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Projections of the Advance in the Start of the Growing Season during the 21st Century Based on CMIP5 Simulations


doi: 10.1007/s00376-014-4125-0

  • It is well-known that global warming due to anthropogenic atmospheric greenhouse effects advanced the start of the vegetation growing season (SOS) across the globe during the 20th century. Projections of further changes in the SOS for the 21st century under certain emissions scenarios (Representative Concentration Pathways, RCPs) are useful for improving understanding of the consequences of global warming. In this study, we first evaluate a linear relationship between the SOS (defined using the normalized difference vegetation index) and the April temperature for most land areas of the Northern Hemisphere for 1982-2008. Based on this relationship and the ensemble projection of April temperature under RCPs from the latest state-of-the-art global coupled climate models, we show the possible changes in the SOS for most of the land areas of the Northern Hemisphere during the 21st century. By around 2040-59, the SOS will have advanced by -4.7 days under RCP2.6, -8.4 days under RCP4.5, and -10.1 days under RCP8.5, relative to 1985-2004. By 2080-99, it will have advanced by -4.3 days under RCP2.6, -11.3 days under RCP4.5, and -21.6 days under RCP8.5. The geographic pattern of SOS advance is considerably dependent on that of the temperature sensitivity of the SOS. The larger the temperature sensitivity, the larger the date-shift-rate of the SOS.
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Manuscript received: 18 June 2014
Manuscript revised: 08 October 2014
通讯作者: 陈斌, bchen63@163.com
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Projections of the Advance in the Start of the Growing Season during the 21st Century Based on CMIP5 Simulations

  • 1. Key Laboratory of Regional Climate-Environment for East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
  • 2. Ministry of Environmental Protection of Jiuzhaigou Country, Sichuan 623400
  • 3. Climatic Research Unit, University of East Anglia, Norwich, UK
  • 4. Center of Excellence for Climate Change Research and Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia
  • 5. Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong

Abstract: It is well-known that global warming due to anthropogenic atmospheric greenhouse effects advanced the start of the vegetation growing season (SOS) across the globe during the 20th century. Projections of further changes in the SOS for the 21st century under certain emissions scenarios (Representative Concentration Pathways, RCPs) are useful for improving understanding of the consequences of global warming. In this study, we first evaluate a linear relationship between the SOS (defined using the normalized difference vegetation index) and the April temperature for most land areas of the Northern Hemisphere for 1982-2008. Based on this relationship and the ensemble projection of April temperature under RCPs from the latest state-of-the-art global coupled climate models, we show the possible changes in the SOS for most of the land areas of the Northern Hemisphere during the 21st century. By around 2040-59, the SOS will have advanced by -4.7 days under RCP2.6, -8.4 days under RCP4.5, and -10.1 days under RCP8.5, relative to 1985-2004. By 2080-99, it will have advanced by -4.3 days under RCP2.6, -11.3 days under RCP4.5, and -21.6 days under RCP8.5. The geographic pattern of SOS advance is considerably dependent on that of the temperature sensitivity of the SOS. The larger the temperature sensitivity, the larger the date-shift-rate of the SOS.

1. Introduction
  • Understanding how plant species have responded to global warming provides background information for predicting future changes in ecosystems. A number of previous studies have reported linear relationships between temperature and vegetation growing season indices for different regions. Regression results suggest that an increase of 1°C in the average of an appropriate combination of monthly temperatures leads to different shifts of the timing of the growing season (especially for the start of the growing season, SOS) for various species and locations across the world (Menzel and Fabian, 1999; Sparks, 2000; Chen and Pan, 2002; Sparks and Menzel, 2002; Fitter and Fitter, 2002; Matsumoto et al., 2003; Chmielewski et al., 2004; Piao et al., 2006; Dai et al., 2013, 2014; Ge et al., 2014b; Wang et al., 2014). These results were obtained using phenological observations (such as first-flowering data and bud-burst-date data) as well as satellite-measured normalized difference vegetation index (NDVI) data. (Shen et al., 2014) defined the temperature sensitivity of SOS as the phenological change per unit temperature and demonstrated that both the magnitude of the pre-season temperature increase and the temperature sensitivity of the SOS can influence the advance of the SOS, which has been shown by observations during the last few decades (Linderholm, 2006; Jeong et al., 2011). It is implied that an appropriate combination of monthly temperatures may be used as a proxy of the SOS when phenological data are not available for the past, or for the future.

