Impacts of climate change on the vegetation of Africa: an adaptive dynamic vegetation modelling approach

Recent IPCC projections suggest that Africa will be subject to particularly severe changes in atmospheric conditions. How the vegetation of Africa and particularly the grassland–savanna–forest complex will respond to these changes has rarely been investigated. Most studies on global carbon cycles use vegetation models that do not adequately account for the complexity of the interactions that shape the distribution of tropical grasslands, savannas and forests. This casts doubt on their ability to reliably simulate the future vegetation of Africa. We present a new vegetation model, the adaptive dynamic global vegetation model (aDGVM) that was specifically developed for tropical vegetation. The aDGVM combines established components from existing DGVMs with novel process‐based and adaptive modules for phenology, carbon allocation and fire within an individual‐based framework. Thus, the model allows vegetation to adapt phenology, allocation and physiology to changing environmental conditions and disturbances in a way not possible in models based on fixed functional types. We used the model to simulate the current vegetation patterns of Africa and found good agreement between model projections and vegetation maps. We simulated vegetation in absence of fire and found that fire suppression strongly influences tree dominance at the regional scale while at a continental scale fire suppression increases biomass in vegetation by a more modest 13%. Simulations under elevated temperature and atmospheric CO₂ concentrations predicted longer growing periods, higher allocation to roots, higher fecundity, more biomass and a dramatic shift toward tree dominated biomes. Our analyses suggest that the CO₂ fertilization effect is not saturated at ambient CO₂ levels and will strongly increase in response to further increases in CO₂ levels. The model provides a general and flexible framework for describing vegetation response to the interactive effects of climate and disturbances.     

Implications of future climate and atmospheric CO2 content for regional biogeochemistry,biogeography and ecosystem services across East Africa

We model future changes in land biogeochemistry and biogeography across East Africa. East Africa is one of few tropical regions where general circulation model (GCM) future climate projections exhibit a robust response of strong future warming and general annual‐mean rainfall increases. Eighteen future climate projections from nine GCMs participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment were used as input to the LPJ dynamic global vegetation model (DGVM), which predicted vegetation patterns and carbon storage in agreement with satellite observations and forest inventory data under the present‐day climate. All simulations showed future increases in tropical woody vegetation over the region at the expense of grasslands. Regional increases in net primary productivity (NPP) (18–36%) and total carbon storage (3–13%) by 2080–2099 compared with the present‐day were common to all simulations. Despite decreases in soil carbon after 2050, seven out of nine simulations continued to show an annual net land carbon sink in the final decades of the 21st century because vegetation biomass continued to increase. The seasonal cycles of rainfall and soil moisture show future increases in wet season rainfall across the GCMs with generally little change in dry season rainfall. Based on the simulated present‐day climate and its future trends, the GCMs can be grouped into four broad categories. Overall, our model results suggest that East Africa, a populous and economically poor region, is likely to experience some ecosystem service benefits through increased precipitation, river runoff and fresh water availability. Resulting enhancements in NPP may lead to improved crop yields in some areas. Our results stand in partial contradiction to other studies that suggest possible negative consequences for agriculture, biodiversity and other ecosystem services caused by temperature increases.     

Global climate models and 'dynamic' vegetation changes

Models of global change must come to incorporate changes in terrestrial vegetation. Here we choose a 1- year meshing (coupling) period to link a global climate model to a well-known biophysical representation of the continental surface by means of eleven vegetation functional types. This coupled model is used to answer two questions: Can a ‘standard’ GCM ‘cope' with sudden switches in continental characteristics?’ and Does the climate ‘care’ about the changing underlying vegetation? We find affirmative answers to both questions. Our results also suggest that those content to generate vegetation post facto from climate output have incomplete results.     

