Arktinen kasvillisuus, lumi ja muuttuva ilmasto

The Arctic is warming two to three times faster than the global average. However, climate change is proceeding at different pace between seasons and the warming has been most prominent in winter. For most of the year, majority of the arctic organisms are covered by insulating snowpack. Snow protects arctic plants, bryophytes and lichens from weather events in the free atmosphere and may provide relatively warm and stable overwintering conditions. The importance of snow has been widely acknowledged, but snow information is rather rarely utilized in climate change impact models that predict the future state of the arctic vegetation. This is largely due to missing wintertime datasets and harsh winter conditions that limit field work efforts in the Arctic. Therefore, there has remained a largely unanswered question: what is the role of snow conditions in spatial redistribution of arctic species and vegetation under rapidly warming climate? In this thesis, I address these gaps in knowledge and methodology. I utilise extensive plot-scale vegetation datasets and link these data to detailed microclimatic measurements covering both summer and winter conditions and to satellite-born snow information at fine spatial scales. I use a suite of statistical modelling methods to explore the snow-vegetation relationships in species pools consisting several hundreds of arctic, alpine and boreal vascular plant, bryophyte and lichen species in northern Fennoscandia, Svalbard and western Greenland. These models are further used to predict patterns in species distributions, community and functional trait compositions and biodiversity in space and time, to test the sensitivity of these vegetation properties to concurrent and separate changes in snow conditions and temperatures. I found that snow and winter conditions have a fundamental role in arctic ecosystems by mediating the effects of climate change at local and regional scales. Snow information improves the accuracy of the models of arctic vegetation and reveals possible future trajectories otherwise hidden from climate change impact models if the effects of snow are not quantified. Heterogeneous snow accumulation is one of the main drivers of taxonomic and functional diversity in tundra, and losing the late melting snowbed environments may lead to homogenisation of the tundra and regional extinctions among snow specialist species. It is evident that ignoring the effects of snow can produce biased projections of the future status of arctic vegetation. Given the high ecological importance of snow in the Arctic, it is alarming that the uncertainties in snow projections for the second half of the century are so high. In the upcoming years, the scientific community should pay more attention to plant-snow relationships and interactions and improve the predictions of future snow conditions at fine spatial and temporal scales.Arktiset alueet lämpenevät kaksi, jopa kolme kertaa nopeammin kuin maapallo keskimäärin. Lämpeneminen etenee kuitenkin epätasaisesti vuodenaikojen välillä ja talvet ovat lämmenneet kaikista nopeimmin. Lumipeite suojaa arktisia eliöitä suurimman osan vuodesta. Se eristää lumen alla talvehtivat kasvit ja jäkälät vapaan ilmakehän sääilmiöiltä ja voi luoda verraten lämpimät ja vakaat talviolot. Lumen suuri merkitys pohjoisissa ekosysteemeissä tunnustetaan laajalti, mutta se silti usein sivuutetaan ilmastonmuuttoksen vaikutuksia tutkittaessa ja ennustettaessa. Suurin syy tähän on sopivien talvea ja lunta kuvaavien aineistojen puute. Siksi on laajalti tutkimatta, kuinka muuttuvat lumiolot tulevat vaikuttamaan arktisten lajien levinneisyyksiin ja runsauksiin tulevassa ilmastossa. Tässä työssä tilkitsen näitä aukkoja tiedoissamme. Tutkimusryhmämme on kerännyt kasvillisuusaineistoja pohjoisessa Fennoskandiassa, Huippuvuorilla ja Grönlannissa. Väitöskirjassani linkitän nämä kasvillisuustiedot tarkkoihin mittauksiin niin kesän kuin talven pienilmastosta sekä toistuvista satelliittikuvista irrotettuun lumi-informaatioon. Käytän tilastollisia malleja selvittämään, kuinka nämä ympäristötekijät vaikuttavat satojen pohjoisten putkilokasvi-, sammal- ja jäkälälajien esiintymiseen ja arktisen luonnon monimuotoisuuden alueelliseen jakautumiseen. Tutkin ja mallinnan, kuinka herkkä arktinen kasvillisuus on muutoksille lumipeitteessä erottamalla lumen vaikutukset suorista lämpötilannousun seurauksista. Sain selville, että talvi- ja lumiolosuhteet määräävät ratkaisevalla tavalla, kuinka ilmastonmuutoksen vaikutukset tulevat ilmenemään pohjoisessa luonnossa paikallisilla ja alueellisilla mittakaavatasoilla. Tiedot lumipeitteestä tai talvisesta pienilmastosta parantavat arktisten lajien levinneisyysmalleja ja voivat paljastaa tulevaisuudenkuvia, jotka jäisivät ennustamatta, jos talven olosuhteet jätetään huomiotta. Lumen vaihteleva kasautuminen ja sulaminen avoimella tundralla on yksi tärkeimmistä pohjoisen luonnon monimuotoisuutta ylläpitävistä tekijöistä. Erityisesti myöhään sulavien lumenviipymien katoaminen hävittäisi samalla suuren joukon tähän habitaattiin erikoistuneita lajeja ja yksipuolistaisi tunturimaisemia ja niiden eliöstöä. Näyttää selvältä, että lumen vaikutusten unohtaminen voi tuottaa harhaisia ennusteita pohjoisen luonnon tulevaisuudesta ja siksi tarvitsemme myös aiempaa tarkemman käsityksen siitä, kuinka lumiolot tulevat kehittymään kuluvan vuosisadan aikana

