Search results for " thermodynamics and nonlinear dynamics"

showing 2 items of 2 documents

Inferring causation from time series in earth system sciences

2019

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.

0301 basic medicineEarth scienceAquatic Ecology and Water Quality ManagementDynamical systems theoryComputer science530 PhysicsDatenmanagement und AnalyseSciencereviewGeneral Physics and Astronomyheart02 engineering and technologyGeneral Biochemistry Genetics and Molecular Biology03 medical and health sciencesDatabasesLife ScienceCausationStatistical physics thermodynamics and nonlinear dynamicsintermethod comparisonlcsh:Scienceresearch workScientific enterpriseMultidisciplinaryWIMEKSeries (mathematics)QComputational sciencefeasibility study500General ChemistryAquatische Ecologie en Waterkwaliteitsbeheersimulation021001 nanoscience & nanotechnologyData sciencecausal inference climateEarth system scienceEnvironmental sciences030104 developmental biologytime series analysisCausal inferencePerspectiveBenchmark (computing)Observational studylcsh:Qconceptual frameworkdata management0210 nano-technologyClimate sciences
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Inferring causal relations from observational long-term carbon and water fluxes records

2022

AbstractLand, atmosphere and climate interact constantly and at different spatial and temporal scales. In this paper we rely on causal discovery methods to infer spatial patterns of causal relations between several key variables of the carbon and water cycles: gross primary productivity, latent heat energy flux for evaporation, surface air temperature, precipitation, soil moisture and radiation. We introduce a methodology based on the convergent cross-mapping (CCM) technique. Despite its good performance in general, CCM is sensitive to (even moderate) noise levels and hyper-parameter selection. We present a robust CCM (RCCM) that relies on temporal bootstrapping decision scores and the deri…

MultidisciplinaryScienceStatisticsQRMedicineCarbon cycleHydrologyStatistical physics thermodynamics and nonlinear dynamicsArticleScientific Reports
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