Editorial Type:
Article
Article Type:
Research Article

Quantifying the Uncertainties of Reanalyzed Arctic Cloud and Radiation Properties Using Satellite Surface Observations

Yiyi Huang Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

Search for other papers by Yiyi Huang in
Current site
Google Scholar
PubMed Close
,
Xiquan Dong Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

Search for other papers by Xiquan Dong in
Current site
Google Scholar
PubMed Close
,
Baike Xi Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Baike Xi in
Current site
Google Scholar
PubMed Close
,
Erica K. Dolinar Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Erica K. Dolinar in
Current site
Google Scholar
PubMed Close
,
Ryan E. Stanfield Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

Search for other papers by Ryan E. Stanfield in
Current site
Google Scholar
PubMed Close
, and
Shaoyue Qiu Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

Search for other papers by Shaoyue Qiu in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Oct 2017
DOI:
https://doi.org/10.1175/JCLI-D-16-0722.1
Page(s):
8007–8029
Restricted access

Abstract

Reanalyses have proven to be convenient tools for studying the Arctic climate system, but their uncertainties should first be identified. In this study, five reanalyses (JRA-55, 20CRv2c, CFSR, ERA-Interim, and MERRA-2) are compared with NASA CERES–MODIS (CM)-derived cloud fractions (CFs), cloud water paths (CWPs), top-of-atmosphere (TOA) and surface longwave (LW) and shortwave (SW) radiative fluxes over the Arctic (70°–90°N) over the period of 2000–12, and CloudSat–CALIPSO (CC)-derived CFs from 2006 to 2010. The monthly mean CFs in all reanalyses except JRA-55 are close to or slightly higher than the CC-derived CFs from May to September. However, wintertime CF cannot be confidently evaluated until instrument simulators are implemented in reanalysis products. The comparison between CM and CC CFs indicates that CM-derived CFs are reliable in summer but not in winter. Although the reanalysis CWPs follow the general seasonal variations of CM CWPs, their annual means are only half or even less than the CM-retrieved CWPs (126 g m−2). The annual mean differences in TOA and surface SW and LW fluxes between CERES EBAF and reanalyses are less than 6 W m−2 for TOA radiative fluxes and 16 W m−2 for surface radiative fluxes. All reanalyses show positive biases along the northern and eastern coasts of Greenland as a result of model elevation biases or possible CM clear-sky retrieval issues. The correlations between the reanalyses and CERES satellite retrievals indicate that all five reanalyses estimate radiative fluxes better than cloud properties, and MERRA-2 and JRA-55 exhibit comparatively higher correlations for Arctic cloud and radiation properties.

Current affiliation: Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona.

© 2017 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: Dr. Xiquan Dong, xdong@email.arizona.edu

Abstract

Reanalyses have proven to be convenient tools for studying the Arctic climate system, but their uncertainties should first be identified. In this study, five reanalyses (JRA-55, 20CRv2c, CFSR, ERA-Interim, and MERRA-2) are compared with NASA CERES–MODIS (CM)-derived cloud fractions (CFs), cloud water paths (CWPs), top-of-atmosphere (TOA) and surface longwave (LW) and shortwave (SW) radiative fluxes over the Arctic (70°–90°N) over the period of 2000–12, and CloudSat–CALIPSO (CC)-derived CFs from 2006 to 2010. The monthly mean CFs in all reanalyses except JRA-55 are close to or slightly higher than the CC-derived CFs from May to September. However, wintertime CF cannot be confidently evaluated until instrument simulators are implemented in reanalysis products. The comparison between CM and CC CFs indicates that CM-derived CFs are reliable in summer but not in winter. Although the reanalysis CWPs follow the general seasonal variations of CM CWPs, their annual means are only half or even less than the CM-retrieved CWPs (126 g m−2). The annual mean differences in TOA and surface SW and LW fluxes between CERES EBAF and reanalyses are less than 6 W m−2 for TOA radiative fluxes and 16 W m−2 for surface radiative fluxes. All reanalyses show positive biases along the northern and eastern coasts of Greenland as a result of model elevation biases or possible CM clear-sky retrieval issues. The correlations between the reanalyses and CERES satellite retrievals indicate that all five reanalyses estimate radiative fluxes better than cloud properties, and MERRA-2 and JRA-55 exhibit comparatively higher correlations for Arctic cloud and radiation properties.

