Journal articles: 'Global mean'

1

Britos, Patricia. "What does mean “global justice”?" Justicia 21, no. 29 (2016): 86–98. http://dx.doi.org/10.17081/just.21.29.1235.

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2

Salzmann, Marc. "Global warming without global mean precipitation increase?" Science Advances 2, no. 6 (2016): e1501572. http://dx.doi.org/10.1126/sciadv.1501572.

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Global climate models simulate a robust increase of global mean precipitation of about 1.5 to 2% per kelvin surface warming in response to greenhouse gas (GHG) forcing. Here, it is shown that the sensitivity to aerosol cooling is robust as well, albeit roughly twice as large. This larger sensitivity is consistent with energy budget arguments. At the same time, it is still considerably lower than the 6.5 to 7% K−1 decrease of the water vapor concentration with cooling from anthropogenic aerosol because the water vapor radiative feedback lowers the hydrological sensitivity to anthropogenic forcings. When GHG and aerosol forcings are combined, the climate models with a realistic 20th century warming indicate that the global mean precipitation increase due to GHG warming has, until recently, been completely masked by aerosol drying. This explains the apparent lack of sensitivity of the global mean precipitation to the net global warming recently found in observations. As the importance of GHG warming increases in the future, a clear signal will emerge.

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3

Hocke, Klemens. "Oscillations of global mean TEC." Journal of Geophysical Research: Space Physics 113, A4 (2008): n/a. http://dx.doi.org/10.1029/2007ja012798.

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4

Vermeij, Geerat J., and Lindsey R. Leighton. "Does global diversity mean anything?" Paleobiology 29, no. 1 (2003): 3–7. http://dx.doi.org/10.1666/0094-8373(2003)029<0003:dgdma>2.0.co;2.

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A major goal of paleobiological research since the early 1960s has been the reconstruction in quantitative terms of the history of biological diversity. Spearheaded by Valentine (1969), Raup (1972, 1976a, b), and Sepkoski (1979, 1981, 1984, 1990, 1993), this effort has yielded estimates of global diversity through time, as well as calculations of global rates and magnitudes of extinction and diversification. A consensus emerging in the early 1980s (Sepkoski et al. 1981) indicated that global marine invertebrate diversity rose through the Cambrian and Ordovician periods to a plateau, which with brief extinction-related interruptions was maintained from the mid-Paleozoic to the mid-Mesozoic. Beginning in the Cretaceous, diversity rose again, reaching a peak in the late Neogene. The five mass extinctions of the Phanerozoic, and more or less distinct episodes of diversification, were identified and distinguished from many lesser events (Raup and Sepkoski 1982). Comparable studies, with varying results, were conducted on land vertebrates (Benton 1985, 1989), land plants (Knoll et al. 1979; Niklas et al. 1980, 1983; Tiffney 1981; Knoll 1984), early protistans (Knoll 1994), insects (Labandeira and Sepkoski 1993), and life as a whole (Van Valen 1984, 1985; Van Valen and Maiorana 1985; Signor 1990; Valentine et al. 1991; Benton 1995; Courtillot and Gaudemer 1996; Miller and Foote 1996).

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Ishihara, Koji. "Estimation of Global Mean Surface Temperature." Japanese Journal of Biometrics 32, Special_Issue (2010): S65—S75. http://dx.doi.org/10.5691/jjb.32.s65.

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6

Wen, Xinyu, Guoli Tang, Shaowu Wang, and Jianbin Huang. "Comparison of Global Mean Temperature Series." Advances in Climate Change Research 2, no. 4 (2011): 187–92. http://dx.doi.org/10.3724/sp.j.1248.2011.00187.

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7

Campbell, Jean A. "What Does the Global Perspective Mean?" Dialogue and Universalism 27, no. 1 (2017): 43–54. http://dx.doi.org/10.5840/du20172715.

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8

Trenberth, Kevin E., John R. Christy, and James W. Hurrell. "Monitoring Global Monthly Mean Surface Temperatures." Journal of Climate 5, no. 12 (1992): 1405–23. http://dx.doi.org/10.1175/1520-0442(1992)005<1405:mgmmst>2.0.co;2.

