NCAR Community Atmospheric Model Version Research Articles

Sharp decline of dust events induces regional wetting over arid and semi-arid Northwest China in the NCAR Community atmosphere model

Multiple lines of observational evidence have indicated a significant wetting over the arid and semi-arid Northwest China (NWC) during recent decades, coinciding with a simultaneous sharp decline of dust events. Although recent studies have attributed NWC wetting to different anthropogenic and natural forcings, the mechanisms are not definitive and the regional wetting has been greatly underestimated in the Coupled Model Intercomparison Project historical simulations. Based on sensitivity experiments with different dust emission amounts using the NCAR Community Atmospheric Model version 5 (CAM5), here we find that decreasing dusts exert significant impacts on mixed-phase clouds through reducing the concentration of ice nucleating particles, increase the NWC precipitation and thus induce regional wetting through enhancing convection precipitation. A possible convection invigoration mechanism whereby the atmospheric vertical temperature gradient and convective instability are strengthened by reduced dusts, leading to convection invigoration and increased precipitation. These results are reinforced by simulations over the dust region in North Africa where mixed-phase and ice clouds are rare and reduced dusts do not increase precipitation. This study highlights the possible mechanism of dust-ice cloud interactions in recent NWC wetting and future regional climate change.

Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations

Abstract. There has been a growing concern that most climate models predict precipitation that is too frequent, likely due to lack of reliable subgrid variability and vertical variations in microphysical processes in low-level warm clouds. In this study, the warm-cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017–2018. The 8-month single-column model (SCM) simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary layer cloud structure, major macrophysical properties, and their transition. The improvement in warm-cloud properties from the Community Atmospheric Model 5 and 6 (CAM5 to CAM6) physics can be found through comparison with the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft-observed ones in the summer but are overestimated by a factor of 2 in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations in droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of subgrid variations in cloud properties in CAM6 cloud microphysics, which further suppresses the scheme's sensitivity to individual warm-rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces an aerosol indirect effect that is too strong. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations.

Climate Impacts of Convective Cloud Microphysics in NCAR CAM5

Abstract We improved the treatments of convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3) by 1) implementing new terminal velocity parameterizations for convective ice and snow particles, 2) adding graupel microphysics, 3) considering convective snow detrainment, and 4) enhancing rain initiation and generation rate in warm clouds. We evaluated the impacts of improved microphysics on simulated global climate, focusing on simulated cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation. Compared to CAM5.3 with the default convective microphysics, the too-strong cloud shortwave radiative forcing due primarily to excessive convective cloud liquid is largely alleviated over the tropics and midlatitudes after rain initiation and generation rate is enhanced, in better agreement with the CERES-EBAF estimates. Geographic distributions of graupel occurrence are reasonably simulated over continents; whereas the graupel occurrence remains highly uncertain over the oceanic storm-track regions. When evaluated against the CloudSat–CALIPSO estimates, the overestimation of convective ice mass is alleviated with the improved convective ice microphysics, among which adding graupel microphysics and the accompanying increase in hydrometeor fall speed play the most important role. The probability distribution function (PDF) of rainfall intensity is sensitive to warm rain processes in convective clouds, and enhancement in warm rain production shifts the PDF toward heavier precipitation, which agrees better with the TRMM observations. Common biases of overestimating the light rain frequency and underestimating the heavy rain frequency in GCMs are mitigated.

Description and Evaluation of a New Deep Convective Cloud Model Considering In‐Cloud Inhomogeneity

AbstractConvections still need to be parameterized in general circulation models (GCMs) in the coming decades. Performances of GCMs are significantly influenced by the convection schemes used. In contrast to most conventional cloud models that ignore in‐cloud inhomogeneities, a new convective cloud model explicitly considering in‐cloud inhomogeneities is developed. The new model adopts a single bulk plume approach, but divides the plume into a series of interacting sub‐plumes in order to mimic the transition from the plume core to its edges. We implemented the new cloud model in NCAR Community Atmosphere Model version 5.0 (CAM5) and evaluated its performance. Single‐column tests show a stronger mass flux profile and low‐level detrainment with the new model. In global simulations, the long‐lasting wet bias in the tropical free troposphere of CAM5 is alleviated. Spatial pattern and intensity distribution of precipitation is improved, but the global hydrological cycle is over‐enhanced. The diurnal cycle of tropical precipitation is also better captured though the precipitation peak still occurs earlier than the observations and the amplitude of the cycle tends to weaken. MJO simulation is slightly improved with the new scheme. In addition, the new cloud model generated more tropical cyclones, in better agreement with observations.

