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- Author or Editor: Kenneth E. Kunkel x
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Abstract
Many climatological locations report only maximum and minimum temperatures. However, in certain applications, such as estimation of design temperatures, the frequency distribution of hourly temperatures is required. For this reason, a method is developed for estimating the mathematical form of the upper half of the cumulative probability distribution function (CDF) for hourly temperatures from the CDF for daily maximum temperatures for the summer months of June, July and August. In this method, an exponential function is fitted to the daily maximum CDF. A procedure for estimating the hourly CDF from the daily CDF is presented. This method is used to estimate summer design temperatures for a number of stations in New Mexico.
Abstract
Many climatological locations report only maximum and minimum temperatures. However, in certain applications, such as estimation of design temperatures, the frequency distribution of hourly temperatures is required. For this reason, a method is developed for estimating the mathematical form of the upper half of the cumulative probability distribution function (CDF) for hourly temperatures from the CDF for daily maximum temperatures for the summer months of June, July and August. In this method, an exponential function is fitted to the daily maximum CDF. A procedure for estimating the hourly CDF from the daily CDF is presented. This method is used to estimate summer design temperatures for a number of stations in New Mexico.
Abstract
A series of simple procedures are presented for extrapolating climatic averages of humidity variables from a reference location with long-term humidity measurements to nearby higher elevation locations. The extrapolation of monthly average dewpoint temperature is accomplished by using an exponential function for the height decrease of water vapor pressure. This procedure results in a lapse rate of dewpoint temperature which is to a first approximation a constant. lie root-mean-square (rms) error in estimating dewpoint temperature at nine higher elevation locations averaged 1.I°C. Summer wet-bulb design temperatures are estimated using a region-wide lapse rate of 4.4°C km−1. The rms error in estimating 1%, 2.5%, and 5% wet-bulb design temperatures at 12 higher elevation locations was 0.9°C. Three-hour monthly average wet-bulb temperatures and relative humidity are estimated from the 3-hour monthly average dewpoint temperature and the 3-hour monthly average air temperature at the reference location with the following procedure. An estimate of the 3-hour monthly average air temperature at the desired location is obtained using an average lapse rate calculated from the monthly mean maximum and minimum temperatures at the two locations. An estimate of 3-hour monthly average dewpoint temperature is obtained using the same procedure that was developed for monthly average dewpoint temperature. Estimates of 3-hour monthly average wet-bulb temperature and relative humidity are then calculated from the estimated air and dewpoint temperatures. A test of this procedure for two station pairs resulted in good agreement for 3-hour monthly average air and wet-bulb temperatures with rms values of 0.8°C and 0.7°C, respectively. The rms error for 3-hour monthly average relative humidity was 5%, however, with some individual errors around 10%.
Abstract
A series of simple procedures are presented for extrapolating climatic averages of humidity variables from a reference location with long-term humidity measurements to nearby higher elevation locations. The extrapolation of monthly average dewpoint temperature is accomplished by using an exponential function for the height decrease of water vapor pressure. This procedure results in a lapse rate of dewpoint temperature which is to a first approximation a constant. lie root-mean-square (rms) error in estimating dewpoint temperature at nine higher elevation locations averaged 1.I°C. Summer wet-bulb design temperatures are estimated using a region-wide lapse rate of 4.4°C km−1. The rms error in estimating 1%, 2.5%, and 5% wet-bulb design temperatures at 12 higher elevation locations was 0.9°C. Three-hour monthly average wet-bulb temperatures and relative humidity are estimated from the 3-hour monthly average dewpoint temperature and the 3-hour monthly average air temperature at the reference location with the following procedure. An estimate of the 3-hour monthly average air temperature at the desired location is obtained using an average lapse rate calculated from the monthly mean maximum and minimum temperatures at the two locations. An estimate of 3-hour monthly average dewpoint temperature is obtained using the same procedure that was developed for monthly average dewpoint temperature. Estimates of 3-hour monthly average wet-bulb temperature and relative humidity are then calculated from the estimated air and dewpoint temperatures. A test of this procedure for two station pairs resulted in good agreement for 3-hour monthly average air and wet-bulb temperatures with rms values of 0.8°C and 0.7°C, respectively. The rms error for 3-hour monthly average relative humidity was 5%, however, with some individual errors around 10%.
