Surface Albedo Research Articles

Liquid-Liquid Phase Separation in Single Suspended Aerosol Microdroplets.

Liquid-liquid phase separation (LLPS) is ubiquitous in ambient aerosols. This specific morphology exerts substantial impacts on the physicochemical properties and atmospheric processes of aerosols, particularly on the gas-particle mass transfer, the interfacial heterogeneous reaction, and the surface albedo. Although there are many studies on the LLPS of aerosols, a clear picture of LLPS in individual aerosols is scarce due to the experimental difficulties of trapping a single particle and mimicking the suspended state of real aerosols. Here, we investigate the phase separation in individual contactless microdroplets by a self-constructed laser tweezer/Raman spectroscopy system. The dynamic transformation of the morphology of optically trapped droplets over the course of humidity cycles is detected by the time-resolved cavity-enhanced Raman spectra. The impacts of pH and inorganic components on LLPS in aerosols are discussed. The results show that the increasing acidity can enhance the miscibility between the hydrophilic and hydrophobic phases and decrease the separation relative humidity of aerosols. Moreover, the inorganic components also have various impacts on the aerosol phase state, whose influence depends on their different salting-out capabilities. It brings possible implications on the morphology of actual atmospheric particles, particularly for those dominated by internal mixtures of inorganic and organic components.

Comparison of methods to estimate aerosol effective radiative forcings in climate models

Abstract. Uncertainty in the effective radiative forcing (ERF) of climate primarily arises from the unknown contribution of aerosols, which impact radiative fluxes directly and through modifying cloud properties. Climate model simulations with fixed sea surface temperatures but perturbed atmospheric aerosol loadings allow for an estimate of how strongly the planet's radiative energy budget has been perturbed by the increase in aerosols since pre-industrial times. The approximate partial radiative perturbation (APRP) technique further decomposes the contributions to the direct forcing due to aerosol scattering and absorption and to the indirect forcing due to aerosol-induced changes in cloud scattering, amount, and absorption, as well as the effects of aerosols on surface albedo. Here we evaluate previously published APRP-derived estimates of aerosol effective radiative forcings from these simulations conducted in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and find that they are biased as a result of two large coding errors that – in most cases – fortuitously compensate. The most notable exception is the direct radiative forcing from absorbing aerosols, which is more than 40 % larger averaged across CMIP6 models in the present study. Correcting these biases eliminates the residuals and leads to better agreement with benchmark estimates derived from double calls to the radiation code. The APRP method – when properly implemented – remains a highly accurate and efficient technique for diagnosing aerosol ERF in cases where double radiation calls are not available, and in all cases it provides quantification of the individual contributors to the ERF that are highly useful but not otherwise available.

Modeling Impacts of Urbanization on Winter Boundary Layer Meteorology and Aerosol Pollution in the Central Liaoning City Cluster, China.

The influence of urbanization on the frequent winter aerosol pollution events in Northeast China is not fully understood. The Weather Research and Forecasting Model with Chemistry (WRF-Chem) coupled with urban canopy (UC) models was used to simulate the impact of urbanization on an aerosol pollution process in the Central Liaoning city cluster (CLCC), China. To investigate the main mechanisms of urban expansion and UC on the winter atmospheric environment and the atmospheric diffusion capacity (ADC) in the CLCC, three simulation cases were designed using land-use datasets from different periods and different UC schemes. A comparative analysis of the simulation results showed that the land-use change (LU) and both LU and UC (LUUC) effects lead to higher surface temperature and lower relative humidity and wind speed in the CLCC by decreasing surface albedo, increasing sensible heat flux, and increasing surface roughness, with a spatial distribution similar to the distribution of LU. The thermal effect leads to an increase in atmospheric instability, an increase in boundary layer height and diffusion coefficient, and an increase in the ADC. The LU and LUUC effects lead to a significant decrease in near-surface PM2.5 concentrations in the CLCC due to changes in meteorological conditions and ADC within the boundary layer. The reduction in surface PM2.5 concentrations due to the LU effect is stronger at night than during daytime, while the LUUC effect leads to a greater reduction in surface PM2.5 concentrations during the day, mainly due to stronger diffusion and dilution caused by the effect of urban turbulence within different levels caused by the more complex UC scheme. In this study, the LU and LUUC effects result in greater thermal than dynamic effects, and both have a negative impact on surface PM2.5 concentrations, but redistribute pollutants from the lower urban troposphere to higher altitudes.

