Surface Albedo Research Articles

Geospatial Insights into Aridity Conditions: MODIS Products and GIS Modeling in Northeast Brazil

Northeast Brazil (NEB), particularly its semiarid region, represents an area highly susceptible to the impacts of climate change, including severe droughts, and intense anthropogenic activities. These stresses may be accelerating environmental degradation and desertification of soil in NEB. The main aim of this study was to gain geospatial insights into the biophysical parameters of surface energy balance and actual evapotranspiration on a multi-temporal scale, aiming to detect and analyze the spectral behavioral patterns of areas vulnerable to degradation processes, based on thematic maps at the surface, for NEB and mainly the semiarid region of NEB from 2000 to 2019. Geospatial data from 8-day MODIS sensor products were used, such as surface reflectance (Terra/MOD09A1 and Aqua/MYD09A1), surface temperature (Terra/MOD11A2 and Aqua/MYD11A2), and actual evapotranspiration (Terra/MOD16A2 and Aqua/MYD16A2), version 6. Therefore, in this study, pixel-to-pixel values were processed by calculating the average pixel statistics for each year. From the reflectance product, digital processing of the surface albedo and spectral vegetation indices was also carried out, using computational programming scripts and machine learning algorithms developed via the Google Earth Engine (GEE) platform. The study also presents a seasonal analysis of these components and their relationships over 20 years. Through vegetation indices and statistical correlations, a new predictive model of actual evapotranspiration was developed. The quantitative and spatiotemporal spectral patterns of the parameters were assessed through descriptive statistics, measures of central tendency and dispersion, and statistical error analyses and correlation indices. Thematic maps highlighted the pixel-to-pixel results, with patterns of high temperature distribution mainly in the central and northeastern part of NEB and the semiarid region of NEB, highlighting the formation of persistent heat islands over time. Meanwhile, in these areas, the maps of actual evapotranspiration showed a drastic reduction due to the lesser availability of energy. Over time, the semiarid region of NEB presented areas with little and/or no vegetation cover, which were highly well-defined between the years 2012 and 2019, confirming that these areas are extremely vulnerable to degradation and desertification processes due to significant loss of vegetative and water resilience. The components of energy balance were highly interconnected to climatological and environmental conditions, showing the severe results of drought and accentuation of the water deficit in NEB, presenting a greater condition of aridity in the semiarid region of NEB over time.

Chemistry-albedo feedbacks offset up to a third of forestation's CO2 removal benefits.

Forestation is widely proposed for carbon dioxide (CO2) removal, but its impact on climate through changes to atmospheric composition and surface albedo remains relatively unexplored. We assessed these responses using two Earth system models by comparing a scenario with extensive global forest expansion in suitable regions to other plausible futures. We found that forestation increased aerosol scattering and the greenhouse gases methane and ozone following increased biogenic organic emissions. Additionally, forestation decreased surface albedo, which yielded a positive radiative forcing (i.e., warming). This offset up to a third of the negative forcing from the additional CO2 removal under a 4°C warming scenario. However, when forestation was pursued alongside other strategies that achieve the 2°C Paris Agreement target, the offsetting positive forcing was smaller, highlighting the urgency for simultaneous emission reductions.

Four decades of global surface albedo estimates in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) climate data record

Abstract. We present the surface albedo data in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) data record family. The temporal coverage of this edition is extended from 1979 until the near-present day. The core algorithms and data format remain unchanged from previous editions, but now white- and blue-sky albedo estimates are also available for the first time in CLARA data. We present an overview of the retrieval, followed by an assessment of the accuracy and stability of the data record, based on collocated comparisons with reference surface albedo measurements and intercomparisons with preceding satellite-based albedo data records. Specific attention is paid to addressing the spatial representativeness problem inherent in the “point-to-pixel” validation of satellite-based coarse surface albedo estimates against in situ measurements. We find the CLARA-A3 albedo data to match or improve upon the accuracy and robustness of the predecessor record (CLARA-A2), with good agreement found when compared to in situ measurements. In cases of a large bias, the spatial representativeness of the measurement site typically explains most of the increase. We conclude with a summarizing discussion on the observed strengths and weaknesses of the new data record, including guidance for potential users. The data are available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b).

