International Satellite Cloud Climatology Project Data Research Articles

Diurnal cycle of rainfall and convection over the Maritime Continent using TRMM and ISCCP

AbstractThis study investigates the diurnal cycle of rainfall, convection, and precipitation features (PFs) over the Maritime Continent (MC). The study uses Tropical Rainfall Measuring Missions (TRMM) Multi‐satellite Precipitation Analysis (TMPA; product 3b42), TRMM PFs, and convective classifications from the International Satellite Cloud Climatology Project (ISCCP) data. Together, these satellites dataset paint a comprehensive picture of the diurnal cycle of rainfall and convection over the MC consistent with past research. Isolated convection initiates around midday over the higher terrain of the large islands (Java, Borneo, and Papua New Guinea). The convection becomes more organized through the afternoon and evening, leading to peak rainfall over the islands around 1800–2100 local standard time (LST). Over the next few hours, some of that rainfall transitions to stratiform rain over land. The convection then propagates offshore overnight with rainfall peaking along the coast around 0300–0600 LST and then over ocean around 0600–0900 LST. ISCCP data suggests that the overnight and early morning convection is more associated with isolated convective cells than the remnants of mesoscale convective systems. The coastal and oceanic diurnal ranges also seem to be larger in stratiform rainfall, in contrast to land where convective rainfall dominates. Seasonally the diurnal variation of rainfall, convection, and PFs over the region have greater amplitude during DJF (December, January, and February) than JJA (June, July, and August). Given the MC's critical role in the global climate, examining variations in these cycles with respect to the Madden–Julian Oscillation and equatorial waves may ultimately lead to improved subseasonal weather forecasts.

An analysis of high cloud variability: imprints from the El Niño–Southern Oscillation

Using data from the International Satellite Cloud Climatology Project (ISCCP), we examine how near-global (60°N–60°S) high cloud fraction varies over time in the past three decades. Our focus is on identifying dominant modes of variability and associated spatial patterns, and how they are related to sea surface temperature. By performing the principal component analysis, we find that the first two principal modes of high cloud distribution show strong imprints of the two types of El Nino–Southern Oscillation (ENSO)—the canonical ENSO and the ENSO Modoki. Comparisons between ISCCP data and 14 models from the Atmospheric Model Intercomparison Project Phase 5 (AMIP5) show that models simulate the spatial pattern and the temporal variations of high cloud fraction associated with the canonical ENSO very well but the magnitudes of the canonical ENSO vary among the models. Furthermore, the multi-model mean of the second principal mode in the AMIP5 simulations appears to capture the temporal behavior of the second mode but individual AMIP5 models show large discrepancies in capturing observed temporal variations. A new metric, defined by the relative variances of the first two principal components, suggests that most of the AMIP5 models overestimate the second principal mode of high clouds.

Variability and Trends in U.S. Cloud Cover: ISCCP, PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather Observations

Abstract Variability and trends in total cloud cover for 1982–2009 across the contiguous United States from the International Satellite Cloud Climatology Project (ISCCP), AVHRR Pathfinder Atmospheres–Extended (PATMOS-x), and EUMETSAT Satellite Application Facility on Climate Monitoring Clouds, Albedo and Radiation from AVHRR Data Edition 1 (CLARA-A1) satellite datasets are assessed using homogeneity-adjusted weather station data. The station data, considered as “ground truth” in the evaluation, are generally well correlated with the ISCCP and PATMOS-x data and with the physically related variables diurnal temperature range, precipitation, and surface solar radiation. Among the satellite products, overall, the PATMOS-x data have the highest interannual correlations with the weather station cloud data and those other physically related variables. The CLARA-A1 daytime dataset generally shows the lowest correlations, even after trends are removed. For the U.S. mean, the station dataset shows a negative but not statistically significant trend of −0.40% decade−1, and satellite products show larger downward trends ranging from −0.55% to −5.00% decade−1 for 1984–2007. PATMOS-x 1330 local time trends for U.S. mean cloud cover are closest to those in the station data, followed by the PATMOS-x diurnally corrected dataset and ISCCP, with CLARA-A1 having a large negative trend contrasting strongly with the station data. These results tend to validate the usefulness of weather station cloud data for monitoring changes in cloud cover, and they show that the long-term stability of satellite cloud datasets can be assessed by comparison to homogeneity-adjusted station data and other physically related variables.

