Data Analysis Project Research Articles

Remote Sensing of Global Sea Surface pH Based on Massive Underway Data and Machine Learning

Seawater pH is a direct proxy of ocean acidification, and monitoring the global pH distribution and long-term series changes is critical to understanding the changes and responses of the marine ecology and environment under climate change. Owing to the lack of sufficient global-scale pH data and the complex relationship between seawater pH and related environmental variables, generating time-series products of satellite-derived global sea surface pH poses a great challenge. In this study, we solved the problem of the lack of sufficient data for pH algorithm development by using the massive underway sea surface carbon dioxide partial pressure (pCO2) dataset to structure a large data volume of near in situ pH based on carbonate calculation between underway pCO2 and calculated total alkalinity from sea surface salinity and relevant parameters. The remote sensing inversion model of pH was then constructed through this massive pH training dataset and machine learning methods. After several tests of machine learning methods and groups of input parameters, we chose the random forest model with longitude, latitude, sea surface temperature (SST), chlorophyll a (Chla), and Mixed layer depth (MLD) as model inputs with the best performance of correlation coefficient (R2 = 0.96) and root mean squared error (RMSE = 0.008) in the training set and R2 = 0.83 (RMSE = 0.017) in the testing set. The sensitivity analysis of the error variation induced by the uncertainty of SST and Chla (SST ≤ ±0.5 °C and Chla ≤ ±20%; RMSESST ≤ 0.011 and RMSEChla ≤ 0.009) indicated that our sea surface pH model had good robustness. Monthly average global sea surface pH products from 2004 to 2019 with a spatial resolution of 0.25° × 0.25° were produced based on the satellite-derived SST and Chla products and modeled MLD dataset. The pH model and products were validated using another independent station-measured pH dataset from the Global Ocean Data Analysis Project (GLODAP), showing good performance. With the time-series pH products, refined interannual variability and seasonal variability were presented, and trends of pH decline were found globally. Our study provides a new method of directly using remote sensing to invert pH instead of indirect calculation based on the construction of massive underway calculated pH data, which would be made useful by comparing it with satellite-derived pCO2 products to understand the carbonate system change and the ocean ecological environments responding to the global change.

Relative Importance of Physical Activity and Body Composition on Insulin Resistance in Older Adult Population

This secondary data analysis project examined the relative importance of physical activity (PA) and body composition on homeostatic model assessment of insulin resistance (HOMA-IR) in older adults and investigated possible racial and/or ethnic differences in these relationships. The analysis included 717 participants from the 2017-2018 National Health and Nutrition Examination Survey study who were 55 years of age or older without diabetes. Results show that HOMA-IR increased with increasing levels of recreational PA, with 1 exception: HOMA-IR decreased with increasing levels of recreational PA in individuals with lower body mass index levels. Racial/ethnic differences in these relationships were identified.

Impact of Recreational Physical Activity And Body Composition On Insulin Resistance In Older Adult Population

Social support in old age has been linked to psychological wellbeing outcomes, such as depressive symptoms. However, insufficient attention has been paid to implications of social support for different domains of psychological wellbeing. In this study, we explored these associations among 797 older adults (mean age = 78.61 years) living in a retirement community in Florida from the ECRC study. Our findings show that measures of social support and connectedness have varying influences on psychological wellbeing. Loneliness was associated with lower life satisfaction (b=- -1.12, p<0.001) and higher depressive symptoms (b=3.52, p<0.001). Higher self-rated social support was associated with higher life satisfaction (b= 1.66, p<0.001) but did not predict depressive symptoms. Depressive symptoms, however, were significantly higher (b=-1.45) among individuals who reported that they don’t have anyone who they can turn to if they feel lonely and want to talk. Feeling lonely also predicted lower positive affects among these older adults (b=-0.65, p<0.001). Similarly, loneliness also predicted higher negative affects (b=1.28, p<0.001). Negative affects were also significantly higher among women (b=-1.15, p<0.001) but lower among those who were living alone (b=-1.06, p<0.001). Overall, our findings underscore the importance of social support and connectedness for psychological wellbeing in later life. This finding is consistent with prior research demonstrating significance of social support in later life for the overall psychological wellbeing of the older adults.

