S. A. Klein

Exposing global cloud biases in the Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators

AbstractSatellite observations and their corresponding instrument simulators are used to document global cloud biases in the Community Atmosphere Model (CAM) versions 4 and 5. The model–observation comparisons show that, despite having nearly identical cloud radiative forcing, CAM5 has a much more realistic representation of cloud properties than CAM4. In particular, CAM5 exhibits substantial improvement in three long-standing climate model cloud biases: 1) the underestimation of total cloud, 2) the overestimation of optically thick cloud, and 3) the underestimation of midlevel cloud. While the increased total cloud and decreased optically thick cloud in CAM5 result from improved physical process representation, the increased midlevel cloud in CAM5 results from the addition of radiatively active snow. Despite these improvements, both CAM versions have cloud deficiencies. Of particular concern, both models exhibit large but differing biases in the subtropical marine boundary layer cloud regimes that are kn...

The Transpose-AMIP II Experiment and Its Application to the Understanding of Southern Ocean Cloud Biases in Climate Models

AbstractThe Transpose-Atmospheric Model Intercomparison Project (AMIP) is an international model intercomparison project in which climate models are run in “weather forecast mode.” The Transpose-AMIP II experiment is run alongside phase 5 of the Coupled Model Intercomparison Project (CMIP5) and allows processes operating in climate models to be evaluated, and the origin of climatological biases to be explored, by examining the evolution of the model from a state in which the large-scale dynamics, temperature, and humidity structures are constrained through use of common analyses.The Transpose-AMIP II experimental design is presented. The project requests participants to submit a comprehensive set of diagnostics to enable detailed investigation of the models to be performed. An example of the type of analysis that may be undertaken using these diagnostics is illustrated through a study of the development of cloud biases over the Southern Ocean, a region that is problematic for many models. Several models s...

On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models

AbstractThe present study examines the correspondence between short- and long-term systematic errors in five atmospheric models by comparing the 16 five-day hindcast ensembles from the Transpose Atmospheric Model Intercomparison Project II (Transpose-AMIP II) for July–August 2009 (short term) to the climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and AMIP for the June–August mean conditions of the years of 1979–2008 (long term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach.The analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by day 5 in ensemble average hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitud...

The parametric sensitivity of CAM5's MJO

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 a 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 the 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 autoconversion of cloud to rainwater 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 CAM5s parameterization suite are needed in order to simultaneously well simulate the MJO and mean-state climate.

Metrics and Diagnostics for Precipitation-Related Processes in Climate Model Short-Range Hindcasts

AbstractIn this study, several metrics and diagnostics are proposed and implemented to systematically explore and diagnose climate model biases in short-range hindcasts and quantify how fast hindcast biases approach to climate biases with an emphasis on tropical precipitation and associated moist processes. A series of 6-day hindcasts with NCAR and the U.S. Department of Energy Community Atmosphere Model, version 4 (CAM4) and version 5 (CAM5), were performed and initialized with ECMWF operational analysis every day at 0000 UTC during the Year of Tropical Convection (YOTC). An Atmospheric Model Intercomparison Project (AMIP) type of ensemble climate simulations was also conducted for the same period. The analyses indicate that initial drifts in precipitation and associated moisture processes (“fast processes”) can be identified in the hindcasts, and the biases share great resemblance to those in the climate runs. Comparing to Tropical Rainfall Measuring Mission (TRMM) observations, model hindcasts produce ...

Assessment of marine boundary layer cloud simulations in the CAM with CLUBB and updated microphysics scheme based on ARM observations from the Azores

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 Program (ARM) 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 CLUBBs consistent treatment of these variables through a probability distribution function (PDF) approach. Subcloud 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 (CRESW).

Atmospheric Radiation Measurement Program Science Plan Current Status and Future Directions of the ARM Science Program

The Atmospheric Radiation Measurement (ARM) Program has matured into one of the key programs in the U.S. Climate Change Science Program. The ARM Program has achieved considerable scientific success in a broad range of activities, including site and instrument development, atmospheric radiative transfer, aerosol science, determination of cloud properties, cloud modeling, and cloud parameterization testing and development. The focus of ARM science has naturally shifted during the last few years to an increasing emphasis on modeling and parameterization studies to take advantage of the long time series of data now available. During the next 5 years, the principal focus of the ARM science program will be to: • Maintain the data record at the fixed ARM sites for at least the next five years. • Improve significantly our understanding of and ability to parameterize the 3-D cloud-radiation problem at scales from the local atmospheric column to the global climate model (GCM) grid square. • Continue developing techniques to retrieve the properties of all clouds, with a special focus on ice clouds and mixed-phase clouds. • Develop a focused research effort on the indirect aerosol problem that spans observations, physical models, and climate model parameterizations. • Implement and evaluate an operational methodology to calculate broad-band heating rates in the atmospheric columns at the ARM sites. • Develop and implement methodologies to use ARM data more effectively to test atmospheric models, both at the cloud-resolving model scale and the GCM scale. • Use these methodologies to diagnose cloud parameterization performance and then refine these parameterizations to improve the accuracy of climate model simulations. In addition, the ARM Program is actively developing a new ARM Mobile Facility (AMF) that will be available for short deployments (several months to a year or more) in climatically important regions. The AMF will have much of the same instrumentation as the remote facilities at ARM’s Tropical Western Pacific and the North Slope of Alaska sites. Over time, this new facility will extend ARM science to a much broader range of conditions for model testing.

