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Which of the Following Events is Not Expected if Global Warming Continues

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1. Introduction

Climate models driven by different emission scenarios have repeatedly been applied to the question of how our climate will change in future. The results clearly show that there will be changes in the spatial and temporal patterns of temperature and precipitation distributions (Cassman & Wood 2005). How the patterns will change, and to which degree, however, is still subject to large uncertainties in many cases (IPCC 2007b). This poses some problems to impact analysts, as even small shifts in the mean or variability of climatic parameter distributions can lead to very large percentage changes in extremes (Trenberth 2012). In crop science, for example, not only the occurrence, but also the timing of extreme events is crucial to determine the accurate prediction of yields, as unseasonal extreme events have much stronger impacts (Collier et al. 2008; Luo 2011). These very immediate and sometimes catastrophic impacts have made the study of the relationship between weather variation and growth responses of plants the topic of many talks and publications (Monteith 2007).

In forest science, the focus lies more on the impacts of long-term changes in the mean of temperature and precipitation distributions on factors such as species composition and timber yield (e.g. Solomon 1986; Shugart et al. 2003; Eggers et al. 2008; Kellomaki et al. 2008; Dale et al. 2010). This is hardly surprising, considering the relative immunity of mature trees to changes in climate. Variability gains in importance, however, the closer species are to their physiological tolerances. Even short amounts of time beyond certain thresholds may limit successful regeneration (Honnay et al. 2002) or cause diebacks (Bigler et al. 2006) and thus affect species' ranges. Species distribution models using measures of extremes – in terms of inter-annual variability of climate parameters – thus show a clear improvement over models using only means when explaining species' range limits (Zimmermann et al. 2009). Species distribution modelling and related fields are special cases, however, because a change in variability has to be included explicitly as an explanatory variable, whereas modellers in other fields just use climate data series as input to run their models, and thus simulate changes in mean and variability as present in the data automatically. The problem with this is the aforementioned uncertainty about variabilities in the generated climate data series.

In this paper, I therefore want to carry out a sensitivity analysis to determine how important it is for impact analyses in the forest sector to account for the uncertainty in variability changes. Biomass, for example, decreases with an increasing variability in the precipitation distribution, and pine trees gain dominance over spruce trees on more water-limited sites (Bugmann & Pfister 2000). How sensitive other forest ecosystem services (ES) are to changes in variability has not been explored systematically yet, however. I thus want to expand on current knowledge, and study the influence of changes in climate variability on the provision of regulating, cultural, and supporting ES in the temperate forest zone of Europe. Furthermore, I want to determine whether diverse stands and monocultures of Fagus sylvatica L. and Picea abies Karst. show different sensitivities, and whether there are thresholds in the severity of change that may lead to drastically different responses. And lastly, I want to identify a possible 'worst case' and a corresponding 'best case' scenario of change for each forest type and ES.

The results of this study may lead to a clearer understanding of the importance of accounting for uncertainties in climate parameter variability and thus to a better understanding of the ranges of change in ES provision that may be expected in future.

2. Material and methods

2.1. Ecosystem services

Forests provide many goods for human use, such as timber, fibres, and non-wood forest products, but they also provide many services that ultimately benefit humankind: habitat for more than 50% of the world's known terrestrial plant and animal species, storage of about 50% of the world's terrestrial carbon stock, and protection of more than 75% of the world's freshwater (Shvidenko et al. 2005). A classification of these goods and services was attempted in the Millennium Ecosystem Assessment report (Millennium Ecosystem Asssessment 2003), which distinguishes provisioning, regulating, cultural, and supporting services. As only natural forests were simulated to avoid confounding impacts of forest management with impacts of climate change, no provisioning service (of timber) was included in the evaluation. Biomass per hectare (t∙ha −1∙a −1) was chosen to represent regulating services (potential for carbon storage and thus climate regulation), and cultural services were represented by a structural diversity index (Staudhammer & Lemay 2001), illustrating the common perception that diverse forests are more aesthetically pleasing than even-aged stands (e.g. Carvalho-Ribeiro & Lovett 2011). Shannon's diversity index is based on the diameter at breast height (DBH =pi ) of each species-specific cohort (all cohorts =s):

Lastly, productivity in terms of volume growth (m3∙ha −1∙a −1) was chosen to represent the supporting services that underpin the provision of all other ES.

