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

Modelling spatio-temporal soil moisture dynamics in mountain tundra

Abstract Soil moisture has a fundamental influence on the processes and functions of tundra ecosystems. Yet, the local dynamics of soil moisture are often ignored, due to the lack of fine resolution, spatially extensive data. In this study, we modelled soil moisture with two mechanistic models, SpaFHy (a catchment-scale hydrological model) and JSBACH (a global land surface model), and examined the results in comparison with extensive growing-season field measurements over a mountain tundra area in northwestern Finland. Our results show that soil moisture varies considerably in the study area and this variation creates a mosaic of moisture conditions, ranging from dry ridges (growing season average 12 VWC%, Volumetric Water Content) to water-logged mires (65 VWC%). The models, particularly SpaFHy, simulated temporal soil moisture dynamics reasonably well in parts of the landscape, but both underestimated the range of variation spatially and temporally. Soil properties and topography were important drivers of spatial variation in soil moisture dynamics. By testing the applicability of two mechanistic models to predict fine-scale spatial and temporal variability in soil moisture, this study paves the way towards understanding the functioning of tundra ecosystems under climate change. This article is protected by copyright. All rights reserved.Peer reviewe

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km(2) resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km(2) pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10 degrees C (mean = 3.0 +/- 2.1 degrees C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 +/- 2.3 degrees C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 +/- 2.3 degrees C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.Peer reviewe

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world\u27s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Soil moisture in process-based modeling in cold environments

Maaperän kosteus vaikuttaa lukuisiin ympäristöprosesseihin ja ilmastollisiin tekijöihin. Se on tärkeä osa hydrologista kiertoa, mutta sen alueelliseen ja ajalliseen vaihteluun vaikuttavat tekijät ovat monimutkaisia ja toisiinsa kytkeytyneitä. Maaperän kosteus vaikuttaa usein myös itse sitä säätelevien prosessien voimakkuuteen. Nämä tekijät vaikeuttavat maaperän kosteuden havainnointia ja ennustamista. Kylmissä ympäristöissä kosteuden ennustaminen on erityisen hankalaa, sillä sen on osoitettu vaihtelevan hyvin pienillä mittakaavatasoilla sekä vaikuttavan olennaisesti moniin ekosysteemiprosesseihin. Näiden vaihteluiden ja prosessien vaikutusten tarkasteluun on kehitetty lukuisia hydrologisia prosessimalleja, jotka käsittelevät maaperän kosteutta eri tavoin. Mallien erojen ja lähestymistapojen ymmärtäminen on tärkeää, jotta niitä voidaan käyttää oikein. Tätä tutkimusta varten valittiin kolme prosessimallia, jotka kuvailevat maaperän kosteutta eri tavoin ja jotka on kehitetty erilaisia tutkimuskysymyksiä varten. Näillä malleilla simuloitiin maaperän kosteuden alueellista ja ajallista vaihtelua pienellä tutkimusalueella Luoteis-Lapissa. Näistä JSBACH on globaali maanpinnan geofysikaalisia ja -kemiallisia prosesseja kuvaava malli, jota käytetään mallintamaan maanpinnan ja ilmakehän välisen rajapinnan olosuhteita. SpaFHy on hydrologinen valuma-aluemalli, joka on kehitetty kuvaamaan boreaalisten metsien vesitasetta ja haihduntaa. Ecohydrotools puolestaan on hienon spatiaalisen skaalan vaihteluun keskittyvä hydrologinen malli. Mallitulokset osoittavat selkeitä yhteneväisyyksiä sekä eroja verrattuna toisiinsa ja kentällä tehtyihin kosteusmittauksiin. Mallit kykenivät hahmottamaan kosteiden ja kuivien alueiden laajan skaalan sijoittumisen alueella, vaikkakin kosteuden suuruus vaihteli mallien välillä merkittävästi. Kaikilla malleilla oli ongelmia hienon resoluution alueellisen vaihtelun kanssa, erityisesti kuivemmilla alueilla. Ajallinen vaihtelu osoitti enemmän yhteneväisyyksiä mallien välillä, mutta mittausten ja mallien välillä oli myös selkeitä eroja. Nämä tulokset osoittavat, että monet tekijät vaikuttavat mallin kykyyn mallintaa maaperän kosteuden vaihtelua. Vaaditut ympäristömuuttujat, mallien sisältämät kuvailut prosesseista sekä mallien rakenne ja käyttötarkoitus vaikuttavat kaikki lopputuloksiin ja johtavat vaihteleviin arvioihin maaperän kosteudesta. Mallitulosten kehittäminen kylmillä alueilla vaatii parempaa ymmärrystä maaperän kosteuteen vaikuttavista prosesseista sekä yksityiskohtaisempaa tietoa olennaisista ympäristömuuttujista.Soil moisture influences various environmental and climatological processes and is an important part of the hydrological cycle. The processes influencing its spatial and temporal variation are complex and linked with each other as well as influenced by soil moisture itself which makes observing them challenging. This is especially true in cold regions where soil moisture has shown strong fine scale variation and influences numerous ecosystem processes. To test different hypotheses related to soil moisture and to simulate its variation, several hydrological process-based models have been developed. Understanding how these models differ from each other and how they describe soil moisture is crucial in order to use them effectively. For this study, three process-based models representing varying model approaches and answering different research questions were chosen and used to simulate the spatial and temporal variation of soil moisture in a small study area in northwestern Finland. JSBACH is a global-scale land surface model that simulates various geophysical and geochemical processes over land and in the boundary layer between land surface and the atmosphere. SpaFHy is a catchment scale hydrological model developed to simulate water balance and evapotranspiration in boreal forests. Ecohydrotools is a hydrological model used to study fine scale spatial variation in soil hydrology. The model results show clear similarities as well as differences when compared with each other and with field measurements of soil moisture. The strongest similarities are in distinguishing wetter and drier areas in the study area, although the actual moisture content estimations vary between the models. All models show difficulties in simulating finer scale spatial variation, particularly in drier areas. Temporal variation shows more similarities between the models, although there are also clear discrepancies with measurements and the models. These simulations show that there are several things influencing a model’s capability to simulate soil moisture variation. Varying data requirements, included processes as well as model design and purpose all influence the results, leading to varying estimations of soil moisture. Improving model predictions in cold environments requires better understanding of the underlying processes as well as more detailed information on the environmental variables influencing soil moisture

