{"id":2289,"date":"2025-03-10T11:00:03","date_gmt":"2025-03-10T11:00:03","guid":{"rendered":"https:\/\/research.reading.ac.uk\/lemontree\/?p=2289"},"modified":"2025-03-10T11:05:20","modified_gmt":"2025-03-10T11:05:20","slug":"integrating-acclimation-into-noah-mp","status":"publish","type":"post","link":"https:\/\/research.reading.ac.uk\/lemontree\/integrating-acclimation-into-noah-mp\/","title":{"rendered":"Integrating Acclimation into the NOAH-MP Model: Progress Towards Better Climate-Carbon Predictions"},"content":{"rendered":"

At LEMONTREE, one of our core objectives is to enhance land surface models by incorporating the dynamic responses of plant processes to environmental changes using the Eco-Evolutionary Optimality theory. The hope is to produce land surface models that are simpler with fewer parameters, but equally, if not more robust and reliable than our current models. Our latest achievement in this area is the successful integration of plant respiration into the NOAH-MP land surface model, a critical tool for simulating the carbon cycle. Led by Yanghang Ren from Tsinghua University, this breakthrough improves how we model photosynthesis and leaf respiration\u2014two key processes influencing carbon storage and climate feedback. The findings were recently published in the Journal of Advances in Modelling Earth Systems<\/em><\/a>, and we\u2019re excited to share the results with you!<\/p>\n

The Challenge: Understanding Plant Responses to Environmental Change<\/h2>\n

The terrestrial biosphere plays a pivotal role in mitigating climate change by acting as a carbon sink, absorbing roughly one-third of human-made CO2<\/sub> emissions. This process is driven by photosynthesis, which fuels plant growth and gross primary productivity (GPP), while canopy respiration (Rcanopy<\/sub>) releases CO2<\/sub> back into the atmosphere. These processes are highly sensitive to changes in the environment, such as temperature, moisture, and CO2<\/sub> levels.<\/p>\n

Traditional land surface models (LSMs) often assume that the photosynthetic capacity and respiration rates of plants remain constant, regardless of shifting environmental conditions. However, research has shown that plants can acclimate to long-term environmental changes, adjusting key physiological traits like Vcmax<\/sub> (maximum photosynthetic rate) and R25<\/sub> (basal leaf respiration) in response to temperature and other factors. Ignoring this acclimation leads to inaccuracies in simulating carbon fluxes and, ultimately, global climate-carbon feedbacks.<\/p>\n

The EEO-Based Approach: A Dynamic Solution<\/h2>\n
\"\"
Figure 1 Flowchart of trait-acclimation scheme incorporated in Noah MP. The inputs are air temperature (T), air pressure (Pres.), atmospheric CO2 concentration (CO2), vapour pressure deficit (VPD), solar radiation (Srad), the fraction of absorbed photosynthetically active radiation (fPAR), and the factor of soil moisture limitation (\u03b2). Tmidday and Tnight are the average midday and night-time (when the sun elevation was < 0\u02da) temperature over the past 15 days, respectively.<\/figcaption><\/figure>\n

 <\/p>\n

Our newly developed EEO-based scheme addresses these gaps by incorporating the concept of acclimation into the NOAH-MP model. The EEO approach builds on the idea that plants optimise their physiological traits to balance energy and resource costs. It focuses on three key mechanisms:<\/p>\n

    \n
  1. Coordination Hypothesis<\/strong>: Photosynthetic capacity is adjusted to balance carbon fixation and energy use.<\/li>\n
  2. Least Cost Hypothesis<\/strong>: Plants optimise their stomatal behaviour to minimise the trade-off between water loss and carbon gain.<\/li>\n
  3. Vcmax<\/sub>-Rcanopy<\/sub> Coupling<\/strong>: Leaf respiration is linked to photosynthetic capacity, ensuring that both processes adjust in harmony with environmental conditions.<\/li>\n<\/ol>\n

    By adding these dynamic, environmentally responsive traits into the model, we can more accurately simulate the varying impacts of temperature, radiation, soil moisture, and atmospheric CO2<\/sub> on plant function.<\/p>\n

