Estimation of 1-km Resolution All-Sky Instantaneous Erythemal UV-B with MODIS Data Based on a Deep Learning Method

Although ultraviolet-B (UV-B) radiation reaching the ground represents a tiny fraction of the total solar radiant energy, it significantly affects human health and global ecosystems. Therefore, erythemal UV-B monitoring has recently attracted significant attention. However, traditional UV-B retrieval methods rely on empirical modeling and handcrafted features, which require expertise and fail to generalize to new environments. Furthermore, most traditional products have low spatial resolution. To address this, we propose a deep learning framework for retrieving all-sky, kilometer-level erythemal UV-B from Moderate Resolution Imaging Spectroradiometer (MODIS) data. We designed a deep neural network with a residual structure to cascade high-level representations from raw MODIS inputs, eliminating handcrafted features. We used an external random forest classifier to perform the final prediction based on refined deep features extracted from the residual network. Compared with basic parameters, extracted deep features more accurately bridge the semantic gap between the raw MODIS inputs, improving retrieval accuracy. We established a dataset from 7 Surface Radiation Budget Network (SURFRAD) stations and 1 from 30 UV-B Monitoring and Research Program (UVMRP) stations with MODIS top-of-atmosphere reflectance, solar and view zenith angle, surface reflectance, altitude, and ozone observations. A partial SURFRAD dataset from 2007–2016 trained the model, achieving an R2 of 0.9887, a mean bias error (MBE) of 0.19 mW/m2, and a root mean square error (RMSE) of 7.42 mW/m2. The model evaluated on 2017 SURFRAD data shows an R2 of 0.9376, an MBE of 1.24 mW/m2, and an RMSE of 17.45 mW/m2, indicating the proposed model accurately generalizes the temporal dimension. We evaluated the model at 30 UVMRP stations with different land cover from those of SURFRAD and found most stations had a relative RMSE of 25% and an MBE within ±5%, demonstrating generalization in the spatial dimension. This study demonstrates the potential of using MODIS data to accurately estimate all-sky erythemal UV-B with the proposed algorithm.

Radiative closure and cloud effects on the radiation budget based on satellite and ship-borne observations during the Arctic summer research cruise PS106

For understanding Arctic climate change, it is critical to quantify and address uncertainties in climate data records on clouds and radiative fluxes derived from long-term passive satellite observations. A unique set of observations collected during the research vessel Polarstern PS106 expedition (28 May to 16 July 2017) by the OCEANET facility is exploited here for this purpose and compared with the CERES SYN1deg Ed. 4.1 satellite remote sensing products. Mean cloud fraction (CF) of 86.7 % for CERES and 76.1 % for OCEANET were found for the entire cruise. The difference of CF between both data sets is due to different spatial resolution and momentary data gaps due to technical limitations of the set of ship-borne instruments. A comparison of radiative fluxes during clear-sky conditions enables radiative closure for CERES products by means of independent radiative transfer simulations. Several challenges were encountered to accurately represent clouds in radiative transfer under cloudy conditions, especially for ice-containing clouds and low-level stratus (LLS) clouds. During LLS conditions, the OCEANET retrievals were in particular compromised by the altitude detection limit of 155 m of the cloud radar. Radiative fluxes from CERES show a good agreement with ship observations, having a bias (standard deviation) of −6.0 (14.6) W m−2 and 23.1 (59.3) W m−2 for the downward longwave (LW) and shortwave (SW) fluxes, respectively. Based on CERES products, mean values of the radiation budget and the cloud radiative effect (CRE) were determined for the PS106 cruise track and the central Arctic region (70°–90° N). For the period of study, the results indicate a strong influence of the SW flux in the radiation budget, which is reduced by clouds leading to a net surface CRE of −8.8 W m−2 and −9.3 W m−2 along the PS106 cruise and for the entire Arctic, respectively. The similarity of local and regional CRE supports that the PS106 cloud observations can be considered to be representative of Arctic cloudiness during early summer.

