The recent intercomparisons of the performance of atmospheric models by the Intergovernmental Panel on Climate Change (IPCC) with climatological sea-surface temperatures (Gates et al., 1990, 1992) show that although there is continuing disagreement among current models (and between models and the corresponding observations), there has been an overall narrowing of the range of model results and a reduction in the models' systematic errors as a whole. A compilation of model systematic errors in the seasonal mean sea-level pressure, temperature, zonal wind and precipitation as simulated by 14 atmospheric models has also recently been completed under WGNE auspices (Boer et al., 1991, 1992). In this study it was found, for example, that a large-scale error common to all current atmospheric GCMs is colder than observed air in the lower troposphere in the tropics and in the upper troposphere in higher latitudes. A corresponding WGNE study of extended-range predictions with 8 atmospheric models shows a similar common error (Bourke et al., 1991).
The basic purpose of AMIP is to undertake the systematic intercomparison and validation of the performance of atmospheric GCMs on seasonal and interannual time scales under as realistic conditions as possible, and to support the in-depth diagnosis and interpretation of the model results. In particular, the simulation of the mean climate and the sequence of shorter-term climatic states, and the simulation of specific atmospheric processes and phenomena are of interest to both the climate and weather prediction communities. Such analyses and intercomparisons require that all models simulate the same time period under comparable experimental conditions, and that the same diagnostic measures of performance be calculated for all models. As simple as it sounds, the decision to undertake such a structured or standardized simulation is a major step forward in climate model intercomparison. In terms of the WGNE's definitions of model intercomparison shown in Figure 1, AMIP is a level 2 intercomparison in which the models' climate is specifically generated for the purpose of intercomparison, in contrast to model intercomparisons in which results are taken from uncoordinated and possibly disparate runs (i.e., level 1 intercomparisons). An earlier example of a level 2 diagnostic intercomparison is that of Cess et al. (1989), while the reports of Boer et al. (1991) and Neelin et al. (1992) are examples of level 1 model intercomparisons.
Atmospheric GCMs represent the land surface character and its behavior in a wide variety of ways, including the possible interaction with vegetation. In order to keep the AMIP specifications as simple as possible, it was decided to make no common specification of the land surface. Thus, over land (as determined by each model's land-sea distribution) surface properties such as the albedo, emissivity, roughness, soil moisture and snow/ice cover, and the possible effects of surface vegetation, are left entirely up to each modeling group. (The validation and intercomparison of land surface parameterization schemes in atmospheric models is being undertaken by the joint WGNE/GEWEX project PILPS (Henderson-Sellers, 1992). Neither has any attempt been made to use common surface elevation data or to specify the values of geophysical constants such as gravity and the orbital parameters, although standard values of the atmospheric carbon dioxide concentration (345 ppm) and solar constant (1365 W/m^2) have been specified; these values are close to the averages observed during the AMIP period.
The initial conditions for the AMIP integrations beginning on 1 January 1979 were also not specified since these would presumably have no significant effect beyond the first month. The atmospheric forecast models in AMIP generally use an operational analysis for 1 January 1979 as initial AMIP conditions, while most of the atmospheric climate models use either climatological January conditions or the results of earlier model runs appropriate for January. In any case, the integrations are carried out over the 3653-day period 1 January 1979 to 31 December 1988, inclusive.
A common list of standard monthly-averaged output has been established as shown in the Table. Set 1 consists of the monthly mean global geographical distributions of selected surface and vertically integrated variables (sea-level pressure, ground and surface air temperature, cloudiness, precipitable water, soil moisture and snow mass). In addition, the monthly means of selected components of the atmospheric heat and hydrologic budgets and the surface wind stress are accumulated at each physics time step in the models' integrations. Set 1 also includes the monthly mean cloud radiative forcing as found by subtracting the net outgoing radiation at the top of the atmosphere in the (artificial) case without clouds from that in the (normal) case with clouds. Set 2 of the AMIP standard output consists of the monthly mean global geographical distributions of selected three-dimensional variables (temperature, geopotential height, specific humidity, the zonal and meridional wind and the associated stream function and velocity potential) at the 850, 500 and 200 hPa levels. In addition, set 2 includes the geographical distributions of the monthly variance (defined as the variance about the monthly mean of the daily averages found from 6 hourly values) of all variables in the set, while set 1 includes the monthly variance of selected variables (sea-level pressure, ground and surface air temperature). Set 3 of the AMIP standard output consists of the monthly means of the zonally-averaged distributions of selected variables in the meridional-vertical plane (temperature, specific and relative humidity, cloudiness, zonal and meridional wind, and the mean meridional streamfunction).
In anticipation of the use of the AMIP results in a wide variety of diagnostic studies, some of which may require information not contained in the monthly averaged standard output sets, each participating modeling group is requested to generate a 6-hourly history-of-state consisting of the prognostic variables of the model, along with the 6-hourly sub-totals of those quantities accumulated for the standard output. For those models without a diurnal cycle, a once daily history is appropriate.
In support of AMIP and the broader interests of the atmospheric modeling community, PCMDI is developing a computerized database of the properties of the models participating in AMIP, as a subset of a more comprehensive information system of the principal historical versions of atmospheric GCMs. PCMDI is also assembling an observational database for validation of the AMIP simulations and related diagnostic studies (see section 5).
In testimony to the widespread interest in model validation and intercomparison in both the forecasting and climate modeling communities, there are presently more than 30 modeling groups participating in the AMIP, some with multiple model entries. The principal characteristics of the AMIP models are summarized by Phillips et al. (1995).
