Relative e ff ects of precipitation variability and warming on grassland ecosystem function

Relative effects of precipitation variability and warming on grassland ecosystem function P. A. Fay, J. M. Blair, M. D. Smith, J. B. Nippert, J. D. Carlisle, and A. K. Knapp USDA ARS Grassland Soil and Water Research Laboratory, 808 E Blackland Rd., Temple, Texas, 76502, USA Division of Biology, Kansas State University, Manhattan, Kansas, 66506, USA Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, 06520, USA Utah Climate Center, Utah State University, Logan, Utah, 84322, USA Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, Colorado, 80523, USA

ity, and warming.We present results from an experiment applying increased growing season rainfall variability and year round warming in native perennial grassland.During ten years of study, total growing season rainfall varied 2-fold, and we found ∼50-200 % interannual variability in plant growth and aboveground net primary productivity, leaf carbon assimilation (A CO 2 ), and soil CO 2 efflux (J CO 2 ) despite only ∼40 % variation in mean volumetric soil water content (0-15 cm, Θ 15 ).Interannual variation in soil moisture was thus amplified in most measures of ecosystem response.Differences between years in Θ 15 explained the greatest portion (14-52 %) of the variation in these processes.Experimentally increased intra-annual rainfall variability doubled the amplitude of intra-annual soil moisture variation and reduced Θ 15 by 15 %, causing most ecosystem processes to decrease 8-40 % in some or all years with increased rainfall variability compared to ambient rainfall timing, suggesting reduced ecosystem rainfall use efficiency.Warming treatments increased 5 cm soil temperature, particularly during spring, fall, and winter.Warming advanced canopy green up in spring, increased winter J CO 2 , and reduced summer J CO 2 and forb ANPP, suggesting that the effects of warming differed in cooler versus warmer parts of the year.We conclude that (1) major ecosystem processes in this grassland may be substantially altered by predicted changes in interannual climate variability, intra-annual rainfall variability, and temperature, (2) interannual climate variation was a larger source of variation in ecosystem function than intra-annual rainfall variability and warming, and (3) effects of increased growing season rainfall variability and warming were small, but ecologically important.The relative effects of these climate drivers are likely to vary for different ecosystem processes and in wetter or drier ecosystems.

Introduction
Terrestrial ecosystems account for the largest exchanges of carbon (C) with the atmosphere (Denman et al., 2007), but the control of these fluxes by climate remains poorly understood.Precipitation and temperature are two primary elements of climate regulating ecosystem function.Important variation in precipitation and temperature, from an ecosystem perspective, occurs on daily to decadal time scales (Bonan, 2002;Goodin et al., 2002).Recent analyses of long-term weather records show that temperatures are warming at a rate not seen in the last century (Trenberth et al., 2007), and that areas of the northern hemisphere have experienced increased total annual precipitation, a greater proportion of precipitation in large events, and longer periods of drought (Groisman et al., 2005;Groisman and Knight, 2008).Projected increases in atmospheric CO 2 and other greenhouse gases are expected to reinforce these trends (Karl et al., 2009).These observed and expected changes in the means and variability of precipitation and temperature on inter-and intra-annual time scales will likely have important impacts on terrestrial ecosystem structure and function, but these have been largely unexplored.
The conceptual framework for understanding ecosystem responses to precipitation variability originates in research on arid ecosystems showing that ecosystem responses to rainfall patterns depend on the temporal separation of rainfall pulses and the extent of inactivity between pulses (Noy-Meir, 1973).Soils play a crucial role by capturing discontinuous inputs of precipitation, and making it available for plant and microbial function in amounts and durations determined, in part, by soil physical properties, vegetation, and disturbance (Reynolds et al., 2004;Rodriguez-Iturbe and Porporato, 2004).Ecosystem responses to altered precipitation variability may differ among wet or dry systems or years, depending on how often thresholds of too little or too much soil moisture are exceeded (Knapp et al., 2008).For example, Heisler-White et al. (2009) found that increased growing season rainfall variability resulted in increased net primary productivity in semiarid grasslands, but decreased it in more mesic grasslands.Previous studies suggest that variation in precipitation at different temporal scales affects different aspects of ecosystem structure and function (Schwinning and Sala, 2004).For example, several studies have linked 2-fold or more interannual variation in aboveground net primary productivity (ANPP) to interannual variability in precipitation.(Briggs and Knapp, 1995;Knapp et al., 2001;Huxman et al., 2004).Variation in the size and spacing of precipitation events within a growing season also affects numerous processes.In tallgrass prairie, a pattern of larger growing season rainfall events separated by longer dry intervals caused increased soil moisture variation and reduced ANPP, leaf carbon assimilation, and soil CO 2 efflux compared to the same total rainfall quantity distributed in smaller more frequent events (Knapp et al., 2002;Fay et al., 2003a;Harper et al., 2005;Nippert et al., 2009).Increased variability in soil moisture can directly reduce average rates of water sensitive processes like soil CO 2 flux and photosynthesis (Mielnick and Dugas, 2000).
Previous studies have found varying ecosystem responses to warming.Earlier spring greenup and flowering was reported in several studies (Badeck et al., 2004;Cleland et al., 2006;Sherry et al., 2007).Increased soil respiration with warming is common across biomes (Rustad et al., 2001), but both increases (Zhou et al., 2006), decreases (Liu et al., 2009), and soil moisture-dependent responses (Almagro et al., 2009) have been reported in grasslands.Similarly, warming generally increased aboveground biomass in a cross-biome meta-analysis (Rustad et al., 2001), but studies in grassland have reported no response (de Valpine and Harte, 2001;Dukes et al., 2005;Xia et al., 2009) or decreased aboveground productivity (De Boeck et al., 2008).Varying responses to warming in grassland likely reflect complex interactions among temperature, soil water availability and the temperature and moisture sensitivities of key plant and microbial physiological processes.
The influence of interannual variability in rainfall and temperature can make it difficult to assess the effects of intra-annual climatic variation (Nippert et al., 2006b).Interactions between warming and intra-annual rainfall variability may amplify interannual variation and create threshold changes in ecosystem structure (CCSP, 2010).An Introduction

