Timothy J. Osborn

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their partitioning among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, supporting the development of climate policies, and project future climate change. Here we describe and synthesise datasets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy and cement production data. Emissions from land-use change (ELUC) are estimated by bookkeeping models based on land-use data. The global atmospheric CO2 growth rate (GATM) is computed from changes in concentration measured at surface stations. The global net uptake of CO2 by the ocean (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based fCO2-products. The global net uptake of CO2 by the land (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence are provided by atmospheric inversions, atmospheric oxygen measurements, ocean interior observation-based estimates, and Earth System Models. The sum of all sources and sinks results in the carbon budget imbalance (BIM), a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2025, EFOS increased by 1.2% relative to 2024, with fossil emissions at 10.5 ± 0.5 GtC yr-1 (including the cement carbonation sink, 0.2 GtC yr-1), ELUC was 1.2 ± 0.7 GtC yr-1, for total anthropogenic CO2 emissions of 11.7 ± 0.9 GtC yr-1 (42.8 ± 3.2 GtCO2 yr-1). Also, for 2025, GATM was 4.4 ± 0.2 GtC yr-1 (2.06 ± 0.1 ppm yr-1), 3.6 GtC lower than the record high 2024 growth rate. SOCEAN was 3.2 ± 0.7 GtC yr-1 and SLAND was 3.6 ± 0.9 GtC yr-1, with a small BIM (0.5 GtC yr-1). The global atmospheric CO2 concentration averaged over 2025 reached 425.6 ± 0.1 ppm. Preliminary data for 2026 suggest a decrease in EFOS relative to 2025 of 0.40% (–1.5% to +0.6%) globally, and atmospheric CO2 concentration increasing by 2.2 ppm reaching 427.7 ppm, 54% above the pre-industrial level (around 278 ppm in 1750). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2025, with a small positive overall budget imbalance, (0.6 GtC yr-1 averaged over the period 1959–2025), although discrepancies of up to around 1 GtC yr-1 persist for the representation of annual to decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows: (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different estimates on the magnitude of the land CO2 flux, particularly in the northern extra-tropics, and (3) a discrepancy between the different estimates on the mean ocean sink and its trend. This living data update documents changes in methods and datasets applied to this most-recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2026 (Friedlingstein et al., 2026b).

The South Pacific Convergence Zone (SPCZ) is the dominant perennial rainfall feature of the Southern Hemisphere, yet the physical mechanisms driving its variability on decadal to multidecadal time‐scales are poorly understood. Using prescribed sea‐surface temperature (SST) perturbations in the atmosphere‐only Intermediate Global Circulation Model version 4 (IGCM4), we investigate the direct atmospheric mechanisms by which three major modes of low‐frequency climate variability—the Interdecadal Pacific Oscillation (IPO), Atlantic Multidecadal Variability (AMV), and Southern Ocean SST‐driven midlatitude jet shifts—modulate the South Pacific hydroclimate. IPO forcing produces the most substantial and spatially coherent SPCZ response: a positive IPO anomaly drives a northeastward shift in the SPCZ, while a negative IPO shifts the SPCZ southwestward. This behaviour arises from coupled dynamic and thermodynamic changes, with anomalous moisture convergence—rather than altered Rossby‐wave refraction—emerging as the dominant control on SPCZ position. By contrast, AMV‐forced atmospheric teleconnections exert only weak and statistically insignificant impacts on South Pacific precipitation; any apparent signal is best interpreted as an alias of IPO‐like SST anomalies in the Pacific. Southern Ocean SST anomalies induce significant shifts in the Southern Hemisphere midlatitude jet and associated Hadley–Ferrel cell structure, but these changes do not generate a coherent SPCZ displacement. Instead, precipitation changes reflect large‐scale regions of anomalous ascent and descent, driven by Hadley and Ferrel cell shifts, rather than modifications to SPCZ dynamics.

The UKCP18 Convection-permitting model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localised storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we apply and evaluate a quantile mapping (QM) bias correction method for UKCP18-CPM hourly precipitation (with diurnal correction) and daily temperature over England. We quantify how closely the bias corrected simulations align with observations relative to the raw simulations. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from −0.87 to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R2 values for the 95th percentile of hourly precipitation from 0.80–0.88 to 0.98 and achieving near-perfect alignment (R2 = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1 %–39.1 % and mean daily temperature by 3.1 to 4.5 °C, highlighting the potential for more intense climate-related events. Overall, the applied bias-correction method brings UKCP18-CPM simulations into closer agreement with observations for both mean behaviour and extremes, providing a more reliable basis for high-resolution impact modelling and assessments that require hourly precipitation forcing.

