4.5.2 Large and Global-Scale Analyses

Records of glacier length changes (WGMS(ICSI-IAHS), various years-a) go far back in time – written reports as far back as 1600 in a few cases – and are directly related to low-frequency climate change. From 169 glacier length records, Oerlemans (2005) compiled mean length variations of glacier tongues for large-scale regions between 1700 and 2000 (Figure 4.13). Although much local, regional and high-frequency variability is superimposed, the smoothed series give an apparently homogeneous signal. General retreat of glacier tongues started after 1800, with considerable mean retreat rates in all regions after 1850 lasting throughout the 20th century. A slow down of retreat between about 1970 and 1990 is more evident in the raw data (Oerlemans, 2005). Retreat was again generally rapid in the 1990s; the Atlantic and the SH curves reflect precipitation-driven growth and advances of glaciers in western Scandinavia and New Zealand during the late 1990s (Chinn et al., 2005).

Records of directly measured glacier mass balances are few and stretch back only to the mid-20th century. Because of the very intensive fieldwork required, these records are biased towards logistically and morphologically ‘easy’ glaciers. Uncertainty in directly measured annual mass balance is typically ±200 kg m^–2 yr^–1 due to measurement and analysis errors (Cogley, 2005). Mass balance data are archived and distributed by the World Glacier Monitoring Service (WGMS(ICSI-IAHS), various years-b). From these and from several other new and historical sources, quality checked time series of the annual mean specific mass balance (the total mass balance of a glacier or ice cap divided by its total surface area) for about 300 individual glaciers have been constructed, analysed and presented in three databases (Ohmura, 2004; Cogley, 2005; Dyurgerov and Meier, 2005). Dyurgerov and Meier (2005) also incorporated recent findings from repeat altimetry of glaciers and ice caps in Alaska (Arendt et al., 2002) and Patagonia (Rignot et al., 2003). Only a few individual series stretch over the entire period. From these statistically small samples, global estimates have been obtained as five-year (pentadal) means by arithmetic averaging (C05a in Figure 4.14), area-weighted averaging (DM05 and O04) and spatial interpolation (C05i). Although mass balances reported from individual glaciers include the effect of changing glacier area, deficiencies in the inventories do not allow for general consideration of area changes. The effect of this inaccuracy is considered minor. Table 4.4 summarises the data plotted in Figure 4.14.

Figure 4.13. Large-scale regional mean length variations of glacier tongues (Oerlemans, 2005). The raw data are all constrained to pass through zero in 1950. The curves shown are smoothed with the Stineman (1980) method and approximate this. Glaciers are grouped into the following regional classes: SH (tropics, New Zealand, Patagonia), northwest North America (mainly Canadian Rockies), Atlantic (South Greenland, Iceland, Jan Mayen, Svalbard, Scandinavia), European Alps and Asia (Caucasus and central Asia).

Figure 4.14. Pentadal (five-year) average mass balance of the world’s glaciers and ice caps excluding those around the ice sheets of Greenland and Antarctica. Mean specific mass balance (left axis) is converted to total mass balance and to SLE (right axis) using the total ice surface area of 546 × 10³ km² (Table 4.3) and the ocean surface area of 362 × 10⁶ km². C05a is an arithmetic mean over all annual measurements within each pentade (Cogley, 2005); the grey envelope is the 90% confidence level of the C05a data and represents the spatial variability of the measured mass balances. The number of measurements in each time period is given at the top of the graph. C05i is obtained by spatial interpolation (Cogley, 2005), while DM05 (Dyurgerov and Meier, 2005) and O04 (Ohmura, 2004) are area-weighted global numbers. MB is the arithmetic mean of C05i, DM05 and O04, and its uncertainty (red shading) combines the spatial variability and the structural uncertainty calculated for the 90% confidence level. This does not include uncertainties that derive from uncertainties in the glacier area inventories. The authors performed area weighting and spatial interpolation only after 1960, when up to 100 measured mass balances were available. The most recent period consists of four years only (2000/2001 to 2003/2004).

