MPI_ESM_LR: MPI-ESM-LR temperature and forcing dataset.

In eirikmn/INLA.climate: Full Bayesian analysis of radiative forcing climate model

MPI-ESM-LR temperature and forcing dataset.

Description

This is the MPI-ESM-LR temperature dataset. Also includes radiative forcing data obtained by combining the MPI-ESM-LR forcing and Hansen et al. (2010) such that the 18-yr moving averages are equal. The forcing slope coefficient when assuming a 1% annual increase of CO2, which is used to estimate the TCR, is 4.1.

Usage

data(MPI_ESM_LR)

Format

The data is a list that contains these objects:

Year

Time index denoting the year of the observations.

Temperature

Annual global mean surface temperature.

Forcing

Annual adjusted radiative forcing.

References

Giorgetta, M. A., et al. ( 2013), Climate and carbon cycle changes from 1850 to 2100 in MPI‐ESM simulations for the Coupled Model Intercomparison Project phase 5, J. Adv. Model. Earth Syst., 5, 572– 597, doi:10.1002/jame.20038.

Hansen, J. and Ruedy, R. and Sato, M. and and Lo, K. 2010: Global surface temperature change, Rev. Geophys., 48, RG4004, doi:10.1029/2010RG000345.

Forster, P. M., T. Andrews, P. Good, J. M. Gregory, L. S. Jackson, and M. Zelinka, 2013: Evaluating adjusted forcing and modelspread for historical and future scenarios in the cmip5 generation of climate models. Journal of Geophysical Research: Atmo-spheres, 118 (3), 1139–1150, doi:10.1002/jgrd.50174

Examples

# Load data
data(MPI_ESM_LR, package = "INLA.climate")

#Plot temperature
plot(MPI_ESM_LR$Year,MPI_ESM_LR$Temperature)

#Plot forcing
plot(MPI_ESM_LR$Year,MPI_ESM_LR$Forcing)

eirikmn/INLA.climate documentation built on Feb. 6, 2023, 11:41 a.m.