Is the high N_d of springtime Arctic water clouds an indication of ice algae – aerosol – cloud – Arctic melting feedback?
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clouds↗
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Is the High Droplet Number Density of Late Spring Arctic Water Clouds an Indication of Snow/Ice Algae – Cloud – Arctic Melting Feedback?
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Yongxiang Hu↗
The Mixed-Phase Arctic Cloud Experiment (M-PACE)
The Mixed-Phase Arctic Cloud Experiment (M-PACE) was conducted September 27 through October 22, 2004 on the North Slope of Alaska. The primary objective was to collect a data set suitable to study interactions between microphysics, dynamics and radiative transfer in mixed-phase Arctic clouds. Observations taken during the 1997/1998 Surface Heat and Energy Budget of the Arctic (SHEBA) experiment revealed that Arctic clouds frequently consist of one (or more) liquid layers precipitating ice. M-PACE sought to investigate the physical processes of these clouds utilizing two aircraft (an in situ aircraft to characterize the microphysical properties of the clouds and a remote sensing aircraft to constraint the upwelling radiation) over the Department of Energy s Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) on the North Slope of Alaska. The measurements successfully documented the microphysical structure of Arctic mixed-phase clouds, with multiple in situ profiles collected in both single-layer and multi-layer clouds over two ground-based remote sensing sites. Liquid was found in clouds with temperatures down to -30 C, the coldest cloud top temperature below -40 C sampled by the aircraft. Remote sensing instruments suggest that ice was present in low concentrations, mostly concentrated in precipitation shafts, although there are indications of light ice precipitation present below the optically thick single-layer clouds. The prevalence of liquid down to these low temperatures could potentially be explained by the relatively low measured ice nuclei concentrations.
Verlinde, J.↗
Arctic Cloud Radiative Forcing in Contemporary Atmospheric Reanalyses
Arctic clouds play an important role in modifying the surface energy balance. In the Arctic, clouds are thought to influence the underlying sea ice cover through changing downwelling longwave radiative fluxes to the surface and through the selective reflection of the shortwave flux in summer. Atmospheric reanalyses are generally thought to have a poor representation of cloud processes at high latitudes, although the representation of trends over the perennial Arctic sea ice pack is less well known. Here, atmospheric energy fluxes are examined at the top of the atmosphere from contemporary reanalyses in comparison to satellite measurements from the CERES-EBAF version 4.1 product. The principal reanalyses examined are the NASA MERRA-2, the ECMWF ERA5 and ERA-Interim, the JRA-55, and the regional Arctic System Reanalysis version 2. In agreement with previous observation-based studies, changes with time in the shortwave cloud radiative forcing in reanalyses are found to be negligible despite strong trends in the absorbed shortwave. Over the full satellite period, there is large disagreement in the seasonality of longwave cloud forcing trends. These trends are reduced during the CERES-EBAF observing period (2003-present). An examination of these trends with respect to sea ice cover changes in each of the reanalyses is conducted.
Cullather, Richard I.↗
FIRE Arctic Clouds Experiment
An overview is given of the First ISCCP Regional Experiment (FIRE) Arctic Clouds Experiment that was conducted in the Arctic during April through July, 1998. The principal goal of the field experiment was to gather the data needed to examine the impact of arctic clouds on the radiation exchange between the surface, atmosphere, and space, and to study how the surface influences the evolution of boundary layer clouds. The observations will be used to evaluate and improve climate model parameterizations of cloud and radiation processes, satellite remote sensing of cloud and surface characteristics, and understanding of cloud-radiation feedbacks in the Arctic. The experiment utilized four research aircraft that flew over surface-based observational sites in the Arctic Ocean and Barrow, Alaska. In this paper we describe the programmatic and science objectives of the project, the experimental design (including research platforms and instrumentation), conditions that were encountered during the field experiment, and some highlights of preliminary observations, modelling, and satellite remote sensing studies.
Curry, J. A.↗
Tropical and Arctic Cloud Observations From CALIPSO Between 2008-2014 and its Implications
There has been studies (e.g., Schweiger, 2004) suggest significant increase of Arctic clouds during the last three decades, especially in the western Arctic region. Such studies are based on passive remote sensing that are not highly reliable due to the lack of contrasts in temperature and reflectance between clouds and snow/ice surfaces. Changes in the Arctic clouds can be evaluated more accurately using the space-based lidar measurements from CALIPSO during the last nine years since CALIPSO can provide much more accurate detection and classification of both water and ice clouds in the Arctic. Time series of Arctic cloud properties (e.g., cloud fraction, cloud thermodynamic phase, water cloud depolarization ratio and droplet number concentration) from CALIPSO data are analyzed in this study. This study reveals the changes in both cloud fraction and cloud microphysical properties during the last nine years when CALIPSO data are available. We will evaluate the changes in Arctic cloud fraction and cloud microphysical properties, their seasonal and spatial characteristics and the potential impact on the energy budget and the climate of the Arctic.