Implementation and Evaluation of a Double-Plume Convective Parameterization in NCAR CAM5

AbstractPerformance of global climate models (GCMs) is strongly affected by the cumulus parameterization (CP) used. Similar to the approach in GFDL AM4, a double-plume CP, which unifies the deep and shallow convection in one framework, is implemented and tested in the NCAR Community Atmospheric Model version 5 (CAM5). Based on the University of Washington (UW) shallow convection scheme, an additional plume was added to represent the deep convection. The shallow and deep plumes share the same cloud model, but use different triggers, fractional mixing rates, and closures. The scheme was tested in single-column, short-term hindcast, and AMIP simulations. Compared with the default combination of the Zhang–McFarlane scheme and UW scheme in CAM5, the new scheme tends to produce a top-heavy mass flux profile during the active monsoon period in the single-column simulations. The scheme increases the intensity of tropical precipitation, closer to TRMM observations. The new scheme increased subtropical marine boundary layer clouds and high clouds over the deep tropics, both in better agreement with observations. Sensitivity tests indicate that regime-dependent fractional entrainment rates of the deep plume are desired to improve tropical precipitation distribution and upper troposphere temperature. This study suggests that a double-plume approach is a promising way to combine shallow and deep convections in a unified framework.

Understanding the Roles of Convective Trigger Functions in the Diurnal Cycle of Precipitation in the NCAR CAM5

AbstractThe wrong diurnal cycle of precipitation is a common weakness of current global climate models (GCMs). To improve the simulation of the diurnal cycle of precipitation and understand what physical processes control it, we test a convective trigger function described in Xie et al. with additional optimizations in the NCAR Community Atmosphere Model version 5 (CAM5). The revised trigger function consists of three modifications: 1) replacing the convective available potential energy (CAPE) trigger with a dynamic CAPE (dCAPE) trigger, 2) allowing convection to originate above the top of planetary boundary layer [i.e., the unrestricted air parcel launch level (ULL)], and 3) optimizing the entrainment rate and threshold value of the dynamic CAPE generation rate for convection onset based on observations. Results from 1° resolution simulations show that the revised trigger can alleviate the long-standing GCM problem of too early maximum precipitation during the day and missing the nocturnal precipitation peak that is observed in many regions, including the U.S. southern Great Plains (SGP). The revised trigger also improves the simulation of the propagation of precipitation systems downstream of the Rockies and the Amazon region. A further composite analysis over the SGP unravels the mechanisms through which the revised trigger affects convection. Additional sensitivity tests show that both the peak time and the amplitude of the diurnal cycle of precipitation are sensitive to the entrainment rate and dCAPE threshold values.

Analysis of climate sensitivity via high-dimensional principal component regression

Uncertainty in the sensitivity of global mean air temperature to changes in atmospheric CO2 concentration arises from factors controlling the response of cloud amounts. Here, we present an analysis of these factors within an ensemble consisting of 164 variations of the NCAR Community Atmosphere Model version 3.1, each of which differs slightly in their treatment of convection and cloud radiative properties. The problem is to uniquely relate observable aspects of model output, which are inherently high dimensional such as the 64,000 spatially mapped values of 11 fields generated from each CAM3.1 model to a scalar prediction of each model’s sensitivity to CO2 forcing. We approach this problem using principal component regression and provide maps of observable predictors. Furthermore, based on this approach, we develop prediction intervals and show that they closely match their advertised coverage.

Importance of the resolution of surface topography vis-à-vis atmospheric and surface processes in the simulation of the climate of Himalaya-Tibet highland

As Himalaya-Tibet Highland (HTH), a spatially extensive and complex terrain, plays influential roles in the regional and global climate, its representation in climate models is a decisive factor in climate simulations. It is established that higher spatial resolution improves the simulation of several seasonal mean features over this region, but how much improvement comes from the better representation of surface topography and how much comes from the better modeling of the atmospheric and surface processes are still not clear. To understand this, three sets of 6-member ensemble simulations are conducted using the NCAR Community Atmosphere Model version 5.1 (CAM5.1) at: (1) 1.9° × 2.5° resolution (Coarse), (2) 0.47° × 0.63° resolution (Fine), and (3) topography is prescribed at the Coarse resolution and rest of the model processes are computed at the Fine resolution (Hybrid). The Coarse resolution overlooks most of the intricate features of the topography with a severe bias of ~ 1–2 km but Fine resolution does a much better job in their representations. Surface air temperatures are found to be strongly dependent on the resolution of topography and therefore it is desirable to resolve topography in models for the realistic simulation and projection of temperatures. Rest of the variables, viz. seasonal mean, seasonal cycle and probability distribution of precipitation; tropospheric temperature and moisture; Tibetan anticyclone, show remarkable improvements with the increase in resolution of the atmospheric and surface processes. Influence of the resolution of topography is found to be limited to the model levels adjacent to the surface, and for higher model levels, the resolution of the atmospheric and surface processes is noted to play a more crucial role.

