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- Author or Editor: Keith Dixon x
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Abstract
Most projections of greenhouse gas–induced climate change indicate a weakening of the thermohaline circulation (THC) in the North Atlantic in response to increased freshening and warming in the subpolar region. These changes reduce high-latitude upper-ocean density and therefore weaken the THC. Using ensembles of numerical experiments with a coupled ocean–atmosphere model, it is found that this weakening could be delayed by several decades in response to a sustained upward trend in the Arctic/North Atlantic oscillation during winter, such as has been observed over the last 30 years. The stronger winds over the North Atlantic associated with this trend extract more heat from the ocean, thereby cooling and increasing the density of the upper ocean and thus opposing the previously described weakening of the THC. This result is of particular importance if the positive trend in the Arctic/North Atlantic oscillation is a response to increasing greenhouse gases, as has been recently suggested.
Abstract
Most projections of greenhouse gas–induced climate change indicate a weakening of the thermohaline circulation (THC) in the North Atlantic in response to increased freshening and warming in the subpolar region. These changes reduce high-latitude upper-ocean density and therefore weaken the THC. Using ensembles of numerical experiments with a coupled ocean–atmosphere model, it is found that this weakening could be delayed by several decades in response to a sustained upward trend in the Arctic/North Atlantic oscillation during winter, such as has been observed over the last 30 years. The stronger winds over the North Atlantic associated with this trend extract more heat from the ocean, thereby cooling and increasing the density of the upper ocean and thus opposing the previously described weakening of the THC. This result is of particular importance if the positive trend in the Arctic/North Atlantic oscillation is a response to increasing greenhouse gases, as has been recently suggested.
Abstract
The predictive abilities of NOAA normals and running means of 2–30 years length are tested statistically. Heating degree-day (HDD) data from six northern United States sites are tested using root-mean-square error of prediction (RMSE) tests, mean absolute error (MAE) tests, and a “best versus worst” predictor methodology. Monte Carlo tests using biased and unbiased numbers are presented for the RMSE and “best versus worst” analyses. Results are consistent with past research in showing that running means 10–30 years in length perform better than shorter averaging periods for predictive purposes. The MAE values are generally found to be lowest for running mean lengths shorter than that for the RMSE statistic at the six sites. For the 30 years studied, NOAA HDD normals performed well along the east coast, indicating a possible regional difference that requires more detailed investigation. Limitations of the “best versus worst” predictor method are discussed, and it is suggested that such a procedure should not be solely relied on in determining the optimum length of prediction.
Abstract
The predictive abilities of NOAA normals and running means of 2–30 years length are tested statistically. Heating degree-day (HDD) data from six northern United States sites are tested using root-mean-square error of prediction (RMSE) tests, mean absolute error (MAE) tests, and a “best versus worst” predictor methodology. Monte Carlo tests using biased and unbiased numbers are presented for the RMSE and “best versus worst” analyses. Results are consistent with past research in showing that running means 10–30 years in length perform better than shorter averaging periods for predictive purposes. The MAE values are generally found to be lowest for running mean lengths shorter than that for the RMSE statistic at the six sites. For the 30 years studied, NOAA HDD normals performed well along the east coast, indicating a possible regional difference that requires more detailed investigation. Limitations of the “best versus worst” predictor method are discussed, and it is suggested that such a procedure should not be solely relied on in determining the optimum length of prediction.
Abstract
The prediction of winter in the United States from Pacific sea surface temperatures was examined using a jackknifed regression scheme and a measure of intraseasonal atmospheric circulation variability. Employing a jackknifed regression methodology when deriving objective prediction equations allowed forecast to be better quantified than in past studies by greatly increasing the effective independent sample size. The procedures were repeated on three datasets: 1) all winters in the period 1950–79 (30 winters), 2) the 15 winters having the highest Variability Index (VI), and 3) the 15 winters having the lowest VI. The Variability Index was constructed to measure the intraseasonal variability of five-day period mean 700 mb heights for a portion of the Northern Hemisphere. Verification results showed that statistically significant skill was achieved in the complete sample (overall mean percent correct of 39 and 59 for three- and two-category forecasts respectively), but improved somewhat for the low VI sample. In that case, corresponding scores were (34 and 64 percent correct. In contrast, the high VI sample scores were lower (34 and 58 percent correct) than for the complete sample, indicating that skill is likely dependent on the degree of interaseasonal circulation variability.
