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In this study, we analyzed changes in the predicted flowering date (PFD) for cherry blossom trees under changing climate conditions by simulating the PFDs for six sites on the Korean Peninsula between 1981 and 2010. The spatial downscaled climate data from the Representative Concentration Pathways (RCP) 8.5 scenarios of 30 global climate models (GCMs) were used in the analysis. Here, we present the range of uncertainty in the PFDs, which were calculated by comparing the simulated PFDs to the observed flowering dates. We determined that the root-mean-square errors (RMSEs) of PFDs from individual GCMs, at 7-15 days, were greater in range than those of the mean PFDs from multiple GCMs, at 7-8 days. During three future periods of 2011-2040, 2041-2070, and 2071-2100, the standard deviations (SD), the interquartile ranges (IQRs), and the relative changes in the mean predicted flowering dates (MPFDs) were calculated to quantify the uncertainty levels inherent from the climate scenarios of multiple GCMs. Distinctive changes in the SDs and IQRs of MPFD were found among the analyzed sites. The SDs increased with time between each future period in Seoul, Incheon, and Jeonju, whereas those in Daegu, Busan, and Mokpo decreased with time. In addition, the IQRs increased with time at Seoul, Incheon, Jeonju, and Daegu but not at Busan and Mokpo. The relative changes in the MPFDs at all six sites became greater with time toward the year 2100. Therefore, combining multiple GCM scenarios may not contribute largely to reduce the uncertainty in the PFDs under changing climate conditions, although it may be useful in quantifying the uncertainty in order to make better decisions based on more accurate information.

It is highly anticipated that warming temperature resulting from global climate change will affect the phenological pattern of kiwifruit, which has been commercially grown in Korea since the early 1980s. Here, we present the potential impacts of climate change on the variations of flowering day of a gold kiwifruit cultivar, Haegeum, in the Jeonnam Province, Korea. By running six global climate models (GCM), the results from this study emphasize the uncertainty in climate change scenarios. To predict the flowering day of kiwifruit, we obtained three parameters of the ‘Chill-day’ model for the simulation of Haegeum: 6.3 o C for the base temperature ( Tb ), 102.5 for chill requirement ( Rc ), and 575 for heat requirement ( Rh ). Two separate validations of the resulting ‘Chill-day’ model were conducted. First, direct comparisons were made between the observed flowering days collected from 25 kiwifruit orchards for two years (2014-15) and the simulated flowering days from the ‘Chill-day’ model using weather data from four weather stations near the 25 orchards. The estimation error between the observed and simulated flowering days was 5.2 days. Second, the model was simulated using temperature data extracted, for the 25 orchards, from a high-resolution digital temperature map, resulting in the error of 3.4 days. Using the RCP 4.5 and 8.5 climate change scenarios from six GCMs for the period of 2021-40, the future flowering days were simulated with the ‘Chill-day’ model. The predicted flowering days of Haegeum in Jeonnam were advanced more than 10 days compared to the present ones from multi-model ensemble, while some individual models resulted in quite different magnitudes of impacts, indicating the multi-model ensemble accounts for uncertainty better than individual climate models. In addition, the current flowering period of Haegeum in Jeonnam Province was predicted to expand northward, reaching over Jeonbuk and Chungnam Provinces. This preliminary result will provide a basis for the local impact assessment of climate change as more phenology models are developed for other fruit trees.

The agro-climatic index is one of the ways to assess the climate resources of particular agricultural areas on the prospect of agricultural production; it can be a key indicator of agricultural productivity by providing the basic information required for the implementation of different and various farming techniques and practicalities to estimate the growth and yield of crops from the climate resources such as air temperature, solar radiation, and precipitation. However, the agro-climate index can always be changed since the index is not the absolute. Recently, many studies which consider uncertainty of future climate change have been actively conducted using multi-model ensemble (MME) approach by developing and improving dynamic and statistical downscaling of Global Climate Model (GCM) output. In this study, the agro-climatic index of Korean Peninsula, such as growing degree day based on , plant period based on , crop period based on , and frost free day were calculated for assessment of the spatio-temporal variations and uncertainties of the indices according to climate change; the downscaled historical (1976-2005) and near future (2011-2040) RCP climate sceneries of AR5 were applied to the calculation of the index. The result showed four agro-climatic indices calculated by nine individual GCMs as well as MME agreed with agro-climatic indices which were calculated by the observed data. It was confirmed that MME, as well as each individual GCM emulated well on past climate in the four major Rivers of South Korea (Han, Nakdong, Geum, and Seumjin and Yeoungsan). However, spatial downscaling still needs further improvement since the agro-climatic indices of some individual GCMs showed different variations with the observed indices at the change of spatial distribution of the four Rivers. The four agro-climatic indices of the Korean Peninsula were expected to increase in nine individual GCMs and MME in future climate scenarios. The differences and uncertainties of the agro-climatic indices have not been reduced on the unlimited coupling of multi-model ensembles. Further research is still required although the differences started to improve when combining of three or four individual GCMs in the study. The agro-climatic indices which were derived and evaluated in the study will be the baseline for the assessment of agro-climatic abnormal indices and agro-productivity indices of the next research work.

