In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. We track twelve key sets of indicators of the state of the climate system, closely following Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment report (AR6) methods, to produce our fourth annual publication. One of the indicators, the Earth's energy imbalance (EEI) provides a crucial integrative measure of the overall heating of the planet and the pace of climate change - this has more than doubled since the 1976-1995 period. A newly added indicator of temperature extremes, the number of days experiencing marine heatwaves, has more than tripled between 1991 and 2025.For the 2016-2025 decade average, observed warming relative to 1850-1900 was 1.26 [1.13 to 1.36] degrees C, of which 1.24 [1.0 to 1.5] degrees C was human-induced. Human-induced warming reached 1.37 degrees C relative to 1850-1900 in the year 2025, increasing at a rate of 0.27 [0.2-0.4] degrees C per decade over 2016-2025. This high rate of warming, which matches the all-time high seen last year in the instrumental record, was caused by a combination of greenhouse gas emissions being at an all-time high of 54.6 +/- 5.5 GtCO2e yr-1 over the last decade (2015-2024), as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that CO2 emission growth is slowing. The continuation of these annual updates could track decreases or increases in the rate of human influence and climatic changes presented here, reflecting the outcomes of societal choices during the critical 2020s decade.The data presented herein can provide a useful reference point for the drafting of the IPCC seventh assessment report. In total, we employ analysis from over 40 global datasets (10.5281/zenodo.20499280, Smith et al., 2026a). Future monitoring of these indicators, such as ocean and satellite measurements of the Earth's energy imbalance, are threatened by geopolitical and public funding decisions. Our ability to consistently track many of the indicators requires the continuity of observation programs and coordination mechanisms, including the Global Climate Observing System (GCOS) program, that enable their effective integration and use.

In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets (published at https://doi.org/10.5281/zenodo.15639576; Smith et al., 2025a) to produce updated estimates for key indicators of the state of the climate system: net emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. This year, we additionally include indicators for sea-level rise and land precipitation change. We follow methods as closely as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One report. The indicators show that human activities are increasing the Earth's energy imbalance and driving faster sea-level rise compared to the AR6 assessment. For the 2015–2024 decade average, observed warming relative to 1850–1900 was 1.24 [1.11 to 1.35] °C, of which 1.22 [1.0 to 1.5] °C was human-induced. The 2024-observed best estimate of global surface temperature (1.52 °C) is well above the best estimate of human-caused warming (1.36 °C). However, the 2024 observed warming can still be regarded as a typical year, considering the human-induced warming level and the state of internal variability associated with the phase of El Niño and Atlantic variability. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.27 [0.2–0.4] °C per decade over 2015–2024. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 53.6±5.2 Gt CO2e yr−1 over the last decade (2014–2023), as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track decreases or increases in the rate of the climatic changes presented here.

Earth’s energy imbalance (EEI) is a fundamental metric of global Earth system change, quantifying the cumulative impact of natural and anthropogenic radiative forcings and feedback. To date, the most precise measurements of EEI change are obtained through radiometric observations at the top of the atmosphere (TOA), while the quantification of EEI absolute magnitude is facilitated through heat inventory analysis, where 90

Abstract Global mean near surface temperature change is the key metric by which our warming climate is monitored and for which international climate policy is set. At the end of each year the Met Office issues a global mean temperature forecast for the coming year. Following on from the new record in 2023, we predict that 2024 will likely (76% chance) be a new record year with a 1‐in‐3 chance of exceeding 1.5°C above pre‐industrial. Whilst a one‐year temporary exceedance of 1.5°C would not constitute a breach of the Paris Agreement target, our forecast highlights how close we are now to this. Our 2024 forecast is primarily driven by the strong warming trend of +0.2°C/decade (1981–2023) and secondly by the lagged warming effect of a strong tropical Pacific El Niño event. We highlight that 2023 itself was significantly warmer than the Met Office DePreSys3 forecast, with much of this additional observed warming coming from the southern hemisphere, the cause of which requires further understanding.

