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    Mean summer (JJA) precipitation (mm day−1) during 1979–2004 for (a) CRU and (b) GPCP observations; reanalyses from (c) ERA-Interim and (d) NARR; and GCMs: (e) ens_GCMs, (f) CanESM2, (g) CNRM-CM5, (h) HadGEM2-ES, (i) MIROC-ESM-CHEM, (j) MPI-ESM-LR, and (k) MRI-CGCM3. The NAM region considered here is the area limited by 23°–36°N, 104°–114°W.

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    Mean precipitation rate and metrics of skill (mm day−1) in the NAM region (dashed area in Fig. 1) during 1979–2004. (a) Annual cycle for CRU, GPCP, NARR, ERA-Interim, and the ens_GCMs; (b) annual cycle for CRU, ens_GCMs, CanESM2, HadGEM2-ES, CNRM-CM5, MIROC-ESM-CHEM, MPI-ESM-LR, and MRI-CGCM3; (c) JJA precipitation for the CRU, GPCP, NARR, ERA-Interim, and the ens_GCMs; and (d) measures of skill: standard deviation (STD) for the observations (CRU and GPCP), reanalyses (ERA-Interim and NARR), and the GCMs and MAE for the GCMs as compared to CRU. Note the difference in the y-axis scale between (a) and (b).

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    Mean summer (JJA) precipitation (mm day−1) during 1979–2004 for (a) GPCP, (b) ERA-Interim, (c) ens_GCMs, (d) CanESM2, (e) CNRM-CM5, (f) HadGEM2-ES, (g) MIROC-ESM-CHEM, (h) MPI-ESM-LR, and (i) MRI-CGCM3.

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    Mean annual cycles of Tskin (°C) for regions over the sea (black) and over land (gray) linked to the NAM LSTC for (a) ERA-Interim (solid line) and the ens_GCMs data (dashed line) and (b) NARR (solid line) and the ens_GCMs (dashed line) during the historical period (1979–2004). The vertical arrows indicate the mean calendar date during the annual cycle on which the continent becomes warmer than the ocean (i.e., a positive LSTC). From the inset map, the oceanic (dark) region over the eastern tropical Pacific is bounded by 15°–23°N, 105°–110°W, and the continental (gray) region over the Arizona Sonoran Desert is bounded by 32°–34°N, 112°–116°W.

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    Mean seasonal vertically integrated moisture flux (vectors; kg m−1 s−1) and its convergence (shading; mm day−1) for (a),(d) ERA-Interim, (b),(e) NARR, and (c),(f) ens_GCMs during the historical period (1979–2004) for (top) December–February (DJF) and (bottom) JJA.

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    (a) Mean annual cycles of an LSTC index estimated from Tskin (land minus sea Tskin averaged over regions in Fig. 4) for ERA-Interim during the historical period (1979–2004; solid line) and for the ens_GCMs for both the historical period (dashed line) and the future RCP8.5 projections (2075–99; dotted line). (b) As in (a), but for a midtropospheric LSTC index based on the 500–1000-hPa thickness. Positive values indicate that the atmosphere over the continent has greater heat content than over the ocean. (c) Mean changes in the annual cycle of the precipitation rate over the NAM region shown as the RCP8.5 (2075–99) minus historical (1979–2004) projections. The median change of the ens_GCMs is also indicated.

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    Mean JJA changes in precipitation rate (mm day−1) expressed as the difference between the RCP8.5 projection of the ens_GCMs and the results for each of the six GCMs during the historical period (1979–2004): (a) CanESM2, (b) CNRM-CM5, (c) HadGEM2-ES, (d) MIROC-ESM-CHEM, (e) MPI-ESM-LR, (f) MRI-CGCM3, and (g) ens_GCMs.

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    Seasonal precipitation changes expressed as the percentage difference between the mean ens_GCMs of the 2075–99 RCP8.5 projections and the historical period (1979–2004) for (a) DJF, (b) March–May (MAM), (c) JJA, and (d) September–November (SON). The contour interval is 2 mm day−1. The gray-shaded areas indicate grid points for which at least four of the six models agree on the sign of the change.

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    July changes in the meridionally averaged (25°–30°N) mean vertical velocity ω (Pa s−1), expressed as the difference between the mean ens_GCMs of the RCP8.5 projection (2075–99) and the historical period (1979–2004). Negative values indicate increased magnitude of ascending vertical velocity. The gray-shaded areas indicate grid points for which at least four of the six models agree on the sign of the change. Dark areas represent the mountains.

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    Mean seasonal differences between the ens_GCMs RCP8.5 projections (2075–99) and the historical period (1979–2004) for the vertically integrated moisture flux (vectors; kg m−1 s−1) and its convergence (shading; mm day−1).

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    Mean DJF zonal wind (m s−1) at the 200-hPa level for (a) ERA-Interim, (b) the historical ens_GCMs (1979–2004), and (c) the RCP8.5 projection (2075–99) minus the historical ens_GCMs mean. The contour interval is 5 m s−1 for (a),(b) and 1 m s−1 for (c).

