The Meteorological Office, abbreviated as the Met Office, is the United Kingdom's national weather service. It is an executive agency and trading fund of the Department for Business, Energy and Industrial Strategy and is led by CEO Penelope Endersby, who took on the role as Chief Executive in December 2018 and is the first woman to do so. The Met Office makes meteorological predictions across all timescales from weather forecasts to climate change.
Phase 2 of the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP2) is a registered model intercomparison project (MIP) of the Coupled Model Intercomparison Project phase 7 (CMIP7). The focus of AerChemMIP2 is the quantification of the atmospheric composition, biogeochemical feedbacks, air quality and climate responses to changes in emissions of chemically reactive gases, aerosol particles, and land use. AerChemMIP2 aims to facilitate a better understanding of their relative contributions to changes in atmospheric composition, radiative forcing, and the climate response and feedbacks from the pre-industrial period to the present day and for projected future emission pathways. Some experiments from the first phase of AerChemMIP are requested in the second phase to track changes in the results of CMIP7 compared to phase six of CMIP. New experiments in AerChemMIP2 open scientific opportunities to address knowledge gaps and persistent uncertainties. Specifically, AerChemMIP2 requests experiments (1) to assess the dependence of effective radiative forcing for aerosols on the fidelity of resolved processes and the simulated base climate, (2) to provide first estimates of forcing for hydrogen and individual volatile organic compounds in the context of CMIP, (3) to enable studies on non-linearity in the Earth system response, (4) to understand the response of wild fires to historical forcings, and (5) to quantify the influence of desert dust increases on climate change. AerChemMIP2 further requests variants of the ScenarioMIP-CMIP7 high-end and overshoot scenarios to quantify future responses to policy implementations for air quality management. Diagnostic requests of AerChemMIP2 are made from CMIP7 core experiments to facilitate offline experiments for chemistry and aerosols. The experimental protocol of AerChemMIP2 presented here closely aligns with the CMIP7 core experimental design, and its other registered MIPs. Selected AerChemMIP2 experiments are performed in the Assessment Fast Track (AFT) of CMIP7. Participation of modelling centres in AerChemMIP2 would help to gain new insights for atmospheric composition and implications for air quality in a warming world with rapidly changing emissions.
We demonstrate that assimilating neural network (NN) predicted surface nitrate leads to a major improvement in phytoplankton short-range (1-5 day) dynamical model forecasts for the Northwest European Shelf (NWES) seas. We show that assimilation of only ocean-colour chlorophyll- in the current Met Office NWES operational system can lead to excess surface nitrate concentrations in the post-spring bloom period and these are a major reason behind some known, fast-growing biases in NWES phytoplankton forecasts during late spring and summer. Assimilating observations of nitrate would potentially help to address this, but NWES nitrate data are typically not available in sufficient abundance to be assimilated effectively. We have therefore used a recently developed and validated NN model predicting surface nitrate concentrations from a range of observable variables and assimilated the NN-predicted nitrate within a research and development version of the Met Office's NWES operational forecasting system. As a result of nitrate assimilation, the phytoplankton five-day forecast skill improves by up to 30%. We show that, although much of this improvement can be achieved by using a weekly nitrate climatology predicted by the NN model, there is a clear advantage in using flow-dependent nitrate data. We discuss the impacts of this improvement on a range of additional eutrophication indicators, such as dissolved inorganic phosphorus and sea-bottom oxygen. We argue that it should be feasible to upgrade this approach to a fully hybrid machine-learning-data assimilation within the near-real-time NWES operational forecasting system.
South America is highly vulnerable to storms and extreme precipitation. Mesoscale convective systems (MCSs), a prevalent storm type in tropical and subtropical South America, can be particularly damaging due to the organized, deep convection that fuels heavy precipitation over wide areas. Here, we track mature-stage MCSs in multiyear convection-permitting regional climate model simulations over South America [the South America Convection-Permitting Regional Climate Model (SA-CPRCM) simulations; Halladay et al. 2023] run by the Met Office to assess the representation of simulated present-day MCSs compared with satellite observations using a cloud-tracking algorithm [Tracking Object-Based Analysis of Clouds (tobac)]. The simulations perform well at capturing the observed MCS climatology, cluding spatial distribution pattern and diurnal and seasonal cycles. However, the simulations overestimate MCS frequency over the Amazon basin by a factor of 1.5 and, with a smaller bias, underestimate MCS frequency over the La Plata basin, likely due to weaker simulated moisture flux from the Amazon to the subtropics by the low-level jet. In general, regional variations in MCS characteristics are correctly simulated, but precipitation-related characteristics show larger model-observed differences. Simulated MCSs overestimate precipitation intensity by a factor of 1.5-2 and underestimate precipitation area by a factor of 2-2.5. This results in an underestimation of the MCS contribution to total rainfall of 20%-30% the model, particularly in subtropical South America. The results from this work highlight the benefits and limitations using the Met Office kilometer-scale climate simulations over South America to simulate MCSs and contribute to broader understanding of modeling challenges in this region. SIGNIFICANCE STATEMENT: The purpose of this study is to assess the representation of large-scale organized convection (specifically mesoscale convective systems) in convection-permitting climate simulations over South America. This is important because these storms frequently generate high-impact weather events such as flooding, hail, and high wind speeds yet are poorly represented in global climate models. Our results provide an evaluation of mesoscale convective system representation over South America in Met Office kilometer-scale simulations and contribute to broader understanding of modeling challenges in the region.
Wildfires can be important drivers of poor air quality. Numerical atmosphere models are routinely used to estimate pollutant concentrations emitted from a wide range of sources, including from wildfires. Such models often take the Eulerian field or Lagrangian particle method for representing mass and transport in the atmosphere. Using the Saddleworth Moor and Winter Hill fires that occurred in North West England in 2018 as a case study, we compared these two methods consistently within the same model framework. We also explored the impact of model spatial resolution on predicted concentrations and health impacts. In the Eulerian simulations, as the horizontal resolution was made finer (from 12 km to 1 km) the horizontal spread of the downwind wildfire pollution decreased substantially, leading a smaller geographical area and population being impacted by the smoke. The estimated number of people exposed to poor air quality due to wildfire from the 1 km Eulerian simulation was 30% lower than from the 12 km Eulerian simulation. A health impact assessment found a similar relative decrease for the estimated excess mortality due to short-term PM2.5 exposure when going from 12 km to 1 km horizontal resolution. Estimated air quality impacts were also found to be sensitive to horizontal resolution for the Lagrangian simulations but to a lesser degree (similar to 10% decrease from 12 km to 1 km). We recommend that model spatial resolution should be considered as a source of uncertainty for wildfire air quality impact assessments, particularly when an Eulerian model is used.
We describe Global Atmosphere 8.0 and Global Land 9.0 (GA8GL9) that are science configurations of the Met Office Unified Model and Joint UK Land Environment Simulator (JULES) land surface model developed for use across weather and climate timescales. GA8GL9 builds upon GA7GL7. It not only consolidates the changes made for the climate branch configuration GA7.1GL7.1 (the atmosphere and land components of the physical model used in HadGEM3-GC3.1, UKESM1 and UKESM1.1 which were all used in the Met Office's CMIP6 submissions) and NWP branch configuration GA7.2GL8.1 (the operational global NWP model at the Met Office between 2019 and 2022), but also includes developments to most areas of the science. Some of the key changes include: prognostic-based entrainment, which adds convective memory and improves precipitation rates and spatial structures; time-smoothed convective increments, which improves the convection-dynamics coupling and greatly reduces the detrimental dynamical effects of convective intermittency; a new riming parametrisation, which increases the amount of supercooled water and hence reduces Southern Ocean biases; and a package of land surface changes, which improves the forecast of near-surface fields and hence removes the need for the aggregate surface tile in NWP applications. Several changes are made that reduce numerical artefacts and improve the numerical stability of the model. The NWP and climate performance of GA8GL9 is evaluated against the previous configuration, GA7GL7. In NWP tests GA8GL9 is shown have reduced errors and improved spatial structure. The mean climate in GA8GL9 is shown to be improved relative to GA7GL7 with notable improvements in the top of atmosphere outgoing shortwave radiation. GA8GL9 is the atmosphere and land component of GC4, and GC4 has been used as the operational global NWP model at the Met Office since May 2022.
