• Beck, H. E., and et al. , 2017a: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrol. Earth Syst. Sci., 21, 62016217, https://doi.org/10.5194/hess-21-6201-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., A. I. J. M. Van Dijk, V. Levizzani, J. Schellekens, D. G. Miralles, B. Martens, and A. De Roo, 2017b: MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci., 21, 589615, https://doi.org/10.5194/hess-21-589-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., and et al. , 2019a: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS. Hydrol. Earth Syst. Sci., 23, 207224, https://doi.org/10.5194/hess-23-207-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., E. F. Wood, M. Pan, C. K. Fisher, D. G. Miralles, A. I. J. M. van Dijk, T. R. McVicar, and R. F. Adler, 2019b: MSWEP V2 global 3-hourly 0.1° precipitation: Methodology and quantitative assessment. Bull. Amer. Meteor. Soc., 100, 473500, https://doi.org/10.1175/BAMS-D-17-0138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Becker, E., H. V. den Dool, and Q. Zhang, 2014: Predictability and forecast skill in NMME. J. Climate, 27, 58915906, https://doi.org/10.1175/JCLI-D-13-00597.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cash, B. A., J. V. Manganello, and J. L. Kinter, 2019: Evaluation of NMME temperature and precipitation bias and forecast skill for South Asia. Climate Dyn., 53, 73637380, https://doi.org/10.1007/s00382-017-3841-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, X., and K. K. Tung, 2018: Global-mean surface temperature variability: Space-time perspective from rotated EOFs. Climate Dyn., 51, 17191732, https://doi.org/10.1007/s00382-017-3979-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chung, C., and S. Nigam, 1999: Weighting of geophysical data in principle component analysis. J. Geophys. Res., 104, 16 92516 928, https://doi.org/10.1029/1999JD900234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., I. Y. Fung, A. D. Del Genio, A. Dai, I. Y. Fung, and A. D. Del Genio, 1997: Surface observed global land precipitation variations during 1900–88. J. Climate, 10, 29432962, https://doi.org/10.1175/1520-0442(1997)010<2943:SOGLPV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DelSole, T., X. Yang, and M. K. Tippett, 2013: Is unequal weighting significantly better than equal weighting for multi-model forecasting? Quart. J. Roy. Meteor. Soc., 139, 176183, https://doi.org/10.1002/qj.1961.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doblas-Reyes, F. J., R. Hagedorn, and T. N. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting — II. Calibration and combination. Tellus, 57A, 234252, https://doi.org/10.3402/tellusa.v57i3.14658.

    • Search Google Scholar
    • Export Citation
  • Drewitt, G., A. A. Berg, W. J. Merryfield, and W. S. Lee, 2012: Effect of realistic soil moisture initialization on the Canadian CanCM3 seasonal forecast model. Atmos.–Ocean, 50, 466474, https://doi.org/10.1080/07055900.2012.722910.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Finan, C., H. Wang, and J. Schemm, 2016: Evaluation of an NMME-based hybrid prediction system for Eastern North Pacific basin tropical cyclones. 41st NOAA Annual Climate Diagnostics and Prediction Workshop, Orono, ME, NOAA/NWS, 3 pp., https://www.nws.noaa.gov/ost/climate/STIP/41CDPW/41cdpw-CFinan.pdf.

  • Giorgi, F., and R. Francisco, 2000: Uncertainties in regional climate change prediction: A regional analysis of ensemble simulations with the HADCM2 coupled AOGCM. Climate Dyn., 16, 169182, https://doi.org/10.1007/PL00013733.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greene, A. M., M. Hellmuth, and T. Lumsden, 2012: Stochastic decadal climate simulations for the Berg and Breede water management areas, Western Cape province, South Africa. Water Resour. Res., 48, W06504, https://doi.org/10.1029/2011WR011152.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gupta, H. V., H. Kling, K. K. Yilmaz, and G. F. Martinez, 2009: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J. Hydrol., 377, 8091, https://doi.org/10.1016/j.jhydrol.2009.08.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagedorn, R., F. J. Doblas-Reyes, and T. N. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting - I. Basic concept. Tellus, 57A, 219233, https://doi.org/10.3402/tellusa.v57i3.14657.