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC). Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open-data, open-science approach, is to make annually updated reliable global climate indicators available in the public domain (https://doi.org/10.5281/zenodo.11388387, Smith et al., 2024a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced. For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023. This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53±5.4 Gt CO2e yr−1 over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for some of the indicators presented here.

Ocean heat content (OHC) is one of the most relevant metrics tracking the current global heating. There-fore, simulated OHC time series are a cornerstone for assessing the scientific performance of Earth system models and global climate models. Here we present a detailed analysis of OHC change in simulations of the historical climate (1850-2014) performed with two pairs of CMIP6 models: U.K. Earth System Model 1 (UKESM1.0) and HadGEM3-GC3.1-LL, and CNRM-ESM2-1 and CNRM-CM6-1. The small number of models enables us to analyze OHC change globally and for individual ocean basins, making use of a novel ensemble of observational products. For the top 700 m of the global ocean, the two CNRM models reproduce the observed OHC change since the 1960s closely. The two U.K. models (UKESM1.0-LL and HadGEM3-GC3.1-LL) compensate a lack of warming in the 0-700 m layer in the 1970s and 1980s with warming below 2000 m. The observed warming between 700 and 2000 m is substantially underestimated by all models. An increased rele-vance for ocean heat uptake in the Atlantic after 1991-suggested by observations-is picked up by the U.K. models but less so by the CNRM models, probably related to an AMOC strengthening in the U.K. models. The regional ocean heat uptake characteristics differ even though all four models share the same ocean component (NEMO ORCA1). Differences in the simulated global, full-depth OHC time series can be attributed to differences in the model's total effective radiative forcing.

Historical in situ ocean temperature profile measurements are important for a wide range of ocean and climate research activities. A large proportion of the profile observations have been recorded using expendable bathythermographs (XBTs), and required bias corrections for use in climate change studies. It is generally accepted that the bias, and therefore bias correction, depends on the type of XBT used. However, poor historical metadata collection practices mean the XBT probe type information is often missing, for 59% of profiles between 1967 and 2000, limiting the development of reliable bias corrections. We develop a process of estimating missing instrument type metadata (the combination of both model and manufacturer) systematically, constructing a machine learning pipeline based on thorough data exploration to inform these choices. The predicted instrument type, where missing, will facilitate improved XBT bias corrections. The new approach improves the accuracy of the XBT type classification compared to previous approaches from a recall value of 0.75–0.94. We also develop an approach to account for the uncertainty associated with metadata assignments using ensembles of decision trees, which could feed into an ensemble approach to creating ocean temperature datasets. We describe the challenges arising from the nature of the dataset in applying standard machine learning techniques to the problem. We have implemented this in a portable, reproducible way using standard data science tools, with a view to these techniques being applied to other similar problems in climate science.

This paper describes a new homogenization algorithm validation methodology, and its use to assess the skill of eight different algorithms, when applied to synthetic daily temperature time series. These algorithms were ACMANT, Climatol (in both daily and monthly configurations), DAP, HOM, MAC‐D, MASH and SpliDHOM. Algorithms were tested on benchmark data replicating daily temperature variability in four regions in North America: Wyoming, the South East, the North East and the South West. These benchmarks contained plausible spatial and temporal correlation, differing station densities and both abrupt and gradual inhomogeneities. Algorithm ability was assessed both according to detection of inhomogeneities and correction of their effects, investigating bias, root‐mean‐square error (RMSE), linear trend recovery, station extremes and variability recovery. Inhomogeneities with a magnitude greater than 1°C were those most commonly detected with smaller inhomogeneities, and those that were not constant over time, proving harder to identify. Regional RMSE was always reduced by all algorithms and regional bias was reduced in over half of the region/scenario pairs. Trend recovery was variable, but the correct sign of regional trends was retained by all algorithms. Areas for future algorithm improvement include working with autocorrelated data and correcting moments higher than the mean. The data are available from https://www.metoffice.gov.uk/hadobs/benchmarks and the validation code from https://github.com/RachelKillick/Daily_benchmarks allowing the extension of this work and its application to new algorithms.

