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On the first day of spring, a young New Yorker, with her mother and aunts, takes the train to the ocean to make an offering of flowers and fruit to Mama Ocean, thanking her for her magnificence, and welcoming the new year.

Global hydrographic and air–sea freshwater flux datasets are used to investigate ocean salinity changes over 1950–2010 in relation to surface freshwater flux. On multi-decadal timescales, surface salinity increases (decreases) in evaporation (precipitation) dominated regions, the Atlantic–Pacific salinity contrast increases, and the upper thermocline salinity maximum increases while the salinity minimum of intermediate waters decreases. Potential trends in E–P are examined for 1950–2010 (using two reanalyses) and 1979–2010 (using four reanalyses and two blended products). Large differences in the 1950–2010 E–P trend patterns are evident in several regions, particularly the North Atlantic. For 1979–2010 some coherency in the spatial change patterns is evident but there is still a large spread in trend magnitude and sign between the six E–P products. However, a robust pattern of increased E–P in the southern hemisphere subtropical gyres is seen in all products. There is also some evidence in the tropical Pacific for a link between the spatial change patterns of salinity and E–P associated with ENSO. The water cycle amplification rate over specific regions is subsequently inferred from the observed 3-D salinity change field using a salt conservation equation in variable isopycnal volumes, implicitly accounting for the migration of isopycnal surfaces. Inferred global changes of E–P over 1950–2010 amount to an increase of 1 ± 0.6 % in net evaporation across the subtropics and an increase of 4.2 ± 2 % in net precipitation across subpolar latitudes. Amplification rates are approximately doubled over 1979–2010, consistent with accelerated broad-scale warming but also coincident with much improved salinity sampling over the latter period.

A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system generates global, daily, gap-filled foundation sea surface temperature (SST) fields from satellite data and in situ observations. The SSTs have uncertainty information provided with them and an ice concentration (IC) analysis is also produced. Additionally, a global, hourly diurnal skin SST product is output each day. The system is run in near real time to produce data for use in applications such as numerical weather prediction. Data production is monitored routinely and outputs are available from the Copernicus Marine Environment Monitoring Service (CMEMS; marine.copernicus.eu). As an operational product, the OSTIA system is continuously under development. For example, since the original descriptor paper was published, the underlying data assimilation scheme that is used to generate the foundation SST analyses has been updated. Various publications have described these changes but a full description is not available in a single place. This technical note focuses on the production of the foundation SST and IC analyses by OSTIA and aims to provide a comprehensive description of the current system configuration.

A 42-year climate data record of global sea surface temperature (SST) covering 1980 to 2021 has been produced from satellite observations, with a high degree of independence from in situ measurements. Observations from twenty infrared and two microwave radiometers are used, and are adjusted for their differing times of day of measurement to avoid aliasing and ensure observational stability. A total of 1.5 × 10 13 locations are processed, yielding 1.4 × 10 12 SST observations deemed to be suitable for climate applications. The corresponding observation density varies from less than 1 km −2 yr −1 in 1980 to over 100 km −2 yr −1 after 2007. Data are provided at their native resolution, averaged on a global 0.05° latitude-longitude grid (single-sensor with gaps), and as a daily, merged, gap-free, SST analysis at 0.05°. The data include the satellite-based SSTs, the corresponding time-and-depth standardised estimates, their standard uncertainty and quality flags. Accuracy, spatial coverage and length of record are all improved relative to a previous version, and the timeseries is routinely extended in time using consistent methods.

