Overview of the Clouds and the Earth's Radiant Energy System (CERES) Experiment

This paper presents an overview of the CERES project. It demonstrates how algorithm improvements have lead to improved top-of-atmosphere (TOA) radiative flux accuracy. CERES shortwave flux anomalies are compared with those from Earthshine and ISCCP-FD

Spectrally Resolved Flux Derived from Collocated AIRS and CERES Observations and its Application in Model Validation

Spectrally resolved outgoing IR flux, the integrand of the outgoing longwave radiation (OLR), has its unique value in evaluating model simulations. Here we describe an algorithm of deriving such clear-sky outgoing spectral flux through the whole IR region from the collocated Atmospheric Infrared Sounder (AIRS) and the Clouds & the Earth's Radiant Energy System (CERES) measurements over the tropical oceans. Based on the scene types and corresponding angular distribution models (ADMs) used in the CERES Single Satellite Footprint (SSF) dataset, spectrally-dependent ADMs are developed and used to estimate the spectral flux at each AIRS channel. A multivariate linear prediction scheme is then used to estimate spectral fluxes at frequencies not covered by the AIRS instrument. The whole algorithm is validated using synthetic spectra as well as the CERES OLR measurements. Using the GFDL AM2 model simulation as a case study, the application of the derived clear-sky outgoing spectral flux in model evaluation is illustrated. By comparing the observed and simulated spectral flux in 2004, compensating errors in the simulated OLR from different absorption bands can be revealed, so does the errors from frequencies within a given absorption band. Discrepancies between the simulated and observed spatial distributions and seasonal evolutions of the spectral fluxes at different spectral ranges are further discussed. The methodology described in this study can be applied to other surface types as well as cloudy-sky observations and corresponding model evaluations

Top-of-Atmosphere Albedo Estimation from Angular Distribution Models Using Scene Identification from Satellite Cloud Property Retrievals

International audienceThe next generation of earth radiation budget satellite instruments will routinely merge estimates of global top-of-atmosphere radiative fluxes with cloud properties. This information will offer many new opportunities for validating radiative transfer models and cloud parameterizations in climate models. In this study, five months of Polarization and Directionality of the Earth's Reflectances 670-nm radiance measurements are considered in order to examine how satellite cloud property retrievals can be used to define empirical angular distribution models (ADMs) for estimating top-of-atmosphere albedo. ADMs are defined for 19 scene types defined by satellite retrievals of cloud fraction and cloud optical depth. Two approaches are used to define the ADM scene types. The first assumes there are no biases in the retrieved cloud properties and defines ADMs for fixed discrete intervals of cloud fraction and cloud optical depth (fixed-τ approach). The second approach involves the same cloud fraction intervals, but uses percentile intervals of cloud optical depth instead (percentile-τ approach). Albedos generated using these methods are compared with albedos inferred directly from the mean observed reflectance field

Intercomparison of Recent Anomaly Time-Series of OLR as Observed by CERES and Computed Using AIRS Products

This paper compares recent spatial and temporal anomaly time series of OLR as observed by CERES and computed based on AIRS retrieved surface and atmospheric geophysical parameters over the 7 year time period September 2002 through February 2010. This time period is marked by a substantial decrease of OLR, on the order of +/-0.1 W/sq m/yr, averaged over the globe, and very large spatial variations of changes in OLR in the tropics, with local values ranging from -2.8 W/sq m/yr to +3.1 W/sq m/yr. Global and Tropical OLR both began to decrease significantly at the onset of a strong La Ni a in mid-2007. Late 2009 is characterized by a strong El Ni o, with a corresponding change in sign of both Tropical and Global OLR anomalies. The spatial patterns of the 7 year short term changes in AIRS and CERES OLR have a spatial correlation of 0.97 and slopes of the linear least squares fits of anomaly time series averaged over different spatial regions agree on the order of +/-0.01 W/sq m/yr. This essentially perfect agreement of OLR anomaly time series derived from observations by two different instruments, determined in totally independent and different manners, implies that both sets of results must be highly stable. This agreement also validates the anomaly time series of the AIRS derived products used to compute OLR and furthermore indicates that anomaly time series of AIRS derived products can be used to explain the factors contributing to anomaly time series of OLR