Microclimate temperature variations from boreal forests to the tundra

Abstract 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 °C) while the corresponding differences for soil temperatures ranged from 10.0 °C (middle boreal forest) to 27.1 °C (tundra). Instantaneous differences in wintertime air temperatures were the largest in the tundra (up to 25.6°C, median 4.2 °C), while in summer the differences were largest in the southern boreal forest (13.1°C, median 4.8°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

Modelling spatio-temporal soil moisture dynamics in mountain tundra

Abstract Soil moisture has a fundamental influence on the processes and functions of tundra ecosystems. Yet, the local dynamics of soil moisture are often ignored, due to the lack of fine resolution, spatially extensive data. In this study, we modelled soil moisture with two mechanistic models, SpaFHy (a catchment-scale hydrological model) and JSBACH (a global land surface model), and examined the results in comparison with extensive growing-season field measurements over a mountain tundra area in northwestern Finland. Our results show that soil moisture varies considerably in the study area and this variation creates a mosaic of moisture conditions, ranging from dry ridges (growing season average 12 VWC%, Volumetric Water Content) to water-logged mires (65 VWC%). The models, particularly SpaFHy, simulated temporal soil moisture dynamics reasonably well in parts of the landscape, but both underestimated the range of variation spatially and temporally. Soil properties and topography were important drivers of spatial variation in soil moisture dynamics. By testing the applicability of two mechanistic models to predict fine-scale spatial and temporal variability in soil moisture, this study paves the way towards understanding the functioning of tundra ecosystems under climate change

Data and code for "High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra"

Repository structure The zipped folder includes the following subfolders: data In-situ measurement data from the plots. Remotely-sensed data could not be included in the repository due to their large size. src R codes to reproduce the data cleaning, prosessing, and statistical analysis steps. results Model parameters, performance statistics, model files, figures, edited tables together with some summary tables produces from upscaling results. raster data and upscaled results Averaged flux, soil moisture and temperature maps for the growing season (July 1-August 2nd, 8 am - 8 pm) as well as static maps produced in this study. All the upscaled results could not be included in the repository due to their large size. Note that the analysis to produce the vegetation classification map are described here: https://github.com/poniitty/kilpisjarvi_vegclas

Using Atmospheric Inverse Modelling of Methane Budgets with Copernicus Land Water and Wetness Data to Detect Land Use-Related Emissions

Climate change mitigation requires countries to report their annual greenhouse gas (GHG) emissions and sinks, including those from land use, land use change, and forestry (LULUCF). In Finland, the LULUCF sector plays a crucial role in achieving net-zero GHG emissions, as the sector is expected to be a net sink. However, accurate estimates of LULUCF-related GHG emissions, such as methane (CH4), remain challenging. We estimated LULUCF-related CH4 emissions in Finland in 2013–2020 by combining national land cover and remote-sensed surface wetness data with CH4 emissions estimated by an inversion model. According to our inversion model, most of Finland’s CH4 emissions were attributed to natural sources such as open pristine peatlands. However, our research indicated that forests with thin tree cover surrounding open peatlands may also be a significant source of CH4. Unlike open pristine peatlands and pristine peatlands with thin tree cover, surrounding transient forests are included in the Finnish GHG inventory if they meet the criteria used for forest land. The current Finnish national GHG inventory may therefore underestimate CH4 emissions from forested organic soils surrounding open peatlands, although more precise methods and data are needed to verify this. Given the potential impact on net GHG emissions, CH4 emissions from transitional forests on organic soils should be further investigated. Furthermore, the results demonstrate the potential of combining atmospheric inversion modelling of GHGs with diverse data sources and highlight the need for methods to more easily combine atmospheric inversions with national GHG inventories