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Willén, Ulrika
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Publications (10 of 42) Show all publications
Stephens, G. L., Hakuba, M. Z., Kato, S., Gettleman, A., Dufresne, J.-L., Andrews, T., . . . Mauritsen, T. (2022). The changing nature of Earth's reflected sunlight. Proceedings of the Royal Society. Mathematical, Physical and Engineering Sciences, 478(2263), Article ID 20220053.
Open this publication in new window or tab >>The changing nature of Earth's reflected sunlight
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2022 (English)In: Proceedings of the Royal Society. Mathematical, Physical and Engineering Sciences, ISSN 1364-5021, E-ISSN 1471-2946, Vol. 478, no 2263, article id 20220053Article in journal (Refereed) Published
National Category
Climate Research
Research subject
Climate; Climate
Identifiers
urn:nbn:se:smhi:diva-6323 (URN)10.1098/rspa.2022.0053 (DOI)000832697100003 ()
Available from: 2022-08-17 Created: 2022-08-17 Last updated: 2023-01-19Bibliographically approved
Eliasson, S., Karlsson, K.-G. & Willén, U. (2020). A simulator for the CLARA-A2 cloud climate data record and its application to assess EC-Earth polar cloudiness. Geoscientific Model Development, 13(1), 297-314
Open this publication in new window or tab >>A simulator for the CLARA-A2 cloud climate data record and its application to assess EC-Earth polar cloudiness
2020 (English)In: Geoscientific Model Development, ISSN 1991-959X, E-ISSN 1991-9603, Vol. 13, no 1, p. 297-314Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-5637 (URN)10.5194/gmd-13-297-2020 (DOI)000510389700004 ()
Available from: 2020-02-25 Created: 2020-02-25 Last updated: 2020-05-04Bibliographically approved
Loeb, N. G., Wang, H., Allan, R. P., Andrews, T., Armour, K., Cole, J. N. S., . . . Wyser, K. (2020). New Generation of Climate Models Track Recent Unprecedented Changes in Earth's Radiation Budget Observed by CERES. Geophysical Research Letters, 47(5), Article ID e2019GL086705.
Open this publication in new window or tab >>New Generation of Climate Models Track Recent Unprecedented Changes in Earth's Radiation Budget Observed by CERES
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2020 (English)In: Geophysical Research Letters, ISSN 0094-8276, E-ISSN 1944-8007, Vol. 47, no 5, article id e2019GL086705Article in journal (Refereed) Published
Abstract [en]

We compare top-of-atmosphere (TOA) radiative fluxes observed by the Clouds and the Earth's Radiant Energy System (CERES) and simulated by seven general circulation models forced with observed sea-surface temperature (SST) and sea-ice boundary conditions. In response to increased SSTs along the equator and over the eastern Pacific (EP) following the so-called global warming "hiatus" of the early 21st century, simulated TOA flux changes are remarkably similar to CERES. Both show outgoing shortwave and longwave TOA flux changes that largely cancel over the west and central tropical Pacific, and large reductions in shortwave flux for EP low-cloud regions. A model's ability to represent changes in the relationship between global mean net TOA flux and surface temperature depends upon how well it represents shortwave flux changes in low-cloud regions, with most showing too little sensitivity to EP SST changes, suggesting a "pattern effect" that may be too weak compared to observations.

