Abstract
Weather disturbances are the manifestation of mean atmospheric energy cascading into eddies, thus identifying atmospheric energy structure is of fundamental importance to understand the weather variability in a changing climate. The question is whether our observational data can lead to a consistent diagnosis on the energy conversion characteristics. Here we investigate the atmospheric energy cascades by a simple framework of Lorenz energy cycle, and analyze the energy distribution in mean and eddy fields as forms of potential and kinetic energy. It is found that even the widely utilized independent reanalysis datasets, NCEP-DOE AMIP-II Reanalysis (NCEP2) and ERA-Interim (ERA-INT), draw different conclusions on the change of weather variability measured by eddy-related kinetic energy. NCEP2 shows an increased mean-to-eddy energy conversion and enhanced eddy activity due to efficient baroclinic energy cascade, but ERA-INT shows relatively constant energy cascading structure between the 1980s and the 2000s. The source of discrepancy mainly originates from the uncertainties in hydrological variables in the mid-troposphere. Therefore, much efforts should be made to improve mid-tropospheric observations for more reliable diagnosis of the weather disturbances as a consequence of man-made greenhouse effect.
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References
Bond NA, Overland JE, Spillane M, Stabeno P (2003) Recent shifts in the state of the North Pacific. Geophy Res Lett 30(23):2183. doi:10.1029/2003GL018597
Dai A, Fyfe JC, Xie S-P, Dai X (2015) Decadal modulation of global surface temperature by internal climate variability Nature. Clim Change 5:555–559
Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597
Fischer EM, Knutti R (2015) Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes Nature. Clim Change 5:560–564
Gettelman A, Collins WD, Fetzer EJ, Eldering A, Irion FW, Duffy PB, Bala G (2006) Climatology of upper-tropospheric relative humidity from the atmospheric infrared sounder and implications for climate. J Clim 19:6104–6121
Held IM, Soden BJ (2006) Robust responses of the hydrological cycle to global warming. J Clim 19:5686–5699
Holland MM, Bitz CM (2003) Polar amplification of climate change in coupled models. Clim Dyn 21:221–232
Holton JR, Hakim GJ (2012) An introduction to dynamic meteorology, vol 88. Academic press, Boston
Hoskins BJ, Draghici I, Davies HC (1978) A new look at the ω-equation. Q J R Meteorol Soc 104:31–38
Kanamitsu M, Ebisuzaki W, Woollen J, Yang SK, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP-DOE AMIP-II reanalysis (R-2). Bull Am Meteorol Soc 83:1631–1644
Kang SM, Lu J (2012) Expansion of the Hadley cell under global warming: winter versus summer. J Clim 25:8387–8393
Kim Y-H, Kim M-K (2013) Examination of the global Lorenz energy cycle using MERRA and NCEP-reanalysis 2. Clim Dyn 40:1499–1513
Kjellsson J (2014) Weakening of the global atmospheric circulation with global warming. Clim Dyn 45:975–988
Kosaka Y, Xie S-P (2013) Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501:403–407
Lee S (2014) A theory for polar amplification from a general circulation perspective Asia-Pacific. J Atmos Sci 50:31–43
Li L, Ingersoll AP, Jiang X, Feldman D, Yung YL (2007) Lorenz energy cycle of the global atmosphere based on reanalysis datasets. Geophys Res Lett 34:L16813
Lorenz EN (1955) Available potential energy and the maintenance of the general circulation. Tellus 7:157–167
Lu J, Vecchi GA, Reichler T (2007) Expansion of the Hadley cell under global warming Geophy Res Lett 34(6):L06805. doi:10.1029/2006GL028443
Marques CAF, Rocha A, Corte-Real J, Castanheira JM, Ferreira J, Melo-Gonçalves P (2009) Global atmospheric energetics from NCEP–Reanalysis 2 and ECMWF–ERA40 Reanalysis. Int J Climatol 29:159–174
Marques CAF, Rocha A, Corte-Real J (2010) Comparative energetics of ERA-40, JRA-25 and NCEP-R2 reanalysis, in the wave number domain. Dyn Atmos Oceans 50:375–399
Marques CAF, Rocha A, Corte-Real J (2011) Global diagnostic energetics of five state-of-the-art climate models. Clim Dyn 36:1767–1794
Miloshevich LM, Vömel H, Whiteman DN, Lesht BM, Schmidlin FJ, Russo F (2006) Absolute accuracy of water vapor measurements from six operational radiosonde types launched during AWEX-G and implications for AIRS validation. J Geophy Res 111:D09S10. doi:10.1029/2005jd006083
Murakami S (2011) Atmospheric local energetics and energy interactions between mean and eddy fields. Part I Theory J Atmos Sci 68:760–768
Murakami S, Ohgaito R, Abe-Ouchi A (2010) Atmospheric local energetics and energy interactions between mean and eddy fields. Part II An example for the last glacial maximum climate. J Atmos Sci 68:533–552
Oort AH (1964) On Estimates of the atmospheric energy cycle. Mon Weather Rev 92:483–493
