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Climate changes over eastern China during the last millennium in simulations and reconstructions
Quaternary International 208 (2009) 11–18
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Climate changes over eastern China during the last millennium in simulations and reconstructions Youbing Peng a, b, Ying Xu b, *, Liya Jin a a b
Key Laboratory of Western China’s Environmental Systems (Ministry of Education), Lanzhou University, Lanzhou 730000, China National Climate Center, China Meteorological Administration, No. 46 Zhongguancun, Beijing 100081, China
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 27 February 2009
Climate change during the last millennium is simulated using fully coupled three-dimensional model CCSM 2.0.1. The model is driven by the natural and anthropogenic forcings. Simulated temperatures over the whole of China and over the Eastern part of China, from combined forcing correlate to some extend with the proxy data, while simulated precipitation in East China (East of 105 E, 25–40 N) and the middle and lower Yangtze River Valley (106–122 E, 26–34 N) shows some similarities with the reconstructions in some periods of time. Both simulated and reconstructed temperature anomalies indicate that the 20th century warming is anomalous in a long-term context. The model indicates that the wet and dry conditions appear alternately in the Medieval Warm Period over eastern China. Dry conditions dominate in the Little Ice Age, whereas wet conditions exist since 1890. The correlation of precipitation between simulated and reconstructed is better in the middle and lower Yangtze River Valley than in East China, especially before 1850. Regional differences are present in East China during the past thousand years and there are obviously no fixed modes of climate changes (warm–wet, cold–wet, warm–dry or cold–dry). The climate change over eastern China is affected by external factors and internal climate process. The changes of temperature and precipitation over eastern China are controlled mainly by the changes of effective solar radiation and volcanic activity during the last one thousand years, while the increase of the contents of greenhouse gases plays a big role on the fast warming over the past one hundred and fifty years. Ó 2009 Elsevier Ltd and INQUA. All rights reserved.
1. Introduction The modeling approach, as a powerful tool, allows us to study the characteristics of the Medieval Warm Period (WMP) and the Little Ice Age (LIA) and provides important insights into the mechanisms that cause these climate variations. The spectrum of climate models ranges from the simplest energy balance models (EBMs) to the most complex coupled atmosphere–ocean general circulation models (AOGCMs). The EBM study by Crowley (2000) indicates that the model can reproduce temperature anomalies remarkably well for the past millennium, using revised forcing for solar activity, volcanic activity, anthropogenic greenhouse gases and aerosols. Simulations of climate change over the past thousand years have also been performed using two-dimensional or ‘‘intermediate complexity’’ climate models (Bauer et al., 2003). But those model’s results could not be used to gain information on a regional
* Corresponding author. Tel.: þ86 10 68400075; fax: þ86 10 58995956. E-mail address: [email protected] (Y. Xu). 1040-6182/$ – see front matter Ó 2009 Elsevier Ltd and INQUA. All rights reserved. doi:10.1016/j.quaint.2009.02.013
basis, not even at a continental scale (Goosse et al., 2005), while such information could be obtained from the comprehensive, three-dimensional general circulation models (GCMs). GCM simulations are used to compare simulated response deduced from reconstructions, and to study the climate response due to the external forcing (Ammann et al., 2007) and the physical processes which amplify the climate response to strong external forcing (Shindell et al., 2001). The results of these simulations are remarkably consistent with the proxy reconstructions of NH mean temperatures over the past millennia and lead to remarkably similar conclusions (at hemispheric and global scales) regarding the factors controlling climate changes. Most of the previous studies only focused on the spatial and temporal details of temperature changes at hemispheric and global scales during the last thousand years. Nevertheless, it is also important to check whether the general conclusions obtained for continental scales are valid on smaller scales. Furthermore, the temperature history of the past few millennia is just one facet of the past changes in the climate system, and it is important to have some insights into past changes of other societal relevant climate variables, like the
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long-term changes in precipitation and the links to the controlling circulation influences (Jones and Mann, 2004). In addition, because of the high computer-time requirements, there are not any GCM simulations including completely at the same time the unforced simulation, the single external forcing simulation and the fullforcing simulation. In the present study, we estimate the evolution of climate including temperature, precipitation in East China (East of 105 E, 25–40 N) and the middle and lower Yangtze River Valley (109–122 E, 26–34 N) over the last millennium based on fullforcing simulation and reconstructions, and we study the climate temporal distribution response to external forcing. The four reconstructing temperature series used here are: the annual temperature series over the whole of China reconstructed by Yang et al. (2002), the temperature series for the whole of China and specifically for eastern China reconstructed by Wang et al. (2007), and the winter-half year temperature series over eastern China reconstructed by Ge et al. (2003), respectively. Two precipitation proxy data are the yearly dry–wet index over eastern China and in the middle and lower Yangtze River Valley (hereafter MLYRV) both reconstructed by Zheng et al. (2006). 