- Regional changes in land-atmosphere CO2 exchange over recent decades were modeled using Dynamic Global Vegetation Models (DGVMs) as part of the TRENDY project. - The models show decreasing carbon sink trends in some regions due to increasing drought, with products generally agreeing on declining productivity in about half of global lands under climate trends. - At the global scale, temperature variations dominate land carbon sink variations, but moisture is more important at local scales. - DGVMs captured about two-thirds of the reduction in tropical land carbon uptake during the 2015-2016 El Niño, mainly due to reduced productivity, with recovery in 2017 under neutral conditions.

1. Michael O’Sullivan, Stephen Sitch, Pierre Friedlingstein, Ana Bastos, TRENDY Modellers
Regional changes in land-atmosphere CO2
exchange over recent decades using DGVMs

2. 31%
11.6 GtCO2/yr
Fate of anthropogenic CO2 emissions (2006-2015)
Source: CDIAC; NOAA-ESRL; Houghton et al 2012; Giglio et al 2013; Le Quéré et al 2016; Global Carbon Budget 2016
26%
9.7 GtCO2/yr
34.1 GtCO2/yr
91%
9%
3.5 GtCO2/yr
16.4 GtCO2/yr
44%
Sources = Sinks

3. Trendy-v7
Aim
Provide annual SLAND and ELUC estimates to GCP Global Carbon Budget 2018;
Contribute regional C fluxes, attribute to processes, evaluation (RECCAP-2/ESA)
Models (17 DGVMs expected – 14 delivered so far)
CABLE, CLASS-CTEM, CLM5.0, ISAM, JSBACH, JULES, LPJ-GUESS, LPJ,
LPX, OCN, ORCHIDEE-CNP, SDGVM, SURFEX, VISIT
Experiments
S0 1700-2017 control / no forcing change
S1 1700-2017 CO2 only
S2 1700-2017 CO2 + Climate (CRUJRA)
S3 1700-2017 CO2 + Climate + LULCC
S4 1700-2017 LULCC
SLAND = NBP S2
ELUC = NBP (S3-S2) & investigate (S4-S0)

4. Decreasing Regional Land Carbon Sink Trends
1990-2009
Sitch et al., BG, 2015
 Increasing Global land
carbon sink
 Decreasing regional C
sink trends
 Regional drying trends
acting on plant
productivity
Trend
Mean

5. Climate driven changes in productivity
O’Sullivan et al. Glob Chan Bio (2018), in prep
• Models and observations disagree on global/regional GPP trends.
• Strong positive northern increase in TRENDY models
• Declining tropical GPP trends in light-use efficiency model
 LUE model captures
climate response?
 FLUXCOM systematically
underestimates IAV and
trends

6. Climate driven changes in productivity
 Products agree on direction of trend on 50% of land surface.
 Drying trends reduced productivity
O’Sullivan et al. Glob Chan Bio (2018), in prep

7. Temperature dominate global land carbon sink variations,
but moisture most important at local scale
Global Scale
Local Scale
Sink
Source
Jung et al. Nature, 2017
Response to moisture
dominates at local scale
Inter-annual variability, net C exchange
Temperature dominates at
global scale
Jung et al. Nature, 2017

8. Compensating moisture effects make temperature
dominate global land carbon sink variations
Jung et al. Nature, 2017
NEETEMP
NEEWAI
RelativedominanceofNEETEMP/WAI

9. El Niño 2015-16
 El Niño induced a strong
3PgC reduction of
tropical land C uptake
over the two years (GCP
estimate from
emissions, atm.
increase, ocean uptake).
 DGVMs can explain
about 65% of this
(1.8PgC for July 2015-
March 2016).
 Anomalies in fire
emissions contribute to
about 10% (0.3PgC)
 According to TRENDY,
this is largely due to a
decrease in GPP (~5%
drop across the tropics).
Bastos et al. Phil Trans Roy. Soc, submitted
GPP
TER
Sink
Source

10. 2017 land sink recovery
 Neutral ENSO conditions -> sink recovery in tropics /
south.
 Contribute to GCP 2018 budget.

11. Systematic Trendy DGVM evaluation with iLamb
Courtesy: Eddy Robertson, Andy Wiltshire
 Benchmark models
against a variety of
observations
 Provide absolute and
relative scores of the
mean state of model
output
 Idea to inform
modelling groups
where to improve

12. Systematic Trendy DGVM evaluation with iLamb
Courtesy: Eddy Robertson, Andy Wiltshire
 Compare relationships between
variables.
 Easy to use / visualize!

