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Holger Pohlmann

Abteilung Klimavariabilität
Gruppe Modellierung des Erdsystems und Vorhersagen
Position Research Scientist
Telefon +49 40 41173-156
Email holger.pohlmann@mpimet.mpg.de
Raum B 224

 

 

Forschungsinteressen

Entwicklung von Klimavorhersagesystemen

Ich arbeite an der Entwicklung und Verbesserung von saisonalen und dekadischen Klimavorhersagesystemen. Retrospektive Klimavorhersagen über die vergangenen (30 bis 60) Jahre werden durchgeführt, um die Vorhersagegüte von Klimavorhersagesystemen zu bestimmen und international zu vergleichen. Die Qualität der Klimavorhersagen ist abhängig von der Qualität des zugrunde liegenden Klimamodells, der Initialisierung mit Beobachtungsdaten und dem Prä- und Postprozessieren der Klimavorhersagen. In meiner beruflichen Laufbahn habe ich zu allen genannten Punkten beigetragen. Zurzeit arbeite ich an der Entwicklung des neuen Klimamodells ICON-ESM mit. Darüber hinaus untersuche ich, wie externe Forcingdaten (z.B. Treibhausgase, Aerosole, usw.) Klimavorhersagen beeinflussen. Dazu werden große Ensembles sogenannter “Single Forcing” Simulationen im Rahmen des EU-Projekts ASPECT erstellt und analysiert, um das Verständnis der Klimavorhersagen und ihrer Mechanismen zu verbessern.

Eingereicht

  • Huo, W., T. Spiegl, S. Wahl, K. Matthes, U. Langematz, H. Pohlmann, J. Kröger, 2024: Assessment of the 11-year solar cycle signals in the middle atmosphere in multiple-model ensemble simulations. Atmospheric Chemistry and Physics
  • Mueller; W., B. Früh, P. Korn, R. Potthast, J. Baehr, J.-M. Bettems, G. Bölöni, S. Brienen, K. Fröhlich, J. Helmert, J. Jungclaus, M. Köhler; S. Lorenz, A. Schneidereit, R. Schnur, J.-P. Schulz, L. Schlemmer; C. Sgoff, T. Pham, H. Pohlmann, B. Vogel; H. Vogel, R. Wirth, S. Zaehle, G. Zängl; B. Stevens, J. Marotzke, 2024: ICON: Towards vertically integrated model configurations for numerical weather prediction, climate prediction and projections. BAMS

2024

  • Lott, F., R. Rani, C. McLandress, A. Podglagen, A. Bushell, M. Bramberger, H.-K. Lee, J. Alexander, J. Anstey, H.-Y. Chun, A. Hertzog, N. Butchart, Y.-H. Kim, Y. Kawatani, B. Legras, E. Manzini, H. Naoe, S. Osprey, R. Plougonven, H. Pohlmann, J. H. Richter, J. Scinocca, J. García-Serrano, F. Serva, T. Stockdale, S. Versick, S. Watanabe, K. Yoshida, 2024: Comparison between non orographic gravity wave parameterizations used in QBOi models and Strateole 2 constant level balloons. JGR Atmosphere, 150, 3721-3736. doi:10.1002/qj.4793

2023

  • O’Kane, T., A. A. Scaife, Y. Kushnir, A. Brookshaw, C. Buontempo, D. Carlin, R. K. Connell, F. Doblas-Reyes, N. Dunstone, K. Förster, A. Graca, A. J. Hobday, V. Kitsios, L. van der Laan, J. Lockwood, W. J. Merryfield, A. Paxian, M. R. Payne, M. C. Reader, G. R. Saville, D. Smith, B. Solaraju-Murali, N. Caltabiano, J. Carman, E. Hawkins, N. Keenlyside, A. Kumar, D. Matei, H. Pohlmann, S. Power, M. Raphael, M. Sparrow, B. Wu, 2023: Recent applications and potential of near-term (interannual to decadal) climate predictions. Frontiers in Climate, 5:1121626. doi:10.3389/fclim.2023.1121626
  • Pohlmann, H., S. Brune, K. Fröhlich, J. H. Jungclaus, C. Sgoff, J. Baehr, 2023: Impact of Ocean Data Assimilation on Climate Predictions with ICON-ESM. Climate Dynamics, 61, 357-373. doi:10.1007/s00382-022-06558-w
  • Polkova, I., D. Swingedouw, L. Hermanson, A. Köhl, D. Stammer, D. Smith., J. Kröger, I. Bethke, X. Yang, L. Zhang, D. Nicoli, P. J. Athanasiadis, M. P. Karami, K. Pankatz, H. Pohlmann, B. Wu, R. Bilbao, P. Ortega, S. Yang, R. Sospedra-Alfonso, W. Merryfield, T. Kataoka, H. Tatebe, Y. Imada, M. Ishii and R. J. Matear, 2023: Initialization shock in the ocean circulation reduces skill in decadal predictions of the Atlantic subpolar gyre. Frontiers in Climate, 5:1273770. doi: 10.3389/fclim.2023.1273770
  • Spiegl, T. C., U. Langematz, H. Pohlmann, J. Kröger, 2023: A critical evaluation of decadal solar cycle imprints in the MiKlip historical ensemble simulations. Weather and Climate Dynamics, 4, 789–807. doi:10.5194/wcd-4-789-2023

2022

  • Hermanson, L., D. Smith, M. Seabrook, R. Bilbao, F. Doblas-Reyes, W. Merryfield, R. Sospedra-Alfonso, N. Dunstone, R. Eade, A. Scaife, T. Mochizuki, T. Kruschke, T. Koenigk, S. Yang, T. Tian, M. Collier, T. O’Kane, K. Pankatz, W. Müller, H. Pohlmann, L. Zhang, T. Delworth, Y. Wang, F. Counillon, N. Keenlyside, I. Bethke, 2022: WMO Global Annual to Decadal Climate Update: A forecast for 2021-2025. BAMS, 103, E1117-1129. doi:10.1175/BAMS-D-20-0311.1
  • Jungclaus, J. H., S. J. Lorenz, H. Schmidt, V. Brovkin, N. Brüggemann, F. Chegini, V. Gayler, M. A. Giorgetta, O. Gutjahr, H. Haak, S. Hagemann, M. Hanke, T. Ilyina, P. Korn, J. Kröger, L. Linardakis, C. Mehlmann, U. Mikolajewicz, W. A. Müller, D. Notz, H. Pohlmann, D. A. Putrasahan, T. Raddatz, L. Ramme, R. Redler, C. H. Reick, T. Riddick, T. Sam, R. Schneck, R. Schnur, M. Schupfner, J.-S. von Storch, F. Wachsmann, K.-H. Wieners, F. Ziemen, B. Stevens, J. Marotzke, and M. Claussen, 2022: The ICON Earth System Model Version 1.0. JAMES, 14, e2021MS002814. doi:10.1029/2021MS002813

2021

  • Fröhlich, K., M. Dobrynin, K. Isensee, C. Gessner, A. Paxian, H. Pohlmann, H. Haak, S. Brune, B. Früh, J. Baehr, 2021: The German climate forecast system: GCFS. JAMES, 13, e2020MS002101. doi:10.1002/2020MS002101
  • Liu, F., U. Daewel, A. Samuelsen, S. Brune, U. Hanz, H. Pohlmann, J. Baehr, C. Schrumm, 2021: Can environmental conditions at North Atlantic deep-sea habitats be predicted several years ahead? - Taking sponge habitats as an example. Front. Mar. Sci., 8, 703297. doi: 10.3389/fmars.2021.703297