Comparative Analysis and High−Precision Modeling of Tropospheric CH4 in the Yangtze River Delta of China Obtained from the TROPOMI and GOSAT

Remote sensing satellite monitoring involving the use of shortwave infrared (SWIR) solar backscatter radiation to measure atmospheric CH4 column concentrations provides wide−ranging and accurate data for quantitatively determining atmospheric CH4 emissions and is highly important for human studies of atmospheric composition and environmental protection. The ESA−launched Sentinel−2 satellite equipped with a tropospheric monitoring instrument (TROPOMI) can provide the concentration of CH4 columns in every piece of the global atmosphere every day. However, these data may be affected by surface albedo, SWIR, aerosols, cirrus cloud scattering, and other factors. The greenhouse gas observing satellite (GOSAT) launched by Japan has fairly accurate data that are minimally affected by the aforementioned factors; however, its data density is much less than that of the TROPOMI. In this study, we propose a CH4 model that combines the TROPOMI and GOSAT data. We construct the model by analyzing the data from the TROPOMI and GOSAT at the same location at the same time. Then, we apply the proposed model to a certain location at a certain time with TROPOMI data but without GOSAT data to obtain a large range of high−precision CH4 data. The most developed urban agglomeration in the Yangtze River Delta in China was selected for model construction and the correlations between the TROPOMI and GOSAT data and their spatial and temporal trends were analyzed. First, we analyzed the CH4 concentrations in the same area measured by both models. The results revealed a high degree of temporal and spatial correlation in the YRD region. The correlation coefficient reached 0.71 in the metropolitan area of the YRD. At the small−city scale, the correlation is much more significant, with the correlation reaching 0.80, 0.79, and 0.71 for Nanjing, Shanghai, and Ningbo, respectively. The most accurate model was screened through comparative construction to calibrate the TROPOMI data and high−precision and high−coverage CH4 concentration information was obtained for the study area. Five models (linear model, quadratic term model, cubic term model, lognormal model, and logistic model) were used to select the best−fitting model. The magnitudes of the differences in the CH4 concentrations calculated by each model were compared. The final results showed that the linear model, as the prediction model, had the highest accuracy, with a coefficient of determination (R22) of 0.542. To avoid the specificity of the constructed model, we used the same method in several simulations to validate. The coefficient of determination of the model constructed with different stochastic data was greater than 0.5. Subsequently, we used Nanjing as the study area and applied the same method to construct the model. The coefficient of determination of the model (R22) was approximately 0.601. The model constructed in this research can be used not only for data conversion between the same products from different sensors to obtain high−precision data products but also for calibrating newly developed satellite data products that utilize mature data products.

Measurements of biologically effective solar radiation using erythemal weighted broadband meters.

In this paper, we describe conversion factors (CF) for the calculation of biologically effective irradiances (BEI) from erythemal irradiance for three effects: photosynthesis of previtamin D3, psoriasis healing, and inactivation of the SARS-Cov-2 virions. CFs were empirically derived from measurements of spectral solar ultraviolet (UV) radiation during all sky conditions at four mid-latitude sites in the Northern Hemisphere, namely Aosta, Belsk, Reading, and San Diego. These CFs were found to depend on solar zenith angle (SZA) and total column ozone, but are largely independent (within ± 5% for SZA < 60°) of local conditions such as surface albedo, visibility and other local atmospheric patterns. The values of these empirical CFs are consistent with analytical CFs derived with radiative transfer calculations (model FastRT) for clear-sky and overcast conditions. To validate these analytical CFs, one-hour radiant exposures for the three biological effects were calculated from erythemal measurements at Reading between 2012 and 2021 and compared with similar exposures calculated directly from the spectral UV measurements. The two datasets agreed within 10% for SZA < 65°, demonstrating the utility of the conversion method. These results suggest that the proposed analytical CFs can be used with confidence to estimate radiant exposures for the three biological effects from measurements of the UV Index at any northern mid-latitude site.

