Longwave Cloud Forcing Research Articles

Eccentric orbits may enhance the habitability of Earth-like exoplanets

ABSTRACT The detection and characterization of Earth-like planets around Sun-like stars is an important goal of exoplanetary research, given their promise for hosting potentially habitable conditions. Key orbital parameters, such as eccentricity, can influence a planet’s climate response and, as a consequence, affect its potential habitability. Utilizing the Earth System Model – the Whole Atmosphere Community Climate Model (WACCM6), we simulated Earth-like exoplanets with two different orbital parameters: one circular ($e = 0$) and another highly eccentric ($e = 0.4$), both with zero obliquity but fixing the annual mean insolation. The highly eccentric case exhibits a 1.9 K warmer surface temperature due to lower surface and cloud albedo and a weaker longwave cloud forcing. Exploring the annual global mean climate difference, we analysed latitudinal and seasonal variations in hydrological cycle variables, such as sea ice, land snow, and clouds. Land habitability metrics based on temperature and precipitation reveal that the $e=0.4$ case has over 25 per cent more habitable land area for more than 80 per cent of its orbit, compared with the $e=0$ case. Additionally, the global circulation pattern shifts from a three-cell to a two-cell system in the $e=0.4$ case, expanding the Hadley cell to higher latitudes, enhancing meridional latent heat transport, and improving land habitability at higher latitudes. Our study suggests that Earth-like exoplanets with high eccentricity orbiting Sun-like stars may have greater land habitability than their circular counterparts, due to seasonally warmer surface temperatures and more evenly distributed precipitation over land.

Observations of Nighttime Clouds Over Chiba, Japan, Using Digital Cameras and Satellite Images

AbstractNighttime cloud cover (CC) is analyzed from the data observed for 34 months over Chiba, Japan, using continuously operated digital cameras. The normal camera works in the visible region, providing quantitative and temporal changes of nighttime CC. The second camera captures sky images without the near‐infrared cut filter, and by applying the thresholding method, we obtain information on the temporal changes of thin (pixel values between 72 and 115) and thick (>115) clouds. Comparing the CC from the visible camera with cloud fraction from Atmospheric Infrared Sounder (AIRS) shows that long‐term data from the camera are valuable for correcting the underestimation of the cloud fraction product from AIRS for low‐level clouds. Furthermore, a comparison between the near‐ground temperature and CC results in estimating the seasonal changes of feedback effects of upward thermal radiation from the ground. The current results reveal that thick clouds contribute to longwave cloud forcing (LWCF) below 100 Wm−2, while thin clouds with CC below 30% contribute to LWCF below 10.2 Wm−2.

Impact of Stochastic Entrainment in the NCAR CAM Deep Convection Parameterization on the Simulation of South Asian Summer Monsoon

Model simulations are highly sensitive to the formulation of the atmospheric mixing process or entrainment in the deep convective parameterizations used in their atmospheric component. In this paper, we have implemented stochastic entrainment in the deep convection scheme of NCAR CAM5 and analyzed the improvements in model simulation, focusing on the South Asian Summer Monsoon (SASM), as compared to the deterministic entrainment formulation in the default version of the model. Simulations using stochastic entrainment (StochCAM5) outperformed default model simulations (DefCAM5), as inferred from multiple metrics associated with the SASM. StochCAM5 significantly alleviated some of the longstanding SASM biases seen in DefCAM5, such as precipitation pattern and magnitude over the Arabian Sea and western Equatorial Indian ocean, early monsoon withdrawal, and the overestimation in the frequency of light precipitation and the underestimation in the frequency of large-to-extreme precipitation. Related SASM dynamical and thermodynamical features, such as Somali Jet, low-level westerly winds, and meridional tropospheric temperature gradient (MTTG), are improved in StochCAM5. Further, the simulation of monsoon intra-seasonal oscillation (MISO), Madden Julian Oscillation (MJO), and equatorial Kelvin waves are improved in StochCAM5. Many essential climate variables, such as shortwave and longwave cloud forcing, cloud cover, relative and specific humidity, and precipitable water, show significant improvement in StochCAM5.

