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Antarctic sea ice is projected to decrease in response to increasing greenhouse gas concentrations. Limited studies so far have examined the coupled atmosphere–ocean response to Antarctic sea ice loss. Here, we isolate the response to Antarctic sea ice loss in the atmosphere and ocean using bespoke sea ice albedo perturbation experiments with HadGEM3-GC3.1-LL, provide the first detailed examination of the global ocean response, and quantify the importance of atmosphere–ocean coupling, through comparison to uncoupled experiments with prescribed Antarctic sea ice loss. Lower-tropospheric warming and moistening over regions of sea ice loss and the nearby Southern Ocean are simulated in both coupled and uncoupled configurations but are of greater magnitude in the coupled model. A weakening and equatorward shift of the tropospheric westerly jet are simulated in both configurations, but are also larger in the coupled model. Ocean coupling allows the warming response to spread northward, and by poleward atmospheric energy transport, back to the Antarctic interior. Warmer tropical sea surface temperatures enhance atmospheric convection, driving upper-tropospheric warming and triggering atmospheric teleconnections to the extratropics, including a weakened Aleutian low. A 20% reduction in Antarctic Circumpolar Current transport and a weakening of the shallow tropical convergence cell are simulated. Surface waters warm and freshen globally, becoming more stratified and stable in the Southern Ocean, with similar changes, but of lesser magnitude, in the Arctic Ocean, where sea ice declines. Our results suggest that the climate effects of Antarctic sea ice loss stretch from pole to pole and from the heights of the tropical troposphere to the depths of the Southern Ocean.

© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development. The results discussed in this article were supported by the project APPLICATE (727862), funded by the European Union's Horizon 2020 research and innovation programme. PO was additionally supported by the Spanish fellowship RYC-2017-22772. Peer Reviewed Article signat per 29 autors/es: Pablo Ortega (1), Edward W. Blockley (2), Morten Køltzow (3), François Massonnet (4), Irina Sandu (5), Gunilla Svensson (6), Juan C. Acosta Navarro (1), Gabriele Arduini (5), Lauriane Batté (7), Eric Bazile (7), Matthieu Chevallier (8), Rubén Cruz-García (1), Jonathan J. Day (5), Thierry Fichefet (4), Daniela Flocco (9), Mukesh Gupta (4), Kerstin Hartung (6,10), Ed Hawkins (9), Claudia Hinrichs (11), Linus Magnusson (5), Eduardo Moreno-Chamarro (1), Sergio Pérez-Montero (1), Leandro Ponsoni (4), Tido Semmler (11), Doug Smith (2), Jean Sterlin (4), Michael Tjernström (6), Ilona Välisuo (7,12), and Thomas Jung (11,13) // (1) Barcelona Supercomputing Center, Barcelona, Spain | (2) Met Office, Exeter, UK | (3) Norwegian Meteorological Institute, Oslo, Norway | (4) Université catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Louvain-la-Neuve, Belgium | (5) European Centre for Medium-Range Weather Forecasts, Reading, UK | (6) Department of Meteorology, Stockholm University, Stockholm, Sweden | (7) CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France | (8) Météo-France, Toulouse, France | (9) National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, UK. | (10) Now at: Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany | (11) Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany | (12) Now at: Meteorology Unit, Finnish Meteorological Institute, Helsinki, Finland | (13) Department of Physics and Electrical Engineering, University of Bremen, Bremen, Germany

