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179 results for “niños”
mom6 cobalt model result for oceanic carbon response to El Niños
<p>GEOS_Chem atmospheric transport model, monthly, 2.5 degree resolution in tropical Pacific Ocean (120-300E, 30N-30S)<br> 1. GEOS_Chem_tropical_pacific_monthly_clim_1992_2017.nc<br> 2. GEOS_Chem_tropical_pacific_monthly_iav_1992_2017.nc<br> 1 and 2 are GEOS_Chem atmospheric transport model results</p> <p>MOM6 COBALT model results<br> Note1: region range is 120-300E, 30N-30S with a resolution of half degree, monthly result from 1982.1.1 to 2018.1.1<br> Note2: if the file name has a label "_detrend_deseason", this file is detrended and deseasonized using full value in 1982-2018 with CDO<br> "cdo -ymonsub -detrend $fin -ymonmean -detrend $fin $fout"<br> Note3: ocean/sea surface is the first layer which is 1 meter deep<br> Note4: MLD_001_temp/salt/dic/alk/kd is the vertical mean value in the mixing layer depth (criterial of 0.01 kg/m3)</p> <p>MOM6_COBALT_tropical_pacific_monthly_dic_deltap_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized delta pCO2 (ocean pCO2 minus air pCO2) in the ocean surface)<br> MOM6_COBALT_tropical_pacific_monthly_dic_deltap_1982_2018.nc<br> (delta pCO2 (ocean pCO2 minus air pCO2) in the ocean surface)<br> MOM6_COBALT_tropical_pacific_monthly_dic_stf_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized air-sea CO2 flux, positive to ocean)<br> MOM6_COBALT_tropical_pacific_monthly_dic_stf_1982_2018.nc<br> (air-sea CO2 flux, positive to ocean)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_1982_2018.nc<br> (Mixing layer depth with a density criterial of 0.01 kg/m3)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_alk_1982_2018_detrend_deseason.nc<br> (first compute vertical mean alkilinity in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_alk_zgradient_1982_2018_detrend_deseason.nc<br> (first compute vertical mean alkilinity gradient (dalk/dz) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_dic_1982_2018_detrend_deseason.nc<br> (first compute vertical mean dissolved inorganic carbon (DIC) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_dic_zgradient_1982_2018_detrend_deseason.nc<br> (first compute vertical mean DIC (ddic/dz) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_Kd_interface_1982_2018_detrend_deseason.nc<br> (first compute vertical mean Diapycnal diffusivity at interfaces layers (kd_interface) in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_salt_1982_2018_detrend_deseason.nc<br> (first compute vertical mean salinity in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_temp_1982_2018_detrend_deseason.nc<br> (first compute vertical mean temperature in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_001_u_1982_2018_detrend_deseason.nc<br> (first compute vertical mean velocity u in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> MOM6_COBALT_tropical_pacific_monthly_MLD_001_v_1982_2018_detrend_deseason.nc<br> (first compute vertical mean velocity v in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_003_1982_2018.nc<br> (Mixing layer depth with a density criterial of 0.03 kg/m3)<br> MOM6_COBALT_tropical_pacific_monthly_pco2surf_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized sea surface pCO2 (ocean pCO2))</p> <p>MOM6_COBALT_tropical_pacific_monthly_pco2surf_1982_2018.nc<br> (sea surface pCO2 (ocean pCO2))<br> MOM6_COBALT_tropical_pacific_monthly_sfc_chl_1982_2018.nc<br> (sea surface chlorophyll)<br> MOM6_COBALT_tropical_pacific_monthly_sfc_dic_1982_2018.nc<br> (sea surface dissolved inorganic carbon (DIC))<br> MOM6_COBALT_tropical_pacific_monthly_sfc_no3_1982_2018.nc<br> (sea surface nitrate, NO3)<br> MOM6_COBALT_tropical_pacific_monthly_sfc_po4_1982_2018.nc<br> (sea surface phosphate, PO4)<br> MOM6_COBALT_tropical_pacific_monthly_SSS_1982_2018.nc<br> (sea surface salinity)<br> MOM6_COBALT_tropical_pacific_monthly_SST_1982_2018.nc<br> (sea surface