The Arctic is a vital component of the global climate, and its rapid environmental evolution is an important element of climate change around the world. To detect and diagnose the changes occurring to the coupled Arctic climate system, a state-of-the-art synthesis for assessment and monitoring is imperative. This paper presents the Arctic System Reanalysis, version 2 (ASRv2), a multiagency, university-led retrospective analysis (reanalysis) of the greater Arctic region using blends of the polar-optimized version of the Weather Research and Forecasting (Polar WRF) Model and WRF three-dimensional variational data assimilated observations for a comprehensive integration of the regional climate of the Arctic for 2000–12. New features in ASRv2 compared to version 1 (ASRv1) include 1) higher-resolution depiction in space (15-km horizontal resolution), 2) updated model physics including subgrid-scale cloud fraction interaction with radiation, and 3) a dual outer-loop routine for more accurate data assimilation. ASRv2 surface and pressure-level products are available at 3-hourly and monthly mean time scales at the National Center for Atmospheric Research (NCAR). Analysis of ASRv2 reveals superior reproduction of near-surface and tropospheric variables. Broadscale analysis of forecast precipitation and site-specific comparisons of downward radiative fluxes demonstrate significant improvement over ASRv1. The high-resolution topography and land surface, including weekly updated vegetation and realistic sea ice fraction, sea ice thickness, and snow-cover depth on sea ice, resolve finescale processes such as topographically forced winds. Thus, ASRv2 permits a reconstruction of the rapid change in the Arctic since the beginning of the twenty-first century–complementing global reanalyses. ASRv2 products will be useful for environmental models, verification of regional processes, or siting of future observation networks.
The new regional 15-km Arctic System Reanalysis, version 2, provides the accuracy and details necessary for many Arctic climate studies over the period 2000–12.
The Arctic is in the midst of rapid change in its physical environment with pronounced increases in surface air temperature, especially for winter and spring over subarctic land areas (Serreze and Francis 2006; Screen et al. 2012), as well as over the Arctic Ocean (e.g., Comiso 2003; Kohnemann et al. 2017). Arctic sea ice extent has declined throughout the satellite era, with the record September minimum extent in 2012 (Fig. 1) and the smallest maximum extent in March 2017 (National Snow and Ice Data Center 2017). Sea ice cover has thinned dramatically (Kwok and Untersteiner 2011), as historical evidence suggests that the recent sea ice minima are unmatched across the Arctic back to 1850 (Walsh et al. 2017). Spring snow-cover extents (SCE) over Eurasia and North America have significantly declined since 2005 (National Oceanic and Atmospheric Administration 2016), with Arctic SCE declining more rapidly than September minimum sea ice extent (e.g., approximately −18% for June over the period 1967–2016; Derksen et al. 2017). Subsurface warming of the permafrost has also been observed in borehole measurements (e.g., Romanovsky et al. 2010). The area of the Greenland Ice Sheet experiencing summer melt has increased, and in mid-July 2012 some 99% of the surface area was melting according to satellite observations, a highly unusual but not unique event (Nghiem et al. 2012). There has also been accelerated movement of Greenland outlet glaciers and increased runoff to the ocean (e.g., Rignot et al. 2011) as Greenland remains the largest land ice mass contributor to sea level rise (Harig and Simons 2016). However, glacier loss in other areas such as the Gulf of Alaska and the Canadian Archipelago are also significant contributors to sea level rise (Harig and Simons 2016) and may not be recoverable this century (Lenaerts et al. 2013). The symptoms of accelerated Arctic climate change are seemingly pervasive (Intergovernmental Panel on Climate Change 2013).
These changes may represent early signs of the expected Arctic amplification of the effects of increasing greenhouse gases (e.g., Screen and Simmonds 2010). However, the Arctic climate system is also home to strong natural variability (Kay et al. 2011; Ding et al. 2017), such as that associated with the North Atlantic Oscillation, the Arctic Oscillation, the Pacific decadal oscillation, and other atmospheric patterns (Thompson and Wallace 2000; Rogers et al. 2001; Rigor and Wallace 2004; Hartmann and Wendler 2005; Overland and Wang 2005). Indeed, the increase in sea ice volume in 2013 following the record minimum raises questions concerning the resilience of the Arctic sea ice cover (Tilling et al. 2015). While there is some evidence that the signature of greenhouse gas forcing has emerged in the Arctic over the last few decades (Fyfe et al. 2013), continued research to separate the forced response from intrinsic variability is needed. There is growing need to improve polar prediction and observing capacity, exemplified by the most recent polar endeavor, the Year of Polar Prediction (YOPP; Jung et al. 2016). This internationally coordinated effort of intensive observing and modeling activities will improve representation of polar processes in models and refine derived satellite products, among other benefits.
