Changes in the Amplitude of the Temperature Annual Cycle in China and Their Implication for Climate Change Research

2011 ◽  
Vol 24 (20) ◽  
pp. 5292-5302 ◽  
Author(s):  
Cheng Qian ◽  
Congbin Fu ◽  
Zhaohua Wu
Keyword(s):  
Climate Change ◽  
Annual Cycle ◽  
Linear Trend ◽  
Surface Air Temperature ◽  
Mean Amplitude ◽  
Warming Trend ◽  
Ensemble Empirical Mode Decomposition ◽  
Surface Solar Radiation ◽  
Climate Change Research ◽  
Annual Cycles

Abstract Climate change is not only reflected in the changes in annual means of climate variables but also in the changes in their annual cycles (seasonality), especially in the regions outside the tropics. In this study, the ensemble empirical mode decomposition (EEMD) method is applied to investigate the nonlinear trend in the amplitude of the annual cycle (which contributes 96% of the total variance) of China’s daily mean surface air temperature for the period 1961–2007. The results show that the variation and change in the amplitude are significant, with a peak-to-peak annual amplitude variation of 13% (1.8°C) of its mean amplitude and a significant linear decrease in amplitude by 4.6% (0.63°C) for this period. Also identified is a multidecadal change in amplitude from significant decreasing (−1.7% decade−1 or −0.23°C decade−1) to significant increasing (2.2% decade−1 or 0.29°C decade−1) occurring around 1993 that overlaps the systematic linear trend. This multidecadal change can be mainly attributed to the change in surface solar radiation, from dimming to brightening, rather than to a warming trend or an enhanced greenhouse effect. The study further proposes that the combined effect of the global dimming–brightening transition and a gradual increase in greenhouse warming has led to a perceived warming trend that is much larger in winter than in summer and to a perceived accelerated warming in the annual mean since the early 1990s in China. It also notes that the deseasonalization method (considering either the conventional repetitive climatological annual cycle or the time-varying annual cycle) can also affect trend estimation.

Temperature Annual Cycle Variation and Response to Solar Radiation during 1960 to 2016 in China

2020 ◽  
Author(s):  
Runze Zhao ◽  
Kaicun Wang ◽  
Guocan Wu ◽  
Chunlue Zhou
Keyword(s):  
Solar Radiation ◽  
Air Temperature ◽  
Annual Cycle ◽  
Surface Air Temperature ◽  
Seasonal Temperature ◽  
Surface Solar Radiation ◽  
Seasonal Temperature Variation ◽  
Phase Amplitude ◽  
Original Time ◽  
Effect Of Surface

<p>The change of its annual cycle is extremely important due to global warming. A widely used method to analyze the changes of temperature annual cycle is based on the decomposition to phase, amplitude and baseline terms. Solar radiation as the leading energy source of temperature changes can directly influence temperature annual cycle. In this study, we investigate the phase, amplitude and baseline of temperature and solar radiation annual cycle after Fourier transform during 1960-2016 in China. The results show that annual cycle of maximum, minimum and mean surface air temperature are advancing in time (-0.08, -0.27 and -0.33 days per ten years), decreasing in range (-0.07, -0.25 and -0.18 degrees per ten years) and rising in baseline (0.20, 0.34 and 0.25 degrees per ten years). To further quantify the effect of surface solar radiation to temperature, we remove the effect from its original time series of maximum and mean temperature, based on a linear regression. The compare of raw and adjusted temperature shows that surface solar radiation advancing the time by 0.19 and 0.19 days per ten years, reduces the range by 0.14 and 0.13 degrees per ten years, and reduces the baseline by 0.08 and 0.04 degrees per ten years, for surface maximum and mean daily air temperature. The result can explain parts of seasonal temperature variation. Effect of surface solar radiation is most obvious Yunnan-Guizhou Plateau for maximum phase. The low phase value in this area is corrected and well-match with other same latitude area after adjusted.</p>

scholarly journals Survival of Liparis loeselii (L.) as an early successional species in Engure region described based on ecological peculiarities DURING the annual cycle

