global climate model scenarios downscaled for … - presentation - march 14.pdfglobal climate model...

Global climate model scenarios downscaled for Canada

David Price Northern Forestry Centre, Edmonton

CIF e-Lecture, 14 March 2012

Downscaled climate scenarios for Canada - 14 March 2012

Acknowledgements Slide 2

Dan McKenney Marty Siltanen Pia Papadopol Kevin Lawrence John Pedlar Kathy Campbell Yonghe Wang (all the above are with CFS)

Mike Hutchinson (Australian National University, Canberra)

Linda Joyce (USDA Forest Service, Ft Collins CO)

Dave Coulson (USDA Forest Service, Ft Collins CO)

Canadian Centre for Climate Modelling and Analysis (CCCMA)

US National Center for Atmospheric Research (NCAR)

G. Strand (UCAR) Australia: Commonwealth Scientific and

Industrial Research Organisation (CSIRO) M. Collier, M. Dix, and T. Hirst

(CSIRO Marine and Atmospheric Research Division)

Japan: Center for Climate System Research, University of Tokyo, National Institute for Environmental Studies, Frontier Research Center for Global Change

Many reviewers in Canada and the USA of two reports published in 2011.


Outline

Techniques: Downscaling and ANUSPLIN Selecting GCMs and GHG scenarios

Results Maps Graphs

Sample applications Testing the climate models Biophysical variables and moisture indices

Concluding remarks Publications & data distribution

Slide 3


Outline


Results Maps Graphs

Sample applications Testing the climate models Biophysical variables and moisture indices


Slide 4


What is Downscaling? Slide 5

Diagram from: D. Viner, CRU, University of East Anglia

―Downscaling‖ is using coarse spatial resolution data to generate information that is more useful at smaller scales

Dynamical: physically consistent simulations of weather at GCM timesteps but at higher spatial resolution. RCMs are the prime example.

Statistical: many methods to relate GCM results to observed weather and climate.

Spatial interpolation:

relatively simple and largely empirical, but robust!


About ANUSPLIN

Mike Hutchinson at Australian National University (Canberra)

FORTRAN program for application of multi-variate thin-plate splines

(typically up to 4 independent variables, more covariates)

Most applications routinely incorporate a spatially (and temporally)

varying dependence on elevation

A key element is that ANUSPLIN minimizes General Cross Validation (GCV)

statistic – an objective method to select the best interpolation models,

optimize data smoothing, and provide estimates of predictive error

Diagnostics help to identify data errors

ANUSPLIN continually being updated Check out http://fennerschool.anu.edu.au/publications/software/anusplin.php Numerous applications worldwide, many independent of Hutchinson’s group

Lots of references available: see CFS GLFC web site for a list and links: http://cfs.nrcan.gc.ca/subsite/glfc-climate

Slide 6

http://fennerschool.anu.edu.au/publications/software/anusplin.php

http://cfs.nrcan.gc.ca/subsite/glfc-climate




Ideally, we want to capture the climate change signal generated by the GCM, but corrected for the GCM’s ―inaccuracies‖ in representing reality

We use the ―Delta method‖, based on a reference period for which we also have observed data (e.g., 1961-1990)

Interpolated long-term monthly means (30-year ―normals‖) provide a reference data set with spatial detail

We then add the change signal (i.e., the temperature difference) to the observed climate normals for the reference period, interpolated to the same coordinates.

Requires processing of GCM data to convert them to deltas WRT the same period in the simulation.

Slide 7

Normalizing the GCM data


Correcting for GCM ―inaccuracy‖ Slide 8

Step 1: Determine mean of observations for reference period.


Slide 9

Correcting for GCM ―inaccuracy‖


Step 2: Determine mean of GCM projection for reference period.


Slide 10




Step 3: Calculate delta values by subtracting (or dividing by) the GCM mean from Step 2


Slide 11




Step 3: Calculate delta values by subtracting (or dividing by) the GCM mean from Step 2

Step 4: Calculate corrected GCM data by adding (or multiplying by) the observed mean from Step 1


Selected GCM Scenarios

Projection data generated by GCMs from CCCma (Canada), CSIRO (Australia), NCAR (USA) and NIES (Japan). [Data also available from IPCC 3rd Assessment (TAR, 2001) (CCCma, CSIRO, Hadley Centre (UK) and NCAR.] SRES A2: increasing population, little technological change, greater

deforestation, pollution and CO2 emissions SRES B1: as A2, but rapid global shift towards resource-efficient technologies

and reduced GHG emissions SRES B2: as B1, but more local efforts to increase resource efficiency and

reduce emissions SRES A1B: higher population growth than A2, with balance of energy from fossil

and renewable sources

Monthly time series extending from 1961 to 2100, gridded to 5 arcminute (1/12 degree lat/lon) resolution – about 10 km.

