Objective and probabilistic long-range forecasts of summertime air temperatures in South Korea based on Gaussian processes

We propose the objective long-range forecasting model based on Gaussian processes (OLRAF-GP), focusing on summertime near-surface air temperatures in June (1-month lead), July (2-month lead), and August (3-month lead). The predictors were objectively selected based on their relationships with the target variables, either from observations (GP-OBS) or from observations and dynamical climate model results from APEC Climate Center multi-model ensemble (APCC MME) for the period with no observed data (GP-MME). The performances of the OLRAF-GP models were compared with the model with pre-determined predictors from observations (GP-PD). Both GP-MME and GP-OBS outperformed GP-PD in June (Heidke skill score; HSS = 0.46, 0.72, and 0.16 for mean temperature) and July (HSS = 0.53, 0.3, and 0.07 for mean temperature). Furthermore, GP-MME mostly outperformed GP-OBS and GP-PD in August (HSS = 0.52, 0.28, and 0.5, respectively, for mean temperature), implying larger contributions of the additional predictors from MME. OLRAF-GP models, especially GP-MME, are expected to better forecast summertime temperatures in regions where existing models have been struggling. We find that the physical processes associated with the notable predictors are aligned with those in previous studies, such as the attribution of the La Niña conditions in the previous winter, the related Indian Ocean capacitor effect, and the impacts of wintertime Polar/Eurasia pattern. These results imply that the mechanisms of the objectively selected predictors can be physically meaningful, and their inclusion can improve model performance and efficiency.

The Current and Future Potential Geographical Distribution and Evolution Process of Catalpa bungei in China

Catalpa bungei C. A. Mey. (C. bungei) is one of the recommended native species for ecological management in China. It is a fast-growing tree of high economic and ecological importance, but its rare resources, caused by anthropogenic destruction and local climatic degradation, have not satisfied the requirements. It has been widely recommended for large-scale afforestation of ecological management and gradually increasing in recent years, but the impact mechanism of climate change on its growth has not been studied yet. Studying the response of species to climate change is an important part of national afforestation planning. Based on combinations of climate, topography, soil variables, and the multiple model ensemble (MME) of CMIP6, this study explored the relationship between C. bungei and climate change, then constructed Maxent to predict its potential distribution under SSP126 and SSP585 and analyzed its dominant environmental factors. The results showed that C. bungei is widely distributed in Henan, Hebei, Hubei, Anhui, Jiangsu, and Shaanxi provinces and others where it covers an area of 2.96 × 106 km2. Under SSP126 and SSP585, its overall habitat area will increase by more than 14.2% in 2080–2100, which mainly indicates the transformation of unsuitable areas into low suitable areas. The center of its distribution will migrate to the north with a longer distance under SSP585 than that under SSP126, and it will transfer from the junction of Shaanxi and Hubei province to the north of Shaanxi province under SSP585 by 2100. In that case, C. bungei shows a large-area degradation trend in the south of the Yangtze River Basin but better suitability in the north of the Yellow River Basin, such as the Northeast Plain, the Tianshan Mountains, the Loess Plateau, and others. Temperature factors have the greatest impact on the distribution of C. bungei. It is mainly affected by the mean temperature of the coldest quarter, followed by precipitation of the wettest month, mean diurnal range, and precipitation of the coldest quarter. Our results hence demonstrate that the increase of the mean temperature of the coldest quarter becomes the main reason for its degradation, which simultaneously means a larger habitat boundary in Northeast China. The findings provide scientific evidence for the ecological restoration and sustainable development of C. bungei in China.

Assessing Snow Phenology and Its Environmental Driving Factors in Northeast China

Snow cover is an important water source and even an Essential Climate Variable (ECV) as defined by the World Meteorological Organization (WMO). Assessing snow phenology and its driving factors in Northeast China will help with comprehensively understanding the role of snow cover in regional water cycle and climate change. This study presents spatiotemporal variations in snow phenology and the relative importance of potential drivers, including climate, geography, and the normalized difference vegetation index (NDVI), based on the MODIS snow products across Northeast China from 2001 to 2018. The results indicated that the snow cover days (SCD), snow cover onset dates (SCOD) and snow cover end dates (SCED) all showed obvious latitudinal distribution characteristics. As the latitude gradually increases, SCD becomes longer, SCOD advances and SCED delays. Overall, there is a growing tendency in SCD and a delayed trend in SCED across time. The variations in snow phenology were driven by mean temperature, followed by latitude, while precipitation, aspect and slope all had little effect on the SCD, SCOD and SCED. With decreasing temperature, the SCD and SCED showed upward trends. The mean temperature has negatively correlation with SCD and SCED and positively correlation with SCOD. With increasing latitude, the change rate of the SCD, SCOD and SCED in the whole Northeast China were 10.20 d/degree, −3.82 d/degree and 5.41 d/degree, respectively, and the change rate of snow phenology in forested areas was lower than that in nonforested areas. At the same latitude, the snow phenology for different underlying surfaces varied greatly. The correlations between the snow phenology and NDVI were mainly positive, but weak correlations accounted for a large proportion.

