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  • 1
    Publication Date: 2022-12-10
    Description: Forests mitigate climate change by storing carbon and reducing emissions via substitution effects of wood products. Additionally, they provide many other important ecosystem services (ESs), but are vulnerable to climate change; therefore, adaptation is necessary. Climate‐smart forestry combines mitigation with adaptation, whilst facilitating the provision of many ESs. This is particularly challenging due to large uncertainties about future climate. Here, we combined ecosystem modeling with robust multi‐criteria optimization to assess how the provision of various ESs (climate change mitigation, timber provision, local cooling, water availability, and biodiversity habitat) can be guaranteed under a broad range of climate futures across Europe. Our optimized portfolios contain 29% unmanaged forests, and implicate a successive conversion of 34% of coniferous to broad‐leaved forests (11% vice versa). Coppices practically vanish from Southern Europe, mainly due to their high water requirement. We find the high shares of unmanaged forests necessary to keep European forests a carbon sink while broad‐leaved and unmanaged forests contribute to local cooling through biogeophysical effects. Unmanaged forests also pose the largest benefit for biodiversity habitat. However, the increased shares of unmanaged and broad‐leaved forests lead to reductions in harvests. This raises the question of how to meet increasing wood demands without transferring ecological impacts elsewhere or enhancing the dependence on more carbon‐intensive industries. Furthermore, the mitigation potential of forests depends on assumptions about the decarbonization of other industries and is consequently crucially dependent on the emission scenario. Our findings highlight that trade‐offs must be assessed when developing concrete strategies for climate‐smart forestry.
    Description: Plain Language Summary: Forests help mitigate climate change by storing carbon and via avoided emissions when wood products replace more carbon‐intensive materials. At the same time, forests provide many other “ecosystem services (ESs)” to society. For example, they provide timber, habitat for various species, and they cool their surrounding regions. They are, however, also vulnerable to ongoing climate change. Forest management must consider all these aspects, which is particularly challenging considering the uncertainty about future climate. Here, we propose how this may be tackled by computing optimized forest management portfolios for Europe for a broad range of future climate pathways. Our results show that changes to forest composition are necessary. In particular, increased shares of unmanaged and broad‐leaved forests are beneficial for numerous ESs. However, these increased shares also lead to decreases in harvest rates, posing a conflict between wood supply and demand. We further show that the mitigation potential of forests strongly depends on how carbon‐intensive the replaced materials are. Consequently, should these materials become “greener” due to new technologies, the importance of wood products in terms of climate change mitigation decreases. Our study highlights that we cannot optimize every aspect, but that trade‐offs between ESs need to be made.
    Description: Key Points: Strategies for climate‐smart forestry under a range of climate scenarios always lead to trade‐offs between different ecosystem services (ESs). Higher shares of unmanaged and broad‐leaved forests are beneficial for numerous ESs, but lead to decreased timber provision. The mitigation potential of forests strongly relies on substitution effects which depend on the carbon‐intensity of the alternative products.
