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Abstract
The result of a stochastic dynamic prediction is the expected values of the model parameters and the covariances among all the parameters. By adopting a Bayesian approach to the problem of analysis and making certain assumptions, one can utilize the vast amount of information in a stochastic dynamic prediction along with the information contained in observations. By making simulated observations of a pre-defined atmosphere, it is shown that the uncertainty in the analyzed values is substantially less than either the uncertainty in the forecast or in the observation. In addition, the results indicate that the effects of the limiting assumptions are minimal. Further experiments are performed in which only heights or only temperatures are actually observed, and in each case it is possible to obtain an analysis for all the parameters in the model. The method is particularly useful for assessing the value and impact of different amounts or types of data.
Abstract
The result of a stochastic dynamic prediction is the expected values of the model parameters and the covariances among all the parameters. By adopting a Bayesian approach to the problem of analysis and making certain assumptions, one can utilize the vast amount of information in a stochastic dynamic prediction along with the information contained in observations. By making simulated observations of a pre-defined atmosphere, it is shown that the uncertainty in the analyzed values is substantially less than either the uncertainty in the forecast or in the observation. In addition, the results indicate that the effects of the limiting assumptions are minimal. Further experiments are performed in which only heights or only temperatures are actually observed, and in each case it is possible to obtain an analysis for all the parameters in the model. The method is particularly useful for assessing the value and impact of different amounts or types of data.
concerning possible effects of air pollution on climate
testimony before the Subcommittee on the Environment and the Atmosphere of the Committee on Science and Technology, U.S. House of Representatives, 13–14 November 1975
Abstract
A precipitation climatology has been developed for the relative frequencies of zero, one, or two or more days with measurable precipitation within 5-day periods. In addition, the distribution of precipitation amounts is given for the one wet day in five and for the more than one wet day in five categories. The purpose of the climatology is to provide background for the development and introduction of extended-range (6–10 day forecast period) precipitation forecasts in terms of the probabilities of the three categories.
The climatology is based on 36 years of precipitation data at 146 stations in the contiguous United States. Details of the treatment of the data are provided. Diagrams are developed to display the seasonal patterns of frequency and amount for individual stations. The frequency diagram is a nomogram based on a simple Markov chain model for precipitation occurrences. It can be used to infer—from the frequencies of 0 and exactly 1 wet day in 5—single-day climatological precipitation probabilities or the probabilities conditional on precipitation falling on the previous day (or to infer—from the daily climatology and knowledge of the persistence—the probability of the three categories of the 5-day period). These diagrams are useful (as will be demonstrated by example) for describing and comparing precipitation climatologies. They should also aid the forecaster in making and interpreting probability forecasts of precipitation frequency for the 6–10 day period, where day-by-day forecasts are unfeasible.
Abstract
A precipitation climatology has been developed for the relative frequencies of zero, one, or two or more days with measurable precipitation within 5-day periods. In addition, the distribution of precipitation amounts is given for the one wet day in five and for the more than one wet day in five categories. The purpose of the climatology is to provide background for the development and introduction of extended-range (6–10 day forecast period) precipitation forecasts in terms of the probabilities of the three categories.
The climatology is based on 36 years of precipitation data at 146 stations in the contiguous United States. Details of the treatment of the data are provided. Diagrams are developed to display the seasonal patterns of frequency and amount for individual stations. The frequency diagram is a nomogram based on a simple Markov chain model for precipitation occurrences. It can be used to infer—from the frequencies of 0 and exactly 1 wet day in 5—single-day climatological precipitation probabilities or the probabilities conditional on precipitation falling on the previous day (or to infer—from the daily climatology and knowledge of the persistence—the probability of the three categories of the 5-day period). These diagrams are useful (as will be demonstrated by example) for describing and comparing precipitation climatologies. They should also aid the forecaster in making and interpreting probability forecasts of precipitation frequency for the 6–10 day period, where day-by-day forecasts are unfeasible.
Abstract
With the future utilization of new-type upper tropospheric observations in mind, the estimation of 500-mb. geopotential height from 300-mb. data is accomplished by least squares regression. The regression coefficients form latitudinal patterns which can be expressed by linear relationships in low latitudes and parabolic relationships else-where. From three year's mid-seasonal-month grid data, measures of extrapolation error are obtained over half of the Northern Hemisphere. Verifying tests with radiosonde station data indicate that the error in low latitudes is substantially due to analysis noise in the 500-mb. grid data.
When the same techniques are applied to 200-mb. information, further error studies show considerably less feasibility of extrapolation from that level to 500 mb. However, the temperature at 200 mb. is found to be valuable in predicting the simultaneous temperature at 500 mb.
Abstract
With the future utilization of new-type upper tropospheric observations in mind, the estimation of 500-mb. geopotential height from 300-mb. data is accomplished by least squares regression. The regression coefficients form latitudinal patterns which can be expressed by linear relationships in low latitudes and parabolic relationships else-where. From three year's mid-seasonal-month grid data, measures of extrapolation error are obtained over half of the Northern Hemisphere. Verifying tests with radiosonde station data indicate that the error in low latitudes is substantially due to analysis noise in the 500-mb. grid data.
