Quantifying phosphorus effects on land carbon uptake

Daniel S. Goll

1

INCyTE seminar series 2021

Friedlingstein et al. ESSD 2019

Land is a sink for anthr. CO2

Land

car

bon

sink

[Gt y

r-1]

Indications of a weakening sink

Wang et al. Science 2021

CO2 fertilization effect

Brienen et al. Nature 2015

Declining biomass sink

Increasing biomass sink

CO2 C Biomass carbon sink

Phosphorus - a limiting factor

Norby et al. New Phyt. 2016

Increasing leaf P concentration Increasing soil P availability

CO2C

Turner et al. Science 2018

Phosphorus - a limiting factor ?

Year 2100

pessimistic optimistic

P availability

Sun et al. 2017Friedlingstein et al. 2014

Controls of P availability

Rock weathering> 103 years

Root uptakehours

Biomass growthyears

Soil P sorption< hour

Mineralisationdecades

P stress

Photosynthesis

+

+“Belowground investments”

-

Various timescales Feedbacks

+CO2

-

Land surface models

● simulate coupled cycles of energy, water, carbon and nutrients (incl. feedbacks)

● resolve processes on their intrinsic timescale

● are based on theoretical understanding and observational data

Land surface models

Goll et al. 2012

● cycles are represented by pools and fluxes

● stoichiometric ratios couple nutrients to carbon cycling

● balance of external fluxes control the nutrient capital

● assume biogeochemical cycles were equilibrated to pre-industrial conditions

Challenge #1: soil P availability

Plant requirement: 0.4 g m-2 yr-1

P in the soil: 14 g m-2Wang et al. 2017

Weihrauch & Opp 2020

Soil P availability

Oxisols

Molisols

USDA

Inorganic P transformation - example

Incr

easi

ng P

lim

itatio

n

Goll et al. 2012

year=2100

Simulated P limitation during 21st century

Model simulations in which only a single type of soil exists.

Goll unpubl.

year 2010

X

X

X

year 2100 ( -1g P)

X

Inorganic P transformation: (de)sorption

Helfenstein et al. 2020 / Wang et al. 2010

Residence times

ModelsReality

Inorganic P transformation: ‘slow P’

Models lack behind understanding/data

Organic P transformation: phosphatases

Potential phosphatase activity

Qualitative observations

Scarce data

Scarce data

Sun et al. 2020

Challenge #2: model evaluation

Friedlingstein et al 2019,2020

e.g. iLAMB

Plant resource use

waternitrogen

light

carbon

phosphorus

FP = plant P uptakeGPP = gross primary productivity

Sun et al. 2021

Phosphorus use efficiency (PUE)

CNP model model-data fusion (Wang et al 2017)

‘Observation’ (Gill & Finzi 2016)

tropical forest - temperate forests - boreal forest

Carbon use efficiency (CUE)

Sun et al. 2021

NPP = net primary productivityGPP = gross primary productivity

tropical forest - temperate forests - boreal forest

CNP model C-only model

MODIS (‘obs’)

Resource use efficiencies

Sun et al. 2021

MODIS C-only model CNP model

Conclusion

Missing processes rudimentary model evaluation

Missing uncertainty assessment

Poor model calibration

Conclusion

Lack of dataLack of process understanding

Missing processes rudimentary model evaluation

Missing uncertainty assessment

Lack of metrics for model evaluation

Low computational efficiency

Poor model calibration

Lack of process understanding

Targeted experiments (e.g. model-data synthesis)

Identification of large-scale drivers & patterns

...

Ways forward

Lack of data Lack of metrics for model evaluation

Compilation of observations (e.g. TRY, FRED)

Linking observable to modelled variables

Machine learning / model-data fusion to bridge gap between obs. and model

Exchange between modelers and ecologists

INCyTE