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9

Nerem, R. S. "Global Mean Sea Level Change: Correction." Science 275, no. 5303 (1997): 1049i—1053. http://dx.doi.org/10.1126/science.275.5303.1049i.

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10

Sarvey, Sharon I. "What Does “Global” Mean at NABN?" Bariatric Nursing and Surgical Patient Care 6, no. 3 (2011): 151–52. http://dx.doi.org/10.1089/bar.2011.9955.

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11

Kiehl, J. T., and Kevin E. Trenberth. "Earth's Annual Global Mean Energy Budget." Bulletin of the American Meteorological Society 78, no. 2 (1997): 197–208. http://dx.doi.org/10.1175/1520-0477(1997)078<0197:eagmeb>2.0.co;2.

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12

Hunt, B. G. "The stationarity of global mean climate." International Journal of Climatology 24, no. 7 (2004): 795–806. http://dx.doi.org/10.1002/joc.1016.

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13

Stephens, Graeme L., and Todd D. Ellis. "Controls of Global-Mean Precipitation Increases in Global Warming GCM Experiments." Journal of Climate 21, no. 23 (2008): 6141–55. http://dx.doi.org/10.1175/2008jcli2144.1.

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Abstract This paper examines the controls on global precipitation that are evident in the transient experiments conducted using coupled climate models collected for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The change in precipitation, water vapor, clouds, and radiative heating of the atmosphere evident in the 1% increase in carbon dioxide until doubled (1pctto2x) scenario is examined. As noted in other studies, the ensemble-mean changes in water vapor as carbon dioxide is doubled occur at a rate similar to that predicted by the Clausius–Clapeyron relationship. The ratio of global changes in precipitation to global changes in water vapor offers some insight on how readily increased water vapor is converted into precipitation in modeled climate change. This ratio ɛ is introduced in this paper as a gross indicator of the global precipitation efficiency under global warming. The main findings of this paper are threefold. First, increases in the global precipitation track increase atmospheric radiative energy loss and the ratio of precipitation sensitivity to water vapor sensitivity is primarily determined by changes to this atmospheric column energy loss. A reference limit to this ratio is introduced as the rate at which the emission of radiation from the clear-sky atmosphere increases as water vapor increases. It is shown that the derived efficiency based on the simple ratio of precipitation to water vapor sensitivities of models in fact closely matches the sensitivity derived from simple energy balance arguments involving changes to water vapor emission alone. Second, although the rate of increase of clear-sky emission is the dominant factor in the change to the energy balance of the atmosphere, there are two important and offsetting processes that contribute to ɛ in the model simulations studied: One involves a negative feedback through cloud radiative heating that acts to reduce the efficiency; the other is the global reduction in sensible heating that counteracts the effects of the cloud feedback and increases the efficiency. These counteracting feedbacks only apply on the global scale. Third, the negative cloud radiative heating feedback occurs through reductions of cloud amount in the middle troposphere, defined as the layer between 680 and 440 hPa, and by slight global cloud decreases in the lower troposphere. These changes act in a manner to expose the warmer atmosphere below to high clouds, thus resulting in a net warming of the atmospheric column by clouds and a negative feedback on the precipitation.

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14

Fay, A. R., and G. A. McKinley. "Global ocean biomes: mean and temporal variability." Earth System Science Data Discussions 7, no. 1 (2014): 107–28. http://dx.doi.org/10.5194/essdd-7-107-2014.

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Abstract. Large-scale studies of ocean biogeochemistry and carbon cycling have often partitioned the ocean into regions along lines of latitude and longitude despite the fact that spatially more complex boundaries would be closer to the true biogeography of the ocean. Herein, we define 17 open-ocean biomes defined by environmental envelopes incorporating 4 criteria: sea surface temperature (SST), spring/summer chlorophyll a concentrations (Chl), ice fraction, and maximum mixed layer depth (maxMLD) on a one-by-one degree grid (doi:10.1594/PANGAEA.828650). By considering interannual variability for each input, we create dynamic ocean biome boundaries that shift annually between 1998 and 2010. Additionally we create a core biome map, which includes only the gridcells that do not change biome assignment across the 13 years of the time-varying biomes. These ocean biomes can be used in future studies to distinguish large-scale ocean regions based on biogeochemical function.