Effects of marine organic aerosols as sources of immersion-mode ice-nucleating particles on high-latitude mixed-phase clouds

Abstract. Mixed-phase clouds are frequently observed in high-latitude regions and have important impacts on the surface energy budget and regional climate. Marine organic aerosol (MOA), a natural source of aerosol emitted over ∼ 70 % of Earth's surface, may significantly modify the properties and radiative forcing of mixed-phase clouds. However, the relative importance of MOA as a source of ice-nucleating particles (INPs) in comparison to mineral dust, and MOA's effects as cloud condensation nuclei (CCN) and INPs on mixed-phase clouds are still open questions. In this study, we implement MOA as a new aerosol species into the Community Atmosphere Model version 6 (CAM6), the atmosphere component of the Community Earth System Model version 2 (CESM2), and allow the treatment of aerosol–cloud interactions of MOA via droplet activation and ice nucleation. CAM6 reproduces observed seasonal cycles of marine organic matter at Mace Head and Amsterdam Island when the MOA fraction of sea spray aerosol in the model is assumed to depend on sea spray biology but fails when this fraction is assumed to be constant. Model results indicate that marine INPs dominate primary ice nucleation below 400 hPa over the Southern Ocean and Arctic boundary layer, while dust INPs are more abundant elsewhere. By acting as CCN, MOA exerts a shortwave cloud forcing change of −2.78 W m−2 over the Southern Ocean in the austral summer. By acting as INPs, MOA enhances the longwave cloud forcing by 0.35 W m−2 over the Southern Ocean in the austral winter. The annual global mean net cloud forcing changes due to CCN and INPs of MOA are −0.35 and 0.016 W m−2, respectively. These findings highlight the vital importance for Earth system models to consider MOA as an important aerosol species for the interactions of biogeochemistry, hydrological cycle, and climate change.

Contrasting Aerosol Effects on Long‐Wave Cloud Forcing in South East Asia and Amazon Simulated With Community Atmosphere Model Version 5.3

AbstractAerosols modify cloud microphysical and radiative properties and thus impact the shortwave and long‐wave cloud forcing (LWCF). This study first reports the finding of contrasting aerosol effects on LWCF in South East Asia and Amazon in the Community Atmosphere Model version 5.3 (CAM5.3), which corresponds to the sum of LW indirect and semi‐direct effects investigated in Ghan et al. (2012, http://doi.org/10.1175/jcli-d-11-00650.1). A series of numerical experiments are conducted to decompose the complex aerosol effects on LWCF. Our analysis indicates that the cooling (negative aerosol effects on LWCF) in Amazon is due mainly to the aerosol effects on warm clouds and the inhibition of vertical motion by the aerosol‐induced radiative cooling. In contrast, the warming (positive aerosol effects on LWCF) in South East Asia is due mainly to the aerosol effect on homogeneous freezing, thus, reducing the ice particle size and prolonging the existence of ice cloud. Our results emphasize that a comprehensive analysis of integrated aerosol effects on both warm and ice clouds is necessary for better understanding the aerosol effects on LWCF.

Uncertainty quantification based cloud parameterization sensitivity analysis in the NCAR community atmosphere model

Using uncertainty quantification techniques, we carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes. The LLNL PSUADE software is used to identify the most sensitive parameters by performing sensitivity analysis. Using Morris One-At-a-Time (MOAT) method, we find that the simulations of global annual mean total precipitation, convective, large-scale precipitation, cloud fractions (total, low, mid, and high), shortwave cloud forcing, longwave cloud forcing, sensible heat flux, and latent heat flux are very sensitive to the threshold-relative-humidity-for-stratiform-low-clouds (rhminl) and the auto-conversion-size-threshold-for-ice-to-snow left( {dcs} right). The seasonal and regime specific dependence of some parameters in the simulation of precipitation is also found for the global monsoons and storm track regions. Through sensitivity analysis, we find that the Somali jet strength and the tropical easterly jet associated with the south Asian summer monsoon (SASM) show a systematic dependence on dcs and rhminl. The timing of the withdrawal of SASM over India shows a monotonic increase (delayed withdrawal) with an increase in dcs. Overall, we find that rhminl, dcs, ai, and as are the most sensitive cloud parameters and thus are of high priority in the model tuning process, in order to reduce uncertainty in the simulation of past, present, and future climate.

