Abstract
A band of intense rainfall extends more than 1,000 km along Mexico’s west coast during Northern Hemisphere summer, constituting the core of the North American monsoon1,2. As in other tropical monsoons, this rainfall maximum is commonly thought to be thermally forced by emission of heat from land and elevated terrain into the overlying atmosphere3,4,5, but a clear understanding of the fundamental mechanism governing this monsoon is lacking. Here we show that the core North American monsoon is generated when Mexico’s Sierra Madre mountains deflect the extratropical jet stream towards the Equator, mechanically forcing eastward, upslope flow that lifts warm and moist air to produce convective rainfall. These findings are based on analyses of dynamic and thermodynamic structures in observations, global climate model integrations and adiabatic stationary wave solutions. Land surface heat fluxes do precondition the atmosphere for convection, particularly in summer afternoons, but these heat fluxes alone are insufficient for producing the observed rainfall maximum. Our results indicate that the core North American monsoon should be understood as convectively enhanced orographic rainfall in a mechanically forced stationary wave, not as a classic, thermally forced tropical monsoon. This has implications for the response of the North American monsoon to past and future global climate change, making trends in jet stream interactions with orography of central importance.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The ERA5 monthly averaged data by hour of day were downloaded from the Copernicus Climate Change Service Climate Data Store (identifiers cited in Methods). MERRA-2 and GPM data were downloaded from the NASA Goddard Earth Sciences Data and Information Services Center (identifiers cited in Methods). ETOPO1 data were downloaded from the National Centers for Environmental Information at the National Oceanic and Atmospheric Administration (identifiers cited in Methods). David K. Adams provided access to GPS Hydromet 2017 data; Trans-boundary, Land and Atmosphere Long-term Observational and Collaborative Network data; and GPS Transect Experiment 2013 data. The time-mean summer climatology from the GCM and time-mean output from the stationary wave model are archived at https://doi.org/10.5281/zenodo.5076509.
Code availability
The Community Earth System Model, which is supported primarily by the National Science Foundation, was obtained from https://www.cesm.ucar.edu. Isla Simpson provided code for the stationary wave model, the original version of which was written by Mingfang Ting and Linhai Yu.
References
Douglas, M. W., Maddox, R. A., Howard, K. & Reyes, S. The Mexican monsoon. J. Clim. 6, 1665–1677 (1993).
Adams, D. K. & Comrie, A. C. The North American monsoon. Bull. Am. Meteorol. Soc. 78, 2197–2213 (1997).
Tang, M. & Reiter, E. R. Plateau monsoons of the Northern Hemisphere: a comparison between North America and Tibet. Mon. Weather Rev. 112, 617–637 (1984).
Vera, C. et al. Toward a unified view of the American monsoon systems. J. Clim. 19, 4977–5000 (2006).
Mechoso, C. R., Robertson, A. W., Ropelewski, C. F. & Grimm, A. M. in The Global Monsoon System: Research and Forecast (eds Chang, C.-P. et al.) Tropical Meteorology Research Programme Report No. 70; 197–206 (No. 1266, WMO/TD, 2005); https://doi.org/10.7916/D8ST803T.
Bryson, R. A. & Lowry, W. P. Synoptic climatology of the Arizona summer precipitation singularity. Bull. Amer. Meteor. Soc. 36, 329–339 (1955).
Krishnamurti, T. N. Tropical east-west circulations during the northern summer. J. Atmos. Sci. 28, 1342–1347 (1971).
Broccoli, A. J. & Manabe, S. The effects of orography on midlatitude Northern Hemisphere dry climates. J. Clim. 5, 1181–1201 (1992).
Stensrud, D., Gall, R., Mullen, S. & Howard, K. Model climatology of the Mexican monsoon. J. Clim. 8, 1775–1794 (1995).
Schmitz, J. T. & Mullen, S. L. Water vapor transport associated with the summertime North American monsoon as depicted by ECMWF analyses. J. Clim. 9, 1621–1634 (1996).
Johnson, R. H., Ciesielski, P. E., McNoldy, B. D., Rogers, P. J. & Taft, R. K. Multiscale variability of the flow during the North American Monsoon Experiment. J. Clim. 20, 1628–1648 (2007).