Microclimate relationships of intraspecific trait variation in sub-Arctic plants

Within-species trait variation is a substantial part of plant functional diversity. However, this intraspecific trait variation (ITV) is rarely investigated in relation to a key characteristic of the Arctic and alpine ecosystems: fine-scale microclimatic heterogeneity. Here, we quantified the influence of microclimate (soil moisture, snow and local temperatures) on plant functional traits, specifically on ITV. We focused on six widespread northern latitude vascular plant species, and measured four traits: plant height, leaf area, leaf dry matter content (LDMC) and specific leaf area (SLA). We related ITV to field and remotely sensed microclimate data from 150 study plots within six study grids. The grids were located within a 76-m altitudinal belt in three environments: the tundra, tundra-forest ecotone and mountain birch forest in Kilpisjarvi, northwestern Finland. We compared the range of trait values between this local trait dataset (n = 5493) and global trait databases (n = 10 383). We found that information in the local dataset covers a relatively large portion of the trait ranges in global databases. The proportion varies among traits and species; the largest portion was 74% for variation in leaf area of Vaccinium uliginosum, and the lowest was 19% for LDMC of Betula nana. We found that ITV in height was mostly related to local temperatures, whereas SLA and LDMC were more related to soil moisture and snow conditions. However, species showed contrasting relationships with the microclimate drivers. We conclude that microclimate profoundly shapes ITV in northern latitude plants and that even a very compact geographic area can contain a large amount of ITV. The influence of the microclimatic conditions varies among functional traits and species, which indicates that plastic response or adaptive potential of the species to climate change may also vary across species, but that necessary variation may often be present within local plant populations.Peer reviewe

