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INFORMATION SHEET ON FUTURE CLIMATE AND IMPACTS IN THE URBAN CASE STUDIES: ATHENS, GREECE Summary ► ► ► ► ► ► ► Daily minimum and maximum air temperatures in Athens increase during the period 1950-2050. The number of hot days and nights also show a sharp increase during the 21st century. Precipitation is increasing slightly in the present climate, while it is decreasing significantly in the ‘mid-century’ climate. Precipitation extremes do not show a clear trend. For Attica and the surrounding forest areas, a significant increase in peri-urban forest fires risk is expected. Mortality associated with high temperatures could increase significantly, especially for the period 2071-2100. A gain in energy demand is expected for the colder half of the year (November to April), especially during the latter part of the 21st century. For the warmer half of the year (May to October), an increasing trend is evident which is double in the hot summer months of July and August when the demand for air conditioning peaks. An increase in ozone episodes is evident, while at the end of the century ozone exceedance days could increase by almost a month. 1. Introduction This information sheet is the third in a series on the present and future impacts of climate change for the Athens urban case study. The first information sheet focused on observed climate (Giannakopoulos et al., 2009). It showed that mean air temperature exhibits a tendency towards warmer years, with significantly warmer summer maximum temperatures of about 1.8 °C since the late 19th century. The number of hot days/nights shows a ‘virtually certain’ increasing trend, especially in the last decade. A slight increasing trend in total rainfall has been observed. The second information sheet (Giannakopoulos et al., 2010) reported that higher forest fire risks are direct consequences of increases in maximum temperature and decreases in rainfall and relative humidity during the summer. Heat waves have readily discernible health impacts as substantial heat-related deaths, occurred at very high temperatures for Athens. Energy mainly peaks in winter; however, a second significant peak is also apparent in summer. In the city of Athens, ozone enhances with temperature although there are still high ozone concentrations at lower temperatures. This information sheet focuses on climate scenarios for the present and future periods in Athens (1950-2050), based on a set of the most recent model simulations including for the first time a coupling of the Mediterranean Sea with the atmosphere at a high spatial resolution. The set of atmospheric variables simulated by the models are presented and compared to observed ones, if available. Biogeophysical impacts are investigated using future projections of the observed relationship between climate and biogeophysical variables. For the Athens urban case study, biogeophysical and vulnerability indicators based on system thresholds were calculated using regional climate model output both from the CIRCE project and from the earlier EU ENSEMBLES project (van der Linden and Mitchell, 2009). The response of biogeophysical and social systems to past extremes in climate can be used as an analogue for the potential consequences of climate change in a region. Future climate indicators are presented for: ► ► ► ► ► ► ► ► Maximum temperature Number of hot days and nights Annual rainfall Rainfall extremes Peri-urban forest fires All-cause daily mortality Energy consumption Ozone exceedance days 1 Climate models The CIRCE project climate model runs (Information sheet on future climate: climate projections for the CIRCE case studies, http://www.cru.uea.ac.uk/projects/circe/projections_models.html) are used to investigate present and future climate changes in the Greater Athens Area for the period 1950-2050. After the year 2000, the A1B scenario for greenhouse gas emissions is used (Nakićenović and Swart, 2000). The CIRCE climate models are of differing spatial resolution and their gridpoints around Athens are shown in Figure 1. INGV (Global model) ~80 km ENEA (Regional model), 30 km IPSL (Global and regional model) 30 km. The IPSL model used is IPSL2 MPI (Regional model), 25 km CNRM (Stretched grid global model), 50 km Figure 1: The Athens area covered by the atmospheric components of the five CIRCE climate models and their horizontal resolution. Red star indicates NOA meteorological station 2 Mean annual maximum and minimum temperature What is it? Mean annual maximum and minimum temperature (Tx and Tn) were used as core climate indicators for the case studies. Observed and projected mean annual Tx and Tn temperature anomalies (Figure 2) for Athens are calculated using output from the ENEA, MPI and IPSL models, for the period 1950-2050. Furthermore, the projected changes (for 2021-2050 with respect to 1961-1990) in these indicators are presented in Section 5: Uncertainties. Athens_Mean Annual Tmax anomalies Athens_Mean Annual Tmin anomalies 4.0 obs ipsl model mean 3.0 2.0 4.0 enea mpi obs ipsl model mean 3.0 2.0 1.0 enea mpi 1.0 0.0 0.0 1 2050 2030 2010 1990 1970 1950 1890 2050 2030 2010 1990 1970 1950 1930 -4.0 1910 -3.0 -4.0 1890 -2.0 -3.0 1930 -1.0 -2.0 