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A 38‐year aerosol optical thickness (AOT) satellite product from the Advanced Very High‐Resolution Radiometer (AVHRR) is used to investigate the aerosol variabilities in the North Indian Ocean (NIO). A dipolar mode with a notable meridional contrast between the equatorial and northern NIO is revealed by the second mode of Empirical Orthogonal Function (EOF) analysis with a sharp rise over the Arabian Sea (AS) and Bay of Bengal (BoB) since 2002. Our results show that the aerosol dipolar variation is primarily modulated by an El Niño–Southern Oscillation (ENSO) during 1983–2001 (ID1) and by a warm phase of Atlantic Multi‐decadal Oscillation (AMO) during 2002–2020 (ID2), leading to a dry‐and‐warming condition spanning the coasts of East Africa, the Arabian Peninsula (AP), and South Asia (SA) that favors increasing aerosol emission and a longer life cycle in the upstream regions. A warm phase of the AMO tends to excite an anomalous cyclone in western Siberia and reinforces anticyclonic circulation in the Tibetan Plateau. Correlation analyses between the second EOF mode time series and other parameters in ID1 and ID2 show that an interannual dry‐and‐warming condition over the AP–SA is associated with a noticeable north‐south contrast of convection between the equatorial NIO and AP–SA in ID1. But in ID2, a zonal contrast of anomalous convective activities between the AP–SA and the BoB‐South China Sea is dominant, partially due to the warming AMO, which aids the development of anticyclonic cells over the TP and the strengthened subtropical westerly jet streams. Plain Language Summary: Using satellite aerosol observations during 1983–2020 and a multivariate statistical technique for temporal‐spatial analysis, we explored the aerosol long‐term and year‐by‐year variations in the North Indian Ocean (NIO). Our results show a significant rise in springtime aerosol over the Arabian Sea and Bay of Bengal since the early 2000s, which is attributed to intensification of a dry‐and‐warming condition in the coasts of East Africa, the Arabian Peninsula, and South Asia. A relatively low aerosol concentration before the early 2000s is mainly modulated by the anomalies of sea surface temperature (SST) in both the West and East Pacific associated with an El Niño or La Niña event, but an increasing aerosol concentration after the early 2000s is linked to warmer SST associated with a phenomenon called Atlantic Multi‐decadal Oscillation (AMO). A warm phase of the AMO may aid the development of the remote teleconnection pattern in the midlatitude regions of the Northern Hemisphere, and thus reinforce the anomalies of atmospheric circulation in the subtropical regions. Key Points: A notable springtime aerosol rise over the Arabian Sea and Bay of Bengal (ASBoB) since the early 2000s is revealed by using satellite dataThe El Niño events may cause a relatively low aerosol loading over the ASBoB during 1983–2001A warm‐phase Atlantic Multi‐decadal Oscillation may favor a notable high aerosol loading over the ASBoB during 2002–2020 [ABSTRACT FROM AUTHOR]

NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) and other similar sea surface temperature (SST) products indicate that the globally averaged SST set a new daily record in March 2023. The record‐high SST in March was immediately broken in April, and new daily records were set again in July and August 2023. The SST anomaly (SSTA) persisted at a record high from mid‐March to the remainder of 2023. Our analysis indicates that the record‐high SSTs, and associated marine heatwaves (MHWs) and even super‐MHWs, are attributed to three factors: (a) a long‐term warming trend, (b) a shift to the warm phase of the multi‐decadal Pacific‐Atlantic‐Arctic (PAA) mode, and (c) the transition from the triple‐dip succession of La Niña events to the 2023–24 El Niño event. Plain Language Summary: Observation‐based analyses such as the NOAA DOISST show that global mean SST reached a record high in April 2023, breaking the previous record of global mean SST set in March 2016, and the April 2023 record of global mean SST was broken again in July and August 2023. Our study indicates that these record‐breaking SSTs in 2023 resulted from record‐high SSTs over much of the global oceans and associated with widespread marine heatwaves (MHWs). Further analyses show that the record‐high SSTs are attributed to a long‐term‐warming trend associated with increasing greenhouse gases, a shift to the warm phase of a multidecadal Pacific‐Atlantic‐Arctic (PAA) mode, and a warming associated with the transition from 2020–23 La Niña events to the 2023–24 El Niño event. Key Points: Global mean sea surface temperature (SST) in 2023 set a record highRecord‐high SST was associated with widespread marine heatwaves (MHWs) and super‐MHWs throughout the oceansRecord‐high SST was attributed to the multi‐decadal warming trend, warm phase of Pacific‐Atlantic‐Arctic mode, and 2023–24 El Niño event [ABSTRACT FROM AUTHOR]

