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Indicator-based frameworks for assessing farms’ environmental performance have become a resource for environmental knowledge regarding the impacts of agricultural practices. The present study explores whether a novel indicator-based direct payment system, which focuses on the farms’ environmental impact, could better target Swiss agricultural policy and help achieve its environmental goals. The system covers the environmental topics of biodiversity, nutrients and climate, plant protection products, and soil. Despite high direct payments, simulations with an agent-based agricultural sector model show that such indicator-based payments have a limited impact. For example, the decrease in the animal population is only moderate. Though direct payments alone can hardly lead to the desired reduction in Switzerland's environmental pollution, they could help make important contributions to a more targeted distribution of environmentally oriented direct payments and steer agricultural production in a more environmentally friendly way.

In recent decades, many sustainability indicators and methods have been developed at farm level, but a validated set of quantitative and scientifically-sound indicators covering all three dimensions of sustainability is still needed. For this reason, the sustainability method SALCAsustain was developed in order to estimate the environmental impact and economic and social situation of farms using a manageable number of indicators. The primary aim of this study was to assess the feasibility, explanatory power, and acceptability to farmers of the SALCAsustain methodical framework. To achieve this goal, SALCAsustain was applied for the first time to selected Swiss farms. In-depth personal feedback interviews were conducted to gain more insights into the feasibility and farmers’ acceptance of the method. The results showed that SALCAsustain is a feasible, acceptable and robust method for assessing farm sustainability based on a set of indicators. Correlation analysis demonstrated that the number of environmental indicators can be reduced due to high correlation, but that the correlation between environmental impact and socioeconomic indicators was generally low. Evaluation of responses to questionnaires and semi-structured interviews with farmers revealed that the majority would adjust their medium and long-term planning to achieve higher sustainability scores. Additional efforts are needed to speed up data collection and to refine plausibility checks, through exploiting the increasing digitalisation in agriculture. Recommendations and instructions on actions for more sustainable farm management are also needed.

A sample of 239 farm year observations of Swiss farms was assessed at the product group level for analyzing the relationship between environmental and economic performance and correlations between product groups (Milk, Cattle, Cereal, Beets, and Potatoes). The farms cover the production regions valley, hill and mountains and practice organic production or proof of ecological performance (PEP), the Swiss standard production. The environmental dimension was covered by nine impact categories calculated by the Swiss Agricultural Life Cycle Assessment method (SALCA). The impacts were aggregated using a data envelopment analysis (DEA). The economic dimension is assessed by the family workforce income per product group calculated from a full cost data set from the Swiss farm accountancy data network (FADN). Hereby, all indirect costs, which cannot be directly attributed to the product groups, were allocated using standard costs. We also included productivity as a third dimension in our analysis, quantified as output per area for crop products and output per animal livestock unit for the animal product groups. No trade-offs between the environmental efficiency and the economic performance were identified. On the contrary, for Cattle and Milk we found significant synergies (1.5 times more observations show synergies than no effect or trade-offs). Furthermore we found that productivity correlated positively with environmental efficiency for Milk (coefficient = 0.27), Cattle (coefficient = 0.38) and Cereals (coefficient = 0.30), but only for Cattle (coefficient = 0.17) and Potatoes (coefficient = 0.47) it correlated with economic performance. For all product groups except Cereals, the organic farming system had 5% to 10 higher environmental efficiency and 5%–26% higher economic performance than the PEP farms. Although the differences were not significant, a consistent decrease up to −20% in environmental performance and productivity was observed between the valley/hill and the mountain region. Our results show no indication that farmers maximize their productivity or economic performance at the cost of environmental efficiency. However, the large variability suggests that there is a) room for improvement in several dimension simultaneously, and b) that maximizing productivity does not seem to be a necessity for these improvements., Journal of Cleaner Production, 388, ISSN:0959-6526

