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- Author or Editor: Peter J. Lamb x
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Abstract
Changes in visibility and the occurrence of smoke or haze during the last three decades are identified for eight locations in and around Illinois. The analyses utilize individual daily data and are performed on both seasonal and annual bases. Visibility variation is investigated using cumulative percentiles and mean ridits.
Summer is the season that experienced the greatest 1950–80 visibility change. Except at Chicago, this was dominated by a pronounced overall decline that coincided with a marked increase in the frequency of smoke/haze. Superimposed on these trends are 1) a strong early-1960s visibility maximum and smoke/haze minimum for Indianapolis and the northern half of Illinois and 2) particularly pronounced visibility degradation and increased smoke/haze occurrence during the late 1960s at most stations. The 1950–80 summer visibility decline at Chicago was much smaller than elsewhere and coincided with a marked downward smoke/hue frequency trend.
The extra-Chicago visibility results for spring are less pronounced versions of their summer counterparts; those for autumn contain the same overall decline, but not the foregoing smaller-scale variations. The spring and autumn occurrence of smoke/haze outside of Chicago exhibits little spatially coherent trend for the study period. Chicago's spring visibility improved slightly during 1950–80 and was accompanied by a stronger decrease in the number of smoke/haze days than occurred for summer. Autumn is the season in which Chicago visibility has degraded most in the last three decades, even though the concurrent reduction in the frequency of smoke/haze has exceeded that of summer and spring.
The winter results differ substantially from those for the other seasons. The 1950–80 winter visibility trends for individual stations range between a moderate decrease and a noticeable improvement, and are associated with strong reductions in smoke/haze frequency. These favorable changes are greatest at Chicago. Superimposed on them are 1) strong visibility maxima and smoke/haze minima during the mid-1950s and mid-1960s at most stations, 2) marked visibility degradation and increased smoke/haze occurrence outside of Chicago (especially in northwestern Illinois) in the late 1960s, and 3) some improvement in that situation in the early 1970s, followed by renewed deterioration.
The extra-Chicago annual results are determined by the similar patterns for summer (especially), spring and autumn. Their Chicago counterparts are the product of larger season-to-season variation, and accordingly reflect winter results to a greater extent.
Abstract
Changes in visibility and the occurrence of smoke or haze during the last three decades are identified for eight locations in and around Illinois. The analyses utilize individual daily data and are performed on both seasonal and annual bases. Visibility variation is investigated using cumulative percentiles and mean ridits.
Summer is the season that experienced the greatest 1950–80 visibility change. Except at Chicago, this was dominated by a pronounced overall decline that coincided with a marked increase in the frequency of smoke/haze. Superimposed on these trends are 1) a strong early-1960s visibility maximum and smoke/haze minimum for Indianapolis and the northern half of Illinois and 2) particularly pronounced visibility degradation and increased smoke/haze occurrence during the late 1960s at most stations. The 1950–80 summer visibility decline at Chicago was much smaller than elsewhere and coincided with a marked downward smoke/hue frequency trend.
The extra-Chicago visibility results for spring are less pronounced versions of their summer counterparts; those for autumn contain the same overall decline, but not the foregoing smaller-scale variations. The spring and autumn occurrence of smoke/haze outside of Chicago exhibits little spatially coherent trend for the study period. Chicago's spring visibility improved slightly during 1950–80 and was accompanied by a stronger decrease in the number of smoke/haze days than occurred for summer. Autumn is the season in which Chicago visibility has degraded most in the last three decades, even though the concurrent reduction in the frequency of smoke/haze has exceeded that of summer and spring.
The winter results differ substantially from those for the other seasons. The 1950–80 winter visibility trends for individual stations range between a moderate decrease and a noticeable improvement, and are associated with strong reductions in smoke/haze frequency. These favorable changes are greatest at Chicago. Superimposed on them are 1) strong visibility maxima and smoke/haze minima during the mid-1950s and mid-1960s at most stations, 2) marked visibility degradation and increased smoke/haze occurrence outside of Chicago (especially in northwestern Illinois) in the late 1960s, and 3) some improvement in that situation in the early 1970s, followed by renewed deterioration.
