FMI's Climate Bulletin Research Letters 2/2022

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Climate data supports the adaptation of reindeer husbandry to climate change in Finland — 32 Daily gridded evapotranspiration data for Finland for 1981–2020 — 35

FMI’S CLIMATE BULLETIN: RESEARCH LETTERS Volume 4 Issue 2 ISSN: 2341-6408 DOI: 10.35614/ISSN-23416408-IK-2022-09-RL

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PUBLISHER Finnish Meteorological Institute (FMI) P.O. BOX 503 FI-00101 HELSINKI www.ilmastokatsaus.fi researchletters@fmi.fi

FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2022

EDITOR IN CHIEF Hilppa Gregow

DESIGN Marko Myllyaho

EDITORIAL COMMITTEE Juha A. Karhu Anna Luomaranta Kaisa Juhanko

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-2022-10-RL Received 1 Feb. 2022, accepted 9 May 2022, first online 30 Jun. 2022, published 20 Dec. 2022

Climate data supports the adaptation of reindeer husbandry to climate change in Finland The journal Poromies, a professional journal for reindeer herders published by the Reindeer Herders’ Association in Finland, mentioned climate change explicitly for the first time in 2004. Since then, the discussion on changing seasonal weather, climate risks and adaptation needs for climate change has become an everyday topic among the herders.

SIRPA RASMUS1, ILARI LEHTONEN2, JOUKO KUMPULA3, MIA LANDAUER1, ILONA METTIÄINEN1,3, JAANA SORVALI3, HEIKKI TUOMENVIRTA2, MINNA TURUNEN1 1 Arctic Centre, University of Lapland, 2Finnish Meteorological Institute, 3Natural Resources Institute Finland

Changing climate of the reindeer management area. Weather is a key factor in the working environment of reindeer herders. Each season is characterized by particular weather-related risks which require strategic responses from the herders. Various coping strategies to deal with adverse weather and pasture conditions are largely based on traditional knowledge. The reindeer management area is almost entirely located in the rapidly warming subarctic climate zone (AMAP, 2017). Climate change, manifesting itself as long-term warming and changes in precipitation and snow conditions, is expected to have both positive and negative impacts on reindeer husbandry. For example, warmer winters can help to keep reindeers fit, but on the other hand, can also lead to an increased number of rain-on-snow events and thaw-freeze cycles causing more frequent occurrence of basal ice and icing of wet snow, which can hinder reindeers’ access to ground lichens. Climate change can also boost spreading of new zoonotic diseases or lead to vegetation shifts. Eventually, traditional knowledge and skills of reindeer herders may become insufficient. Rapidly changing climatological conditions also place new demands on the production of scientific knowledge 32

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FIG 1: The annual number of hot days with daily mean temperature above 20 °C in 1961–1990 (a) and 1991–2020 (b). The change in the annual number of hot days from 1961–1990 to 1991–2020 is shown in (c) and the linear trend in the annual number of hot days in 1961–2020 in (d). Stippling in (c) and (d) indicates a significant change or trend at the 5% risk level.

FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2022

as it may be challenging to define what constitutes “normal”, “rare” or “exceptional” weather conditions.

In Finland, the recent “Act on compensation of damages caused to reindeer husbandry” was for the first time put into action after the exceptional winter of 2019–2020. During this winter, snow cover formed early in October on unfrozen and warm ground causing mold to form on vegetation and, later in winter, icing of bottom snow on pastures (Kumpula et al., 2020). Moreover, during this winter snow cover was very thick and heavy containing several icy layers. Due to relatively cold springtime temperatures, the snow melt did not take place until late May or early June.

This combination of difficult grazing and herding conditions led to severe economic burden on herders because of the necessity to intensify the supplementary feeding and due to high reindeer mortality. Similar difficulties were experienced around northern Fennoscandia. In Norway, for instance, the Red Cross and the military forces supported the transportation of supplementary feed. Worrying conditions were experienced again in 2021 when icy snow cover was formed early in winter in parts of Lapland.

Data and tools for herders and herding communities. Knowledge and learning are important components of adaptive capacity. Different knowledge sets can be used together to create new understanding of the effects of climate change on nature-based livelihoods, such as reindeer husbandry. Co-produced knowledge supports decision-making on adaptation to climate change. Nevertheless, the information provided for herding communities needs to be relevant and understandable.

Recently, Rasmus et al. (2020) put together a set of 14 climatic indices relevant for reindeer husbandry. The indices were related to temperature, precipitation and snow conditions and were based on literature on the livelihood and interviews with the herders.