The 1991–2020 climate normals for sea surface temperature (SST) are computed based on the NOAA Daily Optimum Interpolation SST dataset. This is the first time that high‐resolution SST normals with global coverage can be achieved in the satellite SST era. Normals are one of the fundamental parameters in describing and understanding weather and climate and provide decision‐making information to industry, public, and scientific communities. This product suite includes SST mean, standard deviation, count and extreme parameters at daily, monthly, seasonal and annual time scales on 0.25° spatial grids. The main feature of the SST mean state revealed by the normals is that in the Tropics, the Indo‐Pacific Ocean is dominated by the warm pool (SST ≥ 28°C) while the eastern Pacific is characterized by the cold tongue (SST ≤ 24°C); in the midlatitudes, SSTs are in zonal patterns with high meridional gradients. Daily SST standard deviations are generally small (<1.0°C) except in frontal zones (>1.5°C) mostly associated with ocean currents such as the Gulf Stream, Kuroshio and Equatorial Currents. Compared to the 1982–2011 climatology, the 1991–2020 mean SSTs increased over most global areas but obvious cooling is seen in the Southern Ocean, eastern tropical South Pacific Ocean and North Atlantic warming hole. The Indo‐Pacific warm pool (IPWP) is found to have strengthened in both intensity and coverage since 1982–2011. By a count parameter criterion of ≥300 days annually with SST ≥ 28°C, the IPWP coverage increased 33% from 1982–2011 to 1991–2020. The global mean SST of 1991–2020 is warmer than that of 1982–2011, and the warming rate over 1991–2020 doubles that over 1901–2020. [ABSTRACT FROM AUTHOR]

Our study shows that the intercomparison among sea surface temperature (SST) products is influenced by the choice of SST reference, and the interpolation of SST products. The influence of reference SST depends on whether the reference SSTs are averaged to a grid or in pointwise in situ locations, including buoy or Argo observations, and filtered by first-guess or climatology quality control (QC) algorithms. The influence of the interpolation depends on whether SST products are in their original grids or preprocessed into common coarse grids. The impacts of these factors are demonstrated in our assessments of eight widely used SST products (DOISST, MUR25, MGDSST, GAMSSA, OSTIA, GPB, CCI, CMC) relative to buoy observations: (i) when the reference SSTs are averaged onto 0.25° × 0.25° grid boxes, the magnitude of biases is lower in DOISST and MGDSST (<0.03°C), and magnitude of root-mean-square differences (RMSDs) is lower in DOISST (0.38°C) and OSTIA (0.43°C); (ii) when the same reference SSTs are evaluated at pointwise in situ locations, the standard deviations (SDs) are smaller in DOISST (0.38°C) and OSTIA (0.39°C) on 0.25° × 0.25° grids; but the SDs become smaller in OSTIA (0.34°C) and CMC (0.37°C) on products' original grids, showing the advantage of those high-resolution analyses for resolving finer-scale SSTs; (iii) when a loose QC algorithm is applied to the reference buoy observations, SDs increase; and vice versa; however, the relative performance of products remains the same; and (iv) when the drifting-buoy or Argo observations are used as the reference, the magnitude of RMSDs and SDs become smaller, potentially due to changes in observing intervals. These results suggest that high-resolution SST analyses may take advantage in intercomparisons. Significance Statement: Intercomparisons of gridded SST products be affected by how the products are compared with in situ observations: whether the products are in coarse (0.25°) or original (0.05°–0.10°) grids, whether the in situ SSTs are in their reported locations or gridded and how they are quality controlled, and whether the biases of satellite SSTs are corrected by localized matchups or large-scale patterns. By taking all these factors into account, our analyses indicate that the NOAA DOISST is among the best SST products for the long period (1981–present) and relatively coarse (0.25°) resolution that it was designed for. [ABSTRACT FROM AUTHOR]