2014 ◽  
Vol 68 (1-2) ◽  
pp. 93-100 ◽  
Author(s):  
Daina Roze ◽  
Gunta Jakobsone ◽  
Dace Megre ◽  
Inta Belogrudova ◽  
Amanda Karlovska
Keyword(s):  
Climate Change ◽  
Annual Cycle ◽  
Growing Season ◽  
Annual Cycles ◽  
Previous Season ◽  
Melt Water ◽  
Successional Species ◽  
Perennial Herbs ◽  
Survival Potential ◽  
Liparis Loeselii

Abstract We present some results of a six-year (2008-2013) study in two localities of Liparis loeselii (L.) Rich. near Lake Engure. The annual cycle of L. loeselii, an early successional species, may indicate its potential survival in its typical wet habitat with fluctuating levels of water. Flowering of L. loeselii usually begins in the first decade of June and lasts for several weeks. If the initiation of development was delayed, leaves and inflorescence started to grow almost simultaneously. Development of the first fruit began during flowering and continued to August. Ripening of fruit and seeds occurred in September-October, and they were dispersed mostly by melt water of snow in spring, which is very important for populations in sites overgrowing with perennial herbs. The previous season capsules of L. loeselii remained till the middle of the next growing season; a part of the seeds remained in capsules and less than 1% of seeds had viable embryos. This may increase the survival potential of the population. The studies of herbarium records of L. loeselii in the area of Lake Engure showed that the annual cycles of L. loeselii have been similar and that the species has not responded drastically to climate change.

scholarly journals Hurricane annual cycle controlled by both seeds and genesis probability

2021 ◽  
Vol 118 (41) ◽  
pp. e2108397118
Author(s):  
Wenchang Yang ◽  
Tsung-Lin Hsieh ◽  
Gabriel A. Vecchi
Keyword(s):  
Climate Change ◽  
North Atlantic ◽  
Annual Cycle ◽  
Climate Models ◽  
Essential Elements ◽  
Abrupt Increase ◽  
Annual Cycles ◽  
The North ◽  
Concise Representation ◽  
The North Atlantic

Understanding tropical cyclone (TC) climatology is a problem of profound societal significance and deep scientific interest. The annual cycle is the biggest radiatively forced signal in TC variability, presenting a key test of our understanding and modeling of TC activity. TCs over the North Atlantic (NA) basin, which are usually called hurricanes, have a sharp peak in the annual cycle, with more than half concentrated in only 3 mo (August to October), yet existing theories of TC genesis often predict a much smoother cycle. Here we apply a framework originally developed to study TC response to climate change in which TC genesis is determined by both the number of pre-TC synoptic disturbances (TC “seeds”) and the probability of TC genesis from the seeds. The combination of seeds and probability predicts a more consistent hurricane annual cycle, reproducing the compact season, as well as the abrupt increase from July to August in the NA across observations and climate models. The seeds-probability TC genesis framework also successfully captures TC annual cycles in different basins. The concise representation of the climate sensitivity of TCs from the annual cycle to climate change indicates that the framework captures the essential elements of the TC climate connection.

Correction of Inhomogeneities in Observed Land Surface Temperatures over China

2020 ◽  
Vol 33 (20) ◽  
pp. 8885-8902
Author(s):  
Jizeng Du ◽  
Kaicun Wang ◽  
Baoshan Cui ◽  
Shaojing Jiang
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Cmip5 Models ◽  
Near Surface ◽  
Land Surface Temperatures ◽  
Long Term Trends ◽  
The Difference

AbstractLand surface temperature Ts and near-surface air temperature Ta are two main metrics that reflect climate change. Recently, based on in situ observations, several studies found that Ts warmed much faster than Ta in China, especially after 2000. However, we found abnormal jumps in the Ts time series during 2003–05, mainly caused by the transformation from manual to automatic measurements due to snow cover. We explore the physical mechanism of the differences between automatic and manual observations and develop a model to correct the automatic observations on snowy days in the observed records of Ts. Furthermore, the nonclimatic shifts in the observed Ts were detected and corrected using the RHtest method. After corrections, the warming rates for Ts-max, Ts-min, and Ts-mean were 0.21°, 0.34°, and 0.25°C decade−1, respectively, during the 1960–2014 period. The abnormal jump in the difference between Ts and Ta over China after 2003, which was mentioned in existing studies, was mainly caused by inhomogeneities rather than climate change. Through a combined analysis using reanalyses and CMIP5 models, we found that Ts was consistent with Ta both in terms of interannual variability and long-term trends over China during 1960–2014. The Ts minus Ta (Ts − Ta) trend is from −0.004° to 0.009°C decade−1, accounting for from −3.19% to 5.93% (from −3.09% to 6.39%) of the absolute warming trend of Ts (Ta).

scholarly journals To what extent does the detection of climate change in Hungary depend on the choice of statistical methods?

