Michael Meier

Last Name

Meier

First Name

Michael

ORCID

0000-0003-1846-6071

Show all metadata

Search Results

Publications 1 - 8 of 8

Climate change-induced shifts in leaf phenology of trees: past and future trends
Meier, Michael (2023)
Forests are coupled to the atmosphere, for example by the carbon and water cycles, which results in forest-atmosphere interactions. Regarding the carbon cycle, forests assimilate and respire CO2 during processes such as photosynthesis and decomposition. Thereby, forests may function as a carbon sink or source, depending on their productivity and net assimilation. In addition, sugars are synthesised during photosynthesis and assigned to growth, reproduction, and defence, which affects the fitness of the trees and eventually the distribution of species. Climate change has strong impacts on these processes, altering the productivity of forests and the distribution of species, which in turn feeds back to the climate. To improve our understanding of these interactions, I studied changes in the photosynthetically active period of trees (i.e., the ‘growing season’) and changes in the climatic conditions to which trees are exposed during their growing season (i.e., the ‘bioclimate’). For deciduous trees, the beginning and end of the growing season relate to spring and autumn leaf phenology (i.e., leaf unfolding and leaf senescence), respectively. Climate change alters the timing of leaf unfolding and leaf senescence, which in turn influences the timing and length of the growing season. Thus, the bioclimate is directly influenced by climate change and indirectly by changing growing seasons. While past changes in leaf phenology can be analysed by in-situ observations, the underlying drivers and particularly possible future changes can be determined by evaluating simulations with process-oriented models. In my doctoral thesis, I assessed (1) changes in the growing season and bioclimate in Switzerland during recent decades, (2) the impact of the calibration approach on simulated leaf senescence for the past and the future in Central Europe, and (3) biases in these model simulations, together with the resulting distortions in their accuracy and projections. Chapter 1 focuses on past trends in the growing season and bioclimate, which were estimated with linear-mixed effects models. I studied leaf unfolding and leaf senescence as well as the resulting length of the growing season, while the bioclimate was described by sunshine duration, degree-days, precipitation, and a drought index (i.e., the number of days with a negative atmospheric water balance). The growing season has predominately lengthened, depending on elevation, which was primarily caused by changes in leaf senescence. Drought intensity has generally increased, and the increase has been amplified by changes in leaf unfolding and leaf senescence at low elevations, but weakened at high elevations. Chapter 2 analyses past and future simulations of leaf senescence by paying special attention to the impact of the calibration approach (i.e., optimisation algorithm and sampling design). The simulations were based on 21 state-of-the-art process-oriented models that were calibrated with five optimisation algorithms, each executed in a normal and an extensive mode, based on at least seven sampling designs. Since some models are driven by leaf unfolding, I assumed a moderate advancement of 0.2 days per year for the projections. To assess model performance, I calculated the root mean squared error. To assess model projections, I calculated the shift between the observations during 1980-1999 and the simulation during 2080-2099. The structure of the leaf senescence models had the largest influence on model performance, followed by optimisation algorithms. Best performance was obtained with the Generalized Simulated Annealing algorithm and with systematically balanced or stratified samples. Compared to 1980-1999, the models predicted a shift in leaf senescence between -13 and +20 days. Chapter 3 concentrates on distortions in model performance and model projections due to the bias towards the mean in state-of-the-art process-oriented models. This bias results in simulated values that are closer to the average than the observations. Therefore, I expected the model error to increase with increasing difference in the average leaf senescence between the calibration and validation sample. Similarly, I expected the projected shifts to decrease with increasing difference in average leaf senescence between the calibration sample and the observations that served as reference for the shifts. Model performance was assessed by the mean bias, root mean squared error, and Nash-Sutcliffe efficiency. Model projections were assessed by the shift between reference observations (1980-1999) and future simulations (2080-2099). As expected, model performance eroded at model-specific rates with increasing difference between the calibration and validation sample. Different models performed best on validation samples with identical vs. very different average leaf senescence to the calibration sample. The projected shifts of leaf senescence became significantly smaller when the difference between the calibration sample and the reference observations increased. Undistorted shifts were 10-13 days larger than distorted shifts and revealed that leaf senescence was mostly delayed under moderate and extreme climate warming. Overall, my thesis extends our knowledge on the combined impact of climate and leaf phenology on the bioclimate during the growing season of trees and on the impacts of model structure, calibration approach and bias towards the mean on simulated leaf senescence. Chapter 1 demonstrates that the effect of phenological changes on the bioclimate should be included when studying past and future forest productivity and species composition. Chapter 2 supports conclusions from process-oriented models on underlying processes and drivers of leaf senescence and shows which calibration approaches lead to the most accurate simulations. Chapter 3 illustrates the need and potential for the development of more accurate process-oriented models. Until then, I have to conclude that current state-of-the-art models are unsuitable for reliable projections of leaf senescence.
Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections
Meier, Michael; Bigler, Christof (2023)
Geoscientific Model Development
Autumn leaf phenology marks the end of the growing season, during which trees assimilate atmospheric CO₂. The length of the growing season is affected by climate change because autumn phenology responds to climatic conditions. Thus, the timing of autumn phenology is often modeled to assess possible climate change effects on future CO₂-mitigating capacities and species compositions of forests. Projected trends have been mainly discussed with regards to model performance and climate change scenarios. However, there has been no systematic and thorough evaluation of how performance and projections are affected by the calibration approach. Here, we analyzed >2.3 million performances and 39 million projections across 21 process-oriented models of autumn leaf phenology, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for beech, oak, and larch from 500 central European sites each. Phenology models had the largest influence on model performance. The best-performing models were (1) driven by daily temperature, day length, and partly by seasonal temperature or spring leaf phenology; (2) calibrated with the generalized simulated annealing algorithm; and (3) based on systematically balanced or stratified samples. Autumn phenology was projected to shift between −13 and +20 d by 2080–2099 compared to 1980–1999. Climate scenarios and sites explained more than 80 % of the variance in these shifts and thus had an influence 8 to 22 times greater than the phenology models. Warmer climate scenarios and better-performing models predominantly projected larger backward shifts than cooler scenarios and poorer models. Our results justify inferences from comparisons of process-oriented phenology models to phenology-driving processes, and we advocate for species-specific models for such analyses and subsequent projections. For sound calibration, we recommend a combination of cross-validations and independent tests, using randomly selected sites from stratified bins based on mean annual temperature and average autumn phenology, respectively. Poor performance and little influence of phenology models on autumn phenology projections suggest that current models are overlooking relevant drivers. While the uncertain projections indicate an extension of the growing season, further studies are needed to develop models that adequately consider the relevant processes for autumn phenology.
Phenological shifts induced by climate change amplify drought for broad-leaved trees at low elevations in Switzerland
Meier, Michael; Vitasse, Yann; Bugmann, Harald; et al. (2021)
Agricultural and Forest Meteorology
Climate change alters the bioclimatic conditions during the growing period of trees directly, but also indirectly by causing shifts in spring and autumn leaf phenology that lead to changes in the timing and length of the growing period. Several studies researched the ecological consequences of direct climate change effects on bioclimatic conditions during the growing period of trees. However, the complementary and indirect effects through phenological shifts on these conditions have been insufficiently investigated. We analysed 49088 leaf unfolding and leaf colouring dates of six major European tree species from Switzerland, observed between 200 and 1900 m a.s.l. during 1961–2018. We estimated phenological trends, the resulting changes in bioclimatic conditions during the growing period, and the relative contributions of phenological shifts towards these changes. Our results show that climate change advanced leaf unfolding by up to –3.0 days/decade since 1985. Leaf colouring was mainly delayed at low elevations and was advanced or delayed at high elevations with species-specific rates between –3.1 and +4.0 days/decade. While the length of the growing period and growing degree-days increased for most species after 1985, precipitation during the growing period predominantly decreased by up to –43.6 mm/decade. Furthermore, drought intensity during the growing period (based on the number of days with negative water balance) increased significantly for most species, reaching +6.7 days/decade at low elevations. Phenological shifts amplified the trends towards drier conditions by up to +81% at low elevations for beech, rowan, and sycamore, but weakened them by up to –84% at high elevations for beech, rowan, sycamore, and larch. These findings indicate widely increased drought stress, especially at lower elevations. Further, we conclude that future forest net ecosystem productivity in Central Europe will depend strongly on elevation and species composition, despite a general lengthening of the growing period for trees.
Coincidence of temperature extremes and phenological events of grapevines
Templ, Barbara; Templ, Matthias; Barbieri, Roberto; et al. (2021)
OENO One
