Predicting the mesosphere

What MesoS2D does

MesoS2D uses powerful radars and satellites to study a little-understood layer of the atmosphere (the mesosphere), helping scientists predict how space weather and climate affect our atmosphere from weeks to decades ahead.

Why this matters

To predict how space weather and climate variability affect the whole atmosphere, we need accurate models of every layer.

The mesosphere (about 50–95 km altitude) is the least understood region. It sits at the boundary between the climate system and space weather, making it especially difficult to model.

The mesosphere both influences, and is influenced by, processes from above and below. Atmospheric waves and tides move upward from lower layers, while space weather effects drive changes downward. The mesosphere is tightly coupled with the ionosphere and other regions, so changes in one part can affect the whole system.

Until recently, there were too few observations to improve this. But in the past decade, new satellite missions have created a surge of middle atmosphere data. MesoS2D combines these observations with one of the world’s most advanced whole-atmosphere models to study variability and its drivers.

For climate models, we can already predict variability on short (around 2 weeks) and long (about decadal) scales. But we cannot yet do this in the mesosphere. Currently, prediction skill in the mesosphere is no better than using long-term averages.

Who is involved

MesoS2D is led by UK researchers with international partners. The project brings together expertise in atmospheric science, space weather, and climate modelling.

The team makes use of world-class facilities, including the £50 million EISCAT 3D radar, the most advanced ionospheric radar ever built.

How MesoS2D works

The project combines modern observations and high-resolution models:

• using the highly instrumented region of Scandinavia alongside satellite data to track mesosphere and lower ionosphere variability
• using EISCAT 3D to make unprecedented fine-scale measurements of atmospheric changes
• running high-resolution whole-atmosphere model simulations to identify drivers of variability
• linking observations and models to improve predictability on sub-seasonal to decadal scales

Science objectives

MesoS2D aims to:

• quantify the variability of the mesosphere and its drivers over sub-seasonal to decadal timescales
• use new radar and satellite data to characterise the mesosphere more precisely than ever before
• test how coupled processes (waves, tides, and space weather effects) shape variability
• provide a foundation for improving whole-atmosphere prediction systems

These objectives are a crucial step towards accurate modelling of the mesosphere as part of a fully coupled climate–space weather system.

Planned activities

MesoS2D will use existing and new ground-based observational datasets over Scandinavia to study the atmosphere. Satellite observations add global coverage, while EISCAT 3D enables high-resolution radar measurements of the middle atmosphere.

Alongside this, advanced model simulations help link observations to underlying processes. Together, these activities will provide the first robust pathway to predictability of the mesosphere on sub-seasonal to decadal scales.

The primary objective of MesoS2D is:

To quantify the physical drivers of mesosphere variability at all timescales, opening a
pathway to improved predictability of weather and climate processes in the Earth system.
Our project builds from regional observationally-driven studies to whole-atmospheric dynamical-
science advances via four closely-linked sub-objectives.
We will:

1. Identify and quantify the key physical drivers of observed mesosphere variability, as driven
   by mechanisms above, below and in-situ.
2. Characterize MLTI variability at timescales from hours to decades over Scandinavia, an
   exceptionally well-instrumented testbed region where we will benefit directly from NERC’s
   recent investment in the state-of-the-art EISCAT-3D (E3D) radar.
3. Based on these quantified drivers, develop and implement the improvements needed to
   advance mesosphere predictability in the WACCM global atmospheric model, and provide
   the insight needed to apply this to other models. 
4. Use our observations and new model capabilities to quantify how the mesosphere couples
   to and preconditions dynamical variability in the ionosphere and underlying atmosphere,
   with impacts for GNSS, radio communications, and weather prediction.

By the end of this three-year project, MesoS2D will have produced three key outputs:

1. An integrated physical understanding of the dynamics and chemistry of the mesosphere,
   facilitating the next generation of whole-atmosphere weather and climate modelling.
2. Quantitative constraints on coupling between the neutral and charged atmosphere,
   providing key constraints for enhanced GNSS accuracy and space weather forecasting.
3. Explanations for the drivers of mesosphere variability at both long and short-timescales,
   improving model predictive skill in a very poorly-understood part of the Earth system.

Investigator team:

1. Grant to understand future impacts on atmospheric prediction

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