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  • 1. Björkman, Mats P.
    et al.
    Kuhnel, Rafael
    Partridge, Daniel G.
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Roberts, Tjarda J.
    Aas, Wenche
    Mazzola, Mauro
    Viola, Angelo
    Hodson, Andy
    Ström, Johan
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Isaksson, Elisabeth
    Nitrate dry deposition in svalbard2013In: Tellus. Series B, Chemical and physical meteorology, ISSN 0280-6509, E-ISSN 1600-0889, Vol. 65, p. 19071-Article in journal (Refereed)
    Abstract [en]

    Arctic regions are generally nutrient limited, receiving an extensive part of their bio-available nitrogen from the deposition of atmospheric reactive nitrogen. Reactive nitrogen oxides, as nitric acid (HNO3) and nitrate aerosols (p-NO3), can either be washed out from the atmosphere by precipitation or dry deposited, dissolving to nitrate (NO3-). During winter, NO3- is accumulated in the snowpack and released as a pulse during spring melt. Quantification of NO3- deposition is essential to assess impacts on Arctic terrestrial ecology and for ice core interpretations. However, the individual importance of wet and dry deposition is poorly quantified in the high Arctic regions where in-situ measurements are demanding. In this study, three different methods are employed to quantify NO3- dry deposition around the atmospheric and ecosystem monitoring site, Ny-Alesund, Svalbard, for the winter season (September 2009 to May 2010): (1) A snow tray sampling approach indicates a dry deposition of -10.27 +/- 3.84 mg m(-2) (+/- S.E.); (2) A glacial sampling approach yielded somewhat higher values -30.68 +/- 12.00 mg m(-2); and (3) Dry deposition was also modelled for HNO3 and p-NO3 using atmospheric concentrations and stability observations, resulting in a total combined nitrate dry deposition of -10.76 +/- 1.26 mg m(-2). The model indicates that deposition primarily occurs via HNO3 with only a minor contribution by p-NO3. Modelled median deposition velocities largely explain this difference: 0.63 cm s(-1) for HNO3 while p-NO3 was 0.0025 and 0.16 cm s(-1) for particle sizes 0.7 and 7 mm, respectively. Overall, the three methods are within two standard errors agreement, attributing an average 14% (total range of 2-44%) of the total nitrate deposition to dry deposition. Dry deposition events were identified in association with elevated atmospheric concentrations, corroborating recent studies that identified episodes of rapid pollution transport and deposition to the Arctic.

  • 2. Ghan, Steven
    et al.
    Wang, Minghuai
    Zhang, Shipeng
    Ferrachat, Sylvaine
    Gettelman, Andrew
    Griesfeller, Jan
    Kipling, Zak
    Lohmann, Ulrike
    Morrison, Hugh
    Neubauer, David
    Partridge, Daniel G.
    Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry. University of Oxford, United Kingdom.
    Stier, Philip
    Takemura, Toshihiko
    Wang, Hailong
    Zhang, Kai
    Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability2016In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 113, no 21, p. 5804-5811Article in journal (Refereed)
    Abstract [en]

    A large number of processes are involved in the chain from emissions of aerosol precursor gases and primary particles to impacts on cloud radiative forcing. Those processes are manifest in a number of relationships that can be expressed as factors dlnX/dlnY driving aerosol effects on cloud radiative forcing. These factors include the relationships between cloud condensation nuclei (CCN) concentration and emissions, droplet number and CCN concentration, cloud fraction and droplet number, cloud optical depth and droplet number, and cloud radiative forcing and cloud optical depth. The relationship between cloud optical depth and droplet number can be further decomposed into the sum of two terms involving the relationship of droplet effective radius and cloud liquid water path with droplet number. These relationships can be constrained using observations of recent spatial and temporal variability of these quantities. However, we are most interested in the radiative forcing since the preindustrial era. Because few relevant measurements are available from that era, relationships from recent variability have been assumed to be applicable to the preindustrial to present-day change. Our analysis of Aerosol Comparisons between Observations and Models (AeroCom) model simulations suggests that estimates of relationships from recent variability are poor constraints on relationships from anthropogenic change for some terms, with even the sign of some relationships differing in many regions. Proxies connecting recent spatial/temporal variability to anthropogenic change, or sustained measurements in regions where emissions have changed, are needed to constrain estimates of anthropogenic aerosol impacts on cloud radiative forcing.

