Surface Radiation Budget Research Articles

Observational study on the surface radiation budget and energy balance closure of grassland in the eastern arid region of Northwest China

Based on the observation data of the meteorological environment monitoring system (QT-1010) and the open vorticity correlation system (LI-7500DS), we used the block aerodynamics and energy balance ratio method to analyze the surface radiation budget and energy balance closure characteristics of the grassland in the eastern arid region of Northwest China at different time scales.We found that (1) The daily variation curve of the surface radiation flux is unimodal, and the variation amplitude of each component is as follows: Rs↓> RL↑> RL↓> Rs↑. (2) The monthly peak of each radiation component occurs in summer, and the time of reaching the peak of different components has a lag. The changes of radiation components in sunny and cloudy days are multi-modal. In the precipitation weather, the fluctuation of each radiation flux is unimodal.The average annual surface albedo () is 0.24, which is higher in winter than in summer. There is a linear relationship between surface albedo and soil moisture. (3) The Net radiation (Rnet) dominates the sensible heat in spring and the latent heat in summer. H/Rnet and Gn/Rnet are both positive in a day, H/Rnet and LE/Rnet fluctuate wildly before and after sunrise, and Gn is dominant at night. (4) The energy closure is manifested as summer > winter > Autumn > spring, and the energy closure is good throughout the year.

An analytical model for daily‐periodic slope winds. Part 1: Surface radiation budget

AbstractThis article presents an analytical model for calculating representative daily‐periodic cycles of net surface radiation on a slope with any angle () and orientation, at any latitude and elevation (up to 2500 m), and for all seasons. Starting from the basic equations for estimating the daytime direct and diffuse components of the incoming solar radiation at the Earth's surface under clear‐sky conditions, a Fourier series expansion is derived to get an overall daily‐periodic expression, that is, covering both daytime and nighttime, which does not require data from observations, differently from most models available in the literature. Among various possible applications, this formulation is preliminary to evaluating the radiation and energy budgets driving daily‐periodic wind systems on a simple slope, as reproduced by the model presented in Part 2.

Investigation and Validation of Split-Window Algorithms for Estimating Land Surface Temperature from Landsat 9 TIRS-2 Data

Land surface temperature (LST) is important in a variety of applications, such as urban thermal environment monitoring and water resource management. In this paper, eleven candidate split-window (SW) algorithms were adapted to Thermal Infrared Sensor-2 (TIRS-2) data of the Landsat 9 satellite for estimating the LST. The simulated dataset produced by extensive radiative transfer modeling and five global atmospheric profile databases was used to determine the SW algorithm coefficients. Ground measurements gathered at Surface Radiation Budget Network sites were used to confirm the efficiency of the SW algorithms after their performance was initially examined using the independent simulation dataset. Five atmospheric profile databases perform similarly in training accuracy under various subranges of total water vapor. The candidate SW algorithms demonstrate superior performance compared to the radiative transfer equation algorithm, exhibiting a reduction in overall bias and RMSE by 1.30 K and 1.0 K, respectively. It is expected to provide guidance for the generation of the Landsat 9 LST using the SW algorithms.

Quantifying Changes in the Arctic Shortwave Cloud Radiative Effects

AbstractThe shortwave cloud radiative effect (SWCRE) is important for the Arctic surface radiation budget and is a major source of inter‐model spread in simulating Arctic climate. To better understand the individual contributions of various radiative processes to changes in SWCRE, we extend the existing Approximate Partial Radiative Perturbation (APRP) method by adding the absorptivity for the upward beam, considering differences in reflectivity between upward and downward beams, and analyzing the cloud masking effect resulting from changes in surface albedo. Using data from CMIP model experiments, the study decomposes the SWCRE over the Arctic surface and analyzes inter‐model differences in quadrupled CO2 simulations. The study accounts for the influence of surface albedo, cloud amount, and cloud microphysics in the response of SWCRE to Arctic warming. In the sunlit season, CMIP models exhibit a strong, negative SWCRE with a large inter‐model spread. Arctic clouds dampen the surface albedo feedback by reflecting incoming solar radiation and further decrease the shortwave radiation reflected by surface, a fraction of which is scattered back to the surface by clouds. Specifically, this accounts for the majority of the inter‐model spread in SWCRE. In addition, increased (decreased) cloud amount and cloud liquid water reduce (increase) incoming shortwave fluxes at the surface, but they are found to be not critical to the Arctic surface radiation budget and its inter‐model variation. Overall, the extended APRP method offers a useful tool for analyzing the complex interactions between clouds and radiative processes, accurately decomposes the individual SWCRE responses at the Arctic surface.

