The Arctic System Reanalysis, Version 2

D. H. Bromwich Polar Meteorology Group, Byrd Polar and Climate Research Center, and Atmospheric Sciences Program, Department of Geography, The Ohio State University, Columbus, Ohio

Search for other papers by D. H. Bromwich in
Current site
Google Scholar
PubMed
Close
,
A. B. Wilson Polar Meteorology Group, Byrd Polar and Climate Research Center, The Ohio State University, Columbus, Ohio

Search for other papers by A. B. Wilson in
Current site
Google Scholar
PubMed
Close
,
L. Bai Polar Meteorology Group, Byrd Polar and Climate Research Center, The Ohio State University, Columbus, Ohio

Search for other papers by L. Bai in
Current site
Google Scholar
PubMed
Close
,
Z. Liu Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Z. Liu in
Current site
Google Scholar
PubMed
Close
,
M. Barlage Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by M. Barlage in
Current site
Google Scholar
PubMed
Close
,
C.-F. Shih Computational Information Systems Laboratory, National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by C.-F. Shih in
Current site
Google Scholar
PubMed
Close
,
S. Maldonado Polar Meteorology Group, Byrd Polar and Climate Research Center, and Atmospheric Sciences Program, Department of Geography, The Ohio State University, Columbus, Ohio

Search for other papers by S. Maldonado in
Current site
Google Scholar
PubMed
Close
,
K. M. Hines Polar Meteorology Group, Byrd Polar and Climate Research Center, The Ohio State University, Columbus, Ohio

Search for other papers by K. M. Hines in
Current site
Google Scholar
PubMed
Close
,
S.-H. Wang Polar Meteorology Group, Byrd Polar and Climate Research Center, The Ohio State University, Columbus, Ohio

Search for other papers by S.-H. Wang in
Current site
Google Scholar
PubMed
Close
,
J. Woollen National Centers for Environmental Prediction, College Park, Maryland

Search for other papers by J. Woollen in
Current site
Google Scholar
PubMed
Close
,
B. Kuo UCAR Community Programs, University Corporation for Atmospheric Research, Boulder, Colorado

Search for other papers by B. Kuo in
Current site
Google Scholar
PubMed
Close
,
H.-C. Lin Mesoscale and Microscale Meteorology Laboratory, National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by H.-C. Lin in
Current site
Google Scholar
PubMed
Close
,
T.-K. Wee Constellation Observing System for Meteorology, Ionosphere, and Climate, University Corporation for Atmospheric Research, Boulder, Colorado

Search for other papers by T.-K. Wee in
Current site
Google Scholar
PubMed
Close
,
M. C. Serreze National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado

Search for other papers by M. C. Serreze in
Current site
Google Scholar
PubMed
Close
, and
J. E. Walsh International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, Alaska

Search for other papers by J. E. Walsh in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

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.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Aaron B. Wilson, wilson.1010@osu.edu

Abstract

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.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

CORRESPONDING AUTHOR: Aaron B. Wilson, wilson.1010@osu.edu
Save
  • Alapaty, K., J. A. Herwehe, T. L. Otte, C. G. Nolte, O. R. Bullock, M. S. Mallard, J. S. Kain, and J. Dudhia, 2012: Introducing subgrid-scale cloud feedbacks to radiation for regional meteorological and climate modeling. Geophys. Res. Lett., 39, L24809, https://doi.org/10.1029/2012GL054031.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Auligné, T., A. P. McNally, and D. P. Dee, 2007: Adaptive bias correction for satellite data in a numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 133, 631642, https://doi.org/10.1002/qj.56.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bamber, J. L., S. Ekholm, and W. B. Krabill, 2001: A new, high-resolution digital elevation model of Greenland fully validated with airborne laser altimeter data. J. Geophys. Res., 106, 67336745, https://doi.org/10.1029/2000JB900365.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barker, D. M., W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional (3DVAR) data assimilation system for use with MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897914, https://doi.org/10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barker, D. M., and Coauthors, 2012: The Weather Research and Forecasting Model’s Community Variational/Ensemble Data Assimilation System: WRFDA. Bull. Amer. Meteor. Soc., 93, 831843, https://doi.org/10.1175/BAMS-D-11-00167.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barlage, M., and Coauthors, 2010: Noah land model modifications to improve snowpack prediction in the Colorado Rocky Mountains. J. Geophys. Res., 115, D22101, https://doi.org/10.1029/2009JD013470.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bengtsson, L., and J. Shukla, 1988: Integration of space and in situ observations to study global climate change. Bull. Amer. Meteor. Soc., 69, 11301143, https://doi.org/10.1175/1520-0477(1988)069<1130:IOSAIS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bengtsson, L., S. Hagemann, and K. I. Hodges, 2004a: Can climate trends be calculated from reanalysis data? J. Geophys. Res., 109, D11111, https://doi.org/10.1029/2004JD004536.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bengtsson, L., K. I. Hodges, and S. Hagemann, 2004b: Sensitivity of the ERA-40 reanalysis to the observing system: Determination of the global atmospheric circulation from reduced observations. Tellus, 56A, 456471, https://doi.org/10.3402/tellusa.v56i5.14466.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouttier, F., and G. Kelly, 2001: Observing-system experiments in the ECMWF 4D-Var data assimilation system. Quart. J. Roy. Meteor. Soc., 127, 14691488, https://doi.org/10.1002/qj.49712757419.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bromwich, D. H., K. M. Hines, and L.-S. Bai, 2009: Development and testing of Polar WRF: 2. Arctic Ocean. J. Geophys. Res., 114, D08122, https://doi.org/10.1029/2008JD010300.

