ORCID Profile
0000-0002-2275-0713
Current Organisation
Utrecht University
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Publisher: American Geophysical Union (AGU)
Date: 03-2022
DOI: 10.1029/2021EA002119
Abstract: This article is composed of three independent commentaries about the state of I ntegrated, C oordinated, O pen, N etworked (ICON) principles in the American Geophysical Union Biogeosciences section, and discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (a) Global collaboration, technology transfer, and application (Section 2), (b) Community engagement, community science, education, and stakeholder involvement (Section 3), and (c) Field, experimental, remote sensing, and real‐time data research and application (Section 4). We discuss needs and strategies for implementing ICON and outline short‐ and long‐term goals. The inclusion of global data and international community engagement are key to tackling grand challenges in biogeosciences. Although recent technological advances and growing open‐access information across the world have enabled global collaborations to some extent, several barriers, ranging from technical to organizational to cultural, have remained in advancing interoperability and tangible scientific progress in biogeosciences. Overcoming these hurdles is necessary to address pressing large‐scale research questions and applications in the biogeosciences, where ICON principles are essential. Here, we list several opportunities for ICON, including coordinated experimentation and field observations across global sites, that are ripe for implementation in biogeosciences as a means to scientific advancements and social progress.
Publisher: Wiley
Date: 26-10-2021
DOI: 10.1111/GCB.15905
Abstract: High‐quality atmospheric CO 2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO 2 . In this study, we present the first 6 years (2014–2019) of continuous, high‐precision measurements of atmospheric CO 2 at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO 2 regional signal ( ) that has a marked seasonal cycle with an litude of about 4 ppm. At both seasonal and inter‐annual scales, we find differences in phase between and the local eddy covariance net ecosystem exchange (EC‐NEE), which is interpreted as an indicator of a decoupling between local and non‐local drivers of . In addition, we present how the 2015–2016 El Niño‐induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter‐annual variability of together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO 2 exchange. We use both non‐optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and litude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO 2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data‐driven non‐optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle litude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.
Publisher: Wiley
Date: 11-08-2022
Abstract: Ecological forecasting provides a powerful set of methods for predicting short‐ and long‐term change in living systems. Forecasts are now widely produced, enabling proactive management for many applied ecological problems. However, despite numerous calls for an increased emphasis on prediction in ecology, the potential for forecasting to accelerate ecological theory development remains underrealized. Here, we provide a conceptual framework describing how ecological forecasts can energize and advance ecological theory. We emphasize the many opportunities for future progress in this area through increased forecast development, comparison and synthesis. Our framework describes how a forecasting approach can shed new light on existing ecological theories while also allowing researchers to address novel questions. Through rigorous and repeated testing of hypotheses, forecasting can help to refine theories and understand their generality across systems. Meanwhile, synthesizing across forecasts allows for the development of novel theory about the relative predictability of ecological variables across forecast horizons and scales. We envision a future where forecasting is integrated as part of the toolset used in fundamental ecology. By outlining the relevance of forecasting methods to ecological theory, we aim to decrease barriers to entry and broaden the community of researchers using forecasting for fundamental ecological insight.
No related grants have been discovered for Gerbrand Koren.