World Data Center Research Articles

High-resolution and bias-corrected CMIP5 projections for climate change impact assessments

Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a ‘perfect sibling’ framework and show that it reduces climate model bias by 50–70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment.

МЕТОДИКА ОЦЕНКИ СЛОЖНОФОРМИРУЕМЫХ ЭЛЕМЕНТОВ ВОДНОГО БАЛАНСА (СУММАРНОГО ИСПАРЕНИЯ И ВЛАГОЗАПАСОВ) РЕЧНОГО БАССЕЙНА

There is considered the determination of total evaporation and the change in basin moisture reserves at given annual and seasonal values of precipitation and river runoff over a long-term period of hydro meteorological observations. In the engineering hydrology practice,the most common approach to determining total evaporation using only data on runoff and precipitation is to estimate it by the difference in total precipitation and river runoff. Moreover, the assumption is made that the change in basin moisture reserves for medium and large river basins over a sufficiently long period of time is close to zero. Based on this, in this work, we developed a technique that allows, when determining the total annual and seasonal evaporation from the land surface and reservoirs, to take into account the change in basin moisture reserves, since under real conditions the formed basin moisture reserves of the previous period are involved in the formation of both river runoff and total evaporation of next period. As the main mathematical tool, the work uses the correlation equation interlinking the considered elements of the water balance. Equations are obtained that determine the share of moisture reserves involved in the formation of river flow and total evaporation. Equations have been obtained that determine the proportion of basin moisture reserves involved in the formation of river runoff and total evaporation. Equations are proposed of the connection between runoff and precipitation and changes in annual moisture reserves with the share of moisture reserves involved in the formation of river runoff. The equations allowed estimating the values of total evaporation and moisture reserves in the basin in relation to the Don basin. Equations of the river runoff dependence on total precipitation of the Don basin have been obtained. As the initial hydro meteorological material there were used the data of the State Water Registry, surface water resources and hydrological yearbooks, as well as observational data from information and reference systems, such as the website of the All-Union Research Institute of Hydro meteorological Information – World Data Center – www. meteo.ru. As a result of the conjugated timing analysis of the elements of the water balance of the Don River basin, it has been established that in the basin the influence of natural factors is dominated.

METHANE IN THE SURFACE LAYER OF THE ATMOSPHERE: CURRENT CONTENT, LONG-TERM TRENDS, AND INTRA-ANNUAL VARIABILITY

The article deals with the empirical analysis of series of monthly mean concentrations of methane in the near-surface layer of the atmosphere from the global network of monitoring stations. They operate within the Global Atmosphere Watch (GAW) under the auspices of the World Meteorological Organization (WMO). The data is freely available at the World Data Center for Greenhouse Gases GAW/WMO (WDCGG) on its website https://gaw.kishou.go.jp/. The temporal coverage is from the 1980s. Data series from 69 stations are considered, of which 22 stations represent global background conditions. The rest of the stations are regional. Long-term trends in concentrations and the intra-annual (inter-monthly) deviations of monthly mean concentrations from long-term trends were studied. The multi-year trend was estimated using a series of 12-month running averages. To exclude systematic differences in methane concentrations, these series were adjusted to the series for the high-latitude Arctic station Alert (82° 30' N, 62° 21' W). The analysis showed that long-term trends are non-linear (in particular, a known pause in the growth of methane levels in 1999-2006 is observed), but are similar at most stations under consideration. Exceptions are six regional stations classified as “abnormal” in terms of methane. Possibly, this abnormality is due to the influence of certain sources of methane (anthropogenic or natural). Long-term trends at the rest of the stations just slightly differ from the average trend for the global stations. The series of intra-annual (inter-monthly) deviations of monthly mean concentrations from long-term trends for many stations (even those located at very significant distances from each other) show high correlative similarity. However, this similarity manifests itself at an optimal time shift from 5 months towards earlier dates up to 6 months towards later dates. The results of the analysis are consistent with the assumption that the intra-annual variability in methane concentration is largely driven by seasonal factors that are significantly related to latitude, such as vertical mixing in the atmosphere and destruction in the troposphere in reactions with hydroxyl. The root-mean-square values of intra-annual (inter-monthly) fluctuations in methane concentration depend significantly on latitude. In general, the higher the latitude is, the greater is the value. The maximum values are reached in the latitudinal belt within 45-50° N, and further to the North the values decrease. This feature of intra-annual fluctuations in the level of methane content may be explained, among other things, by significant inter-seasonal fluctuations in anthropogenic methane emissions occurring at the indicated latitudes in the countries with developed economies located in North America and Western Europe. The correlations of the series of intra-annual (inter-monthly) fluctuations of the monthly mean concentrations of CH4 and CO2 were estimated as rather high, about 0.8, at optimal time shifts, which is observed both at some polar stations and at tropical ones. This confirms the assumption that natural seasonal biogeochemical and geophysical processes play a significant role in the formation of intra-annual (inter-monthly) deviations of methane and carbon dioxide content in the near-surface layer from long-term trends. These processes include vertical mixing of air, CO2 absorption on the Earth’s surface, and destruction of methane in the troposphere in reactions with hydroxyl.

