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Home » Process orientated metrics of land surface-atmospheric interactions for diagnosing coupled model simulations of land surface hydro-meteorological extremes

Process orientated metrics of land surface-atmospheric interactions for diagnosing coupled model simulations of land surface hydro-meteorological extremes

“Introduction to the Problem: Hydro-meteorological extremes such as droughts and heat waves have enormous impacts on water resources, agriculture, health, energy production and infrastructure. Understanding how these events have varied in the past and how they are expected to change in the future are key to mitigation and adaptation. Land-atmosphere (L-A) interactions and feedbacks are increasingly acknowledged as important processes that contribute to climate variability and can amplify droughts and heat waves through changes in partitioning of surface fluxes and interactions with the atmospheric boundary layer. Climate models are central to understanding future changes, but they continue to show problems in depicting climate extremes and the processes that lead to their development and persistence, despite incremental improvements in model resolution and more comprehensive treatment of physical processes. Their future projections are therefore inherently uncertain, especially as L-A interactions are expected to intensify in the future and play a more important role in modulating these extremes.

Rationale: Given the potential for high impacts of droughts and heat waves, and our general lack of understanding of future changes in these extreme events, there is a pressing need to evaluate coupled models for their representation of surface fluxes and L-A interactions at the process level. We propose to develop and test a suite of process-based metrics to diagnose the coupling and feedbacks between the land and the atmosphere, and apply these to climate models to help identify deficiencies in parameterizations. This has potential to improve our understanding of the contribution of the land to climate variability and its role in amplifying extreme events, as well as to lead to model developments that provide better understanding of past changes and reduction of uncertainties in future projected changes. This work leverages from the PI’s experience and ongoing activities in understanding large-scale variability of the land surface and its feedbacks with climate, particularly changes in extreme events.

Summary of work to be completed: We will evaluate the observational uncertainties in surface climate, hydrology and L-A interactions from it-situ, remote sensing and observationally constrained models, globally, with a focus on the U.S. We will develop and test a suite of process-based diagnostic metrics on the observational data, with a focus on droughts and heat waves. These metrics will be applied to the CMIP5 ensemble for the historical simulation and a set of future scenarios. The metrics will be used to identify deficiencies in coupled model parameterizations, attribute historic and future changes to L-A interactions, and link the robustness of future projections to historic performance.

Relevance to the Competition and NOAA’s long-term climate goal: This work is central to the mission of the Climate Program Office’s MAPP program to “enhance the Nation’s capability to predict variability and change in Earth’s climate system” by focusing on improvement of climate models in the realm of L-A interactions that is not well understood but is increasingly acknowledged as being important. As such it directly adheres to NOAA’s long-term climate goal of adaptation and mitigation, by specifically addressing the goal to “improve scientific understanding of the changing climate system and its impacts” by evaluating coupled climate models at the process level so that past changes can be diagnosed and the uncertainties in future changes evaluated.”

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