Integrating various variables into a whole is helpful when envisioning the whole picture, and Land System Models are providing some good tool to this end. However, such an approach is time costly when simulating by combining several models. This is especially true when each separate model’s code is unfamiliar to us. I found hope when I recently came across CMIP6 – a most convenient way to combine many models in generating various simulations.
So what’s CMIP6 you may ask? It stands for, Coupled Model Inter-comparison Project started in 1995 under the auspices of the World Climate Research Programme (WCRP), and is now in its sixth phase, hence CMIP6. It coordinates independent model inter-comparison activities and their experiments, adopting a common infrastructure for collecting, organizing, and distributing output from models performing common sets of experiments. I recommend a paper which briefly introduces the structure of CMIP6 data (see http://www.climatechange.cn/article/2019/1673-1719/1673-1719-15-5-445.shtml). In short, CMIP6 provides results from different variable, including land, ocean and atmosphere ecosystems conducted under different experimental conditions (such as double carbon dioxide levels), sourced from different models. This means getting used to CMIP6 not only familiarizes us with the mechanisms of model development, but also inspires us to generate new ideas from long-term simulations.
And how to use CMIP6? The official website ( https://esgf-node.llnl.gov/projects/cmip6/ ) provides basic answers in the Guidance for Data Users (https://pcmdi.llnl.gov/CMIP6/Guide/dataUsers.html). The easiest way to download data is an HTTP DOWNLOAD, where all data is formatee in NCDF. We may get somewhat lost when looking at large amount of tips, instead I suggest to pay attention to three important key words: Source ID (which is the model name, check details here, https://docs.google.com/spreadsheets/d/13DHeTEH_8G08vxTMX1Fs-WbAA6SamBjDdh0fextdcGE/edit#gid=165882553 ), experiment ID (which shows control conditions, check details here, https://docs.google.com/spreadsheets/d/1SktYsKYhRxQFjUsGYbKxu6G4egpQePbQ1Y5bgERRg3M/edit#gid=1894225558), and variables ( containing all output variables found in CMIP6).
CMIP6 allows you to choose different scenarios or experiments, as well as different models. As a result, we can have a rough picture of the variables’ trend from historical to future as shown in Figure 1. One last tip for downloading missing data: If you find the link has expired or shows a connection error, it may help to check the box “Show All Replicas”.
Jansen J, Woolway RI, Kraemer BM, Albergel C, Bastviken D, Weyhenmeyer GA, Marcé R, Sharma S, Sobek S, Tranvik LJ, Perroud M, Golub M, Moore TN, Råman Vinnå L, La Fuente S, Grant L, Pierson DC, Thiery W, Jennings E. Global increase in methane production under future warming of lake bottom waters. Glob Chang Biol. 2022 Sep;28(18):5427-5440. doi: 10.1111/gcb.16298. Epub 2022 Jun 24. PMID: 35694903; PMCID: PMC9546102.