2. Methods 2.1. Model description The model used in this study is the Community Climate System Model (CCSM) version 2.0.1 developed by the National Center for Atmospheric Research (NCAR, Kiehl and Gent, 2004). The model comprises four components: atmosphere, ocean, land surface, and sea ice. The components are linked via a flux coupler, and they are coupled without flux corrections. There are two different resolutions of the model officially supported by NCAR, and for the T31/gx1v3 the lower-resolution setting is used here. The atmospheric component is a global atmospheric general circulation model (AGCM) and is a primitive equation model solved with the spectral transform method in the horizontal and with 26 hybrid-coordinate levels in the vertical. The atmospheric resolution is T31 (an equivalent grid spacing of approximately 3.75 in latitude and longitude). The land model is integrated on the same horizontal grid as the atmosphere, although each grid box is further divided into a hierarchy of land units, soil columns, and plant types. There are 10 sub-surface soil layers in which temperature and moisture (water and ice) are computed, and thickness-dependent multiple snow layers with a maximum of 5. Land units represent the largest spatial patterns of subgrid heterogeneity and include 5 different surface types (glacier, lake, wetland, urban, and vegetated) with 4–16 different vegetation types. The ocean component is the NCAR implementation of POP (Parallel Ocean Program) and is a three-dimensional primitive equation model for which the finite-difference method is employed for the discretization. It has a longitudinal resolution of w3.6 and variable latitudinal resolutions, which are refined in the Tropics up to w0.9 with an average of w1.8 . Numerical poles are located in Greenland and Antarctica. The vertical dimension is treated using a depth (z) coordinated with 25 levels extending to 5 km. The sea ice model is integrated on the same horizontal grid as the ocean model. It is a dynamic–thermodynamic model, which includes multicategory ice thickness distribution, thermodynamics, and dynamics and employs the elastic–viscous–plastic ice rheology. 2.2. Experimental design The three types of model simulations that were conducted are: first, the present-day control run; second the 1000 AD steady run;
and lastly the transient run with different external factors for the last thousand years. In the present-day control simulation, the model is integrated for three hundred years starting from the modern climate state based on observations with fixed external forcings set to presentday values (Table 1). For the 1000 AD steady simulation, we spin up the model, starting from the steady state of present day with the forcing conditions of 1000 AD (Table 1). The model is integrated for three hundred years until the upper w100 m of the ocean temperature reached an approximate steady state, and we used the data of the year 300 as initial conditions for the transient simulations. The model is further integrated for another one thousand years (hereafter CTR) to be use in our analysis. In the transit simulations, the model is forced with external forcing changes of solar constant (S), volcanism (V) and greenhouse gas (G). The simulations are driven by a fourth factor, the Milankovitch forcing (M) which might affect the millennium scale trend (Bertrand et al, 2002). Solar and volcanic forcing series are taken from Crowley et al. (2003). The solar forcing serial is a combination of the observed sunspot numbers and an ice core record of the cosmogenic isotope 10 Be (Fig. 1a). Volcanic activity is represented as radiative forcing by multiplying the aerosol optical depth estimates made from ice cores by a factor of 21 (Hansen et al., 2002), and is applied as a negative deviation from the solar constant (Fig. 1b). Greenhouse gas changes follow as past millennial simulations (850 AD–1999 AD) done by Ammann et al. (2007). Pre-20th century greenhouse gas concentrations (CO2, CH4, N2O) are specified using available ice core data. CFC-11 and CFC-12 are added during the 20th century following SRES guidelines. Further description with the greenhouse gas data is available online at the NCAR CCSM web page (http:// www.cgd.ucar.edu/ccr/ammann/millennium/csm/csm_forcing_ser ies.html). The global and seasonal change of the orbital insolation is computed with the algorithm according to Berger (1978). It should be noted that a number of potentially important forcings are not considered in the simulations studied here: the slow forcing from land-use changes and the negative forcing due to the indirect aerosol effect as well as absorbing aerosols.
3. Results 3.1. Time evolution in the full-forcing simulation and proxy data A comparison of temperature changes over the whole of China and eastern China between full-forcing simulation and proxybased reconstructions is given in Fig. 2. The running average is imposed to remove the high frequency variability. The time evolution of simulated temperatures are similar to the reconstructions except for Ge et al. (2003) with relatively warm conditions ending before 1300 AD which is known as the Medieval Warm Period (MWP). The relatively cold conditions from the end of the 14th century to the early of the 19th century is known as the Little Ice Age (LIA) which ends with the warming in the 20th century. The
Table 1 Forcing used in the 1990 AD control run and the 1000 AD steady run. Forcing
1990 AD
1000 AD
Solar constant CO2 CH4 N2O CFC-11 CFC-12 Orbit year
1367.0 W m2 355 ppmv 1714 ppbv 311 ppbv 0.280 ppbv 0.503 ppbv 1990
1365.0 W m2 280 ppmv 680 ppbv 266 ppbv 0 ppbv 0 ppbv 1000
Y. Peng et al. / Quaternary International 208 (2009) 11–18
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Fig. 1. Natural and anthropogenic climate forcings. Solar constant values related to (a) solar activity and (b) volcanic eruptions. (c) Concentrations of the CO2 and N2O well-mixed greenhouse gases. (d) Estimation of atmospheric CH4 concentration.
