Articles | Volume 12, issue 7
Atmos. Chem. Phys., 12, 3419–3435, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article 11 Apr 2012
Research article | 11 Apr 2012
Modelling atmospheric structure, cloud and their response to CCN in the central Arctic: ASCOS case studies
C. E. Birch et al.
Related subject area
Subject: Clouds and Precipitation | Research Activity: Atmospheric Modelling | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)Cold cloud microphysical process rates in a global chemistry–climate modelPrecipitation enhancement in stratocumulus clouds through airborne seeding: sensitivity analysis by UCLALES-SALSASecondary ice production in summer clouds over the Antarctic coast: an underappreciated process in atmospheric modelsOpinion: Cloud-phase climate feedback and the importance of ice-nucleating particlesOn the ice-nucleating potential of warm hydrometeors in mixed-phase cloudsThe enhancement of droplet collision by electric charges and atmospheric electric fieldsCloud adjustments dominate the overall negative aerosol radiative effects of biomass burning aerosols in UKESM1 climate model simulations over the south-eastern AtlanticDependence of predictability of precipitation in the northwestern Mediterranean coastal region on the strength of synoptic controlThe decomposition of cloud–aerosol forcing in the UK Earth System Model (UKESM1)Sensitivity of warm clouds to large particles in measured marine aerosol size distributions – a theoretical studyHectometric-scale simulations of a Mediterranean heavy-precipitation event during the Hydrological cycle in the Mediterranean Experiment (HyMeX) first Special Observation Period (SOP1)Urbanization-induced land and aerosol impacts on sea-breeze circulation and convective precipitationShallow Cumulus Cloud Feedback in Large Eddy Simulations – Bridging the Gap to Storm Resolving ModelsSnow-induced buffering in aerosol–cloud interactionsEnvironmental sensitivities of shallow-cumulus dilution – Part 1: Selected thermodynamic conditionsEmploying airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the ArcticCloud droplet diffusional growth in homogeneous isotropic turbulence: bin microphysics versus Lagrangian superdroplet simulationsAn idealized model sensitivity study on Dead Sea desertification with a focus on the impact on convectionModelling mixed-phase clouds with the large-eddy model UCLALES–SALSADevelopment of aerosol activation in the double-moment Unified Model and evaluation with CLARIFY measurementsSize dependence in chord characteristics from simulated and observed continental shallow cumulusImpact of aerosols and turbulence on cloud droplet growth: an in-cloud seeding case study using a parcel–DNS (direct numerical simulation) approachDiffusional growth of cloud droplets in homogeneous isotropic turbulence: DNS, scaled-up DNS, and stochastic modelDifferences in tropical high clouds among reanalyses: origins and radiative impactsThe importance of Aitken mode aerosol particles for cloud sustenance in the summertime high Arctic: A simulation study supported by observational dataThe behavior of high-CAPE summer convection in large-domain large-eddy simulations with ICONVertical redistribution of moisture and aerosol in orographic mixed-phase cloudsImproving the Southern Ocean cloud albedo biases in a general circulation modelSensitivity of mixed-phase moderately deep convective clouds to parameterisations of ice formation – An ensemble perspectiveEvaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observationsEnsemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditionsAerosol indirect effects on the temperature–precipitation scalingThe vertical structure and spatial variability of lower-tropospheric water vapor and clouds in the tradesDetection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic modelTo what extents do urbanization and air pollution affect fog?Impacts of Cloud Microphysics Parameterizations on Simulated Aerosol–Cloud-Interactions for Deep Convective Clouds over HoustonThe effects of cloud–aerosol interaction complexity on simulations of presummer rainfall over southern ChinaGlobal response of parameterised convective cloud fields to anthropogenic aerosol forcingAtmospheric energy budget response to idealized aerosol perturbation in tropical cloud systemsUntangling causality in midlatitude aerosol–cloud adjustmentsTechnical note: Fundamental aspects of ice nucleation via pore condensation and freezing including Laplace pressure and growth into macroscopic iceThe relationship between low-level cloud amount and its proxies over the globe by cloud typeImpact of poleward heat and moisture transports on Arctic clouds and climate simulationImpact of resolution on large-eddy simulation of midlatitude summertime convectionThe diurnal stratocumulus-to-cumulus transition over land in southern West AfricaThe impacts of biomass burning activities on convective systems over the Maritime ContinentTechnical note: Deep learning for creating surrogate models of precipitation in Earth system modelsComparing the impact of environmental conditions and microphysics on the forecast uncertainty of deep convective clouds and hailEvaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactionsThe challenge of simulating the sensitivity of the Amazonian cloud microstructure to cloud condensation nuclei number concentrations
Sara Bacer, Sylvia C. Sullivan, Odran Sourdeval, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Atmos. Chem. Phys., 21, 1485–1505,Short summary
We investigate the relative importance of the rates of both microphysical processes and unphysical correction terms that act as sources or sinks of ice crystals in cold clouds. By means of numerical simulations performed with a global chemistry–climate model, we assess the relevance of these rates at global and regional scales. This estimation is of fundamental importance to assign priority to the development of microphysics parameterizations and compare model output with observations.
Juha Tonttila, Ali Afzalifar, Harri Kokkola, Tomi Raatikainen, Hannele Korhonen, and Sami Romakkaniemi
Atmos. Chem. Phys., 21, 1035–1048,Short summary
The focus of this study is on rain enhancement by deliberate injection of small particles into clouds (
cloud seeding). The particles, usually released from an aircraft, are expected to enhance cloud droplet growth, but its practical feasibility is somewhat uncertain. To improve upon this, we simulate the seeding effects with a numerical model. The model reproduces the main features seen in field observations, with a strong sensitivity to the total mass of the injected particle material.
Georgia Sotiropoulou, Étienne Vignon, Gillian Young, Hugh Morrison, Sebastian J. O'Shea, Thomas Lachlan-Cope, Alexis Berne, and Athanasios Nenes
Atmos. Chem. Phys., 21, 755–771,Short summary
Summer clouds have a significant impact on the radiation budget of the Antarctic surface and thus on ice-shelf melting. However, these are poorly represented in climate models due to errors in their microphysical structure, including the number of ice crystals that they contain. We show that breakup from ice particle collisions can substantially magnify the ice crystal number concentration with significant implications for surface radiation. This process is currently missing in climate models.