The extra-Chicago annual results are determined by the similar patterns for summer (especially), spring and autumn. Their Chicago counterparts are the product of larger season-to-season variation, and accordingly reflect winter results to a greater extent.
Abstract
This paper presents the results of climatic pattern analyses of three- and seven-day summer (May–August) rainfall totals for the central United States. A range of eigenvectorial methods is applied to 1949–80 data for a regularly spaced network of 402 stations that extends from the Rocky to the Appalachian Mountains and from the Gulf Coast to the Canadian border. The major objectives are to quantitatively assess the sensitivity of eigenvectorial results to several parameters that have hitherto been the subject of considerable qualitative concern, and to identify the potential applications of those results.
The entire domain variance fractions cumulatively explained by a) the first 10 correlation-based unrotated Principal Components (PCs) and b) the 10 orthogonally rotated (VARIMAX criterion) PCs derived from them are identical for the same data. They vary between 35–47 percent depending on the data time scale and form, being higher for seven- than three-day totals and further enhanced when those totals are square-root (especially) and log10 transformed. The (highly contrasting) sets of unrotated and VARIMAX PC spatial loading patterns are invariant with respect to data time scale and form. They receive strong statistical support from analyses performed on subsets of the data, their covariance- and cross-products-based equivalents, counterpart common factor patterns, and (for VARIMAX) an obliquely rotated (Hanis–Kaiser Case II B′B criterion) PC analysis. The unrotated PC loading patterns very closely resemble the set that Buell claimed would tend to characterize a domain of the present rectangular shape, irrespective of the meteorological parameter treated. They receive little physical support from analyses performed separately for subareas of the domain or from comparison with the interstation correlation matrix from which they are derived. The VARIMAX PC loading patterns, in contrast, derive strong physical support from those verifications. Each of these patterns emphasizes a relatively strong anomaly in a different part of the domain; they collectively yield a regionalization of the domain into 10 subareas within which three- and seven-day summer rainfall tends to be spatially coherent. The regionalization is suggested to be of considerable potential utility for crop-yield modeling, short-range weather prediction, and research into climatic variation and change.
Abstract
This paper presents the results of climatic pattern analyses of three- and seven-day summer (May–August) rainfall totals for the central United States. A range of eigenvectorial methods is applied to 1949–80 data for a regularly spaced network of 402 stations that extends from the Rocky to the Appalachian Mountains and from the Gulf Coast to the Canadian border. The major objectives are to quantitatively assess the sensitivity of eigenvectorial results to several parameters that have hitherto been the subject of considerable qualitative concern, and to identify the potential applications of those results.
The entire domain variance fractions cumulatively explained by a) the first 10 correlation-based unrotated Principal Components (PCs) and b) the 10 orthogonally rotated (VARIMAX criterion) PCs derived from them are identical for the same data. They vary between 35–47 percent depending on the data time scale and form, being higher for seven- than three-day totals and further enhanced when those totals are square-root (especially) and log10 transformed. The (highly contrasting) sets of unrotated and VARIMAX PC spatial loading patterns are invariant with respect to data time scale and form. They receive strong statistical support from analyses performed on subsets of the data, their covariance- and cross-products-based equivalents, counterpart common factor patterns, and (for VARIMAX) an obliquely rotated (Hanis–Kaiser Case II B′B criterion) PC analysis. The unrotated PC loading patterns very closely resemble the set that Buell claimed would tend to characterize a domain of the present rectangular shape, irrespective of the meteorological parameter treated. They receive little physical support from analyses performed separately for subareas of the domain or from comparison with the interstation correlation matrix from which they are derived. The VARIMAX PC loading patterns, in contrast, derive strong physical support from those verifications. Each of these patterns emphasizes a relatively strong anomaly in a different part of the domain; they collectively yield a regionalization of the domain into 10 subareas within which three- and seven-day summer rainfall tends to be spatially coherent. The regionalization is suggested to be of considerable potential utility for crop-yield modeling, short-range weather prediction, and research into climatic variation and change.
