Publications

Auroral emissions have been extensively observed at the Earth, Jupiter, and Saturn. These planets all have appreciable atmospheres and strong magnetic fields, and their auroras predominantly originate from a region encircling each magnetic pole. However, Jupiter’s auroras poleward of these “main” emissions are brighter and more dynamic, and the drivers responsible for much of these mysterious polar auroras have eluded identification to date. We propose that part of the solution may stem from Jupiter’s stronger magnetic field. We model large-scale Alfvénic perturbations propagating through the polar magnetosphere toward Jupiter, showing that the resulting <0.1° deflections of the magnetic field closest to the planet could trigger magnetic reconnection as near as ∼0.2 Jupiter radii above the cloud tops. At Earth and Saturn this physics should be negligible, but reconnection electric field strengths above Jupiter’s poles can approach ∼1 V m−1, typical of the solar corona. We suggest this near-planet reconnection could generate beams of high-energy electrons capable of explaining some of Jupiter’s polar auroras.

Journal article

Ceppi P, Nowack P, 2021,

Observational evidence that cloud feedback amplifies global warming

, Proceedings of the National Academy of Sciences, Vol: 118, ISSN: 0027-8424

Global warming drives changes in Earth’s cloud cover, which, in turn, may amplify or dampen climate change. This “cloud feedback” is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)—the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 ± 0.35 W⋅m<jats:sup>−2</jats:sup>⋅K<jats:sup>−1</jats:sup> (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts.

Journal article

Nakamura TKM, Hasegawa H, Genestreti KJ, Denton RE, Phan TD, Stawarz JE, Nakamura R, Nystrom WDet al., 2021,

Fast cross‐scale energy transfer during turbulent magnetic reconnection

, Geophysical Research Letters, Vol: 48, Pages: 1-8, ISSN: 0094-8276

Magnetic reconnection is a key fundamental process in collisionless plasmas that explosively converts magnetic energy to plasma kinetic and thermal energies through a change of magnetic field topology in a central electron-scale region called the electron diffusion region (EDR). Past simulations and observations demonstrated that this process causes efficient energy conversion through the formation of multiple macro-scale or micro-scale magnetic islands/flux ropes. However, the coupling of these phenomena on different spatiotemporal scales is still poorly understood. Here, based on a new large-scale fully-kinetic simulation with a realistic, initially-fluctuating magnetic field, we demonstrate that macro-scale evolution of turbulent reconnection involving merging of macro-scale islands induces repeated, quick formation of new electron-scale islands within the EDR which soon grow to larger scales. This process causes an efficient cross-scale energy transfer from electron- to larger-scales, and leads to strong electron energization within the growing islands.

Journal article

Thomas C, Voulgarakis A, Lim G, Haigh J, Nowack Pet al., 2021,

An unsupervised learning approach to identifying blocking events:the case of European summer

, Weather and Climate Dynamics, Vol: 2, ISSN: 2698-4016

Atmospheric blocking events are mid-latitudeweather patterns, which obstruct the usual path of the polar jet streams. They are often associated with heat wavesin summer and cold snaps in winter. Despite being centralfeatures of mid-latitude synoptic-scale weather, there is nowell-defined historical dataset of blocking events. Variousblocking indices (BIs) have thus been suggested for automatically identifying blocking events in observational and inclimate model data. However, BIs show significant regionaland seasonal differences so that several indices are typicallyapplied in combination to ensure scientific robustness. Here,we introduce a new BI using self-organizing maps (SOMs),an unsupervised machine learning approach, and compare itsdetection skill to some of the most widely applied BIs. Toenable this intercomparison, we first create a new groundtruth time series classification of European blocking basedon expert judgement. We then demonstrate that our method(SOM-BI) has several key advantages over previous BIs because it exploits all of the spatial information provided in theinput data and reduces the dependence on arbitrary thresholds. Using ERA5 reanalysis data (1979–2019), we find thatthe SOM-BI identifies blocking events with a higher precision and recall than other BIs. In particular, SOM-BI alreadyperforms well using only around 20 years of training data sothat observational records are long enough to train our newmethod. We present case studies of the 2003 and 2019 European heat waves and highlight that well-defined groups ofSOM nodes can be an effective tool to diagnose such weatherevents, although the domain-based approach can still lead toerrors in the identification of certain events in a fashion similar to the other BIs. We further test the red blocking detectionskill of SOM-BI depending on the meteorological variableused to study blocking, including geopotential height, sealevel pressure and four variables related to potential vorticity,and t

