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Climate changes of the past millennium reconstructed from high-resolution pollen records

By: K. Gajewski
Département de géographie
Université d'Ottawa
Ottawa, Ontario
K1N 6N5 Canada

January 1996

Citation for this paper: K. Gajewski. 1998. Climate changes of the past millennium reconstructed from high-resolution pollen records. in: Don C MacIver and Rebecca E Meyer, eds. Proceedings of the workshop on decoding Canada's environmental past: Climate variations and biodiversity change during the last millenium.  Atmospheric Environment Service. Downsview.

Abstract

The pollen assemblages in lake sediments can be used to reconstruct changes through time in the vegetation around the site. Since the vegetation composition is a function, in part, of the regional climate, climate changes can be estimated from these pollen diagrams. The temporal resolution of the vegetation and climate histories depends on a number of factors including sedimentation rate and precision of the chronology. With varved sediments, accurate chronologies can be secured, and high-resolution vegetation and climate histories can be obtained.

Using an example of a series of sites from the conifer-hardwood forest of eastern North America, vegetation changes of the past 1000 years are illustrated. High-resolution pollen diagrams contain variations of several scales, which may arise from different causes. Methods for analyzing these data will be discussed, and climate changes estimated from these data.

Introduction

Of the many scales of climate change, those from decade to century have proven among the most difficult to understand (Gajewski, 1993). The impact of climate variations of this scale on vegetation is also little understood. Understanding the climate and vegetation of the past 1000 years may help us to determine how future climate changes may impact the vegetation.

Records of vegetation change through time are recorded in the pollen buried in lake sediments. What is the potential resolution of these records? A number of processes operate on vegetation at timescales of decades to centuries, including climate changes, fires, and biological interactions such as reproduction cycles and succession. The question of interest here is how well rapid climate changes are recorded by pollen records. Do these climate variations impact the vegetation, and, if so, can we use this to better understand their spatial and temporal patterns.

High resolution records of vegetation change

Records of past vegetation can be found in lake sediments, where the pollen can be extracted and analyzed. Pollen grains are identifiable, although rarely to species, and are very resistant to decay, especially in acidic or anoxic conditions. The pollen grains are well mixed in the air, and thus the pollen arriving in a lake is an integration of the pollen from the catchment area. Since pollen grains are small and many are produced, these grains are a statistical sample of the pollen rain. The composition of the pollen assemblages is related to the vegetation (Birks and Birks, 1980).

Pollen diagrams can therefore be used to determine changes in the vegetation through time, within certain spatial and temporal limits of resolution (e.g. Solomon and Webb, 1985; articles in Huntley and Webb, 1988). Determining the spatial and temporal resolution of past vegetation that can be obtained from pollen diagrams is an important area of research at the present time. The spatial resolution of the vegetation that can be interpreted is a complex function of many factors including sediment type, lake size and vegetation type (e.g. Bradshaw, 1988 and references therein).

The temporal resolution of a pollen diagram depends on a number of factors, including the sedimentation rate, post-depositional bioturbation and the interval (vertical distance) between samples (Green, 1983; Green and Dolman, 1988; Turner and Peglar, 1988). One of the limits to fine-interval pollen interval pollen analysis comes from the calculation of sedimentation rates. Radiocarbon dates, the most common method for dating sediments have an error that is on the order of at least 50 years in recent sediments, and up to a couple of hundred years for older sediment.

In some lakes, laminated sediments provide an opportunity for more precise chronologies. Varves - annual layers in sediments - can provide more precise dating control, and also permit fine-interval sampling. It seems that a number of mechanisms are responsible for varve formation, and reviews of varve-forming processes are available (O'Sullivan, 1983; Saarnisto, 1986). When sediments are laminated, the sedimentation rate is accurately known. Bioturbation is not a problem in these lakes, although there may be some redeposition of pollen from one part of the lake to another. A third factor that may limit the temporal resolution of pollen diagrams the sample interval. This is under the direct control of the palynologist, and mainly limited by the time it takes to count the samples.