Abstract
An operational soil moisture monitoring capability for the midwestern United States is developed using a multilayer soil water balance model which incorporates daily weather data to calculate precipitation, soil evaporation, plant transpiration, runoff, and drainage through the soil profile. The effects of vegetation on soil evaporation and plant transpiration are incorporated through the use of a model for the growth and development of corn. Data requirements include daily observations of maximum temperature, minimum temperature, and precipitation and hourly observations of cloud cover, humidity, and wind speed; these data are collected in real time and aggregated on a climate division scale. The average characteristics of the dominant soils in each climate division are used as representative of that climate division. Using these weather and soils data, the model makes estimates of the current soil moisture status on a climate division basis updated daily. Historical soil moisture estimates using this same model were generated for the period 1949–89 to provide an historical perspective on current soil moisture estimates. This information is accessible to the public through a dial-up computer information system.
Abstract
An operational soil moisture monitoring capability for the midwestern United States is developed using a multilayer soil water balance model which incorporates daily weather data to calculate precipitation, soil evaporation, plant transpiration, runoff, and drainage through the soil profile. The effects of vegetation on soil evaporation and plant transpiration are incorporated through the use of a model for the growth and development of corn. Data requirements include daily observations of maximum temperature, minimum temperature, and precipitation and hourly observations of cloud cover, humidity, and wind speed; these data are collected in real time and aggregated on a climate division scale. The average characteristics of the dominant soils in each climate division are used as representative of that climate division. Using these weather and soils data, the model makes estimates of the current soil moisture status on a climate division basis updated daily. Historical soil moisture estimates using this same model were generated for the period 1949–89 to provide an historical perspective on current soil moisture estimates. This information is accessible to the public through a dial-up computer information system.
Abstract
A simple index of extreme surface (2 m) monthly temperature was analyzed over the conterminous United States for 13 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) hindcast (1981–2010) and prediction (2006–35) datasets as well as the U.S. climate division dataset, version 2 (nClimDiv), as observations for 1981–2010. Results are analyzed for regions defined in the recent Third U.S. National Climate Assessment. There is good agreement between models and observations for all regions for the annual warm and cold indices except for the warm index in the Northwest. For seasonal values of the temperature index, model simulations generally agree with the sign of the observed seasonal trends in all regions except for the Northwest and a few seasons in the “warming hole” areas of the central and southeastern United States. Most individual ensemble member simulations agree with the sign of the observed trend. However, in all regions and seasons, some simulations, in the range of 10%–40% of all ensemble members, show opposite signs, indicating that even overall skillful projections can have substantial uncertainty. These results indicate that there is potential skill in use of GCMs to provide projections of hot and cold extremes on the 30-yr time scale. However, it is important to note that natural variability is comparable to the forced signal on this time scale and thus introduces uncertainty. Analysis of the future simulations (2006–35) indicates that warm extremes increase rapidly while cold extremes become substantially more rare.
Abstract
A simple index of extreme surface (2 m) monthly temperature was analyzed over the conterminous United States for 13 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) hindcast (1981–2010) and prediction (2006–35) datasets as well as the U.S. climate division dataset, version 2 (nClimDiv), as observations for 1981–2010. Results are analyzed for regions defined in the recent Third U.S. National Climate Assessment. There is good agreement between models and observations for all regions for the annual warm and cold indices except for the warm index in the Northwest. For seasonal values of the temperature index, model simulations generally agree with the sign of the observed seasonal trends in all regions except for the Northwest and a few seasons in the “warming hole” areas of the central and southeastern United States. Most individual ensemble member simulations agree with the sign of the observed trend. However, in all regions and seasons, some simulations, in the range of 10%–40% of all ensemble members, show opposite signs, indicating that even overall skillful projections can have substantial uncertainty. These results indicate that there is potential skill in use of GCMs to provide projections of hot and cold extremes on the 30-yr time scale. However, it is important to note that natural variability is comparable to the forced signal on this time scale and thus introduces uncertainty. Analysis of the future simulations (2006–35) indicates that warm extremes increase rapidly while cold extremes become substantially more rare.
