The Twomey effect describes the radiative forcing associated with a change in cloud albedo due to an increase in anthropogenic aerosol emissions. It is driven by the perturbation in cloud droplet number concentration (1Nd; ant) in liquid-water clouds and is currently understood to exert a cooling effect on climate. The Twomey effect is the key driver in the effective radiative forcing due to aerosol–cloud interactions, but rapid adjustments also contribute. These adjustments are essentially the responses of cloud fraction and liquid water path to 1Nd; ant and thus scale approximately with it. While the fundamental physics of the influence of added aerosol particles on the droplet concentration (Nd) is well described by established theory at the particle scale (micrometres), how this relationship is expressed at the large-scale (hundreds of kilometres) perturbation, 1Nd; ant, remains uncertain. The discrepancy between process understanding at particle scale and insufficient quantification at the climate-relevant large scale is caused by co-variability of aerosol particles and updraught velocity and by droplet sink processes. These operate at scales on the order of tens of metres at which only localised observations are available and at which no approach yet exists to quantify the anthropogenic perturbation. Different atmospheric models suggest diverse magnitudes of the Twomey effect even when applying the same anthropogenic aerosol emission perturbation. Thus, observational data are needed to quantify and constrain the Twomey effect. At the global scale, this means satellite data. There are four key uncertainties in determining 1Nd; ant, namely the quantification of (i) the cloud-active aerosol – the cloud condensation nuclei (CCN) concentrations at or above cloud base, (ii) Nd, (iii) the statistical approach for inferring the sensitivity of Nd to aerosol particles from the satellite data and (iv) uncertainty in the anthropogenic perturbation to CCN concentrations, which is not easily accessible from observational data. This review discusses deficiencies of current approaches for the different aspects of the problem and proposes several ways forward: in terms of CCN, retrievals of optical quantities such as aerosol optical depth suffer from a lack of vertical resolution, size and hygroscopicity information, non-direct relation to the concentration of aerosols, difficulty to quantify it within or below clouds, and the problem of insufficient sensitivity at low concentrations, in addition to retrieval errors. A future path forward can include utilising co-located polarimeter and lidar instruments, ideally including high-spectral-resolution lidar capability at two wavelengths to maximise vertically resolved size distribution information content. In terms of Nd, a key problem is the lack of operational retrievals of this quantity and the inaccuracy of the retrieval especially in broken-cloud regimes. As for the Nd-to-CCN sensitivity, key issues are the updraught distributions and the role of Nd sink processes, for which empirical assessments for specific cloud regimes are currently the best solutions. These considerations point to the conclusion that past studies using existing approaches have likely underestimated the true sensitivity and, thus, the radiative forcing due to the Twomey effect.

Variability and change of the Earth\'s climate are of fundamental importance to humankind. In particular anthropogenic climate change has been considered widely as one of the most urgent concerns for the society (United Nations, 1992, 2002). It is therefore vital to improve the understanding of the Earth\'s climate system and its variability.

Parameterization development and evaluation ideally takes a two-step approach (Lohmann et al., 2007). Insight into new processes, and initial parameterization formulation should be guided by theory, process-level observations (laboratory experiments or field studies) or, if these are unavailable, by high-resolution modelling. However, once implemented into large-scale atmospheric models, a thorough testing and evaluation is required in order to assure that the parameterization works satisfactorily for all weather situations and at the scales the model is applied to. Satellite observations are probably the most valuable source of information for this purpose, since they offer a large range of parameters over comparatively long time series and with a very large, to global, coverage. However, satellites usually retrieve parameters in a rather indirect way, and some quantities (e.g., vertical wind velocities) are unavailable. It is thus essential for model evaluation 1. to assure comparability; and, 2. to develop and apply metrics that circumvent the limitations of satellite observations and help to learn about parameterizations. In terms of comparability, the implementation of so-called \"satellite simulators\" has emerged as the approach of choice, in which satellite retrievals are emulated, making use of model information about the subgrid-scale variability of clouds, and creating summary statistics (Bodas-Salcedo et al., 2011; Nam and Quaas, 2012; Nam et al., 2014). In terms of process-oriented metrics, a large range of approaches has been developed, e.g. investigating the life cycle of cirrus from convective detrainment (Gehlot and Quaas, 2012), or focusing on the details of microphysical processes (Suzuki et al., 2011). Besides such techniques focusing on individual parameterizations, the data assimilation technique might be exploited, by objectively adjusting convection parameters and learning about parameter choices and parameterizations in this way (Schirber et al., 2013).In this chapter, we will first introduce the available satellite data, consider their limitations and the approaches to account for these, and then discuss observations-based process-oriented metrics that have been developed so far.

