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940 records · Page 53

The UK Met Office GCM with a Sophisticated Radiation Scheme Applied to the Hot Jupiter HD 209458b

To study the complexity of hot Jupiter atmospheres revealed by observations of increasing quality, we have adapted the UK Met Office Global Circulation Model (GCM), the Unified Model (UM), to these exoplanets. The UM solves the full 3D Navier-Stokes equations with a height-varying gravity, avoiding the simplifications used in most GCMs currently applied to exoplanets. In this work we present the coupling of the UM dynamical core to an accurate radiation scheme based on the two-stream approximation and correlated-k method with state-of-the-art opacities from ExoMol. Our first application of this model is devoted to the extensively studied hot Jupiter HD 209458b. We derive synthetic emission spectra and phase curves, and compare them to both previous models also based on state-of-the-art radiative transfer, and to observations. We find a reasonable a agreement between observations and both our days side emission and hot spot offset, however, our night side emissions is too large. Overall our results are qualitatively similar to those found by Showman et al. (2009) with the SPARCMIT gcm, however, we note several quantitative differences: Our simulations show significant variation in the position of the hottest part of the atmosphere with pressure, as expected from simple timescale arguments, and in contrast to the vertical coherency found by Showman et al. (2009). We also see significant quantitative differences in calculated synthetic observations. Our comparisons strengthen the need for detailed intercomparisons of dynamical cores, radiation schemes and post-processing tools to understand these differences. This effort is necessary in order to make robust conclusions about these atmospheres based on GCM results.

radiation↗

Performance of JAXA’s SGLI standard ocean color products for oceanic to coastal waters: chlorophyll a concentration and light absorption coefficients of colored dissolved organic matter

We present the performance of JAXA’s SGLI standard algorithms for estimating chlorophyll a (chl a) concentration and the light absorption coefcients of colored dissolved organic matter (CDOM) using recently compiled high-quality data obtained from oceanic to coastal waters. Prior to the evaluation of the algorithms, we frst compare two forward models (Gordon et al. in J Geophys Res 93(D9):10909-10924, 1988: G88 and Park and Ruddick in Appl Opt 44(7):1236–1249, 2005: PR05) using a wide range of inherent optical properties (IOPs) to (1) examine if the water refectance is appropriately reproduced and (2) correct measured refectance in terms of its bidirectionality. Based on a good reproducibility of water refectance using the PR05, the optimized IOPs are further used for explaining errors in estimates of chl a concentration and CDOM absorption when using the SGLI, the GSM (Maritorena et al. in Appl Opt 41:2705–2714, 2002), and the QAA (Lee et al. in Appl Opt 41:5755, 2002) inversion algorithms. Results show that the mean error for estimating chl a concentration using the SGLI algorithm is 110% for our dataset. Although this error is lower than that of the GSM and the QAA algorithms, a signifcant underestimate at chl a higher than 2.0 mg m−3 is observed, which is further shown by independent match-up analyses. Another SGLI CDOM product includes the mean error of 50% and shows deviation at high CDOM range (>1.0 per m). A similar trend is observed for the GSM but not for the QAA when a global relationship of CDOM to colored detrital matter is applied. The sources of errors are discussed for potentially improving the retrievals

Second-generation GLobal Imager (SGLI)↗

Use of Machine Learning and Principal Component Analysis to Retrieve Nitrogen Dioxide (NO 2 ) With Hyperspectral Imagers and Reduce Noise in Spectral Fitting

Nitrogen dioxide (NO 2 ) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO 2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO 2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO 2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO 2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO 2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO 2 retrievals. Nevertheless, the NO 2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO 2 retrievals. Once trained, NNs can also substantially speed up NO 2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.

NO2↗

Estimating Major Ion and Nutrient Concentrations in Mangrove Estuaries in Everglades National Park Using Leaf and Satellite Reflectance

Coastal mangrove ecosystems are under duress worldwide because of urban development, sea-level rise, and climate change, processes that are capable of changing the salinity and nutrient concentration of the water utilized by the mangroves. This study correlates long-term water chemistry in mangrove environments, located in Everglades National Park,with mangrove spectral reflectance measurements made at both the leaf and canopy scales. Spectral reflectance measurements were collected using a handheld spectrometer for leaf-level measurements and Landsat 5TMdata for regional coverage. Leaf-level reflectance data were collected from three mangrove species (i.e., red, black and white mangroves) across two regions; a tall mangrove (approximately 18m) and dwarf mangrove (1- 2 m) region. The reflectance data were then used to calculate a wide variety of biophysical reflectance indices (e.g., NDVI, EVI, SAVI) to determine signs of stress. Discrete, quarterly water samples from the surface water, groundwater, and pore water (20 and 85 cm depths) and daily autonomous surface water samples were collected at each site and analyzed for major anions (Cl(−) and SO(2(-)/4)), cations (Na(+), K(+), Mg(sup 2(+)), and Ca(sup 2(+)), total nitrogen (TN) and total phosphorus (TP).Mangrove sites that exhibited the highest salinity and ionic concentrations in the surface and subsurface water also had the lowest near-infrared reflectance at both the leaf and satellite levels. Seasonal reflectance responses were measured in the near-infrared (NIR) wavelengths at both the leaf and canopy scales and were strongly correlated with nutrient and ionic concentrations in the surface and subsurface water, even though there was no significant separability between the three mangrove species. Study sites that experienced the greatest variability in surface and subsurface water ionic concentrations also exhibited the greatest fluctuations in NIR spectral reflectance. Landsat 5TM images were able to detect tall and dwarf mangroves by the differences in spectral indices (e.g., NDVI, NDWI, and EVI) because of the variability in the background conditions amongst the environments. In addition, Landsat 5TM images spanning 16 years (1993- 2009) were successfully used to estimate the seasonal variability in ionic concentrations in the surface water across the Florida Coastal mangrove ecotone. This study has shown that water chemistry can be estimated indirectly by measuring the change in spectral response at the leaf- or satellite-scale. Furthermore, the results of this research may be extrapolated to similar coastal mangrove systems throughout the Caribbean and world-wide wherever red, black, and white mangroves occur.

Mangroves↗