Impacts of external forcing on the 20th century global warming

Transcription

1 Chinese Science Bulletin 2007 SCIENCE IN CHINA PRESS Springer Impacts of external forcing on the 20th century global warming LI LiJuan 1,2, WANG Bin 1 & ZHOU TianJun 1 1 LASG, Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing , China; 2 Graduate University of the Chinese Academy of Sciences, Beijing , China The impacts of external forcing, including natural and anthropogenic, on the 20th century global warming were assessed with the use of the Grid Atmospheric Model of IAP/LASG Version 1.1.0, following the standard coordinated experiment design of the Climate Variability and Predictability (CLIVAR) International Climate of the Twentieth Century Project (C20C), Phase II. The results indicate that external forcing plays an important role in the evolution of the land surface air temperature on interannual, decadal, and interdecadal time scales, and contributes greatly to the global warming in the following two periods: the early twentieth century between the 1910s and the 1940s and the late twentieth century after the 1970s. External forcing also has strong impact on the regional temperature change during the two warming periods except for parts of the Eurasia and the North America continents. In the cooling period, however, the impact of internal variability is dominant. external forcing, global warming, climate simulation Climate change is the result of a series of complex interactions within and between five major components of the earth climate system, i.e. the atmosphere, the hydrosphere, the cryosphere, the geosphere, and the biosphere. The global coupled climate system model is an essential tool for us to understand the present climate characteristics, study past climates, and predict future climate conditions [1]. Simulations of the 20th century climate change provide not only an important criterion to evaluate the performance of climate models, but also a measure of the credibility of the model predictions for the future climate. Three possible forcing agents have been identified to be contributors to the 20th century global warming. The first is human activities, including anthropogenic greenhouse gases (CO 2, CH 4, N 2 O, CFC, etc.), sulfate aerosols and ozone change. The second is natural forcing, such as the evolution of solar irradiance and explosive volcanic eruptions. The last is internal variability of the climate system itself, for example, North Atlantic Oscillation (NAO) and El Nino- Southern Oscillation (ENSO). Many coupled atmosphere-ocean climate model simulations [2 5] revealed the important role of the solar irradiance change in producing the early 20th century warming (1910s 1940s) and the major contribution of the human-induced increase in greenhouse gases to the late warming (1970s to the end of the century). The external forcing agents (EFA) are also shown to be important to both decadal and interdecadal variations of surface air temperature (SAT), whereas the roles of internal variability and other yet unknown factors are not negligible [6,7]. Previous studies, however, have not explored the impacts of EFA and internal variability on the regional SAT change nor their influences on SAT interannual variation. This could be partly due to the fact that most coupled models could not reproduce the SAT interannual variability. Received March 12, 2007; accepted June 30, 2007 doi: /s y Corresponding author ( ljli@mail.iap.ac.cn) Supported by the National 973 Project (Grant No. 2005CB321703), the National Natural Science Foundation of China (Grant No ), and the Chinese Academy of Sciences International Partnership Creative Group The Climate System Model Development and Application Studies Chinese Science Bulletin November 2007 vol. 52 no

