790 research outputs found
Data and scripts for the RaFSIP scheme
No full textThis repository contains microphysics routines, scripts, and processed data from the Weather Research and Forecasting (WRF) model simulations presented in the paper "RaFSIP: Parameterizing ice multiplication in models using a machine learning approach", by Paraskevi Georgakaki and Athanasios Nenes. RaFSIP is a data-driven parameterization designed to streamline the representation of Secondary Ice Production (SIP) in large-scale models. Preprint available on Authorea: https://doi.org/10.22541/essoar.170365383.34520011/v1LAPIv
The relative contribution of secondary ice processes in Alpine mixed-phase clouds
No full textIn-situ observations of mixed-phase clouds (MPCs) forming over mountain tops regularly reveal that ice crystal number concentrations (ICNCs) are orders of magnitude higher than ice-nucleating particle concentrations. This discrepancy has often been attributed to the influence of surface processes such as blowing snow and airborne hoar frost. Ιn-cloud secondary ice production (SIP) processes may also explain this discrepancy, but their contribution has received less attention. Here we explore the potential role of SIP processes on orographic MPCs observed during the Cloud and Aerosol Characterization Experiment (CLACE) 2014 campaign at the mountain-top site of Jungfraujoch in the Swiss Alps using the Weather Research and Forecasting model (WRF). The Hallett-Mossop (H-M) mechanism, included in the default version of the Morrison scheme in WRF, is ruled out since the simulated clouds were outside the active temperature range for this process. This study investigates if the implementation of two additional SIP mechanisms in WRF, namely collisional break-up (BR) between ice hydrometeors and frozen droplet shattering (DS), can bridge the gap between observed and modeled ICNCs. DS is inefficient in the examined conditions due to a lack of sufficiently large raindrops to trigger this process. The BR mechanism is likely important in Alpine MPCs, but the process is activated only within seeder-feeder situations, when precipitation particles are seeding the low-level MPCs inducing their glaciation. At times when a cloud exists near the ground, blowing snow ice particles may be mixed among supercooled liquid droplets and thus contribute significantly to ice growth, but they cannot account for the observed ICNCs. Our findings indicate that outside the H-M temperature range, ice-seeding and blowing snow can initiate ice multiplication in the Alps through the BR mechanism, which is found to elevate the modeled ICNCs up to 3 orders of magnitude, providing a better agreement with in-situ measurements. This highlights the importance of considering both SIP and surface-based processes in weather-prediction and climate models.LAPILT
The impact of secondary ice processes on orographic mixed-phase clouds
No full textGround based and airborne observations of orographic mixed-phase clouds (MPCs) forming over mountain top research stations have long reported a discrepancy between the measured ice crystal number concentrations (ICNCs) and the concentration of ice nucleating particles, the former being several orders of magnitude higher (e.g., Lloyd et al., 2015). Additionally, model simulations of Alpine clouds are frequently found to underestimate the amount of ice compared with observations (Farrington et al., 2016). Although surface-based processes such as blowing snow and hoar frost have been suggested to explain this discrepancy, the potential role of secondary ice production (SIP) processes – especially mechanical breakup of cloud ice and droplet fragmentation during freezing – has been less studied. In this study we utilize the Weather Research and Forecasting model (WRF) to explore the potential contribution of SIP processes on the orographic MPCs observed during the Cloud and Aerosol Characterization Experiment (CLACE) 2014 campaign at the mountain-top site of Jungfraujoch in the Swiss Alps. The only SIP mechanism included in the default version of WRF is the Hallett–Mossop process (H-M), which is however ruled out since the recorded temperatures were generally colder than -8 ˚C. We modified the default WRF to include parameterizations of two additional SIP mechanisms, namely the collisional break-up (BR) upon collisions between ice particles and droplet shattering (DS), in order to investigate if the performance of the model is improved. Simulations suggest that the DS mechanism is not a significant source of ICNCs. The BR mechanism however is quite active, elevating the predicted ICNCs by up to 3 orders of magnitude, which is consistent with observations. The initiation of the BR mechanism is primarily associated with the occurrence of seeder-feeder situations, which are widespread phenomena over Switzerland (Proske et al., 2021). Including a source of ice crystals from the effect of blowing snow episodically affects cloud ICNCs; the numbers reaching cloud base is not large, but the concentrations are multiplied through the action of the BR mechanism. Our findings highlight the importance of considering both secondary ice and an “external” seeding mechanism – primarily falling ice from above and to a lesser degree blowing ice from the surface - in weather-prediction models in order to predict correctly the amount of liquid and ice in MPCs, which is in turn critical for the accurate representation of radiation processes and precipitation patterns. Farrington, R. J., Connolly, P. J., Lloyd, G., Bower, K. N., Flynn, M. J., Gallagher, M. W., Field, P. R., Dearden, C., and Choularton, T. W. (2016). Comparing model and measured ice crystal concentrations in orographic clouds during the INUPIAQ campaign. Atmos. Chem. Phys., 16, 4945–4966, https://doi.org/10.5194/acp-16-4945-2016 Lloyd, G., Choularton, T. W., Bower, K. N., Gallagher, M. W., Connolly, P. J., Flynn, M., Farrington, R., Crosier, J., Schlenczek, O., Fugal, J. and Henneberger, J. (2015). The origins of ice crystals measured in mixed-phase clouds at the high-alpine site Jungfraujoch. Atmos. Chem. Phys., 15, 12953–12969. https://doi.org/10.5194/acp-15-12953-2015 Proske, U., Bessenbacher, V., Dedekind, Z., Lohmann, U., and Neubauer, D. (2021). How frequent is natural cloud seeding from ice cloud layers ( < −35 °C) over Switzerland?. Atmos. Chem. Phys., 21, 5195–5216. https://doi.org/10.5194/acp-21-5195-2021LAPILT
Chemical evolution of primary and secondary biomass burning aerosols during daytime and nighttime
No full textFine particulate matter (PM) affects visibility, climate, and public health. Biomass burning (BB) in the forms of residential wood burning, wildfires, and prescribed burning is a major source of primary and secondary organic matter (OM, an important fraction fine PM), and brown and black carbon (BrC and BC). The contribution of BB to the atmospheric fine PM is only expected to increase in the foreseeable future. Recent studies have highlighted the enhancement in the biomass burning organic aerosol (bbOA) concentrations with aging and reported on the chemical composition of the secondary biomass burning organic aerosol (bbSOA) formed under different conditions. However, the chemical processing of the primary biomass burning organic aerosol (bbPOA) with aging is not well characterized. This chemical processing can potentially alter the chemical composition of bbOA drastically and render its identification and quantification in the atmosphere difficult. We used aerosol mass spectrometry (AMS) and Fourier transform infrared spectroscopy (FTIR) as two complementary methods to quantify the bbPOA aging in this study. AMS measures the bulk composition of OM with a relatively high temporal resolution but provides limited parent compound information due to the extensive fragmentation. FTIR, carried out on PTFE filter samples, provides detailed information about the functional group composition of the OM and certain bbOA makers at the expense of a relatively low temporal resolution. In a series of aging experiments at the Center for Studies of Air Qualities and Climate Change (C-STACC), primary emissions from wood and pellet stoves were injected into an environmental simulation chamber. Primary emissions were aged using hydroxyl and nitrate radicals simulating the atmospheric day-time and night-time oxidation. A high-resolution time-of-flight (HR-TOF) AMS was used to identify the composition of non-refractory PM1. PM1 was also collected on PTFE filters before and after aging for the off-line FTIR analysis. AMS and FTIR agreed well in terms of the measured bbOA mass concentrations, elemental ratios, and the evolution of biomass burning tracers. We developed a procedure to quantify the bbPOA aging using AMS and FTIR. Using AMS, we found that up to 17 % of the bbPOA mass underwent some form of transformation with aging. This transformation was more intense under day-time conditions. FTIR detected a more extensive oxidation (up to two times that of AMS), suggesting a substantial processing of bbPOA, and revealing the limitations of AMS to capture bbPOA aging due to the extensive fragmentation. Different bbOA-related ion fragments were observed to decay at different rates under different conditions (e.g., oxidants and relative humidity). These different decay rates can potentially be used to identify the extent of bbPOA aging in the atmosphere. The bbSOA formed during the daytime oxidation was dominated by acid contributions, resembling certain atmospheric biomass burning samples. The unique, acid-dominated FTIR spectrum of bbSOA can potentially be used as another indicator of the aged bbOA in the atmosphere.LAP
Toward an Understanding of the Indirect Climatic Effect of Aerosols
Get PDFThis thesis is motivated by the need to improve our understanding of the aerosol indirect effect. The activation of aerosol into cloud droplets has been extensively studied, using a comprehensive numerical cloud droplet activation model. Using this model, the effect of water vapor mass transfer limitations on the cloud droplet activation process was first studied; it was found that mass transfer limitations are important for activation under polluted conditions. The potential effect of (currently unresolved) "chemical effects" on cloud droplet number (e.g., the presence soluble gases and surface active species) was also assessed. It was seen that small changes in aerosol and gas-phase composition can have a strong effect on cloud droplet number, and should be included in future estimates of the aerosol indirect effect.