Hu, Yongxiang↗
Studies of Ice Nucleating Aerosol Particles in Arctic Cloud Systems
The focus of this research is to improve the understanding of ice nucleating aerosol particles (IN) and the role they play in ice formation in Arctic clouds. IN are important for global climate issues in a variety of ways. The primary effect is their role in determining the phase (liquid or solid) of cloud particles. The microscale impact is on cloud particle size, growth rate, shape, fall speed, concentration, radiative properties, and scavenging of gases and aerosols. On a larger scale, ice formation affects the development of precipitation (rate, amount, type, and distribution), latent heat release (rate and altitude), ambient humidity, the persistence of clouds, and cloud albedo. The overall goals of our FIRE 3 research are to characterize the concentrations and variability of Arctic IN during the winter-spring transition, to compare IN measurements with ice concentrations in Arctic clouds, and to examine selected IN samples for particle morphology and chemical there are distinguishable chemical signatures. The results can be combined with other measurements of aerosols, gaseous species, and cloud characteristics in order to understand the processes that determine the phase and concentration of cloud particles.
Rogers, David C.↗
Isolating the Surface Type Influence on Arctic Cloud Properties
In response to anthropogenic climate change, substantial declines in sea ice have been observed. An urgent question is whether and how clouds respond to the changing Arctic surface. Due to the important radiative influence of clouds, the response of clouds to sea ice cover change constitutes a potentially important climate feedback within the Arctic. The sign of this feedback is strongly influenced by the seasonality of the cloud response to sea ice loss. Given the importance of this phenomenon, previous research focused on using observations of inter-annual variability to quantify the cloud-sea ice relationship. However, the reliance on inter-annual variability to assess the covariance between clouds and sea ice make it challenging to control for the influence of large-scale meteorology on the clouds. We have devised an approach that applies an in situ, airborne observational strategy to quantify the influence of surface type on Arctic clouds to satellite data. Our event-based method composites cloud property differences for ocean, marginal ice zone, and sea ice surface types for >5000 MIZ crossing events. We find that cloud properties in non-summer months below ~1.5 km are influenced by surface type such that greater cloud fraction and water content are found over ocean relative to sea ice regions. Results are statistically significantly at the 95% confidence level. During summer, the surface type does not influence the cloud properties. The primary cause of the surface type dependent cloud property differences is the thermodynamic profiles differences that occur between the two surface types: namely, ocean footprints are warmer, moister, have more positive surface turbulent fluxes and are less stable than their sea ice counterparts. We find that the differences in thermodynamic profiles by surface type that correspond to the cloud property differences also explains a significant portion of the variability across events. Our analysis provides evidence that the influence of surface type on cloud properties fundamentally occurs due to the inherent surface temperature differences between the surface types. Significant surface type influences on cloud properties are only found in the presence of surface temperature differences between the surface types and only non-summer months meet this criterion. A conceptual model of this process is as follows, fundamental properties of the ocean and sea ice surface types (e.g., surface albedo, heat capacity/thermal inertia, surface roughness, and thermal conductance) lead to systematic differences in the surface temperature by surface type. In response to this surface temperature difference, atmosphere boundary layer processes (including turbulent mixing and radiation) communicate the surface temperature differences to the lower atmosphere driving the greater stability over sea ice than ocean. These differences in the background thermodynamic state profiles between the surface types yield atmospheric conditions that are more conducive to cloud development over ocean than sea ice. The details of these boundary layer processes are sensitive to the surface type but may not be fundamental to the resulting thermodynamic structure and cloud differences. Thus, our results indicate that to accurately model that cloud response to sea ice loss in climate models the surface type properties that yield the systematic differences in ocean and sea ice temperature (e.g., surface albedo, surface turbulent fluxes parameters, and thermal conductance) must be accurately represented.
