Surface Longwave Research Articles

Regimes of Convective Self-Aggregation in Convection-Permitting Beta-Plane Simulations

Abstract The spontaneous self-aggregation (SA) of convection in idealized model experiments highlights the importance of interactions between tropical convection and the surrounding environment. The authors have shown that SA fundamentally changes with the background rotation in previous f-plane simulations, in terms of both the resulting forms of organized convection and the relative roles of the physical feedbacks driving them. This study considers the dependence of SA on rotation in one large domain on the β plane, introducing an additional layer of complexity. Simulations are performed with uniform thermal forcing and explicit convection. Focuses include statistical and structural analysis of the convective modes, process-oriented diagnostics of how they develop, and resulting mean states. Two regimes of SA emerge within the first 15 days, separated by a critical zone where f is analogous to 10°–15° latitude. Organized convection at near-equatorial values of f primarily consists of convectively coupled Kelvin waves. Wind speed–surface enthalpy flux feedbacks are the dominant process driving moisture variability early on, then clear-sky shortwave radiative feedbacks are strongest in wave maintenance. In contrast, at higher f, numerous tropical cyclones develop and coexist, dominated by surface flux and longwave processes. Tropical cyclogenesis is most pronounced at intermediate f (analogous to 25°–40°), but are longer-lived at higher f. The resulting modes of SA at low f differ between these β-plane simulations (convectively coupled waves) and prior f-plane simulations (weak tropical cyclones or nonrotating clusters). Otherwise, these results provide further evidence for the changing roles of radiative, surface flux, and advective processes in influencing SA as f changes, as found in our previous study. Significance Statement In model simulations, convection often self-organizes due to interactions with its surrounding environment. These interactions are relevant in the real-world organization of rainfall and clouds, and may thus be useful to understand for improved prediction of tropical weather and climate. Previous work using a set of simple model experiments with constant Coriolis force showed that at different latitudes, different processes dominate, and different types of organized convection result. This study verifies that finding using a more complex and realistic model, where the Coriolis force varies within the domain to resemble different latitudes. Specifically, the convection here self-organizes into atmospheric waves (periodic disturbances) at low latitudes, and tropical cyclones at high latitudes.

Variability and trends in land surface longwave radiation fluxes from six satellite and reanalysis products

ABSTRACT Earth surface longwave radiation (SLR), including downward (DLR), upward (ULR), and net longwave radiation (NLR), significantly impacts the surface radiation budget and global climate evolution. However, the spatiotemporal variation in SLR remains poorly understood. In this study, three satellite products (GLASS-MODIS V40, GLASS-AVHRR, and CERES-SYN) and three reanalysis datasets (ERA5, MERRA-2, and GLDAS) were validated using ground measurements from 288 sites at seven observation networks. The mean biases and root mean square errors of the monthly DLR (ULR, NLR) estimates from the six products were −6.36 (−3.56, −2.86) Wm-2 and 16.63 (14.33, 13.38) Wm-2, respectively. Large differences in the spatial distribution of the SLR were mainly observed at high-latitude, high-altitude and desert/barren-covered regions. Large interannual variability was detected at high latitudes. GLASS-AVHRR and ERA5 better captured the long-term variability in DLR and ULR, whereas GLASS-AVHRR and MERRA-2 better detected trends in NLR. An increasing trend in DLR and ULR was observed between 1982 and 2015, followed by a decreasing trend from 2016 to 2021; the NLR flux did not exhibit a significant trend. Overall, the GLASS-AVHRR and ERA5 SLR estimates were more accurate and stable than those of the other products in accuracy, spatiotemporal distribution, and trend analysis.

