Near-infrared Albedo Research Articles

Melt pond detection on landfast sea ice using dual co-polarized Ku-band backscatter

The onset of melt ponds on landfast first-year and multiyear sea ice is detected using dual co-polarized Ku-band spaceborne scatterometer data. Twenty-nine locations in the Canadian Arctic Archipelago are used to develop and test the methodology. The daily ScatSat-1 sigma-nought backscatter composite product with a spatial resolution of 2.216 km is used. Vertical (VV) and horizontal (HH) polarizations of these data are used to derive the co-polarized ratio (VV/HH). An increase in VV/HH is associated with an increase in melt pond fraction on the sea ice. This occurs because there is a larger increase in resonant Bragg scattering for VV than for HH when melt pond surfaces exhibit wind-generated capillary waves. VV backscatter magnitude is evaluated in time series using a peak-finding methodology to initiate detection of melt ponds with VV/HH. The peak-finding is based on geophysical controls on backscatter from snow-covered sea ice during the melt season. Daily time-series data from ScatSat-1 are used in conjunction with ERA-5 reanalysis data (2-m air temperature and wind) and a semi-empirical snowmelt model. Air temperature data are used to identify snow melt onset and to force the snowmelt model. Wind data are used to evaluate if the wind speed is sufficient for capillary wave formation. The NSCAT-4DS geophysical model function is used to model Ku-band backscatter from water, which is then used in a sea ice mixture model to simulate the VV/HH magnitude for a range of pond fractions and wind speeds. The potential for detecting pond onset is evaluated in relation to the noise inherent in the ScatSat-1 VV/HH sea ice measurements, which is found to be approximately 0.16 dB. When the melt pond fraction is sufficiently large to cause VV/HH to exceed the noise, pond onset is detected. Cloud-free Sentinel-2 optical satellite imagery is used for validation; specifically, the near-infrared albedo. We find an overall mean bias of −0.5 days, and an absolute deviation of 1.4 days, when comparing our method to a near-infrared method for detecting pond onset. The mean biases (absolute deviations) for first-year and multiyear sea ice are −0.3 (1.5) and −0.8 (1.2) days, respectively. Results show that the temporal resolution, spatial resolution, and efficacy of this method can provide robust sea ice melt pond onset timing for landfast sea ice regions of the Arctic.

Megadunes in Antarctica: migration and characterization from remote and in situ observations

Abstract. Megadunes are peculiar features formed by the interaction between the atmosphere and cryosphere and are known to be present only on the East Antarctic Plateau and other planets (Mars and Pluto). In this study, we have analysed the glaciological dynamic of megadunes, their spectral properties and morphology on two sample areas of the East Antarctic Plateau where in the past international field activities were carried out (EAIIST, East Antarctic International Ice Sheet Traverse; It-ITASE, Italian International Trans-Antarctic Scientific Expedition). Using satellite images spanning 7 years, we analysed the spatial and temporal variability in megadune surface characteristics, i.e. near-infrared (NIR) albedo, thermal brightness temperature (BT) and slope along the prevailing wind direction (SPWD), useful for mapping them. These parameters allowed us to characterize and perform an automated detection of the glazed surfaces, and we determined the influence of the SPWD by evaluating different combinations of these parameters. The inclusion of the SPWD significantly increased the accuracy of the method, doubling it in certain analysed scenes. Using remote and field observations, for the first time we surveyed all the components of upwind migration (absolute, sedimentological and ice flow), finding an absolute value of about 10 m a−1. The analysis shows that the migration is driven by the snow accumulation on the crest and trough prograding upwind on the previous windward flanks characterized by glazed surface. Our results present significant implications for the surface mass balance estimation, paleo-climate reconstruction using ice cores, and the measurements using optical and radar images/data in the megadune areas.

