Abstract

The effects of two different evaporation parameterizations on the sensitivity of simulated climate to solar constant variations are investigated by using a zonally averaged climate model. One parameterization is a nonlinear formulation in which the evaporation is nonlinearly proportional to the sensible heat flux, with the Bowen ratio determined by the predicted vertical temperature and humidity gradients near the earth's surface (model A). The other is the formulation of Saltzman (1968) with the evaporation linearly proportional to the sensible heat flux (model B). The computed climates of models A and B are in good agreement except for the energy partition between sensible and latent heat at the earth's surface. The difference in evaporation parameterizations causes a difference in the response of temperature lapse rate to solar constant variations and a difference in the sensitivity of longwave radiation to surface temperature which leads to a smaller sensitivity of surface temperature to solar constant variations in model A than in model B. The results of model A are qualitatively in agreement with those of the general circulation model calculations of Wetherald and Manabe (1975).

Abstract

A parameterization of quasi-geostrophic eddy transport that takes into account the time variation of the eddy transfer coefficients according to Green's theory is studied. A relation proposed by Green connects the vertical integral of the meridional heat flux at 50°N with the second power of the 500 mb temperature difference between the boundaries of baroclinic activity. It is found that the fourth power in this relation, rather than the original second power, is obtained from analysis of zonal/monthly-mean observational data at 500 mb. For the temperature difference at 1 000 mb, however, the same analysis yields a power of 1.5.

The differences in the seasonal simulation of different powers in the eddy transfer relation are explored in a two-level statistical dynamical zonally averaged model (SDZAM), and it is found that an appropriate choice of power may be of special importance if the model is devised to simulate the seasonal climate cycle, or to test astronomical changes inducing different seasonalities. With the second power in 500 rob, the particular SDZAM being tested simulates an oversensitivity in the high latitude temperature response to the seasonal cycle/astronomical changes, due to its undersensitivity in the simulation of changes in the meridional eddy heat flux. A comparison of the results of a second power at the surface level vs a fourth power at 500 mb is difficult due to the need to retune the model, but a certain advantage to the latter model is detected.

Abstract

An efficient longwave scheme for climate models originally suggested by Sasamori is modified to correctly simulate the water vapor-temperature feedback mechanism. It is found that the modified scheme with a fixed cloud-top altitude (FCA) correctly simulates the longwave sensitivity to surface temperature, ∂ F ^†/∂ T _s , over the clear portion of the sky, but that over the cloud portion of the sky ∂ F ^†/∂ T _<s remains too high. The fixed cloud-top temperature (FCT) method is similarly reviewed and tested. Comparisons with observational Budyko-type correlations are shown to be indecisive over the question of FCA vs FCT.

A direct observational correlation between the effective cloud-top and surface temperatures, based on annually averaged cloud statistics data, suggests a variable cloud-top temperature (VCT) model. In such a model, the temperature of the elective cloud-top layer is varied according to changes in the surface temperature at a rate which is intermediate between that of the FCA and FCT models. This model results in a reasonable ∂ F ^†/∂ T _s over the cloud portion of the sky.

The modified longwave scheme is implemented into a zonally averaged dynamic climate model. It is shown that when the VCT mechanism is invoked, climate sensitivity is doubled compared to that simulated with the FCA model. The importance of simulating not only the correct longwave flux, but also the correct longwave sensitivity to temperature changes is therefore stressed for radiation schemes in studies involved with climate change.

Abstract

Long-term trends in the climate system are always partly obscured by naturally occurring interannual variability. All else being equal, the larger the natural variability, the less precisely one can estimate a trend in a time series of data. Measurement uncertainty, though, also obscures long-term trends. The way in which measurement uncertainty and natural interannual variability interact in inhibiting the detection of climate trends using simple linear regression is derived and the manner in which the interaction between the two can be used to formulate accuracy requirements for satellite climate benchmark missions is shown. It is found that measurement uncertainty increases detection times, but only when considered in direct proportion to natural variability. It is also found that detection times depend critically on the correlation time of natural variability and satellite lifetime. As a consequence, requirements on satellite climate benchmark accuracy and mission lifetime must be directly related to the natural variability of the climate system and its associated correlation times.

Measuring the small changes associated with long-term global climate change from space is a daunting task. The satellite instruments must be capable of observing atmospheric and surface temperature trends as small as 0.1°C decade⁻¹, ozone changes as little as 1% decade⁻¹, and variations in the sun's output as tiny as 0.1% decade⁻¹. To address these problems and recommend directions for improvements in satellite instrument calibration, the National Institute of Standards and Technology (NIST), National Polar-orbiting Operational Environmental Satellite System–Integrated Program Office (NPOESS-IPO), National Oceanic and Atmospheric Administration (NOAA), and National Aeronautics and Space Administration (NASA) organized a workshop at the University of Maryland Inn and Conference Center, College Park, Maryland, 12–14 November 2002. Some 75 scientists participated including researchers who develop and analyze long-term datasets from satellites, experts in the field of satellite instrument calibration, and physicists working on state-of-the-art calibration sources and standards.

The workshop defined the absolute accuracies and long-term stabilities of global climate datasets that are needed to detect expected trends, translated these dataset accuracies and stabilities to required satellite instrument accuracies and stabilities, and evaluated the ability of current observing systems to meet these requirements. The workshop's recommendations include a set of basic axioms or overarching principles that must guide high quality climate observations in general, and a road map for improving satellite instrument characterization, calibration, intercalibration, and associated activities to meet the challenge of measuring global climate change. The workshop also recommended that a follow-up workshop be conducted to discuss implementation of the road map developed at this workshop.