Journal article

Thomas C, Voulgarakis A, Lim G, Haigh J, Nowack Pet al., 2021,

An unsupervised learning approach to identifying blocking events: the case of European summer

, Weather and Climate Dynamics, Vol: 2, Pages: 581-608, ISSN: 2698-4016

Atmospheric blocking events are mid-latitude weather patterns, which obstruct the usual path of the polar jet streams. They are often associated with heat waves in summer and cold snaps in winter. Despite being central features of mid-latitude synoptic-scale weather, there is no well-defined historical dataset of blocking events. Various blocking indices (BIs) have thus been suggested for automatically identifying blocking events in observational and in climate model data. However, BIs show significant regional and seasonal differences so that several indices are typically applied in combination to ensure scientific robustness. Here, we introduce a new BI using self-organizing maps (SOMs), an unsupervised machine learning approach, and compare its detection skill to some of the most widely applied BIs. To enable this intercomparison, we first create a new ground truth time series classification of European blocking based on expert judgement. We then demonstrate that our method (SOM-BI) has several key advantages over previous BIs because it exploits all of the spatial information provided in the input data and reduces the dependence on arbitrary thresholds. Using ERA5 reanalysis data (1979–2019), we find that the SOM-BI identifies blocking events with a higher precision and recall than other BIs. In particular, SOM-BI already performs well using only around 20 years of training data so that observational records are long enough to train our new method. We present case studies of the 2003 and 2019 European heat waves and highlight that well-defined groups of SOM nodes can be an effective tool to diagnose such weather events, although the domain-based approach can still lead to errors in the identification of certain events in a fashion similar to the other BIs. We further test the red blocking detection skill of SOM-BI depending on the meteorological variable used to study blocking, including geopotential height, sea level pressure and four variables related to

Journal article

Zazzeri G, Xu X, Graven H, 2021,

Efficient sampling of atmospheric methane for radiocarbon analysis and quantification of fossil methane.

, Environmental Science and Technology (Washington), Vol: 55, Pages: 8535-8541, ISSN: 0013-936X

Radiocarbon (14C) measurements offer a unique investigative tool to study methane emissions by identifying fossil-fuel methane in air. Fossil-fuel methane is devoid of 14C and, when emitted to the atmosphere, causes a strong decrease in the ratio of radiocarbon to total carbon in methane (Δ14CH4). By observing the changes in Δ14CH4, the fossil fraction of methane emissions can be quantified. Presently, there are very few published Δ14CH4 measurements, mainly because it is challenging to collect and process the large volumes of air needed for radiocarbon measurements. We present a new sampling system that collects enough methane carbon for high precision Δ14CH4 measurements without having to transport large volumes of air. The system catalytically combusts CH4 into CO2 and adsorbs the combustion-derived CO2 onto a molecular sieve trap, after first removing CO2, CO, and H2O. Tests using reference air show a Δ14CH4 measurement repeatability of 5.4‰, similar or better than the precision in the most recent reported measurements. We use the system to produce the first Δ14CH4 measurements in central London and show that day-to-day differences in Δ14CH4 in these samples can be attributed to fossil methane input. The new system could be deployed in a range of settings to investigate CH4 sources.

Journal article

Runov A, Grandin M, Palmroth M, Battarbee M, Ganse U, Hietala H, Hoilijoki S, Kilpua E, Pfau-Kempf Y, Toledo-Redondo S, Turc L, Turner Det al., 2021,

Ion distribution functions in magnetotail reconnection: global hybrid-Vlasov simulation results

, ANNALES GEOPHYSICAE, Vol: 39, Pages: 599-612, ISSN: 0992-7689

Journal article

Greaves JS, Richards AMS, Bains W, Rimmer PB, Sagawa H, Clements DL, Seager S, Petkowski JJ, Sousa-Silva C, Ranjan S, Drabek-Maunder E, Fraser HJ, Cartwright A, Mueller-Wodarg I, Zhan Z, Friberg P, Coulson I, Lee E, Hoge Jet al., 2021,

Phosphine gas in the cloud deck of Venus (vol 5, pg 655, 2021)

, Nature Astronomy, Vol: 5, Pages: 726-728, ISSN: 2397-3366

Journal article

Joyce CJ, McComas DJ, Schwadron NA, Vourlidas A, Christian ER, McNutt RL, Cohen CMS, Leske RA, Mewaldt RA, Stone EC, Mitchell DG, Hill ME, Roelof EC, Allen RC, Szalay JR, Rankin JS, Desai M, Giacalone J, Matthaeus WH, Niehof JT, de Wet W, Winslow RM, Bale SD, Kasper JCet al., 2021,