Current affiliation: Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona.

© 2017 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: Dr. Xiquan Dong, xdong@email.arizona.edu
Save
Cover Journal of Climate
Journal of Climate

  • Bacmeister, J. T., M. J. Suarez, and F. R. Robertson, 2006: Rain reevaporation, boundary layer–convection interactions, and Pacific rainfall patterns in an AGCM. J. Atmos. Sci., 63, 3383–3403, doi:10.1175/JAS3791.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Barton, N. P., S. A. Klein, J. S. Boyle, and Y. Y. Zhang, 2012: Arctic synoptic regimes: Comparing domain-wide Arctic cloud observations with CAM4 and CAM5 during similar dynamics. J. Geophys. Res., 117, D15205, doi:10.1029/2012JD017589.

    • Search Google Scholar
    • Export Citation

  • Bodas-Salcedo, A., and Coauthors, 2011: COSP: Satellite simulation software for model assessment. Bull. Amer. Meteor. Soc., 92, 1023–1043, doi:10.1175/2011BAMS2856.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Boeke, R. C., and P. C. Taylor, 2016: Evaluation of the Arctic surface radiation budget in CMIP5 models. J. Geophys. Res. Atmos., 121, 8525–8548, doi:10.1002/2016JD025099.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bosilovich, M., and Coauthors, 2015: MERRA-2: Initial evaluation of climate. NASA Tech. Memo. NASA/TM-2015-104606/Vol. 43, 145 pp., https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich803.pdf.

  • Bromwich, D. H., and S. H. Wang, 2005: Evaluation of the NCEP–NCAR and ECMWF 15- and 40-yr reanalyses using rawinsonde data from two independent Arctic field experiments. Mon. Wea. Rev., 133, 3562–3578, doi:10.1175/MWR3043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bromwich, D. H., R. I. Cullather, and M. C. Serreze, 2000: Reanalyses depictions of the Arctic atmospheric moisture budget. The Freshwater Budget of the Arctic Ocean, E. Lewis et al., Eds., NATO Science Series, Vol. 70, Springer, 163–196, doi:10.1007/978-94-011-4132-1_8.

    • Crossref
    • Export Citation

  • Bromwich, D. H., S.-H. Wang, and A. J. Monaghan, 2002: ERA-40 representation of the Arctic atmospheric moisture budget. ECMWF Workshop on Re-Analysis, Reading, United Kingdom, ECMWF, 287–298.

  • Bromwich, D. H., R. L. Fogt, K. I. Hodges, and J. E. Walsh, 2007: A tropospheric assessment of the ERA-40, NCEP, and JRA-25 global reanalyses in the polar regions. J. Geophys. Res., 112, D10111, doi:10.1029/2006JD007859.

    • Search Google Scholar
    • Export Citation

  • CERES, 2014: CERES_EBAF_Ed2.8 Data Quality Summary. 46 pp. [Available online at http://ceres.larc.nasa.gov/documents/DQ_summaries/CERES_EBAF_Ed2.8_DQS.pdf.]

  • CERES, 2015: CERES_EBAF-Surface_Ed2.8 Data Quality Summary. CERES Rep., 42 pp. [Available online at http://ceres.larc.nasa.gov/documents/DQ_summaries/CERES_EBAF-Surface_Ed2.8_DQS.pdf.]

  • Chou, M. D., and M. J. Suarez, 1999: A solar radiation parameterization for atmospheric studies. NASA Tech. Memo. NASA/TM-1999-104606, Vol. 15, 51 pp. [Available online at https://ntrs.nasa.gov/search.jsp?R=19990060930.]

  • Chou, M. D., M. J. Suarez, C.-H. Ho, M. M.-H. Yan, and K.-T. Lee, 1998: Parameterizations for cloud overlapping and shortwave single-scattering properties for use in general circulation and cloud ensemble models. J. Climate, 11, 202–214, doi:10.1175/1520-0442(1998)011<0202:PFCOAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Chou, M. D., M. J. Suarez, X.-Z. Liang, and M. M.-H. Yan, 2001: A thermal infrared radiation parameterization for atmospheric studies. NASA Tech. Memo. NASA/TM-2001-104606, Vol. 19, 56 pp. [Available online at https://ntrs.nasa.gov/search.jsp?R=20010072848.]

