Contributors: Hans Hooyberghs (VITO), Julie Berckmans (VITO), Filip Lefebre (VITO), Koen De Ridder (VITO)
1. Heat waves and cold spells
1.1. Background
A heat wave or cold spell is a prolonged period of extremely high or extremely low temperature for a particular region. However, there is a lack of rigorous definitions for heat waves and cold spells. Annual number of heat wave days and cold spell days are calculated based on definitions which are used in the climate and health community, at a European-scale and at a national/regional/local scale.
1.2. Heat wave definitions
1.2.1. European-wide definitions
1.2.1.1. Climatological EURO-CORDEX
For the climatological definition, we rely on the EURO-CORDEX project, and use one of the two definitions that has been put forward in this project (Jacob et al. 2014). A heat wave is thereby considered as a period of at least three consecutive days on which the daily maximal temperature exceeds the 99th percentile of the daily maximal temperatures of the May to September season of the control period of 1971 to 2000.
1.2.1.2. Euroheat project
For the health-related EU-wide definition, we rely on the results of the EUROheat project (Michelozzi et al. 2007; WHO 2009). For the summer period of June to August, heat waves were defined as days in which the maximal apparent temperature (Tappmax) exceeds the threshold (90th percentile of Tappmax for each month) and the minimum temperature (Tmin) exceeds its threshold (90th percentile of Tmin for each month) for at least two days. The apparent temperature is a measure of relative discomfort due to combined heat and high humidity, developed on the basis of physiological studies on evaporative skin cooling. It can be calculated as a combination of air and dew point temperature (Steadman 1979). The calculation of the apparent temperature is described in detail in 2.2.3.
1.2.2. National definitions
1.2.2.1. Definition for Belgium
A heat wave day is a day on which both the three-day running daily minimal and maximal temperature exceed a threshold of 18.2 °C and 29.6 °C respectively between April and September. This condition has to be met for three consecutive days. The source of the heat wave definition is the Belgian federal public health agency.
1.2.2.2. Definition for Hungary
A heat wave day is a day as part of three consecutive days exceeding the 90th percentile of the daily mean temperature from 16th of May to 15th of September. The reference period should be a fixed
period of 10 consecutive years. This period should not be too hot nor too cold compared to the long-term temperature average. Therefore, the period of 1981-1990 was selected. The source of the heat wave definition is the National institute for public health Hungary.
1.2.2.3. Definition for Italy
One single definition is applied for Italy, but with different thresholds for each city per month (Table 1). Therefore, values for heat wave days only exist at city-scale for Italy, not at regional or country scale. A heat wave day for the current climate corresponds to a heat warning and is triggered from day 1 when daily maximum apparent temperature exceeds threshold of the daily maximum apparent temperature between 15th of May and 15th of September. The calculation of the apparent temperature is described in detail in 2.2.3. The source of the heat warning definition is the Department of Epidemiology of the Regional Health service.
Table 1: Apparent temperature threshold (°C) for Italian cities per month between May and September.
