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Abstract In situ observations and output from a numerical model are utilized to examine three dust outbreaks that occurred in the northwestern Sonoran Desert. Via analysis of these events, it is shown that trapped waves generated in the lee of an upwind mountain range produced high surface wind speeds along the desert floor and the observed dust storms. Based on analysis of observational and model output, general characteristics of dust outbreaks generated by trapped waves are suggested, including dust-layer depths and concentrations that are dependent upon wave phase and height above the surface, emission and transport associated with the presence of a low-level jet, and wave-generated high wind speeds and thus emission that occurs far downwind of the wave source. Trapped lee waves are ubiquitous in Earth’s atmosphere and thus it is likely that the meteorological aspects of the dust storms examined here are also relevant to understanding dust in other regions. These dust outbreaks occurred near the Salton Sea, an endorheic inland body of water that is rapidly drying due to changes in water-use management. As such, these findings are also relevant in terms of understanding how future changes in size of the Salton Sea will impact dust storms and air quality there. Significance Statement Dust storms are ubiquitous in Earth’s atmosphere, yet the physical processes underlying dust emission and subsequent transport are not always understood, in part due to the wide variety of meteorological processes that can generate high winds and dust. Here we use in situ measurements and numerical modeling to demonstrate that vertically trapped atmospheric waves generated by air flowing over a mountain are one such mechanism that can produce dust storms. We suggest several features of these dust outbreaks that are specific to their production by trapped waves. As the study area is a region undergoing rapid environmental change, these results are relevant in terms of predicting future dust there. 

Abstract Here we present retrievals of aerosol optical depth τ from an Aerosol Robotic Network (AERONET) station in the southeastern corner of California, an area where dust storms are frequent. By combining AERONET data with collocated ceilometer measurements, camera imagery, and satellite data, we show that during significant dust outbreaks the AERONET cloud-screening algorithm oftentimes classifies dusty measurements as cloud contaminated, thus removing them from the aerosol record. During dust storms we estimate that approximately 85% of all dusty retrievals of τ and more than 95% of retrievals when τ > 0.1 are rejected, resulting in a factor-of-2 reduction in dust-storm averaged τ . We document the specific components in the screening algorithm responsible for the misclassification. We find that a major reason for the loss of these dusty measurements is the high temporal variability in τ during the passage of dust storms over the site, which itself is related to the proximity of the site to the locations of emission. We describe a method to recover these dusty measurements that is based on collocated ceilometer measurements. These results suggest that AERONET sites that are located close to dust source regions may require ancillary measurements to aid in the identification of dust. Significance Statement In this study we demonstrate that, during dust storms, measurements made with a sun photometer at an AERONET site in the western Sonoran Desert are frequently classified as cloud contaminated by the network’s processing algorithm. We identify the various algorithmic tests that result in the misclassification and discuss the physical reasons why dust typically fails those tests. We then present a method to restore these data that utilizes measurements from a collocated ceilometer. This work highlights the challenges, and one solution, to operating an AERONET site in a region that is close to the sources of airborne dust. 

Abstract. A global in situ data set for validation of ocean colour productsfrom the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented.This version of the compilation, starting in 1997, now extends to 2021,which is important for the validation of the most recent satellite opticalsensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprisesin situ observations of the following variables: spectral remote-sensingreflectance, concentration of chlorophyll-a, spectral inherent opticalproperties, spectral diffuse attenuation coefficient, and total suspendedmatter. Data were obtained from multi-project archives acquired via openinternet services or from individual projects acquired directly from dataproviders. Methodologies were implemented for homogenization, qualitycontrol, and merging of all data. Minimal changes were made on the originaldata, other than conversion to a standard format, elimination of some points,after quality control and averaging of observations that were close in timeand space. The result is a merged table available in text format. Overall,the size of the data set grew with 148 432 rows, with each row representing aunique station in space and time (cf. 136 250 rows in previous version;Valente et al., 2019). Observations of remote-sensing reflectance increasedto 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There wasalso a near tenfold increase in chlorophyll data since 2016. Metadata ofeach in situ measurement (original source, cruise or experiment, principalinvestigator) are included in the final table. By making the metadataavailable, provenance is better documented and it is also possible toanalyse each set of data separately. The compiled data are available athttps://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022). 