Geophysical Datascapes of the Cold War: Politics and Practices of International Data Centers

The International Geophysical Year or IGY (1957–1958) was conceived against a background of nuclear secrecy intensified by Cold War political tensions, but the IGY provided the impulse for constructing the distinct data regime which took hold in Soviet and American World Data Centers in the 1950s and 1960s — a regime that turned data into a form of currency traded by the political players in the Cold War. This essay examines that data regime in detail by taking up the issues of secrecy and access, sharing and exchange, accumulation and archiving, and finally the handling and use of the IGY data. Features of the IGY’s data centers, such as the notion of centralized storage of open data freely accessible to users from around the world, played an important role in establishing the practices of data governance that continue today in the form of Big Data. These practices, however, were outcomes of the politics, visions, and accompanying technologies that were embedded in and supported by the political culture of the Cold War. By revisiting the drawbacks and challenges that accompanied that Big Data moment in the early Cold War, this essay explores the multiple meanings of data and the ways in which data circulated in a veiled Cold War political economy that ran parallel to their use (or neglect) in the pursuit of knowledge.

МЕТАН В ПРИПОВЕРХНОСТНОМ СЛОЕ АТМОСФЕРЫ: СОВРЕМЕННОЕ СОДЕРЖАНИЕ, МНОГОЛЕТНИЕ ТРЕНДЫ И ВНУТРИГОДОВАЯ ИЗМЕНЧИВОСТЬ

The article is devoted to the empirical analysis of series of monthly mean concentrations of methane in the near-surface layer of the atmosphere from the global network of monitoring stations. They operate within the Global Atmosphere Watch (GAW) under the auspices of the World Meteorological Organization (WMO). The data is freely available at the World Data Center for Greenhouse Gases GAW/WMO (WDCGG) on its website https://gaw.kishou.go.jp/ . The temporal coverage is from the 1980s. Data series from 69 stations are considered, of which 22 stations represent the global background conditions. The rest of the stations are regional. Long-term trends in concentrations and intraannual (inter-monthly) deviations of mean monthly concentrations from long-term trends were studied. The multi-year trend was estimated using a series of 12-month running averages. To exclude systematic differences in methane concentrations, these series were adjusted to the series for the high-latitude Arctic station Alert (82° 30' N, 62° 21' W). The analysis showed that long-term trends are non-linear (including with a known pause in the growth of methane levels in 1999-2006), but are similar at most of the stations under consideration. Exceptions are 6 regional stations classified as ‘abnormal’ for methane. A possible cause of the abnormality is being under the influence of certain sources of methane (anthropogenic or natural). Long-term trends at the rest of the stations are just slightly differ from the average trend for global stations. The series of intra-annual (inter-monthly) deviations of mean monthly concentrations from long-term trends for many stations, even those located at very significant distances from each other, show high correlative similarity. However, it manifests itself at an optimal time shift from 5 months. towards earlier dates up to 6 months. towards later dates. The results of the analysis are consistent with the assumption that the intra-annual variability in methane concentration is largely driven by seasonal factors that are significantly related to latitude, including vertical mixing in the atmosphere and destruction in the troposphere in reactions with hydroxyl. The root-mean-square values of the intra-annual (inter-monthly) fluctuations in methane concentration significantly depend on latitude. The higher the latitude, the greater the overall value. Maximum values are reached in the latitudinal belt 45-50° N, and further, to the north, the Кузовкин В.