Stochastic Models to Generate Geospatial-, Temporal-, and Cross-Correlated Daily Maximum and Minimum Temperatures

Weather generators are tools used to downscale monthly to seasonal climate forecasts, from numerical climate models to daily values for use as inputs for crop and other environmental models. One main limitation of most of weather generators is that they do not incorporate neither the spatial/temporal correlations between/within sites nor the cross-correlations between variables, characteristics specially important when aggregating, for example, simulated crop yields, freeze events, or heat waves in a watershed or region.Three models were developed to generate realization of daily maximum and minimum temperatures for multiple sites. The first model incorporates only spatial correlation, whereas temporal correlation using a 1-day lag and cross-correlation between variables were added to model one, respectively, by the other two models. Vectors of correlated random numbers were rescaled to temperature values by multiplying each element with the standard deviation and adding the mean of the corresponding weather station. An extension of Crout’s algorithm was developed to enable the factorization of non positive definite matrices. Monthly spatial correlations of generated daily maximum and minimum temperatures between all pairs of weather stations closely matched their observed counterparts. Performance was analyzed by comparing the root mean squared error, temporal semi variograms, correlation/cross-correlation matrices, multi annual monthly means, and standard deviations

ENSO Teleconnection Pattern Changes over the Southeastern United States under a Climate Change Scenario in CMIP5 Models

A strong teleconnection exists between the sea surface temperature (SST) over the tropical Pacific and the winter precipitation in the southeastern United States (SE US).This feature is adopted to validate the fidelity of Coupled Model Intercomparison Project Phase 5 (CMIP5) in this study. In addition, the authors examine whether the teleconnection pattern persists in the future under a global warming scenario. Generally, most of the eight selected models show a positive correlation between November SST over Ni˜no 3 region and December–February (DJF) mean daily precipitation anomalies over the SE US, consistent with the observation. However, the models with poor realization of skewness of Ni˜no indices fail to simulate the realistic teleconnection pattern in the historical simulation. In the Representative Concentration Pathways 8.5 (RCP8.5) run, all of the models maintain positive and slightly increased correlation patterns. It is noteworthy that the region with strong teleconnection pattern shifts northward in the future. Increased variance of winter precipitation due to the SST teleconnection is shown over Alabama and Georgia rather than over Florida under the RCP8.5 scenario in most of themodels, differing fromthe historical run in which the precipitation in Florida is the most attributable to the eastern Pacific SST

ENSO Teleconnection Pattern Changes over the Southeastern United States under a Climate Change Scenario in CMIP5 Models

A strong teleconnection exists between the sea surface temperature (SST) over the tropical Pacific and the winter precipitation in the southeastern United States (SE US).This feature is adopted to validate the fidelity of Coupled Model Intercomparison Project Phase 5 (CMIP5) in this study. In addition, the authors examine whether the teleconnection pattern persists in the future under a global warming scenario. Generally, most of the eight selected models show a positive correlation between November SST over Ni˜no 3 region and December–February (DJF) mean daily precipitation anomalies over the SE US, consistent with the observation. However, the models with poor realization of skewness of Ni˜no indices fail to simulate the realistic teleconnection pattern in the historical simulation. In the Representative Concentration Pathways 8.5 (RCP8.5) run, all of the models maintain positive and slightly increased correlation patterns. It is noteworthy that the region with strong teleconnection pattern shifts northward in the future. Increased variance of winter precipitation due to the SST teleconnection is shown over Alabama and Georgia rather than over Florida under the RCP8.5 scenario in most of themodels, differing fromthe historical run in which the precipitation in Florida is the most attributable to the eastern Pacific SST

The Fingerprint of Climate Change and Urbanization in South Korea

Understanding long-term changes in precipitation and temperature patterns is important in the detection and characterization of climate change, as is understanding the implications of climate change when performing impact assessments. This study uses a statistically robust methodology to quantify long-, medium- and short-term changes for evaluating the degree to which climate change and urbanization have caused temporal changes in precipitation and temperature in South Korea. We sought to identify a fingerprint of changes in precipitation and temperature based on statistically significant differences at multiple-timescales. This study evaluates historical weather data during a 40-year period (1973–2012) and from 54 weather stations. Our results demonstrate that between 1993–2012, minimum and maximum temperature trends in the vicinity of urban and agricultural areas are significantly different from the two previous decades (1973–1992). The results for precipitation amounts show significant differences in urban areas. These results indicate that the climate in urbanized areas has been affected by both the heat island effect and global warming-caused climate change. The increase in the number of rainfall events in agricultural areas is highly significant, although the temporal trends for precipitation amounts showed no significant differences. Overall, the impacts of climate change and urbanization in South Korea have not been continuous over time and have been expressed locally and regionally in terms of precipitation and temperature changes

Assessing Predictability of Cotton Yields in the Southeastern United States Based on Regional Atmospheric Circulation and Surface Temperatures

