The Air Resources Laboratory cooperates with NOAA’s Earth System Research Laboratory to measure fluxes of energy, water, and carbon dioxide at the air-land interface to improve understanding of the Earth’s surface energy balance. It is this balance that drives weather, climate, and ocean circulation, and therefore must be accurately reproduced in climate models in order for decision-makers to make sound choices regarding environmental and economic policy. Accurate understanding and simulation of this balance is also important for weather prediction, including short-term and seasonal predictions of water resources.

The Surface Energy Budget Network (SEBN) is a consolidation of several independent but closely related observing systems into a single, cost-effective and efficient network. SEBN seeks to explain why climate variables (e.g., air temperature, precipitation) have changed. Data, which includes the input of moisture and heat to the atmosphere, are used by NOAA scientists to provide detailed examination of the land-surface feedbacks and related radiative processes that can drive regional climate and to improve weather predictions. Currently, NOAA has three SEBN stations in operation to cover representative eco-regions (forests, grasslands, crops, etc) in the U.S.

The SEBN supports NOAA’s mission of providing high-value routine measurements of surface energy, water, and carbon budgets along with other climate variables in regional vegetation systems across the continental United States. The objective of the SEBN is to provide data that will improve predictions of water (precipitation, soil moisture, evapotranspiration) from days to months by focusing on a predictive understanding and response of the land surface to significant climate events for major land-use types in the U.S.  SEBN data are used by NOAA’s National Centers for Environmental Prediction (NCEP) Land Surface Modeling group for testing and evaluation purposes.

Other uses of SEBN data include: (i) improved parameterizations of the land surface model physics that ultimately improves seasonal predictability of water resources; (ii) better understanding of the critical land surface processes that control the seasonal and annual water and carbon budgets for various ecosystem types and the impacts of extreme climatic events on the land surface responses; and (iii) support validation efforts for NOAA’s new water initiative.