Downwelling Longwave Radiation Research Articles

Recent autumn sea ice loss in the eastern Arctic enhanced by summer Asian-Pacific Oscillation

Recent rapid Arctic sea ice loss was documented as combined results from anthropogenic forcing and climate system internal variability. However, the role of internal variability is not well understood. Here, we propose that the Asian-Pacific Oscillation (APO), an intrinsic atmospheric mode featuring out-of-phase variations in upper-tropospheric temperatures between Asia and the North Pacific, is one driver for autumn sea ice variability in the eastern Arctic. The positive summer APO favors warming of the mid-latitude North Atlantic sea surface temperatures. This warming persists to autumn and in turn triggers strong anticyclonic anomalies over the Barents-Kara-Laptev Seas and weak lower-tropospheric cyclonic anomalies over the East Siberian Sea, enhancing moisture transport into the eastern Arctic. Such changes consequently increase lower-tropospheric humidity, downwelling longwave radiation, and surface air temperature in the eastern Arctic, thereby melting sea ice. Hence, a recent tendency of the summer APO towards the positive phase accelerates autumn sea ice loss in the eastern Arctic.

Forcing for varying boundary layer stability across Antarctica

Abstract. The relative importance of changes in radiative forcing (downwelling longwave radiation) and mechanical mixing (20 m wind speed) in controlling boundary layer stability annually and seasonally at five study sites across the Antarctica continent is presented. From near neutral to extremely strong near-surface stability, radiative forcing decreases with increasing stability, as expected, and is shown to be a major driving force behind variations in near-surface stability at all five sites. Mechanical mixing usually decreases with increasing near-surface stability for regimes with weak to extremely strong stability. For the cases where near neutral, very shallow mixed, and weak stability occur, the wind speed in the very shallow mixed case is usually weaker compared to the near neutral and weak stability cases, while radiative forcing is largest for the near neutral cases. This finding is an important distinguishing factor for the unique case where a very shallow mixed layer is present, indicating that weaker mechanical mixing in this case is likely responsible for the shallower boundary layer that defines the very shallow mixed stability regime. For cases with enhanced stability above a layer of weaker near-surface stability, lower downwelling longwave radiation promotes the persistence of the stronger stability aloft, while stronger near-surface winds act to maintain weaker stability immediately near the surface, resulting in this two-layer boundary layer stability regime.

Seasonal Variations and Spatial Patterns of Arctic Cloud Changes in Association with Sea Ice Loss during 1950–2019 in ERA5

Abstract The dynamic and thermodynamic mechanisms that link retreating sea ice to increased Arctic cloud amount and cloud water content are unclear. Using the fifth generation of the ECMWF Reanalysis (ERA5), the long-term changes between years 1950–79 and 1990–2019 in Arctic clouds are estimated along with their relationship to sea ice loss. A comparison of ERA5 to CERES satellite cloud fractions reveals that ERA5 simulates the seasonal cycle, variations, and changes of cloud fraction well over water surfaces during 2001–20. This suggests that ERA5 may reliably represent the cloud response to sea ice loss because melting sea ice exposes more water surfaces in the Arctic. Increases in ERA5 Arctic cloud fraction and water content are largest during October–March from ∼950 to 700 hPa over areas with significant (≥15%) sea ice loss. Further, regions with significant sea ice loss experience higher convective available potential energy (∼2–2.75 J kg−1), planetary boundary layer height (∼120–200 m), and near-surface specific humidity (∼0.25–0.40 g kg−1) and a greater reduction of the lower-tropospheric temperature inversion (∼3°–4°C) than regions with small (<15%) sea ice loss in autumn and winter. Areas with significant sea ice loss also show strengthened upward motion between 1000 and 700 hPa, enhanced horizontal convergence (divergence) of air, and decreased (increased) relative humidity from 1000 to 950 hPa (950–700 hPa) during the cold season. Analyses of moisture divergence, evaporation minus precipitation, and meridional moisture flux fields suggest that increased local surface water fluxes, rather than atmospheric motions, provide a key source of moisture for increased Arctic clouds over newly exposed water surfaces during October–March. Significance Statement Sea ice loss has been shown to be a primary contributor to Arctic warming. Despite the evidence linking large sea ice retreat to Arctic warming, some studies have suggested that enhanced downwelling longwave radiation associated with increased clouds and water vapor is the primary reason for Arctic amplification. However, it is unclear how sea ice loss is linked to changes in clouds and water vapor in the Arctic. Here, we investigate the relationship between Arctic sea ice loss and changes in clouds using the ERA5 dataset. Improved knowledge of the relationship between Arctic sea ice loss and changes in clouds will help further our understanding of the role of the cloud feedback in Arctic warming.

