Quantifying temporal-spatial variability of land-surface temperature anomalies using DP-based algorithm

Using land-surface temperature anomaly time series during the period from Jan. 1880 to Feb. 2014 (reference period 1901-2000) produced by Berkeley Earth, the temporal-spatial variability of land-surface temperature anomalies are quantified by three DP-based (DP, AUG and mDP) time series segmentation algorithm. The only difference of the above mentioned time series segmentation algorithm is the execution time. Segmentation results show that all the temperature anomaly time series have obvious increasing trend, and the uptrend rates for annual and seasonal (spring, summer, autumn and winter) time series are 0.0101°C/year, 0.0109°C/year, 0.0079°C/year, 0.0092°C/year and 0.0122°C/year respectively. Winter temperature anomalies have the largest uptrend rate in four seasons. The change points of temperature anomalies time series are determined to identify sudden transitions from one period to another with partial linear trend sign. Annual and seasonal land-surface temperature anomaly time series has accelerating tendencies since 1964, 1954, 1967, 1974 and 1964, and the uptrend rates are 0.0244°C/year, 0.0224°C/year, 0.0240°C/year, 0.0323°C/year and 0.0240°C/year. Based on the results of temporal variation of temperature anomalies, the spatial variation of annual and seasonal temperature anomaly time series is analyzed. The spatial trend analysis show that the annual and seasonal temperature anomaly time series in the Northern Hemisphere possessed a more sensitive response to climate change, especially in high latitude region of the Northern Hemisphere have an obviously rapid increase rates.