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Article

Assessing Climate Change Projections through High-Resolution Modelling: A Comparative Study of Three European Cities

by
Ana Ascenso
1,*,
Bruno Augusto
1,
Sílvia Coelho
1,
Isilda Menezes
1,
Alexandra Monteiro
1,
Sandra Rafael
2,
Joana Ferreira
1,
Carla Gama
1,
Peter Roebeling
1 and
Ana Isabel Miranda
1
1
CESAM & Department of Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal
2
IDAD—Institute of Environment and Development, University of Aveiro, 3810-193 Aveiro, Portugal
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(17), 7276; https://doi.org/10.3390/su16177276 (registering DOI)
Submission received: 11 July 2024 / Revised: 17 August 2024 / Accepted: 21 August 2024 / Published: 23 August 2024
(This article belongs to the Special Issue Benefits of Green Infrastructures on Air Quality in Urban Spaces)
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Abstract

:
Climate change is expected to influence urban living conditions, challenging cities to adopt mitigation and adaptation measures. This paper assesses climate change projections for different urban areas in Europe –Eindhoven (The Netherlands), Genova (Italy) and Tampere (Finland)—and discusses how nature-based solutions (NBS) can help climate change adaptation in these cities. The Weather Research and Forecasting Model was used to simulate the climate of the recent past and the medium-term future, considering the RCP4.5 scenario, using nesting capabilities and high spatial resolution (1 km2). Climate indices focusing on temperature-related metrics are calculated for each city: Daily Temperature Range, Summer Days, Tropical Nights, Icing Days, and Frost Days. Despite the uncertainties of this modelling study, it was possible to identify some potential trends for the future. The strongest temperature increase was found during winter, whereas warming is less distinct in summer, except for Tampere, which could experience warmer summers and colder winters. The warming in Genova is predicted mainly outside of the main urban areas. Results indicate that on average the temperature in Eindhoven will increase more than in Genova, while in Tampere a small reduction in annual average temperature was estimated. NBS could help mitigate the increase in Summer Days and Tropical Nights projected for Genova and Eindhoven in the warmer months, and the increase in the number of Frost Days and Icing Days in Eindhoven (in winter) and Tampere (in autumn). To avoid undesirable impacts of NBS, proper planning concerning the location and type of NBS, vegetation characteristics and seasonality, is needed.

1. Introduction

Climate change (CC) is a global threat of this century as the foreseen increased frequency, intensity, and/or duration of extreme weather events will impact natural and human systems. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) [1], climate risks are appearing faster and will get more severe sooner, highlighting the importance of adaptation measures such as nature-based solutions (NBS). NBS, including ecosystem-based adaptation, can reduce risks for ecosystems and benefit people, providing they are planned and implemented in the right way and place [1]. To rightfully plan NBS it is valuable to know the risks and challenges that cities are expected to encounter in the medium-term future.
Europe has experienced a more rapid increase in average annual temperatures than any other World Meteorological Organization (WHO) region since the 1980s, with temperatures rising twice as much as the global average [2]. Projections show that warming will continue in Europe throughout the 21st century: the frequency of heat extremes will likely increase, with a more pronounced rise in the southern regions and the frequency of cold spells and frost days will likely keep decreasing. Agricultural, ecological and hydrological droughts will likely increase in the Mediterranean region by mid- and far end of the century under all RCPs except RCP2.6 [3]. Even though the increase in temperature is expected to result in a longer growing season, food security will be increasingly affected by projected future climate change. Warming compounded by dryness has led to significant decreases in yields in parts of the Mediterranean. Productivity is also negatively affected by increased pests and diseases, and pollinator disruptions due to climate change [4].
The interaction between climate change and the urban environment is widely recognized [5,6,7]. In urban areas, climate change is projected to increase risks for people, assets, economies and ecosystems, including risks from heat stress, higher mean and night-time temperatures, storms and extreme precipitation, inland and coastal flooding, landslides, air pollution, drought, water scarcity, sea-level rise and storm surges [8]. Moreover, cities are highly vulnerable to climate change effects as extreme weather events can be especially disruptive to complex urban systems due to the high level of urbanization and demographic growth [9]. Understanding urbanization, associated risks and vulnerability distributions is crucial for an effective response to climate change threats and their impacts. It is argued that extreme weather events are the aspect of climate change that will have the most direct and obvious impact on the greatest number of people in their lifetimes [10].
Climate impact assessments and the development of regional to local-scale adaptation strategies require the availability of high-resolution climate change scenarios. The EURO-CORDEX initiative [11] has provided regional climate projections for Europe, for different Representative Concentration Pathways (RCPs) at different horizontal resolutions (from 50 to 12.5 km) using a multi-model multi-scenario ensemble of regional climate simulations [11]. The RCPs describe different radiative forcing scenarios based on different assumptions about population, economic growth, energy consumption and sources, and land use over this century [12].
Studies aiming to improve model performance by refining the horizontal grid spacing of regional climate models (RCM) (<5 km) have been underway [13,14,15,16]. Schär et al. [13] refer that the main challenge for kilometer-scale climate simulation relates to the growth in output volumes making it impractical or impossible to store the data. Keppas et al. [15] investigated the climate change impact on urban heat island (UHI) for two Mediterranean cities, Rome and Thessaloniki, their results indicate that the average temperature under the business-as-usual scenario is expected to increase by ~1.5 °C and ~1.3 °C in summer and ~0.8 °C and ~0.6 °C in winter until 2050 in the domains of Thessaloniki and Rome, respectively. Tölle et al. [14] studied how the temperature fields depend on the horizontal resolution, his results for Germany show that the increase in resolution is accompanied by less future warming in summer by 1 °C. A common result from these studies is a stronger mean precipitation over most of Europe as compared to global climate model simulations. The increased resolution can be regarded as an added value to regional climate simulations. Regional climate model simulations also provide higher daily precipitation intensities, which global climate model simulations have difficulties reproducing. Higher-resolution simulations can produce more detailed spatial patterns as physical processes, such as convection and heavy precipitation, are better resolved while surface characteristics and their spatial variability are better represented [11,17].
This work aims to evaluate the climate change impacts at the city level (1 km2 horizontal resolution), for the RCP4.5 scenario. The focus is on the period 2040–2069 under RCP4.5, which approximately corresponds to a 2 °C global warming relative to the preindustrial era. The RCP4.5 is considered a medium stabilization scenario, it contemplates a peak in greenhouse gas emissions in 2050 and then a rapid decline over 30 years, due to the imposition of emissions mitigation policies, leading to a stabilized radiative forcing (the change in the balance between incoming and outgoing radiation to the atmosphere caused primarily by changes in atmospheric composition) of 4.5 W m−2 from 2080 to the end of the century [18]. Three European urban areas with different locations, characteristics and vulnerabilities were chosen as case studies—Eindhoven (Netherlands), Genova (Italy) and Tampere (Finland)—which are the case studies of the H2020 UNaLab Project (https://unalab.eu (accessed on 2 August 2024)) aimed to assess NBS as climate change adaptation and mitigation strategies. In this context, the current assessment is based on the calculation of climate indices, focusing mainly on temperature-related metrics, as this is the most relevant parameter influenced by NBS [19,20,21], and are important when assessing the effectiveness of NBS cooling effect [22]. Knowing that the effectiveness of NBS is highly dependent on the local context, and the design of NBS projects should build on forward-looking studies of projected climate change impacts, this study allows to better understand climate change impacts in cities with different characteristics and to support NBS planning under expected CC.

