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.

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.

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 concentration. 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.

Meckel-gruber syndrome: About an evocative case in French Guiana-When ultrasound is insufficient for accurate diagnosis in a complex syndrome.

In modern medicine, prenatal diagnosis can no longer be sufficient by ultrasound examination. The genetic technical progress and its contribution may remain a challenge in isolated sites with the consequences that this implies in perinatal health.

Predictors of abnormal cytology among HPV-infected women in remote territories of French Guiana.

Background: Cervical cancer prevention using cervical cytology is insufficiently sensitive, a significant proportion of HPV-infected women having normal cytology. The objective of the present study was to try to identify factors associated with abnormal cytology in HPV-infected women living in remote areas of French Guiana.

Methods: A study was conducted in women aged 20-65 years having HPV infections confirmed by HPV DNA detection using the GREINER-BIO-ONE kit.