Arktis-alpiininen kasvillisuus ja maaperän kosteus

Soil moisture regulates a wide range of ecosystem processes at high latitude ecosystems. Soil moisture and temperature control carbon cycle in arctic soils and therefore had impacts on many climate change feedback loops. Arctic-alpine vegetation is adapted for cold and often dry or water saturated soil conditions though the adaptations are species specific. Therefore the aim of this study is to examine how soil conditions, especially moisture, affect on species fine scale distributions in low energy ecosystems. The data contains 21 study grids and holds 378 study plots (1 m2 each) in total. The data is collected during three summers in 2011-2013 at Saana massif in northwestern Finland. Vascular plant, moss and lichen species are sampled from all plots. Soil moisture and temperature are measured in situ, pH is determined from soil samples at a laboratory and radiation is calculated based on the fine scale topography. NMDS-ordination and nominal GBM-models are carried out to study how the explanatory variables affect on species composition. Species richness and diversity are examined by comparing GLM, GAM, and GBM models with the base variables to models which soil moisture is added as a fourth explanatory variable. The same two variable combinations are used to model distributions of individual species in biomod2 platform. Soil moisture and pH were the most effective variables that control vascular plant species composition. Soil moisture was alone the most important variable for mosses but none of the variables showed importance over others in case of lichens. Vascular plant and moss species richness increased with increasing soil moisture. Lichens showed an opposed trend. The community evenness is highest in moist habitats excluding lichens that showed the highest evenness in the driest end of the moisture gradient. Including soil moisture as an explanatory variable into the models improved the predictions of species distribution models in every species group. Vascular plants, mosses, forbs and decidious dwarf shrubs responded positively to soil moisture. Lichens had mostly negative and evergreen dwarf shrubs mostly unimodal response curves towards soil moisture. Soil moisture was the single most important variable in species distribution models but there was a lot of variation between the individual species. Soil moisture is the most important environmental variable that controls species distributions and vegetation characteristics at fine scale in the arctic-alpine environment. The moist habitats maintain the highest vascular plant and moss species pools and are therefore the most important ones for diversity on landscape level. Lichens are distributed more randomly and variables used in this study failed to model lichens as accurately as the other species groups. Most of the lichen species favored dry and acidic soil conditions but results could be due to low competition through low productivity, not direct effects on lichens survival. The individual species vary strongly in their responses to the environmental variables but the different growth forms appears to react quite similarly towards the explanatory variables. This research supports the idea of environmental heterogenity as an important factor for species distributions and confirms the need for local and fine scale studies. According to results of this study soil moisture should be included in species distribution models when predicting climate change effects on the arctic-alpine vegetation.Maaperän kosteus vaihtelee voimakkaasti jo lyhyellä välimatkalla topografisesti heterogeenisessä arktis-alpiinisessa ympäristössä. Maaperän kosteus vaikuttaa moniin ekosysteemiprosesseihin ja resursseihin puskuroimalla lämpötilan vaihtelua ja säätelemällä hajotuksen nopeutta