Current convective parameterization produces bias in extreme rainfall events resulting inunderestimation of Amazon rainfall

Figure: Seasonal 3-hour mean of number of extreme events from TRMM 3h (thick black line) and HighResMIP models used.

 

The Science

Our results showed that Eleven out of seventeen HighResMIP models showed the observed association between rainfall and the number of extreme events at the annual and seasonal scales. Two models better captured the spatial pattern of extreme events at the seasonal and annual scales (higher r values) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals.  Our results suggest that resolution is necessary but not sufficient to improve extreme precipitation. This is particularly true in cases where convective precipitation is the main contributor to extreme precipitation, It is important to also note that convective precipitation in HighResMIP is parameterized and these parameterizations are tuned to ensure that energy is balanced, but they are not designed to capture precipitation extremes. Measurements and understanding of extreme rainfall events in tropical forests are research areas that deserve further attention.

The Impact

While improving model resolution is necessary, it alone is insufficient to enhance rainfall modeling accuracy in the Amazon. Additionally, there is an urgent need for measurements to better understand extreme rainfall events.

Summary

Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3 hourly predictions in the High-Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project, CMIP6) represent extreme rainfall events at annual, seasonal, and sub-daily time scales. TRMM 3B42 (Tropical Rainfall Measuring Mission) 3-hour data were used as observations. Our results showed that Eleven out of seventeen HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher r values) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties, and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.

Contact
Robinson Negron-Juarez
Lawrence Berkeley National Laboratory
robionson.inj@lbl.gov

Funding
This study was supported by the Office of Science, Office of Biological and Environmental Research of the US Department of Energy, Agreement Grant DE-AC02-05CH11231, Next Generation Ecosystem Experiments-Tropics, and the Office of Science’s Regional and Global
Model Analysis of the US Department of Energy, Agreement Grant DE-AC02-05CH11231, Reducing Uncertainties in Biogeochemical Interactions through Synthesis Computation Scientific Focus Area (RUBISCO SFA).

Publication
Negron-Juarez R, Wehner M, Silva Dias M, Ullrich P, Chambers J, Riley W. Coupled Model Intercomparison Project Phase 6 (CMIP6) High Resolution Model Intercomparison Project (HighResMIP) Bias in Extreme Rainfall Drives Underestimation of Amazonian Precipitation.
Environmental Research Communications, DOI: https://doi.org/10.1088/2515-7620/ad6ff9.

Investigating the metabolic responses of Populus trichocarpa leaves to varying light, CO2, and temperature conditions.

Fig: Overview of the experimental setup used to measure oxygen production and isoprene emission in Populus trichocarpa leaves together with traditional CO 2 /H 2 O fluxes and chlorophyll florescence under varying environmental conditions. Also shown are the graphical abstract (O 2 /CO 2 -isoprene yinyang) and the assimilatory quotient AQ calculated as the ratio of net CO 2 flux/net O 2 flux. Images courtesy of K. Jardine, Lawrence Berkeley National Laboratory.

The Science

Thermotolerance mechanisms that protect photosynthesis during heat stress are not well understood. CO2 fixation and production only provide part of the story. O2 production, electron transport, and lipid synthesis provide important mechanistic information on thermal optima. This study developed a novel method to simultaneously measure net oxygen production (NOP) and isoprene emissions in poplar leaves during photosynthesis under varying environmental conditions. 

The Findings
The findings provide a comprehensive view of how light, CO2 and temperature affect the photosynthetic redox budget, revealing key insights into how these variables influence ATP/NADPH utilization and thermotolerance mechanisms in plants including CO2 and O2 recycling and lipid synthesis.

The Impact

• In this study, we present the first coupled observations of leaf net CO2 assimilation (Anet), net oxygen production (NOP), δ18O in O2 and isoprene emissions together with traditional CO2/H2O gas exchange fluxes allowing estimates of ETR and the assimilatory quotient (AQ = Anet/NOP).

• The results confirm a tight connection between water oxidation and ETR and support a model where light-dependent lipid synthesis is primarily driven by photosynthetic ATP/NADPH not consumed by the Calvin-Benson cycle, as an important thermotolerance mechanism linked with high rates of (photo)respiration and CO2/O2 recycling.

