ORCID Profile
0000-0003-0158-2957
Current Organisation
Deutsches Klimarechenzentrum GmbH
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Publisher: American Geophysical Union (AGU)
Date: 04-2019
DOI: 10.1029/2018MS001400
Publisher: Copernicus GmbH
Date: 08-07-2013
Abstract: Abstract. An error in the calculation of the emitted number of primary sulfate particles for a given mass of emitted elementary sulfur has recently been identified in HAM, i.e. the aerosol module utilised in the ECHAM-HAM aerosol climate model. Correcting for this error substantially alters the estimates of top-of-atmosphere radiative forcing due to aerosol indirect effects from global shipping emissions (year 2000) as presented in Peters et al. (2012). Here, we shortly present these new results.
Publisher: Copernicus GmbH
Date: 05-02-2019
DOI: 10.5194/GMD-2019-5
Abstract: Abstract. We present a Lagrangian convective transport scheme developed for Chemistry and Transport Models and ensemble trajectory simulations. Similar to existing schemes in other Lagrangian models, it is based on a statistical approach of calculating parcel displacements by convection. These schemes redistribute air parcels within a fixed time step by calculating probabilities for entrainment and the altitude of detrainment. Our scheme extends this approach by modelling vertical updraft velocities and the time that an air parcel spends inside the convective event, which is important for simulating the tropospheric chemistry of short-lived species, e.g. it determines the time available for heterogeneous processes on the surface of cloud droplets. Two different schemes for determining the vertical updraft velocities are introduced, which are based on constant or random convective area fraction profiles, respectively. SO2 is used as an ex le to show that there is a significant effect on species mixing ratios when modelling the time spent in convective updrafts compared to a nearly instantaneous redistribution of air parcels. The scheme is driven by convective mass fluxes and detrainment rates that originate from an external convective parameterization, which can be obtained from meteorological analysis data or General Circulation Models. Validation runs driven by ECMWF ERA Interim reanalysis data are performed with the scheme implemented into the ATLAS Chemistry and Transport Model. These include long-term global trajectory simulations of Radon-222 that are compared to measurements, and runs testing mass conservation and the reproduction of the convective mass fluxes and detrainment rates of ERA Interim. Simulated vertical updraft velocities are validated by wind profiler measurements in Darwin.
Publisher: American Geophysical Union (AGU)
Date: 27-12-2011
DOI: 10.1029/2011JD016531
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-9811
Abstract: & & In a collaborative effort, data management specialists at the German Climate Computing Centre (Deutsches Klimarechenzentrum, DKRZ) and researchers at the Max Planck Institute for Meteorology (MPI-M) are joining forces to achieve long-term and effective global availability of a high-volume flagship climate model dataset: the MPI-M Grand Ensemble (MPI-GE, Maher et al. 2019& sup& & /sup& ), which is the largest ensemble of a single state-of-the-art comprehensive climate model (MPI-ESM1.1-LR) currently available. The MPI-GE has formed the basis for a number of scientific publications over the past 4 years& sup& & /sup& . However, the wealth of data available from the MPI-GE simulations was essentially invisible to potential data users outside of DKRZ and MPI-M.& & & & In this contribution, we showcase the adopted strategy, experiences made and the current status of FAIR long-term preservation of the MPI-GE dataset in the World Data Center for Climate (WDCC), hosted at DKRZ. The importance of synergistic cooperation between domain-expert data providers and knowledgeable repository staff will be highlighted.& & & & Recognising the demand for MPI-GE data access outside of its native environment, the development of a strategy to make MPI-GE data globally available began in mid 2018. A two-stage dissemination reservation process was decided upon.& & & & In a first step, MPI-GE data would be published and made globally available via the Earth System Grid Federation (ESGF) infrastructure. Second, the ESGF-published data would be transferred to DKRZ& #8217 s long-term and FAIR archiving service WDCC. Datasets preserved in the WDCC can be made accessible via ESGF - global access via the established system would thus still be ensured.& & & & To date, the first stage of the above process is completed and data are available via the ESGF& sup& & /sup& . Data published in the ESGF has to comply with strict data standards in order to ensure efficient data retrieval and interoperability of the dataset. Standardization of the MPI-GE data required selection of an applicable data standard (CMIP5 in this case) and an appropriate variable subset, adaptation and application of fit-for-purpose DKRZ-supplied post-processing software and of course the post-processing of the data itself. All steps required dedicated communication and collaboration between DKRZ and MPI-M staff and required significant time resources. Currently, some 87 TB, comprised of more than 55 000 records, of standardized MPI-GE data are available for search and download from the ESGF. About three to four thousand records with an accumulated volume of several hundred GB are downloaded by ESGF users each month.& & & & The long-term archival of the standardized MPI-GE data using DKRZ& #8217 s WDCC-service is planned to begin within the first half of 2020. All preparatory work done so far, especially the data standardization, significantly reduces the effort and resources required for achieving FAIR MPI-GE data preservation in the WDCC.& & & & & sup& & /sup& Maher, N. et al. ( 2019). J. Adv. Model Earth Sy., 11, 2050& #8211 2069. 0.1029/2019MS001639& & & & & sup& & /sup& www.mpimet.mpg.de/en/grand-ensemble ublications/& & & & & sup& & /sup& esgf-data.dkrz.de rojects/mpi-ge/& & & & & br& & br& & &
Publisher: Copernicus GmbH
Date: 18-10-2019
Abstract: Abstract. We present a Lagrangian convective transport scheme developed for global chemistry and transport models, which considers the variable residence time that an air parcel spends in convection. This is particularly important for accurately simulating the tropospheric chemistry of short-lived species, e.g., for determining the time available for heterogeneous chemical processes on the surface of cloud droplets. In current Lagrangian convective transport schemes air parcels are stochastically redistributed within a fixed time step according to estimated probabilities for convective entrainment as well as the altitude of detrainment. We introduce a new scheme that extends this approach by modeling the variable time that an air parcel spends in convection by estimating vertical updraft velocities. Vertical updraft velocities are obtained by combining convective mass fluxes from meteorological analysis data with a parameterization of convective area fraction profiles. We implement two different parameterizations: a parameterization using an observed constant convective area fraction profile and a parameterization that uses randomly drawn profiles to allow for variability. Our scheme is driven by convective mass fluxes and detrainment rates that originate from an external convective parameterization, which can be obtained from meteorological analysis data or from general circulation models. We study the effect of allowing for a variable time that an air parcel spends in convection by performing simulations in which our scheme is implemented into the trajectory module of the ATLAS chemistry and transport model and is driven by the ECMWF ERA-Interim reanalysis data. In particular, we show that the redistribution of air parcels in our scheme conserves the vertical mass distribution and that the scheme is able to reproduce the convective mass fluxes and detrainment rates of ERA-Interim. We further show that the estimated vertical updraft velocities of our scheme are able to reproduce wind profiler measurements performed in Darwin, Australia, for velocities larger than 0.6 m s−1. SO2 is used as an ex le to show that there is a significant effect on species mixing ratios when modeling the time spent in convective updrafts compared to a redistribution of air parcels in a fixed time step. Furthermore, we perform long-time global trajectory simulations of radon-222 and compare with aircraft measurements of radon activity.