Conclusions References
Tables Figures

Back Close
Full understanding of these interactions and their consequences for ecosystems requires long-term field experiments.Although there are long-term warming experiments (e.g., Saleska et al., 1999;An et al., 2005;Sherry et al., 2009), we know of no experiments in perennial grasslands that have manipulated growing season rainfall variability and warming for long enough to compare their effects to those of interannual climate variation.Grasslands are important and tractable systems for examining these issues because they cover a sizeable portion of terrestrial land masses, are rich and dynamic in biodiversity, are a globally important agricultural resource, and are at risk from degradation and habitat conversion.Understanding their basic functional responses to multiple climate drivers over different time scales is critical for predicting impacts of future climate regimes on these systems and their ability to sustainably provide ecosystem goods and services, such as food, fiber, and clean water, while maintaining biological diversity (Hoekstra et al., 2005).
Here we report results from the first 10 yr (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) of an ongoing experiment in a water-limited perennial grassland in Kansas, in the middle of the North American Central Plains.In 1998, we began an experiment featuring an altered rainfall timing treatment that increased growing season rainfall variability, relative to ambient rainfall patterns, without changing total rainfall amount.In 2003, the increased rainfall variability regime was combined with a warming treatment.Here we extend previous studies (Knapp et al., 2002;Fay et al., 2003a;Harper et al., 2005;Nippert et al., 2009) by evaluating (1) the relative responsiveness of ecosystem processes to interannual climate variability vs. responses to increased within-growing season (intra-annual) rainfall variability, and (2) how the effects of increased inter-and intra-annual rainfall variability on ecosystem processes interact with and compare to those of experimental warming.
These objectives were addressed through measurements of the timing of plant growth and senescence, rates of plant biomass accumulation and flowering, leaf photosynthesis, and soil CO 2 flux.We hypothesized that interannual climate variability, as expressed by variation in rainfall amount and mean soil moisture among years, would be the primary driver of interannual variability in ANPP, while increased intra-annual rainfall Introduction

Conclusions References
Tables Figures

Back Close
Full variability, as reflected by variability in soil moisture within growing seasons, would be the primary driver of average rates of leaf photosynthesis and soil CO 2 efflux.We also hypothesized that interannual climate variability and increased intra-annual rainfall variability would have stronger effects on most ecosystem processes than warming in this grassland, which is in the center of the thermal range of the dominant species.

Methods
This study was conducted in the Rainfall Manipulation Plots (RaMPs) facility at the Konza Prairie Biological Station (KPBS) in northeastern Kansas, USA (39  (Knapp et al., 1998).
The RaMPs facility consists of twelve 14 × 9 m fixed-location rainout shelters covered from 1 May through 31 October by a clear polyethylene roof.The shelters exclude natural rainfall from the plots and divert the excluded rainfall to storage tanks for application to the plots using overhead sprinklers.Each shelter covers a hydrologically isolated 6 × 6 m sampling plot.See Fay et al. (2000) for additional details on the rainout shelter design.Introduction

Conclusions References
Tables Figures

Back Close
Full

Treatments
During Phase I, 1998-2001, four experimental rainfall treatments were applied in three replicates.The treatments were factorial combinations of two growing season rainfall quantities combined with two growing season rainfall patterns, as follows: Ambient: Each time a natural rainfall event occurred, the quantity of rain that fell was applied to the plots within 24 h, replicating the naturally occurring rainfall regime in number and sizes of rainfall events, length of dry intervals between events, and total growing season amount.
Reduced quantity: As in ambient, except 70 % of each rainfall event was applied.Altered pattern: Intra-annual rainfall variability was increased by accumulating the collected rainfall until the dry interval was 50 % longer than the ambient dry interval.
Then the accumulated rainfall was applied as a single large event, at a rate that insured all rainfall entered the soil profile.The total growing season amount of rainfall applied in this treatment was identical to ambient, while the number of rain events was reduced and the size of rain events and length of dry intervals were increased.
Reduced quantity and altered pattern: As in altered pattern, except only 70 % of the accumulated rainfall was applied, which imposed both drought and increased rainfall variability.
In 2002, the reduced quantity treatment was discontinued as a transition to prepare for Phase II.All plots continued to receive their assigned rainfall timing treatment but all now received 100 % of growing season rainfall amounts, n = 6 per rainfall treatment.
Phase II began in 2003 with the initiation of a warming treatment.Infrared heating lamps (HS-2420, Kalglo Electronics Co, Bethlehem, PA, USA) were installed in two randomly chosen 2 × 2 m subplots within the 6 × 6 m rainfall treatment plots.The lamps were operated continuously year round, and emitted a constant 20-25 W m −2 of downward infrared radiation.Lamps were placed 1.2 m above the soil surface at the beginning of each growing season and raised periodically to maintain that height above the plant canopy.The lamps are identical to those of other grassland warming Introduction

