• Acharya, S. C., R. Nathan, Q. J. Wang, C.-H. Su, and N. Eizenberg, 2019: An evaluation of daily precipitation from a regional atmospheric reanalysis over Australia. Hydrol. Earth Syst. Sci., 23, 33873403, https://doi.org/10.5194/hess-23-3387-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aguilar, E., and Coauthors, 2005: Changes in precipitation and temperature extremes in Central America and northern South America, 1961–2003. J. Geophys. Res., 110, D23107, https://doi.org/10.1029/2005JD006119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ávila, A., F. Justino, A. Wilson, D. Bromwich, and M. Amorim, 2016: Recent precipitation trends, flash floods and landslides in southern Brazil. Environ. Res. Lett., 11, 114029, https://doi.org/10.1088/1748-9326/11/11/114029.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ávila, A., F. Guerrero, Y. Escobar, and F. Justino, 2019: Recent precipitation trends and floods in the Colombian Andes. Water, 11, 379, https://doi.org/10.3390/w11020379.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Avila-Diaz, A., G. Abrahão, F. Justino, R. Torres, and A. Wilson, 2020: Extreme climate indices in Brazil: Evaluation of downscaled Earth system models at high horizontal resolution. Climate Dyn., 54, 50655088, https://doi.org/10.1007/s00382-020-05272-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bador, M., and Coauthors, 2020: Impact of higher spatial atmospheric resolution on precipitation extremes over land in global climate models. J. Geophys. Res. Atmos., 125, e2019JD032184, https://doi.org/10.1029/2019JD032184.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barlow, M., and Coauthors, 2019: North American extreme precipitation events and related large-scale meteorological patterns: A review of statistical methods, dynamics, modeling, and trends. Climate Dyn., 53, 68356875, https://doi.org/10.1007/s00382-019-04958-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., and Coauthors, 2017: 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., E. Wood, M. Pan, C. Fisher, D. Miralles, A. van Dijk, T. McVicar, and R. Adler, 2019a: 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
  • Beck, H. E., and Coauthors, 2019b: 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
  • Bengtsson, L., 2010: The global atmospheric water cycle. Environ. Res. Lett., 5, 025202, https://doi.org/10.1088/1748-9326/5/2/025202.

  • Betts, A., M. Köhler, and Y. Zhang, 2009: Comparison of river basin hydrometeorology in ERA-Interim and ERA-40 reanalyses with observations. J. Geophys. Res., 114, D02101, https://doi.org/10.1029/2008JD010761.

    • Search Google Scholar
    • Export Citation
  • Bhuiyan, M., E. Nikolopoulos, and E. Anagnostou, 2019: Machine learning-based blending of satellite and reanalysis precipitation datasets: A multi-regional tropical complex terrain evaluation. J. Hydrometeor., 20, 21472161, https://doi.org/10.1175/JHM-D-19-0073.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bliss, A. C., M. Steele, G. Peng, W. N. Meier, and S. Dickinson, 2019: Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record. Environ. Res. Lett., 14, 045003, https://doi.org/10.1088/1748-9326/aafb84.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boisvert, L., and J. Stroeve, 2015: The Arctic is becoming warmer and wetter as revealed by the Atmospheric Infrared Sounder. Geophys. Res. Lett., 42, 44394446, https://doi.org/10.1002/2015GL063775.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Booth, E., J. Byrne, and D. Johnson, 2012: Climatic changes in western North America, 1950–2005. Int. J. Climatol., 32, 22832300, https://doi.org/10.1002/joc.3401.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Box, G. E. P., and D. A. Pierce, 1970: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Amer. Stat. Assoc., 65, 15091526, https://doi.org/10.1080/01621459.1970.10481180.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brennan, M. K., G. J. Hakim, and E. Blanchard-Wrigglesworth, 2020: Arctic sea-ice variability during the instrumental era. Geophys. Res. Lett., 47, e2019GL086843, https://doi.org/10.1029/2019GL086843.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bromwich, D., A. Wilson, L. S. Bai, G. Moore, and P. Bauer, 2016: A comparison of the regional Arctic System Reanalysis and the global ERA-Interim reanalysis for the Arctic. Quart. J. Roy. Meteor. Soc., 142, 644658, https://doi.org/10.1002/qj.2527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bromwich, D., and Coauthors, 2018: The Arctic System Reanalysis, version 2. Bull. Amer. Meteor. Soc., 99, 805828, https://doi.org/10.1175/BAMS-D-16-0215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chaney, N., J. Sheffield, G. Villarini, and E. Wood, 2014: Development of a high-resolution gridded daily meteorological dataset over sub-Saharan Africa: Spatial analysis of trends in climate extremes. J. Climate, 27, 58155835, https://doi.org/10.1175/JCLI-D-13-00423.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J., 2016: An observational analysis: Tropical relative to Arctic influence on midlatitude weather in the era of Arctic amplification. Geophys. Res. Lett., 43, 52875294, https://doi.org/10.1002/2016GL069102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J., J. Foster, M. Barlow, K. Saito, and J. Jones, 2010: Winter 2009–2010: A case study of an extreme Arctic Oscillation event. Geophys. Res. Lett., 37, L17707, https://doi.org/10.1029/2010GL044256.