Vegetation sensitivity to climate change

How much of a climate change is needed to cause a change in vegetation? What is the relative importance of magnitude and temporal duration of a climate change in affecting vegetation? Addressing these questions involves multiple approaches, including analysis of vegetation dynamics through experimental or observational studies, modelling vegetation dynamics in various ways, and use of the fossil record to determine how the vegetation has changed through time, in response to past climate changes. For vegetation dynamics of more than a few decades, we are limited to using the fossil record or models of vegetation dynamics, as long-term observational studies are rarely available. Several studies have modeled long-term vegetation dynamics (below) but these models need to be verified by the fossil record to ensure that the appropriate dynamics are included.

Several studies have attempted to see if rapid changes in vegetation have occurred. These "fine-resolution" studies (Green, 1983) are concerned with changes on the order of decades to centuries, as opposed to more typical pollen studies which depict migration of species or slow changes of the vegetation which occur on the order of millennia. The problem can be divided into two parts. Firstly, do rapid and relatively small climate changes, such as the Little Ice Age, impact the vegetation in some way, for example, by changing the abundance of plants on the landscape. Secondly are changes such as these registered in the fossil record.

There is some evidence that the answer to the second question is that pollen record is quite sensitive to small and rapid changes in plant abundance on the landscape. Fires and postfire successions are recorded in high-resolution pollen diagrams. Green (1981) and Swain (1978), among others, showed sequences of pollen peaks occurring repeatedly in the past that suggest succession after large local fires. There is a large literature on the analysis of fire frequency from fossil pollen studies, but interpretation of charcoal in lake sediments remains difficult (Clark, 1988a & b; Clark, 1990; Clark and Royall, 1995a; Swain, 1973). It seems that rapid changes in vegetation are quite faithfully recorded in the pollen rain; continued work will further refine our knowledge of the limits to this interpretation. The simulations of Green (1983) and Green and Dolman (1988) provide guides for the interpretation of these series.

The more important question is how the vegetation itself responds to century-scale climate changes. There is some controversy surrounding the interpretation of vegetation records at this time scale. This question is distinct from that of the response of individual tree growth, as measured by tree-ring properties, to a change in climate, as it involves replacement of canopy trees by those of a different species, differential survival and growth, etc.

It has also been suggested that human activity may have has an impact on vegetation. Impacts of farming or human-induced fires have been identified (McAndrews and Boyko-Diakonow, 1989; Clark and Royall, 1995b), but the importance of this is disputed (Campbell and McAndrews, 1995; Clark, 1995). We need to separate the climate signal from that of other aspects of vegetation dynamics or other impacts.

A key aspect of any paleoclimate analysis is that geographic arrays of data are needed to separate climatic from local causes of variation in proxy-climate series. Mapping of pollen assemblages through time has proven quite successful in interpreting past climates from arrays of data (Bernabo and Webb, 1977; articles in Wright et al., 1993). Networks of sites are not available with fine resolution analyses of the past 1000 years, however, and other methods are needed to separate climatic from other causes of variation in these series (Gajewski, 1993).

An example will be used to demonstrate climate impacts on the vegetation for the past 1000 years. The data for this example comes from 7 sites (Fig 1) where fine resolution pollen diagrams are available (Gajewski, 1985).

All 7 lakes have laminated sediments and thus have an accurate chronology for the past 1000 to 2000 calendar years. The samples integrate 10 years of sediment and the between-sample interval is typically every 40 years (Gajewski et al., 1985, 1987; Swain, 1973, 1978). Four of the pollen diagrams are shown in Fig 2 and 3; others can be found in the above references.

To determine climate impacts in these different regions, we can try to compare the diagrams, and search for similarities among all sites. Examining the two diagrams from Maine (Fig 2a ; Fig 2b) we can observe some similarities.

Picea (spruce) increases, especially in the past 500 years, at the expense of Tsuga (hemlock) and Fagus (beech). Comparing these sequences to those from the midwest (Fig 3a, Fig 3b) is difficult, however.