The community has long relied on global atmospheric reanalyses to explore climate system behavior. These syntheses merge a wide variety of surface, atmospheric, and satellite remote sensing data into gridded analyses that are important resources for investigating Arctic climate change and accompanying variability during recent decades, most often since 1979 (e.g., Lindsay et al. 2014). There are nevertheless some important caveats to using global reanalyses for climate change assessment. While the use of a fixed data assimilation system and forecasting model eliminates spurious shifts in the output caused by model upgrades (e.g., Bengtsson and Shukla 1988), the reanalyses remain sensitive to changes in the observing system (e.g., Bengtsson et al. 2004a,b). For example, the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim, hereafter ERAI; Dee et al. 2011) has artificial temporal trends due to the assimilation of rain-affected radiances from satellite passive microwave observations. The National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) (Rienecker et al. 2011) and the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) (Saha et al. 2010) exhibit discontinuities associated with the start of the modern microwave sounder [Advanced Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (ATOVS)] era (Cullather and Bosilovich 2011; Zhang et al. 2012). Major temporal discontinuities have been largely resolved in MERRA, version 2 (Gelaro et al. 2017). ERAI, MERRA, and CFSR showed significant errors in temperature, moisture, and wind speed in the lowest 800 m over the Arctic Ocean when compared to independent sounding observations (Jakobson et al. 2012).
The Arctic System Reanalysis (ASR) is a demonstration regional reanalysis for the greater Arctic (see Fig. 1) and an exercise well aligned with the goals of YOPP. ASR, version 2 (ASRv2), spans the region poleward of the headwaters of the major rivers that flow northward into the Arctic Ocean and help maintain the low salinity of its near-surface layer. In Eurasia, these rivers are the Ob, Yenisei, Lena, and Kolyma, while the Mackenzie is the largest such river in North America. Also the major oceanic storm tracks are included in the ASR domain. Particular attention has been paid to specifying realistic ocean and land surface conditions. Horizontal resolution is finer than the global reanalyses (35 km and coarser grids) and comparable time resolution is used. Optimal polar physics are used where possible. Currently, the period of assimilation is 2000–12, which starts with the launch of the NASA Earth Observing System satellite Terra (and later Aqua) that supplies several of the input datasets. As a result, ASR is particularly suitable for detailed investigations of near-surface characteristics during the period of rapid Arctic change but lacks the multidecadal perspective of the global reanalyses. Thus, these different reanalyses are complementary to each other. ASR, version 1 (ASRv1), at 30-km grid spacing was outlined by Bromwich et al. (2016); the present manuscript describes ASRv2 at 15-km grid spacing and illustrates its performance in relation to ASRv1 and ERAI.
The regional forecast model used for ASRv2 is based on the Weather Research and Forecasting (WRF) Model, version 3.6.0 (Skamarock et al. 2008), utilizing the Advanced Research version of the WRF (ARW) solver for fully compressible nonhydrostatic equations (Table 1). WRF has been optimized for polar environments (known as Polar WRF; http://polarmet.osu.edu/PWRF/) including improving the heat transfer through snow and ice (Hines and Bromwich 2008), the inclusion of fractional sea ice (Bromwich et al. 2009), the ability to specify variable sea ice thickness, snow depth on sea ice, and sea ice albedo (Hines et al. 2011, 2015; Wilson et al. 2011, 2012), and other optimizations included in the Noah land surface model (LSM; Barlage et al. 2010). With the aid of the Mesoscale and Microscale Meteorology Division at the National Center for Atmospheric Research (NCAR), many of these routines developed by the Polar Meteorology Group of the Byrd Polar and Climate Research Center at The Ohio State University are now part of the standard release of WRF (Powers et al. 2017; www.mmm.ucar.edu/weather-research-and-forecasting-model).
The ASRv2 domain is the same as ASRv1 (Bromwich et al. 2016), consisting of a one-way nest, with an outer domain covering most of the Northern Hemisphere (NH) that provides smooth meteorological fields at the lateral boundaries of the inner domain (Fig. 1). The inner domain covers approximately 1.2 × 108 km2, or about 50% of the NH. Care has been taken to avoid placing the inner-domain boundaries across the highest topography ensuring a seamless transition of meteorological parameters. Polar WRF uses a staggered Arakawa C grid with 721 × 721 grid points on a polar stereographic projection and 15-km horizontal resolution for the inner domain. In the vertical direction, Polar WRF uses a terrain-following dry hydrostatic pressure coordinate system with 71 model levels and a constant pressure surface at the top of the model of 10 hPa. The lowest full model level is 4 m above ground level (AGL), with over 25 levels below 850 hPa, 0.5-km level spacing in the midtroposphere, and approximately 0.8 km from the tropopause to the top of the model.
Initial and lateral boundary conditions for the outer domain in Polar WRF are provided by ERAI surface and upper-level model data. To avoid model drift in atmospheric circulation (Glisan et al. 2013; Hines et al. 2015), spectral nudging is implemented on temperature, geopotential height, and wind components above 100 hPa (top 20 vertical levels) on the inner domain (all levels in the outer domain). We use wavenumber 11 to impact only the large-scale synoptic conditions (wavelengths > 1,000 km), and setting the nudging coefficients for all three variables to 10 times the strength of ASRv1 removes additional upper-level model bias in the initial forecast. The top 8 km of Polar WRF are damped and the gravity wave drag option is selected to suppress gravity wave interference at the top of the model.