Arctic radiation budget↗
Projected Regime Shift in Arctic Cloud and Water Vapor Feedbacks
The Arctic climate is changing faster than any other large-scale region on Earth. A variety of positive feedback mechanisms are responsible for the amplification, most of which are linked with changes in snow and ice cover, surface temperature (T(sub s)), atmospheric water vapor (WV), and cloud properties. As greenhouse gases continue to accumulate in the atmosphere, air temperature and water vapor content also increase, leading to a warmer surface and ice loss, which further enhance evaporation and WV. Many details of these interrelated feedbacks are poorly understood, yet are essential for understanding the pace and regional variations in future Arctic change. We use a global climate model (Goddard Institute for Space Studies, Atmosphere-Ocean Model) to examine several components of these feedbacks, how they vary by season, and how they are projected to change through the 21st century. One positive feedback begins with an increase in T(sub s) that produces an increase in WV, which in turn increases the downward longwave flux (DLF) and T(sub s), leading to further evaporation. Another associates the expected increases in cloud cover and optical thickness with increasing DLF and T(sub s). We examine the sensitivities between DLF and other climate variables in these feedbacks and find that they are strongest in the non-summer seasons, leading to the largest amplification in Ts during these months. Later in the 21st century, however, DLF becomes less sensitive to changes in WV and cloud optical thickness, as they cause the atmosphere to emit longwave radiation more nearly as a black body. This regime shift in sensitivity implies that the amplified pace of Arctic change relative to the northern hemisphere could relax in the future.
Chen, Yonghua↗
Arctic Cloud Response to a Perturbation in Sea Ice Concentration: The North Water Polynya
Surface and atmosphere energy exchanges play an important role in the Arctic climate system by influencing the lower atmospheric stability and humidity, sea ice melt and growth, and surface temperature. Sea ice significantly alters the character of these energy exchanges relative to ice-free ocean. The observed decline in Arctic sea ice since 1979 motivates questions related to the evolving role of surface-atmosphere coupling and potential feedbacks on the Arctic system. Due to the strong wintertime cloud warming effect, a critical question concerns the potential response of low clouds to Arctic sea ice decline. Previous approaches relied on interannual variability to investigate the cloud response to sea ice decline. However, the covariation between atmospheric conditions and sea ice makes it difficult to define an observational control when using interannual variability. To circumvent this difficulty, we exploit the recurring North Water polynya, an episodic opening in the northern Baffin Bay sea ice, as a natural laboratory to isolate the cloud response to a rapid, near-step perturbation in sea ice. Our results show that during the event, (a) low-cloud cover is 10%–33% larger over the polynya than nearby sea ice, (b) cloud liquid water content is up to 400% larger over the polynya than nearby sea ice, and (c) the surface cloud radiative effect is 18 W/sq. m larger over the polynya than nearby sea ice. Our results provide evidence that the low-cloud response during a polynya is a positive feedback lengthening the event.
Emily E. Monroe↗
The Influence of Sea ice on Arctic Cloud Properties: What Can We Learn by Applying a In Situ Observational Strategy to Satellite Data?
Sea ice is declining because of anthropogenic climate change. This change alters many aspects of the Arctic climate system, including the way that the surface and atmosphere interact. Atmosphere-surface coupling processes represent an important cloud feedback mechanism that can alter the Arctic surface energy budget. For the Arctic, it has been hypothesized that a reduction in sea ice cover could lead to an increase in clouds. If this process were to occur as originally hypothesized, sea ice loss in all seasons would lead to an increase in clouds. Recent observational studies find a cloud response to sea ice loss in non-summer months and no cloud response in summer months. However, previous studies rely on inter-annual variability and reanalysis to control for the influence of meteorology, reducing the confidence in the resulting conclusions. We adopt a phenomenological, event-based approach that does not need to use meteorological reanalysis. The approach analyzes cloud properties derived from CALIPSO-CloudSat over sea ice and adjacent ice-free footprints by compositing individual satellite ground tracks that cross the Arctic sea ice edge. The underlying assumption, which we verify, is that footprints that are close to each other in space and time experience similar large-scale meteorological conditions. Our results show larger cloud fraction and more cloud liquid water over ice-free than over sea ice footprints and provide additional evidence for a seasonal dependence of cloud-sea ice coupling that is in line with previous work. We find a different result where the maximum cloud property differences between sea ice and ice-free ocean occurs in spring, not fall as earlier studies suggest. We argue that these cloud differences between sea ice and ice-free ocean are primarily caused by the influence of the surface type on the thermodynamic stability of the lower troposphere and not principally from an increase in surface evaporation. In addition, we explore the sensitivity of these results to marginal ice zone width, season, Atlantic vs. Pacific sector, and the cloud property dependence on the distance from the sea ice edge. Overall, our results provide further evidence that cloud-sea ice coupling processes are not offsetting the observed surface energy budget perturbation due to sea ice loss in summer; thus, the cloud response to declining sea ice appears to contribute to amplified Arctic warming.
Patrick C Taylor↗
The influence of sea ice on Arctic cloud properties: What can we learn by applying an in situ observational strategy to satellite data?
