Reto Stockli

European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset

Vegetation distribution and state have been measured since 1981 by the AVHRR (Advanced Very High Resolution Radiometer) instrument through satellite remote sensing. In this study a correction method is applied to the Pathfinder NDVI (Normalized Difference Vegetation Index) data to create a continuous European vegetation phenology dataset of a 10-day temporal and 0.1° spatial resolution; additionally, land surface parameters for use in biosphere–atmosphere modelling are derived. The analysis of time-series from this dataset reveals, for the years 1982–2001, strong seasonal and interannual variability in European land surface vegetation state. Phenological metrics indicate a late and short growing season for the years 1985–1987, in addition to early and prolonged activity in the years 1989, 1990, 1994 and 1995. These variations are in close agreement with findings from phenological measurements at the surface; spring phenology is also shown to correlate particularly well with anomalies in winter temperature and winter North Atlantic Oscillation (NAO) index. Nevertheless, phenological metrics, which display considerable regional differences, could only be determined for vegetation with a seasonal behaviour. Trends in the phenological phases reveal a general shift to earlier (−0.54 days year−1) and prolonged (0.96 days year−1) growing periods which are statistically significant, especially for central Europe.

Evaluation of forest snow processes models (SnowMIP2)

Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up t ...

Cloud observations in Switzerland using hemispherical sky cameras

We present observations of total cloud cover and cloud type classification results from a sky camera network comprising four stations in Switzerland. In a comprehensive intercomparison study, records of total cloud cover from the sky camera, long-wave radiation observations, Meteosat, ceilometer, and visual observations were compared. Total cloud cover from the sky camera was in 65–85% of cases within ±1 okta with respect to the other methods. The sky camera overestimates cloudiness with respect to the other automatic techniques on average by up to 1.1 ± 2.8 oktas but underestimates it by 0.8 ± 1.9 oktas compared to the human observer. However, the bias depends on the cloudiness and therefore needs to be considered when records from various observational techniques are being homogenized. Cloud type classification was conducted using the k-Nearest Neighbor classifier in combination with a set of color and textural features. In addition, a radiative feature was introduced which improved the discrimination by up to 10%. The performance of the algorithm mainly depends on the atmospheric conditions, site-specific characteristics, the randomness of the selected images, and possible visual misclassifications: The mean success rate was 80–90% when the image only contained a single cloud class but dropped to 50–70% if the test images were completely randomly selected and multiple cloud classes occurred in the images.

North American gross primary productivity: regional characterization and interannual variability

Seasonality and interannual variability in North American photosynthetic activity reflect potential patterns of climate variability. We simulate 24 yr (1983–2006) and evaluate regional and seasonal contribution to annual mean gross primary productivity (GPP) as well as its interannual variability. The highest productivity occurs in Mexico, the southeast United States and the Pacific Northwest. Annual variability is largest in tropical Mexico, the desert Southwest and the Midwestern corridor. We find that no single region or season consistently determines continental annual GPP anomaly. GPP variability is dependent upon soil moisture availability in low- and mid-latitudes, and temperature in the north. Soilmoisture is a better predictor than precipitation as it integrates precipitation events temporally. The springtime anomaly is the most frequent seasonal contributor to the annual GPP variability. No climate mode (i.e. ENSO, NAM) can be associated with annual or seasonal variability over the entire continent. We define a region extending from the Northeast United States through the midwest and into the southwestern United States and northernMexico that explains a significant fraction of the variability in springtime GPP. We cannot correlate this region to a single mechanism (i.e. temperature, precipitation or soil moisture) or mode of climate variability.

Impact of Scale and Aggregation on the Terrestrial Water Exchange: Integrating Land Surface Models and Rhône Catchment Observations

Land surface models (LSMs) used in climate modeling include detailed above-ground biophysics but usually lack a good representation of runoff. Both processes are closely linked through soil moisture. Soil moisture however has a high spatial variability that is unresolved at climate model grid scales. Physically based vertical and horizontal aggregation methods exist to account for this scaling problem. Effects of scaling and aggregation have been evaluated in this study by performing catchment-scale LSM simulations for the Rhone catchment. It is found that evapotranspiration is not sensitive to soil moisture over the Rhone but it largely controls total runoff as a residual of the terrestrial water balance. Runoff magnitude is better simulated when the vertical soil moisture fluxes are resolved at a finer vertical resolution. The use of subgrid-scale topography significantly improves both the timing of runoff on the daily time scale (response to rainfall events) and the magnitude of summer baseflow (from seasonal groundwater recharge). Explicitly accounting for soil moisture as a subgrid-scale process in LSMs allows one to better resolve the seasonal course of the terrestrial water storage and makes runoff insensitive to the used grid scale. However, scale dependency of runoff to above-ground hydrology cannot be ignored: snowmelt runoff from the Alpine part of the Rhone is sensitive to the spatial resolution of the snow scheme, and autumnal runoff from the Mediterranean part of the Rhone is sensitive to the spatial resolution of precipitation.

Albedo climatology for European land surfaces retrieved from AVHRR data (1990–2014) and its spatial and temporal analysis from green‐up to vegetation senescence

Satellite-based, long-term records of surface albedo characterization that accurately capture spatial and temporal patterns are essential to develop climate models and to monitor the impact of land use changes on the terrestrial energy and water balance. This study presents the first Bidirectional Reflectance Distribution Function (BRDF) and albedo data set derived from the Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage reflectance data acquired on board National Oceanic and Atmospheric Administration and Meteorological Operational platforms from 1990 to 2014 over Europe. The objectives of this paper are to describe the data sets surface albedo climatology and anomalies in the visible, near-infrared, and shortwave broadbands for the growing season months of May to September in order to facilitate utilization of the data by the climate modeling communities. The results demonstrate that the AVHRR BRDF and albedo data have temporal and spatial patterns that are appropriate for the underlying predominant land cover type and accurately reflect the associated climate variation. Visible and near-infrared broadband albedo anomalies are found to be contrasting in most years, and their spatial distributions depict responses of vegetation to climate events (e.g., heat waves). Visible albedo of crops and near-infrared albedo of pastures show a higher interannual variation than respective albedos of other snow-free land covers, while the interannual standard deviations are found to be lower than 0.015. Our findings indicate the importance of taking into account the spectrally distinct variability of surface albedo when analyzing its complex spatiotemporal dynamics in climate-related research.