1. Observational needs for global carbon cycle modelling Chris Jones Met Office Hadley CentreESA CCI CMUG Fourth Integration Meeting, Exeter, June 2014

2. © Crown copyright Met Office Importance of carbon cycle in climate models and projections Large Uncertainty Better evaluation needed Role of EO and ESA-CCI Requirements for CMIP6 Introduction

3. © Crown copyright Met Office Motivation – why are carbon cycle projections important? Carbon cycle key new element in CMIP5 modelling Makes projections more relevant and useful “TCRE” – critical new outcome of AR5 What emissions (reductions) required to achieve given pathway? But large uncertainty hinders usefulness Warming link to cumulative emissions AR5, WG1, SPM.10 compatible emissions pathways for the RCPs. Fig 6.25; Jones et al., 2013

4. But what are the key processes and uncertainties? ANOVA decomposition of spread between models and scenarios Scenario differences dominate compatible fossil emissions After mid-century emissions pathways separate almost completely by scenario Hewitt et al., 2013 submitted

5. ANOVA decomposition of spread between models and scenarios Scenario differences dominate compatible fossil emissions Similar for ocean uptake, but not for land Land uncertainty large in models through 21 st century Hewitt et al., 2013 submitted ocean spread largely due to scenarios “Low confidence on the magnitude of modelled future land carbon changes” “very high confidence, ocean carbon uptake of anthropogenic CO2 emissions will continue” But what are the key processes and uncertainties?

6. ANOVA decomposition of spread between models and scenarios Scenario differences dominate compatible fossil emissions Similar for ocean uptake, but not for land Caveat – not true regionally for ocean… Global ocean N. Atlantic S. ocean Hewitt et al., 2013 submitted

7. Missing processes in CMIP5 models? ● Permafrost carbon ● Permafrost thaw “virtually certain” [Ch. 12] ● “low confidence” on the magnitude of carbon losses ● N-cycle: “very likely, …, that nutrient shortage will limit... future land carbon sinks” ● Wetlands: “ [CH4 emissions] likely to increase... low confidence in magnitude” ● Land-management ● fire Fig 6.36; O'Connor et al., 2013

8. Evaluation background Model development has moved towards greater complexity Carbon-cycle, chemistry, more interactive aerosols now common place in CMIP5-class models Evaluation not necessarily kept apace OceanAtmos IceLand Ecosystems Chemistry Aerosol AOIL well evaluated ESM less well evaluated

9. Evaluation Taken here in its widest sense Understanding the system and implementing improvements in the models Goes far beyond simple beauty context of comparing datasets side-by-side Top-down Need to look at whole-system outputs. “get the right answer…” Bottom-up Process understanding and evaluation. “…for the right reason” Emergent constraints A posterior constraint on outputs – determining which observations matter

10. CMIP5 Biogeochemistry Evaluation Anav et al. (2013, J. Clim) began an activity to systematically evaluate carbon cycle in CMIP5 models

11. © Crown copyright Met Office Anav et al, 2013 Global soil and biomass carbon stores

12. © Crown copyright Met Office N. Hemi model spread: factor 4 tropics model spread: factor 2 Model spread in biomass 540 ± 220 PgC Global soil and biomass carbon stores Anav et al, 2013

13. © Crown copyright Met Office Global soil and biomass carbon stores Anav et al, 2013 N. Hemi model spread: factor 10 tropics model spread: factor 5 Model spread in soil carbon 1510 ± 790 PgC

14. © Crown copyright Met Office EO requirements Long list LAI/NDVI Phenology, seasonal cycle and trends Land cover Especially for land-use/change Biomass Evaluating/monitoring stock changes, land use emissions Atmospheric Composition CO2, CH4 Soil moisture, fire Drivers of terrestrial carbon changes Ocean colour Biological activity, location of nutrients

15. CCI example: Land-cover ESA CCI land-cover project and new dataset coming out of this Being used to evaluate new PFTs map Example of working directly with EO community to influence format/quality of products courtesy Anna Harper, Andy Hartley

16. Emergent Constraints  First coined in the context of climate projections by Allen & Ingram (2002) (?)  Emergent Constraint : a relationship between an Earth System sensitivity to anthropogenic forcing and an observable (or already observed) feature of the ES.  Emergent because it emerges from the ensemble of ESMs.  Constraint because it enables an observation to constrain the estimate of the ES sensitivity in the real world.  Fluctuation Dissipation Theorem – so we think we can trust links across timescales from variability to sensitivity...

17. Archetypal Example of an Emergent Constraint Hall & Qu (2006) Slide courtesy Peter Cox

18. Relationship between CO2 Growth-rate and Tropical Temperature - Observations Slide courtesy Peter Cox

19. Constrained distribution of tropical land carbon Prior C4MIP PDF After IAV Constraint Slide courtesy Peter Cox

20. © Crown copyright Met Office Emergent Constraints: caveats and potential Not a silver bullet Not intended to replace “traditional” evaluation But fine balance of carbon processes leads to high risk that model improvement won't narrow uncertainty... c.f. Cloud feedbacks and climate sensitivity EMCs provide a complimentary approach But Carbon IAV only uses 1 data point! Mauna Loa CO2 site Spatial information may allow regional constraints Also apply to CH4 IAV to assess future sensitivity

21. © Crown copyright Met Office Future requirements of ESM Evaluation CMIP6 Idea of satellite “MIP”s around a smaller core Each MIP to be responsible for own set of process experiments Must all have strong evaluation focus courtesy Eyring & Stouffer

22. © Crown copyright Met Office Requirements and priorities for CMIP6 CMIP6 will devolve experiment design/evaluation activities back to component communities Crude history: 2000-2009: “carbon cycle is important” 2009-2014: “included in CMIP models. Large spread” 2015-2020: “must improve” Not just make progress But be able to demonstrate/quantify progress © Crown copyright Met Office C4MIP OCMIPLUMIP GHGs Ocean colour Biomass GHGsLand cover MIP activities CCI datasets Future datasets

23. © Crown copyright Met Office Conclusions Carbon cycle crucial in current / next-generation climate models But only if we can make demonstrable progress in evaluation and improvement Evaluation need to keep pace with added complexity Vision for CMIP6 Leading role of MIPs in ensuring evaluation focus Multiple carbon-related MIP activities EO / CCI data will prove invaluable