Krause, A.; Papastefanou, P.; Gregor, K.; Layritz, L.; Zang, C.; Buras, A.; Li, X.; Xiao, J.; Rammig, A. (2022)
Krause, A.; Papastefanou, P.; Gregor, K.; Layritz, L.; Zang, C.; Buras, A.; Li, X....
Scientific Reports 12, 18398.
Historically, humans have cleared many forests for agriculture. While this substantially reduced ecosystem carbon storage, the impacts of these land cover changes on terrestrial gross primary productivity (GPP) have not been adequately resolved yet. Here, we combine high-resolution datasets of satellite-derived GPP and environmental predictor variables to estimate the potential GPP of forests, grasslands, and croplands around the globe. With a mean GPP of 2.0 kg C m−2 yr−1 forests represent the most productive land cover on two thirds of the total area suitable for any of these land cover types, while grasslands and croplands on average reach 1.5 and 1.8 kg C m−2 yr−1, respectively. Combining our potential GPP maps with a historical land-use reconstruction indicates a 4.4% reduction in global GPP from agricultural expansion. This land-use-induced GPP reduction is amplified in some future scenarios as a result of ongoing deforestation (e.g., the large-scale bioenergy scenario SSP4-3.4) but partly reversed in other scenarios (e.g., the sustainability scenario SSP1-1.9) due to agricultural abandonment. Comparing our results to simulations from state-of-the-art Earth System Models, we find that all investigated models deviate substantially from our estimates and from each other. Our maps could be used as a benchmark to reduce this inconsistency, thereby improving projections of land-based climate mitigation potentials.
Ewald, J.; Ammer, C.; Blaschke, M.; Hagge, J.; Henkel, A.; Hese, S.; Klamm, A.; Reger, B.; Rothe, A.; Seibold, S.; Seidl, R.; Zang, C.; Olleck, M. (2022)
Ewald, J.; Ammer, C.; Blaschke, M.; Hagge, J.; Henkel, A.; Hese, S.; Klamm, A....
Posterpräsentation zum Projektauftakt auf dem Waldklimafonds-Kongress 2022 in Göttingen.
Papastefanou, P.; Zang, C.; Angelov, Z.; Anderson de Castro, A.; Jimenez, J.; Campos De Rezende, L.; Ruscica, R.; Sakschewski, B.; Sörensson, A.; Thonicke, K.; Vera, C.; Viovy, N.; von Randow, C.; Rammig, A. (2022)
Papastefanou, P.; Zang, C.; Angelov, Z.; Anderson de Castro, A.; Jimenez, J....
Biogeosciences 19, S. 3843–3861.
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 ). 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 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.
Bodesheim, P.; Babst, F.; Frank, D.; Hartl, C.; Zang, C.; Jung, M.; Reichstein, M.; Mahecha, M. (2022)
Bodesheim, P.; Babst, F.; Frank, D.; Hartl, C.; Zang, C.; Jung, M.; Reichstein, M....
Environmental Data Science 1, e9.
Tree-ring chronologies encode interannual variability in forest growth rates over long time periods from decades to centuries or even millennia. However, each chronology is a highly localized measurement describing conditions at specific sites where wood samples have been collected. The question whether these local growth variabilites are representative for large geographical regions remains an open issue. To overcome the limitations of interpreting a sparse network of sites, we propose an upscaling approach for annual tree-ring indices that approximate forest growth variability and compute gridded data products that generalize the available information for multiple tree genera. Using regression approaches from machine learning, we predict tree-ring indices in space and time based on climate variables, but considering also species range maps as constraints for the upscaling. We compare various prediction strategies in cross-validation experiments to identify the best performing setup. Our estimated maps of tree-ring indices are the first data products that provide a dense view on forest growth variability at the continental level with 0.5° and 0.0083° spatial resolution covering the years 1902–2013. Furthermore, we find that different genera show very variable spatial patterns of anomalies. We have selected Europe as study region and focused on the six most prominent tree genera, but our approach is very generic and can easily be applied elsewhere. Overall, the study shows perspectives but also limitations for reconstructing spatiotemporal dynamics of complex biological processes. The data products are available at https://www.doi.org/10.17871/BACI.248.
Buras, A.; Ovenden, T.; Rammig, A.; Zang, C. (2022)
Dendrochronologia 74, 125964.
