Environ Monit Assess (2014) 186:8473–8486 DOI 10.1007/s10661-014-4031-z

Quantifying the effect of trend, fluctuation, and extreme event of climate change on ecosystem productivity Yupeng Liu & Deyong Yu & Yun Su & Ruifang Hao

Received: 17 November 2013 / Accepted: 15 August 2014 / Published online: 11 September 2014 # Springer International Publishing Switzerland 2014

Y. Su e-mail: [email protected]

indicated that the total contribution of the temperature, precipitation, and PAR to NPP variation from 2001 to 2011 in Hunan province is 85 %, and individual contributions of the temperature, precipitation, and PAR to NPP variation are 44 % (including 34 % trend contribution and 10 % fluctuation contribution), 5 % (including 4 % trend contribution and 1 % fluctuation contribution), and 36 % (including 30 % trend contribution and 6 % fluctuation contribution), respectively. The contributions of temperature fluctuation-driven NPP were higher in the north and lower in the south, and the contributions of precipitation trend-driven NPP and PAR fluctuation-driven NPP are higher in the west and lower in the east. As an instance of occasionally triggered disturbance in 2008, extremely low temperatures and a freezing disaster produced an abrupt decrease of NPP in forest and grass ecosystems. These results prove that the climatic trend change brought about great impacts on ecosystem productivity and that climatic fluctuations and extreme events can also alter the ecosystem succession process, even resulting in an alternative trajectory. All of these findings could improve our understanding of the impacts of climate change on the provision of ecosystem functions and services and can also provide a basis for policy makers to apply adaptive measures to overcome the unfavorable influence of climate change.

Y. Liu : D. Yu : Y. Su : R. Hao State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing 100875, People’s Republic of China

Keywords Net primary productivity . Ecosystem vulnerability . Remote sensing . Signal decomposition . Contribution assessment . Hunan, China

Abstract Climate change comprises three fractions of trend, fluctuation, and extreme event. Assessing the effect of climate change on terrestrial ecosystem requires an understanding of the action mechanism of these fractions, respectively. This study examined 11 years of remotely sensed-derived net primary productivity (NPP) to identify the impacts of the trend and fluctuation of climate change as well as extremely low temperatures caused by a freezing disaster on ecosystem productivity in Hunan province, China. The partial least squares regression model was used to evaluate the contributions of temperature, precipitation, and photosynthetically active radiation (PAR) to NPP variation. A climatic signal decomposition and contribution assessment model was proposed to decompose climate factors into trend and fluctuation components. Then, we quantitatively evaluated the contributions of each component of climatic factors to NPP variation. The results Y. Liu : D. Yu (*) : Y. Su : R. Hao Center for Human-Environment System Sustainability, Beijing Normal University, Beijing 100875, People’s Republic of China e-mail: [email protected] Y. Liu e-mail: [email protected]