Vegetation controls on northern high latitude snow‐albedo feedback: observations and CMIP5 model simulations

The snow‐masking effect of vegetation exerts strong control on albedo in northern high latitude ecosystems. Large‐scale changes in the distribution and stature of vegetation in this region will thus have important feedbacks to climate. The snow‐albedo feedback is controlled largely by the contrast between snow‐covered and snow‐free albedo (Δα), which influences predictions of future warming in coupled climate models, despite being poorly constrained at seasonal and century time scales. Here, we compare satellite observations and coupled climate model representations of albedo and tree cover for the boreal and Arctic region. Our analyses reveal consistent declines in albedo with increasing tree cover, occurring south of latitudinal tree line, that are poorly represented in coupled climate models. Observed relationships between albedo and tree cover differ substantially between snow‐covered and snow‐free periods, and among plant functional type. Tree cover in models varies widely but surprisingly does not correlate well with model albedo. Furthermore, our results demonstrate a relationship between tree cover and snow‐albedo feedback that may be used to accurately constrain high latitude albedo feedbacks in coupled climate models under current and future vegetation distributions.     

Adapting a patch model to simulate the sensitivity of Central-Canadian boreal ecosystems to climate variability

Aim To investigate effects of within-season and interannual climate variability on the behaviour of boreal forest ecosystems as simulated by the FORSKA2 patch model. Location Eleven climate station locations distributed along a transect across the boreal zone of central Canada. Methods FORSKA2′s water balance submodel was modified to enable it to behave more realistically under a varying climate. Long-term actual monthly time-series of temperature and precipitation data were detrended and used to drive the modified model. Long-term monthly averages of the same detrended data were used to drive the unmodified model. Results Modifications created significant improvements when simulating species composition at sites in boreal Canada. Simulated forest biomass values were slightly higher than those obtained from the unmodified model using averaged climate records, but resembled the observed distribution of vegetation more closely. Main conclusions Modified FORSKA2 suggests that boreal forest composition and distribution may be more sensitive to changes in monthly rainfall data than to changes in temperature. Climate variability affects seasonal water balances and should be considered when using patch models to forecast vegetation dynamics during and following a period of climate transition. The modified model provided improved representation of the latitudinal trend in spatially averaged biomass density in this region.     

A synthesis: The role of nutrients as constraints on carbon balances in boreal and arctic regions

As in many ecosystems, carbon (C) cycling in arctic and boreal regions is tightly linked to the cycling of nutrients: nutrients (particularly nitrogen) are mineralized through the process of organic matter decomposition (C mineralization), and nutrient availability strongly constrains ecosystem C gain through primary production. This link between C and nutrient cycles has implications for how northern systems will respond to future climate warming and whether feedbacks to rising concentrations of atmospheric CO₂ from these regions will be positive or negative. Warming is expected to cause a substantial release of C to the atmosphere because of increased decomposition of the large amounts of organic C present in high-latitude soils (a positive feedback to climate warming). However, increased nutrient mineralization associated with this decomposition is expected to stimulate primary production and ecosystem C gain, offsetting or even exceeding C lost through decomposition (a negative feedback to climate warming). Increased primary production with warming is consistent with results of numerous experiments showing increased plant growth with nutrient enrichment. Here we examine key assumptions behind this scenario: (1) temperature is a primary control of decomposition in northern regions, (2) increased decomposition and associated nutrient release are tightly coupled to plant nutrient uptake, and (3) short-term manipulations of temperature and nutrient availability accurately predict long-term responses to climate change.     

Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change

Aim Climate change threatens to shift vegetation, disrupting ecosystems and damaging human well‐being. Field observations in boreal, temperate and tropical ecosystems have detected biome changes in the 20th century, yet a lack of spatial data on vulnerability hinders organizations that manage natural resources from identifying priority areas for adaptation measures. We explore potential methods to identify areas vulnerable to vegetation shifts and potential refugia. Location Global vegetation biomes. Methods We examined nine combinations of three sets of potential indicators of the vulnerability of ecosystems to biome change: (1) observed changes of 20th‐century climate, (2) projected 21st‐century vegetation changes using the MC1 dynamic global vegetation model under three Intergovernmental Panel on Climate Change (IPCC) emissions scenarios, and (3) overlap of results from (1) and (2). Estimating probability density functions for climate observations and confidence levels for vegetation projections, we classified areas into vulnerability classes based on IPCC treatment of uncertainty. Results One‐tenth to one‐half of global land may be highly (confidence 0.80–0.95) to very highly (confidence ≥ 0.95) vulnerable. Temperate mixed forest, boreal conifer and tundra and alpine biomes show the highest vulnerability, often due to potential changes in wildfire. Tropical evergreen broadleaf forest and desert biomes show the lowest vulnerability. Main conclusions Spatial analyses of observed climate and projected vegetation indicate widespread vulnerability of ecosystems to biome change. A mismatch between vulnerability patterns and the geographic priorities of natural resource organizations suggests the need to adapt management plans. Approximately a billion people live in the areas classified as vulnerable.     