correlation coefficients between the simulations and reconstructions by Yang et al. (2002) and Wang et al. (2007) for the whole of China, and Wang et al. (2007) for eastern China are 0.63, 0.63 and 0.43, respectively. We notice that the peaks and valleys in these time series do not run parallel exactly between model and proxy data. The maximum warming during MWP appears at the 11th century in reconstructions, while it appears at the 12th century in simulations which lags behind the former about one hundred years. During the LIA period, although a cooling trend can be seen in the model and proxy data, the main cold time intervals with greater negative anomalies’ occurrence is not synchronous between the model (during the period of 1650–1750 AD) and the reconstruction (during the period of 1600–1690 AD). Another noticeable difference is that the magnitude of temperature change in proxy data appears larger than that in simulation. The absolute anomalies during the great warm period of the MWP in reconstructions are higher than 0.1–0.7 C, compared to the simulations, while during the cold period of the LIA they are higher than 0.2–0.5 C over the whole of China and are comparable with the simulation over eastern China in reconstruction by Wang et al. (2007). The increase of temperature during the 20th century is significant both in the simulation and proxy data. There is little correlation between the model simulation and Ge et al. (2003) in the pre-industrial period. However, recent work (Liu et al., 2005) with ECHO-G model includes the similar set of solar, volcanic and anthropogenic (CO2
and CH4) forcings as our study shows a much higher correlation coefficient. Due to the absent response to the land-use changes and the indirect aerosol effect as well as absorbing aerosols in our simulations the warming during 1951–1980 which is used as a reference period is overestimated in simulation. Thus the simulated temperature in the pre-industrial period is lower than that for the period 1951–1980. Reconstructions show that the MWP over eastern China is not a steady period, with two principal warming phases, while simulation shows only a principal warming phase. Fig. 3a shows a comparison of yearly precipitation between reconstruction and full-forcing simulation in East China during the last millennium. In order to present low-frequency precipitation changes, a 30-yr running average is used for smoothing the detrended dry–wet index series and the simulation time series. The simulation produces a time temporal variability similar to that observed in proxy data. The model indicates that the wet and dry conditions appear alternately before 1400, while the proxy data shows droughts dominating in this period. Both the simulation and reconstruction show dry conditions dominating between the 15th and 17th centuries, whereas wet conditions have appeared since 1890 in simulation and since 1750 in reconstruction. The precipitation has decreased since 1970s in simulation related to the observed climate shift, however, it is still to be considered at wet condition. The model reveals four major wet periods in 1130–1180 AD, 1280–1330 AD, 1740–1810 AD and 1890–1999 AD, and four
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Fig. 2. Comparison of temperature changes between (a) the full-forcing simulation (solid line) and proxy-based data by Yang et al. (2002) (dashed line) and by Wang et al. (2007) (dotted line) over the whole China, (b) the full-forcing simulation (solid line) and proxy-based data by Wang et al. (2007) (dashed line) over eastern China, (c) the full-forcing simulation (solid line) and proxy-based data by Ge et al. (2003) (dashed line) over eastern China. The simulated series are related to the 1000–1999 mean and smoothed using the 10-yr running average in (a), (b) and (c) are related to 1951–1980 and smoothed using the 30-yr running average.
major dry periods in 1000–1080 AD, 1150–1280 AD, 1400–1500 AD and 1810–1890 AD. However, on the centennial time scales, the dry–wet index shows three dry epochs (1000–1230 AD, 1430–1530 AD and 1920–1990 AD) and two wet epochs (1240–1420 AD and 1540–1910 AD) in East China during the last millennium, with multi-decadal fluctuations in each epoch. Similar temporal patterns of precipitation in some periods are also found in other long historical documents that were reconstructed for the same region (Jiang et al., 1997; Song, 2000), such as the dry period from 1150 to 1280 and the wet period from 1280 to 1330 which are roughly consistent with those identified by Jiang et al. (1997) and the wet period from 1740 to 1810 which is consistent with that
reconstructed by Song (2000). It is interesting to note that all of the reconstructions show the precipitation shifts from wet phase to dry phase around 1890, in contrast to the dry phase to wet phase in simulation. Zheng et al. (2006) suggest that strong regional differences appear in East China during the past fifteen hundred years. A further comparison between the reconstruction and model is needed in the MLYRV (Fig. 3b). A better correlation between simulation and reconstruction is gained in the MLYRV than in East China, especially before 1850. The peaks and valleys in these time series run parallel to each other, for example the wettest conditions both appear during 1280–1310 AD. The reconstruction indicates that it has been
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Fig. 3. Yearly precipitation changes in the (a) proxy-based reconstruction (dash line) of Zheng et al. (2006) and full-forcing simulation (solid line) over eastern China, (b) proxybased reconstruction (dash line) of Zheng et al. (2006) and full-forcing simulation (solid line) in the MLYRV. The simulated series are related to 1000–1999 mean and smoothed by 30-yr running average.
relatively wet since 1550 in the MLYRV although the climate turned dry after 1880. While the precipitation in the simulation has increased since 1850 as warmer conditions appeared.
3.2. Spatial patterns in the full-forcing simulation In order to obtain complementary information, the spatial distribution of the changes for particularly warm and cold periods is analyzed. According to the time evolution of the annual temperature anomalies (related to 1000–1999 AD) over eastern China (Fig. 2b), we compute 31-yr averages over the particularly warm period (1120–1150 AD) and the particularly cold period (1680–1710 AD) in East China. Fig. 4 illustrates warmer conditions that appear over all eastern China during 1120–1150 AD and cool conditions that appear over all eastern China during 1680–1710 AD. The magnitude of the response differs according to the regions. The variability of temperature in the middle lower Yellow River valley is larger than that in the middle lower Yangtze River Valley. The positive anomalies during the particularly warm period reach up to 0.4 C and the negative anomalies during the particularly cold period reach up to 0.6 C. Both types of anomalies appear in the middle lower Yellow River valley. In the particularly warm period the precipitation reaches a mean level over the last millennium, while a decrease of 0.2 mm/day occurs over eastern China in the particularly cold period (not shown). This indicates that there are obviously no fixed modes of climate changes (warm–wet, cold– wet, warm–dry or cold–dry) as suggested by Zhang et al. (2007).
3.3. Climate responding to external forcing Temperature changes in each simulation are distinctive, and the effect of external forcing on temperature changes over eastern China is obvious (Fig. 5). Similar changes of temperature appear between CTR and M simulations which indicate that there is a slight effect of variations in orbital parameters. There is a positive correlation between the temperature response of the model and solar forcing. In the SM simulation, the shapes of temperature changes are similar to solar forcing reconstruction where warm condition existed before 1400 corresponding to the relatively higher solar irradiance in the same period, and cold conditions existed during Spo¨rer (1450–1534 AD) minimum. The coldest episode with temperature anomalies of up to 0.35 C appears at the later Spo¨rer in the SM simulation and the SVGM simulation. The timing of the cold episodes is not synchronous between the SM and SVGM simulations with the departures in the SVGM simulation from the SM simulation being prominent during the years of major volcanic eruptions and then indicating that volcanic events is another factor which dominates the temperature changes in East China. The factor V causes marked cooling in East China. Before 1125, because of inactive volcanic activities during this period the temperature changes in the M and VM simulations are in correlation with each other. Greenhouse gas forcing dominates the 20th century rise in the winter-half temperature over eastern China. The increasing amplitude since 1850 is 1.2 C both in the SVGM and GM simulations.
