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A diagnostic tool for determining surface and atmospheric contributions to interannual variations in top-of-atmosphere (TOA) reflected shortwave (SW) and net downward SW surface radiative fluxes is introduced. The method requires only upward and downward radiative fluxes at the TOA and surface as input and therefore can readily be applied to both satellite-derived and model-generated radiative fluxes. Observations from the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Edition 4.0 product show that 81% of the monthly variability in global mean reflected SW TOA flux anomalies is associated with atmospheric variations (mainly clouds), 6% is from surface variations, and 13% is from atmosphere–surface covariability. Over the Arctic Ocean, most of the variability in both reflected SW TOA flux and net downward SW surface flux anomalies is explained by variations in sea ice and cloud fraction alone (r² = 0.94). Compared to CERES, variability in two reanalyses—the ECMWF interim reanalysis (ERA-Interim) and NASA’s Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2)—show large differences in the regional distribution of variance for both the atmospheric and surface contributions to anomalies in net downward SW surface flux. For MERRA-2 the atmospheric contribution is 17% too large compared to CERES while ERA-Interim underestimates the variance by 15%. The difference is mainly due to how cloud variations are represented in the reanalyses. The overall surface contribution in both ERA-Interim and MERRA-2 is smaller than CERES EBAF by 15% for ERA-Interim and 58% for MERRA-2, highlighting limitations of the reanalyses in representing surface albedo variations and their influence on SW radiative fluxes.

This study analyzes a 20-year dataset (2003–2022) to understand long-term trends in radiative effects and optical properties of cirrus clouds. The research was conducted at NASA's Goddard Space Flight Center in Greenbelt, Maryland, USA, the primary location of the Micropulse Lidar Network (MPLNET) project. Analysis of net cloud radiative effects (CREs) at both the top-of-the-atmosphere (TOA) and surface (SFC) reveals decreases in radiative flux by −0.019 and −0.037 W m−2 yr−1 and −0.031 and −0.068 W m−2 yr−1, respectively, based on constrained solutions for lidar-derived 523/527/532 nm extinction coefficient (m−1) solved for lidar ratios bounded by 20 and 30 sr. Currently, key cloud properties such as boundary temperature and altitude, as well as integrated optical depth, remained stable with only minor seasonal changes. This study also uncovers a persistent decline in surface albedo, with a derived trend of −0.00036 yr−1. We further find that the interrelationship between CRE and surface albedo variation intensifies notably during winter months. This leads to speculation that a decrease in the number of days of snow and ice is the main driver of the decrease in surface albedo. The observed trends show a complex relationship between albedo, radiative flux, and climate, highlighting the need for continued monitoring due to their significant impact on future climate and weather patterns. We further quantify trend uncertainty with block-bootstrapped 95 % confidence intervals and evaluate sensitivity to solar zenith angle (SZA), finding that TOA trend magnitudes are partially explained by increasing SZA while surface trends remain robust.

Albedo is a key factor in modulating the absorption of solar radiation on ice surfaces. Satellite measurements have shown a general reduction in albedo across the Greenland ice sheet over the past few decades, particularly along the western margin of the ice sheet, a region known as the Dark Zone (albedo < 0.45). Here we chose a combination of Landsat 4–8 and Sentinel 2 imagery to enable us to derive the longest record of albedo variations in the Dark Zone, running from 1984 to 2020. We developed a simple, pragmatic and efficient sensor transformation to provide a long time series of consistent, harmonized satellite imagery. Narrow to broadband conversion algorithms were developed from regression models of harmonized satellite data and in situ albedo from the Program for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather stations. The albedo derived from the harmonized Landsat and Sentinel 2 data shows that the maximum extent of the Dark Zone expanded rapidly between 2005 and 2007, increasing to ~280% of the average annual maximum extent of 2900 km2 to ~8000 km2 since. The Dark Zone is continuing to darken slowly, with the average annual minimum albedo decreasing at a rate of $\\sim \\!-0.0006 \\pm 0.0004 \\, {\\rm a}^{-1}$ (p = 0.16, 2001–2020).

