Tropical cyclone rapid intensification events often cause destructive hurricane landfalls because they are associated with the strongest storms and forecasts with the highest errors. Multi-decade ...observational datasets of tropical cyclone behavior have recently enabled documentation of upward trends in tropical cyclone rapid intensification in several basins. However, a robust anthropogenic signal in global intensification trends and the physical drivers of intensification trends have yet to be identified. To address these knowledge gaps, here we compare the observed trends in intensification and tropical cyclone environmental parameters to simulated natural variability in a high-resolution global climate model. In multiple basins and the global dataset, we detect a significant increase in intensification rates with a positive contribution from anthropogenic forcing. Furthermore, thermodynamic environments around tropical cyclones have become more favorable for intensification, and climate models show anthropogenic warming has significantly increased the probability of these changes.
Previous studies found large biases between individual observational and model estimates of historical ocean anthropogenic carbon uptake. We show that the largest bias between the Coupled Model ...Intercomparison Project phase 5 (CMIP5) ensemble mean and between two observational estimates of ocean anthropogenic carbon is due to a difference in start date. After adjusting the CMIP5 and observational estimates to the 1791–1995 period, all three carbon uptake estimates agree to within 3 Pg of C, about 4% of the total. The CMIP5 ensemble mean spatial bias compared to the observations is generally smaller than the observational error, apart from a negative bias in the Southern Ocean and a positive bias in the Southern Indian and Pacific Oceans compensating each other in the global mean. This dipole pattern is likely due to an equatorward and weak bias in the position of Southern Hemisphere westerlies and lack of mode and intermediate water ventilation.
Key Points
Observations and model simulations of ocean anthropogenic carbon assume different start dates
Once referenced to the same period, 1971–1995, models and observations of ocean anthropogenic carbon agree to within 4%
A model bias in the mean position of Southern Hemisphere westerlies results in a bias in the pattern of Southern Hemisphere carbon uptake
Observed and predicted increases in Southern Ocean winds are thought to upwell deep ocean carbon and increase atmospheric CO2. However, Southern Ocean dynamics affect biogeochemistry and circulation ...pathways on a global scale. Using idealized Massachusetts Institute of Technology General Circulation Model (MITgcm) simulations, we demonstrate that an increase in Southern Ocean winds reduces the carbon sink in the North Atlantic subpolar gyre. The increase in atmospheric CO2 due to the reduction of the North Atlantic carbon sink is shown to be of the same magnitude as the increase in atmospheric CO2 due to Southern Ocean outgassing. The mechanism can be described as follows: The increase in Southern Ocean winds leads to an increase in upper ocean northward nutrient transport. Biological productivity is therefore enhanced in the tropics, which alters the chemistry of the subthermocline waters that are ultimately upwelled in the subpolar gyre. The results demonstrate the influence of Southern Ocean winds on the North Atlantic carbon sink and show that the effect of Southern Ocean winds on atmospheric CO2 is likely twice as large as previously thought in past, present, and future climates.
Key Points
Increased Southern Ocean winds reduce the North Atlantic carbon sink
The effect of Southern Ocean winds on atmospheric pCO2 is doubled by the nonlocal feedback
Atlantic tropical biology affects the North Atlantic subpolar gyre Revelle buffer factor
Abstract
Previous studies found large biases between individual observational and model estimates of historical ocean anthropogenic carbon uptake. We show that the largest bias between the Coupled ...Model Intercomparison Project phase 5 (CMIP5) ensemble mean and between two observational estimates of ocean anthropogenic carbon is due to a difference in start date. After adjusting the CMIP5 and observational estimates to the 1791–1995 period, all three carbon uptake estimates agree to within 3 Pg of C, about 4% of the total. The CMIP5 ensemble mean spatial bias compared to the observations is generally smaller than the observational error, apart from a negative bias in the Southern Ocean and a positive bias in the Southern Indian and Pacific Oceans compensating each other in the global mean. This dipole pattern is likely due to an equatorward and weak bias in the position of Southern Hemisphere westerlies and lack of mode and intermediate water ventilation.
Key Points
Observations and model simulations of ocean anthropogenic carbon assume different start dates
Once referenced to the same period, 1971–1995, models and observations of ocean anthropogenic carbon agree to within 4%
A model bias in the mean position of Southern Hemisphere westerlies results in a bias in the pattern of Southern Hemisphere carbon uptake
The warming induced by anthropogenic carbon emissions affects the climate system through a multitude of physical mechanisms. Changes in the dynamics, thermodynamics and biogeochemistry of the ocean ...alter the different ocean carbon reservoirs, potentially resulting in further carbon emissions and a climate- carbon feedback. Surface wind stress and surface warming are two of the most influential forcings acting on the ocean carbon system in past, present and future climates due to their influence on the mixed layer dynamics and the large scale ocean circulation. This thesis quantifies the climate-carbon feedback of wind stress and surface warming, with a particular focus on the mechanisms driving the feedbacks and the role of the Southern Ocean and the North Atlantic. To study the feedbacks, a set of theoretical scalings and a hierarchy of numerical simulations are used. Of the climate feedbacks examined, increased surface warming is likely to result in large atmospheric CO2 anomalies while the effects of North Atlantic wind stress are likely to be negligible. The atmospheric feedback of surface warming is constrained by compensating changes in separate ocean carbon reservoirs as a result of warming-induced circulation changes. Southern Ocean winds affect atmospheric CO2 through both local upwelling of carbon as well as the remote modification of Equatorial and North Atlantic chemistry. As a result, the net Southern Ocean wind stress feedback could be significant and even comparable to the temperature feedback. This thesis provides a quantification of regional and global climate-carbon feedbacks due to ocean dynamics. The estimates of carbon-climate feedbacks are useful tools for understanding past, present and future climates.