Abstract
The increased U.S. natural gas price volatility since the mid-to-late-1980s deregulation generally is attributed to the deregulated market being more sensitive to temperature-related residential demand. This study therefore quantifies relations between winter (November–February; December–February) temperature and residential gas consumption for the United States east of the Rocky Mountains for 1989–2000, by region and on monthly and seasonal time scales. State-level monthly gas consumption data are aggregated for nine multistate subregions of three Petroleum Administration for Defense Districts of the U.S. Department of Energy. Two temperature indices [days below percentile (DBP) and heating degree-days (HDD)] are developed using the Richman–Lamb fine-resolution (∼1° latitude–longitude) set of daily maximum and minimum temperatures for 1949–2000. Temperature parameters/values that maximize DBP/HDD correlations with gas consumption are identified. Maximum DBP and HDD correlations with gas consumption consistently are largest in the Great Lakes–Ohio Valley region on both monthly (from +0.89 to +0.91) and seasonal (from +0.93 to +0.97) time scales, for which they are based on daily maximum temperature. Such correlations are markedly lower on both time scales (from +0.62 to +0.80) in New England, where gas is less important than heating oil, and on the monthly scale (from +0.55 to +0.75) across the South because of low January correlations. For the South, maximum correlations are for daily DBP and HDD indices based on mean or minimum temperature. The percentiles having the highest DBP index correlations with gas consumption are slightly higher for northern regions than across the South. This is because lower (higher) relative (absolute) temperature thresholds are reached in warmer regions before home heating occurs. However, these optimum percentiles for all regions are bordered broadly by surrounding percentiles for which the correlations are almost as high as the maximum. This consistency establishes the robustness of the temperature–gas consumption relations obtained. The reference temperatures giving the highest HDD correlations with gas consumption are lower for the colder northern regions than farther south where the temperature range is truncated. However, all HDD reference temperatures greater than +10°C (+15°C) yield similar such correlations for northern (southern) regions, further confirming the robustness of the findings. This robustness, coupled with the very high correlation magnitudes obtained, suggests that potentially strong gas consumption predictability would follow from accurate seasonal temperature forecasts.
Abstract
The increased U.S. natural gas price volatility since the mid-to-late-1980s deregulation generally is attributed to the deregulated market being more sensitive to temperature-related residential demand. This study therefore quantifies relations between winter (November–February; December–February) temperature and residential gas consumption for the United States east of the Rocky Mountains for 1989–2000, by region and on monthly and seasonal time scales. State-level monthly gas consumption data are aggregated for nine multistate subregions of three Petroleum Administration for Defense Districts of the U.S. Department of Energy. Two temperature indices [days below percentile (DBP) and heating degree-days (HDD)] are developed using the Richman–Lamb fine-resolution (∼1° latitude–longitude) set of daily maximum and minimum temperatures for 1949–2000. Temperature parameters/values that maximize DBP/HDD correlations with gas consumption are identified. Maximum DBP and HDD correlations with gas consumption consistently are largest in the Great Lakes–Ohio Valley region on both monthly (from +0.89 to +0.91) and seasonal (from +0.93 to +0.97) time scales, for which they are based on daily maximum temperature. Such correlations are markedly lower on both time scales (from +0.62 to +0.80) in New England, where gas is less important than heating oil, and on the monthly scale (from +0.55 to +0.75) across the South because of low January correlations. For the South, maximum correlations are for daily DBP and HDD indices based on mean or minimum temperature. The percentiles having the highest DBP index correlations with gas consumption are slightly higher for northern regions than across the South. This is because lower (higher) relative (absolute) temperature thresholds are reached in warmer regions before home heating occurs. However, these optimum percentiles for all regions are bordered broadly by surrounding percentiles for which the correlations are almost as high as the maximum. This consistency establishes the robustness of the temperature–gas consumption relations obtained. The reference temperatures giving the highest HDD correlations with gas consumption are lower for the colder northern regions than farther south where the temperature range is truncated. However, all HDD reference temperatures greater than +10°C (+15°C) yield similar such correlations for northern (southern) regions, further confirming the robustness of the findings. This robustness, coupled with the very high correlation magnitudes obtained, suggests that potentially strong gas consumption predictability would follow from accurate seasonal temperature forecasts.
