Explore the vast range of titles available.

Climate change in the Arctic serves as a pivotal indicator of alterations in the global climate system, with clouds playing an essential role in regulating the surface radiative energy balance in the Arctic. Elucidating the patterns of Arctic cloud variability and the underlying mechanisms is of paramount scientific importance for understanding Arctic climate change. Spatiotemporal analysis of Arctic cloud characteristics reveals that since the onset of the 21st century, low clouds have predominantly comprised the Arctic summer cloud fraction (approximately 60%), followed by middle clouds (approximately 30%). The total-cloud fraction has exhibited a marked increasing trend, especially in the Beaufort Sea and Chukchi Sea (0.45%/yr). An attribution analysis suggests that the changes in the Arctic cloud fraction are chiefly driven by trends in two atmospheric circulation modes: The Arctic Oscillation (AO) and the Arctic dipole anomaly (DA). During positive phases of the AO, the cloud fraction increases across all Arctic basins. Conversely, in the positive phases of the DA, the cloud fraction decreases in the Beaufort Sea, Chukchi Sea, and Greenland Sea, whereas it increases in the East Siberian Sea, Kara Sea, and Barents Sea, indicating an “east-west” dipole distribution. Since 2000, the AO has been on an upward trend, whereas the DA has been declining. The combined effect of these two modes has resulted in a significant increase in the cloud fraction within the Beaufort Sea region. Further examination of cloud radiative effects indicates that an increase in the cloud fraction intensifies both longwave warming and shortwave cooling effects, leading to an overall net negative radiative effect. Analyzing the long-term trends in Arctic summer clouds enhances our comprehension of Arctic climate change.

The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes. Plain Language Summary The cloud feedback is the process by which cloud property changes in a warming climate can either further enhance warming or damp it. The Arctic is warming faster than the rest of the globe, and one of the largest sources of uncertainty in its climate projections is the cloud feedback. There are two popular methods to calculate the cloud feedback: the Adjusted Cloud Radiative Effect technique, and the Cloud Radiative Kernel technique. In this paper we compare the two methods in a suite of climate models by considering the extent to which changes in Arctic sea ice impact the cloud feedbacks. From this analysis we conclude that the Cloud Radiative Kernel method is less affected by sea ice loss. We then apply the Cloud Radiative Kernel technique to data from the two most recent generations of global climate models to investigate how polar day Arctic cloud feedbacks have changed between these generations. We find that the median value of these Arctic feedbacks is slightly positive in the newest generation of models, a change from slightly negative in the previous generation that is largely fueled by a weakening of the feedback associated with changes in cloud optical depth. Key Points The Cloud Radiative Kernel method is less sensitive to surface albedo changes than the Adjusted Cloud Radiative Effect technique The Cloud Radiative Kernel method provides poor radiative closure in a suite of global climate models The median shortwave Arctic cloud feedback in recent climate models is slightly positive due to a weakened cloud optical depth feedback

Shortwave cloud radiative effect (SWCRE), known as the cooling effect triggered by cloud, plays a vital role in adjusting the global radiation budget. As the Arctic gets warmer, it may become a more indispensable factor curbing this warming tendency. Research has pointed out a significant relationship between sea ice cover (SIC) and SWCRE over the Arctic during summer (June–August). Although no evidence has been found on cloud response to SIC during summer on the average of the Arctic, this study regards cloud as an inter-connection which can regulate SIC and SWCRE in a particular place: Barents and Kara Sea region (15°E–85°E, 70°N–80°N). Its SWCRE and SIC vary significantly, with their trends being 5.85 w∙m−2 and − 5.87% per decade compared to those of the Arctic mean (2.93 w∙m−2 and − 4.65% per decade). In this area, we find that the growing number of low-level cloud which is resulted from the loss on SIC may be accountable for the increase in SWCRE, as is shown in the correlation coefficient between low-level cloud and SIC reaches − 0.4. The correlation coefficient between low-level cloud and SWCRE is 0.6. It reflects a SIC-cloud-SWCRE negative feedback. Moreover, a regression fitting model is being established to quantify the contribution of Arctic cloud in the process of slowing down the Arctic warming. It reveals that this specific region would turn into an ice-free region with sea surface temperature (SST) 1.5 °C higher than reality during 2001 if we stop the increase in SWCRE. This result presents how fascinating the contribution cloud has been making in its way slowing down the warming pace.

