Global and Planetary Change 148 (2017) 153–165
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Global and Planetary Change journal homepage: www.elsevier.com/locate/gloplacha
Modeling the dynamics of distribution, extent, and NPP of global terrestrial ecosystems in response to future climate change Chengcheng Gang a,b,c,⁎, Yanzhen Zhang c, Zhaoqi Wang c, Yizhao Chen c, Yue Yang c, Jianlong Li c, Jimin Cheng a,b, Jiaguo Qi d, Inakwu Odeh e a
Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi 712100, China Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, China The Global Change Research Institute, School of Life Sciences, Nanjing University, Nanjing 210093, China d The Center for Global Change & Earth Observations, Michigan State University, East Lansing 48823, USA e Department of Environmental Science, Faculty of Agricultural and Environment, the University of Sydney, Sydney 2006, Australia b c
a r t i c l e
i n f o
Article history: Received 15 April 2016 Received in revised form 23 November 2016 Accepted 6 December 2016 Available online 18 December 2016 Keywords: Comprehensive and sequential classification system (CSCS) Coupled model intercomparison project phase five (CMIP5) Representative concentration pathways (RCPs) Multi-model ensemble (MME) Net primary productivity (NPP) Terrestrial ecosystems
a b s t r a c t Understanding how terrestrial ecosystems would respond to future climate change can substantially contribute to scientific evaluation of the interactions between vegetation and climate. To reveal the future climate impacts might on the nature and magnitude of global vegetation, the spatiotemporal distribution and net primary productivity (NPP) of global terrestrial biomes and their dynamics in this century were quantitatively simulated and compared by using the improved Comprehensive and Sequential Classification System and the segmentation model. The 33 general circulation models under the four scenarios of Representative Concentration Pathways (RCPs) were utilized to simulate the future climate change. The multi-model ensemble results showed that at the global scale, the distribution of forests and deserts would expand by more than 2% and 4% over this century, respectively. By contrast, more than 11% of grassland regions would shrink. Despite the considerable differences in the simulated responses of the biomes, the poleward movement or expansion of temperate forest were prominent features across all the scenarios. Meanwhile, the terrestrial NPP was projected to increase by 7.44, 9.51, 9.46, and 12.02 Pg DW·a−1 in 2070s relative to 1970s in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The largest NPP decrease would occur in tundra & alpine steppe. NPP in the Tropical Zone, the North Temperate Zone, and the North Frigid Zone was estimated to increase in this century, whereas NPP in the South Temperate Zone was projected to decrease slightly across all scenarios. Overall, ecosystems in the mid-/high latitudes would be more vulnerable to future climate change in terms of distribution ranges and primary productivity despite the existing uncertainties. Some vegetation would benefit from the warmer and wetter climate. However, most of these plants would suffer and experience irreversible changes, particularly in the northern hemisphere. © 2016 Elsevier B.V. All rights reserved.
1. Introduction The interactions between climate change and terrestrial ecosystems have long been recognized as one of the major issues in global change research. Climate variables, especially temperature and precipitation, are the main factors that affect the development and ranges of ecosystems. Meanwhile, changes in vegetation distribution and growth can in turn affect climate through biogeophysical and biogeochemical processes (Betts, 2000; Bonan et al., 1992; Cox et al., 2000; Levis et al., 1999; Wang and AB Eltahir, 2002). Observational evidence shows that many natural systems are affected by climate change (Biermann, 2007; Field et al., 2007a; Lavorel, 1999; Mayle et al., 2004; McGlone et al., 2001). ⁎ Corresponding author at: Institute of Soil and Water Conservation, Northwest A&F University, Xinong Road 26, Yangling 712100, PR China. E-mail address:
[email protected] (C. Gang).
http://dx.doi.org/10.1016/j.gloplacha.2016.12.007 0921-8181/© 2016 Elsevier B.V. All rights reserved.
