• Abatzoglou, J. T., K. T. Redmond, and L. M. Edwards, 2009: Classification of regional climate variability in the state of California. J. Appl. Meteor. Climatol., 48, 15271541, https://doi.org/10.1175/2009JAMC2062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Abatzoglou, J. T., D. E. Rupp, and P. W. Mote, 2014: Seasonal climate variability and change in the Pacific Northwest of the United States. J. Climate, 27, 21252142, https://doi.org/10.1175/JCLI-D-13-00218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adegoke, J. O., R. A. Pielke, J. Eastman, R. Mahmood, and K. G. Hubbard, 2003: Impact of irrigation on midsummer surface fluxes and temperature under dry synoptic conditions: A regional atmospheric model study of the U.S. High Plains. Mon. Wea. Rev., 131, 556564, https://doi.org/10.1175/1520-0493(2003)131<0556:IOIOMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alter, R. E., Y. Fan, B. R. Lintner, and C. P. Weaver, 2015: Observational evidence that Great Plains irrigation has enhanced summer precipitation intensity and totals in the midwestern United States. J. Hydrometeor., 16, 17171735, https://doi.org/10.1175/JHM-D-14-0115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alter, R. E., H. C. Douglas, J. M. Winter, and E. A. B. Eltahir, 2017: 20th-century regional climate change in the central United States attributed to agricultural intensification. Geophys. Res. Lett., 45, 15861594, https://doi.org/10.1002/2017GL075604.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Asseng, S., and et al. , 2013: Uncertainty in simulating wheat yields under climate change. Nat. Climate Change, 3, 827832, https://doi.org/10.1038/nclimate1916.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Asseng, S., and et al. , 2015: Rising temperatures reduce global wheat production. Nat. Climate Change, 5, 143147, https://doi.org/10.1038/nclimate2470.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Avissar, R., and R. A. Pielke, 1989: A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology. Mon. Wea. Rev., 117, 21132136, https://doi.org/10.1175/1520-0493(1989)117<2113:APOHLS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Avissar, R., and R. A. Pielke, 1991: The impact of plant stomatal control on mesoscale atmospheric circulations. Agric. For. Meteor., 54, 353372, https://doi.org/10.1016/0168-1923(91)90013-G.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Betts, A. K., R. Desjardins, and D. Worth, 2013a: Cloud radiative forcing of the diurnal cycle climate of the Canadian Prairies. J. Geophys. Res. Atmos., 118, 89358953, https://doi.org/10.1002/jgrd.50593.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Betts, A. K., R. Desjardins, D. Worth, and D. Cerkowniak, 2013b: Impact of land use change on the diurnal cycle climate of the Canadian Prairies. J. Geophys. Res. Atmos., 118, 11 99612 011, https://doi.org/10.1002/2013JD020717.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonetti, S., G. Manoli, J. Domec, M. Putti, M. Marani, and G. G. Katul, 2015: The influence of water table depth and the free atmospheric state on convective rainfall predisposition. Water Resour. Res., 51, 22832297, https://doi.org/10.1002/2014WR016431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonsal, B. R., X. Zhang, and W. D. Hogg, 1999: Canadian Prairie growing season precipitation variability and associated atmospheric circulation. Climate Res., 11, 191208, https://doi.org/10.3354/cr011191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bradshaw, B., H. Dolan, and B. Smit, 2004: Farm-level adaptation to climatic variability and change: Crop diversification in the Canadian Prairies. Climatic Change, 67, 119141, https://doi.org/10.1007/s10584-004-0710-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bright, R. M., E. Davin, T. O’Halloran, J. Pongratz, K. Zhao, and A. Cescatti, 2017: Local temperature response to land cover and management change driven by non-radiative processes. Nat. Climate Change, 7, 296302, https://doi.org/10.1038/nclimate3250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Butler, E. E., N. D. Mueller, and P. Huybers, 2018: Peculiarly pleasant weather for US maize. Proc. Natl. Acad. Sci. USA, 115, 11 93511 940, https://doi.org/10.1073/pnas.1808035115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campbell, C. A., R. P. Zentner, S. Gameda, B. Blomert, and D. D. Wall, 2002: Production of annual crops on the Canadian prairies: Trends during 1976–1998. Can. J. Soil Sci., 82, 4557, https://doi.org/10.4141/S01-046.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carbone, R. E., and J. D. Tuttle, 2008: Rainfall occurrence in the U.S. warm season: The diurnal cycle. J. Climate, 21, 41324146, https://doi.org/10.1175/2008JCLI2275.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chug, D., and F. Dominguez, 2019: Isolating the observed influence of vegetation variability on the climate of La Plata River Basin. J. Climate, 32, 44734490, https://doi.org/10.1175/JCLI-D-18-0677.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collow, T. W., A. Robock, and W. Wu, 2014: Influences of soil moisture and vegetation on convective precipitation forecasts over the United States Great Plains. J. Geophys. Res. Atmos., 119, 93389358, https://doi.org/10.1002/2014JD021454.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Couzin, J., 1999: Landscape changes make regional climate run hot and cold. Science, 283, 317319, https://doi.org/10.1126/science.283.5400.317.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Recent climatology, variability, and trends in global surface humidity. J. Climate, 19, 35893606, https://doi.org/10.1175/JCLI3816.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davin, E. L., S. I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proc. Natl. Acad. Sci. USA, 111, 97579761, https://doi.org/10.1073/pnas.1317323111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeAngelis, A., F. Dominguez, Y. Fan, A. Robock, M. D. Kustu, and D. Robinson, 2010: Evidence of enhanced precipitation due to irrigation over the Great Plains of the United States. J. Geophys. Res., 115, D15115, https://doi.org/10.1029/2010JD013892.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Desai, A. R., K. J. Davis, C. J. Senff, S. Ismail, E. V. Browell, D. R. Stauffer, and B. P. Reen, 2006: A case study on the effects of heterogeneous soil moisture on mesoscale boundary-layer structure in the Southern Great Plains, U.S.A. Part I: Simple prognostic model. Bound.-Layer Meteor., 119, 195238, https://doi.org/10.1007/s10546-005-9024-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012: Uncertainty in climate change projections: The role of internal variability. Climate Dyn., 38, 527546, https://doi.org/10.1007/s00382-010-0977-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dickens, J. M., B. T. Forbes, D. S. Cobean, and S. Tadayon, 2011: Documentation of methods and inventory of irrigation data collected for the 2000 and 2005 U.S. Geological Survey Estimated use of water in the United States, comparison of USGS-compiled irrigation data to other sources, and recommendations for future compilations. U.S. Geological Survey Scientific Investigations Rep. 2011–5166, 60 pp., http://pubs.usgs.gov/sir/2011/5166/.

    • Crossref
    • Export Citation
  • Diffenbaugh, N. S., 2009: Influence of modern land cover on the climate of the United States. Climate Dyn., 33, 945958, https://doi.org/10.1007/s00382-009-0566-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dominguez, F., G. Miguez-Macho, and H. Hu, 2016: WRF with water vapor tracers: A study of moisture sources for the North American monsoon. J. Hydrometeor., 17, 19151927, https://doi.org/10.1175/JHM-D-15-0221.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Draper, C. S., R. H. Reichle, and R. D. Koster, 2018: Assessment of MERRA-2 land surface energy flux estimates. J. Climate, 31, 671691, https://doi.org/10.1175/JCLI-D-17-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fall, S., D. Niyogi, A. Gluhovsky, R. A. Pielke, E. Kalnay, and G. Rochon, 2010: Impacts of land use land cover on temperature trends over the continental United States: Assessment using the North American Regional Reanalysis. Int. J. Climatol., 30, 19801993, https://doi.org/10.1002/joc.1996.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., L. R. Leung, S. Hagos, R. A. Houze, C. D. Burleyson, and K. Balaguru, 2016: More frequent intense and long-lived storms dominate the springtime trend in central US rainfall. Nat. Commun., 7, 13429, https://doi.org/10.1038/ncomms13429.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ficklin, D. L., and K. A. Novick, 2017: Historic and projected changes in vapor pressure deficit suggest a continental-scale drying of the United States atmosphere. J. Geophys. Res. Atmos., 122, 20612079, https://doi.org/10.1002/2016JD025855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Findell, K. L., and E. A. B. Eltahir, 2003a: Atmospheric controls on soil moisture–boundary layer interactions. Part I: Framework development. J. Hydrometeor., 4, 552569, https://doi.org/10.1175/1525-7541(2003)004<0552:ACOSML>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Findell, K. L., and E. B. Eltahir, 2003b: Atmospheric controls on soil moisture–boundary layer interactions. Part II: Feedbacks within the continental United States. J. Hydrometeor., 4, 570583, https://doi.org/10.1175/1525-7541(2003)004<0570:ACOSML>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Francis, J. A., and S. J. Vavrus, 2015: Evidence for a wavier jet stream in response to rapid Arctic warming. Environ. Res. Lett., 10, 014005, https://doi.org/10.1088/1748-9326/10/1/014005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang, 2010: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114, 168182, https://doi.org/10.1016/j.rse.2009.08.016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gameda, S., B. Qian, C. A. Campbell, and R. L. Desjardins, 2007: Climatic trends associated with summerfallow in the Canadian Prairies. Agric. For. Meteor., 142, 170185, https://doi.org/10.1016/j.agrformet.2006.03.026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, H., and G. Jia, 2013: Assessing disagreement and tolerance of misclassification of satellite-derived land cover products used in WRF model applications. Adv. Atmos. Sci., 30, 125141, https://doi.org/10.1007/s00376-012-2037-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garcia-Carreras, L., D. J. Parker, C. M. Taylor, C. E. Reeves, and J. G. Murphy, 2010: Impact of mesoscale vegetation heterogeneities on the dynamical and thermodynamic properties of the planetary boundary layer. J. Geophys. Res., 115, D03102, https://doi.org/10.1029/2009JD012811.

    • Search Google Scholar
    • Export Citation
  • Gauthier, D. A., and E. Wiken, 2003: Monitoring the conservation of grassland habitats, Prairie Ecozone, Canada. Environ. Monit. Assess., 88, 343364, https://doi.org/10.1023/A:1025585527169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gentine, P., A. A. M. Holtslag, F. D’Andrea, and M. Ek, 2013: Surface and atmospheric controls on the onset of moist convection over land. J. Hydrometeor., 14, 14431462, https://doi.org/10.1175/JHM-D-12-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerken, T., G. T. Bromley, B. L. Ruddell, S. Williams, and P. C. Stoy, 2018a: Convective suppression before and during the United States Northern Great Plains flash drought of 2017. Hydrol. Earth Syst. Sci., 22, 41554163, https://doi.org/10.5194/hess-22-4155-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerken, T., G. T. Bromley, and P. C. Stoy, 2018b: Surface moistening trends in the northern North American Great Plains increase the likelihood of convective initiation. J. Hydrometeor., 19, 227244, https://doi.org/10.1175/JHM-D-17-0117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimberg, B., S. Ahmed, C. Ellis, Z. Miller, and F. Menalled, 2018: Climate change perceptions and observations of agricultural stakeholders in the Northern Great Plains. Sustainability, 10, 1687, https://doi.org/10.3390/su10051687.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hammermeister, A. M., 2001: An ecological analysis of prairie rehabilitation on petroleum wellsites in southeast Alberta. Ph.D. thesis, University of Alberta, 107 pp.