This study uses geo-spatial crop modeling to quantify the biophysical impact of weather extremes. More specifically, the study analyzes the weather extreme which affected maize production in the USA in 2012; it also estimates the effect of a similar weather extreme in 2050, using future climate scenarios. The secondary impact of the weather extreme on food security in the developing world is also assessed using trend analysis.Many studies have reported on the significant reduction in maize production in the USA due to the extreme weather event (combined heat wave and drought) that occurred in 2012. However, most of these studies focused on yield and did not assess the potential effect of weather extremes on food prices and security. The overall goal of this study was to use geo-spatial crop modeling and trend analysis to quantify the impact of weather extremes on both yield and, followed food security in the developing world.We used historical weather data for severe extreme events that have occurred in the USA. The data were obtained from the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA). In addition we used five climate scenarios: the baseline climate which is typical of the late 20th century (2000s) and four future climate scenarios which involve a combination of two emission scenarios (A1B and B1) and two global circulation models (CSIRO-Mk3.0 and MIROC 3.2). DSSAT 4.5 was combined with GRASS GIS for geo-spatial crop modeling. Simulated maize grain yield across all affected regions in the USA indicates that average grain yield across the USA Corn Belt would decrease by 29% when the weather extremes occur using the baseline climate. If the weather extreme were to occur under the A1B emission scenario in the 2050s respectively, average grain yields would decrease by 38% and 57%, under the CSIRO-Mk3.0 and MIROC 3.2 global climate models, respectively.The weather extremes that occurred in the USA in 2012 resulted in a sharp increase in the world maize price. In addition, it likely played a role in the reduction in world maize consumption and trade in 2012/13, compared to 2011/12. The most vulnerable countries to the weather extremes are poor countries with high maize import dependency ratios including those countries in the Caribbean, northern Africa and western Asia. Other vulnerable countries include low-income countries with low import dependency ratios but which cannot afford highly-priced maize. The study also highlighted the pathways through which a weather extreme would affect food security, were it to occur in 2050 under climate change.Some of the policies which could help vulnerable countries counter the negative effects of weather extremes consist of social protection and safety net programs. Medium- to long-term adaptation strategies include increasing world food reserves to a level where they can be used to cover the production losses brought by weather extremes.

Many of the irrigated spring wheat regions in the world are also regions with high poverty. The impacts of temperature increase on wheat yield in regions of high poverty are uncertain. A grain yield–temperature response function combined with a quantification of model uncertainty was constructed using a multimodel ensemble from two key irrigated spring wheat areas (India and Sudan) and applied to all irrigated spring wheat regions in the world. Southern Indian and southern Pakistani wheat-growing regions with large yield reductions from increasing temperatures coincided with high poverty headcounts, indicating these areas as future food security ‘hot spots’. The multimodel simulations produced a linear absolute decline of yields with increasing temperature, with uncertainty varying with reference temperature at a location. As a consequence of the linear absolute yield decline, the relative yield reductions are larger in low-yielding environments (e.g., high reference temperature areas in southern India, southern Pakistan and all Sudan wheat-growing regions) and farmers in these regions will be hit hardest by increasing temperatures. However, as absolute yield declines are about the same in low- and high-yielding regions, the contributed deficit to national production caused by increasing temperatures is higher in high-yielding environments (e.g., northern India) because these environments contribute more to national wheat production. Although Sudan could potentially grow more wheat if irrigation is available, grain yields would be low due to high reference temperatures, with future increases in temperature further limiting production.

