Skip to main content

Advertisement

Log in

Skill assessment of global climate model wind speed from CMIP5 and CMIP6 and evaluation of projections for the Bay of Bengal

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Atmospheric and oceanic parameters derived from global climate model (GCM) simulations have received wide global attention and importance in representing the future world under different scenarios of greenhouse gas emissions. The present study deals with near-surface wind speed in the Bay of Bengal (BoB) obtained from CMIP5 and the upcoming CMIP6 GCMs and validation exercise clearly signify improved performance of CMIP6 GCMs over CMIP5. Multi-model ensemble mean corresponding to the four emission scenarios are constructed using the best performing models of CMIP6 family. The study reveals that near-future changes in wind speed in the BoB are moderate under the low-end scenario of SSP1-2.6. Projected wind speeds in the head BoB are expected to increase or decrease by 20% during June–July–August and December–January–February under high-end scenario by the end of twenty-first century. A positive change up to 30% in the northeast monsoon winds under SSP5-8.5 is projected in the central BoB. Irrespective of the seasons, a net increase amounting to 0.6–0.8 m/s is observed along the east coast of India under SSP2-4.5 scenario by the mid and end of the century. Maximum rise by 25% (0.5–1 m/s) in wind speed is predicted under SSP3-7.0 scenario in the near future. Further, the study points out a decline in wind speed by 0.2–0.8 m/s in the central and southern BoB under the extreme scenario of SSP5-8.5. Strengthening and weakening of winds over the BoB accounts the projected variations in temperature that resulted from global warming and subsequent changes in atmospheric circulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

(Source URL: https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained)

Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Abram NJ, Wright NM, Ellis B, Dixon BC, Wurtzel JB, England MH, Ummenhofer CC, Philibosian B, Cahyarini SY, Yu TL, Shen CC (2020) Coupling of Indo-Pacific climate variability over the last millennium. Nature 579(7799):385–392

    Google Scholar 

  • Bentamy A, Denis CF (2012) Gridded surface wind fields from Metop/ASCAT measurements. Int J Remote Sen. https://doi.org/10.1080/01431161.2011.600348

    Article  Google Scholar 

  • Bentamy A, Croize-Fillon D, Perigaud C (2008) Characterization of ASCAT measurements based on Buoy and QuikSCAT wind vector observations. Ocean Sci 4(4):265–274

    Google Scholar 

  • Bhaskaran PK (2019) Challenges and future directions in ocean wave modeling: a review. J Extreme Events. https://doi.org/10.1142/S2345737619500040

    Article  Google Scholar 

  • Bhaskaran PK, Gupta N, Dash MK (2014) Wind-wave climate projections for the Indian Ocean from Satellite observations. J Mar Sci Res Dev S11:005. https://doi.org/10.4172/2155-9910.S11-005

    Article  Google Scholar 

  • Bhat GS, Gadgil S, Hareesh Kumar PV, Kalsi SR, Madhusoodanan P, Murty VSN, Prasada Rao CVK, Ramesh Babu V, Rao LVG, Rao RR, Ravichandran M, Reddy KG, Sanjeeva Rao P, Sengupta D, Sikka DR, Swain J, Vinayachandran PN (2001) BOBMEX: the Bay of Bengal monsoon experiment. Bull Am Meteorol Soc 82(10):2217–2243

    Google Scholar 

  • Birsan MV, Lenuta M, Alexandru D (2013) Seasonal changes in wind speed in Romania. Rom Rep Phys 65(4):1479–1484

    Google Scholar 

  • Brands S, Herrera S, Fernández J, Gutiérrez JM (2013) How well do CMIP5 earth system models simulate present climate conditions in Europe and Africa? Clim Dyn 41(3–4):803–817

    Google Scholar 

  • Brower MC, Barton MS, Lledó L, Dubois J, (2013) A study of wind speed variability using global reanalysis data. AWS Truepower. https://aws-dewi.ul.com/assets/A-Study-of-Wind-Speed-Variability-Using-Global-Reanalysis-Data2.pdf. Accessed 14 Jan 2020