Summary

Our study shows that coupling O2 and isoprene exchange to traditional CO2/H2O gas exchange is possible, using cavity ringdown spectroscopy (O2) and proton transfer-reaction mass spectrometery (isoprene). This configuration allows a more complete picture of the photosynthetic redox budget via photosynthetic production of O2, electron transport rate (ETR), and isoprene biosynthesis. This opens avenues for useful measurements during photosynthesis, such as the temperature sensitivity of gross oxygen production (GOP) using 18O-water labeling, and the assimilatory quotient (AQ) which appears to be suppressed at high leaf temperature.  Also, our findings may help resolve some confusion in the literature as to whether isoprene emissions and perhaps lipid synthesis in chloroplasts in general, may or may not be directly linked to net photosynthesis. In agreement with numerous previous studies, we found that isoprene emission can be uncoupled from Anet, i.e., at low Ci and high temperature, and thus it is unlikely that lipid biosynthesis in chloroplasts strictly depends on photosynthesis rate or carbon provision by photosynthates. Therefore, our results suggest that (i) isoprene synthesis (and potentially lipid synthesis in general) in chloroplasts is related to electron generation by photolysis and thus probably via excess photosynthetic ATP/NADPH (not consumed by the Calvin cycle, the photorespiratory cycle, and other pathways acting in parallel like the malate/oxaloacetate shuttle), and (ii) is carbon-limited only when gross photosynthesis declines considerably. The results confirm a tight connection between water oxidation and ETR and support a view of light-dependent lipid synthesis primarily driven by photosynthetic ATP/NADPH not consumed by the Calvin-Benson cycle, as an important thermotolerance mechanism linked with high rates of (photo)respiration and CO2/O2 recycling. Simplified metabolic model of primary CO2 and O2 metabolism at elevated leaf temperatures (e.g. 35 ºC) in poplar leaves (accelerated metabolism). Elevated temperature leads to a suppression of stomatal conductance (gs), net oxygen production (NOP), and net atmospheric CO2 uptake (Anet) and a stimulation of photosynthesis, (photo)respiration, and internal CO2/O2 recycling and isoprenoid synthesis consuming ATP/NADPH. Note the activity of the water-water cycle is depicted as the cycling between O2 and H2O2. 

Contact

Dr. Kolby Jeremiah Jardine
Climate and Ecosystem Sciences Division
Lawrence Berkeley National Laboratory
Email: kjjardine@lbl.gov

Funding

This research was supported by the U.S. Department of Energy (DOE) Office of Science, Office of Biological and Environmental Research (BER), Biological System Science Division (BSSD), Early Career Research Program under Award number FP00007421. Additional support was provided by the Next Generation Ecosystem Experiments-Tropics (NGEE-Tropics) through contract No. DE-AC02-05CH11231.

Publications

Jardine KJ, Som S, Gallo LB, Demus J, Domingues TF, Wistrom CM, Gu L, Tcherkez G, Niinemets Ü. Concurrent Measurement of O2 Production and Isoprene Emission During Photosynthesis: Pros, Cons and Metabolic Implications of Responses to Light, CO2 and Temperature. Plant, cell & environment. 2024. 

https://doi.org/10.1111/pce.15124

Harnessing machine learning to enhance plant coexistence in a vegetation demographic model

Image by Justin Clark:  Harmonious plant coexistence in a vibrant tropical forest ecosystem.

The Science

Tropical forests are critical components of the global carbon, water, and energy cycles that feature the highest biodiversity on Earth. However, modeling the coexistence of different plant types—a key feature of biodiversity—in these forests remains challenging. Researchers used a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), integrated with the Energy Exascale Earth System Model land model (ELM) to improve modeling plant coexistence. The team employed advanced machine learning (ML) techniques to optimize key trait parameters in FATES, resulting in a remarkable enhancement in simulating plant coexistence. The ML approach also improved the accuracy of FATES simulations of water, energy, and carbon fluxes and aboveground biomass.

The Impact

By harnessing the power of ML, this study significantly enhanced scientists’ ability to model the coexistence of different plant types in tropical forests. Artificial intelligence-enhanced ecosystem models hold the potential to accurately predict the effects of environmental changes on diverse ecosystems, fostering effective strategies for sustainable development, carbon sequestration, and achieving carbon-neutral and net-zero emissions goals. Moreover, this research highlights the need for advancing vegetation demographic models to refine the simulation of coexisting plants to capture intricate ecosystem interactions.

Summary

A research team employed two approaches to optimize trait parameters in FATES: 1) leveraging field-based plant trait relationships, and 2) utilizing ML surrogate models. Ensembles of FATES experiments were conducted of a tropical forest site near Manaus, Brazil, in the Amazon basin. The ML-based surrogate models were used to optimize the trait parameters in FATES to improve plant functional type (PFT): sets of plants that have similar environmental responses and ecosystem roles, coexistence, and achieve better model-observation agreements. Considering only observed trait relationships improved the water, energy, and carbon simulations, but degraded PFT coexistence in ELM-FATES simulations. The ML approach significantly enhanced PFT coexistence in the FATES experiments, increasing its occurrence from 21 to 73 percent. After applying observation constraints to identify small simulation biases, the ML-guided simulations retained 33 percent of the coexistence experiments, showing a 23.6-fold increase in PFT coexistence compared to the default experiments. The ML approach also improved FATES simulations of water, energy, and carbon fluxes, as well as aboveground biomass. Based on these results, researchers proposed a reproducible method that utilizes ML to improve model fidelity and PFT coexistence in vegetation demography models. This research highlights the potential of using ML in Earth system modeling of ecosystem dynamics and their response and feedback to climate change impacts.