Extreme summer temperature anomalies over Greenland largely result from clear-sky radiation and circulation anomalies

The polar regions have been undergoing amplified warming in recent years. In particular, Greenland has experienced anomalously warm summers with intense melt rates. We employ a surface radiation budget framework to examine the causes for positive and negative summer temperature anomaly events over Greenland from 1979 to 2021. We found a dominant contribution of the clear-sky downwelling longwave radiation and the surface albedo feedback to temperature anomalies. Atmospheric temperature perturbations dominate the effect of anomalous emissivity on clear-sky downwelling longwave radiation. In warm years, enhanced turbulent heat exchange due to increased surface temperature and diabatic warming in the troposphere induces adiabatic heating of the atmosphere, enhanced moisture advection, and a high-pressure anomaly with a blocking-like anti-cyclonic circulation anomaly following peak temperature days. Different modes of natural climate variability, in particular, related to blocking over Greenland, can further amplify or dampen the ongoing warming trend, causing extreme temperature events.

Hybrid solar irradiance nowcasting and forecasting with the SCOPE method and convolutional neural networks

We use a full year (2018) of GOES-R satellite data to produce 5-minute resolved information on cloud coverage for 7 Surface Radiation Budget Network (SURFRAD). The remote sensing images are then processed using the Spectral Cloud Optical Property Estimation (SCOPE) method in conjunction with Convolution Neural Networks (CNNs) for nowcasts and 1-hour ahead forecasts. We propose and compare two CNN based models for GHI now-casting with a 1-hour forecast horizon and a time resolution that processes data every 5 minutes: one enhanced with the SCOPE method for cloud estimation and another without it. The inclusion of SCOPE-derived information significantly improves model performance, yielding an average RMSE of 44.7 versus 68.9 W/m2 for the model without SCOPE information. The present work underscores the efficacy of even basic CNNs in interpreting satellite imagery combined with atmospheric models of the atmosphere to estimate ground irradiance accurately. The hybrid SCOPE-CNN model outperforms the basic CNN model that relies solely on 10 longwave channels, indicating the relevance of SCOPE’s physical features in enhancing predictions across various solar micro-climates. Further advancements using Convolutional Long Short-Time Memory (ConvLSTM) schemes lead to the development of a SCOPE-CNN-ConvLSTM model, showcasing significant enhancements over smart persistence across all conditions.

Ocean surface radiation measurement best practices

Ocean surface radiation measurement best practices have been developed as a first step to support the interoperability of radiation measurements across multiple ocean platforms and between land and ocean networks. This document describes the consensus by a working group of radiation measurement experts from land, ocean, and aircraft communities. The scope was limited to broadband shortwave (solar) and longwave (terrestrial infrared) surface irradiance measurements for quantification of the surface radiation budget. Best practices for spectral measurements for biological purposes like photosynthetically active radiation and ocean color are only mentioned briefly to motivate future interactions between the physical surface flux and biological radiation measurement communities. Topics discussed in these best practices include instrument selection, handling of sensors and installation, data quality monitoring, data processing, and calibration. It is recognized that platform and resource limitations may prohibit incorporating all best practices into all measurements and that spatial coverage is also an important motivator for expanding current networks. Thus, one of the key recommendations is to perform interoperability experiments that can help quantify the uncertainty of different practices and lay the groundwork for a multi-tiered global network with a mix of high-accuracy reference stations and lower-cost platforms and practices that can fill in spatial gaps.

Increased summertime wildfire as a major driver of the clear-sky dimming in the Siberian Arctic from 2000 to 2020

