A Comparison of Three Methods for Estimating Internal Variability of Near-Surface Air Temperature
Received:September 25, 2018  
View Full Text  View/Add Comment  Download reader
KeyWord:Internal variability  Near-surface air temperature  Pre-industrial control simulations  Polynomial fit  Analysis of variance  Time of emergence
Author NameAffiliationE-mail
LU Jingwen State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
University of Chinese Academy of Sciences, Beijing 100049 
 
ZHOU Tianjun State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
University of Chinese Academy of Sciences, Beijing 100049 
zhoutj@lasg.iap.ac.cn 
HUANG Xin State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
University of Chinese Academy of Sciences, Beijing 100049 
 
ZHANG Wenxia State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029  
ZOU Liwei State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029  
Hits: 254
Download times: 135
Abstract:
      The estimated internal variability of near-surface air temperature was compared using three widely adopted methods [pre-industrial control (piControl) simulations, polynomial fit method, and analysis of variance method , based on 37 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 40 large-ensemble simulations from the Community Earth System Model (CESM). The associated influences on the time of emergence (TOE) of near-surface air temperature in future climate projections were also quantified. The results showed that for multimodels from the CMIP5, the estimated internal variability was comparable based on the piControl simulations and the polynomial fit method, while variability estimated by the analysis of variance method was exaggerated in terms of the magnitude because of its inclusion of model uncertainty. Polar amplification was evident in the spatial distribution of estimated internal variability of surface temperature, with considerably larger magnitudes in the mid- to high-latitudes than the low-latitudes. The internal variability of surface temperature did not vary significantly with time or emission scenarios, except for in the tropics, estimated by the analysis of variance method. Moreover, the estimated internal variability showed high consistency among the three methods, based on large-ensemble simulations from the CESM. The different estimated internal variabilities further affected the TOE in future climate projections, mainly in the North Atlantic Labrador Sea and the Weddell and Ross Seas in the Southern Ocean where deep overturning circulations occur. Specifically, the internal variability was estimated to be less than 15% of the forced signals over China based on all three methods in the CESM large-ensemble simulations. This result was comparable to those estimated by the piControl simulations and polynomial fit method based on the CMIP5 multimodels but tended to be overestimated by the analysis of variance method.