Homogenising meteorological variables: Impact on trends and associated climate indices

Adeyeri, Oluwafemi E.; Laux, Patrick; Ishola, Kazeem A.; Zhou, Wen; Balogun, Ifeoluwa Adebowale; Adeyewa, Zechariah Debo; Kunstmann, Harald

doi:10.1016/j.jhydrol.2022.127585

Cited by 28 publications

(13 citation statements)

References 57 publications

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“…Furthermore, rising temperatures worsen heavy precipitation by increasing atmospheric moisture, promoting the precipitation event through moisture convergence at low altitudes 65 , 66 and increasing evapotranspiration rates arid areas 46 . This will lead to more intense hydrometeorological situations, such as floods and droughts 5 and significantly impact the amount and quality of available water as well as river discharge timing and amplitude 67 . As a result, humans, society and natural systems are at risk 67 , 68 .…”

Section: Discussionmentioning

confidence: 99%

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The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Adeyeri

Zhou

Wang

et al. 2022

Sci Rep

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Climate change could exacerbate extreme climate events. This study investigated the global and continental representations of fourteen multisectoral climate indices during the historical (1979–2014), near future (2025–2060) and far future (2065–2100) periods under two emission scenarios, in eleven Coupled Model Intercomparison Project (CMIP) General Circulation Models (GCM). We ranked the GCMs based on five metrics centred on their temporal and spatial performances. Most models followed the reference pattern during the historical period. MPI-ESM ranked best in replicating the daily precipitation intensity (DPI) in Africa, while CANESM5 GCM ranked first in heatwave index (HI), maximum consecutive dry days (MCCD). Across the different continents, MPI-LR GCM performed best in replicating the DPI, except in Africa. The model ranks could provide valuable information when selecting appropriate GCM ensembles when focusing on climate extremes. A global evaluation of the multi-index causal effects for the various indices shows that the dry spell total length (DSTL) was the most crucial index modulating the MCCD for all continents. Also, most indices exhibited a positive climate change signal from the historical to the future. Therefore, it is crucial to design appropriate strategies to strengthen resilience to extreme climatic events while mitigating greenhouse gas emissions.

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Section: Discussionmentioning

confidence: 99%

“…This will lead to more intense hydrometeorological situations, such as floods and droughts 5 and significantly impact the amount and quality of available water as well as river discharge timing and amplitude 67 . As a result, humans, society and natural systems are at risk 67 , 68 .…”

Section: Discussionmentioning

confidence: 99%

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Adeyeri

Zhou

Wang

et al. 2022

Sci Rep

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Section: Discussionmentioning

confidence: 99%

“…A comparison of solar radiation, wind speed and relative humidity before and after homogenization revealed significant trend discrepancies. Therefore, the detection and correction of inhomogeneities in the observed time series are crucial for the reliable estimation and assessment of long‐term ET, as emphasized in homogenization studies (Adeyeri et al., 2022; L. Cao & Yan, 2012; Peterson et al., 1998; Venema et al., 2012; Yan et al., 2014). Previous studies have attributed increasing ET to various factors such as vegetation changes (G. Wu et al., 2023; X. Wu et al., 2023), environmental factors (J. Liu et al., 2016; W. Liu et al., 2016), land use/cover changes (G. Li et al., 2017), or combinations of these factors (Zheng et al., 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Inhomogeneity may arise from a variety of factors, such as instrument replacement, instrument sensitivity drift, station relocation, changing observation routines, or changing methods of preliminary data processing (W. Xu et al., 2019; Yan & Jones, 2008). These factors can cause systematic biases in the observed data, leading to distorted climate trends (Adeyeri et al., 2022). Detecting and correcting such inhomogeneity in ET data sets is necessary to obtain reliable long‐term trends.…”

Section: Introductionmentioning

confidence: 99%

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Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

Mao,

Bai,

et al. 2024

JGR Atmospheres

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Due to limited direct measurements, regional or global terrestrial evapotranspiration (ET) is generally derived from a combination of meteorological and satellite observations. Although the inhomogeneity of the observed climate data has been widely reported, its impact on the calculated ET has not been adequately quantified. This study aimed to calculate ET using the modified Penman‐Monteith (MPM) model with raw and homogenized meteorological data. Additionally, we compared the calculated ET with those estimates from variable methods (water balance, satellite‐based, and reanalysis) in China and its six major river basins from 1982 to 2020. During the overlapping period of 1997–2018, ET calculated from raw input data decreased slightly at −0.39 mm yr−2 (p = 0.64) in China, whereas homogenized ET showed a significant increasing trend of 0.93 mm yr−2 (p = 0.02), with a better agreement with water balance ET (1.93 mm yr−2, p = 0). Global Land Evaporation Amsterdam Model (GLEAM) and Modern‐Era Retrospective Analysis for Research and Applications, version 2 (MERRA2) could reproduce the increasing trends with 2.08 mm yr−2 (p = 0) and 2.59 mm yr−2 (p = 0). The intercomparison of input variables (solar radiation, relative humidity, wind speed, precipitation, and air temperature) among ET products revealed substantial differences, which can account for the discrepancies in ET estimates. Homogenized ET, GLEAM and MERRA2 exhibited significant increasing trends in China and most river basins from 1982 to 2020. Our findings underscore the importance of utilizing homogenized input data for more accurate ET estimation.

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Joint probabilistic behaviour of climate extremes over the Godavari River basin, India

Kumar,

Das,

Mandal

et al. 2024

Intl Journal of Climatology

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Climate change has an adverse effect on the global hydrological cycle and hence climatic extremes are becoming more persistent. Therefore, the present study analyses the characteristics of the compound extremes in India using the Godavari River basin as a test bed. The study examines the individual return period (IRP) and pairwise joint return period (JRP) of several climate extreme indices such as consecutive dry days (CDD), consecutive wet days (CWD), percentage rainfall of very dry days (R5Tot) and very hot days (TX95) considering historical and future period of two scenarios, SSP245 and SSP585, from the CMIP6 consortium. Both SSPs indicates an upward trend in the indices for CWD and TX95, whilst showcasing a declining trend for CDD and R5Tot at 5% significance level. Under SSP585, the values of JRPs , , and ranges from 3 to 5 years, 14–55 years, 35–274 years and 104 to >1000 during epoch‐1 (2023–2060). During epoch‐2 (2061–2100), the JRPs values are generally higher. Regions displaying lower JRPs values suggest a higher likelihood of simultaneous occurrences of extremes such as floods and droughts, as well as very dry days and warm days within the same year. Under SSP585, the maximum (minimum) value of change rate (CR) of JRP for and during epoch‐1 is more than 900% (−80%) and 1000% (−90%), whereas during epoch‐2, the corresponding CR values are greater than 700% (−80%) and 1000% (−90%). The future possibility of co‐occurrence of ‘CDD and CWD’ and ‘R5Tot and TX95’, both exceeding their 10‐ and 25‐year IRP, in a single year, is highly likely for the regions with low CR values and unlikely for the regions with high CR values. The study would help in understanding the spatial and temporal variability of compound climate extremes over the Godavari River basin.

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Homogenising meteorological variables: Impact on trends and associated climate indices

Cited by 28 publications

References 57 publications

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

Joint probabilistic behaviour of climate extremes over the Godavari River basin, India

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