Three of the indices (the number of ice days, the largest daily precipitation, and the number of heavy precipitation days) belonged to the core set of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (e.g., Sillmann et al., 2013). The snow-related indices were selected from a set of indices examined by Luomaranta et al.

(2019), and the indices for prolonged warm and wet periods were originally defined by Peltonen-Sainio et al.

(2016) for agricultural purposes. The indices were calculated from gridded climate data presented on a 10 km × 10 km grid over the period 1981–2010 (Aalto et al., 2016). Presently, we are updating this dataset by including climate data up to 2020. As the gridded

climate data for Finland starts from 1961, the updated maps enable the comparison of two successive 30-year periods, 1961–1990 and 1991–2020.

The latter period has been adopted as a new climatological normal period (WMO, 2017) by many national weather services throughout the world in 2021, including the Finnish Meteorological Institute. Moreover, this allows us to extend the trend analysis presented by Rasmus et al. (2020) for the 30-year period 1981–2010 to cover the whole 60-year period 1961–2020.

As an example, updated maps are presented for the annual number of hot days with daily mean temperature above 20 °C and for cold days with mean temperature below -25 °C. For the number of hot days, there has been

FIG 2: The annual number of cold days with daily mean temperature below -25 °C in 1961– 1990 (a) and 1991–2020 (b). The change in the annual number of cold days from 1961–1990 to 1991–2020 is shown in (c) and the linear trend in the annual number of cold days in 1961–2020 in (d). Stippling in (c) and (d) indicates a significant change or trend at the 5% risk level.

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no statistically significant change between the 30-year periods within the reindeer management area (Fig. 1).

The number of cold days, by contrast, has decreased substantially between the two time periods throughout the area, and the decreasing trend over the 60-year period 1961–2020 is statistically significant in large areas, particularly in the north (Fig. 2).

Gridded meteorological data are objective and have full spatial coverage on the area. However, fine-scale environmental variability may cause errors and uncertainties into the interpolation of climate data. Moreover, changes in the observation network during the interpolation period may introduce local artificial trends. Still, the maps constitute an easy starting point for discussions with local practitioners, and high spatial resolution enables bringing the data to the scale of the herders’ experiences which are very much local in nature.

Data and tools for adaptation planning and decision makers. In Finland, national adaptation to climate change is guided by The National Climate Change Adaptation Plan 2022 (Ministry of Agriculture and Forestry, 2014). Reindeer husbandry does not have a common adaptation plan, al-

though a need for this has been acknowledged. Suggested adaptation actions consist of, for example, maintaining the uniformity and diversity of the pasture areas, improving reindeer health, and limiting the expansion of invasive alien species.

Adaptation actions are part of daily work in herding communities, indicating that adaptation actions need to be acceptable and actionable by herders.

The legislation, governmental subsidy and compensation policies regarding adaptation measures have significant roles, as well as education and guidance of the livelihood. In local herding communities, deliberate planning of adaptation is rare. To mitigate the adverse effects of climate change in the long term, political support is needed for planned adaptation.

The Ministry of Agriculture and Forestry is presently chairing a multi-stakeholder working group called “Future of reindeer husbandry in Finland”. Discussions on the adaptation to changing climate are an important part of that work. Experiences, views, and expectations of herders are being gathered in several research projects.

One important element in adaptation is data consisting of reliable and relevant information about experienced

and expected changes in climate.

Providing relevant data requires further discussions with herders: how to define a good and bad herding season, what are the critical seasons, what are the indices to be calculated from the observations or looked for in the climate scenario output?

In addition to knowledge and discussions within the livelihood, adaptation also requires collaboration and dialogue with other land-users within the reindeer management area. In practice, actions of other land-users and climate change mitigation, for example in the form of mines and wind parks, may cause additional burdens on herding communities. Further modifications in pasture use and herding practices may be needed. Adaptation may be necessary, but also mean cultural changes and losses, both to Sámi and Finnish herders and herding communities.

Acknowledgements. The study contributes to the CLIMINI project (project number A75777) funded by the European Union’s European Regional Development Fund. We also warmly thank several reindeer herders for valuable discussions and co-developing the material presented here.

Aalto, J., P. Pirinen, and K. Jylhä, 2016: New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. J. Geophys. Res. Atmos., 121, 3807–3823, https://doi.org/10.1002/2015JD024651.

Arctic Monitoring and Assessment Programme (AMAP), 2017: Adaptation Actions for a Changing Arctic - Perspectives from the Barents Area, xiv + 267 pp, https://www.amap.no/documents/doc/adaptation-actions-for-a-changing-arctic-perspectives-from-the-barents-area/1604.