    • Search Google Scholar
    • Export Citation
  • Bromwich, D. H., A. B. Wilson, L. Bai, G. W. K. Moore, and P. Bauer, 2016: A comparison of the regional Arctic System Reanalysis and the global ERA-Interim reanalysis for the Arctic. Quart. J. Roy. Meteor. Soc., 142, 644658, https://doi.org/10.1002/qj.2527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model and implementation and sensitivity. Mon. Wea. Rev., 129, 569585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clough, S. A., M. W. Shephard, E. J. Mlawer, J. S. Delamere, M. J. Iacono, K. Cady-Pereira, S. Boukabara, and P. D. Brown, 2005: Atmospheric radiative transfer modeling: A summary of the AER codes. J. Quant. Spectrosc. Radiat. Transfer, 91, 233244, https://doi.org/10.1016/j.jqsrt.2004.05.058.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Comiso, J., 2003: Warming trends in the Arctic from clear-sky satellite observations. J. Climate, 16, 34983510, https://doi.org/10.1175/1520-0442(2003)016<3498:WTITAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cullather, R. I., and M. G. Bosilovich, 2011: The moisture budget of the polar atmosphere in MERRA. J. Climate, 24, 28612879, https://doi.org/10.1175/2010JCLI4090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and S. M. Uppala, 2009: Variational bias correction of satellite radiance data in the ERA-Interim reanalysis. Quart. J. Roy. Meteor. Soc., 135, 18301841, https://doi.org/10.1002/qj.493.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derber, J. C., and W.-S. Wu, 1998: The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Mon. Wea. Rev., 126, 22872299, https://doi.org/10.1175/1520-0493(1998)126<2287:TUOTCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derksen, C., R. Brown, L. Mudryk, and K. Luojus, 2017: Terrestrial snow cover [in “State of the Climate in 2016”]. Bull. Amer. Meteor. Soc., 98(8), S93S98.

    • Search Google Scholar
    • Export Citation
  • Devine, K. A., and È. Mekis, 2008: Field accuracy of Canadian rain measurements. Atmos.–Ocean, 46, 213227, https://doi.org/10.3137/ao.460202.

  • Ding, Q., and Coauthors, 2017: Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat. Climate Change, 7, 289295, https://doi.org/10.1038/nclimate3241.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Easterling, D. R., and T. C. Peterson, 1995: A new method for detecting undocumented discontinuities in climatological time series. Int. J. Climatol., 15, 369377, https://doi.org/10.1002/joc.3370150403.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fyfe, J. C., K. von Salzen, N. P. Gillett, V. K. Arora, G. Flato, and J. R. McConnell, 2013: One hundred years of Arctic surface temperature variation due to anthropogenic influence. Nat. Sci. Rep., 3, 2645, https://doi.org/10.1038/srep02645.

    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glisan, J. M., W. J. Gutowski, J. J. Cassano, and M. E. Higgins, 2013: Effects of spectral nudging in WRF on Arctic temperature and precipitation simulations. J. Climate, 26, 39853999, https://doi.org/10.1175/JCLI-D-12-00318.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hall, D. K., G. A. Riggs, V. V. Salomonson, N. E. DiGirolamo, and K. A. Bayr, 2002: MODIS snow-cover products. Remote Sens. Environ., 83, 181194, https://doi.org/10.1016/S0034-4257(02)00095-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, Y., P. van Delst, Q. Liu, F. Weng, B. Yan, R. Treadon, and J. Derber, 2006: Community Radiative Transfer Model (CRTM): Version 1. NOAA/NESDIS Tech. Rep. 122, 33 pp.

