Six driver scenarios that can be passed as inputs to run_fredi() and run_fredi_sv()
Source: R/data.R
gcamScenarios.RdA dataframe containing six driver scenarios that can be passed as inputs to run_fredi() and run_fredi_sv(). This data frame has four columns -- year, temp_C, slr_cm, and scenario -- respectively containing:
Format
A data frame with 546 rows and 4 columns:
- year
Year
- temp_C
Global temperature (in degrees Celsius) for the associated year and scenario
- slr_cm
Global Mean Sea Level Rise (in centimeters) for the associated year and scenario
- scenario
Associated scenario identifier
Details
- year
The Common Era (CE) year associated with the observation.
- temp_C
Global temperature (i.e., degrees of warming above the baseline year of 1995) in degrees Celsius, for the associated year and scenario.
- slr_cm
Global Mean Sea Level Rise (i.e., SLR) in centimeters for the associated year and scenario.
- scenario
Associated scenario identifier.
The scenarios in this dataframe were created using Hector, an open-source, reduced-form global carbon-cycle climate model (Hartin et al., 2015) to model temperatures associated with emissions scenarios from the Global Change Analysis Model v5.3 (GCAM). The Global Change Analysis Model v5.3 (GCAM) is an open source model that represents the linkages between energy, water, land, climate and economic systems (Calvin et al., 2019). Scenario identifiers in the scenario column of gcamScenarios() have the string "Hector_GCAM_v5.3_ECS_" as a prefix, followed by the average warming temperature and the suffix "_REF" (e.g., "Hector_GCAM_v5.3_ECS_3.0_REF" for the default scenario for run_fredi() and run_fredi_sv().
These six temperature scenarios represent global temperatures; however, run_fredi() and run_fredi_sv() require temperature scenarios for the contiguous U.S. (CONUS). Therefore, to use the gcamScenarios with run_fredi() or run_fredi_sv(), users must first convert temperatures in the temp_C column to CONUS temperatures via the convertTemps function -- with argument from = "global" (e.g., gcamScenarios |> mutate(temp_C = temp_C |> FrEDI::convertTemps(from = "global"))).
The GCAM scenarios can be passed directly to the SV module via the driverInput argument (e.g., run_fredi_sv(driverInput = gcamScenarios)), since run_fredi_sv() is designed to run multiple scenarios. In contrast, run_fredi() is intended to be run with a single scenario; gcamScenarios should be subset to a specific scenario before passing the temperature scenario to run_fredi() (e.g., run_fredi(list(tempInput=gcamScenarios |> dplyr::filter(scenario=="Hector_GCAM_v5.3_ECS_3.0_REF"))) to use the default scenario).
Calvin, K., Patel, P., Clarke, L., et al. 2019. GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems, Geosci. Model Dev., 12:677–698. https://doi.org/10.5194/gmd-12-677-2019.
Hartin, C.A., Patel, P., Schwarber, A., Link, R.P. and Bond-Lamberty, B.P., 2015. A simple object-oriented and open-source model for scientific and policy analyses of the global climate system–Hector v1. 0. Geoscientific Model Development, 8(4), pp.939-955.