The physics parameterizations chosen for ASRv2 are based on extensive development and testing of Polar WRF over a wide range of Arctic environments including the Greenland Ice Sheet and the Arctic Ocean (Hines and Bromwich 2008; Bromwich et al. 2009; Hines et al. 2011; Wilson et al. 2011, 2012; Hines et al. 2015). The Goddard microphysics scheme is utilized for the cloud microphysics with ice, snow, and graupel processes represented (Tao and Simpson 1993; Tao et al. 2003). We use the Kain–Fritsch scheme (Kain and Fritsch 1990, 1993; Kain 2004) for the cumulus parameterization along with the climate-model-ready update to the global climate model (GCM) version of the Rapid Radiative Transfer Model (RRTMG) for longwave (LW) and shortwave (SW) radiation (Clough et al. 2005; Iacono et al. 2008). Different from ASRv1, however, we implement the new subgrid-scale cloud fraction interaction with radiation that allows for more realistic shortwave and longwave, improving additional weather parameters (Alapaty et al. 2012; Zheng et al. 2016). The Noah LSM (Chen and Dudhia 2001) and the Mellor–Yamada–Nakanishi–Niino (MYNN) (Nakanishi 2001; Nakanishi and Niino 2004, 2006) 2.5-level planetary boundary layer (PBL) and complementary surface-layer schemes are also utilized.
WRFDA system overview.
NCAR’s community WRF data assimilation (WRFDA, formerly WRF-Var) system is adopted for the component of atmospheric analysis in the ASR project. Over recent years, WRFDA has been extended to include a broad range of data assimilation (DA) techniques, including three- and four-dimensional variational data assimilation (3DVAR and 4DVAR) and hybrid ensemble–variational (EnVar) approaches (Huang et al. 2009; Barker et al. 2012). ASR uses the 3DVAR technique that was more mature than other schemes (4DVAR and hybrid EnVar) in WRFDA at the time the project was originally proposed. WRFDA-3DVAR is based upon the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) 3DVAR system (Barker et al. 2004), but the basic software framework is fully updated for ARW (Skamarock et al. 2008). It has been successfully implemented for operational/real-time applications at several numerical weather prediction centers and research institutes (Barker et al. 2012), including the Antarctic Mesoscale Prediction System (Powers et al. 2012).
WRFDA produces analyses of surface pressure and 3D atmospheric temperature, moisture, and wind fields on the WRF Model grid by assimilating many types of observations, including most conventional (both surface and upper air) and remote-retrieval observations as well as radiance data from a number of satellite platforms (Barker et al. 2012). [For a more detailed description of WRFDA, see Skamarock et al. (2008).] All observations used in ASR are provided by NCEP in the binary universal form for the representation of meteorological data (BUFR) format. Figure 2 shows the typical coverage of nonradiance observations used in the ASR within a ±1.5-h data assimilation time window. High-latitude Arctic regions as well as ocean areas are sparsely monitored by conventional observations. Instead, nonradiance observations here are largely satellite atmospheric motion vectors and GPS radio occultation observations (assimilated as refractivity) providing upper-air information along with surface ocean winds (at 10 m) from the Quick Scatterometer (QuikSCAT).
Satellite radiance assimilation.
In addition to those nonradiance observations, radiance data from 12 microwave sensors [6 Advanced Microwave Sounding Unit (AMSU)-A, 3 AMSU-B, and 3 Microwave Humidity Sounder (MHS)] on board 7 polar-orbiting satellites, which have been proven to have a large positive impact on global medium-range forecast performance (e.g., Bouttier and Kelly 2001) and tropical storm forecasting using WRF (e.g., Liu et al. 2012; Schwartz et al. 2012), are also assimilated in ASR. Radiance observations are the major data source providing vertical temperature and moisture soundings over those regions with sparse conventional data coverage. For ASR, only the channels 5–9 (temperature sensitive) of AMSU-A and the channels 3–5 (moisture sensitive) of AMSU-B/MHS are used. High-peaking and surface-sensitive channels are not used because of the relatively low ASR model top (10 hPa) and inaccurate input of surface emissivity and skin temperature. Figure 3 depicts the time series over a period of 13 years (2000–12) of global statistics of bias (left panels) and standard derivation (right panels) of observed minus calculated brightness temperatures using the Community Radiative Transfer Model (CRTM) (Han et al. 2006) with ERAI as input for AMSU-A channels 5–9. These monitoring statistics were obtained using WRFDA’s “offline” variational bias correction (VarBC) option as described by Auligné et al. (2007) and Liu et al. (2012).