An examination of the seasonal and spatial distribution of Polar Stratospheric Clouds (PSCs) inferred from standard temperature profiles in the lower-middle atmosphere above Antarctica, as derived from the Earth Observing System (EOS) Aura Microwave Limb Sounder (MLS) satellite observations and NCEP/NCAR assimilations, is provided. Chemical volume mixing ratio (VMR) observations of EOS Aura MLS v2.2 hydrogen chloride (HCl) were used to show the interannual variability of PSC formation with respect to stratospheric chlorine partitioning during five Southern Hemisphere Antarctic seasons from 2005 to 2009. A remarkable first set of results, obtained from an algorithm developed for modelling HCl depletion areas in the Antarctic polar vortex region, and based on satellite observations, is presented. In particular, the analysis of HCl concentration data obtained from 2006 indicated that the area processed for HCl was larger than the area of PSC during some periods of Antarctic winter, and that this result was robust with respect to the various PSC formation and HCl depletion thresholds utilized. The results suggest that an underestimation in chlorine activation area can occur when temperature thresholds for PSC formation thresholds are employed. The work presented here also evaluated chlorine activation via sulfate aerosol (SA) in the Southern Hemisphere 2006 stratosphere, based on satellite measurements of water vapor (H2O) and constant values of SA, by implementing the TACL formula of Drdla and Müller [2010] in contrast to the TNAT formula of Hanson and Mauersberger [1988]. The results indicated that the former formula was not completely sufficient for accurately modeling areas of depleted HCl and chlorine deactivation for all pressure surfaces in the Antarctic stratosphere. Based on the results of this study, the role of SA in chlorine activation appears to be more important at lower altitudes than for areas higher in the stratosphere.

Measurements with satellite and sounding rocket borne instruments contain important information on remote and local processes in regions containing matter in the plasma state. The characteristic features of the particle distributions can be used to explain the morphology and dynamics of the different plasma populations. Charged particles are lost from a region due to precipitation into the atmosphere, charge exchange processes, or convection to open magnetic field lines. The sources of the Earth’s magnetospheric plasma are mainly ionization and extraction of upper atmosphere constituents, and entry of solar wind plasma. The intensity and distribution of auroral precipitation is controlled in part by the conditions of the interplanetary magnetic field causing different levels of auroral activity. Acceleration of electrons and positive ions along auroral field lines play an important role in magnetospheric physics. Electric fields that are quasi-steady during particle transit times, as well as fluctuating fields, are important for our understanding of the behaviour of the plasma in the auroral region. High-resolution data from the Swedish Viking and the Swedish/German Freja satellites have increased our knowledge considerably about the interaction processes between different particle populations and between particles and wave fields. This thesis describes acceleration processes influencing both ions and electrons and is based on in-situ measurements in the auroral acceleration/heating region, with special emphasis on; processes at very high latitudes, the role of fluctuating electric fields in producing so called electron conics, and positive ion heating transverse to the geomagnetic field lines. / <p>Diss. (sammanfattning) Umeå : Umeå universitet, 1994, härtill 6 uppsatser.</p> / digitalisering@umu.se