Assessing albedo dynamics and its environmental controls of grasslands over the Tibetan Plateau

Land surface albedo, as an essential biophysical factor, plays an essential role in surface energy balance. Identifying environmental drivers of albedo in the Tibetan Plateau (TP) helps to understand the role of the “third pole” in responding to environmental change and regulating regional climate. Based on remotely sensed data of albedo, Normalized Difference Vegetation Index (NDVI), snow cover, and soil moisture, this study investigated the effects of land cover (i.e., vegetation, snow) and soil on albedo from the perspectives of spatial, temporal, and spectral (visible, near-infrared, and shortwave) changes of albedo. Generally, changes in shortwave (SW) albedo were primarily driven by changes in snow cover during the growing season (from May to September), predominantly in May and June. The NDVI had larger contributions to visible (VIS) albedo change and was identified as the foremost important driver for VIS albedo in July and August. The correlations between the near-infrared (NIR) albedo and NDVI were positive in the mid- and late growing season in eastern TP. Soil moisture was negatively correlated with albedo throughout the growing season and was identified as the foremost important driver in August. The NIR albedo was more susceptible than the VIS albedo to changes in soil moisture. The correlations between NDVI and albedo varied across different categories of aridity caused by changing correlations between NIR albedo and NDVI along the aridity gradients, and consequently the VIS and NIR albedo counterbalance can further limit the contributions of vegetation greenness on SW albedo in sub-humid and humid region. Our findings are expected to improve understandings of energy budget simulations over TP region in land surface models.

Classification of Tree Functional Types in a Megadiverse Tropical Mountain Forest from Leaf Optical Metrics and Functional Traits for Two Related Ecosystem Functions

Few plant functional types (PFTs) with fixed average traits are used in land surface models (LSMs) to consider feedback between vegetation and the changing atmosphere. It is uncertain if highly diverse vegetation requires more local PFTs. Here, we analyzed how 52 tree species of a megadiverse mountain rain forest separate into local tree functional types (TFTs) for two functions: biomass production and solar radiation partitioning. We derived optical trait indicators (OTIs) by relating leaf optical metrics and functional traits through factor analysis. We distinguished four OTIs explaining 38%, 21%, 15%, and 12% of the variance, of which two were considered important for biomass production and four for solar radiation partitioning. The clustering of species-specific OTI values resulted in seven and eight TFTs for the two functions, respectively. The first TFT ensemble (P-TFTs) represented a transition from low to high productive types. The P-TFT were separated with a fair average silhouette width of 0.41 and differed markedly in their main trait related to productivity, Specific Leaf Area (SLA), in a range between 43.6 to 128.2 (cm2/g). The second delineates low and high reflective types (E-TFTs), were subdivided by different levels of visible (VIS) and near-infrared (NIR) albedo. The E-TFTs were separated with an average silhouette width of 0.28 and primarily defined by their VIS/NIR albedo. The eight TFT revealed an especially pronounced range in NIR reflectance of 5.9% (VIS 2.8%), which is important for ecosystem radiation partitioning. Both TFT sets were grouped along elevation, modified by local edaphic gradients and species-specific traits. The VIS and NIR albedo were related to altitude and structural leaf traits (SLA), with NIR albedo showing more complex associations with biochemical traits and leaf water. The TFTs will support LSM simulations used to analyze the functioning of mountain rainforests under climate change.

Solar position confounds the relationship between ecosystem function and vegetation indices derived from solar and photosynthetically active radiation fluxes

Vegetation indices derived from solar and photosynthetically active radiation (PAR) sensors (i.e. radiation derived) have been under-utilized in inferring ecosystem function, despite measurement capability at hundreds of sites. This under-utilization may be attributed to reported mismatches among the seasonality of radiation- and satellite-derived vegetation indices and canopy photosynthesis; herein referred to as measurement biases. Here biases in radiation derived reflectance and vegetation indices were assessed using a decadal record of satellite and ground based spectroradiometer data, ecosystem phenology and CO2 fluxes, and radiation derived vegetation indices (i.e. the Normalized Difference Vegetation Index [NDVI], the two band Enhanced Vegetation Index [EVI2]) from a high latitude tundra site (i.e. Imnaviat). At Imnaviat, we found poor correspondence between the three types of reflectance and vegetation indices, especially during the latter part of the growing season. Radiation derived vegetation indices resulted in incorrect estimates of phenological timing of up to a month and poor relationships with canopy photosynthesis (i.e. Gross Ecosystem Exchange (GEE)). These mismatches were attributed to solar position (i.e. solar zenith and azimuth angle) and a method, based on the diel visible and near-infrared albedo variation, was developed to improve the performance of the vegetation indices. The ability of radiation derived vegetation indices to infer GEE and phenological dates drastically improved once radiation derived vegetation indices were corrected for solar position associated biases at Imnaviat. Moreover, radiation derived vegetation indices became better aligned with MODerate resolution Imaging Spectroradiometer (MODIS) satellite estimates after solar position associated biases were corrected at Imnaviat and at 25 Fluxnet sites (~90 site years) across North America. Corrections developed here provide a way forward in understanding daily ecosystem function or filling large gaps in eddy covariance data at a significant number of Fluxnet sites.