The starting dates from which radiance data become available are marked in Fig. 3 for the different instruments. The only sensor covering the whole ASR period is NOAA-15 AMSU-A. Monitoring statistics are a powerful tool for identification of bad channels within the lifetime of sensors, which have to be blacklisted in the data assimilation. For instance, Meteorological Operational-2 (now known as MetOp-B) AMSU-A channel 7 had a substantially increased standard deviation from January 2009 onward, which was known to suffer from increasing instrument noise and was turned off by operational data assimilation systems. The jump of both bias and standard deviation for NOAA-19 AMSU-A channel 8 can also be clearly seen from Fig. 3. The radiance blacklist table used in ASR is a combination of our monitoring results and blacklist tables used by NCEP operations (see www.emc.ncep.noaa.gov/mmb/data_processing/Satellite_Historical_Documentation.htm) and ERAI (P. Poli 2012, personal communication). Some important radiance blacklist decisions in ASR are marked in the right panel of Fig. 3. For instance, iuse(:) = −1 means all channels are turned off, and iuse(8) = −1 denotes that channel 8 is not used. It is evident that radiance bias characteristics of different channels have been evolving with time and exhibit, to a different extent, seasonal variations, posing the need for a time-evolving and adaptive bias correction scheme. A state-of-the-art VarBC scheme was implemented in WRFDA and used for ASR, which is similar to that used at NCEP (Derber and Wu 1998) and ECMWF (Dee and Uppala 2009). Offline monitoring statistics also provide pretrained bias correction coefficients for individual channels, which are used as the initial condition of the cycling VarBC scheme in different streams of ASR production runs and can minimize the spinup effect of bias correction adjustment (Liu et al. 2012; Schwartz et al. 2012).
Surface observation assimilation.
WRFDA does not directly analyze the screen-level atmospheric parameters (i.e., temperature T and moisture Q at 2 m and wind at 10 m), which are important variables commonly used for climate trend analysis. Instead, WRFDA analyzes atmospheric variables at the lowest model level by assimilating 2-m temperature and moisture and 10-m zonal (U) and meridional (V) wind observations from surface stations [surface synoptic observations (SYNOP), aviation routine weather reports (METAR), ship observations (SHIP), buoy observations (BUOY)]. The lowest model level of the ASR domain is at about 4 m, which allows 10-m-wind analysis accurately derived from a vertical interpolation and 2-m T and Q analysis extrapolated using the model’s local lapse rate. To account for the difference between model terrain and surface station elevation, terrain corrections are applied to surface observations (also including surface pressure) before they are assimilated. Note that 2-m temperature and moisture and 10-m U and V wind are the diagnostic, not prognostic, variables in the WRF Model. Therefore, their analyses do not affect the subsequent WRF Model forecast during the ASR data assimilation/forecast cycles.
Atmospheric background error covariances.
Another important aspect is the background error covariance (BEC) statistics that constrain (together with observation errors) the weight between the model background (i.e., a 3-h forecast from previous cycle’s analysis) and the observations and also propagate information from observed to unobserved areas/variables in both the horizontal and vertical through spatial and multivariate correlations implied in the BEC. BECs for ASR were generated using the so-called National Meteorological Center (NMC) method (Parrish and Derber 1992), which takes differences between forecasts of different lengths valid at common times. ASR uses the differences between the 24- and 12-h WRF forecasts, initialized from ERAI interpolated into the ASR grid and valid at either 0000 or 1200 UTC over different months.
Land surface models coupled to mesoscale meteorological models have been shown to perform poorly during cold-season processes, such as snowpack physics and soil heat diffusion, leading to an inadequate representation of spring snowmelt timing and the soil temperature profile, two major metrics of climate change in the Arctic (Slater et al. 2007; Pan et al. 2003; Barlage et al. 2010). Addressing these model issues through data assimilation into land surface models is limited by the paucity of quality state variables at high latitudes. In the ASR, several existing global-scale satellite observations have been identified to improve the representation of the land surface. These data are either integrated directly into the model or used to develop new datasets consistent with the Noah land model infrastructure.
Currently in the WRF–Noah model, land surface properties, such as green vegetation fraction and albedo, are prescribed climatological values based on historical Advanced Very High Resolution Radiometer (AVHRR) satellite data. With the launch of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board the NASA Terra and Aqua platforms in 1999 and 2002 and real-time vegetation monitoring by the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service (NOAA/NESDIS), the availability of high-spatial- and high-temporal-resolution remotely sensed land surface properties improved substantially. The primary concern in assimilating a wide variety of products is that they are consistent. For example, surface albedo is tightly coupled to snow cover so the system must consider this.
The Noah land surface model treats albedo as a mixture of snow-free and snow-covered surfaces with the weighting based on model-diagnosed snow-cover fraction. Satellite-based albedo observations are a combination of all surfaces present in the observation pixel. To use the satellite albedo within the Noah LSM, a disaggregation must be done, since the Noah LSM requires both a snow-covered and snow-free albedo regardless of the presence of snow. The Noah LSM also requires a snow-cover and snow-free albedo everywhere at all times (e.g., snow-covered albedo in the tropics).
