Showing papers by "Naval Postgraduate School published in 2023"

Encrypting for Time- and/or Location-Based Decryption

Abstract: Encrypting and decrypting documents is routinely performed by thousands of computers practically every moment. We describe a time- and location-based decryption algorithm using elliptic curve public-key cryptography that caters for a flexible and accurate range of decryption times and/or locations.

Urban Sustainability in Action - Multi-disciplinary Approach through Jointly Organised Research Schools (URSA MAJOR)

Abstract: Smart cities, sustainable and resilient urban centres, are now being designed and implemented all over the world – including in the Arctic. They are a major part of the European Union's Green Deal transformation and the Sustainable Development Goal (SDG) 11 (sustainable cities and communities), but opinions of those living in such cities can be divided. Additionally, most Smart City frameworks have focused on technological advances and have excluded climate change and environmental aspects. The URSA MAJOR project targets education and science communication to future urban stakeholders, such as civil engineers, ecologists, urban architects, city managers and administrators. The holistic educational approach includes digitalising, collecting, storing and analysis of social and environmental information, visualising in different ways through digital technology, and education and training to use the data.Aspects of the project include eLearning opportunities, urban modelling, citizen science, use of open available data and climate change education. The educational aspects are focused on university students, but the local communities in four cities, as well as Arctic stakeholders are also part of the scope. This presentation will focus on the four main working packages of the project, the needs of an interdisciplinary project team and the results of the science communication efforts, which are now two years in.

Reducing environmental impact at NERSC (Bergen, Norway).

Abstract: NERSC is a non-profit research foundation established in Bergen (Norway) in 1986. Besides cutting-edge research in climate science, the NERSC has for a long time been very attentive to the working conditions, diversity, inclusion, and environmental impact of its activities.In this poster, we will present the different efforts taken at the NERSC initiative to take the path of reducing its environmental impact, especially the GHG footprint, of its activities. We have divided the analysis into big compartments including the travels, the numerical computations, the field campaigns, and the premises. For each compartment, we are in the process to assess the GHG emissions, and some actions have already been taken to already minimize the impacts, for example, a travel policy, and opportunity campaigns.In addition to the details of the work done by NERSC for reducing GHG emissions, we will reflect on challenges and problems encountered while taking those actions, some being general to the scientific or geoscience field (e.g., travel injunction), others being specific to NERSC (fundings, geographic location, low-carbon electricity).Finally, we will draw perspective to the experience, and try to bring recommendations into the debate, such as a better inclusion of climate impact in the European research calls.

Necessity to Use the True Gravity in Large Scale Atmospheric Modeling

Abstract: Newton’s law of universal gravitation applies between two point-masses. True gravitation of solid Earth is volume integration of gravitation of all point-masses inside the solid Earth on a point-mass in atmosphere. However, in meteorology the Earth “shrinks” into a point-mass located at Earth center with entire Earth mass to identify the Earth gravitation (untrue). Combination of untrue gravitational and centrifugal accelerations gives effective gravity (geff). Combination of true gravitational and centrifugal accelerations leads to true gravity (g). The true gravity g minus the effective gravity geff is the gravity disturbance vector, δg = g – geff. With the true gravity g used in the basic equations, seven non-dimensional numbers are proposed to identify the importance of δg versus traditional forcing terms such as horizontal pressure gradient force and Coriolis force. These non-dimensional numbers are calculated from two publicly available datasets with the geoid undulation (N) from the static gravity field model EIGEN-6C4 and long-term mean geopotential height (Z), wind velocity (u, v), and temperature (Ta) at 12 pressure levels in troposphere from the NCEP/NCAR reanalyzed climatology. The results demonstrate δg nonnegligible in hydrostatic equilibrium, geostrophic wind, geostrophic vorticity, Ekman pumping, Q vector, and Omega equation, but negligible in thermal wind relation.

Driving Mechanisms of an Extreme Winter Sea Ice Breakup Event in the Beaufort Sea

Abstract: The loss of thick multiyear sea ice in the Arctic leads to weaker sea ice that is more easily broken up by strong winds. As a consequence, extreme sea ice breakup events may become more frequent, even during the middle of winter when the sea ice cover is frozen solid. This can lead to an earlier onset of the melt season and potentially accelerate Arctic sea ice loss. Such extreme breakup events are generally not captured by climate models, potentially limiting our confidence in projections of Arctic sea ice. We investigated the driving forces behind sea ice breakup events during winter and how they change in a future climate. Our sea ice model is the first to reproduce such breakup events and reveals that the combination of strong winds and thin sea ice is a key factor for these breakups. We found that winter breakups have a large effect on local heat and moisture transfer and cause enhanced sea ice production, but also increase the overall movement of the sea ice cover, making it more vulnerable. Finally, we show that if the Arctic sea ice continues to thin, these extreme breakup events could become even more frequent.