An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2021

Abstract. The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical bottle data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2021 is an update of the previous version, GLODAPv2.2020 (Olsen et al., 2020). The major changes are as follows: data from 43 new cruises were added, data coverage was extended until 2020, all data with missing temperatures were removed, and a digital object identifier (DOI) was included for each cruise in the product files. In addition, a number of minor corrections to GLODAPv2.2020 data were performed. GLODAPv2.2021 includes measurements from more than 1.3 million water samples from the global oceans collected on 989 cruises. The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but updated to World Ocean Circulation Experiment (WOCE) exchange format and (ii) as a merged data product with adjustments applied to minimize bias. For this annual update, adjustments for the 43 new cruises were derived by comparing those data with the data from the 946 quality controlled cruises in the GLODAPv2.2020 data product using crossover analysis. Comparisons to estimates of nutrients and ocean CO2 chemistry based on empirical algorithms provided additional context for adjustment decisions in this version. The adjustments are intended to remove potential biases from errors related to measurement, calibration, and data handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent with to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete CO2 fugacity (fCO2), were not subjected to bias comparison or adjustments. The original data, their documentation, and DOI codes are available at the Ocean Carbon Data System of NOAA NCEI (https://www.ncei.noaa.gov/access/ocean-carbon-data-system/oceans/GLODAPv2_2021/, last access: 7 July 2021). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under https://doi.org/10.25921/ttgq-n825 (Lauvset et al., 2021). These bias-adjusted product files also include significant ancillary and approximated data and can be accessed via https://www.glodap.info (last access: 29 June 2021). These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2021 methods and provides a broad overview of the secondary quality control procedures and results.

Promoting Organizational Justice In Cross-Cultural Data Collection, Analysis, And Interpretation: Towards An Emerging Conceptual Model

Human performance improvement (HPI) practitioners, including Instructional designers (IDs), typically strive to inform inclusive, equitable, and socially just organizational development, workplace learning, and performance improvement decisions when working across cultures. The intention behind these types of decisions is to avoid causing harm to organizational members and the larger societies they serve. One way researchers, IDs, and HPI practitioners can support inclusive, equitable, and socially just organizational decision-making is by operating under organizational justice theory. In this work, we describe how organizational justice theory can be applied by practitioners in cross-cultural data collection, analysis, and interpretation project work.

New and updated global empirical seawater property estimation routines

AbstractWe introduce three new Empirical Seawater Property Estimation Routines (ESPERs) capable of predicting seawater phosphate, nitrate, silicate, oxygen, total titration seawater alkalinity, total hydrogen scale pH (pHT), and total dissolved inorganic carbon (DIC) from up to 16 combinations of seawater property measurements. The routines generate estimates from neural networks (ESPER_NN), locally interpolated regressions (ESPER_LIR), or both (ESPER_Mixed). They require a salinity value and coordinate information, and benefit from additional seawater measurements if available. These routines are intended for seawater property measurement quality control and quality assessment, generating estimates for calculations that require approximate values, original science, and producing biogeochemical property context from a data set. Relative to earlier LIR routines, the updates expand their functionality, including new estimated properties and combinations of predictors, a larger training data product including new cruises from the 2020 Global Data Analysis Project data product release, and the implementation of a first‐principles approach for quantifying the impacts of anthropogenic carbon on DIC and pHT. We show that the new routines perform at least as well as existing routines, and, in some cases, outperform existing approaches, even when limited to the same training data. Given that additional training data has been incorporated into these updated routines, these updates should be considered an improvement over earlier versions. The routines are intended for all ocean depths for the interval from 1980 to ~2030 c.e., and we caution against using the routines to directly quantify surface ocean seasonality or make more distant predictions of DIC or pHT.