  • Carvalho D, Rocha A, Gómez-Gesteira M, Silva Santos C (2014a) Comparison of reanalyzed, analyzed, satellite-retrieved and NWP modelled winds with Buoy data along the Iberian Peninsula Coast. Remote Sens Environ 152:480–492

    Google Scholar 

  • Carvalho D, Rocha A, Gómez-Gesteira M, Silva Santos C (2014b) Offshore wind energy resource simulation forced by different reanalyses: comparison with observed data in the Iberian Peninsula. Appl Energy 134:57–64. https://doi.org/10.1016/j.apenergy.2014.08.018

    Article  Google Scholar 

  • Carvalho D, Rocha A, Gómez-Gesteira M, Santos CS (2017) Potential impacts of climate change on European wind energy resource under the CMIP5 future climate projections. Renew Energy 101:29–40

    Google Scholar 

  • Celik DF, Cengiz E (2014) Wind speed trends over Turkey from 1975 to 2006. Int J Climatol 34(6):1913–1927

    Google Scholar 

  • Chelton DB, Schlax MG, Freilich MH, Milliff RF (2004) Satellite measurements reveal persistent small-scale features in ocean winds. Science 303(5660):978–983

    Google Scholar 

  • Chu PC, Qi Y, Chen Y, Shi P, Mao Q (2004) South China sea wind-wave characteristics. Part 1: validation of wavematch-III using TOPEX/poseidon data. J Atmos Ocean Technol 21(11):1718–1733. https://doi.org/10.1175/JTECH1661.1

    Article  Google Scholar 

  • Crawford CG, Slack JR, Hirsch RM (1983) Nonparametric tests for trend in water quality data using the statistical analysis system. Open Report no. 83-550, US Geological Survey, USA

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Holm EV, Isaksen L, Kallberg P, Kohler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C, de Rosnay P, Tavolato C, Thepaut J-N, Vitart F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597

    Google Scholar 

  • Dong L, Zhou T, Wu B (2014) Indian Ocean warming during 1958–2004 simulated by a climate system model and its mechanism. Clim Dyn 42:203–217. https://doi.org/10.1007/s00382-013-1722-z

    Article  Google Scholar 

  • Ebuchi N, Graber HC, Caruso MJ (2002) Evaluation of wind vectors observed by QuikSCAT/SeaWinds using Ocean Buoy Data. J Atmos Ocean Technol. https://doi.org/10.1175/1520-0426(2002)019<2049:EOWVOB>2.0.CO;2

    Article  Google Scholar 

  • Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9(5):1937–1958

    Google Scholar 

  • Figa-Saldaña J, Wilson JJW, Attema E, Gelsthorpe R, Drinkwater MR, Stoffelen A (2002) The advanced scatterometer (Ascat) on the meteorological operational (MetOp) platform: a follow on for European Wind Scatterometers. Can J Remote Sens. https://doi.org/10.5589/m02-035

    Article  Google Scholar 

  • Ghorbani MA, Khatibi R, FazeliFard MH, Naghipour L, Makarynskyy O (2016) Short-term wind speed predictions with machine learning techniques. Meteorol Atmos Phys 128(1):57–72

    Google Scholar 

  • Gidden MJ, Riahi K, Smith SJ, Fujimori S, Luderer G, Kriegler E, van Vuuren DP, van den Berg M, Feng L, Klein D, Calcin K, Doelman JC, Frank S, Fircko O, Harmsen M, Hasegawa T, Havlik P, Hilaire J, Hoesly R, Horing J, Popp A, Stehfest E, Takahashi K (2019) Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geosci Model Dev 12(4):1443–1475

    Google Scholar 

  • Gocic M, Trajkovic S (2013) Analysis of changes in meteorological variables using Mann–Kendall and Sen’s slope estimator statistical tests in Serbia. Glob Planet Change 100:172–182. https://doi.org/10.1016/j.gloplacha.2012.10.014

    Article  Google Scholar 

  • Goswami BN, Rao AS, Sengupta D, Chakravorty S (2016) Monsoons to mixing in the Bay of Bengal: multiscale air–sea interactions and monsoon predictability. Oceanography 29(2):18–27