Empirical Removal of Artifacts from the ISCCP and PATMOS-x Satellite Cloud Records

AbstractThe International Satellite Cloud Climatology Project (ISCCP) dataset and the Pathfinder Atmospheres–Extended (PATMOS-x) dataset are two commonly used multidecadal satellite cloud records. Because they are constructed from weather satellite measurements lacking long-term stability, ISCCP and PATMOS-x suffer from artifacts that inhibit their use for investigating cloud changes over recent decades. The present study describes and applies a post hoc method to empirically remove spurious variability from anomalies in total cloud fraction at each grid box. Spurious variability removed includes that associated with systematic changes in satellite zenith angle, drifts in satellite equatorial crossing time, and unrealistic large-scale spatially coherent anomalies associated with known and unidentified problems in instrument calibration and ancillary data. The basic method is to calculate for each grid box the least squares best-fit line between cloud anomalies and artifact factor anomalies, and to let the residuals from the best-fit line be the newly corrected data. After the correction procedure, the patterns of regional trends in ISCCP and PATMOS-x total cloud fraction appear much more natural. The corrected data cannot be used for studies of globally averaged cloud change, however, because the methods employed remove any real cloud variability occurring on global scales together with spurious variability. An examination of Moderate Resolution Imaging Spectroradiometer (MODIS) total cloud fraction data indicates that removing global-scale variability has little impact on regional patterns of cloud change. Corrected ISCCP and PATMOS-x data are available from the Research Data Archive at NCAR.

Evaluation of ISCCP cloud amount with MODIS observations

The goal of the International Satellite Cloud Climatology Project (ISCCP) is to provide global cloud amount statistics for atmospheric radiation flux modeling, which is a key element of climate change studies. However, ISCCP estimates rely on two spectral channels only, while the most advanced satellite sensors offer over 20 spectral bands, and thus a higher probability of correct cloud detection. We validated the accuracy of ISCCP mean monthly cloud amount statistics using the state-of-the-art, 36-spectral channel Moderate-resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites. Based on the MODIS Level 2 Cloud Mask we developed a dedicated Level 3 product for Central Europe (2004–2009). For the first time, MODIS swath data were projected onto an ISCCP equal-area grid, which guaranteed an exact geometrical agreement between both climatologies. Results showed that there was a close correlation between ISCCP and MODIS data (ρ=0.872, α=0.99), especially at warmer part of the year (ρ≥0.940, α>0.99). However, ISCCP estimations were found to be unreliable in wintertime when surface was covered with snow. The presence of snow resulted in a significant underestimate of cloud amount by 0.45 for individual ISCCP grid boxes. Our results suggest that MODIS cloud climatology is more reliable when estimates of mean monthly cloud amount are required.

Cloud-Aerosol-Radiation (CAR) ensemble modeling system: Overall accuracy and efficiency

The Cloud-Aerosol-Radiation (CAR) ensemble modeling system has recently been built to better understand cloud/aerosol/radiation processes and determine the uncertainties caused by different treatments of cloud/aerosol/radiation in climate models. The CAR system comprises a large scheme collection of cloud, aerosol, and radiation processes available in the literature, including those commonly used by the world’s leading GCMs. In this study, detailed analyses of the overall accuracy and efficiency of the CAR system were performed. Despite the different observations used, the overall accuracies of the CAR ensemble means were found to be very good for both shortwave (SW) and longwave (LW) radiation calculations. Taking the percentage errors for July 2004 compared to ISCCP (International Satellite Cloud Climatology Project) data over (60°N, 60°S) as an example, even among the 448 CAR members selected here, those errors of the CAR ensemble means were only about −0.67% (−0.6 W m−2) and −0.82% (−2.0 W m−2) for SW and LW upward fluxes at the top of atmosphere, and 0.06% (0.1 W m−2) and −2.12% (−7.8 W m−2) for SW and LW downward fluxes at the surface, respectively. Furthermore, model SW frequency distributions in July 2004 covered the observational ranges entirely, with ensemble means located in the middle of the ranges. Moreover, it was found that the accuracy of radiative transfer calculations can be significantly enhanced by using certain combinations of cloud schemes for the cloud cover fraction, particle effective size, water path, and optical properties, along with better explicit treatments for unresolved cloud structures.

