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  • View in gallery

    Model domain and elevation (m) for (a) China and (b) Australia, including white and black dots identifying the grid cells within the buffer zone and inner domain, respectively. (c),(d) Annual mean LAI (m2 m−2) over both domains. (e),(f) Dominant vegetation classifications applied in the model. The regions of imposed LAI anomalies for ensemble experiments are identified with red boxes in (c),(d).

  • View in gallery

    Seasonal cycle of mean bias, spatial correlation, and RMSD in RegCM4-simulated (a)–(c) overland air temperature (°C), (d)–(f) overland precipitation (mm day−1), (g)–(i) overocean precipitation (mm day−1), and (j)–(l) cloud-cover fraction (over land and ocean) for the China domain during 2011/12, compared to the University of Delaware, TRMM, and PATMOS-x products. Results are shown from 21 simulations of 2010–12, with the first year discarded as spinup. The red lines indicate results from the China19 simulation, whose simulation was considered of highest quality and whose configuration was used for the extended 1960–2013 control simulation.

  • View in gallery

    As in Fig. 2, but for the Australian domain, with the red line representing the Aust16 simulation.

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    Mean air temperature (°C) from (a),(d),(g),(j) RegCM4 and (b),(e),(h),(k) the University of Delaware observations, along with the (c),(f),(i),(l) mean bias, for (a)–(c) DJF, (d)–(f) MAM, (g)–(i) JJA, and (j)–(l) SON for 1960–2013 across the China domain. The RegCM4 control simulation is produced based on the configuration used in the China19 run.

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    As in Fig. 4, but for the Australian domain, and the RegCM4 control simulation is produced based on the configuration used in the Aust16 run.

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    Mean precipitation (mm day−1) from (a),(d),(g),(j) RegCM4 and (b),(e),(h),(k) the University of Delaware observations, along with the (c),(f),(i),(l) mean bias, for (a)–(c) DJF, (d)–(f) MAM, (g)–(i) JJA, and (j)–(l) SON for 1960–2013 across the China domain. The RegCM4 control simulation is produced based on the configuration used in the China19 run.

  • View in gallery

    As in Fig. 6, but for the Australian domain, and the RegCM4 control simulation is produced based on the configuration used in the Aust16 run.

  • View in gallery

    Local difference (ENSINC − ENSDEC) in (a),(d),(g) surface albedo (fraction LAI−1), (b),(e),(h) 2-m air temperature (°C LAI−1), and (c),(f),(i) 2-m DTR (°C LAI−1) in (a)–(c) April, (d)–(f) June, and (g)–(i) August across the Chinese monsoon region (blue lines) and (a)–(c) November, (d)–(f) January, and (g)–(i) March across the Australian monsoon region (red lines) between ENSINC and ENSDEC. Results are shown as a function of the number of ensemble members on the x axis, ranging from 1 to 30. White dots indicate the differences between ENSINC and ENSDEC are statistically significant (p < 0.05). Among 10 000 random iterations, results are shown for the 10th, 50th, and 90th percentiles, with a thicker lines used for the 50th percentile.

  • View in gallery

    Local responses in (a) LAI (m2 m−2), (b) surface albedo (fraction LAI−1), (c) wind stress (N m−2 LAI−1), (d) 10-m wind speed (m s−1 LAI−1), (e) ET (mm day−1 LAI−1), (f) SHF (W m−2 LAI−1), (g) LHF (W m−2 LAI−1), (h) 2-m mean air temperature (°C LAI−1), (i) 2-m max air temperature (°C LAI−1), (j) 2-m min air temperature (°C LAI−1), (k) DTR (°C LAI−1), (l) ground temperature (°C LAI−1), (m) 2-m specific humidity (g kg−1 LAI−1), (n) PBL height (m LAI−1), (o) vertical motion at sigma level 0.83 (hPa s−1 LAI−1), and (p) precipitation (mm day−1 LAI−1) across the Chinese monsoon region in April–September (blue) and Australian monsoon region in November–April (red) to an LAI increase of 1 m2 m−2, based on ENSINC minus ENSDEC. Green and yellow dots identify statistically significant differences at p < 0.1 and p < 0.05, respectively.

  • View in gallery

    Linear response in (a) surface albedo (fraction), (b) DTR (°C), (c) wind stress (N m−2), (d) 10-m wind speed (m s−1), (e) ET (mm day−1), and (f) 2-m specific humidity (g kg−1) to an increase in LAI across the Chinese monsoon region during June, based on (ENSINC − ENSDEC)/2. Only statistically significant differences (p < 0.05) are shown.

