Simulation of subtropical marine stratocumulus clouds in convection-resolving models

2020 ◽  
Author(s):  
Christoph Heim ◽  
Laureline Hentgen ◽  
Nikolina Ban ◽  
Christoph Schär
Keyword(s):  
Cloud Cover ◽  
Large Scale ◽  
Climate Models ◽  
Inversion Layer ◽  
Heat Fluxes ◽  
Global Climate Models ◽  
The Other ◽  
Limited Area ◽  
Stratocumulus Clouds ◽  
The Mean

<p>Even though the complexity and resolution of global climate models (GCMs) has increased over the last decades, the inter-model spread of equilibrium climate sensitivity has not narrowed. The representation of subtropical low-level clouds and their associated radiative feedbacks in climate models still poses a major challenge. A fundamental problem underlying the simulation of such clouds is their multiscale nature. On the one hand, current GCMs allow to capture the large-scale processes but are too coarse to represent the mesoscale and microscale dynamical processes governing their formation and dissipation. On the other hand, large eddy simulations (LES) resolving the micro scale are bound to small domains and thus lack a robust representation of the large-scale flow and the mesoscale organisation of the clouds. Convection-resolving models (CRMs) are an attractive compromise between the former two since they allow for simulations at much higher resolution than in conventional GCMs and on larger domains than in LES.</p><p>Here we analyse how CRMs simulate stratocumulus decks and investigate causes for inter-model differences. We consider a set of ten CRMs (nine GCMs that are run at convection-resolving resolution during a short time period as part of the pioneering DYAMOND initiative, and the limited area model COSMO run by ourselves) used to simulate stratocumulus clouds over the South-East Atlantic during a 40 day period. The simulations cover a range of horizontal grid spacings between 5 and 1 km.</p><p>We find pronounced differences in the mean cloud cover among the analysed CRMs. In comparison to observed radiation (CERES), most of them underestimate cloud cover, in particular the low-lying stratus decks close to the African coast. Nevertheless, the simulated mesoscale cloud organisation is realistic and similar in the set of CRMs, with few exceptions showing organisation on larger scales than in the other models. In general, the simulated cloud field appears to be more sensitive to the model choice than to the horizontal resolution.</p><p>Despite the differences in the cloud cover, most models capture the subtropical inversion and its spatial structure relatively well. Therefore, differences in the inversion strengths do not suffice to explain variability in the simulated cloud cover fraction between models. However, we find a relation between the mean height of the stratocumulus layer (or inversion layer) and its cloud cover fraction: Models with higher inversions tend to simulate a higher cloud cover fraction, bringing them closer to observations. Similarly, stronger vertical mixing within the boundary layer and enhanced surface latent heat fluxes appear to be related to higher cloud cover. Such relations may help to determine the physical processes responsible for the differences among CRMs in the simulated stratocumulus field.</p>

scholarly journals Stratocumulus Clouds in Southeastern Pacific Simulated by Eight CMIP5–CFMIP Global Climate Models

2014 ◽  
Vol 27 (8) ◽  
pp. 3000-3022 ◽  
Author(s):  
Jia-Lin Lin ◽  
Taotao Qian ◽  
Toshiaki Shinoda
Keyword(s):  
Cloud Cover ◽  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Cloud Feedback ◽  
Global Climate Models ◽  
Temperature Structure ◽  
Model Intercomparison ◽  
Stratocumulus Clouds ◽  
Intercomparison Project

Abstract This study examines the stratocumulus clouds and associated cloud feedback in the southeast Pacific (SEP) simulated by eight global climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and Cloud Feedback Model Intercomparison Project (CFMIP) using long-term observations of clouds, radiative fluxes, cloud radiative forcing (CRF), sea surface temperature (SST), and large-scale atmosphere environment. The results show that the state-of-the-art global climate models still have significant difficulty in simulating the SEP stratocumulus clouds and associated cloud feedback. Comparing with observations, the models tend to simulate significantly less cloud cover, higher cloud top, and a variety of unrealistic cloud albedo. The insufficient cloud cover leads to overly weak shortwave CRF and net CRF. Only two of the eight models capture the observed positive cloud feedback at subannual to decadal time scales. The cloud and radiation biases in the models are associated with 1) model biases in large-scale temperature structure including the lack of temperature inversion, insufficient lower troposphere stability (LTS), and insufficient reduction of LTS with local SST warming, and 2) improper model physics, especially insufficient increase of low cloud cover associated with larger LTS. The two models that arguably do best at simulating the stratocumulus clouds and associated cloud feedback are the only ones using cloud-top radiative cooling to drive boundary layer turbulence.