Spectral albedo of coastal landfast sea ice in Prydz Bay, Antarctica

AbstractThe surface spectral albedo was measured over coastal landfast sea ice in Prydz Bay (off Zhongshan Station), East Antarctica from 5 October to 26 November of 2016. The mean albedo decreased from late-spring to early-summer, mainly responding to the change in surface conditions from dry (phase I) to wet (phase II). The evolution of the albedo was strongly influenced by the surface conditions, with alternation of frequent snowfall events and katabatic wind that induce snow blowing at the surface. The two phases and day-to-day albedo variability were more pronounced in the near-infrared albedo wavelengths than in the visible ones, as the near-infrared photons are more sensitive to snow metamorphism, and to changes in the uppermost millimeters and water content of the surface. The albedo diurnal cycle during clear sky conditions was asymmetric with respect to noon, decreasing from morning to evening over full and patchy snow cover, and decreasing more rapidly in the morning over bare ice. We conclude that snow and ice metamorphism and surface melting dominated over the solar elevation angle dependency in shaping the albedo evolution. However, we realize that more detailed surface observations are needed to clarify and quantify the role of the various surface processes.

Dust emission, extinction, and scattering in LDN 1642

Context. LDN 1642 is a rare example of a star-forming, high-latitude molecular cloud. The dust emission of LDN 1642 has already been studied extensively in the past, but its location also makes it a good target for studies of light scattering. Aims. We wish to study the near-infrared (NIR) light scattering in LDN 1642, its correlation with the cloud structure, and the ability of dust models to simultaneously explain observations of sub-millimetre dust emission, NIR extinction, and NIR scattering. Methods. We used observations made with the HAWK-I instrument to measure the NIR surface brightness and extinction in LDN 1642. These data were compared with Herschel observations of dust emission and, with the help of radiative transfer modelling, with the predictions calculated for different dust models. Results. We find, for LDN 1642, an optical depth ratio τ(250 μm)∕τ(J) ≈ 10−3, confirming earlier findings of enhanced sub-millimetre emissivity. The relationships between the column density derived from dust emission and the NIR colour excesses are linear and consistent with the shape of the standard NIR extinction curve. The extinction peaks at AJ = 2.6 mag, and the NIR surface brightness remains correlated with N(H2) without saturation. Radiative transfer models are able to fit the sub-millimetre data with any of the tested dust models. However, these predict an NIR extinction that is higher and an NIR surface brightness that is lower than based on NIR observations. If the dust sub-millimetre emissivity is rescaled to the observed value of τ(250 μm)∕τ(J), dust models with high NIR albedo can reach the observed level of NIR surface brightness. The NIR extinction of the models tends to be higher than in the direct extinction measurements, which is also reflected in the shape of the NIR surface brightness spectra. Conclusions. The combination of emission, extinction, and scattering measurements provides strong constraints on dust models. The observations of LDN 1642 indicate clear dust evolution, including a strong increase in the sub-millimetre emissivity, which has not been fully explained by the current dust models yet.

