Historical snow measurements in the central and southern Apennine Mountains: climatology, variability, and trend by V. Capozzi, F. Serrapica, A. Rocco, C. Annella, C. Annella, G. Budillon

“…For clusters including only stations above 1000 m above the sea level, a significant (at 90 % or 95 % confidence levels) decreasing trend has been found in the winter season (i.e. from December to February), with <span class="inline-formula">−3.2</span> [<span class="inline-formula">−6.0</span> to 0.0] d per 10 years for snow cover duration and <span class="inline-formula">−1.6</span> [<span class="inline-formula">−2.5</span> to <span class="inline-formula">−0.6</span>] d per 10 years for number of days with snowfall. …”
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Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5) by N. Eingrüber, A. Domm, W. Korres, K. Schneider

“…On temporal average for the 3 d heat event, statistically significant mean temperature differences of <span class="inline-formula">−5.8</span> K for surface temperature and <span class="inline-formula">−1.1</span> K for air temperature were simulated. …”
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The European summer heatwave of 2019 – a regional storyline perspective by T. Klimiuk, P. Ludwig, A. Sanchez-Benitez, H. F. Goessling, P. Braesicke, P. Braesicke, J. G. Pinto

“…Firstly, the large-scale circulation in the free troposphere was spectrally nudged to the ERA5 reanalyses within the global coupled climate model AWI-CM-1.1-MR for the recent period of 2017–2022, corresponding to global warming of <span class="inline-formula">+1.4</span> K. This process was then repeated under pre-industrial conditions, as well as under <span class="inline-formula">+2</span>, <span class="inline-formula">+3</span>, and <span class="inline-formula">+4</span> K global-warming climates. …”
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Quantifying the potential of using Soil Moisture Active Passive (SMAP) soil moisture variability to predict subsurface water dynamics by A. K. Nayak, X. Xu, X. Xu, S. K. Frey, S. K. Frey, O. Khader, O. Khader, A. R. Erler, D. R. Lapen, H. A. J. Russell, E. A. Sudicky, E. A. Sudicky

“…Simulated subsurface SM and GW storage show lagged and smoother characteristics relative to SMAP SSM variability with an optimal delay of <span class="inline-formula">∼1</span> d for the 25–50 cm SM, <span class="inline-formula">∼6</span> d for the 50–100 cm SM, and <span class="inline-formula">∼11</span> d for the GW storage for both scales. …”
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AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE by X. Wang, L. Bi, H. Wang, Y. Wang, W. Han, X. Shen, X. Zhang

“…Compared to the external-mixing scheme, the direct radiative effect (DRE) induced by the NSIH effect reached <span class="inline-formula">+1.6</span> W m<span class="inline-formula">⁻²</span> at the top of the atmosphere (TOA) and <span class="inline-formula">−2.9</span> W m<span class="inline-formula">⁻²</span> at the surface. …”
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Accurate space-based NO_<i>x</i> emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes by F. Cifuentes, F. Cifuentes, H. Eskes, E. Dammers, E. Dammers, C. Bryan, F. Boersma, F. Boersma

“…However, when the coarse <span class="inline-formula">1×1</span>° TM5-MP <span class="inline-formula">NO₂</span> profile used in the retrieval is replaced by the high-resolution profile of LOTOS-EUROS, the TROPOMI <span class="inline-formula">NO_<i>x</i></span> emissions are enhanced by 22 % and are in better agreement with the inventory for the Netherlands. …”
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The Geometric Series Hypothesis of Leaf Area Distribution and Its Link to the Calculation of the Total Leaf Area per Shoot of <i>Sasaella kongosanensis</i> ‘Aureostriatus’ by Yong Meng, David A. Ratkowsky, Weihao Yao, Yi Heng, Peijian Shi

“…We found that there were 367 out of 388 shoots (94.6%) where 1 < <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mover accent="true"><mrow><mi>q</mi></mrow><mo>¯</mo></mover></mrow><mrow><mi>A</mi></mrow></msub></mrow></semantics></math></inline-formula> < 1.618 and MAPE < 15%, and these 367 shoots were defined as valid samples. …”
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HERA: a high-resolution pan-European hydrological reanalysis (1951–2020) by A. Tilloy, D. Paprotny, S. Grimaldi, G. Gomes, A. Bianchi, S. Lange, H. Beck, C. Mazzetti, L. Feyen

