Lingyun Wu

Land-atmosphere coupling and summer climate variability over East Asia

[1]xa0Two long-term simulations with the weather research and forecasting model are conducted to assess the contribution of land-atmosphere coupling to interannual variability of summer climate over East Asia. The control experiment (CTL) uses a fully coupled land surface model, while an additional experiment replaces soil moisture evolution at each time step with the climatology of CTL and thus removes the interannual variability of soil moisture. CTL is able to reproduce relatively well climatic means and interannual variability of summer climate over East Asia though some biases exist. It is found that land-atmosphere coupling plays a critical role in influencing summer climate variability, in particular over the climatic and ecological transition zones. Interactive soil moisture strongly amplifies daily mean temperature variability over the southern Siberia–northern Mongolia region, the region from northeast China to central China, and the eastern part of South Asia, accounting for half or more of the total variance. Soil moisture is found to exert substantially stronger impacts on daily maximum temperature variability than on daily mean temperature variability but generally has small effects on daily minimum temperature except for the eastern Tibetan Plateau and some other areas. Soil moisture makes a dominant contribution to precipitation variability over the climatic and ecological transition zones of the southern Siberia–northern Mongolia region and northern China and many areas of western China. While soil moisture-temperature coupling is largely determined by the ability of soil moisture to affect surface fluxes, soil moisture–precipitation coupling also depends on other physical processes, particularly moisture convection.

Land-atmosphere coupling and diurnal temperature range over the contiguous United States

[1]xa0Soil moisture influences on daily maximum (Tmax) and minimum (Tmin) temperatures, and thus the diurnal temperature range (DTR) in summer, are statistically quantified across the contiguous Unites States using soil moisture from the Global Land Data Assimilation System and observational temperatures. A soil moisture feedback parameter is computed based on lagged covariance ratios. Over the zone from California through the Midwest to the Southeast, the soil moisture exhibits a negative feedback on DTR mainly through its damping effect on Tmax. In contrast, a positive feedback on DTR dominates Arizona and New Mexico as the soil moisture exerts a stronger negative forcing on Tmin relative to Tmax. The feedback-induced variability accounts for typically 10–20% of the total DTR variance over regions where strong feedbacks are identified. The results provide a useful benchmark for evaluating climate model simulations, although the employed data and method have limitations that should be recognized.

The role of May vegetation greenness on the southeastern Tibetan Plateau for East Asian summer monsoon prediction

Received 21 September 2010; revised 11 December 2010; accepted 3 January 2011; published 5 March 2011. [1] It is well known that the slowly varying oceanic processes provide the primary source for East Asian summer monsoon (EASM) predictability. However, the memory inherent in the land surface state is less well understood or applied toward the EASM prediction. Here we investigate the role of antecedent vegetation conditions over East Asia for the EASM variation and prediction using March, April, May, and spring mean satellite‐sensed Normalized Difference Vegetation Index (NDVI) for the period of 1982–2006. Results show that May vegetation greenness on the southeastern Tibetan Plateau (TP) is most closely linked to the EASM, accounting for about half of the total EASM variance. May vegetation greenness on the southeastern TP has significant and positive correlations with summer rainfall over the southeastern TP, East Asian summer subtropical frontal region, and many areas of northern China. We further discuss the possible physical mechanism explaining our findings. It is proposed that increased TP vegetation greenness enhances surface thermal effects, which subsequently warm atmospheric temperature, as well as strengthen ascending motion, convergence at the lower layers and divergence at the higher layers, and summer monsoon circulation. Finally, a linear regression model is developed to predict the EASM strength by combination of El Nino–Southern Oscillation (ENSO) and the vegetation greenness. Hindcast for the period 1982–2006 shows that the use of the southeastern TP vegetation information can highly improve the EASM prediction skill compared to that using ENSO alone.

Relationships between large‐scale circulation patterns and carbon dioxide exchange by a deciduous forest