Understanding Cold Climate Cities: Implications for Urban Sustainability

Abstract: Studying urban climate in cold climate cities is essential for understanding the implications for urban sustainability. Recent research has found that urban areas in high northern latitudes experience intense and persistent positive temperature anomalies, known as urban heat islands (UHI). Heat accumulation from year to year creates a cumulative UHI effect that fundamentally changes the environment and soil properties, affecting vegetation productivity and the length of the growing season. The warmer urban climate has significant implications for the urban economy, environment, and human health, particularly in light of global climate change in the cold climate regions. This study analyzes land surface temperature data in the seven largest cities in the European Arctic, ranging in population from 50,000 to nearly 300,000 and spanning four countries and three bioclimatic zones. The results indicate persistent temperature anomalies every season in the 1–5 ◦C range, with the largest city, Murmansk (Russia), showing the highest values. The study also finds a strong inverse relationship between surface UHI intensity and temporal variability. The more substantial the surface UHI, the more stable it is and the lower its temporal variability. We found no general direction of surface UHI change in the long term. The study also suggests that compact and dense urban infrastructure has a more significant impact than the geographical setting of the city on UHI. Understanding the intensity and variability of UHI in these regions is crucial for developing sustainable urban planning and management strategies to mitigate the negative impacts of UHI on the environment and human health.

The thermodynamic differences between winter cyclones from midlatitudes and high latitudes

Abstract: Cyclones carry heat and moisture that impact local conditions along their path. Cyclones with different origins can, however, have different life cycles and cause different impacts. To quantify differences in the thermodynamic evolution of cyclones originating from different latitudes during wintertime, we separate the cyclones according to their origin (cyclogenesis location):  midlatitude (ML) cyclones originating in the North Atlantic and high-latitude (HL) cyclones originating in the Nordic Seas and Barents Seas. It is found that HL cyclones generally carry lower thermodynamic energy as they originate in a cold environment. In contrast, ML cyclones have much higher thermodynamic energy throughout their lifecycle, even though they lose a large amount of heat as they travel long distances from their origin towards the Arctic. For a given region in the high latitudes (e.g., the Barents Sea), the mean vertical profiles of temperature and moisture from the HL group are colder and drier compared to the ones from the ML group, but the maximum values in the HL group can reach those of the ML group. Further analysis for the top 10% warmest profiles in the HL group suggests that these HL cyclones form in a preconditioned warm and moist environment. The precondioning is set up by the large-scale circulation with influences from the upstream North Atlantic. Under special conditions, the formation of high latitude cyclones in a preconditioned warm and moist environment can lead to extreme warming events in the deep Arctic like the one during New Year’s 2015/16.

North Africa and Italy

Abstract: Abstract From a global war perspective, the notion, current into the twentieth-first century, that the Mediterranean was an opportunistic and incremental theater of marginal strategic impact, that competed for resources with more strategically lucrative fronts, can no longer stand close scrutiny. Far from being lured into the Mediterranean to salvage Britain’s sputtering war effort there and reinforce Churchill’s imperial ambitions, the U.S. commitment reinforced Roosevelt’s “Europe first” strategy. It offered a place where the Western Allies could learn to fight at diminished risk and at a strategic advantage against overextended Axis forces. It seriously attrited Axis resources, and even provided the theater that unhinged the Axis. It became the venue for France’s return to the war on the Allied side. It bought time for Roosevelt to build up U.S. military might, and hence increase American influence in the postwar reconfiguration of Europe. For better or worse, the Mediterranean campaign initiated the political transformation of the region. The Mediterranean commitment was central to Roosevelt’s vision of building a stable, liberal, democratic postwar European order, one that avoided the risks and dangers of a premature engagement in Northern Europe. For their part, Axis leaders proved unable to formulate a similar comprehensive strategic vision for the Mediterranean.