All these simulated results indicate that the consistently rising temperature and redistributed rainfall patterns during the past decades have significantly influenced distribution, phenology, productivity, and growth of vegetation. These effects posed a great threat to some species that are vulnerable and sensitive to climate change during the readaption of ecosystems to new habitats (Foley et al., 2000; Gang et al., 2013; Horion et al., 2013). The impacts of climate on vegetation greatly rely on the nature and magnitude of the climate changes. Although uncertainties exist in future greenhouse gas (GHG) emissions, the resulting global climate change and projections of the possible extent of these impacts on terrestrial ecosystems are essential for climate adaptation. Surface modeling of the terrestrial ecosystems has been conducted to explore the effects of climate change on vegetation at the regional and global scales (Sykes et al., 1996; Yue et al., 2011; Zhang et al., 2011). Olson et al. (2001) developed a world terrestrial eco-region dataset, which consisted of 14 biomes and 8
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biogeographic ranges embedded with 867 eco-regions. The concept of potential natural vegetation (PNV) mainly refers to the expected state of mature vegetation in the absence of human intervention (Chiarucci et al., 2010; Zerbe, 1998). The PNV has been widespread studied to evaluate the impacts of past and future climate changes on ecosystems at multiple scales (Brzeziecki et al., 1993; Hickler et al., 2012; Pfister et al., 1977; Yue et al., 2011). The models, such as biogeographic models (e.g., Holdridge Life Zone) and equilibrium vegetation models (e.g., BIOME), have been used to simulate the PNV at multiple spatial scales, providing a wealth of useful information on the understanding of interactions between climate change and vegetation (Cramer et al., 2001; Holdridge, 1947; Salzmann et al., 2008; Sitch et al., 2003; Sykes et al., 1996). The development of dynamic global vegetation models (DGVM) include both vegetation dynamics and land processes, which greatly enrich our knowledge on the biogeochemical feedbacks between climate and terrestrial vegetation (Cramer et al., 2001; Sitch et al., 2003, 2008; Salzmann et al., 2008). However, the complex input data required may sometimes prevent their wide application, particularly in regions where lacking historical or collected data. The Comprehensive Sequential Classification System (CSCS) was established based on the relationships of climate, soil, and vegetation according to water and thermal conditions of a certain environment. The system has been successfully used in modeling biomes at various scales since its development and optimization (Gang et al., 2013; Liang et al., 2012; Lin et al., 2013a; Ren et al., 2008). It is with high confidence that the resilience of many ecosystems is likely to be exceeded by an unprecedented combination of changes in climate, associated disturbances during the course of this century if GHG emissions and other changes continue at or above current rates (Fischlin et al., 2007). A newly developed generation of socio-economic scenarios, known as Representative Concentration Pathways (RCPs), were implemented under the framework of the Coupled Model Intercomparison Project Phase five of the
World Climate Research Programme in the IPCC AR5. The four selected RCPs were produced that lead to radiative forcing levels of 8.5, 6.0, 4.5, and 2.6 W/m 2 by the end of the century, including one very low forcing level mitigation scenario (RCP2.6), two medium stabilization scenarios (RCP4.5, RCP6.0), and one very high baseline emission scenarios (RCP8.5) (IPCC, 2012; Moss et al., 2010). Given the comprehensiveness of the sources covered, as well as in their spatial detail, RCPs provide a unique use as input for climate modeling, mitigation analysis, impact assessment and formation of an analytical thread (Van Vuuren et al., 2011). In the current body of research on climate change and its effects on the terrestrial ecosystems, most of the studies on projected changes are based on regional or local scale ecosystem modeling, future changes in global vegetation under the RCP scenarios at the global scale are only modeled minimally (Anav and Mariotti, 2011; Hickler et al., 2012; Scholze et al., 2006; Thomas et al., 2004; Yu et al., 2014). This situation promotes strong motivation for future intercomparison studies, particularly between different GCMs and RCPs. Therefore, considering the likely changes in global vegetation induced by future climate changes under different RCPs is necessary to fill the gaps in this field. The current analysis was made to model the global surface vegetation at the global scale, focusing particularly on the dynamics of the distribution and NPP of terrestrial ecosystems in consequence of climate change during this century. This research holds a significant potential in providing insights into ways on how terrestrial ecosystems would respond to upcoming decades of climate change. In addition, as an example of potential implications for climate change adaptation, the outcomes may be partly complement to the IPCC report. Furthermore, the methods used in this paper can serve as guidance in past and future global change research, especially for regions or periods when data are difficult to obtain.
2. Materials and methods 2.1. Climate variables Climate scenario comparison or reference that can be used in determining the scenarios and climate change projections usually corresponds to the period 1961–1990. Therefore, the two time periods 1961–1990 (1970s) and 1981–2010 (1990s) were set as the baseline scenario and current scenario, respectively. The climate dataset CRU_TS_3.23 for 1970s and 1990s was obtained from the Climate Research Unit (CRU), which is provided by the British Atmospheric Data Centre. These gridded dataset extends from 1901 to 2014, and covers the global land surface (excluding Antarctica) at a 0.5° resolution. These data provide the best estimates for month-by-month variations in climate variables (Harris et al., 2014). The time periods for future climate scenarios were divided into three time slices, namely 2020–2049 (2030s), 2040–2069 (2050s), and 2060–2089 (2070s). These statistically downscaled (delta method) projected climate data were with 2.5 arc-minute resolution (~5 km resolution) and downloaded for the global simulation from the International Center for Tropical Agriculture (CIAT) climate change portal (http://ccafs-climate. org/). These data were provided and pre-processed by Tyndall institute using ClimGen. The entire list and key characteristics of these atmosphere– ocean general circulation models (GCMs) are described in the Table 1. Climate input for the projected mean annual precipitation (MAP) and mean annual temperature (MAT) were incorporated from the gridded datasets of the mean monthly temperature and precipitation. 2.2. The potential natural vegetation model – the modified CSCS The CSCS is established through the grouping or clustering of units with similar moisture and temperature properties. The system is composed of three levels: class, subclass, and type (Liang et al., 2012; Lin et al., 2013b; Ren et al., 2008). The class level is the basic unit, which is relatively stable and mainly determined by bioclimatic conditions. Classification within the same class is supposed to have a consistent interpretation even in different geographic locations. The subclass level is with intermediate stability, and is determined by the edaphic conditions (including landscape and soil). The type level is “less stable” in the hierarchy and is classified by vegetation characteristics. The class level is mainly indexed by annual cumulative temperature above 0 °C (Σθ), which reflects the natural occurrence of vegetation ecosystems. The parameters employed in the CSCS were easily defined and measured. A humidity index (K), the function of MAP and GDD0, is expressed by: X K ¼ MAP= 0:1 θ
where MAP is the mean annual precipitation (mm) and 0.1 is an empirical parameter.