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Introduction Climate change alters long-term climatic trends and is increasing the magnitude of fluctuations (Bradford 2011). The Intergovernmental Panel on Climate Change (IPCC) stated in its Fourth Assessment Report that the annual median surface air temperature will increase by 1.8 to 4.0 °C and precipitation will alter spatial and temporal patterns in the future (IPCC 2007). The IPCC AR4 report only emphasizes the change of climatic trends, but it is not sufficient to understand the fluctuation process. Both the changes in climatic trends and fluctuations are considered to have immediate effects on ecosystems and can affect the provisioning of their functions and services (Knapp et al. 2002; Fan et al. 2010). Of all changing factors, temperature is the greatest limitation on ecosystems (Bala et al. 2007; Sitch et al. 2003; Lindner et al. 2010). The minimum temperature determines the distribution boundaries of plants (Jentsch et al. 2007; Ferrez et al. 2011), and extreme temperature is the controlling factor at the beginning and the end of the growing season (Yuan et al. 2007; Coops et al. 2009). According to future climate change scenarios, the predictions of general circulation models (GCMs) demonstrate that the increase in the magnitude of minimum temperature is greater than the maximum temperature (IPCC 2007). A decrease in the frequency of extreme cold temperatures and a lengthening of the growing season are likely beneficial to plant growth (Jentsch et al. 2007). However, a higher degree of global warming also may produce higher risks in the provision of ecosystem functions and services (Scholze et al. 2006; King et al. 2013; Tatarinov and Cienciala 2009). Precipitation is another important driving force for ecosystems, and it is predicted to increase in boreal and tropical latitudes but decrease in the mid-latitudes in the future according to GCMs (IPCC 2007). The changed precipitation pattern may alter the supply of water for forest, agriculture, and other ecosystems (Murray et al. 2012; Evangelista et al. 2011), and the appropriate timing of the precipitation is the most important controlling factor for plant growing seasons (Craine et al. 2012). In some wet tropical regions where temperatures and water availability are usually adequate, incident solar radiation is the primary limiting climatic factor on ecosystem (Nemani et al. 2003). The colimitation of temperature, precipitation, and radiation may occur in temperate mid-latitude regions (Running et al. 2004), in

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high latitude regions (Nemani et al. 2003), or in monsoon regions (Nemani et al. 2003). All these studies illustrate that trends and fluctuations of climate change as well as extreme events have different impacts on ecosystem services and vulnerability. Furthermore, the determination of the effect mechanism of climate change in regard to ecosystem trends, fluctuations, and extreme events is necessary. Long-term average climatic conditions represent the climate trend, and fluctuation can be expressed as the deviations from mean values (Bradford 2011). The former being used as a powerful indicator of climatic conditions has a long history (Easterling et al. 2000; Jentsch et al. 2007; Bradford 2011; Scholze et al. 2006; Twine and Kucharik 2009). However, the smoothing effect of gradual trends makes it hard to reflect the dynamic impacts on the ecosystem, and furthermore, future climate change is likely to increase the magnitude of fluctuations. Intensification of climatic fluctuations and extreme events can accelerate shifts in species distribution, alter the composition of communities, and change the provision of ecosystem functions and services, thereby affecting ecosystem vulnerability (Jentsch et al. 2007; Liu et al. 2014; Milne et al. 2013). To distinguish the different trend-driven and fluctuation-driven impacts on the ecosystem, the periodic acquisition of remotely sensed data is needed to provide useful information (Cramer et al. 1999; Ito 2011; Nemani et al. 2003; Zurlini et al. 2013; Bellassen et al. 2011). Periodic vegetation indices (VIs) exhibit a strong interacting relationship with temperature and precipitation (Li and Kafatos 2000; Xu et al. 2012). The coupling of VIs and climatic factor has been used to identify the impacts of climate change and anthropogenic disturbances and assess the magnitude and duration of the disturbances on ecosystems (Mildrexler et al. 2007; Mildrexler et al. 2009; Wen et al. 2013). It is difficult to assess the discrete effects of trends and fluctuations due to the nonlinear relationship between the outcome calculated by origin climatic factors and their decomposed components (Wei et al. 2012; Scheffer et al. 2009). At present, few publications have fully dealt with the effects of the three aspects of climate change on the ecosystem, together. Our work presented here underlines the integrated impacts of both climatic trends and fluctuations. Concretely, we try to (1) propose a model that can decompose climate change into trend and

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fluctuation components and assess their contributions to the ecosystem quality variation; (2) choose the temperature, precipitation, photosynthetically active radiation (PAR), and net primary productivity (NPP) as represented by climatic factors and ecosystem quality, respectively, to implement the model in a typical region of China; and (3) introduce an alternative assessment framework of ecosystem vulnerability differing from the currently universal framework.

