From bc1f7596ec7da0d2aef954ffdb7a54e436133f3b Mon Sep 17 00:00:00 2001
From: bkatiemills <you@example.com>
Date: Wed, 17 Dec 2025 16:08:36 -0500
Subject: [PATCH] citation update

---
 argovis/src/pages/citations.jsx | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/argovis/src/pages/citations.jsx b/argovis/src/pages/citations.jsx
index b307561..35f4a4d 100644
--- a/argovis/src/pages/citations.jsx
+++ b/argovis/src/pages/citations.jsx
@@ -34,22 +34,23 @@ class ArgoAbout extends React.Component {
                         <p><b>In addition to citing Argovis, please cite the reference for the dataset you are using, enumerated below:</b></p>
 						<ul>
 							<li><b>Argo data</b>: see <a href='https://argo.ucsd.edu/data/acknowledging-argo/#:~:text=To%20acknowledge%20Argo%2C%20please%20use,ocean%2Dops.org).' target="_blank" rel="noreferrer"> guidance from the Argo collaboration</a></li>
-							<li><b>Ship-based profiles</b>: CCHDO Hydrographic Data Office (2023). CCHDO Hydrographic Data Archive, Version 2023-09-01. In CCHDO Hydrographic Data Archive. UC San Diego Library Digital Collections. <a href='https://doi.org/10.6075/J0CCHDT8'>https://doi.org/10.6075/J0CCHDT8</a></li>
-							<li><b>Global Drifter Program</b>: Cite as: Elipot, Shane; Sykulski, Adam; Lumpkin, Rick; Centurioni, Luca; Pazos, Mayra (2022). Hourly location, current velocity, and temperature collected from Global Drifter Program drifters world-wide. [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. <a href='https://doi.org/10.25921/x46c-3620' target="_blank" rel="noreferrer">https://doi.org/10.25921/x46c-3620</a>. Accessed [date].</li>
+							<li><b>Argone float location forecasts</b>: Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix, Journal of Atmospheric and Oceanic Technology vol. 40 issue 9 (2023), <a href="https://doi.org/10.1175/JTECH-D-22-0070.1">https://doi.org/10.1175/JTECH-D-22-0070.1</a></li>
+                            <li><b>Atmospheric rivers</b>: based on the methodology described in Rutz, J. J., W. J. Steenburgh, and F. M. Ralph, 2014: Climatological Characteristics of Atmospheric Rivers and Their Inland Penetration over the Western United States. Mon. Wea. Rev., 142, 905–921, <a href='https://doi.org/10.1175/MWR-D-13-00168.1' target="_blank" rel="noreferrer">https://doi.org/10.1175/MWR-D-13-00168.1</a>.</li>
+                            <li><b>Copernicus sea level anomaly timeseries</b>: Copernicus Climate Change Service, Climate Data Store, (2018): Sea level gridded data from satellite observations for the global ocean from 1993 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href='https://doi.org/10.24381/cds.4c328c78' target="_blank" rel="noreferrer">10.24381/cds.4c328c78</a> (Accessed on 06-Jul-2023)</li>
+                            <li><b>Global Drifter Program</b>: Cite as: Elipot, Shane; Sykulski, Adam; Lumpkin, Rick; Centurioni, Luca; Pazos, Mayra (2022). Hourly location, current velocity, and temperature collected from Global Drifter Program drifters world-wide. [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. <a href='https://doi.org/10.25921/x46c-3620' target="_blank" rel="noreferrer">https://doi.org/10.25921/x46c-3620</a>. Accessed [date].</li>
+                            <li><b>GLODAPv2.2016b mapped data product</b>: Lauvset, S. K, R. M. Key, A. Olsen, S. van Heuven, A. Velo, X. Lin, C. Schirnick, A. Kozyr, T. Tanhua, M. Hoppema, S. Jutterström, R. Steinfeldt, E. Jeansson, M. Ishii, F. F. Pérez, T. Suzuki & S. Watelet: A new global interior ocean mapped climatology: the 1°x1° GLODAP version 2, Earth Syst. Sci. Data, 8, 325–340, 2016, <a href='https://doi.org/10.5194/essd-8-325-2016'>doi:10.5194/essd-8-325-2016</a></li>