Daily temperature data produced by harmonic analysis of monthly climate summary have been used as an input to plant phenology model. This study was carried out to evaluate the performance of the harmonic based daily temperature data in prediction of major phenological developments and to apply the results in improving decision support for agricultural production in relation to the climate change scenarios. Daily maximum and minimum temperature data for a climatological normal year (Jan. 1 to Dec. 31, 1971-2000) were produced by harmonic analysis of the monthly climate means for Seoul weather station. The data were used as inputs to a thermal time - based phenology model to predict dormancy, budburst, and flowering of Japanese cherry in Seoul. Daily temperature measurements at Seoul station from 1971 to 2000 were used to run the same model and the results were compared with the harmonic data case. Leaving no information on annual variation aside, the harmonic based simulation showed 25 days earlier release from endodormancy, 57 days longer period for maximum cold tolerance, delayed budburst and flowering by 14 and 13 days, respectively, compared with the simulation based on the observed data. As an alternative to the harmonic data, 30 years daily temperature data were generated by a stochastic process (SIMMETEO + WGEN) using climatic summary of Seoul station for 1971-2000. When these data were used to simulate major phenology of Japanese cherry for 30 years, deviations from the results using observed data were much less than the harmonic data case: 6 days earlier dormancy release, 10 days reduction in maximum cold tolerance period, only 3 and 2 days delay in budburst and flowering, respectively. Inter-annual variation in phenological developments was also in accordance with the observed data. If stochastically generated temperature data could be used in agroclimatic mapping and zoning, more reliable and practical aids will be available to climate change adaptation policy or decision makers.

This study was carried out to quantify potential effects of the surrounding ocean on the observed air temperature at coastal weather stations in the Korean Peninsula. Daily maximum and minimum temperature data for 2001-2009 were collected from 66 Korea Meteorological Administration (KMA) stations and the monthly averages were calculated for further analyses. Monthly data from 27 inland sites were used to generate a gridded temperature surface for the whole Peninsula based on an inverse distance weighting and the local temperature at the remaining 39 sites were estimated by recent techniques in geospatial climatology which are widely used in correction of small - scale climate controls like cold air drainage, urban heat island, topography as well as elevation. Deviations from the observed temperature were regarded as the `apparent` sea effect and showed a quasi-logarithmic relationship with the distance of each site from the nearest coastline. Potential effects of the sea on daily temperature might exceed cooling in summer and warming in winter according to this relationship. We classified 25 sites within the 10 km distance from the nearest coastline into `coastal sites` and the remaining 15 `fringe sites`. When the average deviations of the fringe sites ( for daily maximum and for daily minimum temperature) were used as the `noise` and subtracted from the `apparent` sea effects of the coastal sites, maximum cooling effects of the sea were identified as on the west coast and on the east and the south coast in summer months. The warming effects of the sea in winter ranged from on the west and on the south and east coasts.

While global solar radiation is an essential input variable in crop models, the observation stations are relatively sparse compared with other meteorological elements. Instead of using measured solar radiation, the Angstrom-Prescott model estimates have been widely used. Monthly data for solar radiation and sunshine duration are a convenient basis for deriving Angstrom-Prescott coefficients (a, b), but it is uncertain whether daily solar radiation could be estimated with a sufficient accuracy by the monthly data - derived coefficients. We derived the Angstrom-Prescott coefficients from the 25 years observed global solar radiation and sunshine duration data at 18 locations across South Korea. In order to figure out any improvements in estimating daily solar radiation by replacing monthly data with daily data, the coefficients (a, b) for each month were derived separately from daily data and monthly data. Local coefficients for eight validation sites were extracted from the spatially interpolated maps of the coefficients and used to estimate daily solar radiation from September 2008 to August 2009 when, pyranometers were operated at the same sites for validation purpose. Comparison with the measured radiation showed a better performance of the daily data - derived coefficients in estimating daily global solar radiation than the monthly data - derived coefficients, showing 9.3% decrease in the root mean square error (RMSE).

