Biases in precipitation records found in parallel measurements

Presentación realizada en: 10th EUMETNET Data Management Workshop celebrado en St. Gallen, Suiza, del 28 al 30 de octubre de 2015.In this work we investigate biases introduced by the transition from Conventional to automatic precipitation measurements. This is another study in the framework of The Parallel Observations Scientific Team (POST, http://www.surfacetemperatures.org/databank/parallel_measurements), which is a newly created group of the International Surface Temperature Initiative (ISTI) supported by the World Meteorological Organization (WMO). The goals of POST are the study of climate data inhomogeneities at the daily and sub-daily level. Long instrumental climate records are usually affected by non-climatic changes, due to various reasons like relocations, changes in instrumentation, measurements schemes etc. Such inhomogeneities may distort the climate signal and can influence the assessment of trends and variability. For studying climatic changes it is important to accurately distinguish non-climatic from climatic signals. This can be achieved by studying the differences between two parallel measurements. These need to be sufficiently close together to be well correlated. One important ongoing worldwide transition is the one from manual to automated measurements. We need to study the impact of automated measurements urgently because sooner or later this will affect most of the stations in individual national networks. Similar to temperature series, we study the transition from conventional manual measurements (CON) to Automatic Weather Stations (AWS), using several parallel datasets distributed over Europe and America. The ratio series AWS-CON are subject to quality control, and before the analysis obvious errors are removed. Further, the series are inspected for internal inhomogeneities and– if necessary –the records are split into two or more homogeneous segments. Finally, each segment is studied to understand the biases introduced by the transition, its seasonality as well as changes in the empirical distributions. When additional variables are available, an attempt is made to study the effects of other variables on the observed biases

Description of the bias introduced by the transition from Conventional Manual Measurements to Automatic Weather Station through the analysis of European and American parallel datasets (+ Australia, Israel & Kyrgyzstan)

Presentación realizada en: 10th EUMETNET Data Management Workshop celebrado en St. Gallen, Suiza, del 28 al 30 de octubre de 2015.In this work, we approach the description of the biases introduced by automation in temperature records. This is one of the first studies in the framework of The Parallel Observations Scientific Team (POST). POST is a newly created group of the International Surface Temperature Initiative (ISTI), with the support of the World Meteorological Organization (WMO). The goals of POST (http://www.surfacetemperatures.org/databank/parallel_measurements) are the study of climate data inhomogeneities at the daily and sub-daily level through the compilation and analysis of parallel measurements. Long instrumental climate records are usually affected by non-climatic changes, due to, e.g., relocations and changes in instrumentation, instrument height or data collection and manipulation procedures. These so-called inhomogeneities distort the climate signal and can hamper the assessment of trends and variability. Thus to study climatic changes we need to accurately distinguish non-climatic and climatic signals. The most direct way to study the influence of non-climatic changes on the distribution and to understand the reasons for these biases is the analysis of parallel measurements. A parallel measurement is composed of two or more time series, which measure a climatic variable with two different systems (for example, Montsouris and Stevenson Screens) or in two different locations (for example, city centre and airport). They mimic the situation “before” and “after” a homogeneity break. Most parallel measurements are obtained from collocated or nearly collocated series and can help us to understand the size and shape of different typical sources of inhomogeneity, which affect the climate series. Here we study the transition from conventional temperature manual measurements (CON) to Automatic Weather Stations (AWS), using several parallel datasets distributed over Europe and America. The variables studied in the analysis presented here are daily maximum and minimum temperature. First of all, the metadata – when available - is gathered to gain knowledge on the exact setting of the parallel series. Secondly, the difference (temperature) series AWS-CON are submitted to quality control, to remove obvious errors and inspected to detect internal inhomogeneities and split if necessary. In a third step, each segment is studied to understand the bias introduced by the transition, its seasonality as well as changes in the empirical distributions. When additional variables are available, an attempt is made to study the effects of other variables on the observed biases.With the support of Grant CGL2012-32193, Ministerio de Economía y Competitividad, MINECO, España and FP7-SPACE-2013-1 grand 607193, Uncertainties in Ensembles of Regional Reanalyses (UERRA)

Warming and wetting signals emerging from analysis of changes in climate extreme indices over South America

Here we show and discuss the results of an assessment of changes in both area-averaged and station-based climate extreme indices over South America (SA) for the 1950-2010 and 1969-2009 periods using high-quality daily maximum and minimum temperature and precipitation series. A weeklong regional workshop in Guayaquil (Ecuador) provided the opportunity to extend the current picture of changes in climate extreme indices over SA.Our results provide evidence of warming and wetting across the whole SA since the mid-20th century onwards. Nighttime (minimum) temperature indices show the largest rates of warming (e.g. for tropical nights, cold and warm nights), while daytime (maximum) temperature indices also point to warming (e.g. for cold days, summer days, the annual lowest daytime temperature), but at lower rates than for minimums. Both tails of night-time temperatures have warmed by a similar magnitude, with cold days (the annual lowest nighttime and daytime temperatures) seeing reductions (increases). Trends are strong and moderate (moderate to weak) for regional-averaged (local) indices, most of them pointing to a less cold SA during the day and warmer night-time temperatures.Regionally-averaged precipitation indices show clear wetting and a signature of intensified heavy rain events over the eastern part of the continent. The annual amounts of rainfall are rising strongly over south-east SA (26.41. mm/decade) and Amazonia (16.09. mm/decade), but north-east Brazil and the western part of SA have experienced non-significant decreases. Very wet and extremely days, the annual maximum 5-day and 1-day precipitation show the largest upward trends, indicating an intensified rainfall signal for SA, particularly over Amazonia and south-east SA. Local trends for precipitation extreme indices are in general less coherent spatially, but with more general spatially coherent upward trends in extremely wet days over all SA. © 2012 Elsevier B.V