A Diagnostic PDF Cloud Scheme to Improve Subtropical Low Clouds in NCAR Community Atmosphere Model (CAM5)

AbstractLow clouds strongly impact the radiation budget of the climate system, but their simulation in most GCMs has remained a challenge, especially over the subtropical stratocumulus region. Assuming a Gaussian distribution for the subgrid‐scale total water and liquid water potential temperature, a new statistical cloud scheme is proposed and tested in NCAR Community Atmospheric Model version 5 (CAM5). The subgrid‐scale variance is diagnosed from the turbulent and shallow convective processes in CAM5. The approach is able to maintain the consistency between cloud fraction and cloud condensate and thus alleviates the adjustment needed in the default relative humidity‐based cloud fraction scheme. Short‐term forecast simulations indicate that low cloud fraction and liquid water content, including their diurnal cycle, are improved due to a proper consideration of subgrid‐scale variance over the southeastern Pacific Ocean region. Compared with the default cloud scheme, the new approach produced the mean climate reasonably well with improved shortwave cloud forcing (SWCF) due to more reasonable low cloud fraction and liquid water path over regions with predominant low clouds. Meanwhile, the SWCF bias over the tropical land regions is also alleviated. Furthermore, the simulated marine boundary layer clouds with the new approach extend further offshore and agree better with observations. The new approach is able to obtain the top of atmosphere (TOA) radiation balance with a slightly alleviated double ITCZ problem in preliminary coupled simulations. This study implies that a close coupling of cloud processes with other subgrid‐scale physical processes is a promising approach to improve cloud simulations.

Distinct Contributions of Ice Nucleation, Large‐Scale Environment, and Shallow Cumulus Detrainment to Cloud Phase Partitioning With NCAR CAM5

AbstractMixed‐phase clouds containing both liquid droplets and ice particles occur frequently at high latitudes and in the midlatitude storm track regions. Simulations of the cloud phase partitioning between liquid and ice hydrometeors in state‐of‐the‐art global climate models are still associated with large biases. In this study, the phase partitioning in terms of liquid mass phase ratio (MPRliq, defined as the ratio of liquid mass to total condensed water mass) simulated from the NCAR Community Atmosphere Model version 5 (CAM5) is evaluated against the observational data from A‐Train satellite remote sensors. Modeled MPRliq is significantly lower than observations on the global scale, especially in the Southern Hemisphere (e.g., Southern Ocean and the Antarctic). Sensitivity tests with CAM5 are conducted to investigate the distinct contributions of heterogeneous ice nucleation, shallow cumulus detrainment, and large‐scale environment (e.g., winds, temperature, and water vapor) to the low MPRliq biases. Our results show that an aerosol‐aware ice nucleation parameterization increases the MPRliq especially at temperatures colder than −20°C and significantly improves the model agreements with observations in the Polar regions in summer. The decrease of threshold temperature over which all detrained cloud water is liquid from 268 to 253 K enhances the MPRliq and improves the MPRliq mostly over the Southern Ocean. By constraining water vapor in CAM5 toward reanalysis, modeled low biases in many geographical regions are largely reduced through a significant decrease of cloud ice mass mixing ratio.

Influence of altered low cloud parameterizations for seasonal variation of Arctic cloud amount on climate feedbacks

This study investigates the alteration of climate feedbacks due to overestimated wintertime low-level cloud amount bias over the Arctic region (60°N–90°N) in a climate model. The climate feedback was quantitatively examined through radiative kernels that are pre-calculated radiative responses of climate variables to doubling of carbon dioxide concentration in NCAR Community Atmosphere Model version 3 (CAM3). Climate models have various annual cycle of the Arctic cloud amount at the low-level particularly with large uncertainty in winter and CAM3 may tend to overestimate the Arctic low-level cloud. In this study, the seasonal variation of low-level cloud amount was modified by reducing the wintertime cloud amount by up to 35 %, and then compared with the original without seasonal variation. Thus, we investigate how that bias may affect climate feedbacks and the projections of future Arctic warming. The results show that the decrease in low-level cloud amount slightly affected the radiation budgets because of a small amount of incident solar insolation in winter, but considerably changed water vapor and temperature profiles. Consequently, the most distinctive was decreases in water vapor feedback and contribution of heat transport (by −0.20 and −0.55 W m−2 K−1, respectively) and increases in the lapse rate feedback and cloud feedback (by 0.13 and 0.58 W m−2 K−1, respectively) during winter in this model experiment. This study suggests that the change in Arctic cloud amount effectively reforms the contributions of individual climate feedbacks to Arctic climate system and leads to opposing effects on different feedbacks, which cancel out in the model.

