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We describe two unprecedented large (100-member), longterm (61-year) ensembles based on MRI-AGCM3.2, which were driven by historical and non-warming climate forcing. These ensembles comprise the "Database for Policy Decision making for Future climate change (d4PDF)". We compare these ensembles to large ensembles based on another climate model, as well as to observed data, to investigate the influence of anthropogenic activities on historical changes in the numbers of record-breaking events, including: the annual coldest daily minimum temperature (TNn), the annual warmest daily maximum temperature (TXx) and the annual most intense daily precipitation event (Rx1day). These two climate model ensembles indicate that human activity has already had statistically significant impacts on the number of record-breaking extreme events worldwide mainly in the Northern Hemisphere land. Specifically, human activities have altered the likelihood that a wider area globally would suffer record-breaking TNn, TXx and Rx1day events than that observed over the 2001- 2010 period by a factor of at least 0.6, 5.4 and 1.3, respectively. However, we also find that the estimated spatial patterns and amplitudes of anthropogenic impacts on the probabilities of record-breaking events are sensitive to the climate model and/or natural-world boundary conditions used in the attribution studies.

Extremely heavy precipitation occurred from central to western Japan in July 2018, which was named the 'heavy rain event of July 2018' by the Japan Meteorological Agency (JMA). The JMA [...]

The record low sunshine over Japan during August 20/7 was mainly attributed to a blocking high and Pacific-Japan pattern. Anthropogenic warming and decaying El Nino contributed to an increase in [...]

Single model initial-condition large ensembles (LEs) are a useful approach to understand the roles of forced responses and internal variability in historical and future climate change. Here, we produce one of the largest ensembles thus far using the MIROC6 coupled atmosphere–ocean global climate model (MIROC6-LE). The total experimental period of MIROC6-LE is longer than 76 000 years. MIROC6-LE consists of a long preindustrial control run, 50-member historical simulations, 8 single forcing historical experiments with 10 or 50 members, 5 future scenario experiments with 50 members and 3 single forcing future experiments with 50 members. Here, we describe the experimental design. The output data of most of the experiments are freely available to the public. This dataset would be useful to a wide range of research communities. We also demonstrate some examples of initial analyses. Specifically, we confirm that the linear additivity of the forcing-response relationship holds for the 1850–2020 trends of the annual mean values and extreme indices of surface air temperature and precipitation by analyzing historical fully forced runs and the sum of single forced historical runs. To isolate historical anthropogenic signals of annual mean and extreme temperature for 2000–2020 relative to 1850–1900, ensemble sizes of 4 and 15, respectively, are sufficient in most of the world. Historical anthropogenic signals of annual mean and extreme precipitation are significant with the 50-member ensembles in 76 % and 69 % of the world, respectively. Fourteen members are sufficient to examine differences in changes in annual mean values and extreme indices of temperature and precipitation between the shared socioeconomic pathways (ssp), ssp585 and ssp126, in most of the world. Ensembles larger than 50 members are desirable for investigations of differences in annual mean and extreme precipitation changes between ssp126 and ssp119. Historical and future changes in internal variability, represented by departures from the ensemble mean, are analyzed with a focus on the El Niño/Southern Oscillation (ENSO) and global annual mean temperature and precipitation. An ensemble size of 31 is large enough to detect ENSO intensification from preindustrial conditions to 1951–2000, from 1951–2000 to 2051–2100 in all future experiments, and from low- to high-emission future scenario experiments. The single forcing historical experiments with 27 members can isolate ENSO intensification due to anthropogenic greenhouse gas and aerosol forcings. Future changes in the global mean temperature variability are discernible with 23 members under all future experiments, while 50 members are not sufficient for detecting changes in the global mean precipitation variability in ssp119 and ssp126. We also confirm that these temperature and precipitation variabilities are not precisely analyzed when detrended anomalies from the long-term averages are used due to interannual climate responses to the historical natural forcing, which highlights the importance of large ensembles for assessing internal variability. [ABSTRACT FROM AUTHOR]