Investigation of the Earth’s Albedo using Meteorological Parameters over Nguru, Nigeria

Estimation and investigation of an earth’s albedo is significant in the evaluation and design of solar energy collectors, atmospheric radiative transfer and studies that relates to atmospheric thermal balance. This study employed the shortwave solar energy balancing at the edge of the Earth’s atmosphere to estimate and compare the variation of albedo for Nguru situated in the Sahelian region of Nigeria, using measured monthly mean daily meteorological parameter of global solar radiation obtained from the National Aeronautics and Space Administration (NASA) during the period of thirty-eight years (1984 – 2021). The variation of albedo with surface temperature, maximum wavelength, clearness index, global solar radiation, relative humidity and mean temperature for this location was investigated. The results in this study show that the estimated surface albedo depicts a direct opposite relationship with the clearness index, an inverse relationship with the emitting Earth’s surface temperature and a direct relationship with the wavelength for the studied location. The highest and lowest values of albedo for the location was in the months of August and January with 0.4628 and 0.3403 respectively. The emitting Earth surface temperature ranged between 238.5837 K in August and 251.1607 K in January. These values agreed closely to the standard emitting Earth surface temperature value (255.0000 K). The maximum emitting wavelength values for the location revealed that the radiation is longwave and is found within the infrared region of the electromagnetic spectrum.

Effects of Artificial Green Land on Land–Atmosphere Interactions in the Taklamakan Desert

Land–atmosphere interactions are influenced by the earth’s complex underlying subsurface, which in turn indirectly affects atmospheric motion and climate change. Human activities are increasingly exerting an influence on desert ecosystems, and artificial green land with clear functional orientation has been established in many desert areas. Consequently, the previously dominant, shifting, sand-covered, underlying surface in these desert regions is gradually transforming. This transformation has significant implications for the characteristics of land–atmosphere interactions, causing them to deviate from their original state. At present, existing studies still have not presented a systematic understanding of this change and have ignored the impact of human activities on land–atmosphere interactions in artificial green land. To address these research gaps, this study specifically targets artificial green land in the Tazhong region of Taklamakan Desert. We carried out observation experiments on land–atmosphere interactions in three different functional units from outside to inside: natural shifting sands, the shelter forest, and the living area. We also analyzed the differences and attribution of land–atmosphere interactions characteristics of different functional units. Compared with the natural shifting sands, the daily average maximum values of wind speed in the shelter forest decreased by 78%, and the daily average maximum air temperature and soil (0 cm) temperature decreased by 2.6 °C and 7 °C, respectively. Additionally, the soil moisture level was significantly increased throughout the green land due to the shelter forest. The surface albedo experienced a decrease, with an annual average of 0.21. Furthermore, the aerodynamic roughness and bulk transport coefficient increased by two orders of magnitude. The daily average maximum values of sensible heat flux and soil heat flux (G05) decreased by 18.7% and 75%, respectively, and the daily average maximum value of latent heat flux increased by 70.3%. This effectively improved the microclimate environment of the green land. The living area was greatly reduced by the shelter forest coverage and influenced by the buildings. Consequently, the environmental improvement was not as large as it was inside the shelter forest. However, it still provided a good shelter for production and living in the desert area. Throughout the year, a total of 4.60 × 105 t water was consumed through evapotranspiration in the artificial green land. The findings of this study have the potential to enhance our comprehension of land–atmosphere interactions in desert regions, thereby offering valuable insights for the establishment and effective management of artificial desert green lands.