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Fig. 4. Simulated annual temperature anomalies (relative to 1000–1990 AD) over eastern China for the particularly warm period during 1125–1150 AD (left) and the particularly cold period during 1680–1720 AD (right).
The precipitation response due to the different factors over eastern China during the last millennium is shown in Fig. 6. The response of the temporal evolution due to factors in precipitation is similar to the temperature changes, that is, sudden drops due to
volcanic eruptions, higher values correlates to higher solar irradiance, and an increasing trend after 1850 due to the increase of the concentration of greenhouse gas. This differs when the temperature responds linearly to the increase of the concentration of
Fig. 5. Yearly temperature response over eastern China with 10-yr running average due to factors (a) SVGM, (b) GM, (c) VM, (d) SM, (e) M and (f) in the CTR simulations.
Y. Peng et al. / Quaternary International 208 (2009) 11–18
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Fig. 6. Same as Fig. 5 but for precipitation smoothed with 30-yr running average.
greenhouse gas and the precipitation increases after 1850 with a couple of short-scale fluctuations. The shapes of precipitation changes and solar forcing reconstruction are more similar after 1600 than before in the SM simulation. In the CTR simulation, the precipitation changes show 50–70 yr fluctuations which are consistent with the presence of lower frequency variability in observed PDO behavior. Shen et al. (2008) related the pentadecadal oscillation of simulated precipitation variation in East China with internal climate processes such as the Pacific Decadal Oscillation (PDO). This relationship between precipitation over East China and the PDO is presently under investigation and shall not be discussed here.
4. Conclusions A fully coupled three-dimensional model has been used to investigate the effects of the natural and anthropogenic forcings on climate changes over eastern China during the last millennium. The simulated temperature and precipitation with full time varying forcing are in correlation to some extend with proxybased data on decadal time scales in the whole of China and in East China, even in the MLYRV which is at a smaller scale. The temperature of simulation and reconstruction shows that the MWP, LIA periods occur in East China, and these two terms are unsteady periods with multi-decadal fluctuations. During the MWP and LIA, the temperature amplitude of simulated is not as large as reconstructed. The warming conditions occur in East China since 1850, and the amplitude of the temperature increase between simulation and reconstruction is comparative. Except for the winter temperature reconstruction, all the other reconstructed and simulated temperature anomalies show that the highest temperature occurs in the 20th century. It indicates that the 20th
century warming is anomalous during the last millennium over eastern China. The model indicates that the wet and dry conditions appear alternately in the MWP over eastern China. Dry conditions dominated in the LIA, whereas wet conditions have occurred since 1890. The correlation of precipitation before 1850 between simulation and reconstruction is much better in the MLYRV than that after 1850, which indicates that the human influence of land-use changes and aerosol effects should be considered. The spatial pattern analysis in particularly warm and cold periods shows that the variability of temperature in the middle lower Yellow River valley is larger than that in the middle lower Yangtze River Valley and that there are obviously no fixed modes of climate changes (warm–wet, cold–wet, warm–dry or cold–dry). The response of the temporal evolution due to factors in temperature and precipitation is sudden drops due to volcanic eruptions, higher values correlated to higher solar irradiance, and an increasing trend after 1850 due to the increase of the concentration of greenhouse gas. According to our results, the combination of solar variability and volcanic eruptions explains the major temperature and precipitation fluctuations before 1850 and the increase of the contents of greenhouse gases dominates the 20th century warming. Compared to the reconstructions by Yang et al. (2002) and Ge et al. (2003), the simulated temperature variability is too weak. Several sources of uncertainties could explain it. The internal variability may play an important role on the climate changes at regional scales. It can notably explain why the peak temperature during the MWP or LIA between different locations is not synchronous and why the model underestimates the level of temperature variability compared to the variability of the reconstructions. In order to reduce the level of internal climate variability and get a credible result, a large number of ensemble simulations, which are driven by the same forcing and differ only
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in their initial conditions, should be performed and the ensemble mean should be used to make the comparison with the reconstructions. In addition, the fact that stratospheric sulphate aerosols absorb near-infrared and infrared radiation is missing in our implementation of volcanic forcing and the fact that the model belongs to the lower climate sensitivity group may make the model underestimate the response (Yoshimori et al., 2005). Furthermore, proxy data used here do not provide a perfect record of past climate evolution over eastern China. The magnitude of reconstruction by Ge et al. (2003) shows higher warming during the MWP and cooling during the LIA than other reconstructions. It indicates that the reconstruction derived from a proxy, such as a historical document, would capture the different temperature signals to those from multiproxy indicators. Even between two multiproxy reconstructions for the whole of China, the peaks and valleys in these time series do not run parallel exactly, i.e. the opposite phase changes appear during the last one hundred years. As argued by Goosse et al. (2005), these may result from the effects of various climatic and non-climatic records, the different spectral sensitivity of different proxies to climatic variations and the influence of the local climate. Thus, more multiproxy long and verifiable reconstructions are required to extend our knowledge of past climatic changes and achieve a meaningful comparison between model simulations and regional reconstructions. Due to the uncertainties in models of chaotic components of internal variability and in the forcing reconstructions, it is not realistic to anticipate that models reproduce the exact variability registered in proxy data. What is more, the D/F index has been criticized. It was pointed that it is a qualitative rather than a quantitative reconstruction of precipitation and not as useful as other proxy data in detailing truly large-scale precipitation variations and model-data comparisons. Thus, it is noted that the results in our work just provide some insights into precipitation variability.
Acknowledgments This research is jointly supported by the National Key Program for Developing Basic Sciences (2006CB403707, 2007BAC03A01 and 2009CB421407) of China and Climate Change Study Fund of the China Meteorological Administration (CCSF2006-11). We are grateful to all of the authors of the cited reconstructions in making their data available to the community. We would also like to thank Jingyun Zheng and Shaowu Wang for providing their proxy data.