    Description: European Forest Institute (EFI) Networking Fund http://dx.doi.org/10.13039/501100013942
    Description: Bayerisches Staatsministerium für Wissenschaft und Kunst, Bayerisches Netzwerk für Klimaforschung (BayKliF) http://dx.doi.org/10.13039/501100004563
    Description: Swedish Research Council Formas
    Description: German Federal Office for Agriculture and Food (BLE)
    Description: https://doi.org/10.5281/zenodo.6667489
    Description: https://doi.org/10.5281/zenodo.6612953
    Keywords: ddc:634.9 ; forest management ; climate change mitigation ; substitution effects ; climate‐smart forestry ; ecosystem services ; robust optimization
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2022-03-21
    Language: English
    Type: info:eu-repo/semantics/article
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  • 3
    Publication Date: 2022-09-22
    Description: Southeastern South America is subject to considerable precipitation variability on seasonal to decadal timescales and has undergone very heavy land-cover changes since the middle of the past century. The influence of local land-cover change and precipitation as drivers of regional evapotranspiration long-term trends and variability remains largely unknown in the region. Here, ensembles of stand-alone Dynamic Global Vegetation Models with different atmospheric forcings are used to disentangle the influence of those two drivers on austral summer evapotranspiration from 1950 to 2010. This paper examines the influence of both the ENSO and the dipole-like first-mode of southeastern South American precipitation variability (EOF1) on regional evapotranspiration. We found that in the lower La Plata Basin, evapotranspiration was driven by precipitation variability and showed a positive summer trend. Moreover, the region showed marked seasonal anomalies during El Niño and La Niña summers but mainly during EOF1 phases. On the contrary, in the upper La Plata Basin, land-cover changes forced the negative summer evapotranspiration trend and particularly reduced the summer anomalies of the late 1990s, a period of ENSO and EOF1-positive phases. In the South Atlantic Convergence Zone region, the high evapotranspiration uncertainty across ensemble members impeded finding robust results, which highlights the importance of using multiple DGVMs and atmospheric forcings instead of relying on single model/forcing results.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 4
    Publication Date: 2022-11-22
    Description: Forests mitigate climate change by storing carbon and reducing emissions via substitution effects of wood products. Additionally, they provide many other important ecosystem services (ESs), but are vulnerable to climate change; therefore, adaptation is necessary. Climate-smart forestry combines mitigation with adaptation, whilst facilitating the provision of many ESs. This is particularly challenging due to large uncertainties about future climate. Here, we combined ecosystem modeling with robust multi-criteria optimization to assess how the provision of various ESs (climate change mitigation, timber provision, local cooling, water availability, and biodiversity habitat) can be guaranteed under a broad range of climate futures across Europe. Our optimized portfolios contain 29% unmanaged forests, and implicate a successive conversion of 34% of coniferous to broad-leaved forests (11% vice versa). Coppices practically vanish from Southern Europe, mainly due to their high water requirement. We find the high shares of unmanaged forests necessary to keep European forests a carbon sink while broad-leaved and unmanaged forests contribute to local cooling through biogeophysical effects. Unmanaged forests also pose the largest benefit for biodiversity habitat. However, the increased shares of unmanaged and broad-leaved forests lead to reductions in harvests. This raises the question of how to meet increasing wood demands without transferring ecological impacts elsewhere or enhancing the dependence on more carbon-intensive industries. Furthermore, the mitigation potential of forests depends on assumptions about the decarbonization of other industries and is consequently crucially dependent on the emission scenario. Our findings highlight that trade-offs must be assessed when developing concrete strategies for climate-smart forestry.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 5
    Publication Date: 2022-12-02
    Description: Inter-annual climate variability (hereafter climate variability) is increasing in many forested regions due to climate change. This variability could have larger near-term impacts on forests than decadal shifts in mean climate, but how forests will respond remains poorly resolved, particularly at broad scales. Individual trees, and even forest communities, often have traits and ecological strategies—the legacies of exposure to past variable conditions—that confer tolerance to subsequent climate variability. However, whether local legacies also shape global forest responses is unknown. Our objective was to assess how past and current climate variability influences global forest productivity. We hypothesized that forests exposed to large climate variability in the past would better tolerate current climate variability than forests for which past climate was relatively stable. We used historical (1950–1969) and contemporary (2000–2019) temperature, precipitation, and vapor