2. Data and Methods

This section presents the model setup and the climate indices used in the study. Following Coelho et al. [23], the methodology comprised two main sets of downscaling meteorological simulations. The first used reanalysis data (blend of observations with past short-range weather forecasts) to define and evaluate the model setup (ERA-WRF), and the second used global climate projection scenario data to assess climate change impacts for medium-term future (MPI-CLWRF). These simulations were performed for representative years of the recent past and medium-term future which were selected based on the RCP4.5 scenario and EURO-CORDEX data for each of the case studies. Figure 1 shows a summary of the study methodology.

2.1. Study Areas

This paper focuses on three European cities: Eindhoven (Netherlands), Genova (Italy) and Tampere (Finland), from the Mediterranean region to northern Europe.
Eindhoven is located in the southern Netherlands and it is the country’s fifth-largest city with 238,307 inhabitants on 1 January 2022, and an area of 87.66 km2 (population density of 2719/km2) [24]. The Eindhoven landscape is characterized by sand ridges and valleys with a maximum elevation of 57 m. Most of the area of Eindhoven comprises urban, commercial and industrial areas. Eindhoven’s climate is classified as a temperate oceanic climate, in the Köppen classification, and it is characterized by cool summers, moderate winters and typically high humidity. The average annual temperature is 10.4 °C and the yearly average rainfall is 750 mm [25]. The city’s high population density and rapid growth are the main causes of some of the problems affecting the city, like heat stress, the UHI effect, flooding, densification and urban sprawl—some of which have been studied previously [26,27,28,29,30].
Genova is a coastal city located in northern Italy and it is the largest city in the Liguria Region, with 560,688 inhabitants on 1 January 2022, and an area of 240.3 km2 (population density of 2333/km2) [31]. Genova port is currently the busiest in the Mediterranean Sea. Genova is characterized by a narrow coastal zone with hills and steep mountains in the backcountry (maximum elevation of 1688 m). Genova climate is classified as Mediterranean Csa, or hot temperate, in the Köppen classification, and it is characterized by a limited dry season which is restricted to 1 or 2 summer months [32]. The average annual temperature is 15.6 °C and the yearly average rainfall is 1071 mm [33]. The most common environmental problem identified in Genova, by its citizens, is air pollution [34], followed by flooding, biodiversity loss, and socio-economic issues [35]. Due to the morphology of the Ligurian Golf, where Genova is located, it is prone to particularly intense rainfalls, especially at the end of the summer or autumn [36].
Tampere is located in central Finland and it is the third-largest city in Finland, with 244,223 inhabitants on 1 January 2022, and an area of 524 km2 (population density of 465.2/km2) [37]. Tampere is located on a narrow isthmus between two large lakes and has a maximum elevation of 215 m. Tampere climate is classified as a humid continental/subarctic climate, in the Köppen classification, and it is characterized by cool summers and cold and snowy winters. The average annual temperature is 4.4 °C and the yearly average rainfall is 598 mm. Despite having multiple green areas and outdoor spaces, Tampere is concerned about preserving the water quality in the numerous surrounding lakes of varying sizes, which have been impacted by active urbanization processes. Moreover, maintaining or enhancing biodiversity and addressing flooding were among its environmental challenges [35,38].

