Academic literature on the topic 'Multiannual dataset'

Abstract. Drought forecasting and early warning systems for water resource extremes are increasingly important tools in water resource management in Europe where increased population density and climate change are expected to place greater pressures on water supply. In this context, the North Atlantic Oscillation (NAO) is often used to indicate future water resource behaviours (including droughts) over Europe, given its dominant control on winter rainfall totals in the North Atlantic region. Recent hydroclimate research has focused on the role of multiannual periodicities in the NAO in driving low frequency behaviours in some water resources, suggesting that notable improvements to lead-times in forecasting may be possible by incorporating these multiannual relationships. However, the importance of multiannual NAO periodicities for driving water resource behaviour, and the feasibility of this relationship for indicating future droughts, has yet to be assessed in the context of known non-stationarities that are internal to the NAO and its influence on European meteorological processes. Here we quantify the time–frequency relationship between the NAO and a large dataset of water resources records to identify key non-stationarities that have dominated multiannual behaviour of water resource extremes over recent decades. The most dominant of these is a 7.5-year periodicity in water resource extremes since approximately 1970 but which has been diminishing since 2005. Furthermore, we show that the non-stationary relationship between the NAO and European rainfall is clearly expressed at multiannual periodicities in the water resource records assessed. These multiannual behaviours are found to have modulated historical water resource anomalies to an extent that is comparable to the projected effects of a worst-case climate change scenario. Furthermore, there is limited systematic understanding in existing atmospheric research for non-stationarities in these periodic behaviours which poses considerable implications to existing water resource forecasting and projection systems, as well as the use of these periodic behaviours as an indicator of future water resource drought.

Abstract Guðmundsson, K., Heath, M. R., and Clarke, E. D. 2009. Average seasonal changes in chlorophyll a in Icelandic waters. – ICES Journal of Marine Science, 66: 2133–2140. The standard algorithms used to derive sea surface chlorophyll a concentration from remotely sensed ocean colour data are based almost entirely on the measurements of surface water samples collected in open sea (case 1) waters which cover ∼60% of the worlds oceans, where strong correlations between reflectance and chlorophyll concentration have been found. However, satellite chlorophyll data for waters outside the defined case 1 areas, but derived using standard calibrations, are frequently used without reference to local in situ measurements and despite well-known factors likely to lead to inaccuracy. In Icelandic waters, multiannual averages of 8-d composites of SeaWiFS chlorophyll concentration accounted for just 20% of the variance in a multiannual dataset of in situ chlorophyll a measurements. Nevertheless, applying penalized regression spline methodology to model the spatial and temporal patterns of in situ measurements, using satellite chlorophyll as one of the predictor variables, improved the correlation considerably. Day number, representing seasonal variation, accounted for substantial deviation between SeaWiFS and in situ estimates of surface chlorophyll. The final model, using bottom depth and bearing to the sampling location as well as the two variables mentioned above, explained 49% of the variance in the fitting dataset.

Intense atmospheric disturbances, which impact directly on the sea surface causing a significant increase in wave height and sometimes strong storm surges, have become increasingly frequent in recent years in the Mediterranean Sea, producing extreme concern in highly populated coastal areas, such as the Gulf of Naples (Western Mediterranean Sea, Central Tyrrhenian Sea). In this work, fifty-six months of wave parameters retrieved by an HF radar network are integrated with numerical outputs to analyze the seasonality of extreme events in the study area and to investigate the performance of HF radars while increasing their distances from the coast. The model employed is the MWM (Mediterranean Wind-Wave Model), providing a wind-wave dataset based on numerical models (the hindcast approach) and implemented in the study area with a 0.03° spatial resolution. The integration and comparison with the MWM dataset, carried out using wave parameters and spectral information, allowed us to analyze the availability and accuracy of HF sampling during the investigated period. The statistical comparisons highlight agreement between the model and the HF radars during episodes of sea storms. The results confirm the potential of HF radar systems as long-term monitoring observation platforms, and allow us to give further indications on the seasonality of sea storms under different meteorological conditions and on their energy content in semi-enclosed coastal areas, such as the Gulf of Naples.

