scholarly journals Large-scale industrial cloud perturbations confirm bidirectional cloud water responses to anthropogenic aerosols

2020 ◽  
Author(s):  
Heido Trofimov ◽  
Velle Toll
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Climate Models ◽  
Hot Spot ◽  
Cloud Water ◽  
Global Climate Models ◽  
Small Scale ◽  
Liquid Water Path ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol

<p>Aerosols offset poorly quantified fraction of anthropogenic greenhouse gas warming, whereas the aerosol impact on clouds is the most uncertain mechanism of anthropogenic climate forcing. In this research, we extend satellite observations of polluted cloud tracks from Toll et al. (2019, Nature, https://doi.org/10.1038/s41586-019-1423-9) with analysis of larger scale polluted cloud areas detected in MODerate-resolution Imaging Spectroradiometer satellite images. We demonstrate that large-scale anthropogenic aerosol-induced cloud perturbations exist at various major industrial aerosol source regions. The areal extent of the polluted cloud areas detected in MODIS satellite images extended to hundreds by hundreds of kilometres. Polluted clouds detected in satellite images in the global anthropogenic air pollution hot spot of Norilsk, Russia, and in other regions show close compensation between aerosol-induced cloud water increases and decreases. On average, there is relatively weak decrease in cloud water in the large areas with strong decreases in cloud droplet radii. This is in very good agreement with previous results based on small-scale polluted cloud tracks (Toll et al., 2019) and strongly disagrees with unidirectionally increased liquid water path in global climate models.</p>

Real-world laboratories for studying anthropogenic aerosol impacts on clouds and Earth’s climate

2020 ◽  
Author(s):  
Velle Toll ◽  
Heido Trofimov ◽  
Jorma Rahu
Keyword(s):  
Real World ◽  
Power Plants ◽  
Satellite Images ◽  
Climate Models ◽  
Cloud Droplet ◽  
Cloud Water ◽  
Global Climate Models ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol ◽  
Oil Refineries

<p>The cooling of the Earth’s climate through the effects of anthropogenic aerosols on clouds offsets an unknown fraction of greenhouse gas warming. We discuss how causal relationship between aerosols and clouds can be derived from contrast between clouds polluted by anthropogenic aerosols and nearby unpolluted clouds. Ship tracks have been long considered to be real-world laboratories of aerosol-cloud interactions. More recently, polluted cloud tracks induced by aerosols emitted from volcanoes and wildfires and various industrial sources - such as oil refineries, smelters, coal-fired power plants, and cities have been analysed (Toll et al. 2019; Nature, https://doi.org/10.1038/s41586-019-1423-9). In this research, we extend satellite observations of polluted cloud tracks from Toll et al. (2019) with analysis of smaller and larger scale polluted cloud areas detected in satellite images.</p><p> </p><p>Polluted clouds are detected in MODIS and SEVIRI satellite images as areas with strongly increased cloud droplet number concentrations. Polluted cloud tracks can be utilized to study frequency and magnitude of anthropogenic cloud droplet number perturbations and subsequent cloud adjustments. Anthropogenic aerosol perturbations on liquid-water clouds are detected in various major global industrial areas. Both tens of kilometres wide ship-track-like polluted cloud tracks and hundreds by hundreds of kilometres wide polluted cloud areas show that cloud water can both increase and decrease in response to aerosols depending on meteorological conditions. On average, there is relatively weak decrease in cloud water. Polluted cloud tracks also show that cloud fraction can both increase and decrease compared to nearby less polluted clouds. Applicability of pollution tracks to study impact of absorbing aerosols situated above clouds on below-lying clouds is discussed. We expect that utilization of real-world laboratories of aerosol impacts on clouds will lead to improved physical parameterizations in global climate models and more reliable projections of the future climate.</p>

scholarly journals Aerosol indirect effects in a multi-scale aerosol-climate model PNNL-MMF

2011 ◽  
Vol 11 (1) ◽  
pp. 3399-3459 ◽  
Author(s):  
M. Wang ◽  
S. Ghan ◽  
M. Ovchinnikov ◽  
X. Liu ◽  
R. Easter ◽  
...  
Keyword(s):  
Indirect Effects ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol ◽  
Aerosol Indirect Effects ◽  
Multi Scale

