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- Author or Editor: Nicolas C. Jourdain x
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Abstract
Recent observational studies have suggested that negative and positive Indian Ocean dipole (IOD) events (nIOD and pIOD, respectively) favor a transition toward, respectively, El Niño and La Niña events one year later. These statistical inferences are however limited by the length and uncertainties in the observational records. This paper compares observational datasets with twenty-one 155-yr historical simulations from phase 5 of CMIP (CMIP5) to assess IOD and El Niño–Southern Oscillation (ENSO) properties along with their synchronous and delayed relationships. In the observations and most CMIP5 models, it is shown that El Niños tend to be followed by La Niñas but not the opposite, that pIODs co-occur more frequently with El Niños than nIODs with La Niñas, that nIODs tend to be followed by El Niños one year later less frequently than pIODs by La Niñas, and that including an IOD index in a linear prediction based on the Pacific warm water volume improves ENSO peak hindcasts at 14 months lead. The IOD–ENSO delayed relationship partly results from a combination of ENSO intrinsic properties (e.g., the tendency for El Niños to be followed by La Niñas) and from the synchronous IOD–ENSO relationship. The results, however, reveal that this is not sufficient to explain the high prevalence of pIOD–Niña transitions in the observations and 75% of the CMIP5 models, and of nIOD–Niño transitions in 60% of CMIP5 models. This suggests that the tendency of IOD to lead ENSO by one year should be explained by a physical mechanism that, however, remains elusive in the CMIP5 models. The ability of many CMIP5 models to reproduce the delayed influence of the IOD on ENSO is nonetheless a strong incentive to explore extended-range dynamical forecasts of ENSO.
Abstract
Recent observational studies have suggested that negative and positive Indian Ocean dipole (IOD) events (nIOD and pIOD, respectively) favor a transition toward, respectively, El Niño and La Niña events one year later. These statistical inferences are however limited by the length and uncertainties in the observational records. This paper compares observational datasets with twenty-one 155-yr historical simulations from phase 5 of CMIP (CMIP5) to assess IOD and El Niño–Southern Oscillation (ENSO) properties along with their synchronous and delayed relationships. In the observations and most CMIP5 models, it is shown that El Niños tend to be followed by La Niñas but not the opposite, that pIODs co-occur more frequently with El Niños than nIODs with La Niñas, that nIODs tend to be followed by El Niños one year later less frequently than pIODs by La Niñas, and that including an IOD index in a linear prediction based on the Pacific warm water volume improves ENSO peak hindcasts at 14 months lead. The IOD–ENSO delayed relationship partly results from a combination of ENSO intrinsic properties (e.g., the tendency for El Niños to be followed by La Niñas) and from the synchronous IOD–ENSO relationship. The results, however, reveal that this is not sufficient to explain the high prevalence of pIOD–Niña transitions in the observations and 75% of the CMIP5 models, and of nIOD–Niño transitions in 60% of CMIP5 models. This suggests that the tendency of IOD to lead ENSO by one year should be explained by a physical mechanism that, however, remains elusive in the CMIP5 models. The ability of many CMIP5 models to reproduce the delayed influence of the IOD on ENSO is nonetheless a strong incentive to explore extended-range dynamical forecasts of ENSO.
Abstract
Climate models often exhibit spurious long-term changes independent of either internal variability or changes to external forcing. Such changes, referred to as model “drift,” may distort the estimate of forced change in transient climate simulations. The importance of drift is examined in comparison to historical trends over recent decades in the Coupled Model Intercomparison Project (CMIP). Comparison based on a selection of metrics suggests a significant overall reduction in the magnitude of drift from phase 3 of CMIP (CMIP3) to phase 5 of CMIP (CMIP5). The direction of both ocean and atmospheric drift is systematically biased in some models introducing statistically significant drift in globally averaged metrics. Nevertheless, for most models globally averaged drift remains weak compared to the associated forced trends and is often smaller than the difference between trends derived from different ensemble members or the error introduced by the aliasing of natural variability. An exception to this is metrics that include the deep ocean (e.g., steric sea level) where drift can dominate in forced simulations. In such circumstances drift must be corrected for using information from concurrent control experiments. Many CMIP5 models now include ocean biogeochemistry. Like physical models, biogeochemical models generally undergo long spinup integrations to minimize drift. Nevertheless, based on a limited subset of models, it is found that drift is an important consideration and must be accounted for. For properties or regions where drift is important, the drift correction method must be carefully considered. The use of a drift estimate based on the full control time series is recommended to minimize the contamination of the drift estimate by internal variability.