Periodized nutrition is necessary to optimize training and enhance performance through the season. The Athlete&rsquo, s Plate (AP) is a nutrition education tool developed to teach athletes how to design their plates depending on training load (e.g., volume &times, intensity), from easy (E), moderate (M) to hard (H). The AP was validated, confirming its recommendations according to international sports nutrition guidelines. However, the AP had significantly higher protein content than recommended (up to 2.9 &plusmn, 0.5 g&middot, kg&minus, 1&middot, d&minus, 1, p &lt, 0.001 for H male). The aim of this study was to quantify the environmental impact (EnvI) of the AP and to evaluate the influence of meal type, training load, sex and registered dietitian (RD). The nutritional contents of 216 APs created by 12 sport RDs were evaluated using Computrition Software (Hospitality Suite, v. 18.1, Chatsworth, CA, USA). The EnvI of the AP was analyzed by life cycle assessment (LCA) expressed by the total amount of food on the AP, kg, and kcal, according to the Swiss Agricultural Life Cycle Assessment (SALCA) methodology. Higher EnvI is directly associated with higher training load when the total amount of food on the plate is considered for E (5.7 &plusmn, 2.9 kg CO2 eq/day), M (6.4 &plusmn, 1.5 kg CO2 eq/day), and H (8.0 &plusmn, 2.1 kg CO2 eq/day). Global warming potential, exergy and eutrophication are driven by animal protein and mainly beef, while ecotoxicity is influenced by vegetable content on the AP. The EnvI is influenced by the amount of food, training load and sex. This study is the first to report the degree of EnvI in sports nutrition. These results not only raise the need for sustainability education in sports nutrition in general, but also the urgency to modify the AP nutrition education tool to ensure sports nutrition recommendations are met, while not compromising the environment.

Describing the impact of farming on soil quality is challenging, because the model should consider changes in the physical, chemical, and biological status of soils. Physical damage to soils through heavy traffic was already analyzed in several life-cycle assessment studies. However, impacts on soil structure from grazing animals were largely ignored, and physically based model approaches to describe these impacts are very rare. In this study, we developed a new modeling approach that is closely related to the stress propagation method generally applied for analyzing compaction caused by off-road vehicles. We tested our new approach for plausibility using a comprehensive multi-year dataset containing detailed information on pasture management of several hundred Swiss dairy farms. Preliminary results showed that the new approach provides plausible outcomes for the two physical soil indicators &ldquo, macropore volume&rdquo, and &ldquo, aggregate stability&rdquo

The online version of the original article can be found at https://doi.org/10.1007/s11367-020-01790-0

Agricultural life cycle analysis (LCA) provides information about the environmental footprint of farming. Life cycle sustainability assessment (LCSA) includes social and economic indicators. As a contribution to LCSA, we developed an indicator measuring the impact of individual farms on visual landscape quality based on state-of-the-art theory for landscape aesthetic assessment and conforming to general LCA principles. The indicator is a composite consisting of two independent sub-indicators, the aggregated diversity indicator (ADI) and the area-weighted preference value (AWPV). Both sub-indicators are based on the preference values of the Swiss population for the most frequent crop types and farmland features. The two sub-indicators were calculated when the land-use types with an available preference value represented 75% of a farms’ utilised agricultural area. Following this rule, we were able to evaluate 91% of Swiss farms. The ADI measures a farm’s contribution to land-use diversity and to seasonal diversity, while the AWPV measures a farm’s contribution to perceived naturalness. The two sub-indicators are combined to form the composite landscape indicator (CLI). The two sub-indicators were computed from Swiss farm structure data for 2015 without additional data collection. Two scenarios were defined to test the independence of the two sub-indicators from each other and the response of the CLI against landscape changes. The first scenario enriched the crop diversity of the farms to test the response of the ADI. The second scenario increased the number of standard trees on farms to test the AWPV. The results showed that the two sub-indicators complement one another by responding to different changes in landscape quality. In both cases, the CLI showed on average increasing values after enriching crop diversity or increasing the number of standard trees on selected farms. The solid conceptual grounding of the two sub-indicators in landscape aesthetic theory combined with LCA principles renders them reproducible and independent of the observer.

