Search Results
You are looking at 1 - 4 of 4 items for
- Author or Editor: Jules B. Kajtar x
- Refine by Access: All Content x
Abstract
The Pacific and Indian Oceans are connected by an oceanic passage called the Indonesian Throughflow (ITF). In this setting, modes of climate variability over the two oceanic basins interact. El Niño–Southern Oscillation (ENSO) events generate sea surface temperature anomalies (SSTAs) over the Indian Ocean that, in turn, influence ENSO evolution. This raises the question as to whether Indo-Pacific feedback interactions would still occur in a climate system without an Indonesian Throughflow. This issue is investigated here for the first time using a coupled climate model with a blocked Indonesian gateway and a series of partially decoupled experiments in which air–sea interactions over each ocean basin are in turn suppressed. Closing the Indonesian Throughflow significantly alters the mean climate state over the Pacific and Indian Oceans. The Pacific Ocean retains an ENSO-like variability, but it is shifted eastward. In contrast, the Indian Ocean dipole and the Indian Ocean basinwide mode both collapse into a single dominant and drastically transformed mode. While the relationship between ENSO and the altered Indian Ocean mode is weaker than that when the ITF is open, the decoupled experiments reveal a damping effect exerted between the two modes. Despite the weaker Indian Ocean SSTAs and the increased distance between these and the core of ENSO SSTAs, the interbasin interactions remain. This suggests that the atmospheric bridge is a robust element of the Indo-Pacific climate system, linking the Indian and Pacific Oceans even in the absence of an Indonesian Throughflow.
Abstract
The Pacific and Indian Oceans are connected by an oceanic passage called the Indonesian Throughflow (ITF). In this setting, modes of climate variability over the two oceanic basins interact. El Niño–Southern Oscillation (ENSO) events generate sea surface temperature anomalies (SSTAs) over the Indian Ocean that, in turn, influence ENSO evolution. This raises the question as to whether Indo-Pacific feedback interactions would still occur in a climate system without an Indonesian Throughflow. This issue is investigated here for the first time using a coupled climate model with a blocked Indonesian gateway and a series of partially decoupled experiments in which air–sea interactions over each ocean basin are in turn suppressed. Closing the Indonesian Throughflow significantly alters the mean climate state over the Pacific and Indian Oceans. The Pacific Ocean retains an ENSO-like variability, but it is shifted eastward. In contrast, the Indian Ocean dipole and the Indian Ocean basinwide mode both collapse into a single dominant and drastically transformed mode. While the relationship between ENSO and the altered Indian Ocean mode is weaker than that when the ITF is open, the decoupled experiments reveal a damping effect exerted between the two modes. Despite the weaker Indian Ocean SSTAs and the increased distance between these and the core of ENSO SSTAs, the interbasin interactions remain. This suggests that the atmospheric bridge is a robust element of the Indo-Pacific climate system, linking the Indian and Pacific Oceans even in the absence of an Indonesian Throughflow.
Abstract
Marine heatwaves (MHWs) off Western Australia (110°E–116°E, 22°S–32°S; hereafter, WA MHWs) can cause devastating ecological impacts, as was evidenced by the 2011 extreme event. Previous studies suggest that La Niña is the major large-scale driver of WA MHWs, while Indian Ocean Dipole (IOD) may also play a role. Here, we investigate historical WA MHWs and their connections to these large-scale climate modes in an ocean model (ACCESS–OM2) simulation driven by a prescribed atmosphere from JRA55–do over 1959– 2018. Rather than analysing sea surface temperature, the WA MHWs and climate mode indices were characterized and investigated in vertically averaged temperature (VAT) to ~300m depth to afford the longer ocean dynamic time scales, including remote oceanic connections. We develop a cyclostationary linear inverse model (CS-LIM; from 35°S–10°N, across the Indo-Pacific Ocean), to investigate the relative contributions of La Niña VAT and positive IOD VAT to the predictability of WA VAT MHWs. Using a large ensemble of CS-LIM simulations, we found that ~50% of WA MHWs were preceded about 5 months by La Niña, and 30% of the MHWs by positive IOD about 20 months prior. While precursor La Niña or positive IOD, on their own, were found to correspond with increased WA MHW likelihood in the months following (~2.7 times or ~1.5 times more likely than by chance, respectively), in combination these climate mode phases were found to produce the largest enhancement in MHW likelihood (~3.2 times more likely than by chance). Additionally, we found that stronger and longer La Niña and/or positive IOD tend to lead stronger and longer WA MHWs.
