International Comprehensive Ocean-Atmosphere Data Set Research Articles

MERRA-2 reanalysis-aided sea fog detection based on CALIOP observation over North Pacific

Sea fog can lead to a dangerous meteorological hazard over oceans and coastal areas. Cloud-aerosol Lidar with Orthogonal Polarization (CALIOP) is an effective tool to detect sea fog, however it is ineffective for dense fog detection because the lidar signal has been fully attenuated before reaching the layer base. To address this issue by improving dense fog detection, a reanalysis-aided sea fog detection method using CALIOP lidar measurements and MERRA-2 reanalysis dataset is proposed taking the Northwestern Pacific (NP) as study area. In this method, CALIOP lidar measurements are used to locate the ceiling and base of the hydrometeor layers, and extract the candidate fog layers in the Marine Atmospheric Boundary Layer (MABL). Reanalysis dataset are used to identify real fog from the candidate fog layers based on the near-surface meteorological difference under different weather conditions (sea fog, stratus and stratus precipitation). Method validation using in-situ weather records from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) and five climate stations, shows the detection accuracy and false alarm rate of 89.4% and 2.0%, respectively. Most importantly, about 87.4% of dense fogs are successfully discriminated with the aid of reanalysis data. Model application to CALIOP data from 2007 to 2019 shows pronounced spatial and seasonal characteristics of sea fog distribution over NP, with the highest frequency (>40%) found over the Kuril Islands, Sea of Okhotsk and Bering Sea from June to August. The example demonstrates that by using time series CALIOP observations and the proposed method, one may expect spatio-temporally complete sea fog information, which is important especially for remote regions with scarce in-situ measurements (e.g. polar regions). Our research proposes and proves a novel idea of sea fog detection from space by using satellite lidar and reanalysis data, which may benefit the sea fog forecasting and dynamics analysis, by providing unprecedented large-scale high-quality observations, especially over dense fog conditions.

The monthly average distributions of sea-surface wind speeds and directions in the East Vietnam Sea

This paper analyzes a monthly global gridded sea-surface wind dataset from Jan 1991 to Dec 2020, sourced from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). The goal is to construct schematic maps depicting the monthly average distributions of sea-surface wind speeds and directions in the East Vietnam Sea. These maps provide a visual representation of the monsoon patterns at different points and areas in the region. The statistical and analytical findings reveal that the East Vietnam Sea is primarily influenced by two types of monsoons: Northeast and Southwest. When one is strong, the other is weak, and vice versa. The Northeast monsoon dominates from September to April of the following year, while the Southwest monsoon is more dominant from May to August. The intensity of the Northeast monsoon is usually greater than that of the Southwest monsoon. These results provide an overview of the monsoon distributions in the East Vietnam Sea, which can assist scientists and managers in proposing policies and measures suitable to the climate environment and wind seasons. These findings also help in the effective exploitation of abundant wind energy resources in coastal Vietnam and the East Vietnam Sea. Furthermore, the results are consistent with previous studies of monsoons in the East Vietnam Sea, indicating that the data source used in this study is reliable and can be utilized as input data for tidal, currents, or oil spread models.

Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method

A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this study, a new monthly gridded Arctic SAT dataset dated back to 1979 was reconstructed with a deep learning method by combining surface air temperatures from multiple data sources. The source data include the observations from land station of GHCN (Global Historical Climatology Network), ICOADS (International Comprehensive Ocean-Atmosphere Data Set) over the oceans, drifting ice station of Russian NP (North Pole), and buoys of IABP (International Arctic Buoy Programme). The last two are crucial for improving the representation of the in-situ observed temperatures within the Arctic. The newly reconstructed dataset includes monthly Arctic SAT beginning in 1979 and daily Arctic SAT beginning in 2011. This dataset would represent a new improvement in developing observational temperature datasets and can be used for a variety of applications.

Blending TAC and BUFR Marine In Situ Data for ICOADS Near-Real-Time Release 3.0.2

Abstract 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.