ð1Þ
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Table 1 The brief introduction of the GCMs used in the four RCP scenarios. GCMs
RCP2.6
RCP4.5
RCP6.0
RCP8.5
Modeling group
Country
bcc_csm1_1 bcc_csm1_1_m bnu_esm fio_esm lasg_fgoals_g2 cccma_canesm2 csiro_access1_0 csiro_access1_3 csiro_mk3_6_0 ec_earth cesm1_bgc cesm1_cam5 gfdl_cm3 gfdl_esm2g gfdl_esm2m giss_e2_h_cc giss_e2_r giss_e2_r_cc ncar_ccsm4 inm_cm4 ipsl_cm5a_lr ipsl_cm5b_lr ipsl_cm5a_mr miroc_esm miroc_esm_chem miroc_miroc5 mri_cgcm3 mohc_hadgem2_cc mohc_hadgem2_es mpi_esm_lr mpi_esm_mr ncc_noresm1_m nimr_hadgem2_ao
· · · · · ·
· · · · · · · · ·
· ·
· · · · · · · · · · · · · · · · ·
The Beijing Climate Center Climate model
China
Beijing Normal University - Earth System Model The First Institute of Oceanography-Earth System Model Institute of Atmospheric Physics, Chinese Academy of Sciences The Coupled Global Climate Model, Canadian Centre for Climate Modelling and Analysis The Commonwealth Scientific and Industrial Research Organization
Canada Australia
EC-EARTH consortium The Community Earth System Model, Community Atmosphere Model
Netherlands U.S.A.
·
· · · · · · · · · · · · · · · · · ·
· · · · · · · · · · · · · · · · · · · · · ·
·
·
· · · · · · · ·
· · · · ·
· ·
· · · · · · · · · · · · · · ·
The Geophysical Fluid Dynamic Laboratory
NASA Goddard Institute for Space Studies
The National Center for Atmospheric Research Institute for Numerical Mathematics The Institute Pierre et Simon Laplace
Russia France
Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute
Japan
Meteorological Research Institute Met Office Hadley Centre
U.K.
The Max Planck Institute for Meteorology coupled climate model
Germany
Norwegian Climate Centre National Institute of Meteorological Research/Korea Meteorological Administration
Norway Korea
·means this GCM was used in this scenario.
In the original version of CSCS, the ice and snow were identified in the tundra & alpine steppe class. To reduce this error, the polar/nival type was recognized in the improved CSCS. To further and explicitly reflect the spatial distribution of PNV at a global scale, 48 classes were regrouped into 11 vegetation types, i.e. polar/nival, tundra &alpine steppe, cold desert, semi-desert, steppe, temperate humid grassland, warm desert, savanna, temperate forest, subtropical forest, tropical forest. The polar/nival type was not included in this study. (The index chart for the CSCS is available in the Appendix Fig. A.1). 2.3. NPP estimation – the segmentation model NPP of terrestrial vegetation was simulated by using the segmentation model, which was established according to the humidity index (K) in Eq. (1). The model was established based on evapotranspiration and photosynthesis process of plants (Zhou et al. 1998, Zhang et al. 2011, Uchijima and Seino 1985). The model integrated the interaction among many variables, and was expressed as follows: 8 2 2 h pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffii > > RDI MAP Rn MAP þ Rn þ MAP Rn > < exp − ð9:87 þ 6:25RDIÞ 100 ðKb1:2Þ 2 2 ð2Þ NPP ¼ ðMAP þ Rn Þ MAP þ Rn > > > : 0:29 exp −0:216RDI2 R ðK N1:2Þ n
RDI ¼ 0:629 þ 0:237 PER‐0:00313 PER2
ð3Þ
Rn ¼ RDI MAP L 2:38 10−4
ð4Þ
PER ¼ PET=MAP ¼ 1:6145=K
ð5Þ
where MAP is the mean annual precipitation (mm), RDI is radioactive dryness index which can be calculated by PER, PER is the rate of evapotranspiration, Rn is the intercepted net radiation (J·cm−1·yr−1), L is latent heat (2503 J·g−1), PET is potential evapotranspiration (mm), K is humidity index calculated by Eq. (1). NPP is calculated in unit of g DW·m−2·yr−1. The modeled NPP results have been tested by using the Global Primary Production Data Initiative dataset from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), which represents the majority of global biomes (Olson et al. 2012). The modeled NPP results showed well consistency with the field observation data (Fig. 1).
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Fig. 1. Comparison of modeled NPP value and observed data (p b 0.001). The observed data were collected from the Oak Ridge National Laboratory. These data, and further information about the study sites, are publicly available at www.daac.ornl.gov/NPP/.
