World's Best Scientists 2026 revealed!
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Environmental Sciences
China
2023

D-Index & Metrics

Environmental Sciences

D-Index
64
Citations
14601
World Ranking
2326
National Ranking
234

In-Sik Kang publication distribution in Environmental Sciences in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Environmental Sciences in 2026. The highlighted bar marks where In-Sik Kang sits on this spectrum.

41–50 publications: 21 scientists 51–60 publications: 62 scientists 61–70 publications: 133 scientists 71–80 publications: 257 scientists 81–90 publications: 361 scientists 91–100 publications: 440 scientists 101–110 publications: 492 scientists 111–120 publications: 541 scientists 121–130 publications: 617 scientists 131–140 publications: 544 scientists 141–150 publications: 541 scientists 151–160 publications: 539 scientists 161–170 publications: 444 scientists 171–180 publications: 444 scientists 181–190 publications: 400 scientists 191–200 publications: 377 scientists 201–210 publications: 318 scientists 211–220 publications: 283 scientists 221–230 publications: 263 scientists 231–240 publications: 220 scientists 241–250 publications: 217 scientists 251–260 publications: 180 scientists 261–270 publications: 181 scientists 271–280 publications: 155 scientists 281–290 publications: 130 scientists 291–300 publications: 127 scientists 301–310 publications: 130 scientists 311–320 publications: 85 scientists 321–330 publications: 106 scientists 331–340 publications: 80 scientists 341–350 publications: 83 scientists 351–360 publications: 75 scientists 361–370 publications: 69 scientists 371–380 publications: 52 scientists 381–390 publications: 54 scientists 391–400 publications: 56 scientists 401–410 publications: 44 scientists 411–420 publications: 40 scientists 421–430 publications: 36 scientists 431–440 publications: 25 scientists 441–450 publications: 25 scientists 451–460 publications: 32 scientists 461–470 publications: 29 scientists 471–480 publications: 21 scientists 481–490 publications: 26 scientists 491–500 publications: 25 scientists 501–510 publications: 17 scientists 511–520 publications: 18 scientists 521–530 publications: 15 scientists 531–540 publications: 22 scientists 541–550 publications: 12 scientists 551–560 publications: 15 scientists 561–570 publications: 11 scientists 571–580 publications: 19 scientists 581–590 publications: 9 scientists 591–600 publications: 9 scientists 601–610 publications: 7 scientists 611–620 publications: 11 scientists 621–630 publications: 5 scientists 631–640 publications: 5 scientists 641–650 publications: 6 scientists 651–660 publications: 3 scientists 661–670 publications: 3 scientists 671–680 publications: 4 scientists 681–686 publications: 3 scientists 687+ publications: 100 scientists
41 publications 687+

This scientist: 276 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 687 publications or more.

In-Sik Kang D-index placement in Environmental Sciences in 2026

The chart shows the D-index (discipline H-index) distribution of Environmental Sciences scientists ranked by Research.com in 2026. The highlighted bar marks where In-Sik Kang sits on this spectrum.

