Search Results
You are looking at 1 - 10 of 33 items for
- Author or Editor: Roland A. Madden x
- Refine by Access: All Content x
Abstract
An examination of 30 mb data from eight winter seasons reveals that out-of-phase temperature oscillations occur regularly on either side of 60°N. The typical time scale of these oscillations is 1–3 weeks. Evidence is presented indicating that these out-of-phase oscillations occur because fluctuations in horizontal eddy heat transport across 60°N are a dominant mechanism controlling zonal mean temperature variations in this period range. The interaction between quasi-stationary and transient planetary-scale waves is shown to be capable of producing a large fraction of these fluctuations in eddy transport.
Abstract
An examination of 30 mb data from eight winter seasons reveals that out-of-phase temperature oscillations occur regularly on either side of 60°N. The typical time scale of these oscillations is 1–3 weeks. Evidence is presented indicating that these out-of-phase oscillations occur because fluctuations in horizontal eddy heat transport across 60°N are a dominant mechanism controlling zonal mean temperature variations in this period range. The interaction between quasi-stationary and transient planetary-scale waves is shown to be capable of producing a large fraction of these fluctuations in eddy transport.
Abstract
Estimates of the natural variability of monthly-mean sea-level pressure are made based on a 74-year, grid-point data set. The natural variability of monthly means is defined as those interannual fluctuations that can be ascribed to the effects of statistical sampling alone. That is, the natural variability of monthly means is that variability resulting from the variance and autocorrelation associated with daily weather fluctuations. The natural variability does not reflect a “climate change,” but rather it is the variability within an “unchanging climate.” As such it is a measure of the “climatic noise.” Comparisons between natural and actual interannual variability are discussed in the context of potential long-range predictability. A characteristic time between independent estimates is determined.
Abstract
Estimates of the natural variability of monthly-mean sea-level pressure are made based on a 74-year, grid-point data set. The natural variability of monthly means is defined as those interannual fluctuations that can be ascribed to the effects of statistical sampling alone. That is, the natural variability of monthly means is that variability resulting from the variance and autocorrelation associated with daily weather fluctuations. The natural variability does not reflect a “climate change,” but rather it is the variability within an “unchanging climate.” As such it is a measure of the “climatic noise.” Comparisons between natural and actual interannual variability are discussed in the context of potential long-range predictability. A characteristic time between independent estimates is determined.
Abstract
A simple method for approximating the variance of meteorological time averages is presented. Graphs of the characteristic time between independent estimates and the ratio of the variance of time-averaged data to that of unaveraged data for a first-order autoregressive process are shown.
Abstract
A simple method for approximating the variance of meteorological time averages is presented. Graphs of the characteristic time between independent estimates and the ratio of the variance of time-averaged data to that of unaveraged data for a first-order autoregressive process are shown.
Abstract
It is hypothesized that the interference of stationary and traveling waves of the same longitudinal can cause some of the observed time variations in the large-scale circulation. To explore this hypothesis the eight-winter average structure of a regularly occurring, westward propagating disturbance which we earlier called the “16-day wave” is further documented. Energy quantities are calculated as this 16-day wave moves in and out of phase with the stationary or time-mean wave. The resulting time variations are similar to some already reported in the literature. Eddy heat momentum transport associated with energy conversions have phase relationships between pressure levels that can be approximately predicted by a simple linear superposition of the observed stationary waves and traveling external Rossby waves. In further support of the hypothesis, cross-spectral results determined from independent data show a reasonable agreement with these predictions.
Abstract
It is hypothesized that the interference of stationary and traveling waves of the same longitudinal can cause some of the observed time variations in the large-scale circulation. To explore this hypothesis the eight-winter average structure of a regularly occurring, westward propagating disturbance which we earlier called the “16-day wave” is further documented. Energy quantities are calculated as this 16-day wave moves in and out of phase with the stationary or time-mean wave. The resulting time variations are similar to some already reported in the literature. Eddy heat momentum transport associated with energy conversions have phase relationships between pressure levels that can be approximately predicted by a simple linear superposition of the observed stationary waves and traveling external Rossby waves. In further support of the hypothesis, cross-spectral results determined from independent data show a reasonable agreement with these predictions.
Abstract
Daily rawinsonde data from 19 near-equatorial stations are examined to learn more about annual variations of the 40–50 day oscillations. Lengths of the available time series range from 5 to 28 years. A technique is devised to isolate spectral and cross-spectral quantities as a function of season. It is determined that a variance of the zonal wind in a relatively broad band centered on 47-day periods generally exceeds that in adjacent lower and higher frequency bands by the largest amount during December January and February (DJF) and at stations in the Indian and western Pacific Oceans during all seasons. The coherence between lower-and upper-tropospheric zonal winds tends to be largest in the summer hemisphere for stations in the Indian and western Pacific Oceans. Upper tropospheric zonal and meridional winds are coherent and out of (in) phase at several stations there during DJF [June, July and August (JJA) These results. coupled with composited wind and outgoing longwave radiation data, lead us to conclude that in the Indian and western Pacific Oceans the eastwardd-waving regions of enhanced convection associated with the 40-50 day oscillation force a Kelvin-like wave to the east and anticyclonic, Rossby-like waves to the west. The anticyclonic eddies are found in the summer hemisphere during solstice seasons and cause local surges in upper-level southeasterlies (northeasterlies) during DJF (JJA).