HF radar systems have the potential to measure the wind direction, in addition to surface currents and wave fields. However, studies on HF radar for wind direction determination are rare in the scientific literature. Starting with the results presented in Saviano et al. (2021), we here expand on the reliability of the multiannual wind direction data retrieved over two periods, from May 2008 to December 2010 and from January to December 2012, by a network of three SeaSonde high-frequency (HF) radars operating in the Gulf of Naples (Central Tyrrhenian Sea, Western Mediterranean Sea). This study focuses on the measurements obtained by each antenna over three range cells along a coast–offshore transect, pointing to any potential geographically dependent measurement. The scarcity of offshore wind measurements requires the use of model-generated data for comparative purposes. The data here used are obtained from the Mediterranean Wind–Wave Model, which provides indications for both wave and wind parameters, and the ERA5@2km wind dataset obtained by dynamically downscaling ERA5 reanalysis. These data are first compared with in situ data and subsequently with HF-retrieved wind direction measurements. The analysis of the overall performance of the HF radar network in the Gulf of Naples confirms that the HF radar wind data show the best agreement when the wind speed exceeds a 5 m/s threshold, ensuring a sufficiently energetic surface wave field to be measured. The results obtained in the study suggest the necessity of wind measurements in offshore areas to validate the HF radar wind measurements and to improve the extraction algorithms. The present work opens up further investigations on the applications of wind data from SeaSonde HF radars as potential monitoring platforms, both in coastal and offshore areas.

GnpIS is a data repository for plant phenomics that stores whole field and greenhouse experimental data including environment measures. It allows long-term access to datasets following the FAIR principles: Findable, Accessible, Interoperable, and Reusable, by using a flexible and original approach. It is based on a generic and ontology driven data model and an innovative software architecture that uncouples data integration, storage, and querying. It takes advantage of international standards including the Crop Ontology, MIAPPE, and the Breeding API. GnpIS allows handling data for a wide range of species and experiment types, including multiannual perennial plants experimental network or annual plant trials with either raw data, i.e., direct measures, or computed traits. It also ensures the integration and the interoperability among phenotyping datasets and with genotyping data. This is achieved through a careful curation and annotation of the key resources conducted in close collaboration with the communities providing data. Our repository follows the Open Science data publication principles by ensuring citability of each dataset. Finally, GnpIS compliance with international standards enables its interoperability with other data repositories hence allowing data links between phenotype and other data types. GnpIS can therefore contribute to emerging international federations of information systems.

Abstract. Relict permafrost presents an ideal opportunity to understand the impacts of climatic warming on the ground thermal regime since it is characterized by a mean annual ground temperature close to 0 ∘C and relatively thin permafrost. The long-term and continuous observations of permafrost thermal state and climate background are of great importance to reveal the links between the energy balance on hourly to annual timescales, to evaluate the variations in permafrost thermal state over multiannual periods and to validate the remote sensing dataset. We present 11 years of meteorological and soil data from the Mahan Mountain relict permafrost site of northeastern Qinghai–Tibet Plateau. The meteorological data comprise air and land surface temperature, relative humidity, wind speed and direction, shortwave and longwave downwards and upwards radiation, water vapor pressure, and precipitation on a half-hour timescale. The active layer data include daily soil temperature and soil volumetric water content at five different depths. The permafrost data consist of the ground temperature at 20 different depths up to 28.4 m. The high-quality and long-term datasets are expected to serve as accurate forcing data in land surface models and evaluate remote-sensing products for a broader geoscientific community. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Cryos.tpdc.271838, Wu and Xie, 2021).

AbstractStrong and thick temperature inversions are key components of the Arctic climate system and it is important to study and better understand them. The present study quantifies the temporal and spatial variability of surface-based inversions (SBIs) and elevated inversions (EIs) over Greenland, as derived from the ERA-Interim (ERA-I) dataset for the period 1979–2017. The seasonal and multiannual variability of inversion strength, thickness, and frequency are examined. Our results clearly show regional as well as seasonal patterns of both SBIs and EIs. SBIs are more frequent and stronger than EIs, and the spatial variability of inversions is larger during winter and smaller during summer. Furthermore, during summer, there has been a trend toward stronger (0.3 K decade−1), thicker (12 m decade−1), and more frequent (3% decade−1) SBIs in the southern part of Greenland, especially in the past two decades. Evidently, the strengthening of the anticyclone over Greenland causes a reduction of cloud cover, which manifests in an increase in SBI strength and thickness, particularly in the southern part of Greenland.