2020

  • Hermanson, L.,  R. Bilbao, N. Dunstone, M. Menegoz, P. Ortega, H. Pohlmann, J. Robson, D. Smith, W. Strand, C. Timmreck, S. Yeager, G. Danabasoglu, 2020: Robust multi-year climate impacts of volcanic eruptions in decadal prediction systems. JGR Atmospheres, 125, e2019JD031739. doi:10.1029/2019JD031739
  • Smith, D. M., R. Eade, A. A. Scaife, L.-P. Caron, G. Danabasoglu, T. M. DelSole, T. Delworth, F. J. Doblas-Reyes, N. J. Dunstone, L. Hermanson, V. Kharin, M. Kimoto, W. J. Merryfield, T. Mochizuki, W. A. Müller, H. Pohlmann, S. Yeager and X. Yang, 2020: Author correction: Robust skill of decadal climate predictions. Climate and Atmospheric Science, 3, 15. doi:10.1038/s41612-020-0118-0
  • Smith, D. M., A. A. Scaife, R. Eade, P. Athanasiadis, A. Bellucci, I. Bethke, R. Bilbao, L. F. Borchert, L.-P. Caron, F. Counillon, G. Danabasoglu, T. Delworth, F. J. Doblas-Reyes, N. J. Dunstone, V. Estella-Perez, S. Flavoni, L. Hermanson, N. Keenlyside, V. Kahrin, M. Kimoto, W. J. Merryfield, J. Mignot, T. Mochizuki, K. Modali, P.-A. Monerie, W. A. Müller, D. Nicoli, P. Ortega, K. Pankatz, H. Pohlmann, J. Robson, P. Ruggieri, R. Sospedra-Alfonso, D. Swingedouw, Y. Wang, S. Wild, S. Yeager, X. Yang, L. Zhang, 2020: North Atlantic climate far more predictable than models imply. Nature, 583, 796-800. doi:10.1038/s41586-020-2525-0
  • Stockdale, T. N., Y.-H. Kim, J. A. Anstey, F. Palmeiro, N. Butchart, A. A. Scaife, M. Andrews, A. C. Bushell, M. Dobrynin, J. Garcia-Serrano, K. Hamilton, Y. Kawatani, F. Lott, C. McLandress, H. Naoe, S. Osprey, H. Pohlmann, J. Scinocca, S. Watanabe, K. Yoshida and S. Yukimoto, 2020: Prediction of the quasi-biennial oscillation with a multi-model ensemble of QBO-resolving models. QJRMS, 1–22. doi:10.1002/qj.3919

2019

  • Borchert, L. F., H. Pohlmann, J. Baehr, N.-C. Neddermann, L. Suarez-Gutierrez, W. A. Müller, 2019: Decadal predictions of the probability of occurrence for warm summer temperature extremes. Geophys. Res. Lett., 46, 14042-14051. doi:10.1029/2019GL08538546
  • Feldmann, H., J. G. Pinto, N. Laube, M. Uhlig, J. Momken,A. Pasternack, B. Früh, H. Pohlmann, C. Kottmeier, 2019: Skill and added value of the Miklip regional decadal prediction system for temperature over Europe. Tellus A: Dynamic Meteorology and Oceanography, 71, 1-19, doi:10.1080/16000870.2019.1618678
  • Mauritsen, T., J. Bader, T. Becker, J. Behrens, M. Bittner, R. Brokopf, V. Brovkin, M. Claussen, T. Crueger, M. Esch, I. Fast, S. Fiedler, D. Fläschner, V. Gayler, M. Giorgetta, D. S. Goll, H. Haak, S. Hagemann, C. Hedemann, C. Hohenegger, T. Ilyina, T. Jahns, D. J. de la Cuesta Otero, J. Jungclaus, T. Kleinen, S. Kloster, D. Kracher, S. Kinne, D. Kleberg, G. Lasslop, L. Kornblueh, J. Marotzke, D. Matei, K. Meraner, U. Mikolajewicz, K. Modali, B. Möbis, W. A. Müller, J. E. M. S. Nabel, C. C. W. Nam, D. Notz, S.-S. Nyawira, H. Paulsen, K. Peters, R. Pincus, H. Pohlmann, J. Pongratz, M. Popp, T. Raddatz, S. Rast, R. Redler, C. Reick, T. Rohrschneider, V. Schemann, H. Schmidt, R. Schnur, U. Schulzweida, K. D. Six, L. Stein, I. Stemmler, B Stevens, J.-S. von Storch, F. Tian, A. Voigt, P. de Vrese, K.-H. Wieners, S. Wilkenskjeld, A. Winkler and E. Roeckner, 2019: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. JAMES, 11, 998-1038, doi:10.1029/2018MS001400
  • Paeth, H., J. Li, F. Pollinger, W. A. Müller, H. Pohlmann, H. Feldmann, H.-J. Panitz, 2018: An effective drift correction for dynamical downscaling of decadal global climate predictions. Clim. Dyn., 52, 1343-1357. doi:10.1007/s00382-018-4195-2
  • Paxian, A., M. Ziese, F. Kreienkamp, K. Pankatz, S. Brand, A. Pasternack, H. Pohlmann, K. Modali, and B. Früh, 2018: User-oriented global predictions of the GPCC drought index for the next decade. Met. Zeitschrift, 28, 3-21. doi:10.1127/metz/2018/0912
  • Pohlmann, H., W. A. Müller, M. Bittner, S. Hettrich, K. Modali, K. Pankatz, J. Marotzke, 2019: Realistic quasi-biennial oscillation variability in historical and decadal hindcast simulations using CMIP6 forcing. Geophys. Res. Lett., 46, 14118-14125. doi:10.1029/2019GL084878
  • Schuster , M., J. Grieger, A. Richling, T. Schartner, S. Illing, C. Kadow, W. A. Müller, H. Pohlmann, S. Pfahl, and U. Ulbrich, 2019: Improvement in the decadal prediction skill of the northern hemisphere extra-tropical winter circulation through increased model resolution. Earth System Dynamics, 10, 901-917. doi:10.5194/esd-2019-18
  • Smith, D. M., R. Eade, A. A. Scaife, L.-P. Caron, G. Danabasoglu, T. M. DelSole, T. Delworth, F. J. Doblas-Reyes, N. J. Dunstone, L. Hermanson, V. Kharin, M. Kimoto, W. J. Merryfield, T. Mochizuki, W. A. Müller, H. Pohlmann, S. Yeager and X. Yang, 2019: Robust skill of decadal climate predictions. Climate and Atmospheric Science, 2, 13. doi:10.1038/s41612-019-0071-y

