Time trends and persistence in European temperature anomalies

Lenti, Jacopo; Gil-Alana, Luís A.

doi:10.1002/joc.7090

Cited by 2 publications

(3 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, on time scales longer than 1 month, the lead‐lag correlations between near‐surface temperatures may be related to the variables with long memory. Previous researches on the lead‐lag relationships between near‐surface air temperatures mainly focused on the persistence of air temperature anomalies, using methods including autocorrelation (Leasor et al., 2019), fractional integration (Lenti & Gil‐Alana, 2021), the detrended fluctuation analysis (Capparelli et al., 2011), etc. However, these studies mainly analyzed the characteristics of temperature persistence, and lacked the analysis of corresponding influential factors and physical processes.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…There have been some studies on the persistence of surface air temperatures, that is, the lag correlations between air temperatures. Previous studies showed that long memory in temperature is present and cannot be neglected (Gil‐Alaña et al., 2022; Lenti & Gil‐Alana, 2021; Li et al., 2021), temperature itself is a skillful predictor of the temperatures 1 month ahead (Kolstad et al., 2015), and persistence forecasts can aid in the subseasonal‐to‐seasonal prediction of temperatures (Kolstad et al., 2017; Leasor et al., 2019). However, the understanding and research on the lead‐lag correlations between near‐surface air temperatures remain very limited (Kolstad et al., 2017; Li et al., 2021; Lin et al., 2022; Rypdal et al., 2013).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Soil Temperature Controls the Month‐To‐Month Lead‐Lag Correlations of Near‐Surface Air Temperatures in the Middle and Lower Reaches of the Yangtze River Basin

Song,

Chen,

Wang

et al. 2023

JGR Atmospheres

View full text Add to dashboard Cite

The relationships between the variables at different times are crucial in climate prediction. This study explores the lead‐lag correlations between monthly near‐surface air temperatures, and analyzes the associated influencing factors and processes using mathematical physics equations and statistical methods, based on monthly observations and ERA5 reanalysis data spanning from 1979 to 2013. The results reveal high lead‐lag correlations of near‐surface air temperatures in the middle and lower reaches of the Yangtze River basin, characterized by high frequency and intensity of heatwaves. The lead‐lag correlations between near‐surface temperatures are mainly due to the lead‐lag relationships between soil temperatures (STs). Current and accumulated net solar radiation, current latent heat flux and antecedent ST of several or many months ago are the main factors affecting the lead‐lag relationships between STs. The processes associated with lead‐lag correlations between STs are triggered by antecedent ST, concurrent and accumulated influence factors, respectively. The anomalies of antecedent ST can lead to the anomalies of current one by the persistence of anomaly signals in soil. The effects of accumulated or concurrent influential variables on the lead‐lag correlations between STs are attributed to the signals similar to antecedent STs in these influential variables, and the signals may arise from the response of atmosphere to land or ocean forcing. Moreover, the use of reanalysis data increases the uncertainty of the results. The study reveals influential factors and possible physical processes associated with lead‐lag correlations between near‐surface temperatures, which is helpful for understanding lead‐lag relationship between land surface and the atmosphere.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soil Temperature Controls the Month‐To‐Month Lead‐Lag Correlations of Near‐Surface Air Temperatures in the Middle and Lower Reaches of the Yangtze River Basin

Song,

Chen,

Wang

et al. 2023

JGR Atmospheres

View full text Add to dashboard Cite

show abstract

“…The literature is very extensive, and the behavior of long memory in the study of temperature series should not be neglected (Lenti and Gil-Alana, 2021 ). In fact, long memory, and specifically fractional differentiation, has been widely used in the analysis of temperatures (Gil-Alana, 2005 , 2006 , 2017 ; Vyushin and Kushner, 2009 ; Zhu et al 2010 ; Rea et al 2011 ; Franzke, 2012 ; Yuan et al 2013 ).…”

Section: A Review Of the Literaturementioning

confidence: 99%

Temperature and precipitation in the US states: long memory, persistence, and time trend

Gil-Alana

Gupta

Sauci

et al. 2022

Theor Appl Climatol

View full text Add to dashboard Cite

This paper investigates the time series properties of the temperature and precipitation anomalies in the contiguous USA by using fractional differentiation. This methodology allows to capture time trend components along with properties such as long-range dependence and the degree of persistence. For aggregated data, we find out that long memory is present in both precipitation and temperature since the integration order is significantly positive in the two cases. The time trend is also positive, being higher for the temperature. In addition, observing disaggregated data by states, for the temperature, there are only seven states where the time trend is not significant, with most of them located in Southeast areas, while for the rest of cases, the time trend is significantly positive. All cases exhibit long-range dependence, though the differencing parameter substantially changes from one state to another, ranging from 0.09 in Nebraska and Kansas to 0.18 in Florida and Michigan. For precipitation, the time trend is insignificant in a large number of cases, and the integration order is smaller than for the temperature. In fact, short memory cannot be rejected in fourteen states, and the highest orders of differencing are obtained in Arizona (d = 0.11) and Texas (0.12). In general, we highlight that one cannot draw conclusions about persistence and trends in these two climate-related variables based on aggregate information of the overall USA, given widespread heterogeneity across the states. Tentatively, the degree of dependence across the states seems to be negatively correlated with their level of climate-related risks and the associated preparedness in terms of handling climate change, but this conclusion requires more elaborate research in the future.

show abstract

Time trends and persistence in European temperature anomalies

Cited by 2 publications

References 54 publications

Soil Temperature Controls the Month‐To‐Month Lead‐Lag Correlations of Near‐Surface Air Temperatures in the Middle and Lower Reaches of the Yangtze River Basin

Soil Temperature Controls the Month‐To‐Month Lead‐Lag Correlations of Near‐Surface Air Temperatures in the Middle and Lower Reaches of the Yangtze River Basin

Temperature and precipitation in the US states: long memory, persistence, and time trend

Contact Info

Product

Resources

About