Biases in droplet radii and optical depths of marine stratocumulus retrieved from MODIS imagery

The 2007 Intergovernmental Panel on Climate Change assessment established that the effect of clouds on climate contributes the largest uncertainty in predicting the future climate. Satellite observations provide an opportunity for learning about the behavior of clouds. This thesis seeks to assess the accuracy of cloud properties retrieved from multispectral satellite imagery and test the usefulness of satellite data in verifying conclusions based on aircraft observations that marine stratus appear to be formed through the nearly adiabatic ascent of moist air. Retrievals of cloud optical depth, a measure of cloud thickness, using 0.64-μm reflectances and droplet radii using separately 1.6, 2.1, and 3.7-μm reflectances were obtained with the MODerateresolution Imaging Spectroradiometer (MODIS). Owing to different amounts of absorption by liquid water in the near infrared, with the least at 1.6 μm and the most at 3.7 μm, the growth of droplet radius with cloud thickness should result in the largest droplet radii retrieved using the 3.7-μm reflectances and the smallest using 1.6-μm reflectances. Droplet radii retrieved using the 2.1-μm reflectances, however, are often the largest, and those retrieved using 3.7-μm reflectances are often the smallest. In addition aircraft observations indicate that the relationships between droplet radius, re, and optical depth, t, d ln r / d lnt e ,should be approximately equal to 0.2 for marine stratocumulus. While satellite observations in optically thick, overcast regions yielded d ln re/d ln t consistent with this result, retrievals for regions with broken clouds often yielded d ln r / d lnt e substantially smaller than 0.2. Clouds often exhibit large horizontal and vertical variability. In this thesis a simple radiative transfer model was used to predict reflectances at visible and near infrared wavelengths for clouds formed through the adiabatic ascent of moist air, and then a retrieval scheme based on vertically uniform clouds was used to determine if the departures from the behavior expected for adiabatic clouds might be caused by the assumption of spatial uniformity used in the retrievals. The simulations indicated that at all cloud thicknesses the progression of droplet size using 1.6, 2.1, and 3.7-μm reflectances followed that suggested by the absorptive properties of liquid water. The simulation also indicated, however, contrary to the observations, that when the clouds were optically thin, d ln r / d lnt e should be greater than 0.2, the value expected for the adiabatic ascent of moist air. The simulation was adapted to account for the effects of horizontal variations within clouds. Each 1-km pixel was given a subpixel distribution of optical depths based on a gamma distribution with mean taken from MODIS pixel-scale optical depths and variance given by Kato et al. (2006), obtained from Large Eddy Simulations of marine stratocumulus. Each subpixel was allowed to develop vertically following the adiabatic ascent of moist air. The average of the reflectances for the subpixels was used to retrieve the cloud properties for the pixels, and these properties were compared with the average properties of the subpixels. The retrievals obtained using 1.6-μm reflectances were most strongly affected by the addition of subpixel variations in optical depth and droplet radius. Mean droplet radius retrieved in the simulation was largest when using 1.6-μm reflectances, followed by that retrieved using 2.1 and 3.7-μm reflectances. The simulation of large droplet radii at the shorter wavelengths indicated that the large droplets observed in MODIS may result from horizontal variations within the 1-km MODIS pixel. The values of d ln r / d lnt e calculated for horizontally heterogeneous clouds were close to 0.2, but showed variations that extend both above and below this value, consistent with the MODIS observations. To illustrate the findings based on the simulations, visible optical depth and droplet radius were retrieved for MODIS 500-m pixels overcast by marine stratocumulus. The 500-m pixels were used to represent the subpixel variability within 2-km pixels constructed from the 500-m pixels. Differences between cloud properties retrieved using average radiances and averages of the subpixel properties were compared for overcast and broken-cloud regions using both MODIS and the Partly Cloudy Pixel Retrieval (PCPR) schemes for identifying the overcast 2-km pixels. In the regions overcast by optically thick marine stratocumulus, both methods of identifying overcast pixels led to small biases in the retrieved droplet radii and optical depths. Values of d ln r / d lnt e obtained in the overcast regions using the PCPR identifications were closer to the value of 0.2 expected for adiabatic clouds than those obtained using MODIS identifications. Additionally, values of d ln r / d lnt e calculated using the 500-m overcast pixels yielded better results than those calculated using the 2-km pixels. In regions containing broken clouds, the PCPR identification provided a smaller bias than the MODIS identification, however both methods showed greater biases than those calculated for regions overcast by optically thick marine stratocumulus. Values of d ln r / d lnt e for the regions containing broken clouds showed a positive value when using the PCPR scheme to identify overcast pixels, and a negative value when using the MODIS cloud mask to identify overcast pixels. Consistent with the results from the overcast regions, the d ln r / d lnt e obtained for the 500-m overcast pixels were in closer conformity with adiabatic clouds than the values obtained for the 2-km pixels using both the MODIS and PCPR identifications

Estimating Contrail Climate Effects from Satellite Data

An automated contrail detection algorithm (CDA) is developed to exploit six of the infrared channels on the 1-km MODerate-resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites. The CDA is refined and balanced using visual error analysis. It is applied to MODIS data taken by Terra and Aqua over the United States during 2006 and 2008. The results are consistent with flight track data, but differ markedly from earlier analyses. Contrail coverage is a factor of 4 less than other retrievals and the retrieved contrail optical depths and radiative forcing are smaller by approx.30%. The discrepancies appear to be due to the inability to detect wider, older contrails that comprise a significant amount of the contrail coverage. An example of applying the algorithm to MODIS data over the entire Northern Hemisphere is also presented. Overestimates of contrail coverage are apparent in some tropical regions. Methods for improving the algorithm are discussed and are to be implemented before analyzing large amounts of Northern Hemisphere data. The results should be valuable for guiding and validating climate models seeking to account for aviation effects on climate

