id,title,description,date_created,date_modified,date_published,original_publication_date,publication_doi,provider,is_published,reviews_state,version,is_latest_version,preprint_doi,license,tags_list,tags_data,contributors_list,contributors_data,first_author,subjects_list,subjects_data,download_url,has_coi,conflict_of_interest_statement,has_data_links,has_prereg_links,prereg_links,prereg_link_info,last_updated
2p9wg_v1,"Uncertainties in Projected Rainfall over Brazil: The Role of Climate Model, Bias Correction and Emission Scenario","The aim of this study is to answer four main questions: How well the Eta regional climate model simulates past precipitation over Brazil? What is the impact of bias correction on the reduction of model’s biases? What is the contribution of climate models, bias correction and emission scenarios to the total uncertainty of projected precipitation? And finally, what is the projected change in precipitation over Brazil?
New hydrological insights for the region
The performance of raw simulations of the Eta regional climate models vary spatially over Brazil, being the Amazon and North region the regions with the highest biases. However, while the model fails in accuracy, it represents well the annual cycle of the precipitation and the signal of the future changes is robust (that is, it agrees with the signal of the changes after the bias correction and the models agree with each other). The bias correction presented a great impact in 
the bias reduction. Greater uncertainty levels are attributed to the bias correction followed by the climate models and interaction between climate model and bias correction. The emission scenario is the less contributor to the total uncertainty. Projected precipitation changes indicated a decrease in the daily precipitation and extreme precipitation in the Amazon and North Brazil and increase in the daily precipitation in Southern. The precipitation in winter is expected to increase. Under the IPCC scenario RCP 8.5, homogeneoulsly drier conditions are projected for the entire country.",2020-09-01T13:02:36.107761,2021-02-08T21:35:25.699357,2020-09-01T16:01:06.541515,,,eartharxiv,0,withdrawn,1,1,https://doi.org/10.31223/osf.io/2p9wg,CC-By Attribution 4.0 International,RCP4.5; RCP8.5; bias correction; climate models; emission scenarios; eta model; uncertainties,"[""RCP4.5"", ""RCP8.5"", ""bias correction"", ""climate models"", ""emission scenarios"", ""eta model"", ""uncertainties""]",Carolina Natel de Moura; Jan Seibert; Miriam Rita Moro Mine,"[{""id"": ""r2fzu"", ""name"": ""Carolina Natel de Moura"", ""index"": 0, ""orcid"": ""0000-0003-3103-6789"", ""bibliographic"": true}, {""id"": ""42dv9"", ""name"": ""Jan Seibert"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""25bzc"", ""name"": ""Miriam Rita Moro Mine"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}]",Carolina Natel de Moura,Physical Sciences and Mathematics; Earth Sciences; Hydrology,"[{""id"": ""59ea64a954be8111216c1395"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""59ea64aa54be8111216c13d9"", ""text"": ""Earth Sciences""}, {""id"": ""59ea64aa54be8111216c13eb"", ""text"": ""Hydrology""}]",,0,,no,no,[],,2025-04-09T20:04:06.083736
eayph_v1,What does the NDVI really tell us about crops? Insight from proximal spectral field sensors,"The use of remote sensing in agriculture is expanding due to innovation in sensors and platforms. Drones, high resolution instruments on CubeSats, and robot mounted proximal phenotyping sensors all feature in this drive. Common threads include a focus on high spatial and spectral resolution coupled with the use of machine learning methods for relating observations to crop parameters. As the best-known vegetation index, the Normalized Difference Vegetation Index (NDVI), which quantifies the difference in canopy scattering in the near-infrared and photosynthetic light absorption in the red, is spearheading this drive. Importantly, there are decades of research on the physical principals of the NDVI, relating to soil, structural and measurement geometry effects. Here we bridge the gap between the historical research, grounded in physically based theory, and