Benjamin J. Murray, Kenneth S. Carslaw, and Paul R. Field
Atmos. Chem. Phys., 21, 665–679,Short summary
The balance between the amounts of ice and supercooled water in clouds over the world's oceans strongly influences how much these clouds can dampen or amplify global warming. Aerosol particles which catalyse ice formation can dramatically reduce the amount of supercooled water in clouds; hence we argue that we need a concerted effort to improve our understanding of these ice-nucleating particles if we are to improve our predictions of climate change.
Michael Krayer, Agathe Chouippe, Markus Uhlmann, Jan Dušek, and Thomas Leisner
Atmos. Chem. Phys., 21, 561–575,Short summary
We address the phenomenon of ice enhancement in the vicinity of warm hydrometeors using highly accurate flow simulation techniques. It is found that the transiently supersaturated zones induced by the hydrometeor's wake are by far larger than what has been previously estimated. The ice enhancement is quantified on the micro- and macroscale, and its relevance is discussed. The results provided may contribute to a (currently unavailable) parametrization of the phenomenon.
Shian Guo and Huiwen Xue
Atmos. Chem. Phys., 21, 69–85,Short summary
Observations in previous studies show that cloud droplets carry electric charges. We are curious about whether the electric interaction enhances the collision of cloud droplets. The effect of the electric charge and atmospheric electric field on the raindrop-formation process is studied numerically. Results indicate that a cloud with a small droplet size is more sensitive to an electric charge and field, which could significantly trigger droplet collision and accelerate raindrop formation.
Haochi Che, Philip Stier, Hamish Gordon, Duncan Watson-Parris, and Lucia Deaconu
Atmos. Chem. Phys., 21, 17–33,Short summary
The south-eastern Atlantic is semi-permanently covered by some of the largest stratocumulus clouds and is influenced by one-third of the biomass burning emissions from African fires. A UKEMS1 model simulation shows that the absorption effect of biomass burning aerosols is the most significant on clouds and radiation. The dominate cooling and rapid adjustments induced by the radiative effects of biomass burning aerosols result in an overall cooling in the south-eastern Atlantic.
Christian Keil, Lucie Chabert, Olivier Nuissier, and Laure Raynaud
Atmos. Chem. Phys., 20, 15851–15865,Short summary
During strong synoptic control, which dominates the weather on 80 % of the days in the 2-month HyMeX-SOP1 period, the domain-integrated precipitation predictability assessed with the normalized ensemble standard deviation is above average, the wet bias is smaller and the forecast quality is generally better. In contrast, the spatial forecast quality of the most intense precipitation in the afternoon, as quantified with its 95th percentile, is superior during weakly forced synoptic regimes.
Daniel P. Grosvenor and Kenneth S. Carslaw
Atmos. Chem. Phys., 20, 15681–15724,Short summary
Particles arising from human activity interact with clouds and affect how much of the Sun's energy is reflected away. Lack of understanding about how to represent this in models leads to large uncertainties in climate predictions. We quantify cloud responses to particles in the latest UK Met Office climate model over the North Atlantic Ocean, showing that, in contrast to suggestions elsewhere, increases in cloud coverage and thickness are important over large areas.
Tom Dror, J. Michel Flores, Orit Altaratz, Guy Dagan, Zev Levin, Assaf Vardi, and Ilan Koren
Atmos. Chem. Phys., 20, 15297–15306,Short summary
We used in situ aerosol measurements over the Atlantic, Caribbean, and Pacific to initialize a cloud model and study the impact of aerosol concentration and sizes on warm clouds. We show that high aerosol concentration increases cloud mass and reduces surface rain when giant particles (diameter > 9 µm) are present. The large aerosols changed the timing and magnitude of internal cloud processes and resulted in an enhanced evaporation below cloud base and dramatically reduced surface rain.
Olivier Nuissier, Fanny Duffourg, Maxime Martinet, Véronique Ducrocq, and Christine Lac
Atmos. Chem. Phys., 20, 14649–14667,Short summary
This present article demonstrates how numerical simulations with very high horizontal resolution (150 m) can contribute to better understanding the key physical processes (turbulence and microphysics) that lead to Mediterranean heavy precipitation.
Jiwen Fan, Yuwei Zhang, Zhanqing Li, Jiaxi Hu, and Daniel Rosenfeld
Atmos. Chem. Phys., 20, 14163–14182,Short summary
We investigate the urbanization-induced land and aerosol impacts on convective clouds and precipitation over Houston. We find that Houston urbanization notably enhances storm intensity and precipitation, with the anthropogenic aerosol effect more significant. Urban land effect strengthens sea-breeze circulation, leading to a faster development of warm cloud into mixed-phase cloud and earlier rain. The anthropogenic aerosol effect accelerates the development of storms into deep convection.
Jule Radtke, Thorsten Mauritsen, and Cathy Hohenegger
Atmos. Chem. Phys. Discuss.,
Revised manuscript accepted for ACPShort summary
Shallow trade wind clouds are a key source of uncertainty to projections of the Earth's changing climate. We perform high resolution simulations of trade cumulus and investigate how the representation and climate feedback of these clouds depends on the specific grid spacing. We find that the cloud feedback is positive when simulated with kilometer but near zero when simulated with hectometer grid spacing. These findings suggest that storm resolving models may exaggerate the trade cloud feedback.
Takuro Michibata, Kentaroh Suzuki, and Toshihiko Takemura
Atmos. Chem. Phys., 20, 13771–13780,Short summary
This work reveals that prognostic precipitation significantly reduces the magnitude of aerosol–cloud interactions (ERFaci), mainly due to the collection process associated with snowflakes and underlying cloud droplets. This precipitation-driven buffering effect, which is missing in traditional GCMs, can explain the model–observation discrepancy in ERFaci. These results underscore the necessity for a prognostic precipitation framework in GCMs for more reliable climate simulations.
Sonja Drueke, Daniel J. Kirshbaum, and Pavlos Kollias
Atmos. Chem. Phys., 20, 13217–13239,Short summary
This numerical study provides insights into selected environmental sensitivities of shallow-cumulus dilution. Among the parameters under consideration, the dilution of the cloud cores is strongly sensitive to continentality and cloud-layer relative humidity and weakly sensitive to subcloud- and cloud-layer depths. The impacts of all four parameters are interpreted using a similarity theory of shallow cumulus and buoyancy-sorting arguments.