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15

Shao, Barret Pengyuan, and Svetlozar T. Rachev. "Mean-ETL Optimization of a Global Portfolio." Journal of Investing 22, no. 4 (2013): 115–19. http://dx.doi.org/10.3905/joi.2013.22.4.115.

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16

Wan Nik, W. B., M. Z. Ibrahim, K. B. Samo, and A. M. Muzathik. "Monthly mean hourly global solar radiation estimation." Solar Energy 86, no. 1 (2012): 379–87. http://dx.doi.org/10.1016/j.solener.2011.10.008.

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17

Gunst, Richard F., Sabyasachi Basu, and Robert Brunell. "Defining and Estimating Global Mean Temperature Anomalies." Journal of Climate 6, no. 7 (1993): 1368–74. http://dx.doi.org/10.1175/1520-0442(1993)006<1368:daegmt>2.0.co;2.

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18

Thompson, Grahame F. "Global Corporate Citizenship: What Does it Mean?" Competition & Change 9, no. 2 (2005): 131–52. http://dx.doi.org/10.1179/102452905x45418.

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This article investigates the relationship between corporate social responsibility and a phrase that is fast becoming a preferred description of much the same thing but now set in an international context, namely global corporate citizenship. It is argued that the distinction between these two has not been clearly enough made in the literature. In clarifying the difference, the political nature of the idea of citizenship is focused upon and the politics of introducing triple-line considerations into the activity of transnational corporations is explored. An engagement with a wide range of civil society actors by corporations to further the ‘ethical’ agenda, a reconsideration of ‘corporate democracy’ in an international context, and the idea of a ‘progressive capitalist’ group of companies that might spear-head genuine corporate citizenship are concentrated upon in this assessment. Finally, the politics of an alliance for global corporate citizenship is broached that would take companies well beyond the limited agenda of just noting and attending to their social and environmental responsibilities.

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19

Hess, R. F., and A. G. Zaharia. "Global motion processing: Invariance with mean luminance." Journal of Vision 10, no. 13 (2010): 22. http://dx.doi.org/10.1167/10.13.22.

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20

Gordon, A. H. "Interhemispheric contrasts of mean global temperature anomalies." International Journal of Climatology 12, no. 1 (1992): 1–9. http://dx.doi.org/10.1002/joc.3370120102.

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21

Tokic, Damir, and Stijepko Tokic. "Would a rising yen mean global turmoil?" Journal of Corporate Accounting & Finance 18, no. 6 (2007): 5–11. http://dx.doi.org/10.1002/jcaf.20334.

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22

Kora, Ahmed Dooguy, Ibrahima CISSE, and Jean-Pierre Cances. "Global approach of mean service satisfaction assessment." Journal of Engineering 2014, no. 1 (2014): 24–29. http://dx.doi.org/10.1049/joe.2013.0089.

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23

Hantson, Stijn, Gitta Lasslop, Silvia Kloster, and Emilio Chuvieco. "Anthropogenic effects on global mean fire size." International Journal of Wildland Fire 24, no. 5 (2015): 589. http://dx.doi.org/10.1071/wf14208.

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Wildland fires are an important agent in the earth’s system. Multiple efforts are currently in progress to better represent wildland fires in earth system models. Although wildland fires are a natural disturbance factor, humans have an important effect on fire occurrence by directly igniting and suppressing fires and indirectly influencing fire behaviour by changing land cover and landscape structure. Although these factors are recognised, their quantitative effect on fire growth and burned area are not well understood and therefore only partly taken into account in current process-based fire models. Here we analyse the influence of humans on mean fire size globally. The mean fire size was extracted from the global Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product MCD45. We found a linear decreasing trend between population density and observed mean fire size over the globe, as well as a negative effect of cropland cover and net income. We implemented the effect of population density on fire growth in a global vegetation model including a process-based fire model (SPITFIRE–JSBACH). When including this demographic control, spatial trends in modelled fraction of burned area generally improved when compared with satellite-derived burned area data. More process-based solutions to limit fire spread are needed in the future, but the empirical relations described here serve as an intermediate step to improve current fire models.

