Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4305-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-4305-2020
© 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
| 
16 Sep 2020
Development and technical paper |
| 16 Sep 2020
| 16 Sep 2020
Efficient ensemble data assimilation for coupled models with the Parallel Data Assimilation Framework: example of AWI-CM (AWI-CM-PDAF 1.0)
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
Longjiang Mu
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Bremerhaven, Germany
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Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.
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The East Siberian Sea has nearly 80 % of the subsea permafrost worldwide. The cold layer with a temperature around −1.5 ºC above the seafloor prevents heat transporting from above to melt permafrost and release methane from sediments. However, we observed a warming trend at the seafloor caused by wave-induced vertical mixing in the shelf. The intensified mixing can transport enormous heat downward, leading to warming of more than 3 °C at the bottom, putting the subsea permafrost at high risk.
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Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
Short summary
Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, https://doi.org/10.5194/tc-17-2509-2023, 2023
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Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
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We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Hao-Cheng Yu, Yinglong Joseph Zhang, Lars Nerger, Carsten Lemmen, Jason C. S. Yu, Tzu-Yin Chou, Chi-Hao Chu, and Chuen-Teyr Terng
EGUsphere, https://doi.org/10.5194/egusphere-2022-114, https://doi.org/10.5194/egusphere-2022-114, 2022
Preprint archived
Short summary
Short summary
We develop a new data assimilative approach by combining two parallel frameworks: PDAF and ESMF. This allows maximum flexibility and easy implementation of data assimilation for fully coupled earth system model applications. It is also validated by using a simple benchmark and applied to a realistic case simulation around Taiwan. The real case test shows significant improvement for temperature, velocity and surface elevation before, during and after typhoon events.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
Short summary
Short summary
Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Xuewei Li, Qinghua Yang, Lejiang Yu, Paul R. Holland, Chao Min, Longjiang Mu, and Dake Chen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-359, https://doi.org/10.5194/tc-2020-359, 2021
Preprint withdrawn
Short summary
Short summary
The Arctic sea ice thickness record minimum is confirmed occurring in autumn 2011. The dynamic and thermodynamic processes leading to the minimum thickness is analyzed based on a daily sea ice thickness reanalysis data covering the melting season. The results demonstrate that the dynamic transport of multiyear ice and the subsequent surface energy budget response is a critical mechanism actively contributing to the evolution of Arctic sea ice thickness in 2011.
Chao Min, Qinghua Yang, Longjiang Mu, Frank Kauker, and Robert Ricker
The Cryosphere, 15, 169–181, https://doi.org/10.5194/tc-15-169-2021, https://doi.org/10.5194/tc-15-169-2021, 2021
Short summary
Short summary
An ensemble of four estimates of the sea-ice volume (SIV) variations in Baffin Bay from 2011 to 2016 is generated from the locally merged satellite observations, three modeled sea ice thickness sources (CMST, NAOSIM, and PIOMAS) and NSIDC ice drift data (V4). Results show that the net increase of the ensemble mean SIV occurs from October to April with the largest SIV increase in December, and the reduction occurs from May to September with the largest SIV decline in July.
Qian Shi, Qinghua Yang, Longjiang Mu, Jinfei Wang, François Massonnet, and Matthew R. Mazloff
The Cryosphere, 15, 31–47, https://doi.org/10.5194/tc-15-31-2021, https://doi.org/10.5194/tc-15-31-2021, 2021
Short summary
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The ice thickness from four state-of-the-art reanalyses (GECCO2, SOSE, NEMO-EnKF and GIOMAS) are evaluated against that from remote sensing and in situ observations in the Weddell Sea, Antarctica. Most of the reanalyses can reproduce ice thickness in the central and eastern Weddell Sea but failed to capture the thick and deformed ice in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis in Antarctic climate research.
Cited articles
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and
Arellano, A.: The Data Assimilation Research Testbed: A Community Facility,
B. Am. Meteorol. Soc., 90, 1283–1296, 2009. a
Browne, P. A., de Rosnay, P., Zuo, H., Bennett, A., and Dawson, A.: Weakly
coupled ocean–atmosphere data assimilation in the ECMWF NWP system,
Remote Sensing, 11, 234, https://doi.org/10.3390/rs11030234, 2019. a
Burgers, G., van Leeuwen, P. J., and Evensen, G.: On the Analysis Scheme in the
Ensemble Kalman Filter, Mon. Weather Rev., 126, 1719–1724, 1998. a
Chang, Y.-S., Zhang, S., Rosati, A., Delworth, T. L., and Stern, W. F.: An
assessment of oceanic variability for 1960–2010 from the GFDL ensemble
coupled data assimilation, Clim. Dynam., 40, 775–803, 2013. a
Danilov, S., Kivman, G., and Schröter, J.: A finite-element ocean model:
Principles and evaluation, Ocean Model., 6, 125–150, 2004. a
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J. Geophys.