Two new time-varying albedo datasets are created for snow-free and snow-covered surfaces using the MODIS 8-day 0.05° global albedo product (MODIS product code MCD43C3; Schaaf et al. 2002) along with the MODIS snow-cover products (MOD10C2/MYD10C2; Hall et al. 2002). The MODIS data are first filtered using the albedo product-quality flag and then using the snow product “cloud obscured” flag (data are rejected if cloud cover is greater than 80%). To determine the snow-covered albedo, the MODIS snow products must report at least 70% snow cover on the non-cloud-covered portion. Likewise, to be considered snow free, snow cover must be less than 10%. Since only one albedo observation is used to determine two necessary model inputs, a forward-in-time and backward-in-time filling procedure is done using the nearest (in time) quality observation of either snow-covered or snow-free albedo for each global location. The resulting product for 2007 over a north Alaskan grid point (68.8°N, 154.9°W) is shown in Fig. 4. These albedo products have been produced for 2000–12.
Investigation of the above MODIS albedo (MCD43C3) over Greenland showed an unusual and unrealistic albedo time series. After analyzing a daily albedo dataset based on the MODIS daily snow-cover product (MOD10A1/MYD10A1; Hall et al. 2002), the ASR albedo assimilation replaced the MCD43C3-based product with the MOD10A1-based product over the permanent ice portions of Greenland.
NOAA/NESDIS green vegetation fraction.
A real-time dataset of green vegetation fraction is produced weekly in near–real time by NOAA/NESDIS (Jiang et al. 2008). This dataset is available for the entire ASR processing period at 0.144° spatial resolution. This product is consistent with the current vegetation fraction data used in Noah. Therefore, no further parameter tuning is needed when using this product other than to reset the maximum and minimum annual vegetation fraction range.
ASRv2 data are available from the NCAR Computational Information Systems Laboratory (CISL) Research Data Archive (NCAR/UCAR/OSU 2017).
We compare near-surface variables from ASRv1, ASRv2, and ERAI to observations from ∼5,000 surface stations provided by the National Centers for Environmental Information (www.ncdc.noaa.gov/; counts vary by UTC hour, season, and year) and the Greenland Climate Network (GC-Net; http://cires1.colorado.edu/steffen/gcnet/) for the period January 2000–December 2010 to compare the broadscale performance of ASR at increasing horizontal resolution (Table 2; from 80 km for ERAI to 15 km for ASRv2). All observed time series were screened for outliers and discontinuities. The results reflect reanalysis performance at 3-h intervals in relation to surface observations that are mostly assimilated (except for GC-Net stations), and therefore are not entirely independent. Reanalysis values are spatially interpolated to the station locations from the surrounding four grid points. ASR is available every 3 h while the ERAI is linearly interpolated between analysis times (0000, 0600, 1200, and 1800 UTC) to produce intermediate values (at 0300, 0900 UTC, etc.). Table values are 11-yr averages for each month derived from averaging the results for all 5,000 stations. Lower bias, smaller root-mean-square error (rmse), and higher correlation show a better fit of the reanalysis to the observed time series. The 11-yr mean is very similar to that obtained for each year.
Annual-mean 10-m wind speed biases are smaller in the ASR products compared to ERAI, though a positive (negative) bias is demonstrated by ASRv2 (ASRv1). The annual spatial distribution of 10-m wind speed bias at the observation sites (Fig. 5) shows that ASRv2 almost everywhere has a reduced bias in comparison to ERAI, apart from Scandinavia, Europe, and the U.S. Midwest. Terrain variations not well resolved at 15 km (Fig. 1) may be partly responsible for the reanalysis challenges in Scandinavia and Europe. Table 2 reveals that there is a substantial improvement in rmse and correlation between ERAI and ASRv2, where ASRv2 captures two-thirds of the 3-hourly wind speed variance. Performance is better in summer than winter when the speeds are higher. As described in Bromwich et al. (2016), the improvements in near-surface wind are tied to the finer resolution in ASR and the improved skill in capturing local wind effects near complex terrain. ASRv1 (30 km) wind fields have been shown to be well represented, including wind related to topographically forced wind events (Moore et al. 2016) and Arctic cyclones (Tilinina et al. 2014). The present results along with Moore et al. (2016) for ASRv2 demonstrate that local wind effects are even better captured by ASRv2 at 15-km resolution. (See “Nares Strait flow” sidebar.)
Strong low-level winds are a common cold-season feature in Nares Strait, located between the high terrain of Greenland and Ellesmere Island (Samelson and Barbour 2008). The strong ageostrophic winds are due to orographic channeling down the pressure gradient between high pressure over the Arctic Ocean (Lincoln Sea) and low pressure over Baffin Bay. They may play a key role in generating the persistent winter North Water polynya in northern Baffin Bay. Samelson and Barbour (2008) modeled these winds with Polar MM5 (predecessor to Polar WRF) with a resolution of 6 km. Figure SB1 shows an example of these events that occurred on 9 February 2007 captured by the ASRv1 and ASRv2. The 15-km ASRv2 does a much better job resolving the orography of Nares Strait, and thus the winds are much stronger (>20 m s–1) and more continuous than at the 30-km resolution (∼15 m s–1). The katabatic winds over Greenland feed into the wind flow at two locations in ASRv2. Notice the multiple centers in the low over Baffin Bay compared to the single center in ASRv1. The high over the Arctic Ocean is more clearly captured by the 15-km ASRv2. This case illustrates that topographically forced winds are much better captured by the finer resolution of ASRv2.