Determining the best set of molecular descriptors for a Toxicity classification problem

The safety norms for drug design are very strict with at least three stages of trials. One test, early on in the trials, is about the cardiotoxicity of the molecules, that is, whether the compound blocks any heart channel. Chemical libraries contain millions of compounds. Accuratea prioriandin silicoclassification of non-blocking molecules, can reduce the screening for an effective drug, by half. The compound has to be checked for other risk factors alongside its therapeutic effect; these tests can also be done using a computer. Actual screening in a research laboratory is very expensive and time consuming. To enable the computer modelling, the molecules are provided in Simplified Molecular Input Line Entry (SMILE) format. In this study, they have been decoded using the chem-informatics development kit written in the Java language. The kit is accessed in the R statistical software environment through therJavapackage, that is further wrapped in thercdkpackage. The strings representing the molecular structure, are parsed by thercdkfunctions, to provide structure-activity descriptors, that are known, to be good predictors of biological activity. These descriptors along with the known blocking behaviour of the molecule, constitute the input to the Decision Tree, Random Forest, Gradient Boosting, Support-Vector-Machine, Logistic Regression, and Artificial Neural Network algorithms. This paper reports the results of the data analysis project with shareware tools, to determine the best subset of molecular descriptors, from the large set that is available.

Individual Differences and Features of Self-reported Memory Lapses as Risk Factors for Alzheimer Disease Among Adults Aged 50 Years and Older: Protocol for a Coordinated Analysis Across Two Longitudinal Data Sets.

BackgroundIncreasing evidence has promoted the clinical utility of self-reported memory problems for detecting early impairment associated with Alzheimer disease (AD). However, previous studies investigating memory problems often conflated the types of problems (ie, retrospective and prospective) with their features (ie, frequency and consequences). This bias limits the specificity of traditional measures of memory problems and minimizes their ability to detect differential trajectories associated with cognitive decline. In this study, we use a novel measure of self-reported memory problems that uses daily reports of memory lapses to disentangle types from features for analyzing the impact of each dimension in two longitudinal data sets. Furthermore, this study explores the individual difference factors of age and gender as potential moderators of the relationships between self-reported memory lapses and objective cognitive decline.ObjectiveThe aim of this study is to describe the protocol for a secondary data analysis project that explores the relationship between experiences of daily memory lapses and their associations with cognitive decline in middle-aged and older adults.MethodsThis study uses multilevel, coordinated analyses across two measurement burst data sets to examine the links between features and consequences of memory lapses (retrospective and prospective) and their association with objective cognitive decline. This study’s sample (N=392; aged 50-85 years; n=254, 64.8% women) is drawn from two ongoing, nationally funded research studies: The Effects of Stress on Cognitive Aging, Physiology, and Emotion study and the Einstein Aging Study. Both studies assess the daily experience of memory lapses, including the type as well as the emotional and functional outcomes, and objective measures of cognition, such as processing speed and episodic memory. We will use multilevel modeling to test our conceptual model demonstrating that differences in frequency and types of memory lapses show differential trends in their relationships with cognitive decline and that these relationships vary by the age and gender of participants.ResultsThis project was funded in August 2019. The approval for secondary data analysis was given by the institutional review board in February 2020. Data analysis for this project has not yet started.ConclusionsThe early and accurate identification of individuals most at risk for cognitive decline is of paramount importance. Previous research exploring self-reported memory problems and AD is promising; however, limitations in measurement may explain previous reports of inconsistences. This study addresses these concerns by examining daily reports of memory lapses, how these vary by age and gender, and their relationship with objective cognitive performance. Overall, this study aims to identify the key features of daily memory lapses and the differential trajectories that best predict cognitive decline to help inform future AD risk screening tools.International Registered Report Identifier (IRRID)DERR1-10.2196/25233

Easyreporting simplifies the implementation of Reproducible Research layers in R software.

During last years "irreproducibility" became a general problem in omics data analysis due to the use of sophisticated and poorly described computational procedures. For avoiding misleading results, it is necessary to inspect and reproduce the entire data analysis as a unified product. Reproducible Research (RR) provides general guidelines for public access to the analytic data and related analysis code combined with natural language documentation, allowing third-parties to reproduce the findings. We developed easyreporting, a novel R/Bioconductor package, to facilitate the implementation of an RR layer inside reports/tools. We describe the main functionalities and illustrate the organization of an analysis report using a typical case study concerning the analysis of RNA-seq data. Then, we show how to use easyreporting in other projects to trace R functions automatically. This latter feature helps developers to implement procedures that automatically keep track of the analysis steps. Easyreporting can be useful in supporting the reproducibility of any data analysis project and shows great advantages for the implementation of R packages and GUIs. It turns out to be very helpful in bioinformatics, where the complexity of the analyses makes it extremely difficult to trace all the steps and parameters used in the study.

