The Global Coupled model version 4 (GC4) is an upgraded configuration of the MetUM system, building upon its predecessor, GC3.0/3.1. It incorporates various improvements and changes in the atmospheric and land components (Global Atmosphere 8 and Global Land 9 - GA8GL9) while keeping the ocean component (Global Ocean 6 - GO6) unchanged, except for minor bug fixes. The GC4 model introduces several enhancements, such as the drag package for land surface and hydrology, improvements in radiation and large-scale precipitation parametrisations, advancements in the boundary layer and convection representation (including the prognostic-based convective entrainment rate - ProgEnt), and updates in aerosol properties. Additionally, the inclusion of a multi-grid solver in the dynamics module aims to improve model stability and reduce computational costs. Key improvements in GC4 include better representation of the diurnal cycle of convection over land, reduced Southern Ocean warm bias, increased rainfall over India during the JJA season, improved distribution of precipitation, enhanced representation of low-medium clouds over Northern Europe, and positive impacts of atmosphere-ocean coupling on NWP scores. However, challenges and areas for further improvement persist, including excessive global precipitation, warm biases over coastal regions of East Asia, wet biases over East Asia, weak cloud forcing over certain regions, hydrological cycle discrepancies, biases in gross primary productivity, persistent Southern Ocean biases, enhanced warming and weakened trade winds in the equatorial east Pacific, excessive surface warming in the North Atlantic, weakening of monsoon low-pressure systems and tropical cyclones, drying over Africa, and excessive thick cloud biases in mid-latitudes. The next version of GC (GC5) will attempt to address some of these biases in the next development and assessment cycle with inputs from relevant evaluation groups and partners.

Gravity-wave (GW) parameterizations from 12 general circulation models (GCMs) participating in the Quasi-Biennial Oscillation initiative (QBOi) are compared with Strateole 2 balloon observations made in the tropical lower stratosphere from November 2019-February 2020 (phase 1) and from October 2021-January 2022 (phase 2). The parameterizations employ the three standard techniques used in GCMs to represent subgrid-scale non-orographic GWs, namely the two globally spectral techniques developed by Warner and McIntyre (1999) and Hines (1997), as well as the "multiwaves" approaches following the work of Lindzen (1981). The input meteorological fields necessary to run the parameterizations offline are extracted from the ERA5 reanalysis and correspond to the meteorological conditions found underneath the balloons. In general, there is fair agreement between amplitudes derived from measurements for waves with periods less than 1$$ 1 $$ h and parameterizations. The correlation between the daily observations and the corresponding results of the parameterization can be around 0.4, which is 99%$$ 99\% $$ significant, since 1200 days of observations are used. Given that the parameterizations have only been tuned to produce a quasi-biennial oscillation (QBO) in the models, the 0.4 correlation coefficient of the GW momentum fluxes is surprisingly good. These correlations nevertheless vary between schemes and depend little on their formulation (globally spectral versus multiwaves for instance). We therefore attribute these correlations to dynamical filtering, which all schemes take into account, whereas only a few relate the gravity waves to their sources. Statistically significant correlations are mostly found for eastward-propagating waves, which may be due to the fact that during both Strateole 2 phases the QBO is easterly at the altitude of the balloon flights. We also found that the probability density functions (pdfs) of the momentum fluxes are represented better in spectral schemes with constant sources than in schemes ("spectral" or "multiwaves") that relate GWs only to their convective sources. Three standard non-orographic gravity-wave (GW) parameterizations tuned to produce a realistic tropical quasi-biennial oscillation in 12 global climate models are used to predict in situ balloon observations. On average, we show that the parameterized GWs needed in large-scale models have realistic amplitudes in the tropical lower stratosphere. We also show that balloon averaged and daily values of GW momentum fluxes can correlate with observations when the parameterized GWs come from the lower and middle troposphere. The probability density distributions can also be reproduced realistically, but a problem arises for parameterizations that try to relate gravity waves only to their convective sources. image

A multi-model study is carried out to investigate the ability of models to predict the evolution of the quasi-biennial oscillation (QBO) up to 12 months in advance. All models are initialised from common reanalysis data, and forecasts run for a common set of 30 start dates over 15 years. All models have high skill in predicting the phase evolution of the QBO at 20-30 hPa, with slightly more variable results at higher and lower levels. Other aspects of the predicted QBO are of variable quality, and in some cases are consistently poor. QBO easterlies are too weak in all models at 20-50 hPa, while westerlies can be either too strong or too weak. This results in both a reduced amplitude of the QBO and a westerly bias in zonal-mean winds, notably at 30 hPa. At 70 hPa models tend to have reduced QBO amplitude and an easterly bias. Despite these failings, a multi-model ensemble of bias- and variance-corrected forecasts can be used to give accurate and reliable QBO forecasts up to at least a year ahead. Analysis of the zonal momentum budget during the first month of the forecast shows that large-scale forcing from Eliassen-Palm flux divergence and vertical advection are handled fairly well by the models, although vertical advection terms tend to be weaker than reanalysis estimates. Total tendencies show common errors, suggesting common failings in gravity-wave drag treatments. Teleconnections from the QBO to Northern Hemisphere winter circulation are also examined, and do not appear to be realistic beyond the first month. Analysis of initialised forecasts is a powerful tool for diagnosing the accuracy of model processes driving the QBO.