2018

  • Brune, S., A. Düsterhus, H. Pohlmann, W. A. Müller, J. Baehr, 2018: Time dependency of the prediction skill for the North Atlantic subpolar gyre in initialized decadal hindcasts. Clim. Dyn., 51, 1947-1970. doi:10.1007/s00382-017-3991-4
  • Bunzel, F., W. A. Müller, T. Stacke, S. Hagemann, M. Dobrynin, J. Baehr, K. Fröhlich, H. Pohlmann, 2018: Improved seasonal prediction of European summer temperatures with new 5-layer soil-hydrology scheme. Geophys. Res. Lett., 45, 346-353. doi:10.1002/2017GL076204
  • Butchart, N., J. A. Anstey, K. Hamilton, S. Osprey, C. McLandress, A. C. Bushell, Y. Kawatani, Y.-H. Kim, F. Lott, J. Scinocca, T. N. Stockdale, M. Andrews, O. Bellprat, P. Braesicke, C. Cagnazzo, C.-C. Chen, H.-Y. Chun, M. Dobrynin, R. R. Garcia, J. Garcia-Serrano, L. J. Gray, L. Holt, T. Kerzenmacher, H. Naoe, H. Pohlmann, J. H. Richter, A. A. Scaife, V. Schenzinger, F. Serva, S. Versick, S. Watanabe, K. Yoshida, and S. Yukimoto, 2018: Overview of experiment design and comparison of models participating in phase 1 of the SPARC Quasi-Biennial Oscillation initiative (QBOi), Geosci. Model Dev., 11, 1009-1032, doi:10.5194/gmd-2017-187
  • Dobrynin, M., D. I. V. Domeisen, W. A. Müller, L. Bell, S. Brune, F. Bunzel, A. Düsterhus, K. Fröhlich, H. Pohlmann, J. Baehr, 2018: Improved teleconnection-based dynamical seasonal predictions of boreal winter. Geophys. Res. Lett., 45, 3605-3614. doi:10.1002/2018GL077209
  • Illing, S., C. Kadow, H. Pohlmann, C. Timmreck, 2018: Assessing the Impact of a Future Volcanic Eruption on Decadal Predictions. Earth Syst. Dynam., 9, 701-715, doi:10.5194/esd-9-701-2018
  • Kröger, J., H. Pohlmann, F. Sienz, J. Marotzke, J. Baehr, A. Köhl, K. Modali, I. Polkova, D. Stammer, F. S. E. Vamborg, W. A. Müller, 2018: Full-field initialized decadal predictions with the MPI earth system model: an initial shock in the North Atlantic. Clim. Dyn., 51, 2593-2608. doi:10.1007/s00382-017-4030-1
  • Müller, W. A., J. H. Jungclaus, T. Mauritsen, J. Baehr, M. Bittner, R. Budich, F. Bunzel, M. Esch, R. Ghosh, H. Haak, T. Ilyina, T. Kleine, L. Kornblueh, H. Li, K. Modali,, D. Notz, H. Pohlmann, E. Roeckner, I. Stemmler, F. Tian, J. Marotzke, 2018: A higher-resolved version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR). JAMES, 10, 1383-1413, doi:10.1029/2017MS001217
  • Smith, D. M., A. A. Scaife, E. Hawkins, R. Bilbao, G. J. Boer, M. Caian, L.-P. Caron, G. Danabasoglu, T. Delworth, F. J. Doblas-Reyes, R. Doescher, N. J. Dunstone, R. Eade, L. Hermanson, M. Ishii, V. Kharin, M. Kimoto, T. Koenigk, Y. Kushnir, D. Matei, G. A. Meehl, M. Menegoz, W. J. Merryfield, T. Mochizuki, W. A. Müller, H. Pohlmann, S. Power, M. Rixen, R. Sospedra-Alfonso, M. Tuma, K. Wyser, X. Yang and S. Yeager, 2018: Predicted chance that global warming will temporarily exceed 1.5ºC. Geophys. Res. Lett., 45, 11895-11903, doi:10.1029/2018GL079362

2017

  • Müller, V., H. Pohlmann, A. Düsterhus, D. Matei, J. Marotzke, W. A. Müller, M. Zeller, J. Baehr, 2017: Hindcast skill for the Atlantic meridional overturning circulation at 26.5°N within two MPI-ESM decadal climate prediction systems. Clim. Dyn., 49, 2975-2990. doi:10.1007/s00382-016-3482-z
  • Pohlmann, H., J. Kröger, R. J. Greatbatch, W. A. Müller, 2017: Initialization shock in decadal hindcasts due to errors in wind stress over the tropical Pacific. Clim. Dyn., 49, 2685-2693. doi:10.1007/s00382-016-3486-8

2016

  • Kadow, C., S. Illing, O. Kunst, H. W. Rust, H. Pohlmann, W. A. Müller and U. Cubasch, 2016: Evaluation of forecasts by accuracy and spread in the MiKlip decadal climate prediction system. Met. Zeitschrift, 25, 631-643. doi:10.1127/metz/2015/0639
  • Kruschke, T., H. W. Rust, C. Kadow, W. A. Müller, H. Pohlmann, G. C. Leckebusch, U. Ulbrich, 2016: Probabilistic evaluation of decadal prediction skill regarding Northern Hemisphere winter storms. Met. Zeitschrift, 25, 721-738. doi:10.1127/metz/2015/0641
  • Marotzke, J., W. A. Müller, F. S. E. Vamborg, P. Becker, U. Cubasch, H. Feldmann, F. Kaspar, C. Kottmeier, C. Marini, I. Polkova, K. Prömmel, H. W. Rust, D. Stammer, U. Ulbrich,C. Kadow, A. Köhl, J. Kröger, T. Kruschke, J. G. Pinto, H. Pohlmann, M. Reyers, M Schröder, F. Sienz, C. Timmreck, M. Ziese, 2016: MiKlip - a National Research Project on Decadal Climate Prediction. Bull. Amer. Meteor. Soc., 97, 2379-2394. doi:10.1175/BAMS-D-15-00184.1
  • Mohino, E., N. Keenlyside., H. Pohlmann, 2016: Decadal prediction of Sahel rainfall: where does the skill (or lack thereof) come from? Clim. Dyn., 47, 3593-3612. doi:10.1007/s00382-016-3416-9
  • Pattatanús-Ábrahám, M., C. Kadow, S. Illing,W. A. Müller, H. Pohlmann, W. Steinbrecht, 2016: Bias and drift of the mid-range decadal climate prediction system (MiKlip) validated by European radiosonde data. Met Zeitschrift, 25, 709-720. doi:10.1127/metz/2016/0803
  • Sienz, F., W. A. Müller, H. Pohlmann, 2016: Ensemble size impact on the decadal predictive skill assessment. Met. Zeitschrift, 25, 645-655. doi:10.1127/metz/2016/0670
  • Timmreck, C., H. Pohlmann, S. Illing, C. Kadow, 2016: The impact of stratospheric volcanic aerosol on decadal-scale climate predictions. Geophys. Res. Lett., 43, 834-842. doi:10.1002/2015GL067431

