Salient features of maximum temperature distribution over Tamil Nadu

In this paper an attempt is made to study the spatial variations of maximum temperature over Tamil Nadu. From the data of daily maximum temperature of eleven stations in Tamil Nadu for the first six months from 1981 to 1997, it is observed that there are two separate regions namely high maximum temperature region of interior stations and low maximum temperature region of coastal stations from January to May and the distinction disappears in June. Hill station Kodaikanal recorded less maximum temperature that Ootacamund from January to May and it reverses in June. During onset phase of southwest monsoon, maximum temperature decreases over Coimbatore, Pamban and Kanyakumari. In the presence of cyclonic storm over the Bay of Bengal the maximum temperatures are fallen during the period when the storm affects Tamil Nadu or Andhra coast in May and June whereas when the storms moves farther away from the coast maximum temperature over interior places decreases and increases over north coastal stations.

Synergies between Urban Heat Island and Urban Heat Wave Effects in 9 Global Mega–Regions from 2003 to 2020

Global urbanization significantly impacts the thermal environment in urban areas, yet urban heat island (UHI) and urban heat wave (UHW) studies at the mega–region scale have been rare, and the impact study of urbanization is still lacking. In this study, the MODIS land surface temperature (LST) product was used to depict the UHI and UHW in nine mega–regions globally between 2003 and 2020. The absolute and percentile–based UHW thresholds were adopted for both daily and three–day windows to analyze heat wave frequency, and UHW magnitude as well as frequency were compared with UHI variability. Results showed that a 10% increase in urban built-up density led to a 0.20 °C to 0.95 °C increase in LST, a 0.59% to 7.17% increase in hot day frequency, as well as a 0.08% to 0.95% increase in heat wave number. Meanwhile, a 1 °C increase in UHI intensity (the LST differences between the built-up and Non-built-up areas) led to a 2.04% to 92.15% increase in hot day frequency, where daytime LST exceeds 35 °C and nighttime LST exceeds 25 °C, as well as a 3.30% to 33.67% increase in heat wave number, which is defined as at least three consecutive days when daily maximum temperature exceeds the climatological threshold. In addition, the increasing rates of UHW magnitudes were much faster than the expansion rates of built-up areas. In the mega–regions of Boston, Tokyo, São Paulo, and Mexico City in particular, the increasing rates of UHW hotspot magnitudes were over 2 times larger than those of built-up areas. This indicated that the high temperature extremes, represented by the increase in UHW frequency and magnitudes, were concurrent with an increase in UHI under the context of climate change. This study may be beneficial for future research of the underlying physical mechanisms on urban heat environment at the mega–region scale.

Variation in Extreme Temperature and Its Instability in China

In this study, the instability of extreme temperatures is defined as the degree of perturbation of the spatial and temporal distribution of extreme temperatures, which is to show the uncertainty of the intensity and occurrence of extreme temperatures in China. Based on identifying the extreme temperatures and by analyzing their variability, we refer to the entropy value in the entropy weight method to study the instability of extreme temperatures. The results show that TXx (annual maximum value of daily maximum temperature) and TNn (annual minimum value of daily minimum temperature) in China increased at 0.18 °C/10 year and 0.52 °C/10 year, respectively, from 1966 to 2015. The interannual data of TXx’ occurrence (CTXx) and TNn’ occurrence (CTNn), which are used to identify the timing of extreme temperatures, advance at 0.538 d/10 year and 1.02 d/10 year, respectively. In summary, extreme low-temperature changes are more sensitive to global warming. The results of extreme temperature instability show that the relative instability region of TXx is located in the middle and lower reaches of the Yangtze River basin, and the relative instability region of TNn is concentrated in the Yangtze River, Yellow River, Langtang River source area and parts of Tibet. The relative instability region of CTXx instability is distributed between 105° E and 120° E south of the 30° N latitude line, while the distribution of CTNn instability region is more scattered; the TXx’s instability intensity is higher than TNn’s, and CTXx’s instability intensity is higher than CTNn’s. We further investigate the factors affecting extreme climate instability. We also find that the increase in mean temperature and the change in the intensity of the El Niño phenomenon has significant effects on extreme temperature instability.