Publisher: Philipps-Universität Marburg
Date: 2020
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-3579
Abstract: & & Climate models are known to have biases in tropical precipitation. We assessed to what extent simulations of tropical precipitation have improved in the new Coupled Model Intercomparison Project (CMIP) phase six, using state-of-the-art observational products and model results from the earlier CMIP phases three and five. We characterize tropical precipitation with different well-established metrics. Our assessment includes (1) general aspects of the mean climatology like precipitation associated with the Intertropical Convergence Zone and shallow cloud regimes in the tropics, (2) solar radiative effects including the summer monsoons and the time of occurrence of tropical precipitation in the course of the day, (3) modes of internal variability such as the Madden-Julian Oscillation and the El Ni& #241 o Southern Oscillation, and (4) changes in the course of the 20th century. The results point to improvements of CMIP6 models for some metrics, e.g., the occurrence of drizzle events and consecutive dry days. However, no improvements of CMIP6 models are identified for other aspects of tropical precipitation. These include the area and intensity of the global summer monsoon as well as the diurnal cycle of the tropical precipitation amount, frequency and intensity.& & & & All our metrics taken together, CMIP6 models show no systematic improvement of tropical precipitation across different temporal and spatial scales. The model biases in the spatial distribution of tropical precipitation are typically larger than the changes associated with anthropogenic warming. Given the pace of climate change as compared to the pace of climate model improvements, we suggest to use novel modeling approaches to understand the responseof tropical precipitation to changes in atmospheric composition.& &
Publisher: Ubiquity Press, Ltd.
Date: 2022
DOI: 10.5334/DSJ-2022-007
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-11103
Abstract: & & Some disciplines, especially those that look at the Earth system, work with large to very large amounts of data. Storing this data, but also processing it, places completely new demands on scientific work itself.& & & & Let's take the ex le of climate research and specifically climate modelling. In addition to long-term meteorological measurements in the recent past, results from climate models form the main basis for research and statements on past and possible future global, regional and local climate. Climate models are very complex numerical models that require high-performance computing. However, with the current and future increasing spatial and temporal resolution of the models, the demand for computing resources and storage space is also increasing. Previous working methods and processes no longer hold up and need to be rethought.& & & & Taking the German Climate Computing Centre (DKRZ) as an ex le, we analysed the users, their goals and working methods. DKRZ provides the climate science community with resources such as high-performance computing (HPC), data storage and specialised services and hosts the World Data Center for Climate (WDCC). In analysing users, we distinguish between two groups: those who need the HPC system to run resource-intensive simulations and then analyse them, and those who reuse, build on and analyse existing data. Each group sub ides into subgroups. We have analysed the workflows for each identified user and found identical parts in an abstracted form and derived Canonical Workflow Modules.& & & & In the process, we critically examined the possible use of so-called FAIR Digital Objects (FDOs) and checked to what extent the derived workflows and workflow modules are actually future-proof.& & & & The vision is that the global integrated data space is formed by standardised, independent and persistent entities that contain all information about erse data objects (data, documents, metadata, software, etc.) so that human and, above all, machine agents can find, access, interpret and reuse (FAIR) them in an efficient and cost-saving way. At the same time, these units become independent of technologies and heterogeneous organisation of data, and will contain a built-in mechanism that supports data sovereignty. This will make the handling of data sustainable and secure.& & & & So, each step in a research workflow can be a FDO. In this case, the research is fully reproducible, but parts can also be exchanged and, e.g. experiments can be varied transparently. FDOs can easily be linked to others. The redundancy of data is minimised and thus also the susceptibility to errors is reduced. FDOs open up the possibility of combining data, software or whole parts of workflows in a new and simple but at all times comprehensible way. FDOs will make an important contribution to the reproducibility of research results, but they are also crucial for saving storage space. There are already data that are FDOs, but also self-contained frameworks that store data via tracking workflows. Similar to the TCP/IP standard, DO interface protocols are already developed. However, there are still some open points that are currently being worked on and defined with regard to FDOs in order to make them a globally functioning system.& &
Publisher: Zenodo
Date: 2020
Publisher: American Meteorological Society
Date: 21-06-2017
Abstract: The influence of surface conditions in the form of changing surface temperatures on fully developed mesoscale convective systems (MCSs) is investigated using a cloud-system-resolving setup of the Icosahedral Nonhydrostatic (ICON) model (1-km grid spacing). The simulated MCSs take the form of squall lines with trailing stratiform precipitation. After the squall lines have reached a quasi-steady state, secondary convection is triggered ahead of the squall line, resulting in an increase of squall-line propagation speed, also known as discrete propagation. The higher propagation speed is then maintained for the remainder of the simulations because secondary convection ahead of the squall line acts to reduce the environmental wind shear over the depth of the squall line’s cold pool. The surface conditions have only a marginal effect on the squall lines themselves. This is so because the surface fluxes cannot significantly affect the cold pool, which is continuously replenished by midtropospheric air. The midtroposphere remains similar given the use of identical initial profiles. The only effect of the surface fluxes consists in an earlier acceleration of the squall line due to earlier initiation of secondary convection with higher surface temperature. Finally, a conceptual model to estimate the change in surface temperature needed to achieve a change in onset time of prefrontal secondary convection and the associated discrete propagation events given the environmental conditions is presented.