Abstract
The prediction of winter in the United States from Pacific sea surface temperatures was examined using a jackknifed regression scheme and a measure of intraseasonal atmospheric circulation variability. Employing a jackknifed regression methodology when deriving objective prediction equations allowed forecast to be better quantified than in past studies by greatly increasing the effective independent sample size. The procedures were repeated on three datasets: 1) all winters in the period 1950–79 (30 winters), 2) the 15 winters having the highest Variability Index (VI), and 3) the 15 winters having the lowest VI. The Variability Index was constructed to measure the intraseasonal variability of five-day period mean 700 mb heights for a portion of the Northern Hemisphere. Verification results showed that statistically significant skill was achieved in the complete sample (overall mean percent correct of 39 and 59 for three- and two-category forecasts respectively), but improved somewhat for the low VI sample. In that case, corresponding scores were (34 and 64 percent correct. In contrast, the high VI sample scores were lower (34 and 58 percent correct) than for the complete sample, indicating that skill is likely dependent on the degree of interaseasonal circulation variability.
Abstract
The analyses presented here focus on the Southern Ocean as simulated in a set of global coupled climate model control experiments conducted by several international climate modeling groups. Dominated by the Antarctic Circumpolar Current (ACC), the vast Southern Ocean can influence large-scale surface climate features on various time scales. Its climatic relevance stems in part from it being the region where most of the transformation of the World Ocean’s water masses occurs. In climate change experiments that simulate greenhouse gas–induced warming, Southern Ocean air–sea heat fluxes and three-dimensional circulation patterns make it a region where much of the future oceanic heat storage takes place, though the magnitude of that heat storage is one of the larger sources of uncertainty associated with the transient climate response in such model projections. Factors such as the Southern Ocean’s wind forcing, heat, and salt budgets are linked to the structure and transport of the ACC in ways that have not been expressed clearly in the literature. These links are explored here in a coupled model context by analyzing a sizable suite of preindustrial control experiments associated with the forthcoming Intergovernmental Panel on Climate Change’s Fourth Assessment Report. A framework is developed that uses measures of coupled model simulation characteristics, primarily those related to the Southern Ocean wind forcing and water mass properties, to allow one to categorize, and to some extent predict, which models do better or worse at simulating the Southern Ocean and why. Hopefully, this framework will also lead to increased understanding of the ocean’s response to climate changes.
Abstract
The analyses presented here focus on the Southern Ocean as simulated in a set of global coupled climate model control experiments conducted by several international climate modeling groups. Dominated by the Antarctic Circumpolar Current (ACC), the vast Southern Ocean can influence large-scale surface climate features on various time scales. Its climatic relevance stems in part from it being the region where most of the transformation of the World Ocean’s water masses occurs. In climate change experiments that simulate greenhouse gas–induced warming, Southern Ocean air–sea heat fluxes and three-dimensional circulation patterns make it a region where much of the future oceanic heat storage takes place, though the magnitude of that heat storage is one of the larger sources of uncertainty associated with the transient climate response in such model projections. Factors such as the Southern Ocean’s wind forcing, heat, and salt budgets are linked to the structure and transport of the ACC in ways that have not been expressed clearly in the literature. These links are explored here in a coupled model context by analyzing a sizable suite of preindustrial control experiments associated with the forthcoming Intergovernmental Panel on Climate Change’s Fourth Assessment Report. A framework is developed that uses measures of coupled model simulation characteristics, primarily those related to the Southern Ocean wind forcing and water mass properties, to allow one to categorize, and to some extent predict, which models do better or worse at simulating the Southern Ocean and why. Hopefully, this framework will also lead to increased understanding of the ocean’s response to climate changes.