The impact of performance filtering on climate feedbacks in a perturbed parameter ensemble

A key contribution to the latest generation of climate projections for the UK (UKCP18) was a perturbed parameter ensemble (PPE) of global coupled models based on HadGEM3-GC3.05. Together with 13 CMIP5 simulations, this PPE provides users with a dataset that samples modelling uncertainty and is ideal for use in impacts studies. Evaluations of global mean surface temperatures for this PPE have shown twenty-first century warming rates consistently at the top end of the CMIP5 range. Here we investigate one potential contributory factor to this lack of spread: that the methodology to select plausible members from a larger, related PPE of atmosphere-only experiments preferentially ruled out those predicted to have more negative climate feedbacks (i.e. lower climate sensitivities). We confirm that this is indeed the case. We show that performance in extratropical long-wave cloud forcing played a key role in this by constraining ice cloud parameters, which in turn constrained the feedback distribution (though causal links are not established). The relatively weak relationship driving this constraint is shown to arise from stronger relationships for the long-wave and short-wave cloud feedback components, which largely cancel out due to changes in tropical high clouds. Moreover, we show that the strength of these constraints is due to a structural bias in extratropical long-wave cloud forcing across the PPE. We discuss how choices made in the methodology to pick the plausible PPE members may result in an overly strong constraint when there is a structural bias and possible improvements to this methodology for the future.

Propagation of Error and the Reliability of Global Air Temperature Projections

The reliability of general circulation climate model (GCM) global air temperature projections is evaluated for the first time, by way of propagation of model calibration error. An extensive series of demonstrations show that GCM air temperature projections are just linear extrapolations of fractional greenhouse gas (GHG) forcing. Linear projections are subject to linear propagation of error. A directly relevant GCM calibration metric is the annual average 12.1% error in long-wave cloud forcing (LWCF) produced within CMIP5 climate models. This error is strongly pair-wise correlated across models, implying a source in deficient theory. Climate model LWCF error introduces an annual average ±4 Wm-2 uncertainty into the simulated tropospheric thermal energy flux. This annual ±4 Wm-2 simulation uncertainty is 114 larger than the annual average ~0.035 Wm-2 change in tropospheric thermal energy flux produced by increasing GHG forcing since 1979. Tropospheric thermal energy flux is the determinant of global air temperature. Uncertainty in simulated tropospheric thermal energy flux imposes uncertainty on projected air temperature. Propagation of LWCF thermal energy flux error through the historically relevant 1988 projections of GISS Model II scenarios A, B, and C, the IPCC SRES scenarios CCC, B1, A1B, and A2, and the RCP scenarios of the 2013 IPCC Fifth Assessment Report, uncovers a ±15 C uncertainty in air temperature at the end of a centennial-scale projection. Analogously large but previously unrecognized uncertainties must therefore exist in all the past and present air temperature projections and hindcasts of even advanced climate models. The unavoidable conclusion is that an anthropogenic air temperature signal cannot have been, nor presently can be, evidenced in climate observables.

Dust Radiative Effects on Climate by Glaciating Mixed‐Phase Clouds

AbstractMineral dust plays an important role in the primary formation of ice crystals in mixed‐phase clouds by acting as ice nucleating particles (INPs). It can influence the cloud phase transition and radiative forcing of mixed‐phase clouds, both of which are crucial to global energy budget and climate. In this study, we investigate the dust indirect effects on mixed‐phase clouds through heterogeneous ice nucleation with the U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). Dust and INP concentrations simulated from two versions of E3SM with three ice nucleation parameterizations were evaluated against observations in the Northern Hemisphere. Constrained by these observations, E3SM shows that dust INPs induce a global mean net cloud radiative effect of 0.05 to 0.26 W/m2 with the predominant warming appearing in the Northern Hemisphere midlatitudes. However, a cooling effect is found in the Arctic due to reduced longwave cloud forcing.

Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method

Abstract. Traditional trial-and-error tuning of uncertain parameters in global atmospheric general circulation models (GCMs) is time consuming and subjective. This study explores the feasibility of automatic optimization of GCM parameters for fast physics by using short-term hindcasts. An automatic workflow is described and applied to the Community Atmospheric Model (CAM5) to optimize several parameters in its cloud and convective parameterizations. We show that the auto-optimization leads to 10 % reduction of the overall bias in CAM5, which is already a well-calibrated model, based on a predefined metric that includes precipitation, temperature, humidity, and longwave/shortwave cloud forcing. The computational cost of the entire optimization procedure is about equivalent to a single 12-year atmospheric model simulation. The tuning reduces the large underestimation in the CAM5 longwave cloud forcing by decreasing the threshold relative humidity and the sedimentation velocity of ice crystals in the cloud schemes; it reduces the overestimation of precipitation by increasing the adjustment time in the convection scheme. The physical processes behind the tuned model performance for each targeted field are discussed. Limitations of the automatic tuning are described, including the slight deterioration in some targeted fields that reflect the structural errors of the model. It is pointed out that automatic tuning can be a viable supplement to process-oriented model evaluations and improvement.

Understanding Land–Atmosphere–Climate Coupling from the Canadian Prairie Dataset

Analysis of the hourly Canadian Prairie data for the past 60 years has transformed our quantitative understanding of land–atmosphere–cloud coupling. The key reason is that trained observers made hourly estimates of the opaque cloud fraction that obscures the sun, moon, or stars, following the same protocol for 60 years at all stations. These 24 daily estimates of opaque cloud data are of sufficient quality such that they can be calibrated against Baseline Surface Radiation Network data to yield the climatology of the daily short-wave, long-wave, and total cloud forcing (SWCF, LWCF and CF, respectively). This key radiative forcing has not been available previously for climate datasets. Net cloud radiative forcing changes sign from negative in the warm season, to positive in the cold season, when reflective snow reduces the negative SWCF below the positive LWCF. This in turn leads to a large climate discontinuity with snow cover, with a systematic cooling of 10 °C or more with snow cover. In addition, snow cover transforms the coupling between cloud cover and the diurnal range of temperature. In the warm season, maximum temperature increases with decreasing cloud, while minimum temperature barely changes; while in the cold season with snow cover, maximum temperature decreases with decreasing cloud, and minimum temperature decreases even more. In the warm season, the diurnal ranges of temperature, relative humidity, equivalent potential temperature, and the pressure height of the lifting condensation level are all tightly coupled to the opaque cloud cover. Given over 600 station-years of hourly data, we are able to extract, perhaps for the first time, the coupling between the cloud forcing and the warm season imbalance of the diurnal cycle, which changes monotonically from a warming and drying under clear skies to a cooling and moistening under cloudy skies with precipitation. Because we have the daily cloud radiative forcing, which is large, we are able to show that the memory of water storage anomalies, from precipitation and the snowpack, goes back many months. The spring climatology shows the memory of snowfall back through the entire winter, and the memory in summer, goes back to the months of snowmelt. Lagged precipitation anomalies modify the thermodynamic coupling of the diurnal cycle to the cloud forcing, and shift the diurnal cycle of the mixing ratio, which has a double peak. The seasonal extraction of the surface total water storage is a large damping of the interannual variability of precipitation anomalies in the growing season. The large land-use change from summer fallow to intensive cropping, which peaked in the early 1990s, has led to a coupled climate response that has cooled and moistened the growing season, lowering cloud-base, increasing equivalent potential temperature, and increasing precipitation. We show a simplified energy balance of the Prairies during the growing season, and its dependence on reflective cloud.

