scholarly journals Improving the treatment of subgrid cloud variability in warm rain simulation in CESM2

2021 ◽  
Author(s):  
Hao Wang ◽  
Minghuai Wang ◽  
Daniel Rosenfeld ◽  
Yannian Zhu ◽  
Zhibo Zhang
Keyword(s):  
Liquid Water ◽  
Climate Models ◽  
Global Climate ◽  
Liquid Water Content ◽  
Global Climate Models ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Warm Rain ◽  
Rain Simulation ◽  
Warm Rain Process

<p>Representing subgrid variability of cloud properties has always been a challenge in global climate models (GCMs). In microphysics schemes, the effects of subgrid cloud variability on warm rain process rates calculated based on mean cloud properties are usually accounted for by scaling process rates by an enhancement factor (EF) that is derived from the subgrid variance of cloud water. In our study, we find that the EF derived from Cloud Layers Unified by Binormals (CLUBB) in Community Earth System Model Version 2 (CESM2) is severely overestimated in most of the oceanic areas, which leads to the strong overestimation in the autoconversion rate. Through an EF formula based on empirical fitting of MODIS, we improve the EF in the liquid phase clouds. Results show that the model has a more reasonable relationship between autoconversion rate, cloud liquid water content (LWC), and droplet number concentration (CDNC) in warm rain simulation. The annual mean liquid cloud fraction (LCF), liquid water path (LWP), and CDNC show obvious increases for marine stratocumulus, where the probability of precipitation (POP) shows an obvious decrease. The annual mean LCF, cloud optical thickness (COT), and shortwave cloud forcing (SWCF) match better with observation. The sensitivity of LWP to aerosol decreases obviously. The sensitivities of LCF, LWP, cloud top droplet effective radius (CER), and COT to aerosol are in better agreement with MODIS, but the model still underestimates the response of cloud albedo to aerosol. These results indicate the importance of representing reasonable subgrid cloud variabilities in the simulation of cloud properties and aerosol-cloud interaction in climate models.</p>

scholarly journals Vertical Dependence of Horizontal Variation of Cloud Microphysics: Observations from the ACE-ENA field campaign and implications for warm rain simulation in climate models

2020 ◽  
Author(s):  
Zhibo Zhang ◽  
Qianqian Song ◽  
David Mechem ◽  
Vincent Larson ◽  
Jian Wang ◽  
...  
Keyword(s):  
Climate Models ◽  
Marine Boundary Layer ◽  
Global Climate ◽  
Cloud Microphysics ◽  
Global Climate Models ◽  
Strong Positive Correlation ◽  
Field Campaign ◽  
Warm Rain ◽  
Cloud Water Content ◽  
Warm Rain Process

Abstract. In the current global climate models (GCM), the nonlinearity effect of subgrid cloud variations on the parameterization of warm rain process, e.g., the autoconversion rate, is often treated by multiplying the resolved-scale warm ran process rates by a so-called enhancement factor (EF). In this study, we investigate the subgrid-scale horizontal variations and covariation of cloud water content (qc) and cloud droplet number concentration (Nc) in marine boundary layer (MBL) clouds based on the in-situ measurements from a recent field campaign, and study the implications for the autoconversion rate EF in GCMs. Based on a few carefully selected cases from the field campaign, we found that in contrast to the enhancing effect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in a suppressing effect that makes tends to make EF 

scholarly journals Vertical dependence of horizontal variation of cloud microphysics: observations from the ACE-ENA field campaign and implications for warm-rain simulation in climate models

2021 ◽  
Vol 21 (4) ◽  
pp. 3103-3121
Author(s):  
Zhibo Zhang ◽  
Qianqian Song ◽  
David B. Mechem ◽  
Vincent E. Larson ◽  
Jian Wang ◽  
...  
Keyword(s):  
Climate Models ◽  
Marine Boundary Layer ◽  
Global Climate ◽  
Cloud Microphysics ◽  
Global Climate Models ◽  
Strong Positive Correlation ◽  
Field Campaign ◽  
Warm Rain ◽  
Cloud Water Content ◽  
Warm Rain Process