A growing number of studies have highlighted the consequences of climate change on agriculture, including the impacts of climate extremes such as drought, heat waves and frost. The aim of this study was to assess the influence of temperature extremes on various phenological events of grapevine varieties in Southwest Switzerland (Leytron, Canton of Valais). We aimed to capture the occurrence of extreme events in specific years in various grapevine varieties and at different phenological phases to rank the varieties based on their sensitivity to temperature extremes and thus quantify their robustness. Phenological observations (1978–2018) of six Vitis vinifera varieties (Arvine, Chardonnay, Chasselas, Gamay, Pinot noir and Syrah) were subjected to event coincidence analysis. Extreme events were defined as values in the uppermost or lowermost percentiles of the timing of the phenophases and daily temperatures within a 30-day window before the phenophase event occurred. Significantly more extreme temperature and phenological events occurred in Leytron between 2003 and 2017 than in the earlier years, with the years 2007, 2011, 2014 and 2017 being remarkable in terms of the number of extreme coincidence events. Moreover, bud development and flowering experienced significantly more extreme coincidence events than other phenophases; however, the occurrence rate of extreme coincidence events was independent of the phenophase. Based on the total number of extreme events, the varieties did not differ in their responses to temperature extremes. Therefore, event coincidence analysis is an appropriate tool to quantify the occurrence of extreme events. The occurrence of extreme temperature events clearly affected the advancement of the timings of phenological events in various grapevines. However, there were no varietal differences in terms of response to extreme temperatures; thus, additional research is warranted to outline the best adaptation measures. © 2021 International Viticulture and Enology Society
Process‐oriented models of leaf senescence are biased towards the mean: Impacts on model performance and future projections
Meier, Michael; Bugmann, Harald; Bigler, Christof (2024)
Global Change Biology
The timing of leaf senescence in deciduous trees influences carbon uptake and the resources available for tree growth, defense, and reproduction. Therefore, simulated biosphere-atmosphere interactions and, eventually, estimates of the biospheric climate change mitigation potential are affected by the accuracy of process-oriented leaf senescence models. However, current leaf senescence models are likely to suffer from a bias towards the mean (BTM). This may lead to overly flat trends, whereby errors would increase with increasing difference from the average timing of leaf senescence, ultimately distorting model performance and projected future shifts. However, such effects of the BTM on model performance and future shifts have rarely been investigated. We analyzed >17 × 10⁶ past dates and >49 × 10⁶ future shifts of leaf senescence simulated by 21 process-oriented models that had been calibrated with >45,000 observations from Central Europe for three major European tree species. The surmised effects on model performance and future shifts occurred in all 21 models, revealing strong model-specific BTM. In general, the models performed only slightly better than a null model that just simulates the average timing of leaf senescence. While standard comparisons of model performance favored models with stronger BTM, future shifts of leaf senescence were smaller when projected by models with weaker BTM. Overall, the future shifts for 2090–2099 relative to 1990–1999 increased by an average of 13–14 days after correcting for the BTM. In conclusion, the BTM substantially affects simulations by state-of-the-art leaf senescence models, which compromises model comparisons and distorts projections of future shifts. Smaller shifts result from flatter trends associated with stronger BTM. Therefore, smaller shifts according to models with weaker BTM illustrate the considerable uncertainty in current leaf senescence projections. It is likely that state-of-the-art projections of future biosphere behavior under global change are distorted by erroneous leaf senescence models.
Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections
Meier, Michael; Bigler, Christof (2023)
EGUsphere
Autumn leaf phenology marks the end of the growing season, during which trees assimilate atmospheric CO2. Since autumn leaf phenology responds to climatic conditions, climate change affects the length of the growing season. Thus, autumn leaf phenology is often modelled to assess possible climate change effects on future CO2 mitigating capacities and species compositions of forests. Projected trends have been mainly discussed with regards to model performance and climate change scenarios. However, there has been no systematic and thorough evaluation of how performance and projections are affected by the calibration approach. Here, we analyzed >2.3 million performances and 39 million projections across 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for beech, oak, and larch from 500 Central European sites each. Phenology models had the largest influence on model performance. The best performing models were (1) driven by daily temperature, day length, and partly by seasonal temperature or spring leaf phenology and (2) calibrated with the Generalized Simulated Annealing algorithm (3) based on systematically balanced or stratified samples. Assuming an advancing spring phenology, projected autumn phenology shifts between 13 and +20 days by 2080–2099, resulting in a lengthening of the growing season by 7–40 days. Climate scenarios and sites explained more than 80 % of the variance in these shifts and thus had eight to 22 times the influence of phenology models. Warmer climate scenarios and better performing models predominantly extended the growing season more than cooler scenarios and poorer models. Our results justify inferences from comparisons of process-oriented phenology models to phenology-driving processes and we advocate species-specific models for such analyses and subsequent projections. For sound calibration, we recommend a combination of cross-validations and independent tests, using randomly selected sites from stratified bins based on mean annual temperature and average autumn phenology, respectively. Poor performance and little influence of phenology models on autumn phenology projections suggest that the models are overlooking relevant drivers. While the uncertain projections indicate an extension of the growing season, further studies are needed to develop models that adequately consider the relevant processes for autumn phenology.