Conclusions References
Tables Figures

Back Close
Full experiments (Luo et al., 2001), and here increased day (0.4-0.6 • C) and night (1.0-1.5 • C) canopy temperatures of a 2 × 2 m area during May through August.Two additional 2 × 2 m subplots were unwarmed and thus experienced ambient temperatures.
One unwarmed subplot contained a dummy lamp to control for effects from the physical presence of the lamps and associated infrastructure.

Microclimate and ecosystem function measurements
Sensors were installed to measure rainfall, soil temperature, and soil water content.Natural rainfall quantities were measured with six manual rain gauges.Soil temperature was measured with thermocouples at 5 cm depth (T soil 05 ) in two ambient and two altered rainfall plots during 1998-2003, and in the center of each of the four subplots in three ambient and three altered plots during 2004-2007.T soil 05 was logged every 30 min and stored as 1 h averages on data loggers (CR10X, Campbell Scientific, Logan, UT, USA).Soil volumetric water content was measured at 0-15 cm depth (Θ 15 ) using time domain reflectometry (TDR).During 1998During -2004, a pair of 15 cm long stainless steel rods were inserted vertically at the soil surface at four locations per plot (Phase I), or in the center of each subplot (Phase II).Θ 15 was measured weekly using a Tektronix cable tester.Beginning in 2005, Θ 15 was measured with 30 cm long probes (CS616, Campbell Scientific, Logan, UT, USA) inserted at 30 • .Θ 15 was logged every 10 minutes and stored as 30 min averages on data loggers.We used the daily average Θ 15 of one day

Canopy greenness
Spring canopy green up and fall canopy senescence was quantified in the 2005-2007 growing seasons by measuring percent green cover with a digital canopy camera (Firstgrowth, Decagon Devices Inc., Pullman, WA, USA).Each subplot was imaged between 09:00 h and 12:00 h every four to seven days following the first appearance of new spring growth (∼ late April) until canopy closure (mid-late June), and from the onset of canopy decline until frost (∼ late August-October).

Canopy light penetration
Light interception by the canopy was determined at midseason as a proxy of accumulated aboveground biomass.Vertical profiles of photosynthetic photon flux density (PPFD) were measured with a ceptometer (Decagon Devices Inc., Pullman, WA, USA) at 10 cm increments in the canopy.Profiles were measured between 12:00 h and 14:00

Flowering
Flowering culms of Andropogon gerardii and Sorghastrum nutans were counted each September in two locations per plot during 1999-2002, and once per subplot during 2003-2007.

Soil CO 2 fluxes
Soil CO 2 efflux (J CO 2 ) was measured with an infrared gas analyzer (LI-6200, LI-Cor Biosciences Inc., Lincoln, NE).During the growing season, J CO 2 was measured weekly each May through October.

Data analysis
Intra-annual rainfall variability was quantified by computing the coefficient of variation between successive individual rainfall events (∆Θ 15 ), which describes the absolute amplitude of variation in Θ 15 .Statistical analyses of soil moisture and ecosystem process responses to treatments and year were conducted using linear mixed models procedures in SAS 9.1 (SAS Institute Inc, 2003) in two steps.First, a repeated measures model was fit to data from all ten years with rainfall pattern (ambient vs. altered) as a fixed effect in a randomized complete block design (RCB), year as the repeated effect, and plot as the experimental unit.Type III sums of squares were used to orthogonally compare the effect of interannual climate variability versus effects of increased intra-annual rainfall variability.
Second, separate repeated measures models were fit to the two phases of the experiment.The model for Phase I (1998-2002) contained rainfall pattern and quantity and their interaction as fixed effects in RCB, year as the repeated effect, and plot as the experimental unit.Type III sums of squares were again used to compare effects of interannual climate variation against effects of intra-annual variability and quantity treatments.The model for Phase II (2003II ( -2007) ) contained rainfall pattern as a whole-plot fixed effect in RCB, warming as a subplot fixed effect, and year as the repeated effect.This model compared interannual climate variation to increased rainfall variability and warming effects.For responses measured multiple times during the growing season (i.e., soil moisture means and variability, midseason biomass, A CO 2 , J CO 2 ), the growing season mean was used for analysis, calculated by averaging values from individual sample dates in each year.Transformations were applied to response variables where needed to meet assumptions of normality and/or equal variances.Means separations were performed using the LSMEANS statement with the DIFF option.Full ANOVA results are presented in the Appendix (Tables A1, A2).We used multiple regression (MR) analysis to determine which among mean Θ 15 , CV Θ15 , or T soil 05 explained more variation in key ecosystem responses.Grass and forb ANPP, A CO 2 , and J CO 2 were analyzed with a stepwise procedure with p = 0.10 required for variable retention.Variance inflation factors for the predictor variables ranged from 1.2 (T soil 05 ) to 1.7 (CV Θ15 ), suggesting that multicollinearity among the Introduction

Conclusions References
Tables Figures

Back Close
Full predictor variables was low.Univariate regression analyses of these variables were also conducted.