Importance of radiative transfer processes in urban climate models: a study based on the PALM 6.0 model system

Including radiative transfer processes within the urban canopy layer into microscale urban climate models (UCMs) is essential to obtain realistic model results. These processes include the interaction of buildings and vegetation with shortwave and longwave radiation, thermal emission, and radiation reflections. They contribute differently to the radiation budget of urban surfaces. Each process requires different computational resources and physical data for the urban elements. This study investigates how much detail modellers should include to parameterize radiative transfer in microscale building-resolving UCMs. To that end, we introduce a stepwise parameterization method to the Parallelized Large-eddy Simulation Model (PALM) system 6.0 to quantify individually the effects of the main radiative transfer processes on the radiation budget and on the flow field. We quantify numerical simulations of both simple and realistic urban configurations to identify the major and the minor effects of radiative transfer processes on the radiation budget. The study shows that processes such as surface and vegetation interaction with shortwave and longwave radiation will have major effects, while a process such as multiple reflections will have minor effects. The study also shows that radiative transfer processes within the canopy layer implicitly affect the incoming radiation since the radiative transfer model is coupled to the radiation model. The flow field changes considerably in response to the radiative transfer processes included in the model. The study identified those processes which are essentially needed to assure acceptable quality of the flow field. These processes are receiving radiation from atmosphere based on the sky-view factors, interaction of urban vegetation with radiation, radiative transfer among urban surfaces, and considering at least single reflection of radiation. Omitting any of these processes may lead to high uncertainties in the model results.

Testing the efficacy of atmospheric boundary layer height detection algorithms using uncrewed aircraft system data from MOSAiC

During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, meteorological conditions over the lowest 1 km of the atmosphere were sampled with the DataHawk2 (DH2) fixed wing uncrewed aircraft system (UAS). Of particular interest is the atmospheric boundary layer (ABL) height, as ABL structure can be closely coupled to cloud properties, surface fluxes, and the atmospheric radiation budget. The high temporal resolution of the UAS observations allows us to subjectively identify ABL height for 65 out of the total 89 flights conducted over the central Arctic Ocean between 23 March and 26 July 2020 by visually analyzing profiles of virtual potential temperature, humidity, and bulk Richardson number. Comparing this subjective ABL height with the ABL heights identified by various previously published objective methods allows us to determine which objective methods are most successful at accurately identifying ABL height in the central Arctic environment. The objective methods we use are the Liu-Liang, Heffter, virtual potential temperature gradient maximum, and bulk Richardson number methods. In the process of testing these objective methods on the DH2 data, numerical thresholds were adapted to work best for the UAS-based sampling. To determine if conclusions are robust across different measurement platforms, the subjective and objective ABL height determination processes were repeated using the radiosonde profile closest in time to each DH2 flight. For both the DH2 and radiosonde data, it is determined that the bulk Richardson number method is the most successful at identifying ABL height, while the Liu-Liang method is least successful.

Solar radiation in the cloud-free atmosphere

<p>The quantification of Earth’s solar radiation budget and its temporal changes is essential for the understanding of the genesis and evolution of climate on our planet. While the solar radiative fluxes in and out of the climate system can be accurately tracked and quantified from space by satellite programs such as CERES or SORCE, the disposition of solar energy within in the climate system is afflicted with larger uncertainties. A better quantification of the solar radiative fluxes not only under cloudy, but also under cloud-free conditions can help to reduce these uncertainties and is essential for example for the determination of cloud radiative effects or for the understanding of  temporal changes in the solar radiative components of the climate system.</p> <p>We combined satellite observations of Top of Atmosphere fluxes with the information contained in surface flux observations and climate models to infer the absorption of solar radiation in the atmosphere, which we estimated at 73 Wm<sup>-2</sup> globally under cloud-free conditions (Wild et al. 2019 Clim Dyn). The latest generation of climate models participating in CMIP6 is now able to reproduce this magnitude surprisingly well, whereas in previous climate model  generations the cloud-free atmosphere was typically too transparent for solar radiation, which stated a long-standing modelling issue (Wild 2020 Clim Dyn, Wild et al. 1995 JClim).</p> <p>With respect to changes in solar fluxes, there is increasing evidence that the substantial long-term decadal variations in surface solar radiation known as dimming and brightening occur not only under all-sky, but similarly also under clear-sky conditions (Manara et al. 2016 ACP, Yang et al. 2019 JClim; Wild et al. 2021 GRL). This points to aerosol radiative effects as major factor for the explanation of this phenomenon.</p>

