Yang Song

Interests

Research

1. The interactions between environmental change and terrestrial biosphere processes, including land- surface energy balance, carbon, nitrogen, and phosphorus cycles, and the hydrological cycle.2. Using machine learning approaches to identify metagenomics-informed soil enzyme functional groups and their climate response at the regional and global scale.3. Using metagenomic information to improve the representation of microbially-mediated soil carbon, nitrogen, and phosphorus biogeochemical cycles in the Earth system models.4. Incorporating mechanisms informed with multi-scale observations and understandings of terrestrial processes into Earth system models to better simulate biospheric responses and feedbacks to environmental change.5. The impacts of ecosystem-climate-human interactions on ecosystem goods and services, focusing on agricultural production, bioenergy feedstock, carbon sequestration, and water supply.6. Development of big data and decision support tools for agriculture and bioenergy industry.

Courses

Scholarly Contributions

Journals/Publications

• Neri, P., Gu, L., & Song, Y. (2024). Temperature acclimation of photosystem II Efficiency across plant functional types and climate. EGU Biogeoscience. doi:https://bg.copernicus.org/preprints/bg-2023-163/
• Gu, L., Grodzinski, B., Han, J., Marie, T., Zhang, Y., Song, Y., & Sun, Y. (2023). An exploratory steady‐state redox model of photosynthetic linear electron transport for use in complete modelling of photosynthesis for broad applications. Plant, Cell & Environment. doi:https://doi.org/10.1111/pce.14563
• Lin, T., Kheshgi, H., Song, Y., Vorosmarty, C. J., & Jain, A. K. (2023). Which crop has the highest bioethanol yield in the United States?. Frontiers in Energy Research, 11. doi:https://doi.org/10.3389/fenrg.2023.1070186
• Gu, L., Grodzinski, B., Han, J., Marie, T., Zhang, Y., Song, Y., & Sun, Y. (2022). Granal thylakoid structure and function: explaining an enduring mystery of higher plants. New Phytologist. doi:https://doi.org/10.1111/nph.18371
• Lin, T., Song, Y., Lawrence, P., Kheshgi, H. S., & Jain, A. (2021). Worldwide Maize and Soybean Yield Response to Environmental and Management Factors Over the 20th and 21st Centuries. JGR Biogeosciences, 126. doi:https://doi.org/10.1029/2021JG006304
  More info

  A land process model, Integrated Science Assessment Model, is extended to simulate contemporary soybean and maize crop yields accurately and changes in yields over the period 1901–2100 driven by environmental factors (atmospheric CO2 level ([CO2]) and climate), and management factors (nitrogen input and irrigation). Over the twentieth century, each factor contributes to global yield increase; increasing nitrogen fertilization rates is the strongest driver for maize, and increasing [CO2] is the strongest for soybean. Over the 21st century, crop yields are projected under two future scenarios, RCP4.5-SSP2 and RCP8.5-SSP5; the warmer temperature drives yields lower, while rising [CO2] drives yields higher. The adverse warmer temperature effect of maize and soybean is offset by other drivers, particularly the increase in [CO2], and resultant changes in the phenological events due to climate change, particularly planting dates and harvesting times, by 2090s under both scenarios. Global yield for maize increases under RCP4.5-SSP2, which experiences continued growth in [CO2] and higher nitrogen input rates. For soybean, yield increases at a similar rate. However, in RCP8.5-SSP5, maize yield declines because of greater climate warming, extreme heat stress conditions, and weaker nitrogen fertilization than RCP4.5-SSP2, particularly in tropical and subtropical regions, suggesting that application of advanced technologies, and stronger management practices, in addition to climate change mitigation, may be needed to intensify crop production over this century. The model also projects spatial variations in yields; notably, the higher temperatures in tropical and subtropical regions limit photosynthesis rates and reduce light interception, resulting in lower yields, particularly for soybean under RCP8.5-SSP5.
• Lin, T., Song, Y., Jain, A., Lawrence, P., & Kheshgi, H. S. (2020). Effects of environmental and management factors on worldwide maize and soybean yields over the 20th and 21st centuries. Biogeosciences Discuss. doi:https://doi.org/10.5194/bg-2020-68