Abstract. We assessed the influence of the marine diazotrophic cyanobacterium Trichodesmium on the bio-optical properties of western tropical South Pacific (WTSP) waters (18–22°S, 160°E–160°W) during the February–March 2015 OUTPACE cruise. We performed measurements of backscattering and absorption coefficients, irradiance, and radiance in the euphotic zone with a Satlantic MicroPro free-fall profiler and took Underwater Vision Profiler 5 (UPV5) pictures for counting the largest Trichodesmium spp. colonies. Pigment concentrations were determined by fluorimetry and high-performance liquid chromatography and picoplankton abundance by flow cytometry. Trichome concentration was estimated from pigment algorithms and validated by surface visual counts. The abundance of large colonies counted by the UVP5 (maximum 7093coloniesm⁻³) was well correlated to the trichome concentrations (maximum 2093trichomesL⁻¹) with an aggregation factor of 600. In the Melanesian archipelago, a maximum of 4715trichomesL⁻¹ was enumerated in pump samples (3.2m) at 20°S,16730°E. High Trichodesmium abundance was always associated with absorption peaks of mycosporine-like amino acids (330, 360nm) and high particulate backscattering, but not with high Chl a fluorescence or blue particulate absorption (440nm). Along the west-to-east transect, Trichodesmium together with Prochlorococcus represented the major part of total chlorophyll concentration; the contribution of other groups were relatively small or negligible. The Trichodesmium contribution to total chlorophyll concentration was the highest in the Melanesian archipelago around New Caledonia and Vanuatu (60%), progressively decreased to the vicinity of the islands of Fiji (30%), and reached a minimum in the South Pacific Gyre where Prochlorococcus dominated chlorophyll concentration. The contribution of Trichodesmium to zeaxanthin was respectively 50, 40 and 20% for these regions. During the OUTPACE cruise, the relationship between normalized water-leaving radiance (nL_w) in the ultraviolet and visible and chlorophyll concentration was similar to that found during the BIOSOPE cruise in the eastern tropical Pacific. Principal component analysis (PCA) of OUTPACE data showed that nL_w at 305, 325, 340, 380, 412 and 440nm was strongly correlated to chlorophyll and zeaxanthin, while nL_w at 490 and 565nm exhibited lower correlations. These results, as well as differences in the PCA of BIOSOPE data, indicated that nL_w variability in the greenish blue and yellowish green during OUTPACE was influenced by other variables associated with Trichodesmium presence, such as backscattering coefficient, phycoerythrin fluorescence and/or zeaxanthin absorption, suggesting that Trichodesmium detection should involve examination of nL_w in this spectral domain.

 

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel. 

Abstract. A global compilation of in situ data is useful to evaluate thequality of ocean-colour satellite data records. Here we describe the datacompiled for the validation of the ocean-colour products from the ESA OceanColour Climate Change Initiative (OC-CCI). The data were acquired fromseveral sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD,MERMAID, AMT, ICES, HOT and GeP&CO) and span the period from 1997 to 2018.Observations of the following variables were compiled: spectralremote-sensing reflectances, concentrations of chlorophyll a, spectralinherent optical properties, spectral diffuse attenuation coefficients andtotal suspended matter. The data were from multi-project archives acquiredvia open internet services or from individual projects, acquired directlyfrom data providers. Methodologies were implemented for homogenization,quality control and merging of all data. No changes were made to theoriginal data, other than averaging of observations that were close in timeand space, elimination of some points after quality control and conversionto a standard format. The final result is a merged table designed forvalidation of satellite-derived ocean-colour products and available in textformat. Metadata of each in situ measurement (original source, cruise orexperiment, principal investigator) was propagated throughout the work andmade available in the final table. By making the metadata available,provenance is better documented, and it is also possible to analyse each setof data separately. This paper also describes the changes that were made tothe compilation in relation to the previous version (Valente et al., 2016).The compiled data are available athttps://doi.org/10.1594/PANGAEA.898188 (Valente et al., 2019).