Note on predictors (large scale atmospheric variables)

Introduction

The two statistical downscaling models, SDSM (Wilby et al., 2002) and ASD (Automatic Statistical Downscaling; Hessami et al., 2007), calculate statistical relationships based on multiple linear regression techniques, between large-scale (the predictors) and local (the predictand) climate. These relationships are developed using observed weather data. Assuming that these relationships remain valid in the future, they can be used to obtain downscaled local information for some future time period by driving the relationships with GCM-derived predictors.

Normalization

Both the observed and GCM-derived predictor variables have been normalised with respect to their 1961-1990 means and standard deviations. That is, the mean and standard deviation for the 1961-1990 period were calculated and the mean subtracted from each daily value before dividing by the standard deviation. Since GCMs do not always perform well at simulating the climate of a particular region, there may be large differences between observed and GCM-simulated conditions (i.e. GCM bias or error) which could potentially violate the statistical assumptions associated with the statistical downscaling model and give poor results if the predictor data were not normalised. The normalisation process ensures that the distributions of observed and GCM-derived predictors are in closer agreement than those of the raw observed and raw GCM data.

The predictor variables supplied here have been normalised over the complete 1961-1990 period. Other options include normalisation by season, which may improve the performance of the statistical downscaling model, particularly in regions where there is a distinct seasonal climate pattern. The volume of data involved means that it is not possible to supply a number of predictor datasets which have been prepared using a number of different normalisation options. However, by obtaining the raw observed and raw GCM ouput, it is possible for you to construct your own predictor datasets using different normalisation options.

Predictors

The observed large-scale predictors have been derived from the NCEP reanalysis dataset (Kalnay et al., 1996). For a full description of the project which sourced this dataset, please visit the NCEP/NCAR CDAS, Reanalysis Project Site. The source data were provided by the NOAA-CIRES Climate Diagnostics Center, from where you can obtain a full description of the data. Raw data for the construction of additional predictors may be obtained from these sites. If you wish to construct your own predictors, make sure that you follow the same naming convention outlined in the SDSM User Manual.

Note: the daily NCEP values are the average of 4 values taken at 0Z, 6Z, 12Z and 18Z (Universal Time/Greenwich Mean Time). You should ensure that the time of observation of the predictand data corresponds with the time of the NCEP daily values. It may be necessary to lag (both forward and backward options exist in SDSM) the NCEP predictor data so that it corresponds more closely with the timing of the observed predictand data.

The NCEP-derived predictor data have been interpolated onto the same grid as the GCM; for each different GCM, the NCEP-derived predictor data will be slightly different and you will need to re-calibrate SDSM each time you change to a different GCM. The interpolation has been carried out in this manner since we have more confidence in finer-resolution data interpolated to a coarser resolution than we do in interpolating coarse-resolution (i.e. GCM) data to a finer resolution.

The predictors supplied here may vary slightly from GCM to GCM depending on the data available, but in general we try and supply the following predictors:

VARIABLE

DESCRIPTION

ADDITIONAL NOTES

temp

Mean temperature at 2m

 

mslp

Mean sea level pressure

 

p500

500 hPa geopotential height

The height of this surface will vary depending on the temperature of the atmospheric column: warmer=higher; cooler=lower

p850

850 hPa geopotential height

The height of this surface will vary depending on the temperature of the atmospheric column: warmer=higher; cooler=lower

rhum

Near surface relative humidity

The vapour content of air as a percentage of the vapour content needed to saturate air at the same temperature

r500

Relative humidity at 500 hPa height

 

r850

Relative humidity at 850 hPa height

 

shum

Near surface specific humidity

The mass of water vapour as a proportion of the total mass of moist air of which it is a part; can be used for tracking air masses

s500

Specific humidity at 500 hPa height

 

s850

Specific humidity at 850 hPa height

 

DERIVED VARIABLES

The following variables have been derived using the geostrophic approximation

 

**_f

Geostrophic air flow velocity

 

**_z

Vorticity

A measure of the rotation of the air

**_u

Zonal velocity component

Velocity component along a line of latitude (i.e. east-west)

**_v

Meridional velocity component

Velocity component along a line of longitude (i.e. north-south)

**zh

Divergence1

Relates to the stretching of a fluid, and usually refers to the outflow of air from the base of an anticyclone in meteorology

**th

Wind direction

This is the only variable which is NOT normalised

** refers to different atmospheric levels: the surface (p_), 850 hPa height (p8) and 500 hPa height (p5).

1 For the divergence calculation see the note related to error in the values computed in HadCM3 and CGCM1, as new calculation without the geostrophic approximation as been made for CGCM2. Potential additional predictors have been tested to improve the downscaling of precipitation as for example in the study of Choux (2005).

Predictors are available for the second generation of Earth System Model CanESM2. CanESM2 is the fourth generation coupled global climate model developed by the Canadian Centre for Climate Modelling and Analysis (CCCma) of Environment and Climate Change Canada. Moreover, CanESM2 represents the Canadian contribution to the IPCC Fifth Assessment Report (AR5).

References

Boer, G.J., G. Flato, M.C. Reader and D. Ramsden (2000a): A transient climate change simulation with greenhouse gas and aerosol forcing: experimental design and comparison with the instrumental record for the 20th century. Climate Dynamics 16:405-425.

Boer, G.J., G. Flato D. and Ramsden (2000b): A transient climate change simulation with greenhouse gas and aerosol forcing: projected climate to the 21st century. Climate Dynamics 16: 427-450.

Choux, M. (2005): Development of new predictor variables for the statistical downscaling of precipitation. Degree Master of Engineering, Department of Civil Engineering and Applied Mechanics, McGill University. (Dec. 2005).

Flato, G. and G.J. Boer (2001): Warming asymmetry in climate change simulations. Geophysical Research Letters 28: 195-198.

Flato, G., G.J. Boer, W.G. Lee, N.A. McFarlane, D. Ramsden, M.C. Reader and A.J. Weaver (2000): The Canadian Centre for Climate Modelling and Analysis Global Coupled Model and its Climate. Climate Dynamics 16: 451-467.

Gordon, C., C. Cooper, C.A. Senior, H. Banks, J.M. Gregory, T.C. Johns, J.F.B. Mitchell and R.A. Wood, 2000: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics 16: 147-168.

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne and D. Joseph (1996): The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77: 437-471.

Pope, V.D., M.L. Gallani, P.R. Rowntree and R.A. Stratton, 2000: The impact of new physical parameterizations in the Hadley Centre climate model – HadAM3. Climate Dynamics 16: 123-146.

Wilby, R.L., C.W. Dawson and E.M. Barrow (2002): SDSM - a decision support tool for the assessment of regional climate change impacts. Environmental Modelling Software 17: 145-157.

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