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Record mild winter of 2019/2020 in most of Finland — 4 The exceptional Nordic forest fire season 2018 in the context of climate change — 8 Windstorm Aila 2020: wind forecasts and discussion on climate change — 11 Windstorm Aila 2020: societal impacts — 15 Climate Security in an Interdependent World - Examining Climate Change in Finland’s Comprehensive Security Model Context — 18 Battle between mitigation and adaptation: the future challenge of climate change — 20 The socio-spatial patterns of heat stress exposure in Helsinki on two hot days of 2018 and 2019 — 22
FMI’S CLIMATE BULLETIN: RESEARCH LETTERS Volume 3 Issue 1 ISSN: 2341-6408 DOI: 10.35614/ISSN-23416408-IK-2021-01-RL
PUBLISHER Finnish Meteorological Institute (FMI) P.O. BOX 503 FI-00101 HELSINKI www.ilmastokatsaus.fi ilmastokatsaus@fmi.fi
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EDITOR IN CHIEF Hilppa Gregow
DESIGN Marko Myllyaho
EDITORIAL COMMITTEE Hada Ajosenpää Juha A. Karhu Anna Luomaranta
Please mention the source when citing the content. A DOI is available for each research letter article.
REVIEW BOARD ECRA members
© FMI
DOI: 10.35614/ISSN-2341-6408-IK-2021-02-RL Received 18 Sep. 2020, accepted 9 June 2021, first online 22 June 2021, published 24 June 2021, corrected 2 Aug. 2021
Record mild winter of 2019/2020 in most of Finland The winter of 2019/2020 saw unprecedented mild weather in most of Finland. The winter was moreover characterized by exceptionally thick snow cover in Lapland. At the same time, there was hardly any snow in southern Finland. ILARI LEHTONEN Finnish Meteorological Institute
In Europe, the large-scale atmospheric circulation pattern during the winter of 2019/2020 was characterized by anomalously strong westerlies due to a low-pressure anomaly centred over the Norwegian Sea and northern Scandinavia and a high-pressure anomaly over the Mediterranean Basin (Fig. 1). Persistent westerlies thus pushed mild maritime air from the Atlantic to Northern Europe and all the way to Siberia in the east. In north-western Russia and southern Finland, the mean December to February temperature was over large areas approximately 6–7 °C above the long-term average. In Finland, it was the mildest winter on record in the southern and central parts of the country. Temperatures in Lapland were closer to the typical values, but even there the mean temperature was 2–5 °C above the longterm average. The estimated country average for the mean December to February temperature surpassed the previous records from the winters of 1924/1925 and 2007/2008 by 0.3 °C (Fig. 2a). In Fig. 2, further temperature and snow-related winter weather statistics from two weather stations, one in the south (Kaisaniemi, Helsinki at the Finnish southern coast) and one in the north (Tähtelä, Sodankylä in central Lapland), are illustrated for the past 120 years. In Helsinki, like elsewhere in the southern parts of Finland, 2019/2020 was the mildest
FIG 1: December to February mean temperature and sea-level pressure anomalies for the winter of 2019/2020. Anomalies are relative to the period 1981–2010 based on the ERA5 data (Hersbach et al., 2020). The colour-coded shading shows the temperature anomaly (°C) while the sea-level pressure anomaly (hPa) is shown in white contours. Contour interval is 2.5 hPa and solid/dashed contours denote positive/negative values, respectively.
winter on record. The mean temperature of the winter in Helsinki was 2.3 °C, surpassing the previous record from the winter of 2007/2008 by
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0.9 °C. In Sodankylä, the mean temperature was -8.0 °C making 2019/2020 the seventh mildest winter on record, and 0.6 °C warmer than
FIG 2: Winter weather statistics for Helsinki in southern Finland and for Sodankylä in northern Finland for the winters from 1899/1900 to 2019/2100. (a) December to February mean temperature, (b) the lowest temperature during the winter, (c) number of ice days with daily maximum temperature below 0 °C during the winter, (d) number of snow-covered days during the winter, and (e) the maximum snow depth during the winter. For mean temperature, also the country average according to Tietäväinen et al. (2010) and Aalto et al. (2016) is shown. On the x-axis, e.g., the label 1900 refers to the winter of 1899/1900.
the record mild winter of 2007/2008. One striking feature of the winter weather was a complete lack of even short cold periods in southern Finland. In Helsinki, the lowest temperature during the whole winter was as high as -8.2 °C measured on 29 February (Fig. 2b). It was the second winter on record when temperature did not drop even once below -15 °C in
the Finnish capital. In the north, there were some relatively cold, though short, periods as well. However, an increasing long-term trend can be seen in the winter minimum temperatures in Sodankylä as the lowest temperature of the winter has rarely dropped below -40 °C after the winter of 1998/1999 when the record cold temperature of -49.5 °C was measured.
The lack of cold periods in southern Finland during the winter of 2019/2020 is evident from the low numbers of ice days, i.e., days with the maximum temperature below freezing (Fig. 2c), and snow-covered days (Fig. 2d). In Helsinki, there were only four ice days while the smallest number of ice days during a winter had previously been 18. Snow cover
FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021 | 5
STATION
ADMINISTRATIVE REGION
DATE
SNOW DEPTH
Kilpisjärvi, Enontekiö
Lapland
19 April 1997
190 cm
Kilpisjärvi, Enontekiö
Lapland
19 January 1992
176 cm
Kilpisjärvi, Enontekiö
Lapland
14 March 1993
170 cm
Northern Ostrobothnia
23 April 1993
168 cm
Kilpisjärvi, Enontekiö
Lapland
31 March 1991
159 cm
Kilpisjärvi, Enontekiö
Lapland
5 April 2014
158 cm
Kilpisjärvi, Enontekiö
Lapland
24 February 1964
155 cm
Pirkanmaa
12 February 1984
155 cm
Pokka, Kittilä
Lapland
16 April 1997
155 cm
Haapovaara, Suomussalmi
Kainuu
5 March 2000
151 cm
Kilpisjärvi, Enontekiö
Lapland
14 March 1963
150 cm
Pokka, Kittilä
Lapland
22 February 1974
148 cm
Kursu, Salla
Lapland
5 March 2000
147 cm
Kilpisjärvi, Enontekiö
Lapland
30 March 1989
146 cm
Pokka, Kittilä
Lapland
16 March 1998
145 cm
Pisavaara, Rovaniemen maalaiskunta
Lapland
4 April 1965
143 cm
Kilpisjärvi, Enontekiö
Lapland
27 March 2017
143 cm
Näkkälä, Enontekiö
Lapland
29 March 1967
142 cm
Ylimaa, Ranua
Lapland
6 April 1969
142 cm
Suomussalmi kk
Kainuu
22 March 1962
140 cm
Pallasjärvi, Kittilä
Lapland
28 March 1967
140 cm
Näljänkä, Suomussalmi
Kainuu
5 March 2000
140 cm
Maanselkä Kurkijärvi, Kuusamo
Riuttaskylä, Kuru
TABLE 1: Highest snow depth observations in Finland since 1961. Only one measurement per station during a single winter has been listed. Some of the observations may be subject to snowdrift due to wind, although the obvious cases have been removed.
