Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model

Frozen and unfrozen surfaces exhibit different longwave surface emissivities with different spectral characteristics, and outgoing longwave radiation and cooling rates are reduced for unfrozen scenes relative to frozen ones. Here physically realistic modeling of spectrally resolved surface emissivit...

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Published in:Journal of Geophysical Research: Atmospheres
Main Authors: Kuo, Chaincy, Feldman, Daniel R., Huang, Xianglei, Flanner, Mark, Yang, Ping, Chen, Xiuhong
Format: Article in Journal/Newspaper
Language:unknown
Published: Cambridge University Press 2018
Subjects:
Online Access:https://hdl.handle.net/2027.42/142486
https://doi.org/10.1002/2017JD027595
id ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/142486
record_format openpolar
institution Open Polar
collection University of Michigan: Deep Blue
op_collection_id ftumdeepblue
language unknown
topic climate feedback
emissivity
longwave
radiative kernel
temporal
Atmospheric and Oceanic Sciences
Science
spellingShingle climate feedback
emissivity
longwave
radiative kernel
temporal
Atmospheric and Oceanic Sciences
Science
Kuo, Chaincy
Feldman, Daniel R.
Huang, Xianglei
Flanner, Mark
Yang, Ping
Chen, Xiuhong
Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model
topic_facet climate feedback
emissivity
longwave
radiative kernel
temporal
Atmospheric and Oceanic Sciences
Science
description Frozen and unfrozen surfaces exhibit different longwave surface emissivities with different spectral characteristics, and outgoing longwave radiation and cooling rates are reduced for unfrozen scenes relative to frozen ones. Here physically realistic modeling of spectrally resolved surface emissivity throughout the coupled model components of the Community Earth System Model (CESM) is advanced, and implications for model high‐latitude biases and feedbacks are evaluated. It is shown that despite a surface emissivity feedback amplitude that is, at most, a few percent of the surface albedo feedback amplitude, the inclusion of realistic, harmonized longwave, spectrally resolved emissivity information in CESM1.2.2 reduces wintertime Arctic surface temperature biases from −7.2 ± 0.9 K to −1.1 ± 1.2 K, relative to observations. The bias reduction is most pronounced in the Arctic Ocean, a region for which Coupled Model Intercomparison Project version 5 (CMIP5) models exhibit the largest mean wintertime cold bias, suggesting that persistent polar temperature biases can be lessened by including this physically based process across model components. The ice emissivity feedback of CESM1.2.2 is evaluated under a warming scenario with a kernel‐based approach, and it is found that emissivity radiative kernels exhibit water vapor and cloud cover dependence, thereby varying spatially and decreasing in magnitude over the course of the scenario from secular changes in atmospheric thermodynamics and cloud patterns. Accounting for the temporally varying radiative responses can yield diagnosed feedbacks that differ in sign from those obtained from conventional climatological feedback analysis methods.Plain Language SummaryClimate models have exhibited a persistent cold‐pole bias, whereby they systematically underestimate the average temperature and the amplification of climate change at high latitudes. A number of different explanations have been advanced for cold‐pole biases, which can be broadly divided into radiative and dynamic ...
format Article in Journal/Newspaper
author Kuo, Chaincy
Feldman, Daniel R.
Huang, Xianglei
Flanner, Mark
Yang, Ping
Chen, Xiuhong
author_facet Kuo, Chaincy
Feldman, Daniel R.
Huang, Xianglei
Flanner, Mark
Yang, Ping
Chen, Xiuhong
author_sort Kuo, Chaincy
title Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model
title_short Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model
title_full Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model
title_fullStr Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model
title_full_unstemmed Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model
title_sort time‐dependent cryospheric longwave surface emissivity feedback in the community earth system model
publisher Cambridge University Press
publishDate 2018
url https://hdl.handle.net/2027.42/142486
https://doi.org/10.1002/2017JD027595
geographic Arctic
Arctic Ocean
geographic_facet Arctic
Arctic Ocean
genre albedo
Arctic
Arctic
Arctic Ocean
Climate change
genre_facet albedo
Arctic
Arctic
Arctic Ocean
Climate change
op_relation Kuo, Chaincy; Feldman, Daniel R.; Huang, Xianglei; Flanner, Mark; Yang, Ping; Chen, Xiuhong (2018). "Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model." Journal of Geophysical Research: Atmospheres 123(2): 789-813.
