Land water contributions from GRACE to sea level rise over 2002-2006
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Abstract This study evaluates the intraseasonal variation of winter precipitation over the western United States in 14 coupled general circulation models (GCMs) participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Eight years of each model’s twentieth-century climate simulation are analyzed. The focus is on the two dominant intraseasonal modes for the western U.S. precipitation: the 40-day mode and the 22-day mode. The results show that the models tend to overestimate the northern winter (November–April) seasonal mean precipitation over the western United States and Canada. The models also tend to produce overly strong intraseasonal variability in western U.S. wintertime precipitation, in spite of the overly weak tropical intraseasonal variability in most of the models. All models capture both the 40-day mode and the 22-day mode, usually with overly large variances. For the 40-day mode, models tend to reproduce its deep barotropic vertical structure and three-cell horizontal structure, but only 5 of the 14 models capture its northward propagation, and only 2 models simulate its teleconnection with the Madden–Julian oscillation in the tropical Pacific. For the 22-day mode, 8 of the 14 models reproduce its coherent northward propagation, and 9 models capture its teleconnection with precipitation in the tropical Pacific.
Teleconnection
Barotropic fluid
Mode (computer interface)
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Abstract Analysis of the retrospective ensemble predictions (hindcasts) of the NCEP Climate Forecast System (CFS) indicates that the model successfully simulates many major features of the Asian summer monsoon including the climatology and interannual variability of major precipitation centers and atmospheric circulation systems. The model captures the onset of the monsoon better than the retreat of the monsoon, and it simulates the seasonal march of monsoon rainfall over Southeast Asia more realistically than that over South Asia. The CFS predicts the major dynamical monsoon indices and monsoon precipitation patterns several months in advance. It also depicts the interactive oceanic–atmospheric processes associated with the precipitation anomalies reasonably well at different time leads. Overall, the skill of monsoon prediction by the CFS mainly comes from the impact of El Niño–Southern Oscillation (ENSO). The CFS produces weaker-than-observed large-scale monsoon circulation, due partially to the cold bias over the Asian continent. It tends to overemphasize the relationship between ENSO and the Asian monsoon, as well as the impact of ENSO on the Asian and Indo-Pacific climate. A higher-resolution version of the CFS (T126) captures the climatology and variability of the Asian monsoon more realistically than does the current resolution version (T62). The largest improvement occurs in the simulations of precipitation near the Tibetan Plateau and over the tropical Indian Ocean associated with the zonal dipole mode structure. The analysis suggests that NCEP’s next operational model may perform better in simulating and predicting the monsoon climate over Asia and the Indo-Pacific Oceans.
East Asian Monsoon
Climate Forecast System
Tropical monsoon climate
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Abstract The onset of the summer monsoon associated with global warming is of great concern to the scientific community. While observational data diagnosis has shown the impact of intraseasonal oscillation (ISO) on the monsoon onset, how the ISO may affect the onset of monsoon under global warming remains unknown. Here, by analyzing the onset of the summer monsoon over the South Asian marginal seas projected by models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) under the SSP5–8.5 scenario, we show evidence that the majority of models (>70%) project an earlier onset over the Arabian Sea (ArS) but a delayed onset over the Bay of Bengal (BoB) and the South China Sea (SCS). The temporal shifts of the monsoon onset are attributed to the changes in the premonsoon northward migration of equatorial ISO (NMISO), which is a trigger of monsoon onset and will be advanced (postponed) over the ArS (BoB and SCS). The subtropical upper-level westerly anomaly, inducing delayed occurrence of easterly shear, acts to delay the NMISO over the entire Indian Ocean. However, the intensified low-level southerly wind over the ArS, as well as its induced asymmetric pattern of boundary layer moisture work together to advance the premonsoon NMISO in the area, outweighing the delayed impact from vertical shear. These large-scale circulation changes are driven by tropical warming in the upper troposphere, land warming over the Arabian Peninsula, and ocean warming over the eastern Pacific. This analysis enriches monsoon onset projections by highlighting the role of ISO in influencing the future changes in monsoon onset.