2.2. The Modelling Setup and Evaluation

The Weather Research and Forecasting Model (WRF) [39], version 3.7, with the modifications performed by Fita et al. [40] for regional climate simulations (CLWRF), was applied in this work to perform simulations for the cities of Eindhoven, Genova and Tampere through dynamical downscaling. The WRF setup includes three domains 2-way nested with increasing resolution at a downscaling ratio of five: domain 1 (D1), common to all case studies, with a spatial resolution of 25 km and covering Europe and part of the North Atlantic Ocean (with 180 × 155 horizontal grid cells); domain 2 (D2), focused on the countries of each case study, with a spatial resolution of 5 km (with 96 × 91 horizontal grid cells for Tampere and Eindhoven, and 141 × 106 for Genova), and; domain 3 (D3), over the area of each city, with a spatial resolution of 1 km (with 46 × 36 horizontal grid cells for Tampere, 51 × 41 for Eindhoven, and 41 × 36 for Genova). Figure 2 shows the location of the model domains. All domains were resolved with 30 vertical levels extending up to 50 hPa, with the lowest level at approximately 20 m above the surface.
Information regarding land use was taken from the Coordination of Information on the Environment Land Cover (CORINE land cover 2006, [41]), and re-categorized to be compatible with the model processes in the US Geological Survey (USGS) 24-category land use dataset. The remapping of the CORINE land cover to USGS 24 land use categories followed the methodology proposed by Pineda et al. [42].
For each case study, three sets of simulations were conducted based on different periods, and initial and boundary conditions. Firstly, the WRF model was initialized with global meteorological fields from the European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-Interim) model data with 1° spatial resolution and temporal resolution of 6 h for the Earth surface and pressure levels (ERA-WRF). The ERA-WRF simulations were used to establish the model setup and evaluate its performance. The model results were compared with measured values retrieved from meteorological monitoring databases of each city [25,43,44]. The location of each station can be seen in Figure 2, more detail on the name and location of the station can be found in Table S1 of the Supplementary Material. The model evaluation analysis was performed by comparing results and monitored values for daily average temperature and daily accumulated rainfall, based on the temporal resolution of the monitoring datasets. Comparative time series plots are presented and discussed and statistical parameters (Pearson correlation coefficient (r), bias and root mean square error (RMSE)) were calculated to evaluate the model performance, following the recommendations from Borrego et al. [45] and the COST 728 guidelines [46].
The physical configuration applied included the following parametrizations: WRF Single-Moment 6-class Microphysical Scheme [47]; Dudhia Shortwave radiation scheme [48]; Rapid Radiative Transfer Model longwave radiation model (RRTMG) [49]; MM5 similarity surface layer scheme [50]; Noah Land Surface Model [51] with soil temperature and moisture in four layers, fractional snow cover and frozen soil physics; Yonsei University Planetary Boundary Layer scheme [52]; and Grell-Freitas Ensemble Scheme for cumulus parametrization [53] (this last, only for D1 and D2).
The climate simulations were performed for the recent past and medium-term future scenarios, with the initial and boundary conditions from the MPI-ESM-LR model (Max Planck Institute for Meteorology Earth System Model; [54]), with 1.9° spatial resolution and with the first ensemble member (the first realization, initialization, and set of perturbed physics, which is denoted “r1i1p1”) (MPI-WRF). For these simulations, the Representative Concentration Pathway Scenario RCP4.5 was adopted [55]. The RCP4.5 was chosen in alignment with the Paris Agreement and it provides a common platform for climate models to explore the climate system response to stabilizing the anthropogenic components of radiative forcing. The difference between the MPI-CLWRF simulation results was used to assess the climate change impact.
Two reference years were considered for this study, which are statistically representative of each period of 20 years, of the medium-term future climate scenario and of the recent past climate, used as a baseline scenario. EURO-CORDEX data [11] for each city was used to select the reference year, following the methodology described in Rafael et al. [56] and Coelho et al. [23]. Seasonal and annual anomalies were calculated for the mean, minimum and maximum temperatures. The year closest to the long-term average (anomaly closest to zero) was selected, giving preference to years that have a lower interannual variability. The reference year for the baseline and medium-term scenarios, for each case study, is presented in Table 1.

2.3. Climate Change Indices

This paper focuses on using temperature indices to assess the future climate in the case-study regions. The climate indices selected were based on the World Meteorological Organization’s guidelines on analyzing extremes in a changing climate. These guidelines support informed decisions for adaptation and were recommended by the Expert Team on Climate Change Detection and Indices [57]. Table 2 shows a summary and description of the selected indices.
Additionally, indicators suggested in the European Commission publication on evaluating the impact of NBS [58] were also utilized: the average daily temperature (Tmean), daily maximum temperature (Tx), daily minimum temperature (Tn) and Daily Temperature Range (DTR). These indicators enable a consistent comparison between analyses performed by different authors, promote informed planning, and are a base to assess the effect of NBS on climate resilience. In dense urban areas, in addition to reducing peak temperatures due to evapotranspiration and the creation of shaded areas, NBS are known to reduce temperatures during the night, due to the lesser thermal inertia of urban vegetation [26,59]. Therefore, indices that have Tn as a base of their calculation—Daily Temperature Range (DTR), Tropical nights (TR) and Frost days (FD)—are also relevant in the scope of NBS planning. DTR is an important meteorological index associated with mortality and morbidity. If the nighttime temperature remains high after a hot day, the human body may struggle to recover. In this sense, a high DTR is desirable and NBS could contribute to this.

3. Results and Discussion

In the next section, the results of the model performance evaluation (Section 3.1), the results for the recent past and medium-term future regarding temperature (Section 3.2) and climate indices (Section 3.3) are presented and discussed, for each case study. The indices are investigated annually and by season. For the purpose of this study, winter was considered the average of December, January and February (DJF); spring the average of March, April and May (MAM); summer the average of June, July and August (JJA), and autumn the average of September, October and November (SON).

