|
B019 |
Improving Earth System Predictability: New Mechanisms, Feedbacks, and Approaches for Predicting Global Biogeochemical Cycles in Earth System Models II Posters |
Forrest M. Hoffman, Cheryl S. Harrison, Cheng-En Yang |
07:00–23:59 EST 04:00–20:59 PST |
Predictions of future atmospheric CO2 levels are influenced by global carbon and nutrient cycles, climate interactions, and feedbacks to the Earth system. Relevant processes operate at different spatial and temporal scales and vary across terrestrial, coastal, and marine ecosystems. Uncertain biogeochemical feedbacks may be altered by anthropogenic disturbance agents, including tropospheric O3, acceleration of nutrient and hydrological cycles, eutrophication, acidification, land cover/land use change, and potential climate intervention strategies. This session focuses on integrated understanding of feedback mechanisms that improve Earth system predictability, methods for evaluating and benchmarking process representations in Earth system models, approaches for constraining future climate projections (e.g., emergent constraints), and novel applications of artificial intelligence and machine learning for improving predictive understanding of global biogeochemical cycles.
Type: Poster
Primary Convener: Forrest M. Hoffman (Oak Ridge National Laboratory)
Conveners: Cheryl S. Harrison (University of Texas Rio Grande Valley) Cheng-En Yang (University of Tennessee)
Chairs: Forrest M. Hoffman (Oak Ridge National Laboratory) Cheryl S. Harrison (University of Texas Rio Grande Valley) Cheng-En Yang (University of Tennessee)
OSPA Liaison: Cheryl S. Harrison (University of Texas Rio Grande Valley)
Index Terms: 0428 Carbon cycling 0439 Ecosystems, structure and dynamics 1615 Biogeochemical cycles, processes, and modeling 1622 Earth system modeling
Neighborhoods: 3. Earth Covering
SWIRLs and Tracks: Climate - SWIRL
Cross-Listed: OS - Ocean Sciences GC - Global Environmental Change
Co-Sponsored: ESA: Ecological Society of America
https://agu.confex.com/agu/fm20/meetingapp.cgi/Session/104004
|
|
B019-0003 |
Country-Level Carbon Sequestration Potential by the Middle of the 21st Century |
Lifen Jiang, Junyi Liang, Xingjie Lu, Enqing Hou, Forrest M. Hoffman, Yiqi Luo |
07:00–23:59 EST 04:00–20:59 PST |
Countries have long been making efforts by reducing emissions of greenhouse gases to mitigate climate change. In the agreements of the United Nations Framework Convention on Climate Change, involved countries have committed to reduction targets. However, carbon (C) sink by natural ecosystems has been difficult to quantify. Using a transient traceability framework, we quantified country-level C sequestration potential by natural terrestrial ecosystems by the middle of the 21$^st century based on simulations of 12 CMIP5 Earth System Models under RCP8.5. The top 20 countries that have the highest C sequestration potential has the potential to sequester 62 Pg C by the middle of this century. Among the top 20 countries, Russia, Canada, United States, China, and Brazil sequester the most. The dominant forces to drive carbon sequestration are changes in net primary production and C residence time. Our results highlight that model-based estimates of land C sequestration may potentially offset a substantial proportion of greenhouse-gas emissions, especially for countries with a large change in NPP and long inherent residence time.
Authors:
Lifen Jiang (Northern Arizona University)
Junyi Liang (China Agricultural University)
Xingjie Lu (Northern Arizona University)
Enqing Hou (Northern Arizona University)
Forrest M. Hoffman (Oak Ridge National Laboratory)
Yiqi Luo (Northern Arizona University)
https://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/706677
|
|
B019-0005 |
Dissolved Organic Carbon in Arctic Rivers: Reduced Model with Functional Groups |
Amadini Mendis Jayasinghe, Scott Elliott, Anastasia Piliouras, Jaclyn L. Clement-Kinney, Georgina Gibson, Nicole Jeffery, Forrest M. Hoffman, Jitendra Kumar, Oliver W. Wingenter |
07:00–23:59 EST 04:00–20:59 PST |
Boreal river systems play a crucial role in high latitude change as they carry the highest terrestrial input of all aquatic flow to the sea. This includes a massive dissolved organic flux, injected directly to the climatologically sensitive Arctic Ocean. The dissolved organics imply chemical functional groups that interact with coastal and open ocean biophysical properties such as light attenuation, surface tension, trace metal chelation and aerosol formation. We have performed reduced kinetic modeling for organic matter evolution along an idealized Siberian river. We studied reactivity, networking and fate for the major macromolecular groups based on their diverse structures: sugar, lipids, proteins, heteropolycondensate and humic substance are all considered. We found that along the stream course, chemical reactivity is slow relative to the coastal or open ocean, but mixing at tributary nodes plays a dominant role. Concentrations for the various carbon compounds stagger at connecting points based specifically on sourcing from the different Arctic sub-ecosystems: taiga, tundra, woodland, peat, bog and others. Even so, photochemical and microbial losses contribute to the final mix and along coastlines biophysical impacts are extreme. For example the chromophoric dissolved organic matter or CDOM attenuates at a one versus ten meter e-fold depending on upstream ecology. Soil-runoff and deltaic (pre- versus post-) processing also exert discrimination on the functional distribution and aquatic chemical influence. Further investigation is necessary and ongoing, through an increase in the number of connection points dictating dilution and mixing. And we are hoping to investigate the interaction of humics as flocculants with mineral particles, since they are capable of removing turbidity as ionic strength rises in the plume.
