Complementing ERA5 and E-OBS with high-resolution river discharge over Europe

Hagemann, Stefan; Stacke, Tobias

doi:10.1016/j.oceano.2022.07.003

Artykuł - szczegóły

Tytuł artykułu

Complementing ERA5 and E-OBS with high-resolution river discharge over Europe

Autorzy

Hagemann Stefan , Stacke Tobias

Wybrane pełne teksty z tego czasopisma

Identyfikatory

DOI

10.1016/j.oceano.2022.07.003

Warianty tytułu

Języki publikacji

Abstrakty

The 0.5° resolution of many global observational or quasi-observational datasets is not sufficient for the evaluation of current state-of-the-art regional climate models or the forcing of ocean model simulations over Europe. While higher resolved products are available for meteorological data, e.g. ERA5 reanalysis and the E-OBS vs 22 (EOBS22) datasets, they lack crucial information at the land-ocean boundary. ERA5 is frequently used to force regional climate models (RCMs) or ocean models and both datasets are commonly used as reference datasets for the evaluation of RCMs. Therefore, we extended both datasets with high-resolution river discharge for the period 1979–2018. On the one hand, our discharge data close the water cycle at the land-ocean interface so that the discharges can be used as lateral freshwater input for ocean models applied in the European region. On the other hand, the data can be used to identify trends in discharge that are induced by recent climate change as ERA5 and EOBS22 are rather independent datasets. The experimental setup to generate the discharges was chosen in a way that it could be easily adapted in a climate or Earth system modelling framework. Consequently, the recently developed 5 Min. horizontal resolution version of the hydrological discharge (HD) model was used to simulate discharge. It has already been applied in multiple climate modelling studies and is coupled within several global and regional Earth system models. As the HD model currently does not regard direct human impacts of the river runoff, it is well suited to investigate climate change-related discharge trends. In order to calculate the necessary gridded input fields for the HD model from ERA5 and EOBS22 data, we used the HydroPy global hydrological model. For both experiments, we found that the general behavior of discharge is captured well for many European rivers, which is consistent to earlier results. For the EOBS22 based discharges, a widespread low bias in simulated discharge occurs, which is likely caused by the missing undercatch correction in the underlying precipitation data. The analysis of trends over Southeastern Europe was hampered by missing data in EOBS22 after 2004. Using both experiments, we identified consistent trend patterns in various discharge statistics, with increases in low flow characteristics over Northern Europe and general drying trends over Central and Southern Europe. In summary, we introduced an experimental setup that is useful to generate high-resolution river runoff data consistent with the meteorological forcing for historical periods and future scenarios from any climate model data instead of having to rely on observed time series.

Słowa kluczowe

discharge trends high resolution Europe ERA5 E-OBS

Wydawca

Polish Academy of Sciences, Institute of Oceanology
Elsevier

Czasopismo

Oceanologia

Rocznik

2023

Tom

Vol. 65 (1)

Strony

230--248

Opis fizyczny

Bibliogr. 62 poz., map., rys., tab., wykr.

Twórcy

autor

Hagemann Stefan

stefan.hagemann@hereon.de

Institute of Coastal Systems - Analysis and Modelling, Helmholtz-Zentrum Hereon, Geesthacht, Germany

https://orcid.org/0000-0001-5444-2945

autor

Stacke Tobias

Institute of Coastal Systems - Analysis and Modelling, Helmholtz-Zentrum Hereon, Geesthacht, Germany
Max Planck Institute for Meteorology, Hamburg, Germany

