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Based on the China Meteorological Administration Land Data Assimilation System (CLDAS) reanalysis data and 12–72 h forecasts of the surface (2-m) air temperature (SAT) from the European Centre for Medium-Range Weather Forecasts (ECMWF) and three numerical weather prediction (NWP) models of the China Meteorological Administration (CMA-GFS, CMA-SH, and CMA-MESO), multi-model ensemble forecasts are conducted with a convolutional neural network (CNN) and a feed-forward neural network (FNN) to improve the SAT forecast in Henan Province, China. The results show that there are large errors in the 12–72 h forecasts of SAT from the CMA, while the ECMWF outperforms the other raw NWP models, especially in eastern and southern Henan. The CNN has the best short-term forecasting skills. The difference in the geographical distribution of the CNN forecast errors is small, without any apparent large-value areas. The CNN shows its advantages in its bias correction in the mountainous region (western Henan), indicating that the CNN can capture the spatial features of the atmospheric fields and is therefore more robust in regions with varied topography. In addition, the CNN can extract data features through the convolution kernel and focus on local features; it can assimilate the local features at a higher level and obtain global features. Therefore, the CNN takes advantage of the four models in the SAT forecast and further improves the forecast skill. [ABSTRACT FROM AUTHOR]

In this study, two pattern projection methods, i.e., the stepwise pattern projection method (SPPM) and the newly proposed neighborhood pattern projection method (NPPM), are investigated to improve forecast skills of daily maximum and minimum temperatures (Tmax and Tmin) over East Asia with lead times of 1–7 days. Meanwhile, the decaying averaging method (DAM) is conducted in parallel for comparison. These postprocessing methods are found to effectively calibrate the temperature forecasts on the basis of the raw ECMWF output. Generally, the SPPM is slightly inferior to the DAM, while its insufficiency decreases with increasing lead times. The NPPM shows manifest superiority for all lead times, with the mean absolute errors of Tmax and Tmin decreased by ~0.7° and ~0.9°C, respectively. Advantages of the two pattern projection methods are both mainly concentrated on the high-altitude areas such as the Tibetan Plateau, where the raw ECMWF forecasts show the most conspicuous biases. In addition, aiming at further assessments of these methods on extreme event forecasts, two case experiments are carried out toward a heat wave and a cold surge, respectively. The NPPM is retained as the optimal with the highest forecast skills, which reduces most of the biases to <2°C for both Tmax and Tmin over all the lead days. In general, the statistical pattern projection methods are capable of effectively eliminating spatial biases in forecasts of surface air temperature. Compared with the initial SPPM, the NPPM not only produces more powerful forecast calibrations, but also provides more pragmatic calculations and greater potential economic benefits in practical applications. [ABSTRACT FROM AUTHOR]

Changes of surface air temperature (SAT) over the Indochina Peninsula (ICP) under the Representative Concentration Pathway (RCP) 8.5 scenario are projected for wet and dry seasons in the short-term (2020–2049) and long-term (2070–2099) future of the twenty-first century. A first analysis on projections of the SAT by the state-of-the-art regionally coupled atmosphere-ocean model ROM, including exchanges of momentum, heat, and water fluxes between the atmosphere (Regional Model) and ocean (Max Planck Institute Ocean Model) models, shows the following results: (i) In both seasons, the highest SAT occurs over the southern coastal area while the lowest over the northern mountains. The highest warming magnitudes are located in the northwestern part of the ICP. The regionally averaged SAT over the ICP increases by 2.61 °C in the wet season from short- to long-term future, which is slightly faster than that of 2.50 °C in the dry season. (ii) During the short-term future, largest SAT trends occur over the southeast and northwest ICP in wet and dry seasons, respectively. On regional average, the wet season is characterized by a significant warming rate of 0.22 °C decade−1, while it is non-significant with 0.11 °C decade−1 for the dry season. For the long-term future, the rapid warming is strengthened significantly over whole ICP, with trends of 0.51 °C decade−1 and 0.42 °C decade−1 in wet and dry seasons, respectively. (iii) In the long-term future, more conspicuous warming is noted, especially in the wet season, due to the increased downward longwave radiation. Higher CO2 concentrations enhancing the greenhouse effect can be attributed to the water vapor–greenhouse feedback, which, affecting atmospheric humidity and counter radiation, leads to the rising SAT. [ABSTRACT FROM AUTHOR]

