2020
DOI: 10.1007/s00382-020-05521-x
|View full text |Cite
|
Sign up to set email alerts
|

An observation-based scaling model for climate sensitivity estimates and global projections to 2100

Abstract: We directly exploit the stochasticity of the internal variability, and the linearity of the forced response to make global temperature projections based on historical data and a Green’s function, or Climate Response Function (CRF). To make the problem tractable, we take advantage of the temporal scaling symmetry to define a scaling CRF characterized by the scaling exponent H, which controls the long-range memory of the climate, i.e. how fast the system tends toward a steady-state, and an inner scale $$\tau \ap… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

3
56
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
4
3

Relationship

2
5

Authors

Journals

citations
Cited by 23 publications
(59 citation statements)
references
References 90 publications
3
56
0
Order By: Relevance
“…We show that the model is most sensitive to changes in CO 2 and ice distribution, but the obliquity and land-sea mask have significant influence on modeled temperatures as well. We tune TransEBM to reproduce the 1960-1989 CE global mean temperature and the Equator-to-pole and seasonal temperature gradients of ERA-20CM reanalysis (Hersbach et al, 2015). The resulting latitudinal and seasonal temperature distributions agree well with reanalysis and the general circulation model (GCM) HadCM3 for a simulation of the past millennium (Bühler et al, 2020).…”
mentioning
confidence: 79%
See 1 more Smart Citation
“…We show that the model is most sensitive to changes in CO 2 and ice distribution, but the obliquity and land-sea mask have significant influence on modeled temperatures as well. We tune TransEBM to reproduce the 1960-1989 CE global mean temperature and the Equator-to-pole and seasonal temperature gradients of ERA-20CM reanalysis (Hersbach et al, 2015). The resulting latitudinal and seasonal temperature distributions agree well with reanalysis and the general circulation model (GCM) HadCM3 for a simulation of the past millennium (Bühler et al, 2020).…”
mentioning
confidence: 79%
“…Observational records of climate and proxies are limited in both time and space (Schmidt et al, 2014;Hersbach et al, 2015). As such, climate models are vital for climate research since they can fill in gaps left by observational records.…”
Section: Introductionmentioning
confidence: 99%
“…Depending on the space-time statistics of the anomaly forcing, the HEBE justifies currentbased macroweather (monthly, seasonal) temperature forecasts (Lovejoy et al, 2015;Lovejoy, 2019, 2021a, b) that are effectively high-frequency approximations to the FEBE. Similarly, the low-frequency (asymptotic) power-law part can produce climate projections with significantly lower uncertainties than current general circulation model (GCM)-based alternatives (Hebert, 2017;Hébert et al, 2021) and work in progress directly using the HEBE (Procyk et al, 2020).…”
Section: Discussionmentioning
confidence: 99%
“…For example, the global value h = 0.5 ± 0.2 was found for the long-time behavior needed to project the Earth's temperature to 2100. Hebert (2017), Hébert et al (2021), andProcyk et al (2020), also using centennial-scale global temperature estimates but using the FEBE directly, found the less uncertain h = 0.38 ± 0.05. Using data at monthly and seasonal scales, Del Rio Amador and found and used the value h = 0.42 ± 0.03.…”
Section: Some Features Of Stochastic Forcingmentioning
confidence: 99%
See 1 more Smart Citation