Arctic System Reanalysis
David. H. Bromwich1,2, and Keith M. Hines1
1

Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University, USA
Atmospheric Sciences Program, Department of Geography, The Ohio State University, USA
Correspondence: bromwich.1@osu.edu

2

INTRODUCTION
In the polar regions, its is difficult to place current weather and climate trends in a long-term climatologicalperspective, primarily because the meteorological records there are limited in time and space in comparison withother regions of the globe. The low spatial density of polar meteorological data makes it challenging to separate local
changes from regional or even continental-scale changes. Reanalyses, which assimilate all available observations into
physically-consistent, regularly-spaced and comprehensive datasets, can be especially helpful in these latitudes. The
timeliness of such efforts is especially pronounced given the recently-observed dramatic changes in Arctic land
ice, sea ice, and permafrost regions.
In response to the Arctic’s importance for climate change, the Study of Environmental Arctic Change (SEARCH)
project inspired extensive, interdisciplinary, multi-scale studies of high northern latitudes (Overland et al. 2003). To
integrate observations and modeling efforts into a comprehensive picture of the regional climate and synoptic
meteorology, SEARCH supported efforts toward a multi-year reanalysis of the Arctic that would employ all available
remote-sensing and in-situ data. The U.S. National Oceanic and Atmospheric Administration (NOAA) provided seedmoney to lay the groundwork for an Arctic System Reanalysis (ASR).
A new physically-consistent integration of Arctic data will be achieved through the high-resolution reanalysis ofthe northern high latitude region, spanning poleward from the headwaters of the northward flowing rivers. The ASR isa collaboration of the Ohio State University's Byrd Polar Research Center (BPRC) and Ohio Supercomputer Center(OSC) along with the National Center Atmospheric Research (NCAR), the University of Colorado, the University ofIllinois, the University of Alaska Fairbanks, and the Arctic Region Supercomputing Center (ARSC). The production
phase of the initial ASR was recently funded by the U.S. National Science Foundation as an International Polar Year(IPY 2007-2009) project. The ASR will provide a high resolution description in space (~15 km) and time (1-3 h) ofthe coupled atmosphere-sea ice-land surface system of the Arctic. Ingested historical data streams, along withmeasurements of the physical components of the Arctic Observing Network being developed as part of IPY will drive
the ASR. Gridded output fields from the ASR will serve a variety of uses such drivers for coupled ice-ocean, land
surface and other models, and will offer a focal point for coordinated model inter-comparison efforts. The ASR will
permit detailed reconstructions of the Arctic system's variability and change, thereby complementing efforts of theglobal reanalyses. The project will also shape the legacy observing network of the IPY by providing a vehicle forobserving system sensitivity studies of the Sustained Arctic Observing Network (SAON). To achieve its goals, theASR will require an Arctic-friendly atmospheric numerical model with state-of-the-art dynamics.
BRIEF SUMMARY OF THE ASR
The first generation ASR will span the years 2000-2010 including the IPY. The ASR will be based on apolar-optimized version of the state-of-the-art Weather Research and Forecasting (WRF, Skamarock et al. 2006)
model and the WRF data assimilation capabilities being developed. Various input data consideration issues,reanalysis verification and reanalysis output tasks are being addressed by the University of Colorado and theUniversity of Illinois. Computing platforms to be utilized include those of OSC and the ARSC. The Arctic model
development is being carried out by Polar Meteorology Group of the BPRC. Data assimilation capabilities forWRF (WRF-Var) and ASR are developed by the WRF-Var Development Team of NCAR’s Mesoscale andMicroscale Meteorology Division (MMM). The NCAR MMM is also the main portal for the distribution anddevelopmental organization of the Advanced Research WRF (WRF-ARW). The current data assimilation plan for
ASR is to use 3DVAR initially, with a transition to ensemble Kalman filter (EnKF, Wang et al. 2007) being
considered. An advantage to EnKF is that it estimates reanalysis uncertainty.
The ASR encompasses a broad surface domain within the high-resolution region inside the boundaries. The
planned ASR high-resolution grid shown in Fig. 1 includes all of the watersheds of the northward flowing rivers
emptying into the Arctic Ocean. A lower resolution outer grid may be used to feed the high resolution domain.
The third-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-Interim)or the U.S. National Centers for Environmental Prediction Global Forecast System (GFS) will also be used todrive the ASR. To treat the Arctic land regions, the ASR will also include optimized high-resolution land data
assimilation (HRLDAS) capabilities. Current work at NCAR on the Noah land surface model (LSM), a feature within
the WRF model (Chen and Dudhia 2001; Skamarock et al. 2006), includes HRLDAS development for the ASR. Thisinvolves the blending of atmospheric and land-surface observations with the LSM, with the goal of providing along-term evolution of soil and vegetation features, the surface hydrologic cycle, and the surface energy cycle. TheHRLDAS runs off-line from WRF in between interacting with WRF at intervals of a few hours. Additionally, a new
improvement to the Penman evaporation in version 2.2 of the Noah LSM results in reduced sublimation under stableconditions and a reduced specific humidity bias.