temperature)<br> MOM6_COBALT_tropical_pacific_monthly_taux_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized wind stress in zonal direction)<br> MOM6_COBALT_tropical_pacific_monthly_taux_1982_2018.nc<br> (wind stress in zonal direction)<br> MOM6_COBALT_tropical_pacific_monthly_tauy_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized wind stress in meridional direction)<br> MOM6_COBALT_tropical_pacific_monthly_tauy_1982_2018.nc<br> (wind stress in meridional direction)<br> MOM6_COBALT_tropical_pacific_monthly_tc_depth_1982_2018.nc<br> (thermocline depth defined as depth where the temperature equals 20oC)</p> <p>JRA_rain_tropical_pacific_monthly_prrn_1982_2018_detrend_deseason.nc<br> (detrended and deseasonalized JRA rainfall)<br> </p> <p>Budget terms based MOM6 COBALT model results<br> Note1: region range is 120-300E, 30N-30S with a resolution of half degree, monthly result from 1982.1.1 to 2018.1.1<br> Note2: this is the vertical mean result in the mixing layer depth (criterial of 0.01 kg/m3) after detrend and deseasonalize</p> <p>MOM6_COBALT_tropical_pacific_monthly_MLD_001_pco2w_budget_1982_2018_detrend_deseason.nc<br> (budget terms used for ocean pCO2 budget analysis, vertical mean in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br> pco2_hadv_hdif: horizontal transport term, H_circ;<br> dpco2_hadvx: zonal advection term;<br> dpco2_hadvy: meridional advection term;<br> dpco2_hdif: horizontal diffusivity term;<br> dpco2_vadv_vdif: vertical transport term;<br> dpco2_dic_vdif_vdif: vertical transport term induced by DIC;<br> dpco2_alk_vdif: vertical transport term induced by Alk;<br> dpco2_bio: biological term<br> dpco2_rain: surface freshwater term<br> dpco2_sst: thermal term<br> dpco2_dt: pco2 response term<br> dpco2_flux: CO2 flux response term<br> </p>
El Niño Enhances Snowline Rise and Ice Loss on the Quelccaya Ice Cap, Peru
<p>El Niño Enhances Snowline Rise on the Quelccaya Ice Cap, Peru (in-review)</p> <p>Kara A. Lamantia, Laura J. Larocca, Lonnie G. Thompson, Bryan Mark</p> <p>Exported results from automated snow cover area detection on the Quelccaya Ice Cap (QIC). Further calculated results are detailed in the supplementary documentation in the draft manuscript. See READ ME.txt file for details</p> <p>Sample Code available for Landsat 8 imagery here at the following URL: https://code.<br>earthengine.google.com/cfcbd0780ff3f09b0698035cd6dd678a</p>
Data and code: Disentangling the impact of Atlantic Niño on sea-air CO2 flux
<p>This repository share (1) post-processed data of Norwegian Earth system model version 2 (LM) and (2) codes for calculating carbon system from NorESM variables (MATLAB format). </p>
MCR LTER: Coral Reef: Recruitment during El Niño, Edmunds PLOS1 2017
The negative implications of the thermal sensitivity of reef corals became clear with coral bleaching throughout the Caribbean in the 1980’s, and later globally, with the severe El Niño of 1998 and extensive seawater warming in 2005. These events have substantially contributed to declines in coral cover, and therefore the El Niño of 2016 raised concerns over the implications for coral reefs; on the Great Barrier Reef these concerns have been realized. A different outcome developed in Mo’orea, French Polynesia, where in situ seawater temperature from 15 March 2016 to 15 April 2016 was an average of 0.4°C above the upper 95% CI of the decadal mean temperature, and the NOAA Degree Heating Weeks (DHW) metric supported a Level 1 bleaching alert (DHW ≥ 4.0). Starting 1 September 2016 and for the rest of the year (122 d), in situ seawater temperature was an average of 0.4°C above the 95% CI of long-term values, although DHW remained at zero. Minor coral bleaching (0.2–2.6% of the coral) occurred on the outer reef (10-m and 17-m depth) in April 2016, by May 2016 it had intensified to affect 1.3– 16.8% of the coral, but by August 2016, only 1.4–3.0% of the coral was bleached. Relative to the previous decade, recruitment of scleractinians to settlement tiles on the outer- (10 