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- Author or Editor: David R. Legates x
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Abstract
January and July surface air temperature fields simulated by the GFDI, OSU, GISS, and UKMO general circulation models (GCMS) are compared to the global surface air temperature climatology compiled by Legates and Willmott. Legates and Willmott's climatology was selected as the verification standard because it provides better spatial and temporal coverage than its predecessors, such as the frequently employed RAND climatology compiled in the early 1970s. Difference maps between each GCM-simulated field and the Legates and Willmott climatology are presented and evaluated. Zonal averages by 10° latitudinal bands for each GCM as well as for the Legates and Willmott and RAND climatologies also are examined.
Results indicate that surface air temperature simulations are greatly influenced by model representations of topography, sea level pressure, and precipitation. Inclusion of the diurnal cycle and the type of ocean model used also impact simulated surface air temperatures. Mean January and July surface air temperatures are well simulated by the GISS and UKMO models, whereas temperatures are overestimated by the OSU GCM and underestimated by the GFDL GCM. GISS and UKMO simulations seem even more accurate, on the average, than the data contained in the RAND observation-based climatology. Simulated equatorial air temperatures are slightly higher than observed, particularly in the Southern Hemisphere. Model simulated air temperatures between 30°S and 60°S are usually lower than observed, while air temperatures poleward of 60°S are overestimated. Northern Hemisphere temperatures are generally better simulated than their Southern Hemisphere counterparts.
Abstract
January and July surface air temperature fields simulated by the GFDI, OSU, GISS, and UKMO general circulation models (GCMS) are compared to the global surface air temperature climatology compiled by Legates and Willmott. Legates and Willmott's climatology was selected as the verification standard because it provides better spatial and temporal coverage than its predecessors, such as the frequently employed RAND climatology compiled in the early 1970s. Difference maps between each GCM-simulated field and the Legates and Willmott climatology are presented and evaluated. Zonal averages by 10° latitudinal bands for each GCM as well as for the Legates and Willmott and RAND climatologies also are examined.
Results indicate that surface air temperature simulations are greatly influenced by model representations of topography, sea level pressure, and precipitation. Inclusion of the diurnal cycle and the type of ocean model used also impact simulated surface air temperatures. Mean January and July surface air temperatures are well simulated by the GISS and UKMO models, whereas temperatures are overestimated by the OSU GCM and underestimated by the GFDL GCM. GISS and UKMO simulations seem even more accurate, on the average, than the data contained in the RAND observation-based climatology. Simulated equatorial air temperatures are slightly higher than observed, particularly in the Southern Hemisphere. Model simulated air temperatures between 30°S and 60°S are usually lower than observed, while air temperatures poleward of 60°S are overestimated. Northern Hemisphere temperatures are generally better simulated than their Southern Hemisphere counterparts.
Abstract
In applications where a monthly temporal resolution is employed, an important variable is the proportion of monthly precipitation that falls in solid form. Globally applicable equations for estimating this variable developed by previously published research are reevaluated. A revised equation developed 20 years ago by the lead author for climatological analysis works well for long-term data but not for actual monthly averages. A new equation, therefore, is developed for use with monthly data using an Arctic database of stations above 50°N latitude. These two equations have mean absolute fit errors of 0.0569 and 0.0614, respectively. The data were split into four regions—North America, northern Europe, northern Asia, and Greenland—and were also evaluated for the effect of elevation or seasonality influences. Results suggest that seasonality also is an important variable, particularly to differentiate between midwinter and transition months (i.e., October and April).
Abstract
In applications where a monthly temporal resolution is employed, an important variable is the proportion of monthly precipitation that falls in solid form. Globally applicable equations for estimating this variable developed by previously published research are reevaluated. A revised equation developed 20 years ago by the lead author for climatological analysis works well for long-term data but not for actual monthly averages. A new equation, therefore, is developed for use with monthly data using an Arctic database of stations above 50°N latitude. These two equations have mean absolute fit errors of 0.0569 and 0.0614, respectively. The data were split into four regions—North America, northern Europe, northern Asia, and Greenland—and were also evaluated for the effect of elevation or seasonality influences. Results suggest that seasonality also is an important variable, particularly to differentiate between midwinter and transition months (i.e., October and April).
Precipitation measurements in the United States (as well as all other countries) are adversely affected by the gauge undercatch bias of point precipitation measurements. When these measurements are used to obtain areal averages, particularly in mountainous terrain, additional biases may be introduced because most stations are at lower elevations in exposed sites.
Gauge measurements tend to be underestimates of the true precipitation, largely because of wind-induced turbulence at the gauge orifice and wetting losses on the internal walls of the gauge. These are not trivial as monthly estimates of this bias often vary from 5% to 40%. Biases are larger in winter than in summer and increase to the north in the United States due largely to the deleterious effect of the wind on snowfall.
Simple spatial averaging of data from existing networks does not provide an accurate evaluation of the area-mean precipitation over mountainous terrain (e.g., over much of the western United States) since most stations are located at low elevations. This tends to underestimate area averages since, in mountainous terrain, precipitation generally increases with elevation.
Temporal precipitation trends for the United States, as well as seasonal and annual averages, are presented. Estimates of unbiased (or less biased) precipitation over the northern Great Plains provide a regional analysis.