    Google Scholar 

  • Gupta N, Bhaskaran PK (2016) Inter-dependency of wave parameters and directional analysis of ocean wind-wave climate for the Indian Ocean. Int J Climatol 37:3036–3043. https://doi.org/10.1002/joc.4898

    Article  Google Scholar 

  • Gupta N, Bhaskaran PK, Dash MK (2015) Recent trends in wind-wave climate for the Indian Ocean. Curr Sci 108(12):2191–2201

    Google Scholar 

  • Gupta N, Bhaskaran PK, Dash MK (2017) Dipole behavior in maximum significant wave height over the Southern Indian Ocean. Int J Climatol 37:4925–4937. https://doi.org/10.1002/joc.5133

    Article  Google Scholar 

  • Hasager CB, Dellwik E, Nielsen M, Furevik BR (2004) Validation of ERS-2 SAR offshore wind-speed maps in the North Sea. Int J Remote Sens 25(19):3817–3841

    Google Scholar 

  • Helsel DR, Hirsch RM (1992) Statistical methods. Water Resour. https://doi.org/10.3133/twri04A3

    Article  Google Scholar 

  • IPCC (2013) Summary for Policymakers. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change. The physical science basis. Contributions of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

    Google Scholar 

  • Jadhav SK, Munot AA (2009) Warming SST of Bay of Bengal and decrease in formation of cyclonic disturbances over the Indian Region during southwest monsoon season. Theor Appl Climatol 96(3–4):327–336

    Google Scholar 

  • Kamranzad B, Mori N (2019) Future wind and wave climate projections in the Indian Ocean based on a super-high-resolution MRI-AGCM3.2S model projection. Clim Dyn 53(3–4):2391–2410. https://doi.org/10.1007/s00382-019-04861-7

    Article  Google Scholar 

  • Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin, London

    Google Scholar 

  • Khan TMA, Singh OP, Rahman MS (2000) Recent sea level and sea surface temperature trends along the Bangladesh Coast in relation to the frequency of intense cyclones. Mar Geod 23(2):103–116

    Google Scholar 

  • Klein SA, Soden BJ, Lau N-C (1999) Remote sea surface temperature variations during ENSO: evidence for a tropical atmospheric bridge. J Clim 12:917–932

    Google Scholar 

  • Krishnamurthy V, Kirtman BP (2003) Variability of the Indian Ocean: relation to monsoon and ENSO. Q J R Meteorol Soc 129(590 Part A):1623–1646

    Google Scholar 

  • Krishnan A, Bhaskaran PK (2019a) Performance of CMIP5 wind speed from global climate models for the Bay of Bengal region. Int J Climatol. https://doi.org/10.1002/joc.6404

    Article  Google Scholar 

  • Krishnan A, Bhaskaran PK (2019b) CMIP5 wind speed comparison between satellite altimeter and reanalysis products for the Bay of Bengal. Environ Monit Assess. https://doi.org/10.1007/s10661-019-7729-0

    Article  Google Scholar 

  • Kulkarni S, Huang HP (2014) Changes in surface wind speed over North America from CMIP5 model projections and implications for wind energy. Ad Meteorol. https://doi.org/10.1155/2014/292768

    Article  Google Scholar 

  • Kumar P, Min SK, Weller E, Lee H, Wang XL (2016) Influence of climate variability on extreme ocean surface wave heights assessed from ERA-interim and ERA-20C. J Clim 29(11):4031–4046. https://doi.org/10.1175/JCLI-D-15-0580.1

    Article  Google Scholar 

  • Kumar P, Kaur S, Weller E, Min SK (2019) Influence of natural climate variability on the extreme Ocean surface wave heights over the Indian Ocean. J Geophys Res Oceans 124(8):6176–6199

    Google Scholar 

  • Lee T, Waliser DE, Li J-LF, Landerer FW, Gierach MM (2013) Evaluation of CMIP3 and CMIP5 wind stress climatology using satellite measurements and atmospheric reanalysis products. J Clim 26(16):5810–5826

    Google Scholar 

  • Lehner S, Schulz-Stellenfleth J, Schattler B, Breit H, Horstmann J (2000) Wind and wave measurements using complex ERS-2 SAR wave mode data. IEEE Trans Geosci Remote Sens 38(5 I):2246–2257