2 Theoretically, a coupled climate model should be an ideal tool to study the contributions of the EFA and internal variability to climate change. However, the analyses of the 20th century climate change simulations from nineteen coupled climate models participated in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) showed that the sensitivity to the EFA could be affected by the performance of the coupled models. In particular, remarkable model climate drifts could even exceed the signal of the model response to the EFA. The CLIVAR C20C set up a set of experiments from atmospheric general circulation models (AGCM) and fully coupled models to determine the roles of various forcing agents in the 20th century climate change [8]. To validate the capability of an AGCM to reproduce the 20th century climate, C20C experiments were designed with AGCM driven by the prescribed sea surface temperature (SST) only in Phase I, and by prescribed SST as well as natural and anthropogenic forcing agents in Phase II. Both would be good references for comparisons with results from coupled models. Based on the Phase II results of the C20C experiment from the Grid Atmospheric Model of IAP/LASG Version (GAMIL1.1.0, simply GAMIL hereinafter), we will examine the impacts of the EFA on the 20th century global warming in this study. 1 Experimental design and observational data The model used in this study is GAMIL, with the horizontal resolution of 2.8 and 26 vertical layers. Its key dynamical part includes a finite difference scheme that satisfies conservation laws of total mass and effective energy for solving the primitive hydrostatic equations of baroclinic atmosphere [9]. Except for a modified Tiedtke convective scheme [10], its physical parameterizations are the same as those in the National Center for Atmospheric Research (NCAR) Community Atmospheric Model, Version 2 (CAM2) [11]. Note that the Community Land Model (CLM) is used in GAMIL [12]. According to the standard C20C Phase II experiment design, four members of ensemble simulations were performed with GAMIL for the period between 1871 and The initial conditions of these members were the model states on January 1 of four different years in the 30-year control run of GAMIL driven by climatological SST. The EFA consists of two classes, natural and anthropogenic. The natural class includes SST and sea-ice anomalies, Milankovitch cycles, variations in the total solar irradiance and stratospheric volcanic aerosols. The anthropogenic class comprises changing atmospheric concentrations of greenhouse gases, changes in tropospheric and stratospheric ozone, the direct and indirect effects of atmospheric sulfate aerosols and changes in land surface properties. In this study, however, volcanic aerosols, Milankovitch forcing, the indirect effects of sulfate aerosols, and land use changes are not included because of the small contribution of Milankovitch forcing to climate variation on the time scale of hundred-year, and incapability of the model to deal with the indirect effects of sulfate aerosols and the dynamic vegetation. The sea ice and SST data set is obtained from Rayner et al. [13], which is on a 1 latitude-longitude grid from 1870 to Present. The SST data are produced by incorporating the Met Office Marine Data Bank (MDB), the Global Telecommunications System (GTS) and the Comprehensive Ocean-Atmosphere Data Set (COADS). The sea ice data are collected from a variety of sources including digitized sea ice charts and passive microwave retrievals. After an interpolation from 1 1 into model grids using the area weighting method, these data are interpolated from monthly to mid month using Karl Taylor procedure. The solar constant series is reconstructed based on the observed numbers of sunspots [14]. Both greenhouse gases and sulfate aerosol datasets are provided by the Hadley Center, with the latter being from the Boucher data set [15]. Two sets of different resolution SAT datasets are used for model verification, namely the CRUTEM3 [16] (5 5 ) from January 1850 to June 2006 and the CRUTS2.1 [17] ( ) from January 1901 to December Results Figure 1 shows the observed and simulated variations from 1871 to 2002 of the globally averaged annual mean land SAT anomalies. GAMIL performs well in simulating the globally averaged land SAT evolution in the 20th century, such as the warming over the periods of and , and the slight cooling over the period In the last 30 years ( ), the linear trend in the observed land SAT is /10 a, whereas the trend in the ensemble mean is slightly lower, /10 a. In particular, both the observation and the PROGRESS ATMOSPHERIC SCIENCES LI LiJuan et al. Chinese Science Bulletin November 2007 vol. 52 no

3 Figure 1 Variations from 1871 to 2002 of annual mean of globally averaged land surface air temperature anomalies: observation (bold dashed line), ensemble mean simulation (solid line) and four members (other dashed line). Unit is. simulation show that 1998 is the warmest year of the 20th century in both hemispheres as well as in China, although the model produced weaker amplitudes. In 1998, the global mean SAT anomaly is [18], while it is nearly +0.9 over the land, indicating a higher warming trend over the land than over the ocean owing to their different rates of heat uptake [2]. Stott et al. [2] suggested that the correlations between the ensemble mean and the observed SAT time series over a range of time scales indicate the roles of EFA in the climate change. The results from the HadCM3 model revealed that EFA were important for decadal and interdecadal variations. Zhou and Yu [5], based on the results of the nineteen coupled models of IPCC AR4, found that the natural forcing agents and greenhouse gases affected the reproduction of the observed temperature trend and the simulation of decadal and interdecadal climate variations. No signal of interannual variability was captured by those models although the inclusion of solar forcing and volcanic aerosols had evidently improved their performances. Our results show that the correlation coefficient of global land SAT between the ensemble mean simulation by GAMIL and the observation is 0.884, much higher than that (0.5) from CAM2 only forced by SST [5]. Is this high correlation due to the prominent warming trends exhibited in both the simulation and the observation (Figure 1)? To answer this question, we used a band-filter to separate the simulated and observed land SAT time series into three parts: the linear trend and two kinds of variations on the time scales, respectively, longer and shorter than 10 years, and then calculated the correlation coefficients of these parts between the simulation and observation. After linear detrending, the correlation between the simulated and observed time series decreases to 0.698, while the correlation between the simulated and observed variations on the time scales longer (shorter) than 10 a is (0.841). All these values are statistically significant at the 5% confidence level, although they are a little smaller than that of unfiltered series. The results suggest that not only the linear trend but also the interannual, decadal and interdecadal variations of the land SAT are closely linked to the SST and EFA. To reveal the time scale dependence of the land SAT, Morlet wavelet analyses were applied to both the simulation and observation (Figure 2). Significant interannual, decadal, and interdecadal fluctuations appeared in the observation were well captured by GAMIL, but with weaker amplitude on interannual time scales (such as 2 a and 5 a) and stronger amplitude on decadal/interdecadal time scales (such as 10 a, 20 a and 40 a). These interannual, decadal, and interdecadal variations, however, were not steady. For instance, the power band centered at 5 a appeared in both the simulation and the observation from 1871 to 1885, from 1910 to 1930, around 1950 and from 1980 to 2000, but not in other periods. In brief, driven by the prescribed SST and EFA, GAMIL successfully produced not only the global land warming trend and the decadal/interdecadal land SAT variations, but also the interannual fluctuations despite of a weaker magnitude than the observation. The interannual variability was derived from the observed SST forcing, which was confirmed by the simulation of the CAM2 forced only by SST [5]. Note that the high correlation of the land SAT between the simulation and the observation is closely related to the prescribed ocean conditions. The global warming has distinctive regional features. To examine the role of EFA in the regional SAT change, Figure 3 presents the zonal mean anomalies of land SAT from the observation and the four individual model experiments. The observation shows distinct two warming periods in the last century. The early warming occurred from the 1910s to the 1940s, in which the pronounced SAT increase was observed in middle-high latitudes of the Northern Hemisphere, with the maximum 1.2 in the region to the north of 60 N. The late warming since 1980s was a general one in global scale with the largest warming also in middle-high latitudes. The regional structure of warming was well simulated but with a weaker magnitude than the observed. The actual cause 3150 LI LiJuan et al. Chinese Science Bulletin November 2007 vol. 52 no