The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite enables the accurate determination of atmospheric methane (CH4) and carbon monoxide (CO) abundances at high spatial resolution and global daily sampling. Due to its wide swath and sampling, the global distribution of both gases can be determined in unprecedented detail. The scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFMD) has proven valuable in simultaneously retrieving the atmospheric column-averaged dry-air mole fractions XCH4 and XCO from TROPOMI's radiance measurements in the shortwave infrared (SWIR) spectral range.Here we present recent improvements of the algorithm which have been incorporated into the current version (v1.8) of the TROPOMI/WFMD product. This includes processing adjustments such as increasing the polynomial degree to 3 in the fitting procedure to better account for possible spectral albedo variations within the fitting window and updating the digital elevation model to minimise topography-related biases. In the post-processing, the machine-learning-based quality filter has been refined using additional data when training the random forest classifier to further reduce scenes with residual cloudiness that are incorrectly classified as good. In particular, the cloud filtering over the Arctic ocean is considerably improved. Furthermore, the machine learning calibration, addressing systematic errors due to simplifications in the forward model or instrumental issues, has been optimised. By including an additional feature associated with the fitted polynomial when training the corresponding random forest regressor, spectral albedo variations are better accounted for. To remove vertical stripes in the XCH4 and XCO data, an efficient orbit-wise destriping filter based on combined wavelet–Fourier filtering has been implemented, while optimally preserving the original spatial trace gas features. The temporal coverage of the data records has been extended to the end of April 2022, covering a total length of 4.5 years since the start of the mission, and will be further extended in the future.Validation with the ground-based Total Carbon Column Observing Network (TCCON) demonstrates that the implemented improvements reduce the pseudo-noise component of the products, resulting in an improved random error. The XCH4 and XCO products have similar spatial coverage from year to year including high latitudes and the oceans. The analysis of annual growth rates reveals accelerated growth of atmospheric methane during the covered period, in line with observations at marine surface sites of the Global Monitoring Division of NOAA's Earth System Research Laboratory, which reported consecutive annual record increases over the past 2 years of 2020 and 2021.

From the discovery that Venus has an atmosphere during the 1761 transit by M. Lomonosov to the current exploration of the planet by the Akatsuki orbiter, we continue to learn about the planet’s extreme climate and weather. This chapter attempts to provide a comprehensive but by no means exhaustive review of the results of the atmospheric thermal structure and radiative balance since the earlier works published in Venus and Venus II books from recent spacecraft and Earth based investigations and summarizes the gaps in our current knowledge. There have been no in-situ measurements of the deep Venus atmosphere since the flights of the two VeGa balloons and landers in 1985 (Sagdeev et al., Science 231:1411–1414, 1986 ). Thus, most of the new information about the atmospheric thermal structure has come from different remote sensing (Earth based and spacecraft) techniques using occultations (solar infrared, stellar ultraviolet and orbiter radio occultations), spectroscopy and microwave, short wave and thermal infrared emissions. The results are restricted to altitudes higher than about 40 km, except for one investigation of the near surface static stability inferred by Meadows and Crisp (J. Geophys. Res. 101:4595–4622, 1996 ) from 1  μ m observations from Earth. Little information about the lower atmospheric structure is possible below about 40 km altitude from radio occultations due to large bending angles. The gaps in our knowledge include spectral albedo variations over time, vertical variation of the bulk composition of the atmosphere (mean molecular weight), the identity, properties and abundances of absorbers of incident solar radiation in the clouds. The causes of opacity variations in the nightside cloud cover and vertical gradients in the deep atmosphere bulk composition and its impact on static stability are also in need of critical studies. The knowledge gaps and questions about Venus and its atmosphere provide the incentive for obtaining the necessary measurements to understand the planet, which can provide some clues to learn about terrestrial exoplanets.