Impact of climate change on snowpack dynamics in coastal Central-Western Greenland

Snow patterns in ice-free areas of Greenland play important roles in ecosystems. Within a changing climate, a comprehensive understanding of the snow responses to climate change is of interest to anticipate forthcoming dynamics in these areas. In this study, we analyze the future snowpack evolution of a polar maritime Arctic location, Qeqertarsuaq (Disko Island, Central-Western Greenland). A physically-based snow model (FSM2) is validated and forced with CMIP6 projections for SSP2–4.5 and SSP5–8.5 greenhouse gasses emission scenarios, using two models: CanESM5 and MIROC6. The future snowpack evolution is assessed through four key seasonal (October to May) snow climate indicators: snow depth, snow days, snowfall fraction and ablation rate. Comparison against the observed air temperature for the reference climate period demonstrates superior accuracies for MIROC6 SSP2.4–5, with anomalies at 19 %, compared to CanESM5 SSP5.8–5 (25 %) and CanESM5 SSP2.4–5 (78 %). In terms of precipitation, CanESM5 SSP2.4–5 and SSP2.4–5 exhibit smaller anomalies against the observed data (5 %) in contrast to MIROC6 SSP2.4–5 (15 %) and MIROC6 SSP2.8–5 (17 %). Results demonstrate distinct snowpack responses to climate change depending on the model and emission scenario. For CanESM5, seasonal snow depth anomalies with respect to the reference period range from – 38 % (SSP2–4.5, 2040–2050 period) to – 74 % (SSP5–8.5, 2090–2100 period). MIROC6 projects lower snowpack reductions, with a decrease ranging from – 38 % (SSP2–4.5, 2040–2050 period) to – 57 % (SSP5–8.5, 2090–2100 period). Similar reductions are anticipated for snowfall and snow days. Changes in the snowpack evolution are primarily driven by positive trends in downwelling longwave radiation and air temperature. The projected increase in precipitation by the mid to late 21st century will lead to more frequent rain-on-snow events, intensifying snowpack melting. These findings help enhance the comprehension of future snow dynamics in the ice-free zones of Greenland, as well as the associated hydrological and ecological changes.

Understanding variations in downwelling longwave radiation using Brutsaert's equation

Abstract. A dominant term in the surface energy balance and central to global warming is downwelling longwave radiation (Rld). It is influenced by radiative properties of the atmospheric column, in particular by greenhouse gases, water vapor, clouds, and differences in atmospheric heat storage. We use the semi-empirical equation derived by Brutsaert (1975) to identify the leading terms responsible for the spatial–temporal climatological variations in Rld. This equation requires only near-surface observations of air temperature and humidity. We first evaluated this equation and its extension by Crawford and Duchon (1999) with observations from FLUXNET, the NASA-CERES dataset, and the ERA5 reanalysis. We found a strong spatiotemporal correlation between estimated Rld and the datasets above, with r2 ranging from 0.87 to 0.98 across the datasets for clear-sky and all-sky conditions. We then used the equations to show that changes in lower atmospheric heat storage explain more than 95 % and around 73 % of diurnal range and seasonal variations in Rld, respectively, with the regional contribution decreasing with latitude. Seasonal changes in the emissivity of the atmosphere play a second role, which is controlled by anomalies in cloud cover at high latitudes but dominated by water vapor changes at midlatitudes and subtropics, especially over monsoon regions. We also found that as aridity increases over the region, the contributions from changes in emissivity and lower atmospheric heat storage tend to offset each other (−40 and 20–30 W m−2, respectively), explaining the relatively small decrease in Rld with aridity (−(10–20) W m−2). These equations thus provide a solid physical basis for understanding the spatiotemporal variability of surface downwelling longwave radiation. This should help us to better understand and interpret climatological changes, such as those associated with extreme events and global warming.

