Leo J. Donner

The Community Climate System Model Version 4

AbstractThe fourth version of the Community Climate System Model (CCSM4) was recently completed and released to the climate community. This paper describes developments to all CCSM components, and documents fully coupled preindustrial control runs compared to the previous version, CCSM3. Using the standard atmosphere and land resolution of 1° results in the sea surface temperature biases in the major upwelling regions being comparable to the 1.4°-resolution CCSM3. Two changes to the deep convection scheme in the atmosphere component result in CCSM4 producing El Nino–Southern Oscillation variability with a much more realistic frequency distribution than in CCSM3, although the amplitude is too large compared to observations. These changes also improve the Madden–Julian oscillation and the frequency distribution of tropical precipitation. A new overflow parameterization in the ocean component leads to an improved simulation of the Gulf Stream path and the North Atlantic Ocean meridional overturning circulati...

Tropical Intraseasonal Variability in 14 IPCC AR4 Climate Models Part I: Convective Signals

Abstract This study evaluates the tropical intraseasonal variability, especially the fidelity of Madden–Julian oscillation (MJO) simulations, in 14 coupled general circulation models (GCMs) participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Eight years of daily precipitation from each model’s twentieth-century climate simulation are analyzed and compared with daily satellite-retrieved precipitation. Space–time spectral analysis is used to obtain the variance and phase speed of dominant convectively coupled equatorial waves, including the MJO, Kelvin, equatorial Rossby (ER), mixed Rossby–gravity (MRG), and eastward inertio–gravity (EIG) and westward inertio–gravity (WIG) waves. The variance and propagation of the MJO, defined as the eastward wavenumbers 1–6, 30–70-day mode, are examined in detail. The results show that current state-of-the-art GCMs still have significant problems and display a wide range of skill in simulating the tropical intraseasonal va...

The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3

AbstractThe Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for the atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol–cloud interactions, chemistry–climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical system component of earth system models and models for decadal prediction in the near-term future—for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model. Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud droplet activation by aerosols, subgrid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emiss...

Clouds and the Earth's Radiant Energy System (CERES): algorithm overview

The Clouds and the Earths Radiant Energy System (CERES) is part of NASAs Earth Observing System (EOS), CERES objectives include the following. (1) For climate change analysis, provide a continuation of the Earth Radiation Budget Experiment (ERBE) record of radiative fluxes at the top-of-the-atmosphere (TOA), analyzed using the same techniques as the existing ERBE data. (2) Double the accuracy of estimates of radiative fluxes at TOA and the Earths surface. (3) Provide the first long-term global estimates of the radiative fluxes within the Earths atmosphere. (4) Provide cloud property estimates collocated in space and time that are consistent with the radiative fluxes from surface to TOA. In order to accomplish these goals, CERES uses data from a combination of spaceborne instruments: CERES scanners, which are an improved version of the ERBE broadband radiometers, and collocated cloud spectral imager data on the same spacecraft. The CERES cloud and radiative flux data products should prove extremely useful in advancing the understanding of cloud-radiation interactions, particularly cloud feedback effects on the Earths radiation balance. For this reason, the CERES data should be fundamental to the ability to understand, detect, and predict global climate change. CERES results should also be very useful for studying regional climate changes associated with deforestation, desertification, anthropogenic aerosols, and ENSO events. This overview summarizes the Release 3 version of the planned CERES data products and data analysis algorithms. These algorithms are a prototype for the system that will produce the scientific data required for studying the role of clouds and radiation in the Earths climate system.

MJO Simulation Diagnostics

The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the MJO in climate and weather systems, current global circulation models (GCMs) exhibit considerable shortcomings in representing this phenomenon. These shortcomings have been documented in a number of multimodel comparison studies over the last decade. However, diagnosis of model performance has been challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of variance and correlation to more sophisticated space–time spectral and empirical orthogonal function analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.

