ALBERT

Description: To assess marine boundary layer (MBL) cloud simulations in three versions of the Community Atmosphere Model (CAM), three sets of short-term global hindcasts are performed and compared to Atmospheric Radiation Measurement (ARM) program observations on Graciosa Island in the Azores from June 2009 to December 2010. The three versions consist of CAM5.3 with default schemes (CAM5.3), CAM5.3 with Cloud Layers Unified By Binormals (CLUBB-MG1), and CAM5.3 with CLUBB and updated microphysics scheme (CLUBB-MG2). Our results show that relative to CAM5.3 default schemes, simulations with CLUBB better represent MBL cloud-base height, the height of the major cloud layer, and the daily cloud cover variability. CLUBB also better simulates the relationship of cloud fraction to cloud liquid water path (LWP) most likely due to CLUBB's consistent treatment of these variables through a probability distribution function (PDF) approach. Sub-cloud evaporation of precipitation is substantially enhanced in simulations with CLUBB-MG2 and is more realistic based on the limited observational estimate. Despite these improvements, all model versions underestimate MBL cloud cover. CLUBB-MG2 reduces biases in in-cloud LWP (clouds are not too bright) but there are still too few of MBL clouds due to an underestimate in the frequency of overcast scenes. Thus combining CLUBB with MG2 scheme better simulates MBL cloud processes, but because biases remain in MBL cloud cover CLUBB-MG2 does not improve the simulation of the surface shortwave cloud radiative effect ( CRE SW ).

Description: Abstract This study analyzes the summertime precipitation bias over the Central United States and its relationship to the simulated large‐scale environment and the convection scheme in the Energy Exascale Earth System Model Atmosphere Model version 1. This relationship is mainly examined in a set of short‐term hindcasts initialized with realistic large‐scale conditions for the summer of 2011. Besides the uniform 1° model resolution, we adopt Regionally Refined Meshes to increase the model resolution to 0.25° over the contiguous United States. Additional five‐year Atmospheric Model Intercomparison Project simulations are conducted to confirm that the results from the hindcasts are consistent with the climate runs. We find that the summertime dry precipitation bias over the Great Plains and the wet bias over the Rockies cannot be reduced simultaneously by changing resolution or tuning parameters. As for the diurnal cycle, Energy Exascale Earth System Model Atmosphere Model version 1 captures the general diurnal variation of the large‐scale moisture transport and the large‐scale upward motion over the Central United States. However, the diurnal cycle of precipitation over the Great Plains is out of phase with the diurnal variation of the large‐scale environment because the convective precipitation dominates the total precipitation and its diurnal cycle, and it does not directly respond to the local moisture convergence and the large‐scale upward motion. These results reemphasize the importance of improving the coupling of the convection to the large‐scale environment in reducing the summer precipitation bias over the Central United States in climate models with the resolution of ~0.25°.

Description: We systematically explore the ability of the Community Atmospheric Model Version 5 (CAM5) to simulate the Madden-Julian Oscillation (MJO), through an analysis of MJO metrics calculated from an 1100-member perturbed parameter ensemble of 5-year simulations with observed sea-surface temperatures. Parameters from the deep convection scheme make the greatest contribution to variance in MJO simulation quality with a much smaller contribution from parameters in the large-scale cloud, shallow convection and boundary layer turbulence schemes. Improved MJO variability results from a larger lateral entrainment rate and a reduction in the precipitation efficiency of deep convection that was achieved by a smaller auto-conversion of cloud to rain water and a larger evaporation of convective precipitation. Unfortunately, simulations with an improved MJO also have a significant negative impact on the climatological values of low-level cloud and absorbed shortwave radiation, suggesting that structural in addition to parametric modifications to CAM5's parameterization suite are needed in order to simultaneously well simulate the MJO and mean-state climate.