Berbery, E. H. Mesoscale moisture analysis of the North American monsoon. J. Clim. 14, 121–137 (2001).
Nesbitt, S., Gochis, D. & Lang, T. The diurnal cycle of clouds and precipitation along the Sierra Madre Occidental observed during NAME-2004: implications for warm season precipitation estimation in complex terrain. J. Hydrometeorol. 9, 728–743 (2008).
Ting, M. & Wang, H. The role of the North American topography on the maintenance of the Great Plains summer low-level jet. J. Atmos. Sci. 63, 1056–1068 (2006).
Wexler, H. A boundary layer interpretation of the low-level jet. Tellus 13, 368–378 (1961).
Barlow, M., Nigam, S. & Berbery, E. Evolution of the North American Monsoon System. J. Clim. 11, 2238–2257 (1997).
Collier, J. C. & Zhang, G. J. Effects of increased horizontal resolution on simulation of the North American monsoon in the NCAR CAM3: an evaluation based on surface, satellite, and reanalysis data. J. Clim. 20, 1843–1861 (2007).
Pascale, S. et al. The impact of horizontal resolution on North American monsoon Gulf of California moisture surges in a suite of coupled global climate models. J. Clim. 29, 7911–7936 (2016).
Varuolo-Clarke, A. M., Reed, K. A. & Medeiros, B. Characterizing the North American monsoon in the Community Atmosphere Model: sensitivity to resolution and topography. J. Climate 32, 8355–8372 (2019).
Hu, S. & Boos, W. The physics of orographic elevated heating in radiative–convective equilibrium. J. Atmos. Sci. 74, 2949–2965 (2017).
Gill, A. E. Some simple solutions for heat-induced tropical circulation. Q. J. R. Meteorol. Soc. 106, 447–462 (1980).
Rodwell, M. J. & Hoskins, B. J. Monsoons and the dynamics of deserts. Q. J. R. Meteorol. Soc. 122, 1385–1404 (1996).
Simpson, I. R., Seager, R., Shaw, T. A. & Ting, M. Mediterranean summer climate and the importance of Middle East topography. J. Clim. 28, 1977–1996 (2015).
Simpson, I. R., Seager, R., Ting, M. & Shaw, T. A. Causes of change in Northern Hemisphere winter meridional winds and regional hydroclimate. Nat. Clim. Change 6, 65–70 (2016).
Rodwell, M. J. & Hoskins, B. J. Subtropical anticyclones and summer monsoons. J. Clim. 14, 3192–3211 (2001).
Emanuel, K. A. Atmospheric Convection (Oxford Univ. Press, 1994).
Sobel, A. H. & Bretherton, C. S. Modeling tropical precipitation in a single column. J. Clim. 13, 4378–4392 (2000).
Privé, N. C. & Plumb, R. A. Monsoon dynamics with interactive forcing. Part II: impact of eddies and asymmetric geometries. J. Atmos. Sci. 64, 1431–1442 (2007).
Nie, J., Boos, W. R. & Kuang, Z. Observational evaluation of a convective quasi-equilibrium view of monsoons. J. Clim. 23, 4416–4428 (2010).
Higgins, R. W., Chen, Y. & Douglas, A. V. Interannual variability of the North American warm season precipitation regime. J. Clim. 12, 653–680 (1999).
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
European Centre for Medium-Range Weather Forecasts ERA5 Reanalysis (0.25 Degree Latitude-Longitude Grid) (Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, accessed 30 April 2020); https://doi.org/10.5065/BH6N-5N20.
Hersbach, H. et al. ERA5 Monthly Averaged Data on Single Levels from 1979 to Present (Copernicus Climate Change Service Climate Data Store, accessed 25 February 2021); https://doi.org/10.24381/cds.f17050d7.
Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).
Global Modeling and Assimilation Office MERRA-2 tavgM_2d_slv_Nx: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4 (Goddard Earth Sciences Data and Information Services Center, accessed 1 March 2021); https://doi.org/10.5067/AP1B0BA5PD2K.