A Comprehensive Evaluation of Three Global Surface Longwave Radiation Products

Surface longwave radiation is sensitive to climate change on Earth. This study first comprehensively evaluates the accuracies of surface longwave upward radiation (SLUR) and surface longwave downward radiation (SLDR) among the mainstream surface longwave (LW) radiation products (GLASS, CERES SYN and ERA5); then, the global annual mean values of surface LW radiation as well as its temporal variations from 2003 to 2020 are quantified. The ERA5 SLUR and SLDR show the best accuracies by direct validation, with biases/Stds/RMSEs of −1.05/18.34/18.37 W/m2 and −9.41/24.15/25.92 W/m2, respectively. The GLASS SLUR has the best accuracy under clear-sky conditions with a bias/Std/RMSE of −6.73/14.21/15.72 W/m2. The accuracy of the GLASS SLDR is comparable to CERES SYN. The merit of the GLASS LW radiation is that it can provide rich spatial details due to its high spatial resolution. The global annual mean SLUR is 399.77/398.92/398.19 W/m2, and that of the SLDR is 342.64/347.98/340.47 W/m2 for GLASS, CERES SYN and ERA5, respectively. The interannual variation trends for the three products produce substantially growing long-term trends for the global mean SLUR and SDLR over the globe and land, while there are almost no trends over the ocean. The long-term trends of the seasonal mean SLUR and SDLR in the Northern and Southern Hemispheres are asymmetrical. Our comprehensive evaluation and trend analysis of the mainstream surface LW radiation products can aid in understanding the global energy balance and climate change.

Comparison of Clouds and Cloud Feedback between AMIP5 and AMIP6

We examine the changes in clouds and cloud feedback between Phase 5 (AMIP5) and Phase 6 (AMIP6) of the Atmospheric Model Intercomparison Project. Each model is perturbed by uniformly increasing the sea surface temperature by 4 K. The simulated cloud fraction, the perturbed states and cloud radiative kernels are used to derive cloud feedback in the shortwave (SW), longwave (LW) and their sum (Net). Compared to AMIP5, the cloud fraction in AMIP6 increases by 9.1%, while the perturbation leads to a 0.25% decrease. The Net cloud feedback at the top of the atmosphere (TOA) is almost double (174%). Statistical tests support that this change is mainly due to an increase in the surface SW cloud feedback caused by optically thick, middle and low clouds. The contribution of the atmospheric Net component (12%) stems from the increase in the atmospheric LW cloud feedback, likely to play a role in weakening (strengthening) the northward (southward) meridional atmospheric energy transport, while the opposite is true for the surface LW and Net cloud feedback in the meridional oceanic energy transport. The substantial increase in cloud feedback at the TOA primarily contributes to the higher climate sensitivity. The cloud feedback spread in AMIP6 is comparable to that in AMIP5.

A daily 5-km all-sky sea-surface longwave radiation product based on statistically modified deep neural network and spatiotemporal analysis for 1981–2018

Longwave radiation components, including downward, upward, and net longwave radiation (DLR, ULR, and NLR, respectively), are essential parameters in heat flux exchange across the ocean–atmosphere interface. However, few long-term, high-resolution, and accurate sea–surface longwave radiation (SSLR) products are available. We generated the first high-resolution (5-km) all-sky daily SSLR product from Advanced Very High-Resolution Radiometer (AVHRR) top-of-atmosphere observations, combined with the European Center for Medium-Range Weather Forecasts Reanalysis V5 near-surface meteorological variables and National Oceanic and Atmospheric Administration sea–surface temperatures from 1981 to 2018. We coupled the densely connected convolutional neural network and bidirectional long short-term memory neural network as a retrieval algorithm. The training dataset was generated using integrated SSLR samples from 2002 to 2012 at 437 globally distributed locations. The archived product, SSLR_AVHRR, showed a high accuracy against 81,546 buoy-based observations from eight observation networks, with an R2 of 0.96 (1.00, 0.77), root mean square error of 10.27 (4.51, 9.27) Wm−2, and mean bias error of −1.30 (0.30, −0.72) Wm−2 for DLR (ULR, NLR) retrievals. Based on SSLR_AVHRR, the global DLR (ULR) flux exhibited a significantly (p-value < 0.05) increasing trend of 1.03 (1.08) Wm−2/decade during 1982–2018. The trend was 0.24 (0.34) Wm−2/decade during 1982–2000, which increased to 1.79 (1.45) Wm−2/decade during 2001–2018. This globally increasing trend was dominantly impacted by the significant increases in high latitude, particularly in the Arctic Ocean. Trend variations at low latitudes, which were more frequent than at middle and high latitudes because of the El Niño-Southern Oscillation, mitigated the increasing rates of global DLR and ULR after strong El Niño years, whereas the global NLR flux remained relatively stable throughout the study period. This method can be extended and applied to estimate other air-sea fluxes based on a unified estimating framework to help mitigate imbalanced energy and freshwater budgets at the air-sea interface to some degree.

