Snow cover variability in a forest ecotone of the Oregon Cascades via MODIS Terra products

Snowcover pattern and persistence have important implications for planetary energy balance, climate sensitivity to forcings, and vegetation structure, function, and composition. Variability in snow cover within mountainous regions of the Pacific Northwest, USA is attributable to a combination of anthropogenic climate change and climate oscillations. However, snowcovered areas can be heterogeneous and patchy, requiring more detailed mapping of snow trends to understand their potential influences on montane forests. We used standard dailyMODIS snow products (MOD10A1.5) to investigate the 15-year record (2000–2014) of snow cover in the predominant forest ecotone of the Oregon Western Cascades. We modeled the ecotone using field data from the H.J. Andrews Experimental Forest, and only considered forested MODIS Terra pixels located within the mapped ecotone of a five-county region. Three snow cover metrics were developed using both binary and fractional snow cover data: mean ecotone snow cover percent, number of snow covered days during the melt season, and day of snow disappearance. Snow cover and depletion dates exhibited large interannual variability and no significant linear trends. This variability is likely influenced by the preceding wintertime states of the Pacific Decadal Oscillation (PDO) and the El Niño/Southern Oscillation (ENSO), which tend to covary. We improve and generalize existing methods for power analysis of trend estimation and quantify the number of uninterrupted observations of the snowmetrics thatwould be needed to distinguish trends of different magnitudes fromnoise variance, taking possible autocorrelation into account. Sensitivity analyses of the results to some of our heuristic choices are conducted, and challenges associated with optical remote sensing of snow in a dense montane forest are discussed

Increasing biomass in the warm oceans: unexpected new insights from SeaWiFS

Marine phytoplankton biomass and community structure are expected to change under global warming, with potentially significant impacts on ocean carbon, nutrient cycling, and marine food webs. Previous studies have indicated decreases of primary production and chlorophyll a concentrations and oligotrophic gyre expansions from satellite ocean‐color measurements, purportedly due to global warming. We review this topic via a reanalysis of a novel backscattering‐based phytoplankton functional type and phytoplankton biomass time series over the 1997–2010 period. Unlike previous work, we find that globally the biomass and the percent of large (small) phytoplankton increase (decrease). The oligotrophic gyres contract or expand depending on the chlorophyll a threshold definition employed. In the subtropical gyres, chlorophyll a trends are likely due to physiological changes, while the increasing biomass trends are due to winds and relevant mixing length scale increases

Inter-comparison of phytoplankton functional type phenology metrics derived from ocean color algorithms and earth system models

Ocean color remote sensing of chlorophyll concentration has revolutionized our understanding of the biology of the oceans. However, a comprehensive understanding of the structure and function of oceanic ecosystems requires the characterization of the spatio-temporal variability of various phytoplankton functional types (PFTs), which have differing biogeochemical roles. Thus, recent bio-optical algorithm developments have focused on retrieval of various PFTs. It is important to validate and inter-compare the existing PFT algorithms; however direct comparison of retrieved variables is non-trivial because in those algorithms PFTs are defined differently. Thus, it is more plausible and potentially more informative to focus on emergent properties of PFTs, such as phenology. Furthermore, ocean color satellite PFT data sets can play a pivotal role in informing and/or validating the biogeochemical routines of Earth System models. Here, the phenological characteristics of 10 PFT satellite algorithms and 7 latest-generation climate models from the Coupled Model Inter-comparison Project (CMIP5) are inter-compared as part of the International Satellite PFT Algorithm Inter-comparison Project. The comparison is based on monthly satellite data (mostly SeaWiFS) for the 2003-2007 period. The phenological analysis is based on the fraction of microplankton or a similar variable for the satellite algorithms and on the carbon biomass due to diatoms for the climate models. The seasonal cycle is estimated on a per-pixel basis as a sum of sinusoidal harmonics, derived from the Discrete Fourier Transform of the variable time series. Peak analysis is then applied to the estimated seasonal signal and the following phenological parameters are quantified for each satellite algorithm and climate model: seasonal amplitude, percent seasonal variance, month of maximum, and bloom duration. Secondary/double blooms occur in many areas and are also quantified. The algorithms and the models are quantitatively compared based on these emergent phenological parameters. Results indicate that while algorithms agree to a first order on a global scale, large differences among them exist; differences are analyzed in detail for two Longhurst regions in the North Atlantic: North Atlantic Drift Region (NADR) and North Atlantic Subtropical Gyre West (NASW). Seasonal cycles explain the most variance in zonal bands in the seasonally-stratified subtropics at about 30 latitude in the satellite PFT data. The CMIP5 models do not reproduce this pattern, exhibiting higher seasonality in mid and high-latitudes and generally much more spatially homogeneous patterns in phenological indices compared to satellite data. Satellite data indicate a complex structure of double blooms in the Equatorial region and mid-latitudes, and single blooms on the poleward edges of the subtropical gyres. In contrast, the CMIP5 models show single annual blooms over most of the ocean except for the Equatorial band and Arabian Sea