Rainfall
Total growing season rainfall inputs (May-September) varied 1.8-fold between years, from 334 mm in 2005 to 600 mm in 1998 (Fig. 1a).The altered rainfall treatment dramatically changed growing season rainfall regimes.Event sizes were larger, small events became infrequent, and dry intervals increased compared to the ambient rainfall pattern (Fig. 1b).As a result, the CV of individual rainfall events was significantly greater in 9 out of 10 yr in the altered treatment compared to ambient (Fig. 1c, p < 0.0001, Table A1).The reduced quantity treatment during Phase I caused only minor though significant (p = 0.01) variation in rainfall CV.

Mean soil moisture
There were significant interannual differences in growing season mean soil moisture.
Under ambient rainfall, Θ 15 ranged from 27 % in 2002 to 38 % in 1999 (Fig. 1d, p < 0.0001, Table A1).Altered rainfall patterns reduced Θ 15 to about 86 % of ambient values for the 10 yr combined (p = 0.0025), even though the treatments received the same total rainfall amounts.The reduced quantity treatment reduced Θ 15 to 90 % of ambient values (p = 0.01), similar to the magnitude of the altered rainfall effect.

Conclusions References
Tables Figures

Back Close
Full

Soil moisture variability
Altered rainfall patterns increased CV Θ15 (16 %) and ∆Θ 15 (2-fold) (Fig. 1E, p < 0.01, Table A1), indicating greater soil moisture variability during the growing season and a greater amplitude of soil moisture change between sequential rainfall events.CV Θ15 was a decreasing function of Θ 15 (Fig. 2, R 2 = 0.38, p < 0.0001), indicating that lower mean soil moisture was often accompanied by greater growing season soil moisture variability.∆Θ 15 was weakly correlated with Θ 15 (R 2 = 0.07, p < 0.0001), indicating that the amplitude of soil moisture change between events was only loosely associated with mean soil moisture, and likely more dependent on rain event size.∆Θ 15 and CV Θ15 were unaffected by reduced rainfall quantity (0.06 < p < 0.19, Table A1) and ∆Θ 15 was unaffected by warming (p = 0.23).

Canopy greenness
Warming was the largest factor affecting canopy green up during spring.During late April and May (weeks 15-20) when green up was most rapid, warming increased greenness by 13-96 % compared to unwarmed subplots (Fig. 4, p < 0.0001, Table A2).
Differences among years in canopy greenness were highly significant (p < 0.0001) but much smaller (3 %, data not shown) than the effect of warming.Altered rainfall patterns caused no significant effects on greenness during weeks 15-20 (p = 0.34).However, as the season progressed (early June, weeks 20-22), the warming effect diminished and altered rainfall patterns reduced greenness ∼8 % compared to ambient rainfall (p = 0.0079).Canopy senescence in the late summer/fall showed small but significant (p < 0.0001) differences among years, but no significant warming or rainfall effects (Table A2).

Midseason aboveground biomass
There were large differences among years in canopy light levels at midseason (x 0 , height of interception of 50 % of the light), a direct measure of canopy structure and a proxy for aboveground biomass.Across all years, x 0 varied almost 2-fold (Fig. 5a, p < 0.0001, Table A2), while altered rainfall patterns reduced x 0 17 % (p = 0.016), indicating less aboveground biomass at midseason.Altered rainfall reduced x 0 in all years during Phase I (p = 0.04), while during Phase II x 0 was significantly reduced in 2004 and 2005 (p < 0.001).Warming only affected x 0 in 2007, increasing it by 15 % (p = 0.0002).

Aboveground net primary productivity
For all years combined, total ANPP varied 2-fold among years (Fig. 5b, p < 0.0001, Table A2), while altered rainfall reduced total ANPP by an average of 10 % compared Introduction

Conclusions References
Tables Figures

Back Close
Full to ambient rainfall (p = 0.0098, Table A2).During Phase I, altered pattern and reduced quantity treatments both reduced total ANPP by 15 % compared to ambient (0.004 < p < 0.03, Table A2).However, total ANPP was not affected by altered rainfall patterns during Phase II (p ≥ 0.12).Warming caused a ∼5 % reduction in phase II ANPP (p < 0.039).
Grass ANPP accounted for 80 % of total ANPP and varied to a similar degree among years (p < 0.0001, Fig. 5c).Grass ANPP was not affected by altered rainfall patterns, but increased in 2007 in response to warming (p = 0.017, Table A2).Forb ANPP showed little variation among years except for an increase in 2007 (Fig. 5d, p < 0.0001, Table A2).Warming had the strongest effects on forb ANPP, reducing it 23 % compared to unwarmed subplots (p = 0.04), with no significant difference in warming effects among years (p = 0.91).There were no significant effects of rainfall treatments on forb ANPP (p > 0.40).