Abstract
The aim of this work is to test the potential capabilities of 3D sonar technology for studying small-scale processes in the near-surface layer of the ocean, using the centerline wake of ships of opportunity as the object of study. The first tests conducted in Tampa Bay, Florida, with the 3D sonar have demonstrated the ability of this technology to observe the shape of the centerline wake in great detail starting from centimeter scale, using air bubbles as a proxy. An advantage of the 3D sonar technology is that it allows quantitative estimates of the ship wake geometry, which presents new opportunities for validation of hydrodynamic models of the ship wake. Three-dimensional sonar is also a potentially useful tool for studies of air-bubble dynamics and turbulence in breaking surface waves.
Abstract
The aim of this work is to test the potential capabilities of 3D sonar technology for studying small-scale processes in the near-surface layer of the ocean, using the centerline wake of ships of opportunity as the object of study. The first tests conducted in Tampa Bay, Florida, with the 3D sonar have demonstrated the ability of this technology to observe the shape of the centerline wake in great detail starting from centimeter scale, using air bubbles as a proxy. An advantage of the 3D sonar technology is that it allows quantitative estimates of the ship wake geometry, which presents new opportunities for validation of hydrodynamic models of the ship wake. Three-dimensional sonar is also a potentially useful tool for studies of air-bubble dynamics and turbulence in breaking surface waves.
Abstract
Statistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.
We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.
Abstract
Statistical downscaling (SD) is commonly used to provide information for the assessment of climate change impacts. Using as input the output from large-scale dynamical climate models and observation-based data products, SD aims to provide a finer grain of detail and to mitigate systematic biases. It is generally recognized as providing added value. However, one of the key assumptions of SD is that the relationships used to train the method during a historical period are unchanged in the future, in the face of climate change. The validity of this assumption is typically quite difficult to assess in the normal course of analysis, as observations of future climate are lacking. We approach this problem using a “perfect model” experimental design in which high-resolution dynamical climate model output is used as a surrogate for both past and future observations.
We find that while SD in general adds considerable value, in certain well-defined circumstances it can produce highly erroneous results. Furthermore, the breakdown of SD in these contexts could not be foreshadowed during the typical course of evaluation based on only available historical data. We diagnose and explain the reasons for these failures in terms of physical, statistical, and methodological causes. These findings highlight the need for caution in the use of statistically downscaled products and the need for further research to consider other hitherto unknown pitfalls, perhaps utilizing more advanced perfect model designs than the one we have employed.
Abstract
South Korea has among the highest rates of suicide in the world, and previous research suggests that suicide frequency increases with anomalously high temperatures, possibly as a result of increased sunshine. However, it is unclear whether this temperature–suicide association exists throughout the entire year. Using distributed lag nonlinear modeling, which effectively controls for nonlinear and delayed effects, we examine temperature–suicide associations for both a warm season (April–September) and a cool season (October–March) for three cities across South Korea: Seoul, Daegu, and Busan. We find consistent, statistically significant, mostly linear relationships between relative risk of suicide and daily temperature in the cool season but few associations in the warm season. This seasonal signal of statistically significant temperature–suicide associations only in the cool season exists among all age segments, but especially for those 35 and older, along with both males and females. We further use distributed lag nonlinear modeling to examine cloud cover–suicide associations and find few significant relationships. This result suggests that that high daily temperatures in the cool season, and not exposure to sun, are responsible for the strong temperature–suicide associations found in South Korea.
Abstract
South Korea has among the highest rates of suicide in the world, and previous research suggests that suicide frequency increases with anomalously high temperatures, possibly as a result of increased sunshine. However, it is unclear whether this temperature–suicide association exists throughout the entire year. Using distributed lag nonlinear modeling, which effectively controls for nonlinear and delayed effects, we examine temperature–suicide associations for both a warm season (April–September) and a cool season (October–March) for three cities across South Korea: Seoul, Daegu, and Busan. We find consistent, statistically significant, mostly linear relationships between relative risk of suicide and daily temperature in the cool season but few associations in the warm season. This seasonal signal of statistically significant temperature–suicide associations only in the cool season exists among all age segments, but especially for those 35 and older, along with both males and females. We further use distributed lag nonlinear modeling to examine cloud cover–suicide associations and find few significant relationships. This result suggests that that high daily temperatures in the cool season, and not exposure to sun, are responsible for the strong temperature–suicide associations found in South Korea.