Effects of Convective Microphysics Parameterization on Large-Scale Cloud Hydrological Cycle and Radiative Budget in Tropical and Midlatitude Convective Regions

Abstract A two-moment microphysics scheme for deep convection was previously implemented in the NCAR Community Atmosphere Model version 5 (CAM5) by Song et al. The new scheme improved hydrometeor profiles in deep convective clouds and increased deep convective detrainment, reducing the negative biases in low and midlevel cloud fraction and liquid water path compared to observations. Here, the authors examine in more detail the impacts of this improved microphysical representation on regional-scale water and radiation budgets. As a primary source of cloud water for stratiform clouds is detrainment from deep and shallow convection, the enhanced detrainment leads to larger stratiform cloud fractions, higher cloud water content, and more stratiform precipitation over the ocean, particularly in the subtropics where convective frequency is also increased. This leads to increased net cloud radiative forcing. Over land regions, cloud amounts are reduced as a result of lower relative humidity, leading to weaker cloud forcing and increased OLR. Comparing the water budgets to cloud-resolving model simulations shows improvement in the partitioning between convective and stratiform precipitation, though the deep convection is still too active in the GCM. The addition of convective microphysics leads to an overall improvement in the regional cloud water budgets.

Regional Assessments of Low Clouds against Large-Scale Stability in CAM5 and CAM-CLUBB Using MODIS and ERA-Interim Reanalysis Data

Abstract Daily gridded cloud data from MODIS and ERA-Interim reanalysis have been assessed to examine variations of low cloud fraction (CF) and cloud-top height and their dependence on large-scale dynamics and a measure of stability. To assess the stratocumulus (Sc) to cumulus (Cu) transition (STCT), the observations are used to evaluate two versions of the NCAR Community Atmosphere Model version 5 (CAM5), both the base model and a version that has implemented a new subgrid low cloud parameterization, Cloud Layers Unified by Binormals (CLUBB). The ratio of moist static energy (MSE) at 700–1000 hPa (MSEtotal) is a skillful predictor of median CF of screened low cloud grids. Values of MSEtotal less than 1.00 represent either conditionally or absolutely unstable layers, and probability density functions of CF suggest a preponderance of either trade Cu (median CF < 0.4) or transitional Sc clouds (0.4 < CF < 0.9). With increased stability (MSEtotal > 1.00), an abundance of overcast or nearly overcast low clouds exists. While both MODIS and ERA-Interim indicate a fairly smooth transition between the low cloud regimes, CAM5-Base simulates an abrupt shift from trade Cu to Sc, with trade Cu covering both too much area and occurring over excessively strong stabilities. In contrast, CAM-CLUBB simulates a smoother trade Cu to Sc transition (CTST) as a function of MSEtotal, albeit with too extensive coverage of overcast Sc in the primary northeastern Pacific subsidence region. While the overall CF distribution in CAM-CLUBB is more realistic, too few transitional clouds are simulated for intermediate MSEtotal compared to observations.

Estimating the regional climate responses over river basins to changes in tropical sea surface temperature patterns

We investigate how to identify and assess teleconnection signals between anomalous patterns of sea surface temperature (SST) changes and climate variables related to hydrologic impacts over different river basins. The regional climate sensitivity to tropical SST anomaly patterns is examined through a linear relationship given by the global teleconnection operator (GTO, also generally called a sensitivity matrix or an empirical Green’s function). We assume that the GTO defines a multilinear relation between SST forcing and regional climate response of a target area. The sensitivities are computed based on data from a large ensemble of simulations using the NCAR Community Atmospheric Model version 3.1 (CAM 3.1). The linear approximation is evaluated by comparing the linearly reconstructed response with both the results from the full non-linear atmospheric model and observational data. The results show that the linear approximation can capture regional climate variability that the CAM 3.1 AMIP-style simulations produce at seasonal scales for multiple river basins. The linear method can be used potentially for estimating drought conditions, river flow forecasting, and agricultural water management problems.