Figure 1 Tentative domains for the Arctic System Reanalysis

Figure 2 Domain of Polar WRF simulationsof the North Atlantic and Greenland

POLAR WRF
Earlier work by the Polar Meteorology Group of the BPRC resulted in a polar-optimized version of the 5thgeneration Penn State/NCAR Mesoscale Model (MM5). Tests of “Polar MM5” showed that the model achieved amuch improved performance for both Arctic and Antarctic regions (e.g., Bromwich et al. 2001). To advance this work
into the future, a polar-optimized version of WRF-ARW has been very recently developed by the Polar Meteorology
Group. "Polar WRF" will serve as the base model for the ASR, and will require evaluations and optimizations for theboundary layer parameterization, cloud physics, snow surface physics and sea ice treatment, analogous to the methods
used to develop Polar MM5 (Bromwich et al. 2001). The model is undergoing tests for the Greenland and Antarctic
ice sheets, the Arctic Ocean, and Arctic land environments.
ARCTIC RESULTS WITH POLAR WRF
Testing and development work for Polar WRF began with simulations for ice sheet surface conditions using theGreenland area domain shown in Fig. 2 with 24-km horizontal resolution. The blue curves in Fig, 2 show theDecember locations of the southern sea ice boundary. A detailed description of the simulations and results ispresented by Hines and Bromwich (2008). The winter month December 2002, and the summer month June 2001 aresimulated in a series of integrations initialized daily at 0000 UTC. The initial 12 hours are taken as model spin-up
time for the atmospheric hydrology and boundary layer processes. The output from hours 12-36 of the simulations isconnected into a month-long representation at 3 hour intervals. The results motivated several improvements to Polar
WRF, especially to the Noah LSM and the snowpack treatment. Best results are achieved the use of the modified
Noah LSM, the Mellor-Yamada-Janjic atmospheric boundary layer formulation, and WRF single-moment 5-classmicrophysics scheme.

Figure 3 Domain for the Polar WRF simulations of thewestern Arctic. Squares show station locations. Marks inthe Arctic Ocean show the location of Ice Station SHEBA
during January (blue), June (green) and August (red) 1998.

Figure 4 Color scale of average sea ice fraction during
August 1998. The track of Ice Station SHEBA during
August is shown next to the arrow. Land and ice-freegrid points are unshaded.

