It will be important to know when global warming has reached 1.5°C, as this will be a key marker in global policy given the ambition to pursue efforts to limit warming to this level. But how should the temperature increase be defined in this context? The Global Stocktake agreed at COP28 in Dubai noted “global warming of about 1.1 °C” based on the IPCC 6th Assessment Report, but this number applies to the average of 2011-2020 and hence is already out of date. We propose that the metric for current global warming should allow immediate of identification of passing particular levels of global warming, such as 1.5°C, to avoid unnecessary delays in responding to the exceedance. We also propose that the metric should be consistent with the definition of future Global Warming Levels in the IPCC 6th Assessment Report, which uses 20-year means of projected temperature anomalies with an exceedance year defined as the mid-point of the 20-year period. Without this consistency, the apparent time of reaching 1.5°C could differ from the time previously projected by the IPCC merely because of differences in the definition, which could be misinterpreted as indicting that global warming had reached 1.5°C either earlier or later than projected. This could either undermine confidence in projections or misinform discussions on action to address climate change.While various indicators are already in use that provide a more instantaneous measure of global warming, none are consistent with the IPCC definition of future GWLs nor are suitable for use as a baseline for impacts assessments. To address this, we propose a new metric, the Current Global Warming Level (CGWL), which uses a 20-year average over the previous 10 years from observations and the next 10 years from a forecast or projections. Here we compare the CGWL with the various indicators currently in use for quantifying the current level of global mean temperature change, and compare their indications of global temperature change over recent decades and of the current level of global warming. We also compare the year of exceeding past global warming levels of 0.5°C, 1.0°C and 1.2°C for each indicator. We use a combined observational dataset following IPCC methods and process the indicators from this. For each indicator, we explain potential difficulties that could arise from its use to assess when global warming reaches 1.5°C relative to pre-industrial, and explain the rationale for our proposed indicator, the Current Global Warming Level.

It will be important to know when global warming has reached 1.5°C, as this will be a key marker in global policy given the ambition to pursue efforts to limit warming to this level. But how should the temperature increase be defined in this context? The Global Stocktake agreed at COP28 in Dubai noted “global warming of about 1.1 °C” based on the IPCC 6th Assessment Report, but this number applies to the average of 2011-2020 and hence is already out of date. We propose that the metric for current global warming should allow immediate of identification of passing particular levels of global warming, such as 1.5°C, to avoid unnecessary delays in responding to the exceedance. We also propose that the metric should be consistent with the definition of future Global Warming Levels in the IPCC 6th Assessment Report, which uses 20-year means of projected temperature anomalies with an exceedance year defined as the mid-point of the 20-year period. Without this consistency, the apparent time of reaching 1.5°C could differ from the time previously projected by the IPCC merely because of differences in the definition, which could be misinterpreted as indicting that global warming had reached 1.5°C either earlier or later than projected. This could either undermine confidence in projections or misinform discussions on action to address climate change. While various indicators are already in use that provide a more instantaneous measure of global warming, none are consistent with the IPCC definition of future GWLs nor are suitable for use as a baseline for impacts assessments. To address this, we propose a new metric, the Current Global Warming Level (CGWL), which uses a 20-year average over the previous 10 years from observations and the next 10 years from a forecast or projections. Here we compare the CGWL with the various indicators currently in use for quantifying the current level of global mean temperature change, and compare their indications of global temperature change over recent decades and of the current level of global warming. We also compare the year of exceeding past global warming levels of 0.5°C, 1.0°C and 1.2°C for each indicator. We use a combined observational dataset following IPCC methods and process the indicators from this. For each indicator, we explain potential difficulties that could arise from its use to assess when global warming reaches 1.5°C relative to pre-industrial, and explain the rationale for our proposed indicator, the Current Global Warming Level.

