ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Publication Date: 2022-05-25
    Description: Author Posting. © American Meteorological Society, 2013. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 26 (2013): 2833–2844, doi:10.1175/JCLI-D-12-00181.1.
    Description: The Community Climate System Model, version 4 (CCSM4) is used to assess the climate impact of wind-generated near-inertial waves (NIWs). Even with high-frequency coupling, CCSM4 underestimates the strength of NIWs, so that a parameterization for NIWs is developed and included into CCSM4. Numerous assumptions enter this parameterization, the core of which is that the NIW velocity signal is detected during the model integration, and amplified in the shear computation of the ocean surface boundary layer module. It is found that NIWs deepen the ocean mixed layer by up to 30%, but they contribute little to the ventilation and mixing of the ocean below the thermocline. However, the deepening of the tropical mixed layer by NIWs leads to a change in tropical sea surface temperature and precipitation. Atmospheric teleconnections then change the global sea level pressure fields so that the midlatitude westerlies become weaker. Unfortunately, the magnitude of the real air-sea flux of NIW energy is poorly constrained by observations; this makes the quantitative assessment of their climate impact rather uncertain. Thus, a major result of the present study is that because of its importance for global climate the uncertainty in the observed tropical NIW energy has to be reduced.
    Description: This research was funded as part of the Climate Process Team on internal wave-driven mixing with NSF Grant Nr E0968771 at NCAR.
    Description: 2013-11-01
    Keywords: Fronts ; Inertia-gravity waves ; Mesoscale processes ; Mixing ; Nonlinear dynamics
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 2
    Publication Date: 2022-05-25
    Description: Author Posting. © American Meteorological Society, 2011. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 24 (2011): 4973–4991, doi:10.1175/2011JCLI4083.1.
    Description: The fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Niño–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulation. Changes to the CCSM4 land component lead to a much improved annual cycle of water storage, especially in the tropics. The CCSM4 sea ice component uses much more realistic albedos than CCSM3, and for several reasons the Arctic sea ice concentration is improved in CCSM4. An ensemble of twentieth-century simulations produces a good match to the observed September Arctic sea ice extent from 1979 to 2005. The CCSM4 ensemble mean increase in globally averaged surface temperature between 1850 and 2005 is larger than the observed increase by about 0.4°C. This is consistent with the fact that CCSM4 does not include a representation of the indirect effects of aerosols, although other factors may come into play. The CCSM4 still has significant biases, such as the mean precipitation distribution in the tropical Pacific Ocean, too much low cloud in the Arctic, and the latitudinal distributions of shortwave and longwave cloud forcings.
    Description: National Science Foundation, which sponsors NCAR and the CCSM Project. The project is also sponsored by the U.S. Department of Energy (DOE). Thanks are also due to the many other software engineers and scientists who worked on developing CCSM4, and to the Computational and Information Systems Laboratory at NCAR, which provided the computing resources through the Climate Simulation Laboratory. Hunke was supported within theClimate, Ocean and Sea Ice Modeling project at Los Alamos National Laboratory, which is funded by the Biological and Environmental Research division of the DOE Office of Science. The Los Alamos National Laboratory is operated by theDOENationalNuclear Security Administration under Contract DE-AC52-06NA25396. Raschwas supported by theDOEOffice of Science, Earth System Modeling Program, which is part of the DOE Climate Change Research Program. The Pacific Northwest National Laboratory is operated forDOEbyBattelle Memorial Institute under Contract DE-AC06-76RLO 1830. Worley was supported by the Climate Change Research Division of the Office of Biological and Environmental Research and by the Office ofAdvanced Scientific Computing Research, both in the DOE Office of Science, under Contract DE-AC05-00OR22725 with UT-Batelle, LLC.
    Keywords: Climate models ; Madden–Julian oscillation ; Sea ice ; Model evaluation/performance ; Meridional overturning circulation ; Convection ; Tropics
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 3
    Publication Date: 2022-03-01
    Description: To examine the atmospheric responses to Arctic sea ice variability in the Northern Hemisphere cold season (from October to the following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily varying sea ice, sea surface temperature, and radiative forcings prescribed during the 1979–2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multimodel ensemble mean (MMEM) shows decreasing sea level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drive a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual covariability between sea ice extent in the Barents–Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the covariability in MMEMs. The interannual sea ice decline followed by a negative North Atlantic Oscillation–like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.