Monsoon Planet: Bimodal Rainfall Distribution due to Barrier Structure in Pressure Fields

Abstract Monsoon systems transport water and energy across the globe, making them a central component of the global circulation system. Changes in different forcing parameters have the potential to fundamentally change the monsoon characteristics as indicated in various paleoclimatic records. Here, we use the Atmosphere Model developed at the Geophysical Fluid Dynamics Laboratory (GFDL-AM2) and couple it with a slab ocean in order to analyze the monsoon’s sensitivity to changes in different parameters on a planet with idealized topography (varying land position, slab depth, atmospheric CO2 concentration, solar radiation, sulfate aerosol concentration, and surface albedo). This Monsoon Planet concept of an aquaplanet with a broad zonal land stripe allows us to reduce the influence of topography and to access the relevant meridional monsoon dynamics. In simulations with monsoon dynamics, a bimodal rainfall distribution develops during the monsoon months with one maximum over the tropical ocean and the other one over land. The intensity and extent of the monsoon depend on the relative height of a local maximum in the surface pressure field that is acting as a barrier and is determining the coastward moisture transport. Changes in the barrier height occur during the course of one year but can also be induced when varying different parameters in the sensitivity analysis (e.g., the increase of atmospheric CO2 reduces the barrier height, resulting in an increase of rainfall, while aerosols have the opposing effect). This bimodal rainfall structure separated by a pressure barrier is also present in reanalysis data of the West African monsoon. Significance Statement Monsoon rainfall directly impacts the livelihood of millions of individuals in the tropics. Because monsoons transport energy and water around the globe, their influence reaches far beyond the tropics, and changes in their dynamics affect the climate both locally and globally. The individual monsoon systems are subject to various forcing factors that determine the monsoon characteristics in partly opposing ways. Here, we implement a model setup of the Monsoon Planet to study the monsoon’s sensitivity to various forcings in a simplified design, allowing us to gain new insights into monsoon dynamics. We find that a local maximum in the pressure field is acting as a barrier for moisture transport and thus determines the monsoon characteristics over land.

The Respective Roles of Ocean Heat Transport and Surface Heat Fluxes in Driving Arctic Ocean Warming and Sea Ice Decline

Abstract Arctic Ocean warming and sea ice loss are closely linked to increased ocean heat transport (OHT) into the Arctic and changes in surface heat fluxes. To quantitatively assess their respective roles, we use the 100-member Community Earth System Model, version 2 (CESM2), Large Ensemble over the 1920–2100 period. We first examine the Arctic Ocean warming in a heat budget framework by calculating the contributions from heat exchanges with atmosphere and sea ice and OHT across the Arctic Ocean gateways. Then we quantify how much anomalous heat from the ocean directly translates to sea ice loss and how much is lost to the atmosphere. We find that Arctic Ocean warming is driven primarily by increased OHT through the Barents Sea Opening, with additional contributions from the Fram Strait and Bering Strait OHTs. These OHT changes are driven mainly by warmer inflowing water rather than changes in volume transports across the gateways. The Arctic Ocean warming driven by OHT is partially damped by increased heat loss through the sea surface. Although absorbed shortwave radiation increases due to reduced surface albedo, this increase is compensated by increasing upwelling longwave radiation and latent heat loss. We also explicitly calculate the contributions of ocean–ice and atmosphere–ice heat fluxes to sea ice heat budget changes. Throughout the entire twentieth century as well as the early twenty-first century, the atmosphere is the main contributor to ice heat gain in summer, though the ocean’s role is not negligible. Over time, the ocean progressively becomes the main heat source for the ice as the ocean warms. Significance Statement Arctic Ocean warming and sea ice loss are closely linked to increased ocean heat transport (OHT) into the Arctic and changes in surface heat fluxes. Here we use 100 simulations from the same climate model to analyze future warming and sea ice loss. We find that Arctic Ocean warming is primarily driven by increased OHT through the Barents Sea Opening, though the Fram and Bering Straits are also important. This increased OHT is primarily due to warmer inflowing water rather than changing ocean currents. This ocean heat gain is partially compensated by heat loss through the sea surface. During the twentieth century and early twenty-first century, sea ice loss is mainly linked to heat transferred from the atmosphere; however, over time, the ocean progressively becomes the most important contributor.