Interannual Variability of OLR as Observed by AIRS and CERES

This paper compares spatial anomaly time series of OLR (Outgoing Longwave Radiation) and OLR(sub CLR) (Clear Sky OLR) as determined using observations from CERES Terra and AIRS over the time period September 2002 through June 2011. Both AIRS and CERES show a significant decrease in global mean and tropical mean OLR over this time period. We find excellent agreement of the anomaly time-series of the two OLR data sets in almost every detail, down to 1 deg X 1 deg spatial grid point level. The extremely close agreement of OLR anomaly time series derived from observations by two different instruments implies that both sets of results must be highly stable. This agreement also validates to some extent the anomaly time series of the AIRS derived products used in the computation of the AIRS OLR product. The paper also examines the correlations of anomaly time series of AIRS and CERES OLR, on different spatial scales, as well as those of other AIRS derived products, with that of the NOAA Sea Surface Temperature (SST) product averaged over the NOAA Nino-4 spatial region. We refer to these SST anomalies as the El Nino Index. Large spatially coherent positive and negative correlations of OLR anomaly time series with that of the El Nino Index are found in different spatial regions. Anomalies of global mean, and especially tropical mean, OLR are highly positively correlated with the El Nino Index. These correlations explain that the recent global and tropical mean decreases in OLR over the period September 2002 through June 2011, as observed by both AIRS and CERES, are primarily the result of a transition from an El Nino condition at the beginning of the data record to La Nina conditions toward the end of the data period. We show that the close correlation of global mean, and especially tropical mean, OLR anomalies with the El Nino Index can be well accounted for by temporal changes of OLR within two spatial regions which lie outside the NOAA Nino-4 region, in which anomalies of cloud cover and mid-tropospheric water vapor are both highly negatively correlated with the El Nino Index. Agreement of the AIRS and CERES OLR(sub CLR) anomaly time series is less good, which may be a result of the large sampling differences in the ensemble of cases included in each OLR(sub CLR) data set

The Relationship Between El Nino/La Nina Oscillations and Recent Anomaly Time Series of OLR Determined by CERES and AIRS

This paper compares recent spatial anomaly time series of OLR (Outgoing Longwave Radiation) and OLRCLR (Clear Sky OLR) as determined using CERES and AIRS observations over the time period September 2002 through June 2010. We find excellent agreement in OLR anomaly time series of both data sets in almost every detail, down to the 1 x 1 spatial grid point level. This extremely close agreement of OLR anomaly time series derived from observations by two different instruments implies that both sets of results must be highly stable. This agreement also validates to some extent the anomaly time series of the AIRS derived products used in the computation of the AIRS OLR product. The paper then examines anomaly time series of AIRS derived products over the extended time period September 2002 through April 2011. We show that OLR anomalies during this period are closely in phase with those of an El Nino index, and that the recent global and tropical mean decreases in OLR and OLRCLR are a result of a transition from an El Nino condition at the beginning of the data record to La Nina conditions toward the end of the data period. We show that the relationship between global mean, and especially tropical mean, OLR anomalies to the El Nino index can be explained by temporal changes of the distribution of mid-tropospheric water vapor and cloud cover in two spatial regions that are in direct response to El Nino/La Nina activity which occurs outside these spatial regions

Determination of CERES TOA Fluxes Using Machine Learning Algorithms. Part I: Classification and Retrieval of CERES Cloudy and Clear Scenes

Continuous monitoring of the earth radiation budget (ERB) is critical to the understanding of Earths climate and its variability with time. The Clouds and the Earths Radiant Energy System (CERES) instrument is able to provide a long record of ERB for such scientific studies. This manuscript, which is the first of a two-part paper, describes the new CERES algorithm for improving the clear/cloudy scene classification without the use of coincident cloud imager data. This new CERES algorithm is based on a subset of the modern artificial intelligence (AI) paradigm called machine learning (ML) algorithms. This paper describes the development and application of the ML algorithm known as random forests (RF), which is used to classify CERES broadband footprint measurements into clear and cloudy scenes. Results from the RF analysis carried using the CERES Single Scanner Footprint (SSF) data for January and July are presented in the manuscript. The daytime RF misclassification rate (MCR) shows relatively large values (>30%) for snow, sea ice, and bright desert surface types, while lower values (<10%) for the forest surface type. MCR values observed for the nighttime data in general show relatively larger values for most of the surface types compared to the daytime MCR values. The modified MCR values show lower values (<4%) for most surface types after thin cloud data are excluded from the analysis. Sensitivity analysis shows that the number of input variables and decision trees used in the RF analysis has a substantial influence on determining the classification error

