A physics-based correction model for homogenizing sub-daily temperature series

A new physics-based technique for correcting inhomogeneities present in sub-daily temperature records is proposed. The approach accounts for changes in the sensor-shield characteristics that affect the energy balance dependent on ambient weather conditions (radiation, wind). An empirical model is formulated that reflects the main atmospheric processes and can be used in the correction step of a homogenization procedure. The model accounts for short- and long-wave radiation fluxes (including a snow cover component for albedo calculation) of a measurement system, such as a radiation shield. One part of the flux is further modulated by ventilation. The model requires only cloud cover and wind speed for each day, but detailed site-specific information is necessary. The final model has three free parameters, one of which is a constant offset. The three parameters can be determined, e.g., using the mean offsets for three observation times. The model is developed using the example of the change from the Wild screen to the Stevenson screen in the temperature record of Basel, Switzerland, in 1966. It is evaluated based on parallel measurements of both systems during a sub-period at this location, which were discovered during the writing of this paper. The model can be used in the correction step of homogenization to distribute a known mean step-size to every single measurement, thus providing a reasonable alternative correction procedure for high-resolution historical climate series. It also constitutes an error model, which may be applied, e.g., in data assimilation approaches.

Volcanic forcing for climate modeling: a new microphysics-based data set covering years 1600–present

As the understanding and representation of the impacts of volcanic eruptions on climate have improved in the last decades, uncertainties in the stratospheric aerosol forcing from large eruptions are now linked not only to visible optical depth estimates on a global scale but also to details on the size, latitude and altitude distributions of the stratospheric aerosols. Based on our understanding of these uncertainties, we propose a new model-based approach to generating a volcanic forcing for general circulation model (GCM) and chemistry–climate model (CCM) simulations. This new volcanic forcing, covering the 1600–present period, uses an aerosol microphysical model to provide a realistic, physically consistent treatment of the stratospheric sulfate aerosols. Twenty-six eruptions were modeled individually using the latest available ice cores aerosol mass estimates and historical data on the latitude and date of eruptions. The evolution of aerosol spatial and size distribution after the sulfur dioxide discharge are hence characterized for each volcanic eruption. Large variations are seen in hemispheric partitioning and size distributions in relation to location/date of eruptions and injected SO2 masses. Results for recent eruptions show reasonable agreement with observations. By providing these new estimates of spatial distributions of shortwave and long-wave radiative perturbations, this volcanic forcing may help to better constrain the climate model responses to volcanic eruptions in the 1600–present period. The final data set consists of 3-D values (with constant longitude) of spectrally resolved extinction coefficients, single scattering albedos and asymmetry factors calculated for different wavelength bands upon request. Surface area densities for heterogeneous chemistry are also provided.

Understanding the performance of the FLake model over two African Great Lakes

The ability of the one-dimensional lake model FLake to represent the mixolimnion temperatures for tropical conditions was tested for three locations in East Africa: Lake Kivu and Lake Tanganyika's northern and southern basins. Meteorological observations from surrounding automatic weather stations were corrected and used to drive FLake, whereas a comprehensive set of water temperature profiles served to evaluate the model at each site. Careful forcing data correction and model configuration made it possible to reproduce the observed mixed layer seasonality at Lake Kivu and Lake Tanganyika (northern and southern basins), with correct representation of both the mixed layer depth and water temperatures. At Lake Kivu, mixolimnion temperatures predicted by FLake were found to be sensitive both to minimal variations in the external parameters and to small changes in the meteorological driving data, in particular wind velocity. In each case, small modifications may lead to a regime switch, from the correctly represented seasonal mixed layer deepening to either completely mixed or permanently stratified conditions from similar to 10 m downwards. In contrast, model temperatures were found to be robust close to the surface, with acceptable predictions of near-surface water temperatures even when the seasonal mixing regime is not reproduced. FLake can thus be a suitable tool to parameterise tropical lake water surface temperatures within atmospheric prediction models. Finally, FLake was used to attribute the seasonal mixing cycle at Lake Kivu to variations in the near-surface meteorological conditions. It was found that the annual mixing down to 60m during the main dry season is primarily due to enhanced lake evaporation and secondarily to the decreased incoming long wave radiation, both causing a significant heat loss from the lake surface and associated mixolimnion cooling.

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.