Clouds play an important role in Arctic amplification. This term represents the recently observed enhanced warming of the Arctic relative to the global increase of near-surface air temperature. However, there are still important knowledge gaps regarding the interplay between Arctic clouds and aerosol particles, and surface properties, as well as turbulent and radiative fluxes that inhibit accurate model simulations of clouds in the Arctic climate system. In an attempt to resolve this so-called Arctic cloud puzzle, two comprehensive and closely coordinated field studies were conducted: the Arctic Cloud Observations Using Airborne Measurements during Polar Day (ACLOUD) aircraft campaign and the Physical Feedbacks of Arctic Boundary Layer, Sea Ice, Cloud and Aerosol (PASCAL) ice breaker expedition. Both observational studies were performed in the framework of the German Arctic Amplification: Climate Relevant Atmospheric and Surface Processes, and Feedback Mechanisms (AC) project. They took place in the vicinity of Svalbard, Norway, in May and June 2017. ACLOUD and PASCAL explored four pieces of the Arctic cloud puzzle: cloud properties, aerosol impact on clouds, atmospheric radiation, and turbulent dynamical processes. The two instrumented Polar 5 and Polar 6 aircraft; the icebreaker Research Vessel (R/V) Polarstern; an ice floe camp including an instrumented tethered balloon; and the permanent ground-based measurement station at Ny-Ålesund, Svalbard, were employed to observe Arctic low- and mid-level mixed-phase clouds and to investigate related atmospheric and surface processes. The Polar 5 aircraft served as a remote sensing observatory examining the clouds from above by downward-looking sensors; the Polar 6 aircraft operated as a flying in situ measurement laboratory sampling inside and below the clouds. Most of the collocated Polar 5/6 flights were conducted either above the R/V Polarstern or over the Ny-Ålesund station, both of which monitored the clouds from below using similar but upward-looking remote sensing techniques as the Polar 5 aircraft. Several of the flights were carried out underneath collocated satellite tracks. The paper motivates the scientific objectives of the ACLOUD/PASCAL observations and describes the measured quantities, retrieved parameters, and the applied complementary instrumentation. Furthermore, it discusses selected measurement results and poses critical research questions to be answered in future papers analyzing the data from the two field campaigns.

Mixed‐phase clouds (MPCs) have been identified as significant contributors to uncertainties in climate projections, attributable to model representation of processes controlling the formation and loss of supercooled water droplets and ice particles from the atmosphere. Arctic MPCs are commonly widespread and long‐lived, with sustained ice crystal formation processes that challenge current understanding. This study examines the ice‐nucleating particle (INP) reservoir dynamics governing immersion‐mode heterogeneous freezing in an observed case of Arctic MPCs using a simplified 1D aerosol‐cloud model. The model setup includes prescribed dynamical forcings and thermodynamic profiles, and represents INPs as multicomponent and polydisperse particle size distributions. Diagnostic and prognostic approaches to immersion freezing parameterization are compared, including time‐independent (singular) number‐ and surface area‐based descriptions and a time‐dependent description following classical nucleation theory (CNT). The choice of freezing parameterization defines the size of the INP reservoir. The CNT‐based description yields an orders of magnitude larger INP reservoir than the singular parameterizations, which is the dominant factor for sustained ice crystal formation. The efficiency of the freezing process and cloud cooling are of secondary importance. A diagnostic treatment neglecting INP loss is only accurate when the INP reservoir size is large and INP depletion weak. Since a larger INP reservoir sustains ice crystal formation substantially longer, and ice water path scales with ice crystal concentrations for the conditions considered, resolving the source of differences in INP reservoir dynamics due to model implementation is a high priority for advancing climate model physics. Plain Language Summary Knowledge gaps regarding long‐lived Arctic mixed‐phase clouds, wherein supercooled droplets and ice crystals coexist, lead to significant uncertainties when assessing Earth's surface warming from increasing greenhouse gases. The longevity of such clouds, sustaining both liquid and ice crystal formation over many hours, is poorly represented across global climate models. Application of a simplified column model shows that the underlying freezing parameterization defines the number of ice‐nucleating particles (INPs) available for ice formation, termed INP reservoir in this work. A time‐dependent freezing description yields a substantially greater INP reservoir than time‐independent approaches, and therefore greater ice formation over 10 hr, whereas other factors are less important. Future work will extend to additional environmental conditions and modeling approaches. Key Points A 1‐D model informed by a large‐eddy simulation allows detailed study of immersion ice‐nucleating particles (INPs) Stochastic immersion freezing yields a greater INP reservoir and more sustained ice formation than singular approaches The efficiency of the freezing process and cloud cooling are of secondary importance for the sustenance of ice crystal formation

