Expanding understanding of optical variability in Lake Superior with a 4-year dataset

Lake Superior is one of the largest freshwater lakes on our planet, but few optical observations have been made to allow for the development and validation of visible spectral satellite remote sensing products. The dataset described here focuses on coincidently observing inherent and apparent optical properties along with biogeochemical parameters. Specifically, we observe remote sensing reflectance, absorption, scattering, backscattering, attenuation, chlorophyll concentration, and suspended particulate matter over the ice-free months of 2013–2016. The dataset substantially increases the optical knowledge of the lake. In addition to visible spectral satellite algorithm development, the dataset is valuable for characterizing the variable light field, particle, phytoplankton, and colored dissolved organic matter distributions, and helpful in food web and carbon cycle investigations. The compiled data can be freely accessed at https://seabass.gsfc.nasa.gov/archive/URI/Mouw/LakeSuperior/

Meeting Mentoring Needs in Physical Oceanography: An Evaluation of the Impact of MPOWIR

After a decade of program offerings, the Mentoring Physical Oceanography Women to Increase Retention (MPOWIR) program initiated a community-wide survey to (1) assess the impact MPOWIR has had on retention of women in the field of physical oceanography, and (2) gauge where needs are being met and where gaps still exist. To investigate the impact of MPOWIR, we compare MPOWIR participants with male and female cohorts that did not participate in MPOWIR but were at a similar career stage. The survey results indicate MPOWIR has had a substantial impact by aiding individuals in finding and developing mentoring relationships. MPOWIR women are far more likely to have a mentor, and they report having mentors in addition to their advisors, indicating proactive seeking of mentoring relationships. Survey results identify many unmet mentoring needs for both men and women, but MPOWIR participants appear to be receiving more from their mentoring relationships than their non-MPOWIR cohorts. The majority of survey respondents reported there were challenges to achieving career goals, but MPOWIR participants were significantly more likely to have attained their career goals, even though they had received their PhDs more recently. Eighty-eight percent of survey respondents with PhDs were employed in oceanography, irrespective of participation in MPOWIR. MPOWIR women indicate the program has had a large impact on their lives, with the greatest effect being expansion of professional networks and exposure to professional development skills. Senior participants in the program (who serve as mentors to junior scientists) also reported significant professional and personal growth from being involved. Data obtained independently of the survey show that, of the 173 women who have participated in MPOWIR, the recent PhDs are predominantly in postdoctoral positions as expected, but for participants receiving their PhDs prior to 2012, an impressive 80% are in faculty or university/government/nonprofit research positions. Thus, MPOWIR appears to have had an important impact on retention and career satisfaction of its participants

Deriving inherent optical properties from decomposition of hyperspectral non-water absorption

Semi-analytical algorithms (SAAs) developed for multispectral ocean color sensors have benefited from a variety of approaches for retrieving the magnitude and spectral shape of inherent optical properties (IOPs). SAAs generally follow two approaches: 1) simultaneous retrieval of all IOPs, resulting in pre-defined bio-optical models and spectral dependence between IOPs and 2) retrieval of bulk IOPs (absorption and backscattering) first followed by decomposition into separate components, allowing for independent retrievals of some components. Current algorithms used to decompose hyperspectral remotely-sensed reflectance into IOPs follow the first strategy. Here, a spectral deconvolution algorithm for incorporation into the second strategy is presented that decomposes at-w(λ) from in situ measurements and estimates absorption due to phytoplankton (aph(λ)) and colored detrital material (adg(λ)) free of explicit assumptions. The algorithm described here, Derivative Analysis and Iterative Spectral Evaluation of Absorption (DAISEA), provides estimates of aph(λ) and adg(λ) over a spectral range from 350 to 700 nm. Estimated aph(λ) and adg(λ) showed an average normalized root mean square difference of \u3c30% and \u3c20%, respectively, from 350 to 650 nm for the majority of optically distinct environments considered. Estimated Sdg median difference was \u3c20% for all environments considered, while distribution of Sdg uncertainty suggests that biogeochemical variability represented by Sdg can be estimated free of bias. DAISEA results suggest that hyperspectral satellite ocean color data will improve our ability to track biogeochemical processes affiliated with variability in adg(λ) and Sdg free of explicit assumptions

The impact of mpowir a decade of investing in mentoring women in physical oceanography

MPOWIR (Mentoring Physical Oceanography Women to Increase Retention) is a US communityinitiated and community-led mentoring program aimed at improving the retention of women physical oceanographers in academic and/or research positions. This article describes the MPOWIR program elements designed by the US physical oceanography community, quantifies the participation in these programs, describes MPOWIR’s impact to date, and outlines future directions. An examination of surveys to date indicates that MPOWIR, several years after its implementation, is having a positive impact on the retention of junior women in physical oceanography, primarily by giving them a broad professional network and focused mentoring

A Satellite Assessment of Environmental Controls of Phytoplankton Community Size Structure