1910 -1.0 Figure 2: Observed and projected mean annual maximum (left) and minimum (right) temperature anomalies for Athens calculated using output from the ENEA, MPI and IPSL models, 1950-2050. What does this show? For temperature, the observed trends and climate change signal are stronger than for precipitation. Figure 2 shows observed and simulated (ENEA, MPI and IPSL models) mean annual maximum and minimum temperature for Athens, as anomalies from the 1961-1990 baseline. In general, the projected changes appear as an extension of the observed positive trends, while the long-term 1950-2050 trend for both Tx and Tn is 0.26°C/decade. For Athens maximum temperature, the projected changes do not extend much beyond the upper range of the observations. The influence of the hot summer of 2007 is evident in the 2007 observed Tx value for Athens which appears as an outlier with respect to the observed series but falls centrally within the model range by 2050. Why is it relevant? This indicator is of prime importance, especially for a Mediterranean city such as Athens, since it determines the thermal comfort and cooling demands. There is a strong relationship between the stress experienced by organisms exposed to high temperatures and daily temperature extremes. Moreover, the rate of energy demand depends on the ambient temperature and hence, summer maximum temperature is a valuable estimator of changes in energy consumption for air conditioning of buildings. Changes in temperature may have critical implications in an urban area for surface water resources, periurban forestry, infrastructure, industry, and most notably population heat stress and health (Giannakopoulos et al., 2009). 3 Hot days and nights What is it? Hot nights act synergistically with hot days to contribute to human discomfort during a heat wave. Here, the threshold of the 95th percentile of Tx and Tn for the 1961-1990 period for each of the National Observatory of Athens (NOA) observational records and model (ENEA, MPI. IPSL and model mean) projections were used to define a very hot day and a very hot night, respectively. Athens_Number of Days with Tx>95 thp 100 obs ipsl model mean 90 80 70 Athens_Number of Days with Tn>95 thp 100 obs ipsl model mean 90 enea mpi 80 70 60 50 60 40 40 30 30 20 10 20 0 0 enea mpi 50 2050 2040 2030 2020 2010 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 1900 1890 2050 2040 2030 2020 2010 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 1900 1890 10 Figure 3: Observed and projected annual number of very hot summer days (Tx95) - left; and very hot summer nights (Tn95) – right, for Athens, calculated using output from the ENEA, MPI and IPSL models, 1950-2050. 2050 2040 2030 2020 2010 2000 1890 2050 2040 2030 2020 2010 2000 1990 1980 1970 1960 1950 1940 1930 1920 -20 1910 -20 1900 -10 1890 0 -10 1990 10 0 1980 20 10 1970 30 20 1960 40 30 1950 50 40 enea mpi 1940 60 1930 50 obs ipsl model mean 70 enea mpi 1920 obs ipsl model mean 60 1910 70 Athens_Number of Days with Tn>95 thp anomalies 80 1900 Athens_Number of Days with Tx>95 thp anomalies 80 Figure 4: Observed and projected anomalies of left: annual number of very hot summer days (Tx95), and right: very hot summer nights (Tn95) for Athens, using output from ENEA, MPI and IPSL models, 1950-2050. What does this show? Similar features to mean temperatures, are seen for temperature extremes – very hot days and very hot nights (Figures 3 and 4), although the inter-annual variability is greater and the upper range of the observations is not exceeded very frequently in the future except for the IPSL model. Why is it relevant? An increase in the frequency of heat waves has been observed, particularly in southern Europe (IPCC, 2007). These events intensify in urban areas such as Athens, and are a familiar feature of Greek summers. Heat-wave days have negative effects on human comfort and contribute significantly to heat stress especially if associated with high levels of humidity. In addition, stagnant air masses encourage the build-up of air pollutants, which act synergistically with higher air temperature to increase mortality. Warm nights following a hot day and accompanied by high humidity can be particularly uncomfortable to urban residents. 4 Annual total rainfall What is it? Annual total precipitation was also selected as a climate indicator (Figure 5). Observed and projected annual total rainfall anomalies for Athens are calculated using output from the ENEA, MPI and IPSL models, for the period 1950-2050. Athens_Total Annual Rainfall 1200 obs ipsl model mean 1000 Athens_Total Annual Rainfall anomalies 700 enea mpi 500 300 800 100 600 -100 400 -300 obs ipsl model mean -500 enea mpi 2050 2030 2010 1990 1970 1950 1930 -700 1890 2050 2040 2030 2020 2010 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 1900 1890 0 1910 200 Figure 5: Observed and projected total annual rainfall (left) and total annual rainfall anomalies (right) for the Athens case study calculated using output from the ENEA, MPI and IPSL models, 1950-2050. What does this show? The CIRCE ensemble mean changes for rainfall for the period 1950-2050 in Athens are negative, about 77 mm in absolute terms, while the change in percentage terms is -16 %. The inter-model spread is quite large. The MPI model indicates a particularly large decrease in annual precipitation of -43% for Athens. However, the magnitude of change does not vary systematically from model to model as is the case of temperature. The large inter-annual variability of precipitation makes it difficult to identify statistically significant trends in observed or simulated precipitation-based climate indicators. Why is it relevant? Rainfall is a limiting factor in biological systems, so the examination of total rainfall on an annual and seasonal basis is important, particularly for Mediterranean regions. Changes in annual total rainfall may have important implications for the availability of water resources, which in turn can affect agriculture, forestry and water supply. 5 Precipitation extremes What is it? Figure 6 shows projected changes for three indices in Athens calculated using daily output from the ENEA, MPI and IPSL models for the period 1950-2050: the maximum dry spell length (consecutive dry days: cdd), heavy precipitation (the 90th percentile of daily precipitation: pq90) and maximum three-day precipitation (px3d), where a threshold of > 0.5 mm defined a rain day. Consecutive Dry Days anomalies 200.0 90th percentile of daily precipitation anomalies 30.0 150.0 20.0 100.0 10.0 50.0 0.0 0.0 -50.0 -10.0 -100.0 2050 2040 2030 2020 2010 1990 2050 2000 1980 ipsl mean model 1970 1960 1950 2050 2040 2030 2020 2010 -30.0 2000 1990 1980 enea mpi 2040 -20.0 ipsl mean model 1970 1950 -200.0 1960 enea mpi -150.0 maximum 3-days precipitation anomalies 200.0 150.0 100.0 50.0 0.0 -50.0 -100.0 2030 2020 2010 2000 1990 1980 ipsl mean model 1970 1950 -200.0 1960 enea mpi -150.0 Figure 6: Projected precipitation extremes indices for Athens calculated using daily output from the ENEA, MPI and IPSL models: the maximum dry spell length (consecutive dry days: cdd), heavy precipitation (the 90th percentile of daily precipitation: pq90) and maximum three-day precipitation (px3d). What does this show? It is difficult to draw clear messages even concerning the direction of change for precipitation extremes, particularly given the strong inter-annual variability of precipitation. ENEA and IPSL models suggest a slight increase in the maximum three-day precipitation, while MPI exhibits a decrease. For consecutive dry days, all models indicate an increase. In general, the intermodel spread is quite large and the signal of change cannot be considered robust in contrast to the fairly robust projections for longer dry periods across the Mediterranean, particularly in summer, emerging from other studies (Beniston et al. 2007, Giorgi and Lionello 2008, May 2008, Giannakopoulos et al. 2009). These studies tend to focus on the end of the century and it may be that any signal of change has not yet emerged from the noise due to natural variability by 2021-2050. See Section 5 for further discussion of uncertainties. Why is it relevant? Changes in precipitation extremes are particularly relevant for urban areas. An increase in the incidence and intensity of rainfall events may provoke natural disasters such as localised flash flooding in urban areas. Intense rainfall events lead to greater erosion rates and a higher risk of flash flooding in urban and peri-urban areas with occasional serious loss of life and property. 6 Peri-urban forest fires What is it? Future fire risk for the Greater Athens Area and the surrounding forest area in the Attica peninsula was assessed using the Canadian Fire Weather Index (FWI, Van Wanger 1987). This a numerical rating of fire intensity calculated using daily maximum temperature, relative humidity, wind and precipitation. Although the FWI was originally developed for Canadian forests, several studies have shown its suitability for the Mediterranean basin (e.g., Viegas et al. 1999). The FWI was estimated for future periods using daily output from six ENSEMBLES RCMs (KNMI-RAMCO2, CNRM, ETHZ, MPI, METO and METNO) simulations. Ensemble-mean changes (with respect to the 1961-1990 baseline) for two 30-year future periods (2021-2050 and 2071-2100) were calculated. Model output covers Europe at a resolution of about 25 km, for the A1B emissions scenario. Unlike the CIRCE model runs these are not coupled atmosphereocean regional models. Future FWI projections have also been investigated with two CIRCE models simulations (ENEA and MPI) for the mid-century future period 2021-2050. Figure 7: Projected changes (2021-2050 minus 1961-1990) in the number of days with fire risk FWI > 15 (left) and extreme fire risk FWI > 30 (right), based on the mean of six ENSEMBLES RCM (KNMI-RAMCO2, CNRM, ETHZ, MPI, METO and METNO) simulations. 