scholarly journals A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales

2021 ◽  
Vol 14 (1) ◽  
pp. 73-90
Author(s):  
Hsi-Yen Ma ◽  
Chen Zhou ◽  
Yunyan Zhang ◽  
Stephen A. Klein ◽  
Mark D. Zelinka ◽  
...  
Keyword(s):  
Short Range ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Heat Fluxes ◽  
Model Errors ◽  
Moist Processes ◽  
The Mean ◽  
Mean State

Abstract. We present a multi-year short-range hindcast experiment and its experimental design for better evaluation of both the mean state and variability of atmospheric moist processes in climate models from diurnal to interannual timescales and facilitate model development. We used the Community Earth System Model version 1 as the base model and performed a suite of 3 d hindcasts initialized every day starting at 00:00 Z from 1997 to 2012. Three processes – the diurnal cycle of clouds during different cloud regimes over the central US, precipitation and diabatic heating associated with the Madden–Julian Oscillation (MJO), and the response of precipitation, surface radiative and heat fluxes, as well as zonal wind stress to sea surface temperature anomalies associated with the El Niño–Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from this experiment to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under well-controlled large-scale environment because these phenomena are either a result of interannual climate variability or only happen a few times in a given year (e.g., MJO, or cloud regime types). Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model's climate error directly comes from fast errors in the parameterizations of moist processes. As demonstrated here, model biases in the mean state and variability associated with parameterized moist processes usually develop within a few days and manifest within weeks to affect the simulations of large-scale circulation and ultimately the climate mean state and variability. Therefore, model developers can achieve additional useful understanding of the underlying problems in model physics by conducting a multi-year hindcast experiment.

scholarly journals The Response of the Northwest Atlantic Ocean to Climate Change

2020 ◽  
Vol 33 (2) ◽  
pp. 405-428 ◽  
Author(s):  
Michael A. Alexander ◽  
Sang-ik Shin ◽  
James D. Scott ◽  
Enrique Curchitser ◽  
Charles Stock
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Atlantic Ocean ◽  
Gulf Stream ◽  
Large Scale ◽  
Climate Models ◽  
Gulf Of Maine ◽  
Global Climate Models ◽  
The Other ◽  
Surface Salinity

AbstractROMS, a high-resolution regional ocean model, was used to study how climate change may affect the northwestern Atlantic Ocean. A control (CTRL) simulation was conducted for the recent past (1976–2005), and simulations with additional forcing at the surface and lateral boundaries, obtained from three different global climate models (GCMs) using the RCP8.5 scenario, were conducted to represent the future (2070–99). The climate change response was obtained from the difference between the CTRL and each of the three future simulations. All three ROMS simulations indicated large increases in sea surface temperatures (SSTs) over most of the domain except off the eastern U.S. seaboard resulting from weakening of the Gulf Stream. There are also substantial intermodel differences in the response, including a southward shift of the Gulf Stream in one simulation and a slight northward shift in the other two, with corresponding changes in eddy activity. The depth of maximum warming varied among the three simulations, resulting in differences in the bottom temperature response in coastal regions, including the Gulf of Maine and the West Florida Shelf. The surface salinity decreased in the northern part of the domain and increased in the south in all three experiments, although the freshening extended much farther south in one ROMS simulation relative to the other two, and also relative to the GCM that provided the large-scale forcing. Thus, while high resolution allows for a better representation of currents and bathymetry, the response to climate change can vary considerably depending on the large-scale forcing.

