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The leaf economics spectrum[1,2] and the global spectrum of plant forms and functions[3] revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species[2]. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities[4]. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability[4,5]. Here we derive a set of ecosystem functions[6] from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems[7,8]. © 2021. The Author(s).

The response of evapotranspiration (ET) to warming is of critical importance to the water and carbon cycle of the boreal biome, a mosaic of land cover types dominated by forests and peatlands. The effect of warming-induced vapour pressure deficit (VPD) increases on boreal ET remains poorly understood because peatlands are not specifically represented as plant functional types in Earth system models. Here we show that peatland ET increases more than forest ET with increasing VPD using observations from 95 eddy covariance tower sites. At high VPD of more than 2 kPa, peatland ET exceeds forest ET by up to 30%. Future (2091–2100) mid-growing season peatland ET is estimated to exceed forest ET by over 20% in about one-third of the boreal biome for RCP4.5 and about two-thirds for RCP8.5. Peatland-specific ET responses to VPD should therefore be included in Earth system models to avoid biases in water and carbon cycle projections.Climate warming increases evapotranspiration (ET) more in boreal peatlands than in forests. Observations show that peatland ET can exceed forest ET by up to 30%, indicating a stronger warming response in peatlands. Earth system models do not fully account for peatlands and hence may underestimate future boreal ET.

Freshwaters bring a notable contribution to the global carbon budget by emitting both carbon dioxide (CO2) and methane (CH4) to the atmosphere. Global estimates of freshwater emissions traditionally use a wind-speed-based gas transfer velocity, kCC (introduced by Cole and Caraco, 1998), for calculating diffusive flux with the boundary layer method (BLM). We compared CH4 and CO2 fluxes from BLM with kCC and two other gas transfer velocities (kTE and kHE), which include the effects of water-side cooling to the gas transfer besides shear-induced turbulence, with simultaneous eddy covariance (EC) and floating chamber (FC) fluxes during a 16-day measurement campaign in September 2014 at Lake Kuivajärvi in Finland. The measurements included both lake stratification and water column mixing periods. Results show that BLM fluxes were mainly lower than EC, with the more recent model kTE giving the best fit with EC fluxes, whereas FC measurements resulted in higher fluxes than simultaneous EC measurements. We highly recommend using up-to-date gas transfer models, instead of kCC, for better flux estimates. BLM CO2 flux measurements had clear differences between daytime and night-time fluxes with all gas transfer models during both stratified and mixing periods, whereas EC measurements did not show a diurnal behaviour in CO2 flux. CH4 flux had higher values in daytime than night-time during lake mixing period according to EC measurements, with highest fluxes detected just before sunset. In addition, we found clear differences in daytime and night-time concentration difference between the air and surface water for both CH4 and CO2. This might lead to biased flux estimates, if only daytime values are used in BLM upscaling and flux measurements in general. FC measurements did not detect spatial variation in either CH4 or CO2 flux over Lake Kuivajärvi. EC measurements, on the other hand, did not show any spatial variation in CH4 fluxes but did show a clear difference between CO2 fluxes from shallower and deeper areas. We highlight that while all flux measurement methods have their pros and cons, it is important to carefully think about the chosen method and measurement interval, as well as their effects on the resulting flux.

The Arctic–Boreal Zone is rapidly warming, impacting its large soil carbon stocks. Here we use a new compilation of terrestrial ecosystem CO 2 fluxes, geospatial datasets and random forest models to show that although the Arctic–Boreal Zone was overall an increasing terrestrial CO 2 sink from 2001 to 2020 (mean ± standard deviation in net ecosystem exchange, −548 ± 140 Tg C yr −1 ; trend, −14 Tg C yr −1 ; P  < 0.001), more than 30% of the region was a net CO 2 source. Tundra regions may have already started to function on average as CO 2 sources, demonstrating a shift in carbon dynamics. When fire emissions are factored in, the increasing Arctic–Boreal Zone sink is no longer statistically significant (budget, −319 ± 140 Tg C yr −1 ; trend, −9 Tg C yr −1 ), and the permafrost region becomes CO 2 neutral (budget, −24 ± 123 Tg C yr −1 ; trend, −3 Tg C yr −1 ), underscoring the importance of fire in this region. How the carbon stocks of the Arctic–Boreal Zone change with warming is not well understood. Here the authors show that wildfires and large regional differences in net carbon fluxes offset the overall increasing CO 2 uptake.

A one-dimensional (1-D) model for an enclosed basin (lake) is presented, which reproduces temperature, horizontal velocities, oxygen, carbon dioxide and methane in the basin. All prognostic variables are treated in a unified manner via a generic 1-D transport equation for horizontally averaged property. A water body interacts with underlying sediments. These sediments are represented by a set of vertical columns with heat, moisture and CH4 transport inside. The model is validated vs. a comprehensive observational data set gathered at Kuivajärvi Lake (southern Finland), demonstrating a fair agreement. The value of a key calibration constant, regulating the magnitude of methane production in sediments, corresponded well to that obtained from another two lakes. We demonstrated via surface seiche parameterization that the near-bottom turbulence induced by surface seiches is likely to significantly affect CH4 accumulation there. Furthermore, our results suggest that a gas transfer through thermocline under intense internal seiche motions is a bottleneck in quantifying greenhouse gas dynamics in dimictic lakes, which calls for further research.