В., Семенов С.М. 20 values decrease. This character of intra-annual fluctuations in the level of methane content may be explained, among other things, by significant inter-seasonal fluctuations in anthropogenic methane emissions at the indicated latitudes in countries with developed economies located in North America and Western Europe. The estimates of correlations of the series of intra-annual (inter-monthly) fluctuations of the average monthly concentrations of CH4 and CO2 showed that at optimal time shifts, they are rather high, about 0.8. Moreover, this is observed both at some polar stations and at tropical ones. This confirms the assumption that natural seasonal biogeochemical and geophysical processes play a significant role in the formation of intra-annual (inter-monthly) deviations of the methane and carbon dioxide content in the near-surface layer from long-term trends, namely, vertical mixing of air, CO2 absorption on the Earth's surface, destruction of methane in the troposphere in reactions with hydroxyl.

Sensitivity of the simulated CO2 concentration to inter-annual variations of its sources and sinks over East Asia

The study on how the variations in CO2 sources and sinks can affect the CO2 concentration over East Asia would be useful to provide information for policymaker concerning carbon emission reduction. In this study, a nested-grid version of global chemical transport model (GEOS-Chem) is employed to assess the impacts of variations in meteorological parameters, terrestrial fluxes, fossil fuel emissions, and biomass burning on inter-annual variations of CO2 concentrations over East Asia in 2004–2012. Simulated CO2 concentrations are compared with observations at 14 surface stations from the World Data Centre for Greenhouse Gases (WDCGG) and satellite-derived CO2 column density (XCO2) from the Gases Observing SATellite (GOSAT). The comparison shows that the simulated CO2 column density is generally higher than that of GOSAT by 1.33 × 10−6 (annual mean point by point biases averaged over East Asia). The model reasonably captures the temporal variations of CO2 concentrations observed at the ground-based stations, but it is likely to underestimate the peaks-to-troughs amplitude of the seasonal cycle by 50% or more. The simulated surface CO2 concentration in East Asia exhibits the largest inter-annual variation in December-January–February (DJF). The regional mean absolute deviation (MAD) values over East Asia are within (4.4–5.0) × 10−6 for all seasons. Model sensitivity simulations indicate that the inter-annual variations of surface CO2 concentrations are mainly driven by variations of meteorological parameters, and partly modulated by the inter-annual variations of terrestrial fluxes and fossil fuel emissions in local regions. The variations of the terrestrial fluxes and fossil fuel emissions may account for ∼28% of the inter-annual variation of surface CO2 concentration in southern China. The inter-annual variations of the peaks-to-troughs amplitude are dependent on variations of meteorological parameters, terrestrial fluxes and fossil fuel emissions in local regions. The influence of biomass burning emissions is relatively weak.