Spruce is not even found in these sites, hemlock increases at Hells Kitchen Lake, and is not found in Dark Lake.

Part of the reason for this complexity is illustrated in Fig 4, which is an example from the boreal forest.

Fig 4 illustrates response surfaces summarizing the abundance of pollen as a function of summer temperature and annual precipitation. The lines are contours of equal abundance of pollen percentage. In this example, an identical climate change occurring in three different places are illustrated by arrows, and these would have different impacts on the local vegetation. At site 1, corresponding approximately to the region of Great Slave Lake, a warming of a degree C and an increase in annual precipitation of about 10 cm would cause pine to increase in abundance, while having little effect on spruce. At site 2, (e.g. northern Manitoba) an identical change in climate (starting here from another initial condition) would have little effect on either pine or spruce. In central Québec (arrow 3) this same climate change could be expected to decrease spruce while having little effect on pine.

Thus, identical climate changes could impact forests in different regions in dissimilar ways (Clark, 1991). And, returning to the example above, we would not expect the same climate change in the Great Lakes and eastern North America, introducing another source of complexity. Thus simply comparing pollen curves may not be appropriate.

Several alternative methods are available to compare pollen diagrams from different regions. The seven pollen diagrams were objectively zoned using a stratigraphically constrained cluster analysis (Gordon and Birks, 1972). A pollen zone is a portion of the diagram where the pollen spectra within the zone are more similar to one another than to spectra in adjacent zones. Zone boundaries should correspond to times of change in the pollen diagram and thus in the vegetation. We would anticipate that a climate change should affect a number of sites across a broad geographic area, and that the times of change should be comparable. If, on the other hand, the vegetation dynamics is dominated by local processes such as fire, the zone boundaries should be randomly distributed.

Figure 5 summarizes the results from these 7 sites. At all sites, there is a zone boundary between AD1400 to AD1500. There is a suggestion of another transition somewhere between AD700 to AD1000, but this is not as clear. This suggests that a climate change occurred around 500 years ago at this site, and this corresponds in time with the Little Ice Age. Note that the uppermost levels, after AD1800, show the influence of European settlement of the area, and this also is a clear zone boundary.

Campbell and McAndrews (1991) used a cluster analysis to group 33 lakes in southern Ontario and adjacent areas. Subjectively defining trends in the individual pollen curves and clustering similar pollen diagrams (as opposed to zones in one diagram as is done here), they identified the impact of the Little Ice Age on the region.

Another method to help determine if there is a climate signal in these data is the use of multivariate ordination methods such as principal components analysis. Each of the 7 lakes discussed above was analyzed separately, and the component scores plotted against time. The principal components have separated changes in the pollen assemblages by scale, and these different scale events can be explained by the ecology of the different sites. There was a long-term trend in these data, medium frequency fluctuations on the order of centuries, and higher frequency changes. The long-term cooling expressed by these components (not shown, see Gajewski, 1987) is a well-known phenomena. At treeline in northern Québec, pollen records indicate an opening of the forest-tundra (Gajewski et al., 1993; Richard, 1981) during this time. Changes in the composition of the forests is noted in pollen diagrams from many other sites (e.g. Russell et al., 1993).

The medium frequency fluctuations are of interest to us here (Figure 6; details of this analysis are found in Gajewski, 1987).

These show fluctuations remarkably in phase at all sites, and the taxa strongly loaded (+ & -) on these components is indicated. In areas with similar vegetation and (presumably) disturbance dynamics (e.g. Conroy Lake, Basin Pond) the loadings are similar, indicating fluctuations in similar species and we can infer similar climate changes. Because of the different vegetation at other sites, the species involved differs. Still, the component scores show parallel fluctuations. Interestingly, the fluctuations are coherent even though the fire disturbance regime is different in the eastern and midwestern sites.