Surface wind speeds are an important and revealing component of the structure of tropical cyclones (TCs). To understand the asymmetric structure of surface winds in TCs associated with differences in formation region, environmental wind shear, storm forward motion, and TC strength and intensification, a twelve year database of satellite scatterometer data are utilized to produce composite total wind speed and Fourier-derived, low wavenumber analyses. A quantified asymmetry is determined as a function of TC intensity and reveals the tropical storms are influenced by wind shear at all TC-centric radii but only for areas away from the radius of maximum wind in hurricanes. Additionally, an increase of absolute angular momentum flux has a preference for the downshear-right quadrant, and the low wavenumber maximum develops downwind of this momentum transport. Further evaluation of the asymmetric structure with respect to wind shear’s relation to motion and impacts during TC intensity change are also considered. A composite rapid intensification event is produced and compared to overlapping satellite rain estimates. Results indicate that the TC becomes more symmetric during intensification and the phase of the maximum asymmetry rotates from a downshear-left direction to upshear-left direction after the intensification slows. The rain or convective maximum is generally located upwind of the surface wind maximum at the early stages of intensification and is coincident with the region of large angular momentum transport, which supports the idea that the surface wind asymmetry is likely a consequence of convective or other processes. Using data from a regional TC model, it is also determined that the scatterometer data are useful for model verification of tropical storms and non-major hurricanes and performs similar to or better than the standard tool at forecast lead times up to 60 hours. Preliminary comparisons of model-derived surface wind asymmetry relative to rain generally confirm the observational results.

Les nuages sont une composante majeure du système Terre-océan-atmosphère car ils couvrent en moyenne 40% de la surface du globe. Ils contribuent à refroidir la troposphère car ils réfléchissent une part importante du rayonnement solaire (effet d’albédo), mais ils contribuent également à la réchauffer car ils réémettent une partie du rayonnement infrarouge terrestre (effet de serre naturel). La détermination des effets radiatifs des nuages a été identifiée par le GIEC comme l’une des sources principales d’incertitude sur la prévision du climat. Dans ce contexte, la question se pose quant au rôle de la variabilité décennale de ces propriétés nuageuses. Plus particulièrement, on cherche à identifier une possible évolution ou variation des propriétés des nuages et à comprendre l'impact de cette variabilité sur celle du climat régional (température au sol), et inversement : ici, la région d'étude est l'Europe. On sait en effet qu'au 1er ordre le climat régional européen est contrôlé par la circulation atmosphérique de grande échelle (Cattiaux et al., ou Cassou et al., 2005) mais celle-ci ne suffit pas à expliquer certaines anomalies ou extrêmes de température. Des anomalies de propriétés nuageuses sont donc une piste importante à étudier pour expliquer ce type d’événement extrême. Durant cette thèse, nous proposons donc d'appréhender cette question à partir d'observations spatiales et de simulations. Les observations seront celles de l'Aqua-Train : (1) l'instrument MODIS à bord du satellite Aqua permet, à l'aide d'algorithmes développés à la Nasa, de caractériser le forçage radiatif des nuages depuis 9 ans (i.e. leur capacité à refroidir ou à réchauffer); (2) le lidar du satellite CALIPSO couplé au radar du satellite CloudSat nous renseigne sur la structure verticale de ces mêmes couches nuageuses, leurs propriétés précipitantes et microphysiques depuis 7 ans. Les simulations utilisées pour compléter ces observations sont déjà existantes : elles ont été réalisées avec le modèle régional WRF et couvrent l'ensemble de l'Europe sur une période suffisamment longue pour pouvoir travailler sur la variabilité interannuelle à décennale des nuages. L’objet de la thèse est d'analyser à l'échelle régionale les relations entre les anomalies nuageuses et les anomalies de température. On cherchera à comprendre si les propriétés des nuages sont perturbées pour un régime synoptique donné et si ces perturbations peuvent expliquer certaines anomalies de température via leur effet radiatif direct ou plus indirect dans le cas des nuages convectifs. La démarche suivante sera appliquée : (1) Les observations et les simulations seront analysées conjointement pour mieux caractériser les propriétés nuageuses et surtout leur variabilité spatiale et interannuelle. (2) Ce travail de caractérisation des propriétés nuageuses devra se faire pour chaque régime synoptique identifié. Les saisons d'hiver et d'été seront caractérisées grâce aux régimes de l'Oscillation Nord Atlantique (NAO) (en collaboration avec le LSCE), tandis qu'un travail sur les régimes des saisons intermédiaires sera nécessaire. (3) Nous chercherons ensuite à comprendre comment ces propriétés typiques d’un régime sont perturbées. A régime fixé, nous tenterons de relier des anomalies de couverture nuageuse à des anomalies de température, et les périodes identifiées seront étudiées en détail pour comprendre quels sont les mécanismes qui permettent de passer d’une anomalie de l’un à une anomalie de l’autre : en d’autres termes, il s’agira d’estimer si le forçage radiatif de ces « anomalies nuageuses » peut conduire à l’anomalie de température détectée. Les comparaisons entre simulations et observations seront utiles pour analyser ces liens (existent dans les 2, ou uniquement dans l'un et pourquoi). Le travail de thèse pourra alors consister à réaliser de nouvelles simulations afin de mieux comprendre ces relations. / We characterize the seasonal and inter-annual variabilities of the clouds fraction profiles in both observations and simulation since they are critical to better assess the impact of clouds on climate variability. The spaceborne lidar onboard CALIPSO, providing cloud vertical profiles since 2006, is used together with a 23-year WRF simulation at 20 km resolution. A lidar simulator helps to compare consistently model with observations. The bias in observations due to the satellite under-sampling is first estimated. Then we examine the vertical variability of both occurrence and properties of clouds. It results that observations indicate a similar occurrence of low and high clouds over continent, and more high than low clouds over the sea except in summer. The simulation shows an overestimate (underestimate) of high (low) clouds comparing to observations, especially in summer. However the seasonal variability of the cloud vertical profiles is well captured by WRF. Concerning inter-annual variability, observations show that in winter, it is twice more important for high clouds than for low clouds, which is well simulated. In summer, the observed inter-annual variability is vertically more homogeneous while the model still simulates more variability for high clouds than for low clouds. The good behavior of the simulation in winter allows us to use the 23 years of simulation and 8 years of observations to estimate the time period required to characterize the natural variability of the cloud fraction profile in winter, i.e the time period required to detect significant anomalies and trends.

