Climate Model Sensitivity with Respect to Parameters and External Forcing

Soldatenko, Sergei; Chichkine, Denis

doi:10.5772/64710

Cited by 6 publications

(8 citation statements)

References 36 publications

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“…However, this topic is beyond the scope of this paper, since it is not related to weather and climate modification and control. A fairly complete and comprehensive survey of sensitivity analysis for nonlinear dynamical systems exhibiting, under certain conditions, chaotic behavior can be found in previously published papers (e.g., [27,28]).…”

Section: Sensitivity Analysis Of Atmospheric and Climate Modelsmentioning

confidence: 99%

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An Optimal Control Perspective on Weather and Climate Modification

Soldatenko

Yusupov

2021

Mathematics

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Intentionally altering natural atmospheric processes using various techniques and technologies for changing weather patterns is one of the appropriate human responses to climate change and can be considered a rather drastic adaptation measure. A fundamental understanding of the human ability to modify weather conditions requires collaborative research in various scientific fields, including, but not limited to, atmospheric sciences and different branches of mathematics. This article being theoretical and methodological in nature, generalizes and, to some extent, summarizes our previous and current research in the field of climate and weather modification and control. By analyzing the deliberate change in weather and climate from an optimal control and dynamical systems perspective, we get the ability to consider the modification of natural atmospheric processes as a dynamic optimization problem with an emphasis on the optimal control problem. Within this conceptual and unified theoretical framework for developing and synthesizing an optimal control for natural weather phenomena, the atmospheric process in question represents a closed-loop dynamical system described by an appropriate mathematical model or, in other words, by a set of differential equations. In this context, the human control actions can be described by variations of the model parameters selected on the basis of sensitivity analysis as control variables. Application of the proposed approach to the problem of weather and climate modification is illustrated using a low-order conceptual model of the Earth’s climate system. For the sake of convenient interpretation, we provide some weather and climate basics, as well as we give a brief glance at control theory and sensitivity analysis of dynamical systems.

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Section: Sensitivity Analysis Of Atmospheric and Climate Modelsmentioning

confidence: 99%

“…additional necessary conditions for optimality, the complementary slackness condition, must be specified. The meaning of the path constraint condition (28) is that the global mean surface temperature anomaly should not exceed the value of threshold parameter C T , which is set a priori.…”

Section: Illustrative Examplementioning

confidence: 99%

An Optimal Control Perspective on Weather and Climate Modification

Soldatenko

Yusupov

2021

Mathematics

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“…In addition to the absolute sensitivity functions, S α (α = σ, Λ, u T ), relative sensitivity functions, S R α , are used to estimate the relative influence of the model parameters on the growth rate of unstable waves, which allow ranking the parameters by the degree of influence of their variations on the variations on the growth rate. Relative sensitivity function is defined by [80]…”

Section: Sensitivity Functions For Estimating the Influence Of Globalmentioning

confidence: 99%

Estimated Impacts of Climate Change on Eddy Meridional Moisture Transport in the Atmosphere