    • Search Google Scholar
    • Export Citation
  • Cornes, R., and P. Jones, 2013: How well does the ERA-Interim reanalysis replicate trends in extremes of surface temperature across Europe? J. Geophys. Res. Atmos., 118, 10 26210 276, https://doi.org/10.1002/jgrd.50799.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Croitoru, A.-E., A. Piticar, and D. C. Burada, 2016: Changes in precipitation extremes in Romania. Quat. Int., 415, 325335, https://doi.org/10.1016/j.quaint.2015.07.028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, P., and B. Tan, 2017: The nature of the Arctic Oscillation and diversity of the extreme surface weather anomalies it generates. J. Climate, 30, 55635584, https://doi.org/10.1175/JCLI-D-16-0467.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., M. Halbleib, J. Smith, W. Gibson, M. Doggett, G. Taylor, J. Curtis, and P. Pasteris, 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 20312064, https://doi.org/10.1002/joc.1688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., M. Balmaseda, G. Balsamo, R. Engelen, A. Simmons, and J. Thépaut, 2014: Toward a consistent reanalysis of the climate system. Bull. Amer. Meteor. Soc., 95, 12351248, https://doi.org/10.1175/BAMS-D-13-00043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Diaconescu, E. P., A. Mailhot, R. Brown, and D. Chaumont, 2017: Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas. Climate Dyn., 50, 20612085, https://doi.org/10.1007/s00382-017-3736-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donat, M., and Coauthors, 2013: Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. J. Geophys. Res. Atmos., 118, 20982118, https://doi.org/10.1002/jgrd.50150.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donat, M., J. Sillmann, S. Wild, L. Alexander, T. Lippmann, and F. Zwiers, 2014: Consistency of temperature and precipitation extremes across various global gridded in situ and reanalysis datasets. J. Climate, 27, 50195035, https://doi.org/10.1175/JCLI-D-13-00405.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donat, M., L. Alexander, N. Herold, and A. Dittus, 2016: Temperature and precipitation extremes in century-long gridded observations, reanalyses, and atmospheric model simulations. J. Geophys. Res. Atmos., 121, 11 17411 189, https://doi.org/10.1002/2016JD025480.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • El Kenawy, A., J. I. López-Moreno, and S. M. Vicente-Serrano, 2011: Recent trends in daily temperature extremes over northeastern Spain (1960–2006). Nat. Hazards Earth Syst. Sci., 11, 25832603, https://doi.org/10.5194/nhess-11-2583-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fujiwara, M., and Coauthors, 2017: Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems. Atmos. Chem. Phys., 17, 14171452, https://doi.org/10.5194/acp-17-1417-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gallagher, M. R., H. Chepfer, M. D. Shupe, and R. Guzman, 2020: Warm temperature extremes across Greenland connected to clouds. Geophys. Res. Lett., 47, e2019GL086059, https://doi.org/10.1029/2019GL086059.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grotjahn, R., and Coauthors, 2016: North American extreme temperature events and related large-scale meteorological patterns: A review of statistical methods, dynamics, modeling, and trends. Climate Dyn., 46, 11511184, https://doi.org/10.1007/s00382-015-2638-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gupta, H., S. Sorooshian, and P. Yapo, 1999: Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. J. Hydrol. Eng., 4, 135143, https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamed, K. H., and A. Rao, 1998: A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol., 204, 182196, https://doi.org/10.1016/S0022-1694(97)00125-X.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