The years since 2000 have been a golden age in in situ ocean observing with the proliferation and organization of autonomous platforms such as surface drogued buoys and subsurface Argo profiling floats augmenting ship-based observations. Global time series of mean sea surface temperature and ocean heat content are routinely calculated based on data from these platforms, enhancing our understanding of the ocean's role in Earth's climate system. Individual measurements of meteorological, sea surface, and subsurface variables directly improve our understanding of the Earth system, weather forecasting, and climate projections. They also provide the data necessary for validating and calibrating satellite observations. Maintaining this ocean observing system has been a technological, logistical, and funding challenge. The global COVID-19 pandemic, which took hold in 2020, added strain to the maintenance of the observing system. A survey of the contributing components of the observing system illustrates the impacts of the pandemic from January 2020 through December 2021. The pandemic did not reduce the short-term geographic coverage (days to months) capabilities mainly due to the continuation of autonomous platform observations. In contrast, the pandemic caused critical loss to longer-term (years to decades) observations, greatly impairing the monitoring of such crucial variables as ocean carbon and the state of the deep ocean. So, while the observing system has held under the stress of the pandemic, work must be done to restore the interrupted replenishment of the autonomous components and plan for more resilient methods to support components of the system that rely on cruise-based measurements. [ABSTRACT FROM AUTHOR]

This paper describes the new International Comprehensive Ocean–Atmosphere Data Set (ICOADS) near-real-time (NRT) release (R3.0.2), with greatly enhanced completeness over the previous version (R3.0.1). R3.0.1 had been operationally produced monthly from January 2015 onward, with input data from the World Meteorological Organization (WMO) Global Telecommunication Systems (GTS) transmissions in the Traditional Alphanumeric Codes (TAC) format. Since the release of R3.0.1, however, many observing platforms have changed, or are in the process of transitioning, to the Binary Universal Form for Representation of Meteorological Data (BUFR) format. R3.0.2 combines input data from both BUFR and TAC formats. In this paper, we describe input data sources; the BUFR decoding process for observations from drifting buoys, moored buoys, and ships; and the data quality control of the TAC and BUFR data streams. We also describe how the TAC and BUFR streams were merged to upgrade R3.0.1 into R3.0.2 with duplicates removed. Finally, we compare the number of reports and spatial coverage of essential climate variables (ECVs) between R3.0.1 and R3.0.2. ICOADS NRT R3.0.2 shows both quantitative and qualitative gains from the inclusion of BUFR reports. The number of observations in R3.0.2 increased by nearly 1 million reports per month, and the coverage of buoy and ship sea surface temperatures (SSTs) on monthly 2° × 2° grids increased by 20%. The number of reported ECVs also increased in R3.0.2. For example, observations of SST and sea level pressure (SLP) increased by around 30% and 20%, respectively, as compared to R3.0.1, and salinity is a new addition to the ICOADS NRT product in R3.0.2. Significance Statement: The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) is the largest collection of surface marine observations spanning from 1662 to the present. A new version, ICOADS near-real-time 3.0.2, includes data transmitted in the Binary Universal Form for Representation of Meteorological Data (BUFR) format, in combination with Traditional Alphanumeric Codes (TAC) data. Many of the organizations that report observations in near–real time have moved to BUFR, so this update brings ICOADS into alignment with collections and archives of these international data distributions. By including the BUFR reports, the number of observations in the upgraded version of ICOADS increased by nearly one million reports per month and spatial coverage of buoy and ship SSTs increased by 20% over the previous version. [ABSTRACT FROM AUTHOR]