    • Search Google Scholar
    • Export Citation
  • Hao, Z., X. Yuan, Y. Xia, F. Hao, and V. P. Singh, 2017: An overview of drought monitoring and prediction systems at regional and global scales. Bull. Amer. Meteor. Soc., 98, 18791896, https://doi.org/10.1175/BAMS-D-15-00149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harnos, D. S., J.-K. E. Schemm, H. Wang, and C. A. Finan, 2017: NMME-based hybrid prediction of Atlantic hurricane season activity. Climate Dyn., 53, 72677285, https://doi.org/10.1007/s00382-017-3891-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2014: Updated high-resolution grids of monthly climatic observations - The CRU TS3.10 dataset. Int. J. Climatol., 34, 623642, https://doi.org/10.1002/joc.3711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jia, L., and et al. , 2015: Improved seasonal prediction of temperature and precipitation over land in a high-resolution GFDL climate model. J. Climate, 28, 20442062, https://doi.org/10.1175/JCLI-D-14-00112.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kawamura, R., 1994: A rotated EOF analysis of global sea surface temperature variability with interannual and interdecadal scales. J. Phys. Oceanogr., 24, 707715, https://doi.org/10.1175/1520-0485(1994)024<0707:AREAOG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khajehei, S., A. Ahmadalipour, and H. Moradkhani, 2017: An effective post-processing of the North American multi-model ensemble (NMME) precipitation forecasts over the continental US. Climate Dyn., 51, 457472, https://doi.org/10.1007/s00382-017-3934-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirtman, B. P., and et al. , 2014: The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585601, https://doi.org/10.1175/BAMS-D-12-00050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kling, H., M. Fuchs, and M. Paulin, 2012: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. J. Hydrol., 424–425, 264277, https://doi.org/10.1016/j.jhydrol.2012.01.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krakauer, N. Y., 2017: Temperature trends and prediction skill in NMME seasonal forecasts. Climate Dyn., 53, 72017213, https://doi.org/10.1007/s00382-017-3657-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, S., and H. Wang, 2015: Seasonal prediction systems based on CCSM3 and their evaluation. Int. J. Climatol., 35, 46814694, https://doi.org/10.1002/joc.4316.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, F., and et al. , 2016: Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China. Int. J. Climatol., 36, 132144, https://doi.org/10.1002/joc.4333.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raftery, A. E., D. Madigan, and J. A. Hoeting, 1997: Bayesian model averaging for linear regression models. J. Amer. Stat. Assoc., 92, 179191, https://doi.org/10.1080/01621459.1997.10473615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rings, J., J. A. Vrugt, G. Schoups, J. A. Huisman, and H. Vereecken, 2012: Bayesian model averaging using particle filtering and Gaussian mixture modeling: Theory, concepts, and simulation experiments. Water Resour. Res., 48, W05520, https://doi.org/10.1029/2011WR011607.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roy, T., A. Serrat-Capdevila, H. Gupta, and J. Valdes, 2017a: A platform for probabilistic Multimodel and Multiproduct Streamflow Forecasting, Water Resour. Res., 53, 376399, https://doi.org/10.1002/2016WR019752.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roy, T., A. Serrat-Capdevila, J. Valdes, M. Durcik, and H. Gupta, 2017b: Design and implementation of an operational multimodel multiproduct real-time probabilistic streamflow forecasting platform. J. Hydroinf., 19, 911919, https://doi.org/10.2166/hydro.2017.111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roy, T., J. B. Valdés, B. Lyon, E. M. C. Demaria, A. Serrat-Capdevila, H. V. Gupta, R. Valdés-Pineda, and M. Durcik, 2018: Assessing hydrological impacts of short-term climate change in the Mara River basin of East Africa. J. Hydrol., 566, 818829, https://doi.org/10.1016/j.jhydrol.2018.08.051.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roy, T., J. B. Valdés, A. Serrat-Capdevila, M. Durcik, E. Demaria, R. Valdés-Pineda, and H. Gupta, 2020: Detailed Overview of the multimodel multiproduct streamflow forecasting platform. J. Appl. Water Eng. Res., https://doi.org/10.1080/23249676.2020.1799442, in press.

