[en] The presence of clouds in the Arctic regulates the surface energy budget (SEB) over the sea‐ice surface and the ice‐free ocean. Following several previous field campaigns, the cloud–radiation relationship, including cloud vertical structure and phase, has been elucidated; however, modeling of this relationship has matured slowly. In recognition of the recent decline in the Arctic sea‐ice extent, representation of the cloud system in numerical models should consider the effects of areas covered by sea ice and ice‐free areas. Using an in situ stationary meteorological observation data set obtained over the ice‐free Arctic Ocean by the Japanese Research Vessel Mirai (September 2014), coordinated evaluation of six regional climate models (RCMs) with nine model runs was performed by focusing on clouds and the SEB. The most remarkable findings were as follows: (1) reduced occurrence of unstable stratification with low‐level cloud water in all models in comparison to the observations, (2) significant differences in cloud water representations between single‐ and double‐moment cloud schemes, (3) extensive differences in partitioning of hydrometeors including solid/liquid precipitation, and (4) pronounced lower‐tropospheric air temperature biases. These issues are considered as the main sources of SEB uncertainty over ice‐free areas of the Arctic Ocean. The results from a coupled RCM imply that the SEB is constrained by both the atmosphere and the ocean (and sea ice) with considerable feedback. Coordinated improvement of both stand‐alone atmospheric and coupled RCMs would promote a more comprehensive and improved understanding of the Arctic air–ice–sea coupled system.
Research Center/Unit :
Sphères - SPHERES
Disciplines :
Earth sciences & physical geography
Author, co-author :
Inoue, J.
Sato, K.
Rinke, A.
Cassano, J.
Fettweis, Xavier ; Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie
Heinemann, G.
Matthes, H.
Orr, A.
Phillips, T.
Seefeldt, M.
Solomon, A.
Webster, S.
Language :
English
Title :
Clouds and radiation processes in regional climate models evaluated using observations over the ice-free Arctic Ocean
Publication date :
2021
Journal title :
Journal of Geophysical Research. Atmospheres
ISSN :
2169-897X
eISSN :
2169-8996
Publisher :
Wiley, Hoboken, United States - New Jersey
Peer reviewed :
Peer Reviewed verified by ORBi
Tags :
CÉCI : Consortium des Équipements de Calcul Intensif
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Barrett, A. I., Hogan, R. J., & Forbes, R. M. (2017). Why are mixed-phase altocumulus clouds poorly predicted by large-scale models? Part 1. Physical processes. Journal of Geophysical Research: Atmospheres, 122(18), 9903–9926. https://doi.org/10.1002/2016JD026321
Curry, J. A., & Lynch, A. H. (2002). Comparing arctic regional climate model. EOS, Transactions American Geophysical Union, 83(9), 87. https://doi.org/10.1029/2002EO000051
Curry, J. A., Schramm, J. L., Rossow, W. B., & Randall, D. (1996). Overview of arctic cloud and radiation characteristics. Journal of Climate, 9(8), 1731–1764. https://doi.org/10.1175/1520-0442(1996)009⟨1731:OOACAR⟩2.0.CO;2
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The era-interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137, 553–597.