m) and back- (2 m) reef over 2016/17 was high, both from January 2016 to August 2016, and from August 2016 to January 2017, with increased relative abundances of pocilloporids on the outer reef, and acroporids in the back reef. The 2016 El Niño created a distinctive signature in seawater temperature for Mo’orea, but it did not cause widespread coral bleaching or mortality, rather, it was associated with high coral recruitment. While the 2016 El Niño has negatively affected other coral reefs in the Indo-Pacific, the coral communities of Mo’orea continue to show signs of resilience, thus cautioning against general statements regarding the effects of the 2015/16 El Niño on coral reefs in the region. This wo
FIGURE 4 in Heterobranch Sea Slug Range Shifts in the Northeast Pacific Ocean associated with the 2015-16 El Niño
FIGURE 4. Abundance of the Bulla gouldiana and Aplysia vaccaria in the low rocky intertidal at Naples, Santa Barbara, California 2006–2017.
Effects of the 2015-2016 El Niño on Currents and Temperatures on the Northern Peruvian Coast
<p>Temperatures and currents were measured using equipment moored on an oil platform (4°26.90' S, 81° 20.13' W) off Talara, Peru. The depth of the platform was 45 m at the center and approximately 50 m around the edge. A thermistor chain (RBRconcerto) with 3 m intervals was moored at the center of the platform to determine variations in the vertical thermal structure. An acoustic Doppler current profiler (ADCP, RDI WH300) was moored on the bottom about 80 m to the west of the platform. The sampling intervals were 10 minutes for the thermistor chain and 30 minutes for the ADCP. The equipment was deployed on 6 December 2014, and maintained until 27 May 2016, for one year and five months. As the mooring line of the thermistor chain was lost, however, the thermistor chain data were obtained until 30 December 2015. To obtain the daily data, a median filter was applied to the raw temperature and a two-day moving average to the raw current data. The data in MATLAB binary format and the explanation file were uploaded.</p>
MOM6-COBALT2 model results for asymmetrical Ocean Carbon Responses to La Niña and El Niño in the Tropical Pacific Ocean
<p>1. MOM6 COBALT2 model results</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_fco2_decomposition_1990_2021.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_fco2_decomposition_1990_2021.nc</a></p> <p>(air-sea carbon flux decomposition)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_dic_stf_gas_1980_2023_detrend_deseason_matlab_tp.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_dic_stf_gas_1980_2023_detrend_deseason_matlab_tp.nc</a></p> <p> (detrended and deseasonalized air-sea CO2 flux, positive to ocean)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_chl_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_chl_1980_2023.nc</a></p> <p> (sea surface chlorophyll)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSH_1990_2021_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSH_1990_2021_detrend_deseason_matlab.nc</a></p> <p> (detrended and deseasonalized sea surface height)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_no3_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_no3_1980_2023.nc</a></p> <p>(sea surface nitrate, NO3)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SST_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SST_1980-2023.nc</a></p> <p> (sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_sfc_po4_1980_2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_sfc_po4_1980_2023.nc</a></p> <p> (sea surface phosphate, PO4)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSS_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSS_1980-2023.nc</a></p> <p> (sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SSS_1990-2023_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SSS_1990-2023_detrend_deseason_matlab.nc</a></p> <p>(detrended and deseasonalized sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_SST_1990-2023_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_SST_1990-2023_detrend_deseason_matlab.nc</a></p> <p>(detrended and deseasonalized sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_u_1990-2021_detrend_deseason_matlab.nc.