The interdecadal variations in extratropical anticyclone activity over East Asia during the winters of 1958–2013 were analysed using an objective identification and tracking algorithm for anticyclones. The influence of the interdecadal variation in anticyclone activity over the Mongolian Plateau on the interdecadal variation in East Asian surface air temperature and its possible mechanism was studied. The results showed that the Mongolian Plateau is the key region of anticyclonic activity in East Asia, and the occurrence frequency of anticyclones presented significantly opposite distribution forms in the higher and lower latitudes. An abrupt change in anticyclone activity over the Mongolian Plateau occurred in the late 1970s. From 1958 to the late 1970s, the main anticyclone activity occurred farther northward, whereas the activity was farther southward from the 1980s to 2013. Further study showed that in the period when the anticyclone occurrence frequency in the higher latitudes north of 50°N was lower (higher), while that in the lower latitudes south of 50°N was higher (lower), the surface air temperature of most parts of East Asia, especially the middle and high latitudes, was abnormally high (low). This was due to the anticyclonic activities, the Siberian high faded in the northwest part and expanded in southeast, while the East Asia trough and Ural Mountain blocking high weakened. This was also manifested as the variation in the geopotential height tendency forcing by transient waves. As the anticyclonic wave-breaking intensifies from western Mongolia to Northeast China, the high-frequency momentum flux over this area was significantly enhanced, strengthening the west wind in the higher latitudes from the north side and weakening the west wind in the middle latitudes from the south side. Under the combined force of the above activity, the southward movement of polar cold air was weakened, and the warm air in the middle and low latitudes was more easily transported to the north, resulting in warmer surface air temperatures over most parts of East Asia. [ABSTRACT FROM AUTHOR]

Based on near-term climate simulations for IPCC-AR5 (The Fifth Assessment Report), probabilistic multimodel ensemble prediction (PMME) of decadal variability of surface air temperature in East Asia (20°-50°N, 100°-145°E) was conducted using the multivariate Gaussian ensemble kernel dressing (GED) methodology. The ensemble system exhibited high performance in hindcasting the decadal (1981-2010) mean and trend of temperature anomalies with respect to 1961-90, with a RPS of 0.94 and 0.88 respectively. The interpretation of PMME for future decades (2006-35) over East Asia was made on the basis of the bivariate probability density of the mean and trend. The results showed that, under the RCP4.5 (Representative Concentration Pathway 4.5 W m) scenario, the annual mean temperature increases on average by about 1.1-1.2 K and the temperature trend reaches 0.6-0.7 K (30 yr). The pattern for both quantities was found to be that the temperature increase will be less intense in the south. While the temperature increase in terms of the 30-yr mean was found to be virtually certain, the results for the 30-yr trend showed an almost 25% chance of a negative value. This indicated that, using a multimodel ensemble system, even if a longer-term warming exists for 2006-35 over East Asia, the trend for temperature may produce a negative value. Temperature was found to be more affected by seasonal variability, with the increase in temperature over East Asia more intense in autumn (mainly), faster in summer to the west of 115°E, and faster still in autumn to the east of 115°E. [ABSTRACT FROM AUTHOR]

Myanmar is located in a tropical region where temperature rises very fast and hence is highly vulnerable to climate change. The high variability of the air temperature poses potential risks to the local community. Thus, the current study uses 42 synoptic meteorological stations to assess the spatiotemporal changes in air temperature over Myanmar during 1971–2013. The nonparametric sequential Mann-Kendall (SqMK), linear regression, empirical orthogonal function (EOF), Principal Component Analysis (PCA), and composite analysis were used to assess the long-term trends in maximum (Tmax) and minimum (Tmin) temperature series and their possible mechanism over the study region. The results indicate that the trend of Tmax has significantly increased at the rates of 90% in summer season, while the Tmin revealed a substantial positive trend in winter season time series with the magnitude of 30%, respectively. Moreover, during a rapid change of climate (1995–2013) we observed an air temperature increase of 0.7 °C. The spatial distributions of EOF revealed relatively warmer temperatures over the whole region except the south in the summer; however, a similar pattern can be seen for the rainy season and winter, implying warming in the central part and cooling in the northern and southern parts. Furthermore, the Indian Ocean Dipole (IOD) influence on air temperature over Myanmar is more prevalent than that of the El Niño Southern Oscillation (ENSO). The result implies that the positive phase of the IOD and negative phase of the Southern Oscillation Index (SOI; El Niño) events led to the higher temperature, resulting in intense climatic extremes (i.e., droughts and heatwaves) over the target region. Therefore, this study's findings can help policymakers and decision-makers improve economic growth, agricultural production, ecology, water resource management, and preserving the natural habitat in the target region. [ABSTRACT FROM AUTHOR]