When the same techniques are applied to 200-mb. information, further error studies show considerably less feasibility of extrapolation from that level to 500 mb. However, the temperature at 200 mb. is found to be valuable in predicting the simultaneous temperature at 500 mb.
A long-time series (1895–1984) of mean areally averaged winter temperatures in the contiguous United States depicts an unprecedented spell of abnormal winters beginning with the winter of 1975–76. Three winters during the eight-year period, 1975–76 through 1982–83, are defined as much warmer than normal (abnormal), and the three consecutive winters, 1976–77 through 1978–79, much colder than normal (abnormal). Abnormal is defined here by the least abnormal of these six winters based on their normalized departures from the mean. When combined, these two abnormal categories have an expected frequency close to 21%. Assuming that the past 89 winters (1895–1984) are a large enough sample to estimate the true interannual temperature variability between winters, we find, using Monte Carlo simulations, that the return period of a series of six winters out of eight being either much above or much below normal is more than 1000 years. This event exceeds the calculated return period of the three consecutive much colder than normal winters (1976–77 through 1978–79) all falling into a much below normal category, i.e., one that is expected to contain approximately 10% of the data. The more moderate winters of 1981–82 and 1983–84 can also be considered abnormal by relaxing the limits necessary for an abnormal classification, but this gives a return period of 467 years for the spell of eight abnormal winters in the nine consecutive winters 1975–76 through 1983–84.
A long-time series (1895–1984) of mean areally averaged winter temperatures in the contiguous United States depicts an unprecedented spell of abnormal winters beginning with the winter of 1975–76. Three winters during the eight-year period, 1975–76 through 1982–83, are defined as much warmer than normal (abnormal), and the three consecutive winters, 1976–77 through 1978–79, much colder than normal (abnormal). Abnormal is defined here by the least abnormal of these six winters based on their normalized departures from the mean. When combined, these two abnormal categories have an expected frequency close to 21%. Assuming that the past 89 winters (1895–1984) are a large enough sample to estimate the true interannual temperature variability between winters, we find, using Monte Carlo simulations, that the return period of a series of six winters out of eight being either much above or much below normal is more than 1000 years. This event exceeds the calculated return period of the three consecutive much colder than normal winters (1976–77 through 1978–79) all falling into a much below normal category, i.e., one that is expected to contain approximately 10% of the data. The more moderate winters of 1981–82 and 1983–84 can also be considered abnormal by relaxing the limits necessary for an abnormal classification, but this gives a return period of 467 years for the spell of eight abnormal winters in the nine consecutive winters 1975–76 through 1983–84.
history, policy, and future of industrial meteorology
Papers presented at Session 4 of the 56th Annual Meeting of the AMS, 20 January 1976, Philadelphia, Pa.
Abstract
Linkages between diminishing Arctic sea ice and changes in Arctic terrestrial ecosystems have not been previously demonstrated. Here, the authors use a newly available Arctic Normalized Difference Vegetation Index (NDVI) dataset (a measure of vegetation photosynthetic capacity) to document coherent temporal relationships between near-coastal sea ice, summer tundra land surface temperatures, and vegetation productivity. The authors find that, during the period of satellite observations (1982–2008), sea ice within 50 km of the coast during the period of early summer ice breakup declined an average of 25% for the Arctic as a whole, with much larger changes in the East Siberian Sea to Chukchi Sea sectors (>44% decline). The changes in sea ice conditions are most directly relevant and have the strongest effect on the villages and ecosystems immediately adjacent to the coast, but the terrestrial effects of sea ice changes also extend far inland. Low-elevation (<300 m) tundra summer land temperatures, as indicated by the summer warmth index (SWI; sum of the monthly-mean temperatures above freezing, expressed as °C month−1), have increased an average of 5°C month−1 (24% increase) for the Arctic as a whole; the largest changes (+10° to 12°C month−1) have been over land along the Chukchi and Bering Seas. The land warming has been more pronounced in North America (+30%) than in Eurasia (16%). When expressed as percentage change, land areas in the High Arctic in the vicinity of the Greenland Sea, Baffin Bay, and Davis Strait have experienced the largest changes (>70%). The NDVI has increased across most of the Arctic, with some exceptions over land regions along the Bering and west Chukchi Seas. The greatest change in absolute maximum NDVI occurred over tundra in northern Alaska on the Beaufort Sea coast [+0.08 Advanced Very High Resolution Radiometer (AVHRR) NDVI units]. When expressed as percentage change, large NDVI changes (10%–15%) occurred over land in the North America High Arctic and along the Beaufort Sea. Ground observations along an 1800-km climate transect in North America support the strong correlations between satellite NDVI observations and summer land temperatures. Other new observations from near the Lewis Glacier, Baffin Island, Canada, document rapid vegetation changes along the margins of large retreating glaciers and may be partly responsible for the large NDVI changes observed in northern Canada and Greenland. The ongoing changes to plant productivity will affect many aspects of Arctic systems, including changes to active-layer depths, permafrost, biodiversity, wildlife, and human use of these regions. Ecosystems that are presently adjacent to year-round (perennial) sea ice are likely to experience the greatest changes.