Surface Albedo Assimilation and Its Impact on Surface Radiation Budget in Beijing

Surface albedo is a crucial parameter in land surface radiation budget. As bias exists between the model simulated and observed surface albedo, data assimilation is an important method to improve the simulation results. Moreover, surface albedo is associated with the wavelength of the sunlight. So, solar radiation partitioning is important to parameterize the surface albedo. In this paper, the moderate resolution imaging spectroradiometer- (MODIS-) retrieved direct visible, direct near-infrared, diffuse visible, and diffuse near-infrared surface albedos were assimilated into the integrated urban land model (IUM). The solar radiation partitioning method was introduced to parameterize the surface albedo. Based on the albedo data from MODIS and the solar radiation partitioning method, the surface albedo data set for the Beijing municipal area was generated. Based on the surface albedo data set and the IUM, the impacts of the surface albedo on the surface radiation budget were discussed quantitatively. Surface albedo is inversely proportional to the net radiation. For urban areas, after assimilation, the annual average net radiation decreases about 5.6%. For cropland, grassland, and forest areas, after assimilation, the annual average net radiations increase about 20.2%, 24.3%, and 18.7%, respectively.

Impact of rainfall extremes on energy exchange and surface temperature anomalies across biomes in the Horn of Africa

Precipitation extremes have a strong influence on the exchange of energy and water between the land surface and the atmosphere. Although the Horn of Africa has faced recurrent drought and flood events in recent decades, it is still unclear how these events impact energy exchange and surface temperature across different ecosystems. Here, we analyzed the impact of precipitation extremes on spectral albedo (total shortwave, visible, and near-infrared (NIR) broadband albedos), energy balance, and surface temperature in four natural vegetation types: forest, savanna, grassland, and shrubland. We used remotely sensed observations of surface biophysical properties and climate from 2001 to 2016. Our results showed that, in forests and savannas, precipitation extremes led to divergent spectral changes in visible and NIR albedos, which cancelled each other limiting shortwave albedo changes. An exception to this pattern was observed in shrublands and grasslands, where both visible and NIR albedo increased during drought events. Given that shrublands and grasslands occupy a large fraction of the Horn of Africa (52%), our results unveil the importance of these ecosystems in driving the magnitude of shortwave radiative forcing in the region. The average regional shortwave radiative forcing during drought events (−0.64 W m−2, SD 0.11) was around twice that of the extreme wet events (0.33 W m−2, SD 0.09). Such shortwave forcing, however, was too small to influence the surface–atmosphere coupling. In contrast, the surface feedback through turbulent flux changes was strong across vegetation types and had a significant (P < 0.05) impact on the surface temperature and net radiation anomalies, except in forests. The strongest energy exchange and surface temperature anomalies were observed over grassland and the smallest over forest, which was shown to be resilient to precipitation extremes. These results suggest that land management activities that support forest preservation, afforestation, and reforestation can help to mitigate the impact of drought through their role in modulating energy fluxes and surface temperature anomalies in the region.

Intercomparison and improvement of two-stream shortwave radiative transfer schemes in Earth system models for a unified treatment of cryospheric surfaces

Abstract. Snow is an important climate regulator because it greatly increases the surface albedo of middle and high latitudes of the Earth. Earth system models (ESMs) often adopt two-stream approximations with different radiative transfer techniques, the same snow therefore has different solar radiative properties depending whether it is on land or on sea ice. Here we intercompare three two-stream algorithms widely used in snow models, improve their predictions at large zenith angles, and introduce a hybrid model suitable for all cryospheric surfaces in ESMs. The algorithms are those employed by the SNow ICe and Aerosol Radiative (SNICAR) module used in land models, dEdd–AD used in Icepack, the column physics used in the Los Alamos sea ice model CICE and MPAS-Seaice, and a two-stream discrete-ordinate (2SD) model. Compared with a 16-stream benchmark model, the errors in snow visible albedo for a direct-incident beam from all three two-stream models are small (&lt;±0.005) and increase as snow shallows, especially for aged snow. The errors in direct near-infrared (near-IR) albedo are small (&lt;±0.005) for solar zenith angles θ&lt;75∘, and increase as θ increases. For diffuse incidence under cloudy skies, dEdd–AD produces the most accurate snow albedo for both visible and near-IR (&lt;±0.0002) with the lowest underestimate (−0.01) for melting thin snow. SNICAR performs similarly to dEdd–AD for visible albedos, with a slightly larger underestimate (−0.02), while it overestimates the near-IR albedo by an order of magnitude more (up to 0.04). 2SD overestimates both visible and near-IR albedo by up to 0.03. We develop a new parameterization that adjusts the underestimated direct near-IR albedo and overestimated direct near-IR heating persistent across all two-stream models for θ&gt;75∘. These results are incorporated in a hybrid model SNICAR-AD, which can now serve as a unified solar radiative transfer model for snow in ESM land, land ice, and sea ice components.