  • 3.
    Lowe, Samuel
    et al.
    Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry. University of Oxford, UK.
    Partridge, Daniel G.
    Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry. University of Oxford, UK.
    Topping, David
    Stier, Philip
    Inverse modelling of Kohler theory - Part 1: A response surface analysis of CCN spectra with respect to surface-active organic species2016In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 16, no 17, p. 10941-10963Article in journal (Refereed)
    Abstract [en]

    In this study a novel framework for inverse modelling of cloud condensation nuclei (CCN) spectra is developed using Kohler theory. The framework is established by using model-generated synthetic measurements as calibration data for a parametric sensitivity analysis. Assessment of the relative importance of aerosol physicochemical parameters, while accounting for bulk-surface partitioning of surface-active organic species, is carried out over a range of atmospherically relevant supersaturations. By introducing an objective function that provides a scalar metric for diagnosing the deviation of modelled CCN concentrations from synthetic observations, objective function response surfaces are presented as a function of model input parameters. Crucially, for the chosen calibration data, aerosol-CCN spectrum closure is confirmed as a well-posed inverse modelling exercise for a subset of the parameters explored herein. The response surface analysis indicates that the appointment of appropriate calibration data is particularly important. To perform an inverse aerosol-CCN closure analysis and constrain parametric uncertainties, it is shown that a high-resolution CCN spectrum definition of the calibration data is required where single-valued definitions may be expected to fail. Using Kohler theory to model CCN concentrations requires knowledge of many physicochemical parameters, some of which are difficult to measure in situ on the scale of interest and introduce a considerable amount of parametric uncertainty to model predictions. For all partitioning schemes and environments modelled, model output showed significant sensitivity to perturbations in aerosol log-normal parameters describing the accumulation mode, surface tension, organic : inorganic mass ratio, insoluble fraction, and solution ideality. Many response surfaces pertaining to these parameters contain well-defined minima and are therefore good candidates for calibration using a Monte Carlo Markov Chain (MCMC) approach to constraining parametric uncertainties. A complete treatment of bulk-surface partitioning is shown to predict CCN spectra similar to those calculated using classical Kohler theory with the surface tension of a pure water drop, as found in previous studies. In addition, model sensitivity to perturbations in the partitioning parameters was found to be negligible. As a result, this study supports previously held recommendations that complex surfactant effects might be neglected, and the continued use of classical Kohler theory in global climate models (GCMs) is recommended to avoid an additional computational burden. The framework developed is suitable for application to many additional composition-dependent processes that might impact CCN activation potential. However, the focus of this study is to demonstrate the efficacy of the applied sensitivity analysis to identify important parameters in those processes and will be extended to facilitate a global sensitivity analysis and inverse aerosol-CCN closure analysis.

  • 4.
    Partridge, Daniel
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Inverse Modeling of Cloud – Aerosol Interactions2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The role of aerosols and clouds is one of the largest sources of uncertainty in understanding climate change. The primary scientific goal of this thesis is to improve the understanding of cloud-aerosol interactions by applying inverse modeling using Markov Chain Monte Carlo (MCMC) simulation.

    Through a set of synthetic tests using a pseudo-adiabatic cloud parcel model, it is shown that a self adaptive MCMC algorithm can efficiently find the correct optimal values of meteorological and aerosol physiochemical parameters for a specified droplet size distribution and determine the global sensitivity of these parameters. For an updraft velocity of 0.3 m s-1, a shift towards an increase in the relative importance of chemistry compared to the accumulation mode number concentration is shown to exist somewhere between marine (~75 cm-3) and rural continental (~450 cm-3) aerosol regimes.

    Examination of in-situ measurements from the Marine Stratus/Stratocumulus Experiment (MASE II) shows that for air masses with higher number concentrations of accumulation mode (Dp = 60-120 nm) particles (~450 cm-3), an accurate simulation of the measured droplet size distribution requires an accurate representation of the particle chemistry. The chemistry is relatively more important than the accumulation mode particle number concentration, and similar in importance to the particle mean radius. This result is somewhat at odds with current theory that suggests chemistry can be ignored in all except for the most polluted environments. Under anthropogenic influence, we must consider particle chemistry also in marine environments that may be deemed relatively clean.

    The MCMC algorithm can successfully reproduce the observed marine stratocumulus droplet size distributions. However, optimising towards the broadness of the measured droplet size distribution resulted in a discrepancy between the updraft velocity, and mean radius/geometric standard deviation of the accumulation mode. This suggests that we are missing a dynamical process in the pseudo-adiabatic cloud parcel model.  