Public Expenditures in the Selected Economic Industries: Policy Implications for the Period of the COVID-19 Pandemic Crisis

The paper presents the constrained multiplier methodology in semi-input-output analysis and quantifies the effect of the government support to the industries mostly affected by COVID-19 pandemic in Slovakia. In contrary to traditional input-output analysis, the methodology in this paper allows to drop assumptions of the unlimited industries’ supply. The analysis is conducted for 57 economic industries, households, governments, and the foreign using the Global Trade Analysis Project data. The analysis not only identifythat the most stricken industry is the recreation and other services together with transport industry, it also enumerates the effects of the government subsidy. Each Euro of the government support to the Recreation sector increases the Gross Domestic Product by 1.21 Euro, to the Transport industry by 0.93 Euro, while to the automotive industry included to this analysis by 0.54 Euro. The government subsidy should aim the Recreation industry not only for the highest decline of sales during the pandemic, also for the highest number of firms at risk and because of very effective return of the government support which almost triples the total output and generates more than one Euro of the GDP.

IMDAA: High Resolution Satellite-era Reanalysis for the Indian Monsoon Region

AbstractA high resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12 km resolution reanalysis covering the satellite-era from 1979 to 2018 using 4D-Var data assimilation method and the UK Met Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some were not used in any previous reanalyses. Various aspects of this reanalysis, like quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semi-permanent summer monsoon features (e.g., Low-level Jet and Tropical Easterly Jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, inter-annual, and intra-seasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

Factors Controlling Coral Skeletal U/Ca Ratios with Implications for their Use as a Proxy for Past Ocean Conditions

Seawater temperature, salinity and carbonate chemistry have been shown to influence the uranium/calcium (U/Ca) ratios of scleractinian coral skeletons. This apparent sensitivity of U/Ca to multiple environmental parameters calls into question whether there is one environmental variable that most strongly controls coral U/Ca, and whether U/Ca can be straightforwardly applied as a paleoenvironmental proxy due to the tendency of environmental variables to covary in space and time. In this study, uranium concentration data from an existing compilation of tropical scleractinian coral U-series measurements is paired with environmental data from the World Ocean Atlas (WOA) and the Global Ocean Data Analysis Project (GLODAP) to examine the sensitivity of coral skeletal U/Ca to multiple seawater properties including temperature, salinity, pH, and saturation state. First, univariate linear regressions and multiple linear regressions were used to compare relationships between uranium and environmental parameters in the dataset with relationships observed in previous studies. Next, principal component analysis and regularized regression were used to identify the most likely predictors of coral U/Ca in order to create a multiple linear regression model. Results indicate that pH, Ω, alkalinity, and temperature are all significant predictors of uranium concentrations in coral. The magnitude and strength of relationships between U/Ca and environmental variables also differ across different genera. Seawater properties with strong correlations and small ranges make interpretation of these results difficult. However, results of these analyses indicate that U/Ca is dependent on multiple environmental parameters and that previously developed univariate regressions may be insufficient to characterize the full range of variables that influence coral [238U]. KEYWORDS: Coral; Paleoceanography; Proxy Calibration; Uranium; Multiple Linear Regression; Regularized Regression; Environmental Change; Oceanographic Databases