Recent observations show that dust emitted within the Arctic (Arctic dust) has a remarkably high ice nucleating ability, especially between −20°C and −5°C, but its impacts on the number concentrations of ice nucleating particles (INPs) and radiative balance in the Arctic are not well understood. Here we incorporate an observation‐based ice‐nucleation parameterization indicating the high ice nucleating ability of Arctic dust into a global aerosol‐climate model. A simulation using this parameterization better reproduces INP observations in the Arctic and estimates >100 times higher dust INP number concentrations with ∼100% contribution from Arctic dust in the Arctic lower troposphere (>60°N and >700 hPa) during summer and fall (June–November) than a simulation applying a standard ice‐nucleation parameterization suitable for desert dust to Arctic dust. Our results demonstrate the importance of considering an ice‐nucleation parameterization suitable for Arctic dust when simulating INPs and their effects on aerosol‐cloud interactions in the Arctic. Plain Language Summary Dust is an important aerosol type acting as “ice nucleating particles,” which initiate the formation of ice crystals within mixed‐phase clouds (consisting of both supercooled water droplets and ice crystals) and influence the cloud lifetime and distribution. Recent observations show that dust is emitted from ice‐ and vegetation‐free areas in the Arctic region (hereafter Arctic dust), which has a remarkably high ice nucleating ability, compared with desert dust such as Asian dust and Saharan dust, because of the presence of certain organic matter. However, the impacts of Arctic dust with high ice nucleating ability on ice nucleating particles and mixed‐phase clouds in the Arctic are unknown. In this study, we investigate the importance of Arctic dust with high ice nucleating ability for ice nucleating particles in the Arctic using a global aerosol‐climate model. Our simulation results show that Arctic dust accounts for almost all dust ice nucleating particles in the Arctic lower troposphere (>60°N and about 0–3 km) during summer and fall (June–November). This study demonstrates the importance of considering the high ice nucleating ability of Arctic dust when simulating ice nucleating particles and their impacts on mixed‐phase clouds and radiative balance in the Arctic. Key Points Arctic dust, emitted within the Arctic, accounts for most of dust ice nucleating particles in the Arctic lower troposphere in summer to fall Importance of Arctic dust as ice nucleating particles in the Arctic strongly depends on its high ice nucleating ability at high temperatures Considering an ice‐nucleation parameterization suitable for Arctic dust is crucial for aerosol‐cloud‐climate simulations in the Arctic

Our study shows that, during 2001–2017, when the sea ice was melting rapidly, cyclone days accounted for more than 50% of the total autumn days at the sounding stations in the Arctic marginal seas north of the Eurasian continent and almost 50% of the total autumn days at the sounding station on the northern coast of Canada. It is necessary to investigate the influence of Arctic cyclones on the cloud fraction in autumn when the sea ice refreezes from its summer minimum and the infrared cloud radiative effect becomes increasingly important. Cyclones at the selected stations are characterized by a narrow maximum rising zone with vertically consistent high relative humidity (RH) and a broad region outside the high RH zone with low RH air from the middle troposphere covering the low troposphere’s high relative humidity air. Consequently, on approximately 40% of the cyclone days, the cloud formation condition was improved from the near surface to the upper troposphere due to the cooling of strong rising warm humid air. Therefore, cyclones lead to middle cloud increases and sometimes high cloud increases, since the climatological Arctic autumn clouds are mainly low clouds. On approximately 60% of the cyclone days, only low cloud formed, but the low cloud formation condition was suppressed due to the mixing ratio decrease induced by cold dry air sinking. As a result, cyclones generally lead to a decrease in low clouds. However, the correlation between the cyclones and low clouds is complex and varies with surface ice conditions.