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    As in Fig. 10, but for the northern Australian monsoon region during January.

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    Linear response in 10-m wind vectors (m s−1) during (a) November, (b) December, (c) January, (d) February, (e) March, and (f) April to an increase in LAI across the northern Australian monsoon region, based on (ENSINC − ENSDEC)/2. Only statistically significant differences (p < 0.05) are shown. The area of modified LAI is identified with a red box.

  • View in gallery

    Vertical profile (model sigma level on y axis) of the linear response in (a),(b) specific humidity (g kg−1), (c),(d) air temperature (°C), (e),(f) vertical motion (105 hPa s−1), and (g),(h) cloud-cover fraction to an increase in LAI across the (a),(c),(e),(g) Chinese monsoon region during April–September or (b),(d),(f),(h) Australian monsoon region during November–April, based on (ENSINC − ENSDEC)/2. Statistically significant differences (p < 0.05) are dotted.

  • View in gallery

    Scatterplots of the difference (ENSINC − ENSDEC) in monthly ET (x axis; mm day−1) vs monthly precipitation (y axis; mm day−1) in ENSINC compared to ENSDEC for the (a)–(f) Chinese monsoon region during April–September and the (g)–(l) Australian monsoon region during November–April. Each red dot represents one of 30 ensemble members. Correlation coefficients are provided in each panel label (statistically significant correlations, at p < 0.05, are in boldface italics). Blue shading indicates increases in both precipitation and ET.

  • View in gallery

    Scatterplots of the difference (ENSINC − ENSDEC) in monthly vertical motion at sigma level 0.825 (x axis; 105 hPa s−1) vs monthly precipitation (y axis; mm day−1) in ENSINC compared to ENSDEC for the (a)–(f) Chinese monsoon region during April–September and the (g)–(l) Australian monsoon region during November–April. Each red dot represents one of 30 ensemble members. Correlation coefficients are provided in each panel label (statistically significant correlations, at p < 0.05, are in boldface italics).

  • View in gallery

    Percent difference in the frequency of 6-hourly precipitation totals for bins of 0, 0.1–2, 2.1–4, …, 50.1–52 mm, across the (a) Chinese monsoon region during April–September and the (b) northern Australian monsoon region during November–April, due to an increase in regional LAI. Percent difference is computed as (ENSINC − ENSDEC)/(ENSDEC × 100).

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Regional Climate Modeling of Vegetation Feedbacks on the Asian–Australian Monsoon Systems

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  • 1 Nelson Institute Center for Climatic Research, University of Wisconsin–Madison, Madison, Wisconsin
  • | 2 National Center for Atmospheric Research, Boulder, Colorado
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Abstract

This study explores the hypothesis that subtropical and tropical monsoon regions exhibit unique responses to vegetation feedbacks. Using the Community Climate System Model (CCSM), M. Notaro et al. concluded that reduced vegetation cover led to an earlier subtropical Chinese monsoon and a delayed, weaker tropical Australian monsoon, yet significant climate and leaf area index (LAI) biases obfuscated the hypothesis’s reliability. To address these concerns, the Regional Climate Model, version 4 (RegCM4), likewise coupled to the Community Land Model but with “observed” LAI boundary conditions, is applied across China and Australia. The model matches the observed dominance of crops, grass, and evergreen trees in southern China and grass and shrubs in northern Australia. The optimal model configuration is determined and applied in control runs for 1960–2013. Monsoon region LAI is modified in a RegCM4 ensemble, aimed at contrasting vegetation feedbacks between tropical and subtropical regions. Greater LAI supports reductions in albedo, temperature, wind speed, boundary layer height, ascending motion, and midlevel clouds and increases in diurnal temperature range (DTR), wind stress, evapotranspiration (ET), specific humidity, and low clouds. In response to greater LAI, rainfall is enhanced during Australia’s pre-to-midmonsoon season but not for China. Modified LAI leads to dramatic changes in the temporal distribution and intensity of Australian rain events. Heterogeneous responses to biophysical feedbacks include amplified impacts (e.g., increased ET and DTR) across China’s croplands and Australia’s shrublands. Inconsistencies between China’s monsoonal responses in the present RegCM4 study and prior CCSM study of M. Notaro et al. are attributed to CCSM’s excessive forest cover and LAI, exaggerated roughness mechanism, and deficient ET response.

