Weakened amplitude and delayed phase of the future temperature seasonal cycle over China during the twenty‐first century

Hu, Dan; Jiang, Dabang; Tian, Zhan; Lang, Xianmei

doi:10.1002/joc.7634

Cited by 6 publications

(6 citation statements)

References 46 publications

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“…We addressed the annual cycle of GCMs using Fourier transform equations. The amplitude and phase of annual cycle was estimated by fitting the annual Fourier harmonic (Ding et al 2023;Hu et al 2022) to the monthly series that were previously smoothed using spline cubic functions. To appraise how well the Fourier transform effectively describes the seasonal cycle of the monthly series, we evaluated the performance of Fourier predictions against the original precipitation and temperature data across space and time for each of the studied subregions.…”

Section: Discussionmentioning

confidence: 99%

CMIP6 precipitation and temperature projections for Chile

Salazar,

Thatcher,

Goubanova

et al. 2023

Clim Dyn

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Precipitation and near-surface temperature from an ensemble of 36 new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) are evaluated over Chile’s climate. The analysis is focused on four distinct climatic subregions: Northern Chile, Central Chile, Northern Patagonia, and Southern Patagonia. Over each of the subregions, first, we evaluate the performance of individual global climate models (GCMs) against a suit of precipitation and temperature observation-based gridded datasets over the historical period (1986–2014) and then we analyze the models’ projections for the end of the century (2080–2099) for four different shared socioeconomic pathways scenarios (SSP). Although the models are characterized by general wet and warm mean bias, they reproduce realistically the main spatiotemporal climatic variability over different subregions. However, none of the models is best across all subregions for both precipitation and temperature. Moreover, among the best performing models defined based on the Taylor skill score, one finds the so-called “hot models” likely exhibiting an overestimated climate sensitivity, which suggests caution in using these models for accessing future climate change in Chile. We found robust (90% of models agree in the direction of change) projected end-of-the-century reductions in mean annual precipitation for Central Chile (~ − 20 to ~ − 40%) and Northern Patagonia (~ − 10 to ~ − 30%) under scenario SSP585, but changes are strong from scenario SSP245 onwards, where precipitation is reduced by 10–20%. Northern Chile and Southern Patagonia show non-robust changes in precipitation across the models. Yet, future near-surface temperature warming presented high inter-model agreement across subregions, where the greatest increments occurred along the Andes Mountains. Northern Chile displays the strongest increment of up to ~ 6 °C in SSP585, followed by Central Chile (up to ~ 5 °C). Both Northern and Southern Patagonia show a corresponding increment by up to ~ 4 °C. We also briefly discuss about the environmental and socio-economic implications of these future changes for Chile.

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Section: Discussionmentioning

confidence: 99%

CMIP6 precipitation and temperature projections for Chile

Salazar,

Thatcher,

Goubanova

et al. 2023

Clim Dyn

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“…Based on the flexible framework GAMLSS with p-spline and carefully chosen distribution settings, smooth, stable and credible SCs and SCETs have been constructed. Since previous works have paid enough attention to the changes in SCs mean temperature (Hu et al, 2022; F I G U R E 4 Estimation biases of the percentage of daily temperature records below the 10th percentile or beyond the 90th percentile SCET to all observation records during the base period. Zhao et al, 2020), it is meaningful to investigate relevant climatic changes of the smooth SCETs during the past decades over China, because SCETs are also indicative in climate system other than measures for the detection of temperature extremes.…”

Section: Climatic Changes Of the Smooth Scets And Pdfsmentioning

confidence: 99%

“…Based on the flexible framework GAMLSS with p‐spline and carefully chosen distribution settings, smooth, stable and credible SCs and SCETs have been constructed. Since previous works have paid enough attention to the changes in SCs mean temperature (Hu et al, 2022; Qian, Fu, et al, 2011; Zhao et al, 2020), it is meaningful to investigate relevant climatic changes of the smooth SCETs during the past decades over China, because SCETs are also indicative in climate system other than measures for the detection of temperature extremes. We continued to use the representative 10th and 90th percentiles recommended by the ETCCDI indices as extreme thresholds.…”

Section: Climatic Changes Of the Smooth Scets And Pdfsmentioning

confidence: 99%

“…In general, previous studies on SCs and their smoothing have mostly focused on mean temperatures, for example, in the applications of amplitude and phase changes and timing of seasons (Hu et al, 2022; López‐Franca et al, 2022; Qian, Fu, et al, 2011). The cyclic characteristics of extreme temperatures could be very different from mean temperatures and should also receive attention.…”

Section: Introductionmentioning

confidence: 99%

“…Wilks (2011) has summarized elementary examples of describing the smooth SC based on harmonic analysis. A commonly used method is to use cosine and Fourier transform to obtain the smooth SC (Gubler et al, 2023; Hu et al, 2022; Zhao et al, 2020). However, this framework is easily influenced by inter‐seasonal and intra‐seasonal natural variabilities and is very vulnerable to overfitting (Bosshard et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding seasonal cycle of daily extreme temperatures based on generalized additive model for location, scale and shape with smoothing spline

Tu,

Yan,

Qian

2024

Intl Journal of Climatology

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The seasonal cycle (SC) of surface air temperature is a substantial element in climatology. Some basic and important topics in climate change studies are based on accurate and reliable estimation of SCs, such as percentile‐based indices of extremes and probability density function (PDF) changes of daily temperatures. For both of them, most studies characterized SCs using averages of multi‐days windows, which is not smooth and accurate enough representing the extreme thresholds and climatological normal of SCs. It is necessary to construct smooth and reasonable SCs for more accurate estimation of temperature changes on extreme thresholds and PDFs. In this study, we propose a flexible method based on generalized additive models for location scale and shape and penalized b‐spline smoothing technique with respective distributions to construct smooth SCs and SC for extreme temperatures (SCETs). The accuracy of the constructed smooth SCETs is good with the estimation biases of percentiles tending to be zero. The constructed smooth SCET also exhibits good stability over time, such that the magnitude changes of temperatures on each calendar day are close to climatic changes of mean temperature when the concerned period shifts. Based on the constructed smooth SCs, climatic changes by seasons and PDFs between two periods, 1961–1990 and 1991–2020, are examined over China. The increase of thresholds for hot extremes in spring during the recent period is prominent, while the increase of thresholds for daytime cold extremes in summer over a part of central to southern China is also notable. The smooth SCs and SCETs based on our flexible statistical modelling framework can characterize daily extreme temperatures reasonably and accurately, and should be expected to have more applications for a better understanding of climate changes related to distributions and seasonal cycles.

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Optimal reliability ensemble averaging approach for robust climate projections over China

Gao,

Yu,

Zhou

et al. 2024

Intl Journal of Climatology

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Accurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China.

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Weakened amplitude and delayed phase of the future temperature seasonal cycle over China during the twenty‐first century

Cited by 6 publications

References 46 publications

CMIP6 precipitation and temperature projections for Chile

CMIP6 precipitation and temperature projections for Chile

Understanding seasonal cycle of daily extreme temperatures based on generalized additive model for location, scale and shape with smoothing spline

Optimal reliability ensemble averaging approach for robust climate projections over China

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