This latter observation contrasts with modelling results of Overpeck et al (1990) who suggested that increased disturbance (as is present in the midwestern sites, based on charcoal analysis of the sediments; Swain, 1978) should make forests more susceptible to change. These century scale climate changes are also registered at all sites, although they are not located or chosen from particularly "sensitive" sites, in contrast to modelling results of Davis and Botkin (1985). This suggests that more modelling work is needed to better understand vegetation dynamics of this scale; for example Clark (1995) has questioned the parameterizations used. Alternatively, this interpretation of the data may be inaccurate. For example, Campbell and McAndrews (1993) compared a forest simulation to a pollen diagram from southern Ontario. They feel the vegetation response to climate change is very slow, leading to a "disequilibrium" between the vegetation and climate. Of course, this result is quite sensitive to the climate forcing they assumed for their model. More verification of vegetation model output with the fossil record is needed.

Reconstructing climate changes from pollen records

Analyzing the individual pollen diagrams, we can propose possible climate changes, and even suggest, qualitatively, the direction of the change (warming, drying, etc). However, to compare these climate inferences to other data such as tree-ring records or ice cores, quantitative climate reconstructions are needed; that is, these time series in the form of pollen percentages need to be transformed to units of temperature, precipitation, etc. If these climate inferences are to be used to validate climate models, these must be derived objectively (Gajewski, 1993).

To transform pollen records into climate units of temperature or precipitation, various statistical transfer function methods can be used (Fig 7; Bernabo, 1981; Gajewski, 1988; Anderson et al., 1991).

As the vegetation changes at these sites are not too great, regression methodology can be used. This has the advantage of a large number of diagnostic tools which ensure that the models are statistically valid (e.g. Arigo et al., 1986). When summer temperatures are estimated for the 7 sites, a long-term cooling is found for the past 2000 years of about 0.6° C/1000 years for the sites in the northeast, and 0.3° C/1000 years for sites in the midwest. Deviations from this cooling ( Fig 8 ) however, differ even across this small region, although nearby sites are similar.

Reconstructing atmospheric circulation changes using multiple proxy records

When the pollen assemblages are calibrated in terms of past temperature and precipitation, they can be compared to other proxy climate records. Several proxy-climate series were taken from the literature (Fig 9) to relate to the pollen derived summer temperatures discussed above.

Series considered by the original authors to indicate summer temperatures were used. Several long tree-ring series from western United States are considered to indicate summer temperatures (Lamarche and Stockton 1974; Lamarche 1974a, b); the tree-ring indices were normalized and used with no further transformation. Three normalized ice-melt series from northern Canada and Greenland were also used without further transformation (Fisher & Koerner 1983; Koerner, 1977; Herron et al, 1981). Averages were taken to make all series comparable to the lowest resolution data, the pollen-derived summer temperatures.

When these proxy climate records of the past 2000 years are compared (Fig 10), regional differences are revealed.

However, it is not clear how much of this difference is due to regional differences in climate, and how much due to differences between the properties of the proxy-climate sensors, properties such as response time, seasonal averaging of the climate signal, etc. Developing multiple different paleoclimate sensors from one region may help to determine this.

One way to synthesize these series would be to determine the patterns of circulation for the past (Kutzbach and Guetter, 1980). This can be done statistically (Fig 11), by estimating the relationship between modern day temperature and pressure patterns using a regression analysis, and applying these estimated parameters to fossil temperature reconstructions.

A preliminary result using the above-mentioned data is shown in Fig 12. (Actually, the techniques proceeds through a principal components analysis of the temperature and pressure fields. The regression equations explained 30% of the variance between the pressure and temperature fields of 1899-1978; between 17% and 48% of the individual variance of the first 6 SLP components was explained by the first 6 temperature components.)

This example is shown only to illustrate the methodology, as there are some problems with these preliminary results. Tree-ring series may not record long-term climate trends due in part to the process of chronology development, as well as the relatively short lifespan of most species. The time-lag in the response of the pollen series to climate changes has not been accounted for. The relationship of ice melt percentages in ice cores to the mean conditions is not clear, and there is a possibility of some loss of record during very warm years. Thus, these circulation patterns may not be realistic, although the decreased importance of the subtropical high pressure cell which is indicated for the Little Ice Age seems a reasonable result. However, reconstructions such as these provide hypotheses of past circulation patterns that can be compared to climate model output or to climate interpretations of new independent series.

