CFMIP Phase 1 project details.

1. Introduction and Purpose
2. Scope and Approach
3. Principal Research Areas

   3.1International Satellite Cloud Climatology - Simulator

   3.2Model Experiments:
      3.2.1SST Perturbation Experiments
      3.2.2Equilibrium 1*CO2 and 2*CO2 Slab Ocean Experiments
      3.2.3Climate Change Experiments with cloud resolving models

   3.3Analysis Methodologies
      3.3.1Feedbacks
      3.3.2Clouds

4. Summary
5. References

1. Introduction and Purpose

Despite a decade of intense research on the role of water vapour and clouds in climate change, the Third Assessment Report of the IPCC concluded that “the sign of net cloud feedback is still a matter of uncertainty, and the various models exhibit a large spread” and also that “the balance of evidence favours a positive clear sky water vapour feedback of a magnitude comparable to that found in simulations”. Furthermore the IPCC recommended further work was needed to “understand and characterise more completely the dominant processes and feedbacks (e.g.from clouds and sea ice) in the atmosphere”.
Similarly at a recent World Climate Research Programme Workshop on Cloud Processes and Cloud feedbacks a conclusion was that “reducing the uncertainty in cloud-climate feedbacks is one of the toughest challenges facing atmospheric scientists”.

The anthropogenic climate change component of the CLIVAR Initial Implementation Plan (WCRP, 1998) called for a specially focussed research project to identify the mechanisms of climate change and understand the reasons for differences in model response. In response the JSC/CLIVAR Working Group on Climate Models is attempting to encourage coordinated research in the area of feedbacks in climate models. A previous attempt at intercomparing a large ensemble of models (Le Treut and McAvaney, 2000) was very restricted by the limited amount of diagnostic information available and hence had a very limited capacity to study the different cloud behaviour exhibited by the models.

The study of cloud feedbacks raises a number of scientific questions that need to be addressed systematically using both the current generation of climate models and cloud resolving models (CRMs) together with a close connection to observations. Some of the broad questions are:

• How well do current climate models simulate the distribution and behaviour of clouds?
• What is the relationship between feedbacks on different timescales (seasonal and longer)?
• What is the reason for the wide range in climate and hydrologic sensitivity in climate models?
• To what extent do the processes producing feedbacks in cloud resolving models resemble those in climate models?

This document contains a strategic plan of action, a set of specific proposals and some more detailed plans for a sub-set of the proposals. Strategic issues will depend on the assessment of scientific colleagues as to how well scientific questions are put for substantial progress to be made. In cases where specific proposals are made but implementation plans are not well advanced, further input from interested scientific parties is sought.

2. Scope and Approach

Cloud feedbacks in climate models can be evaluated in climate models through the two basic steps; (a) linking simulated feedbacks to the observational record (especially the cloud archives) or to results from cloud resolving models and (b) relating the spread in results from ocean-atmosphere models to the spread in feedbacks found in a hierarchy of simpler models (eg slab ocean, perturbed SST).

This requires a carefully crafted and interlocking set of intercomparisons that optimises the benefits from existing well designed intercomparisons (AMIP and CMIP), especially through their associated diagnostic sub-projects. Where necessary, additional new simulations are proposed in order to diagnose the components of cloud feedback in a more comprehensive manner and to relate those components to observations. The wider availability of ISCCP cloud observations and the associated ISCCP simulator have now made it possible to compare, in much more detail, the behaviour of clouds in different dynamical regimes in models against observations. The input from, and close association with, the clouds (GCSS) and radiation components (GRP) of GEWEX is obviously crucial. Because of the current highly evolved state of cloud resolving models input from, and collaboration with, the GCSS area is particularly important.

3. Principal Research Areas

Since systematic investigation of cloud feedbacks in climate models remains very much in its infancy only a few specific research areas have emerged. There have been some purely modelling based approaches but, until very recently, attempts to link such studies with observations have been very few. The remainder of this section attempts to outline some key research areas and point out the links between them (and other components of WCRP). Success in any of the areas presented below will depend on the level of commitment of resources from individual diagnostic and modelling groups to fully explore those details of models that are important for cloud feedback.

