Changes in vegetative growing seasons are dominant indicators of the dynamic response of ecosystems to climate change. Therefore, knowledge of growing seasons over the past decades is essential to predict ecosystem changes. In this study, the long-term changes in the growing seasons of temperate vegetation over the Northern Hemisphere were examined by analyzing satellite-measured normalized difference vegetation index and reanalysis temperature during 1982-2008. Results showed that the length of the growing season (LOS) increased over the analysis period; however, the role of changes at the start of the growing season (SOS) and at the end of the growing season (EOS) differed depending on the time period. On a hemispheric scale, SOS advanced by 5.2 days in the early period (1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999) but advanced by only 0.2 days in the later period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008). EOS was delayed by 4.3 days in the early period, and it was further delayed by another 2.3 days in the later period. The difference between SOS and EOS in the later period was due to less warming during the preseason (January-April) before SOS compared with the magnitude of warming in the preseason (June-September) before EOS. At a regional scale, delayed EOS in later periods was shown. In North America, EOS was delayed by 8.1 days in the early period and delayed by another 1.3 days in the later period. In Europe, the delayed EOS by 8.2 days was more significant than the advanced SOS by 3.2 days in the later period. However, in East Asia, the overall increase in LOS during the early period was weakened in the later period. Admitting regional heterogeneity, changes in hemispheric features suggest that the longer-lasting vegetation growth in recent decades can be attributed to extended leaf senescence in autumn rather than earlier spring leaf-out.

No abstract

Through the past 60 years, forests, now of various age classes, have been established in the southern part of the Korean Peninsula through nationwide efforts to reestablish forests since the Korean War (1950–53), during which more than 65% of the nation's forest was destroyed. Careful evaluation of long-term changes in vegetation growth after reforestation is one of the essential steps to ensuring sustainable forest management. This study investigated nationwide variations in vegetation phenology using satellite-based growing season estimates for 1982–2008. The start of the growing season calculated from the normalized difference vegetation index (NDVI) agrees reasonably with the ground-observed first flowering date both temporally (correlation coefficient, r = 0.54) and spatially (r = 0.64) at the 95% confidence level. Over the entire 27-year period, South Korea, on average, experienced a lengthening of the growing season of 4.5 days decade−1, perhaps due to recent global warming. The lengthening of the growing season is attributed mostly to delays in the end of the growing season. The retrieved nationwide growing season data were used to compare the spatial variations in forest biomass carbon density with the time-averaged growing season length for 61 forests. Relatively higher forest biomass carbon density was observed over the regions having a longer growing season, especially for the regions dominated by young (<30 year) forests. These results imply that a lengthening of the growing season related to the ongoing global warming may have positive impacts on carbon sequestration, an important aspect of large-scale forest management for sustainable development.

Regional warming, owing to urbanization, leads to earlier spring phenological events and may expose plants to hard freeze damage. This study examined the influence of urbanization on the risk of frost damage to spring flowers in South Korea from 1973 to 2015. For the analysis period, we categorized 25 cities into two groups: those showing rapid population growth (rPG) ≥ 200,000, including 13 cities, and those showing no or decreased population growth (nPG), including 12 cities. We then investigated the time from the last frost dates (LFDs) in spring to the first flowering dates (FFDs) for each group. The rPG group experienced significant spring warming of 0.47°C per decade, resulting in earlier LFDs and FFDs. For this group, the advancement of LFD was more rapid than that of FFD, and the days between these two dates increased from 0.42 to 0.47 days per decade, implying a reduced risk of frost damage. Spring warming and the advancement of FFDs and LFDs were relatively small for the nPG group, and the LFDs were rather delayed. Consequently, the days between LFDs and FFDs were reduced from −1.05 to −1.67 days per decade, indicating an increased risk of frost damage. The contrasting changes in the frost-damage risk between the two city groups can be attributed to distinct urban warming at night, which makes the LFDs substantially earlier in the rPG group. Therefore, this study suggests that the warming associated with urbanization may lessen the risk of spring frost damage to plants in rapidly growing urban areas.

Abstract. Snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas LSMs evaluate the snow cover, snow albedo, and snow depth through interaction with atmospheric conditions. Most LSMs include parameters based on empirical relations, resulting in uncertainties in model solutions. When the initially developed empirical parameters are local or inadequate, we need to optimize the parameter sets for a certain region. In this study, we seek the optimal parameter values in the snow-related processes – snow cover, snow albedo, and snow depth – of the Noah LSM, for South Korea, using the micro-genetic algorithm and the in situ surface observations and remotely sensed satellite data. Snow data from observation stations representing five land cover types – deciduous broadleaf forest, mixed forest, woody savanna, cropland, and urban and built-up lands – are used to optimize five snow-related parameters that calculate the fractional snow cover, maximum snow albedo of fresh snow, and fresh snow density associated with the snow depth. Another parameter, reflecting the dependence of fractional snow cover on the land cover types, is also optimized. Optimization of these six snow-related parameters has led to improvement in the root mean squared errors by 17.0 %, 6.2 %, and 3.3 % in snow depth, snow albedo, and fractional snow cover, respectively. In terms of the mean bias, the underestimation problems of snow depth and overestimation problems of snow albedo have been alleviated through optimization of parameters calculating the fresh snow by about 44.2 % and 31.0 %, respectively.