Milieux environnementaux, transferts et interactions dans les hydrosystèmes et les sols

Abstract This study presents the global climate model IPSL‐CM6A‐LR developed at Institut Pierre‐Simon Laplace (IPSL) to study natural climate variability and climate response to natural and anthropogenic forcings as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This article describes the different model components, their coupling, and the simulated climate in comparison to previous model versions. We focus here on the representation of the physical climate along with the main characteristics of the global carbon cycle. The model's climatology, as assessed from a range of metrics (related in particular to radiation, temperature, precipitation, and wind), is strongly improved in comparison to previous model versions. Although they are reduced, a number of known biases and shortcomings (e.g., double Intertropical Convergence Zone [ITCZ], frequency of midlatitude wintertime blockings, and El Niño–Southern Oscillation [ENSO] dynamics) persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL‐CM5A‐LR used in CMIP5. A large ensemble of more than 30 members for the historical period (1850–2018) and a smaller ensemble for a range of emissions scenarios (until 2100 and 2300) are also presented and discussed.

Nitrogen (N) is crucial for crop productivity. However nowadays more than\nhalf of the N added to cropland is lost to the environment, wasting the\nresource, producing threats to air, water, soil and biodiversity, and\ngenerating greenhouse gas emissions. Based on FAO data, we have\nreconstructed the trajectory followed, in the past 50 years, by 124\ncountries in terms of crop yield and total nitrogen inputs to cropland\n(manure, synthetic fertiliser, symbiotic fixation and atmospheric\ndeposition). During the last five decades the response of agricultural\nsystems to increased nitrogen fertilization has evolved differently in the\ndifferent world countries. While some countries have improved their agroenvironmental\nperformances, in others the increased fertilization has\nproduced low agronomical benefits and higher environmental losses. Our\ndata also suggest that, in general, those countries using a higher\nproportion of N inputs from symbiotic N fixation rather than from synthetic\nfertilizer have a better N use efficiency.

Livestock production systems currently occupy around 28% of the land surface of the European Union (equivalent to 65% of the agricultural land). In conjunction with other human activities, livestock production systems affect water, air and soil quality, global climate and biodiversity, altering the biogeochemical cycles of nitrogen, phosphorus and carbon. Here, we quantify the contribution of European livestock production to these major impacts. For each environmental effect, the contribution of livestock is expressed as shares of the emitted compounds and land used, as compared to the whole agricultural sector. The results show that the livestock sector contributes significantly to agricultural environmental impacts. This contribution is 78% for terrestrial biodiversity loss, 80% for soil acidification and air pollution (ammonia and nitrogen oxides emissions), 81% for global warming, and 73% for water pollution (both N and P). The agriculture sector itself is one of the major contributors to these environmental impacts, ranging between 12% for global warming and 59% for N water quality impact. Significant progress in mitigating these environmental impacts in Europe will only be possible through a combination of technological measures reducing livestock emissions, improved food choices and reduced food waste of European citizens.

IntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent of coronavirus disease (COVID-19). People infected with SARS-CoV-2 may exhibit no or mild non-specific symptoms; thus, they may contribute to silent circulation of the virus among humans. Since SARS-CoV-2 RNA can be detected in stool samples, monitoring SARS-CoV-2 RNA in waste water (WW) has been proposed as a complementary tool to investigate virus circulation in human populations.AimTo test if the quantification of SARS-CoV-2 genomes in WW correlates with the number of symptomatic or non-symptomatic carriers.MethodWe performed a time-course quantitative analysis of SARS-CoV-2 by RT-qPCR in raw WW samples collected from several major WW treatment plants in Greater Paris. The study period was 5 March to 23 April 2020, including the lockdown period in France (from 17 March).ResultsWe showed that the increase of genome units in raw WW accurately followed the increase of human COVID-19 cases observed at the regional level. Of note, the viral genome could be detected before the epidemic grew massively (around 8 March). Equally importantly, a marked decrease in the quantities of genome units was observed concomitantly with the reduction in the number of new COVID-19 cases, 29 days following the lockdown.ConclusionThis work suggests that a quantitative monitoring of SARS-CoV-2 genomes in WW could generate important additional information for improved monitoring of SARS-CoV-2 circulation at local or regional levels and emphasises the role of WW-based epidemiology.