could only be measured in Helsinki on nine days, while the previous record for the smallest number of snow-covered days was 45. January, February and March were completely snow free, although it must be noted that automized weather stations have difficulties in measuring very shallow snow cover. Hence, the observations from the recent years are not completely comparable to earlier manual observations. Nevertheless, the highest snow depth in Helsinki during the winter of 2019/2020 was only 3 cm, measured as late as on 16
April, while previously snow depth had reached at least 15 cm in each winter. In Sodankylä in northern Finland, snow statistics were also record-breaking during the winter of 2019/2020. However, this was not due to a lack of snow but because of an excess of snow. The peak snow depth in Sodankylä was 127 cm, measured on 15 April, and it was the highest snow depth on record at the station. An even higher snow depth of 138 cm was measured in Saariselkä, Inari on 18 April. Although
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these were new snow depth records in this part of the country, even higher snow depths have been previously measured elsewhere in Finland, most often in the very northwesternmost edge of Lapland, where also the national snow depth record, 190 cm, was measured in the village of Kilpisjärvi on 19 April 1997 (Table 1). In other parts of Finland, higher snow depths than those measured in Central Lapland during the winter of 2019/2020 were measured last time in Kainuu in early March 2000. Historically, the most renowned snowy
winter in Finland has been the winter of 1898/1899 when snow depth reached in March approximately 150 cm in the regions of Northern Savonia and North Karelia (Alfthan, 1911). Also in March 1900, there were approximately 140 cm of snow as south as locally in Uusimaa (Kersalo and Pirinen, 2009). During the winter of 2019/2020, snow cover also persisted much longer than is typical for northern Finland, although the count of snow-covered days in Sodankylä, 223 days, was not a new record. It was still the highest count of snow-covered days there since the winter of 1995/1996. Due to global warming, equally
mild winters as 2019/2020 are expected to become more frequent in the future. According to high-emission scenarios under the Representative Concentration Pathway (RCP) 8.5, the winter mean temperature could most likely rise by approximately 4 °C by circa 2050 relative to 1981–2010 and by even 8 °C by 2100 (Ruosteenoja et al., 2016). These scenarios could occur when the global mean temperature increases by approximately 4 °C during the current century. If the rate of global warming could be slowed down, the warming of the Finnish winters would remain smaller too. For example, under the RCP4.5, the global mean tempera-
ture would rise most likely almost by 2 °C during the 21st century and the Finnish winters would become by about 4–5 °C warmer at the same time. Moreover, as most climate models show a cold bias over Northern Europe and the models with most severe cold bias tend to indicate the most intense warming, the models on average might somewhat overestimate the warming in Finland in winter (Räisänen and Ylhäisi, 2015). Acknowledgements: Copernicus Climate Change Service is acknowledged for making the ERA5 reanalysis data available at https://doi. org/10.24381/cds.adbb2d47.
Aalto, J., et al., 2016: New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. J. Geophys. Res. Atmos., 121, 3807–3823. Alfthan, M., 1911: Suomen kartasto 1910. Suomen Maantieteellinen Seura, Helsinki. Hersbach, H., et al., 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteorol. Soc., 146, 1999–2049. Kersalo, J., and Pirinen, P., 2009: Suomen maakuntien ilmasto. Finnish Meteorological Institute Reports 2009:8, Helsinki. Räisänen, J., and Ylhäisi, J. S., 2015: CO2-induced climate change in northern Europe: CMIP2 versus CMIP3 versus CMIP5. Clim. Dynam., 45, 1877–1897. Ruosteenoja, K., et al., 2016: Climate projections for Finland under the RCP forcing scenarios. Geophysica, 51, 17–50. Tietäväinen, H., et al., 2010: Annual and seasonal mean temperatures in Finland during the last 160 years based on gridded temperature data. Int. J. Climatol., 30, 2247–2256.
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DOI: 10.35614/ISSN-2341-6408-IK-2021-03-RL Received 18 Sep. 2020, accepted 11 June 2021, first online 22 June 2021, published 24 June 2021
The exceptional Nordic forest fire season 2018 in the context of climate change A number of large forest fires made headlines in Sweden during the exceptionally warm and dry summer of 2018. Also, in Finland, fire departments were busy with numerous wildfires. It appears that weather conditions in 2018 were very favourable for the occurrence of fires. In the future, similar summers are expected to occur somewhat more often. ILARI LEHTONEN, ARI VENÄLÄINEN Finnish Meteorological Institute
On the one hand, fire is a natural phenomenon in boreal forests and has an essential role in maintaining biodiversity. On the other hand, wildfires pose a threat to property and infrastructure, and even to people’s lives. This was recently demonstrated during the exceptionally warm and dry summer of 2018 when several large fires burned almost 25,000 hectares of forest in Sweden (Sjöström and Granström, 2020) with the four largest individual fires burning 8,400 hectares of forest in Kårböle, 3,500 hectares in Lillåsen-Fågelsjö, 2,500 hectares in Trängslet and 850 hectares in Stor-Brättan (Björheden and Johannesson, 2019). It was the largest annual burned area in Sweden in a quarter of a century and firefighters from multiple countries were involved in fighting the fires. Numerous forest fires occurred also in Finland but due to efficient fire suppression measures, the burned forest area remained as low as approximately 1,200 hectares and the largest individual fire burned only 50 hectares of forest in Pyhäranta (Lehtonen and Venäläinen, 2020). Yet, it was the largest annual burned area and most active fire season in Finland since 2006. In addition, other regions in north-western Europe, like the United Kingdom, for instance (Sibley, 2019), were similarly affected by warm and dry weather and experienced numerous wildfires.
(a) Temperature anomaly (°C)
(b) Precipitation anomaly (mm/day)
FIG 1: (a) May to September temperature anomaly (°C) in 2018. (b) Precipitation anomaly (mm/day) from May to September in 2018. Anomalies are relative to the period 1979–2019 based on the ERA5 data (Hersbach et al., 2020). Adapted from Lehtonen and Venäläinen (2020). Climate change is predicted to dramatically alter the fire regime in the circumboreal region (e.g., Kasischke and Stocks, 2012). In general, increasing temperatures leading to enhanced evaporation are expected to increase the fire risk. As the summer of 2018 was roughly as warm as a majority of summers might be in the late 21st century according to the climate projections (Lehtonen and Pirinen, 2019), an interesting question is whether forest fire seasons as in 2018 would also occur more regularly in the future. In 2018, the mean May to September temperature was in most of northern Europe approximately 2 °C above the long-term average (Fig. 1a). At the same time, precipitation levels were considerably below the aver-
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age in all the regions adjacent to the Baltic Sea (Fig. 1b). Already May was very dry and the warmest on record in many places. The most intense heat wave took place during the latter half of July (Sinclair et al., 2019). This was also the time when the largest fires occurred. In order to evaluate the fire risk related to these warm and dry conditions, we applied the Fire Weather Index (FWI) system following Van Wagner and Pickett (1985). The FWI system, developed originally in Canada, is one of the most widely used fire risk rating systems worldwide, and for the Finnish conditions, it has proven to perform approximately as well as the forest-fire index that is used operationally in Finland (Vajda et al., 2013).