Materials and methods Study area Hunan province is located in the middle part of China between 108°47′ and 114°13′ E, ranging between 24° 39′ and 30° 08′ N (Fig. 1). The total area is approximately 210,000 km2. The population was up to more than 70 million in 2011. The topography has a relatively less amount of flat land, with many hilly and mountainous lands. The annual precipitation of Hunan province ranges from 1,200 to 1,700 mm, and the mean annual temperature is approximately 17 °C. Hunan province is influenced by the typical East Asian monsoon weather pattern. This special climatic pattern leads Hunan province to be a seriously natural disaster-prone province, particularly in regard to seasonal drought, flood, extremely low temperatures, and freezing disasters. From January to February in 2008, extremely low temperatures and freezing disasters had great impacts on the social ecosystem in Hunan province. Because of the above features, it is a rational site to detect the influence of trends, fluctuations, and extreme climate change events on the ecosystem.

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To a pixel, the relationship between dependent variable and independent variables can be formulated by multiple linear regression as follows: Yb t ¼ b1 X 1;t þ b2 X 2;t þ b3 X 3;t

ð1Þ

where Yb t is the normalize sequences NPP in a time serial of t (t=1,…,n), X1,t, X2,t, and X3,t are the normalize sequences temperature, precipitation, and PAR, respectively, b1, b2, and b3 are the regression coefficients, respectively. There is the following relationship: m 2 ¼ b1 ρ 1 þ b2 ρ 2 þ b3 ρ 3

ð2Þ

where m is multiple correlation coefficient and ρ1, ρ2, and ρ3 are the correlation coefficients of NPP with the three climatic factors (X1, X2, and X3), respectively. The left part of Eq. (2) is the total contribution of the three climatic factors to NPP variation, and each item of the right part is the individual contribution of X1, X2, and X3 to NPP variation, respectively.

Climatic signal decomposition and contribution assessment model We propose a model to evaluate the discrete responses of the quality parameters of the ecosystem in terms of climatic trend and fluctuation changes and assess their contributions to the ecosystem, respectively. The climatic factors (temperature, precipitation, and PAR) include two components, namely, the linear trend component and nonlinear fluctuation component. Each component has increased, decreased, and stable statuses. The combination of each status produces a total of nine modes (Fig. 2). In this study, the climatic factor is decomposed into linear trend component and nonlinear fluctuation component, namely

Partial least squares regression model

X t ¼ xT;t þ x F;t

To quantify the effects of climatic factors on ecosystem, we choose the monthly mean temperature, monthly total precipitation, and monthly total PAR as the represented climatic factors and choose NPP as the represented quality parameter of ecosystem. The correlation coefficients of the three climatic factors are 0.536, 0.697, and 0.007, respectively (Table 1). We used partial least squares regression model (Geladi and Kowalski 1986) to solve the collinearity problem of climatic factors.

where Xt is the climatic factor in a time serial of t (t= 1,…,n), xT,t is the trend component, xF,t is the fluctuation component. The xF,t can be further partitioned into two components: x F;t ¼ xreg;t þ xres;t

ð3Þ

ð4Þ

where xreg,t is the regular (predictable) fluctuation component and xres,t is the residual (unpredictable) fluctuation component. The xT,t is considered as a piecewise

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Fig. 1 The location of Hunan province in China

linear function between two trend break points (Verbesselt et al. 2010a), such that xT;t ¼ αi þ βi t

ð5Þ

where i=1,…,n, αi and βi are the intercept and slope of the linear function, respectively. The xreg,t is a harmonic function between the two break points of

Table 1 The correlation coefficients among climate factors Climatic factor

Temperature

Temperature

1.00

Precipitation PAR

Precipitation

PAR

0.536**

0.697**

1.00

0.007 1.00

**The correlation is significant at the significance level of 0.01

fluctuation and can be formulated as (Verbesselt et al. 2010b; Verbesselt et al. 2012): xreg;t ¼