National Category
Climate Research
Research subject
Climate
Identifiers
urn:nbn:se:smhi:diva-5681 (URN)10.1029/2019GL086705 (DOI)000529112700018 ()
Available from: 2020-05-13 Created: 2020-05-13 Last updated: 2020-09-07Bibliographically approved
Eliasson, S., Karlsson, K.-G., van Meijgaard, E., Meirink, J. F., Stengel, M. & Willén, U. (2019). The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model. Geoscientific Model Development, 12(2), 829-847
Open this publication in new window or tab >>The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model
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2019 (English)In: Geoscientific Model Development, ISSN 1991-959X, E-ISSN 1991-9603, Vol. 12, no 2, p. 829-847Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-5301 (URN)10.5194/gmd-12-829-2019 (DOI)000459423200001 ()
Available from: 2019-07-31 Created: 2019-07-31 Last updated: 2020-05-04Bibliographically approved
Stengel, M., Schlundt, C., Stapelberg, S., Sus, O., Eliasson, S., Willén, U. & Meirink, J. F. (2018). Comparing ERA-Interim clouds with satellite observations using a simplified satellite simulator. Atmospheric Chemistry And Physics, 18(23), 17601-17614
Open this publication in new window or tab >>Comparing ERA-Interim clouds with satellite observations using a simplified satellite simulator
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2018 (English)In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 18, no 23, p. 17601-17614Article in journal (Refereed) Published
National Category
Climate Research Meteorology and Atmospheric Sciences
Research subject
Climate; Remote sensing
Identifiers
urn:nbn:se:smhi:diva-5026 (URN)10.5194/acp-18-17601-2018 (DOI)000452860300003 ()
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved
Bennartz, R., Hoschen, H., Picard, B., Schroder, M., Stengel, M., Sus, O., . . . Willén, U. (2017). An intercalibrated dataset of total column water vapour and wet tropospheric correction based on MWR on board ERS-1, ERS-2, and Envisat. Atmospheric Measurement Techniques, 10(4), 1387-1402
Open this publication in new window or tab >>An intercalibrated dataset of total column water vapour and wet tropospheric correction based on MWR on board ERS-1, ERS-2, and Envisat
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2017 (English)In: Atmospheric Measurement Techniques, ISSN 1867-1381, E-ISSN 1867-8548, Vol. 10, no 4, p. 1387-1402Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4093 (URN)10.5194/amt-10-1387-2017 (DOI)000399302900001 ()
Available from: 2017-05-11 Created: 2017-05-11 Last updated: 2017-05-11Bibliographically approved
Lauer, A., Eyring, V., Righi, M., Buchwitz, M., Defourny, P., Evaldsson, M., . . . Willén, U. (2017). Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool. Remote Sensing of Environment, 203, 9-39
Open this publication in new window or tab >>Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool
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2017 (English)In: Remote Sensing of Environment, ISSN 0034-4257, E-ISSN 1879-0704, Vol. 203, p. 9-39Article in journal (Refereed) Published
National Category
Climate Research
Research subject
Climate
Identifiers
urn:nbn:se:smhi:diva-4459 (URN)10.1016/j.rse.2017.01.007 (DOI)000418464200003 ()
Available from: 2018-01-09 Created: 2018-01-09 Last updated: 2018-01-09Bibliographically approved
Stengel, M., Stapelberg, S., Sus, O., Schlundt, C., Poulsen, C., Thomas, G., . . . Hollmann, R. (2017). Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project. Earth System Science Data, 9(2), 881-904
Open this publication in new window or tab >>Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project
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2017 (English)In: Earth System Science Data, ISSN 1866-3508, E-ISSN 1866-3516, Vol. 9, no 2, p. 881-904Article in journal (Refereed) Published
National Category
Remote Sensing
Research subject
Remote sensing
Identifiers
urn:nbn:se:smhi:diva-4451 (URN)10.5194/essd-9-881-2017 (DOI)000416002500001 ()
Available from: 2017-12-12 Created: 2017-12-12 Last updated: 2020-05-04Bibliographically approved
Koenigk, T., Brodeau, L., Graversen, R. G., Karlsson, J., Svensson, G., Tjernstrom, M., . . . Wyser, K. (2013). Arctic climate change in 21st century CMIP5 simulations with EC-Earth. Climate Dynamics, 40(11-12), 2719-2743
Open this publication in new window or tab >>Arctic climate change in 21st century CMIP5 simulations with EC-Earth
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2013 (English)In: Climate Dynamics, ISSN 0930-7575, E-ISSN 1432-0894, Vol. 40, no 11-12, p. 2719-2743Article in journal (Refereed) Published
Abstract [en]