3. Results 3.1. Changes in climatic variables Temperature and precipitation are two dominant factors that affect the occurrence and development of terrestrial vegetation. Therefore, learning how the climate factors would vary during this century is crucial. The projected MAT and MAP in different periods of these scenarios gave their respective changing trends (Fig. 2). The MAT would continue to rise over the 21st century under all of the RCPs. The highest increase of 5.083 °C would occur in the RCP8.5 scenario, whereas the lowest increase of 2.169 °C would take place in the RCP2.6 scenario. The MAT would rise by 3.187 °C and 3.378 °C across the globe at the end of this century in the RCP4.5 and RCP6.0 scenario, respectively. The MAT is expected to increase more rapidly in the RCP4.5 scenario than in the RCP6.0 scenario during the 2030s–2050s period, whereas it would slow down during the 2050s–2070s period, leading to a higher MAT in the 2070s in the RCP6.0 scenario than in the RCP4.5 scenario. Spatially, regions showing the ascending trend for MAT would be widespread globally during this century in the RCP4.5, RCP6.0 and RCP8.5 scenarios. The MAP is also projected to increase over this century, by 3.05%, 5.11%, 5.22% and 7.92% across the globe in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. More than a half of the regions would experience an increasing MAP as well. In general, the world would become warmer and wetter over this century, as simulated by most GCMs.
3.2. Changes in natural vegetation Forests are the most distributed vegetation, covering nearly 47% of the total lands. Based on the results, the total area of forests is estimated to increase during this century (Fig. 3). The highest increase of 335.91 × 104 km2 would occur in RCP4.5, whereas the lowest increase of 276.36 × 104 km2 would be observed in the RCP8.5. Temperate forest, the most extensively distributed forest, would increase the most among the three forest types. The highest and lowest increases in temperate forest area are projected in the RCP4.5 and RCP2.6 scenarios with 333.17 and 286.82 × 104 km2, respectively. Tropical forest is also predicted to expand. The highest increase would occur in the RCP8.5 scenario with 220.33 × 104 km2, whereas the lowest increase is projected in the RCP2.6 scenario with 135.17 × 104 km2. By contrast, the area of subtropical forest would decrease consistently over this century, by
Fig. 2. Dynamics of the global MAT and MAP in different time periods.
140.61, 190.40, 195.07, and 274.51 × 104 km2 in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. Grasslands, the second largest distributed vegetation, are projected to shrink in this century (Fig. 3). The most decrease of 518.55 × 104 km2 would occur in the RCP4.5, whereas the least decrease would be in the RCP2.6 with 421.33 × 104 km2. The warmer and wetter trend in climate would cause the substantial dieback of tundra & alpine steppe. The largest and least decreases are projected for the RCP8.5 and RCP2.6 with 1045.34 and 593.42 × 10 4 km 2 , respectively. Similarly, the area of temperate humid grassland would also decrease, by 43.17, 67.96, 63.36, and 77.81 × 104 km2 in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. By contrast, the distribution of steppe and savanna would expand continuously. The area of steppe would increase by 31.90, 41.89, 44.62, and 60.24 × 10 4 km 2 in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The largest expansion of savanna would occur in the RCP8.5 by 551.00 × 104 km2 , and the least increase is expected to happen in RCP2.6 by 184.17 × 104 km2. Deserts are the least distributed vegetation, which is projected to expand in this century. The total area of deserts would increase by 166.93, 213.79, 194.96, and 293.40 × 10 4 km2 in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. Among the three desert types, the area of cold desert would decrease the most in the RCP8.5 by 84.67 × 104 km2 at the end of this century, and by 21.34, 51.07, and 55.77 × 10 4 km2 in the RCP2.6, RCP4.5, and RCP6.0, respectively. For semi-desert, the largest decrease is also predicted in the RCP8.5 by 62.83 × 104 km 2, whereas the least decrease would occur in the RCP2.6 scenario by 42.70 × 10 4 km 2 . Warm desert is the most widely distributed desert, which accounts for nearly 70%
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Fig. 3. Dynamics of each natural vegetation area in proportion to the total area of vegetation in different time periods. The numbers in blue color in 2070s indicate an increasing trend relative to 1970s, whereas those in red color indicate a decreasing trend. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
of the total deserts. The advancement of warm desert would occur in all scenarios, by 230.96, 308.25, 306.07, and 440.90 × 104 km2 in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively. The expansion of warm desert would offset the contradiction of cold desert and semi-desert, leading to the overall ascending trend of the desert area. (The spatial distribution of terrestrial vegetation in 1970s, 1990s, and 2070s of the four RCPs is available in the Appendix Fig. A.2. The vegetation maps in 2070s of the four RCPs was simulated by climate data from ncar_ccsm4.) 3.3. Changes in terrestrial NPP The increasing terrestrial NPP during this century is projected across all RCP scenarios (Fig. 4). The terrestrial NPP is projected to increase from 120.11 Pg DW·a− 1 in 1970s to 127.55, 129.62, 129.57, and 132.13 Pg DW·a− 1 in 2070s in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The increasing rate would accelerate in the 1990s–2030s period. The rising NPP would slow down after 2050s in the RCP2.6, whereas it would continue to increase aggressively in the RCP8.5. As to vegetation types, NPP of forests and grasslands would both increase during this century, whereas NPP of desert would show different changing trends in the four RCPs. NPP of forests account for nearly 77% of the total terrestrial NPP. The largest increase is projected for the tropical forest, which would increase by 4.20, 6.25, 6.14 and 7.96 Pg DW a − 1 in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. Similarly, NPP of temperate forest would also