    • Search Google Scholar
    • Export Citation
  • Sabeerali, C. T., R. S. Ajayamohan, and S. A. Rao, 2019: Loss of predictive skill of Indian summer monsoon rainfall in NCEP CFSv2 due to misrepresentation of Atlantic zonal mode. Climate Dyn., 52, 45994619, https://doi.org/10.1007/s00382-018-4390-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Setiawan, A. M., Y. Koesmaryono, A. Faqih, and D. Gunawan, 2017: North American Multi Model Ensemble (NMME) performance of monthly precipitation forecast over South Sulawesi, Indonesia. IOP Conf. Ser. Earth Environ. Sci., 58, 012035, https://doi.org/10.1088/1755-1315/58/1/012035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shukla, S., J. Roberts, A. Hoell, C. C. Funk, F. Robertson, and B. Kirtman, 2016: Assessing North American Multimodel Ensemble (NMME) seasonal forecast skill to assist in the early warning of anomalous hydrometeorological events over East Africa. Climate Dyn., 53, 74117427, https://doi.org/10.1007/s00382-016-3296-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slater, L. J., G. Villarini, and A. A. Bradley, 2016: Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA. Climate Dyn., 53, 73817396, https://doi.org/10.1007/s00382-016-3286-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thober, S., R. Kumar, J. Sheffield, J. Mai, D. Schäfer, and L. Samaniego, 2015: Seasonal soil moisture drought prediction over Europe using the North American Multi-Model Ensemble (NMME). J. Hydrometeor., 16, 23292344, https://doi.org/10.1175/JHM-D-15-0053.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, D., M. Pan, and E. F. Wood, 2018: Assessment of a high-resolution climate model for surface water and energy flux simulations over global land: An intercomparison with reanalyses. J. Hydrometeor., 19, 11151129, https://doi.org/10.1175/JHM-D-17-0156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., and R. E. Dickinson, 1972: Empirical orthogonal representation of time series in the frequency domain. Part I: Theoretical considerations. J. Appl. Meteor., 11, 887892, https://doi.org/10.1175/1520-0450(1972)011<0887:EOROTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wanders, N., and E. F. Wood, 2016: Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations. Environ. Res. Lett., 11, 094007, https://doi.org/10.1088/1748-9326/11/9/094007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wanders, N., and et al. , 2017: Forecasting the hydroclimatic signature of the 2015/16 El Niño event on the western United States. J. Hydrometeor., 18, 177186, https://doi.org/10.1175/JHM-D-16-0230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., 2014: Evaluation of monthly precipitation forecasting skill of the National Multi-model Ensemble in the summer season. Hydrol. Processes, 28, 44724486, https://doi.org/10.1002/hyp.9957.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. Academic Press, 467 pp.

  • Winter, C. L., and D. Nychka, 2010: Forecasting skill of model averages. Stochastic Environ. Res. Risk Assess., 24, 633638, https://doi.org/10.1007/s00477-009-0350-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, E. F., 1978: Analyzing hydrologic uncertainty and its impact upon decision making in water resources. Adv. Water Resour., 1, 299305, https://doi.org/10.1016/0309-1708(78)90043-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, L., N. Chen, X. Zhang, Z. Chen, C. Hu, and C. Wang, 2019: Improving the North American Multi-Model Ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning. Climate Dyn., 53, 601615, https://doi.org/10.1007/s00382-018-04605-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, M.-N., and X. Yuan, 2018: Evaluation of summer drought ensemble prediction over the Yellow River basin. Atmos. Ocean. Sci. Lett., 11, 314321, https://doi.org/10.1080/16742834.2018.1484253.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, X., J. K. Roundy, E. F. Wood, and J. Sheffield, 2015: Seasonal forecasting of global hydrologic extremes: System development and evaluation over GEWEX basins. Bull. Amer. Meteor. Soc., 96, 18951912, https://doi.org/10.1175/BAMS-D-14-00003.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, T., Y. Zhang, and I. Chen, 2018: Predictive performance of NMME seasonal forecasts of global precipitation: A spatial-temporal perspective. J. Hydrol., 570, 1725, https://doi.org/10.1016/j.jhydrol.2018.12.036.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, Y., and H.-M. Kim, 2018: Prediction of atmospheric rivers over the North Pacific and its connection to ENSO in the North American Multi-Model Ensemble (NMME). Climate Dyn., 51, 16231637, https://doi.org/10.1007/s00382-017-3973-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Global Evaluation of Seasonal Precipitation and Temperature Forecasts from NMME