We describe and evaluate historical simulations which use the third Hadley Centre Global Environment Model in the Global Coupled configuration 3.1 (HadGEM3‐GC3.1) and which form part of the UK's contribution to the sixth Coupled Model Intercomparison Project, CMIP6. These simulations, run at two resolutions, respond to historically evolving forcings such as greenhouse gases, aerosols, solar irradiance, volcanic aerosols, land use, and ozone concentrations. We assess the response of the simulations to these historical forcings and compare against the observational record. This includes the evolution of global mean surface temperature, ocean heat content, sea ice extent, ice sheet mass balance, permafrost extent, snow cover, North Atlantic sea surface temperature and circulation, and decadal precipitation. We find that the simulated time evolution of global mean surface temperature broadly follows the observed record but with important quantitative differences which we find are most likely attributable to strong effective radiative forcing from anthropogenic aerosols and a weak pattern of sea surface temperature response in the low to middle latitudes to volcanic eruptions. We also find evidence that anthropogenic aerosol forcings play a role in driving the Atlantic Multidecadal Variability and the Atlantic Meridional Overturning Circulation, which are key features of the North Atlantic ocean. Overall, the model historical simulations show many features in common with the observed record over the period 1850–2014 and so provide a basis for future in‐depth study of recent climate change.

Fully coupled climate models have long shown a wide range of Antarctic sea ice states and evolution over the satellite era. Here, we present a high-level evaluation of Antarctic sea ice in 40 models from the most recent phase of the Coupled Model Intercomparison Project (CMIP6). Many models capture key characteristics of the mean seasonal cycle of sea ice area (SIA), but some simulate implausible historical mean states compared to satellite observations, leading to large intermodel spread. Summer SIA is consistently biased low across the ensemble. Compared to the previous model generation (CMIP5), the intermodel spread in winter and summer SIA has reduced, and the regional distribution of sea ice concentration has improved. Over 1979-2018, many models simulate strong negative trends in SIA concurrently with stronger-than-observed trends in global mean surface temperature (GMST). By the end of the 21st century, models project clear differences in sea ice between forcing scenarios.

An assessment of the risks of a seasonally ice-free Arctic at 1.5 and 2.0 ∘C global warming above pre-industrial levels is undertaken using model simulations with solar radiation management to achieve the desired temperatures. An ensemble of the CMIP5 model HadGEM2-ES uses solar radiation management (SRM) to achieve the desired global mean temperatures. It is found that the risk for a seasonally ice-free Arctic is reduced for a target temperature for global warming of 1.5 ∘C (0.1 %) compared to 2.0 ∘C (42 %), in general agreement with other methodologies. The SRM produced more ice loss, for a specified global temperature, than for CO2 mitigation scenarios, as SRM produces a higher polar amplification.

A new sea ice configuration, GSI8.1, is implemented in the Met Office global coupled configuration HadGEM3-GC3.1 which will be used for all CMIP6 (Coupled Model Intercomparison Project Phase 6) simulations. The inclusion of multi-layer thermodynamics has required a semi-implicit coupling scheme between atmosphere and sea ice to ensure the stability of the solver. Here we describe the sea ice model component and show that the Arctic thickness and extent compare well with observationally based data.

The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Amongst other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific description of these components are in companion papers), and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present-day forcing. The suitability of the configuration for predictabiity on shorter timescales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2-AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2-AO, many aspects of the present-day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extra-tropical variability and top-of-atmosphere & surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking.

This paper presents an investigation of the robustness of correlations between characteristics of Arctic summer cyclones and September Arctic sea ice extent. A cyclone identification and tracking algorithm is run for output from 100-year coupled climate model simulations at two resolutions and for 30 years of reanalysis data, using two different tracking variables (mean sea-level pressure, MSLP; and 850 hPa vorticity) for identification of the cyclones. The influence of the tracking variable, the spatial resolution of the model, and spatial and temporal sampling on the correlations is then explored. We conclude that the correlations obtained depend on all of these factors and that care should be taken when interpreting the results of such analyses. Previous studies of this type have used around 30 years of reanalysis and observational data, analysed with a single tracking variable. Our results therefore cast some doubt on the conclusions drawn in those studies.

Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea ice cover observed over the last few decades, which is projected to continue. The prospect of increased human industrial activity in the region, as well as scientific interest in the predictability of sea ice, provides important motivation for understanding, and improving, the skill of Arctic predictions. Several operational forecasting centres now routinely produce seasonal predictions of sea ice cover using coupled atmosphere-ocean-sea ice models. Although assimilation of sea ice concentration into these systems is commonplace, sea ice thickness observations, being much less mature, are typically not assimilated. However many studies suggest that initialisation of winter sea ice thickness could lead to improved prediction of Arctic summer sea ice. Here, for the first time, we directly assess the impact of winter sea ice thickness initialisation on the skill of seasonal summer forecasts by assimilating CryoSat-2 thickness data into the Met Office's coupled seasonal forecasting system (GloSea). We show a significant improvement in predictive skill of Arctic sea ice extent and ice-edge location for forecasts of September Arctic sea ice made from the beginning of the melt season. The improvements in sea ice cover lead to further improvement of near-surface air temperature and pressure fields across the region. A clear relationship between modelled winter thickness biases and summer extent errors is identified which supports the theory that Arctic winter thickness provides some predictive capability for summer ice extent, and further highlights the importance that modelled winter thickness biases can have on the evolution of forecast errors through the melt season.

An assessment of the risks of a seasonally ice-free arctic at 1.5 and 2.0 °C global warming above pre-industrial is undertaken using model simulations with solar radiation management to achieve the desired temperatures. An ensemble, of the CMIP5 model HadGEM2-ES, is used to reduce the internal variability and produce a probability density function of an ice-free state. It is found that the continuing loss of Arctic sea ice can be halted if the Paris Agreement temperature goal of 1.5 °C is achieved. A comparison with other methodologies and models shows that the result is robust.

Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue. The prospect of increased human industrial activity in the region,as well as scientific interest in the predictability of sea ice,provides important motivation for understanding, and improving, the skill of Arctic predictions. Several operationalforecasting centres now routinely produce seasonal predictions of sea-ice cover using coupled atmosphere–ocean–sea ice models. Although assimilation of sea-ice concentration into these systems is commonplace, sea-ice thickness observations,being much less mature, are typically not assimilated. However, many studies suggest that initialisation of winter sea-ice thickness could lead to improved prediction of Arctic summer sea ice. Here, for the first time, we directly assess the impact of winter sea-ice thickness initialisation on the skill of summer seasonal predictions by assimilating CryoSat-2 thickness data into the Met Office’s coupled seasonal prediction system (GloSea). We show a significant improvementin predictive skill of Arctic sea-ice extent and ice-edge location for forecasts of September Arctic sea ice made from the beginning of the melt season. The improvements in sea-ice cover lead to further improvement of near-surface air temperature and pressure fields across the region. A clear relationship between modelled winter thickness biases and summer extent errors is identified which supports the theory that Arctic winter thickness provides some predictive capability for summer ice extent, and further highlights the importance that modelled winter thickness biases can have on the evolution of forecast errors through the melt season.

Temperature and salinity profiles are being obtained from instrumented marine mammals in near real-time. The mammals, mostly elephant seals, sample to depths of up to 2000 m in high-latitude regions where there are very few other in situ observations. This study investigates the impact of mammal temperature and salinity observations on the UK Met Office's global ocean forecasting model, focussing on impacts in the Southern Ocean region. Three reanalyses were performed, each assimilating a varying amount of seal data whilst holding all other conditions constant. Assimilation of both temperature and salinity profiles from mammals has a detrimental effect on the model's temperature and salinity fields due to a high bias in the salinity values. Restricting assimilation to temperature profiles only has benefits for both temperature and salinity fields in regions of the ocean where seals are sampling, improving root-mean-square error statistics by between 1 and 6% compared to the case where no mammal data were assimilated. In addition, the assimilation of seal temperature profiles alters the mixed-layer depth and repositions and increases gradients of some fronts compared to the no-seals case.