Doms, G., Förstner, J., Heise, H., Herzog, H.-J., Mironov, D., Raschendorfer, M., et al. (2013). A description of the nonhydrostatic regional COSMO-model. Part II. Physical parameterizations. Retrieved from http://www.cosmo-model.org/content/model/documentation/core/cosmo_physics_5.00.pdf
Edson, J., Jampana, V., Weller, R., Bigorre, S., Plueddemann, A., Fairall, C., et al. (2013). On the exchange of momentum over the open ocean. Journal of Physical Oceanography, 43, 1589–1610. https://doi.org/10.1175/JPO-D-12-0173.1
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. https://doi.org/10.1175/JCLI-D-13-00608.1
Fairall, C. W., Bradley, E. F., Rogers, D. P., Edson, J. B., & Young, G. S. (1996). Bulk parameterization of air-sea fluxes for tropical ocean-global atmosphere coupled-ocean atmosphere response experiment. Journal of Geophysical Research, 101(C2), 3747–3764. https://doi.org/10.1029/95JC03205
Fettweis, X., Box, J., Agosta, C., Amory, C., Kittel, C., Lang, C., et al. (2017). Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate mar model. The Cryosphere, 11, 1015–1033. https://doi.org/10.5194/tc-11-1015-2017
Forbes, R. M., & Ahlgrimm, M. (2014). On the representation of high-latitude boundary layer mixed-phase cloud in the ECMWF global model. Monthly Weather Review, 142(9), 3425–3445. https://doi.org/10.1175/MWR-D-13-00325.1
Furtado, K., & Field, P. (2017). The role of ice microphysics parametrizations in determining the prevalence of supercooled liquid water in high-resolution simulations of a Southern Ocean midlatitude cyclone. Journal of the Atmospheric Sciences, 74(6), 2001–2021. https://doi.org/10.1175/JAS-D-16-0165.1
Gallée, H. (1995). Simulation of the mesocyclonic activity in the Ross sea, Antarctica. Monthly Weather Review, 123(7), 2051–2069. https://doi.org/10.1175/1520-0493(1995)123h2051:SOTMAI>2.0.CO;2
Gilbert, E., Orr, A., King, J. C., Renfrew, I. A., Lachlan-Cope, T., Field, P. F., & Boutle, I. A. (2020). Summertime cloud phase strongly influences surface melting on the Larsen C ice shelf, Antarctica. Quarterly Journal of the Royal Meteorological Society, 146(729), 1575–1589. https://doi.org/10.1002/qj.3753
Giorgi, F., Jones, C., & Asrar, G. (2009). Addressing climate information needs at the regional level: The CORDEX framework. World Meteorological Organization Bulletin, 58(3), 175–183.
Gutjahr, O., & Heinemann, G. (2018). A model-based comparison of extreme winds in the arctic and around Greenland. International Journal of Climatology, 38(14), 5272–5292. https://doi.org/10.1002/joc.5729
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
Hong, S.-Y., Noh, Y., & Dudhia, J. (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 134(9), 2318–2341. https://doi.org/10.1175/MWR3199.1
Inoue, J. (2014). R/V Mirai Cruise Report MR14-05. Retrieved from http://www.godac.jamstec.go.jp/catalog/data/doc_catalog/media/MR14-05_all.pdf
Inoue, J. (2020). Review of forecast skills for weather and sea ice in supporting arctic navigation. Polar Science, 100523. https://doi.org/10.1016/j.polar.2020.100523
Inoue, J., Enomoto, T., & Hori, M. (2013). The impact of radiosonde data over the ice-free arctic ocean on the atmospheric circulation in the northern hemisphere. Geophysical Research Letters, 40, 864–869. https://doi.org/10.1002/grl.50207
Inoue, J., & Hori, M. E. (2011). Arctic cyclogenesis at the marginal ice zone: A contributory mechanism for the temperature amplification? Geophysical Research Letters, 38(12), L12502. https://doi.org/10.1029/2011GL047696
Inoue, J., Kosović, B., & Curry, J. (2005). Evolution of a storm-driven cloudy boundary layer in the arctic. Boundary-Layer Meteorology, 117, 213–230. https://doi.org/10.1007/s10546-004-6003-2
Inoue, J., Liu, J., Pinto, J. O., & Curry, J. A. (2006). Intercomparison of Arctic regional climate models: Modeling clouds and radiation for SHEBA in May 1998. Journal of Climate, 19(17), 4167–4178. https://doi.org/10.1175/JCLI3854.1
Inoue, J., Sato, K., & Oshima, K. (2018). Comparison of the arctic tropospheric structures from the era-interim reanalysis with in situ observations. Okhotsk Sea and Polar Oceans Research, 2, 7–12.