3.1. Model Evaluation for Recent Past

The chosen recent past year, per city, was the basis for the model evaluation. The results from the statistical analysis (r, bias and RMSE) are presented in Table 3. Time series comparisons for daily average air temperature at 2 m height and daily precipitation totals are shown in Figure 3 and Figure 4, respectively.
The model has a good performance in estimating temperature for all cities; it successfully reproduced the high temperatures in Eindhoven and Genova (around 30 °C) and the low temperatures in Tampere (down to −20 °C). The statistical parameters show an adequate performance of the WRF model, following the COST 728 guidelines [46] (bias ± 2 °C). Among the three case studies, for temperature, the lowest correlation factor was 0.98, the median bias was 0.23 °C, and the average RMSE was 1.7 °C. The highest bias and RMSE were calculated for Genova (~2 °C) at the highest elevation station (360 m). Moreover, the stations closest to water, in Genova and Tampere, were the only stations where temperature was underestimated (bias < 0). Air temperature is strongly tied to surface properties, complex surface properties like coastal and mountainous areas are associated with higher model deviations, due to model deficiencies to capture the local conditions [46].
Regarding precipitation, model is slightly overestimating precipitation values for Tampere, particularly during hotter months, and underestimating precipitation values during heavy rainfall events, for Genova. The average correlation factor is 0.65, the median bias is 0.55 mm and the RMSE varies between 4 and 9 mm, across the 3 case studies. Previous studies have recognized the difficulty of models to simulate precipitation in Europe, especially in summer, when precipitation is mainly a result of mesoscale convection, because this region is exposed to intense synoptic perturbations from the Atlantic and to moisture-rich inflows from the Mediterranean and is characterized by complex orographic features [60,61,62,63]. In addition, air temperature observation and precipitation totals especially in urban areas can also have uncertainties and potential inhomogeneities [64]. Overall, Eindhoven is the city that is most accurately simulated.

3.2. Daily Mean, Maximum and Minimum Temperature

The temperature indicators were evaluated using a probability density function (PDF), a statistical expression that defines the probability that some outcome will occur, each, in this case, is a temperature value. Percentile values are also calculated: 10th percentile (P10)—the value below which 10% of the data, when sorted, fall; 50th percentile (P50)—or median and 90th percentile (P90)—the value below which 90% of the data, when sorted, fall.
Figure 5 shows the PDF with the percentiles P10, P50, and P90 of the recent-past and medium-term future WRF simulation results concerning daily mean (Tmean), maximum (Tx), and minimum temperatures (Tn), for Eindhoven, Genova and Tampere. The spatial distribution of the average annual differences between medium-term future and recent past for the same variables is shown in Figure 6.
The recent past and medium-term future PDF for all cities are different in shape, especially for Tampere. In Eindhoven, the future temperature is higher for all variables, with a higher P50 than in the recent past. Also, Eindhoven’s annual maps (Figure 6) indicate an average increase in Tx and Tmean, and in the urban areas for Tn. Among the case studies, Genova shows the least marked increase, with a very similar P50 but a higher P10 and smaller P90, for all variables. On average Tmean and Tx increased, while Tn decreased in the densely built areas. Wang et al. [65] also projected a decrease in Tn values (up to −2 °C) for 2040 in a coastal area. Even though the temperatures are usually higher in the Mediterranean, the results suggest that on average the temperature in Eindhoven (western Europe) will increase more than in the Mediterranean city (Genova), possibly due to the attenuating effect of the sea, a similar trend was also reported by previous studies [66].
Tampere presents a marked decrease in P10 and an increase in P90 and P50, for all variables. In this city, the average annual Tmean, Tx, and Tn show a decrease between the medium-term future and the recent past, consistent with the PDFs; the areas with smaller differences are the characteristic Tampere lakes (see Figure 6).
Table 4 presents the average seasonal differences for Tmean, Tx and Tn, between the medium-term future and recent past, for the three cities.
The projected temperature tendencies for each city are consistent between variables but differ considerably between cities and seasons. The increase in temperature in the south and central European cities is bigger in winter and spring, this is consistent with other high-resolution RCM studies [67]. For Tampere, even though annually the climate signal points to a decrease in temperature, warmer summers and falls are expected. Tampere is the city with the most contrasting results between seasons; the increase in temperature from July to November and the decrease from December to May indicates a shift in the thermal seasons, also reported by Ruosteenoja et al. [68].
Table 5 compiles results from other climate change studies, for the medium-term future, which included the same region/countries and climate scenario. The results in Table 5 concern different reference years, as well as different model domains, resolutions and input data, therefore these studies should not be directly compared.
The results for temperature show the same signal as published literature, except for the annual mean in Tampere and Eindhovem with respect to EURO-CORDEX results. The decrease in temperature projected for the northern European city contrasts with results from global climate models (GCM) [11,73,74]. Despite this discrepancy, the CMIP5 model (FIO-ESM) also simulates widespread cooling over the Northern North Atlantic and its surroundings, including northern Europe, under the RCP4.5 scenario [75]. Moreover, RCM studies indicate that the climate change signal may change in very high-resolution simulations. Additionally, Scandinavia is characterized by a very large scatter in the local temperature change, therefore, high-resolution simulation results could differ from the results of coarser grids [76]. RCM simulations, on average, estimate a smaller temperature increase in summer compared to GCM simulations [73,77,78,79,80]. Nonetheless, the increase in Tampere’s mean temperature foreseen in summer coincides with the range of values from CMIP5 RCP4.5 projection [81]: +1 °C to +3.5 °C for the south of Finland compared to our projection of +1.77 °C for Tampere.