2.2. Gradient of environmental conditions

For the systematic analysis of changes in ES provision caused by changes in temperature and precipitation distributions, an artificial gradient of environmental conditions covering the temperate zone of Europe was constructed. The site-specific weather variables for each month were determined as follows: First, a previously utilized gradient spanning environmental conditions from the cold to the dry tree line in Central Europe (e.g. Bugmann & Solomon 2000) was scrutinized for its climatic range, and the coldest measured long-term mean monthly temperature for each month was chosen to form the base series T1. Values in this series were then increased nine times in steps of 1 degree Celsius, resulting in 10 temperature series (). The same was carried out for precipitation, with the lowest measured long-term monthly precipitation sums chosen for series P1, and then increased nine times in steps of 24 cm per year (2 cm per month), also resulting in 10 series of baseline climate conditions ().

Table 1. Artificial gradient of long-term monthly mean temperatures, with series T1 based on the minimum values of an actual environmental gradient located in Switzerland. For series T2–T10 the mean monthly temperature was increased stepwise by one degree.

Table 2. Artificial gradient of long-term monthly precipitation sums, with series P1 based on the minimum values of an actual environmental gradient located in Switzerland. For series P2–P10 the monthly precipitation sum was increased stepwise by 2 cm per month (24 cm per year).

Each temperature series was subsequently combined once with each precipitation series, resulting in weather data for 100 distinct sites. Average soil characteristics with a water-holding capacity ('bucket size') of 15 cm and an intermediate nutrient availability of 70 kg nitrogen per hectare were universally assumed. In a first run of the 100 sites, all 30 native species currently parameterized for Europe ( in Rasche et al. 2012) were allowed to establish, in a second run the species list was limited to only F. sylvatica, and in a third one to P. abies, the two economically most important species in Europe.

2.3. Weather data for current and future climatic conditions

As described in Section 2.2, I used a previously defined environmental gradient as basis for the artificial gradient. The weather data for the original gradient were obtained from the Landscape Dynamics Unit at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), who used the DAYMET model (Thornton et al. 1997) to interpolate daily climate data to a resolution of 1 ha for the time period 1930–2006. The same institution also provided several scenarios of climate change for the gradient (Zimmermann et al. 2013), from which the trends and ranges of change were adopted.

For the scenarios of climate change, I first increased only the mean of the temperature distribution four times in steps of 1 degree Celsius (+0.5 in the winter and spring months, and +1.5 in the summer and fall ones; and thus on average +1 per annum), then only the standard deviation of the distribution four times in steps of 0.5, and then mean and standard deviation of the temperature distribution combined (Figure 1). Second, I changed the precipitation distribution in steps of 5% (+5% in the winter and spring months and –5% in the summer and fall ones; and thus on average ±0), then only the standard deviation in steps of 0.5, and then both combined. Lastly I changed both temperature and precipitation distributions simultaneously, first both means, then both standard deviations, and lastly all together (), thus resulting in 36 scenarios of climate change, and 10,800 simulations overall, when applied to each of the 100 site files and each of the three species scenarios. Each simulation was initialized under current climate conditions and run for 1500 years to reach a quasi-equilibrium. Then climatic conditions were changed linearly over a period of 100 years, until they reached the desired magnitude of change. Simulations were continued for another 1400 years, to again reach a quasi-equilibrium with the new climatic conditions. Values for biomass, productivity, and structural diversity were sampled from these two equilibria.

Figure 1. Presumed changes in the distribution of temperatures (adapted from IPCC 2012), with a) changes in the mean (μ), b) changes in the variability (σ), and c) changes in both. Numbers 1–4 refer to the different degrees of change (+1 to +4°C, +0.5 to +2 standard deviations).