Huffman, G., Stocker, E., Bolvin, D., Nelkin, E. & Tan, J. GPM IMERG Final Precipitation L3 1 Day 0.1 Degree x 0.1 Degree V06 (ed. Savtchenko, A.) (Goddard Earth Sciences Data and Information Services Center, accessed 3 May 2021); https://doi.org/10.5067/GPM/IMERGDF/DAY/06.
Schneider, U. et al. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 115, 15–40 (2013).
Schneider, U. et al. GPCC Full Data Monthly Product Version 7.0 at 0.5°: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historic Data (Federal Ministry of Transport and Digital Infrastructure, accessed 1 April 2020); https://doi.org/10.5676/DWD_GPCC/FD_M_V7_050.
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
CRU TS4.00: Climatic Research Unit (CRU) Time-Series (TS) Version 4.00 of High-Resolution Gridded Data of Month-by-Month Variation in Climate, Precipitation Monthly Means (The Centre for Environmental Data Analysis UK, accessed 3 April 2020); https://doi.org/10.5072/edf8febfdaad48abb2cbaf7d7e846a86.
NOAA National Geophysical Data Center ETOPO1 1 Arc-Minute Global Relief Model (NOAA National Centers for Environmental Information, accessed 14 January 2021).
Amante, C. & Eakins, B. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis NOAA Technical Memorandum NESDIS NGDC-24 (National Geophysical Data Center, NOAA, accessed 14 January 2021); https://doi.org/10.7289/V5C8276M.
Serra, Y. L. et al. The North American Monsoon GPS Transect Experiment 2013. Bull. Am. Meteorol. Soc. 97, 2103–2115 (2016).
Pérez-Ruiz, E. R. et al. Landscape controls on water-energy-carbon fluxes across different ecosystems during the North American monsoon. J. Geophys. Res. Biogeosci. 126, e2020JG005809 (2021).
Cabral-Cano, E. et al. TLALOCNet: a continuous GPS-Met backbone in Mexico for seismotectonic and atmospheric research. Seismol. Res. Lett.89, 373–381 (2018).
Neale, R. B. et al. Description of the NCAR Community Atmosphere Model (CAM 5.0) No. NCAR/TN-464+STR (NCAR, 2012); https://doi.org/10.5065/D6N877R0.
Oleson, K. W. et al. Technical Description of Version 4.0 of the Community Land Model (CLM) No. NCAR/TN-478+STR (NCAR, 2010).
Wehner, M. F. et al. Resolution dependence of future tropical cyclone projections of CAM5.1 in the U.S. CLIVAR Hurricane Working Group idealized configuration. J. Clim. 28, 3905–3925 (2015).
Wehner, M. F., Reed, K. A., Loring, B., Stone, D. & Krishnan, H. Changes in tropical cyclones under stabilized 1.5 and 2.0°C global warming scenarios as simulated by the Community Atmospheric Model under the HAPPI protocols. Earth Syst. Dynam. 9, 187–195 (2018).
Mo, K. C., Juang, H. M. H., Higgins, R. W. & Song, Y. Impact of model resolution on the prediction of summer precipitation over the United States and Mexico. J. Clim. 18, 3910–3927 (2005).
Hales, J. E. Surges of maritime tropical air northward over the Gulf of California. Mon. Weather Rev. 100, 298–306 (1972).
Brenner, I. S. A surge of maritime tropical air–Gulf of California to the southwestern United States. Mon. Weather Rev. 102, 375–389 (1974).
Turrent, C. & Cavazos, T. Role of the land-sea thermal contrast in the interannual modulation of the North American Monsoon. Geophys. Res. Lett. 36, L02808 (2009).
Finch, Z. O. & Johnson, R. H. Observational analysis of an upper-level inverted trough during the 2004 North American Monsoon Experiment. Mon. Weather Rev. 138, 3540–3555 (2010).
Liang, X., Zhu, J., Kunkel, K. E., Ting, M. & Wang, J. X. L. Do CGCMs simulate the North American monsoon precipitation seasonal-interannual variability? J. Clim. 21, 4424–4448 (2008).
Geil, K. L., Serra, Y. L. & Zeng, X. Assessment of CMIP5 model simulations of the North American monsoon system. J. Clim. 26, 8787–8801 (2013).
Pascale, S. et al. Weakening of the North American monsoon with global warming. Nat. Clim. Change 7, 806–812 (2017).