  • 5.
    Partridge, Daniel G.
    et al.
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Vrugt, J. A.
    Tunved, Peter
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Ekman, A. M. L.
    Stockholm University, Faculty of Science, Department of Meteorology .
    Gorea, D.
    Univ Amsterdam, Inst Biodivers & Ecosyst Dynam, Amsterdam, Netherlands .
    Sorooshian, A.
    Univ Arizona, Dept Chem & Environm Engn, Tucson, AZ USA .
    Inverse modeling of cloud-aerosol interactions: Part 1: Detailed response surface analysis2011In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 11, no 14, p. 7269-7287Article in journal (Refereed)
    Abstract [en]

    New methodologies are required to probe the sensitivity of parameters describing cloud droplet activation. This paper presents an inverse modeling-based method for exploring cloud-aerosol interactions via response surfaces. The objective function, containing the difference between the measured and model predicted cloud droplet size distribution is studied in a two-dimensional framework, and presented for pseudo-adiabatic cloud parcel model parameters that are pair-wise selected. From this response surface analysis it is shown that the susceptibility of cloud droplet size distribution to variations in different aerosol physiochemical parameters is highly dependent on the aerosol environment and meteorological conditions. In general the cloud droplet size distribution is most susceptible to changes in the updraft velocity. A shift towards an increase in the importance of chemistry for the cloud nucleating ability of particles is shown to exist somewhere between marine average and rural continental aerosol regimes. We also use these response surfaces to explore the feasibility of inverse modeling to determine cloud-aerosol interactions. It is shown that the "cloud-aerosol" inverse problem is particularly difficult to solve due to significant parameter interaction, presence of multiple regions of attraction, numerous local optima, and considerable parameter insensitivity. The identifiability of the model parameters will be dependent on the choice of the objective function. Sensitivity analysis is performed to investigate the location of the information content within the calibration data to confirm that our choice of objective function maximizes information retrieval from the cloud droplet size distribution. Cloud parcel models that employ a moving-centre based calculation of the cloud droplet size distribution pose additional difficulties when applying automatic search algorithms for studying cloud-aerosol interactions. To aid future studies, an increased resolution of the region of the size spectrum associated with droplet activation within cloud parcel models, or further development of fixed-sectional cloud models would be beneficial. Despite these improvements, it is demonstrated that powerful search algorithms remain necessary to efficiently explore the parameter space and successfully solve the cloud-aerosol inverse problem.

  • 6.
    Partridge, Daniel G.
    et al.
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Vrugt, J. A.
    Univ Calif Irvine, Dept Civil & Environm Engn, Henry Samueli Sch Engn, Irvine, CA USA .
    Tunved, Peter
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Ekman, Annica
    Stockholm University, Faculty of Science, Department of Meteorology .
    Struthers, Hamish
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM). Stockholm University, Faculty of Science, Department of Meteorology .
    Sorooshian, A.
    Univ Arizona, Dept Atmospher Sci, Tucson, AZ USA .
    Inverse modeling of cloud-aerosol interactions: Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte carlo based simulation approach2012In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 12, no 6, p. 2823-2847Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel approach to investigate cloud-aerosol interactions by coupling a Markov Chain Monte Carlo (MCMC) algorithm to a pseudo-adiabatic cloud parcel model. Despite the number of numerical cloud-aerosol sensitivity studies previously conducted few have used statistical analysis tools to investigate the sensitivity of a cloud model to input aerosol physiochemical parameters. Using synthetic data as observed values of cloud droplet number concentration (CDNC) distribution, this inverse modelling framework is shown to successfully converge to the correct calibration parameters. The employed analysis method provides a new, integrative framework to evaluate the sensitivity of the derived CDNC distribution to the input parameters describing the lognormal properties of the accumulation mode and the particle chemistry. To a large extent, results from prior studies are confirmed, but the present study also provides some additional insightful findings. There is a clear transition from very clean marine Arctic conditions where the aerosol parameters representing the mean radius and geometric standard deviation of the accumulation mode are found to be most important for determining the CDNC distribution to very polluted continental environments (aerosol concentration in the accumulation mode >1000 cm−3) where particle chemistry is more important than both number concentration and size of the accumulation mode. The competition and compensation between the cloud model input parameters illustrate that if the soluble mass fraction is reduced, both the number of particles and geometric standard deviation must increase and the mean radius of the accumulation mode must increase in order to achieve the same CDNC distribution. For more polluted aerosol conditions, with a reduction in soluble mass fraction the parameter correlation becomes weaker and more non-linear over the range of possible solutions (indicative of the sensitivity). This indicates that for the cloud parcel model used herein, the relative importance of the soluble mass fraction appears to decrease if the number or geometric standard deviation of the accumulation mode is increased. This study demonstrates that inverse modelling provides a flexible, transparent and integrative method for efficiently exploring cloud-aerosol interactions efficiently with respect to parameter sensitivity and correlation.