    Google Scholar 

  • Lin F, Chen X, Yao H (2017) Evaluating the use of Nash–Sutcliffe efficiency coefficient in goodness-of-fit measures for daily runoff simulation with SWAT. J Hydrol Eng 22(11):1–9

    Google Scholar 

  • Lindzen RS, Nigam S (1987) On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics. J Atmos Sci 44(17):2418–2436

    Google Scholar 

  • Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259

    Google Scholar 

  • Manwell JF, McGowan JG, Rogers AL (2010) Wind energy explained: theory, design and application, 2nd edn. Wiley, UK, p 705

    Google Scholar 

  • McPhaden MJ, Meyers G, Ando K, Masumoto Y, Murty VSN, Ravichandran M, Syamsudin F, Vialard J, Yu L, Yu W (2009) RAMA: the research moored array for African–Asian–Australian monsoon analysis and prediction. Bull Am Meteorol Soc 90(4):459–480

    Google Scholar 

  • McVicar TR, Donohue L, Jianguo VN, Thomas T, Paul G, Jurgen J, Deepak H, Youcef M, Natalie MK, Andries DY (2012) Global review and synthesis of trends in observed terrestrial near-surface wind speeds: implications for evaporation. J Hydrol 416–417:182–205. https://doi.org/10.1016/j.jhydrol.2011.10.024

    Article  Google Scholar 

  • Mohan S, Bhaskaran PK (2019a) Evaluation and bias correction of global climate models in the CMIP5 over the Indian Ocean Region. Environ Monit Assess 191:806. https://doi.org/10.1007/s10661-019-7700-0

    Article  Google Scholar 

  • Mohan S, Bhaskaran PK (2019b) Evaluation of CMIP5 climate model projections for surface wind speed over the Indian Ocean Region. Clim Dyn 53(9–10):5415–5435. https://doi.org/10.1007/s00382-019-04874-2

    Article  Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900. https://doi.org/10.13031/2013.23153

    Article  Google Scholar 

  • Morim J, Hemer M, Andutta F, Shimura T, Cartwright N (2020) Skill and uncertainty in surface wind fields from general circulation models: intercomparison of bias between AGCM, AOGCM and ESM global simulations. Int J Climatol 40(5):2659–2673

    Google Scholar 

  • Muthige M, Malherbe J, Engelbrecht F, Grab S, Beraki A, Maisha TR, Merwe JVD (2018) Projected changes in tropical cyclones over the South West Indian Ocean under different extents of global warming. Environ Res Lett. https://doi.org/10.1088/1748-9326/aabc60

    Article  Google Scholar 

  • Nagababu G, Kachhwaha SS, Naidu NK, Savsani V (2017) Application of reanalysis data to estimate offshore wind potential in EEZ of India based on marine ecosystem considerations. Energy 118:622–631

    Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models. Part I—a discussion of principles. J Hydrol 27(3):282–290

    Google Scholar 

  • Nayak S, Bhaskaran PK, Venkatesan R, Dasgupta S (2013) Modulation of local wind-waves at Kalpakkam from remote forcing effects of Southern Ocean swells. Ocean Eng 64:23–35. https://doi.org/10.1016/j.oceaneng.2013.02.010

    Article  Google Scholar 

  • Parvathi V, Suresh I, Lengaigne M, Izumo T, Vialard J (2017) Robust projected weakening of winter monsoon winds over the Arabian sea under climate change. Geophys Res Lett 44(19):9833–9843

    Google Scholar 

  • Parvathy KG, Bhaskaran PK (2019) Nearshore modelling of wind-waves and its attenuation characteristics over a mud dominated shelf in the Head Bay of Bengal. Reg Stud Mar Sci. https://doi.org/10.1016/j.rsma.2019.100665

    Article  Google Scholar 

  • Patra A, Bhaskaran PK (2016) Trends in Wind-wave climate over the head Bay of Bengal region. Int J Climatol 36(13):4222–4240

    Google Scholar 

  • Patra A, Bhaskaran PK (2017) Temporal variability in wind-wave climate and its validation with ESSO-NIOT wave atlas for the head Bay of Bengal. Clim Dyn 49(4):1271–1288

