Atmospheric General Circulation Model Ensemble Research Articles

Variability of Tropical Cyclone Frequency Over the Western North Pacific in 2018–2020

In this study, we examine the tropical cyclone (TC) activity over the western North Pacific (WNP) in 2018–2020 and its relationship with planetary scale convection and circulation anomalies, which play an important role for TC genesis. To determine the sea surface temperature (SST)-forced atmospheric variability, atmospheric general circulation model (AGCM) ensemble simulations are executed along with the observed SST. For AGCM experiments, we use two different convection schemes to examine uncertainty in convective parameterization and robustness of simulated atmospheric response. The observed TC activity and genesis potential demonstrated consistent features. In our AGCM ensemble simulations, the updated convection scheme improves the simulation ability of observed genesis potential as well as planetary scale convection and circulation features, e.g., in September–October–November (SON), a considerable increase in the genesis potential index over the WNP in SON 2018, WNP in SON 2019, and South China Sea (SCS) in SON 2020, which were not captured in the Emanuel scheme, have been simulated in the updated convection scheme.

Potential Predictability of the Tropical Cyclone Frequency Over the Western North Pacific With 50‐km AGCM Ensemble Experiments

AbstractThe potential predictability determined by sea surface temperature (SST) of the frequency distribution of tropical cyclones (TCs) is studied using ensemble simulations with a 50‐km resolution atmospheric general circulation model (AGCM). In this experiment, the interannual variability of the TC frequency over the western North Pacific (WNP) is primarily determined by SST, as the simulated ensemble mean signal is dominant over the ensemble spread and effectively follows the observed TC frequency in June‐July‐August (JJA). In contrast, the correlation between the observed and simulated TC frequency variability is less significant in September‐October‐November (SON). To explore the TC frequency‐SST linkage, the relationship between the TC frequency variability over the WNP and climate modes determined by SST indices is investigated. Through a correlation analysis, we found that El Niño Modoki is the major driver of the TC frequency variability over the WNP in JJA. Partial correlation analysis further reveals that internal atmospheric dynamics are important for the TC frequency variability over the WNP in SON. These results suggest that the tropical climate mode is responsible for the seasonal predictability of the TC frequency variability over the WNP and that the prediction skill is seasonally dependent with a high (low) skill in JJA (SON).

Impact of air\u2013sea coupling on the probability of occurrence of heat waves in Japan

In extreme event attribution, which aims to answer whether and to what extent a particular extreme weather event can be attributed to global warming, the probability of an event is generally estimated through large ensemble simulations, using an atmospheric general circulation model (AGCM). In islands, such as Japan, it has been considered that surface air temperature (SAT) can be significantly affected by the surrounding sea surface temperature (SST), which mostly is affected by atmospheric circulation at mid- and high-latitudes. Therefore, the absence of SST responses to atmospheric variability in AGCMs impacts the estimation of the occurrence of extreme events, such as heat waves in Japan. In this study, we examined the impact of air–sea coupling on the probability of occurrence of severe heat waves that occurred in Japan in the summer of 2010 by analyzing the probability differences obtained from AGCM and coupled general circulation model (CGCM) large-ensemble experiments. The observed ocean temperature, salinity, and sea ice were assimilated in the 100-member CGCM experiments, as they were assigned as boundary conditions in the 100-member AGCM experiments. The SAT around Japan in the northern summer is largely related to the Bonin high, whose interannual variability is largely affected by the Silk Road and Pacific-Japan (PJ) pattern teleconnections in the real world. The SAT anomaly over Japan was related to the pressure variability due to the Silk Road and PJ patterns in the CGCM experiment. By contrast, the SAT over Japan simulated by AGCM was less sensitive to such pressure variability, and the SAT ensemble spread became narrower in AGCM. The results suggest that the probability of occurrence of the 2010 heat wave in Japan would tend to be underestimated by the AGCM ensemble compared to the CGCM ensemble, provided that the ensemble averages of the SAT anomalies were equal between CGCM and AGCM experiments. This study raised the issue of the absence of SST response to atmospheric variability in AGCMs, which can critically impact the estimation of extreme event probability, particularly in mid-latitude islands, such as Japan.

Effective sample size for precipitation estimation in atmospheric general circulation model ensemble experiments: dependence on temporal and spatial averaging scales

The accuracy of climate projections is improved by increasing the number of samples from ensemble experiments, leading to a decrease in the confidence interval of a target climatological variable. The improvement in the accuracy depends on the degree of independence of each ensemble member in the experiments. When the members of ensemble experiments are dependent on each other, the introduction of an effective sample size (ESS) is necessary to correctly estimate the confidence interval. This study is the first attempt to estimate the ESS for precipitation as a function of the number of ensemble members, although some previous studies have investigated another type of ESS in terms of the length of simulation period. The ESS in the present study is intrinsic to the atmospheric general circulation models (AGCM) forced by the ocean boundary condition because the outputs of AGCM ensemble members are similar or dependent on each other due to the commonly used boundary condition, i.e., the distribution of sea surface temperature, sea ice concentration, and sea ice thickness. Looking at the values of ESS as a function of geographical location, those in the tropics and over the ocean are smaller than those at higher latitudes and over continents; precipitation events in areas with smaller (larger) ESS are strongly (weakly) constrained by the ocean boundary condition. The increase in temporal and spatial averaging scales for precipitation estimation leads to the decrease in the ESS, of whose trend is attributed to the spatio-temporal characteristics of the precipitation events as represented by the power spectrum and co-spectrum.