Farm accountancy data network normally serves two objectives: estimating the national agricultural income and providing detailed data at production branch level. This study proposes a new sampling design with two independent samples A and B each addressing one of these objectives. The design is based on stratified sampling along with a combination of optimal and power allocation. Violations of precision targets will be avoided by the collapsing of strata. We assess the accuracy of key structural and economic variables by means of a Monte Carlo simulation. Multiple linear regression has been shown to be a powerful tool for imputing financial data to individual census farms. The results illustrate that the proposed design meets prescribed precision and feasibility restrictions at both the single-strata and national levels. It is further demonstrated that unifying samples A and B helps to significantly reduce the survey costs.

Process-based crop models are widely used in decision support systems or to assess impacts of climate change on agriculture at different spatial scales. They include crop and/or cultivar-specific parameters that need to be calibrated. However, the availability of reference data is often limited. An alternative is to use yield records from widely available Farm Accountancy Data Network (FADN). The goal of this study was therefore to propose and test a crop model calibration procedure that makes use of FADN data. To account for the lack of management information in the FADN databases, the concept of ‘Meteorologically Possible Yield’ (MPY) was adopted. This concept is particularly relevant in the context of climate impact studies as MPYs are by definition only driven by climate variability. As an example, the procedure was applied to calibrate the generic crop model CropSyst for a region located in north-eastern Switzerland. Validation using data from a long-term field trial with detailed information on fertilizer applications showed that the proposed procedure provides robust simulation results and is therefore suitable for climate impact studies in regions where detailed experimental data are scarce. In a case study application, the transferability of the local calibration to a site with drier conditions was tested and simulation results for this new site were compared to results obtained using local recalibration. Results showed that predicted yields can differ substantially and the differences can be strongly amplified when impacts of climate change are considered. This highlights the need for adjusting model calibrations to local site conditions and for considering parameter uncertainties in climate impact studies.

We present an analysis of the climatologies and the interannual variations of 2 m temperature and precipitation for ensemble simulations performed with the CCLM for Europe. Special focus is put on the analysis of the intra-ensemble variabilities. CCLM predicts a pronounced cold bias in spring and summer over large parts of Europe north of approximately 40N. In contrast, the model suffers from a distinct warm bias in Northern Europe in winter. Precipitation is clearly overestimated in Europe during all seasons, with the exception of a dry summer bias extending from Italy to the Black Sea. CCLM distinctly overestimates the precipitation variations over most of Europe while temperature variations tend to be underestimated in Northern Europe and overestimated in the Mediterranean. The study shows that temperature and precipitation biases for both the mean and interannual variability are critically dependent on the ensemble member that is selected for the evaluation. It is therefore essential to use ensemble simulations for model validation in order to avoid random model biases for both the mean and interannual variability of temperature and precipitation. The results suggest that the intra-ensemble variability relative to the model bias is higher for interannual variability than for the mean. It can thus be concluded that the model's performance in predicting climate extremes cannot be properly evaluated using only one model simulation. Finally, we also compare several extreme indices (largest number of consecutive frost days, summer days, wet days, and dry days). The study shows that the cold summer bias leads to an underestimation of the largest number of consecutive summer days over major parts of the model domain. The wet model bias in Central and Northern Europe leads to a distinct underestimation of the largest number of consecutive dry days.