  • Hoffmann, L., and Coauthors, 2019: From ERA-Interim to ERA5: The considerable impact of ECMWF’s next-generation reanalysis on Lagrangian transport simulations. Atmos. Chem. Phys., 19, 30973124, https://doi.org/10.5194/acp-19-3097-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, L., and G. Huang, 2020: The changes of high-temperature extremes and their links with atmospheric circulation over the Northern Hemisphere. Theor. Appl. Climatol., 139, 261274, https://doi.org/10.1007/s00704-019-02970-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., and C. Deser, 2009: North Atlantic climate variability: The role of the North Atlantic Oscillation. J. Mar. Syst., 78, 2841, https://doi.org/10.1016/j.jmarsys.2008.11.026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hurrell, J. W., Y. Kushnir, G. Ottersen, and M. Visbeck, 2003: An overview of the North Atlantic Oscillation. The North Atlantic Oscillation: Climatic Significance and Environmental Impact, Geophys. Monogr., Vol. 134, Amer. Geophys. Union, 1–35.

    • Crossref
    • Export Citation
  • Justino, F., A. B. Wilson, D. H. Bromwich, A. Avila, L.-S. Bai, and S.-H. Wang, 2019: Northern Hemisphere extratropical turbulent heat fluxes in ASRv2 and global reanalyses. J. Climate, 32, 21452166, https://doi.org/10.1175/JCLI-D-18-0535.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kendall, M., 1975: Rank Correlation Methods. 4th ed. Charles Griffin, 202 pp.

  • 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
  • Koenigk, T., P. Berg, and R. Döscher, 2015: Arctic climate change in an ensemble of regional CORDEX simulations. Polar Res., 34, 24603, https://doi.org/10.3402/polar.v34.24603.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koyama, T., and J. Stroeve, 2019: Greenland monthly precipitation analysis from the Arctic System Reanalysis (ASR): 2000–2012. Polar Sci., 19, 112, https://doi.org/10.1016/j.polar.2018.09.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kunkel, K., and J. Angel, 1999: Relationship of ENSO to snowfall and related cyclone activity in the contiguous United States. J. Geophys. Res., 104, 19 42519 434, https://doi.org/10.1029/1999JD900010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lader, R., U. S. Bhatt, J. E. Walsh, T. S. Rupp, and P. A. Bieniek, 2016: Two-meter temperature and precipitation from atmospheric reanalysis evaluated for Alaska. J. Appl. Meteor. Climatol., 55, 901922, https://doi.org/10.1175/JAMC-D-15-0162.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lader, R., J. Walsh, U. Bhatt, and P. Bieniek, 2017: Projections of twenty-first-century climate extremes for Alaska via dynamical downscaling and quantile mapping. J. Appl. Meteor. Climatol., 56, 23932409, https://doi.org/10.1175/JAMC-D-16-0415.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, X., X. Wang, and V. Babovic, 2018: Analysis of variability and trends of precipitation extremes in Singapore during 1980–2013. Int. J. Climatol., 38, 125141, https://doi.org/10.1002/joc.5165.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lindsay, R., M. Wensnahan, A. Schweiger, and J. Zhang, 2014: Evaluation of seven different atmospheric reanalysis products in the Arctic. J. Climate, 27, 25882606, https://doi.org/10.1175/JCLI-D-13-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, B., D. Luo, A. Dai, and L. Wu, 2020: Combined influences on North American winter air temperature variability from North Pacific blocking and the North Atlantic Oscillation: Sub-seasonal and interannual timescales. J. Climate, 33, 71017123, https://doi.org/10.1175/JCLI-D-19-0327.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, H. B., 1945: Nonparametric tests against trend. Econometrica, 13, 245, https://doi.org/10.2307/1907187.

