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Author Topic: Modelling the Anthropocene  (Read 18281 times)

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #100 on: February 18, 2017, 04:10:21 PM »
The linked (open access) reference indicates that the current IPCC assessment models miss critical dynamic human-earth systems feedback mechanisms that means that our current assessments significantly underestimate coming impacts (see image of different types of possible collapses under our influence).

Safa Motesharrei et al, Modeling Sustainability: Population, Inequality, Consumption, and Bidirectional Coupling of the Earth and Human Systems, National Science Review (2016). DOI: 10.1093/nsr/nww081 

https://academic.oup.com/nsr/article-lookup/doi/10.1093/nsr/nww081

Abstract: “Over the last two centuries, the impact of the Human System has grown dramatically, becoming strongly dominant within the Earth System in many different ways. Consumption, inequality, and population have increased extremely fast, especially since about 1950, threatening to overwhelm the many critical functions and ecosystems of the Earth System. Changes in the Earth System, in turn, have important feedback effects on the Human System, with costly and potentially serious consequences. However, current models do not incorporate these critical feedbacks. We argue that in order to understand the dynamics of either system, Earth System Models must be coupled with Human System Models through bidirectional couplings representing the positive, negative, and delayed feedbacks that exist in the real systems. In particular, key Human System variables, such as demographics, inequality, economic growth, and migration, are not coupled with the Earth System but are instead driven by exogenous estimates, such as United Nations population projections. This makes current models likely to miss important feedbacks in the real Earth–Human system, especially those that may result in unexpected or counterintuitive outcomes, and thus requiring different policy interventions from current models. The importance and imminence of sustainability challenges, the dominant role of the Human System in the Earth System, and the essential roles the Earth System plays for the Human System, all call for collaboration of natural scientists, social scientists, and engineers in multidisciplinary research and modeling to develop coupled Earth–Human system models for devising effective science-based policies and measures to benefit current and future generations.”

Significance Statement: “The Human System has become strongly dominant within the Earth System in many different ways. However, in current models that explore the future of humanity and environment, and guide policy, key Human System variables, such as demographics, inequality, economic growth, and migration, are not coupled with the Earth System but are instead driven by exogenous estimates such as United Nations (UN) population projections. This makes the models likely to miss important feedbacks in the real Earth–Human system that may result in unexpected outcomes requiring very different policy interventions. The importance of humanity's sustainability challenges calls for collaboration of natural and social scientists to develop coupled Earth–Human system models for devising effective science-based policies and measures.”

See also the associated linked article entitled: “It's more than just climate change”.

https://phys.org/news/2017-02-climate_1.html


Extract: “Co-author Matthias Ruth, Director and Professor at the School of Public Policy and Urban Affairs, Northeastern University, said: "The result of not dynamically modeling these critical Human-Earth System feedbacks would be that the environmental challenges humanity faces may be significantly underestimated. Moreover, there's no explicit role given to policies and investments to actively shape the course in which the dynamics unfold. Rather, as the models are designed now, any intervention—almost by definition—comes from the outside and is perceived as a cost. Such modeling, and the mindset that goes with it, leaves no room for creativity in solving some of the most pressing challenges."

"The paper correctly highlights that other human stressors, not only the climate ones, are very important for long-term sustainability, including the need to reduce inequality'', said Carlos Nobre (not a co-author), one of the world's leading Earth System scientists, who recently won the prestigious Volvo Environment Prize in Sustainability for his role in understanding and protecting the Amazon. "Social and economic equality empowers societies to engage in sustainable pathways, which includes, by the way, not only the sustainable use of natural resources but also slowing down population growth, to actively diminish the human footprint on the environment."

Michael Mann, Distinguished Professor and Director of the Earth System Science Center at Penn State University, who was not a co-author of the paper, commented: "We cannot separate the issues of population growth, resource consumption, the burning of fossil fuels, and climate risk. They are part of a coupled dynamical system, and, as the authors show, this has dire potential consequences for societal collapse. The implications couldn't be more profound."”
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

sidd

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Re: Modelling the Anthropocene
« Reply #101 on: February 18, 2017, 06:57:20 PM »
Thanks for the Motasharrei et. al link to human influenced models. I reviewed his 2014 publication (ref 134 in this one, http://www.sciencedirect.com/science/article/pii/S0921800914000615 which oddly enuf has no doi) while reading this one, especially the graphs (including Fig 5 in the present review which you attached.)

