The importance of temporal collocation for the evaluation of aerosol models with observations

This is the final version of the article. Available from European Geosciences Union (EGU) and Copernicus Publications via the DOI in this record.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 (Ångström 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.This work was supported by the Natural Environmental Research Council grant nr NE/J024252/1 (Global Aerosol Synthesis And Science Project). Computational resources for the ECHAM-HAM runs were made available by Deutsches Klimarechenzentrum (DKRZ) through support from the Bundesministerium für Bildung und Forschung (BMBF). The ECHAM-HAMMOZ model is developed by a consortium composed of ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford, the Finnish Meteorological Institute and the Leibniz Institute for Tropospheric Research, and managed by the Center for Climate Systems Modeling (C2SM) at ETH Zurich. P. Stier would like to acknowledge funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) ERC project ACCLAIM (grant agreement no. FP7-280025). HadGEMUKCA was run on the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk). The development of the UKCA model (www.ukca.ac.uk) was supported by the UK’s Natural Environment Research Council (NERC) through the NERC Centres for Atmospheric Science (NCAS) initiative. MIROC-SPRINTARS was run on the SX-9 supercomputer at NIES (CGER) in Japan. The figures in this paper were prepared using David W. Fanning’s coyote library for IDL. The authors thank an anonymous reviewer and in particular Andrew Sayer for useful comments that helped improve the manuscript

Satellite observations of cloud regime development: the role of aerosol processes

This is the final version of the article. Available from European Geosciences Union via the DOI in this record.Many different interactions between aerosols and clouds have been postulated, based on correlations between satellite retrieved aerosol and cloud properties. Previous studies highlighted the importance of meteorological covariations to the observed correlations. In this work, we make use of multiple temporally-spaced satellite retrievals to observe the development of cloud regimes. The observation of cloud regime development allows us to account for the influences of cloud fraction (CF) and meteorological factors on the aerosol retrieval. By accounting for the aerosol index (AI)-CF relationship, we reduce the influence of meteorological correlations compared to “snapshot” studies, finding that simple correlations overestimate any aerosol effect on CF by at least a factor of two. We find an increased occurrence of transitions into the stratocumulus regime over ocean with increases in MODIS AI, consistent with the hypothesis that aerosols increase stratocumulus persistence. We also observe an increase in transitions into the deep convective regime over land, consistent with the aerosol invigoration hypothesis. We find changes in the transitions from the shallow cumulus regime in different aerosol environments. The strength of these changes is strongly dependent on Low Troposphere Static Stability and 10 m windspeed, but less so on other meteorological factors. Whilst we have reduced the error due to meteorological and CF effects on the aerosol retrieval, meteorological covariation with the cloud and aerosol properties is harder to remove, so these results likely represent an upper bound on the effect of aerosols on cloud development and CF.This work was supported by a UK Natural Environment Research Council (NERC) DPhil studentship and funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement no. FP7-280025

Links between satellite-retrieved aerosol and precipitation

This is the final version of the article. Available from EGU via the DOI in this recordMany theories have been proposed detailing how aerosols might impact precipitation, predicting both increases and decreases depending on the prevailing meteorological conditions and aerosol type. In convective clouds, increased aerosol concentrations have been speculated to invigorate convective activity. Previous studies have shown large increases in precipitation with increasing aerosol optical depth, concluding an aerosol effect on precipitation. Our analysis reveals that these studies may have been influenced by cloud effects on the retrieved aerosol, as well as by meteorological covariations. We use a regime-based approach to separate out different cloud regimes, allowing for the study of aerosol–cloud interactions in individual cloud regimes. We account for the influence of cloud properties on the aerosol retrieval and make use of the diurnal sampling of the TRMM satellite and the TRMM merged precipitation product to investigate the precipitation development. We find that whilst there is little effect on precipitation at the time of the aerosol retrieval, in the 6 h after the aerosol retrieval, there is an increase in precipitation from cloud in high-aerosol environments, consistent with the invigoration hypothesis. Increases in lightning flash count with increased aerosol are also observed in this period. The invigoration effect appears to be dependent on the cloud-top temperature, with clouds with tops colder than 0 °C showing increases in precipitation at times after the retrieval, as well as increases in wet scavenging. Warm clouds show little change in precipitation development with increasing aerosol, suggesting ice processes are important for the invigoration of precipitation.This work was supported by a UK Natural Environment Research Council (NERC) DPhil studentship and funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. FP7-280025