Jan Kretzschmar, Johannes Stapf, Daniel Klocke, Manfred Wendisch, and Johannes Quaas
Atmos. Chem. Phys., 20, 13145–13165,Short summary
This study compares simulations with the ICON model at the kilometer scale to airborne radiation and cloud microphysics observations that have been derived during the ACLOUD aircraft campaign around Svalbard, Norway, in May/June 2017. We find an overestimated surface warming effect of clouds compared to the observations in our setup. This bias was reduced by considering subgrid-scale vertical motion in the activation of cloud condensation nuclei in the two-moment microphysical scheme used.
Wojciech W. Grabowski and Lois Thomas
Atmos. Chem. Phys. Discuss.,
Revised manuscript accepted for ACPShort summary
This paper presents a modeling study that investigates the impact of cloud turbulence on the diffusional growth of cloud droplets and compares modeling results to analytic solutions published in the past. The focus is on comparing the two microphysics modeling methodologies, the Eulerian bin microphysics and Lagrangian particle-based microphysics, and exposing their limitations.
Samiro Khodayar and Johannes Hoerner
Atmos. Chem. Phys., 20, 12011–12031,
Jaakko Ahola, Hannele Korhonen, Juha Tonttila, Sami Romakkaniemi, Harri Kokkola, and Tomi Raatikainen
Atmos. Chem. Phys., 20, 11639–11654,Short summary
In this study, we present an improved cloud model that reproduces the behaviour of mixed-phase clouds containing liquid droplets and ice crystals in more detail than before. This model is a convenient computational tool that enables the study of phenomena that cannot fit into a laboratory. These clouds have a significant role in climate, but they are not yet properly understood. Here, we show the advantages of the new model in a case study focusing on Arctic mixed-phase clouds.
Hamish Gordon, Paul R. Field, Steven J. Abel, Paul Barrett, Keith Bower, Ian Crawford, Zhiqiang Cui, Daniel P. Grosvenor, Adrian A. Hill, Jonathan Taylor, Jonathan Wilkinson, Huihui Wu, and Ken S. Carslaw
Atmos. Chem. Phys., 20, 10997–11024,Short summary
The Met Office's Unified Model is widely used both for weather forecasting and climate prediction. We present the first version of the model in which both aerosol and cloud particle mass and number concentrations are allowed to evolve separately and independently, which is important for studying how aerosols affect weather and climate. We test the model against aircraft observations near Ascension Island in the Atlantic, focusing on how aerosols can "activate" to become cloud droplets.
Philipp J. Griewank, Thijs Heus, Neil P. Lareau, and Roel A. J. Neggers
Atmos. Chem. Phys., 20, 10211–10230,Short summary
The idea that larger shallow cumulus clouds have stronger updrafts than small shallow cumulus clouds is as intuitive as it is old. In this paper we gather years of upward-pointing laser measurements from a plain in Oklahoma and combine them with 28 d of high-resolution simulations. Our approach, which has much more data than previous studies, confirms that updraft strength and cloud size are linked and that the simulations reproduce the observed cloud wind and moisture structure.
Sisi Chen, Lulin Xue, and Man-Kong Yau
Atmos. Chem. Phys., 20, 10111–10124,Short summary
This study employs a parcel–DNS (direct numerical simulation) modeling framework to accurately resolve the aerosol–droplet–turbulence interactions in an ascending air parcel. The effect of turbulence, aerosol hygroscopicity, and aerosol mass loading on droplet growth and rain formation is investigated through a series of in-cloud seeding experiments in which hygroscopic particles were seeded near the cloud base.
Lois Thomas, Wojciech W. Grabowski, and Bipin Kumar
Atmos. Chem. Phys., 20, 9087–9100,Short summary
This work presents an extension of a classical small-scale modeling approach, direct numerical simulation (DNS), to large computational volumes, tens and hundreds of meters on the side. Diffusional growth of cloud droplets is more significantly affected by large scales of turbulent motions because vertical velocity perturbations associated with those scales result in larger and longer-lasting supersaturation perturbations that affect the spread of the droplet spectrum.
Jonathon S. Wright, Xiaoyi Sun, Paul Konopka, Kirstin Krüger, Bernard Legras, Andrea M. Molod, Susann Tegtmeier, Guang J. Zhang, and Xi Zhao
Atmos. Chem. Phys., 20, 8989–9030,Short summary
High clouds are influential in tropical climate. Although reanalysis cloud fields are essentially model products, they are indirectly constrained by observations and offer global coverage with direct links to advanced water and energy cycle metrics, giving them many useful applications. We describe how high cloud fields are generated in reanalyses, assess their realism and reliability in the tropics, and evaluate how differences in these fields affect other aspects of the reanalysis state.
Ines Bulatovic, Adele L. Igel, Caroline Leck, Jost Heintzenberg, Ilona Riipinen, and Annica M. L. Ekman
Atmos. Chem. Phys. Discuss.,
Revised manuscript accepted for ACPShort summary
We use detailed numerical modelling to show that small aerosol particles (diameters ~ 25–80 nm, so-called Aitken mode particles) significantly influence low-level cloud properties in the clean summertime high Arctic. The small particles can help sustain clouds when the concentration of larger particles is low (< 10–20 cm−3). Measurements from four different observational campaigns in the high Arctic support the modelling results as they indicate that Aitken mode aerosols are frequently activated.
Harald Rybka, Ulrike Burkhardt, Martin Köhler, Ioanna Arka, Luca Bugliaro, Ulrich Görsdorf, Ákos Horváth, Catrin I. Meyer, Jens Reichardt, Axel Seifert, and Johan Strandgren
Atmos. Chem. Phys. Discuss.,
Revised manuscript accepted for ACPShort summary
Current state of the art regional numerical weather prediction models employ kilometre scale horizontal grid resolutions, thereby still parametrizing convection. In this study, we use a high-resolution model to study summertime convection comparing to different ground and satellite based observational data sets. The results suggest a very close agreement to observations regarding timing, geometrical structure and cloud ice water path, supplying information for parametrization development.
Annette K. Miltenberger, Paul R. Field, Adrian H. Hill, and Andrew J. Heymsfield
Atmos. Chem. Phys., 20, 7979–8001,Short summary
Orographic wave clouds offer a natural laboratory to investigate cloud microphysical processes and their representation in atmospheric models. They impact the larger-scale flow by a vertical redistribution of moisture and aerosol. We use detailed observations from the ICE-L campaign to evaluate the representation of these clouds in a state-of-the-art numerical weather prediction model and explore the impact of environmental conditions on the vertical redistribution of moisture.
