Indirect forcing

From Earth Science Information Partners (ESIP)

AeroCom wiki discussion entry

Go back to AeroCom/Working group structure

See also summary of AeroCom/Recommendations

AeroCom working group Indirect forcing

Status

An AEROCOM-IND model intercomparison has taken place under lead of Joyce Penner in 2004/2005. Fourteen simulations have been carried out with each model, each one for five years with the atmospheric GCM, for seven model configurations under present-day and pre-industrial aerosol emission scenarios. The seven configurations varied the degree of freedom of the individual models for the aerosol indirect effect from a common treatment of all relevant parametrisations to individual treatments of all (see [Protocol] for details). Three modelling groups participated (U Oslo, Trude Storelvmo; U Kyushu, Toshi Takemura; LMD Paris, Johannes Quaas). The main result was that the different treatments of aerosol life cycles and of the parameterizations of CDNC and of autoconversion rate lead to large inter-model differences in the simulated all-sky short-wave radiative effects of anthropogenic aerosols, while the influence of the differently simulated cloud fields and aerosol direct effects is minor (Penner et al., 2006).

At the 2004 AEROCOM meeting where the AEROCOM-IND experiment has been proposed, 14 groups expressed interest in participating. Only one out of these, plus two others, actually contributed. This fact, and the experience of the three participating groups, suggest that the requirements were possibly too demanding.


Ideas for a new initiative

  • Focus on the cloud albedo indirect effect for low-level liquid water clouds (models may include other effects, but evaluation would focus on the cloud albedo effect)
  • Use of cloud-aerosol statistical relationships as metrics of the aerosol indirect effect
  • Propose few additional model diagnostics for comparison to data-tied constraints


At present, various aerosol indirect effects have been proposed. These include the cloud albedo effect (increase in CDNC at constant cloud liquid water content), the cloud lifetime effect (delay of collision/coalescence processes due to smaller CDR, thus enhancement of cloud cover and cloud liquid water path) and the semi-direct effect (evaporation of cloud water or inhibition of cloud formation due to warming or stabilisation of the atmosphere due to absorption of solar radiation). So far, these effects have mostly been studied for low-level liquid water clouds. More recently, effects on convective clouds, ice- and mixed-phase clouds have been proposed, too (Lohmann and Feichter, 2005). Modelling studies suggest that effects of anthropogenic aerosols on ice- and mixed-phase clouds are small compared to the ones on liquid water clouds (Lohmann et al., 2007). However, the former effects are even more uncertain than the latter ones. Models also suggest that the semi-direct effect is smaller than the cloud albedo and cloud lifetime effects (Lohmann and Feichter, 2001). The cloud albedo effect is a prerequisite for the cloud lifetime effect. For these reasons, the proposed initiative should focus on the cloud albedo effect on low-level liquid water clouds as the probably most fundamental and best-understood one.

Parametrizations of the cloud albedo effect (in the following just called the aerosol indirect effect, AIE) for GCMs have been derived usually from aircraft data (e.g., Boucher and Lohmann, 1995; Lin and Leaitch, 1997). Comparisons of satellite-derived statistics and GCM simulations show that the parametrisations derived at the small scale are not suitable to be applied directly at the large GCM grid scale making adjustments and evaluation necessary (Lohmann et al., 2007).

The AIE can be defined as

Eq. 1 <math>AIE=\frac{\partial\ln r_e}{\partial\ln\tau_a}</math>

with <math>r_e</math> the CDR and <math>\tau_a</math> the AOD (Feingold et al., 2003). In this way, the AIE has been derived from satellite data (Nakajima et al., 2001; Bréon et al., 2002; Sekiguchi et al., 2003; Quaas et al., 2004; Kaufman et al., 2005; Quaas and Boucher, 2005) and from ground-based remote sensing data (Feingold et al., 2003). An even more appropriate proxy for the AIE might be the relationship between CDNC and AOD as proposed by Quaas et al. (2006). Assuming adiabaticity for low-level liquid-water clouds, CDNC can be derived from satellite data. Such satellite-derived relationships have served in a few studies with single GCMs to examine parameterizations of the AIE (Lohmann and Lesins, 2002; Sekiguchi et al., 2003; Quaas et al., 2004; Quaas and Boucher, 2005; Storelvmo et al., 2006; Quaas et al., 2006).

These AIE metrics are available from POLDER (CDR – aerosol index) and MODIS (CDR – AOD, CDNC – AOD), where MODIS retrievals from NASA-LaRC (Minnis et al., 2004) and NASA-Goddard (Platnick et al., 2003) are both used. MODIS-derived statistics are available at a regional and seasonal basis. As part of the proposed initiative, the metrics could be derived from other satellite sensors and ground-based remote sensing as well.

Model diagnostics requirements

Minimum

  • AOD
  • Cloud-top droplet effective radius for low-level liquid water clouds
  • Cloud droplet number concentration for low-level liquid water clouds
  • Cloud fraction
  • Fractional coverage by low-level liquid water clouds
  • Cloud liquid water path for low-level liquid water clouds
  • Planetary albedo
  • SW and LW ToA radiative fluxes
  • SW and LW cloud-free ToA radiative fluxes
  • Cloud-top temperature
  • Potential temperature @ 700 hPa and surface
  • Total IWP
  • Total LWP
  • Cloud-top ice crystal radius
  • Cloud-top droplet effective radius for all liquid clouds


Recommended

  • ISCCP simulator output
  • CICCS simulator output
  • CCN number concentration at cloud base (or in-cloud CCN concentration)
  • Aerosol mass concentration at cloud base (or in-cloud aerosol concentration)


CICCS: CFMIP ISCCP Calipso-Cloudsat Simulator (CFMIP: cloud forcing model intercomparison project, lead by Mark Webb, UK Met Office; ISCCP: International Cloud Climatology Project) available as beta release spring 2008 from the UK Met Office (via AEROCOM).


