Related papers: On Spurious Causality, CO2, and Global Temperature
The climate response to anthropogenic forcing has long been one of the dominant uncertainties in predicting future climate change (Houghton et al, 2001). Many observationally-based estimates of climate sensitivity (S) have been presented in…
We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual predictions. In particular, we exploit the fact that…
Could cosmic structure have formed by the action of causal physics within the standard hot big bang, or was a prior period of inflation required? Recently there has been some discussion of whether causal sources could reproduce the pattern…
Surface heat flow is a key parameter for the geothermal structure, rheology, and hence the dynamics of continents. However, the coverage of heat flow measurements is still poor in many continental areas. By transforming the stable nonlinear…
A set of idealized experiments are performed to analyze the competing effects of declining atmospheric CO2 concentrations, the opening of an ocean gateway, and varying orbital parameters. These forcing mechanisms, which influence the global…
Inferring nonlinear and asymmetric causal relationships between multivariate longitudinal data is a challenging task with wide-ranging application areas including clinical medicine, mathematical biology, economics and environmental…
Using parametric analysis (curve fitting) we find a persistent temperature bump, coincident with World War II (WW2), in eight independent time series, four land- and four ocean-based. We fit the data with a Gaussian on a quadratic…
It is often known, from modelling studies, that a certain mode of climate tipping (of the oceanic thermohaline circulation, for example) is governed by an underlying fold bifurcation. For such a case we present a scheme of analysis that…
As a consequence of greenhouse forcing, all state of the art general circulation models predict a positive temperature trend that is greater for the troposphere than the surface. This predicted positive trend increases in value with…
Climate change impacts a broad spectrum of human resources and activities, necessitating the use of climate models to project long-term effects and inform mitigation and adaptation strategies. These models generate multiple datasets by…
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…
We propose a non-steady state model of the global temperature change. The model describes Earth's surface temperature dynamics under main climate forcing. The equations were derived from basic physical relationships and detailed assessment…
Liang information flow is a quantity widely used in classical network theory to quantify causation, and has been applied widely, for example, to finance and climate. The most striking aspect here is to freeze/subtract a certain node of the…
Accurate estimation of counterfactual outcomes in high-dimensional data is crucial for decision-making and understanding causal relationships and intervention outcomes in various domains, including healthcare, economics, and social…
Causality is receiving increasing attention by the artificial intelligence and machine learning communities. This paper gives an example of modelling a recommender system problem using causal graphs. Specifically, we approached the causal…
A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented. Invariant Causal Prediction was used to develop tools to understand the causal drivers of pyroCb formation.…
Determining changes in global temperature and precipitation that may indicate climate change is complicated by annual variations. One approach for finding potential climate change indicators is to train a model that predicts the year from…
Causal structure learning from observational data remains a non-trivial task due to various factors such as finite sampling, unobserved confounding factors, and measurement errors. Constraint-based and score-based methods tend to suffer…
One major drawback of state-of-the-art artificial intelligence is its lack of explainability. One approach to solve the problem is taking causality into account. Causal mechanisms can be described by structural causal models. In this work,…
This paper investigates the implications of the future continuation of the demonstrated past (1960-2012) strong correlation between first-difference atmospheric CO2 and global surface temperature. It does this, for the period from the…