Soil moisture revamps the temperature extremes in a warming climate over India

Ganeshi, Naresh; Mujumdar, Milind; Takaya, Yuhei; Goswami, Mangesh; Singh, Bhupendra Bahadur; Krishnan, R.; Terao, Toru

doi:10.1038/s41612-023-00334-1

Cited by 15 publications

(15 citation statements)

References 54 publications

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“…As a result, their influence can be seen on large‐scale weather phenomena to small‐scale eddies. More than 70% of the area of the Indian landmass has experienced significant changes in characteristics of extreme temperature due to SM perturbations (Ganeshi et al., 2023). For arid and semi‐arid regions, the surface soil heat flux and the turbulent sensible heat fluxes dominated the surface energy budget because of the lack of precipitation (Singh & Chudasama, 2021; Surendran et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Challenges in Simulating Prevailing Fog Types Over Urban Region of Delhi

Parde,

Ghude,

Dhangar

et al. 2024

JGR Atmospheres

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Accurately predicting fog is challenging due to interplay of myriad processes in its formation and high spatiotemporal variability. This study compares the performance of the Weather Research and Forecasting model with control (CNTL‐WRF) and assimilated fine‐grid (HRLDAS‐WRF) soil fields in the Ingo‐Gangetic Plain (IGP) over a 2‐years winter period (2019–2020 and 2020–2021). Results show HRLDAS‐WRF enhances accuracy in representing surface fog's heterogeneity and lifecycle across the IGP, demonstrating a spatial skill improvement of approximately 18% with a Fraction Skill Score of 0.44, compared to CNTL‐WRF's (0.36). Employing fog classification algorithm identifies 25 dense fog episodes (Vis < 500 m) over Delhi's urban boundary layer, including 14 radiation (RAD), 5 cloud‐base lowering (CBL), 3 advection + radiation (ADV + RAD), and 3 evaporation (EVA) episodes. CNTL‐WRF predicts 20 episodes but misses five due to a dry bias in the initial moisture conditions. However, HRLDAS‐WRF demonstrates limited vertical fog growth in various occurrences, highlighting the crucial role of fine‐gridded soil states for enhanced land‐surface feedback. Detailed analysis shows a 40% reduction in mean onset error for RAD fog occurrences in HRLDAS‐WRF when compared to CNTL‐WRF. In CBL fog episodes, both models exhibit significant radiative cooling and inversion before fog onset, leading to inaccurate predictions as RAD fog. Similarly, forecasting the abrupt development of ADV + RAD fog episodes is challenging as models struggle to replicate moisture intrusion over radiatively cooled surfaces in windy conditions. Predicting EVA fog, forms within an hour after sunrise, remains difficult due to the current model parameterization that rapidly dissipates fog soon after sunrise.

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Section: Introductionmentioning

confidence: 99%

Challenges in Simulating Prevailing Fog Types Over Urban Region of Delhi

Parde,

Ghude,

Dhangar

et al. 2024

JGR Atmospheres

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“…In addition, soil temperature (T s ) plays an important role in many critical processes [5]. It strongly influences a wide range of biotic and abiotic processes, plays an important role in the exchange of energy and matter between the soil and the air, and even affects the local climate [6][7][8]. T s is usually influenced by many factors [9], such as meteorological and topographical conditions [10].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau

Deng,

Liu,

Guo

et al. 2024

Agronomy

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Soil temperature directly affects the germination of seeds and the growth of crops. In order to accurately predict soil temperature, this study used RF and MLP to simulate shallow soil temperature, and then the shallow soil temperature with the best simulation effect will be used to predict the deep soil temperature. The models were forced by combinations of environmental factors, including daily air temperature (Tair), water vapor pressure (Pw), net radiation (Rn), and soil moisture (VWC), which were observed in the Hejiashan watershed on the Loess Plateau in China. The results showed that the accuracy of the model for predicting deep soil temperature proposed in this paper is higher than that of directly using environmental factors to predict deep soil temperature. In testing data, the range of MAE was 1.158–1.610 °C, the range of RMSE was 1.449–2.088 °C, the range of R2 was 0.665–0.928, and the range of KGE was 0.708–0.885 at different depths. The study not only provides a critical reference for predicting soil temperature but also helps people to better carry out agricultural production activities.

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“…The persistence of dry soil can increase the likelihood of droughts (e.g. Koster et al 2004), extreme temperatures (Nicholls and Larsen 2011, Whan et al 2015, Berg et al 2016, Ganeshi et al 2023, and wildfires (O et al 2020). On the other hand, higher soil moisture can modulate changes in flood timing (Sharma et al 2018, Wasko et al 2020 and magnitude (Wasko and Nathan 2019).…”

Section: Introductionmentioning

confidence: 99%

Assessing the soil moisture-precipitation feedback in Australia: CYGNSS observations

Bui,

Li,

Sherwood

et al. 2023

Environ. Res. Lett.

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Previous modelling and case studies highlight the impacts of antecedent soil moisture on precipitation, showing the connection between the anomalous land surface and atmospheric conditions. However, observational evidence is lacking, especially on daily timescales, primarily due to the difficulty in assessing the interaction between soil moisture and atmospheric variability and dataset quality. Using satellite retrievals, this study investigates the relationship between soil moisture and next-day precipitation in Australia. Analysing the 5 year soil moisture data from the Cyclone Global Navigation Satellite System, we find that soil moisture anomalies influence next-day precipitation probability where higher soil moisture is associated with a higher probability of precipitation, even allowing for precipitation persistence. We also find that this feedback is generally positive in northern Australia but slightly negative in the southern regions, suggesting regional dependence. Linkages between the persistence of dry/wet soil moisture days and the possibility of wildfires and floods are also discussed. These findings have direct implications for the management and predictions of extreme conditions.

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Soil moisture revamps the temperature extremes in a warming climate over India

Cited by 15 publications

References 54 publications

Challenges in Simulating Prevailing Fog Types Over Urban Region of Delhi

Challenges in Simulating Prevailing Fog Types Over Urban Region of Delhi

Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau

Assessing the soil moisture-precipitation feedback in Australia: CYGNSS observations

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