EARTHSYSTEMS: Lisbon Doctoral School on Earth System Science, PhD Projects

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 (Fischer et al. 2007) that, despite their limited duration, may have long lasting effects in natural ecosystems. Earth Observations (EO) and the advances in retrieval algorithms brought high spatial and temporal resolution observations of key land-surface characteristics such as the land surface temperature (LST). However, the emergence of EO products has not been fully explored by the weather and climate communities to constrain their models. Most of land surface related EO data is used to validate, but not in data assimilation or to constrain and develop models. A clear example is the LST which, despite its quality, is not assimilated in any global operational numerical weather prediction model. This can be partially due to scientific and technical challenges involved in model optimization and data assimilation in complex models, which can imply redesign of the model structure and high computational costs. In the case of the LST, a striking barrier is the large systematic errors amongst models and EO data on the diurnal range in some biomes (e.g. semi-arid). The primary objective of this work is to address the following question: What is the added value of using EO LST data in model development and data assimilation? The work will be based on two hypotheses:(i) models must represent key processes related to the observed EO variable and (ii) parameter estimation is paramount to constrain the conceptual models. The frontier-research work will use the state-of-the-art European Center for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction model and the EC-EARTH climate model to develop and test the hypotheses presented, together with the newly available Land-SAF EO data.