Radiative heating of the ISCCP upper level cloud regimes and its impact on the large‐scale tropical circulation

Radiative heating profiles of the International Satellite Cloud Climatology Project (ISCCP) cloud regimes (or weather states) were estimated by matching ISCCP observations with radiative properties derived from cloud radar and lidar measurements from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) sites at Manus, Papua New Guinea, and Darwin, Australia. Focus was placed on the ISCCP cloud regimes containing the majority of upper level clouds in the tropics, i.e., mesoscale convective systems (MCSs), deep cumulonimbus with cirrus, mixed shallow and deep convection, and thin cirrus. At upper levels, these regimes have average maximum cloud occurrences ranging from 30% to 55% near 12 km with variations depending on the location and cloud regime. The resulting radiative heating profiles have maxima of approximately 1 K/day near 12 km, with equal heating contributions from the longwave and shortwave components. Upper level minima occur near 15 km, with the MCS regime showing the strongest cooling of 0.2 K/day and the thin cirrus showing no cooling. The gradient of upper level heating ranges from 0.2 to 0.4 K/(day∙km), with the most convectively active regimes (i.e., MCSs and deep cumulonimbus with cirrus) having the largest gradient. When the above heating profiles were applied to the 25‐year ISCCP data set, the tropics‐wide average profile has a radiative heating maximum of 0.45 K day‐1 near 250 hPa. Column‐integrated radiative heating of upper level cloud accounts for about 20% of the latent heating estimated by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The ISCCP radiative heating of tropical upper level cloud only slightly modifies the response of an idealized primitive equation model forced with the tropics‐wide TRMM PR latent heating, which suggests that the impact of upper level cloud is more important to large‐scale tropical circulation variations because of convective feedbacks rather than direct forcing by the cloud radiative heating profiles. However, the height of the radiative heating maxima and gradient of the heating profiles are important to determine the sign and patterns of the horizontal circulation anomaly driven by radiative heating at upper levels.

Evaluation of ISCCP Multisatellite Radiance Calibration for Geostationary Imager Visible Channels Using the Moon

Since 1983, the International Satellite Cloud Climatology Project (ISCCP) has collected Earth radiance data from the succession of geostationary and polar-orbiting meteorological satellites operated by weather agencies worldwide. Meeting the ISCCP goals of global coverage and decade-length time scales requires consistent and stable calibration of the participating satellites. For the geostationary imager visible channels, ISCCP calibration provides regular periodic updates from regressions of radiances measured from coincident and collocated observations taken by Advanced Very High Resolution Radiometer instruments. As an independent check of the temporal stability and intersatellite consistency of ISCCP calibrations, we have applied lunar calibration techniques to geostationary imager visible channels using images of the Moon found in the ISCCP data archive. Lunar calibration enables using the reflected light from the Moon as a stable and consistent radiometric reference. Although the technique has general applicability, limitations of the archived image data have restricted the current study to Geostationary Operational Environmental Satellite and Geostationary Meteorological Satellite series. The results of this lunar analysis confirm that ISCCP calibration exhibits negligible temporal trends in sensor response but have revealed apparent relative biases between the satellites at various levels. However, these biases amount to differences of only a few percent in measured absolute reflectances. Since the lunar analysis examines only the lower end of the radiance range, the results suggest that the ISCCP calibration regression approach does not precisely determine the intercept or the zero-radiance response level. We discuss the impact of these findings on the development of consistent calibration for multisatellite global data sets.

Cloud frequency climatology at the Andes/Amazon transition: 1. Seasonal and diurnal cycles

Tropical montane regions present a complex local climate but one that may be very sensitive to local and global change. Therefore, it is important to assess their current climatological state, and to understand how the large‐scale circulation may affect local‐scale cloud patterns. We examine the cloud climatology of a tropical Andean montane region in the context of tropical South American climate in terms of seasonal/diurnal cycles using a corrected ISCCP (International Satellite Cloud Climatology Project) DX cloud product (1983–2008), MODIS (Moderate Resolution Imaging Spectroradiometer) MOD35 visible cloud flags (2000–2008) and ground‐based cloud observations. Cloud climatologies were compared for three elevation zones: highlands (puna grassland), eastern slope (the montane forest) and lowlands. We found that in the dry season (JJA) the study area is part of a localized region of higher cloud frequency relative to other parts the eastern slope, and also relative to the adjacent highlands and lowlands. The highlands exhibited the greatest amplitude mean annual cycle of cloud frequency, with a minimum in June for all times of day. There were contrasts between the three zones with regard to the month in which the minimum cloud frequency occurs between different times of day. Higher lowland and eastern slope cloud frequencies compared with those on the puna in the early hours in the wet season suggest low‐level convergence at lower elevations. Comparisons between satellite products show that ISCCP and MODIS produce very similar annual cycles although the absolute cloud frequencies are higher in ISCCP data.

