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Tropical tropopause layer temperature biases and lower stratospheric humidity biases from 27-year N96 and N216 atmosphere/land-only climate simulations of GA6.0 and GA7.0 versus MERRA and ERA-Interim reanalyses (following Hardiman et al., 2015).
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We describe Global Atmosphere 7.0 and Global Land 7.0 (GA7.0/GL7.0), the latest science configurations of the Met Office Unified Model (UM) and the Joint UK Land Environment Simulator (JULES) land surface model developed for use across weather and climate timescales. GA7.0 and GL7.0 include incremental developments and targeted improvements that, b...
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... To this end, prognostic parameterisation for precipitation (PROG) has been incorporated into some state-of-the-art GCMs [11][12][13][14][15] . Employing PROG improves representations of aerosol-cloud interactions 16 , cloud-to-rain conversion 17 , and cloud feedback 18 . ...
Radiative forcing is an essential metric for accurate climate prediction. Clouds are a well-known source of uncertainty, but the radiative effects of precipitation (REP) are poorly understood and excluded from most general circulation models (GCMs). This is because conventional GCMs treat precipitation diagnostically, and thus, are transparent to shortwave and longwave radiation. In this study, we investigated the REP at global and regional scales by employing three sub-models incorporating (1) diagnostic precipitation, (2) prognostic precipitation without REP, and (3) prognostic precipitation with REP. We found that REP alters not only the local thermodynamic profile but also the remote precipitation rate and distribution through changes in atmospheric circulation. The polar surface temperature increases by more than 1 K in the winter when considering REP. The 34 CMIP6 models show systematic differences in Arctic amplification depending on REP, emphasising that GCMs should include REP to improve confidence in simulating atmosphere-ocean-cryosphere interactions.
... This scheme simulates various aerosol species across five lognormal size modes: nucleation mode, soluble Aitken mode, accumulation mode, coarse mode, and insoluble Aitken mode (Mulcahy et al., 2020). Typically, aerosols with a radius of ≥25 nm (Aitken mode) are activated into cloud condensation nuclei (CCN) and cloud droplets (Abdul-Razzak & Ghan, 2000;Walters et al., 2019). A constant cloud water pH of 5.0 is used in the UKESM1, which is important for aqueous-phase chemistry (Turnock et al., 2019). ...
Dimethyl sulfide (DMS) is the largest source of natural sulfur in the atmosphere and undergoes oxidation reactions resulting in gas‐to‐particle conversion to form sulfate aerosol. Climate models typically use independent chemical schemes to simulate these processes, however, the sensitivity of sulfate aerosol to the schemes used by CMIP6 models has not been evaluated. Current climate models offer oversimplified DMS oxidation pathways, adding to the ambiguity surrounding the global sulfur burden. Here, we implemented seven DMS and sulfate chemistry schemes, six of which are from CMIP6 models, in an atmosphere‐only Earth system model. A large spread in aerosol optical depth (AOD) is simulated (0.077), almost twice the magnitude of the pre‐industrial to present‐day increase in AOD. Differences are largely driven by the inclusion of the nighttime DMS oxidation reaction with NO3, and in the number of aqueous phase sulfate reactions. Our analysis identifies the importance of DMS‐sulfate chemistry for simulating aerosols. We suggest that optimizing DMS/sulfur chemistry schemes is crucial for the accurate simulation of sulfate aerosols.
... It is seen in the multi-model mean over several iterations of climate model inter-comparison studies (Tian and Dong, 2020). It is also a pattern of precipitation bias which has been present in several versions of the Met Office global climate model (Walters et al, 2019). ...
The under-representation of cloud formation is a long-standing bias associated with climate simulations. Parameterisation schemes are required to capture cloud processes within current climate models but have known biases. We overcome these biases by embedding a Multi-Output Gaussian Process (MOGP) trained on high resolution Unified Model simulations to represent the variability of temperature and specific humidity within a climate model. A trained MOGP model is coupled in-situ with a simplified Atmospheric General Circulation Model named SPEEDY. The temperature and specific humidity profiles of SPEEDY are perturbed at fixed intervals according to the variability predicted from the MOGP. Ten-year predictions are generated for both control and ML-hybrid models. The hybrid model reduces the global precipitation bias by 18\% and over the tropics by 22\%. To further understand the drivers of these improvements, physical quantities of interest are explored, such as the distribution of lifted index values and the alteration of the Hadley cell. The control and hybrid set-ups are also run in a plus 4K sea-surface temperature experiment to explore the effects of the approach on patterns relating to cloud cover and precipitation in a warmed climate setting.