Spatial, temporal, and spectral variations in albedo due to vegetation changes in China’s grasslands

Changes in Earth’s albedo due to vegetation dynamics, snow cover, and land cover change have attracted much attention. However, the effects of vegetation dynamics on albedo have not been comprehensively documented according to its spatial (regional), temporal (within growing season), and spectral (visible, near-infrared, and shortwave) characteristics. This study examined the effects of vegetation greenness on albedo from 2000 to 2014 in China’s grasslands, which have considerable intra- and inter-annual variations, using remote sensing-based albedo and two-band Enhanced Vegetation Index (EVI2) data. Generally, we found an insignificant negative correlation between the shortwave (SW) albedo and EVI2 for grasslands in China. However, the visible (VIS) albedo was more sensitive to changes in vegetation greenness than near-infrared (NIR) albedo in China’s grasslands. The relationship between the NIR albedo and EVI2 was more complicated, especially in the Tibetan Plateau (TP), where the correlation was negative in the early growing season and positive in the late growing season; while the correlation between the NIR albedo and EVI2 was always negative in main part of Inner Mongolia (IM). The different albedo-EVI2 relationships in IM and TP may be related to differences in soil albedos. The higher sensitivity of the SW albedo to vegetation greenness change in IM, the stronger effect on land surface radiation budget. Our finding about vegetation-induced changes in albedo differ in space, time and spectral bands is expected to contribute to the improvement of land surface models.

Tropical forest leaves may darken in response to climate change

Tropical forest leaf albedo (reflectance) greatly impacts how much energy the planet absorbs; however; little is known about how it might be impacted by climate change. Here, we measure leaf traits and leaf albedo at ten 1-ha plots along a 3,200-m elevation gradient in Peru. Leaf mass per area (LMA) decreased with warmer temperatures along the elevation gradient; the distribution of LMA was positively skewed at all sites indicating a shift in LMA towards a warmer climate and future reduced tropical LMA. Reduced LMA was significantly (P < 0.0001) correlated with reduced leaf near-infrared (NIR) albedo; community-weighted mean NIR albedo significantly (P < 0.01) decreased as temperature increased. A potential future 2 °C increase in tropical temperatures could reduce lowland tropical leaf LMA by 6-7 g m-2 (5-6%) and reduce leaf NIR albedo by 0.0015-0.002 units. Reduced NIR albedo means that leaves are darker and absorb more of the Sun's energy. Climate simulations indicate this increased absorbed energy will warm tropical forests more at high CO2 conditions with proportionately more energy going towards heating and less towards evapotranspiration and cloud formation.

Color study of asteroid families within the MOVIS catalog

The aim of this work is to study the compositional diversity of asteroid families based on their near-infrared colors, using the data within the MOVIS catalog. As of 2017, this catalog presents data for 53 436 asteroids observed in at least two near-infrared filters (Y,J,H, orKs). Among these asteroids, we find information for 6299 belonging to collisional families with bothY−JandJ−Kscolors defined. The work presented here complements the data from SDSS and NEOWISE, and allows a detailed description of the overall composition of asteroid families. We derived a near-infrared parameter, theML*, that allows us to distinguish between four generic compositions: two different primitive groups (P1 and P2), a rocky population, and basaltic asteroids. We conducted statistical tests comparing the families in the MOVIS catalog with the theoretical distributions derived from ourML*in order to classify them according to the above-mentioned groups. We also studied the background populations in order to check how similar they are to their associated families. Finally, we used this parameter in combination with NEOWISE and SDSS to check for possible bimodalities in the data. We found 43 families withML*err&lt; 0.071 and with at least 8 asteroids observed: 5 classified as P1, 10 classified as P2, 19 families associated with the rocky population, and 9 families that were not linked to any of the previous populations. In these cases, we compared our samples with different combinations of these theoretical distributions to find the one that best fits the family data. We also show, using the data from MOVIS and NEOWISE, that the Bapistina family presents a two-cluster distribution in the near-infrared albedo vs.ML*parameter space that might be related to a common differentiated parent body. Finally, we show that the backgrounds we defined seem to be linked to their associated families.

