Climate Change Centre Austria, Vienna (Austria)
The aerosol-cloud interaction has been much explored in recent studies because of its high uncertain contribution to the anthropogenic forcing of climate change. The high uncertainty relies on the high difficulty in understanding the multiple processes and players involved, related to the non-linearity between the change in aerosols and multiple cloud properties. Earth system models are an indispensable tool to predict the future climate. They are process-based models, and they are directly affected by the uncertainty in the understanding of the aerosol-cloud dynamics. The present work investigates the ability of the CMIP6 models to reproduce the relationship between the cloud droplet number concentration (CDNC) and the aerosol optical depth (AOD), taken as a proxy of the number of aerosols (or CCN, cloud condensation nuclei). First a comparison between the modelled and observed AOD (data was not available for CDNC) was conducted to individuate the best performance within the models in reproducing this variable. The observational data were from MODIS data and the comparison was done on the climatological mean over the 2000-2014 period. Using different statistical parameters, a ranking was produced and the model GFDL-ESM4 resulted to better perform over the other models. Afterwards, in order to account for the non-linearity of the process, joint histogram have been used to reproduce the relationship between AOD and CDNC. The objective was to compare the modelled results on a global and local scale with the MODIS data from the study Gryspeerdt et al 2016, in order to see if the model could capture the complex relationship. The different time resolution didn’t allow to proceed to a direct comparison of the plots, but they result to be compatible, leading to the conclusion that GFDL-ESM4 model is able to well reproduce this interaction. The importance of separating the analysis into the liquid water content and the ice content is also appreciated, which is even more evident in regional analysis. Further investigations are needed to better compare and quantify the performance of the CMIP6 models in reproducing the observed aerosol-cloud interaction.
OCTOPUS project - explOring aerosol-Cloud inTeractiOns in CMIP6 models Using joint-histogramS
Adele Zaini, and Matthias Schwarz. "OCTOPUS project - explOring aerosol-Cloud inTeractiOns in CMIP6 models Using joint-histogramS." ROHub. Apr 27 ,2022. https://w3id.org/ro-id/dd948b04-bfa4-44b0-814b-19f7daff6b8c.
This Jupyter notebook has been used to generate the Research Object and also to update it e.g. add all the internal and external resources used and generated during the eScience course.
Jupyter Notebook for creating RO and aggregating all the resources used/generated
Overview of the study areas using data from GFDL-ESM4 climatological mean (2000 - 2014) of AOD at 550 nm.
This is the final report written for the FORCeS eScience course 'Tools in Climate Science: Linking Observations with Modelling'. It has been built from the Jupyter Notebook and also contains the scientific discussion of the results as well as references.
Final Report on 'explOring aerosol-Cloud inTeractiOns in CMIP6 models Using joint-histogramS'
This jupyter notebook support the report written for the FORCeS eScience course 'Tools in Climate Science: Linking Observations with Modelling'
Jupyter Notebook for explOring aerosol-Cloud inTeractiOns in CMIP6 models Using joint-histogramS.
Plotting of statistical analysis of the comparison between CMIP6 models and MODIS observations.
Statistical analysis of the comparison CMIP6-MODIS
Overview of the AOD data with a) MODIS: Climatological mean (2000-2014) of AOD; b) KACE-1-0-G: climatological mean (2000-2014) of AOD.
This GeoJSON file shows the entire globe e.g. the analysis has been done on a global scale.
Plotting of the joint histograms figures for different regions and models.
Joint histograms AOD-CDNC of 'GFDL-ESM4' model
This is a Python script where all the functions used in the Jupyter Notebook were gathered. This Python script needs to be downloaded with the Jupyter Notebook and must be in the same folder than the Jupyter Notebook itself.
Python script containing all the functions used in the Jupyter Notebook
The aerosol-cloud interaction has been much explored in recent studies because of its high uncertain contribution to the anthropogenic forcing of climate change.
They are process-based models, and they are directly affected by the uncertainty in the understanding of the aerosol-cloud dynamics.
OCTOPUS project - explOring aerosol-Cloud inTeractiOns in CMIP6 models Using joint-histogramS.
Department of Geosciences, University of Oslo (Norway)