    Google Scholar 

  • Patra A, Bhaskaran PK, Jose F (2018) Time evolution of atmospheric parameters and their influence on sea level pressure over the head Bay of Bengal. Clim Dyn 50(11–12):4583–4598

    Google Scholar 

  • Rahaman H, Srinivasu U, Panickal S, Durgadoo JV, Griffies SM, Ravichandran M, Bozec A, Cherchi A, Voldoire A, Sidorenko D, Chassignet EP, Danabasoglu G, Tsujino H, Getzlaff K, Ilicak M, Bentsen M, Long MC, Fogli PG, Farneti R, Danilov S, Marsland SJ, Valcke S, Yeager SG, Wang Q (2020) An assessment of the Indian Ocean mean state and seasonal cycle in a suite of interannual CORE-II simulations. Ocean Model 145:101503

    Google Scholar 

  • Rani SI, Das Gupta M, Sharma P, Prasad VS (2014) Intercomparison of Oceansat-2 and ASCAT winds with in situ Buoy observations and short-term numerical forecasts. Atmos Ocean 52(1):92–102. https://doi.org/10.1080/07055900.2013.869191

    Article  Google Scholar 

  • Reguero BG, Menéndez M, Méndez FJ, Mínguez R, Losada IJ (2012) A global ocean wave (GOW) calibrated reanalysis from 1948 onwards. Coast Eng 65:38–55

    Google Scholar 

  • Rehman S (2013) Long-term wind speed analysis and detection of its trends using Mann–Kendall test and linear regression method. Arab J Sci Eng 38(2):421–437

    Google Scholar 

  • Ritter A, Muñoz-Carpena R (2013) Performance evaluation of hydrological models: statistical significance for reducing subjectivity in goodness-of-fit assessments. J Hydrol 480(1):33–45. https://doi.org/10.1016/j.jhydrol.2012.12.004

    Article  Google Scholar 

  • Roxy MK, Ritika K, Terray P, Masson S (2014) The curious case of Indian Ocean warming. J Clim 27(22):8501–8509

    Google Scholar 

  • Sahoo B, Bhaskaran PK (2016) Assessment on historical cyclone tracks in the Bay of Bengal, East Coast of India. Int J Climatol 36(1):95–109

    Google Scholar 

  • Sandhya KG, Bala Krishnan Nair TM, Bhaskaran PK, Sabique L, Arun N, Jeykumar K (2013) Wave forecasting system for operational use and its validation at coastal Puducherry, East Coast of India. Ocean Eng 80:64–72. https://doi.org/10.1016/j.oceaneng.2014.01.009

    Article  Google Scholar 

  • Semedo A, Sušelj K, Rutgersson A, Sterl A (2011) A global view on the wind sea and swell climate and variability from ERA-40. J Clim 24(5):1461–1479

    Google Scholar 

  • Semedo A, Weisse R, Behrens A, Sterl A, Bengtsson L, Günther H (2013) Projection of global wave climate change toward the end of the twenty-first century. J Clim 26:8269–8288. https://doi.org/10.1175/JCLI-D-12-00658.1

    Article  Google Scholar 

  • Sempreviva AM, Furevik B, Cheruy F, Barthelmie RJ, Jimenez B, Transerici C (2006) Estimating off-shore wind climatology in the Mediterranean area, comparison of QuikSCAT data with other methodologies. OWEMES 2006, 20–22 April, Civitavecchia, Italy

  • Shanas PR, Sanil Kumar V (2015) Trends in surface wind speed and significant wave height as revealed by ERA-interim wind wave Hindcast in the Central Bay of Bengal. Int J Climatol 35(9):2654–2663

    Google Scholar 

  • Simmons AJ, Berrisford P, Dee DP, Hersbach H, Hirahara S, Thépaut JN (2017) A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets. Q J R Meteorol Soc 143(702):101–119

    Google Scholar 

  • Sinha M, Jha S, Chakraborty P (2020) Indian Ocean wind speed variability and global teleconnection patterns. Oceanologia 62(2):126–138. https://doi.org/10.1016/j.oceano.2019.10.002