Abstract
Climate models often exhibit spurious long-term changes independent of either internal variability or changes to external forcing. Such changes, referred to as model “drift,” may distort the estimate of forced change in transient climate simulations. The importance of drift is examined in comparison to historical trends over recent decades in the Coupled Model Intercomparison Project (CMIP). Comparison based on a selection of metrics suggests a significant overall reduction in the magnitude of drift from phase 3 of CMIP (CMIP3) to phase 5 of CMIP (CMIP5). The direction of both ocean and atmospheric drift is systematically biased in some models introducing statistically significant drift in globally averaged metrics. Nevertheless, for most models globally averaged drift remains weak compared to the associated forced trends and is often smaller than the difference between trends derived from different ensemble members or the error introduced by the aliasing of natural variability. An exception to this is metrics that include the deep ocean (e.g., steric sea level) where drift can dominate in forced simulations. In such circumstances drift must be corrected for using information from concurrent control experiments. Many CMIP5 models now include ocean biogeochemistry. Like physical models, biogeochemical models generally undergo long spinup integrations to minimize drift. Nevertheless, based on a limited subset of models, it is found that drift is an important consideration and must be accounted for. For properties or regions where drift is important, the drift correction method must be carefully considered. The use of a drift estimate based on the full control time series is recommended to minimize the contamination of the drift estimate by internal variability.
Abstract
The representation of the El Niño–Southern Oscillation (ENSO) under historical forcing and future projections is analyzed in 34 models from the Coupled Model Intercomparison Project phase 5 (CMIP5). Most models realistically simulate the observed intensity and location of maximum sea surface temperature (SST) anomalies during ENSO events. However, there exist systematic biases in the westward extent of ENSO-related SST anomalies, driven by unrealistic westward displacement and enhancement of the equatorial wind stress in the western Pacific. Almost all CMIP5 models capture the observed asymmetry in magnitude between the warm and cold events (i.e., El Niños are stronger than La Niñas) and between the two types of El Niños: that is, cold tongue (CT) El Niños are stronger than warm pool (WP) El Niños. However, most models fail to reproduce the asymmetry between the two types of La Niñas, with CT stronger than WP events, which is opposite to observations. Most models capture the observed peak in ENSO amplitude around December; however, the seasonal evolution of ENSO has a large range of behavior across the models. The CMIP5 models generally reproduce the duration of CT El Niños but have biases in the evolution of the other types of events. The evolution of WP El Niños suggests that the decay of this event occurs through heat content discharge in the models rather than the advection of SST via anomalous zonal currents, as seems to occur in observations. No consistent changes are seen across the models in the location and magnitude of maximum SST anomalies, frequency, or temporal evolution of these events in a warmer world.
Abstract
The representation of the El Niño–Southern Oscillation (ENSO) under historical forcing and future projections is analyzed in 34 models from the Coupled Model Intercomparison Project phase 5 (CMIP5). Most models realistically simulate the observed intensity and location of maximum sea surface temperature (SST) anomalies during ENSO events. However, there exist systematic biases in the westward extent of ENSO-related SST anomalies, driven by unrealistic westward displacement and enhancement of the equatorial wind stress in the western Pacific. Almost all CMIP5 models capture the observed asymmetry in magnitude between the warm and cold events (i.e., El Niños are stronger than La Niñas) and between the two types of El Niños: that is, cold tongue (CT) El Niños are stronger than warm pool (WP) El Niños. However, most models fail to reproduce the asymmetry between the two types of La Niñas, with CT stronger than WP events, which is opposite to observations. Most models capture the observed peak in ENSO amplitude around December; however, the seasonal evolution of ENSO has a large range of behavior across the models. The CMIP5 models generally reproduce the duration of CT El Niños but have biases in the evolution of the other types of events. The evolution of WP El Niños suggests that the decay of this event occurs through heat content discharge in the models rather than the advection of SST via anomalous zonal currents, as seems to occur in observations. No consistent changes are seen across the models in the location and magnitude of maximum SST anomalies, frequency, or temporal evolution of these events in a warmer world.