This study evaluates the skin temperature (ST) datasets of the International Satellite Cloud Climatology Project (ISCCP) D satellite product, the ISCCP FD satellite product, the 40-yr ECMWF Re-Analysis (ERA-40), the NCEP–NCAR Reanalysis, and the NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis. The monthly anomalies of all the datasets are correlated to each other and to most of the ground-truth stations with correlation coefficients >0.50. To evaluate their qualities, the 5 ST datasets are used to calculate clear-sky (CS) outgoing longwave radiation (OLR) and upward surface longwave radiation (USLR); the results are compared with the Earth Radiation Budget Experiment (ERBE) satellite observation and 14 surface stations. The satellite-derived STs and ERA-40 ST tend to bias high on hot deserts (e.g., Sahara Desert), and the reanalyzed STs tend to bias low in mountain areas (e.g., Tibet). In Northern Hemisphere high-latitude regions (tundra, wetlands, deciduous needle-leaf forests, and sea ice), the CS OLR anomalies calculated using the satellite-derived STs have higher correlations and lower root-mean-squared errors with the ERBE satellite observation than those derived from using the reanalyzed STs. ERA-40 underestimates the amplitude of the seasonal ST over glaciers. All the reanalysis products (ERA-40, NCEP–NCAR, and NCEP–DOE AMIP-II) overestimate the ST during partial sea ice–covered periods in the middle-high-latitude oceans. Nonetheless, suspected spurious noises with an amplitude of 2 K in the satellite-derived STs produce a physically unviable anomaly over earth’s surface where the amplitude of the anomaly is weak (such as open-water bodies, croplands, rain forest, grasslands, hot deserts, and cold deserts). Better land–ocean–ice schemes for a reanalysis should be developed for desert regions, high plateaus, fractional sea ice–covered oceans, and seasonally snow-covered lands, where the largest ST errors are identified.

Changes of the summer evapotranspiration regime under increased levels of atmospheric greenhouse gases are discussed for three Alpine river basins on the basis of a new set of simulations carried out with a high-resolution hydrological model. The climate change signal was inferred from the output of two simulations with a state-of-the-art global climate model (GCM), a reference run valid for 1961–1990 and a time-slice simulation valid for 2071–2100 under forcing from the A2 IPCC emission scenario. In this particular GCM experiment and with respect to the Alpine region summer temperature was found to increase by 3 to 4°C, whereas precipitation was found to decrease by 10 to 20%. Global radiation and water vapor pressure deficit were found to increase by about 5% and 2 hPa, respectively. On this background, an overall increase of potential evapotranspiration of about 20% relative to the baseline was predicted by the hydrological model, with important variations between but also within individual basins. The results of the hydrological simulations also revealed a reduction in the evapotranspiration efficiency that depends on altitude. Accordingly, actual evapotranspiration was found to increase at high altitudes and to the south of the Alps, but to decrease in low elevation areas of the northern forelands and in the inner-Alpine domain. Such a differentiation does not appear in the GCM scenario, which predicts an overall increase in evapotranspiration over the Alps. This underlines the importance of detailed simulations for the quantitative assessment of the regional impact of climate change on the hydrological cycle.

Land surface albedo, snow cover fraction (SCF), and snow depth (SD) from two versions of the ECHAM climate model are compared to available ground-based and remote-sensed climatologies. ECHAM5 accurately reproduces the annual cycle of SD and correctly captures the timing of the snowmelt. ECHAM4, in contrast, simulates an excessive Eurasian snow mass in spring due to a delayed snowmelt. Annual cycles of continental snow cover area (SCA) are captured fairly well in both ECHAM4 and ECHAM5. The negative SCA trend observed during the last two decades of the twentieth century is evident also in the ECHAM5 simulation but less pronounced. ECHAM5 captures the interannual variability of SCA reasonably well, which is in contrast with results that were reported earlier for second-phase Atmospheric Model Intercomparison Project (AMIP II) models. An error analysis revealed that, for studies on SCA, it is essential to test the data records for their homogeneity and trends. The second part of the paper compares simulated surface albedos with remote-sensed climatologies derived from PINKER and the Moderate Resolution Imaging Spectroradiometer (MODIS). ECHAM5 is in better agreement with observations in the Himalayan–Tibetan area than ECHAM4. In contrast, the positive surface albedo bias over boreal forests under snow conditions in ECHAM4 is even more pronounced in ECHAM5. This deficiency is mainly due to the neglect of the snow-masking effect of stems and branches after trees have lost their foliage. The analysis demonstrates that positive biases in the SCA are not necessarily related to positive albedo biases. Furthermore, an overestimation of the area-averaged SD is not always related to positive SCF anomalies since the relationship between SD and SCF is highly nonlinear.