A warming Arctic is expected to exacerbate wildfires in Siberia, potentially creating a critical feedback to the Arctic climate change. However, our understanding of these fire-climate interactions remains limited. This study investigated changes in East Siberian wildfires and their influence on fire emissions, aerosol optical depth (AOD), and the surface clear-sky insolation across the Siberian Arctic from 2000 to 2020 using satellite observations. Our analysis reveals a substantial increase in wildfires, with fire counts doubling (a 114% increase) and fire radiative power surging by 8.4 × 106 MW compared to the early 21st century. Over 93% of this increase occurred during the boreal summer months. These intensified wildfires led to a significant rise in aerosol emission (organic carbon, PM2.5, and black carbon) exceeding 75% in East Siberia. Consequently, fire-season AOD in the Siberian Arctic increased by 33% (6.0 × 10−2), with 85% (5.1 × 10−2) attributable to wildfire changes. The wildfire-associated increase in AOD resulted in enhanced clear-sky dimming of 4.1 ± 3.2 W m−2 across the Siberian Arctic from 2000 to 2020. These findings suggest a critical feedback mechanism: a warming Arctic drives increased wildfires in Siberia, which in turn significantly impact the Arctic surface radiative budget through enhanced clear-sky dimming. Future simulations and projections for the Arctic should prioritize incorporating the feedback effects of intensifying wildfires.

Evaluation of Downward Surface Longwave Flux Estimates Using Meteosat Cloud Observations

AbstractThe downward surface longwave flux (DSLF) plays a relevant role in the Earth’s surface radiative budget, which is crucial to monitor, understand and model the impact of changes at local and global scales on surface temperature and surface conditions. This study focuses on the evaluation and intercomparison of four DSLF products: (a) a recently developed all‐weather DSLF product based on the multivariate adaptive regression splines (MARS) algorithm driven by satellite cloud information from the Meteosat Second Generation (MSG) and ERA5 reanalysis screen variables; (b) the Satellite Application Facility on Land Surface Analysis (LSA SAF); (c) CERES Synoptic top‐of‐atmosphere and surface fluxes and clouds (CERES‐SYN1deg) and (d) ERA5 reanalysis. The study covers the period 2005–2021 and the MSG region focusing on monthly means. The evaluation performed against 48 ground stations from the Baseline Surface Radiation Network (BSRN) and FLUXNET2015 networks showed that the MARS product outperforms the remaining products, particularly the LSA SAF, while ERA5 and CERES show similar performance metrics. The fours products are intercompared in terms of their mean spatial variability and temporal mean annual cycles and inter‐annual variability in four selected regions, showing a high level of agreement, particularly between MARS, ERA5 and CERES. Our results highlight the clear added value of MARS with respect to LSA SAF, while providing higher spatial resolution (0.05°), constrained by satellite cloud information, when compared with ERA5 (0.25°) or CERES (1°).

Downscaling Land Surface Temperature Derived from Microwave Observations with the Super-Resolution Reconstruction Method: A Case Study in the CONUS

Optical sensors cannot penetrate clouds and can cause serious missing data problems in optical-based Land Surface Temperature (LST) products. Under cloudy conditions, microwave observations are usually utilized to derive the land surface temperature. However, microwave sensors usually have coarse spatial resolutions. High-Resolution (HR) LST data products are usually desired for many applications. Instead of developing and launching new high-resolution satellite sensors for LST observations, a more economical and practical way is to develop proper methodologies to derive high-resolution LSTs from available Low-Resolution (LR) datasets. This study explores different algorithms to downscale low-resolution LST data to a high resolution. The existing regression-based downscaling methods usually require simultaneous observations and ancillary data. The Super-Resolution Reconstruction (SRR) method developed for traditional image enhancement can be applicable to high-resolution LST generation. For the first time, we adapted the SRR method for LST data. We specifically built a unique database of LSTs for the example-based SRR method. After deriving the LST data from the coarse-resolution passive microwave observations, the AMSR-E at 25 km and/or AMSR-2 at 10 km, we developed an algorithm to downscale them to a 1 km spatial resolution with the SRR method. The SRR downscaling algorithm can be implemented to obtain high-resolution LSTs without auxiliary data or any concurrent observations. The high-resolution LSTs are validated and evaluated with the ground measurements from the Surface Radiation (SURFRAD) Budget Network. The results demonstrate that the downscaled microwave LSTs have a high correlation coefficient of over 0.92, a small bias of less than 0.5 K, but a large Root Mean Square Error (RMSE) of about 4 K, which is similar to the original microwave LST, so the errors in the downscaled LST could have been inherited from the original microwave LSTs. The validation results also indicate that the example-based method shows a better performance than the self-similarity-based algorithm.