    Global warming caused an extension of the growing season during the 20th century (Linderholm, 2006). According to (IPCC, 2013), global warming since 1950 is very likely due to the observed increase in anthropogenic atmospheric greenhouse gas (GHG) concentrations, and this increase is almost certain to continue in the future. The long-lived GHG concentrations exert a strong control on the radiative forcing of the climate system, and then the ecosystem. It is beneficial to analyze possible impacts of global warming on the growing season in the future. The Coupled Model Intercomparison Project Phase 5 (CMIP5) projections of climate change are forced by GHG emission scenarios consistent with the Representative Concentration Pathways (RCPs), which were formulated based on the projected population growth, technological development, and societal responses in the future (Moss et al., 2010). The RCP scenarios provide a rough estimate of the fixed radiative forcing by 2100 (Taylor et al., 2012). The output of climate models under the RCP scenarios provides a base for studying possible changes in the SOS for the 21st century.

    A key question is whether the temperature sensitivity of the SOS remains constant at the century scale. In (Shen et al., 2014), the temperature sensitivity was determined using the NDVI-derived SOS and effective pre-season temperature during 1982-2008. (Wang et al., 2014) argued that the temperature sensitivity of a spring phenology index may change in time over a long period. However, (Xia et al., 2013) found that if only the April temperature is used, the temperature sensitivity is robust throughout a century-scale period at different sites for most of the land areas of the Northern Hemisphere. In the present study, we investigate changes in the SOS under the RCP scenarios using the knowledge obtained from (Xia et al., 2013).

    It is clear that climate models are not perfect (Reichler and Kim, 2008; Knutti, 2008), but there have been substantial improvements from one generation of models to another [e.g. from CMIP1 to CMIP3, as summarized by (Reichler and Kim, 2008), and from CMIP3 to CMIP5, as summarized by (Knutti and Sedlacek, 2013)]. In fact, many coupled climate models with all radiative forcings perform well in simulating the global and northern hemispheric mean surface air temperature evolutions of the 20th century (Cubasch et al., 2001; Zhou and Yu, 2006; Meehl et al., 2007; Hegerl et al., 2007; Randall et al., 2007; Knutti et al., 2008). The consistency among models suggests that the projected changes for large-scale climate features are structurally stable (McWilliams, 2007).

    The aim of this study is to reveal possible shifts of the SOS during the 21st century in the land areas of the Northern Hemisphere by using the established linear relationship and CMIP5 monthly temperature simulations. The study includes the following steps:

    (1) Evaluate the relationship between the SOS and April temperature (temperature sensitivity of SOS) based on observational data for 1982-2008 and reconstruct the SOS time series for the same period.

    (2) Evaluate the performance of CMIP5 coupled models by comparing the observed changes in monthly mean temperature and the simulated results for the past.

    (3) Quantify the possible changes in SOS for 2006-99 under the RCP scenarios with an uncertainty assessment.

2. Data
  • This study uses the global 0.5°× 0.5° gridded land surface monthly (April) mean air temperature dataset (TS 3.10) for 1901-2008, developed at the Climatic Research Unit (CRU) (Harris et al., 2014). This dataset were obtained from the Climatic Research Unit, University of East Anglia, via their website (accessed 16 March 2014): http://badc.nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__dataent_1256223773328276.

  • NDVI data have been developed from the Advanced Very High-Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting satellites. The data are calibrated and processed by the National Aeronautics and Space Administration's (NASA) Global Inventory Monitoring and Modeling Systems (GIMMS) group, with a spatial resolution of 8 km ×8 km and a temporal resolution of 15 days, for 1982-2008. Further data corrections (e.g. aerosol, cloud, volcanic, and sensor degradation) are also applied to improve the data quality (Tucker et al., 2005). The temperate vegetation areas of the Northern Hemisphere are chosen as the study area, where the seasonality of vegetation is evident (Slayback et al., 2003; Piao et al., 2006). The NDVI changes could be used to describe the "greenness" of vegetation. In this study, for each gird, the thresholds for the SOS are defined from the NDVI climatology. In the climatological annual cycle of NDVI, the SOS is determined by detecting the inflection date when this NDVI annual cycle time series begins to rise for a specific year (Zeng et al., 2011; Jeong et al., 2011).

  • The simulations of global coupled ocean-atmosphere general circulation models (CGCMs) provide the primary base for studying possible future climate changes (Kharin et al., 2007). The CMIP5 projections of climate change are forced by GHG emission scenarios consistent with the RCPs, as described in (Moss et al., 2010).