A breviary of Earth’s climate changes using Stephan-Boltzmann law

Earth’s surface temperature oscillated greatly throughout time. From near congelation during “snowball Earth” 2.9Gya to an ice-free world in the Paleocene-Eocene Thermal maximum 55Mya. These changes have been forced by internal (e.g. changes in the chemical composition of the atmosphere) or external (e.g. changes in solar luminosity) drivers that varied through time. Thus, if we understand how the radiation budget evolved in different times, we can closely calculate past global climate; a fundamental comparison to situate current climate change in the context Earth’s history. Here I present an analytical framework employing a simple energy balance derived from the Stephan-Boltzmann law, that allows for quick comparison between drivers of global temperature and at multiple moments in the history of our planet. My results show that current rates of increase in global temperature are at least four times faster than any previous warming event.

Quantifying the influence of management strategies on surface radiation budgets and energy patterns in tea fields

The surface energy balance from canopy to landscape scales in crop fields plays an essential role in surface-atmosphere interactions, and it is strongly influenced by the management strategies and field practices of farmers. To characterize how different agricultural practices of farmers affect the microenvironment in perennial crop fields, long-term observation of the radiation budget and energy components under different field practices was undertaken in two neighboring tea fields with different management strategies (a conventional operation and an organic-certified field managed by different farmers) in northern Taiwan. The results showed that the difference in the radiation budget in these two tea fields was minor (only 1% for net radiation), but the differences in the energy components were more significant (sensible heat was 10% lower and latent heat was 25% higher in the organic-certified field than in the conventional field) due to highly distinct practices in these two fields. This finding implies that the organic-certified application could lower the partitioning of sensible heat flux and increase the latent heat flux, thereby reducing the temperature variation and decreasing the vapor pressure deficit. The organic-certified field reduced the surface heating in terms of the long-term energy patterns. This study’s findings also indicate that field practices in conventional field can increase the sensible heat flux (51.5% at noon time) on short-term time scales, compared with only 9.6% in organic-certified field. Furthermore, this study offers a comprehensive understanding of tea field practices, a scientific basis for in-field water conservation, and a quantitative analysis for modeling from micro to regional scales.

Cross-Comparison of Global Surface Albedo Operational Products—MODIS, GLASS, and CGLS

Surface albedo, as an important parameter for land surface geo-biophysical and geo-biochemical processes, has been widely used in the research communities involved in surface energy balance, weather forecasting, atmospheric circulation, and land surface process models. In recent years, operational products using satellite-based surface albedo have, from time to time, been rapidly developed, contributing significantly to the estimation of energy balance at regional or global scales. The increasing number of research topics on dynamic monitoring at a decades-long scale requires a combination of albedo products generated from various sensors or programs, while the quantitative assessment of agreement or divergence among different surface albedo products still needs further understanding. In this paper, we investigated the consistency of three classical operational surface albedo products that have been frequently used by researchers globally via the official issued datasets-MODIS, GLASS (Global LAnd Surface Satellite), and CGLS (Copernicus Global Land Service). The cross-comparison was performed on all the identical dates available during 2000–2017 to represent four season-phases. We investigated the pixel-based validity of each product, consistency of global annual mean, spatial distribution and different temporal dynamics among the discussed products in white-sky (WSA) and black-sky (BSA) albedo at visible (VIS), near-infrared (NIR), and shortwave (SW) regimes. Further, varying features along with the change of seasons was also examined. In addition, the variation in accuracy of shortwave albedo magnitude was explored using ground measurements collected by the Baseline Surface Radiation Network (BSRN) and the Surface Radiation Budget Network (SUFRAD). Results show that: (1) All three products can provide valid long-term albedo for dominant land surface, while GLASS can provide additional estimation over sea surfaces, with the highest percentage of valid land surface pixels, at up to 93% in October 24. The invalid pixels mainly existed in the 50°N–60°N latitude belt in December for GLASS, Central Africa in April and August for MODIS, and northern high latitudes for CGLS. (2) The global mean albedo of CGLS at the investigated bands has significantly higher values than those of MODIS and GLASS, with a relative difference of ~20% among the three products. The global mean albedo of MODIS and GLASS show a generally increasing trend from April to December, with an abrupt rise at NIR and SW of CGLS in June of 2014. Compared with SW and VIS bands, the linear temporal trend of the NIR global albedo mean in three products continues to increase, but the slope of CGLS is 10–100 times greater than that of the other two products. (3) The differences in albedo, which are higher in April, October, and December than in August, exhibit a small variation over the main global land surface regions, except for Central Eurasia, North Africa, and middle North America. The magnitude of global absolute difference among the three products usually varies within 0.02–0.06, but with the largest value occasionally exceeding 0.1. The relative difference is mainly within 10%–20%, and can deviate more than 40% away from the baseline. In addition, CGLS has a greater opportunity to achieve the largest difference compared with MODIS and GLASS. (4) The comparison with ground measurements indicates that MODIS generally performs better than GLASS and CGLS at the sites discussed. This study demonstrates that apparent differences exist among the three investigated albedo products due to the ingested source data, algorithm, atmosphere correction etc., and also points at caution regarding data fusion when multiple albedo products were organized to serve the following applications.

