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Register a new userIncluding climate system feedbacks in calculations of the social cost of methane
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Integrated assessment models (IAMs) are valuable tools that consider the interactions between socioeconomic systems and the climate system. Decision-makers and policy analysts employ IAMs to calculate the marginalized monetary cost of climate damages resulting from an incremental emission of a greenhouse gas. Used within the context of regulating anthropogenic methane emissions, this metric is called the social cost of methane (SC-CH$_4$). Because several key IAMs used for social cost estimation contain a simplified model structure that prevents the endogenous modeling of non-CO$_2$ greenhouse gases, very few estimates of the SC-CH$_4$ exist. For this reason, IAMs should be updated to better represent methane cycle dynamics that are consistent with comprehensive Earth System Models. We include feedbacks of climate change on the methane cycle to estimate the SC-CH$_4$. Our expected value for the SC-CH$_4$ is $1163/t-CH$_4$ under a constant 3.0% discount rate. This represents a 44% increase relative to a mean estimate without feedbacks on the methane cycle.
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When the climate system is forced, e.g. by emission of greenhouse gases, it responds on multiple time scales. As temperatures rise, feedback processes might intensify or weaken. Current methods to analyze feedback strength, however, do not take such state dependency into account; they only consider changes in (global mean) temperature and assume all feedbacks are linearly related to that. This makes (transient) changes in feedback strengths almost intangible and generally leads to underestimation of future warming. Here, we present a multivariate (and spatially explicit) framework that facilitates dissection of climate feedbacks over time scales. Using this framework, information on the composition of projected (transient) future climates and feedback strengths can be obtained. Moreover, it can be used to make projections for many emission scenarios through linear response theory. The new framework is illustrated using the Community Earth System Model version 2 (CESM2).
Increases in atmospheric CO2 and CH4 result from a combination of forcing from anthropogenic emissions and Earth System feedbacks that reduce or amplify the effects of those emissions on atmospheric concentrations. Despite decades of research carbon-climate feedbacks remain poorly quantified. The impact of these uncertainties on future climate are of increasing concern, especially in the wake of recent climate negotiations. Emissions, long concentrated in the developed world, are now shifting to developing countries, where the emissions inventories have larger uncertainties. The fraction of anthropogenic CO2 remaining in the atmosphere has remained remarkably constant over the last 50 years. Will this change in the future as the climate evolves? Concentrations of CH4, the 2nd most important greenhouse gas, which had apparently stabilized, have recently resumed their increase, but the exact cause for this is unknown. While greenhouse gases affect the global atmosphere, their sources and sinks are remarkably heterogeneous in time and space, and traditional in situ observing systems do not provide the coverage and resolution to attribute the changes to these greenhouse gases to specific sources or sinks. In the past few years, space-based technologies have shown promise for monitoring carbon stocks and fluxes. Advanc
Modern weather and climate models share a common heritage, and often even components, however they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should reflect this. While the use of machine learning to emulate weather forecast models is a relatively new endeavour there is a rich history of climate model emulation. This is primarily because while weather modelling is an initial condition problem which intimately depends on the current state of the atmosphere, climate modelling is predominantly a boundary condition problem. In order to emulate the response of the climate to different drivers therefore, representation of the full dynamical evolution of the atmosphere is neither necessary, or in many cases, desirable. Climate scientists are typically interested in different questions also. Indeed emulating the steady-state climate response has been possible for many years and provides significant speed increases that allow solving inverse problems for e.g. parameter estimation. Nevertheless, the large datasets, non-linear relationships and limited training data make Climate a domain which is rich in interesting machine learning challenges. Here I seek to set out the current state of climate model emulation and demonstrate how, despite some challenges, recent advances in machine learning provide new opportunities for creating useful statistical models of the climate.
Though the Boltzmann-Gibbs framework of equilibrium statistical mechanics has been successful in many arenas, it is clearly inadequate for describing many interesting natural phenomena driven far from equilibrium. The simplest step towards that goal is a better understanding of nonequilibrium steady-states (NESS). Here we focus on one of the distinctive features of NESS, persistent probability currents, and their manifestations in our climate system. We consider the natural variability of the steady-state climate system, which can be approximated as a NESS. These currents must form closed loops, which are odd under time reversal, providing the crucial difference between systems in thermal equilibrium and NESS. Seeking manifestations of such current loops leads us naturally to the notion of probability angular momentum and oscillations in the space of observables. Specifically, we will relate this concept to the asymmetric part of certain time-dependent correlation functions. Applying this approach, we propose that these current loops give rise to preferred spatio-temporal patterns of natural climate variability that take the form of climate oscillations such as the El-Ni~{n}o Southern Oscillation (ENSO) and the Madden-Julien Oscillation (MJO). In the space of climate indices, we observe persistent currents and define a new diagnostic for these currents: the probability angular momentum. Using the observed climatic time series of ENSO and MJO, we compute both the averages and the distributions of the probability angular momentum. These results are in good agreement with the analysis from a linear Gaussian model. We propose that, in addition to being a new quantification of climate oscillations across models and observations, the probability angular momentum provides a meaningful characterization for all statistical systems in NESS.
There is ongoing interest in the global entropy production rate as a climate diagnostic and predictor, but progress has been limited by ambiguities in its definition; different conceptual boundaries of the climate system give rise to different internal production rates. Three viable options are described, estimated and investigated here, two of which -- the material and the total radiative (here planetary) entropy production rates -- are well-established and a third which has only recently been considered but appears very promising. This new option is labelled the transfer entropy production rate and includes all irreversible processes that transfer heat within the climate, radiative and material, but not those involved in the exchange of radiation with space. Estimates in three model climates put the material rate in the range $27$-$48$ mW/m$^2$K, the transfer rate $67$-$76$ mW/m$^2$K, and the planetary rate $1279$-$1312$ mW/m$^2$K. The climate-relevance of each rate is probed by calculating their responses to climate changes in a simple radiative-convective model. An increased greenhouse effect causes a significant increase in the material and transfer entropy production rates but has no direct impact on the planetary rate. When the same surface temperature increase is forced by changing the albedo instead, the material and transfer entropy production rates increase less dramatically and the planetary rate also registers an increase. This is pertinent to solar radiation management as it demonstrates the difficulty of reversing greenhouse gas-mediated climate changes by albedo alterations. It is argued that the transfer perspective has particular significance in the climate system and warrants increased prominence.
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