3.3. Climate Change Indices

To further evaluate temperature changes and the potential role of NBS for climate change adaptation in urban areas, a set of climate indices was analyzed: Daily Temperature Range (DTR), summer days (SU), Tropical nights (TR), Icing days (ID) and Frost days (FD). Figure 7 shows the spatial distribution of the selected climate indices for the differences between the medium-term future and recent past. Table 5 shows these indices as mean values per season.
Figure 7 shows that the annual average of the DTR is expected to increase in the urban areas of the 3 cities, and overall in Eindhoven. The spatial differences in Genova and Tampere are mostly due to their unique topography, as the Tx in Tampere’s lakes will decrease less than in the surrounding areas and Tn in the mountainous regions of Genova will increase more than in the coastal area (more urbanized, low elevation and larger attenuating effect of the sea), resulting in a smaller temperature range. The seasonal average (Table 6) indicates an increase in the DTR during spring and a decrease in summer and autumn for Genova and Tampere. The differences between day and night-time temperatures are lessened due to two main factors, cloud coverage, which reduces the total incident solar radiation, and night-time infrared cooling. During winter and autumn, the decreased temperature range in northern Europe can be partly explained by a reduction of synoptic-scale temperature variability, which is supported by the day-to-day temperature fluctuations [82]. Additionally, in the north of Europe, there is a weak solar-induced diurnal temperature cycle in the early to mid-winter. At the peak of solar radiation (summer), warming values are mostly dependent on the variation in cloud coverage and greenhouse gas forcing, with the former being the most important factor for incident solar radiation changes [73]. An increase in solar radiation values positively affects the diurnal temperature variations, which can explain the correlation between average temperature and its daily range.
The number of SU per year will decrease in Eindhoven due to the reduction in Tx during fall, however, spring and Summer are expected to have more SU. A similar tendency is seen in the number of TR, though, there is an increase of TR in the built-up areas, an indication that the UHI effect in Eindhoven will increase with climate change. High-density urban areas become “islands” of higher temperatures relative to rural areas—structures such as buildings, roads, and other infrastructures absorb and re-emit the sun’s heat more than natural landscapes. This effect is more noticeable during nighttime, thus affecting TR values more than SU.
In Genova, the averaged values suggest an increase in SU during Summer, a decrease in Autumn, and a decrease in TR in both seasons. The spatial distribution shows a reduction in SU and TR values in the urban areas (near the sea and with low elevation). This is likely due to the relationship between sea and land calculated with high resolution and because water has a higher specific heat than land, the seas take longer to heat. Moreover, the increase in SU and TN over the mountainous region demonstrates the impact of elevation on the climate change signal. The high-resolution simulations allowed the use of non-hydrostatic equations for the atmosphere and thus the elevation effect could be captured. Previous studies have documented that the temperature increase is amplified in high-elevation regions [83]. This effect is more noticeable for minimum temperatures, in warmer months and at 1000 to 1300 m above sea level (equivalent to the mountainous regions in Genova). According to Warscher et al. [67], the warming could be explained by several mechanisms: (i) the snow-albedo feedback around the mean elevation of the snowline causing a decrease in snow cover duration, which in turn leads to enhanced absorption of solar radiation, resulting in higher near-surface temperature (this effect can be amplified by the deposition of dark particles); (ii) the temperature sensitivity to radiative forcing is higher for low temperatures; (iii) long-wave radiation non-linearly depends on specific humidity such that an increase in the latter has a disproportionally large warming effect in drier conditions typical for higher elevations; (iv) increased release of latent heat above the condensation level of a warmer and moister atmosphere results in additional warming in the respective elevation; and (v) the cooling effect of aerosols is reduced in higher elevations.
The number of SU and TR, according to the definitions followed in this study, will not change in Tampere since P90 is less than 20 °C for all temperature variables analyzed (Figure 5).
Regarding the number of ID and FD, the spatial distribution is homogeneous in all cities. Eindhoven shows an increase in both indices, especially during spring and autumn. Tampere is also expected to increase overall the number of ID, mainly due to its increase during the first half of the year. On the other hand, the number of FD per year will decrease in Tampere, especially during fall. In Genova, the number of FD will also decrease, due to changes in winter and spring.
Despite the potential value of the results, the authors acknowledge the unavoidable uncertainty. The analysis was limited to one RCM simulation and, hence, quantifying the uncertainty was not possible. The sources of uncertainty in temperature projection include: (i) the unexpected evolution of the greenhouse gases emissions and concentrations; and (ii) modelling uncertainty (from initial and boundary conditions; downscaling method and other input data).

4. Nature-Based Solutions Potential

Nature-based solutions have different impacts depending on location and type; by considering these aspects NBS can be a way to address some of the foreseen changes in temperature. Oswald et al. [84] reported that green surfaces can slightly decrease the average number of SU (maximum reductions of up to 1.9%) and locally can have a strong “cooling” effect with maximum reductions of up to 10%. The largest average impact, however, was obtained through afforestation in the direct vicinity of the case study city (−10% of SU). Moreover, de Wit et al. [85] obtained the biggest reduction of SU (−9 days) by changing the roof albedo to 0.7 in the city centre. Gál et al. [86] reported that the largest reduction of temperature was achieved with large urban parks comprised of dense trees near the city centre, and grasslands promoted the largest nighttime cooling. However, large dense tree areas in the outskirts of the city led to night warming in the inner areas (due to the blocking of the cool air inflow into the city). For the simulated case cities, Tx will increase more than Tn, and thus the most effective proposed NBS are urban parks (with more trees and shrubs than grassland) to be implemented in the areas of the city with the highest temperature. Green or high albedo roofs are a good alternative if cities do not have the area available for such implementation. In the context of climate change mitigation, Eindhoven is the city that will likely benefit the most from NBS implementation because it is the city with the largest increase in temperature and has an evident UHI effect. Genova’s warming, on the other hand, is expected to occur mainly outside of the main urban area, where NBS will be less effective in regulating temperature.
Moreover, NBS not only promote a decrease in temperature in summer and spring, but also increase the temperature in winter and autumn [87,88,89]. Therefore, NBS have the potential to mitigate the increase in the number of FD and ID in Eindhoven (in winter) and Tampere (in autumn). They will also prevent the increase in SU and TR in warmer months for these cities, assuming that the NBS effects will accompany the shift in the seasons projected for Tampere.
However, the warmer effect could also exacerbate the increase in temperature expected in Eindhoven and Genova during winter. These less desirable impacts can be avoided with appropriate planning, e.g., by selecting a specific type of vegetation. Feasible, integrated mitigation and adaptation solutions can be tailored to specific locations while avoiding conflict with sustainable development objectives and managing risks and trade-offs [21,89]. It is also important to note that NBS are themselves vulnerable to climate change impacts. Nature-based solutions cannot deliver the full range of benefits unless they are based on functioning, resilient ecosystems and developed taking account of adaptation principles. NBS approaches to climate change mitigation should also take into account climate change adaptation if they are to remain effective [1].