Methane (CH4) is the second most important anthropogenic greenhouse gas with a significant impact on radiative forcing, tropospheric air quality, and stratospheric water vapor. Remote sensing observations enable the detection and quantification of local methane emissions across large geographical areas, which is a critical step for understanding local flux distributions and subsequently prioritizing mitigation strategies. Obtaining methane column concentration measurements with low noise and minimal surface interference has direct consequences for accurately determining the location and emission rates of methane sources. The quality of retrieved column enhancements depends on the choices of the instrument and retrieval parameters. Here, we studied the changes in precision error and bias as a result of different spectral resolutions, instrument optical performance, and detector exposure times by using a realistic instrument noise model. In addition, we formally analyzed the impact of spectrally complex surface albedo features on retrievals using the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) algorithm. We built an end-to-end modeling framework that can simulate observed radiances from reflected solar irradiance through a simulated CH4 plume over several natural and artificial surfaces. Our analysis shows that complex surface features can alias into retrieved methane abundances, explaining the existence of retrieval biases in current airborne methane observations. The impact can be mitigated with higher spectral resolution and a larger polynomial degree to approximate surface albedo variations. Using a spectral resolution of 1.5 nm, an exposure time of 20 ms, and a polynomial degree of 25, a retrieval precision error below 0.007 mole m−2 or 1.0 % of total atmospheric CH4 column can be achieved for high albedo cases, while minimizing the bias due to surface interference such that the noise is uncorrelated among various surfaces. At coarser spectral resolutions, it becomes increasingly harder to separate complex surface albedo features from atmospheric absorption features. Our modeling framework provides the basis for assessing tradeoffs for future remote sensing instruments and algorithmic designs. For instance, we find that improving the spectral resolution beyond 0.2 nm would actually decrease the retrieval precision, as detector readout noise will play an increasing role. Our work contributes towards building an enhanced monitoring system that can measure CH4 concentration fields to determine methane sources accurately and efficiently at scale.

Land surface albedo plays a crucial role in the surface energy balance of climate systems and remains one of the most uncertain components in the radiation budget of climate models. Vegetation significantly influences climate regulation by altering the earth’s surface albedo. The plant water status affects the radiation balance of the earth’s surface by changing albedo; however, our understanding of how plant water status influences albedo variations in terrestrial ecosystems is limited. This study aims to investigate the spatial and temporal features of albedo and its affecting variables. Specifically, we analyze the relationships between albedo and affecting factors, including microwave emissivity difference vegetation index (EDVI), Normalized Difference Vegetation Index (NDVI), and volumetric soil moisture content (VSWC). Additionally, we examine the effect of vegetation water content (VWC) on albedo changes in the Xishuangbanna evergreen broad-leaved forest (BNS) and Changbaishan temperate mixed forest (CBS) in China. We use in-situ measurements and satellite observation datasets. EDVI, a new parameter developed to using on the land surface emissivity difference between two wavelengths to indicate VWC. Our results show that albedo increased during the early growing season, peaked in summer, and subsequently decreased in both forests. The multiple-year mean albedo was relatively higher in the BNS compared to the CBS forest during the study period from May to October. VWC and other variables significantly affect albedo at both forest sites. Throughout the whole year study period, we found a negative correlation between albedo and both EDVI and NDVI, as well as VSWC in the BNS forest. In contrast, at the CBS forest, albedo was positively correlated with EDVI and NDVI while being negatively correlated with VSWC from May to October. VWC influences albedo indirectly by affecting other variables that affects albedo. Our analysis revealed a decreasing trend in albedo as NDVI and VSWC increased under both high and low VWC status, demonstrating a negative correlation with these indices. However, the pattern of albedo change was lower under high VWC status in the BNS forest, suggesting that VWC lower albedo by absorbing more incoming solar radiation. In contrast, at the CBS forest, albedo continued to increase with rising VWC during the growing season, showing a positive correlation with NDVI, which indicates that albedo changes were primarily driven by vegetation greenness. Overall, we concluded that vegetation greenness is the main driver of albedo change under high VWC status in the BNS forest, while VSWC becomes as the most important driver of albedo change under low VWC status. In the CBS forest, NDVI is the primary driver of albedo change, regardless of VWC status, during the growing season. This study highlights the usefulness of EDVI as an indicator of VWC on understanding its effect on albedo changes and enhances our understanding of the mechanisms underlying land-atmosphere interactions in forest ecosystems.