Presentations

• Fan, C., & Song, Y. (2024, Sep). The effect of changing hydroclimate on soil microbial functions involved in soil C nutrient cycling. GSA Connects 2024. Anaheim, CA.
• Neri, P., Song, Y., & Gu, L. (2024, Winter). Light reactions: A vision for a statistical and mechanistic model of environmental regulation on SIF and GPP at the leaf level. AGU 2024. Washington DC.
• Song, Y. (2024, June). Integrating multiscale data, artificial intelligence, and Earth System Model to predict environmental feedback of microbially-mediated soil carbon dynamics. AOGS annual meeting. Gangwon-do, Korea.
• Song, Y. (2024, November). Gene-to-Earth system modeling: opportunities, challenges, and uncertainty. The Ecosystem Genomics GIDP program Seminar Series. Tucson, AZ, USA.
• Song, Y. (2024, Sep). Advancing irrigated agriculture water use and resource assessment in NextGen and USGS Agricultural water use framework: the upper Colorado river basin as a testing case. CRIOH Science Meeting 2024. Tuscaloosa, AL: NOAA/USGS.
• Song, Y. (2024, September). Harness the power of AI in predicting biosphere-climate interactions. UA College of Science AI Forum. Tucson AZ.
• Song, Y., & Fan, C. (2023, Apr). Integrating omics, machine learning, and process-based land surface model to predict hydroclimate feedbacks of microbial functions and its implication for soil carbon emission. EGU General Assembly. Vienna, Austria.
• Song, Y., Fan, c., & Wilson, S. (2024, Nov). Leveraging Gene-to-Ecosystem MONet observations and artificial intelligence to unveil environmental regulation on diverse microbial functions in soil carbon and nutrient cycles. 2024 ASA-CSSA-SSSA International Annual Meeting. San Antonio, TX.
• Wilson, S., Mishra, U., & Song, Y. (2024, September). Parameterizing biochar effect on soil greenhouse gas emission and carbon sequestration using artificial intelligence and land surface model. GSA Connects 2024. Anaheim, CA.
• Wilson, S., Wilson, U., & Song, Y. (2024, Dec). Parameterizing biochar effect on climate-smart agriculture using artificial intelligence and land surface model. AGU 2024. Washington DC.
• Fan, C., & Song, Y. (2023, Dec). Deep learning unravels spatiotemporal dynamics of soil microbial functional diversity and consequent soil carbon emission under changing environment. AGU2023. San Francisco, CA.
• Fan, C., & Song, Y. (2023, Nov). Harness the power of NEON observations and artificial intelligence to predict the hydroclimate regulation on microbial functional composition across the CONUS (The second prize of the graduate poster). The 18th Annual Research Insights in Semiarid Ecosystem (RISE) Symposium. Tucson, AZ.
• Song, Y. (2023, July). Biological and hydraulic controls on soil water dynamics and its implication for Arizona grassland carbon cycle under changing hydroclimate. The 9th COAA International Conference on Atmosphere, Ocean, and Climate Change (ICAOCC23). Singapore.
• Song, Y., Changpeng, F., & Sabrina, W. (2023, Dec). Needs, challenges, and opportunities for representing environmental feedback of microbial composition in the Earth system models. AGU2023. San Francisco, CA.
• Song, Y., Fan, C., & Hu, T. (2023, Dec). Integrating molecular-to-ecosystem observations to advancing couped soil hydrological and biogeochemical cycles in the dryland. DOE RUBISCO SOC Working group meeting. San Francisco, CA.
• Song, Y., Fan, C., & Weintraub-Leff, S. (2023, Dec). Integration NEON and MoNet observations to predict environmental feedback of microbial functional composition and its implication for soil carbon emission. AGU2023. San Francisco, CA.
• Song, Y., Hu, T., & Zeng, X. (2023, Aug). Biological and hydraulic controls on soil water dynamics and its implication for dryland carbon cycle under changing hydroclimate. Asia Oceania Geosciences Society 20th Annual Meeting. Singapore.
• Wilson, S., Gautam, S., Mishra, U., & Song, Y. (2023, Dec). Parameterizing Biochar Effect on Soil Decomposition Processes Using Artificial Intelligence and Process Based Modeling. AGU2023. San Francisco, CA.
• Fan, C., & Song, Y. (2022, Dec). Mapping microbial functional diversity and predicting its environmental response. AUG2022. Chicago, IL.
• Song, Y. (2022). The challenge of genomics for ecosystem modeling. The Ecosystem Genomics Initiative Seminar Series. Tucson: NSF.
• Song, Y. (2022, June). Mitigating uncertainty in predicting climate-carbon feedback. . The 5th Land Model Training Courses, Northern Arizona University. Flagstaff, USA.: Northern Arizona University.
• Song, Y. (2022, June). Needs and challenges of representing microbial functional diversity in the Earth system models. DOE Sandia National Lab Soils in the climate crisis workshop. Online: DOE.
• Song, Y. (2022, Nov). Mitigating uncertainty in predicting climate-carbon feedback from the perspective of biosphere-climate interaction. Geoscience department seminar. Tucson.
• Song, Y. (2022, Oct). Harness the power of machine learning and omics to identify microbial functional composition across diverse environments. EMSL Integration 2022. Online (Hybrid).
• Song, Y., Fan, C., & Yang, X. (2022, Dec). Integration omics, machine learning, and process-based land surface model to predict environmental feedbacks of microbial functions and its implication for soil carbon emission. AUG2022. Chicago, IL.
• Hu, T., Biederman, J., Smith, W. K., Zeng, X., & Song, Y. (2021, Dec). The feedback of Arizona Grassland to Longer Seasonal Droughts and its Implication for Dryland Carbon Cycling: Insights from Model-Experiment Integration. AGU2021. Online.
• Song, Y. (2021, Fall). Environmental resilience of vegetation and microbial communities from scientific understanding to real-world application. Civil & Architectural Engineering & Mechanics Department, University of Arizona. Tucson, AZ.
• Song, Y. (2021, Spring). Identifying enzyme functional composition across diverse environments for parameterizing microbial function in land surface models. RUBISCO Soil Carbon Work Group Meeting. Online: DOE.
• Song, Y., & Neri, P. (2021, Aug). The Climate Feedback of Light Reaction: A Meta-Analysis Utilizing SIF. DOE ESS PI meeting 2021. Online: DOE.
• Song, Y., Fan, C., Gautam, S., & Mishra, U. (2021, Dec). Microbial functional composition across diverse environments: a dataset to parameterize and benchmark microbial-mediated soil organic matter decomposition models. AGU2021. Online.
• Song, Y., Hu, T., Neri, P., Biederman, J., & Smith, W. K. (2021, Fall). The feedback of Arizona Grassland to Longer Seasonal Droughts and its Implication for Dryland Carbon Cycling: Insights from Model-Experiment Integration. 2021 Symposium on Resilience Research for Global Development Challenges. Tucson, AZ.
• Song, Y. (2020, 08/2020). Modeling microbial functional diversity mitigates projected soil carbon loss in response to climate warming. ESA 2020. Online.