INVESTIGATION OF CLOUDS AND CLOUD RADIATIVE FORCING ON THE WINDWARD SIDE OF THE MADAGASCAR MOUNTAIN CHAINS

Abstract. Clouds affect the radiative energy balance of the earth–atmosphere system by reflecting and trapping the radiation. The cooling occurs over the earth by reflecting the incoming solar radiation and warming by trapping the outgoing longwave terrestrial radiation. In this paper an attempt has been carried out to understand the clouds and cloud radiative forcing over the windward side of the Madagascar mountain chain. The study was carried out using the Clouds and Earth’s Radiant Energy System (CERES) data June–September from 2000 to 2016. Over the windward side, clouds tend to cool whereas on the leeward side, clouds tend to warm marginally. During this period, peak value of shortwave cloud forcing and the longwave cloud forcing are −45 W m−2 and +15 W m−2 respectively. Generally, the clouds are restricted to low level in the windward side. We also examined the association between the cloud radiative forcing and cloud physical properties such as cloud optical depth, cloud, cloud top temperature and cover amount. The cloud optical depth (−0.74 correlation value) and cloud cover amount (−0.51 correlation value) show better correlation with net cloud radiative cooling. The surface pressure of the Madagascar is also correlated with the net cooling over the windward side.

Sensitivity Study of Anthropogenic Aerosol Indirect Forcing through Cirrus Clouds with CAM5 Using Three Ice Nucleation Parameterizations

Quantifying the radiative forcing due to aerosol–cloud interactions especially through cirrus clouds remains challenging because of our limited understanding of aerosol and cloud processes. In this study, we investigate the anthropogenic aerosol indirect forcing (AIF) through cirrus clouds using the Community Atmosphere Model version 5 (CAM5) with a state-of-the-art treatment of ice nucleation. We adopt a new approach to isolate anthropogenic AIF through cirrus clouds in which ice nucleation parameterization is driven by prescribed pre-industrial (PI) and present-day (PD) aerosols, respectively. Sensitivities of anthropogenic ice AIF (i.e., anthropogenic AIF through cirrus clouds) to different ice nucleation parameterizations, homogeneous freezing occurrence, and uncertainties in the cloud microphysics scheme are investigated. Results of sensitivity experiments show that the change (PD minus PI) in global annual mean longwave cloud forcing (i.e., longwave anthropogenic ice AIF) ranges from 0.14 to 0.35 W m–2, the change in global annual mean shortwave cloud forcing (i.e., shortwave anthropogenic ice AIF) from –0.47 to –0.20 W m–2, and the change in net cloud forcing from –0.12 to 0.05 W m–2. Our results suggest that different ice nucleation parameterizations are an important factor for the large uncertainty of anthropogenic ice AIF. Furthermore, improved understanding of the spatial and temporal occurrence characteristics of homogeneous freezing events and the mean states of cirrus cloud properties are also important for constraining anthropogenic ice AIF.

Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

Abstract. In this study, a regional coupled climate–chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting – Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006–2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2 m temperature (T2) in this study (with a mean bias of −0.6 °C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4 % in 2013) and O3 in summer (with an NMB of 18.2 % in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate–chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in east Asia (e.g., SWDOWN and T2 decreased by 21.8 W m−2 and 0.45 °C, respectively, and most pollutant concentrations increased by 4.8–9.5 % in January over China's major cities) were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled models over east Asia for regional climate and air quality modeling and to study the important climate–chemistry interactions. This work established a baseline for WRF-CMAQ simulations for a future period under the RCP4.5 climate scenario, which will be presented in a future paper.

Aerosol impacts on radiative and microphysical properties of clouds and precipitation formation

Through modifying the number concentration and size of cloud droplets, aerosols have intricate impacts on radiative and microphysical properties of clouds, which together influence precipitation processes. Aerosol-cloud interactions for a mid-latitude convective cloud system are investigated using a two-moment aerosol-aware bulk microphysical scheme implemented into the Weather Research and Forecasting (WRF) model. Three sensitivity experiments with initial identical dynamic and thermodynamic conditions, but different cloud-nucleating aerosol concentrations were conducted. Increased aerosol number concentration has resulted in more numerous cloud droplets of overall smaller sizes, through which the optical properties of clouds have been changed. While the shortwave cloud forcing is significantly increased in more polluted experiments, changes in the aerosol number concentration have negligible impacts on the longwave cloud forcing. For the first time, it is found that polluted clouds have higher cloud base heights, the feature that is caused by more surface cooling due to a higher shortwave cloud forcing, as well as a drier boundary layer in the polluted experiment compared to the clean. The polluted experiment was also associated with a higher liquid water content (LWC), caused by an increase in the number of condensation of water vapor due to higher concentration of hygroscopic aerosols acting as condensation nuclei. The domain-averaged accumulated precipitation is little changed under both polluted and clean atmosphere. Nevertheless, changes in the rate of precipitation are identified, such that under polluted atmosphere light rain is reduced, while both moderate and heavy rain are intensified, confirming the fact that if an ample influx of water vapor exists, an increment of hygroscopic aerosols can increase the amount of precipitation.

Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1

Abstract. The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 with the Carbon Bond 2005 (CB05) gas-phase mechanism is evaluated for its first decadal application during 2001–2010 using the Representative Concentration Pathway 8.5 (RCP 8.5) emissions to assess its capability and appropriateness for long-term climatological simulations. The initial and boundary conditions are downscaled from the modified Community Earth System Model/Community Atmosphere Model (CESM/CAM5) v1.2.2. The meteorological initial and boundary conditions are bias-corrected using the National Center for Environmental Protection's Final (FNL) Operational Global Analysis data. Climatological evaluations are carried out for meteorological, chemical, and aerosol–cloud–radiation variables against data from surface networks and satellite retrievals. The model performs very well for the 2 m temperature (T2) for the 10-year period, with only a small cold bias of −0.3 °C. Biases in other meteorological variables including relative humidity at 2 m, wind speed at 10 m, and precipitation tend to be site- and season-specific; however, with the exception of T2, consistent annual biases exist for most of the years from 2001 to 2010. Ozone mixing ratios are slightly overpredicted at both urban and rural locations with a normalized mean bias (NMB) of 9.7 % but underpredicted at rural locations with an NMB of −8.8 %. PM2.5 concentrations are moderately overpredicted with an NMB of 23.3 % at rural sites but slightly underpredicted with an NMB of −10.8 % at urban/suburban sites. In general, the model performs relatively well for chemical and meteorological variables, and not as well for aerosol–cloud–radiation variables. Cloud-aerosol variables including aerosol optical depth, cloud water path, cloud optical thickness, and cloud droplet number concentration are generally underpredicted on average across the continental US. Overpredictions of several cloud variables over the eastern US result in underpredictions of radiation variables (such as net shortwave radiation – GSW – with a mean bias – MB – of −5.7 W m−2) and overpredictions of shortwave and longwave cloud forcing (MBs of ∼ 7 to 8 W m−2), which are important climate variables. While the current performance is deemed to be acceptable, improvements to the bias-correction method for CESM downscaling and the model parameterizations of cloud dynamics and thermodynamics, as well as aerosol–cloud interactions, can potentially improve model performance for long-term climate simulations.

The effect of large‐scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized GCM

AbstractThe effect of global climate model (GCM) time step—which also controls how frequently global and embedded cloud resolving scales are coupled—is examined in the Superparameterized Community Atmosphere Model ver 3.0. Systematic bias reductions of time‐mean shortwave cloud forcing (∼10 W/m2) and longwave cloud forcing (∼5 W/m2) occur as scale coupling frequency increases, but with systematically increasing rainfall variance and extremes throughout the tropics. An overarching change in the vertical structure of deep tropical convection, favoring more bottom‐heavy deep convection as a global model time step is reduced may help orchestrate these responses. The weak temperature gradient approximation is more faithfully satisfied when a high scale coupling frequency (a short global model time step) is used. These findings are distinct from the global model time step sensitivities of conventionally parameterized GCMs and have implications for understanding emergent behaviors of multiscale deep convective organization in superparameterized GCMs. The results may also be useful for helping to tune them.