Variations in solar radiation incident at Earth's surface profoundly affect the human and terrestrial environment. A decline in solar radiation at land surfaces has become apparent in many observational records up to 1990, a phenomenon known as global dimming. Newly available surface observations from 1990 to the present, primarily from the Northern Hemisphere, show that the dimming did not persist into the 1990s. Instead, a widespread brightening has been observed since the late 1980s. This reversal is reconcilable with changes in cloudiness and atmospheric transmission and may substantially affect surface climate, the hydrological cycle, glaciers, and ecosystems.

Snow cover fraction (SCF) has a significant influence on the surface albedo and thus on the radiation balance and surface climate. Long-term three dimensional simulations with general circulation models (GCMs) show that the SCF greatly affects the climate in the Northern Hemisphere. By means of both ground observations and remotely sensed data, several deficiencies in the SCF simulated by the current ECHAM4 GCM were identified: over mountainous areas a substantial overestimation in the SCF was found whereas flat areas showed a distinctly underestimated SCF. This work proposes a new parametrization of the SCF for use in GCMs. Evaluations illustrate that it is beneficial to distinguish between the following three terrains: (1) flat, non-forested areas, (2) mountainous regions and (3) forests. The modified SCF parametrization for flat, non-forested areas was derived by using global datasets of ground-based snow depth and remote sensing observations of snow cover data. A 3-dimensional ECHAM4 simulation showed that this modification raises the SCF by up to approximately 20%, mainly in areas with a relatively thin snow cover. The comparison between remotely sensed and simulated mean monthly surface albedo revealed a significant overestimation of the surface albedo in snow-covered mountainous areas. An extension of the current SCF parametrization in ECHAM4 to take into account mountain effects, based on the French climate model Arpege, yielded a close agreement with satellite-derived surface albedo. The adoption of the submodel for snow albedo, as used in the Canadian Land Surface Scheme (CLASS), combined with a newly developed simple snow interception model, demonstrated the ability to capture the main physical processes of snow-covered canopies, including the albedo. The validation of the new parametrization with Boreal Ecosystem-Atmosphere Study (BOREAS) field data showed that the modification is appropriate to capture the main features of the albedo over snow-covered forests during and after heavy snowfall events. Furthermore, the proposed modification has a beneficial impact on the delayed snow melt in spring, a well-known problem in many current GCMs: The simulated surface albedo over the boreal forests decreases by approximately 0.1 during winter and spring, which is in better agreement with ground-based observations. This induces a significant rise in the surface temperature over extended parts of Eurasia and North America in late spring, which subsequently yields a faster snowmelt and an accelerated retreat of the snow line.

Annual cycles of monthly albedos simulated with a general circulation model (GCM) are compared with surface observations. The data observed at 35 stations are retrieved from the Global Energy Balance Archive (GEBA) and drawn from the soil moisture and meteorological observations in the former Soviet Union. The model data are obtained with the ECHAM4 GCM in a ten-year simulation of the present-day climate at T106 resolution. The model calculated albedo values are modified before they are compared with the surface observations: They are interpolated to the stations and adjusted to account for altitude differences and fractional forest area. During the snow-free period, the model underestimates the albedo by up to 0.05 at the stations (with values between 0.2 and 0.25 measured over short grass) because the albedo for grassland is too low in the model. During the period with seasonal snow cover, the model underestimates the albedo by up to 0.2 at stations in Russia, Scandinavia and Canada, which experience severe winters. This underestimation is due to an oversimplified parameterization of the snow covered grid fraction and an inadequate linear relation between snow albedo and temperature. The derivative of albedo with respect to the forest fraction implemented in ECHAM is in line with the observations, although a small overestimation of the model’s gradient has been detected.