Abstract
Historical (1901–79) temperature and precipitation data for four Illinois stations were used to determine the frequency with which summer and winter averages for periods of various length (i.e., different climatic normals) are closest to the value for the next year, and hence its best predictor. The normal achieving the highest frequency in this regard is considered the best for characterizing the recent climate for a given point in time and assessing the abnormality of the following year.
Normals for 5, 10, 15, 20 and 25 years were investigated, along with the 30-year ones generally used. Five-year normals most frequently provided the closest estimate of the next year's value for both parameters in both seasons. Ten-year normals also have a high probability of being the best predictors, whereas 20-year normals have a particularly low probability of such success. The standard 30-year normals also perform poorly in this regard. These results contrast strongly with earlier suggestions that 15–25 year normals are “optimum” for prediction because they possess the minimum extrapolation variance when normals are employed as predictors. This difference between the two sets of results indicated that 5-year normals tend to possess larger prediction errors when they are not the best predictors, than do other normals on the greater number of occasions they are not the best predictors. The present findings were used by the Illinois Commerce Commission in evaluating weather normalization rate adjustments proposed by utility companies in 1979–80.
An investigation also is made into the nature of the climatic variation occurring when each normal is the best predictor. Five-year normals tend to attain this position for precipitation when the difference from the preceding year and the departures from longer-term averages are all moderate-to-small. When 5-year normals are the best temperature predictors, in contrast, the departures from this normal (and hence prediction errors) are very large. The frequency with which various normals were the best predictors shows no marked temporal variation during the study period.
Abstract
Historical (1901–79) temperature and precipitation data for four Illinois stations were used to determine the frequency with which summer and winter averages for periods of various length (i.e., different climatic normals) are closest to the value for the next year, and hence its best predictor. The normal achieving the highest frequency in this regard is considered the best for characterizing the recent climate for a given point in time and assessing the abnormality of the following year.
Normals for 5, 10, 15, 20 and 25 years were investigated, along with the 30-year ones generally used. Five-year normals most frequently provided the closest estimate of the next year's value for both parameters in both seasons. Ten-year normals also have a high probability of being the best predictors, whereas 20-year normals have a particularly low probability of such success. The standard 30-year normals also perform poorly in this regard. These results contrast strongly with earlier suggestions that 15–25 year normals are “optimum” for prediction because they possess the minimum extrapolation variance when normals are employed as predictors. This difference between the two sets of results indicated that 5-year normals tend to possess larger prediction errors when they are not the best predictors, than do other normals on the greater number of occasions they are not the best predictors. The present findings were used by the Illinois Commerce Commission in evaluating weather normalization rate adjustments proposed by utility companies in 1979–80.
An investigation also is made into the nature of the climatic variation occurring when each normal is the best predictor. Five-year normals tend to attain this position for precipitation when the difference from the preceding year and the departures from longer-term averages are all moderate-to-small. When 5-year normals are the best temperature predictors, in contrast, the departures from this normal (and hence prediction errors) are very large. The frequency with which various normals were the best predictors shows no marked temporal variation during the study period.
Abstract
A semiphysical solar radiation (SR) model is implemented to generate a new historical daily SR database for 53 locations in nine Midwestern and six adjacent states (available from the Midwestern Climate Center). This model estimates daily SR using standard hourly meteorological observations (surface atmospheric pressure and dewpoint temperature; cloud height and fractional sky cover by layer) as well as time of day, day of year, latitude/longitude, and the daily presence/absence of snow cover as input. Because of an extensive effort to interpolate for missing input (especially cloud) data, the daily SR dataset generated is 92% complete for all 53 stations for 1948–91, and 99% complete for the 43 stations with continuous hourly meteorological observations that commenced during 1945–50 and extended through 1991. Consistent with previous work, the model validates favorably against sets of daily SR measurements from (three) contrasting parts of the study region, and so its output is used here without adjustment.
