(a) Annual mean sea-surface temperature anomalies (SSTAs) in 2023 (°C) and (b) difference of annual mean SSTAs from the previous year (2023 minus 2022; °C). SSTAs are based on the 1991–2020 climatologies. The stippled areas in panel (b) indicate that the 2023-minus-2022 SSTA difference is significant at 95% confidence.
Seasonal mean sea-surface temperature anomalies (SSTAs) of ERSSTv5 (°C; shading) for (a) Dec 2022–Feb 2023, (b) Mar–May 2023, (c) Jun–Aug 2023, and (d) Sep–Nov 2023. The normalized seasonal mean SSTAs based on the seasonal mean standard deviations (SDs) over 1991–2020 are indicated by contours of −2 (dashed white), −1 (dashed black), 1 (solid black), and 2 (solid white).
Annual mean sea-surface temperature anomalies (SSTAs; °C) of ERSSTv5, (solid white) and 2 standard deviations (SDs, gray shading) of ERSSTv5, SSTAs of HadSST.4.0.1.0 (solid red), and SSTAs of DOISST (solid green) for the period 1950–2023 except for (b) and (f). (a) Global ocean, (b) global ocean in 1880–2023, (c) tropical Pacific, (d) North Pacific, (e) tropical Indian, (f) North Atlantic for 1880–2023, (g) tropical Atlantic, and (h) Southern oceans (30°S–60°S). The 2-SD envelope was derived from a 500-member ensemble analysis based on ERSSTv5 and centered on SSTAs of ERSSTv5. The years 2000 and 1950 are indicated by dotted vertical black lines.
Global marine heatwave (MHW) coverage during three years characterized by the onset of major El Niño events: (a) 1997, (b) 2015, and (c) 2023. Colors indicate the number of months in each year for which an MHW was present at each OISSTv2.1 grid cell (0.25° resolution). The deepest blues, surrounded by a gray contour, indicate regions that experienced MHWs in only one month or not at all. MHWs were calculated using a 90% threshold of monthly sea-surface temperature anomalies relative to the 1991–2020 climatology from OISSTv2.1.
Intensity of 2023 marine heatwaves (MHWs) relative to historical conditions during the satellite record. Colors indicate the rank of 2023’s highest monthly sea-surface temperature anomalies (SSTAs) relative to SSTAs for all months from 1982 to 2023 (N=504). SSTAs were calculated relative to the 1991–2020 climatology from OISSTv2.1. White areas did not experience any MHWs in 2023 based on monthly SSTAs.
(a) RFROM v2 estimate of 0-m–2000-m ocean heat content anomaly (OHCA; × 109 J m−2) for 2023 analyzed following Lyman and Johnson (2023) with v2 improvements as in https://www.pmel.noaa.gov/rfrom/. In situ OHCA profiles are used as training data and satellite sea-surface height, satellite sea-surface temperatures, overlying layer properties, location, and time are predictors. Values are displayed relative to a 1993–2022 baseline. (b) 2023-minus-2022 combined estimates of OHCA expressed as a local surface heat flux equivalent (W m−2). For (a) and (b) comparisons, note that 95 W m−2 applied over one year results in a 3 × 109 J m−2 change of OHCA. (c) Linear trend for 1993–2023 annual OHCA (W m−2). Areas with statistically insignificant trends at 5%–95% confidence (taking into account the decorrelation time scale of the residuals when estimating effective degrees of freedom) are stippled.
(a) Near-global (66.5°S–81.5°N, but excluding seasonally ice-covered regions within that latitude range) average monthly ocean temperature anomalies (°C; from RFROM v2, Lyman and Johnson [2023]) relative to 2004–23 average monthly values, smoothed with a five-month Hanning filter and contoured at 0.025°C intervals (see color bar) vs. pressure and time. (b) Linear trend of temperature anomalies over time for 2004–23 in (a) plotted vs. pressure in °C decade−1 (blue line).
