Synthesis

A key finding of this analysis is that the historic temperature and precipitation record at Konza Prairie displays periodicities similar to those for ENSO, NAO, and NP. Periods of stronger NAO (i.e. larger positive index values) were associated with warmer winters, periods of stronger ENSO with wetter winters, and periods of stronger NP with warmer summers. The growing-season course of aboveground biomass accumulation appears to be limited initially by temperature, then later by soil moisture.

Warmer or wetter winters caused by ENSO or NAO activity may thus influence aboveground biomass accumulation by promoting an earlier start to the growing season than in colder, drier winters, thereby lengthening the rapid first phase of biomass accumulation. Warmer summers caused by a strengthened NP index may reduce ANPP by lowering accumulation rates during summer, when rainfall is relatively sparse, evaporative demands are at their maximum, and soil moisture has been depleted from its springtime maximum. This scenario implies that the juxtaposition of ENSO, NAO, and NP events may be as important as the strength of any single process because of their potential to either augment or offset each other. For example, a strong NAO/ENSO combined with a weak NP influence may promote peak ANPP years; the reverse (weak NAO/ENSO with strong NP influence) could cause low-productivity years.

The key synthesis question is then, How might these climate teleconnections, acting in concert, affect ecological response in tallgrass prairie? A comprehensive treatment of this question is beyond the scope of this chapter, but the results presented here provide some insight. Our results imply that the primary effects of atmospheric teleconnections on tallgrass prairie productivity are indirect. For example, the ENSO indexes were correlated with precipitation during fall and winter (table 20.1), which recharges the soil profile with moisture but which is otherwise weakly linked to ANPP compared to growing-season precipitation (Briggs and Knapp 1995). Similarly, since NAO correlates with non-growing-season temperatures, its effect on ANPP is also indirect (table 20.1).

In contrast, the NP may have more direct impacts on ANPP because it was cor-

Table 20.1 Seasonal correlations between climate variables and teleconnection indices

Season

Season

Table 20.1 Seasonal correlations between climate variables and teleconnection indices

Index

JFM

AMJ

JAS

OND

Temperature

Niño 1,2

0.00

-0.08

-0.03

-0.05

Niño 3

-0.01

-0.07

-0.01

0.00

NAO

0.47*

0.25*

0.15

0.26*

Sunspots

-0.09

-0.07

0.03

-0.01

Insolation

0.12

0.12

-0.02

0.08

NP

0.10

0.22*

0.28*

0.02

Precipitation

Niño 1,2

0.31*

0.06

0.01

0.16

Niño 3

0.33*

-0.01

-0.02

0.19*

NAO

-0.09

-0.19*

-0.07

0.13

Sunspots

0.10

-0.04

-0.04

0.00

Insolation

0.17

0.09

0.01

0.11

NP

-0.04

0.05

0.01

0.23*

*Indicates significant correlation (p < 0.05).

*Indicates significant correlation (p < 0.05).

related with temperature during the active growing season (table 20.1). Temperature is significantly related to NAO throughout most of the year, including the early growing season (April-June; r = 0.25, p < 0.05). Temperature is also positively correlated with NP during these months (r = 0.23, p < 0.05). The NAO operates mainly at quasi-quintennial timescales (periods ~5 and 9 years), whereas NP is an inter-decadal-scale oscillation (period ~12 years). The similar correlations values between NAO and temperature and NP and temperature are surprising, given the differing timescales of these indexes. However, NP and NAO are significantly correlated during June (r = 0.21, p < 0.005). This correlation suggests that early growing-season temperature is responding to an atmospheric circulation process that affects both indices.

For the precipitation data, significant negative correlations are found with NAO during the growing season (r = -0.19, p < 0.05). Early growing-season (May, June) precipitation should have little effect on ANPP accumulation (see previous discussion), however, precipitation dynamics in July might affect total productivity. Correlation between July NAO and precipitation is significant (r = -0.20, p < 0.05). The negative correlation indicates that higher values of NAO associate with low precipitation totals and vice versa. Although the negative correlation values for NAO and precipitation are opposite in sign from those between NAO and temperature, they may actually represent a coherent influence on productivity. Early season temperatures (positively related to NAO phase) would affect the timing of peak values, whereas moisture conditions later in the season (negatively correlated to precipitation) would limit upper ANPP values (see figure 20.4A).

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