3.1 International Satellite Cloud Climatology - Simulator

The cloud information obtained from the International Satellite Climate Climatology Project (ISCCP; Rossow and Schiffer, 1991) is a very valuable source of information that has not, in general, been extensively used as part of standard model evaluation exercises because of problems in relating the clouds diagnosed within a model to those observed by the satellites. Tools now exist that, using model variables, produce clouds that are consistent with the satellite algorithms (Webb et al 2001). The ISCCP cloud simulator is now available from http: evaluation of model performance against both ISCCP and ERBE is then practical.

With “ISCCP clouds” available from the model it will be possible to compare the behaviour of various types of model clouds with observations under differing dynamical regimes. Differing dynamical regimes in the current climate may be a suitable proxy for climate change since, as the earth warms, cloud feedbacks may arise through a transition from one dynamically induced cloud regime to another. Confidence in the ability of models to simulate climate change will be increased if models correctly simulate the contrast between cloud regimes for the current climate. Tselioudis and Jakob (2001) have obtained interesting results for two different models using this approach. Bony et al (2003) have also performed an interesting comparison of cloud changes due to dynamic and thermodynamic components by compositing in different vertical velocity bins.

There are also a number of statistical relationships between cloud amount and albedo and cloud amount and outgoing long wave radiation that have been found using combined information from ERBE and ISCCP. Since these relationships are likely to affect the sensitivity of the radiation budget to perturbations in cloud amount it is reasonable to assume that climate models that fail to capture these relationships are likely to misrepresent those cloud feedbacks that depend upon cloud amount. (Webb et al 2001). Other statistical relationships between radiative fluxes and sea surface temperature (or SST anomalies) have been found in observations and the assessment of the ability of models to reproduce them has been attempted (Bony et al, 1997).

3.2 Model Experiments:

The ±2K SST perturbation experiments reported by Cess et al (1990, 1996) still represent the only systematic study of the strength of cloud feedback across a range of models used for climate projection. This experiment is quite simple to perform and, because it is for a fixed season (July), the computational demands are very modest. Most modelling groups already include the cloud radiative forcing diagnostic and its components as part of their standard model. This experiment is the simplest experiment called for in the CLIVAR Implementation Plan. There is great interest amongst the broad scientific community in discovering whether the spread in model results in the earlier (1990s) experiments has altered in any way. Since the total feedback is the result of a diverse set of compensating effects, in some models, despite the greater complexity in the parametrisation of cloud radiation interactions, the strength of the cloud feedback may be relatively unchanged (eg Yao and Del Genio, 1999). While many variants of the classic ±2K perturbation experiment have been proposed, the overwhelming reason for conducting this experiment is connection with past experiments hence enabling continuation of the monitoring begun in 1990.

Slab ocean experiments are usually performed by all modelling groups as part of their overall model development strategy. They represent the simplest climate change experiment with a responsive ocean. The common IPCC definition of model ‘climate sensitivity’ as the change in surface air temperature at the time of equilibrium at with 2*CO2 forcing requires such a calibrating experiment. There has been little formal agreement on the form of slab ocean experiments but common practices such as using a 50m deep slab ocean and a ‘q-flux’ technique need to be standardised where necessary. These experiments are amenable to a great deal of analysis little of which has been conducted systematically under a common framework.

It is strongly urged that the modelling community undertake 1*CO2 and 2*CO2 slab ocean experiments under an agreed framework and that sufficient information be saved that an analysis of the strength of, at least, the cloud feedback can be made for each model. It is also strongly urged that the behaviour of the clouds in the simulated warmer climate be evaluated against similar cloud regimes in the 1*CO2 case. In order to maximise the information obtained from such an experiment it is important that the atmospheric component of the slab ocean experiment be as similar as possible to that submitted to AMIP and should also be the atmospheric component of coupled model simulations submitted to CMIP and the new Climate of 20th Century Project.

The SST Perturbation and slab ocean experiments form the core of CFMIP and modelling centres are encouraged to submit to at least one and, if possible, both experiments. Details of these experiments are included on the subsequent pages.

Cloud resolving Models have proven to be a very valuable tool in exploring the complexity of the structure of cloud systems. Up to now there has been little work in exploring the response of cloud resolving models within a climate change framework. With the computer capacity now available it should be possible to investigate the response of Cloud resolving models to idealised climate change forcing (eg response to ±2K SST perturbation) and to explore the complexity of the cloud radiation interaction. It is also noted that some experimentation has begun using 2D cloud resolving models embedded in 3D climate models, while the computational cost of such a configuration is high, results from an idealised climate change experiment using such a combination would be of great scientific interest.