On the Lessons Learned from the Operations of the ERBE Nonscanner Instrument in Space and the Production of the Nonscanner TOA Radiation Budget Dataset

Monitoring the flow of radiative energy at top-of-atmosphere (TOA) is essential for understanding the Earths climate and how it is changing with time. The determination of TOA global net radiation budget using broadband nonscanner instruments has received renewed interest recently due to advances in both instrument technology and the availability of small satellite platforms. The use of such instruments for monitoring Earths radiation budget was attempted in the past from satellite missions such as the Nimbus 7 and the Earth Radiation Budget Experiment (ERBE). This paper discusses the important lessons learned from the operation of the ERBE nonscanner instrument and the production of the ERBE nonscanner TOA radiation budget data set that have direct relevance to current nonscanner instrument efforts

Climate Quality Broadband and Narrowband Solar Reflected Radiance Calibration Between Sensors in Orbit

vAs the potential impacts of global climate change become more clear [1], the need to determine the accuracy of climate prediction over decade-to-century time scales has become an urgent and critical challenge. The most critical tests of climate model predictions will occur using observations of decadal changes in climate forcing, response, and feedback variables. Many of these key climate variables are observed by remotely sensing the global distribution of reflected solar spectral and broadband radiance. These "reflected solar" variables include aerosols, clouds, radiative fluxes, snow, ice, vegetation, ocean color, and land cover. Achieving sufficient satellite instrument accuracy, stability, and overlap to rigorously observe decadal change signals has proven very difficult in most cases and has not yet been achieved in others [2]. One of the earliest efforts to make climate quality observations was for Earth Radiation Budget: Nimbus 6/7 in the late 1970s, ERBE in the 1980s/90s, and CERES in 2000s are examples of the most complete global records. The recent CERES data products have carried out the most extensive intercomparisons because if the need to merge data from up to 11 instruments (CERES, MODIS, geostationary imagers) on 7 spacecraft (Terra, Aqua, and 5 geostationary) for any given month. In order to achieve climate calibration for cloud feedbacks, the radiative effect of clear-sky, all-sky, and cloud radiative effect must all be made with very high stability and accuracy. For shortwave solar reflected flux, even the 1% CERES broadband absolute accuracy (1-sigma confidence bound) is not sufficient to allow gaps in the radiation record for decadal climate change. Typical absolute accuracy for the best narrowband sensors like SeaWiFS, MISR, and MODIS range from 2 to 4% (1-sigma). IPCC greenhouse gas radiative forcing is approx. 0.6 W/sq m per decade or 0.6% of the global mean shortwave reflected flux, so that a 50% cloud feedback would change the global reflected flux by approx. 0.3 W/sq m or 0.3% per decade in broadband SW calibration change. Recent results comparing CERES reflected flux changes with MODIS, MISR, and SeaWiFS narrowband changes concluded that only SeaWiFS and CERES were approaching sufficient stability in calibration for decadal climate change [3]. Results using deep convective clouds in the optically thick limit as a stability target may prove very effective for improving past data sets like ISCCP. Results for intercalibration of geostationary imagers to CERES using an entire month of regional nearly coincident data demonstrates new approaches to constraining the calibration of current geostationary imagers. The new Decadal Survey Mission CLARREO is examining future approaches to a "NIST-in-Orbit" approach of very high absolute accuracy reference radiometers that cover the full solar and infrared spectrum at high spectral resolution but at low spatial resolution. Sampling studies have shown that a precessing CLARREO mission could calibrate other geo and leo reflected solar radiation and thermal infrared sensors