5. Summary and Conclusions

The main purpose of this study was to assess climate change projections for three European cities with high horizontal resolution (1 km2) WRF simulations, to foster NBS planning and serve as a base for future NBS studies. A first simulation was performed to evaluate the model performance, and the reanalysis simulation had a very good performance in reproducing temperature for all case study regions. The future climate simulations were performed for a representative year of the medium-term future climate and the intermediate climate change scenario RCP4.5. The future simulations did not include any future changes in the land use of the three cities and future changes in urban pollutant emissions were not considered. To evaluate temperature changes and the potential role of NBS for climate change adaptation in urban areas, a set of climate indices was analyzed: Daily Temperature Range, Summer Days, Tropical Nights, Icing Days, and Frost Days. In addition, the average daily temperature, average daily maximum temperature and average daily minimum temperature were also evaluated.
Results indicate that on average the temperature in Eindhoven (western Europe) will increase more than in the southern European city (Genova), while in Tampere (northern Europe) a slight reduction in temperature was estimated. The strongest temperature increase was found during winter, whereas warming is less distinct in summer, except for Tampere, which will experience warmer summers and colder winters. The warming in Genova is seen mainly outside of the main urban areas.
In Eindhoven, the increase in temperature during summer and spring will lead to more summer days (+49%) and tropical nights (+43%). Genova results show the smallest climate signal; the largest change is the reduction in the number of tropical nights (−15%), which will mainly happen in autumn. In Tampere, frost days will decrease by 75% while icing days will increase by 16%.
The results from this paper were discussed in the context of the current scientific knowledge on NBS for temperature reduction. We estimate that NBS could help mitigate the increase in Summer Days and Tropical Nights projected for Genova and Eindhoven in the warmer months, and the increase in the number of Frost Days and Icing Days in Eindhoven (in winter) and Tampere (in autumn). However, NBS could also exacerbate the increase in temperature expected in Eindhoven and Genova during winter. To optimize the benefits of NBS and avoid undesirable impacts, proper planning concerning the location and type of NBS, including vegetation characteristics and seasonality, is needed. The most effective type of NBS to be implemented will be urban parks (with more trees and shrubs than grassland) in the areas of the city with the highest temperature. Green or high albedo roofs are a good alternative if cities don’t have the area available for such implementation.
In the context of NBS for climate change mitigation, the results indicate that Eindhoven is the city that will likely benefit most from NBS implementation. It is the city with the largest increase in temperature and has an evident urban heat island UHI effect. Genova’s warming is expected to occur outside of the main urban area, where NBS will be less effective in regulating temperature.
Notwithstanding the usefulness of these results, this study has some limitations. The presented analyses are limited to only one RCM simulation for one single RCP scenario, and a representative year was used instead of a 30-year simulation, but high–resolution spatio-temporal data is provided and cities can become more resilient by improving the design and discussion of context-specific measures.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16177276/s1, Table S1: Location and altitude of the weather monitoring station used in the model validation.

