ICOS Science Conference 2020, Session 16

1. Operational decision tools for
climate change mitigation:
a case study for agricultural systems
17.9.2020
ICOS
Istem Fer
Finnish Meteorological Institute
istem.fer@fmi.fi
Session 16: Carbon exchange between atmosphere and reservoirs with
long-term storage potential – research, monitoring and verification

2. Outline
Storing Carbon in the terrestrial biosphere
Model-data integration platform
Modelling & uncertainties
Qvidja example: near-real time
Synthetic data experiments
2

3. Fuss et al., 2018
Soil C sequestration
● Large potential
● Permanence
● Lower cost
● Positive socio-economic,
environmental, biophysical impacts
● Fewer negative side-effects
● Early saturation
● Premature reversal
● Hard to monitor and verify
● Large uncertainties

4. 1) Experiments & monitoring
Aim: Quantifying additional Carbon
sequestration in agricultural soils and
associate it with carbon-smart practices:
• cover crops
• intercropping
• harvesting time & amount
• tilling time & amount
• fertilizer timing & amount &
composition
Carbon-
smart Baseline
Time
SoilC
Carbon-action plots
🔼
3

5. 2) Modelling*
Rescale spatially
can’t measure everything
everywhere all the time
Rescale temporally
decisions are about the future
synthesize
4* process-based dynamic computer simulators of natural systems

6. 8
Beyond Modeling: A Roadmap to Community Cyberinfrastructure for Ecological
Data-Model Integration, 2020, https://www.preprints.org/manuscript/202001.0176/v1

7. Carbon Action Farms

8. Intensive management
(business-as-usual)
Moderate management
(carbon-smart)
Calibrate here Apply (predict) here
Eddy-Covariance (EC) plot
A demonstration from Qvidja, FI
Time
SoilC
Carbon-action (CA) plots
🔼Model:
Basic Grassland Model
(BASGRA)
10

9. Carbon science, policy, and management informed by the
best available data and models
Dietze, Ecological Forecasting, 2017 9

10. Data as drivers, parameters and initial conditions
Dietze, Ecological Forecasting, 2017
Soil carbon
Soil water
Soil nitrogen
Phenological stage
...
Temperature
Precipitation
Radiation
...
Specific Leaf Area
Rubisco content
C:N ratio
Turnover rates
...
Meteorological drivers
Field measurements
Chamber
measurements
Remote sensing
Phenocams
Eddy covariance
...

11. Initial Conditions @ Qvidja - as of 2018
phenology carbon nitrogen water
11

12. Bayesian calibration
Dietze, Ecological Forecasting, 2017

13. Predicted - Observed : Before & After

14. Forecast
Dietze, Ecological Forecasting, 2017
Apply (predict) here

15. When to cut? now or next week

17. 54 81

18. Scenario development & forecast
Dietze, Ecological Forecasting, 2017

19. Pseudo-obs
If we were to collect annual soil organic carbon samples until 2100,
(assuming the treatments work) we would expect to see data points
such as this one:

20. As future has not become present yet,
we need models for future projections
16

21. Forecast & Data assimilation
Dietze, Ecological Forecasting, 2017

22. As future becomes present, we can inform
model predictions with observations
17
Assimilate every year, 2019-2030

23. As future becomes present, we can inform
model predictions with observations
17
Assimilate every year, 2019-2030

24. As future becomes present, we can inform
model predictions with observations
17
Assimilate every year, 2019-2030

25. Pseudo-data experiments
1. SOC every year between 2019-2030
2. SOC every 5-yrs: 2020, 2025, 2030
Assimilate;

26. 17

27. Pseudo-data experiments
1. SOC every year between 2019-2030
2. SOC every 5-yrs: 2020, 2025, 2030
3. Technological advancement in 5 years: allows more precise
measurements for years 2025 and 2030

30. Future steps
• Assimilating other data types: Satellite-derived
observations, inventory etc.
• Including other models: other species, other
management practices
• Adding other scenarios, expanding to other sites etc.

31. Summary
• We need both model and data for a more reliable MRV of soil C
sequestration.
• A technological advancement that allows more frequent
measurements could be as useful as one that allows more precise
measurements.
• Model-data simulation experiments help setting research and
development priorities, and inform future observational design and
carbon accounting options.

32. FMI AgriCarbo Group: Jari Liski, Tuula Aalto, Tuomas Laurila, Liisa Kulmala, Istem Fer, Olli
Nevalainen, Toni Viskari, Jarmo Mäkelä, Julius Vira, Henriikka Vekuri, Laura Heimsch,
Annalea Lohila, Stephanie Gerin, Jussi Heinonsalo, Miia Salminen, Layla Höckerstedt, Janne
Pusa and research assistants
PEcAn Group: Michael Dietze, Rob Kooper, Istem Fer, Ankur Desai, David LeBauer,
Ann Raiho, Alexey Shiklomanov, Hamze Dokoohaki, Chris Black, Elizabeth Cowdery,
Shawn Serbin, Bailey Morrison, Kristina Riemer, Tony Gardella
Acknowledgements
FieldObservatory Group (FMI, SYKE, HAMK, BSAG): Iivari Kunttu, Joni Kukkamaki,
Tuomas Mattila, Olli Niemitalo, Antti Juntunen, Olli Koskela, Jari Liski, Istem Fer, Olli
Nevalainen, Layla Höckerstedt, Pieta Jarva, Laura Mäkelä, Laura Hoijer