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Record W4295671955 · doi:10.5194/acp-22-11889-2022

Newly identified climatically and environmentally significant high-latitude dust sources

2022· article· en· W4295671955 on OpenAlexaffabout
Outi Meinander, Pavla Dagsson‐Waldhauserová, P. V. Amosov, Elena Aseyeva, Cliff Atkins, Alexander Baklanov, Clarissa Baldo, Sarah Barr, Barbara Barzycka, Liane G. Benning, Bojan Cvetković, Polina Enchilik, D. М. Frolov, Santiago Gassó, Konrad Kandler, Н.С. Касимов, Jan Kavan, James King, Tatyana Koroleva, V. V. Krupskaya, Markku Kulmala, Monika A. Kusiak, Hanna K. Lappalainen, Michał Laska, Jérôme Lasne, Marek Lewandowski, Bartłomiej Luks, James B. McQuaid, Beatrice Moroni, Benjamin J. Murray, Ottmar Möhler, Adam Nawrót, Slobodan Ničković, Norman T. O’Neill, Goran Pejanović, Olga Popovicheva, Keyvan Ranjbar, Manolis N. Romanías, Olga Samonova, Alberto Sánchez-Marroquín, Kerstin Schepanski, И. Н. Семенков, Anna Sharapova, Elena Shevnina, Zongbo Shi, Mikhail Sofiev, Frédéric Thévenet, Þröstur Þorsteinsson, М. А. Тимофеев, Nsikanabasi Silas Umo, Andreas Uppstu, Darya Urupina, György Varga, Tomasz Werner, Ólafur Arnalds

Bibliographic record

VenueAtmospheric chemistry and physics · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversité de SherbrookeUniversité de Montréal
FundersUlkoministeriöNatural Environment Research CouncilSight Research UK
KeywordsLatitudeEnvironmental scienceDust stormStormAtmospheric sciencesArcticMineral dustDeposition (geology)Physical geographyClimatologyAerosolGeologyGeographyOceanographyMeteorologyGeomorphology

Abstract

fetched live from OpenAlex

Dust particles from high latitudes have a potentially large local, regional, and global significance to climate and the environment as short-lived climate forcers, air pollutants, and nutrient sources. Identifying the locations of local dust sources and their emission, transport, and deposition processes is important for understanding the multiple impacts of high-latitude dust (HLD) on the Earth's systems. Here, we identify, describe, and quantify the source intensity (SI) values, which show the potential of soil surfaces for dust emission scaled to values 0 to 1 concerning globally best productive sources, using the Global Sand and Dust Storms Source Base Map (G-SDS-SBM). This includes 64 HLD sources in our collection for the northern (Alaska, Canada, Denmark, Greenland, Iceland, Svalbard, Sweden, and Russia) and southern (Antarctica and Patagonia) high latitudes. Activity from most of these HLD sources shows seasonal character. It is estimated that high-latitude land areas with higher (SI ≥0.5), very high (SI ≥0.7), and the highest potential (SI ≥0.9) for dust emission cover >1 670 000 km 2 , >560 000 km 2 , and >240 000 km 2 , respectively. In the Arctic HLD region (≥60 ∘ N), land area with SI ≥0.5 is 5.5 % (1 035 059 km 2 ), area with SI ≥0.7 is 2.3 % (440 804 km 2 ), and area with SI ≥0.9 is 1.1 % (208 701 km 2 ). Minimum SI values in the northern HLD region are about 3 orders of magnitude smaller, indicating that the dust sources of this region greatly depend on weather conditions. Our spatial dust source distribution analysis modeling results showed evidence supporting a northern HLD belt, defined as the area north of 50 ∘ N, with a “transitional HLD-source area” extending at latitudes 50–58 ∘ N in Eurasia and 50–55 ∘ N in Canada and a “cold HLD-source area” including areas north of 60 ∘ N in Eurasia and north of 58 ∘ N in Canada, with currently “no dust source” area between the HLD and low-latitude dust (LLD) dust belt, except for British Columbia. Using the global atmospheric transport model SILAM, we estimated that 1.0 % of the global dust emission originated from the high-latitude regions. About 57 % of the dust deposition in snow- and ice-covered Arctic regions was from HLD sources. In the southern HLD region, soil surface conditions are favorable for dust emission during the whole year. Climate change can cause a decrease in the duration of snow cover, retreat of glaciers, and an increase in drought, heatwave intensity, and frequency, leading to the increasing frequency of topsoil conditions favorable for dust emission, which increases the probability of dust storms. Our study provides a step forward to improve the representation of HLD in models and to monitor, quantify, and assess the environmental and climate significance of HLD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.175
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations105
Published2022
Admission routes2
Has abstractyes

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