The FWI rating is a dimensionless quantity indicating the likely intensity of a fire. It can be further converted into daily severity rating (DSR) that is expected to reflect the required efforts for fire suppression more accurately than FWI by emphasizing higher FWI values through a power relation as follows: (1) DSR=0.0272×FWI1.77 DSR, averaged over a certain time period, is called a seasonal severity rating (SSR) which is generally used in evaluating the severity of a fire season. By using the ERA5 reanalysis data (Hersbach et al., 2020), we calculated SSRs averaged from May to September for the period 1979–2019 over northern Europe. Then, we defined the return levels for SSRs according to the generalized extreme value (GEV) distribution (Gilleland and Katz, 2005). The analysis indicated that fire risk in 2018 indeed was exceptionally high over large areas of northern Europe (Fig. 2). Over large areas both in Finland and Sweden, including the regions where the largest fires raged, the recurrence interval of such a high seasonally averaged forest-fire risk was more than 50 years. This agrees with the analysis of Sjöström and Granström (2020) showing that the number of days with high fire risk was in large parts of Sweden much higher in 2018 than in any other recent year. Building on a previous work, we evaluated how climate change is expected to affect recurrence intervals of fire seasons as difficult as 2018 (Leh tonen and Venäläinen, 2020). This inspection was restricted to Finland, since we used the FWI values calculated by Lehtonen et al. (2016) over the Finnish domain under climate projections extending from 1980 to 2099. We used data from five different climate models (listed in Table 1) participating in the Coupled Model Intercomparison Project phase 5 (CMIP5) under the Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 (van Vuuren et al., 2011). The model data were downscaled on a 0.1° ×
Return time of SSR in 2018 (in years)
FIG 2: Estimated recurrence interval of the seasonal severity rating (SSR) from May to September in 2018. Bonfire symbols indicate locations of the four largest fires in Sweden and the largest fire in Finland. MODEL
COUNTRY OF ORIGIN
HORIZONTAL RESOLUTION (LONG × LAT)
CanESM2
Canada
1.875° × 1.875°
CNRM-CM5
France
1.4° × 1.4°
GFDL-CM3
United States
2.5° × 2.0°
United Kingdom
1.25° × 1.875°
Japan
1.4° × 1.4°
HadGEM2-ES MIROC5
TABLE 1: CMIP5 models used in this study. For more information about the models, please see Table 9.A.1 in Flato et al. (2013). 0.2° latitude–longitude grid covering Finland as described by Lehtonen et al. (2016). We then split the scenario period 1980–2099 into three 40-years periods, 1980–2019, 2020–2059 and 2060–2099, and defined the return levels for SSRs individually for each period and model simulation. The return levels over the model period 1980–2019 were used to match with the return levels calculated from the ERA5 data over the period 1979–2019. For each grid cell in each model simulation, we defined the value of SSR corresponding to the recurrence interval of modelled SSR over the 1980–2019 period matching with the recurrence interval of the SSR in 2018 evaluated
from the ERA5 data. Then, we used the recurrence estimates for the two future periods to estimate how often as severe a fire season as in 2018 would occur in the future. Fig. 3 shows that in the future the recurrence intervals for fire seasons like 2018 are expected to decrease. Nevertheless, according to the multi-model mean estimate, in the near-future period 2020–2059, the recurrence interval remains above 50 years in large areas in central parts of Finland, although this area shrinks compared to the estimated recurrence intervals of the fire season 2018. Also, in the far-future period 2060–2099, the recurrence interval of fire seasons like 2018 is projected
FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021 | 9
to remain in most of Finland between 10 and 50 years. Thus, it can be concluded that fire seasons comparable to 2018 will most likely remain quite rare even in forthcoming decades. However, estimated recurrence intervals between the five climate models diverged considerably and the simulations with most pronounced warming indicated much smaller future recurrence intervals for fire seasons like 2018 than the multi-model mean. Moreover, we acknowledge that substantial uncertainty is involved in the return level estimates of larger than 40 years as they exceed the length of the data chunk used in deriving the return levels. Also, the use of only five climate simulations makes a small sample associated with substantial uncertainties. Nevertheless, we conclude that if summer temperatures increase several degrees of Celsius, it is possible that fire seasons comparable to 2018, and even more severe fire seasons, may start to occur frequently. Acknowledgements: We acknowledge Fire Protection Fund (grant agreement SMDno-2019-988) for the support for this study. We are also grateful for Copernicus Climate Change Service for making the ERA5 reanalysis data available at https:// doi.org/10.24381/cds.adbb2d47. Moreover, we acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table 1 of this paper) for producing their
Return time of SSR in 2018 (in years) (a) 2020–2059 RCP4.5
(b) 2020–2059 RCP8.5
(c) 2060–2099 RCP4.5
(d) 2060–2099 RCP8.5
FIG 3: Multi-model mean estimate for the recurrence interval of the seasonal severity rating (SSR) similar to 2018 under the RCP4.5 scenario during 2020– 2059 (a), under the RCP8.5 scenario during 2020–2059 (b), under the RCP4.5 scenario during 2060–2099 (c), and under the RCP8.5 scenario during 2060– 2099 (d). Adapted from Lehtonen and Venäläinen (2020). model output and making it available. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided
coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
Björheden, R., and Johannesson, T., 2019: The effects on Swedish forestry of the summer 2018. Skogforsk, Uppsala. [in Swedish with an abstract in English] Flato, G., et al., 2013: Evaluation of Climate Models. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T. F., et al. (eds.)]. Cambridge University Press, Cambridge, and New York. Gilleland, E., and Katz, R., 2005: Extreme Toolkit (extRemes): Weather and Climate Applications of Extreme Value Statistics. National Science Foundation. Hersbach, H., et al., 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteorol. Soc., 146, 1999–2049. Kasischke, E. S., and Stocks, B. J., 2012: Fire, Climate Change, and Carbon Cycling in the Boreal Forest. Springer Science & Business Media, New York. Lehtonen, I., and Pirinen, P., 2019: 2018: An exceptionally warm thermal growing season in Finland. FMI’s Clim. Bull. Res. Lett., 1(1), 5. Lehtonen, I., and Venäläinen, A., 2020: Forest fire season 2018 in a changing climate – an exceptional year or new normal? Finnish Meteorological Institute Reports 2020:2, Helsinki. [in Finnish with an abstract in English] Lehtonen, I., et al., 2016: Risk of large-scale fires in boreal forests of Finland under changing climate. Nat. Hazards Earth Syst. Sci., 16, 239–253. Sibley, A. M., 2019: Wildfire outbreaks across the United Kingdom during summer 2018. Weather, 74, 397–402. Sinclair, V. A., et al., 2019: The summer 2018 heatwave in Finland. Weather, 74, 403–409. Sjöström, J., and Granström, A., 2020: Wildfires in Sweden – trends and patterns during recent decades. Swedish Civil Contingencies Agency, Karlstad. [in Swedish with an abstract in English] Vajda, A., et al., 2013: Assessment of forest fire danger in a boreal forest environment: description and evaluation of the operational system applied in Finland. Meteorol. Appl., 21, 879–887. van Vuuren, D. P., et al., 2011: The representative concentration pathways: an overview. Clim. Change, 109, 5–31. Van Wagner, C. E., and Pickett, T. L., 1985: Equations and FORTRAN Program for the Canadian Forest Fire Weather Index System. Canadian Forestry Service, Forestry Technical Report 33, Ottawa. 10 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021
DOI: 10.35614/ISSN-2341-6408-IK-2021-04-RL Received 22 Sep. 2020, accepted 11 May 2021, first online 25 May, published 24 June 2021
Windstorm Aila 2020: wind forecasts and discussion on climate change The most severe weather warnings are not given lightly, but when they are, everyone should take care and act. This case study presents warnings, forecasts and observations during Storm Aila 16–17 September 2020 in Finland and compares it to the past windstorms. It demonstrates why weather and climate change knowledge should be seamlessly presented to improve our understanding of possible impacts of climate change on storms in Finland. HILPPA GREGOW, TERHI K LAURILA, MIKA RANTANEN, EERIK SAARIKALLE, ILONA LÁNG, CARL FORTELIUS Finnish Meteorological Institute
Preparing for the storm. In mid-September 2020, we were preparing our society for potential storm impacts by increasing communication in the social media and by official communication means. The strongest warnings were issued already on 14 September 2020 about an extreme windstorm that would cause abundant rain and extreme northerly storm winds in Finland on 16–17 September 2020. The storm was named as Aila. Meteorologists of the Finnish Meteorological Institute (FMI) also used Twitter to give
constant updates of the maximum wind speed and heavy precipitation forecasts. The highest red-level warnings for wind gusts and rough waves were issued as early as three days before Storm Aila arrived. In the following days, the highest-level warnings were further extended to also cover land areas (Fig. 1). Insurance companies informed about typical consequences of a storm of this magnitude and how one can be prepared before and after the storm, especially if damages have materialized. For instance,
the following actions were listed: take care of pipes outside to let the water run freely; check the condition of your property; make sure you know how to cut off electricity if the storm has caused damage to wires; remove such trees from your yard that are at risk to fall before the storm; move any items that may fly away in the air; after the storm, take photos of the damages and then cover the property to avoid creation of further damage; ask support from the rescue services if the damages are extensive.
FIG 1: Weather warnings issued at around 17:30 local time on (a) 14 September, (b) 15 September, and (c) 16 September 2020, i.e., one to three days before storm Aila hit Finland on the night between 16–17 September 2020. FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021 | 11
FIG 2: Deterministic forecasts for wind speed (colors), mean sea level pressure (black solid lines), station-based 24 h maximum wind gusts (on land stations), and station-based 24 h maximum 10-min wind speeds (numbers over coastal stations) valid on 17 September at 12:00 local time from MEPS model runs on (a) 15 September, (b) 16 September, and (c) morning of 17 September 2020.