  • Harding, K. J., and P. K. Snyder, 2012: Modeling the atmospheric response to irrigation in the Great Plains. Part II: The precipitation of irrigated water and changes in precipitation recycling. J. Hydrometeor., 13, 16871703, https://doi.org/10.1175/JHM-D-11-099.1.

    • 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
  • Hohenegger, C., P. Brockhaus, C. S. Bretherton, and C. Schär, 2009: The soil moisture–precipitation feedback in simulations with explicit and parameterized convection. J. Climate, 22, 50035020, https://doi.org/10.1175/2009JCLI2604.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Q., A. Weiss, S. Feng, and P. S. Baenziger, 2005: Earlier winter wheat heading dates and warmer spring in the U.S. Great Plains. Agric. For. Meteor., 135, 284290, https://doi.org/10.1016/j.agrformet.2006.01.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huber, D., D. Mechem, and N. Brunsell, 2014: The effects of Great Plains irrigation on the surface energy balance, regional circulation, and precipitation. Climate, 2, 103128, https://doi.org/10.3390/cli2020103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. T. F. Stocker et al., Eds., Cambridge University Press, 1535 pp.

  • IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability Part A: Global and Sectoral Aspects. Cambridge University Press, 1132 pp., http://www.ipcc.ch/pdf/assessment-report/ar5/wg2/WGIIAR5-PartA_FINAL.pdf.

  • Jones, P., 2016: The reliability of global and hemispheric surface temperature records. Adv. Atmos. Sci., 33, 269282, https://doi.org/10.1007/s00376-015-5194-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Juang, J. Y., A. Porporato, P. C. Stoy, M. S. Siqueira, A. C. Oishi, M. Detto, H. S. Kim, and G. G. Katul, 2007: Hydrologic and atmospheric controls on initiation of convective precipitation events. Water Resour. Res., 43, W03421, https://doi.org/10.1029/2006WR004954.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanamori, H., T. Kumagai, H. Fujinami, T. Hiyama, and T. Yasunari, 2018: Effects of long- and short-term atmospheric water cycles on the water balance over the Maritime Continent. J. Hydrometeor., 19, 14131427, https://doi.org/10.1175/JHM-D-18-0052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and et al. , 2015: The Community Earth System Model (CESM) Large Ensemble Project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keller, M., D. S. Schimel, W. W. Hargrove, and F. M. Hoffman, 2008: A continental strategy for the National Ecological Observatory Network. Front. Ecol. Environ., 6, 282284, https://doi.org/10.1890/1540-9295(2008)6[282:ACSFTN]2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klemm, T., and R. A. McPherson, 2017: The development of seasonal climate forecasting for agricultural producers. Agric. For. Meteor., 232, 384399, https://doi.org/10.1016/j.agrformet.2016.09.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kucharik, C. J., 2008: Contribution of planting date trends to increased maize yields in the central United States. Agron. J., 100, 328336, https://doi.org/10.2134/agronj2007.0145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lanning, S. P., K. Kephart, G. R. Carlson, J. E. Eckhoff, R. N. Stougaard, D. M. Wichman, J. M. Martin, and L. E. Talbert, 2010: Climatic change and agronomic performance of hard red spring wheat from 1950 to 2007. Crop Sci., 50, 835841, https://doi.org/10.2135/cropsci2009.06.0314.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lark, T. J., J. M. Salmon, and H. K. Gibbs, 2015: Cropland expansion outpaces agricultural and biofuel policies in the United States. Environ. Res. Lett., 10, 044003, https://doi.org/10.1088/1748-9326/10/4/044003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrimore, J. H., M. J. Menne, B. E. Gleason, C. N. Williams, D. B. Wuertz, R. S. Vose, and J. Rennie, 2011: An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3. J. Geophys. Res., 116, D19121, https://doi.org/10.1029/2011JD016187.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., Y. Li, B. Bonsal, A. H. Manson, and L. Scaff, 2018: Combined impacts of ENSO and MJO on the 2015 growing season drought on the Canadian Prairies. Hydrol. Earth Syst. Sci., 22, 50575067, https://doi.org/10.5194/hess-22-5057-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D. B., and C. Bonfils, 2008: The effect of irrigation on regional temperatures: A spatial and temporal analysis of trends in California, 1934–2002. J. Climate, 21, 20632071, https://doi.org/10.1175/2007JCLI1755.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D. B., G. Bala, and P. B. Duffy, 2006: Biogeophysical impacts of cropland management changes on climate. Geophys. Res. Lett., 33, L06708, https://doi.org/10.1029/2005GL025492.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D. B., C. J. Bonfils, L. M. Kueppers, and M. A. Snyder, 2008: Irrigation cooling effect on temperature and heat index extremes. Geophys. Res. Lett., 35, L09705, https://doi.org/10.1029/2008GL034145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D. B., M. J. Roberts, W. Schlenker, N. Braun, B. B. Little, R. M. Rejesus, and G. L. Hammer, 2014: Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest. Science, 344, 516519, https://doi.org/10.1126/science.1251423.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, Y., K. Harding, and L. Kueppers, 2017: Irrigation effects on land–atmosphere coupling strength in the United States. J. Climate, 30, 36713685, https://doi.org/10.1175/JCLI-D-15-0706.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahmood, R., K. G. Hubbard, R. D. Leeper, and S. A. Foster, 2008: Increase in near-surface atmospheric moisture content due to land use changes: Evidence from the observed dewpoint temperature data. Mon. Wea. Rev., 136, 15541561, https://doi.org/10.1175/2007MWR2040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahmood, R., T. Keeling, S. A. Foster, and K. G. Hubbard, 2013: Did irrigation impact 20th century air temperature in the High Plains aquifer region? Appl. Geogr., 38, 1121, https://doi.org/10.1016/j.apgeog.2012.11.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahmood, R., and et al. , 2014: Land cover changes and their biogeophysical effects on climate. Int. J. Climatol., 34, 929953, https://doi.org/10.1002/joc.3736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manoli, G., J. C. Domec, K. Novick, A. C. Oishi, A. Noormets, M. Marani, and G. Katul, 2016: Soil–plant–atmosphere conditions regulating convective cloud formation above southeastern US pine plantations. Global Change Biol., 22, 22382254, https://doi.org/10.1111/gcb.13221.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Millett, B., W. C. Johnson, and G. Guntenspergen, 2009: Climate trends of the North American prairie pothole region 1906–2000. Climatic Change, 93, 243267, https://doi.org/10.1007/s10584-008-9543-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mishra, V., J. M. Wallace, and D. P. Lettenmaier, 2012: Relationship between hourly extreme precipitation and local air temperature in the United States. Geophys. Res. Lett., 39, L16403, https://doi.org/10.1029/2012GL052790.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, N. D., E. E. Butler, K. A. McKinnon, A. Rhines, M. Tingley, N. M. Holbrook, and P. Huybers, 2016: Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Climate Change, 6, 317322, https://doi.org/10.1038/nclimate2825.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, N. D., A. Rhines, E. E. Butler, D. K. Ray, S. Siebert, N. M. Holbrook, and P. Huybers, 2017: Global relationships between cropland intensification and summer temperature extremes over the last 50 years. J. Climate, 30, 75057528, https://doi.org/10.1175/JCLI-D-17-0096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muhlbauer, A., P. Spichtinger, and U. Lohmann, 2009: Application and comparison of robust linear regression methods for trend estimation. J. Appl. Meteor. Climatol., 48, 19611970, https://doi.org/10.1175/2009JAMC1851.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Novick, K. A., and et al. , 2016: The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Climate Change, 6, 10231027, https://doi.org/10.1038/nclimate3114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Partridge, T. F., J. M. Winter, E. C. Osterberg, D. W. Hyndman, A. D. Kendall, and F. J. Magilligan, 2018: Spatially distinct seasonal patterns and forcings of the U.S. warming hole. Geophys. Res. Lett., 45, 20552063, https://doi.org/10.1002/2017GL076463.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., 2001: Influence of the spatial distribution of vegetation and soils on the predictions of cumulus convective rainfall. Rev. Geophys., 39, 151177, https://doi.org/10.1029/1999RG000072.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., R. Avissar, M. Raupach, A. J. Dolman, X. Zeng, and A. S. Denning, 1998: Interactions between the atmosphere and terrestrial ecosystems: Influence on weather and climate. Global Change Biol., 4, 461475, https://doi.org/10.1046/j.1365-2486.1998.t01-1-00176.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., J. Adegoke, A. Beltrán-Przekurat, C. A. Hiemstra, J. Lin, U. S. Nair, D. Niyogi, and T. E. Nobis, 2007: An overview of regional land-use and land-cover impacts on rainfall. Tellus, 59B, 587601, https://doi.org/10.1111/j.1600-0889.2007.00251.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prasanna, V., and T. Yasunari, 2009: Time–space characteristics of seasonal and interannual variations of atmospheric water balance over South Asia. J. Meteor. Soc. Japan, 87, 263287, https://doi.org/10.2151/jmsj.87.263.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., and et al. , 2015: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges. Rev. Geophys., 53, 323361, https://doi.org/10.1002/2014RG000475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., C. Liu, K. Ikeda, S. B. Trier, R. M. Rasmussen, G. J. Holland, and M. P. Clark, 2017a: Increased rainfall volume from future convective storms in the US. Nat. Climate Change, 7, 880884, https://doi.org/10.1038/s41558-017-0007-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prein, A. F., R. M. Rasmussen, K. Ikeda, C. Liu, M. P. Clark, and G. J. Holland, 2017b: The future intensification of hourly precipitation extremes. Nat. Climate Change, 7, 4852, https://doi.org/10.1038/nclimate3168.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quiring, S. M., and T. N. Papakyriakou, 2005: Characterizing the spatial and temporal variability of June–July moisture conditions in the Canadian prairies. Int. J. Climatol., 25, 117138, https://doi.org/10.1002/joc.1104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Quiring, S. M., and D. B. Kluver, 2009: Relationship between winter/spring snowfall and summer precipitation in the northern Great Plains of North America. J. Hydrometeor., 10, 12031217, https://doi.org/10.1175/2009JHM1089.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raddatz, R. L., 1998: Anthropogenic vegetation transformation and the potential for deep convection on the Canadian prairies. Can. J. Soil Sci., 78, 657666, https://doi.org/10.4141/S98-011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raddatz, R. L., 2000: Summer rainfall recycling for an agricultural region of the Canadian prairies. Can. J. Soil Sci., 80, 367373, https://doi.org/10.4141/S99-016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ramsey, F., and D. Schafer, 2012: The Statistical Sleuth: A Course in Methods of Data Analysis. 4th ed. Brooks/Cole, 48 pp.

