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Two picrite flows from the SW rift zone of Mauna Loa contain xenoliths of dunite, harzburgite, lherzolite, plagioclase-bearing lherzolite and harzburgite, troctolite, gabbro, olivine gabbro, and gabbronorite. Textures and olivine compositions preclude a mantle source for the xenoliths, and rare earth element concentrations of xenoliths and clinopyroxene indicate that the xenolith source is not old oceanic crust, but rather a Hawaiian, tholeiitic-stage magma. Pyroxene compositions, phase assemblages and textural relationships in xenoliths indicate at least two different crystallization sequences. Calculations using the pMELTS algorithm show that the two sequences result from crystallization of primitive Mauna Loa magmas at 6 kbar and 2 kbar. Independent calculations of olivine Ni–Fo compositional variability in the plagioclase-bearing xenoliths over these crystallization sequences are consistent with observed olivine compositional variability. Two parents of similar bulk composition, but which vary in Ni content, are necessary to explain the olivine compositional variability in the dunite and plagioclase-free peridotitic xenoliths. Xenoliths probably crystallized in a small magma storage area beneath the rift zone, rather than the large sub-caldera magma reservoir. Primitive, picritic magmas are introduced to isolated rift zone storage areas during periods of high magma flux. Subsequent eruptions reoccupy these areas, and entrain and transport xenoliths to the surface. [ABSTRACT FROM PUBLISHER]

Reports on the discovery of exotic, strontium-enriched melt inclusions in Mauna Loa olivines in Hawaii Island. Complete trace-element patterns resembling those of layered gabbros found in ophiolites; Observation of recycled oceanic crust in `ghost plagioclase' within the source of Mauna Loa lavas.

The COVID-19 pandemic is causing projected annual CO2 emission reductions up to −8% for 2020. This approximately matches the reductions required year on year to fulfill the Paris agreement. We pursue the question whether related atmospheric concentration changes may be detected by the Total Carbon Column Observing Network (TCCON), and brought into agreement with bottom-up emission-reduction estimates. We present a mathematical framework to derive annual growth rates from observed column-averaged carbon dioxide (XCO2) including uncertainties. The min–max range of TCCON growth rates for 2012–2019 was [2.00, 3.27] ppm/yr with a largest one-year increase of 1.07 ppm/yr for 2015/16 caused by El Niño. Uncertainties are 0.38 [0.28, 0.44] ppm/yr limited by synoptic variability, including a 0.05 ppm/yr contribution from single-measurement precision. TCCON growth rates are linked to a UK Met Office forecast of a COVID-19-related reduction of −0.32 ppm yr−2 in 2020 for Mauna Loa. The separation of TCCON-measured growth rates vs. the reference forecast (without COVID-19) is discussed in terms of detection delay. A 0.6 [0.4, 0.7]-yr delay is caused by the impact of synoptic variability on XCO2, including a ≈1-month contribution from single-measurement precision. A hindrance for the detection of the COVID-19-related growth rate reduction in 2020 is the ±0.57 ppm/yr uncertainty for the forecasted reference case (without COVID-19). Only assuming the ongoing growth rate reductions increasing year-on-year by −0.32 ppm yr−2 would allow a discrimination of TCCON measurements vs. the unperturbed forecast and its uncertainty—with a 2.4 [2.2, 2.5]-yr delay. Using no forecast but the max–min range of the TCCON-observed growth rates for discrimination only leads to a factor ≈2 longer delay. Therefore, the forecast uncertainties for annual growth rates must be reduced. This requires improved terrestrial ecosystem models and ocean observations to better quantify the land and ocean sinks dominating interannual variability. [ABSTRACT FROM AUTHOR]

Predicting lava flow pathways is important for understanding effusive eruptions and for volcanic hazard assessment. One particular challenge is understanding the interplay between flow pathways and substrate topography that is often rough on a variety of scales (< 1 m to 10 s km). To study this problem, we develop a lava flow modeling framework that combines spectral analysis of substrate roughness with a new lava flow model (MULTIFLOW). The MUTLIFLOW model includes a multiple flow direction routing algorithm in conjunction with a thresholding function that limits the extent of the flow. Comparison of MULTIFLOW results with recent flows on Mt. Etna, Mauna Loa, and Ki̅lauea suggests that MULTIFLOW accurately predicts the influence of topography on lava flow paths and morphology. Spectral filtering of pre-flow digital elevation models allow us to quantify which topographic wavelengths are most important for influencing lava flow pathways. Filtering constrains the height of pre-existing objects that flow overtop as a measure of flow thickness, and aids in identifying divergence of flow pathways from pre-eruptive topography that would result from modification of the substrate by the flow. Low-pass filter cutoffs in the range of ~ 10–100 m significantly improve the fit between modeled flow and real flow paths, suggesting a sensitivity of lava flow paths to such mesoscale topographic wavelengths. The three case studies are generalized with a parameter investigation of lava flow branching on synthetic red-noise topography. We demonstrate that the tendency of a flow to branch or not can be predicted on the basis of spectral characteristics of the underlying surface. We end by examining spectral signatures associated with volcanic and erosional processes on the Island of Hawai'i that may aid in forecasting lava flow pathways and deconvolving competing topographic construction and erosion in volcanic landscapes on longer timescales. [ABSTRACT FROM AUTHOR]

In this work, we exploited large DInSAR and GPS datasets to create a 4D image of the magma transfer processes at Mauna Loa Volcano (Island of Hawai’i) from 2005 to 2015. The datasets consist of 23 continuous GPS time series and 307 SAR images acquired from ascending and descending orbits by ENVISAT (ENV) and COSMO-SkyMed (CSK) satellites. Our results highlight how the joint use of SAR data acquired from different orbits (thus with different look angles and wavelengths), together with deformation data from GPS networks and geological information can significantly improve the constraints on the geometry and location of the sources responsible for the observed deformation. The analysis of these datasets has been performed by using an innovative method that allows building a complex source configuration. The results suggest that the deformation pattern observed from 2005 to 2015 has been controlled by three deformation sources: the ascent of magma along a conduit, the opening of a dike and the slip along the basal decollement. This confirms that the intrusion of the magma within a tabular system (rift dikes) may trigger the sliding of the SE portion of the volcanic edifice along the basal decollement. This case study confirms that it is now possible to exploit large geodetic datasets to improve our knowledge of volcano dynamics. The same approach could also be easily applied in other geodynamical contexts such as geothermal reservoirs and regions with complex tectonics. [ABSTRACT FROM AUTHOR]