Abstract
All long historical climate records are based on measurements that experienced shifts in instrumentation, site characteristics, or locations. How such changes affect the quality of past data remains an uncertainty for the thousands of historical records, confounding efforts to assess climate change. Fortunately, one station in Illinois with 118 yr of records has also kept detailed records of all such shifts plus overlapping measurements of temperatures and precipitation, allowing exact measurements of how conditions changed over time. This study examined these data and found varying discontinuities of 0.1°–0.9°C in annual temperatures due to various shifts, but no changes in daily precipitation related to site shifts. However, hourly precipitation amounts from recording rain gauges did undergo a considerable shift due to changes in rain gauge types. Similar studies need to be made of other stations with comparable historical records of station and instrument shifts and with overlapping measurements when shifts were made.
Abstract
All long historical climate records are based on measurements that experienced shifts in instrumentation, site characteristics, or locations. How such changes affect the quality of past data remains an uncertainty for the thousands of historical records, confounding efforts to assess climate change. Fortunately, one station in Illinois with 118 yr of records has also kept detailed records of all such shifts plus overlapping measurements of temperatures and precipitation, allowing exact measurements of how conditions changed over time. This study examined these data and found varying discontinuities of 0.1°–0.9°C in annual temperatures due to various shifts, but no changes in daily precipitation related to site shifts. However, hourly precipitation amounts from recording rain gauges did undergo a considerable shift due to changes in rain gauge types. Similar studies need to be made of other stations with comparable historical records of station and instrument shifts and with overlapping measurements when shifts were made.
Abstract
The CMIP5 decadal hindcast (“Hindcast”) and prediction (“Predict”) experiment simulations from 11 models were analyzed for the United States with respect to two metrics of extreme precipitation: the 10-yr return level of daily precipitation, derived from the annual maximum series of daily precipitation, and the total precipitation exceeding the 99.5th percentile of daily precipitation. Both Hindcast simulations and observations generally show increases for the 1981–2010 historical period. The multimodel-mean Hindcast trends are statistically significant for all regions while the observed trends are statistically significant for the Northeast, Southeast, and Midwest regions. An analysis of CMIP5 simulations driven by historical natural (“HistoricalNat”) forcings shows that the Hindcast trends are generally within the 5th–95th-percentile range of HistoricalNat trends, but those outside that range are heavily skewed toward exceedances of the 95th-percentile threshold. Future projections for 2006–35 indicate increases in all regions with respect to 1981–2010. While there is good qualitative agreement between the observations and Hindcast simulations regarding the direction of recent trends, the multimodel-mean trends are similar for all regions, while there is considerable regional variability in observed trends. Furthermore, the HistoricalNat simulations suggest that observed historical trends are a combination of natural variability and anthropogenic forcing. Thus, the influence of anthropogenic forcing on the magnitude of near-term future changes could be temporarily masked by natural variability. However, continued observed increases in extreme precipitation in the first decade (2006–15) of the “future” period partially confirm the Predict results, suggesting that incorporation of increases in planning would appear prudent.