  • Matthes, H., A. Rinke, and K. Dethloff, 2016: Recent changes in Arctic temperature extremes: Warm and cold spells during winter and summer. Environ. Res. Lett., 11, 029501, https://doi.org/10.1088/1748-9326/11/2/029501.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McEvoy, D. J., J. F. Mejia, and J. L. Huntington, 2014: Use of an observation network in the Great Basin to evaluate gridded climate data. J. Hydrometeor., 15, 19131931, https://doi.org/10.1175/JHM-D-14-0015.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Melvin, A., J. Murray, B. Boehlert, J. Martinich, L. Rennels, and T. Rupp, 2017: Estimating wildfire response costs in Alaska’s changing climate. Climatic Change, 141, 783795, https://doi.org/10.1007/s10584-017-1923-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343360, https://doi.org/10.1175/BAMS-87-3-343.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moore, G. W. K., I. A. Renfrew, B. E. Harden, and S. H. Mernild, 2015: The impact of resolution on the representation of southeast Greenland barrier winds and katabatic flows. Geophys. Res. Lett., 42, 30113018, https://doi.org/10.1002/2015GL063550.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moriasi, D., J. Arnold, M. Van Liew, R. Binger, R. Harmel, and T. Veith, 2007: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE, 50, 885900, https://doi.org/10.13031/2013.23153.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murdock, T., S. Sobie, F. Zwiers, and H. Eckstrand, 2013: Climate change and extremes in the Canadian Columbia basin. Atmos.–Ocean, 51, 456469, https://doi.org/10.1080/07055900.2013.816932.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NCAR, 2017: Arctic System Reanalysis version 2. National Center for Atmospheric Research Computational and Information Systems Laboratory, accessed 5 June 2019, https://doi.org/10.5065/D6X9291B.