Observation of aerosol induced ‘lower tropospheric cooling’ over Indian core monsoon region

Aerosols play a significant role in regional scale pollution that alters the cloud formation process, radiation budget, and climate. Here, using long-term (2003-2019) observations from multi-satellite and ground-based remote sensors, we show robust aerosol-induced instantaneous daytime lower tropospheric cooling during the pre-monsoon season over the Indian core monsoon region (ICMR). Quantitatively, an average cooling of -0.82±0.11 °C to -1.84±0.25 °C is observed in the lower troposphere. The observed cooling is associated with both aerosol-radiation and aerosol-cloud-radiation interactions processes. The elevated dust and polluted-dust layers cause extinction of the incoming solar radiation, thereby decreasing the lower tropospheric temperature. The aerosol-cloud interactions also contribute to enhancement of cloud fraction which further contributes to the lower tropospheric cooling. The observed cooling results in a stable lower tropospheric structure during polluted conditions, which can also feedback to cloud systems. Our findings suggest that aerosol induced lower tropospheric cooling can strongly affect the cloud distribution and circulation dynamics over the ICMR, a region of immense hydroclimatic importance.

Linking Future Hydroclimatological Changes With Past Climatic Conditions In Southeastern Iran: Insights From Models And Observations

We compare the predicted results of future hydrological changes based on a thirty-year (1989-2019) weather dataset with paleoclimatic changes inferred based on established proxies from the Jazmurian playa in southeastern Iran. Parallels between expected changes in the future were compared to past climatic conditions to trace the impact this region has undergone in the distant past. The study area is affected by the Indian Ocean Summer Monsoon (IOSM) and the Mid-Latitude Westerlies (MLW). The maximum and minimum temperatures and precipitation were predicted for the future (2061- 2080) by statistical downscaling outputs of 5 GCM models (EC-EARTH, GFDL-CM3, HadGEM2-ES, MIROC5, MPI-ESM-MR) under RCP 4.5 and RCP 8.5. The results show that the 20-years average of the mean temperatures ((Tmax + Tmin)/2) will increase in the range of 3.2 to 4.6 °C under RCP 8.5 compared to the base period. The trends suggest that the region will experience drier conditions than the baseline period in the future under both scenarios. In addition, the GCM predicts a considerable decline in MLW precipitation and little change in future IOSM precipitation under both scenarios compared to the baseline. The decrease in MLW precipitation is consistent with other GCM predictions and real paleoclimatic changes that happened during past warm/wet periods in the region. However, considering the close relationship between the increase in the Earth’s radiation budget and enhanced IOSM precipitation in southeast Iran since the late Pleistocene, we postulate that more intensive IOSM activity can be expected in the future.