Land cover changes across Greenland dominated by a doubling of vegetation in three decades

Land cover responses to climate change must be quantified for understanding Arctic climate, managing Arctic water resources, maintaining the health and livelihoods of Arctic societies and for sustainable economic development. This need is especially pressing in Greenland, where climate changes are amongst the most pronounced of anywhere in the Arctic. Ice loss from the Greenland Ice Sheet and from glaciers and ice caps has increased since the 1980s and consequently the proglacial parts of Greenland have expanded rapidly. Here we determine proglacial land cover changes at 30 m spatial resolution across Greenland during the last three decades. Besides the vastly decreased ice cover (− 28,707 km2 ± 9767 km2), we find a doubling in total areal coverage of vegetation (111% ± 13%), a quadrupling in wetlands coverage (380% ± 29%), increased meltwater (15% ± 15%), decreased bare bedrock (− 16% ± 4%) and increased coverage of fine unconsolidated sediment (4% ± 13%). We identify that land cover change is strongly associated with the difference in the number of positive degree days, especially above 6 °C between the 1980s and the present day. Contrastingly, absolute temperature increase has a negligible association with land cover change. We explain that these land cover changes represent local rapid and intense geomorphological activity that has profound consequences for land surface albedo, greenhouse gas emissions, landscape stability and sediment delivery, and biogeochemical processes.

Assessing the Sensitivity of Snow Depth Simulations to Land Surface Parameterizations within Noah-MP in Northern Xinjiang, China

Snow cover plays a crucial role in the surface energy balance and hydrology and serves as a key indicator of climate change. In this study, we conducted an ensemble simulation comprising 48 members generated by randomly combining the parameterizations of five physical processes within the Noah-MP model. Utilizing the variance-based Sobol total sensitivity index, we quantified the sensitivity of regional-scale snow depth simulations to parameterization schemes. Additionally, we analyzed the spatial patterns of the parameterization sensitivities and assessed the uncertainty of the multi-parameterization scheme ensemble simulation. The results demonstrated that the differences in snow depth simulation results among the 48 scheme combinations were more pronounced in mountain regions, with melting mechanisms being the primary factor contributing to uncertainty in ensemble simulation. Contrasting mountain regions, the sensitivity index for the physical process of partitioning precipitation into rainfall and snowfall was notably higher in basin areas. Unexpectedly, the sensitivity index of the lower boundary condition of the physical process of soil temperature was negligible across the entire region. Surface layer drag coefficient and snow surface albedo parameterization schemes demonstrated meaningful sensitivity in localized areas, while the sensitivity index of the first snow layer or soil temperature time scheme exhibited a high level of sensitivity throughout the entire region. The uncertainty of snow depth ensemble simulation in mountainous areas is predominantly concentrated between 0.2 and 0.3 m, which is significantly higher than that in basin areas. This study aims to provide valuable insights into the judicious selection of parameterization schemes for modeling snow processes.

Multidecadal Changes in the Flow Velocity and Mass Balance of the Hailuogou Glacier in Mount Gongga, Southeastern Tibetan Plateau

Maritime glaciers in the southeastern Tibetan Plateau (TP) have experienced important changes in mass and dynamics over the past decades, challenging the regional water supply and glacier-related hazards. However, knowledge about long-term variations in the surface velocity and mass balance of maritime glaciers remains incomplete due to the lack of representative observations in the southeastern TP. In this study, offset tracking is employed to measure spatiotemporal variation in the surface velocity of the Hailuogou Glacier (HLG) in Mount Gongga of the southeastern TP using Sentinel-1A imagery, while the time series of the HLG mass balance is reconstructed since 1950 by a physically based energy–mass balance model. Our satellite-based results find that HLG surface velocity shows significant spatial heterogeneity with a double-peak pattern along the flow line, and sustained slowdown below the icefall zone has been observed during the past nearly 40 years, although the icefall zone and the area above it have become relatively active. Our modeling indicates a persistent increase in mass loss over the last seven decades with an average rate of −0.58 m water equivalent (w.e.) year−1, which has accelerated in the past two decades. Sustained slowdown on the glacier is concomitant with pronounced negative mass balance, thereby enhancing glacier wastage in recent decades. The long-term trend in HLG mass loss is mainly driven by an increase in positive air temperature that decreases surface albedo and solid precipitation ratio and increases longwave incoming radiation, besides the influence of supraglacial debris cover. Large-scale atmospheric circulation patterns in the Eurasian region provide important implications for regional-to-local climate variability, unsustainably intensifying the trend of the negative mass balance of the HLG in the southeastern TP in the past two decades.