Description: This study validates the cloud ice water content (IWC, non-precipitating ice/non-snow) produced by a unique prognostic cloud ice parameterization when used in the UCLA atmospheric general circulation model against CloudSat observations, and also compares it with the ERA-Interim reanalysis. A distinctive aspect of this parameterization is the novel treatment of the conversion of cloud ice to precipitating snow. The ice-to-snow autoconversion time scale is a function of differential infrared radiative heating and environmental static stability. The simulated IWC is in agreement with CloudSat observations in terms of its magnitude and three-dimensional structure. The annual and seasonal means of the zonal-mean IWC profiles from the simulations both show a local maximum in the upper troposphere in the tropics associated with deep convection, and other local maxima in the mid-troposphere in midlatitudes in both hemispheres associated with storm tracks. In contrast to the CloudSat values, the reanalysis shows much smaller IWC values in the tropics and much larger values in the lower troposphere in midlatitudes. The different vertical structures and magnitudes of IWC between the simulations and the reanalysis are likely due to differences in the parameterization of various processes in addition to the ice-to-snow autoconversion, including ice sedimentation, temperature thresholds for ice deposition and cumulus detrainment of cloud ice. However, a series of sensitivity experiments supports the conclusion that the model with a constant autoconversion time scale cannot reproduce the correct IWC distribution in both the tropics and midlatitudes, which strongly suggests the importance of physically based effects on the autoconversion timescale.

Description: [1]  During the last two decades, significant progress has been made in assessing the Earth Radiation Balance from satellite observations. Yet, satellite based estimates differ from each other and from those provided by numerical models. Major issues are related to quality of satellite observations, such as the frequent changes in satellite observing systems, degradation of sensors, restricted spectral intervals and viewing geometry of sensors, and changes in the quality of atmospheric inputs that drive the inference schemes. To reduce differences among the satellite based estimates requires, among others, updates to inference schemes so that most recent auxiliary information can be fully utilized. This paper reports on improvements introduced to a methodology developed at the University of Maryland to estimate shortwave (SW) radiative fluxes within the atmosphere system from satellite observations, the implementation of the approach with newly available auxiliary information, evaluation of the downwelling SW flux against ground observations, and comparison with independent satellite methods and numerical models. Specifically, introduced are: new Narrow to Broadband (N/B) transformations and new Angular Distribution Models (ADM) for clear and cloudy sky that incorporate most recent land use classifications; improved aerosol treatment; separation of clouds by phase; improved sun-earth geometry; and implementation at 0.5° spatial resolution at 3-hourly intervals integrated to daily and monthly time scales. When compared to an earlier version of the model as implemented at 2.5° at global scale and against observations from the globally distributed Baseline Surface Radiation Network (BSRN) stations for a period of six years (at monthly time scale), the bias was reduced from 8.6 (4.6%) to −0.5 (0.3%) W/m 2 , the standard deviation from 16.6 (8.9%) to 14.5 (7.8%) W/m 2 while the correlation remained high at 0.98 in both cases. Evaluation was also done over oceanic sites as available from the Pilot Research Moored Array in the Tropical Atlantic (PIRATA) moorings and from the Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON) moorings in the tropical Pacific Ocean. Overall, results over oceans were not as good as over land for all the satellite retrievals compared in this study.

Description: We perform an observationally based evaluation of the cloud ice water content (CIWC) and path (CIWP) of present-day GCMs, notably 20th century CMIP5 simulations, and compare these results to CMIP3 and two recent reanalyses. We use three different CloudSat + CALIPSO ice water products and two methods to remove the contribution from the convective core ice mass and/or precipitating cloud hydrometeors with variable sizes and falling speeds so that a robust observational estimate can be obtained for model evaluations. The results show that for annual mean CIWP, there are factors of 2–10 in the differences between observations and models for a majority of the GCMs and for a number of regions. However, there are a number of CMIP5 models, including CNRM-CM5, MRI, CCSM4 and CanESM2, as well as the UCLA CGCM, that perform well compared to our past evaluations. Systematic biases in CIWC vertical structure occur below the mid-troposphere where the models overestimate CIWC, with this bias arising mostly from the extratropics. The tropics are marked by model differences in the level of maximum CIWC (∼250–550 hPa). Based on a number of metrics, the ensemble behavior of CMIP5 has improved considerably relative to CMIP3, although neither the CMIP5 ensemble mean nor any individual model performs particularly well, and there are still a number of models that exhibit very large biases despite the availability of relevant observations. The implications of these results on model representations of the Earth radiation balance are discussed, along with caveats and uncertainties associated with the observational estimates, model and observation representations of the precipitating and cloudy ice components, relevant physical processes and parameterizations.