Sensitive temperature changes on the Tibetan Plateau in response to global warming

In recent years, rapid global warming has led to a dramatic cryosphere retreat and caused environmental disasters on the Tibetan Plateau (TP). To better understand the near-surface air temperature variation on the TP in response to global warming in the 21st century, we systematically detected temperature changes on the TP relative to global warming regarding warming time, warming amplitude, and elevation-dependent warming (EDW) using the multi-model ensemble (MME) means of 24 CMIP6 models. The results showed that the warming rate on the TP is 1.3–1.45 times more than that of mean global warming from 2015 to 2100, with the TP having already warmed by 1.5 °C above the pre-industrial levels in 2016/2015 and will warm by 2 °C by 2028/2027 under the Shared Socioeconomic Pathway (SSP) scenarios, SSP2–4.5 and SSP5–8.5. In addition, the warming amplitude on the TP in response to global warming levels of 1.5 and 2 °C would be 1.97 and 2.69 °C under the SSP2–4.5 scenario and 1.94 and 2.68 °C under the SSP5–8.5 scenario. Furthermore, EDW amplifies the warming amplitude at high elevations, mainly because of the decrease in surface albedo. The most sensitive warming amplitude in the cryosphere region, with an elevation of 5–5.5 km, would exceed 2.06, 2.79, 4.29, and 5.73 °C when global warming reaches 1.5, 2, 3, and 4 °C under the SSP5–8.5 scenario. This additional 0.56–1.73 °C warming in the high elevation relative to the global mean will intensify the cryosphere ablation on the TP. Thus, considering the projected larger warming trend and amplitude, the high-elevation region on the TP would be particularly sensitive and vulnerable, which is unfavorable for the TP from the perspective of the Paris Agreement warming target.

Effective Factors for Reducing Land Surface Temperature in Each Local Climate Zone Built Type in Tokyo and Shanghai

Identifying the main factors influencing the land surface temperature (LST) of each local climate zone (LCZ) built type is of great significance for controlling LST. This study investigated the main factors influencing the LST of each LCZ built type in two Asian megacities: Tokyo and Shanghai. Each area in both megacities was classified according to the LCZ scheme. The diurnal LST, pervious surface fraction (PSF), surface albedo (SA), average building height (⟨BH⟩), and gross building coverage ratio (λp) of each LCZ were also calculated. Finally, the influence of the properties of each LCZ built type on LST was investigated. The results demonstrated that the main factors influencing LST of different LCZ built types differed in Tokyo and Shanghai. ⟨BH⟩ was the main factor influencing LST for compact mid-rise and open high-rise types in Tokyo, and the compact mid-rise type in Shanghai; PSF was the main factor influencing LST for other LCZ built types. Moreover, both ⟨BH⟩ and PSF negatively correlated with LST. Based on the above results and characteristics of each LCZ built type, specific LST mitigation strategies for each LCZ built type were proposed for Tokyo and Shanghai. The approach in this study can contribute to perspectives for urban planners and policymakers to develop highly feasible and reasonable LST mitigation strategies.

Heterogeneity of Surface Heat Exchange of Slopes and Potential Drivers of the Initiation of Thaw Slump, Qinghai-Tibet Plateau

In the mountainous permafrost area, most thaw slumps are distributed in north or northeast-facing shady slope areas. It is commonly known that there is a heterogeneity in permafrost between different slope aspects, but there has been a lack of detailed measured data to quantitatively evaluate their relationships, and in-depth understandings on how the slope aspects are linked to the distribution of thaw slumps. This study examined the heterogenous thermal regime, soil moisture content, and surface radiation at two slope sites with opposing aspects in a warming permafrost region on the Qinghai-Tibet Plateau (QTP). The results indicate that similar air temperatures (Ta) were monitored on the two slopes, but there were significant differences in ground temperature and moisture content in the active layer from 2016 to 2021. The sunny slope exhibited a higher mean annual ground surface temperature (Ts), and over the five years the mean annual temperature at the top of permafrost was 1.3–1.4℃ warmer on the sunny slope than the shady slope. On the contrary, the near-surface soil moisture content was about 10–13% lower on the sunny slope (~22–27%) than the shady slope (~35–38%) during the thawing season (June–September). Radiation data indicate that significantly higher shortwave downward radiation (DR) appeared at the sunny slope site. However, due to the greater surface albedo, the net radiation (Rn) was lower on the sunny slope. Slope aspect also affects the ground ice content due to its influence on ground temperature, freeze-thaw cycles, and soil moisture. Shady slopes have a shallower burial of ice-rich permafrost compared to sunny slopes. The results highlight greatly different near-surface ground thermal conditions at the two slope sites with different aspects in a mountainous permafrost region. This helps identify the slope-related causes of increasing thaw slumps and provides a basis for predicting their future development.