Studies have indicated that marine heatwaves (MHWs) have had severe impacts on the marine ecosystem in the Pacific, Atlantic, and Indian Oceans, but there have been few studies focused on MHWs in the Arctic. On the other hand, amplified rapid warming in the Arctic region makes it a hotspot strategically and economically worldwide. In this study, we documented that the average intensity of MHWs in the Arctic was comparable with that in the other regions of the global oceans. The annual intensity, frequency, duration, and areal coverage of MHWs have increased significantly in recent decades. The increase of the annual duration is mainly owing to the postponed end time, thus the prolonged periods, of the MHW seasons. Our analysis indicates that the increasing trends of the annual intensity, frequency, duration, and areal coverage in the Arctic are closely associated with the increasing surface air temperature and decreasing sea‐ice concentration under the global warming environment. These features are robust across three different sea surface temperature (SST) products and using different MHW criteria. Plain Language Summary: Events of extremely warm waters in the oceans are known as marine heatwaves (MHWs). Past MHW research has used sea surface temperature (SST) to diagnose MHWs and has focused on the tropical and subtropical oceans. The questions we address here are: (a) Are there any MHW events in the Arctic region where SSTs are generally low? and (b) Are the MHWs weaker or stronger in the Arctic than in the other oceans? Our study indicates that: (a) MHWs do exist in the Arctic, (b) their strengths increase with time, and (c) they are stronger than those in the other oceans in the most recent decades. These MHWs may have a strong impact on the Arctic bio‐ecosystem due to their low heat tolerance since the seasonal variation of SST in the Arctic is small. Key Points: The average marine heatwaves (MHWs) in the Arctic are as strong as in the other ocean basins in 1982–2020The annual intensity are stronger in 2000–2020 than in 1982–2000, which is consistent with changes in air temperature, sea‐ice, and cloudThe increase of annual duration is largely associated with the postponed end time of the MHW seasons [ABSTRACT FROM AUTHOR]

The NOAA Daily Optimum Interpolation Sea Surface Temperature dataset (DOISST) has recently been updated to v2.1 (January 2016–present). Its accuracy may impact the climate assessment, monitoring and prediction, and environment-related applications. Its performance, together with those of seven other well-known sea surface temperature (SST) products, is assessed by comparison with buoy and Argo observations in the global oceans on daily 0.25° × 0.25° resolution from January 2016 to June 2020. These seven SST products are NASA MUR25, GHRSST GMPE, BoM GAMSSA, UKMO OSTIA, NOAA GPB, ESA CCI, and CMC. Our assessments indicate that biases and root-mean-square difference (RMSDs) in reference to all buoys and all Argo floats are low in DOISST. The bias in reference to the independent 10% of buoy SSTs remains low in DOISST, but the RMSD is slightly higher in DOISST than in OSTIA and CMC. The biases in reference to the independent 10% of Argo observations are low in CMC, DOISST, and GMPE; also, RMSDs are low in GMPE and CMC. The biases are similar in GAMSSA, OSTIA, GPB, and CCI whether they are compared against all buoys, all Argo, or the 10% of buoy or 10% of Argo observations, while the RMSDs against Argo observations are slightly smaller than those against buoy observations. These features indicate a good performance of DOISST v2.1 among the eight products, which may benefit from ingesting the Argo observations by expanding global and regional spatial coverage of in situ observations for effective bias correction of satellite data. [ABSTRACT FROM AUTHOR]

A joint effort between the Copernicus Climate Change Service (C3S) and the Group for High Resolution Sea Surface Temperature (GHRSST) has been dedicated to an intercomparison study of eight global gap-free sea surface temperature (SST) products to assess their accurate representation of the SST relevant to climate analysis. In general, all SST products show consistent spatial patterns and temporal variability during the overlapping time period (2003–18). The main differences between each product are located in the western boundary current and Antarctic Circumpolar Current regions. Linear trends display consistent SST spatial patterns among all products and exhibit a strong warming trend from 2012 to 2018 with the Pacific Ocean basin as the main contributor. The SST discrepancy between all SST products is very small compared to the significant warming trend. Spatial power spectral density shows that the interpolation into 1° spatial resolution has negligible impacts on our results. The global mean SST time series reveals larger differences among all SST products during the early period of the satellite era (1982–2002) when there were fewer observations, indicating that the observation frequency is the main constraint of the SST climatology. The maturity matrix scores, which present the maturity of each product in terms of documentation, storage, and dissemination but not the scientific quality, demonstrate that ESA-CCI and OSTIA SST are well documented for users' convenience. Improvements could be made for MGDSST and BoM SST. Finally, we have recommended that these SST products can be used for fundamental climate applications and climate studies (e.g., El Niño). [ABSTRACT FROM AUTHOR]

The NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian, South Pacific, and South Atlantic Oceans that is due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the traditional alphanumeric codes (TAC) to the binary universal form for the representation of meteorological data (BUFR). The cold bias against Argo was about −0.14°C on global average and −0.28°C in the Indian Ocean from January 2016 to August 2019. We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5-m depth. The impact of using the satellite MetOp-B instead of NOAA-19 was notable for high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying a freezing point instead of regressed ice-SST proxy. This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to −0.07° and −0.14°C in the global ocean and Indian Ocean, respectively, when compared with independent Argo observations and are reduced to −0.04° and −0.08°C in the global ocean and Indian Ocean, respectively, when compared with dependent Argo observations. The difference against the Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) product is reduced from −0.09° to −0.01°C in the global oceans and from −0.20° to −0.04°C in the Indian Ocean. [ABSTRACT FROM AUTHOR]

Previous research has shown that the 1877/78 El Niño resulted in great famine events around the world. However, the strength and statistical significance of this El Niño event have not been fully addressed, largely due to the lack of data. We take a closer look at the data using an ensemble analysis of the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5). The ERSSTv5 standard run indicates a strong El Niño event with a peak monthly value of the Niño-3 index of 3.5°C during 1877/78, stronger than those during 1982/83, 1997/98, and 2015/16. However, an analysis of the ERSSTv5 ensemble runs indicates that the strength and significance (uncertainty estimates) depend on the construction of the ensembles. A 1000-member ensemble analysis shows that the ensemble mean Niño-3 index has a much weaker peak of 1.8°C, and its uncertainty is much larger during 1877/78 (2.8°C) than during 1982/83 (0.3°C), 1997/98 (0.2°C), and 2015/16 (0.1°C). Further, the large uncertainty during 1877/78 is associated with selections of a short (1 month) period of raw-data filter and a large (20%) acceptance criterion of empirical orthogonal teleconnection modes in the ERSSTv5 reconstruction. By adjusting these two parameters, the uncertainty during 1877/78 decreases to 0.5°C, while the peak monthly value of the Niño-3 index in the ensemble mean increases to 2.8°C, suggesting a strong and statistically significant 1877/78 El Niño event. The adjustment of those two parameters is validated by masking the modern observations of 1981–2017 to 1861–97. Based on the estimated uncertainties, the differences among the strength of these four major El Niño events are not statistically significant. [ABSTRACT FROM AUTHOR]

This analysis estimates uncertainty in the NOAA global surface temperature (GST) version 5 (NOAAGlobalTemp v5) product, which consists of sea surface temperature (SST) from the Extended Reconstructed SST version 5 (ERSSTv5) and land surface air temperature (LSAT) from the Global Historical Climatology Network monthly version 4 (GHCNm v4). Total uncertainty in SST and LSAT consists of parametric and reconstruction uncertainties. The parametric uncertainty represents the dependence of SST/LSAT reconstructions on selecting 28 (6) internal parameters of SST (LSAT), and is estimated by a 1000-member ensemble from 1854 to 2016. The reconstruction uncertainty represents the residual error of using a limited number of 140 (65) modes for SST (LSAT). Uncertainty is quantified at the global scale as well as the local grid scale. Uncertainties in SST and LSAT at the local grid scale are larger in the earlier period (1880s–1910s) and during the two world wars due to sparse observations, then decrease in the modern period (1950s–2010s) due to increased data coverage. Uncertainties in SST and LSAT at the global scale are much smaller than those at the local grid scale due to error cancellations by averaging. Uncertainties are smaller in SST than in LSAT due to smaller SST variabilities. Comparisons show that GST and its uncertainty in NOAAGlobalTemp v5 are comparable to those in other internationally recognized GST products. The differences between NOAAGlobalTemp v5 and other GST products are within their uncertainties at the 95% confidence level. [ABSTRACT FROM AUTHOR]