Simulation-based indices for a climate-resilient agriculture - insights from ADAPTER

<p>Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned to adapt to interannual climate variability and extremes. In the ADAPTER project, we collaborate with stakeholders from these groups and generate practically relevant information, tailored climate change indices and usable information products.</p> <p> </p> <p>The challenges of climate change for agriculture are manifold. The genetic traits of crops need to be adapted to a new climatic average, for instance by breeding new sorts of crops that are specialised for warmer and dryer conditions (i.e. maximising average yields). Agricultural practises need to be adapted to changing seasonal weather patterns under changing climate conditions. It is also vital to ensure the resilience to climate extremes by aiming for a low inter-annual yield variability, in order to prevent price shocks or food shortages.</p> <p> </p> <p>In order to adequately determine the optimal balance between specialisation and risk diversification, the agricultural sector hence requires knowledge not only about changes in the mean climate, but also on the variance around the changing mean. In this contribution, we focus on this second aspect by analysing the potential impact of forced changes in climate variability on the stability of crop yields in central Europe.</p> <p> </p> <p>We analyse the changing climate variability in 85 regional climate model projections from Coordinated Downscaling Experiments over Europe (EURO-CORDEX). We first show how the projections indicate a general increase in climate variability during critical development stages of wheat, rapeseed and maize in Europe. Second, we determine several more specific agronomic climate indices that capture events that have previously been shown to be critical for yields, for instance the occurrence of high daily maximum temperature, the seasonal sum of rainfall, the number of dry days, or the occurrence of compound events with simultaneous drought and increased temperatures. Finally, we illustrate how the results can be made accessible to practitioners in the agriculture sector by co-designing interactive browser applications, thus directly supporting the adaptation of the agricultural system to climate change.</p> <p> </p>

Determination of fractal dimension of chaotic at tractor for Maximum temperature over Madras

The dimensions of attractors of daily maximum temperature (during March-May) recorded by the two observatories of Madras, viz., Nungarnbakkarn and Meenambakkarn are estimated from phase space trajectories by the method of deterministic chaos, The dimensions provide the basic information on the minimum number of parameters required to understand the complex dynamical system and also the upper bound (degrees of freedom) of such parameters that are sufficient to model the system, The fractal dimension for the weather event, viz. maximum temperature over Madras is between 3.5 and 3.9 suggesting 4 parameters are necessary to model the system and a maximum of 19 parameters are sufficient.

On forecasting daily summer maximum temperature at Madras

Forecasting schemes based on statistical techniques have been developed to forecast daily summer (March-May) maximum temperatures of Madras. A set of optimal number of predictors were chosen from a large number of parameters by employing stepwise forward screening. Separate forecasting schemes for Madras city and airport, with lead time of 24 and 9 hr were developed from the data of 12 years and tested in an independent sample of 4 years. Maximum temperature of the previous day, normal daily maximum temperature, temperature advection index and morning zonal wind at Madras at 900 hPa level were among the predictors selected. The schemes yielded good results providing 77-87% correct, forecasts with skill scores of 0.29-0.57.

GRNN Prediction Model for Temperature-Induced Deformation of CRTS II Unballasted Slab Track

The General Regression Neural Network (GRNN) is one of the algorithms of artificial neural networks (ANN) that receives much attention in prediction applications. This research used the GRNN to predict the temperatureinduced deformation of unballasted track structures based on experimental data considering external weather conditions, such as sunshine duration, rain conditions, daily maximum temperature, daily minimum temperature, and daily average wind speed. The GRNN network predicts the average absolute error of the prediction results (0.0318 ℃), the maximum absolute error (1.7729 ℃), and the GRNN prediction sample mean squared error (0.070701). The average relative error is 0.32%. The finding of this study shows that the GRNN prediction method has good accuracy and robustness. Furthermore, it can promote the research of unballasted track temperature fields that are related to concrete structures.