Description: During the last two decades, significant progress has been made in assessing the Earth Radiation Balance from satellite observations. Yet, satellite based estimates differ from each other and from those provided by numerical models. Major issues are related to quality of satellite observations, such as the frequent changes in satellite observing systems, degradation of sensors, restricted spectral intervals and viewing geometry of sensors, and changes in the quality of atmospheric inputs that drive the inference schemes. To reduce differences among the satellite based estimates requires, among others, updates to inference schemes so that most recent auxiliary information can be fully utilized. This paper reports on improvements introduced to a methodology developed at the University of Maryland to estimate shortwave (SW) radiative fluxes within the atmosphere system from satellite observations, the implementation of the approach with newly available auxiliary information, evaluation of the downwelling SW flux against ground observations, and comparison with independent satellite methods and numerical models. Specifically, introduced are: new Narrow to Broadband (N/B) transformations and new Angular Distribution Models (ADM) for clear and cloudy sky that incorporate most recent land use classifications; improved aerosol treatment; separation of clouds by phase; improved sun-earth geometry; and implementation at 0.5° spatial resolution at 3-hourly intervals integrated to daily and monthly time scales. When compared to an earlier version of the model as implemented at 2.5° at global scale and against observations from the globally distributed Baseline Surface Radiation Network (BSRN) stations for a period of six years (at monthly time scale), the bias was reduced from 8.6 (4.6%) to −0.5 (0.3%) W/m2, the standard deviation from 16.6 (8.9%) to 14.5 (7.8%) W/m2 while the correlation remained high at 0.98 in both cases. Evaluation was also done over oceanic sites as available from the Pilot Research Moored Array in the Tropical Atlantic (PIRATA) moorings and from the Tropical Atmosphere Ocean/Triangle Trans-Ocean Buoy Network (TAO/TRITON) moorings in the tropical Pacific Ocean. Overall, results over oceans were not as good as over land for all the satellite retrievals compared in this study.

Description: We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen-temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from 9 institutes, perform a series of 5-day hindcasts, each initialised from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally, a strong diurnal cycle in the screen-temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the error at SGP. This suggests that conclusions drawn from detailed evaluation of models using instruments located at SGP will be representative of errors that are prevalent over a larger spatial scale.

Description: Many numerical weather prediction and climate models exhibit too warm lower tropospheres near the mid-latitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in 9 model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor and aerosols are quantified, using an array of radiation measurement stations near the ARM SGP site. Furthermore, an in-depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although non-negligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud-radiative effects dominate in the cloud-related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud, but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud-radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.

Description: Many weather forecast and climate models simulate warm surface air temperature (T 2m ) biases over mid-latitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multi-model intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to the T 2m bias using a short-term hindcast approach during the spring and summer of 2011. Observations are mainly from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) sites. The present study examines the contributions of surface energy budget errors. All participating models simulate too much net shortwave and longwave fluxes at the surface but with no consistent mean bias sign in turbulent fluxes over the Central U.S. and SGP. Nevertheless, biases in the net shortwave and downward longwave fluxes, as well as surface evaporative fraction (EF) are contributors to T 2m bias. Radiation biases are largely affected by cloud simulations, while EF bias is largely affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation based upon the surface energy budget is derived to further quantify the magnitudes of radiation and EF contributions to T 2m bias. Our analysis ascribes that a large EF underestimate is the dominant source of error in all models with a large positive temperature bias, whereas an EF overestimate compensates for an excess of absorbed shortwave radiation in nearly all the models with the smallest temperature bias.