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Publications (10 of 117) Show all publications
Lötstedt, P. (2019). The linear noise approximation for spatially dependent biochemical networks. Bulletin of Mathematical Biology, 81, 2873-2901
Open this publication in new window or tab >>The linear noise approximation for spatially dependent biochemical networks
2019 (English)In: Bulletin of Mathematical Biology, ISSN 0092-8240, E-ISSN 1522-9602, Vol. 81, p. 2873-2901Article in journal (Refereed) Published
National Category
Computational Mathematics Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-351228 (URN)10.1007/s11538-018-0428-0 (DOI)
Projects
eSSENCE
Available from: 2018-04-11 Created: 2018-05-21 Last updated: 2019-08-07Bibliographically approved
Marchenko, S., Cheng, G., Lötstedt, P., Pohjola, V., Pettersson, R., van Pelt, W. & Reijmer, C. (2019). Thermal conductivity of firn at Lomonosovfonna, Svalbard, derived from subsurface temperature measurements. The Cryosphere, 13, 1843-1859
Open this publication in new window or tab >>Thermal conductivity of firn at Lomonosovfonna, Svalbard, derived from subsurface temperature measurements
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2019 (English)In: The Cryosphere, ISSN 1994-0416, E-ISSN 1994-0424, Vol. 13, p. 1843-1859Article in journal (Refereed) Published
Abstract [en]

Accurate description of snow and firn processes is necessary for estimating the fraction of glacier surface melt that contributes to runoff. Most processes in snow and firn are to a great extent controlled by the temperature therein and in the absence of liquid water, the temperature evolution is dominated by the conductive heat exchange. The latter is controlled by the effective thermal conductivity k. Here we reconstruct the effective thermal conductivity of firn at Lomonosovfonna, Svalbard, using an optimization routine minimizing the misfit between simulated and measured subsurface temperatures and densities. The optimized k* values in the range from 0.2 to 1.6 W (m K)−1 increase downwards and over time. The results are supported by uncertainty quantification experiments, according to which k* is most sensitive to systematic errors in empirical temperature values and their estimated depths, particularly in the lower part of the vertical profile. Compared to commonly used density-based parameterizations, our k values are consistently larger, suggesting a faster conductive heat exchange in firn.