increase, by 3.28, 3.70, 3.67 and 4.00 Pg DW·a− 1 in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. By contrast, NPP of the sub-tropical forest is projected to decrease across all scenarios. NPP of grasslands account for nearly 20% of total terrestrial NPP, which is also projected to increase during this century. NPP of savanna would contribute the most, which is projected to increase consistently by 2.65, 3.80, 4.15 and 6.67 Pg DW·a − 1 in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. NPP of steppe would also present an overall increasing trend over the whole study period. The largest NPP decrease would occur in the tundra & alpine steppe, which would decrease by 1.78, 2.55, 2.58 and 3.56 Pg DW·a− 1 in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The descending NPP of temperate humid grassland would be relatively smaller. NPP of deserts, which amount to nearly 3% of the total terrestrial NPP, is estimated to decrease slightly in the RCP2.6 and RCP6.0, whereas it would increase in the RCP4.5 and RCP8.5 during this century. NPP of semi-desert and cold desert would all decrease during this century. By contrast, NPP of warm desert would increase by 0.22, 0.29, 0.28 and 0.47 Pg DW·a− 1 in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. From the climate zone perspective, all the GCMs agreed that vegetation NPP would all increase vigorously in the North Temperate Zone (NTZ), as well as in the North Frigid Zone (NFZ) and the Tropical Zone (TZ). By contrast, the vegetation NPP would decrease slightly in the South Temperate Zone (STZ) (Fig. 5). In the NTZ, NPP of temperate forest, accounting for more than a half of terrestrial NPP in this zone, would contribute the most to the NPP increase in this zone among all
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Fig. 4. Dynamics of the NPP of vegetation types in different time periods.
Fig. 5. Terrestrial NPP dynamics in the four climate zones in different time periods.
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Fig. 6. Vegetation NPP changes in 2070s relative to 1970s in the NTZ.
ecosystems. NPP of sub-tropical forest and tropical forest are also projected to increase during this century. In the three desert types, NPP of cold desert would show a decreasing trend, whereas that of
warm desert and semi-desert would increase. As to grasslands, NPP of tundra & alpine steppe and temperate humid grassland would both decrease. NPP of the tundra & alpine steppe would decrease the most,
Fig. 7. Vegetation NPP changes in 2070s relative to 1970s in the STZ.
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Fig. 8. Vegetation NPP changes in 2070s relative to 1970s in the TZ.
especially in the RCP8.5 scenario. The increase in NPP would occur in savanna and steppe (Fig. 6). In the STZ, the largest increase would occur in the savanna. Similarly, NPP of warm desert and tropical forest are also projected to increase. By contrast, NPP of all the other vegetation would decrease in this century, in which NPP of sub-tropical forest would decrease the most (Fig. 7).
In the TZ, NPP of the tropical forest covers more than 70% of total NPP in this zone. Results indicate that the tropical forest NPP would increase during this century (Fig. 8). NPP of savanna and warm desert are projected to increase across all the scenarios. NPP of all the other vegetation types would present a decreasing trend, in which the NPP of subtropical forest would decrease the most. In the NFZ, NPP of the tundra &
Fig. 9. Vegetation NPP changes in 2070s relative to 1970s in the NFZ.
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alpine steppe, which account for more than 90% of vegetation NPP in this region, is projected to decrease in all GCMs, especially in the RCP8.5 scenario (Fig. 9). The encroachment of temperate forest in this zone would lead to a drastic increase in NPP. Similarly, the total NPP of temperate humid grassland and steppe would also increase in this century. (The area change for each vegetation in 2070s relative to 1970s in the four climate zones are available in the Appendix Table A.1)
4. Discussion 4.1. Discussion of the methodology The climate dataset CRU_TS_3.23 used for reconstructing the baseline scenario (1970s) and current scenario (1990s) was obtained from the CRU. The error source of this dataset has been reported to possibly come from the homogenization and interpolation methods. Given the need for checking the inhomogeneities, meteorological records obtained from areas or periods with low density were added to the database without checking. In addition, the interpolation method is weak in detecting abrupt data, which cannot be detected unless they are widespread (New et al., 2000). Nonetheless, the error in the CRU dataset is substantially smaller than the climate trends believed to have been occurring during the twentieth century. Thus, CRU_TS_3.23 is capable of correctly reflecting climate conditions. Such data are more appropriate for large scale studies than regional analysis. Uncertainties also exist in the GCMs which were used to simulate future climate change. GCMs represent but cannot fully capture the interactions between atmospheric and hydrological processes (Knutti and Sedláček, 2013). The physical processes involved in and different parameterization schemes would cause various GCM sensitivities to radioactive forcing, which caused the discrepancies in projecting the future climate trends among different GCMs (Zhang et al., 2013). The GCMs used in this study, which were obtained from the CIAT, were processed by a downscaling method. The anomalies between centroids of GCM cells were interpolated, and then were added to high resolution baseline climate surfaces (Hijmans et al., 2005). This method assumes that changes in climates are only relevant at coarse scales, and the relationships between variables may be maintained toward the future (Ramirez-Villegas and Jarvis, 2010). These assumptions may not be applicable given the larger errors of highly heterogeneous landscapes with complex topographic conditions. However, the assumption is useful for homogeneous landscapes or large spatial scale (Bellard et al., 2014; Plank et al., 2016). To reduce errors and biased results, all available GCMs of the four RCPs were utilized instead of selecting a subset of GCMs alone. CSCS was used in this paper to simulate the vegetation maps in different periods and their dynamics. The CSCS was established by linking vegetation with their climatic and edaphic factors (Ren et al., 2008). Humidity index, determined by MAP and cumulative temperature, is the main parameter in the CSCS. This system does not consider the terrain effects, which may reduce the system's accuracy in regions with complicated underlying surfaces, such as mountainous regions. In addition, the precipitation data do not take into account the supply of underground water and melt water, which may cause the underestimation of water input in high latitude and elevated regions. In this study, the vegetation maps were simulated at a 30-year period at the global scale, and the 48 classes were integrated into 11 broad vegetation types to substantially avoid the potential errors. The CSCS enables a feasible approach of demonstrating natural vegetation maps and their spatial zonal distribution under climate conditions globally. Thus, the CSCS presents promising applications in the research of past and future global change, especially for regions or periods with lacks of collected data.