2015

  • Baehr, J., K. Fröhlich, M. Botzet, D. I. V. Domeisen, L. Kornblueh, D. Notz, R. Piontek, H. Pohlmann, S. Tietsche, W. A. Müller, 2015: The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model. Clim. Dyn., 44, 2723-2735. doi:10.1007/s00382-014-2399-7
  • Bellucci, A., R. Haarsma, S. Gualdi, P. J. Athanasiadis, M. Caian, C. Cassou, E. Fernandez, A. Germe, J. Jungclaus, J. Kröger, D. Matei, W. Müller, H. Pohlmann, D. Salas y Melia, E. Sanchez, D. Smith, L. Terray, K. Wyser, S. Yang, 2015: An assessment of a multi-model ensemble of decadal climate predictions. Clim. Dyn., 44, 2787-2806. doi:10.1007/s00382-014-2164-y
  • Corti, S., T. Palmer, M. Balmaseda, A. Weisheimer, S. Drijfhout, N. Dunstone, W. Hazeleger, J. Kröger, H. Pohlmann, D. Smith, J.-S. von Storch, B. Wouters, 2015: Impact of initial conditions versus external forcing in decadal climate predictions: A sensitivity experiment. J. Climate, 28, 4454-4470. doi:10.1175/JCLI-D-14-00671.1

2014

  • Meehl, G. A., L. Goddard, G. Boer, R. Burgman, G. Branstator, C. Cassou, S. Corti, G. Danabasoglu, F. Doblas-Reyes, E. Hawkins, A. Karspeck, M. Kimoto, A. Kumar, D. Matei, J. Mignot, R. Msadek, A. Navarra, H. Pohlmann, M. Rienecker, T. Rosati, E. Schneider, D. Smith, R. Sutton, H. Teng, G. J. van Oldenborgh, G. Vecchi, S. Yeager, 2014: Decadal climate prediction: An update from the trenches. Bull. Amer. Meteor. Soc., 95, 243-267. doi:10.1175/BAMS-D-12-00241.1
  • Müller, W. A., H. Pohlmann, F. Sienz, D. Smith, 2014: Decadal climate predictions for the period 1901-2010 with a coupled climate model. Geophys. Res. Lett., 41, 2100-2107. doi:10.1002/2014GL059259
  • Scaife, A. A., M. Athanassiadou, M. Andrews, A. Arribas, M. Baldwin, N. Dunstone, J. Knight, C. MacLachlan, E. Manzini, W. A. Müller, H. Pohlmann, D. Smith., T. Stockdale, A. Williams, 2014: Predictability of the quasi-biennial oscillation and its northern winter teleconnection on seasonal to decadal timescales. Geophys. Res. Lett., 41, 1752-1758. doi:10.1002/2013GL059160
  • Smith, D. M., N. J. Dunstone, R. Eade, D. Fereday, L. Hermanson, J. M. Murphy, H. Pohlmann, N. Robinson, A. A. Scaife, 2014: Comments on “Multi-year Predictions of North Atlantic Hurricane Frequency: Promise and limitations.” J. Climate, 27, 487-489. doi:10.1175/JCLI-D-13-00220.1

2013

  • Hazeleger, W., B. Wouters, G. J. van Oldenborgh, S. Corti, T. Palmer, D. Smith, N. Dunstone, J. Kröger, H. Pohlmann, J.-S. von Storch, 2013: Predicting multi-year North Atlantic Ocean variability. J. Geophys. Res. Oceans, 118, 1087-1098. doi:10.1002/jgrc.20117
  • Pohlmann, H., W. A. Müller, K. Kulkarni, M. Kameswarrao, D. Matei, F. S. E. Vamborg, C. Kadow, S. Illing, J. Marotzke, 2013: Improved forecast skill in the tropics in the new MiKlip decadal climate predictions. Geophys. Res. Lett., 40, 5798-5802. doi:10.1002/2013GL058051
  • Pohlmann, H., D. M. Smith, M. A. Balmaseda, N. S. Keenlyside, S. Masina, D. Matei, W. A. Müller, P. Rogel, 2013: Predictability of the mid-latitude Atlantic meridional overturning circulation in a multi-model system. Clim. Dyn., 41, 775-785. doi:10.1007/s00382-013-1663-6
  • Smith, D. M., R. Eade, H. Pohlmann, 2013: A comparison of anomaly and full field initialization for seasonal to decadal climate prediction. Clim Dyn., 41, 3325-3338. doi:10.1007/s00382-013-1683-2
  • Smith, D. M., A. A. Scaife, G. J. Boer, M. Caian, F. J. Doblas-Reyes, V. Guemas, E. Hawkins, W. Hazeleger, L. Hermanson, C. K. Ho, M. Ishii, V. Kharin, M. Kimoto, B. Kirtman, J. Lean, D. Matei, W. A. Müller, H. Pohlmann, A. Rosati, B. Wouters, K. Wyser, 2013: Real-time multi-model decadal predictions. Clim. Dyn, 41, 2875-2888. doi:10.1007/s00382-1600-0

2012

  • Matei, D., H. Pohlmann, J. Jungclaus, W. Müller, H. Haak, J. Marotzke, 2012: Two tales of initializing decadal climate prediction experiments with the ECHAM5/MPI-OM model. J. Climate, 25, 8502-8523. doi:10.1175/JCLI-D-11-00633.1
  • Müller, W. A., J. Baehr, H. Haak, J. H. Jungclaus, J. Kröger, D. Matei, D. Notz, H. Pohlmann,  J.-S. von Storch, J. Marotzke, 2012: Forecast skill of multi-year seasonal means in the decadal prediction system of the Max Planck Institute for Meteorology. Geophys. Res. Lett., 39, L22707. doi:10.1029/2012GL053326

2011

  • Hawkins, E., R. S. Smith, L. C. Allison, J. M. Gregory, T. J. Woolings, H. Pohlmann, B. de Cuevas, 2011: Bistability of the Atlantic overturning circulation in a global climate model and links to ocean freshwater transport. Geophys. Res. Lett., 38, L10605. doi:10.1029/2011GL047208
  • Hawkins, E., R. S. Smith, L. C. Allison, J. M. Gregory, T. J. Woolings, H. Pohlmann, B. de Cuevas, 2011: Correction to "Bistability of the Atlantic overturning circulation in a global climate model and links to ocean freshwater transport". Geophys. Res. Lett., 38, L16699. doi:10.1029/2011GL048997

2010

  • Hurrell, J. W., T. Delworth, G. Danabasoglu, H. Drange, K. Drinkwater, S. Griffies, N. Holbrook, B. Kirtman, N. Keenlyside, M. Latif, J. Marotzke, J. Murphy, G. A. Meehl, T. Palmer, H. Pohlmann, T. Rosati, R. Seager, D. Smith, R. Sutton, A. Timmermann, K. E. Trenberth, J. Tribbia, M. Visbeck, 2010: "Decadal Climate Prediction: Opportunities And Challenges" in Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society (Vol. 2), Venice, Italy, 21-25 September 2009, J. Hall, D. E. Harrison, and D. Stammer, Eds., ESA Publication WPP-306. doi:10.5270/OceanObs09.cwp.45
  • Matei, D., H. Pohlmann, W. Müller, H. Haak, J. Jungclaus, J. Marotzke, 2010: "Quantifying the role of ocean initial conditions in decadal prediction" in Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society (Annex), Venice, Italy, 21-25 September 2009, J. Hall, D. E. Harrison, and D. Stammer, Eds., ESA Publication WPP-306. doi:10.5270/OceanObs09
  • Smith, D. M., R. Eade, N. J. Dunstone, D. Fereday, J. M. Murphy, H. Pohlmann, A. Scaife, 2010: Skilful multi-year predictions of Atlantic hurricane frequency. Nat. Geosci., 3, 846-849. doi:10.1038/ng1004