Res., 99, 10143–10162, 1994. a
Fournier, A., Nerger, L., and Aubert, J.: An ensemble Kalman filter for the
time-dependent analysis of the geomagnetic field, Geochemistry Geophysics
Geosystems, 14, 4035–4043, 2013. a
Frolov, S., Bishop, C. H., Holt, T., Cummings, J., and Kuhl, D.: Facilitating
strongly coupled ocean-atmosphere data assimilation with an interface solver,
Mon. Weather Rev., 144, 3–20, 2016. a
Gaspari, G. and Cohn, S. E.: Construction of Correlation Functions in Two and
Three Dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, 1999. a
Gillet-Chaulet, F.: Assimilation of surface observations in a transient marine ice sheet model using an ensemble Kalman filter, The Cryosphere, 14, 811–832, https://doi.org/10.5194/tc-14-811-2020, 2020. a
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: quality controlled ocean
temperature and salinity profiles and monthly objective analyses with
uncertainty estimates, J. Geophys. Res.-Oceans, 118, 6704–6716, 2013. a
Han, G., Wu, X., Zhang, S., Liu, Z., and Li, W.: Error Covariance Estimation
for Coupled Data Assimilation Using a Lorenz Atmosphere and a Simple
Pycnocline Ocean Model, J. Climate, 26, 10218–10231, 2013. a
Harlim, J. and Hunt, B. R.: Four-dimensional local ensemble transform Kalmn
filter: numerical experiments with a global corculation model, Tellus, 59A,
731–748, 2007. a
Houtekamer, P. L. and Mitchell, H. L.: Data Assimilation Using an Ensemble
Kalman Filter Technique, Mon. Weather Rev., 126, 796–811, 1998. a
Karspeck, A. R., Danabasoglu, G., Anderson, J., Karol, S., Collins, N.,
Vertenstein, M., Raeder, K., Hoar, T., Neale, R., Edwards, J., and Craig, A.:
A global coupled ensemble data assimilation system using the Community
Earth System Model and the Data Assimilation Research Testbed,
Q. J. Roy. Meteor. Soc., 144, 2404–2430, https://doi.org/10.1002/qj.3308, 2018. a, b, c, d, e, f
Kirchgessner, P., Toedter, J., Ahrens, B., and Nerger, L.: The smoother
extension of the nonlinear ensemble transform filter, Tellus A, 69, 1327766, https://doi.org/10.1080/16000870.2017.1327766,
2017. a, b
Kunii, M., Ito, K., and Wada, A.: Preliminary Test of a Data Assimilation
System with a Regional High-Resolution Atmosphere-Ocean Coupled Model Based
on an Ensemble Kalman Filter, Mon. Weather Rev., 145, 565–581, 2017. a
Kurtz, W., He, G., Kollet, S. J., Maxwell, R. M., Vereecken, H., and Hendricks Franssen, H.-J.: TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model, Geosci. Model Dev., 9, 1341–1360, https://doi.org/10.5194/gmd-9-1341-2016, 2016. a, b, c, d, e
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, 2016. a
Lawrence, B. N., Rezny, M., Budich, R., Bauer, P., Behrens, J., Carter, M., Deconinck, W., Ford, R., Maynard, C., Mullerworth, S., Osuna, C., Porter, A., Serradell, K., Valcke, S., Wedi, N., and Wilson, S.: Crossing the chasm: how to develop weather and climate models for next generation computers?, Geosci. Model Dev., 11, 1799–1821, https://doi.org/10.5194/gmd-11-1799-2018, 2018. a
Lea, D. J., Mirouze, I., Martin, M. J., King, R. R., Hines, A., Walters, D.,
and Thurlow, M.: Assessing a new coupled data assimilation system based on
the Met Office coupled atmosphere-land-ocean-sea ice model, Mon. Weather
Rev., 143, 4678–4694, 2015. a
Liu, Z., Wu, S., Zhang, S., Liu, Y., and Rong, X.: Ensemble data assimilation
in a simple coupled climate model: The role of ocean-atmopshere interaction,
Adv. Atmos. Sci., 30, 1235–1248, 2013. a
Mu, L., Yang, Q., Losch, M., Losa, S. N., RIcker, R., Nerger, L., and Liang,
X.: Improving sea ice thickness estimates by assimilating CryoSat-2 and
SMOS sea ice thickness data simultaneously, Q. J. Roy. Meteor. Soc., 144,
529–538, 2018. a
Mu, L., Nerger, L., Tang, Q., Losa, S. N., Sidorenko, D., Wang, Q., Semmler,
T., Zampieri, L., Losch, M., and Goessling, H. F.: Toward a data assimilation
system for seamless sea ice prediction based on the AWI climate model, J.