© 2017 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 e-mail: Michael Notaro, mnotaro@wisc.edu

Abstract

This study explores the hypothesis that subtropical and tropical monsoon regions exhibit unique responses to vegetation feedbacks. Using the Community Climate System Model (CCSM), M. Notaro et al. concluded that reduced vegetation cover led to an earlier subtropical Chinese monsoon and a delayed, weaker tropical Australian monsoon, yet significant climate and leaf area index (LAI) biases obfuscated the hypothesis’s reliability. To address these concerns, the Regional Climate Model, version 4 (RegCM4), likewise coupled to the Community Land Model but with “observed” LAI boundary conditions, is applied across China and Australia. The model matches the observed dominance of crops, grass, and evergreen trees in southern China and grass and shrubs in northern Australia. The optimal model configuration is determined and applied in control runs for 1960–2013. Monsoon region LAI is modified in a RegCM4 ensemble, aimed at contrasting vegetation feedbacks between tropical and subtropical regions. Greater LAI supports reductions in albedo, temperature, wind speed, boundary layer height, ascending motion, and midlevel clouds and increases in diurnal temperature range (DTR), wind stress, evapotranspiration (ET), specific humidity, and low clouds. In response to greater LAI, rainfall is enhanced during Australia’s pre-to-midmonsoon season but not for China. Modified LAI leads to dramatic changes in the temporal distribution and intensity of Australian rain events. Heterogeneous responses to biophysical feedbacks include amplified impacts (e.g., increased ET and DTR) across China’s croplands and Australia’s shrublands. Inconsistencies between China’s monsoonal responses in the present RegCM4 study and prior CCSM study of M. Notaro et al. are attributed to CCSM’s excessive forest cover and LAI, exaggerated roughness mechanism, and deficient ET response.

© 2017 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 e-mail: Michael Notaro, mnotaro@wisc.edu

1. Introduction

Vegetation influences the regional climate directly through biophysical feedbacks, comprising exchanges in moisture, energy, and momentum with the atmosphere and resulting atmospheric circulation changes, and indirectly through biogeochemical processes that alter the concentration of atmospheric carbon dioxide (Charney 1975; Shukla and Mintz 1982; Dickinson 1984; Sud and Smith 1985; Dickinson et al. 1986, 1991; Sellers et al. 1986, 1996; Sud et al. 1988; Dickinson and Henderson-Sellers 1988; Nobre et al. 1991; Bonan et al. 1992; Bonan 1994, 2002; Pielke et al. 1998; McPherson 2007). Models have indicated the sensitivity of regional climate to surface albedo (Charney 1975; Charney et al. 1977; Dirmeyer and Shukla 1994; Gallimore and Kutzbach 1996), soil moisture (Shukla and Mintz 1982; Koster et al. 2006), leaf area (Chase et al. 1996; Kang et al. 2007), and surface roughness (Sud and Smith 1985; Sud et al. 1988; Chen et al. 2012).

Vegetation feedbacks are complex, with evidence that their sign and strength vary depending on biome type, geographical region, and background climatology (Zheng and Eltahir 1998; Hoffmann and Jackson 2000; Osborne et al. 2004; Liu et al. 2010; Notaro et al. 2011a; Notaro and Gutzler 2012). For example, in response to deforestation, climate models have produced warming in the tropics, because of the evapotranspiration (ET) feedback (Henderson-Sellers et al. 1993; Costa and Foley 2000; Werth and Avissar 2002; Gibbard et al. 2005), and cooling at the high latitudes, because of the vegetation–snow albedo feedback (Bonan et al. 1992; Betts 2000; Govindasamy et al. 2001; Gallimore et al. 2005; Notaro and Liu 2008). The pronounced strength of climate–vegetation biophysical process interactions over the West African, South and East Asian, and South American monsoon regions was noted by Xue et al. (2010). Other studies have concluded that individual biomes produce unique feedbacks to climate (Snyder et al. 2004; Gibbard et al. 2005; Liu et al. 2010; Notaro and Gutzler 2012). Among the global biomes, Snyder et al. (2004) found that boreal forests, savannas, and tropical forests have the greatest influence on global temperatures, local precipitation, and remote precipitation, respectively.