sekä hiilen kiertoa maaperän ja ilmakehän välillä. Arktis-alpiininen kasvillisuus on lajikohtaisesti sopeutunut kuivuuden tai veden kyllästämän maan aiheuttamaan stressiin, joten voi olettaa, että maaperän kosteus säätelee kasvillisuuden piirteiden ja yksittäisten lajien hienon mittakaavan esiintymiskuvaa. Kasvillisuuden piirteiden ja yksittäisten lajien esiintymistä tutkittiin 378 yhden neliömetrin kasvillisuusruutua kattavalla aineistolla, joka on kerätty kesinä 2011-2013 Kilpisjärven Saanatunturin rinteiltä. Jokaiselta tutkimusruudulta on kartoitettu putkilokasvi-, sammal- ja jäkälälajien peittävyydet, mitattu maaperän kosteus ja lämpötila sekä otettu maaperänäyte pH-analyysiä varten. Ruutujen vieton ja viettosuunnan mukaan ruuduille on laskettu vuosittaisen potentiaalisen saapuvan auringonsäteilyn määrä. Lajikoostumukseen ja -yhteisöön vaikuttavien ympäristömuuttujien merkitystä on tutkittu NMDS-ordinaatiomenetelmällä ja GBM-malleilla kasvillisuusluokkien esiintymistä mallintaen. Lajirunsauden ja yhteisön tasaisuuden vaihtelua tutkittiin kilpailuttamalla GLM-, GAM- ja GBM-malleissa perusmallia ja maaperänkosteudella lisättyä mallia keskenään koko aineistolla sekä kasvillisuusluokkien ja topografialuokkien sisällä. Kosteusluokkia ja kumuloituvia laji-alue-käyriä tarkastelemalla tutkittiin, miten ympäristömuuttujat vaikuttavat lajipoolin kokoon. Yksittäisten lajien esiintymistä mallinnettiin kymmentä levinneisyysmallia käyttäen perus- ja täysmalleilla, joiden ennustekykykyjen eroja vertailtiin. Lajien vastekäyrien muotoja ja ympäristömuuttujien suhteellisia tärkeyksiä tarkasteltiin lajiryhmien ja ekologisten jakojen kautta. Putkilokasveilla kosteus ja pH säätelevät lajikoostumusta. Sammalilla kosteus on yksin tärkein muuttuja, kun taas jäkälillä yksikään muuttuja ei selitä voimakkaasti lajiyhteisön koostumusta. Putkilokasveilla ja sammalilla lajirunsaus ja yhteisön tasaisuus kasvavat maaperän kosteuden lisääntyessä. Jäkälillä kosteuden efekti on päinvastainen. Maaperän kosteus selittää myös kasviyhteisöjen sisäistä lajirunsauden vaihtelua, mutta ei jokaisessa yhteisössä tai mesotopografisen gradientin osassa. Maaperän kosteus parantaa yksittäisten lajien levinneisyysmallien hyvyyttä jokaisella lajiryhmällä. Putkilokasveilla, sammalilla, ruohoilla ja kesävihannilla varvuilla vaste kosteuden muutokseen oli yleisimmin positiivinen, jäkälillä negatiivinen ja talvivihannilla varvuilla yksihuippuinen. Graminoideilla löytyi tasaisesti monenmuotoisia vasteita kosteuden muutokseen. Kosteudella on keskimäärin suurin vaikutus lajien esiintymiseen, mutta yksittäisten lajien välillä on suuria eroja vastekäyrien muodoissa ja muuttujien suhteellisissa tärkeyksissä myös kasvumuotojen sisällä. Kosteuden hienopiirteinen vaihtelu on tärkein yksittäinen ympäristötekijä arktis-alpiinisen kasvillisuuden piirteiden ja yksittäisten lajien esiintymiskuvien säätelijänä. Kosteat alueet ylläpitävät suurempaa lajipoolia putkilokasveilla ja sammalilla, kun taas jäkälillä lajiyhteisö käyttäytyy ympäristöön nähden satunnaisemmin. Jäkälien lajirunsaus on korkeimmillaan kuivemmissa habitaateissa, joissa muiden lajiryhmien aiheuttama kilpailu on kuivuusstressin takia pienimmillään. Eri lajit ovat sopeutuneet erilaiseen ympäristön tuomaan stressiin, eikä yhden lajiryhmän perusteella voida ennustaa toisen lajiryhmän, taikka yksittäisen lajin esiintymistä. Kasvumuodot kuitenkin reagoivat ympäristöönsä kohtuullisen samankaltaisesti. Maaperän kosteus ja sen hienopiirteinen vaihtelu on otettava huomioon arvioitaessa ilmastonmuutoksen vaikutuksia pohjoiseen kasvillisuuteen. Kokeellista tutkimusta on suunnattava osin lämpötilasta kosteuden manipulointiin ja ympäristömuuttujien yhteisvaikutusten arviointiin. Makroilmaston muutosten vaikutus paikalliseen maaperän kosteuteen on selvitettävä nykyistä tarkemmin