The potential to predict cotton yields up to one month before planting in the southeastern United States is assessed in this research. To do this, regional atmospheric variables that are related to historic summer rainfall and cotton yields were identified. The use of simulations of those variables from a global circulation model (GCM) for estimating cotton yields was evaluated. The authors analyzed detrended cotton yields (1970–2004) from 48 counties in Alabama and Georgia, monthly rainfall from 53 weather stations, monthly reanalysis data of 850- and 200-hPa winds and surface temperatures over the southeast U.S. region, and monthly predictions of the same variables from the ECHAM 4.5 GCM. Using the reanalysis climate data, it was found that meridional wind fields and surface temperatures around the Southeast were significantly correlated with county cotton yields (explaining up to 52% of the interannual variability of observed yields), and with rainfall over most of the region, especially during April and July. The tendency for cotton yields to be lower during years with atmospheric circulation patterns that favor higher humidity and rainfall is consistent with increased incidence of disease in cotton during flowering and harvest periods under wet conditions. Cross-validated yield estimations based on ECHAM retrospective simulations of wind and temperature fields forced by observed SSTs showed significant predictability skill (up to 55% and 60% hit skill scores based on terciles and averages, respectively). It is concluded that there is potential to predict cotton yields in the Southeast by using variables that are forecast by numerical climate models

Stochastic Models to Generate Geospatial-, Temporal-, and Cross-Correlated Daily Maximum and Minimum Temperatures

Weather generators are tools used to downscale monthly to seasonal climate forecasts, from numerical climate models to daily values for use as inputs for crop and other environmental models. One main limitation of most of weather generators is that they do not incorporate neither the spatial/temporal correlations between/within sites nor the cross-correlations between variables, characteristics specially important when aggregating, for example, simulated crop yields, freeze events, or heat waves in a watershed or region.Three models were developed to generate realization of daily maximum and minimum temperatures for multiple sites. The first model incorporates only spatial correlation, whereas temporal correlation using a 1-day lag and cross-correlation between variables were added to model one, respectively, by the other two models. Vectors of correlated random numbers were rescaled to temperature values by multiplying each element with the standard deviation and adding the mean of the corresponding weather station. An extension of Crout’s algorithm was developed to enable the factorization of non positive definite matrices. Monthly spatial correlations of generated daily maximum and minimum temperatures between all pairs of weather stations closely matched their observed counterparts. Performance was analyzed by comparing the root mean squared error, temporal semi variograms, correlation/cross-correlation matrices, multi annual monthly means, and standard deviations

The Fingerprint of Climate Change and Urbanization in South Korea

Understanding long-term changes in precipitation and temperature patterns is important in the detection and characterization of climate change, as is understanding the implications of climate change when performing impact assessments. This study uses a statistically robust methodology to quantify long-, medium- and short-term changes for evaluating the degree to which climate change and urbanization have caused temporal changes in precipitation and temperature in South Korea. We sought to identify a fingerprint of changes in precipitation and temperature based on statistically significant differences at multiple-timescales. This study evaluates historical weather data during a 40-year period (1973–2012) and from 54 weather stations. Our results demonstrate that between 1993–2012, minimum and maximum temperature trends in the vicinity of urban and agricultural areas are significantly different from the two previous decades (1973–1992). The results for precipitation amounts show significant differences in urban areas. These results indicate that the climate in urbanized areas has been affected by both the heat island effect and global warming-caused climate change. The increase in the number of rainfall events in agricultural areas is highly significant, although the temporal trends for precipitation amounts showed no significant differences. Overall, the impacts of climate change and urbanization in South Korea have not been continuous over time and have been expressed locally and regionally in terms of precipitation and temperature changes

Comparing theoretical irrigation requirement and actual irrigation for citrus in Florida

Florida ranks first in citrus production, with nearly 68% of all U.S. citrus growing in the season 2005-2006. Most of the citrus groves are located from central to south Florida, and agricultural irrigation permitting is regulated by three of Florida's five water management districts. Most of the permitting for citrus production in Highlands, Polk and Hillsborough counties is conducted by the Southwest Florida Water Management District (SWFWMD), and quantities are based on the District's AGMOD computer program. In 2003, the SWFWMD implemented new permit criteria so that permitted amounts were more representative of actual water use. This paper compares grower reported citrus irrigation water use in Highlands, Polk and Hillsborough counties from 1994 through 2005 with permitted and theoretical irrigation requirements calculated by a daily water balance. Two different sets of crop coefficients (Kc's) developed for citrus in Florida were compared in the daily soil water balance calculation of theoretical irrigation requirements. The percentage of irrigated area considered in this study ranged from 40 to 60% to simulate a range of grower practices. Meteorological data from two weather stations and additional rainfall information from 50 locations within the three counties was used in the water balance. Missing and error values in the meteorological historical record data were filled with weather generators. The multiannual average water consumption (including cold protection water use) from growers ranged from 243 (Hillsborough) to 406 mm (Highlands) and the multiannual average permitted irrigation requirement (without cold protection) ranged from 295 to 557 mm. The simulated gross irrigation requirements under different scenarios of location-Kc-wetted area were variable but mostly lower than the limits established by the district, except for some scenarios in Polk County, whose maximum simulated irrigation value reached 578 mm year-1. In general, permitted limits recommended by the SWFWMD seem to be reasonable for the actual water use by growers in these counties.Weather generator Water balance Permitted irrigation values Uncertainty