Multi‐model, multi‐sensor estimates of global evapotranspiration: climatology, uncertainties and trends

AbstractEstimating evapotranspiration (ET) at continental to global scales is central to understanding the partitioning of energy and water at the earth's surface and the feedbacks with the atmosphere and biosphere, especially in the context of climate change. Recent evaluations of global estimates from remote sensing, upscaled observations, land surface models and atmospheric reanalyses indicate large uncertainty across the datasets of the order of 50% of the global annual mean value. In this paper, we explore the uncertainties in global land ET estimates using three process‐based ET models and a set of remote sensing and observational based radiation and meteorological forcing datasets. Input forcings were obtained from International Satellite Cloud Climatology Project (ISCCP) and Surface Radiation Budget (SRB). The three process‐based ET models are: a surface energy balance method (SEBS), a revised Penman–Monteith (PM) model, and a modified Priestley–Taylor model. Evaluations of the radiation products from ISCCP and SRB show large differences in the components of surface radiation, and temporal inconsistencies that relate to changes in satellite sensors and retrieval algorithms. In particular, step changes in the ISCCP surface temperature and humidity data lead to spurious increases in downward and upward longwave radiation that contributes to a step change in net radiation, and the ISCCP data are not used further. An ensemble of global estimates of land surface ET are generated at daily time scale and 0.5 degree spatial resolution for 1984–2007 using two SRB radiation products (SRB and SRBqc) and the three models. Uncertainty in ET from the models is much larger than the uncertainty from the radiation data. The largest uncertainties relative to the mean annual ET are in transition zones between dry and humid regions and monsoon regions. Comparisons with previous studies and an inferred estimate of ET from long‐term inferred ET indicate that the ensemble mean value is reasonable, but generally biased high globally. Long‐term changes over 1984–2007 indicate a slight increase over 1984–1998 and decline thereafter, although uncertainties in the forcing radiation data and lack of direct linkage with soil moisture limitations in the models prevents attribution of these changes. Copyright © 2011 John Wiley & Sons, Ltd.

An analysis of cloud cover in multiscale modeling framework global climate model simulations using 4 and 1 km horizontal grids

Over the past few years, a new type of global climate model (GCM) has emerged in which a two‐dimensional or small three‐dimensional cloud resolving model is embedded into each grid cell of a GCM. This approach is frequently called the multiscale modeling framework (MMF) but is also known as a cloud‐resolving convection parameterization or a superparameterization. In this article, we compare joint histograms of cloud top height and optical depth from the MMF with those being produced by the International Satellite Cloud Climatology Project (ISCCP) and from the Multiangle Imaging Spectroradiometer (MISR). While the form of the ISCCP and MISR data sets is conceptually similar, the satellite sensors and the algorithms differ, with the result that the joint histograms can differ quite significantly even when viewing exactly the same clouds. The analysis takes advantages of the strengths of each data set, as well as the differences in these data (which, for example, allow one to characterize the amount of some multilayer clouds). MMF simulation runs with three different resolutions are analyzed. One simulation is run using a 4 km horizontal grid with 26 vertical levels (on a stretched grid), a second simulation is run with a 1 km horizontal grid and the same 26 vertical levels, and a third simulation is run with a 1 km horizontal grid and 52 vertical levels. The analysis shows that the MMF reproduces the broad pattern of tropical convergence zones, subtropical belts, and midlatitude storm tracks as observed by ISCCP and MISR. However, the model has several significant shortcomings. Perhaps most seriously, it significantly underpredicts the amount of low‐level cloud in most regions. The simulation with a 1 km horizontal grid and 52 vertical layers is found to improve modestly several aspect of the MMF low‐level cloud cover. The model output is obtained using ISCCP and MISR instrument simulators and the role of horizontal resolution in the instrument simulators is examined.