Phytoplankton play a key role as the base of the marine food web and a crucial component in the Earth\u27s carbon cycle. There have been a few regional studies that have utilized satellite‐estimated phytoplankton functional type products in conjunction with other environmental metrics. Here we expand to a global perspective and ask, what are the physical drivers of phytoplankton composition variability? Using a variety of satellite‐observed ocean color products and physical properties spanning 1997–2015, we characterize spatial and temporal variability in phytoplankton community size structure in relation to satellite‐based physical drivers. We consider the relationships globally and by major thermal regimes (cold and warm), dominant size distribution, and chlorophyll concentration variability. Globally, euphotic depth is the most important parameter driving phytoplankton size variability and also over the majority of the high‐latitude ocean and the central gyres. In all other regions, size variability is driven by a balance of light and mode of nutrient delivery. We investigated the relationship between size composition and chlorophyll concentration and the physical drivers through correlation analysis. Changes in size composition over time are regionally varying and explained by temporal shifts in the varying physical conditions. These changes in phytoplankton size composition and the varying underlying physical drivers will ultimately impact carbon export and food web processes in our changing ocean

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications

Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks

Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human populations, hydroclimatic information can be a potential tool for disease preparedness. In this study, we conducted a multivariate temporal and spatial assessment of 34 climate indices calculated from ground and satellite Earth observations to examine the role of temperature and rainfall extremes on the seasonality of rotavirus transmission in Bangladesh. We extracted rainfall data from the Global Precipitation Measurement and temperature data from the Moderate Resolution Imaging Spectroradiometer sensors to validate the analyses and explore the potential of a satellite‐based seasonal forecasting model. Our analyses found that the number of rainy days and nighttime temperature range from 16°C to 21°C are particularly influential on the winter transmission cycle of rotavirus. The lower number of wet days with suitable cold temperatures for an extended time accelerates the onset and intensity of the outbreaks. Temporal analysis over Dhaka also suggested that water logging during monsoon precipitation influences rotavirus outbreaks during a summer transmission cycle. The proposed model shows lag components, which allowed us to forecast the disease outbreaks 1 to 2 months in advance. The satellite data‐driven forecasts also effectively captured the increased vulnerability of dry‐cold regions of the country, compared to the wet‐warm regions

Global evaluation of particulate organic carbon flux parameterizations and implications for atmospheric pCO\u3csub\u3e2\u3c/sub\u3e

The shunt of photosynthetically derived particulate organic carbon (POC) from the euphotic zone and deep remineralization comprises the basic mechanism of the “biological carbon pump.” POC raining through the “twilight zone” (euphotic depth to 1 km) and “midnight zone” (1 km to 4 km) is remineralized back to inorganic form through respiration. Accurately modeling POC flux is critical for understanding the “biological pump” and its impacts on air‐sea CO2 exchange and, ultimately, long‐term ocean carbon sequestration. Yet commonly used parameterizations have not been tested quantitatively against global data sets using identical modeling frameworks. Here we use a single one‐dimensional physical‐biogeochemical modeling framework to assess three common POC flux parameterizations in capturing POC flux observations from moored sediment traps and thorium‐234 depletion. The exponential decay, Martin curve, and ballast model are compared to data from 11 biogeochemical provinces distributed across the globe. In each province, the model captures satellite‐based estimates of surface primary production within uncertainties. Goodness of fit is measured by how well the simulation captures the observations, quantified by bias and the root‐mean‐square error and displayed using “target diagrams.” Comparisons are presented separately for the twilight zone and midnight zone. We find that the ballast hypothesis shows no improvement over a globally or regionally parameterized Martin curve. For all provinces taken together, Martin\u27s b that best fits the data is [0.70, 0.98]; this finding reduces by at least a factor of 3 previous estimates of potential impacts on atmospheric pCO2 of uncertainty in POC export to a more modest range [−16 ppm, +12 ppm]

Expanding understanding of optical variability in Lake Superior with a 4-year dataset

Lake Superior is one of the largest freshwater lakes on our planet, but few optical observations have been made to allow for the development and validation of visible spectral satellite remote sensing products. The dataset described here focuses on coincidently observing inherent and apparent optical properties along with biogeochemical parameters. Specifically, we observe remote sensing reflectance, absorption, scattering, backscattering, attenuation, chlorophyll concentration, and suspended particulate matter over the ice-free months of 2013-2016. The dataset substantially increases the optical knowledge of the lake. In addition to visible spectral satellite algorithm development, the dataset is valuable for characterizing the variable light field, particle, phytoplankton, and colored dissolved organic matter distributions, and helpful in food web and carbon cycle investigations. The compiled data can be freely accessed at https://seabass.gsfc.nasa.gov/archive/URI/Mouw/LakeSuperior/

Global ocean particulate organic carbon flux merged with satellite parameters

Particulate organic carbon (POC) flux estimated from POC concentration observations from sediment traps and 234 Th are compiled across the global ocean. The compilation includes six time series locations: CARIACO, K2, OSP, BATS, OFP, and HOT. Efficiency of the biological pump of carbon to the deep ocean depends largely on biologically mediated export of carbon from the surface ocean and its remineralization with depth; thus biologically related parameters able to be estimated from satellite observations were merged at the POC observation sites. Satellite parameters include net primary production, percent microplankton, sea surface temperature, photosynthetically active radiation, diffuse attenuation coefficient at 490 nm, euphotic zone depth, and climatological mixed layer depth. Of the observations across the globe, 85% are concentrated in the Northern Hemisphere with 44% of the data record overlapping the satellite record. Time series sites accounted for 36% of the data, while 71% of the data are measured at≥ 500m with the most common deployment depths between 1000 and 1500m. This data set is valuable for investigations of CO2 drawdown, carbon export, remineralization, and sequestration. The compiled data can be freely accessed at doi.org/10.1594/PANGAEA.855600