  • Reidmiller, D. R., C. W. Avery, D. R. Easterling, K. E. Kunkel, K. L. M. Lewis, T. K. Maycock, and B. C. Stewart, 2018: Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II. U.S. Global Change Research Program, https://nca2018.globalchange.gov/.

    • Search Google Scholar
    • Export Citation
  • Rodell, M., and J. S. Famiglietti, 2002: The potential for satellite-based monitoring of groundwater storage changes using GRACE: The High Plains aquifer, Central US. J. Hydrol., 263, 245256, https://doi.org/10.1016/S0022-1694(02)00060-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodell, M., J. S. Famiglietti, D. N. Wiese, J. T. Reager, H. K. Beaudoing, F. W. Landerer, and M. H. Lo, 2018: Emerging trends in global freshwater availability. Nature, 557, 651659, https://doi.org/10.1038/s41586-018-0123-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rohde, R., and et al. , 2013: A new estimate of the average Earth surface land temperature spanning 1753 to 2011. Geoinfo. Geostat. Overview, 1, https://doi.org/10.4172/2327-4581.1000101.

    • Search Google Scholar
    • Export Citation
  • Rossum, G., 1995: Python Reference Manual. CWI (Centre for Mathematics and Computer Science), https://ir.cwi.nl/pub/5008.

  • Sacks, W. J., and C. J. Kucharik, 2011: Crop management and phenology trends in the U.S. Corn Belt: Impacts on yields, evapotranspiration and energy balance. Agric. For. Meteor., 151, 882894, https://doi.org/10.1016/j.agrformet.2011.02.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Santer, B. D., T. M. L. Wigley, J. S. Boyle, D. J. Gaffen, J. J. Hnilo, D. Nychka, D. E. Parker, and K. E. Taylor, 2000: Statistical significance of trends and trend differences in layer-average atmospheric temperature time series. J. Geophys. Res., 105, 73377356, https://doi.org/10.1029/1999JD901105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seabold, S., and J. Perktold, 2010: Statsmodels: Econometric and statistical modeling with Python. Proc. Ninth Python Sci. Conf., Austin, TX, 57–61, https://conference.scipy.org/proceedings/scipy2010/pdfs/seabold.pdf.

    • Crossref
    • Export Citation
  • Seager, R., N. Lis, J. Feldman, M. Ting, A. P. Williams, J. Nakamura, H. Liu, and N. Henderson, 2018a: Whither the 100th meridian? The once and future physical and human geography of America’s arid–humid divide. Part I: The story so far. Earth Interact., 22, 122, https://doi.org/10.1175/EI-D-17-0011.1.

    • Search Google Scholar
    • Export Citation
  • Seager, R., J. Feldman, N. Lis, M. Ting, A. P. Williams, J. Nakamura, H. Liu, and N. Henderson, 2018b: Whither the 100th meridian? The once and future physical and human geography of America’s arid–humid divide. Part II: The meridian moves east. Earth Interact., 22, 124, https://doi.org/10.1175/EI-D-17-0012.1.

    • Search Google Scholar
    • Export Citation
  • Segal, M., Z. Pan, R. W. Turner, and E. S. Takle, 1998: On the potential impact of irrigated areas in North America on summer rainfall caused by large-scale systems. J. Appl. Meteor., 37, 325331, https://doi.org/10.1175/1520-0450-37.3.325.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., D. Lüthi, M. Litschi, and C. Schär, 2006: Land–atmosphere coupling and climate change in Europe. Nature, 443, 205209, https://doi.org/10.1038/nature05095.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., and et al. , 2018: Land radiative management as contributor to regional-scale climate adaptation and mitigation. Nat. Geosci., 11, 8896, https://doi.org/10.1038/s41561-017-0057-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sertel, E., A. Robock, and C. Ormeci, 2010: Impacts of land cover data quality on regional climate simulations. Int. J. Climatol., 30, 19421953, https://doi.org/10.1002/joc.2036.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and et al. , 2008: A description of the Advanced Research WRF version 3, NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Smith, W. N., and et al. , 2013: Assessing the effects of climate change on crop production and GHG emissions in Canada. Agric. Ecosyst. Environ., 179, 139150, https://doi.org/10.1016/j.agee.2013.08.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spera, S. A., J. M. Winter, and J. W. Chipman, 2018: Evaluation of agricultural land cover representations on regional climate model simulations in the Brazilian Cerrado. J. Geophys. Res. Atmos., 123, 51635176, https://doi.org/10.1029/2017JD027989.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stein, A. F., R. R. Draxler, G. D. Rolph, B. J. B. Stunder, M. D. Cohen, and F. Ngan, 2015: NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System. Bull. Amer. Meteor. Soc., 96, 20592077, https://doi.org/10.1175/BAMS-D-14-00110.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stoy, P. C., and et al. , 2018: Opportunities and trade-offs among BECCS and the food, water, energy, biodiversity, and social systems nexus at regional scales. BioScience, 68, 100111, https://doi.org/10.1093/biosci/bix145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Q., C. Miao, Q. Duan, H. Ashouri, S. Sorooshian, and K.-L. Hsu, 2018: A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Rev. Geophys., 56, 79107, https://doi.org/10.1002/2017RG000574.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vick, E. S. K., P. C. Stoy, A. C. I. Tang, and T. Gerken, 2016: The surface–atmosphere exchange of carbon dioxide, water, and sensible heat across a dryland wheat-fallow rotation. Agric. Ecosyst. Environ., 232, 129140, https://doi.org/10.1016/j.agee.2016.07.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wiken, E. B., and et al. , Eds., 1996: A perspective on Canada’s ecosystems: An overview of the terrestrial and marine ecozones. Canadian Council on Ecological Areas, 95 pp.

  • Xie, S.-P., and et al. , 2015: Towards predictive understanding of regional climate change. Nat. Climate Change, 5, 921930, https://doi.org/10.1038/nclimate2689.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Z., F. Dominguez, X. Zeng, H. Hu, H. Gupta, and B. Yang, 2017: Impact of irrigation over the California Central Valley on regional climate. J. Hydrometeor., 18, 13411357, https://doi.org/10.1175/JHM-D-16-0158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yin, J., J. D. Albertson, J. R. Rigby, and A. Porporato, 2015: Land and atmospheric controls on initiation and intensity of moist convection: CAPE dynamics and LCL crossings. Water Resour. Res., 51, 84768493, https://doi.org/10.1002/2015WR017286.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    A map of the region considered to be the northern North American Great Plains (NNAGP) for the purposes of this study, which comprises the NEON northern plains domain 9 in the United States and the Environment Canada Prairies Terrestrial Ecozone. Landcover as ascertained by MODIS are broken up into categories using the International Geosphere–Biosphere Programme (IGBP) classification system (Friedl et al. 2010).

  • View in gallery

    Trends in 2-m air temperature Tair from 1970 to 2015 across the NNAGP and surrounding regions. Stippling indicates a significant trend at p < 0.05 after correcting for autocorrelation [Eq. (1)] against a null hypothesis of no trend.

  • View in gallery

    As in Fig. 2, but for vapor pressure deficit.

  • View in gallery

    As in Fig. 2, but for precipitation.

  • View in gallery

    Monthly trends in near-surface (2 m) air temperature between 1970 and 2015 for the NNAGP (Fig. 1) from the CRU dataset. The orange lines indicate the median trend and the blue line indicates zero trend.

  • View in gallery

    May and June trends in (a) 2-m air temperature, (b) vapor pressure deficit, and (c) precipitation from 1970 to 2015 in the NNAGP (black outline; see Fig. 1). In (a) the stippling indicates significant (p < 0.05) cooling after correction for temporal autocorrelation [Eq. (1)] versus mean global warming of 0.2°C decade−1 during the commensurate period. In (b) and (c) the stippling indicates significant trends compared to a null hypothesis of no trend.

  • View in gallery

    As in Fig. 5, but for vapor pressure deficit (VPD).

  • View in gallery

    As in Fig. 5, but for precipitation.

  • View in gallery

    Trends in near-surface air temperature from the CESM-LE. The boxplots show the median (green line) and variation in trends from the 39 ensemble members by month between 1970 and 2015. The trend estimates are based on averages over the grid cells that contain the NNAGP. The dark blue line indicates where a zero trend would fall.

  • View in gallery

    Trends in air temperature from 1970 to 2015 in the NNAGP using Berkley Earth Surface Temperature data. The blue line indicates zero trend. The orange lines indicate the median of the distribution of trends.

  • View in gallery

    Near-surface (2 m) air temperature trends from 270 GHCN sites within the NNAGP. Sites with at least 75% daily data availability from 1925 to 2015 were chosen for analysis. Maximum and minimum temperatures were averaged together to obtain average daily temperature. Daily data were aggregated to the May and June period and trends were calculated for each site with varying trend start years until 2015. The elongation of the violin plots is due to the shorter record from which trends were calculated, leading to more variability in trends. The distributions of the 270 trends are shown in the violin plot for each trend start year.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 322 322 46
PDF Downloads 214 214 55

Recent Trends in the Near-Surface Climatology of the Northern North American Great Plains

View More View Less
  • 1 Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana
  • | 2 Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, and Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania
  • | 3 National Center for Atmospheric Research, Boulder, Colorado
  • | 4 Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, and Department of Biological Systems Engineering, University of Wisconsin–Madison, Madison, Wisconsin
© Get Permissions
Open access

Abstract

We examined climate trends in the northern North American Great Plains (NNAGP) from 1970 to 2015, a period that aligns with widespread land-use changes in this globally important agricultural region. Trends were calculated from the Climatic Research Unit (CRU) and other climate datasets using a linear regression model that accounts for temporal autocorrelation. The NNAGP warmed on an annual basis, with the largest change occurring in winter (DJF) at 0.4°C decade−1. January in particular warmed at nearly 0.9°C decade−1. The NNAGP cooled by −0.18°C decade−1 during May and June, nearly the opposite of global warming trends during the study period. The atmospheric vapor pressure deficit (VPD), which can limit crop growth, decreased in excess of −0.4 hPa decade−1 during climatological summer in the southeastern part of the study domain. Precipitation P increased in the eastern portion of the NNAGP during all seasons except fall and increased during May and June in excess of 8 mm decade−1. Climate trends in the NNAGP largely followed global trends except during the early warm season (May and June) during which 2-m air temperature Tair became cooler, VPD lower, and P greater across large parts of the study region. These changes are consistent with observed agricultural intensification during the study period, namely the reduction of summer fallow and expansion of agricultural land use. Global climate model simulations indicate that observed Tair trends cannot be explained by natural climate variability. However, further climate attribution experiments are necessary to understand if observed changes are caused by increased agricultural intensity or other factors.

