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Analysis code for paper: Changes in clouds and thermodynamics under solar geoengineering and implications for required solar reduction
Russotto, Rick;
Russotto, Rick;
Analysis code for paper: Changes in clouds and thermodynamics under solar geoengineering and implications for required solar reduction
Analysis and plotting scripts for paper by R.D. Russotto and T.P. Ackerman in Atmos. Chem. Phys. special issue on the Geoengineering Model Intercomparison Project. (new version: adds "shellKernelCalculations.py", which had been accidentally omitted from the upload) DOI for paper: 10.5194/acp-2018-345 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 http://www.aos.wisc.edu/~dvimont/matlab/. If you use any of this code, please acknowledge where it came from. Python scripts were run using Python 2.7.9. Versions of packages used: -Matplotlib 1.5.1 -NumPy 1.8.2 -NetCDF4 1.1.0 Which scripts make which figures in the paper: Figure 1: isG1ReductionCorrelatedWithECS.py Figure 2: taZonalMeanProfiles.py Figure 3: husZonalMeanProfiles.py Figure 4: cloudFractionZonalMeanProfiles.py Figure 5: multiModelMeanCloudsV2.py Figure 6: multiModelMeanPredictorsV2.py Figure 7: multiModelMeanAPRP.py Figures 8, S9, S10, S11: analyzeKernelResults.py Figures 9, S12: mapLWCRE.py Figures 10, 11: barGraphsV2.py Figures S1, S2, S3: cloudFractionMaps.py Figures S4, S5: lowCloudPredictorMaps.py Figures S6, S7, S8: scriptUsingAPRPonGeoMIP.py Figure S13: rapidVsFeedbackAPRP.py Other scripts and modules that the above scripts depend on: APRP.py calculateClimatologiesForRadiativeKernels.py correctCESM_rlut.py find_rlut_correction.py geomipFunctions.py saveModelLatsLons.py shellKernelCalculations.py zonalMeanCloudFraction_CSIRO.py zonalMeanCloudFraction_HadGEM2-ES.py A standalone version of the APRP code can be found at https://github.com/rdrussotto/pyAPRP, with further documentation.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average views 209 downloads 327 citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average - 209views327downloads
citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP!influenceInfluence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
Impulse provided by BIP!viewsViews provided by UsageCounts
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209
327
Analysis and plotting scripts for paper by R.D. Russotto and T.P. Ackerman in Atmos. Chem. Phys. special issue on the Geoengineering Model Intercomparison Project. (new version: adds "shellKernelCalculations.py", which had been accidentally omitted from the upload) DOI for paper: 10.5194/acp-2018-345 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 http://www.aos.wisc.edu/~dvimont/matlab/. If you use any of this code, please acknowledge where it came from. Python scripts were run using Python 2.7.9. Versions of packages used: -Matplotlib 1.5.1 -NumPy 1.8.2 -NetCDF4 1.1.0 Which scripts make which figures in the paper: Figure 1: isG1ReductionCorrelatedWithECS.py Figure 2: taZonalMeanProfiles.py Figure 3: husZonalMeanProfiles.py Figure 4: cloudFractionZonalMeanProfiles.py Figure 5: multiModelMeanCloudsV2.py Figure 6: multiModelMeanPredictorsV2.py Figure 7: multiModelMeanAPRP.py Figures 8, S9, S10, S11: analyzeKernelResults.py Figures 9, S12: mapLWCRE.py Figures 10, 11: barGraphsV2.py Figures S1, S2, S3: cloudFractionMaps.py Figures S4, S5: lowCloudPredictorMaps.py Figures S6, S7, S8: scriptUsingAPRPonGeoMIP.py Figure S13: rapidVsFeedbackAPRP.py Other scripts and modules that the above scripts depend on: APRP.py calculateClimatologiesForRadiativeKernels.py correctCESM_rlut.py find_rlut_correction.py geomipFunctions.py saveModelLatsLons.py shellKernelCalculations.py zonalMeanCloudFraction_CSIRO.py zonalMeanCloudFraction_HadGEM2-ES.py A standalone version of the APRP code can be found at https://github.com/rdrussotto/pyAPRP, with further documentation.