Flowering
Flowering culm production in A. gerardii and S. nutans was low in most years but was abundant in 1999 and 2004 (Fig. 5e-f, p < 0.0001, Table A2). A. gerardii flowering did not differ between rainfall or warming treatments (0.16 < p < 0.70, Table A2).In contrast, altered rainfall patterns reduced S. nutans flowering by 50 % for all years combined (p = 0.0036), as well as in both Phase I and II (0.006 < p < 0.04, Table A2).Warming treatments had no effects on flowering culm production for S. nutans.

Leaf carbon assimilation
There were large differences among years in A CO 2 for the codominant grasses A. gerardii and S. nutans.Mean A CO 2 in A. gerardii varied 3-fold among years (Fig. 6a, p < 0.0001, Table A1), and was reduced 8 % by altered rainfall patterns for all years Introduction

Conclusions References
Tables Figures

Back Close
Full  A1).For S. nutans, A CO 2 varied 80 % among years (Fig. 6b, p < 0.0001), and was reduced by altered rainfall patterns in 2002 (p = 0.007).Warming had no effect on A CO 2 for either grass.In contrast, S. canadensis showed little interannual variation in A CO 2 and no response to altered rainfall patterns.However, the reduced quantity treatment decreased S. canadensis A CO 2 by 10 % compared to ambient quantity (p < 0.05, Table A1), mainly because of a large decrease in 2000 (Fig. 6b).For all three species, in years when alterations in rainfall timing and/or quantity caused significant reductions in A CO 2 , Θ 15 was reduced by 8 to 38 % (0.07 > p > 0.0001).

Conclusions References
Tables Figures

Back Close
Full A CO 2 in A. gerardii and S. nutans were increasing functions of T soil 05 (R 2 = 0.04−0.12,0.007 < p < 0.04, data not shown).
Θ 15 was the first variable to enter multiple regression models for total ANPP, grass ANPP, and A CO 2 , accounting for 18-52 % of the variation (0.0001 < p < 0.009, Table 1).CV Θ15 was the second variable to enter the MR model for grass and forb ANPP, and A. gerardii A CO 2 , explaining an additional 2-7 % of variation in these variables (0.0001 < p < 0.003).CV Θ15 did not enter models for A CO 2 of S. canadensis or S. nutans.Thus, Θ 15 consistently explained more of the variation in these ecosystem processes.J CO 2 followed a different pattern.CV Θ15 was first to enter the MR model (R 2 = 0.27, p < 0.0001) followed by Θ 15 (R 2 = 0.11, p < 0.0001).T soil 05 entered the MR models last or not at all, accounting for only 1-6 % of variation in total ANPP, forb ANPP, J CO 2 , and S. nutans A CO 2 (0.0001 < p < 0.02), and not entering models for grass ANPP or A CO 2 of A. gerardii or S. canadensis.

Discussion
Results from ten years of experimental rainfall manipulation and five years of warming treatments encompassing a wide range of natural climatic variability show that interannual climate variation, increased intra-annual (growing season) rainfall variability, and warming all affected key ecosystem processes.There was more interannual variation in ecosystem function than there was from intra-annual rainfall variability and warming.However the relatively smaller effects of intra-annual rainfall variability and warming still caused significant effects on some processes.

Interannual variability caused greater effects than increased intra-annual rainfall variability on most ecosystem processes
Our analyses demonstrated that large (nearly 2-fold) interannual variation in total (May-September) rainfall resulted in ∼40 % interannual variation in growing season mean soil moisture (Θ 15 ).The magnitude of interannual rainfall variability was greater than that of Introduction

Conclusions References
Tables Figures

Back Close
Full interannual mean soil moisture variability because of the limited capacity for soil to store rainfall (Brady and Weil, 2002;Rodriguez-Iturbe and Porporato, 2004).Nonetheless, variation in Θ 15 was associated with 50 to 300 % variation in rates of key ecosystem processes.High interannual variation in total ANPP resulted from high variation in grass ANPP (Fig. 5a, b), which is consistent with previous studies (Briggs and Knapp, 1995;Knapp et al., 2001).In contrast, forb ANPP was relatively constant among years (Fig. 5d), as reported in previous studies (Knapp et al., 2001).Interannual variation in flowering of the codominant grasses was also high, but qualitatively different from that of total ANPP.Flowering was high only during the two years with the highest Θ 15 (1999 and 2004), and was consistently low in other years, suggesting there is a threshold total rainfall requirement for flowering in these grasses.Craine et al. (2010) also found a threshold requirement for grass flowering in this grassland.A rainfall regime with more frequent drought years could result in fewer flowering events, potentially lowering future inputs to the seed bank from these grasses.Interannual variability in A CO 2 of the grasses was of comparable magnitude to that of total ANPP.We expected interannual variability to have a smaller effect on A CO 2 relative to that of intra-annual rainfall variability, because A CO 2 in these grasses decreases strongly with soil moisture depletion, and recovery is often slow when soil moisture is restored, especially after extended drought (Knapp, 1985;Heckathorn et al., 1997).
Leaf level photosynthesis has been associated with long-term plant success in this grassland (McAllister et al., 1998), and the ability to track soil moisture variability is crucial to the success of the grasses (Swemmer et al., 2006;Nippert et al., 2006a).The finding that A CO 2 was strongly correlated with Θ 15 (Table 1) suggests that on average, A CO 2 was strongly coupled to interannual climate variation and associated interannual differences in soil water supply.
High responsiveness in grass A CO 2 to interannual variation was consistent with grass growth responses.For example, grass ANPP and A CO 2 was more highly correlated with Θ 15 than was forb ANPP and A CO 2 (Table 1).C 4 grasses such as A. gerardii and S. nutans typically have higher photosynthetic rates and experience greater variation Introduction