3.3 Analysis Methodologies

Only a relatively few analysis techniques have been devised thus far to quantify the feedbacks operating in climate models and the analysis has proceeded following the engineering analogy of feedback in an amplifier or a simple first order partial differentiation approach. The full richness of feedbacks in non-linear climate system remains to be explored. Use of satellite derived cloud information in evaluating climate models has been hampered by the differing definitions of clouds n climate models and in satellite derived products, there is a continuing need to ensure that data from climate models is compared with observations from all sources in a more optimal manner (i.e. climate model information is presented in the form that is as close as possible to the original observation).

The cloud forcing methodology of Cess et al (1990) provides the most straightforward measure of cloud feedback. This method computes a total derivative so that complete separation of cloud feedback from other feedbacks (particularly water vapour and lapse rate) is not possible (Zhang et al., 1996). The cloud forcing approach has bee extended a little further by Watterson et al (1999) and has proved useful in a more regional intercomparison, particularly allowing estimates of snow and ice albedo feedback; investigation of the potential for further separation.

It is also desirable to perform a quantitative analysis of all of the feedback processes acting in a climate change experiment. A suitable methodology has been developed by Wetherald and Manabe (1988) and used with many variants by some modelling groups (e.g. Le Treut et al (1991), Zhang et al (1994), Colman and McAvaney (1995), Watterson and Dix (1996) and Colman (2001)). Although very powerful, using such techniques may not be possible for all of the participating models due to the disparity of model design, particularly in terms of cloud specification for radiative algorithms and the inconsistencies between radiation transfer codes.

Another technique has used simple one dimensional radiative convection models (RCMs) (eg. Hansen et al, 1984) however this technique still requires consistency of radiation and convection codes and suffers from the additional limitation that considerable temporal and spatial averaging is required.

It is highly desirable that the model equivalent of ISCCP clouds (Webb et al, 2001) should be determined for the control climate of each participating climate model. At the current time the use of the ISCCP simulator "in line" mode is strongly encouraged to determine the various ISCCP cloud types from the model, however, it is also possible to run the simulator "off line". The behaviour of these model clouds under different dynamical regimes and when subject to changes in forcing should also be investigated. Supporting information for the installation of the ISCCP simulator is given here. In addition, a mailing list exists which we suggest participants join and use to email queries etc. To subscribe, send a message to majordormo@metoffice.com with the following message body:
subscribe isccp-simulator-projects your.email@address.com

A vectorised version of the ISCCP simulator has now been produced making it much more computationally effective for modelling groups with vector computers to include the ISCCP simulator "in line" in their models.

4. Summary

A systematic intercomparison of cloud feedbacks in climate models is proposed as part of a programme to provide continuing documentation of the strength of cloud feedbacks in climate models and an evaluation of the performance of climate models in simulating aspects of clouds that are important in cloud feedback.

The CFMIP project includes both a heavy link between models and observations and an intercomparison between models.

The Diagnostic Subproject section suggests some initial investigations for which the data may be used. In general this requires the diagnosis, within models, of simulated clouds in a manner that is consistent with the ISCCP cloud algorithms. Systematic investigation of cloud behaviour in different dynamical regimes across a range of climate models as compared to the behaviour of ISCCP clouds in dynamical regimes determined from reanalysis products should aid in categorising the potential for climate models to produce realistic cloud feedbacks.

The Experimental Protocols section describes in more detail the two specific experiments required for CFMIP. The first type (using perturbation to the SST as a "forcing" to an atmosphere only model) is mainly to provide a link to previous intercomparisons conducted by Cess and collaborators (Cess, 1990 and 1996), the second type (using a slab ocean model interacting with an atmospheric model) is expected to become the "standard" over the next decade.calls for a more complete investigation of the behaviour of clouds in climate models as compared to ISCCP data.

Suggestions relating existing model intercomparison projects (AMIP and CMIP) and other projects (e.g. ARM, CRM investigations) are included in Project Extensions.

It is hoped that this intercomparison will serve as an important contribution to the IPCC aim to "characterise more completely the dominant processes in cloud feedback" in climate models.

5. References

Aires F., and W. B. Rossow, 2003, Inferring instantaneous, multivariate and nonlinear sensitivities for the analysis of feedback processes in a dynamical system: Lorenz model case-study Quarterly J Roy Met Soc 129(A) 239-278.

Boer, G.J, 1993, Climate change and the regulation of the surface moisture and energy budgets, Clim. Dyn. 8, 225-239.