Atmospheric impacts of an Arctic sea ice minimum as seen in the Community Atmosphere Model

AbstractThere is growing recognition that reductions in Arctic sea ice extent will influence patterns of atmospheric circulation both within and beyond the Arctic. We explore the impact of 2007 ice conditions (the second lowest Arctic sea ice extent in the satellite era) on atmospheric circulation and surface temperatures and fluxes through a series of model experiments with the NCAR Community Atmospheric Model version 3 (CAM3). Two 30‐year simulations were performed; one using climatological sea ice extent for the end of the 20th century and other using observed sea ice extent from 2007. Circulation differences over the Northern Hemisphere were most prominent during autumn and winter with lower sea level pressure (SLP) and tropospheric pressure simulated over much of the Arctic for the 2007 sea ice experiment. The atmospheric response to 2007 ice conditions was much weaker during summer, with negative SLP anomalies simulated from Alaska across the Arctic to Greenland. Higher temperatures and larger surface fluxes to the atmosphere in areas of anomalous open water were also simulated. CAM3 experiment results were compared to observed SLP anomalies from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data. The observed SLP anomalies during spring are nearly opposite to those simulated. In summer, large differences were shown between the observed and simulated SLP also, suggesting that the sea ice conditions in the months preceding and during the summer of 2007 were not responsible for creating an atmospheric circulation pattern which favoured the large observed sea ice loss. The simulated and observed atmospheric circulation anomalies during autumn and winter were more similar than spring and summer, with the exception of a strong high pressure system in the Beaufort Sea which was not simulated, suggesting that the forced atmospheric response to reduced sea ice was in part responsible for the observed atmospheric circulation anomalies during autumn and winter.

Impacts of Snow Initialization on Subseasonal Forecasts of Surface Air Temperature for the Cold Season

Abstract The present study examines the impacts of snow initialization on surface air temperature by a number of ensemble seasonal predictability experiments using the NCAR Community Atmosphere Model version 3 (CAM3) AGCM with and without snow initialization. The study attempts to isolate snow signals on surface air temperature. In this preliminary study, any effects of variations in sea ice extent are ignored and do not explicitly identify possible impacts on atmospheric circulation. The Canadian Meteorological Center (CMC) daily snow depth analysis was used in defining initial snow states, where anomaly rescaling was applied in order to account for the systematic bias of the CAM3 snow depth with respect to the CMC analysis. Two suites of seasonal (3 months long) ensemble hindcasts starting at each month in the colder part of the year (September–April) with and without the snow initialization were performed for 12 recent years (1999–2010), and the predictability skill of surface air temperature was estimated. Results show that considerable potential predictability increases up to 2 months ahead can be attained using snow initialization. Relatively large increases are found over East Asia, western Russia, and western Canada in the later part of this period. It is suggested that the predictability increases are sensitive to the strength of snow–albedo feedback determined by given local climate conditions; large gains tend to exist over the regions of strong snow–albedo feedback. Implications of these results for seasonal predictability over the extratropical Northern Hemisphere and future direction for this research are discussed.

A numerical study of the effect of different aerosol types on East Asian summer clouds and precipitation

In this study, the anthropogenic aerosol impact on the summer monsoon clouds and precipitation in East Asia is investigated using the NCAR Community Atmospheric Model version 5 (CAM5), a state-of-the-art climate model considering aerosol direct, semi-direct and indirect effects. The effects of all anthropogenic aerosols, and anthropogenic black carbon (BC), sulfate, and primary organic matter (POM) are decomposed from different sensitivity simulations. Anthropogenic sulfate and POM reduce the solar flux reaching the surface directly by scattering the solar radiation, and indirectly by increasing the cloud droplet number concentration and cloud liquid water path over East China. The surface air temperature over land is reduced, and the precipitation in North China is suppressed. Unlike anthropogenic sulfate and POM, anthropogenic BC does not have a significant effect on the air temperature at the surface, because of the reduction of the cloud liquid water path and the weakening of shortwave cloud forcing by its semi-direct effect. The anthropogenic BC strengthens the southwesterly wind over South China and leads to stronger deep convection at the 25°N–30°N latitudinal band. The effect of all anthropogenic aerosols on air temperature, clouds, and precipitation is not a linear summation of effects from individual anthropogenic sulfate, BC and POM. Overall all anthropogenic aerosols suppress the precipitation in North China and enhance the precipitation in South China and adjacent ocean regions.