The horizontal resolution of the models being used for AMIP ranges from 2.5 degrees latitude x 3.75 degrees longitude to about 4 degrees latitude x 6 degrees longitude among the 12 finite-difference models represented, and from R15 to R40/T63 among the participating 19 spectral models, while the number of vertical levels (usually in sigma or hybrid coordinates) ranges from 2 to 30. Although most models include a diurnal cycle, there are wide variations in the schemes used in models' parameterization of radiation, convection, clouds, frictional effects and soil properties.
From the monthly-averaged standard output (see Table), the PCMDI, in cooperation with the participating modeling groups, will undertake the preparation of a series of reports summarizing and intercomparing the models' results, together with an estimate of the models' systematic errors on the basis of the observational data bank being assembled for AMIP model validation (described below). While much important information on the model's individual and collective performance will be provided by these statistics, insight into the models' portrayal of specific physical mechanisms requires a deeper and more revealing diagnosis of the results.
The PCMDI will assist the diagnostic subprojects in the acquisition of the required model data from the AMIP standard output and the model histories (which are expected to be available at LLNL in DRS format), and will provide computational assistance to the extent feasible. Proposals for additional AMIP diagnostic subprojects are welcome, and should be sent to the author as soon as possible.
The availability of observational data with which to validate the models' performance is essential to the success of AMIP. Although data from a wide variety of sources are currently available, they are generally not for the specific AMIP decade or in a format readily useful in model validation and diagnosis. To develop a model-oriented observational database in support of AMIP, the PCMDI is acquiring gridded global data sets for as many of the variables in the AMIP standard output and for as many months of the AMIP period as possible. These data include temperature, geopotential, wind and relative humidity from both ECMWF and NMC analyses, and the cloudiness, radiation and precipitation as generated by the WCRP International Satellite Cloud Climatology Project (ISCCP), the WCRP/NASA Earth Radiation Budget Experiment (ERBE), and the WCRP Global Precipitation Climatology Project (GPCP) observational programs, respectively. It is planned to store these data (and other relevant data that may become available) at PCMDI in uniformly-formatted DRS files for ease of access and use in connection with AMIP analyses. It is recognized that when a reanalysis of the period 1979-1988 is performed with an advanced data assimilation system (as currently under consideration by both the ECMWF and NMC), it will be possible to generate consistent observational estimates of the AMIP monthly-averaged standard output variables as well as a complete history. AMIP therefore provides an important justification for carrying out such a reanalysis in a timely fashion over the next few years (Bengtsson and Shukla, 1988).
In addition to its obvious and unprecedented value in the comprehensive validation of the current generation of atmospheric GCMs, the results of AMIP can serve as a reference for the systematic documentation of model improvements by the repetition of some or all of the AMIP simulation with new model versions, and may also provide a useful yardstick for sensitivity and predictability studies with atmospheric models. AMIP also compliments the intercomparison of ocean models being undertaken by the WCRP TOGA Numerical Experimentation Group (D. Anderson and T. Stockdale, personal communication), and may serve as a prototype for intercomparisons of coupled atmosphere-ocean GCMs. In the future it may be useful to extend the AMIP period beyond 1988 (and perhaps before 1979), and to repeat the AMIP integrations with improved boundary conditions as well as with improved models. Independent realizations of the AMIP integration with different initial conditions have already been recognized by several modeling groups as an important source of information on natural variability and climate predictability.
AMIP may be regarded as the first "electronic" model intercomparison, in the sense that its results and diagnoses (along with the corresponding observed data) will be electronically available for analysis and display. The greatest obstacle to achieving this goal lies in the development of efficient techniques for the storage, retrieval and visualization of extremely large databases. This is a major challenge to the computational sciences community on whose skills climate modeling studies increasingly depend.
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Boer, G.J., K. Arpe, M. Blackburn, M. Déqué, W.L. Gates, T.L. Hart, H. Le Treut, E. Roeckner, D.A. Sheinin, I. Simmonds, R.N.B. Smith, T. Tokioka, R.T. Wetherald and D. Williamson, 1991: An intercomparison of the climates simulated by 14 atmospheric general circulation models. CAS/JSC Working Group on Numerical Experimentation, WCRP-58, WMO/TD-No. 425, World Meteorological Organization, Geneva.
Boer, G.J.,1992: Some results from and an intercomparison of the climates simulated by 14 atmospheric general circulation models. J.Geophys. Res., 97, 12771-12786.
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Cess, R.D., G.L. Potter, J.P. Blanchet, G.J. Boer, S.J. Ghan, J.T. Kiehl, H. Le Treut, Z.-X. Li, X.-Z. Liang, J.F.B. Mitchell, J.-J. Morcrette, D.A. Randall, M.R. Riches, E. Roeckner, U. Schlese, A. Slingo, K.E. Taylor, W.M. Washington, R.T. Wetherald and I. Yagai, 1989: Interpretation of cloud-climate feedback as produced by 14 atmospheric general circulation models. Science, 245, 513-516.
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Neelin, J.D., M. Latif, M.A.F. Allaart, M.A. Cane, U. Cubasch, W.L. Gates, P.R. Gent, M.Ghil, C. Gordon, N.C. Lau, C.R. Mechoso, G.A. Meehl, J.M. Oberhuber, S.G.H. Philander, P.S. Schopf, K.R. Sperber, A. Sterl, T. Tokioka, J. Tribbia and S.E. Zebiak, 1992: Tropical air-sea interaction in general circulation models. Climate Dynamics, 7, 73-104.
Phillips, T.J., 1994: A summary documentation of the AMIP models. PCMDI Report No. 18, 343 pp.
Phillips, T.J., R. Anderson, and M. Brösius, 1995: Hypertext summary documentation of the AMIP models. UCRL-MI-116384, Lawrence Livermore National Laboratory, Livermore, CA (URL http://www-pcmdi.llnl.gov/projects/modeldoc/amip/).
Last update April 17, 1997. For further information, contact Larry Gates (firstname.lastname@example.org).