Modeling studies of vegetation’s influence on the global monsoons have largely focused on the long-term climatic impacts of land use and yielded contradictory findings. For example, Barlage and Zeng (2004) and Narisma and Pitman (2003, 2006) reached opposite conclusions about the impact of reduced LAI from Australian land use on temperature and latent heat fluxes (LHFs). Miller et al. (2005) concluded that aboriginal fire practices and related vegetation losses limited the Australian monsoon’s ability to penetrate inland. The impacts of land-use change in China were modeled by Liu et al. (2008), Lee et al. (2011), Ma et al. (2013), and Yu et al. (2013), with the consensus that expanded vegetation cover supports greater ET and rainfall. Modeling studies have suggested that land-use changes may have triggered substantial feedbacks over northern China (Zheng et al. 2002; Chen et al. 2004) and the Yellow River basin (Li and Xue 2005) and modest effects in eastern China (Findell et al. 2007), providing evidence that vegetation feedbacks produce a heterogeneous response across China. Therefore, a high-resolution regional climate model (RCM), with accurate representation of vegetation characteristics, may be needed to accurately study land–atmosphere interactions over such geographically and ecologically complex regions. Few studies have examined the influence of intraseasonal-to-interannual LAI variations on regional climate, despite its potential importance to short-term climate prediction.

Modeling studies by Xue et al. (2010) and Notaro et al. (2011a) suggested that the global monsoon regions respond uniquely to vegetation feedbacks. Using a coarse-resolution (1.9° × 2.5°) fully coupled global climate model (GCM), the National Center for Atmospheric Research (NCAR) Community Climate System Model, version 3.5 (CCSM3.5; Collins et al. 2006; Gent et al. 2010), coupled to the Community Land Model (CLM; Bonan et al. 2002; Levis et al. 2004; Dickinson et al. 2006), Notaro et al. (2011a) explored the role of vegetation feedbacks on the global monsoons at the subannual time scale. Through initial value ensemble experiments, the climatic response was analyzed to an imposed reduction in vegetation cover across each major monsoon region. The monsoon regions exhibited some consistent responses, including reductions in leaf area index (LAI), turbulent fluxes, and atmospheric moisture; anomalous subsidence; and increases in surface air temperature. While midmonsoon rainfall typically did not respond much to reduced vegetation cover, there were often distinct impacts on vertical motion, precipitable water, and rainfall within the flank months (pre- and postmonsoon) of the monsoon season, consistent with studies by Osborne et al. (2004) and Xu et al. (2015). Notaro et al. (2011a) suggested that tropical monsoon regions respond to vegetation cover perturbations with hydrologic feedbacks (e.g., modified ET and LHF) and subtropical monsoon regions respond with thermal feedbacks [modified surface albedo, sensible heat flux (SHF), and land–ocean temperature contrast]. Reductions in vegetation cover led to opposite responses in China and northern Australia. The Chinese summer monsoon shifted earlier, as a result of greater ocean–land thermal contrast, with enhanced springtime ascending motion and rainfall and anomalous descent in September. In contrast, the Australian summer monsoon was weakened and delayed, as a result of diminished ET, with declines in precipitable water and rainfall in spring and anomalous descent during November–January (Notaro et al. 2011a,b; Wyrwoll et al. 2013). Unfortunately, the coarse-resolution CCSM3.5 model exhibited substantial regional climate biases, with excessive monsoon rainfall that penetrated too far inland (e.g., +136% rainfall bias in northern Australia; Notaro et al. 2011a), and large positive LAI biases across the monsoon regions, including excessive forest cover across China because of the absence of land use in the model. These biases obfuscate the reliability of the findings on land–atmosphere interactions and resulting hypothesis on the unique responses to vegetation feedbacks within tropical versus subtropical monsoon regions.

In the present study, the potentially unique atmospheric responses to subseasonal-to-seasonal variations in LAI between the subtropical Chinese (mean latitude of 30°N) and tropical Australian (mean latitude of 15°S) monsoon regions, as suggested by the CCSM3.5-based study of Notaro et al. (2011a), are explored using a high-resolution RCM, forced with realistic, interannually varying LAI. Because of a higher spatial resolution and advanced physics parameterizations, RCMs generally improve the spatial pattern of simulated temperature and precipitation, compared to their driving reanalysis or GCM (Giorgi et al. 2001; Roads et al. 2003; Liang et al. 2004, 2006, 2008; Diffenbaugh et al. 2005; Zhu and Liang 2007; Wang et al. 2009; Kanamitsu and DeHaan 2011; Feser et al. 2011). Their high spatial resolution permits a detailed representation of topography, coastlines, and land cover (Mearns 2003; Mearns et al. 2003) and their influence on local precipitation (Leung and Wigmosta 1999; Hay et al. 2006). Unlike GCMs, RCMs can produce realistic simulations of daily weather phenomena and their associated statistics compared to observations (Früh et al. 2010; Kunz et al. 2010; Semmler and Jacob 2004), including the seasonal distribution of heavy rainfall events and their amounts (Frei et al. 2003; Semmler and Jacob 2004; Hohenegger et al. 2008; Gutowski et al. 2010; Hanel and Buishand 2010; Kawazoe and Gutowski 2013). While GCMs have notoriously struggled at simulating the East Asian monsoon (Notaro et al. 2011a; Feng et al. 2014), the benefits of dynamical downscaling have been demonstrated (Sato and Xue 2012), including the capability to reproduce the seasonal progression and interannual variability of the mei-yu front (Wang et al. 2003; Hu and Ding 2010).

