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Software . 2025
License: CC BY
Data sources: Datacite
ZENODO
Software . 2025
License: CC BY
Data sources: Datacite
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AnjaKatzenberger/Monsoon_Hysteresis_reveals_Atmospheric_Memory: Codes for Publication 'Monsoon Hysteresis reveals Atmospheric Memory'

Authors: Anja Katzenberger;

AnjaKatzenberger/Monsoon_Hysteresis_reveals_Atmospheric_Memory: Codes for Publication 'Monsoon Hysteresis reveals Atmospheric Memory'

Abstract

Codes for the PNAS Publication "Monsoon Hysteresis reveals Atmospheric Memory" from Katzenberger, Anja and Levermann, Anders This repository contains codes and the virtual environment to reproduce the results for the publication "Monsoon Hysteresis reveals Atmospheric Memory". The README is structured as below: 1.) Description of codes 2.) Description of data sets 3.) List of codes to create individual figures 4.) List of datasets as required by codes 5.) Requirements and versions All results were produced using Python (version 3.11.7). In order to reproduce specific figures, select the required code from the list given in (3.) and adapt the paths to datasets following (4.). You will also have to adapt the savedir-path by giving a directory where you wish to save the created figures. The codes were run in the virtual environment new_venv. To simplify the process of installing the required Python packages according to this environment, you can install the requirements following requirements.txt or the list in (5.). The command to install the virtual environment packages on the target machine is pip install -r requirements.txt wind.py: This code creates the wind direction and precipitation distribution on the Monsoon Planet for every month hysteresis_10N_monthly.ipynb: This notebook explores key variables on the Monsoon Planet as precipitation, evaporation, surface temperature, solar radiation, etc. based on monthly data. It also creates the hysteresis plot based on monthly values. hysteresis_10N_daily.py: This code creates hysteresis plot based on daily data that is smoothened using singular spectrum analysis. 3D_hysteresis.ipynb: This notebook creates the interactive 3D hysteresis plot depending on upper (solar radiation) and lower (surface temperature) boundary conditions. Hysteresis_real_monsoon.py: This code creates hysteresis plot of observed monsoon systems based on satellite data and observed rainfall. MonsoonSeasonalCycle.py: This code implements the Moisture Advection Feedback and creates the 'theory hysteresis plot'. MSC_recalHyst_withPnWadjustment.py: As in MonsoonSeasonalCycle.py but with an implemented delay as described in the equations. hysteresis_observations_model_theory.py: This code plots the hysteresis plot of real monsoon systems, the Monsoon Planet hysteresis as well as the theory hystereis plot in one figure to simplify comparison. memory.py: This code helps to explore the memory experiments and quantify the memory effect within the atmosphere. bistability.py: This code creates the bistability plot and visualizes the experimental setup. Precip_Slider_SW_2D_depending_on_swvalue.py, Precip_Slider_tsurf_2D_depending_on_tsurfvalue.py: These two codes create a visualisation of the hysteresis on the Monsoon Planet depending on upper and lower boundary conditions. robustness_check_sol.py, robustness_check_co2.py, robustness_check_aerosol.py, robustness_check_albedo.py: These four codes check the robustness of the hysteresis with regard to changes in the baseline setup.

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selected citations
These citations are derived from selected sources.
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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average