    In this study, the RCP2.6, RCP4.5 and RCP8.5 scenarios are chosen to represent a range of possible outcomes for 2006-99 (Table 1). The radiative forcing under RCP8.5 increases throughout the 21st century, and reaches a level of about 8.5 W m-2 by 2100. In addition to this "high" RCP8.5 scenario, there is an intermediate scenario, RCP4.5, which reaches a level of about 4.5 W m-2 by 2100. The RCP2.6 scenario is described as a peak-and-decay low scenario, in which the radiative forcing reaches a maximum near the middle of the 21st century before decreasing to an eventual low level of 2.6 W m-2 (Taylor et al., 2012).

    A collection of models can be used to determine the uncertainty range in projections (Tebaldi and Knutti, 2007). In this study, we select 16 CMIP5 models developed by 12 scientific organizations from 8 countries around the world (Table 2). The average of multiple models (multi-model ensemble, MME) performs better than any single model when compared with observations (Gillett et al., 2002; Gleckler et al., 2008; Reichler and Kim, 2008). In this study, we calculate the MME by averaging the results of the 16 models. Details of all the experiments and output of the models used in this study were obtained from the Working Group on Coupled Modelling (WGCM) under the World Climate Research Programme (WCRP), via their website (accessed 1 September 2013): http://cmip-pcmdi.llnl.gov/cmip5.

3. Method
  • To evaluate the performance of the CGCMs from CMIP5 in reproducing the current climate, simulations of the 20th century experiments, termed "historical", are compared with the observations, for 1901-2004. The historical runs are driven by observed atmospheric composition changes (including both anthropogenic and natural sources). The historical scenarios cross much of the industrial period (from the 19th century to the present). Based on these results, the projection of monthly mean temperature change (then the changes in growing season indices) over the globe in the 21st century is discussed.

    For convenience of comparison with high-resolution NDVI data, we assume that large-scale climate change in temperature is in general of good spatial homogeneity, and hence the CRU observational data and the CGCMs' output are interpolated into a common grid of 8 km ×8 km. This may lose some NDVI information at the finer-scale grids, but hopefully this does not have much effect on the results at the large-scale (hemispheric scale) for the pattern of SOS changes.

    For each grid, the linear regression coefficient between shifts of the SOS and changes in corresponding April mean temperature is quantified for 1982-2008, using least-squares methods. The coefficient is defined as the temperature sensitivity of the SOS for each grid. Only grids showing significant correlation (with a significance level of 0.05) are considered in this study. Figure 1a shows an example of the temperature sensitivity of the SOS averaged over the analyzed areas (all the grids showing significant correlation). It indicates that for a 1°C warming of the April mean temperature, there should be an advancement of 2.1 days for the SOS.

    Figure 1.  Temperature sensitivity of the SOS, e.g. the regression coefficients of the SOS onto April temperature for 1982-2008, (a) averaged over the analyzed areas and (b) at each grid. Only grids that show a significant linear relationship are marked (significance level: 0.05). Units: d °C-1.

    Figure 2.  (a) Spatial distribution of the linear trends of the observed SOS. (b) The same as (a), except for the expected (reconstructed) SOS. Units: d (10 yr)-1.

    This linear relationship, i.e. the temperature sensitivity of the SOS, enables us to straightforwardly reconstruct the SOS time series. First, the April temperature anomalies are calculated with respect to the whole period for each grid. Then, the April temperature anomalies multiplied by the temperature sensitivity of the SOS defines the reconstructed SOS series for a given grid.

4. Results
  • The regression coefficients of the SOS on the April temperature range from -0.8 to -5 d °C-1 (5% to 95%) for most of the land areas of the Northern Hemisphere (Fig. 1b, mainly located between 30°N and 70°N). These linear regression coefficients are considered to represent the temperature sensitivity of SOS. The spatial distributions of the phenological sensitivity span a latitudinal gradient over the continents and a longitudinal gradient in the coastal zones, showing larger values in southwestern North America, western Europe, and southern Asia. This pattern is consistent with the previous study of (Xia et al., 2013).

    In response to climate warming, shifts of the SOS in the temperate zones are determined not only by the temperature level in time but also by the temperature sensitivity. Having considered both factors, we reconstruct the time series of the SOS (the expected SOS). From 1982 to 2008, most of the analyzed areas experience advanced SOS, for both the observed (Fig. 2a) and expected (Fig. 2b) SOS. This is consistent with the previous study of (Jeong et al., 2011). The geographical distribution of the trends derived from the expected SOS (Fig. 2b) is broadly consistent spatially with those of the observed trends, though in general the expected trends are smaller in magnitude than the observed.