Conclusions References
Tables Figures

Back Close
Full in plant water status than forbs (Knapp, 1984;Turner et al., 1995;McAllister et al., 1998;Nippert et al., 2006a), consistent with isotopic studies showing that grasses rely proportionally more than forbs on shallow, more variable soil moisture during dry periods (Nippert and Knapp, 2007).Grasses as a group are also more active than many forbs during the hot dry period of midsummer, when water deficits are most common and severe.These differential responses could provide a mechanism for changes in community structure under more variable rainfall regimes.

Increased intra-annual rainfall variability reduced rates of ecosystem processes, other things being equal
The altered rainfall timing treatment markedly changed the statistical structure of rainfall inputs, increasing the variability in rainfall by creating longer dry periods and larger rainfall events.This translated directly to increased soil moisture variability and reduced mean Θ 15 in some or all years, caused by the prolonged periods of low soil moisture during the longer dry periods.This confirms and extends our previous findings of the effects of increased rainfall variability on soil moisture dynamics in this grassland (Knapp et al., 2002;Fay et al., 2003aFay et al., , 2008)).Since total rainfall inputs were unchanged, increased rainfall variability reduced the effective storage of rainfall in the upper part of the soil profile.We found that soil moisture variability was a decreasing function of mean soil moisture (Fig. 2).We previously reported that soil moisture variability was independent of mean soil moisture in our experiment (Knapp et al., 2002;Fay et al., 2003a).However, the current finding is based on a longer data set, emphasizing the need for long-term manipulations that capture enough natural rainfall variability to correctly show the relationship of interannual variation in Θ 15 with intra-annual variation in soil moisture (Davidowitz, 2002).
Increased intra-annual rainfall variability significantly affected most ecosystem processes compared to an equal amount of rainfall at ambient variability.However, the effects of increased intra-annual rainfall variability were smaller than interannual climate Introduction

Conclusions References
Tables Figures

Back Close
Full variation for soil moisture (Fig. 9a inset) and most ecosystem processes.Increased rainfall variability reduced plant growth and leaf and soil CO 2 fluxes only 8-17 % despite the large increase in the amplitude of soil moisture variability (∆Θ 15 ).This confirms and extends our earlier findings (Knapp et al., 2002).The reduction in rates of ecosystem processes with altered rainfall timing suggest that increased rainfall variability increased water limitation in this grassland.The result of increased water limitation may be a grassland that is more sensitive to interannual climate variation (Huxman et al., 2004).
The magnitude of interannual variation in J CO 2 (∼46 %) was less than interannual variation in leaf carbon assimilation or total ANPP.A similar magnitude of interannual variation in J CO 2 was reported in a semiarid grassland by Liu et al. (2009) and in an annual grassland by Chou et al. (2009).However, in the multiple regression analysis, CV Θ 15 explained more variation in J CO 2 than did Θ 15 , suggesting that J CO 2 was actually more strongly associated with intra-annual rainfall variability.This result could be explained if soil moisture variability disproportionately affected belowground processes such as allocation of current photosynthate to roots, root biomass, litter decomposition, or microbial biomass or substrate availability (Luo and Zhou, 2006), resulting in greater reductions in overall belowground metabolic activity compared to those from mean soil moisture.

Warming effects occur at different times of year than intra-annual rainfall effects
The warming treatment raised soil temperature, especially during spring, fall, and winter.This outcome was consistent with that of a similar warming experiment in Oklahoma tallgrass prairie, where mean annual soil temperature increased approximately 2 • C with only weak reductions in soil moisture (Wan et al., 2005).
The most apparent effects of warming were found in canopy greenness, soil respiration, and forb ANPP.The marked increase in spring canopy greenness indicates Introduction