(a) Annual average global integrals of in situ estimates of upper (0-m–700-m) ocean heat content anomaly (OHCA; ZJ; 1 ZJ = 1021 J) for the period 1993–2022 with standard errors of the mean. The Meteorological Research Institute (MRI)/Japan Meteorological Agency (JMA) estimate is an update of Ishii et al. (2017). The Pacific Marine Environmental Laboratory (PMEL)/Jet Propulsion Laboratory (JPL)/Joint Institute for Marine and Atmospheric Research estimate is computed from RFROM v2 after Lyman and Johnson (2023). The Met Office Hadley Centre estimate is computed from gridded monthly temperature anomalies following Palmer et al. (2007) and Good et al. (2013). Both the PMEL and Met Office estimates use Cheng et al. (2014) eXpendable BathyThermograph (XBT) corrections and Gouretski and Cheng (2020) mechanical bathythermograph corrections. The NCEI estimate follows Levitus et al. (2012). The Institute of Atmospheric Physics (IAP)/Chinese Academy of Sciences (CAS) estimate was substantially revised from previous years as reported in Cheng et al. (2024). The CU/CMU estimate is detailed in Giglio et al. (2024). See Johnson et al. (2014) for details on uncertainties, methods, and datasets. For comparison, all estimates have been individually offset (vertically on the plot), first to their individual 2005–22 means (the best sampled time period) and then to their collective 1993 mean. (b) Annual average global integrals of in situ estimates of intermediate (700-m–2000-m) OHCA for 1993–2022 (ZJ) with standard errors of the mean and a long-term trend with one standard error uncertainty shown from September 1992 to July 2013 for deep and abyssal (z>2000 m) OHCA following Purkey and Johnson (2010) but updated using all repeat hydrographic section data available from https://cchdo.ucsd.edu/ as of Jan 2024.
(a) Map of the 2023 annual surface salinity anomaly (colors, PSS-78) with respect to monthly climatological 1955–2012 salinity fields from WOA13v2 (yearly average; gray contours at 0.5 intervals, PSS-78). (b) Difference of 2023 and 2022 surface salinity maps (colors, PSS-78 yr−1). White ocean areas are too data-poor (retaining <80% of a large-scale signal) to map. (c) Map of local linear trends estimated from annual surface salinity anomalies for 2005–23 (colors, PSS-78 yr−1). Areas with statistically insignificant trends at 5%–95% confidence (taking into account the decorrelation time scale of the residuals when estimating effective degrees of freedom) are stippled. All maps are made using Argo data.
Seasonal maps of sea-surface salinity (SSS) anomalies (colors) from monthly blended maps of satellite and in situ salinity data (BASS; Xie et al. 2014) relative to monthly climatological 1955–2012 salinity fields from WOA13v2 for (a) Dec 2022–Feb 2023, (b) Mar–May 2023, (c) Jun–Aug 2023, and (d) Sep–Nov 2023.
Average monthly salinity anomalies from 0 m to 1000 m for the period 2014–23 for the (a) Atlantic, (d) Pacific, and (g) Indian basins. Change in salinity from 2022 to 2023 for the (b) Atlantic, (e) Pacific, and (h) Indian basins. Change in the 0-m–500-m zonal-average salinity from 2022 to 2023 in the (c) Atlantic, (f) Pacific, and (i) Indian basins with areas of statistically insignificant change, defined as <±1 std. dev. and calculated from all year-to-year changes between 2005 and 2023, stippled in dark gray. Data were smoothed using a three-month running mean. Anomalies are relative to the long-term (1955–2017) WOA18 monthly salinity climatology (Zweng et al. 2018).
Linear trend of zonally-averaged salinity from 2005 to 2023 over the upper 1000 m for the (a) Atlantic, (b) Pacific, and (c) Indian basins. The salinity trend is per decade and computed using least squares regression. Areas that are stippled in dark gray are not significant at the 95% confidence interval.
(a) Surface heat flux (Qnet) anomalies (W m−2) for 2023 relative to the 2001–15 climatology. Positive values denote ocean heat gain. (b) 2023-minus-2022 difference for (b) Qnet, (c) net surface radiation shortwave (SW) + longwave (LW), and (d) turbulent heat fluxes latent heat (LH) + sensitive heat (SH), respectively. Positive tendencies denote more ocean heat gain in 2023 than in 2022. LH+SH are from WHOI OAFlux2, and SW+LW is from the NASA FLASHFlux version 4B adjusted to CERES Surface EBAF Ed4.2. Net radiative fluxes defined as the difference between the incoming and outgoing radiation (positive indicates radiative flux into the ocean).
