Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections

Documenting the uncertainty of climate change projections is a fundamental objective of the inter-comparison exercises organized to feed into the Intergovernmental Panel on Climate Change (IPCC) reports. Usually, each modeling center contributes to these exercises with one or two configurations of its climate model, corresponding to a particular choice of "free parameter" values, resulting from a long and often tedious "model tuning" phase. How much uncertainty is omitted by this selection and how might readers of IPCC reports and users of climate projections be misled by its omission? We show here how recent machine learning approaches can transform the way climate model tuning is approached, opening the way to a simultaneous acceleration of model improvement and parametric uncertainty quantification. We show how an automatic selection of model configurations defined by different values of free parameters can produce different "warming worlds," all consistent with present-day observations of the climate system.

Coupling radiative, conductive and convective heat-transfers in a single Monte Carlo algorithm: A general theoretical framework for linear situations

It was recently shown that radiation, conduction and convection can be combined within a single Monte Carlo algorithm and that such an algorithm immediately benefits from state-of-the-art computer-graphics advances when dealing with complex geometries. The theoretical foundations that make this coupling possible are fully exposed for the first time, supporting the intuitive pictures of continuous thermal paths that run through the different physics at work. First, the theoretical frameworks of propagators and Green's functions are used to demonstrate that a coupled model involving different physical phenomena can be probabilized. Second, they are extended and made operational using the Feynman-Kac theory and stochastic processes. Finally, the theoretical framework is supported by a new proposal for an approximation of coupled Brownian trajectories compatible with the algorithmic design required by ray-tracing acceleration techniques in highly refined geometry.

Are general circulation models obsolete?

Traditional general circulation models, or GCMs-that is, three-dimensional dynamical models with unresolved terms represented in equations with tunable parameters-have been a mainstay of climate research for several decades, and some of the pioneering studies have recently been recognized by a Nobel prize in Physics. Yet, there is considerable debate around their continuing role in the future. Frequently mentioned as limitations of GCMs are the structural error and uncertainty across models with different representations of unresolved scales and the fact that the models are tuned to reproduce certain aspects of the observed Earth. We consider these shortcomings in the context of a future generation of models that may address these issues through substantially higher resolution and detail, or through the use of machine learning techniques to match them better to observations, theory, and process models. It is our contention that calibration, far from being a weakness of models, is an essential element in the simulation of complex systems, and contributes to our understanding of their inner workings. Models can be calibrated to reveal both fine-scale detail and the global response to external perturbations. New methods enable us to articulate and improve the connections between the different levels of abstract representation of climate processes, and our understanding resides in an entire hierarchy of models where GCMs will continue to play a central role for the foreseeable future.

The "teapot in a city": A paradigm shift in urban climate modeling

Urban areas are a high-stake target of climate change mitigation and adaptation measures. To understand, predict, and improve the energy performance of cities, the scientific community develops numerical models that describe how they interact with the atmosphere through heat and moisture exchanges at all scales. In this review, we present recent advances that are at the origin of last decade's revolution in computer graphics, and recent breakthroughs in statistical physics that extend well-established path-integral formulations to nonlinear coupled models. We argue that this rare conjunction of scientific advances in mathematics, physics, computer, and engineering sciences opens promising avenues for urban climate modeling and illustrate this with coupled heat transfer simulations in complex urban geometries under complex atmospheric conditions. We highlight the potential of these approaches beyond urban climate modeling for the necessary appropriation of the issues at the heart of the energy transition by societies.

Increased risk of near term global warming due to a recent AMOC weakening

Some of the new generation CMIP6 models are characterised by a strong temperature increase in response to increasing greenhouse gases concentration¹. At first glance, these models seem less consistent with the temperature warming observed over the last decades. Here, we investigate this issue through the prism of low-frequency internal variability by comparing with observations an ensemble of 32 historical simulations performed with the IPSL-CM6A-LR model, characterized by a rather large climate sensitivity. We show that members with the smallest rates of global warming over the past 6-7 decades are also those with a large internally-driven weakening of the Atlantic Meridional Overturning Circulation (AMOC). This subset of members also matches several AMOC observational fingerprints, which are in line with such a weakening. This suggests that internal variability from the Atlantic Ocean may have dampened the magnitude of global warming over the historical era. Taking into account this AMOC weakening over the past decades means that it will be harder to avoid crossing the 2 °C warming threshold.