Leveraging Latency

Abstract: Abstract Over the last seven decades, some states successfully leveraged the threat of acquiring atomic weapons to compel concessions from superpowers. For many others, however, this coercive gambit failed to work. When does nuclear latency—the technical capacity to build the bomb—enable states to pursue effective coercion? In Leveraging Latency, Tristan A. Volpe argues that having greater capacity to build weaponry doesn’t translate to greater coercive advantage. Volpe finds that there is a trade-off between threatening proliferation and promising nuclear restraint. States need just enough bomb-making capacity to threaten proliferation, but not so much that it becomes too difficult for them to offer nonproliferation assurances. The boundaries of this sweet spot align with the capacity to produce the fissile material at the heart of an atomic weapon. To test this argument, Volpe includes comparative case studies of four countries that leveraged latency against superpowers: Japan, West Germany, North Korea, and Iran. In doing so, Volpe identifies a generalizable mechanism—the threat-assurance trade-off—that explains why more power often makes compellence less likely to work. This framework illuminates how technology shapes broader bargaining dynamics and helps to refine policy options for inhibiting the spread of nuclear weapons. As nuclear technology continues to cast a shadow over the global landscape, Leveraging Latency provides a systematic assessment of its coercive utility.

Reply on RC2

Abstract: Abstract. Multiyear sea ice (MYI) cover in the Arctic has been monitored for decades using increasingly sophisticated remote sensing techniques, and these have documented a significant decline in MYI over time. However, such techniques are unable to differentiate between the processes affecting the evolution of the MYI. Further, estimating the thickness, and thus the volume of MYI remains challenging. In this study we employ a sea ice-ocean model to investigate the changes to MYI over the period 2000–2018. We exploit the Lagrangian framework of the sea ice model to introduce a new method of tracking MYI area and volume, which is based on identifying MYI during freeze onset each autumn. The model is found to successfully reproduce the spatial distribution and evolution of observed MYI extent. We discuss the balance of the processes (melt, ridging, export, and replenishment) linked to the general decline in MYI cover. The model suggests that rather than one process dominating the losses, there is an episodic imbalance between the different sources and sinks of MYI. We identify those key to the significant observed declines of 2007 and 2012; while melt and replenishment are important in 2012, sea ice dynamics play a significant role in 2007. Notably, the model suggests that convergence of the ice, through ridging, can result in large reductions of MYI area without a corresponding loss of MYI volume. This highlights the benefit of using models alongside satellite observations to aid interpretation of the observed MYI evolution in the Arctic.

The departure from mixed-layer similarity during the afternoon decay of turbulence in the free-convective boundary layer: results from Large-Eddy Simulations

Abstract: We investigate the departure from mixed-layer similarity during the afternoon decay of turbulence. More specifically, we aim to characterize the time-development of the departure of the velocity-variances profiles from their mixed-layer similarity reference state, for vastly-different idealistic shapes and time scales of the prescribed surface heat flux decay. For that purpose, we carry out idealized large-eddy simulations of the homogeneous free-convective boundary layer, where the prescribed surface kinematic heat flux (H) follows a sinusoidal or an exponential decay. The duration between the maximum and the zero surface heat flux, τf, is taken equal to 6 h or 2 h. A reference simulation with prescribed constant surface heat flux is also performed in order to derive the mixed-layer similarity profiles (self-similar profiles) for the vertical and horizontal velocity variances. The methodology is based on analyzing the collapse of the normalized velocity-variances profiles from different runs, while they depart from the self-similar profiles. Within the descriptive frames where the time is tracked solely by either one of the forcing time scales τf or τf _tilde = (1/H.dH/dt)-1, we find that the velocity-variances profiles from different runs do not collapse while they depart from the self-similar profiles, suggesting that the departure is dependent on the shape of the surface heat flux decay. As the mixed-layer similarity relies on the assumption that the CBL is in a quasi-equilibrium, which is maintained as long as the adjustment time scale of the largest eddies (i.e. the convective eddy-turnover time scale, t*) is much smaller than the characteristic time scale of the surface heat flux decay, we successively consider the ratios r = τf / t* and r_tilde  = τf _tilde / t* (instead of the forcing time scale alone) for tracking the time and characterizing the departure from mixed-layer similarity. As the velocity-variances profiles from different runs depart from mixed-layer similarity, we find them to collapse in the only case where the parameter r_tilde is used for tracking the time, supporting the independence of the departure from the shape of the surface heat flux decay. As a consequence of this result, the knowledge of r_tilde is sufficient to predict the velocity variances and evaluate their departure from the quasi-steady state, irrespective of the shape of the surface heat flux decay.

