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[1] Decadal changes in shortwave irradiance at the Earth's surface are estimated for the period from approximately 1960 through to 2000 from pyranometer records stored in the Global Energy Balance Archive. For this observational period, estimates could be calculated for a total of 140 cells of the International Satellite Cloud Climatology Project grid (an equal area 2.5° × 2.5° grid at the equator) using regression models allowing for station effects. In large regions worldwide, shortwave irradiance decreases in the first half of the observational period, recovers from the decrease in the 1980s, and thereafter increases, in line with previous reports. Years of trend reversals are determined for the grid cells which are best described with a second-order polynomial model. This reversal of the trend is observed in the majority of the grid cells in the interior of Europe and in Japan. In China, shortwave irradiance recovers during the 1990s in the majority of the grid cells in the southeast and northeast from the decrease observed in the period from 1960 through to 1990. A reversal of the trend in the 1980s or early 1990s is also observed for two grid cells in North America, and for the grid cells containing the Kuala Lumpur (Malaysia), Singapore, Casablanca (Morocco), Valparaiso (Chile) sites, and, noticeably, the remote South Pole and American Samoa sites. Negative trends persist, i.e., shortwave radiation decreases, for the observational period 1960 through to 2000 at the European coasts, in central and northwest China, and for three grid cells in India and two in Africa.

[1] This paper describes how a statistical scale-space technique can be used for evaluating climate models. A difference image between model and validation data is used as input. Hypothesis testing is performed at each difference pixel for a broad range of image resolutions (or scales). This approach circumvents some of the classical problems of hypothesis testing. An area, at a particular scale, is claimed to be significant if it is sufficiently different from zero in the difference image. such differences are called features. As the scale gradually increases from fine to coarse, features are created, they grow and merge and may finally annihilate. The scale-space algorithm produces maps for statistical inference and the degree of significance at different locations. The adapted scale-space technique was applied for validation of ECHAM5 Global Circulation Model surface albedo against a remote sensing surface albedo climatology. Overall, the largest discrepancies were detected over snow and ice-covered areas, and ECHAM5 was found to overestimate the albedo compared to the albedo climatology for all scales in March. Successively coarser spatial scales resulted in more and larger significant areas in the difference image. At the finest scales (280 km) very few areas of significant albedo differences were detected because of relatively high interannual variability for the areas of largest difference. At 1100 km, significant albedo differences were found in the southern part of the Arctic Ocean adjacent to the ice edge, probably because of the different positions of the ice edge in the two data sets. A scale of 2500 km was found to be reasonable for validating albedo as the statistical significance agrees well with differences meaningful from a climatologist's point of view. At this scale most of the snow covered regions in Northern Eurasia with high positive differences and relatively low interannual variability were found to be significant.

[1] Surface albedo (ALB), snow cover fraction (SCF) and snow water equivalent (SWE) of state-of-the-art coupled climate models are compared and validated against ground-based and remote-sensed climatologies. Most IPCC AR4 climate models predict excessive snow mass in spring and suffer from a delayed spring snow melt while the onset of the snow accumulation is generally well captured. This positive SWE bias is mainly caused by too heavy snowfall during the winter and spring season. Seasonal cycles of snow cover area (SCA) at continental scales are captured reasonably well by most participating models. Two models clearly overestimate SCA over both Eurasia and North America. Year-to-year variations are reasonably well captured over both Eurasia and North America in winter and spring. The most pronounced underestimation in the interannual SCA variability is generally simulated during snow melt. The pronounced negative SCA trend that has been observed from 1979 to 2000 is only partly reproduced in the AR4 model simulations. Furthermore, the computed trends show a large spread among the models. Results from time slice simulations with the ECHAM5 climate model suggest that accurate sea surface temperatures are vital for correctly predicting SCA trends. Simulated global mean annual surface albedos are slightly above the remote-sensed surface albedo estimates. The participating AR4 models generally reproduce the seasonal cycle of the surface albedo with sufficient accuracy while systematic albedo biases are predicted over both snow-free and snow-covered areas, with the latter being distinctly more pronounced. The study shows that the surface albedo over snow-covered forests is probably too high in various state-of-the-art global climate models. The analysis demonstrates that positive biases in SCA are not necessarily related to positive albedo biases. Furthermore, an overestimation of area-averaged SWEs is not necessarily related to positive SCA anomalies since the relationship between SWE and SCF is highly nonlinear.

[1] Global land surface albedo data sets derived from the Terra Moderate-Resolution Imaging Spectroradiometer (MODIS) from March 2000 to present have been completed for ready use by the global modeling community. This paper describes these albedo and bidirectional reflectance distribution function Climate Modeling Grid products and their variability within major global vegetation types. Preliminary results based on collection 4 data reveal that these coarse resolution (0.05°), geographic (latitude/longitude), global albedos have spatial and temporal patterns appropriate for the underlying land cover classes, further encouraging modelers to introduce albedos as functions of ground cover, geographic location, temporal season, and spatial resolution in the various climate-modeling schemes.

[1] Moderate-Resolution Imaging Spectroradiometer (MODIS) surface albedo at high spatial and spectral resolution is compared with other remotely sensed climatologies, ground-based data, and albedos simulated with the European Center/Hamburg 4 (ECHAM4) global climate model at T42 resolution. The study demonstrates the importance of MODIS data in assessing and improving albedo parameterizations in weather forecast and climate models. The remotely sensed PINKER surface albedo climatology follows the MODIS estimates fairly well in both the visible and near-infrared spectra, whereas ECHAM4 simulates high positive albedo biases over snow-covered boreal forests and the Himalayas. In contrast, the ECHAM4 albedo is probably too low over the Sahara sand desert and adjacent steppes. The study clearly indicates that neglecting albedo variations within T42 grid boxes leads to significant errors in the simulated regional climate and horizontal fluxes, mainly in mountainous and/or snow-covered regions. MODIS surface albedo at 0.05 resolution agrees quite well with in situ field measurements collected at Baseline Surface Radiation Network (BSRN) sites during snow-free periods, while significant positive biases are found under snow-covered conditions, mainly due to differences in the vegetation cover at the BSRN site (short grass) and the vegetation within the larger MODIS grid box. Black sky (direct beam) albedo from the MODIS bidirectional reflectance distribution function model captures the diurnal albedo cycle at BSRN sites with sufficient accuracy. The greatest negative biases are generally found when the Sun is low. A realistic approach for relating albedo and zenith angle has been proposed. Detailed evaluations have demonstrated that ignoring the zenith angle dependence may lead to significant errors in the surface energy balance.

[1] This study investigates the impact of spectrally resolved surface albedo on the total surface albedo. The neglect of albedo variation within the shortwave spectrum may lead to substantial errors as the atmospheric water greatly influences the spectral distribution of the incoming radiation. It is shown that ignoring the spectral dependence of the surface albedo will affect the predicted climate. The study reveals substantial changes in the climate over northern Africa when modifying the surface albedo of the Sahara deserts. Detailed information is given how the European Center/Hamburg General Circulation Model (ECHAM4) can be extended to include surface boundary conditions for both the visible and near-infrared incoming radiation. This comprises global climatologies for both the visible and near-infrared albedo for snow-free conditions, as well as the corresponding albedo values over snow, land-/sea ice and over snow covered forests. Comparisons between several available surface albedo climatologies and a newly compiled albedo data set show substantial scatter in estimated albedos. The largest albedo differences are found in snow covered forest regions as well as in arid and semi-arid terrains.