Effects of improved interpolation in the wet-scavenging scheme of FLEXPART

2021 ◽  
Author(s):  
Anne Tipka ◽  
Petra Seibert
Keyword(s):  
Water Content ◽  
Dispersion Model ◽  
Mass Conservation ◽  
Cloud Water ◽  
Time Interval ◽  
Checkerboard Pattern ◽  
Specific Test ◽  
Cloud Water Content ◽  
Wet Scavenging ◽  
The Impact

<p>The Lagrangian dispersion model FLEXPART v10.4 uses cloud water content, temperature, and precipitation rates to calculate wet scavenging. Currently, only precipitation fields are interpolated spatially to the particle positions. A simple nearest-neighbour approach is used for cloud parameters and temperature. This is made worse by the fact that precipitation fields from the European Centre for Medium Range Weather Forecasts (ECMWF) are temporal integrals whereas all the other parameters refer to a specific time. The pre-processor flex_extract disaggregates the precipitation fields to construct point values that can preserve the integral quantity when interpolated in FLEXPART. However, this method does not preserve precipitation in each time interval, leading to smoothing, or even shifting precipitation into dry periods.</p><p>We have implemented interpolation of all fields relevant for wet scavenging in FLEXPART v10.4 as well as the option to use our improved precipitation disaggregation scheme (https://doi.org/10.5194/gmd-11-2503-2018). It introduces two additional subgrid points within one original time interval. This secures consistency, continuity and mass conservation of precipitation within each time interval.</p><p>These updates lead to a massive improvement of the wet deposition fields in a specific test case where we applied a high-resolution outgrid that makes the effects of interpolation issues more visible. Originally, a kind of checkerboard pattern was visible, as well as a banded structure due to the finite time interval between meteorological input fields. Both features are mostly eliminated now. Additionally, the influence of varying the temporal and spatial resolution of the ECMWF input fields was investigated, and the benefit of using the ECMWF cloud water content instead of parametrised values. We also look at the impact of the new version on other, previously used test cases, for example, a lifetime analysis of aerosol particles as well as transport of mineral dust and black carbon.</p>

scholarly journals Monte Carlo-based subgrid parameterization of vertical velocity and stratiform cloud microphysics in ECHAM5.5-HAM2

2013 ◽  
Vol 13 (15) ◽  
pp. 7551-7565 ◽  
Author(s):  
J. Tonttila ◽  
P. Räisänen ◽  
H. Järvinen
Keyword(s):  
Monte Carlo ◽  
Water Content ◽  
Vertical Velocity ◽  
Cloud Microphysics ◽  
Cloud Water ◽  
Radiative Effects ◽  
Reference Simulation ◽  
Cloud Water Content ◽  
Cloud Radiative Effects ◽  
The Impact

Abstract. A new method for parameterizing the subgrid variations of vertical velocity and cloud droplet number concentration (CDNC) is presented for general circulation models (GCMs). These parameterizations build on top of existing parameterizations that create stochastic subgrid cloud columns inside the GCM grid cells, which can be employed by the Monte Carlo independent column approximation approach for radiative transfer. The new model version adds a description for vertical velocity in individual subgrid columns, which can be used to compute cloud activation and the subgrid distribution of the number of cloud droplets explicitly. Autoconversion is also treated explicitly in the subcolumn space. This provides a consistent way of simulating the cloud radiative effects with two-moment cloud microphysical properties defined at subgrid scale. The primary impact of the new parameterizations is to decrease the CDNC over polluted continents, while over the oceans the impact is smaller. Moreover, the lower CDNC induces a stronger autoconversion of cloud water to rain. The strongest reduction in CDNC and cloud water content over the continental areas promotes weaker shortwave cloud radiative effects (SW CREs) even after retuning the model. However, compared to the reference simulation, a slightly stronger SW CRE is seen e.g. over mid-latitude oceans, where CDNC remains similar to the reference simulation, and the in-cloud liquid water content is slightly increased after retuning the model.

