Thoughts on Pattern Scaling

During the last week I have been consider ideas put forward at the

Pattern Scaling, Climate Model Emulators and their Application to the new Scenario Process
NCAR, Boulder Colorado, April 23-25 2014

and

Lopez et. la. 2013 Robustness of pattern scaled climate change scenarios for adaption decision support

Major Aims

• "Fit empirical / statistical relation b/w impact relevant climate variables and large scale quantities obtainable through simple models"
• "Run simple models under arbitrary scenarios and recover impact relevant outcomes by applying those relations"

• User Needs
• Impact research
• policy makers
• Social Economic
• higher resolution
• 
  
• Uncertainty
• Handling
• Quantitating 
• sufficiently low uncertainty for outcome information produced to be useful.

Standard Pattern Scaling

• Developed, tested and applied for 20 years
• provide a simplified representation of climate system responses.
• 
  
  • local (or regional) changes in these variables tend to increase linearly with the global warming over the coming century.
  1. local change can be seen as a 'response' to the global warming (GW)
  
  critical assumption is the there is linear relationship b/w a scalar, and a geographical response pattern

Flaws / Concerns

• main climate mechanisms are not linear
• feedback
• timescale in response change
• patterns evolve
• 
  

Uncertainty 

Uncertainty hard to capture

• model uncertainty
• Multimodel ensemble can reduce uncertainty
• scenario uncertainty
• depend on statistical assumption
• analysis of variance 
• map std dev.

NCL Regional Temperature Anomolies

Data
HadCRUT 4
Surface Temperature Anomalies (C with respect to 1961-1990)

Choose a region

Getting my regions in a reusable, correctly projected, and regridded  was more difficult than I thought it would be.
Publicly available global region based netCDF files are not common
In the end, I used a shape file to create a netCDF with a single time dimension, and  integer layer of region ids at each 1 degree

Australia

Just going with continents at this stage and Australia is the country I was born.

Start with a single day.
; read only desired  time 1970
 x  = fin->temperature_anomaly(1440,:,:)

 xr = where(region.eq.2,x,x@_FillValue)

 print(avg(xr))

Then time series for a single year. Oct 2012 to Sept 2013 

; Time Series average for Australia 2013
 x1=fin->temperature_anomaly(1953:1964,:,:)
 rconform = conform(x1, region, (/1,2/))
 xr1 =mask(x1, rconform, 2)

 xa1 = dim_avg_n(xr1,(/1 , 2/))
 t1 = ispan(0,12,1)

 wks   = gsn_open_wks ("x11","xy")                ; open workstation
 res                  = True                     ; plot mods desired
 res@tiMainString     = "Average Australian Temperature Anomaly 2013" ;

Result similar to BOM 

Then annual time series for a decade or so

 yStart = 1990
 yEnd = 2013
 tStart  = (yStart - T_OFFSET ) * 12;
 tEnd = (yEnd - T_OFFSET) * 12 + 11
 x2=fin->temperature_anomaly(tStart:tEnd,:,:)
    
 rconform2 = conform(x2, region, (/1,2/))
 xr2 =mask(x2, rconform2, 2)
 copy_VarCoords(x2, xr2) ; need dim metadata retained for clim functions
 xa2 = dim_avg_n(xr2,(/1 , 2/))
  xannual = month_to_annual(xa2, 1)  ;Annual Average Temperautre             

  printVarSummary(xannual)  

 wks   = gsn_open_wks ("ps","xy")                ; open workstation
 res                  = True                     ; plot mods desired
 res@tiMainString     = "Annual Mean  Australian Temperature  1990-2013" ;
 res@tiYAxisString = "Anomalies" ; y-axis label      

 res@gsnYRefLine           = 0.              ; reference line   
 res@gsnXYBarChart         = True            ; create bar chart 
 res@gsnAboveYRefLineColor = "red"           ; above ref line fill red
 res@gsnBelowYRefLineColor = "blue"          ; below ref line fill blue

res@tiXAxisString = "Year"

 plot  = gsn_csm_xy (wks,ispan(yStart,yEnd,1),xannual,res) ; create plot  

Then summer time series for a decade

 ; Summer Time Average Temperature anomaly for 2000's
 yStart = 2000
 yEnd = 2013
 tStart  = (yStart - T_OFFSET ) * 12 ;
 tEnd = (yEnd - T_OFFSET) * 12 + 11
 x3=fin->temperature_anomaly(tStart:tEnd,:,:)
    
 rconform3 = conform(x3, region, (/1,2/)) ;Create Mask grid with time dim of data
 xr3 =mask(x3, rconform3, 2) ; mask out data not in region 2 , Australia
 copy_VarCoords(x3, xr3) ; need dim metadata retained for clim functions

 xseasonal = month_to_seasonN(xr3, (/ "DJF"/))  ; Seasonal Average Temperautre       
 printVarSummary(xseasonal)  