Soldatenko

2019

Applied Sciences

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Research findings suggest that water (hydrological) cycle of the earth intensifies in response to climate change, since the amount of water that evaporates from the ocean and land to the atmosphere and the total water content in the air will increase with temperature. In addition, climate change affects the large-scale atmospheric circulation by, for example, altering the characteristics of extratropical transient eddies (cyclones), which play a dominant role in the meridional transport of heat, moisture, and momentum from tropical to polar latitudes. Thus, climate change also affects the planetary hydrological cycle by redistributing atmospheric moisture around the globe. Baroclinic instability, a specific type of dynamical instability of the zonal atmospheric flow, is the principal mechanism by which extratropical cyclones form and evolve. It is expected that, due to global warming, the two most fundamental dynamical quantities that control the development of baroclinic instability and the overall global atmospheric dynamics—the parameter of static stability and the meridional temperature gradient (MTG)—will undergo certain changes. As a result, climate change can affect the formation and evolution of transient extratropical eddies and, therefore, macro-exchange of heat and moisture between low and high latitudes and the global water cycle as a whole. In this paper, we explore the effect of changes in the static stability parameter and MTG caused by climate change on the annual-mean eddy meridional moisture flux (AMEMF), using the two classical atmospheric models: the mid-latitude f-plane model and the two-layer β-plane model. These models are represented in two versions: “dry,” which considers the static stability of dry air alone, and “moist,” in which effective static stability is considered as a combination of stability of dry and moist air together. Sensitivity functions were derived for these models that enable estimating the influence of infinitesimal perturbations in the parameter of static stability and MTG on the AMEMF and on large-scale eddy dynamics characterized by the growth rate of unstable baroclinic waves of various wavelengths. For the base climate change scenario, in which the surface temperature increases by 1 °C and warming of the upper troposphere outpaces warming of the lower troposphere by 2 °C (this scenario corresponds to the observed warming trend), the response of the mass-weighted vertically averaged annual mean MTG is -0.2 ℃ per 1000 km. The dry static stability increases insignificantly relative to the reference climate state, while on the other hand, the effective static stability decreases by more than 5.4%. Assuming that static stability of the atmosphere and the MTG are independent of each other (using One-factor-at-a-time approach), we estimate that the increase in AMEMF caused by change in MTG is about 4%. Change in dry static stability has little effect on AMEMF, while change in effective static stability leads to an increase in AMEMF of about 5%. Thus, neglecting atmospheric moisture in calculations of the atmospheric static stability leads to tangible differences between the results obtained using the dry and moist models. Moist models predict ~9% increase in AMEMF due to global warming. Dry models predict ~4% increase in AMEMF solely because of the change in MTG. For the base climate change scenario, the average temperature of the lower troposphere (up to ~4 km), in which the atmospheric moisture is concentrated, increases by ~1.5 ℃. This leads to an increase in specific humidity of about 10.5%. Thus, since both AMEMF and atmospheric water vapor content increase due to the influence of climate change, a rather noticeable restructuring of the global water cycle is expected.

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“…As an important application of the phenomena introduced in the previous sections, we consider the coupled ocean atmosphere model used in climate studies [44,45]. In this context, it is usual to consider low dimensional Lorenz system as the basic dynamics and couple two versions of the same , one with fast and other with slow time scales, to model the interactive dynamics of a fast oscillating atmosphere and slow-fluctuating ocean.…”

Section: Coupled Ocean Atmosphere Modelmentioning

confidence: 99%

“…In this context, it is usual to consider low dimensional Lorenz system as the basic dynamics and couple two versions of the same , one with fast and other with slow time scales, to model the interactive dynamics of a fast oscillating atmosphere and slow-fluctuating ocean. The equations representing coupled convective dynamics studied earlier are given below [45].…”

Section: Coupled Ocean Atmosphere Modelmentioning

confidence: 99%

Suppression of dynamics and frequency synchronization in coupled slow and fast dynamical systems

Gupta

Ambika

2016

Eur. Phys. J. B

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Abstract. We present our study on the emergent states of two interacting nonlinear systems with differing dynamical time scales. We find that the inability of the interacting systems to fall in step leads to difference in phase as well as change in amplitude. If the mismatch is small, the systems settle to a frequency synchronized state with constant phase difference. But as mismatch in time scale increases, the systems have to compromise to a state of no oscillations. We illustrate this for standard nonlinear systems and identify the regions of quenched dynamics in the parameter plane. The transition curves to this state are studied analytically and confirmed by direct numerical simulations. As an important special case, we revisit the well-known model of coupled ocean atmosphere system used in climate studies for the interactive dynamics of a fast oscillating atmosphere and slowly changing ocean. Our study in this context indicates occurrence of multi stable periodic states and steady states of convection coexisting in the system, with a complex basin structure.

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Climate Model Sensitivity with Respect to Parameters and External Forcing

Cited by 6 publications

References 36 publications

An Optimal Control Perspective on Weather and Climate Modification

An Optimal Control Perspective on Weather and Climate Modification

Estimated Impacts of Climate Change on Eddy Meridional Moisture Transport in the Atmosphere

Suppression of dynamics and frequency synchronization in coupled slow and fast dynamical systems

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