Thomas Jackson

Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups

Over the past decade, techniques have been presented to derive the community structure of phytoplankton at synoptic scales using satellite ocean-colour data. There is a growing demand from the ecosystem modelling community to use these products for model evaluation and data assimilation. Yet, from the perspective of an ecosystem modeller these products are of limited use unless: (i) the phytoplankton products provided by the remote-sensing community match those required by the ecosystem modellers; and (ii) information on per-pixel uncertainty is provided to evaluate data quality. Using a large dataset collected in the North Atlantic, we re-tune a method to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size (pico- (20μm)). The method is modified to account for the influence of sea surface temperature, also available from satellite data, on model parameters and on the partitioning of microphytoplankton into diatoms and dinoflagellates, such that the phytoplankton groups provided match those simulated in a state of the art marine ecosystem model (the European Regional Seas Ecosystem Model, ERSEM). The method is validated using another dataset, independent of the data used to parameterise the method, of more than 800 satellite and in situ match-ups. Using fuzzy-logic techniques for deriving per-pixel uncertainty, developed within the ESA Ocean Colour Climate Change Initiative (OC-CCI), the match-up dataset is used to derive the root mean square error and the bias between in situ and satellite estimates of the chlorophyll for each phytoplankton group, for 14 different optical water types (OWT). These values are then used with satellite estimates of OWTs to map uncertainty in chlorophyll on a per pixel basis for each phytoplankton group. It is envisaged these satellite products will be useful for those working on the validation of, and assimilation of data into, marine ecosystem models that simulate different phytoplankton groups.

On the Potential of Surfers to Monitor Environmental Indicators in the Coastal Zone

The social and economic benefits of the coastal zone make it one of the most treasured environments on our planet. Yet it is vulnerable to increasing anthropogenic pressure and climate change. Coastal management aims to mitigate these pressures while augmenting the socio-economic benefits the coastal region has to offer. However, coastal management is challenged by inadequate sampling of key environmental indicators, partly due to issues relating to cost of data collection. Here, we investigate the use of recreational surfers as platforms to improve sampling coverage of environmental indicators in the coastal zone. We equipped a recreational surfer, based in the south west United Kingdom (UK), with a temperature sensor and Global Positioning System (GPS) device that they used when surfing for a period of one year (85 surfing sessions). The temperature sensor was used to derive estimates of sea-surface temperature (SST), an important environmental indicator, and the GPS device used to provide sample location and to extract information on surfer performance. SST data acquired by the surfer were compared with data from an oceanographic station in the south west UK and with satellite observations. Our results demonstrate: (i) high-quality SST data can be acquired by surfers using low cost sensors; and (ii) GPS data can provide information on surfing performance that may help motivate data collection by surfers. Using recent estimates of the UK surfing population, and frequency of surfer participation, we speculate around 40 million measurements on environmental indicators per year could be acquired at the UK coastline by surfers. This quantity of data is likely to enhance coastal monitoring and aid UK coastal management. Considering surfing is a world-wide sport, our results have global implications and the approach could be expanded to other popular marine recreational activities for coastal monitoring of environmental indicators.

Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a data

In this work, trend estimates are used as indicators to compare the multi-annual variability of different satellite chlorophyll-a (Chla) data and to assess the fitness-for-purpose of multi-mission Chla products as climate data records (CDR). Under the assumption that single-mission products are free from spurious temporal artifacts and can be used as benchmark time series, multi-mission CDRs should reproduce the main trend patterns observed by single-mission series when computed over their respective periods. This study introduces and applies quantitative metrics to compare trend distributions from different data records. First, contingency matrices compare the trend diagnostics associated with two satellite products when expressed in binary categories such as existence, significance and signs of trends. Contingency matrices can be further summarized by metrics such as Cohens κ index that rates the overall agreement between the two distributions of diagnostics. A more quantitative measure of the discrepancies between trends is provided by the distributions of differences between trend slopes. Thirdly, maps of the level of significance P of a t-test quantifying the degree to which two trend estimates differ provide a statistical, spatially-resolved, evaluation. The proposed methodology is applied to the multi-mission Ocean Colour-Climate Change Initiative (OC-CCI) Chla data. The agreement between trend distributions associated with OC-CCI data and single-mission products usually appears as good as when single-mission products are compared. As the period of analysis is extended beyond 2012 to 2015, the level of agreement tends to be degraded, which might be at least partly due to the aging of the MODIS sensor on-board Aqua. On the other hand, the trends displayed by the OC-CCI series over the short period 2012–2015 are very consistent with those observed with VIIRS. These results overall suggest that the OC-CCI Chla data can be used for multi-annual time series analysis (including trend detection), but with some caution required if recent years are included, particularly in the central tropical Pacific. The study also recalls the challenges associated with creating a multi-mission ocean color data record suitable for climate research.

Impact of El Niño Variability on Oceanic Phytoplankton

Oceanic phytoplankton respond rapidly to a complex spectrum of climate-driven perturbations, confounding attempts to isolate the principal causes of observed changes. A dominant mode of variability in the Earth-climate system is that generated by the El Nino phenomenon. Marked variations are observed in the centroid of anomalous warming in the Equatorial Pacific under El Nino, associated with quite different alterations in environmental and biological properties. Here, using observational and reanalysis datasets, we differentiate the regional physical forcing mechanisms, and compile a global atlas of associated impacts on oceanic phytoplankton caused by two extreme types of El Nino. We find robust evidence that during Eastern Pacific (EP) and Central Pacific (CP) types of El Nino, impacts on phytoplankton can be felt everywhere, but tend to be greatest in the tropics and subtropics, encompassing up to 67% of the total affected areas, with the remaining 33% being areas located in high-latitudes. Our analysis also highlights considerable and sometimes opposing regional effects. During EP El Nino, we estimate decreases of -56 TgC/y in the tropical eastern Pacific Ocean, and -82 TgC/y in the western Indian Ocean, and increase of +13 TgC/y in eastern Indian Ocean, whereas during CP El Nino, we estimate decreases -68 TgC/y in the tropical western Pacific Ocean and -10 TgC/y in the central Atlantic Ocean. We advocate that analysis of the dominant mechanisms forcing the biophysical under El Nino variability may provide a useful guide to improve our understanding of projected changes in the marine ecosystem in a warming climate and support development of adaptation and mitigation plans.