the recent field-based developments, to ask the question: What does field sensed NDVI tell us about crops? We answer this question with data from two crop field sites featuring field mounted spectral reflectance sensors and a drone-based spectroscopy system. The results show how ecosystem processes can be followed using the NDVI, but also how crop structure and soil reflectance controls data collected in wavelength space.",2020-09-01T06:32:38.429035,2021-02-08T21:35:24.900939,2020-09-01T15:54:38.013484,,,eartharxiv,0,withdrawn,1,1,https://doi.org/10.31223/osf.io/eayph,CC-By Attribution 4.0 International,NDVI; crops; drone; remote sensing,"[""NDVI"", ""crops"", ""drone"", ""remote sensing""]",Jon Atherton; Chao Zhang; jaakko Oivukkamäki; Liisa Kulmala; Shan Xu; Teemu Hakala; Eija Honkavaara; Alasdair MacArthur; Albert Porcar-Castell,"[{""id"": ""d4t7c"", ""name"": ""Jon Atherton"", ""index"": 0, ""orcid"": null, ""bibliographic"": true}, {""id"": ""r8hxe"", ""name"": ""Chao Zhang"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""m9da5"", ""name"": ""jaakko Oivukkam\u00e4ki"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}, {""id"": ""y6jfq"", ""name"": ""Liisa Kulmala"", ""index"": 3, ""orcid"": null, ""bibliographic"": true}, {""id"": ""ct4d5"", ""name"": ""Shan Xu"", ""index"": 4, ""orcid"": null, ""bibliographic"": true}, {""id"": ""7uvp6"", ""name"": ""Teemu Hakala"", ""index"": 5, ""orcid"": null, ""bibliographic"": true}, {""id"": ""rkhfd"", ""name"": ""Eija Honkavaara"", ""index"": 6, ""orcid"": null, ""bibliographic"": true}, {""id"": ""serkx"", ""name"": ""Alasdair MacArthur"", ""index"": 7, ""orcid"": null, ""bibliographic"": true}, {""id"": ""6spj8"", ""name"": ""Albert Porcar-Castell"", ""index"": 8, ""orcid"": null, ""bibliographic"": true}]",Jon Atherton,Physical Sciences and Mathematics; Other Physical Sciences and Mathematics; Environmental Sciences; Environmental Monitoring,"[{""id"": ""59ea64a954be8111216c1395"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""59ea64aa54be8111216c13c0"", ""text"": ""Other Physical Sciences and Mathematics""}, {""id"": ""59ea64aa54be8111216c13c1"", ""text"": ""Environmental Sciences""}, {""id"": ""59ea64aa54be8111216c13c7"", ""text"": ""Environmental Monitoring""}]",,0,,no,no,[],,2025-04-09T20:04:19.099532
n8hz7_v1,"A continental-scale assessment of density, size, distribution, and historical trends of Australian farm dams","Farm dams are a cornerstone of modern agriculture and are ubiquitous across rural landscapes, but their intensifying effects on the environment remain unclear. As a striking example, Australia is the second driest continent on Earth, yet most farm dams are unmonitored and unregulated. As a result, there are no nation-wide statistics on how many farm dams exist or their historical trends. In this study, we trained a deep-learning convolutional neural network model to carry out the first continental-scale assessment on density, distribution, and historical trends of Australian farm dams using satellite imagery. We estimated that Australia has 1.765 million farm dams occupying an area of 4,678 Km2 and storing 11,396 GL of water. The State of New South Wales recorded the highest number of farm dams (654,983; 37% of the total) and Victoria the highest overall density (1.73 dams Km-2). We also estimated that 202,119 farm dams (11.5%) remain omitted from any records, especially in South Australia, Western Australia, and the Northern Territory. Three decades of historical records revealed an ongoing decrease in the construction rate of farm dam, from >3% per annum before 2000, to ~1% after 2000, to <0.05% after 2010 – except in the Australian Capital Territory where rates have remained relatively high. To facilitate sharing information with the Government, scientists, managers, and the local community, we developed AusDams.org: a free interactive portal to visualise the distribution of farm dams and generate statistics for any area of Australia.",2020-08-31T22:35:53.734576,2021-02-08T21:35:30.138219,2020-09-01T16:00:34.109739,,,eartharxiv,0,withdrawn,1,1,https://doi.org/10.31223/osf.io/n8hz7,CC-By Attribution 4.0 International,Australian