2009

  • Pohlmann, H., J. H. Jungclaus, A. Köhl, D. Stammer, J. Marotzke, 2009: Initializing decadal climate predictions with the GECCO oceanic synthesis: Effects on the North Atlantic. J. Climate, 22, 3926-3938. doi:10.1175/2009JCLI2535.1

2007

  • Latif, M., C. W. Böning, J. Willebrand, A. Biastoch, F. Alvarez-Garcia, N. Keenlyside, H. Pohlmann, 2007: Decadal to multidecadal variability of the Atlantic MOC: mechanisms and predictability. AGU Geophysical Monograph 173 "Ocean Circulation: Mechanisms and Impacts - Past and Future Changes of Meridional Overturning", A. Schmittner, J. Chiang, and S. Hemming (Eds.), American Geophysical Union, Washington DC, 149-166. ISBN: 978-0-87590-438-2

2006

  • Collins, M., M. Botzet, A. Carril, H. Drange, A. Jouzeau, M. Latif, O. H. Otteraa, H. Pohlmann, A. Sorteberg, R. Sutton, L. Terray, 2006: Interannual to decadal climate predictability: A multimodel-ensemble study. J. Climate, 19, 1195-1203. doi:10.1175/JCLI3654.1
  • Latif, M., M. Collins, H. Pohlmann, N. Keenlyside, 2006: A review of predictability studies of Atlantic sector climate on decadal time scales. J. Climate, 19, 5971-5987. doi:10.1175/JCLI3945.1
  • Latif, M., H. Pohlmann, W. Park, 2006: Predictability of the North Atlantic thermohaline circulation. In "Predictability of Weather and Climate", T. N. Palmer and R. Hagedorn (Eds.), Cambridge University Press, 343-364
  • Pohlmann, H., R. J. Greatbatch, 2006: Discontinuities in the late 1960's in different atmospheric data products. Geophys. Res. Lett., 33, L22803. doi:10.1029/2006GL027644
  • Pohlmann, H., F. Sienz, M. Latif, 2006: Influence of the multidecadal Atlantic meridional overturning circulation variability on European climate. J. Climate, 19, 6062-6067. doi:10.1175/JCLI3941.1

2005

  • Pohlmann, H., M. Latif, 2005: Atlantic versus Indo-Pacific influence on Atlantic-European climate. Geophys. Res. Lett., 32, L05707. doi:10.1029/2004GL021316

2004

  • Pohlmann, H., M. Botzet, M. Latif, A. Roesch, M. Wild, P. Tschuck, 2004: Estimating the decadal predictability of a coupled AOGCM. J. Climate, 17, 4463-4472. doi:10.1175/3209.1
  • Rodwell, M. J., M. Drévillon, C. Frankignoul, J. W. Hurrell, H. Pohlmann, M. Stendel, R. T. Sutton, 2004: North Atlantic forcing of climate and its uncertainty from a multi-model experiment. Quart. J. Roy. Meteor. Society, 130, 2013-2032. doi:10.1256/qj.03.207

2024

  • Brune, S., D. Krieger, M. B. Uddin, H. Pohlmann, J. Baehr, 2024: GCFS2.2 extended seasonal predictions 24 month, May and November starts 1980-2023, monthly mean 2D variables. DOKU at DKRZ. https://hdl.handle.net/21.14106/71b8a51d3f8001c0eee20e7f8983ca198e3a5c50
  • Müller, W. A., H. Pohlmann, X. Zhu, C. Febre, S. Lorenz, 2024: Report ENSO for Climate Prediction (ENSO-CP). MPI-M internal report
  • Pohlmann, H., D. Dommenget, X. Zhu, W. A. Müller, 2024: ENSO tuning in ICON-Seamless. ICON/COSMO/CLM/ART USER Seminar (ICCARUS), DWD, Offenbach, Germany, 4-6 March 2024
  • Pohlmann, H. and Müller, W. A., 2024: The North Atlantic climate variability in single-forcing large ensemble simulations with MPI-ESM-LR, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1411, https://doi.org/10.5194/egusphere-egu24-1411
  • Pohlmann, H., S. Brune, 2024: CMIP6_supplemental DAMIP MPI-ESM1-2-LR. World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/C6s_5275364
  • Pohlmann, H., Müller, W. A., Athanasiadis, P., 2024: Opportunities and challeges of seasonal-to-decadal predictions of European mean climate and extremes. ASPECT Workshop for National Met Services. Online, 22-23 October 2024, https://www.aspect-project.eu/from-seasons-to-decades-a-workshop-for-national-meteorological-services/
  • Polkova, I. and Co-Authors, 2024: Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre. EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15358, https://doi.org/10.5194/egusphere-egu24-15358
  • Polkova, I., and Co-Authors, 2024: Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre. Workshop on Climate Predicon and Services over the Atlantic-Arctic region, Bergen, Norway, 27-30 May 2024
  • Rani, R., et al. 2024: Comparison between non orographic gravity wave drag parameterizations used in QBOi, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2472, https://doi.org/10.5194/egusphere-egu24-2472
  • Sgoff, C., H. Pohlmann, S. Brune, T. Van Pham, A. Schneidereit, T. Steinert, K. Fröhlich, 2024: Weakly coupled data assimilation for climate predictions with ICON-Seamless. ICON/COSMO/CLM/ART USER Seminar (ICCARUS), DWD, Offenbach, Germany, 4-6 March 2024
  • Sgoff, C., H. Pohlmann, S. Brune, T. Van Pham, A. Schneidereit, T. Steinert, K. Fröhlich, 2024: Weakly coupled data assimilation for climate predictions with ICON-Seamless. Workshop on Climate Predicon and Services over the Atlantic-Arctic region, Bergen, Norway, 27-30 May 2024
  • Sgoff, C., H. Pohlmann, S. Brune, T. Van Pham, A. Schneidereit, T. Steinert, K. Fröhlich, 2024: Weakly coupled data assimilation for climate predictions with ICON-XPP. EMS Annual Meeting, Barcelona, Spain, 2-6 September 2024, https://meetingorganizer.copernicus.org/EMS2024/EMS2024-950.html