Controls on Surface Warming by Winter Arctic Moist Intrusions in Idealized Large-Eddy Simulations

Abstract The main energy input to the polar regions in winter is the advection of warm, moist air from lower latitudes. This makes the polar climate sensitive to the temperature and moisture of extrapolar air. Here, we study this sensitivity from an air-mass transformation perspective. We perform simulations of an idealized maritime air mass brought into contact with sea ice employing a three-dimensional large-eddy simulation model coupled to a one-dimensional multilayer sea ice model. We study the response of cloud dynamics and surface warming during the air-mass transformation process to varying initial temperature and humidity conditions of the air mass. We find in all cases that a mixed-phase cloud is formed, initially near the surface but rising continuously with time. Surface warming of the sea ice is driven by downward longwave surface fluxes, which are largely controlled by the temperature and optical depth of the cloud. Cloud temperature, in turn, is robustly constrained by the initial dewpoint temperature of the air mass. Since dewpoint only depends on moisture, the overall result is that surface warming depends almost exclusively on initial humidity and is largely independent of initial temperature. We discuss possible climate implications of this result—in particular, for polar amplification of surface warming and the role played by atmospheric energy transports.

Estimation of downwelling surface longwave radiation for all-weather skies from FengYun-4A geostationary satellite data

ABSTRACT Fengyun-4A (FY-4A) is the latest generation of China’s geostationary satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4A can provide high-precision, high-frequency observation data, which makes a new possibility for estimating the downwelling surface longwave radiation (DSLR) with high spatial and temporal resolution. This work presents a new method for estimating DSLRs under all-sky conditions using a genetic algorithm–artificial neural network (GA-ANN) algorithm based on brightness temperature (BT) from the FY-4A AGRI infrared channels and near-surface air temperature and dew point temperature from ERA5 reanalysis data. Based on the verification results of two independent observation sites, it is shown that the bias and RMSE are - 4.31 W/m2 and 35.28 W/m2, respectively. Compared the CERES SYN all-sky DSLR product with the DSLR estimated by the new method, the bias and RMSE are 0.86 W/m2 and 26.87 W/m2, respectively, and the new method has a higher spatial resolution (4 km), which can display more details of spatial variation.

Aerosol direct radiative forcing over China: A 40-year MERRA-2-based evaluation

Aerosol direct radiative forcing (ADRF) has substantial impacts on regional and global climate changes, whereas it remains one of the largest uncertainties among various climate forcing factors. The 40-year detailed clear-sky ADRFs over China from 1981 to 2020 are systematically studied based on the MERRA-2 satellite reanalysis data, which hopefully benefit for the evaluation of the performances of aerosol-climate models. The clear-sky shortwave ADRFs show diverse spatial distributions with strong top of atmosphere (TOA) and surface cooling and atmospheric heating over the Taklamakan desert, eastern China, and southern China. A high clear-sky longwave surface ADRF reaching ∼6.5 W/m2 over dust source regions is found, and continuous increase of ADRFs over the Taklamakan desert during the past four decades may indicate that dust episodes therein are becoming severe. The mean clear-sky shortwave surface, TOA and atmospheric ADRFs, and longwave surface ADRF over China during 1981–2020 are found to be −12.3, −5.1, 7.2, and 1.0 W/m2, respectively. The seasonality of clear-sky ADRFs shows strongest mean values in spring, moderate forcings in summer and fall, and weakest levels in winter. The monthly average clear-sky surface and TOA ADRFs over China vary by approximately twofold, ranging the weakest values of −7.8 and −3.9 W/m2 in December to the strongest −17.6 and −7.1 W/m2 in April, respectively. Distinctive seasonal and monthly patterns of clear-sky ADRFs are generally seen among the Beijing-Tianjin-Hebei region, Yangtze River Delta, Pearl River Delta and Tarim Basin in China, while the monthly ADRF patterns over a typical area of these four regions are similar during four different decades. The clear-sky ADRFs over China are highly correlated with aerosol optical thickness, and dust has strong influences on clear-sky shortwave aerosol direct radiative forcing among column aerosol compositions. Our study indicates general underestimations of clear-sky atmospheric ADRF over China by aerosol-climate models and stronger impacts of aerosol scattering than absorption on the TOA radiation budgets in China.