We compare the response of the quasi-biennial oscillation (QBO) to a warming climate in eleven atmosphere general circulation models that performed time-slice simulations for present-day, doubled, and quadrupled CO2 climates. No consistency was found among the models for the QBO period response, with the period decreasing by eight months in some models and lengthening by up to thirteen months in others in the doubled CO2 simulations. In the quadruped CO2 simulations a reduction in QBO period of 14 months was found in some models, whereas in several others the tropical oscillation no longer resembled the present day QBO, although could still be identified in the deseasonalized zonal mean zonal wind timeseries. In contrast, all the models projected a decrease in the QBO amplitude in a warmer climate with the largest relative decrease near 60 hPa. In simulations with doubled and quadrupled CO2 the multi-model mean QBO amplitudes decreased by 36\% and 51\%, respectively. Across the models the differences in the QBO period response were most strongly related to how the gravity wave momentum flux entering the stratosphere and tropical vertical residual velocity responded to the increases in CO2 amounts. Likewise it was found that the robust decrease in QBO amplitudes was correlated across the models to changes in vertical residual velocity, parameterized gravity wave momentum fluxes, and to some degree the resolved upward wave flux. We argue that uncertainty in the representation of the parameterized gravity waves is the most likely cause of the spread among the eleven models in the QBO's response to climate change.

Stratospheric water vapor affects the Earth's radiative balance and stratospheric chemistry, yet its future changes are uncertain and not fully understood. The influence of deep convection on stratospheric water vapor remains subject to debate. This letter presents a detailed process‐based model study of the impact of convective ice sublimation on stratospheric water vapor in response to CO 2 forced climate change. The influence of convective injection is found to be limited by the vertical profile of temperature and saturation vapor pressure in the tropical tropopause layer, not by the frequency of occurrence. Lagrangian trajectory analysis shows the relative contributions to stratospheric water vapor from sublimation and large‐scale transport are approximately unchanged when CO 2 is increased. The results indicate the role of convective ice injection for stratospheric water vapor in a warmer climate remains constrained by large‐scale temperatures.

Concentrations of water vapour entering the tropical lower stratosphere are primarily determined by conditions that air parcels encounter as they are transported through the tropical tropopause layer (TTL). Here we quantify the relative roles of variations in TTL temperatures and transport in determining seasonal and interannual variations of stratospheric water vapour. Following previous studies, we use trajectory calculations with the water vapour concentration set by the Lagrangian dry point (LDP) along trajectories. To assess the separate roles of transport and temperatures, the LDP calculations are modified by replacing either the winds or the temperatures with those from different years to investigate the wind or temperature sensitivity of water vapour to interannual variations and, correspondingly, with those from different months to investigate the wind or temperature sensitivity to seasonal variations. Both ERA-Interim reanalysis data for the 1999–2009 period and data generated by a chemistry–climate model (UM-UKCA) are investigated. Variations in temperatures, rather than transport, dominate interannual variability, typically explaining more than 70 % of variability, including individual events such as the 2000 stratospheric water vapour drop. Similarly seasonal variation of temperatures, rather than transport, is shown to be the dominant driver of the annual cycle in lower stratospheric water vapour concentrations in both the model and reanalysis, but it is also shown that seasonal variation of transport plays an important role in reducing the seasonal cycle maximum (reducing the annual range by about 30 %). The quantitative role in dehydration of sub-seasonal and sub-monthly Eulerian temperature variability is also examined by using time-filtered temperature fields in the trajectory calculations. Sub-monthly temperature variability reduces annual mean water vapour concentrations by 40 % in the reanalysis calculation and 30 % in the model calculation. As with other aspects of dehydration, simple Eulerian measures of variability are not sufficient to quantify the implications for dehydration, and the Lagrangian sampling of the variability must be taken into account. These results indicate that, whilst capturing seasonal and interannual variation of temperature is a major factor in modelling realistic stratospheric water vapour concentrations, simulation of seasonal variation of transport and of sub-seasonal and sub-monthly temperature variability are also important and cannot be ignored.

The Quasi‐biennial Oscillation (QBO) dominates the interannual variability of the tropical stratosphere and influences other regions of the atmosphere. The high predictability of the QBO implies that its teleconnections could lead to increased skill of seasonal and decadal forecasts provided the relevant mechanisms are accurately represented in models. Here modelling and sampling uncertainties of QBO teleconnections are examined using a multi‐model ensemble of QBO‐resolving atmospheric general circulation models that have carried out a set of coordinated experiments as part of the Stratosphere‐troposphere Processes And their Role in Climate (SPARC) QBO initiative (QBOi). During Northern Hemisphere winter, the stratospheric polar vortex in most of these models strengthens when the QBO near 50 hPa is westerly and weakens when it is easterly, consistent with, but weaker than, the observed response. These weak responses are likely due to model errors, such as systematically weak QBO amplitudes near 50 hPa, affecting the teleconnection. The teleconnection to the North Atlantic Oscillation is less well captured overall, but of similar strength to the observed signal in the few models that do show it. The models do not show clear evidence of a QBO teleconnection to the Northern Hemisphere Pacific‐sector subtropical jet.

