AMIP II Diagnostic Subproject
21
Surface Climatologies
Project coordinator:
P.D. Jones
M. Hulme
T.Osborn
Climatic Research Unit, University of East Anglia
Norwich NR4 7TJ UK
Background
Objectives
Methodology
Data Requirements
References
Background
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).
Objectives
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.
Methodology
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.
References
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
(amip@pcmdi.llnl.gov).
Last update: 23 June 1999. This page is maintained
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