    • Crossref
    • Export Citation
  • Ning, L., and R. S. Bradley, 2015: Winter climate extremes over the northeastern United States and southeastern Canada and teleconnections with large-scale modes of climate variability. J. Climate, 28, 24752493, https://doi.org/10.1175/JCLI-D-13-00750.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Overland, J., and M. Wang, 2018: Arctic–midlatitude weather linkages in North America. Polar Sci., 16, 19, https://doi.org/10.1016/j.polar.2018.02.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Papritz, L., 2020: Arctic lower-tropospheric warm and cold extremes: Horizontal and vertical transport, diabatic processes, and linkage to synoptic circulation features. J. Climate, 33, 9931016, https://doi.org/10.1175/JCLI-D-19-0638.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peng, H. T., C. Q. Ke, X. Shen, M. Li, and Z. De Shao, 2020: Summer albedo variations in the Arctic sea ice region from 1982 to 2015. Int. J. Climatol., 40, 30083020, https://doi.org/10.1002/joc.6379.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Przybylak, R., and P. Wyszyński, 2020: Air temperature changes in the Arctic in the period 1951–2015 in the light of observational and reanalysis data. Theor. Appl. Climatol., 139, 7594, https://doi.org/10.1007/s00704-019-02952-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qu, M., J. Wan, and X. Hao, 2014: Analysis of diurnal air temperature range change in the continental United States. Wea. Climate Extremes, 4, 8695, https://doi.org/10.1016/j.wace.2014.05.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rapaić, M., R. Brown, M. Markovic, and D. Chaumont, 2015: An evaluation of temperature and precipitation surface-based and reanalysis datasets for the Canadian Arctic, 1950–2010. Atmos.–Ocean, 53, 283303, https://doi.org/10.1080/07055900.2015.1045825.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., Q. Liu, R. D. Koster, C. S. Draper, S. P. P. Mahanama, and G. S. Partyka, 2017: Land surface precipitation in MERRA-2. J. Climate, 30, 16431664, https://doi.org/10.1175/JCLI-D-16-0570.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rivière, G., and M. Drouard, 2015: Understanding the contrasting North Atlantic oscillation anomalies of the winters of 2010 and 2014. Geophys. Res. Lett., 42, 68686875, https://doi.org/10.1002/2015GL065493.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roberts, M. J., and Coauthors, 2018: The benefits of global high resolution for climate simulation process understanding and the enabling of stakeholder decisions at the regional scale. Bull. Amer. Meteor. Soc., 99, 23412359, https://doi.org/10.1175/BAMS-D-15-00320.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schoof, J., and S. Robeson, 2016: Projecting changes in regional temperature and precipitation extremes in the United States. Wea. Climate Extremes, 11, 2840, https://doi.org/10.1016/j.wace.2015.09.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 13791389, https://doi.org/10.1080/01621459.1968.10480934.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheffield, J., G. Goteti, and E. F. Wood, 2006: Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Climate, 19, 30883111, https://doi.org/10.1175/JCLI3790.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shepherd, T. G., 2016: Effects of a warming Arctic. Science, 353, 989990, https://doi.org/10.1126/science.aag2349.

  • Sheridan, S., and C. Lee, 2018: Temporal trends in absolute and relative extreme temperature events across North America. J. Geophys. Res. Atmos., 123, 11 88911 898, https://doi.org/10.1029/2018JD029150.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Simmons, A. J., and P. Poli, 2015: Arctic warming in ERA-Interim and other analyses. Quart. J. Roy. Meteor. Soc., 141, 11471162, https://doi.org/10.1002/qj.2422.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Siqueira, V. A., and Coauthors, 2018: Toward continental hydrologic–hydrodynamic modeling in South America. Hydrol. Earth Syst. Sci., 22, 48154842, https://doi.org/10.5194/hess-22-4815-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skansi, M., and Coauthors, 2013: Warming and wetting signals emerging from analysis of changes in climate extreme indices over South America. Global Planet. Change, 100, 295307, https://doi.org/10.1016/j.gloplacha.2012.11.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tarek, M., F. P. Brissette, and R. Arsenault, 2020: Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America. Hydrol. Earth Syst. Sci., 24, 25272544, https://doi.org/10.5194/hess-24-2527-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 12971300, https://doi.org/10.1029/98GL00950.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., and J. M. Wallace, 2000a: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 10001016, https://doi.org/10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., and J. M. Wallace, 2000b: Annular modes in the extratropical circulation. Part II: Trends. J. Climate, 13, 10181036, https://doi.org/10.1175/1520-0442(2000)013<1018:AMITEC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thornton, P., S. Running, and M. White, 1997: Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol., 190, 214251, https://doi.org/10.1016/S0022-1694(96)03128-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thornton, P., M. M. Thornton, B. W. Mayer, Y. Wei, R. Devarakonda, R. S. Vose, and R. B. Cook, 2018: Daymet: Daily Surface Weather Data on a 1-km Grid for North America, version 3. Oak Ridge National Laboratory Distributed Active Archive Center, accessed 25 June 2019, https://doi.org/10.3334/ORNLDAAC/1392.