Abstract

Maps are presented showing the mean annual sensitivities of longwave and net radiation at the top of the atmosphere to changes in cloud amount for the region 60°N to 60°S. The maps are based on an analysis of a 45-month set of monthly mean radiation budget data for the years 1974–78 derived from the NOAA satellite scanning radiometers. The analysis technique is based on the regression method of Ohring and Clapp (1980), with some minor modifications. Both regionally and globally, the maps show that the albedo effect of clouds is greater than their greenhouse effect. The maps also suggest that the longwave sensitivity parameter might serve as a useful measure of the geographical distribution of effective cloud heights.

As part of the joint National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) Pathfinder program, the NOAA National Environmental Satellite, Data, and Information Service (NESDIS) has created a research-quality global atmospheric dataset through the reprocessing of Advanced Very High Resolution Radiometer (AVHRR) observations since 1981. The AVHRR is an imaging radiometer that flies on NOAA polar-orbiting operational environmental satellites (POES) measuring radiation reflected and emitted by the earth in five spectral channels. Raw AVHRR observations were recalibrated using a vicarious calibration technique for the reflectance channels and an appropriate treatment of the nonlinearity of the infrared channels. The observations are analyzed in the Pathfinder Atmosphere (PATMOS) project to obtain statistics of channel radiances, cloud amount, top of the atmosphere radiation budget, and aerosol optical thickness over ocean. The radiances and radiation budget components are determined for clear-sky and all-sky conditions. The output products are generated on a quasi-equalarea grid with an approximate 110 km × 110 km spatial resolution and twice-a-day temporal resolution, and averaged over 5-day (pentad) and monthly time periods. PATMOS data span the period from September 1981 through June 2001. Analyses show that the PATMOS data in their current archived form are sufficiently accurate for studies of the interaction of clouds and aerosol with solar and terrestrial radiation, and of climatic phenomena with large signals (e.g., the annual cycle, monsoons, ENSOs, or major volcanic eruptions). Global maps of the annual average of selected products are displayed to illustrate the capability of the dataset to depict the climatological fields and the spatial detail and relationships between the fields, further demonstrating how PATMOS is a unique resource for climate studies. Smaller climate signals, such as those associated with global warming, may be more difficult to detect due to the presence of artifacts in the time series of the products. Principally, these are caused by the drift of each satellite's observation time over its mission. A statistical method, which removes most of these artifacts, is briefly discussed. Quality of the products is assessed by comparing the adjusted monthly mean time series for each product with those derived from independent satellite observations. The PATMOS dataset for the monthly means is accessible at www.saa.noaa.gov/.

Abstract

As part of the joint National Oceanic and Atmospheric Administration–National Aeronautics and Space Administration (NOAA–NASA) Pathfinder program, the NOAA/National Environmental Satellite, Data and Information Service (NESDIS) has created a research-quality atmospheric, climate-scale dataset through the reprocessing of archived Advanced Very High Resolution Radiometer (AVHRR) observations from four afternoon satellites, in orbit since 1981. The raw observations were recalibrated using a vicarious calibration technique for the AVHRR reflectance channels and an improved treatment of the nonlinearity of the three infrared emittance channels. State-of-the-art algorithms are used in the Pathfinder Atmosphere (PATMOS) project to process global AVHRR datasets into statistics of channel radiances, total cloud amount, components of the earth's radiation budget, and aerosol optical thickness over oceans. The radiances and earth radiation budget components are determined for clear-sky and all-sky conditions. The output products are generated on a quasi-equal-area grid with a spatial resolution of approximately 110 km, with twice-a-day temporal resolution, and averaged over 5-day (pentad) and monthly time periods. The quality of the products is assessed relative to independent surface or satellite observations of these parameters. This analysis shows that the PATMOS data are sufficiently accurate for studies of the interaction of clouds and aerosol with solar and terrestrial radiation, and of climatic phenomena with large signals, for example, the annual cycle, monsoons, and the four ENSOs and two major volcanic eruptions that occurred during the 19-yr PATMOS period. Analysis also indicates that smaller climate signals, such as those associated with longer-term trends in surface temperature, may be difficult to detect due to the presence of artifacts in the time series that result from the drift of each satellite's observation time over its mission. However, a simple statistical method is employed to remove much of the effect caused by orbital drift. The uncorrected PATMOS dataset is accessible electronically.

Abstract

To date, the assimilation of satellite measurements in numerical weather prediction (NWP) models has focused on the clear atmosphere. But satellite observations in the visible, infrared, and microwave provide a great deal of information on clouds and precipitation. This special collection describes how to use this information to initialize clouds and precipitation in models. Since clouds and precipitation often occur in sensitive regions for forecast impacts, such improvements are likely necessary for continuing to acquire significant gains in weather forecasting.

This special collection of the Journal of the Atmospheric Sciences is devoted to articles based on papers presented at the International Workshop on Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models, in Lansdowne, Virginia, in May 2005. This introduction summarizes the findings of the workshop. The special collection includes review articles on satellite observations of clouds and precipitation (Stephens and Kummerow), parameterizations of clouds and precipitation in NWP models (Lopez), radiative transfer in cloudy/precipitating atmospheres (Weng), and assimilation of cloud and precipitation observations (Errico et al.), as well as research papers on these topics.