Abstract
The Weather Research and Forecast model at ⅓° resolution is used to simulate the statistics of tropical cyclone (TC) activity in the present climate of the South Pacific. In addition to the large-scale conditions, the model is shown to reproduce a wide range of mesoscale convective systems. Tropical cyclones grow from the most intense of these systems formed along the South Pacific convergence zone (SPCZ) and sometimes develop into hurricanes. The three-dimensional structure of simulated tropical cyclones is in excellent agreement with dropsondes and satellite observations. The mean seasonal and spatial distributions of TC genesis and occurrence are also in good agreement with the Joint Typhoon Warning Center (JTWC) data. It is noted, however, that the spatial pattern of TC activity is shifted to the northeast because of a similar bias in the environmental forcing. Over the whole genesis area, 8.2 ± 3.5 cyclones are produced seasonally in the model, compared with 6.6 ± 3.0 in the JTWC data. Part of the interannual variability is associated with El Niño–Southern Oscillation (ENSO). ENSO-driven displacement of the SPCZ position produces a dipole pattern of correlation and results in a weaker correlation when the opposing correlations of the dipole are amalgamated over the entire South Pacific region. As a result, environmentally forced variability at the regional scale is relatively weak, that is, of comparable order to stochastic variability (±1.7 cyclones yr−1), which is estimated from a 10-yr climatological simulation. Stochastic variability appears essentially related to mesoscale interactions, which also affect TC tracks and the resulting occurrence.
Abstract
The Weather Research and Forecast model at ⅓° resolution is used to simulate the statistics of tropical cyclone (TC) activity in the present climate of the South Pacific. In addition to the large-scale conditions, the model is shown to reproduce a wide range of mesoscale convective systems. Tropical cyclones grow from the most intense of these systems formed along the South Pacific convergence zone (SPCZ) and sometimes develop into hurricanes. The three-dimensional structure of simulated tropical cyclones is in excellent agreement with dropsondes and satellite observations. The mean seasonal and spatial distributions of TC genesis and occurrence are also in good agreement with the Joint Typhoon Warning Center (JTWC) data. It is noted, however, that the spatial pattern of TC activity is shifted to the northeast because of a similar bias in the environmental forcing. Over the whole genesis area, 8.2 ± 3.5 cyclones are produced seasonally in the model, compared with 6.6 ± 3.0 in the JTWC data. Part of the interannual variability is associated with El Niño–Southern Oscillation (ENSO). ENSO-driven displacement of the SPCZ position produces a dipole pattern of correlation and results in a weaker correlation when the opposing correlations of the dipole are amalgamated over the entire South Pacific region. As a result, environmentally forced variability at the regional scale is relatively weak, that is, of comparable order to stochastic variability (±1.7 cyclones yr−1), which is estimated from a 10-yr climatological simulation. Stochastic variability appears essentially related to mesoscale interactions, which also affect TC tracks and the resulting occurrence.
Abstract
Leading patterns of observed monthly extreme rainfall variability in Australia are examined using an empirical orthogonal teleconnection (EOT) method. Extreme rainfall variability is more closely related to mean rainfall variability during austral summer than in winter. The leading EOT patterns of extreme rainfall explain less variance in Australia-wide extreme rainfall than is the case for mean rainfall EOTs. The authors illustrate that, as with mean rainfall, the El Niño–Southern Oscillation (ENSO) has the strongest association with warm-season extreme rainfall variability, while in the cool season the primary drivers are atmospheric blocking and the subtropical ridge. The Indian Ocean dipole and southern annular mode also have significant relationships with patterns of variability during austral winter and spring. Leading patterns of summer extreme rainfall variability have predictability several months ahead from Pacific sea surface temperatures (SSTs) and as much as a year in advance from Indian Ocean SSTs. Predictability from the Pacific is greater for wetter-than-average summer months than for months that are drier than average, whereas for the Indian Ocean the relationship has greater linearity. Several cool-season EOTs are associated with midlatitude synoptic-scale patterns along the south and east coasts. These patterns have common atmospheric signatures denoting moist onshore flow and strong cyclonic anomalies often to the north of a blocking anticyclone. Tropical cyclone activity is observed to have significant relationships with some warm-season EOTs. This analysis shows that extreme rainfall variability in Australia can be related to remote drivers and local synoptic-scale patterns throughout the year.
