Mapping Europe into local climate zones

Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives

Heat risk assessment for the Brussels capital region under different urban planning and greenhouse gas emission scenarios

Urban residents are exposed to higher levels of heat stress in comparison to the rural population. As this phenomenon could be enhanced by both global greenhouse gas emissions (GHG) and urban expansion, urban planners and policymakers should integrate both in their assessment. One way to consider these two concepts is by using urban climate models at a high resolution. In this study, the influence of urban expansion and GHG emission scenarios is evaluated at 100 m spatial resolution for the city of Brussels (Belgium) in the near (2031-2050) and far (2081-2100) future. Two possible urban planning scenarios (translated into local climate zones, LCZs) in combination with two representative concentration pathways (RCPs 4.5 and 8.5) have been implemented in the urban climate model UrbClim. The projections show that the influence of GHG emissions trumps urban planning measures in each period. In the near future, no large differences are seen between the RCP scenarios; in the far future, both heat stress and risk values are twice as large for RCP 8.5 compared to RCP 4.5. Depending on the GHG scenario and the LCZ type, heat stress is projected to increase by a factor of 10 by 2090 compared to the present-day climate and urban planning conditions. The imprint of vulnerability and exposure is clearly visible in the heat risk assessment, leading to very high levels of heat risk, most notably for the North Western part of the Brussels Capital Region. The results demonstrate the need for mitigation and adaptation plans at different policy levels that strive for lower GHG emissions and the development of sustainable urban areas safeguarding livability in cities

A non-linear Granger-causality framework to investigate climate-vegetation dynamics

Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate-vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate-vegetation dynamics

The Air-temperature Response to Green/blue-infrastructure Evaluation Tool (TARGET v1.0) : an efficient and user-friendly model of city cooling

The adverse impacts of urban heat and global climate change are leading policymakers to consider green and blue infrastructure (GBI) for heat mitigation benefits. Though many models exist to evaluate the cooling impacts of GBI, their complexity and computational demand leaves most of them largely inaccessible to those without specialist expertise and computing facilities. Here a new model called The Air-temperature Response to Green/blue-infrastructure Evaluation Tool (TARGET) is presented. TARGET is designed to be efficient and easy to use, with fewer user-defined parameters and less model input data required than other urban climate models. TARGET can be used to model average street-level air temperature at canyon-to-block scales (e.g. 100 m resolution), meaning it can be used to assess temperature impacts of suburb-to-city-scale GBI proposals. The model aims to balance realistic representation of physical processes and computation efficiency. An evaluation against two different datasets shows that TARGET can reproduce the magnitude and patterns of both air temperature and surface temperature within suburban environments. To demonstrate the utility of the model for planners and policymakers, the results from two precinct-scale heat mitigation scenarios are presented. TARGET is available to the public, and ongoing development, including a graphical user interface, is planned for future work

Urban pollution and ecosystem services

Urban pollutants can degrade and inhibit ecological functions and processes. Those natural processes provide vital benefits and services to humans. The ‘services’ range from food and water provision, to aesthetics, cultural benefits, health and recreation opportunities, and also climate regulation (including water and air quality regulation and flood regulation). These services are referred to collectively as ecosystem services (ES). To understand the impacts of urban pollution, and the opportunities for mitigating its effects, it is important to explore the relationship between pollution and ES. This can lead to better decision-making for urban infrastructure and spaces and can provide increased opportunities for gaining multiple ES from urban environments

Global hydro-climatic biomes identified via multitask learning

The most widely used global land cover and climate classifications are based on vegetation characteristics and/or climatic conditions derived from observational data. However, these classification schemes do not directly stem from the characteristic interaction between the local climate and the biotic environment. In this work, we model the dynamic interplay between vegetation and local climate in order to delineate ecoregions that share a coherent response to hydro-climate variability. Our novel framework is based on a multitask learning approach that discovers the spatial relationships among different locations by learning a low-dimensional representation of predictive structures. This low-dimensional representation is combined with a clustering algorithm that yields a classification of biomes with coherent behaviour. Experimental results using global observation-based datasets indicate that, without the need to prescribe any land cover information, the identified regions of coherent climate-vegetation interactions agree well with the expectations derived from traditional global land cover maps. The resulting global "hydro-climatic biomes" can be used to analyse the anomalous behaviour of specific ecosystems in response to climate extremes and to benchmark climate-vegetation interactions in Earth system models

The diurnal evolution of the urban heat island of Paris: a model-based case study during Summer 2006

The urban heat island (UHI) over Paris during summer 2006 was simulated using the Advanced Regional Prediction System (ARPS) updated with a simple urban parametrization at a horizontal resolution of 1 km. Two integrations were performed, one with the urban land cover of Paris and another in which Paris was replaced by cropland. The focus is on a five-day clear-sky period, for which the UHI intensity reaches its maximum. The diurnal evolution of the UHI intensity was found to be adequately simulated for this five day period. The maximum difference at night in 2 m temperature between urban and rural areas stemming from the urban heating is reproduced with a relative error of less than 10%. The UHI has an ellipsoidal shape and stretches along the prevailing wind direction. The maximum UHI intensity of 6.1 K occurs at 23:00 UTC located 6 km downstream of the city centre and this largely remains during the whole night. An idealized one-column model study demonstrates that the nocturnal differential sensible heat flux, even though much smaller than its daytime value, is mainly responsible for the maximum UHI intensity. The reason for this nighttime maximum is that additional heat is only affecting a shallow layer of 150 m. An air uplift is explained by the synoptic east wind and a ramp upwind of the city centre, which leads to a considerable nocturnal adiabatic cooling over cropland. The idealized study demonstrates that the reduced vertical adiabatic cooling over the city compared to cropland induces an additional UHI build-up of 25%. The UHI and its vertical extent is affected by the boundary-layer stability, nocturnal low-level jet as well as radiative cooling. Therefore, improvements of representing these boundary-layer features in atmospheric models are important for UHI studies

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

A new regional climate model for POLAR-CORDEX : evaluation of a 30-year hindcast with COSMO-CLM2 over Antarctica

Continent-wide climate information over the Antarctic Ice Sheet (AIS) is important to obtain accurate information of present climate and reduce uncertainties of the ice sheet mass balance response and resulting global sea level rise to future climate change. In this study, the COSMO-CLM2 Regional Climate Model is applied over the AIS and adapted for the specific meteorological and climatological conditions of the region. A 30-year hindcast was performed and evaluated against observational records consisting of long-term ground-based meteorological observations, automatic weather stations, radiosoundings, satellite records, stake measurements and ice cores. Reasonable agreement regarding the surface and upper-air climate is achieved by the COSMO-CLM2 model, comparable to the performance of other state-of-the-art climate models over the AIS. Meteorological variability of the surface climate is adequately simulated, and biases in the radiation and surface mass balance are small. The presented model therefore contributes as a new member to the COordinated Regional Downscaling EXperiment project over the AIS (POLAR-CORDEX) and the CORDEX-CORE initiative