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_u_1990-2021_detrend_deseason_matlab.nc.nc</a></p> <p>(detrended and deseasonalized sea surface zonal velocity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_MLD_003_1980-2023.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_MLD_003_1980-2023.nc</a></p> <p>(Mixing layer depth with a density criterial of 0.03 kg/m3)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_v_1990-2021_detrend_deseason_matlab.nc.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_v_1990-2021_detrend_deseason_matlab.nc.nc</a></p> <p>(detrended and deseasonalized sea surface meridional velocity)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_pco2surf_1980_2023_detrend_deseason_matlab_tp.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_pco2surf_1980_2023_detrend_deseason_matlab_tp.nc</a></p> <p> (detrended and deseasonalized ocean pCO2 in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/hist_gcb_simA_ocean_static.nc/content" target="_blank" rel="noopener noreferrer">hist_gcb_simA_ocean_static.nc</a></p> <p>(grid of model)</p> <p><a href="../api/records/13325632/draft/files/all_gcb_simA_monthly_budget_mld001_pco2surf_1990_2021_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">all_gcb_simA_monthly_budget_mld001_pco2surf_1990_2021_detrend_deseason_matlab.nc</a></p> <p> (budget terms used for ocean pCO2 budget analysis, vertical mean in the mixing layer depth (criterial of 0.01 kg/m3), then do detrended and deseasonalized)<br>pco2_hadv_hdif: horizontal transport term, H_circ;<br>dpco2_vadv_vdif: vertical transport term,V_circ;<br>dpco2_bio: biological term<br>dpco2_rain: surface freshwater term<br>dpco2_sst: thermal term<br>dpco2_dt: pco2 response term<br>dpco2_flux: CO2 flux response term</p> <p>2. dataset</p> <p><a href="../api/records/13325632/draft/files/co2_products_Rodenbeck_v2022_monthly_all_detrend_deseason_by_filter.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Rodenbeck_v2022_monthly_all_detrend_deseason_by_filter.nc</a></p> <p>(CO2 based MLS: detrended and deseasonalized ocean pCO2 and air-sea carbon flux in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/co2_products_Rodenbeck_v2022_monthly_area.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Rodenbeck_v2022_monthly_area.nc</a></p> <div>(the grid of MLS)</div> <p><a href="../api/records/13325632/draft/files/co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_detrend_deseason_matlab.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_detrend_deseason_matlab.nc</a></p> <p>(CO2 based SOM-FFN: detrended and deseasonalized ocean pCO2 and air-sea carbon flux in the ocean surface)</p> <p><a href="../api/records/13325632/draft/files/co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_area.nc/content" target="_blank" rel="noopener noreferrer">co2_products_Landschutzer_v2022_MPI_SOM_FFN_2022_NCEI_OCADS_area.nc</a></p> <p>(the grid of SOM-FFN)</p> <p><a href="../api/records/13325632/draft/files/precipitation_JRA55-do-v1.5merge_res_all_monthly_prra.JRA.1.5.monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">precipitation_JRA55-do-v1.5merge_res_all_monthly_prra.JRA.1.5.monthly_detrend_deseason.nc</a></p> <p>(JRA: detrended and deseasonalized precipitation)</p> <p><a href="../api/records/13325632/draft/files/phosphate_all_woa18_all_p_monthly.nc/content" target="_blank" rel="noopener noreferrer">phosphate_all_woa18_all_p_monthly.nc</a></p> <p>(WOA: sea surface phosphate, PO4)</p> <p><a href="../api/records/13325632/draft/files/ssh_AVISO_all_sla_tp_twosat_phy_l4_vDT2018_monthly_1994_2020_03_01_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">ssh_AVISO_all_sla_tp_twosat_phy_l4_vDT2018_monthly_1994_2020_03_01_detrend_deseason.nc</a></p> <p>(AVISO: detrended and deseasonalized sea surface height)</p> <p><a href="../api/records/13325632/draft/files/sss_satellite_all_oisss_v1_sss_201109-202203_monthly.nc/content" target="_blank" rel="noopener noreferrer">sss_satellite_all_oisss_v1_sss_201109-202203_monthly.nc</a></p> <p>(OISSS: sea surface salinity)</p> <p><a href="../api/records/13325632/draft/files/sss_satellite_all_oisss_v1_sss_201109-202203_monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">sss_satellite_all_oisss_v1_sss_201109-202203_monthly_detrend_deseason.nc</a></p> <div>(OISSS: detrended and deseasonalized sea surface salinity)</div> <p><a href="../api/records/13325632/draft/files/co2_SOCAT_SOCATv2022_tracks_gridded_monthly.nc/content" target="_blank" rel="noopener noreferrer">co2_SOCAT_SOCATv2022_tracks_gridded_monthly.nc</a></p> <p>(SOCAT: ocean pCO2)</p> <p><a href="../api/records/13325632/draft/files/mld_obs_mld_DR003_c1m_reg2.0_rewrite_nanvalue.nc/content" target="_blank" rel="noopener noreferrer">mld_obs_mld_DR003_c1m_reg2.0_rewrite_nanvalue.nc</a></p> <p>(Mixing layer depth with a density criterial of 0.03 kg/m3)</p> <p><a href="../api/records/13325632/draft/files/nitrate_all_woa18_all_n_monthly.nc/content" target="_blank" rel="noopener noreferrer">nitrate_all_woa18_all_n_monthly.nc</a></p> <p>(WOA: sea surface nitrate, NO3)</p> <p><a href="../api/records/13325632/draft/files/sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly.nc/content" target="_blank" rel="noopener noreferrer">sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly.nc</a></p> <p> (OISST: sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">sst_OISST_allsst.avhrr-only-v2.1_1990_2021_monthly_detrend_deseason.nc</a></p> <p>(OISST: detrended and deseasonalized sea surface temperature)</p> <p><a href="../api/records/13325632/draft/files/chl_OC_CCI_all_OC_CCI_momthly_chlor_a_4km_fv5.0_199709_202112_clim.nc/content" target="_blank" rel="noopener noreferrer">chl_OC_CCI_all_OC_CCI_momthly_chlor_a_4km_fv5.0_199709_202112_clim.nc</a></p> <p>(GlobColour: sea surface chlorophyll)</p> <p><a href="../api/records/13325632/draft/files/uwind_JRA55-do-v1.5merge_res_all_monthly_uas.JRA.1.5.monthly_detrend_deseason.nc/content" target="_blank" rel="noopener noreferrer">uwind_JRA55-do-v1.5merge_res_all_monthly_uas.JRA.1.5.monthly_detrend_deseason.nc</a></p> <p>(JAR: detrended and deseasonalized zonal wind velocity)</p> <p><a href="../api/records/13325632/draft/files/TAO_CO2_tao_pco2_13stations_monthly_deseason_1997_2017.nc/content" target="_blank" rel="noopener noreferrer">TAO_CO2_tao_pco2_13stations_monthly_deseason_1997_2017.nc</a></p> <p>(TAO: sea surface temperature, sea surface salinity, and ocean pco2)</p> <p>3. Figure</p> <p>Figure1-4, FigureS1-S11 are data analysis files used python</p> <p> </p>
Daily Anomalies and High Productivity Zone Mask for Northern Peruvian Coastal Marine Ecosystem during the 2017 Coastal El Niño (December 2016 - May 2017)
<p>This dataset is part of the manuscript entitled "Chlorophyll Response and High Productivity Zone Contraction in Northern Perú During the 2017 Coastal El Niño."</p> <p>The dataset is designed to assess the atmospheric and oceanographic drivers of productivity changes during the 2017 Coastal El Niño. It allows detailed analysis of the interactions between physical processes (e.g., wind-driven upwelling rates, heat flux changes) and biological responses (e.g., chlorophyll concentration variations) in a region highly susceptible to ENSO-related variability. This comprehensive dataset provides valuable insight into the physical-biological coupling and the impacts of rapid climate events on marine ecosystems. The dataset, covering the period from December 1, 2016, to May 31, 2017, includes:</p> <p>1. Chlorophyll-a Anomalies (chla): Represents deviations in surface chlorophyll concentrations, a proxy for phytoplankton biomass, highlighting variations in primary productivity during the event.