Could the cloud cover in future climate models be improved by incorporating the role of sea spray in surface heat fluxes?

2020 ◽  
Author(s):  
Yajuan Song ◽  
Fangli Qiao ◽  
Qi Shu ◽  
Jiping Liu ◽  
Ying Bao ◽  
...  
Keyword(s):  
Latent Heat ◽  
Cloud Cover ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Heat Fluxes ◽  
Global Climate Models ◽  
Shortwave Radiation ◽  
Sea Spray ◽  
Low Level

<p>Accurate cloud cover and radiative effect simulation remains a long-standing challenge for global climate models (GCMs). The Southern Ocean (SO) cloud cover is substantially underestimated by most GCMs. Therefore, too much shortwave radiation is absorbed by oceans, which causes an overly warm sea surface temperature (SST) bias over the SO. For the first time, sea spray effects on latent and sensible heat fluxes are considered in a climate model. The most notable sea spray impacts on heat fluxes occur over the SO, with anomalous latent heat fluxes up to -7.74 W m<sup>-2</sup>. Enhanced latent heat release lead to SST cooling. In addition, more clouds are formed over the SO to reflect excessive downward shortwave radiation, especially low-level clouds at 1.51% increments. Our results provide a feasible solution to mitigate the lack of low-level clouds and overly warm SST biases over the SO in GCMs.</p>

Distinct Patterns of Cloud Changes Associated with Decadal Variability and Their Contribution to Observed Cloud Cover Trends

2019 ◽  
Vol 32 (21) ◽  
pp. 7281-7301
Author(s):  
Yong-Jhih Chen ◽  
Yen-Ting Hwang ◽  
Mark D. Zelinka ◽  
Chen Zhou
Keyword(s):  
Cloud Cover ◽  
Large Scale ◽  
Climate Models ◽  
Decadal Variability ◽  
Global Climate ◽  
Global Climate Models ◽  
Central Pacific ◽  
The Pacific ◽  
Low Cloud ◽  
Low Cloud Cover

Abstract With the goal of understanding the relative roles of anthropogenic and natural factors in driving observed cloud trends, this study investigates cloud changes associated with decadal variability including the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO). In the preindustrial simulations of CMIP5 global climate models (GCMs), the spatial patterns and the vertical structures of the PDO-related cloud cover changes in the Pacific are consistent among models. Meanwhile, the models show consistent AMO impacts on high cloud cover in the tropical Atlantic, subtropical eastern Pacific, and equatorial central Pacific, and on low cloud cover in the North Atlantic and subtropical northeast Pacific. The cloud cover changes associated with the PDO and the AMO can be understood via the relationships between large-scale meteorological parameters and clouds on interannual time scales. When compared to the satellite records during the period of 1983–2009, the patterns of total and low cloud cover trends associated with decadal variability are significantly correlated with patterns of cloud cover trends in ISCCP observations. On the other hand, the pattern of the estimated greenhouse gas (GHG)-forced trends of total cloud cover differs from that related to decadal variability, and may explain the positive trends in the subtropical southeast Pacific, negative trends in the midlatitudes, and positive trends poleward of 50°N/S. In most models, the magnitude of the estimated decadal variability contribution to the observed cloud cover trends is larger than that contributed by GHG, suggesting the observed cloud cover trends are more closely related to decadal variability than to GHG-induced warming.

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.

scholarly journals A new world natural vegetation map for global change studies

2008 ◽  
Vol 80 (2) ◽  
pp. 397-408 ◽  
Author(s):  
David M. Lapola ◽  
Marcos D. Oyama ◽  
Carlos A. Nobre ◽  
Gilvan Sampaio
Keyword(s):  
Large Scale ◽  
New World ◽  
Climate Models ◽  
Global Climate ◽  
Horizontal Resolution ◽  
Vegetation Classification ◽  
Natural Vegetation ◽  
Global Climate Models ◽  
Vegetation Map ◽  
Vegetation Maps

