Jan Cermak

Challenges in Combining Projections from Multiple Climate Models

Recent coordinated efforts, in which numerous general circulation climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multimodel ensembles sample initial conditions, parameters, and structural uncertainties in the model design, and they have prompted a variety of approaches to quantifying uncertainty in future climate change. International climate change assessments also rely heavily on these models. These assessments often provide equal-weighted averages as best-guess results, assuming that individual model biases will at least partly cancel and that a model average prediction is more likely to be correct than a prediction from a single model based on the result that a multimodel average of present-day climate generally outperforms any individual model. This study outlines the motivation for using multimodel ensembles and discusses various challenges in interpreting them. Among these challenges are that the number of models in these ensembles is usually small, their distribution in the model or parameter space is unclear, and that extreme behavior is often not sampled. Model skill in simulating present-day climate conditions is shown to relate only weakly to the magnitude of predicted change. It is thus unclear by how much the confidence in future projections should increase based on improvements in simulating present-day conditions, a reduction of intermodel spread, or a larger number of models. Averaging model output may further lead to a loss of signal— for example, for precipitation change where the predicted changes are spatially heterogeneous, such that the true expected change is very likely to be larger than suggested by a model average. Last, there is little agreement on metrics to separate ‘‘good’’ and ‘‘bad’’ models, and there is concern that model development, evaluation, and posterior weighting or ranking are all using the same datasets. While the multimodel average appears to still be useful in some situations, these results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models.

Ground Fog Detection from Space Based on MODIS Daytime Data—A Feasibility Study

Abstract The distinction made by satellite data between ground fog and low stratus is still an open problem. A proper detection scheme would need to make a determination between low stratus thickness and top height. Based on this information, stratus base height can be computed and compared with terrain height at a specific picture element. In the current paper, a procedure for making the distinction between ground fog and low-level stratus is proposed based on Moderate Resolution Imaging Spectroradiometer (MODIS, flying on board the NASA Terra and Aqua satellites) daytime data for Germany. Stratus thickness is alternatively derived from either empirical relationships or a newly developed retrieval scheme (lookup table approach), which relies on multiband albedo and radiative transfer calculations. A trispectral visible–near-infrared (VIS–NIR) approach has been proven to give the best results for the calculation of geometrical thickness. The comparison of horizontal visibility data from synoptic observing...

A feasibility study of daytime fog and low stratus detection with TERRA/AQUA‐MODIS over land

A scheme for the detection of fog and low stratus over land during daytime based on data of the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument is presented. The method is based on an initial threshold test procedure in the MODIS solar bands 1–7 (0.62–2.155µm). Fog and low stratus detection generally relies on the definition of minimum and maximum fog and low stratus properties, which are converted to spectral thresholds by means of radiative transfer calculations (RTC). Extended sensitivity studies reveal that thresholds mainly depend on the solar zenith angle and, hence, illumination-dependent threshold functions are developed. Areas covered by snow, ice and mid-/high-level clouds as well as bright/hazy land surfaces are omitted from the initial classification result by means of a subsequent cloud-top height test based on MODIS IR band 31 (at 12 µm) and a NIR/VIS ratio test. The validation of the final fog and low stratus mask generally shows a satisfactory performance of the scheme. Validation problems occur due to the late overpass time of the TERRA platform and the time lag between SYNOP and satellite observations. Apparent misclassifications are mainly found at the edge of the fog layers, probably due to over- or underestimation of fog and low stratus cover in the transition zone from fog to haze. Copyright

Detecting ground fog from space-a microphysics-based approach

The detection of ground fog from satellite data is of interest in operational nowcasting applications, as well as in studies of the climate system. A discrimination between fog at the ground and other low-stratus situations from satellite data requires information on cloud vertical geometry to establish whether the cloud touches the ground. This article introduces a technique that allows for the discrimination between low stratus and (ground) fog on the basis of geostationary satellite imagery. The cloud-base height is derived using a subadiabatic model of cloud microphysics. In this model, the cloud base is varied until model liquid–water path matches that retrieved from satellite data. The performance of this technique is shown to be good in a comparison with METeorological Aerodrome Report data comprising 1030 satellite scenes. With a hit rate of 81% and a threat score of 0.62, the skill is satisfactory.