Abstract
A coupled climate model with poleward-intensified westerly winds simulates significantly higher storage of heat and anthropogenic carbon dioxide by the Southern Ocean in the future when compared with the storage in a model with initially weaker, equatorward-biased westerlies. This difference results from the larger outcrop area of the dense waters around Antarctica and more vigorous divergence, which remains robust even as rising atmospheric greenhouse gas levels induce warming that reduces the density of surface waters in the Southern Ocean. These results imply that the impact of warming on the stratification of the global ocean may be reduced by the poleward intensification of the westerlies, allowing the ocean to remove additional heat and anthropogenic carbon dioxide from the atmosphere.
Abstract
A coupled climate model with poleward-intensified westerly winds simulates significantly higher storage of heat and anthropogenic carbon dioxide by the Southern Ocean in the future when compared with the storage in a model with initially weaker, equatorward-biased westerlies. This difference results from the larger outcrop area of the dense waters around Antarctica and more vigorous divergence, which remains robust even as rising atmospheric greenhouse gas levels induce warming that reduces the density of surface waters in the Southern Ocean. These results imply that the impact of warming on the stratification of the global ocean may be reduced by the poleward intensification of the westerlies, allowing the ocean to remove additional heat and anthropogenic carbon dioxide from the atmosphere.
Abstract
Using simulations performed with 18 coupled atmosphere–ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of the Northern Hemisphere snowfall under the representative concentration pathway (RCP4.5) scenario are analyzed for the period 2006–2100. These models perform well in simulating twentieth-century snowfall, although there is a positive bias in many regions. Annual snowfall is projected to decrease across much of the Northern Hemisphere during the twenty-first century, with increases projected at higher latitudes. On a seasonal basis, the transition zone between negative and positive snowfall trends corresponds approximately to the −10°C isotherm of the late twentieth-century mean surface air temperature, such that positive trends prevail in winter over large regions of Eurasia and North America. Redistributions of snowfall throughout the entire snow season are projected to occur—even in locations where there is little change in annual snowfall. Changes in the fraction of precipitation falling as snow contribute to decreases in snowfall across most Northern Hemisphere regions, while changes in total precipitation typically contribute to increases in snowfall. A signal-to-noise analysis reveals that the projected changes in snowfall, based on the RCP4.5 scenario, are likely to become apparent during the twenty-first century for most locations in the Northern Hemisphere. The snowfall signal emerges more slowly than the temperature signal, suggesting that changes in snowfall are not likely to be early indicators of regional climate change.
Abstract
Using simulations performed with 18 coupled atmosphere–ocean global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5), projections of the Northern Hemisphere snowfall under the representative concentration pathway (RCP4.5) scenario are analyzed for the period 2006–2100. These models perform well in simulating twentieth-century snowfall, although there is a positive bias in many regions. Annual snowfall is projected to decrease across much of the Northern Hemisphere during the twenty-first century, with increases projected at higher latitudes. On a seasonal basis, the transition zone between negative and positive snowfall trends corresponds approximately to the −10°C isotherm of the late twentieth-century mean surface air temperature, such that positive trends prevail in winter over large regions of Eurasia and North America. Redistributions of snowfall throughout the entire snow season are projected to occur—even in locations where there is little change in annual snowfall. Changes in the fraction of precipitation falling as snow contribute to decreases in snowfall across most Northern Hemisphere regions, while changes in total precipitation typically contribute to increases in snowfall. A signal-to-noise analysis reveals that the projected changes in snowfall, based on the RCP4.5 scenario, are likely to become apparent during the twenty-first century for most locations in the Northern Hemisphere. The snowfall signal emerges more slowly than the temperature signal, suggesting that changes in snowfall are not likely to be early indicators of regional climate change.
