Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5277-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-13-5277-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 Nov 2020
Development and technical paper |
| 05 Nov 2020
| 05 Nov 2020
Flex_extract v7.1.2 – a software package to retrieve and prepare ECMWF data for use in FLEXPART
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Aerosol Physics & Environmental Physics, University of Vienna, Vienna, Austria
previously published under the name Anne Philipp
Leopold Haimberger
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Petra Seibert
Institute of Meteorology, University of Natural Resources and Life Sciences, Vienna, Austria
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The A-LIFE aircraft field experiment was carried out in the eastern Mediterranean in 2017. Using A-LIFE data, we studied the change in mineral dust optical properties due to mixing with anthropogenic aerosols. We found that increasing pollution affects dust optical properties, which has implications for identifying dust events and understanding their climate effects. We also show that optical properties of Saharan and Arabian dust are similar when comparing cases with equal pollution content.
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
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This preprint is open for discussion and under review for Ocean Science (OS).
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Ocean reanalyses combine models and observations to reconstruct past ocean conditions. We evaluate their performance against detailed measurements from the subpolar North Atlantic at the OSNAP section. While reanalyses capture long-term averages and broad circulation patterns, they miss some more regional features and variability. This highlights both their value and their limitations, stressing the need for improved observations and higher-resolution models.
Marilena Teri, Josef Gasteiger, Katharina Heimerl, Maximilian Dollner, Manuel Schöberl, Petra Seibert, Anne Tipka, Thomas Müller, Sudharaj Aryasree, Konrad Kandler, and Bernadett Weinzierl
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The A-LIFE aircraft field experiment was carried out in the eastern Mediterranean in 2017. Using A-LIFE data, we studied the change in mineral dust optical properties due to mixing with anthropogenic aerosols. We found that increasing pollution affects dust optical properties, which has implications for identifying dust events and understanding their climate effects. We also show that optical properties of Saharan and Arabian dust are similar when comparing cases with equal pollution content.
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Silke Groß, Volker Freudenthaler, Moritz Haarig, Albert Ansmann, Carlos Toledano, David Mateos, Petra Seibert, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Josef Gasteiger, Maximilian Dollner, Anne Tipka, Manuel Schöberl, Marilena Teri, and Bernadett Weinzierl
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The tropical tropopause layer (TTL) is a crucial region where the troposphere transitions into the stratosphere, influencing air mass transport. This study examines temperature trends in the TTL and lower stratosphere using data from weather balloons, satellites, and reanalysis datasets. We found cooling trends in the TTL from 1980–2001, followed by warming from 2002–2023. These shifts are linked to changes in atmospheric circulation and impact water vapor transport into the stratosphere.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
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Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data are complicated by the distortion of the used modelling grids and the large number of different grid types. We present two new methods that allow the calculation of oceanic fluxes of volume, heat, salinity, and ice through almost arbitrary sections for various models and reanalyses that are independent of the used modelling grids.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
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This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
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This study investigates the temporal stability and reliability of winter-month trends of air–sea heat fluxes from ERA5 forecasts over the North Atlantic basin for the period 1950–2019. Driving forces of trends and the impact of modes of climate variability and analysis increments on air–sea heat fluxes are investigated. Finally, a new and independent estimate of the Atlantic Meridional Overturning Circulation weakening is provided and associated with a decrease in air–sea heat fluxes.
Magdalena Fritz, Michael Mayer, Leopold Haimberger, and Susanna Winkelbauer
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The interaction between the Indonesian Throughflow (ITF) and regional climate phenomena indicates the high relevance for monitoring the ITF. Observations remain temporally and spatially limited; hence near-real-time monitoring is only possible with reanalyses. We assess how well ocean reanalyses depict the intensity of the ITF via comparison to observations. The results show that reanalyses agree reasonably well with in situ observations; however, some aspects require higher-resolution products.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
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Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
Hydrol. Earth Syst. Sci., 26, 279–304, https://doi.org/10.5194/hess-26-279-2022, https://doi.org/10.5194/hess-26-279-2022, 2022
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We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.
Noemi Imfeld, Leopold Haimberger, Alexander Sterin, Yuri Brugnara, and Stefan Brönnimann
Earth Syst. Sci. Data, 13, 2471–2485, https://doi.org/10.5194/essd-13-2471-2021, https://doi.org/10.5194/essd-13-2471-2021, 2021
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Upper-air data form the backbone of reanalysis products, particularly in the pre-satellite era. However, historical upper-air data are error-prone because measurements at high altitude were especially challenging. Here, we present a collection of data from historical intercomparisons of radiosondes and error assessments reaching back to the 1930s that may allow us to better characterize such errors. The full database, including digitized data, images, and metadata, is made publicly available.