(a) Surface freshwater precipitation (P) – evaporation (E) flux anomalies (cm yr−1) for 2023 relative to the 1988–2015 climatology. Positive values denote ocean freshwater gain. 2023-minus-2022 differences for (b) P–E, (c) E, and (d) P. Positive (negative) values denote ocean freshwater gain (loss). P is the GPCP version 2.3 product, and E is from WHOI OAFlux2.
(a) Wind stress magnitude (shaded) and vector anomalies (N m−2) for 2023 relative to a 2001–22 climatology. (b) 2023-minus-2022 differences in wind stress. (c) Ekman vertical velocity (WEK; cm day−1) anomalies for 2023 relative to a 1988–2015 climatology. Positive (negative) values denote upwelling (downwelling). (d) 2023-minus-2022 differences of WEK. Wind stress and WEK fields are from WHOI OAFlux2.
Annual mean time series of global ocean-surface (a) net surface heat flux (Qnet; W m−2) from a combination of CERES EBAF4.2 shortwave (SW) + longwave (LW) and WHOI OAFlux2 latent heat (LH) + sensitive heat (SH). The 2023 Qnet is based on FLASHFlux SW+LW as adjusted to EBAF and OAFlux2 LH+SH. (b) Net freshwater flux anomaly (P–E; cm yr−1) from a combination of GPCP P and OAFlux2 E. (c) Wind stress magnitude anomalies (N m−2) from WHOI OAFlux2. Error bars denote one standard deviation of annual-mean variability.
(a) Global mean sea level (GMSL; mm) observed by satellite altimeters (1993–2023) produced with support from the NASA Sea Level Change and Ocean Surface Topography Science Teams (black). Monthly global ocean mass (2005–23) from GRACE and GRACE-FO calculated from mass concentrations produced by the NASA Jet Propulsion Laboratory (blue). GRACE and GRACE-FO data within 300 km of land were excluded in both ocean mass time series. Monthly global mean steric sea level (2005–23) from Scripps Institute of Oceanography (SIO) Argo data (red). Shading around the mass and steric series represents a 95% confidence range based on Gaussian process regressions, which are used to produce a 95% confidence range for the sum of global ocean mass plus steric (purple). (b) Total local sea-level change (mm) during 1993–2023 as measured by satellite altimetry (contours) and tide gauges (circles). Hatching indicates local changes that differ from the change in GMSL by more than one standard deviation. The trend map was generated using gridded delayed-mode and near-real-time altimetry data produced by the Copernicus Climate Change Service and obtained from the Copernicus Marine Service. Tide-gauge observations were obtained from the University of Hawaii Sea Level Center Fast Delivery database.
(a) Annual average sea-level anomaly during 2023 relative to average sea level at each location during 1993–2022. (b) Average 2023-minus-2022 sea-level anomaly. (c) Average sea-level anomaly during DJF 2023 relative to the 1993–2022 DJF average. (d) Same as (c), but for SON. Units are given in cm. Global mean sea level was subtracted from panels (c),(d) to emphasize regional, non-secular change. These maps were generated using gridded delayed-mode and near-real-time altimetry data produced by the Copernicus Climate Change Service and obtained from the Copernicus Marine Service.
(a) Number of extreme sea-level events from tide gauges during 2023. (b) Counts in (a) as a function of annual sea-level anomaly during 2023. Square markers in (a) and (b) highlight locations with more than 10 extreme events. (c) Change in the number of extreme sea-level events from 2022 to 2023. (d) Counts in (c) as a function of the change in annual sea level from 2022 to 2023. Square markers in (c) and (d) highlight locations where the magnitudes of changes in counts of extreme events were greater than 10. Counts of extreme sea-level events were calculated from hourly tide-gauge observations obtained from the University of Hawaii Sea Level Center Fast Delivery database. Only records with at least 80% completeness during 1993–2023 and 80% completeness during both 2022 and 2023, individually, were analyzed.
Annually-averaged geostrophic zonal current anomalies with respect to the seasonal climatology (cm s−1) for (a) 2023 and (b) 2023-minus-2022 anomalies, derived from a synthesis of drifters, altimetry, and winds. Values are only shown where they are significantly different from zero.