    • Search Google Scholar
    • Export Citation
  • Harig, C., and F. J. Simons, 2016: Ice mass loss in Greenland, the Gulf of Alaska, and the Canadian Archipelago: Seasonal cycles and decadal trends. Geophys. Res. Lett., 43, 31503159, https://doi.org/10.1002/2016GL067759.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartmann, B., and G. Wendler, 2005: The significance of the 1976 Pacific climate shift in the climatology of Alaska. J. Climate, 18, 48244839, https://doi.org/10.1175/JCLI3532.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hegner, H., G. Müller, V. Nespor, A. Ohmura, R. Steigrad, and H. Gilgen, 1998: World Climate Research Program WCRP (WMO/ICSU/IOC) Baseline Surface Radiation Network (BSRN): Update of the technical plan for BSRN data management, version 1.0. World Radiation Monitoring Center Tech. Rep. 2, 38 pp.

  • Hines, K. M., and D. H. Bromwich, 2008: Development and testing of Polar Weather Research and Forecasting (WRF) Model. Part I: Greenland Ice Sheet meteorology. Mon. Wea. Rev., 136, 19711989, https://doi.org/10.1175/2007MWR2112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hines, K. M., and D. H. Bromwich, 2017: Simulation of late summer Arctic clouds during ASCOS with Polar WRF. Mon. Wea. Rev., 145, 521541, https://doi.org/10.1175/MWR-D-16-0079.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hines, K. M., D. H. Bromwich, L.-S. Bai, M. Barlage, and A. S. Slater, 2011: Development and testing of Polar WRF. Part III: Arctic land. J. Climate, 24, 2648, https://doi.org/10.1175/2010JCLI3460.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hines, K. M., D. H. Bromwich, L.-S. Bai, C. M. Bitz, J. G. Powers, and K. W. Manning, 2015: Sea ice enhancements to Polar WRF. Mon. Wea. Rev., 143, 23632385, https://doi.org/10.1175/MWR-D-14-00344.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, X.-Y., and Coauthors, 2009: Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Mon. Wea. Rev., 137, 299314, https://doi.org/10.1175/2008MWR2577.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shepard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Search Google Scholar
    • Export Citation
  • Intergovernmental Panel on Climate Change, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp.

  • Jakobson, E., T. Vihma, T. Palo, L. Jakobson, H. Keernik, and J. Jaagus, 2012: Validation of atmospheric reanalyses over the central Arctic Ocean. Geophys. Res. Lett., 39, L10802, https://doi.org/10.1029/2012GL051591.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, L., D. Tarpley, K. E. Mitchell, S. Zhou, F. N. Kogan, and W. Guo, 2008: Adjusting for long-term anomalous trends in NOAA’s Global Vegetation Index data sets. IEEE Trans. Geosci. Remote Sens., 46, 409422, https://doi.org/10.1109/TGRS.2007.902844.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jung, T., and Coauthors, 2016: Advancing polar prediction capabilities on daily to seasonal time scales. Bull. Amer. Meteor. Soc., 97, 16311647, https://doi.org/10.1175/BAMS-D-14-00246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain-Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci., 47, 27842802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 165–170.

    • Crossref
    • Export Citation
  • Kato, S., N. G. Loeb, F. G. Rose, D. R. Doelling, D. A. Rutan, T. E. Caldwell, L. Yu, and R. A. Weller, 2013: Surface irradiances consistent with CERES-derived top-of-atmosphere shortwave and longwave irradiances. J. Climate, 26, 27192740, https://doi.org/10.1175/JCLI-D-12-00436.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., M. M. Holland, and A. Jahn, 2011: Inter-annual to multi-decadal Arctic sea ice extent trends in a warming world. Geophys. Res. Lett., 38, L15708, https://doi.org/10.1029/2011GL048008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kohnemann, S. H. E., G. Heinemann, D. H. Bromwich, and O. Gutjahr, 2017: Extreme warming in the Kara Sea and Barents Sea during the winter period 2000–16. J. Climate, 30, 89138927, https://doi.org/10.1175/JCLI-D-16-0693.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwok, R., and N. Untersteiner, 2011: The thinning of Arctic sea ice. Phys. Today, 64, 3641, https://doi.org/10.1063/1.3580491.