Albedo estimated from remote sensing correlates with ecosystem multifunctionality in global drylands

Monitoring ecosystem functioning is a significant step towards detecting changes in ecosystem attributes that could be linked to land degradation and desertification in drylands worldwide. Remote sensing-based vegetation indices (VIs) and land surface albedo are two favorite indicators to monitor desertification process due to their close relationship with ecosystem status and to their increasing applicability over multiple spatiotemporal scales. While VIs are routinely used to monitor ecosystem attributes and functions such as vegetation cover and productivity, no previous study has evaluated whether remote sensing-measured albedo is related to the simultaneous provision of multiple ecosystem functions (multifunctionality) in global drylands. In this study, we evaluated the correlation of six albedo metrics (shortwave black-sky albedo, shortwave white-sky albedo, visible black-sky albedo, visible white-sky albedo, near-infrared black-sky albedo and near-infrared white-sky albedo) and two VIs (Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) with multifunctionality indices related to carbon, nitrogen and phosphorus cycling measured in 61 dryland ecosystems from all continents except Antarctica. We found a negative relationship between land surface albedo and multifunctionality. Black-sky albedo had a stronger correlation with multifunctionality than white-sky albedo. Visible black-sky albedo showed the strongest correlation with multifunctionality (MUL, −0.314), as well as with functions related to carbon (CCY, −0.216) and nitrogen cycling (NCY, −0.410), while near-infrared (−0.339) and shortwave black-sky albedo (−0.325) showed stronger correlations with functions related to phosphorus cycling (PCY) than visible black-sky albedo (−0.233) did. VIs showed significant positive correlations with MUL, CCY, and NCY, and the magnitudes were higher than those observed between albedo metrics and the multifunctionality indices. However, VIs were not correlated with PCY, which had significant correlations with both shortwave and near-infrared albedo. Though the magnitudes of the correlations observed were not high, which may result from the wide variability in soil and vegetation types in our dataset, our findings indicate that remotely sensed albedo correlates to multifunctionality, which has been linked to alternative states in global drylands. As such, albedo has the potential to monitor changes in dryland ecosystem functioning, which can inform us about the onset of desertification in these areas.

A multilayer physically based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow

Abstract. Light-absorbing impurities (LAIs) decrease snow albedo, increasing the amount of solar energy absorbed by the snowpack. Its most intuitive and direct impact is to accelerate snowmelt. Enhanced energy absorption in snow also modifies snow metamorphism, which can indirectly drive further variations of snow albedo in the near-infrared part of the solar spectrum because of the evolution of the near-surface snow microstructure. New capabilities have been implemented in the detailed snowpack model SURFEX/ISBA-Crocus (referred to as Crocus) to account for impurities' deposition and evolution within the snowpack and their direct and indirect impacts. Once deposited, the model computes impurities' mass evolution until snow melts out, accounting for scavenging by meltwater. Taking advantage of the recent inclusion of the spectral radiative transfer model TARTES (Two-stream Analytical Radiative TransfEr in Snow model) in Crocus, the model explicitly represents the radiative impacts of light-absorbing impurities in snow. The model was evaluated at the Col de Porte experimental site (French Alps) during the 2013–2014 snow season against in situ standard snow measurements and spectral albedo measurements. In situ meteorological measurements were used to drive the snowpack model, except for aerosol deposition fluxes. Black carbon (BC) and dust deposition fluxes used to drive the model were extracted from simulations of the atmospheric model ALADIN-Climate. The model simulates snowpack evolution reasonably, providing similar performances to our reference Crocus version in terms of snow depth, snow water equivalent (SWE), near-surface specific surface area (SSA) and shortwave albedo. Since the reference empirical albedo scheme was calibrated at the Col de Porte, improvements were not expected to be significant in this study. We show that the deposition fluxes from the ALADIN-Climate model provide a reasonable estimate of the amount of light-absorbing impurities deposited on the snowpack except for extreme deposition events which are greatly underestimated. For this particular season, the simulated melt-out date advances by 6 to 9 days due to the presence of light-absorbing impurities. The model makes it possible to apportion the relative importance of direct and indirect impacts of light-absorbing impurities on energy absorption in snow. For the snow season considered, the direct impact in the visible part of the solar spectrum accounts for 85 % of the total impact, while the indirect impact related to accelerated snow metamorphism decreasing near-surface specific surface area and thus decreasing near-infrared albedo accounts for 15 % of the total impact. Our model results demonstrate that these relative proportions vary with time during the season, with potentially significant impacts for snowmelt and avalanche prediction.