Abstract
Linkages between diminishing Arctic sea ice and changes in Arctic terrestrial ecosystems have not been previously demonstrated. Here, the authors use a newly available Arctic Normalized Difference Vegetation Index (NDVI) dataset (a measure of vegetation photosynthetic capacity) to document coherent temporal relationships between near-coastal sea ice, summer tundra land surface temperatures, and vegetation productivity. The authors find that, during the period of satellite observations (1982–2008), sea ice within 50 km of the coast during the period of early summer ice breakup declined an average of 25% for the Arctic as a whole, with much larger changes in the East Siberian Sea to Chukchi Sea sectors (>44% decline). The changes in sea ice conditions are most directly relevant and have the strongest effect on the villages and ecosystems immediately adjacent to the coast, but the terrestrial effects of sea ice changes also extend far inland. Low-elevation (<300 m) tundra summer land temperatures, as indicated by the summer warmth index (SWI; sum of the monthly-mean temperatures above freezing, expressed as °C month−1), have increased an average of 5°C month−1 (24% increase) for the Arctic as a whole; the largest changes (+10° to 12°C month−1) have been over land along the Chukchi and Bering Seas. The land warming has been more pronounced in North America (+30%) than in Eurasia (16%). When expressed as percentage change, land areas in the High Arctic in the vicinity of the Greenland Sea, Baffin Bay, and Davis Strait have experienced the largest changes (>70%). The NDVI has increased across most of the Arctic, with some exceptions over land regions along the Bering and west Chukchi Seas. The greatest change in absolute maximum NDVI occurred over tundra in northern Alaska on the Beaufort Sea coast [+0.08 Advanced Very High Resolution Radiometer (AVHRR) NDVI units]. When expressed as percentage change, large NDVI changes (10%–15%) occurred over land in the North America High Arctic and along the Beaufort Sea. Ground observations along an 1800-km climate transect in North America support the strong correlations between satellite NDVI observations and summer land temperatures. Other new observations from near the Lewis Glacier, Baffin Island, Canada, document rapid vegetation changes along the margins of large retreating glaciers and may be partly responsible for the large NDVI changes observed in northern Canada and Greenland. The ongoing changes to plant productivity will affect many aspects of Arctic systems, including changes to active-layer depths, permafrost, biodiversity, wildlife, and human use of these regions. Ecosystems that are presently adjacent to year-round (perennial) sea ice are likely to experience the greatest changes.
Abstract
The mechanisms driving trends and variability of the normalized difference vegetation index (NDVI) for tundra in Alaska along the Beaufort, east Chukchi, and east Bering Seas for 1982–2013 are evaluated in the context of remote sensing, reanalysis, and meteorological station data as well as regional modeling. Over the entire season the tundra vegetation continues to green; however, biweekly NDVI has declined during the early part of the growing season in all of the Alaskan tundra domains. These springtime declines coincide with increased snow depth in spring documented in northern Alaska. The tundra region generally has warmed over the summer but intraseasonal analysis shows a decline in midsummer land surface temperatures. The midsummer cooling is consistent with recent large-scale circulation changes characterized by lower sea level pressures, which favor increased cloud cover. In northern Alaska, the sea-breeze circulation is strengthened with an increase in atmospheric moisture/cloudiness inland when the land surface is warmed in a regional model, suggesting the potential for increased vegetation to feedback onto the atmospheric circulation that could reduce midsummer temperatures. This study shows that both large- and local-scale climate drivers likely play a role in the observed seasonality of NDVI trends.
Abstract
The mechanisms driving trends and variability of the normalized difference vegetation index (NDVI) for tundra in Alaska along the Beaufort, east Chukchi, and east Bering Seas for 1982–2013 are evaluated in the context of remote sensing, reanalysis, and meteorological station data as well as regional modeling. Over the entire season the tundra vegetation continues to green; however, biweekly NDVI has declined during the early part of the growing season in all of the Alaskan tundra domains. These springtime declines coincide with increased snow depth in spring documented in northern Alaska. The tundra region generally has warmed over the summer but intraseasonal analysis shows a decline in midsummer land surface temperatures. The midsummer cooling is consistent with recent large-scale circulation changes characterized by lower sea level pressures, which favor increased cloud cover. In northern Alaska, the sea-breeze circulation is strengthened with an increase in atmospheric moisture/cloudiness inland when the land surface is warmed in a regional model, suggesting the potential for increased vegetation to feedback onto the atmospheric circulation that could reduce midsummer temperatures. This study shows that both large- and local-scale climate drivers likely play a role in the observed seasonality of NDVI trends.