Boer, G.J. and B. Yu, 2003, Climate sensitivity and Response, Clim. Dyn. , 20, 415-429.

Bony S., Duvel J.P., Le Treut H., 1995: Observed dependence of the water vapour and clear-sky greenhouse effect on sea surface temperature: comparison with climate warming experiments, Climate Dynamics, 11, 307-320.

Bony S., K.-M. Lau and Y.C. Sud, 1997: Sea-Surface Temperature and Large-Scale Circulation Influences on Tropical Greenhouse Effect and Cloud Radiative Forcing, J. Climate, 10, 2055- 2077.

Bony S, J-L Dufresne, H. Le Treut, J.-J. Morcrette, and C. Senior, 2003, On dynamic and thermodynamic components of cloud changes. Climate Dynamics (submitted)

Cess, R.D. and G.L. Potter, 1988, A methodology for understanding and intercomparing atmospheric climate feedback processes in GCMs. J. Geophys. Res., 93, 8305-8314.

Cess, R.D. Potter, G.L., Blanchet, J.P., Boer, G.J., Ghan, S.J., Hansen, J., Kiehl, J.T., Le Treut, H., Li, Z.-X., Liang, X-Z, McAvaney, B.J., Meleshko, V.P., Mitchell, J.F.B., Morcrette, J-J, Randall, D.A., Rikus, L.J., Roeckner, E., Schlese, U., Sheinin, D.A., Slingo, A., Sokolov, A.P., Taylor, K.E., Washington, W.M., Wetherald, R.T., Yagai, I, 1990, Intercomparison and interpretation of cloud-climate feedback processes in nineteen atmospheric general circulation models. J. Geophys. Res., 95, 16601-16615.

Cess, R. D. M.-H. Zhang, Potter, G.L., Barker, H., Colman, R.A., Dazlich, D.A., Del Genio, A.D., Esch, M., Fraser, J.R., Galin, V., Gates, W.L., Hack, J.J., Kiehl, J.T., Lacis, A.A., Liang, X-Z, Mahfouf, J.-F., McAvaney, B.J., Meleshko, V.P., Mitchell, J.F.B., Morcrette, J-J, Randall, D.A., Roeckner, E., Royer, J.-F., Sheinin, D.A., Sokolov, A.P., Taylor, K.E., Wang, W.-C., Wetherald, R.T., 1993: Uncertainties in carbon dioxide radiative forcing in atmospheric general circulation models. Science, 262, 1252--1255.

Cess, R.D, Zhang, M.H., Ingram, W.J., Potter, G.L., Alekseev, V., Barker, H.W., Cohen-Solal, E., Colman, R.A., Dazlich, D.A., Del Genio, A.D., Dix, M.R., Dymnikov, V., Esch, M., Fowler, L.D., Fraser. J.R., Galin, V., Gates, W.L., Hack, J.J., Kiehl, J.T., Le Treut, H., Lo, K.K.-W., McAvaney, B.J., Meleshko, V.P., Morcrette, J-J, Randall, D.A., Roeckner, E., Royer, J.-F., Schlesinger, M.E., Sporyshev, P.V., Timbal, B., Taylor, K.E., Volodin, E.M., Wang, W., and Wetherald, R.T., 1996, Cloud feedback in atmospheric general circulation models: An update. J. Geophys. Res., 101, 12791-12794.

Colman, R.A. and B.J. McAvaney, 1997, A study of GCM climate feedbacks from perturbed SST experiments. Clim Dynamics 13, 717-731.

Colman, R.A. 2001, On the vertical extent of atmospheric feedbacks. Climate Dynamics, 17, 391-405.

Gregory, J.M, W.J. Ingram, M.A. Palmer, G.S. Jones, P.A. Stott, R.B. Thorpe, J.A. Lowe, T.C. Johns and K.D. Williams, 2003, A new method for diagnosing radiative forcing and climate sensitivity, Geophys. Res. Lett. (in press)

Hallegatte S., A. Lahellec, J-Y Grandpeix, 2003, The characterization of the water feedback dynamics in climate change using a one box model (submitted to J. Climate)

Hansen , J.D., A. Lacis, D. Rind, G. Russell, P. Stone, I. Fung, R. Ruedy and J. Lerner, 1984, in ‘Climate Processes and Climate Sensitivity’ J.E. Hansen and T. Takahashi, Eds American Geophysical Union, Washington DC, 130-163.