The Forecast Ocean Assimilation Model (FOAM) is an operational ocean analysis and forecast system run daily at the Met Office. FOAM provides modelling capability in both deep ocean and coastal shelf sea regimes using the NEMO (Nucleus for European Modelling of the Ocean) ocean model as its dynamical core. The FOAM Deep Ocean suite produces analyses and 7-day forecasts of ocean tracers, currents and sea ice for the global ocean at 1/4° resolution. Satellite and in situ observations of temperature, salinity, sea level anomaly and sea ice concentration are assimilated by FOAM each day over a 48 h observation window. The FOAM Deep Ocean configurations have recently undergone a major upgrade which has involved the implementation of a new variational, first guess at appropriate time (FGAT) 3D-Var, assimilation scheme (NEMOVAR); coupling to a different, multi-thickness-category, sea ice model (CICE); the use of coordinated ocean-ice reference experiment (CORE) bulk formulae to specify the surface boundary condition; and an increased vertical resolution for the global model. In this paper the new FOAM Deep Ocean system is introduced and details of the recent changes are provided. Results are presented from 2-year reanalysis integrations of the Global FOAM configuration including an assessment of short-range ocean forecast accuracy. Comparisons are made with both the previous FOAM system and a non-assimilative FOAM system. Assessments reveal considerable improvements in the new system to the near-surface ocean and sea ice fields. However there is some degradation to sub-surface tracer fields and in equatorial regions which highlights specific areas upon which to focus future improvements.

This work was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101), the UK Public Weather Service research program, and the European Union Framework 7 SPECS project. Leon Hermanson was funded as part of his Research Fellowship by Willis as part of Willis Research Network (WRN).

Following the 2015 UNFCCC Conference of Parties in Paris there is renewed interest in understanding and avoiding potentially dangerous climate change. The loss of Arctic sea ice is one of the most directly visible aspects of climate change and the question is frequently asked: when can we expect the Arctic to be ice-free in summer? We argue here that this question may not be the most useful one to inform decisions on climate change mitigation or adaptation in the Arctic. The development of a community-wide consensus on a robust definition of "ice-free", may reduce confusion in the community and amongst the public.

Ocean data assimilation systems can take into account time and space scale variations by representing background error covariance functions with more complex shapes than the classical Gaussian function. In particular, the construction of the correlation functions can be improved to give more flexibility. We describe a correlation operator that features high correlations within a short scale and weak correlations within a larger scale. This multiple length scale correlation operator is defined as a linear combination of Whittle–Matern functions with different length scales. The main characteristics of the resulting correlation function are described. In particular, a focus is given on features that might be of interest to determine the parameters of the model: the Daley length scale, the normalised spectrum inflexion point and the kurtosis coefficient. The multiple length scale operator has been implemented in NEMOVAR, a variational ocean data assimilation system. A dual length scale formulation was tested in a one-year reanalysis and compared with a single length scale formulation. The results emphasise the importance of estimating with great care the factors used within the combination. They also demonstrate the potential of the dual length scale formulation, in particular through a decrease of the innovation statistics for salinity profiles. The dual length scale formulation is now operational at the Met Office. Keywords: background error, covariance, correlations, length scales, diffusion equation, recursive filter, kurtosis (Published: 25 February 2016) Citation: Tellus A 2016, 68, 29744, http://dx.doi.org/10.3402/tellusa.v68.29744

Surface currents from 2007–2008 hindcasts of the Forecast Ocean Assimilation Model (FOAM) Global and Indian Ocean models are assessed against observations at 46 global tropical moored buoy array sites. Zonal (u) currents are less challenging to model than meridional flows (v) due to their lower frequency variability. The assimilative global model has reasonable skill for zonal currents but less skill for meridional currents. The assimilative models have higher skill than the corresponding non-assimilative models. A too-strong westward bias of the order of 20cm/s is evident along the equator in all model versionsused in this study. No extra skill is evident in the high resolution (1/12°) regional model compared to the coarser resolution (1/4°) global model.