Authors:
Amadini Mendis Jayasinghe (New Mexico Tech)
Scott Elliott (Los Alamos National Laboratory)
Anastasia Piliouras (Los Alamos National Laboratory)
Jaclyn L. Clement-Kinney (Los Alamos National Laboratory)
Georgina Gibson (University of Alaska Fairbanks)
Nicole Jeffery (Los Alamos National Laboratory)
Forrest M. Hoffman (Oak Ridge National Laboratory)
Jitendra Kumar (Oak Ridge National Laboratory)
Oliver W. Wingenter (New Mexico Tech)
https://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/686453
|
|
B019-0009 |
Detection and Attribution of Climate-Driven Extremes in Net Biome Productivity from 1850 through 2100 |
Bharat Sharma, Forrest M. Hoffman, Jitendra Kumar, Auroop R. Ganguly |
07:00–23:59 EST 04:00–20:59 PST |
Terrestrial ecosystems take up about one-third of total anthropogenic carbon emissions, providing a check on rising atmospheric CO2 concentration. While increases in CO2 fertilization and water use efficiency increase vegetation productivity under rising atmospheric CO2 levels, rising surface temperature often leads to a reduction in available soil moisture and an increase in plant respiration. This results in varying spatial and temporal responses of net biome production (NBP) and the strength of the land carbon sink. The latest generation of Earth system models and observations have shown that the increase in vegetation productivity could reach a tipping point beyond which the respiration losses could be higher than photosynthetic capacity, as the surface temperatures get higher than the optimum growing temperature of plants. However, the impacts of future climate on extremes in NBP is unknown. We investigated NBP extremes in the Community Earth System Model (CESM2) from 1850 through 2100 and attributed the NBP extremes to individual and compound effects of climate drivers. Preliminary results showed a net increase in the frequency of negative extremes in NBP, with anomalous reductions in soil moisture as the most dominant climate driver. We found increased variability in vegetation growth due to rising CO2 emissions through the study of extremes in NBP. A larger increase in the frequency and intensity of negative extremes in NBP than positive extremes in NBP indicates persistent extremes-driven reductions in vegetation growth in the future, and this imbalance could lead to a net reduction in terrestrial carbon uptake capacity and carbon storage when ecosystem respiration exceeds photosynthesis. The consequences of declining NBP and increasing negative extremes in NBP may result in global reduction in plant productivity and crop yield, even as the demand for vegetation is increasing due to rising demand for food, fiber, fuel, and building material.
Authors:
Bharat Sharma (Northeastern University)
Forrest M. Hoffman (Oak Ridge National Laboratory)
Jitendra Kumar (Oak Ridge National Laboratory)
Auroop R. Ganguly (Northeastern University)
https://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/756624
|
|
B019-0010 |
Have Land Surface and Carbon Cycle Processes in Earth System Models Improved Over Time? |
Forrest M. Hoffman, Nathan Collier, Mingquan Mu, Cheng-En Yang, Charles D. Koven, David M. Lawrence, Gretchen Keppel-Aleks, Min Xu, Qing Zhu, Weiwei Fu, Jiafu Mao, Hyungjun Kim, J. Keith Moore, William J. Riley, James T. Randerson |
07:00–23:59 EST 04:00–20:59 PST |
Better representation of biogeochemistry–climate feedbacks and ecosystem processes in Earth system models (ESMs) is essential for reducing uncertainties associated with projections of climate change during the remainder of the 21st century and beyond. Model–data comparison and integration activities are required to inform improvement of land carbon cycle models and the design of new measurement campaigns aimed at reducing uncertainties associated with key land surface processes. The International Land Model Benchmarking (ILAMB) Package was designed to facilitate systematic and comprehensive model–data comparison and improve understanding of factors influencing model fidelity. We used ILAMB to benchmark and intercompare terrestrial carbon cycle models coupled within ESMs used to conduct historical simulations for the Fifth and Sixth Phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Results indicate that the suite of CMIP6 land models exhibits better performance than the suite of CMIP5 land models in comparison with observations for a variety of biogeochemical, hydrological, and energy-related variables. These improvements are partially attributed to reductions of biases in temperature, precipitation, and incoming radiation, suggesting that free-running atmosphere models in these ESMs also improved; however, biases in some regions increased. An analysis of forcing variables, prognostic land variables, and relationships from variable-to-variable comparisons indicate an overall improvement in most CMIP6 models, with relationships for some models exhibiting the greatest improvement in ILAMB scores, suggesting that improved model process representation in some models, and likely increased model complexity, contributed to improved model performance. We further analyze the degree to which the range of model uncertainties may have been reduced for CMIP6 land models as compared with CMIP5 land models.
