Development of statistical seasonal prediction models of Arctic Sea Ice concentration using CERES absorbed solar radiation

Statistical seasonal prediction models for the Arctic sea ice concentration (SIC) were developed for the late summer (August-October) when the downward trend is dramatic. The absorbed solar radiation (ASR) at the top of the atmosphere in June has a significant seasonal leading role on the SIC. Based on the lagged ASR-SIC relationship, two simple statistical models were established: the Markovian stochastic and the linear regression models. Crossvalidated hindcasts of SIC from 1979 to 2014 by the two models were compared with each other and observation. The hindcasts showed general agreement between the models as they share a common predictor, ASR in June and the observed SIC was well reproduced, especially over the relatively thin-ice regions (of one- or multi-year sea ice). The robust predictability confirms the functional role of ASR in the prediction of SIC. In particular, the SIC prediction in October was quite promising probably due to the pronounced icealbedo feedback. The temporal correlation coefficients between the predicted SIC and the observed SIC were 0.79 and 0.82 by the Markovian and regression models, respectively. Small differences were observed between the two models; the regression model performed slightly better in August and September in terms of temporal correlation coefficients. Meanwhile, the prediction skills of the Markovian model in October were higher in the north of Chukchi, the East Siberian, and the Laptev Seas. A strong non-linear relationship between ASR in June and SIC in October in these areas would have increased the predictability of the Markovian model.     

Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere

An ensemble data assimilation system using the 4-dimensional Local Ensemble Transform Kalman Filter is implemented to a global non-hydrostatic Numerical Weather Prediction model on the cubed-sphere. The ensemble data assimilation system is coupled to the Korea Institute of Atmospheric Prediction Systems Package for Observation Processing, for real observation data from diverse resources, including satellites. For computational efficiency in a parallel computing environment, we employ some advanced software engineering techniques in the handling of a large number of files. The ensemble data assimilation system is tested in a semi-operational mode, and its performance is verified using the Integrated Forecast System analysis from the European Centre for Medium-Range Weather Forecasts. It is found that the system can be stabilized effectively by additive inflation to account for sampling errors, especially when radiance satellite data are additionally used.     

广东冬季水资源的预测和因子背景场分析

为预报广东冬季水资源量,利用500hPa高度场和太平洋海温场作为气候变化的相关因子场,并应用了主分量逐步回归预测模型进行了计算。利用各标准化主分量的预测结果,按照主分量逐步回归模型进行回算得到预测场。结果表明,2004年和2006年预测结果与实况比较相似;2005年冬季水资源预测值和实测值分布相差较大。为了分析预测因子的影响,对进入广东冬季水资源场前4个主分量预测方程的因子进行分析,并对个别关键因子的背景场做了细致的研究。     

西太平洋赤道海域的热量平衡

本文利用1986年11月中国科学院实验3号考察船在西太平洋赤道海域(0°～5°N,130°～150°E)考察所获得的29天辐射资料和同期取得的温、压、湿、风和探空等资料,计算了这一海域11月份的热量平衡各量值和整层气柱的加热量。结果表明:该海域是一个强热源区,其上空大气也是一个强热源区。     

A dynamic link between the basin-scale and zonal modes in the Tropical Indian Ocean

Summary The interannual variability of sea surface temperature (SST) anomalies in the tropical Indian Ocean is dominated mainly by a basin-scale mode (BM) and partly by an east–west contrast mode (zonal mode, ZM). The BM reflects the basin-scale warming or cooling and is highly correlated with El Nino with 3- to 6-month lags, while the ZM is marginally correlated with El Nino with 9-month lags.During an El Nino, large-scale anomalous subsidence over the maritime continent occurs as a result of an eastward shift in the rising branch of the Walker circulation suppresses convection over the eastern Indian Ocean, allowing more solar radiation over the eastern Indian Ocean. At the same time, the anomalous southeasterly wind over the equatorial Indian Ocean forces the thermocline over the western Indian Ocean to deepen, especially in the southern part. As a result, SST over the whole basin increases. As El Nino decays, the subsidence over the maritime continent ceases and so does the anomalous southeasterly wind. However, the thermocline perturbation does not quickly shoal back to normal because of inertia and it disperses as Rossby waves. These Rossby waves are reflected back as an equatorial Kelvin wave, causing deepening of the thermocline in the eastern Indian Ocean, and preventing SSTs from cooling in that region. Moreover, the weaker wind speed of the monsoon circulation results in less latent heat loss, and thus warms the eastern Indian Ocean. These two processes therefore help to maintain warm SSTs over the eastern Indian Ocean until fall. During the fall, the warm SST over the eastern Indian Ocean and the cold SST over the western Indian Ocean are enhanced by air–sea interaction and the ZM returns. The ZM dissipates through the seasonal reversal of the monsoon atmospheric circulation and the boundary-reflected Kelvin wave. In the same manner, a basin-scale cooling in the tropical Indian Ocean can induce the ZM warming in the west and cooling in the east.     

