Climate change multi-model projections in CMIP6 scenarios in Central Hokkaido, Japan

Peng, Shilei; Wang, Qianqian; Li, Zhan; Mihara, Kunihito; Kuramochi, Kanta; Toma, Yo; Hatano, Ryusuke

doi:10.1038/s41598-022-27357-7

Cited by 40 publications

(18 citation statements)

References 81 publications

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“…Results show that ALL forcing cause substantial wetting under the SSP2-4.5 scenario in most of the Northern Hemisphere (NH) and drying in the Southern Hemisphere (SH), which was consistent with the results shown in the CMIP5 (Chen & Frauenfeld, 2014;Liu et al, 2017;Roy, 2017; and CMIP6 (Peng et al, 2023;Scoccimarro & Gualdi, 2020;Song et al, 2022). This is mainly due to the nonuniform heating of the GHG.…”

Section: Discussionsupporting

confidence: 85%

Influence of anthropogenic and natural forcings on future changes in precipitation projected by the CMIP6–DAMIP models

Wang

Zhao

Hua

et al. 2023

Intl Journal of Climatology

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Warming has already changed the patterns of precipitation and increased the risk of extreme climate events. An assessment of the influences from anthropogenic and natural forcings on future changes in precipitation is therefore essential to define appropriate mitigation and adaptation policies. We assess the effects of natural (NAT) and anthropogenic forcings (greenhouse gases [GHG] and anthropogenic aerosols [AA]) on precipitation over land areas simulated under the Coupled Model Intercomparison Project Phase 6 (CMIP6) SSP2‐4.5 scenario. Our results indicate that changes in precipitation due to GHG forcing, AA forcing and all external forcings (ALL) are projected to increase by about 3.0%–24.8%, 1.1%–6.9% and 4.2%–31.2%, respectively, across most land areas in the twenty‐first century, especially over the mid‐ and high latitudes of the Northern Hemisphere and central Africa, with an increase in the mean and flattening of the probability distribution functions (PDFs). By contrast, the NAT‐induced changes are relatively small, with a slight increase in the mean of the PDFs. The frequencies of both future extreme precipitation and dry events are projected to increase by 11%–101% and 3%–72%, respectively, over many continental regions under the influence of external forcings, especially under GHG forcing. Fingerprinting detection shows that the signals of GHG (except for Australia), AA and ALL forcing are detectable at the likely level (signal‐to‐noise ratio >0.95; 66% confidence) over most land areas. The GHG signals are stronger and detected earlier (after 2010s, likely level) than the AA signals (after 2060s, likely level).

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Section: Discussionsupporting

confidence: 85%

Influence of anthropogenic and natural forcings on future changes in precipitation projected by the CMIP6–DAMIP models

Wang

Zhao

Hua

et al. 2023

Intl Journal of Climatology

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“…Saddique et al (2020) observed in their study that there was a significant increase in both warming and precipitation, with the increase being more pronounced from the RCP4.5 to the RCP8.5 scenario in Pakistan. In contrast to this, Peng et al (2023) found that a lower increase in precipitation is projected under the higher scenario (ssp585) as compared to the lower scenario (ssp119) for the Ishikari River Basin (IRB) of Japan. In the Shandong Plain, the mean annual precipitation is expected to increase by 11.2% during the mid-term period 2050 (2041-2060), and by 15.2% during the long-term period 2080 (2071-2090).…”

Section: Precipitation Projection 361 Mean Annual Precipitation Changementioning

confidence: 73%

“…The study illustrated that in the long term, there is a projected increase in annual precipitation of 10.41%, 10.66%, and 15.80% under the scenarios ssp126, ssp245, and ssp585, respectively. In contrast, the study by Peng et al (2023) in Japan indicated lower precipitation was projected in the 2070s as compared to the 2040s. Furthermore, this increase is expected to be highest under the highest emission scenario, ssp585, compared to the medium scenario, ssp245, and the lowest scenario, ssp126.…”

Section: Precipitation Projection 361 Mean Annual Precipitation Changementioning

confidence: 86%

Trend Analysis and Projection of Climate Variables Using the LARS-WG Model in Coastal Areas of China

Disasa,

Yan,

Wang

et al. 2024

Preprint

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The rising air temperature and shifting precipitation patterns threaten crop production and water distribution worldwide. The coastal region of China, specifically the Huaibei and Shandong Plains, is recognized as one of the most vulnerable areas among those impacted due to the complex interplay of land, sea, and atmospheric dynamics. The study utilized traditional trend analysis methods (Mann-Kendall and Sen's Slope) along with an innovative polygon trend analysis (IPTA) to predict the baseline arithmetic mean and standard deviation of the monthly precipitation trend. Moreover, the latest version of the Long Ashton Research Station Weather Generator (LARS-WG 7) model was used to predict average mean monthly precipitation and maximum and minimum temperatures for two future times: midterm 2050 (2041–2060) and long-term 2080 (2071–2090). The performance of each GCM incorporated in LARS-WG was evaluated independently and compared to a multi-model ensemble. All of the meteorological stations that were analyzed using the MK method (except for Suzhou, Dangshan, and Mengcheng) showed a significant decreasing trend in the arithmetic mean of monthly precipitation in March. However, for the majority of the remaining months, the study indicated a non-significant decreasing trend. In contrast, the IPTA method demonstrated a significant decreasing trend in most months, highlighting its superior ability to detect hidden trends compared to the MK method. The projections showed that mean annual precipitation is likely to increase at all meteorological stations in the Huaibei Plains and Shandong Plains during two periods: 2050 (2041–2060) and 2080 (2071–2090). A maximum increase in average mean annual precipitation is projected at the highest emission scenario (ssp585) as compared to the medium (ssp245) and low emission (ssp126) scenarios, and at the long-term period 2080 (2071-2090) as compared to the mid-term period 2050 (2041-2060). The mean annual precipitation in the Shandong Plain is projected to increase by 10.4%, 14.5%, and 14.8% under the ssp126, ssp245, and ssp585 scenarios, respectively. Similarly, in the Huaibei Plain, the projected increases are 10.9%, 13.6%, and 15.1% under the ssp126, ssp245, and ssp585 scenarios, respectively. The anticipated increase in mean precipitation per decade is expected to be 2.0% (= 1.96 mm/decade) in the Huaibei Plain and 1.31% (= 0.63 mm/decade) in the Shandong Plain. Both maximum and minimum temperatures are projected to increase persistently across all meteorological stations during two time periods: 2050 (2041–2060) and 2080 (2071–2090) under three different SSPs (ssp126, ssp245, and ssp585). The long-term period 2080 (2071–2090) is projected to experience the highest increase in both maximum and minimum temperatures, surpassing the increases observed in the midterm period 2050 (2041–2060). Among the different SSPs, the greatest increase in both maximum and minimum temperature was projected under the highest forcing emission scenario, SSP 585. With a persistent increase in air temperature and precipitation patterns fluctuating under a future climate scenario in the coastal area of China, climate change can influence all aspects of life, especially water resource distribution and agricultural water management. This study provides valuable insight for water resources planners and agricultural experts in the coastal region of China, as this area is a very vulnerable area to climate change and is also the main staple food-producing area in China.

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“…However, in the case of the northern districts of Kerala, currently (year 2020), the highly suitable area is less, but, for the future scenarios, in many of the northern districts, some of the areas turn into highly suitable, which is a positive trend. Projected changes in future climate scenarios showed changes in temperature and rainfall, which favour turmeric cultivation in certain areas 41‐43 . These suggest that Northern districts could experience more favorable conditions for turmeric cultivation as a result of projected changes in climate parameters.…”

Section: Resultsmentioning

confidence: 96%

“…Projected changes in future climate scenarios showed changes in temperature and rainfall, which favour turmeric cultivation in certain areas. [41][42][43] These suggest that Northern districts could experience more favorable conditions for turmeric cultivation as a result of projected changes in climate parameters.…”

Section: Resultsmentioning

confidence: 99%

Land suitability analysis for turmeric crop for humid tropical Kerala, India, under current and future climate scenarios using advanced geospatial techniques

Banu,

Krishnamurthy,

Srinivasan

et al. 2024

J Sci Food Agric

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BACKGROUNDTurmeric cultivation primarily thrives in India, followed by Bangladesh, Cambodia, Thailand, China, Malaysia, Indonesia and the Philippines. India leads globally in both area and production of turmeric. Despite this, there is a recognized gap in research regarding the impact of climate change on site suitability of turmeric. The primary objective of the present study was to evaluate both the present and future suitability of turmeric cultivation within the humid tropical region of Kerala, India, by employing advanced geospatial techniques. The research utilized meteorological data from the Indian Meteorological Department for the period of 1986–2020 as historical data and projected future data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Four climatic scenarios of shared socioeconomic pathway (SSP) from the Intergovernmental Panel on Climate Change AR6 model of MIROC6 for the year 2050 (SSP 1–2.6, SSP 2–4.5, SSP 3–7.0 and SSP 5–8.5) were used.RESULTSThe results showed that suitable area for turmeric cultivation is declining in future scenario and this decline can be primarily attributed to fluctuations in temperature and an anticipated increase in rainfall in the year 2050. Notable changes in the spatial distribution of suitable areas over time were observed through the application of geographic information system (GIS) techniques. Importantly, as per the suitability criteria provided by ICAR‐National Bureau of Soil Survey and Land Use Planning (ICAR‐NBSS & LUP), all the districts in Kerala exhibited moderately suitable conditions for turmeric cultivation. With the GIS tools, the study identified highly suitable, moderately suitable, marginally suitable and not suitable areas of turmeric cultivation in Kerala. Presently 28% of area falls under highly suitable, 41% of area falls under moderately suitable and 11% falls under not suitable for turmeric cultivation. However, considering the projected scenarios for 2050 under the SSP framework, there will be a significant decrease in highly suitable area by 19% under SSP 5–8.5. This reduction in area will have an impact on the productivity of the crop as a result of changes in temperature and rainfall patterns.CONCLUSIONThe outcome of the present research suggests that the state of Kerala needs to implement suitable climate change adaptation and management strategies for sustaining the turmeric cultivation. Additionally, the present study includes a discussion on potential management strategies to address the challenges posed by changing climatic conditions for optimizing turmeric production in the region. © 2024 Society of Chemical Industry.

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Climate change multi-model projections in CMIP6 scenarios in Central Hokkaido, Japan

Cited by 40 publications

References 81 publications

Influence of anthropogenic and natural forcings on future changes in precipitation projected by the CMIP6–DAMIP models

Influence of anthropogenic and natural forcings on future changes in precipitation projected by the CMIP6–DAMIP models

Trend Analysis and Projection of Climate Variables Using the LARS-WG Model in Coastal Areas of China

Land suitability analysis for turmeric crop for humid tropical Kerala, India, under current and future climate scenarios using advanced geospatial techniques

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