2022

  • Brune, S., H. Pohlmann, W. Müller, D. M. Nielsen, L. Hövel, J. Baehr, 2022: MPI-ESM-LR 1.2.01 decadal prediction localEnKF LE monthly. https://www.wdc-climate.de/ui/entry?acronym=DKRZ_LTA_1075_ds00016
  • Brune, S., H. Pohlmann, W. Müller, D. M. Nielsen, L. Hövel, J. Baehr, 2022: MPI-ESM-LR 1.2.01 decadal prediction localEnKF LE daily. https://www.wdc-climate.de/ui/entry?acronym=DKRZ_LTA_1075_ds00015
  • Brune, S., H. Pohlmann, W. Müller, D. M. Nielsen, L. Hövel, J. Baehr, 2022: MPI-ESM-LR 1.2.01 decadal prediction localEnKF LE 3hrly atmos surface. https://www.wdc-climate.de/ui/entry?acronym=DKRZ_LTA_1075_ds00014
  • Düsterhus, A., L. Borchert, B. Meyer, V. Koul, H. Pohlmann, S. Brune, J. Baehr, 2022: What can the last century teach us about climate models? EGU, doi:10.5194/egusphere-egu22-5377
  • Früh, B., R. Potthast, W. Müller, P. Korn, S. Brienen, K. Fröhlich, J. Helmert, M. Köhler, S. Lorenz, T. v. Pham, H. Pohlmann, L. Schlemmer, R. Schnur, J.-P. Schulz, C. Sgoff, B. Vogel, R. Wirth, G. Zängl, 2022: ICON-Seamles, the development of a novel earth system model based on ICON from time scales from weather to climate. doi:10.5194/ems2022-292
  • Köhler, M., D. Zobel, J.-O. Beismann, H. Pohlmann, 2022: ICON Ocean: potential synergies between Vectorization and GPU port. DWD internal report.
  • Kröger, J., H. Pohlmann, 2022: SOLCHECK MPI-M MPI-ESM1-2-HR CMIP6 retrospective forecasts (hindcasts) without solar and ozone variability. World Data Center for Climate (WDCC) at DKRZ. https://www.wdc-climate.de/ui/entry?acronym=DKRZ_LTA_1148_dsg0001
  • Kröger, J., H. Pohlmann, 2022: SOLCHECK MPI-M MPI-ESM1-2-HR CMIP6 historical simulation with low-pass filtered solar and ozone variability. World Data Center for Climate (WDCC) at DKRZ. doi:1026050/WDCC/SOLCHECK_MPI-ESM-HR_C6_lowpass
  • Leps, N., B. Früh, H. Pohlmann, 2022: Klima- und Umweltberatung trägt zum 6. Sachstandsbericht des Weltklimarates bei. Mitarbeiterzeitung (MAZ) des DWD
  • Potthast, R., W. Müller, B. Früh, P. Korn, S. Brienen, K. Fröhlich, J. Helmert, M. Köhler, S. Lorenz, T. van Pham, H. Pohlmann, L. Schlemmer, R. Schnur, J.-P. Schulz, C. Sgoff, B. Vogel, R. Wirth, G. Zängl, 2022: ICON-seamless: die Entwicklung eines neuen Erdsystemmodells basierend auf ICON für alle Zeitskalen, von Wettervorhersagen bis Klimaprognosen. DACH, doi:10.5194/dach2022-28

2021

  • Brune, S., H. Pohlmann, W. Müller, D. M. Nielsen, L. Hövel, J. Baehr, 2021: MPI-ESM-LR 1.2.01 decadal prediction localEnKF monthly. https://www.wdc-climate.de/ui/entry?acronym=DKRZ_LTA_1075_ds00008
  • Brune, S., H. Pohlmann, W. Müller, D. M. Nielsen, L. Hövel, J. Baehr, 2021: MPI-ESM-LR 1.2.01 decadal prediction localEnKF daily. https://www.wdc-climate.de/ui/entry?acronym=DKRZ_LTA_1075_ds00007
  • Brune, S., V. Koul, D. M. Nielsen, L. Hövel, H. Pohlmann, A. Düsterhus, J. Baehr, 2021: A large ensemble decadal prediction system with MPI-ESM. EGU, doi:10.5194/egusphere-egu21-9263
  • Brune, S., V. Koul, D. M. Nielsen, L. Hövel, H. Pohlmann, A. Düsterhus, J. Baehr, 2021: A large ensemble decadal prediction system with MPI-ESM. EGU, doi:10.5194/egusphere-egu21-9263
  • Düsterhus, A., L. Borchert, V. Koul, H. Pohlmann, S. Brune, 2021: Variability of the North Atlantic Oscillation in the 20th century. EGU, doi:10.5194/egusphere-egu21-12580
  • Linardakis, L., J.-O. Beismann, H. Pohlmann, 2021: The ICON-O performance on the DWD NEC Aurora machine.
  • Lorenz, S., J. Jungclaus, H. Schmidt, H. Haak, C. Reick, M. Schupfner, ..., H. Pohlmann, ..., T. Ilyina, 2021: MPI-M ICON-ESM-LR model output prepared for CMIP6 CMIP 1pctCO2. doi:10.22033/ESGF/CMIP6.6433
  • Lorenz, S., J. Jungclaus, H. Schmidt, H. Haak, C. Reick, M. Schupfner, ..., H. Pohlmann, ..., T. Ilyina, 2021: MPI-M ICON-ESM-LR model output prepared for CMIP6 CMIP historical. doi:10.22033/ESGF/CMIP6.6593
  • Lorenz, S., J. Jungclaus, H. Schmidt, H. Haak, C. Reick, M. Schupfner, ..., H. Pohlmann, ..., T. Ilyina, 2021: MPI-M ICON-ESM-LR model output prepared for CMIP6 CMIP abrupt-4xCO2. doi:10.22033/ESGF/CMIP6.6457
  • Lorenz, S., J. Jungclaus, H. Schmidt, H. Haak, C. Reick, M. Schupfner, ..., H. Pohlmann, ..., T. Ilyina, 2021: MPI-M ICON-ESM-LR model output prepared for CMIP6 CMIP piControl.doi:10.22033/ESGF/CMIP6.6673
  • Pohlmann, H., W. Müller, K. Modali, S. Lorenz, H. Schmidt, 2021: Ruby-1 retrospective. MPI-M internal report.
  • Pohlmann, H., 2021: Metadata for 'SOLCHECK MPI-M MPI-ESM1-2-HR CMIP6 historical simulation without solar and ozone variability'. doi:10.26050/WDCC/SOLCHECK_MPI-ESM-HR_C6_hist