SUMMARYMulticomponent cyclicity in influenza (flu) incidence had been observed in various countries (e.g. periods T = 1, 2–3, 5–6, 8·0, 10·6–11·3, 13, 18–19 years) and its close similarity with cycles in natural environmental phenomena as meteorological factors and heliogeophysical activity (HGA) suggested. This report aimed at verifying previous results on cyclic patterns of flu incidence by exploring whether flu annual cyclicity (seasonality) and trans-year (13 to <24 months) and/or multiannual (long-term, ⩾24 months) cycles might be present. For this purpose, a relatively long monthly flu incidence dataset consisting of absolute numbers of new cases from the Grand Baku area, Azerbaijan, for the years 1976–2000 (300 months) was analysed. The exploration of underlying chronomes or, time structures, was done by linear and nonlinear parametric regression models, autocorrelation, spectral analysis and periodogram regression analysis. We analysed temporal dynamics and described multicomponent cyclicity, determining its statistical significance. The analysis, considering the flu data specifically stratified in three distinct intervals (1976–1990, 1991–1995, 1996–2000), and also combinations thereof, indicated that the main cyclic pattern was a seasonal one, with a period of T = 12 months. Further, a number of multiannual cycles with periods T in the ranges of 26–36, 62–85 or 113–162 months were observed, i.e. average periods of 2·5, 6·1 and 11·5 years, respectively. Indeed, most of these cycles correspond to similar cyclic parameters of HGA and further analyses are warranted to investigate such relationships. In conclusion, our study revealed the presence of multicomponent cyclic dynamics in influenza incidence by using relatively long time-series of monthly data. The specific cyclic patterns of flu incidence in Azerbaijan allows further, more specific modelling and correlations with environmental factors of similar cyclicity, e.g. HGA, to be explored. These results might contribute more widely to a better understanding of influenza dynamics and its aetiology as well as to the derivation of more precise forecasted estimates for planning and prevention purposes.

Abstract. A scientific approach is presented to aggregate and harmonize a set of 60 geophysical variables at hourly timescale over a decade, and to allow multiannual and multi-variable studies combining atmospheric dynamics and thermodynamics, radiation, clouds and aerosols from ground-based observations. Many datasets from ground-based observations are currently in use worldwide. They are very valuable because they contain complete and precise information due to their spatio-temporal co-localization over more than a decade. These datasets, in particular the synergy between different type of observations, are under-used because of their complexity and diversity due to calibration, quality control, treatment, format, temporal averaging, metadata, etc. Two main results are presented in this article: (1) a set of methods available for the community to robustly and reliably process ground-based data at an hourly timescale over a decade is described and (2) a single netCDF file is provided based on the SIRTA supersite observations. This file contains approximately 60 geophysical variables (atmospheric and in ground) hourly averaged over a decade for the longest variables. The netCDF file is available and easy to use for the community. In this article, observations are “re-analyzed”. The prefix “re” refers to six main steps: calibration, quality control, treatment, hourly averaging, homogenization of the formats and associated metadata, as well as expertise on more than a decade of observations. In contrast, previous studies (i) took only some of these six steps into account for each variable, (ii) did not aggregate all variables together in a single file and (iii) did not offer an hourly resolution for about 60 variables over a decade (for the longest variables). The approach described in this article can be applied to different supersites and to additional variables. The main implication of this work is that complex atmospheric observations are made readily available for scientists who are non-experts in measurements. The dataset from SIRTA observations can be downloaded at http://sirta.ipsl.fr/reobs.html (last access: April 2017) (Downloads tab, no password required) under https://doi.org/10.14768/4F63BAD4-E6AF-4101-AD5A-61D4A34620DE.

