Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator) by M. R. Russo, M. R. Russo, S. L. Bartholomew, S. L. Bartholomew, D. Hassell, D. Hassell, A. M. Mason, E. Neininger, A. J. Perman, D. A. J. Sproson, D. Watson-Parris, D. Watson-Parris, N. L. Abraham, N. L. Abraham

“…By interpolating global gridded hourly files onto observation locations, we reduce data output for a typical climate resolution run, from <span class="inline-formula">∼3</span> Gb per model variable per month to <span class="inline-formula">∼15</span> Mb per model variable per month (a 200-times reduction in data volume). …”
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Southern Ocean control on atmospheric CO₂ changes across late Pliocene Marine Isotope Stage M2 by S. Hou, L. Toebrock, M. van der Linden, F. Rothstegge, M. Ziegler, L. J. Lourens, P. K. Bijl

“…<p>During the Pliocene, atmospheric <span class="inline-formula">CO₂</span> concentrations (<i>p</i><span class="inline-formula">CO₂</span>) were probably sometimes similar to today's, and global average temperature was <span class="inline-formula">∼3 °C</span> higher than preindustrial. However, the relationships and phasing between variability in climate and <i>p</i><span class="inline-formula">CO₂</span> on orbital timescales are not well understood. …”
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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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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

“…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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A sub-fossil coral Sr∕Ca record documents northward shifts of the Tropical Convergence Zone in the eastern Indian Ocean by M. Pfeiffer, H. Takayanagi, H. Takayanagi, L. Reuning, T. K. Watanabe, T. K. Watanabe, S. Ito, D. Garbe-Schönberg, T. Watanabe, T. Watanabe, T. Watanabe, C.-C. Wu, C.-C. Shen, C.-C. Shen, J. Zinke, G.-J. A. Brummer, S. Y. Cahyarini

“…U<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M4" display="inline" overflow="scroll" dspmath="mathml"><mo>/</mo></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="8pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="57ee8123d9c9aefcf23d9c7f6463c158"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="cp-21-211-2025-ie00005.svg" width="8pt" height="14pt" src="cp-21-211-2025-ie00005.png"/></svg:svg></span></span>Th dating shows that the <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow><mrow class="chem"><mi mathvariant="normal">Sr</mi></mrow><mo>/</mo><mrow class="chem"><mi mathvariant="normal">Ca</mi></mrow></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="33pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="90dc7a8a55bd37b8673bdad01c3cf421"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="cp-21-211-2025-ie00006.svg" width="33pt" height="14pt" src="cp-21-211-2025-ie00006.png"/></svg:svg></span></span>-based SST record extends from 1869–1918 and from 1824–1862 with a relative age uncertainty of <span class="inline-formula">±3</span> years (<span class="inline-formula">2<i>σ</i></span>). …”
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APG mergence and topological potential optimization based heuristic user association strategy by Zhirui HU, Meihua BI, Fangmin XU, Meilin HE, Changliang ZHENG

“…Therefore, it is reasonable to model the problem of improving network scalable degree as minimizing network coupling degree,and it is feasible to improve network scalable degree by reducing network coupling degree.2)The upper limit of computational complexity of the proposed algorithm is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"> <mi mathvariant="script">O</mi><mo stretchy="false">(</mo><mi>K</mi><mi>N</mi><msub> <mi>log</mi> <mn>2</mn> </msub> <mi>N</mi><mo>+</mo><msup> <mi>k</mi> <mn>2</mn> </msup> <mo>+</mo><mi>N</mi><mi>N</mi><msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>p</mtext> </msub> <mo stretchy="false">)</mo></math></inline-formula>,while that of directly solving the optimization problem is<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="script">O</mi><mo stretchy="false">(</mo><msup> <mi>N</mi> <mrow> <msub> <mover accent="true"> <mi>N</mi> <mo>¯</mo> </mover> <mtext>u</mtext> </msub> <mi>K</mi></mrow> </msup> <mo stretchy="false">)</mo></math></inline-formula>.3)For theoretical analysis of the network scalable degree,take Fig.3 as an example.If AP2 changes,12 APs in Fig. 3(a)are affected and the network scalable degree is η₂=0.51,while 4 APs in Fig.3(c)are affected and the network scalable degree is η₂=0.79.4)Fig.5 shows the simulation results of network scalable degree.Compared with the traditional strategy,the network scalable degree is improved by 9.59% with 4.43% user rate loss.Compared with the strategy in[10],the network scalable degree is improved by 22.15% with 4.99% user rate loss. 5) The algorithm parameters, the threshold β₀of overlap rate and the upper limit number N₀of AP associated, effect the performance.As shown in Fig.6,with β₀or N₀decreases,η increases and the total user rate decreases. …”
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Diurnal variations of NO₂ tropospheric vertical column density over the Seoul metropolitan area from the Geostationary Environment Monitoring Spectrometer (GEMS): season... by S. Seo, S.-W. Kim, S.-W. Kim, K.-M. Kim, A. Richter, K. Lange, J. P. Burrows, J. Park, J. Park, H. Hong, H. Lee, U. Jeong, J.-H. Woo, J.-H. Woo, J. Kim

“…Moderate (wind speed <span class="inline-formula">≥3</span> <span class="inline-formula">m s⁻¹</span>) or strong wind (wind speed <span class="inline-formula">&gt;5</span> <span class="inline-formula">m s⁻¹</span>) reduced the magnitude of the diurnal behavior in January, implying that the <span class="inline-formula">NO₂</span> plumes were transported downwind. …”
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