Influence of tropical convective enhancement in Pacific on the trend of stratospheric sudden warmings in Northern Hemisphere

The exploration of the trend in stratospheric sudden warmings (SSWs) is conducive to predict SSWs in the future. Utilizing the National Centre for Environmental Prediction Reanalysis (NCEP) (1948–2020) and Japanese 55-year Reanalysis (JRA55) (1958–2020), we investigated the duration and strength of SSWs in the Northern Hemisphere occurred in the boreal winter (December–February). We found the duration of SSWs tends to increase and the strength of SSWs tends to strengthen from 1948 to 2003. After 2003, these trends did not continue. We utilized the observed cloudiness from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) to find that the convective activities in the tropical Central Pacific were enhanced during 1948–2003, and the enhancement of the convective activities did not continue after 2003. The circulation anomalies caused by the enhanced convective activities propagate to the high latitudes through wave trains. The anomalies of circulation and the climatological circulation at high latitudes interfere with each other and superimpose, which has a significant impact on planetary wave 1 (PW1). As a result, the PW1 also showed an increasing trend from 1948 to 2003 and a decreasing trend after 2003. After the stratosphere filters out the planetary wave with a large wavenumber, PW1 accounts for more proportion of planetary waves, which causes the trend in SSWs to change.

Decadal shift in sea fog frequency over the northern South China Sea in spring: Interdecadal variation and impact of the Pacific Decadal Oscillation

Sea fog has an important impact on marine activities, coastal ecological environments, and regional radiation budgets. In the present study, we analyzed the characteristics of sea fog frequency (SFF) interdecadal variations over the northern South China Sea (NSCS) and the role of the Pacific Decadal Oscillation (PDO) in these changes, using independent datasets. The SFFs in the fog season derived from International Comprehensive Ocean–Atmosphere Data Set (ICOADS) observations and ERA-5 cross-validate a significant decadal transition from negative to positive phase in the late 1970s, which was further verified by a moving t-test. The composite fields after 1980s, including wind, low-atmospheric stability and moisture supply, were much more favorable for an increase in SFF than those before 1980s. These conditions were found to be closely associated with the PDO, with a significant correlation coefficient between the SFF derived from ICOADS (ERA-5) and the PDO at 0.53 (0.48), and even reaching 0.94 (0.92) after applying an 18-year moving average from 1950 to 1999. An anticyclonic circulation anomaly over the North western Pacific associated with positive PDO phase enhances the stability in the lower atmosphere and the water vapor supply due to a southerly wind anomaly at 850 hPa over the NSCS. With the strengthening of the winter monsoon in the positive PDO phase, northerly wind anomalies over the East Asian continent converge with southerly wind anomalies from the ocean, increasing the humidity in the NSCS, which helps to incease the SFF. In the negative PDO phase, the oceanic and atmospheric conditions slightly decrease the frequency of sea fog occurrence. This study improves the understanding of the mechanism on the interdecadal variation of SFF as modulated by the PDO, and has useful implications for further work on sea fog projection.

A Completeness and Complementarity Analysis of the Data Sources in the NOAA In Situ Sea Surface Temperature Quality Monitor (iQuam) System

In situ sea surface temperatures (SST) are the key component of the calibration and validation (Cal/Val) of satellite SST retrievals and data assimilation (DA). The NOAA in situ SST Quality Monitor (iQuam) aims to collect, from various sources, all available in situ SST data, and integrate them into a maximally complete, uniform, and accurate dataset to support these applications. For each in situ data type, iQuam strives to ingest data from several independent sources, to ensure most complete coverage, at the cost of some redundancy in data feeds. The relative completeness of various inputs and their consistency and mutual complementarity are often unknown and are the focus of this study. For four platform types customarily employed in satellite Cal/Val and DA (drifting buoys, tropical moorings, ships, and Argo floats), five widely known data sets are analyzed: (1) International Comprehensive Ocean-Atmosphere Data Set (ICOADS), (2) Fleet Numerical Meteorology and Oceanography Center (FNMOC), (3) Atlantic Oceanographic and Meteorological Laboratory (AOML), (4) Copernicus Marine Environment Monitoring Service (CMEMS), and (5) Argo Global Data Assembly Centers (GDACs). Each data set reports SSTs from one or more platform types. It is found that drifting buoys are more fully represented in FNMOC and CMEMS. Ships are reported in FNMOC and ICOADS, which are best used in conjunction with each other, but not in CMEMS. Tropical moorings are well represented in ICOADS, FNMOC, and CMEMS. Some CMEMS mooring reports are sampled every 10 min (compared to the standard 1 h sampling in all other datasets). The CMEMS Argo profiling data set is, as expected, nearly identical with those from the two Argo GDACs.

