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 not 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 need to be eliminated, as much as possible, from time series prior to their application, especially their application in climate trends assessment.
Such factors can influence different climate elements (temperature, precipitation, pressure, wind, etc.) from different observing network characteristics/densities, including various satellite observing systems but we also draw attention to particular issues relating to sub-daily rainfall series. An intensification of short-duration rainfall extremes arising due to climate change could lead to an increase in flash flooding in urban areas and fast-responding catchments. Projections of change from very high resolution climate models are providing new evidence for such increases in a number of areas. However, our understanding of sub-daily rainfall - its drivers and recent and future trends - is limited, primarily through the lack of high quality observations, a lack of understanding of the relevant physical processes and the availability of suitable models and model integrations.
This session calls for contributions that are related to:
· Bias correction and quality control/homogenization of climate data, including bias correction and validation of climate data from satellite observations and from GCM and RCM simulations, including on sub-daily timescales.
· The development and availability of new datasets and their analysis (spatial and temporal characteristics, particularly of extremes) and 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)
· Identify and assess relevant sub-daily indices or generate other derived sub-daily data products (e.g. gridded products or merged products which combine ground-based observations with other types of measurement including remotely sensed data and radar).
· Improve understanding of the drivers of extreme events, including large-scale circulation conditions and local thermodynamics (e.g. using the Clausius-Clapeyron relationship).