Projection on elevation-dependent and latitude-dependent warming over Antarctica from CMIP6 under different socioeconomic scenarios

Many studies have focused on elevation-dependent warming (EDW) across high mountains, but few studies have examined both EDW and LDW (latitude-dependent warming) on Antarctic warming. This study analyzed the Antarctic amplification (AnA) with respect to EDW and LDW under SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5 from Coupled Model Intercomparison Project Phase 6 (CMIP6) during the period 2015–2100. The results show that the AnA appears under all socioeconomic scenarios, and the greatest signal appears in austral autumn. In the future, Antarctic warming is not only elevation-dependent, but also latitude-dependent. Generally, positive EDW of mean temperature (Tmean), maximum temperature (Tmax) and minimum temperature (Tmin) appear in the range of 1.0–4.5 km, and the corresponding altitudinal amplification trends are 0.012/0.012/0.011 (SSP1–2.6)—0.064/0.065/0.053 (SSP5–8.5) °C decade−1•km−1. Antarctic EDW demonstrates seasonal differences, and is strong in summer and autumn and weak in winter under SSP3–7.0 and SSP5–8.5. For Tmean, Tmax and Tmin, the feature of LDW is varies in different latitude ranges, and also shows seasonal differences. The strongest LDW signal appears in autumn, and the warming rate increases with increasing latitude at 64–79°S under SSP1–2.6. The similar phenomenon can be observed at 68–87°S in the other cases. Moreover, the latitude component contributes more to the warming of Tmean and Tmax relative to the corresponding altitude component, which may relates to the much larger range of latitude (∼2600 km) than altitude (∼4.5 km) over Antarctica, while the EDW contributes more warming than LDW in the changes in Tmin in austral summer. Moreover, surface downwelling longwave radiation, water vapor and latent heat flux are the potential factors influencing Antarctic EDW, and the variation in surface downwelling longwave radiation can also be considered as an important influencing factor for Antarctic LDW. Our results provide preliminary insights into EDW and LDW in Antarctica.

Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model

Abstract. As a long-standing problem in climate models, large positive shortwave radiation biases exist at the surface over the Southern Ocean, impacting the accurate simulation of sea surface temperature, atmospheric circulation, and precipitation. Underestimations of low-level cloud fraction and liquid water content are suggested to predominantly contribute to these radiation biases. Most model evaluations for radiation focus on summer and rely on satellite products, which have their own limitations. In this work, we use surface-based observations at Macquarie Island to provide the first long-term, seasonal evaluation of both downwelling surface shortwave and longwave radiation in the Australian Community Climate and Earth System Simulator Atmosphere-only Model version 2 (ACCESS-AM2) over the Southern Ocean. The capacity of the Clouds and the Earth’s Radiant Energy System (CERES) product to simulate radiation is also investigated. We utilize the novel lidar simulator, the Automatic Lidar and Ceilometer Framework (ALCF), and all-sky cloud camera observations of cloud fraction to investigate how radiation biases are influenced by cloud properties. Overall, we find an overestimation of +9.5±33.5 W m−2 for downwelling surface shortwave radiation fluxes and an underestimation of -2.3±13.5 W m−2 for downwelling surface longwave radiation in ACCESS-AM2 in all-sky conditions, with more pronounced shortwave biases of +25.0±48.0 W m−2 occurring in summer. CERES presents an overestimation of +8.0±18.0 W m−2 for the shortwave and an underestimation of -12.1±12.2 W m−2 for the longwave in all-sky conditions. For the cloud radiative effect (CRE) biases, there is an overestimation of +4.8±28.0 W m−2 in ACCESS-AM2 and an underestimation of -7.9±20.9 W m−2 in CERES. An overestimation of downwelling surface shortwave radiation is associated with an underestimated cloud fraction and low-level cloud occurrence. We suggest that modeled cloud phase is also having an impact on the radiation biases. Our results show that the ACCESS-AM2 model and CERES product require further development to reduce these radiation biases not just in shortwave and in all-sky conditions, but also in longwave and in clear-sky conditions.