Adv. Model. Earth Sy., 12, 359, https://doi.org/10.1029/2019MS001937 , 2020. a, b, c
Nerger, L., Hiller, W., and Schröter, J.: PDAF - The Parallel
Data Assimilation Framework: Experiences with Kalman filtering., in:
Use of High Performance Computing in Meteorology – Proceedings of the 11.
ECMWF Workshop, edited by: Zwieflhofer, W. and Mozdzynski, G.,
World Scientific, 63–83, 2005. a, b, c
Nerger, L., Danilov, S., Hiller, W., and Schröter, J.: Using sea level data
to constrain a finite-element primitive-equation ocean model with a local
SEIK filter, Ocean Dynam., 56, 634–649, 2006. a
Nerger, L., Janjić, T., Schröter, J., and Hiller, W.: A regulated
localization scheme for ensemble-based Kalman filters, Q. J. Roy. Meteor.
Soc., 138, 802–812, 2012a. a
Nerger, L., Schulte, S., and Bunse-Gerstner, A.: On the influence of model
nonlinearity and localization on ensemble Kalman smoothing, Q. J. Roy.
Meteor. Soc., 140, 2249–2259, 2014. a
Nerger, L., Tang, Q., and Mu, L.: The PDAF model binding for AWI-CM
(AWI-CM-PDAF version 1.0), Zenodo, https://doi.org/10.5281/zenodo.3822030,
2019a. a
Nerger, L., Tang, Q., and Mu, L.: Efficient ensemble data assimilation for
coupled models with the Parallel Data Assimilation Framework: Example of
AWI-CM – output files and plot scripts, Zenodo,
https://doi.org/10.5281/zenodo.3823816, 2019b. a
OpenMP: OpenMP Application Program Interface Version 3.0, available at:
http://www.openmp.org/ (last access: 14 September 2020), 2008. a
Pardini, F., Corradini, S., Costa, A., Ongari, T. E., Merucci, L., Neri, A.,
Stelitano, D., and deḾichieli Vitturi, M.: Ensemble-Based Data
Assimilation of Volcanic Ash Clouds from Satellite Observations: Application
to the 24 December 2018 Mt. Etna Explosive Eruption, Atmosphere, 11, 359, https://doi.org/10.3390/atmos11040359,
2020. a
Penny, S. G., Akella, S., Alves, O., Bishop, C., Buehner, M., Chevalier, M.,
Counillon, F., Drper, C., Frolov, S., Fujii, Y., Kumar, A., Laloyaux, P.,
Mahfouf, J.-F., MArtin, M., Pena, M., de Rosnay, P., Subramanian, A., Tardif,
R., Wang, Y., and Wu, X.: Coupled data assimilation for integrated Earth
system analysis and prediction: Goals, Challenges and Recommendations, Tech.