Karina von Schuckmann, Lijing Cheng, Matthew D. Palmer, James Hansen, Caterina Tassone, Valentin Aich, Susheel Adusumilli, Hugo Beltrami, Tim Boyer, Francisco José Cuesta-Valero, Damien Desbruyères, Catia Domingues, Almudena García-García, Pierre Gentine, John Gilson, Maximilian Gorfer, Leopold Haimberger, Masayoshi Ishii, Gregory C. Johnson, Rachel Killick, Brian A. King, Gottfried Kirchengast, Nicolas Kolodziejczyk, John Lyman, Ben Marzeion, Michael Mayer, Maeva Monier, Didier Paolo Monselesan, Sarah Purkey, Dean Roemmich, Axel Schweiger, Sonia I. Seneviratne, Andrew Shepherd, Donald A. Slater, Andrea K. Steiner, Fiammetta Straneo, Mary-Louise Timmermans, and Susan E. Wijffels
Earth Syst. Sci. Data, 12, 2013–2041, https://doi.org/10.5194/essd-12-2013-2020, https://doi.org/10.5194/essd-12-2013-2020, 2020
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Understanding how much and where the heat is distributed in the Earth system is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This study is a Global Climate Observing System (GCOS) concerted international effort to obtain the Earth heat inventory over the period 1960–2018.
Cited articles
Brandl, G.: Sphinx - Python Documentation Generator, available at:
http://www.sphinx-doc.org/en/master/, last access: 12 December 2019. a
Buizza, R., Richardson, D. S., and Palmer, T. N.: Benefits of increased
resolution in the ECMWF ensemble system and comparison with poor-man's
ensembles, Q. J. Roy. Meteor. Soc., 129,
1269–1288, https://doi.org/10.1256/qj.02.92,
2003. a
Copernicus: How to use the CDS API, available at:
https://cds.climate.copernicus.eu/api-how-to, last access: 3 September 2019. a
ECMWF: ERA-Interim: What is the spatial reference, available at:
https://confluence.ecmwf.int/display/CKB/ERA-Interim%3A+What+is+the+spatial+reference (last access: 26 August 2019),
2016a. a
ECMWF: ERA5: What is the spatial reference, available at:
https://confluence.ecmwf.int/display/CKB/ERA5%3A+What+is+the+spatial+reference (last access: 26 August 2019),
2016b. a
ECMWF: OpenIFS: Horizontal Resolution Configurations, available at:
https://confluence.ecmwf.int/display/OIFS/4.2+OpenIFS%3A+Horizontal+Resolution+Configurations (last access: 8 June 2020),
2017. a
ECMWF: ECMWF, available at: https://www.ecmwf.int, last access: 5 August 2019a. a
ECMWF: Forecasting system upgrade set to improve global weather forecasts, available at:
https://www.ecmwf.int/en/about/media-centre/news/2019/forecasting-system-upgrade-set-improve-global-weather-forecasts,
last access: 24 August 2019c. a
ECMWF: Data spatial coordinate systems, available at:
https://www.ecmwf.int/en/forecasts/documentation-and-support/data-spatial-coordinate-systems
(last access: 26 August 2019), 2019d. a
ECMWF: Changes in ECMWF model, available at:
https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model,
last access: 27 August 2019f. a
ECMWF: IFS Documentation, available at:
https://www.ecmwf.int/en/publications/ifs-documentation,
last access: 26 August 2019g. a
ECMWF: ERA5 data documentation, available at:
https://confluence.ecmwf.int/display/CKB/ERA5+data+documentation,
last access: 27 August 2019h. a
ECMWF: ECMWF Web API Home, available at:
https://confluence.ecmwf.int/display/WEBAPI/ECMWF+Web+API+Home,
last access: 3 September 2019k. a
ECMWF: What are the changes from ERA-Interim to ERA5?, available at: https://confluence.ecmwf.int/pages/viewpage.action?pageId=74764925, last access: 16 October 2020l. a
ECMWF: Tutorials, available at:
https://confluence.ecmwf.int/display/METV/Tutorials, last access: 18 December 2019m. a
Foord, M. and the mock team: Mock object library, available at:
https://mock.readthedocs.io/en/latest/, last access: 12 December 2019. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Munoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Radnoti, G., de Rosnay, P., Rozum, I.,
Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global
Reanalysis, Quart. J. Roy. Met. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hoyer, S. and Hamman, J.: xarray: N-D labeled arrays and datasets in
Python, Journal of Open Research Software, 5, https://doi.org/10.5334/jors.148, 2017. a
Krekel, H.: pytest Documentation, available at:
https://buildmedia.readthedocs.org/media/pdf/pytest/latest/pytest.pdf, last access: 12 December 2019. a
Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K., and Janssen, P.: A coupled
data assimilation system for climate reanalysis, Q. J. Roy. Meteor. Soc., 142, 65–78, https://doi.org/10.1002/qj.2629, 2016. a, b
Laloyaux, P., de Boisseson, E., Balmaseda, M., Bidlot, J.-R., Broennimann, S.,