Inverse modelling of Köhler theory – Part 1: A response surface analysis of CCN spectra with respect to surface-active organic species

This is the final version of the article. Available from European Geosciences Union (EGU) and Copernicus Publications via the DOI in this record.In this study a novel framework for inverse modelling of cloud condensation nuclei (CCN) spectra is developed using Köhler 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 Köhler 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 Köhler 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 Köhler 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.This work was supported by the UK Natural Environment Research Council grants NE/I020148/1 (AerosolCloud Interactions – A Directed Programme to Reduce Uncertainty in Forcing) and NE/J024252/1 (Global Aerosol Synthesis And Science Project). P. Stier would like to acknowledge funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) ERC project ACCLAIM (grant agreement no. FP7-280025)

Inverse modeling of cloud-aerosol interactions – Part 1: Detailed response surface analysis

This is the final version of the article. Available from EGU via the DOI in this record.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.We gratefully acknowledge the financial support of the Bert Bolin Centre for Climate research. We gratefully appreciate G. J. Roelofs, IMAU, Utrecht, the Netherlands, for providing us with the pseudo-adiabatic cloud parcel model used in this study. We gratefully acknowledge Hamish Struthers valuable discussions and his help to improve the readability of the manuscript. Some of the calculations made during the course of this study have been made possible using the LISA cluster from the SARA centre for parallel computing at the University of Amsterdam, the Netherlands. AS acknowledges support from an Office of Naval Research YIP award (N00014-10-1-0811).The authors acknowledge the Swedish Environmental Monitoring Program a

Revising the hygroscopicity of inorganic sea salt particles

This is the final version of the article. Available from Springer Nature via the DOI in this record.Sea spray is one of the largest natural aerosol sources and plays an important role in the Earth's radiative budget. These particles are inherently hygroscopic, that is, they take-up moisture from the air, which affects the extent to which they interact with solar radiation. We demonstrate that the hygroscopic growth of inorganic sea salt is 8-15% lower than pure sodium chloride, most likely due to the presence of hydrates. We observe an increase in hygroscopic growth with decreasing particle size (for particle diameters <150 nm) that is independent of the particle generation method. We vary the hygroscopic growth of the inorganic sea salt within a general circulation model and show that a reduced hygroscopicity leads to a reduction in aerosol-radiation interactions, manifested by a latitudinal-dependent reduction of the aerosol optical depth by up to 15%, while cloud-related parameters are unaffected. We propose that a value of κs=1.1 (at RH=90%) is used to represent the hygroscopicity of inorganic sea salt particles in numerical models.P.Z. was partially financed by an Advanced Postdoc.Mobility fellowship of the Swiss National Science Foundation (grant no. P300P2_147776). M.E.S., C.L. and I.R. were financed by the Nordic Center of Excellence on Cryosphere-Atmosphere-Cloud-Climate-Interactions (NCoE CRAICC) and the Swedish Research Council (Vetenskapsradet). O.V. and A.V. were supported by the Academy of Finland Centre of Excellence (grant no. 272041) and The Doctoral School of the University of Eastern Finland. J.C.C. and M.G. received financial support from the European Research Commission via the ERC grant ERC-CoG 615922-BLACARAT. A.N. acknowledges support from a Georgia Power Scholar chair and a Cullen-Peck faculty fellowship. S.B. and M.M.-F. acknowledge funding by the Swiss National Science Foundation (grant no. 200020_146760/1). I. Tegen (TROPOS, Germany) is acknowledged for providing help with the sea spray source functions. We thank D. Eklöf and Z. Bacsik from the Department of Materials and Environmental Chemistry at Stockholm University for their assistance in the pycnometre and Fourier transform infrared spectrometer measurements. The ECHAM-HAMMOZ model is developed by a consortium composed of ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford, the Finnish Meteorological Institute and the Leibniz Institute for Tropospheric Research, and managed by the Center for Climate Systems Modeling (C2SM) at ETH Zurich

On the characteristics of aerosol indirect effect based on dynamic regimes in global climate models