Many of the current, Coupled Model Intercomparison Project 6 (CMIP6), General Circulation Models (GCMs) show climate sensitivity higher than currently accepted uncertainty ranges. There is a weak correlation between increases in vertical resolution and in climate sensitivity from CMIP5. In particular, the MO Hadley Centre GCM’s vertical resolution has more than doubled, and its climate sensitivity has also increased substantially. We therefore compare estimates of climate sensitivity from the CMIP6 model HadGEM3‐GC3.1‐LL, with 85 levels, and a version with 242 levels. This is far higher resolution than in any previously published simulations with a mainstream GCM, though still less than many scales important for clouds. The climate sensitivity and feedbacks, including the cloud feedback, change little. This suggests that vertical resolution did not drive recent increases in GCM climate sensitivity, though this result should be further tested in other GCMs and over a broader range of resolutions.

We analyze the stratospheric waves in models participating in phase 1 of the Stratosphere–troposphere Processes And their Role in Climate (SPARC) Quasi-Biennial Oscillation initiative (QBOi). All models have robust Kelvin and mixed Rossby-gravity wave modes in winds and temperatures at and represent them better than most of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. There is still some spread among the models, especially concerning the mixed Rossby-gravity waves. We attribute the variability in equatorial waves among the QBOi models in part to the varying horizontal and vertical resolutions, to systematic biases in zonal winds, and to the considerable variability in convectively coupled waves in the troposphere among the models: only roughly half of the QBOi models have realistic convectively coupled Kelvin waves and only a few models have convectively coupled mixed Rossby-gravity waves. The models with stronger convectively coupled waves produce larger zonal mean forcing due to resolved waves in the QBO region. Finally we evaluate the Eliassen-Palm (EP) flux and EP flux divergence of the resolved waves in the QBOi models. We find that there is a large spread in the forcing from resolved waves in the QBO region, and the resolved wave forcing has a robust correlation with model vertical resolution

The Coupled Model Intercomparison Project 6 protocol suggests prescribing preindustrial ozone concentrations in abrupt‐4xCO2 simulations. This leads to a mismatch between the thermal tropopause, which rises due to climate change, and the ozone tropopause, which remains fixed. The result is unphysically high ozone concentrations in the upper troposphere, leading to a warm bias in cold point temperature and increased stratospheric water vapor. In the U.K. physical climate model HadGEM3‐GC3.1 this increases the surface climate sensitivity. In the future, other climate models without interactive ozone schemes may face similar problems. We describe a method to interactively redistribute ozone in climate simulations, which removes the inconsistency between the thermal and ozone tropopause heights while retaining the prescribed ozone distribution as closely as possible. This removes unphysical consequences of the tropopause mismatch, while still allowing a fair comparison against other Coupled Model Intercomparison Project 6 model simulations. After each model year, the monthly mean, zonal mean, thermal tropopause is formed based on the previous two model years. The ozone tropopause is defined at 1 km below the thermal tropopause by setting ozone concentrations there to 80 ppbv, and smoothing appropriately. The mass of ozone removed from the troposphere is added to the stratosphere thus conserving the total mass of ozone. This redistribution is then applied proportionally to the 3‐D monthly mean ozone concentrations. The climate model is run for the following year, using this redistributed ozone, and then the whole process is repeated. Results are presented from preindustrial and abrupt‐4xCO2 simulations, but this method can be used for any climate simulation.

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, between them, address four critical errors identified in previous configurations: excessive precipitation biases over India, warm and moist biases in the tropical tropopause layer (TTL), a source of energy non-conservation in the advection scheme and excessive surface radiation biases over the Southern Ocean. They also include two new parametrisations, namely the UK Chemistry and Aerosol (UKCA) GLOMAP-mode (Global Model of Aerosol Processes) aerosol scheme and the JULES multi-layer snow scheme, which improve the fidelity of the simulation and were required for inclusion in the Global Atmosphere/Global Land configurations ahead of the 6th Coupled Model Intercomparison Project (CMIP6). In addition, we describe the GA7.1 branch configuration, which reduces an overly negative anthropogenic aerosol effective radiative forcing (ERF) in GA7.0 whilst maintaining the quality of simulations of the present-day climate. GA7.1/GL7.0 will form the physical atmosphere/land component in the HadGEM3–GC3.1 and UKESM1 climate model submissions to the CMIP6.

The Stratosphere–troposphere Processes And their Role in Climate (SPARC) Quasi-Biennial Oscillation initiative (QBOi) aims to improve the fidelity of tropical stratospheric variability in general circulation and Earth system models by conducting coordinated numerical experiments and analysis. In the equatorial stratosphere, the QBO is the most conspicuous mode of variability. Five coordinated experiments have therefore been designed to (i) evaluate and compare the verisimilitude of modelled QBOs under present-day conditions, (ii) identify robustness (or alternatively the spread and uncertainty) in the simulated QBO response to commonly imposed changes in model climate forcings (e.g. a doubling of CO2 amounts), and (iii) examine model dependence of QBO predictability. This paper documents these experiments and the recommended output diagnostics. The rationale behind the experimental design and choice of diagnostics is presented. To facilitate scientific interpretation of the results in other planned QBOi studies, consistent descriptions of the models performing each experiment set are given, with those aspects particularly relevant for simulating the QBO tabulated for easy comparison.