2169-897X
2169-8996
https://hdl.handle.net/2027.42/142486
doi:10.1002/2017JD027595
Journal of Geophysical Research: Atmospheres
Qu, X., & Hall, A. ( 2006 ). Assessing snow albedo feedback in simulated climate change. Journal of Climate, 19 ( 11 ), 2617 – 2630.
Li, J. ( 2000 ). Gaussian quadrature and its application to infrared radiation. Journal of the Atmospheric Sciences, 57 ( 5 ), 753 – 765. https://doi.org/10.1175/1520-0469(2000)057<0753:GQAIAT>2.0.CO;2
Massom, R. A., Eicken, H., Hass, C., Jeffries, M. O., Drinkwater, M. R., Sturm, M., … Morris, K. ( 2001 ). Snow on Antarctic sea ice. Reviews of Geophysics, 39 ( 3 ), 413 – 445.
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M., & Mikolajewicz, U. ( 2012 ). Tuning the climate of a global model. Journal of Advances in Modeling Earth Systems, 4, M00A01. https://doi.org/10.1029/2012MS000154
Meehl, G. A., Covey, C., Taylor, K. E., Delworth, T., Stouffer, R. J., Latif, M., … Mitchell, J. F. ( 2007 ). The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bulletin of the American Meteorological Society, 88 ( 9 ), 1383 – 1394.
Mishchenko, M. I. ( 1994 ). Asymmetry parameters of the phase function for densely packed scattering grains. Journal of Quantitative Spectroscopy and Radiative Transfer, 52 ( 1 ), 95 – 110.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. ( 1997 ). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated‐k model for the longwave. Journal of Geophysical Research, 102 ( D14 ), 16,663 – 16,682.
Neftel, A., Friedli, H., Moor, E., Lötscher, H., Oeschger, H., Siegenthaler, U., & Stauffer, B. ( 1994 ). Historical CO 2 record from the Siple Station ice core, Trends: A compendium of data on global change. Carbon dioxide information analysis center. Oak Ridge, TN: Oak Ridge National Laboratory, U.S. Department of Energy.
NOAA ESRL Global Monitoring Division ( 2015 ). Updated annually. Atmospheric carbon dioxide dry air mole fractions from quasi‐continuous measurements at Mauna Loa, Hawaii. Compiled by K.W. Thoning, D.R. Kitzis, and A. Crotwell. National Oceanic and Atmospheric Administration (NOAA), Earth System Research Laboratory (ESRL), Global Monitoring Division (GMD), Boulder, CO. Version 2015‐12 at https://doi.org/10.7289/V54X55RG
Otto‐Bliesner, B. L., Brady, E. C., Fasullo, J., Jahn, A., Landrum, L., Stevenson, S., … Strand, G. ( 2016 ). Climate variability and change since 850 CE: An ensemble approach with the Community Earth System Model. Bulletin of the American Meteorological Society, 97 ( 5 ), 735 – 754. https://doi.org/10.1175/BAMS-D-14-00233.1
Park, T. W., Deng, Y., Cai, M., Jeong, J. H., & Zhou, R. ( 2014 ). A dissection of the surface temperature biases in the Community Earth System Model. Climate dynamics, 43 ( 7‐8 ), 2043 – 2059.
Qu, X., & Hall, A. ( 2007 ). What controls the strength of snow‐albedo feedback? Journal of Climate, 20 ( 15 ), 3971 – 3981.
Qu, X., & Hall, A. ( 2014 ). On the persistent spread in snow‐albedo feedback. Climate Dynamics, 42 ( 1‐2 ), 69 – 81.
Sanderson, B. M., Shell, K. M., & Ingram, W. ( 2010 ). Climate feedbacks determined using radiative kernels in a multi‐thousand member ensemble of AOGCMs. Climate Dynamics, 35 ( 7 ), 1219 – 1236.
