Journal cover Journal topic
Advances in Statistical Climatology, Meteorology and Oceanography An international open-access journal on applied statistics
Journal topic

Journal metrics

CiteScore value: 0.1
CiteScore
0.1
Scimago H <br class='widget-line-break'>index value: 1
Scimago H
index1
Definitions
Download
Short summary
We present new probabilistic estimates of model parameters in the MIT Earth System Model using more recent data and an updated method. Model output is compared to observed climate change to determine which sets of model parameters best simulate the past. In response to increasing surface temperatures and accelerated heat storage in the ocean, our estimates of climate sensitivity and ocean diffusivity are higher. Using a new interpolation algorithm results in smoother probability distributions.
Read more
ASCMO | Articles | Volume 4, issue 1/2
ASCMO | Articles | Volume 4, issue 1/2
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 19–36, 2018
https://doi.org/10.5194/ascmo-4-19-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 19–36, 2018
https://doi.org/10.5194/ascmo-4-19-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ASCMO | Articles | Volume 4, issue 1/2

  30 Nov 2018

30 Nov 2018

Estimates of climate system properties incorporating recent climate change

Alex G. Libardoni et al.

Related authors

Baseline evaluation of the impact of updates to the MIT Earth System Model on its model parameter estimates
Alex G. Libardoni, Chris E. Forest, Andrei P. Sokolov, and Erwan Monier
Geosci. Model Dev., 11, 3313–3325, https://doi.org/10.5194/gmd-11-3313-2018,https://doi.org/10.5194/gmd-11-3313-2018, 2018
Short summary

Related subject area

Climate research
A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020,https://doi.org/10.5194/ascmo-6-223-2020, 2020
Short summary
A protocol for probabilistic extreme event attribution analyses
Sjoukje Philip, Sarah Kew, Geert Jan van Oldenborgh, Friederike Otto, Robert Vautard, Karin van der Wiel, Andrew King, Fraser Lott, Julie Arrighi, Roop Singh, and Maarten van Aalst
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ascmo-6-177-2020,https://doi.org/10.5194/ascmo-6-177-2020, 2020
Short summary
The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models
Mark D. Risser and Michael F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 115–139, https://doi.org/10.5194/ascmo-6-115-2020,https://doi.org/10.5194/ascmo-6-115-2020, 2020
Short summary
A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series
Donald P. Cummins, David B. Stephenson, and Peter A. Stott
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 91–102, https://doi.org/10.5194/ascmo-6-91-2020,https://doi.org/10.5194/ascmo-6-91-2020, 2020
Short summary
Robust regional clustering and modeling of nonstationary summer temperature extremes across Germany
Meagan Carney and Holger Kantz
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 61–77, https://doi.org/10.5194/ascmo-6-61-2020,https://doi.org/10.5194/ascmo-6-61-2020, 2020
Short summary
More articles (8)

Cited articles

Aldrin, M., Holden, M., Guttorp, P., Skeie, R. B., Myhre, G., and Bernstein, T. K.: Bayesian estimation of climate sensitivity based on a simple climate model fitted to observations of hemispheric temperatures and global ocean heat content, Environmetrics, 23, 253–271, 2012. a, b
Andrews, D. G. and Allen, M. R.: Diagnosis of climate models in terms of transient climate response and feedback response time, Atmos. Sci. Lett., 9, 7–12, 2008. a
Andronova, N. G. and Schlesinger, M. E.: Objective estimation of the probability density function for climate sensitivity, J. Geophys. Res., 106, 22605–22612, 2001. a
Bayes, T.: An essay towards solving a problem in the doctrine of chances, Phil. Trans. Roy. Soc., 53, 370–418, 1763. a
Bindoff, N. L., Stott, P. A., AchutaRao, K. M., Allen, M. R., Gillett, N., Gutzler, D., Hansingo, K., Hegerl, G., Gu, Y., Jain, S., Mokhov, I. I., Overland, J., Perlwitz, J., Sebbari, R., and Zhang, X.: Detection and attribution of climate change: From global to regional, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P., 867–952, Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013. a
More articles (45)
Publications Copernicus
Download
PDF XML
Short summary
We present new probabilistic estimates of model parameters in the MIT Earth System Model using more recent data and an updated method. Model output is compared to observed climate change to determine which sets of model parameters best simulate the past. In response to increasing surface temperatures and accelerated heat storage in the ocean, our estimates of climate sensitivity and ocean diffusivity are higher. Using a new interpolation algorithm results in smoother probability distributions.
Read more
Citation
Advances in Statistical Climatology, Meteorology and Oceanography

An international open-access journal on applied statistics

All site content, except where otherwise noted, is licensed under the Creative Commons Attribution 4.0 License.
Contact
|
Imprint
|
Data protection