  • Lenaerts, J. T. M., J. H. van Angelen, M. R. van den Broeke, A. S. Gardner, B. Wouters, and E. van Meijgaard, 2013: Irreversible mass loss of Canadian Arctic Archipelago glaciers. Geophys. Res. Lett., 40, 870874, https://doi.org/10.1002/grl.50214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • L’Heureux, M. L., A. Kumar, G. D. Bell, M. S. Halpert, and R. W. Higgins, 2008: Role of the Pacific–North American (PNA) pattern in the 2007 Arctic sea ice decline. Geophys. Res. Lett., 35, L20701, https://doi.org/10.1029/2008GL035205.

    • Search Google Scholar
    • Export Citation
  • Lindsay, R., M. Wensnahan, A. Schweiger, and J. Zhang, 2014: Evaluation of seven different atmospheric reanalysis products in the Arctic. J. Climate, 27, 25882606, https://doi.org/10.1175/JCLI-D-13-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Z., C. S. Schwartz, C. Snyder, and S. Ha, 2012: Impact of assimilating AMSU-A radiances on forecasts of 2008 Atlantic tropical cyclones initialized with a limited-area ensemble Kalman filter. Mon. Wea. Rev., 140, 40174034, https://doi.org/10.1175/MWR-D-12-00083.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. Smith, D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Toward optimal closure of the Earth’s top-of-atmosphere radiation budget. J. Climate, 22, 748766, https://doi.org/10.1175/2008JCLI2637.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, Q., K. C. Wang, and M. Wild, 2015: Impact of geolocations of validation data on the evaluation of surface incident shortwave radiation from earth system models. J. Geophys. Res. Atmos., 120, 68256844, https://doi.org/10.1002/2014JD022572.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maslanik, J. A., C. Fowler, J. Stroeve, S. Drobot, J. Zwally, D. Yi, and W. Emery, 2007: A younger, thinner Arctic ice cover: Increased potential for rapid, extensive sea-ice loss. Geophys. Res. Lett., 34, L24501, https://doi.org/10.1029/2007GL032043.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maslanik, J. A., J. Stroeve, C. Fowler, and W. Emery, 2011: Distribution and trends in Arctic sea ice age through spring 2011. Geophys. Res. Lett., 38, L13502, https://doi.org/10.1029/2011GL047735.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mekis, È., 2005: Adjustments for trace measurements in Canada. 15th Conf. on Applied Climatology/13th Symp. on Meteorological Observations and Instrumentation, Savannah, GA, Amer. Meteor. Soc., J3.7, https://ams.confex.com/ams/15AppClimate/techprogram/paper_92155.htm.

  • Mekis, È., and W. D. Hogg, 1999: Rehabilitation and analysis of Canadian daily precipitation time series. Atmos.–Ocean, 37, 5385, https://doi.org/10.1080/07055900.1999.9649621.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mekis, È., and R. Hopkinson, 2004: Derivation of an improved snow water equivalent adjustment factor map for application on snowfall ruler measurements in Canada. Proc. 14th Conf. on Applied Climatology, Seattle, WA, Amer. Meteor. Soc., 7.12, https://ams.confex.com/ams/84Annual/techprogram/paper_68724.htm.

  • Moore, G. W. K., D. H. Bromwich, A. B. Wilson, I. Renfrew, and L. Bai, 2016: Arctic System Reanalysis improvements in topographically-forced winds near Greenland. Quart. J. Roy. Meteor. Soc., 142, 20332045, https://doi.org/10.1002/qj.2798.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., 2001: Improvement of the Mellor–Yamada turbulence closure model based on large-eddy simulation data. Bound.-Layer Meteor., 99, 349378, https://doi.org/10.1023/A:1018915827400.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2004: An improved Mellor–Yamada level-3 model with condensation physics: Its design and verification. Bound.-Layer Meteor., 112, 131, https://doi.org/10.1023/B: BOUN.0000020164.04146.98.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and H. Niino, 2006: An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Bound.-Layer Meteor., 119, 397407, https://doi.org/10.1007/s10546-005-9030-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • National Center for Atmospheric Research/University Corporation for Atmospheric Research, and Polar Meteorology Group/Byrd Polar and Climate Research Center/The Ohio State University, 2017: Arctic System Reanalysis version 2. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, CO, https://doi.org/10.5065/D6X9291B.