4 PROGRESS The Morlet wavelet power spectrum of the observed (a) and the ensemble mean simulated (b) global mean land surface air temperature anoma- of the early warming has not been clear yet. Delworth et al.[6] proposed that it results from the natural internal climate variability. The results from GAMIL, however, suggest that it be mainly a result of the EFA since the contribution by internal variability is insignificant. The spread among GAMIL ensemble runs, calculated according to Equation 8 in Zhou and Yu[5], is generally less than 0.05 in most regions, with the maximum of 0.2 in the northern high latitudes, which is much less than the magnitude of the warming (0.6 ) due to the EFA. Hence, based on the results from GAMIL, we believe that the EFA plays an important role in reproducing the first warming, consistent with the findings from coupled models[5], which suggested that the warming was due to changes in solar irradiance. Most of previous studies[2,3,6] have emphasized the contribution by the increased greenhouse gases to the warming in recent decades, which is supported by the GAMIL outputs again since the spread ( ) among ensemble members was much smaller than the ensemble mean ( ). The simulated SAT variations by GAMIL show that the warming in the two periods could both result from the EFA, although the model failed to reproduce the positive anomalies north to the equator from 1900 to 1940 and negative anomalies south to the equator from 1880 to These discrepancies could be related to the surface energy imbalance in the model[10]. The warming trend varies not only with region but also with time. In the observation (Figure 1), the evolution of the land SAT in the 20th century can be divided into three periods starting from 1910, 1940 and 1970, respectively[2]. The global mean land SAT increased in the first and the last periods but decreased in the second period. Figure 4 shows the distribution of the linear trend and the ratio of noise (internal variability among ensemble members) to signal (or ensemble mean) in the three periods. In the first period, the observed trend is 0.66 /30 a, larger than the ensemble mean, 0.57 /30 a (Table 1). GAMIL reproduces the basic distribution of the land SAT trend with the warmest regions in the northern high latitudes, but with the simulated warming amplitude LI LiJuan et al. Chinese Science Bulletin November 2007 vol. 52 no ATMOSPHERIC SCIENCES Figure 2 lies.

5 Figure 3 Zonal mean land surface air temperature anomalies for the observation (a), the ensemble mean (b) and the four experiments. All values are subjected to a 10-year low-pass filter and plotted at the ending year of the 10-year period. (a) CRUTEM3, (b) Ensemble, (c) RUN1, (d) RUN2, (e) RUN3, (f) RUN4. Table 1 Linear trends of global mean land surface air temperature in the observations and the simulations in three periods during OBS ( /30 a) Ensemble mean ( /30 a) weaker than the observation, even 1 2 /30 a smaller in the northern Eurasia. The weaker model response to the EFA could be a result of the experimental design, namely, the lacking of the response of SST to EFA because of the use of the prescribed SST. In contrast to the early warming, the latest warming spreads more widely. The trend in the observation is /30 a, larger than the ensemble mean, 0.77 /30 a (Table 1). The maximum warming is located in the North American and Asian continents. The largest negative difference be3152 tween the simulated and the observed is found in the Asian continent. During the two warming periods, the ratio of noise to signal is less than 0.5 in most regions except for parts of the Eurasia and the North America continents, indicating that the increase in land SAT results mainly from EFA. In the cooling period, the observed trend is /30 a, while the ensemble mean is 0.23 /30 a (Table 1). The range and magnitude of the simulated cooling are smaller than those of the observed. In Siberia, Northern America and China, the ratio of noise to signal exceeds 1.0, indicating that the internal variability is dominant in this period. 3 Conclusions We assessed the contributions of external forcing agents, LI LiJuan et al. Chinese Science Bulletin November 2007 vol. 52 no

6 LI LiJuan et al. Chinese Science Bulletin November 2007 vol. 52 no ATMOSPHERIC SCIENCES PROGRESS Figure 4 Linear trends of the surface air temperature for three 30-year periods spanning 1910 to 1999 from the observations (left), the ensemble mean (middle) and the ratio of internal noise to signal (ensemble mean) (right). Unit is /30 a.