K X k¼1



2πkt þ δ j;k θ j;k sin f

 ð6Þ

where, θj,k and δj,k are the amplitude and phase at frequency f/k, f is 12 for a monthly time series each year, k is the number of harmonic terms, and one can use three harmonic terms (e.g., k=3) to robustly detect the climate change (Geerken 2009). The xres,t is the residual variability beyond that noted in the trend component and regular fluctuation component. The linear trend component and nonlinear fluctuation component of the climatic factor have discrete impacts on the quality parameters of the ecosystem (illustrated in Fig. 3). The shadow area in Fig. 3a represents the linear

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Fig. 2 The combinations of climatic trend and fluctuation components. The dotted line represents the climatic trend component, and the solid line represents the climatic fluctuation component. The climatic variability is usually fluctuating around the trend

fraction of the ecosystem quality parameter affected by the climatic trend component. The shadow area in Fig. 3b represents the nonlinear fraction of the ecosystem quality parameter affected by the climatic fluctuation component. Figure 3c represents the overlay of the linear fraction and the nonlinear fraction of the quality parameter of the ecosystem, and the repeated fractions are removed from the total variation. The total impact of the climatic factor on the quality parameters of the ecosystem equals the sum of the impacts from each fraction, namely Z y ¼ f ð X Þ ¼ f T ð xT Þ þ

xT þx F xT

f ‵F ðx F Þdx

ð7Þ

where y is the quality parameter of the ecosystem, xT and xF are the decomposed components of climatic factor by equation (3), where f (X) represents the action relationship between the climatic factor X and the quality parameter of the ecosystem y, f T (xT) is the linear fraction of the quality parameter of the ecosystem affected by xT,f‵F(xF ) is the change rate of nonlinear fraction of the quality parameter of the ecosystem per xT þx F

unit of xF, and ∫xT

f ‵F ðx F Þdx is the nonlinear

fraction of the quality parameter of the ecosystem affected by xF. The contributions of each fraction to the total contribution are given by the following: 8  Z > > < C T ¼ f T ðxT Þ= f T ðxT Þ þ > > :

Z CF ¼

xT þx F

f xT

F

0

xT þx F xT

 f ‵F ðx F Þdx  100%

 ðx F Þdx= f T ðxT Þ þ

Z

xT þx F xT

 f ‵F ðx F Þdx  100%

ð8Þ where CT and CF are the contributions of the linear trend fraction and nonlinear fluctuation fraction to the total contribution, respectively. In this study, the quality parameter of the ecosystem is represented by NPP (gCm−2), which is a sensitive signal indicator of the terrestrial ecosystem to climate change and is simulated based on the improved Carnegie-Ames-Stanford Approach (CASA) model (Yu et al. 2009a; Yu et al. 2009b). The simulated NPP from the CASA model has been proven to be consistent with field-measured NPP observations (Yu et al. 2009a; Yu et al. 2009b; Potter et al. 1993) and the simulations by other ecosystem models in regard to temporal and

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Fig. 3 The effects of the climate linear trend component and nonlinear fluctuation component on the ecosystem quality parameter. The dotted line represents the linear trend of climate change, and the solid line represents the nonlinear fluctuation of climate change

spatial variability (Cramer et al. 1999; Ito 2011; McCallum et al. 2009; Nayak et al. 2010; Sasai et al. 2011). The CASA model is a production efficiency model (PEM) and calculates NPP as a function of the absorbed photosynthetically active solar radiation (APAR) and the light utilization efficiency (ε) (Potter et al. 1993): NPP ¼ APAR  ε

ð9Þ

where APAR is a function of normalized difference vegetation index (NDVI) and PAR and εis a function of the maximum light-use efficiency (LUE) and adjusted factors with temperature and water stress, so that the model can be further partitioned into six factors (Yu et al. 2009a; Yu et al. 2009b; Yu et al. 2008): NPP ¼ f ðNDVIÞ  PAR  εmax  T ε1  T ε2  W ε