Madden–Julian oscillation
Oscillation (cell signaling)
East Asian Monsoon
Teleconnection
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This paper examines the contribution of tropical–extratropical cloudbands, and of interactions between tropical cloud and mid-latitude systems, to cool season (April–October) rainfall in agriculturally marginal areas of Australia. A following paper describes inter- and intra-annual variability of these features. A classification scheme for these tropical influences based on GMS satellite imagery is described, and used to compile a 15-year archive of events. It is shown that cloudbands extending from the tropical oceans bordering Australia ('Oceanic' Cloudbands) are most frequent and influential between April and July, but decrease sharply after August, at which time bands originating over the continental interior ('Continental' Cloudbands) increase. The contribution of these systems to rainfall at stations representing agriculturally marginal areas is assessed. Oceanic Cloudbands originating west of 120°E contribute 70–90 per cent of cool-season rain in north-western Australia, with the contribution decreasing to the south and east. North-eastern Australia receives a significant portion of its rain from Cloudbands originating east of 120°E. Tropical–mid-latitude interactions are more important over eastern than western Australia, and produce some 30–40 per cent of rain over much of inland eastern Australia. The overall tropical influence (Cloudbands plus interactions) on rainfall is least in South Australia and western Victoria, but still amounts to some 35–40 per cent of cool-season rain in those areas. The proportion of events producing significant rainfall (>10 mm) is also examined: almost two-thirds of the Oceanic Cloudbands to affect western Australia produce significant rain, and about half of those affecting eastern Australia. © 1997 by the Royal Meteorological Society. Int. J. Climatol., 17: 807–829 (1997) (No. of Figures: 10. No. of Tables: 4. No. of References: 33.)
Extratropical cyclone
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Abstract The climatology and variability of the January to February (JF) season in eastern Africa's (EA) precipitation are examined during the 1960–2020 period, as off‐season climate could have dire consequences, considering agricultural practices tie to the seasonal cycle of precipitation. The analysis in this study is divided into four parts. The first is the climatological background of variability during the JF season. Second, the spatiotemporal variability of the leading mode of the JF precipitation is described using an empirical orthogonal function (EOF) method. Third, anomalous atmospheric circulations linked to the variability of the JF precipitation were examined through composite analysis. Fourth, the link between JF precipitation and sea surface temperature (SST) is explored using composite and correlation analyses. The leading mode (EOF1) shows a monopole variation, with a positive anomaly in the entire region accounting for 55.1% of the total variance. EOF1 is linked to the SST anomaly (SSTA) over the tropical Indian Ocean (TIO). A warm (cool) SSTA in the TIO induces diabatic warming/adiabatic cooling (diabatic cooling/adiabatic warming). This leads to the rising (sinking) of warm and moist air (cold and dry air) from the lower to higher (higher to lower) troposphere via the ascending (descending) branch of the Walker circulation and contributes to the upper warm (cold) temperature anomaly centred at ~300 hPa. The warm (cold) anomaly is closely associated with the upper‐level westerly (easterly) and divergence (convergence) anomalies at the upper side of the warm (cold) core, coupled with ascending (descending) and deep wet (dry) anomalies below the warm (cold) core. This induces moisture convergence (divergence) and unstable (stable) conditions that favour (suppresses) precipitation over EA. Consequently, this study may facilitate the prediction of the JF precipitation and decrease in socio‐economic losses in EA.