7 Figure 8: Projected changes (2021-2050 minus 1961-1990) in the number of days with fire risk FWI > 15 (left) and extreme fire risk FWI > 30 (right), based on the average of two CIRCE models (ENEA and MPI)). What does this show? In order to identify a relationship between FWI values and fire occurrence for the Attica region, meteorological data for the period 1983-1990 were obtained from the Greek National Meteorological Service and daily fire frequency and area burnt in the regions of the meteorological stations were obtained from the Forest Research Institute of Athens. Fire occurrence was found to be low for FWI less than about 15 and tends to increase with increasing FWI. A threshold of FWI = 30 was used to define extreme fire risk (Giannakopoulos et al. 2010). For Attica and the surrounding forest areas, fire risk is projected to increase in both future periods. For the near-future period 2021-2050, the number of days with fire risk (FWI>15) increases by up to 17-20 days or more in the north western parts of the study area, while extreme fire risk (FWI>30) is more spatially variable, with an increase of about 10 days in the vicinity of Athens (Figure 7). By the end of the century (2071-2100), the increase is 50 days (for FWI>15) and 40 days for extreme fire risk (not shown). The same analysis based on the two CIRCE simulations shows a different spatial distribution for the FWI. In peri-urban regions, the future fire risk for FWI>15 is larger in the Great Athens Area than for the ENSEMBLES simulations. In contrast, extreme fire risk shows smaller increases for the CIRCE simulations compared to the ENSEMBLES simulations (Figure 8). Why is this important? Peri-urban forest fires are highly sensitive to climate change since fuel moisture is affected by precipitation, relative humidity, air temperature and wind speed. The contribution of meteorological factors to fire risk is simulated by various non-dimensional indices of fire risk. The destruction of forests is of great concern, since this has many side effects, e.g. floods, soil erosion and consequent loss of fertility. Furthermore, peri-urban forests fires play a fundamental role in regulating the air temperature and wind circulation in the surrounding city. Thus peri-urban forest fires aside from the general side effects may also contribute to an increase of temperature in the city during the summer months and an intensification of the urban heat island. 8 All-cause daily mortality What is it? A linear model was used to project heat-related mortality to the future climate (2021-2050 and 20712100) using temperature output from the RACMO2 RCM ENSEMBLES simulation for the A1B emissions scenario. An ‘adaptation’ or acclimatisation factor of 1°C per 30 years (Dessai, 2002) was included to allow for physiological and behavioural adjustment to higher temperatures. The same model was also applied for the CIRCE simulations (ENEA, MPI and IPSL) for the 2021-2050 period. Figure 9: Excess deaths (right axis; red and dark blue bars for present and future climate with adaptation, respectively) and daily temperature frequencies (left axis; light blue bars) in Athens. Top: for the future periods 2021-2050 (left) and 2071-2100 (right), using the RACMO2 RCM ENSEMBLES simulation; bottom: for the future period 2021-2050 derived using the ENEA, MPI and IPSL CIRCE models. What does this show? Even after adjustment for adaptation, there are significant increases in excess heat-related mortality for the periods 2021-2050 and especially for the period 2071-2100 (Figure 9). This increase is shown by the shift to the right and the longer tails of the simulated dark-blue bars compared to the red observed bars. There are significant differences in projected mortality between the three CIRCE models. The IPSL model fails to capture the high summer temperatures recorded in Athens, and thus shows much less excess mortality in the future. ENEA and MPI have a much better fit to observations. Why is it relevant? Heat waves have readily discernible health impacts because they result in a large number of deaths and affect relatively large, heterogeneous areas simultaneously. Not all heat waves have a similar impact on mortality; the intensity, duration and timing of the event are particularly important. Illnesses recognisable as the direct results of exposure to prolonged periods of high temperature are heatstroke, heat exhaustion, and heat cramps. Heat-related mortality in large urban areas disproportionately affects the elderly, young people with pre-existing illness, and low-income groups. 9 Energy consumption What is it? Future energy demand in Athens was estimated using temperature output from the RACMO2 RCM ENSEMBLES simulation for the A1B emissions scenario and a simple extrapolation of the non-linear trend between temperature and energy load from the present day (Giannakopoulos et al., 2010). It is assumed that technology use (UNDP, 2002) is the same in the control (1961-1990) and future periods (2021-2050 and 2071-2100). The same model was also applied for two CIRCE simulations (ENEA, MPI and IPSL). Figure 10: Athens energy demand (bars, right axis) and daily maximum temperature (triangles, left axis) for the colder (blue bars) and warmer (red bars) halves of the year for 1961-1990, 2021-2050 periods for, top: the RACMO2 RCM ENSEMBLES simulation; bottom: CIRCE simulations (ENEA, MPI, IPSL). What does this show? Figure 10 (top) shows that for the colder half of the year (November to April), energy demand decreases, especially during the latter part of the 21st century. The saving in energy demand is about 2% for 2021-2050 and about 5% for 20712100. For the warmer half of the year (May to October), an increasing trend is evident: a 5% increase in demand for 2021-2050 