management policies; GIS technology; artificial water bodies; deep learning classification model; freshwater reserves; invasive species; land-use change; spatially explicit dataset; urbanisation rates; water security,"[""Australian management policies"", ""GIS technology"", ""artificial water bodies"", ""deep learning classification model"", ""freshwater reserves"", ""invasive species"", ""land-use change"", ""spatially explicit dataset"", ""urbanisation rates"", ""water security""]",Martino E. Malerba; Nicholas Wright; Peter Macreadie,"[{""id"": ""45gct"", ""name"": ""Martino E. Malerba"", ""index"": 0, ""orcid"": ""0000-0002-7480-4779"", ""bibliographic"": true}, {""id"": ""b3rpf"", ""name"": ""Nicholas Wright"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""3u9yb"", ""name"": ""Peter Macreadie"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}]",Martino E. Malerba,Physical Sciences and Mathematics; Environmental Sciences; Environmental Monitoring; Natural Resources Management and Policy; Water Resource Management; Natural Resources and Conservation,"[{""id"": ""59ea64a954be8111216c1395"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""59ea64aa54be8111216c13c1"", ""text"": ""Environmental Sciences""}, {""id"": ""59ea64aa54be8111216c13c7"", ""text"": ""Environmental Monitoring""}, {""id"": ""59ea64aa54be8111216c13c8"", ""text"": ""Natural Resources Management and Policy""}, {""id"": ""59ea64aa54be8111216c13ca"", ""text"": ""Water Resource Management""}, {""id"": ""59ea64aa54be8111216c13cc"", ""text"": ""Natural Resources and Conservation""}]",,0,,no,not_applicable,[],,2025-04-09T20:04:15.759367
g2uxy_v1,"Structure and age relationship of joint sets on the Lilstock Benches, UK, based on mapping a full resolution UAV-based image","Outcrop studies of fracture networks are important to understand such networks in the subsurface, but complete
maps of all fractures in large outcrops are rare due to limitations of outcrop and image resolution. We present the
first full-resolution UAV-based, Gigapixel dataset and DEM of the wave-cut Lilstock Benches in the southern Bristol Channel basin, a classic outcrop of layer-bound fracture networks in limestones. With this dataset, we mapped the patterns and age relationships of successive generations of joints in dm-thick limestone layers separated by claystone beds. Using well-defined interpretation criteria based on crosscutting relationships and joint length, up to eight generations of joints were mapped. Results show that joint geometry and interrelations are fully resolved in the whole outcrop. Different joint generations have unique characteristics in terms of shape, orientation, spatial distribution and cross-cutting relations. The presence of low-angle crossings and junctions of joints suggest periods of partial joint cementation and reactivation. The dataset and interpretations are proposed as a benchmark of a large scale, complete fracture network to test digital fracture network models.",2020-08-29T06:44:19.320236,2021-02-08T21:35:25.406041,2020-08-31T17:31:26.144975,,,eartharxiv,0,withdrawn,1,1,https://doi.org/10.31223/osf.io/g2uxy,Academic Free License (AFL) 3.0,Abutment; Lilstock; UAV; fracturing; joints,"[""Abutment"", ""Lilstock"", ""UAV"", ""fracturing"", ""joints""]",Martijn Passchier; Janos Urai; Cees Passchier,"[{""id"": ""pqnsd"", ""name"": ""Martijn Passchier"", ""index"": 0, ""orcid"": null, ""bibliographic"": true}, {""id"": ""tw2fu"", ""name"": ""Janos Urai"", ""index"": 1, ""orcid"": ""0000-0001-5299-6979"", ""bibliographic"": true}, {""id"": ""xaj5w"", ""name"": ""Cees Passchier"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}]",Martijn Passchier,Physical Sciences and Mathematics; Environmental Sciences; Other Environmental Sciences,"[{""id"": ""59ea64a954be8111216c1395"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""59ea64aa54be8111216c13c1"", ""text"": ""Environmental Sciences""}, {""id"": ""59ea64aa54be8111216c13c2"", ""text"": ""Other Environmental Sciences""}]",,0,,no,no,[],,2025-04-09T20:04:19.972199
