globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2268
论文题名:
Arctic amplification decreases temperature variance in northern mid- to high-latitudes
作者: James A. Screen
刊名: Nature Climate Change
ISSN: 1758-1272X
EISSN: 1758-7392
出版年: 2014-06-15
卷: Volume:4, 页码:Pages:577;582 (2014)
语种: 英语
英文关键词: Climate change ; Atmospheric science
英文摘要:

Changes in climate variability are arguably more important for society and ecosystems than changes in mean climate, especially if they translate into altered extremes1, 2, 3. There is a common perception and growing concern that human-induced climate change will lead to more volatile and extreme weather4. Certain types of extreme weather have increased in frequency and/or severity5, 6, 7, in part because of a shift in mean climate but also because of changing variability1, 2, 3, 8, 9, 10. In spite of mean climate warming, an ostensibly large number of high-impact cold extremes have occurred in the Northern Hemisphere mid-latitudes over the past decade11. One explanation is that Arctic amplification—the greater warming of the Arctic compared with lower latitudes12 associated with diminishing sea ice and snow cover—is altering the polar jet stream and increasing temperature variability13, 14, 15, 16. This study shows, however, that subseasonal cold-season temperature variability has significantly decreased over the mid- to high-latitude Northern Hemisphere in recent decades. This is partly because northerly winds and associated cold days are warming more rapidly than southerly winds and warm days, and so Arctic amplification acts to reduce subseasonal temperature variance. Previous hypotheses linking Arctic amplification to increased weather extremes invoke dynamical changes in atmospheric circulation11, 13, 14, 15, 16, which are hard to detect in present observations17, 18 and highly uncertain in the future19, 20. In contrast, decreases in subseasonal cold-season temperature variability, in accordance with the mechanism proposed here, are detectable in the observational record and are highly robust in twenty-first-century climate model simulations.

Arctic amplification is clearly identified in autumn zonal-mean land near-surface temperature anomalies since the year 1979 in a contemporary reanalysis (Fig. 1a) and gridded station observations (Supplementary Fig. 1). In the last decade, positive zonal-mean temperature anomalies are particularly evident across the entire mid- to high-latitude Northern Hemisphere, but notably becoming larger in magnitude with increasing latitude. The linear trend for the period 1979–2013 is 0.86 °C per decade at latitudes 70°–80° N compared with only 0.30 °C per decade at 30°–40° N (Fig. 1e; green line). Arctic amplification is observed in all seasons except summer12, but because it is largest in autumn, the focus of the main material is on this season with results from the other seasons provided in the Supplementary Information. Coincident with Arctic amplification, the zonal-mean variance of autumn daily temperature anomalies has decreased in both the reanalysis (Fig. 1b) and observations (Supplementary Fig. 1). Here and in what follows, the variance is calculated at each grid point before area averaging (Methods). Negative variance anomalies emerge in the last decade for latitudes 40°–80° N. The negative linear trend in zonal-mean autumn variance is statistically significant for latitudes 60°–80° N (Fig. 1e; black line). Decreases in grid-point variance are observed over large parts of the extratropical Northern Hemisphere, with the largest declines found over Canada and northern Siberia (Supplementary Figs 2 and 3). Zonal-mean temperature anomalies for the 5% coldest (that is, most negative daily anomalies) and 5% warmest (that is, most positive daily anomalies) days per autumn, reveal asymmetric warming tendencies. Cold autumn days have warmed substantially with the largest changes in high latitudes (Fig. 1c and Supplementary Fig. 1). Warm autumn days have also warmed (Fig. 1d and Supplementary Fig. 1), but at a slower rate, especially at higher latitudes (Fig. 1e; blue and red lines). The geographical regions with decreased variance well match those where cold autumn days have warmed faster than warm autumn days (Supplementary Figs 2 and 3).

Figure 1: Changing mean temperature and variability.
Changing mean temperature and variability.

ad, Zonal-mean autumn mean temperature (a), subseasonal temperature variance (b), mean cold autumn day temperature (c) and mean warm autumn day temperature (d) anomalies, 1979–2013. Variance is calculated at each grid point before area averaging. Anomalies are calculated for 10° latitude bands and are relative to the 1980–1999 mean. e, Linear trends of zonal-mean autumn mean temperature (green), subseasonal temperature variance (black), cold autumn day temperature (blue) and warm autumn day temperature (red). The cross-hatching denotes 10° latitude bands for which the variance trend is statistically significant at the 95% confidence level. f,g, Probability density functions (f) and cumulative distribution functions (g) for autumn daily-mean temperature anomalies over latitudes 55°–80° N for the periods 1979–1988 (black) and 2004–2013 (green). In f and g, the blue and red lines denote the 5% and 95% thresholds of the distributions (based on the 1979–1988 period in f).