Seasonally averaged zonal geostrophic anomalies with respect to seasonal climatology for (a) Dec 2022–Feb 2023, (b) Mar–May 2023, (c) Jun–Aug 2023, and (d) Sep–Nov 2023. Values are only shown where they are significantly different from zero.
(a) Climatological geostrophic surface current speed (cm s−1) from Mulet et al. (2021) in the Kuroshio Current region (box) and surrounding regions. (b) 2023 mean geostrophic currents from MDT13 and Copernicus near-real-time altimetry. (c) Latitude of maximum zonal currents averaged in the Kuroshio Current region. (d) Mean eddy kinetic energy (m2 s−2) in the Kuroshio Current region.
The Atlantic meridional overturning circulation (MOC) observing system: moored arrays (solid red and black lines) and sections across which the MOC and meridional heat transport (MHT) are estimated by synthesizing in situ measurements (Argo, eXpendable BathyThermograph [XBT]) with satellite altimetry data (dashed red lines). The red lines show the sections that have updates covered in this report, while the black lines show the sections for which updates are pending. The record mean MOC and MHT values are shown in parentheses.
The meridional overturning circulation (MOC) and meridional heat transport (MHT) estimates at the (a) Subpolar North Atlantic Program (OSNAP), (b) 41°N, and (c) RAPID (26.5°N) sections in the North Atlantic. The light blue (pink) lines show the monthly MOC (MHT) time series and the bold blue (red) lines show their yearly moving averages.
The synthetic meridional overturning circulation (MOC) and meridional heat transport (MHT) estimates at the AXMOC section at (a) 22.5°S and at the (b) 25°S and (c) 34.5°S sections in the South Atlantic. The light blue (pink) lines show the monthly MOC (MHT) time series and the bold blue (red) lines show their yearly moving averages.
Spatial distribution of average monthly (a) VIIRS-N20 chlorophyll-a (Chla) anomalies (%), (b) VIIRS-N20 phytoplankton carbon (Cphy) anomalies (%), and (c) MODIS-A sea-surface temperature (SST) anomalies (°C) for Oct 2022–Sep 2023, where monthly differences were derived relative to the MODIS-A climatological record (Oct 2002–Sep 2022). Chla and Cphy are stated as % difference from climatology, while SST is shown as an absolute difference. Also shown in each panel is the location of the mean 15°C SST isotherm (black lines) delineating the permanently stratified ocean (PSO). Differences in the SST anomalies here versus in Fig. 3.1 are owing to differences in climatological periods, smoothing, and data sources.
Distribution of Oct 2022–Sep 2023 monthly means (red circles) for (a) VIIRS-N20 chlorophyll-a (Chla) and (b) VIIRS-N20 phytoplankton carbon (Cphy) for the permanently stratified ocean (PSO) region (see Fig. 3.26), superimposed on the climatological values as derived from the combined time series of SeaWiFS, MODIS-A, and VIIRS-N20 over the period of Oct 1998–Sep 2022. Gray boxes show the interquartile range of the climatology, with a black line for the median value and whiskers extending to minimum and maximum values. Subsequent panels show latitudinally segregated subsets of the PSO for the (c),(d) Northern Hemisphere (NH; above tropics), (e),(f) tropical ±23.5° latitude subregion (EQ), and (g),(h) Southern Hemisphere (SH; below tropics). Units for (a), (c), (e), and (g) are Chla (mg m−3) and those for (b), (d), (f), and (h) are Cphy (mg m−3).
Twenty-six-year, multi-mission record of chlorophyll-a (Chla; mg m−3) and phytoplankton carbon (Cphy; mg m−3) averaged over the permanently stratified ocean (PSO; Oct 1997–Sep 2023). (a) Monthly Chla, with the horizontal line indicating the multi-mission mean Chla concentration for the entire PSO region. (b) Monthly Chla anomalies after subtraction of the multi-mission climatological mean (Fig. 3.25a). (c) Monthly Cphy, with the horizontal line indicating the multi-mission mean Cphy concentration for the entire PSO region. (d) Monthly Cphy anomalies after subtraction of the multi-mission climatological mean (Fig. 3.25b). Shaded blue and red colors show the Multivariate El Niño–Southern Oscillation (ENSO) Index (MEI), inverted and scaled to match the range of the Chla and Cphy anomalies, where blue indicates La Niña and red indicates El Niño conditions.</