Upscaling of longwave downward radiation from instantaneous to any temporal scale: Algorithms, validation, and comparison

Surface longwave downward radiation (LWDR) is a key factor affecting the surface energy balance. The daily LWDR and the diurnal variations of LWDR are of great significance for studies of climate change and surface processes. How to obtain LWDR at an averaged temporal scale from instantaneous LWDR is one of the long-standing problems in the field of radiation budget from remote sensing. In this paper, two temporal upscaling methods are introduced, namely, a method based on the diurnal variations of LWDR (diurnal variation based, DVB) and a method based on random forest regression (RFR). The results reveal that: (1) The DVB method has a global hourly and daily LWDR root-mean-square error (RMSE) of less than 21 W/m2 and 15 W/m2, respectively, and the RMSE of the daily LWDR based on RFR is less than 7 W/m2; (2) When compared with four existing statistical interpolation methods, the DVB method can not only ensure the accuracy, but also can overcome the problem of missing samples and/or an abnormal samples during upscaling; (3) Except for directly predict daily LWDR, the DVB methods can also obtain more accurate LWDR diurnal variations such as hourly, half-hourly etc. The RFR method enables high-efficiency and accurate estimation of daily averaged LWDR from instantaneous measurements. Compared with existing methods and products, the proposed methods are not only efficient, but also have a superior applicability and reliable accuracy. The proposed strategies provide new ideas for the community in estimating LWDR at continuous temporal scales from remotely sensed measurements.

A New Bottom-of-Atmosphere (BOA) Radiance-Based Hybrid Method for Estimating Clear-Sky Surface Longwave Upwelling Radiation From MODIS Data

Surface longwave upwelling radiation (SLUR) is a critical parameter for studying the water-energy balance between the land surface and atmosphere. In this study, we proposed a new hybrid method for estimating the clear-sky SLUR from the bottom-of-atmosphere (BOA) radiance (new BOA-hybrid method) of the Moderate Resolution Imaging Spectroradiometer (MODIS). There are two key parts in the developed new BOA-hybrid method. First, a physical four-channel algorithm was developed to estimate the atmospheric terms (atmospheric upwelling radiance and transmittance), which were used to calculate MODIS BOA radiance. Second, a linear model linking SLUR and MODIS BOA channel radiances was constructed using a large number of samples generated by extensive radiative transfer simulations. In situ measurements from twenty-seven sites in four flux networks were collected to validate the new BOA-hybrid method. The average bias and RMSE were 1.65 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 16.23 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the new BOA-hybrid method, which was superior to the original BOA-hybrid method whose bias and RMSE were 3.52 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 18.51 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and slightly better than the classical hybrid method that estimates SLUR using the top-of-atmosphere (TOA) radiance (TOA-hybrid method) whose bias and RMSE were -1.64 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 17.44 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The performance of the new and original BOA-hybrid methods was similar when the total precipitable water vapor (TPW) was less than 1.5 cm. However, under a large TPW (i.e., TPW>3 cm), especially in the case of a large viewing zenith angle (i.e., VZA>45°), the accuracy of the original BOA-hybrid method decreased significantly, while the physical four-channel algorithm could effectively improve the accuracy of atmospheric correction and the RMSE of the SLUR estimated by BOA-hybrid could be reduced by more than 10 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In summary, the developed new BOA-hybrid method can estimate SLUR accurately and has great potential to produce a long-term high spatial resolution environmental data record of SLUR.

Estimating All-Weather Surface Longwave Radiation from Satellite Passive Microwave Data

Surface longwave radiation (SLR) is an essential geophysical parameter of Earth’s energy balance, and its estimation based on thermal infrared (TIR) remote sensing data has been extensively studied. However, it is difficult to estimate cloudy SLR from TIR measurements. Satellite passive microwave (PMW) radiometers measure microwave radiation under the clouds and therefore can estimate SLR in all weather conditions. We constructed SLR retrieval models using brightness temperature (BT) data from an Advanced Microwave Scanning Radiometer 2 (AMSR2) based on a neural network (NN) algorithm. SLR from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) product was used as the reference. NN-based models were able to reproduce well the spatial variability of SLR from ERA5 at the global scale. Validations indicate a reasonably good performance was found for land sites, with a bias of 1.32 W/m2, root mean squared error (RMSE) of 35.37 W/m2, and coefficient of determination (R2) of 0.89 for AMSR2 surface upward longwave radiation (SULR) data, and a bias of −2.26 W/m2, RMSE of 32.94 W/m2, and R2 of 0.82 for AMSR2 surface downward longwave radiation (SDLR) data. AMSR2 SULR and SDLR retrieval accuracies were higher for oceanic sites, with biases of −2.98 and −4.04 W/m2, RMSEs of 6.50 and 13.42 W/m2, and R2 values of 0.83 and 0.66, respectively. This study provides a solid foundation for the development of a PMW SLR retrieval model applicable at the global scale to generate long-term continuous SLR products using multi-year satellite PMW data and for future research with a higher spatiotemporal resolution.