Validation and Spatiotemporal Analysis of Surface Net Radiation from CRA/Land and ERA5-Land over the Tibetan Plateau

High spatial–temporal resolution surface net radiation (RN) data are of great significance to the study of climate, ecology, hydrology and cryosphere changes on the Tibetan Plateau (TP), but the verification of the surface net radiation products on the plateau is not sufficient. In this study, the China Meteorological Administration Global Land Surface Reanalysis Products (CRA/Land) and ECMWF Land Surface Reanalysis version 5 (ERA5-Land) RN data were validated using ground measurements at daily and monthly time scales, and the spatiotemporal patterns were also analyzed. The results indicate the following: (1) CRA/Land overestimated while ERA5-Land underestimated RN, but CRA/Land RN outperformed ERA5-Land in observations at the daily and monthly scale. (2) The CRA/Land RN data had a larger error in the central part and a smaller error in the northeast of the TP, while ERA5-Land showed the opposite. (3) The spatial patterns of RN revealed by CRA/Land and ERA5-Land data showed differences in most regions. The CRA/Land data showed that the RN of the TP had a downward trend during 2000 and 2020 with a slope of −0.112 W·m−2/a, while the ERA5-Land data indicated an upward trend with a change rate of 0.016 W·m−2/a. (4) Downwelling shortwave radiation (DSR), upwelling shortwave radiation (USR), downwelling longwave radiation (DLR) and upwelling longwave radiation (ULR) are the four components of RN, and the evaluation results indicate that the DSR, DLR and ULR recorded via CRA/Land and ERA5-Land are consistent with the observed data, but the consistency between the USR recorded via CRA/Land and ERA5-Land and the observed data is poor. (5) The inconsistency of the USR data is the main reason for the large differences in the spatiotemporal distribution of CRA/Land and ERA5-Land RN data across the TP.

Differences Evaluation among Three Global Remote Sensing SDL Products

At present, a variety of global remote sensing surface downwelling longwave radiation (SDL) products are used for atmospheric science research; however, there are few studies on the quantitative evaluation of differences among different SDL products. In order to evaluate the differences among different SDL products quantitatively, we have selected three commonly used SDL products—Clouds and the Earth’s Radiant Energy System-Synoptic Radiative Fluxes and Clouds (CERES-SYN), the European Centre for Medium Range Weather Forecasts-Surface Radiation Budget (ECMWF-SRB) and the Global Energy and Water Exchanges Project-Surface Radiation Budget (GEWEX-SRB)—to comprehensively study in this paper. The results show that there are significant differences among the three SDL products in some areas, such as in the Arctic, the Antarctic, the Sahara, the Tibet Plateau, and Greenland. The maximum absolute root mean square error (RMSEab) in these areas is greater than 20 Wm−2, the maximum relative root mean square error (RMSEre) is greater than 20%, the maximum and minimum absolute mean bias error (MBEab) are about 20 Wm−2 and −20 Wm−2, respectively, and the maximum and minimum relative mean bias error (MBEre) are about 10% and −10%, respectively. Among the three SDL products, the difference between the ECMWF-SRB and GEWEX-SRB is the most significant. In addition, this paper also analyzed the differences among different SDL products based on three aspects. Firstly, the differences among the three SDL products show that there is significant seasonality, and the differences among different months may vary greatly. However, the differences are not sensitive to years. Secondly, there are some differences in cloud-forcing radiative fluxes (CFRFs) of different SDL products, which is also an important factor affecting the difference between different SDL products. Finally, in the process of converting high temporal resolution SDL products into monthly SDL products, data processing also affects the difference between different SDL products.

