Research in urban evolution requires that the features of cities are accurately captured for input into evolutionary models. Until recently, the evolutionary effects of cities have often been addressed using single sites, dichotomous urban–rural contrasts or, to a lesser extent, using urban gradients. However, urbanization does not produce a homogenous spatial continuum: cities are highly heterogeneous environments, with sharp and often non-linear environmental changes related to the amount of impervious surface, green vegetation, air pollution, light, noise, or contrasted temperature profiles. The comprehensive quantification of urban heterogeneity in space and time is essential for exploring the origins of organismal variation and adaptation in cities, and to best identify the strength and directionality of selective pressures and neutral processes occurring in populations of urban organisms. This chapter reviews frameworks that can be used to describe and quantify urbanization—these include classical ecological frameworks, the understudied temporal dimension of urban evolutionary biology, and the concept of replicated insight into urban-driven evolutionary processes. The chapter further discusses how axes of variation capturing the urban environment can be quantified with univariate and multivariate approaches, and presents quantitative results on how urbanization is captured in published studies of urban evolution. Finally, it discusses study design and statistical approaches of interest when testing for urban evolution: these include the question of model selection and variable fitting, spatial autocorrelation, and appropriate scale use in studies of urban evolution.

The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOVs) as either “confident clear”, “probably clear”, “probably cloudy”, or “confident cloudy”. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by the MODIS Science Team assumes that confident clear and probably clear IFOVs are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this “best-guess” approach is unreliable, especially on a regional/local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with Aqua MODIS IFOV. Based on 33 793 648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in polar regions at night, and in selected locations over the Northern Hemisphere (e.g. China, the north-west coast of Africa, and eastern parts of the United States). Consequently, applications of MODIS data on a regional/local scale should first assess the extent of the uncertainty. We suggest using CALIPSO-based cloud fractions to improve MODIS cloud amount estimates. This approach can also be used for Terra MODIS data, and other passive cloud imagers, where the footprint is collocated with CALIPSO.

The joint CloudSat–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) climatology remains the only dataset that provides a global, vertically-resolved cloud amount statistic. However, data are affected by uncertainty that is the result of a combination of infrequent sampling, and a very narrow, pencil-like swath. This study provides the first global assessment of these uncertainties, which are quantified using bootstrapped confidence intervals. Rather than focusing on a purely theoretical discussion, we investigate empirical data that span a five-year period between 2006 and 2011. We examine the 2B-Geometric Profiling (GEOPROF)-LIDAR cloud product, at typical spatial resolutions found in global grids (1.0°, 2.5°, 5.0°, and 10.0°), four confidence levels (0.85, 0.90, 0.95, and 0.99), and three time scales (annual, seasonal, and monthly). Our results demonstrate that it is impossible to estimate, for every location, a five-year mean cloud amount based on CloudSat–CALIPSO data, assuming an accuracy of 1% or 5%, a high confidence level (>0.95), and a fine spatial resolution (1°–2.5°). In fact, the 1% requirement was only met by ~6.5% of atmospheric volumes at 1° and 2.5°, while the more tolerant criterion (5%) was met by 22.5% volumes at 1°, or 48.9% at 2.5° resolution. In order for at least 99% of volumes to meet an accuracy criterion, the criterion itself would have to be lowered to ~20% for 1° data, or to ~8% for 2.5° data. Our study also showed that the average confidence interval: decreased four times when the spatial resolution increased from 1° to 10°; doubled when the confidence level increased from 0.85 to 0.99; and tripled when the number of data-months increased from one (monthly mean) to twelve (annual mean). The cloud regime arguably had the most impact on the width of the confidence interval (mean cloud amount and its standard deviation). Our findings suggest that existing uncertainties in the CloudSat–CALIPSO five-year climatology are primarily the result of climate-specific factors, rather than the sampling scheme. Results that are presented in the form of statistics or maps, as in this study, can help the scientific community to improve accuracy assessments (which are frequently omitted), when analyzing existing and future CloudSat–CALIPSO cloud climatologies.

Most satellite cloud climatologies come in the form of global, low‐resolution datasets: so‐ called ‘gridded’ Level 3 products, resulting from the reprojection and spatio‐temporal aggregation of swath (Level 2) data. Their coarse resolution means that global datasets are of limited usefulness in regional studies. In this paper we develop and evaluate a new, regional cloud climatology over Poland and its neighbouring countries (∼10% of the area covered by Europe), based on observations performed with the state‐of‐the‐art cloud imager, the moderate resolution imaging spectroradiometer (MODIS). In contrast to the operational, globalMODIScloud climatology, which is delivered as a Level 3 product at a spatial resolution of 1° × 1°, this regional climatology maintains theMODISnadir spatial resolution of 1 km/pixel. The resulting high‐spatial‐resolution climatology is compared withAVHRRandSEVIRIdatasets, and surface‐based (SYNOP) observations at the level of monthly and annual means. The results shows that the standardMODISLevel 2 cloud mask productMOD35/MYD35can be successfully used to develop a regional, high‐resolution cloud climatology.MODISprovides reliable estimates of cloud amount at the national scale (annual mean: 64.0% or 70.8%, depending on theMODISdata interpretation scheme), and correctly reproduces the annual cloud amount cycle (correlation between monthly means withSEVIRI/AVHRR>0.98). A comparison with monthly mean surface observations reveals a bias ranging from −1.1% up to 5.9%, and a root mean square error of 4.2% − 6.6%.MODISdata also correctly indicates the spatial distribution of clouds. However, local anomalies were detected that were identified as artifacts of theMODIScloud detection algorithm. Those artifacts covered 9% of the study area, but had no impact on spatially‐averaged metrics.