The next round of testing is over the Arctic Ocean using a western Arctic grid with 25-km resolution (Bromwich etal. 2008). The 3500×2750 domain for the simulations is displayed in Fig. 3. A crucial new component is themodified ocean surface treatment that now allows for fractional sea ice between 2-100% coverage. The atmosphericsurface layer routine is called for all horizontal grid points in the domain, including separate calls for the ice andopen-water components of pack ice grid points. Over the oceans, the LSM is called only for the ice portion of packice grid points. The new simulations also include the fully-two-moment ice and liquid water microphysics of Morrison
et al. (2005). Figure 4 shows the fraction of sea ice for ocean grid points with model domain during August 1998,
when the Arctic open water fraction is considerably larger than during most other months. For this round ofsimulations, the initial spin-up time for the simulation is increased to 24 hours, and the model output from hours 24-48
is combined into the month-long fields. Arctic conditions are simulated for the selected months: January 1998, June1998, and August 1998 representing mid-winter, early summer and late summer conditions, respectively from theSurface Heat Budget of the Arctic (SHEBA, Persson et al. 2002; Uttal et al. 2002) observational study. High quality
observations are available for many atmospheric and oceanic fields during SHEBA (e.g., Persson et al. 2002).
Relevant locations of Ice Station SHEBA are shown in Figs. 3 and 4. Over the Arctic pack ice, the ice surface
conditions change greatly over the course of late spring, then summer, and finally onto Autumn (Perovich et al. 2007).
Based upon in-situ and remote-sensing observations, the albedo of sea ice is specified as a function of time andlatitude for June and as a function of time for August. Details are presented in Bromwich et al. (2008). Simulation
results are compared with the observations of the drifting ice station SHEBA in the Arctic ice pack. The Polar WRF
simulations show good agreement with observations for all three months. Model performance statistics are presentedin Table 1. Figure 5 shows the excellent agreement between observed surface pressure and simulated surfacepressure during the winter month January. Similar simulation quality, with correlations of 0.97 or larger, for surfacepressure occurs during the early summer month June and the late summer month August (Table 1). The very good
agreement between the simulated and observed surface pressure demonstrates that Polar WRF is well capturing thesynoptic variability. Due to the strong influence of local boundary layer processes and the surface energy balance,
however, surface temperature is a more difficult field to simulate than surface pressure, hence the temperaturecorrelations are much smaller in Table 1. Figure 6, which shows the time series of surface temperature, reflects thesecomplications, as the high-frequency fluctuations of temperature are not necessary well-captured by Polar WRF. The
high-frequency fluctuations in temperature seen in Fig. 6 are primarily due to variability in water and ice clouds nearSHEBA (Bromwich et al. 2008). This is not surprising as Arctic clouds are frequently problematic for numericalsimulations. Nevertheless, the temperature biases are small during June and August, and the simulations show very
good promise for their ability to capture the synoptic variability in the Arctic. Overall, we find Polar WRF to be askillful tool for studies of Arctic Ocean meteorology. Furthermore, Hines and Bromwich (2008) show that PolarWRF simulates a superior surface energy balance over Greenland than the earlier generation Polar MM5.

Table 1. Model performance statistics for Polar WRF during January, June and August 1998 incomparison to Ice Station SHEBA observations
Correlation
BiasRoot Mean Square Error
VariableJanuaryJuneAugust January June August January June
AugustSurface Pressure (hPa)
0.98
0.97
0.99
0.5
1.1
1.6
2.2
2.0
1.5
Surface Temperature (°C)
0.83
0.38
0.46
-2.2
0.4
0.2
4.3
1.1
1.1
*
2.0/2.5 m Temperature
0.82
0.45
-1.8
0.2
4.0
1.1(°C)
10 m Temperature (°C)
0.80
0.49
0.55
-2.1
0.3
0.1
4.2
1.1
1.1
10 m Wind Speed
0.88
0.71
0.83
-0.6
-0.6
-0.6
1.5
1.5
1.6
*
2.0/2.5 m Specific-0.04
0.13
0.0+
0.12
0.29
0.27+
0.820.63
0.62+-1
Humidity (g kg )Incident Longwave
0.84
-0.19
0.29
-10.2
-9.4
2.8
24.3
173.7
24.6
Radiation (W m-2)
Incident Shortwave
0.47
0.38
-6.3
5.9
171.4 114.1
Radiation (W m-2)
* Observations were available at 2.5 m from tower observations and are used to verify 2.0 m Polar WRF results.
+ Observations at 2.5 m were not available for August so the 2.0 m Polar WRF specific humidity was comparedagainst tower observations at 10-m.

ACKNOWLEDGMENTS. This research is supported by NSF IPY Grant 0733023, NOAA CIFAR Grant
UAF04-0047, NASA Award GRT962800, and UCAR Subcontract S01-22961.