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The segmentation model used in this paper is based on the water use efficiency of vegetation. The efficiency was mainly determined by the ratio of the CO2 flux equation to vapor flux equations. The synthetic model is an actual evapotranspiration model that connects between water balance and heat balance, and reflects the effects of energy and water on the rate of evaporation. The former part (K b 1.2) works well in modeling vegetation NPP in arid/semi-arid regions, whereas the latter part (K N 1.2) is superior in simulating vegetation NPP in humid regions. The humid index K is integrated into the model, which links the CSCS and the segmentation model. The segmentation model is capable of detecting global NPP in response to long term climate change according to the validation results. In addition, for the projections of future vegetation maps and NPP, the multi-model ensemble mean is used to reduce the error that may be caused by a single model. Admittedly, the CSCS and the segmentation model did not incorporate the direct effect of CO2 fertilization and nitrogen deposition affecting on plant growth and competition. The influence of potential climate-induced changes in disturbance (e.g. fire, insect outbreaks, and pathogens) or human-related activities (e.g. grazing, agricultural development, urbanization, logging, or irrigation) was also not explicitly included. The simulations for all RCP scenarios agree with the NPP increases under the future climate conditions, which are consistent with the findings from other previous studies (Alo and Wang, 2008; Cao and Woodward, 1998; Cramer et al., 2001). Results presented in this paper are not predictions of the future state of the terrestrial biosphere, but are some possible changes in distributions and primary productivities of natural vegetation in response to the future climate change under different scenarios. Such results may provide suggestions in decision making on mitigating potential climate impacts and reducing vulnerability.
4.2. Effects of future climate change on terrestrial ecosystems The ultimate objective of this study was to identify the possible signs and magnitude of vegetation changes in response to future climate change across the globe during the 21st century. In this study, the variation in future climate variables were first examined, and then the likely impacts of these changes on the spatiotemporal pattern, variations and NPP of terrestrial vegetation were simulated under the state-of-the-art RCP scenarios. A consensus is found across the four RCP scenarios, indicating that a rising temperature and increased rainfall would occur across the globe under future climate. The world would become warmer and wetter in this century. Temperature and precipitation, as well as their spatial dynamic patterns are the main factors that influence the trends in simulated vegetation distribution and NPP. The warmer climate shown in all the RCP scenarios has a strong positive impact on vegetation growth, especially in regions which are initially cold under the baseline climate. Based on the multi-model ensemble results, by the end of this century, the ecosystems in mid- and high latitude/elevations, such as tundra and cold desert, would be forced to move toward higher latitude/elevations, and their distribution ranges would decrease continuously, especially in the RCP8.5 scenario. The large expansion of temperate forest is projected to displace the original tundra ecosystem by the end of the century, whereas the northward movement of the tundra would in turn decrease polar biomes. These conclusions about the likely nature would happen across all the scenarios, which are consistent with the results of previous studies (Cramer et al., 2001; Gerber et al., 2004; Joos et al., 2001; Lucht et al., 2006; Wookey, 2008). In Europe, the forest area is expected to expand toward the north, shrinking the current tundra area by 2100 (Kljuev, 2001; MNRRF, 2003; Shiyatov et al., 2005). Similarly changing patterns have also been projected in North America (Field et al., 2007b). The driving factor for the expansion of temperate forest may be attributed to the longer growing seasons and
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Table 2 Comparisons of future terrestrial NPP between our research and other publications. Periods
Models
NPP (Pg C·yr−1)
Sources
140 yr simulation 2100 2050–2070
A simplified terrestrial carbon cycle model Six DGVMs Carbon Exchange between Vegetation, Soil, and the Atmosphere (CEVSA)
Hajima et al., 2014 Cramer et al., 2001 Cao and Woodward, 1998
NA 2100
The Terrestrial Ecosystem Model National Center for Atmospheric Research Community Land Model
2100 2070s
Five DGVMs CSCS & the segmentation model
Increase by 43.3 ± 31.0 70–90 54.8 (climate only) 69.6 (doubled CO2) 61.2–64.3 NPP enhancement (no exact value) Increase in vegetation biomass 57.40–59.46