    • Crossref
    • Export Citation
  • National Oceanic and Atmospheric Administration, 2016: Arctic Report Card. Accessed 27 April 2017, http://arctic.noaa.gov/Report-Card.

  • National Snow and Ice Data Center, 2017: Arctic sea ice news and analysis. Accessed 27 April 2017, http://nsidc.org/arcticseaicenews/.

  • Nghiem, S. V., and Coauthors, 2012: The extreme melt across the Greenland ice sheet in 2012. Geophys. Res. Lett., 39, L20502, https://doi.org/10.1029/2012GL053611.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ohmura, A., and Coauthors, 1998: Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for climate research. Bull. Amer. Meteor. Soc., 79, 21152136, https://doi.org/10.1175/1520-0477(1998)079<2115:BSRNBW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Overland, J. E., and M. Wang, 2005: The Arctic climate paradox: The recent decrease of the Arctic Oscillation. Geophys. Res. Lett., 32, L06701, https://doi.org/10.1029/2004GL021752.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, M., and Coauthors, 2003: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 2. Evaluation of model simulated snow water equivalent. J. Geophys. Res., 108, 8850, https://doi.org/10.1029/2003JD003994.

    • Search Google Scholar
    • Export Citation
  • Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 17471763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., and D. R. Easterling, 1994: Creation of homogeneous composite climatological reference series. Int. J. Climatol., 14, 671679, https://doi.org/10.1002/joc.3370140606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., and R. S. Vose, 1997: An overview of the Global Historical Climatology Network temperature database. Bull. Amer. Meteor. Soc., 78, 28372849, https://doi.org/10.1175/1520-0477(1997)078<2837:AOOTGH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Powers, J. G., K. W. Manning, D. H. Bromwich, J. J. Cassano, and A. M. Cayette, 2012: A decade of Antarctic science support through AMPS. Bull. Amer. Meteor. Soc., 93, 16991712, https://doi.org/10.1175/BAMS-D-11-00186.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Powers, J. G., and Coauthors, 2017: The Weather Research and Forecasting Model: Overview, system efforts, and future directions. Bull. Amer. Meteor. Soc., 98, 17171737, https://doi.org/10.1175/BAMS-D-15-00308.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 36243648, https://doi.org/10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rignot, E., I. Velicogna, M. R. van den Broeke, A. Monaghan, and J. T. M. Lenaerts, 2011: Acceleration of the contribution of the Greenland and Antarctic ice sheets to sea level rise. Geophys. Res. Lett., 38, L05503, https://doi.org/10.1029/2011GL047109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rigor, I. G., and J. M. Wallace, 2004: Variations in the age of Arctic sea-ice and summer sea-ice extent. Geophys. Res. Lett., 31, L09401, https://doi.org/10.1029/2004GL019492.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, A. N., D. H. Bromwich, E. N. Sinclair, and R. I. Cullather, 2001: The atmospheric hydrologic cycle over the Arctic basin from reanalyses. Part II: Interannual variability. J. Climate, 14, 24142429, https://doi.org/10.1175/1520-0442(2001)014<2414:TAHCOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Romanovsky, V. E., S. L. Smith, and H. H. Christiansen, 2010: Permafrost thermal state in the polar Northern Hemisphere during the International Polar Year 2007–2009: A synthesis. Permafrost Periglacier Processes, 21, 105116, https://doi.org/10.1002/ppp.689.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Samelson, R. M., and P. L. Barbour, 2008: Low-level jets, orographic effects, and extreme events in Nares Strait: A model-based mesoscale climatology. Mon. Wea. Rev., 136, 47464759, https://doi.org/10.1175/2007MWR2326.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schaaf, C. B., and Coauthors, 2002: First operational BRDF, albedo and nadir reflectance products from MODIS. Remote Sens. Environ., 83, 135148, https://doi.org/10.1016/S0034-4257(02)00091-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., Z. Liu, Y. Chen, and X.-Y. Huang, 2012: Impact of assimilating microwave radiances with a limited-area ensemble data assimilation system on forecasts of Typhoon Morakot. Wea. Forecasting, 27, 424437, https://doi.org/10.1175/WAF-D-11-00033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2010: The central role of diminishing sea ice in recent Arctic temperature amplification. Nature, 464, 13341337, https://doi.org/10.1038/nature09051.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., C. Deser, and I. Simmonds, 2012: Local and remote controls on observed Arctic warming. Geophys. Res. Lett., 39, L10709, https://doi.org/10.1029/2012GL051598.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and A. P. Barrett, 2011: Characteristics of the Beaufort Sea high. J. Climate, 24, 159182, https://doi.org/10.1175/2010JCLI3636.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serreze, M. C., and J. Francis, 2006: The Arctic amplification debate. Climatic Change, 76, 241264, https://doi.org/10.1007/s10584-005-9017-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Search Google Scholar
    • Export Citation
  • Slater, A. G., T. J. Bohn, J. L. McCreight, M. C. Serreze, and D. P. Lettenmaier, 2007: A multimodel simulation of pan-Arctic hydrology. J. Geophys. Res., 112, G04S45, https://doi.org/10.1029/2006JG000303.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stöckli, R., E. Vermote, N. Saleous, R. Simmon, and D. Herring, 2005: The Blue Marble Next Generation: A true color Earth dataset including seasonal dynamics from MODIS. NASA Earth Observatory, accessed 26 April 2017, https://earthobservatory.nasa.gov/Features/BlueMarble/?src=ve.