Output is needed as daily averages. Ideally, the overpass time of the polar-orbiting satellite is sampled to provide a diurnal coverage. Simple codes for the cloud-top sampling and for the sampling of the satellite overpass can be provided.


Modelling groups which expressed interest to participate

  • Georgia Tech (Thanos Nenes)
  • ETH Zurich (Trude Storelvmo)
  • U Oslo (Jón-Egill Kristjánsson)
  • NCAR (Andrew Gettelman)
  • U Leeds (David Ridley)
  • LSCE/IPSL (Yves Balkanski)
  • UK Met Office (Nicolas Bellouin)
  • MPI Hamburg (Johannes Quaas)


References

Boucher, O., and U. Lohmann, The sulfate-CCN-cloud albedo effect - a sensitivity study with two general circulation models, Tellus, 47B, 281-300, 1995.

Bréon, F.-M., D. Tanré and S. Generoso, Aerosol effect on cloud droplet size monitored from satellite, Science, 295, 834 – 838, 2002.

Feingold, G., W. L. Eberhard, D. E. Veron, and M. Previdi, First measurements of the Twomey indirect effect using ground-based remote sensors, Geophys. Res. Lett., 30(6), 1287, doi:10.1029/2002GL016633, 2003.

Kaufman, Y. J., L. A. Remer, D. Tanré, R.-R. Li, R. Kleidman, S. Mattoo, R. Levy, T. Eck, B. N. Holben, C. Ichoku, V. Martins, and I. Koren, A critical examination of the residual cloud contamination and diurnal sampling effects on MODIS estimates of aerosol over ocean, Proc. Natl. Acad. Sci., 102, 11207-11212, 2005.

Lin, H, and W. R. Leaitch: Development of an in-cloud aerosol activation parameterization for climate modelling. Proceedings of the WMO Workshop on Measurement of Cloud Properties for Forecasts of Weather, Air Quality and Climate, Mexico City, June, pp. 328-335, 1997.

Lohmann, U. and J. Feichter, Can the direct and semi-direct aerosol effect compete with the indirect effect on a global scale? Geophys. Res. Lett., 28, 159-161, 2001.

Lohmann, U., and G. Lesins, Stronger constraints on the anthropogenic indirect aerosol effect, Science, 298, 1012-1015, 2002.

Lohmann, U., and J. Feichter, Global indirect aerosol effects: A Review, Atmos. Chem. Phys., 5, 715-737, 2005.

Lohmann, U., J. Quaas, S. Kinne, and J. Feichter, Different approaches for constraining global climate models of the anthropogenic indirect aerosol effect, Bull. Am. Meteorol. Soc., 88, 243–249, 2007.

Minnis, P., D. F. Young, S. Sun-Mack, P. W. Heck, D. R. Doelling, and Q. Z. Trepte, CERES cloud property retrievals from imagers on TRMM, Terra, and Aqua, Proc. SPIE 10th International Symposium on Remote Sensing: Conference on Remote Sensing of Clouds and the Atmosphere VII, 37-48, 5235, Barcelona, Spain, 8-12 September 2004.

Nakajima, T., A. Higurashi, K. Kawamoto, and J. E. Penner: A possible correlation between satellite-derived cloud and aerosol microphysical parameters, Geophys. Res. Lett., 28, 1171-1174, 2001.

Penner, J., J. Quaas, T. Storelvmo, T. Takemura, O. Boucher, H. Guo, A. Kirkevåg, J. E. Kristjánsson, and Ø. Seland, Model intercomparison of indirect aerosol effects, Atmos. Chem. Phys., 6, 3391-3405, SRef-ID: 1680-7324/acp/2006-6-3391, 2006.

Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riédi et R. A. Frey, The MODIS Cloud Products: Algorithms and Examples from Terra, IEEE Transactions on Geoscience and Remote Sensing, 41, 459-473, 2003.

Quaas, J., O. Boucher and F.-M. Bréon, Aerosol indirect effects in POLDER satellite data and in the LMDZ GCM, J. Geophys. Res., 109, D08205, doi:10.1029/2003JD004317, 2004.

Quaas, J., and O. Boucher, Constraining the first aerosol indirect radiative forcing in the LMDZ GCM using POLDER and MODIS satellite data, Geophys. Res. Lett., 32, L17814, doi:10.1029/2005GL023850, 2005.

Quaas, J., O. Boucher and U. Lohmann, Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data, Atmos. Chem. Phys., 6, 947-955, 2006.

Sekiguchi, M., T. , K. Suzuki, K. Kawamoto, A. Higurashi, D. Rosenfeld, I. Sano, and S. Mukai: A study of the direct and indirect effects of aerosols using global satellite data sets of aerosol and cloud parameters, J. Geophys. Res., 108(D22), 4699, doi:10.1029/2002JD003359, 2003.

Storelvmo, T., J. E. Kristjansson, G. Myhre, M. Johnsrud, and F. Stordal, Combined observational and modeling based study of the aerosol indirect effect., Atmos. Chem. Phys., 6, 3583-3601, 2006.