Correcting Observational Biases in Sea Surface Temperature Observations Removes Anomalous Warmth during World War II

AbstractMost historical sea surface temperature (SST) estimates indicate warmer World War II SSTs than expected from forcing and internal climate variability. If real, this World War II warm anomaly (WW2WA) has important implications for decadal variability, but the WW2WA may also arise from incomplete corrections of biases associated with bucket and engine room intake (ERI) measurements. To better assess the origins of the WW2WA, we develop five different historical SST estimates (reconstructions R1–R5). Using uncorrected SST measurements from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) version 3.0 (R1) gives a WW2WA of 0.41°C. In contrast, using only buckets (R2) or ERI observations (R3) gives WW2WAs of 0.18° and 0.08°C, respectively, implying that uncorrected biases are the primary source of the WW2WA. We then use an extended linear-mixed-effect method to quantify systematic differences between subsets of SSTs and develop groupwise SST adjustments based on differences between pairs of nearby SST measurements. Using all measurements after applying groupwise adjustments (R4) gives a WW2WA of 0.13°C [95% confidence interval (c.i.): 0.01°–0.26°C] and indicates that U.S. and U.K. naval observations are the primary cause of the WW2WA. Finally, nighttime bucket SSTs are found to be warmer than their daytime counterparts during WW2, prompting a daytime-only reconstruction using groupwise adjustments (R5) that has a WW2WA of 0.09°C (95% c.i.: −0.01° to 0.18°C). R5 is consistent with the range of internal variability found in either the CMIP5 (95% c.i.: −0.10° to 0.10°C) or CMIP6 ensembles (95% c.i.: −0.11° to 0.10°C). These results support the hypothesis that the WW2WA is an artifact of observational biases, although further data and metadata analyses will be important for confirmation.

A New Technique for Century-Scale Wind Component Indices

Advancing the understanding of how variations in the climate over the ocean influences the weather over the United States can be aided by developing marine climatic indices. Herein, wind component indices are developed using nearly 125 years of wind observations from ships. A new technique using probability density functions for the values of meridional and zonal wind components is developed to create indices for a user-selected region and accumulation interval (e.g., annual or seasonal) over a climatological period. The index is a measure of the shift in the likelihood of values above or below a threshold for a given season or year as compared to the long-term (e.g., 125 year) probability distribution. The new index method is demonstrated using ship-based wind observations for select regions of the Atlantic Ocean. Ship observations are extracted from release 3.0.0 of the International Comprehensive Ocean-Atmosphere Data Set. Prior to index creation, an assessment of wind data quality is completed, and suspect observations are removed. The method to create a probabilistic wind component index is described along with a metric of the uncertainty in the calculated index. Two wind component indices, for regions in the north Atlantic and eastern Gulf of Mexico, are presented to demonstrate the technique. Using the Gulf of Mexico index as a case study, we compare the wind component indices to precipitation measured over the Gulf coastal states and identify several relationships between multi-year changes in winds in the eastern Gulf of Mexico and precipitation on a seasonal basis. Exploring the spatiotemporal patterns of the onshore/offshore component wind indices derived from seasonal wind forecasts could provide a metric for future prediction of seasonal or annual precipitation to support the agricultural sector. The index method demonstrated can be applied to other spatiotemporal regions for different parameters and using other source datasets.

Late 19th century navigational uncertainties and their influence on sea surface temperature estimates