City | Month | Tappmax | City | Month | Tappmax |
Bolzano | 5 | 31.5 | Ancona | 5 | 30.5 |
Bolzano | 6 | 32.5 | Ancona | 6 | 32.5 |
Bolzano | 7 | 32.5 | Ancona | 7 | 35.5 |
Bolzano | 8 | 32.5 | Ancona | 8 | 35.5 |
Bolzano | 9 | 32.5 | Ancona | 9 | 35.5 |
Torino | 5 | 29.5 | Pescara | 5 | 29.5 |
Torino | 6 | 31.5 | Pescara | 6 | 34.5 |
Torino | 7 | 33.5 | Pescara | 7 | 36.5 |
Torino | 8 | 35.5 | Pescara | 8 | 37.5 |
Torino | 9 | 35.5 | Pescara | 9 | 37.5 |
Milano | 5 | 33.5 | Roma | 5 | 29.5 |
Milano | 6 | 36.5 | Roma | 6 | 34.5 |
Milano | 7 | 37.5 | Roma | 7 | 35.5 |
Milano | 8 | 37.5 | Roma | 8 | 35.5 |
Milano | 9 | 37.5 | Roma | 9 | 35.5 |
Brescia | 5 | 31.5 | Campobasso | 5 | 29.5 |
Brescia | 6 | 32.5 | Campobasso | 6 | 29.5 |
Brescia | 7 | 34.5 | Campobasso | 7 | 30.5 |
Brescia | 8 | 34.5 | Campobasso | 8 | 30.5 |
Brescia | 9 | 34.5 | Campobasso | 9 | 30.5 |
Verona | 5 | 33.5 | Bari | 5 | 31.5 |
Verona | 6 | 35.5 | Bari | 6 | 33.5 |
Verona | 7 | 36.5 | Bari | 7 | 36.5 |
Verona | 8 | 37.5 | Bari | 8 | 36.5 |
Verona | 9 | 37.5 | Bari | 9 | 36.5 |
Venezia | 5 | 31.5 | Napoli | 5 | 30.5 |
Venezia | 6 | 34.5 | Napoli | 6 | 35.5 |
Venezia | 7 | 35.5 | Napoli | 7 | 37.5 |
Venezia | 8 | 35.5 | Napoli | 8 | 37.5 |
Venezia | 9 | 35.5 | Napoli | 9 | 37.5 |
Trieste | 5 | 30.5 | Palermo | 5 | 31.5 |
Trieste | 6 | 34.5 | Palermo | 6 | 33.5 |
Trieste | 7 | 35.5 | Palermo | 7 | 35.5 |
Trieste | 8 | 35.5 | Palermo | 8 | 36.5 |
Trieste | 9 | 35.5 | Palermo | 9 | 36.5 |
Genova | 5 | 28.5 | Messina | 5 | 29.5 |
Genova | 6 | 33.5 | Messina | 6 | 36.5 |
Genova | 7 | 34.5 | Messina | 7 | 37.5 |
Genova | 8 | 35.5 | Messina | 8 | 39.5 |
Genova | 9 | 35.5 | Messina | 9 | 39.5 |
Bologna | 5 | 30.5 | Reggio | 5 | 29.5 |
Bologna | 6 | 34.5 | Reggio | 6 | 34.5 |
Bologna | 7 | 35.5 | Reggio | 7 | 37.5 |
Bologna | 8 | 36.5 | Reggio Calabria | 8 | 39.5 |
Bologna | 9 | 36.5 | Reggio | 9 | 39.5 |
Firenze | 5 | 31.5 | Catania | 5 | 30.5 |
Firenze | 6 | 34.5 | Catania | 6 | 35.5 |
Firenze | 7 | 35.5 | Catania | 7 | 37.5 |
Firenze | 8 | 36.5 | Catania | 8 | 39.5 |
Firenze | 9 | 36.5 | Catania | 9 | 39.5 |
Perugia | 5 | 29.5 | Cagliari | 5 | 30.5 |
Perugia | 6 | 32.5 | Cagliari | 6 | 35.5 |
Perugia | 7 | 33.5 | Cagliari | 7 | 36.5 |
Perugia | 8 | 35.5 | Cagliari | 8 | 37.5 |
Perugia | 9 | 35.5 | Cagliari | 9 | 37.5 |
1.2.2.4. Definition for United Kingdom (UK)
One single definition is applied for the UK, but with different thresholds for each region at NUTS level 1 (Table 2). Therefore, values for heat wave days only exist at regional scale for UK, not at country scale. A heat wave day is a day when daily minimum and daily maximum temperature exceed threshold of the daily minimum and daily maximum temperature between 1st of June and 15th of September, for three consecutive days. The source of the heat warning definition is Public Health England.
Table 2: Daily maximum and daily minimum temperature thresholds for the regions at NUTS level 1 in the UK.
