An eleven year record of XCO₂ estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over it's first eleven years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inverse modeling systems (models). In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million (M) soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2M out of 37M) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (< 1M out of 24M). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (one sigma) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Similarly, global mean biases are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of order 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the data. This dataset should be especially useful for studies of carbon cycle phenomena that span a full decade or more, and may serve as a useful complement to the shorter OCO-2 v10 dataset, which begins in September 2014.

Integrating Data to Find Links Between Environment and Health

Several obstacles stand in the way of integrating social, health, and Earth science data for vital geohealth studies, but there are tools and opportunities to overcome these obstacles.

Job analyses of Earth science data librarians and data managers

This study’s purpose is to capture the skills of Earth science data managers and librarians through interviews with current job holders. Job analysis interviews were conducted of fourteen participants –six librarians and eight data managers—to assess the types and frequencies of job tasks. Participants identified tasks related to communication, including collaboration, teaching, and project management activities. Data specific tasks included data discovery, processing, and curation, which require an understanding of the data, technology, and information infrastructures to support data use, re-use, and preservation. Most respondents had formal science education and six had a master’s degree in Library and Information Sciences. Most of the knowledge, skills, and abilities for these workers were acquired through on-the-job experience, but future professionals in these careers may benefit from tailored education informed through job analyses.

Ordination analyses in sedimentology, geochemistry and paleoenvironment - current trends and recommendations

&lt;p&gt;Ordination is a family of multivariate exploratory data analysis methods. With the advent of high-throughput data acquisition protocols, community databases, and multiproxy studies, the use of ordination in Earth sciences has snowballed. As data management and analytical tools expand, this growing body of knowledge opens new possibilities of meta-analyses and data-mining across studies. This requires the analyses to be chosen adequately to the character of Earth science data, including pre-treatment consistent with the precision and accuracy of the variables, as well as appropriate documentation. To investigate the current situation in Earth sciences, we surveyed 174 ordination analyses in 163 publications in the fields of geochemistry, sedimentology and palaeoenvironmental reconstruction and monitoring. We focussed on studies using Principal Component Analysis (PCA), Non-Metric Multidimensional Scaling (NMDS) and Detrended Correspondence Analysis (DCA).&lt;/p&gt;&lt;p&gt;PCA was the most ubiquitous type of analysis (84%), with the other two accounting for ca. 12% each. Of 128 uses of PCA, only 5 included a test for normality, and most of these cases were not applied or documented correctly. Common problems include: (1) not providing information on the dimensions of the analysed matrix (16% cases); (2) using a larger number of variables than observations (24 cases); (3) not documenting the distance metric used in NMDS (55% cases); and (4) lack of information on the software used (38% cases). The majority (53%) of surveyed studies did not provide the data used for analysis at all and a further 35% provided data sets in a format that does not allow immediate, error-free reuse, e.g. as data table directly in the article text or in PDF appendix. The &amp;#8220;golden standard&amp;#8221; of placing a curated data set in an open access repository was followed only by 6 (3%) of the analyses. Among analyses which reported using code-based statistical environments such as R Software, SAS or SPSS, none provided the code that would allow reproducing the analyses.&lt;/p&gt;&lt;p&gt;Geochemical and Earth science data sets require expert knowledge which should support analytical decisions and interpretations. Data analysis skills attract students to Earth sciences study programmes and offer a viable research alternative when field- or lab-based work is limited. However, many study curricula and publishing process have not yet endorsed this methodological progress, leading to situations where mentors, reviewers and editors cannot offer quality assurance for the use of ordination methods. We provide a review of solutions and annotated R Software code for PCA, NMDA and DCA of geochemical data sets in the freeware R Software environment, encouraging the community to reuse and further develop a reproducible ordination workflow.&lt;/p&gt;

Science storms the cloud

&lt;p&gt;The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte datasets. Datasets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence (AI/ML) are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to the data without specialized cloud computing knowledge. Practically, for scientists, the effect of these changes is to vastly shrink the amount of time spent acquiring and processing data, freeing up more time for science. This shift in paradigm is lowering the threshold for entry, expanding the science community, and increasing opportunities for collaboration, while promoting scientific innovation, transparency, and reproducibility. These changes are increasing the speed of science, broadening the possibilities of what questions science can answer, and expanding participation in science.&lt;/p&gt;

Enhancing Food Security Through Earth Science Data

When most agriculture in the world is rural, getting crucial geoscience information to farmers is a technical challenge that a few organizations are just starting to figure out.