Accurate estimates of historical changes in sea surface temperatures (SSTs) and their uncertainties are important for documenting and understanding historical changes in climate. A source of uncertainty that has not previously been quantified in historical SST estimates stems from position errors. A Bayesian inference framework is proposed for quantifying errors in reported positions and their implications on SST estimates. The analysis framework is applied to data from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS3.0) in 1885, a time when astronomical and chronometer estimation of position was common but predated the use of radio signals. Focus is upon a subset of 943 ship tracks from ICOADS3.0 that report their position every two hours to a precision of 0.01∘ longitude and latitude. These data are interpreted as positions determined by dead reckoning that are periodically updated by celestial correction techniques. The posterior medians of uncertainties in celestial correction are 33.1 km (0.30∘ on the equator) in longitude and 24.4 km (0.22∘) in latitude, respectively. Celestial navigation uncertainties being smaller in latitude than longitude is qualitatively consistent with the relative difficulty of obtaining astronomical estimates. The posterior medians for two-hourly dead reckoning uncertainties are 19.2% for ship speed and 13.2∘ for ship heading, leading to random position uncertainties with median 0.18∘ (20 km on the equator) in longitude and 0.15∘ (17 km) in latitude. Reported ship tracks also contain systematic position uncertainties relating to precursor dead-reckoning positions not being updated after obtaining celestial position estimates, indicating that more accurate positions can be provided for SST observations. Finally, we translate position errors into SST uncertainties by sampling an ensemble of SSTs from the Multiscale Ultrahigh Resolution Sea Surface Temperature (MURSST) data set. Evolving technology for determining ship position, heterogeneous reporting and archiving of position information, and seasonal and spatial changes in navigational uncertainty and SST gradients together imply that accounting for positional error in SST estimates over the span of the instrumental record will require substantial additional effort.

Minimizing heterogeneity of VOS observations for improving estimates of short-wave radiation over the oceans

The paper describes the development of climatologies of short-wave radiation over the ocean using visually observed cloud cover from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). For producing climatology of short-wave radiation, we applied bulk parameterization (known as SAIL/IOARAS). For a proper computation of short-wave radiation values special effort was taken to reduce sampling errors in cloud cover from ICOADS. Specifically, we consider the uncertainties associated with temporal inhomogeneity of observations and potentially implying biases in short-wave radiation estimates related to the astronomy factors critical for short-wave radiation. For this purpose, we propose simple and effective algorithms.

Systematic Differences in Bucket Sea Surface Temperatures Caused by Misclassification of Engine Room Intake Measurements

AbstractDifferences in sea surface temperature (SST) biases among groups of bucket measurements in the International Comprehensive Ocean–Atmosphere Dataset, version 3.0 (ICOADS3.0), were recently identified that introduce offsets of as much as 1°C and have first-order implications for regional temperature trends. In this study, the origin of these groupwise offsets is explored through covariation between offsets and diurnal cycle amplitudes. Examination of an extended bucket model leads to expectations for offsets and amplitudes to covary in either sign, whereas misclassified engine room intake (ERI) temperatures invariably lead to negative covariance on account of ERI measurements being warmer and having a smaller diurnal amplitude. An analysis of ICOADS3.0 SST measurements that are inferred to come from buckets indicates that offsets after the 1930s primarily result from the misclassification of ERI measurements in points of five lines of evidence. 1) Prior to when ERI measurements become available in the 1930s, offset–amplitude covariance is weak and generally positive, whereas covariance is stronger and generally negative subsequently. 2) The introduction of ERI measurements in the 1930s is accompanied by a wider range of offsets and diurnal amplitudes across groups, with 3) approximately 20% of estimated diurnal amplitudes being significantly smaller than buoy and drifter observations. 4) Regressions of offsets versus amplitudes intersect independently determined end-member values of ERI measurements. 5) Offset-amplitude slopes become less negative across all regions and seasons between 1960 and 1980, when ERI temperatures were independently determined to become less warmly biased. These results highlight the importance of accurately determining measurement procedures for bias corrections and reducing uncertainty in historical SST estimates.

Phenological shuffling of major marine phytoplankton groups over the last six decades