In this study, the quality of near-surface current forecasts from the FOAM ocean forecasting system is assessed using the trajectories of Lagrangian drifting buoys. A method is presented for deriving pseudo-Eulerian estimates of ocean currents from the positions of Surface Velocity Program drifters and the resulting data are compared to velocities observed by the global tropical moored buoy array. A quantitative analysis of the global FOAM velocities is performed for the period 2007 and 2008 using currents derived from over 3000 unique drifters (providing an average of 650 velocity observations per day). A potential bias is identified in the Southern Ocean which appears to be caused by wind-slip in the drifter dataset as a result of drogue loss. The drifter-derived currents are also used to show how the data assimilation scheme and a recent system upgrade impact upon the quality of FOAM current forecasts.

We investigate some complex solutions a(x,t) of the heterogeneous complex-Ginzburg‐Landau equation @a/@t = [( x) + ix | a| 2 ]a + @ 2 a/@x 2

Operational ocean forecast systems provide routine marine products to an ever-widening community of users and stakeholders. The majority of users need information about the quality and reliability of the products to exploit them fully. Hence, forecast centres have been developing improved methods for evaluating and communicating the quality of their products. Global Ocean Data Assimilation Experiment (GODAE) OceanView, along with the Copernicus European Marine Core Service and other national and international programmes, has facilitated the development of coordinated validation activities among these centres. New metrics, assessing a wider range of ocean parameters, have been defined and implemented in real-time. An overview of recent progress and emerging international standards is presented here.

The RAPID-MOCHA array has observed the Atlantic Meridional overturning circulation (AMOC) at 26.5°N since 2004. During 2009/2010, there was a transient 30% weakening of the AMOC driven by anomalies in geostrophic and Ekman transports. Here, we use simulations based on the Met Office Forecast Ocean Assimilation Model (FOAM) to diagnose the relative importance of atmospheric forcings and internal ocean dynamics in driving the anomalous geostrophic circulation of 2009/10. Data assimilating experiments with FOAM accurately reproduce the mean strength and depth of the AMOC at 26.5°N. In addition, agreement between simulated and observed stream functions in the deep ocean is improved when we calculate the AMOC using a method that approximates the RAPID observations. The main features of the geostrophic circulation anomaly are captured by an ensemble of simulations without data-assimilation. These model results suggest that the atmosphere played a dominant role in driving recent interannual variability of the AMOC.

The ocean plays a key role in modulating the climate\ud of the Earth system (ES). At the present time it is also\ud a major sink both for the carbon dioxide (CO2) released\ud by human activities and for the excess heat driven by the\ud resulting atmospheric greenhouse effect. Understanding the\ud ocean’s role in these processes is critical for model projections\ud of future change and its potential impacts on human\ud societies. A necessary first step in assessing the credibility of\ud such future projections is an evaluation of their performance\ud against the present state of the ocean. Here we use a range\ud of observational fields to validate the physical and biogeochemical\ud performance of the ocean component of UKESM1,\ud a new Earth system model (ESM) for CMIP6 built upon\ud the HadGEM3-GC3.1 physical climate model. Analysis focuses\ud on the realism of the ocean’s physical state and circulation,\ud its key elemental cycles, and its marine productivity.\ud UKESM1 generally performs well across a broad spectrum\ud of properties, but it exhibits a number of notable biases.\ud Physically, these include a global warm bias inherited from\ud model spin-up, excess northern sea ice but insufficient southern\ud sea ice and sluggish interior circulation. Biogeochemical\ud biases found include shallow remineralization of sinking\ud organic matter, excessive iron stress in regions such as\ud the equatorial Pacific, and generally lower surface alkalinity\ud that results in decreased surface and interior dissolved inorganic\ud carbon (DIC) concentrations. The mechanisms driving\ud these biases are explored to identify consequences for the behaviour\ud of UKESM1 under future climate change scenarios\ud and avenues for model improvement. Finally, across key biogeochemical\ud properties, UKESM1 improves in performance\ud relative to its CMIP5 precursor and performs well alongside\ud its fellow members of the CMIP6 ensemble.