The warming induced by anthropogenic carbon emissions affects the climate system through a multitude of physical mechanisms. Changes in the dynamics, thermodynamics and biogeochemistry of the ocean ...alter the different ocean carbon reservoirs, potentially resulting in further carbon emissions and a climate- carbon feedback. Surface wind stress and surface warming are two of the most influential forcings acting on the ocean carbon system in past, present and future climates due to their influence on the mixed layer dynamics and the large scale ocean circulation. This thesis quantifies the climate-carbon feedback of wind stress and surface warming, with a particular focus on the mechanisms driving the feedbacks and the role of the Southern Ocean and the North Atlantic. To study the feedbacks, a set of theoretical scalings and a hierarchy of numerical simulations are used. Of the climate feedbacks examined, increased surface warming is likely to result in large atmospheric CO2 anomalies while the effects of North Atlantic wind stress are likely to be negligible. The atmospheric feedback of surface warming is constrained by compensating changes in separate ocean carbon reservoirs as a result of warming-induced circulation changes. Southern Ocean winds affect atmospheric CO2 through both local upwelling of carbon as well as the remote modification of Equatorial and North Atlantic chemistry. As a result, the net Southern Ocean wind stress feedback could be significant and even comparable to the temperature feedback. This thesis provides a quantification of regional and global climate-carbon feedbacks due to ocean dynamics. The estimates of carbon-climate feedbacks are useful tools for understanding past, present and future climates.
The ability of Earth System Models to accurately simulate the seasonal cycle of the partial pressure of CO2 in surface water (
pCO2SW) has important implications for projecting future ocean carbon ...uptake. Here we develop objective model skill score metrics and assess the abilities of 18 CMIP5 models to simulate the seasonal mean, amplitude, and timing of
pCO2SW in biogeographically defined ocean biomes. The models perform well at simulating the monthly timing of the seasonal minimum and maximum of
pCO2SW, but perform somewhat worse at simulating the seasonal mean values, particularly in polar and equatorial regions. The results also illustrate that a single “best” model can be difficult to determine, despite an analysis restricted to the seasonality of a single variable. Nonetheless, groups of models tend to perform better than others, with significant regional differences. This suggests that particular models may be better suited for particular regions, though we find no evidence for model tuning. Timing and amplitude skill scores display a weak positive correlation with observational data density, while the seasonal mean scores display a weak negative correlation. Thus, additional mapped
pCO2SW data may not directly increase model skill scores; however, improved knowledge of the dominant mechanisms may improve model skill. Lastly, we find skill score variability due to internal model variability to be much lower than variability within the CMIP5 intermodel spread, suggesting that mechanistic model differences are primarily responsible for differences in model skill scores.
Key Points:
Model performance highly variable across biomes
Particular models better suited for specific biomes and metrics
Internal variability low compared to intermodel variability
The ability of Earth System Models to accurately simulate the seasonal cycle of the partial pressure of CO sub(2) in surface water (pCO sub(2) super(sw)) has important implications for projecting ...future ocean carbon uptake. Here we develop objective model skill score metrics and assess the abilities of 18 CMIP5 models to simulate the seasonal mean, amplitude, and timing of pCO sub(2) super(sw) in biogeographically defined ocean biomes. The models perform well at simulating the monthly timing of the seasonal minimum and maximum of pCO sub(2) super(sw), but perform somewhat worse at simulating the seasonal mean values, particularly in polar and equatorial regions. The results also illustrate that a single "best" model can be difficult to determine, despite an analysis restricted to the seasonality of a single variable. Nonetheless, groups of models tend to perform better than others, with significant regional differences. This suggests that particular models may be better suited for particular regions, though we find no evidence for model tuning. Timing and amplitude skill scores display a weak positive correlation with observational data density, while the seasonal mean scores display a weak negative correlation. Thus, additional mapped pCO sub(2) super(sw) data may not directly increase model skill scores; however, improved knowledge of the dominant mechanisms may improve model skill. Lastly, we find skill score variability due to internal model variability to be much lower than variability within the CMIP5 intermodel spread, suggesting that mechanistic model differences are primarily responsible for differences in model skill scores. Key Points: * Model performance highly variable across biomes * Particular models better suited for specific biomes and metrics * Internal variability low compared to intermodel variability
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