Search

Permafrost degradation due to rapid increase in ground surface temperature (GST) in recent years may strongly affect boreal forest ecosystems. However, comparatively few studies have explored how changes in permafrost affect tree growth dynamics in boreal forest. Here, we used a tree ring network of 12 Dahurian larch (Larix gmelinii) sites across permafrost regions in northeast China. We observed an increase in L. gmelinii growth over the past decade, seemingly linked to a shift in their climatic limitations, where winter GST has become the most strongly limiting factor for L. gmelinii growth. The recent increase in growth was particularly strong in older trees (>300 years), which could be related to older trees having a more developed root system. GST was the main limiting factor for tree growth. While summer GST had a somewhat consistently positive correlation with tree growth, winter GST has shifted from a negative to a strongly positive correlation with growth in the last decade, coincidental with a sharp increase in winter GST since 2004. Winter GST is also strongly correlated with the rapidly thawing permafrost dynamics. Overall, our results suggest a link between recent changes in the permafrost and shifts in climate-growth correlations for one of the main boreal tree species. As a result, L. gmelinii has experienced an important increase in radial growth that may indicate that, unlike what has been reported for other boreal species, it may temporally benefit from warming climate in the continuous permafrost region of the Asian boreal forests. [ABSTRACT FROM AUTHOR]

The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In Finland, a forested country, over 50% of the current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high-spatial-resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20m spatial resolution CORINE land-use dataset and high-resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalyzed data. Using a geospatial re-mapping technique the data were downscaled to 26 meteorological station locations to represent very diverse environments. Applying a comparison, we find that the downscaled 10-year return levels represent 66% of the observed variation among the stations examined. In addition, the spatial variation in wind-multiplier-downscaled 10-year return level wind was compared with the WAsP model-simulated wind. The heterogeneous test area was situated in northern Finland, and it was found that the major features of the spatial variation were similar, but in some locations, there were relatively large differences. The results indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations with the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations. [ABSTRACT FROM AUTHOR]

The bioeconomy has an increasing role to play in climate change mitigation and the sustainable development of national economies. In a forested country, such as Finland, over 50 % of its current bioeconomy relies on the sustainable management and utilization of forest resources. Wind storms are a major risk that forests are exposed to and high spatial resolution analysis of the most vulnerable locations can produce risk assessment of forest management planning. Coarse spatial resolution estimates of the return levels of maximum wind speed based, e.g., on reanalysed meteorological data or climate scenarios can be downscaled to forest stand levels with the help of land cover and terrain elevation data. In this paper, we examine the feasibility of the wind multiplier approach for downscaling of maximum wind speed, using 20 meter spatial resolution CORINE-land use dataset and high resolution digital elevation data. A coarse spatial resolution estimate of the 10-year return level of maximum wind speed was obtained from the ERA-Interim reanalysed data. These data were downscaled to 26 meteorological station locations to represent very diverse environments: Open Baltic Sea islands, agricultural land, forested areas, and Northern Finland treeless fells. Applying a comparison, the downscaled 10-year return levels explained 77 % of the observed variation among the stations examined. In addition, the spatial variation of wind multiplier downscaled 10-year return level wind was compared with the WAsP- model simulated wind. The heterogeneous test area was situated in Northern Finland, and it was found that the major features of the spatial variation were similar, but in the details, there were relatively large differences. However, for areas representing a typical Finnish forested landscape with no major topographic variation, both of the methods produced very similar results. Further fine-tuning of wind multipliers could improve the downscaling for the locations with large topographic variation. However, the current results already indicate that the wind multiplier method offers a pragmatic and computationally feasible tool for identifying at a high spatial resolution those locations having the highest forest wind damage risks. It can also be used to provide the necessary wind climate information for wind damage risk model calculations, thus making it possible to estimate the probability of predicted threshold wind speeds for wind damage and consequently the probability (and amount) of wind damage for certain forest stand configurations. [ABSTRACT FROM AUTHOR]

In order to explore the trends in sunshine duration in the low-latitude belt of South China, long-term sunshine duration, total clouds, low clouds, and visibility were investigated for the period 1961-2005. The results show significant declines in sunshine duration, occurring on the average of −3.2% and −2.8% per decade under all-sky and clear-sky conditions, respectively. It is noted that increased air aerosol loading due to rapid socio-economic development is the drive behind the sharp declines in sunshine duration in the study area. Though cloud is the primary regulator of sunshine duration under all-sky conditions, sunshine is strongly correlated with visibility under clear-sky conditions. Relational analysis between sunshine and cloud amount suggests 0.4% change in clouds per decade, which is well in agreement with the trend of decline in sunshine duration under all-sky conditions in the study area. Increasing low-cloud opacity could be the primary factor driving the decline in sunshine duration in the low-latitude belt of South China. [ABSTRACT FROM AUTHOR]

The seasonal patterns of total cloud amount (TCA) responsible for El Niño/La Niña-Southern Oscillation (ENSO) Sea Surface Temperature (SST) anomalies were investigated using the ISCCP-D2 cloud and NOAA OI.v2 SST datasets for the period of July 1983 to June 2008. The results show three main ENSO-sensitive regions obtained by spatial overlapping of seasonal correlations, two in the western tropical Pacific and one in the central tropical Pacific. These regions were named WTP1, WTP2, and CTP. In all three regions, except the JJA (June-August) WTP2, the TCA changes were significantly correlated with the Niño 3.4 anomalies during the four seasons (December-January-February (DJF), March-April-May, JJA, and September-October-November (SON)). Remarkable differences in the seasonal variability of TCA were observed in these regions. In the WTP1, the DJF TCA always remained the highest value among the four seasons in all years. In the WTP2, the maximum TCA occurred during JJA in most years. In the CTP, the extreme value of TCA was mainly observed in DJF or SON near the peak time of ENSO. Seasonal cross-correlation analyses also showed significant relations between TCA and Niño 3.4 SST in these regions, which may be helpful for forecasting the evolution of ENSO. [ABSTRACT FROM AUTHOR]