  • Tao, W.-K., and J. Simpson, 1993: The Goddard Cumulus Ensemble model. Part I. Model description. Terr. Atmos. Ocean. Sci., 4, 1954, https://doi.org/10.3319/TAO.1993.4.1.35(A).

    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., and Coauthors, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys., 82, 97137, https://doi.org/10.1007/s00703-001-0594-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., and J. M. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 10001016, https://doi.org/10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tilinina, N., S. K. Gulev, and D. H. Bromwich, 2014: New view of Arctic cyclone activity from the Arctic system reanalysis. Geophys. Res. Lett., 41, 17661772, https://doi.org/10.1002/2013GL058924.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tilling, R. L., A. Ridout, A. Shepherd, and D. J. Wingham, 2015: Increased Arctic sea ice volume after anomalously low melting in 2013. Nat. Geosci., 8, 643646, https://doi.org/10.1038/ngeo2489.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walsh, J. E., F. Fetterer, J. S. Stewart, and W. L. Chapman, 2017: Database for depicting Arctic sea ice variations back to 1850. Geogr. Rev., 107, 89107, https://doi.org/10.1111/j.1931-0846.2016.12195.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wild, M., D. Folini, C. Schär, N. Loeb, E. Dutton, and G. König-Langlo, 2013: The global energy balance from a surface perspective. Climate Dyn., 40, 31073134, https://doi.org/10.1007/s00382-012-1569-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wild, M., and Coauthors, 2015: The energy balance over land and oceans: An assessment based on direct observations and CMIP5 climate models. Climate Dyn., 44, 33933429, https://doi.org/10.1007/s00382-014-2430-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, A. B., D. H. Bromwich, and K. M. Hines, 2011: Evaluation of Polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air analysis. J. Geophys. Res., 116, D11112, https://doi.org/10.1029/2010JD015013.

    • Search Google Scholar
    • Export Citation
  • Wilson, A. B., D. H. Bromwich, and K. M. Hines, 2012: Evaluation of Polar WRF forecasts on the Arctic System Reanalysis domain: 2. Atmospheric hydrologic cycle. J. Geophys. Res., 117, D04107, https://doi.org/10.1029/2011JD016765.

    • Search Google Scholar
    • Export Citation
  • Zhang, L., A. Kumar, and W. Wang, 2012: Influence of changes in observations on precipitation: A case study for the Climate Forecast System Reanalysis (CFSR). J. Geophys. Res., 117, D08105, https://doi.org/10.1029/2011JD017347.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., S. Liang, M. Wild, and B. Jiang, 2015: Analysis of surface incident shortwave radiation from four satellite products. Remote Sens. Environ., 165, 186202, https://doi.org/10.1016/j.rse.2015.05.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., S. Liang, G. Wang, Y. Yao, B. Jiang, and J. Cheng, 2016: Evaluation of the reanalysis surface incident shortwave radiation products from NCEP, ECMWF, GSFC, and JMA using satellite and surface observations. Remote Sens., 8, 225, https://doi.org/10.3390/rs8030225.

    • Search Google Scholar
    • Export Citation
  • Zheng, Y., K. Alapaty, J. A. Herwehe, A. D. Del Genio, and D. Niyogi, 2016: Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain–Fritsch scheme. Mon. Wea. Rev., 144, 833860, https://doi.org/10.1175/MWR-D-15-0005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1514 513 30
PDF Downloads 1584 252 18