    Description: Published
    Description: 8419–8443
    Description: 2A. Fisica dell'alta atmosfera
    Description: JCR Journal
    Keywords: Arctic ; Sea ice ; Atmospheric circulation ; Climate models ; 01.01. Atmosphere
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 4
    Publication Date: 2022-06-06
    Description: Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Liang, Y.-C., Frankignoul, C., Kwon, Y.-O., Gastineau, G., Manzini, E., Danabasoglu, G., Suo, L., Yeager, S., Gao, Y., Attema, J. J., Cherchi, A., Ghosh, R., Matei, D., Mecking, J., Tian, T., & Zhang, Y. Impacts of Arctic sea ice on cold season atmospheric variability and trends estimated from observations and a multimodel large ensemble. Journal of Climate, 34(20), (2021): 8419–8443, https://doi.org/10.1175/JCLI-D-20-0578.s1.
    Description: To examine the atmospheric responses to Arctic sea ice variability in the Northern Hemisphere cold season (from October to the following March), this study uses a coordinated set of large-ensemble experiments of nine atmospheric general circulation models (AGCMs) forced with observed daily varying sea ice, sea surface temperature, and radiative forcings prescribed during the 1979–2014 period, together with a parallel set of experiments where Arctic sea ice is substituted by its climatology. The simulations of the former set reproduce the near-surface temperature trends in reanalysis data, with similar amplitude, and their multimodel ensemble mean (MMEM) shows decreasing sea level pressure over much of the polar cap and Eurasia in boreal autumn. The MMEM difference between the two experiments allows isolating the effects of Arctic sea ice loss, which explain a large portion of the Arctic warming trends in the lower troposphere and drive a small but statistically significant weakening of the wintertime Arctic Oscillation. The observed interannual covariability between sea ice extent in the Barents–Kara Seas and lagged atmospheric circulation is distinguished from the effects of confounding factors based on multiple regression, and quantitatively compared to the covariability in MMEMs. The interannual sea ice decline followed by a negative North Atlantic Oscillation–like anomaly found in observations is also seen in the MMEM differences, with consistent spatial structure but much smaller amplitude. This result suggests that the sea ice impacts on trends and interannual atmospheric variability simulated by AGCMs could be underestimated, but caution is needed because internal atmospheric variability may have affected the observed relationship.
    Description: We acknowledge support by the Blue-Action Project (the European Union’s Horizon 2020 research and innovation programme, #727852, http://www.blue-action.eu/index.php?id=3498). The WHOI–NCAR group was supported by the U.S. National Science Foundation (NSF) Office of Polar Programs Grants 1736738 and 1737377. Their computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by the Computational and Information Systems Laboratory at NCAR. NCAR is a major facility sponsored by the U.S. NSF under Cooperative Agreement No. 1852977. Guillaume Gastineau was granted access to the HPC resources of TGCC under the allocations A5-017403 and A7-017403 made by GENCI. The SST and SIC data were downloaded from the U.K. Met Office Hadley Centre Observations Datasets (http://www.metoffice.gov.uk/hadobs/hadisst). The work by NLeSC was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. The simulations of IAP AGCM were supported by the National Key R&D Program of China 2017YFE0111800. The NorESM2-CAM6 simulations were performed on resources provided by UNINETT Sigma2–the National Infrastructure for High Performance Computing and Data Storage in Norway (nn2343k, NS9015K).
    Keywords: Arctic ; Sea ice ; Atmospheric circulation ; Climate models
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 5
    Publication Date: 2022-05-26
    Description: Author Posting. © American Meteorological Society, 2012. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 25 (2012): 1361–1389, doi:10.1175/JCLI-D-11-00091.1.