Detecting pointer years in tree-ring data is a central aspect of dendroecology. Pointer years are usually represented by extraordinary secondary tree growth, which is often interpreted as a response to abnormal environmental conditions such as late-frosts or droughts. Objectively identifying pointer years in larger tree-ring networks and relating those to specific climatic conditions will allow for refining our understanding of how trees perform under extreme climate and consequently, under anticipated climate change. Recently, Buras et al. (2020) demonstrated that frequently used pointer-year detection methods were either too sensitive or insensitive for such large scale analyses. In their study, Buras et al. (2020) proposed a novel approach for detecting pointer years – the standardized growth change (SGC) method which outperformed other pointer-year detection methods in pseudopopulation trials. Yet, the authors concluded that SGC could be improved further to account for the inability to detect pointer years following successive growth decline. Under this framework, we here present a refined version of the SGC-method – the bias-adjusted standardized growth change method (BSGC). The methodological adjustment to the SGC approach comprises conflated probabilities derived from standardized growth changes with probabilities derived from a time-step specific global standardization of growth changes. In addition, BSGC allows for estimating the length of the deflection period, i.e. the period before extraordinary growth values have reached normal levels. Application of BSGC to simulated and measured tree-ring data indicated an improved performance in comparison to SGC which allows for the identification of pointer years following years of successive growth decline. Also, deflection period lengths were estimated well and revealed plausible results for an existing tree-ring data set. Based on these validations, BSGC can be considered a further refinement of pointer-year detection, allowing for a more accurate identification and consequently better understanding of the radial growth response of trees to extreme events.
Dorado-Liñán, I.; Ayarzagüena, B.; Babst, F.; Xu, G.; Gil, L.; Battipaglia, G.; Buras, A.; Čada, V.; Camarero, J.; Cavin, L.; Claessens, H.; Drobyshev, I.; Garamszegi, B.; Grabner, M.; Hacket-Pain, A.; Hartl, C.; Hevia, A.; Janda, P.; Jump, A.; Kazimirovic, M.; Keren, S.; Kreyling, J.; Land, A.; Latte, N.; Levanič, T.; van der Maaten, E.; van der Maaten-Theunissen, M.; Martínez-Sancho, E.; Menzel, A.; Mikoláš, M.; Motta, R.; Muffler, L.; Nola, P.; Panayotov, M.; Petritan, A.; Petritan, I.; Popa, I.; Prislan, P.; Roibu, C.; Rydval, M.; Sánchez-Salguero, R.; Scharnweber, T.; Stajič, B.; Svoboda, M.; Tegel, W.; Teodosiu, M.; Toromani, E.; Trotsiuk, V.; Turcu, D.; Weigel, R.; Wilmking, M.; Zang, C.; Zlatanov, T.; Trouet, V. (2022)
Dorado-Liñán, I.; Ayarzagüena, B.; Babst, F.; Xu, G.; Gil, L.; Battipaglia, G....
Nature Communications 13, 2015.
The mechanistic pathways connecting ocean-atmosphere variability and terrestrial productivity are well-established theoretically, but remain challenging to quantify empirically. Such quantification will greatly improve the assessment and prediction of changes in terrestrial carbon sequestration in response to dynamically induced climatic extremes. The jet stream latitude (JSL) over the North Atlantic-European domain provides a synthetic and robust physical framework that integrates climate variability not accounted for by atmospheric circulation patterns alone. Surface climate impacts of north-south summer JSL displacements are not uniform across Europe, but rather create a northwestern-southeastern dipole in forest productivity and radial-growth anomalies. Summer JSL variability over the eastern North Atlantic-European domain (5-40E) exerts the strongest impact on European beech, inducing anomalies of up to 30% in modelled gross primary productivity and 50% in radial tree growth. The net effects of JSL movements on terrestrial carbon fluxes depend on forest density, carbon stocks, and productivity imbalances across biogeographic regions.
Martinez del Castillo, E.; Zang, C.; Buras, A.; Hacket-Pain, A.; Esper, J.; Serrano-Notivoli, R.; Hartl, C.; Weigel, R.; Klesse, S.; Resco de Rios, V.; Scharnweber, T.; Dorado-Liñán, I.; van der Maaten-Theunissen, M.; van der Maaten, E.; Jump, A.; Mikac, S.; Banzragch, B.; Beck, W.; Cavin, L.; Claessens, H.; Čada, V.; Cufar, K.; Dulamsuren, C.; Gricar, J.; Gil-Pelegrín, E.; Janda, P.; Kazimirovic, M.; Kreyling, J.; Latte, N.; Leuschner, C.; Longares, L.; Menzel, A.; Merala, M.; Motta, R.; Muffler, L.; Nola, P.; Petritan, A.; Petritan, I.; Prislan, P.; Rubio-Cuadadro, Á.; Rydval, M.; Stajič, B.; Svoboda, M.; Toromani, E.; Trotsiuk, V.; Wilmking, M.; Zlatanov, T.; de Luis, M. (2022)
Martinez del Castillo, E.; Zang, C.; Buras, A.; Hacket-Pain, A.; Esper, J....
Communications Biology 5, 163.
The growth of past, present, and future forests was, is and will be affected by climate variability. This multifaceted relationship has been assessed in several regional studies, but spatially resolved, large-scale analyses are largely missing so far. Here we estimate recent changes in growth of 5800 beech trees (Fagus sylvatica L.) from 324 sites, representing the full geographic and climatic range of species. Future growth trends were predicted considering state-of-the-art climate scenarios. The validated models indicate growth declines across large region of the distribution in recent decades, and project severe future growth declines ranging from −20% to more than −50% by 2090, depending on the region and climate change scenario (i.e. CMIP6 SSP1-2.6 and SSP5-8.5). Forecasted forest productivity losses are most striking towards the southern distribution limit of Fagus sylvatica, in regions where persisting atmospheric high-pressure systems are expected to increase drought severity. The projected 21st century growth changes across Europe indicate serious ecological and economic consequences that require immediate forest adaptation.
Buras, A.; Rammig, A.; Zang, C. (2021)
Frontiers in Plant Science 12, 689220.
Forest decline, in course of climate change, has become a frequently observed phenomenon. Much of the observed decline has been associated with an increasing frequency of climate change induced hotter droughts while decline induced by flooding, late-frost, and storms also play an important role. As a consequence, tree mortality rates have increased across the globe. Despite numerous studies that have assessed forest decline and predisposing factors for tree mortality, we still lack an in-depth understanding of (I) underlying eco-physiological mechanisms, (II) the influence of varying environmental conditions related to soil, competition, and micro-climate, and (III) species-specific strategies to cope with prolonged environmental stress. To deepen our knowledge within this context, studying tree performance within larger networks seems a promising research avenue. Ideally such networks are already established during the actual period of environmental stress. One approach for identifying stressed forests suitable for such monitoring networks is to assess measures related to tree vitality in near real-time across large regions by means of satellite-borne remote sensing. Within this context, we introduce the European Forest Condition monitor (EFCM)—a remote-sensing based, freely available, interactive web information tool. The EFCM depicts forest greenness (as approximated using NDVI from MODIS at a spatial resolution of roughly 5.3 hectares) for the pixel-specific growing season across Europe and consequently allows for guiding research within the context of concurrent forest performance. To allow for inter-temporal comparability and account for pixel-specific features, all observations are set in relation to normalized difference vegetation index (NDVI) records over the monitoring period beginning in 2001. The EFCM provides both a quantile-based and a proportion-based product, thereby allowing for both relative and absolute comparison of forest greenness over the observational record. Based on six specific examples related to spring phenology, drought, late-frost, tree die-back on water-logged soils, an ice storm, and windthrow we exemplify how the EFCM may help identifying hotspots of extraordinary forest greenness. We discuss advantages and limitations when monitoring forest condition at large scales on the basis of moderate resolution remote sensing products to guide users toward an appropriate interpretation.
Senf, C.; Buras, A.; Zang, C.; Rammig, A.; Seidl, R. (2020)
Nature Communications 11, 6200.
Meyer, B.; Buras, A.; Rammig, A.; Zang, C. (2020)
Dendrochronologia 64, 125780.
Zang, C. (2020)
Shekhar, A.; Chen, J.; Bhattacharjee, S.; Buras, A.; Castro, A.; Zang, C.; Rammig, A. (2020)
Shekhar, A.; Chen, J.; Bhattacharjee, S.; Buras, A.; Castro, A.; Zang, C....
Remote Sensing 12, 3249 (19).
Buras, A.; Rammig, A.; Zang, C. (2020)
Dendrochronologia 63, 125746.
Schuldt, B.; Buras, A.; Arend, M.; Vitasse, Y.; Beierkuhnlein, C.; Damm, A.; Gharun, M.; Grams, T.; Hauck, M.; Hajek, P.; Hartmann, H.; Hiltbrunner, E.; Hoch, G.; Holloway-Phillips, M.; Körner, C.; Larysch, E.; Lübbe, T.; Nelson, D.; Rammig, A.; Rigling, A.; Rose, L.; Ruehr, N.; Schumann, K.; Weiser, F.; Werner, C.; Wohlgemuth, T.; Zang, C.; Kahmen, A. (2020)
Schuldt, B.; Buras, A.; Arend, M.; Vitasse, Y.; Beierkuhnlein, C.; Damm, A.; Gharun, M....
Basic and Applied Ecology 45, S. 86-103.
Papastefanou, P.; Zang, C.; Pugh, T.; Liu, D.; Grams, T.; Hickler, T.; Rammig, A. (2020)
Frontiers in Plant Science 11, 373, S. 1-13.
Castro, A.; Chen, J.; Zang, C.; Shekhar, A.; Jimenez, J.; Bhattacharjee, S.; Kindu, M.; Morales, V.; Rammig, A. (2020)
Castro, A.; Chen, J.; Zang, C.; Shekhar, A.; Jimenez, J.; Bhattacharjee, S.; Kindu, M....
Remote Sensing 12, 1202 (7).
Buras, A.; Rammig, A.; Zang, C. (2020)
Biogeosciences 17 (6), S. 1655-1672.
Zang, C.; Buras, A.; Esquivel-Muelbert, A.; Jump, A.; Rigling, A.; Rammig, A. (2020)
Global Change Biology 26 (2), S. 322-324.
Buras, A.; Meyer, B.; Zang, C. (2020)
Ländlicher Raum, 02/2020, S. 14-16.
Land, A.; Remmele, S.; Hofmann, J.; Reichle, D.; Eppli, M.; Zang, C.; Buras, A.; Hein, S.; Zimmermann, R. (2019)
Land, A.; Remmele, S.; Hofmann, J.; Reichle, D.; Eppli, M.; Zang, C.; Buras, A....
Climate of the Past 15 (5), S. 1677-1690.