Evaluation of EPIC oxygen bands stability with radiative transfer simulations over the South Pole

The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite orbiting the Sun at the Lagrange-1 point was launched without onboard calibration systems. Vicarious calibration is conducted for 8 of the 10 UV/VIS/NIR channels using other low earth orbiting satellite instruments, while its two O2 bands (688 nm and 764 nm) rely on indirect moon-view calibrations because the same narrow-band O2 bands are not readily available from other in-flight instruments. This study compares EPIC measurements from the four O2 bands aiming at examining sensor stability over a uniquely suited location, i.e., the permanently snow-covered South Pole. The study utilizes radiative transfer model simulations with in-situ atmospheric soundings taken at South Pole during months of December and January from 2015 to 2022. The absolute discrepancy between the model simulations and observations is less than 1.0% for the two reference bands, but 5.75% and 15.63% for the 688 nm, and 764 nm absorption bands, respectively. The simulated A-band and B-band ratios are 16.09% and 4.74% higher than that from the observations. Various sensitivities are conducted to estimate possible contributions to the discrepancies from input atmospheric profiles, spectral surface albedos and surface BRDF. While none of the input uncertainties is likely to account for the large discrepancies in the oxygen absorption bands, a small shift in the instrument response function could be the main reason for these biases. On the other hand, the model simulations are able to capture systematic variations with observed angular measurements and explain the multi-year trends found in observed O2 band ratios due to satellite orbit shifting. When model simulated contributions from the angle variations are deducted from the observed O2 band ratios, the residual O2 band ratios are found to be stable since 2015.

Deforestation and Its Effect on Surface Albedo and Weather Patterns

Deforestation is an important environmental problem and a key promoter of regional climate change through modifying the surface albedo. The objective of this research was to characterize the impact of deforestation and land use changes on surface albedo (α) and climate patterns in a tropical highland region of Mexico, between the years 2014 and 2021. The main land cover types are coniferous forests (CF), oak and gallery woodlands (OGW), and annual agriculture (AA), which represent more than 88% of the regional territory. We used 2014 and 2021 Landsat 8 OLI images with topographic and atmospheric correction in order to develop an inventory of albedo values for each land cover type in both time scenarios. Albedo images were generated by using the equation proposed by Liang in 2001, which is based on the reflectance of the bands 2, 3, 4, 5, and 7. Differences in albedo values were calculated between the years 2014 and 2021, and those differences were correlated with variations in climate parameters, for which we used climate data derived from the WRF model. In addition, the different land use changes found were classified in terms of triggers for increasing or decreasing surface albedo. We used the Mann–Whitney U Test to compare the 2021 − 2014 climatic deviations in two samples: Sample A, which included sites without albedo change in 2021; and Sample B, including sites with albedo change in 2021. Results showed that between 2014 and 2021, at least 38 events of land use change or deforestation occurred, with albedo increments between 1 and 11%, which triggered an average increment of 2.16% (p < 0.01; Mann–Whitney U Test) of the regional surface albedo in comparison to the 2014 scenario. In this period, the albedo for CF, OGW, and AA also increased significantly (p < 0.001; Mann–Whitney U Test) by +79, +12, and +9%, respectively. In addition, the regional albedo increment was found to be significant and negatively correlated (p < 0.01 Spearman’s coefficient) with relative humidity (RH), maximum temperature (Tmax), and minimum temperature (Tmin), and correlated (p < 0.01) positively with diurnal temperature range (DTR). The Mann–Whitney U Test revealed that 2021 climatic variations in Sample B sites are statistically different (p < 0.05) to 2021 climatic variations in Sample A sites, which demonstrates that albedo changes are linked to a decrease in minimum temperature and relative humidity and an increase in DTR. Conversion of CF and OGW into AA, perennial agriculture (PA), or grassland (GR) always yielded an albedo increment, whilst the conversion of AA to irrigation agriculture (IA) or PA triggered a decrease in albedo, and finally, the pass from GR or AA to protected agriculture (PA) caused albedo to increase or decrease, depending on the greenhouse covers materials. Reducing deforestation of CF and OGW, conversion of AA or GR into PA, and selecting adequate greenhouse covers could help to mitigate regional climate change.