warmer winters, which no longer restrict the northern distribution of temperate trees (Woodward, 1987). Meanwhile, the area of subtropical forest would also decrease, the original habitat would be replaced by tropical forest in Asia and savanna in South America, and the habitat of subtropical forest in North America would be encroached by the temperate forest. The projected future simulation also indicates the expansion of savanna and warm desert. As a consequence of climate warming, the mean annual rainfall would decrease along the Mediterranean coast, extending into the northern Sahara and along the west coast to 15°N and subsequently leading to the expansion of warm desert (Boko et al., 2007). The expansion of savanna into the northern Great Plains region was predicted to occur by the end of the century using Lund-Potsdam-Jena dynamic global vegetation model (Bachelet et al., 2003). Similarly, in South America, some tropical forests would probably be replaced by ecosystems, such as tropical savannas, which are more resistant to multiple stresses caused by temperature increase, droughts and fires (Magrin et al., 2007). In central Australia, shifts in rainfall patterns would likely favor the establishment of desert vegetation and encroachment of unpalatable shrublands. In addition to the effects of CO2, water supply, and increasing fire occurrences, grazing practice would probably be critical in the rangeland regions (Gifford and Howden, 2001; Hughes, 2003). In the tropics, moisture condition changes induced by increase in evapotranspiration rate, or decrease in precipitation, or both would favor the establishment of drought-resistance vegetation types, leading to the wide spread of warm desert and tropical savannas and the enhancement of NPP. Replacement of tropical forest by savannas is expected in eastern Amazonia and central and southern Mexico, along with replacement of semiarid vegetation by arid vegetation caused by synergistic effects of climate changes (Magrin et al., 2007; Nobre et al., 2005). Although all GCMs simulations indicated that terrestrial NPP would increase by the end of this century in response to future climate changes, the magnitude of this increase varies markedly among RCPs and within each RCP. Several studies have recorded increasing NPP in response to future climate and atmospheric CO2 concentration (Alo and Wang, 2008; Cramer et al., 2001). Nevertheless, large uncertainties among models still exists. We compared our simulated future terrestrial NPP with previous studies (Table 2). The major differences may include the following aspects: differences in parameterization, recognition of the vegetation types, and the land surface processes that evolved. Firstly, different parameter combinations have been reported to be capable of recreating the historical records and presenting them well, but their behavior in projecting future status are debatable (Jones et al., 2006). Results show that the errors of simulations would be greater in the 2070s than in 2030s, implying the differences among GCMs caused by parameterization amplified over time. Secondly, previous studies focused more on the responses of trees to climate change than other vegetation types (Alo and Wang, 2008; Cramer et al., 2001). Plant functional types are central to DGVMs, as their parameters vary with respect to ecosystem processes (Cramer et al., 2001). As a consequence, identification of vegetation types can also lead to discrepancies among
Melillo et al., 1993 Alo and Wang, 2008 Sitch et al., 2008 This paper
the models. Thirdly, differences in land surface processes caused by model complexity or specific process formulations contribute mostly to the overall uncertainties among the models (Cramer et al., 2001). Finding a specific ecological process that can be identified as the main source of the overall uncertainties is still difficult, even in DGVMs. These uncertainties in projecting the future changes are exiting unavoidably. Therefore, these differences should be investigated further, and local observations are required for constraining and optimizing the models. 5. Conclusion This analysis concludes that a range of climate impacts on global vegetation would occur by the end of the 21st century, and effects would vary greatly among different ecosystems. The MME results showed that as a consequence of a warming climate, the terrestrial vegetation would respond to climate changes with a considerable poleward shift or advancement of temperate forests in the northern high latitudes at the expense of contraction of tundra, as well as a substantial dieback of vegetation cover in the tropics. All GCMs simulate cumulative vegetation NPP by 2070s in response to changes in future climate for all RCP scenarios. Both forest and grassland NPP would increase, whereas desert NPP is projected to decrease slightly in the RCP2.6 and RCP6.0 but increase in the RCP4.5 and RCP8.5. The consistent decrease in the tundra & alpine steppe NPP would occur across all the scenarios because of the contraction of its distribution. By contrast, the largest NPP increase would occur in the savanna, which can be explained by its expansion and the ascending mean NPP. In the four climate zones, NPP would increase obviously in the TZ, NFZ, and the NTZ, especially in the RCP8.5 scenario. However, NPP variation in the STZ would be relatively small, showing a slight decrease. In general, all GCMs agree that this century would witness a warmer and wetter world, which would benefit the expansion and growth of vegetation to a certain extent. However, some ecosystems would suffer from these changes, especially those in the mid-/high-latitudes or elevations. Although uncertainties exist, this paper presents possible scenarios for future terrestrial ecosystems. Understanding the terrestrial biosphere processes is paramount to enhancing our ability to predict future climate change-terrestrial ecosystems interactions. Acknowledgements This work was supported by “National Natural Science Foundation of China (31602004)”, “the National Key Research and Development Program of China (2016YFC0501707)”, “Key cultivation project of Chinese Academy of Sciences (The promotion and management of ecosystem functions of restored vegetation in Loess Plateau, China)”, “Special Foundation for State Basic Research Program of China (2014YF210100)”, “the Doctoral Start-up Fund of Northwest A&F University (2452015339)”. We also appreciate the British Atmospheric Data Centre (BADC) and the International Center for Tropical Agriculture (CIAT) for sharing datasets.
