Scientific consensus on climate change

There is a strong scientific consensus that the Earth is warming and that this warming is mainly caused by human activities. This consensus is supported by various studies of scientists' opinions and by position statements of scientific organizations, many of which explicitly agree with the Intergovernmental Panel on Climate Change (IPCC) synthesis reports. Nearly all actively publishing climate scientists say humans are causing climate change. Surveys of the scientific literature are another way to measure scientific consensus. A 2019 review of scientific papers found the consensus on the cause of climate change to be at 100%, and a 2021 study concluded that over 99% of scientific papers agree on the human cause of climate change. The small percentage of papers that disagreed with the consensus often contain errors or cannot be replicated. Consensus points The current scientific consensus is that:

Earth's climate has warmed significantly since

A Generic Framework to Produce Probabilistic Rainfall Scenarios Incorporating Climate Change

Xavier Beuchat

The present work addresses the problematic of forecasting impacts of climate change on future rainfall regimes and their consequences on urban stormwater infrastructures. Researches carried out allowed to develop an integrated framework for producing high resolution probabilistic rainfall projections suitable for studying hydrological processes at the scale of urban drainage. Downscaling is at the core of the methodology as the predictions of the numerical General Circulation Models (GCMs) employed by the climate scientific community to model climate evolution are too coarse for hydrological impact studies. The proposed downscaling approach respects the scales of the physical processes characterizing precipitations and consists in three steps: i) Daily rainfall series at the location of interest are downscaled from coarse-gridded monthly GCMs projections (scale of weather events); ii) The generated daily series are further downscaled to the hourly time-step (scale of storms dynamics); iii) Finally, hourly series are disaggregated to sub-hourly level (scale of raincells). Daily downscaling is achieved by a statistical procedure, based on Generalized Linear Models (GLMs), seeking to relate large-scale atmospheric variables, corresponding to the scale of GCMs, to local daily rainfall series. The proposed methodology is assessed using three contrasted situations in Switzerland (Geneva, Sion and Säntis) and is shown to perform well in reproducing historical rainfall statistics (including extremes and inter-annual variability) in the present-day climate; furthermore, projections were shown to be consistent with the simulations of physically-based dynamical models (i.e. Regional Climate Models). Projections for the second part of the 21th century indicate considerably drier summers, but no significant tendency toward more extreme events was detected except for Säntis. Finally, extensions of the methodology were presented allowing to downscale other atmospheric variables than rainfall. Sub-daily rainfall downscaling is achieved using a stochastic hourly rainfall generator based on Poisson clusters model which aims at conceptualizing storm dynamics in a simple way. To provide sensible results such generators have to be fitted on historical rainfall statistics computed at different levels of temporal aggregation. In the present context, this raises a fundamental problem as the required fitting statistics at the sub-daily time-scale are not available for the future. Shortcomings of existing methods led us to develop a novel approach based on Multivariate Adaptive Regression Splines (MARS) which were so far seldom used in hydrology. The proposed MARS models are conditioned on climate and fit thus particularly well in the general downscaling framework. In addition, atmospheric predictors allow to account naturally for seasonal variations meaning that a single MARS model holds for the whole year, whereas existing models are specific to each month of the year and are therefore not robust against the seasonal changes that might induce global warming. The methodology was applied to generate hourly rainfall series from daily data simulated by the GLMs at Geneva for the end of the 21th century. Climate change was found to impact significantly summer storm dynamics: raincells are predicted to be shorter but more intense, and storms are projected to be less frequent. A frequency analysis made on the simulated hourly rainfall series revealed a significant increase of hourly rainfall return levels. Hourly rainfall series are further disaggregated to the 10-minute level using a cascade-based model. Using case studies in the Geneva area, the performances of this sub-hourly rainfall disagreggator (in particular the reproduction of extreme values) were shown to be equivalent when fitted on statistics derived from the temporal levels 10-minute to 1-hour (sub-hourly fitting set), or from the 1-hour to 3-hour levels (supra-hourly fitting set). In consequence, the supra-hourly statistics of the hourly rainfall series generated by the stochastic Poisson clusters model can be used to fit the disagreggator model in order to simulate 10-minute rainfall series. Projections of 10-minute rainfall at Geneva for the end of the 21th century indicate an increase of extreme events intensities. Uncertainties in the proposed downscaling procedure are dealt with using, whenever feasible, probabilistic models (i.e. GLMs, hourly rainfall generator, MARS model and sub-hourly disagreggator), and relying on a large number of General Circulation Models projections conditioned on various greenhouse gases emissions scenarios. The present work concludes with a case study illustrating how the developed downscaling methodology may be used to evaluate different strategies of sustainable stormwater management. The Industrial Zone of Plan-les-Ouates (ZIPLO) taken as example is a small urbanized area of Geneva. Urban drainage was characterized using a semi-distributed rainfall-runoff model, and climate change (under the higher greenhouse gas emissions scenario) was shown to increase significantly the peak discharge flows at the ZIPLO outlet. Different sustainable stormwater options were then evaluated in order to limit the peak discharge flows under the joint scenario of climate change and projected urbanization increase.