Abstract. Much of the large uncertainty in estimates of anthropogenic aerosol effects on climate arises from the multi-scale nature of the interactions between aerosols, clouds and large-scale dynamics, which are difficult to represent in conventional global climate models (GCMs). In this study, we use a multi-scale aerosol-climate model that treats aerosols and clouds across multiple scales to study aerosol indirect effects. This multi-scale aerosol-climate model is an extension of a multi-scale modeling framework (MMF) model that embeds a cloud-resolving model (CRM) within each grid cell of a GCM. The extension allows the explicit simulation of aerosol/cloud interactions in both stratiform and convective clouds on the global scale in a computationally feasible way. Simulated model fields, including liquid water path (LWP), ice water path, cloud fraction, shortwave and longwave cloud forcing, precipitation, water vapor, and cloud droplet number concentration are in agreement with observations. The new model performs quantitatively similar to the previous version of the MMF model in terms of simulated cloud fraction and precipitation. The simulated change in shortwave cloud forcing from anthropogenic aerosols is −0.77 W m−2, which is less than half of that in the host GCM (NCAR CAM5) (−1.79 W m−2) and is also at the low end of the estimates of most other conventional global aerosol-climate models. The smaller forcing in the MMF model is attributed to its smaller increase in LWP from preindustrial conditions (PI) to present day (PD): 3.9% in the MMF, compared with 15.6% increase in LWP in large-scale clouds in CAM5. The much smaller increase in LWP in the MMF is caused by a much smaller response in LWP to a given perturbation in cloud condensation nuclei (CCN) concentrations from PI to PD in the MMF (about one-third of that in CAM5), and, to a lesser extent, by a smaller relative increase in CCN concentrations from PI to PD in the MMF (about 26% smaller than that in CAM5). The smaller relative increase in CCN concentrations in the MMF is caused in part by a smaller increase in aerosol lifetime from PI to PD in the MMF, a positive feedback in aerosol indirect effects induced by cloud lifetime effects. The smaller response in LWP to anthropogenic aerosols in the MMF model is consistent with observations and with high resolution model studies, which may indicate that aerosol indirect effects simulated in conventional global climate models are overestimated and point to the need to use global high resolution models, such as MMF models or global CRMs, to study aerosol indirect effects. The simulated total anthropogenic aerosol effect in the MMF is −1.05 W m−2, which is close to the Murphy et al. (2009) inverse estimate of −1.1 ± 0.4 W m−2 (1σ) based on the examination of the Earth's energy balance. Further improvements in the representation of ice nucleation and low clouds are needed.

scholarly journals Differences between downscaling with spectral and grid nudging using WRF

2012 ◽  
Vol 12 (8) ◽  
pp. 3601-3610 ◽  
Author(s):  
P. Liu ◽  
A. P. Tsimpidi ◽  
Y. Hu ◽  
B. Stone ◽  
A. G. Russell ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Wrf Model ◽  
Global Climate Models ◽  
Small Scale ◽  
Data Set ◽  
Spectral Nudging

Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

scholarly journals Partitioning Internal Variability and Model Uncertainty Components in a Multimember Multimodel Ensemble of Climate Projections

2014 ◽  
Vol 27 (17) ◽  
pp. 6779-6798 ◽  
Author(s):  
Benoit Hingray ◽  
Mériem Saïd
Keyword(s):  
Model Uncertainty ◽  
Lead Time ◽  
Large Scale ◽  
Climate Models ◽  
Internal Variability ◽  
Global Climate Models ◽  
Small Scale ◽  
Climate Projections ◽  
Multimodel Ensemble ◽  
Total Uncertainty

Abstract A simple and robust framework is proposed for the partitioning of the different components of internal variability and model uncertainty in an unbalanced multimember multimodel ensemble (MM2E) of climate projections obtained for a suite of statistical downscaling models (SDMs) and global climate models (GCMs). It is based on the quasi-ergodic assumption for transient climate simulations. Model uncertainty components are estimated from the noise-free signals of the different modeling chains using a two-way analysis of variance (ANOVA) framework. The residuals from the noise-free signals are used to estimate the large- and small-scale internal variability components associated with each considered GCM–SDM configuration. This framework makes it possible to take into account all members available from any climate ensemble of opportunity. Uncertainty is quantified as a function of lead time for projections of changes in temperature and precipitation produced for a mesoscale alpine catchment. Internal variability accounts for more than 80% of total uncertainty in the first decades. This proportion decreases to less than 10% at the end of the century for temperature but remains greater than 50% for precipitation. Small-scale internal variability is negligible for temperature; however, it is similar to the large-scale component for precipitation, whatever the projection lead time. SDM uncertainty is always greater than GCM uncertainty for precipitation. It is also greater for temperature in the middle of the century. The response-to-uncertainty ratio is very high for temperature. For precipitation, it is always less than one, indicating that even the sign of change is uncertain.