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Land–Sea Thermal Contrast and Intensity of the North American Monsoon under Climate Change Conditions

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  • 1 Department of Physical Oceanography, Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, Mexico
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Abstract

The hypothesis that global warming during the twenty-first century will increase the land–sea thermal contrast (LSTC) and therefore the intensity of early season precipitation of the North American monsoon (NAM) is examined. To test this hypothesis, future changes (2075–99 minus 1979–2004 means) in LSTC, moisture flux convergence (MFC), vertical velocity, and precipitation in the region are analyzed using six global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) under the representative concentration pathway 8.5 (RCP8.5) emission scenario. A surface LSTC index shows that the continent becomes warmer than the ocean in May in the North American Regional Reanalysis (NARR) and ECMWF Interim Re-Analysis (ERA-Interim) and in June in the mean ensemble of the GCMs (ens_GCMs), and the magnitude of the positive LSTC is greater in the reanalyses than in the ens_GCMs during the historic period. However, the reanalyses underestimate July–August precipitation in the NAM region, while the ens_GCMs reproduces the peak season surprisingly well but overestimates it the rest of the year. The future ens_GCMs projects a doubling of the magnitude of the positive surface LSTC and an earlier start of the continental summer warming in mid-May. Contrary to the stated hypothesis, however, the mean projection suggests a slight decrease of monsoon coastal precipitation during June–August (JJA), which is attributed to increased midtropospheric subsidence, a reduced midtropospheric LSTC, and reduced MFC in the NAM coastal region. In contrast, the future ens_GCMs produces increased MFC and precipitation over the adjacent mountains during JJA and significantly more rainfall over the entire NAM region during September–October, weakening the monsoon retreat.

Corresponding author address: Tereza Cavazos, Department of Physical Oceanography, Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana 3918, Zona Playitas, 22860 Ensenada, Baja California, Mexico. E-mail: tcavazos@cicese.mx

Abstract

The hypothesis that global warming during the twenty-first century will increase the land–sea thermal contrast (LSTC) and therefore the intensity of early season precipitation of the North American monsoon (NAM) is examined. To test this hypothesis, future changes (2075–99 minus 1979–2004 means) in LSTC, moisture flux convergence (MFC), vertical velocity, and precipitation in the region are analyzed using six global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) under the representative concentration pathway 8.5 (RCP8.5) emission scenario. A surface LSTC index shows that the continent becomes warmer than the ocean in May in the North American Regional Reanalysis (NARR) and ECMWF Interim Re-Analysis (ERA-Interim) and in June in the mean ensemble of the GCMs (ens_GCMs), and the magnitude of the positive LSTC is greater in the reanalyses than in the ens_GCMs during the historic period. However, the reanalyses underestimate July–August precipitation in the NAM region, while the ens_GCMs reproduces the peak season surprisingly well but overestimates it the rest of the year. The future ens_GCMs projects a doubling of the magnitude of the positive surface LSTC and an earlier start of the continental summer warming in mid-May. Contrary to the stated hypothesis, however, the mean projection suggests a slight decrease of monsoon coastal precipitation during June–August (JJA), which is attributed to increased midtropospheric subsidence, a reduced midtropospheric LSTC, and reduced MFC in the NAM coastal region. In contrast, the future ens_GCMs produces increased MFC and precipitation over the adjacent mountains during JJA and significantly more rainfall over the entire NAM region during September–October, weakening the monsoon retreat.

Corresponding author address: Tereza Cavazos, Department of Physical Oceanography, Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana 3918, Zona Playitas, 22860 Ensenada, Baja California, Mexico. E-mail: tcavazos@cicese.mx

1. Introduction

Evaluations of the global climate models (GCMs) participating in both phases 3 (e.g., Seager et al. 2007; Seth et al. 2011; Cavazos and Arriaga-Ramirez 2012) and 5 (Cook and Seager 2013; Seth et al. 2013; Maloney et al. 2014) of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively) have indicated that North American monsoon (NAM) precipitation may decrease in the future. Specifically, Seth et al. (2011, 2013) and Cook and Seager (2013) suggest that future changes in the annual cycle of monsoon precipitation will be associated with an increase in tropospheric stability during winter and spring, reinforced by a decrease in available surface moisture. Dry conditions during spring would lead to a convective barrier, which could reduce early season rainfall. Furthermore, Fasullo (2012) argues that future atmospheric warming over the continent will cause the saturation specific humidity to increase (i.e., a greater amount of water vapor per unit volume will be necessary to reach saturation).

Monsoonal circulations modulate regional precipitation through seasonal changes in the wind caused by an initial large-scale land–sea thermal contrast (LSTC) (Li and Yanai 1996; Rodwell and Hoskins 2001; Zhu et al. 2007; Turrent and Cavazos 2009, 2012). For the NAM, Turrent and Cavazos (2009) found that the intensity of the regional LSTC modulates the low-level moisture transport over the Gulf of California and accounts for roughly half of all early season (June–July) monsoon precipitation variability over northwestern Mexico. Sutton et al. (2007) concluded from an evaluation of the CMIP3 GCMs that global surface temperatures should be expected to increase faster over the continents than over the oceans, with the greatest land/sea warming ratios occurring in subtropical latitudes, regardless of the greenhouse gas (GHG) emissions scenario. Based on those results alone, early season monsoon circulations would be expected to intensify under global warming conditions. However, most of the GCMs of the CMIP3 and CMIP5 simulations suggest that by the end of the twenty-first century the NAM region may become drier and, according to the CMIP3 results, this will be mainly due to the expansion and weakening of the large-scale tropical circulation patterns (viz., the Walker and Hadley cells; Held and Soden 2006; Lu et al. 2007). The CMIP5 models also present a weakening of the Walker circulation but, in contrast to CMIP3 results, they project a strengthening of the Hadley cell during boreal winter (Lee and Wang 2014). The response of the surface temperatures and the LSTC to the radiative forcings associated to increased concentrations of GHG and aerosols in CMIP5 probably adds additional complexity to the regional climatic response in the phase 5 simulations (e.g., Villarini and Vecchi 2012), with regards to CMIP3.