Ting, M. & Yu, L. Steady response to tropical heating in wavy linear and nonlinear baroclinic models. J. Atmos. Sci. 55, 3565–3582 (1998).
Ting, M. & Held, I. M. The stationary wave response to a tropical SST anomaly in an idealized GCM. J. Atmos. Sci. 47, 2546–2556 (1990).
Ting, M. The stationary wave response to a tropical SST anomaly in an idealized GCM. J. Atmos. Sci. 51, 3286–3308 (1994).
Held, I. M., Ting, M. & Wang, H. Northern winter stationary waves: theory and modeling. J. Clim. 15, 2125–2144 (2002).
Hoskins, B. J. & Rodwell, M. J. A model of the Asian summer monsoon. Part I: the global scale. J. Atmos. Sci. 52, 1329–1340 (1995).
Acknowledgements
This material is based on work supported by the US Department of Energy, Office of Science, Office of Biological and Environmental Research, Climate and Environmental Sciences Division, Regional and Global Model Analysis Program, under Award DE-SC0019367. It used resources of the National Energy Research Scientific Computing Center, which is a Department of Energy Office of Science User Facility. W.R.B. acknowledges support from the Miller Institute for Basic Research in Science at the University of California, Berkeley. This paper benefited from discussions with D. Adams, Q. Nicolas, I. Fung and J. C. H. Chiang. We thank M. Wehner for advice on running an older configuration of CAM5 at 0.25° resolution.
Author information
Authors and Affiliations
Contributions
W.R.B. conceived the study, devised and performed the GCM and stationary wave model integrations, and analysed model output. S.P. assessed the GCM bias. Both authors analysed observations and contributed to writing the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information Nature thanks Jane Baldwin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Main geographic features of the North American monsoon.
The blue line delimits land area used for area-averaging precipitation (NAM domain) in Extended Data Fig. 2a, while the dashed black curve outlines the Gulf of California region used for area-averaging the coast-parallel moisture flux in Extended Data Fig. 2b. Mapping software: IDL. Adapted from Pascale et al. (2017).
Extended Data Fig. 2 Seasonal cycles of NAM precipitation and along-shore moisture flux in the Gulf of California (GoC) simulated by the high-resolution GCM largely fall within the range of observed interannual variability.
a) Lines show the seasonal cycle of monthly precipitation averaged over the North American monsoon land domain (shown in Extended Data Fig. 1) and over the period 1980–2009 in two observational datasets (CRU in blue and GPCC in purple) and in the Control GCM (CESM; black). Shading bounds the 5th and 95th percentiles of GPCC interannual variability. The GCM lacks the large positive bias in autumn precipitation commonly seen in lower-resolution ocean-atmosphere coupled GCMs. b) Lines show the coast-parallel component of the 10-m moisture flux in the GoC for 1980–2009 in two reanalyses (MERRA2 in blue and ERA5 in purple) and the lowest model-level moisture flux in the Control GCM (CESM; black, about 7 hPa above the surface). Shading bounds the 5th and 95th percentiles of ERA5 interannual variability. The coast-parallel moisture flux is obtained by projecting the vector field along the coast-parallel direction (34° anticlockwise from north), then averaging over the Gulf of California domain shown in Extended Data Fig. 1.
Extended Data Fig. 3 The high-resolution GCM captures the northward low-level wind and the tongue of high moist static energy (MSE) air over the Gulf of California.
Vectors show 10-m horizontal wind from both a) ERA5 and b) MERRA2 (both 1980–2019 means), and c) the lowest model level wind from the Control GCM (CESM; roughly 7 hPa above the surface). Shading in all panels shows 2-m MSE, normalized by the specific heat of dry air to cast this variable in units of K. Mapping software: IDL.
Extended Data Fig. 4 Time-mean winds produce moisture convergence that balances precipitation in the Control GCM.
a) Vertically integrated moisture flux converged by summer-mean winds in the Control GCM, in mm day−1. This has a highly similar spatial pattern to that of the summer-mean difference between precipitation and surface evaporation (b), which must closely approximate the total vertically integrated moisture flux convergence. The larger magnitude of (a) compared to (b) indicates that transient eddies dry the core NAM precipitation maximum. Convergence of the moisture flux was computed using spherical harmonics truncated at wavenumber 288 to reduce spectral ringing around orography. Mapping software: Cartopy with Natural Earth shapefiles.