Analysis reveals that ERAI and ASR products have small annual-mean 2-m temperature biases, with the smallest biases represented by ASRv2. However, ASRv2 is colder than both ASRv1 and ERAI with small negative biases from January through October. However, these biases are well within the statistical error inherent in the model version change between ASRv1 and ASRv2. The annual spatial bias (Fig. 5) confirms the bias magnitude reduction in ASRv2 in comparison to ERAI except in the same problematic areas as for wind speed (Scandinavia, Europe, and U.S. Midwest). Nearly halving the annual-mean rmse value from ERAI to ASRv2 (Table 2) indicates that ASRv2 shows a much closer fit to the observations and the standard deviation of unexplained variance is small. This is further supported by the increasing skill indicated by higher correlation.
Annual-mean 2-m dewpoint biases are similar between the reanalyses. Negative monthly dewpoint biases but small positive 2-m temperature biases for ASRv1 from April through October indicate drier than observed conditions. Negative 2-m temperature biases but positive dewpoint biases during the summer months in ASRv2 reflect ample moisture due to the improved cloud processes implemented in ASRv2. Again, lower annual-mean rmse and higher correlation in ASRv2 show an improvement in overall fit and skill.
All three reanalyses capture the surface pressure (atmospheric circulation) very well with very small biases, low rmses, and very high correlations. Consistent with other near-surface variables, the rmse decreases from ERAI to ASRv2. To summarize, ASRv2 at 15 km shows a close fit to the surface observations throughout the year with the “large-scale parameter” surface pressure being the most skillful and the “more localized parameter” surface wind speed being less so.
For analysis of the upper-air variables in ASRv2, we have selected 500-hPa temperature and 700-hPa relative humidity for comparison with ERAI for the period December 2006–November 2007 (Fig. 6). Figure 6a shows the annual-mean 500-hPa temperature in ASRv2. The pattern aligns closely with the expected mean large-scale circulation. The coldest temperatures are located in the vicinity of the largest troughs, centered over Canada (∼75°W) and Siberia (∼140°E). A weaker trough is indicated over eastern Europe (∼45°E) as well, with the strongest gradients throughout the midlatitudes within the major troughs. Figure 6b shows the differences between ASRv2 and ERAI, which are generally within ±0.1°C. This is similar to the radiosonde comparison conducted by Bromwich et al. (2016) for ASRv1 and ERAI. The differences do not reveal systematic biases with scattered differences likely tied to small local variations between the reanalyses’ assimilations. The greatest differences occur throughout the North Pacific, North Atlantic, and in areas of complex terrain.
Relative humidity at 700 hPa illustrates the midtroposphere (∼3,000 m), which is the level at which most weather systems are steered across the NH. Figure 6a depicts the annual-mean relative humidity for December 2006–November 2007 showing a general low-to-high-latitude gradient. The lowest relative humidity is found in the arid desert regions of the U.S. Southwest (30°–35°N, 110°–125°W) and the Middle East (30°N, 50°E) and near the influence of the subtropical high in the Pacific. Higher relative humidity north of 40°N is associated with the major NH storm tracks and cooler cloudier environments. The onshore flow along the west coast of North America (55°–65°N, 115°–165°W) is highlighted by the higher relative humidity in this location, along with areas in southwest Greenland (60°–65°N, 30°–45°W) and western Scandinavia (60°–70°N, 5°–40°E) and across Siberia (55°–70°N, 75°–165°E). An area of slightly lower relative humidity is located in the vicinity of the Beaufort Sea high (70°–80°N, 110°W–180°), which was exceptionally strong during this period and has been linked to a number of teleconnections and summer sea ice decline (L’Heureux et al. 2008; Serreze and Barrett 2011). Figure 6d shows the differences between ASRv2 and ERAI, where magnitudes are generally within ±4%. ASRv2 has higher relative humidity across the main oceanic storm-track regions of the North Pacific and Atlantic and smaller positive differences compared to ERAI across much of the Arctic. ASRv2 demonstrates lower relative humidity across much of the continental areas of Eurasia. Compared to the ASRv1 analysis with radiosondes (Bromwich et al. 2016), these results are comparable to an average 2% deficit in the RH across the domain with slightly higher RH in ASRv2 than ERAI.
We compare ASRv2 mean annual total forecast precipitation to ERAI for the 2000–10 period (Fig. 7). ASRv2 mean precipitation (Fig. 7a) clearly depicts the major storm tracks of the North Pacific and Atlantic where over 2,000 mm of annual precipitation falls. Greater amounts are also shown along the higher terrain of western North America. Much lighter amounts (<600 mm) fall across much of the Arctic basin and in the desert regions of the Middle East. Figure 7b shows that differences between ASRv2 and ERAI across much of the domain are generally ±10%. Both storm-track regions show up to 10% less annual precipitation in ASRv2 than in ERAI. The greatest differences between the two reanalyses occur over the highest terrain in western North America, the higher elevations throughout central Asia, and Greenland where differences are in excess of 50%. Across much of the Arctic, differences are small, though ASRv2 is dry (∼15%) relative to ERAI throughout much of the western Arctic basin.