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Appendix A
Fig. A.1 The index chart for the modified CSCS. The modified CSCS documents 48 classes, and the 48 classes are combined into 11 PNV units. Classes in the same color are regrouped in the one PNV unit, i.e. I: Polar/Nival; II: Tundra &alpine steppe; III: Cold desert; VI: Semi-desert; V: Steppe; VI: Temperate humid grassland; VII: Warm desert; VIII: Savanna; IX: Temperate forest; X: Subtropical forest XI: Tropical forest.
Fig. A.2. The spatial pattern of global potential natural vegetation in 1970s, 1990s, and 2070s in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The vegetation for 2070s in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5 was simulated by climate data from ncar_ccsm4.
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Table A.1 The area change for each vegetation in the four climate zones in 2070s relative to 1970s (Unit: ×104 km2).
Tundra & alpine steppe
Cold desert
Semi-desert
Steppe
Temperate humid grassland
Warm desert
Savanna
Temperate forest
Sub-tropical forest
Tropical forest
RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5 RCP2.6 RCP4.5 RCP6.0 RCP8.5
North temperate zone
South temperate zone
Tropical zone
North frigid zone
−378.06 ± 143.80 −513.80 ± 123.40 −516.45 ± 134.82 −683.36 ± 101.27 −24.28 ± 34.61 −55.95 ± 32.50 −60.57 ± 33.93 −90.73 ± 30.37 −6.93 ± 26.24 8.67 ± 32.62 1.61 ± 27.41 22.18 ± 49.93 11.09 ± 26.66 29.08 ± 26.18 33.30 ± 27.08 57.94 ± 34.78 −59.77 ± 47.80 −98.86 ± 52.57 −94.17 ± 69.27 −134.62 ± 70.33 −8.02 ± 43.31 42.62 ± 52.46 49.07 ± 54.69 121.09 ± 61.22 76.25 ± 36.43 130.64 ± 41.87 145.40 ± 43.71 245.81 ± 67.31 133.56 ± 148.20 189.40 ± 130.55 192.01 ± 134.46 199.92 ± 112.07 69.88 ± 32.21 93.93 ± 32.10 91.13 ± 33.38 104.43 ± 30.17 20.11 ± 16.45 43.82 ± 25.38 44.90 ± 26.42 105.29 ± 48.65
−2.89 ± 1.08 −4.63 ± 1.24 −4.91 ± 1.05 −7.41 ± 0.86 2.29 ± 7.15 4.90 ± 9.92 5.29 ± 10.73 6.86 ± 9.48 −36.04 ± 13.05 −52.19 ± 18.12 −56.25 ± 16.05 −82.77 ± 15.85 −15.99 ± 4.34 −21.75 ± 4.58 −23.20 ± 3.88 −30.85 ± 3.89 −8.92 ± 0.73 −9.22 ± 0.69 −9.23 ± 0.81 −9.68 ± 0.61 52.76 ± 48.52 69.18 ± 53.80 72.36 ± 51.11 107.82 ± 68.34 38.15 ± 53.86 51.51 ± 49.02 48.74 ± 41.37 72.90 ± 57.66 −26.02 ± 5.87 −36.99 ± 6.02 −38.97 ± 5.30 −56.38 ± 5.33 −19.96 ± 15.88 −28.96 ± 20.51 −24.08 ± 20.94 −56.38 ± 27.15 17.16 ± 6.09 28.96 ± 9.42 31.28 ± 10.05 53.48 ± 12.37
1.59 ± 1.07 0.03 ± 0.92 −0.33 ± 0.54 −1.41 ± 0.23 −1.79 ± 3.09 −2.49 ± 2.86 −2.96 ± 3.42 −3.27 ± 3.02 −8.60 ± 2.55 −8.75 ± 3.10 −9.53 ± 2.74 −11.00 ± 3.27 −5.92 ± 1.93 −7.15 ± 1.23 −7.36 ± 1.24 −8.61 ± 1.36 −4.08 ± 0.61 −4.68 ± 0.44 −4.81 ± 0.35 −5.41 ± 0.37 −7.48 ± 62.26 2.69 ± 60.61 −9.11 ± 75.69 18.00 ± 87.30 145.83 ± 61.83 181.99 ± 77.55 201.71 ± 76.34 303.85 ± 119.09 8.60 ± 3.49 5.26 ± 4.07 5.59 ± 4.90 −1.23 ± 5.38 −149.42 ± 39.29 −214.27 ± 33.41 −220.85 ± 25.57 −281.41 ± 19.10 −29.77 ± 52.88 −6.45 ± 64.27 −4.17 ± 67.17 59.94 ± 121.81
−83.77 ± 57.48 −133.53 ± 73.32 −135.52 ± 81.40 −223.16 ± 88.02 ×
×
0.34 ± 0.47 0.32 ± 0.36 0.46 ± 0.56 0.44 ± 1.31 21.08 ± 16.13 36.16 ± 24.92 36.21 ± 26.37 61.92 ± 46.61 ×
×
92.28 ± 63.59 142.18 ± 72.20 145.33 ± 82.22 223.16 ± 90.85 ×
×
× means this vegetation type was not shown in this zone.
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