Characteristics of Aerosols and Clouds and Their Role in Earth’s Energy Budget

Abstract Clouds and aerosols provide the greatest uncertainty in estimating and interpreting Earth’s energy budget. This study not only focuses on surface brightening/dimming, but also explores Earth’s energy balance. The validation results of the CERES-SYN, ISCCP-FH, and GEWEX-SRB datasets with Baseline Surface Radiation Network (BSRN), Surface Radiation Budget Network (SURFRAD), and CMA observations show that CERES data have the highest accuracy and the longest temporal coverage. The role of clouds and aerosols in Earth’s energy budget was explained using CERES and MERRA-2 products. The results show that Earth’s energy increases at a rate of 0.63 W m−2 decade−1 in 2000–21. The global surface brightens at a rate of 0.57 W m−2 decade−1, with surface energy decreasing at a rate of 0.19 W m−2 decade−1. Brightening was found over Australia, central Asia, and southern Africa, mainly associated with cloud reduction, with aerosol emissions reductions contributing to the East Asian surface brightening. The surface brightening in South America and Southeast Asia is also due to the reduction of clouds. The increase of aerosols in South Asia is the main factor for its surface dimming, while we infer that the climatic effect from the increase of black carbon (BC) aerosols in South Asia is the inducing factor for the dimming in southern China. The surface darkening in West Asia is the result of the combined effect of clouds and aerosols, while in northern Africa it may be related to the increase of clouds caused by the decrease of dust aerosols. Surface energy increases only in Southeast Asia, South America, and Europe. Significance Statement Clouds and aerosols provide the greatest uncertainty in estimating and interpreting Earth’s changing energy budget. Moreover, the relative importance of clouds and aerosols is variable, depending on the regions and time scales of studies. This study shows that the energy inflow to the Earth system is greater than the energy outflow in 2000–21, with radiation on Earth increasing at a rate of 0.63 W m−2 decade−1 with the surface energy decreases at a rate of 0.19 W m−2 decade−1. Brightening was found over Southeast Asia, South America, Australia, and southern Africa, with the areas of surface darkening occurring mainly in Asia and northern Africa.

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>0.99 for monthly data and R2>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.

Low cloud response to aerosol‐radiation‐cloud interactions: Idealized WRF numerical experiments for EUREC4A project

AbstractAerosols significantly affect cloud microphysics and energy budget in different ways. The contribution of the direct, semi‐direct, and indirect effects of aerosols on radiation are here investigated over the North Atlantic tropical ocean under different aerosol loadings. The Weather Research and Forecasting Model is used to perform a set of numerical idealized experiments, which are forced with prescribed aerosol profiles. We evaluate the effects of aerosols on modeled shallow clouds and surface radiative budget. The results indicate that large aerosol loadings are associated with enhanced cloudiness and reduced precipitation. While the change in rainfall is mainly due to the larger number of smaller droplets, the change in cloudiness is attributed to the effects of absorbing aerosols, mainly dust particles, which are responsible for a rise of temperature that feeds back onto specific humidity. As in the boundary layer the increase of moisture dominates, the net effect is a higher relative humidity, which favors the formation of thin low non‐precipitating clouds. The feedback accounts for a dynamical change in the lower troposphere: shortwave radiation absorption increases temperature at the top of the marine atmospheric boundary‐layer and reduces entrainment of warm and dry air, increasing low level moisture content. Despite the overall increase in cloudiness, daytime cloud cover is reduced. The semi‐direct effect of aerosols on clouds results in a warming of the surface, opposite to the indirect effect.

Quantitation of the Surface Shortwave and Longwave Radiative Effect of Dust with an Integrated System: A Case Study at Xianghe.

Aerosols play a crucial role in the surface radiative budget by absorbing and scattering both shortwave and longwave radiation. While most aerosol types exhibit a relatively minor longwave radiative forcing when compared to their shortwave counterparts, dust aerosols stand out for their substantial longwave radiative forcing. In this study, radiometers, a sun photometer, a microwave radiometer and the parameterization scheme for clear-sky radiation estimation were integrated to investigate the radiative properties of aerosols. During an event in Xianghe, North China Plain, from 25 April to 27 April 2018, both the composition (anthropogenic aerosol and dust) and the aerosol optical depth (AOD, ranging from 0.3 to 1.5) changed considerably. A notable shortwave aerosol radiative effect (SARE) was revealed by the integrated system (reaching its peak at -131.27 W·m-2 on 26 April 2018), which was primarily attributed to a reduction in direct irradiance caused by anthropogenic aerosols. The SARE became relatively consistent over the three days as the AODs approached similar levels. Conversely, the longwave aerosol radiative effect (LARE) on the dust days ranged from 8.94 to 32.93 W·m-2, significantly surpassing the values measured during the days of anthropogenic aerosol pollution, which ranged from 0.35 to 28.67 W·m-2, despite lower AOD values. The LARE increased with a higher AOD and a lower Ångström exponent (AE), with a lower AE having a more pronounced impact on the LARE than a higher AOD. It was estimated that, on a daily basis, the LARE will offset approximately 25% of the SARE during dust events and during periods of heavy anthropogenic pollution.

