気象庁

Marine heatwaves (MHWs) are characterized by extremely high water temperatures persisting for at least several consecutive days, and their severe ecological and economic impacts have been increasing. In this study, the impacts of interannual-to-decadal sea surface temperature (SST) variability on MHWs in 10 areas around Japan were evaluated using daily satellite-based SST data from 1983 to 2022. To evaluate these impacts, MHWs were detected using SST without the interannual-to-decadal variability, while keeping the threshold for the MHW detection unchanged. As a result, the annual MHW days averaged for the 10 areas were reduced by 53

Extreme precipitation has intensified under global warming, and Japan is especially vulnerable due to its complex topography and climate. This study examines annual maximum daily precipitation recorded at 51 synoptic stations across Japan for 1901–2020. Annual maxima were modeled within an extreme-value framework using both GEV and Gumbel distributions. To represent possible temporal change, we considered both time-invariant and time-varying formulations, where the location parameter, the scale parameter, or both were allowed to change over time after stationarity assessment. The main objective of the study is to compare three estimation approaches for these models: maximum likelihood estimation (MLE), ordinary least squares (OLS), and weighted least squares (WLS). A supplementary Monte Carlo experiment under stationary GEV settings further indicates that OLS and WLS generally outperform MLE in terms of overall parameter error when contamination becomes stronger. Based on multiple goodness-of-fit criteria, the full GEV is preferred over the Gumbel model at 45 of 51 stations, and the non-stationary models provided the best fit at 42 stations. For estimation, least-squares approaches are selected more often than MLE at 43 of 51 stations (OLS: 24; WLS: 19; MLE: 8), indicating improved robustness to highly irregular rainfall. Return levels for 10-, 20-, 50-, and 100-year periods reveal a clear southward increase; for example, the 100-year return level is 593.1 mm at Naze versus 171.4 mm at Akita. These findings support region-specific risk assessment and mitigation planning across Japan.

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support 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. This year, we introduced corrections on the ELUC, SOCEAN and SLAND estimates. 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 2024, EFOS increased by 1.1 % relative to 2023, with fossil emissions at 10.3 ± 0.5 GtC yr−1 (including the cement carbonation sink, 0.2 GtC yr−1), ELUC was 1.3 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.6 ± 0.9 GtC yr−1 (42.4 ± 3.2 GtCO2 yr−1). Also, for 2024, GATM was 7.9 ± 0.2 GtC yr−1 (3.73 ± 0.1 ppm yr−1), 2.2 GtC above the 2023 growth rate. SOCEAN was 3.4 ± 0.4 GtC yr−1 and SLAND was 1.9 ± 1.1 GtC yr−1, leaving a large negative BIM (−1.7 GtC yr−1), suggesting that the total sink or GATM is strongly overestimated in 2024. The global atmospheric CO2 concentration averaged over 2024 reached 422.8 ± 0.1 ppm. Preliminary data for 2025 suggest an increase in EFOS relative to 2024 of +1.0 % (0.2 % to 1.7 %) globally, and atmospheric CO2 concentration increasing by 2.1 ppm reaching 425.6 ppm, 53 % 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 1959–2024, with a near-zero overall budget imbalance, 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 methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the mean ocean sink. 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-2025 (Friedlingstein et al., 2025c).

In early August 2023, Typhoon Khanun exhibited stagnant motion around the Ryukyu Islands, moving northwestward, eastward, and then northward. This study examined the environmental factors controlling its complex trajectory using atmospheric data assimilation and typhoon forecast model results. The northwestward-to-eastward shift was primarily caused by the weakening of the subtropical high north of the typhoon and its nearly simultaneous strengthening to the south, while midlatitude westerlies had limited influence. The subsequent eastward-to-northward turn was driven by the intensification of the subtropical high to the typhoon’s east. These changes of the subtropical high were linked to the propagation of upper-level Rossby wave packets from an anticyclonic circulation anomaly over northeastern China, which developed under the influence of a tropical depression that evolved from Typhoon Doksuri.

The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is a joint Japanese-European satellite observation mission for understanding the interaction between cloud, aerosol, and radiation processes and improving the accuracy of climate change predictions. The EarthCARE satellite was equipped with four sensors, a 355 nm high-spectral-resolution lidar with depolarization measurement capability (ATLID) as well as a cloud profiling radar, a multi-spectral imager, and a broadband radiometer, to observe the global distribution of clouds, aerosols, and radiation. In this study, we have developed algorithms to produce ATLID Level 2 aerosol products using ATLID Level 1 data. The algorithms estimated the following four products: (1) Layer identifiers such as aerosols, clouds, clear-skies, or surfaces were estimated by the combined use of vertically variable criteria and spatial continuity methods developed for the CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) analysis. (2) Aerosol optical properties such as extinction coefficient, backscatter coefficient, depolarization ratio, and lidar ratio at 355 nm were optimized to ATLID L1 data by the method of maximum likelihood. (3) Six aerosol types, namely smoke, pollution, marine, pristine, dusty-mixture, and dust were identified based on a two-dimensional diagram of the lidar ratio and depolarization ratio at 355 nm developed by cluster-analysis using the AERONET (AErosol RObotic NETwork) dataset with ground-based lidar data. (4) The planetary boundary layer height was determined using the improved wavelet covariance transform method for the ATLID analysis. The performance of various algorithms was evaluated using pseudo ATLID Level 1 data generated by Joint-Simulator (Joint Simulator for Satellite Sensors), which incorporates aerosol and cloud distributions simulated by numerical models. Results from applying the algorithms to the pseudo ATLID Level 1 data with realistic signal noise added for aerosol or cloud predominant cases revealed: (1) misidentification of aerosol and cloud layers was relatively low, approximately 10 %; (2) the retrieval errors of aerosol optical properties were 0.08×10-7±1.12×10-7m-1sr-1 (2±34 % in relative error) for backscatter coefficient and 0.01±0.07 (4±27 % in relative error) for depolarization ratio; (3) aerosol type classification was generally performed well. These results indicate that the algorithm's capability to provide valuable insights into the global distribution of aerosols and clouds, facilitating assessments of their climate impact through atmospheric radiation processes.