Kumpula, J., M. Jokinen, J. Siitari, and S. Siitari, 2020: Talven 2019–2020 sää-, lumi- ja luonnonolosuhteiden poikkeuksellisuus ja vaikutukset poronhoitoon [in Finnish]. Luonnonvara- ja biotalouden tutkimus, 58/2020, 57 pp, http://urn.fi/URN:ISBN:978-952-380-023-6.

Luomaranta, A., J. Aalto, and K. Jylhä, 2019: Snow cover trends in Finland over 1961–2014 based on gridded snow depth observations. Int. J. Climatol., 39, 3147–3159, https://doi.org/10.1002/joc.6007.

Ministry of Agriculture and Forestry, 2014: Finland’s National Climate Change Adaptation Plan 2022, 41 pp, https://mmm.fi/en/national-climatechange-adaptation-plan.

Peltonen-Sainio, P., and Coauthors, 2016: Harmfulness of weather events and adaptive capacity of farmers at high latitudes of Europe. Clim. Res., 67, 221–240, https://doi.org/10.3354/cr01378.

Rasmus, S., M. Turunen, A. Luomaranta, S. Kivinen, K. Jylhä, and J. Räihä, 2020: Climate change and reindeer management in Finland: Co-analysis of practitioner knowledge and meteorological data for better adaptation. Sci. Total Environ., 710, 136229, https://doi.org/10.1016/j.scitotenv.2019.136229.

Sillmann, J., V. V. Kharin, X. Zhang, F. W. Zwiers, and D. Bronaugh, 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J. Geophys. Res. Atmos., 118, 1716–1733, https://doi.org/10.1002/jgrd.50203.

World Meteorological Organization (WMO), 2017: WMO Guidelines on the Calculation of Climate Normals. 19 pp, https://library.wmo.int/index. php?lvl=notice_display&id=20130#.YrBICzlBy-Y.

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D O I : 1 0. 3561 4/ I SS N - 2 341 - 6 4 0 8- I K- 2 022-1 1 -RL

Received 3 Mar. 2022, accepted 3 Jun. 2022, first online 6 Jul. 2022, published 20 Dec. 2022

Daily gridded evapotranspiration data for Finland for 1981–2020 In this paper, we present daily gridded evapotranspiration dataset covering Finland over the 40-year period 1981–2020. The dataset representing evapotranspiration from a reference surface is freely available in a 1 km × 1 km grid.

PENTTI PIRINEN¹, ILARI LEHTONEN¹, RISTO K. HEIKKINEN², KAISU AAPALA², JUHA AALTO¹ ¹Finnish Meteorological Institute, ²Finnish Environment Institute

Introduction. Evapotranspiration is the combination of two separate processes, evaporation and plant transpiration. Evaporation accounts for the vaporisation of liquid water to the atmosphere from evaporative surfaces, such as the soil and water bodies, while transpiration accounts for the vaporisation of the liquid water from plant tissues to the atmosphere as part of life cycle of the plants. Both processes occur simultaneously and there is no simple way to distinguish between them.

Evapotranspiration is a central part of the hydrological cycle. Thus, it is one of the most important hydrometeorological variables in agrometeorology, and also plays a key role in assessing the susceptibility of natural ecosystems to climate change and land use driven phenomena, particularly droughts (Fisher et al., 2011; Yang et al., 2015). Evapotranspiration is affected by weather parameters, water availability and the evaporative surface. The most important weather parameters influencing evapotranspiration are air temperature, relative humidity, wind speed and solar radiation.

Materials and methods. Several methods have been developed to estimate evapotranspiration by using different climatic variables (McMahon et al., 2013; Guo et al., 2016). Here, we used the standard method recommended by the Food and Agricultur-

al Organization of the United Nations (FAO) for calculating the reference crop evapotranspiration (ET₀) from daily weather data. This so-called FAO Penman–Monteith equation can be written as follows (Allen et al., 1998, 2006): (1) where Rn is the net radiation at the crop surface (MJ m-2 day-1), G the soil heat flux density (MJ m-2 day-1), T the air temperature at 2 m height (°C), u2 the wind speed at 2 m height (m s-1), es the vapour pressure of the air at

saturation (kPa), ea the actual vapour pressure (kPa), Δ the slope of the vapour pressure curve (kPa °C-1), Cn and Cd are coefficients related to reference surface and calculation time step and γ is the psychrometric constant (kPa °C-1) as defined below. In daily time step computations, G may be ignored (Allen et al., 1998). Following the FAO recommendations for calculations with daily time step, we used values of Cn= 900 and Cd = 0.34 characterising a reference surface with a crop height of 0.12 m, a fixed surface resistance of 70 s m-1 and an albedo of 0.23. Surface resistance describes the restriction of

FIG 1: Comparison of calculated reference crop evapotranspiration (ET0) values to pan evaporation measurements in Jokioinen, Jyväskylä and Mikkeli as averaged over the period 1981–1995. Positive (negative) values indicate higher (lower) calculated ET0 compared to measured pan evaporation. Light bars represent ET0 calculated from station data and dark bars from the created gridded dataset using the data from the grid cells closest to the stations in question. The locations of Jokioinen, Jyväskylä and Mikkeli are shown in Fig. 2a.