Inoue, J., Yamazaki, A., Ono, J., Dethloff, K., Maturilli, M., Neuber, R., et al. (2015). Additional arctic observations improve weather and sea-ice forecasts for the Northern Sea Route. Scientific Reports, 5, 16868. https://doi.org/10.1038/srep16868
Intrieri, J. M., Fairall, C. W., Shupe, M. D., Persson, P. O. G., Andreas, E. L., Guest, P. S., & Moritz, R. E. (2002). An annual cycle of Arctic surface cloud forcing at SHEBA. Journal of Geophysical Research, 107(C10), SHE 13-1–SHE 13-14. https://doi.org/10.1029/2000JC000439
JAMSTEC (2015). Mirai mr14-05 cruise data. Retrieved from http://www.godac.jamstec.go.jp/darwin/cruise/mirai/mr14-05/e; https://doi.org/10.17596/0001861
Kawaguchi, Y., Nishino, S., Inoue, J., Maeno, K., Takeda, H., & Oshima, K. (2016). Enhanced diapycnal mixing due to near-inertial internal waves propagating through an anticyclonic eddy in the ice-free Chukchi Plateau. Journal of Physical Oceanography, 46, 2457–2481. https://doi.org/10.1175/JPO-D-15-0150.1
Kay, J., L'Ecuyer, T., Chepfer, H., Loeb, N., Morrison, A., & Cesana, G. (2016). Recent advances in arctic cloud and climate research. Current Climate Change Reports, 2, 159–169. https://doi.org/10.1007/s40641-016-0051-9
Morrison, H., Thompson, G., & Tatarskii, V. (2009). Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Monthly Weather Review, 137(3), 991–1007. https://doi.org/10.1175/2008MWR2556.1
MOSAiC Science Plan Writing Team (2016). Multidisciplinary drifting observatory for the study of arctic climate science plan. Retrieved from https://www.iasc.info/images/news/MosaicSciencePlan2016.pdf
Nakanishi, M., & Niino, H. (2006). An improved Mellor–Yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog. Boundary-Layer Meteorology, 119, 397–407. https://doi.org/10.1007/s10546-005-9030-8
Nishino, S., Kawaguchi, Y., Inoue, J., Yamamoto-Kawai, M., Aoyama, M., Harada, N., & Kikuchi, T. (2019). Do strong winds impact water mass, nutrient, and phytoplankton distributions in the ice-free Canada basin in the fall?. Journal of Geophysical Research: Oceans, 125, e2019JC015428. https://doi.org/10.1029/2019JC015428
Nose, T., Waseda, T., Kodaira, T., & Inoue, J. (2020). Satellite-retrieved sea ice concentration uncertainty and its effect on modeling wave evolution in marginal ice zones. The Cryosphere, 14, 2029–2052. https://doi.org/10.5194/tc-14-2029-2020
Nose, T., Webb, A., Waseda, T., Inoue, J., & Sato, K. (2018). Predictability of storm wave heights in the ice-free Beaufort Sea. Ocean Dynamics, 68, 1383–1402. https://doi.org/10.1007/s10236-018-1194-0
Ono, J., Inoue, J., Yamazaki, A., Dethloff, K., & Yamaguchi, H. (2016). The impact of radiosonde data on forecasting sea-ice distribution along the Northern Sea Route during an extremely developed cyclone. Journal of Advances in Modeling Earth Systems, 8, 292–303. https://doi.org/10.1002/2015MS000552
Orr, A., Phillips, T., Webster, S., Elvidge, A., Weeks, M., Hosking, S., & Turner, J. (2014). Met office unified model high-resolution simulations of a strong wind event in Antarctica. Quarterly Journal of the Royal Meteorological Society, 140, 2287–2297. https://doi.org/10.1002/qj.2296
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., et al. (2017). The weather research and forecasting model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society, 98(8), 1717–1737. https://doi.org/10.1175/BAMS-D-15-00308.1
Rinke, A., Dethloff, K., Cassano, J., Christensen, J., Curry, J., Du, P., et al. (2006). Evaluation of an ensemble of arctic regional climate models: Spatiotemporal fields during the SHEBA year. Climate Dynamics, 26, 459–472. https://doi.org/10.1007/s00382-005-0095-3
Sato, K., Inoue, J., Kodama, Y.-M., & Overland, J. (2012). Impact of arctic sea-ice retreat on the recent change in cloud-base height during autumn. Geophysical Research Letters, 39, L10503. https://doi.org/10.1029/2012GL051850
