Earth system models (ESMs) allow numerical simulations of the Earth's climate system. Driven by the need to better understand climate change and its impacts, these models have become increasingly sophisticated over time, generating vast amounts of data. To effectively evaluate the complex state-of-the-art ESMs and ensure their reliability, new tools for comprehensive analysis are essential. The open-source community-driven Earth System Model Evaluation Tool (ESMValTool) addresses this critical need by providing a software package for scientists to assess the performance of ESMs using common diagnostics and metrics. In this paper, we describe recent significant improvements of ESMValTool's computational efficiency, which allow a more effective evaluation of these complex ESMs and also high-resolution models. These optimizations include parallel computing (execute multiple computation tasks simultaneously), out-of-core computing (process data larger than available memory), and distributed computing (spread computation tasks across multiple interconnected nodes or machines). When comparing the latest ESMValTool version with a previous not yet optimized version, we find significant performance improvements for many relevant applications running on a single node of a high-performance computing (HPC) system, ranging from 2.3 times faster runs in a multi-model setup up to 23 times faster runs for processing a single high-resolution model. By utilizing distributed computing on two nodes of an HPC system, these speedup factors can be further improved to 3.0 and 44, respectively. Moreover, evaluation runs with the latest version of ESMValTool also require significantly less computational resources than before, which in turn reduces power consumption and thus the overall carbon footprint of ESMValTool runs. For example, the previously mentioned use cases use 2.3 (multi-model evaluation) and 23 (high-resolution model evaluation) times less resources compared to the reference version on one HPC node. Finally, analyses which could previously only be performed on machines with large amounts of memory can now be conducted on much smaller hardware through the use of out-of-core computation. For instance, the high-resolution single-model evaluation use case can now be run with 8 GB of available memory despite an input data size of 35 GB, which was not possible with earlier versions of ESMValTool. This enables running much more complex evaluation tasks on a personal laptop than before.

The signal-to-noise paradox that climate models are better at predicting the real world than their own ensemble forecast members highlights a serious and currently unresolved model error, adversely affecting climate predictions and introducing uncertainty into climate projections. By computing the magnitude of feedback between transient eddies and large-scale flow anomalies in multiple seasonal forecast systems, this study shows that current systems underestimate this positive eddy feedback, and that this deficiency is strongly linked to weak signal-to-noise ratios in ensemble mean predictions. Improved eddy feedback is further shown to be linked to greater teleconnection strength between the El Niño Southern Oscillation and the Arctic Oscillation and to stronger predictable signals. We also present a technique to estimate the potential gain in skill that may come from eliminating eddy feedback deficiency, showing that skill could double in some extratropical regions, significantly improving predictions of the Arctic Oscillation.

Abstract Boreal winter (December–February) 2020/2021 in the North Atlantic/European region was characterised by a negative North Atlantic Oscillation (NAO) index. Although this was captured within the ensemble spread of predictions from the Met Office Global Seasonal forecast system (GloSea5), with 17% of ensemble members predicting an NAO less than zero, the forecast ensemble mean was shifted towards a positive NAO phase. The observed monthly NAO anomalies were particularly negative in January and February, following an early January sudden stratospheric warming (SSW), and a prolonged period of Phase 6 or 7 of the Madden Julian Oscillation (MJO) in late January/early February. In contrast, predictions showed the expected teleconnection from the observed La Niña, with a positive NAO signal resulting from a weakening of the Aleutian Low leading to a reduction in tropospheric wave activity, an increase in polar vortex strength and a reduced chance of an SSW. Forecasts initialised later in the winter season successfully predicted the negative NAO in January and February once the SSW and MJO were within the medium range timescale. GloSea5 likely over‐predicted the strength of the La Niña which we estimate caused a small negative bias in the SSW probability. However, this error is smaller than the uncertainty in SSW probability from the finite forecast ensemble size, emphasising the need for large forecast ensembles. This case study also demonstrates the advantage of continuously updated lagged ensemble forecasts over a ‘burst’ ensemble started on a fixed date, since a change in forecast signal due to events within the season can be detected early and promptly communicated to users.

Angular momentum is fundamental to the structure and variability of the atmosphere and hence regional weather and climate. Total atmospheric angular momentum (AAM) is also directly related to the rotation rate of the Earth and hence the length of day. However, the long-range predictability of fluctuations in the length of day, atmospheric angular momentum and the implications for climate prediction are unknown. Here we show that fluctuations in AAM and the length of day are predictable out to more than a year ahead and that this provides an atmospheric source of long-range predictability of surface climate. Using ensemble forecasts from a dynamical climate model we demonstrate predictable signals in the atmospheric angular momentum field that propagate slowly and coherently polewards into the northern and southern hemisphere due to wave-mean flow interaction within the atmosphere. These predictable signals are also shown to precede changes in extratropical surface climate via the North Atlantic Oscillation. These results provide a novel source of long-range predictability of climate from within the atmosphere, greatly extend the lead time for length of day predictions and link geodesy with climate variability.