Polluted cloud lines in satellite snapshots and satellite climatologies

2021 ◽  
Author(s):  
Velle Toll ◽  
Heido Trofimov ◽  
Jorma Rahu ◽  
Piia Post
Keyword(s):  
Power Plants ◽  
Climate Models ◽  
Spatial Scales ◽  
Cloud Water ◽  
Global Climate Models ◽  
Anthropogenic Aerosols ◽  
Oil Refineries ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions ◽  
Cloud Response

<p>It is challenging to separate the cause from effect in aerosol-cloud interactions. Anomalous cloud lines polluted by anthropogenic aerosols help distinguish the cause from effect as properties of polluted clouds can be directly compared to nearby unpolluted clouds’ properties. Pollution tracks in clouds induced by localised aerosol emissions (Toll et al. 2019, Nature, https://doi.org/10.1038/s41586-019-1423-9)  are visually detectable ship-track-like quasi-linear polluted cloud features in satellite snapshots. We detected similar anomalous polluted cloud lines in the long-term average satellite data, where cloud response to aerosol over a long time is recorded. Polluted cloud tracks are induced by various aerosol sources like oil refineries, smelters, coal-fired power plants, smaller industry towns, ships, and volcanoes. We detected polluted cloud tracks at spatial scales varying from tens of kilometres to thousands of kilometres (Trofimov et al. 2020; JGR Atmospheres, https://doi.org/10.1029/2020JD032575).  </p><p> </p><p>Polluted cloud tracks detected in satellite snapshots are excellent for the process-level understanding of aerosol-cloud interactions. Polluted cloud tracks recorded in satellite climatologies are great for estimating the average cloud response to aerosols. MODIS snapshots of polluted cloud tracks show relatively weak cloud water response to aerosols at various spatial scales. High-resolution analysis of South-East Atlantic shipping corridor shows partial off-set of the Twomey effect by decreased cloud water. Cloud fraction sometimes increases in the polluted cloud tracks and sometimes decreases compared to the nearby unpolluted clouds. The temporal evolution of cloud responses in pollution tracks estimated from geostationary SEVIRI data and meteorological conditions favourable for pollution track occurrence is presented. We expect that the utilisation of these real-world laboratories of aerosol impacts on clouds helps to improve global climate models’ physical parameterisations.</p>

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

<p>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 ±0.15 W m<sup>-2</sup> K<sup>-1</sup>. 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.</p>

scholarly journals Technical Note: On the use of nudging for aerosol-climate model intercomparison studies

2014 ◽  
Vol 14 (7) ◽  
pp. 10311-10343 ◽  
Author(s):  
K. Zhang ◽  
H. Wan ◽  
X. Liu ◽  
S. J. Ghan ◽  
G. J. Kooperman ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Cloud Amount ◽  
Temperature Fields ◽  
Specific Humidity ◽  
Radiation Budget ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol ◽  
The Impact

Abstract. Nudging is an assimilation technique widely used in the development and evaluation of climate models. Constraining the simulated wind and temperature fields using global weather reanalysis facilitates more straightforward comparison between simulation and observation, and reduces uncertainties associated with natural variabilities of the large-scale circulation. On the other hand, the forcing introduced by nudging can be strong enough to change the basic characteristics of the model climate. In the paper we show that for the Community Atmosphere Model version 5, due to the systematic temperature bias in the standard model and the sensitivity of simulated ice formation to anthropogenic aerosol concentration, nudging towards reanalysis results in substantial reductions in the ice cloud amount and the impact of anthropogenic aerosols on longwave cloud forcing. In order to reduce discrepancies between the nudged and unconstrained simulations and meanwhile take the advantages of nudging, two alternative experimentation methods are evaluated. The first one constrains only the horizontal winds. The second method nudges both winds and temperature, but replaces the long-term climatology of the reanalysis by that of the model. Results show that both methods lead to substantially improved agreement with the free-running model in terms of the top-of-atmosphere radiation budget and cloud ice amount. The wind-only nudging is more convenient to apply, and provides higher correlations of the wind fields, geopotential height and specific humidity between simulation and reanalysis. This suggests nudging the horizontal winds but not temperature is a good strategy for the investigation of aerosol indirect effects through ice clouds, since it provides well-constrained meteorology without strongly perturbing the model's mean climate.

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 Differences between downscaling with spectral and grid nudging using WRF

2012 ◽  
Vol 12 (1) ◽  
pp. 1191-1213 ◽  
Author(s):  
P. Liu ◽  
A. P. Tsimpidi ◽  
Y. Hu ◽  
B. Stone ◽  
A. G. Russell ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Regional Climate ◽  
Wrf Model ◽  
Global Climate Models ◽  
Small Scale ◽  
Data Set ◽  
Spectral Nudging

Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data using Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.

scholarly journals Global climatology and trends in convective environments from ERA5 and rawinsonde data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mateusz Taszarek ◽  
John T. Allen ◽  
Mattia Marchio ◽  
Harold E. Brooks
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Convective Available Potential Energy ◽  
Global Climate Models ◽  
Convective Precipitation ◽  
The Past ◽  
The Tropics ◽  
Rawinsonde Data

AbstractGlobally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing.

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