The potential global distribution of an emerging forest pathogen, Lecanosticta acicola, under a changing climate

Brown spot needle blight (BSNB), caused by Lecanosticta acicola (Thüm.) Syd., is an emerging forest disease of Pinus species originating from North America and introduced to Europe and Asia. Severity and spread of the disease has increased in the last two decades in North America and Europe as a response to climate change. No modeling work on spread, severity, climatic suitability, or potential distribution has been done for this important emerging pathogen. This study utilizes a global dataset of 2,970 independent observations of L. acicola presence and absence from the geodatabase, together with Pinus spp. distribution data and 44 independent climatic and environmental variables. The objectives were to (1) identify which bioclimatic and environmental variables are most influential in the distribution of L. acicola; (2) compare four modeling approaches to determine which modeling method best fits the data; (3) examine the realized distribution of the pathogen under climatic conditions in the reference period (1971–2000); and (4) predict the potential future global distribution of the pathogen under various climate change scenarios. These objectives were achieved using a species distribution modeling. Four modeling approaches were tested: regression-based model, individual classification trees, bagging with three different base learners, and random forest. Altogether, eight models were developed. An ensemble of the three best models was used to make predictions for the potential distribution of L. acicola: bagging with random tree, bagging with logistic model trees, and random forest. Performance of the model ensemble was very good, with high precision (0.87) and very high AUC (0.94). The potential distribution of L. acicola was computed for five global climate models (GCM) and three combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (SSP-RCP): SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5. The results of the five GCMs were averaged on combined SSP-RCP (median) per 30-year period. Eight of 44 studied factors determined as most important in explaining L. acicola distribution were included in the models: mean diurnal temperature range, mean temperature of wettest quarter, precipitation of warmest quarter, precipitation seasonality, moisture in upper portion of soil column of wettest quarter, surface downwelling longwave radiation of driest quarter, surface downwelling shortwave radiation of warmest quarter and elevation. The actual distribution of L. acicola in the reference period 1971–2000 covered 5.9% of Pinus spp. area globally. However, the model ensemble predicted potential distribution of L. acicola to cover an average of 58.2% of Pinus species global cover in the reference period. Different climate change scenarios (five GCMs, three SSP-RCPs) showed a positive trend in possible range expansion of L. acicola for the period 1971–2100. The average model predictions toward the end of the century showed the potential distribution of L. acicola rising to 62.2, 61.9, 60.3% of Pinus spp. area for SSP1-RCP2.6, SSP2-RCP4.5, SSP5-RCP8.5, respectively. However, the 95% confidence interval encompassed 35.7–82.3% of global Pinus spp. area in the period 1971–2000 and 33.6–85.8% in the period 2071–2100. It was found that SSP-RCPs had a little effect on variability of BSNB potential distribution (60.3–62.2% in the period 2071–2100 for medium prediction). In contrast, GCMs had vast impact on the potential distribution of L. acicola (33.6–85.8% of global pines area). The maps of potential distribution of BSNB will assist forest managers in considering the risk of BSNB. The results will allow practitioners and policymakers to focus surveillance methods and implement appropriate management plans.

Canopy Structure and Air Temperature Inversions Impact Simulation of Sub‐Canopy Longwave Radiation in Snow‐Covered Boreal Forests

AbstractLongwave radiation is often the dominant source of energy for snowmelt in forests. Measurements at forest sites of varying density in Sweden and Finland show that downwelling longwave radiation is enhanced under forest canopies, even for sparse canopies and particularly for clear skies. Canopy density must be estimated accurately to predict this enhancement. Linear regression with above‐canopy longwave radiation and air temperature as predictors of sub‐canopy radiation gives good predictions of sub‐canopy longwave radiation with weightings for transmission through canopy gaps that are close to measured sky view fractions. Air temperature serves here as a proxy for effective canopy radiative temperature. Adding above‐canopy shortwave radiation as a predictor gives little improvement in the predictions, suggesting that daytime heating of trunks above the air temperature (“hot trees”) has limited influence on longwave radiation under these continuous canopies. The influence of canopy temperatures falling below the above‐canopy air temperature (“cold trees”) on calm, clear nights, however, is apparent. Decoupling of canopy and above‐canopy air temperatures in an energy balance model of the type used in large‐scale land surface modeling allows this cooling.