30 D-Index: 12 scientists 31 D-Index: 26 scientists 32 D-Index: 51 scientists 33 D-Index: 88 scientists 34 D-Index: 123 scientists 35 D-Index: 163 scientists 36 D-Index: 206 scientists 37 D-Index: 267 scientists 38 D-Index: 265 scientists 39 D-Index: 275 scientists 40 D-Index: 321 scientists 41 D-Index: 343 scientists 42 D-Index: 305 scientists 43 D-Index: 336 scientists 44 D-Index: 330 scientists 45 D-Index: 348 scientists 46 D-Index: 291 scientists 47 D-Index: 275 scientists 48 D-Index: 272 scientists 49 D-Index: 273 scientists 50 D-Index: 263 scientists 51 D-Index: 232 scientists 52 D-Index: 266 scientists 53 D-Index: 217 scientists 54 D-Index: 199 scientists 55 D-Index: 177 scientists 56 D-Index: 202 scientists 57 D-Index: 204 scientists 58 D-Index: 166 scientists 59 D-Index: 177 scientists 60 D-Index: 166 scientists 61 D-Index: 152 scientists 62 D-Index: 143 scientists 63 D-Index: 150 scientists 64 D-Index: 124 scientists 65 D-Index: 119 scientists 66 D-Index: 120 scientists 67 D-Index: 118 scientists 68 D-Index: 82 scientists 69 D-Index: 98 scientists 70 D-Index: 94 scientists 71 D-Index: 105 scientists 72 D-Index: 74 scientists 73 D-Index: 84 scientists 74 D-Index: 70 scientists 75 D-Index: 67 scientists 76 D-Index: 78 scientists 77 D-Index: 60 scientists 78 D-Index: 59 scientists 79 D-Index: 52 scientists 80 D-Index: 47 scientists 81 D-Index: 38 scientists 82 D-Index: 48 scientists 83 D-Index: 42 scientists 84 D-Index: 42 scientists 85 D-Index: 43 scientists 86 D-Index: 29 scientists 87 D-Index: 37 scientists 88 D-Index: 29 scientists 89 D-Index: 30 scientists 90 D-Index: 34 scientists 91 D-Index: 20 scientists 92 D-Index: 22 scientists 93 D-Index: 17 scientists 94 D-Index: 19 scientists 95 D-Index: 24 scientists 96 D-Index: 21 scientists 97 D-Index: 20 scientists 98 D-Index: 24 scientists 99 D-Index: 17 scientists 100 D-Index: 17 scientists 101 D-Index: 21 scientists 102 D-Index: 25 scientists 103 D-Index: 18 scientists 104 D-Index: 26 scientists 105 D-Index: 19 scientists 106 D-Index: 15 scientists 107 D-Index: 10 scientists 108 D-Index: 13 scientists 109 D-Index: 15 scientists 110 D-Index: 12 scientists 111 D-Index: 8 scientists 112 D-Index: 7 scientists 113 D-Index: 9 scientists 114 D-Index: 6 scientists 115 D-Index: 12 scientists 116 D-Index: 7 scientists 117 D-Index: 8 scientists 118 D-Index: 3 scientists 119 D-Index: 5 scientists 120 D-Index: 7 scientists 121 D-Index: 2 scientists 122 D-Index: 4 scientists 123 D-Index: 8 scientists 124 D-Index: 7 scientists 125+ D-Index: 99 scientists
30 D-Index 125+

This scientist: 64 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 125 D-Index or more.

Research.com Recognitions

  • 2023 - Research.com Environmental Sciences in China Leader Award

Overview

In-Sik Kang is affiliated with the Second Institute of Oceanography in China and has contributed extensively to the fields of Earth and Planetary Sciences as well as Environmental Science. Their research spans various subfields including Global and Planetary Change, Oceanography, Atmospheric Science, Computer Vision and Pattern Recognition, and Information Systems and Management.

The scientist's main research topics involve Climate variability and models, Oceanographic and Atmospheric Processes, Meteorological Phenomena and Simulations, Arctic and Antarctic ice dynamics, Scientific Computing and Data Management, Distributed and Parallel Computing Systems, and Environmental and Agricultural Sciences.

Frequent publication venues where Kang's work appears include the Journal of Climate, npj Climate and Atmospheric Science, Proceedings of the National Academy of Sciences, Nature Geoscience, and Earth System Science Data.

  • Journal of Climate
  • npj Climate and Atmospheric Science
  • Proceedings of the National Academy of Sciences
  • Nature Geoscience
  • Earth system science data

Kang has collaborated frequently with several researchers, among them:

  • Sarah M. Kang
  • Yue Yu
  • Paulo Ceppi
  • Fred Kucharski
  • Saroj K. Mishra

Recent notable papers authored or co-authored by In-Sik Kang include:

  • Global impacts of recent Southern Ocean cooling, 2023, Proceedings of the National Academy of Sciences
  • Separating the Indian and Pacific Ocean Impacts on the Euro-Atlantic Response to ENSO and Its Transition from Early to Late Winter, 2020, Journal of Climate
  • Recent global climate feedback controlled by Southern Ocean cooling, 2023, Nature Geoscience
  • Atlantic Ocean influence on Middle East summer surface air temperature, 2020, npj Climate and Atmospheric Science
  • Earth Virtualization Engines (EVE), 2024, Earth system science data