Author Contributions

Conceptualization, A.A., S.C., S.R., A.M., J.F. and A.I.M.; methodology, A.A. and S.C.; software, A.A. and B.A.; validation, A.A. and C.G.; formal analysis, A.A.; investigation, A.A.; writing—original draft preparation, A.A., J.F., S.C., I.M. and S.R.; writing—review and editing, A.I.M., C.G. and P.R.; visualization, A.A. and C.G.; supervision, A.I.M., P.R. and C.G.; project administration, P.R.; funding acquisition, P.R. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the UNaLab project (Grant Agreement No. 730052, Topic: SCC-2-2016-2017: Smart Cities and Communities Nature-based solutions). Acknowledgement for the financial support to the PhD grants of A. Ascenso (SFRH/BD/136875/2018), S. Coelho (SFRH/BD/137999/2018), B. Augusto (2020.06293.BD), the contract granted to J. Ferreira (http://doi.org/10.54499/2020.00622.CEECIND/CP1589/CT0020), Carla Gama (grant no. 2021.00732.CEECIN) and CESAM (UIDB/50017/2020+UIDP/50017/2020), to FCT/MCTES through national funds, and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors would like to thank the municipalities of Eindhoven, Genova and Tampere for providing information on the case studies.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Summary of the study methodology.
Figure 1. Summary of the study methodology.
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Figure 2. Model domain configuration used in the WRF implementation and location of the meteorological monitoring stations (●) for the cities of Tampere, Eindhoven and Genova (from top to bottom). The model ran in 2-way nesting mode with increasing domain resolutions of 25 km (D1), 5 km (D2) and 1 km (D3).
Figure 2. Model domain configuration used in the WRF implementation and location of the meteorological monitoring stations (●) for the cities of Tampere, Eindhoven and Genova (from top to bottom). The model ran in 2-way nesting mode with increasing domain resolutions of 25 km (D1), 5 km (D2) and 1 km (D3).
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Figure 3. Average daily air temperature at 2 m height modelled (purple) and observed (blue) values for the selected meteorological monitoring stations in Eindhoven, Genova, and Tampere, for the recent past year.
Figure 3. Average daily air temperature at 2 m height modelled (purple) and observed (blue) values for the selected meteorological monitoring stations in Eindhoven, Genova, and Tampere, for the recent past year.
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Figure 4. Daily precipitation totals modelled (purple) and observed (blue) values for the selected meteorological monitoring stations in Eindhoven, Genova, and Tampere, for the recent past year.
Figure 4. Daily precipitation totals modelled (purple) and observed (blue) values for the selected meteorological monitoring stations in Eindhoven, Genova, and Tampere, for the recent past year.
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Figure 5. Probability density functions (PDF) of the recent-past (green) and medium-term future (red; RCP4.5) WRF simulation results concerning mean (top panel), maximum (middle panel) and minimum temperatures (bottom panel), for Eindhoven (left), Genova (middle) and Tampere (right). In the PDF, the lines are the P10 (dashed), P50 (solid), and P90 (dotted) percentiles.
Figure 5. Probability density functions (PDF) of the recent-past (green) and medium-term future (red; RCP4.5) WRF simulation results concerning mean (top panel), maximum (middle panel) and minimum temperatures (bottom panel), for Eindhoven (left), Genova (middle) and Tampere (right). In the PDF, the lines are the P10 (dashed), P50 (solid), and P90 (dotted) percentiles.
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Figure 6. Average annual differences between medium-term future (RCP4.5) and recent past for daily mean temperature (Tmean; °C; top panel), daily maximum temperature (Tx; °C; middle panel) and daily minimum temperature (Tn; °C; bottom panel), for Eindhoven (left), Genova (middle) and Tampere (right).
Figure 6. Average annual differences between medium-term future (RCP4.5) and recent past for daily mean temperature (Tmean; °C; top panel), daily maximum temperature (Tx; °C; middle panel) and daily minimum temperature (Tn; °C; bottom panel), for Eindhoven (left), Genova (middle) and Tampere (right).
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Figure 7. Differences between medium-term future (RCP4.5) and recent past for Daily Temperature Range (DTR; °C; first row), number of Summer days (SU; days per year; second row), number of tropical nights (TR; nights per year; third row), number of icing days (ID; days per year; fourth row), number of frost days (FD; days per year; fifth row), for Eindhoven (left), Genova (middle) and Tampere (right).
Figure 7. Differences between medium-term future (RCP4.5) and recent past for Daily Temperature Range (DTR; °C; first row), number of Summer days (SU; days per year; second row), number of tropical nights (TR; nights per year; third row), number of icing days (ID; days per year; fourth row), number of frost days (FD; days per year; fifth row), for Eindhoven (left), Genova (middle) and Tampere (right).
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Table 1. Simulated years for recent past and medium-term future, for each case study.
Table 1. Simulated years for recent past and medium-term future, for each case study.
Simulation PeriodEindhovenGenovaTampere
Recent past 201320132012
Medium-term future204820512052
Table 2. List of climate change indices analyzed in this study (based on Klein Tank et al. [57] and European Commission [58]).
Table 2. List of climate change indices analyzed in this study (based on Klein Tank et al. [57] and European Commission [58]).
IndexNameDefinition
DTRDaily Temperature RangeDifference between daily maximum and minimum temperatures
SUSummer daysNumber of days where the daily maximum temperature is higher than 25 °C
TRTropical nightsNumber of days where the daily minimum temperature is higher than 20 °C
