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The long-term variations in meteorological drought and its large-scale climate patterns in each season in Central Asia from 1901 to 2015 remain unclear. Here, this issue is addressed using meteorology measurements and reanalysis data through correlation and composite analyses. The drought intensity index (DII) and extent index (DEI) do not exhibit significant linear trends from 1901 to 2015 but do exhibit interannual to interdecadal variations. Both the DII and DEI are highly correlated with the tropical Nino 4 sea surface temperature (SST) and extratropical atmospheric teleconnections, including the East Atlantic (EA) pattern, the East Atlantic/West Russia (EAWR) pattern, and the Arctic oscillation (AO), but with seasonal discrepancies in terms of the degree of influence. The winter drought is strongly linked to both the negative EA and negative EAWR patterns, while spring and autumn drought are strongly linked to the negative EAWR and negative EA patterns, respectively. In the winter, spring, and autumn, drought is also closely linked to below normal Nino 4 SST. The links to EA and EAWR patterns are mainly derived from their impacts on precipitation in the central and northern sectors, while the link to Nino 4 SST is mainly derived from its impacts on precipitation in the southern sector. By considering both drought intensity and drought extent, the ten extreme drought years for each season are selected and, through composite analysis, their large-scale climate patterns are studied. The extreme drought generally occurs in the contexts of a negative EA pattern in winter, a negative EAWR pattern in spring, and a negative AO pattern in autumn. As an exception, summer drought is weakly correlated with Nino 4 SST and is not correlated with extratropical atmospheric teleconnections.

The climate warming that has occurred over the past decades may benefit plant growth and development because it reduces the severity of frost events. However, these rising temperatures may also lead to diminished frost hardiness in plants due to their insufficient hardening. Despite climate warming exerting such dual effects on frost damage, how this might change the frost damage of woody plants remains unknown. Here, we conducted a laboratory experiment that used the relative electrolyte leakage method to derive species-specific model parameters for frost hardiness and a damage model. Then we simulated the daily frost hardiness and damage of five typical temperate tree species (Ulmus pumila, Robinia pseudoacacia, Fraxinus chinensis, Salix babylonica, and Armeniaca vulgaris), from 1980 to 2015, in Beijing, China. The root mean square error (RMSE) between observed and predicted frost damage ranged from 3.58% to 7.65%. According to our simulation results, frost hardiness has declined over this 36-year period due to insufficient cold hardening of plants in autumn coupled with rapid dehardening in spring; however, the percentage of frost damage incurred by the five species showed a declining trend because of the reduced frequency and intensity of frost events. Thus, decreased frost severity may, to a large extent, offset the negative effects of diminished frost hardiness such that the frost risk faced by temperate forests may well remain constant or decline with continued climate warming.

Phenological changes in crops affect efficient agricultural production and can be used as important biological indicators of local and regional climate change. Although crop phenological changes and their responses to climate change, especially temperature, have been investigated, the impact of agronomic practice such as cultivar shifts and planted date changes on crop phenology remains unclear. Here, we used a long-term dataset (1981–2010) of wheat phenology and associated local weather data from 48 agro-meteorological stations in four temperature zones in China to analyze phenological changes of spring and winter wheat. Trend analysis method was used to estimate changes in the date of growth stages and the duration of growth phases, while sensitivity analysis method was used to qualify the response of growth phase duration to mean temperature (Tmean), total precipitation (PRE), and total sunshine duration (SSD). Using the Crop Environment Resource Synthesis-wheat model, we isolated the impacts of climate change, cultivar selection, and sowing date on phenological change of wheat. Results show that phenological changes were greatest in the warm-temperate zone. Sensitivity analysis indicates that growth phase duration was generally negatively related to Tmean and positively related to PRE and SSD. The positive sensitivity response to Tmean occurred in the tillering to jointing and sowing to maturity growth periods in the warmer temperature zones, suggesting that warmer temperatures during the overwintering period hampered effective vernalization in winter wheat. Modeling results further indicate that reductions in wheat growth duration caused by climate change could be offset by the introduction of new cultivars with high thermal requirements and accelerated with delayed sowing date.

