Climate variability from annual to multi-decadal timescales in a
                        two-layer stochastic energy balance model: analytic solutions and
                        implications for general circulation models

Soldatenko, Sergei; Colman, Robert

doi:10.1080/16000870.2018.1554421

Cited by 17 publications

(18 citation statements)

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“…Values for these parameters were estimated based on a CMIP5 AOGCM analysis and are rounded multi-model means. The ranges of feedback λ and climate system inertia C, which characterize the uncertainty in the corresponding parameter, were as follows: 0.61 ≤ λ ≤ 1.70 W m −2 K −1 and 4.7 ≤ C ≤ 8.6 W yr m −2 K −1 [94].…”

Section: Using Low-order Simple Models To Study Climate Variabilitymentioning

confidence: 99%

Mathematical Modelling of Climate Change and Variability in the Context of Outdoor Ergonomics

2021

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The current climate change, unlike previous ones, is caused by human activity and is characterized by an unprecedented rate of increase in the near-surface temperature and an increase in the frequency and intensity of hazardous weather and climate events. To survive, society must be prepared to implement adaptation strategies and measures to mitigate the negative effects of climate change. This requires, first of all, knowledge of how the climate will change in the future. To date, mathematical modelling remains the only method and effective tool that is used to predict the climate system’s evolution under the influence of natural and anthropogenic perturbations. It is important that mathematics and its methods and approaches have played a vital role in climate research for several decades. In this study, we examined some mathematical methods and approaches, primarily, mathematical modelling and sensitivity analysis, for studying the Earth’s climate system, taking into account the dependence of human health on environmental conditions. The essential features of stochastic climate models and their application for the exploration of climate variability are examined in detail. As an illustrative example, we looked at the application of a low-order energy balance model to study climate variability. The effects of variations in feedbacks and the climate system’s inertia on the power spectrum of global mean surface temperature fluctuations that characterized the distribution of temperature variance over frequencies were estimated using a sensitivity analysis approach. Our confidence in the obtained results was based on the satisfactory agreement between the theoretical power spectrum that was derived from the energy balance model and the power spectrum that was obtained from observations and coupled climate models, including historical runs of the CMIP5 models.

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Section: Using Low-order Simple Models To Study Climate Variabilitymentioning

confidence: 99%

Mathematical Modelling of Climate Change and Variability in the Context of Outdoor Ergonomics

2021

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“…РЕЗУЛьТАТы И Их ОБСУжДЕНИЕ В двухслойной модели (1)-(2) временной масштаб климатической изменчивости (межгодовая, междекадная) определяется эффективной теплоемкостью «быстрой» подсистемы, т.е. параметром С [26,35,45]. При рассмотрении межгодовой изменчивости климата «быстрая» подсистема включает в себя атмосферу и квазиоднородный слой океана.…”

Section: T T Dt G T a G T T B Tunclassified

“…Для этого случая параметр С принимает значения в диапазоне от 4.7 до 8.6 Вт год м -2 K -1 при среднем межмодельном значении 7.3 Вт год м -2 K -1 [38]. При моделировании междекадной изменчивости полагаем, что значения параметра C находятся в интервале от 15 до 25 Вт год м -2 K -1 [26,36,45], а в качестве характерного значения примем С = 20 Вт год м -2 K -1 . Параметр λ, характеризующий обратные связи в ЗКС, также задается в соответствии со значениями, приведенными в [37]: 0.61 ≤ λ ≤ 1.70 Вт м -2 K -1 при среднем межмодельном значении 1.13 Вт м -2 K -1 .…”

Section: T T Dt G T a G T T B Tunclassified

“…Однако в отличие от межмодельных расхождений чувствительности климата, причины, обуславливающие широкий диапазон оценок климатической изменчивости, до сих пор остаются не совсем понятными (см. [26] и др.). Тем не менее признается, что обратные связи играют существенную роль в процессах, обуславливающих изменчивость глобального климата [22,[27][28][29].…”

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“…/ ; / . Умножая обе части уравнения (5) на xx T и интегрируя затем полученное выражение по всем возможным значениям x, получим уравнение для вторых моментов xx T [26,43], которое в покомпонентной форме эквивалентно следующей системе уравнений:…”

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Estimating the influence of thermal inertia and feedbacks in the atmosphere–ocean system on the variability of global surface temperature

Soldatenko

Yusupov

2019

Известия Российской академии наук. Физика атмосферы и океана

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The present climate is characterized not only by the trend due to the increase in the concentrations of greenhouse gases in the atmosphere, but also by fluctuations covering a wide range of frequencies and scales. The global climate variability, calculated via the computational results from the Coupled Model Intercomparison Project Phase 5 of the World Climate Research Program, show significant inter-model differences. In particular, inter-model distinctions in decadal anomalies of the global and hemispheric temperatures reach four times the value. However, unlike the inter-model differences in climate sensitivity, the reasons for a wide range of estimates of climate variability are still unclear. Based on the two-component energy-balance stochastic model, the paper analyzes the inter-annual and inter-decadal variability of the mean global surface temperature (GST) to feedback and the thermal inertia of the atmosphere-ocean system, assuming that the external forcing is random fluctu-ations of the radiation balance at the top of the atmosphere. Using the obtained ab-solute and relative sensitivity functions, the influence of thermal inertia and feed-backs in the climate system on the inter-annual and inter-decimal variability (variance) of the GST and its spectrum is estimated.

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Understanding the links between climate feedbacks, variability and change using a two-layer energy balance model

Colman

Soldatenko

2020

Clim Dyn

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Climate variability from annual to multi-decadal timescales in a two-layer stochastic energy balance model: analytic solutions and implications for general circulation models

Abstract: View related articles View Crossmark data Citing articles: 3 View citing articles Climate variability from annual to multi-decadal timescales in a two-layer stochastic energy balance model: analytic solutions and implications for general circulation models

Cited by 17 publications

References 50 publications

Mathematical Modelling of Climate Change and Variability in the Context of Outdoor Ergonomics

Mathematical Modelling of Climate Change and Variability in the Context of Outdoor Ergonomics

Estimating the influence of thermal inertia and feedbacks in the atmosphere–ocean system on the variability of global surface temperature

Understanding the links between climate feedbacks, variability and change using a two-layer energy balance model

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