Abstract
The CMIP5 decadal hindcast (“Hindcast”) and prediction (“Predict”) experiment simulations from 11 models were analyzed for the United States with respect to two metrics of extreme precipitation: the 10-yr return level of daily precipitation, derived from the annual maximum series of daily precipitation, and the total precipitation exceeding the 99.5th percentile of daily precipitation. Both Hindcast simulations and observations generally show increases for the 1981–2010 historical period. The multimodel-mean Hindcast trends are statistically significant for all regions while the observed trends are statistically significant for the Northeast, Southeast, and Midwest regions. An analysis of CMIP5 simulations driven by historical natural (“HistoricalNat”) forcings shows that the Hindcast trends are generally within the 5th–95th-percentile range of HistoricalNat trends, but those outside that range are heavily skewed toward exceedances of the 95th-percentile threshold. Future projections for 2006–35 indicate increases in all regions with respect to 1981–2010. While there is good qualitative agreement between the observations and Hindcast simulations regarding the direction of recent trends, the multimodel-mean trends are similar for all regions, while there is considerable regional variability in observed trends. Furthermore, the HistoricalNat simulations suggest that observed historical trends are a combination of natural variability and anthropogenic forcing. Thus, the influence of anthropogenic forcing on the magnitude of near-term future changes could be temporarily masked by natural variability. However, continued observed increases in extreme precipitation in the first decade (2006–15) of the “future” period partially confirm the Predict results, suggesting that incorporation of increases in planning would appear prudent.
During the last 20 years the use of climate data and information in agriculture and water resources has increased dramatically. This has resulted from vastly improved access to comprehensive datasets and climate information made available by wide use of personal computers, as well as ease of access due to Internet connections to computer systems containing specially developed climate databases and information packages. Furthermore, the recent development of better, more sophisticated information about how climate conditions affect various physical conditions and economic outcomes has enabled more informed decisions by managers, who, in turn, developed a greater awareness of how to utilize climate information. The demand for information has grown as a result of increasing economic pressures and because certain agricultural and water management activities and their infrastructure have become more sensitive to certain climate aberrations. These factors have led to the development of new suppliers of data and information, including regional climate centers to handle the quick assembly of updated databases, and the expansion of the private sector into the provision of specialized climate information needed by a wide variety of users. Key new uses relate to near-real-time access to constantly updated interpreted data and to availability of sophisticated information products relating current and future climate conditions to specific outcomes. In sum, these advances represent major improvements in the service of atmospheric sciences to the nation, helping to improve the economy and environmental management.
During the last 20 years the use of climate data and information in agriculture and water resources has increased dramatically. This has resulted from vastly improved access to comprehensive datasets and climate information made available by wide use of personal computers, as well as ease of access due to Internet connections to computer systems containing specially developed climate databases and information packages. Furthermore, the recent development of better, more sophisticated information about how climate conditions affect various physical conditions and economic outcomes has enabled more informed decisions by managers, who, in turn, developed a greater awareness of how to utilize climate information. The demand for information has grown as a result of increasing economic pressures and because certain agricultural and water management activities and their infrastructure have become more sensitive to certain climate aberrations. These factors have led to the development of new suppliers of data and information, including regional climate centers to handle the quick assembly of updated databases, and the expansion of the private sector into the provision of specialized climate information needed by a wide variety of users. Key new uses relate to near-real-time access to constantly updated interpreted data and to availability of sophisticated information products relating current and future climate conditions to specific outcomes. In sum, these advances represent major improvements in the service of atmospheric sciences to the nation, helping to improve the economy and environmental management.
Abstract
A diagnostic analysis of relationships between central U.S. climate characteristics and various flow and scalar fields was used to evaluate nine global coupled ocean–atmosphere general circulation models (CGCMs) participating in the Coupled Model Intercomparison Project (CMIP). To facilitate identification of physical mechanisms causing biases, data from 21 models participating in the Atmospheric Model Intercomparison Project (AMIP) were also used for certain key analyses.
Most models reproduce basic features of the circulation, temperature, and precipitation patterns in the central United States, although no model exhibits small differences from the observationally based data for all characteristics in all seasons. Model ensemble means generally produce better agreement with the observationally based data than any single model. A fall precipitation deficiency, found in all AMIP and CMIP models except the third-generation Hadley Centre CGCM (HadCM3), appears to be related in part to slight biases in the flow on the western flank of the Atlantic subtropical ridge. In the model mean, the ridge at 850 hPa is displaced slightly to the north and to the west, resulting in weaker southerly flow into the central United States.