ktcde_v1,An analytical solution to the Navier–Stokes equation for incompressible flow around a solid sphere,"This paper is concerned with obtaining a formulation for the flow past a sphere in a viscous and incompressible fluid, building upon previously obtained well-known solutions that were limited to small Reynolds numbers. Using a method based on a summation of separation of variables, we develop a general analytical solution to the Navier--Stokes equation for the special case of axially symmetric two-dimensional flow around a sphere. For a particular set of mathematical conditions, the solution can be expressed generally as a hypergeometric function. It reproduces streamlines and flow velocities close to a moving sphere, and provides the angular location immediately behind the sphere where there is a separation between laminar flow and a stagnant region. To produce eddies around a fast-moving sphere, we present a solution obtained using a variable substitution that does not require the separation of variables and is a function of Bessel functions of the first and second kind. For particular boundary conditions, it exhibits eddies behind a fast-moving sphere.",2020-08-22T18:09:49.189512,2021-02-08T21:35:24.270946,2020-08-25T16:24:08.582526,,,eartharxiv,0,withdrawn,1,1,https://doi.org/10.31223/osf.io/ktcde,GNU Lesser General Public License (LGPL) 2.1,Bessel functions of the first kind; Bessel functions of the second kind; Legendre functions of the first kind; Legendre functions of the second kind; Navier–Stokes equation; angle of separation; associated Legendre function of the first kind; hypergeometric function; modified Bessel functions of the first kind; modified Bessel functions of the second kind; partial differential equation; stream function,"[""Bessel functions of the first kind"", ""Bessel functions of the second kind"", ""Legendre functions of the first kind"", ""Legendre functions of the second kind"", ""Navier\u2013Stokes equation"", ""angle of separation"", ""associated Legendre function of the first kind"", ""hypergeometric function"", ""modified Bessel functions of the first kind"", ""modified Bessel functions of the second kind"", ""partial differential equation"", ""stream function""]",Ahmad Talaei; Timothy J. Garrett,"[{""id"": ""wrq26"", ""name"": ""Ahmad Talaei"", ""index"": 0, ""orcid"": ""0000-0003-4603-5666"", ""bibliographic"": true}, {""id"": ""kcuz8"", ""name"": ""Timothy J. Garrett"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}]",Ahmad Talaei,Physical Sciences and Mathematics; Applied Mathematics; Partial Differential Equations; Special Functions; Earth Sciences; Physics; Fluid Dynamics; Engineering; Mechanical Engineering; Other Mechanical Engineering,"[{""id"": ""59ea64a954be8111216c1395"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""59ea64a954be8111216c13b0"", ""text"": ""Applied Mathematics""}, {""id"": ""59ea64a954be8111216c13b1"", ""text"": ""Partial Differential Equations""}, {""id"": ""59ea64a954be8111216c13b5"", ""text"": ""Special Functions""}, {""id"": ""59ea64aa54be8111216c13d9"", ""text"": ""Earth Sciences""}, {""id"": ""59ea64ab54be8111216c13f8"", ""text"": ""Physics""}, {""id"": ""59ea64ab54be8111216c1401"", ""text"": ""Fluid Dynamics""}, {""id"": ""59ea64b354be8111216c15d9"", ""text"": ""Engineering""}, {""id"": ""59ea64b354be8111216c15da"", ""text"": ""Mechanical Engineering""}, {""id"": ""59ea64b354be8111216c15dd"", ""text"": ""Other Mechanical Engineering""}]",,0,,no,not_applicable,[],,2025-04-09T20:03:48.858774
qhtb6_v1,The composition and weathering of the continents over geologic time,"The composition of continental crust records a history of construction by tectonics and destruction by physical and chemical erosion. Quantitative constraints on how both igneous addition and chemical weathering have modified the continents' bulk composition are essential for understanding the evolution of geodynamics and climate. We have extracted temporal trends in sediments' protolith composition and weathering intensity from the largest available compilation of sedimentary major-element compositions, of ~ 15,000 samples from 4.0 Ga to the present. To do this we used a new analytical method which inverts whole sedimentary compositions for protolith composition and