Ocean Color Algorithm for the Retrieval of the Particle Size Distribution and Carbon-Based Phytoplankton Size Classes Using a Two-Component Coated-Spheres Backscattering Model

The particle size distribution (PSD) of suspended particles in near-surface seawater is a key property linking biogeochemical and ecosystem characteristics with optical properties that affect ocean color remote sensing. Phytoplankton size affects their physiological characteristics and ecosystem and biogeochemical roles, e.g. in the biological carbon pump, which has an important role in the global carbon cycle and thus climate. It is thus important to develop capabilities for measurement and predictive understanding of the structure and function of oceanic ecosystems, including the PSD, phytoplankton size classes (PSCs) and phytoplankton functional types (PFTs). Here, we present an ocean color satellite algorithm for the retrieval of the parameters of an assumed power-law PSD. The forward optical model considers two distinct particle populations (particle assemblage categories) &mdash; phytoplankton and non-algal particles (NAP). Phytoplankton are modeled as coated spheres following the Equivalent Algal Populations (EAP) framework, and NAP are modeled as homogeneous spheres. The forward model uses Mie and Aden-Kerker scattering computations, for homogeneous and coated spheres (for phytoplankton and NAP, respectively) to model the total particulate spectral backscattering coefficient as the sum of phytoplankton and NAP backscattering. The PSD retrieval is achieved via Spectral Angle Mapping (SAM) which uses backscattering end-members created by the forward model. The PSD is used to retrieve size-partitioned absolute and fractional phytoplankton carbon concentrations (i.e. carbon-based PSCs), as well as particulate organic carbon (POC), using allometric coefficients. The EAP-based formulation allows for the estimation of chlorophyll-a concentration via the retrieved PSD, as well as the estimation of the percent of backscattering due to NAP vs. phytoplankton. The PSD algorithm is operationally applied to the merged Ocean Colour Climate Change Initiative (OC-CCI) v5.0 ocean color data set. Results of an initial validation effort are also presented, using PSD, POC, and pico-phytoplankton carbon in-situ measurements. Validation results indicate the need for an empirical tuning for the absolute phytoplankton carbon concentrations; however these results and comparison with other phytoplankton carbon algorithms are ambiguous as to the need for the tuning. The latter finding illustrates the continued need for high-quality, consistent, large global data sets of phytoplankton carbon and related variables to facilitate future algorithm improvements.</p

Cannabidiol improves memory and decreases IL-1β serum levels in rats with lipopolysaccharide-induced inflammation

