ALBERT

Description: Three long-term temperature data series measured in Portugal were studied to detect and correct non-climatic homogeneity breaks and are now available for future studies of climate variability. Series of monthly minimum (Tmin) and maximum (Tmax) temperatures measured in the three Portuguese meteorological stations of Lisbon (from 1856 to 2008), Coimbra (from 1865 to 2005) and Porto (from 1888 to 2001) were studied to detect and correct non-climatic homogeneity breaks. These series together with monthly series of average temperature (Taver) and temperature range (DTR) derived from them were tested in order to detect homogeneity breaks, using, firstly, metadata, secondly, a visual analysis and, thirdly, four widely used homogeneity tests: von Neumann ratio test, Buishand test, standard normal homogeneity test and Pettitt test. The homogeneity tests were used in absolute (using temperature series themselves) and relative (using sea-surface temperature anomalies series obtained from HadISST2 close to the Portuguese coast or already corrected temperature series as reference series) modes. We considered the Tmin, Tmax and DTR series as most informative for the detection of homogeneity breaks due to the fact that Tmin and Tmax could respond differently to changes in position of a thermometer or other changes in the instrument's environment; Taver series have been used, mainly, as control. The homogeneity tests show strong inhomogeneity of the original data series, which could have both internal climatic and non-climatic origins. Homogeneity breaks which have been identified by the last three mentioned homogeneity tests were compared with available metadata containing data, such as instrument changes, changes in station location and environment, observing procedures, etc. Significant homogeneity breaks (significance 95% or more) that coincide with known dates of instrumental changes have been corrected using standard procedures. It was also noted that some significant homogeneity breaks, which could not be connected to the known dates of any changes in the park of instruments or stations location and environment, could be caused by large volcanic eruptions. The corrected series were again tested for homogeneity: the corrected series were considered free of non-climatic breaks when the tests of most of monthly series showed no significant (significance 95% or more) homogeneity breaks that coincide with dates of known instrument changes. Corrected series are now available in the frame of ERA-CLIM FP7 project for future studies of climate variability.

Description: For the 2007 to 2014 period, we use a statistical approach to evaluate the performance of Tsyganenko and Sitnov [2005, doi:10.1029/2004JA010798] semi-empirical model (TS05) in estimating the magnetospheric transient signal observed at four Northern Hemisphere mid-latitude ground stations: Coimbra, Portugal; Panagyurishte, Bulgary; Novosibirsk, Russia and Boulder, USA. We find that the TS05 performance is clearly better for the X (North-South) than for the Y (East-West) field components and for more geomagnetically active days as determined by local K-indices. For ~ 50% (X) and ~ 30% (Y) of the total number of geomagnetically active days, correlation values yield r 〉 0.7. During more quiet conditions, only ~30% (X) and ~15% (Y) of the number of analysed days yield r 〉 0.7. We compute separate contributions from different magnetospheric currents to data time variability and to signal magnitude. During more active days, all Tail, Symmetric Ring and Partial Ring currents contribute to the time variability of X while the Partial Ring and Field Aligned currents contribute most to the time variability of Y. The Tail and Symmetric Ring currents are main contributors to the magnitude of X. In the best case estimations when r 〉 0.7, remaining differences between observations and TS05 predictions could be explained by global induction in the Earth's upper layers and crustal magnetization. The closing of Field Aligned currents through the Earth's center in the TS05 model seems to be mainly affecting the Y magnetospheric field predictions.