Transient hysteresis of near-surface permafrost response to external forcing

Елисеев, А. В.; Demchenko, P. F.; Arzhanov, M. M.; Мохов, И. И.

doi:10.1007/s00382-013-1672-5

Cited by 22 publications

(12 citation statements)

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“…It is one experiment type in the multirun simulation environment SimEnv for sensitivity and uncertainty analysis of model output (Flechsig et al, 2013) which we use for all calibration experiments. A schematic plot of the optimization process is shown in Sect.…”

Section: Calibrationmentioning

confidence: 99%

“…5 we introduce our specific calibration method. For parameter optimization of the wind velocities, we use simulated annealing, which approximates the global minimum of a high-dimensional function (Flechsig et al, 2013). In Sect.…”

mentioning

confidence: 99%

“…Thus, following classical statistical-dynamical approaches (Dobrovolski, 2000;Imkeller and von Storch, 2012), the key assumption is that the wind velocity field V can be split into a synoptic-scale component V (2-to 6-day period) and a large-scale, longterm component V (Fraedrich and Böttger, 1978) such that…”

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confidence: 99%

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The dynamical core of the Aeolus 1.0 statistical–dynamical atmosphere model: validation and parameter optimization

et al. 2018

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Abstract. We present and validate a set of equations for representing the atmosphere's large-scale general circulation in an Earth system model of intermediate complexity (EMIC). These dynamical equations have been implemented in Aeolus 1.0, which is a statistical-dynamical atmosphere model (SDAM) and includes radiative transfer and cloud modules (Coumou et al., 2011;Eliseev et al., 2013). The statistical dynamical approach is computationally efficient and thus enables us to perform climate simulations at multimillennia timescales, which is a prime aim of our model development. Further, this computational efficiency enables us to scan large and high-dimensional parameter space to tune the model parameters, e.g., for sensitivity studies.Here, we present novel equations for the large-scale zonalmean wind as well as those for planetary waves. Together with synoptic parameterization (as presented by Coumou et al., 2011), these form the mathematical description of the dynamical core of Aeolus 1.0.We optimize the dynamical core parameter values by tuning all relevant dynamical fields to ERA-Interim reanalysis data forcing the dynamical core with prescribed surface temperature, surface humidity and cumulus cloud fraction. We test the model's performance in reproducing the seasonal cycle and the influence of the El Niño-Southern Oscillation (ENSO). We use a simulated annealing optimization algorithm, which approximates the global minimum of a high-dimensional function.With non-tuned parameter values, the model performs reasonably in terms of its representation of zonal-mean circulation, planetary waves and storm tracks. The simulated annealing optimization improves in particular the model's representation of the Northern Hemisphere jet stream and storm tracks as well as the Hadley circulation.The regions of high azonal wind velocities (planetary waves) are accurately captured for all validation experiments. The zonal-mean zonal wind and the integrated lower troposphere mass flux show good results in particular in the Northern Hemisphere. In the Southern Hemisphere, the model tends to produce too-weak zonal-mean zonal winds and a too-narrow Hadley circulation. We discuss possible reasons for these model biases as well as planned future model improvements and applications.

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Section: Calibrationmentioning

confidence: 99%

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The dynamical core of the Aeolus 1.0 statistical–dynamical atmosphere model: validation and parameter optimization

et al. 2018

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“…Using another EMIC, IAP RAS CM, Eliseev et al . [] demonstrated hysteresis in the permafrost system, showing that permafrost melted faster in a warming world than it grew in a cooling world at the same temperature.…”

Section: Climatic Tipping Elementsmentioning

confidence: 99%

Tipping elements and climate–economic shocks: Pathways toward integrated assessment

Kopp

Shwom²,

Wagner³

et al. 2016

Earth's Future

164

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The literature on the costs of climate change often draws a link between climatic "tipping points" and large economic shocks, frequently called "catastrophes." The phrase "tipping points" in this context can be misleading. In popular and social scientific discourse, "tipping points" involve abrupt state changes. For some climatic "tipping points," the commitment to a state change may occur abruptly, but the change itself may be rate-limited and take centuries or longer to realize. Additionally, the connection between climatic "tipping points" and economic losses is tenuous, although emerging empirical and process-model-based tools provide pathways for investigating it. We propose terminology to clarify the distinction between "tipping points" in the popular sense, the critical thresholds exhibited by climatic and social "tipping elements," and "economic shocks." The last may be associated with tipping elements, gradual climate change, or nonclimatic triggers. We illustrate our proposed distinctions by surveying the literature on climatic tipping elements, climatically sensitive social tipping elements, and climate-economic shocks, and we propose a research agenda to advance the integrated assessment of all three.

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“…A common approach for parameter tuning is simulated annealing (Ingber, 1996;Kirkpatrick, 1984). It is one experiment type in the multirun simulation environment SimEnv for sensitivity and uncertainty analysis of model output (Flechsig et al, 2013) which we use for all calibration experiments. A schematic plot of the optimization process is shown in Sect.…”

Section: Calibrationmentioning

confidence: 99%

response to anonymous referee 1

Totz¹

2017

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We present and validate a set of equations for representing the atmosphere's large-scale general circulation in an Earth system model of intermediate complexity (EMIC). These dynamical equations have been implemented in Aeolus 1.0, which is a statistical-dynamical atmosphere model (SDAM) and includes radiative transfer and cloud modules (Coumou et al., 2011; Eliseev et al., 2013). The statistical dynamical approach is computationally efficient and thus enables us to perform climate simulations at multimillennia timescales, which is a prime aim of our model development. Further, this computational efficiency enables us to scan large and high-dimensional parameter space to tune the model parameters, e.g., for sensitivity studies. Here, we present novel equations for the large-scale zonalmean wind as well as those for planetary waves. Together with synoptic parameterization (as presented by Coumou et al., 2011), these form the mathematical description of the dynamical core of Aeolus 1.0. We optimize the dynamical core parameter values by tuning all relevant dynamical fields to ERA-Interim reanalysis data (1983-2009) forcing the dynamical core with prescribed surface temperature, surface humidity and cumulus cloud fraction. We test the model's performance in reproducing the seasonal cycle and the influence of the El Niño-Southern Oscillation (ENSO). We use a simulated annealing optimization algorithm, which approximates the global minimum of a high-dimensional function. With non-tuned parameter values, the model performs reasonably in terms of its representation of zonal-mean circulation, planetary waves and storm tracks. The simulated annealing optimization improves in particular the model's representation of the Northern Hemisphere jet stream and storm tracks as well as the Hadley circulation. The regions of high azonal wind velocities (planetary waves) are accurately captured for all validation experiments. The zonal-mean zonal wind and the integrated lower troposphere mass flux show good results in particular in the Northern Hemisphere. In the Southern Hemisphere, the model tends to produce too-weak zonal-mean zonal winds and a too-narrow Hadley circulation. We discuss possible reasons for these model biases as well as planned future model improvements and applications. 1 Introduction Numerical models of the Earth system play a key role in our understanding of physical processes in Earth and atmosphere and can be used to simulate past and future climate changes.

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Transient hysteresis of near-surface permafrost response to external forcing

Cited by 22 publications

References 45 publications

The dynamical core of the Aeolus 1.0 statistical–dynamical atmosphere model: validation and parameter optimization

The dynamical core of the Aeolus 1.0 statistical–dynamical atmosphere model: validation and parameter optimization

Tipping elements and climate–economic shocks: Pathways toward integrated assessment

response to anonymous referee 1

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