{"title":"彩虹与气候变化:气候模型诊断和参数化教程","authors":"A. Gettelman","doi":"10.5194/gmd-16-4937-2023","DOIUrl":null,"url":null,"abstract":"Abstract. Earth system models (ESMs) must represent processes below the grid scale of a model using representations (parameterizations) of physical and\nchemical processes. As a tutorial exercise to understand diagnostics and parameterization, this work presents a representation of rainbows for an\nESM: the Community Earth System Model version 2 (CESM2). Using the “state” of the model, basic physical laws, and some assumptions, we generate a\nrepresentation of this unique optical phenomenon as a diagnostic output. Rainbow occurrence and its possible changes are related to cloud occurrence\nand rain formation, which are critical uncertainties for climate change prediction. The work highlights issues which are typical of many diagnostic\nparameterizations such as assumptions, uncertain parameters, and the difficulty of evaluation against uncertain observations. Results agree\nqualitatively with limited available global “observations” of rainbows. Rainbows are seen in expected locations in the subtropics over the ocean\nwhere broken clouds and frequent precipitation occur. The diurnal peak is in the morning over ocean and in the evening over land. The\nrepresentation of rainbows is found to be quantitatively sensitive to the assumed amount of cloudiness and the amount of stratiform rain. Rainbows\nare projected to have decreased, mostly in the Northern Hemisphere, due to aerosol pollution effects increasing cloud coverage since 1850. In the\nfuture, continued climate change is projected to decrease cloud cover, associated with a positive cloud feedback. As a result the rainbow diagnostic\nprojects that rainbows will increase in the future, with the largest changes at midlatitudes. The diagnostic may be useful for assessing cloud\nparameterizations and is an exercise in how to build and test parameterizations of atmospheric phenomena.\n","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":" ","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rainbows and climate change: a tutorial on climate model diagnostics and parameterization\",\"authors\":\"A. Gettelman\",\"doi\":\"10.5194/gmd-16-4937-2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. Earth system models (ESMs) must represent processes below the grid scale of a model using representations (parameterizations) of physical and\\nchemical processes. As a tutorial exercise to understand diagnostics and parameterization, this work presents a representation of rainbows for an\\nESM: the Community Earth System Model version 2 (CESM2). Using the “state” of the model, basic physical laws, and some assumptions, we generate a\\nrepresentation of this unique optical phenomenon as a diagnostic output. Rainbow occurrence and its possible changes are related to cloud occurrence\\nand rain formation, which are critical uncertainties for climate change prediction. The work highlights issues which are typical of many diagnostic\\nparameterizations such as assumptions, uncertain parameters, and the difficulty of evaluation against uncertain observations. Results agree\\nqualitatively with limited available global “observations” of rainbows. Rainbows are seen in expected locations in the subtropics over the ocean\\nwhere broken clouds and frequent precipitation occur. The diurnal peak is in the morning over ocean and in the evening over land. The\\nrepresentation of rainbows is found to be quantitatively sensitive to the assumed amount of cloudiness and the amount of stratiform rain. Rainbows\\nare projected to have decreased, mostly in the Northern Hemisphere, due to aerosol pollution effects increasing cloud coverage since 1850. In the\\nfuture, continued climate change is projected to decrease cloud cover, associated with a positive cloud feedback. As a result the rainbow diagnostic\\nprojects that rainbows will increase in the future, with the largest changes at midlatitudes. The diagnostic may be useful for assessing cloud\\nparameterizations and is an exercise in how to build and test parameterizations of atmospheric phenomena.\\n\",\"PeriodicalId\":12799,\"journal\":{\"name\":\"Geoscientific Model Development\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoscientific Model Development\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.5194/gmd-16-4937-2023\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscientific Model Development","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/gmd-16-4937-2023","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
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.
期刊介绍:
Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication:
* geoscientific model descriptions, from statistical models to box models to GCMs;
* development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results;
* new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data;
* papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data;
* model experiment descriptions, including experimental details and project protocols;
* full evaluations of previously published models.