Global climate model scenarios downscaled for Canada David Price - - PowerPoint PPT Presentation

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Global climate model scenarios downscaled for Canada David Price - - PowerPoint PPT Presentation

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Global climate model scenarios downscaled for Canada David Price Northern Forestry Centre, Edmonton CIF e-Lecture, 14 March 2012 Slide 2 Acknowledgements Dan McKenney Canadian Centre for Climate Modelling and Marty Siltanen Analysis

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Global climate model scenarios downscaled for Canada

David Price Northern Forestry Centre, Edmonton CIF e-Lecture, 14 March 2012

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Downscaled climate scenarios for Canada - 14 March 2012

Acknowledgements

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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.

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Downscaled climate scenarios for Canada - 14 March 2012

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

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Downscaled climate scenarios for Canada - 14 March 2012

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

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Downscaled climate scenarios for Canada - 14 March 2012

What is Downscaling?

Slide 5 Diagram from: D. Viner, CRU, University of East Anglia

 ―D

  • wnscaling‖ 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!

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Downscaled climate scenarios for Canada - 14 March 2012

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,

  • ptimize 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

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Downscaled climate scenarios for Canada - 14 March 2012

 Ideally, we want to capture the climate change signal generated by the GCM, but corrected for the GCM’s ―ina ccuracies‖ 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.

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Normalizing the GCM data

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Downscaled climate scenarios for Canada - 14 March 2012

Correcting for GCM ―inac

curacy‖

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Step 1: Determine mean of

  • bservations for reference period.
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Correcting for GCM ―inac

curacy‖

Step 1: Determine mean of

  • bservations for reference period.

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

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Downscaled climate scenarios for Canada - 14 March 2012

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Correcting for GCM ―inac

curacy‖

Step 1: Determine mean of

  • bservations for reference period.

Step 2: Determine mean of GCM projection for reference period. Step 3: Calculate delta values by subtracting (or dividing by) the GCM mean from Step 2

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Downscaled climate scenarios for Canada - 14 March 2012

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Correcting for GCM ―inac

curacy‖

Step 1: Determine mean of

  • bservations for reference period.

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

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Downscaled climate scenarios for Canada - 14 March 2012

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.

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Downscaled climate scenarios for Canada - 14 March 2012

IPCC AR4 GHG Scenarios

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GCM spatial resolutions vary

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Downscaled climate scenarios for Canada - 14 March 2012

GCM input data sets

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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

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Downscaled climate scenarios for Canada - 14 March 2012

Outline Techniques:

  • Downscaling and ANUSPLIN
  • Selecting GCMs and GHG scenarios

Results

  • Maps
  • Graphs

Applications

  • Testing the climate models
  • Biophysical variables and moisture indices

Concluding remarks

  • Publications & data distribution

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

Projected changes in Tmax

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Projected changes in Precip

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These maps show ―ab solute‖ 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.

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Changes in annual means (SRES A2, 2080s)

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Temperature Increase (°C) Precipitation Change (ratio)

CGCM3.1

  • Canada

CSIRO Mk 3.5

  • Australia

MIROC3.2

  • Japan

NCAR CCSM3

  • USA
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Analysis by Canadian ecozones

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Annual Mean Daily Tmin Prairies subhumid ecozone (Parkland) Temperature (°C)

(Historical data ~45 years)

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Winter (DJF) Mean Daily Tmin Boreal Plains ecozone Temperature (°C)

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Summer (JJA) Mean Daily Tmax Atlantic Maritime ecozone Temperature (°C)

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Summer (JJA) Mean Vapour Pressure Atlantic Maritime ecozone Vapour pressure (kPa)

(No historical record)

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Fall (SON) Total Precipitation Mixedwood Plains ecozone Total Precipitation (mm)

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M

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Winter (DJF) Mean Daily Solar Radn Boreal Shield W ecozone MJ m-2 day-1

(No historical record)

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

10-year moving averages

MJ m-2 day-1

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Pacific Maritime Montane Cordillera Atlantic Maritime Prairies Aspen parkland Mixedwood Plains 2050 2090

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

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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….

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IPCC AR4 GHG Scenarios

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Taiga Plains Hudson Plains Aspen Parkland Boreal Shield E Annual Mean Daily Tmin (°C) Boreal Plains

Temperature trends by ecozone — 1960 to 2100

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Taiga Plains Hudson Plains Aspen Parkland Boreal Shield E Annual Mean Daily Tmin (°C) Boreal Plains

Temperature trends by ecozone — 1960 to 2100

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Precipitation trends by ecozone — 1960 to 2100

Taiga Plains Hudson Plains Aspen Parkland Boreal Shield E Annual Total Precipitation (mm) Boreal Plains

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Downscaled climate scenarios for Canada - 14 March 2012

Outline Techniques:

  • Downscaling and ANUSPLIN
  • Selecting GCMs and GHG scenarios

Results

  • Maps
  • Graphs

Applications

  • Testing the climate models
  • Biophysical variables and moisture indices

Concluding remarks

  • Publications & data distribution

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Do GCMs really work?

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www.globalwarmingart.com Data from: Meehl et al. 2004. J. Clim. 17: 3721-3727. Jones and Moberg. 2003. J.

  • Clim. 16: 206-223.
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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)

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Biophysical variables

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  • 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)

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 Range of approaches to calculating balance of annual precipitation and evapotranspiration, the latter a function

  • f 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

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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

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Projections of Hogg CMI

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1961-1990 2011-2040 2041-2070 2071-2100 SRES A2 averages of four GCMs

Maps prepared by Y. Wang, NRCan

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Downscaled climate scenarios for Canada - 14 March 2012

Outline Techniques:

  • Downscaling and ANUSPLIN
  • Selecting GCMs and GHG scenarios

Results

  • Maps
  • Graphs

Applications

  • Testing the climate models
  • Biophysical variables and moisture indices

Concluding remarks

  • Publications & data distribution

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Data distribution - GLFC

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

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Data distribution - GLFC

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

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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!

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Downscaled climate scenarios for Canada - 14 March 2012

Publications

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