0 (blue line), and RASM1.0.1 (purple line), and reanalyses (spread shown as gray
732
shading) at CA-Man (far left column), US-Ivo (center left column), FI-Hyy (center right
733
column), and RU-Che (far right column).
40
734
735
FIG. 10. The mean annual cycle over a water year (October-September) of snow depth averaged
736
for the 2  2 boxes defined in Fig. S2, the middle of Alaska (ALASKA MID, top), southern
737
Alaska (ALASKA SOUTH, middle), and the Adirondacks (bottom), as in upscaled observations
738
and in RASM1.0.0 and RASM1.0.1.
41
739
740
FIG. 11. Comparison of monthly mean (a) latent heat (LH) flux, (b) sensible heat (SH) flux, (c)
741
downward shortwave (SW) radiation, (d) upward SW radiation, (e) downward longwave (LW)
742
radiation, and (f) upward LW radiation from Surface Heat Budget of the Arctic (SHEBA)
743
observations with RASM1.0.0, RASM1.0.1, CESM1, and reanalyses. Observational uncertainty
744
(1 standard deviation) is indicated by the vertical lines extending from the circles, and the
745
spread in reanalyses is shown by the gray shading.
42
746
747
FIG. 12. Comparison of monthly mean (a) surface temperature and (b) wind speed from SHEBA
748
observations with RASM1.0.0, RASM1.0.1, CESM1, and reanalyses. Observational uncertainty
749
(1 standard deviation) is indicated by the vertical lines extending from the circles, and the
750
spread in reanalyses is shown by the gray shading.
43
751
752
FIG. 13. Monthly biases in RASM1.0.1 (a,b) sea surface temperature (SST) from HadSST, (c,d)
753
2-m surface air temperature (SAT) from Wang and Zeng (2013), and (e,f) precipitation from
754
CMAP for January (top) and July (bottom).
755
44