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-8463
Abstract: & & The generation of high quality research data is expensive. The FAIR principles were established to foster the reuse of such data for the benefit of the scientific community and beyond. Publishing research data with metadata and DataCite DOIs in public repositories makes them findable and accessible (FA of FAIR). However, DOIs and basic metadata do not guarantee the data are actually reusable without discipline-specific knowledge: if data are saved in proprietary or undocumented file formats, if detailed discipline-specific metadata are missing and if quality information on the data and metadata are not provided. In this contribution, we present ongoing work in the AtMoDat project, -a consortium of atmospheric scientists and infrastructure providers, which aims on improving the reusability of atmospheric model data.& br& & & & br& Consistent standards are necessary to simplify the reuse of research data. Although standardization of file structure and metadata is well established for some subdomains of the earth system modeling community & #8211 e.g. CMIP & #8211 , several other subdomains are lacking such standardization. Hence, scientists from the Universities of Hamburg and Leipzig and infrastructure operators cooperate in the AtMoDat project in order to advance standardization for model output files in specific subdomains of the atmospheric modeling community. Starting from the demanding CMIP6 standard, the aim is to establish an easy-to-use standard that is at least compliant with the Climate and Forecast (CF) conventions. In parallel, an existing netCDF file convention checker is extended to check for the new standards. This enhanced checker is designed to support the creation of compliant files and thus lower the hurdle for data producers to comply with the new standard. The transfer of this approach to further sub-disciplines of the earth system modeling community will be supported by a best-practice guide and other documentation. A showcase of a standard for the urban atmospheric modeling community will be presented in this session. The standard is based on CF Conventions and adapts several global attributes and controlled vocabularies from the well-established CMIP6 standard.& br& & & & br& Additionally, the AtMoDat project aims on introducing a generic quality indicator into the DataCite metadata schema to foster further reuse of data. This quality indicator should require a discipline-specific implementation of a quality standard linked to the indicator. We will present the concept of the generic quality indicator in general and in the context of urban atmospheric modeling data.& & &
Publisher: MIT Press - Journals
Date: 2022
DOI: 10.1162/DINT_A_00127
Abstract: In this paper we present the derivation of Canonical Workflow Modules from current workflows in simulation-based climate science in support of the elaboration of a corresponding framework for simulation-based research. We first identified the different users and user groups in simulation-based climate science based on their reasons for using the resources provided at the German Climate Computing Center (DKRZ). What is special about this is that the DKRZ provides the climate science community with resources like high performance computing (HPC), data storage and specialised services, and hosts the World Data Center for Climate (WDCC). Therefore, users can perform their entire research workflows up to the publication of the data on the same infrastructure. Our analysis shows, that the resources are used by two primary user types: those who require the HPC-system to perform resource intensive simulations to subsequently analyse them and those who reuse, build-on and analyse existing data. We then further sub ided these top-level user categories based on their specific goals and analysed their typical, idealised workflows applied to achieve the respective project goals. We find that due to the sub ision and further granulation of the user groups, the workflows show apparent differences. Nevertheless, similar “Canonical Workflow Modules” can be clearly made out. These modules are “Data and Software (Re)use”, “Compute”, “Data and Software Storing”, “Data and Software Publication”, “Generating Knowledge” and in their entirety form the basis for a Canonical Workflow Framework for Research (CWFR). It is desirable that parts of the workflows in a CWFR act as FDOs, but we view this aspect critically. Also, we reflect on the question whether the derivation of Canonical Workflow modules from the analysis of current user behaviour still holds for future systems and work processes.
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-5965
Abstract: & & Large scale transient climate simulations and their intercomparison with paleo data within the German initiative PalMod (www.palmod.de, currently in phase II) provides an exclusive ex le of applying a Data Management Plan (DMP) to conceptualise& data workflows within and outside a large multidisciplinary project. PalMod-II data products include output of three state-of-the-art climate models with various coupling complexities and spatial resolutions& simulating the climate of the past 130,000 years. Additional to the long time series of model data, a comprehensive compilation of paleo-observation data (including a model-observation-comparison toolbox, Baudouin et al, 2021 EGU-CL1.2) is envisaged for validation.& & & & & Owing to the enormous amount of data coming from models and observations, produced and handled by different groups of scientists spread across various institutions, a dedicated DMP as a living document provides a data-workflow framework for exchange and sharing of data within and outside the PalMod community. The DMP covers the data life cycle within the project starting from its generation (data formats and standards), analysis (intercomparison with models and observations), publication (usage, licences), dissemination (standardised, via ESGF) and finally archiving after the project lifetime. As an active and continually updated document, the DMP ensures the ownership and responsibilities of data subsets of various working groups along with their data sharing/reuse regulations within the working groups in order to ensure a sustained progress towards the project goals.& & & & & This contribution discusses the current status and challenges of the DMP for PalMod-II which covers the details of data produced within various working groups, project-wide workflow strategy for sharing and exchange of data, as well as a definition of a PalMod-II variable list for ESGF standard publication. The FAIR (Findability, Accessibility, Interoperability, and Reusability) data principles play a central role and are proposed for the entire life cycle of PalMod-II data products (model and proxy paleo data) for sharing/reuse during and after the project lifetime.& & & & & br& & br& & &
Publisher: Stockholm University Press
Date: 2014
Publisher: Copernicus GmbH
Date: 02-2017
Abstract: Abstract. A simple plume implementation of the second version (v2) of the Max Planck Institute Aerosol Climatology, MACv2-SP, is described. MACv2-SP provides a prescription of anthropogenic aerosol optical properties and an associated Twomey effect. It was created to provide a harmonized description of post-1850 anthropogenic aerosol radiative forcing for climate modeling studies. MACv2-SP has been designed to be easy to implement, change and use, and thereby enable studies exploring the climatic effects of different patterns of aerosol radiative forcing, including a Twomey effect. MACv2-SP is formulated in terms of nine spatial plumes associated with different major anthropogenic source regions. The shape of the plumes is fit to the Max Planck Institute Aerosol Climatology, version 2, whose present-day (2005) distribution is anchored by surface-based observations. Two types of plumes are considered: one predominantly associated with biomass burning, the other with industrial emissions. These differ in the prescription of their annual cycle and in their optical properties, thereby implicitly accounting for different contributions of absorbing aerosol to the different plumes. A Twomey effect for each plume is prescribed as a change in the host model's background cloud-droplet population density using relationships derived from satellite data. Year-to-year variations in the litude of the plumes over the historical period (1850–2016) are derived by scaling the plumes with associated national emission sources of SO2 and NH3. Experiments using MACv2-SP are performed with the Max Planck Institute Earth System Model. The globally and annually averaged instantaneous and effective aerosol radiative forcings are estimated to be −0.6 and −0.5 W m−2, respectively. Forcing from aerosol–cloud interactions (the Twomey effect) offsets the reduction of clear-sky forcing by clouds, so that the net effect of clouds on the aerosol forcing is small hence, the clear-sky forcing, which is more readily measurable, provides a good estimate of the total aerosol forcing.