Table 3. Simulated changes in seasonal climate variables (μ average, σ standard deviation, T Temperature, and P precipitation). The following combinations were considered: ΔμT, ΔσT, (ΔμT + ΔσT), ΔμP, ΔσP, (ΔμP + ΔσP), (ΔμT + ΔμP), (ΔσT + ΔσP), and (ΔμT + ΔσT + ΔμP + ΔσP).

2.4. The forest model

The concept of the model ForClim (Bugmann 1996), which was used in this study, is based on the theory of forest succession (Watt 1947), which states that stand dynamics as a whole can be represented by the individual succession of trees on small patches of land (Shugart 1984). Based on this reasoning, ForClim simulates establishment, growth, and mortality of trees on typically 50–200 patches, each with the size of 800 m2, small enough to be potentially dominated by a single tree.

The biotic processes simulated in ForClim are driven by the abiotic environment, represented for each site by the soil variables water-holding capacity and available nitrogen, and the monthly weather variables long-term mean of temperature and its standard deviation, long-term mean sum of precipitation and its standard deviation, and cross-correlation of both temperature and precipitation. Monthly values are necessary to account for different growing season lengths of deciduous and evergreen species. Actual values for each month are generated by stochastically sampling from the respective long-term statistics of monthly temperature means and precipitation sums. A different realization is generated for each of the 500 patches simulated per site. This implementation facilitates an easy manipulation of the mean of the distribution, the standard deviation, or both, but it should also be mentioned that it makes it impossible to portray very short, very extreme events. Only extreme years or clusters of extreme years can be simulated. Considering the model's time step of one year and the long simulation time, however, this limitation can be considered as negligible.

The variables of the abiotic environment are translated into bioclimatic variables that influence establishment and growth of trees. Establishment is determined by minimum winter temperature, growing season temperature, growing season soil moisture, and light availability, all of which have to be favourable for establishment to succeed. Tree growth is simulated according to Moore's (1989) carbon budget approach, modified by Risch et al. (2005), Didion et al. (2009), and Rasche et al. (2012), where an optimal growth rate is reduced based on light and nitrogen availability, growing season temperature, growing season soil moisture, and crown length. The resulting volume growth is allocated dynamically to height and diameter growth, based on available light and the shade tolerance of the trees. Tree mortality is triggered by an age-related and a stress-induced component.

The model was tested and validated against a variety of empirical data (e.g. national forest inventories, long-term growth, and yield research plots), in terms of both general applicability and precision of single processes, and showed good results in terms of simulated species composition, growth rates, biomass, harvested timber, diameter and height distributions, and several other tree-based variables (see, e.g., Bugmann & Cramer 1998; Wehrli et al. 2007; Didion et al. 2009; Heiri 2009; Rasche et al. 2011, 2012). A detailed mathematical description of the model can be found in Bugmann (1994).

3. Results

The importance of changes in climate parameter variability can only be determined in relation to the impacts of changes in the mean. For this reason, both cases are discussed in the following.

3.1. Diverse forests

An increase in mean temperature has mainly positive effects on all three ES in diverse forests (Figure 2). The main cause of this effect is a gain in productivity on currently temperature-limited sites through a general lengthening of growing season and a shift in species composition towards more productive species. With increasing severity of change, however, negative effects increase in number on warm and dry sites (> 9°C, < 116 cm) when considering productivity and structural diversity. A shift in mean precipitation results in a rising number of negative effects on biomass and productivity on the drier sites. Structural diversity is almost insensitive to shifts in mean precipitation. In the combination simulations of both mean temperature and precipitation changes, the effects of temperature shift dominate for structural diversity and productivity, whereas on the drier sites of the gradient the response of biomass is directed by the negative influence of the precipitation shift.

Figure 2. (colour online) Simulated current levels of ES (small plots on left-hand side) and significant changes in ES levels (main plots) on multi-species sites. Each square represents 100 sites with different environmental conditions, ranging from cold and dry (5°C, 44 cm) to warm and moist (14°C, 260 cm). Simulated were changes in the mean (μ), the variability (σ), and a combination of both of only the temperature distribution (T), only the precipitation distribution (P), and both simultaneously. Numbers on the column tops refer to the severities of change (see ). White squares did not experience a significant change (Student's t-test), green squares indicate significant positive changes, and red squares with points indicate significant negative changes.

