Using observed daily temperatures in China, three independent types of heat waves (HWs), including daytime HWs, nighttime HWs, and compound HWs (with both extreme daily maxima and minima), were defined. Different types of HWs showed distinctive preferences in occurrence locations and timing. However, spatial patterns of accompanying relative humidity were generally independent of categorization, except for closer association of nighttime events with high humidity level. Compound HWs and nighttime HWs experienced significant increases in frequency, participating days, mean duration, intensity and areal extent. Conversely, significant decreasing trends of above indicators prevailed in daytime HWs, especially in central-eastern China. Tendency of relative humidity changes didn’t vary with HW types. Instead it caused an interesting phenomenon that dry HWs in the west became more humid and humid events in the east got dryer, as manifested most obviously in compound type. Thorough comparisons highlight the evolutionary dominance of HW types. Specifically, previously-dominating independent daytime HWs have been increasingly replaced by independent nighttime events in central-eastern China, and by compound HWs in southern China. That’s the very reason for negative trends of independent daytime HWs in eastern China, even in a warming climate.

An Inter-comparison of Three Heat Wave Types in China during 1961-2010: Observed Basic Features and Linear Trends

Evaluation of NASA POWER and ERA5-Land for estimating tropical precipitation and temperature extremes

Abstract Continued warming trends lead to an increasing risk of exposure to extreme heatwaves, which threaten the health of urban residents, especially the ageing population. Here, we project the spatiotemporal trend of future exposure risk across 9188 global urban settlements between 2020 and 2100 under the shared socioeconomic pathway (SSP) 2-4.5 and SSP5-8.5 scenarios. Results show that urban heatwave exposure risk increases by 619% and 1740% for SSP2-4.5 and SSP5-8.5, respectively, and by 1642% to 5529% for the elderly. Notably, 69% of the elderly exposure risk comes from middle-income countries, where the increasing trend on the regional average is 1.2 times higher than that of high-income countries. There is an increasing trend towards greater concentration on large cities, especially in low- and lower-middle-income countries. In high-income countries, climate effects contribute 39% to 58% of increasing exposure for elderly individuals, whereas ageing effects play more prominent role in lower-income countries. This emphasizes the disproportionately higher heat-related burden for elderly individuals and inequitable trends in lower income countries. Understanding the vulnerable and priority regions in future heatwave exposure will inform adaptation strategies to support urban climate-resilient development. 

/pdf/rising-vulnerability-of-compound-risk-inequality-to-ageing-2mb0lfem.pdf

Rising vulnerability of compound risk inequality to ageing and extreme heatwave exposure in global cities

Abstract We investigate the contribution of anthropogenic forcing to the extreme temperature and precipitation events in Central Asia (CA) during the last 60 years. We bias-adjust and downscale two Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) ensemble outputs, with natural (labelled as hist-nat , driven only by solar and volcanic forcing) and natural plus anthropogenic forcing (labelled as hist , driven by all-forcings), to $$0.25^{\circ } \times 0.25 ^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0</mml:mn> <mml:mo>.</mml:mo> <mml:msup> <mml:mn>25</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> <mml:mo>×</mml:mo> <mml:mn>0</mml:mn> <mml:mo>.</mml:mo> <mml:msup> <mml:mn>25</mml:mn> <mml:mo>∘</mml:mo> </mml:msup> </mml:mrow> </mml:math> spatial resolution. Each ensemble contains six models from ISIMIP, based on the Coupled Model Inter-comparison Project phase 6 (CMIP6). The presented downscaling methodology is necessary to create a reliable climate state for regional climate impact studies. Our analysis shows a higher risk of extreme heat events (factor 4 in signal-to-noise ratio) over large parts of CA due to anthropogenic influence. Furthermore, a higher likelihood of extreme precipitation over CA, especially over Kyrgyzstan and Tajikistan, can be attributed to anthropogenic forcing (over 100 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> changes in intensity and 20 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> in frequency). Given that these regions show a high risk of rainfall-triggered landslides and floods during historical times, we report that human-induced climate warming can contribute to extreme precipitation events over vulnerable areas of CA. Our high-resolution data set can be used in impact studies focusing on the attribution of extreme events in CA and is freely available to the scientific community. 

/pdf/anthropogenic-influence-on-extreme-temperature-and-135zqwjt.pdf

Anthropogenic influence on extreme temperature and precipitation in Central Asia

The cross-boundary of land degradation in Mongolia and China and achieving its neutrality - challenges and opportunities

Precipitation is an important component of the hydrological cycle and has significant impact on ecological environment and social development, especially in arid areas where water resources are scarce. As a typical arid and semi-arid region, the Mongolian Plateau is ecologically fragile and highly sensitive to climate change. Reliable global precipitation data is urgently needed for the sustainable development over this gauge-deficient region. With high-quality estimates, fine spatiotemporal resolutions, and wide coverage, the state-of-the-art Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and European Center for Medium-range Weather Forecasts Reanalysis 5 (ERA5) have great potential for regional climatic, hydrological, and ecological applications. However, how they perform has not been well investigated on the Mongolian Plateau. Therefore, this study evaluated the performance of three IMERG V06 datasets (ER, LR and FR), two ERA5 products (ERA5-HRES and ERA5-Land), and their predecessors (TMPA-3B42 and ERA-Interim) over the region across 2001-2018. The results showed that all products broadly characterized seasonal precipitation cycles and spatial patterns, but only the three reanalysis products, IMERG FR and TMPA-3B42 could capture interannual and decadal variability. When describing daily precipitation, dataset performances ranked ERA5-Land > ERA5-HRES > ERA-Interim > IMERG FR > IMERG LR > IMERG ER > TMPA-3B42. All products showed deficiencies in overestimating weak precipitation and underestimating high-intensity precipitation. Besides, products performed best in agricultural lands and forests along the northern and south-eastern edges, followed by urban areas and grasslands closer to the center, and worst in the sparse vegetation and bare areas of the south-west. Due to a negative effect of topographic complexity, IMERG showed poor detection capabilities in forests. Accordingly, this research currently supports the applicability of reanalysis ERA5 data over the arid, topographically complex Mongolian Plateau, which can inform regional applications with different requirements. 

/pdf/evaluation-of-imerg-and-era5-precipitation-products-over-the-1j4hwzu0.pdf

Evaluation of IMERG and ERA5 precipitation products over the Mongolian Plateau

Changes in Global Heat Waves and Its Socioeconomic Exposure in a Warmer Future

Temperature is the most concerned factor in the human–environment interactions. Apparent temperature accounting for other meteorological variables, such as humidity, wind speed and solar radiation, is the equivalent temperature, and it is a more accurate indicator to reflect human's environmental temperature perception. High‐quality apparent temperature data are urgently needed for the further research on human–environment interactions. At the same time, as global heat waves continue to increase in frequency, duration and intensity, understanding the impact of heat waves on human health needs human perception‐based heat wave data. Using ERA5 hourly data on single levels of 2 m temperature, wind speed, dewpoint temperature and solar radiation, this study developed a global apparent temperature and heat wave (GATHW) toolbox based on the Climate Data Store (CDS) online platform. This toolbox allows using three methods to calculate daily apparent temperature and heat wave at three spatial resolutions of 0.25°, 0.5° and 1° respectively. It can realize online calculation, display and real‐time download and is updated in near real‐time. The global daily apparent temperature and annual heat wave dataset from 2006 to 2020 calculated by the toolbox can be obtained from https://www.doi.org/10.5281/zenodo.4764325. After evaluation, this dataset can well reflect the typical extreme temperatures and heat wave events, and is more accurate, with higher resolution and faster update frequency than similar data products, which can provide data support for the study of human–environmental interactions and extreme climate events.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/gdj3.155

Global near real‐time daily apparent temperature and heat wave dataset

The fragile ecological environment of the Mongolian Plateau (MP) is sensitive to climate change. It is necessary to fully understand the temperature and precipitation changes on the MP to ensure regional sustainable development. Most existing studies are conducted based on station observations which suffer from sparse distribution and limited spatial representativeness and cannot perfectly represent the climatic conditions of the whole region. By contrast, the long‐term, spatially continuous reanalysis products offer new opportunities for MP climate change research. However, the coarse spatial resolution and large uncertainties of reanalysis data limit their applicability to provide reliable climate information at finer scales. Within the delta downscaling framework, we downscale and correct the state‐of‐the‐art European Centre for Medium‐range Weather Forecasts ReAnalysis 5 land portion (ERA5‐Land) using a high‐resolution WorldClim reference climatology and generate a long‐term (1950–2020) 1‐km monthly air temperature and precipitation downscaled dataset. During the downscaling process, the results obtained by nearest neighbour, bilinear and bicubic interpolation methods are evaluated and compared. Air temperature result of bilinear method and precipitation result of nearest neighbour method, which have the best accuracy, are taken as the final downscaled data. The evaluation shows that the downscaled data has acceptable accuracy (overall MBE = −0.41°C, Corr = 0.997, RMSE = 1.51°C for air temperature, and MBE = 1.58 mm·month−1, Corr = 0.747, RMSE = 24.24 mm·month−1 for precipitation), and outperforms the original ERA5‐Land and widely used Climatic Research Unit (CRU) data. In addition, the downscaled data can also provide accurate and detailed temperature and precipitation change trends over the MP. With fine spatial resolution, long‐time span and good spatial continuity, the downscaled air temperature and precipitation will be a useful data source to explore climate change over the MP.

One‐kilometre monthly air temperature and precipitation product over the Mongolian Plateau for 1950–2020

Corrigendum to “Diagnose the dominant climate factors and periods of spring phenology in Qinling Mountains, China” [Ecol. Indic. 131 (2021) 108211]

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