Forecast validation. To validate the forecasts, the wind speed and wind gust predictions from MetCoOp Enemble Prediction System (MEPS) were compared with the wind observations. MEPS is a short-range convection permitting limited area ensemble prediction system based on the HARMONIE-AROME weather model (Bengtsson et al. 2017). It has a horizontal resolution of 2.5 km and it produces forecasts with 66 h lead time. At the lateral edges of its domain, MEPS uses information from a global medium to extended-range ensemble prediction system (IFSENS) which has 18 km horizontal resolution and is based on the Integrated Forecast System (IFS) and operated by the European Centre for Medium-Range Weather Forecasts (ECMWF). MEPS is jointly operated by the FMI, the Swedish Meteorological and Hydrological Institute (SMHI) and the Norwegian
Meteorological Institute (Met Norway) and the Estonian Weather Service (ENVIR). When comparing MEPS wind gusts (Fig. 2) to observations (Fig. 3) it turned out that the forecasts were rather good in the heavily impacted areas in Western Finland. Difficulties in forecasting the magnitude and severity of the storm occurred in the southeast during the time when the cyclone movement was slowing down. As MEPS was not the only forecast model in use, the meteorologists succeeded in their warnings by interpreting MEPS and the other models based on their experience. For example, the forecasts based on the IFS model predicted the formation of Storm Aila very well (Rantanen et al. 2021). The highest observed 10-min sustained wind speed was 29.4 m/s (Rauma Kylmäpihlaja; Fig. 3a) and the highest wind gust was 35.3 m/s (Pietarsaari Kallan; Fig. 3b).
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Comparison to past storm events. October to March is the typical time window for occurrence of extreme extratropical cyclones and storm winds in Finland (FMI 2021). In the current storm severity classifications starting from 1994 in Finland, a storm with maximum 10-min sustained wind speeds of 29 m/s or above is considered severe. Such windstorms occur in Finland around every fourth year on average. Windstorms in September are less common and have not reached this category in Finland over the coastal areas before 2020. Additionally, only one of the past September windstorms, post-tropical storm Mauri in 1982 (Laurila et al. 2020), has been a severe one, as regards impacts. While Storm Mauri had tropical origins (Laurila et al. 2020), Aila was a classic baroclinic storm (Rantanen et al. 2021). What was remarkable in Mauri was that its maximum 10-min
average storm winds (23 m/s) were reported from land areas and not from the sea as for Aila. However, it must be noted that there were much less wind speed observations in 1982 than in 2020 and therefore it is possible that the strongest winds in Mauri were not reported. Typically, severe windstorms occur during late autumn and winter, e.g., Storm Laina (2/2015) and Storm Seija (12/2013). Nonetheless, it is important to note that the storm impacts do not only depend on the wind speed but also for example ground frost and affected areas have an important role. The impacts of Storm Aila are discussed in another article in this Research Letter issue (Láng et al. 2021). Observed storm day statistics. Storm day in Finland is defined as a day with observed 10-min average wind speed equal to or higher than 21 m/s on at least one of the coastal weather stations. During 2020, Finland experienced 44 storm days which is the highest annual number of storm days in the period of 2006–2020 (FMI 2021). The second highest annual number of storm days was 37 in 2007 and the annual average of storm days is 27 for the period of 2006–2020 (FMI, 2021). Discussion on climate change. The risk for windstorm impacts on Finland is increasing based on several factors: 1) decrease of soil frost during winter, 2) Norway spruce being the most vulnerable tree species for windthrow without soil frost, 3) leaves on trees in summer and autumn adding the wind load for deciduous trees as birch is taking over Norway spruce in south (Peltola et al. 2010), 4) snow loads on branches of the coniferous trees in autumn, winter and spring (Gregow et al. 2011 and references therein). As regards the impact of climate change on strong winds one needs to be careful when assessing variability and trends, because the chosen time period from which the trend is calculated has a significant effect on the magnitude and sign of the trend. Based on Gre-
FIG 3: (a) Maximum wind speeds and (b) maximum wind gusts on 16–17 September 2020 based on observations from FMI’s weather stations.
gow et al. (2008), when investigating the variability of the strength of the storms (with respect to the observed 10-min mean wind speeds) in Finland, one could see, based on four stations, that there was decadal variation but no significant trend in the frequency of storms in 1959–2007. Similarly, Laurila et al. (2021) found no significant trends for mean and extreme 10-meter wind speeds in Finland from ERA5 reanalysis (gridded wind speeds) in 1979–2018. The other investigations in Gregow et al. (2008) focused on the historical severity classification of windstorms (covering decades 1959–1999). The increase in the number of measurement stations was also considered. A storm was ranked strong or severe if, correspondingly, 25–27.9 m/s and 28 m/s or more was measured during the storm by at least one Finnish coastal station. It was found that in 1959–1969 out of 94 observed windstorms (OW), alto-
gether (9) 10 % were strong and (2) 2 % were severe (Fig. 4). In 1970–1979, out of 143 OW altogether (24) 17 % were strong and (3) 2 % were severe (Fig. 4). In 1980–1989, out of 158 OW, altogether (33) 21 % were strong and (8) 5 % were severe and in 1990–1999, out of 205 OW altogether (44) 21 % were strong and (7) 3 % severe (Fig. 4). Gregow et al. (2008) noticed that the number of storms increased as the measurement network got denser (Fig. 4). The decadal assessments were conducted to see whether there was a predictable decadal variation in the occurrence of strong and severe storms and if there was a change in the spatial extent of the storms impacting Finnish territories. As regards the future, there is little agreement on how the windiness is going to change in Finland during the 21st century, because of the existing large uncertainties in the response of atmospheric circulation to
FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021 | 13
anthropogenic climate change (Gregow et al. 2020). For example, based on 21 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models, only minor changes in the strong winds are expected in Finland (Ruosteenoja et al. 2019): the change for September, October and November in Finland and its surroundings is 0–2.5 % by the end of the 21st century. Moreover, this result was obtained using Representative Concentration Pathway 8.5 (RCP8.5) scenario, which is nowadays considered as an unlikely, worst-case scenario (Hausfather and Peters 2020). With a more realistic, RCP4.5 scenario, the future changes in wind speeds in autumn were found to be even weaker (Ruosteenoja et al. 2019). Aila was the strongest windstorm recorded in Finland for the month of September with the present-day network of observation stations (Rantanen et al. 2021). However, as Gregow et al. (2008) showed, the denser the measuring network, the better we can assess the details with respect to the number and severity of storms. Thus, the direct comparison to the storm records of previous years must be done with caution. While many future studies on windiness have estimated the time-mean changes of strong winds under the anthropogenic climate change (e.g. Ruosteenoja et al. 2019), more infor-
FIG 4: Number of measurement stations and percentage of strong (25–27.9 m/s) and severe storms (greater than or equal to 28 m/s). This figure has been drawn based on the investigations of Gregow et al. (2008).
mation on the behaviour of extreme winds associated with windstorms, such as Aila, could be potentially obtained by extracting and analysing the wind speeds along the cyclone tracks in climate model simulations (e.g. Priestley et al. 2020). This would give knowledge on how the extreme winds specifically in the mid-latitude cyclones are possibly going to change in the future, which is crucial information when assessing how to adapt to climate change. Thus, although Aila
made a new record for September, we emphasize that further research is needed to investigate whether severe windstorms in autumn such as Aila are becoming more common in the future. Acknowledgement: We thank ERA4CS for the Windsurfer project. And we thank Antti Mäkelä for proof-reading.