Water masses in the Atlantic Ocean: characteristics and distributions

Abstract. A large number of water masses are presented in the Atlantic Ocean, and knowledge of their distributions and properties is important for understanding and monitoring of a range of oceanographic phenomena. The characteristics and distributions of water masses in biogeochemical space are useful for, in particular, chemical and biological oceanography to understand the origin and mixing history of water samples. Here, we define the characteristics of the major water masses in the Atlantic Ocean as source water types (SWTs) from their formation areas, and map out their distributions. The SWTs are described by six properties taken from the biased-adjusted Global Ocean Data Analysis Project version 2 (GLODAPv2) data product, including both conservative (conservative temperature and absolute salinity) and non-conservative (oxygen, silicate, phosphate and nitrate) properties. The distributions of these water masses are investigated with the use of the optimum multi-parameter (OMP) method and mapped out. The Atlantic Ocean is divided into four vertical layers by distinct neutral densities and four zonal layers to guide the identification and characterization. The water masses in the upper layer originate from wintertime subduction and are defined as central waters. Below the upper layer, the intermediate layer consists of three main water masses: Antarctic Intermediate Water (AAIW), Subarctic Intermediate Water (SAIW) and Mediterranean Water (MW). The North Atlantic Deep Water (NADW, divided into its upper and lower components) is the dominating water mass in the deep and overflow layer. The origin of both the upper and lower NADW is the Labrador Sea Water (LSW), the Iceland–Scotland Overflow Water (ISOW) and the Denmark Strait Overflow Water (DSOW). The Antarctic Bottom Water (AABW) is the only natural water mass in the bottom layer, and this water mass is redefined as Northeast Atlantic Bottom Water (NEABW) in the north of the Equator due to the change of key properties, especially silicate. Similar with NADW, two additional water masses, Circumpolar Deep Water (CDW) and Weddell Sea Bottom Water (WSBW), are defined in the Weddell Sea region in order to understand the origin of AABW.

OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification

Abstract. Ocean acidification has profoundly altered the ocean's carbonate chemistry since preindustrial times, with potentially serious consequences for marine life. Yet, no long-term, global observation-based data set exists that allows us to study changes in ocean acidification for all carbonate system parameters over the last few decades. Here, we fill this gap and present a methodologically consistent global data set of all relevant surface ocean parameters, i.e., dissolved inorganic carbon (DIC), total alkalinity (TA), partial pressure of CO2 (pCO2), pH, and the saturation state with respect to mineral CaCO3 (Ω) at a monthly resolution over the period 1985 through 2018 at a spatial resolution of 1∘×1∘. This data set, named OceanSODA-ETHZ, was created by extrapolating in time and space the surface ocean observations of pCO2 (from the Surface Ocean CO2 Atlas, SOCAT) and total alkalinity (TA; from the Global Ocean Data Analysis Project, GLODAP) using the newly developed Geospatial Random Cluster Ensemble Regression (GRaCER) method (code available at https://doi.org/10.5281/zenodo.4455354, Gregor, 2021). This method is based on a two-step (cluster-regression) approach but extends it by considering an ensemble of such cluster regressions, leading to improved robustness. Surface ocean DIC, pH, and Ω were then computed from the globally mapped pCO2 and TA using the thermodynamic equations of the carbonate system. For the open ocean, the cluster-regression method estimates pCO2 and TA with global near-zero biases and root mean squared errors of 12 µatm and 13 µmol kg−1, respectively. Taking into account also the measurement and representation errors, the total uncertainty increases to 14 µatm and 21 µmol kg−1, respectively. We assess the fidelity of the computed parameters by comparing them to direct observations from GLODAP, finding surface ocean pH and DIC global biases of near zero, as well as root mean squared errors of 0.023 and 16 µmol kg−1, respectively. These uncertainties are very comparable to those expected by propagating the total uncertainty from pCO2 and TA through the thermodynamic computations, indicating a robust and conservative assessment of the uncertainties. We illustrate the potential of this new data set by analyzing the climatological mean seasonal cycles of the different parameters of the surface ocean carbonate system, highlighting their commonalities and differences. Further, this data set provides a novel constraint on the global- and basin-scale trends in ocean acidification for all parameters. Concretely, we find for the period 1990 through 2018 global mean trends of 8.6 ± 0.1 µmol kg−1 per decade for DIC, −0.016 ± 0.000 per decade for pH, 16.5 ± 0.1 µatm per decade for pCO2, and −0.07 ± 0.00 per decade for Ω. The OceanSODA-ETHZ data can be downloaded from https://doi.org/10.25921/m5wx-ja34 (Gregor and Gruber, 2020).