Carbonization in Titan Tholins: implication for low albedo on surfaces of Centaurs and trans-Neptunian objects

AbstractAstronomical observations of Centaurs and trans-Neptunian objects (TNOs) yield two characteristic features – near-infrared (NIR) reflectance and low geometric albedo. The first feature apparently originates due to complex organic material on their surfaces, but the origin of the material contributing to low albedo is not well understood. Titan tholins synthesized to simulate aerosols in the atmosphere of Saturn's moon Titan have also been used for simulating the NIR reflectances of several Centaurs and TNOs. Here, we report novel detections of large polycyclic aromatic hydrocarbons, nanoscopic soot aggregates and cauliflower-like graphite within Titan tholins. We put forth a proof of concept stating the surfaces of Centaurs and TNOs may perhaps comprise of highly ‘carbonized’ complex organic material, analogous to the tholins we investigated. Such material would apparently be capable of contributing to the NIR reflectances and to the low geometric albedos simultaneously.

Modified treatment of intercepted snow improves the simulated forest albedo in the Canadian Land Surface Scheme

The Canadian Land Surface Scheme (CLASS) was modified to correct an underestimation of the winter albedo in evergreen needleleaf forests. Default values for the visible and near-infrared albedo of a canopy with intercepted snow, αVIS,cs and αNIR,cs, respectively, were too small, and the fraction of the canopy covered with snow, fsnow, increased too slowly with interception, producing a damped albedo response. A new model for fsnow is based on zI*, the effective depth of newly intercepted snow required to increase the canopy albedo to its maximum, which corresponds in the model with fsnow = 1. Snow unloading rates were extracted from visual assessments of photographs and modelled based on relationships with meteorological variables, replacing the time-based method employed in CLASS. These parameterizations were tested in CLASS version 3.6 at boreal black spruce and jack pine forests in Saskatchewan, Canada, a subalpine Norway spruce and silver fir forest at Alptal, Switzerland, and a boreal maritime forest at Hitsujigaoka, Japan. Model configurations were assessed based on the index of agreement, d, relating simulated and observed daily albedo. The new model employs αVIS,cs = 0.27, αNIR,cs = 0.38 and zI* = 3 cm. The best single-variable snow unloading algorithm, determined by the average cross-site d, was based on wind speed. Two model configurations employing ensemble averages of the unloading rate as a function of total incoming radiation and wind speed, and air temperature and wind speed, respectively, produced larger minimum cross-site d values but a smaller average. The default configuration of CLASS 3.6 produced a cross-site average d from October to April of 0.58. The best model employing a single parameter (wind speed at the canopy top) for modelling the unloading rate produced an average d of 0.86, while the two-parameter ensemble-average unloading models produced a minimum d of 0.81 and an average d of 0.84. © 2015 Her Majesty the Queen in Right of Canada. Hydrological Processes published by John Wiley & Sons, Ltd.

