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Climate Data Homogenization and Analysis of Climate Variability, Trends and Extremes
Convener: Xiaolan Wang  | Co-Conveners: Rob Roebeling , Petr Stepanek , Enric Aguilar , Cesar Azorin-Molina 
 / Fri, 13 Apr, 13:30–15:00
 / Attendance Fri, 13 Apr, 17:30–19:00

Accurate, homogeneous, and long-term climate data records are indispensable for many aspects of climate research and services. Realistic and reliable assessments of historical climate trends and climate variability are possible with accurate, homogeneous and long-term time series of climate data and their quantified uncertainties. Such climate data are also indispensable for assimilation in a reanalysis, as well as for the calculation of statistics that are needed to define the state of climate and to analyze climate extremes. Unfortunately, many kinds of changes (such as instrument and/or observer changes, changes in station location and/or environment, observing practices, and/or procedures) that took place during data collection period could cause non-climatic changes (artificial shifts) in the data time series. Such shifts could have huge impacts on the results of climate analysis, especially when it concerns climate trend analysis. Therefore, artificial shifts need to be eliminated, as much as possible, from long-term climate data records prior to their application.

The above described factors can influence different essential climate variables, including atmospheric (e.g., temperature, precipitation, wind speed), oceanic (e.g., sea surface temperature), and terrestrial (e.g., albedo, snow cover) variables from in-situ observing networks, satellite observing systems, and climate/earth-system model simulations. Our session calls for contributions that are related to:

· Correction of biases, quality control, homogenization, and validation of essential climate variables data records.

· Development of new datasets and their analysis (spatial and temporal characteristics, particularly of extremes), examining observed trends and variability, as well as studies that explore the applicability of techniques/algorithms to data of different temporal resolutions (annual, monthly, daily, sub-daily).

· Rescue and analysis of centennial meteorological observations, with focus on wind data prior to the 1960s, as a unique source to fill in the gap of knowledge of wind variability over century time-scales and to better understand the observed slowdown (termed “stilling”) of near-surface winds in the last 30-50 years.