Publisher: Wiley
Date: 17-01-2020
Publisher: American Geophysical Union (AGU)
Date: 07-2018
DOI: 10.1029/2017MS001233
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-13189
Abstract: & & The project PalMod II is the second phase of Germany& #8217 s national paleoclimate modelling initiative (www.palmod.de) whose aim is to model the transient climate evolution from the last interglacial to the anthropocene with state of the art earth system models. The second phase more precisely wants to perform simulations for the last glacial inception, the marine isotope stage 3, and the last deglaciation. It further plans to compile paleo-observational proxy data over the full glacial cycle from about 130,000 years before present until today. Models of differing complexity (fully-coupled earth system models and models of intermediate complexity) will be used to assess the scientific questions posed in PalMod II. Model output will be combined with the compiled paleo-proxy data for validation purposes. The sheer data amount in excess of several petabytes and different data handling practices of the participating communities require dedicated management of the data workflow both in- and outside of the immediate PalMod community.& & & & The PalMod II data management takes place in close collaboration between data management specialists and the scientists. The objectives include the standardisation of each simulation and proxy dataset, the facilitation of data sharing and data reuse between work packages, the access channels for external collaborators, and the long-term preservation of the data. The data management follows the concept of the & quot Active Data Management Plan& quot , which foresees a continuous development of the data management plan (DMP), starting with an initial basic version. The DMP covers the entire life cycle of the research data generated in the project, from generation and analysis to data publication and archiving. This includes aspects such as data formats, metadata standards and data usage licenses. Ownership and responsibilities for simulation and paleo data sets as well as the input data during and after the end of the project will also be considered.& & & & This contribution will present the initial DMP for PalMod II. It will describe the amount of data produced in the project, highlight how the above mentioned aspects will be dealt with, and present how the project aims to ensure the Findability, Accessibility, Interoperability, and Reusability, i.e. the FAIR data principles, of simulation output, post-processed model data, and paleo-proxy data from PalMod II.& & & & * This contribution presents results of the full PalMod II initiative, the authors present them on behalf of the initiative.& &
Publisher: Wiley
Date: 02-10-2015
DOI: 10.1002/QJ.2655
Publisher: American Geophysical Union (AGU)
Date: 25-01-2017
DOI: 10.1002/2016MS000809
Abstract: We implement a Stochastic Multicloud Model (SMCM) in an observation‐informed configuration into the convection scheme of the state‐of‐the‐art GCM ECHAM6.3. The SMCM configuration we use here has been tuned to represent observed tropical convection by associating the occurrence and strength of deep convection to midtropospheric vertical velocity and relative humidity. We show that compared to the ECHAM6.3 standard model, the SMCM‐modified version shows improved capacity to simulate features of tropical intraseasonal variability, including MJO‐like disturbances, without significantly distorting the mean model climate. This improvement goes in hand with ameliorated coupling of atmospheric convection to tropospheric moisture and spatiotemporal coherence of tropical convection compared to reanalysis and observations. We attribute these effects to (i) improved coupling of triggering and suppression of deep convective events to the model's large‐scale environment and (ii) the observations‐informed closure formulation which leads to an overall reduction of deep convective mass fluxes. Sensitivity tests show that while (ii) improves the convection‐moisture relationship, it is (i) which improves the spatiotemporal coherence of tropical rainfall and is important for MJO simulation. Further, the simulated spatiotemporal coherence of tropical rainfall is an intrinsic property of the convection schemes themselves and not of their parameters. We stress that this study serves as a proof‐of‐concept and motivates further efforts towards building a novel convection parameterization with the SMCM as a central element.
Publisher: Zenodo
Date: 2022
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-15391
Abstract: German climate research initiative PalMod phase II (www.palmod.de) is presented here as an exclusive ex le where the project end-product is unique, scientific paleo-climate data. PalMod-II data products include output from three state-of-the-art coupled climate models of varying complexity and spatial resolutions simulating the climate of the past 130,000 years. In addition to the long time series of modeling data, a comprehensive compilation of paleo-observation data is prepared to facilitate model-model and model-proxy intercomparison and evaluation. Being a large multidisciplinary project, a dedicated RDM (Research Data Management) approach is applied within the cross-cutting working group for PalMod-II. The DMP (Data Management Plan), as a living document, is used for documenting the data-workflow framework that defines the details of paleo-climate data life-cycle. The workflow containing the organisation, storage, preservation, sharing and long-term curation of the data is defined and tested.& In order to make the modeling data inter-comparable across the PalMod-II models and easily analyzable by the global paleo-climate community, model data standardization (CMORization) workflows are defined for in idual PalMod models and their sub-models. The CMORization workflows contain setup, definition, and quality assurance testing of CMIP61 based standardization processes adapted to PalMod-II model simulation output requirements with a final aim of data publication via ESGF2. PalMod-II data publication via ESGF makes the paleo-climate data an asset which is (re-)usable beyond the project life-time.The PalMod-II RDM infrastructure enables common research data management according to the FAIR3 data principles across all the working groups of PalMod-II using common workflows for the exchange of data and information along the process chain. Applying data management planning within PalMod-II made sure that all the data related workflows were defined, continuously updated if needed and made available to the project stakeholders. End products of PalMod-II which consist of unique long term scientific paleo-climate data (model as well as paleo-proxy data) are made available for re-use via the paleo-climate research community as well as other research disciplines (e.g., land-use, socio-economic studies etc.).1. Coupled Model Intercomparison Project phase 6 (gcm-cmip/wgcm-cmip6)2. Earth System Grid Federation (esgf.llnl.gov)3. Findable, Accessible, Interoperable, Reusable
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-13285
Abstract: & & In 2019 the Universit& #228 t& Hamburg was awarded funding for 4 clusters of excellence in the Excellence Strategy of the Federal and State Governments. One of these clusters funded by the German Research Foundation (DFG) is & #8220 CliCCS & #8211 Climate, Climatic Change, and Society& #8221 .& The scientific objectives of CliCCS are achieved within three intertwined research themes, on the Sensitivity and Variability in the Climate System, Climate-Related Dynamics of Social Systems, and Sustainable Adaption Scenarios. Each theme is structured into multiple projects addressing sub-objectives of each theme. More than 200 researchers the Hamburg University, but also other connected research centers and partner institutions & are involved and almost all of them are using but mainly produce new data.& & & & Research data is produced with great effort and is therefore one of the valuable assets of scientific institutions. It is part of good scientific practice to make research data freely accessible and available in the long term as a transparent basis for scientific statements.& & & & Within the interdisciplinary cluster of excellence CliCCS, the type of research data is very different. The data range from results from physically dynamic ocean and atmosphere models, to measurement data in the coastal area, to survey and interview data in the field of sociology.& & & & & The German Climate Computing Center (DKRZ) is taking care on the Research Data Management and supports the researchers in creating data management plans, keeping naming conventions or simply finding the optimal repository to publish the data. The goal is to store and long-term archiving of the data, but also to ensure the quality of the data and thus to facilitate potential reuse.& &