Observations of Fog‐Aerosol Interactions Over Central Greenland

AbstractSupercooled fogs can have an important radiative impact at the surface of the Greenland Ice Sheet, but they are difficult to detect and our understanding of the factors that control their lifetime and radiative properties is limited by a lack of observations. This study demonstrates that spectrally resolved measurements of downwelling longwave radiation can be used to generate retrievals of fog microphysical properties (phase and particle effective radius) when the fog visible optical depth is greater than ∼0.25. For 12 cases of fog under otherwise clear skies between June and September 2019 at Summit Station in central Greenland, nine cases were mixed‐phase. The mean ice particle (optically‐equivalent sphere) effective radius was 24.0 ± 7.8 µm, and the mean liquid droplet effective radius was 14.0 ± 2.7 µm. These results, combined with measurements of aerosol particle number concentrations, provide evidence supporting the hypotheses that (a) low surface aerosol particle number concentrations can limit fog liquid water path, (b) fog can act to increase near‐surface aerosol particle number concentrations through enhanced mixing, and (c) multiple fog events in quiescent periods gradually deplete near‐surface aerosol particle number concentrations.

Radiation as the dominant cause of high-temperature extremes on the eastern Tibetan Plateau

Temperature extremes have been related to anomalies in large-scale circulation, but how these alter the surface energy balance is less clear. Here, we attributed high extremes in daytime and nighttime temperatures of the eastern Tibetan Plateau (ETP) to anomalies in the surface energy balance. We find that daytime high-temperature extremes are mainly caused by altered solar radiation, while nighttime ones are controlled by changes in downwelling longwave radiation. These radiation changes are largely controlled by cloud variations, which are further associated with certain large-scale circulations that modulate vertical air motion and horizontal cloud convergence. In addition, driven by a high-pressure system, strengthened downward solar radiation tends to decrease the snow albedo, which then plays an important role in reducing upward solar radiation, especially during winter and for compounding warm events. The results during winter and summer are generally similar but also present significant differences in terms of the contribution of variations in snow albedo, surface turbulent fluxes, and horizontal advection of cloud, which hence need further attention in simulating the high-temperature extreme events in the ETP. Our work indicates the importance to attribute different temperature extremes separately from the perspective of energy balance.

Intraseasonal Sea Ice Concentration Variability Over the Weddell Sea During Austral Autumn

AbstractThe sea ice concentration (SIC) over the Weddell Sea displays obvious intraseasonal variability during austral autumn with a dominant frequency of 5–20 days. The intraseasonal SIC variability is manifested as development of sea ice anomalies from the northeastern Antarctic Peninsula toward the central Weddell Sea. Rossby wave train associated with the internal atmospheric variability featuring circumglobal zonal wavenumber 4 pattern induces the development of anomalous surface winds and sea ice drifting, leading to the SIC anomalies directly. The thermodynamical processes associated with the intraseasonal SIC variability include the air temperature, the moisturizing of the lower troposphere, the latent and sensible heating, and the downwelling longwave radiation, which contribute to the variation of the local greenhouse effect over the Weddell Sea, and thus lead to the sea ice variation. The investigation of the intraseasonal SIC variability helps the understanding of sea‐ice‐atmosphere interaction over the Antarctic region.

Uncertain role of clouds in shaping summertime atmosphere-sea ice connections in reanalyses and CMIP6 models

Downwelling longwave radiation (DLR) driven by the atmospheric and cloud conditions in the troposphere is suggested to be a dominant factor to determine the summertime net surface energy budget over the Arctic Ocean and thus plays a key role to shape the September sea ice. We use reanalyses and the self-organizing map (SOM) method to distinguish CMIP6 model performance in replicating the observed strong atmosphere-DLR connection. We find all models can reasonably simulate the linkage between key atmosphere variables and the clear sky DLR but behave differently in replicating the atmosphere-DLR connection due to cloud forcing. In ERA5 and strongly coupled models, tropospheric high pressure is associated with decreased clouds in the mid- and high-levels and increased clouds near the surface. This out-of-phase structure indicates that DLR cloud forcing is nearly neutral, making the clear sky DLR more important to bridge JJA circulation to late-summer sea ice. In MERRA-2 and weakly coupled models, tropospheric clouds display a vertically homogeneous reduction; the cloud DLR is thus strongly reduced due to the cooling effect, which partially cancels out the clear sky DLR and makes the total DLR less efficient to translate circulation forcing to sea ice. The differences of cloud vertical distribution in CMIP6 appear to be differentiated by circulation related relative humidity. Therefore, a better understanding of the discrepancy of different reanalyses and remote sensing products is critical to comprehensively evaluate simulated interactions among circulation, clouds, sea ice and energy budget at the surface in summer.