Even cooler insights: On the power of forests to (water the Earth and) cool the planet.

Scientific innovation is overturning conventional paradigms of forest, water, and energy cycle interactions. This has implications for our understanding of the principal causal pathways by which tree, forest, and vegetation cover (TFVC) influence local and global warming/cooling. Many identify surface albedo and carbon sequestration as the principal causal pathways by which TFVC affects global warming/cooling. Moving toward the outer latitudes, in particular, where snow cover is more important, surface albedo effects are perceived to overpower carbon sequestration. By raising surface albedo, deforestation is thus predicted to lead to surface cooling, while increasing forest cover is assumed to result in warming. Observational data, however, generally support the opposite conclusion, suggesting surface albedo is poorly understood. Most accept that surface temperatures are influenced by the interplay of surface albedo, incoming shortwave (SW) radiation, and the partitioning of the remaining, post-albedo, SW radiation into latent and sensible heat. However, the extent to which the avoidance of sensible heat formation is first and foremost mediated by the presence (absence) of water and TFVC is not well understood. TFVC both mediates the availability of water on the land surface and drives the potential for latent heat production (evapotranspiration, ET). While latent heat is more directly linked to local than global cooling/warming, it is driven by photosynthesis and carbon sequestration and powers additional cloud formation and top-of-cloud reflectivity, both of which drive global cooling. TFVC loss reduces water storage, precipitation recycling, and downwind rainfall potential, thus driving the reduction of both ET (latent heat) and cloud formation. By reducing latent heat, cloud formation, and precipitation, deforestation thus powers warming (sensible heat formation), which further diminishes TFVC growth (carbon sequestration). Large-scale tree and forest restoration could, therefore, contribute significantly to both global and surface temperature cooling through the principal causal pathways of carbon sequestration and cloud formation.

Winter cover cropping increases albedo and latent heat flux in a Texas High Plains agroecosystem

AbstractWinter cover crops represent a land cover change that may sequester carbon in the soil and improve agricultural sustainability. Their adoption may also change the Earth's radiative balance and result in biophysical feedbacks to climate through alterations in albedo and latent heat fluxes. Understanding the mechanisms underlying these alterations to the radiative balance is important for making reliable future climate projections. However, data on cover crop biophysics are limited, requiring models to rely on data from summer plants for parameterization, likely biasing predictions. To address this gap, we measured the albedo and stomatal conductance of two summer crops and three winter crops on farms in the High Plains region of Texas. We also established a winter cover crop field experiment with two cover crops and fallow fields to estimate the change in albedo and latent heat flux that results from a switch to winter cover cropping. We found that albedo was significantly higher in winter‐like conditions than in summer‐like conditions due to an increase in plant albedo and a reduction in leaf area index. The albedo of winter cover crops was higher than the soil albedo, resulting in an increase in top‐of‐atmosphere reflected radiation of 7%–14% when converting from fallow fields to winter cover cropped fields. There was an additional cooling effect through doubling of the estimated latent heat flux caused by the presence of cover crops. The combined changes in albedo and latent heat resulted in a change in the surface energy balance that is associated with an overall cooling effect of winter cover crops on surface atmospheric temperatures. While this effect is likely to be region‐specific, these results strongly indicate that winter cover crops alter the surface albedo and latent heat flux of agricultural fields and provide a direct cooling effect in the High Plains region of Texas.