ð10Þ

The improved CASA model we used in this paper requires three key inputs: (1) The first key input was remote sensing inputs including MODIS-based NDVI data and land cover data. Standard MODIS NDVI products—MOD13A3 (collection 5) dataset—provided monthly composite atmospherically corrected and a nadir-adjusted dataset at 1-km spatial resolution (downloaded at https://lpdaac.usgs.gov/ ). The land cover dataset came from the Joint Research Centre (JRC) and was compiled by the Institute of Remote Sensing Application, Chinese Academy of Sciences. The resolution of this dataset was 1 km, and the land cover types included forest, shrub, grass, cropland, urban, and water body. The forest type includes broadleaved and needle-leaved, evergreen, and deciduous trees. The shrub type includes evergreen and deciduous shrubs and bushes. The grass type includes pastures, slope grasslands, plain grasslands, and meadows. The cropland type includes paddies and drylands. The urban type includes artificial surfaces and associated areas.

The water body type included rivers, lakes, and ponds. All these data were projected to Universal Transverse Mercator (UTM) zone 49 north, by using World Geodetic System-84 (WGS-84) datum. (2) The second key input was monthly surface meteorological inputs including monthly solar radiation, which was used to estimate APAR, monthly mean temperature and monthly total precipitation, which were used to estimate the temperature stress coefficients (Tε1, Tε2), and moisture stress coefficient (Wε). The monthly mean temperature and monthly total precipitation were derived from the records of existing 25 meteorological stations spread all over the Hunan province during 2001–2011 (downloaded from http://cdc.cma.gov.cn ). The photosynthetically active radiation (PAR) was calculated from the sunshine hours. All these data were validated with the missing and suspicious data eliminated and then interpolated using the Kriging method with 1 km× 1 km resolution and projection to match the MODISbased NDVI data. (3) The third key input was biomespecific coefficients (observed NPP, ε and εmax). Based on the land cover data, the observed NPP, the temperature stress coefficients (Tε1, Tε2) and the moisture stress coefficient (Wε), the maximal light use efficacy (εmax) of the vegetation type was estimated to produce light use efficiency (ε) of the vegetation type, which was then used with the APAR to predict monthly NPP. The monthly mean temperature, monthly total precipitation, and monthly total PAR were decomposed into the linear trend component and nonlinear fluctuation component by the climatic signal decomposition model, respectively, which is represented by Eqs. (3)–(6). The relationship between the climate factors and NPP could be represented by f (X) in Eq. (7). Except for the climate factors, the other inputs in the improved CASA model were directly based on the observed data. We used the improved CASA model to calculate the trend-driven NPP and fluctuation-driven NPP, respectively, and added them to obtain total NPP

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(namely, NPPtotal =NPPtrend +NPPfluctuation) and subsequently used Eq. (8) to assess their contributions to total NPP variation, respectively.

Results Simulated NPP The simulated NPP indicated that the annual NPP in Hunan province was 575 gCm−2 a−1 and ranged from 10 to 1,255 gCm−2 a−1 during 2001–2011. The comparisons of the minimum value, maximum value, mean value, standard deviation of NPP (gCm−2 a−1), area (km2), and regional NPP (TgCa−1) among the different land cover types are shown in Table 2. The forest had the highest mean value of NPP and largest area in Hunan province. The urban had the lowest mean value of NPP and a higher standard deviation than the shrub, grass, and cropland. The forest contributed 79.32 TgCa−1, occupying 67 % of the regional NPP of Hunan province, and the shrub took second place, occupying 16 %. The impacts of climate factors on NPP variation The results indicated that the total contribution of the temperature, precipitation, and PAR to NPP variation from 2001 to 2011 in Hunan province was 85 %, and individual contributions of the temperature, precipitation, and PAR to NPP variation were 44, 5, and 36 %, respectively (Fig. 4). The total contributions of climatic factors to forest, shrub, grass, and cropland NPP variation were higher than those to the urban area (Table 3). The mean contributions of temperature to forest, shrub, and grass NPP variation were lower than cropland and urban areas, but the mean contributions of PAR were higher (Table 3).