Information on the expected geographical shift of suitable zones for growing crops under future climate is a starting point of adaptation planning in agriculture and is attracting much concern from policy makers as well as researchers. Few practical schemes have been developed, however, because of the difficulty in implementing the site-selection concept at an analytical level. In this study, we suggest site-selection criteria for quality Fuji apple production and integrate geospatial data and information available in public domains (e.g., digital elevation model, digital soil maps, digital climate maps, and predictive models for agroclimate and fruit quality) to implement this concept on a GIS platform. Primary criterion for selecting sites suitable for Fuji apple production includes land cover, topography, and soil texture. When the primary criterion is satisfied, climatic conditions such as the length of frost free season, freezing risk during the overwintering period, and the late frost risk in spring are tested as the secondary criterion. Finally, the third criterion checks for fruit quality such as color and shape. Land attributes related to these factors in each criterion were implemented in ArcGIS environment as relevant raster layers for spatial analysis, and retrieval procedures were automated by writing programs compatible with ArcGIS. This scheme was applied to the A1B projected climates for South Korea in the future normal years (2011-2040, 2041-2070, and 2071-2100) as well as the current climate condition observed in 1971-2000 for selecting the sites suitable for quality Fuji apple production in each period. Results showed that this scheme can figure out the geographical shift of suitable zones at landscape scales as well as the latitudinal shift of northern limit for cultivation at national or regional scales.

The geographical distribution of freeze risk determines the latitudinal and altitudinal limits and the maximum acreage suitable for fruit production. Any changes in its pattern can affect the policy for climate change adaptation in fruit industry. High-definition digital maps for such applications are not available yet due to uncertainty in the combined responses of temperature and dormancy depth under the future climate scenarios. We applied an empirical freeze risk index, which was derived from the combination of the dormancy depth and threshold temperature inducing freeze damage to dormant buds of `Changhowon Hwangdo` peach trees, to the high-definition digital climate maps prepared for the current (1971-2000), the near future (2011-2040) and the far future (2071-2100) climate scenarios. According to the geospatial analysis at a landscape scale, both the safe and risky areas will be expanded in the future and some of the major peach cultivation areas may encounter difficulty in safe overwintering due to weakening cold tolerance resulting from insufficient chilling. Our test of this method for the two counties representing the major peach cultivation areas in South Korea demonstrated that the migration of risky areas could be detected at a sub-grid scale. The method presented in this study can contribute significantly to climate change adaptation planning in agriculture as a decision aids tool.

Most deciduous trees in temperate zone are dormant during the winter to overcome cold and dry environment. Dormancy of deciduous fruit trees is usually separated into a period of rest by physiological conditions and a period of quiescence by unfavorable environmental conditions. Inconsistent and fewer budburst in pear orchards has been reported recently in South Korea and Japan and the insufficient chilling due to warmer winters is suspected to play a role. An accurate prediction of the flowering time under the climate change scenarios may be critical to the planning of adaptation strategy for the pear industry in the future. However, existing methods for the prediction of budburst depend on the spring temperature, neglecting potential effects of warmer winters on the rest release and subsequent budburst. We adapted a dormancy clock model which uses daily temperature data to calculate the thermal time for simulating winter phenology of deciduous trees and tested the feasibility of this model in predicting budburst and flowering of Niitaka pear, one of the favorite cultivars in Korea. In order to derive the model parameter values suitable for Niitaka, the mean time for the rest release was estimated by observing budburst of field collected twigs in a controlled environment. The thermal time (in chill-days) was calculated and accumulated by a predefined temperature range from fall harvest until the chilling requirement (maximum accumulated chill-days in a negative number) is met. The chilling requirement is then offset by anti-chill days (in positive numbers) until the accumulated chill-days become null, which is assumed to be the budburst date. Calculations were repeated with arbitrary threshold temperatures from to (at an interval of 0.1), and a set of threshold temperature and chilling requirement was selected when the estimated budburst date coincides with the field observation. A heating requirement (in accumulation of anti-chill days since budburst) for flowering was also determined from an experiment based on historical observations. The dormancy clock model optimized with the selected parameter values was used to predict flowering of Niitaka pear grown in Suwon for the recent 9 years. The predicted dates for full bloom were within the range of the observed dates with 1.9 days of root mean square error.