Comment on “Metrics to describe the dynamical evolution of atmospheric moisture: Intercomparison of model (NARR) and observations (ISCCP)” by Kun Tao and Ana P. Barros

Comment on “Metrics to describe the dynamical evolution of atmospheric moisture: Intercomparison of model (NARR) and observations (ISCCP)” by Kun Tao and Ana P. Barros

Long-Term Regional Estimates of Evapotranspiration for Mexico Based on Downscaled ISCCP Data

Abstract The development and evaluation of a long-term high-resolution dataset of potential and actual evapotranspiration for Mexico based on remote sensing data are described. Evapotranspiration is calculated using a modified version of the Penman–Monteith algorithm, with input radiation and meteorological data from the International Satellite Cloud Climatology Project (ISCCP) and vegetation distribution derived from Advanced Very High Resolution Radiometer (AVHRR) products. The ISCCP data are downscaled to ⅛° resolution using statistical relationships with data from the North American Regional Reanalysis (NARR). The final product is available at ⅛°, daily, for 1984–2006 for all Mexico. Comparisons are made with the NARR offline land surface model and measurements from approximately 1800 pan stations. The remote sensing estimate follows well the seasonal cycle and spatial pattern of the comparison datasets, with a peak in late summer at the height of the North American monsoon and highest values in low-lying and coastal regions. The spatial average over Mexico is biased low by about 0.3 mm day−1, with a monthly rmse of about 0.5 mm day−1. The underestimation may be related to the lack of a model for canopy evaporation, which is estimated to be up to 30% of total evapotranspiration. Uncertainties in both the remote sensing–based estimates (because of input data uncertainties) and the true value of evapotranspiration (represented by the spread in the comparison datasets) are up to 0.5 and 1.2 mm day−1, respectively. This study is a first step in quantifying the long-term variation in global land evapotranspiration from remote sensing data.

A near‐global, 2‐hourly data set of atmospheric precipitable water from ground‐based GPS measurements

A 2‐hourly data set of atmospheric precipitable water (PW) has been produced from the zenith path delay (ZPD) derived from ground‐based Global Positioning System (GPS) measurements. The PW data are available every 2 hours from 80 to 268 International GNSS Service (IGS, formally International GPS Service) ground stations from 1997 to 2004. The accuracy of the IGS ZPD product is roughly 4 mm. An analysis technique is developed to convert ZPD to PW on a global scale. Special efforts are made on deriving surface pressure (Ps) and water‐vapor‐weighted atmospheric mean temperature (Tm), which are two key parameters for converting ZPD to PW. Ps is derived from global, 3‐hourly surface synoptic observations with temporal, vertical and horizontal adjustments. Tm is calculated from NCEP/NCAR reanalysis with temporal, vertical and horizontal interpolations. The derived Ps and Tm at the GPS location and height have root‐mean‐square (rms) errors of 1.65 hPa and 1.3 K, respectively. A theoretical error analysis concludes that typical PW error associated with the errors in ZPD, Tm and Ps is on the order of 1.5 mm. The PW data set is compared with radiosonde, microwave radiometer (MWR) and satellite data. The GPS and radiosonde PW comparisons at 98 stations around the globe show a mean difference of 1.08 mm (drier for radiosonde data) with a standard deviation of differences of 2.68 mm, which corresponds to mean percentage difference and standard deviation of 5.5% and 10.6%, respectively. The bias is primarily due to known dry biases in the Vaisala radiosonde data. The RMS difference between GPS and radiosonde/MWR data ranges from 1.2 mm to 2.83 mm. The latitudinal and seasonal variations of PW derived from the GPS data agree well with that from International Satellite Cloud Climatology Project (ISCCP) data if the ISCCP data are sampled only at grid boxes containing GPS stations. The large difference between GPS and ISCCP data in the subtropics is interesting, but is not easily explained. The comparisons did not reveal any systematic bias in GPS PW data and show that a RMS difference of less than 3 mm between GPS‐derived PW and other data sets is achieved. The comparison study also illustrates the value of GPS‐estimated PW for examining the quality of other data sets, such as those from radiosondes and MWR. Preliminary analysis of this data set shows interesting and significant diurnal variations in PW in four different regions.