weathering intensity simultaneously. We find that the average Archean upper continental crust was silica rich and had a similar compositional diversity to modern continents. This is consistent with an early-Archean, or earlier, onset of plate tectonics. In the Archean, chemical weathering was ~ 25 % more efficient at sequestering CO2 than in subsequent time periods. Since 2.0 Ga, over long (> 0.5 Ga) timescales, the crustal weathering intensity has remained largely constant. On shorter timescales over the Phanerozoic, the intensity of weathering is correlated to global climate state, consistent with silicate weathering feedback acting in response to changes in CO2 outgassing.",2020-08-21T12:06:59.468242,2024-04-08T12:33:02.688055,2020-08-21T16:35:18.385147,,,eartharxiv,0,withdrawn,1,1,https://doi.org/10.31223/osf.io/qhtb6,GNU Lesser General Public License (LGPL) 2.1,archean continents; crustal evolution; phanerozoic climate; plate tectonics; provenance; sedimentary geochemistry; weathering,"[""archean continents"", ""crustal evolution"", ""phanerozoic climate"", ""plate tectonics"", ""provenance"", ""sedimentary geochemistry"", ""weathering""]",Alex Lipp; Oliver Shorttle; Erik Sperling; Jochen J Brocks; Devon Cole; Peter  Crockford; Lucas Del Mouro; Keith Dewing; Stephen Q Dornbos; Joseph F. Emmings,"[{""id"": ""pax9c"", ""name"": ""Alex Lipp"", ""index"": 0, ""orcid"": ""0000-0003-2130-8576"", ""bibliographic"": true}, {""id"": ""hb8cv"", ""name"": ""Oliver Shorttle"", ""index"": 1, ""orcid"": ""0000-0002-8713-1446"", ""bibliographic"": true}, {""id"": ""peb9k"", ""name"": ""Erik Sperling"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}, {""id"": ""y2bgk"", ""name"": ""Jochen J Brocks"", ""index"": 3, ""orcid"": null, ""bibliographic"": true}, {""id"": ""bxvaz"", ""name"": ""Devon Cole"", ""index"": 4, ""orcid"": null, ""bibliographic"": true}, {""id"": ""sf69j"", ""name"": ""Peter  Crockford"", ""index"": 5, ""orcid"": ""0000-0002-0770-6482"", ""bibliographic"": true}, {""id"": ""5utvr"", ""name"": ""Lucas Del Mouro"", ""index"": 6, ""orcid"": null, ""bibliographic"": true}, {""id"": ""adx9m"", ""name"": ""Keith Dewing"", ""index"": 7, ""orcid"": null, ""bibliographic"": true}, {""id"": ""bjrwy"", ""name"": ""Stephen Q Dornbos"", ""index"": 8, ""orcid"": null, ""bibliographic"": true}, {""id"": ""nb97v"", ""name"": ""Joseph F. Emmings"", ""index"": 9, ""orcid"": null, ""bibliographic"": true}]",Alex Lipp,Physical Sciences and Mathematics; Earth Sciences; Geochemistry,"[{""id"": ""59ea64a954be8111216c1395"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""59ea64aa54be8111216c13d9"", ""text"": ""Earth Sciences""}, {""id"": ""59ea64aa54be8111216c13e3"", ""text"": ""Geochemistry""}]",,0,,available,no,[],,2025-04-09T20:04:14.905971
t8dm4_v1,GARPOS: analysis software for the GNSS-A seafloor positioning with simultaneous estimation of sound speed structure,"Global Navigation Satellite System – Acoustic ranging combined seafloor geodetic technique (GNSS-A) has extended the geodetic observation network into the ocean. The key issue for analyzing the GNSS-A data is how to correct the effect of sound speed variation in the seawater. We constructed a generalized observation equation and developed a method to directly extract the gradient sound speed structure by introducing appropriate statistical properties in the observation equation, especially the data correlation term. In the proposed scheme, we calculate the posterior probability based on the empirical Bayes approach using the Akaike’s Bayesian Information Criterion (ABIC) for model selection. This approach enabled us to suppress the overfitting of sound speed variables and thus to extract simpler sound speed field and stable seafloor positions from the GNSS-A dataset. The proposed procedure is implemented in the Python-based software “GARPOS” (GNSS-Acoustic Ranging combined POsitioning Solver).",2020-08-21T11:04:59.219674,2021-02-08T21:35:22.053554,2020-08-21T16:34:31.108353,,,eartharxiv,0,withdrawn,1,1,https://doi.org/10.31223/osf.io/t8dm4,CC-By Attribution 4.0 International,GNSS-A; GNSS-A methodology; GNSS-A oceanography; seafloor geodesy; sound