PanoMRT: Panoramic infrared thermography to model human thermal exposure and comfort

As summer heat waves become the new normal worldwide, modeling human thermal exposure and comfort to assess and mitigate urban overheating is crucial to uphold livability in cities. We introduce PanoMRT, an open source human-biometeorological model to calculate Mean Radiant Temperature (TMRT), Physiologically Equivalent Temperature (PET), and the Universal Thermal Climate Index (UTCI) from thermal equirectangular 360° panoramas and standard weather information (air temperature, relative humidity, wind speed). We validated the model for hot, dry, clear summer days in Tempe, Arizona, USA with in-situ observations using a FLIR Duo Pro R thermal camera on a rotating arm and the mobile human-biometeorological instrument platform MaRTy. We observed and modeled TMRT and thermal comfort for 19 sites with varying ground cover (grass, concrete, asphalt), sky view factor, exposure (sun, shade), and shade type (engineered, natural) six times per day. PanoMRT performed well with a Root Mean Square Error (RMSE) of 4.1 °C for TMRT, 2.6 °C for PET, and 1.2 °C for UTCI, meeting the accuracy requirement of ±5 °C set in the ISO 7726 standard for heat and cold stress studies. RayMan reference model runs without measured surface temperature forcing reveal that accurate longwave radiative flux estimations are crucial to meet the ±5 °C threshold, particularly for shaded locations and during midday when surface temperatures peak and longwave modeling errors are largest. This study demonstrates the importance of spatially resolved 3D surface temperature data for thermal exposure and comfort modeling to capture complex longwave radiation exposure patterns resulting from heterogeneity in built configuration and material radiative and thermal properties in the built environment.

Impact of Clouds and Blowing Snow on Surface and Atmospheric Boundary Layer Properties Over Dome C, Antarctica.

Clouds and blowing snow (BLSN) occur frequently over Antarctica, where it is critical to understand their feedbacks to surface and atmospheric boundary layer processes. Dome C, an elevated East Antarctic station, dominated by lengthy periods of surface longwave (LW) radiative cooling, is selected to reveal cloud and BLSN impacts within a largely stable environment. The sky condition is classified as clear, cloudy, or BLSN, using 3years of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite data. Co-located and contemporaneous in situ observations are used to investigate the relationship of sky condition with surface and atmospheric boundary layer thermal structure, focusing on seasonal variability. Results show that increased downwelling LW radiation from clouds abate surface radiative cooling losses, contributing to warming during all seasons. An increase of 3°C in the mean surface air temperature is observed during spring, whereas, a more dramatic rise (around 10°C), due to accompanying large-scale subsidence, is observed during fall and winter in association with clouds. For all seasons, the wind speed and wind speed shear are strongest during BLSN events, and the surface-based inversion is weakened by cooling which peaks in a shallow above-surface turbulent layer. The stronger background stability during fall and winter seasons, restricts turbulence and BLSN depths generally to the lowest tens of meters. The Earth's cryosphere is among the most rapidly evolving yet least well-observed regions, and knowledge of clouds and BLSN interactions with the typical stable atmospheric boundary layer can help further understand energy and moisture exchanges.

Impacts of greenhouse gases and anthropogenic aerosols changes on surface air temperature in East Asia under different post-pandemic period emission scenarios