Vidya Varma, Olaf Morgenstern, Paul Field, Kalli Furtado, Jonny Williams, and Patrick Hyder
Atmos. Chem. Phys., 20, 7741–7751,Short summary
The present generation of global climate models has an insufficiently reflected short-wave radiation, especially over the Southern Ocean. This leads to an excessive heating of the ocean surface in the model, creating sea surface temperature biases and subsequent problems with atmospheric dynamics. Misrepresentation of clouds could be attributed to this radiation bias; we try to address this issue by slowing the growth rate of ice crystals and improving the supercooled liquid clouds in the model.
Annette K. Miltenberger and Paul R. Field
Atmos. Chem. Phys. Discuss.,
Revised manuscript accepted for ACP
Peter Kuma, Adrian J. McDonald, Olaf Morgenstern, Simon P. Alexander, John J. Cassano, Sally Garrett, Jamie Halla, Sean Hartery, Mike J. Harvey, Simon Parsons, Graeme Plank, Vidya Varma, and Jonny Williams
Atmos. Chem. Phys., 20, 6607–6630,Short summary
We evaluate clouds over the Southern Ocean in the climate model HadGEM3 and reanalysis MERRA-2 using ship-based ceilometer and radiosonde observations. We find the models underestimate cloud cover by 18–25 %, with clouds below 2 km dominant in reality but lacking in the models. We find a strong link between clouds, atmospheric stability and sea surface temperature in observations but not in the models, implying that sub-grid processes do not generate enough cloud in response to these conditions.
Guy Dagan and Philip Stier
Atmos. Chem. Phys., 20, 6291–6303,Short summary
Ensemble daily simulations for two separate month-long periods over a region near Barbados were conducted to investigate aerosol effects on cloud properties and the atmospheric energy budget. For each day, two simulations were conducted with low and high cloud droplet number concentrations representing clean and polluted conditions, respectively. These simulations are used to distinguish between properties that are robustly affected by changes in aerosol concentrations and those that are not.
Nicolas Da Silva, Sylvain Mailler, and Philippe Drobinski
Atmos. Chem. Phys., 20, 6207–6223,Short summary
Microphysical effects of aerosols were found to weaken precipitation in a Euro-Mediterranean area. The present numerical study quantifies the processes that may be involved through the use of the temperature–precipitation relationship. It shows larger aerosol effects at low temperatures. At these temperatures, the process that contributes most is the increase in atmospheric stability through an enhanced aerosol cooling effect in the lower troposphere compared to the upper troposphere.
Ann Kristin Naumann and Christoph Kiemle
Atmos. Chem. Phys., 20, 6129–6145,Short summary
The interaction of water vapor and cloudiness poses challenges for weather and climate models. In this study we compare airborne lidar measurements from two field campaigns in the tropical Atlantic with high-resolution simulations. We find that at kilometer-scale grid spacing, the simulations show good skill in reproducing the water vapor distribution in the trades but struggle to capture the transition from cloud-free to low cloud fraction with increasing moisture.
Montserrat Costa-Surós, Odran Sourdeval, Claudia Acquistapace, Holger Baars, Cintia Carbajal Henken, Christa Genz, Jonas Hesemann, Cristofer Jimenez, Marcel König, Jan Kretzschmar, Nils Madenach, Catrin I. Meyer, Roland Schrödner, Patric Seifert, Fabian Senf, Matthias Brueck, Guido Cioni, Jan Frederik Engels, Kerstin Fieg, Ksenia Gorges, Rieke Heinze, Pavan Kumar Siligam, Ulrike Burkhardt, Susanne Crewell, Corinna Hoose, Axel Seifert, Ina Tegen, and Johannes Quaas
Atmos. Chem. Phys., 20, 5657–5678,Short summary
The impact of anthropogenic aerosols on clouds is a key uncertainty in climate change. This study analyses large-domain simulations with a new high-resolution model to investigate the differences in clouds between 1985 and 2013 comparing multiple observational datasets. The differences in aerosol and in cloud droplet concentrations are clearly detectable. For other quantities, the detection and attribution proved difficult, despite a substantial impact on the Earth's energy budget.
Shuqi Yan, Bin Zhu, Yong Huang, Jun Zhu, Hanqing Kang, Chunsong Lu, and Tong Zhu
Atmos. Chem. Phys., 20, 5559–5572,Short summary
The development of China has caused rapid urbanization and severe air pollution. However, the extent of their individual and combined effects on fog is not well understood. Through numerical experiments, we find that urbanization suppresses low-level fog but probably promotes upper-level fog. Additional aerosols generally promote fog. Urbanization affects fog to a much larger extent than aerosols do.
Yuwei Zhang, Jiwen Fan, Zhanqing Li, and Daniel Rosenfeld
Atmos. Chem. Phys. Discuss.,
Revised manuscript accepted for ACPShort summary
Impacts of anthropogenic aerosols on deep convective clouds (DCCs) and precipitation are investigated using both the Morrison bulk and spectral-bin microphysics (SBM) schemes. With the SBM scheme, anthropogenic aerosols notably invigorate convective intensity and precipitation, making the simulated DCCs agreeing better with observations whereas such effect is absent with Morrison, mainly due to limitations of the saturation adjustment approach for droplet condensation and evaporation.
Kalli Furtado, Paul Field, Yali Luo, Tianjun Zhou, and Adrian Hill
Atmos. Chem. Phys., 20, 5093–5110,Short summary
By combining observations with simulations from a weather forecasting model, new insights are obtained into extreme rainfall processes. We use a model which includes the effects of aerosols on clouds in a fully consistent way. This greater complexity improves realism but raises the computational cost. We address the cost–benefit relationship of this and show that cloud–aerosol interactions have important, measurable benefits for simulating climate extremes.