Weather and climate forecasting are demanding computational fluid dynamics (CFD) problems - complex, high-resolution, coupled models that are being tested through repeatedly assimilating new observations to produce forecasts within a narrow operational window, every day of the year. Similarly, climate predictions, for example UKCP18 (the UK Climate Predictions 2018), require vast computers to run the weather models into future climate scenarios to provide advice to citizens, private sector and Governments. In this talk I will describe the process of making a weather and climate forecast, and how consistent investment in CFD has led to a consistent improvement in predictive capability. We shall also explore some of the remaining challenges, such as the representation of the moist convection that yields powerful tropical thunderstorms, and then scales up to affecting our weather in the mid latitudes, or the 2d turbulence in the ocean that is so important for the overall ocean circulation, and then the carbon cycle, so important for climate. Finally, I shall introduce some very recent results from AI deep learning emulators of weather models, which point to a major disruption in how operational centres produce weather forecasts, and may well disrupt the whole fluid dynamics endeavour!

The turbulent ocean surface boundary layer is a key part of the climate system affecting both the energy and carbon cycles. Accurately simulating the boundary layer is critical in improving climate model performance, which deeply relies on our understanding of the turbulence in the boundary layer. Turbulent energy sources in the boundary layer are traditionally believed to be dominated by waves, winds and convection. Recently, submesoscale phenomena with spatial scales of 0.1 similar to 10 km at ocean fronts have been shown to also make a contribution. Here, by applying a non-dimensional turbulent kinetic energy budget equation, we show that the submesoscale geostrophic shear production at fronts is a significant turbulent energy source within the ocean boundary layer away from the sea surface. The contribution reaches 34% of the total dissipation in winter and 17% in summer at the mid-depth of the boundary layer, despite its intermittency in space and time. This work indicates fundamental deficiencies in previous conceptions of ocean boundary layer turbulence, and invites a reappraisal of the sampling scale in observations, model resolution and parameterizations, and other consequences of the global energy budget.

The UK is committed to substantially increasing offshore wind capacity in its drive to decarbonise electricity production and achieve net zero. If low wind episodes – or “wind drought” events – occur during high energy demand periods, energy security may be threatened without alternative supply. To ensure resilience of the power system now and in the coming years as offshore wind generation grows, better understanding of the severity, frequency and duration of low wind episodes would be useful. Variability in winds is likely to dominate over trends in the next few decades, and hence having improved information on present day characteristics of wind drought is valuable.Here we focus our attention on the North Sea as a centre of current and planned offshore wind resource for the UK and a number of other European countries, and on the winter season, given the occurrence of weather patterns that risk security of supply. We use a large ensemble of initialised climate model simulations to provide a synthetic but realistic event set that greatly increases the sample size of extreme events compared with that available from reanalysis data, and gives more robust information about their likelihood and properties. Using the basic unit of a week of low winds as the timescale of analysis, we report on the frequency and duration of wind drought events. In addition, we examine the wider conditions associated with wind drought events to investigate what remote factors may contribute to prolonged wind drought.

Assessing global mean temperature rise using the average warming over the previous one or two decades will delay formal recognition of when Earth breaches the Paris agreement’s 1.5 °C guard rail. Here is what’s needed to avoid the wait. Assessing global mean temperature rise using the average warming over the previous one or two decades will delay formal recognition of when Earth breaches the Paris agreement’s 1.5 °C guard rail. Here is what’s needed to avoid the wait.

Current global climate models struggle to represent precipitation and related extreme events, with serious implications for the physical evidence base to support climate actions. A leap to kilometre-scale models could overcome this shortcoming but requires collaboration on an unprecedented scale.

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.