  • Christensen, M. W., A. Behrangi, T. S. L’Ecuyer, N. B. Wood, M. D. Lebsock, and G. L. Stephens, 2016: Arctic observation and reanalysis integrated system: A new data product for validation and climate study. Bull. Amer. Meteor. Soc., 97, 907–916, doi:10.1175/BAMS-D-14-00273.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis Project. Quart. J. Roy. Meteor. Soc., 137, 1–28, doi:10.1002/qj.776.

  • Curry, J. A., J. L. Schramm, W. B. Rossow, and D. Randall, 1996: Overview of Arctic cloud and radiation characteristics. J. Climate, 9, 1731–1764, doi:10.1175/1520-0442(1996)009<1731:OOACAR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • De Boer, G., and Coauthors, 2014: Near-surface meteorology during the Arctic Summer Cloud Ocean Study (ASCOS): Evaluation of reanalyses and global climate models. Atmos. Chem. Phys., 14, 427–445, doi:10.5194/acp-14-427-2014.

    • 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, 553–597, doi:10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Deng, M., G. G. Mace, Z. Wang, and E. Berry, 2015: CloudSat 2C-ICE product update with a new Ze parameterization in lidar-only region. J. Geophys. Res. Atmos., 120, 12 198–12 208, doi:10.1002/2015JD023600.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dolinar, E. K., X. Dong, B. Xi, J. H. Jiang, and H. Su, 2015: Evaluation of CMIP5 simulated clouds and TOA radiation budgets using NASA satellite observations. Climate Dyn., 44, 2229–2247, doi:10.1007/s00382-014-2158-9.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dong, X., and G. G. Mace, 2003: Arctic stratus cloud properties and radiative forcing derived from ground-based data collected at Barrow, Alaska. J. Climate, 16, 445–461, doi:10.1175/1520-0442(2003)016<0445:ASCPAR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dong, X., B. Xi, K. Crosby, and P. Minnis, 2006: A climatology of midlatitude continental clouds from the ARM SGP Central Facility. Part II: Cloud fraction and surface radiative forcing. J. Climate, 19, 1765–1783, doi:10.1175/JCLI3710.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dong, X., P. Minnis, B. Xi, S. Sun-Mack, and Y. Chen, 2008: Comparison of CERES-MODIS stratus cloud properties with ground-based measurements at the DOE ARM Southern Great Plains site. J. Geophys. Res., 113, D03204, doi:10.1029/2007JD008438.

    • Search Google Scholar
    • Export Citation

  • Dong, X., B. Xi, K. Crosby, C. N. Long, R. S. Stone, and M. D. Shupe, 2010: A 10 year climatology of Arctic cloud fraction and radiative forcing at Barrow, Alaska. J. Geophys. Res., 115, D17212, doi:10.1029/2009JD013489.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Dong, X., B. Xi, K. Crosby, S. Qiu, P. Minnis, S. Sun-Mack, and F. Rose, 2016: A radiation closure study of Arctic cloud microphysical properties using the collocated satellite-surface data and Fu–Liou radiative transfer model. J. Geophys. Res. Atmos., 121, 10 175–10 198, doi:10.1002/2016JD025255.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., and J. A. Curry, 1993: An intermediate one-dimensional thermodynamic sea ice model for investigating ice–atmosphere interactions. J. Geophys. Res., 98, 10 085–10 109, doi:10.1029/93JC00656.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • English, J. M., J. E. Kay, A. Gettelman, X. Liu, Y. Wang, Y. Zhang, and H. Chepfer, 2014: Contributions of clouds, surface albedos, and mixed-phase ice nucleation schemes to Arctic radiation biases in CAM5. J. Climate, 27, 5174–5197, doi:10.1175/JCLI-D-13-00608.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • English, J. M., A. Gettelman, and G. R. Henderson, 2015: Arctic radiative fluxes: Present-day biases and future projections in CMIP5 models. J. Climate, 28, 6019–6038, doi:10.1175/JCLI-D-14-00801.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • ECMWF, 2014: IFS Documentation-Cy40r1 Part IV: Physical processes. ECMWF, 190 pp. [Available online at https://www.ecmwf.int/sites/default/files/IFS_CY40R1_Part4.pdf.]