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24

Bertsch, G. F., and K. Hagino. "Mean-field theory for global binding systematics." Physics of Atomic Nuclei 64, no. 4 (2001): 588–94. http://dx.doi.org/10.1134/1.1368217.

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25

Erlykin, A. D., T. Sloan, and A. W. Wolfendale. "Solar activity and the mean global temperature." Environmental Research Letters 4, no. 1 (2009): 014006. http://dx.doi.org/10.1088/1748-9326/4/1/014006.

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26

Jin, Taoyong, Jiancheng Li, and Weiping Jiang. "The global mean sea surface model WHU2013." Geodesy and Geodynamics 7, no. 3 (2016): 202–9. http://dx.doi.org/10.1016/j.geog.2016.04.006.

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27

Storch, Hans von, Eduardo Zorita, and Jesús F. González-Rouco. "Relationship between global mean sea-level and global mean temperature in a climate simulation of the past millennium." Ocean Dynamics 58, no. 3-4 (2008): 227–36. http://dx.doi.org/10.1007/s10236-008-0142-9.

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28

Tai, Chang-Kou. "Inferring the global mean sea level from a global tide gauge network." Acta Oceanologica Sinica 30, no. 4 (2011): 102–6. http://dx.doi.org/10.1007/s13131-011-0140-5.

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29

Lin, Jia Lin, and Tao Tao Qian. "Solar Energy and Global Climate Change." Advanced Materials Research 875-877 (February 2014): 1767–70. http://dx.doi.org/10.4028/www.scientific.net/amr.875-877.1767.

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Previous studies have shown that the solar energy input to the earth system underwent significant decadal variations at individual surface energy budget stations, with a global dimming from 1950s to 1980s, but a global brightening from 1980s to 2000s, and a mixed tendency at different locations thereafter. Here we use a new global gridded solar irradiance dataset to show that the previous results from individual stations represent well the regional means but not the global mean or hemisphere means. The global mean has a decadal variation that is quite different from the individual station results reported in previous studies, which comes from the fact that the southern hemisphere mean has an opposite trend with the northern hemisphere mean. No long-term global dimming trend is found associated with global warming

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30

Fay, A. R., and G. A. McKinley. "Global open-ocean biomes: mean and temporal variability." Earth System Science Data 6, no. 2 (2014): 273–84. http://dx.doi.org/10.5194/essd-6-273-2014.

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Abstract. Large-scale studies of ocean biogeochemistry and carbon cycling have often partitioned the ocean into regions along lines of latitude and longitude despite the fact that spatially more complex boundaries would be closer to the true biogeography of the ocean. Herein, we define 17 open-ocean biomes classified from four observational data sets: sea surface temperature (SST), spring/summer chlorophyll a concentrations (Chl a), ice fraction, and maximum mixed layer depth (maxMLD) on a 1° × 1° grid (available at doi:10.1594/PANGAEA.828650). By considering interannual variability for each input, we create dynamic ocean biome boundaries that shift annually between 1998 and 2010. Additionally we create a core biome map, which includes only the grid cells that do not change biome assignment across the 13 years of the time-varying biomes. These biomes can be used in future studies to distinguish large-scale ocean regions based on biogeochemical function.

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31

Poppick, Andrew, Elisabeth J. Moyer, and Michael L. Stein. "Estimating trends in the global mean temperature record." Advances in Statistical Climatology, Meteorology and Oceanography 3, no. 1 (2017): 33–53. http://dx.doi.org/10.5194/ascmo-3-33-2017.