A comprehensive in situ and remote sensing data set from the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign

Abstract. The Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign was carried out north-west of Svalbard (Norway) between 23 May and 6 June 2017. The objective of ACLOUD was to study Arctic boundary layer and mid-level clouds and their role in Arctic amplification. Two research aircraft (Polar 5 and 6) jointly performed 22 research flights over the transition zone between open ocean and closed sea ice. Both aircraft were equipped with identical instrumentation for measurements of basic meteorological parameters, as well as for turbulent and radiative energy fluxes. In addition, on Polar 5 active and passive remote sensing instruments were installed, while Polar 6 operated in situ instruments to characterize cloud and aerosol particles as well as trace gases. A detailed overview of the specifications, data processing, and data quality is provided here. It is shown that the scientific analysis of the ACLOUD data benefits from the coordinated operation of both aircraft. By combining the cloud remote sensing techniques operated on Polar 5, the synergy of multi-instrument cloud retrieval is illustrated. The remote sensing methods were validated using truly collocated in situ and remote sensing observations. The data of identical instruments operated on both aircraft were merged to extend the spatial coverage of mean atmospheric quantities and turbulent and radiative flux measurement. Therefore, the data set of the ACLOUD campaign provides comprehensive in situ and remote sensing observations characterizing the cloudy Arctic atmosphere. All processed, calibrated, and validated data are published in the World Data Center PANGAEA as instrument-separated data subsets (Ehrlich et al., 2019b, https://doi.org/10.1594/PANGAEA.902603).

Adaptive model predictions of daily total column ozone over the Amazon Inter-Tropical Confluence Zone

The aim of this paper is to broaden the scope of a recent adaptive model in order to obtain predictions of total column ozone (TCO) trends over the Amazon Inter-Tropical Confluence Zone (ITCZ). The adaptive model makes daily TCO predictions over the tropical equator-Andes-Region, relying on seasonal patterns and the solar cycle. This study uses daily observations of the sunspot number cycle, given by the World Data Center for the production, preservation and dissemination of the international sunspot number (Royal Observatory of Belgium), and satellite total-column ozone data, collected by NASA (January 1979 to April 2018), for two Colombian locations: one in and one adjacent to the ITCZ. The agreement between daily total-column predictions by the adaptive model and satellite observations is excellent. Daily averaged relative errors around of 3.7 % and 2.8 % for both locations are reported herein.

Data mining of sustainable development process with using nightlight indicators

The object of research is the process of sustainable development of territorial units on the example of the regions of Ukraine. The concept of sustainable development has become a leading development strategy for most countries in the world. One of the biggest challenges is the availability of complete and verified data for sustainable development assessment models. The method used to calculate the index of sustainable development, developed in the World Data Center for Geoinformatics and Sustainable Development of the National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute». This methodology is based on calculating the metric of the index of sustainable development based on measurements of the quality of life of the population and the components of the safety of living for individual countries and regions. For the application of the methodology at the regional level, it was suggested to use information on the night lighting of the territory received by means of remote sensing of the Earth from satellites. The character and cohesiveness of the connection between the brightness of night lighting and indicators of sustainable development are studied. It is established that the most significant link exists between indicators of the economic development index, the index of influence on climate change and night lighting of the regions of Ukraine. Based on geographic information analysis of ArcGIS software, ESRI has applied statistical zoning tools that provide opportunities for statistical processing of satellite images within the regionally or regionally allocated areas.On the basis of the mathematical apparatus of the intellectual data analysis, a global and local regression analysis of the relationships between the revealed indicators was carried out. Consideration of the cramped nature of this connection in the territorial section revealed areas with varying degrees of close ties, which is explained by the peculiarities of the socio-economic development of the territories. Due to this, it is possible to obtain the calculation of similar indicators at more detailed territorial levels, corresponding to separate regions or cities of regional significance.

