Second Workshop on Estimation with the RDBES data model (WKRDB-EST2; outputs from 2020 meeting)

This report shows how the new RDBES that is currently in development will be better able to support the recast EU Data Collection Framework (Regulation (EU) 2017/1004) than the existing RDB. The RDBES is an essential platform for MS and RCGs to fulfil their obligations towards documenting and improving data quality and designing and implementing regional sampling designs. The evaluation of data precision was performed using two complementary techniques. For relatively simple sampling designs it is possible to use analytical functions to calculate the precision (or a related statistical measure such as variance) of a statistical estimate. These calculations and implementations of them in R code are presented in this report. For more complicated sampling designs, the use of analytical functions is usually not feasible. In these cases, it is necessary to evaluate precision using numerical techniques, the main one of which is bootstrapping. This report discussed when bootstrapping is appropriate and gives several worked examples describing how bootstrapping can be applied in different cases. The evaluation of bias is a difficult subject and is hard to quantify. The approach followed in this report was to build on the previous work available in the ICES literature and identify and enumerate the main common sources of bias in catch sampling programs they describe. The information was collated and an evaluation performed as to whether data stored using the RDBES data format and reports issues from them can inform about the potential for bias in catch estimates. A set of example reports was coded that demonstrates the utility of the RDBES in relation to bias issues and can already help member states to identify how deviations in their sampling programmes and sampling variability may potentially lead to bias in their catch estimates

The Third Workshop on Population of the RDBES Data Model (WKRDB-POP3)

The aims of this workshop were to explain the data model developed for the commercial fisheries Regional Database and Estimation System (RDBES), assist in populating it with real data for the second test data call for the RDBES, and encourage participants to take part in ongoing testing of the RDBES data submission system. This report documents the progress that participants have done to prepare their institutes for future use of the RDBES system. Some issues with data conversion have been identified and are documented in this report. None of the identified issues are thought to be serious impediments to moving forward with the RDBES development according to the roadmap decided by the Steering Committee of the Regional Fisheries Database in 2020. The RDBES Core Group (the group of people developing the RDBES data model) and ICES Data Centre will look at the results of this workshop and either respond to individual questions or adapt the data model and documentation as required. The workshop concluded and reported before the deadline of the test data call. For a complete test of the data model, all participants were encouraged to complete the data call. A report on the degree of completion of the data call may be expected from WGRDBESGOV which convenes after the data call deadline

High-energy neutrinos in the context of multimessenger physics

The field of astroparticle physics is currently developing rapidly, since new experiments challenge our understanding of the investigated processes. Three messengers can be used to extract information on the properties of astrophysical sources: photons, charged Cosmic Rays and neutrinos. This review focuses on high-energy neutrinos (E>100 GeV) with the main topics as follows. The production mechanism of high-energy neutrinos in astrophysical shocks. The connection between the observed photon spectra and charged Cosmic Rays is described and the source properties as they are known from photon observations and from charged Cosmic Rays are presented. High-energy neutrino detection. Current detection methods are described and the status of the next generation neutrino telescopes are reviewed. In particular, water and ice Cherenkov detectors as well as radio measurements in ice and with balloon experiments are presented. In addition, future perspectives for optical, radio and acoustic detection of neutrinos are reviewed. Sources of neutrino emission. The main source classes are reviewed, i.e. galactic sources, Active Galactic Nuclei, starburst galaxies and Gamma Ray Bursts. The interaction of high energy protons with the cosmic microwave background implies the production of neutrinos, referred to as GZK neutrinos. Implications of neutrino flux limits. Recent limits given by the AMANDA experiment and their implications regarding the physics of the sources are presented.Comment: accepted for publication by Physics Reports, 127 pages, 29 figure

Workshop on Raising Data using the RDBES and TAF (WKRDBESRaiseTAF; outputs from 2022 meeting)