Weather Variables Associated with Spore Dispersal of Lecanosticta acicola Causing Pine Needle Blight in Northern Spain

In the last decade, the impact of needle blight fungal pathogens on the health status of forests in northern Spain has marked a turning point in forest production systems based on Pinus radiata species. Dothistroma needle blight caused by Dothistroma septosporum and D. pini, and brown spot needle blight caused by Lecanosticta acicola, coexist in these ecosystems. There is a clear dominance of L. acicola with respect to the other two pathogens and evidence of sexual reproduction in the area. Understanding L. acicola spore dispersal dynamics within climatic determinants is necessary to establish more efficient management strategies to increase the sustainability of forest ecosystems. In this study, spore counts of 15 spore traps placed in Pinus ecosystems were recorded in 2019 and spore abundance dependency on weather data was analysed using generalised additive models. During the collection period, the model that best fit the number of trapped spores included the daily maximum temperature and daily cumulative precipitation, which was associated to higher spore counts. The presence of conidia was detected from January and maximum peaks of spore dispersal were generally observed from September to November.

A Stepwise-Clustered Simulation Approach for Projecting Future Heat Wave Over Guangdong Province

A heat wave is an important meteorological extreme event related to global warming, but little is known about the characteristics of future heat waves in Guangdong. Therefore, a stepwise-clustered simulation approach driven by multiple global climate models (i.e., GCMs) is developed for projecting future heat waves over Guangdong under two representative concentration pathways (RCPs). The temporal-spatial variations of four indicators (i.e., intensity, total intensity, frequency, and the longest duration) of projected heat waves, as well as the potential changes in daily maximum temperature (i.e., Tmax) for future (i.e., 2006–2095) and historical (i.e., 1976–2005) periods, were analyzed over Guangdong. The results indicated that Guangdong would endure a notable increasing annual trend in the projected Tmax (i.e., 0.016–0.03°C per year under RCP4.5 and 0.027–0.057°C per year under RCP8.5). Evaluations of the multiple GCMs and their ensemble suggested that the developed approach performed well, and the model ensemble was superior to any single GCM in capturing the features of heat waves. The spatial patterns and interannual trends displayed that Guangdong would undergo serious heat waves in the future. The variations of intensity, total intensity, frequency, and the longest duration of heat wave are likely to exceed 5.4°C per event, 24°C, 25 days, and 4 days in the 2080s under RCP8.5, respectively. Higher variation of those would concentrate in eastern and southwestern Guangdong. It also presented that severe heat waves with stronger intensity, higher frequency, and longer duration would have significant increasing tendencies over all Guangdong, which are expected to increase at a rate of 0.14, 0.83, and 0.21% per year under RCP8.5, respectively. Over 60% of Guangdong would suffer the moderate variation of heat waves to the end of this century under RCP8.5. The findings can provide decision makers with useful information to help mitigate the potential impacts of heat waves on pivotal regions as well as ecosystems that are sensitive to extreme temperature.

Spatiotemporal Changes in Temperature and Precipitation in West Africa. Part I: Analysis with the CMIP6 Historical Dataset

Climate variability and change constitute major challenges for Africa, especially West Africa (WA), where an important increase in extreme climate events has been noticed. Therefore, it appears essential to analyze characteristics and trends of some key climatological parameters. Thus, this study addressed spatiotemporal variabilities and trends in regard to temperature and precipitation extremes by using 21 models of the Coupled Model Intercomparison Project version 6 (CMIP6) and 24 extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). First, the CMIP6 variables were evaluated with observations (CHIRPS, CHIRTS, and CRU) of the period 1983–2014; then, the extreme indices from 1950 to 2014 were computed. The innovative trend analysis (ITA), Sen’s slope, and Mann–Kendall tests were utilized to track down trends in the computed extreme climate indices. Increasing trends were observed for the maxima of daily maximum temperature (TXX) and daily minimum temperature (TXN) as well as the maximum and minimum of the minimum temperature (TNX and TNN). This upward trend of daily maximum temperature (Tmax) and daily minimum temperature (Tmin) was enhanced with a significant increase in warm days/nights (TX90p/TN90p) and a significantly decreasing trend in cool days/nights (TX10p/TN10p). The precipitation was widely variable over WA, with more than 85% of the total annual water in the study domain collected during the monsoon period. An upward trend in consecutive dry days (CDD) and a downward trend in consecutive wet days (CWD) influenced the annual total precipitation on wet days (PRCPTOT). The results also depicted an upward trend in SDII and R30mm, which, additionally to the trends of CDD and CWD, could be responsible for localized flood-like situations along the coastal areas. The study identified the 1970s dryness as well as the slight recovery of the 1990s, which it indicated occurred in 1992 over West Africa.