Abstract. In the current global climate models (GCMs), the nonlinearity effect of subgrid cloud variations on the parameterization of warm-rain process, e.g., the autoconversion rate, is often treated by multiplying the resolved-scale warm-rain process rates by a so-called enhancement factor (EF). In this study, we investigate the subgrid-scale horizontal variations and covariation of cloud water content (qc) and cloud droplet number concentration (Nc) in marine boundary layer (MBL) clouds based on the in situ measurements from a recent field campaign and study the implications for the autoconversion rate EF in GCMs. Based on a few carefully selected cases from the field campaign, we found that in contrast to the enhancing effect of qc and Nc variations that tends to make EF > 1, the strong positive correlation between qc and Nc results in a suppressing effect that tends to make EF < 1. This effect is especially strong at cloud top, where the qc and Nc correlation can be as high as 0.95. We also found that the physically complete EF that accounts for the covariation of qc and Nc is significantly smaller than its counterpart that accounts only for the subgrid variation of qc, especially at cloud top. Although this study is based on limited cases, it suggests that the subgrid variations of Nc and its correlation with qc both need to be considered for an accurate simulation of the autoconversion process in GCMs.

scholarly journals Reducing the aerosol forcing uncertainty using observational constraints on warm rain processes

2020 ◽  
Vol 6 (22) ◽  
pp. eaaz6433 ◽  
Author(s):  
Johannes Mülmenstädt ◽  
Christine Nam ◽  
Marc Salzmann ◽  
Jan Kretzschmar ◽  
Tristan S. L’Ecuyer ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Observational Constraints ◽  
Observational Constraint ◽  
Liquid Water Path ◽  
Anthropogenic Aerosols ◽  
Aerosol Forcing ◽  
Warm Rain ◽  
Atmospheric State

Global climate models (GCMs) disagree with other lines of evidence on the rapid adjustments of cloud cover and liquid water path to anthropogenic aerosols. Attempts to use observations to constrain the parameterizations of cloud processes in GCMs have failed to reduce the disagreement. We propose using observations sensitive to the relevant cloud processes rather than only to the atmospheric state and focusing on process realism in the absence of aerosol perturbations in addition to the process susceptibility to aerosols. We show that process-sensitive observations of precipitation can reduce the uncertainty on GCM estimates of rapid cloud adjustments to aerosols. The feasibility of an observational constraint depends on understanding the precipitation intensity spectrum in both observations and models and also on improving methods to compare the two.

scholarly journals A Bootstrap Technique for Testing the Relationship between Local-Scale Radar Observations of Cloud Occurrence and Large-Scale Atmospheric Fields

2006 ◽  
Vol 63 (11) ◽  
pp. 2813-2830 ◽  
Author(s):  
Roger Marchand ◽  
Nathaniel Beagley ◽  
Sandra E. Thompson ◽  
Thomas P. Ackerman ◽  
David M. Schultz
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Initial Conditions ◽  
Global Climate ◽  
Stable State ◽  
Local Scale ◽  
Global Climate Models ◽  
Scale Model ◽  
Synoptic Scale ◽  
Cloud Properties

Abstract A classification scheme is created to map the synoptic-scale (large scale) atmospheric state to distributions of local-scale cloud properties. This mapping is accomplished by a neural network that classifies 17 months of synoptic-scale initial conditions from the rapid update cycle forecast model into 25 different states. The corresponding data from a vertically pointing millimeter-wavelength cloud radar (from the Atmospheric Radiation Measurement Program Southern Great Plains site at Lamont, Oklahoma) are sorted into these 25 states, producing vertical profiles of cloud occurrence. The temporal stability and distinctiveness of these 25 profiles are analyzed using a bootstrap resampling technique. A stable-state-based mapping from synoptic-scale model fields to local-scale cloud properties could be useful in three ways. First, such a mapping may improve the understanding of differences in cloud properties between output from global climate models and observations by providing a physical context. Second, this mapping could be used to identify the cause of errors in the modeled distribution of clouds—whether the cause is a difference in state occurrence (the type of synoptic activity) or the misrepresentation of clouds for a particular state. Third, robust mappings could form the basis of a new statistical cloud parameterization.

scholarly journals An Investigation of the Influence of Droplet Number Concentration and Giant Aerosol Particles upon Supercooled Large Drop Formation in Wintertime Stratiform Clouds