Publisher: Pensoft Publishers
Date: 12-10-2022
DOI: 10.3897/RIO.8.E95817
Abstract: Motivation Results of simulations with climate models form the most important basis for research and statements about possible changes in the future global, regional and local climate. These output volumes are increasing at an exponential rate (Balaji et al. 2018, Stevens et al. 2019). Efficiently handling these amounts of data is a challenge for researchers, mainly because the development of novel data and workflow handling approaches have not proceeded at the same rate as data volume has been increasing. This problem will only become more pronounced with the ever increasing performance of High Performance Computing (HPC) - systems used to perform weather and climate simulations (Lawrence et al. 2018). For ex le, in the framework of the European Commission's Destination Earth program the Digital Twins (Bauer et al. 2021) are expected to produce hundreds of terabytes of model output data every day at the EuroHPC computing sites. The described data challenge can be dissected into several aspects, two of which we will focus on in this contribution. Available data in the Earth System Sciences (ESS) are increasingly made openly accessible by various institutions, such as universities, research centres and government agencies, in addition to subject-specific repositories. Further, the exploitability of weather and climate simulation output beyond the expert community by humans and automated agents (as described by the FAIR data principles (F-Findable, A-Accessable, I-Interoperable, R-Reusable), Wilkinson et al. 2016) is currently very limited if not impossible due to disorganized metadata or incomplete provenance information. Additionally, developments regarding globally available and FAIR workflows in the spirit of the FAIR Digital Object (FDO) framework (Schultes and Wittenburg 2019, Schwardmann 2020) are just at the beginning. Cultural Change In order to address the challenges with respect to data mentioned above, current efforts at DKRZ (German Climate Computing Center) are aimed at a complete restructuring of the way research is performed in simulation-based climate research (Anders et al. 2022, Mozaffari et al. 2022, Weigel et al. 2020). DKRZ is perfectly suited for this endeavor, because researchers have the resources and services available to conduct the entire suite of their data-intensive workflows - ranging from planning and setting up of model simulations, analyzing the model output, reusing existing large-volume datasets to data publication and long-term archival. At the moment, DKRZ-users do not have the possibility to orchestrate their workflows via a central service, but rather use a plethora of different tools to piece them together. Framework Environment Freva The central element of the new workflow environment at DKRZ shall be represented by the Freva (Free Evaluation System Framework) software infrastructure, which offers standardized data and tool solutions in ESS and is optimized for use on high-performance computer systems (Kadow et al. 2021). Freva is designed to be very well suited to the use of the FDO framework. The crucial aspects here are: the standardisation of data objects as input for analysis and processing, the already implemented remote access to data via a Persisitent Identifier (PID), the currently still system-internal capture of analysis provenance and the possibility of sharing results but also workflows by research groups up to large communities. the standardisation of data objects as input for analysis and processing, the already implemented remote access to data via a Persisitent Identifier (PID), the currently still system-internal capture of analysis provenance and the possibility of sharing results but also workflows by research groups up to large communities. It is planned to extend the functionality of Freva so that the system automatically determines the data required for a specific analysis from a researcher’s research question (provided to the system via some interface), enquires available databases (local disk or tape, cloud or federated resources) for that data and retrieves the data if possible. If data are not available (yet), Freva shall be able to automatically configure, set up and submit model simulations to the HPC-System, so that the required data is created and becomes available (cf. Fig. 1). These data will in turn be ingested into Freva’s data catalog for reuse. Next, Freva shall orchestrate and document the analysis performed. Results will be provided either as numerical fields, images or animations depending on the researcher’s need. As a final step, the applied workflow and/or underlying data are published in accordance with the FAIR data guiding principles. FDOs - towards a global integrated Data Space To make the process sketched out above a reality, application of the FDO concept is essential (Schwardmann 2020, Schultes and Wittenburg 2019). There is a long tradition in the ESS community of global dissemination and reuse of large-volume climate data sets. Community standards like those developed and applied in the framework of internationally coordinated model intercomparison studies (CMIP) allow for low-barrier reuse of data (Balaji et al. 2018). Globally resolvable PIDs are provided on a regular basis. Current community ESS standards and workflows are already close to being compatible with implementing FDOs, however, now we also have to work on open points in the FDO concept, which are: the clear definition of community-specific FDO requirements including PID Kernel Types specifications, the operation of data type registries and the technical implementation requirements for global access to FDOs. the clear definition of community-specific FDO requirements including PID Kernel Types specifications, the operation of data type registries and the technical implementation requirements for global access to FDOs. With these in place and implemented in Freva following standardized implementation recommendations, automated data queries across spatially distributed or different types of local databases become possible. We introduce the concept of implementations in Freva and also use it to highlight the challenges we face. Using an ex le, we show the vision of the work of a scientist in earth system science .
Publisher: Meteorological Society of Japan
Date: 2019
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-12099
Abstract: & & The German Climate Computing Center (DKRZ) is an established topical IT service provider serving the needs of the German climate science community and their associated partners. At DKRZ, climate researchers have the means available to cover every aspect of the research life cycle, ranging from planning, model development and testing, model execution on the in-house HPC cluster (16 PFlops mainly CPU-based, 130 PB disk storage), data analysis (batch jobs, Jupyter, Freva), data publication and dissemination via the Earth System Grid Federation (ESGF) as well as long-term data preservation either at the project-level (little curation) or in the CoreTrustSeal certified World Data Center for Climate (WDCC) (extensive curation along the FAIR data principles). A plethora of user support services offered by domain-expert staff complement DKRZ& #8217 s portfolio.& & & & & & & & & With the new HPC system coming online in early 2022 and a number of funded and to-be funded projects exploiting the available computational resources for conducting e.g. global storm-resolving (grid spacing & em& O& /em& (1-3km)) simulations on climatic timescales, the current interplay DKRZ& #8217 s services needs to be revisited to devise a unified workflow that will be able to handle the upcoming challenges.& & & & & & & & & & This is why the above mentioned projects will supply a significant amount of funds to conceive a framework to efficiently orchestrate the entire model development, model execution and data handling workflow at DKRZ in close collaboration with the climate science community.& & & & & & & & & In this contribution, we will detail our vision of a rev ed and versatile ESM orchestration framework at DKRZ. Currently, this vision is based on having the orchestration performed by the Freva System (0.5334/jors.253), in which users will be able to kick-off model compilation, compute and analysis jobs. Furthermore, Freva enables seamless provenance tracking of the entire workflow. Together with the implementation of data publication, long-term archiving and data dissemination workflows, the envisioned system provides a complete package of FAIR Digital Objects (FDOs) to researchers and allows for reproducibility, transparency and reduction of data redundancy.& & &
