The information system of the French Peatland Observation Service: Service National d'Observation Tourbières – A valuable tool to assess the impact of global changes on the hydrology and biogeochemistry of temperate peatlands through long term monitoring

Mitigating and adapting to global changes requires a better understanding of the response of the Biosphere to these environmental variations. Human disturbances and their effects act in the long term (decades to centuries) and consequently, a similar time frame is needed to fully understand the hydrological and biogeochemical functioning of a natural system. To this end, the ‘Centre National de la Recherche Scientifique’ (CNRS) promotes and certifies long-term monitoring tools called national observation services or ‘Service National d'Observation’ (SNO) in a large range of hydrological and biogeochemical systems (e.g., cryosphere, catchments, aquifers). The SNO investigating peatlands, the SNO ‘Tourbières’, was certified in 2011 ( https://www.sno-tourbieres.cnrs.fr/ ). Peatlands are mostly found in the high latitudes of the northern hemisphere and French peatlands are located in the southern part of this area. Thus, they are located in environmental conditions that will occur in northern peatlands in coming decades or centuries and can be considered as sentinels. The SNO Tourbières is composed of four peatlands: La Guette (lowland central France), Landemarais (lowland oceanic western France), Frasne (upland continental eastern France) and Bernadouze (upland southern France). Thirty target variables are monitored to study the hydrological and biogeochemical functioning of the sites. They are grouped into four datasets: hydrology, fluvial export of organic matter, greenhouse gas fluxes and meteorology/soil physics. The data from all sites follow a common processing chain from the sensors to the public repository. The raw data are stored on an FTP server. After operator or automatic processing, data are stored in a database, from which a web application extracts the data to make them available ( https://data-snot.cnrs.fr/data-access/ ). Each year at least, an archive of each dataset is stored in Zenodo, with a digital object identifier (DOI) attribution ( https://zenodo.org/communities/sno_tourbieres_data/ ).     

Effects of variable winds on biological productivity on continental shelves in coastal upwelling systems

The production and distribution of biological material in wind-driven coastal upwelling systems are of global importance, yet they remain poorly understood. Production is frequently presumed to be proportional to upwelling rate, yet high winds can lead to advective losses from continental shelves, where many species at higher trophic levels reside. An idealized mixed-layer conveyor (MLC) model of biological production from constant upwelling winds demonstrated previously that the amount of new production available to shelf species increased with upwelling at low winds, but declined at high winds [Botsford, L.W., Lawrence, C.A., Dever, E.P., Hastings, A., Largier, J., 2003. Wind strength and biological productivity in upwelling systems: an idealized study. Fisheries Oceanography 12, 245–259]. Here we analyze the response of this model to time-varying winds for parameter values and observed winds from the Wind Events and Shelf Transport (WEST) study region. We compare this response to the conventional view that the results of upwelling are proportional to upwelled volume. Most new production per volume upwelled available to shelf species occurs following rapid increases in shelf transit time due to decreases in wind (i.e. relaxations). However, on synoptic, event time-scales shelf production is positively correlated with upwelling rate. This is primarily due to the effect of synchronous periods of low values in these time series, paradoxically due to wind relaxations. On inter-annual time-scales, computing model production from wind forcing from 20 previous years shows that these synchronous periods of low values have little effect on correlations between upwelling and production. Comparison of model production from 20 years of wind data over a range of shelf widths shows that upwelling rate will predict biological production well only in locations where cross-shelf transit times are greater than the time required for phytoplankton or zooplankton production. For stronger mean winds (narrower shelves), annual production falls below the peak of constant wind prediction [Botsford et al., 2003. Wind strength and biological productivity in upwelling systems: an idealized study. Fisheries Oceanography 12, 245–259], then as winds increase further (shelves become narrower) production does not decline as steeply as the constant wind prediction.     

Microphysical properties of stratocumulus clouds during ACE‐2

Microphysical measurements performed during 8 flights of the CLOUDYCOLUMN component of ACE‐2, with the Meteo‐France Merlin‐IV, are analyzed in terms of droplet number concentration and size. The droplet concentration is dependent upon the aerosol properties within the boundary layer. Its mean value over a flight varies from 55 cm⁻³, for the cleanest conditions, to 244 cm⁻³, for the most polluted one. For each flight, the variability of the concentration, in selected cloud regions that are not affected by mixing with dry air or drizzle scavenging, ranges from 0.5 to 1.5 of the mean value. The mean volume diameter increases with altitude above cloud base according to the adiabatic cloud model. The frequency distribution of mean droplet volume normalized by the adiabatic value, for the selected regions, shows the same dispersion as the distribution of normalized concentration. The values of droplet concentration versus mean volume diameter are then examined in sub‐adiabatic samples to characterize the effects of mixing and drizzle scavenging. Finally, the ratio of mean volume diameter to effective diameter is analyzed and a simple relationship between these 2 crucial parameters is proposed.     

