Climate change and the future distribution of palsa mires : ensemble modelling, probabilities and uncertainties

Palsas are mounds with a permafrost core covered by peat. They occur in subarctic palsa mires, which are ecologically valuable mire complexes located at the outer margin of the permafrost zone. Palsas are expected to undergo rapid changes under global warming. This study presents an assessment of the potential impacts of climate change on the spatial distribution of palsa mires in northern Fennoscandia during the 21st century. A large ensemble of statistical climate envelope models was developed, each model defining the relationship between palsa occurrences and a set of temperature- and precipitation-based indicators. The models were used to project areas suitable for palsas in the future. The sensitivity of these models to changes in air temperature and precipitation was analysed to construct impact response surfaces. These were used to assess the behaviour of models when extrapolated into changed climate conditions, so that new criteria, in addition to conventional model evaluation statistics, could be defined for determining model reliability. A special focus has been on comparing alternative methods of representing future climate, applying these with impact models and quantifying different sources of uncertainty in the assessment. Climate change projections were constructed from output of coupled atmosphere-ocean general circulation models as well as finer resolution regional climate models and uncertainties in applying these with impact models were explored. New methods were developed to translate probabilistic climate change projections to probabilistic estimates of impacts on palsas. In addition to future climate, structural differences in impact models appeared to be a major source of uncertainty. However, using the model judged most reliable according to the new criteria, results indicated that the area with suitable climatic conditions for palsas can be expected to shrink considerably during the 21st century, disappearing entirely for an increase in mean annual air temperature of 4°C relative to the period 1961-1990. The risk of this occurring by the end of the 21st century was quantified to be between 43% (for the B1 low emissions scenario) and 100% (for the A2 high emissions scenario). The projected changes in areas suitable for palsas are expected to have a significant influence on the biodiversity of subarctic mires and are likely to affect the regional carbon budget

Rapid spread of the wasp spider Argiope bruennichi across Europe: a consequence of climate change?

Numerous species are expanding their ranges towards the North Pole, a pattern that is usually explained with climate change. However, few studies have actually tested the potential role of climate in such range expansions. Here, we studied the wasp spider Argiope bruennichi, which has multiplied its range in Central and Northern Europe during the 20th century and is still spreading. Using current and historical climate data, we analysed whether this spread can be explained by climate warming, increasing cold tolerance or if it is unrelated to temperature. Spatial partial regression showed that the spread of A. bruennichi into formerly cooler areas is independent of spatial autocorrelation, indicating that it is driven by temperature. Some aspects of the spread, as e.g. the patchy distribution at the beginning of the century are likely to be relicts of climate fluctuations before our study period. From the middle of the 20th century until the 1980s, A. bruennichi was recorded from gradually cooler climates, while temperature was relatively constant. This indicates that A. bruennichi either increased its cold tolerance or that the spread continued with a time lag following an earlier warming event, due to dispersal limitation. In the last two decades, temperature rose sharply. The temperatures at which A. bruennichi was newly recorded increased as well, indicating that the spider is dispersal limited and that the spread will continue even in the absence of further climate warmin

Ilmastonmuutos ja vieraslajien leviäminen Suomeen – Tutkimustiedon synteesi ja suurilmastollinen vertailu

Vieraslajit ovat maailmanlaajuinen ympäristöongelma, joilla on huomattavia ekologisia, taloudellisia ja terveydellisiä haittavaikutuksia. Aggressiivisesti leviävät haitalliset vieraslajit ovat elinympäristöjen häviämisen jälkeen toiseksi suurin syy luonnon monimuotoisuuden vähenemiseen. Ilmastonmuutos tulee voimistamaan useilla alueilla vieraslajien leviämistä, sekä vahvistamaan niiden kykyä muodostaa elinvoimaisia populaatioita luonnossa ja aiheuttaa merkittäviä haittoja alkuperäiselle lajistolle. Tätä kehitystä voimistaa kasvava kansainvälinen kauppa ja liikenne. Tässä työssä selvitetään ilmastonmuutoksen ja vieraslajien yhteyksiä sekä ilmastonmuutoksen vaikutuksia Suomen vieraslajien tilanteeseen kolmesta eri näkökulmasta; (1) Laaja kirjallisuusselvitys kokoaa yhteen tuoreen tiedon Euroopan vieraslajeista. Erityisesti tarkastellaan mihin seikkoihin vieraslajien voimakas leviäminen perustuu, minkälaisista lajeista tulee haitallisesti luonnonympäristöihin leviäviä vieraslajeja ja miten ilmastonmuutos vaikuttaa vieraslajien leviämiseen ja niiden torjuntaan; (2) Euroopan ilmastoskenaarioiden perusteella arvioidaan sitä, miltä alueilta uusia vieraslajeja voi levitä Suomeen ilmastonmuutoksen myötä ja mitkä näistä lajeista ovat haittavaikutuksiltaan merkittävimpiä; (3) Globaalien suurilmastollisten vertailujen avulla selvitetään mitkä maantieteelliset alueet ovat kaikkein todennäköisimpiä haitallisten vieraslajien lähtöalueita eli miltä alueilta voi nykyään ja tulevaisuudessa levitä uusia Suomen luonnossa menestyviä vieraslajeja

Decomposing sources of uncertainty in climate change projections of boreal forest primary production