Over the past few decades, the oceans have stored the majority of the excess heat in the climate system resulting from anthropogenic emissions. An accurate, long-term sea surface temperature (SST) dataset is essential for monitoring and researching the changes to the global oceans. A variety of SST datasets have been produced by various institutes over the years, and here, we present a new SST data record produced originally within the Copernicus Marine Environment Monitoring Service (which is therefore named CMEMS v2.0) and assess: (1) its accuracy compared to independent observations; (2) how it compares with the previous version (named CMEMS v1.2); and (3) its performance during two major volcanic eruptions. By comparing both versions of the CMEMS datasets using independent in situ observations, we show that both datasets are within the target accuracy of 0.1 K, but that CMEMS v2.0 is closer to the ground truth. The uncertainty fields generated by the two analyses were also compared, and CMEMS v2.0 was found to provide a more accurate estimate of its own uncertainties. Frequency and vector analysis of the SST fields determined that CMEMS v2.0 feature resolution and horizontal gradients were also superior, indicating that it resolved oceanic features with greater clarity. The behavior of the two analyses during two volcanic eruption events (Mt. Pinatubo and El Chichón) was examined. A comparison with the HadSST4 gridded in situ dataset suggested a cool bias in the CMEMS v2.0 dataset versus the v1.2 dataset following the Pinatubo eruption, although a comparison with sparser buoy-only observations yielded less clear results. No clear impact of the El Chichón eruption (which was a smaller event than Mt. Pinatubo) on CMEMS v2.0 was found. Overall, with the exception of a few specific and extreme events early in the time series, CMEMS v2.0 possesses high accuracy, resolution, and stability and is recommended to users.

Warming in the oceans The upper ocean acts as a giant heat sink and has absorbed the majority of excess energy generated by anthropogenic greenhouse gasses. This makes ocean heat content, potentially, a key indicator of climate change. But to be useful for evaluating the global energy balance and as a constraint on climate models, the measurement uncertainties of such a key indicator need to be well understood. At present the magnitude of the oceanic heat uptake is highly uncertain, with patterns of inter-annual variability in particular differing among estimates. In a major international collaboration, Lyman et al . compare the available upper-ocean heat content anomaly curves and examine the sources of uncertainly attached to them — including the difficulties in correcting bias in expendable bathythermograph data. They find that, uncertainties notwithstanding, there is clear and robust evidence for a warming trend of 0.64 watts per square metre between 1993 and 2008. The upper 300 m of the world's oceans act as a giant heat sink and have absorbed the majority of the excess energy generated by anthropogenic greenhouse gases. But the magnitude of the oceanic heat uptake is uncertain, and differing estimates have led to questions regarding the closure of the global energy budget. Here, a comparison of ocean heat content estimates is presented; the conclusion is that a robust warming of 0.64 W m −2 occurred from 1993 to 2008. A large (∼10 23  J) multi-decadal globally averaged warming signal in the upper 300 m of the world’s oceans was reported roughly a decade ago 1 and is attributed to warming associated with anthropogenic greenhouse gases 2 , 3 . The majority of the Earth’s total energy uptake during recent decades has occurred in the upper ocean 3 , but the underlying uncertainties in ocean warming are unclear, limiting our ability to assess closure of sea-level budgets 4 , 5 , 6 , 7 , the global radiation imbalance 8 and climate models 5 . For example, several teams have recently produced different multi-year estimates of the annually averaged global integral of upper-ocean heat content anomalies (hereafter OHCA curves) or, equivalently, the thermosteric sea-level rise 5 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 . Patterns of interannual variability, in particular, differ among methods. Here we examine several sources of uncertainty that contribute to differences among OHCA curves from 1993 to 2008, focusing on the difficulties of correcting biases in expendable bathythermograph (XBT) data. XBT data constitute the majority of the in situ measurements of upper-ocean heat content from 1967 to 2002, and we find that the uncertainty due to choice of XBT bias correction dominates among-method variability in OHCA curves during our 1993–2008 study period. Accounting for multiple sources of uncertainty, a composite of several OHCA curves using different XBT bias corrections still yields a statistically significant linear warming trend for 1993–2008 of 0.64 W m -2 (calculated for the Earth’s entire surface area), with a 90-per-cent confidence interval of 0.53–0.75 W m -2 .

