What is known is that regressing climate
data against the same tree-growth data, alternatively represented
at annual resolution or after common low-pass smoothing of
the climate and growth data, can result in very different scaling factors
and, hence, reconstructed climate series with notably different
long-term variability
What is known is that regressing climate data against the same tree-growth data, alternatively represented at annual resolution or after common low-pass smoothing of the climate and growth data, can result in very different scaling factors and, hence, reconstructed climate series with notably different long-term variability
众所周知气候不断退化导致其数据变动而年轮所显示的气候数据相对滞后产生矛盾,其解决变法有二:1以年为时间尺度来提供数据 2找出两者一致对应较小的尺度,这将导致对其动因进行大不同放缩比例,因此这将对重建气候数据序列产生长远及显著的变化。仅供参考