An increase in temperature variability mainly has negative effects, and most strongly influences structural diversity, to a lesser degree productivity, and, with increasing severity of change, biomass on the drier sites. An increase in precipitation variability negatively influences biomass on the drier half of the gradient and productivity on the warmer sites, increasing in extent with increasing severity of change, and positively influences structural diversity on the drier sites, and productivity on the very dry (44 cm) sites. A closer analysis of the results revealed that the dominant species on these sites was simulated to be Pinus sylvestris L., the most drought-tolerant species currently parameterized for ForClim. As the trees were already drought stressed in the simulations under current climate, their productivity was not significantly reduced under the scenario of change. It actually increased when the greater variability in precipitation occasionally produced values above the species' drought tolerance. In the combination simulations of temperature and precipitation variability changes, the results indicate a true combination of both effects, with neither temperature nor precipitation dominating.

In combination simulations of mean and variability changes, the effect of a shift in the mean dominates the effect of a shift in variability, at least concerning productivity and structural diversity. Biomass, on the other hand, is strongly influenced by an increase in the variability of precipitation, and consequently the effects of this change dominate the results in the combination simulations. In both cases, there is no cumulation of the effects of changes in mean and variability.

3.2. F. sylvatica monocultures

An increase in mean temperature has mainly positive effects on all three ES, with the exception of biomass and productivity on the driest sites warmer than 8°C (Figure 3). A shift in mean precipitation has only few significant effects on all three ES, but those are almost exclusively negative: on dry sites (68 cm and lower) values of ES decrease significantly, affecting more sites with an increasing severity of change. In the combination simulations of both temperature and precipitation mean changes the effects are dominated by the changes in mean temperature.

Figure 3. (colour online) Simulated current levels of ES (small plots on left-hand side) and significant changes in ES levels (main plots) in F. sylvatica monocultures. See caption Figure 2 for more details.

An increase in temperature variability has positive effects on the colder sites of the gradient (<8°C), as more years reaching the species-specific required minimal number of degree days for growth are simulated, thus resulting in significant positive changes. Negative impacts are more abundant, however, beginning at sites with average annual temperatures of 8°C and continuing towards sites with higher temperatures as the severity of change increases. An analysis of simulation results revealed that this slightly surprising pattern could be explained by a higher abundance of winters with temperatures below what can be tolerated by F. sylvatica seedlings for successful establishment. The bigger the variability was simulated to be, the warmer the sites that occasionally experienced very cold winters tended to get. Colder sites were not concerned, as winter temperatures already influenced establishment numbers under the current climate. An increase in precipitation variability negatively influences all three ES on the drier sites, increasing in extent with increasing strength, as the drought tolerance of F. sylvatica trees is exceeded more often. On the very dry sites (<68 cm), however, an increase is simulated for all three ES, showing that on sites where drought is severe already in the beginning, a change in variability results in more years where drought is not as extreme, and thus growth is enhanced. This shows the importance of extreme events on the upper end of the precipitation distribution. In the combination simulations of temperature and precipitation variability change, the influence of temperature variability dominates the results, but the positive changes on very dry sites due to precipitation variability changes are also visible.

In combination simulations of mean and variability changes, a shift in the mean dominates over a shift in variability; yet if the effect of increasing variability is negative (e.g. for biomass), this negative effect shows as well.

3.3. P. abies monocultures

An increase in mean temperature has positive effects for biomass and productivity on the colder and moister part of the gradient (<9°C, >68 cm) and negative effects on the warmer sites (Figure 4). The negative changes on the warm sites are due to failed establishments caused by a lack of chilling in winter, which indicates that the distribution boundary of P. abies was reached. The effects on structural diversity are overwhelmingly negative, leaving almost no site untouched. A shift in mean precipitation has few impacts; however, on the dry sites (<92 cm) there are increasingly negative impacts for all three ES, and occasional positive impacts on structural diversity. Owing to the overall low impact precipitation changes have, in the combination simulations of both temperature and precipitation changes the changes in mean temperature dominate the overall effects.