Integration of a physics-based direct normal irradiance (DNI) model to enhance the National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

Derivation and compilation of lower-atmospheric properties relating to temperature, wind, stability, moisture, and surface radiation budget over the central Arctic sea ice during MOSAiC

Abstract. Atmospheric measurements taken over the span of an entire year between October 2019 and September 2020 during the icebreaker-based Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provide insight into processes acting in the Arctic atmosphere. Through the merging of disparate yet complementary in situ observations, we can derive information about these thermodynamic and kinematic processes with great detail. This paper describes methods used to create a lower-atmospheric properties dataset containing information on several key features relating to the central Arctic atmospheric boundary layer, including properties of temperature inversions, low-level jets, near-surface meteorological conditions, cloud cover, and the surface radiation budget. The lower-atmospheric properties dataset was developed using observations from radiosondes launched at least four times per day, a 10 m meteorological tower and radiation station deployed on the sea ice near the research vessel Polarstern, and a ceilometer located on the deck of the Polarstern. This lower-atmospheric properties dataset, which can be found at https://doi.org/10.1594/PANGAEA.957760 (Jozef et al., 2023), contains metrics which fall into the overarching categories of temperature, wind, stability, clouds, and radiation at the time of each radiosonde launch. The purpose of the lower-atmospheric properties dataset is to provide a consistent description of general atmospheric boundary layer conditions throughout the MOSAiC year, which can aid in research applications with the overall goal of gaining a greater understanding of the atmospheric processes governing the central Arctic and how they may contribute to future climate change.

CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023

Abstract. This paper presents the third edition of The Satellite Application Facility on Climate Monitoring's (CM SAF) cloud, albedo, and surface radiation dataset from advanced very-high-resolution radiometer (AVHRR) data, CLARA-A3. The content of earlier CLARA editions, namely cloud, surface albedo, and surface radiation products, has been extended with two additional surface albedo products (blue- and white-sky albedo), three additional surface radiation products (net shortwave and longwave radiation, and surface radiation budget), and two top of atmosphere radiation budget products (reflected solar flux and outgoing longwave radiation). The record length is extended to 42 years (1979–2020) by also incorporating results from the first version of the advanced very high resolution radiometer imager (AVHRR/1). A continuous extension of the climate data record (CDR) has also been implemented by processing an interim climate data record (ICDR) based on the same set of algorithms but with slightly changed ancillary input data. All products are briefly described together with validation results and intercomparisons with currently existing similar CDRs. The extension of the product portfolio and the temporal coverage of the data record, together with product improvements, is expected to enlarge the potential of using CLARA-A3 for climate change studies and, in particular, studies of potential feedback effects between clouds, surface albedo, and radiation. The CLARA-A3 data record is hosted by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) CM SAF and is freely available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003 (Karlsson et al., 2023b).

Thermodynamic and kinematic drivers of atmospheric boundary layer stability in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC)

Abstract. Observations collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) provide a detailed description of the impact of thermodynamic and kinematic forcings on atmospheric boundary layer (ABL) stability in the central Arctic. This study reveals that the Arctic ABL is stable and near-neutral with similar frequencies, and strong stability is the most persistent of all stability regimes. MOSAiC radiosonde observations, in conjunction with observations from additional measurement platforms, including a 10 m meteorological tower, ceilometer, microwave radiometer, and radiation station, provide insight into the relationships between atmospheric stability and various atmospheric thermodynamic and kinematic forcings of ABL turbulence and how these relationships differ by season. We found that stronger stability largely occurs in low-wind (i.e., wind speeds are slow), low-radiation (i.e., surface radiative fluxes are minimal) environments; a very shallow mixed ABL forms in low-wind, high-radiation environments; weak stability occurs in high-wind, moderate-radiation environments; and a near-neutral ABL forms in high-wind, high-radiation environments. Surface pressure (a proxy for synoptic staging) partially explains the observed wind speeds for different stability regimes. Cloud frequency and atmospheric moisture contribute to the observed surface radiation budget. Unique to summer, stronger stability may also form when moist air is advected from over the warmer open ocean to over the colder sea ice surface, which decouples the colder near-surface atmosphere from the advected layer, and is identifiable through observations of fog and atmospheric moisture.