2008 ◽  
Vol 47 (10) ◽  
pp. 2659-2678 ◽  
Author(s):  
Sonia Lasher-Trapp ◽  
Sarah Anderson-Bereznicki ◽  
Ashley Shackelford ◽  
Cynthia H. Twohy ◽  
James G. Hudson
Keyword(s):  
Particle Number ◽  
Aerosol Particles ◽  
Liquid Water Content ◽  
Cloud Properties ◽  
Droplet Concentration ◽  
Warm Rain ◽  
Stratiform Clouds ◽  
Initial Comparison ◽  
Warm Rain Process ◽  
Small Cloud

Abstract Supercooled large drops (SLD) can be a significant hazard for aviation. Past studies have shown that warm-rain processes are prevalent, or even dominant, in stratiform clouds containing SLD, but the primary factors that control SLD production are still not well understood. Giant aerosol particles have been shown to accelerate the formation of the first drizzle drops in some clouds and thus are a viable source of SLD, but observational support for testing their effectiveness in supercooled stratiform clouds has been lacking. In this study, new observations collected during six research flights from the Alliance Icing Research Study II (AIRS II) are analyzed to assess the factors that may be relevant to SLD formation, with a particular emphasis on the importance of giant aerosol particles. An initial comparison of observed giant aerosol particle number concentrations with the observed SLD suggests that they were present in sufficient numbers to be the source of the SLD. However, microphysical calculations within an adiabatic parcel model, initialized with the observed aerosol distributions and cloud properties, suggest that the giant aerosol particles were only a limited source of SLD. More SLD was produced in the modeled clouds with low droplet concentrations, simply by an efficient warm-rain process acting at temperatures below 0°C. For cases in which the warm-rain process is limited by a higher droplet concentration and small cloud depth/liquid water content, the giant aerosol particles were then the only source of SLD. The modeling results are consistent with the observed trends in SLD across the six AIRS II cases.

The Role of Mesoscale Cellular Convective Cloud Morphologies in Low Cloud Feedbacks 

2021 ◽  
Author(s):  
Isabel L. McCoy ◽  
Daniel T. McCoy ◽  
Robert Wood ◽  
Paquita Zuidema ◽  
Frida A.-M. Bender
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Convective Cloud ◽  
Static Stability ◽  
Reanalysis Data ◽  
Global Climate Models ◽  
Liquid Water Path ◽  
Radiative Impacts ◽  
Low Cloud

&lt;p&gt;Mesoscale cellular convective (MCC) clouds occur in large-scale patterns over the ocean, are prevalent in sub-tropical cloud regions and mid-latitudes, and have important radiative impacts on the climate system. On average, closed MCC clouds have higher albedos than open or disorganized MCC clouds for the same cloud fraction which suggests differences in micro- and macro-physical characteristics between MCC morphologies. Marine cold air outbreaks (MCAOs) influence the development of open MCC clouds and the transition from closed to open MCC clouds in the mid-latitudes. A MCAO index, M, combines atmospheric surface forcing and static stability and can be used to examine global MCC morphology dependencies. MCC cloud morphology occurrence is also expected to shift with sea surface temperature (SST) changes as the climate warms. Analysis of MCC identifications (derived from a neural network classifier applied to MODIS satellite collection 6 liquid water path retrievals) and ECMWF ERA5 reanalysis data shows that closed MCC cloud occurrence shifts to open or disorganized MCC within an M-SST space. Global climate models (GCMs) predict that M will change regionally in strength as SSTs increase. Based on our derived MCC-M-SST relationship in the current climate, closed MCC occurrence frequency is expected to increase with a weakening of M but decrease with an increase in SSTs. This results in a shift to cloud morphologies with lower albedos. Cloud controlling factor analysis is used to estimate the resulting low cloud morphology feedback which is found to be spatially varied and between &amp;#177;0.15 W m&lt;sup&gt;-2&lt;/sup&gt; K&lt;sup&gt;-1&lt;/sup&gt;. Because the morphology feedback is estimated to be positive in the extra-tropics and is not currently represented in GCMs, this implies a higher climate sensitivity than GCMs currently estimate.&lt;/p&gt;

Assessment of AOD by 16 CMIP6 Models Based on Satellite-Derived Dataset from 2000-2014 over Eastern Center China