Figure 4. (colour online) Simulated current levels of ES (small plots on left-hand side) and significant changes in ES levels (main plots) in P. abies monocultures. See caption Figure 2 for more details.

An increase in temperature variability has negative impacts on the very cold sites (<6°C) for biomass and structural diversity and an increasingly positive effect on warm sites (>10°C) for all three ES. An analysis of the results revealed that lower simulated maximum winter temperatures were responsible for this, as they allowed higher establishment rates, and as a consequence higher values of ES. This shows the importance of extreme events on the lower end of the temperature distribution, as successful establishment and sapling growth of P. abies trees have to occur in years colder than the future norm. An increase in precipitation variability has positive effects on the very dry sites (44 cm) for all three ES, showing that on sites where drought is severe already in the beginning, a change in variability results in more years where drought is not as extreme and thus growth is enhanced, and occasionally positive impacts on the rest of the sites for structural diversity. Negative effects are overall more abundant, however, growing in number as the severity of change increases and affecting the dry/warm sites for biomass and productivity. When both temperature and precipitation variabilities are changed simultaneously, precipitation variability dominates the response of biomass, whereas temperature variability dominates the response of productivity and structural diversity.

When both the mean and the standard deviation of the climate variable distributions are changed, the effect of a shift in mean dominates the effect of a shift in variability, at least concerning productivity and structural diversity. Biomass is again more strongly influenced by an increase in variability. On the warmer sites (>10°C), a higher variability in temperature can slightly ameliorate the negative effects a higher mean temperature has on the level of ES.

3.4. Comparison of the impact on different ES

In diverse stands, productivity and structural diversity were negatively affected mostly on hot sites by an increase in temperature variability and positively by an increase in mean temperature on colder ones. Biomass, on the one hand, reacted most strongly to a change in precipitation. In beech monocultures, ES were affected in the same broad patterns. In spruce monocultures, biomass and productivity reacted similarly as well, yet biomass clearly experienced a higher impact through precipitation changes than productivity. Structural diversity, on the other hand, was much more sensitive to changes in mean temperature than the other two ES. Nearly every site experienced a significant loss in structural diversity, even on sites where biomass and productivity were simulated to rise. An analysis of the diameter distributions before and after climate change revealed that a reduction of trees in the smaller diameter classes was responsible for this. On formerly temperature-limited sites, trees grew to be bigger and prohibited larger establishment numbers through shading, and on drought-limited sites lack of precipitation was the cause.

4. Discussion

The results show that changes in variability yield a lower number of significant changes in forest ES levels than changes in the mean, regardless of forest type. Yet with the exception of P. abies monocultures, these changes are projected to be almost exclusively negative and to increase in number the greater the change in variability becomes. Negative changes are also not exclusively focused on the distributional edges of the species; in the diverse forest scenario, losses in structural diversity and productivity can be observed over a broad gradient of environmental conditions. Almost the exact opposite is true for changes in the mean of climate parameter distributions: A high number of significant and often positive changes are projected to take place, which are focused mainly on currently temperature-limited sites, indicating a gain in growth potential due to a lengthening of the growing season and shifts towards more productive species in diverse stands (cf. e.g. Eggers et al. 2008; Albert & Schmidt 2010; Lindner et al. 2010).

The implication of this result for impact analyses regarding forest ES is that the overall trend in ES provision is well represented even if only the means of climate parameter distributions are changed, but that at the physiological limits of species, changes in variability have to be accounted for in order to realistically assess impacts (cf. Zimmermann et al. 2009).

This sensitivity study also highlights possible best and worst case scenarios for the level of ES provided by stands of different species. For the ES produced by diverse and pure F. sylvatica stands, it would be fortuitous if climate change mainly involved a change in the mean of the temperature distribution (cf. Bergh et al. 2003; Albert & Schmidt 2010), and less favourable if a change in variability was involved – temperature variability for structural diversity, and variability of the precipitation distribution for biomass (cf. Bugmann & Pfister 2000) and productivity. However, there is some evidence that species' genetic diversity – a factor not included in the ForClim model – may be sufficient for a good performance even under extreme conditions (Beierkuhnlein et al. 2011).