We present results from the first 6 years of this major U.K. government funded project to accelerate and enhance collaborative research and development in climate science, forge a strong strategic partnership between U.K. and Chinese climate scientists, and demonstrate new climate services developed in partnership. The development of novel climate services is described in the context of new modeling and prediction capability, enhanced understanding of climate variability and change, and improved observational datasets. Selected highlights are presented from over 300 peer reviewed studies generated jointly by U.K. and Chinese scientists within this project. We illustrate new observational datasets for Asia and enhanced capability through training workshops on the attribution of climate extremes to anthropogenic forcing. Joint studies on the dynamics and predictability of climate have identified new opportunities for skillful predictions of important aspects of Chinese climate such as East Asian summer monsoon rainfall. In addition, the development of improved modeling capability has led to profound changes in model computer codes and climate model configurations, with demonstrable increases in performance. We also describe the successes and difficulties in bridging the gap between fundamental climate research and the development of novel real-time climate services. Participation of dozens of institutes through subprojects in this program, which is governed by the Met Office Hadley Centre, the China Meteorological Administration, and the Institute of Atmospheric Physics, is creating an important legacy for future collaboration in climate science and services.

Five upper ocean mixed layer models driven by ERA-Interim surface forcing are compared with a year of hydrographic observations of the upper 1000 m, taken at the Porcupine Abyssal Plain observatory site using profiling gliders. All the models reproduce sea surface temperature (SST) fairly well, with annual mean warm biases of 0.11 degrees C (PWP model), 0.24 degrees C (GLS), 0.31 degrees C (TKE), 0.91 degrees C (KPP) and 0.36 degrees C (OSMOSIS). The main exception is that the KPP model has summer SSTs which are higher than the observations by nearly 3 degrees. Mixed layer salinity (MLS) is not reproduced well by the models and the biases are large enough to produce a nontrivial density bias in the Eastern North Atlantic Central Water which forms in this region in winter. All the models develop mixed layers which are too deep in winter, with average winter mixed layer depth (MLD) biases between 160 and 228 m. The high variability in winter MLD is reproduced more successfully by model estimates of the depth of active mixing and/or boundary layer depth than by model MLD based on water column properties. After the spring restratification event, biases in MLD are small and do not appear to be related to the preceding winter biases. There is a very clear relationship between MLD and local wind stress in all models and in the observations during spring and summer, with increased wind speeds leading to deepening mixed layers, but this relationship is not present during autumn and winter. We hypothesize that the deepening of the MLD in autumn is so strongly driven by the annual cycle in surface heat flux that the winds are less significant in the autumn. The surface heat flux drives a diurnal cycle in MLD and SST from March onwards, though this effect is much more significant in the models than in the observations. We are unable to identify one model as definitely better than the others. The only clear differences between the models are KPP's inability to accurately reproduce summer SSTs, and the OSMOSIS model's more accurate reproduction of MLS.

Abstract We describe the approach taken to develop the United Kingdom's first community Earth system model, UKESM1. This is a joint effort involving the Met Office and the Natural Environment Research Council (NERC), representing the U.K. academic community. We document our model development procedure and the subsequent U.K. submission to CMIP6, based on a traceable hierarchy of coupled physical and Earth system models. UKESM1 builds on the well‐established, world‐leading HadGEM models of the physical climate system and incorporates cutting‐edge new representations of aerosols, atmospheric chemistry, terrestrial carbon, and nitrogen cycles and an advanced model of ocean biogeochemistry. A high‐level metric of overall performance shows that both models, HadGEM3‐GC3.1 and UKESM1, perform better than most other CMIP6 models so far submitted for a broad range of variables. We point to much more extensive evaluation performed in other papers in this special issue. The merits of not using any forced climate change simulations within our model development process are discussed. First results from HadGEM3‐GC3.1 and UKESM1 include the emergent climate sensitivity (5.5 and 5.4 K, respectively) which is high relative to the current range of CMIP5 models. The role of cloud microphysics and cloud‐aerosol interactions in driving the climate sensitivity, and the systematic approach taken to understand this role, is highlighted in other papers in this special issue. We place our findings within the broader modeling landscape indicating how our understanding of key processes driving higher sensitivity in the two U.K. models seems to align with results from a number of other CMIP6 models.