[1]xa0In this study, we focus on a deciduous forest in central Massachusetts and investigate the relationships between global climate indices and CO2 exchange using eddy-covariance flux measurements from 1992 to 2007. Results suggest that large-scale circulation patterns influence the annual CO2 exchange in the forest through their effects on the local surface climate. Annual gross ecosystem exchange (GEE) in the forest is closely associated with spring El Nino–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), previous fall Atlantic Multidecadal Oscillation (AMO), and previous winter East Pacific–North Pacific (EP-NP) pattern. Annual net ecosystem exchange (NEE) responds to previous fall AMO and PDO, while annual respiration (R) is impacted by previous fall ENSO and Pacific/North American Oscillation (PNA). Regressions based on these relationships are developed to simulate the annual GEE, NEE, and R. To avoid problems of multicollinearity, we compute a “Composite Index for GEE (CIGEE)” based on a linear combination of spring ENSO and PDO, fall AMO, and winter EP-NP and a “Composite Index for R (CIR)” based on a linear combination of fall ENSO and PNA. CIGEE, CIR, and fall AMO and PDO can explain 41, 27, and 40% of the variance of the annual GEE, R, and NEE, respectively. We further apply the methodology to two other northern midlatitude forests and find that interannual variabilities in NEE of the two forests are largely controlled by large-scale circulation patterns. This study suggests that global climate indices provide the potential for predicting CO2 exchange variability in the northern midlatitude forests.

Influence of human population movements on urban climate of Beijing during the Chinese New Year holiday

The population movements for the Chinese New Year (CNY) celebrations, known as the world’s largest yearly migration of human beings, have grown rapidly in the past several decades. The massive population outflows from urban areas largely reduce anthropogenic heat release and modify some other processes, and may thus have noticeable impacts on urban climate of large cities in China. Here, we use Beijing as an example to present observational evidence for such impacts over the period of 1990–2014. Our results show a significant cooling trend of up to 0.55u2009°C per decade, particularly at the nighttime during the CNY holiday relative to the background period. The average nighttime cooling effect during 2005–2014 reaches 0.94u2009°C relative to the 1990s, significant at the 99% confidence level. The further analysis supports that the cooling during the CNY holiday is attributable primarily to the population outflow of Beijing. These findings illustrate the importance of population movements in influencing urban climate despite certain limitations. As the world is becoming more mobile and increasingly urban, more efforts are called for to understand the role of human mobility at various spatial and temporal scales.

Assessing population movement impacts on urban heat island of Beijing during the Chinese New Year holiday: effects of meteorological conditions

Chinese New Year (CNY), or Spring Festival, is the most important of all festivals in China. We use daily observations to show that Beijing’s urban heat island (UHI) effects largely depend on precipitation, cloud cover, and water vapor but are insensitive to wind speed, during the CNY holiday season. Non-precipitating, clear, and low humidity conditions favor strong UHI effects. The CNY holiday, with some 3 billion journeys made, provides a living laboratory to explore the role of population movements in the UHI phenomenon. Averaged over the period 2004–2013, with the Olympic year of 2008 excluded, Beijing’s UHI effects during the CNY week decline by 0.48xa0°C relative to the background period (4xa0weeks including 2 to 3xa0weeks before, and 2 to 3xa0weeks after, the CNY week). With combined effects of precipitation, large cloud cover, and high water vapor excluded, the UHI effects during the CNY week averaged over the study period decline by 0.76xa0°C relative to the background period, significant at the 99% confidence level by Student’s t test. These results indicate that the impacts of population movements can be more easily detected when excluding unfavorable meteorological conditions to the UHI. Population movements occur not only during the CNY holiday, but also during all the time across the globe. We suggest that better understanding the role of population movements will offer new insight into anthropogenic climate modifications.

Prediction of summer hot extremes over the middle and lower reaches of the Yangtze River valley

Substantial efforts have been made in recent decades to understand the characteristics and variations of summer hot extremes and the underlying physical processes involved. However, the seasonal prediction of summer hot extremes remains challenging. The populous middle and lower reaches of the Yangtze River valley (MLYR) in China are severely influenced by hot extremes during summer. This study presents seasonal predictions of summer hot extremes over the MLYR for the period 1979–2016 based on three preceding predictors that are closely linked to hot extremes over this region: spring soil moisture over the southeastern Indochina Peninsula (SIP); spring sea surface temperature (SST) over the western tropical Pacific (WTP); and the difference of Nino 3.4 SST data in May and the previous December. The soil moisture deficit over the SIP and warm SST over the WTP in spring, as well as the difference of Niño 3.4 SST data in May and the previous December, tend to result in positive geopotential height anomalies over the MLYR, which may favor hot extremes by enhancing downward solar radiation, subsidence warming and local soil moisture–temperature coupling associated with precipitation reduction. Using these three predictors, we demonstrate with cross validation that the temporal variations of hot extremes over the MLYR can be skillfully predicted for the study period (i.e., 1979–2016), while biases exist in the magnitude. Hindcast experiments for 2012–2016 show that high prediction skill can be achieved for the spatial patterns of hot extremes, with pattern correlation coefficients of 0.83–0.99. Our findings are expected to facilitate the practical prediction of hot extremes over the MLYR.