Oort AH (1983) Global atmospheric circulation statistics, 1958–1973. vol 14. US Department of Commerce, National Oceanic and Atmospheric Administration
Paltridge G, Arking A, Pook M (2009) Trends in middle- and upper-level tropospheric humidity from NCEP reanalysis data. Theor Appl Climatol 98:351–359
Peixóto JP, Oort AH (1974) The annual distribution of atmospheric energy on a planetary scale. J Geophys Res 79:2149–2159
Rahmstorf S, Coumou D (2011) Increase of extreme events in a warming world. Proc Natl Acad Sci 108:17905–17909
Stocker T et al (2013) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change
Trenberth KE, Fasullo JT (2013) An apparent hiatus in global warming? Earth’s Future 1:19–32
Vecchi GA, Soden BJ (2007) Global warming and the weakening of the tropical circulation. J Clim 20:4316–4340
Watanabe M, Shiogama H, Tatebe H, Hayashi M, Ishii M, Kimoto M (2014) Contribution of natural decadal variability to global warming acceleration and hiatus Nature. Clim Change 4:893–897
Yeo S-R, Yeh S-W, Kim K-Y, Kim W (2016) The role of low-frequency variation in the manifestation of warming trend and ENSO amplitude. Clim Dyn 1–17. doi:10.1007/s00382-016-3376-0
Acknowledgements
This study was supported by RP-Grant 2015 of Ewha Womans University, Korea and by “Development of cloud algorithms” project, funded by ETRI, which is a subproject of “Development of Geostationary Meteorological Satellite Ground Segment (NMSC-2016-01)” program funded by NMSC of KMA. W. Kim acknowledges the support from the APEC Climate Center. Y.-S. Choi is supported by Jet Propulsion Laboratory, California Institute of Technology.
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Appendices
Appendix 1: governing equations
We start from a set of primitive equations for non-divergent hydrostatic dry air:
where \(\lambda\), \(\varphi\), \(p\), and \(t\) is longitude, latitude, pressure, and time, respectively; \(U=\left(u,v,w\right)\) is 3-dimensional wind velocity, \(\theta\) is potential temperature (\(={\left({p}_{0}/p\right)}^{\kappa }T\)), \(\Phi\) is geopotential height, \(\alpha\) is specific volume; \(a\) is the radius of the earth, \(f\) is the Coriolis parameter, \(F=\left({F}_{\lambda },{F}_{\varphi },0\right)\) is friction, \(Q\) is diabatic heating, \({p}_{0}\) is reference pressure (1000 hPa). All other notations follow general form.
Appendix 2: time-averaged energy terms
Monthly means of available potential energy and kinetic energy are expressed as follows:
where \(\left\langle X \right\rangle\) denotes global averages over a given pressure level and \(\gamma =-\kappa /p{{\left( {{p}_{0}}/p \right)}^{\kappa }}{{\left( d\left\langle {\bar{\theta }} \right\rangle /dp \right)}^{-1}}\) is the static stability of the dry atmosphere. P and K denote available potential energy and kinetic energy, while subscripts M and E indicate mean and eddy energy, respectively. It can be inferred from the equation that the available potential energy is a function of temperature deviation from its surrounding, so that relatively warmer (colder) air can potentially induce air motion. Due to strong hydrostatic constraints kinetic energy is solely expressed by the horizontal winds.
Appendix 3: time-averaged conversion terms
Conversion of energy C(A, B) from A to B is calculated as
The conversion of available potential energy to kinetic energy [positive C(P M , K M ) or C(P E , K E )] results when relatively warm air (positive \({\alpha }=\frac{RT}{p}\) rises (negative \(\omega\)). The transfer of mean energy into (from) eddy energy is split into two parts that incorporate interaction term whose appropriate time mean is zero. Here, the subscript I stands for interaction energy that links between the mean energy and the eddy energy, but it disappears after monthly averaging. A detailed discussion of the interaction energy can be found in Murakami (2011). Regarding the calculation of energy conversion between the eddy terms, C(P E , K E ), the \(\omega \bullet \alpha\) formula is utilized to focus on the thermodynamic aspect of energy conversion (Kim and Kim 2013).
Appendix 4: time-averaged generation, dissipation, and boundary flux terms
Generation, dissipation, and boundary fluxes are as follows. In this study, however, generation terms were not calculated explicitly, but as a residual of fluxes. As the following terms are highly sensitive to reanalysis configuration, they are not extensively discussed in this research.
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Kim, W., Choi, YS. Long-term change of the atmospheric energy cycles and weather disturbances. Clim Dyn 49, 3605–3617 (2017). https://doi.org/10.1007/s00382-017-3533-0
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DOI: https://doi.org/10.1007/s00382-017-3533-0