  • 7.
    Partridge, Daniel
    et al.
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Tunved, Peter
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Vrugt, Jasper
    Ekman, Annica
    Stockholm University, Faculty of Science, Department of Meteorology .
    Sorooshian, Armin
    Roelofs, Geert-Jan
    Jonsson, Haf
    A study of marine stratocumulus clouds using an inverse modelling approachManuscript (preprint) (Other academic)
    Abstract [en]

    This paper presents a Bayesian inverse modelling approach to simultaneously assess the ability of a pseudo-adiabatic cloud parcel model to match in-situ measurements of the droplet size distribution in a cloud as well as model parameters describing the updraft and different aerosol microphysical properties (herein termed calibration parameters). Our methodology is tested using observations from two clean (average accumulation mode number concentration < 60 cm-3) and two polluted clouds (average accumulation mode number concentration > 100 cm-3) observed during the Marine Stratus/Stratocumulus Experiment (MASE II) campaign. Our framework capitalizes on recent developments in Markov Chain Monte Carlo (MCMC) simulation and retrieves the most likely parameter values and their underlying posterior probability density function. This distribution provides necessary information to efficiently and in a statistically robust manner, assess both the global sensitivity of aerosol physiochemical and meteorological parameters, and the suitability of cloud parcel models to comprehensively describe the evolution of cloud droplet size distributions in stratocumulus clouds.

    We demonstrate that the updraft velocity is the most important calibration parameter for describing the observed droplet distribution for each cloud case, corroborating previous findings. The accumulation mode number, shape and size are found to be more important than chemistry except for the most polluted conditions (average accumulation mode number concentration ~455 cm-3). This highlights that conditions exist for marine stratocumulus clouds in which an accurate description of the aerosol chemistry is a pre-requisite for the accurate representation of cloud microphysical properties.

    Overall, the MCMC algorithm successfully matches the observed droplet size distribution for each cloud case. In doing so, however, the subsequent agreement between the derived and measured calibration parameters is generally poor. An important result from this analysis is that for certain calibration parameters, consistent patterns of deviation were found in the posterior distributions for all the clouds included in this study. This finding indicates that either there is systematic sampling or averaging artefacts in our observations, or our pseudo-adiabatic cloud parcel model omits or consistently misrepresents processes and/or parameter(s) required to accurately simulate the droplet size distributions of the observed marine stratocumulus. By repeating our inverse methodology with more calibration parameters of which current measurements are uncertain (surface tension, mass accommodation coefficient), we find that it is likely that the process description within the current formulation of the pseudo-adiabatic cloud model used in this study misses a dynamical process rather than parameter(s).

  • 8. Schutgens, N. A. J.
    et al.
    Partridge, Daniel G.
    Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry.
    Stier, P.
    The importance of temporal collocation for the evaluation of aerosol models with observations2016In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 16, no 2, p. 1065-1079Article in journal (Refereed)
    Abstract [en]

    It is often implicitly assumed that over suitably long periods the mean of observations and models should be comparable, even if they have different temporal sampling. We assess the errors incurred due to ignoring temporal sampling and show that they are of similar magnitude as (but smaller than) actual model errors (20-60 %). Using temporal sampling from remote-sensing data sets, the satellite imager MODIS (MODerate resolution Imaging Spectroradiometer) and the ground-based sun photometer network AERONET (AErosol Robotic NETwork), and three different global aerosol models, we compare annual and monthly averages of full model data to sampled model data. Our results show that sampling errors as large as 100% in AOT (aerosol optical thickness), 0.4 in AE (angstrom ngstrom Exponent) and 0.05 in SSA (single scattering albedo) are possible. Even in daily averages, sampling errors can be significant. Moreover these sampling errors are often correlated over long distances giving rise to artificial contrasts between pristine and polluted events and regions. Additionally, we provide evidence that suggests that models will underestimate these errors. To prevent sampling errors, model data should be temporally collocated to the observations before any analysis is made. We also discuss how this work has consequences for in situ measurements (e.g. aircraft campaigns or surface measurements) in model evaluation. Although this study is framed in the context of model evaluation, it has a clear and direct relevance to climatologies derived from observational data sets.