Denotes content that is immediately available upon publication as open access.

© 2019 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: Gabriel T. Bromley, gabriel.bromley@montana.edu

Abstract

We examined climate trends in the northern North American Great Plains (NNAGP) from 1970 to 2015, a period that aligns with widespread land-use changes in this globally important agricultural region. Trends were calculated from the Climatic Research Unit (CRU) and other climate datasets using a linear regression model that accounts for temporal autocorrelation. The NNAGP warmed on an annual basis, with the largest change occurring in winter (DJF) at 0.4°C decade−1. January in particular warmed at nearly 0.9°C decade−1. The NNAGP cooled by −0.18°C decade−1 during May and June, nearly the opposite of global warming trends during the study period. The atmospheric vapor pressure deficit (VPD), which can limit crop growth, decreased in excess of −0.4 hPa decade−1 during climatological summer in the southeastern part of the study domain. Precipitation P increased in the eastern portion of the NNAGP during all seasons except fall and increased during May and June in excess of 8 mm decade−1. Climate trends in the NNAGP largely followed global trends except during the early warm season (May and June) during which 2-m air temperature Tair became cooler, VPD lower, and P greater across large parts of the study region. These changes are consistent with observed agricultural intensification during the study period, namely the reduction of summer fallow and expansion of agricultural land use. Global climate model simulations indicate that observed Tair trends cannot be explained by natural climate variability. However, further climate attribution experiments are necessary to understand if observed changes are caused by increased agricultural intensity or other factors.

Denotes content that is immediately available upon publication as open access.

© 2019 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: Gabriel T. Bromley, gabriel.bromley@montana.edu

1. Introduction

Global near-surface temperatures have increased by nearly 0.2°C decade−1 since the mid-1970s, with concurrent increases in global atmospheric moisture content (IPCC 2013; Dai 2006). Global climate trends are important to understand, but regional climate often departs from global trends due to regional variations in water and energy dynamics. Characterizing regional climate change is an important first step toward understanding the mechanisms that underlie these changes to further improve our understanding of the climate system and to provide information for decision makers (Abatzoglou et al. 2009; Xie et al. 2015). Of particular importance is an improved understanding of climate dynamics in food-producing regions, identified by the World Climate Research Programme as the Water for the Food Baskets of the World grand challenge.

At regional scales, climate is often strongly influenced by land surface processes (Seneviratne et al. 2006) in addition to the general circulation. Anthropogenic modifications including irrigation and other agricultural land-use and land-cover changes can alter this connection (Couzin 1999), and numerous recent studies have reiterated that agricultural intensification (i.e., cropland expansion and/or increasing crop yields) often results in regional cooling during the vegetative growing season (Mahmood et al. 2014; Mueller et al. 2016; Alter et al. 2017; Mueller et al. 2017; Gameda et al. 2007). Increases in agricultural intensity often alter the surface energy balance such that latent heat flux to the atmosphere is increased, sensible heat flux is reduced, and maximum temperatures are suppressed (Huber et al. 2014; Diffenbaugh 2009). These changes to the surface energy balance can increase precipitation by increasing atmospheric moisture content and enhancing convective initiation through increased moist static energy (Pielke 2001; Pielke et al. 2007; Gerken et al. 2018b; Vick et al. 2016; Gentine et al. 2013; Gameda et al. 2007). The increase in latent heat flux decreases the Bowen ratio, which lowers the height of the planetary boundary layer (PBL) and simultaneously lowers the lifting condensation level (LCL) (Juang et al. 2007; Alter et al. 2015; Gerken et al. 2018a). LCLs that exceed the PBL height are associated with cloud development and constitute a “necessary but not sufficient condition” for convective precipitation (Juang et al. 2007; Bonetti et al. 2015; Manoli et al. 2016), noting additional requirements such as a minimum CAPE of ~400 J kg−1 (Yin et al. 2015) and positive convective triggering potential (Findell and Eltahir 2003a,b). Changes in PBL processes from agricultural management also modify local and downwind air temperature Tair and precipitation (Diffenbaugh 2009; Lu et al. 2017), emphasizing their importance to regional climate. Understanding how agriculture interacts with regional climate processes is a critical step for developing sustainable management practices to help avoid deleterious impacts of climate change on agricultural production (e.g., Asseng et al. 2013, 2015), which have already been observed across multiple crop producing regions (IPCC 2014).

The Canadian Prairies (CP) are a notable example of a region that has experienced a simultaneous change in agricultural intensity and regional climate. The CP have undergone substantial changes in land surface composition since the 1970s, driven by a shift away from wheat/summer fallow cropping sequences to more diversified cropping systems that replaced summer fallow with oilseeds, pulses, and other crops (Campbell et al. 2002; Bradshaw et al. 2004). The area of summer fallow in the CP has decreased from nearly 160 000 km2 in 1970 to less than 20 000 km2 at present (Vick et al. 2016), which has increased moisture flux to the atmosphere and altered regional climate during the vegetative growing season (Gameda et al. 2007). Maximum summer temperatures have decreased by 1.2°C since the 1970s, driven by a reduction in surface net radiation of −6 W m−2 (Betts et al. 2013b). The decrease in net radiation is attributed to increased cloud cover as a result of shifting land use and increases in surface relative humidity of up to 7% (Betts et al. 2013a,b; Mahmood et al. 2008). These changes in near-surface moisture have also increased the potential for convective precipitation during the growing season (Raddatz 1998, 2000).

Summer fallow in the United States decreased gradually from over 160 000 to 60 000 km2 during a similar time frame but starting in the mid-1980s (Vick et al. 2016), a large part of which has occurred in the U.S. Northern Great Plains (NGP) adjacent to the CP (Fig. 1). Convective likelihood has increased across parts of the NGP during the vegetative growing season (Gerken et al. 2018a), but it is otherwise unclear if the mechanisms causing regional climate changes in the NGP have followed those of the CP. It is difficult to understand the mechanisms causing regional cooling in the combined CP and NGP [called herein the northern North American Great Plains (NNAGP)] without first characterizing regional climate observations. Here, we describe changes in observed near-surface (2 m) air temperature Tair, vapor pressure deficit (VPD), and precipitation P in the NNAGP at annual, seasonal, and monthly time scales from 1970 to 2015 by analyzing global climatic databases. Changes in Tair and P are commonly studied, and we additionally study VPD given its important role in crop yields (Lobell et al. 2014) and increasingly important role in controlling water transport from the soil through transpiration (Novick et al. 2016). VPD is the difference between vapor pressure and saturation vapor pressure at the surface such that a decrease in 2-m VPD can be interpreted as more atmospheric moisture near the surface, all else being equal. We focus on the period from the 1970s until the present to study the time period characterized by increasing agricultural intensity (Alter et al. 2017; Vick et al. 2016) to improve our understanding of regional climate changes that have impacted—and appear to be impacted by—land and water management in the NNAGP.

Fig. 1.
Fig. 1.

A map of the region considered to be the northern North American Great Plains (NNAGP) for the purposes of this study, which comprises the NEON northern plains domain 9 in the United States and the Environment Canada Prairies Terrestrial Ecozone. Landcover as ascertained by MODIS are broken up into categories using the International Geosphere–Biosphere Programme (IGBP) classification system (Friedl et al. 2010).

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

2. Methods

a. Study area

The NNAGP has no formal definition. For the purposes of this study, we consider it to be the area encompassed by the National Ecological Observatory Network (NEON) northern plains domain in the United States (domain 9) (Keller et al. 2008) and the Prairies Ecozone in Canada (Fig. 1) (Wiken et al. 1996). The entire domain largely encompasses what is often called the “Prairie Pothole” region with the inclusion of semiarid areas dominated by rangeland toward the west (Millett et al. 2009) and the “Northern Short Grasslands” region defined by the World Wide Fund for Nature. The northern plains domain encompasses most of the Upper Missouri River basin (Stoy et al. 2018) as well as the U.S. portions of the Red River of the North and the Nebraska Sandhills. The Prairies Ecozone encompasses most major grassland and agricultural regions of the Prairie Provinces with the exception of the Peace River Country in Alberta and British Columbia.

The NNAGP was comprised largely of native shortgrass and mixed-grass prairie before the advent of widespread agriculture. Row-crop agriculture, largely maize (hereafter corn) and soy, now dominate the eastern region of the NNAGP. A diverse mixture of wheat, pulse crops, oilseeds, and cover crops now dominate the northern and western regions, with rangeland dominating in areas unsuited to row-crop agriculture, largely in the western part of the study region, with minor contributions of forests, urban areas, and lands developed for energy extraction, largely overlying the Bakken formation and within the Powder River basin of Wyoming and Montana. The Canadian Prairies are broken into two distinct zones: the semiarid prairies and the temperate prairies, with the latter forming the boundary between prairie and boreal forest (Gauthier and Wiken 2003; Hammermeister 2001).

b. Data

Station observations and gridded observational data products are used to study trends in Tair, VPD, and P in the NNAGP. Gridded monthly Tair and P come from the Climatic Research Unit (CRU) TS 4.01 (Harris et al. 2014). VPD was calculated by subtracting vapor pressure obtained from the CRU dataset from saturation vapor pressure calculated using the CRU Tair data product. The CRU dataset is in good agreement with other commonly used temperature and precipitation datasets (Sun et al. 2018; Jones 2016) though it does not contain as many source observations (Harris et al. 2014). The CRU dataset is also not homogeneous, though the source observations are often homogenized by the original data collection organization, and thus better suited for trend analysis (Harris et al. 2014). The CRU dataset compiles observations from up to eight climate stations per grid cell from World Meteorological Organization (WMO) stations, a density that is achieved for most of North America. We use the independent Berkeley Earth dataset (Rohde et al. 2013) to corroborate the major findings from the CRU dataset in the appendixes.