Glacial-interglacial mean sea level pressure change due to sea level, ice sheet and atmospheric mass changes

The change in the global mean atmospheric pressure between glacial and interglacial periods is evaluated at sea level. This change originates in a modification of topography and in a possible variation in the atmospheric mass. In this calculation the atmosphere is at hydrostatic equilibrium, and the parameters describing the glacial period are varied in a plausible range. The result, with constant atmospheric mass, is a mean sea level pressure decrease of 9–15 hPa linked with the deglaciation. The corresponding pressure change at the reference level corresponding to the present day sea level does not exceed one hPa. When considering only the change in the atmospheric mass, an increase which does not exceed 2 hPa is found, linked with the deglaciation.     

Fossil hyrax dung and evidence of Late Pleistocene and Holocene vegetation types in the Namib Desert

Pollen was derived from fossil dung of herbivorous hyraxes, deposited in a rock shelter on the highest mountain in Namibia, Dâures or Brandberg, situated on the Namib Desert margin. Radiocarbon dating ranging in age between modern times and 30 000 yr BP showed it represents the first empirical pollen evidence of continental palaeovegetation during the Late Pleistocene along the western escarpment of southern Africa. The initial results indicate Last Glacial Maximum vegetation differed totally from the current pattern as vegetation types were dominated by small Asteraceae shrubs, in contrast to those of the Holocene and modern times which show more succulents, grass and woody elements (arboreal pollen). The results suggest that Cape floral communities did not reach into the tropics along the western escarpment of Africa, despite such pollen types occurring in marine cores. Copyright © 2004 John Wiley & Sons, Ltd.     

THE FREE-WILSON PARADIGM REDUX:SIGNIFICANCE OF THE FREE-WILSON COEFFICIENTS,INSIGNIFICANCE OF COEFFICIENT ‘UNCERTAINTIES’ AND STATISTICAL SINS

The Free-Wilson paradigm is an established and powerful tool for quantitatively relating activity withchemical structure.Current implementations of the paradigm,however,are flawed both conceptually andin execution.As part of an attempt to more fully realize the promise of the paradigm,it was necessaryto examine these limitations in detail.This report introduces a robust,theory-founded Free-Wilson implementation:stepwise principalcomponents regression analysis(SPCRA).SPCRA is computationally superior to previousimplementations but does not in itself correct their conceptual flaws.The development of SPCRA did,however,facilitate derivation of a simple and chemically significantinterpretation of the Free-Wilson structure-activity model.A number of statistical aspects of this modelcommonly misused in previous applications are discussed at length.These discussions provide criticalbackground for the development of an alternative implementation of the Free-Wilson paradigm.     

A Deep-Learning-Based Geological Parameterization for History Matching Complex Models

A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN–PCA method is inspired by recent developments in computer vision using deep learning. CNN–PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN–PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN–PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multipoint statistics is introduced. The metrics are based on summary statistics of the nonlinear filter responses of geological models to a pre-trained deep CNN. In addition, in the CNN–PCA formulation presented here, a convolutional neural network is trained as an explicit transform function that can post-process PCA models quickly. CNN–PCA is shown to provide both unconditional and conditional realizations that honor the geological features present in reference SGeMS geostatistical realizations for a binary channelized system. Flow statistics obtained through simulation of random CNN–PCA models closely match results for random SGeMS models for a demanding case in which O-PCA models lead to significant discrepancies. Results for history matching are also presented. In this assessment CNN–PCA is applied with derivative-free optimization, and a subspace randomized maximum likelihood method is used to provide multiple posterior models. Data assimilation and significant uncertainty reduction are achieved for existing wells, and physically reasonable predictions are also obtained for new wells. Finally, the CNN–PCA method is extended to a more complex nonstationary bimodal deltaic fan system, and is shown to provide high-quality realizations for this challenging example.