IAMC time-series data template

Modeling comparison exercises are useful to generate robust insights that are not only based on results from individual models and specific methodological approaches, but are targeting a set of scientific or policy relevant questions by undertaking a modeling experiment with several integrated assessment models. Often modeling comparison exercises also include a capacity building component by bringing modeling communities with different backgrounds together which learn from each other (e.g., global and national modeling groups). These modeling comparison activities typically involve collecting input as well as output data from the involved modeling groups for the analysis which, depending on the scope and questions of the experiment, can be very labor intensive. This is particularly true if, as done in the past, every modeling comparison exercise designs its own data template that the modeling groups have to adapt to. In addition, compiling and cleaning data as a necessary step before any insightful analysis can be done is labor intensive as well.

In response to the these challenges, the first IAMC data template was developed in the context of the IAMC scientific working group on data management and protocols is the initial Asian Modeling Exercise (AME) template which dates back to 2009/2010. This first AME template has been revised since and dozens of other model intercomparison projects as well as a number of IPCC assessments have adopted the IAMC data template format and extended the original set of variables (see more comprehensive list below).

While the development of a standardized data template and publicly accessible databases that serve as central repositories to document results of model intercomparison projects are not scientific activities per se, large benefits for research activities of these developments have materialized. First, participation in model intercomparison projects has become less resource intensive for modeling groups due to the use of standardized data exchange formats. Second, within model intercomparison projects, more time can be spent on analysis of data rather than on data handling and cleaning when utilizing a system with automated quality checks. Third, making data from modeling exercises and individual scenario development activities publicly available improves the documentation and transparency of scenarios in the literature. It also makes it possible to perform meta analysis of scenario data which otherwise would be outside reach, because of the prohibitively labor intensive data collection and data processing that are necessary steps before being able to perform such meta analysis.

Additional background information and documentation on the IAMC data template and the web-based database infrastructure hosted by the International Institute for Applied Systems Analysis (IIASA) can be found in a dedicated nomenclature repository hosted on GitHub that supersedes an earlier website.