January Surface Pressure at Ice Station SHEBA
1050

P r e s s u r e (h P a )

1045
1040
1035
1030
1025

Correlation: 0.98
Bias:
0.5 hPa
RMSE:
2.2 hPa

1020
1015

Observations
Polar WRF

1010
1005
1000
1611162126

31

Figure 5 Surface pressure (hPa) from observations and Polar WRF at Ice Station SHEBA for January 1998
REFERENCES
Bromwich, D.H., J.J. Cassano, T. Klein, G. Heinemann, K.M. Hines, K. Steffen and J.E. Box 2001: Mesoscalemodeling of katabatic winds over Greenland with the Polar MM5. Mon. Wea. Rev., 129, 2290-2309.
Bromwich, D.H., K.M. Hines and L.-S. Bai 2008: Development and testing of Polar WRF. Part II. The Arctic
Ocean. J. Geophys. Res., to be submitted.
Chen, F., and J. Dudhia 2001: Coupling an advanced land-surface/ hydrology model with the Penn State/ NCAR
MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569-585.
Hines, K.M., and D.H. Bromwich 2008: Development and testing of Polar WRF. Part I. Greenland Ice Sheet
Meteorology. Mon. Wea. Rev., 136, in press.
Morrison, H., J.A. Curry and V.I. Khvorostyanov 2005: A new double-moment microphysics parameterization forapplication in cloud and climate models. Part I: Description. J. Atmos. Sci., 62, 1665–1677.
Overland, J., J. Calder, F. Fetterer, D. McGuire, J. Morison, J. Richter-Menge, N. Soriede and J. Walsh 2003:
SEARCH workshop on large-scale atmosphere/cryosphere observations. Bull. Amer. Meteor. Soc., 83, 1077-1082.
Perovich, D.K., S.V. Nghiem, T. Markus and A. Schweiger 2007: Seasonal evolution and interannual variability of thelocal solar energy absorbed by the Arctic sea ice–ocean system. J. Geophys. Res., 112, C03005,
doi:10.1029/2006JC003558.
Persson, P.O.G., C.W. Fairall, E.L. Andreas, P.S. Guest and D.K. Perovich 2002: Measurements near the AtmosphericSurface Flux Group Tower at SHEBA: Near-surface conditions and surface energy budget. J. Geophys. Res., 107,8045, doi:10.1029/2000JC000705.
Skamarock, W.C., J.B. Klemp, J. Dudhia, D.O. Gill, D.M. Barker, W. Wang and J.G. Powers 2006: A Description ofthe Advanced Research WRF Version 2. NCAR/TN–468+STR, 88 pp.
Uttal, T., and 27 Coauthors, 2002, Surface Heat Budget of the Arctic Ocean. Bull. Amer. Meteor. Soc., 83, 255–275.
Wang, X., T.M. Hamill, J.S. Whitaker, and C.H. Bishop 2007: A comparison of hybrid ensemble transform Kalmanfilter-optimum interpolation and ensemble square root filter analysis schemes. Mon. Wea. Rev., 135, 1055-1076.

aa

T e m p e r a tu r e (C )-15January
SurfaceTemperature
IceStationStation
SHEBA
January
SurfaceTemperature atatIce
SHEBA
Correlation: 0.83
Bias:
-2.20°C
RMSE:
4.29° C-20-25-30-35

Polar WRF
Observations-40-451611162126

31January
January1998
1998

bb

5
4

Correlation: 0.38
Bias:
0.42°C
RMSE:
1.05°C

3

Temperature (C)June
SurfaceTemperature
IceStation
StationSHEBA
SHEBA
June
SurfaceTemperature atat Ice
Polar WRF
Observations

2
10-1-2-3-4-5
1611162126

31

T e m p e r a tu r e (C )June1998
1998
Junec210-1-2-3-4-5-6-7

AugustIceStation
StationSHEBA
SHEBA
AugustSurface
SurfaceTemperature
Temperature atatIce

Correlation: 0.46
Bias:0.20°C
RMSE: 1.15°C

Polar WRF
Observations
161116212631

AugustAugust1998
1998
Figure 6 Time series of surface temperature (°C) from observations (solid lines) and the Polar WRF simulations(dashed lines) at Ice Station SHEBA for (a) January, (b) June and (c) August 1998. Correlation, bias and root
mean square error (RMSE) statistics are shown for each month.