A systematic warm bias in the tropical tropopause layer (TTL) is commonly found in both climate and numerical weather prediction models. In this study, the nature of this temperature bias is examined by integrating the MetOffice Unified Model (MetUM) with various ozone concentrations in the TTL. Like other models, the long‐term integration of MetUM with the Atmospheric Model Intercomparison Project (AMIP) configuration shows a notable warm bias (∼2 K) in the TTL with a comparable cold bias in the tropical stratosphere above ∼70 hPa. We demonstrate that these biases are particularly sensitive to the tropical ozone concentration prescribed in the model. By replacing the background ozone, which is typically used for AMIP‐type simulations, with the Southern Hemisphere ADditional OZonesondes (SHADOZ) measurements or the Binary Data Base of Profiles (BDBP), the dipolar temperature biases in the TTL and tropical stratosphere are significantly reduced. Further sensitivity tests show that the tropical ozone amount in a 14–20 km layer is a key contributor to this change. These results suggest that accurate ozone forcing in the TTL is crucial for reliable weather and climate simulations.

We describe Global Atmosphere 6.0 and Global Land 6.0 (GA6.0/GL6.0): the latest science configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) land surface model developed for use across all timescales. Global Atmosphere 6.0 includes the ENDGame (Even Newer Dynamics for General atmospheric modelling of the environment) dynamical core, which significantly increases mid-latitude variability improving a known model bias. Alongside developments of the model's physical parametrisations, ENDGame also increases variability in the tropics, which leads to an improved representation of tropical cyclones and other tropical phenomena. Further developments of the atmospheric and land surface parametrisations improve other aspects of model performance, including the forecasting of surface weather phenomena. We also describe GA6.1/GL6.1, which includes a small number of long-standing differences from our main trunk configurations that we continue to require for operational global weather prediction. Since July 2014, GA6.1/GL6.1 has been used by the Met Office for operational global numerical weather prediction, whilst GA6.0/GL6.0 was implemented in its remaining global prediction systems over the following year.