Energetic particle evolution during coronal mass ejection passage from 0.3 to 1 AU

, ASTRONOMY & ASTROPHYSICS, Vol: 651, ISSN: 0004-6361

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Citations: 9

Journal article

Chen Y-J, Hwang Y-T, Ceppi P, 2021,

The impacts of cloud-radiative changes on poleward atmospheric and oceanic energy transport in a warmer climate

, Journal of Climate, Vol: 34, Pages: 7857-7874, ISSN: 0894-8755

Based on theory and climate model experiments, previous studies suggest most of the uncertainties in projected future changes in meridional energy transport and zonal mean surface temperature can be attributed to cloud feedback. To investigate how radiative and dynamical adjustments modify the influence of cloud-radiative changes on energy transport, this study applies a cloud-locking technique in a fully-coupled climate model, CESM. Under global warming, the impacts of cloud-radiative changes on the meridional energy transport are asymmetric in the two hemispheres. In the Northern Hemisphere, the cloud-radiative changes have little impact on energy transport, because 89% of the cloud-induced heating is balanced locally by increasing outgoing longwave radiation. In the Southern Hemisphere, on the other hand, cloud-induced dynamical changes in the atmosphere and the ocean cause enhanced poleward energy transport, accounting for most of the increase in energy transport under warming. Our experiments highlight that the local longwave radiation adjustment induced by temperature variation can partially offset the impacts of cloud-radiative changes on energy transport, making the estimated impacts smaller than those obtained from directly integrating cloud-radiative changes in previous studies. It is also demonstrated that the cloud-radiative impacts on temperature and energy transport can be significantly modulated by the oceanic circulation, suggesting the necessity of considering atmospheric-oceanic coupling when estimating the impacts of cloud-radiative changes on the climate system.

Journal article

Duan D, He J, Bowen TA, Woodham LD, Wang T, Chen CHK, Mallet A, Bale SDet al., 2021,

Anisotropy of solar wind turbulence in the inner heliosphere at kinetic scales: PSP observations

, Letters of the Astrophysical Journal, Vol: 915, Pages: 1-7, ISSN: 2041-8205

The anisotropy of solar wind turbulence is a critical issue in understanding the physics of energy transfer between scales and energy conversion between fields and particles in the heliosphere. Using the measurement of Parker Solar Probe (PSP), we present an observation of the anisotropy at kinetic scales in the slow, Alfvénic, solar wind in the inner heliosphere. The magnetic compressibility behaves as expected for kinetic Alfvénic turbulence below the ion scale. A steepened transition range is found between the inertial and kinetic ranges in all directions with respect to the local background magnetic field direction. The anisotropy of k⊥ ≫ k∥ is found evident in both transition and kinetic ranges, with the power anisotropy P⊥/P∥ > 10 in the kinetic range leading over that in the transition range and being stronger than that at 1 au. The spectral index varies from αt∥ = −5.7 ± 1.0 to αt⊥ = −3.7 ± 0.3 in the transition range and αk∥ = −3.12 ± 0.22 to αk⊥ = −2.57 ± 0.09 in the kinetic range. The corresponding wavevector anisotropy has the scaling of ${k}_{\parallel }\sim {k}_{\perp }^{0.71\pm 0.17}$ in the transition range, and changes to ${k}_{\parallel }\sim {k}_{\perp }^{0.38\pm 0.09}$ in the kinetic range, consistent with the kinetic Alfvénic turbulence at sub-ion scales.

Journal article

Farrell WM, Rasca AP, MacDowall RJ, Gruesbeck JR, Bale SD, Kasper JCet al., 2021,

Switchback Boundary Dissipation and Relative Age

, ASTROPHYSICAL JOURNAL, Vol: 915, ISSN: 0004-637X

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Citations: 2

Journal article

Madanian H, Schwartz SJ, Fuselier SA, Burgess D, Turner DL, Chen L-J, Desai MI, Starkey MJet al., 2021,

Direct Evidence for Magnetic Reflection of Heavy Ions from High Mach Number Collisionless Shocks

, ASTROPHYSICAL JOURNAL LETTERS, Vol: 915, ISSN: 2041-8205

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Citations: 5

Journal article

Gristey JJ, Su W, Loeb NG, Vonder Haar TH, Tornow F, Schmidt KS, Hakuba MZ, Pilewskie P, Russell JEet al., 2021,

Shortwave radiance to irradiance conversion for earth radiation budget satellite observations: a review