Best Publications

  • The Asian Summer Monsoon: An Intercomparison of CMIP5 vs. CMIP3 Simulations of the Late 20th Century

    K. R. Sperber;H. Annamalai;I. Kang;A. Kitoh

  • Fundamental challenge in simulation and prediction of summer monsoon rainfall

    Bin Wang;Qinghua Ding;Xiouhua Fu;In-Sik Kang

  • Current status of ENSO prediction skill in coupled ocean-atmosphere models

    Emilia K. Jin;James L. Kinter;B. Wang;C.-K. Park

  • Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region

    June Yi Lee;Bin Wang;Matthew C. Wheeler;Xiouhua Fu

  • Intercomparison of the climatological variations of Asian summer monsoon precipitation simulated by 10 GCMs

    I.-S. Kang;K. Jin;B. Wang;K.-M. Lau

  • Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

    Bin Wang;June Yi Lee;In Sik Kang;J. Shukla

  • MJO Simulation Diagnostics

    D Waliser;K Sperber;H Hendon;D Kim

  • Ensemble Simulations of Asian–Australian Monsoon Variability by 11 AGCMs*

    Bin Wang;In-Sik Kang;June-Yi Lee

  • Application of MJO Simulation Diagnostics to Climate Models

    D. Kim;K. Sperber;W. Stern;D. Waliser

  • Interactive Feedback between ENSO and the Indian Ocean

    Jong-Seong Kug;In-Sik Kang

  • AGCM simulations of intraseasonal variability associated with the Asian summer monsoon

    D. E. Waliser;K. Jin;I.-S. Kang;W. F. Stern

  • Western tropical Pacific multidecadal variability forced by the Atlantic multidecadal oscillation.

    Cheng Sun;Fred Kucharski;Jianping Li;Fei-Fei Jin

  • El Niño and La Niña sea surface temperature anomalies: Asymmetry characteristics associated with their wind stress anomalies

    In-Sik Kang;Jong-Seong Kug

  • Potential Predictability of Summer Mean Precipitation in a Dynamical Seasonal Prediction System with Systematic Error Correction

    In-Sik Kang;June-Yi Lee;Chung-Kyu Park

  • Principal Modes of Climatological Seasonal and Intraseasonal Variations of the Asian Summer Monsoon

    In-Sik Kang;Chang-Hoi Ho;Young-Kwon Lim;K. M. Lau

  • A Systematic Relationship between Intraseasonal Variability and Mean State Bias in AGCM Simulations

    Daehyun Kim;Adam H. Sobel;Eric D. Maloney;Dargan M. W. Frierson

  • MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis

    Min-Seop Ahn;Daehyun Kim;Kenneth R. Sperber;In-Sik Kang;In-Sik Kang

  • How are seasonal prediction skills related to models’ performance on mean state and annual cycle?

    June-Yi Lee;Bin Wang;I.-S. Kang;J. Shukla

  • Role of the ENSO–Indian Ocean coupling on ENSO variability in a coupled GCM

    Jong Seong Kug;Tim Li;Soon Il An;In Sik Kang

  • How accurately do coupled climate models predict the leading modes of Asian-Australian monsoon interannual variability?

    Bin Wang;June-Yi Lee;I.-S. Kang;J. Shukla

  • CGILS: Results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models

    Minghua Zhang;Christopher S. Bretherton;Peter N. Blossey;Phillip H. Austin

Frequent Co-Authors

Jong-Seong Kug
Jong-Seong Kug Seoul National University
Fred Kucharski
Fred Kucharski International Centre for Theoretical Physics
Fei-Fei Jin
Fei-Fei Jin University of Hawaii at Manoa
Soon-Il An
Soon-Il An Yonsei University
June-Yi Lee
June-Yi Lee Pusan National University
Daehyun Kim
Daehyun Kim University of Washington
Yoo-Geun Ham
Yoo-Geun Ham Seoul National University
Bin Wang
Bin Wang University of Hawaii at Manoa
Mansour Almazroui
Mansour Almazroui King Abdulaziz University
Myong-In Lee
Myong-In Lee Ulsan National Institute of Science and Technology

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