Poster Presentations

• Fan, C., & Song, Y. (2024, Dec). The effect of changing hydrothermal condition on soil microbial functions involved in soil C nutrient cycling. AGU2024. Washington DC.
• Gyo, J. C., Song, Y., & Mishra, U. (2024, Dec). Influence of soil microbes on spatial-temporal dynamics of soil carbon: coupling machine learning with ESM. AGU2024. Washington DC.
• Song, Y. (2024, Dec). Assessing the impact of climate change and extreme weather on microbial functional composition and soil carbon emissions: an integrated AI-ESM study. DOE RUBISCO SOC Working group meeting. Washington DC.
• Hu, T., Smith, W., Biederman, J., Zeng, X., & Song, Y. (2022, Nov). The feedback of Arizona grassland to changing hydroclimate and its impact on equilibrium state of carbon and water fluxes over time: A scenario analysis using CLM5.0.. The 18th Annual Research Insights in Semiarid Ecosystem (RISE) Symposium. Tucson, AZ: USDA.
• Neri, P., Hu, T., Zhang, X., Fan, C., Gu, L., & Song, Y. (2022, Dec). Modeling of Climate Feedbacks of Light-Dependent Photophysical and Photochemical Reactions and their Implication for Terrestrial Carbon Assimilation: Global Analysis Using the Community Land Model. AGU2022. Chicago, IL.
• Song, Y. (2022, Aug). Harness the power of machine learning and omics to identify microbial functional composition across diverse environments. JGI2022. San Francisco, CA.
• Hu, T., Biederman, J., Smith, W. K., Zeng, X., & Song, Y. (2021, Nov). The effect of Biological and Physical Processes on Soil Water Dynamics and its Feedback to Arizona Grassland. The 17th Annual Research Insights in Semiarid Ecosystem Symposium. Tucson AZ: USDA.
• Neri, P. J., Gu, L., & Song, Y. (2021, Dec). The Climate Feedback of Potential Photosynthetic Efficiency: A Meta-Analysis Utilizing SIF. AGU 2021. Online.