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FIG 2: Monthly average reference crop evapotranspiration (ET0) in Finland in 1991–2020 in (a) April, (b) May, (c) June, (d) July, (e) August, and (f) September. vapour from land surface in the water exchange process, without using land surface temperature data, and albedo describes the proportion of solar radiation reflected from the surface. The psychometric constant γ is defined as follows: (2) where cp is specific heat of air at constant pressure (1.013×103 MJ kg-1 °C-1), p is the atmospheric pressure (kPa), rMW is the ratio of molecular weight of water vapour to dry air (0.622) and λ is latent heat of water vaporisation (2.45 MJ kg-1).

In the constructed dataset, daily values of ET₀ over the period 1981–2020 from April to September 36

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were incorporated in a 1 km × 1 km lattice system covering Finland. The weather data required for calculating ET₀ were extracted from the Finnish daily gridded climatological dataset (Aalto et al., 2016), except the wind data, which were interpolated from the ERA5 reanalysis (Hersbach et al., 2020). Wind speed provided in the ERA5 at 10 m height was adjusted to 2 m height using a conversion factor of 0.748, as suggested by Allen et al. (1998) assuming a logarithmic wind speed profile. The wind data were originally presented in a 0.25° × 0.25° spatial resolution, while the other weather variables were interpolated into the 1 km × 1 km grid from station observa-

FMI’S CLIMATE BULLETIN: RESEARCH LETTERS 2/2022

tions using kriging with external drift.

Net radiation data were an exception, since Rn values used in the interpolation were mainly estimated based on cloudiness observations prior the mid-1990s as described by Venäläinen and Heikinheimo (1997). However, when measurements of Rn were available, we used the measured values and from 2008 onwards, only measured data for Rn was used. Saturation vapour pressure deficit (es­- ea) was calculated separately with daily maximum and minimum temperatures, assuming constant relative humidity throughout a day, and the daily average saturation vapour pressure deficit was then calculated as an average of the two daily values.

Discussion and evaluation of the results. Monthly ET₀ values in the gridded dataset were compared with evaporation measurements conducted by the Finnish Environment Institute with standard Class-A evaporation pans (e.g., Jovanovic et al., 2008) from May to September over the period 1981–1995 on three locations: Jokioinen, Jyväskylä and Mikkeli. We also calculated ET₀ values from station observations on these locations by using an R package developed by Guo et al.

(2016). Fig. 1 shows that ET₀ values in the created gridded dataset were close to those calculated from station data, though ET₀ tended to be slightly higher in the gridded data, particularly in late season. The comparison to pan measurements shows that from May to July, calculated ET₀ values were consistently somewhat lower than those measured with evaporation pans, the difference being approximately 20% in Jokioinen and Jyväskylä and 5–10% in Mikkeli. In August and September, calculated ET₀ values were closer to measured pan evaporation, except in Mikkeli where calculated ET₀ was clearly higher in September. This was because in September measured pan evaporation was approximately one third lower in Mikkeli compared to the two other locations.

In contrast, calculated ET0 values were close to each other at all locations (not

shown), even though in Jokioinen and Jyväskylä Rn was measured, while in Mikkeli it was estimated from cloudiness observations. It is also noteworthy that from May to August measured pan evaporation was lower in Mikkeli than in the two other locations although there is no climatological reason for this difference suggesting that there would be a high uncertainty related to the pan evaporation measurements.

It has been previously recognized that pan data should be treated with caution if used to approximate daily potential evapotranspiration. This is because correlations between daily estimates of ET₀ with Eq. (1) and pan data can be poor, though they improve over longer timer periods (Chiew and McMahon, 1992). Moreover, studies conducted in Florida and Canada have indicated that when daily weather data is used with Eq. (1), the equation overestimates ET₀ on annual scale by about 10% compared to pan measurements (Irmak and Haman, 2003; Xing et al., 2008). However, the results of Irmak and Haman (2003) from Florida showed that this overestimation increases with decreasing daylight time. According to their results, from May to July calculated ET₀ was even underestimated by 3%, while the largest overestimation of 52% took place in December. This agrees with our findings, showing the larg-

est underestimation of calculated ET₀ from May to July when daylight time is the longest, actually much longer in Finland than in Florida, which is in accordance with larger underestimation of calculated ET₀. However, in using the constructed evapotranspiration data, it should be kept in mind that calculated ET₀ represents evapotranspiration from a reference surface which may differ to various degree from real environment in Finland.