    • Crossref
    • Export Citation
  • Timmermans, B., M. Wehner, D. Cooley, T. O’Brien, and H. Krishnan, 2019: An evaluation of the consistency of extremes in gridded precipitation data sets. Climate Dyn., 52, 66516670, https://doi.org/10.1007/s00382-018-4537-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uotila, P., and Coauthors, 2019: An assessment of ten ocean reanalyses in the polar regions. Climate Dyn., 52, 16131650, https://doi.org/10.1007/s00382-018-4242-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vogel, B., and R. C. L. Bullock, 2020: Institutions, indigenous peoples, and climate change adaptation in the Canadian Arctic. GeoJournal, https://doi.org/10.1007/s10708-020-10212-5, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walsh, J., P. Bieniekb, B. Brettschneidera, E. Euskirchenc, R. Lader, and R. Thomand, 2017: The exceptionally warm winter of 2015/16 in Alaska. J. Climate, 30, 20692088, https://doi.org/10.1175/JCLI-D-16-0473.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walther, B. A., and J. L. Moore, 2005: The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography, 28, 815829, https://doi.org/10.1111/j.2005.0906-7590.04112.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C., R. M. Graham, K. Wang, S. Gerland, and M. A. Granskog, 2019: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: Effects on sea ice thermodynamics and evolution. Cryosphere, 13, 16611679, https://doi.org/10.5194/tc-13-1661-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., H. M. Kim, and E. K. M. Chang, 2017: Changes in Northern Hemisphere winter storm tracks under the background of Arctic amplification. J. Climate, 30, 37053724, https://doi.org/10.1175/JCLI-D-16-0650.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J. A., D. Sulla-Menashe, C. E. Woodcock, O. Sonnentag, R. F. Keeling, and M. A. Friedl, 2020: Extensive land cover change across Arctic–boreal northwestern North America from disturbance and climate forcing. Global Change Biol., 26, 807822, https://doi.org/10.1111/gcb.14804.

    • Search Google Scholar
    • Export Citation
  • Wanner, H., S. Brönnimann, C. Casty, D. Gyalistras, J. Luterbacher, C. Schmutz, D. B. Stephenson, and E. Xoplaki, 2001: North Atlantic oscillation—Concepts and studies. Surv. Geophys., 22, 321381, https://doi.org/10.1023/A:1014217317898.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wazneh, H., M. A. Arain, and P. Coulibaly, 2020: Climate indices to characterize climatic changes across southern Canada. Meteor. Appl., 27, 119, https://doi.org/10.1002/met.1861.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wettstein, J. J., and L. O. Mearns, 2002: The influence of the North Atlantic–Arctic Oscillation on mean, variance, and extremes of temperature in the northeastern United States and Canada. J. Climate, 15, 35863600, https://doi.org/10.1175/1520-0442(2002)015<3586:TIOTNA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, A. B., D. H. Bromwich, and K. M. Hines, 2011: Evaluation of polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air analysis. J. Geophys. Res., 116, D11112, https://doi.org/10.1029/2010JD015013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wong, J. S., S. Razavi, B. R. Bonsal, H. S. Wheater, and Z. E. Asong, 2017: Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada. Hydrol. Earth Syst. Sci., 21, 21632185, https://doi.org/10.5194/hess-21-2163-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, B., 2017: Winter atmospheric circulation anomaly associated with recent Arctic winter warm anomalies. J. Climate, 30, 84698479, https://doi.org/10.1175/JCLI-D-17-0175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Y., T. Y. Gan, and X. Tan, 2019: Spatiotemporal changes in precipitation extremes over Canada and their teleconnections to large-scale climate patterns. J. Hydrometeor., 20, 275296, https://doi.org/10.1175/JHM-D-18-0004.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yin, H., M. G. Donat, L. V. Alexander, and Y. Sun, 2015: Multi-dataset comparison of gridded observed temperature and precipitation extremes over China. Int. J. Climatol., 35, 28092827, https://doi.org/10.1002/joc.4174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, A., P. Higuera, J. Abatzoglou, P. Duffy, and F. Hu, 2019: Consequences of climatic thresholds for projecting fire activity and ecological change. Global Ecol. Biogeogr., 28, 521532, https://doi.org/10.1111/geb.12872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yue, S., P. Pilon, and G. Cavadias, 2002: Power of the Mann-Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J. Hydrol., 259, 254271, https://doi.org/10.1016/S0022-1694(01)00594-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., L. A. Vincent, W. D. Hogg, and A. Niitsoo, 2000: Temperature and precipitation trends in Canada during the 20th century. Atmos.–Ocean, 38, 395429, https://doi.org/10.1080/07055900.2000.9649654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., L. Alexander, G. Hegerl, P. Jones, A. Klein Tank, T. Peterson, B. Trewin, and F. Zwiers, 2011: Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip. Rev.: Climate Change, 2, 851870, https://doi.org/10.1002/wcc.147.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 437 437 148
Full Text Views 88 88 22
PDF Downloads 123 123 41

