Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
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Published:2023-09-01
Issue:17
Volume:16
Page:4937-4956
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Abstract
Abstract. Earth system models (ESMs) must represent processes below the grid scale of a model using representations (parameterizations) of physical and
chemical processes. As a tutorial exercise to understand diagnostics and parameterization, this work presents a representation of rainbows for an
ESM: the Community Earth System Model version 2 (CESM2). Using the “state” of the model, basic physical laws, and some assumptions, we generate a
representation of this unique optical phenomenon as a diagnostic output. Rainbow occurrence and its possible changes are related to cloud occurrence
and rain formation, which are critical uncertainties for climate change prediction. The work highlights issues which are typical of many diagnostic
parameterizations such as assumptions, uncertain parameters, and the difficulty of evaluation against uncertain observations. Results agree
qualitatively with limited available global “observations” of rainbows. Rainbows are seen in expected locations in the subtropics over the ocean
where broken clouds and frequent precipitation occur. The diurnal peak is in the morning over ocean and in the evening over land. The
representation of rainbows is found to be quantitatively sensitive to the assumed amount of cloudiness and the amount of stratiform rain. Rainbows
are projected to have decreased, mostly in the Northern Hemisphere, due to aerosol pollution effects increasing cloud coverage since 1850. In the
future, continued climate change is projected to decrease cloud cover, associated with a positive cloud feedback. As a result the rainbow diagnostic
projects that rainbows will increase in the future, with the largest changes at midlatitudes. The diagnostic may be useful for assessing cloud
parameterizations and is an exercise in how to build and test parameterizations of atmospheric phenomena.
Publisher
Copernicus GmbH
Reference35 articles.
1. Albrecht, B. A.:
Aerosols, Cloud Microphysics and Fractional Cloudiness, Science, 245, 1227–1230, 1989. a 2. Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P., Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau, A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman, A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T., Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein, M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T., Toll, V., Winker, D., and Stevens, B.:
Bounding Global Aerosol Radiative Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660, https://doi.org/10.1029/2019RG000660, 2020. a, b, c 3. Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., and Schanen, D. P.:
Higher-Order Turbulence Closure and Its Impact on Climate Simulation in the Community Atmosphere Model, J. Climate, 26, 9655–9676, https://doi.org/10.1175/JCLI-D-13-00075.1, 2013. a 4. Bogenschutz, P. A., Gettelman, A., Hannay, C., Larson, V. E., Neale, R. B., Craig, C., and Chen, C.-C.:
The path to CAM6: coupled simulations with CAM5.4 and CAM5.5, Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, 2018. a 5. Businger, S.:
The Secrets of the Best Rainbows on Earth, B. Am. Meteorol. Soc., 102, E338–E350, https://doi.org/10.1175/BAMS-D-20-0101.1, 2021. a, b, c, d, e, f, g
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