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Abstract. Given uncertainties in physical theory and numerical climate simulations, the historical temperature record is often used as a source of empirical information about climate change. Many historical trend analyses appear to de-emphasize physical and statistical assumptions: examples include regression models that treat time rather than radiative forcing as the relevant covariate, and time series methods that account for internal variability in nonparametric rather than parametric ways. However, given a limited data record and the presence of internal variability, estimating radiatively forced temperature trends in the historical record necessarily requires some assumptions. Ostensibly empirical methods can also involve an inherent conflict in assumptions: they require data records that are short enough for naive trend models to be applicable, but long enough for long-timescale internal variability to be accounted for. In the context of global mean temperatures, empirical methods that appear to de-emphasize assumptions can therefore produce misleading inferences, because the trend over the twentieth century is complex and the scale of temporal correlation is long relative to the length of the data record. We illustrate here how a simple but physically motivated trend model can provide better-fitting and more broadly applicable trend estimates and can allow for a wider array of questions to be addressed. In particular, the model allows one to distinguish, within a single statistical framework, between uncertainties in the shorter-term vs. longer-term response to radiative forcing, with implications not only on historical trends but also on uncertainties in future projections. We also investigate the consequence on inferred uncertainties of the choice of a statistical description of internal variability. While nonparametric methods may seem to avoid making explicit assumptions, we demonstrate how even misspecified parametric statistical methods, if attuned to the important characteristics of internal variability, can result in more accurate uncertainty statements about trends.

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32

Tol, Richard S. J. "Population and trends in the global mean temperature." Atmósfera 30, no. 2 (2017): 121–35. http://dx.doi.org/10.20937/atm.2017.30.02.04.

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33

Hsiung, Jane. "Mean surface energy fluxes over the global ocean." Journal of Geophysical Research 91, no. C9 (1986): 10585. http://dx.doi.org/10.1029/jc091ic09p10585.

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34

Miller, J. A. "Washington Watch: Does coral bleaching mean global warming?" BioScience 41, no. 2 (1991): 77. http://dx.doi.org/10.1093/bioscience/41.2.77.

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35

Eweda, Eweda. "Global Stabilization of the Least Mean Fourth Algorithm." IEEE Transactions on Signal Processing 60, no. 3 (2012): 1473–77. http://dx.doi.org/10.1109/tsp.2011.2177976.

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36

Burki, Talha Khan. "What will COP21 mean for global respiratory health?" Lancet Respiratory Medicine 4, no. 2 (2016): 96. http://dx.doi.org/10.1016/s2213-2600(15)00541-x.

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37

Le, Q., J. P. Franc, and J. M. Michel. "Partial Cavities: Global Behavior and Mean Pressure Distribution." Journal of Fluids Engineering 115, no. 2 (1993): 243–48. http://dx.doi.org/10.1115/1.2910131.

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The results of an experimental work concerning the behavior of flows with partial cavities are presented. The tests were carried out using a plano-convex foil placed in the free surface channel of the I.M.G. Hydrodynamic Tunnel. The experimental conditions concerning ambient pressure, water velocity, and body size were such that various and realistic kinds of flows could be realized. The main flow regimes are described and correlated to the values of foil incidence and cavitation parameter. Attention is paid to the shedding of large vapor pockets into the cavity wake and its possible periodic character. Aside from classical consideration to the cavity length and shedding frequency in the periodic regime, results concerning the wall pressure distribution in the rear part of the cavity are given. They lead to distinguish thin, stable, and closed cavities from the thick ones in which the reentrant jet plays a dominant role for the shedding of vortical structures and the flow unsteadiness.

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38

Natarov, Svetlana I., Mark A. Merrifield, Janet M. Becker, and Phillip R. Thompson. "Regional influences on reconstructed global mean sea level." Geophysical Research Letters 44, no. 7 (2017): 3274–82. http://dx.doi.org/10.1002/2016gl071523.

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39

Kaczkowski, Stephen. "Mathematical Models for Global Mean Sea Level Rise." College Mathematics Journal 48, no. 3 (2017): 162–69. http://dx.doi.org/10.4169/college.math.j.48.3.162.

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40

Piecuch, Christopher G., and Rui M. Ponte. "Mechanisms of Global-Mean Steric Sea Level Change." Journal of Climate 27, no. 2 (2014): 824–34. http://dx.doi.org/10.1175/jcli-d-13-00373.1.

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Abstract Global-mean sea level change partly reflects volumetric expansion of the oceans because of density change, otherwise known as global-mean steric sea level change. Owing to nonlinearities in the equation of state of seawater, the nature of processes contributing to recent observed global-mean steric sea level changes has not been well understood. Using a data-constrained ocean state estimate, global-mean steric sea level change over 1993–2003 is revisited, and contributions from ocean transports and surface exchanges are quantified using closed potential temperature and salinity budgets. Analyses demonstrate that estimated decadal global-mean steric sea level change results mainly from a slight, time-mean imbalance between atmospheric forcing and ocean transports over the integration period: surface heat and freshwater exchanges produce a trend in global-mean steric sea level that is mainly offset by the redistribution of potential temperature and salinity through small-scale diffusion and large-scale advection. A set of numerical experiments demonstrates that global-mean steric sea level changes simulated by ocean general circulation models are sensitive to the regional distribution of ocean heat and freshwater content changes.