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Beatrix Izsák ◽  
Tamás Szentimrey
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Linear Trend ◽  
Meteorological Data ◽  
Warming Trend ◽  
Data Series ◽  
Data Sets ◽  
Raw Data ◽  
Temperature Trends ◽  
Trend Data

AbstractThe trend analysis of meteorological time series has gained prominence in recent decades, the most common method being the so-called ‘linear analytical trend analysis’. Until the mid-1990s, trend analysis was commonly performed on non-homogenized data sets, which frequently led to erroneous conclusions. Nowadays, only homogenized data sets are examined, so it really is possible to detect climate change in long meteorological data sets. In this paper, the methodology of linear trend analysis is summarized, the way in which the model can be validated is demonstrated, and there is a discussion of the results obtained if unjustified discontinuities caused by changing measurement conditions, such as the relocation of stations, changes in measurement time, or instrument change occur. On the basis of an examination of records for the preceding 118 years, it is possible to state that both annual and seasonal mean temperature trends display a significant warming trend. In the case of homogenized data series, the change is significant over the entire territory of Hungary; in the case of raw data series, however, the change is not significant everywhere. The validity of the linear model is tested using the F-test, a task as yet carried out on the entire Hungarian data series, series comprising records for over 100 years. Furthermore, neither has a comparison been made of the trend data for raw data series and the homogenized data series with the help of information on station history to explore the causes of inhomogeneity.

scholarly journals Climate Change in Rwanda: The Observed Changes in Daily Maximum and Minimum Surface Air Temperatures during 1961–2014

2021 ◽  
Vol 9 ◽  
Author(s):  
Jean Paul Ngarukiyimana ◽  
Yunfei Fu ◽  
Celestin Sindikubwabo ◽  
Idrissa Fabien Nkurunziza ◽  
Faustin Katchele Ogou ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Scales ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Temporal Variations ◽  
Least Square ◽  
Daily Maximum ◽  
Spatial And Temporal Variability ◽  
Positive Trend ◽  
Minimum Surface

Rwanda has experienced high temperature rising phenomena over the last decades and hence, highly vulnerable to climate change. This paper examined the spatial and temporal variations of daily maximum and minimum surface air temperature (Tmin and Tmax) and diurnal temperature range (DTR). It studied variables at monthly, seasonal and annual time-scales from 1961 to 2014. The study applied various statistical methods such as ordinary least-square fitting, Mann-Kendall, Sen’ slope and Sequential Mann-Kendall statistical test to the new reconstructed ENACTS dataset that cover the period from 1983 to 2014 while pre-1983s recorded data from 24 meteorological stations have been added to complete the lengthiness of ENACTS data. The January to February season did not show a significant trend at seasonal time-scales. The authors decided only to consider March-to-May, June-to-August and October-to-December seasons for further analyses. Topography impacts on temperature classified stations into three regions: region one (R1) (1,000–1,500 m), region two (R2) (1,500–2,000 m) and region three (R3) (≥2,000 m). With high confidence, the results indicate a significant positive trend in both Tmin and Tmax in all three regions during the whole study period. However, the magnitude rate of temperatures change is different in three regions and it varies in seasonal and annual scale. The spatial distributions of Tmax and Tmin represent a siginificant warming trend over the whole country notably since the early 1980s. Surprisingly, Tmin increased at a faster rate than Tmax in R3 (0.27 vs. 0.07°C/decade in March-to-May) and (0.29 vs. 0.04°C/decade in October-to-December), resulting in a significant decrease in the DTR. This is another confirmation of warming in Rwanda. The mutation test application exhibited most of the abrupt changes in the seasonal and annual Tmax and Tmin trends between 1984 and 1990. The present work mainly focus on the spatial and temporal variability of Tmin, Tmax and DTR in Rwanda and their relationship with elevation change, leaving a gap in other potential cause factors explored in the future.