Changes in satellite‐derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006

Monitoring changes in vegetation growth has been the subject of considerable research during the past several decades, because of the important role of vegetation in regulating the terrestrial carbon cycle and the climate system. In this study, we combined datasets of satellite‐derived Normalized Difference Vegetation Index (NDVI) and climatic factors to analyze spatio‐temporal patterns of changes in vegetation growth and their linkage with changes in temperature and precipitation in temperate and boreal regions of Eurasia (> 23.5°N) from 1982 to 2006. At the continental scale, although a statistically significant positive trend of average growing season NDVI is observed (0.5 × 10^?3 year^?1, P = 0.03) during the entire study period, there are two distinct periods with opposite trends in growing season NDVI. Growing season NDVI has first significantly increased from 1982 to 1997 (1.8 × 10^?3 year^?1, P < 0.001), and then decreased from 1997 to 2006 (?1.3 × 10^?3 year^?1, P = 0.055). This reversal in the growing season NDVI trends over Eurasia are largely contributed by spring and summer NDVI changes. Both spring and summer NDVI significantly increased from 1982 to 1997 (2.1 × 10^?3 year^?1, P = 0.01; 1.6 × 10^?3 year^?1P < 0.001, respectively), but then decreased from 1997 to 2006, particularly summer NDVI which may be related to the remarkable decrease in summer precipitation (?2.7 mm yr^?1, P = 0.009). Further spatial analyses supports the idea that the vegetation greening trend in spring and summer that occurred during the earlier study period 1982–1997 was either stalled or reversed during the following study period 1997–2006. But the turning point of vegetation NDVI is found to vary across different regions.     

Tree‐ring analysis and modeling approaches yield contrary response of circumboreal forest productivity to climate change

Circumboreal forest ecosystems are exposed to a larger magnitude of warming in comparison with the global average, as a result of warming‐induced environmental changes. However, it is not clear how tree growth in these ecosystems responds to these changes. In this study, we investigated the sensitivity of forest productivity to climate change using ring width indices (RWI) from a tree‐ring width dataset accessed from the International Tree‐Ring Data Bank and gridded climate datasets from the Climate Research Unit. A negative relationship of RWI with summer temperature and recent reductions in RWI were typically observed in continental dry regions, such as inner Alaska and Canada, southern Europe, and the southern part of eastern Siberia. We then developed a multiple regression model with regional meteorological parameters to predict RWI, and then applied to these models to predict how tree growth will respond to twenty‐first‐century climate change (RCP8.5 scenario). The projections showed a spatial variation and future continuous reduction in tree growth in those continental dry regions. The spatial variation, however, could not be reproduced by a dynamic global vegetation model (DGVM). The DGVM projected a generally positive trend in future tree growth all over the circumboreal region. These results indicate that DGVMs may overestimate future wood net primary productivity (NPP) in continental dry regions such as these; this seems to be common feature of current DGVMs. DGVMs should be able to express the negative effect of warming on tree growth, so that they simulate the observed recent reduction in tree growth in continental dry regions.     

