Gcam Tool

[Emerson Mata]

GCAMis an open source, publicly available human-Earth systems model that simulates the co-evolution of the economic, energy system, land use, and climate systems, including how this co-evolution is shaped by technologies, policies and other factors. Outputs from GCAM include the market shares of energy technologies and fuels, air pollutant and greenhouse gas emissions, policy costs, water use, and climate impacts. Work is ongoing to add air quality-related health impacts. GCAM development is led by the Joint Global Change Research Institute of Pacific Northwest National Laboratory.

GLIMPSE acts as a graphical user interface for GCAM, bringing this complex and powerful model to a much broader set of users. GLIMPSE consists of the Scenario Builder and a Results Viewer. Using the Scenario Builder, decision-makers and analysts at the national, regional, and state levels can construct technology and policy scenarios, run the GCAM model, and track its execution. Users can create a rich set of scenarios by modifying technology assumptions and by representing a broad range of energy, air pollutant, and climate policies. Tools are available for diagnosing problems with scenarios and archiving scenarios such that they can be examined again in the future. The Results Viewer includes tools for comparing and visualizing the scenario results, as well as for highlighting the outputs that change the most from one scenario to another.

Flexible: Many of the assumptions about the future can be changed. For example, users could explore alternative assumptions about population and economic growth, technology availability, and future policies.

Comprehensive: By simulating the interactions among human and earth systems, GLIMPSE can identify important unintended consequences and other policy considerations that may not otherwise be apparent.

Note: While GLIMPSE makes GCAM much easier to use, it is still a highly complex model that requires an investment of time and effort from the user to understand how it works and to interpret the results.

GLIMPSE currently runs on Windows computers. At least 16 GB of RAM is recommended, as well as 100 GB or more of free hard disk space. Future versions of GLIMPSE will be compatible with Linux and Apple operating systems.

The GCAM ecosystem of models and tools provides a suite of options for interfacing with GCAM inputs and outputs. The following are a list of models and tools created for the GCAM user community with their descriptions and location:

The gcamrpt R package provides functions for converting GCAM output into the format used by most IAM experiments to enter results into their databases. Users provide a table of desired outputs, along with options (such as filtering and aggregation), and the package runs the necessary GCAM queries (no more than once per query) and passes the results to the functions that produce the output.

The moirai Land Data System (Moirai LDS) is designed to produce recent historical land data inputs for the AgLU module of GCAM data system1, but the Moirai LDS outputs could also be used by other models/applications. The moirai are the Greek Fates, and this software is named Moirai to represent the fundamental influence of land data inputs on model outcomes. The primary function of the Moirai LDS is to combine spatially explicit input data (e.g., raster images) with tabular input data (e.g., crop price table) to generate tabular output data for a suite of variables. Some of these outputs replace the data provide by the Global Trade Analysis Project (GTAP), and other data replace and augment the original GCAM GIS processing. The Moirai LDS output data are aggregated by Geographic Land Unit (GLU)2 within each country. The GLU coverage is an input to the Moirai LDS (as a thematic raster image and an associated CSV file that maps the thematic integers to names), and the GLU boundaries can be determined arbitrarily. Previous versions of GCAM (and Moirai LDS) used only bioclimatic Agro-Ecological Zones (AEZs) and corresponding data that were provided by GTAP, as the GLUs. As a result, some AEZ terminology still exists in the code, but this terminology now refers more generally to GLUs. The Moirai LDS now enables any set of boundaries to be used as GLUs (including AEZs), allowing for more flexible generation of land use region boundaries (defined as the intersection of GLUs with geopolitical regions). The current default set of GLUs is the same set of 235 global watersheds as used by the GCAM water module. The GCAM 5.1 geopolitical regions (32 or 14) are included and used as inputs to Moirai to generate a mapping file between the Moirai outputs, which are at the level of the intersection between the GLUs and the country boundaries, and the geopolitical regions. The diagnostics scripts use this geopolitical region mapping in some cases. Moirai can also recalibrate three of the outputs (crop production, harvested area, and land rent) to a specified year that is the center of a five-year averaging window. No recalibration retains the circa 2000, 7-year average of the source data. The cucrrency-year for land rent can also be specified, and the default is 2001 to match the GTAP data.

hector is an open source, object-oriented, simple global climate carbon-cycle model. It runs essentially instantaneously while still representing the most critical global scale earth system processes, and is one of a class of models heavily used for for emulating complex climate models and uncertainty analyses.