The hypothesis from Notaro et al. (2011a) that vegetation feedbacks produce unique responses between subtropical and tropical monsoon regions is examined here using the International Centre for Theoretical Physics (ICTP) Regional Climate Model, version 4 (RegCM4; Giorgi et al. 2012), with focus on the Chinese and northern Australian summer monsoons. The paper is organized as follows. Section 2 outlines the details of the RCM, boundary conditions, experimental design, and observations used to evaluate the model. Model results are presented in section 3, followed by a summary of conclusions, study limitations, and implications in section 4.

2. Data and methods

a. Model setup

All simulations use the state-of-the-art RegCM4, which is a compressible, finite-difference model that is constrained to hydrostatic balance and consists of a dynamical core based on the Fifth-generation Pennsylvania State University (PSU)–NCAR Mesoscale Model (MM5; Grell et al. 1994). The source of atmospheric boundary conditions is the National Centers for Environmental Prediction (NCEP)–NCAR reanalysis (Kalnay et al. 1996). The sources of oceanic boundary conditions are the global Met Office Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) for 1960–80 (Rayner et al. 2006) and the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation Sea Surface Temperature (OISST) dataset for 1981–present (Reynolds et al. 2002). A horizontal grid spacing of 40 km and 23 vertical sigma levels are applied, with 157 and 172 grid cells in the y and x directions, respectively, for both the Chinese and Australian domains (Figs. 1a,b). This represents roughly 30 times higher spatial resolution than applied by Notaro et al. (2011a) using CCSM3.5. Lateral boundary conditions are provided to the buffer zone, which surrounds the inner domain and has a width of 18 grid cells. Rather large domains are applied so that the buffer zone is far from the monsoon regions, where the land surface is perturbed in ensemble experiments (Notaro and Zarrin 2011). The Chinese domain is characterized by much greater topographic complexity, exceeding 5 km in elevation across the Tibetan Plateau (Figs. 1c,d). RegCM4 has been successfully applied to East Asia (Zheng et al. 2002; Singh et al. 2006; Giorgi et al. 2012; Liu et al. 2014; Gao et al. 2016; Qin and Xie 2016) and Australia (Song et al. 2008). The NCAR Community Land Model, version 3.5 (CLM3.5; Oleson et al. 2008; Steiner et al. 2009; Tawfik and Steiner 2011; Mei et al. 2013), with 10 soil layers, serves as the land surface component for the present study. RegCM4–CLM correctly matches the observed dominance of cropland (26%), C3 grassland (18%), and temperate needleleaf evergreen forest (15%) in the Chinese monsoon region (20°–40°N, 105°–123°E) and of C4 grassland (37%), temperate broadleaf deciduous shrubland (34%), and bare ground (16%) in the Australian monsoon region (20°–10°S, 120°–150°E) (Figs. 1e,f), thereby addressing the limitation of excessive forest cover in China as simulated by CCSM3.5 in the study by Notaro et al. (2011a).

Fig. 1.
Fig. 1.

Model domain and elevation (m) for (a) China and (b) Australia, including white and black dots identifying the grid cells within the buffer zone and inner domain, respectively. (c),(d) Annual mean LAI (m2 m−2) over both domains. (e),(f) Dominant vegetation classifications applied in the model. The regions of imposed LAI anomalies for ensemble experiments are identified with red boxes in (c),(d).

