Statistical Downscaling of General Circulation Models to Produce Climate Change Scenarios for

Halifax, NSLee Titus; BSc (physics), Dmet, MSc Candidate (physics)

Environment CanadaMeteorological Service of Canada

Climate Change Section In collaboration with Dr. Richard Greatbatch, Dr. Jinyu Sheng and Dr. Ian Folkins in the Department of Atmospheric Science at Dalhousie University

1

Typical grid spacing in a GCM

4From IPCC 2001

METHOD

• Remove seasonal cycle from predictand/predictors.• Focus on Winter (DJF)• Predictor Selection• Principal component analysis (PCA).• Multiple linear regression on Tmax and NCEP PC’s.• Validation• Use CGCM3 predictors to hindcast historical distribution.• Use future CGCM3 predictors to make projections.

Seasonal cycle removal

Predictor Selectionmean sea level Pressure *** Redundancy inspired the removal of 500hpa geopotential height500hpa zonal windspeed.500hpa meridional windspeed total windspeed500hpa vorticity 850hpa geopotential height 850hpa zonal windspeed 850hpa meridional windspeed wind direction850hpa vorticity surface zonal windspeed

surface meridional windspeed surface vorticity divergence500hpa specific humidity 850hpa specific humidity surface specific humidity surface mean temperature

2122 )( ggg VUU

g

g

U

Varctan

gg Vf

U

PCA/MLR BASICS

YTXXTX 1

PCA

mean sea level pressure -0.17500hpa geopotential height 0.14500hpa zonal windspeed -0.05500hpa meridional windspeed 0.48500hpa vorticity -0.14 ***CORRELATION = 0.62850hpa geopotential height -0.01 40% of the variation in TMAX is 850hpa zonal windspeed -0.08 explained by temperature advection850hpa meridional windspeed 0.58 850hpa vorticity 0.10surface zonal windspeed -0.13surface meridional windspeed 0.52surface vorticity 0.24

PCA

1

212 ln

p

p

g

TRZZZ ????MSLP

500GPH

850GPH

REGRESSION INFO

Explained variance (percent) 79Regression error variance 0.07Number of predictors (PC's) 10Gamma squared 0.21

VALIDATION

NCEP PREDICTION

NEXT

• Once the best historical model from NCEP has been made…hindcast using CGCM3 predictors.

• Project the CGCM3 predictors onto the eigenvectors created from NCEP.

CGCM3 PREDICTION

PROJECTIONS

1

SUMMARY

• Predictor Selection/PCA improves regression• Reduces autocorrelation of regression errors

(removal of seasonal cycle).• Identify governing physics by season (gives

confidence in the future).• Normal distributions are a much better assumption

using seasons compared to annual.

My Next Steps

• FDEOFR to get regression coefficients as a function of frequency.

• FDEOFR gives candidate downscaling frequencies and helps sort out physics (climate vs. weather)

CLIMATE QUOTES“The reasonable man tries to adapt to the world around him, while the

unreasonable tries to adapt the world to himself. Therefore all progress depends on the unreasonable man”

--George Bernard Shaw

“The real problem is not global warming. It is in fact the majority’s level of awareness. As if we are somehow separate from nature”

--Lee Titus

“The laws of Congress and the laws of physics have grown increasingly divergent, and the laws of physics are not likely to change."--Bill McKibben