Long-term responses of boreal vegetation to global change: an experimental and modelling investigation

The response of boreal ecosystems to future global change is an uncertain but potentially critical component of the feedback between the terrestrial biosphere and the atmosphere. To reduce some of the uncertainties in predicting the responses of this key ecosystem, the climate change experiment (CLIMEX) exposed an entire undisturbed catchment of boreal vegetation to CO₂ enrichment (560 ppmv) and climate change (+ 5 °C in winter, + 3 °C in summer) for three years (1994–96). This paper describes the leaf metabolic responses of the vegetation to the experimental treatment and model simulations of possible future changes in the hydrological and carbon balance of the site. Randomized intervention analysis of the leaf gas exchange measurements for the dominant species indicated Pinus sylvestris had significantly (P < 0.01) higher photosynthetic rates and Betula pubescens and Vaccinium myrtillus had significantly (P < 0.01) lower stomatal conductances after three years treatment compared to the controls. These responses led to sustained increases in leaf water-use efficiency of all species of trees and ground shrubs, as determined from carbon isotope analyses. Photosynthesis (A) vs. intercellular CO₂ (c_i) response curves (A/c_i responses), RuBisCo analysis and leaf nitrogen data together suggested none of the species investigated exhibited down-regulation in photosynthetic capacity. At the whole ecosystem level, the improved water economy of the plants did not translate into increased catchment runoff. Modelling simulations for the site indicate this was most likely brought about by a compensatory increase in evapotranspiration. In terms of the carbon budget of the site, the ecosystem model indicates that increased CO₂ and temperature would lead to boreal ecosystems of the type used in CLIMEX, and typical of much of southern Norway, acting as moderate net sinks for CO₂.     

Bud-burst modelling in Siberia and its impact on quantifying the carbon budget

Vegetation phenology is affected by climate change and in turn feeds back on climate by affecting the annual carbon uptake by vegetation. To quantify the impact of phenology on terrestrial carbon fluxes, we calibrate a bud‐burst model and embed it in the Sheffield dynamic global vegetation model (SDGVM) in order to perform carbon budget calculations. Bud‐burst dates derived from the VEGETATION sensor onboard the SPOT‐4 satellite are used to calibrate a range of bud‐burst models. This dataset has been recently developed using a new methodology based on the normalized difference water index, which is able to distinguish snowmelt from the onset of vegetation activity after winter. After calibration, a simple spring warming model was found to perform as well as more complex models accounting for a chilling requirement, and hence it was used for the carbon flux calculations. The root mean square difference (RMSD) between the calibrated model and the VEGETATION dataset was 6.5 days, and was 6.9 days between the calibrated model and independent ground observations of bud‐burst available at nine locations over Siberia. The effects of bud‐burst model uncertainties on the carbon budget were evaluated using the SDGVM. The 6.5 days RMSD in the bud‐burst date (a 6% variation in the growing season length), treated as a random noise, translates into about 41 g cm^?2 yr^?1 in net primary production (NPP), which corresponds to 8% of the mean NPP. This is a moderate impact and suggests the calibrated model is accurate enough for carbon budget calculations. In addition to random differences between the calibrated model and VEGETATION data, systematic errors between the calibrated bud‐burst model and true ground behaviour may occur, because of bias in the temperature dataset or because the bud‐burst detected by VEGETATION is because of some other phenological indicator. A systematic error of 1 day in bud‐burst translates into a 10 g cm^?2 yr^?1 error in NPP (about 2%). Based on the limited available ground data, any systematic error because of the use of VEGETATION data should not lead to significant errors in the calculated carbon flux. In contrast, widely used methods based on the normalized difference vegetation index from the advanced very high resolution radiometer satellite are likely to confuse snowmelt and vegetation greening, leading to errors of up to 15 days in bud‐burst date, with consequent large errors in carbon flux calculations.     

Dynamic global vegetation modelling for prediction of plant functional types and biogenic trace gas fluxes