Implementation of a soil albedo scheme in the CABLEv1.4b land surface model and evaluation against MODIS estimates over Australia

Abstract. Land surface albedo, the fraction of incoming solar radiation reflected by the land surface, is a key component of the Earth system. This study evaluates snow-free surface albedo simulations by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model with the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Satellite Pour L'Observation de la Terre (SPOT) albedo. We compare results from offline simulations over the Australian continent. The control simulation has prescribed background snow-free and vegetation-free soil albedo derived from MODIS whilst the experiments use a simple parameterisation based on soil moisture and colour, originally from the Biosphere Atmosphere Transfer Scheme (BATS), and adopted in the Common Land Model (CLM). The control simulation, with prescribed soil albedo, shows that CABLE simulates overall albedo over Australia reasonably well, with differences compared to MODIS and SPOT albedos within ±0.1. Application of the original BATS scheme, which uses an eight-class soil classification, resulted in large differences of up to −0.25 for the near-infrared (NIR) albedo over large parts of the desert regions of central Australia. The use of a recalibrated 20-class soil colour classification from the CLM, which includes a higher range for saturated and VIS (visible) and NIR soil albedos, reduced the underestimation of the NIR albedo. However, this soil colour mapping is tuned to CLM soil moisture, a quantity which is not necessarily transferrable between land surface models. We therefore recalibrated the soil color map using CABLE's climatological soil moisture, which further reduced the underestimation of the NIR albedo to within ±0.15 over most of the continent as compared to MODIS and SPOT albedos. Small areas of larger differences of up to −0.25 remained within the central arid parts of the continent during summer; however, the spatial extent of these large differences is substantially reduced as compared to the simulation using the default eight-class uncalibrated soil colour map. It is now possible to use CABLE coupled to atmospheric models to investigate soil-moisture–albedo feedbacks, an important enhancement of the model.

Forest summer albedo is sensitive to species and thinning: how should we account for this in Earth system models?

Abstract. Although forest management is one of the instruments proposed to mitigate climate change, the relationship between forest management and canopy albedo has been ignored so far by climate models. Here we develop an approach that could be implemented in Earth system models. A stand-level forest gap model is combined with a canopy radiation transfer model and satellite-derived model parameters to quantify the effects of forest thinning on summertime canopy albedo. This approach reveals which parameter has the largest affect on summer canopy albedo: we examined the effects of three forest species (pine, beech, oak) and four thinning strategies with a constant forest floor albedo (light to intense thinning regimes) and five different solar zenith angles at five different sites (40° N 9° E–60° N 9° E). During stand establishment, summertime canopy albedo is driven by tree species. In the later stages of stand development, the effect of tree species on summertime canopy albedo decreases in favour of an increasing influence of forest thinning. These trends continue until the end of the rotation, where thinning explains up to 50% of the variance in near-infrared albedo and up to 70% of the variance in visible canopy albedo. The absolute summertime canopy albedo of all species ranges from 0.03 to 0.06 (visible) and 0.20 to 0.28 (near-infrared); thus the albedo needs to be parameterised at species level. In addition, Earth system models need to account for forest management in such a way that structural changes in the canopy are described by changes in leaf area index and crown volume (maximum change of 0.02 visible and 0.05 near-infrared albedo) and that the expression of albedo depends on the solar zenith angle (maximum change of 0.02 visible and 0.05 near-infrared albedo). Earth system models taking into account these parameters would not only be able to examine the spatial effects of forest management but also the total effects of forest management on climate.

Developing a gross primary production model for coniferous forests of northeastern USA from MODIS data

Accurate estimation of ecosystem carbon fluxes is crucial for understanding the feedbacks between the terrestrial biosphere and the atmosphere and for making climate-policy decisions. A statistical model is developed to estimate the gross primary production (GPP) of coniferous forests of northeastern USA using remotely sensed (RS) radiation (land surface temperature and near-infra red albedo) and ecosystem variables (enhanced vegetation index and global vegetation moisture index) acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This GPP model (called R-GPP-Coni), based only on remotely sensed data, was first calibrated with GPP estimates derived from the eddy covariance flux tower of the Howland forest main tower site and then successfully transferred and validated at three other coniferous sites: the Howland forest west tower site, Duke pine forest and North Carolina loblolly pine site, which demonstrate its transferability to other coniferous ecoregions of northeastern USA. The proposed model captured the seasonal dynamics of the observed 8-day GPP successfully by explaining 84–94% of the observed variations with a root mean squared error (RMSE) ranging from 1.10 to 1.64gC/m2/day over the 4 study sites and outperformed the primary RS-based GPP algorithm of MODIS.