Arctic sea surface temperatures (SSTs) are estimated mostly from satellite sea ice concentration (SIC) estimates. In regions with sea ice the SST is the temperature of open water or of the water under the ice. A number of different proxy SST estimates based on SIC have been developed. In recent years more Arctic quality-control buoy SSTs have become available, allowing better validation of different estimates and the development of improved proxy estimates. Here proxy SSTs from different approaches are evaluated and an improved proxy SST method is shown. The improved proxy SSTs were tested in an SST analysis, and showed reduced bias and random errors compared to the Arctic buoy SSTs. Almost all reduction in errors is in the warm melt season. In the cold season the SIC is typically high and all estimates tend to have low errors. The improved method will be incorporated into an operational SST analysis. [ABSTRACT FROM AUTHOR]

The relative roles of buoy and Argo observations in two sea surface temperature (SST) analyses are studied in the global ocean and tropical Pacific Ocean over 2000–16 using monthly Extended Reconstructed SST version 5 (ERSSTv5) and Daily Optimum Interpolation SST version 2 (DOISST). Experiments show an overall higher impact by buoys than Argo floats over the global oceans and an increasing impact by Argo floats. The impact by Argo floats is generally larger in the Southern Hemisphere than in the Northern Hemisphere. The impact on trends and anomalies of globally averaged SST by either one is small when the other is used. The warming trend over 2000–16 remains significant by including either buoys or Argo floats or both. In the tropical Pacific, the impact by buoys was large over 2000–05 when the number of Argo floats was low, and became smaller over 2010–16 when the number and area coverage of Argo floats increased. The magnitude of El Niño and La Niña events decreases when the observations from buoys, Argo floats, or both are excluded. The impact by the Tropical Atmosphere Ocean (TAO) and Triangle Trans-Ocean Buoy Network (TRITON) is small in normal years and during El Niño events. The impact by TAO/TRITON buoys on La Niña events is small when Argo floats are included in the analysis systems, and large when Argo floats are not included. The reason for the different impact on El Niño and La Niña events is that the drifting buoys are more dispersed from the equatorial Pacific region by stronger trade winds during La Niña events. [ABSTRACT FROM AUTHOR]

The difficulty in effectively evaluating sea surface temperature (SST) analyses is finding independent observations, since most available observations have been used in the SST analyses. In this study, the ocean profile measurements [from reverse thermometer, CTD, mechanical bathythermograph (MBT), and XBT] above 5-m depth over 1950–2016 from the World Ocean Database (WOD) are used (data labeled pSSTW). The biases of MBT and XBT are corrected based on currently available algorithms. The bias-corrected pSSTW over 1950–2016 and satellite-based SST from the European Space Agency (ESA) Climate Change Initiative (CCI) over 1992–2010 are used to evaluate commonly available SST analyses. These SST analyses are the Extended Reconstructed SST (ERSST), versions 5, 4, and 3b, the Met Office Hadley Centre Sea Ice and SST dataset (HadISST), and the Japan Meteorological Administration (JMA) Centennial In Situ Observation-Based Estimates of SST version 2.9.2 (COBE-SST2). Our comparisons show that the SST from COBE-SST2 is the closest to pSSTW and CCI in most of the Pacific, Atlantic, and Southern Oceans, which may result from its unique bias correction to ship observations. The SST from ERSST version 5 is more consistent with pSSTW than its previous versions over 1950–2016, and is more consistent with CCI than its previous versions over 1992–2010. The better performance of ERSST version 5 over its previous versions is attributed to its improved bias correction applied to ship observations with a baseline of buoy observations, and is seen in most of the Pacific and Atlantic. [ABSTRACT FROM AUTHOR]