1. A Dynamic Global Vegetation Model (DGVM) has been developed as a new feature of the NASA-CASA (Carnegie Ames Stanford Approach) ecosystem production and trace gas model. This DGVM includes seasonal phenology algorithms calibrated using historical interannual data sets derived from the Advanced Very High Resolution (AVHRR) satellite ‘greenness’ index. 2. The coupled CASA-DGVM design is based conceptually on two main elements of Tilman's resource-ratio hypothesis of vegetation change, namely: 1) plant competition for resources (water and light) over relatively short time periods of months and seasons; and 2) the long-term pattern in the supply of growth-limiting resources such as water and nutrients, i.e. the resource-supply trajectory. This simulation model generates global gridded estimates of primary production, above and below ground biomass, leaf area index (LAI), and trace gas fluxes from soil. 3. Eight distributed test locations for the DGVM were evaluated initially to represent a variety of climate conditions ranging from Arctic (64°N Alaska) to tropical and subtropical (24°S southern Africa) latitude zones. At all test locations, the predicted plant functional type (PFT) matched closely with the actual reported PFT. 4. In the process of running the model to steady state PFTs, most forest locations showed a rapid progression of transient states, from bare ground to grassland, to grasses with shrub cover, and finally to the forest PFT. From this first global application, the DGVM correctly predicts the presence of forest classes in approximately 75–95% of all cases worldwide, and grasslands in approximately 58% of all cases. 5. The effects of two hypothetical climate change scenarios were evaluated. Scenario I was set by warming air surface temperatures linearly to 4 °C above average over a 25-year simulation period. Scenario II was set by decreasing annual rainfall amounts linearly to 50% below average over a 25-year simulation period. 6. The warming scenario I resulted in PFT at high-latitude forest and boreal forest sites changing to mixed coniferous forest, accompanied by increase in canopy LAI. The drought scenario II resulted in PFT at the boreal forest and savanna sites changing to grasslands. At locations where PFT did not change with climate, however, soil water and canopy LAI were predicted to decline progressively under the warming scenario, beginning from steady-state temperate and tropical zone PFTs. They also declined under the drought scenario beginning from practically any steady state PFT.     

Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.     

Taxonomy,together with ontogeny and growing conditions,drives needleleaf species' sensitivity to climate in boreal North America

Currently, there is no consensus regarding the way that changes in climate will affect boreal forest growth, where warming is occurring faster than in other biomes. Some studies suggest negative effects due to drought‐induced stresses, while others provide evidence of increased growth rates due to a longer growing season. Studies focusing on the effects of environmental conditions on growth–climate relationships are usually limited to small sampling areas that do not encompass the full range of environmental conditions; therefore, they only provide a limited understanding of the processes at play. Here, we studied how environmental conditions and ontogeny modulated growth trends and growth–climate relationships of black spruce (Picea mariana) and jack pine (Pinus banksiana) using an extensive dataset from a forest inventory network. We quantified the long‐term growth trends at the stand scale, based on analysis of the absolutely dated ring‐width measurements of 2,266 trees. We assessed the relationship between annual growth rates and seasonal climate variables and evaluated the effects of various explanatory variables on long‐term growth trends and growth–climate relationships. Both growth trends and growth–climate relationships were species‐specific and spatially heterogeneous. While the growth of jack pine barely increased during the study period, we observed a growth decline for black spruce which was more pronounced for older stands. This decline was likely due to a negative balance between direct growth gains induced by improved photosynthesis during hotter‐than‐average growing conditions in early summers and the loss of growth occurring the following year due to the indirect effects of late‐summer heat waves on accumulation of carbon reserves. For stands at the high end of our elevational gradient, frost damage during milder‐than‐average springs could act as an additional growth stressor. Competition and soil conditions also modified climate sensitivity, which suggests that effects of climate change will be highly heterogeneous across the boreal biome.     

Analysis of vegetation distribution in Interior Alaska and sensitivity to climate change using a logistic regression approach

Aim To understand drivers of vegetation type distribution and sensitivity to climate change. Location Interior Alaska. Methods A logistic regression model was developed that predicts the potential equilibrium distribution of four major vegetation types: tundra, deciduous forest, black spruce forest and white spruce forest based on elevation, aspect, slope, drainage type, fire interval, average growing season temperature and total growing season precipitation. The model was run in three consecutive steps. The hierarchical logistic regression model was used to evaluate how scenarios of changes in temperature, precipitation and fire interval may influence the distribution of the four major vegetation types found in this region. Results At the first step, tundra was distinguished from forest, which was mostly driven by elevation, precipitation and south to north aspect. At the second step, forest was separated into deciduous and spruce forest, a distinction that was primarily driven by fire interval and elevation. At the third step, the identification of black vs. white spruce was driven mainly by fire interval and elevation. The model was verified for Interior Alaska, the region used to develop the model, where it predicted vegetation distribution among the steps with an accuracy of 60–83%. When the model was independently validated for north‐west Canada, it predicted vegetation distribution among the steps with an accuracy of 53–85%. Black spruce remains the dominant vegetation type under all scenarios, potentially expanding most under warming coupled with increasing fire interval. White spruce is clearly limited by moisture once average growing season temperatures exceeded a critical limit (+2 °C). Deciduous forests expand their range the most when any two of the following scenarios are combined: decreasing fire interval, warming and increasing precipitation. Tundra can be replaced by forest under warming but expands under precipitation increase. Main conclusion The model analyses agree with current knowledge of the responses of vegetation types to climate change and provide further insight into drivers of vegetation change.     