In some of the 2014 results we see oscillations, and in others we do not. In the old days we used to call these underdamped and overdamped cases. It might be interesting to apply the methods of Sugihara(2012, doi:10.1126/science.1227079 ) and Ye(2015, http://www.pnas.org/cgi/doi/10.1073/pnas.1417063112 ) directly to the underdamped cases, while the overdamped ones might require rotation to imaginary time and back after the calculation.

I think i have some of the code written ... I may have to take a look

sidd
« Last Edit: February 18, 2017, 07:02:48 PM by sidd »

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #102 on: February 18, 2017, 09:00:29 PM »
I think i have some of the code written ... I may have to take a look


sidd,

If you get your model calibrated, see if you can include the impacts of a human-climate interaction mechanism coded as a strange attractor feeding into a meltdown of the entire financial system (maybe you can numerically show the elite that they suffer more losses than their collective guts are telling them):

See the linked article entitled: "Climate change could threaten entire financial system, APRA warns"

http://www.abc.net.au/news/2017-02-17/climate-change-could-threaten-entire-financial-system-apra/8281436

Extract: "Climate change could threaten the stability of the entire financial system, the prudential regulator has warned, as it prepares to apply climate change "stress tests" to the nation's financial institutions.

In its first major speech on climate change, the Australian Prudential Regulation Authority chastised companies for a lack of action on the risks it poses."

Best regards,
ASLR
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #103 on: February 18, 2017, 09:47:57 PM »
The linked reference examines the sensitivity of Shared Socioeconomic Pathways to changes in key parameters & finds that energy intensity and economic growth are the two most important determinants of future GHG emissions.  Now all we have to do is to convince people (including elites) to use less energy (of all types) and to accept less economic growth.

G. Marangoni, et. al. (2017), "Sensitivity of projected long-term CO2 emissions across the Shared Socioeconomic Pathways", Nature Climate Change, Volume: 7, Pages: 113–117, doi:10.1038/nclimate3199

http://www.nature.com/nclimate/journal/v7/n2/full/nclimate3199.html

Abstract: "Scenarios showing future greenhouse gas emissions are needed to estimate climate impacts and the mitigation efforts required for climate stabilization. Recently, the Shared Socioeconomic Pathways (SSPs) have been introduced to describe alternative social, economic and technical narratives, spanning a wide range of plausible futures in terms of challenges to mitigation and adaptation. Thus far the key drivers of the uncertainty in emissions projections have not been robustly disentangled. Here we assess the sensitivities of future CO2 emissions to key drivers characterizing the SSPs. We use six state-of-the-art integrated assessment models with different structural characteristics, and study the impact of five families of parameters, related to population, income, energy efficiency, fossil fuel availability, and low-carbon energy technology development. A recently developed sensitivity analysis algorithm allows us to parsimoniously compute both the direct and interaction effects of each of these drivers on cumulative emissions. The study reveals that the SSP assumptions about energy intensity and economic growth are the most important determinants of future CO2 emissions from energy combustion, both with and without a climate policy. Interaction terms between parameters are shown to be important determinants of the total sensitivities."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

sidd

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Re: Modelling the Anthropocene
« Reply #104 on: February 18, 2017, 10:14:58 PM »
My ideas at the moment  to take the model output (HANDY) in Motasharrei(2014), then real world data and

1)  use the empirical dynamic method in Ye(2015) to see if in fact there is at all evidence for the efficacy of the Sugihara(2012) method     
`
2) if there is, use cross convergence as in Sugihara(2012) to attemp to untangle causality between various outcomes

3) Either 1) or 2) could be used for prediction

4) Coupling financial systems into HANDY is not something i would untertake lightly. I have a more general idea in mind toward those ends, which would take me to far afield to explain fully at the moment. But briefly, the idea is to investigate the stability of the dynamical orbits describing the system (stability of projections into lower dimensional spaces from the full manifold, if you like). Once that is done i think coupling to other dynamical systems such as financial/economic/... ones would be much easier, since one could establish at a glance (well, a few glances and based on the independent behaviour of each system you were trying to couple) what the effect of the coupling on each would be.

