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1. Global water level variability observed after the Hunga Tonga-Hunga Ha'apai volcanic tsunami of 2022

   https://doi.org/10.5194/os-19-517-2023  

   Devlin, Adam T. ; Jay, David A. ; Talke, Stefan A. ; Pan, Jiayi ( January 2023 , Ocean Science)

   Abstract. The eruption of the Hunga Tonga-Hunga Ha'apai volcano on 15 January 2022 provided a rare opportunity to understand global tsunamiimpacts of explosive volcanism and to evaluate future hazards, includingdangers from “volcanic meteotsunamis” (VMTs) induced by the atmosphericshock waves that followed the eruption. The propagation of the volcanic andmarine tsunamis was analyzed using globally distributed 1 min measurementsof air pressure and water level (WL) (from both tide gauges and deep-waterbuoys). The marine tsunami propagated primarily throughout the Pacific,reaching nearly 2 m at some locations, though most Pacific locationsrecorded maximums lower than 1 m. However, the VMT resulting from theatmospheric shock wave arrived before the marine tsunami and propagatedglobally, producing water level perturbations in the Indian Ocean, theMediterranean, and the Caribbean. The resulting water level response of manyPacific Rim gauges was amplified, likely related to wave interaction withbathymetry. The meteotsunami repeatedly boosted tsunami wave energy as itcircled the planet several times. In some locations, the VMT was amplifiedby as much as 35-fold relative to the inverse barometer due to near-Proudmanresonance and topographic effects. Thus, a meteotsunami from a largereruption (such as the Krakatoa eruption of 1883) could yield atmosphericpressure changes of 10 to 30 mb, yielding a 3–10 m near-field tsunami thatwould occur in advance of (usually) larger marine tsunami waves, posingadditional hazards to local populations. Present tsunami warning systems donot consider this threat. 

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2. The global seismographic network reveals atmospherically coupled normal modes excited by the 2022 Hunga Tonga eruption

   https://doi.org/10.1093/gji/ggac284  

   Ringler, A T ; Anthony, R E ; Aster, R C ; Taira, T ; Shiro, B R ; Wilson, D C ; De Angelis, S ; Ebeling, C ; Haney, M ; Matoza, R S ; et al ( November 2022 , Geophysical Journal International)

   SUMMARY The eruption of the submarine Hunga Tonga-Hunga Haʻapai (Hunga Tonga) volcano on 15 January 2022, was one of the largest volcanic explosions recorded by modern geophysical instrumentation. The eruption was notable for the broad range of atmospheric wave phenomena it generated and for their unusual coupling with the oceans and solid Earth. The event was recorded worldwide across the Global Seismographic Network (GSN) by seismometers, microbarographs and infrasound sensors. The broad-band instrumentation in the GSN allows us to make high fidelity observations of spheroidal solid Earth normal modes from this event at frequencies near 3.7 and 4.4 mHz. Similar normal mode excitations were reported following the 1991 Pinatubo (Volcanic Explosivity Index of 6) eruption and were predicted, by theory, to arise from the excitation of mesosphere-scale acoustic modes of the atmosphere coupling with the solid Earth. Here, we compare observations for the Hunga Tonga and Pinatubo eruptions and find that both strongly excited the solid Earth normal mode 0S29 (3.72 mHz). However, the mean modal amplitude was roughly 11 times larger for the 2022 Hunga Tonga eruption. Estimates of attenuation (Q) for 0S29 across the GSN from temporal modal decay give Q = 332 ± 101, which is higher than estimates of Q for this mode using earthquake data (Q = 186.9 ± 5). Two microbarographs located at regional distances (<1000 km) to the volcano provide direct observations of the fundamental acoustic mode of the atmosphere. These pressure oscillations, first observed approximately 40 min after the onset of the eruption, are in phase with the seismic Rayleigh wave excitation and are recorded only by microbarographs in proximity (<1500 km) to the eruption. We infer that excitation of fundamental atmospheric modes occurs within a limited area close to the site of the eruption, where they excite select solid Earth fundamental spheroidal modes of similar frequencies that are globally recorded and have a higher apparent Q due to the extended duration of atmospheric oscillations. 