AbstractAimPhytoplankton form the basis of the marine food web and are responsible for approximately 50% of the world's photosynthesis. Changes to their ecology are, therefore, important: here, we examined seasonal patterns in ocean phytoplankton abundance for 45 taxa over 59 years collected from circa 410,000 km of line‐transect sampling at temperate latitudes.LocationThe North Sea.MethodsFor our analysis we used plankton abundance data from the Continuous Plankton Recorder (CPR) survey, sea surface temperature measurements from the Hadley Centre, UK Meteorological Office and wind speed data from the International Comprehensive Ocean–Atmosphere Data Set, NOAA.ResultsWe found large differences in changes in the timing of peak abundance between the major phytoplankton groups. Late‐summer blooming dinoflagellates (n = 10 taxa) tended to show a large seasonal advancement, the timing of peak abundance for dinoflagellates as group advancing 39 days over these six decades. By contrast diatoms (n = 35) did not show any change as a group in their timing of peak abundance over the time series. Granger causality testing suggested a major driver of these phenological changes has been ocean warming in general but more specifically the rate of spring temperature rise as the most important factor. We also found differences in the timing of peak abundance of harmful algal bloom taxa, with some showing peak abundance earlier while others have moved later.Main conclusionsThere has been a fundamental transformation of the classic seasonal progression from blooms of diatoms to dinoflagellates, which lies at the heart of temperate marine food chains, as the classic bimodal diatom and dinoflagellate seasonal peaks are eroded to a more continuous, single, longer‐lasting phytoplankton peak. This phenological shuffling within and between major taxonomic groups is likely to have profound implications for the transfer of energy to higher trophic levels.

A new compilation of globally gridded night‐time marine air temperatures: The UAHNMATv1 dataset

AbstractNear‐surface air temperature over the oceans is a relatively unused parameter in understanding the current state of climate, but is useful as an independent temperature metric over the oceans and serves as a geographical and physical complement to near‐surface air temperature over land. Although one complete version of this dataset exists (HadNMAT2), it has been strongly recommended that various groups generate climate records independently, which is one motivation here. This University of Alabama in Huntsville (UAH) study began with the construction of monthly night‐time marine air temperature (UAHNMATv1) values from the early‐twentieth century through to the present era using air temperatures on ships. Data from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) Release 3.0 (R3.0) were used to compile a complete time series of gridded UAHNMATv1. The observations required detailed homogenization procedures since there are many biases to account for such as increasing ship height and changing observing practices. The UAHNMATv1 dataset, once homogenized, is gridded to 5.0° monthly anomalies from 1900 to 2018. This study will present results which quantify the variability and trends and compare to current trends of other related datasets that include HadNMAT2 and sea‐surface temperatures (HadISST & ERSSTv4). This new dataset has broad overall agreement both globally and regionally with HadNMAT2, HadISST, and ERSSTv4 datasets.

The International Comprehensive Ocean-Atmosphere Data Set – Meeting Users Needs and Future Priorities

The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) is a collection and archive of in situ marine observations, which has been developed over several decades as an international project and recently guided by formal international partnerships and the ICOADS Steering Committee. ICOADS contains observations from many different observing systems encompassing the evolution of measurement technology since the 18th century. ICOADS provides an integrated source of observations for a range of applications, is widely used for the development of research and climate monitoring, and forms the main marine in situ surface data source, e.g. near-surface ocean observations and lower atmospheric marine-meteorological observations from buoys, ships, coastal stations, and oceanographic sensors, for oceanic and atmospheric research and reanalysis. ICOADS has developed ways to incorporate user and reanalyses feedback information associated with permanent unique identifiers, and is also the main repository for data that have been rescued from ships’ logbooks and other marine data digitisation activities. ICOADS has been adopted widely because it provides convenient access to a range of observation types, globally, and through the entire marine instrumental record. ICOADS has for decades provided a secure home for such observations. Because of the increased volume of observations, particularly those available in near-real-time, and an expansion of their diversity, the ICOADS processing system now requires extensive modernisation. Based on user feedback, we will outline the improvements that are required, the challenges to their implementation, and the benefits of upgrading this important and diverse marine archive and distribution activity.

Assembling an infrastructure for historic climate data recovery: data friction in practice