Level set L85(50 t ,35 s ) 85 &VERTLEVS z t o p o f m o d e l = 8 5 0 0 0 .0 0 , f i r s t c o n s t a n t r r h o l e v e l = 5 1 , e t a t h e t a= .0 0 0 0 0 0 0 E+00 , 0 . 2 3 5 2 9 4 1 E-03 , 0 .6 2 7 4 5 1 0 E-03 , 0 . 1 1 7 6 4 7 1 E-02 , 0 . 1 8 8 2 3 5 3 E-02 , . 2 7 4 5 0 9 8 E-02 , 0 .3 7 6 4 7 0 6 E-02 , 0 .4 9 4 1 1 7 6 E-02 , 0 .6 2 7 4 5 1 0 E-02 , 0 .7 7 6 4 7 0 5 E-02 , .9 4 1 1 7 6 4 E-02 , 0 . 1 1 2 1 5 6 9 E-01 , 0 . 1 3 1 7 6 4 7 E-01 , 0 . 1 5 2 9 4 1 2 E-01 , 0 . 1 7 5 6 8 6 3 E-01 , . 2 0 0 0 0 0 0 E-01 , 0 . 2 2 5 8 8 2 3 E-01 , 0 . 2 5 3 3 3 3 3 E-01 , 0 . 2 8 2 3 5 2 9 E-01 , 0 .3 1 2 9 4 1 1 E-01 , . 3 4 5 0 9 8 0 E-01 , 0 .3 7 8 8 2 3 5 E-01 , 0 .4 1 4 1 1 7 6 E-01 , 0 .4 5 0 9 8 0 4 E-01 , 0 .4 8 9 4 1 1 8 E-01 , .5 2 9 4 1 1 7 E-01 , 0 .5 7 0 9 8 0 4 E-01 , 0 .6 1 4 1 1 7 6 E-01 , 0 .6 5 8 8 2 3 5 E-01 , 0 .7 0 5 0 9 8 0 E-01 , .7 5 2 9 4 1 1 E-01 , 0 .8 0 2 3 5 2 9 E-01 , 0 .8 5 3 3 3 3 3 E-01 , 0 .9 0 5 8 8 2 3 E-01 , 0 .9 6 0 0 0 0 1 E-01 , . 1 0 1 5 6 8 7 E+00 , 0 . 1 0 7 2 9 4 2 E+00 , 0 . 1 1 3 1 7 6 7 E+00 , 0 . 1 1 9 2 1 6 1 E+00 , 0 . 1 2 5 4 1 2 7 E+00 , . 1 3 1 7 6 6 6 E+00 , 0 . 1 3 8 2 7 8 1 E+00 , 0 . 1 4 4 9 4 7 6 E+00 , 0 . 1 5 1 7 7 5 7 E+00 , 0 . 1 5 8 7 6 3 3 E+00 , . 1 6 5 9 1 1 5 E+00 , 0 . 1 7 3 2 2 2 1 E+00 , 0 . 1 8 0 6 9 6 9 E+00 , 0 . 1 8 8 3 3 9 0 E+00 , 0 . 1 9 6 1 5 1 8 E+00 , . 2 0 4 1 4 0 0 E+00 , 0 . 2 1 2 3 0 9 3 E+00 , 0 . 2 2 0 6 6 7 1 E+00 , 0 . 2 2 9 2 2 2 2 E+00 , 0 . 2 3 7 9 8 5 6 E+00 , . 2 4 6 9 7 0 9 E+00 , 0 . 2 5 6 1 9 4 2 E+00 , 0 . 2 6 5 6 7 5 2 E+00 , 0 . 2 7 5 4 3 7 2 E+00 , 0 . 2 8 5 5 0 8 0 E+00 , . 2 9 5 9 2 0 3 E+00 , 0 .3 0 6 7 1 2 8 E+00 , 0 .3 1 7 9 3 0 7 E+00 , 0 .3 2 9 6 2 6 6 E+00 , 0 .3 4 1 8 6 1 5 E+00 , . 3 5 4 7 0 6 1 E+00 , 0 .3 6 8 2 4 1 6 E+00 , 0 .3 8 2 5 6 1 3 E+00 , 0 .3 9 7 7 7 1 7 E+00 , 0 .4 1 3 9 9 4 4 E+00 , . 4 3 1 3 6 7 5 E+00 , 0 .4 5 0 0 4 7 4 E+00 , 0 .4 7 0 2 1 0 9 E+00 , 0 .4 9 2 0 5 7 1 E+00 , 0 .5 1 5 8 0 9 8 E+00 , .5 4 1 7 2 0 1 E+00 , 0 .5 7 0 0 6 8 6 E+00 , 0 .6 0 1 1 6 8 8 E+00 , 0 .6 3 5 3 6 9 7 E+00 , 0 .6 7 3 0 5 9 0 E+00 , .7 1 4 6 6 7 1 E+00 , 0 .7 6 0 6 7 0 1 E+00 , 0 .8 1 1 5 9 4 4 E+00 , 0 .8 6 8 0 2 0 8 E+00 , 0 .9 3 0 5 8 8 4 E+00 , . 