Understanding and Reducing Warm and Dry Summer Biases in the Central United States: Analytical Modeling to Identify the Mechanisms for CMIP Ensemble Error Spread

Abstract Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases.

Concurrence of blowing snow and polynya enhances arctic surface–atmosphere interaction: a modeling study with an extreme wind event in 2018

Snow, a critical element influencing surface energy/mass balance of the Arctic, can also drift in the air to complicate the surface–atmosphere interaction. This complexity can be further enhanced when the surface includes polynya. These processes, however, have not been well studied and are often unrepresented in climate and weather models. We address this by applying a snow/ice-enhanced version of the Weather Research and Forecasting model to examine the impacts of blowing snow and polynya on the surface–atmosphere interaction during an extreme Arctic wind event in February 2018, when an unprecedented polynya occurred off the north coast of Greenland. The results indicate that blowing snow and the polynya contribute opposite signs to the changes of surface sensible/latent heat fluxes, but both cause enhanced downwelling longwave radiation. Process analysis shows that the thermodynamic moistening/cooling effects due to the blowing snow sublimation are amplified by increased surface winds, reduced temperature inversion, and upward wind anomaly associated with the polynya. Enhanced surface–atmosphere interaction over a polynya due to blowing snow sublimation can potentially sustain the continuing development of the polynya.

Agrivoltaics: Modeling the relative importance of longwave radiation from solar panels.

Agrivoltaics, which integrate photovoltaic power production with agriculture in the same plot of land, have the potential to reduce land competition, reduce crop irrigation, and increase solar panel efficiency. To optimize agrivoltaic systems for crop growth, energy pathways must be characterized. While the solar panels shade the crops, they also emit longwave radiation and partially block the ground from downwelling longwave radiation. A deeper understanding of the spatial variation in incoming energy would enable controlled allocation of energy in the design of agrivoltaic systems. The model also demonstrates that longwave energy should not be neglected when considering a full energy balance on the soil under solar panels.

A central arctic extreme aerosol event triggered by a warm air-mass intrusion

Frequency and intensity of warm and moist air-mass intrusions into the Arctic have increased over the past decades and have been related to sea ice melt. During our year-long expedition in the remote central Arctic Ocean, a record-breaking increase in temperature, moisture and downwelling-longwave radiation was observed in mid-April 2020, during an air-mass intrusion carrying air pollutants from northern Eurasia. The two-day intrusion, caused drastic changes in the aerosol size distribution, chemical composition and particle hygroscopicity. Here we show how the intrusion transformed the Arctic from a remote low-particle environment to an area comparable to a central-European urban setting. Additionally, the intrusion resulted in an explosive increase in cloud condensation nuclei, which can have direct effects on Arctic clouds’ radiation, their precipitation patterns, and their lifetime. Thus, unless prompt actions to significantly reduce emissions in the source regions are taken, such intrusion events are expected to continue to affect the Arctic climate.

Atmospheric forcing dominates winter Barents-Kara sea ice variability on interannual to decadal time scales

The last two decades have seen a dramatic decline and strong year-to-year variability in Arctic winter sea ice, especially in the Barents-Kara Sea (BKS), changes that have been linked to extreme midlatitude weather and climate. It has been suggested that these changes in winter sea ice arise largely from a combined effect of oceanic and atmospheric processes, but the relative importance of these processes is not well established. Here, we explore the role of atmospheric circulation patterns on BKS winter sea ice variability and trends using observations and climate model simulations. We find that BKS winter sea ice variability is primarily driven by a strong anticyclonic anomaly over the region, which explains more than 50% of the interannual variability in BKS sea-ice concentration (SIC). Recent intensification of the anticyclonic anomaly has warmed and moistened the lower atmosphere in the BKS by poleward transport of moist-static energy and local processes, resulting in an increase in downwelling longwave radiation. Our results demonstrate that the observed BKS winter sea-ice variability is primarily driven by atmospheric, rather than oceanic, processes and suggest a persistent role of atmospheric forcing in future Arctic winter sea ice loss.