Publisher: University of Hamburg
Date: 2011
DOI: 10.17617/2.1404657
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-19340
Abstract: & & Earth System observational and model data volumes are constantly increasing and it can be challenging to discover, download, and analyze data if scientists do not have the required computing and storage resources at hand. This is especially the case for detection and attribution studies in the field of climate change research since we need to perform multi-source and cross-disciplinary comparisons for datasets of high-spatial and large temporal coverage. Researchers and end-users are therefore looking for access to cloud solutions and high performance compute facilities. The Earth System Grid Federation (ESGF, esgf.llnl.gov/) maintains a global system of federated data centers that allow access to the largest archive of model climate data world-wide. ESGF portals provide free access to the output of the data contributing to the next assessment report of the Intergovernmental Panel on Climate Change through the Coupled Model Intercomparison Project. In order to support users to directly access to high performance computing facilities to perform analyses such as detection and attribution of climate change and its impacts, the EU Commission funded a new service within the infrastructure of the European Network for Earth System Modelling (ENES, ata/data-metadata-service/analysis-platforms). This new service is designed to reduce data transfer issues, speed up the computational analysis, provide storage, and ensure the resources access and maintenance. Furthermore, the service is free of charge, only requires a lightweight application. We will present a demo on how flexible it is to calculate climate indices from different ESGF datasets covering a wide range of temporal and spatial scales using cdo (Climate Data Operators, code.mpimet.mpg.de rojects/cdo/) and Jupyter notebooks running directly on the ENES partners: the DKRZ (Germany), JASMIN (UK), CMCC(Italy), and IPSL (France) high performance computing centers.& &
Publisher: Zenodo
Date: 2021
Publisher: Zenodo
Date: 2022
Publisher: American Meteorological Society
Date: 31-10-2013
Abstract: The aim for a more accurate representation of tropical convection in global circulation models is a long-standing issue. Here, the relationships between large and convective scales in observations and a stochastic multicloud model (SMCM) to ultimately support the design of a novel convection parameterization with stochastic elements are investigated. Observations of tropical convection obtained at Darwin and Kwajalein are used here. It is found that the variability of observed tropical convection generally decreases with increasing large-scale forcing, implying a transition from stochastic to more deterministic behavior with increasing forcing. Convection shows a more systematic relationship with measures related to large-scale convergence compared to measures related to energetics (e.g., CAPE). Using the observations, the parameters in the SMCM are adjusted. Then, the SMCM is forced with the time series of the observed large-scale state and the simulated convective behavior is compared to that observed. It is found that the SMCM cloud fields compare better with observations when using predictors related to convergence rather than energetics. Furthermore, the underlying framework of the SMCM is able to reproduce the observed functional dependencies of convective variability on the imposed large-scale state—an encouraging result on the road toward a novel convection parameterization approach. However, establishing sound cause-and-effect relationships between tropical convection and the large-scale environment remains problematic and warrants further research.
Publisher: Copernicus GmbH
Date: 13-07-2012
Abstract: Abstract. In this study, we employ the global aerosol-climate model ECHAM-HAM to globally assess aerosol indirect effects (AIEs) resulting from shipping emissions of aerosols and aerosol precursor gases. We implement shipping emissions of sulphur dioxide (SO2), black carbon (BC) and particulate organic matter (POM) for the year 2000 into the model and quantify the model's sensitivity towards uncertainties associated with the emission parameterisation as well as with the shipping emissions themselves. Sensitivity experiments are designed to investigate (i) the uncertainty in the size distribution of emitted particles, (ii) the uncertainty associated with the total amount of emissions, and (iii) the impact of reducing carbonaceous emissions from ships. We use the results from one sensitivity experiment for a detailed discussion of shipping-induced changes in the global aerosol system as well as the resulting impact on cloud properties. From all sensitivity experiments, we find AIEs from shipping emissions to range from −0.32 ± 0.01 W m−2 to −0.07 ± 0.01 W m−2 (global mean value and inter-annual variability as a standard deviation). The magnitude of the AIEs depends much more on the assumed emission size distribution and subsequent aerosol microphysical interactions than on the magnitude of the emissions themselves. It is important to note that although the strongest estimate of AIEs from shipping emissions in this study is relatively large, still much larger estimates have been reported in the literature before on the basis of modelling studies. We find that omitting just carbonaceous particle emissions from ships favours new particle formation in the boundary layer. These newly formed particles contribute just about as much to the CCN budget as the carbonaceous particles would, leaving the globally averaged AIEs nearly unaltered compared to a simulation including carbonaceous particle emissions from ships.
Publisher: MDPI AG
Date: 27-08-2019
Abstract: Representing mesoscale convective systems (MCSs) and their multi-scale interaction with the large-scale atmospheric dynamics is still a major challenge in state-of-the-art global numerical weather prediction (NWP) models. This results in potentially defective forecasts of synoptic-scale dynamics in regions of high MCS activity. Here, we quantify this error by comparing simulations performed with a very large-domain, convection-permitting NWP model to two operational global NWP models relying on parameterized convection. We use one month’s worth of daily forecasts over Western Africa and focus on land regions only. The convection-permitting model matches remarkably well the statistics of westward-propagating MCSs compared to observations, while the convection-parameterizing NWP models misrepresent them. The difference in the representation of MCSs in the different models leads to measurably different synoptic-scale forecast evolution as visible in the wind fields at both 850 and 650 hPa, resulting in forecast differences compared to the operational global NWP models. This is quantified by computing the correlation between the differences and the number of MCSs: the larger the number of MCSs, the larger the difference. This fits the expectation from theory on MCS–mean flow interaction. Here, we show that this effect is strong enough to affect daily limited-area forecasts on very large domains.
