Software Open Access
{ "files": [ { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/analyzeKernelResults.py" }, "checksum": "md5:5f081a7b19add6ab7bcbe5d43635288a", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "analyzeKernelResults.py", "type": "py", "size": 35989 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/APRP.py" }, "checksum": "md5:07df5a23dd3b97176a2be4eee09c3b78", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "APRP.py", "type": "py", "size": 26753 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/barGraphsV2.py" }, "checksum": "md5:2af1bab571320fbc1ef07751fdb950cf", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "barGraphsV2.py", "type": "py", "size": 21229 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/calculateClimatologiesForRadiativeKernels.py" }, "checksum": "md5:8580dd1c53994253a5bc1117713abc74", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "calculateClimatologiesForRadiativeKernels.py", "type": "py", "size": 26427 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/cloudFractionMaps.py" }, "checksum": "md5:ab6cd5df0d64c4502d270d2930e10539", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "cloudFractionMaps.py", "type": "py", "size": 26594 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/cloudFractionZonalMeanProfiles.py" }, "checksum": "md5:aa7c201a1993514b23832fe2f3fe27e7", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "cloudFractionZonalMeanProfiles.py", "type": "py", "size": 12307 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/correctCESM_rlut.py" }, "checksum": "md5:299e377bff92effb4d828b83135a7f44", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "correctCESM_rlut.py", "type": "py", "size": 2596 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/find_rlut_correction.py" }, "checksum": "md5:43d5235759e7e0e13a299e4d6b9e531e", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "find_rlut_correction.py", "type": "py", "size": 5382 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/geomipFunctions.py" }, "checksum": "md5:92dfe6518bc89061c64fe5256dd1b72a", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "geomipFunctions.py", "type": "py", "size": 38039 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/husZonalMeanProfiles.py" }, "checksum": "md5:8c3f9e230896fcf1e0ff05ee4a5ded00", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "husZonalMeanProfiles.py", "type": "py", "size": 12218 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/isG1ReductionCorrelatedWithECS.py" }, "checksum": "md5:c336087bfa8dc43b0b2ffbf1edafb3f8", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "isG1ReductionCorrelatedWithECS.py", "type": "py", "size": 13395 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/LICENSE" }, "checksum": "md5:d3812ce9b8c6fecc64f5849315f08c05", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "LICENSE", "type": "", "size": 70 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/lowCloudPredictorMaps.py" }, "checksum": "md5:bac84f5b75a2591163cf7399c05eb9b7", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "lowCloudPredictorMaps.py", "type": "py", "size": 25957 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/mapLWCRE.py" }, "checksum": "md5:70a5764bf28bb7de9a497d19db593103", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "mapLWCRE.py", "type": "py", "size": 26057 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/multiModelMeanAPRP.py" }, "checksum": "md5:20e6499f3dac4d66f9ddb7d35a532677", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "multiModelMeanAPRP.py", "type": "py", "size": 16158 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/multiModelMeanCloudsV2.py" }, "checksum": "md5:90504930edb0c57190d15450560a0869", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "multiModelMeanCloudsV2.py", "type": "py", "size": 12329 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/multiModelMeanPredictorsV2.py" }, "checksum": "md5:cf67f3ea8ab92140f6ddad613be4141b", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "multiModelMeanPredictorsV2.py", "type": "py", "size": 7983 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/rapidVsFeedbackAPRP.py" }, "checksum": "md5:d67614db5cb42968a0ae8d97ac76d908", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "rapidVsFeedbackAPRP.py", "type": "py", "size": 21370 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/saveModelLatsLons.py" }, "checksum": "md5:1c1a11277ec40641ea5b1308dae9d1e9", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "saveModelLatsLons.py", "type": "py", "size": 2374 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/scriptUsingAPRPonGeoMIP.py" }, "checksum": "md5:3f4bfa3b8a70e58f119eae223b91d9dd", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "scriptUsingAPRPonGeoMIP.py", "type": "py", "size": 46613 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/taZonalMeanProfiles.py" }, "checksum": "md5:2c0e80958b9622a4ff9ca2f3f1d2396a", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "taZonalMeanProfiles.py", "type": "py", "size": 12377 