Cloud microphysics and aerosol indirect effects in the global climate model ECHAM5-HAM

The double-moment cloud microphysics scheme from ECHAM4 has been coupled to the size-resolved aerosol scheme ECHAM5-HAM. ECHAM5-HAM predicts the aerosol mass and number concentrations and the aerosol mixing state. This results in a much better agreement with observed vertical profiles of the black carbon and aerosol mass mixing ratios than with the previous version ECHAM4, where only the different aerosol mass mixing ratios were predicted. Also, the simulated liquid, ice and total water content and the cloud droplet and ice crystal number concentrations as a function of temperature in stratiform mixed-phase clouds between 0 and –35°C agree much better with aircraft observations in the ECHAM5 simulations. ECHAM5 performs better because more realistic aerosol concentrations are available for cloud droplet nucleation and because the Bergeron-Findeisen process is parameterized as being more efficient. The total anthropogenic aerosol effect includes the direct, semi-direct and indirect effects and is defined as the difference in the top-of-the-atmosphere net radiation between present-day and pre-industrial times. It amounts to –1.8 W m^−2 in ECHAM5, when a relative humidity dependent cloud cover scheme and present-day aerosol emissions representative for the year 2000 are used. It is larger when either a statistical cloud cover scheme or a different aerosol emission inventory are employed

Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model

Aerosol particles influence cloud formation and properties. Hence climate models that aim for a physical representation of the climate system include aerosol modules. In order to represent more and more processes and aerosol species, their representation has grown increasingly detailed. However, depending on one's modelling purpose, the increased model complexity may not be beneficial, for example because it hinders understanding of model behaviour. Hence we develop a simplification in the form of a climatology of aerosol concentrations. In one approach, the climatology prescribes properties important for cloud droplet and ice crystal formation, the gateways for aerosols to enter the model cloud microphysics scheme. Another approach prescribes aerosol mass and number concentrations in general. Both climatologies are derived from full ECHAM-HAM simulations and can serve to replace the HAM aerosol module and thus drastically simplify the aerosol treatment. The first simplification reduces computational model time by roughly 65 %. However, the naive mean climatological treatment needs improvement to give results that are satisfyingly close to the full model. We find that mean cloud condensation nuclei (CCN) concentrations yield an underestimation of cloud droplet number concentration (CDNC) in the Southern Ocean, which we can reduce by allowing only CCN at cloud base (which have experienced hygroscopic growth in these conditions) to enter the climatology. This highlights the value of the simplification approach in pointing to unexpected model behaviour and providing a new perspective for its study and model development.</p

Aerosol size-dependent below-cloud scavenging by rain and snow in the ECHAM5-HAM

Wet deposition processes are highly efficient in the removal of aerosols from the atmosphere, and thus strongly influence global aerosol concentrations, and clouds, and their respective radiative forcings. In this study, physically detailed size-dependent below-cloud scavenging parameterizations for rain and snow are implemented in the ECHAM5-HAM global aerosol-climate model. Previously, below-cloud scavenging by rain in the ECHAM5-HAM was simply a function of the aerosol mode, and then scaled by the rainfall rate. The below-cloud scavenging by snow was a function of the snowfall rate alone. The global mean aerosol optical depth, and sea salt burden are sensitive to the below-cloud scavenging coefficients, with reductions near to 15% when the more vigorous size-dependent below-cloud scavenging by rain and snow is implemented. The inclusion of a prognostic rain scheme significantly reduces the fractional importance of below-cloud scavenging since there is higher evaporation in the lower troposphere, increasing the global mean sea salt burden by almost 15%. Thermophoretic effects are shown to produce increases in the global and annual mean number removal of Aitken size particles of near to 10%, but very small increases (near 1%) in the global mean below-cloud mass scavenging of carbonaceous and sulfate aerosols. Changes in the assumptions about the below-cloud scavenging by rain of particles with radius smaller than 10 nm do not cause any significant changes to the global and annual mean aerosol mass or number burdens, despite a change in the below-cloud number removal rate for nucleation mode particles by near to five-fold. Annual and zonal mean nucleation mode number concentrations are enhanced by up to 30% in the lower troposphere with the more vigourous size-dependent below-cloud scavenging. Closer agreement with different observations is found when the more physically detailed below-cloud scavenging parameterization is employed in the ECHAM5-HAM model