Albedo-dominated biogeophysical warming effects induced by vegetation restoration on the Loess Plateau, China

Vegetation restoration can reduce surface albedo and retain more heat on the land surface. This feedback would partially offset the cooling effect of vegetation by absorbing CO2. Ignoring the climate feedback from changes in albedo will lead us to overestimate the contribution of increased vegetation to warming mitigation. Utilizing remote sensing data and employing a radiative forcing algorithm, we quantified the climatic feedback associated with vegetation restoration on the Chinese Loess Plateau (CLP). The surface albedo on the CLP decreased from 2001 to 2017, correlating with increased vegetation greenness, resulting in positive radiative forcing (multi-year average: 1.58 W/m2, average growth rate: 0.125 W/m2/yr). The radiative feedback of reduced albedo due to vegetation restoration is equivalent to the warming effect of emitting 3.7 kgCO2/m2. Contrary to expectations, CLP vegetation restoration efforts lacked a significant cooling effect, potentially increasing surface warming risks from albedo changes. These results emphasize the dominant role of biogeophysical feedback mechanisms induced by vegetation restoration. It will contribute to a comprehensive assessment of the climate benefits of large-scale vegetation increases in temperate regions. Decision makers can provide diverse vegetation management and restoration strategies depending on the differences in climate feedback for vegetation restoration at different locations.

East Asian methane emissions inferred from high-resolution inversions of GOSAT and TROPOMI observations: a comparative and evaluative analysis

Abstract. We apply atmospheric methane column retrievals from two different satellite instruments (Greenhouse gases Observing SATellite – GOSAT; TROPOspheric Monitoring Instrument – TROPOMI) to a regional inversion framework to quantify East Asian methane emissions for 2019 at 0.5∘ × 0.625∘ horizontal resolution. The goal is to assess if GOSAT (relatively mature but sparse) and TROPOMI (new and dense) observations inform consistent methane emissions from East Asia with identically configured inversions. Comparison of the results from the two inversions shows similar correction patterns to the prior inventory in central northern China, central southern China, northeastern China, and Bangladesh, with less than 2.6 Tg a−1 differences in regional posterior emissions. The two inversions, however, disagree over some important regions, particularly in northern India and eastern China. The methane emissions inferred from GOSAT observations are 7.7 Tg a−1 higher than those from TROPOMI observations over northern India but 6.4 Tg a−1 lower over eastern China. The discrepancies between the two inversions are robust against varied inversion configurations (i.e., assimilation window and error specifications). We find that the lower methane emissions from eastern China inferred by the GOSAT inversion are more consistent with independent ground-based in situ and total column (TCCON) observations, indicating that the TROPOMI retrievals may have high XCH4 biases in this region. We also evaluate inversion results against tropospheric aircraft observations over India during 2012–2014 by using a consistent GOSAT inversion of earlier years as an intercomparison platform. This indirect evaluation favors lower methane emissions from northern India inferred by the TROPOMI inversion. We find that in this case the discrepancy in emission inference is contributed by differences in data coverage (almost no observations by GOSAT vs. good spatial coverage by TROPOMI) over the Indo-Gangetic Plain. The two inversions also differ substantially in their posterior estimates for northwestern China and neighboring Kazakhstan, which is mainly due to seasonally varying biases between GOSAT and TROPOMI XCH4 data that correlate with changes in surface albedo.