La Niña persisted from 2020 to 2023, but its mechanisms are still unclear. In this study, atmosphere and ocean reanalysis and 100‐member initialized forecasts using a state‐of‐the‐art climate model were analyzed to identify factors contributing to the persistence of the first‐ to second‐year La Niña during 2020–2022. We found that North Pacific high pressure anomalies in the winter of 2020/2021 forced a negative phase of the Pacific meridional mode through the following spring, forming the broader structure of La Niña. The resultant broader La Niña pattern slowed down the recharge‐discharge process by Ekman transport, persisting La Niña. Ensemble forecast sensitivity analysis revealed that the meridional extent of La Niña explains its forecast spread, reaffirming the importance of La Niña spatial pattern. Advancing predictive understanding of 2020–2022 multi‐year La Niña can help to improve the extended seasonal forecast. Plain Language Summary: Almost 3 years have passed since the sea surface temperature in the central‐eastern equatorial Pacific became a cooler‐than‐normal in 2020. This event, called La Niña, can influence our lives through causing anomalous weather conditions globally. Therefore, it is important to know why the La Niña event has lasted so long. Using the computer simulation of climate, we repeated La Niña forecasts started from November 2020 a hundred times. We found that when the shape of La Niña was broader, the forecast was successful 1 year ahead. The shape of La Niña in 2021 depended on the atmospheric condition in the North Pacific. This study suggests that checking La Niña shape informs whether La Niña continues or declines. This knowledge may improve future climate forecasts. Key Points: The 100‐member ensemble forecasts initialized in November 2020 by MIROC6 captured prolonged La Niña in 2021/2022The forecast sensitivity analysis to initial states of ensemble members revealed a critical role of the North Pacific highNorth Pacific high in winter 2020/2021, causing negative North Pacific Meridional Mode spring, formed the spatially broader La Niña, resulting in long persistence [ABSTRACT FROM AUTHOR]

The attribution of 2022's high temperatures in Japan to human-induced climate change exhibits significant regional variability, with FARs of 0.33–1, influenced by large-scale wind changes and regional topography. [ABSTRACT FROM AUTHOR]

The 2016 extreme warmth across Asia would not have been possible without climate change. The 2015/16 El Nino also contributed to regional warm extremes over Southeast Asia and the Maritime [...]

Knowledge of regional differences in future climate projections is important for effective adaptation strategies. Extreme events often arise regionally, but multiscale factors likely act together. Hence, we need discussion of multiple scales for the regional characteristics of future changes of extremes. In this study, using a large ensemble climate simulation database (d4PDF) created by global and regional climate models, the change in the temperature extreme defined as the top 10% of summertime daily maximum temperature in Japan is investigated under a globally 2- and 4-K-warmer climate, with emphasis on its regionality. Under global warming, the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A simple composite analysis of extreme events shows that the high temperature occurs under a site-specific spatial pattern of sea level pressure (SLP), with a common feature of a warm anomaly up to the upper troposphere over the sites. The SLP pattern reflects the local topography and favors a foehnlike wind that increases the near-surface temperature. The impact of climate change in SLP on the foehn-inducing pattern varies with site, leading to regional differences in high-temperature changes. Therefore, the dynamic response of SLP to global warming results in a characteristic spatial distribution for the high-temperature change, which differs from the distribution for the mean-temperature change that generally shows the thermodynamic response. The characteristic is expected to appear in mountainous regions of the world, and this study helps in understanding future projections of high temperature there. Significance Statement: Regionality in future climate projections strongly influences the usefulness of adaptation strategies to climate change. This study indicates that the increase in extremely high temperature has a different spatial distribution from that of mean temperature. A site-specific spatial pattern of sea level pressure (SLP) reflecting the local topography contributes to the location of high temperature via a foehnlike wind. The impact of climate change in SLP on the pattern varies with site and leads to the regionality in high-temperature changes, which is the dynamic response to global warming unlike the thermodynamic response appearing on the mean-temperature change. This study helps us to understand future projections of temperature extreme in mountainous regions and their surroundings around the world. [ABSTRACT FROM AUTHOR]