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/zonalMeanCloudFraction_CSIRO.py" }, "checksum": "md5:ee6654aabed65efc8290130fba4a22ed", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "zonalMeanCloudFraction_CSIRO.py", "type": "py", "size": 16718 }, { "links": { "self": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048/zonalMeanCloudFraction_HadGEM2-ES.py" }, "checksum": "md5:a4481c2f6b82f9c88f219c70ae6f8c91", "bucket": "068d9228-ae18-43f1-b6c2-911848f57048", "key": "zonalMeanCloudFraction_HadGEM2-ES.py", "type": "py", "size": 25196 } ], "owners": [ 41634 ], "doi": "10.5281/zenodo.1328272", "stats": { "version_unique_downloads": 290.0, "unique_views": 181.0, "views": 196.0, "version_views": 271.0, "unique_downloads": 221.0, "version_unique_views": 239.0, "volume": 10736026.0, "version_downloads": 572.0, "downloads": 456.0, "version_volume": 13665983.0 }, "links": { "doi": "https://doi.org/10.5281/zenodo.1328272", "conceptdoi": "https://doi.org/10.5281/zenodo.1328271", "bucket": "https://zenodo.org/api/files/068d9228-ae18-43f1-b6c2-911848f57048", "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.1328271.svg", "html": "https://zenodo.org/record/1328272", "latest_html": "https://zenodo.org/record/3490985", "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.1328272.svg", "latest": "https://zenodo.org/api/records/3490985" }, "conceptdoi": "10.5281/zenodo.1328271", "created": "2018-08-03T21:24:22.956294+00:00", "updated": "2020-01-25T07:27:23.102944+00:00", "conceptrecid": "1328271", "revision": 7, "id": 1328272, "metadata": { "access_right_category": "success", "doi": "10.5281/zenodo.1328272", "description": "<p>Analysis and plotting scripts for paper by R.D. Russotto and T.P. Ackerman in <em>Atmos. Chem. Phys.</em> special issue on the Geoengineering Model Intercomparison Project.</p>\n\n<p>DOI for paper: <a href=\"https://doi.org/10.5194/acp-2018-345\">10.5194/acp-2018-345</a></p>\n\n<p>Python code was written by Rick Russotto. The APRP.py module was based in part on Matlab scripts provided by Yen-Ting Hwang. The vertical regridding code was based in part on the "convert_sigma_to_pres" algorithm by Dan Vimont, available at <a href=\"http://www.aos.wisc.edu/~dvimont/matlab/\">http://www.aos.wisc.edu/~dvimont/matlab/</a>.</p>\n\n<p>If you use any of this code, please acknowledge where it came from.</p>\n\n<p>Python scripts were run using Python 2.7.9. Versions of packages used: <br>\n-Matplotlib 1.5.1 <br>\n-NumPy 1.8.2 <br>\n-NetCDF4 1.1.0</p>\n\n<p> </p>\n\n<p>Which scripts make which figures in the paper:</p>\n\n<p><strong>Figure 1: </strong><br>\nisG1ReductionCorrelatedWithECS.py</p>\n\n<p><strong>Figure 2: </strong><br>\ntaZonalMeanProfiles.py</p>\n\n<p><strong>Figure 3: </strong><br>\nhusZonalMeanProfiles.py</p>\n\n<p><strong>Figure 4: </strong><br>\ncloudFractionZonalMeanProfiles.py</p>\n\n<p><strong>Figure 5: </strong><br>\nmultiModelMeanCloudsV2.py</p>\n\n<p><strong>Figure 6: </strong><br>\nmultiModelMeanPredictorsV2.py</p>\n\n<p><strong>Figure 7: </strong><br>\nmultiModelMeanAPRP.py</p>\n\n<p><strong>Figures 8, S9, S10, S11: </strong><br>\nanalyzeKernelResults.py</p>\n\n<p><strong>Figures 9, S12: </strong><br>\nmapLWCRE.py</p>\n\n<p><strong>Figures 10, 11: </strong><br>\nbarGraphsV2.py</p>\n\n<p><strong>Figures S1, S2, S3: </strong><br>\ncloudFractionMaps.py</p>\n\n<p><strong>Figures S4, S5: </strong><br>\nlowCloudPredictorMaps.py</p>\n\n<p><strong>Figures S6, S7, S8: </strong><br>\nscriptUsingAPRPonGeoMIP.py</p>\n\n<p><strong>Figure S13:</strong><br>\nrapidVsFeedbackAPRP.py</p>\n\n<p><strong>Other scripts and modules that the above scripts depend on: </strong><br>\nAPRP.py <br>\ncalculateClimatologiesForRadiativeKernels.py <br>\ncorrectCESM_rlut.py <br>\nfind_rlut_correction.py <br>\ngeomipFunctions.py <br>\nsaveModelLatsLons.py <br>\nzonalMeanCloudFraction_CSIRO.py <br>\nzonalMeanCloudFraction_HadGEM2-ES.py </p>\n\n<p> </p>\n\n<p>A standalone version of the APRP code can be found at <a href=\"https://github.com/rdrussotto/pyAPRP\">https://github.com/rdrussotto/pyAPRP</a>, with further documentation.</p>", "language": "eng", "title": "Analysis code for paper: Changes in clouds and thermodynamics under solar geoengineering and implications for required solar reduction", "license": { "id": "other-at" }, "journal": { "title": "Atmospheric Chemistry and Physics" }, "relations": { "version": [ { "count": 2, "index": 0, "parent": { "pid_type": "recid", "pid_value": "1328271" }, "is_last": false, "last_child": { "pid_type": "recid", "pid_value": "3490985" } } ] }, "publication_date": "2018-08-03", "creators": [ { "orcid": "0000-0002-7981-735X", "affiliation": "University of Washington", "name": "Russotto, Rick" } ], "access_right": "open", "resource_type": { "type": "software", "title": "Software" }, "related_identifiers": [ { "scheme": "doi", "identifier": "10.5194/acp-2018-345", "relation": "isSupplementTo" }, { "scheme": "doi", "identifier": "10.5281/zenodo.1328271", "relation": "isVersionOf" } ] } }
All versions | This version | |
---|---|---|
Views | 271 | 196 |
Downloads | 572 | 456 |
Data volume | 13.7 MB | 10.7 MB |
Unique views | 239 | 181 |
Unique downloads | 290 | 221 |