Bengtsson, L., et al., 2017: The HARMONIE–AROME Model Configuration in the ALADIN–HIRLAM NWP System. Mon. Weather Rev., 145, 1919–1935, https://doi.org/10.1175/MWR-D-16-0417.1. FMI, 2021: Tuulitilastot. Accessed 14 April 2021, https://www.ilmatieteenlaitos.fi/tuulitilastot. Gregow, H., et al., 2008: Vaaraa aiheuttavista sääilmiöistä Suomen muuttuvassa ilmastossa. Raportteja 2008:3. Ilmatieteen laitos, 99 s. Gregow, H., et al., 2020: Review on winds, extratropical cyclones and their impacts in Northern Europe and Finland, Finnish Meteorological Institute Reports, 2020(3), http://hdl.handle.net/10138/320298. Gregow, H., et al., 2011: Combined occurrence of wind, snow loading and soil frost with implications for risks to forestry in Finland under the current and changing climatic conditions, Silva Fenn., 45, 35–54, https://doi.org/10.14214/sf.30. Hausfather, Z. and Peters, G., 2020: Emissions–the ‘business as usual’ story is misleading, Nature, 577, 618–620, https://doi.org/10.1038/d41586020-00177-3. Láng, I., et al., 2021: Windstorm Aila 2020: Societal Impacts, FMI’s Clim. Bull. Res. Lett., 3(1), 15–17, https://doi.org/10.35614/ISSN-2341-6408-IK2021-05-RL. Laurila, T. K., et al., 2020: The Extratropical Transition of Hurricane Debby (1982) and the Subsequent Development of an Intense Windstorm over Finland, Mon. Weather Rev., 148, 377–401, https://doi.org/10.1175/MWR-D-19-0035.1. Laurila, T. K., et al., 2021: Climatology, variability, and trends in near-surface wind speeds over the North Atlantic and Europe during 1979–2018 based on ERA5, Int. J. Climatol., 41(4), 2253–2278, https://doi.org/10.1002/joc.6957. Peltola, H., et al., 2010: Impacts of climate change on timber production and regional risks of wind-induced damage to forests in Finland, For. Ecol. and Manag., 260(5), 833–845. Priestley, M. D. K., et al., 2020: An Overview of the Extratropical Storm Tracks in CMIP6 Historical Simulations, J. Climate, 33(15), 6315–6343, https:// doi.org/10.1175/JCLI-D-19-0928.1. Rantanen, M., et al., 2021: Storm Aila: An unusually strong autumn storm in Finland, Weather, https://doi.org/10.1002/wea.3943. Ruosteenoja, K. et al., 2019: Projected changes in European and North Atlantic seasonal wind climate derived from CMIP5 simulations, J. Climate, 32(19), 6467–6490, https://doi.org/10.1175/JCLI-D-19-0023.1. 14 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021
DOI: 10.35614/ISSN-2341-6408-IK-2021-05-RL Received 22 Sep. 2020, accepted 19 Apr. 2021, first online 29 Apr. 2021, published 24 June 2021, corrected 2 Aug 2021
Windstorm Aila 2020: societal impacts In densely forested countries such as Finland, windstorms are among the most significant natural hazards, causing the widest damages for society in the form of power outages, damages to forest and property, emergency tasks and traffic delays. In this paper we describe the impacts of windstorm Aila in September 2020. ILONA LÁNG, ANTTI MÄKELÄ, PAAVO KORPELA, HILPPA GREGOW Finnish Meteorological Institute
Most of the intense windstorms occur typically during late autumn and winter (October-March), while strong windstorms in September are less common. Regarding wind speed (maximum average wind speed 29.4 m/s), Aila was the strongest windstorm occurring in September in the past 20 years (Gregow et al. 2021; FMI 2020). With respect to impacts, Aila’s return period is around 5–10 years. In this paper, we discuss the level of preparedness and present some of the main societal impacts of Windstorm Aila occurring on the 16–17 September 2020. Regarding the analysis of the forecast and the climatological aspect of this storm, please see other articles in this Research Letter issue. In total, the society was well-prepared for windstorm Aila. The issued warnings were on the highest possible level (red) as early as three days before windstorm Aila arrived in Finland and for the first time the national 112-mobile application was used to distribute warning messages to inform citizens on severe weather. As part of their responsibilities before a significant windstorm, the duty forecasters include a brief impact estimation in the so called LUOVA1 weather bulletin (FMI 2016). During Aila, the issued LUOVA bulletin estimated the impacts to reach at least 100,000 power outages nationally, which means a significant 1
Named windstorms with at least 100,00 households outages (2005-2018)
FIG 1: Comparison of the most impactful named windstorms (2005-2018) and windstorm Aila. The households left without electricity due to windstorm Aila reached 160,000 (red bar; Finnish Energy, 2020). In contrast, the household outages of the reference dataset (grey bars; Láng et al., 2021) express the overall number of outages experienced by the households during past windstorms i.e. one household may experience several outages during a windstorm. The household outages (this data is not yet available for Aila) is comparable with the households without electricity, however the number of household outages is often higher than the number of households without electricity. Therefore, it is likely that windstorm Aila would rank higher among the most impactful storms than shown in this figure.
LUOVA is an official warning report of FMI tailored for authorities which spreads information on natural hazards nationally and globally. FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021 | 15
FIG 2: a) The development of the power outages (households without electricity) during windstorm Aila. b) The 173 municipalities where the power outages occurred on the 17th of September 14:00 local time. storm for the energy sector. Regarding the overall socioeconomic impacts, the preliminary estimation showed that windstorm Aila ranked high among the past two decades’ windstorms (Fig. 1), however the impacts for different sectors varied. For instance, the power outage amounts of windstorm Aila (~160,000 households without electricity) are comparably standard for this magnitude of storm, however the number of emergency tasks during Aila (nearly 3,000) reached the second highest place in the record. The good predictability of windstorm Aila in combination of precise warnings and effective communication for the authorities and public resulted in one of the decade’s strongest storms being handled reasonably well in the Finnish society. The environmental conditions during the late summer and early autumn increase the risk of extensive forest damages as the soil is unfrozen and trees have leaves, which both make trees fall easier during a storm (Valta et al. 2019). In addition, during windstorm Aila, the soil was wet on wide areas in Central Finland because of an exceptionally rainy September (rain accumulation until mid-September was 50–100 mm). As Kamimura et al. (2012) state, also wet soil influences
FIG 3: Emergency tasks during Windstorm Aila. 2950 emergency tasks were recorded between the 16th of September 4 pm and the 18th of September 4 pm local time. The red dots on the map indicate the locations of the emergency tasks. Note: The emergency task data is highly biased with the population density.
16 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021
the wind damage and decreases the tree anchorage to the ground. All these factors, together with strong wind gusts of Aila, favored it to become a significant windstorm regarding the impacts. Energy. The number of households left without electricity started to increase from the evening of 16th Sep, reaching the peak (>90 000 customers without electricity simultaneously) in the evening on 17th Sep and decreased below 10 000 about 24 hours later (Fig. 2a). Nearly 200 municipalities were affected by the storm (Fig. 2b). The impacts on electricity grids were considerable (see Fig. 1), but not as massive as they could have been, taking into account the magnitude and unusual timing of the windstorm. Preliminary examination suggests that less faults occurred in the regions with more ground-cabling compared to air transmission lines. The strongest wind gusts hit the western part of the country where a big share of the power grid is already underground, compared to the eastern part of the country where for example windstorm Rauli hit with full power (Fig. 1). In addition, at the time of windstorm Rauli in 2016, only 35.6 % of the national power grid was underground and in 2019 the percentage of ground cabling was already 44.7 %. This shows that Finland is investing in improving the preparedness for storms and in windstorm cases as Aila, the investment also seems to pay off. Emergency services. Regarding emergency tasks, Aila was ranked to be the third busiest windstorm day for emergency services with 2950 emergency tasks during 48 hours (Fig. 3) (the final emergency task numbers might change once the data is consolidated). Windstorm Tapani (2011),
INDICATOR/ STORM
AILA
RAULI
LYYLI
VALIO
EINO
SEIJA
TAPANI
(MONTH,
(SEP,
(AUGUST,
(MAY,
(OCT,
(NOV,
(DEC,
(DEC,
YEAR)
2020)
2019)
2015)
2015)
2013)
2013)
2011)
0.5–1.5
1.5
~1
3
Damaged forest (Mm³)
~0.4–0.7 ~0.1–0.2 0.1–0.2
Emergency tasks
~3000
2200
~500
1600
1800
2100
5800
Max gust, inland (m/s)
~30
23
24
26
27
30
32
Soil frost
no
no
partly
no
no
partly
no
Leaves on trees
yes
yes
partly
no
no
no
no
TABLE 1: Comparing windstorm Aila with another six significant windstorms in the past decade: their impacts, maximum wind gusts and environmental conditions during the windstorms. known also as Cyclone Dagmar (Kufeoglu and Lehtonen 2014), has the highest number of emergency tasks (5800; Table 1), and windstorm Janika (2001) being close to Tapani’s impacts, however the impact observations are less reliable and therefore left out of the comparison of this article. Regarding emergency tasks, Aila stands closest to windstorm Rauli’s impacts (2200; Table 1). The spatial occurrence of emergency call tasks (Fig. 3) indicates the largest density of rescue tasks in general in the coastal areas, in the regions of the strongest wind gusts and in the areas with highest population (cities). In the morning of the 17th of Sep, the emergency tasks were rapidly increasing especially in the western and southwestern Finland but during the course of the day, more emergency tasks started to occur in southern Finland as well. Eastern and northern parts of the country remained with less damage since the storm was weaken-
ing while moving eastwards. In total, no casualties related to the storm were reported. Other impacts. The Finnish Forest Center estimated that the storm did not cause large-scale forest damages, but mostly scattered and smaller damages especially in the southern borders of open areas. The total estimated amount of fallen trees was 0.4 to 0.7 million m3 equal to 15–20 million Euros. Furthermore, one fallen tree in Tampere caused the whole railroad system to cease between Tampere and Helsinki. Overall, Aila’s ranking is among the strongest windstorms of the 2000s with respect to the impacts. This study highlights the importance of efficient early-warning measures to mitigate the storm impacts, especially related to human safety and critical infrastructure. Acknowledgements: We thank ERA4CS for the WINDSURFER project.