  • Fels, S. B., and M. D. Schwarzkopf, 1975: The simplified exchange approximation: A new method for radiative transfer calculations. J. Atmos. Sci., 32, 1475–1488, doi:10.1175/1520-0469(1975)032<1475:TSEAAN>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Gorodetskaya, I. V., and L. B. Tremblay, 2008: Arctic cloud properties and radiative forcing from observations and their role in sea ice decline predicted by the NCAR CCSM3 Model during the 21st century. Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications, Geophys. Monogr., Vol. 180, Amer. Geophys. Union, 47–62, doi:10.1029/180GM05.

    • Crossref
    • Export Citation

  • Halliwell, D., 2012: Basic and other measurements of radiation at station Alert (2004–2008). AeroCan, Wilcox, PANGAEA, accessed 10 May 2016, doi:10.1594/PANGAEA.788590.

    • Crossref
    • Export Citation

  • Hirahara, S., M. Ishii, and Y. Fukuda, 2014: Centennial-scale sea surface temperature analysis and its uncertainty. J. Climate, 27, 57–75, doi:10.1175/JCLI-D-12-00837.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hou, Y., K. Campana, and S. Yang, 1996: Shortwave radiation calculations in the NCEP’s global model. Proc. Int. Radiation Symp. 1996, Fairbanks, AK, IRS, 19–24.

  • Hou, Y., S. Moorthi, K. Campana, 2002: Parameterization of solar radiation transfer in the NCEP models. NCEP Office Note 441, 46 pp.

  • Huang, Y., X. Dong, B. Xi, E. K. Dolinar, and R. E. Stanfield, 2017: The footprints of 16 year trends of Arctic springtime cloud and radiation properties on September sea ice retreat. J. Geophys. Res. Atmos., 122, 2179–2193, doi:10.1002/2016JD026020.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp.

  • JMA, 2013: Outline of the operational numerical weather prediction at the Japan Meteorological Agency. JMA. [Available online at http://www.jma.go.jp/jma/jma-eng/jma-center/nwp/outline2013-nwp/index.htm.]

  • Karlsson, J., and G. Svensson, 2011: The simulation of Arctic clouds and their influence on the winter surface temperature in present-day climate in the CMIP3 multi-model dataset. Climate Dyn., 36, 623–635, doi:10.1007/s00382-010-0758-6.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kato, S., S. Sun-Mack, W. F. Miller, F. G. Rose, Y. Chen, P. Minnis, and B. A. Wielicki, 2010: Relationships among cloud occurrence frequency, overlap, and effective thickness derived from CALIPSO and CloudSat merged cloud vertical profiles. J. Geophys. Res., 115, D00H28, doi:10.1029/2009JD012277.

    • Search Google Scholar
    • 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, 2719–2740, doi:10.1175/JCLI-D-12-00436.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kay, J. E., and T. L’Ecuyer, 2013: Observational constraints on Arctic Ocean clouds and radiative fluxes during the early 21st century. J. Geophys. Res. Atmos., 118, 7219–7236, doi:10.1002/jgrd.50489.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Kobayashi, S., and Coauthors, 2015: The JRA-55 Reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5–48, doi:10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Komurcu, M., and Coauthors, 2014: Intercomparison of the cloud water phase among global climate models. J. Geophys. Res. Atmos., 119, 3372–3400, doi:10.1002/2013JD021119.

    • Crossref
    • 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, 2588–2606, doi:10.1175/JCLI-D-13-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liu, Y., and J. R. Key, 2016: Assessment of Arctic cloud cover anomalies in atmospheric reanalysis products using satellite data. J. Climate, 29, 6065–6083, doi:10.1175/JCLI-D-15-0861.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. 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, 748–766, doi:10.1175/2008JCLI2637.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mace, G. G., Y. Zhang, S. Platnick, M. D. King, P. Minnis, and P. Yang, 2005: Evaluation of cirrus cloud properties from MODIS radiances using cloud properties derived from ground-based data collected at the ARM SGP site. J. Appl. Meteor., 44, 221–240, doi:10.1175/JAM2193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Minnis, P., D. F. Young, B. A. Wielicki, P. W. Heck, X. Dong, L. L. Stowe, and R. M. Welch, 1999: CERES cloud properties derived from multispectral VIRS data. Satellite Remote Sensing of Clouds and the Atmosphere IV, J. E. Russell, Ed., International Society for Optics and Photonics (SPIE Proceedings, Vol. 3867), doi:10.1117/12.373047.