CYCLOCIM: A 4-D variational assimilation system for the climatological mean seasonal cycle of the ocean circulation

We describe a new 4-D variational assimilation system, called CYCLOCIM, to estimate the climatological seasonal cycle of the residual mean ocean circulation. CYCLOCIM assimilates monthly mean potential temperature and salinity data from the World Ocean Atlas, and CFC-11, CFC-12 and natural radiocarbon measurements for the deep ocean from the Global Data Analysis Project, Version 2. CYCLOCIM’s control parameters include: (i) a seasonally varying 3-D field of unresolved eddy-stress divergences that appear in the horizontal momentum equations, (ii) seasonally varying 2-D correction fields for the surface heat and freshwater fluxes, and (iii) a constant scaling factor for the air–sea flux of CFCs. The influence of initial conditions on a fully spun-up ocean model vanishes. Thus, unlike other 4-D variational assimilation systems, CYCLOCIM does not include initial conditions as control parameters. A Bayesian procedure is used to formulate the inverse problem, which is solved by finding the maximum of the posterior probability distribution. The optimization process used to find the maximum includes a forward simulation to calculate the flow velocities and tracer distributions followed by a backward (“adjoint” ) simulation to compute the gradient of the posterior. A quasi-Newton search algorithm is used to find the set of parameters to maximize the posterior probability. We find that by resolving the seasonal cycle the model is able to better fit the observations in the upper ocean compared to a previous steady-state version of the model. The main output from CYCLOCIM is a set of 12 data-constrained monthly tracer transport matrices that will provide a useful circulation model for global marine biogeochemical cycle studies.

Promoting Organizational Justice In Cross-Cultural Data Collection, Analysis, And Interpretation: Towards An Emerging Conceptual Model

Human performance improvement (HPI) practitioners, including Instructional designers (IDs), typically strive to inform inclusive, equitable, and socially just organizational development, workplace learning, and performance improvement decisions when working across cultures. The intention behind these types of decisions is to avoid causing harm to organizational members and the larger societies they serve. One way researchers, IDs, and HPI practitioners can support inclusive, equitable, and socially just organizational decision-making is by operating under organizational justice theory. In this work, we describe how organizational justice theory can be applied by practitioners in cross-cultural data collection, analysis, and interpretation project work.

An updated version of the global interior ocean biogeochemical data product, GLODAPv2.2020

Abstract. The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2020 is an update of the previous version, GLODAPv2.2019. The major changes are data from 106 new cruises added, extension of time coverage to 2019, and the inclusion of available (also for historical cruises) discrete fugacity of CO2 (fCO2) values in the merged product files. GLODAPv2.2020 now includes measurements from more than 1.2 million water samples from the global oceans collected on 946 cruises. The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4) have undergone extensive quality control with a focus on systematic evaluation of bias. The data are available in two formats: (i) as submitted by the data originator but updated to WOCE exchange format and (ii) as a merged data product with adjustments applied to minimize bias. These adjustments were derived by comparing the data from the 106 new cruises with the data from the 840 quality-controlled cruises of the GLODAPv2.2019 data product using crossover analysis. Comparisons to empirical algorithm estimates provided additional context for adjustment decisions; this is new to this version. The adjustments are intended to remove potential biases from errors related to measurement, calibration, and data-handling practices without removing known or likely time trends or variations in the variables evaluated. The compiled and adjusted data product is believed to be consistent to better than 0.005 in salinity, 1 % in oxygen, 2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µmol kg−1 in dissolved inorganic carbon, 4 µmol kg−1 in total alkalinity, 0.01–0.02 in pH (depending on region), and 5 % in the halogenated transient tracers. The other variables included in the compilation, such as isotopic tracers and discrete fCO2, were not subjected to bias comparison or adjustments. The original data and their documentation and DOI codes are available at the Ocean Carbon Data System of NOAA NCEI (https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2020/, last access: 20 June 2020). This site also provides access to the merged data product, which is provided as a single global file and as four regional ones – the Arctic, Atlantic, Indian, and Pacific oceans – under https://doi.org/10.25921/2c8h-sa89 (Olsen et al., 2020). These bias-adjusted product files also include significant ancillary and approximated data. These were obtained by interpolation of, or calculation from, measured data. This living data update documents the GLODAPv2.2020 methods and provides a broad overview of the secondary quality control procedures and results.