</p> <p>2. Sea Surface Temperature Anomalies (sst): Captures changes in sea surface temperatures relative to the climatological mean, providing insight into the warming pattern typical of marine heatwaves associated with the Coastal El Niño.</p> <p>3. Sea Level Anomaly (sla): Indicates changes in sea surface height, which reflects thermal expansion of water masses and potential contributions from coastal trapped waves propagating along the Peruvian coast.</p> <p>4. Wind Component Anomalies (u,v): Daily anomalies for both zonal (east-west) and meridional (north-south) wind components, which are critical for understanding changes in wind patterns including upwelling and Ekman transport processes.</p> <p>5. Ekman Pumping Anomalies (w): Represents variations in vertical water movement forced by wind stress curl, highlighting the suppression or enhancement of upwelling during the event.</p> <p>6. Latent Heat Flux Anomalies (lathf): Indicates deviations in heat loss from the ocean surface due to evaporation, affecting surface temperature regulation.</p> <p>7. Shortwave Radiation Anomalies (swrad): Shows changes in solar radiation (and also a proxy for PAR) reaching the ocean surface, influencing upper ocean heat content and the light availability for phytoplankton.</p> <p>8. High Productivity Zone (mask): A binary mask with daily values of 1 indicating areas meeting the HPZ criterion and 0 otherwise, allowing for spatial tracking of the HPZ's extent during the period of study.</p> <p> </p>
Dataset for manuscript "El Niño Southern Oscillation and Tropical Basin Interaction in Idealized Worlds"
<p>Dataset accompanying the manuscript<br>Dommenget and Hutchinson, 2025: El Niño Southern Oscillation and Tropical Basin Interaction in Idealized Worlds,<strong> Climate Dynamics, </strong>63, p274 doi: <a href="https://doi.org/10.1007/s00382-025-07759-9">10.1007/s00382-025-07759-9</a></p> <p>This dataset contains the following tar files:<br>control-a55c1.tar<br>coralsea.tar<br>hermanito2.tar<br>inf-trop.tar<br>solo100.tar<br>solo150.tar<br>solo200.tar<br>solo250.tar<br>solo300.tar<br>solo350.tar<br>solo50.tar<br>trio120.tar<br>trio160.tar<br>trio200.tar<br>trio.tar<br>twins.tar<br><br></p> <p>Each tar file is a folder corresponding to an experiment, described in the manuscript. There are 4 subfolders in each experiment:<br>ancil <br>input <br>results <br>scripts</p> <p><br>The `results` folder contains post-processed data which forms the analysis in the manuscript. These are in compressed netcdf form, with self-describing variables.<br>The `scripts` folder contains a `create.ancil.files.*` script, which was used to generate the input boundary conditions, and a `gfdl-run.cold.start.*` script, which was used to run the experiment on Gadi (nci.org.au).<br>The `ancil` and `input` folders contain various input files that are used as inputs to each experiment. These inputs are included for reproducibility, but are by no means easy to understand without expert knowledge of the GFDL model.</p> <p>We anticipate that the `results` folder is self-explanatory, while the `ancil`, `input` and `scripts` folders are not easy to understand unless you have experience with running GFDL CM2.1. Contact the authors if you want information on how to run the experiments using these inputs.</p>
Fig. 6 in Population dynamics of the migratory fish Prochilodus lineatus in a neotropical river: the relationships with river discharge, flood pulse, El Niño and fluvial megafan behaviour
Fig. 6. Above: Retreat of the Pilcomayo River and dynamic creation of new flood plains due to self-blockage (silting up) of the river channel. This caused a retreat of hundreds of kilometers of the choke point in a few decades (indicated by the black arrow) and an upstream migration of the flood plains. Bullets indicate migrating Sábalo population in the Pilcomayo River (white) and Sábalo population in the La Plata basin (black). Below: Breakthrough of Pilcomayo River bank inundating new areas in the Chaco floodplain area.