Buizza, R., Dalhgren, P., Dee, D., Haimberger, L., Hersbach, H., Kosaka, Y.,
Martin, M., Poli, P., Rayner, N., Rustemeier, E., and Schepers, D.: CERA-20C:
A Coupled Reanalysis of the Twentieth Century, J Adv. Model. Earth Sy., 10, 1172–1195, https://doi.org/10.1029/2018MS001273,
2018. a
McKinney, W.: Data Structures for Statistical Computing in Python, in:
Proceedings of the 9th Python in Science Conference, Austin, Texas, 28 June–3 July 2010, edited by: van der Walt,
S. and Millman, J., 51–56, https://doi.org/10.25080/Majora-92bf1922-00a, 2010. a
Palmer, T. N., Barkmeijer, J., Buizza, R., and Petroliagis, T.: The ECMWF
Ensemble Prediction System, Meteorol. Appl., 4, 301–304,
https://doi.org/10.1017/S1350482797000649, 1997. a
Pisso, I., Sollum, E., Grythe, H., Kristiansen, N. I., Cassiani, M., Eckhardt, S., Arnold, D., Morton, D., Thompson, R. L., Groot Zwaaftink, C. D., Evangeliou, N., Sodemann, H., Haimberger, L., Henne, S., Brunner, D., Burkhart, J. F., Fouilloux, A., Brioude, J., Philipp, A., Seibert, P., and Stohl, A.: The Lagrangian particle dispersion model FLEXPART version 10.4, Geosci. Model Dev., 12, 4955–4997, https://doi.org/10.5194/gmd-12-4955-2019, 2019. a, b, c, d
Ritchie, H., Temperton, C., Simmons, A., Hortal, M., Davies, T., Dent, D., and
Hamrud, M.: Implementation of the Semi–Lagrangian Method in a
High–Resolution Version of the ECMWF Forecast Model, Mon. Weather Rev.,
123, 489–514, https://doi.org/10.1175/1520-0493(1995)123<0489:IOTSLM>2.0.CO;2, 1995. a
Simmons, A. J. and Burridge, D. M.: An Energy and Angular–Momentum Conserving
Vertical Finite-Difference Scheme and Hybrid Vertical Coordinates, Mon.
Weather Rev., 109, 758–766, https://doi.org/10.1175/1520-0493(1981)109<0758:AEAAMC>2.0.CO;2, 1981. a, b
Sneed, H. M., Seidl, R., and Baumgartner, M.: Software in Zahlen, Hanser, Munich, ISBN 978-3-446-42175-2, 357 pp., 2010. a
Spillner, Andreas; Linz, T.: Basiswissen Softwaretest, 5th edn., dpunkt, Heidelberg, ISBN 978-3-86490-024-2, 290 pp., 2012. a
Stohl, A. and Seibert, P.: Accuracy of trajectories as determined from the
conservation of meteorological tracers, Q. J. Roy. Meteor. Soc., 125,
1465–1584, https://doi.org/10.1002/qj.49712454907, 1998. a, b, c
Stohl, A., Wotawa, G., Seibert, P., and Kromp-Kolb, H.: Interpolation errors in
wind fields as a function of spatial and temporal resolution and their impact
on different types of kinematic trajectories, J. Appl. Meteorol., 34,
2149–2165, https://doi.org/10.1175/1520-0450(1995)034<2149:IEIWFA>2.0.CO;2, 1995. a, b, c
Stohl, A., Hittenberger, M., and Wotawa, G.: Validation of the
Lagrangian particle dispersion model FLEXPART against large-scale tracer
experiment data, Atmos. Environ., 32, 4245–4264,
https://doi.org/10.1016/S1352-2310(98)00184-8, 1998. a, b, c
Stohl, A., Haimberger, L., Scheele, M., and Wernli, H.: An intercomparison of
results from three trajectory models, Meteorol. Appl., 8, 127–135, https://doi.org/10.1017/S1350482701002018,
2001. a
Stohl, A., Forster, C., Frank, A., Seibert, P., and Wotawa, G.: Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2, Atmos. Chem. Phys., 5, 2461–2474, https://doi.org/10.5194/acp-5-2461-2005, 2005 a, b, c
Thénault, S.: Pylint, available at: https://www.pylint.org/ (last access: 11 December 2019), 2001. a
van Rossum, G., Warsaw, B., and Coghlan, N.: PEP 8 – Style Guide for Python
Code, available at: https://www.python.org/dev/peps/pep-0008/ (last access: 16 October 2020),
2001. a
Walt, S. v. d., Colbert, S. C., and Varoquaux, G.: The NumPy array: A
structure for efficient numerical computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011.
a
Wolff, E.: Continuous Delivery: Der pragmatische Einstieg, Dpunkt Verlag,
Heidelberg, ISBN 978-3-86490-208-6, 282 pp., 2014. a
Wotawa, G., Stohl, A., and Neininger, B.: The urban plume of Vienna:
comparisons between aircraft measurements and photochemical model results,
Atmos. Environ., 32, 2479–2489,
https://doi.org/10.1016/S1352-2310(98)00021-1,
1998. a
Short summary
Flex_extract v7.1 is an open-source software to retrieve and prepare meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) MARS archive to serve as input for the FLEXTRA–FLEXPART atmospheric transport modelling system. It can be used by public as well as member-state users and enables the retrieval of a variety of different data sets, including the new reanalysis ERA5. Instructions are given for installation along with typical usage scenarios.
Flex_extract v7.1 is an open-source software to retrieve and prepare meteorological fields from...
Special issue