41 páginasThe Workshop on Raising Data using the RDBES and TAF (WKRDBES-Raise&TAF) met online (26–30 of September 2022) to evaluate the use of the Regional Database and Estimation System (RDBES) format to reproduce the 2022 InterCatch input and output, identifying a Transparent Assessment Framework (TAF) structure to organize the intermediate steps and to propose standardized output formats. The main outcomes of WKRDBES-Raise&TAF were: · RDBES provides sufficient support for current national estimation protocols. However, some minor issues were reported that hampered an exact reproduction of the estimates. Therefore, adaptations of the data model should not be excluded completely. · All the input to stock assessment that InterCatch currently provides, could be reproduced. The participants started from the current stock extracts that can be downloaded from InterCatch. · A workflow was proposed with a national TAF repository for each country, a stock estimation repository and a stock assessment repository. The intermediate output of those repositories will be stored in an ‘intermediate output database’ and depending on the user role, you will get access to the relevant stages in this workflow. · The following requirements for the standard output formats were defined: they cannot be more restrictive than the InterCatch input and output format; they should present measures of uncertainty and sample sizes (for national estimates) and should have a configurable domain definition (for national estimates). Despite those successful outcomes, the current plan for transition to an operational system was concluded to be too optimistic. WKRDBES-Raise&TAF therefore recommends to the Working Group on Governance of the Regional Database and Estimation System (WGRDBESGOV) to revise the roadmap and allow RDBES to be in a test phase also for 2023. WKRDBES-Raise&TAF felt the need to test the proposed workflow on a small scale and therefore recommends to the WGRDBESGOV to arrange a workshop where two stocks (pok.27.3a46 (Saithe (Pollachius virens) in Subareas 4, 6 and Division 3.a (North Sea, Rockall and West of Scotland, Skagerrak and Kattegat) and wit.27.3a47d (Witch (Glyptocephalus cynoglossus) in Subarea 4 and Divisions 3.a and 7.d (North Sea, Skagerrak and Kattegat, eastern English Channel)) will be set up to go through the whole flow.Peer reviewe

Admissible dissimilarity value (ADV) as a measure of subsampling reliability: case study North Sea cod (Gadus morhua)

The shape of the length frequency distribution (LFD) is an important input for stock assessments and one of the most important features in studies of fish population dynamics, providing estimates of growth parameters. In practice, oversampling may occur when sampling commercially important species. At times of more and more limited resources, the length sample size can be optimized at some stages of national or regional sampling programmes, without reducing the quality of stock assessments. The main objective of this study is to demonstrate a general distribution-free methodological approach for an optimization of sample size developed as an alternative to both analytical and bootstrap approaches. A novel framework to identify the reduced but still informative sample and to quantify the (dis) similarity between reduced and original samples is proposed. The identification procedure is based on the concept of reference subsample, which represents a theoretical minimal representative subsample that despite smaller sample size still preserves a reasonably precise LFD for certain species. The difference between the original sample and the reference subsample called admissible dissimilarity value (ADV) serves as the upper threshold and can be used to quantify the reliability of derived subsamples. Monte Carlo simulations were conducted to validate the approach under various LFD shapes. We illustrate in case studies how ADV can support to evaluate adequate sampling effort. The case studies focus on length samples from the German commercial vessels fishing for North Sea cod (Gadus morhua)

Permafrost warming and surface energy balance processes at two contrasting sites: Samoylov, Lena Delta (Siberia), Bayelva (Spitsbergen)

Predicting the vulnerability of permafrost carbon (C) to climate change requires simulation of the permafrost’s annual dynamics coupled with the C cycle, as well as the soil water status which determines aerobic or anaerobic decomposition of organic matter. Quantitative long-term water and energy balance studies are particularly scarce circumpolar and almost absent from Siberia, but are of great importance for the validation of climate and permafrost surface schemes within climate models. Furthermore, due to the complex nature and non linearity of processes, quantitative predictions vary largely. Within the research group SPARC (Sensitivity of the permafrost system’s water and energy balance under changing climate: A multiscale perspective) the carbon, water, and heat flux cycles in the complex Arctic landscapes at scales that range from metres to kilometres are investigated. Two field sites, located in the continuous permafrost environment are studied in detail since 1998: Bayelva (close to Ny-Alesund, Spitsbergen) and Samoylov (Lena Delta, Siberia). We combine field measurements of permafrost processes, pools, and fluxes, with remote sensing data and numerical climate models at local and regional scales. A quantitative process understanding is developed by identifying key processes of the seasonal and annual energy, water and carbon balance and the factors that affect these processes. This includes: representation of sub-grid cell variability in the landcover (especially with regard to water bodies), the quantification of the annual surface energy balance, the importance of snow cover formation and ablation for the permafrost thermal regime and the relationships between thermal and hydrologic processes and carbon cycle. The data collection as well as process understanding is also used within the large collaborative EU 7th framework project PAGE21 (Changing Permafrost in the Arctic and its Global Effects in the 21st Century (PAGE21). This project aims to understand and quantify the vulnerability of permafrost environments to a changing global climate, and to investigate the feedback mechanisms associated with increasing greenhouse gas emissions from permafrost zones