1 0 0 0 0 0 0 E+01 , e t a r h o= . 1 1 7 6 4 7 1 E-03 , 0 .4 3 1 3 7 2 6 E-03 , 0 .9 0 1 9 6 0 8 E-03 , 0 . 1 5 2 9 4 1 2 E-02 , 0 . 2 3 1 3 7 2 5 E-02 , . 3 2 5 4 9 0 2 E-02 , 0 .4 3 5 2 9 4 1 E-02 , 0 .5 6 0 7 8 4 3 E-02 , 0 .7 0 1 9 6 0 7 E-02 , 0 .8 5 8 8 2 3 5 E-02 , . 1 0 3 1 3 7 3 E-01 , 0 . 1 2 1 9 6 0 8 E-01 , 0 . 1 4 2 3 5 2 9 E-01 , 0 . 1 6 4 3 1 3 7 E-01 , 0 . 1 8 7 8 4 3 1 E-01 , . 2 1 2 9 4 1 2 E-01 , 0 . 2 3 9 6 0 7 8 E-01 , 0 . 2 6 7 8 4 3 1 E-01 , 0 . 2 9 7 6 4 7 0 E-01 , 0 .3 2 9 0 1 9 6 E-01 , . 3 6 1 9 6 0 8 E-01 , 0 .3 9 6 4 7 0 6 E-01 , 0 .4 3 2 5 4 9 0 E-01 , 0 .4 7 0 1 9 6 0 E-01 , 0 .5 0 9 4 1 1 8 E-01 , .5 5 0 1 9 6 1 E-01 , 0 .5 9 2 5 4 9 0 E-01 , 0 .6 3 6 4 7 0 5 E-01 , 0 .6 8 1 9 6 0 7 E-01 , 0 .7 2 9 0 1 9 6 E-01 , .7 7 7 6 4 7 0 E-01 , 0 .8 2 7 8 4 3 1 E-01 , 0 .8 7 9 6 0 7 8 E-01 , 0 .9 3 2 9 4 1 2 E-01 , 0 .9 8 7 8 4 3 3 E-01 , . 1 0 4 4 3 1 4 E+00 , 0 . 1 1 0 2 3 5 4 E+00 , 0 . 1 1 6 1 9 6 4 E+00 , 0 . 1 2 2 3 1 4 4 E+00 , 0 . 1 2 8 5 8 9 7 E+00 , . 1 3 5 0 2 2 4 E+00 , 0 . 1 4 1 6 1 2 8 E+00 , 0 . 1 4 8 3 6 1 6 E+00 , 0 . 1 5 5 2 6 9 5 E+00 , 0 . 1 6 2 3 3 7 4 E+00 , . 1 6 9 5 6 6 8 E+00 , 0 . 1 7 6 9 5 9 5 E+00 , 0 . 1 8 4 5 1 8 0 E+00 , 0 . 1 9 2 2 4 5 4 E+00 , 0 . 2 0 0 1 4 5 9 E+00 , . 2 0 8 2 2 4 7 E+00 , 0 . 2 1 6 4 8 8 2 E+00 , 0 . 2 2 4 9 4 4 6 E+00 , 0 . 2 3 3 6 0 3 9 E+00 , 0 . 2 4 2 4 7 8 3 E+00 , . 2 5 1 5 8 2 6 E+00 , 0 . 2 6 0 9 3 4 7 E+00 , 0 . 2 7 0 5 5 6 2 E+00 , 0 . 2 8 0 4 7 2 6 E+00 , 0 . 2 9 0 7 1 4 1 E+00 , . 3 0 1 3 1 6 6 E+00 , 0 .3 1 2 3 2 1 8 E+00 , 0 .3 2 3 7 7 8 7 E+00 , 0 .3 3 5 7 4 4 1 E+00 , 0 .3 4 8 2 8 3 8 E+00 , . 3 6 1 4 7 3 9 E+00 , 0 .3 7 5 4 0 1 4 E+00 , 0 .3 9 0 1 6 6 5 E+00 , 0 .4 0 5 8 8 3 1 E+00 , 0 .4 2 2 6 8 1 0 E+00 , . 4 4 0 7 0 7 5 E+00 , 0 .4 6 0 1 2 9 2 E+00 , 0 .4 8 1 1 3 4 0 E+00 , 0 .5 0 3 9 3 3 4 E+00 , 0 .5 2 8 7 6 4 9 E+00 ,