The widely distributed glaciers in the greater Himalayan region have generally experienced rapid shrinkage since the 1850s. As invaluable sources of water and because of their scarcity, these glaciers are extremely important. Beginning in the twenty-first century, new methods have been applied to measure the mass budget of these glaciers. Investigations have shown that the albedo is an important parameter that affects the melting of Himalayan glaciers. The surface albedo based on the Moderate Resolution Imaging Spectroradiometer (MODIS) data over the Hindu Kush, Karakoram and Himalaya (HKH) glaciers is surveyed in this study for the period 2000-2011. The general albedo trend shows that the glaciers have been darkening since 2000. The most rapid decrease in the surface albedo has occurred in the glacial area above 6000 m, which implies that melting will likely extend to snow accumulation areas. The mass-loss equivalent (MLE) of the HKH glacial area caused by surface shortwave radiation absorption is estimated to be 10.4 Gt yr-1, which may contribute to 1.2% of the global sea level rise on annual average (2003-2009). This work probably presents a first scene depicting the albedo variations over the whole HKH glacial area during the period 2000-2011. Most rapidly decreasing in albedo has been detected in the highest area, which deserves to be especially concerned.

Snow and ice albedo schemes in present-day climate models often lack a sophisticated radiation penetration scheme and do not explicitly include spectral albedo variations. In this study, we evaluate a new snow albedo scheme in the Regional Atmospheric Climate Model (RACMO2) for the Greenland ice sheet, version 2.3p3, that includes these processes. The new albedo scheme uses the Two-streAm Radiative TransfEr in Snow (TARTES) model and the Spectral-to-NarrOWBand ALbedo (SNOWBAL) module, version 1.2. Additionally, the bare-ice albedo parameterization has been updated. The snow and ice broadband and narrowband albedo output of the updated version of RACMO2 is evaluated using the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and Kangerlussuaq transect (K-transect) in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) remote-sensing observations. Generally, the modeled narrowband and broadband albedo is in very good agreement with satellite observations, leading to a negligible domain-averaged broadband albedo bias for the interior. Some discrepancies are, however, observed close to the ice margin. Compared to the previous model version, RACMO2.3p2, the broadband albedo is considerably higher in the bare-ice zone during the ablation season, as atmospheric conditions now alter the bare-ice broadband albedo. For most other regions, however, the updated broadband albedo is lower due to spectral effects, radiation penetration or enhanced snow metamorphism.

Increasing the albedo of urban surfaces, through strategies like white roof installations, has emerged as a promising approach for urban climate adaptation. Yet, modeling these strategies on a large scale is limited by the use of static urban surface albedo representations in the Earth system models. In this study, we developed a new transient urban surface albedo scheme in the Community Earth System Model and evaluated evolving adaptation strategies under varying urban surface albedo configurations. Our simulations model a gradual increase in the urban surface albedo of roofs, impervious roads, and walls from 2015 to 2099 under the SSP3‐7.0 scenario. Results highlight the cooling effects of roof albedo modifications, which reduce the annual‐mean canopy urban heat island intensity from 0.8°C in 2015 to 0.2°C by 2099. Compared to high‐density and medium‐density urban areas, higher albedo configurations are more effective in cooling environments within tall building districts. Additionally, urban surface albedo changes lead to changes in building energy consumption, where high albedo results in more indoor heating usage in urban areas located beyond 30°N and 25°S. This scheme offers potential applications like simulating natural albedo variations across urban surfaces and enables the inclusion of other urban parameters, such as surface emissivity. Plain Language Summary Higher albedo surfaces reflect more sunlight, which helps cool down cities. Yet, research into how altering the albedo of urban surfaces on a global scale can aid climate adaptation is limited. It either relies on empirical analysis, oversimplifying urban physical processes, or assumes that urban surface albedo remains constant over time. These limitations hinder our understanding of how changes in urban surfaces can impact the urban thermal environment. In this study, we developed a new option that allows urban surface albedo to vary over time within a global climate model. By gradually increasing global urban surface albedo, we quantified the cooling effects of implementing high urban albedo in a more realistic way. This new option sets the stage for future exploration of scenarios like painting roofs white or how materials age, shedding light on effective urban climate adaptation strategies. Key Points We developed a new representation scheme of transient urban surface albedo in Community Earth System Model (CESM) to improve urban climate adaptation modeling The new scheme enables CESM to assess evolving adaptation strategies for roofs, impervious roads, and walls over time Simulations show increasing roof albedo cools cities more effectively than increasing wall or impervious road albedo