CMIP2 Project:

Analysis of climate variability and change using simple global indices

Prof David Karoly
CRC for Southern Hemisphere Meteorology
Monash University
Clayton VIC 3168
AUSTRALIA

phone: +61-3-9905 9669
fax: +61-3-9905 9689
email: djk@vortex.shm.monash.edu.au

Some early studies of global climate change sought to identify significant changes in global mean surface temperature and to attribute such changes to human influences. However, cause and effect are difficult to identify unequivocally in such a simple globally-averaged measure. More recent climate change attribution studies have focussed on fingerprint methods, which make use of the spatial patterns of temperature change to try to attribute the observed changes to one or more climate forcing factors. However, fingerprint methods also make use of more complex multi-variate statistics and the results may be harder to interpret or to communicate than those using global mean temperature.

In this project, we seek to compare the internal climate variability in a number of climate model control simulations with the climate changes in transient greenhouse simulations using a small number of global indices. These indices have been selected based on earlier studies of climate change detection and on some of the key features identified in the climate change fingerprints that have been used commonly. They include global mean temperature, global mean temperature contrast between land and ocean, mean inter-hemispheric temperature contrast, the mean meridional temperature gradient between high and low latitudes, and the mean magnitude of the seasonal temperature cycle and of the diurnal temperature cycle on land.

The Lead Authors of Chapter 2 "Observed Climate Change" of IPCC TAR are producing the following indices and an observed data mask (in additional to time series of global mean temperature):

  1. Land-Ocean temperature difference time series
  2. Winter-Summer temperature difference time series (both hemispheres)
  3. N.H./S.H. temperature difference time series
  4. Diurnal temperature range time series
  5. 700 mb/50 mb temperature difference time series
  6. Upper ten percentile of daily precipitation events time series (based on a fixed threshold for each grid cell)
  7. Zonal temp differences 20S-20N - 50-70N.
We propose to compute time series of the global mean temperature and indices 1, 2, 3, and 7 above from the time series of monthly mean surface air temperature from the long control simulations and the transient greenhouse simulations available in CMIP2. The model time series will be computed using an observed data mask consistent with that used in IPCC.

Estimates of the natural variability of these indices from the control simulations will be compared between models. Preliminary analysis has shown that several of these indices are nearly independent. The correlations between the indices in each model's control simulation will be compared between the different models. The trends in these indices in the transient greenhouse simulations will be compared between models and with the control simulations.

It is expected that the combination of these simple global indices will have more power to detect and attribute climate change than does the use of global mean temperature alone.