A comprehensive aerosol activation parameterization was developed for use in a first-principle assessment of the aerosol indirect effect. This new parameterization introduces the concept of "population splitting," in which the droplets are separated into two populations, each with its own growth characteristics. The effect of water vapor mass transfer limitations is explicitly introduced. The parameterization allows for treatment of chemical effects. The new parameterization shows excellent and robust agreement with a detailed numerical parcel model.
Previously unidentified mechanisms of aerosol-cloud interactions were also explored. Aerosol, when it contains black carbon, can absorb light and heat the droplet enough to affect its activation behavior. This can affect cloud properties in numerous and counterintuitive ways; black carbon heating can dissipate clouds, but may also increase cloud lifetime (and lead to a climatic cooling) by decreasing the probability of drizzle formation.
Finally, the performance of instruments used for measuring the concentration of cloud condensation nuclei (CCN) was assessed. Each design exhibits different limitations and sources of uncertainty, but all show decreased ability to measure CCN of low critical supersaturation (<0.1%). The performance of the instrumentation can be very sensitive to the operating conditions. Therefore, an in-depth theoretical understanding of the instrumentation is necessary; otherwise, CCN measurements may be subject to considerable uncertainty.</p
Droplet activation parameterization: The population-splitting concept revisited
No full textIn this work, we postulate, implement and evaluate modifications to the "population-splitting" concept, introduced by Nenes and Seinfeld (2003), for calculation of water-condensation rates in droplet-activation parameterizations. The population-splitting approximation consists of dividing the population of growing droplets into two categories: those that experience significant growth after exposed to a supersaturation larger than their critical supersaturation, and those that do not grow much larger than their critical diameter. The modifications introduced here lead to an improved accuracy and precision of the parameterization-derived maximum supersaturation, smax, and droplet-number concentration, Nd, as determined by comparing against those of detailed numerical simulations of the activation process. A numerical computation of the first-order derivatives ∂ Nd/∂ χj of the parameterized Nd to input variables χi was performed and compared against the corresponding parcel-model-derived sensitivities, providing a thorough evaluation of the impacts of the introduced modifications in the parameterization ability to respond to aerosol characteristics. An evaluation of the parameterization computation of Nd and smax against detailed numerical simulations of the activation process showed a relative error of -6.0% ± 6.2% for smax, and -2.7% ± 4.8% for Nd, which represents a considerable reduction in prediction bias when compared to earlier versions of the parameterization. The proposed modifications require only minor changes for their numerical implementation in existing codes based on the population-splitting concept. © 2014 Author(s).LAP
Parameterization of cloud droplet formation in large-scale models: Including effects of entrainment
No full textThis work offers for the first time a comprehensive parameterization suitable for large-scale models which is robust, computationally efficient, and from first principles links chemical effects, aerosol heterogeneity and entrainment with cloud droplet formation. The parameterization is based on the entraining ascending parcel model framework; mixing of outside air is parameterized in terms of a per-length entrainment rate. The integration of the droplet growth is done using the "population splitting" concept of Nenes and Seinfeld (2003). Formulations for lognormal and sectional aerosol representations are given, as well as simplifications that allow the treatment of entrainment with high computational efficiency without loss of accuracy. The concept of "critical entrainment," a value beyond which droplet activation is not favored, is introduced and shown that it is important for defining (1) whether or not entrainment effects have an impact on droplet formation and (2) the characteristic temperature and pressure for cloud droplet formation. The performance of the parameterization was evaluated against a detailed numerical parcel model over a comprehensive range of droplet formation conditions. The agreement is always very good (mean relative error 2.3% ± 21%); errors tend to increase as entrainment approaches the critical value, but are never above 40%. Copyright 2007 by the American Geophysical Union.LAP
Continued development of a cloud droplet formation parameterization for global climate models
No full textThis study presents continued development of the Nenes and Seinfeld (2003) cloud droplet activation parameterization. First, we expanded the formulation to (1) allow for a lognormal representation of aerosol size distribution, and (2) include a size-dependant mass transfer coefficient for the growth of water droplets to accommodate the effect of size (and potentially organic films) on the droplet growth rate. The performance of the new scheme is evaluated by comparing the parameterized cloud droplet number concentration with that of a detailed numerical activation cloud parcel model. The resulting modified parameterization robustly and closely tracks the parcel model simulations, even for low values of the accommodation coefficient (average error 4.1 ± 1.3%). The modifications to include the effect of accommodation coefficient do not increase the computational cost but substantially improve the parameterization performance. This work offers a robust, computationally efficient and first-principles approach for directly linking complex chemical effects (e.g., surface tension depression, changes in water vapor accommodation, solute contribution from partial solubility) on aerosol activation within a global climate modeling framework. Copyright 2005 by the American Geophysical Union.LAP