Data from the Community Earth System Model Large Ensemble (CESM-LE) experiment (Kay et al. 2015) were used to determine if observed trends can be explained by natural climate variability (e.g., Deser et al. 2012). The ensemble contains 39 simulations with a 1° horizontal grid spacing, a length scale twice that of the CRU dataset, and uses historical forcings until 2005, at which point they are run under the RCP8.5 scenario. Each model’s initial conditions were slightly varied to create an ensemble that captures internal climate variability (Kay et al. 2015). The grid points containing the NNAGP were selected and averaged creating monthly time series for each model. Linear trends were then calculated from the monthly data.

c. Analysis

Anomalies in Tair, VPD, and P were calculated by first averaging monthly data for each grid point in the CRU dataset for the period 1980–2010. The means were then subtracted from the values of the time period of interest to create the anomaly value. Trends in the anomalies were calculated for the variables of interest using an ordinary least squares (OLS) regression for the time period 1970–2015. The OLS function from the Statsmodels (Seabold and Perktold 2010) package in Python (Rossum 1995) was used to calculate trends. For the case of Tair during May and June, a one-sided lower-tailed Student’s t test was used to compare the calculated trends to the observed global annual average Tair trend of 0.2°C decade−1 for the study period calculated using the CRU database, a similar but more conservative trend than the 0.26°C decade−1 that is found by the IPCC (IPCC 2013). A two-tailed Student’s t test was used when comparing against a null trend for VPD, P, and Tair for seasonal analyses. We use the adjusted standard error to account for effects of temporal autocorrelation on standard error (Ramsey and Schafer 2012):
Sadj=S1+r11r1,
where S is the standard error and r1 is the lag-1 autocorrelation coefficient calculated using the “acf” function in the Statsmodels package. Without the adjustment, the calculated p values are often too low, resulting in more frequently rejecting the null hypothesis of no trend or, for the case of Tair, rejecting significantly different trends from the observed global increase of 0.2°C decade−1 (Santer et al. 2000). We study trends in Tair, VPD, and P for standard climatological seasons as well as individual months to better identify periods that are exhibiting exceptional change. We also test the sensitivity of trends to the start date of the study period using the Global Historical Climatology Network (GHCN) dataset (Lawrimore et al. 2011) in the appendixes.

3. Results

a. Seasonal trends

The greatest increases in Tair occurred during climatological winter (DJF), with some areas experiencing significant (p < 0.05) warming in excess of 0.4°C decade−1 (Fig. 2), especially in the CP and the eastern portion of the NGP. Spring (MAM) and summer (JJA) show no significant positive or negative warming trends in aggregate but fall (SON) warmed at approximately the global average trend of 0.2°C decade−1 over the measurement period, which is significantly different from no trend across most of the U.S. northern plains.

Fig. 2.
Fig. 2.

Trends in 2-m air temperature Tair from 1970 to 2015 across the NNAGP and surrounding regions. Stippling indicates a significant trend at p < 0.05 after correcting for autocorrelation [Eq. (1)] against a null hypothesis of no trend.

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

VPD decreased across most of the NNAGP during winter and across its eastern half during summer (Fig. 3). The southeastern NGP experienced the greatest negative trend of −0.4 hPa decade−1 or less. The mountainous regions to the west of the NNAGP experienced large and significant positive trends of VPD in excess of 0.4 hPa decade−1 during summer. VPD decreased during Winter across the entire region, with the greatest decrease in the south. Spring and fall experienced no significant trends in VPD in the study area over the measurement period.

Fig. 3.
Fig. 3.

As in Fig. 2, but for vapor pressure deficit.

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

Notably, P increased by 1 mm decade−1 or more across the eastern Dakotas during winter, by 2 mm decade−1 or more in western Minnesota during MAM, and by 4 mm decade−1 or more across the central and eastern Dakotas during summer (Fig. 4).

Fig. 4.
Fig. 4.

As in Fig. 2, but for precipitation.

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

b. Monthly trends

Analyzing Tair trends by month across the measurement period for the NNGAP reveals patterns of significant warming and cooling that are not apparent in the analysis by climate season (Fig. 5). Median Tair across the entire NNAGP increased by ~0.9°C decade−1 in January. February experienced no significant trend and March warmed by 0.3°C decade−1. April experienced no Tair trend, but May and June cooled by about −0.2°C decade−1 on average. In other words, warming in March masked cooling in May in the analysis by climatological spring (MAM; Fig. 2). July and August have warmed by ~0.2°C decade−1 on average, masking June cooling in the seasonal analysis. Likewise, the lack of Tair trend in October masked warming in September and November on the order of 0.3°–0.4°C decade−1, with similar mean warming in December, slightly greater than the global annual warming trend across the study period on the order of 0.2°C decade−1.

Fig. 5.
Fig. 5.

Monthly trends in near-surface (2 m) air temperature between 1970 and 2015 for the NNAGP (Fig. 1) from the CRU dataset. The orange lines indicate the median trend and the blue line indicates zero trend.

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

The greatest May and June cooling trends on the order of −0.25°C decade−1 are found in eastern Montana, western North Dakota, and the CP, and extend to the Canadian Shield north of the study domain and the Rocky Mountains to the west of the study domain (Fig. 6a). The Rocky Mountains are important for storm generation in the NNAGP (Carbone and Tuttle 2008) but are outside of the study area. Mean cooling across the entire NNAGP during May and June from 1970 to 2015 averaged −0.1°C decade−1 compared to a mean global warming of 0.3°C decade−1 over the same period.

Fig. 6.
Fig. 6.

May and June trends in (a) 2-m air temperature, (b) vapor pressure deficit, and (c) precipitation from 1970 to 2015 in the NNAGP (black outline; see Fig. 1). In (a) the stippling indicates significant (p < 0.05) cooling after correction for temporal autocorrelation [Eq. (1)] versus mean global warming of 0.2°C decade−1 during the commensurate period. In (b) and (c) the stippling indicates significant trends compared to a null hypothesis of no trend.

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

VPD decreased by −0.2 hPa decade−1 in May and −0.3 hPa decade−1 in June, which, coupled with decreases in Ta, resulted in cooler and moister conditions (Fig. 6b). VPD in July and August show high spatial variability with large positive and negative VPD trends across different parts of the study region (Fig. 7). The dipole feature of Fig. 3c explains why there is so much variability in July and August: The eastern half of the study area exhibited negative VPD trends and the western half experienced positive trends. It was found that P increased across the entire NNAGP at 3 and 4 mm decade−1 during May and June, respectively, and P in eastern North Dakota has increased in excess of 8 mm decade−1 (Fig. 6c). October also exhibits a positive trend in P of 2 mm decade−1, but trends in P during other months are not different from zero (Fig. 8).

Fig. 7.
Fig. 7.

As in Fig. 5, but for vapor pressure deficit (VPD).

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

Fig. 8.
Fig. 8.

As in Fig. 5, but for precipitation.

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

4. Discussion

The research conducted here demonstrates that the annual climate of the NNAGP followed global Tair trends from 1970 to 2015 with important seasonal exceptions that require further analysis to ascertain the responsible mechanisms. The analysis of standard climatological seasons shows that warming occurred across the study area, exceeding the global average trend during most of the winter but less than the global average trend during early summer. The Northern Hemisphere winter is warming at the greatest rate globally, particularly in the extratropical and Arctic regions (IPCC 2013). The NNAGP follows this pattern, with the greatest winter warming trend exceeding 0.4°C decade−1 in the northern part of the study domain (Abatzoglou et al. 2014) (Fig. 2). VPD has decreased during winter and summer, with the largest changes occurring in the southern part of the NNAGP during summer (Fig. 3), and P has increased during much of the warm season, primarily in the eastern NNAGP, with the only significant negative trend in P in the western half of the study area during winter (Fig. 4).

To provide context to the results, we analyzed the temperature trends for the NNAGP using 39 members from the CESM-LE ensemble, an experiment designed to put bounds on internal climate variability (Kay et al. 2015). The trends show that the observed May–June cooling trends are outside the spread of simulated trends and the ensembles of Tair trends are higher for most months than observations with a median of 0.6°–1.25°C decade−1 (Fig. 9). This indicates that the observed trends in the NNAGP are outside of natural climate variability and might be forced by processes that are not captured in the CESM-LE simulations, including those that are local in nature. These could include an inaccurate representation of agricultural intensification and other land-use changes that have occurred over the study period (e.g., Stoy et al. 2018) and errors in simulating convective processes (e.g., Prein et al. 2015) and associated land surface atmosphere feedbacks (Hohenegger et al. 2009) due to the coarse model resolution. It is important to note that the CESM-LE simulations were run at 1° horizontal grid spacing and are limited in their ability to simulate the mechanisms that give rise to convective cloud formation and precipitation, which are known to be important features of the growing season climate of the NNAGP (Betts et al. 2013a). One can infer therefore that important climate mechanisms in the NNAGP are not captured by existing global climate models, at least during some seasons.

Fig. 9.
Fig. 9.

Trends in near-surface air temperature from the CESM-LE. The boxplots show the median (green line) and variation in trends from the 39 ensemble members by month between 1970 and 2015. The trend estimates are based on averages over the grid cells that contain the NNAGP. The dark blue line indicates where a zero trend would fall.

