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Parameterisation, ground-truthing and
benchmarking: the importance of in-situ
data for global sustainability
Sandy P. Harr...
We live in a data-
rich world
ESA Sentinel 1
But data comes
in all shapes
and sizes
IPCC 5th Assessment Report
In-situ measurements
Unpublished work by Dr Yang Na
Ground-
truthing:
exploiting
data for
“product”
validation
Unpublished work by Colin Prentice and Wang Han
Ground-truthing: exploiting data for
model validation
!
!
!
!
!
!
Comparis...
%drylightning
% wet days
Original
New LPX
Mean
100
0
0 100Lightning flashes /km2/day
%CGlightning
Original
Modified
50
5
0...
Kelley et al., 2014, GMD
Parameterisation:
exploiting data for model
development
11/29/2014 thick_bark.jpg (400×270)
http:...
Models based on
current
understanding,
parameterized
with modern
observations,
tested using
modern data …
and expected to
...
Bartlein, Harrison and a cast of thousands, Climate Dynamics, 2012
Old lake deposits – Megafezzan, Sahara
Quantitative rec...
Izumi et al., 2013 GRL; Izumi et al., 2014 Clim Dynamics
Land-ocean contrast Polar amplification
Can models predict the bi...
Perez Sanz et al., 2014, CoP
Do models simulate regional
climates well?
Sign usually ✔
Magnitude definitely ✗
Are models getting any
better?
definitely ✗
Harrison et al., in press, NCC
Data source has
disappeared ….
Database not
up-to-date
Data not
“public”
Sheer amount
and diversityCMIP1: 1 gigabyte
CMIP2: 500 gigabytes
CMIP3: 36 terabytes
CMIP5: 3.3 petabytes
Take-home
messages
• Data-rich world but access to data is often
problematic, vital to foster open access and
recognition ...
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Parameterisation, ground-truthing and benchmarking: the importance of in-situ data for global sustainability, S. Harrison

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S. Harrison (University of Reading, Reading, United Kingdom), at the Our Common Future Under Climate Change conference, July 7-10 in Paris, France.

More at http://www.commonfuture-paris2015.org/

Published in: Science
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Parameterisation, ground-truthing and benchmarking: the importance of in-situ data for global sustainability, S. Harrison

  1. 1. Parameterisation, ground-truthing and benchmarking: the importance of in-situ data for global sustainability Sandy P. Harrison Centre for Past Climate Change
  2. 2. We live in a data- rich world ESA Sentinel 1 But data comes in all shapes and sizes
  3. 3. IPCC 5th Assessment Report In-situ measurements
  4. 4. Unpublished work by Dr Yang Na Ground- truthing: exploiting data for “product” validation
  5. 5. Unpublished work by Colin Prentice and Wang Han Ground-truthing: exploiting data for model validation ! ! ! ! ! ! Comparison of simulated and observed ci:ca, across multiple plant functional types! Experimentally determined and simulated Jmax:Vcmax against growth temperature!
  6. 6. %drylightning % wet days Original New LPX Mean 100 0 0 100Lightning flashes /km2/day %CGlightning Original Modified 50 5 0.5 0.05 0.001 0.1 10,000 %drydays withlightning dry lightning (strikes/km2/day) 100 0 0 14 • Partition lightning between C-C and C-G • Successful ignitions more likely on dry days • How many strikes on dry days? • TARGET – successful simulation of burnt area Parameterisation: exploiting data for model development Kelley et al., 2014, GMD
  7. 7. Kelley et al., 2014, GMD Parameterisation: exploiting data for model development 11/29/2014 thick_bark.jpg (400×270) http://learnline.cdu.edu.au/units/env207/resources/thick_bark.jpg 1/1 11/29/2014 eucalyptus-tree-bark.jpg (1024×768) http://www.mooseyscountrygarden.com/the-garden-gallery/eucalyptus-tree-bark.jpg 1/2 014 Schnitt_durch_Stamm_Korkeiche.JPG (4320×3240) load.wikimedia.org/wikipedia/commons/e/ef/Schnitt_durch_Stamm_Korkeiche.JPG 1/2
  8. 8. Models based on current understanding, parameterized with modern observations, tested using modern data … and expected to predict climate changes well outside “modern” envelope Benchmarking: the need for palaeodata
  9. 9. Bartlein, Harrison and a cast of thousands, Climate Dynamics, 2012 Old lake deposits – Megafezzan, Sahara Quantitative reconstructions of times when climate was very different from today
  10. 10. Izumi et al., 2013 GRL; Izumi et al., 2014 Clim Dynamics Land-ocean contrast Polar amplification Can models predict the big climate-change signals? ✔ Ratios remarkably consistent in future and past climates Ratios confirmed by palaeo and historic observations
  11. 11. Perez Sanz et al., 2014, CoP Do models simulate regional climates well? Sign usually ✔ Magnitude definitely ✗
  12. 12. Are models getting any better? definitely ✗ Harrison et al., in press, NCC
  13. 13. Data source has disappeared …. Database not up-to-date Data not “public”
  14. 14. Sheer amount and diversityCMIP1: 1 gigabyte CMIP2: 500 gigabytes CMIP3: 36 terabytes CMIP5: 3.3 petabytes
  15. 15. Take-home messages • Data-rich world but access to data is often problematic, vital to foster open access and recognition of products • In-situ data are vital but it is important to maintain/expand observational networks and this requires coordination • Historical and palaeo-records vital but synthesis and archiving required • Significant role for trusted data repositories and services, but these need support • Global sustainability will require combining natural and human data sources, and data and model data, but ways to do this under-explored

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