PurposeThe purpose of this paper is to adopt an assemblage theory lens to examine the socio-material forces shaping the development of an infrastructure for the recovery of archived historical marine weather records for use in contemporary climate data sets.Design/methodology/approachThe authors adopted a data journeys approach to research design, conducting in-depth semi-structured interviews with climate scientists, citizen scientists and a climate historian who were engaged at key sites across the journey of data from historical record to the International Comprehensive Ocean-Atmosphere Data Set database. Interview data were complemented by further qualitative data collected via observations of working practices, a digital ethnography of citizen scientists’ online forums, and documentation relevant to the circulation and governance of climate data across emergent data infrastructures. Data were thematically analysed (Ryan and Bernard, 2003), with themes being informed primarily by the theoretical framework.FindingsThe authors identify and critically examine key points of friction in the constitution of the data recovery infrastructure and the circulation of data through it, and identify the reflexive and adaptive nature of the beliefs and practices fostered by influential actors within the assemblage in order to progress efforts to build an infrastructure despite significant challenges. The authors conclude by addressing possible limitations of some of these adaptive practices within the context of the early twenty-first century neoliberal state, and in light of current debates about data justice.Originality/valueThe paper draws upon original empirical data and a novel theoretical framework that draws together Deleuze and Guattari’s assemblage theory with key concepts from the field of critical data studies (data journeys, data friction and data assemblage) to illuminate the socio-material constitution of the data recovery infrastructure within the context of the early twenty-first century neoliberal state.

A 60-Year Time Series Analyses of the Upwelling along the Portuguese Coast

Coastal upwelling has a significant local impact on marine coastal environment and on marine biology, namely fisheries. This study aims to evaluate climate and environmental changes in upwelling trends between 1950 and 2010. Annual, seasonal and monthly upwelling trends were studied in three different oceanographic areas of the Portuguese coast (northwestern—NW, southwestern—SW, and south—S). Two sea surface temperature datasets, remote sensing (RS: 1985–2009) and International Comprehensive Ocean—Atmosphere Data Set (ICOADS: 1950–2010), were used to estimate an upwelling index (UPWI) based on the difference between offshore and coastal sea surface temperature. Time series analyses reveal similar yearly and monthly trends between datasets A decrease of the UPWI was observed, extending longer than 20 years in the NW (1956–1979) and SW (1956–1994), and 30 years in the S (1956–1994). Analyses of sudden shifts reveal long term weakening and intensification periods of up to 30 years. This means that in the past 60 years a normal climate UPWI occurred along the Portuguese coast. An intensification of UPWI was recorded in recent decades regardless of the areas (RS: 1985–2009). Such an intensification rate (linear increase in UPWI) is only significant in S in recent decades (increase rate: ICOADS = 0.02 °C decade-1; RS = 0.11 °C decade-1) while in NW and SW the increase rate is meaningless. In NW more stable UPWI conditions were recorded, however average UPWI values increased in autumn and winter in NW in recently decades (RS: 1985–2009). An intensification rate of UPWI was recorded during summer (July, August and September) in SW and S in latter decades (RS: 1985–2009). The average UPWI values increased in recent decades in autumn in S. Marked phenological changes were observed in S in summer (before downwelling conditions prevail whilst recently when UPWI regimes prevail) with UPWI seasonal regime in S in recent decades becoming similar to those found in SW and NW. Results of this work can contribute to a better understanding of how upwelling dynamics affect/are correlated with biological data.

Systematic Differences in Bucket Sea Surface Temperature Measurements among Nations Identified Using a Linear-Mixed-Effect Method

AbstractThe International Comprehensive Ocean–Atmosphere Dataset (ICOADS) is a cornerstone for estimating changes in sea surface temperatures (SST) over the instrumental era. Interest in determining SST changes to within 0.1°C makes detecting systematic offsets within ICOADS important. Previous studies have corrected for offsets among engine room intake, buoy, and wooden and canvas bucket measurements, as well as noted discrepancies among various other groupings of data. In this study, a systematic examination of differences in collocated bucket SST measurements from ICOADS3.0 is undertaken using a linear-mixed-effect model according to nations and more-resolved groupings. Six nations and a grouping for which nation metadata are missing, referred to as “deck 156,” together contribute 91% of all bucket measurements and have systematic offsets among one another of as much as 0.22°C. Measurements from the Netherlands and deck 156 are colder than the global average by −0.10° and −0.13°C, respectively, both at p < 0.01, whereas Russian measurements are offset warm by 0.10°C at p < 0.1. Furthermore, of the 31 nations whose measurements are present in more than one grouping of data (i.e., deck), 14 contain decks that show significant offsets at p < 0.1, including all major collecting nations. Results are found to be robust to assumptions regarding the independence and distribution of errors as well as to influences from the diurnal cycle and spatially heterogeneous noise variance. Correction for systematic offsets among these groupings should improve the accuracy of estimated SSTs and their trends.