... In this study, we demonstrate the use of pyPDAF in a coupled data assimilation (CDA) setup. The research on CDA is motivated by the use of coupled earth system models, especially for the coupled atmosphere and ocean simulations (Eyring et al.,60 2016; Walters et al., 2019). Traditionally, each model component is assimilated individually and the state of each model component interacts with the others only in the coupled model forecast. ...
Data assimilation (DA) is an essential component of numerical weather and climate prediction. Efficient implementation of DA benefits both operational prediction and research. Currently, a variety of DA software programs are available. One of the notable DA libraries is the Parallel Data Assimilation Framework (PDAF) designed for ensemble data assimilation. The DA framework is widely used with complex high-dimensional climate models and is applied for research on atmosphere, ocean, sea ice and marine ecosystem modelling, as well as operational ocean forecasting. Meanwhile, there exists increasing need for flexible and efficient DA implementations using Python due to the increasing amount of intermediate complexity models as well as machine learning based models coded in Python. To accommodate for such needs, here, we introduce a Python interface to PDAF, pyPDAF. The Python interface allows for flexible DA system development while retaining the efficient implementation of the core DA algorithms in the Fortran-based PDAF. The ideal use-case of pyPDAF is a DA system where the model integration is independent from the DA program, which reads the model forecast ensemble, produces a model analysis and update the restart files of the model, or a DA system where the model can be used in Python. With implementations of both PDAF and pyPDAF, this study demonstrates the use of pyPDAF and PDAF for coupled data assimilation (CDA) in a coupled atmosphere and ocean model, the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM). Using both weakly and strongly CDA, we demonstrate that pyPDAF allows for the utilisation of Python-based user-supplied functions in the Fortran-based DA framework. We also show that the Python-based user-supplied routine can be a main reason for the slow-down of the DA system based on pyPDAF. Our CDA experiments confirm the benefit of strongly coupled data assimilation compared to the weakly coupled data assimilation. We also demonstrate that the CDA not only improves the instantaneous analysis but also the long-term trend of the coupled dynamical system.
... Idealized aquaplanet simulations are performed using the Global Atmosphere 7.0 configuration of the Met Office Unified Model (Walters et al., 2019) in an atmosphere-only GCM setup with prescribed SSTs. The model employs a semi-implicit, semi-Lagrangian formulation to solve the three-dimensional non-hydrostatic, compressible equations of motion, and utilizes physical parameterizations to represent subgrid-scale processes such as atmospheric radiation, boundary-layer turbulence, convection, clouds, and precipitation. ...
... The model employs a semi-implicit, semi-Lagrangian formulation to solve the three-dimensional non-hydrostatic, compressible equations of motion, and utilizes physical parameterizations to represent subgrid-scale processes such as atmospheric radiation, boundary-layer turbulence, convection, clouds, and precipitation. The model setup employs a single-moment microphysics scheme as described in Walters et al. (2019) that is primarily based on a modified Wilson and Ballard (1999) microphysics scheme. Here, only the mass mixing ratio of the hydrometeor species is determined prognostically, while the number concentration of the respective hydrometeor species is diagnosed. ...
... The last 15 years of simulation data are analyzed, discounting the initial 5 years to account for the spin-up period. More details on the model physics and parameterization schemes can be found in Boutle et al. (2017) and Walters et al. (2019). ...
Tropical high cloud cover decreases with surface warming in most general circulation models. This reduction, according to the “stability‐iris” hypothesis, is thermodynamically controlled and linked to a decrease in the radiatively‐driven clear‐sky convergence, when the peak anvil clouds rise because of the rising isotherms. The influence of the large‐scale dynamical changes on the tropical high cloud fraction remains difficult to disentangle from the local thermodynamic influence, given that the mean meridional circulation remains inextricably tied to the local thermodynamic structure of the atmosphere. However, using idealized general circulation model simulations, we propose a novel method to segregate the dynamical impact from the thermodynamic impact on the tropical high cloud fraction. To this end, our investigation primarily focuses on the mechanisms underpinning changes in the high cloud cover in the deep tropics in response to extratropical surface warming, when the tropical sea surface temperatures remain invariant. Net convective detrainment of ice cloud condensates decreases at the peak detrainment region, without a rise in its altitude. We also find that the importance of depositional growth of ice cloud condensates in controlling the high cloud fraction response in the deep tropics varies with altitude.
... It consists of the Met Office Unified Model (MetUM) atmospheric model in the GA8 configuration, the Joint UK Land Environment Simulator land-surface model in the GL9 configuration, the Nucleus for European Modelling of the Ocean (NEMO) ocean model in the GO6 configuration (Storkey et al., 2018), and the CICE sea-ice model in the GSI8.1 configuration (Ridley et al., 2018). Earlier versions of the atmosphere and land model components were described by Walters et al. (2019), with the following key changes since then: prognostic based entrainment, which adds convective memory and improves precipitation rates and spatial structures; time-smoothed convective increments, which improve the convection-dynamics coupling and greatly reduce the 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 surface changes, which improves the forecast of near-surface winds and removes the need for the aggregate tile in NWP. The atmosphere and land are run in one executable, and the ocean and sea ice are run in a second executable. ...
... However, these are very computationally expensive to run, so the experiments described here use lower resolution versions of N640 (∼20 km) for the deterministic forecasts and N320 (∼40 km) for the ensemble forecasts. The model uses a terrain-following height coordinate, with 70 levels in the vertical, of which 50 are below 18 km (Walters et al., 2019). The ocean and sea-ice model horizontal resolution is 1/4 • (∼25 km) for both deterministic and ensemble components, both operationally and in the experiments described here. ...
Operational data assimilation systems for coupled atmosphere–ocean prediction are usually “weakly coupled”, in which there is no explicit interaction between the atmosphere and ocean within the data assimilation step. Explicitly allowing for cross‐correlations between the ocean and the atmosphere may have potential benefits in improving the consistency of atmosphere and ocean analyses, as well as allowing a better use of observations at the interface. To understand whether such correlations are significant on the time‐scales of numerical weather prediction, we investigate the atmosphere–ocean cross‐correlations of short‐term forecast errors from the Met Office coupled prediction system, considering their temporal and spatial variability. We find that significant correlations exist between atmosphere and ocean forecast errors on these time‐scales, and that these vary diurnally, from day to day, spatially and synoptically. For correlations between errors in the atmospheric wind and ocean temperature, positive correlations in the North Atlantic region are found to be synoptically dependent, with correlation structures extending into the ocean throughout the deep mixed layer, beyond a depth of 100 m. In contrast, negative correlations over the Indian Ocean are very shallow and are associated with the diurnal cycle of solar radiation. The significance and variability of cross‐correlations indicates that there should be a benefit from including them in data assimilation systems, but it will be important to allow for some flow‐dependence in the correlations. Furthermore, the differing vertical extents of the cross‐correlations in different regions implies the need for situation‐dependent localisation of ensemble correlations when including them in coupled data assimilation systems.
... The atmospheric model component is the MetUM. Model science settings for MC12 are as per the Global Atmosphere and Land 7 (GAL7) configuration (Walters et al., 2019) and include a mass flux parametrisation for atmospheric convection based on Gregory and Rowntree (1990). Lateral-boundary conditions are obtained from ERA5 (Hersbach et al., 2020) and update every 6 h. ...
A multi-season convection-permitting regional climate simulation of the Maritime Continent (MC) using the Met Office Unified Model (MetUM) with 2.2 km grid spacing is presented and evaluated. The simulations pioneer the use of atmosphere–ocean coupling with the multi-column K profile parametrisation (KPP) mixed-layer ocean model in atmospheric convection-permitting climate simulations. Comparisons are made against a convection-parametrised simulation in which it is nested and which in turn derives boundary conditions from the ERA5 reanalysis. This paper describes the configuration, performance of the mean state and variability in the two simulations compared against observational datasets. The models have both minor sea surface temperature (SST) and wet precipitation biases. The diurnal cycle, representation of equatorial waves, and relationship between SST and precipitation are all improved in the convection-permitting model compared to the convection-parametrised model. The Madden–Julian oscillation (MJO) is present in both models with a faster-than-observed propagation speed. However, it is unclear whether fidelity of the MJO simulation is inherent to the model or whether it predominantly arises from the forcing at the boundaries.
