CL5.7Climate Data Homogenization and Climate Trend and Variability Assessment
|Convener: Xiaolan Wang | Co-Conveners: Rob Roebeling , Petr Stepanek , Enric Aguilar , Wouter Dorigo|
The accuracy and homogeneity of climate data are indispensable for many aspects of climate research. In particular, a realistic and reliable assessment of historical climate trends and variability is hardly possible without a long-term, homogeneous time series of climate data. Accurate and homogeneous climate data are also indispensable for the calculation of related statistics that are needed and used to define the state of climate and climate extremes. Unfortunately, many kinds of changes (such as instrument and/or observer changes, and changes in station location and environment, observing practices and procedure, etc.) that took place in the period of data record could cause non-climatic changes (artificial shifts) in the data time series. Such artificial shifts could have huge impacts on the results of climate analysis, especially those of climate trend analysis. Therefore, artificial changes shall be eliminated, to the extent possible, from the time series prior to its application, especially its application in climate trends assessment.
This session calls for contributions that are related to bias correction and homogenization of climate data, including bias correction and validation of various climate data from satellite observations and from GCM and RCM simulations, as well as quality control/assurance of observations of various variables in the Earth system. It also calls for contributions that use high quality, homogeneous climate data to assess climate trends and variability and to analyze climate extremes, including the use of bias-corrected GCM or RCM simulations in statistical downscaling. This session will include studies that inter-compare different techniques and/or propose new techniques/algorithms for bias-correction and homogenization of climate data, for assessing climate trends and variability and analysis of climate extremes (including all aspects of time series analysis), as well as studies that explore the applicability of techniques/algorithms to data of different temporal resolutions (annual, monthly, dailyâ¦) and of different climate elements (temperature, precipitation, pressure, wind, etc) from different observing network characteristics/densities, including various satellite observing systems.