Analyses of the dataset document the basic Midwestern spatial and temporal SR variability since the mid-to late 1940s. The spatial variation of calendar monthly mean SR is dominated by a near-meridional (north-eastward) decrease in fall and winter. This fundamental pattern is substantially perturbed from midspring through summer by subregional-to-mesoscale variability around and across the Great Lakes. Time series of individual monthly station mean SR values exhibit a pronounced, regionwide 1945–91 downtrend for August–November. This decline is strongest (∼12%) and most statistically significant (>99% level) for October in a belt extending east-southeastward from west-central Wisconsin across southern lake Michigan and western Lake Erie to western Pennsylvania. The SR trends for December–July are largely positive but of lesser spatial coherence, temporal consistency, and statistical significance.
Abstract
A semiphysical solar radiation (SR) model is implemented to generate a new historical daily SR database for 53 locations in nine Midwestern and six adjacent states (available from the Midwestern Climate Center). This model estimates daily SR using standard hourly meteorological observations (surface atmospheric pressure and dewpoint temperature; cloud height and fractional sky cover by layer) as well as time of day, day of year, latitude/longitude, and the daily presence/absence of snow cover as input. Because of an extensive effort to interpolate for missing input (especially cloud) data, the daily SR dataset generated is 92% complete for all 53 stations for 1948–91, and 99% complete for the 43 stations with continuous hourly meteorological observations that commenced during 1945–50 and extended through 1991. Consistent with previous work, the model validates favorably against sets of daily SR measurements from (three) contrasting parts of the study region, and so its output is used here without adjustment.
Analyses of the dataset document the basic Midwestern spatial and temporal SR variability since the mid-to late 1940s. The spatial variation of calendar monthly mean SR is dominated by a near-meridional (north-eastward) decrease in fall and winter. This fundamental pattern is substantially perturbed from midspring through summer by subregional-to-mesoscale variability around and across the Great Lakes. Time series of individual monthly station mean SR values exhibit a pronounced, regionwide 1945–91 downtrend for August–November. This decline is strongest (∼12%) and most statistically significant (>99% level) for October in a belt extending east-southeastward from west-central Wisconsin across southern lake Michigan and western Lake Erie to western Pennsylvania. The SR trends for December–July are largely positive but of lesser spatial coherence, temporal consistency, and statistical significance.
Abstract
Ice storms are an infrequent but significant hazard in the U.S southern Great Plains. Common synoptic profiles for freezing precipitation reveal advection of low-level warm moist air from the Gulf of Mexico (GOM), above a shallow Arctic air mass ahead of a midlevel trough. Because the GOM is the proximal basin and major moisture source, this study investigates impacts of varying GOM sea surface temperature (SST) on the thermodynamic evolution of a winter storm that occurred during 28–30 January 2010, with particular emphasis on the modulation of freezing precipitation. A high-resolution, nested ARW sensitivity study with a 3.3-km inner domain is performed, using six representations of GOM SST, including control, climatological mean, uniform ±2°C from control, and physically constrained upper- and lower-bound basin-average anomalies from a 30-yr dataset. The simulations reveal discernable impacts of SST on the warm-layer inversion, precipitation intensity, and low-level dynamics. Whereas total precipitation for the storm increased monotonically with SST, the freezing-precipitation response was more varied and nonlinear, with the greatest accumulation decreases occurring for the coolest SST perturbation, particularly at moderate precipitation rates. Enhanced precipitation and warm-layer intensity promoted by warmer SST were offset for the highest perturbations by deepening of the weak 850-hPa low circulation and faster eastward progression associated with enhanced baroclinicity and diabatic generation of potential vorticity. Air-parcel trajectories terminating within the freezing-precipitation region were examined to identify airmass sources and modification. These results suggest that GOM SST can affect the severity of concurrent ice-storm events in the southern Great Plains, with warmer basin SST potentially exacerbating the risk of damaging ice accumulations.
