Data Model

The IAMC timeseries format for scenario data

Over the past decade, the Integrated Assessment Modeling Consortium (IAMC) developed a standardised tabular timeseries format to exchange scenario data. Previous high-level use cases include reports by the Intergovernmental Panel on Climate Change (IPCC) and model comparison exercises within the Energy Modeling Forum (EMF) hosted by Stanford University.

The table below shows a typical example of integrated-assessment scenario data following the IAMC format from the CD-LINKS project. The pyam package is geared for analysis and visualization of any scenario data provided in this structure.


Illustrative example of IAMC-format timeseries data
via the IAMC 1.5°C Scenario Explorer ([1])

Refer to for more information on the IAMC format and a full list of previous use cases.

The variable column

The ‘variable’ column implements a “semi-hierarchical” structure using the | character (pipe, not l or i) to indicate the depth.

Semi-hierarchical means that a hierarchy can be imposed, e.g., one can enforce that the sum of Emissions|CO2|Energy and Emissions|CO2|Other must be equal to Emissions|CO2 (if there are no other Emissions|CO2|… variables). However, this is not mandatory, e.g., the sum of Primary Energy|Coal, Primary Energy|Gas and Primary Energy|Fossil should not be equal to Primary Energy because this would double-count fossil fuels.

Refer to the variable list in the documentation pages of the IAMC 1.5°C Scenario Explorer to see the full list of variables used in the recent IPCC Special Report on Global Warming of 1.5 ºC (SR15).

The year column

In its original design, the IAMC data format (see above) assumed that the temporal dimension of any scenario data was restricted to full years represented as integer values.

Two additional use cases are currently supported by pyam in development mode (beta):

  • using representative sub-annual timesteps via the “extra_cols” feature (see the section on custom columns in the ‘data’ table)

  • using continuous time via datetime.datetime, replacing the ‘year’ column by a ‘time’ column

Please reach out to the developers to get more information on this ongoing work.

The IamDataFrame class

A pyam.IamDataFrame instance is a wrapper for two pandas.DataFrame instances (i.e., tables, see the pandas docs for more information).

The data table

This table contains the timeseries data related to an ensemble of scenarios. It is structured in long format, where each datapoint is one row. In contrast, the standard IAMC-style format is in wide format (see the example above), where each timeseries is one row and the timesteps are represented as columns.

While long-format tables have advantages for the internal implementation of many pyam functions, wide-format tables are more intuitive to users. The method timeseries() converts between the formats and returns a pandas.DataFrame in wide format. Exporting an IamDataFrame to file using to_excel() or to_csv() also writes the data table in wide format.

The standard columns

The columns of the ‘data’ table are ['model', 'scenario', 'region', 'unit', <time_format>, 'value'], where time_format is ‘year’ when timesteps are given in years (as int) or ‘time’ when time is represented on a continuous scale (as datetime.datetime).

Custom columns of the data table

If an IamDataFrame is initialised with columns that are not in the list above nor interpreted as belonging to the time dimension (in wide format), these columns are included in the ‘data’ table as additional columns (extra_cols). This feature can be used, for example, to distinguish between multiple climate models providing different values for the variable Temperature|Global Mean.


Not all pyam functions currently support the continuous-time format or custom columns in a ‘data’ table. Please reach out via the mailing list or GitHub issues if you are not sure whether your use case is supported.


A word of warning when using custom columns for annotations: pyam drops any data rows where the ‘value’ column is ‘nan’, and it raises an error for ‘nan’ in any other column. Hence, if you are adding variable/region-specific meta information to ‘data’, you need to make sure that you add a value to every single row.

The reason for that implementation is that pandas does not work as expected with ‘nan’ in some situations (see here and here). Therefore, enforcing that there are no ‘nan’s in an IamDataFrame ensures that pyam has a clean dataset on which to operate.

The meta table

This table is intended for categorisation and quantitative indicators at the model-scenario level. Examples in the SR15 context are the warming category (‘Below 1.5°C’, ‘1.5°C with low overshoot’, etc.) and the cumulative CO2 emissions until the end of the century.

When performing operations such as rename() or append(), pyam attempts to keep the information in ‘meta’ consistent with the ‘data’ dataframe.


The ‘meta’ table is not intended for annotations of individual data points. If you want to add meta information at this level (e.g., which stylized climate model provided the variable Temperature|Global Mean, or whether a data point is from the original data source or the result of an operation), this should operate on the ‘data’ table of the IamDataFrame using the custom-columns feature (see custom columns above).


The pyam package provides two methods for filtering scenario data:

An existing IamDataFrame can be filtered using filter(col=...), where col can be any column of the ‘data’ table (i.e., ['model', 'scenario', 'region', 'unit', 'year'/'time'] or any custom columns), or a column of the ‘meta’ table. The returned object is a new IamDataFrame instance.

A pandas.DataFrame (‘data’) with columns or index ['model', 'scenario'] can be filtered by any ‘meta’ columns from an IamDataFrame (df) using pyam.filter_by_meta(data, df, col=..., join_meta=False). The returned object is a pandas.DataFrame down-selected to those models-and-scenarios where the ‘meta’ column satisfies the criteria given by col=... . Optionally, the ‘meta’ columns are joined to the returned dataframe.



Daniel Huppmann, Elmar Kriegler, Volker Krey, Keywan Riahi, Joeri Rogelj, Katherine Calvin, Florian Humpenoeder, Alexander Popp, Steven K. Rose, John Weyant, and et al. IAMC 1.5 °C Scenario Explorer and Data hosted by IIASA (release 2.0). Integrated Assessment Modeling Consortium & International Institute for Applied Systems Analysis, 2019. URL:, doi:10.5281/zenodo.3363345.