Abstract. We describe Global Atmosphere 6.0 and Global Land 6.0: the latest science configurations of the Met Office Unified Model and JULES land surface model developed for use across all timescales. Global Atmosphere 6.0 includes the ENDGame dynamical core, which significantly increases mid-latitude variability improving a known model bias. Alongside developments of the model’s physical parametrisations, ENDGame also increases variability in the tropics, which leads to an improved representation of tropical cyclones and other tropical phenomena. Further developments of the atmospheric and land surface parametrisations improve other aspects of model performance, including the forecasting of surface weather phenomena. We also describe Global Atmosphere 6.1 and Global Land 6.1, which include a small number of long-standing differences from our main trunk configurations that we continue to require for operational global weather prediction. Since July 2014, GA6.1/GL6.1 has been used by the Met Office for operational global NWP, whilst GA6.0/GL6.0 was implemented in its remaining global prediction systems over the following year.

&VERTLEVSz t o p o f m o d e l = 8 5 0 0 0 .0 0 , f i r s t c o n s t a n t r r h o l e v e l = 5 1 , e t a t h e t a= 0 .0 0 0 0 0 0 E+00 , 0 . 2 3 5 2 9 4 1 E-03 , 0 .6 2 7 4 5 1 0 E-03 , 0 . 1 1 7 6 4 7 1 E-02 , 0 . 1 8 8 2 3 5 3 E-02 , 0 . 2 7 4 5 0 9 E-02 , 0 .3 7 6 4 7 0 6 E-02 , 0 .4 9 4 1 1 7 6 E-02 , 0 .6 2 7 4 5 1 0 E-02 , 0 .7 7 6 4 7 0 5 E-02 , 0 .9 4 1 1 7 6 E-02 , 0 . 1 1 2 1 5 6 9 E-01 , 0 . 1 3 1 7 6 4 7 E-01 , 0 . 1 5 2 9 4 1 2 E-01 , 0 . 1 7 5 6 8 6 3 E-01 , 0 . 2 0 0 0 0 0 E-01 , 0 . 2 2 5 8 8 2 3 E-01 , 0 . 2 5 3 3 3 3 3 E-01 , 0 . 2 8 2 3 5 2 9 E-01 , 0 .3 1 2 9 4 1 1 E-01 , 0 .3 4 5 0 9 8 E-01 , 0 .3 7 8 8 2 3 5 E-01 , 0 .4 1 4 1 1 7 6 E-01 , 0 .4 5 0 9 8 0 4 E-01 , 0 .4 8 9 4 1 1 8 E-01 , 0 .5 2 9 4 1 1 E-01 , 0 .5 7 0 9 8 0 4 E-01 , 0 .6 1 4 1 1 7 6 E-01 , 0 .6 5 8 8 2 3 5 E-01 , 0 .7 0 5 0 9 8 0 E-01 , 0 .7 5 2 9 4 1 E-01 , 0 .8 0 2 3 5 2 9 E-01 , 0 .8 5 3 3 3 3 3 E-01 , 0 .9 0 5 8 8 2 3 E-01 , 0 .9 6 0 0 0 0 1 E-01 , 0 . 1 0 1 5 6 8 E+00 , 0 . 1 0 7 2 9 4 2 E+00 , 0 . 1 1 3 1 7 6 7 E+00 , 0 . 1 1 9 2 1 6 1 E+00 , 0 . 1 2 5 4 1 2 7 E+00 , 0 . 1 3 1 7 6 6 E+00 , 0 . 1 3 8 2 7 8 1 E+00 , 0 . 1 4 4 9 4 7 6 E+00 , 0 . 1 5 1 7 7 5 7 E+00 , 0 . 1 5 8 7 6 3 3 E+00 , 0 . 1 6 5 9 1 1 E+00 , 0 . 1 7 3 2 2 2 1 E+00 , 0 . 1 8 0 6 9 6 9 E+00 , 0 . 1 8 8 3 3 9 0 E+00 , 0 . 1 9 6 1 5 1 8 E+00 , 0 . 2 0 4 1 4 0 E+00 , 0 . 2 1 2 3 0 9 3 E+00 , 0 . 2 2 0 6 6 7 1 E+00 , 0 . 2 2 9 2 2 2 2 E+00 , 0 . 2 3 7 9 8 5 6 E+00 , 0 . 2 4 6 9 7 0 E+00 , 0 . 2 5 6 1 9 4 2 E+00 , 0 . 2 6 5 6 7 5 2 E+00 , 0 . 2 7 5 4 3 7 2 E+00 , 0 . 2 8 5 5 0 8 0 E+00 , 0 . 2 9 5 9 2 0 E+00 , 0 .3 0 6 7 1 2 8 E+00 , 0 .3 1 7 9 3 0 7 E+00 , 0 .3 2 9 6 2 6 6 E+00 , 0 .3 4 1 8 6 1 5 E+00 , 0 .3 5 4 7 0 6 E+00 , 0 .3 6 8 2 4 1 6 E+00 , 0 .3 8 2 5 6 1 3 E+00 , 0 .3 9 7 7 7 1 7 E+00 , 0 .4 1 3 9 9 4 4 E+00 , 0 .4 3 1 3 6 7 E+00 , 0 .4 5 0 0 4 7 4 E+00 , 0 .4 7 0 2 1 0 9 E+00 , 0 .4 9 2 0 5 7 1 E+00 , 0 .5 1 5 8 0 9 8 E+00 , 0 .5 4 1 7 2 0 E+00 , 0 .5 7 0 0 6 8 6 E+00 , 0 .6 0 1 1 6 8 8 E+00 , 0 .6 3 5 3 6 9 7 E+00 , 0 .6 7 3 0 5 9 0 E+00 , 0 .7 1 4 6 6 7 E+00 , 0 .7 6 0 6 7 0 1 E+00 , 0 .8 1 1 5 9 4 4 E+00 , 0 .8 6 8 0 2 0 8 E+00 , 0 .9 3 0 5 8 8 4 E+00 , 0 . 1 0 0 0 0 0 E+01 , e t a r h o= 0 . 1 1 7 6 4 7 E-03 , 0 .4 3 1 3 7 2 6 E-03 , 0 .9 0 1 9 6 0 8 E-03 , 0 . 1 5 2 9 4 1 2 E-02 , 0 . 2 3 1 3 7 2 5 E-02 , 0 .3 2 5 4 9 0 E-02 , 0 .4 3 5 2 9 4 1 E-02 , 0 .5 6 0 7 8 4 3 E-02 , 0 .7 0 1 9 6 0 7 E-02 , 0 .8 5 8 8 2 3 5 E-02 , 0 . 1 0 3 1 3 7 E-01 , 0 . 1 2 1 9 6 0 8 E-01 , 0 . 1 4 2 3 5 2 9 E-01 , 0 . 1 6 4 3 1 3 7 E-01 , 0 . 1 8 7 8 4 3 1 E-01 , 0 . 2 1 2 9 4 1 E-01 , 0 . 2 3 9 6 0 7 8 E-01 , 0 . 2 6 7 8 4 3 1 E-01 , 0 . 