This is the final version of the article. Available from EGU via the DOI in this record.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 (ω500), 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 (ω500   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.M. Wang acknowledged the support from the Jiangsu Province Specially-appointed professorship grant and the One Thousand Young Talents Program and the National Natural Science Foundation of China (41575073). The contribution from Pacific Northwest National Laboratory was supported by the US Department of Energy (DOE), Office of Science, Decadal and Regional Climate Prediction using Earth System Models (EaSM program). H. Wang acknowledges support by the DOE Earth System Modeling program. The Pacific Northwest National Laboratory is operated for the DOE by Battelle Memorial Institute under contract DE-AC06-76RLO 1830. The ECHAM-HAMMOZ model is developed by a consortium composed of ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford, the Finnish Meteorological Institute and the Leibniz Institute for Tropospheric Research, and managed by the Center for Climate Systems Modeling (C2SM) at ETH Zurich. D. Neubauer gratefully acknowledges the support by the Austrian Science Fund (FWF): J 3402-N29 (Erwin Schrödinger Fellowship Abroad). The Center for Climate Systems Modeling (C2SM) at ETH Zurich is acknowledged for providing technical and scientific support. This work was supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID s431. D. G. Partridge would like to acknowledge support from the UK Natural Environment Research Council project ACID-PRUF (NE/I020148/1) as well as thanks to N. Bellouin for useful discussions during the course of this work. The development of GLOMAP-mode within HadGEM is part of the UKCA project, which is supported by both NERC and the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). We acknowledge use of the MONSooN system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, a strategic partnership between the Met Office and the Natural Environment Research Council. P. Stier would like to acknowledge support from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. FP7-280025

Robust observational constraint of uncertain aerosol processes and emissions in a climate model and the effect on aerosol radiative forcing

The effect of observational constraint on the ranges of uncertain physical and chemical process parameters was explored in a global aerosol–climate model. The study uses 1 million variants of the Hadley Centre General Environment Model version 3 (HadGEM3) that sample 26 sources of uncertainty, together with over 9000 monthly aggregated grid-box measurements of aerosol optical depth, PM2.5, particle number concentrations, sulfate and organic mass concentrations. Despite many compensating effects in the model, the procedure constrains the probability distributions of parameters related to secondary organic aerosol, anthropogenic SO2 emissions, residential emissions, sea spray emissions, dry deposition rates of SO2 and aerosols, new particle formation, cloud droplet pH and the diameter of primary combustion particles. Observational constraint rules out nearly 98 % of the model variants. On constraint, the ±1σ (standard deviation) range of global annual mean direct radiative forcing (RFari) is reduced by 33 % to −0.14 to −0.26 W m−2, and the 95 % credible interval (CI) is reduced by 34 % to −0.1 to −0.32 W m−2. For the global annual mean aerosol–cloud radiative forcing, RFaci, the ±1σ range is reduced by 7 % to −1.66 to −2.48 W m−2, and the 95 % CI by 6 % to −1.28 to −2.88 W m−2. The tightness of the constraint is limited by parameter cancellation effects (model equifinality) as well as the large and poorly defined “representativeness error” associated with comparing point measurements with a global model. The constraint could also be narrowed if model structural errors that prevent simultaneous agreement with different measurement types in multiple locations and seasons could be improved. For example, constraints using either sulfate or PM2.5 measurements individually result in RFari±1σ ranges that only just overlap, which shows that emergent constraints based on one measurement type may be overconfident

The global aerosol-climate model ECHAM6.3-HAM2.3-Part 2: Cloud evaluation, aerosol radiative forcing, and climate sensitivity