Correction to Beaufort‐estimated wind speeds over the Tropical Indian Ocean

The Beaufort Scale was used to estimate wind speeds (WSs) over oceans before the introduction of ship‐mounted anemometers. Beaufort‐estimated WSs form a major component of most historical data sets such as those of the International Comprehensive Ocean—Atmosphere Dataset (ICOADS) and data obtained from the Indian Meteorological Department (IMD). The Beaufort‐estimated WSs in the individual records of the ICOADS and IMD data are based on the World Meteorological Organization (WMO) 1100 scale, which gives biased WS equivalents. Corrections to this scale have been formulated to give better estimates of the WS. Among others, the corrections proposed by Da Silva and Lindau are found to give least bias. However, the correction scale derived by Da Silva has limited utility over the Indian Ocean. The present paper derives a correction explicitly for the Tropical Indian Ocean (30–100 ° E, 30 ° S–30 ° N) using individual records from the ICOADS and IMD unique data for the period 1985–2005. There is significant improvement in the agreement between Beaufort estimates and the anemometer‐measured WSs based on these corrections. The regression co‐efficients for the new scale are derived using annual and monthly data. The correction scale derived, which is based on the regression co‐efficients for July (July scale), gives less bias compared with the scales of other months. The slope differs by almost 17% when the new July scale is applied to the annual data when compared with the Da Silva scale; the bias showed a reduction from 0.52 to 0.08 m/s.

Over 10 million seawater temperature records for the United Kingdom Continental Shelf between 1880 and 2014 from 17 Cefas (United Kingdom government) marine data systems

Abstract. The datasets described here bring together quality-controlled seawater temperature measurements from over 130 years of departmental government-funded marine science investigations in the UK (United Kingdom). Since before the foundation of a Marine Biological Association fisheries laboratory in 1902 and through subsequent evolutions as the Directorate of Fisheries Research and the current Centre for Environment Fisheries & Aquaculture Science, UK government marine scientists and observers have been collecting seawater temperature data as part of oceanographic, chemical, biological, radiological, and other policy-driven research and observation programmes in UK waters. These datasets start with a few tens of records per year, rise to hundreds from the early 1900s, thousands by 1959, and hundreds of thousands by the 1980s, peaking with > 1 million for some years from 2000 onwards. The data source systems vary from time series at coastal monitoring stations or offshore platforms (buoys), through repeated research cruises or opportunistic sampling from ferry routes, to temperature extracts from CTD (conductivity, temperature, depth) profiles, oceanographic, fishery and plankton tows, and data collected from recreational scuba divers or electronic devices attached to marine animals. The datasets described have not been included in previous seawater temperature collation exercises (e.g. International Comprehensive Ocean–Atmosphere Data Set, Met Office Hadley Centre sea surface temperature data set, the centennial in situ observation-based estimates of sea surface temperatures), although some summary data reside in the British Oceanographic Data Centre (BODC) archive, the Marine Environment Monitoring and Assessment National (MERMAN) database and the International Council for the Exploration of the Sea (ICES) data centre. We envisage the data primarily providing a biologically and ecosystem-relevant context for regional assessments of changing hydrological conditions around the British Isles, although cross-matching with satellite-derived data for surface temperatures at specific times and in specific areas is another area in which the data could be of value (see e.g. Smit et al., 2013). Maps are provided indicating geographical coverage, which is generally within and around the UK Continental Shelf area, but occasionally extends north from Labrador and Greenland to east of Svalbard and southward to the Bay of Biscay. Example potential uses of the data are described using plots of data in four selected groups of four ICES rectangles covering areas of particular fisheries interest. The full dataset enables extensive data synthesis, for example in the southern North Sea where issues of spatial and numerical bias from a data source are explored. The full dataset also facilitates the construction of long-term temperature time series and an examination of changes in the phenology (seasonal timing) of ecosystem processes. This is done for a wide geographic area with an exploration of the limitations of data coverage over long periods. Throughout, we highlight and explore potential issues around the simple combination of data from the diverse and disparate sources collated here. The datasets are available on the Cefas Data Hub (https://www.cefas.co.uk/cefas-data-hub/). The referenced data sources are listed in Sect. 5.