and 15% for 2071-2100. This increases doubles in the hot summer months of July and August when the demand for air conditioning peaks. The energy demand projections based on CIRCE simulations for 2021-2050 (Figure 10, bottom) 10 also indicate a decrease in energy demand during the cold period and an increase during the hot period. In contrast to the ENSEMBLES simulations, the projected change in energy demand is lower during the warm period (not exceeding +2%) compared to the cold (about 2% as in the ENSEMBLES simulations). Why is it relevant? Consumption of electricity is particularly sensitive to weather (especially temperature), since large amounts of electricity cannot be stored and thus the electricity that is generated must be instantly consumed. Average daily electricity demand in most European countries shows a single peak during winter months (Hekkenberg et al. 2009). In Europe, only Spain, Portugal, Italy and Greece currently show an additional peak during summer months (Giannakopoulos and Psiloglou 2006, Hekkenberg et al. 2009). Knowledge of the energy characteristics for an urban area such as Athens has important policy implications for the power industry. Power planning and management needs to take key climatic, demographic and economic factors into consideration to address the pressing issue of energy saving. This will help each local city government to establish policies to encourage the replacement of old inefficient electrical equipment with energy saving equipment. If feasible, introducing renewable energy systems should also be encouraged in the light of increased levels of energy demand due to climate change. Climate change may, in turn, result in summer electricity demand peaks, seen already in Athens, affecting in this way, generation capacity, maintenance scheduling and pricing. The peak in cooling energy demand falls in the dry season. Low water supply can reduce energy production from hydroelectric plants, as well as from conventional power plants, which require water for cooling and for driving the turbines. As a result, production may not match energy demand in the warm period of the year. Additional capacity may need to be installed unless adaptation / mitigation strategies or stricter building regulations to improve insulation are established. Adaptation measures such as building codes could help minimize the increase in energy demand during heat-waves. 11 Ozone exceedance days What is it? The relationship between daily eight-hour maximum temperature and ozone concentration for Athens is shown in Giannakopoulos et al. 2010. The probability of ozone exceedance days (defined as days with maximum 8-hour average ≥ 60 ppb, i.e., according to EU Directive 2008/50/EC) increases over the temperature range 17 to 38°C, mainly due to high activity of photo-chemical processes. Figure 11 (top) depicts the ozone probability distribution for temperatures above 17°C using output from one of the ENSEMBLES RCMs (the KNMI model) in order to estimate the potential impact of increasing future temperatures on ozone exceedance days in the Greater Athens Area. The same approach was also applied for the CIRCE simulations (ENEA, MPI and IPSL) (Figure 11, bottom). Figure 11: Ozone probability distributions for temperature above 17 °C, for top: the observed (1990-1999) (black), the 2021-2050 (green) and the 2071-2100 (red) periods for KNMI model. Bottom: the observed period (1990-1999) (green), and three projections for period 2021-2050 (grey – ENEA; red – IPSL; blackMPI). Values are plotted at the mid-point concentration of each 5ppb ozone concentration bin. 12 What does this show? Figure 11 (top) indicates that the number of ozone exceedance days is projected to increase. For the future period 2021-2050, the percentage increase is about 8%, amounting to about seven extra ozone exceedance days per year. For the end of the 21st century (2071-2100), the percentage increase is about 30%, which is almost an additional month (about 27 days) of ozone exceedance days per year. The same analysis for the CIRCE models simulations indicates that for the period 2021-2050 ozone exceedance days are expected to increase by 4 days for MPI, 8 days for ENEA and 34 days for IPSL (Figure 11, bottom). Why is it relevant? It is already known that day-to-day changes in weather affect both the severity and duration of pollution episodes. In a future climate-change world, faster chemical reactions, increased 4. Socio-economic trends Since climate is not the only driver of change, future changes in non-climate drivers and vulnerabilities can accentuate or diminish future climate change impacts. For urban case studies, population growth, GDP and energy consumption are key issues. In Greece, the urban population is generally projected to increase by 30% by the mid century (Figure 12a). In contrast, a low annual growth rate for Athens indicates that the population will not increase significantly in the future (Figure 12b). Total energy consumption is projected to increase 2.5% by 2030s. However, the domestic sector is estimated to increase 40% (Table 1). Given the increase in urban population, rising domestic energy consumption mainly concerns biogenic emissions, and stagnation may