Abstract
Ice storms are an infrequent but significant hazard in the U.S southern Great Plains. Common synoptic profiles for freezing precipitation reveal advection of low-level warm moist air from the Gulf of Mexico (GOM), above a shallow Arctic air mass ahead of a midlevel trough. Because the GOM is the proximal basin and major moisture source, this study investigates impacts of varying GOM sea surface temperature (SST) on the thermodynamic evolution of a winter storm that occurred during 28–30 January 2010, with particular emphasis on the modulation of freezing precipitation. A high-resolution, nested ARW sensitivity study with a 3.3-km inner domain is performed, using six representations of GOM SST, including control, climatological mean, uniform ±2°C from control, and physically constrained upper- and lower-bound basin-average anomalies from a 30-yr dataset. The simulations reveal discernable impacts of SST on the warm-layer inversion, precipitation intensity, and low-level dynamics. Whereas total precipitation for the storm increased monotonically with SST, the freezing-precipitation response was more varied and nonlinear, with the greatest accumulation decreases occurring for the coolest SST perturbation, particularly at moderate precipitation rates. Enhanced precipitation and warm-layer intensity promoted by warmer SST were offset for the highest perturbations by deepening of the weak 850-hPa low circulation and faster eastward progression associated with enhanced baroclinicity and diabatic generation of potential vorticity. Air-parcel trajectories terminating within the freezing-precipitation region were examined to identify airmass sources and modification. These results suggest that GOM SST can affect the severity of concurrent ice-storm events in the southern Great Plains, with warmer basin SST potentially exacerbating the risk of damaging ice accumulations.
Abstract
The possible contribution of jet aircraft condensation trails (contrails) to recent observed increases in high cloudiness constitutes a potentially important human effect on climate that has received relatively little attention. Very high resolution (0.6 km) thermal-infrared imagery from the Defense Meteorological Satellite Program polar orbiters, concentrated in the nighttime and morning hours, is interpreted to derive a climatic description of contrails over the United States and adjacent areas for the midseason months (April, July, October, and January) of 1977–79. A manual technique of identifying contrails on the imagery is validated by comparison with more recent ground-based observations. Contrail spatial distributions are mapped at a 1° lat × 1° long resolution for monthly and multimonth time periods.
Contrail incidence is widespread over the United States and adjacent areas, with highest frequencies occurring over the following regions: the extreme Southwest (particularly southern California), the Southeast (especially southeast Georgia and northeast Florida), the west coast of British Columbia and Vancouver Island, and the eastern Midwest centered on southeast Indiana and western Kentucky. Contrails are most frequent during the transition-season months (April and October), and are least frequent in July. Latitudinally, contrail incidence peaks over the northern (southern) regions in July (January), suggesting a first-order association with the seasonal variation of upper-tropospheric westerly winds. Analysis of synoptic-scale midtropospheric circulation patterns confirms that the highest contrail frequencies occur in association with baroclinic phenomena, particularly cyclone waves and jet streams. Moreover, contrails tend frequently to occur in conjunction with other clouds, including the cirrus associated with jet-stream and frontal systems.
Analyses of rawinsonde data for three representative contrail “outbreak” (multiple occurrence) events during the study months confirm some earlier studies that suggest contrails form below a cold, elevated tropopause (i.e., around ridgelines in the geopotential height field), in contrast with noncontrail days. Accordingly, the temperature advection in the troposphere accompanying the contrail outbreaks is positive, or warm, and relatively weak. This contrail climatic description provides a context within which recent surface climate changes at regional and subregional scales may be cast.