New Features and Enhancements in Community Land Model (CLM5) Snow Albedo Modeling: Description, Sensitivity, and Evaluation

AbstractWe enhance the Community Land Model (CLM) snow albedo modeling by implementing several new features with more realistic and physical representations of snow‐aerosol‐radiation interactions. Specifically, we incorporate the following model enhancements: (a) updating ice and aerosol optical properties with more realistic and accurate data sets, (b) adding multiple dust types, (c) adding multiple surface downward solar spectra to account for different atmospheric conditions, (d) incorporating a more accurate adding‐doubling radiative transfer solver, (e) adding nonspherical snow grain representations, (f) adding black carbon‐snow and dust‐snow internal mixing representations, and (g) adding a hyperspectral (480‐band vs. the default 5‐band) modeling capability. These model features/enhancements are included as new CLM physics/namelist options, which allows for quantification of model sensitivity to snow albedo processes and for multi‐physics model ensemble analyses for uncertainty assessment. The model updates have been included in the latest released CLM version. Sensitivity analyses reveal strong impacts of using the new adding‐doubling solver, nonspherical snow grains, aerosol‐snow internal mixing, updated aerosol optics, and different dust types. These enhanced snow albedo representations improve the CLM simulated global snowpack evolution and land surface conditions, with reduced biases in simulated snow surface albedo, snow cover, snow water equivalent, snow depth, and surface (2‐m) air temperature over many mid‐latitude mountainous regions and seasonal snowpacks but degraded performance in some northern high‐latitude regions.

Aerosol radiative forcing of forest fires unprecedented in South Korea (2022) captured by Korean geostationary satellites, GK-2A AMI and GK-2B GEMS

The worst forest fires in Korean history broke out on March 4, 2022 and lasted for ten days. In order to monitor the catastrophic forest fires, Geostationary Korea Multi-Purpose Satellite (GK)-2 A Advanced Meteorological Imager (AMI) and GK-2B Geostationary Environment Monitoring Spectrometer (GEMS) data were used in this study. Aerosol optical depth (AOD) irretrievable for the biomass-burning aerosols produced with water vapor classified as could-contaminated, was reconstructed by ultraviolet aerosol index (UVAI). Afterward, aerosol radiative forcing (ARF) at TOA was finally estimated by the correlation of AOD and surface albedo with ARF. Most of the aerosols drifted toward the East Sea by the prevailing westerly winds, and caused a cooling effect on the atmosphere with a maximum daily average radiative forcing of −69.28 Wm-2. Furthermore, the fire-prone conditions for the unprecedented forest fires were discussed in detail as following aspects; 1) the most severe drought caused by a “triple-dip” La Niña; 2) pressure patterns and topographical features that generate strong winds; 3) coniferous forests prone to fires; and 4) increased human activity following the nationwide COVID-19 vaccination. This study demonstrated that the rapid and effective ARF estimation based on the satellite remote sensing can contribute to a better understanding of ARF in the Earth's radiation budget for the global forest fires that will be more frequent, intense, and longer-lasting due to the human-caused climate and environment changes.

Instant Response of Tibetan Plateau Surface Albedo to Snow Coverage and Depth in Snow Season

AbstractTibetan Plateau (TP) snow cover is featured by sub‐seasonal changes, affecting weather and climate in surrounding and downstream areas. Previous studies emphasize the effect of background atmospheric circulation on rapid changes of TP snow cover as a whole. However, spatial discrepant changes of snow cover over the TP with complex topography and uneven snowfall remain unaddressed. Our research indicates that snow cover fraction dominates the rapid changes of surface albedo across the TP, and snow depth also significantly influences surface albedo changes through modulating snow albedo in central and eastern TP with shallow snow. However, the excessive snow amount and empirical snow cover fraction schemes introduce spatially divergent biases of surface albedo changes in simulations. Our research highlights the instant response of TP surface albedo to both snow coverage and depth in snow season, and provides a promising perspective for improving TP snow and surface albedo simulations.

