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Register a new userRobustness of competing climatic states
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The climate is a non-equilibrium system undergoing the continuous action of forcing and dissipation. Under the effect of a spatially inhomogeneous absorption of solar energy, all the climate components dynamically respond by redistributing energy until an approximate steady state is reached. In order to improve the skill of climate models and correct their biases, it is essential to investigate how such dynamical balance is reached. In general, the climate system features multiple steady states for a given set of boundary conditions. Here, we apply the Thermodynamic Diagnostic Tool (TheDiaTo) to investigate the statistical properties of the five co-existing climates, ranging from a snowball to an ice-free aquaplanet, obtained in MITgcm coupled simulations under the same boundary conditions. The aim is to explore the multistability of the climate by highlighting differences in competing steady states and their characteristic signatures regarding the meridional transport of heat and water mass, the Lorenz energy cycle and the material entropy production. Alternative cloud parametrizations and descriptions of energy exchange are also used to investigate how robust such signatures are and, at the same time, how the statistical properties can be improved in the simulated climatic states. Thus we show how the diagnostic tool can help in identifying strengths and weaknesses of a model configuration.
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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.
The impact of extreme climate such as drought and flooding on agriculture, tourism, migration and peace in Nigeria is immense. There is the need to study the trend and statistics for better planning, preparation and adaptation. In this study, the statistical and temporal variation of climatic indices Standardized Precipitation Index (SPI ) and Standardized Precipitation Evapotranspiration Index (SPEI) were computed for eighteen (18) stations covering four climatic zones (Sahel, Midland, Guinea Savannah and Coastal) of tropical Nigeria. Precipitation, minimum and maximum temperature from 1980 - 2010 obtained from the archives of the Nigerian Meteorological Services were used to compute both the SPI and SPEI indices at 1-, 3- 6- and 12-month timescales. The temporal variation of drought indices showed that droughts were more prominent at 6- and 12-months timescales. SPI and SPEI were found to be better correlated at longer timescales than short time scales. Predominant small, positive and significant trend across the region suggest an increasing trend due to climate change.
We propose a second renormalization group method to handle the tensor-network states or models. This method reduces dramatically the truncation error of the tensor renormalization group. It allows physical quantities of classical tensor-network models or tensor-network ground states of quantum systems to be accurately and efficiently determined.
Across many scientific and engineering disciplines, it is important to consider how much the output of a given system changes due to perturbations of the input. Here, we study the robustness of the ground states of $pm J$ spin glasses on random graphs to flips of the interactions. For a sparse graph, a dense graph, and the fully connected Sherrington-Kirkpatrick model, we find relatively large sets of interactions that generate the same ground state. These sets can themselves be analyzed as sub-graphs of the interaction domain, and we compute many of their topological properties. In particular, we find that the robustness of these sub-graphs is much higher than one would expect from a random model. Most notably, it scales in the same logarithmic way with the size of the sub-graph as has been found in genotype-phenotype maps for RNA secondary structure folding, protein quaternary structure, gene regulatory networks, as well as for models for genetic programming. The similarity between these disparate systems suggests that this scaling may have a more universal origin.
Convective and radiative cooling are the two principle mechanisms by which the Earths surface transfers heat into the atmosphere and that shape surface temperature. However, this partitioning is not sufficiently constrained by energy and mass balances alone. We use a simple energy balance model in which convective fluxes and surface temperatures are determined with the additional thermodynamic limit of maximum convective power. We then show that the broad geographic variation of heat fluxes and surface temperatures in the climatological mean compare very well with the ERA-Interim reanalysis over land and ocean. We also show that the estimates depend considerably on the formulation of longwave radiative transfer and that a spatially uniform offset is related to the assumed cold temperature sink at which the heat engine operates.
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