Paleobiodiversity and Earth Science Paleoenvironmental Data

Paleontology lies at the interface of earth sciences, biology and geologic time. When matched to histories of environmental change, such data is ideally suited to understanding how climate/environmental change affects biodiversity, and even how biodiversity change affects climate. Paleontologic and earth science collections, the data derived from them, and the global data infrastructures used in research are distinctively different than those in biology. Geologic time-linked earth science collections are found in geologic surveys, the oil industry and in museums as rock samples and sediment cores; paleontologic collections in museums and (via the microscopic fossils they contain) in sediment core repositories. Much published geologic time-linked earth science data is not tied to any collection material, and collected fossils are mostly only loosely linked to earth science collections or data. The exception is sediment cores and microfossils, where both earth science data and microfossil specimens are taken from the same cores and are thus tightly linked to each other. Lastly, most paleobiodiversity data used in research, even if derived from collection material, is compiled from the published literature rather than from collection databases. Three major types of linked earth science-paleontology data exist, each with its own data infrastructure. Very long time scale (Phanerozoic: 600 million years [myr]-Recent) but low time resolution (ca 10 myr) published fossil data is held in the Paleobiology Database [PBDB] and/or the Geobiodiversity Database [GBDB], together with very limited earth science data (mostly general rock type and approximate rock layer where found). PBDB in particular has been very successful in documenting the history of biodiversity over the last 600 my, and how a few major environmental events have affected it (mass extinctions and recoveries). Poor time resolution, taxonomic coarseness (most records are only for genera, not species) and poor links to earth science data have limited using this type of data for detailed studies of paleodiversity-environmental change. The NSB (formerly 'Neptune') database of deep-sea marine microfossils holds published fossil species occurrence data for the last ca 100 myr from the earth science sediment core collections of the deep-sea drilling programs [DSDP, ODP, IODP] together with detailed geochronologic data used to assign precise geologic ages to samples [.2-.02 myr resolution]. Species names are mostly linked to the separate community taxonomic catalog Mikrotax. All fossil occurrence data is also linked by sample name to the geologic core section material it was derived from. Earth history data from these core sections is not held in a central database, but a significant fraction is archived in the Pangea database, part of the ICSU World Data Center system. Biodiversity and environmental data can be linked directly via their shared sediment core sample locations, and/or by the high precision geologic ages for data in the systems. This type of data has been used to study biodiversity response to environmental change on many time and geographic scales, including rapid warming or cooling events, and response to major environmental catastrophes, e.g. the meteorite impact at the end of the Cretaceous. Lastly, very high temporal resolution (.01-.0001 my), mostly microfossil, plus earth-science data from land sections is held in the Neotoma system. This covers the last few my and is mostly used in local or regional studies of biodiversity and environmental change. New global data networking initiatives such as the Digital Deep Earth [DDE] initiative will create new opportunities to link earth science and biodiversity data to each other.

Data Distribution Centre Support for the IPCC Sixth Assessment

The information provided in the Intergovernmental Panel on Climate Change (IPCC; http://ipcc.ch ) Assessment Reports (ARs) inform climate change policy development. Within the IPCC the scientific coordination of the ARs is conducted by three Working Groups (WGs) comprising of the Bureaus supported by their Technical Support Units (TSUs). Data management support is provided by the IPCC Data Distribution Centre (DDC; http://ipcc-data.org ), which is overseen by the Task Group on Data Support for Climate Change Assessments (TG-Data; formerly TGICA). The DDC is a federated structure that is currently managed by the Centre for Environmental Data Analysis (CEDA; http://www.ceda.ac.uk/ ), United Kingdom; the World Data Center for Climate (WDCC; http://www.wdc-climate.de ), Germany; and the Center for International Earth Science Information Network (CIESIN; http://www.ciesin.columbia.edu/ ) at Columbia University, U.S. For the IPCC Sixth Assessment cycle (AR6), analyses of climate simulations and observations published in scientific literature will be assessed. The reports will include figures and tables prepared from the underlying digital information. The DDC plays an increasingly important role in facilitating the exchange of data, as well as curating the assessed datasets, scripts and provenance records to facilitate the assessment process and to support the traceability of AR6 results through long-term continuity of data management and curation. These issues, among others, are addressed by the DDC support group ( https://cedadev.github.io/ipcc_ddc ) currently consisting of members from the three TSUs and the three DDC managers.