contribute to an increase in the occurrence of ozone pollution episodes. Ozone episodes are closely linked to adverse effects on human health, vegetation and ecosystems, and there are documented relationships between ozone exceedances and hospitalization. It has previously been shown that daily maximum temperature variation accounts for much of the influence of meteorological variables on ozone (Lin et al. 2001, Bloomer et al. 2009). This strong correlation of ozone episodes with temperature is primarily associated with surface air ventilation, since high temperatures are typically a result of stagnant air. The ozonetemperature association also depends on local ozone production chemistry and on temperaturedependent biogenic emissions (Jacob et al. 1993). cities. The only sector indicating future decreases in future energy consumption is the rural sector, explained by an increase in urbanization and decrease in rural population. The increase in energy consumption appears to be in line with the increase in GDP (Figure 12c). IMF projections for the Greek GDP take into account the current economic recession since GDP is estimated to decline slightly in 2010 and 2011. IMF projections show an increase thereafter although the current economic crisis could be deeper and therefore last longer than forecast. Given the rise in GDP, life expectancy is also on the rise, reaching or exceeding 80 years of age by 2050 (Figure 12d). Table 1. Projected energy consumption per activity sector. Source: Hellenic Ministry of Environment, Energy and Climate Change ‘Analysis of energy scenarios for the attainment of 20/20/20 goals’. Total Energy Consumption (ktoe) per sector Rural 2010 2015 2020 2025 2030 1065 1045 1033 1044 1051 Industry 4300 4192 4486 4936 4729 Transportation 8355 8757 9368 10018 10521 Domestic 5753 6009 6865 7544 8089 Tertiary 2059 2193 2436 2680 2884 21532 22195 24187 26222 27274 Total 13 3 2.5 2 Urban annual growth rate (%) 9000 Urba n popul a tion Annual rate of change of percentage urban (%) 8000 Athens popul a tion 7000 Rura l popul a tion 6000 1.5 5000 1 4000 0.5 3000 2040-2045 2030-2035 2020-2025 2010-2015 2000-2005 1990-1995 1980-1985 1970-1975 1960-1965 1950-1955 0 Figure 12a: Annual urban population growth rate, Greece. Source: World Population Prospects: The 2006 Revision and World Urbanization Prospects: The 2007 Revision, http://esa.un.org/unup 23500 23000 22500 Gross domestic product per capita, current prices Gross domestic product, current prices (in billions) 0 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 Figure 12b: Urbanisation (urban and rural population) Source: World Population Prospects: The 2006 Revision and World Urbanization Prospects: The 2007 Revision, http://esa.un.org/unup 100 260 90 255 80 250 70 240 21500 1000 265 245 22000 2000 235 Life expectancy at birth for both sexes (years) 60 50 World 40 Greece 5. Uncertainties Uncertainties are present in all stages of the CIRCE case-study assessment from observations, to emissions, models, projections, impacts and adaptive response. Bias is evident in comparisons of observed and simulated (ENEA, MPI and IPSL models) maximum and minimum annual temperature (Figure 13) and annual precipitation (Figure 14) for the Athens case study. The cold bias is most clearly evident for minimum temperature in all model output. Maximum temperatures are better simulated especially for the ENEA model. Precipitation appears generally well simulated for Athens. The models, however, are not able to capture extreme years: There are no simulated values comparable 2085-2090 2070-2075 2055-2060 2040-2045 2025-2030 2010-2015 1995-2000 Figure 12c: Gross domestic product in billions (red line, right axis) and Gross domestic product per capita (green line, left axis) for Greece. Source: IMF World Economic Outlook Database, April 2010 1980-1985 2008 2009 2010 2011 2012 2013 2014 2015 1965-1970 230 1950-1955 30 21000 Figure 12d: Life expectancy at birth in Greece and the world. Source: UN World Population Prospects: The 2008 Revision, http://esa.un.org/unpp to the exceptionally high total annual precipitation observed in Athens in 2003. This is expected as CIRCE model runs are not reanalysis-forced runs so one would not expect year-to-year consistency, although some extreme years are evident in the simulations. To minimise the effects of bias, CIRCE projections are generally presented in the case-study work as the difference between the future (2021-2050) and control (1961-1990) periods. In the case of extreme events (Figures 15, 16 and 17), biases in the absolute magnitude of events (but not in the shape of the distribution) are minimised by using percentile thresholds calculated from the model control period rather than from observations. 