A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model

Abstract. Most global climate models parameterize separate cloud types using separate parameterizations. This approach has several disadvantages, including obscure interactions between parameterizations and inaccurate triggering of cumulus parameterizations. Alternatively, a unified cloud parameterization uses one equation set to represent all cloud types. Such cloud types include stratiform liquid and ice cloud, shallow convective cloud, and deep convective cloud. Vital to the success of a unified parameterization is a general interface between clouds and microphysics. One such interface involves drawing Monte Carlo samples of subgrid variability of temperature, water vapor, cloud liquid, and cloud ice, and feeding the sample points into a microphysics scheme. This study evaluates a unified cloud parameterization and a Monte Carlo microphysics interface that has been implemented in the Community Atmosphere Model (CAM) version 5.3. Model computational expense is estimated, and sensitivity to the number of subcolumns is investigated. Results describing the mean climate and tropical variability from global simulations are presented. The new model shows a degradation in precipitation skill but improvements in shortwave cloud forcing, liquid water path, long-wave cloud forcing, precipitable water, and tropical wave simulation.

Application of an Online-Coupled Regional Climate Model, WRF-CAM5, over East Asia for Examination of Ice Nucleation Schemes: Part II. Sensitivity to Heterogeneous Ice Nucleation Parameterizations and Dust Emissions

Aerosol particles can affect cloud microphysical properties by serving as ice nuclei (IN). Large uncertainties exist in the ice nucleation parameterizations (INPs) used in current climate models. In this Part II paper, to examine the sensitivity of the model predictions to different heterogeneous INPs, WRF-CAM5 simulation using the INP of Niemand et al. (N12) [1] is conducted over East Asia for two full years, 2006 and 2011, and compared with simulation using the INP of Meyers et al. (M92) [2], which is the original INP used in CAM5. M92 calculates the nucleated ice particle concentration as a function of ice supersaturation, while N12 represents the nucleated ice particle concentration as a function of temperature and the number concentrations and surface areas of dust particles. Compared to M92, the WRF-CAM5 simulation with N12 produces significantly higher nucleated ice crystal number concentrations (ICNCs) in the northern domain where dust sources are located, leading to significantly higher cloud ice number and mass concentrations and ice water path, but the opposite is true in the southern domain where temperatures and moistures play a more important role in ice formation. Overall, the simulation with N12 gives lower downward shortwave radiation but higher downward longwave radiation, cloud liquid water path, cloud droplet number concentrations, and cloud optical depth. The increase in cloud optical depth and the decrease in downward solar flux result in a stronger shortwave and longwave cloud forcing, and decreases temperature at 2-m and precipitation. Changes in temperature and radiation lower surface concentrations of OH, O3, SO42−, and PM2.5, but increase surface concentrations of CO, NO2, and SO2 over most of the domain. By acting as cloud condensation nuclei (CCN) and IN, dust particles have different impacts on cloud water and ice number concentrations, radiation, and temperature at 2-m and precipitation depending on whether the dominant role of dust is CCN or IN. These results indicate the importance of the heterogeneous ice nucleation treatments and dust emissions in accurately simulating regional climate and air quality.

Low clouds suppress Arctic air formation and amplify high-latitude continental winter warming

High-latitude continents have warmed much more rapidly in recent decades than the rest of the globe, especially in winter, and the maintenance of warm, frost-free conditions in continental interiors in winter has been a long-standing problem of past equable climates. We use an idealized single-column atmospheric model across a range of conditions to study the polar night process of air mass transformation from high-latitude maritime air, with a prescribed initial temperature profile, to much colder high-latitude continental air. We find that a low-cloud feedback--consisting of a robust increase in the duration of optically thick liquid clouds with warming of the initial state--slows radiative cooling of the surface and amplifies continental warming. This low-cloud feedback increases the continental surface air temperature by roughly two degrees for each degree increase of the initial maritime surface air temperature, effectively suppressing Arctic air formation. The time it takes for the surface air temperature to drop below freezing increases nonlinearly to ∼ 10 d for initial maritime surface air temperatures of 20 °C. These results, supplemented by an analysis of Coupled Model Intercomparison Project phase 5 climate model runs that shows large increases in cloud water path and surface cloud longwave forcing in warmer climates, suggest that the "lapse rate feedback" in simulations of anthropogenic climate change may be related to the influence of low clouds on the stratification of the lower troposphere. The results also indicate that optically thick stratus cloud decks could help to maintain frost-free winter continental interiors in equable climates.