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

a. Climatological seasons mask large monthly trends

There was little to no trend in Tair in the NNGAP during the summer months in the analysis of standard climatological seasons (Fig. 2). Significant cooling became apparent across much of the NNAGP during May and June after disaggregating the analysis into months to identify if the seasonal analyses were averaging out important information (Fig. 5). Stronger May and June cooling trends result if the 1980s were chosen as a start date (see appendix B), suggesting that the cooling effect is not an artifact of choosing 1970 as a start date. It is difficult to attribute the significant May and June Tair decrease to any particular mechanism in an empirical study except to note that the CESM-LE fails to capture the observations (Fig. 9), but observations are broadly consistent with research on land–atmosphere coupling to date. A reduction in the area of summer fallow since the 1970s has been identified as a contributor to summer cooling and cloud development in the Canadian Prairies (Gameda et al. 2007; Betts et al. 2013b; Raddatz 1998) and is consistent with the increase in likelihood of convective precipitation due in part to the lowering of the Bowen ratio across parts of the U.S. northern plains (Gerken et al. 2018a). That being said, changes in irrigation in the Ogallala Aquifer region south of the study domain, increased land surface albedo due to the advent of no-till agriculture (Seneviratne et al. 2018; Davin et al. 2014; Lobell et al. 2006), ongoing increases in water table height and thereby surface water extent across much of the study region (Rodell et al. 2018), and changes in the general circulation are all likely to interact with land-cover change to cause the unique May and June temperature trends, and we discuss each in the context of regional climate modeling below after discussing trends in P to which trends in Tair are coupled (e.g., Prein et al. 2017b).

b. Trends in precipitation

An increase of P occurred the eastern part of the NNAGP during most of the warm season including climatological spring and summer (Fig. 4). Further evidence of increased P manifests itself in the fact that groundwater storage is increasing across the northern Great Plains (Rodell et al. 2018); these changes in groundwater are likely due to an increase in water inputs to the land surface rather than decreased outputs given that latent heat flux has increased across parts of the NNAGP (Gerken et al. 2018a; Vick et al. 2016). While not explicitly studied here, there is evidence that precipitation intensity has increased more than the Clausius–Clapeyron relationship would suggest, with a 16% increase in rainfall amount occurring during the heaviest precipitation events (Reidmiller et al. 2018; Mishra et al. 2012). Recent studies have demonstrated that increased precipitation and precipitation intensity, especially during the early warm season, are partly due to stronger convective systems (Prein et al. 2017a,b; Feng et al. 2016). It is important to note that the NNAGP is characterized by considerable year-on-year variability in P, which can mask trends (Muhlbauer et al. 2009) and has wide-ranging impacts on agriculture and water resources. A recent reminder of this was given in 2016 and 2017, when parts of the NNAGP experienced 150% of normal P for the 2016 calendar year followed by a severe “flash” drought in the western NGP during the 2017 growing season, noting that our study period ends in 2015 due to data availability. This drought was preceded by a period of anomalously low likelihood of convective precipitation (Gerken et al. 2018b), further emphasizing the important role that locally triggered convective P plays in the NNGAP during climatological spring and summer.

c. Land-cover change

The early warm season of May and June is a period of considerable vegetative activity and alone comprises one-half or more of the total growing-season net carbon uptake by winter and spring wheat in central Montana in the United States, with rapid crop growth during this period giving rise to substantial latent heat fluxes (Vick et al. 2016). The eddy covariance measurements described by Vick et al. (2016) demonstrate that a spring wheat field had nearly double the evapotranspiration as a nearby fallow field and half the sensible heat flux from planting to harvest. The simulated height of the atmospheric boundary layer on account of these differences in surface–atmosphere energy exchange differed by nearly 1 km between the wheat and fallow treatments during the mid–growing season, suggesting a substantial impact of agricultural management on boundary layer climate, consistent with observations in the Canadian Prairies (Betts et al. 2013b; Gameda et al. 2007). Given the decline in summer fallow area in the NNAGP on the order of over 200 000 km2 (Vick et al. 2016), a potential mechanism for the May and June cooling trend emerges if the lower, more humid boundary layer more readily produces clouds and precipitation (Gerken et al. 2018a), which would also help explain May–June decreases in VPD.

That being said, the reduction of summer fallow is not the only change occurring in the NNAGP. Shrubs and croplands have increased in aerial extent the U.S. northern plains since 2001, while the extent of forest, pasture, and Conservation Reserve Program areas have decreased (Stoy et al. 2018). No-till agriculture has increased albedo, but the magnitude and location of these changes are difficult to quantify and the reanalysis data products that may be able to estimate these changes often struggle to correctly estimate changes to net radiation as cloud fields are not sufficiently resolved in the reanalysis modeling systems (e.g., Draper et al. 2018). Warming temperatures associated with global climate change have created conditions conducive to corn and soy agriculture in the NNAGP (Smith et al. 2013; Kucharik 2008; Butler et al. 2018) and corn and soy acreage has also increased in the Prairie Pothole region of the Dakotas since the 2000s (Lark et al. 2015). The growing season for corn/soy as well as wheat has been extended by up to 10 days in some areas contributing to an increase in yields (Lanning et al. 2010; Kucharik 2008; Hu et al. 2005; Sacks and Kucharik 2011). An increase in corn/soy production in the U.S. central plains (i.e., the “Corn Belt”) resulted in a similar cooling trend to what we observe in the NNAGP, although it has occurred later in the warm season (Mueller et al. 2016) consistent with seasonal growth patterns of maize, a C4 plant. It is important to note that the replacement of one land-cover type in favor of another does not always result in a consistent effect on regional temperature and precipitation (Garcia-Carreras et al. 2010; Bright et al. 2017; Fall et al. 2010) and surface–atmosphere interactions may be dominated by soil moisture state rather than vegetation composition (Desai et al. 2006; Collow et al. 2014), highlighting the importance of coupled energy and water fluxes to regional climate processes.

Another influence on atmospheric boundary layer processes is the addition of moisture by way of irrigation, which lowers the Bowen ratio by favoring latent heat flux at the surface (Adegoke et al. 2003; Lobell and Bonfils 2008; Lobell et al. 2008; Mahmood et al. 2013). The addition of surface moisture tends to increase P by way of increasing moist static energy available for convection, particularly downstream of irrigated areas (DeAngelis et al. 2010; Huber et al. 2014; Harding and Snyder 2012; Yang et al. 2017; Segal et al. 1998). Irrigation in the NNAGP is far less common than the U.S. southern plains, especially areas overlying the Ogallala Aquifer (Dickens et al. 2011), but could be influenced by advection of moisture from the south (Rodell and Famiglietti 2002; Rodell et al. 2018)—especially given the influence of the nocturnal jet in the central United States—which might be the water source for the observed increase in P. Such changes to water cycling immediately outside the NNAGP emphasize the importance of understanding external versus internal inputs to the climate system.

Ongoing climate warming has increased the importance of VPD to plant stomatal function, transpiration, and the assimilation of carbon across global biomes (Novick et al. 2016). Much of the United States has exhibited near-surface drying (i.e., an increase in VPD) with climate change—with deleterious consequences for agriculture (Seager et al. 2018b)—with the exception of parts of the U.S. portion of the NNAGP (Ficklin and Novick 2017; Seager et al. 2018a). Our analysis supports the notion that aridity is increasing in the NNAGP on an annual basis because of the increase in Tair and lack of trend in P. However, there are two signals in the monthly VPD trends that are consistent with agricultural intensification or other changes to regional water cycling. First, the decrease in VPD during May and June (Fig. 6b) is consistent with increased evapotranspiration resulting from a larger planted area as noted. Second, there is a significant decrease in VPD in the southeastern part of the study area during climatological summer (Fig. 3c), which is consistent with an increase in agricultural intensification within the study region (Mueller et al. 2016) and/or an increase in moisture transport from the surrounding region that has experienced an increase in irrigation.

d. Toward an understanding of the mechanisms underlying regional early-season cooling

Results here are broadly consistent with the notion that agricultural intensity within and/or external to the NNAGP have caused a cooling trend during parts of the vegetative growing season, but also point to the need for process-based regional climate studies to identify the causes that underlie these observations. The NNAGP is a dynamic region that receives advected moisture and energy from the Arctic, Pacific, and the Gulf of Mexico in addition to internal water and energy cycling (Bonsal et al. 1999; Raddatz 2000; Quiring and Kluver 2009) of which locally recycled convective precipitation is an important part (Raddatz 2000). The NNAGP is also influenced by global oscillations including ENSO and the PDO, and the MJO, and teleconnections that result in variable weather patterns and precipitation (Quiring and Papakyriakou 2005; Bonsal et al. 1999; Li et al. 2018). Climate change is also increasing the variability of the polar jet, which may be partially responsible for changes in surface temperature across the continent due to changes in meridional flow patterns (Francis and Vavrus 2015; Partridge et al. 2018). Despite the notion that early growing season changes in regional climate are consistent with the impacts of an intensifying agriculture system either within or external to the NNAGP, we cannot exclude other features of the climate system that may be responsible for observed changes in Tair, VPD, and P.

Targeted climate change attribution experiments could help to quantify the contribution of different processes to the observed trends by modifying energy and moisture fluxes from the land surface to mimic past, present, and projected future changes in agricultural intensity while controlling for large-scale weather conditions. Of particular importance to the NNAGP is the use of convection-permitting climate models (CPMs) that run on spatial scales of 4 km or less (e.g., Prein et al. 2015) considering the importance of land–atmosphere feedbacks and convective precipitation in the NNAGP (Gerken et al. 2018a; Betts et al. 2013a) and the failure of current climate models to simulate observed May–June Tair trends (Fig. 9). Early simulations demonstrated that parameterizations were inadequate for modeling atmospheric circulations associated with heterogeneous landscapes (Avissar and Pielke 1989, 1991; Pielke et al. 1998). Therefore, to successfully model these changes using a CPM, the model would need to accurately represent the land surface and the resulting land–atmosphere interactions. A commonly used CPM, the Weather Research and Forecasting Model (WRF) (Skamarock et al. 2008), contains land-cover data products that are accurate for homogenous landscapes but often need to be addressed for heterogeneous landscapes and landscapes that exhibit interannual variability such as agricultural areas (Spera et al. 2018; Sertel et al. 2010; Gao and Jia 2013). It is also important to track moisture sources in such a modeling exercise to determine the roles of internal versus external water cycle dynamics in determining ongoing climate changes in the NNAGP. Determining the precipitation recycling ratio (Raddatz 2000; Prasanna and Yasunari 2009; Kanamori et al. 2018) would help with source attribution, as would using Lagrangian methods such as the HYSPLIT model (Stein et al. 2015). Given the use of a convection permitting model, however, tracing water vapor within the model framework might give detailed insight into the water balance of the NNAGP (Dominguez et al. 2016; Chug and Dominguez 2019). Such an effort would require extensive computational resources but would also be critical given the importance of the NNAGP to the global food system.

5. Summary

Regional climate trends influence perception of climate change and subsequent management decisions by local and regional stakeholders (Abatzoglou et al. 2009; Grimberg et al. 2018) for whom subseasonal climate variability is important (Asseng et al. 2015; Klemm and McPherson 2017). The results of this study demonstrate the importance of studying climate trends at time scales that are more finely resolved than standard climatological seasons. From a global perspective, much of the NNAGP has responded to climate change as expected. However, there are stark changes within standard climatological seasons that are not captured by the CESM-LE simulations (Fig. 9): May and June have cooled at nearly the opposite rate of observed global warming, and January warmed much more than the global average (Fig. 5). The May–June cooling trend is centered within the NNAGP, but the warming in the winter and shoulder seasons has occurred in the Rocky Mountains to the west and areas farther north (Fig. 2). VPD is generally not changing with the exception of the warm season; May and June have experienced a decrease in VPD with variable trends during July and August (Fig. 7). The negative summer VPD trend is found across the eastern half of the domain—largest in the southeast—and like many trends observed here spills across the study region, in this case toward the northeast (Fig. 3). Overall P has increased across the early warm season, with the largest positive trends found in May and June (Fig. 8). Generally, the positive trends in P are located in the eastern half of the domain but extend outside of the study area as well (Fig. 6c) and it is unclear if this is part of a common climate trend. Our observational study demonstrates that the NNAGP largely followed annual average global climate trends with many exceptions, including cooler and wetter conditions during May and June, whose causes must be investigated using mechanistic studies to understand if they were caused by agricultural intensification, and to disentangle the interaction between land management and climate in this globally important agricultural region.