2021 ◽  
Author(s):  
xiao li ◽  
minghuai wang ◽  
yawen liu ◽  
yiquan jiang ◽  
xinyi dong
Keyword(s):  
Climate Models ◽  
Comprehensive Evaluation ◽  
Global Climate ◽  
Chemical Properties ◽  
Sulfate Aerosol ◽  
Global Climate Models ◽  
Aerosol Concentration ◽  
Central China ◽  
Liquid Water Path ◽  
Simulated Precipitation

&lt;h3&gt;Knowledge of aerosol concentration, type, and physical and chemical properties is necessary to understand their role in Earth&amp;#8217;s climate system. However, CMIP6 models&amp;#8217; performance of AOD simulation in China lacks a comprehensive evaluation and the potential improvement for CMIP6 models is still unclear. Here, we assess the performance of CMIP6 models in simulating annual mean AOD climatology and its seasonality over China from 2000 to 2014 and explore the underlying reasons for its performance. Compared with CMIP5, CMIP6 models can better capture the annual mean AOD climatology magnitude over Eastern Central China (ECC) with a notable enhancement of 52.98% due to a significant increase in the dominate sulfate aerosol. However, the majority of CMIP6 models fail to capture the observed inverted &amp;#8220;V-like&amp;#8221; pattern that depicts two centers of maximum AOD in spring over northeast China (NEC) and in summer over southeast China (SEC), respectively. The deficiency of two maximums by CMIP6 models is separately due to the negative bias in the simulation of organic aerosol (OA) AOD and sulfate AOD. Our analysis suggests that the deviation of simulated precipitation, relative humidity (RH), and liquid water path (LWP) in CMIP6 models contributes to the deviation of simulated sulfate AOD through affecting sulfate aerosol concentration by wet deposition and aqueous-phase production. Therefore, this study illustrates the urgent need to improve AOD simulation in global climate models.&lt;/h3&gt;

3D Modeling of Supersaturated H2O/H2O2 on Early Mars Climate during the Noachia

2021 ◽  
Author(s):  
Antony Delavois ◽  
François Forget ◽  
Martin Turbet ◽  
Ehouarn Millour
Keyword(s):  
Liquid Water ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Supersaturation Ratio ◽  
3 Dimensional ◽  
High Supersaturation ◽  
1D Model ◽  
Early Mars

&lt;p&gt;The climate of Mars during its first billion years is one of the most intriguing question in our understanding of the Solar System. The planet was host of a large amount of liquid water flowing on the surface throughout the Noachian era, approximatively 4Gya. Geomorphological observations is the main evidence for liquid water since valley networks and lakes are still visible on the surface, although dry nowadays.&lt;/p&gt; &lt;p&gt;Different studies have tried to reproduce the conditions that may have occured on the planet, trying to find an atmospheric process or composition that could solve the Faint Young Sun Paradox. Theses modeling studies, through the use of 3-dimensional Global Climate Models struggled to warm sufficiently the past climate of Mars, even considering different greenhouse gases, the role of clouds, meteoritic impact or even volcanism (XXX). However, the presence of H2 could be an interesting solution for a sustainable warming as some recent studies suggest (Turbet and Forget, 2021). Another recent study (Ito et al. 2020) suggested that H2O2 might be a convincing candidate but has to be in high supersaturation ratio in the atmosphere, even though it only used a simplified 1D model and relatively high supersaturation levels.&lt;/p&gt; &lt;p&gt;We try here to explore more in detail the scenario of supersaturated H2O2 and H2O, that also might be a specy able to provide a sufficient global warming under supersaturated conditions or through the formation of high altitude clouds. Since H2O is the major source of H2O2 in the atmosphere, it is important to assess whether the H2O content in the atmosphere is enough to provide high quantities of H2O2. We also try to constrain the theoritical supersaturation level of H2O/H2O2 that will allow the warming of the climate above 273K, but with a detailled 3D GCM simulation. Even if we do not tackle the question whether the supersaturation hypothesis is realistic or not, these results give a better understanding of&amp;#160; what would be Early Mars' climate under such conditions.&lt;/p&gt;

scholarly journals Cloud feedbacks in extratropical cyclones: insight from long-term satellite data and high-resolution global simulations