7 both natural and anthropogenic, to the 20th century global warming, based on the results of the standard coordinated experiments of the CLIVAR C20C, Phase II from GAMIL. The following three main conclusions are reached: (1) GAMIL performs better than coupled models in simulating the interannual variations of the land SAT. The simulations of GAMIL show important roles of the external forcing agents in producing the interannual, decadal, and interdecadal variations of the land SAT. Our study confirms the contributions of the external forcing to both the early and the recent warming periods. The early warming was mainly caused by natural forcing and the recent warming was generally attributed to anthropogenic forcing. (2) In the two warming periods, external forcing has a strong impact on the regional SAT evolution except for parts of the Eurasia and the North American continents. (3) In the cooling period ( ), the internal variability exceeds the response of the SAT to external forcing. This makes the failure of climate models to reproduce the cooling trend. The results from the GAMIL simulations indicate that the internal variability played a more significant role in the cooling period than in the warming periods. The simulated warming, however, is weaker than the observed, especially in Eurasia. The causes for the negative bias might be two-folded. One is the experiment design, which did not include air-sea interaction, and the other is the possible effect of the systematic bias in the surface energy imbalance ( 13 wm 2 ) on the simulations. Improved simulations are expected in the near future with better experiment designs and the removal of the surface energy imbalance. The model integration is performed on the Lenovo DeepComp 6800 Supercomputer at the supercomputing Center of the Chinese Academy of Sciences. This work also contributes to the CLIVAR C20C project. 1 Zhou T J, Yu R C, Wang Z Z, et al. The Atmospheric General Circulation Model SAMIL and its Associated Coupled Model FGOALS_s (in Chinese). Beijing: Meteorological Press, Stott P A, Tett S F B, Jones G S, et al. External control of 20th century temperature by natural and anthropogenic forcing. Science, 2000, 290: Tett S F B, Stott P A, Allen M A, et al. Causes of twentieth century temperature change. Nature, 1999, 399: Meehl G A, Washington W M, Wigley T M L, et al. Solar and greenhouse gas forcing and climate response in the twentieth century. J Climate, 2003, 16: Zhou T J, Yu R C. Twentieth-century surface air temperature over China and the globe simulated by coupled climate models. J Climate, 2006, 19: Delworth T L, Knutson T R. Simulation of early 20th century global warming. Science, 2000, 287: Natalia G A, Michael E S. Causes of global temperature changes during the 19th and 20th centuries. Geophys Res Lett, 2000, 27(14): Folland C K, Shukla J, Kinter J, et al. C20C: The Climate of the Twentieth Century Project. CLIVAR Exchanges, 2002, 7(2): Wang B, Wan H, Ji Z Z, et al. Design of a new dynamical core for global atmospheric models based on some efficient numerical methods. Sci China Ser A-Math Sci, 2004, 47(Suppl): Li L J, Wang B, Wang Y Q, et al. Improvements in Climate Simulation with Modifications to the Tiedtke Convective Parameterization in the Grid-point Atmospheric Model of IAP LASG (GAMIL). Adv Atmos Sci, 2007, 24: Collins W D, Hack J J, Boville B A, et al. Description of the NCAR Community Atmosphere Model (CAM2). NCAR Technical Notes Dai Y J, Zeng X, Dickinson R E, et al. The Common Land Model (CLM). B Am Meteorol Soc, 2003, 84: Rayner N A, Parker D E, Horton E B, et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geop Res, 2003, 108(14): Lean J, Beer J, Bradley R. Reconstruction of solar irradiance since 1610: Implications for climate change. Geophys Res Lett, 1995, 22(23): Boucher O, Pham M. History of sulfate aerosol radiative forcings. Geophys Res Lett, 2002, 29(9): Brohan P, Kennedy J J, Haris I, et al. Uncertainty estimates in regional and global observed temperature changes: a new dataset from J Geop Res, 2006, 111: D Mitchell T D, Jones P D. An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatology, 2005, 25: Gong D Y, Wang S W. 1998: the warmest year on record of the century in China. Meteorology (in Chinese), 1999, 25(8): LI LiJuan et al. Chinese Science Bulletin November 2007 vol. 52 no