Observational determination of the greenhouse effect

Raval, Abid; Ramanathan, V.

doi:10.1038/342758a0

Cited by 438 publications

(262 citation statements)

References 15 publications

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“…In that investigation the enhancement was due to water-vapor feedback; i.e., as the climate warms the atmosphere contains more water vapor and that amplifies the warming, since water vapor is itself a greenhouse gas. Recently, Raval and Ramanathan [1989] have employed satellite data to quantify this positive feedback, and the present GCM simulations are consistent with this observational study [Cess, 1989]. The important point is that the 19 models produce closely comparable and observationally consistent clear-sky sensitivity parameters.…”

Section: Discussionsupporting

confidence: 56%

Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models

et al. 1990

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The need to understand differences among general circulation model projections of CO2-induced climatic change has motivated the present study, which provides an intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. This intercomparison uses sea surface temperature change as a surrogate for climate change. The interpretation of cloud-climate interactions is given special attention. A roughly threefold variation in one measure of global climate sensitivity is found among the 19 models. The important conclusion is that most of this variation is attributable to differences in the models' depiction of cloud feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as reliable climate predictors. It is further emphasized that cloud feedback is the consequence of all interacting physical and dynamical processes in a general circulation model. The result of these processes is to produce changes in temperature, moisture distribution, and clouds which are integrated into the radiative response termed cloud feedback. INTRODUCTIONProjected increases in the concentration of atmospheric carbon dioxide and other greenhouse gases are expected to have an important impact on climate. The most comprehensive way to infer future climatic change associated with this perturbation of atmospheric composition is by means of three-dimensional general circulation models (GCMs). Schlesinger and Mitchell [1987] have, however, demonstrated that several existing GCMs simulate climate responses to increasing CO2 that differ considerably. Cess and Potter [1988], following a suggestion by Speltnan and Manabe [1984], indicate that differences in global-mean warming, The global-mean direct radiative forcing G of the surfaceatmosphere system is evaluated by holding all other climate parameters fixed. It is this quantity that induces the ensuing climate change, and physically, it represents a change in the net (solar plus infrared) radiative flux at the top of the atmosphere (TOA). For an increase in the CO2 concentration of the atmosphere, to cite one example, G is the reduction in the emitted TOA infrared flux resulting solely from the CO2 increase, and this reduction results in a heating of the surface-atmosphere system. The response process is the change in climate that is then necessary to restore the TOA radiation balance, such that that is either too warm or too cold, then it will respectively produce a climate sensitivity parameter that is too small or too large, and clearly, the intercomparison simulation had to be designed to eliminate this effect. There was also a practical constraint: the CO2 simulations require large amounts of computer time for equilibration of the rather primitive ocean models that have been used in these numerical experiments.An attractive alternative that eliminated both of the above mentioned difficulties was to adopt +_2øK sea surface temperature ( The perpetual July simulation e...

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Section: Discussionsupporting

confidence: 56%

Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models

et al. 1990

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“…The dynamics associated with convective regime [Inamdar and Ramanathan, 1994] contributes to the strong moistening shown in the bottom panel of Figure 7. This is also the same regime (in T s ) experiencing the super greenhouse effect [Raval and Ramanathan, 1989] whereby an increase in T s beyond 297 K, leads to an increase in G a that exceeds that due to an increase in the black body surface emission. The rate of increase of downwelling radiation from the atmosphere to the surface is even larger explaining the sharp increase in the column radiative cooling in the window, which is dominated by the net flux divergence at the surface (middle panel of Figure 7).…”

Section: Interactions Between Column Radiative Cooling Surface Tempementioning

confidence: 95%

“…[21] We consider two basic quantities: (1) G a , which is the net reduction in the outgoing LW flux due to the presence of the atmosphere [Raval and Ramanathan, 1989] and is the difference between the upward flux at the surface and the TOA LW flux, and (2) G* a , the downwelling radiation emitted by the atmosphere to the INAMDAR ET AL. : WATER VAPOR GREENHOUSE EFFECT surface.…”

Section: Physics Of the Window And Nonwindow Fluxes And Column Coolinmentioning

confidence: 99%

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Satellite observations of the water vapor greenhouse effect and column longwave cooling rates: Relative roles of the continuum and vibration‐rotation to pure rotation bands

2004

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[1] The Clouds and the Earth's Radiant Energy System (CERES) instrument on board the Tropical Rainfall Measuring Mission (TRMM) satellite provides, for the first time, a largescale (40 S to 40 N) data set for the atmospheric greenhouse effect and the columnaveraged longwave (LW) radiative cooling rates in the broadband (5-100 microns) and the window (8-12 microns) regions. We demonstrate here that the separation into the window and the nonwindow (5-8 microns and 12-100 microns) fluxes provides the first global-scale data set to exhibit the sensitivity of the atmospheric greenhouse effect to vertical water vapor distribution. The nonwindow greenhouse effect varies linearly with the logarithm of column water vapor amount weighted with the atmospheric pressure, while the window component varies quadratically with the water vapor partial pressure. The column cooling rates range from about À170 to À210 W m À2 for the nonwindow region. The window cooling rates are only about 10% to 20% of the above range and approach rapidly to near-zero values for surface temperatures less than 288 K. The nonwindow component of the greenhouse effect and cooling rates are shown to be more sensitive to upper troposphere water vapor, while the window greenhouse effect and cooling rates are shown to be more sensitive to the lower troposphere water vapor amount. In addition, the data reveal that in tropical regions, with warm sea surface temperatures (greater than 297 K) and elevated upper tropospheric water vapor amounts, the continuum emission in the window region leads to enhanced cooling of the column, while the rotational bands in the nonwindow region lead to a net decrease in the longwave cooling of the atmospheric column.

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“…It is generally believed that the water vapor feedback amplifies global warming resulting from an increase in CO2. This positive feedback is due to stronger convection and more water vapor in the warmer troposphere, including the upper troposphere [Raval and Ramanathan, 1989;Betts, 1990;Rind et al, 1991;Soden and Fu, 1995]. Lindzen [1990], however, has proposed a negative water vapor feedback possibly induced by enhanced compensatory subsidence associated with stronger convection.…”

Section: Introductionmentioning

confidence: 99%

Seasonal variations of upper tropospheric water vapor and high clouds observed from satellites

Chen¹,

Rood²,

Read³

1999

J. Geophys. Res.

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Observational determination of the greenhouse effect

Cited by 438 publications

References 15 publications

Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models

Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models

Satellite observations of the water vapor greenhouse effect and column longwave cooling rates: Relative roles of the continuum and vibration‐rotation to pure rotation bands

Seasonal variations of upper tropospheric water vapor and high clouds observed from satellites

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