Ocean temperature observations are crucial for a host of climate research and forecasting activities, such as climate monitoring, ocean reanalysis and state estimation, seasonal-to-decadal forecasts, and ocean forecasting. For all of these applications, it is crucial to understand the uncertainty attached to each of the observations, accounting for changes in instrument technology and observing practices over time. Here, we describe the rationale behind the uncertainty specification provided for all in situ ocean temperature observations in the International Quality-controlled Ocean Database (IQuOD) v0.1, a value-added data product served alongside the World Ocean Database (WOD). We collected information from manufacturer specifications and other publications, providing the end user with uncertainty estimates based mainly on instrument type, along with extant auxiliary information such as calibration and collection method. The provision of a consistent set of observation uncertainties will provide a more complete understanding of historical ocean observations used to examine the changing environment. Moving forward, IQuOD will continue to work with the ocean observation, data assimilation and ocean climate communities to further refine uncertainty quantification. We encourage submissions of metadata and information about historical practices to the IQuOD project and WOD.

We present a new version of the Met Office Hadley Centre/Climatic Research Unit global surface temperature data set, HadCRUT5. HadCRUT5 presents monthly average near‐surface temperature anomalies, relative to the 1961–1990 period, on a regular 5° latitude by 5° longitude grid from 1850 to 2018. HadCRUT5 is a combination of sea‐surface temperature (SST) measurements over the ocean from ships and buoys and near‐surface air temperature measurements from weather stations over the land surface. These data have been sourced from updated compilations and the adjustments applied to mitigate the impact of changes in SST measurement methods have been revised. Two variants of HadCRUT5 have been produced for use in different applications. The first represents temperature anomaly data on a grid for locations where measurement data are available. The second, more spatially complete, variant uses a Gaussian process based statistical method to make better use of the available observations, extending temperature anomaly estimates into regions for which the underlying measurements are informative. Each is provided as a 200‐member ensemble accompanied by additional uncertainty information. The combination of revised input data sets and statistical analysis results in greater warming of the global average over the course of the whole record. In recent years, increased warming results from an improved representation of Arctic warming and a better understanding of evolving biases in SST measurements from ships. These updates result in greater consistency with other independent global surface temperature data sets, despite their different approaches to data set construction, and further increase confidence in our understanding of changes seen.

Historical ocean temperature measurements are important in studying climate change due to the high proportion of heat absorbed by the ocean. These measurements come from a variety of sources, including Expendable Bathythermographs (XBTs), which are an important source of such data. Their measurements need bias corrections which are dependent on the type of XBT used, but poor metadata collection practices mean the type is often missing, increasing the measurement uncertainty and thus the uncertainty of the downstream dataset. This talk will describe efforts to fill in missing instrument type metadata using machine learning techniques so better bias corrections can be applied and the uncertainty in ocean temperature datasets reduced. I will describe the challenge arising from the nature of the dataset in applying standard ML techniques to the problem. I will also describe how we have used this project to explore the benefits of different platforms for ML and what open reproducible science looks like for Machine Learning projects.