Evaluation of Microphysics Parameterization for Convective Clouds in the NCAR Community Atmosphere Model CAM5

Abstract A physically based two-moment microphysics parameterization scheme for convective clouds is implemented in the NCAR Community Atmosphere Model version 5 (CAM5) to improve the representation of convective clouds and their interaction with large-scale clouds and aerosols. The explicit treatment of mass mixing ratio and number concentration of cloud and precipitation particles enables the scheme to account for the impact of aerosols on convection. The scheme is linked to aerosols through cloud droplet activation and ice nucleation processes and to stratiform cloud parameterization through convective detrainment of cloud liquid/ice water content (LWC/IWC) and droplet/crystal number concentration (DNC/CNC). A 5-yr simulation with the new convective microphysics scheme shows that both cloud LWC/IWC and DNC/CNC are in good agreement with observations, indicating the scheme describes microphysical processes in convection well. Moreover, the microphysics scheme is able to represent the aerosol effects on convective clouds such as the suppression of warm rain formation and enhancement of freezing when aerosol loading is increased. With more realistic simulations of convective cloud microphysical properties and their detrainment, the mid- and low-level cloud fraction is increased significantly over the ITCZ–southern Pacific convergence zone (SPCZ) and subtropical oceans, making it much closer to the observations. Correspondingly, the serious negative bias in cloud liquid water path over subtropical oceans observed in the standard CAM5 is reduced markedly. The large-scale precipitation is increased and precipitation distribution is improved as well. The long-standing precipitation bias in the western Pacific is significantly alleviated because of microphysics–thermodynamics feedbacks.

Effects of convective adjustment time scale on the simulation of tropical climate

This paper describes the effects of convective adjustment time scale (τ) on the simulation of tropical climate. The NCAR-Community Atmosphere Model version 3 (CAM3) has been used for this study. In the default configuration of the model, the prescribed value of τ, a characteristic time scale with which convective available potential energy (CAPE) is removed at an exponential rate by convection, is assumed to be 1 h. However, some recent observational findings suggest that, it is larger by around one order of magnitude. In order to investigate the dependence of tropical climate simulation to this time scale, we conducted two simulations, one with a time scale of 1 h (CTRL) and another with 8 h (EXPT), and examined the differences in simulated climate. For this, we analyzed both the mean as well as transient features, viz., seasonal mean quantities, equatorial waves, and meridional migration of convective disturbances. The spatial distributions of seasonal mean precipitation are found to be better in EXPT. The spatial correlation coefficients of CTRL and EXPT with the observations are 0.79 and 0.83, respectively, for northern hemisphere winter. Similarly, for northern hemisphere summer, the values are 0.67 and 0.79, respectively. In addition, there is also an improvement in the simulation of equatorial waves, specifically, the Kelvin waves, Madden–Julian oscillation, and n = 1 equatorial Rossby waves become more realistic in EXPT. The characteristics of meridional migration of convective activity over tropics also become more reasonable in EXPT. Thus, it is found that there is a clear improvement in some of the key aspects of the simulated tropical climate with the revised convective adjustment time scale.

CAM3 bias over the Arctic region during northern winter studied with a linear stationary model

This study builds upon two prior papers, which examine Arctic region bias of CAM3 (NCAR Community Atmosphere Model version 3) simulations during winter. CAM3 output is compared with ECMWF (European Centre for Medium-Range Weather Forecasts) 40 year reanalysis (ERA-40) data. Our prior papers considered the temperature and the vorticity equation terms and demonstrated that diabatic, transient, and linear terms dominate nonlinear bias terms over most areas of interest. Accordingly, this paper uses a linearized form of the model’s dynamical core equations to study aspects of the forcing that lead to the CAM3 biases. We treat the model’s long term winter bias as a solution to a linear stationary wave model (LSWM). Key features of the bias in the vorticity, temperature, and ln of surface pressure (=q) fields are shown at medium resolution. The important features found at medium resolution are captured at the much lower LSWM resolution. The Arctic q bias has two key features: excess q over the Barents Sea and a missing Beaufort High (negative maximum q bias) to the north of Alaska and eastern Siberia. The forcing fields are calculated by the LSWM. Horizontal advection tends to create multi-polar combinations of negative and positive extrema in the forcing. The positive and negative areas of forcing approximately match corresponding areas in the bias. There is a broad relation between cold bias with elevated q bias, as expected from classical theory. Forcing in related quantities: near surface vorticity and surface pressure combine to produce the sea level pressure bias.