On the Structure of the Lower Troposphere in the Summertime Stratocumulus Regime of the Northeast Pacific

Abstract Data collected in situ as part of the second field study of the Dynamics and Chemistry of Marine Stratocumulus field program are used to evaluate the state of the atmosphere in the region of field operations near 30°N, 120°W during July 2001, as well as its representation by a variety of routinely available data. The routine data include both the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and NCEP–NCAR reanalyses, forecasts from their respective forecast systems (the Integrated and Global Forecast Systems), the 30-km archive from the International Satellite Cloud Climatology Project (ISCCP), the Quick Scatterometer surface winds, and remotely sensed fields derived from radiances measured by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the Advanced Microwave Sounding Unit, and the Advanced Very High Resolution Radiometer. The analysis shows that outside of the boundary layer the state of the lower troposphere is reasonably represented by the reanalysis and forecast products, with the caveat of a slight warm bias at 850 hPa in the NCEP–NCAR products. Within the planetary boundary layer (PBL) the agreement is not as good: both the boundary layer depth and cloud amount are underpredicted, and the boundary layer temperature correlates poorly with the available data, which may be related to a poor representation of SSTs in this region of persistent cloud cover. ERA-40 also suffers from persistently weak zonal winds within the PBL. Among the satellite records the ISCCP data are found to be especially valuable, evincing skill in both predicting boundary layer depth (from cloud-top temperatures and TMI surface temperatures) and cloud liquid water paths (from cloud optical depths). An analysis of interannual variability (among Julys) based on ERA-40 and the 1983–2001 ISCCP record suggests that thermodynamic quantities show similar interannual and synoptic variability, principally concentrated just above the PBL, while dynamic quantities vary much more on synoptic time scales. Furthermore, the analysis suggests that the correlation between stratocumulus cloud amount and lower-tropospheric stability exhibits considerable spatial structure and is less pronounced than previously thought.

Arguments against a physical long‐term trend in global ISCCP cloud amounts

The International Satellite Cloud Climatology Project (ISCCP) multi‐decadal record of cloudiness exhibits a well‐known global decrease in cloud amounts. This downward trend has recently been used to suggest widespread increases in surface solar heating, decreases in planetary albedo, and deficiencies in global climate models. Here we show that trends observed in the ISCCP data are satellite viewing geometry artifacts and are not related to physical changes in the atmosphere. Our results suggest that in its current form, the ISCCP data may not be appropriate for certain long‐term global studies, especially those focused on trends.”

Clear-sky aerosol radiative forcing effects based on multi-site AERONET observations over Europe

One of the great unknowns in climate research is the contribution of aerosols to climate forcing and climate perturbation. In this study, retrievals from AERONET are used to estimate the direct clear-sky aerosol top-of-atmosphere and surface radiative forcing effects for 12 multi-site observing stations in Europe. The radiative transfer code sdisort in the libRadtran environment is applied to accomplish these estimations. Most of the calculations in this study rely on observations which have been made for the years 1999, 2000, and 2001. Some stations do have observations dating back to the year of 1995. The calculations rely on a pre-compiled aerosol optical properties database for Europe. Aerosol radiative forcing effects are calculated with monthly mean aerosol optical properties retrievals and calculations are presented for three different surface albedo scenarios. Two of the surface albedo scenarios are generic by nature bare soil and green vegetation and the third relies on the ISCCP (International Satellite Cloud Climatology Project) data product. The ISCCP database has also been used to obtain clear-sky weighting fractions over AERONET stations. The AERONET stations cover the area 0° to 30° E and 42° to 52° N. AERONET retrievals are column integrated and this study does not make any seperation between the contribution of natural and anthropogenic components. For the 12 AERONET stations, median clear-sky top-of-atmosphere aerosol radiative forcing effect values for different surface albedo scenarios are calculated to be in the range of −4 to −2 W/m2. High median radiative forcing effect values of about −6 W/m2 were found to occur mainly in the summer months while lower values of about −1 W/m2 occur in the winter months. The aerosol surface forcing also increases in summer months and can reach values of −8 W/m2. Individual stations often have much higher values by a factor of 2. The median top-of-atmosphere aerosol radiative forcing effect efficiency is estimated to be about −25 W/m2 and their respective surface efficiency is around −35 W/m2. The fractional absorption coefficient is estimated to be 1.7, but deviates significantly from station to station. In addition, it is found that the well known peak of the aerosol radiative forcing effect at a solar zenith angle of about 75° is in fact the average of the peaks occurring at shorter and longer wavelengths. According to estimations for Central Europe, based on mean aerosol optical properties retrievals from 12 stations, the critical threshold of the aerosol single scattering albedo, between cooling and heating in the presence of an aerosol layer, is close between 0.6 and 0.76.