Fig. 5 in Population dynamics of the migratory fish Prochilodus lineatus in a neotropical river: the relationships with river discharge, flood pulse, El Niño and fluvial megafan behaviour
Fig. 5. (a) Mean annual discharge and Sábalo catches over the years in the Pilcomayo River near Villa Montes. (b) Calculated and observed Sábalo catches based on the data presented in Fig. 4a. Correlations were obtained by stepwise multiple linear regression with backward selection (SPSS v. 15.0). The river discharge of the seven preceding years (Y1-Y7) plus the current year (Y0) were used in the analyses. The solid line is based upon the years 1980-2006. The dashed line is based upon the years 1997-2007. (c) Observed Sábalo catches plotted against the calculated Sábalo catches for the years 1980-1996 and 1997- 2006. Data of Sábalo catches and mean river discharges were obtained from Proyecto Pilcomayo (Tarija, Bolivia).
Fig. 4 in Population dynamics of the migratory fish Prochilodus lineatus in a neotropical river: the relationships with river discharge, flood pulse, El Niño and fluvial megafan behaviour
Fig. 4. (a) Mean annual discharge for the Pilcomayo River since 1960. The values were calculated for the hydrological year, which runs from October of the previous year until September of the current year. Data were obtained from Proyecto Pilcomayo (Tarija, Bolivia). (b) Mean monthly values of the Southern Oscillation Index (dots) and mean annual discharges of the Pilcomayo River (open circles), since 1976. Mean annual discharge values were calculated from data obtained from Proyecto Pilcomayo (Tarija, Bolivia). The values were calculated for the hydrological year, which runs from October of the previous year until September of the current year.
Fig. 3 in Population dynamics of the migratory fish Prochilodus lineatus in a neotropical river: the relationships with river discharge, flood pulse, El Niño and fluvial megafan behaviour
Fig. 3. Total dissolved solids concentration (TDS) in Pilcomayo River water (a) or water temperature (b) and gonadal maturation indices of Sábalo (Prochilodus lineatus) fish versus time (May 1998 until February 1999). The gonadal maturation indices are scaled from 1 to 6 in which 6 represents spawning.
Fig. 6 in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil
Fig. 6. Hybrid multidimensional scaling (HMDS) ordination plot in two dimensions of benthic fish assemblage), River (b; ->7m). This ordination explained ~39% of the variance in the association matrix (r2 = 0,384).
Fig. 5. Estimated species richness E in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil
Fig. 5. Estimated species richness E(Sn) by strata at rio Negro (a-Sep, b-Nov 1997 and c-Feb 1998) and rio Branco (d-Sep
Fig. 3. Estimated species richness E in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil
Fig. 3. Estimated species richness E(Sn) by months of collection for (a) rio Negro and (b) rio Branco.
Fig. 4 in Spatial and temporal variation of benthic fish assemblages during the extreme drought of 1997-98 (El Niño) in the middle rio Negro, Amazonia, Brazil
Fig. 4. Temporal and spatial variation of CPUA in number of individuals (ind.m-2.103 - a and c) and biomass (g.m-2.103 - b and d) captured on the rio Negro (a and b) and rio Branco (c and d) for each trawl in: () Sep; () Nov (just in rio Negro) 1997 and () Feb 1998.