    On average over the analyzed areas, the trends of the observed SOS (Fig. 3) are -1.5 d (10 yr)-1, while those for the expected SOS are -0.8 d (10 yr)-1. The trends of the reconstructed indices explain only about half of the observed trends. This is partly because the SOS is not determined only by temperature, but also other factors such as precipitation and humidity, which may change and/or reinforce the effect on phenology induced by climatic warming. Nevertheless, even based on the same AVHRR dataset, there is large uncertainty in estimating the phenological trends [e.g. they range from 0.4 to 1.9 d (10 yr)-1 for China (Cong et al., 2013)], depending on the different methods applied to quantify the phenological dates. In comparison, the rate of advance of the expected and observed SOS are very similar from the early 1990s. The correlation coefficients are about 0.8 between the observed and expected time series for 1982-2008. It is clear that the reconstructed phenological indices can capture the main features as observed on a hemispheric-scale analysis.

    It can be concluded that this linear relationship enables us to study phenological changes in the SOS by using monthly mean temperature when NDVI data are not available, e.g. the changes in SOS under the RCP scenarios in the 21st century.

  • Many coupled climate models have the ability to simulate both the global and Northern Hemisphere mean surface air temperature evolutions of the 20th century (Zhou and Yu, 2006; Hegerl et al., 2007; Randall et al., 2007; Knutti, 2008; IPCC, 2013). It is clear that the performances of the CGCMs in reproducing the historical changes in April temperature are good enough that they can be used for projecting changes in the SOS in the future with reasonable confidence. In this study, we simply evaluate the behavior of the CGCMs from CMIP5 based on the April temperature, averaged over the Northern Hemisphere.

    Figure 4 shows that the observed April temperature, averaged over the Northern Hemisphere, shows an increasing trend for the whole period studied, with a rate of about 0.11 °C (10 yr)-1. It also shows a multi-decadal variance, which experiences two increasing periods (from 1901 to the mid-1940s and from the mid-1960s to 2004). Between these increasing periods there is a decreasing period. The MME April temperature shows similar variance to the observed one, including the increasing trend for the whole period [0.09 °C (10 yr)-1]. The 5%-95% uncertainty intervals assessed using the output of the 16 climate models, which are also given in Fig. 4, indicate uncertainty intervals of the increasing trends from 0.04°C to 0.14°C (10 yr)-1 for the simulated April temperature for the whole period. The increasing trends of April temperature between the observations and the simulations for 1951-2004 are also compared. The trend is 0.21 °C (10 yr)-1 for the observations, while the trend is 0.19 °C (10 yr)-1 for the MME [with uncertainty intervals of 0.09°C to 0.27°C (10 yr)-1]. This indicates that the CGCMs have the ability to simulate the April temperature changes on a hemispheric scale for different periods.

    Figure 3.  Interannual variations in the observed (blue) and expected (red) SOS. Correlation between the two time series is given in the lower-right corner of the figure.

    Figure 4.  Comparison of the observed (black line) northern hemispheric-scale changes in surface temperature anomalies (relative to the average for the whole period) of April with results simulated (red line) by climate models under the historical scenario for 1901-2004. Yellow shaded bands show the 5% to 95% uncertainty range for these simulations from the 16 climate models.

    Figure 5.  The SOS time series averaged over the Northern Hemisphere during 2006-99 with respect to 1985-2004 (historical scenario), projected by the CMIP5 models under the (a) RCP2.6, (b) RCP4.5, and (c) RCP8.5 scenarios. Negative (positive) values indicate the advance (delay) in the number of days with respect to 1985-2004. Orange shaded bands show the 5% to 95% uncertainty range for these simulations from the 16 climate models. The short horizontal lines indicate the averaged SOS for 2040-59 and 2080-99, with the middle ones being the SOS calculated from the MME, and the upper and lower ones the 5% and 95% ranges, respectively.

    Figure 6.  Changes in the SOS for (a) RCP2.6, (b) RCP4.5 and (c) RCP8.5 for 2040-59 with respect to the results of 1985-2004 under the historical scenario, calculated from MME. Units:\,d.

    Figure 7.  Changes in SOS for (a) RCP2.6, (b) RCP4.5 and (c) RCP8.5 for 2080-99 with respect to the results of 1985-2004 under the historical scenario, calculated from MME. Units: d.