The demand for rainfall data in gridded digital formats has increased in recent years due to the close linkage between hydrological models and decision support systems using the geographic information system. One of the most widely used tools for digital rainfall mapping is the PRISM (parameter-elevation regressions on independent slopes model) which uses point data (rain gauge stations), a digital elevation model (DEM), and other spatial datasets to generate repeatable estimates of monthly and annual precipitation. In the PRISM, rain gauge stations are assigned with weights that account for other climatically important factors besides elevation, and aspects and the topographic exposure are simulated by dividing the terrain into topographic facets. The size of facet or grid cell resolution is determined by the density of rain gauge stations and a grid cell is considered as the lowest limit under the situation in Korea. The PRISM algorithms using a 270m DEM for South Korea were implemented in a script language environment (Python) and relevant weights for each 270m grid cell were derived from the monthly data from 432 official rain gauge stations. Weighted monthly precipitation data from at least 5 nearby stations for each grid cell were regressed to the elevation and the selected linear regression equations with the 270m DEM were used to generate a digital precipitation map of South Korea at 270m resolution. Among 1.25 million grid cells, precipitation estimates at 166 cells, where the measurements were made by the Korea Water Corporation rain gauge network, were extracted and the monthly estimation errors were evaluated. An average of 10% reduction in the root mean square error (RMSE) was found for any months with more than 100mm monthly precipitation compared to the RMSE associated with the original 5km PRISM estimates. This modified PRISM may be used for rainfall mapping in rainy season (May to September) at much higher spatial resolution than the original PRISM without losing the data accuracy.

Temperate zone deciduous tree phenology may be vulnerable to projected temperature change, and associated geographical impact is of concern to ecologists. Although many phenology models have been introduced to evaluate climate change impact, there has been little attempt to show the spatial variation across a geographical region due to contamination by the urban heat island (UHI) effect as well as the insufficient spatial resolution of temperature data. We present a practical method for assessing climate change impact on tree phenology at spatial scales sufficient to accommodate the UHI effect. A thermal time-based two-step phenological model was adapted to simulate and project flowering dates of Japanese cherry (Prunus serrulata var. spontanea) in South Korea under the changing climates. The model consists of two sequential periods: the rest period described by chilling requirements and the forcing period described by heating requirements. Daily maximum and minimum temperature are used to calculate daily chill units until a pre-determined chilling requirement for rest release is met. After the projected rest release date, daily heat units (growing degree days) are accumulated until a pre-determined heating requirement for flowering is achieved. Model parameters were derived from the observed bud-burst and flowering dates of cherry tree at the Seoul station of the Korea Meteorological Administration (KMA), along with daily temperature data for 1923–1948. The model was validated using the observed data at 18 locations across South Korea during 1955–2004 with a root mean square error of 5.1 days. This model was used to project flowering dates of Japanese cherry in South Korea from 1941 to 2100. Gridded data sets of daily maximum and minimum temperature with a 270 m grid spacing were prepared for the climatological normal years 1941–1970 and 1971–2000 based on observations at 56 KMA stations and a geospatial interpolation scheme for correcting urban heat island effect as well as elevation effect. We obtained a 25 km-resolution, 2011–2100 temperature projection data set covering peninsular Korea under the auspices of the Inter-governmental Panel on Climate Change—Special Report on Emission Scenarios A2 from the Meteorological Research Institute of KMA. The data set was converted to 270 m gridded data for the climatological years 2011–2040, 2041–2070 and 2071–2100. The phenology model was run by the gridded daily maximum and minimum temperature data sets, each representing climatological normal years for 1941–1970, 1971–2000, 2011–2040, 2041–2070, and 2071–2100. According to the model calculation, the spatially averaged flowering date for the 1971–2000 normal is earlier than that for 1941–1970 by 5.2 days. Compared with the current normal (1971–2000), flowering of Japanese cherry is expected to be earlier by 9, 21, and 29 days in the future normal years 2011–2040, 2041–2070, and 2071–2100, respectively. Southern coastal areas might experience springs with incomplete or even no flowering caused by insufficient chilling required for breaking bud dormancy.