Finnish Energy, 2020: The power outage map. Accessed 18 September 2020, https://www.sahkokatkokartta.fi/. FMI, 2020: Tuulitilastot. Accessed 18 September 2020, https://www.ilmatieteenlaitos.fi/tuulitilastot. FMI, 2016: LUOVA answers the authorities’ need for information when a natural disaster occurs, https://en.ilmatieteenlaitos.fi/news/286502165. Gregow, H., et al., 2021: Windstorm Aila 2020: wind forecasts and discussion on climate change, in review. Kamimura, K., et al., 2012: Root anchorage of hinoki (Chamaecyparis obtuse (Sieb. Et Zucc.) Endl.) under the combined loading of wind and rapidly supplied water on soil: analyses based on tree-pulling experiments, Eur. J. Forest Res., 131, 219–227, https://link.springer.com/article/10.1007%2 Fs10342-011-0508-2. Kufeoglu, S., and Lehtonen, M., 2014: Cyclone Dagmar of 2011 and its impacts in Finland, IEEE PES Innov. Smart, 10.1109/ISGTEurope.2014.7028868. Láng, I., et al., 2021: Investigating extra-tropical storm impacts on electricity grids by classifying 83 storms in Finland, in review. Valta, H., et al., 2019: Communicating the amount of windstorm induced forest damage by the maximum wind gust speed in Finland, Adv. Sci. Res., 16, 31–37, https://doi.org/10.5194/asr-16-31-2019. FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021 | 17
DOI: 10.35614/ISSN-2341-6408-IK-2021-06-RL Received 22 Sep. 2020, accepted 30 Apr. 2021, first online 28 May 2021, published 24 June 2021
Climate Security in an Interdependent World – Examining Climate Change in Finland’s Comprehensive Security Model Context There is an increasing concern that climate change may have undermining impacts on security, all the way from global to the individual level. Both climate change impacts and the efforts to mitigate and manage climate change have the potential to create threats to people, ecosystems, and the stability of societies. There is a need to build knowledge on climate security impacts, and to evaluate if a new kind of comprehensive security thinking and actors are needed. SANNA ERKAMO1,2, EMMA HAKALA2, HEIKKI TUOMENVIRTA1, JUHA PYYKÖNEN3, KATI BERNINGER4, ORAS TYNKKYNEN4, ANTTO VIHMA2 1Finnish Meteorological Institute 2Finnish Institute of International Affairs 3Security Analysis Oy 4Tyrsky Consulting
Climate change may threaten the stability and security of society in many ways, and its effects are expected to intensify in the coming decades. Both the changing climate, and ambitious mitigation efforts create new uncertainties and risks (Erkamo et al. 2021). These range from adverse health effects caused by extreme weather events to disruptions in the international production chains, and all the way to conflicts related to the use and management of natural resources on a global scale. In addition to direct climate and weather-related risks, climate change also acts as a threat multiplier. Cascading effects can have far-reaching and unpredictable consequences. Local climate impacts can cause transboundary effects around the world through global impact chains. Climate change impacts are so comprehensive that they will inevitably shape
international relations, economies, migration, and inter-group tensions. In order to limit climate change, the emissions of greenhouse gases need to be reduced drastically. Meeting the targets set out in the Paris Agreement also requires removal of greenhouse gases from the atmosphere by deliberate human actions. Therefore, effective climate change mitigation requires structural changes and systemic societal effort. Climate change as a driver for change in the security context has been discussed in Finnish Government Report on Finnish Foreign and Security Policy and the National Risk Assessment (Ministry of Interior 2019). Research on the threats caused by climate change has recently expanded to include climate change as a global security threat. Although Finland, as a relative-
18 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021
ly wealthy and well-functioning society, is quite well-positioned to adapt to local impacts of climate change, it might not be as prepared for societal challenges brought on by the more complex international dynamics. Developments in international climate politics, technological change in mitigation of greenhouse gases, and climate-related disturbances to global markets may have more significant impact for Finnish security than the direct and local effects of climate change. The Finnish Institute of International Affairs (FIIA), the Finnish Meteorological Institute, Security Analysis Oy and Tyrsky Consulting are jointly conducting a research project titled “Finland and Climate Security in an Interdependent World”. The main goal of the project is to examine the wide-ranging climate security effects in Finland and the ways in which the
Finnish Comprehensive Security Model can be utilised in preparing for them. The project will conclude in May 2021. The Comprehensive Security Model is “a Finnish preparedness cooperation model in which the vital functions of society are looked after through cooperation between the authorities, the business community, organisations and citizens.” (Government Resolution 2017) The model standardizes national preparedness across administrative boundaries and guides administrative branches’ specific strategies. Recent research stresses that climate security challenges cannot be met exclusively through measures associated with security policy in the narrow sense. The impacts of climate change on security should be addressed with risk assessments, but it is also necessary to integrate political, economic and societal perspectives into these analyses and preparedness-building in general. (Hakala et al. 2019a)
To fill current knowledge gaps, inter-disciplinary research on the geopolitical and structural environmental security impacts is required. A co-research approach between researchers and policy-makers or practitioners may help to raise awareness on the potential consequences as well as to find actionable, effective policy responses. A wide participation from different administrative sectors is appreciated in the development of solutions because policy recommendations are likely to require crosscutting administrative cooperation. (Hakala et al. 2019b) The “Finland and Climate Security in an Interdependent World” is a participatory research project that consists of four phases: literature review, scenario work, expert workshops, analysis and co-development of policy recommendations. Possible climate security impacts are identified through a literature review, document analysis and expert interviews. The review is used to create practi-
cal security scenarios that will be elaborated together with experts in administration and other stakeholders. Scenarios are used to identify weaknesses in the current policies and practises, and to co-develop potential solutions to improve preparedness in expert workshops. The project will provide a systematic categorization and description of the climate security effects in line with Finland’s Comprehensive Security Model. In addition, the project explores whether the current Comprehensive Security Model is sufficient in preparing for identified climate security impacts and how the Model could be improved. Acknowledgements: We thank the Finnish Government’s 2020 analysis, assessment and research activities (VN TEAS) for their funding and the project’s Steering Group nominated by VN TEAS for their guidance.
Erkamo, S., et al., 2021: Security implications of climate change in Finland. Reports, Finnish Meteorological institute. (Report in Finnish with Abstract in English) in preparation. Government Resolution, 2017: Security Strategy for Society 2017, The Security Committee, Helsinki, https://turvallisuuskomitea.fi/wp-content/uploads/2018/04/YTS_2017_english.pdf. Hakala, E., et al., 2019a: Northern Warning Lights: Ambiguities of Environmental Security in Finland and Sweden, Sustainability, 11(8), 2228, https:// doi.org/10.3390/su11082228. Hakala, E., et al., 2019b: A lot of talk, but little action—The blind spots of Nordic environmental security policy, Sustainability, 11(8), 2379, https://doi. org/10.3390/su11082379. Ministry of Interior, 2019: National risk assessment 2018. Publications of the Ministry of the Interior 2019:5, pp. 70. Helsinki. (Report in Finnish with Abstract in English), http://urn.fi/URN:ISBN:978-952-324-245-6.