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Contents lists available at ScienceDirect
Quaternary International journal homepage: www.elsevier.com/locate/quaint
Climate changes over eastern China during the last millennium in simulations and reconstructions Youbing Peng a, b, Ying Xu b, *, Liya Jin a a b
Key Laboratory of Western China’s Environmental Systems (Ministry of Education), Lanzhou University, Lanzhou 730000, China National Climate Center, China Meteorological Administration, No. 46 Zhongguancun, Beijing 100081, China
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 27 February 2009
Climate change during the last millennium is simulated using fully coupled three-dimensional model CCSM 2.0.1. The model is driven by the natural and anthropogenic forcings. Simulated temperatures over the whole of China and over the Eastern part of China, from combined forcing correlate to some extend with the proxy data, while simulated precipitation in East China (East of 105 E, 25–40 N) and the middle and lower Yangtze River Valley (106–122 E, 26–34 N) shows some similarities with the reconstructions in some periods of time. Both simulated and reconstructed temperature anomalies indicate that the 20th century warming is anomalous in a long-term context. The model indicates that the wet and dry conditions appear alternately in the Medieval Warm Period over eastern China. Dry conditions dominate in the Little Ice Age, whereas wet conditions exist since 1890. The correlation of precipitation between simulated and reconstructed is better in the middle and lower Yangtze River Valley than in East China, especially before 1850. Regional differences are present in East China during the past thousand years and there are obviously no fixed modes of climate changes (warm–wet, cold–wet, warm–dry or cold–dry). The climate change over eastern China is affected by external factors and internal climate process. The changes of temperature and precipitation over eastern China are controlled mainly by the changes of effective solar radiation and volcanic activity during the last one thousand years, while the increase of the contents of greenhouse gases plays a big role on the fast warming over the past one hundred and fifty years. Ó 2009 Elsevier Ltd and INQUA. All rights reserved.
1. Introduction The modeling approach, as a powerful tool, allows us to study the characteristics of the Medieval Warm Period (WMP) and the Little Ice Age (LIA) and provides important insights into the mechanisms that cause these climate variations. The spectrum of climate models ranges from the simplest energy balance models (EBMs) to the most complex coupled atmosphere–ocean general circulation models (AOGCMs). The EBM study by Crowley (2000) indicates that the model can reproduce temperature anomalies remarkably well for the past millennium, using revised forcing for solar activity, volcanic activity, anthropogenic greenhouse gases and aerosols. Simulations of climate change over the past thousand years have also been performed using two-dimensional or ‘‘intermediate complexity’’ climate models (Bauer et al., 2003). But those model’s results could not be used to gain information on a regional
* Corresponding author. Tel.: þ86 10 68400075; fax: þ86 10 58995956. E-mail address: [email protected] (Y. Xu). 1040-6182/$ – see front matter Ó 2009 Elsevier Ltd and INQUA. All rights reserved. doi:10.1016/j.quaint.2009.02.013
basis, not even at a continental scale (Goosse et al., 2005), while such information could be obtained from the comprehensive, three-dimensional general circulation models (GCMs). GCM simulations are used to compare simulated response deduced from reconstructions, and to study the climate response due to the external forcing (Ammann et al., 2007) and the physical processes which amplify the climate response to strong external forcing (Shindell et al., 2001). The results of these simulations are remarkably consistent with the proxy reconstructions of NH mean temperatures over the past millennia and lead to remarkably similar conclusions (at hemispheric and global scales) regarding the factors controlling climate changes. Most of the previous studies only focused on the spatial and temporal details of temperature changes at hemispheric and global scales during the last thousand years. Nevertheless, it is also important to check whether the general conclusions obtained for continental scales are valid on smaller scales. Furthermore, the temperature history of the past few millennia is just one facet of the past changes in the climate system, and it is important to have some insights into past changes of other societal relevant climate variables, like the
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long-term changes in precipitation and the links to the controlling circulation influences (Jones and Mann, 2004). In addition, because of the high computer-time requirements, there are not any GCM simulations including completely at the same time the unforced simulation, the single external forcing simulation and the fullforcing simulation. In the present study, we estimate the evolution of climate including temperature, precipitation in East China (East of 105 E, 25–40 N) and the middle and lower Yangtze River Valley (109–122 E, 26–34 N) over the last millennium based on fullforcing simulation and reconstructions, and we study the climate temporal distribution response to external forcing. The four reconstructing temperature series used here are: the annual temperature series over the whole of China reconstructed by Yang et al. (2002), the temperature series for the whole of China and specifically for eastern China reconstructed by Wang et al. (2007), and the winter-half year temperature series over eastern China reconstructed by Ge et al. (2003), respectively. Two precipitation proxy data are the yearly dry–wet index over eastern China and in the middle and lower Yangtze River Valley (hereafter MLYRV) both reconstructed by Zheng et al. (2006). 