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41

Pangburn, Dan. "Influence of Sunspots on Global Mean Surface Temperature." Energy & Environment 25, no. 8 (2014): 1455–71. http://dx.doi.org/10.1260/0958-305x.25.8.1455.

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42

Mikhailov, G. A., and I. N. Medvedev. "Mean-square optimization for global Monte Carlo algorithms." Doklady Mathematics 82, no. 1 (2010): 514–18. http://dx.doi.org/10.1134/s1064562410040046.

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43

Roble, R. G., E. C. Ridley, and R. E. Dickinson. "On the global mean structure of the thermosphere." Journal of Geophysical Research 92, A8 (1987): 8745. http://dx.doi.org/10.1029/ja092ia08p08745.

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44

Wigley, T. M. L. "Interpretation of High Projections for Global-Mean Warming." Science 293, no. 5529 (2001): 451–54. http://dx.doi.org/10.1126/science.1061604.

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45

Xiao, Sinan, and Zhenzhou Lu. "Global sensitivity analysis based on Gini’s mean difference." Structural and Multidisciplinary Optimization 58, no. 4 (2018): 1523–35. http://dx.doi.org/10.1007/s00158-018-1982-7.

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46

Ma, L. H., Y. B. Han, and Z. Q. Yin. "Periodicities in Global Mean TEC from GNSS Observations." Earth, Moon, and Planets 105, no. 1 (2008): 3–10. http://dx.doi.org/10.1007/s11038-008-9242-2.

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47

Korevaar, Nick, and Rob Kusner. "The global structure of constant mean curvature surfaces." Inventiones Mathematicae 114, no. 1 (1993): 311–32. http://dx.doi.org/10.1007/bf01232673.

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48

Coumou, Dim, Alexander Robinson, and Stefan Rahmstorf. "Global increase in record-breaking monthly-mean temperatures." Climatic Change 118, no. 3-4 (2013): 771–82. http://dx.doi.org/10.1007/s10584-012-0668-1.

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49

Mulet, Sandrine, Marie-Hélène Rio, Hélène Etienne, et al. "The new CNES-CLS18 global mean dynamic topography." Ocean Science 17, no. 3 (2021): 789–808. http://dx.doi.org/10.5194/os-17-789-2021.

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Abstract. The mean dynamic topography (MDT) is a key reference surface for altimetry. It is needed for the calculation of the ocean absolute dynamic topography, and under the geostrophic approximation, the estimation of surface currents. CNES-CLS mean dynamic topography (MDT) solutions are calculated by merging information from altimeter data, GRACE, and GOCE gravity field and oceanographic in situ measurements (drifting buoy velocities, hydrological profiles). The objective of this paper is to present the newly updated CNES-CLS18 MDT. The main improvement compared to the previous CNES-CLS13 solution is the use of updated input datasets: the GOCO05S geoid model is used based on the complete GOCE mission (November 2009–October 2013) and 10.5 years of GRACE data, together with all drifting buoy velocities (SVP-type and Argo floats) and hydrological profiles (CORA database) available from 1993 to 2017 (instead of 1993–2012). The new solution also benefits from improved data processing (in particular a new wind-driven current model has been developed to extract the geostrophic component from the buoy velocities) and methodology (in particular the computation of the medium-scale GOCE-based MDT first guess has been revised). An evaluation of the new solution compared to the previous version and to other existing MDT solutions show significant improvements in both strong currents and coastal areas.

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50

Paeth, Heiko, and Andreas Hense. "Mean versus extreme climate in the Mediterranean region and its sensitivity to future global warming conditions." Meteorologische Zeitschrift 14, no. 3 (2005): 329–47. http://dx.doi.org/10.1127/0941-2948/2005/0036.

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Journal articles on the topic 'Global mean'

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