Constraints in Adapting the Climate Change in Konkan Region of Maharashtra

Changing climate is a serious environmental issue affecting agricultural production all overthe world. India is also facing the problem of increased mean temperature and irregularityof rainfall, and the Konkan region of Maharashtra is also not escaped from this issue. Thestudy was designed and conducted in the northern part of the Konkan region to understandthe constraints experienced by the farmers while adapting the climate change. The studywas carried in four villages of Palghar district with 245 respondents selected byproportionate random sampling method. Major constraints were expressed by the farmerswhile adapting the changing climate were, lack of credence on current weather forecastingsystem, poor accurate weather forecast information, irregular & low voltage capacity powersupply and seven others.

Quantitative Analysis of Vegetation Dynamics in Response to Climatic Factors and Human Activities in Southwest China

We determined the time scale of normalized difference vegetation index (NDVI) response to drought and used trend and correlation analyses to explore the spatial and temporal variability characteristics of the NDVI and SPEI and their sensitivity to climatic factors in southwest China from 2000 to 2020. We used a partial derivative approach to calculate the contributions of six climatic factors and human activities to the interannual variation in the NDVI. The results demonstrated that from 2000 to 2020, the annual mean NDVI in southwest China showed a slight decreasing trend at a rate of 0.0001 y−1. The NDVI had the highest sensitivity to the standardized precipitation and evapotranspiration index on a 12-month time scale. The NDVI exhibited a 1-year delayed response to drought. The SPEI has the highest sensitivity to precipitation. The percentage of pixels with a positive correlation between NDVI and precipitation, mean temperature, temperature difference, mean relative humidity, mean wind speed, and sunshine duration in the study area was 31.73%, 46.81%, 35.49%, 25.76%, 39.36%, and 39.89%, respectively. The average contributions of these six climatic factors to the interannual variation of NDVI were 0.00029, 0.00046, −0.00007, 0.00007, 0.0008, and 0.00001 y−1, respectively. The NDVI had the highest sensitivity to mean temperature and the lowest sensitivity to mean relative humidity. The average contributions of climatic factors and human activities to interannual variability in southwest China were 0.00156 and 0.00012 y−1, respectively. The positive influence of climatic factors on the NDVI was stronger than that of human activities. This study provides a theoretical basis for the sustainable management of the regional ecological environment.

Thermography signs of lung inflammation

The purpose of the study was to find out if infrared thermography of the thorax is the method to select the patients with lung inflammationMaterial, methods: Thermograms were accumulated and processed in the «TVision» cloud storage («Dignosis», Russia). Special regions of interest (ROI) were automatically created: 1. on the front and back of the thorax roughly in the projection of the upper lobe (ULP) and the lower lobe (LLP) of the lung; 2.e lines on the front surface of the thorax. Two types of temperature gradients were calculated: between ULP and LLP (by subtraction mean temperature in LLP from mean temperature in ULP) (ΔT1); between both ULP and both LLP on the back of the thorax (ΔT2). Approximation confidence value for the polynomial trend line (R²) along the marked lines on the front surface of the thorax also calculated. Totally 489 thermograms, were analyzed, included 337 from healthy patients (group 1) and 152 from patients with confirmed diagnosis of lung inflammation (group 2)Results: R² value was higher in the group 1 compare to group 2 (0.58 ± 0.16 vs 0.3 ± 0.2, p < 0.05). ΔT1 value was negative only in patients from group 2, as well as ΔT1 value greater than 0.4 °C.Conclusion: three independent thermographic criteria suitable for detecting lung inflammation were found, so infrared thermography is the valuable method for screening this pathology.