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-12560
Abstract: & & The perceived community demand for research data repositories to provide services ensuring that stored data comply with the FAIR principles requires transparent evaluation of such services. In previous work, the long term archiving service WDCC& sup& & /sup& (World Data Centre for Climate) at DKRZ (German Climate Computing Center, Hamburg) underwent an even-handed self-assessment along the published FAIR principles and the results are published on the DKRZ homepage& sup& & /sup& .& & & & Here, we present results of an overhaul of the previous WDCC FAIRness-assessment by subjecting datasets archived in WDCC to a number of now available objective FAIR assessment approaches which are available as questionnaires or fully-automated web applications& sup& ,4,5& /sup& . In these approaches, FAIRness is assessed using so-called metrics or maturity indicators. While the terminology is more a choice of the test provider - e.g. the term & #8216 metric& #8217 may be off-putting for some - both give quantitative results. First tests show that (meta)data archived in WDCC seem to attain a higher level of FAIRness when evaluated using questionnaires compared to the results obtained from fully-automated applications. Further work is needed to substantiate this finding.& & & & We learn that while neither one of the two evaluation approaches is ideal, they both show merit. Questionnaires & #8211 answered by knowledgeable repository staff & #8211 capture domain- and repository-specific aspects of FAIRness, like the use of controlled vocabularies in the datasets, granularity of archived datasets, reuse documentation or clear assessment of local data access protocols. However, the human-performed evaluation does not capture machine-actionability in terms of FAIR. This aspect is & #8211 naturally & #8211 very well assessed by automatic evaluation approaches, but the results strongly depend on the way the tests for FAIR metrics/maturity indicators are implemented. However, automatic tests often only assess metadata FAIRness, lack domain-specific& & & & FAIRness indicators or yield failed tests if a repositories& #8217 technical properties, e.g. the specification of authentication procedures for data access, are not compatible with what an automatic procedure is built to test for.& & & & Therefore, since WDCC has an over 30 year long history of preserving climate-science related data with a focus on reusability by the community (and beyond), FAIRness evaluations based on human-actionable questionnaires show a high degree of FAIRness. We further learn that there is an urgent need for specifically-designed automatic FAIR testing approaches taking into account domain-specific data standards and structures. Especially the availability of atmospheric and climate science related FAIR metrics/maturity indicators is very limited. We thus recommend compilations of the latter and we will aim at contributing to this effort.& & & & In our contribution, we specifically showcase strong as well as weak aspects of the WDCC service in terms of FAIRness and report on our measures to increase domain-specific FAIRness of WDCC and present recommendations for establishing FAIR indicators for (meta)data common to the Earth System Science community. We will make the results of our assessment openly available on the WDCC homepage as well as produce a corresponding Open Access peer-reviewed publication.& & & & & & & & & References:& & & & & sup& & /sup& cera-www.dkrz.de& & & & & sup& & /sup& cera-www.dkrz.de/WDCC/ui/cerasearch/info?site=fairness& & & & & sup& & /sup& ode/60731/outputs& & & & & & sup& & /sup& fairsharing.github.io/FAIR-Evaluator-FrontEnd/#!/& & & & & & sup& & /sup& www.fairsfair.eu/f-uji-automated-fair-data-assessment-tool& &
Publisher: Wiley
Date: 09-01-2022
Publisher: Copernicus GmbH
Date: 08-2016
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-9686
Abstract: & & This contribution highlights the Python xarray technique in context of a climate specific application (typical formats are NetCDF, GRIB and HDF).& & & & We will see how to use in-file metadata and why they are so powerful for data analysis, in particular by looking at community specific problems, e.g. one can select purely on coordinate variable names. ECAS, the ENES Climate Analytics Service available at Deutsches Klimarechenzentrum (DKRZ), will help by enabling faster access to the high-volume simulation data output from climate modeling experiments. In this respect, we can also make use of & #8220 dask& #8221 which was developed for parallel computing and can smoothly work with xarray. This is extremely useful when we want to exploit fully the advantages of our supercomputer.& & & & Our fully integrated service offers an interface via Jupyter notebooks (ecaslab.dkrz.de). We provide an analysis environment without the need of costly transfers, accessing CF standardized data files and all accessible via the ESGF portal on our nodes (esgf-data.dkrz.de). We can analyse the data of e.g. CMIP5, CMIP6, Grand Ensemble and observation data. ECAS was developed in the frame of European Open Source Cloud (EOSC) hub.& &
Publisher: Copernicus GmbH
Date: 08-2016
DOI: 10.5194/GMD-2016-189
Abstract: Abstract. A simple plume implementation of the second version (v2) of the Max Planck Institute Aerosol Climatology, MACv2-SP, is described. MACv2-SP provides a prescription of anthropogenic aerosol optical properties and an associated Twomey effect for the harmonized use in climate modelling studies. It has been designed to be easy to implement, change and use, and thereby enable studies exploring the climatic effects of different plausible aerosol distributions and their impact on clouds. MACv2-SP is formulated in terms of nine spatial plumes associated with different major anthropogenic source regions. The shape of the plumes is fit to the Max Planck Institute Aerosol Climatology, version 2, which is based on present day (2005) observations. Decadal variations in the litude of the plumes over the historical (post 1850) period is derived by scaling the plumes with associated national emission sources of SO2 and NH3. Two types of plumes are considered: one predominantly associated with biomass burning the other with industrial emissions. The two types of plumes differ in the prescription of their annual cycle and in their optical properties, thereby implicitly accounting for different contributions of absorbing aerosol to the different plumes. A Twomey effect for each plume is prescribed as a change in the host model's background cloud droplet population density using relationships derived from satellite data. Experiments using the simple plume model are performed with the Max Planck Institute Earth System Model. The instantaneous and effective aerosol radiative forcings is estimated to be −0.6 W m−2 and −0.7 W m−2 respectively. Forcing from aerosol-cloud interactions (the Twomey effect) offsets the reduction of clear-sky forcing from clouds, so that the net effect of clouds on the aerosol forcing is small.
Publisher: American Meteorological Society
Date: 20-08-2020
Abstract: The representation of tropical precipitation is evaluated across three generations of models participating in phases 3, 5, and 6 of the Coupled Model Intercomparison Project (CMIP). Compared to state-of-the-art observations, improvements in tropical precipitation in the CMIP6 models are identified for some metrics, but we find no general improvement in tropical precipitation on different temporal and spatial scales. Our results indicate overall little changes across the CMIP phases for the summer monsoons, the double-ITCZ bias, and the diurnal cycle of tropical precipitation. We find a reduced amount of drizzle events in CMIP6, but tropical precipitation occurs still too frequently. Continuous improvements across the CMIP phases are identified for the number of consecutive dry days, for the representation of modes of variability, namely, the Madden–Julian oscillation and El Niño–Southern Oscillation, and for the trends in dry months in the twentieth century. The observed positive trend in extreme wet months is, however, not captured by any of the CMIP phases, which simulate negative trends for extremely wet months in the twentieth century. The regional biases are larger than a climate change signal one hopes to use the models to identify. Given the pace of climate change as compared to the pace of model improvements to simulate tropical precipitation, we question the past strategy of the development of the present class of global climate models as the mainstay of the scientific response to climate change. We suggest the exploration of alternative approaches such as high-resolution storm-resolving models that can offer better prospects to inform us about how tropical precipitation might change with anthropogenic warming.
