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Evaluation of land model simulations across multiple sites and multiple models: Results from the NACP site-level synthesis effort. Peter Thornton 1 , Gautam Bisht 1 , Dan Ricciuto 1 , NACP Site-Level Synthesis Participants

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Evaluation of land model simulations across multiple sites and multiple models: Results from the NACP site-level synthesis effort

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Evaluation of land model simulations across multiple sites and multiple models results from the nacp site level synthesis effort

Evaluation of land model simulations across multiple sites and multiple models:Results from the NACP site-level synthesis effort

Peter Thornton1, Gautam Bisht1, Dan Ricciuto1, NACP Site-Level Synthesis Participants

1 Oak Ridge National Laboratory, Environmental Sciences Division and ORNL Climate Change Science Institute


Sponsors

Sponsors

  • NASA Terrestrial Ecology Program

  • DOE, Office of Biological and Environmental Research, Climate and Environmental Sciences Division, Terrestrial Ecosystem Science Program


Premise

Premise

  • Models can and should serve as tools for the integration and synthesis of our best understanding and knowledge

  • Models can and should provide testable (falsifiable) hypotheses

  • Through model-data synthesis efforts, those hypotheses can and should be tested, and discarded or improved when confidence is shown to be low


Analysis setting

Analysis setting

  • Subset of sites and models from full NACP site-level synthesis effort

  • Forest sites (evergreen and deciduous)

  • Range of climates

  • Models that include diurnal cycle

  • Carbon, sensible heat, latent heat fluxes

  • Diurnal cycle, seasonal cycle, interannual variability, long-term mean

  • Influence of steady-state vs. transient forcings


12 models and 13 sites

CAN-IBIS

CNCLASS

CLM-CN

ECOSYS

ED2

ISOLSM

LOTEC

ORCHIDEE

SIB

SIBCASA

SSIB2

TECO

CA-Ca1 Campbell River

CA-Oas Old aspen

CA-Obs Old black spruce

CA-Ojp Old jack pine

CA-Qfo Mature black spruce

CA-TP4 Turkey Point

US-Dk3 Duke Forest pine

US-Ha1 Harvard Forest main

US-Ho1 Howland main

US-Me2 Metolius intermediate

US-MOz Missouri Ozark

US-NR1 Niwot Ridge

US-UMB U Michigan Bio Stn

12 Models and 13 Sites


Diurnal cycle of gpp us dk3

Diurnal cycle of GPP: US-Dk3

Mean diurnal cycle for June-July-August, y-axis units = umol/m2/s, x-axis is half-hour time step. Results from steady-state simulations


Diurnal cycle of gpp ca obs

Diurnal cycle of GPP: CA-Obs


Diurnal cycle of gpp us umb

Diurnal cycle of GPP: US-UMB


Diurnal cycle of nee ca oas

Diurnal cycle of NEE: CA-Oas


Diurnal cycle of nee us ha1

Diurnal cycle of NEE: US-Ha1


Diurnal cycle of nee us dk3

Diurnal cycle of NEE: US-Dk3


Diurnal cycle of nee clm cn

Diurnal cycle of NEE: CLM-CN


Seasonal cycle of clm cn us ha1

Seasonal cycle of CLM-CN: US-Ha1


Findings 1

Findings: 1

  • Time-scale of N-limitation mechanism in CLM-CN is wrong.

    • Evident at both diurnal and seasonal

    • Original hypothesis that plants respond to N availability on sub-daily time scale should be rejected

    • Introducing new mechanism to buffer N availability in time


Findings 2

Findings: 2

  • Evaluation of LE suggests that current basis for estimation of stomatal conductance in CLM-CN is reasonable

    • This result should be revisited once new N storage mechanism is added


Findings 3

Findings: 3

  • CLM-CN is very sensitive to fine root : leaf allocation patterns

    • Difficult measurement

    • Likely candidate parameter for data assimilation

    • Evidence emerging from global-scale studies and comparison to root turnover data that model fine root longevity needs to be modified

  • Other models sensitive to this as well?


Findings 4 underway

Findings: 4 (underway)

  • Introducing transient forcing (disturbance, rising atmospheric CO2, changing N deposition) seems to improve estimate of decadal-scale NEE

    • Doesn’t seem to change conclusions obtained from steady-state simulations

    • This is the most critical flux for evaluation of long-term climate-carbon cycle feedbacks


Conclusions

Conclusions

  • Approach has proved very useful in identifying strengths and weaknesses in CLM-CN

  • This kind of critical evaluation across multiple models provides a path forward for improved future model generations

  • Improving modelers’ ability to know what to ask for from observationalists and experimentalists.


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