5) Another powerful idea is from the notion of transfer entropy as in Liang(2015, doi:10.1103/PhysRevE.90.052150 ) to detect causality _and_ stabilization between time series. I have already coded to combine transfer entropy with cross convergence and empirical dynamical methods. The strength of this combination is that it winnows the relevant subspaces down to those which capture the essentials of the dynamics. Thus we only have to look at a few possible combinations for the relevant subspaces, neatly evading combinatorial explosion (i hope)

6) I can do all this from realworld time series (as I have already done in some ecological systems) or from the HANDY output. My instinct is to jump right into real data, but i need to get some familiarity with these methods on simple (ha!) models before diving into the cold and deep pools of reality

The program outlined above is very easy to conceive, on a sunny afternoon in Philly, sitting on a deck outside, drinking hi test microbrew, and cheeping back at the birds and miaowing at itinerant cats. How much of it i actually get done I cannot say.

sidd

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #105 on: February 18, 2017, 10:41:25 PM »
The program outlined above is very easy to conceive, on a sunny afternoon in Philly, sitting on a deck outside, drinking hi test microbrew, and cheeping back at the birds and miaowing at itinerant cats. How much of it i actually get done I cannot say.

Godspeed, John Glenn.
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

sidd

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Re: Modelling the Anthropocene
« Reply #106 on: February 19, 2017, 05:32:46 AM »
"Godspeed, John Glenn."

Heehee. I didn't think I was quite so obviously dead yet. 

His funeral was on a bitingly cold day in Columbus, OH. They had an imressive turnout for the weather, but then ohioans, even the soft ones in columbus, are quite stoic. I was in columbus then, but did not attend.

There are little towns in ohio like Cambridge and New Concord where he grew up, and I have been thru many times. They have seen hard times.

sidd

bligh8

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Re: Modelling the Anthropocene
« Reply #107 on: February 21, 2017, 08:24:15 AM »
Hey…..wow, such musings on a Saturday afternoon in April, a breath of fresh air indeed.

Fair Winds
bligh

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #108 on: March 06, 2017, 07:52:22 PM »
The linked open access reference discusses various techniques to expand current ESMs to better human behavior:

Finn Müller-Hansen, Maja Schlüter, Michael Mäs, Rainer Hegselmann, Jonathan F. Donges, Jakob J. Kolb, Kirsten Thonicke, and Jobst Heitzig (2017), "How to represent human behavior and decision making in Earth system models? A guide to techniques and approaches", Earth Syst. Dynam. Discuss., doi:10.5194/esd-2017-18

http://www.earth-syst-dynam-discuss.net/esd-2017-18/esd-2017-18.pdf

Abstract: "In the Anthropocene, humans have a critical impact on the Earth system and vice versa, which can generate complex feedback processes between social and ecological dynamics. Integrating human behavior into formal Earth System Models (ESMs), however, requires crucial modeling assumptions about actors and their goals, behavioral options and decision rules, as well as modeling decisions regarding human social interactions and the aggregation of individuals' behavior. In this tutorial review, we compare existing modeling approaches and techniques from different disciplines and schools of thought dealing with human behavior at various levels of decision making. Providing an overview over social-scientific modeling approaches, we demonstrate modelers' often vast degrees of freedom but also seek to make modelers aware of the often crucial consequences of seemingly innocent modeling assumptions.

After discussing which socio-economic units are potentially important for ESMs, we review models of individual decision making that correspond to alternative behavioral theories and that make diverse modeling assumptions about individuals' preferences, beliefs, decision rules, and foresight. We discuss approaches to model social interaction, covering game theoretic frameworks, models of social influence and network models. Finally, we elaborate approaches to study how the behavior of individuals, groups and organizations can aggregate to complex collective phenomena, discussing agent-based, statistical and representative-agent modeling and economic macro-dynamics. We illustrate the main ingredients of modeling techniques with examples from land-use dynamics as one of the main drivers of environmental change bridging local to global scales."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #109 on: March 15, 2017, 10:26:03 PM »
The linked reference uses Amazonian deep convective clouds as an example of the valued of using the Gamma phase space for modeling clouds:

Cecchini, M. A., Machado, L. A. T., Wendisch, M., Costa, A., Krämer, M., Andreae, M. O., Afchine, A., Albrecht, R. I., Artaxo, P., Borrmann, S., Fütterer, D., Klimach, T., Mahnke, C., Martin, S. T., Minikin, A., Molleker, S., Pardo, L. H., Pöhlker, C., Pöhlker, M. L., Pöschl, U., Rosenfeld, D., and Weinzierl, B.: Illustration of microphysical processes in Amazonian deep convective clouds in the Gamma phase space: Introduction and potential applications, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2017-185, in review, 2017.