Reanalysis.

Historical changes in near-surface temperature and its variance are studied in the European Centre for Medium-range Weather Forecasts’ ERA-Interim reanalysis. The source data were 6-h, globally complete, gridded (1.5° latitude–longitude) fields of 2-m air temperature for the period 1979–2013 inclusively. Daily averages were taken and anomalies calculated by removing the 35-year mean for each day and grid point. This process removed the climatological-mean seasonal cycle. Four quantities were calculated for each grid point, season (defined as December–January–February, winter; March–April–May, spring; June–July–August, summer; September–October–November; autumn) and year: the mean temperature anomaly of all days, the variance of temperature anomalies for all days, the mean temperature anomaly of the 5% coldest days and the mean temperature anomaly of the 5% warmest days. Zonal means of these quantities were then calculated for 10° latitude bands. Oceanic grid points were masked and were not included in the zonal means. Trends were calculated by standard least-squares linear regression and tested for statistical significance using a two-tailed Student’s t-test, accounting for temporal autocorrelation using the effective sample size. Statistical significance is reported at the 95% confidence level.

Observations.

Daily near-surface maximum and minimum temperature anomalies were taken from the HadGHCND observational data set. Data were obtained on a 2.5° latitude by 3.75° longitude grid for the period 1950–2011. Daily-mean temperature anomalies were estimated from the average of the minimum and maximum temperature anomalies each day. Seasonal-mean temperature statistics were calculated as per the reanalysis.

Wind effects on temperature.

Seasonal-mean temperature anomalies, stratified with respect to winds from different directions30 were calculated using daily-mean temperature and wind components at 925 hPa taken from the ERA-Interim reanalysis. For example the autumn temperature anomaly for northerly (from the north) winds is defined as,

where TN is the daily temperature (N represents North) for cases for which the wind has a northerly component (that is, the meridional wind is negative) during the autumn of year y and nN is the number of northerly cases (indexed by i), θN is the angle of the wind vector for winds with a northerly component measured clockwise from due westerly (from the west), T is the temperature irrespective of wind direction during all autumns from 1979 to 2013 and n is the number of days irrespective of wind direction (indexed by j). The sine weighting gives full weight (that is, unity) to winds blowing directly from the north and a smaller weight to winds closer to due easterly or westerly, and can be thought of as the ‘degree of northerliness’. Although this weighting is physically justified (to emphasize meridional heat advection as opposed to zonal heat advection), very similar results are obtained without it. This procedure was applied at each grid point, but oceanic grid points and grid points at which the 925 hPa pressure surface intersected the Earth’s surface (that is, surface pressure was below 925 hPa) were masked. An analogous procedure was used to calculate the temperature anomaly for southerly (from the south) winds.

Models.

Data were obtained from 34 coupled climate models (listed in Supplementary Table 1) that participated in the fifth Coupled Model Intercomparison Project (CMIP5) and for which daily-mean near-surface temperature fields were archived in the Earth System Grid Federation (http://esgf-index1.ceda.ac.uk/esgf-web-fe/) data holdings. This study uses simulations performed with the RCP8.5 future concentrations pathway, which is a high-end (business as usual) scenario with a continuous rise in atmospheric greenhouse gas concentrations throughout the twenty-first century, leading to an atmospheric CO2 concentration of approximately 950 ppm by 2100. Data from 1 January 2006 to 31 December 2099 are used. Some modelling groups have performed multiple iterations, but this study uses only one ensemble member per model. Seasonal-mean temperature statistics were calculated as per the reanalysis and observations (except that the daily anomalies were calculated relative to the 2006–2035 mean) for each model individually on its native grid. Land grid-point values were binned into 10° latitude bands, or 5° latitude–longitude boxes, and then averaged to derive areal means on a common grid, before further averaging across the models. The modelled influence of wind direction on temperature was assessed as per the reanalysis, except that surface air temperature and wind components were used (daily model output was not available for the 925 hPa level). This analysis was performed for 27 CMIP5 models (those listed in Supplementary Table 1, except for models 7, 8, 9, 15, 19, 20 and 34, which did not have the required data available), on the native grid of each model before averaging onto a common grid, and then averaging across the models.

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http://www.nature.com/nclimate/journal/v4/n7/full/nclimate2268.html
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/5095
Appears in Collections:气候变化事实与影响
科学计划与规划
气候变化与战略

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James A. Screen. Arctic amplification decreases temperature variance in northern mid- to high-latitudes[J]. Nature Climate Change,2014-06-15,Volume:4:Pages:577;582 (2014).
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