Monthly maps of average evapotranspiration in Finland. Monthly maps of average ET₀ over the World Meteorological Organization (WMO) normal period 1991–2020 are presented in Fig. 2. In Finland, evapotranspiration is highest in July and June. Moreover, evapotranspiration decreases towards the north in accordance with decreasing temperature and global radiation (Venäläinen and Heikinheimo, 1997; Jokinen et al., 2021).

Data availability. The created gridded dataset will be made freely available for download at https:// en.ilmatieteenlaitos.fi/gridded-observations-on-aws-s3. We will moreover update the dataset to cover also the period 1961–1980.

Acknowledgments: This work was supported by funding provided by the Finnish Ministry of the Environment (SUMI project; decision VN/33334/2021).

Aalto, J., P. Pirinen, and K. Jylhä, 2016: New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. J. Geophys. Res. Atmos., 121, 3807–3823, https://doi.org/10.1002/2015JD024651.

Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration: guidelines for computing crop requirements. FAO Irrigation and Drainage Paper No. 56., https://www.fao.org/3/X0490E/X0490E00.htm.

Allen, R. G., and Coauthors, 2006: A recommendation on standardized surface resistance for hourly calculation of reference ET0 by the FAO56 Penman-Monteith method. Agr. Water Manage., 81, 1–22, https://doi.org/10.1016/j.agwat.2005.03.007.

Chiew, F. H. S. and T. A. McMahon, 1992: An Australian comparison of Penman’s potential evapotranspiration estimates and class A evaporation pan data. Aust. J. Soil. Res., 30, 101–112, https://doi.org/10.1071/SR9920101.

Fisher, J. B., R. J. Whittaker, and Y. Malhi, 2011: ET come home: potential evapotranspiration in geographical ecology. Global Ecol. Biogeogr., 20, 1–18, https://doi.org/10.1111/j.1466-8238.2010.00578.x.

Guo, D., S. Westra, and H. R. Maier, 2016: An R package for modelling actual, potential and reference evapotranspiration. Environ. Modell. Softw., 78, 216–224, https://doi.org/10.1016/j.envsoft.2015.12.019.

Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.

Irmak, S. and D. Z. Haman, 2003: Evaluation of five methods for estimating Class A pan evaporation in a humid climate. HortTechnology, 13, 500–508, https://doi.org/10.21273/HORTTECH.13.3.0500.

Jokinen, P., P. Pirinen, J. Kaukoranta, A. kangas, P. Alenius, P. Eriksson, M. Johansson, and S. Wilkman, 2021: Climatological and oceanographic statistics of Finland 1991–2020. Finnish Meteorological Institute Reports 2021:8, 170 pp, https://doi.org/10.35614/isbn.9789523361485.

Jovanovic, B., D. A. Jones, and D. Collins, 2008: A high-quality monthly pan evaporation dataset for Australia. Clim. Change, 87, 517–535, http:// dx.doi.org/10.1007/s10584-007-9324-6.

McMahon, T. A., M. C. Peel, L. Lowe, R. Srikanthan, and T. R. McVicar, 2013: Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis. Hydrol. Earth Syst. Sci., 17, 1331–1363, https://doi.org/10.5194/hess-17-1331-2013.

Venäläinen, A. and M. Heikinheimo, 1997: The spatial variation of long-term mean global radiation in Finland. Int. J. Climatol., 17, 415–426, https:// doi.org/10.1002/(SICI)1097-0088(19970330)17:4%3C415::AID-JOC138%3E3.0.CO;2-%23.

Xing, Z., L. Chow, F. Meng, H. W. Rees, J. Monteith, and S. Lionel, 2008: Testing reference evaporation estimation methods using evaporation pan and modeling in maritime region of Canada. J. Irrig. Drain. Eng., 134, 417–424, http://doi.org/10.1061/(ASCE)0733-9437(2008)134:4(417).

Yang, H., and Coauthors, 2015: Ecosystem evapotranspiration as a response to climate and vegetation coverage changes in Northwest Yunnan, China. Plos One, 10, e0134795, https://doi.org/10.1371/journal.pone.0134795.

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