Comparison of International Panel on Climate Change Fourth Assessment Report climate model simulations of surface albedo with satellite products over northern latitudes

The surface albedos simulated by seventeen climate models over the northern latitudes of the Western Hemisphere were compared with satellite‐derived albedo products provided by the International Satellite Cloud Climatology Project (ISCCP). Model simulations were conducted in support of the International Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Results show the following: (1) Annual albedo of the region averaged for all models is fairly close to that provided by the ISCCP (0.351 versus 0.334). The difference between model average and ISCCP albedos is well below the standard deviation in albedo among models. (2) Most models simulated seasonal variations in regional albedo reasonably well. In summer, the models systematically overestimated albedo relative to the ISCCP data by as much as 0.05. In winter, large differences were detected among the climate models. (3) The spatial correlations among models, and between models and ISCCP, depend on geographic location, season and surface type. In general, the spatial correlation coefficients between individual models and the ISCCP data were highest for the land surface in midsummer and for the ocean surface in spring. Model bias was smaller for the ocean surface than for the land surface, and smaller in summer than in winter. (4) Unlike the modeling results, the satellite data showed large interannual variations in albedo and a systematic decreasing trend over the 16 year period of 1984–1999. Depending on season, the standard deviation of albedo interannual variation ranged from 0.036 to 0.074, and the linear regression slope of the decreasing trend ranged from −0.02 to −0.05 per decade according to ISCCP results. The large interannual variation and decreasing trend are not reflected in model simulations. Additional efforts are still required to improve surface albedo simulations in GCMs and its mapping from satellite.

A Comparison of Simulated Clouds to ISCCP Data

Abstract An evaluation of the cloud parameterization scheme in the High-Resolution Regional Model (HRM) of the Deutscher Wetterdienst was conducted using data from the International Satellite Cloud Climatology Project (ISCCP). Uncertainties in the model and in the measurements were first quantified. Then, criteria for comparisons of simulated and measured data were chosen in order to identify model deficiencies. The simulated clouds were subsequently classified by their parameterization so model deficiencies could be easily attributed to a certain parameterization scheme. Following this evaluation, an overestimation of simulated mean cloud amount was identified as a deficiency of the HRM. The overestimation occurred mainly during the night and was due to an overprediction of subscale clouds at low-level emissivity heights. At medium-level emissivity heights during the day, the cloud amount is underpredicted. This leads to an underestimation of the diurnal cycle. These deficiencies were connected with the relative humidity parameterization used to characterize subscale cloudiness.

Long-term variability of solar direct and global radiation derived from ISCCP data and comparison with reanalysis data

Annual variations of solar radiation at the Earth’s surface may be strong and could seriously harm the return of investment for solar energy projects. This paper analyzes the long-term variability of broadband surface solar radiation based on 18 years of three-hourly satellite observations from the International Satellite Cloud Climatology Project (ISCCP). Direct normal irradiance (DNI) and global horizontal irradiance (GHI) at the surface are derived through radiative transfer calculations, using different physical input parameters describing the actual composition of the atmosphere. Validation of DNI is performed with two years of high resolution Meteosat-derived irradiance. Monthly averages show an average mean bias deviation of −1.7%. Results for DNI from the 18-year time series indicate strong and significant increases for several regions in the subtropics up to +4 W/m 2 per year, with exception of Australia, where a small decrease in DNI of –1 W/m 2 per year is observed. Inter-annual variability for DNI is very strong and sometimes exceeds 20%. Comparisons of calculations with and without volcanic aerosol reveal a decrease of up to 16% in annual averages due to volcano eruptions. Changes in GHI are much smaller and less significant. Results show a maximum increase of 0.8 W/m 2 per year and an annual variability of less than 4%. Volcano eruptions reduce annual averages of GHI by less than 2.2%. The two reanalysis data sets investigated differ strongly from each other and are far off the validated results derived from satellite data. Trends are weaker and less significant or even of opposite sign.