Tools & Data

Dataset used in publication “Effects of local and remote black carbon aerosols on summer monsoon precipitation over India”

Essential model data used to produce the figures and tables in publication "Effects of local and remote black carbon aerosols on summer monsoon precipitation over India"

EUREC4A

trajectory data for the 59 mobile platforms deployed during EUREC4A https://doi.org/10.25326/165

Dataset used in publication “Effects of local and remote black carbon aerosols on summer monsoon precipitation over India”

Essential model data used to produce the figures and tables in publication "Effects of local and remote black carbon aerosols on summer monsoon precipitation over India"
https://iopscience.iop.org/article/10.1088/2515-7620/ac18d1

Data required for the DAMIP component for paper, Modifying emissions scenario projections to account for the effects of COVID-19: protocol for CovidMIP

Lamboll R, Jones C
doi.org/10.22033/ESGF/input4MIPs.15901 (2021a)
and doi.org/10.22033/ESGF/input4MIPs.15902 (2021b)

Underlying data for emissions modification terms in paper, Modifying emissions scenario projections to account for the effects of COVID-19: protocol for CovidMIP

Forster (2021) data available

Supplementary material: Incremental improvements of 2030 targets insufficient to achieve the Paris Agreement goals

ANDREAS GEIGES; Alexander Nauels; Marina Andrijevic; Peter Pfleiderer

# NDC_ambition_ESD_paper_material

Silicone, an open source Python package

Code for Silicone

Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing

Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing

Priestley-Centre/Near_term_warming: v1.2

Data for Stringent mitigation substantially reduces risk of unprecedented near-term warming rates

Data used in Forster et al (2019) Latest climate models confirm need for urgent mitigation

Data for Fig. 2, and additional figures comparing SSPs and RCPs, are available from https://github.com/Priestley-Centre/CMIP5_CMIP6_FaIR_gsat_data.

Data used in Dimitrelos et al (2020) A Sensitivity Study of Arctic Air‐Mass Transformation Using Large Eddy Simulation

Three-dimensional large eddy simulation and a bulk sea ice model to examine the lifecycle of clouds formed during wintertime advection of moist and warm air over sea ice, following a Lagrangian perspective.

Data used in Vial et al (2019) A New Look at the Daily Cycle of Trade Wind Cumuli

Access to primary data is provided on the MPG site.

Data used in Jones et al (2019) The Zero Emissions Commitment Model Intercomparison Project (ZECMIP) contribution to C4MIP: quantifying committed climate changes following zero carbon emissions

These data in .csv file format are available from:
C4MIP http://www.c4mip.net/index.php?id=3387
CDRMIP https://www.kiel-earth-institute.de/CDR_Model_Intercomparison_Project.html websites.

Model output used in Richardson et al (2019) Efficacy of Climate Forcings in PDRMIP Models

The PDRMIP model output is publicly available on CICERO website

Data from Lee et al (2021) The contribution of global aviation to anthropogenic climate forcing in 2018

Calculation of radiative forcing and effective radiative forcing of global aviation impacts

The HadGEM3-GA7.1 radiative kernels are available Smith (2020)

This dataset provides radiative kernels based on the HadGEM3-GA7.1 atmospheric model. The kernels are produced with respect to a pre-industrial baseline

Code used in Andrews and Forster (2020) Energy budget constraints on historical forcing

The code used to produce the energy budget constraint and Fig. 1 in this study is available via the online repository at https://github.com/timothyandrews/historical-radiative-forcing/

Forster P.M. et al. (2020) Time-series of annual global surface air temperature anomaly data for the CMIP6 historical, SSP1-2.6, and SSP5-8.5 simulations

Anomaly data for the CMIP6 historical, SSP1-2.6, and SSP5-8.5 simulations

Data used in Forster et al Emissions changes in 2020 due to COVID-19 (Forster, 2020)

Data are differences in either kt per day or Mt per day (for CO2) compared to annual averaged baseline emissions. Rows are days in 2020. Columns are ISO_A3 abbreviations of countries. Last two columns are international aviation and shipping.  Two estimates are given: high and mid.  Based on Google mobility data and EDGAR datasets. Data projected from day 120 until Day 366.

Case studies of climate sensitivity with PMIP: Python statistical codes (Renoult et al., 2020)

These 4 codes can be run to estimate climate sensitivity by using the tropical temperature of the models participating in the Paleoclimate Modelling Intercomparison Project (PMIP) and simulating either the Last Glacial Maximum or the mid-Pliocene Warm Period. They link to Renoult et al. (in review), A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP.