Processes occurring at the land surface are known to have an impact in weather and climate variability in a wide range of timescales, from hours to millennia, making land surface models a required component of both weather and climate prediction systems. Of special relevance is the role of land surface processes involving vegetation and soil water in the amplification of extreme weather and climate anomalies, such as the extreme hot summers in Europe in 2003 and 2010 episodes that, despite their limited duration, may have long lasting effects in natural ecosystems.
CONTROL is based on two hypotheses:(i) the models need to represent key processes related to the observed EO variable and (ii) parameter estimation is necessary and can be used to constrain the conceptual models. CONTROL aims at using EO data to constrain current weather forecasting and climate models at the source of their land-atmosphere exchanges. This will be achieved via (i) detailed evaluation of the model at different spatial and temporal scales using in-situ observations, (ii) model changes in key processes related with thermal and hydrological exchanges, (iii) dual state-parameter data assimilation of the EO data and (iv) a range of modelling setups from surface only point simulations to coupled weather forecasts and climate projections.
CONTROL will provide a new insight in the use of EO data to constrain land-surface processes while proposing an outreach plan to increase public awareness on the science behind weather forecasts and climate projections.
Science and Technology Foundation.
Project leader: Emanuel Dutra (IDL-FCUL).
Partners: IDL, IPMA and FCUL-cE3c.