Significant discrepancies of land surface daily net radiation among ten remotely sensed and reanalysis products

ABSTRACT Land surface all-wave net radiation (Rn) is crucial in determining Earth's climate by contributing to the surface radiation budget. This study evaluated seven satellite and three reanalysis long-term land surface Rn products under different spatial scales, spatial and temporal variations, and different conditions. The results showed that during 2000-2018, Global Land Surface Satellite Product (GLASS)-Moderate Resolution Imaging Spectroradiometer (MODIS) performed the best (RMSE=25.54 Wm-2, bias=−1.26 Wm-2), followed by ERA5 (the fifth-generation of European Centre for Medium-Range Weather Forecast Reanalysis) (RMSE=32.17 Wm-2, bias=−4.88 Wm-2) and GLASS-AVHRR (Advanced Very-High-Resolution Radiometer) (RMSE=33.10 Wm-2, bias=4.03 Wm-2). During 1983-2018, GLASS-AVHRR and ERA5 ranked top and performed similarly, with RMSE values of 31.70 and 33.08 Wm-2 and biases of −4.56 and 3.48 Wm-2, respectively. The averaged multi-annual mean Rn over the global land surface of satellite products was higher than that of reanalysis products by about 10∼30 Wm-2. These products differed remarkably in long-term trends variations, particularly pre-2000, but no significant trends were observed. Discrepancies were more frequent in satellite data, while reanalysis products showed smoother variations. Large discrepancies were found in regions with high latitudes, reflectance, and elevation which could be attributed to input radiative components, meteorological variables (e.g., cloud properties, aerosol optical thickness), and applicability of the algorithms used. While further research is needed for detailed insights.

Evaluating the impact of enhanced horizontal resolution over the Antarctic domain using a variable-resolution Earth system model

Abstract. Earth system models are essential tools for understanding the impacts of a warming world, particularly on the contribution of polar ice sheets to sea level change. However, current models lack full coupling of the ice sheets to the ocean and are typically run at a coarse resolution (1∘ grid spacing or coarser). Coarse spatial resolution is particularly a problem over Antarctica, where sub-grid-scale orography is well-known to influence precipitation fields, and glacier models require high-resolution atmospheric inputs. This resolution limitation has been partially addressed by regional climate models (RCMs), which must be forced at their lateral and ocean surface boundaries by (usually coarser) global atmospheric datasets, However, RCMs fail to capture the two-way coupling between the regional domain and the global climate system. Conversely, running high-spatial-resolution models globally is computationally expensive and can produce vast amounts of data. Alternatively, variable-resolution grids can retain the benefits of high resolution over a specified domain without the computational costs of running at a high resolution globally. Here we evaluate a historical simulation of the Community Earth System Model version 2 (CESM2) implementing the spectral element (SE) numerical dynamical core (VR-CESM2) with an enhanced-horizontal-resolution (0.25∘) grid over the Antarctic Ice Sheet and the surrounding Southern Ocean; the rest of the global domain is on the standard 1∘ grid. We compare it to 1∘ model runs of CESM2 using both the SE dynamical core and the standard finite-volume (FV) dynamical core, both with identical physics and forcing, including prescribed sea surface temperatures (SSTs) and sea ice concentrations from observations. Our evaluation reveals both improvements and degradations in VR-CESM2 performance relative to the 1∘ CESM2. Surface mass balance estimates are slightly higher but within 1 standard deviation of the ensemble mean, except for over the Antarctic Peninsula, which is impacted by better-resolved surface topography. Temperature and wind estimates are improved over both the near surface and aloft, although the overall correction of a cold bias (within the 1∘ CESM2 runs) has resulted in temperatures which are too high over the interior of the ice sheet. The major degradations include the enhancement of surface melt as well as excessive cloud liquid water over the ocean, with resultant impacts on the surface radiation budget. Despite these changes, VR-CESM2 is a valuable tool for the analysis of precipitation and surface mass balance and thus constraining estimates of sea level rise associated with the Antarctic Ice Sheet.