AMIP II Diagnostic Subproject 21
Surface Climatologies
Project coordinator:
P.D. Jones
M. Hulme

Climatic Research Unit, University of East Anglia
Norwich NR4 7TJ   UK
Data Requirements


This proposed subproject is an extension of Diagnostic Sub-Project No.21 from AMIP I (see papers Srinivasan et al., 1995; Osborn and Hulme, 1997, 1998). The work proposed will be undertaken through existing research programmes in the Climatic Research Unit funded by the UK Department of the Environment (Dr M.Hulme) and by the US Department of Energy (Professor P.D.Jones).


This subproject will utilise the extensive observed surface climate datasets constructed and held by the Climatic Research Unit to assess the ability of the AMIP II simulations to reproduce a variety of surface climatological characteristics on both monthly and daily timesteps. These characteristics include:

  • mean surface climatological fields and interannual variability (e.g. precip., tmin., tmax., vapour pressure, cloud cover).
  • regional synoptic circulation patterns and relationships with daily weather variables.
  • a number of pressure-based indices of ocean-atmosphere variability (e.g. NAO, SOI, TPI, NPI).
The importance for models to reproduce these features relates to, respectively, the broad-scale patterns (including interannual variability) of modelled surface climates, the internal consistency of AGCMs, and the model representation of key indicators of ocean-atmosphere interactions which govern regional-scale climate anomalies.


Three main areas of analysis are envisaged and these will utilise the following observed validation datasets:

  • global daily MSLP on a 5° by 10° grid
  • global monthly temperatures on a 5° by 5° box grid
  • terrestrial monthly precipitation on a 2.5° by 3.75° box grid
  • a new 1979-1998 interannual monthly terrestrial surface climatology on a 0.5° box grid for precip., wet days, tmax, tmin, vapour pressure and cloud cover (New et al. 1999,2000)
  • daily time series of precip., tmax and tmin for NW Europe
  •  monthly time series of pressure indices for the NAO, SOI, TPI and NPI.
An important point to note is that these observed surface climate datasets are independent of model re-analysis datasets. The three broad areas of analysis are as follows:
  • mean monthly surface climatological fields for 1979-1998 will be compared for global terrestrial areas for precip., wet days, tmin., tmax., vapour pressure, and cloud cover. We will be using a new high resolution 1901-1998 terrestrial climatology for this validation exercise (New et al., 1999,2000), a dataset that will also allow the interannual variability of these climatological properties to be validated.

  • synoptic circulation patterns for selected regions (e.g., the British Isles, NW Europe) will be analysed using airflow typing schemes (e.g., Conway et al., 1996) and their relationships with aspects of daily weather (e.g., precipitation rate and occurrence, diurnal temperature range) compared. The ability of AGCMs to reproduce these features has major importance for GCM downscaling applications in climate scenario construction (Conway and Jones, 1998).

  • a number of surface pressure-based indices of ocean-atmosphere variability (e.g. NAO, SOI, TPI, NPI) will be validated on monthly and seasonal time-scales. The interannual variability of these indices in particular will be validated, as will relationships between the indices and selected regional patterns of precipitation and temperature anomalies. The ability of AGCMs to capture these large-scale co-ordinated variability features also has important consequences for climate scenario development and climate change detection.
Data Requirements

Model output for the following variables for 1979-1998 will be utilised:

  • global monthly time series for MSLP, precip., tmin, tmax, vapour pressure and cloud cover.
  • daily time series for selected regions of MSLP, precip., tmin, tmax.

Conway, D. and Jones,P.D. (1998) The use of weather types and air flow indices for GCM downscaling.  J. Hydrology, 212-3, 348-361.

Conway, D., Wilby,R.L. and Jones,P.D. (1996) Precipitation and air flow indices over the British Isles. Climate Research, 7, 169-183.

Hulme, M. and New,M. (1997) The dependence of large-scale precipitation climatologies on temporal and spatial gauge sampling.  J. Climate, 10, 1099-1113.

Jones, P.D. and Hulme,M. (1996) Calculating regional climatic time series for temperature and precipitation: methods and illustrations. Int. J. Climatol., 16, 361-377

Jones, P.D., Osborn,T.J. and Briffa,K.R. (1997) Estimating sampling errors in large-scale temperature averages, J. Climate, 10, 2548-2568.

New, M., Hulme, M. and Jones P.D. (1999) Representing twentieth century space-time climate variability. Part 1 : Development of a 1961-90 mean monthly climatology. J. Climate, 12, 829-856.

New, M., Hulme, M. and Jones P.D. (2000) Representing twentieth century space-time climate variability. Part 2 : Development of 1901-1998 monthly terrestrial climate fields.  J. Climate (in press).

Osborn,T.J. (1997) Areal and point precipitation intensity changes : Implications for the application of climate models. Geophys. Res. Letts., 24 , 2829-2832.

Osborn, T.J. and Hulme,M. (1997) Development of a relationship between station and gridbox rainday frequencies for climate model validation, J. Climate, 10, 1885-1908.

Osborn, T.J. and Hulme,M. (1998) Evaluation of the daily precipitation characteristics of AMIP atmospheric model simulations over Europe, Int. J. Climatol., 18, 505-522.

Srinivasan, G., Hulme,M. and Jones,C.G. (1995) An evaluation of the spatial and interannual variability of tropical precipitation as simulated by GCMs, Geophys. Res. Letts., 22, 1697-1700

For further information, contact the AMIP Project Office (
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