Zak Kipling, Laurent Labbouz, and Philip Stier
Atmos. Chem. Phys., 20, 4445–4460,
Guy Dagan, Philip Stier, Matthew Christensen, Guido Cioni, Daniel Klocke, and Axel Seifert
Atmos. Chem. Phys., 20, 4523–4544,Short summary
In order to better understand the physical processes behind aerosol effects on the atmospheric energy budget, we analyse numerical simulations of tropical cloud systems. Two sets of simulations, at different dates during the NARVAL 2 field campaign, are simulated with different dominant cloud modes. Our results demonstrate that under different environmental conditions, the response of the atmospheric energy budget to aerosol perturbation could be different.
Daniel T. McCoy, Paul Field, Hamish Gordon, Gregory S. Elsaesser, and Daniel P. Grosvenor
Atmos. Chem. Phys., 20, 4085–4103,Short summary
Incomplete understanding of how aerosol affects clouds degrades our ability to predict future climate. In particular, it is unclear how aerosol affects the lifetime of clouds. Does it increase or decrease it? This confusion is partially because causality flows from aerosol to clouds and clouds to aerosol, and it is hard to tell what is happening in observations. Here, we use simulations to tell us about how clouds affect aerosol and use this to interpret observations, showing increased lifetime.
Atmos. Chem. Phys., 20, 3209–3230,Short summary
Pore condensation and freezing (PCF) is an ice nucleation mechanism explaining ice formation at low ice supersaturation. It is assumed that liquid water condenses in pores of solid aerosol particles below water saturation followed by ice nucleation within the pores. This study discusses conditions of pore filling, homogeneous ice nucleation within the volume of porewater, and growth of ice out of the pores, taking the effect of negative pressure within pores below water saturation into account.
Jihoon Shin and Sungsu Park
Atmos. Chem. Phys., 20, 3041–3060,Short summary
In this work, we show that the previously identified strong spatiotemporal correlation relationship between the low-level cloud amount (LCA) and its large-scale environmental proxy, the estimated low-level cloud fraction (ELF), holds for various low-level cloud types over the globe rather than for a specific cloud type. However, we also identify several weaknesses of the ELF and suggest a potential pathway to further improve it in the future as a global proxy for LCA.
Eun-Hyuk Baek, Joo-Hong Kim, Sungsu Park, Baek-Min Kim, and Jee-Hoon Jeong
Atmos. Chem. Phys., 20, 2953–2966,Short summary
Many general circulation models (GCMs) have difficulty simulating Arctic clouds and climate, causing substantial inter-model spread. By analyzing various model simulation results, we found that the association between the enhanced poleward transports of heat and moisture and an increase in liquid clouds over the Arctic is evident in GCMs. Our study demonstrates that enhanced poleward heat and moisture transport in a model can improve simulations of Arctic clouds and climate.
Christopher Moseley, Ieda Pscheidt, Guido Cioni, and Rieke Heinze
Atmos. Chem. Phys., 20, 2891–2910,Short summary
In this paper, we analyze a climate simulation over Germany of a continuous period in May and June 2016, with resolutions of 600 m, 300 m, and 150 m. This resolution is high enough that strong convective rain events like rain showers and thunderstorms are sufficiently resolved. Our analysis shows that the tendency of convection to organize is improved at higher resolution and that the highest-resolution simulation is closest to weather radar data.
Xabier Pedruzo-Bagazgoitia, Stephan R. de Roode, Bianca Adler, Karmen Babić, Cheikh Dione, Norbert Kalthoff, Fabienne Lohou, Marie Lothon, and Jordi Vilà-Guerau de Arellano
Atmos. Chem. Phys., 20, 2735–2754,Short summary
Using a high-resolution model we simulate the transition from night to day clouds on southern West Africa using observations from the DACCIWA project. We find that the radiative effects of clouds help mantain a thick cloud layer in the night, while the mixing of cloud air with air above during the day, aided by moisture and heat fluxes at the surface, thins this layer and promotes its transition to other clouds. The effect of changing wind with height accelerates the transition.
Hsiang-He Lee and Chien Wang
Atmos. Chem. Phys., 20, 2533–2548,Short summary
This study has demonstrated how biomass burning activities could affect convective systems in the Maritime Continent by altering cloud microphysics and dynamics. Because near-surface heating from the absorption of fire aerosols can enhance the prevailing wind from the ocean during the daytime and further weaken land breeze and surface convergence at nighttime, it changes the diurnal rainfall intensity, especially those low-level wind patterns associated with the weak westerly (WW) regime.
Theodore Weber, Austin Corotan, Brian Hutchinson, Ben Kravitz, and Robert Link
Atmos. Chem. Phys., 20, 2303–2317,Short summary
Climate model emulators can save computer time but are less accurate than full climate models. We use neural networks to build emulators of precipitation, trained on existing climate model runs. By doing so, we can capture nonlinearities and how the past state of a model (to some degree) shapes the future state. Our emulator outperforms a persistence forecast of precipitation.
Constanze Wellmann, Andrew I. Barrett, Jill S. Johnson, Michael Kunz, Bernhard Vogel, Ken S. Carslaw, and Corinna Hoose
Atmos. Chem. Phys., 20, 2201–2219,Short summary
Severe hailstorms may cause damage to buildings and crops. Thus, the forecast of numerical weather prediction (NWP) models should be as reliable as possible. Using statistical emulation, we identify those model input parameters describing environmental conditions and cloud microphysics which lead to large uncertainties in the prediction of deep convection. We find that the impact of the input parameters on the uncertainty depends on the considered output variable.
Giulia Saponaro, Moa K. Sporre, David Neubauer, Harri Kokkola, Pekka Kolmonen, Larisa Sogacheva, Antti Arola, Gerrit de Leeuw, Inger H. H. Karset, Ari Laaksonen, and Ulrike Lohmann
Atmos. Chem. Phys., 20, 1607–1626,Short summary
The understanding of cloud processes is based on the quality of the representation of cloud properties. We compared cloud parameters from three models with satellite observations. We report on the performance of each data source, highlighting strengths and deficiencies, which should be considered when deriving the effect of aerosols on cloud properties.
Pascal Polonik, Christoph Knote, Tobias Zinner, Florian Ewald, Tobias Kölling, Bernhard Mayer, Meinrat O. Andreae, Tina Jurkat-Witschas, Thomas Klimach, Christoph Mahnke, Sergej Molleker, Christopher Pöhlker, Mira L. Pöhlker, Ulrich Pöschl, Daniel Rosenfeld, Christiane Voigt, Ralf Weigel, and Manfred Wendisch
Atmos. Chem. Phys., 20, 1591–1605,Short summary
A realistic representation of cloud–aerosol interactions is central to accurate climate projections. Here we combine observations collected during the ACRIDICON-CHUVA campaign with chemistry-transport simulations to evaluate the model’s ability to represent the indirect effects of biomass burning aerosol on cloud microphysics. We find an upper limit for the model sensitivity on cloud condensation nuclei concentrations well below the levels reached during the burning season in the Amazon Basin.