Abstract
Daily rawinsonde data from 19 near-equatorial stations are examined to learn more about annual variations of the 40–50 day oscillations. Lengths of the available time series range from 5 to 28 years. A technique is devised to isolate spectral and cross-spectral quantities as a function of season. It is determined that a variance of the zonal wind in a relatively broad band centered on 47-day periods generally exceeds that in adjacent lower and higher frequency bands by the largest amount during December January and February (DJF) and at stations in the Indian and western Pacific Oceans during all seasons. The coherence between lower-and upper-tropospheric zonal winds tends to be largest in the summer hemisphere for stations in the Indian and western Pacific Oceans. Upper tropospheric zonal and meridional winds are coherent and out of (in) phase at several stations there during DJF [June, July and August (JJA) These results. coupled with composited wind and outgoing longwave radiation data, lead us to conclude that in the Indian and western Pacific Oceans the eastwardd-waving regions of enhanced convection associated with the 40-50 day oscillation force a Kelvin-like wave to the east and anticyclonic, Rossby-like waves to the west. The anticyclonic eddies are found in the summer hemisphere during solstice seasons and cause local surges in upper-level southeasterlies (northeasterlies) during DJF (JJA).
Abstract
Evidence of regularly propagating, large-scale waves is found in a 73-year record of Northern Hemisphere sea-level pressure data and in a nine-year record of upper air data. Cross-spectrum analyses indicate that south of 50°N, in all seasons, a zonal wavenumber 1 disturbance moves westward around the world in 5 days. In addition, north of 50°N a zonal wavenumber 1 disturbance moves westward around the world in one to three weeks with an average period near 16 days. This disturbance appears to be strongest in winter and spring. The structure of the 16-day wave during winter is studied in detail, and it is shown to be consistent, in many respects, with that of a theoretically predicted free planetary wave, or wave of the second class. A similar conclusion can be made concerning the 5-day wave.
Abstract
Evidence of regularly propagating, large-scale waves is found in a 73-year record of Northern Hemisphere sea-level pressure data and in a nine-year record of upper air data. Cross-spectrum analyses indicate that south of 50°N, in all seasons, a zonal wavenumber 1 disturbance moves westward around the world in 5 days. In addition, north of 50°N a zonal wavenumber 1 disturbance moves westward around the world in one to three weeks with an average period near 16 days. This disturbance appears to be strongest in winter and spring. The structure of the 16-day wave during winter is studied in detail, and it is shown to be consistent, in many respects, with that of a theoretically predicted free planetary wave, or wave of the second class. A similar conclusion can be made concerning the 5-day wave.
Abstract
No abstract available.
Abstract
No abstract available.
Abstract
Several important developments in the 1960s showed the way to using spectral analysis to identify and describe atmospheric waves predicted by theory. Among these waves were normal-mode Rossby–Haurwitz waves (NMRHWs). What follows is, first, a brief outline of how the influence of these developments on analyses of data collected during the Line Islands Experiment led to work on NMRHWs. Next, theoretical expectations of free NMRHWs as described by Kasahara and Kasahara and Puri in the early 1980s are discussed. Finally, spectral analyses of observed vorticity fields are presented for easy comparison with those expectations. The similarity between these relatively simple model predictions and observations is unique in meteorology, where complexity is the general rule. Readily available routines coded in NCAR Command Language (NCL) were used to isolate NMRHWs. It should be noted that, while these routines provide approximations to the theoretical predictions, open-access software for exact solutions has become available.
Abstract
Several important developments in the 1960s showed the way to using spectral analysis to identify and describe atmospheric waves predicted by theory. Among these waves were normal-mode Rossby–Haurwitz waves (NMRHWs). What follows is, first, a brief outline of how the influence of these developments on analyses of data collected during the Line Islands Experiment led to work on NMRHWs. Next, theoretical expectations of free NMRHWs as described by Kasahara and Kasahara and Puri in the early 1980s are discussed. Finally, spectral analyses of observed vorticity fields are presented for easy comparison with those expectations. The similarity between these relatively simple model predictions and observations is unique in meteorology, where complexity is the general rule. Readily available routines coded in NCAR Command Language (NCL) were used to isolate NMRHWs. It should be noted that, while these routines provide approximations to the theoretical predictions, open-access software for exact solutions has become available.
Abstract
Theoretical and modeling studies suggest that increasing greenhouse gases will cause the global mean temperature to rise a few degrees centigrade during the next century. Current global coupled GCMs have shown a distinct pattern of warming associated with this global mean rise. It is important to know how well our observing network will be able to capture the global mean temperature rise associated with this pattern if it occurs. The authors consider if a sampling bias exist as a result of the spatial distribution of observations as they are now located (1950–1979) when detecting a pattern of temperature change that should be typical of a warming due to increasing atmospheric CO2. The observations prove adequate to estimate the globally averaged temperature change associated with the pattern of CO2 warming from a general circulation model with a bias whose absolute value is generally less than 2%.
Abstract
Theoretical and modeling studies suggest that increasing greenhouse gases will cause the global mean temperature to rise a few degrees centigrade during the next century. Current global coupled GCMs have shown a distinct pattern of warming associated with this global mean rise. It is important to know how well our observing network will be able to capture the global mean temperature rise associated with this pattern if it occurs. The authors consider if a sampling bias exist as a result of the spatial distribution of observations as they are now located (1950–1979) when detecting a pattern of temperature change that should be typical of a warming due to increasing atmospheric CO2. The observations prove adequate to estimate the globally averaged temperature change associated with the pattern of CO2 warming from a general circulation model with a bias whose absolute value is generally less than 2%.