2020

2019

  • Brovkin, V., K.-H. Wieners, M. Giorgetta, J. Jungclaus, C. Reick, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPIESM1.2-LR model output prepared for CMIP6 C4MIP. doi:10.22033/ESGF/CMIP6.748
  • Fiedler, S., B. Stevens, K.-H. Wieners, M. Giorgetta, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 RFMIP piClim-control. doi:10.22033/ESGF/CMIP6.6662
  • Fiedler, S., B. Stevens, K.-H. Wieners, M. Giorgetta, C. Reick, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPIESM1.2-LR model output prepared for CMIP6 RFMIP. doi:10.22033/ESGF/CMIP6.784
  • Haak, H., J. Jungclaus, M. Bittner, K.-H. Wieners, F. Wachsmann, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPIESM1.2-HR model output prepared for CMIP6 FAFMIP. doi:10.22033/ESGF/CMIP6.774
  • Jungclaus, J., Bittner, K.-H. Wieners, F. Wachsmann, M. Schupfner, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPIESM1.2-HR model output prepared for CMIP6 CMIP. doi:10.22033/ESGF/CMIP6.741
  • Jungclaus, J., U. Mikolajewicz, M.-L. Kapsch, R. D'Agostino, K.-H. Wieners, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPIESM1.2-LR model output prepared for CMIP6 PMIP.doi:10.22033/ESGF/CMIP6.787
  • Jungclaus, J., M. Bittner, K.-H. Wieners, F. Wachsmann, M. Schupfner,  ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 CMIP historical. doi:10.22033/ESGF/CMIP6.6594
  • Jungclaus, J., M. Bittner, K.-H. Wieners, F. Wachsmann, M. Schupfner,  ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 CMIP amip. doi:10.22033/ESGF/CMIP6.6463
  • Jungclaus, J., M. Bittner, K.-H. Wieners, F. Wachsmann, M. Schupfner, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 CMIP piControl. doi:10.22033/ESGF/CMIP6.6674
  • Müller, W., T. Ilyina, H. Li, C. Timmreck, V. Gayler, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP. doi:10.22033/ESGF/CMIP6.15016
  • Müller, W., T. Ilyina, H. Li, C. Timmreck, V. Gayler, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-GHG. doi:10.22033/ESGF/CMIP6.15022
  • Müller, W., T. Ilyina, H. Li, C. Timmreck, V. Gayler, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-aer. doi:10.22033/ESGF/CMIP6.15024
  • Müller, W., T. Ilyina, H. Li, C. Timmreck, V. Gayler, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-sol. doi:10.22033/ESGF/CMIP6.15030
  • Müller, W., T. Ilyina, H. Li, C. Timmreck, V. Gayler, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-totalO3. doi:10.22033/ESGF/CMIP6.15032
  • Müller, W., T. Ilyina, H. Li, C. Timmreck, V. Gayler, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-volc. doi:10.22033/ESGF/CMIP6.15033
  • Müller, W., T. Ilyina, H. Li, C. Timmreck, V. Gayler, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 DAMIP hist-nat. doi:10.22033/ESGF/CMIP6.15028
  • Niemeier, U., K.-H. Wieners, M. Giorgetta, J. Jungclaus, C. Reick, M. Esch, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPIESM1.2-LR model output prepared for CMIP6 GeoMIP. doi:10.22033/ESGF/CMIP6.751
  • Niemeier, U., K.-H. Wieners, M. Giorgetta, J. Jungclaus, C. Reick, M. Esch, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-HR model output prepared for CMIP6 GeoMIP. doi:10.22033/ESGF/CMIP6.15294
  • Pohlmann, H., W. Müller, K. Modali, K. Pankatz, M. Bittner, ..., E. Roeckner, 2019: MPI-M MPIESM1.2-HR model output prepared for CMIP6 DCPP. doi:10.22033/ESGF/CMIP6.768
  • Pongratz, J., K. Hartung, K.-H. Wieners, M. Giorgetta, J. Jungclaus, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPIESM1.2-LR model output prepared for CMIP6 LUMIP. doi:10.22033/ESGF/CMIP6.772
  • Schupfner, M., K.-H. Wieners, F. Wachsmann, C. Steger, M. Bittner, ..., H. Pohlmann, ..., E. Roeckner, 2019: DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. doi:10.22033/ESGF/CMIP6.2450
  • Schupfner, M., K.-H. Wieners, F. Wachsmann, C. Steger, M. Bittner, ..., H. Pohlmann, ..., E. Roeckner, 2019: DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP ssp585.doi:10.22033/ESGF/CMIP6.4403
  • Schupfner, M., K.-H. Wieners, F. Wachsmann, C. Steger, M. Bittner, ..., H. Pohlmann, ..., E. Roeckner, 2019: DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP ssp245. doi:10.22033/ESGF/CMIP6.4398
  • Schupfner, M., K.-H. Wieners, F. Wachsmann, C. Steger, M. Bittner, ..., H. Pohlmann, ..., E. Roeckner, 2019: DKRZ MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP ssp126. doi:10.22033/ESGF/CMIP6.4397
  • Steger, C., M. Schupfner, K.-H. Wieners, F. Wachsmann, M. Bittner, ..., H. Pohlmann, ..., E. Roeckner, 2019: DWD MPI-ESM1.2-HR model output prepared for CMIP6 ScenarioMIP. doi:10.22033/ESGF/CMIP6.1869
  • Stacke, T., K.-H. Wieners, M. Giorgetta, J. Jungclaus, C. Reick, ..., H. Pohlmann, ... E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 LS3MIP land-hist. Version  doi.org/10.22033/ESGF/CMIP6.6620
  • Stacke, T., K.-H. Wieners, M. Giorgetta, J. Jungclaus, C. Reick, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPIESM1.2-LR model output prepared for CMIP6 LS3MIP. doi:10.22033/ESGF/CMIP6.760
  • Wieners, K.-H., M. Giorgetta, J. Jungclaus, C. Reick, M. Esch, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP historical. doi:10.22033/ESGF/CMIP6.6595
  • Wieners, K.-H., M. Giorgetta, J. Jungclaus, C. Reick, M. Esch, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP. doi:10.22033/ESGF/CMIP6.742
  • Wieners, K.-H., M. Giorgetta, J. Jungclaus, C. Reick, M. Esch, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPIESM1.2-LR model output prepared for CMIP6 ScenarioMIP. doi:10.22033/ESGF/CMIP6.793
  • Wieners, K.-H., M. Giorgetta, J. Jungclaus, C. Reick, M. Esch, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 ScenarioMIP ssp585. doi:10.22033/ESGF/CMIP6.6705
  • Wieners, K.-H., M. Giorgetta, J. Jungclaus, C. Reick, M. Esch, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 ScenarioMIP ssp245. doi:10.22033/ESGF/CMIP6.6693
  • Wieners, K.-H., M. Giorgetta, J. Jungclaus, C. Reick, M. Esch, ..., H. Pohlmann, ..., E. Roeckner, 2019: MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 ScenarioMIP ssp126. doi:10.22033/ESGF/CMIP6.6690
  • Wieners, K.-H., M. Giorgetta, J. Jungclaus, C. Reick, M. Esch, ..., H. Pohlmann, ..., E. Roeckner, 2019:MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP piControl. doi:10.22033/ESGF/CMIP6.6675

2017

  • von Storch, J.-S., D. Putrasahan, K. Lohmann, O. Gutjahr, J. Jungclaus, ..., H. Pohlmann, E. Roeckner, 2017:  MPI-M MPIESM1.2-HR model output prepared for CMIP6 HighResMIP. doi:10.22033/ESGF/CMIP6.762
  • von Storch, J.-S. D. Putrasahan, K. Lohmann, O. Gutjahr, J. Jungclaus, ..., H. Pohlmann, ..., E. Roeckner, 2017: MPI-M MPI-ESM1.2-XR model output prepared for CMIP6 HighResMIP. doi:10.22033/ESGF/CMIP6.10290

2015

  • Böttinger, M., H. Pohlmann, N. Röber, K. Meier-Fleischer, D. Spickermann, 2015: Visualization of 2D uncertainty in decadal climate prediction. In "Workshop on Visualisation in Environmental Sciences (EnvirVis)", A. Middel, K. Rink and G. H. Weber (Eds.), The Eurographics Association. doi:10.2312/envirvis.20151083

2014

2012

  • Bellucci, A., R. Haarsma, S. Gualdi, P. Athanasiadis, M. Caian, C. Cassou, A. Germe, J. Jungclaus, J. Kröger, D. Matei, W. Müller, H. Pohlmann, D. Salas y Melia, E. Sanchez, D. Smith, L. Terray, K. Wyser, 2012: An assessment of a multi-model ensemble of decadal climate predictions performed within the framework of the COMBINE project. COMBINE Technical Report No. 2

2011

  • Doblas-Reyes, F. J., G. J. van Oldenborgh, J. Garcia-Serrano, H. Pohlmann, A. A. Scaife, D. Smith, 2011: CMIP5 near-term climate predictions. CLIVAR Exchanges, 56, 8-11

