Advances in Atmospheric Sciences

The Northern Hemisphere (NH) often experiences frequent cold air outbreaks and heavy snowfalls during La Niña winters. In 2022, a third-year La Niña event has exceeded both the oceanic and atmospheric thresholds since spring and is predicted to reach its mature phase in December 2022. Under such a significant global climate signal, whether the Eurasian Continent will experience a tough cold winter should not be assumed, despite the direct influence of mid- to high-latitude, large-scale atmospheric circulations upon frequent Eurasian cold extremes, whose teleconnection physically operates by favoring Arctic air invasions into Eurasia as a consequence of the reduction of the meridional background temperature gradient in the NH. In the 2022/23 winter, as indicated by the seasonal predictions from various climate models and statistical approaches developed at the Institute of Atmospheric Physics, abnormal warming will very likely cover most parts of Europe under the control of the North Atlantic Oscillation and the anomalous anticyclone near the Ural Mountains, despite the cooling effects of La Niña. At the same time, the possibility of frequent cold conditions in mid-latitude Asia is also recognized for this upcoming winter, in accordance with the tendency for cold air invasions to be triggered by the synergistic effect of a warm Arctic and a cold tropical Pacific on the hemispheric scale. However, how the future climate will evolve in the 2022/23 winter is still subject to some uncertainty, mostly in terms of unpredictable internal atmospheric variability. Consequently, the status of the mid- to high-latitude atmospheric circulation should be timely updated by medium-term numerical weather forecasts and sub-seasonal-to-seasonal prediction for the necessary date information and early warnings.

East Africa is particularly vulnerable to precipitation variability, as the livelihood of much of the population depends on rainfed agriculture. Seasonal forecasts of the precipitation anomalies, when skillful, can therefore improve implementation of coping mechanisms with respect to food security and water management. This study assesses the performance of Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) on forecasting June–September (JJAS) seasonal precipitation anomalies over East Africa. The skill in predicting the JJAS mean precipitation initiated from 1 May for the period of 1982–2019 is evaluated using both deterministic and probabilistic verification metrics on grid cell and over six distinct clusters. The results show that NUIST-CFS1.0 captures the spatial pattern of observed seasonal precipitation climatology, albeit with dry and wet biases in a few parts of the region. The model has positive skill across a majority of Ethiopia, Kenya, Uganda, and Tanzania, whereas it doesn’t exceed the skill of climatological forecasts in parts of Sudan and southeastern Ethiopia. Positive forecast skill is found over regions where the model shows better performance in reproducing teleconnections related to oceanic SST. The prediction performance of NUIST-CFS1.0 is found to be on a level that is potentially useful over a majority of East Africa.

Soil respiration is a key component of the global terrestrial ecosystem carbon cycle. The static opaque chamber method was used to measure the CO2 effluxes from soil of a semiarid Aneurolepidium chinense steppe and a Stipa krylovii steppe in the Xilin River Basin of Inner Mongolia, China from March 2002 to December 2004. The results indicated that the soil respiration rates of the semiarid Aneurolepidium chinense steppe and the Stipa krylovii steppe were both relatively high from mid-May to mid-September of each year and remained low during the rest of the year. The minimum value of soil respiration occurred in December or January and negative effluxes of CO2 appeared for several days during the non-growing season of individual years at the two sampling sites. A high annual variation was found in the two steppes with the coefficients of variance (CV) being over 94%, even high to 131%. The annual sums of soil CO2 efflux of the Aneurolepidium chinense steppe varied between 356.4 gC m−2 yr−1 and 408.8 gC m−2 yr−1, while those of the Stipa krylovii steppe in the three years were in the range of 110.6 gC m−2 yr−1 to 148.6 gC m−2 yr−1. The mean respiration rates of the Aneurolepidium chinense steppe were significantly higher than those of the Stipa krylovii steppe in different statistical periods with the exception of the non-growing season. About 59.9% and 80.6% of the soil respiration variations in both steppes for the whole sampling period were caused by the changes of temperature and soil water content. In the Aneurolepidium chinense steppe, the soil respiration rate has significant or extremely significant positive correlation (r = 0.58–0.85, p < 0.05 or p < 0.01) with air temperature and ground temperature of the topsoil except in 2002; the unique contributions of temperature change to the soil respiration variation of the three years were 53.3%, 81.0% and 58.6%, respectively. But, for the Stipa krylovii steppe in the same time interval, the soil water content (especially that of the 10–20 cm layer) has a greater effect on the change of soil respiration, and the unique contributions of the change of the 10–20 cm soil water content to the variations of soil respiration in 2002 and 2003 were 60.0% and 54.3%, respectively. In 2004, in spite of the higher contribution of temperature than soil water content, the contribution of ground temperature at a depth of 10 cm was only 46.2%, much weaker than that of any single year in the Aneurolepidium chinense steppe.

Using radiosonde temperatures of 92 selected stations in China, the uncertainties in homogenization processes caused by different reference series, including nighttime temperature, the NCEP (National Centers for Environmental Prediction) and ERA-40 (European Centre for Medium-Range Weather Forecasts) forecasting background, are examined via a two-phase regression approach. Although the results showed limited consistency in the temporal and spatial distribution of identified break points (BPs) in the context of metadata events of instrument model change and correction method, significant uncertainties still existed in BP identification, adjustment, and impact on the estimated trend. Reanalysis reference series generally led to more BP identification in homogenization. However, those differences were parts of global climatic shifts, which may have confused the BP calculations. Discontinuities also existed in the reanalysis series due to changes in the satellite input. The adjustment values deduced from the reanalysis series ranged widely and were larger than those from the nighttime series and, therefore, impacted the estimated temperature trend.

By performing error analysis of the information from the 48-hr forecasting charts of the 500-hPa fields by the B model over eastern Asia in the period of July to September 1982 and expansions of the height fields of westerlies and the subtropical zone by use of the Chebyshev polynomial and EOF, respectively, a scheme is developed for predicting the synchronous STH coefficient (i. e. time coefficient) in terms of the Chebyshev one, thus making possible statistical forecasting of the 500-hPa subtropical field within 48 hr. Tests with independent samples indicate that, to a certain extent, this scheme can be used in operational prediction as a reference.