A winter-season warming trend has been observed in eastern Asian countries during the last century. Significant effects on dormancy and the subsequent bud-burst of deciduous fruit trees are expected. However, phenological observations are scant in comparison with long-time climate records in the region. Chill-day accumulation, estimated from daily maximum and minimum temperature, is a reasonable proxy for dormancy depth of temperate-zone fruit trees. A selected chill-day model was parameterized for the Campbell Early grapevine, which is the major cultivar (grown virtually everywhere) in South Korea. To derive model parameters (threshold temperature for chilling and the chilling requirement for breaking dormancy), a controlled-environment experiment using field-sampled twigs of Campbell Early was conducted. The chill-day model to estimate bud-burst dates was adjusted by derived parameters and was applied using 1994–2004 daily temperature data obtained from the automated weather station in the vineyard at the National Horticultural Research Institute. The model gave consistently good performance in predicting bud-burst of Campbell Early (RMSE of 2.5 days). To simulate dormancy depth of Campbell Early at eight locations in South Korea for the last century, the model was applied using data obtained for each location from 1921 to 2004. Calculations showed that the chilling requirement for breaking endodormancy of Campbell Early can be satisfied by mid-January to late February in South Korea, and the date was delayed going either northward or southward from the Daegu–Jeonju line that crosses the middle of South Korea in the east–west direction. Maximum length of the cold tolerant period (the number of days between endodormancy release and the forced dormancy release) showed the same spatial pattern. Dormancy release for 1981–2004 advanced by as much as 15 days relative to that for 1921–50 at all locations except Jeju (located in the southernmost island with a subtropical climate), where an average 15-day delay was predicted. The cold-tolerant period diminished somewhat at six out of eight locations. As a result, bud-burst of Campbell Early in spring was advanced by 6–10 days at most locations, and interannual variation in bud-burst dates increased at all locations. The earlier bud-burst after the 1970s was due to 1) warming in winter that results in earlier dormancy release (Incheon, Mokpo, Gangneung, and Jeonju), 2) warming in early spring that enhances regrowth after breaking dormancy (Busan and Jeju), and 3) a combination of both (Seoul and Daegu).

Urban atmosphere may play as a harbinger for the future climate change with respect to temperature and concentration. The Seoul metropolitan area is unique in rapid urbanization and industrialization during the last several decades, providing a natural dome with increased temperature. This study was carried out to evaluate the feasibility of using the urban-rural environmental gradient in replacement of the IPCC mid-term scenario (after 30-50 years). For this, we measured atmospheric concentration and air temperature at three sites with different degree of urbanization (Seoul, Suwon, and Icheon). Results from 11-month measurement can be summarized as follows: (1) The annual mean concentration across 3 sites was in the order of Seoul (439 ppm) > Suwon (419 ppm) > Icheon (416 ppm), showing a substantial urban-rural environmental gradient. (2) The diurnal fluctuation in concentration was greater in summer than in winter, showing the effect of photosynthesis on local concentration. (3) The daily maximum concentration was observed at 0500 LST in spring and summer, 0800 LST in autumn, and 0900 LST in winter, showing the sunrise-time dependence. (4) The observed hourly maximum concentration averaged for the whole period was 446 ppm in Seoul at 0700 LST, while the minimum was 407 ppm in Suwon at 1500 LST. (5) Compared with the background atmospheric concentration of in Anmyeon-do (377.4 ppm annual mean), concentration of the study sites was higher by 14% in Seoul, by 10% in Suwon, and by 9% in Icheon. The observed concentration in Seoul reached already 98% of the 2030-2040 projection (450 ppm) and 80% of the 2040-2050 projection (550 ppm) under the IPCC BAU scenario, showing a feasibility of using the dome of Seoul as a natural experimental setting for the mid-term climate change impact assessment.

The normalized difference in incident solar energy between a target surface and a level surface (overheating index, OHI) is useful in eliminating estimation error of site-specific maximum temperature in complex terrain. Due to the complexity in its calculation, however, an empirical proxy variable called northern exposure index (NEI) which combines slope and aspect has been used to estimate OHI based on empirical relationships between the two. An experiment with real-world landscape and temperature data was carried out to evaluate performance of the NEI - derived OHI (N-OHI) in reduction of spatial interpolation error for daily maximum temperature compared with that by the original OHI. We collected daily maximum temperature data from 7 sites in a mountainous watershed with a area and a 795m elevation range () in Pyongchang, Kangwon province. Northern exposure index was calculated for the entire 166,050 grid cells constituting the watershed based on a 30-m digital elevation model. Daily OHI was calculated for the same watershed ana regressed to the variation of NEI. The regression equations were used to estimate N-OHI for 15th of each month. Deviations in daily maximum temperature at 7 sites from those measured at the nearby synoptic station were calculated from June 2006 to February 2007 and regressed to the N-OHI. The same procedure was repeated with the original OHI values. The ratio sum of square errors contributable by the N-OHI were 0.46 (winter), 0.24 (fall), and 0.01 (summer), while those by the original OHI were 0.52, 0.37 and 0.15, respectively.