Arctic Climate Changes Based on Historical Simulations (1900‒2013) with the CAMS-CSM

The Chinese Academy of Meteorological Sciences Climate System Model (CAMS-CSM) is a newly developed global climate model that will participate in the Coupled Model Intercomparison Project phase 6. Based on historical simulations (1900?2013), we evaluate the model performance in simulating the observed characteristics of the Arctic climate system, which includes air temperature, precipitation, the Arctic Oscillation (AO), ocean temperature/salinity, the Atlantic meridional overturning circulation (AMOC), snow cover, and sea ice. The model?data comparisons indicate that the CAMS-CSM reproduces spatial patterns of climatological mean air temperature over the Arctic (60°?90°N) and a rapid warming trend from 1979 to 2013. However, the warming trend is overestimated south of the Arctic Circle, implying a subdued Arctic amplification. The distribution of climatological precipitation in the Arctic is broadly captured in the model, whereas it shows limited skills in depicting the overall increasing trend. The AO can be reproduced by the CAMS-CSM in terms of reasonable patterns and variability. Regarding the ocean simulation, the model underestimates the AMOC and zonally averaged ocean temperatures and salinity above a depth of 500 m, and it fails to reproduce the observed increasing trend in the upper ocean heat content in the Arctic. The large-scale distribution of the snow cover extent (SCE) in the Northern Hemisphere and the overall decreasing trend in the spring SCE are captured by the CAMS-CSM, while the biased magnitudes exist. Due to the underestimation of the AMOC and the poor quantification of air–sea interaction, the CAMS-CSM overestimates regional sea ice and underestimates the observed decreasing trend in Arctic sea–ice area in September. Overall, the CAMS-CSM reproduces a climatological distribution of the Arctic climate system and general trends from 1979 to 2013 compared with the observations, but it shows limited skills in modeling local trends and interannual variability.     

UV attenuation in the cloudy atmosphere

Ultraviolet (UV) energy absorption plays a very important role in the Earth–atmosphere system. Based on observational data for Beijing, we suggest that some atmospheric constituents utilize or transfer UV energy in chemical and photochemical (C&P) reactions, in addition to those which absorb UV energy directly. These constituents are primarily volatile organic compounds (VOCs) emitted from both vegetative and anthropogenic sources. The total UV energy loss in the cloudy atmosphere for Beijing in 1990 was 78.9 Wm⁻². This attenuation was caused by ozone (48.3 Wm⁻²), other compounds in the atmosphere (26.6 Wm⁻²) and a scattering factor (4.0 Wm⁻²). Our results for a cloudy atmosphere in the Beijing area show that the absorption due to these other compounds occurs largely through the mediation of water vapor. This fraction of energy loss has not been fully accounted for in previous models. Observations and previous models results suggest that 1) a cloudy atmosphere absorbs 25∼30 Wm⁻² more solar shortwave radiation than models predict; and 2) aerosols can significantly decrease the downward mean UV-visible radiation and the absorbed solar radiation at the surface by up to 28 and 23 Wm⁻², respectively. Thus, quantitative study of UV and visible absorption by atmospheric constituents involved in homogeneous and heterogeneous C&P reactions is important for atmospheric models.     

Estimation of Surface-Layer Scaling Parameters in the Unstable Boundary Layer: Implications for Orographic Flow Speed-Up

A method is proposed for estimating the surface-layer depth \((z_s)\) and the friction velocity \((u_*)\) as a function of stability (here quantified by the Obukhov length, L) over the complete range of unstable flow regimes. This method extends that developed previously for stable conditions by Argaín et al. (Boundary-Layer Meteorol 130:15–28, 2009), but uses a qualitatively different approach. The method is specifically used to calculate the fractional speed-up \((\varDelta S)\) in flow over a ridge, although it is suitable for more general boundary-layer applications. The behaviour of \(z_s \left( L\right) \) and \(u_*\left( L\right) \) as a function of L is indirectly assessed via calculation of \(\varDelta S\left( L\right) \) using the linear model of Hunt et al. (Q J R Meteorol Soc 29:16–26, 1988) and its comparison with the field measurements reported in Coppin et al. (Boundary-Layer Meteorol 69:173–199, 1994) and with numerical simulations carried out using a non-linear numerical model, FLEX. The behaviour of \(\varDelta S\) estimated from the linear model is clearly improved when \(u_*\) is calculated using the method proposed here, confirming the importance of accounting for the dependences of \(z_s\left( L \right) \) and \(u_*\left( L \right) \) on L to better represent processes in the unstable boundary layer.     

商丘市1980-2009年飑线的发生与危害

利用商丘站1980-2009年观测资料,分析了飑线的发生、发展规律及其危害.结果表明,近30 a来商丘几乎年年发生飑线天气,1年最多发生12次,每年夏季(6-8月)飑线活动最为频繁,每一次飑线天气都造成不同程度的人员伤亡和经济损失;20世纪80年代以后,飑线发生次数呈增长趋势.