The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°-0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s-40s when observation instruments changed rapidly. Both long- (1900-2015) and short-term (2000-15) SST trends in ERSSTv5 remain significant as in ERSSTv4. [ABSTRACT FROM AUTHOR]

The uncertainty in Extended Reconstructed SST (ERSST) version 4 (v4) is reassessed based upon 1) reconstruction uncertainties and 2) an extended exploration of parametric uncertainties. The reconstruction uncertainty ( U r) results from using a truncated (130) set of empirical orthogonal teleconnection functions (EOTs), which yields an inevitable loss of information content, primarily at a local level. The U r is assessed based upon 32 ensemble ERSST.v4 analyses with the spatially complete monthly Optimum Interpolation SST product. The parametric uncertainty ( U p) results from using different parameter values in quality control, bias adjustments, and EOT definition etc. The U p is assessed using a 1000-member ensemble ERSST.v4 analysis with different combinations of plausible settings of 24 identified internal parameter values. At the scale of an individual grid box, the SST uncertainty varies between 0.3° and 0.7°C and arises from both U r and U p. On the global scale, the SST uncertainty is substantially smaller (0.03°-0.14°C) and predominantly arises from U p. The SST uncertainties are greatest in periods and locales of data sparseness in the nineteenth century and relatively small after the 1950s. The global uncertainty estimates in ERSST.v4 are broadly consistent with independent estimates arising from the Hadley Centre SST dataset version 3 (HadSST3) and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2). The uncertainty in the internal parameter values in quality control and bias adjustments can impact the SST trends in both the long-term (1901-2014) and 'hiatus' (2000-14) periods. [ABSTRACT FROM AUTHOR]

The monthly Extended Reconstructed Sea Surface Temperature (ERSST) dataset, available on global 2° × 2° grids, has been revised herein to version 4 (v4) from v3b. Major revisions include updated and substantially more complete input data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) release 2.5; revised empirical orthogonal teleconnections (EOTs) and EOT acceptance criterion; updated sea surface temperature (SST) quality control procedures; revised SST anomaly (SSTA) evaluation methods; updated bias adjustments of ship SSTs using the Hadley Centre Nighttime Marine Air Temperature dataset version 2 (HadNMAT2); and buoy SST bias adjustment not previously made in v3b. Tests show that the impacts of the revisions to ship SST bias adjustment in ERSST.v4 are dominant among all revisions and updates. The effect is to make SST 0.1°-0.2°C cooler north of 30°S but 0.1°-0.2°C warmer south of 30°S in ERSST.v4 than in ERSST.v3b before 1940. In comparison with the Met Office SST product [the Hadley Centre Sea Surface Temperature dataset, version 3 (HadSST3)], the ship SST bias adjustment in ERSST.v4 is 0.1°-0.2°C cooler in the tropics but 0.1°-0.2°C warmer in the midlatitude oceans both before 1940 and from 1945 to 1970. Comparisons highlight differences in long-term SST trends and SSTA variations at decadal time scales among ERSST.v4, ERSST.v3b, HadSST3, and Centennial Observation-Based Estimates of SST version 2 (COBE-SST2), which is largely associated with the difference of bias adjustments in these SST products. The tests also show that, when compared with v3b, SSTAs in ERSST.v4 can substantially better represent the El Niño/La Niña behavior when observations are sparse before 1940. Comparisons indicate that SSTs in ERSST.v4 are as close to satellite-based observations as other similar SST analyses. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) observations from satellite-based Advanced Very High Resolution Radiometer (AVHRR) instrument exhibit biases. Adjustments necessary for removing the AVHRR biases have been studied by progressive experiments. These experiments show that the biases are sensitive to various parameters, including the length of the input data window, the base-function empirical orthogonal teleconnections (EOTs), the ship-buoy SST adjustment, and a shift in grid system. The difference in bias adjustments due to these parameters can be as large as 0.3°-0.5°C in the tropical Pacific at the monthly time scale. The AVHRR bias adjustments were designed differently in the daily optimum interpolation SST (DOISST) and the Extended Reconstructed SST datasets that ingest AVHRR SSTs (ERSSTsat). The different AVHRR bias adjustments result in the differences in SST datasets in DOISST and ERSSTsat. Comparisons show that the SST difference between these two datasets results largely from the difference in the AVHRR bias adjustments and little from SST analysis methods in the Niño-3.4 region, as well as in the global oceans. For example, the average difference of the Niño-3.4 SSTs between DOISST and ERSSTsat is approximately 0.12°C due to the bias adjustments and is about 0.01°C due to the analysis methods. This study finds that the DOISST datasets can be improved by using the revised AVHRR bias adjustment of a wider input data window, updated EOTs, and a shifted grid system in DOISST. Improvements can also be made by including a ship-buoy SST adjustment, a zonal SST adjustment, or revised EOTs without damping in the high latitudes in ERSSTsat. [ABSTRACT FROM AUTHOR]