http://www.atmos-chem-phys-discuss.net/acp-2017-185/

Abstract. The behavior of tropical clouds remains a major open scientific question, given that the associated phys-ics is not well represented by models. One challenge is to realistically reproduce cloud droplet size dis-tributions (DSD) and their evolution over time and space. Many applications, not limited to models, use the Gamma function to represent DSDs. However, there is almost no study dedicated to understanding the phase space of this function, which is given by the three parameters that define the DSD intercept, shape, and curvature. Gamma phase space may provide a common framework for parameterizations and inter-comparisons. Here, we introduce the phase-space approach and its characteristics, focusing on warm-phase microphysical cloud properties and the transition to the mixed-phase layer. We show that trajectories in this phase space can represent DSD evolution and can be related to growth processes. Condensational and collisional growth may be interpreted as pseudo-forces that induce displacements in opposite directions within the phase space. The actually observed movements in the phase space are a result of the combination of such pseudo-forces. Additionally, aerosol effects can be evaluated given their significant impact on DSDs. The DSDs associated with liquid droplets that favor cloud glaciation can be delimited in the phase space, which can help models to adequately predict the transition to the mixed phase. We also consider possible ways to constrain the DSD in two-moment bulk microphysics schemes, where the relative dispersion parameter of the DSD can play a significant role. Overall, the Gamma phase-space approach can be an invaluable tool for studying cloud microphysical evolution and can be readily applied in many scenarios that rely on Gamma DSDs.
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #110 on: March 16, 2017, 03:34:21 AM »
The linked reference discusses the use of Wasserstein distance can be used to better understand climate attractors.

Robin, Y., Yiou, P., and Naveau, P.: Detecting Changes in Forced Climate Attractors with Wasserstein Distance, Nonlin. Processes Geophys. Discuss., doi:10.5194/npg-2017-5, in review, 2017.

http://www.nonlin-processes-geophys-discuss.net/npg-2017-5/

Abstract. The climate system can been described by a dynamical system and its associated attractor. The dynamics of this attractor depends on the external forcings that influence the climate. Such forcings can affect the mean values or variances, but regions of the attractor that are seldom visited can also be affected. It is an important challenge to measure how the climate attractor responds to different forcings. Currently, the Euclidean distance or similar measures like the Mahalanobis distance have been favoured to measure discrepancies between two climatic situations. Those distances do not have a natural building mechanism to take into account the attractor dynamics. In this paper, we argue that a Wasserstein distance, stemming from optimal transport theory, offers an efficient and practical way to discriminate between dynamical systems. After treating a toy example, we explore how the Wasserstein distance can be applied and interpreted to detect non-autonomous dynamics from a Lorenz system driven by seasonal cycles and a warming trend.

For the open access pdf see:

http://www.nonlin-processes-geophys-discuss.net/npg-2017-5/npg-2017-5.pdf

“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson

AbruptSLR

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Re: Modelling the Anthropocene
« Reply #111 on: March 22, 2017, 06:23:30 PM »
The linked reference discusses model projected changes in extreme precipitation with global warming; and indicates that rainfall will likely increase through 2100; which to my mind will (among other things) accelerate degradation of permafrost.

Guiling Wang, Dagang Wang, Kevin E. Trenberth, Amir Erfanian, Miao Yu, Michael G. Bosilovich, & Dana T. Parr (2017), "The peak structure and future changes of the relationships between extreme precipitation and temperature", Nature Climate Change, doi:10.1038/nclimate3239

http://www.nature.com/nclimate/journal/vaop/ncurrent/full/nclimate3239.html

Abstract: "Theoretical models predict that, in the absence of moisture limitation, extreme precipitation intensity could exponentially increase with temperatures at a rate determined by the Clausius–Clapeyron (C–C) relationship.  Climate models project a continuous increase of precipitation extremes for the twenty-first century over most of the globe. However, some station observations suggest a negative scaling of extreme precipitation with very high temperatures, raising doubts about future increase of precipitation extremes. Here we show for the present-day climate over most of the globe, the curve relating daily precipitation extremes with local temperatures has a peak structure, increasing as expected at the low–medium range of temperature variations but decreasing at high temperatures. However, this peak-shaped relationship does not imply a potential upper limit for future precipitation extremes. Climate models project both the peak of extreme precipitation and the temperature at which it peaks (Tpeak) will increase with warming; the two increases generally conform to the C–C scaling rate in mid- and high-latitudes, and to a super C–C scaling in most of the tropics. Because projected increases of local mean temperature (Tmean) far exceed projected increases of Tpeak over land, the conventional approach of relating extreme precipitation to Tmean produces a misleading sub-C–C scaling rate."
“It is not the strongest or the most intelligent who will survive but those who can best manage change.”
― Leon C. Megginson