Miocene Antarctic Ice Sheet area adapts significantly faster than volume to CO2-induced climate change

Abstract. The strongly varying benthic δ18O levels of the early and mid-Miocene (23 to 14 Myr ago) are primarily caused by a combination of changes in Antarctic Ice Sheet (AIS) volume and deep-ocean temperatures. These factors are coupled since AIS changes affect deep-ocean temperatures. It has recently been argued that this is due to changes in ice sheet area rather than volume because area changes affect the surface albedo. This finding would be important when the transient AIS grows relatively faster in extent than in thickness, which we test here. We analyse simulations of Miocene AIS variability carried out using the three-dimensional ice sheet model IMAU-ICE forced by warm (high CO2, no ice) and cold (low CO2, large East AIS) climate snapshots. These simulations comprise equilibrium and idealized quasi-orbital transient runs with strongly varying CO2 levels (280 to 840 ppm). Our simulations show a limited direct effect of East AIS changes on Miocene orbital-timescale benthic δ18O variability because of the slow build-up of volume. However, we find that relative to the equilibrium ice sheet size, the AIS area adapts significantly faster and more strongly than volume to the applied forcing variability. Consequently, during certain intervals the ice sheet is receding at the margins, while ice is still building up in the interior. That means the AIS does not adapt to a changing equilibrium size at the same rate or with the same sign everywhere. Our results indicate that the Miocene Antarctic Ice Sheet affects deep-ocean temperatures more than its volume suggests.

Quantification of Shortwave Surface Albedo Feedback Using a Neural Network Approach

Radiative transfer is a nonlinear process. Despite this, most current methods to evaluate radiative feedback, such as the kernel method, rely on linear assumptions. Neural network (NN) models can emulate nonlinear radiative transfer due to their structure and activation functions. This study aims to test whether NNs can be used to evaluate shortwave radiative feedbacks and to assess their performance. This study focuses on the shortwave radiative feedback driven by surface albedo. An NN model is first trained using idealized cases, simulating truth values from a radiative transfer model via the partial radiative perturbation method. Two heuristic cases are analyzed: univariate feedback, perturbing the albedo; and bivariate feedback, perturbing the albedo and cloud cover concurrently. These test the NN’s ability to capture nonlinearity in the albedo–flux and albedo–cloud–flux relationships. We identify the minimal NN structure and predictor variables for accurate predictions. Then, an NN model is trained with realistic radiation flux and atmospheric variable data and is tested with respect to its predictions at different order levels: zero-order for the flux itself, first-order for radiative sensitivity (kernels), and second-order for kernel differences. This paper documents the test results and explains the NN’s ability to reproduce the complex nonlinear relationship between radiation flux and different atmospheric variables, such as surface albedo, cloud optical depth, and their coupling effects.

Radiative Regime According to the New RAD-MSU(BSRN) Complex in Moscow: The Roles of Aerosol, Surface Albedo, and Sunshine Duration

The radiative budget is one of the key factors that influences climate change. The aim of this study was to analyze the radiative regime in Moscow using the RAD-MSU(BSRN) complex and to estimate the radiative effects of the main geophysical factors during the 2021–2023 period. This complex is equipped and maintained according to the recommendations of the Baseline Surface Radiation Network; however, it is not a part of this network. In cloudless conditions, the decrease in global shortwave irradiance (Q) is about 18–22% due to the aerosol content with a pronounced change in the direct to diffuse ratio. In winter, the increase in Q is about 45 W/m2 (or 9%) at h = 30° due to a high surface albedo and reduced aerosol and water vapor contents, while the net shortwave irradiance (Bsh) demonstrates a significant decrease due to the prevailing effects of snow albedo. In cloudy conditions, a nonlinear dependence of Q and Bsh cloud transmittance on the relative sunshine duration is observed. The mean changes in Q for the 2021–2023 against the 1955–2020 period are characterized by negative anomalies (−22%) in winter and positive anomalies in summer (+3%) due to the changes in cloudiness. This is in line with the global tendencies in the long-term changes in shortwave irradiance in moderate climates in Europe in recent years.