Arctic cloud annual cycle biases in climate models

Arctic clouds exhibit a robust annual cycle with maximum cloudiness in fall and minimum cloudiness in winter. These variations affect energy flows in the Arctic with a large influence on the surface radiative fluxes. Contemporary climate models struggle to reproduce the observed Arctic cloud amount annual cycle and significantly disagree with each other. The goal of this analysis is to quantify the cloud-influencing factors that contribute to winter–summer cloud amount differences, as these seasons are primarily responsible for the model discrepancies with observations. We find that differences in the total cloud amount annual cycle are primarily caused by differences in low, rather than high, clouds; the largest differences occur between the surface and 950 hPa. Grouping models based on their seasonal cycles of cloud amount and stratifying cloud amount by cloud-influencing factors, we find that model groups disagree most under strong lower tropospheric stability, weak to moderate mid-tropospheric subsidence, and cold lower tropospheric air temperatures. Intergroup differences in low cloud amount are found to be a function of lower tropospheric thermodynamic characteristics. Further, we find that models with a larger low cloud amount in winter have a larger ice condensate fraction, whereas models with a larger low cloud amount in summer have a smaller ice condensate fraction. Stratifying model output by the specifics of the cloud microphysical scheme reveals that models treating cloud ice and liquid condensate as separate prognostic variables simulate a larger ice condensate fraction than those that treat total cloud condensate as a prognostic variable and use a temperature-dependent phase partitioning. Thus, the cloud microphysical parameterization is the primary cause of inter-model differences in the Arctic cloud annual cycle, providing further evidence of the important role that cloud ice microphysical processes play in the evolution and modeling of the Arctic climate system

Process Drivers, Inter-Model Spread, and the Path Forward: A Review of Amplified Arctic Warming

Arctic amplification (AA) is a coupled atmosphere-sea ice-ocean process. This understanding has evolved from the early concept of AA, as a consequence of snow-ice line progressions, through more than a century of research that has clarified the relevant processes and driving mechanisms of AA. The predictions made by early modeling studies, namely the fall/winter maximum, bottom-heavy structure, the prominence of surface albedo feedback, and the importance of stable stratification have withstood the scrutiny of multi-decadal observations and more complex models. Yet, the uncertainty in Arctic climate projections is larger than in any other region of the planet, making the assessment of high-impact, near-term regional changes difficult or impossible. Reducing this large spread in Arctic climate projections requires a quantitative process understanding. This manuscript aims to build such an understanding by synthesizing current knowledge of AA and to produce a set of recommendations to guide future research. It briefly reviews the history of AA science, summarizes observed Arctic changes, discusses modeling approaches and feedback diagnostics, and assesses the current understanding of the most relevant feedbacks to AA. These sections culminate in a conceptual model of the fundamental physical mechanisms causing AA and a collection of recommendations to accelerate progress towards reduced uncertainty in Arctic climate projections. Our conceptual model highlights the need to account for local feedback and remote process interactions within the context of the annual cycle to constrain projected AA. We recommend raising the priority of Arctic climate sensitivity research, improving the accuracy of Arctic surface energy budget observations, rethinking climate feedback definitions, coordinating new model experiments and intercomparisons, and further investigating the role of episodic variability in AA

On the Increasing Importance of Air-Sea Exchanges in a Thawing Arctic: A Review

Forty years ago, climate scientists predicted the Arctic to be one of Earth’s most sensitive climate regions and thus extremely vulnerable to increased CO2. The rapid and unprecedented changes observed in the Arctic confirm this prediction. Especially significant, observed sea ice loss is altering the exchange of mass, energy, and momentum between the Arctic Ocean and atmosphere. As an important component of air–sea exchange, surface turbulent fluxes are controlled by vertical gradients of temperature and humidity between the surface and atmosphere, wind speed, and surface roughness, indicating that they respond to other forcing mechanisms such as atmospheric advection, ocean mixing, and radiative flux changes. The exchange of energy between the atmosphere and surface via surface turbulent fluxes in turn feeds back on the Arctic surface energy budget, sea ice, clouds, boundary layer temperature and humidity, and atmospheric and oceanic circulations. Understanding and attributing variability and trends in surface turbulent fluxes is important because they influence the magnitude of Arctic climate change, sea ice cover variability, and the atmospheric circulation response to increased CO2. This paper reviews current knowledge of Arctic Ocean surface turbulent fluxes and their effects on climate. We conclude that Arctic Ocean surface turbulent fluxes are having an increasingly consequential influence on Arctic climate variability in response to strong regional trends in the air-surface temperature contrast related to the changing character of the Arctic sea ice cover. Arctic Ocean surface turbulent energy exchanges are not smooth and steady but rather irregular and episodic, and consideration of the episodic nature of surface turbulent fluxes is essential for improving Arctic climate projections

Database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions

Abstract Since the early 2000s, sea ice has experienced an increased rate of decline in thickness, extent and age. This new regime, coined the ‘New Arctic’, is accompanied by a reshuffling of energy flows at the surface. Understanding of the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with sea ice parcel drift tracks in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states, including remotely sensed surface energy budget terms. Additionally, flags indicate when sea ice parcels travel within cyclones, recording cyclone intensity and distance from the cyclone center. The quality of the ice parcel database was evaluated by comparison with sea ice mass balance buoys and correlations are high, which highlights the reliability of this database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic