Analysis on the Changes of Agro-Meteorological Thermal Indices in Northeast China under RCP4.5 Scenario Using the PRECIS2.1

Li, Xin‐Hua; Xu, Yinlong; Chen, Meng; Zhang, Lei; Wang, Chang-Gui

doi:10.3390/atmos9080323

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…For example, climate simulations from statistical downscaling models are assessed through their reproduction of long-term trends of temperature and precipitation, with more attention on local regions rather than on China as a whole (Fan et al 2011(Fan et al , 2013(Fan et al , 2015. Comparatively, with the help of commonly used RCMs, for example, PRECIS and RegCM, a number of works are being done to assess and project climate change and their subsequent impacts, covering main features of the mean climatologies and extreme events (Xu et al 2010;Yang et al 2010;Gao et al 2012Gao et al , 2013Gao et al , 2017Fan et al 2014;Zhang et al 2017;Li et al 2018). Even though these works have been proven to enhance multiscale information, there is still a shortage of systematic analysis on high-resolution scenarios, and few comparisons have been conducted between statistical and dynamic downscaling methods, which need to be enhanced for future climate change assessments.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Statistical and Dynamic Downscaling Techniques in Generating High-Resolution Temperatures in China from CMIP5 GCMs

Zhang

Chen

et al. 2020

Journal of Applied Meteorology and Climatology

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In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information.

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Section: Introductionmentioning

confidence: 99%

Comparison of Statistical and Dynamic Downscaling Techniques in Generating High-Resolution Temperatures in China from CMIP5 GCMs

Zhang

Chen

et al. 2020

Journal of Applied Meteorology and Climatology

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“…The threshold for the start time point of an active agriculture growing season was set at 10 • C, according to the general agreement in previous publications [25,26]. The period between the first date (FD) with daily mean air temperature ≥ 10 • C and the last date (LD) with daily mean air temperature ≤ 10 • C was thus defined as the length of the potential agricultural growing season (PAGS), implying the potential time period for major crop production.…”

Section: Indices For Agro-climatic Change and Variationmentioning

confidence: 99%

The Agro-Climatic Change Characteristics across China during the Latest Decades

Huo

Zhang²,

Kong

et al. 2022

Agriculture

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Climate change has been highlighted over the last decades worldwide, with pronounced higher warming trends for China. Induced by climate change, to some extent, agricultural production has changed, as well as the climatic resources during the agricultural growing season. An obvious longer potential agricultural growing season (PAGS) was detected in the latest decades from 1961–2020. The spatial–temporal characteristics of change and variation of climatic resources during the PAGS were explored, utilizing comparisons of the differences in mean magnitude, standard deviation, and trends. In the period of 1991–2020 relative to 1961–1990, alterations in PAGS were characterized by increases of 0–1.5 °C in mean air temperature overall, 0.8–23.8% increases in precipitation in the southeast, northeast, and west, as well as a decrease of 2.1–10.2% in insolation in central-south regions but an increase of 0.3–6.7% in the north and west. The features were pronounced during the PAGS in the primary agricultural zones as follows: (1) Northeast China, increasing and stable temperature but unstable precipitation and insolation; (2) North, eastern-west, and Southwest China, increasing but unstable temperature, decreasing but stable precipitation as well as decreasing and unstable insolation; (3) Southeast China, increasing but unstable temperature and precipitation as well as decreasing and unstable insolation; (4) West China, increasing but unstable temperature and precipitation as well as increasing but unstable insolation. Further comparisons between agro-climatic change and climate change indicated that temperature alterations during the PAGS were less numerous while precipitation and insolation were more unstable than the changes during the whole year. These findings can assist the understanding of regional agricultural climate changes and guide agricultural production practices in response.

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“…To a large extent, temperature determines optimal regional crop types, cropping systems, as well as farming activities, thus having considerable effects on agricultural output [1][2][3][4]. Related indices, such as agricultural critical temperature, accumulated temperature, and frost-free period, are usually calculated from the available temperature dataset to estimate regional thermal resources and to provide instructions for agricultural production [5][6][7][8][9]. As a result, it is important to evaluate the accuracy and applicability of temperature data for agrometeorological studies.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Accuracy of a Gridded Near-Surface Temperature Dataset over Mainland China

Qiu

Liu

et al. 2019

Atmosphere

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High-resolution meteorological data products are crucial for agrometeorological studies. Here, we study the accuracy of an important gridded dataset, the near-surface temperature dataset from the 5 km × 5 km resolution China dataset of meteorological forcing for land surface modeling (published by the Beijing Normal University). Using both the gridded dataset and the observed temperature data from 590 meteorological stations, we calculate nine universal meteorological indices (mean, maximum, and minimum temperatures of daily, monthly, and annual data) and five agricultural thermal indices (first frost day, last frost day, frost-free period, and ≥0 °C and ≥10 °C active accumulated temperature, i.e., AAT0 and AAT10) of the 11 temperature zones over mainland China. Then, for each meteorological index, we calculate the root mean square errors (RMSEs), correlation coefficient and climate trend rates of the two datasets. The results show that the RMSEs of these indices are usually lower in the north subtropical, mid-subtropical, south subtropical, marginal tropical and mid-tropical zones than in the plateau subfrigid, plateau temperate, and plateau subtropical mountains zones. Over mainland China, the AAT0, AAT10, and mean and maximum temperatures calculated from the gridded data show the same climate trends with those derived from the observed data, while the minimum temperature and its derivations (first frost day, last frost day, and frost-free period) show the opposite trends in many areas. Thus, the mean and maximum temperature data derived from the gridded dataset are applicable for studies in most parts of China, but caution should be taken when using the minimum temperature data.

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Analysis on the Changes of Agro-Meteorological Thermal Indices in Northeast China under RCP4.5 Scenario Using the PRECIS2.1

Cited by 5 publications

References 27 publications

Comparison of Statistical and Dynamic Downscaling Techniques in Generating High-Resolution Temperatures in China from CMIP5 GCMs

Comparison of Statistical and Dynamic Downscaling Techniques in Generating High-Resolution Temperatures in China from CMIP5 GCMs

The Agro-Climatic Change Characteristics across China during the Latest Decades

Evaluating the Accuracy of a Gridded Near-Surface Temperature Dataset over Mainland China

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