 xa3 = dim_avg_n(xseasonal,(/2 , 3/)) ; Average across  long and lat dimension
                                ; average across region as all other values masked 
 copy_VarCoords_2(xseasonal,xa3)
 printVarSummary(xa3)

 print(xa3)

 wks   = gsn_open_wks ("x11","xy")                ; open workstation
 res                  = True                     ; plot mods desired
 res@tiMainString     = "Summer Mean  Australian Temperature  1990-2013" ;
 res@tiYAxisString = "Anomalies" ; y-axis label      

 res@gsnYRefLine           = 0.              ; reference line   
 res@gsnXYBarChart         = True            ; create bar chart 
 res@gsnAboveYRefLineColor = "red"           ; above ref line fill red
 res@gsnBelowYRefLineColor = "blue"          ; below ref line fill blue

res@tiXAxisString = "Year"

 plot  = gsn_csm_xy (wks,ispan(yStart,yEnd,1),xa3(0,:),res) ; create plot 

Happy with this time to move on

Install CDO

Mac OSX Mavericks

Download https://code.zmaw.de/projects/cdo/files

In terminal

gunzip cdo-current.tar.gz

tar -xf cdo-current.tar

cd cdo-1.6.4r5

./configure

make

sudo make install

Projections and Gridding

Today I have been thinking about projections

CMIP5 is netCDF the projection is provided in the Metadata, resolution 0.5° x 0.5°

I want to perform polygon regions analysis.

I need the 'masking' of the CMIP5 to be efficient. Visualisation is not a concern.

What file format for storing polygons should I use ?

• projection 
• resolution

I have been looking at different programming languages, particularly R and NCL.

NCL is built to handle  netCDF data efficiently.

But I am having trouble grasping how I can use my polygons efficiently. The polygons are initially shapefiles. The example polygon code focuses on using polygons in visualisation. The example shapefile masking code seems very inefficient. 

Focusing on NCL

If I know CMIP5 resolution and projection I should be able to create netCDF file with a layer / a variable / or slot in array -  per region , with binary values.

Max and Min Latitude of the region could be used to reduce data extract from the netCDF

; read only desired area & times

x     = in->SST(tStrt:tLast,{latS:latN},{lonL:lonR})

Merge / AND / if / where

@FillValue is kind of like null for this variable, ignored by many functions.

  if(.not.isatt(data,"_FillValue")) then
    data@_FillValue = default_fillvalue(typeof(data))          ;-- make sure "data" has a missing value
  end if

x is CMIP5 netCDF data

regions is all my polygons

 x and regions should have the same dimensions

 xr = where(regions.eq.17, x, x@_FillValue)

Or once I have a single binary object per region

 x = in->SST(tStrt:tLast,{r17.latS:r17.latN},{r17.lonL:r17.lonR})

 xr = where(r17, x, x@_FillValue)

There is memory concern here. 

I am going with the concept by region. that is 

For each region

For each netCDF file

Open netCDF file
Populate variable (X) with netCDF data by region and time period
Close netCDF file
Perform any calculation which will optimise memory footprint 

x <- X

Delete X, keep derived data.


Still got the region shape file. Regrid to standard CMIP5 

Ahhh there is no standard grid ding CMIP5

Initially going for bi-linearly interpolation  to 1x1 rectilinear grid 

Why Bilinear

Focus initially land
Noaa using in this http://www.esrl.noaa.gov/psd/ipcc/ocn/details.html
Linear Interpolation had a good ranking in 

Mora, C., et al. (2013). "Biotic and human vulnerability to projected changes in ocean biogeochemistry over the 21st century." PLoS biology 11(10): e1001682.




Why Rectilinear grid



Simple
Too many people think in rectangles
I don't like how the areas are so different
Consider variation at later stage


Why 1 x 1


Because resolution should be reasonable with all the CMIP5 0.5 degree data

NCL Try outs

Installing NCL

Put this in my .bash_profile file:

# NCL additions

export NCARG_ROOT=/Volumes/Data/Users/fmacgill/Projects/ncl62/

export PATH=$NCARG_ROOT/bin:$PATH

Library Issue

Had issues with a library, solve by installing gcc47 through macports

http://www.ncl.ucar.edu/Download/macosx.shtml#libgomp

Put this in my .bash_profile file:

   export DYLD_FALLBACK_LIBRARY_PATH=/opt/local/lib/gcc47/

clmMonLLT

Decide to try out NCL with this example code.

https://www.ncl.ucar.edu/Applications/climo.shtml

Downloaded 

clim0_4.ncl

https://www.ncl.ucar.edu/Applications/Data/

Downloaded 

xieArkin_T42.nc

Want to see output immediately so changed Line 31

wks   = gsn_open_wks("x11" ,"climo")        ; open ps file

So what going on