The freeze-up of high Arctic ponds and potential impacts on the carbon balance

A considerable part of the global carbon budget is stored in the Arctic permafrost landscapes. Several studies suggest that lakes and ponds play a key role in the carbon turnover of these ecosystems as they are considered to be favourable paths of carbon exchange between surface and atmosphere. The direction and strength of the carbon fluxes from Arctic lakes is controlled by a variety of physical and biochemical processes whose climate interactions are complex and still poorly understood. In some Arctic regions the fractional area of lakes and ponds can be as large as 25% highlighting the importance of water bodies in the Arctic ecosystems. Our long-term studies on the energy balance of a typical Arctic lake landscape reveal that the seasonal freeze-thaw dynamic is highly sensitive to small variations in the winter time radiation budget and the subsurface heat flux, especially at shallow ponds. The time required to completely freeze the water body including the subjacent bottom sediments can vary up to several months. This implies that the period of unfrozen ponds, during which biological activity is favourable, highly depends on factors such as the winter time cloudiness and snow cover. Hence, the close interaction between the winter time surface energy balance and biological processes might strongly affect the production and storage of green house gases of Arctic landscapes. This potential climate feedback mechanism is even more important as small water bodies are usually below the spatial resolution of remote sensing products. Therefore, they are not included in landscape classifications used in recent estimates of the global carbon budget or climate models. Nevertheless, small water bodies can make up a considerable percentage of the tundra surface comparable in size to the area occupied by large (thermokarst) lakes. Further investigation on the role of small water bodies appears to be mandatory for a better understanding of the Arctic carbon balance

Frozen ponds – production and storage of methane during the Arctic winter

Lakes and ponds play a key role in the carbon cycle of permafrost ecosystems. They are considered to be hotspots of carbon dioxide and methane emissions. However, the strength of the emissions is controlled by a variety of physical and biochemical processes whose responses to climate warming are complex and still poorly understood. In some Arctic regions up to 25% of the land surface area are occupied by free water surfaces. Often, a large fraction of these water surfaces is attributed to small lakes and ponds which are highly abundant in lowland tundra landscapes. These small water bodies are usually not accounted for in land surface classifications and calculations of the Arctic carbon balance. Nevertheless, small water bodies receive increased attention since recent studies demonstrate that ponds can strongly contribute to the CO2 and CH4 emissions of tundra ecosystems. In addition, water bodies strongly affect the thermal state of the surrounding permafrost. During the freezing period water bodies prolong the duration in which thawed soil material is available for microbial decomposition. This study presents CH4 production rates of ponds in a typical lowland tundra landscape in northern Siberia during the freezing period. For this purpose the CH4 concentrations in ice cores of different ponds are measured. The production rates of CH4 are calculated by fitting a mass balance model to the measured CH4 concentration profiles. The results reveal strong differences in CH4 production between the ponds. Shallow and intact polygonal ponds show low production rates on the order of 10-11 to 10-10 mol m-2 s-1. Whereas deeper ponds with clear signs of thermal erosion show CH4 production rates on the order of about 10-6 mol m-2 s-1. These production rates equate in magnitude to the summertime CH4 emission rates that are reported for the same site as average for the tundra landscape. Thus, strong CH4 production still occurs long after the tundra soils are completely frozen and microbial productions is supposed to be minimal. The release of the produced CH4 to the atmosphere potentially occurs during the melt period

A satellite based monitoring scheme for lowland permafrost – potentials and uncertainties

In this study we present an operational permafrost monitoring scheme using a multi-satellite approach in combination with a transient permafrost model. The created forcing dataset and the model results are intensively validated against long term observations at a tundra site in the Lena River Delta in Siberia. The first results show that operational permafrost monitoring with reasonable accuracy is possible based on a joint dataset of multiple remote sensing products. However, severe limitations are caused by uncertainties in the parameterizations of the soil and snow thermal properties. The assimilation of further satellite products such as surface moisture from synthetic aperture radar (SAR) might help to reduce these uncertainties of subsurface parameterizations