In order to know how surface air temperature (SAT) changes in East Asia under different emission scenarios after the COVID-19 outbreak, we investigated the impacts of greenhouse gases (GHGs) and anthropogenic aerosols changes on SAT in East Asia by using the aerosol-climate coupled model BCC-AGCM 2.0_CUACE/Aero, combining with the post-pandemic emission scenarios proposed by Covid multi-Earth system model intercomparison project (CovidMIP scenarios for short, including fossil-fueled recovery, moderate green stimulus, strong green stimulus, hereinafter as FFF, MGG, SGG, respectively). We assessed the impacts of changes in GHGs and anthropogenic aerosols together and separately on SAT in East Asia and its typical subregions during 2020–2050. The results show that by mid-21st-century, SAT in East Asia will increase by 0.81 ± 0.083 °C under Baseline (same as SSP2-4.5 scenario), i.e., SAT difference between 2045–2050 and 2020–2025), and there will be more intense warming in all the three scenarios in East Asia, in which the largest SAT difference (SAT-d) compared to Baseline is 0.33 ± 0.11 °C under SGG and the smallest SAT-d is 0.07 ± 0.14 °C under FFF. To further explore the mechanism of these SAT-d, we analyzed the trend of surface longwave and shortwave net radiation flux driven by GHGs and anthropogenic aerosols there. It is found that in early period (2020–2035), the role of aerosol changes is bigger than that of GHG changes in dominating SAT-d, particularly sulfate, whose reduction will become the main contributor to SAT-d by affecting the net solar flux at surface. In later period (2036–2050), because of GHGs’ longer atmospheric lifetime than aerosols, the role of decreasing GHGs concentrations will determine the drop in SAT-d through affecting the net longwave flux at surface.

Effect of imposed shear on the stability of a viscoelastic liquid flowing down a slippery plane

We investigate a linear stability analysis for the Oldroyd-B liquid draining down a slippery inclined plane where a constant shear stress is applied at the liquid surface in the streamwise direction. The aim is to expand the previous study (Shaqfeh et al., 1989) in the presence of imposed shear stress when the bounding plane is slippery. The stability analysis is executed analytically as well as numerically for low to high values of the Reynolds number, which detects the existence of surface and shear modes. In the low Reynolds number regime, we use the analytical long-wave asymptotic expansion to find the onset of primary instability for the surface mode. On the other hand, we implement the numerical Chebyshev spectral collocation technique to determine the short-wave instability when the Reynolds number changes from a moderate to a high value. It is found that the critical Reynolds number for the surface mode decreases with the increasing values of the slip length and imposed shear stress, and thereby, the surface instability occurs at a lower Reynolds number than that when the slip length and the imposed shear stress are excluded. More specifically, the imposed shear stress has a destabilizing effect on the surface mode when acting in the co-flow direction but a stabilizing effect when acting in the counter-flow direction. Furthermore, the viscosity ratio exhibits a stabilizing effect on the surface mode near the onset of instability but exhibits a destabilizing effect far away from the onset of instability. In the high Reynolds number regime, the primary instability induced by the shear mode can be stabilized by the viscosity ratio and slip length, but can be destabilized by applying a constant shear stress in the co-flow direction. In a special case, the result for the surface mode corresponding to Walters’ liquid B ′ ′ can be recovered from that of the Oldroyd-B liquid if the viscoelastic parameter is kept at a very low value. • Analytical and numerical studies of shear-imposed Oldroyd-B liquid over a slippery plane. • The surface mode is destabilized but the shear mode is stabilized by the slip length. • Both the surface and shear modes are destabilized if a constant shear stress is applied in the coflow direction. • Both the surface and shear modes are stabilized if a constant shear stress is applied in the counter-flow direction. • Fully elastic instability exists and can be suppressed by the imposed shear stress if it acts in the counter-flow direction. • Fully elastic instability exists and can be intensified by wall slip. • The result of the long-wave surface mode for Walters’ liquid B ′ ′ is recovered from that of the Oldroyd-B liquid if the viscoelastic parameter is very small.

Generating 5 km resolution 1981–2018 daily global land surface longwave radiation products from AVHRR shortwave and longwave observations using densely connected convolutional neural networks