We determine the annual timing of spring recovery from spaceborne microwave radiometer observations across northern hemisphere boreal evergreen forests for 1979–2014. We find a trend of advanced spring recovery of carbon uptake for this period, with a total average shift of 8.1 d (2.3 d/decade). We use this trend to estimate the corresponding changes in gross primary production (GPP) by applying in situ carbon flux observations. Micrometeorological CO₂ measurements at four sites in northern Europe and North America indicate that such an advance in spring recovery would have increased the January–June GPP sum by 29 g·C·m−2 [8.4 g·C·m−2 (3.7%)/decade]. We find this sensitivity of the measured springtime GPP to the spring recovery to be in accordance with the corresponding sensitivity derived from simulations with a land ecosystem model coupled to a global circulation model. The model-predicted increase in springtime cumulative GPP was 0.035 Pg/decade [15.5 g·C·m−2 (6.8%)/decade] for Eurasian forests and 0.017 Pg/decade for forests in North America [9.8 g·C·m−2 (4.4%)/decade]. This change in the springtime sum of GPP related to the timing of spring snowmelt is quantified here for boreal evergreen forests.

Tundra and boreal ecosystems encompass the northern circumpolar permafrost region and are experiencing rapid environmental change with important implications for the global carbon (C) budget. We analysed multi-decadal time series containing 302 annual estimates of carbon dioxide (CO 2 ) flux across 70 permafrost and non-permafrost ecosystems, and 672 estimates of summer CO 2 flux across 181 ecosystems. We find an increase in the annual CO 2 sink across non-permafrost ecosystems but not permafrost ecosystems, despite similar increases in summer uptake. Thus, recent non-growing-season CO 2 losses have substantially impacted the CO 2 balance of permafrost ecosystems. Furthermore, analysis of interannual variability reveals warmer summers amplify the C cycle (increase productivity and respiration) at putatively nitrogen-limited sites and at sites less reliant on summer precipitation for water use. Our findings suggest that water and nutrient availability will be important predictors of the C-cycle response of these ecosystems to future warming. The future of carbon dynamics in the northern high latitudes is uncertain yet represents an important potential feedback under climate change. This study uses a comprehensive observational dataset to show an increasing carbon sink in non-permafrost systems; in permafrost systems uptake was offset by loss.

Wetland methane (CH 4 ) emissions ( F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between F C H 4 and temperature using observations from the FLUXNET-CH 4 database. Measurements collected across the globe show substantial seasonal hysteresis between F C H 4 and temperature, suggesting larger F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH 4 production are thus needed to improve global CH 4 budget assessments. Wetland methane emissions contribute to global warming, and are oversimplified in climate models. Here the authors use eddy covariance measurements from 48 global sites to demonstrate seasonal hysteresis in methane-temperature relationships and suggest the importance of microbial processes.

Boreal forests comprise 73% of the world’s coniferous forests. Based on forest floor measurements, they have been considered a significant natural sink of methane (CH 4 ) and a natural source of nitrous oxide (N 2 O), both of which are important greenhouse gases. However, the role of trees, especially conifers, in ecosystem N 2 O and CH 4 exchange is only poorly understood. We show for the first time that mature Scots pine ( Pinus sylvestris L.) trees consistently emit N 2 O and CH 4 from both stems and shoots. The shoot fluxes of N 2 O and CH 4 exceeded the stem flux rates by 16 and 41 times, respectively. Moreover, higher stem N 2 O and CH 4 fluxes were observed from wet than from dry areas of the forest. The N 2 O release from boreal pine forests may thus be underestimated and the uptake of CH 4 may be overestimated when ecosystem flux calculations are based solely on forest floor measurements. The contribution of pine trees to the N 2 O and CH 4 exchange of the boreal pine forest seems to increase considerably under high soil water content, thus highlighting the urgent need to include tree-emissions in greenhouse gas emission inventories.

Large variability is inherent to turbulent flux observations. We review different methods used to estimate the flux random errors. Flux errors are calculated using measured turbulent and simulated artificial records. We recommend two flux errors with clear physical meaning: the flux error of the covariance, defining the error of the measured flux as 1 standard deviation of the random uncertainty of turbulent flux observed over an averaging period of typically 30 min to 1 h duration; and the error of the flux due to the instrumental noise. We suggest that the numerical approximation by Finkelstein and Sims (2001) is a robust and accurate method for calculation of the first error estimate. The method appeared insensitive to the integration period and the value 200 s sufficient to obtain the estimate without significant bias for variety of sites and wide range of observation conditions. The filtering method proposed by Salesky et al. (2012) is an alternative to the method by Finkelstein and Sims (2001) producing consistent, but somewhat lower, estimates. The method proposed by Wienhold et al. (1995) provides a good approximation to the total flux random uncertainty provided that independent cross-covariance values far from the maximum are used in estimation as suggested in this study. For the error due to instrumental noise the method by Lenschow et al. (2000) is useful in evaluation of the respective uncertainty. The method was found to be reliable for signal-to-noise ratio, defined by the ratio of the standard deviation of the signal to that of the noise in this study, less than three. Finally, the random uncertainty of the error estimates was determined to be in the order of 10 to 30 % for the total flux error, depending on the conditions and method of estimation.