+                            <li><b>LocalGP potential temperature integrals</b>: Sala, J., Giglio, D., Capotondi, A., Sukianto, T., and Kuusela, M.: Leading dynamical processes of global marine heatwaves in an ocean state estimate, Ocean Sci., 21, 2463–2479, <a href='https://doi.org/10.5194/os-21-2463-2025'>https://doi.org/10.5194/os-21-2463-2025</a>, 2025.</li>
+                            <li><b>NOAA Sea surface temperature timeseries</b>: NOAA Optimum Interpolation (OI) SST V2 data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href='https://psl.noaa.gov' target="_blank" rel="noreferrer">https://psl.noaa.gov</a>; original data available <a href='https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html' target="_blank" rel="noreferrer">here</a>; also see <a href='ftp://ftp.emc.ncep.noaa.gov/cmb/sst/papers/oiv2pap/'>Reynolds, R.W., N.A. Rayner, T.M. Smith, D.C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625.</a></li>
+                            <li><b>REMSS CCMP wind vector timeseries</b>: Mears, C.; Lee, T.; Ricciardulli, L.; Wang, X.; Wentz, F., 2022: RSS Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis on 0.25 deg grid, Version 3.0, Remote Sensing Systems, Santa Rosa, CA. Available at www.remss.com DOI: <a href='https://doi.org/10.56236/RSS-uv6h30' target="_blank" rel="noreferrer">10.56236/RSS-uv6h30</a></li>
+                            <li><b><a href='https://sio-argo.ucsd.edu/RG_Climatology.html' target="_blank" rel="noreferrer">Roemmich-Gilson Argo gridded climatology</a></b>: Roemmich, D. and J. Gilson, 2009: The 2004-2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Progress in Oceanography, 82, 81-100. </li>        
+                            <li><b>Scripps Argo Trajectory-Based Velocity Product: Global Estimates of Absolute Velocity Derived from Core, Biogeochemical, and Deep Argo Float Trajectories at Parking Depth</b>: <a href='https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0065.1/JTECH-D-22-0065.1.xml' target="_blank" rel="noreferrer">https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0065.1/JTECH-D-22-0065.1.xml</a>, DOI: <a href='https://doi.org/10.6075/J0NK3F7V'>https://doi.org/10.6075/J0NK3F7V</a></li>
+                            <li><b>Ship-based profiles</b>: CCHDO Hydrographic Data Office (2023). CCHDO Hydrographic Data Archive, Version 2023-09-01. In CCHDO Hydrographic Data Archive. UC San Diego Library Digital Collections. <a href='https://doi.org/10.6075/J0CCHDT8'>https://doi.org/10.6075/J0CCHDT8</a></li>
 							<li><b>Tropical cyclone data</b>:
 								<ul>
 									<li><b>JTWC</b> (<a href='https://www.metoc.navy.mil/jtwc/jtwc.html?best-tracks' target="_blank" rel="noreferrer">https://www.metoc.navy.mil/jtwc/jtwc.html?best-tracks</a>): Chu, J. H., Sampson, C. R.,  Levine, A. S., & Fukada, E. (2002). The joint typhoon warning center tropical cyclone best-tracks, 1945-2000. Joint Typhoon Warning Center, Technical Report No. NRL/MR/7540-02-16.</li>
 									<li><b>HURDAT</b> (<a href='https://www.nhc.noaa.gov/data/#hurdat' target="_blank" rel="noreferrer">https://www.nhc.noaa.gov/data/#hurdat</a>): Landsea, C. W., & Franklin, J. L. (2013). Atlantic hurricane database uncertainty and presentation of a new database format. Monthly Weather Review, 141(10), 3576–3592.</li>
 								</ul>
-							</li>
