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Register a new userChaotic signature of climate extremes
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Ibiyinka Fuwape
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Understanding the dynamics of climate extreme is important in its prediction and modeling. In this study, linear trends in percentile, threshold, absolute, and duration based temperature and precipitation extremes indicator were obtained for the period 1979 - 2012 using the ETCCDI data set. The pattern of trend was compared with nonlinear measures (Entropy, Hurst Exponent, Recurrence Quantification Analysis) of temperature and precipitation. Regions which show positive trends in temperature based extremes were found to be areas with low entropy and chaotic. Complexity measures also revealed that the dynamics of the southern hemisphere differs from that of the northern hemisphere.
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We construct and analyze climate networks based on daily satellite measurements of temperatures and geopotential heights. We show that these networks are stable during time and are similar over different altitudes. Each link in our network is stable with typical 15% variability. The entire hierarchy of links is about 80% consistent during time. We show that about half of this stability is due to the spatial 2D embedding of the network, and half is due to physical coupling mechanisms. The network stability of equatorial regions is found to be lower compared to the stability of a typical network in non-equatorial regions.
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.
The influence of the Madden Julian Oscillation (MJO) on the precipitation extremes in Indonesia during the rainy season (October to April) has been evaluated using the daily station rain gauge data and the gridded Asian Precipitation Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) from 1987 to 2017 for different phases of the MJO. The results show that MJO significantly modulates the frequency of extreme precipitation events in Indonesia, with the magnitude of the impact varying across regions. Specifically, the convectively active (suppressed) MJO increases (decreases) the probability of extreme precipitation events over the western and central parts of Indonesia by up to 70% (40%). In the eastern part of Indonesia, MJO increases (decreases) extreme precipitation probability by up to 50% (40%). We attribute the differences in the probability of extreme precipitation events to the changes in the horizontal moisture flux convergence induced by MJO. The results indicate that the MJO provides the source of predictability of daily extreme precipitation in Indonesia.
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
We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model and analyze their interplay. First, drawing from the theory of quasipotentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states, and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, specifically manifold learning, we characterize the data landscape of the simulation output to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earths climate.
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