    • Crossref
    • Export Citation

  • Minnis, P., D. F. Young, B. A. Wielicki, S. Sun-Mack, Q. Z. Trepte, Y. Chen, P. W. Heck, and X. Dong, 2002: A global cloud database from VIRS and MODIS for CERES. Optical Remote Sensing of the Atmosphere and Clouds III, H.-L. Huang et al., Eds., International Society for Optics and Photonics (SPIE Proceedings, Vol. 4891), doi:10.1117/12.467317.

    • Crossref
    • Export Citation

  • Minnis, P., and Coauthors, 2008: Cloud detection in nonpolar regions for CERES using TRMM VIRS and Terra and Aqua MODIS data. IEEE Trans. Geosci. Remote Sens., 46, 3857–3884, doi:10.1109/TGRS.2008.2001351.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Minnis, P., and Coauthors, 2011a: CERES edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data—Part I: Algorithms. IEEE Trans. Geosci. Remote Sens., 49, 4374–4400, doi:10.1109/TGRS.2011.2144601.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Minnis, P., and Coauthors, 2011b: CERES edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data—Part II: Examples of average results and comparisons with other data. IEEE Trans. Geosci. Remote Sens., 49, 4401–4430, doi:10.1109/TGRS.2011.2144602.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 663–16 682, doi:10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Moorthi, S., H. L. Pan, and P. Caplan, 2001: Changes to the 2001 NCEP operational MRF/AVN global analysis/forecast system. NWS Tech. Procedures Bull. 484, 14 pp. [Available online at http://www.nws.noaa.gov/om/tpb/484.pdf.]

  • NCAR, 2015: The Climate Data Guide: NOAA 20th-Century Reanalysis, version 2 and 2c. NCAR, accessed 12 June 2016. [Available online at https://climatedataguide.ucar.edu/climate-data/noaa-20th-century-reanalysis-version-2-and-2c.]

  • Ohmura, A., and Coauthors, 1998: Baseline Surface Radiation Network (BSRN/WCRP): New precision radiometry for climate research. Bull. Amer. Meteor. Soc., 79, 2115–2136, doi:10.1175/1520-0477(1998)079<2115:BSRNBW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Reeves Eyre, J. E. J., and X. Zeng, 2017: Evaluation of Greenland near surface air temperature datasets. Cryosphere, 11, 1591–1605, doi:10.5194/tc-11-1591-2017.

    • 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, 3624–3648, doi:10.1175/JCLI-D-11-00015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rutan, D. A., F. G. Rose, N. M. Smith, and T. P. Charlock, 2001: Validation data set for CERES surface and atmospheric radiation budget (SARB). GEWEX News, No. 11 (1), International GEWEX Project Office, Silver Spring, MD, 11–12.

  • Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 3483–3517, doi:10.1175/JCLI3812.1.

  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057, doi:10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 2185–2208, doi:10.1175/JCLI-D-12-00823.1.