This study assesses a new standard-output-based approach for the target population of the Swiss Farm Accountancy Data Network. The analysis depicts that the standard output per farm is an appropriate measure for delimiting the target population. It is shown that a major fraction of the summed census livestock and crop area is covered by the target populations. It is demonstrated that the threshold value significantly impacts on statistical measures of structural variables. Furthermore, the new scheme was compared to three other concepts for the target population. The investigation reveals substantial differences among the four different concepts for the target population, regarding both the coverage of structural variables and the number of farms per farm type.

This paper describes the software metrics analysis of 10 releases of a real-time telephone switching system developed by German telecommunications firm. We developed a metrics program that computes the standard complexity metrics plus the number of global and resource variables changed and referenced. The releases of the real-time software satisfies published laws of software evolution, e.g. continuing change, increasing entropy, and total change is not uniform over the changed modules. The data also supports Harrison and Cook''s program maintenance decision model. We propose the change standard deviation as a threshold for their model. We found that the information flow metrics (global and resource variables referenced or changed) characterize real-time complexity better than the standard software complexity metrics, e.g. Halstead''s Software Science, LOC, McCabe''s Cyclomatic Complexity. We also investigated the relations between programming hours for the various releases and the program changes and changes in metric values.

Atmospheric Measurement Techniques, 4 (5), ISSN:1867-1381, ISSN:1867-8548

The integrity of the Baseline Surface Radiation Network (BSRN) radiation monthly averages are assessed by investigating the impact on monthly means due to the frequency of data gaps caused by missing or discarded high time resolution data. The monthly statistics, especially means, are considered to be important and useful values for climate research, model performance evaluations and for assessing the quality of satellite (time- and space-averaged) data products. The study investigates the spread in different algorithms that have been applied for the computation of monthly means from 1-min values. The paper reveals that the computation of monthly means from 1-min observations distinctly depends on the method utilized to account for the missing data. The intra-method difference generally increases with an increasing fraction of missing data. We found that a substantial fraction of the radiation fluxes observed at BSRN sites is either missing or flagged as questionable. The percentage of missing data is 4.4%, 13.0%, and 6.5% for global radiation, direct shortwave radiation, and downwelling longwave radiation, respectively. Most flagged data in the shortwave are due to nighttime instrumental noise and can reasonably be set to zero after correcting for thermal offsets in the daytime data. The study demonstrates that the handling of flagged data clearly impacts on monthly mean estimates obtained with different methods. We showed that the spread of monthly shortwave fluxes is generally clearly higher than for downwelling longwave radiation. Overall, BSRN observations provide sufficient accuracy and completeness for reliable estimates of monthly mean values. However, the value of future data could be further increased by reducing the frequency of data gaps and the number of outliers. It is shown that two independent methods for accounting for the diurnal and seasonal variations in the missing data permit consistent monthly means to within less than 1 W m−2 in most cases. The authors suggest using a standardized method for the computation of monthly means which addresses diurnal variations in the missing data in order to avoid a mismatch of future published monthly mean radiation fluxes from BSRN. The application of robust statistics would probably lead to less biased results for data records with frequent gaps and/or flagged data and outliers. The currently applied empirical methods should, therefore, be completed by the development of robust methods., Atmospheric Measurement Techniques, 4 (2), ISSN:1867-1381, ISSN:1867-8548

[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.

Climatic Change, 79 (1-2), ISSN:0165-0009, ISSN:1573-1480

[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.

Snow cover fraction (SCF) has a significant influence on the surface albedo and thus on the radiation balance and surface climate. Long-term three dimensional simulations with General Circulation Models (GCMs) showed that the SCF greatly affects the climate in the Northern Hemisphere.

[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.