Abstract
Leading patterns of observed monthly extreme rainfall variability in Australia are examined using an empirical orthogonal teleconnection (EOT) method. Extreme rainfall variability is more closely related to mean rainfall variability during austral summer than in winter. The leading EOT patterns of extreme rainfall explain less variance in Australia-wide extreme rainfall than is the case for mean rainfall EOTs. The authors illustrate that, as with mean rainfall, the El Niño–Southern Oscillation (ENSO) has the strongest association with warm-season extreme rainfall variability, while in the cool season the primary drivers are atmospheric blocking and the subtropical ridge. The Indian Ocean dipole and southern annular mode also have significant relationships with patterns of variability during austral winter and spring. Leading patterns of summer extreme rainfall variability have predictability several months ahead from Pacific sea surface temperatures (SSTs) and as much as a year in advance from Indian Ocean SSTs. Predictability from the Pacific is greater for wetter-than-average summer months than for months that are drier than average, whereas for the Indian Ocean the relationship has greater linearity. Several cool-season EOTs are associated with midlatitude synoptic-scale patterns along the south and east coasts. These patterns have common atmospheric signatures denoting moist onshore flow and strong cyclonic anomalies often to the north of a blocking anticyclone. Tropical cyclone activity is observed to have significant relationships with some warm-season EOTs. This analysis shows that extreme rainfall variability in Australia can be related to remote drivers and local synoptic-scale patterns throughout the year.
Abstract
Tropical cyclones drive intense ocean vertical mixing that explains most of the surface cooling observed in their wake (the “cold wake”). In this paper, the authors investigate the influence of cyclonic rainfall on the cold wake at a global scale over the 2002–09 period. For each cyclone, the cold wake intensity and accumulated rainfall are obtained from satellite data and precyclone oceanic stratification from the Global Eddy-Permitting Ocean Reanalysis (GLORYS2). The impact of precipitation on the cold wake is estimated by assuming that cooling is entirely due to vertical mixing and that an extra amount of energy (corresponding to the energy used to mix the rain layer into the ocean) would be available for mixing the ocean column in the hypothetical case with no rain. The positive buoyancy flux of rainfall reduces the mixed layer depth after the cyclone passage, hence reducing cold water entrainment. The resulting reduction in cold wake amplitude is generally small (median of 0.07 K for a median 1 K cold wake) but not negligible (>19% for 10% of the cases). Despite similar cyclonic rainfall, the effect of rain on the cold wake is strongest in the Arabian Sea and weak in the Bay of Bengal. An analytical approach with a linearly stratified ocean allows attributing this difference to the presence of barrier layers in the Bay of Bengal. The authors also show that the cold wake is generally a “salty wake” because entrainment of subsurface saltier water overwhelms the dilution effect of rainfall. Finally, rainfall temperature has a negligible influence on the cold wake.
Abstract
Tropical cyclones drive intense ocean vertical mixing that explains most of the surface cooling observed in their wake (the “cold wake”). In this paper, the authors investigate the influence of cyclonic rainfall on the cold wake at a global scale over the 2002–09 period. For each cyclone, the cold wake intensity and accumulated rainfall are obtained from satellite data and precyclone oceanic stratification from the Global Eddy-Permitting Ocean Reanalysis (GLORYS2). The impact of precipitation on the cold wake is estimated by assuming that cooling is entirely due to vertical mixing and that an extra amount of energy (corresponding to the energy used to mix the rain layer into the ocean) would be available for mixing the ocean column in the hypothetical case with no rain. The positive buoyancy flux of rainfall reduces the mixed layer depth after the cyclone passage, hence reducing cold water entrainment. The resulting reduction in cold wake amplitude is generally small (median of 0.07 K for a median 1 K cold wake) but not negligible (>19% for 10% of the cases). Despite similar cyclonic rainfall, the effect of rain on the cold wake is strongest in the Arabian Sea and weak in the Bay of Bengal. An analytical approach with a linearly stratified ocean allows attributing this difference to the presence of barrier layers in the Bay of Bengal. The authors also show that the cold wake is generally a “salty wake” because entrainment of subsurface saltier water overwhelms the dilution effect of rainfall. Finally, rainfall temperature has a negligible influence on the cold wake.