Variability and change of the Earth\''s climate are of fundamental importance to humankind. In particular anthropogenic climate change has been considered widely as one of the most urgent concerns for the society (United Nations, 1992, 2002). It is therefore vital to improve the understanding of the Earth\''s climate system and its variability.

Parameterization development and evaluation ideally takes a two-step approach (Lohmann et al., 2007). Insight into new processes, and initial parameterization formulation should be guided by theory, process-level observations (laboratory experiments or field studies) or, if these are unavailable, by high-resolution modelling. However, once implemented into large-scale atmospheric models, a thorough testing and evaluation is required in order to assure that the parameterization works satisfactorily for all weather situations and at the scales the model is applied to. Satellite observations are probably the most valuable source of information for this purpose, since they offer a large range of parameters over comparatively long time series and with a very large, to global, coverage. However, satellites usually retrieve parameters in a rather indirect way, and some quantities (e.g., vertical wind velocities) are unavailable. It is thus essential for model evaluation 1. to assure comparability; and, 2. to develop and apply metrics that circumvent the limitations of satellite observations and help to learn about parameterizations. In terms of comparability, the implementation of so-called \"satellite simulators\" has emerged as the approach of choice, in which satellite retrievals are emulated, making use of model information about the subgrid-scale variability of clouds, and creating summary statistics (Bodas-Salcedo et al., 2011; Nam and Quaas, 2012; Nam et al., 2014). In terms of process-oriented metrics, a large range of approaches has been developed, e.g. investigating the life cycle of cirrus from convective detrainment (Gehlot and Quaas, 2012), or focusing on the details of microphysical processes (Suzuki et al., 2011). Besides such techniques focusing on individual parameterizations, the data assimilation technique might be exploited, by objectively adjusting convection parameters and learning about parameter choices and parameterizations in this way (Schirber et al., 2013).In this chapter, we will first introduce the available satellite data, consider their limitations and the approaches to account for these, and then discuss observations-based process-oriented metrics that have been developed so far.