Summary SARS-CoV-2 is the etiological agent of COVID-19. Most of SARS-CoV-2 carriers are assumed to exhibit no or mild non-specific symptoms. Thus, they may contribute to the rapid and mostly silent circulation of the virus among humans. Since SARS-CoV-2 can be detected in stool samples it has recently been proposed to monitor SARS-CoV-2 in wastewaters (WW) as a complementary tool to investigate virus circulation in human populations. In the present work we assumed that the quantification of SARS-CoV-2 genomes in wastewaters should correlate with the number of symptomatic or non-symptomatic carriers. To test this hypothesis, we performed a time-course quantitative analysis of SARS-CoV-2 by RT-qPCR in raw wastewater samples collected from several major wastewater treatment plant (WWTP) of the Parisian area. The study was conducted from 5 March to 23 April 2020, therefore including the lockdown period in France (since 17 March 2020). We confirmed that the increase of genome units in raw wastewaters accurately followed the increase of human COVID-19 cases observed at the regional level. Of note, the viral genomes could be detected before the beginning of the exponential growth of the epidemic. As importantly, a marked decrease in the quantities of genomes units was observed concomitantly with the reduction in the number of new COVID-19 cases which was an expected consequence of the lockdown. A s a conclusion, this work suggests that a quantitative monitoring of SARS-CoV-2 genomes in wastewaters should bring important and additional information for an improved survey of SARS-CoV-2 circulation at the local or regional scale.

Abstract Groundwater provides critical freshwater supply, particularly in dry regions where surface water availability is limited. Climate change impacts on GWS (groundwater storage) could affect the sustainability of freshwater resources. Here, we used a fully-coupled climate model to investigate GWS changes over seven critical aquifers identified as significantly distressed by satellite observations. We assessed the potential climate-driven impacts on GWS changes throughout the 21 st century under the business-as-usual scenario (RCP8.5). Results show that the climate-driven impacts on GWS changes do not necessarily reflect the long-term trend in precipitation; instead, the trend may result from enhancement of evapotranspiration, and reduction in snowmelt, which collectively lead to divergent responses of GWS changes across different aquifers. Finally, we compare the climate-driven and anthropogenic pumping impacts. The reduction in GWS is mainly due to the combined impacts of over-pumping and climate effects; however, the contribution of pumping could easily far exceed the natural replenishment.

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This paper presents a community effort to develop good practice guidelines for the validation of global coarse-scale satellite soil moisture products. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of statistical results, and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave-based surface soil moisture products. We conclude by identifying research gaps that should be addressed in the near future.

This paper discusses the notion of similarity often used in the regionalization studies of hydrological models. We compare two different visions of similarity: the apparent similarity defined on the basis of observable catchment properties, and behavioral similarity judged through the use of hydrological models. These two visions are generally assumed to be merged in regionalization studies: Catchments having apparently similar physical characteristics are assumed to have a similar hydrological behavior. In this paper, we wished to test the validity of this assumption. To this aim, we defined behavioral (hydrological) similarity on the basis of model parameter transferability. Then pools of hydrologically similar catchments are compared with pools of apparently physically similar catchments, as identified on the basis of physiographic catchment descriptors. The overlap between the two pools of similar catchments is analyzed, making it possible to judge the efficiency of the physical similarity measure and to identify hydrologically similar catchments in an ungauged context. The results show that the overlap between the two pools is significant for only 60% of the catchments. For the other catchments, two major reasons were identified as contributing to the lack of overlap: (1) these catchments often have a quite specific hydrological behavior and (2) the role of the underground properties of the catchment on its hydrological behavior was not found to be accurately described by the available physical descriptors, meaning that more relevant catchment descriptors should be sought to better describe the geological and lithological context in hydrological terms.

Increased human water use combined with climate change have aggravated water scarcity from the regional to global scales. However, the lack of spatially detailed datasets limits our understanding of the historical water use trend and its key drivers. Here, we present a survey-based reconstruction of China’s sectoral water use in 341 prefectures during 1965 to 2013. The data indicate that water use has doubled during the entire study period, yet with a widespread slowdown of the growth rates from 10.66 km 3 ⋅y −2 before 1975 to 6.23 km 3 ⋅y −2 in 1975 to 1992, and further down to 3.59 km 3 ⋅y −2 afterward. These decelerations were attributed to reduced water use intensities of irrigation and industry, which partly offset the increase driven by pronounced socioeconomic development (i.e., economic growth, population growth, and structural transitions) by 55% in 1975 to 1992 and 83% after 1992. Adoptions for highly efficient irrigation and industrial water recycling technologies explained most of the observed reduction of water use intensities across China. These findings challenge conventional views about an acceleration in water use in China and highlight the opposing roles of different drivers for water use projections.

