TY - JOUR
T1 - Validation of CMIP5 multimodel ensembles through the smoothness of climate variables
AU - Lee, Myoungji
AU - Jun, Mikyoung
AU - Genton, Marc G.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2015/5/14
Y1 - 2015/5/14
N2 - Smoothness is an important characteristic of a spatial process that measures local variability. If climate model outputs are realistic, then not only the values at each grid pixel but also the relative variation over nearby pixels should represent the true climate. We estimate the smoothness of long-term averages for land surface temperature anomalies in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and compare them by climate regions and seasons. We also compare the estimated smoothness of the climate outputs in CMIP5 with those of reanalysis data. The estimation is done through the composite likelihood approach for locally self-similar processes. The composite likelihood that we consider is a product of conditional likelihoods of neighbouring observations. We find that the smoothness of the surface temperature anomalies in CMIP5 depends primarily on the modelling institution and on the climate region. The seasonal difference in the smoothness is generally small, except for some climate regions where the average temperature is extremely high or low.
AB - Smoothness is an important characteristic of a spatial process that measures local variability. If climate model outputs are realistic, then not only the values at each grid pixel but also the relative variation over nearby pixels should represent the true climate. We estimate the smoothness of long-term averages for land surface temperature anomalies in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and compare them by climate regions and seasons. We also compare the estimated smoothness of the climate outputs in CMIP5 with those of reanalysis data. The estimation is done through the composite likelihood approach for locally self-similar processes. The composite likelihood that we consider is a product of conditional likelihoods of neighbouring observations. We find that the smoothness of the surface temperature anomalies in CMIP5 depends primarily on the modelling institution and on the climate region. The seasonal difference in the smoothness is generally small, except for some climate regions where the average temperature is extremely high or low.
UR - http://hdl.handle.net/10754/555652
UR - http://www.tellusa.net/index.php/tellusa/article/view/23880
UR - http://www.scopus.com/inward/record.url?scp=84930648150&partnerID=8YFLogxK
U2 - 10.3402/tellusa.v67.23880
DO - 10.3402/tellusa.v67.23880
M3 - Article
SN - 1600-0870
VL - 67
SP - 23880
JO - Tellus, Series A: Dynamic Meteorology and Oceanography
JF - Tellus, Series A: Dynamic Meteorology and Oceanography
IS - 1
ER -