Introduction to special section on Toward Reducing Cloud‐Climate Uncertainties in Atmospheric General Circulation Models

[1] Uncertainties of global warming projections have not changed much in general circulation models (GCMs) in the last 20 years. For example, in the first, second, and third reports of the Intergovernmental Panel on Climate Change (IPCC), the ranges of global warming simulated in GCMs are 1.9 to 5.2 C [Mitchell et al., 1990], 2.1 to 4.6 C [Kattenberg et al., 1996], and 2.0 to 5.1 C [Cubasch et al., 2001] respectively. These discrepancies in model’s climate sensitivities can been largely attributed to differences in their cloud-climate feedback processes [e.g., Cess et al., 1990; Soden et al., 2004]. [2] Many research efforts have therefore been directed to understand how cloud feedbacks operate in a climate change, with the hope to design models that can correctly describe them in a climate change scenario. It is however well known that cloud feedbacks are sensitive to perturbations of physical parameterizations and subgrid-scale processes in the models that are well justifiable at the present time. [3] Thus the search to magically find a ‘‘correct’’ model may well be analogous to the search for ‘‘The Gold in the Orchard,’’ the story of the three sons trying to find the hidden gold in an orchard. In this Italian folk tale, an elderly farmer called his three sons to his deathbed to tell them that there was a pot of gold buried in the family orchard. After his death, the three sons dug up the whole orchard but found no gold. In the next season, however, the olive trees bore a lot more fruits than usual. When they were sold, they gave the sons a whole pot of gold. [4] The story reminds us of two things. First, there might be no such thing as a model with the correct cloud-climate feedback unless all aspects of the model are right. Second, the hard work of digging can lead to real payoff through a different direction. The articles in this special issue represent a glimpse of the efforts of the Cloud Parameterization and Modeling Working Group (CPM) of the Atmospheric Radiation Measurement Program (ARM) of the U. S. Department of Energy in digging the orchard: understanding and improving model clouds by using observations at the process levels, with the purpose of reducing cloud-feedback uncertainties in atmospheric general circulation models. [5] The first paper in this section reports an assessment of the current status of cloud simulations in GCMs, with participations of most climate modeling centers in the United States and in Europe. The remaining seventeen papers can be categorized into four groups that appear in order in this section. The first group focuses on a case study of cloud simulations during the March 2000 ARM Intensive Cloud Field Campaign, designated as the ARM/GCSS (Global Energy and Water Experiments–Cloud System Studies) case 4, following earlier studies of ARM/GCSS case 1 [Ghan et al., 2000] and ARM/GCSS Case 3 [Xie et al., 2002; Xu et al., 2002]. The second group of papers describes developments of cloud parameterization algorithms using ARM data. The third group of papers evaluates model cloud processes against ARM measurements. The last group of papers describes research results concerning measurements of clouds. [6] These papers may have raised more questions than solutions. They, however, collectively expose many issues that have to be dealt with in order to design GCMs that can correctly describe cloud-climate feedback processes: the gold in the orchard.

Introduction to CAUSES: Description of Weather and Climate Models and Their Near‐Surface Temperature Errors in 5 day Hindcasts Near the Southern Great Plains

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 nine institutes, perform a series of 5 day hindcasts, each initialized 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.

A cloudy planetary boundary layer oscillation arising from the coupling of turbulence with precipitation in climate simulations

The Community Atmosphere Model (CAM) adopts Cloud Layers Unified By Binormals scheme (CLUBB) and an updated microphysics (MG2) scheme for a more unified treatment of cloud processes. This makes interactions between parameterizations tighter and more explicit. In this study, a cloudy planetary boundary layer (PBL) oscillation related to interaction between CLUBB and MG2 is identified in CAM. This highlights the need for consistency between the coupled sub-grid processes in climate model development. This oscillation occurs most often in the marine cumulus cloud regime. The oscillation occurs only if the modeled PBL is strongly decoupled and precipitation evaporates below the cloud. Two aspects of the parameterized coupling assumptions between CLUBB and MG2 schemes cause the oscillation: 1) a parameterized relationship between rain evaporation and CLUBBs sub-grid spatial variance of moisture and heat that induces an extra cooling in the lower PBL; and 2) rain evaporation which happens at a too low an altitude because of the precipitation fraction parameterization in MG2. Either one of these two conditions can overly stabilize the PBL and reduce the upward moisture transport to the cloud layer so that the PBL collapses. Global simulations prove that turning off the evaporation-variance coupling and improving the precipitation fraction parameterization effectively reduces the cloudy PBL oscillation in marine cumulus clouds. By evaluating the causes of the oscillation in CAM, we have identified the PBL processes that should be examined in models having similar oscillations. This study may draw the attention of the modeling and observational communities to the issue of coupling between parameterized physical processes.