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3. 2022 Tonga Volcanic Eruption Induced Global Propagation of Ionospheric Disturbances via Lamb Waves

   https://doi.org/10.3389/fspas.2022.871275  

   Zhang, Shun-Rong ; Vierinen, Juha ; Aa, Ercha ; Goncharenko, Larisa P. ; Erickson, Philip J. ; Rideout, William ; Coster, Anthea J. ; Spicher, Andres ( March 2022 , Frontiers in Astronomy and Space Sciences)

   The Tonga volcano eruption at 04:14:45 UT on 2022-01-15 released enormous amounts of energy into the atmosphere, triggering very significant geophysical variations not only in the immediate proximity of the epicenter but also globally across the whole atmosphere. This study provides a global picture of ionospheric disturbances over an extended period for at least 4 days. We find traveling ionospheric disturbances (TIDs) radially outbound and inbound along entire Great-Circle loci at primary speeds of ∼300–350 m/s (depending on the propagation direction) and 500–1,000 km horizontal wavelength for front shocks, going around the globe for three times, passing six times over the continental US in 100 h since the eruption. TIDs following the shock fronts developed for ∼8 h with 10–30 min predominant periods in near- and far- fields. TID global propagation is consistent with the effect of Lamb waves which travel at the speed of sound. Although these oscillations are often confined to the troposphere, Lamb wave energy is known to leak into the thermosphere through channels such as atmospheric resonance at acoustic and gravity wave frequencies, carrying substantial wave amplitudes at high altitudes. Prevailing Lamb waves have been reported in the literature as atmospheric responses to the gigantic Krakatoa eruption in 1883 and other geohazards. This study provides substantial first evidence of their long-duration imprints up in the global ionosphere. This study was enabled by ionospheric measurements from 5,000+ world-wide Global Navigation Satellite System (GNSS) ground receivers, demonstrating the broad implication of the ionosphere measurement as a sensitive detector for atmospheric waves and geophysical disturbances. 

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4. Wind Stress, Wind Stress Curl, and Upwelling Velocities in the Northwest Atlantic (80-45W, 30-45N) during 1980-2019

   https://doi.org/10.5281/zenodo.8200832  

   Gifford, Ian ; Gangopadhyay, Avijit ; Andres, Magdalena ; Gawarkiewicz, Glen ; Oliver, Hilde ; Silver, Adrienne ( January 2023 , Zenodo)

   This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details.   

   After the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy’s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation. 

   The monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020). 

   Corresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details.   

   The files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution.  

   Units of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array. 

   Filenames: 

   monthly_windstress_wsc_upwelling.nc: 480 time steps from 80W to 45W and 30N to 45N.

   seasonal_windstress_wsc_upwelling.nc: 160 time steps from 80W to 45W and 30N to 45N.

   annual_windstress_wsc_upwelling.nc: 40 time steps from 80W to 45W and 30N to 45N.

   Please contact igifford@earth.miami.edu for any queries. {"references": ["Gifford, I.H., 2023. The Synchronicity of the Gulf Stream Free Jet and the Wind Induced Cyclonic Vorticity Pool. MS Thesis, University of Massachusetts Dartmouth. 75pp.", "Gill, A. E. (1982). Atmosphere-ocean dynamics (Vol. 30). Academic Press.", "Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357\u2013362 (2020). DOI: 10.1038/s41586-020-2649-2.", "Japan Meteorological Agency/Japan (2013), JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, https://doi.org/10.5065/D6HH6H41, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colo. (Updated monthly.)", "Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H. and Miyaoka, K., 2015. The JRA-55 reanalysis: General specifications and basic characteristics.\u202fJournal of the Meteorological Society of Japan. Ser. II,\u202f93(1), pp.5-48.", "Large, W.G. and Pond, S., 1981. Open ocean momentum flux measurements in moderate to strong winds.\u202fJournal of physical oceanography,\u202f11(3), pp.324-336.", "Risien, C.M. and Chelton, D.B., 2008. A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data.\u202fJournal of Physical Oceanography,\u202f38(11), pp.2379-2413.", "Schulzweida, Uwe. (2022). CDO User Guide (2.1.0). Zenodo. https://doi.org/10.5281/zenodo.7112925.", "Trenberth, K.E., Large, W.G. and Olson, J.G., 1989. The effective drag coefficient for evaluating wind stress over the oceans.\u202fJournal of Climate,\u202f2(12), pp.1507-1516."]} 

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5. Arctic wildfires at a warming threshold

   https://doi.org/10.1126/science.ade9583  

   Post, Eric ; Mack, Michelle C. ( November 2022 , Science)

   Vast amounts of organic carbon are stored in Arctic soils. Much of this is in the form of peat, a layer of decomposing plant matter. Arctic wildfires release this carbon to the atmosphere as carbon dioxide (CO 2 ) ( 1 ) and contribute to global warming. This creates a feedback loop in which accelerated Arctic warming ( 2 ) dries peatland soils, which increases the likelihood of bigger, more frequent wildfires in the Arctic and releases more CO 2 , which further contributes to warming. Although this feedback mechanism is qualitatively understood, there remain uncertainties about its details. On page 532 of this issue, Descals et al. ( 3 ) analyze data from the 2019 and 2020 wildfire seasons in the Siberian Arctic and predict the extent of carbon-rich soils likely to burn in the area with future warming. Critically, they suggest that even minor increases in temperature above certain thresholds may promote increasingly larger wildfires. 

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