An Exact Solution For Modeling Photoacclimation of the Carbon-to-Chlorophyll Ratio in Phytoplankton

A widely-used theory of the photoacclimatory response in phytoplankton has, until now, been solved using a mathematical approximation that puts strong limitations on its applicability in natural conditions. We report an exact, analytic solution for the chlorophyll-to-carbon ratio as a function of the dimensionless irradiance (irradiance normalized to the photoadaptation parameter for phytoplankton) that is applicable over the full range of irradiance occurring in natural conditions. Application of the exact solution for remote-sensing of phytoplankton carbon at large scales is illustrated using satellite-derived chlorophyll, surface irradiance data and mean photosynthesis-irradiance parameters for the season assigned to every pixel on the basis of ecological provinces. When the exact solution was compared with the approximate one at the global scale, for a particular month (May 2010), the results differed by at least 15% for about 70% of Northern Hemisphere pixels (analysis was performed during the northern hemisphere Spring bloom period) and by more than 50% for 24% percent of Northern Hemisphere pixels (approximate solution overestimates the carbon-to-chlorophyll ratio compared with the exact solution). Generally, the divergence between the two solutions increases with increasing available light, raising the question of the appropriate timescale for specifying the forcing irradiance in ecosystem models.

Short-Term Space-Use Patterns of Translocated Mojave Desert Tortoise in Southern California.

Increasingly, renewable energy comprises a larger share of global energy production. Across the western United States, public lands are being developed to support renewable energy production. Where there are conflicts with threatened or endangered species, translocation can be used in an attempt to mitigate negative effects. For the threatened Mojave desert tortoise (Gopherus agassizii), we sought to compare habitat- and space-use patterns between short-distance translocated, resident, and control groups. We tested for differences in home range size based on utilization distributions and used linear mixed-effects models to compare space-use intensity, while controlling for demographic and environmental variables. In addition, we examined mean movement distances as well as home range overlap between years and for male and female tortoises in each study group. During the first active season post-translocation, home range size was greater and space-use intensity was lower for translocated tortoises than resident and control groups. These patterns were not present in the second season. In both years, there was no difference in home range size or space-use intensity between control and resident groups. Translocation typically resulted in one active season of questing followed by a second active season characterized by space-use patterns that were indistinguishable from control tortoises. Across both years, the number of times a tortoise was found in a burrow was positively related to greater space-use intensity. Minimizing the time required for translocated tortoises to exhibit patterns similar to non-translocated individuals may have strong implications for conservation by reducing exposure to adverse environmental conditions and predation. With ongoing development, our results can be used to guide future efforts aimed at understanding how translocation strategies influence patterns of animal space use.

Validation and Intercomparison of Ocean Color Algorithms for Estimating Particulate Organic Carbon in the Oceans

Particulate Organic Carbon (POC) plays a vital role in the ocean carbon cycle. Though relatively small compared with other carbon pools, the POC pool is responsible for large fluxes and is linked to many important ocean biogeochemical processes. The satellite ocean-colour signal is influenced by particle composition, size, and concentration and provides a way to observe variability in the POC pool at a range of temporal and spatial scales. To provide accurate estimates of POC concentration from satellite ocean colour data requires algorithms that are well validated, with uncertainties characterised. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean colour data provided by the Ocean Colour Climate Change Initiative (OC-CCI) and validated against the largest database of

Expanding Aquatic Observations through Recreation

\textit{in situ}

Satellite Radiation Products for Ocean Biology and Biogeochemistry: Needs, State-of-the-Art, Gaps, Development Priorities, and Opportunities

POC measurements currently available. The results of this validation exercise indicate satisfactory levels of performance from several algorithms (highest performance was observed from the algorithms of \cite{stramski2008} and \cite{loisel2002}) and uncertainties that are within the requirements of the user community. Estimates of the standing stock of the POC can be made by applying these algorithms, and yield an estimated mixed-layer integrated global stock of POC between 0.77 and 1.3 Pg C of carbon. Performance of the algorithms vary regionally, suggesting that blending of region-specific algorithms may provide the best way forward for generating global POC products.

Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

Accurate observations of the Earth system are required to understand how our planet is changing and to help manage its resources. The aquatic environment−including lakes, rivers, wetlands, estuaries, coastal and open oceans−is a fundamental component of the Earth system controlling key physical, biological, and chemical processes that allow life to flourish. Yet, this environment is critically undersampled in both time and space. New and cost-effective sampling solutions are urgently needed. Here, we highlight the potential to improve aquatic sampling by tapping into recreation. We draw attention to the vast number of participants that engage in aquatic recreational activities and argue, based on current technological developments and recent research, that the time is right to employ recreational citizens to improve large-scale aquatic sampling efforts. We discuss the challenges that need to be addressed for this strategy to be successful (e.g. sensor package design, data quality, and citizen motivation), the steps needed to realize its potential, and additional societal benefits that arise when engaging citizens in scientific sampling.