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Climate change response in wintertime widespread fog conditions over the Indo-Gangetic Plains

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Abstract

This study investigates the influence of climate change on widespread fog conditions over the Indo-Gangetic Plains (IGP) of north India using observations, reanalysis data of atmospheric parameters, coupled model inter-comparison project 6 (CMIP6) projections following four future scenarios based on the shared socio-economic pathways (SSP126, SSP245, SSP370, SSP585), and advanced analysis techniques including machine learning. Two parameters fog fraction and widespread fog days (WFDs) are estimated in this study by functional mapping of fog observations with 8 atmospheric parameters for the period 1981–2018 using three empirical/machine learning approaches. Of these, we note that the deep learning convolutional neural network (CNN) exhibits superiority in performance by showing the mapping closer to the observed, and also offers promising potential for operational purposes to provide fog outlooks for the IGP region. Temporal evolution of fog fractions and WFDs is analyzed from the CMIP6 projections following the aforementioned four future scenarios using CNN for the future periods of the twenty-first century. It is noted that there is a substantial enhancement in the CMIP6 projected fog fractions as high as 57% during the period (2015–2045) relative to the historical (1981–2014) period, while the largest increase of 154% is seen in projected WFDs. It is also seen that the near-future period (2015–2045) witnesses a larger prevalence of WFDs, for all scenarios except SSP126, due to the combined effects of air pollution and greenhouse warming. The post-2046 periods, however, generally indicate signatures of decline in foggy days with widespread conditions relative to historical period in most of the scenarios except SSP370. The severity in fog conditions following the high-emission scenarios SSP370 and SSP585 during this period comes from the relative impact of mitigation strategies of pollutants. The findings provide insights into the possible future changes in widespread fog conditions suitable for the IGP region.

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(Observational Source: India Meteorological Department)

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(Observational Source: India Meteorological Department)

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Data availability

The CMIP6 (https://esgf-data.dkrz.de/search/cmip6-dkrz/) model outputs and ERA5 reanalysis datasets (https://cds.climate.copernicus.eu) used in this study are publicly available online. The observational daily summaries used in the study can be obtained from the India Meteorological Department (IMD; http://dsp.imdpune.gov.in/) on request. The code and datasets generated during and/or analysed in this study are available from the corresponding author on reasonable request.

Code availability

On request.

Abbreviations

AO:

Arctic oscillation

AOD:

Aerosol optical depth

CMIP6:

Coupled Model Inter-comparison Project 6

CNN:

Convolutional neural network

CC:

Correlation coefficient

ECMWF:

European Centre for Medium-Range Weather Forecasts

ERA5:

Fifth-generation ECMWF atmospheric reanalysis

EU:

Eurasian pattern

IGP:

Indo-Gangetic Plains

IMD:

India Meteorological Department

IVS:

Inter-annual variability skill score

LR:

Linear regression

RE:

Relative error

SSP126:

Shared socioeconomic pathway 1(radiative forcing level by 2100 is 2.6 W m−2)

SSP245:

Shared socioeconomic pathway 2 (radiative forcing level by 2100 is 4.5 W m−2)

SSP370:

Shared socioeconomic pathway 3 (radiative forcing level by 2100 is 7.0 W m−2)

SSP585:

Shared socioeconomic pathway 5 (radiative forcing level by 2100 is 8.5 W m−2)

STCF:

Short-term climate forcers

SVR:

Support vector regression

TR:

Total Ranking Score

USH:

Zonal wind shear

RH:

Relative humidity

WFD:

Widespread fog days

WMO:

World Meteorological Organization

WS:

Wind speed

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Acknowledgements

Authors of this study acknowledge Director, Indian Institute of Tropical Meteorology (IITM) for encouraging this work. We acknowledge the India Meteorological Department (IMD), Pune, for providing the daily data summaries used in this study. The authors acknowledge the World Climate Research Programme for making the CMIP6 datasets available for research, and thank the Earth System Grid Federation (ESGF) for archiving and providing access to the CMIP6 datasets. ERA5 data obtained from the ECMWF server is acknowledged. Utilization of high performance computing facility at IITM, and open source packages Keras and Scikit-learn Python library for carrying out the analyses is acknowledged. Discussions with Mr. Panini Dasgupta, IITM, are acknowledged. We thank the anonymous reviewers for providing valuable comments. This work is carried out as part of the first author’s doctoral dissertation.

Funding

Indian Institute of Tropical Meteorology (IITM), Pune is an autonomous institute, fully funded by the Ministry of Earth Sciences (MoES), Government of India.

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Authors and Affiliations

  1. Centre for Climate Change Research (CCCR), Indian Institute of Tropical Meteorology, Ministry of Earth Sciences (IITM-MoES), Pune, India

    Dipti Hingmire, Ramesh Vellore, R. Krishnan, Manmeet Singh, A. Metya, T. Gokul & D. C. Ayantika

  2. Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, India

    Dipti Hingmire, A. Metya & T. Gokul

  3. Jackson School of Geosciences, University of Texas at Austin, Austin, TX, 78712, USA

    Manmeet Singh

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  1. Dipti Hingmire
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  2. Ramesh Vellore
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  3. R. Krishnan
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  4. Manmeet Singh
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  5. A. Metya
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  6. T. Gokul
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  7. D. C. Ayantika
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Correspondence to Ramesh Vellore.

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Hingmire, D., Vellore, R., Krishnan, R. et al. Climate change response in wintertime widespread fog conditions over the Indo-Gangetic Plains. Clim Dyn 58, 2745–2766 (2022). https://doi.org/10.1007/s00382-021-06030-1

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  • DOI: https://doi.org/10.1007/s00382-021-06030-1

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