xanthos is an open-source hydrologic model, written in Python, designed to quantify and analyze global water availability. xanthos simulates historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degrees. xanthos was designed to be extensible and used by scientists that study global water supply and work with GCAM. Xanthos uses a user-defined configuration file to specify model inputs, outputs and parameters. xanthos has been tested using actual global data sets and the model is able to provide historical observations and future estimates of renewable freshwater resources in the form of total runoff, average streamflow, potential evapotranspiration, actual evapotranspiration, accessible water, hydropower potential, and more.

gcamfd calculates food demand using the Edmonds et. al model. The Edmonds model divides food consumption into two categories, staples, which represent basic foodstuffs, and nonstaples, which represent higher-quality foods. Demand for staples increases at low income, but eventually peaks and begins to decline with higher income. Demand for nonstaples increases with income over all income ranges; however, total (staple + nonstaple) demand saturates asymptotically at high income.

tethys is a spatiotemporal downscaling model for global water withdrawal from GCAM water demand outputs. This model was created to link xanthos (a global hydrologic modeling framework) and GCAM. The main objective of tethys is to downscale GCAM water demand outputs into monthly gridded (0.5-degree) data by domestic, electricity, irrigation, livestock, manufacturing, and mining sectors.

demeter is a land-use and land-cover disaggregation and change detection model built to downscale GCAM land allocation outputs to a user-desired gridded resolution. Projected land allocation from GCAM is traditionally transferred to Earth System Models (ESMs) in a variety of gridded formats and spatial resolutions as inputs for simulating biophysical and biogeochemical fluxes. Existing tools for performing this translation generally require a number of manual steps which introduces error and is inefficient. Demeter makes this process seamless and repeatable by providing gridded land-use and land-cover change (LULCC) products derived directly from GCAM in a variety of formats and resolutions commonly used by ESMs. Demeter is publicly available via GitHub and has an extensible output module allowing for future ESM needs to be easily accommodated.

The fldgen R package provides functions to learn the spatial, temporal, and inter-variable correlation of the variability in an earth system model (ESM) and generate random two-variable fields (e.g., temperature and precipitation) with equivalent properties.

The gcammaptools package provides functions for plotting GCAM data on world or regional maps. This includes functions for making plots for regional or gridded data, as well as default projection and theme settings that provide a house style for GCAM plots.

The GCAM Dashboard is a scenario explorer for GCAM. Its purpose is to provide a way to give users a quick view of the data in a collection of scenarios. You can get a listing of the scenarios in a data set and the queries available for each scenario, or available jointly for a collection of scenarios. You can plot the queries for a single scenario in a map view or over time, or you can plot the difference in output values between two scenarios in either of the same two views.

The rgis R package facilitates GIS functionality and workflows commonly represented in proprietary and open source GISs. This package does not contain functionality for visualization, but is focused on geospatial algorithms for analysis, conversion, IO, modification, and workflows of spatial files and data structures.

The pygis Python package facilitates GIS functionality and workflows commonly represented in proprietary and open source GISs. This package does not contain functionality for visualization, but is focused on geospatial algorithms for analysis, conversion, IO, modification, and workflows of spatial files and data structures.

There is an urgent need for multi-model studies to characterize uncertainty arising from model heterogeneity. These studies aim to build a more reliable and transparent framework, informing policymakers in the design and implementation of climate policies. In response to this challenge, multiple institutes and organizations have adopted the standardized data template developed by the Integrated Assessment Modeling Consortium (IAMC). This template is maintained by the International Institute for Applied Systems Analysis (IIASA) and aims to standardize and facilitate model intercomparison exercises. For the latest Assessment Report (AR6), the Intergovernmental Panel on Climate Change (IPCC) required all contributors to homogenize their data to enable comparisons and ensure full transparency. This practice has set the foundation for a new open management of the outputs in the area of global scenario analysis.

In the case of the Global Change Analysis Model (GCAM), a well-regarded model that has been extensively used for different international and national scenario analysis, the harmonization code has never been documented nor standardized, making it difficult to reproduce outputs and hindering the transparency of results. To overcome these limitations, the authors of this paper have developed gcamreport, an R package that systematizes the transformations of GCAM outputs, generates figures to facilitate the analysis of the results, and allows user interaction with the produced outputs. Furthermore, the tool can be used embedded in a Docker image, which allows users to use the package in a virtual environment without having to install any specific software or library. Finally, each gcamreport release is linked to either a version of GCAM or a study in which GCAM was used, ensuring reproducibility, interoperability, accessibility, and findability, which is in line with the well-known open science principles FAIR and TRUST.

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