Changes in glacier albedo and the driving factors in the Western Nyainqentanglha Mountains from 2001 to 2020

AbstractGlacier surface albedo dominates glacier energy balance, thus strongly affecting the glacier mass balance. Glaciers in the Western Nyainqentanglha Mountains (WNM) experienced large mass losses in the past two decades, but long-term changes of glacier albedo and its drivers are less understood. In this study, we retrieved glacier albedo with MODIS reflectance data to characterize the spatiotemporal variability of albedo from 2001 to 2020. Air temperature, rainfall, snowfall and deposition of light-absorbing impurities (LAIs) were evaluated as potential drivers of the observed variability in glacier albedo. The results showed that: (1) the glacier albedo experienced large inter-annual fluctuations, with the mean albedo being 0.552 ± 0.002 and a clear decreasing trend of 0.0443 ± 2 × 10−4 dec−1 in the WNM. The fastest decline was observed in autumn and in the vicinity of the equilibrium line altitude, indicating an extended melt season and an expansion of the ablation region to higher elevation; (2) local meteorology and LAIs deposition are the main drivers of glacier albedo change, but their effects on seasonal albedos are different due to different glacier processes. Both air temperature and the balance between liquid and solid precipitation affect summer and autumn albedos due to glacier ablation. Air temperature is the main driver of spring and winter albedos due to sublimation and metamorphism of snow, while snowfall carried by westerlies has limited influence on these two seasonal albedos due to less snowfall. LAIs mainly affect spring albedo due to high concentration coupled with the southerly wind in spring. These findings highlight the significance of changes in glacier albedo and the key role of local meteorology and LAIs deposition in determining such changes, which play an important role in glaciological and cryosphere processes.

Complex refractive index and single scattering albedo of Icelandic dust in the shortwave part of the spectrum

Abstract. Icelandic dust can impact the radiative budget in high-latitude regions directly by affecting light absorption and scattering and indirectly by changing the surface albedo after dust deposition. This tends to produce a positive radiative forcing. However, the limited knowledge of the spectral optical properties of Icelandic dust prevents an accurate assessment of these radiative effects. Here, the spectral single scattering albedo (SSA) and the complex refractive index (m=n-ik) of Icelandic dust from five major emission hotspots were retrieved between 370–950 nm using online measurements of size distribution and spectral absorption (βabs) and scattering (βsca) coefficients of particles suspended in a large-scale atmospheric simulation chamber. The SSA(λ) estimated from the measured βabs and βsca increased from 0.90–0.94 at 370 nm to 0.94–0.96 at 950 nm in Icelandic dust from the different hotspots, which falls within the range of mineral dust from northern Africa and eastern Asia. The spectral complex refractive index was retrieved by minimizing the differences between the measured βabs and βsca and those computed using the Mie theory for spherical and internally homogeneous particles, using the size distribution data as input. The real part of the complex refractive index (n(λ)) was found to be 1.60–1.61 in the different samples and be independent of wavelength. The imaginary part (k(λ)) was almost constant with wavelength and was found to be around 0.004 at 370 nm and 0.002–0.003 at 950 nm. The estimated complex refractive index was close to the initial estimates based on the mineralogical composition, also suggesting that the high magnetite content observed in Icelandic dust may contribute to its high absorption capacity in the shortwave part of the spectrum. The k(λ) values retrieved for Icelandic dust are at the upper end of the reported range for low-latitude dust (e.g., from the Sahel). Furthermore, Icelandic dust tends to be more absorbing towards the near-infrared. In Icelandic dust, k(λ) between 660–950 nm was 2–8 times higher than most of the dust samples sourced in northern Africa and eastern Asia. This suggests that Icelandic dust may have a stronger positive direct radiative forcing on climate that has not been accounted for in climate predictions.