The impacts of the climatic trend and fluctuation components on NPP variation The results indicated that the trend component of the temperature, precipitation, and PAR during 2001–2011, on average, contributed 34, 4, and 30 % to the total NPP variation, respectively. The fluctuation component of the temperature, precipitation, and PAR contributed 10, 1, and 6 %, respectively. The contributions of temperature fluctuation-driven NPP were higher in the north and lower in the south (Fig. 5d). The contributions of precipitation trend-driven NPP and PAR fluctuationdriven NPP were higher in the west and lower in the east (Fig. 5b, f). The impacts of extremely low temperatures and freezing disaster on NPP variation We elucidated two typical change patterns of NPP among the forest and grass when the extremely low temperatures and a freezing disaster occurred in January, 2008 (Fig. 6). The results indicated that the decrease in NPP occurred in the forest and grass in 2008 (Fig. 6a). The NPP of grass recovered immediately after 2008, but the NPP of forest recovered more slowly (Fig. 6b). The increase in NPP variance of grass and forest occurred between 2008 and 2009, and after 2009, the NPP variance of grass decreased but that of the forest not (Fig. 6c).

Discussion Climate change has documented immediate and strong effects on the social ecosystem (IPCC 2007; Twine and Kucharik 2009; Schröter et al. 2005; Scholze et al. 2006; Jentsch et al. 2007). How to adapt to the climate change has become a global issue, which has received a

Table 2 Main statistical characteristics of NPP simulations using the improved CASA model in Hunan province during 2001–2011 Land cover

Minimum (gCm−2 a−1)

Maximum (gCm−2 a−1)

Mean (gCm−2 a−1)

SD (gCm−2 a−1)

Area (km2)

Regional NPP (TgCa−1) 79.32

Forest

324

1,255

638

316

124,326

Shrub

382

581

467

42

41,669

19.46

Grass

103

700

583

52

3,523

2.05

Cropland

92

686

521

63

33,941

17.68

Urban

10

675

397

128

662

0.26

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Fig. 4 Spatial pattern of the contributions (%) of a temperature, b precipitation, c PAR, and d total to NPP variation in Hunan province during 2001–2011. The dotted pixels represent that the correlation was not significant at the level 0.01

great amount of attention and concern from scientists, governments, and the worldwide public. To overcome the unfavorable influence of climate change, the IPCC

published a special report in 2013 —Managing the risks of extreme events and disasters to advance climate change adaptation (SREX) (IPCC 2013). This report

Environ Monit Assess (2014) 186:8473–8486 Table 3 The mean contribution (%) of climatic factors to the NPP variation among land cover in Hunan province during 2001– 2011

Land cover

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Temperature (%)

Precipitation (%)

PAR (%)

Total (%)

Forest

43.29

4.38

37.52

85.19

Shrub

42.57

4.68

39.78

87.03

Grass

43.80

4.65

36.85

85.30

Cropland

48.98

4.24

28.62

81.84

Urban

48.96

2.17

19.10

70.23

emphasizes the impacts of extreme events and disasters on the social ecosystem. In this paper, we demonstrated

that the total influence of climate change comes from the contributions of the trend component, the fluctuation

Fig. 5 Spatial pattern of the mean contributions (%) of a temperature trend, b precipitation trend, c PAR trend, d temperature fluctuation, e precipitation fluctuation, and f PAR fluctuation to NPP variation in Hunan province during 2001–2011

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Fig. 6 Comparison of two typical change patterns of NPP related with extremely low temperatures and a freezing disaster that occurred in 2008 among forest and grass ecosystem in Hunan

