Simulation of the present-day climate with the climate model INMCM5

Volodin, E. M.; Mortikov, Evgeny; Kostrykin, S. V.; Galin, V. Ya.; Lykossov, V. N.; Gritsun, Andrey; Diansky, N. A.; Гусев, А. В.; Iakovlev, Nikolay

doi:10.1007/s00382-017-3539-7

Cited by 135 publications

(43 citation statements)

References 33 publications

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“…Preliminary model output shows that these changes will increase (improve) SAF over forested regions (not shown). One model whose SAF will not change much in CMIP6 is INMCM5, which features a very similar land model to its predecessor (Volodin et al, 2017). This new snow albedo calculation uses a type 1 vegetation masking approach as opposed to type 3, which generally lowers α sc and weakens SAF (Qu & Hall, 2007).…”

Section: Cmip6 Outlookmentioning

confidence: 99%

“…Modeling centers are in the final stages of development for CMIP6 (Eyring et al, 2016), meaning that we can draw informed qualitative conclusions on how the models will evolve. Only a couple of CMIP6 models have published documentation to date (e.g., Volodin et al, 2017;Walters et al, 2017), but several others are actively sharing development progress online (e.g., http://www.cesm. ucar.edu/models/cesm2.0/).…”

Section: Cmip6 Outlookmentioning

confidence: 99%

“…Note that SAF in HadGEM2-series models is slightly stronger than observations. One model whose SAF will not change much in CMIP6 is INMCM5, which features a very similar land model to its predecessor (Volodin et al, 2017). INMCM4 has a α sc (and SAF) that is biased high over NH forests ; Figure 1) because of the vegetation masking scheme used (type 4).…”

Section: Cmip6 Outlookmentioning

confidence: 99%

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Why Do Models Produce Spread in Snow Albedo Feedback?

Thackeray

Hall

2018

Geophysical Research Letters

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Key Points Structurally varying snowpack, vegetation, and albedo parameterizations drive most of the intermodel spread in snow albedo feedback Many models now exhibit a more realistic feedback than in the past, but issues with two models limit the reduction in ensemble spread Preliminary signs from ongoing model development are positive and suggest a likely reduction in spread among upcoming models

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Section: Cmip6 Outlookmentioning

confidence: 99%

Section: Cmip6 Outlookmentioning

confidence: 99%

Section: Cmip6 Outlookmentioning

confidence: 99%

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Why Do Models Produce Spread in Snow Albedo Feedback?

Thackeray

Hall

2018

Geophysical Research Letters

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“…In the numerical experiments discussed below only the first aerosol indirect effect (the influence of aerosol on cloud drop radius) is taken into consider-ation. A model description and analysis of simulations of the present day climate can be found in Volodin et al (2017b).…”

Section: Model and Datamentioning

confidence: 99%

“…There are two wellknown periods of acceleration in 1920-1940 and 1980-2000 and two periods with a stabilization of the global mean temperature in 1950-1975 and 2000-2014. The reason for this oscillatory behavior is still debated. In Wilcox et al (2013) it is shown that the period of climate stabilization in 1950-1975 can be connected with the increase in anthropogenic SO 2 emissions in Europe and North America, as well as with stratospheric volcanic eruptions (Bindoff et al, 2013), while the decrease in warming in 2000-2014 could be attributed to a slowdown of methane and the tropospheric ozone concentration increase rate. On the other hand, the ensemble of CMIP5 model runs (with all the mentioned aspects of aerosol and greenhouse gas forcing taken into account) continues to raise global temperature in 2000-2014 albeit at a slower rate (Checa-Garcia et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Simulation of observed climate changes in 1850–2014 with climate model INM-CM5

Volodin

Gritsun

2018

Earth Syst. Dynam.

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Abstract. Climate changes observed in 1850–2014 are modeled and studied on the basis of seven historical runs with the climate model INM-CM5 under the scenario proposed for the Coupled Model Intercomparison Project Phase 6 (CMIP6). In all runs global mean surface temperature rises by 0.8 K at the end of the experiment (2014) in agreement with the observations. Periods of fast warming in 1920–1940 and 1980–2000 as well as its slowdown in 1950–1975 and 2000–2014 are correctly reproduced by the ensemble mean. The notable change here with respect to the CMIP5 results is the correct reproduction of the slowdown in global warming in 2000–2014 that we attribute to a change in ocean heat uptake and a more accurate description of the total solar irradiance in the CMIP6 protocol. The model is able to reproduce the correct behavior of global mean temperature in 1980–2014 despite incorrect phases of the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation indices in the majority of experiments. The Arctic sea ice loss in recent decades is reasonably close to the observations in just one model run; the model underestimates Arctic sea ice loss by a factor of 2.5. The spatial pattern of the model mean surface temperature trend during the last 30 years looks close to the one for the ERA-Interim reanalysis. The model correctly estimates the magnitude of stratospheric cooling.

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Common error patterns in the regional atmospheric circulation simulated by the CMIP multi-model ensemble

Brands

2022

Preprint

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The ability of global climate models to reproduce recurrent regional atmospheric circulation types is introduced as an overarching concept to explore potential dependencies between these models. If this approach is applied on a sufficiently large spatial domain, the similarity of the resulting error pattern can be compared from one model to another. By computing a pattern correlation matrix for a large multi-model ensemble from the Coupled Model Intercomparison Project Phases 5 and 6, groups of comparatively strong correlation coefficients are obtained for those models working with similar atmospheric components. Thereby, frequent shared error patterns are found within in the ensemble, which also occur for nominally different atmospheric component models. The error pattern correlation coefficients describing these similarities are nearly unrelated to model performance and can be used as statistical dependency weights.

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Simulation of the present-day climate with the climate model INMCM5

Cited by 135 publications

References 33 publications

Why Do Models Produce Spread in Snow Albedo Feedback?

Why Do Models Produce Spread in Snow Albedo Feedback?

Simulation of observed climate changes in 1850–2014 with climate model INM-CM5

Common error patterns in the regional atmospheric circulation simulated by the CMIP multi-model ensemble

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