Computationally efficient upper atmosphere chemistry schemes for a weather and climate model

The importance of the Mesosphere and Lower Thermosphere (MLT, 50-100 km altitude) region has become increasingly apparent in the recent decade. Both the ionosphere and thermosphere are sensitive to forcing from the lower atmosphere. MLT coupling can also affect stratospheric ozone, through chemical changes linked to energetic particle precipitation and associated transport, and the ozone changes can then affect the evolution of troposphere on seasonal to decadal timescales. Within the MLT domain itself, chemistry plays a very important role. Here, heating from exothermic reactions becomes important (especially during polar night) and must be accounted for to correctly simulate temperatures. Accurate modelling of the MLT, and of coupling above and below, thus requires an accurate modelling of the chemistry in that region.

Models that span the Earth’s surface to the MLT are used for both operational space weather forecasts and climate studies. The chemistry schemes developed for these models reflect the computational constraints of the application. Forecast models (e.g. the Whole Atmosphere Model (WAM)) use simplified chemistry schemes to enable the model to run fast enough to produce forecasts in close to near-real-time. Only relatively few major chemical reactions are sufficient to adequately represent the large rise in temperature in the MLT and so WAM focuses on a self-consistent treatment of a few major species like O, O2 and N2 while using simplified ozone chemistry and omitting minor species. Models used for climate studies (e.g. the Whole Atmosphere Community Climate Model, WACCM), on the other hand, adopt a comprehensive, more expensive chemistry scheme which includes ion chemistry and which enables, for example, studies of climate change from 1850 and of climate impacts associated with long‐term ozone change.

A limitation of this approach is that few, if any, sensitivity studies have been carried out to assess whether the WAM (WACCM) scheme would benefit from an increase (decrease) in complexity and to assess the costs and benefits of such changes in complexity to a range of different model applications. Such a study is a major recommendation from the whole atmospheric commentary paper of Jackson et al. (2019).

Simplified schemes, such as that used in WAM, is one way of reducing computational expense. Another way is to exploit recent large advances in machine learning (ML) methods to open up the possibility of emulating more comprehensive chemistry schemes. This would enable the retention of most of the accuracy and adaptability of the comprehensive schemes but at a fraction of the cost. This approach enables us to perform a whole range of experiments hitherto thought to be too expensive, such as ensemble climate simulations, or very high resolution coupled chemistry climate simulations aimed at better understanding chemistry-dynamics interactions.

The Met Office provides operational space weather alerts and forecasts. A strategic goal of the research programme that supports this service is the development of a coupled Sun-to-Earth modelling system for improved forecast capability. An important part of this coupled system is the development of an extended version of the Unified Model (UM) which extends from the Earth’s surface to the thermosphere and ionosphere. The Met Office have developed a new extended version of the UM with a top boundary near 150 km and recent work has added neutral chemical reactions relevant to the MLT (both the major reactions and sodium chemistry) to the UM scheme. Future work is planned to adapt these changes, and the results from this project, for use in the Next Generation Modelling System, which will replace the UM for Met Office weather and climate use by the mid 2020s.

Objectives
• Examine current UM MLT chemistry (limited range of neutral species, parametrized ion heating) for initial assessment of accuracy compared to the comprehensive WACCM scheme.
• Extend UM scheme where necessary (e.g. adding ion chemistry) to improve its applicability and to provide a reference point for development and testing of new computationally efficient schemes.
• Use simplified versions of either or both of the UM and WACCM comprehensive chemistry schemes to complete cost / benefit analysis of different weather / climate applications.
• Use machine learning methods to develop a new chemistry scheme with much of the accuracy and complexity of the comprehensive schemes but at much reduced cost.