Extended Data Fig. 5 Linear stationary wave solution.
Linear solutions were obtained by scaling the Control - FlatMex surface height forcing by 10−6 then multiplying the response by 106, thus rendering quadratic terms in the conservation equations a factor of 10−6 smaller than linear terms. a) Streamfunction of anomalous 700 hPa horizontal wind (shading, in meters; air flows clockwise around maxima). The thick orange line is the zero contour of the basic-state zonal wind, which near 35°N divides westward trade winds from prevailing eastward extratropical flow. Thin blue lines show 700 hPa potential temperature (in K). b) Anomalous zonal wind at 26°N (shading, in m s−1) with isentropes plotted in blue (5 K contour interval); the total zonal wind (basic state plus response to orography) is contoured in orange, with a contour interval of 2 m s−1, negative contours omitted, and zero contour in bold. Streamfunction in (a) has been normalized by the gravitational acceleration and Coriolis parameter at 45°N. Mapping software: Cartopy with Natural Earth shapefiles.
Extended Data Fig. 6 Basic state isentropes and zonal wind, illustrating how steady, lower-tropospheric adiabatic flow must be deflected southward to avoid being blocked by the ground.
Summer-mean zonal wind (shading, m s−1) and potential temperature (blue contours, interval 5 K) at 103°W in the FlatMex integration. Orography is masked in white.
Extended Data Fig. 7 Low-resolution stationary wave solution.
Fully nonlinear response to the Control - FlatMex surface height forcing obtained with the stationary wave model integrated at R30 horizontal resolution (main text Fig. 2c, d showed solutions at R63 resolution). a) Streamfunction of anomalous 700 hPa horizontal wind (shading, in meters; air flows clockwise around maxima). Surface height of 1.5 km is contoured in green, and thick orange line is zero contour of basic state zonal wind, which near 35°N divides westward trade winds from prevailing eastward extratropical flow. Thin blue lines show 700 hPa potential temperature (in K). b) Anomalous zonal wind at 26°N (shading, in m s−1) with isentropes plotted in blue (5 K contour interval) and orography masked in white; the total zonal wind (basic state plus response to orography) is contoured in orange, with a contour interval of 2 m s−1, negative contours omitted, and the zero contour in bold. Streamfunction in (a) has been normalized by the gravitational acceleration and Coriolis parameter at 45°N. Note that total near-surface flow just west of the SMO is westward, unlike in the high-resolution solutions shown in Fig. 2d. Mapping software: Cartopy with Natural Earth shapefiles.
Extended Data Fig. 8 Averaging regions for the seasonal cycle of MSE and wind shown in main text Fig. 3c.
Regions over which a) surface air MSE and b) low-level zonal wind were averaged in our seasonal cycle diagnostics. Mapping software: Cartopy with Natural Earth shapefiles.
Extended Data Fig. 9 Distinct spatial structure of the response to the pure thermal forcing.
Anomalies in summer-mean a) precipitation (mm day−1) and b) surface air MSE (K) in the FlatMexLowAlb model run relative to the FlatMex run. Panels (c) and (d) show the same as (a) and (b) but for the Control run relative to FlatMex. In all panels, only anomalies that are statistically significant at the 5% level by a Student t-test are shown. Mapping software: Cartopy with Natural Earth shapefiles.
Supplementary information
Rights and permissions
About this article
Cite this article
Boos, W.R., Pascale, S. Mechanical forcing of the North American monsoon by orography. Nature 599, 611–615 (2021). https://doi.org/10.1038/s41586-021-03978-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-021-03978-2
This article is cited by
-
Drainage divide migration and implications for climate and biodiversity
Nature Reviews Earth & Environment (2024)
-
Emergence of the modern global monsoon from the Pangaea megamonsoon set by palaeogeography
Nature Geoscience (2023)
-
Wetting and drying trends under climate change
Nature Water (2023)
-
A global survey of diurnal offshore propagation of rainfall
Nature Communications (2022)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.