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The Great Atlantic Sargassum Belt (GASB) first appeared in 2011 and quickly became the largest interconnected floating biome globally. Sargassum spp. requires both phosphorus (P) and nitrogen (N) for growth, yet the sources fueling the GASB are unclear. Here, we use coral–bound nitrogen isotopes from six coral cores to reconstruct N2 fixation, the primary source of bioavailable N to the surface ocean across the wider Caribbean over the past 120 years. Our data indicate that changes in N2 fixation were controlled by multidecadal and interannual changes in the supply of excess P from equatorial upwelling in the Atlantic. We show that the supply of P from equatorial upwelling and N from the N2 fixation response can explain the extent of the GASB since 2011. # Equatorial upwelling of phosphorus drives Atlantic N~2~ fixation and *Sargassum* blooms This Excel file contains time series data combining coral geochemical records (δ¹⁵N and δ¹⁸O), climate indices, Sargassum biomass, and major riverine outflows. The dataset integrates multiple spatially distributed records to examine long-term variability in nutrient dynamics, climate forcing, and ecological responses in the Caribbean and tropical Atlantic. Values that were not available or are missing are indicated as N/A. ## Column Reference Table File: Caribbean_data_for_DRYAD.xlsx | Column Name | Description | | :----------------------------------- | :------------------------------------------------------------------------------------------------- | | **Year\_CR\_Turneffe** | Calendar year of sampling for coral records from Turneffe Atoll (Belize) and Cahuita (Costa Rica). | | **Cahuita Costa Rica\_d18O\_ts** | Coral δ¹⁸O time series from Cahuita, Costa Rica (proxy for SST and freshwater input). | | **d15N\_CR** | Coral-bound δ¹⁵N from Cahuita, Costa Rica (proxy for nitrogen source/processing). | | **Turneffe Atoll\_d18O\_ts** | Coral δ¹⁸O time series from Turneffe Atoll, Belize. | | **d15N\_Turneffe** | Coral-bound δ¹⁵N from Turneffe Atoll. | | **Date\_MQ** | Sampling date for Martinique (MQ) site. | | **d18O\_MQ** | Coral δ¹⁸O from Martinique. | | **d15N\_MQ** | Coral δ¹⁵N from Martinique. | | **Year Bermuda** | Calendar year for Bermuda coral samples. | | **d15N Bermuda** | Coral δ¹⁵N from Bermuda. | | **Year\_CUBA** | Calendar year for Cuban coral records. | | **d15N\_CUBA** | Coral δ¹⁵N from Cuba. | | **d15N\_Mexico** | Coral δ¹⁵N from Mexico. | | **Year\_Tobago** | Calendar year for Tobago coral samples. | | **d15N\_Tobago** | Coral δ¹⁵N from Tobago. | | **Year AMM** | Year corresponding to Atlantic Meridional Mode (AMM) values. | | **AMM\_SST** | Sea Surface Temperature anomalies associated with the AMM. | | **AMM\_Wind** | Wind anomalies associated with the AMM. | | **AMO** | Atlantic Multidecadal Oscillation index value. | | **average\_year** | Averaged year across all coral records included. | | **AVERAGE\_rescaled** | Composite δ¹⁵N record rescaled across sites. | | **error\_propagated** | Propagated error estimate for the rescaled average. | | **AVERAGE\_rescaled\_noCR\_BM\_TB** | Rescaled δ¹⁵N average excluding Costa Rica, Bermuda, and Tobago. | | **error\_propagated2** | Propagated error for the reduced-site average. | | **Months Sargassum** | Month of Sargassum observation. | | **Monthly Sargassum biomass (tons)** | Monthly biomass estimates of pelagic Sargassum (tons). | | **Year\_SST\_SSS** | Year corresponding to SST/SSS data. | | **SST\_10-20N\_20-60W** | Sea Surface Temperature average over 10–20°N, 20–60°W. | | **SSS\_10-20N\_20-60W** | Sea Surface Salinity average over the same region. | | **U\_windstress\_10\_20N\_58\_62W** | Zonal wind stress (10–20°N, 58–62°W). | | **windspeed\_0\_20N\_20\_50W** | Mean wind speed (0–20°N, 20–50°W). | | **Geo\_u\_12\_18N\_60\_80W (CC)** | Geostrophic zonal velocity (12–18°N, 60–80°W), Caribbean Current proxy. | | **DU\_scav\_areaweight** | Dust deposition (scavenging flux, area-weighted). | | **DU\_ddep\_areaweight** | Dust dry deposition (area-weighted). | | **BC\_scav\_areaweight** | Black carbon scavenging flux (area-weighted). | | **Bc\_ddep\_areaweight** | Black carbon dry deposition (area-weighted). | | **BC\_total\_areaweight** | Total black carbon deposition (area-weighted). | | **DU\_total\_areaweight** | Total dust deposition (area-weighted). | | **Obidos\_Amazon\_m3\_s** | Amazon River discharge at Óbidos station (m³/s). | | **Ciudad Bolivar\_Orinoco\_m3\_s** | Orinoco River discharge at Ciudad Bolívar (m³/s). | | **Year Pstar** | Year corresponding to P\* (phosphorus excess) record. | | **Pstar** | Phosphorus excess (indicator of nutrient balance, micro Molar). | | **Amazon\_outflow\_date** | Date of Amazon outflow measurement. | | **Amazon\_outflow\_km3** | Amazon River outflow volume (km³). | | **Orinoco\_outflow\_date** | Date of Orinoco outflow measurement. | | **Orinoco\_outflow\_km3** | Orinoco River outflow volume (km³). | Links to other publicly accessible locations of the data: * [https://climexp.knmi.nl](http://...) Data was derived from the following sources: * Climate Explorer was used for gridded satellite-derived products (SST, SSS, windspeed, windstress) by using the geographical extent as indicated in the manuscript ## Code/Software No software was used for data analysis, and the codes used for figures and data analyses are available on GitHub ([https://github.com/marinejon/](https://github.com/marinejon/)) 

The geological record encodes the relationship between climate and atmospheric carbon dioxide (CO₂) over long and short timescales, as well as potential drivers of evolutionary transitions. However, reconstructing CO₂beyond direct measurements requires the use of paleoproxies and herein lies the challenge, as proxies differ in their assumptions, degree of understanding, and even reconstructed values. In this study, we critically evaluated, categorized, and integrated available proxies to create a high-fidelity and transparently constructed atmospheric CO₂record spanning the past 66 million years. This newly constructed record provides clearer evidence for higher Earth system sensitivity in the past and for the role of CO₂thresholds in biological and cryosphere evolution. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.