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Patrick C. Taylor↗
Role of Arctic Low Clouds in the Arctic Climate System: Isolating the Surface Type Influence on Arctic Low-Clouds
Interactions between sea ice and clouds represent a mechanism through which sea ice influences climate. Understanding the cloud response to the rapidly changing Arctic surface properties is necessary for improving climate simulations and projection of future change inside and outside the Arctic. Towards the goal of understanding the cloud response to sea ice, we composite cloud properties for ice-free, marginal ice zone (MIZ), and ice-covered surfaces during MIZ crossing events to analyze the cloud property differences between surface types. Restricting the analysis to MIZ crossing events enables the isolation of the sea ice effect on clouds from meteorological factors. We find larger cloud fraction and total water concentration below ~1.5 km over ice-free relative to ice-covered surfaces during non-summer months. During summer, the results suggest larger cloud fraction and total water concentration over ice-free surfaces, however differences do not exceed observational uncertainty. Cloud property differences are linked to atmospheric thermodynamic profile differences, namely ice-free surfaces are warmer, moister, less stable, and have more positive surface turbulent fluxes than ice-covered surfaces. Ice-free minus ice-covered cloud property differences scale with surface temperature differences and are only found in the presence of a surface temperature difference. Our results suggest a 0.02 cloud fraction and 0.005 g m 3 total water concentration increase (~5%) at the level of maximum cloud fraction between 2000-2021 due to the observed Arctic sea ice decline in fall, corresponding to ~2 Wm 2 increase in the net surface radiative flux supporting a positive sea ice-cloud radiative feedback in fall and winter and a negative sea ice-cloud radiative feedback in spring. We propose an updated conceptual model where the average surface type influence on cloud properties is mediated by surface temperature differences between ice-free and ice-covered surfaces.
Arctic climate↗
Does a Relationship Between Arctic Low Clouds and Sea Ice Matter?
Arctic low clouds strongly affect the Arctic surface energy budget. Through this impact Arctic low clouds influence important aspects of the Arctic climate system, namely surface and atmospheric temperature, sea ice extent and thickness, and atmospheric circulation. Arctic clouds are in turn influenced by these elements of the Arctic climate system, and these interactions create the potential for Arctic cloud-climate feedbacks. To further our understanding of potential Arctic cloudclimate feedbacks, the goal of this paper is to quantify the influence of atmospheric state on the surface cloud radiative effect (CRE) and its covariation with sea ice concentration (SIC). We build on previous research using instantaneous, active remote sensing satellite footprint data from the NASA A-Train. First, the results indicate significant differences in the surface CRE when stratified by atmospheric state. Second, there is a weak covariation between CRE and SIC for most atmospheric conditions. Third, the results show statistically significant differences in the average surface CRE under different SIC values in fall indicating a 3-5 W m(exp -2) larger LW CRE in 0% versus 100% SIC footprints. Because systematic changes on the order of 1 W m(exp -2) are sufficient to explain the observed long-term reductions in sea ice extent, our results indicate a potentially significant amplifying sea ice-cloud feedback, under certain meteorological conditions, that could delay the fall freeze-up and influence the variability in sea ice extent and volume. Lastly, a small change in the frequency of occurrence of atmosphere states may yield a larger Arctic cloud feedback than any cloud response to sea ice.
Taylor, Patrick C.↗
Factors Controlling the Properties of Multi-Phase Arctic Stratocumulus Clouds
The 2004 Multi-Phase Arctic Cloud Experiment (M-PACE) IOP at the ARM NSA site focused on measuring the properties of autumn transition-season arctic stratus and the environmental conditions controlling them, including concentrations of heterogeneous ice nuclei. Our work aims to use a large-eddy simulation (LES) code with embedded size-resolved aerosol and cloud microphysics to identify factors controlling multi-phase arctic stratus. Our preliminary simulations of autumn transition-season clouds observed during the 1994 Beaufort and Arctic Seas Experiment (BASE) indicated that low concentrations of ice nuclei, which were not measured, may have significantly lowered liquid water content and thereby stabilized cloud evolution. However, cloud drop concentrations appeared to be virtually immune to changes in liquid water content, indicating an active Bergeron process with little effect of collection on drop number concentration. We will compare these results with preliminary simulations from October 8-13 during MPACE. The sensitivity of cloud properties to uncertainty in other factors, such as large-scale forcings and aerosol profiles, will also be investigated. Based on the LES simulations with M-PACE data, preliminary results from the NASA GlSS single-column model (SCM) will be used to examine the sensitivity of predicted cloud properties to changing cloud drop number concentrations for multi-phase arctic clouds. Present parametrizations assumed fixed cloud droplet number concentrations and these will be modified using M-PACE data.