Rep. WWRP 2017-3, World Meteorological Organization, 2017. a
Pradhan, H. K., Voelker, C., Losa, S. N., Bracher, A., and Nerger, L.:
Assimilation of global total chlorophyll OC-CCI data and its impact on
individual phytoplankton fields, J. Geophys. Res.-Oceans, 124, 470–490,
2019. a
Rackow, T., Sein, D. V., Semmler, T., Danilov, S., Koldunov, N. V., Sidorenko, D., Wang, Q., and Jung, T.: Sensitivity of deep ocean biases to horizontal resolution in prototype CMIP6 simulations with AWI-CM1.0, Geosci. Model Dev., 12, 2635–2656, https://doi.org/10.5194/gmd-12-2635-2019, 2019. a
Sakov, P. and Oke, P. R.: A deterministic formulation of hte ensemlbe Kalman
filter: an alternative to ensemble square root filters, Tellus, 60A,
361–371, 2008. a
Sidorenko, D., Rackow, T., Jung, T., Semmler, T., Barbi, D., Danilov, S.,
Dethloff, K., Dorn, W., Firg, K., Goessling, H. F., d. Handorf, Harig, S.,
Hiller, W., Juricke, S., Losch, M., Schröter, J., Sein, D. V., and Wang,
Q.: Towards multi-resolution global climate moeling with ECHAM6-FESOM.
Part I: model formulation and mean climate, Clim. Dynam., 44, 757–780, 2015. a, b, c, d
Sluka, T. C., Penny, S. G., Kalnay, E., and Miyoshi, T.: Assimilating
atmospheric observations into the ocean using strongly coupled ensemble data
assimilation, Geophys. Res. Lett., 43, 752–759, 2016. a
Snyder, C., Bengtsson, T., Bickel, P., and Anderson, J.: Obstacles to
high-dimensional particle filtering, Mon. Weather Rev., 136, 4629–4640, 2008. a
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S.,
Salzmann, M., Schmift, H., an K. Blovk, J. B., Brokopf, R., Fast, I., Kinne,
S., Koernblueh, L., Lohmann, U., Pincus, R., Reichler, T., and Roeckner, E.:
Atmospheric component of the MPI-M Earth system model: ECHAM6., J. Adv.
Model. Earth Sy., 5, 146–172, 2013. a
Tang, Q., Mu, L., Sidorenko, D., Goessling, H., Semmler, T., and Nerger, L.:
Improving the ocean and atmosphere in a coupled ocean-atmosphere model by
assimilating satellite sea surface temperature and subsurface profile data,
Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.3885, in press, 2020. a, b, c
Valcke, S.: The OASIS3 coupler: a European climate modelling community software, Geosci. Model Dev., 6, 373–388, https://doi.org/10.5194/gmd-6-373-2013, 2013. a
van Leeuwen, P. J.: Particle Filtering in Geophysical Systems, Mon. Weather Rev.,
137, 4089–4114, 2009. a
van Leeuwen, P. J.: Nonlinear data assimilation in geosciences: An extremely
efficient particle filter, Q. J. Roy. Meteor. Soc., 136, 1991–1999, 2010. a
van Leeuwen, P. J., Künsch, H. R., Nerger, L., Potthast, R., and Reich, S.:
Particle filters for high-dimensional geoscience applications: a review, Q.
J. Roy. Meteor. Soc., 145, 2335–2365, 2019. a
Vetra-Carvalho, S., van Leeuwen, P. J., Nerger, L., Barth, A., Altaf, M. U.,
Brasseur, P., Kirchgessner, P., and Beckers, J.-M.: State-of-the-art
stochastic data assimilation methods for high-dimensional non-Gaussian
problems, Tellus A, 70, 1445364, https://doi.org/10.1080/16000870.2018.1445364, 2018. a
Wang, Q., Danilov, S., and Schröter, J.: Finite element ocean circulation
model based on triangular prismatic elements with application in studying the
effect of topography representation, J. Geophys. Res., 113, C05015, https://doi.org/10.1029/2007JC004482, 2008. a
Yu, L., Fennel, K., Bertino, L., Gharamti, M. E., and Thompson, K. R.: Insights
on multivariate updates of physical and biogeochemical ocean variables using
an ensemble Kalman filter and an idealized model of upwelling, Ocean Model.,
126, 13–28, 2018. a
Zhang, S., Harrison, M. J., Rosati, A., and Wittenberg, A.: System design and
evaluation of a coupled ensemble data assimilation for global oceanic climate
studies, Mon. Weather Rev., 135, 3541–3564, 2007. a
Short summary
Data assimilation combines observations with numerical models to get an improved estimate of the model state. This work discusses the technical aspects of how a coupled model that simulates the ocean and the atmosphere can be augmented by data assimilation functionality provided in generic form by the open-source software PDAF (Parallel Data Assimilation Framework). A very efficient program is obtained that can be executed on high-performance computers.
Data assimilation combines observations with numerical models to get an improved estimate of the...