Shell, K. M., Kiehl, J. T., & hields, C. A. ( 2008 ). Using the radiative kernel technique to calculate climate feedbacks in NCAR’s Community Atmospheric Model. Journal of Climate, 21 ( 10 ), 2269 – 2282.
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J., … Hecht, M. ( 2010 ). The Parallel Ocean Program (POP) reference manual ocean component of the Community Climate System Model (CCSM) and Community Earth System Model (CESM) ( Rep. LAUR‐01853, 141 ). Boulder, CO: University Corporation for Atmospheric Research.
Soden, B. J., Held, I. M., Colman, R., Shell, K. M., Kiehl, J. T., & Shields, C. A. ( 2008 ). Quantifying climate feedbacks using radiative kernels. Journal of Climate, 21 ( 14 ), 3504 – 3520.
Stroeve, J., Holland, M. M., Meier, W., Scambos, T., & Serreze, M. ( 2007 ). Arctic sea ice decline: Faster than forecast. Geophysical Research Letters, 34, L09501. https://doi.org/10.1029/2007GL029703
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. ( 2012 ). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93 ( 4 ), 485 – 498.
Trenberth, K. E., Fasullo, J. T., & Kiehl, J. ( 2009 ). Earth’s global energy budget. Bulletin of the American Meteorological Society, 90 ( 3 ), 311 – 323. https://doi.org/10.1175/2008bams2634.1
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin, N. N., Aleksandrov, Y. I., & Colony, R. ( 1999 ). Snow depth on Arctic sea ice. Journal of Climate, 12 ( 6 ), 1814 – 1829.
Warren, S. G., & Brandt, R. E. ( 2008 ). Optical constants of ice from the ultraviolet to the microwave: A revised compilation. Journal of Geophysical Research, 113, D14220. https://doi.org/10.1029/2007JD009744
Webster, M. A., Rigor, I. G., Nghiem, S. V., Kurtz, N. T., Farrell, S. L., Perovich, D. K., & Sturm, M. ( 2014 ). Interdecadal changes in snow depth on Arctic sea ice. Journal of Geophysical Research: Oceans, 119, 5395 – 5406. https://doi.org/10.1002/2014JC009985
Wetherald, R. T., & Manabe, S. ( 1988 ). Cloud feedback processes in a general circulation model. Journal of the Atmospheric Sciences, 45 ( 8 ), 1397 – 1416.
Winton, M. ( 2006 ). Surface albedo feedback estimates for the AR4 climate models. Journal of Climate, 19 ( 3 ), 359 – 365.
Arctic Climate Impact Assessment ( 2005 ). Arctic climate impact assessment ( ACIA Overview Report ) (p. 1020 ). New York, NY: Cambridge University Press. ISBN 0 521 86509 3.
Armour, K. C., Bitz, C. M., & Roe, G. H. ( 2013 ). Time‐varying climate sensitivity from regional feedbacks. Journal of Climate, 26 ( 13 ), 4518 – 4534.
Baldridge, A. M., Hook, S. J., Grove, C. I., & Rivera, G. ( 2009 ). The ASTER spectral library version 2.0. Remote Sensing of Environment, 113 ( 4 ), 711 – 715.
Barton, N. P., Klein, S. A., & Boyle, J. S. ( 2014 ). On the contribution of longwave radiation to global climate model biases in Arctic lower tropospheric stability. Journal of Climate, 27 ( 19 ), 7250 – 7269.
Bony, S., Colman, R., Kattsov, V. M., Allan, R. P., Bretherton, C. S., Dufresne, J. L., … Randall, D. A. ( 2006 ). How well do we understand and evaluate climate change feedback processes? Journal of Climate, 19 ( 15 ), 3445 – 3482.
Chen, X., Huang, X., & Flanner, M. G. ( 2014 ). Sensitivity of modeled far‐IR radiation budgets in polar continents to treatments of snow surface and ice cloud radiative properties. Geophysical Research Letters, 41, 6530 – 6537. https://doi.org/10.1002/2014GL061216
Colman, R. A., & McAvaney, B. J. ( 1997 ). A study of general circulation model climate feedbacks determined from perturbed sea surface temperature experiments. Journal of Geophysical Research, 102 ( D16 ), 19383 – 19402.