Volatile organic compound fluxes in the San Joaquin Valley – spatial distribution, source attribution, and inventory comparison

Abstract: Abstract. The San Joaquin Valley is an agricultural region in California that suffers from poor air quality. Since traffic emissions are decreasing, other sources of volatile organic compounds (VOCs) are gaining importance in the formation of secondary air pollutants. Using airborne eddy covariance, we conducted direct, spatially resolved flux observations of a wide range of VOCs in the San Joaquin Valley during June 2021 at 23–36 °C. Through landcover-informed footprint disaggregation, we were able to attribute emissions to sources and identify tracers for distinct source types. VOC mass fluxes were dominated by alcohols, mainly from dairy farms, while oak isoprene and citrus monoterpenes were important sources of reactivity. Comparisons with two commonly used inventories showed that isoprene emissions in the croplands were overestimated, while dairy and highway VOC emissions were generally underestimated in the inventories, and important citrus and biofuel VOC point sources were missing from the inventories. This study thus presents unprecedented insights into the VOC sources in an intensive agricultural region and provides much needed information for the improvement of inventories, air quality predictions and regulations.

Reply on RC2

Abstract: Abstract. Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve the state estimates and consequent forecasts. We interface the deterministic Ensemble Kalman filter (DEnKF) to the Lagrangian sea ice model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble-DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble DA. As a solution, we perform the DEnKF analysis on a fixed reference mesh, where model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea-ice observations on the model's forecast skills of the Arctic sea ice by comparing against satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvement of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. In contrast, the bivariate improvements between SIC and SIT are unobvious. Our ensemble-DA system based on the stand-alone sea ice model is computationally efficient and demonstrates comparable skills to operational forecasting models that use DA.

Citizen science observations capture global patterns of plant traits

Abstract: As global change accelerates, the urgency for a solid understanding of biosphere-environment interactions grows. However, we need more data on plant functional traits to test such relationships reliably across ecosystems. The TRY database contains an impressive collection of plant trait measurements for thousands of species already, and there have been some approaches to spatially extrapolate them using geospatial predictors and remote sensing data; however, the original data is spatially sparse so that extrapolations come with substantial uncertainties. At the same time, citizen scientists have collected increasingly dense observations of species occurrences around the globe. Here, we test if we can link species occurrences from the citizen science project iNaturalist with trait observations from TRY to produce global trait maps without the need for spatial extrapolation. We generated spatial grids for 18 traits, calculating a mean for each grid cell by averaging trait values associated with observations within that cell. We compared mean trait values from iNaturalist observations to community-weighted mean traits from sPlotOpen, a globally sampled dataset of vegetation plot data. Our results show correlations between the two datasets of up to r = 0.69, especially in biomes with higher iNaturalist observation density and those not dominated by trees. Also, we show that iNaturalist-derived maps have higher correlations to sPlotOpen-derived maps than previously published trait maps. This strong correlation between two fundamentally different datasets is astounding and unexpected. iNaturalist is noisy and heterogenous, sampled by citizen scientists who share the species they encounter and find interesting; sPlotOpen is a data collection of vegetation plots that were measured and recorded within the framework of specific research questions. The fact that these two datasets exhibit such a strong resemblance opens up a promising avenue for using the data treasure trove that is crowd-sourced data to help fill the gaps in plant trait data and demonstrates that crowd-sourced data, such as the iNaturalist observations, can be used to complement professional data collection efforts.

Reply on RC1

Abstract: Abstract. Multiyear sea ice (MYI) cover in the Arctic has been monitored for decades using increasingly sophisticated remote sensing techniques, and these have documented a significant decline in MYI over time. However, such techniques are unable to differentiate between the processes affecting the evolution of the MYI. Further, estimating the thickness, and thus the volume of MYI remains challenging. In this study we employ a sea ice-ocean model to investigate the changes to MYI over the period 2000–2018. We exploit the Lagrangian framework of the sea ice model to introduce a new method of tracking MYI area and volume, which is based on identifying MYI during freeze onset each autumn. The model is found to successfully reproduce the spatial distribution and evolution of observed MYI extent. We discuss the balance of the processes (melt, ridging, export, and replenishment) linked to the general decline in MYI cover. The model suggests that rather than one process dominating the losses, there is an episodic imbalance between the different sources and sinks of MYI. We identify those key to the significant observed declines of 2007 and 2012; while melt and replenishment are important in 2012, sea ice dynamics play a significant role in 2007. Notably, the model suggests that convergence of the ice, through ridging, can result in large reductions of MYI area without a corresponding loss of MYI volume. This highlights the benefit of using models alongside satellite observations to aid interpretation of the observed MYI evolution in the Arctic.