Improving modelled albedo over the Greenland ice sheet through parameter optimisation and MODIS snow albedo retrievals

Abstract. Greenland ice sheet mass loss continues to accelerate as global temperatures increase. The surface albedo of the ice sheet determines the amount of absorbed solar energy, which is a key factor in driving surface snow and ice melting. Satellite-retrieved snow albedo allows us to compare and optimise modelled albedo over the entirety of the ice sheet. We optimise the parameters of the albedo scheme in the ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) land surface model for 3 random years taken over the 2000–2017 period and validate over the remaining years. In particular, we want to improve the albedo at the edges of the ice sheet, since they correspond to ablation areas and show the greatest variations in runoff and surface mass balance. By giving a larger weight to points at the ice sheet's edge, we improve the model–data fit by reducing the root-mean-square deviation by over 25 % for the whole ice sheet for the summer months. This improvement is consistent for all years, even those not used in the calibration step. We also show the optimisation successfully improves the model–data fit at 87.5 % of in situ sites from the PROMICE (Programme for Monitoring of the Greenland Ice Sheet) network. We conclude by showing which additional model outputs are impacted by changes to the albedo parameters, encouraging future work using multiple data streams when optimising these parameters.

Modeling medium resolution evapotranspiration using downscaling techniques in north-western part of India

The present investigation provides a modeling solution to downscale MODIS-based evapotranspiration (ET) at a 30 m spatial resolution from its original 500 m spatial resolution using meteorological and Landsat 8 (Operational Land Imager, OLI) data by employing downscaling models. The nine indices namely Surface Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Infrared Index for Band 7 (NDIIB7) were calculated from Lansat 8 data at 30 m spatial resolution. The multiple linear regression (MLR) and Least Square Support Vector Machine (LS-SVM) models were developed to generate the relationship between MODIS 500 m ET and Landsat indices at 500 m scale. Further, these develop models were used to estimate 30 m ET based on 30 m Landsat 8 indices. The performance of developed models (MLR and LS-SVM) was carried out using correlation coefficient (CC), Nash-Sutcliffe coefficient (NASH) efficiency, Root Mean Square Error (RMSE) and Normalised Mean Square Error (NMSE). Penman–Monteith (PM) method was used to estimate the ET using observed station data. The results show that lowest ETO was observed in the month of December while it was maximum in the month of May. Using the performances indices, it was found that LS-SVM model slightly outperformed than MLR model. However, the downscaled model overestimates ET in comparison to the Penman-Monteith method. Further, the significant correlation was found between MODIS ET and LS-SVM ET at all the stations.

A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds Over Reflecting Surfaces

AbstractIn the Earth Sciences, the 3D radiative transfer equation is often solved for by Monte Carlo (MC) methods. They can, however, be computationally taxing, and that can narrow their range of application and limit their use in explorations of model parameter spaces. A novel family of MC algorithms is investigated here in which single simulations provide estimates of both radiative quantities A for a set of parameters , as usual, as well as the overarching functional (x) that can be evaluated, extremely efficiently, at any x. One such algorithm is developed and demonstrated for horizontally averaged broadband solar radiative fluxes as functions of surface albedo for uniform Lambertian surfaces beneath inhomogeneous cloudy atmospheres. Simulations for a high‐resolution synthetic cloud field, at various solar zenith angles, illustrate the potential of the method to gain insights into the nature of 3D radiative effects for complicated atmosphere‐surface conditions using information specially derived from the MC simulation. For simulations performed with a single surface albedo it is found that as surface albedo increases, 3D radiative effects increase, too, with maxima occurring at middling to large values, and then decrease. By utilizing the derived coefficients that describe it was established that these 3D effects stem from differences in fractions of radiation entrapped at successive orders of internal multiple reflections for 1D and 3D transfer.

Regional Impact of Snow‐Darkening on Snow Pack and the Atmosphere During a Severe Saharan Dust Deposition Event in Eurasia

AbstractLight‐absorbing impurities such as mineral dust can play a major role in reducing the albedo of snow surfaces. Particularly in spring, deposited dust particles lead to increased snow melt and trigger further feedbacks at the land surface and in the atmosphere. Quantifying the extent of dust‐induced variations is difficult due to high variability in the spatial distribution of mineral dust and snow. We present an extension of a fully coupled atmospheric and land surface model system to address the impact of mineral dust on the snow albedo across Eurasia. We evaluated the short‐term effects of Saharan dust in a case study. To obtain robust results, we performed an ensemble simulation followed by statistical analysis. Mountainous regions showed a strong impact of dust deposition on snow depth. We found a mean significant reduction of −1.4 cm in the Caucasus Mountains after 1 week. However, areas with flat terrain near the snow line also showed strong effects despite lower dust concentrations. Here, the feedback to dust deposition was more pronounced as increase in surface temperature and air temperature. In the region surrounding the snow line, we found an average significant surface warming of 0.9 K after 1 week. This study shows that the impact of mineral dust deposition depends on several factors. Primarily, these are altitude, slope, snow depth, and snow cover fraction. Especially in complex terrain, it is therefore necessary to use fully coupled models to investigate the effects of mineral dust on snow pack and the atmosphere.