-							<li><b>Argone float location forecasts</b>: Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition Matrix, Journal of Atmospheric and Oceanic Technology vol. 40 issue 9 (2023), <a href="https://doi.org/10.1175/JTECH-D-22-0070.1">https://doi.org/10.1175/JTECH-D-22-0070.1</a></li>
-							<li><b>Scripps Argo Trajectory-Based Velocity Product: Global Estimates of Absolute Velocity Derived from Core, Biogeochemical, and Deep Argo Float Trajectories at Parking Depth</b>: <a href='https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0065.1/JTECH-D-22-0065.1.xml' target="_blank" rel="noreferrer">https://journals.ametsoc.org/view/journals/atot/aop/JTECH-D-22-0065.1/JTECH-D-22-0065.1.xml</a>, DOI: <a href='https://doi.org/10.6075/J0NK3F7V'>https://doi.org/10.6075/J0NK3F7V</a></li>
-							<li><b><a href='https://sio-argo.ucsd.edu/RG_Climatology.html' target="_blank" rel="noreferrer">Roemmich-Gilson Argo gridded climatology</a></b>: Roemmich, D. and J. Gilson, 2009: The 2004-2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Progress in Oceanography, 82, 81-100. </li>
-							<li><b>GLODAPv2.2016b mapped data product</b>: Lauvset, S. K, R. M. Key, A. Olsen, S. van Heuven, A. Velo, X. Lin, C. Schirnick, A. Kozyr, T. Tanhua, M. Hoppema, S. Jutterström, R. Steinfeldt, E. Jeansson, M. Ishii, F. F. Pérez, T. Suzuki & S. Watelet: A new global interior ocean mapped climatology: the 1°x1° GLODAP version 2, Earth Syst. Sci. Data, 8, 325–340, 2016, <a href='https://doi.org/10.5194/essd-8-325-2016'>doi:10.5194/essd-8-325-2016</a></li>
-							<li><b>NOAA Sea surface temperature timeseries</b>: NOAA Optimum Interpolation (OI) SST V2 data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at <a href='https://psl.noaa.gov' target="_blank" rel="noreferrer">https://psl.noaa.gov</a>; original data available <a href='https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html' target="_blank" rel="noreferrer">here</a>; also see <a href='ftp://ftp.emc.ncep.noaa.gov/cmb/sst/papers/oiv2pap/'>Reynolds, R.W., N.A. Rayner, T.M. Smith, D.C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625.</a></li>
-							<li><b>Copernicus sea level anomaly timeseries</b>: Copernicus Climate Change Service, Climate Data Store, (2018): Sea level gridded data from satellite observations for the global ocean from 1993 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href='https://doi.org/10.24381/cds.4c328c78' target="_blank" rel="noreferrer">10.24381/cds.4c328c78</a> (Accessed on 06-Jul-2023)</li>
-							<li><b>REMSS CCMP wind vector timeseries (demo)</b>: Mears, C.; Lee, T.; Ricciardulli, L.; Wang, X.; Wentz, F., 2022: RSS Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis on 0.25 deg grid, Version 3.0, Remote Sensing Systems, Santa Rosa, CA. Available at www.remss.com DOI: <a href='https://doi.org/10.56236/RSS-uv6h30' target="_blank" rel="noreferrer">10.56236/RSS-uv6h30</a></li>
-							<li><b>Atmospheric rivers</b>: based on the methodology described in Rutz, J. J., W. J. Steenburgh, and F. M. Ralph, 2014: Climatological Characteristics of Atmospheric Rivers and Their Inland Penetration over the Western United States. Mon. Wea. Rev., 142, 905–921, <a href='https://doi.org/10.1175/MWR-D-13-00168.1' target="_blank" rel="noreferrer">https://doi.org/10.1175/MWR-D-13-00168.1</a>.</li>
+							</li>	
 							<li><b>Biological Southern Ocean State Estimate (BSOSE, coming soon)</b>:
 								<ul>
 									<li>M. Mazloff, P. Heimbach, and C. Wunsch, 2010: An Eddy-Permitting Southern Ocean State Estimate. J. Phys. Oceanogr., 40, 880-899. doi: <a href='https://doi.org/10.1175/2009JPO4236.1'>10.1175/2009JPO4236.1</a></li>