An intercomparison of cloud-resolving models with the atmospheric radiation measurement summer 1997 intensive observation period data

SUMMARY This paper reports an intercomparison study of midlatitude continental cumulus convection simulated by eight two-dimensional and twothree-dimensional cloud-resolving models (CRMs), driven by observed large-scale advective temperature and moisture tendencies, surface turbulent euxes, and radiative-heating proe les during three sub-periods of the summer 1997 Intensive Observation Period of the US Department of Energy’s Atmospheric Radiation Measurement (ARM) program. Each sub-period includes two or three precipitation events of various intensities over a span of 4 or 5 days. The results can be summarized as follows. CRMs can reasonably simulate midlatitude continental summer convection observed at the ARM Cloud and Radiation Testbed site in terms of the intensity of convective activity, and the temperature and specie c-humidity evolution. Delayed occurrences of the initial precipitation events are a common feature for all three sub-cases among the models. Cloud mass e uxes, condensate mixing ratios and hydrometeor fractions produced by all CRMs are similar. Some of the simulated cloud properties such as cloud liquid-water path and hydrometeor fraction are rather similar to available observations. All CRMs produce large downdraught mass euxes with magnitudes similar to those of updraughts, in contrast to CRM results for tropical convection. Some inter-model differences in cloud properties are likely to be related to those in the parametrizations of microphysical processes. There is generally a good agreement between the CRMs and observations with CRMs being signie cantly better than single-column models (SCMs), suggesting that current results are suitable for use in improving parametrizations in SCMs. However, improvements can still be made in the CRM simulations; these include the proper initialization of the CRMs and a more proper method of diagnosing cloud boundaries in model outputs for comparison with satellite and radar cloud observations.

The GFDL CM3 Coupled Climate Model: Characteristics of the Ocean and Sea Ice Simulations

AbstractThis paper documents time mean simulation characteristics from the ocean and sea ice components in a new coupled climate model developed at the NOAA Geophysical Fluid Dynamics Laboratory (GFDL). The GFDL Climate Model version 3 (CM3) is formulated with effectively the same ocean and sea ice components as the earlier CM2.1 yet with extensive developments made to the atmosphere and land model components. Both CM2.1 and CM3 show stable mean climate indices, such as large-scale circulation and sea surface temperatures (SSTs). There are notable improvements in the CM3 climate simulation relative to CM2.1, including a modified SST bias pattern and reduced biases in the Arctic sea ice cover. The authors anticipate SST differences between CM2.1 and CM3 in lower latitudes through analysis of the atmospheric fluxes at the ocean surface in corresponding Atmospheric Model Intercomparison Project (AMIP) simulations. In contrast, SST changes in the high latitudes are dominated by ocean and sea ice effects absen...

A scheme for parameterizing ice cloud water content in general circulation models

Abstract The optical properties of ice clouds are a primary issue for climate and climate change. Evaluating these optical properties in three-dimensional models for studying climate will require a method to calculate the ice water content of such clouds. A procedure is developed to parameterize ice water content as a function of large-scale meteorological characteristics for use in circulation models in which the ice water content is not calculated by means of a three-dimensional prognostic equation for condensed water. The technique identifies large-scale flows in which ice clouds exist and calculates their ice water content by reconstructing the trajectory associated with cloud formation. As the cloud forms, its ice content changes both by deposition of ice from water vapor and by ice removal by sedimentation. The sedimentation process is found to modify significantly the ice water content expected from deposition alone. Ice water contents predicted by the parameterization are compared with aircraft ob...

A Cumulus Parameterization Including Mass Fluxes, Vertical Momentum Dynamics, and Mesoscale Effects

Abstract A formulation for parameterizing cumulus convection, which treats cumulus vertical momentum dynamics and mass fluxes consistently, is presented. This approach predicts the penetrative extent of cumulus updrafts on the basis of their vertical momentum and provides a basis for treating cumulus microphysics using formulations that depend on vertical velocity. Treatments for cumulus microphysics are essential if the water budgets of convective systems are to be evaluated for treating mesoscale stratiform processes associated with convection, which are important for radiative interactions influencing climate. The water budget (both condensed and vapor) of the cumulus updrafts is used to drive a semi-empirical parameterization for the large-scale effects of the mesoscale circulations associated with deep convection. The parameterization for mesoscale effects invokes mesoscale ascent to redistribute vertically water detrained at the tops of the cumulus updrafts. The local cooling associated with this me...

Climate Effects of Aerosol-Cloud Interactions

Advances in satellite observations and model development are needed to disentangle the complex interactions of aerosols and clouds and their effects on climate. Aerosols counteract part of the warming effects of greenhouse gases, mostly by increasing the amount of sunlight reflected back to space. However, the ways in which aerosols affect climate through their interaction with clouds are complex and incompletely captured by climate models. As a result, the radiative forcing (that is, the perturbation to Earths energy budget) caused by human activities is highly uncertain, making it difficult to predict the extent of global warming (1, 2). Recent advances have led to a more detailed understanding of aerosol-cloud interactions and their effects on climate, but further progress is hampered by limited observational capabilities and coarse-resolution climate models.