Evidence of elevation-dependent warming from the Chinese Tian Shan

The phenomenon in which the warming rate of air temperature is amplified with elevation is termed elevation-dependent warming (EDW). It has been clarified that EDW can accelerate the retreat of glaciers and melting of snow, which can have significant impacts on the regional ecological environment. Owing to the lack of high-density ground observations in high mountains, there is widespread controversy regarding the existence of EDW. Current evidence is mainly derived from typical high-mountain regions such as the Swiss Alps, the Colorado Rocky Mountains, the tropical Andes and the Tibetan Plateau–Himalayas. Rare evidence in other mountain ranges has been reported, especially in arid regions. In this study, EDW features (regional warming amplification and altitude warming amplification) in the Chinese Tian Shan (CTM) were detected using a unique high-resolution (1 km, 6-hourly) air temperature dataset (CTMD) from 1979 to 2016. The results showed that there were significant EDW signals at different altitudes on different timescales. The CTM showed significant regional warming amplification in spring, especially in March, and the warming trends were greater than those of continental China with respect to three temperatures (minimum temperature, mean temperature and maximum temperature). The significance values of EDW above different altitude thresholds are distinct for three temperatures in 12 months. The warming rate of the minimum temperature in winter showed a significant elevation dependence (p<0.01), especially above 3000 m. The greatest altitudinal gradient in the warming rate of the maximum temperature was found above 4000 m in April. For the mean temperature, the warming rates in June and August showed prominent altitude warming amplification but with different significance above 4500 m. Within the CTM, the Tolm Mountains, the eastern part of the Borokoonu Mountains, the Bogda Mountains and the Balikun Mountains are representative regions that showed significant altitude warming amplification on different timescales. This new evidence could partly explain the accelerated melting of snow in the CTM, although the mechanisms remain to be explored.

Artificial intelligence reconstructs missing climate information - newest developments

&lt;p&gt;Historical temperature measurements are the basis of global climate datasets like HadCRUT4. This dataset contains many missing values, particularly for periods before the mid-twentieth century, although recent years are also incomplete. Here we demonstrate that artificial intelligence can skilfully fill these observational gaps when combined with numerical climate model data. We show that recently developed image inpainting techniques perform accurate monthly reconstructions via transfer learning using either 20CR (Twentieth-Century Reanalysis) or the CMIP5 (Coupled Model Intercomparison Project Phase 5) experiments. The resulting global annual mean temperature time series exhibit high Pearson correlation coefficients (&amp;#8805;0.9941) and low root mean squared errors (&amp;#8804;0.0547&amp;#8201;&amp;#176;C) as compared with the original data. These techniques also provide advantages relative to state-of-the-art kriging interpolation and principal component analysis-based infilling. When applied to HadCRUT4, our method restores a missing spatial pattern of the documented El Ni&amp;#241;o from July 1877. With respect to the global mean temperature time series, a HadCRUT4 reconstruction by our method points to a cooler nineteenth century, a less apparent hiatus in the twenty-first century, an even warmer 2016 being the warmest year on record and a stronger global trend between 1850 and 2018 relative to previous estimates. We propose image inpainting as an approach to reconstruct missing climate information and thereby reduce uncertainties and biases in climate records.&lt;/p&gt; &lt;p&gt;As published in:&lt;/p&gt; &lt;p&gt;Kadow, C., Hall, D.M. &amp; Ulbrich, U. Artificial intelligence reconstructs missing climate information. &lt;em&gt;Nat. Geosci.&lt;/em&gt; &lt;strong&gt;13, &lt;/strong&gt;408&amp;#8211;413 (2020). https://doi.org/10.1038/s41561-020-0582-5&lt;/p&gt; &lt;p&gt;Newest developments around the technology will be presented.&lt;/p&gt; &lt;p&gt;&amp;#160;&lt;/p&gt;

Relationship of mean temperatures with screen temperatures

An attempt has been made to study the variation in daily mean temperatures obtained from maximum and minimum temperatures and that obtained from hourly temperatures recorded by the automatic weather station at the Agrometeorological Observatory, Anand (Gujarat). The mean temperatures obtained from the records of daily maximum and minimum temperatures were higher and fluctuated from -1.5 to 1.5 °C during the months of September to May as compared to the respective values obtained from hourly temperatures recorded by the automatic weather station. However, during May to September, these daily mean temperatures were found to be higher than mean temperatures obtained from the automatic weather station. Different coefficients were deduced from the records of the automatic weather station to estimate the hourly temperatures and a model was developed similar to that of William and Logan (1981). The hourly temperatures and the daily mean temperatures so estimated were in good agreement with the respective actual hourly and daily temperatures record by the automatic weather station. Therefore, by using this model one could estimate the true daily mean temperature from the records of maximum and minimum temperatures.

Periodicities in the time-series of the annual mean temperatures of Central England

ABSTRACT .Maximum Entropy spectral Analysis (MESA) of the 8IUlua1 mean temperature series for Central England for 1659-1991 indicated significant periodicilies at T = 7.8, 11.1, 12.5, 15, 18, 23, 32, 37, 68, 81, l09 and 203 years. These compare well with T = 22, 30, 80, 200 years obtained for China. Also, a good comparison is obtained with some periodicities in the sunspot number series.