ACIA: Impacts of a warming Arctic: Arctic Climate Impact Assessment, Cambridge University Press, New York, USA, 2004.
Alexander, M. A., Bhatt, U. S.,Walsh, J. E., Timlin, M., and Miller, J. S.: The atmospheric response to realistic Arctic sea ice anomalies in an AGCM during winter, J. Climate, 17, 890–905, 2004.
Arzel, O., Fichefet, T., and Goosse, H.: Sea ice evolution over the 20th and 21st centuries as simulated by current AOGCMs, Ocean Model., 12, 401–415, 2006.
Bhatt, U. S., Alexander, M. A., Deser, C., Walsh, J. E., Miller, J. S., Timlin, M. S., Scott, J. D., and Tomas, R. A.: The atmospheric response to realistic reduced summer Arctic sea ice anomalies, in Arctic sea ice decline: Observations, projections, mechanisms, and implications, Geophys. Mono. Ser., 180, 91–110, 2008.
Bigg, E. K. and Leck, C.: Cloud-active particles over the central Arctic Ocean, J. Geophys. Res., 106, 32155–32166, 2001.
Bigg, E. K., Leck, C., and Nilsson, E. D.: Sudden changes in arctic atmospheric aerosol concentrations during summer and autumn, Tellus B, 48, 254–271, 1996.
Birch, C. E., Brooks, I. M., Tjernström, M., Milton, S. F., Earnshaw, P., Söderberg, S., and Persson, P. O. G.: The performance of a global and mesoscale model over the central Arctic Ocean during late summer, J. Geophys. Res., 114, D13104, https://doi.org/10.1029/2008JD010790, 2009.
Chapman, W. L. and Walsh, J. E.: Simulations of Arctic temperature and pressure by global coupled models, J. Climate, 20, 609–632, 2007.
Cogley, J. G.: The albedo of water as a function of latitude, Mon. Weather Rev., 107, 775–781, 1979.
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated decline in the Arctic sea ice cover, Geophys. Res. Lett., 35, L01703, https://doi.org/10.1029/2007GL031972, 2008.
Curry, J., Rossow, W., Randall, D., and Schramm, J.: Overview of Arctic Cloud and Radiation Characteristics, J. Climate, 9, 1731–1764, 1996.
Davies, T., Cullen, M. J. P., Malcolm, A. J., Mawson, M. H., Staniforth, A., White, A. A., and Wood, S.: A new dynamical core for the Met Office's global and regional modelling of the atmosphere, Q. J. Roy. Meteorol. Soc., 131, 1759–1782, 2005.
Deser, C., Thomas, R. A., and Peng, A.: The transient atmospheric circulation response to Northern Atlantic SST and sea ice anomalies, J. Climate, 20, 4751–4767, 2007.
Edwards, J. M. and Slingo, A.: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model, Quart. J. Roy. Meteorol. Soc., 122, 689–719, 1996.
Francis, J. A., Chan, W., Leathers, D. J., Miller, J. R., and Veron, D. E.: Winter Northern Hemisphere 565 weather patterns remember summer Arctic sea-ice extent, Geophys. Res. Lett., 36, L07503, https://doi.org/10.1029/2009GL037274, 2009.
Flocco, D., Feltham, D. L., and Turner, A. K.: Incorporation of a physically based melt pond scheme into the sea ice component of a climate model, J. Geophys. Res., 115, C08012, https://doi.org/10.1029/2009JC005568, 2010.
Heintzenberg, J. and Leck, C.: The summer aerosol in the Central Arctic 1991–2008: did it change or not?, Atmos. Chem. Phys. Discuss., 12, 887–933, https://doi.org/10.5194/acpd-12-887-2012, 2012.
Holland, M. M., Bitz, C. M., and Tremblay, L.: Future abrupt reductions in the summer Arctic sea ice, Geophys. Res. Lett., 22, L23503, https://doi.org/10.1029/2006GL028024, 2006.
Intrieri, J., Fairall, C. W., Shupe, M., Persson, P., Andreas, E., Guest, P., and Moritz, R.: An annual cycle of Arctic surface cloud forcing at SHEBA, J. Geophys. Res., 107, 8039, https://doi.org/10.1029/2000JC000439, 2002.
Kahl, J. D.: Characteristics of the low-level temperature inversion
along the Alaskan Arctic coast, Int. J. Climatol., 10, 537–548, 1990.
along the Alaskan Arctic coast, Int. J. Climatol., 10, 537–548, 1990.
Kahl, J. D. and Martinez, D. A.: Long-term variability in the low-level inversion layer over the Arctic Ocean, Int. J. Climatol., 16, 1297–1313, 1996.
Kahl, J. D., Serreze, M. C., and Schnell, R. C.: Tropospheric low-level temperature inversions in the Canadian Arctic, Atmos.-Ocean, 30, 511–529, 1992.
Karlsson, J. and Svensson, G.: The simulation of Arctic clouds and their influence on the winter surface temperature in present-day climate in the CMIP3 multi-model dataset, Clim. Dyn., 36, 623–635, 2010.
Kay, J. E. and Gettelman, A.: Cloud influence on and response to seasonal Arctic sea ice loss, J. Geophys. Res., 114, D18204, https://doi.org/10.1029/2009JD011773, 2009.
Lannerfors, H., Heintzenberg, J., and Hansson, H.-C.: A comprehensive study of physical and chemical parameters of the Arctic summer aerosol results from the Swedish expedition Ymer-80, Tellus B, 35, 40–54, 1983.
Leck, C., Norman, M., Bigg, E. K., and Hillamo, R.: Chemical composition and sources of the high Arctic aerosol relevant for fog and cloud formation, J. Geophys. Res., 107, 4135, https://doi.org/:10.1029/2001JD001463, 2002.
Lenderink, G. and Holtslag, A. A. M.: Evaluation of kinetic energy approach for modeling turbulent fluxes in stratocumulus, Mon. Weather Rev., 128, 244–258, 2000.