Authors:
Forrest M. Hoffman (Oak Ridge National Laboratory)
Nathan Collier (Oak Ridge National Laboratory)
Mingquan Mu (University of California Irvine)
Cheng-En Yang (University of Tennessee)
Charles D. Koven (Lawrence Berkeley National Laboratory)
David M. Lawrence (National Center for Atmospheric Research)
Gretchen Keppel-Aleks (University of Michigan Ann Arbor)
Min Xu (Oak Ridge National Laboratory)
Qing Zhu (Lawrence Berkeley National Laboratory)
Weiwei Fu (University of California Irvine)
Jiafu Mao (Oak Ridge National Laboratory)
Hyungjun Kim (University of Tokyo)
J. Keith Moore (University of California Irvine)
William J. Riley (Lawrence Berkeley National Laboratory)
James T. Randerson (University of California Irvine)
https://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/729408
|
|
B019-0011 |
The Community Land Model (CLM5) Parameter Perturbation Ensemble Project: Towards Comprehensive Understanding of Parametric Uncertainty on the Global Terrestrial Carbon Cycle |
David M. Lawrence, Katie Dagon, Daniel Kennedy, Rosemary A. Fisher, Benjamin Sanderson, Keith W. Oleson, Forrest M. Hoffman, Nathan Collier, Danica L. Lombardozzi, William R. Wieder, Charles D. Koven, Sean C. Swenson |
07:00–23:59 EST 04:00–20:59 PST |
The Community Land Model (CLM5) is widely used by the Earth System Modeling research community to study many aspects of the role of land in climate and weather. In particular, the omodel is frequently used to understand and predict global and regional land carbon stock trajectories, water state trends, and carbon-water interactions and water use efficiency trends. Recent work has demonstrated high uncertainty due to forcing, structural, and parametric uncertainties. Prior efforts to assess CLM parametric uncertainty have been hampered by computational constraints or code limitations, necessarily limited to selected parameters related to specific processes. Here, we present a new community effort to conduct a comprehensive tiered exploration of parameter sensitivity and uncertainty; the CLM5 Parameter Perturbation Ensemble project (CLM5PPE). We have identified 200+ model parameters across processes that control energy, water, carbon, and nitrogen interactions. Phase 1 of the CLM5PPE involves one-at-a-time high/low parameter perturbations for all 200+ parameters on a sparse grid (~250 grid cells) that reasonably captures the main features of global higher-resolution simulations. Each simulation is checked for reasonableness (e.g., vegetation survivability rates). Each parameter perturbation is also run with environmental perturbations (CO2, climate, N-deposition) that span historical and projected values. A set of 50 parameters are selected for further evaluation with the criteria for selection based on their importance in determining the mean, variability, and responses to environmental perturbations for a range of key land climate variables. Phase 2 uses these parameters to run a Latin hypercube sparse-grid 2500-member perturbed parameter ensemble, again repeated for each environmental perturbation. In Phase 3, ~200 best performing parameter sets will be used to run an ensemble of historical and projection period 2° resolution simulations to provide a realistic and comprehensive assessment of parametric uncertainty. All data output from this project as well as the scripting infrastructure to automate parameter perturbations, generate large ensembles, and assess model performance will also be made available to facilitate further parameter exploration of this and future versions of CLM.
Authors:
David M. Lawrence (National Center for Atmospheric Research)
Katie Dagon (National Center for Atmospheric Research)
Daniel Kennedy (National Center for Atmospheric Research)
Rosemary A. Fisher (National Center for Atmospheric Research)
Benjamin Sanderson (National Center for Atmospheric Research)
Keith W. Oleson (National Center for Atmospheric Research)
Forrest M. Hoffman (Oak Ridge National Laboratory)
Nathan Collier (Oak Ridge National Laboratory)
Danica L. Lombardozzi (National Center for Atmospheric Research)
William R. Wieder (National Center for Atmospheric Research)
Charles D. Koven (Lawrence Berkeley National Laboratory)
Sean C. Swenson (National Center for Atmospheric Research)
https://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/761603
|