“…We use the new European set-up of the OS LISFLOOD model, running at 1 <span class="inline-formula">arcmin</span> (<span class="inline-formula">≈1.8</span> <span class="inline-formula">km</span>) with 6-hourly time steps. …”
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Leaf Water Potential in a Mixed Mediterranean Forest from Machine Learning and Unmanned Aerial Vehicle (UAV)-Based Hyperspectral Imaging by Netanel Fishman, Yehuda Yungstein, Assaf Yaakobi, Sophie Obersteiner, Laura Rez, Gabriel Mulero, Yaron Michael, Tamir Klein, David Helman

“…Predictions were best for <i>Cupressus sempervirens</i> (R² = 0.80) and <i>Pistacia lentiscus</i> (R² = 0.49), which had the largest <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ψ</mi></mrow></semantics></math></inline-formula>_leaf variances (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo><mi>σ</mi></mrow></semantics></math></inline-formula> > 1 MPa). Aggregating data at the plot scale in a ‘general’ model markedly improved the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ψ</mi></mrow></semantics></math></inline-formula>_leaf model (R² = 0.79, RMSE = 0.31 MPa; rRMSE = 13%), providing a promising tool for monitoring mixed forest <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ψ</mi></mrow></semantics></math></inline-formula>_leaf. …”
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Andreybulakhite, Ni(C₂O₄)&thinsp; ⋅ &thinsp;2H₂O, the first natural nickel oxalate by O. S. Vereshchagin, S. N. Britvin, S. N. Britvin, D. V. Pankin, M. S. Zelenskaya, M. G. Krzhizhanovskaya, M. A. Kuz'mina, N. S. Vlasenko, O. V. Frank-Kamenetskaya

“…The empirical formula calculated on the basis of (Ni <span class="inline-formula">+</span> Cu <span class="inline-formula">+</span> Mg <span class="inline-formula">+</span> Co) <span class="inline-formula">=1</span> atom per formula unit is (Ni<span class="inline-formula">_0.63</span>Cu<span class="inline-formula">_0.27</span>Mg<span class="inline-formula">_0.08</span>Co<span class="inline-formula">_0.02)_Σ1.00</span>(C<span class="inline-formula">₂</span>O<span class="inline-formula">₄</span>) <span class="inline-formula">⋅</span> 2H<span class="inline-formula">₂</span>O. …”
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SMILE术暗适应下瞳孔直径的影响因素分析 by 林海芹, 刘泉

“…></graphic></alternatives></inline-formula>=1.050<italic>x</italic>-0.223，<italic>r</italic>²=0.771。…”
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Effect of Lymphatic Drainage Manipulation on Knee Joint Swelling after Anterior Cruciate Ligament Reconstruction by MENG Cong, BAO Yong, ZHANG Weiming

“…></graphic></alternatives></inline-formula>=-1.070, -0.880, -0.600, <italic>P</italic>=0.003, 0.003, 0.048, respectively. …”
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Spatial and temporal heterogeneity of soil respiration in a bare-soil Mediterranean olive grove by S. Aranda-Barranco, S. Aranda-Barranco, P. Serrano-Ortiz, P. Serrano-Ortiz, A. S. Kowalski, A. S. Kowalski, E. P. Sánchez-Cañete, E. P. Sánchez-Cañete

“…<span class="inline-formula"><i>R</i>_{s  Under-Tree}</span> represented 39 % of the <span class="inline-formula"><i>R</i>_s</span> of the olive grove. …”
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A synthesis of <i>Sphagnum</i> litterbag experiments: initial leaching losses bias decomposition rate estimates by H. Teickner, H. Teickner, E. Pebesma, K.-H. Knorr

“…Previous research on vascular plants suggests that decomposition rate (<span class="inline-formula"><i>k</i>₀</span>) estimates from litterbag experiments are biased when initial leaching losses (<span class="inline-formula"><i>l</i>₀</span>) are ignored. …”
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A machine-learning reconstruction of sea surface <i>p</i>CO₂ in the North American Atlantic Coastal Ocean Margin from 1993 to 2021 by Z. Wu, Z. Wu, W. Lu, A. Roobaert, L. Song, X.-H. Yan, W.-J. Cai