Colman, R. A. ( 2013 ). Surface albedo feedbacks from climate variability and change. Journal of Geophysical Research: Atmospheres, 118, 2827 – 2834. https://doi.org/10.1002/jgrd.50230
Comiso, J. C., & Nishio, F. ( 2008 ). Trends in the sea ice cover using enhanced and compatible AMSR‐E, SSM/I, and SMMR data. Journal of Geophysical Research, 113, C02S07. https://doi.org/10.1029/2007JC004257
Crook, J. A., & Forster, P. M. ( 2014 ). Comparison of surface albedo feedback in climate models and observations. Geophysical Research Letters, 41, 1717 – 1723. https://doi.org/10.1002/2014GL059280
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., … Bechtold, P. ( 2011 ). The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137 ( 656 ), 553 – 597.
English, J. M., Kay, J. E., Gettelman, A., Liu, X., Wang, Y., Zhang, Y., & Chepfer, H. ( 2014 ). Contributions of clouds, surface albedos, and mixed‐phase ice nucleation schemes to Arctic radiation biases in CAM5. Journal of Climate, 27 ( 13 ), 5174 – 5197.
Feldman, D. R., Collins, W. D., Pincus, R., Huang, X., & Chen, X. ( 2014 ). Far‐infrared surface emissivity and climate. Proceedings of the National Academy of Sciences, 111 ( 46 ), 16,297 – 16,302.
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., & Tschudi, M. A. ( 2011 ). Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008. Nature Geoscience, 4 ( 3 ), 151 – 155.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W. J., … Forest, C. ( 2013 ). Evaluation of climate models. In: Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Climate Change 2013, 5, 741 – 866.
Gettelman, A., Liu, X., Ghan, S. J., Morrison, H., Park, S., Conley, A. J., … Li, J. L. ( 2010 ). Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model. Journal of Geophysical Research, 115, D18216. https://doi.org/10.1029/2009JD013797
Hale, G. M., & Querry, M. R. ( 1973 ). Optical constants of water in the 200‐nm to 200‐μm wavelength region. Applied optics, 12 ( 3 ), 555 – 563.
Hall, A., & Qu, X. ( 2006 ). Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophysical Research Letters, 33, L03502. https://doi.org/10.1029/2005GL025127
Hansen, J., Nazarenko, L., Ruedy, R., Sato, M., Willis, J., Del Genio, A., … Novakov, T. ( 2005 ). Earth’s energy imbalance: Confirmation and implications. Science, 308 ( 5727 ), 1431 – 1435. https://doi.org/10.1126/science.1110252
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spelling ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/142486 2023-08-20T03:59:21+02:00 Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model Kuo, Chaincy Feldman, Daniel R. Huang, Xianglei Flanner, Mark Yang, Ping Chen, Xiuhong 2018-01-27 application/pdf https://hdl.handle.net/2027.42/142486 https://doi.org/10.1002/2017JD027595 unknown Cambridge University Press Wiley Periodicals, Inc. Kuo, Chaincy; Feldman, Daniel R.; Huang, Xianglei; Flanner, Mark; Yang, Ping; Chen, Xiuhong (2018). "Time‐Dependent Cryospheric Longwave Surface Emissivity Feedback in the Community Earth System Model." Journal of Geophysical Research: Atmospheres 123(2): 789-813. 2169-897X 2169-8996 https://hdl.handle.net/2027.42/142486 doi:10.1002/2017JD027595 Journal of Geophysical Research: Atmospheres Qu, X., & Hall, A. ( 2006 ). Assessing snow albedo feedback in simulated climate change. Journal of Climate, 19 ( 11 ), 2617 – 2630. Li, J. ( 2000 ). Gaussian quadrature and its application to infrared radiation. Journal of the Atmospheric Sciences, 57 ( 5 ), 753 – 765. https://doi.org/10.1175/1520-0469(2000)057<0753:GQAIAT>2.0.CO;2 Massom, R. A., Eicken, H., Hass, C., Jeffries, M. O., Drinkwater, M. R., Sturm, M., … Morris, K. ( 2001 ). Snow on Antarctic sea ice. Reviews of Geophysics, 39 ( 3 ), 413 – 445. Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M., & Mikolajewicz, U. ( 2012 ). Tuning the climate of a global model. Journal of Advances in Modeling Earth Systems, 4, M00A01. https://doi.org/10.1029/2012MS000154 Meehl, G. A., Covey, C., Taylor, K. E., Delworth, T., Stouffer, R. J., Latif, M., … Mitchell, J. F. ( 2007 ). The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bulletin of the American Meteorological Society, 88 ( 9 ), 1383 – 1394. Mishchenko, M. I. ( 1994 ). Asymmetry parameters of the phase function for densely packed scattering grains. Journal of Quantitative Spectroscopy and Radiative Transfer, 52 ( 1 ), 95 – 110. Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. ( 1997 ). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated‐k model for the longwave. Journal of Geophysical Research, 102 ( D14 ), 16,663 – 16,682. Neftel, A., Friedli, H., Moor, E., Lötscher, H., Oeschger, H., Siegenthaler, U., & Stauffer, B. ( 1994 ). Historical CO 2 record from the Siple Station ice core, Trends: A compendium of data on global change. Carbon dioxide information analysis center. Oak Ridge, TN: Oak Ridge National Laboratory, U.S. Department of Energy. NOAA ESRL Global Monitoring Division ( 2015 ). Updated annually. Atmospheric carbon dioxide dry air mole fractions from quasi‐continuous measurements at Mauna Loa, Hawaii. Compiled by K.W. Thoning, D.R. Kitzis, and A. Crotwell. National Oceanic and Atmospheric Administration (NOAA), Earth System Research Laboratory (ESRL), Global Monitoring Division (GMD), Boulder, CO. Version 2015‐12 at https://doi.org/10.7289/V54X55RG Otto‐Bliesner, B. L., Brady, E. C., Fasullo, J., Jahn, A., Landrum, L., Stevenson, S., … Strand, G. ( 2016 ). Climate variability and change since 850 CE: An ensemble approach with the Community Earth System Model. Bulletin of the American Meteorological Society, 97 ( 5 ), 735 – 754. https://doi.org/10.1175/BAMS-D-14-00233.1 Park, T. W., Deng, Y., Cai, M., Jeong, J. H., & Zhou, R. ( 2014 ). A dissection of the surface temperature biases in the Community Earth System Model. Climate dynamics, 43 ( 7‐8 ), 2043 – 2059. Qu, X., & Hall, A. ( 2007 ). What controls the strength of snow‐albedo feedback? Journal of Climate, 20 ( 15 ), 3971 – 3981. Qu, X., & Hall, A. ( 2014 ). On the persistent spread in snow‐albedo feedback. Climate Dynamics, 42 ( 1‐2 ), 69 – 81. Sanderson, B. M., Shell, K. M., & Ingram, W. ( 2010 ). Climate feedbacks determined using radiative kernels in a multi‐thousand member ensemble of AOGCMs. Climate Dynamics, 35 ( 7 ), 1219 – 1236. Shell, K. M., Kiehl, J. T., & hields, C. A. ( 2008 ). Using the radiative kernel technique to calculate climate feedbacks in NCAR’s Community Atmospheric Model. Journal of Climate, 21 ( 10 ), 2269 – 2282. Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J., … Hecht, M. ( 2010 ). The Parallel Ocean Program (POP) reference manual ocean component of the Community Climate System Model (CCSM) and Community Earth System Model (CESM) ( Rep. LAUR‐01853, 141 ). Boulder, CO: University Corporation for Atmospheric Research. Soden, B. J., Held, I. M., Colman, R., Shell, K. M., Kiehl, J. T., & Shields, C. A. ( 2008 ). Quantifying climate feedbacks using radiative kernels. Journal of Climate, 21 ( 14 ), 3504 – 3520. Stroeve, J., Holland, M. M., Meier, W., Scambos, T., & Serreze, M. ( 2007 ). Arctic sea ice decline: Faster than forecast. Geophysical Research Letters, 34, L09501. https://doi.org/10.1029/2007GL029703 Taylor, K. E., Stouffer, R. J., & Meehl, G. A. ( 2012 ). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93 ( 4 ), 485 – 498. Trenberth, K. E., Fasullo, J. T., & Kiehl, J. ( 2009 ). Earth’s global energy budget. Bulletin of the American Meteorological Society, 90 ( 3 ), 311 – 323. https://doi.org/10.1175/2008bams2634.1 Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin, N. N., Aleksandrov, Y. I., & Colony, R. ( 1999 ). Snow depth on Arctic sea ice. Journal of Climate, 12 ( 6 ), 1814 – 1829. Warren, S. G., & Brandt, R. E. ( 2008 ). Optical constants of ice from the ultraviolet to the microwave: A revised compilation. Journal of Geophysical Research, 113, D14220. https://doi.org/10.1029/2007JD009744 Webster, M. A., Rigor, I. G., Nghiem, S. V., Kurtz, N. T., Farrell, S. L., Perovich, D. K., & Sturm, M. ( 2014 ). Interdecadal changes in snow depth on Arctic sea ice. Journal of Geophysical Research: Oceans, 119, 5395 – 5406. https://doi.org/10.1002/2014JC009985 Wetherald, R. T., & Manabe, S. ( 1988 ). Cloud feedback processes in a general circulation model. Journal of the Atmospheric Sciences, 45 ( 8 ), 1397 – 1416. Winton, M. ( 2006 ). Surface albedo feedback estimates for the AR4 climate models. Journal of Climate, 19 ( 3 ), 359 – 365. Arctic Climate Impact Assessment ( 2005 ). Arctic climate impact assessment ( ACIA Overview Report ) (p. 1020 ). New York, NY: Cambridge University Press. ISBN 0 521 86509 3. Armour, K. C., Bitz, C. M., & Roe, G. H. ( 2013 ). Time‐varying climate sensitivity from regional feedbacks. Journal of Climate, 26 ( 13 ), 4518 – 4534. Baldridge, A. M., Hook, S. J., Grove, C. I., & Rivera, G. ( 2009 ). The ASTER spectral library version 2.0. Remote Sensing of Environment, 113 ( 4 ), 711 – 715. Barton, N. P., Klein, S. A., & Boyle, J. S. ( 2014 ). On the contribution of longwave radiation to global climate model biases in Arctic lower tropospheric stability. Journal of Climate, 27 ( 19 ), 7250 – 7269. Bony, S., Colman, R., Kattsov, V. M., Allan, R. P., Bretherton, C. S., Dufresne, J. L., … Randall, D. A. ( 2006 ). How well do we understand and evaluate climate change feedback processes? Journal of Climate, 19 ( 15 ), 3445 – 3482. Chen, X., Huang, X., & Flanner, M. G. ( 2014 ). Sensitivity of modeled far‐IR radiation budgets in polar continents to treatments of snow surface and ice cloud radiative properties. Geophysical Research Letters, 41, 6530 – 6537. https://doi.org/10.1002/2014GL061216 Colman, R. A., & McAvaney, B. J. ( 1997 ). A study of general circulation model climate feedbacks determined from perturbed sea surface temperature experiments. Journal of Geophysical Research, 102 ( D16 ), 19383 – 19402. Colman, R. A. ( 2013 ). Surface albedo feedbacks from climate variability and change. Journal of Geophysical Research: Atmospheres, 118, 2827 – 2834. https://doi.org/10.1002/jgrd.50230 Comiso, J. C., & Nishio, F. ( 2008 ). Trends in the sea ice cover using enhanced and compatible AMSR‐E, SSM/I, and SMMR data. Journal of Geophysical Research, 113, C02S07. https://doi.org/10.1029/2007JC004257 Crook, J. A., & Forster, P. M. ( 2014 ). Comparison of surface albedo feedback in climate models and observations. Geophysical Research Letters, 41, 1717 – 1723. https://doi.org/10.1002/2014GL059280 Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., … Bechtold, P. ( 2011 ). The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137 ( 656 ), 553 – 597. English, J. M., Kay, J. E., Gettelman, A., Liu, X., Wang, Y., Zhang, Y., & Chepfer, H. ( 2014 ). Contributions of clouds, surface albedos, and mixed‐phase ice nucleation schemes to Arctic radiation biases in CAM5. Journal of Climate, 27 ( 13 ), 5174 – 5197. Feldman, D. R., Collins, W. D., Pincus, R., Huang, X., & Chen, X. ( 2014 ). Far‐infrared surface emissivity and climate. Proceedings of the National Academy of Sciences, 111 ( 46 ), 16,297 – 16,302. Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., & Tschudi, M. A. ( 2011 ). Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008. Nature Geoscience, 4 ( 3 ), 151 – 155. Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W. J., … Forest, C. ( 2013 ). Evaluation of climate models. In: Climate change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Climate Change 2013, 5, 741 – 866. Gettelman, A., Liu, X., Ghan, S. J., Morrison, H., Park, S., Conley, A. J., … Li, J. L. ( 2010 ). Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model. Journal of Geophysical Research, 115, D18216. https://doi.org/10.1029/2009JD013797 Hale, G. M., & Querry, M. R. ( 1973 ). Optical constants of water in the 200‐nm to 200‐μm wavelength region. Applied optics, 12 ( 3 ), 555 – 563. Hall, A., & Qu, X. ( 2006 ). Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophysical Research Letters, 33, L03502. https://doi.org/10.1029/2005GL025127 Hansen, J., Nazarenko, L., Ruedy, R., Sato, M., Willis, J., Del Genio, A., … Novakov, T. ( 2005 ). Earth’s energy imbalance: Confirmation and implications. Science, 308 ( 5727 ), 1431 – 1435. https://doi.org/10.1126/science.1110252 IndexNoFollow climate feedback emissivity longwave radiative kernel temporal Atmospheric and Oceanic Sciences Science Article 2018 ftumdeepblue https://doi.org/10.1002/2017JD02759510.1175/1520-0469(2000)057<0753:GQAIAT>2.0.CO;210.7289/V54X55RG10.1175/BAMS-D-14-00233.110.1002/jgrd.5023010.1029/2009JD01379710.1126/science.1110252 2023-07-31T20:22:30Z Frozen and unfrozen surfaces exhibit different longwave surface emissivities with different spectral characteristics, and outgoing longwave radiation and cooling rates are reduced for unfrozen scenes relative to frozen ones. Here physically realistic modeling of spectrally resolved surface emissivity throughout the coupled model components of the Community Earth System Model (CESM) is advanced, and implications for model high‐latitude biases and feedbacks are evaluated. It is shown that despite a surface emissivity feedback amplitude that is, at most, a few percent of the surface albedo feedback amplitude, the inclusion of realistic, harmonized longwave, spectrally resolved emissivity information in CESM1.2.2 reduces wintertime Arctic surface temperature biases from −7.2 ± 0.9 K to −1.1 ± 1.2 K, relative to observations. The bias reduction is most pronounced in the Arctic Ocean, a region for which Coupled Model Intercomparison Project version 5 (CMIP5) models exhibit the largest mean wintertime cold bias, suggesting that persistent polar temperature biases can be lessened by including this physically based process across model components. The ice emissivity feedback of CESM1.2.2 is evaluated under a warming scenario with a kernel‐based approach, and it is found that emissivity radiative kernels exhibit water vapor and cloud cover dependence, thereby varying spatially and decreasing in magnitude over the course of the scenario from secular changes in atmospheric thermodynamics and cloud patterns. Accounting for the temporally varying radiative responses can yield diagnosed feedbacks that differ in sign from those obtained from conventional climatological feedback analysis methods.Plain Language SummaryClimate models have exhibited a persistent cold‐pole bias, whereby they systematically underestimate the average temperature and the amplification of climate change at high latitudes. A number of different explanations have been advanced for cold‐pole biases, which can be broadly divided into radiative and dynamic ... Article in Journal/Newspaper albedo Arctic Arctic Arctic Ocean Climate change University of Michigan: Deep Blue Arctic Arctic Ocean Journal of Geophysical Research: Atmospheres 123 2 789 813