... CESM2 is described in Danabasoglu et al. (2020) (Best et al., 2011), ocean biogeochemistry (Yool et al., 2013) and sea-ice (Ridley et al., 2018). Aerosols are simulated by the GLOMAP-mode scheme (Mann et al., 2010) using five log-normal modes for sulfate, sea-salt, black and organic carbon, while a sectional scheme is used to simulate mineral dust (Sellar et al., 2019). suggest that a 25Tg/yr/region emission rate would produce a forcing closest to our target "nominal forcing". ...
A modeling protocol is introduced (defined by a series of model simulations with specified model output). The protocol is designed to improve understanding of climate impacts from Marine Cloud Brightening (MCB) Climate Intervention. The model simulations are not intended to assess consequences from a realistic MCB deployment intended to achieve specific climate targets but instead to expose responses produced by MCB interventions in 6 regions with pervasive cloud systems that are often considered as candidate regions for such a deployment. A calibration step involving simulations with fixed sea surface temperatures is first used to identify a common forcing, and then coupled simulations with forcing in individual regions and combinations of regions are used to examine climate impacts. Synthetic estimates constructed by superposing responses from simulations with forcing in individual regions are considered as a means to approximate the climate impacts produced when MCB interventions are introduced in multiple regions. A few results comparing simulations from 3 modern climate models (CESM2, E3SMv2, UKESM1) are used to illustrate similarities and differences between model behavior and the utility of estimates of MCB climate responses that have been synthesized by summing responses introduced in individual regions. There are substantial differences in the cloud responses to aerosol injections between models, but the models often show strong similarities in precipitation and surface temperature response signatures when forcing is imposed with similar amplitudes in common regions.
... The Lagrangian atmospheric dispersion model NAME was used to reconstruct wind trajectories at a high spatial ( 10 km) and temporal (3h) resolution [20], relative to the domain of interest extending across multiple countries. These NAME simulations are driven using historic analysis meteorology data from the global configuration of the Unified Model (UM), the Met Office's operational Numerical Weather Prediction model [25]. NAME wind trajectories were calculated starting from 3860 source locations spaced on a regular 20km × 20km grid across Ethiopia, Somalia, Eritrea and Kenya (Figure 1(A)). ...
Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here we use the AMIS approach to fit a von Misses distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from a 3D meteorological data. The primary objective is to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.
... We have analyzed the results of two separate meteorological models, the IFS and the UM. A detailed description of the two models can be found in the ECMWF CY47R1 IFS (2020) Documentation, and for the UM in Walters et al. (2019). For most of our research, we have considered the forecast data routinely provided by ECMWF and UKMO, with the average horizontal grid-spacing (resolution) detailed in Table 1. ...
We analyze the performance of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and UK Met Office (UKMO) meteorological models in predicting offshore blowing wind in coastal areas. Our attention is mainly on the Mediterranean coast, up to 200 km distance from the shore. We compare forecast neutral winds with Advanced Scatterometer measurements. The results indicate that the ECMWF forecasts systematically underestimate wind speed with respect to scatterometer data, while the UKMO model tends to overestimate. A cross‐analysis suggests that, better than fetch, model biases are a function of the model horizontal discretization, hence of the number of grid steps the wind runs over the sea. The steepness and roughness of the land orography before entering the sea, together with the related drag parameters, appear to have a strong role in determining the coastal offshore wind values. Surface drag over land tends to reduce the related wind speed, and it takes a few grid points to adjust to the smooth sea surface conditions. This is supported by a detailed study with a high resolution grid, but different orography resolutions. In these simulations, practically identical coastal wind speed distributions are found when the subgrid orography schemes are switched off. Vertical cross‐sections of potential temperature and wind in bora and mistral conditions illustrate the strong role of gravity waves, wind channeling and turbulent diffusion in coastal numerical weather prediction. Besides local wave modeling, this may be relevant for offshore wind farms, typically situated within 5–50 km from the coast.