Shortwave and longwave components of the surface radiation budget measured at the Thule High Arctic Atmospheric Observatory, Northern Greenland

Abstract. The Arctic climate is influenced by the interaction of shortwave (SW) and longwave (LW) radiation with the atmosphere and the surface. The comprehensive evolution of the Surface Radiative Fluxes (SRF) on different time scales is of paramount importance to understanding the complex mechanisms governing the Arctic climate. However, only a few sites located in the Arctic region provide long-term time series of SRF allowing for capturing of the seasonality of atmospheric and surface parameters and for carrying out validation of satellite products and/or reanalyses. This paper presents the daily and monthly SRF record collected at the Thule High Arctic Atmospheric Observatory (THAAO, 76.5∘ N, 68.8∘ W), in North-Western Greenland. The downwelling components of the SW (DSI) and the LW (DLI) irradiances have been measured at THAAO since 2009, whereas the collection of the upwelling SW (USI) and LW (ULI) irradiance was started in 2016, together with additional measurements, such as meteorological parameters and column water vapour. The datasets of DSI (Meloni et al., 2022a; https://doi.org/10.13127/thaao/dsi), USI (Meloni et al., 2022b; https://doi.org/10.13127/thaao/usi), DLI (Meloni et al., 2022c; https://doi.org/10.13127/thaao/dli), ULI (Meloni et al., 2022d; https://doi.org/10.13127/thaao/uli), and near-surface air temperature (Muscari et al., 2018; https://doi.org/10.13127/thaao/met), can be accessed through the THAAO web site (https://www.thuleatmos-it.it/data, last access: 16 January 2024). The DSI is absent (solar zenith angle ≥90∘) from 29 October to 13 February, assuming maxima in June (monthly mean of 277.0 Wm−2), when it is about half of the total incoming irradiance. The USI maximum occurs in May (132.4 Wm−2) owing to the combination of moderately high DSI values and high albedo. The shortwave surface albedo (A) assumes an average of 0.16 during summer, when the surface is free of snow. Differently, during months of snow-covered surface, when solar radiation allows A to be estimated, its values are greater than 0.6. A large interannual variability is observed in May and September, months characterized by rapidly changing surface conditions, having a link with air temperature anomalies. The DLI and ULI maxima occur in July and August, and the minima in February and March. ULI is always larger than DLI and shows a wider annual cycle. ULI is well described by a fourth-order polynomial fit to the air temperature (R2&gt;0.99 for monthly data and R2&gt;0.97 for daily data). The Surface Radiation Budget (SRB) is positive from April to August, when absorption of solar radiation exceeds the infrared net cooling, with a maximum value of 153.2 Wm−2 in June. From November to February, during the polar night, the LW net flux varies between −34.5 and −43.0 Wm−2. In March and September, the negative LW net flux overcomes the positive SW contribution, producing a negative SRB. The THAAO measurements show clear evidence of the influence of several regional weather/climate events, that appear strongly linked with SRF anomalies. These anomalies were found, for example, during summer 2012, when a large ice melting event took place over Greenland, and during winter 2019–2020, which was extraordinarily cold in the Arctic region.

Upgrade and extension of LSA-SAF land surface albedo archive from EPS Metop/AVHRR: description and quality assessment

ABSTRACT ETAL is the operational EPS Ten-Day Albedo product, produced by the EUMETSAT Satellite Application Facility for Land Surface Analysis (LSA SAF). By back-processing the full catalogue of EPS-Metop radiance data from September 2007 to June 2021, we are able to 1) extend the temporal coverage (previously the archive only went back to 2015) and 2) improve the product archive that was based on near-real time (NRT) processing; the second point is achieved by using reanalyses instead of forecasts of atmospheric conditions and by not being exposed to missing data in the NRT radiance inputs. We present this reprocessed part of the ETAL data set, called ETAL-R, and assess its quality and consistency with respect to the original archive of NRT ETAL data (for the overlapping period 2015–2021), as well as its accuracy compared to albedo from MODIS and ground stations. ETAL-R exhibits reliable long-term stability and increased homogeneity compared to the NRT archive, and the comparison against the additional reference data shows satisfactory accuracy. Overall, ETAL-R is shown to be very consistent with the ETAL NRT archive while – under certain circumstances – improving it.