Using Distributed Practice to Improve Students’ Attitudes and Performance in Statistics

Background: Research shows distributed practice enhances learning and skill development, but less is known about the effect on perceptions and attitudes toward the material being learned. Objective: This study examined whether distributed practice could improve performance and attitudes in statistics, a subject that students report finding unpleasant and anxiety-provoking. Method: This quasi-experiment compared statistics students who received distributed practice with Excel throughout the semester to a control group without distributed practice. At the end of the semester, all students completed a major data analysis project with Excel and a self-report measure of their perceptions and attitudes toward the class and statistics. Results: Significant results suggest students who received distributed practice: earned higher project grades; liked statistics more; and perceived the class to be more effective for knowledge and skill development, even though they found statistics to be more difficult than the control group. Conclusion: This study suggests distributed practice helps improve students’ performance and attitudes toward statistics, even though they think it is difficult. Teaching Implications: In addition to improving performance in challenging subjects, distributed practice may be used to help students appreciate and feel more favorably about classes they find difficult.

A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach

Abstract. Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than 2 centuries. As the ocean stores most of the carbon on our planet, there is an important task in unraveling the natural and anthropogenic processes that drive the carbon cycle at different spatial and temporal scales. We contribute to this by designing a global monthly climatology of total dissolved inorganic carbon (TCO2), which offers a robust basis in carbon cycle modeling but also for other studies related to this cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured to extract from the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) and the Lamont–Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set of variables related to the former's variability. The global root mean square error (RMSE) of mapping TCO2 is relatively low for the two datasets (GLODAPv2.2019: 7.2 µmol kg−1; LDEO: 11.4 µmol kg−1) and also for independent data, suggesting that the network does not overfit possible errors in data. The ability of NNGv2LDEO to capture the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in 10 time series stations spread over different regions of the ocean (RMSE: 3.6 to 13.2 µmol kg−1). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity, and oxygen from the World Ocean Atlas 2013 and phosphate, nitrate, and silicate computed from a neural network fed with the previous fields. The resolution is 1∘×1∘ in the horizontal, 102 depth levels (0–5500 m), and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution, and it is centered around the year 1995. The uncertainty of the climatology is low when compared with climatological values derived from measured TCO2 in the largest time series stations. Furthermore, a computed climatology of partial pressure of CO2 (pCO2) from a previous climatology of total alkalinity and the present one of TCO2 supports the robustness of this product through the good correlation with a widely used pCO2 climatology (Landschützer et al., 2017). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/10551, Broullón et al., 2020).

Current estimates of K&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt;* and K&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;* appear inconsistent with measured CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; system parameters in cold oceanic regions

Abstract. Seawater absorption of anthropogenic atmospheric carbon dioxide (CO2) has led to a range of changes in carbonate chemistry, collectively referred to as ocean acidification. Stoichiometric dissociation constants used to convert measured carbonate system variables (pH, pCO2, dissolved inorganic carbon, total alkalinity) into globally comparable parameters are crucial for accurately quantifying these changes. The temperature and salinity coefficients of these constants have generally been experimentally derived under controlled laboratory conditions. Here, we use field measurements of carbonate system variables taken from the Global Ocean Data Analysis Project version 2 and the Surface Ocean CO2 Atlas data products to evaluate the temperature dependence of the carbonic acid stoichiometric dissociation constants. By applying a novel iterative procedure to a large dataset of 948 surface-water, quality-controlled samples where four carbonate system variables were independently measured, we show that the set of equations published by Lueker et al. (2000), currently preferred by the ocean acidification community, overestimates the stoichiometric dissociation constants at temperatures below about 8 ∘C. We apply these newly derived temperature coefficients to high-latitude Argo float and cruise data to quantify the effects on surface-water pCO2 and calcite saturation states. These findings highlight the critical implications of uncertainty in stoichiometric dissociation constants for future projections of ocean acidification in polar regions and the need to improve knowledge of what causes the CO2 system inconsistencies in cold waters.