PROFILING OF CLOUD WATER CONTENT BY ACTIVE-PASSIVE SENSING

2014 ◽  
Vol 73 (13) ◽  
pp. 1141-1152
Author(s):  
Ye. N. Belov ◽  
B. A. Kabanov ◽  
Stanislav I. Khomenko ◽  
G. I. Khlopov ◽  
A. M. Linkova ◽  
...  
Keyword(s):  
Water Content ◽  
Cloud Water ◽  
Passive Sensing ◽  
Cloud Water Content

Understanding the Extratropical Liquid Water Path Feedback in Mixed-Phase Clouds with an Idealized Global Climate Model

2022 ◽  
pp. 1-48
Author(s):  
Yi Ming
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Climate Model ◽  
Global Climate Model ◽  
Cloud Water ◽  
Mixed Phase ◽  
Liquid Water Path ◽  
Water Path ◽  
Cloud Water Content ◽  
Mixed Phase Clouds

Abstract A negative shortwave cloud feedback associated with higher extratropical liquid water content in mixed-phase clouds is a common feature of global warming simulations, and multiple mechanisms have been hypothesized. A set of process-level experiments performed with an idealized global climate model (a dynamical core with passive water and cloud tracers and full Rotstayn-Klein single-moment microphysics) show that the common picture of the liquid water path (LWP) feedback in mixed-phase clouds being controlled by the amount of ice susceptible to phase change is not robust. Dynamic condensate processes—rather than static phase partitioning—directly change with warming, with varied impacts on liquid and ice amounts. Here, three principal mechanisms are responsible for the LWP response, namely higher adiabatic cloud water content, weaker liquid-to-ice conversion through the Bergeron-Findeisen process, and faster melting of ice and snow to rain. Only melting is accompanied by a substantial loss of ice, while the adiabatic cloud water content increase gives rise to a net increase in ice water path (IWP) such that total cloud water also increases without an accompanying decrease in precipitation efficiency. Perturbed parameter experiments with a wide range of climatological LWP and IWP demonstrate a strong dependence of the LWP feedback on the climatological LWP and independence from the climatological IWP and supercooled liquid fraction. This idealized setup allows for a clean isolation of mechanisms and paints a more nuanced picture of the extratropical mixed-phase cloud water feedback than simple phase change.

scholarly journals Climatology of cloud water content associated with different cloud types observed by A-Train satellites

2015 ◽  
Vol 120 (9) ◽  
pp. 4196-4212 ◽  
Author(s):  
Lei Huang ◽  
Jonathan H. Jiang ◽  
Zhien Wang ◽  
Hui Su ◽  
Min Deng ◽  
...  
Keyword(s):  
Water Content ◽  
Cloud Water ◽  
Cloud Water Content

scholarly journals On the Definition of the Cloud Water Content Fluctuations and Its Effects on the Computation of a Second-Order Liquid Water Correlation

2007 ◽  
Vol 64 (2) ◽  
pp. 665-669 ◽  
Author(s):  
E. Bouzereau ◽  
L. Musson-Genon ◽  
B. Carissimo
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Climate Models ◽  
Cloud Water ◽  
Second Order ◽  
Subgrid Scale ◽  
Eddy Flux ◽  
Cloud Models ◽  
Cloud Water Content ◽  
Definition Of

Abstract In subgrid-scale condensation schemes of cloud models, the majority of previous authors have relied on results presented in a paper by Bougeault. In the present paper, second-order liquid water correlations are restated that differ from the former paper but are coherent with the corrigendum of Mellor. These differences are explained here through two different underlying definitions of cloud water content fluctuations; they can be summarized by whether or not unsaturated air within a grid box contributes to the eddy flux of the cloud water content. Taking into account the issue mentioned above, the “buoyancy flux” is also derived. Although the full impact of these changes has not been evaluated here, it may become important for future global cloud-resolving climate models.

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