National Category
Physical Geography
Identifiers
urn:nbn:se:uu:diva-334156 (URN)10.5194/tc-13-1843-2019 (DOI)000474653300002 ()
Funder
Swedish Research Council, 621-2014-3735Swedish Research Council Formas, 214-2013-1600
Available from: 2019-07-09 Created: 2017-11-21 Last updated: 2019-08-15Bibliographically approved
van Dongen, E. C. H., Kirchner, N., van Gijzen, M. B., van de Wal, R. S. W., Zwinger, T., Cheng, G., . . . von Sydow, L. (2018). Dynamically coupling full Stokes and shallow shelf approximation for marine ice sheet flow using Elmer/Ice (v8.3). Geoscientific Model Development, 11, 4563-4576
Open this publication in new window or tab >>Dynamically coupling full Stokes and shallow shelf approximation for marine ice sheet flow using Elmer/Ice (v8.3)
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2018 (English)In: Geoscientific Model Development, ISSN 1991-959X, E-ISSN 1991-9603, Vol. 11, p. 4563-4576Article in journal (Refereed) Published
National Category
Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-363123 (URN)10.5194/gmd-11-4563-2018 (DOI)000450295700001 ()
Projects
eSSENCE
Available from: 2018-11-16 Created: 2018-10-12 Last updated: 2019-01-24Bibliographically approved
Bashardanesh, Z. & Lötstedt, P. (2018). Efficient Green's function reaction dynamics (GFRD) simulations for diffusion-limited, reversible reactions. Journal of Computational Physics, 357, 78-99
Open this publication in new window or tab >>Efficient Green's function reaction dynamics (GFRD) simulations for diffusion-limited, reversible reactions
2018 (English)In: Journal of Computational Physics, ISSN 0021-9991, E-ISSN 1090-2716, Vol. 357, p. 78-99Article in journal (Refereed) Published
National Category
Computational Mathematics Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-338758 (URN)10.1016/j.jcp.2017.12.025 (DOI)000427393800004 ()
Available from: 2017-12-21 Created: 2018-01-12 Last updated: 2018-06-08Bibliographically approved
Engblom, S., Lötstedt, P. & Meinecke, L. (2018). Mesoscopic modeling of random walk and reactions in crowded media. Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, 98, 033304:1-16, Article ID 033304.
Open this publication in new window or tab >>Mesoscopic modeling of random walk and reactions in crowded media
2018 (English)In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 98, p. 033304:1-16, article id 033304Article in journal (Refereed) Published
National Category
Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-360450 (URN)10.1103/PhysRevE.98.033304 (DOI)000444574600010 ()
Projects
UPMARCeSSENCE
Available from: 2018-09-11 Created: 2018-09-13 Last updated: 2018-11-15Bibliographically approved
Cheng, G., Lötstedt, P. & von Sydow, L. (2017). Accurate and stable time stepping in ice sheet modeling. Journal of Computational Physics, 329, 29-47
Open this publication in new window or tab >>Accurate and stable time stepping in ice sheet modeling
2017 (English)In: Journal of Computational Physics, ISSN 0021-9991, E-ISSN 1090-2716, Vol. 329, p. 29-47Article in journal (Refereed) Published
National Category
Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-309278 (URN)10.1016/j.jcp.2016.10.060 (DOI)000390511500003 ()
Projects
eSSENCE
Available from: 2016-11-02 Created: 2016-12-02 Last updated: 2017-11-29Bibliographically approved
Engblom, S., Hellander, A. & Lötstedt, P. (2017). Multiscale simulation of stochastic reaction–diffusion networks. In: Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology: (pp. 55-79). Springer
Open this publication in new window or tab >>Multiscale simulation of stochastic reaction–diffusion networks
2017 (English)In: Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, Springer, 2017, p. 55-79Chapter in book (Refereed)
Place, publisher, year, edition, pages
Springer, 2017
National Category
Computational Mathematics Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-331828 (URN)10.1007/978-3-319-62627-7_3 (DOI)978-3-319-62626-0 (ISBN)
Projects
eSSENCE
Available from: 2017-10-05 Created: 2017-10-18 Last updated: 2018-11-12Bibliographically approved
Hellander, A., Klosa, J., Lötstedt, P. & MacNamara, S. (2017). Robustness analysis of spatiotemporal models in the presence of extrinsic fluctuations. SIAM Journal on Applied Mathematics, 77, 1157-1183
Open this publication in new window or tab >>Robustness analysis of spatiotemporal models in the presence of extrinsic fluctuations
2017 (English)In: SIAM Journal on Applied Mathematics, ISSN 0036-1399, E-ISSN 1095-712X, Vol. 77, p. 1157-1183Article in journal (Refereed) Published
National Category
Computational Mathematics Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-326829 (URN)10.1137/15M1052317 (DOI)000408945700004 ()
Projects
eSSENCE
Available from: 2017-07-27 Created: 2017-07-31 Last updated: 2017-12-08Bibliographically approved
Meinecke, L., Engblom, S., Hellander, A. & Lötstedt, P. (2016). Analysis and design of jump coefficients in discrete stochastic diffusion models. SIAM Journal on Scientific Computing, 38, A55-A83
Open this publication in new window or tab >>Analysis and design of jump coefficients in discrete stochastic diffusion models
2016 (English)In: SIAM Journal on Scientific Computing, ISSN 1064-8275, E-ISSN 1095-7197, Vol. 38, p. A55-A83Article in journal (Refereed) Published
National Category
Computational Mathematics Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-272192 (URN)10.1137/15M101110X (DOI)000371235600003 ()
Projects
UPMARCeSSENCE
Available from: 2016-01-06 Created: 2016-01-12 Last updated: 2018-11-12Bibliographically approved
Ahlkrona, J., Lötstedt, P., Kirchner, N. & Zwinger, T. (2016). Dynamically coupling the non-linear Stokes equations with the shallow ice approximation in glaciology: Description and first applications of the ISCAL method. Journal of Computational Physics, 308, 1-19
Open this publication in new window or tab >>Dynamically coupling the non-linear Stokes equations with the shallow ice approximation in glaciology: Description and first applications of the ISCAL method
2016 (English)In: Journal of Computational Physics, ISSN 0021-9991, E-ISSN 1090-2716, Vol. 308, p. 1-19Article in journal (Refereed) Published
National Category
Computational Mathematics
Identifiers
urn:nbn:se:uu:diva-269822 (URN)10.1016/j.jcp.2015.12.025 (DOI)000369086700001 ()
Projects
eSSENCE
Available from: 2015-12-17 Created: 2015-12-18 Last updated: 2017-12-01Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2143-3078

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