EPFL2011

New and improved methods for integrated models of metabolism and gene expression

Pierre Guy Rémy Salvy

The beginning of the 21st century was marked by the advent of disruptive technologies, which ushered an era of groundbreaking advances in fundamental sciences, carried by the great pace at which computational capabilities spread and evolved. But the new century also came with its fair share of challenges. Anthropogenic climate change brought issues of sustainability in the production chemicals and food. The global improvement of life expectancy and diagnostic methods also saw the increased incidence of illnesses for which age is a risk factor, such as cancer and dementia. Systems biology, and more specifically metabolic engineering, are fields that are interested in engineering living organisms. In particular, they provide tools and methods that are suited to respond to the new requirements of chemical production, food availability, and health and medicine through the understanding and engineering of living cells. Engineered microorganisms are already used in the production of both commodity and specialty chemicals, genetically improved crops are a possible answer to the ever-increasing food demand, and new medical treatments rely on an improved understanding and control of cellular idiosyncrasies. Efficient engineering requires mathematical models. Over the last decades, the increasing availability of full genome sequences and their translation into models of metabolism enabled the emergence of a wide gamut of methods to describe the inner workings of the cells we study. In particular, models of metabolism and gene expression (ME-models) were the first formulation to account simultaneously for cell metabolism, and the expression mechanisms translating genetic information into proteins. In this thesis, I present a new, and improved, formulation for ME-models, and apply it to elucidate the emergence of non-trivial elements of cell physiology. This new ME-model formulation, ETFL, allows the integration of more experimental data than the previous state of the art, while being more efficient than previously published equivalent methods. Then, I show ETFL elucidates complex cellular behaviors. In particular, I demonstrate the preferred consumption of specific carbohydrate by E. coli, or diauxie, is the result of an optimal program of the cell towards growth, under the constraints of proteome limitation. I show that ETFL can be adapted to elucidate the dynamics of the proteome in the cell and the transition from one physiology to another. I also describe the construction of a ME-model of a eukaryotic organism, S. cerevisiae, and how the model produced can account for the emergence of overflow metabolism, or the Crabtree effect. Finally, I build a model of human colon cancer, and present a formulation for ETFL that allows to account for regulatory interactions in ME-models. I use the model to reproduce the known mechanisms of action of the drug metformin, and show it has a dual, dose-dependent action. I also show how such models can be used to predict potential mechanisms of resistance against treatment. In a second part of this thesis, I present open source software pieces I developed and contributed to, to promote open science. This work outlines the potential for ME-models in systems biology, and shows how to use them to elucidate complex cellular physiologies. The methods presented in this work also show how these new and improved ME-models constitute a major step towards systematic, integrated whole-cell modeling.

EPFL2021

The microbiome of cryospheric ecosystems

Tom Ian Battin, Massimo Bourquin, Susheel Bhanu Busi, Leïla Ezzat, Stylianos Fodelianakis, Tyler Joe Kohler, Grégoire Marie Octave Edouard Michoud, Hannes Markus Peter

The cryosphere includes those parts of Earth where water or soil is frozen, such as snow, ice, glaciers and permafrost soils. Here, the authors present a global inventory of cryospheric microbial communities and their genetic repertoires. The melting of the cryosphere is among the most conspicuous consequences of climate change, with impacts on microbial life and related biogeochemistry. However, we are missing a systematic understanding of microbiome structure and function across cryospheric ecosystems. Here, we present a global inventory of the microbiome from snow, ice, permafrost soils, and both coastal and freshwater ecosystems under glacier influence. Combining phylogenetic and taxonomic approaches, we find that these cryospheric ecosystems, despite their particularities, share a microbiome with representatives across the bacterial tree of life and apparent signatures of early and constrained radiation. In addition, we use metagenomic analyses to define the genetic repertoire of cryospheric bacteria. Our work provides a reference resource for future studies on climate change microbiology.