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  • 1 Civil and Environmental Engineering, Princeton University, Princeton, New Jersey
  • | 2 Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona
  • | 3 Civil and Environmental Engineering, Princeton University, Princeton, New Jersey
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Abstract

We present a comprehensive global evaluation of monthly precipitation and temperature forecasts from 16 seasonal forecasting models within the NMME Phase-1 system, using Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEP-V2; precipitation) and Climate Research Unit TS4.01 (CRU-TS4.01; temperature) data as reference. We first assessed the forecast skill for lead times of 1–8 months using Kling–Gupta efficiency (KGE), an objective performance metric combining correlation, bias, and variability. Next, we carried out an empirical orthogonal function (EOF) analysis to compare the spatiotemporal variability structures of the forecasts. We found that, in most cases, precipitation skill was highest during the first lead time (i.e., forecast in the month of initialization) and rapidly dropped thereafter, while temperature skill was much higher overall and better retained at higher lead times, which is indicative of stronger temporal persistence. Based on a comprehensive assessment over 21 regions and four seasons, we found that the skill showed strong regional and seasonal dependencies. Some tropical regions, such as the Amazon and Southeast Asia, showed high skill even at longer lead times for both precipitation and temperature. Rainy seasons were generally associated with high precipitation skill, while during winter, temperature skill was low. Overall, precipitation forecast skill was highest for the NASA, NCEP, CMC, and GFDL models, and for temperature, the NASA, CFSv2, COLA, and CMC models performed the best. The spatiotemporal variability structures were better captured for precipitation than temperature. The simple forecast averaging did not produce noticeably better results, emphasizing the need for more advanced weight-based averaging schemes.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-19-0095.s1.

Current affiliation: Civil and Environmental Engineering, University of Nebraska–Lincoln, Lincoln, Nebraska.

Current affiliation: Water in the West, Woods Institute for the Environment, Stanford University, Stanford, California.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Tirthankar Roy, roy@unl.edu

Abstract

We present a comprehensive global evaluation of monthly precipitation and temperature forecasts from 16 seasonal forecasting models within the NMME Phase-1 system, using Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEP-V2; precipitation) and Climate Research Unit TS4.01 (CRU-TS4.01; temperature) data as reference. We first assessed the forecast skill for lead times of 1–8 months using Kling–Gupta efficiency (KGE), an objective performance metric combining correlation, bias, and variability. Next, we carried out an empirical orthogonal function (EOF) analysis to compare the spatiotemporal variability structures of the forecasts. We found that, in most cases, precipitation skill was highest during the first lead time (i.e., forecast in the month of initialization) and rapidly dropped thereafter, while temperature skill was much higher overall and better retained at higher lead times, which is indicative of stronger temporal persistence. Based on a comprehensive assessment over 21 regions and four seasons, we found that the skill showed strong regional and seasonal dependencies. Some tropical regions, such as the Amazon and Southeast Asia, showed high skill even at longer lead times for both precipitation and temperature. Rainy seasons were generally associated with high precipitation skill, while during winter, temperature skill was low. Overall, precipitation forecast skill was highest for the NASA, NCEP, CMC, and GFDL models, and for temperature, the NASA, CFSv2, COLA, and CMC models performed the best. The spatiotemporal variability structures were better captured for precipitation than temperature. The simple forecast averaging did not produce noticeably better results, emphasizing the need for more advanced weight-based averaging schemes.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-19-0095.s1.

Current affiliation: Civil and Environmental Engineering, University of Nebraska–Lincoln, Lincoln, Nebraska.

Current affiliation: Water in the West, Woods Institute for the Environment, Stanford University, Stanford, California.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Tirthankar Roy, roy@unl.edu

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