Held, I.M. and B.J. Soden, 2000, Water vapour Feedback and global warming, Ann. Rev. Energy Environ., 25, 441-475.

Jakob, C. and G. Tselioudis, 2003, Objective identification of cloud regimes in the Tropical Western Pacific. Geophys. Res. Let., 30, doi:10.1029/2003GL018367.

Le Treut, H, and Z.X. Li, 1991, The sensitivity of an atmospheric general circulation model to prescribed SST changes: Feedback effects associated with the simulation of cloud optical properties. Clim. Dyn., 5, 175-187.

Le Treut H. and B. McAvaney, 2000, A model intercomparison of equilibrium climate change response to CO2 doubling. Notes du Pole Modélisation, No. 18, Institue Pierre Simon Laplace des Sciences de l’Environment Global.

McFarlane et al., 1992, J. Clim., 5(10): 1013-1044.

Norris, J.R. and C.P. Weaver, 2001, Improved techniques for evaluating GCM cloudiness applied to NCAR CCM3. J. Clim., 14, 2540-2550.

Flato, G.M., G.J. Boer, W. Lee, N. McFarlane, D. Ramsden and A. Weaver, 2000, The CCCma global coupled model and its climate, Clim. Dyn., 16, 451-467.

Randall, D.A., Cess, R.D., Blanchet, J.P., Boer, G.J., Dazlich, D.A., Del Genio, A.D., Deque, M., Dymnikov, V., Galin, V., Ghan, S.J., Lacis, A.A., Le Treut, H., Li, Z.-X., Liang, X-Z, McAvaney, B.J., Meleshko, V.P., Mitchell, J.F.B., Morcrette, J-J, Potter, G.L., Rikus, L.J., Roeckner, E., Royer, J.F., Schlese, U., Sheinin, D.A., Slingo, J., Sokolov, A.P., Taylor, K.E., Washington, W.M., Wetherald, R.T., Yagai, I. and Zhang, M.-H., 1992; Intercomparison and Interpretation of Surface Energy Fluxes in Atmospheric General Circulation Models". J. Geophys. Res., 97, 3711-3724.

Rossow, W.B, and R.A. Schiffer, 1991, ISCCP cloud data products, Bull Am. Meteorol. Soc. 72, 2-20.

Senior C.A. and J.F.B. Mitchell, 1993, Carbon dioxide and climate: The impact of cloud parametrization. J. Clim., 6, 393-418.

Tselioudis, G., Y.-C. Zhang and W.B. Rossow, 2000, Cloud and radiation variations associated with Northern Hemisphere midlatitude low and high sea level pressure regimes, J. Clim., 13, 312-327.

Tselioudis, G. and C. Jakob, 2001, Midlatitude cloud property variability and the implications for climate feedback processes, 4^th International Scientific Conference on the Global Energy and Water Cycle, Paris, p 154.

Watterson, I.G., M.R. Dix and R.A. Colman, 1999, A comparison of present and doubled CO2 climates and feedbacks simulated by three general circulation models, J. Geophys. Res., 104, 1943-1956.

WCRP, 1996, CLIVAR Initial Implementation Plan WRCP No 103 WMO/TD 869

Webb, M., C. Senior, S. Bony, J.-J; Morcrette, 2001, Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models, Climate Dynamics, 17, 905-922.

Wetherald, R.T. and S. Manabe, 1988, Cloud feedback processes in a GCM. J. Atmos. Sci. 45, 1397-1415.

Williams, K.D., M.A. Ringer and C.A. Senior, 2003, Evaluating the cloud response to climate change and current climate variability, Clim. Dyn. 20, 705-721.

Yao, M.-S., and A.D. Del Genio, 1999: Effects of cloud parameterization on the simulation of climate changes in the GISS GCM. J. Climate, 12, 761-779

Zhang, M.H. R.D. Cess, J.J. Hack and J.T. Kiehl, 1994, Diagnostic study of climate feedback processed in atmospheric general circulation models. J. Geophys. Res. 99, 5525-5537.

Zhang, M.H., J.T. Kiehl and J.J. Hack, 1996, Cloud-radiative Feedback as produced by different parameterizations of cloud emissivity in CCM2. NATO ASI Series, Vol. I 34, Climate Sensitivity to Radiative Perturbations: Physical Mechanisms and their Validation, Edited by Herve Le Treut, Springer-Verlag Berlin Heidelberg.