Climate change effects on net carbon exchange of a boreal aspen–hazelnut forest: estimates from the ecosystem model ecosys

Although boreal forests are currently sinks for atmospheric C, there is some concern that they may not remain so under hypothesized warming of the boreal climate. The ecosystem model ecosys was used to evaluate possible changes in ecosystem C exchange and accumulation under changes in atmospheric CO₂ concentration (C_a) proposed in emissions scenario IS92a, and accompanying changes in air temperature and precipitation proposed by general circulation models running under IS92a. Ecosys was first tested under current climate by comparing modelled rates of C exchange and accumulation with those measured in a mixed aspen–hazelnut stand in central Saskatchewan. The model was then run with daily increments of C_a, temperature and precipitation, and differences in C exchange and accumulation between current and changing climates were evaluated. Model results indicated that over a 120‐y period, a mixed aspen–hazelnut stand currently accumulates about 14 kg C m^?2. Under the hypothesized changes in climate this stand would accumulate an additional 8.5 kg C m^?2, largely through higher rates of CO₂ fixation and longer growing seasons under higher C_a and temperature. This additional accumulation would be entirely as aspen wood, while soil organic matter would change little. This accumulation would therefore be vulnerable to losses from fire and insects.     

The positive net radiative greenhouse gas forcing of increasing methane emissions from a thawing boreal forest‐wetland landscape

At the southern margin of permafrost in North America, climate change causes widespread permafrost thaw. In boreal lowlands, thawing forested permafrost peat plateaus (‘forest’) lead to expansion of permafrost‐free wetlands (‘wetland’). Expanding wetland area with saturated and warmer organic soils is expected to increase landscape methane (CH₄) emissions. Here, we quantify the thaw‐induced increase in CH₄ emissions for a boreal forest‐wetland landscape in the southern Taiga Plains, Canada, and evaluate its impact on net radiative forcing relative to potential long‐term net carbon dioxide (CO₂) exchange. Using nested wetland and landscape eddy covariance net CH₄ flux measurements in combination with flux footprint modeling, we find that landscape CH₄ emissions increase with increasing wetland‐to‐forest ratio. Landscape CH₄ emissions are most sensitive to this ratio during peak emission periods, when wetland soils are up to 10 °C warmer than forest soils. The cumulative growing season (May–October) wetland CH₄ emission of ~13 g CH₄ m^?2 is the dominating contribution to the landscape CH₄ emission of ~7 g CH₄ m^?2. In contrast, forest contributions to landscape CH₄ emissions appear to be negligible. The rapid wetland expansion of 0.26 ± 0.05% yr^?1 in this region causes an estimated growing season increase of 0.034 ± 0.007 g CH₄ m^?2 yr^?1 in landscape CH₄ emissions. A long‐term net CO₂ uptake of >200 g CO₂ m^?2 yr^?1 is required to offset the positive radiative forcing of increasing CH₄ emissions until the end of the 21st century as indicated by an atmospheric CH₄ and CO₂ concentration model. However, long‐term apparent carbon accumulation rates in similar boreal forest‐wetland landscapes and eddy covariance landscape net CO₂ flux measurements suggest a long‐term net CO₂ uptake between 49 and 157 g CO₂ m^?2 yr^?1. Thus, thaw‐induced CH₄ emission increases likely exert a positive net radiative greenhouse gas forcing through the 21st century.