Modelling soil moisture in a high-latitude landscape using LiDAR and soil data

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non-climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape-scale soil moisture variation by utilizing high-resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high-latitude landscape of mountain tundra in north-western Finland. We measured the plots three times during growing season 2016 with a hand-held time-domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R-2 = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R-2 = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R-2 = 0.47 and RMSE 9.34 VWC%, and for the latter R-2 = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high-resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1m(2) digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine-scale soil moisture variation. In the temporal variation models, the strongest predictor was the field-quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright (c) 2017 John Wiley & Sons, Ltd.Peer reviewe

Spatial confounding in Bayesian species distribution modeling

1) Species distribution models (SDMs) are currently the main tools to derive species niche estimates and spatially explicit predictions for species geographical distribution. However, unobserved environmental conditions and ecological processes may confound the model estimates if they have direct impact on the species and, at the same time, they are correlated with the observed environmental covariates. This, so-called spatial confounding, is a general property of spatial models and it has not been studied in the context of SDMs before. 2) We examine how the estimation accuracy of SDMs depends on the type of spatial confounding. We construct two simulation studies where we alter spatial structures of the observed and unobserved covariates and the level of dependence between them. We fit generalized linear models with and without spatial random effects applying Bayesian inference and recording the bias induced to model estimates by spatial confounding. After this we examine spatial confounding also with real vegetation data from northern Norway. 3) Our results show that model estimates for coarse scale covariates, such as climate covariates, are likely to be biased if a species distribution depends also on an unobserved covariate operating on a finer spatial scale. Pushing higher probability for a relatively weak and smoothly varying spatial random effect compared to the observed covariates improved the model's estimation accuracy. The improvement was independent of the actual spatial structure of the unobserved covariate. 4) Our study addresses the major factors of spatial confounding in SDMs and provides a list of recommendations for pre-inference assessment of spatial confounding and for inference-based methods to decrease the chance of biased model estimates.Peer reviewe

Relationships between above-ground plant traits and carbon cycling in tundra plant communities

The trait composition and trait diversity of plant communities are globally applicable predictors of ecosystem functioning. Yet, it is unclear how plant traits influence carbon cycling. This is an important question in the tundra where vegetation shifts are occurring across the entire biome, and where soil organic carbon stocks are large and vulnerable to environmental change. To study how plant traits affect carbon cycling in the tundra, we built a model that explained carbon cycling (above-ground and soil organic carbon stocks, and photosynthetic and respiratory fluxes) with abiotic conditions (air temperature and soil moisture), and the averages and within-community variabilities of three above-ground traits: plant height, leaf dry matter content (LDMC) and SLA. These functional parameters were represented by abundance-weighted means and standard deviations of species traits. The data were collected from an observational study setting from northern Finland. The explanatory power of the models was relatively high, but a large part of variation in soil organic carbon stocks remained unexplained. Average plant height was the strongest predictor of all carbon cycling variables except soil carbon stocks. Communities of larger plants were associated with larger CO2 fluxes and above-ground carbon stocks. Communities with fast leaf economics (i.e. high SLA and low LDMC) had higher photosynthesis, ecosystem respiration and soil organic carbon stocks. Within-community variability in plant height, SLA and LDMC affected ecosystem functions differently. Variability in SLA and LDMC increased CO2 fluxes and soil organic carbon stocks, while variability in height increased the above-ground carbon stock. The contributions of within-community trait variability metrics to ecosystem functioning within the study area were about as important as those of average SLA and LDMC. Synthesis. Plant height, SLA and LDMC have clear effects on tundra carbon cycling. The importance of within-community trait variability highlights a potentially important mechanism controlling the vast tundra carbon pools that should be better recognized. More research on root traits and decomposer communities is needed to understand the below-ground mechanisms regulating carbon cycling in the tundra.Peer reviewe

Lost at high latitudes : Arctic and endemic plants under threat as climate warms

Aim: Species' biogeographical patterns are already being altered by climate change. Here, we provide predictions of the impacts of a changing climate on species' geographical ranges within high-latitude mountain flora on a sub-continental scale. We then examined the forecasted changes in relation to species' biogeographic histories. Location: Fennoscandia, Northern Europe (55-72 degrees N). Methods: We examined the sensitivity of 164 high-latitude mountain species to changing climate by modelling their distributions in regard to climate, local topography and geology at a 1 km(2) resolution. Using an ensemble of six statistical modelling techniques and data on current (1981-2010) and future (2070-2099) climate based on three Representative Concentration Pathways (RCPs 2.6, 4.5, 8.5), we developed projections of current and future ranges. Results: The average species richness of the mountain flora is predicted to decrease by 15%-47% per 1 km(2) cell, depending on the climate scenario considered. Arctic flora is projected to undergo severe range loss along with non-poleward range contractions, while alpine flora is forecasted to find suitable habitat in a warmer North. A substantial majority (71%-92%) of the studied species are projected to lose more than half of their present range by the year 2100. Species predicted to lose all suitable habitat had ranges centred in the northernmost (>68 degrees N) part of continental Europe. Main conclusions: Climate change is predicted to substantially diminish the extent and richness of Europe's high-latitude mountain flora. Interestingly, species' biogeographic histories affect their vulnerability to climate change. The vulnerability of true Arctic and endemic species marks them as highly important for conservation decisions.Peer reviewe