Regions UK | Tmax | Tmin |
London | 32 | 18 |
South East | 31 | 16 |
South West | 30 | 15 |
Eastern | 30 | 15 |
West Midlands | 30 | 15 |
East Midlands | 30 | 15 |
North West | 30 | 15 |
Yorkshire and Humber | 29 | 15 |
North East | 28 | 15 |
1.2.2.5. Definition for Sweden
A heat wave day is a day on which daily maximum temperature is equal to or exceeds 30 °C for 5 consecutive days, during an unknown period. Therefore, the period between April and September has been selected. The source is the Swedish Meteorological and Hydrological Institute.
1.2.2.6. Definition for Lithuania
Two definitions exist for Lithuania, one that indicates a dangerous heat wave event (karštis) and one that indicates a disastrous heat wave event (kaitra). We present a dangerous heat wave event, which corresponds to a day when the daily maximum temperature is equal to or exceeds 30 °C between May and September. The source of the definition is Lithuanian Hydrometeorological Service.
1.2.2.7. Definition for Latvia
A heat wave day is a day on which the daily maximum temperature exceeds the 90th percentile of daily maximum temperature calculated for the season between May and August in the 30-yr period 1961-1990. This condition has to be met for 6 consecutive days. Since the available EURO-CORDEX dataset starts at 1971, we select the 30-yr period of 1971-2000 as reference period. The source for heat wave definition is the Latvian Meteorological Service.
1.2.2.8. Definition for Estonia
Two definitions exist for Estonia, one that indicates a dangerous heat wave event and one that indicates an extremely dangerous heat wave event. We present a dangerous heat wave event, which corresponds to a day when the daily maximum temperature is equal to and exceeds 30 °C between May and August for two consecutive days. The definition also considers a spatial component, i.e. that 30 % of the territory should fulfill this condition. This additional condition has not been implemented, because it has been developed at country-scale but cannot easily be replicated at regional scale. The source for the heat wave definition is the Estonian Environment Agency.
1.2.2.9. Definition for Finland
Two definitions exist for Finland, one that indicates a heat day and one that indicates a heat warning. We present a heat warning, which corresponds to a day when the daily mean temperature exceeds 20 °C and the daily maximum temperature exceeds 27 °C. The period is not defined so we select the period between April and September. The source for the heat warning definition is the Finnish Meteorological Institute.
1.3. Cold spell definitions
1.3.1. National definitions
1.3.1.1. Definition for Lithuania
A cold spell day is a day on which the daily minimum temperature is equal or below a threshold of - 30 °C (called speigas) or -25 °C (called stiprus šaltis), for a minimum of 1 day. The threshold of -30°C has been selected within this dataset to present the number of cold spell days. The period is not defined so we select the period between November and March. The source of the cold spell definition is the Lithuanian Hydrometeorological Service.
1.3.1.2. Definition for Latvia
A cold spell day is a day on which the daily minimum temperature is below the 10th percentile of daily minimum temperature calculated for a 30-yr control period between 1961 and 1990. Since the available EURO-CORDEX dataset starts at 1971, we select the 30-yr period of 1971-2000 as reference period The condition has to be met for 6 consecutive days. The period is not defined so we select the period between November and March. The source of the cold spell definition is the Latvian Meteorological Service.
1.3.1.3. Definition for Estonia
A cold spell day is a day on which the daily minimum temperature is equal or below -30 °C. The conditions has to be met for either 2 or 3 consecutive days, corresponding to a dangerous or extremely dangerous heat wave event, respectively. The threshold of 2 consecutive days has been selected within this dataset to present the number of cold spell days. The period is not defined so we select the period between November and March. The source of the cold spell definition is the Estonian Environment Agency.
1.3.1.4. Definition for Finland
A warning for a cold day is issued when the daily minimum temperature is equal to or is below a certain threshold, which is dependent on the region. The thresholds are -20 °C for Southern Finland, -25 °C for central parts of Finland and -30 °C of Northern Finland. The source of the cold spell definition is the Finnish Meteorological Institute.