Contact
Ruby Leung
Pacific Northwest National Laboratory
Ruby.Leung@pnnl.gov

Funding

This research was supported by the Department of Energy’s Biological and Environmental Research program as part of the Terrestrial Ecosystem Science program through the Next-Generation Ecosystem Experiments-Tropics project.

Publications

Li, L., Y. Fang, Z. Zheng, M. Shi, M. Longo, C. D. Koven, J. A. Holm, R. A. Fisher, N. G. McDowell, J. Chambers, and L. R. Leung. “A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0),” Geosci. Model Dev., 16, 4017–4040 (2023). [DOI: 10.5194/gmd-16-4017-2023]

Related Links

https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1286/

Airborne laser scanning reveals the impacts of human activities and natural disturbances in a critical region for carbon dynamics in the Earth system.

Image courtesy of KC Cushman (left) and Marcos Longo (right).  Tropical forests are subject to a range of disturbance types, from small-scale mortality from natural processes affecting one or a few trees (left) to anthropogenic clearing of large areas (right).

 

The Science

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric
measurements suggest that it has been releasing more carbon than it has absorbed because of
deforestation and forest degradation. However, attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge. Using repeated high-resolution airborne laser scanning, we found a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y-1 for the study region attributable to both anthropogenic and natural processes. 

The Impact

Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes This study presents a detailed partitioning of aboveground carbon losses and gains in the Amazon forest, improving our understanding of the relative importance of anthropogenic and natural disturbance types. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system. The methodology used here also demonstrates the use of randomized samples with airborne remote sensing for scaling observations from local to regional inference. 

Summary

Human activities and recent changes in regional climate have caused significant changes to the structure, integrity, and biodiversity of tropical forests. The Brazilian Amazon has experienced severe deforestation and degradation, leading to the region becoming a carbon source rather than a sink in recent decades. However, the relative importance of deforestation, degradation, and natural disturbances for regional carbon dynamics are not well understood. Using randomized, repeated, very high-resolution airborne laser scanning surveys, this study attributed carbon losses among anthropogenic and natural disturbance types, and extrapolated results to the entire Amazonian Arc of Deforestation. Extrapolating the lidar-based statistics to the study area (544,300 km2), we found that 24.1, 24.2, and 14.5 Tg C y-1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y-1) and other disturbances (50.3 Tg C y-1) were partially counterbalanced by forest growth (44.1 Tg C y-1). These results highlight the importance of forest degradation—in addition to more commonly studied deforestation—for regional carbon dynamics. 

Contact

Lead author:
Name: Ovidiu Csillik
Institution: Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA – Postdoctoral researcher
Current Institution: Wake Forest University, Winston-Salem, NC – Assistant Professor
Email: ovidiu.csillik@gmail.com
Phone: 213-465-6732

DOE co-author:
Name: KC Cushman
Institution: Oak+++ Ridge National Laboratory
Email: cushmankc@ornl.gov
Phone: 865-924-7364

Funding

• The research of O.C., M.K., A.F., and S.S. carried out at the Jet Propulsion Laboratory, California Institute of Technology, was under a contract with the National Aeronautics and Space Administration (80NM0018D0004).
• The research of K.C.C. was carried out at Oak Ridge National Laboratory, which is managed by the University of Tennessee-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy.
• O.C., M.K., M.L., and K.C.C were supported by the Next Generation Ecosystem Experiments‐Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (DE-AC02-05CH11231).
• E.R.P. was supported by a NASA LCLUC Program grant (20-LCLUC2020-0024).
• Funding for EBA airborne lidar datasets was provided by the Amazon Fund/BNDES (Grant 14.2.0929.1, Improving Biomass Estimation Methods for the Amazon – EBA); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvimento Científico e Tecnológico (Processes 403297/2016-8 and 301661/2019-7).
• Support to generate carbon calibrations was provided by the Sustainable Landscapes Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, and USAID, and the US Department of State.

Publications

10. Csillik et al.(NGEE-Tropics Collaboration), “A large net carbon loss attributed to anthropogenic and natural disturbances in the Amazon Arc of Deforestation” Proceedings of the National Academy of Sciences of the United States of America121 (33), e2310157121 (2024). [DOI: 10.1073/pnas.2310157121] 

Related Links

Deforestation harms climate less than other types of Amazon degradation, study finds, Reuters, August 5, 2024

Forest degradation releases 5 times more Amazon carbon than deforestation, Mongabay, August 9, 2024