https://orcid.org/0000-0003-4637-5337

Bibliografia

1. Akbulut, N.E., Sahin, Y., Bayar, S., Akbulut, A., 2009. Rivers of Turkey. In: Rivers of Europe. Elsevier, 643-672.
2. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO, Rome, Italy.
3. Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Schamm, K., Schneider, U., Ziese, M., 2013. A description of the global landsurface precipitation data products of the global precipitation climatology centre with sample applications including centennial (trend) analysis from 1901-present. Earth Syst. Sci. Data 5 (1), 71-99. https://doi.org/10.5194/essd-5-71-2013
4. Bonaduce, A., Staneva, J., Grayek, S., Bidlot, J.-R., Breivik, Ø., 2020. Sea-state contributions to sea-level variability in the European seas. Ocean Dynam. 70 (12), 1547-1569. https://doi.org/10.1007/s10236-020-01404-1
5. Cornes, R.C., van der Schrier, G., van den Besselaar, E.J.M., Jones, P.D., 2018. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. D: Atmos 123 (17), 9391-9409. https://doi.org/10.1029/2017jd028200
6. Cucchi, M., Weedon, G.P., Amici, A., Bellouin, N., Lange, S., Schmied, H.M., Hersbach, H., Buontempo, C., 2020. WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data 12 (3), 2097-2120. https://doi.org/10.5194/essd-12-2097-2020
7. Dai, A., Qian, T., Trenberth, K.E., Milliman, J.D., 2009. Changes in continental freshwater discharge from 1948 to 2004. J. Climate 22 (10), 2773-2792. https://doi.org/10.1175/2008jcli2592.1
8. Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A.P., Monge-Sanz, B.M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., Vitart, F., 2011. The ERA-interim reanalysis: configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc. 137 (656), 553-597. https://doi.org/10.1002/qj.828
9. Donnelly, C., Andersson, J.C.M., Arheimer, B., 2016. Using flow signatures and catchment similarities to evaluate the E-HYPE multi-basin model across Europe. Hydrol. Sci. J. 61 (2), 255-273. https://doi.org/10.1080/02626667.2015.1027710
10. Dümenil, L., Todini, E., 1992. A rainfall-runoff scheme for use in the Hamburg climate model. In: Advances in Theoretical Hydrology. Elsevier, 129-157.
11. European Environment Agency, 2017. Climate change, impacts and vulnerability in Europe 2016: an indicator based report. European Environment Agency EEA Rep. 01/2017. https://doi.org/10.2800/534806
12. Gupta, H.V., Kling, H., Yilmaz, K.K., Martinez, G.F., 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol. 377 (1-2), 80-91. https://doi.org/10.1016/j.jhydrol.2009.08.003
13. Haddeland, I., Clark, D.B., Franssen, W., Ludwig, F., Voß, F., Arnell, N.W., Bertrand, N., Best, M., Folwell, S., Gerten, D., Gomes, S., Gosling, S.N., Hagemann, S., Hanasaki, N., Harding, R., Heinke, J., Kabat, P., Koirala, S., Oki, T., Polcher, J., Stacke, T., Viterbo, P., Weedon, G.P., Yeh, P., 2011. Multimodelestimate of the global terrestrial water balance: Setup and first results. J. Hydrometeorol. 12 (5), 869-884. https://doi.org/10.1175/2011jhm1324.1
14. Hagemann, S., 2002. An improved land surface parameter dataset for global and regional climate models. Max Planck Inst. For Meteorol. Rep. no. 336. MPI, Hamburg, Germany, 21 pp. http://hdl.handle.net/11858/00-001M-0000-002B-539B-6
15. Hagemann, S., Chen, C., Clark, D.B., Folwell, S., Gosling, S.N., Haddeland, I., Hanasaki, N., Heinke, J., Ludwig, F., Voss, F., Wiltshire, A.J., 2013. Climate change impact on available water resources obtained using multiple global climate and hydrology models. Earth Syst. Dynam. 4 (1), 129-144. https://doi.org/10.5194/esd-4-129-2013
16. Hagemann, S., Dümenil, L., 1998. A parametrization of the lateral waterflow for the global scale. Clim. Dynam. 14 (1), 17-31. https://doi.org/10.1007/s003820050205
17. Hagemann, S., Dümenil Gates, L., 2001. Validation of the hydrological cycle of ECMWF and NCEP reanalyses using the MPI hydrological discharge model. J. Geophys. Res. D: Atmos 106 (D2), 1503-1510. https://doi.org/10.1029/2000JD900568