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DOI: 10.35614/ISSN-2341-6408-IK-2021-07-RL Received 21 Sep. 2020, accepted 23 June 2021, first online 24 June 2021, published 24 June 2021
Battle between mitigation and adaptation: the future challenge of climate change Climate change is caused not only by higher greenhouse gas (GHG) concentrations, but a variety of natural and anthropogenic driving forces affecting the global energy balance, including e.g., land use changes and aerosol emissions. Climate change policy requires both mitigation activities designed to reduce GHG emissions, and adaptation policies addressing changes to natural eco- and climatic- systems caused by climate change. EEVA KUNTSI-REUNANEN Finnish Meteorological Institute
Climate change is linked to virtually all aspects of modern economics: electricity consumption, heating and cooling of buildings, transportation, agriculture, forestry, waste management, use of chemicals, industrial production processes, etc. Climate change, moreover, impacts biodiversity systems, contributes to water scarcity problems, and sea level rise. Climate change is thus, very much a core sustainability issue (VijayaVenkataRaman et al. 2012; IPCC 2013). There are basically three options for the reduction of CO2 emissions; (i) reduction of carbon intensive energy use, (ii) switch to less carbon intensive fuels or (iii) efficiency improvement of the energy system (Kuntsi-Reunanen 2007). The efficiency improvement is related to (i) the socio-cultural development of the society, (ii) economic and structural development and (iii) technological development (Sun and Kuntsi 2004). Within the economic development, it is essential how the role of industrial development and its structure in the globalised economy (e.g. shift of heavy and polluting industry to developing countries) relates to the development of other sectors (e.g. service sector and tourism) (Sun and Kuntsi 2004; Kuntsi-Reunanen 2007). The mitigation also involves reducing the flow of heat-trapping GHGs
into the atmosphere by enhancing the “sinks” that accumulate and store these gases (such as the oceans, forests and soil). Moreover future solution for cutting CO2 emissions might be the nature based solutions (NBS) as they help to remove emissions from the atmosphere. Negative emission technologies (NET) are likely to be needed in order to stabilize global warming at 1,5 °C, the target to which governments committed to, in the Paris Agreement (IPCC 2018). Even if emissions of GHGs are reduced radically, climate will continue to change, at least for the foreseeable future. This is because the interdependent physical, chemical and biological processes in the oceans, atmosphere and on land do not respond instantly to changes in GHGs as they have mean residence times in the atmosphere from decades to over a century. While it is essential that humans reduce their distracting impact on climate and ecosystems, they should act immediately to begin to prepare themselves for local to global climatic changes they have been contributing to since the industrial revolution (Ruth 2010). Adaptation requires adjustment to the actual or expected future climate. The aim is to reduce our vulnerability to the harmful effects of climate change (such as sea level intrusion, stronger
20 | FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021
extreme weather events or food insecurity). It also includes exploiting any beneficial potential opportunities associated with climate change (e.g. longer periods of growth or increased yields growth in some regions). Although climate change is a global issue, it is felt locally. Cities and municipalities are therefore at the frontline of adaptation. In the absence of national or international climate policy direction, cities and local communities around the world have been focusing on solving their own climate change-related problems. They are working to build flood defences, plan for heatwaves and higher temperatures, install water-permeable sidewalks to better deal with floods and rainwater and improve water storage and use (Gandini A. et al. 2021). However, governments at different levels are becoming better at adaptation. Climate change is starting to be taken into account in various development plans: how to manage the increasingly extreme disasters and associated risks, how to protect coastlines and deal with sea-level encroachment, how to best manage land and forests, how to deal with and plan for reduced water availability, how to develop sustainable crop varieties and how to protect energy and public infrastructure etc.
In terms of climate change policy, mitigation continues to receive more attention than adaptation. This can be explained by a number of reasons, such as the urgency of emission reductions, the lack of time of adaptation, the risk and complexity of analysis and methods used in adaptation, and the lack of information on adaptation itself and the measures used in adaptation. The balance between mitigation and adaptation about climate change is challenging for various reasons. Climate change is characterised by large uncertainties, time lags, and large differences in costs and benefits around the world. Furthermore, climate change impacts a number of properties that are difficult to value, including ecosystems, biodiversity, and quality of life, and it has not been possible to establish meaningful and reliable economic estimates of climate change damages. The estimated benefits of substantially reducing GHGs are diffuse across the globe, uncertain or unknown in terms of probability and magnitude, and primarily fall far in the future (Kuntsi-Reunanen 2015). According to Tubi et al. (2012), the failure of international mitigation efforts so far, despite the widespread attention they gained, indicates that climate change is a politically difficult problem to address. Furthermore, it is a global problem, whose solution cannot be achieved through the efforts of any single state or small group of states. In addition, the negative effects of climate change are largely long term, and there-
fore are not readily perceptible at present. Hence, mitigation policies imply that present generations pay for the benefit of future generations. Mitigation requires large-scale behavioural changes, but in many cases, governments lack the incentive or ability to bring them about. Thus, while collective action is needed to tackle climate change, all countries have a dis-incentive to undertake such action as they currently enjoy advantages from the activities that contribute to global warming, but believe they will suffer only a fraction of the environmental costs in the future (Anderson and Bows 2011; Rosen and Guenther 2015). The world has changed remarkably since the climate convention was signed in 1992. Several countries have developed massively since then. Many industrial countries are now more aware of the risks, as well as the difficulties, impacts, and opportunities associated with climate change. Many developing countries have developed economically and some of them have already achieved the level of industrialized countries. At the same time, the direct effects of climate change have become commonplace and have caused serious damages in some vulnerable developing countries. There is an urgent need for adaptation measures (Kuntsi-Reunanen 2015). It should be noted that substantial changes in population size, age structure, and urbanization are expected in many parts of the world this century. Statistical analysis of historical data suggests that population growth has been
one driver of emissions growth over the past several decades and that urbanization and aging can also affect energy use and emissions. As the living standard rises and population continues to grow, energy use and CO2 emissions in city areas do the same. Aging can reduce emissions in the long term by up to 20 %, particularly in industrialized country regions. In contrast, urbanization can lead to an increase in projected emissions by more than 25 %, especially in developing country regions (O’Neill et al. 2010). The pursuit of a carbon-neutral world in 2050 sounds promising but ambitious. Mitigation can have significant local benefits when generating outcomes that make good sense irrespective of climate change. Conversely, local adaptation can have national and global benefits when it frees up productive resources instead of drawing them into disaster mitigation. Furthermore, considerable overlap between climate change mitigation and adaptation actions exists, and spending on one can simultaneously advance the goals of the other (Ruth 2010). Instead of battle between mitigation and adaptation, these two should work hand in hand for a common goal. Acknowledgements: This paper was supported by the Climate change adaptation: regional aspects and policy instruments (SUOMI) project funded by the Ministry of Environment of Finland and Academy of Finland Flagship ACCC (grant number 337552).