Ocean heat content is arguably one of the most relevant metrics for tracking global climate change and in particular the current global heating. Because of its enormous heat capacity, the global ocean stores about 93 percent of the excess heat in the Earth System. Time series of global ocean heat content (OHC) closely track Earth’s energy imbalance as observed as the net radiative balance at the top of the atmosphere. For these reasons simulated OHC time series are a cornerstone for assessing the scientific performance of Earth System models (ESM) and global climate models. Here we present a detailed analysis of the OHC change in simulations of the historical climate (20th century up to 2014) performed with four of the current, state-of-the art generation of ESMs and climate models. These four models are UKESM1, HadGEM3-GC3.1-LL, CNRM-ESM2-1 and CNRM-CM6-1. All four share the same ocean component, NEMO3.6 in the shaconemo eORCA1 configuration, and they all take part in CMIP6, the current Phase 6 of the Coupled Model Intercomparison Project. Analysing a small number of models gives us the opportunity to analyse OHC change for the global ocean as well as for individual ocean basins. In addition to the ensemble means, we focus on some individual ensemble members for a more detailed process understanding. For the global ocean, the two CNRM models reproduce the observed OHC change since the 1960s closely, especially in the top 700 m of the ocean. The two UK models (UKESM1 and HadGEM3-GC3.1-LL) do not simulate the observed global ocean warming in the 1970s and 1980s, and they warm too fast after 1991. We analyse how this varied performance across the models relates to the simulated radiative forcing of the atmosphere. All four models show a smaller ocean heat uptake since 1971, and a larger transient climate response (TCR), than the CMIP5 ensemble mean. Close analysis of a few individual ensemble members indicates a dominant role of heat uptake and deep-water formation processes in the Southern Ocean for variability and change in global OHC. Evaluating OHC change in individual ocean basins reveals that the lack of warming in the UK models stems from the Pacific and Indian basins, while in the Atlantic the OHC change 1971-2014 is close to the observed value. Resolving the ocean warming in depth and time shows that regional ocean heat uptake in the North Atlantic plays a substantial role in compensating small warming rates elsewhere. An opposite picture emerges from the CNRM models. Here the simulated OHC change is close to observations in the Pacific and Indian basins, while tending to be too small in the Atlantic, indicating a markedly different role for the Atlantic meridional overturning circulation (AMOC) and cross-equatorial heat transport in these models.

Global near surface temperature anomalies (degC relative to 1961-1990) for 2019. Data are from the HadCRUT.4.6.0.0 dataset (an update of Moriceet al. 2012). Crosses indicate where temperatures exceed the 90th (small crosses) and 98th percentile (large ones) of the 1961-1990 distribution of annual temperatures. Dashes indicate temperature below the 10th (small) and 2nd (large) percentiles.

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org ) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

AbstractEditor’s note: For easy download the posted pdf of the State of the Climate for 2019 is a low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

Observational estimates of global ocean heat content (OHC) change are used to assess Earth’s energy imbalance over the 20th Century. However, intercomparison studies show that the mapping methods used to interpolate sparse ocean temperature profile data are a key source of uncertainty. We present a new approach to assessing OHC mapping methods using ‘synthetic profiles’ generated from a state-of-the-art global climate model simulation. Synthetic profiles have the same sampling characteristics as the historical ocean temperature profile data but are based on model simulation data. Mapping methods ingest these data in the same way as they would real observations, but the resultant mapped fields can be compared to a model simulation ‘truth’. We use this approach to assess two mapping methods that are used routinely for climate monitoring and initialisation of decadal forecasts. The introduction of the Argo network of autonomous profiling floats during the 2000s drives clear improvements in the ability of these methods to reconstruct the variability and spatial structure of OHC changes. At depths below 2000 m, both methods underestimate the magnitude of the simulated ocean warming signal. Temporal variability and trends in OHC are better captured in the better-observed northern hemisphere than in the southern hemisphere. At all depths, the sampling characteristics of the historical data introduces some spurious variability in the estimates of global OHC on sub-annual to multi-annual timescales. However, many of the large scale spatial anomalies, especially in the upper ocean, are successfully reconstructed even with sparse observations from the 1960s, demonstrating the potential to construct historical ocean analyses for assessing decadal predictions. The value of using accurate global covariances for data-poor periods is clearly seen. The results of this ‘proof-of-concept’ study are encouraging for gaining further insights into the capabilities and limitations of different mapping methods and for quantifying uncertainty in global OHC estimates.