2. Methods 2.1. Model description The model used in this study is the Community Climate System Model (CCSM) version 2.0.1 developed by the National Center for Atmospheric Research (NCAR, Kiehl and Gent, 2004). The model comprises four components: atmosphere, ocean, land surface, and sea ice. The components are linked via a flux coupler, and they are coupled without flux corrections. There are two different resolutions of the model officially supported by NCAR, and for the T31/gx1v3 the lower-resolution setting is used here. The atmospheric component is a global atmospheric general circulation model (AGCM) and is a primitive equation model solved with the spectral transform method in the horizontal and with 26 hybrid-coordinate levels in the vertical. The atmospheric resolution is T31 (an equivalent grid spacing of approximately 3.75 in latitude and longitude). The land model is integrated on the same horizontal grid as the atmosphere, although each grid box is further divided into a hierarchy of land units, soil columns, and plant types. There are 10 sub-surface soil layers in which temperature and moisture (water and ice) are computed, and thickness-dependent multiple snow layers with a maximum of 5. Land units represent the largest spatial patterns of subgrid heterogeneity and include 5 different surface types (glacier, lake, wetland, urban, and vegetated) with 4–16 different vegetation types. The ocean component is the NCAR implementation of POP (Parallel Ocean Program) and is a three-dimensional primitive equation model for which the finite-difference method is employed for the discretization. It has a longitudinal resolution of w3.6 and variable latitudinal resolutions, which are refined in the Tropics up to w0.9 with an average of w1.8 . Numerical poles are located in Greenland and Antarctica. The vertical dimension is treated using a depth (z) coordinated with 25 levels extending to 5 km. The sea ice model is integrated on the same horizontal grid as the ocean model. It is a dynamic–thermodynamic model, which includes multicategory ice thickness distribution, thermodynamics, and dynamics and employs the elastic–viscous–plastic ice rheology. 2.2. Experimental design The three types of model simulations that were conducted are: first, the present-day control run; second the 1000 AD steady run;
and lastly the transient run with different external factors for the last thousand years. In the present-day control simulation, the model is integrated for three hundred years starting from the modern climate state based on observations with fixed external forcings set to presentday values (Table 1). For the 1000 AD steady simulation, we spin up the model, starting from the steady state of present day with the forcing conditions of 1000 AD (Table 1). The model is integrated for three hundred years until the upper w100 m of the ocean temperature reached an approximate steady state, and we used the data of the year 300 as initial conditions for the transient simulations. The model is further integrated for another one thousand years (hereafter CTR) to be use in our analysis. In the transit simulations, the model is forced with external forcing changes of solar constant (S), volcanism (V) and greenhouse gas (G). The simulations are driven by a fourth factor, the Milankovitch forcing (M) which might affect the millennium scale trend (Bertrand et al, 2002). Solar and volcanic forcing series are taken from Crowley et al. (2003). The solar forcing serial is a combination of the observed sunspot numbers and an ice core record of the cosmogenic isotope 10 Be (Fig. 1a). Volcanic activity is represented as radiative forcing by multiplying the aerosol optical depth estimates made from ice cores by a factor of 21 (Hansen et al., 2002), and is applied as a negative deviation from the solar constant (Fig. 1b). Greenhouse gas changes follow as past millennial simulations (850 AD–1999 AD) done by Ammann et al. (2007). Pre-20th century greenhouse gas concentrations (CO2, CH4, N2O) are specified using available ice core data. CFC-11 and CFC-12 are added during the 20th century following SRES guidelines. Further description with the greenhouse gas data is available online at the NCAR CCSM web page (http:// www.cgd.ucar.edu/ccr/ammann/millennium/csm/csm_forcing_ser ies.html). The global and seasonal change of the orbital insolation is computed with the algorithm according to Berger (1978). It should be noted that a number of potentially important forcings are not considered in the simulations studied here: the slow forcing from land-use changes and the negative forcing due to the indirect aerosol effect as well as absorbing aerosols.
3. Results 3.1. Time evolution in the full-forcing simulation and proxy data A comparison of temperature changes over the whole of China and eastern China between full-forcing simulation and proxybased reconstructions is given in Fig. 2. The running average is imposed to remove the high frequency variability. The time evolution of simulated temperatures are similar to the reconstructions except for Ge et al. (2003) with relatively warm conditions ending before 1300 AD which is known as the Medieval Warm Period (MWP). The relatively cold conditions from the end of the 14th century to the early of the 19th century is known as the Little Ice Age (LIA) which ends with the warming in the 20th century. The
Table 1 Forcing used in the 1990 AD control run and the 1000 AD steady run. Forcing
1990 AD
1000 AD
Solar constant CO2 CH4 N2O CFC-11 CFC-12 Orbit year
1367.0 W m2 355 ppmv 1714 ppbv 311 ppbv 0.280 ppbv 0.503 ppbv 1990
1365.0 W m2 280 ppmv 680 ppbv 266 ppbv 0 ppbv 0 ppbv 1000
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Fig. 1. Natural and anthropogenic climate forcings. Solar constant values related to (a) solar activity and (b) volcanic eruptions. (c) Concentrations of the CO2 and N2O well-mixed greenhouse gases. (d) Estimation of atmospheric CH4 concentration.