scholarly journals Extracting Modulated Annual Cycle in Climate and Ocean Time Series Using an Enhanced Harmonic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yibo Zhang ◽  
Haidong Pan ◽  
Shuang Li ◽  
Xianqing Lv
Keyword(s):  
Harmonic Analysis ◽  
Annual Cycle ◽  
Numerical Experiments ◽  
Decomposition Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Annual Cycles ◽  
Mode Decomposition ◽  
Observation Series ◽  
Climate Series ◽  
Modulated Annual Cycle

Accurate extraction of the modulated annual cycle (MAC) is important for climatic and oceanic research. A variety of methods are available to extract the annual cycle with inconsistent results. Since actual annual cycles are unknown in the observation series, the reliability and applicability of the results extracted by these methods are difficult to estimate. In this study, three widely used decomposition methods, ensemble empirical mode decomposition (EEMD), nonlinear mode decomposition (NMD), and enhanced harmonic analysis (EHA), are evaluated by idealized numerical experiments for extracting modulated annual cycles from climate series. Idealized numerical experiments are carried out and show that the recently proposed EHA had the most accuracy in extracting the MAC from the constructed data. The optimal independent point (IP) number, which makes the most accurate result for EHA, can be found in each ideal experiment. In the actual experiment, two IP selection criteria are proposed for EHA to extract MAC from observations.

scholarly journals Contrasting Evolution Patterns of Endorheic and Exorheic Lakes on the Central Tibetan Plateau and Climate Cause Analysis during 1988–2017

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1962
Author(s):  
Zhilong Zhao ◽  
Yue Zhang ◽  
Zengzeng Hu ◽  
Xuanhua Nie
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Climatic Factors ◽  
Global Climate ◽  
Rapid Expansion ◽  
Annual Precipitation ◽  
The Tibetan Plateau ◽  
Lake Area ◽  
Climate Change Research ◽  
Mean Temperature

The alpine lakes on the Tibetan Plateau (TP) are indicators of climate change. The assessment of lake dynamics on the TP is an important component of global climate change research. With a focus on lakes in the 33° N zone of the central TP, this study investigates the temporal evolution patterns of the lake areas of different types of lakes, i.e., non-glacier-fed endorheic lakes and non-glacier-fed exorheic lakes, during 1988–2017, and examines their relationship with changes in climatic factors. From 1988 to 2017, two endorheic lakes (Lake Yagenco and Lake Zhamcomaqiong) in the study area expanded significantly, i.e., by more than 50%. Over the same period, two exorheic lakes within the study area also exhibited spatio-temporal variability: Lake Gaeencuonama increased by 5.48%, and the change in Lake Zhamuco was not significant. The 2000s was a period of rapid expansion of both the closed lakes (endorheic lakes) and open lakes (exorheic lakes) in the study area. However, the endorheic lakes maintained the increase in lake area after the period of rapid expansion, while the exorheic lakes decreased after significant expansion. During 1988–2017, the annual mean temperature significantly increased at a rate of 0.04 °C/a, while the annual precipitation slightly increased at a rate of 2.23 mm/a. Furthermore, the annual precipitation significantly increased at a rate of 14.28 mm/a during 1995–2008. The results of this study demonstrate that the change in precipitation was responsible for the observed changes in the lake areas of the two exorheic lakes within the study area, while the changes in the lake areas of the two endorheic lakes were more sensitive to the annual mean temperature between 1988 and 2017. Given the importance of lakes to the TP, these are not trivial issues, and we now need accelerated research based on long-term and continuous remote sensing data.

Twitter Users’ Displays of Affect in the Global Warming Debate

2021 ◽  
pp. 004728162110078
Author(s):  
Shanna Cameron ◽  
Alexandra Russell ◽  
Luke Brake ◽  
Katherine Fredlund ◽  
Angela Morris
Keyword(s):  
Climate Change ◽  
Social Media ◽  
Global Warming ◽  
Content Analysis ◽  
Technical Communication ◽  
Research Team ◽  
Future Research ◽  
Climate Change Research ◽  
Quantitative Content ◽  
Twitter Users

This article engages with recent discussions in the field of technical communication that call for climate change research that moves beyond the believer/denier dichotomy. For this study, our research team coded 900 tweets about climate change and global warming for different emotions in order to understand how Twitter users rely on affect rhetorically. Our findings use quantitative content analysis to challenge current assumptions about writing and affect on social media, and our results indicate a number of arenas for future research on affect, global warming, and rhetoric.

Sign in / Sign up

Export Citation Format

Share Document