Conclusions References
Tables Figures

Back Close
Full that warming advanced ecosystem phenology.This result is consistent with findings from larger spatial scales (Badeck et al., 2004).Warming effects on canopy greenness diminished and were replaced by altered rainfall effects from late spring through the remainder of the growing season.This result suggests a transition from temperature control of early spring canopy greenness to control by rainfall variability.The lack of late season response to warming contrasts with some studies, where warming led to earlier senescence in annual grassland (Zavaleta et al., 2003) and increased fall green aboveground biomass (Wan et al., 2005).
The warming treatment reduced mean growing season J CO 2 by about 5 %.Liu et al. (2009) also found reduced J CO 2 with experimental warming in semiarid grassland, while warming increased annual J CO 2 in Oklahoma tallgrass prairie (Zhou et al., 2006).An increase in soil respiration of about 20 % with warming was typical across grassland, forest, and desert ecosystems (Rustad et al., 2001).J CO 2 is an increasing function of soil temperature and a quadratic function of soil moisture in our experiment (Harper et al., 2005).Our finding of lower J CO 2 despite warmed soil may mean that lower soil moisture offset the stimulatory effect of warming.The quadratic response of J CO 2 to soil moisture may explain why warming may cause lower J CO 2 with reduced soil moisture in some cases (Liu et al., 2009) or higher J CO 2 with reduced soil moisture in others (Zhou et al., 2006).
In contrast, warming caused a marked increase in J CO 2 during winter.This suggests that winter soil respiration was primarily limited by soil temperature.Similarly, Almagro et al. ( 2009) found that soil respiration increased with soil temperature during moist, cool conditions.The increase in winter CO 2 efflux, while small in absolute terms, would still affect total annual soil CO 2 efflux.As a result, annual responses in soil respiration to global changes cannot be inferred from short-term or growing season measurements, but the entire year must be accounted for.
Reduced forb ANPP was the most consistent effect of the warming treatment on plant growth.Forb ANPP may have been reduced because forb activity peaks during the cooler early and late portions of the growing season, when warming might be Introduction

Conclusions References
Tables Figures

Back Close
Full expected to have stronger effects than during the hotter middle of the growing season.
In addition, because most forbs are C 3 , they may have lost more C to photorespiration at higher temperatures.Because forbs contribute much of the plant diversity in these grasslands, warming may be a stronger driver of biodiversity change over time than increased rainfall variability.However, reduced forb ANPP only translated into reduced total ANPP in two out of five years.This overall lack of strong warming responses in ANPP indicated that following spring green up, rainfall variability was the main driver of biomass accumulation.Warming did not affect biomass production in annual grassland (Dukes et al., 2005), forb biomass in alpine meadow (de Valpine and Harte, 2001), or grass and forb biomass in semiarid Mongolian steppe (Xia et al., 2009).However warming reduced aboveground biomass in experimental grassland assemblages grown in a cool temperate climate, due to lower soil moisture (De Boeck et al., 2008).The weak effect of the warming treatment on total ANPP was consistent with the MR finding that T soil 05 explained little variation in these processes.Wan et al. (2005) and Klein et al. (2005) reported similar results in grasslands.

A conceptual model of the coupling of rainfall variability and warming
The findings from the first ten years of this experiment suggest that interannual climate variation, increased growing season rainfall variability, and warming can be hypothesized to exert effects on this grassland ecosystem in the following ways.
1. Interannual climate variation, mainly in growing season rainfall total, drives interannual variation in average soil moisture and rates of key ecosystem processes (Fig. 9a).3. Within a growing season (Fig. 9b), warming likely stimulates rates of ecosystem processes active during cooler parts of the growing season (e.g., canopy development, winter CO 2 efflux), but during the middle, warmer and water-limited portion of the growing season, increased rainfall variability and warming effects where they occur likely reduce rates of ecosystem processes.This sequential difference in the effects of warming and increased intra-annual rainfall variability changes the seasonal dynamics of ecosystem processes, compared to ambient temperature and variability.
This framework reveals several gaps in our understanding of the effects of rainfall and temperature variability on this grassland, indicating important areas for further research.
1.For the ranges of rainfall amounts and ecosystem responses in this study, we suggest a linear relationship between rainfall amount and average rates of ecosystem processes (Fig. 9a).However over a larger range of rainfall, asymptotic or threshold responses could occur.
2. In the simplest, linear case, increased intra-annual rainfall variability will decrease ecosystem processes equally at all rainfall amounts (Fig. 9a).However if ecosystem responses to rainfall amount prove to be nonlinear, we would expect greater effects of increased rainfall variability at intermediate rainfall amounts.Variability effects will decrease at high rainfall amounts because of less frequent soil moisture deficit, and at low rainfall amounts because of lower overall soil moisture.
3. The interactive effects of rainfall amount and intra-annual variability (Fig. 9a), and the transitions between effects of rainfall variability and warming (Fig. 9b) will likely differ among processes.
In conclusion, major ecosystem processes in this grassland were generally strongly affected by interannual variability, followed by intra-annual rainfall variability and warming.Introduction

Conclusions References
Tables Figures

Back Close
Full The nature of the relationship between intra-annual rainfall variability and warming has important implications for understanding the effects of climate change on this grassland, and its ability to sustainably provide food and fiber while supporting biological diversity and other ecosystem goods and services.Future research should seek explanation for how the interactive effects of these drivers may change in wetter or drier ecosystems (Knapp et al., 2008;Heisler-White et al., 2009), and examine daily to weekly variability, which strongly affects soil moisture and CO 2 fluxes (Fay et al., 2003b;Ogle and Reynolds, 2004;Harper et al., 2005).Introduction