Aim: Memory improving and anti-inflammatory properties of cannabidiol (CBD) were investigated in an experimental model of lipopolysaccharide (LPS)-induced inflammation. Materials and methods: Male Wistar rats were randomly divided into 4 groups: control, LPS control, LPS + CBD 5 mg/kg bw, and LPS + CBD 10 mg/kg bw. Animals were treated with CBD 14 days before LPS administration and throughout the experiment. Step-through passive avoidance task, Y-maze, and novel object recognition test (NORT) were used to assess the memory functions. The following parameters were recorded: latency time, spontaneous alternations percentage (SA%) and recognition index (RI). IL-10, IL-6, TNF-α, and IL-1β serum levels were measured to evaluate the immunomodulatory properties of CBD. Results: LPS led to significant decrease of the recorded parameters in all memory tasks. This demonstrated the memory-impairing effect of LPS-induced inflammation. In the Y-maze and NORT tests, both doses of CBD increased SA% and RI, respectively. Significant difference was found in comparison with the LPS controls. Rats from the CBD treated groups showed increased latency in the step-through passive avoidance task. In the short-term memory test, both CBD doses significantly increased this parameter when compared with both control groups (p&amp;lt;0.05 and p&amp;lt;0.001, respectively), whereas in the long-term memory test, statistical significance was reached only in comparison with the LPS controls (p&amp;lt;0.01). CBD treatment failed to reduce TNF-α and IL-6 serum levels. The lower studied dose significantly decreased IL-10 and IL-1β concentrations compared to LPS controls (p&amp;lt;0.01 and p&amp;lt;0.05, respectively). Conclusions: CBD improved spatial working and recognition memory in rats with LPS-induced inflammation. Suppression of IL-1β production could be attributed to the observed effect

Ocean carbon from space: Current status and priorities for the next decade

The ocean plays a central role in modulating the Earth\u27s carbon cycle. Monitoring how the ocean carbon cycle is changing is fundamental to managing climate change. Satellite remote sensing is currently our best tool for viewing the ocean surface globally and systematically, at high spatial and temporal resolutions, and the past few decades have seen an exponential growth in studies utilising satellite data for ocean carbon research. Satellite-based observations must be combined with in-situ observations and models, to obtain a comprehensive view of ocean carbon pools and fluxes. To help prioritise future research in this area, a workshop was organised that assembled leading experts working on the topic, from around the world, including remote-sensing scientists, field scientists and modellers, with the goal to articulate a collective view of the current status of ocean carbon research, identify gaps in knowledge, and formulate a scientific roadmap for the next decade, with an emphasis on evaluating where satellite remote sensing may contribute. A total of 449 scientists and stakeholders participated (with balanced gender representation), from North and South America, Europe, Asia, Africa, and Oceania. Sessions targeted both inorganic and organic pools of carbon in the ocean, in both dissolved and particulate form, as well as major fluxes of carbon between reservoirs (e.g., primary production) and at interfaces (e.g., air-sea and land–ocean). Extreme events, blue carbon and carbon budgeting were also key topics discussed. Emerging priorities identified include: expanding the networks and quality of in-situ observations; improved satellite retrievals; improved uncertainty quantification; improved understanding of vertical distributions; integration with models; improved techniques to bridge spatial and temporal scales of the different data sources; and improved fundamental understanding of the ocean carbon cycle, and of the interactions among pools of carbon and light. We also report on priorities for the specific pools and fluxes studied, and highlight issues and concerns that arose during discussions, such as the need to consider the environmental impact of satellites or space activities; the role satellites can play in monitoring ocean carbon dioxide removal approaches; economic valuation of the satellite based information; to consider how satellites can contribute to monitoring cycles of other important climatically-relevant compounds and elements; to promote diversity and inclusivity in ocean carbon research; to bring together communities working on different aspects of planetary carbon; maximising use of international bodies; to follow an open science approach; to explore new and innovative ways to remotely monitor ocean carbon; and to harness quantum computing. Overall, this paper provides a comprehensive scientific roadmap for the next decade on how satellite remote sensing could help monitor the ocean carbon cycle, and its links to the other domains, such as terrestrial and atmosphere

Report on IOCCG Workshop Phytoplankton Composition from Space: towards a validation\ud strategy for satellite algorithms