A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations

Climatic variations at decadal scales such as phases of accelerated warming or weak monsoons have profound effects on society and economy. Studying these variations requires insights from the past. However, most current reconstructions provide either time series or fields of regional surface climate, which limit our understanding of the underlying dynamics. Here, we present the first monthly paleo-reanalysis covering the period 1600 to 2005. Over land, instrumental temperature and surface pressure observations, temperature indices derived from historical documents and climate sensitive tree-ring measurements were assimilated into an atmospheric general circulation model ensemble using a Kalman filtering technique. This data set combines the advantage of traditional reconstruction methods of being as close as possible to observations with the advantage of climate models of being physically consistent and having 3-dimensional information about the state of the atmosphere for various variables and at all points in time. In contrast to most statistical reconstructions, centennial variability stems from the climate model and its forcings, no stationarity assumptions are made and error estimates are provided.

Potential predictability of the sea‐surface temperature forced equatorial East African short rains interannual variability in the 20th century

In this article, the predictability of the 20th century sea‐surface temperature (SST) forced East African short rains variability is analyzed using observational data and ensembles of long atmospheric general circulation model (AGCM) simulations. To our knowledge, such an analysis for the whole 20th century using a series of AGCM ensemble simulations is carried out here for the first time. The physical mechanisms that govern the influence of SST on East African short rains in the model are also investigated. It is found that there is substantial skill in reproducing the East African short rains variability, given that the SSTs are known. Consistent with previous recent studies, it is found that the Indian Ocean and in particular the western pole of the Indian Ocean dipole (IOD) play a dominant role for the prediction skill, whereas SSTs outside the Indian Ocean play a minor role. The physical mechanism for the influence of the western Indian Ocean on East African rainfall in the model is consistent with previous findings and consists of a gill‐type response to a warm (cold) anomaly that induces a westerly (easterly) low‐level flow anomaly over equatorial Africa and leads to moisture flux convergence (divergence) over East Africa. On the other hand, a positive El Niño–Southern Oscillation (ENSO) anomaly leads to a spatially non‐coherent reducing effect over parts of East Africa, but the relationship is not strong enough to provide any predictive skill in our model. The East African short rains prediction skill is also analyzed within a model‐derived potential predictability framework and it is shown that the actual prediction skill is broadly consistent with the model potential prediction skill. Low‐frequency variations of the prediction skill are mostly related to SSTs outside the Indian Ocean region and are likely due to an increased interference of ENSO with the Indian Ocean influence on East African short rains after the mid‐1970s climate shift.

SST forcing of decadal Indian Monsoon rainfall variability

Using ensemble integrations of an Atmospheric General Circulation Model (AGCM) forced globally with observed sea surface temperature (SST) anomalies it is shown that the observed decadal variability of Indian monsoon rainfall (IMR) can be reproduced, although amplitudes are somewhat overestimated. A second AGCM ensemble forced only in the Indian Ocean region suggests that the local Indian Ocean SST forcing is contributing significantly to the decadal IMR variability. Here, cold (warm) equatorial SSTs cause low‐level divergence (convergence) that in turn modifies the local Hadley circulation and strengthens (weakens) the Indian monsoon circulation. This result is complementary to previous findings that IMR variability is mainly determined by an atmospheric teleconnection with ENSO.

A method to aid in the determination of the sampling size of AGCM ensemble simulations

Historically, the high computational expense of performing lengthy climate simulations has limited the number of possible realizations. Here, we exploit the computational advantage of a recently developed parallel atmospheric general circulation model (AGCM) to determine the number of realizations required to calculate the model's statistics to a specified degree of certainty. Using standard statistical analysis techniques, this minimum ensemble size is found to be highly dependent on which output field is under examination. A strong dependence on location, season and averaging is also revealed.

Reproducible Forced Modes in AGCM Ensemble Integrations and Potential Predictability of Atmospheric Seasonal Variations in the Extratropics

An approach to assess the potential predictability of the extratropical atmospheric seasonal variations in an ensemble of atmospheric general circulation model (AGCM) integrations has been proposed in this study by isolating reproducible forced modes and examining their contributions to the local ensemble mean. The analyses are based on the monthly mean output of an eight-member ensemble of 10-yr Atmospheric Model Intercomparison Project integrations with a T42L18 AGCM. An EOF decomposition applied to the ensemble anomalies shows that there exist some forced modes that are less affected by the internal process and thus appear to be highly reproducible. By reconstructing the ensemble in terms of the more reproducible forced modes and by developing a quantitative measure, the potential predictability index (PPI), which combines the reproducibility with the local variance contribution, the local ensemble mean over some selective geographic areas in the extratropics was shown to result primarily from reproducible forced modes rather than internal chaotic fluctuations. Over those regions the ensemble mean is potentially predictable. Extratropical potentially predictable regions are found mainly over North America and part of the Asian monsoon regions. Interestingly, the potential predictability over some preferred areas such as Indian monsoon areas and central Africa occasionally results primarily from non-ENSO-related boundary forcing, although ENSO forcing generally dominates over most of the preferred areas. The quantitative analysis of the extratropical potential predictability with PPI has shown that the preferred geographic areas have obvious seasonality. For the 850-hPa temperature, for example, potentially predictable regions during spring and winter are confined to Alaska, northwest Canada, and the southeast United States, the traditional PNA region, while during summer and fall they are favored over the middle part of North America. It has also been shown that the boreal summer season (June–August) possesses the largest potentially predictable area, which seems to indicate that it is a favored season for the extratropical potential predictability. On the contrary, boreal winter (December–February) appears to have a minimum area of extratropical potential predictability. The results have been compared with the more traditional statistical tests for potential predictability and with observations from the National Centers for Environmental Prediction reanalysis, which indicates that the PPI analysis proposed here is successful in revealing extratropical potential predictability determined by the external forcing.