2. Future climate data
2.1. Input data
We use a particular product, containing bias-adjusted EURO-CORDEX model output for 2 metre air temperature. This data was developed within the CLIM4ENERGY project (https://climate.copernicus.eu/clim4energy). The bias correction method is called IPSL-CDFT22 using the reference observational dataset of WFDEI (Weedon et al., 2014) for the period of 1979- 2005. The bias correction methodology uses the general Cumulative Distribution Function transform method (CDFt) explained in Vrac et al. (2012). The bias adjustment was done for 4 Regional Climate Models (RCMs) coupled to 1 Gerenal Circulation Model (GCM), and 1 RCM coupled to 4 GCMs, so a total of 8 models or model-combinations at a horizontal resolution of 0.11 x 0.11 degrees under two scenarios RCP4.5 and RCP8.5 (Table 3).
Within the CLIM4ENERGY project, the bias correction has been applied for (air) temperature and precipitation. Some of the heat-wave definitions also rely on the daily maximal apparent temperatures (e.g. the EU health definition and the Italian definitions). Assuming that no bias- correction is required for the relative humidity, we combine the uncorrected hourly specific humidity at surface with the uncorrected surface pressure and the bias corrected temperature, to obtain an estimate for the bias-corrected dew point and apparent temperatures (see 2.2.3). However, hourly humidity data is missing for the RCA4 regional climate model, we only retain the other 4 models for the definitions relying on the apparent temperature (see Table 3).
Table 3: The models used within the CLIM4ENERGY project that were bias-corrected using the same method of CDFt
Scenario | Period | RCM | Driving model (GCM) | Apparent |
RCP4.5/ RCP8.5 | 1971-2100 | WRF331F | IPSL-IPSL-CM5A-MR | Yes |
ARPEGE51 | CNRM-CERFACS-CNRM-CM5 | Yes | ||
HIRHAM5 | ICHEC-EC-EARTH | Yes | ||
RACMO22E | ICHEC-EC-EARTH | Yes | ||
RCA4 | IPSL-IPSL-CM5A-MR | No | ||
CNRM-CERFACS-CNRM-CM5 | No | |||
ICHEC-EC-EARTH | No | |||
MPI-M-MPI-ESM-LR | No |
2.2. Data processing
2.2.1. Overview
The data processing uses several steps:
- Computation of daily temperature time series
- Computation of daily apparent temperature time series
- Computation of heat-wave days
- Climate averages over 30 years
- Ensemble averages and standard deviations
- Regridding to regular latitude-longitude grid
In the following paragraphs, each step is described in more detail.
2.2.2. Calculation of daily temperature time series
At first, the hourly time series are converted to daily minimal and maximal temperature time series for the period 1971 - 2100.
2.2.3. Computation of daily maximal apparent temperature time series
Some of the heat-wave definitions rely on the daily maximal apparent temperatures (e.g. the EU health definition and the Italian definitions). Within the CLIM4ENERGY project, the bias correction has only been applied for (air) temperature and precipitation. We thus have to calculate bias- corrected future apparent temperatures based on the bias-corrected temperature data and other uncorrected parameters. We assume that no bias correcting is required for the relative humidity reported in the RCM-output, and that we can combine the (uncorrected) relative humidity with the bias-corrected temperature to obtain the bias-corrected dew point temperature and apparent temperature.
According to the CMIP5 standard, the RCMs provide hourly uncorrected specific humidity. Hence, we first have to compute the hourly uncorrected relative humidity based on the hourly uncorrected specific humidity Q, surface pressure P, and temperature 𝑇 (in Kelvin):
Subsequently, we assume that the relative humidity doesn't require a correction, thus 𝑅𝐻𝑏𝑐 = 𝑅𝐻, where the superscript bc refers to bias-corrected variables. The bias-corrected dew point 𝑇𝑏𝑐 is
then computed using the approximation:
for which all the necessary input is now available. Finally, the hourly bias-corrected apparent temperature is obtained as
To reduce the amount of data, we only retain the daily maximal apparent temperature.