pressure deficit (VPD) and the remotely sensed enhanced vegetation index (EVI) to quantify how historical and contemporary climate variability relate to patterns of contemporary forest productivity. Consistent with our hypothesis, forests exposed to large temperature variability in the past were more tolerant of contemporary temperature variability than forests where past temperatures were less variable. Forests were 19-fold times less sensitive to contemporary temperature variability where historical inter-annual temperature variability was 0.66 °C (two standard deviations) greater than the global average historical temperature variability. We also found that larger increases in temperature variability between the two study periods often eroded the tolerance conferred by the legacy effects of historical temperature variability. However, the hypothesis was not supported in the case of precipitation and VPD variability, potentially due to physiological tradeoffs inherent in how trees cope with dry conditions. We conclude that the sensitivity of forest productivity to imminent increases in temperature variability may be partially predictable based on the legacies of past conditions.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 6
    Publication Date: 2023-02-08
    Description: Recent publications indicate that the Amazon may be acting more as a carbon source than a sink in some regions. Moreover, the Amazon is a source of moisture for other regions in the continent, and deforestation over the years may be reducing this function. In this work, we analyze the impacts of elevated CO2 (eCO2) and Land Use Change (LUC) on Gross Primary Productivity (GPP) and evaporation in the Southern Amazon (70S 140S, 660W 510W), which suffered strong anthropogenic influence in the period of 1981‒2010. We ran four Dynamic Global Vegetation Models (DGVMs), isolating historical CO2, constant CO2, LUC, and Potential Natural Vegetation (PNV) scenarios with three climate variable datasets: precipitation, temperature and shortwave radiation. We compared the outputs to five “observational” datasets obtained through eddy covariance, remote sensing, meteorological measurements, and machine learning. The results indicate that eCO2 may have offset deforestation, with GPP increasing by ∼13.5% and 9.3% (dry and rainy seasons, respectively). After isolating the LUC effect, a reduction in evaporation of ∼4% and ∼1.2% (dry and rainy seasons, respectively) was observed. The analysis of forcings in subregions under strong anthropogenic impact revealed a reduction in precipitation of ∼15 and 30 mm, and a temperature rise of 10 and 0.60 C (dry and rainy seasons, respectively). Differences in the implementation of plant physiology and Leaf area Index (LAI) in the DGVMs introduced some uncertainties in the interpretation of the results. Nevertheless, we consider that it was an important exercise given the relevance.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 7
    Publication Date: 2023-12-21
    Description: Over the last decades, the Amazon rainforest has been hit by multiple severe drought events. Here, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon region and their impacts on the regional carbon cycle. As an indicator of drought stress in the Amazon rainforest, we use the widely applied maximum cumulative water deficit (MCWD). Evaluating nine state-of-the-art precipitation datasets for the Amazon region, we find that the spatial extent of the drought in 2005 ranges from 2.2 to 3.0 (mean =2.7) ×106 km2 (37 %–51 % of the Amazon basin, mean =45 %), where MCWD indicates at least moderate drought conditions (relative MCWD anomaly 〈 -0.5). In 2010, the affected area was about 16 % larger, ranging from 3.0 up to 4.4 (mean =3.6) ×106 km2 (51 %–74 %, mean =61 %). In 2016, the mean area affected by drought stress was between 2005 and 2010 (mean = 3.2 x 106 km2; 55 % of the Amazon basin), but the general disagreement between datasets was larger, ranging from 2.4 up to 4.1×106 km2 (40 %–69 %). In addition, we compare differences and similarities among datasets using the self-calibrating Palmer Drought Severity Index (scPDSI) and a dry-season rainfall anomaly index (RAI). We find that scPDSI shows a stronger and RAI a much weaker drought impact in terms of extent and severity for the year 2016 compared to MCWD. We further investigate the impact of varying evapotranspiration on the drought indicators using two state-of-the-art evapotranspiration datasets. Generally, the variability in drought stress is most dependent on the drought indicator (60 %), followed by the choice of the precipitation dataset (20 %) and the evapotranspiration dataset (20 %). Using a fixed, constant evapotranspiration rate instead of variable evapotranspiration can lead to an overestimation of drought stress in the parts of Amazon basin that have a more pronounced dry season (for example in 2010). We highlight that even for well-known drought events the spatial extent and intensity can strongly depend upon the drought indicator and the data sources it is calculated with. Using only one data source and drought indicator has the potential danger of under- or overestimating drought stress in regions with high measurement uncertainty, such as the Amazon basin.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 8
    Publication Date: 2024-01-18