The time series of globally averaged mean specific mass balance from different authors have very similar shapes despite some offsets in magnitude. Around 1970, mass balances were close to zero or slightly positive in most regions (Figure 4.15) and close to zero in the global mean (Figure 4.14), indicating near-equilibration with climate after the strong earlier mass loss. This gives confidence that the glacier wastage in the late 20th century is essentially a response to post-1970 global warming (Greene, 2005). Strong mass losses are indicated for the 1940s but uncertainty is great since the arithmetic mean values (C05a in Figure 4.14) are from only a few glaciers. The most recent period consists of four years only (2000/2001–2003/2004) and does not cover all regions completely. The shortage of data from Alaska and Patagonia likely causes a positive bias on the area-weighted and interpolated analyses (DM05, O04, C05i) due to the large ice areas in these regions. There is probably also a negative bias in the arithmetic mean (C05a), due to the strongly negative northern mid-latitudes mass balances in 2002/2003, particularly in the European Alps (Zemp et al., 2005). Mass loss rates for 1990/1991 to 2003/2004 are roughly double those for 1960/1961 to 1989/1990 (Table 4.4).

Over the last half century, both global mean winter accumulation and summer melting have increased steadily (Ohmura, 2004; Dyurgerov and Meier, 2005; Greene, 2005). At least in the NH, winter accumulation and summer melting correlate positively with hemispheric air temperature, whereas the mean specific mass balance correlates negatively with hemispheric air temperature (Greene, 2005). Dyurgerov and Dwyer (2000) analysed time series of 21 NH glaciers and found a rather uniformly increased mass turnover rate, qualitatively consistent with moderately increased precipitation and substantially increased low-altitude melting. This general trend is also indicated for Alaska (Arendt et al., 2002), the Canadian Arctic Archipelago (Abdalati et al., 2004) and Patagonia (Rignot et al., 2003).

Regional analyses by Dyurgerov and Meier (2005) show strongest negative mean specific mass balances in Patagonia, the northwest USA and southwest Canada, and Alaska, with losses especially rapid in Patagonia and Alaska after the mid-1990s (Figure 4.15a). A cumulative mean specific mass balance of –10 × 10³ kg m^–2 corresponds to a loss of 10 m of water, or about 11 m of ice, averaged over the glacier area; cumulative losses in Patagonia since 1960 are approximately 40 m of ice thickness averaged over the glaciers. Only Europe showed a mean value close to zero, with strong mass losses in the Alps compensated by mass gains in maritime Scandinavia until the end of the 20th century. High spatial variability in climate and, thus, in glacier variations, also exists in other large regions such as in the high mountains of Asia (Liu et al., 2004; Dyurgerov and Meier, 2005). Values for Patagonia and Alaska are mainly derived from altimetry evaluations made by Arendt et al. (2002) and Rignot et al. (2003), and authors of both papers note that the observed mass losses cannot be explained by surface mass loss only, but also include increased ice discharge due to enhanced ice velocity. The latter, in turn, has possibly been triggered by previous negative mass balances of glaciers calving icebergs, as well as by increased melt water production that enhances basal sliding. Some glaciers exhibit quasi-periodic internal instabilities (surging), which can affect data from those glaciers (Arendt et al., 2002; Rignot et al., 2003), but these effects are expected to average very close to zero over large regions and many years or decades. Because of a lack of suitable information, the temporal variation of the mass loss of the Patagonian ice fields has been interpolated to match the time series of Alaskan mass balances assuming similar climate regimes (Dyurgerov and Meier, 2005).

The surface mass balance of snow and ice is determined by a complex interaction of energy fluxes towards and away from the surface and the occurrence of solid precipitation. Nevertheless, glacier fluctuations show a strong statistical correlation with air temperature at least at a large spatial scale throughout the 20th century (Greene, 2005), and a strong physical basis exists to explain why warming would cause mass loss (Ohmura, 2001). Changes in snow accumulation also matter, and may dominate in response to strong circulation changes or when temperature is not changing greatly. For example, analyses of glacier mass balances, volume changes, length variations and homogenised temperature records for the western portion of the European Alps (Vincent et al., 2005) clearly indicate the role of precipitation changes in glacier variations in the 18th and 19th centuries. Similarly, Nesje and Dahl (2003) explained glacier advances in southern Norway in the early 18th century as being due to increased winter precipitation rather than colder temperatures.

Figure 4.15. Cumulative mean specific mass balances (a) and cumulative total mass balances (b) of glaciers and ice caps, calculated for large regions (Dyurgerov and Meier, 2005). Mean specific mass balance shows the strength of climate change in the respective region. Total mass balance is the contribution from each region to sea level rise.

Total mass balances are the integration of mean specific mass balances (which have a climate signal) over the existing glacier area. Consequently, the biggest mass losses and, thus, contributions to sea level rise are from Alaska with 0.11 mm yr^–1 SLE from 1960/1961 to 1989/1990 and 0.24 mm yr^–1 SLE from 1990/1991 to 2002/2003, the Arctic (0.09 and 0.19), and the high mountains of Asia (0.08 and 0.10) (Figure 4.15b).