During June-November 2012, pronounced differences in tropical Pacific sea surface temperature (SST) anomalies were observed between three widely used SST products: the extended reconstructed SST version 3b (ERSSTv3b), and the optimum interpolation SST version 2 analyses (OISST), produced weekly (OISSTwk) and daily (OISSTdy). During June-August 2012, the Niño-3.4 SST anomaly (SSTA) index was 0.2°-0.3°C lower in ERSSTv3b than in OISSTwk and OISSTdy, while it was 0.3°-0.4°C higher from September to November 2012. Such differences in the Niño-3.4 SSTA index can impact the assessment of the status of the El Niño-Southern Oscillation, which is determined using a threshold of ±0.5°C in the Niño-3.4 SSTA index. To investigate the reasons for the differences between ERSSTv3b and OISSTdy/OISSTwk, an experimental analysis (called ERSSTsat) is created that is similar to ERSSTv3b but includes satellite-derived SSTs. However, significant differences in the Niño-3.4 SSTA index remained between ERSSTsat and OISSTdy/OISSTwk. Comparisons of ERSSTsat and OISSTdy indicate that their differences are mostly associated with the different schemes for bias adjustment applied to the satellite-based SSTs. It is therefore suggested that the differences in the Niño-3.4 SSTA index between ERSSTv3b and OISSTdy cannot be solely due to the inclusion of but by the bias adjustment methodology of satellite data in OISSTdy. Finally, the SST products are compared with observations from ships, buoys, and satellites. On the monthly time scale, the area-averaged Niño-3.4 SSTA index in the tropical Pacific is more consistent with in situ observations in ERSSTv3b than in OISSTdy. In contrast, pointwise observations across the tropical Pacific are more consistent with OISSTdy than ERSSTv3b. It is therefore suggested that the differences among SST products are partially due to a structural uncertainty of various SST estimates. [ABSTRACT FROM AUTHOR]

Sea Surface Temperature (SST) is an essential climate variable (ECV) for monitoring the state and detecting changes in the climate. The concept of ECVs, developed by the Global Climate Observing System (GCOS) program of the World Meteorological Organization (WMO), has been broadly adopted in worldwide science and policy circles Besides being a climate change indicator, the global SST field is an essential input for atmospheric models, air-sea exchange studies, understanding marine ecosystems, operational weather, and ocean forecasting, military and defense operations, tourism, and fisheries research. It is, therefore, critical to understand the errors associated with SST measurements from both in situ measurements and satellite observations. The customary way of validating a satellite SST is to compare it with in situ measured SSTs. This method, however, will have inaccuracies due to uncertainties involving both types of measurements. A triple collocation (TC) error analysis can be implemented on three mutually independent error-prone measurements to estimate the root-mean-square error (RMSE) of each measurement. In this study, the error characterization for the Pathfinder SST version 5.3 (PF53) dataset is performed using an extended TC (ETC) method and reported to be in the range of 0.31 to 0.37 K. These values are reasonable, as is evident from corresponding very high (~0.98) unbiased signal-to-noise ratio (SNR) values. [ABSTRACT FROM AUTHOR]