Surface longwave radiation (SLWR) components, including downward longwave radiation (DLR), upward longwave radiation (ULR), and net longwave radiation (NLR), are major contributors to the Earth's surface radiation budget and play important roles in ecological, hydrological, and atmospheric processes. Previous SLWR products have different drawbacks, such as being temporally short (after 2000), spatially coarse (≥ 25 km), and instantaneous values, which hinder their in-depth applications in land surface process modeling and climate trends analysis. Here, we reported the Advanced Very High-Resolution Radiometer (AVHRR)-based Global LAnd Surface Satellites (GLASS-AVHRR) SLWR products over the global land surface at a 5 km spatial resolution and 1 day temporal resolution between 1981 and 2018. These products were generated using multiple densely connected convolutional neural networks (DesCNNs) from the AVHRR top-of-atmosphere (TOA) reflected and emitted observations and European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis (ERA5) near-surface meteorological data. DesCNNs were trained using integrated SLWR samples derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)-based GLASS, Clouds and the Earth's Radiant Energy System Synoptic (CERES-SYN), and ERA5 SLWR products. In situ measurements from 231 globally distributed sites were used to evaluate the GLASS-AVHRR SLWR estimates. The results illustrated the overall high accuracies of GLASS-AVHRR SLWR products with root-mean-square-errors (RMSEs) of 18.66, 14.92, and 16.29 Wm−2, and mean bias errors (MBEs) of −2.69, −3.77, and 0.49 Wm−2 for all-sky DLR, ULR, and NLR, respectively. We found good correlation and consistency between GLASS-AVHRR and both CERES-SYN and ERA5 in terms of spatial patterns, latitudinal gradient, and temporal evolution. Our results revealed the significant contribution of shortwave observations to SLWR estimation owing to the high amounts of clouds over polar regions and water vapor and clouds in tropical areas, which was not previously widely recognized by the remote sensing community. GLASS-AVHRR SLWR products were updated, documented, and made available to the public at www.glass.umd.edu and www.geodata.cn.

The surface longwave cloud radiative effect derived from space lidar observations

Abstract. Clouds warm the surface in the longwave (LW), and this warming effect can be quantified through the surface LW cloud radiative effect (CRE). The global surface LW CRE has been estimated over more than 2 decades using space-based radiometers (2000–2021) and over the 5-year period ending in 2011 using the combination of radar, lidar and space-based radiometers. Previous work comparing these two types of retrievals has shown that the radiometer-based cloud amount has some bias over icy surfaces. Here we propose new estimates of the global surface LW CRE from space-based lidar observations over the 2008–2020 time period. We show from 1D atmospheric column radiative transfer calculations that surface LW CRE linearly decreases with increasing cloud altitude. These computations allow us to establish simple parameterizations between surface LW CRE and five cloud properties that are well observed by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) space-based lidar: opaque cloud cover and altitude and thin cloud cover, altitude, and emissivity. We evaluate this new surface LWCRE–LIDAR product by comparing it to existing satellite-derived products globally on instantaneous collocated data at footprint scale and on global averages as well as to ground-based observations at specific locations. This evaluation shows good correlations between this new product and other datasets. Our estimate appears to be an improvement over others as it appropriately captures the annual variability of the surface LW CRE over bright polar surfaces and it provides a dataset more than 13 years long.

Wavy regime of a colloidal falling film

We explore the linear stability and subsequent formation of nonlinear waves in a colloidal, gravity-driven falling film flow. The problem of a gravity-driven falling film devoid of any microstructure has been well studied. However, the role of particulate suspensions in the stability and dynamics of such systems is less explored. We perform a linear stability analysis and observe that the presence of colloidal particles stabilizes both the long-wave surface and short-wave shear instability modes. We also derive nonlinear models in the framework of long-wave theory using Benney's gradient expansion and the Integral Boundary Layer (IBL) approach to study the systems' wavy dynamics.

Estimation of Downwelling Surface Longwave Radiation with the Combination of Parameterization and Artificial Neural Network from Remotely Sensed Data for Cloudy Sky Conditions

This work proposes a new method for estimating downwelling surface longwave radiation (DSLR) under cloudy-sky conditions based on a parameterization method and a genetic algorithm–artificial neural network (GA-ANN) algorithm. The new method establishes a GA-ANN model based on simulated data, and then combines MODIS satellite data and ERA5 reanalysis data to estimate the DSLR. According to the validation results of the field sites, the bias and RMSE are –9.18 and 34.88 W/m2, respectively. Compared with the existing research, the new method can achieve reasonable accuracy. Parameter analysis using independently simulated data shows that the near-surface air temperature (Ta) and cloud base height (CBH) have an important influence on DSLR estimation under cloudy-sky conditions. With an increase in CBH, DSLR gradually decreases; however, with an increase in Ta, DSLR shows a trend of gradual increase. When estimating DSLR under cloudy-sky conditions, under the influence of clouds, except for cirrus, the change in DSLRs with CBH and Ta is greater than 20 W/m2.

Toward a more realistic representation of surface albedo in NASA CERES-derived surface radiative fluxes

Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm–2 and underestimates the upwelling shortwave flux by –15.70 Wm–2 and downwelling longwave fluxes by –12.58 Wm–2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show that the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm–2) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm–2). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.