Publisher: Meteorological Society of Japan
Date: 2020
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-8903
Abstract: & & Since few years Research Data Management is becoming an increasingly important part of scientific projects regardless of the number of topics or subjects, researchers or institutions involved. The bigger the project, the more are the data organization and data management requirements in order to assure the best outcome of the project. Despite this, projects rarely have clear structures or responsibilities for data management. The importance of clearly defining data management and also budgeting for it is often underestimated and/or neglected. A rather scarce number of reports and documentations explaining the research data management in certain projects and detailing best practice ex les can be found in the current literature. & Additionally, these are often mixed up with topics of the general project management. Furthermore, these ex les are very focused on the certain issues of the described projects and thus, a transferability (or general application) of provided methods is very difficult.& & & & This contribution presents generic concepts of research data management with an effort to separate them from general project management tasks. Project size, details among the ersity of topics and the involved researcher, play an important role in shaping data management and determining which methods of data management can add value to the outcome of a project. We especially focus on different organisation types, including roles and responsibilities for data management in projects of different sizes. Additionally, we show how and when also education should be included, but also how important agreements in a project are.& &
Publisher: Zenodo
Date: 2020
Publisher: Pensoft Publishers
Date: 12-10-2022
DOI: 10.3897/RIO.8.E95268
Abstract: In addition to the previous intensive discussion on the “Data Deluge” with respect to enormous increase of available research data, the 2022 Internet-of-Things conference confirmed that in the near future there will be billions if not trillions of smart IoT devices in a very wide range of applications and locations, many of them with computational capacities. This large number of distributed IoT devices will create continuous streams of data that will require a global framework to facilitate their integration into the Internet to enable controlled access to their data and services, to name but a few aspects. This framework would enable tracking of these IoT devices to measure their resource usage for instance to globally address the UN Sustainable Development Goals. Additionally, policy makers are committed to define regulations to break data monopolies and increase sharing. The result will be an increasingly huge domain of accessible digital data which on the one hand allows addressing new challenges especially cross-sector ones. A key prerequisite for this is to find the right data across domain boundaries supporting a specific task. Digitisation is already being called the fourth industrial revolution and the emerging data and information is the 21 st century's new resource. Currently this vision is mostly unrealised due to the inability of existing data and digital resources to be findable, accessible, interoperable, and reusable despite the progress in providing thematic catalogs. As a result, the capacity of this new resource is latent and mostly underutilized. There is no Internet level infrastructure that currently exists to facilitate the process by which all data and digital resources are made consistently and globally accessible. There are patchworks of localized and limited access to trusted data on the Internet created by specific communities that have been funded or directed to collaborate. To turn digital information into a commodity, description, access to, validation, and processing of data needs to become part of the Internet infrastructure we call the Global Integrated Data Space (GIDS). The main pillars of this approach require that data and services be globally identified and consistently accessed, with predictive descriptions and access control to make them globally findable. Currently researchers are relying partly on informal knowledge such as knowing the labs and persons to maximize the chance to access trustworthy data, but this method is limiting the use of suitable data. In the future data scenario, other mechanisms will become possible. In the public information space Google-like searches using specific key terms have become an accepted solution to find documents for human consumption. This approach however, does not work in the GIDS with large numbers of data contributors from a wide range of institutions, from millions of IoT devices worldwide, and where a wide range of data types and automatic data processing procedures dominate. Indeed, successful labs that apply complex models describing digital surrogates can automatically leverage data and data processing procedures from other labs. This makes the currently often operationally applied manual stitching of data and operations too costly both in time and resources to be a competitive option. A researcher looking for specific brain imaging data for a specific study has a few options: Rely on a network of colleagues. Execute Google-like searches in known registries looking for appropriate departments and researchers. Execute Google-like searches on suitable data. He/she engages an agent to execute profile matching in suitable sub-spaces. Rely on a network of colleagues. Execute Google-like searches in known registries looking for appropriate departments and researchers. Execute Google-like searches on suitable data. He/she engages an agent to execute profile matching in suitable sub-spaces. We assume that data creators will have the capability and be interested to create detailed metadata of different types and that the researchers, who are looking for specific data, will be able to specify precise profiles for data they are looking for. Two of the key characteristics of the future data space will be operations that can carry out profile matching at ultra-high speeds and that will lead to various subspaces according to some facets using self-organizing mechanisms. Of course, this poses high requirements on the metadata quality being used and that creators and potential consumers share knowledge about the semantic space in which they operate, and available semantic mappings used by brokers or self-provided. Metadata must be highly detailed and suitable schemas have been developed already in many communities. In addition to the usual metadata, potential users will need to specify their credentials in the form of trusted roles and their usage purposes to indicate access opportunities. Changing current metadata practices to yield richer metadata as presribed by the FAIR principles will not be simple, especially since we seem to be far away from formalizing roles and usage purposes in a broadly accepted way, but the pressure to create rich and standardized metadata will increase. It should be noted of course that for data streams created by IoT sensors, defining proper metadata is an action that is only requested once or a few times. Why are FDOs special in this automatic profile matching scenario? FDOs are bundling all information required for automatic profile matching in a secure way, i.e., all metadata information are available via the gloablly unique resolvable and persisten identifiers (PID) of the FDO and the PID security mechanisms are at the basis to establish trust. FDOs will be provided with a secure method that is capable of computing efficiently coded profiles representing all properties of an FDO relevant for profile matching. This would speedup profile matching enormously. We will address two major questions characterizing the “FDO Machine” we are envisioning: Which kinds of representations could make profile matching much more efficient? How could FDO-based mechanisms be used to efficiently create sub-spaces that would help the emerging layer of information brokers to offer specialized services addressing specialized needs as for ex le requested by UN’s Sustainable Development Goals? Which kinds of representations could make profile matching much more efficient? How could FDO-based mechanisms be used to efficiently create sub-spaces that would help the emerging layer of information brokers to offer specialized services addressing specialized needs as for ex le requested by UN’s Sustainable Development Goals? Brokers might want to use specialized agents to create subspaces along many different important facets such as domains, trustworthiness, roles, etc. Such subspaces are ephemeral virtual structures on top of the huge global integrated data space.
Publisher: Copernicus GmbH
Date: 16-02-2011
Abstract: Abstract. We present a method for deriving the radiative effects of absorbing aerosols in cloudy scenes from satellite retrievals only. We use data of 2005–2007 from various passive sensors aboard satellites of the "A-Train" constellation. The study area is restricted to the tropical- and subtropical Atlantic Ocean. To identify the dependence of the local planetary albedo in cloudy scenes on cloud liquid water path and aerosol optical depth (AOD), we perform a multiple linear regression. The OMI UV-Aerosolindex serves as an indicator for absorbing-aerosol presence. In our method, the aerosol influences the local planetary albedo through direct- (scattering and absorption) and indirect (Twomey) aerosol effects. We find an increase of the local planetary albedo (LPA) with increasing AOD of mostly scattering aerosol and a decrease of the LPA with increasing AOD of mostly absorbing aerosol. These results allow us to derive the direct aerosol effect of absorbing aerosols in cloudy scenes, with the effect of cloudy-scene aerosol absorption in the tropical- and subtropical Atlantic contributing (+21.2 ± 11.1)×10−3 Wm−2 to the global top of the atmosphere radiative forcing.
No related grants have been discovered for Karsten Peters-von Gehlen.