We are bound to large uncertainties when considering impacts of climate change on forest productivity. Studies formally acknowledging and determining the relative importance of different sources of this uncertainty are still scarce, although the choice of the climate scenario, and e.g. the assumption of the CO2 effects on tree water use can easily result in contradicting conclusions of future forest productivity. In a large scale, forest productivity is primarily driven by two large fluxes, gross primary production (GPP), which is the source for all carbon in forest ecosystems, and heterotrophic respiration. Here we show how uncertainty of GPP projections of Finnish boreal forests divides between input, mechanistic and parametric uncertainty. We used the simple semi-empirical stand GPP and water balance model PRELES with an ensemble of downscaled global circulation model (GCM) projections for the 21st century under different emissions and forcing scenarios (both RCP and SRES). We also evaluated the sensitivity of assumptions of the relationships between atmospheric CO2 concentration (C-a), photosynthesis and water use of trees. Even mean changes in climate projections of different meteorological variables for Finland were so high that it is likely that the primary productivity of forests will increase by the end of the century. The scale of productivity change largely depends on the long-term C-a fertilization effect on GPP and transpiration. However, GCM variability was the major source of uncertainty until 2060, after which emission scenario/pathway became the dominant factor. Large uncertainties with a wide range of projections can make it more difficult to draw ecologically meaningful conclusions especially on the local to regional scales, yet a thorough assessment of uncertainties is important for drawing robust conclusions.We are bound to large uncertainties when considering impacts of climate change on forest productivity. Studies formally acknowledging and determining the relative importance of different sources of this uncertainty are still scarce, although the choice of the climate scenario, and e.g. the assumption of the CO2 effects on tree water use can easily result in contradicting conclusions of future forest productivity. In a large scale, forest productivity is primarily driven by two large fluxes, gross primary production (GPP), which is the source for all carbon in forest ecosystems, and heterotrophic respiration. Here we show how uncertainty of GPP projections of Finnish boreal forests divides between input, mechanistic and parametric uncertainty. We used the simple semi-empirical stand GPP and water balance model PRELES with an ensemble of downscaled global circulation model (GCM) projections for the 21st century under different emissions and forcing scenarios (both RCP and SRES). We also evaluated the sensitivity of assumptions of the relationships between atmospheric CO2 concentration (C-a), photosynthesis and water use of trees. Even mean changes in climate projections of different meteorological variables for Finland were so high that it is likely that the primary productivity of forests will increase by the end of the century. The scale of productivity change largely depends on the long-term C-a fertilization effect on GPP and transpiration. However, GCM variability was the major source of uncertainty until 2060, after which emission scenario/pathway became the dominant factor. Large uncertainties with a wide range of projections can make it more difficult to draw ecologically meaningful conclusions especially on the local to regional scales, yet a thorough assessment of uncertainties is important for drawing robust conclusions.Peer reviewe

Using impact response surfaces to analyse the likelihood of impacts on crop yield under probabilistic climate change

Conventional methods of modelling impacts of future climate change on crop yields often rely on a limited selection of projections for representing uncertainties in future climate. However, large ensembles of climate projections offer an opportunity to estimate yield responses probabilistically. This study demonstrates an approach to probabilistic yield estimation using impact response surfaces (IRSs). These are constructed from a set of sensitivity simulations that explore yield responses to a wide range of changes in temperature and precipitation. Options for adaptation and different levels of future atmospheric carbon dioxide concentration [CO2] defined by representative concentration pathways (RCP4.5 and RCP8.5) were also considered. Model-based IRSs were combined with probabilistic climate projections to estimate impact likelihoods for yields of spring barley (Hordeum vulgare L.) in Finland during the 21st century. Probabilistic projections of climate for the same RCPs were overlaid on IRSs for corresponding [CO2] levels throughout the century and likelihoods of yield shortfall calculated with respect to a threshold mean yield for the baseline (1981–2010). Results suggest that cultivars combining short pre- and long post-anthesis phases together with earlier sowing dates produce the highest yields and smallest likelihoods of yield shortfall under future scenarios. Higher [CO2] levels generally compensate for yield losses due to warming under the RCPs. Yet, this does not happen fully under the more moderate warming of RCP4.5 with a weaker rise in [CO2], where there is a chance of yield shortfall throughout the century. Under the stronger warming but more rapid [CO2] increase of RCP8.5, the likelihood of yield shortfall drops to zero from mid-century onwards. Whilst the incremental IRS-based approach simplifies the temporal and cross-variable complexities of projected climate, it was found to offer a close approximation of evolving future likelihoods of yield impacts in comparison to a more conventional scenario-based approach. The IRS approach is scenario-neutral and existing plots can be used in combination with any new scenario that falls within the sensitivity range without the need to perform new runs with the impact model. A single crop model is used for demonstration, but an ensemble IRS approach could additionally capture impact model uncertainties.peerReviewe

Effects of changing climate on the hydrology of a boreal catchment and lake DOC : probabilistic assessment of a dynamic model chain

Peer reviewe

Modelling population structure in the context of urban land use change in Europe

Population structure and dynamics are important drivers of land use. In this article, we present the methods and outcomes of integrating population projections across multiple spatial scales with an urban growth model. By linking shared socioeconomic pathway (SSP)-specific national population projections to present-day population distributions at a sub-national scale, we describe a downscaling approach that provides input into a regional urban growth (RUG) model for Europe. The allocation of population acts as a key driver for residential urban demand especially in the SSP5-based scenario, and therefore regional (sub-national) urban growth. Sub-national population trends can deviate strongly from national averages stemming from current population age structures: this creates different urban land use patterns and demand for artificial surfaces. We see strong population dependence in the regional development of urban areas across Europe, and the effects caused by age structure and sub-national population dynamics