, Remote Sensing, Vol: 13, ISSN: 2072-4292

Observing the Earth radiation budget (ERB) from satellites is crucial for monitoring and understanding Earth’s climate. One of the major challenges for ERB observations, particularly for reflected shortwave radiation, is the conversion of the measured radiance to the more energetically relevant quantity of radiative flux, or irradiance. This conversion depends on the solar-viewing geometry and the scene composition associated with each instantaneous observation. We first outline the theoretical basis for algorithms to convert shortwave radiance to irradiance, most commonly known as empirical angular distribution models (ADMs). We then review the progression from early ERB satellite observations that applied relatively simple ADMs, to current ERB satellite observations that apply highly sophisticated ADMs. A notable development is the dramatic increase in the number of scene types, made possible by both the extended observational record and the enhanced scene information now available from collocated imager information. Compared with their predecessors, current shortwave ADMs result in a more consistent average albedo as a function of viewing zenith angle and lead to more accurate instantaneous and mean regional irradiance estimates. One implication of the increased complexity is that the algorithms may not be directly applicable to observations with insufficient accompanying imager information, or for existing or new satellite instruments where detailed scene information is not available. Recent advances that complement and build on the base of current approaches, including machine learning applications and semi-physical calculations, are highlighted.

Journal article

Halekas JS, Bercic L, Whittlesey P, Larson DE, Livi R, Berthomier M, Kasper JC, Case AW, Stevens ML, Bale SD, MacDowall RJ, Pulupa MPet al., 2021,

The Sunward Electron Deficit: A Telltale Sign of the Sun's Electric Potential

, ASTROPHYSICAL JOURNAL, Vol: 916, ISSN: 0004-637X

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Citations: 14

Journal article

Wang R, Vasko IY, Mozer FS, Bale SD, Kuzichev IV, Artemyev AV, Steinvall K, Ergun R, Giles B, Khotyaintsev Y, Lindqvist P-A, Russell CT, Strangeway Ret al., 2021,

Electrostatic Solitary Waves in the Earth's Bow Shock: Nature, Properties, Lifetimes, and Origin

, JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS, Vol: 126, ISSN: 2169-9380

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Citations: 17

Journal article

Yao Z, Dunn WR, Woodfield EE, Clark G, Mauk BH, Ebert RW, Grodent D, Bonfond B, Pan D, Rae IJ, Ni B, Guo R, Branduardi-Raymont G, Wibisono AD, Rodriguez P, Kotsiaros S, Ness J-U, Allegrini F, Kurth WS, Gladstone GR, Kraft R, Sulaiman AH, Manners H, Desai RT, Bolton SJet al., 2021,

Revealing the source of Jupiter's x-ray auroral flares

, SCIENCE ADVANCES, Vol: 7, ISSN: 2375-2548

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Citations: 14

Journal article

Chen L, Ma B, Wu D, Zhao G, Tang J, Bale SDet al., 2021,

An Interplanetary Type IIIb Radio Burst Observed by Parker Solar Probe and Its Emission Mechanism

, ASTROPHYSICAL JOURNAL LETTERS, Vol: 915, ISSN: 2041-8205

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Citations: 7

Journal article

Kuhn- Regnier A, Voulgarakis A, Nowack P, Forkel M, Prentice IC, Harrison Set al., 2021,

The importance of antecedent vegetation and drought conditions as global drivers of burnt areas

, Biogeosciences, Vol: 18, Pages: 3861-3879, ISSN: 1726-4170

The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥ 1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (∼ 4 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.

Journal article

Kuhn-Régnier A, Voulgarakis A, Nowack P, Forkel M, Prentice IC, Harrison SPet al., 2021,

Quantifying the Importance of antecedent fuel-related vegetationproperties for burnt area using random forests

, Biogeosciences, Vol: 8, ISSN: 1726-4170

The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence mayhelp to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediateimpact of climate, vegetation, and human influences in agiven month and tested the impact of various combinationsof antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global,climatological out-of-sample R2from 0.579 to 0.701, but theinclusion of antecedent vegetation conditions on timescales≥ 1 year had no impact on simulated burnt area. Currentmoisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were importantfor fuel build-up. The models also enabled the visualisationof interactions between variables, such as the importanceof antecedent productivity coupled with instantaneous drying. The length of the period which needs to be consideredvaries across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longertime periods (∼ 4 months), while moisture-limited regionsare more sensitive to current conditions that regulate fuel drying.

Journal article

Hall RJ, Mitchell DM, Seviour WJM, Wright CJet al., 2021,