Temperature rather than individual growing period length determines radial growth of sessile oak in the Pyrenees
Charlet de Sauvage, Justine; Vitasse, Yann; Meier, Michael; et al. (2022)
Agricultural and Forest Meteorology
Tree growth and leaf phenology are both affected by global warming. Mountain ecosystems are of paramount importance for studying phenological and growth responses of trees to the gradual variation of temperature. However, the relationship between growing season length and tree growth has been little studied at the level of individual trees. Here, we investigated the relationships between leaf phenology, growing degree-days and radial growth of sessile oaks growing in nine populations along an elevational gradient of 1500 m in the French Pyrenees. In each population, leaf unfolding in spring and leaf coloration in autumn were monitored between 2005 and 2015 on 25-30 trees having contrasting spring phenology (i.e. early vs. late flushing trees). These trees were cored in 2013 to analyse annual tree-ring widths. While trees displayed consistent phenological ranks for both leaf unfolding and leaf coloration within their population over the years, the growing period length decreased with increasing elevation, from about 210 days at the lowest elevation (131 m a.s.l.) to 140 days at the highest elevation (1630 m a.s.l.). For a given year, individual leaf coloration dates correlated positively with leaf unfolding dates at lower elevations, but negatively at higher elevations. Radial growth was positively correlated with growing degree-days at higher elevations, but negatively correlated at lower elevations, likely because higher temperatures are often associated with severe droughts in the lowlands of this region. No clear relationship was found between growing period length and radial growth of oaks within their population. This indicates that climatic conditions during the growing period have a more important impact on the secondary growth of sessile oaks than the growing period length. Our findings suggest that the lengthening of the growing period of trees in response to global warming does not necessarily lead to higher radial growth and productivity.
Aging and stress explain the earlier start of leaf senescence in trees in warmer years: translating the latest findings on senescence regulation into the DP3 model (v1.1)
Meier, Michael; Bigler, Christof; Chuine, Isabelle (2025)
Geoscientific Model Development
Leaf senescence ends the growing season of deciduous trees, affecting the amount of atmospheric CO2 sequestered by forests. Therefore, some climate models integrate projected leaf senescence dates to simulate the carbon cycle. Here, we developed a process-oriented model of leaf senescence (the “DP3 model”) by testing 34 formulations of the leaf development process based on the latest findings on the regulation of leaf aging and senescence. The period between leaf unfolding and leaf senescence was separated into the subsequent young, mature, and old leaf phases, with particular reactions to leaf aging and cold stress, photoperiod stress, and dry stress. The DP3 model simulates daily rates of aging and stress to predict dates of transition from young to mature to old leaf, senescence induction dates, and leaf senescence dates. This allows new hypotheses regarding the regulation of leaf senescence to be tested. For example, the DP3 model predicted an earlier onset of senescence in warmer conditions, likely due to earlier leaf unfolding (aging) and increased cold and dry stress in spring, together with longer-lasting senescence, likely due to the later accumulation of photoperiod stress relative to leaf development and decreased cold stress in summer and fall, which can be validated through experiments and in situ observations. The DP3 model and compared previous models were equally accurate but less accurate than the Null model (average senescence date observed in the calibration sample). This lower accuracy of the DP3 and compared models is likely due to noise in the visually observed leaf senescence data, which blurs the signal of the leaf senescence process, and to incorrect model formulations. The model errors were similarly affected by climate conditions and location among compared models (including the Null model) and varied mostly due to the leaf senescence data. Noisy leaf senescence data likely force the models to resort to the mean observation, impeding inferences from accuracy-based model comparisons about the leaf senescence process. This calls for revised observation protocols and methods that measure rather than estimate different senescence stages, such as senescence induction and 50 % of the leaves having changed color, e.g., based on greenness, involving digital cameras and automated image assessment.

Publications 1 - 8 of 8

Michael Meier

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results