We developed a new world natural vegetation map at 1 degree horizontal resolution for use in global climate models. We used the Dorman and Sellers vegetation classification with inclusion of a new biome: tropical seasonal forest, which refers to both deciduous and semi-deciduous tropical forests. SSiB biogeophysical parameters values for this new biome type are presented. Under this new vegetation classification we obtained a consensus map between two global natural vegetation maps widely used in climate studies. We found that these two maps assign different biomes in ca. 1/3 of the continental grid points. To obtain a new global natural vegetation map, non-consensus areas were filled according to regional consensus based on more than 100 regional maps available on the internet. To minimize the risk of using poor quality information, the regional maps were obtained from reliable internet sources, and the filling procedure was based on the consensus among several regional maps obtained from independent sources. The new map was designed to reproduce accurately both the large-scale distribution of the main vegetation types (as it builds on two reliable global natural vegetation maps) and the regional details (as it is based on the consensus of regional maps).

scholarly journals Relationship between Shortwave Cloud Radiative Forcing and Local Meteorological Variables Compared in Observations and Several Global Climate Models

2006 ◽  
Vol 19 (17) ◽  
pp. 4344-4359 ◽  
Author(s):  
Markus Stowasser ◽  
Kevin Hamilton
Keyword(s):  
Relative Humidity ◽  
Vertical Velocity ◽  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Grid Point ◽  
Global Climate Models ◽  
Coupled Models ◽  
Cloud Radiative Forcing ◽  
Cloud Forcing

Abstract The relations between local monthly mean shortwave cloud radiative forcing and aspects of the resolved-scale meteorological fields are investigated in hindcast simulations performed with 12 of the global coupled models included in the model intercomparison conducted as part of the preparation for Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). In particular, the connection of the cloud forcing over tropical and subtropical ocean areas with resolved midtropospheric vertical velocity and with lower-level relative humidity are investigated and compared among the models. The model results are also compared with observational determinations of the same relationships using satellite data for the cloud forcing and global reanalysis products for the vertical velocity and humidity fields. In the analysis the geographical variability in the long-term mean among all grid points and the interannual variability of the monthly mean at each grid point are considered separately. The shortwave cloud radiative feedback (SWCRF) plays a crucial role in determining the predicted response to large-scale climate forcing (such as from increased greenhouse gas concentrations), and it is thus important to test how the cloud representations in current climate models respond to unforced variability. Overall there is considerable variation among the results for the various models, and all models show some substantial differences from the comparable observed results. The most notable deficiency is a weak representation of the cloud radiative response to variations in vertical velocity in cases of strong ascending or strong descending motions. While the models generally perform better in regimes with only modest upward or downward motions, even in these regimes there is considerable variation among the models in the dependence of SWCRF on vertical velocity. The largest differences between models and observations when SWCRF values are stratified by relative humidity are found in either very moist or very dry regimes. Thus, the largest errors in the model simulations of cloud forcing are prone to be in the western Pacific warm pool area, which is characterized by very moist strong upward currents, and in the rather dry regions where the flow is dominated by descending mean motions.

Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

2016 ◽  
Vol 29 (17) ◽  
pp. 6065-6083 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Global Climate ◽  
Correlation Coefficients ◽  
Cloud Amount ◽  
Satellite Observation ◽  
Skill Score ◽  
Global Climate Models ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

scholarly journals Using Empirical Orthogonal Teleconnections to evaluate interannual rainfall variability over China in the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations

2017 ◽  
Author(s):  
Claudia Christine Stephan ◽  
Nicholas P. Klingaman ◽  
Pier Luigi Vidale ◽  
Andrew G. Turner ◽  
Marie-Estelle Demory ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Rainfall Variability ◽  
Reanalysis Data ◽  
Horizontal Resolution ◽  
Unified Model ◽  
Seasonal Precipitation ◽  
Climate Simulations ◽  
Interannual Rainfall Variability ◽  
The Mean

Abstract. Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyze the sensitivity to air-sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ~ 200, 90, and 40 km in the zonal direction at the equator, respectively) are analyzed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China, but improve with finer resolution and coupling. Empirical Orthogonal Teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air-sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal-mean timeseries. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

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