18. Hagemann, S., Dümenil Gates, L., 2003. Improving a subgrid runoff parameterization scheme for climate models by the use of high resolution data derived from satellite observations. Clim. Dynam. 21 (3—4), 349-359. https://doi.org/10.1007/s00382-003-0349-x
19. Hagemann, S., Ho-Hagemann, H.T., 2021. The hydrological discharge model — a river runoff component for offline and coupled model applications (5.0.0). Zenodo. https://doi.org/10.5281/zenodo.4893099
20. Hagemann, S., Stacke, T., 2021. Forcing for HD Model from HydroPy and subsequent HD Model river runoff over Europe based on EOBS22 and ERA5 data. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/EOBS_ERA5-RIVER_RUNOFF
21. Hagemann, S., Stacke, T., Ho-Hagemann, H.T.M., 2020. High resolution discharge simulations over Europe and the Baltic Sea catchment. Front. Earth Sci. 8. https://doi.org/10.3389/feart.2020.00012
22. Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., Pappenberger, F., 2020. GloFAS-ERA5 operational global river discharge reanalysis 1979 present. Earth Syst. Sci. Data 12 (3), 2043-2060. https://doi.org/10.5194/essd-12-2043-2020
23. Hartmann, D., Tank, A.K., Rusticucci, M., Alexander, L., Brönnimann, S., Charabi, Y., Dentener, F., Dlugokencky, E., Easterling, D., Kaplan, A., Soden, B., Thorne, P., Wild, M., Zhai, P., 2013. Observations: Atmosphere and surface. In: Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P. (Eds.), The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
24. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-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., 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., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thépaut, J.-N., 2020. The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc. 146 (730), 1999-2049. https://doi.org/10.1002/qj.3803
25. Ho-Hagemann, H.T.M., Hagemann, S., Grayek, S., Petrik, R., Rockel, B., Staneva, J., Feser, F., Schrum, C., 2020. Internal model variability of the regional coupled system model GCOAST-AHOI. Atmosphere 11 (3), 227. https://doi.org/10.3390/atmos11030227
26. Khublaryan, M.G., 2009. Water resources for sustainable development, with particular reference to Russia. In: Vol. I of Area Studies (Regional Sustainable Development Review): Russia-Volume I. EOLSS Publications, Oxford, UK, 81-94.
27. Klein Tank, A.M.G., Wijngaard, J.B., Können, G.P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., Heino, R., Bessemoulin, P., Müller-Westermeier, G., Tzanakou, M., Szalai, S., Pálsdóttir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., van Engelen, A.F.V., Forland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T., López, J.A., Dahlström, B., Moberg, A., Kirchhofer, W., Ceylan, A., Pachaliuk, O., Alexander, L.V., Petrovic, P., 2002. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol. 22 (12), 1441-1453. https://doi.org/10.1002/joc.773
28. Kling, H., Fuchs, M., Paulin, M., 2012. Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. J. Hydrol. 424-425, 264-277. https://doi.org/10.1016/j.jhydrol.2012.01.011
29. Klok, E.J., Klein Tank, A.M.G., 2009. Updated and extended European dataset of daily climate observations. Int. J. Climatol. 29 (8), 1182-1191. https://doi.org/10.1002/joc.1779
30. Knoben, W.J.M., Freer, J.E., Woods, R.A., 2019. Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrol. Earth Syst. Sci. 23 (10), 4323-4331. https://doi.org/10.5194/hess- 23- 4323- 2019
31. Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., Takahashi, K., 2015. The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteorol. Soc. Jpn. 93 (1), 5-48. https://doi.org/10.2151/jmsj.2015-001
32. Lamarche, C., Santoro, M., Bontemps, S., d’Andrimont, R., Radoux, J., Giustarini, L., Brockmann, C., Wevers, J., Defourny, P., Arino, O., 2017. Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Remote Sens. 9 (1), 36. https://doi.org/10.3390/rs9010036
33. Lehner, B., Döll, P., 2004. Development and validation of a global database of lakes, reservoirs and wetlands. J. Hydrol. 296 (1), 1-22. https://doi.org/10.1016/j.jhydrol.2004.03.028
34. Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., Arheimer, B. 2010. Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrol. Res. 41 (3-4), 295-319. https://doi.org/10.2166/nh.2010.007
35. Llasat, M.C., del Moral, A., Cortès, M., Rigo, T., 2021. Convective precipitation trends in the Spanish Mediterranean region. Atmos. Res. 257, 105581. https://doi.org/10.1016/j.atmosres.2021.105581
36. Merchán, D., Causapé, J., Abrahao, R., 2013. Impact of irrigation implementation on hydrology and water quality in a small agricultural basin in Spain. Hydrol. Sci. J. 58 (7), 1400-1413.
37. Muis, S., Apecechea, M.I., Dullaart, J., de Lima Rego, J., Madsen, K.S., Su, J., Yan, K., Verlaan, M., 2020. A high-resolution global dataset of extreme sea levels, tides, and storm surges, including future projections. Front. Mar. Sci. 7. https://doi.org/10.3389/fmars.2020.00263
38. Peña-Angulo, D., Vicente-Serrano, S.M., Domnguez-Castro, F., Murphy, C., Reig, F., Tramblay, Y., Trigo, R.M., Luna, M.Y., Turco, M., Noguera, I., Aznárez-Balta, M., Garcá-Herrera, R., Tomas-Burguera, M., Kenawy, A.E., 2020. Long-term precipitation in Southwestern Europe reveals no clear trend attributable to anthropogenic forcing. Environ. Res. Lett. 15 (9), 094070. https://doi.org/10.1088/1748-9326/ab9c4f
39. Priestley, C.H.B., Taylor, R.J., 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Mon. Weather Rev. 100 (2), 81-92. https://doi.org/10.1175/1520-0493(1972)100〈0081:otaosh〉2.3.co;2
40. Rakovec, O., Kumar, R., 2022. Mesoscale hydrologic model based historical streamflow simulation over europe at 1/16 degree. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/MHMBASSIMEUR
41. Rockel, B., Will, A., Hense, A., 2008. The regional climate model COSMO-CLM (CCLM). Meteorol. Z. 17 (4), 347-348. https://doi.org/10.1127/0941-2948/2008/0309
42. Samaniego, L., Kumar, R., Attinger, S., 2010. Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resour. Res. 46 (5), W05523. https://doi.org/10.1029/2008WR007327
43. Smakhtin, V., Revenga, C., Döll, P., 2004. A pilot global assessment of environmental water requirements and scarcity. Water Int. 29 (3), 307-317. https://doi.org/10.1080/02508060408691785
44. Stacke, T., Hagemann, S., 2012. Development and evaluation of a global dynamical wetlands extent scheme. Hydrol. Earth Syst. Sci. 16 (8), 2915-2933. https://doi.org/10.5194/hess- 16- 2915- 2012
45. Stacke, T., Hagemann, S., 2021. HydroPy (v1.0): A new global hydrology model written in Python. Geosci. Model Dev. https://doi.org/10.5194/gmd- 2021- 53
46. Stahl, K., Hisdal, H., Hannaford, J., Tallaksen, L.M., van Lanen, H.A.J., Sauquet, E., Demuth, S., Fendekova, M., Jódar, J., 2010. Streamflow trends in Europe: evidence from a dataset of near-natural catchments. Hydrol. Earth Syst. Sci. 14 (12), 2367-2382. https://doi.org/10.5194/hess-14-2367-010
47. Stahl, K., Tallaksen, L.M., Hannaford, J., van Lanen, H.A.J., 2012. Filling the white space on maps of European runoff trends: estimates from a multi-model ensemble. Hydrol. Earth Syst. Sci. 16 (7), 2035-2047. https://doi.org/10.5194/hess- 16- 2035- 2012
48. Suzuki, T., Yamazaki, D., Tsujino, H., Komuro, Y., Nakano, H., Urakawa, S., 2017. A dataset of continental river discharge based on JRA-55 for use in a global ocean circulation model. J. Oceanogr. 74 (4), 421-429. https://doi.org/10.1007/s10872-017-0458-5
49. Svendsen, L. M., Gustafsson, B., 2020. Waterborne nitrogen and phosphorus inputs and water flow to the Baltic Sea 1995—2018. online, viewed 14 June 2022. https://helcom.fi/baltic-sea-trends/environment-fact- sheets/eutrophication/
50. Telteu, C.-E., Schmied, H.M., Thiery, W., Leng, G., Burek, P., Liu, X., Boulange, J.E.S., Andersen, L.S., Grillakis, M., Gosling, S.N., Satoh, Y., Rakovec, O., Stacke, T., Chang, J., Wanders, N., Shah, H.L., Trautmann, T., Mao, G., Hanasaki, N.,Koutroulis, A., Pokhrel, Y., Samaniego, L., Wada, Y., Mishra, V., Liu, J., Döll, P., Zhao, F., Gädeke, A., Rabin, S.S., Herz, F., 2021. Understanding each others models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication. Geosci. Model Dev. 14 (6), 3843-3878. https://doi.org/10.5194/gmd- 14- 3843- 2021