This study offers an overview of the low-frequency (i.e., monthly to seasonal) evolution, dynamics, predictability, and surface impacts of a rare Southern Hemisphere (SH) stratospheric warming that occurred in austral spring 2019. Between late August and mid-September 2019, the stratospheric circumpolar westerly jet weakened rapidly, and Antarctic stratospheric temperatures rose dramatically. The deceleration of the vortex at 10 hPa was as drastic as that of the first-ever-observed major sudden stratospheric warming in the SH during 2002, while the mean Antarctic warming over the course of spring 2019 broke the previous record of 2002 by ∼50% in the midstratosphere. This event was preceded by a poleward shift of the SH polar night jet in the uppermost stratosphere in early winter, which was then followed by record-strong planetary wave-1 activity propagating upward from the troposphere in August that acted to dramatically weaken the polar vortex throughout the depth of the stratosphere. The weakened vortex winds and elevated temperatures moved downward to the surface from mid-October to December, promoting a record strong swing of the southern annular mode (SAM) to its negative phase. This record-negative SAM appeared to be a primary driver of the extreme hot and dry conditions over subtropical eastern Australia that accompanied the severe wildfires that occurred in late spring 2019. State-of-the-art dynamical seasonal forecast systems skillfully predicted the significant vortex weakening of spring 2019 and subsequent development of negative SAM from as early as late July.

Sudden stratospheric warmings (SSWs) are amongst the most dramatic events in the Earth's atmosphere and they drive extreme surface weather conditions. They have been recently linked to the hot and dry weather conditions that favour wildfires over Australia. However, the chance of a southern hemisphere event is unknown because it has only been observed once. Legitimate estimation of the frequency of SSW events requires a large sample of realistic model simulations. Here we show that the chance of an event is close to 4% per year, implying that an event will occur, on average, every 25 yr, using a state-of-the-art model that simulates SSWs accurately. It is thus not surprising that there was a near miss in the September prior to the Australian wildfire of 2019, given the 40 yr of comprehensive satellite records and just one observed Antarctic event. According to this new estimate, it would also not be surprising to see a second SSW event in the coming years in the southern hemisphere. Such a stratospheric warming event might bring further extreme surface weather conditions and natural hazards, as it may raise the risk of increased rainfall in the latitudinal band of 35-50 degrees S. Meanwhile, the associated hot and dry weather conditions over austral subtropical continents might increase the risk of wildfires over these regions.

We assess the impact of atmospheric horizontal resolution on the prediction skill and fidelity of seasonal forecasts. We show the response to an increase of atmospheric resolution from 0.8 to 0.3° horizontal grid spacing in parallel ensembles of forecasts. Changes in the prediction skill of major modes of tropical El Nino Southern Oscillation (ENSO) and extratropical North Atlantic Oscillation (NAO) variability are small and not detected and there is no discernible impact on the weak signal‐to‐noise ratio in seasonal predictions of the winter NAO at this range of resolutions. Although studies have shown improvements in the simulation of tropical cyclones as model resolution is increased, we find little impact on seasonal prediction skill of either their numbers or intensity. Over this range of resolutions it appears that the benefit of increasing atmospheric resolution to seasonal climate predictions is minimal. However, at yet finer scales there appears to be increased eddy feedback which could strengthen weak signals in predictions of the NAO. Until prediction systems can be run operationally at these scales, it may be better to use additional computing resources for other enhancements such as increased ensemble size, for which there is a clear benefit in extratropical seasonal prediction skill.

Predicting monsoon onset is crucial for agriculture and socioeconomic planning in countries where millions rely on the timely arrival of monsoon rains for their livelihoods. In this study we demonstrate useful skill in predicting year-to-year variations in South China Sea summer monsoon onset at up to a three-month lead time using the GloSea5 seasonal forecasting system. The main source of predictability comes from skillful prediction of Pacific sea surface temperatures associated with El Niño and La Niña. The South China Sea summer monsoon onset is a known indicator of the broadscale seasonal transition that represents the first stage of the onset of the Asian summer monsoon as a whole. Subsequent development of rainfall across East Asia is influenced by subseasonal variability and synoptic events that reduce predictability, but interannual variability in the broadscale monsoon onset for East Asian summer monsoon still provides potentially useful information for users about possible delays or early occurrence of the onset of rainfall over East Asia.