Sea surface temperature (SST) data from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard the Sentinel-3 satellites have been used in the Met Office’s Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) since 2019 (Sentinel-3A SST data since March 2019 and Sentinel-3B data since December 2019). The impacts of using SLSTR SSTs and the SLSTR as the reference sensor for the bias correction of other satellite data have been assessed using independent Argo float data. Combining Sentinel-3A and -3B SLSTRs with two Visible Infrared Imaging Radiometer Suite (VIIRS) sensors (onboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership and National Oceanic and Atmospheric Administration-20 satellites) in the reference dataset has also been investigated. The results indicate that when using the SLSTR as the only reference satellite sensor, the OSTIA system becomes warmer overall, although there are mixed impacts in different parts of the global ocean. Using both the VIIRS and the SLSTR in the reference dataset leads to moderate but more consistent improvements globally. Numerical weather prediction (NWP) results also indicate a better performance when using both the VIIRS and the SLSTR in the reference dataset compared to only using the SLSTR at night. Combining the VIIRS and the SLSTR with latitudinal weighting shows the best validation results against Argo, but further investigation is required to refine this method.

Ocean warming accounts for the majority of the earth’s recent energy imbalance. Historic ocean heat content (OHC) changes are important for understanding changing climate. Calculations of OHC anomalies (OHCA) from in situ measurements provide estimates of these changes. Uncertainties in OHCA estimates arise from calculating global fields from temporally and spatially irregular data (mapping method), instrument bias corrections, and the definitions of a baseline climatology from which anomalies are calculated. To investigate sensitivity of OHCA estimates for the upper 700m to these different factors, the same qualitycontrolled dataset is used by seven groups and comparisons are made. Two time periods (1970–2008 and 1993–2008) are examined. Uncertainty due to the mapping method is 16.5 ZJ for 1970–2008 and 17.1 ZJ for 1993–2008 (1 ZJ = 1 × 1021 J). Uncertainty due to instrument bias correction varied from 8.0 to 17.9 ZJ for 1970–2008 and from 10.9 to 22.4 ZJ for 1993–2008, depending on mapping method. Uncertainty due to baseline mean varied from 3.5 to 14.5 ZJ for 1970–2008 and from 2.7 to 9.8 ZJ for 1993–2008, depending on mapping method and offsets. On average mapping method is the largest source of uncertainty. The linear trend varied from 1.3 to 5.0 ZJ yr−1 (0.08–0.31 W m−2) for 1970–2008 and from 1.5 to 9.4 ZJ yr−1 (0.09–0.58 W m−2) for 1993–2008, depending on method, instrument bias correction, and baseline mean. Despite these complications, a statistically robust upper-ocean warming was found in all cases for the full time period.

We present observations of the same magnetic cloud made near Earth by the Advance Composition Explorer (ACE), Wind, and the Acceleration, Reconnection, Turbulence and Electrodynamics of the Moon’s Interaction with the Sun (ARTEMIS) mission comprising the Time History of Events and Macroscale Interactions during Substorms (THEMIS) B and THEMIS C spacecraft, and later by Juno at a distance of 1.2 AU. The spacecraft were close to radial alignment throughout the event, with a longitudinal separation of 3.6 ∘ between Juno and the spacecraft near Earth. The magnetic cloud likely originated from a filament eruption on 22 October 2011 at 00:05 UT, and caused a strong geomagnetic storm at Earth commencing on 24 October. Observations of the magnetic cloud at each spacecraft have been analysed using minimum variance analysis and two flux rope fitting models, Lundquist and Gold–Hoyle, to give the orientation of the flux rope axis. We explore the effect different trailing edge boundaries have on the results of each analysis method, and find a clear difference between the orientations of the flux rope axis at the near-Earth spacecraft and Juno, independent of the analysis method. The axial magnetic field strength and the radial width of the flux rope are calculated using both observations and fitting parameters and their relationship with heliocentric distance is investigated. Differences in results between the near-Earth spacecraft and Juno are attributed not only to the radial separation, but to the small longitudinal separation which resulted in a surprisingly large difference in the in situ observations between the spacecraft. This case study demonstrates the utility of Juno cruise data as a new opportunity to study magnetic clouds beyond 1 AU, and the need for caution in future radial alignment studies.