speed structure,"[""GNSS-A"", ""GNSS-A methodology"", ""GNSS-A oceanography"", ""seafloor geodesy"", ""sound speed structure""]",Shun-ichi Watanabe; Tadashi Ishikawa; Yusuke Yokota; Yuto Nakamura,"[{""id"": ""wkb8u"", ""name"": ""Shun-ichi Watanabe"", ""index"": 0, ""orcid"": ""0000-0002-9276-6112"", ""bibliographic"": true}, {""id"": ""fvqtg"", ""name"": ""Tadashi Ishikawa"", ""index"": 1, ""orcid"": ""0000-0001-6385-7915"", ""bibliographic"": true}, {""id"": ""26ty4"", ""name"": ""Yusuke Yokota"", ""index"": 2, ""orcid"": ""0000-0003-2969-9110"", ""bibliographic"": true}, {""id"": ""fjzgu"", ""name"": ""Yuto Nakamura"", ""index"": 3, ""orcid"": null, ""bibliographic"": true}]",Shun-ichi Watanabe,Physical Sciences and Mathematics; Earth Sciences; Geophysics and Seismology,"[{""id"": ""59ea64a954be8111216c1395"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""59ea64aa54be8111216c13d9"", ""text"": ""Earth Sciences""}, {""id"": ""59ea64aa54be8111216c13e0"", ""text"": ""Geophysics and Seismology""}]",,0,,available,no,[],,2025-04-09T20:03:50.243384
2rxbn_v1,Quantification of non-linear multiphase flow in porous media,"We measure the pressure difference during two-phase flow across a sandstone sample for a range of injection rates and fractional flows of water, the wetting phase, during an imbibition experiment. We quantify the onset of a transition from a linear relationship between flow rate and pressure gradient to a non-linear power-law dependence. We show that the transition from linear (Darcy) to non-linear flow and the exponent in the power-law is a function of fractional flow. We use energy balance to accurately predict the onset of intermittency for a range of fractional flows, fluid viscosities and three rock types, reconciling several literature datasets.",2020-08-20T14:52:14.063702,2021-02-08T21:35:19.895019,2020-08-20T19:15:55.465880,,,eartharxiv,0,withdrawn,1,1,https://doi.org/10.31223/osf.io/2rxbn,GNU Lesser General Public License (LGPL) 2.1,fluid dynamics; multiphase flow; porous media,"[""fluid dynamics"", ""multiphase flow"", ""porous media""]",Yihuai Zhang; Branko Bijeljic; Ying Gao; Qingyang Lin; Martin J. Blunt,"[{""id"": ""9u5fr"", ""name"": ""Yihuai Zhang"", ""index"": 0, ""orcid"": ""0000-0001-5471-3450"", ""bibliographic"": true}, {""id"": ""m68z5"", ""name"": ""Branko Bijeljic"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""vc3gs"", ""name"": ""Ying Gao"", ""index"": 2, ""orcid"": null, ""bibliographic"": true}, {""id"": ""wmxkz"", ""name"": ""Qingyang Lin"", ""index"": 3, ""orcid"": ""0000-0001-5691-9532"", ""bibliographic"": true}, {""id"": ""w9m43"", ""name"": ""Martin J. Blunt"", ""index"": 4, ""orcid"": null, ""bibliographic"": true}]",Yihuai Zhang,Physical Sciences and Mathematics; Earth Sciences; Mineral Physics; Hydrology; Medicine and Health Sciences; Life Sciences; Engineering,"[{""id"": ""59ea64a954be8111216c1395"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""59ea64aa54be8111216c13d9"", ""text"": ""Earth Sciences""}, {""id"": ""59ea64aa54be8111216c13e7"", ""text"": ""Mineral Physics""}, {""id"": ""59ea64aa54be8111216c13eb"", ""text"": ""Hydrology""}, {""id"": ""59ea64ab54be8111216c1410"", ""text"": ""Medicine and Health Sciences""}, {""id"": ""59ea64b054be8111216c1513"", ""text"": ""Life Sciences""}, {""id"": ""59ea64b354be8111216c15d9"", ""text"": ""Engineering""}]",,0,,not_applicable,not_applicable,[],,2025-04-09T20:03:45.787179
huz73_v1,Correcting 19th and 20th century sea surface temperatures improves simulations of Atlantic hurricane activity,"Changes in the statistics of North Atlantic hurricanes are known to depend upon the pattern of tropical sea surface temperatures (SSTs).  Dynamical and statistical models are key tools to predict future hurricane activity, with our confidence in this application rooted in the models’ ability to skillfully reproduce hurricane variations over the past 30-40 years, when satellite data allows accurate reconstruction of observed ocean temperature variations.  Extending the evaluation of simulations forced with historical SSTs against hurricane activity to century scales provides a more complete assessment of predictive skill, but which is limited in part by uncertainty in historical SST estimates.  