2018 ◽  
Author(s):  
Daniel T. McCoy ◽  
Paul R. Field ◽  
Gregory S. Elsaesser ◽  
Alejandro Bodas-Salcedo ◽  
Brian H. Kahn ◽  
...  
Keyword(s):  
Water Vapor ◽  
Liquid Water ◽  
Climate Models ◽  
Moisture Flux ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Extratropical Cyclones ◽  
Liquid Water Path ◽  
Cloud Feedbacks ◽  
Water Path

Abstract. Extratropical cyclones provide a unique set of challenges and opportunities in understanding variability in cloudiness over the extratropics (poleward of 30°). We can gain insight into the shortwave cloud feedback from examining cyclone variability. Here we contrast global climate models (GCMs) with horizontal resolutions from 7 km up to hundreds of kilometers with Multi-Sensor Advanced Climatology Liquid Water Path (MAC-LWP) microwave observations of cyclone properties from the period 1992–2015. We find that inter-cyclone variability in both observations and models is strongly driven by moisture flux along the cyclone's warm conveyor belt (WCB). Stronger WCB moisture flux enhances liquid water path (LWP) within cyclones. This relationship is replicated in GCMs, although its strength varies substantially across models. In the southern hemisphere (SH) oceans 28–42 % of the observed interannual variability in cyclone LWP may be explained by WCB moisture flux variability. This relationship is used to propose two cloud feedbacks acting within extratropical cyclones: a negative feedback driven by Clausius-Clapeyron increasing water vapor path (WVP), which enhances the amount of water vapor available to be fluxed into the cyclone; and a feedback moderated by changes in the life cycle and vorticity of cyclones under warming, which changes the rate at which existing moisture is imported into the cyclone. We show that changes in moisture flux drive can explain the observed trend in Southern Ocean cyclone LWP over the last two decades. Transient warming simulations show that the majority of the change in cyclone LWP can be explained by changes in WCB moisture flux, as opposed to changes in cloud phase. The variability within cyclone composites is examined to understand what cyclonic regimes the mixed phase cloud feedback is relevant to. At a fixed WCB moisture flux cyclone LWP increases with increasing SST in the half of the composite poleward of the low and decreases in the half equatorward of the low in both GCMs and observations. Cloud-top phase partitioning observed by the Atmospheric Infrared Sounder (AIRS) indicates that phase transitions may be driving increases in LWP in the poleward half of cyclones.

Erroneous Relationships among Humidity and Cloud Forcing Variables in Three Global Climate Models

2008 ◽  
Vol 21 (17) ◽  
pp. 4190-4206 ◽  
Author(s):  
Florian Bennhold ◽  
Steven Sherwood
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Global Climate ◽  
Precipitable Water ◽  
Static Stability ◽  
Global Climate Models ◽  
Radiative Properties ◽  
Cloud Forcing ◽  
Joint Distributions ◽  
Cloud Properties

Abstract Links are examined between time-averaged cloud radiative properties, particularly the longwave and shortwave components of cloud radiative forcing (CRF), and properties of the long-term averages of atmospheric soundings, in particular upper-tropospheric humidity (UTH), lower-tropospheric precipitable water (PW), and static stability (SS). The joint distributions of moisture measures and the composite or conditional mean CRF for different moisture and stability combinations are computed. This expands on previous studies that have examined cloud properties versus vertical velocity and surface temperature. These computations are done for satellite observations and for three representative coupled climate models from major modeling centers. Aside from mean biases reported previously, several departures are identified between the modeled and observed joint distributions that are qualitative and significant. Namely, the joint distribution of PW and UTH is very compact in observations but less so in models, cloud forcings are tightly related to PW in the data but to UTH in the models, and strong negative net CRF in marine stratocumulus regions occurs only for high SS and low UTH in the data but violates one or both of these restrictions in each of the models. All three errors are preliminarily interpreted as symptoms of inadequate dependence of model convective development on ambient humidity above the boundary layer. In any case, the character of the errors suggests utility for model testing and future development. A set of scalar metrics for quantifying some of the problems is presented; these metrics can be easily applied to standard model output. Finally, an examination of doubled-CO2 simulations suggests that the errors noted here are significantly affecting cloud feedback in at least some models. For example, in one model a strong negative feedback is found from clouds forming in model conditions that never occur in the observations.

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