51. Teuling, A.J., Hirschi, M., Ohmura, A., Wild, M., Reichstein, M., Ciais, P., Buchmann, N., Ammann, C., Montagnani, L., Richardson, A.D., Wohlfahrt, G., Seneviratne, S.I., 2009. A regional perspective on trends in continental evaporation. Geophys. Res. Lett. 36 (2). https://doi.org/10.1029/2008gl036584
52. Thornthwaite, C.W., 1948. An approach toward a rational classification of climate. Soil Sci. 66 (1), 77. https://doi.org/10.1097/00010694- 194807000- 00007
53. Tsujino, H., Urakawa, S., Nakano, H., Small, R.J., Kim, W.M., Yeager, S.G., Danabasoglu, G., Suzuki, T., Bamber, J.L., Bentsen, M., Böning, C.W., Bozec, A., Chassignet, E.P., Curchitser, E., Dias, F.B., Durack, P.J., Griffies, S.M., Harada, Y., Ilicak, M., Josey, S.A., Kobayashi, C., Kobayashi, S., Komuro, Y., Large, W.G., Sommer, J.L., Marsland, S.J., Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., Yamazaki, D., 2018. JRA-55 based surface dataset for driving ocean sea-ice models (JRA55-do). Ocean Modell. 130, 79-139. https://doi.org/10.1016/j.ocemod.2018.07.002
54. Van Der Knijff, J.M., Younis, J., Roo, A.P.J.D., 2010. LISFLOOD: a GIS-based distributed model for river basin scale water balance and flood simulation. Int. J. Geogr. Inf. Sci. 24 (2), 189-212. https://doi.org/10.1080/13658810802549154
55. Vogt, J., Soille, P., de Jager, A., Rimavicîute, E., Mehl, W., Foisneau, S., Bódis, K., Dusart, J., Paracchini, M.L., Haastrup, P., Bamps, C., 2007. A pan-European river and catchment database. Joint Research Centre (JRC), Ispra, Italy Tech. Rep. 22920 EN. https://doi.org/10.2788/35907
56. Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., Schewe, J., 2014. The inter-sectoral impact model intercom- parison project (ISIMIP): Project framework. Proc. Natl. Acad. Sci. USA 111 (9), 3228-3232. https://doi.org/10.1073/pnas.1312330110
57. Weedon, G.P., Balsamo, G., Bellouin, N., Gomes, S., Best, M.J., Viterbo, P., 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim re-analysis data. Water Resour. Res. 50 (9), 7505-7514. https://doi.org/10.1002/2014WR015638
58. Weedon, G.P., Gomes, S., Viterbo, P., Shuttleworth, W.J., Blyth, E., Österle, H., Adam, J.C., Bellouin, N., Boucher, O., Best, M., 2011. Creation of the WATCH Forcing Data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J. Hydrometeorol. 12 (5), 823-848. https://doi.org/10.1175/2011JHM1369.1
59. Wilson, C., Harle, J.D., Wakelin, S.L., 2019. Development of a regional ocean model for the Caribbean, including 3D dynamics, thermodynamics and full surface flux forcing. National Oceanogr. Centre Res. & Consultancy Rep. 65. National Oceanography Centre, Southampton, UK. Yamazaki, D., de Almeida, G.A.M., Bates, P.D., 2013. Improving computational efficiency in global river models by implementing the local inertial flow equation and a vector-based river network map. Water Resour. Res. 49 (11), 7221-7235. https://doi.org/10.1002/wrcr.20552
60. Yamazaki, D., Kanae, S., Kim, H., Oki, T., 2011. A physically based description of floodplain inundation dynamics in a global river routing model. Water Resour. Res. 47 (4), W04501. https://doi.org/10.1029/2010wr009726
61. Zampieri, L., Kauker, F., Fröhle, J., Sumata, H., Hunke, E.C., Goessling, H.F., 2021. Impact of sea-ice model complexity on the performance of an unstructured-mesh sea-ice/ocean model under different atmospheric forcings. J. Adv. Model. Earth Syst. 13 (5). https://doi.org/10.1029/2020ms002438
62. Zängl, G., Reinert, D., Rípodas, P., Baldauf, M., 2014. The ICON (ICOsahedral non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core. Quart. J. Roy. Meteor. Soc. 141 (687), 563-579. https://doi.org/10.1002/qj.2378

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-aecf428a-3f67-4e10-b6ae-c3b802e4e90a