Understanding the processes that control the evolution of the ocean surface boundary layer (OSBL) is a prerequisite for obtaining accurate simulations of air–sea fluxes of heat and trace gases. Observations of the rate of dissipation of turbulent kinetic energy (ε), temperature, salinity, current structure, and wave field over a period of 9.5 days in the northeast Atlantic during the Ocean Surface Mixing, Ocean Submesoscale Interaction Study (OSMOSIS) are presented. The focus of this study is a storm that passed over the observational area during this period. The profiles of ε in the OSBL are consistent with profiles from large-eddy simulation (LES) of Langmuir turbulence. In the transition layer (TL), at the base of the OSBL, ε was found to vary periodically at the local inertial frequency. A simple bulk model of the OSBL and a parameterization of shear driven turbulence in the TL are developed. The parameterization of ε is based on assumptions about the momentum balance of the OSBL and shear across the TL. The predicted rate of deepening, heat budget, and the inertial currents in the OSBL were in good agreement with the observations, as is the agreement between the observed value of ε and that predicted using the parameterization. A previous study reported spikes of elevated dissipation related to enhanced wind shear alignment at the base of the OSBL after this storm. The spikes in dissipation are not predicted by this new parameterization, implying that they are not an important source of dissipation during the storm.

Abstract Six recent Langmuir turbulence parameterization schemes and five traditional schemes are implemented in a common single‐column modeling framework and consistently compared. These schemes are tested in scenarios versus matched large eddy simulations, across the globe with realistic forcing (JRA55‐do, WAVEWATCH‐III simulated waves) and initial conditions (Argo), and under realistic conditions as observed at ocean moorings. Traditional non‐Langmuir schemes systematically underpredict large eddy simulation vertical mixing under weak convective forcing, while Langmuir schemes vary in accuracy. Under global, realistic forcing Langmuir schemes produce 6% (−1% to 14% for 90% confidence) or 5.2 m (−0.2 m to 17.4 m for 90% confidence) deeper monthly mean mixed layer depths than their non‐Langmuir counterparts, with the greatest differences in extratropical regions, especially the Southern Ocean in austral summer. Discrepancies among Langmuir schemes are large (15% in mixed layer depth standard deviation over the mean): largest under wave‐driven turbulence with stabilizing buoyancy forcing, next largest under strongly wave‐driven conditions with weak buoyancy forcing, and agreeing during strong convective forcing. Non‐Langmuir schemes disagree with each other to a lesser extent, with a similar ordering. Langmuir discrepancies obscure a cross‐scheme estimate of the Langmuir effect magnitude under realistic forcing, highlighting limited understanding and numerical deficiencies. Maps of the regions and seasons where the greatest discrepancies occur are provided to guide further studies and observations.

The ocean surface boundary layer is a critical interface across which momentum, heat, and trace gases are exchanged between the oceans and atmosphere. Surface processes (winds, waves, and buoyancy forcing) are known to contribute significantly to fluxes within this layer. Recently, studies have suggested that submesoscale processes, which occur at small scales (0.1–10 km, hours to days) and therefore are not yet represented in most ocean models, may play critical roles in these turbulent exchanges. While observational support for such phenomena has been demonstrated in the vicinity of strong current systems and littoral regions, relatively few observations exist in the open‐ocean environment to warrant representation in Earth system models. We use novel observations and simulations to quantify the contributions of surface and submesoscale processes to turbulent kinetic energy (TKE) dissipation in the open‐ocean surface boundary layer. Our observations are derived from moorings in the North Atlantic, December 2012 to April 2013, and are complemented by atmospheric reanalysis. We develop a conceptual framework for dissipation rates due to surface and submesoscale processes. Using this framework and comparing with observed dissipation rates, we find that surface processes dominate TKE dissipation. A parameterization for symmetric instability is consistent with this result. We next employ simulations from an ocean front‐resolving model to reestablish that dissipation due to surface processes exceeds that of submesoscale processes by 1–2 orders of magnitude. Together, these results suggest submesoscale processes do not dramatically modify vertical TKE budgets, though such dynamics may be climatically important owing to their ability to remove energy from the ocean.