14 Athens_Annual Maximum Temperature 30 25 20 15 10 obs ipsl model mean 5 enea mpi 2050 2040 2030 2020 2010 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 1900 1890 0 Athens_Annual Minimum Temperature 30 obs ipsl model mean 25 enea mpi 20 15 10 5 2050 2040 2030 2020 2010 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 1900 1890 0 Figure 13: Observed and projected mean annual maximum (top) and minimum (bottom) temperatures for Athens calculated using output from the ENEA, MPI and IPSL models, 1950-2050 Athens_Total Annual Rainfall 1200 obs ipsl model mean 1000 enea mpi 800 600 400 200 2050 2040 2030 2020 2010 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 1900 1890 0 Figure 14: Observed and projected total annual rainfall for the urban case studies using output from the ENEA, MPI and IPSL models, 1950-2050. It is important to consider the ensemble range as well as the ensemble mean. The precipitationbased indicators for the urban case studies (Figure 17) are based on only three of the CIRCE models: ENEA, MPI and IPSL – so caution is needed. Caution is also needed in using the biogeophysical and vulnerability indicators for Athens. While the fire-risk analysis is based on six RCM runs from the ENSEMBLES project, the air pollution, human health and energy demand analyses are all based on a single ENSEMBLES RCM. However, these use projected changes in temperature which are generally more robust than precipitation. All the CIRCE models (and ENSEMBLES RCMs) agree in the direction of temperature change: an increase in both mean and high temperature extremes, although there is some uncertainty in terms of the magnitude of change. While the CIRCE models generally agree in projecting a decrease in mean annual precipitation (Figure 5), the uncertainties are greater at the seasonal scale and particularly with respect to extremes (see Figure 17). This is in part due to the higher inter-annual variability of precipitation, but also to the higher spatial variability of precipitation and the finer spatial scale of the underlying physical processes – particularly with respect to convective events. The large inter-annual variability of precipitation makes it difficult to identify statistically significant trends in observed (Giannakopoulos et al. 2010) or simulated (Figure 5) precipitationbased climate indicators. For temperature, the observed trends and climate change signal are stronger. Figure 2 shows observed and simulated (ENEA, MPI and IPSL models) mean annual Tx and Tn for the urban case studies as anomalies from the 19611990 baseline to correct for the model biases shown in Figure 13. In general, the projected changes appear as an extension of the observed positive trends. Most notably for Athens Tx, the projected changes do not extend much beyond the upper range of the observations. The influence of the hot summer of 2007 is evident in the 2007 observed Tx anomaly for Athens which appears as an outlier with respect to the observed series but falls centrally within the model range by 2050. Similar features are seen in the case of temperature extremes – very hot days and very hot nights (Figure 3), although the inter-annual variability is greater and the upper range of the observations is not exceeded that frequently in the future. 15 seasonal tn95n 60 ensemble enea ingv ipsl mpi cnrm 50 40 30 20 10 0 DJF MAM JJA SON Figure 15: Projected changes (2021-2050 minus 1961-1990) in the number of very hot nights (Tn95n) based on CIRCE model data for Athens. 30.00 summer tx95n 25.00 20.00 15.00 10.00 5.00 0.00 enea ingv ipsl mpi cnrm ensemble Figure 16: Projected seasonal changes (2021-2050 minus 1961-1990) in the number of very hot summer days (Tx95n) based on CIRCE model data for Athens. The ensemble-mean change is shown, together with changes for five individual models. 65.0 55.0 cdd (days) 45.0 pq90 (mm/day) 35.0 px3d (mm) 25.0 15.0 5.0 -5.0 -15.0 -25.0 -35.0 enea ipsl mpi Figure 17: Projected changes in three precipitation extremes indices for Athens calculated using daily output from the ENEA, IPSL and MPI models: the maximum dry spell length (consecutive dry days: cdd), heavy precipitation (the 90th percentile of daily precipitation: pq90) and maximum three-day precipitation (px3d). A threshold of > 0.5 mm was used to define a rain day The quantitative impacts assessments undertaken for the Athens case study are based on statistical rather than process-based modelling. Most of the statistical models are linear. It may be reasonable to assume linearity and stationarity given certain underlying assumptions in the model – which should be made clear to users. In the case of the energy demand model for Athens, for example, it is explicitly assumed that technology use is the same in the control and future periods. Thus no allowance is made for a potential drive towards a more energy efficient economy or for other socioeconomically-driven trends. A somewhat different approach is taken in the Athens health risk work, where an adaptation or acclimatisation factor of 1°C per decade is used in an attempt to account for behavioural and physiological changes. It should also be noted that, when constructing the impacts models, absolute thresholds of temperature were used. This choice gives a clear picture of the underlying impact to relevant stakeholders. However, the projected impact of future climate is very much dependent on the model’s bias in comparison to observations. As an example, the IPSL model has the largest cold bias so it projects virtually no increase in mortality when health impacts are considered. Another option would be to work with percentile rather than absolute thresholds. In this case, IPSL would show the highest increase