Louis, J. F.: A parametric model of vertical eddy fluxes in the atmosphere, Bound.-Layer Meteorol., 17, 187–202, 1979.
Manabe, S. and Wetherald, R. T.: The effects of doubling the CO2 concentration in the climate of a general circulation model, J. Atmos. Sci., 32, 3–15, 1975.
Mann, G. W., Carslaw, K. S., Spracklen, D. V., Ridley, D. A., Manktelow, P. T., Chipperfield, M. P., Pickering, S. J., and Johnson, C. E.: Description and evaluation of GLOMAP-mode: a modal global aerosol microphysics model for the UKCA composition-climate model, Geosci. Model Dev., 3, 519–551, https://doi.org/10.5194/gmd-3-519-2010, 2010.
Martin, M., Chang, R. Y.-W., Sierau, B., Sjogren, S., Swietlicki, E., Abbatt, J. P. D., Leck, C., and Lohmann, U.: Cloud condensation nuclei closure study on summer arctic aerosol, Atmos. Chem. Phys., 11, 11335–11350, https://doi.org/10.5194/acp-11-11335-2011, 2011.
Mauritsen, T., Sedlar, J., Tjernström, M., Leck, C., Martin, M., Shupe, M., Sjogren, S., Sierau, B., Persson, P. O. G., Brooks, I. M., and Swietlicki, E.: An Arctic CCN-limited cloud-aerosol regime, Atmos. Chem. Phys., 11, 165–173, https://doi.org/10.5194/acp-11-165-2011, 2011.
Moran, K. P., Martnew, B. E., Post, M. J., Kropfli, R. A., Welsh, D. C., and Widener, K. B.: An unattended cloud-profiling radar for use in climate research, B. Am. Meteorol. Soc., 79, 443–455, 1998.
Morrison, H. and Pinto, J. O.: Mesoscale modeling of springtime arctic mixed-phase stratiform clouds using a new two-moment bulk microphysics scheme, J. Atmos. Sci., 62, 3683–3704, 2005.
Nghiem, S. V., Rigor, I. G., Perovich, D. K., Clemente-Colón, P., and Weatherly, J. W.: Rapid reduction of Arctic perennial sea ice, Geophys. Res. Lett., 34, L19504, https://doi.org/10.1029/2007GL031138, 2007.
Norris, J. R.: Low cloud type over the ocean from surface observations, Part I: Relationship to surface meteorology and the vertical distribution of temperature and moisture, J. Climate, 11, 369–382, 1998.
NSIDC: Arctic sea ice new and analysis: Summer 2011: Arctic sea ice near record lows, National Snow and Ice Data Centre, http://nsidc.org/arcticseaicenews/, last access: 4 October 2011.
Overland, J. E. and Wang, M.: Large-scale atmospheric circulation changes are associated with the recent loss of Arctic sea ice, Tellus, 62A, 1–9, 2010.
Paluch, I. R. and Lenschow, D. H.: Stratiform cloud formation in the marine boundary layer, J. Atmos. Sci., 48, 2141–2158, 1991.
Parkinson, C. L. and Cavalieri, D. J.: Arctic sea ice variability and trend, 1979–2006, J. Geophys. Res., 113, C07004, https://doi.org/10.1029/2007JC004564, 2008.
Perovich, D. K., Nghiem, S. V., Markus, t., and Schweiger, A.: Seasonal evolution and interannual variability of the local solar energy absorbed by the Arctic sea ice-ocean system, J. Geophys. Res., 112, C03005, https://doi.org/10.1029/2006JC003558, 2007.
Pedersen, C. A., Roeckner, E., Lüthje, M., and Winther, J.-G.: A new sea ice albedo scheme including melt ponds for ECHAM5 general circulation model, J. Geophys. Res., 114, D08101, https://doi.org/10.1029/2008JD010440, 2009.
Perovich, D. K., Nghiem, S. V., Markus, T., and Schweiger, A.: Seasonal evolution and interannual variability of the local solar energy absorbed by the Arctic sea ice-ocean system, J. Geophys. Res., 112, C03005, https://doi.org/10.1029/2006JC003558, 2007.
Persson, P. O. G.: Onset and end of summer melt season over sea ice: Thermal structure and surface energy perspective from SHEBA, Clim. Dyn., https://doi.org/10.1007/s00382-011-1196-9, 2011.
Phillips, T. J., Potter, G. L., Williamson, D. L., Cederwall, R. T., Boyle, J. S., Fiorino, M., Hnilo, J. J., Olson, J. G., Xie, S., and Yio, J.: Evaluating parameterizations in General Circulation Models, B. Am. Meteorol. Soc., 85, 1903–1915, 2004.
Raes, F., Van Dingenen, R., Vignati, E., Wilson, J., Putaud, J. P., Seinfeld, J. H., and Adams, P.: Formation and cycling of aerosols in the global troposphere, Atmos. Environ., 34, 4215–4240, 2000.
Rawlins, F., Ballard, S. P., Bovis, K. J., Clayton, A. M., Li, D., Inverarity, G. W., Lorenc, A. C., and Payne, T. J.: The Met Office Global 4-Dimensional Variational Data Assimilation Scheme, Q. J. Roy. Meteorol. Soc., 133, 347–362, 2007.
Roberts, G. C. and Nenes, A.: A continuous-flow streamwise thermal-gradient CCN chamber for atmospheric measurements, Aerosol Sci. Technol., 39, 206–221, 2005.
Rutledge, S. A. and Hobbs, P. V.: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones, VIII: A model for the "seeder-feeder" process in warm-frontal rainbands, J. Atmos. Sci., 40, 1185–1206, 1983.
Sedlar, J. and Tjernström, M.: Stratiform cloud - inversion characterization during the Arctic melt season, Bound.-Lay. Meteorol., 132, 455–474, 2009.
Sedlar, J., Tjernström, M., Mauritsen, T., Shupe, M. D., Brooks, I. M., Persson, P. O. G., Birch, C. E., Leck, C., Sirevaag, A., and Nicolaus, M.: A transitioning Arctic surface energy budget: the impacts of solar zenith angle, surface albedo and cloud radiative forcing, Clim. Dyn., 37, 1643–1660, https://doi.org/10.1007/s00382-010-0937-5, 2011.