The North Atlantic Oscillation (NAO), the regional manifestation of the Arctic Oscillation (AO), dominates winter climate variability in Europe and North America. Skilful seasonal forecasting of the winter NAO/AO has been demonstrated recently by dynamical prediction systems. However, the role of initial conditions in this predictability remains unknown. Using a latest generation seasonal forecasting system and reanalysis data, we show that the initial upper stratospheric zonal wind anomaly contributes to winter NAO/AO predictability through downward propagation of initial conditions. An initial polar westerly/easterly anomaly in the upper stratosphere propagates down to the troposphere in early winter, favoring a poleward/equatorward shift of the tropospheric mid-latitude jet. This tropospheric anomaly persists well into the late winter and induces the positive/negative phase of NAO/AO in the troposphere. Our results imply that good representation of stratospheric initial condition and stratosphere-troposphere coupling in models is important for winter climate prediction.

We investigate the relationship between the western Pacific subtropical high (WPSH) and tropical cyclone (TC) landfall in the ERA‐Interim reanalysis and two configurations of the UK Met Office Global Seasonal forecasting system version 5 (GloSea5): Global Atmosphere 3.0 (GA3) and Global Coupled configuration 2 (GC2). Both model configurations have the same horizontal and vertical resolution in the ocean and the atmosphere, but differ in terms of model physics. The WPSH strongly modulates TC activity over the subtropical western North Pacific (WNP) and TC landfall over East Asia (Japan, Korea and East China). Here we show that both model configurations GA3 and GC2 show significant skill for predictions of the WPSH and TC variability over the subtropical WNP, as well as TC frequency along the coast of East Asia, during the boreal summer (June–August). An extension of the analysis to include the full WNP typhoon season (June–November) is also examined; however, only a weak significant relationship between the WPSH index and the observed TC frequency over East Asia is found during this period, and no significant relationship is present in either GloSea5 GA3 or GC2. Results highlight the potential for operational seasonal forecasts of TC landfall risk for Japan, Korea and East China over the June–August period using predictions of the WPSH indices from GloSea5.

Seasonal climate forecasts (SCFs) have significant potential to support shorter-term agricultural decisions and longer-term climate adaptation plans, but uptake in Europe has to date been low. Under the European Union funded project, European Provision Of Regional Impacts Assessments on Seasonal and Decadal Timescales (EUPORIAS) we have developed the Land Management Tool (LMTool), a prototype seasonal climate service for land managers, working closely in collaboration with two stakeholder organizations, Clinton Devon Estates (CDE) and the National Farmers Union (NFU). LMTool was one of several prototype climate services selected for development within EUPORIAS, including those for the UK transport network, food security in Ethiopia, renewable energy production, hydroelectric energy production in Sweden, and river management in two French basins. The LMTool provides SCFs (1–3 months ahead) to farmers in the Southwest UK, alongside 14-day site specific weather forecasts during the winter months when the skill of seasonal forecasts is greatest.We describe the processes through which the LMTool was co-designed and developed with the farmers, its technical development and key features; critically examine the lessons learned and their implications for providing future climate services for land managers; and finally assess the feasibility of delivering an operational winter seasonal climate service for UK land managers.A number of key learning points from developing the prototype may benefit future work in climate services for the land management and agriculture sector; many of these points are also valid for climate services in other sectors. Prototype development strongly benefitted from; working with intermediaries to identify representative, engaged land managers; an iterative and flexible process of co-design with the farmer group; and from an interdisciplinary project team. Further work is needed to develop a better understanding of the role of forecast skill in land management decision making, the potential benefits of downscaling and how seasonal forecasts can help support land managers decision-making processes. The prototype would require considerable work to implement a robust operational forecast system, and a longer period to demonstrate the value of the services provided. Finally, the potential for such services to be applied more widely in Europe is not well understood and would require further stakeholder engagement and forecast development.

Abstract The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Among 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 descriptions 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 predictability on shorter time scales (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 extratropical variability and top‐of‐atmosphere and 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.

Skilful climate predictions of the winter North Atlantic Oscillation and Arctic Oscillation out to a few months ahead have recently been demonstrated, but the source of this predictability remains largely unknown. Here we investigate the role of the Tropics in this predictability. We show high levels of skill in tropical rainfall predictions, particularly over the Pacific but also the Indian and Atlantic Ocean basins. Rainfall fluctuations in these regions are associated with clear signatures in tropical and extratropical atmospheric circulation that are approximately symmetric about the Equator in boreal winter. We show how these patterns can be explained as steady poleward propagating linear Rossby waves emanating from just a few key source regions. These wave source ‘hotspots’ become more or less active as tropical rainfall varies from winter to winter but they do not change position. Finally, we show that predicted tropical rainfall explains a highly significant fraction of the predicted year‐to‐year variation of the winter North Atlantic Oscillation.