    The simulated SOS shows sustained advancing trends for 2006-99 under the RCP4.5 (Fig. 5b) and RCP8.5 (Fig. 5c) scenarios. For the RCP2.6 scenario (Fig. 5a), the SOS sustains an advancement rate for 2006-40, while from 2040 onwards the SOS is almost the same. The SOS calculated from the MME advances (negative value) -2.5 days (uncertainty interval calculated from the 16 simulations: -6.6 to 1.2 days) under RCP2.6, -4.5 days (-8.7 to -1.0 days) under RCP4.5, and -5.4 days (-9.8 to -2.0 days) under RCP8.5, when comparing 2040-59 with 1985-2004. The SOS advances -2.3 days (-6.8 to 1.5 days) under RCP2.6, -6.0 days (-11.0 to -1.8 days) under RCP4.5, and -11.5 days (-19.2 to -6.6 days) under RCP8.5, when comparing 2080-99 with 1985-2004. This indicates that the future projections of changes show a high degree of uncertainty, but consistency in the advancement direction.

    Figures 6 and 7 show the changes in the SOS under the RCP scenarios for 2040-59 and 2080-99, respectively, in terms of the results of the historical scenario for 1985-2004. The patterns are similar among these six geographical maps of changes in the SOS. The patterns of changes in the SOS under the RCP scenarios are different from the observed for the last few decades in some aspects. For example, greater advancement trends are observed in Europe (Fig. 2a); while in the CMIP5 simulations, the larger changes in the SOS during the 21st century are in southwestern North America, western Europe, southern Asia, and parts of eastern Canada. It is worth noting that this pattern is similar to the pattern of temperature sensitivity of the SOS (Fig. 1), indicating that the regions with larger SOS temperature sensitivity need to have more attention paid to them.

    In addition, by comparing the land cover categories for 2006 using NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) (MCD12C1, collection V051, were obtained from the Land Processes Distributed Active Archive Center, via their website: https://lpdaac.usgs.gov/products/modis_products_table/mcd12c1, accessed 10 August 2013), we found that the areas with large changes in SOS are mainly covered by evergreen needleleaf forest, mixed forest, and grasslands. For these regions, the simulated changes in the SOS imply considerable influences on the ecosystem structure and function, which deserve further study.

5. Discussion
  • This study investigated the possible changes in the SOS for parts of the land areas of the Northern Hemisphere during the 21st century, based on the temperature sensitivity of the SOS and the April temperature projected under the RCP scenarios from the latest state-of-the-art CGCMs. It was found that the observed trend of the SOS is -1.5 d (10 yr)-1 but the simulated one is -0.8 d (10 yr)-1 for 1982-2008 (Fig. 3), implying that the models underestimate the trend of the SOS by a factor of 1.875. We suggest that the projected SOS trends under the three RCPs scenarios should be multiplied by 1.875. Therefore, the SOS based on the MME simulation will advance by -4.7 days under RCP2.6, -8.4 days under RCP4.5, and -10.1 days under RCP8.5, for 2040-59 relative to 1985-2004. It will advance by -4.3 days under RCP2.6, -11.3 days under RCP4.5, and -21.6 days under RCP8.5 for 2080-99 relative to 1985-2004. It is worth noting that the advance of the SOS during the 21st century shows large regional differences. For example, averaged over the study areas in China (20°-42°N, 95°-120°E), the SOS will advance (the values in Fig. 7 multiplied by 1.875) by -10.7 days under RCP2.6, -18.6 days under RCP4.5, and -37.5 days under RCP8.5 for 2080-99 relative to 1985-2004. These are larger than the results obtained by Ge et al. (2014a), who suggested that, in China during the 21st century, the average number of days by which the first leaf date advances is about -19 days, -11 days and -7.4 days under the (IPCC, 2001) A2, A1B and B2 scenarios, respectively. This discrepancy is partly due to the differences of temperature changes between the RCP scenarios and the previous versions of the scenarios. Comparing the temperature sensitivity of the SOS (Fig. 1) and the changes in the SOS for the 21st century (Figs. 6 and 7), we can conclude that the larger the local temperature sensitivity of the SOS, the more notable the shift of the SOS, i.e. in southwestern North America, western Europe, southern Asia, and parts of eastern Canada.

    Acknowledgements. This work was supported by the CAS Strategic Priority Research Program—Climate Change: Carbon Budget and Relevant Issues (Grant No. XDA05090000), CityU Strategic Research (Grant No. 7004164), and the National Natural Science Foundation of China (Project No. 41405082).

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