Informing Nature-based Climate Solutions for the United States with the best-available science

Drought alters aboveground biomass production efficiency: Insights from two European beech forests

The fraction of photosynthetically assimilated carbon that trees allocate to long-lasting woody biomass pools (biomass production efficiency - BPE), is a key metric of the forest carbon balance. Its apparent simplicity belies the complex interplay between underlying processes of photosynthesis, respiration, litter and fruit production, and tree growth that respond differently to climate variability. Whereas the magnitude of BPE has been routinely quantified in ecological studies, its temporal dynamics and responses to extreme events such as drought remain less well understood. Here, we combine long-term records of aboveground carbon increment (ACI) obtained from tree rings with stand-level gross primary productivity (GPP) from eddy covariance (EC) records to empirically quantify aboveground BPE (= ACI/GPP) and its interannual variability in two European beech forests (Hainich, DE-Hai, Germany; Sorø, DK-Sor, Denmark). We found significant negative correlations between BPE and a daily-resolved drought index at both sites, indicating that woody growth is de-prioritized under water limitation. During identified extreme years, early-season drought reduced same-year BPE by 29 % (Hainich, 2011), 31 % (Sorø, 2006), and 14 % (Sorø, 2013). By contrast, the 2003 late-summer drought resulted in a 17 % reduction of post-drought year BPE at Hainich. Across the entire EC period, the daily-to-seasonal drought response of BPE resembled that of ACI, rather than that of GPP. This indicates that BPE follows sink dynamics more closely than source dynamics, which appear to be decoupled given the distinctive climate response patterns of GPP and ACI. Based on our observations, we caution against estimating the magnitude and variability of the carbon sink in European beech (and likely other temperate forests) based on carbon fluxes alone. We also encourage comparable studies at other long-term EC measurement sites from different ecosystems to further constrain the BPE response to rare climatic events.

The principles of natural climate solutions

Natural climate solutions can mitigate climate change in the near-term, during a climate-critical window. Yet, persistent misunderstandings about what constitutes a natural climate solution generate unnecessary confusion and controversy, thereby delaying critical mitigation action. Based on a review of scientific literature and best practices, we distill five foundational principles of natural climate solutions (nature-based, sustainable, climate-additional, measurable, and equitable) and fifteen operational principles for practical implementation. By adhering to these principles, practitioners can activate effective and durable natural climate solutions, enabling the rapid and wide-scale adoption necessary to meaningfully contribute to climate change mitigation.

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

AmeriFlux BASE data pipeline to support network growth and data sharing

AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux's diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team's quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO₂) across the Chequamegon Ecosystem-Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became weaker with time as non-climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. Higher CO₂ increased maximum net assimilation parameters but not total gross primary productivity. Stand-scale sites were larger net sinks than the landscape tower. Clustered, long-term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.