This is the final version. Available on open access from European Geosciences Union via the DOI in this recordCode availability. The ECHAM-HAMMOZ model is made freely available to the scientific community under the HAMMOZ Software License Agreement, which defines the conditions under which the model can be used. More information can be found at the HAMMOZ website (https://redmine.hammoz.ethz.ch/projects/hammoz, last access: 13 August 2019). Scripts can be found at https://doi.org/10.5281/zenodo.2553891 (Neubauer et al., 2019a).Data availability. Data can be found at https://doi.org/10.5281/zenodo.2541936 (Neubauer et al., 2019b). ESA cloud CCI data can be downloaded from http://www.esa-cloud-cci.org/?q=data_download (Poulsen et al., 2017; Stengel et al., 2017b). MODIS products are available for download from Level 1 and the Atmosphere Archive and Distribution System (LAADS) at https://ladsweb.modaps.eosdis.nasa.gov/search/ (Platnick, 2017). ISCCP histogram data and the CALIPSO-GOCCP product can be obtained from http://climserv.ipsl.polytechnique.fr/cfmip-obs/ (Zhang et al., 2012; Pincus et al., 2012). Cloud-top CDNC can be downloaded from https://doi.org/10.15695/vudata.ees.1 (Bennartz and Rausch, 2016). MAC-LWP data are available at the Goddard Earth Sciences Data and Information Services Center (GES DISC; current hosting: http://disc.sci.gsfc.nasa.gov, Elsaesser et al., 2016). CERES satellite data can be obtained from the NASA Langley Research Center Atmospheric Science Data Center at https://ceres.larc.nasa.gov/order_data.php (last access: 12 February 2018). The IWP satellite data from Li et al. (2012) were obtained from the authors. GPCP Precipitation data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://www.esrl.noaa.gov/psd/ (last access: 16 September 2017).The global aerosol-climate model ECHAM6.3-HAM2.3 (E63H23) as well as the previous model versions ECHAM5.5-HAM2.0 (E55H20) and ECHAM6.1-HAM2.2 (E61H22) are evaluated using global observational datasets for clouds and precipitation. In E63H23, the amount of low clouds, the liquid and ice water path, and cloud radiative effects are more realistic than in previous model versions. E63H23 has a more physically based aerosol activation scheme, improvements in the cloud cover scheme, changes in the detrainment of convective clouds, changes in the sticking efficiency for the accretion of ice crystals by snow, consistent ice crystal shapes throughout the model, and changes in mixed-phase freezing; an inconsistency in ice crystal number concentration (ICNC) in cirrus clouds was also removed. Common biases in ECHAM and in E63H23 (and in previous ECHAM-HAM versions) are a cloud amount in stratocumulus regions that is too low and deep convective clouds over the Atlantic and Pacific oceans that form too close to the continents (while tropical land precipitation is underestimated). There are indications that ICNCs are overestimated in E63H23. Since clouds are important for effective radiative forcing due to aerosol-radiation and aerosol-cloud interactions (ERFariCaci) and equilibrium climate sensitivity (ECS), differences in ERFariCaci and ECS between the model versions were also analyzed. ERFariCaci is weaker in E63H23 (-1:0 W m-2) than in E61H22 (-1:2 W m-2) (or E55H20;-1:1 W m-2). This is caused by the weaker shortwave ERFariCaci (a new aerosol activation scheme and sea salt emission parameterization in E63H23, more realistic simulation of cloud water) overcompensating for the weaker longwave ERFariCaci (removal of an inconsistency in ICNC in cirrus clouds in E61H22). The decrease in ECS in E63H23 (2.5 K) compared to E61H22 (2.8 K) is due to changes in the entrainment rate for shallow convection (affecting the cloud amount feedback) and a stronger cloud phase feedback. Experiments with minimum cloud droplet number concentrations (CDNCmin) of 40 cm-3 or 10 cm-3 show that a higher value of CDNCmin reduces ERFariCaci as well as ECS in E63H23.Swiss National Science FoundationEuropean Union FP7European Research Council (ERC)Academy of Finlan

Predicting infectious complications in neutropenic children and young people with cancer (IPD protocol)

<p>Abstract</p> <p>Background</p> <p>A common and potentially life-threatening complication of the treatment of childhood cancer is infection, which frequently presents as fever with neutropenia. The standard management of such episodes is the extensive use of intravenous antibiotics, and though it produces excellent survival rates of over 95%, it greatly inconveniences the three-fourths of patients who do not require such aggressive treatment. There have been a number of studies which have aimed to develop risk prediction models to stratify treatment. Individual participant data (IPD) meta-analysis in therapeutic studies has been developed to improve the precision and reliability of answers to questions of treatment effect and recently have been suggested to be used to answer questions regarding prognosis and diagnosis to gain greater power from the frequently small individual studies.</p> <p>Design</p> <p>In the IPD protocol, we will collect and synthesise IPD from multiple studies and examine the outcomes of episodes of febrile neutropenia as a consequence of their treatment for malignant disease. We will develop and evaluate a risk stratification model using hierarchical regression models to stratify patients by their risk of experiencing adverse outcomes during an episode. We will also explore specific practical and methodological issues regarding adaptation of established techniques of IPD meta-analysis of interventions for use in synthesising evidence derived from IPD from multiple studies for use in predictive modelling contexts.</p> <p>Discussion</p> <p>Our aim in using this model is to define a group of individuals at low risk for febrile neutropenia who might be treated with reduced intensity or duration of antibiotic therapy and so reduce the inconvenience and cost of these episodes, as well as to define a group of patients at very high risk of complications who could be subject to more intensive therapies. The project will also help develop methods of IPD predictive modelling for use in future studies of risk prediction.</p