2010

  • Brander, K. M., U. Daewel, K. F. Drinkwater, G. Engelhard, A. Flin, M. Lindgren, B. R. MacKenzie, I. Mantzouni, P. Munk, G. Ottersen, H. Pohlmann, B. Rothschild, C. Schrum, J. E. Stiansen, S. Sundby, K. U. Wieland, 2010: Cod and future climate change. ICES Cooperative Research Report, 305, 88pp. ISBN: 978-87-7482-084-0
  • Murphy, J., V. Kattsov, N. Keenlyside, M. Kimoto, G. Meehl, V. Mehta, H. Pohlmann, A. Scaife, D. Smith, 2010: Towards Prediction of Decadal Climate Variability and Change. Procedia Environmental Sciences, 1, 287-304, ISSN 1878-0296. doi:10.1016/j.proenv.2010.09.018

2004

  • Latif, M., M. Collins, R. J. Stouffer, H. Pohlmann, N. Keenlyside, 2004: The physical basis for prediction of Atlantic sector climate on decadal time scales. CLIVAR Exchanges, 31, 6-8
  • Pohlmann, H., N. Keenlyside, 2004: Review: Decadal-multidecadal climate predictability. Students Contest of the CRCES-IPRC Workshop on Decadal Climate Variability, Hawaii, USA

2003

  • Collins, M., A. Carril, H. Drange, H. Pohlmann, R. Sutton, L. Terray, 2003: North Atlantic decadal predictability. CLIVAR Exchanges, 28, 6-7

2002

  • Latif, M., E. Roeckner, M. Botzet, M. Esch, H. Haak, S. Hagemann, J. Jungclaus, S. Legutke, H. Pohlmann, S. Marsland, U. Mikolajewicz, 2002: Predictability of the North Atlantic thermohaline circulation. Proceedings of the ECMWF Seminar on Predictability of Weather and Climate, 265-273

2000

  • Pohlmann, H., M. Harder, T. Martin, P. Lemke, 2000: Comparison of two sea ice simulations forced with ECMWF and NCEP/NCAR re-analyses data. Proceedings of the Second WCRP International Conference on Reanalyses, 229-232, WCRP-109, WMO/TD-NO 985

Aktuelle Projekte

ASPECT - Adaptation-oriented Seamless Predictions of European Climate (Video)

Coming Decade - Mittelfristige Klimaprognosen in Deutschland und Europa

EPESC - Explaining and Predicting Earth System Change

LEADER - Large Ensembles for Attribution of Dynamically-driven Extremes

Vorherige Projekte

IAFE

2019-2023      

Innovation in der angewandten Forschung und Entwicklung (DWD)

ROMIC

2019-2023

Rolle der mittleren Atmosphäre im Klima (BMBF)

MiKlip

2011-2019

Mittelfristige Klimavorhersagen (BMBF)

SPECS

2012-2016

Seasonal to Decadal Climate Prediction for the Improvement of European Climate Services (EU FP7)

COMBINE

2009-2011

Comprehensive Modelling of the Earth System for Better Climate Prediction and Projection (EU FP7)

THOR

2008-2011

Thermohaline Overturning Circulation at Risk? (EU FP7)

ENSEMBLES

2007-2009

Ensembles-Based Predictions of Climate Changes and their Impact (EU FP7)

Nordatlantik

2006-2007

Der Nordatlantik als Teil des Erdsystems (BMBF)

Canadian CLIVAR

2005-2006

Canadian CLIVAR Research Network

DEKLIM

2000-2005

Deutsches Klimaforschungsprogramm (BMBF)

SEALION

2000-2003

Sea Ice in the Antarctic linked with Ocean-Atmosphere Forcing (EU)

seit 2023          Max-Planck-Institut für Meteorologie, Hamburg
2019 - 2023    Deutscher Wetterdienst, Hamburg
2011 - 2019    Max-Planck-Institut für Meteorologie, Hamburg
2007 - 2011    Met Office Hadley Centre, Exeter, UK
2006 - 2007    Max-Planck-Institut für Meteorologie, Hamburg
2005 - 2006    Institute of Oceanography, Dalhousie University, Halifax, Kanada                       

 

Ph.D.

2000 - 2005     Max-Planck-Institut für Meteorologie, Hamburg
2005    Ph.D. in Naturwissenschaften, Universität Hamburg                       

 

Universität

1993-2000       Meteorologie, Universität Kiel                                                       
2000              Diplom in Meteorologie, Universität Kiel                                  

2024

2023

  • Früh, B., R. Potthast, W. Müller, P. Korn, G. Bölöni, S. Brienen, K. Fröhlich, M. Köhler, S. Lorenz, V. Maurer, T. V. Pham, H. Pohlmann, L. Schlemmer, R. Schnur, C. Sgoff, C. Steger, H. Vogel, R. Wirth, G. Zängl, 2023: ICON for climate simulations. ICON/COSMO/CLM/ART USER Seminar (ICCARUS), 6.-9.3.2023, DWD, Offenbach, Germany
  • Pohlmann, H., 2023: DAKLIM: Entwicklung einer Datenassimilation mit ICON-Seamless für Klimavorhersagen. Innovation in der Angewandten Forschung und Entwicklung (IAFE), 21.-23.3.2023, DWD, Offenbach, Germany
  • Romanova, V., G. Geppert, H. Pohlmann, C. Sgoff, N. Noll, T. Hüther, K. Fröhlich, 2023: Integrated atmosphere-ocean-land data assimilation for climate analysis and seasonal prediction. International Symposium on Data Assimilation (ISDA), 16.-20.10.2023, Bologna, Italy
  • Spiegl, T., F. Schmidt, U. Langematz, W. Huo, S. Wahl, K. Matthes, J. Kröger, H. Pohlmann, 2023: The response in the Tropics to decadal and centennial solar variability. International Union for Geodesy and Geophysics (IUGG), 11.-20.7.2023, Berlin, Germany

Gastwissenschaftler seit 2019 Universität Hamburg

GC NTCP

2019-2021

Grand Challenge on Near-term Climate Prediction

DCVP

2016-2019

Decadal Climate Variability and Predictability

QBOi

2016-2019

Quasi-Biennial Oscillation Initiative

Aktuelle Veröffentlichung

Einfluss der Ozeandatenassimilation auf Klimavorhersagen mit ICON-ESM

Schema der Datenassimilation. Über einen Ensemble-Kalman Filter (PDAF) werden Ozean Salzgehalt- und temperaturprofile in ICON-ESM assimiliert. Nach dem Assimilationsschritt folgt ein ICON-ESM Lauf bestehend aus 10 Ensemlemitgliedern über einen Monat. Der Assimilationsschritt wiederholt sich im nächsten Assimilationszyklus. Dadurch wird ein Assimilationslauf über den Zeitraum 1960-2014 erzeugt, der die initialen Bedingungen für die dekadischen Nachhersagen liefert.

[Weiter lesen ...]

Pohlmann, H., S. Brune, K. Fröhlich, J. H. Jungclaus, C. Sgoff, J. Baehr, 2023: Impact of Ocean Data Assimilation on Climate Predictions with ICON-ESM. Climate Dynamics, 61, 357-373. doi:10.1007/s00382-022-06558-w