The by SMHI (Swedish Meteorological and Hydrological Institute) developed snow cover product, that analyses snow cover from satellite images, needs to be validated. A reliable validation method should be developed and concentrated to verify the snow cover analyses from images emerged from the recently operative European MSG-satellite. The validation is done for Europe, and this first validation test to evaluate the validation method is only done for a couple of clear days in March 2004. The snow cover analyses from the MSG images, computed by the snow cover product, are compared to synoptic snow observations and to a similar snow cover product from the NOAA project NESDIS. Every grid point of the MSG analysis area and the reference NESDIS area has been given a snow classification, describing the local status of the snow cover. The synoptic classification is derived from snow depth reports, stored in SMHI database. The product (MSG) classification and the reference classification in every grid point has then been added to a table and presented for manual evaluation. The most exacting work is to prepare the validation data to be comparable. The preparation quality affects the results, especially at the comparison to the synoptic source where the snow cover classification is a delicate problem. The synoptic reference data has shown up to be far too sparse to be used for a serious validation. There are also problems with the interpretation of the snow reports. Using the NESDIS source as reference the result looks better and the validation method is probably reliable. Images of the snow cover from MSG and NESDIS sources have also been sketched and compared. This comparison shows that the snow cover differences might originate from the snow cover product. The temperature of the ground might affect the snow detection; the snow is not detected sufficiently when ground is cold. On the other hand high altitude clouds seems possibly generate false snow detection. From the image comparison could also be presumed that forest might hide the snow cover. A more complete validation is now needed to draw any definitive conclusions if the existing snow cover differences originate from the snow cover product or from the validation method. But the method seems to work. Synoptic source is not recommended to use as validation reference, but the snow cover scenes from NESDIS seems to be a reliable reference source and works well for the validation method. / En produkt för beräkning av snötäckningsgrad har utvecklats av SMHI (Sveriges meteorologiska och hydrologiska institut). Produkten analyserar snötäcke utifrån satellitbilder och en tillförlitlig metod att validera produkten ska utvecklas. Valideringen som sedan ska göras, koncentreras till att verifiera snötäckesanalyser utifrån den nyligen operativa Europeiska MSG-satellitens bilder. Valideringen görs för Europa, och denna första testvalidering för att utvärdera valideringsmetoden görs för ett fåtal dagar med klart väder under mars 2004. Produktens snötäckesanalyser från MSG-satellitens bilder jämförs med synoptiska snöobservationer tillika analyser från en liknande produkt från amerikanska NOAAs projekt NESDIS. MSG- och NESDIS-analysernas snötäckesinformation finns lagrat i ett snöklassificeringsfält motsvarande den geografiska arean (Europa), där alla gridpunkter har tilldelats en klassificering vilket beskriver den lokala statusen på snötäcket i punkten. Snötäckesklassificeringen för de synoptiska observationerna görs utifrån snödjupsrapporter lagrade i SMHIs databas. De olika värdena på MSG-klassificeringen och referensklassificeringen i varje punkt summeras och presenteras i en tabell för utvärdering. Det mest krävande jobbet är att förbehandla indatat från de olika källorna för att få det jämförbart. Kvalitéten på förarbetet påverkar resultatet, speciellt vid jämförelsen mot synoptiska data där snötäckesklassificeringen är komplicerad. Resultattabellen tenderar att visa på ett bra resultat, men produkten för snötäckesanalys verkar ha svårt att detektera snö tillfredställande. Den synoptiska referenskällan har visat sig innehålla alldeles för lite data för att kunna användas i en seriös validering. Det finns även vissa problem med tolkningen av snörapporterna från databasen. Med NESDIS-produktens analys som referens ser resultatet bättre ut och valideringsmetoden kan sannolikt betraktas som tillförlitlig. En jämförelse mellan kartbilder över de två källornas klassificeringar har visat att det är möjligt att avvikelserna i beskrivningen av snötäcket beror på produkten för snötäckesanalys. Produktens snödetektering ser ut att kunna påverkas av marktemperaturen, snön upptäcks inte tillräckligt bra då marken är kall. Även höga moln ser ut att kunna påverka snödetekteringen och ger i så fall falskt klassificeringen snö där det enligt referenskällan är barmark. Utifrån bildjämförelsen kan också antas att skog kan gömma snötäcket. En mer komplett validering krävs för att dra några definitiva slutsatser om skillnaderna i snötäckningsgrad beror på valideringsmetoden eller på produkten för snötäckesanalys. Men metoden ser ut att kunna fungera. Synoptiska observationer rekommenderas inte att använda som referens, men snötäckesanalyser från NESDIS-projektets produkt verkar vara en tillförlitlig referens och fungerar väl för valideringsmetoden.