Assessing Lagrangian inverse modelling of urban anthropogenic CO2 fluxes using in situ aircraft and ground-based measurements in the Tokyo area

BackgroundIn order to use in situ measurements to constrain urban anthropogenic emissions of carbon dioxide (CO2), we use a Lagrangian methodology based on diffusive backward trajectory tracer reconstructions and Bayesian inversion. The observations of atmospheric CO2 were collected within the Tokyo Bay Area during the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) flights, from the Tsukuba tall tower of the Meteorological Research Institute (MRI) of the Japan Meteorological Agency and at two surface sites (Dodaira and Kisai) from the World Data Center for Greenhouse Gases (WDCGG).ResultsWe produce gridded estimates of the CO2 emissions and calculate the averages for different areas within the Kanto plain where Tokyo is located. Using these inversions as reference we investigate the impact of perturbing different elements in the inversion system. We modified the observations amount and location (surface only sparse vs. including aircraft CO2 observations), the background representation, the wind data used to drive the transport model, the prior emissions magnitude and time resolution and error parameters of the inverse model.ConclusionsOptimized fluxes were consistent with other estimates for the unperturbed simulations. Inclusion of CONTRAIL measurements resulted in significant differences in the magnitude of the retrieved fluxes, 13% on average for the whole domain and of up to 21% for the spatiotemporal cells with the highest fluxes. Changes in the background yielded differences in the retrieved fluxes of up to 50% and more. Simulated biases in the modelled transport cause differences in the retrieved fluxes of up to 30% similar to those obtained using different meteorological winds to advect the Lagrangian trajectories. Perturbations to the prior inventory can impact the fluxes by ~ 10% or more depending on the assumptions on the error covariances. All of these factors can cause significant differences in the estimated flux, and highlight the challenges in estimating regional CO2 fluxes from atmospheric observations.

Prediction of maximum amplitude of solar cycle 25 using machine learning

Prediction of maximum amplitude of Solar Cycle 25 is obtained by using four different machine learning regression methods, i.e. Linear Regression (LR), Random Forest (RF), Radial Basis Function (RBF) and Support Vector Machine (SVM). Monthly mean sunspot number data during the 1856-June 2018 (solar cycles 10-24) from the World Data Center SILSO, Royal Observatory of Belgium, Brussels are used as machine learning inputs. According to LR, RF, RBF and SVM, the maximum of Solar Cycle 25 is predicted to occur in September 2023 (sunspot number of 159.4 ± 22.3), in December 2024 (sunspot number of 110.2 ± 12.8), in December 2024 (sunspot number of 95.5 ± 21.9) and in July 2024 (sunspot number of 93.7 ± 23.2), respectively. The prediction using LR method suggested that Solar Cycle 25 maximum will be slightly higher than the current cycle, while RBF and SVM suggested much lower cycles. RF prediction suggested a lower maximum with well-constructed double-peak. It was also found that the Solar Cycle 25 is predicted to begin in the late 2019 or early 2020 according to all four methods.

Critical Issues in Ionospheric Data Quality and Implications for Scientific Studies

AbstractIonospheric data are valuable records of the behavior of the ionosphere, solar activity, and the entire Sun‐Earth system. The data are critical for both societally important services and scientific investigations of upper atmospheric variability. This work investigates some of the difficulties and pitfalls in maintaining long‐term records of geophysical measurements. This investigation focuses on the ionospheric parameters contained in the historical data sets within the National Oceanic and Atmospheric Administration National Geophysical Data Center and Space Physics Interactive Data Resource databases. These archives include data from approximately 100 ionosonde stations worldwide, beginning in the early 1940s. Our study focuses on the quality and consistency of ionosonde data accessible via the primary Space Physics Interactive Data Resource node located within the National Oceanic and Atmospheric Administration National Geophysical Data Center and the World Data Center for Solar‐Terrestrial Physics located in Boulder, Colorado. We find that, although the Space Physics Interactive Data Resource archives contained an impressive amount of high‐quality data, specific problems existed involving missing and noncontiguous data sets, long‐term variations or changes in methodologies and analysis procedures used, and incomplete documentation. The important lessons learned from this investigation are that the data incorporated into an archive must have clear traceability back to the primary source, including scientific validation by the contributors, and that the historical records must have associated metadata that describe relevant nuances in the observations. Although this report only focuses on historical ionosonde data in National Oceanic and Atmospheric Administration databases, we feel that these findings have general applicability to environmental scientists interested in using long‐term geophysical data sets for climate and global change research.