H^∞ Control of a Heated/Cooled Rod Under Point Actuation and Point Sensing

Abstract: Our long term goal is to extend the H ^∞ paradigm to nonlinear, infinite dimensional systems under point actuation and point sensing. This paper is the first step in this direction. We find the minimum L ² gain from the noisy heat flux at one end of a rod to the temperature at an arbitrary point of the rod that can be achieved by state feedback on the heat flux at the other end of the rod. We do this by completing the square to obtain a Riccati partial differential equation whose solution yields a state feedback control law that achieves a given L ² gain. We solve the Riccati PDE by Fourier series. We iterate this process to find the minimum L ² gain.

Dynamic loading on composite structures with fluid-structure interaction

Abstract: As dynamic loading is applied to polymer composite structures which are in contact with water, fluid-structure interaction (FSI) plays an important role on dynamic responses and failure of the composite structures. In particular, the FSI effect can magnify the impact forces and strains applied to composite structures, which results in premature failure. In addition, the dynamic characteristics of the composite structures are altered significantly by the FSI. In other words, the FSI effect must be properly considered in design and analysis of composite structures in order to avoid any unexpected failure.

Sea state contribution to steric sea-level

Abstract: The effect of wave-induced processing on steric sea-level is investigated through ocean-wave coupled simulations. The experiments are performed with a high-resolution configuration of the Geestacht COAstal model SysTem (GCOAST), implemented in the Northeast Atlantic, the North Sea and the Baltic Sea which are considered as connected basins. The GCOAST system accounts for wave-ocean interactions and the ocean circulation relies on the NEMO (Nucleus for European Modelling of the Ocean) ocean model, while ocean-wave simulations are performed using the spectral wind wave model WAM. The objective is to quantify the sea-state contribution to steric sea-level variability and trend over a 26-year period (1992-2017). The ability of wave-ocean coupled simulations to disclose the sea-state contributions to sea-level variability and surge is demonstrated. The contribution of the wave-induced processes (WIPs) to the sea surface dynamics (e.g. temperature and salinity), ocean mixing (mixed layer thickness), and on the modulation of air-sea fluxes (e.g. heat flux) clearly appear both during winter (10-20 %) and summer (10 %), which in turn affect the steric sea-level variability. Investigating the components of steric sea-level signal, the thermosteric sea-level shows larger amplitudes compared to the halosteric component. Significant contributions to the thermosteric sea-level variability (40 %) due to wave-induced processes are observed in the North Atlantic (in summer) and along the Norwegian Trench (in winter). WIPs influence the thermosteric sea-level trends in the North Atlantic up to the order of 1 mm yr-1, both during winter and summer, in the open ocean and at the shelf break, while smaller contributions are observed over the shelf areas of the North Sea.

Hybrid covariance super-resolution data assimilation

Abstract: This work extends the concept of "Super-resolution data assimilation" (SRDA, Barthélémy et al. 2022)) to the case of mixed-resolution ensembles pursuing two goals: (1) emulate the Ensemble Kalman Filter while (2) benefit from high-resolution observations. The forecast step is performed by two ensembles at two different resolutions, high and low-resolution. Before the assimilation step the low-resolution ensemble is downscaled to the high-resolution space, then both ensembles are updated with high-resolution observations. After the assimilation step, the low-resolution ensemble is upscaled back to its low-resolution grid for the next forecast. The downscaling step before the data assimilation step is performed either with a neural network, or with a simple cubic spline interpolation operator. The background error covariance matrix used for the update of both ensembles is a hybrid matrix between the high and low resolution background error covariance matrices. This flavor of the SRDA is called "Hybrid covariance super-resolution data assimilation" (Hybrid SRDA). We test the method with a quasi-geostrophic model in the context of twin-experiments with the low-resolution model being twice and four times coarser than the high-resolution one. The Hybrid SRDA with neural network performs equally or better than its counterpart with cubic spline interpolation, and drastically reduces the errors of the low-resolution ensemble. At equivalent computational cost, the Hybrid SRDA outperforms both the SRDA (8.4%) and the standard EnKF (14%). Conversely, for a given value of the error, the Hybrid SRDA requires as little as  50% of the computational resources of  the EnKF. Finally, the Hybrid SRDA can be formulated as a low-resolution scheme, in the sense that the assimilation is performed in the low-resolution space, encouraging the application of the scheme with realistic ocean models.