Climate Extremes across the North American Arctic in Modern Reanalyses

View More View Less
  • 1 Department of Agricultural Engineering, Universidade Federal de Viçosa, Viçosa, Brazil
  • 2 Polar Meteorology Group, Byrd Polar and Climate Research Center, The Ohio State University, Columbus, Ohio
© Get Permissions
Restricted access

ABSTRACT

Atmospheric reanalyses are a valuable climate-related resource where in situ data are sparse. However, few studies have investigated the skill of reanalyses to represent extreme climate indices over the North American Arctic, where changes have been rapid and indigenous responses to change are critical. This study investigates temperature and precipitation extremes as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) over a 17-yr period (2000–16) for regional and global reanalyses, namely the Arctic System Reanalysis, version 2 (ASRv2); North American Regional Reanalysis (NARR); European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis; Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2); and Global Meteorological Forcing Dataset for Land Surface Modeling (GMFD). Results indicate that the best performances are demonstrated by ASRv2 and ERA5. Relative to observations, reanalyses show the weakest performance over far northern basins (e.g., the Arctic and Hudson basins) where observing networks are less dense. Observations and reanalyses show consistent warming with decreased frequency and intensity of cold extremes. Cold days, cold nights, frost days, and ice days have decreased dramatically over the last two decades. Warming can be linked to a simultaneous increase in daily precipitation intensity over several basins in the domain. Moreover, the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) distinctly influence extreme climate indices. Thus, these findings detail the complexity of how the climate of the Arctic is changing, not just in an average sense, but in extreme events that have significant impacts on people and places.

© 2021 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: Aaron Benjamin Wilson, wilson.1010@osu.edu

ABSTRACT

Atmospheric reanalyses are a valuable climate-related resource where in situ data are sparse. However, few studies have investigated the skill of reanalyses to represent extreme climate indices over the North American Arctic, where changes have been rapid and indigenous responses to change are critical. This study investigates temperature and precipitation extremes as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) over a 17-yr period (2000–16) for regional and global reanalyses, namely the Arctic System Reanalysis, version 2 (ASRv2); North American Regional Reanalysis (NARR); European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis; Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2); and Global Meteorological Forcing Dataset for Land Surface Modeling (GMFD). Results indicate that the best performances are demonstrated by ASRv2 and ERA5. Relative to observations, reanalyses show the weakest performance over far northern basins (e.g., the Arctic and Hudson basins) where observing networks are less dense. Observations and reanalyses show consistent warming with decreased frequency and intensity of cold extremes. Cold days, cold nights, frost days, and ice days have decreased dramatically over the last two decades. Warming can be linked to a simultaneous increase in daily precipitation intensity over several basins in the domain. Moreover, the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) distinctly influence extreme climate indices. Thus, these findings detail the complexity of how the climate of the Arctic is changing, not just in an average sense, but in extreme events that have significant impacts on people and places.

© 2021 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: Aaron Benjamin Wilson, wilson.1010@osu.edu
Save