Dans le cadre de mon travail de thèse, je me suis intéressé aux aérosols atmosphériques et à leur transfert de l’atmosphère vers les surfaces terrestres par les précipitations. La stratégie générale que j’ai suivie repose sur l’observation des dépôts humides sur différentes échelles de temps, interannuelle d’une part et intraévènementielle de l’autre. Elle repose aussi sur leur observation dans des environnements marqués en termes de charge et de composition en aérosols, mais aussi de dynamiques atmosphériques et de précipitations. Le fait de combiner des mesures à la fois sur la composition de l’atmosphère et sur la composition des dépôts humides permet d’identifier la nature des dépôts (intensité, composition, source et provenance) et d’expliquer les phénomènes impliqués dans les dépôts. Cela passe par la documentation complète de différents paramètres (aérosols, dynamique, pluie, dépôt) sur les mêmes périodes de temps, ce qui est néanmoins complexe à mettre en oeuvre. Les deux axes de mon travail portent sur des questions distinctes et complémentaires de l’étude des dépôts humides.Le premier axe s’est porté sur les dépôts humides au Sahel, région semi-aride où le lessivage des poussières minérales de l’atmosphère est un processus clé pour contraindre le bilan atmosphérique en masse de ces composés. Dans cette région marquée par la présence de nombreux systèmes convectifs contrôlant les quantités de précipitations annuelles, la question sur les liens entre dynamiques atmosphériques et dépôts s’est alors posée. La stratégie d’observation long-terme mis en place sur les stations au Sahel dans le cadre du réseau INDAAF, avec une synergie autour de mesures météorologiques, de concentrations et de dépôts d’aérosols, a permis de constituer une base de données très complète. À partir de cette base de données pluriannuelle aux stations de Banizoumbou (Niger) et de Cinzana (Mali) de 2007 et 2015, l’identification de phénomène de cold pools (gouttes froides) à partir de données météorologiques de surface et leur lien avec les retombées de poussières minérales sont discutés. Les ratios de lessivage ont été calculés pour les évènements associés aux cold pools et varient sur plusieurs ordres de grandeur en fonction de l’effet de dilution qui diffère selon les régimes de concentrations atmosphériques en poussière minérale. Les évènements les plus convectifs associés à des concentrations élevées présentent une gamme de valeurs moins dispersée (319 – 766) qui ne dépend pas de la quantité de précipitation.Le second axe s’est focalisé sur l’étude intraévènementielle des dépôts en milieu urbain pour diverses situations de pluie, de concentration et composition en aérosols. Que peut nous apprendre le suivi des dépôts au cours d’un évènement de pluie ? Pour y répondre, j’ai tout d’abord participé au développement d’un collecteur me permettant de collecter les dépôts humides en fractions successives au cours de la pluie. Complétées par un ensemble de mesures colocalisées sur les aérosols et les dynamiques atmosphériques acquises sur le terrain pour 8 cas d’étude, les analyses chimiques des dépôts dissouts et particulaires m’ont permis de discuter à la fois la provenance des aérosols, mais aussi les processus mis en jeu. J’ai pu quantifier la décroissance des concentrations, même de composés traces, dans les dépôts au cours de la pluie. J’ai également pu documenter l’évolution de la solubilité pour les espèces chimiques des dépôts et discuter des poids relatifs des mécanismes de lessivage dans- (rainout) et sous- (washout) le nuage. La variabilité des dépôts observée au cours d’un évènement est au final aussi importante que celle observée entre évènements de pluie
In the work conducted for my thesis, I studied atmospheric aerosols and their transfer from the atmosphere to the surface by precipitation. The main strategy I followed is based on the observation of wet deposition on different time scales, interannual on one hand and intra-event on the other. It also relies on their observation in environments marked in terms of aerosol load and composition, but also in terms of atmospheric dynamics and precipitation. Combining measurements on both atmospheric and wet deposition compositions allows to identify the characteristics of the deposition (intensity, composition, source and origin) and to explain the phenomena involved in the deposition. This requires the complete documentation of different parameters (aerosols, dynamics, rainfall, deposition) over the same periods of time, which is nevertheless complex to implement. The two axes of my work deal with distinct and complementary issues in the study of wet deposition.The first focus has been on wet deposition in the Sahel, a semi-arid region where the scavenging of mineral dust from the atmosphere is a key process to constrain the atmospheric mass balance of these compounds. In this region marked by the presence of numerous convective systems controlling annual precipitation amounts, the question of the links between atmospheric dynamics and deposition was addressed. The long-term observation strategy implemented at stations in the Sahel as part of the INDAAF network, with a synergy of meteorological measurements, aerosol concentrations and deposition, has enabled the creation of a very complete database. From this multi-year dataset at Banizoumbou (Niger) and Cinzana (Mali) stations from 2007 and 2015, the identification of cold pools phenomena from surface meteorological data and their link with mineral dust deposition are discussed. Washout ratios have been calculated for cold pool events and vary over several orders of magnitude depending on the dilution effect which differs according to the levels of atmospheric aerosol concentrations. The most convective events associated with high concentrations have a less scattered range of values (319 – 766) that does not depend on the amount of precipitation.The second axis focused on the intra-event study of wet deposition in urban areas for various rainfall situations, aerosol concentration and composition. The question is: what can we learn from the monitoring of deposition during a rain event? To answer this, I first participated in the development of a collector allowing me to collect wet deposition in successive fractions during the rain event. Complemented by a set of co-located measurements on aerosols and atmospheric dynamics acquired in the field for 8 study cases, the chemical analyses of dissolved and particulate deposition allowed me to discuss both the origin of the aerosols and processes involved. I was able to quantify the decay of concentrations, even of trace compounds, in the deposits during rainfall. I was also able to document the evolution of solubility for chemical species in the deposition and discuss the relative contribution of the rainout and washout mechanisms. The variability of deposition observed during an event is actually as significant as that observed between rain events