Summary

I have suggested that century-scale climate changes, the latest of which is the Little Ice Age, have impacted vegetation composition in eastern North America. Several different methods can be used to identify these changes. Climate changes can be estimated from these changes in pollen abundance, and syntheses of networks of sites can suggest how the atmospheric circulation has changed in the past. This can be used to help choose among alternative hypotheses of the cause of decade to century climate variations (Crowley and Kim, 1993; Rind and Overpeck, 1993).

Acknowledgements

This work is supported by an NSERC Research Grant. This paper is a contribution of Subproject 4 to the Climate System History and Dynamics Programme that is jointly sponsored by the Natural Sciences and Engineering Research Council of Canada and the Atmospheric Environment Service of Canada.

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Figure Captions

Fig 1 (a) Map showing location of sites in northeastern United States. Vegetation zones after Lull (1968).

Fig 1 (b) Map showing location of sites in midwestern United States. Vegetation zones after Barrett (1980).

Fig 2 (a) Percentage pollen diagram from Conroy Lake, northeastern Maine (Gajewski et al., 1987). Location of site is shown in Figure 1. Dating is from varve counts.

Fig 2 (b) Percentage pollen diagram from Basin Pond, southwestern Maine (Gajewski et al., 1987) . Location of site is shown in Figure 1.

Fig 3 (a) Percentage pollen diagram from Hells Kitchen Lake, northeastern Wisconsin (Swain, 1978). Location of site in Figure 1.

Fig 3 (b) Percentage pollen diagram from Dark Lake, northwestern Wisconsin (Gajewski et al., 1985). Location of site in Figure 1.

Fig 4 Response surfaces of Picea (spruce) and Pinus (pine) pollen as a function of July temperature and annual precipitation. Details in Anderson et al. (1991). The arrows signify a climate change of about one ° C and an increase in annual precipitation of about 10 cm.

Fig 5 Zonation of seven high-resolution pollen diagrams (Details in text). See Figure 1 for location of sites.

Fig 6 Principal components analysis of 7 fine-resolution pollen diagrams. The analyses were done on a correlation matrix, and only the components showing medium frequency changes are shown. The series are smoothed, the signs may be reversed for illustrative purposes. The component number is shown above the series. Details in Gajewski (1987).

Fig 7 Scheme describing methods for obtaining quantitative estimates of past climates from fossil pollen assemblages. Spatial arrays of modern pollen assemblages from a series of lakes are related to modern climate data from nearby sites using regression or some similar statistical technique. These equations are then solved for the fossil pollen assemblages to obtain estimates of the "fossil" climate.

Fig 8 Summer temperatures estimated from pollen diagrams for Conroy Lake (northern Maine) and Basin Pond (southern Maine), Clear Pond (northern N.Y.) and Hells Kitchen Lake (eastern Wisconsin). Details of the methodology in Gajewski (1988). A linear trend (cooling) has been removed, and the curves smoothed by a running mean). Location of sites in Figure 1.

Fig 9 Location of proxy climate data with records of summer temperature for the past 2000 years. Data are normalized tree-ring widths (CA, NV, CO), summer temperature estimated from pollen data (WWI, MN, EWI, NME, SME, NY) and normalized ice-melt percentages (WCG, SWG, SEG). Source of data listed in text.

Fig 10 Examples of proxy-climate series for the past 2000 years. Location of sites shown in Fig 9.

Fig 11 Methodology for obtaining estimates of pressure patterns for periods in the past. Modern climate data (e.g. summer temperature) is obtained near to sites where fossil proxy-climate data are available. These time-series are related to the pressure patterns of the same period. The resulting regression parameters are solved using fossil proxy-climate estimates to provide estimates of past pressure patterns. See Kutzbach and Guetter (1980).

Fig 12 Estimates of sea level pressure (JJA) for 3 periods during the past 1000 years. The estimates are of 50-year means, and expressed as deviations from the 1899 to 1978 mean pattern

 

 

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