Empirical orthogonal functions
Anomaly (physics)
Diabatic
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Abstract This study evaluates the subseasonal variability associated with the Asian summer monsoon in 14 coupled general circulation models (GCMs) participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Eight years of each model’s twentieth-century climate simulation are analyzed. The authors focus on the three major components of Asian summer monsoon: the Indian summer monsoon (ISM), the western North Pacific summer monsoon (WNPSM), and the East Asian summer monsoon (EASM), together with the two dominant subseasonal modes: the eastward- and northward-propagating boreal summer intraseasonal oscillation (BSIO) and the westward-propagating 12–24-day mode. The results show that current state-of-the-art GCMs still have difficulties and display a wide range of skill in simulating the subseasonal variability associated with Asian summer monsoon. During boreal summer (May–October), most of the models produce reasonable seasonal-mean precipitation over the ISM region, but excessive precipitation over the WNPSM region and insufficient precipitation over the EASM region. In other words, models concentrate their rain too close to the equator in the western Pacific. Most of the models simulate overly weak total subseasonal (2–128 day) variance, as well as too little variance for BSIO and the 12–24-day mode. Only 4–5 models produce spectral peaks in the BSIO and 12–24-day frequency bands; instead, most of the models display too red a spectrum, that is, an overly strong persistence of precipitation. For the seven models with three-dimensional data available, five reproduce the preconditioning of moisture in BSIO but often with a too late starting time, and only three simulate the phase lead of low-level convergence. Interestingly, although models often have difficulty in simulating the eastward propagation of BSIO, they tend to simulate well the northward propagation of BSIO, together with the westward propagation of the 12–24-day mode. The northward propagation in these models is thus not simply a NW–SE-tilted tail protruding off of an eastward-moving deep-tropical intraseasonal oscillation.
East Asian Monsoon
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Abstract In this paper the extensive integrations produced for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) are used to examine the relationship between ENSO and monsoons at interannual and decadal time scales. The study begins with an analysis of the monsoon simulation in the twentieth-century integrations. Six of the 18 models were found to have a reasonably realistic representation of monsoon precipitation climatology. For each of these six models SST and anomalous precipitation evolution along the equatorial Pacific during El Niño events display considerable differences when compared to observations. Out of these six models only four [Geophysical Fluid Dynamics Laboratory Climate Model versions 2.0 and 2.1 (GFDL_CM_2.0 and GFDL_CM_2.1), Meteorological Research Institute (MRI) model, and Max Planck Institute ECHAM5 (MPI_ECHAM5)] exhibit a robust ENSO–monsoon contemporaneous teleconnection, including the known inverse relationship between ENSO and rainfall variations over India. Lagged correlations between the all-India rainfall (AIR) index and Niño-3.4 SST reveal that three models represent the timing of the teleconnection, including the spring predictability barrier, which is manifested as the transition from positive to negative correlations prior to the monsoon onset. Furthermore, only one of these three models (GFDL_CM_2.1) captures the observed phase lag with the strongest anticorrelation of SST peaking 2–3 months after the summer monsoon, which is partially attributable to the intensity of the simulated El Niño itself. The authors find that the models that best capture the ENSO–monsoon teleconnection are those that correctly simulate the timing and location of SST and diabatic heating anomalies in the equatorial Pacific and the associated changes to the equatorial Walker circulation during El Niño events. The strength of the AIR-Niño-3.4 SST correlation in the model runs waxes and wanes to some degree on decadal time scales. The overall magnitude and time scale for this decadal modulation in most of the models is similar to that seen in observations. However, there is little consistency in the phase among the realizations, suggesting a lack of predictability of the decadal modulation of the monsoon–ENSO relationship. The analysis was repeated for each of the four models using results from integrations in which the atmospheric CO2 concentration was raised to twice preindustrial values. From these “best” models in the double CO2 simulations there are increases in both the mean monsoon rainfall over the Indian subcontinent (by 5%–25%) and in its interannual variability (5%–10%). For each model the ENSO–monsoon correlation in the global warming runs is very similar to that in the twentieth-century runs, suggesting that the ENSO–monsoon connection will not weaken as global climate warms. This result, though plausible, needs to be taken with some caution because of the diversity in the simulation of ENSO variability in the coupled models that have been analyzed. Implications of the present results for monsoon prediction are discussed.
Teleconnection
Predictability
Forcing (mathematics)
Multivariate ENSO index
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