Sedlar, J., Tjernström, M., Rinke, A., Orr, A., Cassano, J., Fettweis, X., et al. (2020). Confronting arctic troposphere, clouds, and surface energy budget representations in regional climate models with observations. Journal of Geophysical Research: Atmospheres, 125(6), e2019JD031783. https://doi.org/10.1029/2019JD031783
Shupe, M. D., & Intrieri, J. M. (2004). Cloud radiative forcing of the arctic surface: The influence of cloud properties, surface albedo, and solar zenith angle. Journal of Climate, 17(3), 616–628. https://doi.org/10.1175/1520-0442(2004)017<0616:CRFOTA>2.0.CO;2
Sommerfeld, A., Nikiema, O., Rinke, A., Dethloff, K., & Laprise, R. (2015). Arctic budget study of intermember variability using HIRHAM5 ensemble simulations. Journal of Geophysical Research: Atmosphere, 120(18), 9390–9407. https://doi.org/10.1002/2015JD023153
Sotiropoulou, G., Sedlar, J., Forbes, R., & Tjernström, M. (2015). Summer arctic clouds in the ECMWF forecast model: An evaluation of cloud parametrization schemes. Quarterly Journal of the Royal Meteorological Society, 142, 387–400. https://doi.org/10.1002/qj.2658
Sundqvist, H., Berge, E., & Kristjánsson, J. (1989). Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Monthly Weather Review, 117, 1641–1657. https://doi.org/10.1175/1520-0493(1989)117<1641:CACPSW>2.0.CO;2
Taylor, P. C., Boeke, R. C., Li, Y., & Thompson, D. W. J. (2019). Arctic cloud annual cycle biases in climate models. Atmospheric Chemistry and Physics, 19(13), 8759–8782. https://doi.org/10.5194/acp-19-8759-2019
Tedesco, M., & Fettweis, X. (2020). Unprecedented atmospheric conditions (1948–2019) drive the 2019 exceptional melting season over the Greenland ice sheet. The Cryosphere, 14(4), 1209–1223. https://doi.org/10.5194/tc-14-1209-2020
Tompkins, A. (2002). A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. Journal of the Atmospheric Sciences, 59, 1917–1942. https://doi.org/10.1175/1520-0469(2002)059<1917:APPFTS>2.0.CO;2
Uttal, T., Curry, J. A., McPhee, M. G., Perovich, D. K., Moritz, R. E., Maslanik, J. A., et al. (2002). Surface heat budget of the Arctic Ocean. Bulletin of the American Meteorological Society, 83(2), 255–276. https://doi.org/10.1175/1520-0477(2002)083<0255:SHBOTA>2.3.CO;2
Vihma, T., Screen, J., Tjernström, M., Newton, B., Zhang, X., Popova, V., et al. (2016). The atmospheric role in the arctic water cycle: A review on processes, past and future changes, and their impacts. Journal of Geophysical Research: Biogeosciences, 121(3), 586–620. https://doi.org/10.1002/2015JG003132
Wilson, D., Bushell, A., Kerr-Munslow, A., Price, J., & Morcrette, C. (2008). PC2: A prognostic cloud fraction and condensation scheme. I: Scheme description. Quarterly Journal of the Royal Meteorological Society, 134, 2093–2107. https://doi.org/10.1002/qj.333
Wood, R., & Bretherton, C. (2006). On the relationship between stratiform low cloud cover and lower-tropospheric stability. Journal of Climate, 19, 6425–6432. https://doi.org/10.1175/jcli3988.1
Wyser, K., Jones, C., Du, P., Girard, É., Willén, U., Cassano, J., et al. (2008). An evaluation of arctic cloud and radiation processes during the SHEBA year: Simulation results from eight Arctic regional climate models. Climate Dynamics, 30, 203–223. https://doi.org/10.1007/s00382-007-0286-1
Yamazaki, A., Inoue, J., Dethloff, K., Maturilli, M., & König-Langlo, G. (2015). Impact of radiosonde observations on forecasting summertime arctic cyclone formation: Arctic radiosondes for cyclone forecast. Journal of Geophysical Research: Atmospheres, 120, 3249–3273. https://doi.org/10.1002/2014JD022925
Yao, B., Liu, C., Yin, Y., Liu, Z., Shi, C., Iwabuchi, H., & Weng, F. (2020). Evaluation of cloud properties from reanalyzes over East Asia with a radiance-based approach. Atmospheric Measurement Techniques, 13, 1033–1049. https://doi.org/10.5194/amt-13-1033-2020
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.