    Key Findings: A More Accurate Carbon Cycle Model<\/h2>\n
      \n
    1. Improved Simulations of Key Traits<\/strong>
      \nThe EEO-based scheme outperformed the standard Noah MP model in capturing the temporal and spatial variations of Vcmax<\/sub> and R25<\/sub> across different biomes. For example, seasonal variations in R25<\/sub> were more accurately modelled, with R<\/em>2<\/sup> of 0.65 for deciduous broadleaf forests, compared to the static predictions of the standard model.<\/li>\n
    2. Better Predictions of GPP and Canopy Respiration<\/strong>
      \nThe EEO-based scheme significantly enhanced GPP predictions across multiple sites. It improved the model’s accuracy, reducing bias from 10.1% to just 4%, and cut down the error in canopy respiration predictions, which were previously overestimated by up to 200%. These improvements lead to a more reliable estimation of carbon sequestration in terrestrial ecosystems.<\/li>\n
    3. Enhanced Carbon-Climatic Feedback Modelling<\/strong>
      \nIncorporating acclimation processes reduces the overestimation of carbon release with warming. For example, the EEO model predicted a more moderate increase in GPP with temperature rise (16.5%) compared to the 41.2% increase in the standard model. This more conservative estimate of carbon cycle responses helps temper the feedback between climate change and carbon emissions, offering a more balanced view of future climate-carbon interactions.<\/li>\n<\/ol>\n
      \"\"
      Figure 2 Temporal variations in R25 and Vcmax,25 for the specific PFT at B4WarmED site. The boxplots indicate the measured (grey box) and EEO simulated (yellow box) trait values weekly-averaged for 2009-2013 (25th percentile, 75th percentile and median; maximum and minimum for the whiskers). The black and yellow curves show the temporal variations in mean trait values for measurements and EEO simulations, respectively. The purple dotted line is the PFT parameter used in the standard Noah MP. R2 and RMSE are calculated using the mean trait values. C.V. is the coefficient of variation of measured mean trait values. DBF: deciduous broadleaf forest; ENF: evergreen needleleaf forest. The seasonal variation of measured Vcmax,25 for ENF was not analysed here due to the limited samplings.<\/figcaption><\/figure>\n

       <\/p>\n

       <\/p>\n

      A Model with Less Complexity, More Precision<\/h2>\n

      One of the most exciting aspects of the EEO-based scheme is its simplicity. Unlike the standard Noah MP model, which requires numerous tuneable parameters for each plant functional type (PFT), the EEO-based scheme only needs three parameters to model acclimation globally. This streamlined approach makes the model computationally efficient while still capturing the crucial dynamics of plant responses to climate variability.<\/p>\n

      Further Enhancements<\/h2>\n

      While the EEO-based scheme shows great promise, there are still areas for improvement. Future iterations of the model may include better accounting for environmental variability and the acclimation of other plant components, such as roots and stems, as well as soil respiration. We also plan to refine the model’s ability to capture the full range of environmental influences on photosynthesis and respiration, ensuring even more accurate projections.<\/p>\n

      Conclusion<\/h2>\n

      This study marks a significant step forward in improving the accuracy and reliability of land surface models. By incorporating dynamic processes like acclimation into the NOAH-MP model, we\u2019ve achieved more accurate simulations of the carbon cycle, with important implications for understanding the carbon-climate feedback loop. As climate models continue to evolve, approaches like ours will play a crucial role in helping us predict and mitigate the impacts of climate change. We have big plans for incorporating even more EEO refined parameters into other LSMs soon.<\/p>\n

      We can read the full paper here for more details on the methods and results:<\/p>\n

      Ren, Y., Wang, H., Harrison, S.P., Prentice, I.C., Mengoli, G., Zhao, L., Reich, P.B., Yang, K. 2024. Incorporating the acclimation of photosynthesis and leaf respiration in the Noah-MP land surface model: model development and evaluation<\/em>. Journal of Advances in Modelling Earth Systems. https:\/\/doi.org\/10.1029\/2024MS004599<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

      At LEMONTREE, one of our core objectives is to enhance land surface models by incorporating the dynamic responses of plant processes to environmental changes using the Eco-Evolutionary Optimality theory. 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