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At least 271 records · Page 15

Sampling the ENSO response to tropical volcanic eruptions in the GISS-E2-1 model: physical & biogeochemical ocean effects

Despite observational evidence that an El-Niño is likely to follow tropical volcanic eruptions (Predybaylo et al, 2017), modelling studies have so far reached no consensus on either the sign or physical mechanism of El Niño response to volcanism (Khodri et al, 2017) . A previous modelling study found that following the Pinatubo eruption, the tropical Pacific developed El Niño-like conditions that triggered a decrease in the upwelling of DIC and increased carbon uptake (Eddebbar et al., 2019). However, the carbon cycle response to volcanisms as it depends on ENSO phase remains unclear. Here we perform thirty ensemble control simulations using the GISS-E2-1-G model to sample the pre-industrial control ENSO cycle and thirty more to superimpose a Pinatubo-like volcanic eruption. We find that the model tends to favor the development of a weak El-Niño following the volcanic eruption only in the case of a volcano that erupts at the start of a neutral ENSO phase. In response to the weak El-Niño, some anomalies have the same sign as those that develop from a strong El-Niño, such as weaker winds, shallower mixed layer depths, less direct but more diffuse radiation at the surface of the ocean, less nitrate, and lower primary productivity. However, other anomalies show the opposite sign as those from a strong El-Niño, such as more precipitation, less surface DIC, and less surface ocean pCO 2 . In contrast to the neutral phase, the volcanic response during positive ENSO and negative ENSO phases are swamped by the model’s internal variability.

ENSO↗

Leveraging observed soil heterotrophic respiration fluxes as a novel constraint on global-scale models

Microbially-explicit models may improve understanding and projections of carbon dynamics in response to future climate change, but their fidelity in simulating global-scale soil heterotrophic respiration (RH), a stringent test for soil biogeochemical models, has never been evaluated. We used statistical global RH products, as well as 7,821 daily site-scale RH measurements, to evaluate the spatio-temporal performance of one first-order decay model (CASA-CNP) and two microbially-explicit biogeochemical models (CORPSE and MIMICS) that were forced by two different climate datasets. CORPSE and MIMICS did not provide any measurable performance improvement; instead, the models were highly sensitive to the meteorological input data used to drive them. Spatial RH variability was generally well simulated except in the northern middle latitudes (~50°N) and arid regions; models captured the seasonal variability of RH well, but showed more divergence in tropic and arctic regions. Our results demonstrate that the next generation of biogeochemical models shows promise, but also needs to be improved for realistic spatio-temporal variability of RH. Finally, we emphasize the importance of net primary production, soil moisture, and soil temperature inputs, and that jointly evaluating soil models for their spatial (global scale) and temporal (site scale) performance provides crucial benchmarks for improving biogeochemical models.

Jian, Jinshi↗

Improving Coastal Ocean Color Validation Capabilities through Application of Inherent Optical Properties (IOPs)

Understanding how the different components of seawater alter the path of incident sunlight through scattering and absorption is essential to using remotely sensed ocean color observations effectively. This is particularly apropos in coastal waters where the different optically significant components (phytoplankton, detrital material, inorganic minerals, etc.) vary widely in concentration, often independently from one another. Inherent Optical Properties (IOPs) form the link between these biogeochemical constituents and the Apparent Optical Properties (AOPs). understanding this interrelationship is at the heart of successfully carrying out inversions of satellite-measured radiance to biogeochemical properties. While sufficient covariation of seawater constituents in case I waters typically allows empirical algorithms connecting AOPs and biogeochemical parameters to behave well, these empirical algorithms normally do not hold for case I1 regimes (Carder et al. 2003). Validation in the context of ocean color remote sensing refers to in-situ measurements used to verify or characterize algorithm products or any assumption used as input to an algorithm. In this project, validation capabilities are considered those measurement capabilities, techniques, methods, models, etc. that allow effective validation. Enhancing current validation capabilities by incorporating state-of-the-art IOP measurements and optical models is the purpose of this work. Involved in this pursuit is improving core IOP measurement capabilities (spectral, angular, spatio-temporal resolutions), improving our understanding of the behavior of analytical AOP-IOP approximations in complex coastal waters, and improving the spatial and temporal resolution of biogeochemical data for validation by applying biogeochemical-IOP inversion models so that these parameters can be computed from real-time IOP sensors with high sampling rates. Research cruises supported by this project provides for collection and processing of seawater samples for biogeochemical (pigments, DOC and POC) and optical (CDOM and POM absorption coefficients) analyses to enhance our understanding of the linkages between in-water optical measurements (IOPs and AOPs) and biogeochemical constituents and to provide a more comprehensive suite of validation products.

Mannino, Antonio↗