Abstract
The possible contribution of jet aircraft condensation trails (contrails) to recent observed increases in high cloudiness constitutes a potentially important human effect on climate that has received relatively little attention. Very high resolution (0.6 km) thermal-infrared imagery from the Defense Meteorological Satellite Program polar orbiters, concentrated in the nighttime and morning hours, is interpreted to derive a climatic description of contrails over the United States and adjacent areas for the midseason months (April, July, October, and January) of 1977–79. A manual technique of identifying contrails on the imagery is validated by comparison with more recent ground-based observations. Contrail spatial distributions are mapped at a 1° lat × 1° long resolution for monthly and multimonth time periods.
Contrail incidence is widespread over the United States and adjacent areas, with highest frequencies occurring over the following regions: the extreme Southwest (particularly southern California), the Southeast (especially southeast Georgia and northeast Florida), the west coast of British Columbia and Vancouver Island, and the eastern Midwest centered on southeast Indiana and western Kentucky. Contrails are most frequent during the transition-season months (April and October), and are least frequent in July. Latitudinally, contrail incidence peaks over the northern (southern) regions in July (January), suggesting a first-order association with the seasonal variation of upper-tropospheric westerly winds. Analysis of synoptic-scale midtropospheric circulation patterns confirms that the highest contrail frequencies occur in association with baroclinic phenomena, particularly cyclone waves and jet streams. Moreover, contrails tend frequently to occur in conjunction with other clouds, including the cirrus associated with jet-stream and frontal systems.
Analyses of rawinsonde data for three representative contrail “outbreak” (multiple occurrence) events during the study months confirm some earlier studies that suggest contrails form below a cold, elevated tropopause (i.e., around ridgelines in the geopotential height field), in contrast with noncontrail days. Accordingly, the temperature advection in the troposphere accompanying the contrail outbreaks is positive, or warm, and relatively weak. This contrail climatic description provides a context within which recent surface climate changes at regional and subregional scales may be cast.
Abstract
The article describes research opportunities associated with evaluating the characteristics of climate forecasts in settings where sequential decisions are made. Illustrative results are provided for corn production in east central Illinois. These results indicate that the production process examined has sufficient flexibility to utilize climate forecasts for specific production seasons but the value of those forecasts is sensitive to economic parameters as well as forecasts characteristics. Forecasts periods of greatest importance, as well as the relationships between forecast value, accuracy, and lead time, are evaluated.
Abstract
The article describes research opportunities associated with evaluating the characteristics of climate forecasts in settings where sequential decisions are made. Illustrative results are provided for corn production in east central Illinois. These results indicate that the production process examined has sufficient flexibility to utilize climate forecasts for specific production seasons but the value of those forecasts is sensitive to economic parameters as well as forecasts characteristics. Forecasts periods of greatest importance, as well as the relationships between forecast value, accuracy, and lead time, are evaluated.
Abstract
A three-step process is proposed to be most efficient for generating skillful climate forecasts which could reduce the adverse socioeconomic effects of climatic variability. These steps involve identifying weather-sensitive economic sectors, documenting the flexibility of these sectors with respect to likely forecast information, and the development of accordingly focused forecast capabilities. An illustration of the types of information needed to identify sector flexibility is provided for Midwest crop production. Finally, a pilot study using actual farmer data for east central Illinois suggests that increased corn yields could have resulted if producers had been forewarned of the benign weather conditions experienced during the 1979 growing season. This implies that skillful, properly structured climate forecasts may be useful to Midwest crop producers.
Abstract
A three-step process is proposed to be most efficient for generating skillful climate forecasts which could reduce the adverse socioeconomic effects of climatic variability. These steps involve identifying weather-sensitive economic sectors, documenting the flexibility of these sectors with respect to likely forecast information, and the development of accordingly focused forecast capabilities. An illustration of the types of information needed to identify sector flexibility is provided for Midwest crop production. Finally, a pilot study using actual farmer data for east central Illinois suggests that increased corn yields could have resulted if producers had been forewarned of the benign weather conditions experienced during the 1979 growing season. This implies that skillful, properly structured climate forecasts may be useful to Midwest crop producers.