The present paper presents results of seasonal‐to‐decadal climate predictions based on a coupled climate model called the Model for Interdisciplinary Research on Climate version 6 (MIROC6) contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). MIROC6 is initialized every year for 1960–2018 by assimilating observed ocean temperature and salinity anomalies and full fields of sea ice concentration and by prescribing atmospheric initial states from reanalysis data. The impacts of updating the system on prediction skill are then evaluated by comparing hindcast experiments between the MIROC6 prediction system and a previous system based on MIROC version 5 (MIROC5). Skill of seasonal prediction is overall improved in association with representation and initialization of El Niño/Southern Oscillation (ENSO), the Quasi‐Biennial Oscillation (QBO), and the Northern Hemisphere sea ice concentration in MIROC6. In particular, the QBO is skillfully predicted up to 3 years ahead with a maximum anomaly correlation exceeding r = 0.8. The prediction skill for the North Atlantic Oscillation in winter is also enhanced, but the prediction still suffers from model's inherent errors. On decadal timescales, MIROC6 has a larger fraction of areas of the globe with better surface temperature skill at all lead times than MIROC5, and it has predictive skill in the annual‐mean sea surface temperature (SST) in the North Atlantic and the Pacific. In particular, MIROC6 hindcasts at 2–5 years lead time are able to capture the spatial structure of SST changes in the North Pacific and the eastern tropical Pacific associated with the 1970s regime shift better than MIROC5 hindcasts. Plain Language Summary: The present paper presents results of seasonal‐to‐decadal climate predictions based on a coupled climate model called the Model for Interdisciplinary Research on Climate version 6 (MIROC6) contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6). The MIROC6 prediction system takes into account observed ocean, sea ice, and atmospheric conditions in the initialization, whereas its predecessor (MIROC5) considers observed ocean conditions only. Experiments with MIROC6 without initialization are also carried out in order to quantify impacts of initialization. Prediction skill of MIROC6 new prediction system several months to a year ahead is improved over MIROC5. In particular, the tropical variability in the stratosphere, which could be a key for predictions of the North American and European winter climate, is well predicted beyond a year. Also, while the overall improvement over MIROC5 is modest, MIROC6 has substantial skill for predictions of annual‐mean sea surface temperatures, particularly over the North Atlantic and the Pacific several years in advance. MIROC6 is able to better predict the variability in the North Pacific and the eastern tropical Pacific than initialized MIROC5 and uninitialized MIROC6 experiments, highlighting the importance of the system updates and observed information. Key Points: A seasonal to decadal prediction system with MIROC6 was developed for contributing to the Coupled Model Intercomparison Project Phase 6Seasonal predictions of El Niño, North Atlantic Oscillation, Quasi‐Biannual Oscillation, and sea ice are improved over its predecessorThe new system better captures the spatial structure of the 1970s Pacific regime shift in sea surface temperatures [ABSTRACT FROM AUTHOR]