“…<p>Insufficient spatiotemporal coverage of observations of the surface partial pressure of CO<span class="inline-formula">₂</span> (<span class="inline-formula"><i>p</i></span>CO<span class="inline-formula">₂</span>) has hindered precise carbon cycle studies in coastal oceans and justifies the development of spatially and temporally continuous <span class="inline-formula"><i>p</i></span>CO<span class="inline-formula">₂</span> data products. …”
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A long-term drought reconstruction based on oxygen isotope tree ring data for central and eastern parts of Europe (Romania) by V. Nagavciuc, V. Nagavciuc, G. Helle, M. Rădoane, C.-C. Roibu, M.-G. Cotos, M. Ionita, M. Ionita

“…A <span class="inline-formula"><i>δ</i>¹⁸</span>O site chronology spanning 1803–2020 was compiled from seven individual time series. …”
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Soybean oil, a linoleic acid source, in lamb diets: metabolic, feeding behavior, and ruminal parameters by V. G. O. Lima, L. O. da Silva, J. E. de Freitas Jr., H. D. R. Alba, V. M. Leite, W. P. Silva, D. D. S. Pina, L. C. Leite, C. S. Rodrigues, S. A. Santos, G. G. P. de Carvalho

“…There was a linear reduction in DM (<span class="inline-formula"><i>P</i></span> <span class="inline-formula">=</span> 0.019) and crude protein (<span class="inline-formula"><i>P</i></span> <span class="inline-formula">=</span> 0.007) intake as well as a linear decrease in non-fibrous carbohydrate intake (<span class="inline-formula"><i>P</i></span> <span class="inline-formula">&lt;</span> 0.001) and digestibility (<span class="inline-formula"><i>P</i></span> <span class="inline-formula">=</span> 0.039) due to increased SO inclusion levels. …”
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Effect of colloidal particle size on physicochemical properties and aggregation behaviors of two alkaline soils by Y. Yan, X. Zhang, C. Xu, C. Xu, J. Liu, F. Hu, F. Hu, Z. Geng, Z. Geng

“…Soil colloidal fractions, from two alkaline soils – Anthrosol and Calcisol – were subdivided into three ranges: <span class="inline-formula"><i>d</i>&lt;2</span> <span class="inline-formula">µ</span>m, <span class="inline-formula"><i>d</i>&lt;1</span> <span class="inline-formula">µ</span>m and <span class="inline-formula"><i>d</i>&lt;100</span> nm. …”
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Dacostaite, K(Mg₂Al)[Mg(H₂O)₆]₂(AsO₄)₂F₆&thinsp; ⋅ &thinsp;2H₂O, a new fluoride–arsenate mi... by C. Biagioni, C. Biagioni, D. Mauro, D. Mauro, J. Sejkora, Z. Dolníček, A. Dini, R. Škoda

“…Dacostaite is monoclinic, with a space group of <span class="inline-formula"><i>C</i></span>2/<span class="inline-formula"><i>m</i></span> and <span class="inline-formula"><i>a</i>=12.474(5)</span>, <span class="inline-formula"><i>b</i>=7.198(3)</span>, <span class="inline-formula"><i>c</i>=13.724(6)</span> Å, <span class="inline-formula"><i>β</i>=99.518(13)</span>°, and <span class="inline-formula"><i>V</i>=1215.3(8)</span> Å<span class="inline-formula">³</span>. …”
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Creep enhancement and sliding in a temperate, hard-bedded alpine glacier by J.-P. Roldán-Blasco, A. Gilbert, L. Piard, F. Gimbert, C. Vincent, O. Gagliardini, A. Togaibekov, A. Togaibekov, A. Walpersdorf, N. Maier

“…<p>Glacier internal deformation is usually described by Glen's flow law using two material parameters: the creep factor (<span class="inline-formula"><i>A</i></span>) and the flow law exponent (<span class="inline-formula"><i>n</i></span>). …”
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