Biblio
Filtre: Første Bokstav I Etternavn er C [Slett Alle Filtre]
Biomass changes and trophic amplification of plankton in a warmer ocean. Global Change Biology 20, (2014).
Building the capacity for forecasting marine biogeochemistry and ecosystems: recent advances and future developments. Journal of operational oceanography. Publisher: The Institute of Marine Engineering, Science & Technology 8, (2015).
Building trust in climate science: data products for the 21st century. Environmetrics 23, 373-381 (2012).
Burgundy regional climate change and its potential impact on grapevines. Climate Dynamics (2012).doi:10.1007/s00382-011-1284
Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter. Journal of Geophysical Research (JGR): Oceans 119, (2014).
The carbon cycle in the Greenland Sea ESOP-2 Final Report. GEOS, Univ. i Bergen to EC FP4 (1999).
Causes of the large warm bias in the Angola–Benguela Frontal Zone in the Norwegian Earth System Model. Climate Dynamics (2017).doi:10.1007/s00382-017-3896-2
Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error. Nonlinear processes in geophysics 25, (2018).
Characterisation of Inland and Coastal Waters with space sensors. NERSC Technical Report No. 164 (1999).
The circlet transform: A robust tool for detecting features with circular shapes. Computers & Geosciences 37, (2011). Abstract
Last ned: sdarticle-1.pdf (1.86 MB)
Climate Change and Silk Road Civilization Evolution in Arid Central Asia: Progress and Issues. Advances in Earth Science 34, (2019).
The Climate Model: An ARCPATH Tool to Understand and Predict Climate Change. Nordic Perspectives on the Responsible Development of the Arctic: Pathways to Action (2020).doi:10.1007/978-3-030-52324-4_8
Cloud cover and cloud types in the Eurasian Arctic in 1936–2012. International Journal of Climatology 39, (2019). Abstract
Combined influence of oceanic and atmospheric circulations on Greenland sea ice concentration. The Cryosphere 15, (2021).
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model. Journal of Computational Science 44, (2020).
Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, (2021). Abstract
Comparing recent changes in the Arctic and the Third Pole: linking science and policy. (2022).doi:10.1080/1088937X.2022.2105969
Comparison of ensemble-based and variational-based data assimilation schemes in a quasi-geostrophic model. 86th AMS annual meeting (2006).
Comparison of Local Ensemble Transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic Model. Monthly Weather Review 137, 693 - 709 (2009).
A comparison of sequential assimilation schemes for ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM): Twin experiments with static forecast error covariances. Ocean Modelling 37, (2011). Abstract
Last ned: sdarticle-4.pdf (3.72 MB)
Connection between distribution of Harp seals and ice cover parameters determined using ERS-2 SAR-2 imagery. In proceedings of the ICES/NAFO Workshop on Survey Methodology for Harp Hooded Seals, Copenhagen (1997).