The Arctic climate change is analyzed in an ensemble of future projection simulations performed with the global coupled climate model EC-Earth2.3. EC-Earth simulates the twentieth century Arctic climate relatively well but the Arctic is about 2 K too cold and the sea ice thickness and extent are overestimated. In the twenty-first century, the results show a continuation and strengthening of the Arctic trends observed over the recent decades, which leads to a dramatically changed Arctic climate, especially in the high emission scenario RCP8.5. The annually averaged Arctic mean near-surface temperature increases by 12 K in RCP8.5, with largest warming in the Barents Sea region. The warming is most pronounced in winter and autumn and in the lower atmosphere. The Arctic winter temperature inversion is reduced in all scenarios and disappears in RCP8.5. The Arctic becomes ice free in September in all RCP8.5 simulations after a rapid reduction event without recovery around year 2060. Taking into account the overestimation of ice in the twentieth century, our model results indicate a likely ice-free Arctic in September around 2040. Sea ice reductions are most pronounced in the Barents Sea in all RCPs, which lead to the most dramatic changes in this region. Here, surface heat fluxes are strongly enhanced and the cloudiness is substantially decreased. The meridional heat flux into the Arctic is reduced in the atmosphere but increases in the ocean. This oceanic increase is dominated by an enhanced heat flux into the Barents Sea, which strongly contributes to the large sea ice reduction and surface-air warming in this region. Increased precipitation and river runoff lead to more freshwater input into the Arctic Ocean. However, most of the additional freshwater is stored in the Arctic Ocean while the total Arctic freshwater export only slightly increases.

Keywords
Arctic climate, Future scenarios, CMIP5, Global coupled atmosphere-ocean modeling, Coupled Arctic climate processes
National Category
Climate Research
Research subject
Climate
Identifiers
urn:nbn:se:smhi:diva-375 (URN)10.1007/s00382-012-1505-y (DOI)000319360800010 ()
Available from: 2015-04-07 Created: 2015-03-31 Last updated: 2017-12-04Bibliographically approved
Ning, T., Elgered, G., Willén, U. & Johansson, J. M. (2013). Evaluation of the atmospheric water vapor content in a regional climate model using ground-based GPS measurements. Journal of Geophysical Research - Atmospheres, 118(2), 329-339
Open this publication in new window or tab >>Evaluation of the atmospheric water vapor content in a regional climate model using ground-based GPS measurements
2013 (English)In: Journal of Geophysical Research - Atmospheres, ISSN 2169-897X, E-ISSN 2169-8996, Vol. 118, no 2, p. 329-339Article in journal (Refereed) Published
Abstract [en]

Ground-based GPS measurements can provide independent data for the assessment of climate models. We use the atmospheric integrated water vapor (IWV) obtained from GPS measurements at 99 European sites to evaluate the regional Rossby Centre Atmospheric climate model (RCA) driven at the boundaries by the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA Interim). The GPS data were compared to the RCA simulation and the ERA Interim data. The comparison was first made using the monthly mean values. Averaged over the domain and the 14 years covered by the GPS data, IWV differences of about 0.47 kg/m(2) and 0.39 kg/m(2) are obtained for RCA-GPS and ECMWF-GPS, respectively. The RCA-GPS standard deviation is 0.98 kg/m(2) whereas it is 0.35 kg/m(2) for the ECMWF-GPS comparison. The IWV differences for RCA are positively correlated to the differences for ECMWF. However, this is not the case for two sites in Italy where a wet bias is seen for ECMWF, while a dry bias is seen for RCA, the latter being consistent with a cold temperature bias found for RCA in that region by other authors. Comparisons of the estimated diurnal cycle and the spatial structure function of the IWV were made between the GPS data and the RCA simulation. The RCA captures the geographical variation of the diurnal peak in the summer. Averaged over all sites, a peak at 17 local solar time is obtained from the GPS data while it appears later, at 18, in the RCA simulation. The spatial variation of the IWV obtained for an RCA run with a resolution of 11 km gives a better agreement with the GPS results than does the spatial variation from a 50 km resolution run. Citation: Ning, T., G. Elgered, U. Willen, and J. M. Johansson (2013), Evaluation of the atmospheric water vapor content in a regional climate model using ground-based GPS measurements, J. Geophys. Res. Atmos., 118, 329-339, doi: 10.1029/2012JD018053.

National Category
Climate Research
Research subject
Climate
Identifiers
urn:nbn:se:smhi:diva-392 (URN)10.1029/2012JD018053 (DOI)000317838100007 ()
Available from: 2015-04-02 Created: 2015-03-31 Last updated: 2017-12-04Bibliographically approved
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