correlation coefficients between the simulations and reconstructions by Yang et al. (2002) and Wang et al. (2007) for the whole of China, and Wang et al. (2007) for eastern China are 0.63, 0.63 and 0.43, respectively. We notice that the peaks and valleys in these time series do not run parallel exactly between model and proxy data. The maximum warming during MWP appears at the 11th century in reconstructions, while it appears at the 12th century in simulations which lags behind the former about one hundred years. During the LIA period, although a cooling trend can be seen in the model and proxy data, the main cold time intervals with greater negative anomalies’ occurrence is not synchronous between the model (during the period of 1650–1750 AD) and the reconstruction (during the period of 1600–1690 AD). Another noticeable difference is that the magnitude of temperature change in proxy data appears larger than that in simulation. The absolute anomalies during the great warm period of the MWP in reconstructions are higher than 0.1–0.7 C, compared to the simulations, while during the cold period of the LIA they are higher than 0.2–0.5 C over the whole of China and are comparable with the simulation over eastern China in reconstruction by Wang et al. (2007). The increase of temperature during the 20th century is significant both in the simulation and proxy data. There is little correlation between the model simulation and Ge et al. (2003) in the pre-industrial period. However, recent work (Liu et al., 2005) with ECHO-G model includes the similar set of solar, volcanic and anthropogenic (CO2
and CH4) forcings as our study shows a much higher correlation coefficient. Due to the absent response to the land-use changes and the indirect aerosol effect as well as absorbing aerosols in our simulations the warming during 1951–1980 which is used as a reference period is overestimated in simulation. Thus the simulated temperature in the pre-industrial period is lower than that for the period 1951–1980. Reconstructions show that the MWP over eastern China is not a steady period, with two principal warming phases, while simulation shows only a principal warming phase. Fig. 3a shows a comparison of yearly precipitation between reconstruction and full-forcing simulation in East China during the last millennium. In order to present low-frequency precipitation changes, a 30-yr running average is used for smoothing the detrended dry–wet index series and the simulation time series. The simulation produces a time temporal variability similar to that observed in proxy data. The model indicates that the wet and dry conditions appear alternately before 1400, while the proxy data shows droughts dominating in this period. Both the simulation and reconstruction show dry conditions dominating between the 15th and 17th centuries, whereas wet conditions have appeared since 1890 in simulation and since 1750 in reconstruction. The precipitation has decreased since 1970s in simulation related to the observed climate shift, however, it is still to be considered at wet condition. The model reveals four major wet periods in 1130–1180 AD, 1280–1330 AD, 1740–1810 AD and 1890–1999 AD, and four
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Fig. 2. Comparison of temperature changes between (a) the full-forcing simulation (solid line) and proxy-based data by Yang et al. (2002) (dashed line) and by Wang et al. (2007) (dotted line) over the whole China, (b) the full-forcing simulation (solid line) and proxy-based data by Wang et al. (2007) (dashed line) over eastern China, (c) the full-forcing simulation (solid line) and proxy-based data by Ge et al. (2003) (dashed line) over eastern China. The simulated series are related to the 1000–1999 mean and smoothed using the 10-yr running average in (a), (b) and (c) are related to 1951–1980 and smoothed using the 30-yr running average.
major dry periods in 1000–1080 AD, 1150–1280 AD, 1400–1500 AD and 1810–1890 AD. However, on the centennial time scales, the dry–wet index shows three dry epochs (1000–1230 AD, 1430–1530 AD and 1920–1990 AD) and two wet epochs (1240–1420 AD and 1540–1910 AD) in East China during the last millennium, with multi-decadal fluctuations in each epoch. Similar temporal patterns of precipitation in some periods are also found in other long historical documents that were reconstructed for the same region (Jiang et al., 1997; Song, 2000), such as the dry period from 1150 to 1280 and the wet period from 1280 to 1330 which are roughly consistent with those identified by Jiang et al. (1997) and the wet period from 1740 to 1810 which is consistent with that
reconstructed by Song (2000). It is interesting to note that all of the reconstructions show the precipitation shifts from wet phase to dry phase around 1890, in contrast to the dry phase to wet phase in simulation. Zheng et al. (2006) suggest that strong regional differences appear in East China during the past fifteen hundred years. A further comparison between the reconstruction and model is needed in the MLYRV (Fig. 3b). A better correlation between simulation and reconstruction is gained in the MLYRV than in East China, especially before 1850. The peaks and valleys in these time series run parallel to each other, for example the wettest conditions both appear during 1280–1310 AD. The reconstruction indicates that it has been
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Fig. 3. Yearly precipitation changes in the (a) proxy-based reconstruction (dash line) of Zheng et al. (2006) and full-forcing simulation (solid line) over eastern China, (b) proxybased reconstruction (dash line) of Zheng et al. (2006) and full-forcing simulation (solid line) in the MLYRV. The simulated series are related to 1000–1999 mean and smoothed by 30-yr running average.
relatively wet since 1550 in the MLYRV although the climate turned dry after 1880. While the precipitation in the simulation has increased since 1850 as warmer conditions appeared.
3.2. Spatial patterns in the full-forcing simulation In order to obtain complementary information, the spatial distribution of the changes for particularly warm and cold periods is analyzed. According to the time evolution of the annual temperature anomalies (related to 1000–1999 AD) over eastern China (Fig. 2b), we compute 31-yr averages over the particularly warm period (1120–1150 AD) and the particularly cold period (1680–1710 AD) in East China. Fig. 4 illustrates warmer conditions that appear over all eastern China during 1120–1150 AD and cool conditions that appear over all eastern China during 1680–1710 AD. The magnitude of the response differs according to the regions. The variability of temperature in the middle lower Yellow River valley is larger than that in the middle lower Yangtze River Valley. The positive anomalies during the particularly warm period reach up to 0.4 C and the negative anomalies during the particularly cold period reach up to 0.6 C. Both types of anomalies appear in the middle lower Yellow River valley. In the particularly warm period the precipitation reaches a mean level over the last millennium, while a decrease of 0.2 mm/day occurs over eastern China in the particularly cold period (not shown). This indicates that there are obviously no fixed modes of climate changes (warm–wet, cold– wet, warm–dry or cold–dry) as suggested by Zhang et al. (2007).
3.3. Climate responding to external forcing Temperature changes in each simulation are distinctive, and the effect of external forcing on temperature changes over eastern China is obvious (Fig. 5). Similar changes of temperature appear between CTR and M simulations which indicate that there is a slight effect of variations in orbital parameters. There is a positive correlation between the temperature response of the model and solar forcing. In the SM simulation, the shapes of temperature changes are similar to solar forcing reconstruction where warm condition existed before 1400 corresponding to the relatively higher solar irradiance in the same period, and cold conditions existed during Spo¨rer (1450–1534 AD) minimum. The coldest episode with temperature anomalies of up to 0.35 C appears at the later Spo¨rer in the SM simulation and the SVGM simulation. The timing of the cold episodes is not synchronous between the SM and SVGM simulations with the departures in the SVGM simulation from the SM simulation being prominent during the years of major volcanic eruptions and then indicating that volcanic events is another factor which dominates the temperature changes in East China. The factor V causes marked cooling in East China. Before 1125, because of inactive volcanic activities during this period the temperature changes in the M and VM simulations are in correlation with each other. Greenhouse gas forcing dominates the 20th century rise in the winter-half temperature over eastern China. The increasing amplitude since 1850 is 1.2 C both in the SVGM and GM simulations.