Acknowledgments

We thank NOAA, NASA, as well as many others for decades of data collection and management and acknowledge support from the National Science Foundation (NSF) Office of Integrated Activities (OIA) Award 1632810, the NSF Division of Environmental Biology (DEB) Award 1552976, the U.S. Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Hatch project 228396 and multistate project W3188, the Graduate School at Montana State University, and the Montana Wheat and Barley Committee. Dr. Ankur Desai, James T. Douglas, Shaelyn Meyer, Rebecca Pappas (deceased), Elizabeth Rehbein, Dr. Amy Trowbridge, and Dr. Perry Miller provided valuable comments on earlier drafts of the manuscript and we thank the referees for constructive comments.

APPENDIX A

Data Coverage

The CRU dataset is a gridded observational dataset that integrates observations from the World Meteorological Organization (WMO) and other sources (Harris et al. 2014). The CRU dataset contains a source observation density data product that shows how many unique stations were interpolated to each grid cell. Each grid cell in the NNAGP (and most of North America) have eight stations contributing to each grid cell, which is the maximum that the CRU algorithms incorporate.

APPENDIX B

Trend Verification

We repeated the analysis of Tair trends using the Berkley Earth Surface Temperature data (Rohde et al. 2013) to critique results from the CRU dataset (Fig. 5). The same monthly Tair patterns hold (Fig. B1). We also used the Global Historical Climatology Network (GHCN) dataset (Lawrimore et al. 2011) to critique the start date of the analysis (Fig. B2). The May and June cooling trend across all GHCN stations in the NNAGP is larger if the analysis starts in 1980, suggesting that beginning the analysis in 1970 results in a conservative interpretation of observed May and June cooling trends.

Fig. B1.
Fig. B1.

Trends in air temperature from 1970 to 2015 in the NNAGP using Berkley Earth Surface Temperature data. The blue line indicates zero trend. The orange lines indicate the median of the distribution of trends.

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

Fig. B2.
Fig. B2.

Near-surface (2 m) air temperature trends from 270 GHCN sites within the NNAGP. Sites with at least 75% daily data availability from 1925 to 2015 were chosen for analysis. Maximum and minimum temperatures were averaged together to obtain average daily temperature. Daily data were aggregated to the May and June period and trends were calculated for each site with varying trend start years until 2015. The elongation of the violin plots is due to the shorter record from which trends were calculated, leading to more variability in trends. The distributions of the 270 trends are shown in the violin plot for each trend start year.

Citation: Journal of Climate 33, 2; 10.1175/JCLI-D-19-0106.1

REFERENCES

  • Abatzoglou, J. T., K. T. Redmond, and L. M. Edwards, 2009: Classification of regional climate variability in the state of California. J. Appl. Meteor. Climatol., 48, 15271541, https://doi.org/10.1175/2009JAMC2062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Abatzoglou, J. T., D. E. Rupp, and P. W. Mote, 2014: Seasonal climate variability and change in the Pacific Northwest of the United States. J. Climate, 27, 21252142, https://doi.org/10.1175/JCLI-D-13-00218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adegoke, J. O., R. A. Pielke, J. Eastman, R. Mahmood, and K. G. Hubbard, 2003: Impact of irrigation on midsummer surface fluxes and temperature under dry synoptic conditions: A regional atmospheric model study of the U.S. High Plains. Mon. Wea. Rev., 131, 556564, https://doi.org/10.1175/1520-0493(2003)131<0556:IOIOMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alter, R. E., Y. Fan, B. R. Lintner, and C. P. Weaver, 2015: Observational evidence that Great Plains irrigation has enhanced summer precipitation intensity and totals in the midwestern United States. J. Hydrometeor., 16, 17171735, https://doi.org/10.1175/JHM-D-14-0115.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alter, R. E., H. C. Douglas, J. M. Winter, and E. A. B. Eltahir, 2017: 20th-century regional climate change in the central United States attributed to agricultural intensification. Geophys. Res. Lett., 45, 15861594, https://doi.org/10.1002/2017GL075604.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Asseng, S., and et al. , 2013: Uncertainty in simulating wheat yields under climate change. Nat. Climate Change, 3, 827832, https://doi.org/10.1038/nclimate1916.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Asseng, S., and et al. , 2015: Rising temperatures reduce global wheat production. Nat. Climate Change, 5, 143147, https://doi.org/10.1038/nclimate2470.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Avissar, R., and R. A. Pielke, 1989: A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology. Mon. Wea. Rev., 117, 21132136, https://doi.org/10.1175/1520-0493(1989)117<2113:APOHLS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Avissar, R., and R. A. Pielke, 1991: The impact of plant stomatal control on mesoscale atmospheric circulations. Agric. For. Meteor., 54, 353372, https://doi.org/10.1016/0168-1923(91)90013-G.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Betts, A. K., R. Desjardins, and D. Worth, 2013a: Cloud radiative forcing of the diurnal cycle climate of the Canadian Prairies. J. Geophys. Res. Atmos., 118, 89358953, https://doi.org/10.1002/jgrd.50593.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Betts, A. K., R. Desjardins, D. Worth, and D. Cerkowniak, 2013b: Impact of land use change on the diurnal cycle climate of the Canadian Prairies. J. Geophys. Res. Atmos., 118, 11 99612 011, https://doi.org/10.1002/2013JD020717.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonetti, S., G. Manoli, J. Domec, M. Putti, M. Marani, and G. G. Katul, 2015: The influence of water table depth and the free atmospheric state on convective rainfall predisposition. Water Resour. Res., 51, 22832297, https://doi.org/10.1002/2014WR016431.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonsal, B. R., X. Zhang, and W. D. Hogg, 1999: Canadian Prairie growing season precipitation variability and associated atmospheric circulation. Climate Res., 11, 191208, https://doi.org/10.3354/cr011191.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bradshaw, B., H. Dolan, and B. Smit, 2004: Farm-level adaptation to climatic variability and change: Crop diversification in the Canadian Prairies. Climatic Change, 67, 119141, https://doi.org/10.1007/s10584-004-0710-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bright, R. M., E. Davin, T. O’Halloran, J. Pongratz, K. Zhao, and A. Cescatti, 2017: Local temperature response to land cover and management change driven by non-radiative processes. Nat. Climate Change, 7, 296302, https://doi.org/10.1038/nclimate3250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Butler, E. E., N. D. Mueller, and P. Huybers, 2018: Peculiarly pleasant weather for US maize. Proc. Natl. Acad. Sci. USA, 115, 11 93511 940, https://doi.org/10.1073/pnas.1808035115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Campbell, C. A., R. P. Zentner, S. Gameda, B. Blomert, and D. D. Wall, 2002: Production of annual crops on the Canadian prairies: Trends during 1976–1998. Can. J. Soil Sci., 82, 4557, https://doi.org/10.4141/S01-046.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carbone, R. E., and J. D. Tuttle, 2008: Rainfall occurrence in the U.S. warm season: The diurnal cycle. J. Climate, 21, 41324146, https://doi.org/10.1175/2008JCLI2275.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chug, D., and F. Dominguez, 2019: Isolating the observed influence of vegetation variability on the climate of La Plata River Basin. J. Climate, 32, 44734490, https://doi.org/10.1175/JCLI-D-18-0677.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collow, T. W., A. Robock, and W. Wu, 2014: Influences of soil moisture and vegetation on convective precipitation forecasts over the United States Great Plains. J. Geophys. Res. Atmos., 119, 93389358, https://doi.org/10.1002/2014JD021454.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Couzin, J., 1999: Landscape changes make regional climate run hot and cold. Science, 283, 317319, https://doi.org/10.1126/science.283.5400.317.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Recent climatology, variability, and trends in global surface humidity. J. Climate, 19, 35893606, https://doi.org/10.1175/JCLI3816.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davin, E. L., S. I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proc. Natl. Acad. Sci. USA, 111, 97579761, https://doi.org/10.1073/pnas.1317323111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeAngelis, A., F. Dominguez, Y. Fan, A. Robock, M. D. Kustu, and D. Robinson, 2010: Evidence of enhanced precipitation due to irrigation over the Great Plains of the United States. J. Geophys. Res., 115, D15115, https://doi.org/10.1029/2010JD013892.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Desai, A. R., K. J. Davis, C. J. Senff, S. Ismail, E. V. Browell, D. R. Stauffer, and B. P. Reen, 2006: A case study on the effects of heterogeneous soil moisture on mesoscale boundary-layer structure in the Southern Great Plains, U.S.A. Part I: Simple prognostic model. Bound.-Layer Meteor., 119, 195238, https://doi.org/10.1007/s10546-005-9024-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deser, C., A. Phillips, V. Bourdette, and H. Teng, 2012: Uncertainty in climate change projections: The role of internal variability. Climate Dyn., 38, 527546, https://doi.org/10.1007/s00382-010-0977-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dickens, J. M., B. T. Forbes, D. S. Cobean, and S. Tadayon, 2011: Documentation of methods and inventory of irrigation data collected for the 2000 and 2005 U.S. Geological Survey Estimated use of water in the United States, comparison of USGS-compiled irrigation data to other sources, and recommendations for future compilations. U.S. Geological Survey Scientific Investigations Rep. 2011–5166, 60 pp., http://pubs.usgs.gov/sir/2011/5166/.

