Bruce A. Wielicki

Clouds and the Earth's Radiant Energy System (CERES) - An Earth Observing System experiment

Abstract Clouds and the Earths Radiant Energy System (CERES) is an investigation to examine the role of cloud/radiation feedback in the Earths climate system. The CERES broadband scanning radiometers are an improved version of the Earth Radiation Budget Experiment (ERBE) radiometers. The CERES instruments will fly on several National Aeronautics and Space Administration Earth Observing System (EOS) satellites starting in 1998 and extending over at least 15 years. The CERES science investigations will provide data to extend the ERBE climate record of top-of-atmosphere shortwave (SW) and longwave (LW) radiative fluxes. CERES will also combine simultaneous cloud property data derived using EOS narrowband imagers to provide a consistent set of cloud/radiation data, including SW and LW radiative fluxes at the surface and at several selected levels within the atmosphere. CERES data are expected to provide top-of-atmosphere radiative fluxes with a factor of 2 to 3 less error than the ERBE data. Estimates of ra...

Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation Budget

Abstract Despite recent improvements in satellite instrument calibration and the algorithms used to determine reflected solar (SW) and emitted thermal (LW) top-of-atmosphere (TOA) radiative fluxes, a sizeable imbalance persists in the average global net radiation at the TOA from satellite observations. This imbalance is problematic in applications that use earth radiation budget (ERB) data for climate model evaluation, estimate the earth’s annual global mean energy budget, and in studies that infer meridional heat transports. This study provides a detailed error analysis of TOA fluxes based on the latest generation of Clouds and the Earth’s Radiant Energy System (CERES) gridded monthly mean data products [the monthly TOA/surface averages geostationary (SRBAVG-GEO)] and uses an objective constrainment algorithm to adjust SW and LW TOA fluxes within their range of uncertainty to remove the inconsistency between average global net TOA flux and heat storage in the earth–atmosphere system. The 5-yr global mean...

Mission to Planet Earth: Role of Clouds and Radiation in Climate

The role of clouds in modifying the earths radiation balance is well recognized as a key uncertainty in predicting any potential future climate change. This statement is true whether the climate change of interest is caused by changing emissions of greenhouse gases and sulfates, deforestation, ozone depletion, volcanic eruptions, or changes in the solar constant. This paper presents an overview of the role of the National Aeronautics and Space Administrations Earth Observing System (EOS) satellite data in understanding the role of clouds in the global climate system. The paper gives a brief summary of the cloud/radiation problem, and discusses the critical observations needed to support further investigations. The planned EOS data products are summarized, including the critical advances over current satellite cloud and radiation budget data. Key advances include simultaneous observation of radiation budget and cloud properties, additional information on cloud particle size and phase, improved detection ...

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.

Satellite Instrument Calibration for Measuring Global Climate Change: Report of a Workshop

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 suns 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 phy...

On the determination of cloud cover from satellite sensors: The effect of sensor spatial resolution

Landsat thematic mapper (TM) data are used to provide a very high spatial resolution source of cloud “truth.” TM atmospheric window channels at wavelengths of 0.83 and 11.5 μm are used to simulate the visible and infrared channels on meteorological satellites. The TM data are spatially averaged to provide spatial resolutions (that is, pixel sizes) ranging from the full resolution 28.5-m data to the 1-, 4-, and 8-km resolutions typical of AVHRR and GOES data. These data are then used to examine the sensitivity of satellite-derived estimates of cloud fractional coverage to changing sensor spatial resolution. Seven different cloud retrieval algorithms are examined, including the International Satellite Cloud Climatology Project (ISCCP), new cloud for Earth (NCLE) radiation budget, hybrid bispectral threshold method (HBTM), spatial coherence, box counting based on fractal theory, reflectance threshold, and temperature threshold methods. The analysis is carried out for 24 cloud fields of varying cloud type: cumulus, stratocumulus, altocumulus, cirrus, and multilayered cloud. Results indicate that estimates of cloud fraction vary greatly as a function of sensor spatial resolution and cloud algorithm assumptions. Even for 28.5-m spatial resolution data, current cloud algorithms give cloud fractions that vary by as much as 0.25. In general, cloud algorithms are sensitive to either sensor resolution (threshold methods such as ISCCP or HBTM) or to assumptions about cloud optical depth (NCLE, spatial coherence). When present, the effect of sensor resolution is small for satellite pixel sizes less than 0.25 km. The effects are large for pixel sizes of 1 km or larger. Results are shown to be a strong function of cloud type. The effect of sensor resolution is strongest for boundary layer clouds and is very weak for cirrus clouds. At the 4- to 8-km spatial resolutions typical of meteorological satellite data, the ISCCP algorithm overestimates cloud fraction for boundary layer cloud by about 0.05 but underestimates thin cirrus by 0.05. The NCLE algorithm underestimates all cloud types by an average of 0.32, and spatial coherence underestimates boundary layer cloud fraction an average of 0.18. For boundary layer clouds the errors are traced to the assumption of cloud-filled pixels for ISCCP, the assumption of optically thick clouds for spatial coherence, and the assumption of a typical cloud albedo for the NCLE method. Using 8-km data, the HBTM and ISCCP methods provide the most accurate cloud fraction, although the HBTM method underestimates thin cirrus by 0.20 and ISCCP overestimates all cloud types but cirrus by about 0.05. The 24 cloud fields examined were chosen to explore some of the more difficult cloud retrieval cases, so the results should not be extrapolated to global average conditions. Nevertheless, the results suggest a critical need for a clearer understanding of the performance of satellite-derived cloud properties.