Sedlar, J., Shupe, M. D., and Tjernström, M.: On the relationship between thermodynamic structure and cloud top, and its climate significance in the Arctic,J. Climate, 25, 2374–2393, https://doi.org/10.1175/JCLI-D-11-00186.1, 2012.
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic amplification: A research synthesis, Global Plant. Change, 77, 85–96, 2011.
Serreze, M. C. and Francis, J.: The Arctic Amplification Debate, Climatic Change, 76, 241–264, 2006.
Serreze, M. C., Kahl, J. D., and Schnell, R. C.: Low-level temperature inversions of the Eurasian Arctic and comparisons with Soviet drifting station data, J. Climate, 5, 615–629, 1992.
Shupe, M. D.: A ground-based multiple remote-sensor cloud phase classifier, Geophys. Res. Lett., 34, L22809, https://doi.org/10.1029/2007GL031008, 2007.
Shupe, M. D.: Clouds at Arctic atmospheric observations. Part II: Thermodynamic phase characteristics, J. App. Meteorol. Clim., 50, 645–661, 2011.
Shupe, M. D. and Intrieri, J.M.: Cloud radiative forcing of the Arctic surface: The influence of cloud properties, surface albedo, and solar zenith angle, J. Climate, 17, 616–628, 2004.
Shupe, M. D., Uttal, T., and Matrosov, S. Y.: Arctic cloud microphysics retrievals from surface-based remote sensors, J. Appl. Meteorol., 44, 1544–1562, 2005.
Shupe, M. D., Matrosov, S. Y., and Uttal, T.: Arctic mixed-phase cloud properties derived from surface-based sensors at SHEBA, J. Atmos. Sci., 63, 697–711, 2006.
Shupe, M. D., Walden, V. P., Eloranta, E., Uttal, T., Campbell, J. R., Starkweather, S. M., and Shiobara, M.: Clouds at Arctic atmospheric observations. Part II: Occurrence and macrophysical properties, J. App. Meteorol. Clim., 50, 626–644, 2011.
Smith, R. N. B.: A scheme for predicting layer clouds and their water content in a general circulation model, Q. J. Roy. Meteorol. Soc., 116, 435–460, 1990.
Soloman, A., Morrison, H., Persson, O., Shupe, M. D., and Bao, J.-. W.: Investigation of the microphysical parameterizations of snow and ice ice Arctic clouds during M-PACE through model-observation comparisons, Mon. Weather Rev., 137, 3110–3127, 2009.
Solomon, A., Shupe, M. D., Persson, P. O. G., and Morrison, H.: Moisture and dynamical interactions maintaining decoupled Arctic mixed-phase stratocumulus in the presence of a humidity inversion, Atmos. Chem. Phys., 11, 10127–10148, https://doi.org/10.5194/acp-11-10127-2011, 2011.
Solomon, S., Qin, D., Manning, M., M., M., Averyt, K., Tignor, M. M. B., Miller, H. L., and Chen, Z., eds.: Climate Change 2007: The Physical Science Basis, IPCC, Cambridge University Press, Cambridge, UK, 2007.
Staniforth, A., White, A., Wood, N., Thuburn, J., Zerroukat, M., Cordero, E., and Davies, T.: Joy of UM 6.3 Model Formulation, Unified Model Documentation Paper, No. 15, 2006.
Stevens, B., Beljaars, A., Bordoni, S., Halloway, C., Köhler, M., Krueger, S., Savic-Jovcic, V., and Zhang, Y.: On the structure of the lower troposphere in the summertime stratocumulus regime of the Northeast Pacific, Mon. Weather Rev., 135, 985–1005, 2007.
Tjernström, M.: Is there a diurnal cycle in the summer cloud-capped Arctic boundary layer?, J. Atmos. Sci., 64, 3970–3986, 2007.
Tjernström, M. and Graversen, R. G.: The vertical structure of the lower Arctic troposphere analysed from observations and the ERA-40 reanalysis, Q. J. Roy. Meteorol. Soc., 135, 431–443, 2009.
Tjernström, M., Leck, C., Persson, O., Jensen, M., Oncley, S., and Targino, A.: The Summertime Arctic Atmosphere. Meteorological Measurements during the Arctic Ocean Experiment 2001, B. Am. Meteorol. Soc., 84, 1305–1321, 2004.
Tjernström, M., Sedlar, J., and Shupe, M. D.: How well do regional climate models reproduce radiation and clouds in the Arctic?, J. App. Meteorol. Clim., 47, 2405–2422, 2008.
Tjernström, M., Birch, C. E., Brooks, I. M., Shupe, M. D., Persson, P. O. G., Sedlar, J., Mauritsen, T., Leck, C., Paatero, J., Szczodrak, M., and Wheeler, C. R.: Central Arctic atmospheric summer conditions during the Arctic Summer Cloud Ocean Study (ASCOS): contrasting to previous expeditions, Atmos. Chem. Phys. Discuss., 12, 4101–4164, https://doi.org/10.5194/acpd-12-4101-2012, 2012.
Uttal, T., Curry, J. A., McPhee, M. G., Perovich, D. K., Moritz, R. E., Maslanik, J. A., Guest, P. S., Stern, H. L., Moore, J. A., Turenne, R., Heiberg, A., Serreze, M. C., Wylie, D. P., Persson, O. G., Paulson, C. A., Halle, C., Morison, J. H., Wheeler, P. A., Makshtas, A., Welch, H., Shupe, M. D., Intrieri, J. M., Stamnes, K., Lindsey, R. W., Pinkel, R., Pegau, W. S., Stanton, T. P., and Grenfeld, T. C.: Surface heat budget of the Arctic Ocean, B. Am. Meteorol. Soc., 83, 255–275, 2002.
Walsh, J., Kattsov, V., Chapman, W., Govorkova, V., and Pavlova, T.: Comparison of Arctic Climate Simulations by Uncoupled and Coupled Global Models, J. Climate, 15, 1429–1446, 2002.
Westwater, E. R., Han, Y., Shupe, M. D., and Matrosov, S. Y.: Analysis of integrated cloud liquid and precipitable water vapour retrievals from microwave radiometers during SHEBA, J. Geophys. Res., 106, 32019–32030, 2001.
Wilson, D. R. and Ballard, S. P.: A microphysically based precipitation scheme for the U.K. Meteorological Office Unified Model, Q. J. Roy. Meteorol. Soc., 125, 1607–1636, 1999.