    Article  Google Scholar 

  • Sreelakshmi S, Bhaskaran PK (2020) Spatio-temporal distribution and variability of high threshold wind speed and significant wave height for the Indian Ocean. Pure Appl Geophys. https://doi.org/10.1007/s00024-020-02462-8

    Article  Google Scholar 

  • Srivastava A, Dwivedi S, Mishra AK (2016) Intercomparison of high-resolution Bay of Bengal circulation models forced with different winds. Mar Geod 39(3–4):271–289

    Google Scholar 

  • Stouffer RJ, Eyring V, Meehl GA, Bony S, Senior C, Stevens B, Taylor KE (2017) CMIP5 scientific gaps and recommendations for CMIP6. Bull Am Meteorol Soc 98(1):95–105

    Google Scholar 

  • Swapna P, Krishnan R, Wallace JM (2014) Indian Ocean and monsoon coupled interactions in a warming environment. Clim Dyn 42:2439–2454. https://doi.org/10.1007/s00382-013-1787-8

    Article  Google Scholar 

  • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106(D7):7183–7192

    Google Scholar 

  • Tokinaga H, Xie SP (2011) Wave- and anemometer-based sea surface wind (WASWind) for climate change analysis. J Clim 24(1):267–285

    Google Scholar 

  • Vandemark D, Vachon PW, Chapron B (1998) Assessment of ERS-1 SAR wind-speed estimates using an airborne altimeter. Earth Obs Q 59:5–8

    Google Scholar 

  • Wang XL, Feng Y, Swail VR (2014) Change in global ocean wave heights as projected using multimodel CMIP5 simulations. Geophys Res Lett 41:1026–1034. https://doi.org/10.1002/2013GL058650

    Article  Google Scholar 

  • Wentz FJ, Ricciardulli L, Hilburn K, Mears C (2007) How much more rain will global warming bring? Science 317(5835):233–235

    Google Scholar 

  • Willmott CJ, Ackleson SG, Davis RE, Feddema JJ, Klink KM, Legates DR, O'Donnell J, Rowe CM (1985) Statistics for the evaluation and comparison of models. J Geophys Res 90(C5):8995–9005

    Google Scholar 

  • Young IR, Ribal A (2019) Multiplatform evaluation of global trends in wind speed and wave height. Science 364(6440):548–552

    Google Scholar 

  • Young IR, Zieger S, Babanin AV (2011) Global trends in wind speed and wave height. Science 332(6028):451–455. https://doi.org/10.1126/science.1197219

    Article  Google Scholar 

  • Zhang X, Vincent LA, Hogg WD, Niitsoo A (2000) Temperature and precipitation trends in Canada during the 20th century. Atmos Ocean 38(3):395–429

    Google Scholar 

  • Zou T, Kaminski ML (2014) Predictions of climate change impact on fatigue assessment of offshore floating structures. Delft: Department of Maritime and Transport Technology, Delft University of Technology, p 10

Download references

Acknowledgements

The authors sincerely thank the Department of Science and Technology (DST), Government of India, for the financial support. This study was conducted under the Centre of Excellence (CoE) in Climate Change studies established at IIT Kharagpur funded by DST, Government of India. The study forms a part of the ongoing project 'Wind-Waves and Extreme Water Level Climate Projections for the East Coast of India'. The authors also acknowledge the World Climate Research Program's Working Group on Coupled Modelling, for providing CMIP5 and CMIP6 multi-model data, Asia-Pacific Data Research Center for the scatterometer data from ERS-1/2, QuikSCAT, and ASCAT satellite missions, the European Centre for Medium-Range Weather Forecasts for the ERA-interim data and Pacific Marine Environmental Laboratory under National Oceanic and Atmospheric Administration (NOAA) for the RAMA buoy observations.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prasad K. Bhaskaran.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Krishnan, A., Bhaskaran, P.K. Skill assessment of global climate model wind speed from CMIP5 and CMIP6 and evaluation of projections for the Bay of Bengal. Clim Dyn 55, 2667–2687 (2020). https://doi.org/10.1007/s00382-020-05406-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-020-05406-z

Keywords

Navigation