A contribution to the PAGES Asia2k working group. ABSTRACT: To investigate climate variability in Asia during the last millennium the spatial and temporal evolution of summer (June–July–August; JJA) temperature in eastern and south central Asia is reconstructed using multi proxy records and the regularized expectation maximization (RegEM) algorithm with truncated total least squares (TTLS) under a point by point regression (PPR) framework. The temperature index reconstructions show that the late 20th century was the warmest period in Asia over the past millennium. The temperature field reconstructions illustrate that temperatures in central eastern and southern China during the 11th and 13th centuries and in western Asia during the 12th century were significantly higher than those in other regions and comparable to levels in the 20th century. Except for the most recent warming all identified warm events showed distinct regional expressions and none were uniform over the entire reconstruction area. The main finding of the study is that spatial temperature patterns have on centennial time scales varied greatly over the last millennium. Moreover seven climate model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) over the same region of Asia are all consistent with the temperature index reconstruction at the 99 confidence level. Only spatial temperature patterns extracted as the first empirical orthogonal function (EOF) from the GISS E2 R and MPI ESM P model simulations are significant and consistent with the temperature field reconstruction over the past millennium in Asia at the 90 confidence level. This indicates that both the reconstruction and the simulations depict the temporal climate variability well over the past millennium. However the spatial simulation or reconstruction capability of climate variability over the past millennium could be still limited. For reconstruction some grid points do not pass validation tests and reveal the need for more proxies with high temporal resolution accurate dating and sensitive temperature signals especially in central Asia and before AD 1400.

Based on the reconstructed temperatures, precipitation changes, and occurrences of extreme climate events, together with historical records on fiscal deterioration, food crises, and the frequencies of popular unrest, rebellions and wars, we identified three principal ways in which climate change contributed to the collapse in the Ming dynasty. Firstly, cooling, aridification, and desertification during a cold period destroyed the military farm system, which was the main supply system for the provisioning of government troops on the northern frontiers; these impacts increased the military expenditure from 64 % of total government expenditure in 1548–1569 to 76 % in 1570–1589 and thus aggravated the national fiscal crisis that occurred during the late Ming dynasty. Secondly, climate deterioration (e.g., cooling, aridification, and an increase in the frequencies of frost- and drought-related disasters, etc.) led to a 20–50 % reduction in the per capita production of raw grain in most areas of China, which resulted in widespread food crises and exacerbated the vulnerability of social structures during the last several decades of the Ming dynasty. Thirdly, the severe droughts occurring in 1627–1643 were a key trigger to the peasantry uprising. These droughts also played a significant role to promote the peasantry uprising, especially reviving the peasantry troops by recruitment of famine victims when they nearly perished in 1633 and 1638, and severely disrupting the food supply for the government troops, resulting in the final defeat of the government troops by the peasantry troops. This study contributes to an understanding of the climate-related mechanisms behind the collapse of the Ming dynasty, and provides a historical case study that enhances our understanding of the nature of interactions between climate change and social vulnerability.

This paper provides new evidence of regional warming trends from local Chinese observations covering the period 1951-2010. We used satellite-derived land data and weighted urban and rural temperature records (a weighted method) and estimate the regional warming trend, which involves natural climate change and human impact. The annual warming rate over the whole of China is 0.21 +/- 0.02 A degrees C/decade. The seasonal warming is 0.30 A +/- 0.05 A degrees C/decade (Winter), 0.24 A degrees C A +/- 0.03 A degrees C/decade (Spring); 0.16 A +/- 0.02 A degrees C/decade (Summer) and 0.21 A +/- 0.03 A degrees C/decade (Autumn). The mean warming trend is lower than previous estimates (e.g. NMIC, CRU-China) using un-weighted methods (arithmetic average of all records). The warming difference between the weighted and un-weighted accounts for 27 % (12 %) of the NMIC (CRU-China) un-weighted estimate on the total warming. This indicates that previous estimations overestimated a regional warming trend. The differences can be partly attributed to the weighting of the urban effect which is taken into consideration in this study, resulting in a much slower temperature increase. Spatially, the northern part of China shows a larger difference than the south especially for winter and spring. We argue that it is of importance to take into consideration the influence of urban land-use change to improve the physical understanding of surface warming in China over past decades.

In light of the observed climate changes in recent decades over eastern China, we studied the changes in spring phenophases of woody plants observed at 16-stations during 1963–1996, and explored the possible link between the spring phenophases changes and climate changes before the phenophase onset. It is found that, in the region north of 33∘N (including Northeast, North China and the lower reaches of the Huaihe River), the phenophase advanced 1.1–4.3 days per decade for early spring and 1.4–5.4 days per decade for late spring, but in the eastern part of the southwest China it was dealyed by 2.9–6.9 days per decade in early spring and 2.4–6.2 days per decade in late spring. One outstanding feature is identified in Guangzhou in south China, where significant advance of 7.5 days per decade in early spring and delay of 4.6 days per decade in late spring were detected. Statistically siginficant correlation was found between the changes of spring phenophase and the temperatures of one or several months before the phenophase onset. The relationship between the trend of phenophase change and temperature change was highly non-linear (more sensitivity to cooling than to warming) and reached an asymptote 0.5∘C per decade, which may have implication in the responses of the ecosystem in a future global warming scenario. In addition, we also examined the link between the spring phenophase, and length and mean temperature of the growing season, and the analyses suggested that they were highly correlated as well.