2 9 7 6 4 7 0 E-01 , 0 .3 2 9 0 1 9 6 E-01 , 0 .3 6 1 9 6 0 E-01 , 0 .3 9 6 4 7 0 6 E-01 , 0 .4 3 2 5 4 9 0 E-01 , 0 .4 7 0 1 9 6 0 E-01 , 0 .5 0 9 4 1 1 8 E-01 , 0 .5 5 0 1 9 6 E-01 , 0 .5 9 2 5 4 9 0 E-01 , 0 .6 3 6 4 7 0 5 E-01 , 0 .6 8 1 9 6 0 7 E-01 , 0 .7 2 9 0 1 9 6 E-01 , 0 .7 7 7 6 4 7 E-01 , 0 .8 2 7 8 4 3 1 E-01 , 0 .8 7 9 6 0 7 8 E-01 , 0 .9 3 2 9 4 1 2 E-01 , 0 .9 8 7 8 4 3 3 E-01 , 0 . 1 0 4 4 3 1 E+00 , 0 . 1 1 0 2 3 5 4 E+00 , 0 . 1 1 6 1 9 6 4 E+00 , 0 . 1 2 2 3 1 4 4 E+00 , 0 . 1 2 8 5 8 9 7 E+00 , 0 . 1 3 5 0 2 2 E+00 , 0 . 1 4 1 6 1 2 8 E+00 , 0 . 1 4 8 3 6 1 6 E+00 , 0 . 1 5 5 2 6 9 5 E+00 , 0 . 1 6 2 3 3 7 4 E+00 , 0 . 1 6 9 5 6 6 E+00 , 0 . 1 7 6 9 5 9 5 E+00 , 0 . 1 8 4 5 1 8 0 E+00 , 0 . 1 9 2 2 4 5 4 E+00 , 0 . 2 0 0 1 4 5 9 E+00 , 0 . 2 0 8 2 2 4 E+00 , 0 . 2 1 6 4 8 8 2 E+00 , 0 . 2 2 4 9 4 4 6 E+00 , 0 . 2 3 3 6 0 3 9 E+00 , 0 . 2 4 2 4 7 8 3 E+00 , 0 . 2 5 1 5 8 2 E+00 , 0 . 2 6 0 9 3 4 7 E+00 , 0 . 2 7 0 5 5 6 2 E+00 , 0 . 2 8 0 4 7 2 6 E+00 , 0 . 2 9 0 7 1 4 1 E+00 , 0 .3 0 1 3 1 6 E+00 , 0 .3 1 2 3 2 1 8 E+00 , 0 .3 2 3 7 7 8 7 E+00 , 0 .3 3 5 7 4 4 1 E+00 , 0 .3 4 8 2 8 3 8 E+00 , 0 .3 6 1 4 7 3 E+00 , 0 .3 7 5 4 0 1 4 E+00 , 0 .3 9 0 1 6 6 5 E+00 , 0 .4 0 5 8 8 3 1 E+00 , 0 .4 2 2 6 8 1 0 E+00 , 0 .4 4 0 7 0 7 E+00 , 0 .4 6 0 1 2 9 2 E+00 , 0 .4 8 1 1 3 4 0 E+00 , 0 .5 0 3 9 3 3 4 E+00 , 0 .5 2 8 7 6 4 9 E+00 , 0 .5 5 5 8 9 4 E+00 , 0 .5 8 5 6 1 8 7 E+00 , 0 .6 1 8 2 6 9 3 E+00 , 0 .6 5 4 2 1 4 4 E+00 , 0 .6 9 3 8 6 3 0 E+00 , 0 .7 3 7 6 6 8 E+00 , 0 .7 8 6 1 3 2 3 E+00 , 0 .8 3 9 8 0 7 5 E+00 , 0 .8 9 9 3 0 4 6 E+00 , 0 .9 6 5 2 9 4 2 E+00 , / 1 . 1 4 0 0 2 9 3 E-01 , 1 .2 2 7 7 8 4 4 E-01 , 1 .3 1 8 7 9 2 3 E-01 , 1 .4 1 3 0 4 9 0 E-01 , 1 .5 1 0 5 5 6 E-01 , 1 .6 1 1 3 1 5 5 E-01 , 1 .7 1 5 3 2 5 1 E-01 , 1 .8 2 2 5 8 7 4 E-01 , 1 .9 3 3 1 1 2 4 E-01 , 2 .0 4 6 9 1 1 E-01 , 2 . 1 6 4 0 1 4 6 E-01 , 2 . 2 8 4 4 6 8 0 E-01 , 2 .4 0 8 3 4 3 7 E-01 , 2 .5 3 5 7 4 7 3 E-01 , 2 .6 6 6 8 3 4 E-01 , 2 .8 0 1 8 1 6 8 E-01 , 2 .9 4 0 9 8 2 0 E-01 , 3 .0 8 4 7 0 8 2 E-01 , 3 .2 3 3 4 8 7 6 E-01 , 3 .3 8 7 9 4 3 E-01 , 3 .5 4 8 8 6 4 4 E-01 , 3 .7 1 7 2 2 5 2 E-01 , 3 .8 9 4 2 2 3 3 E-01 , 4 .0 8 1 3 1 3 3 E-01 , 4 .2 8 0 2 4 2 E-01 , 4 .4 9 3 1 0 3 9 E-01 , 4 .7 2 2 3 7 3 8 E-01 , 4 .9 7 0 9 7 2 5 E-01 , 5 . 2 4 2 3 0 9 0 E-01 , 5 .5 4 0 3 5 3 E-01 , 5 .8 6 9 6 9 7 3 E-01 , 6 . 2 3 5 6 2 9 9 E-01 , 6 .6 4 4 2 0 4 9 E-01 , 7 .1 0 2 3 3 8 7 E-01 , 7 .6 1 7 8 8 4 E-01 , 8 . 1 9 9 7 3 4 7 E-01 , 8 .8 5 7 9 1 7 8 E-01 , 9 .6 0

A warm bias in tropical tropopause temperature is found in the Met Office Unified Model (MetUM), in common with most models from phase 5 of CMIP (CMIP5). Key dynamical, microphysical, and radiative processes influencing the tropical tropopause temperature and lower-stratospheric water vapor concentrations in climate models are investigated using the MetUM. A series of sensitivity experiments are run to separate the effects of vertical advection, ice optical and microphysical properties, convection, cirrus clouds, and atmospheric composition on simulated tropopause temperature and lower-stratospheric water vapor concentrations in the tropics. The numerical accuracy of the vertical advection, determined in the MetUM by the choice of interpolation and conservation schemes used, is found to be particularly important. Microphysical and radiative processes are found to influence stratospheric water vapor both through modifying the tropical tropopause temperature and through modifying upper-tropospheric water vapor concentrations, allowing more water vapor to be advected into the stratosphere. The representation of any of the processes discussed can act to significantly reduce biases in tropical tropopause temperature and stratospheric water vapor in a physical way, thereby improving climate simulations.