SURFACE AIR TEMPERATURE - MODEL BIAS

Figure number: 3.3 From the IPCC Working Group I Contribution to the Sixth Assessment Report: Chapter 3

Contents

• Contents
• Description
• Installation
  • Option 1: Run on Binder (No Installation Needed)
  • Option 2: Local Installation
• Expected image path
• Publication sources
• How to cite
  • Figure Citation
  • Repository Citation
• Disclaimer

Description

The figure shows the annual-mean surface (2 m) air temperature (°C) for the period 1995–2014. (a) Multi-model (ensemble) mean constructed with one realization of the CMIP6 historical experiment from each model. (b) Multi- model mean bias, defined as the difference between the CMIP6 multi-model mean and the climatology of the Fifth generation of ECMWF atmospheric reanalyses of the global climate (ERA5). (c) Multi-model mean of the root mean square error calculated over all months separately and averaged with respect to the climatology from ERA5. (d) Multi-model-mean bias as the difference between the CMIP6 multi-model mean and the climatology from ERA5. Also shown is the multi- model mean bias as the difference between the multi-model mean of (e) high resolution and (f) low resolution simulations of four HighResMIP models and the climatology from ERA5......

Installation

Option 1: Run on Binder (No Installation Needed)

You can quickly run this project in a live, interactive environment without any installation. Click the button below to launch the project on Binder:

This will automatically install the necessary dependencies and launch the Jupyter Notebooks in your browser.

Option 2: Local Installation

If you prefer to run the project locally, follow these steps:

1. Clone the repository: ```bash git clone https://github.com/edsml-mh1123/AR6-WGI-Figure.git cd AR6-WGI-Figure

2. Create and activate the Conda environment:

```bash conda env create -f environment.yml

conda activate fig3.3_env ```

1. Download the required data

Before running the rest of the Jupyter notebooks, you need to download the required data. Run the provided Python script to handle this:

```bash cd data

python download_data.py ```

Expected image path

• recipeipccwg1ar6ch3atmosphereYYYYMMDDHHMMSS/plots/fig33cmip5/fig33/modelbiastasannualclim_CMIP5.eps
• recipeipccwg1ar6ch3atmosphereYYYYMMDDHHMMSS/plots/fig33cmip6/fig33/modelbiastasannualclim_CMIP6.eps

Publication sources

Bock, L., Lauer, A., Schlund, M., Barreiro, M., Bellouin, N., Jones, C., Predoi, V., Meehl, G., Roberts, M., and Eyring, V.: Quantifying progress across different CMIP phases with the ESMValTool, Journal of Geophysical Research: Atmospheres, 125, e2019JD032321. https://doi.org/10.1029/2019JD032321

How to cite

If you use this repository or any of its contents in your work, please cite it appropriately.

Repository Citation

This repository includes a CITATION.cff file for citation. You can generate a citation in your preferred format using:

bash cffconvert --format bibtex

Figure Citation

If you use Figure 3.3 from the IPCC report included in this repository, please cite it as:

Figure 3.2 in IPCC, 2021: Chapter 3. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi: 10.1017/9781009157896.005 .]

Disclaimer

Please note that figures in this repository may differ from those in the published version due to the editorial process. The repository contains the latest available versions prior to publication.