The extended range temperature prediction is of great importance for public health, energy and agriculture. The two machine learning methods, namely, the neural networks and natural gradient boosting (NGBoost), are applied to improve the prediction skills of the 2-m maximum air temperature with lead times of 1–35 days over East Asia based on the Environmental Modeling Center, Global Ensemble Forecast System (EMC-GEFS), under the Subseasonal Experiment (SubX) of the National Centers for Environmental Prediction (NCEP). The ensemble model output statistics (EMOS) method is conducted as the benchmark for comparison. The results show that all the post-processing methods can efficiently reduce the prediction biases and uncertainties, especially in the lead week 1–2. The two machine learning methods outperform EMOS by approximately 0.2 in terms of the continuous ranked probability score (CRPS) overall. The neural networks and NGBoost behave as the best models in more than 90% of the study area over the validation period. In our study, CRPS, which is not a common loss function in machine learning, is introduced to make probabilistic forecasting possible for traditional neural networks. Moreover, we extend the NGBoost model to atmospheric sciences of probabilistic temperature forecasting which obtains satisfying performances. [ABSTRACT FROM AUTHOR]

The high-resolution regional climate model (RCM) REMO has been implemented over the region of Central Asia, including western China. A model run forced by reanalysis data (1/2° resolution), and two runs forced by a GCM (one run with 1/2° and one run with 1/6° resolution) have been realized. The model has been evaluated regarding its ability to simulate the mean climate of the period 1971–2000. It has been found that the spatial pattern of mean temperature and precipitation is simulated well by REMO. The REMO simulations are often closer to observational data than reanalysis data are, and show considerably higher spatial detail. The GCM-forced simulations extend to the year 2100 under the A1B scenario. The climate change signal of temperature is largest in winter in the northern part of the study area and over mountainous terrain. A warming up to 7°C is projected until the end of the 21st century. In summer, warming is strongest over the southern part of Central Asia. Changes in precipitation are spatially more heterogeneous. [ABSTRACT FROM AUTHOR]

Future changes of surface air temperature (SAT) over the Indochina Peninsula (ICP) under the two representative concentration pathways (RCP 4.5 and 8.5) are projected for the 21st century using the state-of-the-art regionally atmosphere-ocean coupled model ROM. Comparisons with the SA-OBS and CMIP5 models show that ROM capture well the present and future SAT changes over the ICP. The following results are obtained: (i) The mean, maximum and minimum temperatures increase slightly over 2011-2055 under the two scenarios. The projected temperature variations under the RCP 4.5 become relatively stable after 2056, whereas the RCP 8.5 projections of mean, maximum and minimum temperatures show robust warming trends of 0.54 °C decade-1, 0.52 °C decade-1 and 0.55 °C decade-1, respectively. (ii) In the period of 2011-2055, the increasing trends of temperatures are higher under the RCP 8.5 than RCP 4.5 for wet seasons, while both are relatively moderate for dry seasons. During 2056-2099, warming rates are more pronounced in both seasons under the RCP 8.5, whereas no significant warming trends are detected under the RCP 4.5. (iii) The more conspicuous warming after 2050s under the RCP 8.5 is predominately attributed to the increased downward longwave radiation. The higher CO2 concentration enhances the greenhouse effect, intensifying the atmospheric counter radiation and subsequently increasing the SAT. (iv) Analysis against the extremes indicates that cold nights and days are decreasing with smaller rates under the RCP 4.5 than RCP 8.5. In contrast, the projected warm nights and days show apparent increasing under the two scenarios, and nights are generally warming faster than days. Evolutions of the extremes are more significant under the RCP 8.5 than RCP 4.5, suggesting that the heat extremes in the reference period of 1981-2005 would become rather normal over the ICP under a high concentration scenario in the late 21st century. [ABSTRACT FROM AUTHOR]