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Climate and climate change data
            portals




Julian Ramirez / Andy Jarvis / Carlos
              Navarro
Contents
• Where does everything
  start?
• Daily weather data
  – Weather station
  – Gridded
• Mean climatology
  – Weather station
  – Interpolated
• Future climate data
  – GCM data
  – Downscaled
Where does everything start?
• We need plants for
  food
• Plants respond to the
  environment
RV & C (2012)
Daily weather data
• Agricultural
  researchers need
  high quality
  weather
  information




                     RV & C (2012)
Sources of daily weather data
• Our own met service
• Global Historical Climatology Network
  (GHCN) -14k weather stations
• CIAT’s weather station database (9k
  stations) –but loads of restrictions
• Global Summary of the Day (GSOD) -10k
  weather stations –uh oh?
• GPCP, GPCC, NASA-Power (1 degree)
  (1995 – now)
• TRMM (1998 – now)
Despite some improvements in data availability

                            16000                                                           2500
                                        All stations
                            14000
                                        Highly reliable




                                                                                                   Number of stations (high quality)
                                                                                            2000
                            12000
 Number of stations (all)




Early
   10000
20th century                                                                                1500

                            8000

                                                                                            1000
                            6000

                            4000
                                                                                            500
                            2000     © Global Historical Climatology Network (GHCN)
                                     http://www.ncdc.noaa.gov/ghcnm/v2.php
                                0                                                           0
                                 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Optimal (mid)
20th century
SSA (WS)
SSA (1DD)
SAS (WS)
SAS (1DD)
Satellite rainfall data (TRMM)



Chitala (MWI)


                TRMM




                observed
MEAN CLIMATOLOGY
Mean climatology
• New et al. (2002) –CRU (10 min
  resolution)




• Hijmans et al. (2005) –WorldClim (0.5 min)
Interpolated
rainfall bias
Interpolated
temperature
bias
Climate model data
BCCR-BCM2.0        CCCMA-CGCM3.1-    CNRM-CM3
                   T47


           Research areas: Available and
CSIRO-MK3.0
               usable climate data
                   CSIRO-MK3.5       GFDL-CM2.0




GFDL-CM2.1         INGV-ECHAM4       INM-CM3.0




IPSL-CM4           MIROC3.2-MEDRES   MIUB-ECHO-G




MPI-ECHAM5         MRI-CGCM2.3.2A    NCAR-CCSM3.0




NCAR-PCM1          UKMO-HADCM3       UKMO-HADGEM1
Global climate models
• Climate model skill (CMIP3)
    1961-1990 Rainfall          1961-1990 Temperature




                          Source: Ramirez and Challinor, 2012
Global Climate Models
                            • Global climate model skill (IPCC 4AR)
                                       Annual     December-February     June-July-August
Diurnal temperature range
          temperature
   MeanRainfall




                                                            Source: Ramirez and Challinor, 2012
Also, we need downscaling
• Even the most precise GCM is too
  coarse (~100km)
• To increase resolution, uniformise,
  provide high resolution and
  contextualised data
• Different methods exist… from
  interpolation to neural networks and
  RCMs
www.ccafs-climate.org
Can high resolution datasets be
   used for ag. Modelling?




                    Navarro et al., in prep
Notes to take
• Various sources of
  weather/climate data exist,
  pick the best for your case… if
  there’s no data…
• Beware of errors in the data
• Improvements to data network
  are needed
• Use downscaled information
  carefully
• Assess the ability of these
  data to make ag. predictions
                                    (c) Neil Palmer (CIAT)