2.2.4. Computation of yearly statistics
In a next step, the relevant number of heat-wave days / cold-spell days is calculated for each year of the period 1971 – 2100, for all the RCMs and scenarios under consideration. The output of this step are yearly time series of all the relevant definitions per model and scenario, which will be further processed in the following steps.
2.2.5. Climate averages over 30 years
To retrieve the climate signal from the annual time series, we take a running average over 30 years. The year-labels always refers to the middle of the 30 year period; we thus report the average of the statistics in the period [x – 15, x +15] for year x. The results are only available for the 100-year time frame 1986 – 2085.
2.2.6. Ensemble averages
To obtain an ensemble average, we calculate for each year the mean over the models under consideration. We assume that all the models have an equal probability and that their results are independent from each other1, and thus apply uniform weights. For the definitions relying on the apparent temperature, we only use four RCMs, for the other definitions we use all the eight RCMs listed in Table 3.
Apart from the average, for each year also the standard deviation over the models is calculated. Since the standard deviation has large interannual variations, we further smooth the standard deviation over 20 years. For the period 1986 – 1995 we use the value of 1995, while for the period 2076 – 2085, the value of 2076 is applied.
2.2.7. Regridding
The original projection from the bias-adjusted EURO-CORDEX data is a rotated pole grid with 424 grid cells in the longitudinal direction and 412 grid cells in the latitudinal direction (Christensen et al., 2014). This format is unfortunately unsuitable to be used in the Climate Data Store toolbox, which can only deal with regular longitude-latitude grids. Therefore, we reproject the ensemble averages and standard deviations bilinearly to a longitude-latitude grid (coordinate system EPSG:4326 / WGS84) with a resolution of 0.1 x 0.1 degrees. The detailed characteristics are given in Table 4.
Table 4: Grid characteristics of the final output grid.
Attribute | Meta data description | Meta data value |
grid_lon_res | longitudinal resolution of regular grid | 0.1 degree |
grid_nlon | number of longitude cells in regular grid | 599 |
grid_lat_res | latitudinal resolution of regular grid | 0.1 degree |
grid_nlat | number of latitude cells in regular grid | 425 |
grid_westb | west bound of regular grid | -24.85 |
grid_eastb | east bound of regular grid | 34.95 |
grid_northb | north bound of regular grid | 72.55 |
grid_southb | south bound of regular grid | 30.05 |
3. References
Christensen, O.B., W.J. Gutowski, G. Nikulin and S. Legutke (2014): CORDEX Archive Design, https://is-enes-data.github.io/cordex_archive_specifications.pdf
Jacob, D. , J. Petersen, B. Eggert, A. Alias, O.B. Christensen, L.M. Bouwer, A. Braun, A. Colette et al. (2014): EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg Environ Change, Vol. 14, pp. 563-578
Michelozzi, P., U. Kirchmayer, K. Katsouyanni, A. Biggeri, G. McGregor, B. Menne, P. Kassomenos, H.R. Anderson, M. Baccini, G. Accetta, A. Analytis and T. Kosatsky (2007): Assessment and prevention of acute health effects of weather conditions in Europe, the PHEWE project: background, objectives, design, Evnironmental Health, Vol. 6:12, pp. 1-10
Steadman, R. G. (1979): The assessment of sultriness. Part II: Effects of wind, extra radiation and barometric pressure on apparent temperature, J. Appl. Meteorol., Vol. 18, pp. 874-885
Vrac, M., P. Drobinski, A. Merlo, M. Herrmann, C. Lavaysse, L. Li and S. Somot (2012): Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment, Nat. Hazards Earth Syst. Sci., Vol. 12, pp. 2769-2784
Weedon, G. P., G. Balsamo, N. Bellouin, S. Gomes, M. J. Best and P. Viterbo (2014): The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., Vol. 50, pp. 7505-7514
WHO, 2009: Improving public health responses to extreme weather/heat-waves-EuroHEAT, Technical summary, Editors: B. Menne and F. Matthies