    Description: The IPCC Assessment Reports offer the scientific foundation for international climate negotiations and constitute an unmatched resource for climate change researchers. However, the assessment cycles take multiple years. As a contribution to cross- and interdisciplinary understanding across diverse climate change research communities, we have streamlined an annual process to identify and synthesise essential research advances. We collected input from experts on different fields using an online questionnaire and prioritised a set of ten key research insights with high policy relevance. This year we focus on: (1) looming overshoot of the 1.5°C warming limit, (2) urgency of phasing-out fossil fuels, (3) challenges for scaling carbon dioxide removal, (4) uncertainties regarding the future of natural carbon sinks, (5) need for join governance of biodiversity loss and climate change, (6) advances in the science of compound events, (7) mountain glacier loss, (8) human immobility in the face of climate risks, (9) adaptation justice, and (10) just transitions in food systems. We first present a succinct account of these Insights, reflect on their policy implications, and offer an integrated set of policy relevant messages. This science synthesis and science communication effort is also the basis for a report targeted to policymakers as a contribution to elevate climate science every year, in time for the UNFCCC COP.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 9
    Publication Date: 2021-10-07
    Description: Land use and climate changes both affect terrestrial ecosystems. Here, we used three combinations of Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1xRCP26, SSP3xRCP60, and SSP5xRCP85) as input to three dynamic global vegetation models to assess the impacts and associated uncertainty on several ecosystem functions: terrestrial carbon storage and fluxes, evapotranspiration, surface albedo, and runoff. We also performed sensitivity simulations in which we kept either land use or climate (including atmospheric CO2) constant from year 2015 on to calculate the isolated land use versus climate effects. By the 2080–2099 period, carbon storage increases by up to 87 ± 47 Gt (SSP1xRCP26) compared to present day, with large spatial variance across scenarios and models. Most of the carbon uptake is attributed to drivers beyond future land use and climate change, particularly the lagged effects of historic environmental changes. Future climate change typically increases carbon stocks in vegetation but not soils, while future land use change causes carbon losses, even for net agricultural abandonment (SSP1xRCP26). Evapotranspiration changes are highly variable across scenarios, and models do not agree on the magnitude or even sign of change of the individual effects. A calculated decrease in January and July surface albedo (up to −0.021 ± 0.007 and −0.004 ± 0.004 for SSP5xRCP85) and increase in runoff (+67 ± 6 mm/year) is largely driven by climate change. Overall, our results show that future land use and climate change will both have substantial impacts on ecosystem functioning. However, future changes can often not be fully explained by these two drivers and legacy effects have to be considered.
    Keywords: 333.7 ; 551.6 ; land use change ; climate change projections ; terrestrial ecosystems ; vegetation modeling ; ecosystem service indicators ; legacy effects
    Language: English
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  • 10
    Publication Date: 2021-10-02
    Description: Ecosystems continuously adapt to interacting environmental drivers that change over time. Consequently, the carbon balance of terrestrial ecosystem may presently still be affected by past anthropogenic disturbances (e.g., deforestation) and other environmental changes (e.g., climate change). However, even though such so-called “legacy effects” are implicitly included in many carbon cycle modeling studies, they are typically not explicitly quantified and therefore scientists might not be aware of their long-term importance. Here, we use the ecosystem model LPJ-GUESS to quantify legacy effects for the 21st century and the respective contributions of the following environmental drivers: climate change, CO2 fertilization, land use change, wood harvest, nitrogen deposition, and nitrogen fertilization. According to our simulations, the combined legacy effects of historical (1850–2015) environmental changes result in a land carbon uptake of +126 Gt C over the future (2015–2099) period. This by far exceeds the impacts of future environmental changes (range −53 Gt C to +16 Gt C for three scenarios) and is comparable in magnitude to historical carbon losses (−154 Gt C). Legacy effects can mainly be attributed to ecosystems still adapting to historical increases in atmospheric CO2 (+65 Gt C) and nitrogen deposition (+33 Gt C), but long-term vegetation regrowth following agricultural abandonment (+8 Gt C) and wood harvest (+19 Gt C) also play a role. The response of the biosphere to historical environmental changes dominates future terrestrial carbon cycling at least until midcentury. Legacy effects persist many decades after environmental changes occurred and need to be considered when interpreting changes and estimating terrestrial carbon uptake potentials.
    Keywords: 333.7 ; ecosystem modeling ; environmental drivers ; carbon sink ; lagged response ; ecosystem equilibrium ; committed change
    Language: English
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