The IOCCG-supported workshop “Phytoplankton Composition from Space: towards a validation strategy for satellite algorithms” was organized as a follow-up to the Phytoplankton Functional Types from Space splinter session, held at the International Ocean Colour Science Meeting (Germany, 2013). The specific goals of the workshop were to: 1. Provide a summary of the status of activities from relevant IOCCG working groups, the 2nd PFT intercomparison working group, PFT validation data sets and other research developments. 2. Provide a PFT validation strategy that considers the different applications of PFT products: and seeks community consensus on datasets and analysis protocols. 3. Discuss possibilities for sustaining ongoing PFT algorithm validation and intercomparison activities. The workshop included 15 talks, breakout sessions and plenary discussions. Talks covered community algorithm intercomparison activity updates, review of established and novel methods for PFT validation, validation activities for specific applications and space-agency requirements for PFT products and validation. These were followed by general discussions on (a) major recommendations for global intercomparison initiative in respect to validation, intercomparison and user’s guide; (b) developing a community consensus on which data sets for validation are optimal and which measurement and analysis protocols should be followed to support sustained validation of PFT products considering different applications; (c) the status of different validation data bases and measurement protocols for different PFT applications, and (d) engagement of the various user communities for PFT algorithms in developing PFT product specifications. From these discussions, two breakout groups provided in depth discussion and recommendations on (1) validation of current algorithms and (2) work plan to prepare for validation of future missions. Breakout group 1 provided an action list for progressing the current international community validation and intercomparison activity. Breakout group 2 provided the following recommendations towards developing a future validation strategy for satellite PFT products: 1. Establish a number of validation sites that maintain measurements of a key set of variables. 2. This set of variables should include: • Phytoplankton pigments from HPLC, phycobilins from spectrofluorometry • Phytoplankton cell counts and ID, volume / carbon estimation and imaging (e.g. from flow cytometry, FlowCam, FlowCytobot type technologies) • Inherent optical properties (e.g. absorption, backscattering, VSF) • Hyperspectral radiometry (both above and in-water) • Particle size distribution • Size-fractionated measurements of pigments and absorption • Genetic / -omics data 3. Undertake an intercomparison of methods / instruments over several years at a few sites to understand our capabilities to fully characterize the phytoplankton community. 4. Organise workshops to address the following topics: • Techniques for particle analysis, characterization and classification • Engagement with modellers and understanding end-user requirements • Data storage and management, standards for data contributors, data challenges In conclusion, the workshop was assessed to have fulfilled its goals. A follow-on meeting will be organized during the International Ocean Colour Science Meeting 2015 in San Francisco. Specific follow-on actions are listed at the end of the report

Size-partitioned phytoplankton carbon concentrations retrieved from ocean color data, links to data in NetCDF format

Owing to their important roles in biogeochemical cycles, phytoplankton functional types (PFTs) have been the aim of an increasing number of ocean color algorithms. Yet, none of the existing methods are based on phytoplankton carbon (C) biomass, which is a fundamental biogeochemical and ecological variable and the "unit of accounting" in Earth system models. We present a novel bio-optical algorithm to retrieve size-partitioned phytoplankton carbon from ocean color satellite data. The algorithm is based on existing methods to estimate particle volume from a power-law particle size distribution (PSD). Volume is converted to carbon concentrations using a compilation of allometric relationships. We quantify absolute and fractional biomass in three PFTs based on size - picophytoplankton (0.5-2 µm in diameter), nanophytoplankton (2-20 µm) and microphytoplankton (20-50 µm). The mean spatial distributions of total phytoplankton C biomass and individual PFTs, derived from global SeaWiFS monthly ocean color data, are consistent with current understanding of oceanic ecosystems, i.e., oligotrophic regions are characterized by low biomass and dominance of picoplankton, whereas eutrophic regions have high biomass to which nanoplankton and microplankton contribute relatively larger fractions. Global climatological, spatially integrated phytoplankton carbon biomass standing stock estimates using our PSD-based approach yield - 0.25 Gt of C, consistent with analogous estimates from two other ocean color algorithms and several state-of-the-art Earth system models. Satisfactory in situ closure observed between PSD and POC measurements lends support to the theoretical basis of the PSD-based algorithm. Uncertainty budget analyses indicate that absolute carbon concentration uncertainties are driven by the PSD parameter No which determines particle number concentration to first order, while uncertainties in PFTs' fractional contributions to total C biomass are mostly due to the allometric coefficients. The C algorithm presented here, which is not empirically constrained a priori, partitions biomass in size classes and introduces improvement over the assumptions of the other approaches. However, the range of phytoplankton C biomass spatial variability globally is larger than estimated by any other models considered here, which suggests an empirical correction to the No parameter is needed, based on PSD validation statistics. These corrected absolute carbon biomass concentrations validate well against in situ POC observations