We present a case study of long range forecasts for Northern Hemisphere winter 2015/2016. This winter produced the strongest El Nino event since 1997/1998 and equatorial Pacific sea surface temperature anomalies exceeded 3 degrees C. Other factors relevant to the Northern Hemisphere extratropical atmosphere circulation included a strong westerly phase of the Quasi- Biennial Oscillation (QBO) and very strong winds in the stratospheric polar night jet in early winter. At the surface, intense cyclonic extratropical circulation anomalies occurred in early winter in both the North Pacific and North Atlantic, consistent with known teleconnections to these phases of ElNino- Southern Oscillation, the QBO and the polar night jet. The midlatitude flow was very westerly in early winter and less westerly and sometimes northerly in late winter, when sudden stratospheric warming events also occurred. We show that initialised climate predictions were able to capture the winter mean flow pattern at seasonal lead times from well before the start of winter. In this special case, not only the winter mean flow pattern, but also some aspects of the sub- seasonal evolution were skilfully predicted. We show that the winter of 1982/1983 was closely analogous to winter 2015/2016 in both the predictable driving factors and the forecast winter circulation. This case study adds to the evidence that the north Atlantic circulation can be predictable on seasonal timescales and advance warning of the increased risk of intense rainfall and storminess which caused extreme flooding in the UK in December was possible in this case.

China suffers from frequent summer floods and droughts, but seasonal forecast skill of corresponding summer rainfall remains a key challenge. In this study, we demonstrate useful levels of prediction skill over the Yangtze river valley for summer rainfall and river flows using a new high resolution forecast system. Further analysis of the sources of predictability suggests that the predictability of Yangtze river valley summer rainfall corresponds to skillful prediction of rainfall in the deep tropics and around the Maritime Continent. The associated dynamical signals favor increased poleward water vapor transport from South China and hence Yangtze river valley summer rainfall and river flow. The predictability and useful level of skill demonstrated by this study imply huge potential for flooding and drought related disaster mitigation and economic benefits for the region based on early warning of extreme climate events.

The population of the Sahel region of West Africa has approximately doubled in the past 50 years, and could potentially double again by the middle of this century. This has led to the northward expansion of agricultural areas at the expense of natural savanna, leading to widespread land use -land cover change (LULCC). Because there is strong evidence of significant surface-atmosphere coupling in this region, one of the main goals of the West African Monsoon Modeling and Evaluation project phase II is to provide basic understanding of LULCC on the regional climate, and to evaluate the sensitivity of the seasonal variability of the West African Monsoon to LULCC. The prescribed LULCC is based on the changes from 1950 through 1990, representing a maximum feasible degradation scenario in the past half century. It is applied to 5 state of the art global climate models (GCMs) over a 6-year simulation period. Multiple GCMs are used because the magnitude of the impact of LULCC depends on model-dependent coupling strength between the surface and the overlying atmosphere, the magnitude of the surface biophysical changes, and how the key processes linking the surface with the atmosphere are parameterized within a particular model framework. Land cover maps and surface parameters may vary widely among models; therefore a special effort was made to impose consistent biogeophysical responses of surface parameters to LULCC using a simple experimental setup. The prescribed LULCC corresponds to degraded vegetation conditions, which mainly cause increases in the Bowen ratio and decreases in the surface net radiation, and result in a significant reduction in surface evaporation (upwards of 1 mm day −1 over a large part of the Sahel). This, in turn, mainly leads to less moisture convergence and precipitation over the LULCC zone. The overall impact is a rainfall reduction with every model, which ranges across models from 4 to 25 % averaged over the Sahel, and a southward shift of the rainfall peak in three of the five models which evokes a precipitation dipole pattern which is consistent with the observed pattern for dry climate anomalies over this region. The African Easterly Jet shifts equator-ward, although the strength of this change varies considerably among the models. In most of the models, the main factor causing diabatic cooling of the upper troposphere and enhanced subsidence over the region of LULCC is the reduction of convective heating rates linked to reduced latent heat flux and moisture flux convergence. In broad agreement with previous studies, the impact of degradation on the regional climate is found to vary among the different models, however, the signal is stronger and more consistent between the models here than in previous inter-comparison projects. This is likely related to our emphasis on prioritizing a consistent impact of LULCC on the surface biophysical properties.