Nocturnal cold air pools, which frequently form in complex terrain under anticyclonic systems, are a hazard to flowering buds of temperate fruit trees, but the spatial resolution to anticipate them exceeds the current weather forecast scale. To supplement the insufficient spatial resolution of official minimum temperature forecasts, a spatial interpolation scheme was developed by incorporating local geographic potential for cold air accumulation. Air temperature was measured at 10-min intervals at eight locations within a 2.1 km × 2.1 km hilly orchard area in South Korea from September 2002 to August 2003. Minimum temperature data for suspected radiative cooling nights were collected, and deviations from estimations by a conventional spatial interpolation were calculated. A digital elevation model with a 10 m cell size was used to calculate the cold air accumulation at eight locations. Zonal averages of the cold air accumulation were computed for each location by increasing the cell radius from 1 to 10. Temperature deviations were regressed to a common logarithm of the smoothed averages of cold air accumulation to derive a linear relationship between the local temperature deviation and the site topography. The highest coefficient of determination was found at a cell radius of 5, which corresponds to an approximately 1 ha boundary surrounding the point of interest. Nocturnal temperature profiles were observed and the inversion cap height was determined using a tethered balloon system. Empirical equations describing the potential effects of cold air accumulation and of the inversion profile on minimum temperature were combined with a conventional lapse rate-corrected inverse distance weighting interpolation scheme. This new interpolation scheme was successfully validated with an independent data set, showing a strong feasibility for development of a site-specific frost warning system for mountainous areas.

Gridded temperature data are necessary to run ecological models at regional scales for climate impact studies and have been generated by spatially interpolating measured values at synoptic stations. Because there are few synoptic stations with long-term records in rural areas in Korea, data from urban stations have been used for this purpose. Due to the overlapping of the rapid urbanization-industrialization period with the global warming era in Korea, climate data from these urbanized areas might be contaminated with urban heat island effect. This study was conducted to differentiate urbanization and regional climate change effects on apparent temperature change. Monthly averages of daily minimum, maximum, and mean temperature at 14 synoptic stations were prepared for 1951-1980 (past normal) and 1971-2000 (current normal) periods, respectively.Differences in two temperature normals were regressed to the logarithm of the population increase at 14 corresponding cities from 1966 to 1985. The regression equations were used to determine potential effects of urbanization and to extract the net contribution of regional climate change to the apparent temperature change. According to the model calculation, urbanization effect was common in all months except April. Up to 0.5° warming of nighttime temperature was induced by urbanization in the current normal period compared with the past normal period. There was little effect of regional climate change on local warming in the warm season (May through November). The cool season was warmed mainly by regionally increased daytime temperature. The results could be used to remove urbanization effects embedded in raw data, helping restore unbiased rural temperature trends in South Korea.

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)...

Cherry blossoms, an icon of spring, are celebrated in many cultures of the temperate region. For its sensitivity to winter and early spring temperatures, the timing of cherry blossoms is an ideal indicator of the impacts of climate change on tree phenology. Here, we applied a process-based phenology model for temperate deciduous trees to predict peak bloom dates (PBD) of flowering cherry trees (Prunus×yedoensis 'Yoshino' and Prunus serrulata 'Kwanzan') in the Tidal Basin, Washington, DC and the surrounding Mid-Atlantic States in response to climate change. We parameterized the model with observed PBD data from 1991 to 2010. The calibrated model was tested against independent datasets of the past PBD data from 1951 to 1970 in the Tidal Basin and more recent PBD data from other locations (e.g., Seattle, WA). The model performance against these independent data was satisfactory (Yoshino: r(2) = 0.57, RMSE = 6.6 days, bias = 0.9 days and Kwanzan: r(2) = 0.76, RMSE = 5.5 days, bias = -2.0 days). We then applied the model to forecast future PBD for the region using downscaled climate projections based on IPCC's A1B and A2 emissions scenarios. Our results indicate that PBD at the Tidal Basin are likely to be accelerated by an average of five days by 2050 s and 10 days by 2080 s for these cultivars under a mid-range (A1B) emissions scenario projected by ECHAM5 general circulation model. The acceleration is likely to be much greater (13 days for 2050 s and 29 days for 2080s) under a higher (A2) emissions scenario projected by CGCM2 general circulation model. Our results demonstrate the potential impacts of climate change on the timing of cherry blossoms and illustrate the utility of a simple process-based phenology model for developing adaptation strategies to climate change in horticulture, conservation planning, restoration and other related disciplines.