Province, China. a Monthly NPP variation. b Annual NPP variation. c Seasonal component-removed NPP variance changes

component, and extreme events, in particular that the climate change risk (CCR) actually equals the total negative effects of tendency variation, fluctuation value, and the extreme events occurring in particular period, in

other words, tendency risk (TR), fluctuation risk (FR), and extreme risk (ER), that is, CCR=TR+FR+ER. The extreme events and disasters which were focused by the IPCC SREX cannot thoroughly reveal the systemic

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features of CCR, which may mislead governments into establishing unnecessary comprehensive defensive measures. The distinction between the trend and fluctuation of climatic conditions is an issue of temporal scaling because mean value is determined with respect to a historical reference period, from month to annual or even century. For ecological investigations, choosing the temporal resolution is determined by the life span of organisms or successional speed of the ecosystems (Wu 1999; Zhang et al. 2013). Our study was based on a view of annual ecosystem productivity variation, and therefore, the trend components of climate factors were examined in terms of gradual annual changes, and fluctuation components represented seasonal cycle. The fluctuation component included regular (predictable) and residual (unpredictable) variability in our work. The regular fluctuation component reflects the seasonal phenology characteristics of the region, and it varies among different climatic districts. The typical East Asian monsoon weather pattern leads to Hunan province having significantly seasonal phenology. The monthly climatic factors facilitate to represent the seasonal phenology and restrain the white noise generated by the observation error. This characteristic is suitable for simulation by a harmonic function because the seasonal cycle of climatic factors can be characterized by the amplitude, phase, and harmonic terms in harmonic function (Eastman et al. 2009; Geerken 2009; Verbesselt et al. 2010b). The amplitude of the harmonic function represents the magnitude of the fluctuation component. The phase of the harmonic function represents the time of occurrence and duration of the fluctuation component. The number of harmonic terms in the harmonic function is determined by the temporal resolution of the observation data and the phenological features we want to detect. For monthly observation climatic data, the phenology variations that occur in a less than 3-month cycle can be eliminated by choosing three harmonic terms (Geerken 2009). Higher harmonic terms represent higher frequency events or less revisiting observation intervals. The residual fluctuation component mainly produces measurement error, signal noise, anthropogenic or natural disturbance, etc. The contribution of the trend components of climatic factors to ecosystem productivity variation was underscored in our study and was consistent with previous studies (Bradford 2011; Sasai et al. 2011). As the precipitation trend increased from west to east in Hunan

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province, the less water stress in east area made the contribution of precipitation trend decrease (Fig. 5b). The explanatory power of climatic fluctuation change with respect to NPP variation should not be neglected. Previous studies have proven that climatic fluctuation has a great impact on the ecosystem (Bradford 2011; Jentsch et al. 2007). In Hunan province, as the temperature fluctuation decreased from north to south, the contribution of temperature fluctuation to NPP variation decreased (Fig. 5d). Extreme events such as drought, flood, heat waves, extremely low temperatures, and freezing disasters also may accelerate ecosystem changes or even trigger an abrupt change of NPP variation (Jentsch et al. 2007; Ferrez et al. 2011). As indicated in Fig. 6, the influence of the extremely low temperatures and the freezing disaster on NPP variation was far beyond its short duration. This phenomenon indicated that the introduction of extreme events can alter the ecosystem succession process and may bring the system into an alternative stable status. Craine et al. (2012) found the importance of the time of occurrence of extreme events in the grass ecosystem. We further illustrated that the effects of the same extreme event varied among grass and forest ecosystem (Fig. 6). The results suggested that the increasing variance (Carpenter and Brock 2006) is a good indicator to indicate the ecosystem stability (Fig. 6c). However, it does not mean that the resilience of forest ecosystem is lower than that of the grass ecosystem. The less decrease in forest NPP than in grass NPP when the extremely low temperatures and a freezing disaster occurred in 2008 indicated that the forest ecosystem with higher resilience can absorb more negative influence from disturbance and lose fewer services (Schröter et al. 2005) but need more recovery times (Fig. 6b). The less resilience of grass ecosystem to the freezing disaster made it lose more NPP than forest in 2008, but short life span of grass made it rapidly recover (Fig. 6b). Ecosystem vulnerability is generally considered in the framework of exposure, sensitivity, and adaptive capacity relying on a particular ecosystem service among time and space (Metzger et al. 2006; Schröter et al. 2005). This type of assessment is not sufficient to reflect the dynamic characteristics of the vulnerability. Thus, we propose an alternative assessment framework of ecosystem vulnerability differing from the current universal framework. This framework starts from the decomposition of climate change into trend and