20 data sets in total. Lots of ways to use these data!

Nakićenović et al. 2000. IPCC Special Report on Emissions Scenarios.

Slide 12


IPCC AR4 GHG Scenarios Slide 13


GCM spatial resolutions vary Slide 14


GCM input data sets Slide 15

GCM1 IPCC AR4 scenario(s)

Monthly variable(s)2 Source3 Time period

CGCM31MR 20C3M, A1B, A2, B1

pr, tas, rsds, hur, huss, psl, uas, vas

CMIP3 1961–2100

CGCM31MR 20C3M, A1B, A2, B1

tasmin, tasmax CCCma 1961–2100

CSIROMK35 20C3M, A1B, A2, B1

pr, tas, tasmin, tasmax, rsds, psl, uas, vas, hur, huss (except B1)

CMIP3 1961–2100

CSIROMK35 B1 huss CSIRO 2001–2100

MIROC32MR 20C3M, A1B, A2, B1

pr, tas, tasmin, tasmax, rsds, hur, huss, psl, uas, vas

CMIP3 1961–2100

NCARCCSM3 20C3M, A1B, B1

pr, tas, tasmin, tasmax, rsds, hur, huss, psl, uas, vas

CMIP3 1961–2099

NCARCCSM3 A2 pr, tas, tasmin, tasmax, rsds, hur, huss, psl, uas, vas

ESG 1961–2099


Outline


Results Maps Graphs

Applications Testing the climate models Biophysical variables and moisture indices


Slide 16


Slide 17

These maps show ―absolute‖ temperatures. It is very hard to see the changes over 100+ simulated years!

Projected changes in Tmax


Projected changes in Precip Slide 18

These maps show ―absolute‖ total annual precipitation. Again, it is very hard to see the changes over 100+ simulated years! It would be just as hard to see differences among different GCMs when forced by the same GHG emissions scenario.


Changes in annual means (SRES A2, 2080s)

Slide 19

Temperature Increase (°C) Precipitation Change (ratio)

CGCM3.1 - Canada

CSIRO Mk 3.5 - Australia

MIROC3.2 - Japan

NCAR CCSM3 - USA


Analysis by Canadian ecozones Slide 20


Slide 21 Annual Mean Daily Tmin Prairies subhumid ecozone (Parkland)

Tem

pera

ture

(°C

)

(Historical data ~45 years)


Slide 22 Winter (DJF) Mean Daily Tmin Boreal Plains ecozone

Tem

pera

ture

(°C

)


Slide 23 Summer (JJA) Mean Daily Tmax Atlantic Maritime ecozone

Tem

pera

ture

(°C

)


Slide 24 Summer (JJA) Mean Vapour Pressure Atlantic Maritime ecozone

Vapo

ur p

ress

ure

(kPa

)

(No historical record)


Slide 25 Fall (SON) Total Precipitation Mixedwood Plains ecozone

Tota

l Pre

cipi

tatio

n (m

m)


M

Slide 26 Winter (DJF) Mean Daily Solar Radn Boreal Shield W ecozone

MJ

m-2

day

-1 (No historical record)


Slide 27 Winter (DJF) Mean Daily Solar Radn Boreal Shield W ecozone

10-year moving averages

MJ

m-2

day

-1


Pacific Maritime Montane Cordillera Atlantic Maritime

Prairies Aspen parkland Mixedwood Plains

2050

2090

Which GCM scenarios are best for a regional study? It depends….

Slide 28


Taiga Plains Boreal Shield W Boreal Shield E

Boreal Cordillera Boreal Plains Hudson Plains

2050 2090

Which GCM scenarios are best for a regional study? It depends….

Slide 29


IPCC AR4 GHG Scenarios Slide 30


Taiga Plains Hudson Plains

Aspen Parkland Boreal Shield E

Ann

ual M

ean

Dai

ly T

min

(°C

)

Boreal Plains

Temperature trends by ecozone — 1960 to 2100

Slide 31




Ann

ual M

ean

Dai

ly T

min

(°C

)

Boreal Plains

Temperature trends by ecozone — 1960 to 2100

Slide 32


Precipitation trends by ecozone — 1960 to 2100



Ann

ual T

otal

Pre

cipi

tatio

n (m

m)

Boreal Plains

Slide 33


Outline


Results Maps Graphs



Slide 34


Do GCMs really work? Slide 35

www.globalwarmingart.com

Data from: Meehl et al. 2004. J. Clim. 17: 3721-3727. Jones and Moberg. 2003. J. Clim. 16: 206-223.

http://www.globalwarmingart.com/

Downscaled climate scenarios for Canada - 14 March 2012 Ensemble_a2_MaxT_July Ensemble_a2_MinT_January

Overestimating Tmax

Underestimating observed Tmin— it was warmer in 1990-2005 than the GCMs predicted!

