An ensemble of bias-adjusted CMIP6 climate simulations based on a high-resolution North American reanalysis.

ESPO-G6-R2 v1.0 is a set of statistically downscaled and bias-adjusted climate simulations based on the Coupled Model Intercomparison Project 6 (CMIP6) models. The dataset is composed of daily timeseries of three variables: daily maximum temperature, daily minimum temperature and daily precipitation. Data are available from 1950 to 2100 over North America. The simulation ensemble is comprised of 14 models driven by two emissions scenarios (SSP2-4.5 and SSP3-7.0). In this paper, we describe the workflow used for the bias-adjustment, which relies on the detrended quantile mapping method and the Regional Deterministic Reforecast System (RDRS) v2.1 reference dataset. Using the framework defined in the VALUE project, we show the improvements made by the bias-adjustment on marginal, temporal and multivariate aspects of the data. We also verify that the bias-adjusted climate data have similar climate change signal to the original climate model simulations. Finally, we provide guidance to users on how to use this dataset.

Latent space of a small genetic network: Geometry of dynamics and information.

The high-dimensional character of most biological systems presents genuine challenges for modeling and prediction. Here we propose a neural network-based approach for dimensionality reduction and analysis of biological gene expression data, using, as a case study, a well-known genetic network in the early Drosophila embryo, the gap gene patterning system. We build an autoencoder compressing the dynamics of spatial gap gene expression into a two-dimensional (2D) latent map. The resulting 2D dynamics suggests an almost linear model, with a small bare set of essential interactions. Maternally defined spatial modes control gap genes positioning, without the classically assumed intricate set of repressive gap gene interactions. This, surprisingly, predicts minimal changes of neighboring gap domains when knocking out gap genes, consistent with previous observations. Latent space geometries in maternal mutants are also consistent with the existence of such spatial modes. Finally, we show how positional information is well defined and interpretable as a polar angle in latent space. Our work illustrates how optimization of small neural networks on medium-sized biological datasets is sufficiently informative to capture essential underlying mechanisms of network function.

Investigation of the Spatial Distribution of Methane Sources in the Greater Toronto Area Using Mobile Gas Monitoring Systems.

For methane emission reduction strategies in urban areas to be effective, large emitters must be identified. Recent studies in U.S. cities have highlighted the contribution of methane emissions from natural gas distribution networks and end use. We present a methane emission source identification and quantification method for the Greater Toronto Area (GTA), the largest metropolitan area in Canada, using mobile gas monitoring systems. From May 2018 to August 2019, we collected 77 surveys of methane mixing ratios, covering a distance of about 6400 km, and sampled emission plumes from sources such as closed landfills, natural gas compressor stations, and waterways. Our results indicate that inactive landfills emit less than inventory estimates. Despite this discrepancy, we confirm that the waste sector is the largest methane emitter in the GTA. We also report that the frequency of methane leaks from the local distribution system ranges between 4 and 22 leaks per 100 km of roadway in downtown Toronto, which is comparable to the range observed in U.S. cities, which have invested in modern natural gas distribution infrastructure. Last, we find that engineered waterways, whose emissions are currently not reported in inventories, may be a significant source of methane.