in mortality since it is the model that presents the greatest differences between future and baseline climate. Obviously, each option presents its advantages and disadvantages but in the Athens case study we have chosen to work with absolute thresholds. Ideally, for the Athens and other urban case studies, impacts and vulnerability indicators would also be considered at high spatial resolution. In terms of the health implications of high ozone days, for example, the distribution of vulnerable groups (the young and elderly, those with pre-existing conditions, outdoor workers) may vary spatially across the city and particular ‘hot spots’ of pollution may occur, for example downwind of industrial areas. Thus there are uncertainties inherent to all stages of the CIRCE case-studies integrated assessments. The uncertainties in the climate 16 projections are perhaps the easiest to quantify and demonstrate to stakeholders. Nonetheless, it is important to consider all aspects of uncertainty in the context of adaptation decision making. 6. Integrated assessment The Greater Athens area, i.e., the city with its suburbs and the Attica peninsula in general, comprises almost half of the Greek population (circa 5 million inhabitants) and consequently represents a major part of the economic, social, cultural and administrative activities of the country. The occurrence of a severe heat wave in Athens can therefore paralyse all socioeconomic sectors and culminate in a substantial increase in energy demand (for air conditioning needs) and a deleterious effect on human health (due to the high population density). In Athens, urbanization constitutes an additional aggravating factor since the urban fabric retains night temperatures at high levels and hinders biological cooling. Additionally, poor air quality contributes to the increased health risks in Athens. Persistent high summer temperatures also increase significantly peri-urban forest fire risk (Giannakopoulos et al., 2009). Table 2 provides a summary of climate indicators values based on the CIRCE model climate simulations for the ‘present’ 1961-1990 and ‘mid-century’ 2021-2050 periods, as well as the changes between the two periods. Table 3 provides a summary of climate trends based on the CIRCE model simulations for these same periods. The negative, though not statistically significant, temperature trends for the present climate are in contrast to the statistically significant increasing trends simulated for the mid-century climate. Table 4 summarises the biogeophysical and social vulnerability indicators for the ‘mid-century’ 2021-2050, and the ‘end of century’ 2071-2100 periods based on numerical simulations derived from the ENSEMBLES and CIRCE models. Table 2: Mean values and trends of some key variables simulated by three coupled models for the Athens area (Figure 1) based on ensemble averages, for the present, 1961-1990 and ‘mid-century’, 2021-2050 periods for a set (ENEA, MPI, IPSL) of numerical simulations. Mean ‘long-term’ changes between 1961-1990 and 20212050 are also shown. Climate indicator present climate (1961-1990) mid-century (2021-2050) Long-term changes from 19611990 to 2021-2050 T max (°C) 20.2±0.3 21.8±0.2 +1.3±0.3 T min (°C) 11.4±0.1 12.9±0.1 +1.6±0.2 Hot days (days) 18±1 39±2 +21±1 Hot nights (days) 18±1 49±2 +30±2 Precipitation (mm) 365±11 288±12 -77±16 17 Table 3: Summary of trends for the present 1961-1990, ‘mid-century’ 2021-2050 and ‘long-term’ 2061-2050 periods, based on a set (ENEA, MPI, and IPSL) of numerical simulations. Climate indicator present climate trend (1961-1990) mid-century trend (2021-2050) long-term trend (1961-2050) T max (°C/decade) -0.07±0.07 +0.62±0.081 +0.27±0.021 T min (°C/decade) -0.03 ±0.06 +0.59±0.071 +0.26±0.021 Hot days (days/decade) +1.3 ±1.0 +8.2±1.51 +3.5±0.31 Hot nights (days/decade) +1.4±1.2 +10.6±1.61 +5.1±0.31 Precipitation (mm/decade) +8.7 ±1.3 -23±101 -12.3±2.11 1 denotes that the trend is significant at the 95% level. The statistical significance of the trends was examined using the Student’s t-test. Table 4: Summary of differences for the biogeophysical and social vulnerability indicators between the ‘midcentury’ 2021-2050 or the ‘end of century’ 2071-2100 and the present climate period based on numerical simulations derived from CIRCE and ENSEMBLES models. Biogeophysical / Social Vulnerability Indicator Fire risk (days/year) Extreme fire risk (days/year) Excess deaths Energy demand – cold season Energy demand – warm season Ozone exceedance days CIRCE mid-century (2021-2050) trends ENSEMBLES mid-century (2021-2050) trends ENSEMBLES end of century (2071-2100) trends (↑) 20 (↑) 14 (↑) 20 (↑) 10 (↑) 50 (↑) 40 (↑) 63%1 (↑) 70% (↑) 60% (↓) 2% (↑) 2% (↓) 2% (↑) 5% (↓) 5% (↑) 15% (↑) +6days2 (↑) 8% (+7days) (↑) 30% (+27 days) Impact significant increase in periurban forest fires increase in heatrelated mortality increase in energy consumption increase in ozone episodes 1 : Except for IPSL that fails to capture high summer temperatures. Future excess deaths are calculated with 1°C adaptation for the mid-future and 2°C for the end of the century. 2 : Average from ENEA and MPI. The projected IPSL increase is +34 days. 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