Increased atmospheric carbon dioxide and climate feedback mechanisms

As a consequence of fossil fuel burning, the atmospheric concentration of carbon dioxide has increased from 314 ppm in 1958, when detailed measurements of this quantity began, to a present value of 335 ppm; and it is estimated that during the next century, the CO2 concentration will double relative to its assumed preindustrial value of 290 ppm. Since CO2 is an infrared-active gas, increases in its atmospheric concentration would lead to a larger infrared opacity for the atmospheric which, by normal logic, would result in a warmer Earth. A number of modeling endeavors suggest a 2 to 4 C increase in global mean surface temperature with doubling of the CO2 concentration. But such estimates of CO2-induced warming are highly uncertain because of a lack of knowledge of climate feedback mechanisms. Interactive influences upon the solar and infrared opacities of the Earth-atmosphere system can either amplify or damp a climate-forcing mechanism such as increasing CO2. Climate feedback mechanisms discussed include climate sensitivity, cloudiness-radiation feedback, climate change predictions, and interactive atmospheric chemistry

Small global-mean cooling due to volcanic radiative forcing

In both the observational record and atmosphere-ocean general circulation model (AOGCM) simulations of the last ∼∼ 150 years, short-lived negative radiative forcing due to volcanic aerosol, following explosive eruptions, causes sudden global-mean cooling of up to ∼∼ 0.3 K. This is about five times smaller than expected from the transient climate response parameter (TCRP, K of global-mean surface air temperature change per W m−2 of radiative forcing increase) evaluated under atmospheric CO2 concentration increasing at 1 % yr−1. Using the step model (Good et al. in Geophys Res Lett 38:L01703, 2011. doi:10.​1029/​2010GL045208), we confirm the previous finding (Held et al. in J Clim 23:2418–2427, 2010. doi:10.​1175/​2009JCLI3466.​1) that the main reason for the discrepancy is the damping of the response to short-lived forcing by the thermal inertia of the upper ocean. Although the step model includes this effect, it still overestimates the volcanic cooling simulated by AOGCMs by about 60 %. We show that this remaining discrepancy can be explained by the magnitude of the volcanic forcing, which may be smaller in AOGCMs (by 30 % for the HadCM3 AOGCM) than in off-line calculations that do not account for rapid cloud adjustment, and the climate sensitivity parameter, which may be smaller than for increasing CO2 (40 % smaller than for 4 × CO2 in HadCM3)

Clouds and the Earth's Radiant Energy System (CERES) algorithm theoretical basis document

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and the Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 1 provides both summarized and detailed overviews of the CERES Release 1 data analysis system. CERES will produce global top-of-the-atmosphere shortwave and longwave radiative fluxes at the top of the atmosphere, at the surface, and within the atmosphere by using the combination of a large variety of measurements and models. The CERES processing system includes radiance observations from CERES scanning radiometers, cloud properties derived from coincident satellite imaging radiometers, temperature and humidity fields from meteorological analysis models, and high-temporal-resolution geostationary satellite radiances to account for unobserved times. CERES will provide a continuation of the ERBE record and the lowest error climatology of consistent cloud properties and radiation fields. CERES will also substantially improve our knowledge of the Earth's surface radiation budget

Cloud feedback in atmospheric general circulation models: An update

Six years ago, we compared the climate sensitivity of 19 atmospheric general circulation models and found a roughly threefold variation among the models; most of this variation was attributed to differences in the models' depictions of cloud feedback. In an update of this comparison, current models showed considerably smaller differences in net cloud feedback, with most producing modest values. There are, however, substantial differences in the feedback components, indicating that the models still have physical disagreements

Cloud inhomogeneity and broadband solar fluxes

Simplified representations of spatially inhomogeneous (three-dimensional (3-D)) clouds in radiative transfer models provide systematic errors when calculating solar broadband radiative fluxes. An example is the neglect of horizontal photon transports as it is the case for the independent column approximation (ICA). The present work tries to quantify and interpret these errors on the basis of a large set of 3-D mixed phase cloud scenarios with 3-D varying extinction coefficients, scattering phase functions, and single-scattering albedos. The cloud cases result from a mesoscale atmospheric circulation model with detailed cloud microphysics. Domain-averaged cloud radiative fluxes are calculated by means of a Monte Carlo radiative transfer model. Depending on cloud type and solar zenith angle (SZA) the differences between 3-D and ICA results range from +20 W m−2 to −30 W m−2 for the upward reflected fluxes and from +10 W m−2 to −7 W m−2 for the absorbed fluxes. The mean (averaged over all cloud realizations) errors of the ICA-based upward fluxes vary between 5 W m−2 overestimation at 15°SZA and 6 W m−2 underestimation at 75°SZA. The ICA underestimates the absorbed flux by ∼1–2 W m−2 for most SZA except for 75°. It is found that neglecting the horizontal variability of the absorption and scattering properties of the cloud hydrometeors leads to a general underestimation of solar broadband absorption by as much as 15 W m−2 with average values between 4 W m−2 at small SZA and 1 W m−2 at large SZA

On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles

Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO 2 doubling climate change experiments, from the CFMIP (Cloud Feedback Model Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level cloud amount) contribute most to this figure in the CFMIP ensemble, while areas of negative cloud feedback (associated with increases in low level cloud amount and optical thickness) contribute most in QUMP. Classes associated with high-top cloud feedbacks are responsible for 33 and 20% of the cloud feedback contribution in CFMIP and QUMP, respectively, while classes where no particular cloud type stands out are responsible for 8 and 21%.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45863/1/382_2006_Article_111.pd