Abstract
Marine heatwaves (MHWs) off Western Australia (110°E–116°E, 22°S–32°S; hereafter, WA MHWs) can cause devastating ecological impacts, as was evidenced by the 2011 extreme event. Previous studies suggest that La Niña is the major large-scale driver of WA MHWs, while Indian Ocean Dipole (IOD) may also play a role. Here, we investigate historical WA MHWs and their connections to these large-scale climate modes in an ocean model (ACCESS–OM2) simulation driven by a prescribed atmosphere from JRA55–do over 1959– 2018. Rather than analysing sea surface temperature, the WA MHWs and climate mode indices were characterized and investigated in vertically averaged temperature (VAT) to ~300m depth to afford the longer ocean dynamic time scales, including remote oceanic connections. We develop a cyclostationary linear inverse model (CS-LIM; from 35°S–10°N, across the Indo-Pacific Ocean), to investigate the relative contributions of La Niña VAT and positive IOD VAT to the predictability of WA VAT MHWs. Using a large ensemble of CS-LIM simulations, we found that ~50% of WA MHWs were preceded about 5 months by La Niña, and 30% of the MHWs by positive IOD about 20 months prior. While precursor La Niña or positive IOD, on their own, were found to correspond with increased WA MHW likelihood in the months following (~2.7 times or ~1.5 times more likely than by chance, respectively), in combination these climate mode phases were found to produce the largest enhancement in MHW likelihood (~3.2 times more likely than by chance). Additionally, we found that stronger and longer La Niña and/or positive IOD tend to lead stronger and longer WA MHWs.
Abstract
Understanding variability of the Indonesian Throughflow (ITF) and its links to El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD), and how they are represented across climate models constitutes an important step toward improved future climate projections. These issues are examined using 20 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and the SODA-2.2.4 ocean reanalysis. It is found that the CMIP5 models overall simulate aspects of ITF variability, such as spectral and vertical structure, that are consistent with the reanalysis, although intermodel differences are substantial. The ITF variability is shown to exhibit two dominant principal vertical structures: a surface-intensified transport anomaly (ITFM1) and an anomalous transport characterized by opposing flows in the surface and subsurface (ITFM2). In the CMIP5 models and reanalysis, ITFM2 is linked to both ENSO and the IOD via anomalous Indo-Pacific Walker circulation. The driver of ITFM1 however differs between the reanalysis and the CMIP5 models. In the reanalysis ITFM1 is a delayed response to ENSO, whereas in the CMIP5 models it is linked to the IOD associated with the overly strong IOD amplitude bias. Further, the CMIP5 ITF variability tends to be weaker than in the reanalysis, due to a tendency for the CMIP5 models to simulate a delayed IOD in response to ENSO. The importance in considering the vertical structure of ITF variability in understanding ENSO and IOD impact is further underscored by the close link between greenhouse-forced changes in ENSO variability and projected changes in subsurface ITF variability.
Abstract
Understanding variability of the Indonesian Throughflow (ITF) and its links to El Niño–Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD), and how they are represented across climate models constitutes an important step toward improved future climate projections. These issues are examined using 20 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and the SODA-2.2.4 ocean reanalysis. It is found that the CMIP5 models overall simulate aspects of ITF variability, such as spectral and vertical structure, that are consistent with the reanalysis, although intermodel differences are substantial. The ITF variability is shown to exhibit two dominant principal vertical structures: a surface-intensified transport anomaly (ITFM1) and an anomalous transport characterized by opposing flows in the surface and subsurface (ITFM2). In the CMIP5 models and reanalysis, ITFM2 is linked to both ENSO and the IOD via anomalous Indo-Pacific Walker circulation. The driver of ITFM1 however differs between the reanalysis and the CMIP5 models. In the reanalysis ITFM1 is a delayed response to ENSO, whereas in the CMIP5 models it is linked to the IOD associated with the overly strong IOD amplitude bias. Further, the CMIP5 ITF variability tends to be weaker than in the reanalysis, due to a tendency for the CMIP5 models to simulate a delayed IOD in response to ENSO. The importance in considering the vertical structure of ITF variability in understanding ENSO and IOD impact is further underscored by the close link between greenhouse-forced changes in ENSO variability and projected changes in subsurface ITF variability.
Abstract
In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.
Abstract
In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.