Emotional Quotient Inventory - Young Version / Inventario de Inteligencia Emocional - Versión para Niños // EMOTIONAL INTELLIGENCE-EQi-YV_Data_IC_T6-Post
<p>Answers given by students in the sixth grade of Primary Education to the 60 items of The Emotional Quotient Inventory: Youth Version (EQ-i: YV) by Bar-On and Parker (2000; Spanish validation by Ferrándiz, Hernández, Bermejo, Ferrando, and Sáinz, 2012). Data collected in public schools in Castellón (Spain) in the spring of the school year 2015-16. The calculation of the 5 Socioemotional Dimensions is included: Intrapersonal, Interpersonal, Stress Management, Adaptability, and General Mood. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p> </p> <p>Respuestas que los alumnos/as de sexto curso de Educación Primaria dan a los 60 ítems del Inventario de Inteligencia Emocional: Versión para Niños (EQ-i:YV) de Bar-On and Parker (2000; validado para población española por Ferrándiz, Hernández, Bermejo, Ferrando, y Sáinz, 2012). Datos recogidos en centros públicos de Castellón (España) en primavera del curso escolar 2015-16. Se incluye el cálculo de las 5 Dimensiones Socioemocionales: Intrapersonal, Interpersonal, Manejo del Estrés, Adaptabilidad y Estado de Ánimo General. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
Emotional Quotient Inventory - Young Version / Inventario de Inteligencia Emocional – Versión para Niños // EMOTIONAL INTELLIGENCE-EQi-YV_Data_IC_T3-Post
<p>Answers given by students in the third grade of Primary Education to the 60 items of The Emotional Quotient Inventory: Youth Version (EQ-i: YV) by Bar-On and Parker (2000; Spanish validation by Ferrándiz, Hernández, Bermejo, Ferrando, and Sáinz, 2012). Data collected in public schools in Castellón (Spain) in the spring of the school year 2012-13. The calculation of the 5 Socioemotional Dimensions is included: Intrapersonal, Interpersonal, Stress Management, Adaptability, and General Mood. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p> </p> <p>Respuestas que los alumnos/as de tercer curso de Educación Primaria dan a los 60 ítems del Inventario de Inteligencia Emocional (EQ-i:YV) de Bar-On and Parker (2000; validado para población española por Ferrándiz, Hernández, Bermejo, Ferrando, y Sáinz, 2012). Datos recogidos en centros públicos de Castellón (España) en primavera del curso escolar 2012-13. Se incluye el cálculo de las 5 Dimensiones Socioemocionales: Intrapersonal, Interpersonal, Manejo del Estrés, Adaptabilidad y Estado de Ánimo General Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
Emotional Quotient Inventory - Young Version / Inventario de Inteligencia Emocional – Versión para Niños // EMOTIONAL INTELLIGENCE-EQi-YV_Data_CC-CS_T6-Post
<p>Answers given by students in the sixth grade of Primary Education to the 60 items of The Emotional Quotient Inventory: Youth Version (EQ-i: YV) by Bar-On and Parker (2000; Spanish validation by Ferrándiz, Hernández, Bermejo, Ferrando, and Sáinz, 2012). Data collected in public schools in Castellón and Seville (Spain) in the spring of the school year 2014-15. The calculation of the 5 Socioemotional Dimensions is included: Intrapersonal, Interpersonal, Stress Management, Adaptability, and General Mood. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p> </p> <p>Respuestas que los alumnos/as de sexto curso de Educación Primaria dan a los 60 ítems del Inventario de Inteligencia Emocional (EQ-i:YV) de Bar-On and Parker (2000; validado para población española por Ferrándiz, Hernández, Bermejo, Ferrando, y Sáinz, 2012). Datos recogidos en centros públicos de Castellón y Sevilla (España) en primavera del curso escolar 2014-15. Se incluye el cálculo de las 5 Dimensiones Socioemocionales: Intrapersonal, Interpersonal, Manejo del Estrés, Adaptabilidad y Estado de Ánimo General Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.