13. Summary
 Models and “observations” disagree on GPP response
to climate trends
 Agreement on direction of GPP trend on 50% of land
surface
 Moisture dominates the land IAV response at the
local scale, but Temperature at the global scale
 DGVMs explain ~65% of the reduced land uptake due
to 2015-16 El Niño, and attribute this to reduced GPP.
 More neutral ENSO conditions in 2017 led to a
recovery of the land sink
 TRENDY is beginning to systematically evaluate
DGVMs with help from friends at iLamb

14. Importance of dry ecosystems in the carbon cycle
Drylands cover 40% of the global land area
>25% terrestrial C (small per unit area, but large-area)
High variability in climate and C uptake and release very
sensitive to variability Sevilleta LTER, Courtesy, Scott Collins

15. Science, May 22, 2015
Drylands important for Trend and IAV in Land C sink

16. Changing semi-arid climate sensitivity
• Invasive species and grazing by domestic animals (Asner et al 2004)
• Fire suppression (Andela et al 2013)
• Increased water-use efficiency (Donohue et al 2013) and shrub expansion (e.g. +6%
across Australia since 1982)
• Climate trends (Dohohue et al 2009)
Poulter et al. Nature, 2014
 Semi-arid carbon uptake
is more transitory than in
forests?
 Increasing sensitivity of
semi-arid regions not
represented in CMIP5
ensemble

17. Systematic Trendy DGVM evaluation with iLamb
Courtesy: Eddy Robertson, Andy Wiltshire

18. Systematic Trendy DGVM evaluation with iLamb
Courtesy: Eddy Robertson, Andy Wiltshire

19. Houghton bookkeeping
7 Process Models
Satellite data (tropics only)
3 Model average
Netland-usechangeCO2emissions(GtCyr-1)
year
forest
regrowth
(1.6±0.5)
gross
deforestation
(2.9±0.5)
Pan et al. (2011)
1990 – 2007
Net
emissions
(1.3±0.7)
1750 – 2011 Cumulative emissions:
180 [100 to 260] GtC
source: Ciais et al. 2013 IPCC AR5
Large Uncertainty in the Land Use Flux

20. Have CO2 emissions from land use change
systematically been underestimated?
Wood Harvest
Grazing and Crop Harvest (grass PFT)
Shifting Cultivation
Crop Management
Arneth et al. Nature Geo, 2017

21. • Have CO2 emissions from land use change
systematically been underestimated?

22. Annex 1 Description of CRU-JRA55
and differences from CRU-NCEP
• New: Ian Harris UEA, in collaboration with Nicolas Viovy, has kindly agreed to merge the “new generation” reanalysis
from JRA-55 (Japanese 55-year Reanalysis) with the CRU dataset.
•
• 1. All JRA-55 data are regridded to the CRU 0.5° grid using appropriate NCL routines based on the Spherepack package,
and masked to give a land-only (excluding Antarctica) dataset.
•
• 2. For the four variables tmp, dswrf, shum and pre, JRA-55 is aligned to CRU TS (v3.26) tmp, cld, vap and pre (also wet)
respectively over land, using the same transformations as previously. The other four variables (pres, ugrd, vgrd, dlwrf)
pass through without further modification.
•
• 3. For years between 1958 and 2017, JRA-55 is used. Alignment to CRU TS occurs where appropriate.
•
• 4. For years between 1901 and 1957, random years from JRA-55 for 1958-1967 are used to fill. Alignment to CRU TS
applies separately to each instance, as appropriate (ie, using the appropriate CRU TS year). It means that we use the
same method as with CRUNCEP except for version V8 where the NCEP 1901-2014 reanalysis was used to generate years
before 1948.
•
• In terms of format, CRUJRA is very similar to CRUNCEP. One exception is that latitude values now run from south to
north.
•
• In addition to the fact that JRA-55 will differ from NCEP in term of meteorological model, an important difference is the
resolution of JRA is 0.5 degree instead of 2.5° for NCEP. This means that now resolution of reanalysis is compatible with
resolution of the CRU dataset. This will not change the monthly fields that are still aligned to CRU TS but obviously it will
change the spatial and high frequency temporal variability of the fields which is expected to be higher (and more
realistic) than in CRUNCEP. So it also means that model results will probably differ from previous years which require
models to redo the simulation over all the full period 1901-2017 (and then not just extend with 2017).

23. Land use data
• Land-use states for the years 850-2012, and land-use transitions for the years 850-2011, are the same
as LUH2 v2h (released for CMIP6).
•
• Land-use states for the years 2013-2018, and land-use transitions for the years 2012-2017, are new,
based on new inputs from HYDE, and new FAO data for the national wood harvest demands, as
described below.
•
• HYDE inputs: The version of HYDE used for LUH2 v2h was based on a previous FAO release that
included data up to and including the year 2012 – those years did not change in this new GCB dataset.
The new data from HYDE, prepared for GCB 2018, is based on the most recent FAO release, which
includes data up to and including the year 2015 (HYDE applied annual changes in FAO data to the year
2012 data from the previous release to get the new 2013-2015 data used for GCB 2018). After the year
2015 HYDE extrapolates the cropland, pasture, and urban data until the year 2018.
•
• Wood harvest inputs: The version of wood harvest data used for LUH2 v2h was based on a previous
FAO release that included data up to and including the year 2014 – those inputs remained the same in
this new GCB dataset. The new wood harvest data, prepared for GCB 2018, is based on the most recent
FAO release, which includes data up to and including the year 2016 (we applied annual changes in FAO
WH data to the year 2014 data from the previous release to get the new 2015-2016 data used for GCB
2018). After the year 2016 we extrapolated the wood harvest data until the year 2018.

24. • Land transition
• Wood harvest flux