In P. abies stands, biomass and productivity are projected to benefit most from a change in temperature variability, whereas structural diversity would benefit most from a change in both temperature and precipitation variability. The worst case would be if both the mean of temperature and precipitation changed, as even today, several P. abies stands are at their natural limit in Europe (Kölling et al. 2009; Yousefpour et al. 2010), and are pushed beyond this threshold in the simulations of climate change. Although drought is an important factor in this development, failed establishment on very warm sites is simulated to be the more important one overall (cf. Young & Hanover 1978; Dumais & Prévost 2007). Only natural succession was simulated in this study, but other studies suggest that planted trees may be affected as well, going so far as to warn that forest management may in future require repeated replanting of sensitive seedlings, regardless of how well-adapted they might be as adults (Fuhrer et al. 2006). However, if only the strength of extreme frost events decrease without too high winter temperatures hindering establishment, an increase in productivity is simulated, and observed (e.g. Rammig et al. 2010).

Some of the 'best case' scenarios are of course highly unlikely. It is certain that mean temperatures have already risen and will further rise in future (IPCC 2007a), so that a change in variability only is improbable. It is also unlikely that only a shift in mean temperature will occur. The temperatures of Europe's 2003 heat summer, for example, can only be explained by a combined shift of the statistical distribution towards warmer temperatures and an increase in variability (Schär et al. 2004). In Austria, however, temperature variability has evolved independently of mean temperature in the last 140 years and decadal trends of variability hardly exceed + /- 1 standard deviation (Hiebl & Hofstätter 2012). Precipitation patterns are subject to even more uncertainty than temperature. It is assumed that there will be a greatly increased risk of extreme precipitation events (Coumou & Rahmstorf 2012), yet which changes in the rainfall distribution will be responsible for this is not sure. Studies of the past climate indicate that the shape parameter of the precipitation distribution may remain relatively stable, whereas the scale parameter varies spatially and temporally (Groisman et al. 1999; Alexander et al. 2006).

In addition to changes in temperature and precipitation, the level of CO2 in the atmosphere is projected to rise (IPCC 2007a), a feature not considered in this study to facilitate the exact attribution of the results to either temperature or precipitation effects, and thus to the underlying growth processes. If this factor and other factors such as pathogens or mechanical disturbances were included, results may look different (cf. Dale et al. 2001; Seidl et al. 2008; Bugmann & Bigler 2011). It should also be mentioned that neither heat waves nor extremes in precipitation would most likely pose the most serious risk to forests, but winter storms will (Fuhrer et al. 2006). However, wind is not conclusively coupled to climate change (Coumou & Rahmstorf 2012), and was therefore not incorporated into this study.

5. Conclusions

For the study of climate change impacts on the levels of forest ES in the temperate zone of Europe, uncertainty about climate parameter variability is of secondary importance. The trend in the provision of ES is well represented with scenarios of mean climate parameter changes only, but on moisture-limited and heat-stressed sites impacts of changes in variability gain in importance. Most of these impacts are negative, and can be observed not only in stands populated with a single species already at its physiological limit, but also in diverse stands, indicating that the current species diversity of temperate forests in Europe is not enough to retain the current levels of ES provision. The sensitivity to the changes is different for different ES, with biomass, for instance, being more influenced by changes in precipitation, and productivity and structural diversity by changes in temperature. Changes in temperature variability ameliorate the negative impacts on structural diversity, whereas they mostly aggravate impacts on the other ES.

The results underline the need to develop adaptation measures tailored to the sites and ES of interest, and also show that although trends are sufficiently described by changes in the mean of climate variables, for the fine-tuning of measures the consideration of climate variability is required.

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Source: https://www.tandfonline.com/doi/full/10.1080/21513732.2014.939719

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