To evaluate monthly and seasonal characteristics of precipitation in ASRv2 and improvements over ASRv1, we repeat our analysis from Bromwich et al. (2016; ASRv1 included here for comparison) for the period December 2006–November 2007 using the Global Historical Climatology Network, version 2 (GHCN2) (Peterson and Vose 1997), and the Adjusted Historical Canadian Climate Data (AHCCD) (Mekis and Hogg 1999) precipitation gauges (Fig. 7a). Each has undergone quality-control procedures to improve wind undercatch, evaporation, and adjustments for trace observations, all particularly important for Arctic precipitation (Peterson and Easterling 1994; Easterling and Peterson 1995; Mekis and Hopkinson 2004; Mekis 2005; Devine and Mekis 2008). We used only stations with complete annual records and divided the analysis between midlatitude (south of 60°N; 296 stations) and polar (north of 60°N; 78 stations).
Compared to the midlatitude stations (Fig. 7c), we note further improvements in the summertime precipitation for this particular season (summer 2007). Monthly biases for April–July are smaller in ASRv2 than in ASRv1 (10%–15%), though still generally overpredicted and higher than those demonstrated by ERAI. While warm-season precipitation is well captured by ASRv2, the cooler season shows drier biases in ASRv2 from August through March. For the polar stations (Fig. 7d), ASRv2 is comparable to ERAI from March through October. Significant improvements over ASRv1 (>10%) occur during the warmer months of May–August. Similar to the midlatitudes; however, the months of November–February are generally drier in ASRv2 than in ASRv1 or ERAI.
Downward radiation at the surface.
Annual-mean incident SW and downwelling LW from Clouds and the Earth’s Radiant Energy System Energy Balance and Filled (CERES-EBAF; Loeb et al. 2009; Kato et al. 2013) monthly 1° × 1° dataset are compared to ASRv2 and ERAI for December 2006–November 2007 (Fig. 8). These data were obtained from the NASA Langley Research Center CERES ordering tool (http://ceres.larc.nasa.gov/). CERES-EBAF has shown greater accuracy compared to other gridded radiation products as it incorporates detailed cloud and aerosol information (Ma et al. 2015; Wild et al. 2013, 2015; Zhang et al. 2015, 2016). Figure 8 also depicts additional ground-based measurements from independent sites (black dots; Abisko, Sweden; Atqasuk, Alaska; Sondankyla, Finland; and Summit, Greenland) and others that are part of the World Climate Research Programme Baseline Surface Radiation Network (BSRN; Hegner et al. 1998; Ohmura et al. 1998). These stations provide a validation of CERES-EBAF and a comparison between ERAI, ASRv1 (Bromwich et al. 2016), and ASRv2 (Table 3). [For a full description of the radiation data, see Wilson et al. (2012).]
Figure 8a shows ASRv2 SW compared to the CERES-EBAF surface product. In general, ASRv2 has too much incident SW at the surface across much of the domain, with differences of 20–50 W m−2. Small negative biases (0 to −20 W m−2) are located over the western Arctic Ocean, Hudson Bay, and some parts of Baffin Bay. Conversely, ERAI has generally too much SW compared to the CERES-EBAF over the midlatitudes (Fig. 8b), but too little across the central Arctic where differences exceed 20 W m−2. Comparing these locations to Table 3, differences are consistent between CERES-EBAF and comparisons made at ground stations. For SW, both ASRv2 and ERAI show an excess of SW, with the greatest differences occurring during the summer months. Though ASRv2 SW biases are greater than ERAI, they are much improved over ASRv1 with a decrease from annual-mean bias of 42 to 27 W m−2 in the midlatitudes. Likewise, rmse is lower (95.3 W m−2) and correlations are greater (0.92) than ERAI. Table 3 also supports the findings demonstrated by Figs. 8a and 8b for the polar stations, with too much shortwave radiation in ASRv2 (annual-mean bias of 14.8 W m−2) and too little in ERAI (annual-mean bias of −6.7 W m−2).
Figure 8c shows that ASRv2 generally predicts too little LW radiation across the domain, with differences between CERES-EBAF in the Arctic region ranging from −10 to −20 W m−2. Coupled with Fig. 8a, and despite the improved model cloud physics in Polar WRF, these biases indicate that additional model improvements are necessary in order to fully capture the radiative cloud effects. Comparatively, ERAI produces too much LW over the Arctic Ocean with differences of up to 20 W m−2 (Fig. 8d) indicative of too much cloud cover or optically thick clouds in that region.