The aim of this study is to examine the changes in the thermodynamical mechanisms associated to the annual cycle of precipitation in the NAM region that are expected for the late twenty-first century (2075–99), as revealed by an ensemble of six CMIP5 GCMs (Table 1). Specifically, changes in the role of the LSTC, moisture flux convergence (MFC), and vertical velocity are evaluated under the most extreme scenario from the CMIP5 dataset [representative concentration pathway 8.5 (RCP8.5); Taylor et al. 2012]. Although there is no relationship between individual CMIP3 and CMIP5 GCMs because of changes in model physics, resolution, and aerosol forcings in the CMIP5 models (Taylor et al. 2012), for consistency the six models selected for the ensemble (ens_GCMs) were chosen based on their ability to adequately resolve the key features of the NAM system in CMIP3 (e.g., Liang et al. 2008; Lin et al. 2008; Cavazos and Arriaga-Ramirez 2012) and CMIP5 studies (e.g., Bukovsky et al. 2013; Geil et al. 2013), as well as key features of the intraseasonal atmospheric variability of the eastern tropical Pacific based on the CMIP5 models (Jiang et al. 2013).

Table 1.

Coupled ocean–atmosphere GCMs from the CMIP5 dataset used in this analysis. The modeling center, the number of model realizations analyzed, and the spatial resolution of the global atmospheric grid are indicated. ESMs include interactive prognostic aerosol, chemistry, and dynamical vegetation (Taylor et al. 2012).

Table 1.

The paper is organized as follows: The data and methodology are described in section 2. Results of the validation of the GCMs through comparison with observational datasets during the historical period (1979–2004) are shown in section 3. Climate change projections for the late twenty-first century (2075–99) based on the six GCMs and their mean ensemble under the RCP8.5 radiative forcing scenario are discussed in section 4. Summary and conclusions are given in section 5.

2. Data and methodology

Observed gauge-based monthly gridded precipitation and surface air temperature, at 0.5° spatial resolution, were obtained from the Climatic Research Unit (CRU) of the University of East Anglia dataset (version 3.1: 1901–2009; available online at http://badc.nerc.ac.uk/browse/badc/cru/data/cru_ts_3.10; Mitchell and Jones 2005). Gridded monthly precipitation was also obtained from the Global Precipitation Climatology Project (GPCP), version 2.1 (Huffman et al. 2009), which is a satellite–gauge combination with a spatial resolution of 2.5°. The GPCP is freely available online (http://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html).

Additionally, model-derived monthly estimates of various surface and atmospheric variables at different vertical levels with 1.5° spatial resolution were obtained for the 1979–2004 historical period from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim; available online at http://data-portal.ecmwf.int/data/d/interim_moda/; Dee et al. 2011). Daily surface variables and atmospheric fields at different levels were also obtained from the North American Regional Reanalysis (NARR), which has a spatial resolution of approximately 0.3° (32 km). NARR is freely available online (http://www.esrl.noaa.gov/psd/data/gridded/data.narr.html). For comparison purposes, all monthly fields were regridded to the 0.5° latitude–longitude CRU grid, using bilinear interpolation.

The monthly averaged output of six GCMs from the CMIP5 dataset (Table 1; Taylor et al. 2012) was obtained from different sites of the CMIP5 webpage (http://cmip-pcmdi.llnl.gov/cmip5/data_getting_started.html). For each GCM, two experiments were chosen: 1) historical simulations that used realistic values of radiative forcing for the late twentieth century and 2) the RCP8.5 projections that simulate future climate change using a high GHG radiative forcing scenario that reaches 8.5 W m−2 by 2100. The monthly GCM data that were analyzed consisted of mean precipitation, surface air temperature (Tas), surface temperature (Tskin), and 500–1000-hPa thickness, together with wind (u, υ, and ω), air temperature (Ta), and specific humidity (q) at vertical levels ranging from 1000 to 200 hPa. Each GCM has a different number of realizations (Table 1). Thus, the monthly realizations were averaged to create a single output for each variable and model and a multimodel ensemble mean was calculated for each variable.

The NAM region considered here is shown in Fig. 1; it includes the core region over northwestern Mexico and the northernmost extension of the NAM over Arizona and New Mexico. To evaluate the annual cycle, precipitation is averaged over the entire NAM region; this results in quantities smaller than the average precipitation for the core monsoon, as implied from Fig. 1. This is why the annual cycle of mean monsoon precipitation and its future changes documented in this study may differ from the quantities reported by other studies that have used different monsoon areas or have only focused on the core monsoon (e.g., Bukovsky et al. 2013; Cook and Seager 2013; Geil et al. 2013; Maloney et al. 2014).

Fig. 1.
Fig. 1.

Mean summer (JJA) precipitation (mm day−1) during 1979–2004 for (a) CRU and (b) GPCP observations; reanalyses from (c) ERA-Interim and (d) NARR; and GCMs: (e) ens_GCMs, (f) CanESM2, (g) CNRM-CM5, (h) HadGEM2-ES, (i) MIROC-ESM-CHEM, (j) MPI-ESM-LR, and (k) MRI-CGCM3. The NAM region considered here is the area limited by 23°–36°N, 104°–114°W.

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

Surface LSTC was evaluated using the annual cycles of the area average of Tskin over the continental and oceanic regions relevant to the NAM (see Fig. 4): namely, 1) the thermal low region spanning the Arizona Sonoran Desert (32°–34°N, 112°–116°W; Tang and Reiter 1984) and 2) an oceanic area in the eastern tropical Pacific, south of the Gulf of California (15°–23°N, 105°–110°W), which is the principal source of low-level moisture for the core monsoon region (Turrent and Cavazos 2009). A midtropospheric LSTC index was also obtained based on the 500–1000-hPa temperature thickness difference between the two regions.

The vertically integrated moisture flux Q was computed from the surface to the 200-hPa level using the following equation:
e1
where g is the acceleration of gravity, q is specific humidity, p is pressure, and v is the horizontal wind vector. Additionally, the horizontal MFC was calculated,
e2

3. Historical validation

a. Precipitation

The spatial pattern of mean summer rainfall over the NAM region estimated by the ens_GCMs is similar to that of the CRU and the GPCP observed datasets and to NARR´s pattern, with maximum precipitation (>3 mm day−1) over the Sierra Madre Occidental (Fig. 1). ERA-Interim has a dry bias over the core monsoon region, with precipitation >3 mm day−1 greatly underestimated. Individually, CNRM-CM5, HadGEM2-ES, MPI-ESM-LR, and MRI-CGCM3 reproduce the spatial rainfall pattern over the Sierra Madre Occidental very well (Fig. 1; see Table 1 for expanded model names). CanESM2 is the driest model (Figs. 1f, 2b), while MPI-ESM-LR is the wettest over the monsoon region (Figs. 1j, 2b). The mean annual cycle of precipitation over the NAM region (Fig. 2a) shows maximum precipitation during July–August (~2.8 mm day−1), which is surprisingly well captured by the ens_GCMs, but it is overestimated (underestimated) by GPCP (by NARR and ERA-Interim).

Fig. 2.
Fig. 2.

Mean precipitation rate and metrics of skill (mm day−1) in the NAM region (dashed area in Fig. 1) during 1979–2004. (a) Annual cycle for CRU, GPCP, NARR, ERA-Interim, and the ens_GCMs; (b) annual cycle for CRU, ens_GCMs, CanESM2, HadGEM2-ES, CNRM-CM5, MIROC-ESM-CHEM, MPI-ESM-LR, and MRI-CGCM3; (c) JJA precipitation for the CRU, GPCP, NARR, ERA-Interim, and the ens_GCMs; and (d) measures of skill: standard deviation (STD) for the observations (CRU and GPCP), reanalyses (ERA-Interim and NARR), and the GCMs and MAE for the GCMs as compared to CRU. Note the difference in the y-axis scale between (a) and (b).

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

The GPCP, NARR, and ERA-Interim datasets agree well with the CRU precipitation annual cycle, with some biases, as seen in Fig. 2a. The dry summer bias in ERA-Interim’s annual cycle is also seen in the spatial pattern in the core monsoon over northwestern Mexico (see the 4 mm day−1 contour in Figs. 1c and 3b) and in the interannual summer precipitation (Fig. 2c), especially after 1990. The ens_GCMs greatly overestimates spring, fall, and winter precipitation, as compared to the observations (Figs. 2a,b). However, the ens_GCMs summer estimate is an improvement with regards to the CMIP3 models, consistent with the results of Geil et al. (2013); many CMIP3 models underestimated summer precipitation and had a late rainfall peak in September that delayed the monsoon retreat (Seth et al. 2011; Cavazos and Arriaga-Ramirez 2012). The CMIP5 models also manifest a similar weak monsoon retreat (Figs. 2a,b), which is particularly evident in the MIROC-ESM-CHEM, MPI-ESM-LR, and MRI-CGCM3 model results (Fig. 2b). Previous studies using a large number of models have also described the late monsoon retreat in CMIP5 (Cook and Seager 2013; Geil et al. 2013). Geil et al. (2013) argued that the inaccuracy of the spatial gradients of the geopotential height fields across a larger region prevents some models to produce a realistic representation of the onset and retreat of the monsoon. In the present study all models, except CanESM2, produce reasonable monsoon onsets at the monthly time scale, although differences may exist at the daily time scale, as documented by Geil et al. (2013). The largest mean absolute errors (MAEs) are produced by MIROC-ESM-CHEM, MPI-ESM-LR, and MRI-CGCM3 (Fig. 2d). HadGEM2-ES is the model that best reproduces the annual cycle of precipitation (Fig. 2b) and has both the lowest MAE and the closest standard deviation to observations (Fig. 2d), consistent with Bukovsky et al. (2013) and Geil et al. (2013), while CanESM2 is unable to reproduce the annual cycle. A recent study by Martinez-Sanchez and Cavazos (2014) showed that HadGEM2-ES includes a very good representation of the annual cycle of sea surface temperatures (SST) of the eastern tropical Pacific, while the SSTs in CanESM2 are highly overestimated from April to December, which may be partially responsible for the dry precipitation bias in the NAM region.

Interannually, June–August (JJA) monsoon precipitation in GPCP, NARR, and ERA-Interim are highly correlated (>0.8) with CRU (Fig. 2d) over the NAM region. ERA-Interim and NARR exhibit a marginal negative trend (p = 0.1) in summer precipitation that it is not observed in the other datasets (Fig. 2d). The negative trend in NARR may be due to problems in the assimilated precipitation in the core monsoon region, which is known to have large gaps after the 1990s (Arriaga-Ramirez and Cavazos 2010). It is possible that ERA-Interim assimilated precipitation suffers from a similar problem. Monsoon precipitation in the observed CRU data also has a weak negative trend since 1979 over several parts of the NAM, especially along the coastal region (not shown). Castro et al. (2007) also found a negative trend of NAM coastal precipitation derived from a regional model and argued that it is possibly related to the recent warming of the eastern tropical Pacific that has been documented in several studies (e.g., Webster et al. 2005; Santer et al. 2006; Elsner et al. 2008; Knutson et al. 2013; Martinez-Sanchez and Cavazos 2014), but the warming cannot be attributed to anthropogenic forcing (Knutson et al. 2013).

An important feature of the summer season is the location and intensity of the intertropical convergence zone (ITCZ), which reaches its most northerly position over the eastern tropical Pacific at the height of the boreal summer. The observations and models both place the core of the ITCZ near 10°N, 110°W during JJA. The ens_GCMs adequately simulates the position and intensity of the ITCZ as compared to the ERA-Interim and GPCP data (Fig.3). However, some models have deficiencies in the simulation of the ITCZ core: CNRM-CM5 and MIROC-ESM-CHEM simulate a weak core, while it is stronger than the observed estimate in HadGEM2-ES and MPI-ESM-LR. It is interesting to note the large-scale precipitation pattern of CanESM2 (the driest ensemble member for the NAM region; Figs. 1f, 2b). Although the mean position and intensity of the ITCZ is well reproduced (Fig. 3d), the 2 mm day−1 contour is too far south in the NAM region, possibly because of other factors such as the intensity and position of the subtropical highs [see Fig. 4 in Maloney et al. (2014)] and a weaker LSTC (due to a warmer ocean, as mentioned above).

Fig. 3.
Fig. 3.

Mean summer (JJA) precipitation (mm day−1) during 1979–2004 for (a) GPCP, (b) ERA-Interim, (c) ens_GCMs, (d) CanESM2, (e) CNRM-CM5, (f) HadGEM2-ES, (g) MIROC-ESM-CHEM, (h) MPI-ESM-LR, and (i) MRI-CGCM3.

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

b. LSTC and MFC

Figure 4 presents a comparison of the mean annual cycles of Tskin over the continental and oceanic regions involved in the NAM LSTC estimated from ERA-Interim, NARR, and the ens_GCMs. During boreal winter and at the peak of the monsoon season, NARR is slightly warmer over land than ERA-Interim. Over the ocean, the mean ens_GCMs is in good agreement with the two reanalyses, being only roughly 1°C warmer from March through September. However, over land the GCMs have a significant cold bias, with Tskin being about 3°C cooler than ERA-Interim and NARR, especially from March through September. The cooler land surface temperatures are a likely consequence of the GCMs wet bias during the fall and winter (Fig. 3a), a problem that was also seen in CMIP3 (Cavazos and Arriaga-Ramirez 2012) and in regional models of the North American Regional Climate Change Assessment Program (NARCCAP) forced by some CMIP5 models (Bukovsky et al. 2013). The month in which the continent becomes warmer, on average, than the ocean (i.e., signaling the start of the positive LSTC that presumably drives the NAM precipitation onset) is mid-May for ERA-Interim (Fig. 4a) and NARR (Fig. 4b) and June for the ens_GCMs (Fig. 4). According to the proposed LSTC hypothesis, the delayed continental warming and weaker seasonal LSTC in the historical ens_GCMs should mean delayed and/or weak early season precipitation (June–July) compared to NARR and ERA-Interim’s precipitation, but this is not the case. In spite of their strong surface LSTC, the two reanalyses have a slight dry bias in July–August (Fig. 2a), while the ens_GCMs mean JJA precipitation agrees well with the observations.

Fig. 4.
Fig. 4.

Mean annual cycles of Tskin (°C) for regions over the sea (black) and over land (gray) linked to the NAM LSTC for (a) ERA-Interim (solid line) and the ens_GCMs data (dashed line) and (b) NARR (solid line) and the ens_GCMs (dashed line) during the historical period (1979–2004). The vertical arrows indicate the mean calendar date during the annual cycle on which the continent becomes warmer than the ocean (i.e., a positive LSTC). From the inset map, the oceanic (dark) region over the eastern tropical Pacific is bounded by 15°–23°N, 105°–110°W, and the continental (gray) region over the Arizona Sonoran Desert is bounded by 32°–34°N, 112°–116°W.

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

Another factor that has an important influence on NAM precipitation is the MFC in the core region (Barlow et al. 1998; Ruiz-Barradas and Nigam 2005; Turrent and Cavazos 2009), which at the onset of the monsoon is partially modulated by the LSTC (Turrent and Cavazos 2012). During boreal winter and spring, the MFC pattern indicates that the ITCZ is weak with strong easterly flux centered at 8°N and west of 120°W (Figs. 5a,c). The winter southward position of the ITCZ and the expansion into the tropics—because of meridional migration of the Hadley cell—of the divergence associated with the North Pacific subtropical high (NPSH) and North Atlantic subtropical high (NASH) generate strong moisture divergence over large areas of Mexico and Central America, causing the dry season over the region. Weak winter moisture convergence is only observed over the mountains in NARR (Fig. 5b) and over eastern Mexico in ERA-Interim and the ens_GCMs (Figs. 5a,c).

Fig. 5.
Fig. 5.

Mean seasonal vertically integrated moisture flux (vectors; kg m−1 s−1) and its convergence (shading; mm day−1) for (a),(d) ERA-Interim, (b),(e) NARR, and (c),(f) ens_GCMs during the historical period (1979–2004) for (top) December–February (DJF) and (bottom) JJA.

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

During the boreal summer (Fig. 5d) and autumn (not shown), ERA-Interim has the core of the ITCZ located over the eastern tropical Pacific with moisture convergence greater than 10 mm day−1 roughly centered near 10°N, 100°W, consistent with the precipitation pattern shown in Fig. 3b. This high moisture pool presumably feeds the NAM core region, where MFC values reach 8 mm day−1. The corresponding summer spatial pattern of the ens_GCMs is weaker over the NAM region than what is observed for ERA-Interim (Fig. 5f). The NARR and ERA-Interim patterns are similar north of 12°N. However, the NARR field has greater detail because of its finer spatial resolution, which generates areas of convergence over the Sierra Madre Occidental and local divergent cells along both sides of the mountains. Extended regions of intense divergence over eastern Mexico (−10 mm day−1), the subtropical eastern Pacific Ocean, the Baja California peninsula, and the U.S. West Coast (−4 mm day−1) are found to the east and west of the NAM core region (Figs. 5d–f).

Overall, the ens_GCMs adequately reproduces the patterns of moisture transport and divergence seen in ERA-Interim during both the cold and warm seasons (Figs. 5c,f). However, during winter the ensemble has a much weaker moisture divergence over the NAM region, which may be partially responsible for the wet bias (Fig. 2a). During summer, the convergence zones in the ens_GCMs are slightly weaker in the NAM core region than in ERA-Interim. Therefore, the stronger LSTC and stronger MFC pattern—but weaker precipitation response—seen in ERA-Interim (as compared to the ens_GCMs) both suggest that the reanalysis may have a dry convective scheme, among other possible factors. The CMIP5 models, especially the Earth system models (ESMs) in Table 1 (Taylor et al. 2012), have made notable improvements to the model physics and aerosol forcing, and some have increased their spatial resolution relative to their CMIP3 versions, which may partially explain the improvement in the warm season precipitation.

4. Climate change projections

a. Changes in the summer LSTC and precipitation

Figure 6a compares the ERA-Interim annual cycle of the surface LSTC (land minus sea Tskin) during the historical period (1979–2004) with the corresponding annual cycles of ens_GCMs for both the historical and the RCP8.5 scenario. The ens_GCMs underestimates ERA-Interim’s surface LSTC by several degrees throughout most of the historical annual cycle (Fig. 6a). ERA-Interim’s LSTC shows a cumulative continental warming (positive values of the thermal difference in Fig. 6a) of 12.9°C, spanning mid-May through September. In contrast, the corresponding value in the historical ens_GCMs is only 4.4°C, with positive values of the thermal difference occurring only from June through mid-August. At the end of the twenty-first century, the surface LSTC is projected to nearly double during the monsoon season, with an earlier start of the continental warming in mid-May. However, the future LSTC in the ens_GCMs is still weaker than the ERA-Interim LSTC in the historical period. The midtropospheric positive LSTC begins in June in both the historical and future projections (Fig. 6b), in synchrony with the onset of monsoon precipitation.

Fig. 6.
Fig. 6.

(a) Mean annual cycles of an LSTC index estimated from Tskin (land minus sea Tskin averaged over regions in Fig. 4) for ERA-Interim during the historical period (1979–2004; solid line) and for the ens_GCMs for both the historical period (dashed line) and the future RCP8.5 projections (2075–99; dotted line). (b) As in (a), but for a midtropospheric LSTC index based on the 500–1000-hPa thickness. Positive values indicate that the atmosphere over the continent has greater heat content than over the ocean. (c) Mean changes in the annual cycle of the precipitation rate over the NAM region shown as the RCP8.5 (2075–99) minus historical (1979–2004) projections. The median change of the ens_GCMs is also indicated.

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

Based on the LSTC hypothesis alone, one would expect that, with an earlier and intensified surface LSTC in the RCP8.5 scenario (Fig. 6a), the early season (June–July) monsoon precipitation would also increase relative to the historical period, but the modeled scenarios actually resulted in a small decrease in July rainfall spread across the entire NAM region (Table 2; Fig. 6c). However, a significant increase in precipitation is projected for the end of the monsoon season in September (Table 2; Fig. 6c) that extends the peak of the monsoon season from July–August to July–September (JAS), postponing or weakening the monsoon’s retreat phase.

Table 2.

Monthly precipitation differences, expressed both in millimeters per day and as a percentage of change, between the median monthly ens_GCMs RCP8.5 scenario projection (2075–99) and the mean monthly values in the historical period (1979–2004).

Table 2.

The lack of statistical significance in the changes of JJA monsoon precipitation averaged over the entire NAM region reflects the discrepancy among the different model projections, which can be clearly seen in Fig. 7. Other studies that have used a larger number of models (e.g., Cook and Seager 2013; Maloney et al. 2014) have found that the uncertainty of the projected precipitation changes is stronger in the northern portion of the NAM region, in the transition to the subtropics (Figs. 7, 8); higher consensus of negative precipitation changes are expected in the core monsoon region during winter, spring (Fig. 8), and summer (Fig. 7g), particularly in July (Fig. 6c), in agreement with Cook and Seager (2013).

Fig. 7.
Fig. 7.

Mean JJA changes in precipitation rate (mm day−1) expressed as the difference between the RCP8.5 projection of the ens_GCMs and the results for each of the six GCMs during the historical period (1979–2004): (a) CanESM2, (b) CNRM-CM5, (c) HadGEM2-ES, (d) MIROC-ESM-CHEM, (e) MPI-ESM-LR, (f) MRI-CGCM3, and (g) ens_GCMs.

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

Fig. 8.
Fig. 8.

Seasonal precipitation changes expressed as the percentage difference between the mean ens_GCMs of the 2075–99 RCP8.5 projections and the historical period (1979–2004) for (a) DJF, (b) March–May (MAM), (c) JJA, and (d) September–November (SON). The contour interval is 2 mm day−1. The gray-shaded areas indicate grid points for which at least four of the six models agree on the sign of the change.

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

Understanding the cause of the apparent inconsistency in the response of the summer monsoon precipitation to increased warming, which leads us to reject the initial LSTC hypothesis, is fundamental for understanding changes in the NAM onset and circulation under global warming conditions. Lee and Wang (2014) attribute the weakening of monsoon precipitation in future projections of CMIP5 to stabilization of the atmosphere due to vertically differential warming. At the height of the monsoon season (July), the ens_GCMs projects increased low-level ascending motion over the foothills and peaks of the Sierra Madre Occidental but a marginal increase in midtropospheric stability throughout the NAM region (Fig. 9). The midtropospheric LSTC analysis, based on the 500–1000-hPa thickness, shows a weakening (−15 m) of the meridional thermal contrast for July in the future RCP8.5 scenario (Fig. 6b), which is possibly related to weaker vertical ascending motion above 700 hPa in the coastal areas (Fig. 9). This increase in midtropospheric stability is the likely cause for the projected reduction in JJA rainfall (Figs. 7g, 8), especially over the coastal plains. The mean negative summer rainfall changes along the Pacific coast are mainly driven by HadGEM2-ES, MPI-ESM-LR, and MIROC-ESM-CHEM and, to a lesser degree, by CanESM2 (Fig. 7).

Fig. 9.
Fig. 9.

July changes in the meridionally averaged (25°–30°N) mean vertical velocity ω (Pa s−1), expressed as the difference between the mean ens_GCMs of the RCP8.5 projection (2075–99) and the historical period (1979–2004). Negative values indicate increased magnitude of ascending vertical velocity. The gray-shaded areas indicate grid points for which at least four of the six models agree on the sign of the change. Dark areas represent the mountains.

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

b. Changes in MFC, seasonal precipitation, and circulation

An increase in moisture flux divergence is projected during the boreal winter, spring, and summer over large regions of the tropical oceans, Mexico, and Central America (Fig. 10). The divergent moisture fluxes over the eastern tropical Pacific, the Gulf of Mexico and Caribbean Sea and the North Atlantic are projected to intensify and migrate toward the coastal regions of Mexico and Central America during JJA. This implies a reduction of the moisture transported from both oceanic basins into the NAM region, limiting the major monsoon convergence to regions over Mexico’s highlands and displacing the ITCZ south of 10°N, which results in a higher (lower) percentage of precipitation over the coastal (mountain) areas of the NAM (Fig. 8c) relative to the annual precipitation. Moreover, summer precipitation in southern Mexico and Central America is projected to decrease in association with increasing precipitation in the southward-displaced ITCZ core region (Fig. 8c). During the fall, the eastern Pacific and North Atlantic divergent circulations (related to the NPSH and NASH) weaken, allowing a northward migration of the ITCZ and moisture transport into the NAM region (Fig. 10d), which can explain the significant increase of precipitation in September throughout the entire intra-American seas region noted above (Table 2; Figs. 6c, 8d) and in other studies (Cook and Seager 2013; Maloney et al. 2014; Seth et al. 2013).

Fig. 10.
Fig. 10.

Mean seasonal differences between the ens_GCMs RCP8.5 projections (2075–99) and the historical period (1979–2004) for the vertically integrated moisture flux (vectors; kg m−1 s−1) and its convergence (shading; mm day−1).

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

The divergent circulations over the eastern tropical Pacific and the North Atlantic, linked to the Hadley cells, intensify equatorward during the boreal winter (Fig. 10a), consistent with Lee and Wang (2014). Winter precipitation over northwestern Mexico and the southwestern United States is strongly influenced by the position and intensity of the subtropical westerly jet and by Pacific teleconnections (e.g., Gershunov and Barnett 1998; Gershunov and Cayan 2003; Cavazos and Rivas 2004; Neelin et al. 2013; Polade et al. 2013). During late fall, the North Pacific jet stream tends to migrate to the subtropics, with intense westerly winds (20–30 m s−1) at the 200-hPa level (Bluestein 1993). The winter patterns of the 200-hPa zonal winds from ERA-Interim and the ens_GCMs are similar during the historical period (Figs. 11a,b), but the ens_GCMs simulates stronger winds and a deeper trough off the western coast of North America, as well as weaker moisture divergence (Fig. 5d); these factors may be linked to the wet winter bias in the historical ens_GCMs (Fig. 3).

Fig. 11.
Fig. 11.

Mean DJF zonal wind (m s−1) at the 200-hPa level for (a) ERA-Interim, (b) the historical ens_GCMs (1979–2004), and (c) the RCP8.5 projection (2075–99) minus the historical ens_GCMs mean. The contour interval is 5 m s−1 for (a),(b) and 1 m s−1 for (c).

Citation: Journal of Climate 27, 12; 10.1175/JCLI-D-13-00557.1

Under the high radiative forcing scenario (RCP8.5), the subtropical westerly jet does not show a significant change in its latitudinal position over the subtropical Pacific but rather an intensification and eastward expansion with a stronger jet off Southern California and over the Gulf of Mexico (Fig. 11c), which is a result consistent with the study by Neelin et al. (2013) that used 12 CMIP5 models. This is in sharp contrast to the CMIP3 results, which projected a significant northward migration of the jet (Yin 2005) and significantly less winter precipitation over the southwestern United States and northwestern Mexico (e.g., Seager and Vecchi 2010; Cavazos and Arriaga-Ramirez 2012). The observed winter pattern results in a north–south precipitation anomaly (Fig. 8a), with increased precipitation over California and reduced rainfall over northwestern Mexico; the dry anomaly over northwestern Mexico is located under the southern flank of the jet stream. Furthermore, increased winter precipitation is expected over the Caribbean region because of an enhanced jet stream over the Gulf of Mexico.

5. Summary and conclusions

The objective of this study was to evaluate the hypothesis that global warming during the twenty-first century will increase the land–sea thermal contrast (LSTC) and consequently the intensity of early monsoon (June–July) circulation and precipitation over the NAM region. The hypothesis was tested with NARR and ERA-Interim, and the mean ensemble (ens_GCMs) of the six CMIP5 models that best reproduced key features of the monsoon during the historical period, according to previous studies. NARR has a slightly larger LSTC than ERA-Interim, but both annual cycles are similar. Thus, the comparisons with the ens_GCMs were done with ERA-Interim, because for other fields NARR has the disadvantage of not covering the ITCZ region.

Even though ERA-Interim has stronger summer LSTC and MFC over the NAM region than the ens_GCMs, the reanalysis underestimates summer precipitation, especially after 1990, which may be associated to a dry convection scheme or data assimilation problems (e.g., Dee et al. 2011), among other possible factors. The historical precipitation in the NAM region was not found to have a significant trend, while NARR and ERA-Interim exhibit a marginal negative trend (p = 0.1) during JJA. Surprisingly, the ens_GCMs of the CMIP5 improved the estimate of summer precipitation, as compared to the CMIP3 simulations; however, the ens_GCMs greatly overestimates fall, winter, and spring CRU precipitation; a similar wet bias was observed in CMIP3 (e.g., Seth et al. 2011; Cavazos and Arriaga-Ramirez 2012) and in other studies based on CMIP5 results (Geil et al. 2013; Seth et al. 2013). The CMIP5 models have improved the representation of key Pacific winter climate modes and teleconnections (Polade et al. 2013), but our results indicate that the ens_GCMs still has large wet and cold biases in the NAM region during the fall and winter seasons, which may be partially attributed to an intense subtropical westerly jet and weak moisture divergence (as compared to ERA-Interim).

During winter, contrary to what was suggested by the CMIP3 models (Lorenz and DeWeaver 2007), the subtropical westerly jet stream in the ens_GCMs does not present a significant northward migration in the future RCP8.5 scenarios. Instead, it has an intensification and eastward expansion of the jet stream than the historical simulations, consistent with Neelin et al. (2013). This results in small positive changes in winter precipitation (4%–8%) over California and stronger subsidence and reduced precipitation (4%–8%) over northwestern Mexico, under the southern flank of the subtropical jet stream. The projected dry anomaly is consistent with other studies that included a larger number of CMIP5 models (e.g., Cook and Seager 2013; Neelin et al. 2013; Seth et al. 2013). The CMIP5 precipitation response to the RCP8.5 forcing scenario is much weaker than the A2 scenarios from CMIP3, which projected winter and spring reductions of 10%–20% over the southwestern United States and northwestern Mexico (Cavazos and Arriaga-Ramirez 2012).

During the boreal summer, the CMIP5 models exhibit regional changes that appear to be linked to a decrease in the midtropospheric LSTC, enhanced atmospheric stability, and a southward displacement of the ITCZ. At the end of the twenty-first century, the surface LSTC is projected to double during the monsoon season under the RCP8.5 scenario, with an earlier onset of the continental warming in mid-May. However, contrary to our initial LSTC hypothesis, early monsoon precipitation is not likely to increase. This apparent inconsistency—the weakening of the monsoon onset response—can be explained by differential changes in the midtropospheric LSTC, which is expected to decrease over the coastal region of the NAM, especially during July. Increased vertically integrated MFC over the mountains produces midtropospheric subsidence on the coastal region of the NAM, in spite of the increased surface LSTC and the corresponding low-level ascending motion. Previous studies that have used the CMIP5 models are in agreement with our summer results and also highlight increased tropospheric stability (Cook and Seager 2013; Seth et al. 2013) and an increase in the height of the lifting condensation level over the continent (Dirmeyer et al. 2013) as major factors involved in the changes to the monsoon dynamics.

Because of the great societal relevance of the onset and retreat of the monsoon rains, it is very important to reduce the large uncertainties that the current and future modeled scenarios present. The role of atmospheric stability and of the surface and midtropospheric LSTCs as major factors for changes to the monsoon onset dynamics needs to be further investigated at the daily time scale (e.g., Geil et al. 2013), as well as the causes of the weak monsoon retreat, with higher-resolution regional climate models that permit dynamical downscaling of the GCMs output (e.g., Liang et al. 2006; Castro et al. 2012). The causes of the weak monsoon retreat and of the wet and cold biases during winter and spring of the historic GCMs need to be understood so the uncertainties can be reduced. Dynamical downscaling initiatives such as the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2009; Bukovsky et al. 2013) and the Coordinated Regional Climate Downscaling Experiment (CORDEX; Giorgi et al. 2009; http://wcrp-cordex.ipsl.jussieu.fr/) for Central America (e.g., Fuentes-Franco et al. 2013) represent good opportunities to further pursue this research.

Acknowledgments

We greatly appreciate the comments and suggestions of two reviewers, which helped to improve this manuscript. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and thank the climate modeling groups for producing and making available their model output. Partial funding for this work was provided by CONACYT through the REDESClim Network and a graduate scholarship to the first author (Registry 242911); additional funding was received through an internal CICESE grant.

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