New climate models show a stronger warming with greenhouse gas emissions than is suggested by observations. Here, the authors argue that internal variability of the Atlantic Ocean may have dampened some of the recent warming, which could explain part of the disagreement between the newer models and observations.

The latitudinal distribution of clouds on Titan

Clouds have been observed recently on Titan, through the thick haze, using near-infrared spectroscopy and images near the south pole and in temperate regions near 40 degrees S. Recent telescope and Cassini orbiter observations are now providing an insight into cloud climatology. To study clouds, we have developed a general circulation model of Titan that includes cloud microphysics. We identify and explain the formation of several types of ethane and methane clouds, including south polar clouds and sporadic clouds in temperate regions and especially at 40 degrees in the summer hemisphere. The locations, frequencies, and composition of these cloud types are essentially explained by the large-scale circulation.

A wind origin for Titan's haze structure

Titan, the largest moon of Saturn, is the only satellite in the Solar System with a dense atmosphere. Titan's atmosphere is mainly nitrogen with a surface pressure of 1.5 atmospheres and a temperature of 95 K (ref. 1). A seasonally varying haze, which appears to be the main source of heating and cooling that drives atmospheric circulation, shrouds the moon. The haze has numerous features that have remained unexplained. There are several layers, including a 'polar hood', and a pronounced hemispheric asymmetry. The upper atmosphere rotates much faster than the surface of the moon, and there is a significant latitudinal temperature asymmetry at the equinoxes. Here we describe a numerical simulation of Titan's atmosphere, which appears to explain the observed features of the haze. The critical new factor in our model is the coupling of haze formation with atmospheric dynamics, which includes a component of strong positive feedback between the haze and the winds.

Simulations of Titan's brightness by a two-dimensional haze model

We have used a 2-D microphysics model to study the effects of atmospheric motions on the albedo of Titan's thick haze layer. We compare our results to the observed variations of Titan's brightness with season and latitude. We use two wind fields; the first is a simple pole-to-pole Hadley cell that reverses twice a year. The second is based on the results of a preliminary Titan GCM. Seasonally varying wind fields, with horizontal velocities of about 1 cm sec-1 at optical depth unity, are capable of producing the observed change in geometric albedo of about 10% over the Titan year. Neither of the two wind fields can adequately reproduce the latitudinal distribution of reflectivity seen by Voyager. At visible wavelengths, where only haze opacity is important, upwelling produces darkening by increasing the particle size at optical depth unity. This is due to the suspension of larger particles as well as the lateral removal of smaller particles from the top of the atmosphere. At UV wavelengths and at 0.89 micrometers the albedo is determined by the competing effects of the gas the haze material. Gas is bright in the UV and dark at 0.89 micrometers. Haze transport at high altitudes controls the UV albedo and transport at low altitude controls the 0.89 micrometers albedo. Comparisons between the hemispheric contrast at UV, visible, and IR wavelengths can be diagnostic of the vertical structure of the wind field on Titan.

Numerical simulation of the general circulation of the atmosphere of Titan

The atmospheric circulation of Titan is investigated with a general circulation model. The representation of the large-scale dynamics is based on a grid point model developed and used at Laboratoire de Météorologie Dynamique for climate studies. The code also includes an accurate representation of radiative heating and cooling by molecular gases and haze as well as a parametrization of the vertical turbulent mixing of momentum and potential temperature. Long-term simulations of the atmospheric circulation are presented. Starting from a state of rest, the model spontaneously produces a strong superrotation with prograde equatorial winds (i.e., in the same sense as the assumed rotation of the solid body) increasing from the surface to reach 100 m sec-1 near the 1-mbar pressure level. Those equatorial winds are in very good agreement with some indirect observations, especially those of the 1989 occultation of Star 28-Sgr by Titan. On the other hand, the model simulates latitudinal temperature contrasts in the stratosphere that are significantly weaker than those observed by Voyager 1 which, we suggest, may be partly due to the nonrepresentation of the spatial and temporal variations of the abundances of molecular species and haze. We present diagnostics of the simulated atmospheric circulation underlying the importance of the seasonal cycle and a tentative explanation for the creation and maintenance of the atmospheric superrotation based on a careful angular momentum budget.