Understanding the contributions of aerosol properties and parameterization discrepancies to droplet number variability in a global climate model
No full textAerosol indirect effects in climate models strongly depend on the representation of the aerosol activation process. In this study, we assess the process-level differences across activation parameterizations that contribute to droplet number uncertainty by using the adjoints of the Abdul-Razzak and Ghan (2000) and Fountoukis and Nenes (2005) droplet activation parameterizations in the framework of the Community Atmospheric Model version 5.1 (CAM5.1). The adjoint sensitivities ofNd to relevant input parameters are used to (i) unravel the spatially resolved contribution of aerosol number, mass, and chemical composition to changes inNd between present-day and pre-industrial simulations and (ii) identify the key variables responsible for the differences inNd fields and aerosol indirect effect estimates when different activation schemes are used within the same modeling framework. The sensitivities are computed online at minimal computational cost. Changes in aerosol number and aerosol mass concentrations were found to contribute toNd differences much more strongly than chemical composition effects. The main sources of discrepancy between the activation parameterizations considered were the treatment of the water uptake by coarse mode particles, and the sensitivity of the parameterizedNd accumulation mode aerosol geometric mean diameter. These two factors explain the different predictions ofNd over land and over oceans when these parameterizations are employed. Discrepancies in the sensitivity to aerosol size are responsible for an exaggerated response to aerosol volume changes over heavily polluted regions. Because these regions are collocated with areas of deep clouds, their impact on shortwave cloud forcing is amplified through liquid water path changes. The same framework is also utilized to efficiently explore droplet number uncertainty attributable to hygroscopicity parameter of organic aerosol (primary and secondary). Comparisons between the parameterization-derived sensitivities of droplet number against predictions with detailed numerical simulations of the activation process were performed to validate the physical consistency of the adjoint sensitivities. © 2014 Author(s).LAP
Using dimensionality reduction and causal inference to constrain precipitation and climate
No full textContinued greenhouse gas emissions will lead to increasing global warming. Effective adaptation and mitigation policies depend on accurately estimating climate sensitivity, the Earth's surface temperature response to increasing CO2 emissions. Global Climate models (GCMs) provide the long-term simulations essential to better understand climate and quantify this temperature change. Despite advances in numerical modelling and theory, model divergence remains significant. The Intergovernmental Panel on Climate Change (IPCC) recognized the "hot model problem" in its Sixth Assessment report (2021). One source of this model uncertainty is parametrization schemes, that encode our physical understanding of subgrid-scale processes, like clouds and convection. Convective precipitation, due to its stochastic nature and dependence to fine-scale processes, is still poorly represented in GCMs.
This thesis uses dimensionality reduction and causal inference methods on multi-model ensemble outputs to address these challenges. Model complexity has become a norm in model development, and hinders their interpretability. Major progress in these areas require a rigorous search for the processes underlying the large interdependent model outputs. The d-MAPS method offers a low-level representation of datasets, while causal inference methods question the relationships between variables. The first application focuses on Sea Surface Temperature (SST) dynamics, and investigates the potential of causal network properties to narrow down the range of plausible climate sensitivity. Chapter 1 develops the methodology for a first ensemble, with the evaluation of patterns and teleconnections in a recent period. Chapter 2 expands the methodology to climate projections. We found that SST effects weaken in warmer climates, with a significant model uncertainty in the eastern Pacific Ocean. Hot models do not exhibit more realistic teleconnection effects within a fixed causal network, but their SST patterns are more realistic with a varying structure. The second application examines the dependency of convective precipitation on its environmental conditions. Chapter 3 aims to quantify interactions among variables within a specific large-scale regime. We analyzed high-resolution model outputs from Global Storm-Resolving Models (GSRM), in which deep convection is explicitly resolved, using a multivariate causal graph. This framework helped us better understand the d-MAPS method, especially its exclusion of sharp humidity gradients. We found consistent control of large-scale variable on the convective precipitation distribution across GSRMs, and a significant uncertainty of the role of vertical velocity.
Both applications aim to better understand processes and quantify the inter-model differences. Ultimately, we use causal effects to constrain climate sensitivity, and to identify the largest control of large-scale humidity on convective precipitation. This research work, based on chain of decisions - from the inference of regions to the potential constraint of a target, through the inference of links and their effects, reveals all the complexity of the climate system, but also shows promising results in the search for robust relationships.LAP
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