Anderson, K. and Bows, A., 2011: Beyond ‘dangerous’ climate change: emission scenarios for a new world, Philosophical Transactions of the Royal Society, 369, 20–44. Gandini, A. et al., 2021: Climate change risk assessment: A holistic multi-stakeholder methodology for the sustainable development of cities, Sustainable Cities and Society, 65, https://doi.org/10.1016/j.scs.2020.102641. IPCC, 2013: Climate change 2013: The physical science basis. Contribution of working group I to the Fifth Assessment Report of the IPCCC. Edited by Stocker, T.F., et al., Cambridge University Press. IPCC, 2018: Global Warming of 1.5°C.An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P., et al.]. In Press Kuntsi-Reunanen, E., 2007: A comparison of Latin American energy-related CO2 emissions from 1970 to 2001, Energy Policy, 35, 586–596. Kuntsi-Reunanen, E., 2015: Climate change and global responsibility – the role of energy consumption, GDP and CO2 emissions, Annales Universitatis Turkuensis, AII, 308. O’Neill, B. C., et al., 2010: Global demographic trends and future carbon emissions, PNAS, 107(41), 17521–17526, https://doi.org/10.1073/ pnas.1004581107. Rosen, R. A., and Guenther, E., 2015: The economics of mitigationg climate change: What can we know?, Technological Forecasting & Social Change, 91, 93–106. Ruth, M., 2010: Economic and social benefits of climate information: Assessing the cost of inaction, Procedia Environmental Sciences, 1, 387–394. Sun, J. W., and Kuntsi, E., 2004: Environmental impact of energy use in Bangladesh, India, Pakistan and Thailand, Global Environmental Change, 14, 161–169. Tubi, A., et al., 2012: The effect of vulnerability on climate change mitigation policies, Global Environmental Change, 22, 471–482. VijayaVenkataRaman, S., et al., 2012: A review of climate change, mitigation and adaptation, Renewable and Sustainable Energy Reviews, 16(1), 878–897. FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 1/2021 | 21
DOI: 10.35614/ISSN-2341-6408-IK-2021-08-RL Received 9 Feb. 2021, accepted 2 June 2021, first online 7 June 2021, published 24 June 2021
The socio-spatial patterns of heat stress exposure in Helsinki on two hot days of 2018 and 2019 Exposure to heat stress varies due to the large-scale weather pattern and local factors such as the urban heat island effect, type of built environment, and—like in Helsinki— impact of the sea and may vary significantly between heatwave events. Moreover, the vulnerability characteristics of the exposed population play a notable role in the health impacts. We present case studies of the socio-spatial exposure patterns to heat stress in the Helsinki metropolitan area during two hot days of the 2018 and 2019 heatwave events. ATHANASIOS VOTSIS, REIJA RUUHELA, HILPPA GREGOW Finnish Meteorological Institute
Heatwaves and urban resilience. Temperature extremes, floods, drought and water scarcity—along with impacts on human health— are indicated by the IPCC as the key climate change risks in urban areas (Revi et al. 2014). Addressing unequal exposure and vulnerability to temperature extremes across demographic groups has direct links to the UN’s Sustainable Development Goals and the notion of systemic resilience. As the climatology of heatwaves is changing, necessitating the updating of warning systems and architectural or urban planning standards, information on the spatiotemporal behavior of heatwaves, interface with the built environment, and impacts on residents is becoming crucial. The urban heat island (UHI) effect may magnify the health impacts of heat stress in urban areas. In Helsinki, the mortality attributable to heatwaves can be about 2.5 times higher than in the surrounding, more rural area (Ruuhela et al. 2021). However, the intensity and spatial distribution of the UHI also
% POPULATION
% ELDERLY 75+
% WORKPLACES
% DWELLINGS
EXPOSED
EXPOSED
EXPOSED
EXPOSED
risk level
2/8/ 2018
28/7/ 2019
2/8/ 2018
28/7/ 2019
2/8/ 2018
28/7/ 2019
2/8/ 2018
28/7/ 2019
–, green
0.8
0.7
0.9
0.8
1.1
1.1
0.9
0.9
hot, yellow
30.9
11.6
33.4
9.2
56.4
6.7
34.7
10.3
very hot, orange
68.4
87.6
65.7
90.0
42.5
92.2
64.4
88.8
TABLE 1: Key exposure and vulnerability indicators during the 2/8/2018 and 28/7/2019 heatwaves. vary depending on the synoptic situation, and in a coastal city like Helsinki, the impact of the sea is substantial. In these case studies of two hot days during heatwaves in 2018 and 2019 with different weather patterns we demonstrate the role of weather, specifically wind direction, on the spatial distribution of exposure to heat stress, and on the severity of the exposure according to selected factors affecting vulnerability of the population.
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Spatial patterns from MEPS gridded operational forecast data. A hot day in Finland is defined as one with Tdmax >= 25.1 °C. A heatwave occurs if this lasts for at least three days, although no official definition exists. Two recent heatwave events in a large Finnish urban region, in the Helsinki metropolitan area, occurred in 2018 and 2019. To benefit from a continuous spatial coverage and account for the non-trivial interaction between
2018-08-02 08:00:00 UTC
2019-07-28 08:00:00 UTC
32.5
2019-08-02 14:00:00 UTC
32.5
30.0
30.0
27.5
27.5
25.0
25.0
22.5
22.5
20.0
20.0
17.5
17.5
15.0
15.0
32.5
2019-07-28 12:00:00 UTC
32.5
30.0
30.0
27.5
27.5
25.0
25.0
22.5
22.5
20.0
20.0
17.5
17.5
15.0
15.0
FIG 1: Morning (left) and afternoon (right) static snapshots of apparent temperature (°C) in the Finnish capital region on 2/8/2018 (top) and 28/7/2019 (bottom).
meteorology and the built environment, we retrieved data from the MetCoOp Ensemble Prediction System (MEPS), an hourly weather forecast product based on HARMONIE-AROME that covers the Nordic Region at a 2.5 km horizontal resolution (Müller et al. 2017). A SURFEX land surface model is coupled to this operational forecast of the Finnish Meteorological Institute, including information on surface physiographic characteristics such as land use, vegetation types, and urban morphology. From the forecast data we produced a gridded representation
of the spatiotemporal variation of thermal comfort conditions on two hot days during the heatwaves using apparent temperature (AT) (Steadman 1994) based on air temperature and relative humidity at 2 m, and wind speed at 10 m. Fig. 1 presents the progression of AT during the hottest days of the two heatwaves: 2/8/2018 and 28/7/2019. These animations show how the direction of the airstream affected the spatial distribution of thermal exposure. During the hot day in 2018 mainly southwesterly wind from the sea prevailed.
This wind direction led to cooler conditions on the shoreline than inland. In addition, the relatively narrow band of the highest thermal exposure north of the most densely built urban area suggests that the southwesterly winds advected warmer air from the area of the strongest UHI. During the hot day in 2019 northerly wind from inland prevailed. This led to a nearly opposite pattern in the spatial distribution of thermal exposure. In this case, the cooling effect of the sea was lacking, and the shoreline was warmer than the inland during both day and night.
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The highest thermal exposure on the shoreline suggests in this case that the northerly wind advected warmer air from the densely built areas. Exposure and social vulnerability. Exposure to heat stress in Helsinki varies due to several factors, notably the urban heat island effect, type of built environment, weather pattern and impact of the sea. An additional factor is the population vulnerability during heatwaves. As an initial overview, we provide in Table 1 an overlay of the gridded socioeconomic data by Statistics Finland with the spatiotemporal distribution of the national heat warning risk levels, namely ‘hot (yellow)’ (if Tdmax >= 27 and Tdmean >= 20 °C) and ‘very hot (orange)’ (if Tdmax >= 30 and Tdmean >= 24 °C). More specifically, the MEPS gridded data enable the calculation of the risk levels in a geographically resolved manner, making it possible to understand the exposure patterns of the population, infrastructure, and
of different vulnerability groups in better detail than with city-wide aggregate data. The selected indicators demonstrate that the exposure of the population to different levels of heat stress may vary substantially in Helsinki—in addition to meteorological factors and characteristics of the built environment—due to the spatial distribution of population and their daily activities. These first case studies on the spatial distribution of exposure to heat stress during heatwaves in Helsinki reveal the need for further studies on vulnerability factors that would benefit health impact studies and urban planning. The use of forecast data from the operational weather prediction model suggests that there is also potential for targeted, tailored forecasts for urban areas to prepare for heatwaves. Lastly, meteorological data from a longer time horizon and the addition of mortality, morbidity and productivity data will enable the detection of possible
location-specific human adaptation or acclimatization trends in the population, thus making a further step in understanding adaptation pathways and sustainability transitions in urban areas. Acknowledgements. The study contributes to SmartLand/SLUPSU project (Smart Land use policy for sustainable urbanization) funded by the Finnish Strategic Research Program (decision No. 327803), URCLIM project (Advance on Urban Climate Services) funded by the EU Era4CS program (grant No. 690462), CHAMPS project (Climate change and Health: Adapting to Mental, Physical and Societal challenges) funded by the Academy of Finland (decision No. 329225), and ACCC (Atmosphere and Climate Competence Center) Flagship project funded by the Academy of Finland (decision No. 337549e). We thank Herman Böök and Carl Fortelius for their support and guidance.
Müller, M., et al., 2017: AROME - MetCoOp: A Nordic convective scale operational weather prediction model, Weather & Forecasting, https://doi.org/10.1175/WAF-D-16-0099.1. Revi, A., et al., 2014: Urban areas, in Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects, Cambridge University Press, 535–612. Ruuhela, R., et al., 2021: Temperature-related mortality in Helsinki compared to its surrounding region over two decades, with special emphasis on intensive heatwaves, Atmosphere, 12(46), https://doi.org/10.3390/atmos12010046. Steadman, R. G., 1994: Norms of apparent temperature in Australia, Aust. Met. Mag., 43, 1–16.
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KUVA: PIXABAY
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Ilmatieteen laitos ilmastokatsaus@fmi.fi www.ilmastokatsaus.fi