One of the key uncertainties in future sea-level projections is attributed to the Greenland ice sheet(GrIS).Studying the response of the GrIS to climate changes during the past warm periods is helpful for understanding future changes in the GrIS.In this study,using three global climate models(Community Atmosphere Model version3.1 and version 4.0 and Norwegian Earth System Model)and a three-dimensional ice sheet model,we investigate the climate and ice sheet changes over Greenland during the mid-Pliocene warm period(*3 Ma BP).The results show that the regionally averaged summer temperature over Greenland is 9.4–13.4°C higher during the mid-Pliocene period than during the pre-industrial era and the annual mean precipitation is 65.2–108.3 mm a-1greater.In response to this warm-wet climate,the GrIS shows a substantial decrease in size during the mid-Pliocene,with little ice existing along the eastern coast of Greenland.Compared to that simulated in the control run,the global sea level is approximately 7.8–8.1 m higher during the mid-Pliocene due to the decrease in the size of the GrIS.In addition,paleoclimate proxies also indicate that it is unlikely that a large-scale ice sheet exists over Greenland during the mid-Pliocene warm period.
The mid-Pliocene, the most recent warm geological period, is thought to be indicative of the fate of the Earth's climate under global warming. Earlier evidence has suggested that permanent El Nio-like conditions existed in the mid-Pliocene, though the concept of a permanent El Nio remains controversial. Here, the authors analyzed Nio 3.4 SST in pre-industrial and mid-Pliocene simulations with the low-resolution version of the Norwegian Earth System Model (NorESM-L). The simulated mid-Pliocene Nio3.4 SST, with a smaller standard deviation, indicated that a weaker ENSO existed in the mid-Pliocene relative to the pre-industrial experiment. Compared with earlier modeling studies, our simulations show that the problem of ENSO's standard deviations in the mid-Pliocene remains unresolved, although the mean and the period of ENSO in the mid-Pliocene have been resolved by earlier geological and modeling studies.
Given that climate extremes in China might have serious regional and global consequences, an increasing number of studies are examining temperature extremes in China using the Coupled Model Intercomparison Project Phase 5(CMIP5)models. This paper investigates recent changes in temperature extremes in China using 25 state-of-the-art global climate models participating in CMIP5. Thirteen indices that represent extreme temperature events were chosen and derived by daily maximum and minimum temperatures, including those representing the intensity(absolute indices and threshold indices),duration(duration indices), and frequency(percentile indices) of extreme temperature. The overall performance of each model is summarized by a "portrait" diagram based on relative root-mean-square error, which is the RMSE relative to the median RMSE of all models, revealing the multi-model ensemble simulation to be better than individual model for most indices. Compared with observations, the models are able to capture the main features of the spatial distribution of extreme temperature during 1986–2005. Overall, the CMIP5 models are able to depict the observed indices well, and the spatial structure of the ensemble result is better for threshold indices than frequency indices. The spread amongst the CMIP5 models in different subregions for intensity indices is small and the median CMIP5 is close to observations; however, for the duration and frequency indices there can be wide disagreement regarding the change between models and observations in some regions.The model ensemble also performs well in reproducing the observational trend of temperature extremes. All absolute indices increase over China during 1961–2005.
This paper provides a review of paleoclimate modeling activities in China. Rather than attempt to cover all topics, we have chosen a few climatic intervals and events judged to be particularly informative to the international community. In historical climate simulations, changes in solar radiation and volcanic activity explain most parts of reconstructions over the last millennium prior to the industrial era, while atmospheric greenhouse gas concentrations play the most important role in the20 th century warming over China. There is a considerable model–data mismatch in the annual and boreal winter temperature change over China during the mid-Holocene [6000 years before present(ka BP)], while coupled models with an interactive ocean generally perform better than atmospheric models. For the Last Glacial Maximum(21 ka BP), climate models successfully reproduce the surface cooling trend over China but fail to reproduce its magnitude, with a better performance for coupled models. At that time, reconstructed vegetation and western Pacific sea surface temperatures could have significantly affected the East Asian climate, and environmental conditions on the Qinghai–Tibetan Plateau were most likely very different to the present day. During the late Marine Isotope Stage 3(30–40 ka BP), orbital forcing and Northern Hemisphere glaciation, as well as vegetation change in China, were likely responsible for East Asian climate change. On the tectonic scale,the Qinghai–Tibetan Plateau uplift, the Tethys Sea retreat, and the South China Sea expansion played important roles in the formation of the East Asian monsoon-dominant environment pattern during the late Cenozoic.
Using Lanczos filtered simulation results from the ECHO-G coupled ocean-atmosphere model,this study analyzes the spatiotemporal structure of temperature and precipitation on centennial time scale to examine how climate change in eastern China responded to external forcing during the last millennium.The conclusions are (1) eastern China experienced a warm-cold-warm climate transition,and the transition from the warm period to the cold period was slower than the cold to warm transition which followed it.There was more rainfall in the warm periods,and the transitional peak and valley of precipitation lag those of temperature.The effective solar radiation and solar irradiance have significant impacts on the temporal variation of both temperature and precipitation.Volcanic activity plays an important role in the sudden drop of temperature before the Present Warm Period (PWP).There is a positive correlation between precipitation and volcanic activity before 1400 A.D.,and a negative relationship between the two thereafter.The concentration of greenhouse gases increases in the PWP,and the temperature and precipitation increase accordingly.(2) The spatial pattern of the first leading empirical orthogonal function (EOF) mode of temperature on centennial time scale is consistent with that on the inter-annual/inter-decadal (IA-ID) time scales;namely,the entirety of eastern China is of the same sign.This pattern has good coherence with effective solar radiation and the concentrations of greenhouse gases.The first leading EOF mode of precipitation on centennial time scale is totally different from that on the IA-ID time scales.The first leading mode of centennial time scale changes consistently over the entirety of eastern China,while the middle and lower reaches of the Yangtze and Yellow Rivers are the opposite to the rest of eastern China is the leading spatial pattern on IA-ID time scale.The distribution of precipitation on centennial time scale is affected by solar irradiance and greenhouse gas concentrations.
WANG HongLiLIU JianWANG ZhiYuanWANG SuMinKUANG XueYuan
Three global datasets, the History Database of the Global Environment (HYDE), Kaplan and Krumhardt (KK) and Pongratz of reconstructed anthropogenic land cover change (ALCC) were introduced and compared in this paper. The HYDE dataset was reconstructed by Goldewijk and his colleagues at the National Institute of Public Health and the Environment in Netherland, covering the past 12 000 years. The KK dataset was reconstructed by Kaplan and his colleagues, the Soil-Vegetation-Atmosphere Research Group at the Institute of Environmental Engineering in Switzerland, covering the past 8000 years. The Pongratz dataset was reconstructed by Pon-gratz and her colleagues at the Max Planck Institute for Meteorology in Germany, covering AD 800-1992. The results show that the reconstructed datasets are quite different from each other due to the different methods used. The three datasets all allocated the historical ALCC according to human population density. The main reason causing the differences among the three datasets lies on the different relationships between population density and land use used in each reconstructed dataset. The KK dataset is better than the other two datasets for two important reasons. First, it used the nonlinear relationship between population density and land use, while the other two used the linear relationship. Second, Kaplan and his colleagues adopted the technological development and intensification parameters and considered the wood harvesting and the long-term fallow area resulted from shifting cultivation, which were neglected in the reconstructions of the other two datasets. Therefore, the KK dataset is more suitable as one of the anthropogenic forcing fields for climate simulation over the past two millennia that is recently concerned by two projects, the National Basic Research Program and the Strategic and Special Frontier Project of Science and Technology of the Chinese Academy of Sciences.
YAN MiWANG ZhiyuanJed Oliver KAPLANLIU JianMIN ShenWANG Sumin