Fridlind, Ann↗
An Observationally-Based Determination of the Arctic Sea Ice-Cloud Feedback Since 2000: Isolating the Arctic Cloud Response to Sea Ice Loss
Arctic sea ice responds to and drives Arctic climate change. The interactions between Arctic sea ice and clouds represent a mechanism through which sea ice can drive climate change. We composite active remote sensing satellite cloud properties for ice-free, marginal ice zone (MIZ), and ice-covered surfaces during MIZ crossing events to investigate the influence of the transition from an ice-covered to an ice-free surface on low-level clouds. We demonstrate that the event-based methodology controls for large-scale meteorological factors and isolates the sea ice effect on clouds. We find larger cloud fraction and total water content below ~1.5 km over ice-free relative to ice-covered surfaces during non-summer months, indicating a low-level cloud sensitivity to Arctic sea ice decline. During summer, results show larger cloud fraction and water content over ice-free surfaces, however the differences are statistically indistinguishable. Evidence is provided that atmospheric thermodynamic profile differences cause the cloud property differences, namely that ice-free footprints are warmer, moister, have more positive surface turbulent fluxes and are less stable than their ice-covered counterparts. Ice-free and ice-covered surface cloud property differences scale with surface temperature differences such that cloud property differences are only found in the presence of a surface temperature difference. We conclude that surface temperature differences modulate the cloud response to sea ice loss through influences on surface turbulent fluxes and lower tropospheric stability. The results imply a positive non-summer sea ice-cloud feedback and that up to a 0.02 cloud fraction and 0.05 g m 3 total water content increase in fall are due to the observed sea ice decline.
Patrick C Taylor↗
Arctic PBL Cloud Height and Motion Retrievals from MISR and MINX
How Arctic clouds respond and feedback to sea ice loss is key to understanding of the rapid climate change seen in the polar region. As more open water becomes available in the Arctic Ocean, cold air outbreaks (aka. off-ice flow from polar lows) produce a vast sheet of roll clouds in the planetary boundary layer (PBl). The cold air temperature and wind velocity are the critical parameters to determine and understand the PBl structure formed under these roll clouds. It has been challenging for nadir visible/IR sensors to detect Arctic clouds due to lack of contrast between clouds and snowy/icy surfaces. In addition) PBl temperature inversion creates a further problem for IR sensors to relate cloud top temperature to cloud top height. Here we explore a new method with the Multiangle Imaging Spectro-Radiometer (MISR) instrument to measure cloud height and motion over the Arctic Ocean. Employing a stereoscopic-technique, MISR is able to measure cloud top height accurately and distinguish between clouds and snowy/icy surfaces with the measured height. We will use the MISR INteractive eXplorer (MINX) to quantify roll cloud dynamics during cold-air outbreak events and characterize PBl structures over water and over sea ice.
Wu, Dong L.↗
The Influence of Sea Ice on Arctic Low Cloud Properties and Radiative Effects
The Arctic is one of the most climatically sensitive regions of the Earth. Climate models robustly project the Arctic to warm 2-3 times faster than the global mean surface temperature, termed polar warming amplification (PWA), but also display the widest range of surface temperature projections in this region. The response of the Arctic to increased CO2 modulates the response in tropical and extra-tropical regions through teleconnections in the atmospheric circulation. An increased frequency of extreme precipitation events in the northern mid-latitudes, for example, has been linked to the change in the background equator-to-pole temperature gradient implied by PWA. Understanding the Arctic climate system is therefore important for predicting global climate change. The ice albedo feedback is the primary mechanism driving PWA, however cloud and dynamical feedbacks significantly contribute. These feedback mechanisms, however, do not operate independently. How do clouds respond to variations in sea ice? This critical question is addressed by combining sea ice, cloud, and radiation observations from satellites, including CERES, CloudSAT, CALIPSO, MODIS, and microwave radiometers, to investigate sea ice-cloud interactions at the interannual timescale in the Arctic. Cloud characteristics are strongly tied to the atmospheric dynamic and thermodynamic state. Therefore, the sensitivity of Arctic cloud characteristics, vertical distribution and optical properties, to sea ice anomalies is computed within atmospheric dynamic and thermodynamic regimes. Results indicate that the cloud response to changes in sea ice concentration differs significantly between atmospheric state regimes. This suggests that (1) the atmospheric dynamic and thermodynamic characteristics and (2) the characteristics of the marginal ice zone are important for determining the seasonal forcing by cloud on sea ice variability.
Taylor, Patrick C.↗