Search Results
You are looking at 1 - 6 of 6 items for :
- Author or Editor: Roland A. Madden x
- Journal of Climate x
- Refine by Access: All Content x
Abstract
No abstract available.
Abstract
No abstract 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%.
Abstract
Optimal averaging is a method to estimate some area mean of datasets with imperfect spatial sampling. The accuracy of the method is tested by application to time series of January temperature fields simulated by the NCAR Community Climate Model. Some restrictions to the application of optimal averaging are given. It is demonstrated that the proper choice of a spatial correlation model is crucial. It is shown that the optimal averaging procedures provide a better approximation to the true mean of a region than simple area-weight averaging does. The inclusion of measurement errors of realistic size at each observation location hardly changes the value of the optimal average nor does it substantially alter the sampling error. of the optimal average.
Abstract
Optimal averaging is a method to estimate some area mean of datasets with imperfect spatial sampling. The accuracy of the method is tested by application to time series of January temperature fields simulated by the NCAR Community Climate Model. Some restrictions to the application of optimal averaging are given. It is demonstrated that the proper choice of a spatial correlation model is crucial. It is shown that the optimal averaging procedures provide a better approximation to the true mean of a region than simple area-weight averaging does. The inclusion of measurement errors of realistic size at each observation location hardly changes the value of the optimal average nor does it substantially alter the sampling error. of the optimal average.
Abstract
No abstract available.
Abstract
No abstract available.
Abstract
Using more than three times as many stations and time series of daily data that am generally 1.5–3.0 times longer than those in a previous study, estimates of the natural variability, also known as climate noise, of surface air temperatures are extended over most North America. The potential for long-range prediction of monthly means is determined by comparing the actual interannual variability of monthly means with the climate noise that is assumed to be unpredictable at long range. The climate noise estimates am typically larger during winter than during the other seasons. Nonetheless, the potential for long-range prediction is, generally, greatest for January and least for April. During January, temperatures nearest the oceans am more predictable than those for the central portions of North America.
The low-frequency white-noise statistical model that is used to estimate the unpredictable climate noise is compared with time series of (near) surface temperatures from a general circulation model to confirm its credibility. The estimates of the potential for prediction are tested further to establish their sensitivities to a critical parameter of the statistical model and to spatial averaging.
Abstract
Using more than three times as many stations and time series of daily data that am generally 1.5–3.0 times longer than those in a previous study, estimates of the natural variability, also known as climate noise, of surface air temperatures are extended over most North America. The potential for long-range prediction of monthly means is determined by comparing the actual interannual variability of monthly means with the climate noise that is assumed to be unpredictable at long range. The climate noise estimates am typically larger during winter than during the other seasons. Nonetheless, the potential for long-range prediction is, generally, greatest for January and least for April. During January, temperatures nearest the oceans am more predictable than those for the central portions of North America.
The low-frequency white-noise statistical model that is used to estimate the unpredictable climate noise is compared with time series of (near) surface temperatures from a general circulation model to confirm its credibility. The estimates of the potential for prediction are tested further to establish their sensitivities to a critical parameter of the statistical model and to spatial averaging.