Comparing these spatial plots to Table 3, again we see consistency as the stations indicate negative LW biases throughout the midlatitudes. ASRv2 improves over both ASRv1 (−11.4 W m−2) and ERAI (−8.8 W m−2) with a mean annual bias of −6.8 W m−2. Unlike the SW, similar negative LW biases occur throughout the year for both ASRv2 and ERAI. In the polar region, consistently low LW biases are evident throughout the annual cycle, and the LW bias in ASRv2 is slightly degraded (−13.9 W m−2) compared to ASRv1 (−11.8 W m−2). Ultimately, these results reflect strongly on the analysis by Hines and Bromwich (2017), who demonstrate that in order to accurately predict Arctic low clouds, models need accurate cloud condensation nuclei predictions.
In this paper we have described ASRv2, a new high-resolution regional reanalysis of the greater Arctic covering the period from January 2000 to December 2012. This paper details the production system for ASRv2, including the Polar WRF specifications, WRFDA data assimilation routine, and observational datasets. Noted enhancements over ASRv1 (Bromwich et al. 2016) include increasing the horizontal resolution to 15 km, upgrading Polar WRF and cloud physics, adding a dual outer-loop routine in the data assimilation to ensure a better fit between the model first guess and observations at analysis time, and additional nudging in the upper levels to remove model biases.
The surface and upper-air analysis fields and forecast precipitation and downward radiation at the surface have been analyzed. Surface analysis with approximately 5,000 surface stations reveals superior comparison in ASRv2, particularly driving down the 10-m wind speed biases and significantly improving the correlations over ASRv1 and ERAI. The upper-air analysis shows an extremely close comparison between ASRv2 and ERAI in 500-hPa temperature and 700-hPa relative humidity, with differences generally within ±0.1°C and ±4%, respectively. Precipitation analysis shows that we have markedly improved summertime precipitation, decreasing the biases during this season by 10%–15%, but a dry bias remains during the cool months. Though comparison between downward radiation at the surface and satellite-derived values reveals that ASRv2 still produces too much shortwave and too little longwave radiation in the forecasts, biases for these values in the midlatitudes are nearly half compared to ASRv1 and the improvement is attributed to the inclusion of subgrid-scale cloud fraction interaction with radiation. Thus, ASRv2 has been shown to be an important synthesis tool for the detection and monitoring of Arctic climate change. (See “Kara and Barents Seas trends” sidebar.) ASRv2 provides important benefits to the research community, in particular those in need of atmospheric data to conduct process studies of Arctic phenomena (e.g., local transport and fluxes) and to drive other environmental models.
Figure SB2a illustrates linear trends in the spatial extent of January sea ice from 2000 to 2012. According to this analysis, the strongest statistically significant trends have occurred in the Kara and Barents Seas around the island of Novaya Zemlya (68°–80°N, 60°–90°E). This is consistent with the analysis by Kohnemann et al. (2017) showing that a reduction of sea ice in this region in late autumn and winter is a driver of enhanced ocean–atmosphere sensible heat flux. The Novaya Zemlya trends for this time period are approximately 40%, nearly 4 times the basinwide sea ice extent decline across the Arctic. Figures SB2b–e show the coupled feedback between this sea ice loss and the atmosphere. Reduced sea ice cover enhances sensible and latent fluxes from the ocean to the atmosphere, leading to an extreme linear change in 2-m temperature over the 13-yr period of nearly 13°C (Fig. SB2b). This energy flux plays a driving role in the evaporation of moisture into the atmospheric boundary layer. Additional moisture in the atmosphere enhances downward longwave radiation at the surface, driving further increases in surface temperature and sea ice melt. Figures SB2c and SB2d support this dynamic relationship with linear changes in downward longwave radiation of 52 to 78 W m−2 and specific humidity between 1.04 and 2.08 g kg–1 for 2000–12, all statistically significant with p values <0.01. Additionally, the increased moisture leads to significant positive cloud and precipitation trends downwind (and consistent with the mean flow) from the strongest sea ice decline east of Novaya Zemlya (Fig. SB2e). Together, these results demonstrate the capacity to use ASRv2 in a detailed analysis of atmospheric processes associated with surface changes in the Arctic.
Looking forward, of immediate concern is updating ASRv2 beyond 2012 to the present. It is important to continue to capture the accelerated climate changes taking place in the Arctic. This includes declining sea ice and snow cover across the Arctic, variables that are likely to be better observed through satellite platforms such as Cryosat-2 and ICESat-2. Likewise, there is growing support within the Arctic community for an extension of ASR back to 1979 with refinements to the atmosphere, land surface, sea ice modeling, and data assimilation. This will provide a longer context from which to compare the most rapidly changing period in the Arctic to changes that occurred prior to 2000.
This work was supported by the National Science Foundation (NSF) through Grants ARC-0733023 and ARC-1144117 and by National Aeronautics and Space Administration (NASA) Grant NNX12AI29G. Byrd Polar and Climate Research Center Contribution Number 1573. The authors thank the Ohio Supercomputer Center (www.osc.edu) for their use of the Oakley, Ruby, and Owens Clusters in order to conduct ASRv2. The authors would also like to acknowledge the early work conducted by the late Andrew Slater on this project, and thank the anonymous reviewers for their feedback and suggestions.