Dwarf Shrubs Impact Tundra Soils : Drier, Colder, and Less Organic Carbon

In the tundra, woody plants are dispersing towards higher latitudes and altitudes due to increasingly favourable climatic conditions. The coverage and height of woody plants are increasing, which may influence the soils of the tundra ecosystem. Here, we use structural equation modelling to analyse 171 study plots and to examine if the coverage and height of woody plants affect the growing-season topsoil moisture and temperature (< 10 cm) as well as soil organic carbon stocks (< 80 cm). In our study setting, we consider the hierarchy of the ecosystem by controlling for other factors, such as topography, wintertime snow depth and the overall plant coverage that potentially influence woody plants and soil properties in this dwarf shrub-dominated landscape in northern Fennoscandia. We found strong links from topography to both vegetation and soil. Further, we found that woody plants influence multiple soil properties: the dominance of woody plants inversely correlated with soil moisture, soil temperature, and soil organic carbon stocks (standardised regression coefficients = - 0.39; - 0.22; - 0.34, respectively), even when controlling for other landscape features. Our results indicate that the dominance of dwarf shrubs may lead to soils that are drier, colder, and contain less organic carbon. Thus, there are multiple mechanisms through which woody plants may influence tundra soils.Peer reviewe

Microclimate temperature variations from boreal forests to the tundra

Microclimate varies greatly over short horizontal and vertical distances, and timescales. This multi-level heterogeneity influences terrestrial biodiversity and ecosystem functions by determining the ambient environment where organisms live in. Fine-scale heterogeneity in microclimate temperatures is driven by local topography, land and water cover, snow, and soil characteristics. However, their relative influence over boreal and tundra biomes and in different seasons, has not been comprehensively quantified. Here, we aim to (1) quantify temperature variations measured at three heights: soil (-6 cm), near-surface (15 cm) and air (150 cm), and (2) determine the relative influence of the environmental variables in driving thermal variability. We measured temperature at 446 sites within seven focus areas covering large macroclimatic, topographic, and ecosystem gradients (tundra, mires, forests) of northern Europe. Our data, consisting of over 60 million temperature readings during the study period of 2019/11-2020/10, reveal substantial thermal variability within and across the focus areas. Near-surface temperatures in the tundra showed the greatest instantaneous differences within a given focus area (32.3 degrees C) while the corresponding differences for soil temperatures ranged from 10.0 degrees C (middle boreal forest) to 27.1 degrees C (tundra). Instantaneous differences in wintertime air temperatures were the largest in the tundra (up to 25.6 degrees C, median 4.2 degrees C), while in summer the differences were largest in the southern boreal forest (13.1 degrees C, median 4.8 degrees C). Statistical analyses indicate that monthly-aggregated temperature variations in boreal forests are closely linked to water bodies, wetlands, and canopy cover, whereas in the tundra, variation was linked to elevation, topographic solar radiation, and snow cover. The results provide new understanding on the magnitude of microclimate temperature variability and its seasonal drivers and will help to project local impacts of climate change on boreal forest and tundra ecosystems.Peer reviewe

Bioclimatic atlas of the terrestrial Arctic

The Arctic is the region on Earth that is warming at the fastest rate. In addition to rising means of temperature-related variables, Arctic ecosystems are affected by increasingly frequent extreme weather events causing disturbance to Arctic ecosystems. Here, we introduce a new dataset of bioclimatic indices relevant for investigating the changes of Arctic terrestrial ecosystems. The dataset, called ARCLIM, consists of several climate and event-type indices for the northern high-latitude land areas > 45 degrees N. The indices are calculated from the hourly ERA5-Land reanalysis data for 1950-2021 in a spatial grid of 0.1 degree (similar to 9 km) resolution. The indices are provided in three subsets: (1) the annual values during 1950-2021; (2) the average conditions for the 1991-2020 climatology; and (3) temporal trends over 1951-2021. The 72-year time series of various climate and event-type indices draws a comprehensive picture of the occurrence and recurrence of extreme weather events and climate variability of the changing Arctic bioclimate.Peer reviewe