Achieving Climate Change Absolute Accuracy in Orbit

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREOs inherently high absolute accuracy will be verified and traceable on orbit to Systeme Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earths thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which...

Reexamination of the Observed Decadal Variability of the Earth Radiation Budget Using Altitude-Corrected ERBE/ERBS Nonscanner WFOV Data

Abstract This paper gives an update on the observed decadal variability of the earth radiation budget (ERB) using the latest altitude-corrected Earth Radiation Budget Experiment (ERBE)/Earth Radiation Budget Satellite (ERBS) Nonscanner Wide Field of View (WFOV) instrument Edition3 dataset. The effects of the altitude correction are to modify the original reported decadal changes in tropical mean (20°N to 20°S) longwave (LW), shortwave (SW), and net radiation between the 1980s and the 1990s from 3.1, −2.4, and −0.7 to 1.6, −3.0, and 1.4 W m−2, respectively. In addition, a small SW instrument drift over the 15-yr period was discovered during the validation of the WFOV Edition3 dataset. A correction was developed and applied to the Edition3 dataset at the data user level to produce the WFOV Edition3_Rev1 dataset. With this final correction, the ERBS Nonscanner-observed decadal changes in tropical mean LW, SW, and net radiation between the 1980s and the 1990s now stand at 0.7, −2.1, and 1.4 W m−2, respectivel...

Cumulus Cloud Properties Derived Using Landsat Satellite Data

Abstract Landsat Multispectral Scanner (MSS) digital data are used to remotely sense cumulus cloud properties such as cloud fraction and cloud reflectance, along with the distribution of cloud number and cloud fraction as a function of cloud size. The analysis is carried out for four cumulus fields covering regions approximately 150 km square. Results for these initial cloud fields indicate that: (i) the common intuitive model of clouds as nearly uniform reflecting surfaces is a poor representation of cumulus clouds, (ii) the cumulus clouds were often multicelled, even for clouds as small as 1 km in diameter, (iii) cloud fractional coverage derived using a simple reflectance threshold is sensitive to the chosen threshold even for 57-meter resolution Landsat data, (iv) the sensitivity of cloud fraction to changes in satellite sensor resolution is less sensitive than suggested theoretically, and (v) the Landsat derived cloud size distributions show encouraging similarities among the cloud fields examined.

Geostationary Enhanced Temporal Interpolation for CERES Flux Products

AbstractThe Clouds and the Earth’s Radiant Energy System (CERES) instruments on board the Terra and Aqua spacecraft continue to provide an unprecedented global climate record of the earth’s top-of-atmosphere (TOA) energy budget since March 2000. A critical step in determining accurate daily averaged flux involves estimating the flux between CERES Terra or Aqua overpass times. CERES employs the CERES-only (CO) and the CERES geostationary (CG) temporal interpolation methods. The CO method assumes that the cloud properties at the time of the CERES observation remain constant and that it only accounts for changes in albedo with solar zenith angle and diurnal land heating, by assuming a shape for unresolved changes in the diurnal cycle. The CG method enhances the CERES data by explicitly accounting for changes in cloud and radiation between CERES observation times using 3-hourly imager data from five geostationary (GEO) satellites. To maintain calibration traceability, GEO radiances are calibrated against Mode...