Reply on RC1

Abstract: Abstract. Advanced data assimilation (DA) methods, widely used in geophysical and climate studies to merge observations with numerical models, can improve the state estimates and consequent forecasts. We interface the deterministic Ensemble Kalman filter (DEnKF) to the Lagrangian sea ice model, neXtSIM. The ensemble is generated by perturbing the atmospheric and oceanic forcing throughout the simulations and randomly initialized ice cohesion. Our ensemble-DA system assimilates sea ice concentration (SIC) from the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) and sea ice thickness (SIT) from the merged CryoSat-2 and SMOS datasets (CS2SMOS). Because neXtSIM is computationally solved on a time-dependent evolving mesh, it is a challenging application for ensemble DA. As a solution, we perform the DEnKF analysis on a fixed reference mesh, where model variables are interpolated before the DA and then back to each member's mesh after the DA. We evaluate the impact of assimilating different types of sea-ice observations on the model's forecast skills of the Arctic sea ice by comparing against satellite observations and a free-run ensemble in an Arctic winter period, 2019–2020. Significant improvements in modeled SIT indicate the importance of assimilating weekly CS2SMOS SIT, while the improvement of SIC and ice extent are moderate but benefit from daily ingestion of the OSI-SAF SIC. In contrast, the bivariate improvements between SIC and SIT are unobvious. Our ensemble-DA system based on the stand-alone sea ice model is computationally efficient and demonstrates comparable skills to operational forecasting models that use DA.

American Civil Religion in the Era of Trump

Abstract: In 1967, Robert Bellah argued that America’s “founding myth”, what he called American civil religion, helps bind American society together by providing its citizens with a sense of origin, direction, and meaning. For evidence, Bellah primarily turned to the inaugural speeches of American presidents. This paper draws on semantic network analysis to empirically examine the inaugural addresses of Presidents Trump and Biden, looking for evidence of what some would consider aspects of American civil religion. As some believe American civil religion to be no more than a thinly veiled form of nationalism, it also considers the importance of words associated with nationalism. It finds that both Trump and Biden employed the language of nationalism and American civil religion in their respective addresses, and while it found no differences in their use of nationalist discourse, it did find that American civil religion figures more prominently in Biden’s address than in Trump’s.

Quantifying the dependence of drop spectrum width on cloud drop number concentration for cloud remote sensing

Abstract: Abstract. In-situ measurements of liquid cloud and precipitation drop size distributions from aircraft-mounted probes are used to examine the relationship of the width of drop size distributions to cloud drop number. The width of the size distribution is quantified in terms of the parameter k=(rv /re)3, where rv is the volume mean radius and re is the effective radius of the distributions. We find that on small spatial scales (~100 m), k is positively correlated with cloud drop number. This correlation is robust across a variety of campaigns using different probe technology. A new parameterization of k versus cloud drop number is developed. This new parameterization of k is used in an algorithm to derive cloud drop number in liquid phase clouds using satellite measurements of cloud optical depth and effective radius from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor on Aqua. This algorithm is compared to the standard approach to derive drop number concentration that assumes a fixed value for k. The general tendency of the parameterization is to narrow the distribution of derived number concentration. The new parameterization generally increases the derived number concentration over ocean, where N is low, and decreases it over land, where N is high. Regional biases are as large as 20 % with the magnitude of the bias closely tracking the regional mean number concentration. Interestingly, biases are smallest in regions of frequent stratocumulus cloud cover, which are a regime of significant interest for study of the aerosol indirect effect on clouds.

The Bounds of Defense

Abstract: Abstract Most people believe that killing someone, while generally morally wrong, can in some cases be a permissible act. Most people similarly believe that war, while awful, can be justified. This book addresses both subjects as equal parts in a larger meditation on the ethics of harm and moral responsibility—whether in war collectively or in individual cases of self-defense—and whatever it is that lies in between the two. The book sets out by examining the moral justification for individual defensive killing and then tests its application to collective war as a natural outgrowth of the former. In seeking sincere answers to these morally vexing questions, it offers a novel theory of liability attribution based upon evidence-relative norms: we should only be held morally responsible for what we could possibly know is wrong based on the evidence before us. In developing this theory of liability as it applies to war, Bounds of Defense also gives a robust apologia of what has been called as “revisionist” just war theory and charts a neo-liberal basis for just war theory grounded on the value of individual autonomy along the way. The entire book is an earnest attempt to take individual rights and responsibilities seriously in our thinking over killing and the horrors of war.