Here we show that recent corrections for systematic offsets in bucket SST measurements improve model skill in reproducing North Atlantic hurricane counts and lead to consistent reproducibility since the late 19th century.  Changes in hurricane frequency introduced by revising historical SST data are of similar magnitude to projected changes for 2081-2100 in response to increasing greenhouse gases, highlighting the importance of accurately assessing SST patterns for both the historical period and the future.",2020-08-20T14:34:54.918584,2021-02-08T21:35:21.440412,2020-08-20T19:14:10.050315,,,eartharxiv,0,withdrawn,1,1,https://doi.org/10.31223/osf.io/huz73,GNU Lesser General Public License (LGPL) 2.1,Bias correction; Hurricane; Sea surface temperature; Simulation,"[""Bias correction"", ""Hurricane"", ""Sea surface temperature"", ""Simulation""]",Duo CHAN; Gabriel A. Vecchi; Wenchang Yang; Peter Huybers,"[{""id"": ""qvzue"", ""name"": ""Duo CHAN"", ""index"": 0, ""orcid"": ""0000-0002-8573-5115"", ""bibliographic"": true}, {""id"": ""apkfb"", ""name"": ""Gabriel A. Vecchi"", ""index"": 1, ""orcid"": null, ""bibliographic"": true}, {""id"": ""83txa"", ""name"": ""Wenchang Yang"", ""index"": 2, ""orcid"": ""0000-0003-0053-9527"", ""bibliographic"": true}, {""id"": ""zh72a"", ""name"": ""Peter Huybers"", ""index"": 3, ""orcid"": ""0000-0002-3734-8145"", ""bibliographic"": true}]",Duo CHAN,Physical Sciences and Mathematics; Oceanography and Atmospheric Sciences and Meteorology; Atmospheric Sciences; Climate,"[{""id"": ""59ea64a954be8111216c1395"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""59ea64a954be8111216c13b9"", ""text"": ""Oceanography and Atmospheric Sciences and Meteorology""}, {""id"": ""59ea64a954be8111216c13bb"", ""text"": ""Atmospheric Sciences""}, {""id"": ""59ea64a954be8111216c13be"", ""text"": ""Climate""}]",,0,,no,no,[],,2025-04-09T20:04:06.960782
ju26e_v1,Identifying and correcting the World War 2 warm anomaly in sea surface temperature measurements,"Most foregoing estimates of historical sea surface temperature (SST) feature warmer global-average SSTs during World War 2 well in excess of climate-model predictions.  This warm anomaly, referred to as the WW2WA, was hypothesized to arise from incomplete corrections of biases associated with rapid changes in measurement instruments and protocols.  Using linear mixed-effects methods we confirm highly significant offsets among specific groups of bucket and engine-room-intake SST measurements that, upon correction, reduce the WW2WA by 0.26°C (95% c.i. 0.15 to 0.38°C).  Furthermore, SST measurements during WW2 coming from buckets are reportedly warmer at night than day, and controlling for this evident bias reduces the WW2WA by another 0.05°C (0.02 to 0.08°C).  Adjusted SSTs give a more stable and smoothly evolving record of historical warming with a WW2WA of 0.09°C (-0.01 to 0.18°C) that is consistent with internal variability in climate models.",2020-08-20T13:30:02.474650,2021-02-08T21:35:21.019437,2020-08-20T19:15:05.982366,,,eartharxiv,0,withdrawn,1,1,https://doi.org/10.31223/osf.io/ju26e,GNU Lesser General Public License (LGPL) 2.1,Bias; Correction; Model-data discrepancy; Sea surface temperature; World War II,"[""Bias"", ""Correction"", ""Model-data discrepancy"", ""Sea surface temperature"", ""World War II""]",Duo CHAN; Peter Huybers,"[{""id"": ""qvzue"", ""name"": ""Duo CHAN"", ""index"": 0, ""orcid"": ""0000-0002-8573-5115"", ""bibliographic"": true}, {""id"": ""zh72a"", ""name"": ""Peter Huybers"", ""index"": 1, ""orcid"": ""0000-0002-3734-8145"", ""bibliographic"": true}]",Duo CHAN,Physical Sciences and Mathematics; Oceanography and Atmospheric Sciences and Meteorology; Oceanography; Climate,"[{""id"": ""59ea64a954be8111216c1395"", ""text"": ""Physical Sciences and Mathematics""}, {""id"": ""59ea64a954be8111216c13b9"", ""text"": ""Oceanography and Atmospheric Sciences and Meteorology""}, {""id"": ""59ea64a954be8111216c13bd"", ""text"": ""Oceanography""}, {""id"": ""59ea64a954be8111216c13be"", ""text"": ""Climate""}]",,0,,no,no,[],,2025-04-09T20:04:02.576025