    Description: The ocean component of the Community Climate System Model version 4 (CCSM4) is described, and its solutions from the twentieth-century (20C) simulations are documented in comparison with observations and those of CCSM3. The improvements to the ocean model physical processes include new parameterizations to represent previously missing physics and modifications of existing parameterizations to incorporate recent new developments. In comparison with CCSM3, the new solutions show some significant improvements that can be attributed to these model changes. These include a better equatorial current structure, a sharper thermocline, and elimination of the cold bias of the equatorial cold tongue all in the Pacific Ocean; reduced sea surface temperature (SST) and salinity biases along the North Atlantic Current path; and much smaller potential temperature and salinity biases in the near-surface Pacific Ocean. Other improvements include a global-mean SST that is more consistent with the present-day observations due to a different spinup procedure from that used in CCSM3. Despite these improvements, many of the biases present in CCSM3 still exist in CCSM4. A major concern continues to be the substantial heat content loss in the ocean during the preindustrial control simulation from which the 20C cases start. This heat loss largely reflects the top of the atmospheric model heat loss rate in the coupled system, and it essentially determines the abyssal ocean potential temperature biases in the 20C simulations. There is also a deep salty bias in all basins. As a result of this latter bias in the deep North Atlantic, the parameterized overflow waters cannot penetrate much deeper than in CCSM3.
    Description: NCAR is sponsored by the National Science Foundation. The CCSM is also sponsored by the Department of Energy. SGY was supported by the NOAA Climate Program Office under Climate Variability and Predictability Program Grant NA09OAR4310163.
    Description: 2012-09-01
    Keywords: Ocean circulation ; Climate models ; General circulation models ; Ocean models
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
  • 6
    Publication Date: 2022-10-26
    Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Stammer, D., Bracco, A., AchutaRao, K., Beal, L., Bindoff, N. L., Braconnot, P., Cai, W., Chen, D., Collins, M., Danabasoglu, G., Dewitte, B., Farneti, R., Fox-Kemper, B., Fyfe, J., Griffies, S. M., Jayne, S. R., Lazar, A., Lengaigne, M., Lin, X., Marsland, S., Minobe, S., Monteiro, P. M. S., Robinson, W., Roxy, M. K., Rykaczewski, R. R., Speich, S., Smith, I. J., Solomon, A., Storto, A., Takahashi, K., Toniazzo, T., & Vialard, J. Ocean climate observing requirements in support of climate research and climate information. Frontiers in Marine Science, 6, (2019): 444, doi:10.3389/fmars.2019.00444.
    Description: Natural variability and change of the Earth’s climate have significant global societal impacts. With its large heat and carbon capacity and relatively slow dynamics, the ocean plays an integral role in climate, and provides an important source of predictability at seasonal and longer timescales. In addition, the ocean provides the slowly evolving lower boundary to the atmosphere, driving, and modifying atmospheric weather. Understanding and monitoring ocean climate variability and change, to constrain and initialize models as well as identify model biases for improved climate hindcasting and prediction, requires a scale-sensitive, and long-term observing system. A climate observing system has requirements that significantly differ from, and sometimes are orthogonal to, those of other applications. In general terms, they can be summarized by the simultaneous need for both large spatial and long temporal coverage, and by the accuracy and stability required for detecting the local climate signals. This paper reviews the requirements of a climate observing system in terms of space and time scales, and revisits the question of which parameters such a system should encompass to meet future strategic goals of the World Climate Research Program (WCRP), with emphasis on ocean and sea-ice covered areas. It considers global as well as regional aspects that should be accounted for in designing observing systems in individual basins. Furthermore, the paper discusses which data-driven products are required to meet WCRP research and modeling needs, and ways to obtain them through data synthesis and assimilation approaches. Finally, it addresses the need for scientific capacity building and international collaboration in support of the collection of high-quality measurements over the large spatial scales and long time-scales required for climate research, bridging the scientific rational to the required resources for implementation.
    Description: This work was partly supported by the DFG funded excellence center CliSAP of the Universituat Hamburg (DS). AB was supported by the National Science Foundation through award NSF-1658174 and by the NOAA through award NA16OAR4310173. SM was supported by the Earth Systems and Climate Change Hub of the Australian Government’s National Environmental Science Program.
    Keywords: Ocean observing system ; Ocean climate ; Earth observations ; In situ measurements ; Satellite observations ; Ocean modeling ; Climate information
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...