We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year‐to‐year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores. We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter‐basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño–Southern Oscillation (ENSO) appear to be reproduced in multi‐model predictions and are responsible for much of the prediction skill. They also explain the relative magnitude of inter‐annual variability, the relative magnitude of predictable rainfall signals and the ranking of prediction skill across different basins. These seasonal tropical rainfall predictions exhibit a severe wet bias, often in excess of 20% of mean rainfall. However, we find little direct relationship between bias and prediction skill. Our results suggest that future prediction systems would be best improved through better model representation of inter‐basin rainfall connections as these are strongly related to prediction skill, particularly in the Indian and West Pacific regions. Finally, we show that predictions of tropical rainfall alone can generate highly skilful forecasts of the main modes of extratropical circulation via linear relationships that might provide a useful tool to interpret real‐time forecasts. Tropical rainfall and its year‐to‐year variability. Seasonal mean climatological rainfall (top left) and inter‐annual variability (top right) of global rainfall from GPCP observations. Tropical rainfall is analysed in the boxed regions for the Indian (45°–100°E, 5°S–10°N), West Pacific (110°–140°E, 5°S–25°N), East Pacific (200 –90 W, 5°S–10°N) and Atlantic (60°–0 W, 5°S–5°N) regions. Mean and standard deviation of ensemble member rainfall over the same years (1993–2012) from a single example prediction system (bottom panels, GloSea5). Note the logarithmic contour interval. Units are mm/day. [ABSTRACT FROM AUTHOR]

The accumulated evidence indicates that agricultural production is being affected by climate change. However, most of the available evidence at a global scale is based on statistical regressions. Corroboration using independent methods, specifically process‐based modelling, is important for improving our confidence in the evidence. Here, we estimate the impacts of climate change on the global average yields of maize, rice, wheat and soybeans for 1981–2010, relative to the preindustrial climate. We use the results of factual and non‐warming counterfactual climate simulations performed with an atmospheric general circulation model that do and do not include anthropogenic forcings to climate systems, respectively, as inputs into a global gridded crop model. The results of a 100‐member ensemble climate and crop simulation suggest that climate change has decreased the global mean yields of maize, wheat and soybeans by 4.1, 1.8 and 4.5%, respectively, relative to the counterfactual simulation (preindustrial climate), even when carbon dioxide (CO2) fertilization and agronomic adjustments are considered. For rice, no significant impacts (−1.8%) are detected. The uncertainties in estimated yield impacts represented by the 90% probability interval that are derived from the ensemble members are −8.5 to +0.5% for maize, −8.4 to −0.5% for soybeans, −9.6 to +12.4% for rice and − 7.5 to +4.3% for wheat. Based on the yield impacts, the estimates of average annual production losses throughout the world for the most recent years of the study (2005–2009) account for 22.3 billion USD (B$) for maize, 6.5 B$ for soybeans, 0.8 B$ for rice and 13.6 B$ for wheat. Our assessment confirms that climate change has modulated recent yields and led to production losses, and our adaptations to date have not been sufficient to offset the negative impacts of climate change, particularly at lower latitudes. The presented study estimates the impacts of historical climate change on the global average yields of maize, rice, wheat and soybean in 1981–2010. The analysis uses the results of factual and counterfactual climate simulations performed with an atmospheric general circulation model that do and do not include anthropogenic forcings to climate systems, respectively, as inputs to a global gridded crop model. Based on the yield impacts, estimates of average annual economic production losses at the global level are quantified. [ABSTRACT FROM AUTHOR]

The article focuses on a study which aims to examine the influence of climate change on heat extremes in Asia in 2016. Topics being presented include analyses of the observed monthly temperature record from the Goddard Institute for Space Studies (GISS) Surface Temperature Analysis as well as several 100-member ensemble experiments of 2016.

Using an international, multi-model suite of historical forecasts from the World Climate Research Programme (WCRP) Climate-system Historical Forecast Project (CHFP), we compare the seasonal prediction skill in boreal wintertime between models that resolve the stratosphere and its dynamics ('high-top') and models that do not ('low-top'). We evaluate hindcasts that are initialized in November, and examine the model biases in the stratosphere and how they relate to boreal wintertime (December-March) seasonal forecast skill. We are unable to detect more skill in the high-top ensemble-mean than the low-top ensemble-mean in forecasting the wintertime North Atlantic Oscillation, but model performance varies widely. Increasing the ensemble size clearly increases the skill for a given model. We then examine two major processes involving stratosphere-troposphere interactions (the El Niño/Southern Oscillation (ENSO) and the Quasi-Biennial Oscillation (QBO)) and how they relate to predictive skill on intraseasonal to seasonal time-scales, particularly over the North Atlantic and Eurasia regions. High-top models tend to have a more realistic stratospheric response to El Niño and the QBO compared to low-top models. Enhanced conditional wintertime skill over high latitudes and the North Atlantic region during winters with El Niño conditions suggests a possible role for a stratospheric pathway. [ABSTRACT FROM AUTHOR]