• Analysis of brGDGT distributions in global peat dataset. • Correlation of brGDGT distributions with peat pH and mean annual air temperature. • Development of peat-specific temperature and pH proxies. Glycerol dialkyl glycerol tetraethers (GDGTs) are membrane-spanning lipids from Bacteria and Archaea that are ubiquitous in a range of natural archives and especially abundant in peat. Previous work demonstrated that the distribution of bacterial branched GDGTs (brGDGTs) in mineral soils is correlated to environmental factors such as mean annual air temperature (MAAT) and soil pH. However, the influence of these parameters on brGDGT distributions in peat is largely unknown. Here we investigate the distribution of brGDGTs in 470 samples from 96 peatlands around the world with a broad mean annual air temperature (−8 to 27 °C) and pH (3–8) range and present the first peat-specific brGDGT-based temperature and pH calibrations. Our results demonstrate that the degree of cyclisation of brGDGTs in peat is positively correlated with pH, pH = 2.49 × CBT peat + 8.07 ( n = 51, R 2 = 0.58, RMSE = 0.8) and the degree of methylation of brGDGTs is positively correlated with MAAT, MAAT peat (°C) = 52.18 × MBT 5me ′ − 23.05 ( n = 96, R 2 = 0.76, RMSE = 4.7 °C). These peat-specific calibrations are distinct from the available mineral soil calibrations. In light of the error in the temperature calibration (∼4.7 °C), we urge caution in any application to reconstruct late Holocene climate variability, where the climatic signals are relatively small, and the duration of excursions could be brief. Instead, these proxies are well-suited to reconstruct large amplitude, longer-term shifts in climate such as deglacial transitions. Indeed, when applied to a peat deposit spanning the late glacial period (∼15.2 kyr), we demonstrate that MAAT peat yields absolute temperatures and relative temperature changes that are consistent with those from other proxies. In addition, the application of MAAT peat to fossil peat (i.e. lignites) has the potential to reconstruct terrestrial climate during the Cenozoic. We conclude that there is clear potential to use brGDGTs in peats and lignites to reconstruct past terrestrial climate.

Abstract. The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (“LMIP”, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (“LFMIP”, building upon the GLACE-CMIP blueprint).

Considerable progress has recently been made in natural language processing: deep learning algorithms are increasingly able to generate, summarize, translate and classify texts. Yet, these language models still fail to match the language abilities of humans. Predictive coding theory offers a tentative explanation to this discrepancy: while language models are optimized to predict nearby words, the human brain would continuously predict a hierarchy of representations that spans multiple timescales. To test this hypothesis, we analysed the functional magnetic resonance imaging brain signals of 304 participants listening to short stories. First, we confirmed that the activations of modern language models linearly map onto the brain responses to speech. Second, we showed that enhancing these algorithms with predictions that span multiple timescales improves this brain mapping. Finally, we showed that these predictions are organized hierarchically: frontoparietal cortices predict higher-level, longer-range and more contextual representations than temporal cortices. Overall, these results strengthen the role of hierarchical predictive coding in language processing and illustrate how the synergy between neuroscience and artificial intelligence can unravel the computational bases of human cognition.

Silicon (Si), in the form of dissolved silicate (DSi), is a key nutrient in marine and continental ecosystems. DSi is taken up by organisms to produce structural elements (e.g., shells and phytoliths) composed of amorphous biogenic silica (bSiO 2 ). A global mass balance model of the biologically active part of the modern Si cycle is derived on the basis of a systematic review of existing data regarding terrestrial and oceanic production fluxes, reservoir sizes, and residence times for DSi and bSiO 2 . The model demonstrates the high sensitivity of biogeochemical Si cycling in the coastal zone to anthropogenic pressures, such as river damming and global temperature rise. As a result, further significant changes in the production and recycling of bSiO 2 in the coastal zone are to be expected over the course of this century.

Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture and vegetation water content, which are related to the Earth water and carbon cycles. Only two space-borne L-band sensors are currently operating: SMOS, launched end of 2009 and thus providing now a 10-year global data set and SMAP, launched beginning of 2015. This study provides a state-of-the-art scientific overview of the SMOS-IC retrieval data set based on the SMOS L-band observations. This SMOS product aims at improved performance and independence of auxiliary data, key features for robust applications. The SMOS-IC product includes both a soil moisture (SM) and a L-band vegetation optical depth (L-VOD) data set which are currently at the basis of several studies evaluating the impact of climate and anthropogenic activities on aboveground carbon stocks. Since the release of the first version, the algorithm has been significantly changed in support to key applications, but no document is available to report these changes. This paper fills this gap by analyzing key science questions related to the product development, reviewing application results and presenting an extensive description of the last version of the product (version 2) considering changes in comparison to the previous version (V105). For the future it is planned to merge the SMOS and SMAP L-VOD data sets to ensure L-VOD data continuity in the event of failure of one of the space-borne SMOS or SMAP sensors.

Vector databases typically manage large collections of embedding vectors. As AI applications are growing rapidly, the number of embeddings that need to be stored and indexed is increasing. The Faiss library is dedicated to vector similarity search, a core functionality of vector databases. Faiss is a toolkit of indexing methods and related primitives used to search, cluster, compress and transform vectors. This paper describes the trade-offs in vector search and the design principles of Faiss in terms of structure, approach to optimization and interfacing. We benchmark key features of the library and discuss a few selected use cases to highlight its broad applicability.

Abstract. This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).

Large lakes of the world are habitats for diverse species, including endemic taxa, and are valuable resources that provide humanity with many ecosystem services. They are also sentinels of global and local change, and recent studies in limnology and paleolimnology have demonstrated disturbing evidence of their collective degradation in terms of depletion of resources (water and food), rapid warming and loss of ice, destruction of habitats and ecosystems, loss of species, and accelerating pollution. Large lakes are particularly exposed to anthropogenic and climatic stressors. The Second Warning to Humanity provides a framework to assess the dangers now threatening the world’s large lake ecosystems and to evaluate pathways of sustainable development that are more respectful of their ongoing provision of services. Here we review current and emerging threats to the large lakes of the world, including iconic examples of lake management failures and successes, from which we identify priorities and approaches for future conservation efforts. The review underscores the extent of lake resource degradation, which is a result of cumulative perturbation through time by long-term human impacts combined with other emerging stressors. Decades of degradation of large lakes have resulted in major challenges for restoration and management and a legacy of ecological and economic costs for future generations. Large lakes will require more intense conservation efforts in a warmer, increasingly populated world to achieve sustainable, high-quality waters. This Warning to Humanity is also an opportunity to highlight the value of a long-term lake observatory network to monitor and report on environmental changes in large lake ecosystems.

Core Ideas OZCAR is a network of sites studying the critical zone. OZCAR covers various disciplines. OZCAR will help disciplines to work together for a better representation and modeling of the critical zone. The French critical zone initiative, called OZCAR (Observatoires de la Zone Critique–Application et Recherche or Critical Zone Observatories–Application and Research) is a National Research Infrastructure (RI). OZCAR‐RI is a network of instrumented sites, bringing together 21 pre‐existing research observatories monitoring different compartments of the zone situated between “the rock and the sky,” the Earth's skin or critical zone (CZ), over the long term. These observatories are regionally based and have specific initial scientific questions, monitoring strategies, databases, and modeling activities. The diversity of OZCAR‐RI observatories and sites is well representative of the heterogeneity of the CZ and of the scientific communities studying it. Despite this diversity, all OZCAR‐RI sites share a main overarching mandate, which is to monitor, understand, and predict (“earthcast”) the fluxes of water and matter of the Earth's near surface and how they will change in response to the “new climatic regime.” The vision for OZCAR strategic development aims at designing an open infrastructure, building a national CZ community able to share a systemic representation of the CZ, and educating a new generation of scientists more apt to tackle the wicked problem of the Anthropocene. OZCAR articulates around: (i) a set of common scientific questions and cross‐cutting scientific activities using the wealth of OZCAR‐RI observatories, (ii) an ambitious instrumental development program, and (iii) a better interaction between data and models to integrate the different time and spatial scales. Internationally, OZCAR‐RI aims at strengthening the CZ community by providing a model of organization for pre‐existing observatories and by offering CZ instrumented sites. OZCAR is one of two French mirrors of the European Strategy Forum on Research Infrastructure (eLTER‐ESFRI) project.

Abstract. The high-latitude regions of the Northern Hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance – those of the permanently frozen soils (permafrost), and of the great expanse of boreal forest – are vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO2 fluxes, in addition to a recently developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input datasets, are extensively evaluated against (i) temperature gradients between the atmosphere and deep soils, (ii) the hydrological components comprising the water balance of the largest high-latitude basins, and (iii) CO2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcing-dependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment.