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Mechanical forcing of the North American monsoon by orography


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.

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Fig. 1: Influence of orography on rain and low-level wind.
Fig. 2: Generation of eastward flow across western Mexico by the mechanically forced stationary wave.
Fig. 3: Diurnal and seasonal cycles in the NAM.
Fig. 4: Response to a pure thermal forcing.

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

Code availability

The Community Earth System Model, which is supported primarily by the National Science Foundation, was obtained from Isla Simpson provided code for the stationary wave model, the original version of which was written by Mingfang Ting and Linhai Yu.


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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




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

Correspondence to William R. Boos.

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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.

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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.

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Boos, W.R., Pascale, S. Mechanical forcing of the North American monsoon by orography. Nature 599, 611–615 (2021).

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