Community Research and Development Information Service - CORDIS

H2020

UACSURF Report Summary

Project ID: 654492
Funded under: H2020-EU.1.3.2.

Periodic Reporting for period 1 - UACSURF (Understanding atmospheric circulation from a surface perspective)

Reporting period: 2015-04-01 to 2017-03-31

Summary of the context and overall objectives of the project

While the thermodynamic effects of rising concentrations of greenhouse gases in the atmosphere are largely well understood, theoretical and experimental work on the effect of a warming planet on atmospheric dynamics is relatively scarce. This limits our ability to detect, understand and predict the effect of climate change on atmospheric circulation and weather regimes, which cause extreme events ranging from storms over heavy precipitation to draughts or heat waves. The present project aims to make a contribution to advance that understanding by examining the effect of drag on atmospheric circulation and its representation in weather and climate models.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

During the project, climate model sensitivity experiments were run and analysed alongside available model output from standard CMIP5 experiments. Theoretical analyses and work with simplified models served to better understand the link between changes in surface drag and changes in atmospheric circulation. The main results were published in an open acces GRL paper (Climate model biases in jet streams, blocking and storm tracks resulting from missing orographic drag, doi: 10.1002/2016GL069551). The researcher was also involved in preparing a workshop on the effects of drag on large-scale circulation held at the European Center for Medium-Range Weather Forecasts in September 2016.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

We were able to show that missing parameterised low-level drag can explain typical biases in CMIP5 models to a large extent. This shows a potential for impriving these models climatologies in a computationally cheap way (improving model formulations instead of increasing numerical resolution). In some cases, model biased are linked to spread in future projections, which makes such improvemens key for predicting future circulation changes with confidence.

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