Mechanisms behind the phenomenon of Arctic amplification are widely discussed. To contribute to this debate, the (AC)³ project was established in 2016 (www.ac3-tr.de/). It comprises modeling and data analysis efforts as well as observational elements. The project has assembled a wealth of ground-based, airborne, shipborne, and satellite data of physical, chemical, and meteorological properties of the Arctic atmosphere, cryosphere, and upper ocean that are available for the Arctic climate research community. Short-term changes and indications of long-term trends in Arctic climate parameters have been detected using existing and new data. For example, a distinct atmospheric moistening, an increase of regional storm activities, an amplified winter warming in the Svalbard and North Pole regions, and a decrease of sea ice thickness in the Fram Strait and of snow depth on sea ice have been identified. A positive trend of tropospheric bromine monoxide (BrO) column densities during polar spring was verified. Local marine/biogenic sources for cloud condensation nuclei and ice nucleating particles were found. Atmospheric–ocean and radiative transfer models were advanced by applying new parameterizations of surface albedo, cloud droplet activation, convective plumes and related processes over leads, and turbulent transfer coefficients for stable surface layers. Four modes of the surface radiative energy budget were explored and reproduced by simulations. To advance the future synthesis of the results, cross-cutting activities are being developed aiming to answer key questions in four focus areas: lapse rate feedback, surface processes, Arctic mixed-phase clouds, and airmass transport and transformation.

An advanced multiwavelength polarization Raman lidar was operated aboard the icebreaker Polarstern during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition to continuously monitor aerosol and cloud layers in the central Arctic up to 30 km height. The expedition lasted from September 2019 to October 2020 and measurements were mostly taken between 85 and 88.5∘ N. The lidar was integrated into a complex remote-sensing infrastructure aboard the Polarstern. In this article, novel lidar techniques, innovative concepts to study aerosol–cloud interaction in the Arctic, and unique MOSAiC findings will be presented. The highlight of the lidar measurements was the detection of a 10 km deep wildfire smoke layer over the North Pole region between 7–8 km and 17–18 km height with an aerosol optical thickness (AOT) at 532 nm of around 0.1 (in October–November 2019) and 0.05 from December to March. The dual-wavelength Raman lidar technique allowed us to unambiguously identify smoke as the dominating aerosol type in the aerosol layer in the upper troposphere and lower stratosphere (UTLS). An additional contribution to the 532 nm AOT by volcanic sulfate aerosol (Raikoke eruption) was estimated to always be lower than 15 %. The optical and microphysical properties of the UTLS smoke layer are presented in an accompanying paper (Ohneiser et al., 2021). This smoke event offered the unique opportunity to study the influence of organic aerosol particles (serving as ice-nucleating particles, INPs) on cirrus formation in the upper troposphere. An example of a closure study is presented to explain our concept of investigating aerosol–cloud interaction in this field. The smoke particles were obviously able to control the evolution of the cirrus system and caused low ice crystal number concentration. After the discussion of two typical Arctic haze events, we present a case study of the evolution of a long-lasting mixed-phase cloud layer embedded in Arctic haze in the free troposphere. The recently introduced dual-field-of-view polarization lidar technique was applied, for the first time, to mixed-phase cloud observations in order to determine the microphysical properties of the water droplets. The mixed-phase cloud closure experiment (based on combined lidar and radar observations) indicated that the observed aerosol levels controlled the number concentrations of nucleated droplets and ice crystals.

The Arctic region is sensitive to climate change and is warming faster than the global average. Aerosol particles change cloud properties by acting as cloud condensation nuclei and ice-nucleating particles, thus influencing the Arctic climate system. Therefore, understanding the aerosol particle properties in the Arctic is needed to interpret and simulate their influences on climate. In this study, we collected ambient aerosol particles using whole-air and PM10 inlets and residual particles of cloud droplets and ice crystals from Arctic low-level clouds (typically, all-liquid or mixed-phase clouds) using a counterflow virtual impactor inlet at the Zeppelin Observatory near Ny-Ålesund, Svalbard, within a time frame of 4 years. We measured the composition and mixing state of individual fine-mode particles in 239 samples using transmission electron microscopy. On the basis of their composition, the aerosol and cloud residual particles were classified as mineral dust, sea salt, K-bearing, sulfate, and carbonaceous particles. The number fraction of aerosol particles showed seasonal changes, with sulfate dominating in summer and sea salt increasing in winter. There was no measurable difference in the fractions between ambient aerosol and cloud residual particles collected at ambient temperatures above 0 ∘C. On the other hand, cloud residual samples collected at ambient temperatures below 0 ∘C had several times more sea salt and mineral dust particles and fewer sulfates than ambient aerosol samples, suggesting that sea spray and mineral dust particles may influence the formation of cloud particles in Arctic mixed-phase clouds. We also found that 43 % of mineral dust particles from cloud residual samples were mixed with sea salt, whereas only 18 % of mineral dust particles in ambient aerosol samples were mixed with sea salt. This study highlights the variety in aerosol compositions and mixing states that influence or are influenced by aerosol–cloud interactions in Arctic low-level clouds.