Influences of in-cloud aerosol scavenging parameterizations on aerosol concentrations and wet deposition in ECHAM5-HAM

A diagnostic cloud nucleation scavenging scheme, which determines stratiform cloud scavenging ratios for both aerosol mass and number distributions, based on cloud droplet, and ice crystal number concentrations, is introduced into the ECHAM5-HAM global climate model. This scheme is coupled with a size-dependent in-cloud impaction scavenging parameterization for both cloud droplet-aerosol, and ice crystal-aerosol collisions. The aerosol mass scavenged in stratiform clouds is found to be primarily (&amp;gt;90%) scavenged by cloud nucleation processes for all aerosol species, except for dust (50%). The aerosol number scavenged is primarily (&amp;gt;90%) attributed to impaction. 99% of this impaction scavenging occurs in clouds with temperatures less than 273 K. Sensitivity studies are presented, which compare aerosol concentrations, burdens, and deposition for a variety of in-cloud scavenging approaches: prescribed fractions, a more computationally expensive prognostic aerosol cloud processing treatment, and the new diagnostic scheme, also with modified assumptions about in-cloud impaction and nucleation scavenging. Our results show that while uncertainties in the representation of in-cloud scavenging processes can lead to differences in the range of 20–30% for the predicted annual, global mean aerosol mass burdens, and near to 50% for accumulation mode aerosol number burden, the differences in predicted aerosol mass concentrations can be up to one order of magnitude, particularly for regions of the middle troposphere with temperatures below 273 K where mixed and ice phase clouds exist. Different parameterizations for impaction scavenging changed the predicted global, annual mean number removal attributed to ice clouds by seven-fold, and the global, annual dust mass removal attributed to impaction by two orders of magnitude. Closer agreement with observations of black carbon profiles from aircraft (increases near to one order of magnitude for mixed phase clouds), mid-troposphere &lt;sup&gt;210&lt;/sup&gt;Pb vertical profiles, and the geographic distribution of aerosol optical depth is found for the new diagnostic scavenging scheme compared to the prescribed scavenging fraction scheme of the standard ECHAM5-HAM. The diagnostic and prognostic schemes represent the variability of scavenged fractions particularly for submicron size aerosols, and for mixed and ice phase clouds, and are recommended in preference to the prescribed scavenging fractions method

Simulating the seeder–feeder impacts on cloud ice and precipitation over the Alps

The ice phase impacts many cloud properties as well as cloud lifetime. Ice particles that sediment into a lower cloud from an upper cloud (external seeder–feeder process) or into the mixed-phase region of a deep cloud from cirrus levels (internal seeder–feeder process) can influence the ice phase of the lower cloud, amplify cloud glaciation and enhance surface precipitation. Recently, numerical weather prediction modeling studies have aimed at representing the ice crystal number concentration in mixed-phase clouds more accurately by including secondary ice formation processes. The increase in the ice crystal number concentration can impact the number of ice particles that sediment into the lower cloud and alter its composition and precipitation formation. In the Swiss Alps, the orography permits the formation of orographic clouds, making it ideal for studying the occurrence of multi-layered clouds and the seeder–feeder process. We present results from a case study on 18 May 2016, showing the occurrence frequency of multi-layered clouds and the seeder–feeder process. About half of all observed clouds were categorized as multi-layered, and the external seeder–feeder process occurred in 10 % of these clouds. Between cloud layers, ≈60 % of the ice particle mass was lost due to sublimation or melting. The external seeder–feeder process was found to be more important than the internal seeder–feeder process with regard to the impact on precipitation. In the case where the external seeder–feeder process was inhibited, the average surface precipitation and riming rate over the domain were both reduced by 8.5 % and 3.9 %, respectively. When ice–graupel collisions were allowed, further large reductions were seen in the liquid water fraction and riming rate. Inhibiting the internal seeder–feeder process enhanced the liquid water fraction by 6 % compared to a reduction of 5.8 % in the cloud condensate, therefore pointing towards the de-amplification in cloud glaciation and a reduction in surface precipitation. Adding to the observational evidence of frequent seeder–feeder situations, at least over Switzerland, our study highlights the extensive influence of sedimenting ice particles on the properties of feeder clouds as well as on precipitation formation.</p

Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability

This is the final version. Available from National Academy of Sciences via the DOI in this recordA large number of processes are involved in the chain from emissions of aerosol precursor gases and primary particles to impacts on cloud radiative forcing. Those processes are manifest in a number of relationships that can be expressed as factors dlnX/dlnY driving aerosol effects on cloud radiative forcing. These factors include the relationships between cloud condensation nuclei (CCN) concentration and emissions, droplet number and CCN concentration, cloud fraction and droplet number, cloud optical depth and droplet number, and cloud radiative forcing and cloud optical depth. The relationship between cloud optical depth and droplet number can be further decomposed into the sum of two terms involving the relationship of droplet effective radius and cloud liquid water path with droplet number. These relationships can be constrained using observations of recent spatial and temporal variability of these quantities. However, we are most interested in the radiative forcing since the preindustrial era. Because few relevant measurements are available from that era, relationships from recent variability have been assumed to be applicable to the preindustrial to present-day change. Our analysis of Aerosol Comparisons between Observations and Models (AeroCom) model simulations suggests that estimates of relationships from recent variability are poor constraints on relationships from anthropogenic change for some terms, with even the sign of some relationships differing in many regions. Proxies connecting recent spatial/temporal variability to anthropogenic change, or sustained measurements in regions where emissions have changed, are needed to constrain estimates of anthropogenic aerosol impacts on cloud radiative forcing.The Pacific Northwest National Laboratory (PNNL) is operated for the Department of Energy (DOE) by Battelle Memorial Institute under Contract DE-AC06-76RLO 1830. Work at PNNL was supported by the US DOE Decadal and Regional Climate Prediction using Earth System Models program and by the US DOE Earth System Modeling program. Work of M.W. and S.Z. performed at Nanjing University was supported by the One Thousand Young Talent Program, Jiangsu Province Specially-Appointed Professor Grant, and the National Natural Science Foundation of China (41575073). A portion of this research was performed using PNNL Institutional Computing resources. The ECHAM6-HAM model was 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 is managed by the Center for Climate Systems Modeling (C2SM) at ETH Zurich. D.N. acknowledges support by the Austrian Science Fund (J 3402-N29, Erwin Schrödinger Fellowship Abroad). C2SM at ETH Zurich is acknowledged for providing technical and scientific support. This work was also supported by a grant from the Swiss National Supercomputing Centre under Project ID s431. D.G.P. and P.S. acknowledge support from the United Kingdom (UK) Natural Environment Research Council Grant NE/I020148/1. P.S. and Z.K. acknowledge funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/2007–2013) ERC project ACCLAIM (Grant Agreement FP7-280025). The development of modal version of the GLObal Model of Aerosol Processes (GLOMAP-mode) within Hadley Center Global Environmental Mode (HadGEM) is part of the United Kingdom Chemistry and Aerosols (UKCA) project, which is supported by both National Environmental Research Council (NERC) and the Joint Department of Energy & Climate Change/Department for Environment, Food & Rural Affairs Meteorology Office Hadley Centre Climate Programme. We acknowledge use of the Met Office and NERC MONSooN high performance computing system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, a strategic partnership between the Met Office and the NERC. Simulations by SPRINTARS were executed with the supercomputer system SX-9/ACE of the National Institute for Environmental Studies, Japan. SPRINTARS is partly supported by the Environment Research and Technology Development Fund (S-12-3) of the Ministry of the Environment, Japan and Japan Society for the Promotion of Science KAKENHI Grants-in-Aid for Scientific Research 15H01728 and 15K12190. Computing resources for CAM5-MG2 simulations were provided by the Climate Simulation Laboratory at National Center for Atmospheric Research (NCAR) Computational and Information Systems Laboratory. NCAR is sponsored by the US National Science Foundation

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

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