The CMIP doubled-CO2 transient runs show warming (1°–5°C) for all models and seasons and variable precipitation changes over the central United States. Temperature (precipitation) changes are larger (mostly less) than the variations that are observed in the twentieth century and the model variations in the control simulations.
Abstract
A diagnostic analysis of relationships between central U.S. climate characteristics and various flow and scalar fields was used to evaluate nine global coupled ocean–atmosphere general circulation models (CGCMs) participating in the Coupled Model Intercomparison Project (CMIP). To facilitate identification of physical mechanisms causing biases, data from 21 models participating in the Atmospheric Model Intercomparison Project (AMIP) were also used for certain key analyses.
Most models reproduce basic features of the circulation, temperature, and precipitation patterns in the central United States, although no model exhibits small differences from the observationally based data for all characteristics in all seasons. Model ensemble means generally produce better agreement with the observationally based data than any single model. A fall precipitation deficiency, found in all AMIP and CMIP models except the third-generation Hadley Centre CGCM (HadCM3), appears to be related in part to slight biases in the flow on the western flank of the Atlantic subtropical ridge. In the model mean, the ridge at 850 hPa is displaced slightly to the north and to the west, resulting in weaker southerly flow into the central United States.
The CMIP doubled-CO2 transient runs show warming (1°–5°C) for all models and seasons and variable precipitation changes over the central United States. Temperature (precipitation) changes are larger (mostly less) than the variations that are observed in the twentieth century and the model variations in the control simulations.
Abstract
A unique rainstorm in northern Illinois produced 43 cm of precipitation in mid-July 1996, the highest 24-h precipitation amount ever recorded officially in the upper Midwest. Rains exceeding 20 cm fell over an area of 4400 km2, creating extremely damaging flash floods in portions of Chicago and its suburbs. Measurements from 496 rain gauges, including 80 recording gauges in the heavy rain area, made it possible to accurately define this storm.
The heavy rains were the result of two massive mesoscale convective systems, one in the afternoon and one at night. These systems formed to the north of a nearby stationary warm front. Several factors contributed to the excessive rainfall. Excessive moisture was present to the southwest of the warm front over Iowa and western Illinois; atmospheric moisture content was enhanced by surface evaporation from a very wet surface created by heavy rains the previous day, creating a conditionally unstable atmosphere. A cool air mass transported by easterly winds off Lake Michigan strengthened and slowed the movement of the warm front. A low-level jet oriented perpendicular to the warm front resulted in rising motion north of the warm front. These factors (instability, moisture availability, lifting mechanism) combined to form intense storms. This paper, the first of a three-part series, describes the storm in detail, including its morphology and causes, and the resulting rainfall distributions.
Abstract
A unique rainstorm in northern Illinois produced 43 cm of precipitation in mid-July 1996, the highest 24-h precipitation amount ever recorded officially in the upper Midwest. Rains exceeding 20 cm fell over an area of 4400 km2, creating extremely damaging flash floods in portions of Chicago and its suburbs. Measurements from 496 rain gauges, including 80 recording gauges in the heavy rain area, made it possible to accurately define this storm.
The heavy rains were the result of two massive mesoscale convective systems, one in the afternoon and one at night. These systems formed to the north of a nearby stationary warm front. Several factors contributed to the excessive rainfall. Excessive moisture was present to the southwest of the warm front over Iowa and western Illinois; atmospheric moisture content was enhanced by surface evaporation from a very wet surface created by heavy rains the previous day, creating a conditionally unstable atmosphere. A cool air mass transported by easterly winds off Lake Michigan strengthened and slowed the movement of the warm front. A low-level jet oriented perpendicular to the warm front resulted in rising motion north of the warm front. These factors (instability, moisture availability, lifting mechanism) combined to form intense storms. This paper, the first of a three-part series, describes the storm in detail, including its morphology and causes, and the resulting rainfall distributions.