IDIcing daysNumber of days where the daily maximum temperature is lower than 0 °C
FDFrost daysNumber of days where the daily minimum temperature is lower than 0 °C
Table 3. Correlation coefficient (r), bias and RMSE for Eindhoven, Tampere and Genova for daily average air temperature at 2 m height and daily precipitation totals, for the recent past year.
Table 3. Correlation coefficient (r), bias and RMSE for Eindhoven, Tampere and Genova for daily average air temperature at 2 m height and daily precipitation totals, for the recent past year.
Daily Average TemperatureDaily Precipitation
CityNamerBias (°C)RMSE (°C)rBias (mm)RMSE (mm)
EindhovenAirport0.990.241.290.610.554.14
GenovaBolzaneto0.980.381.260.75−0.297.57
Castellaccio0.992.142.440.601.8411.63
Centro Funzionale0.98−1.141.650.630.748.56
Gavette------------0.660.019.09
Pegli0.98−1.221.85------------
Pontedecimo0.990.231.280.750.308.70
TampereHarmala------------0.555.3814.17
Airport0.991.422.17------------
Siilinkari0.99−0.831.73------------
Table 4. Average annual and seasonal differences between medium-term future (RCP4.5) and recent past for daily mean temperature (Tmean; °C), daily maximum temperature (Tx; °C), and daily minimum temperature (Tn; °C), for Eindhoven, Genova and Tampere. Red cells show positive values (increase) and blue cells show negative values (decrease).
Table 4. Average annual and seasonal differences between medium-term future (RCP4.5) and recent past for daily mean temperature (Tmean; °C), daily maximum temperature (Tx; °C), and daily minimum temperature (Tn; °C), for Eindhoven, Genova and Tampere. Red cells show positive values (increase) and blue cells show negative values (decrease).
DJFMAMJJASONANNUAL
Tmean (°C)Eindhoven+0.94+0.70+0.29−1.03+0.26
Genova+0.94−0.22−0.38−0.04+0.09
Tampere−2.92−1.25+1.77+0.95−0.31
Tx (°C)Eindhoven+0.91+1.16+0.30−0.93+0.40
Genova+0.940.00−0.57−0.26+0.04
Tampere−2.94−1.03+1.69+0.75−0.33
Tn (°C)Eindhoven+0.73+0.32+0.05−1.27−0.01
Genova+0.81−0.51−0.23+0.07+0.05
Tampere−2.87−1.40+1.77+1.13−0.29
Table 5. Compilation of average temperature projections for medium-term future climate (RCP4.5) for Eindhoven/Netherlands, Genova/Italy and Tampere/Finland.
Table 5. Compilation of average temperature projections for medium-term future climate (RCP4.5) for Eindhoven/Netherlands, Genova/Italy and Tampere/Finland.
Ref.LocationResolution (km2)∆Tmean (°C)
Paper ResultsEindhoven1 × 10.3
EURO-CORDEX *Eindhoven12.5 × 12.5−3.5
KNMI [69]Netherlands11 × 111
Lecœur et al. [70]Netherlands50 × 500.5–1.5
Paper ResultsGenova1 × 10.1
EURO-CORDEX *Genova12.5 × 12.50.4
Cholakian et al. [66]Western Mediterranean50 × 501.77
D’oria et al. [71]Northern Italy12.5 × 12.51.5
D’oria et al. [72]Northern Tuscany12.5 × 12.50.8
Lecœur et al. [70]Italy50 × 500.5–1.5
Paper ResultsTampere1 × 1−0.3
EURO-CORDEX *Tampere12.5 × 12.50.6
Ruosteenoja et al. [73]Finland50 × 501.8
Lecœur et al. [70]Finland50 × 500.5–1.5
* Average values for each city for the years selected in this study.
Table 6. Medium-term future (RCP4.5) average values (differences) per year and season for Daily Temperature Range (DTR; °C), number of Summer days (SU; days per season), number of tropical nights (TR; nights per season), number of icing days (ID; days per season), number of frost days (FD; days per season), for Eindhoven, Genova and Tampere. Red cells show positive values (increase) and blue cells show negative values (decrease).
Table 6. Medium-term future (RCP4.5) average values (differences) per year and season for Daily Temperature Range (DTR; °C), number of Summer days (SU; days per season), number of tropical nights (TR; nights per season), number of icing days (ID; days per season), number of frost days (FD; days per season), for Eindhoven, Genova and Tampere. Red cells show positive values (increase) and blue cells show negative values (decrease).
DJFMAMJJASONANNUAL
DTR
(°C) (%)		Eindhoven4.478.188.226.566.88
(+4%)(+11%)(+3%)(+5%)(+6%)
Genova4.255.454.964.904.89
(+3%)(+11%)(−6%)(−6%)(+0%)
Tampere3.085.055.613.434.30
(−2%)(+8%)(−1%)(−10%)(−1%)
SU
(days per season)		Eindhoven0.002.4122.604.0129.02
(0.00)(+2.37)(+5.80)(−8.13)(+0.04)
Genova0.000.0525.030.9726.04
(0.00)(−0.13)(+6.50)(−7.44)(−1.06)
Tampere0.000.000.500.000.50
(+0.00)(+0.00)(+0.50)(+0.00)(+0.50)
TR
(nights per season)		Eindhoven0.000.374.270.845.48
(+0.00)(+0.37)(+1.02)(−3.10)(−1.71)
Genova0.000.0027.517.3134.83
(+0.00)(+0.00)(−0.63)(−5.81)(−6.43)
Tampere0.000.000.000.000.00
(+0.00)(+0.00)(+0.00)(+0.00)(+0.00)
ID
(days per season)		Eindhoven6.411.460.004.2412.11
(−0.56)(+1.46)(+0.00)(+4.24)(+5.14)
Genova1.930.000.000.001.93
(−0.78)(+0.00)(+0.00)(+0.00)(−0.78)
Tampere61.2326.000.001.0088.00
(+12.87)(+14.51)(+0.00)(−15.23)(+12.16)
FD
(days per season)		Eindhoven24.806.580.008.6740.06
(−7.24)(+4.95)(+0.00)(+8.18)(+5.89)
Genova11.200.440.000.0911.73
(−4.22)(−0.73)(+0.00)(−0.13)(−5.09)
Tampere75.9340.900.005.32122.15
(+2.24)(−2.78)(+0.00)(−16.35)(−16.89)
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Ascenso, A.; Augusto, B.; Coelho, S.; Menezes, I.; Monteiro, A.; Rafael, S.; Ferreira, J.; Gama, C.; Roebeling, P.; Miranda, A.I. Assessing Climate Change Projections through High-Resolution Modelling: A Comparative Study of Three European Cities. Sustainability 2024, 16, 7276. https://doi.org/10.3390/su16177276

AMA Style

Ascenso A, Augusto B, Coelho S, Menezes I, Monteiro A, Rafael S, Ferreira J, Gama C, Roebeling P, Miranda AI. Assessing Climate Change Projections through High-Resolution Modelling: A Comparative Study of Three European Cities. Sustainability. 2024; 16(17):7276. https://doi.org/10.3390/su16177276

Chicago/Turabian Style

Ascenso, Ana, Bruno Augusto, Sílvia Coelho, Isilda Menezes, Alexandra Monteiro, Sandra Rafael, Joana Ferreira, Carla Gama, Peter Roebeling, and Ana Isabel Miranda. 2024. "Assessing Climate Change Projections through High-Resolution Modelling: A Comparative Study of Three European Cities" Sustainability 16, no. 17: 7276. https://doi.org/10.3390/su16177276

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