Formation of Convective Clouds at the Foothills of the Tropical Eastern Andes (South Ecuador)

Abstract This study examines the seasonal and diurnal dynamics of convective cloud entities—small cells and a mesoscale convective complex–like pattern—in the foothills of the tropical eastern Andes. The investigation is based on Geostationary Operational Environmental Satellite-East (GOES-E) satellite imagery (2005–07), images of a scanning X-band rain radar, and data from regular meteorological stations. The work was conducted in the framework of a major ecological research program, the Research Unit 816, in which meteorological instruments are installed in the Rio San Francisco valley, breaching the eastern Andes of south Ecuador. GOES image segmentation to discriminate convective cells and other clouds is performed for a 600 × 600 km2 target area, using the concept of connected component labeling by applying the 8-connectivity scheme as well as thresholds for minimum blackbody temperature, spatial extent, and eccentricity of the extracted components. The results show that the formation of convective c...

How thermodynamic environments control stratocumulus microphysics and interactions with aerosols

Aerosol–cloud interactions are central to climate system changes and depend on meteorological conditions. This study identifies distinct thermodynamic regimes and proposes a conceptual framework for interpreting aerosol effects. In the analysis, ten years (2003–2012) of daily satellite-derived aerosol and cloud products are combined with reanalysis data to identify factors controlling Southeast Atlantic stratocumulus microphysics. Considering the seasonal influence of aerosol input from biomass burning, thermodynamic environments that feature contrasting microphysical cloud properties and aerosol–cloud relations are classified. While aerosol impact is stronger in unstable environments, it is mostly confined to situations with low aerosol loading (aerosol index AI ≲ 0.15), implying a saturation of aerosol effects. Situations with high aerosol loading are associated with weaker, seasonally contrasting aerosol-droplet size relationships, likely caused by thermodynamically induced processes and aerosol swelling.

Short Note: FMet-an integrated framework for Meteosat data processing for operational scientific applications

This paper introduces the FMet software package for the scientific processing of Meteosat SEVIRI data. A number of individual modules handle the processing steps from image format conversion to calibration and product generation and presentation. The package is designed for operational applications. It can be freely extended and configured using a dynamic graphical user interface.

The METCRAX II Field Experiment: A Study of Downslope Windstorm-Type Flows in Arizona’s Meteor Crater

AbstractThe second Meteor Crater Experiment (METCRAX II) was conducted in October 2013 at Arizona’s Meteor Crater. The experiment was designed to investigate nighttime downslope windstorm−type flows that form regularly above the inner southwest sidewall of the 1.2-km diameter crater as a southwesterly mesoscale katabatic flow cascades over the crater rim. The objective of METCRAX II is to determine the causes of these strong, intermittent, and turbulent inflows that bring warm-air intrusions into the southwest part of the crater. This article provides an overview of the scientific goals of the experiment; summarizes the measurements, the crater topography, and the synoptic meteorology of the study period; and presents initial analysis results.

Characterization of Low Clouds With Satellite and Ground-Based Remote Sensing Systems

Satellite and ground-based retrievals of a number of (low) cloud characteristics are compared in this paper in order to assess the performance of the techniques and identify potential synergies. Centred on the COST720 International Comparison Campaign for Temperature, hUmidity and Cloud profiling (TUC), four cases with different meteorological situations are analysed in detail. Parameter agreement (for cloud presence, liquid water path, cloud geometrical thickness and cloud top temperature) is good in general. It is shown that satellite retrievals of liquid water path and cloud thickness could be improved using liquid water content derived from ground-based measurements, while ground-based retrievals can profit from the spatial component in satellite data. Taken together, the combination of instruments and techniques presented in this paper allows for a detailed assessment of complex cloudy atmospheres.

Isolated Mountain Forests in Central Asian Deserts: A Case Study from the Govi Altay, Mongolia

The role of isolated mountain forests in Central Asian drylands has been the subject of a number of recent studies. The present paper examines islands of birch and willow forest in the Mongolian Govi Altay Mountains. Using vegetation surveys, dendrochronological studies and charcoal findings, it is attempted to assess the present-day ecological state of the vegetation as well as the environmental history and ecological trends. One of the three forests studied seems to be expanding rapidly due to declining levels of utilization under climatically favourable conditions. The other forests however, situated about 350 km to the south-east of the former, are threatened by consistently high levels of anthropogenic pressure. Based on ecological evidence, the forest islands’ role as witnesses of a once coherent forest belt that may have been retreating since the mid-Holocene is discussed.