Conclusions References
Tables Figures

Back Close
Full  Full  Full  Full Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | per week from the continuous data to match the sampling frequency of the pre-2005 Θ 15 Discussion Paper | Discussion Paper | Discussion Paper | h at two locations per plot during 1999-2002, and one location per subplot during 2003-2007 on 2-4 clear days each July.Light profiles were fit with an exponential function: PPFD = a/(1 − e (x−x 0 )/b ) (1) Where a = maximum PPFD, x 0 = height of 50 % PPFD and b = slope of PPFD decrease.Smaller x 0 values indicate that the height of interception of 50 % of the light is closer to the ground, and deeper penetration of light into the canopy means less aboveground biomass.Profiles not fitting this equation with p < 0.0001 were omitted from analysisannually from harvests of aboveground biomass at the end of the growing season (mid October).All aboveground biomass was clipped at ground level from 20 × 50 cm sampling quadrats.Ten quadrats per plot were harvested during 1998-2002, and four quadrats per subplot (16 per plot) were harvested during 2003-2007.The plots were burned each spring and ungrazed, so accumulated aboveground biomass represented current year production.Harvested samples were sorted into grasses and forbs, and weighed after drying at 65 • C for at least 48 h.One plot differed markedly in plant species composition from the others and was omitted from the biomass analyses.Data from 1998-2002 were reported in Knapp et al. (2002) and Fay et al. (2003a).
[1998][1999][2000][2001][2002] was measured with two calibrated closed path infrared gas analyzers (LI-6200, LI-Cor Biosciences, Lincoln, NE, USA).A CO 2 was measured on four plants per plot, using fully expanded, recently matured upper canopy leaves, and was completed between 10:00 h-15:00 h.The 1998-1999 weekly measurements usually were conducted on cloud-free days.Measurements were omitted from analysis if PPFD was < 500 µmol m −2 s −1 , and entire plots were omitted from analysis when plot mean PPFD was < 1000 µmol m −2 s −1 .All measurements during 2000 and 2002 were conducted on clear days.Discussion Paper | Discussion Paper | Discussion Paper | A CO 2 during 2005-2006 was measured with two open path infrared gas analyzers (LI-6400, LI-Cor, Biosciences, Lincoln, NE, USA) with red/blue LED light sources and CO 2 injectors.Measurements were conducted in one warmed subplot and one unwarmed subplot per plot.Each measurement used one recently matured leaf per tiller from two tillers per species.Cuvette conditions were 1500 µmol m −2 s −1 PPFD, 370 µmol mol −1 [CO 2 ], and near ambient relative humidity.This PPFD is adequate for light saturation and comparable to light levels in the 1998-2002 data.Measurements were logged when stability (the coefficient of variation of A CO 2 ) was < 1 % over 15 s.Measurements usually stabilized in 5-10 min.Data from 1999 were reported in Fay et al. (2002), and from 2005-2006 in Nippert et al. (2009).
Winter J CO 2 was measured on 7-9 snow-free dates during November through March in 2005/2006 and 2006/2007.J CO 2 was measured at two permanently installed PVC collars at four locations per plot during 1998-2002, and at two collars per subplot from 2003-2007.Data from 1998-2001 were reported in Harper et al. (2005).

(
CV) from the daily rainfall amounts applied to the RaMPs during the growing season (1 May-30 September).Mean growing season Θ 15 was computed by averaging each year's weekly TDR readings.Variability during the growing season in Θ 15 was quantified by computing two variability metrics: (1) the CV of Θ 15 , which expresses temporal variability in mean Θ 15 as a percentage of Θ 15 ; (2) the mean change in soil moisture Introduction Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3 • C during 2004-2007.However, T soil 05 varied considerably more among seasons than among years.Winter T soil 05 averaged 2-4 • C, increasing to 15-17 • C during spring and fall, and 24-26 • C during summer (Fig. 3b-e).T soil 05 differed 2-3 • C between day and night, regardless of season.
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | combined (p = 0.03) because of significant effects in 1998, 2000, and 2002.During Phase I, A. gerardii A CO 2 was unaffected by the reduced quantity treatment (data not shown, p > 0.46, Table Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

2.
Increased intra-annual (growing season) rainfall variability reduces rates of most ecosystem processes compared to ambient rainfall patterns with the same total amount of rainfall.This reduction in ecosystem function (sensu Hui et al., 2003) is an indicator of lower ecosystem rainfall use efficiency resulting from greater temporal variability in growing season soil moisture.Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Table 1 .
Regression parameters and statistics for mean soil moisture at 15 cm depth (Θ 15 ), intra-annual season soil moisture variability at 15 cm depth (CV Θ15 ), and soil temperature at 5 cm depth (T soil 05 ) from univariate and multivariate regression models for ANPP, leaf photosynthesis, and soil CO 2 efflux.

Table A1 .
Analysis of variance F statistics for rainfall and soil moisture variation responses to pattern, quantity, and warming treatments in RaMPs.

Table A2 .
Analysis of variance F statistics for vegetation responses to pattern, quantity, and warming treatments in RaMPs.