GCM “validation” (15 years of observed data vs. simulated) Slide 36


Biophysical variables Slide 37

No. Variable Description

1 Annual Mean Temperature Annual mean of monthly mean temperatures

2 Mean Diurnal Range Annual mean of monthly mean daily temperature ranges

3 Isothermality (2) / (7) 4 Temperature Seasonality Standard deviation of monthly mean

temperature estimates expressed as a percentage of their mean

5 Max Temperature of Warmest Period Highest monthly maximum temperature

6 Min Temperature of Coldest Period Lowest monthly minimum temperature

7 Temperature Annual Range (5) – (6) 8 Mean Temperature of Wettest

Quarter Mean temperature of three wettest months

9 Mean Temperature of Driest Quarter Mean temperature of three driest months

10 Mean Temperature of Warmest Quarter

Mean temperature of three warmest months

11 Mean Temperature of Coldest Quarter

Mean temperature of three coldest months

12 Annual Precipitation Sum of monthly precipitation values

13 Precipitation of Wettest Period Precipitation of the wettest month

14 Precipitation of Driest Period Precipitation of the driest month

15 Precipitation Seasonality Standard deviation of monthly precipitation estimates expressed as a percentage of their mean

16 Precipitation of Wettest Quarter Total precipitation of three wettest months

17 Precipitation of Driest Quarter Total precipitation of three driest months

18 Precipitation of Warmest Quarter Total precipitation of three warmest months

19 Precipitation of Coldest Quarter Total precipitation of three coldest months

20 Start of Growing Season Date when daily mean temperature first meets or exceeds 5 °C for five consecutive days in spring

21 End of Growing Season Date when daily minimum temperature first falls below -2 °C after 1 August

22 Growing Season Length (21) – (20) 23 Total Precipitation for Period 1 Total precipitation of three months prior to

(20) 24 Total Precipitation for Period 3 Total precipitation during (22) 25 Growing Degree Days for Period 3 Total degree days during (22), accumulated

for all days where mean temperature exceeds 5 °C.

26 Annual Minimum Temperature Annual mean of monthly minimum temperatures

27 Annual Maximum Temperature Annual mean of monthly maximum temperatures

28 Mean Temperature for Period 3 Mean temperature during (22) 29 Temperature Range for Period 3 Highest minus lowest temperature during (22)


Range of approaches to calculating balance of annual precipitation and evapotranspiration, the latter a function of temperature and radiation. Climate Moisture Index (Hogg 1994, 1997) PDSI (Palmer 1965)

Considerable interest in projecting how climate change will affect water supplies, availability, and hence ecosystems and communities… E.g., Dai (2011)…

We are investigating implications for Canadian forest regions using our own data

Projecting Future Drought Slide 38


Slide 39 The future global context?

Dai 2011. WIRES Climate Change 2: 45-66. doi: 10.1002/wcc.81

-20 -10 -6 -3 -1 0 +1 +3 +6 +10 +20 10-yr average Palmer Drought Severity Index from IPCC AR4

(A1B scenario, 22 models,)

1955 1980

2005 2035

2065 2095


Projections of Hogg CMI Slide 40

1961-1990 2011-2040

2041-2070 2071-2100

SRES A2 averages of four GCMs

Maps prepared by Y. Wang, NRCan


Outline


Results Maps Graphs



Slide 41


Data distribution - GLFC Slide 42

http://cfs.nrcan.gc.ca/projects/3/3



Data distribution - GLFC Slide 43




Concluding Remarks

We have created a suite of several national-scale climate scenarios which allow us to explore a range of potential impacts

We chose a simple downscaling approach that is easy to understand and provides robust data for application over large regions It is not clear that other methods give results that

are more meaningful, especially considering all the assumptions and errors in current GCMs. (Not suggesting there is anything wrong with other downscaling methods!)

Data are freely available to anyone on request!

Slide 44


Publications Slide 45

http://www.fs.fed.us/rm/pubs/rmrs_gtr263.pdf http://cfs.nrcan.gc.ca/publications?id=32971

http://www.fs.fed.us/rm/pubs/rmrs_gtr263.pdf

http://cfs.nrcan.gc.ca/publications?id=32971

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