Understanding how precipitation responds to temperature change is crucial for anticipating future climate variability. The geological past provides a unique window into rainfall responses to large-scale climate shifts, yet regional responses in the South–Central Pacific remain poorly constrained due to the lack of continuous long-term palaeoclimate records extending beyond the Holocene. To address this gap, we reconstruct temperature and precipitation using biomarker proxies from a 50,000-year peat core from Nuku Hiva, French Polynesia. During the Last Glacial period, increased hydrogen isotopic values of plant waxes indicate drier conditions. Pollen data show increased abundances of drought-adapted herbaceous taxa and reduced cloud forest species. Temperature-sensitive bacterial lipids indicate substantially cooler glacial conditions relative to the Holocene. Notably, reconstructed temperature and precipitation changes are strongly correlated, consistent with data assimilation studies. These results highlight the sensitivity of the tropical Pacific to background climate state and provide important constraints on regional climate dynamics.

The South Pacific Convergence Zone (SPCZ) is the dominant perennial rainfall feature of the Southern Hemisphere, yet the physical mechanisms driving its variability on decadal to multi-decadal timescales remain poorly constrained. Using prescribed sea-surface temperature (SST) perturbations in the atmosphere-only IGCM4 model, we investigate how three major modes of low-frequency climate variability – the Inter-decadal Pacific Oscillation (IPO), Atlantic Multi-decadal Variability (AMV), and Southern Ocean SST-driven mid-latitude jet shifts – modulate South Pacific hydroclimate. IPO forcing produces the most substantial and spatially coherent SPCZ response: a positive (negative) IPO anomaly drives a north-eastward (south-westward) shift in the SPCZ. This behaviour arises from coupled dynamic and thermodynamic dynamic changes, with anomalous moisture convergence – rather than altered Rossby wave refraction – emerging as the dominant control on SPCZ position. By contrast, AMV-forced atmospheric tele-connections exert only weak and statistically insignificant impacts on South Pacific precipitation; any apparent signal is best interpreted as an alias of IPO-like SST anomalies in the Pacific. Southern Ocean SST anomalies induce significant shifts in the Southern Hemisphere mid-latitude jet and associated Hadley–Ferrel cell structure, but these changes do not generate a coherent SPCZ displacement. Instead, precipitation anomalies reflect large-scale regions of anomalous ascent and descent, driven by Hadley and Ferrel cell shifts, rather than modifications to SPCZ dynamics.

The UKCP18 Convection-Permitting Model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localized storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we applied a quantile mapping (QM) method to correct hourly precipitation and daily temperature for four selected ensemble members (EM01, EM04, EM07, EM08) of the UKCP18-CPM for England. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from −0.87 °C to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R² values for the 95th percentile of hourly precipitation from 0.80–0.88 to 0.98 and achieving near-perfect alignment (R² = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1–39.1 % and mean daily temperature by 3.1 °C to 4.5 °C, highlighting the potential for more intense climate-related events. These results emphasize the effectiveness of bias correction in reducing model biases and improving the reliability of the CPM climate projections, thereby supporting more reliable future high-resolution climate and hydrological impact assessments in England.

The UKCP18 Convection-Permitting Model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localized storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we applied a quantile mapping (QM) method to correct hourly precipitation and daily temperature for four selected ensemble members (EM01, EM04, EM07, EM08) of the UKCP18-CPM for England. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from −0.87 °C to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R² values for the 95th percentile of hourly precipitation from 0.80–0.88 to 0.98 and achieving near-perfect alignment (R² = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1–39.1 % and mean daily temperature by 3.1 °C to 4.5 °C, highlighting the potential for more intense climate-related events. These results emphasize the effectiveness of bias correction in reducing model biases and improving the reliability of the CPM climate projections, thereby supporting more reliable future high-resolution climate and hydrological impact assessments in England.

The migration of humans into the eastern Pacific was relatively rapid and focused around 900-1250 CE. Although the causes for this migration are varied, we put forward evidence to suggest that a change in the mean state of the tropical South Pacific from La Niña like to El Niño like conditions accompanied by an increase in climate “shocks” around the period of migration, could have created conditions to promote migration east into the Pacific. We use a range of sediment archives and hydroclimate proxies located in sites within the region of migration, to reconstruct climate conditions in the ‘sending’ islands, and ‘receiving’ archipelagos. Climate in the period immediately prior to the eastward migration was characterised by a drier southwest Pacific. During the period following settlement of the central region of eastern Polynesia, the mean climate state in receiving islands became wetter, with fewer climate “shocks”. Results from socio-hydrological models highlight the sensitivity of growing populations to droughts within a drying climate. Using these lines of evidence, we suggest that as populations grew, particularly in drought-sensitive islands, people with some knowledge of eastern “Gateway islands”, chose to move east, fortuitously at a time when wetter conditions supported their long-term settlement.

The UKCP18 Convection-Permitting Model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localized storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we applied a quantile mapping (QM) method to correct hourly precipitation and daily temperature for four selected ensemble members (EM01, EM04, EM07, EM08) of the UKCP18-CPM for England. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from −0.87 °C to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R² values for the 95th percentile of hourly precipitation from 0.80–0.88 to 0.98 and achieving near-perfect alignment (R² = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1–39.1 % and mean daily temperature by 3.1 °C to 4.5 °C, highlighting the potential for more intense climate-related events. These results emphasize the effectiveness of bias correction in reducing model biases and improving the reliability of the CPM climate projections, thereby supporting more reliable future high-resolution climate and hydrological impact assessments in England.