Precipitation measurements in the United States (as well as all other countries) are adversely affected by the gauge undercatch bias of point precipitation measurements. When these measurements are used to obtain areal averages, particularly in mountainous terrain, additional biases may be introduced because most stations are at lower elevations in exposed sites.
Gauge measurements tend to be underestimates of the true precipitation, largely because of wind-induced turbulence at the gauge orifice and wetting losses on the internal walls of the gauge. These are not trivial as monthly estimates of this bias often vary from 5% to 40%. Biases are larger in winter than in summer and increase to the north in the United States due largely to the deleterious effect of the wind on snowfall.
Simple spatial averaging of data from existing networks does not provide an accurate evaluation of the area-mean precipitation over mountainous terrain (e.g., over much of the western United States) since most stations are located at low elevations. This tends to underestimate area averages since, in mountainous terrain, precipitation generally increases with elevation.
Temporal precipitation trends for the United States, as well as seasonal and annual averages, are presented. Estimates of unbiased (or less biased) precipitation over the northern Great Plains provide a regional analysis.
Abstract
Very few (if any) in situ measurements of rainfall are available in the Pacific ITCZ east of the Line Islands (157°W). Hence, climatological datasets, which are assembled from various in situ sources, and satellite-derived analyses of precipitation are frequently relied upon to provide information on the distribution of rainfall in this important region. A substantial amount of disagreement exists among these information sources as demonstrated in this paper. In particular, the east–west gradient of estimated rainfall intensity in the eastern Pacific ITCZ is quite different during the Northern Hemisphere warm season among six different satellite algorithms (one infrared and five microwave) and two climatologies that are examined. Some of these data suggest that a local minimum in rainfall intensity is located near 140°W in the Pacific ITCZ during northern summer, with increasing intensity toward the east and west, while the others depict steadily decreasing rainfall intensity from west of the Americas to the date line. Conversely, all of the precipitation estimates that are examined depict a rainfall maximum in the Pacific ITCZ near 140°W during the Northern Hemisphere cool season, although the magnitudes vary substantially among them.
The authors examine estimates of seasonal mean rainfall over the eastern Pacific ITCZ (cast of the date line) from two rainfall climatologies and six satellite precipitation estimation techniques during July 1987 through June 1990. Inconsistencies among the precipitation analyses are investigated by examining several independent datasets that include atmospheric circulation data, sea surface temperature data, and ship reports of weather type.
Abstract
Very few (if any) in situ measurements of rainfall are available in the Pacific ITCZ east of the Line Islands (157°W). Hence, climatological datasets, which are assembled from various in situ sources, and satellite-derived analyses of precipitation are frequently relied upon to provide information on the distribution of rainfall in this important region. A substantial amount of disagreement exists among these information sources as demonstrated in this paper. In particular, the east–west gradient of estimated rainfall intensity in the eastern Pacific ITCZ is quite different during the Northern Hemisphere warm season among six different satellite algorithms (one infrared and five microwave) and two climatologies that are examined. Some of these data suggest that a local minimum in rainfall intensity is located near 140°W in the Pacific ITCZ during northern summer, with increasing intensity toward the east and west, while the others depict steadily decreasing rainfall intensity from west of the Americas to the date line. Conversely, all of the precipitation estimates that are examined depict a rainfall maximum in the Pacific ITCZ near 140°W during the Northern Hemisphere cool season, although the magnitudes vary substantially among them.
The authors examine estimates of seasonal mean rainfall over the eastern Pacific ITCZ (cast of the date line) from two rainfall climatologies and six satellite precipitation estimation techniques during July 1987 through June 1990. Inconsistencies among the precipitation analyses are investigated by examining several independent datasets that include atmospheric circulation data, sea surface temperature data, and ship reports of weather type.
Abstract
Previously published claims of large regional (northern vs southern states) differences in risks of fatality associated with tornadoes in the United States are reexamined. This new study extends earlier claims to include 1) data from a much longer time frame, 2) injuries as well as fatalities, and 3) more precise estimates of meteorological features of tornado events (specifically, a precise calculation of daytime vs nighttime and pathlength). The current study also includes formal mediation analyses involving variables that might explain regional differences. Results indicate that significant increases in the risk of fatality and injury do occur in southern states as compared with northern states. Mediation models show that these regional differences remain significant when meteorological factors of nocturnal occurrence and pathlength are included. Thus, these meteorological factors cannot explain regional differences in risk of fatality and injury, a failure that is unlikely to reflect a lack of data or a lack of precision in the measurement of potential mediators.
Abstract
Previously published claims of large regional (northern vs southern states) differences in risks of fatality associated with tornadoes in the United States are reexamined. This new study extends earlier claims to include 1) data from a much longer time frame, 2) injuries as well as fatalities, and 3) more precise estimates of meteorological features of tornado events (specifically, a precise calculation of daytime vs nighttime and pathlength). The current study also includes formal mediation analyses involving variables that might explain regional differences. Results indicate that significant increases in the risk of fatality and injury do occur in southern states as compared with northern states. Mediation models show that these regional differences remain significant when meteorological factors of nocturnal occurrence and pathlength are included. Thus, these meteorological factors cannot explain regional differences in risk of fatality and injury, a failure that is unlikely to reflect a lack of data or a lack of precision in the measurement of potential mediators.