Predictability of El Niño-Southern Oscillation (ENSO) is examined using ensemble hindcasts made with a seasonal prediction system based on the atmosphere and ocean general circulation model, the Model for Interdisciplinary Research on Climate, version 5 (MIROC5). Particular attention is paid to differences in predictive skill in terms of the prediction error for two prominent types of El Niño: the conventional eastern Pacific (EP) El Niño and the central Pacific (CP) El Niño, the latter having a maximum warming around the date line. Although the system adopts ocean anomaly assimilation for the initialization process, it maintains a significant ability to predict ENSO with a lead time of more than half a year. This is partly due to the fact that the system is little affected by the 'spring prediction barrier,' because increases in the error have little dependence on the thermocline variability. Composite analyses of each type of El Niño reveal that, compared to EP El Niños, the ability to predict CP El Niños is limited and has a shorter lead time. This is because CP El Niños have relatively small amplitudes, and thus they are more affected by atmospheric noise; this prevents development of oceanic signals that can be used for prediction. [ABSTRACT FROM AUTHOR]

Predictability of above-normal rainfall over Thailand during the rainy season of 2011 was investigated with a one-tier seasonal prediction system based on an atmosphere-ocean coupled general circulation model (CGCM) combined with a statistical downscaling method. The statistical relationship was derived using singular value decomposition analysis (SVDA) between observed regional rainfall and the hindcast of tropical sea surface temperature (SST) from the seasonal prediction system, which has an ability to forecast oceanic variability for lead times up to several months. The downscaled product of 2011 local rainfall was obtained by combining rainfall patterns derived from significant modes of SVDA. This method has the advantage in terms of flexibility that phenomenon-based statistical relationships, such as teleconnections associated with El Niño-Southern Oscillation (ENSO), Indian Ocean dipole (IOD), or the newly recognized central Pacific El Niño, are considered separately in each SVDA mode. The downscaled prediction initialized from 1 August 2011 reproduced the anomalously intense precipitation pattern over Indochina including northern Thailand during the latter half of the rainy season, even though the direct hindcast from the CGCM failed to predict the local rainfall distribution and intensity. Further analysis revealed that this method is applicable to the other recent events such as heavy rainfall during the rainy seasons of 2002 and 2008 in Indochina. [ABSTRACT FROM AUTHOR]

We produced 100-member event attribution ensembles during 2009-2012 under all forcing conditions and in two different counterfactual worlds without anthropogenic forcing (mainly greenhouse gases and aerosols) and without aerosol emission changes using the MIROC5 atmospheric general circulation model. It seemed that both human influences and the sea surface temperature ( SST) natural variability increased probabilities of the 2010 severe drought in the South Amazon region, and that changes in aerosols emissions had little effect on the drought. It should be noted that our assessments were sensitive to bias corrections according to the relationships between the SST natural variability and precipitation. [ABSTRACT FROM AUTHOR]

The article discusses the extent to which anthropogenic sea surface temperature (SST) and atmospheric circulation changes influenced heavy rainfall in southwestern Japan or Pacific-Japan (PJ) in July 2012. Characteristics of the stronger northwestern Pacific subtropical high (PASH) during the 2012 rainy season include a dominant teleconnection pattern over PJ. It is suggested that Japan's heavy rainfall in 2012 was not greatly influenced by anthropogenic climate change.

A correction to the article "An Event Attribution of the 2010 Drought in the South Amazon Region using the MIROC5 Model" that was published in the January 10, 2017 is presented.