  • 9.
    Tunved, Peter
    et al.
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Partridge, David G.
    Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
    Korhonen, H.
    New trajectory-driven aerosol and chemical process model Chemical and Aerosol Lagrangian Model (CALM)2010In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 10, no 21, p. 10161-10185Article in journal (Refereed)
    Abstract [en]

    A new Chemical and Aerosol Lagrangian Model (CALM) has been developed and tested. The model incorporates all central aerosol dynamical processes, from nucleation, condensation, coagulation and deposition to cloud formation and in-cloud processing. The model is tested and evaluated against observations performed at the SMEAR II station located at Hyytiala (61 degrees 51'N, 24 degrees 17'E) over a time period of two years, 2000-2001. The model shows good agreement with measurements throughout most of the year, but fails in reproducing the aerosol properties during the winter season, resulting in poor agreement between model and measurements especially during December-January. Nevertheless, through the rest of the year both trends and magnitude of modal concentrations show good agreement with observation, as do the monthly average size distribution properties. The model is also shown to capture individual nucleation events to a certain degree. This indicates that nucleation largely is controlled by the availability of nucleating material (as prescribed by the [H2SO4]), availability of condensing material (in this model 15% of primary reactions of monoterpenes (MT) are assumed to produce low volatile species) and the properties of the size distribution (more specifically, the condensation sink). This is further demonstrated by the fact that the model captures the annual trend in nuclei mode concentration. The model is also used, alongside sensitivity tests, to examine which processes dominate the aerosol size distribution physical properties. It is shown, in agreement with previous studies, that nucleation governs the number concentration during transport from clean areas. It is also shown that primary number emissions almost exclusively govern the CN concentration when air from Central Europe is advected north over Scandinavia. We also show that biogenic emissions have a large influence on the amount of potential CCN observed over the boreal region, as shown by the agreement between observations and modeled results for the receptor SMEAR II, Hyytiala, during the studied period.

  • 10. Zhang, Shipeng
    et al.
    Wang, Minghuai
    Ghan, Steven J.
    Ding, Aijun
    Wang, Hailong
    Zhang, Kai
    Neubauer, David
    Lohmann, Ulrike
    Ferrachat, Sylvaine
    Takeamura, Toshihiko
    Gettelman, Andrew
    Morrison, Hugh
    Lee, Yunha
    Shindell, Drew T.
    Partridge, Daniel G.
    Stockholm University, Faculty of Science, Department of Environmental Science and Analytical Chemistry. University of Oxford, UK.
    Stier, Philip
    Kipling, Zak
    Fu, Congbin
    On the characteristics of aerosol indirect effect based on dynamic regimes in global climate models2016In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 16, no 5, p. 2765-2783Article in journal (Refereed)
    Abstract [en]

    Aerosol-cloud interactions continue to constitute a major source of uncertainty for the estimate of climate radiative forcing. The variation of aerosol indirect effects (AIE) in climate models is investigated across different dynamical regimes, determined by monthly mean 500 hPa vertical pressure velocity (omega(500)), lower-tropospheric stability (LTS) and large-scale surface precipitation rate derived from several global climate models (GCMs), with a focus on liquid water path (LWP) response to cloud condensation nuclei (CCN) concentrations. The LWP sensitivity to aerosol perturbation within dynamic regimes is found to exhibit a large spread among these GCMs. It is in regimes of strong large-scale ascent (omega(500)aEuro-aEuro parts per thousand < aEuro-a'25 hPa day(-1)) and low clouds (stratocumulus and trade wind cumulus) where the models differ most. Shortwave aerosol indirect forcing is also found to differ significantly among different regimes. Shortwave aerosol indirect forcing in ascending regimes is close to that in subsidence regimes, which indicates that regimes with strong large-scale ascent are as important as stratocumulus regimes in studying AIE. It is further shown that shortwave aerosol indirect forcing over regions with high monthly large-scale surface precipitation rate (> 0.1 mm day(-1)) contributes the most to the total aerosol indirect forcing (from 64 to nearly 100 %). Results show that the uncertainty in AIE is even larger within specific dynamical regimes compared to the uncertainty in its global mean values, pointing to the need to reduce the uncertainty in AIE in different dynamical regimes.

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