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Fig. 4. Simulated annual temperature anomalies (relative to 1000–1990 AD) over eastern China for the particularly warm period during 1125–1150 AD (left) and the particularly cold period during 1680–1720 AD (right).
The precipitation response due to the different factors over eastern China during the last millennium is shown in Fig. 6. The response of the temporal evolution due to factors in precipitation is similar to the temperature changes, that is, sudden drops due to
volcanic eruptions, higher values correlates to higher solar irradiance, and an increasing trend after 1850 due to the increase of the concentration of greenhouse gas. This differs when the temperature responds linearly to the increase of the concentration of
Fig. 5. Yearly temperature response over eastern China with 10-yr running average due to factors (a) SVGM, (b) GM, (c) VM, (d) SM, (e) M and (f) in the CTR simulations.
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Fig. 6. Same as Fig. 5 but for precipitation smoothed with 30-yr running average.
greenhouse gas and the precipitation increases after 1850 with a couple of short-scale fluctuations. The shapes of precipitation changes and solar forcing reconstruction are more similar after 1600 than before in the SM simulation. In the CTR simulation, the precipitation changes show 50–70 yr fluctuations which are consistent with the presence of lower frequency variability in observed PDO behavior. Shen et al. (2008) related the pentadecadal oscillation of simulated precipitation variation in East China with internal climate processes such as the Pacific Decadal Oscillation (PDO). This relationship between precipitation over East China and the PDO is presently under investigation and shall not be discussed here.
4. Conclusions A fully coupled three-dimensional model has been used to investigate the effects of the natural and anthropogenic forcings on climate changes over eastern China during the last millennium. The simulated temperature and precipitation with full time varying forcing are in correlation to some extend with proxybased data on decadal time scales in the whole of China and in East China, even in the MLYRV which is at a smaller scale. The temperature of simulation and reconstruction shows that the MWP, LIA periods occur in East China, and these two terms are unsteady periods with multi-decadal fluctuations. During the MWP and LIA, the temperature amplitude of simulated is not as large as reconstructed. The warming conditions occur in East China since 1850, and the amplitude of the temperature increase between simulation and reconstruction is comparative. Except for the winter temperature reconstruction, all the other reconstructed and simulated temperature anomalies show that the highest temperature occurs in the 20th century. It indicates that the 20th
century warming is anomalous during the last millennium over eastern China. The model indicates that the wet and dry conditions appear alternately in the MWP over eastern China. Dry conditions dominated in the LIA, whereas wet conditions have occurred since 1890. The correlation of precipitation before 1850 between simulation and reconstruction is much better in the MLYRV than that after 1850, which indicates that the human influence of land-use changes and aerosol effects should be considered. The spatial pattern analysis in particularly warm and cold periods shows that the variability of temperature in the middle lower Yellow River valley is larger than that in the middle lower Yangtze River Valley and that there are obviously no fixed modes of climate changes (warm–wet, cold–wet, warm–dry or cold–dry). The response of the temporal evolution due to factors in temperature and precipitation is sudden drops due to volcanic eruptions, higher values correlated to higher solar irradiance, and an increasing trend after 1850 due to the increase of the concentration of greenhouse gas. According to our results, the combination of solar variability and volcanic eruptions explains the major temperature and precipitation fluctuations before 1850 and the increase of the contents of greenhouse gases dominates the 20th century warming. Compared to the reconstructions by Yang et al. (2002) and Ge et al. (2003), the simulated temperature variability is too weak. Several sources of uncertainties could explain it. The internal variability may play an important role on the climate changes at regional scales. It can notably explain why the peak temperature during the MWP or LIA between different locations is not synchronous and why the model underestimates the level of temperature variability compared to the variability of the reconstructions. In order to reduce the level of internal climate variability and get a credible result, a large number of ensemble simulations, which are driven by the same forcing and differ only
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in their initial conditions, should be performed and the ensemble mean should be used to make the comparison with the reconstructions. In addition, the fact that stratospheric sulphate aerosols absorb near-infrared and infrared radiation is missing in our implementation of volcanic forcing and the fact that the model belongs to the lower climate sensitivity group may make the model underestimate the response (Yoshimori et al., 2005). Furthermore, proxy data used here do not provide a perfect record of past climate evolution over eastern China. The magnitude of reconstruction by Ge et al. (2003) shows higher warming during the MWP and cooling during the LIA than other reconstructions. It indicates that the reconstruction derived from a proxy, such as a historical document, would capture the different temperature signals to those from multiproxy indicators. Even between two multiproxy reconstructions for the whole of China, the peaks and valleys in these time series do not run parallel exactly, i.e. the opposite phase changes appear during the last one hundred years. As argued by Goosse et al. (2005), these may result from the effects of various climatic and non-climatic records, the different spectral sensitivity of different proxies to climatic variations and the influence of the local climate. Thus, more multiproxy long and verifiable reconstructions are required to extend our knowledge of past climatic changes and achieve a meaningful comparison between model simulations and regional reconstructions. Due to the uncertainties in models of chaotic components of internal variability and in the forcing reconstructions, it is not realistic to anticipate that models reproduce the exact variability registered in proxy data. What is more, the D/F index has been criticized. It was pointed that it is a qualitative rather than a quantitative reconstruction of precipitation and not as useful as other proxy data in detailing truly large-scale precipitation variations and model-data comparisons. Thus, it is noted that the results in our work just provide some insights into precipitation variability.
Acknowledgments This research is jointly supported by the National Key Program for Developing Basic Sciences (2006CB403707, 2007BAC03A01 and 2009CB421407) of China and Climate Change Study Fund of the China Meteorological Administration (CCSF2006-11). We are grateful to all of the authors of the cited reconstructions in making their data available to the community. We would also like to thank Jingyun Zheng and Shaowu Wang for providing their proxy data.
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