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fluctuation signals, further evaluating the discrete contributions of trend and fluctuation signals to the ecosystem quality, then assessing ecosystem vulnerability by exploring the cause of abrupt changes of the quality parameter and quantifying the magnitude, duration, and timing of the disturbance. The work presented here explains the effects of temperature, precipitation, and PAR and their trend and fluctuation components on ecosystem productivity. The other environmental driving factors, including CO2 concentration, soil available water, and related factors, tend to be characterized by colimiting nonlinearities (Nemani et al. 2003). In the future, we will seek to explore how environmental driving factors (temperature, precipitation, PAR, CO2 concentration, soil available water, etc.) and anthropogenic influence work together. The different responses of vegetation to environmental driving factors and their trends and fluctuations may allow the key parameters of ecosystem to compensate for each other. All of these findings could improve our understanding of the impacts of climate change on the provision of ecosystem functions and services and could provide a basis for policy makers to apply adaptive measures and to overcome the unfavorable influence of climate change.

Summary and conclusions Climatic trends, fluctuations, and extreme events have great implications for ecosystems and affect the provisioning of their functions and services. It is necessary to quantify these effects and improve our understanding to adapt to the unfavorable influence of climate change. NPP as an important indicator of the terrestrial ecosystem is sensitive to climate change. Studying NPP variation is helpful to understand the feedback mechanism of the ecosystem to climate change. We proposed a model to quantitatively evaluate the discrete impacts of the climatic trend and fluctuation on quality parameters of ecosystem and assess the ecosystem vulnerability, in which the temperature, precipitation, PAR, and NPP were chosen as the represented climatic factors and quality parameter of ecosystem to implement the model in Hunan province, China. The climatic factors were decomposed into trend and fluctuation components, and each of them was used to evaluate its contribution to NPP variation. The results indicated that both the trend and fluctuation of climate

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change as well as extremely climatic events had great impacts on the ecosystems. Compared with the traditional vulnerability assessments, our work pays more attention to process-based monitoring, explores the cause of abrupt changes, and facilities the quantification of the impact of climate change on the ecosystems. The model that we propose is clearly defined and has a rational framework. Most of the datasets used in this study are obtained easily and freely. These features of our model increase the breadth of its potential applications. The model can serve to characterize the effects of climatic trend and fluctuation on ecosystem functions (e.g., biomass, productivity, and runoff) and services (e.g., food, timber, and aesthetic values). This approach also can serve as the basis of producing more reasonable management strategies to adapt to the unfavorable influence of climate change. Currently, the effects of climatic trends and fluctuations on ecosystem functions and services are still obscure (Craine et al. 2012; Jentsch et al. 2007), and we believe that our work may provide a new perspective to help to advance this work. Acknowledgments This research was funded by the Program of National Basic Research Program of China (973 Program) “Global change and environmental risk’s evolution process and its integrated assessment model” (No. 2012CB955402), the 111 project “Hazard and Risk Science Base at Beijing Normal University“ under Grant B08008, Ministry of Education and State Administration of Foreign Experts Affairs, People’s Republic of China, and the Project of State Key Laboratory of Earth Surface Processes and Resources Ecology. Special thanks are given to the referees and editors for their instructive comments and suggestions and editing the manuscript.

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