Variation in albedo and other vegetation characteristics in non-forested northern ecosystems: the role of lichens and mosses

Vegetation has a profound impact on climate through complex interactions and feedback loops, where especially regulation of albedo, the ratio of reflected to incoming solar radiation, is important at high latitudes. How vegetation albedo varies along environmental gradients in tundra ecosystems is still not well understood, particularly for ecosystems dominated by nonvascular vegetation. We studied broadband shortwave albedo of open boreal, arctic, and alpine ecosystems over a 2000 km long latitudinal gradient (60° N–79° N) and contrasted this against species composition, vegetation greenness (normalised difference vegetation index—NDVI), momentary ecosystem CO2 fluxes and reindeer (Rangifer tarandus) grazing pressure. High cover of pale terricolous fruticose lichens was the single most important predictor for vegetation albedo, which had a maximum value of 0.389 under clear sky conditions and solar zenith angle 60°. To our knowledge, this is the highest broadband albedo recorded for a vegetated surface. NDVI was negatively correlated to lichen biomass (rs = −0.56), and albedo (rs = −0.19). Gross primary production and ecosystem respiration varied considerably less between plots and vegetation types than albedo. While it is well-known that Rangifer affects climate-relevant aboveground biomass, we here show that its regulation of surface albedo in northern ecosystems may also be of high importance for land-atmosphere interactions. The data presented here thus advocate for an increased understanding of the important and complex role of herbivores and lichen cover in climate-vegetation interactions.

Comparison between deep learning architectures for the 1 km, 10/15-min estimation of downward shortwave radiation from AHI and ABI

The retrieval of downward shortwave radiation (DSR) with high spatiotemporal resolution and short latency is critical. It is the fundamental driving force of surface energy, carbon, and hydrological circulations, and a key energy source for photovoltaic electricity. However, existing methods face significant challenges owing to cloud heterogeneity and their reliance on other satellite-derived products, which hinder the retrieval of accurate and timely DSR with high spatiotemporal resolution. In addition to the spectral features used in traditional approaches, deep learning (DL) can incorporate the spatial and temporal features of satellite data. This study developed and compared three DL methods, namely the DenseNet, the bidirectional gated recurrent unit without surface albedo as inputs (BiGRUnor), and the convolutional neural network with gated recurrent unit without surface albedo as inputs (CNNGRUnor). These methods were used to estimate DSR at 1 km and 10/15 min resolutions directly from top-of-atmosphere reflectance over the Advanced Himawari Imager (AHI) onboard Himawari-8 and the Advanced Baseline Imager (ABI) onboard GOES-16 coverage, achieving high accuracies. The instantaneous root mean square error (RMSE) and relative RMSE for the three models were 68.4 (16.1%), 69.4 (16.3%), and 67.1 (15.7%) W/m2, respectively, which are lower than the baseline machine learning method, the multilayer perceptron model (MLP), with RMSE at 76.8 W/m2 (18.0%). Hourly accuracies for the three DL methods were 58.6 (14.1%), 57.8 (14.0%), and 57.3 (13.8%) W/m2, which are within the DSR RMSEs that we estimated for existing datasets of the Earth's Radiant Energy System (CERES) (88.8 W/m2, 21.4%) and GeoNEX (77.8 W/m2, 18.8%). The study illustrates that DL models that incorporate temporal information can eliminate the need for surface albedo as an input, which is crucial for timely monitoring and nowcasting of DSR. Incorporating spatial information can enhance retrieval accuracy in overcast conditions, and incorporating infrared bands can further improve the accuracy of DSR estimation.