Climatic prerequisites for changing activity in the natural Crimean-Congo hemorrhagic fever focus in the South of the Russian Federation

Subject of study. Examining an impact of the contemporary climatic changes on the natural CCHF focus as well as CCHF incidence in the South of the European part of the Russian Federation. Materials and methods. Annual Epidemiological Report on CCHF Incidence provided by Departments of the Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing in the subjects of the Southern and the North-Caucasian Federal Districts, CCHF Epizootologic Monitoring Report on the territory of the Volgograd Region, Rostov Region and Stavropol Region were used in the study. Hydrometeorological data were obtained from the database of the All-Russian Research Institute of Hydrometeorological Information World Data Center, weather station archives as well as The 20112016 National Report on the State of Environment in the Stavropol Region. Results. For the first time, climatic factors affecting each phase of the life cycle of ixodid tick Hyalomma marginatum which is the main CCHF virus vector in the South of the European part of the Russian Federation were identified. A direct correlation between seasonal air temperature fluctuations determining ixodid tick count and CCHF incidence was demonstrated. It was found that the average air temperature and accumulated precipitation collectively affect H. marginatum population (e.g., in Stavropol Region). Peak CCHF incidence was demonstrated to correlate with temperature conditions favorable to developing ixodid ticks. In contrast, air temperature fluctuations were noted to negatively impact in the years preceding decreased CCHF incidence. A relation between CCHF incidence and degree of wetting during preceding spring-and-summer as well as autumn seasons affecting vital activity, metamorphosis in preimaginal stages and count of the next-generation H. marginatum was revealed. Moreover, CCHF virus tended to expand geographic range northwards due to an effects of climatic changes. Virus-carrying Ixodidae found on the territory of the administrative districts of the Volgograd Region adjacent to the Volga Federal District evidence about a risk of CCHF virus spreading outside the Southern Regions of the Russian Federation.

Estudo de Ciclos Multidecadais nos Índices Climáticos Usando a Análise de Wavelet para o Norte/Nordeste do Brasil

This study investigates the climatic indexes time series, with a period of the most recent 80 years, with monthly mean values, using the Pacific Decadal Oscillation Index (PDO), Southern Oscillation Index (SOI), and the monthly solar activity time series with the sunspots number (MS), obtained from the National Weather Service’s website - Climate Prediction Center and Sunspot data from the World Data Center SILSO, Royal Observatory of Belgium, Brussels. The statistical software R was used with the WaveletComp package to generate the Morlet wavelet power spectra, bivariate analysis with cross-wavelet and the biwavelet package. As a result, predominant cycles were obtained and order variabilities of 32, 64, 128 and 256 months were observed, with approximately 2.66, 5.33, 10.66 and 21.33 years. These frequencies are observed in the variability of the period from January 1933 to September 2016, in the total of 993 months (82.75 years), characterizing decadal variations for multidecadal. These observed multidecadal cycles (of the order of 10.66 and 21.33 years may show a degree of association with the variability of solar activity (Sunspot) with the climatic variability cycles in the Atmospheric/Ocean system. Data from total rainfall from January 1951 to September 2017 were analyzed information located at Belem, Sao Luiz, Fortaleza, Natal and Fernando de Noronha airports, characterizing a diagonal from the north to the Brazilian northeast, observing that these series show similarity in the decadal and multidecadal cycles found in the SOI/PDO and Sunspot series.

The Global Catalogue of Microorganisms (GCM) 10K type strain sequencing project: providing services to taxonomists for standard genome sequencing and annotation.

The World Federation of Culture Collections and the World Data Center for Microorganisms (wdcm) initiated an international community-led project to sequence and annotate newly described prokaryotic taxa. This sequencing project aims to cooperate with international culture collections and the International Journal of Systematic and Evolutionary Microbiology and contribute to the expansion of whole genome sequencing databases for type strains. It will provide global microbial taxonomists with free standard genome sequencing and annotation services. Taxonomists are encouraged to contact the wdcm and participant culture collections to submit a type strain sequencing proposal.

Long-Term Trends of Atmospheric CH4 Concentration across China from 2002 to 2016

Spatiotemporal variations of atmospheric CH4 from 2002 to 2016 across China were detected, based on the Atmospheric Infrared Sounder (AIRS) sixth-layer CH4 concentration. The CH4 concentration showed good consistency with the ground measurements of surface CH4 concentration from the World Data Centre for Greenhouse Gases (WDCGG) (R2 = 0.83, p < 0.01), indicating that the remotely-sensed CH4 reflected the spatial and temporal variations of surface CH4 concentration. Across China, three hotspots of CH4 concentration were found in northern Xinjiang, the northeast of Inner Mongolia/Heilongjiang, and the Norgay plateau in northwest Sichuan. The CH4 concentration showed obviously seasonal variations, with the maximum CH4 concentration occurring in summer, followed by the autumn, winter, and spring. Furthermore, the CH4 concentration showed significantly increasing trends across China, with the rate of increase ranging from ~0.29 to 0.62 ppb·month−1, which would bring a 0.0019~0.014 mK potential rise in surface temperature response over China. In particular, the most rapidly increasing rates occurred in the Qinghai-Tibet plateau, while relatively low rates occurred in southeast China.

Global Energy Dynamics During Substorms on 9 March 2008 and 26 February 2008 Using Satellite Observations and the WINDMI Model

AbstractWe analyze three substorms that occur on (1) 9 March 2008 05:14 UT, (2) 26 February 2008 04:05 UT, and (3) 26 February 2008 04:55 UT. Using ACE solar wind velocity and interplanetary magnetic field Bz values, we calculate the rectified (southward Bz) solar wind voltage propagated to the magnetosphere. The solar wind conditions for the two events were vastly different, 300 kV for 9 March 2008 substorm, compared to 50 kV for 26 February 2008. The voltage is input to a nonlinear physics‐based model of the magnetosphere called WINDMI. The output is the westward auroral electrojet current which is proportional to the auroral electrojet (AL) index from World Data Center for Geomagnetism Kyoto and the SuperMAG auroral electrojet index (SML). Substorm onset times are obtained from the superMAG substorm database, Pu et al. (2010, https://doi.org/10.1029/2009JA014217), Lui (2011, https://doi.org/10.1029/2010JA016078) and synchronized to Time History of Events and Macroscale Interactions during Substorms satellite data. The timing of onset, model parameters, and intermediate state space variables are analyzed. The model onsets occurred about 5 to 10 min earlier than the reported onsets. Onsets occurred when the geotail current in the WINDMI model reached a critical threshold of 6.2 MA for the 9 March 2008 event, while, in contrast, a critical threshold of 2.1 MA was obtained for the two 26 February 2008 events. The model estimates 1.99 PJ of total energy transfer during the 9 March 2008 event, with 0.95 PJ deposited in the ionosphere. The smaller events on 26 February 2008 resulted in a total energy transfer of 0.37 PJ according to the model, with 0.095 PJ deposited in the ionosphere.

Solar Activity Observations at the World Data Center for Solar–Terrestrial Physics

Solar Activity Observations at the World Data Center for Solar–Terrestrial Physics