    • Crossref
    • Export Citation
  • Diffenbaugh, N. S., 2009: Influence of modern land cover on the climate of the United States. Climate Dyn., 33, 945958, https://doi.org/10.1007/s00382-009-0566-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dominguez, F., G. Miguez-Macho, and H. Hu, 2016: WRF with water vapor tracers: A study of moisture sources for the North American monsoon. J. Hydrometeor., 17, 19151927, https://doi.org/10.1175/JHM-D-15-0221.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Draper, C. S., R. H. Reichle, and R. D. Koster, 2018: Assessment of MERRA-2 land surface energy flux estimates. J. Climate, 31, 671691, https://doi.org/10.1175/JCLI-D-17-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fall, S., D. Niyogi, A. Gluhovsky, R. A. Pielke, E. Kalnay, and G. Rochon, 2010: Impacts of land use land cover on temperature trends over the continental United States: Assessment using the North American Regional Reanalysis. Int. J. Climatol., 30, 19801993, https://doi.org/10.1002/joc.1996.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., L. R. Leung, S. Hagos, R. A. Houze, C. D. Burleyson, and K. Balaguru, 2016: More frequent intense and long-lived storms dominate the springtime trend in central US rainfall. Nat. Commun., 7, 13429, https://doi.org/10.1038/ncomms13429.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ficklin, D. L., and K. A. Novick, 2017: Historic and projected changes in vapor pressure deficit suggest a continental-scale drying of the United States atmosphere. J. Geophys. Res. Atmos., 122, 20612079, https://doi.org/10.1002/2016JD025855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Findell, K. L., and E. A. B. Eltahir, 2003a: Atmospheric controls on soil moisture–boundary layer interactions. Part I: Framework development. J. Hydrometeor., 4, 552569, https://doi.org/10.1175/1525-7541(2003)004<0552:ACOSML>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Findell, K. L., and E. B. Eltahir, 2003b: Atmospheric controls on soil moisture–boundary layer interactions. Part II: Feedbacks within the continental United States. J. Hydrometeor., 4, 570583, https://doi.org/10.1175/1525-7541(2003)004<0570:ACOSML>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Francis, J. A., and S. J. Vavrus, 2015: Evidence for a wavier jet stream in response to rapid Arctic warming. Environ. Res. Lett., 10, 014005, https://doi.org/10.1088/1748-9326/10/1/014005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang, 2010: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114, 168182, https://doi.org/10.1016/j.rse.2009.08.016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gameda, S., B. Qian, C. A. Campbell, and R. L. Desjardins, 2007: Climatic trends associated with summerfallow in the Canadian Prairies. Agric. For. Meteor., 142, 170185, https://doi.org/10.1016/j.agrformet.2006.03.026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, H., and G. Jia, 2013: Assessing disagreement and tolerance of misclassification of satellite-derived land cover products used in WRF model applications. Adv. Atmos. Sci., 30, 125141, https://doi.org/10.1007/s00376-012-2037-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garcia-Carreras, L., D. J. Parker, C. M. Taylor, C. E. Reeves, and J. G. Murphy, 2010: Impact of mesoscale vegetation heterogeneities on the dynamical and thermodynamic properties of the planetary boundary layer. J. Geophys. Res., 115, D03102, https://doi.org/10.1029/2009JD012811.

    • Search Google Scholar
    • Export Citation
  • Gauthier, D. A., and E. Wiken, 2003: Monitoring the conservation of grassland habitats, Prairie Ecozone, Canada. Environ. Monit. Assess., 88, 343364, https://doi.org/10.1023/A:1025585527169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gentine, P., A. A. M. Holtslag, F. D’Andrea, and M. Ek, 2013: Surface and atmospheric controls on the onset of moist convection over land. J. Hydrometeor., 14, 14431462, https://doi.org/10.1175/JHM-D-12-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerken, T., G. T. Bromley, B. L. Ruddell, S. Williams, and P. C. Stoy, 2018a: Convective suppression before and during the United States Northern Great Plains flash drought of 2017. Hydrol. Earth Syst. Sci., 22, 41554163, https://doi.org/10.5194/hess-22-4155-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerken, T., G. T. Bromley, and P. C. Stoy, 2018b: Surface moistening trends in the northern North American Great Plains increase the likelihood of convective initiation. J. Hydrometeor., 19, 227244, https://doi.org/10.1175/JHM-D-17-0117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimberg, B., S. Ahmed, C. Ellis, Z. Miller, and F. Menalled, 2018: Climate change perceptions and observations of agricultural stakeholders in the Northern Great Plains. Sustainability, 10, 1687, https://doi.org/10.3390/su10051687.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hammermeister, A. M., 2001: An ecological analysis of prairie rehabilitation on petroleum wellsites in southeast Alberta. Ph.D. thesis, University of Alberta, 107 pp.

  • Harding, K. J., and P. K. Snyder, 2012: Modeling the atmospheric response to irrigation in the Great Plains. Part II: The precipitation of irrigated water and changes in precipitation recycling. J. Hydrometeor., 13, 16871703, https://doi.org/10.1175/JHM-D-11-099.1.

    • 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
  • Hohenegger, C., P. Brockhaus, C. S. Bretherton, and C. Schär, 2009: The soil moisture–precipitation feedback in simulations with explicit and parameterized convection. J. Climate, 22, 50035020, https://doi.org/10.1175/2009JCLI2604.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Q., A. Weiss, S. Feng, and P. S. Baenziger, 2005: Earlier winter wheat heading dates and warmer spring in the U.S. Great Plains. Agric. For. Meteor., 135, 284290, https://doi.org/10.1016/j.agrformet.2006.01.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huber, D., D. Mechem, and N. Brunsell, 2014: The effects of Great Plains irrigation on the surface energy balance, regional circulation, and precipitation. Climate, 2, 103128, https://doi.org/10.3390/cli2020103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. T. F. Stocker et al., Eds., Cambridge University Press, 1535 pp.

  • IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability Part A: Global and Sectoral Aspects. Cambridge University Press, 1132 pp., http://www.ipcc.ch/pdf/assessment-report/ar5/wg2/WGIIAR5-PartA_FINAL.pdf.

  • Jones, P., 2016: The reliability of global and hemispheric surface temperature records. Adv. Atmos. Sci., 33, 269282, https://doi.org/10.1007/s00376-015-5194-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Juang, J. Y., A. Porporato, P. C. Stoy, M. S. Siqueira, A. C. Oishi, M. Detto, H. S. Kim, and G. G. Katul, 2007: Hydrologic and atmospheric controls on initiation of convective precipitation events. Water Resour. Res., 43, W03421, https://doi.org/10.1029/2006WR004954.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kanamori, H., T. Kumagai, H. Fujinami, T. Hiyama, and T. Yasunari, 2018: Effects of long- and short-term atmospheric water cycles on the water balance over the Maritime Continent. J. Hydrometeor., 19, 14131427, https://doi.org/10.1175/JHM-D-18-0052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and et al. , 2015: The Community Earth System Model (CESM) Large Ensemble Project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keller, M., D. S. Schimel, W. W. Hargrove, and F. M. Hoffman, 2008: A continental strategy for the National Ecological Observatory Network. Front. Ecol. Environ., 6, 282284, https://doi.org/10.1890/1540-9295(2008)6[282:ACSFTN]2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klemm, T., and R. A. McPherson, 2017: The development of seasonal climate forecasting for agricultural producers. Agric. For. Meteor., 232, 384399, https://doi.org/10.1016/j.agrformet.2016.09.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kucharik, C. J., 2008: Contribution of planting date trends to increased maize yields in the central United States. Agron. J., 100, 328336, https://doi.org/10.2134/agronj2007.0145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lanning, S. P., K. Kephart, G. R. Carlson, J. E. Eckhoff, R. N. Stougaard, D. M. Wichman, J. M. Martin, and L. E. Talbert, 2010: Climatic change and agronomic performance of hard red spring wheat from 1950 to 2007. Crop Sci., 50, 835841, https://doi.org/10.2135/cropsci2009.06.0314.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lark, T. J., J. M. Salmon, and H. K. Gibbs, 2015: Cropland expansion outpaces agricultural and biofuel policies in the United States. Environ. Res. Lett., 10, 044003, https://doi.org/10.1088/1748-9326/10/4/044003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawrimore, J. H., M. J. Menne, B. E. Gleason, C. N. Williams, D. B. Wuertz, R. S. Vose, and J. Rennie, 2011: An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3. J. Geophys. Res., 116, D19121, https://doi.org/10.1029/2011JD016187.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., Y. Li, B. Bonsal, A. H. Manson, and L. Scaff, 2018: Combined impacts of ENSO and MJO on the 2015 growing season drought on the Canadian Prairies. Hydrol. Earth Syst. Sci., 22, 50575067, https://doi.org/10.5194/hess-22-5057-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D. B., and C. Bonfils, 2008: The effect of irrigation on regional temperatures: A spatial and temporal analysis of trends in California, 1934–2002. J. Climate, 21, 20632071, https://doi.org/10.1175/2007JCLI1755.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D. B., G. Bala, and P. B. Duffy, 2006: Biogeophysical impacts of cropland management changes on climate. Geophys. Res. Lett., 33, L06708, https://doi.org/10.1029/2005GL025492.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D. B., C. J. Bonfils, L. M. Kueppers, and M. A. Snyder, 2008: Irrigation cooling effect on temperature and heat index extremes. Geophys. Res. Lett., 35, L09705, https://doi.org/10.1029/2008GL034145.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lobell, D. B., M. J. Roberts, W. Schlenker, N. Braun, B. B. Little, R. M. Rejesus, and G. L. Hammer, 2014: Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest. Science, 344, 516519, https://doi.org/10.1126/science.1251423.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, Y., K. Harding, and L. Kueppers, 2017: Irrigation effects on land–atmosphere coupling strength in the United States. J. Climate, 30, 36713685, https://doi.org/10.1175/JCLI-D-15-0706.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahmood, R., K. G. Hubbard, R. D. Leeper, and S. A. Foster, 2008: Increase in near-surface atmospheric moisture content due to land use changes: Evidence from the observed dewpoint temperature data. Mon. Wea. Rev., 136, 15541561, https://doi.org/10.1175/2007MWR2040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahmood, R., T. Keeling, S. A. Foster, and K. G. Hubbard, 2013: Did irrigation impact 20th century air temperature in the High Plains aquifer region? Appl. Geogr., 38, 1121, https://doi.org/10.1016/j.apgeog.2012.11.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahmood, R., and et al. , 2014: Land cover changes and their biogeophysical effects on climate. Int. J. Climatol., 34, 929953, https://doi.org/10.1002/joc.3736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Manoli, G., J. C. Domec, K. Novick, A. C. Oishi, A. Noormets, M. Marani, and G. Katul, 2016: Soil–plant–atmosphere conditions regulating convective cloud formation above southeastern US pine plantations. Global Change Biol., 22, 22382254, https://doi.org/10.1111/gcb.13221.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Millett, B., W. C. Johnson, and G. Guntenspergen, 2009: Climate trends of the North American prairie pothole region 1906–2000. Climatic Change, 93, 243267, https://doi.org/10.1007/s10584-008-9543-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mishra, V., J. M. Wallace, and D. P. Lettenmaier, 2012: Relationship between hourly extreme precipitation and local air temperature in the United States. Geophys. Res. Lett., 39, L16403, https://doi.org/10.1029/2012GL052790.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mueller, N. D., E. E. Butler, K. A. McKinnon, A. Rhines, M. Tingley, N. M. Holbrook, and P. Huybers, 2016: Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Climate Change, 6, 317322, https://doi.org/10.1038/nclimate2825.