  • Schwarzkopf, M. D., and S. B. Fels, 1991: The simplified exchange method revisited: An accurate, rapid method for computation of infrared cooling rates and fluxes. J. Geophys. Res., 96, 9075–9096, doi:10.1029/89JD01598.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Serreze, M. C., and C. M. Hurst, 2000: Representation of mean Arctic precipitation from NCEP–NCAR and ERA Reanalyses. J. Climate, 13, 182–201, doi:10.1175/1520-0442(2000)013<0182:ROMAPF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Serreze, M. C., J. R. Key, J. E. Box, J. A. Maslanik, and K. Steffen, 1998: A new monthly climatology of global radiation for the Arctic and comparisons with NCEP–NCAR reanalysis and ISCCP-C2 fields. J. Climate, 11, 121–136, doi:10.1175/1520-0442(1998)011<0121:ANMCOG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shine, K. P., 1984: Parametrization of the shortwave flux over high albedo surfaces as a function of cloud thickness and surface albedo. Quart. J. Roy. Meteor. Soc., 110, 747–764, doi:10.1002/qj.49711046511.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Shupe, M. D., and J. M. Intrieri, 2004: Cloud radiative forcing of the Arctic surface: The influence of cloud properties, surface albedo, and solar zenith angle. J. Climate, 17, 616–628, doi:10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sommeria, G., and J. W. Deardorff, 1977: Subgrid-scale condensation in models of nonprecipitating clouds. J. Atmos. Sci., 34, 344–355, doi:10.1175/1520-0469(1977)034<0344:SSCIMO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stanfield, R. E., X. Dong, B. Xi, A. Kennedy, A. D. Del Genio, P. Minnis, and J. H. Jiang, 2014: Assessment of NASA GISS CMIP5 and post-CMIP5 simulated clouds and TOA radiation budgets using satellite observations. Part I: Cloud fraction and properties. J. Climate, 27, 4189–4208, doi:10.1175/JCLI-D-13-00558.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stubenrauch, C. J., and Coauthors, 2013: Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX Radiation Panel. Bull. Amer. Meteor. Soc., 94, 1031–1049, doi:10.1175/BAMS-D-12-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Taylor, P. C., S. Kato, K.-M. Xu, and M. Cai, 2015: Covariance between Arctic sea ice and clouds within atmospheric state regimes at the satellite footprint level. J. Geophys. Res. Atmos., 120, 12 656–12 678, doi:10.1002/2015JD023520.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Tiedtke, M., 1993: Representation of clouds in large-scale models. Mon. Wea. Rev., 121, 3040–3061, doi:10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Waliser, D. E., and Coauthors, 2009: Cloud ice: A climate model challenge with signs and expectations of progress. J. Geophys. Res., 114, D00A21, doi:10.1029/2008JD010015.

    • Search Google Scholar
    • Export Citation

  • Walsh, J. E., W. L. Chapman, and D. H. Portis, 2009: Arctic cloud fraction and radiative fluxes in atmospheric reanalyses. J. Climate, 22, 2316–2334, doi:10.1175/2008JCLI2213.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wang, X., and J. R. Key, 2005: Arctic surface, cloud, and radiation properties based on the AVHRR Polar Pathfinder Dataset. Part I: Spatial and temporal characteristics. J. Climate, 18, 2558–2574, doi:10.1175/JCLI3438.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wendler, G., F. D. Eaton, and T. Ohtake, 1981: Multiple reflection effects on irradiance in the presence of Arctic stratus clouds. J. Geophys. Res., 86, 2049–2057, doi:10.1029/JC086iC03p02049.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913–1924, doi:10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wu, X., and R. Grumbine, 2013: Sea ice in the NCEP Climate Forecast System Reanalysis. Science and Technology Infusion Climate Bulletin, 38th NOAA Annual Climate Diagnostics and Prediction Workshop, College Park, MD, NWS, 8 pp. [Available online at http://www.nws.noaa.gov/ost/climate/STIP/38CDPW/38cdpw-XWu.pdf.]

  • Xi, B., X. Dong, P. Minnis, and M. M. Khaiyer, 2010: A 10 year climatology of cloud fraction and vertical distribution derived from both surface and GOES observations over the DOE ARM SPG site. J. Geophys. Res., 115, D12124, doi:10.1029/2009JD012800.

    • Search Google Scholar
    • Export Citation

  • Xi, B., X. Dong, P. Minnis, M. M. Khaiyer, and S. Sun-Mack, 2014: Comparison of marine boundary layer cloud properties from CERES-MODIS edition 4 and DOE ARM AMF measurements at the Azores. J. Geophys. Res. Atmos., 119, 9509–9529, doi:10.1002/2014JD021813.

    • Crossref
    • Export Citation

  • Xu, K.-M., and D. A. Randall, 1996: A semiempirical cloudiness parameterization for use in climate models. J. Atmos. Sci., 53, 3084–3102, doi:10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zib, B. J., X. Dong, B. Xi, and A. Kennedy, 2012: Evaluation and intercomparison of cloud fraction and radiative fluxes in recent reanalyses over the Arctic using BSRN surface observations. J. Climate, 25, 2291–2305, doi:10.1175/JCLI-D-11-00147.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 594 208 10
PDF Downloads 443 123 10