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

Rather than applying a constant or potentially biased LAI seasonal cycle in the RegCM4–CLM simulations, a Moderate Resolution Imaging Spectroradiometer (MODIS)-based global reanalysis dataset of vegetation phenology (Stöckli et al. 2008, 2011) is employed as boundary conditions (Figs. 1c,d). This product contains interannually varying, daily reconstructed plant functional type (PFT)-specific LAI on a 1° × 1° grid for 1960–2010 (Stöckli et al. 2011). The growing season index (GSI) by Jolly et al. (2005) diagnoses vegetation phenology based on low temperatures, evaporative demand, and photoperiod. Stöckli et al. (2008, 2011) developed a GSI-based prognostic phenology model to predict the biophysical vegetation states (e.g., LAI). By combining MODIS LAI (Myneni et al. 2002), remotely sensed PFTs, observed meteorological data, and the prognostic phenology model, the phenology reanalysis was developed. A satellite data assimilation system, based on the ensemble Kalman filter, was applied globally using subgrid-scale representations of PFTs and elevation. Quality-screened observations were used to constrain a PFT-dependent phenological parameter set, which was used to predict global LAI (Stöckli et al. 2011). The daily LAI product specifies the phenology in all simulations here. This approach addresses the concern over excessive LAI across monsoon regions in the CCSM3.5 simulations by Notaro et al. (2011a). Annual mean LAI exhibits a heterogeneous pattern across the two monsoon regions, exceeding 1.8 m2 m−2 across the evergreen forests to the south of China’s Yangtze River and across the Australian grasslands bordering the Arafura Sea while remaining below 1 m2 m−2 across the Chinese croplands to the north of the Yangtze River and across the Australian shrublands, positioned closer to the continental interior (Figs. 1c–f).

b. Model optimization

To determine optimal model configurations for both regions, sets of 21 simulations are produced for the Chinese domain and 26 simulations are produced for the Australian domain for 2010–12, excluding the first year for spinup (Tables 1 and 2). The runs differ in terms of planetary boundary layer (PBL) scheme, explicit moisture scheme, convection scheme, cumulus closure scheme, and numerous coefficients and thresholds. Options for the cumulus convection scheme, which represents small-scale precipitation, include the Grell (Grell 1993), simplified Kuo (Anthes et al. 1987), Massachusetts Institute of Technology (MIT–Emanuel; Emanuel and Zivkovic-Rothman 1999), Tiedtke (Tiedtke 1996), Kain–Fritsch (Kain and Fritsch 1990; Kain 2004), and “mixed convection” schemes (Giorgi et al. 2012), where each scheme has its own tunable parameters (Giorgi et al. 1993, 2012). Within the Grell scheme, the choices for closure assumption include Arakawa–Schubert (Arakawa and Schubert 1974) and Fritsch–Chappell (Fritsch and Chappell 1980). Available explicit moisture schemes, for treating resolvable-scale precipitation and nonconvective clouds, include the subgrid explicit moisture scheme (SUBEX; Pal et al. 2000) and Nogherotto–Tompkins scheme (Nogherotto et al. 2016). The options for PBL physics are the modified Holtslag (Holtslag et al. 1990) and University of Washington (UW-PBL; Bretherton et al. 2004) schemes. Ocean surface flux schemes include the Biosphere–Atmosphere Transfer Scheme, version 1e (BATS1e; Dickinson et al. 1993), and Zeng scheme (Zeng et al. 1998).

Table 1.

Summary of the configurations applied in 21 simulations during 2010–12 over the China domain. Choices of PBL scheme are 1 for modified Holtslag and 2 for the UW-PBL. The max total cloud-cover fraction for radiation is assigned to either its default value, 0.75, or 0.90. Choices of explicit moisture scheme (moist sch) are 1 for SUBEX and 2 for Nogherotto–Tompkins. When applying SUBEX, the relative humidity threshold over the ocean is assigned to either its default value 0.90 or 0.95, and the relative humidity threshold over the land is assigned to either its default value 0.80 or 0.90. Choices for the cumulus convection scheme (conv sch) over land and over ocean are 1 for Kuo, 2 for Grell, 4 for MIT–Emanuel, 5 for Tiedtke, and 6 for Kain–Fritsch; when the scheme differs between land and ocean, this is known as a mixed convection scheme. When applying the Grell convection scheme, choices for cumulus closure scheme (Grell closure) are 1 for Arakawa–Schubert and 2 for Fritsch–Chappell. The min and max precipitation efficiency coefficients over the ocean for the Grell scheme (Grell precip effic ocean) are either assigned to their default values or divided by 2. When applying the Tiedtke convection scheme, the conversion coefficient from cloud water over land (Tiedtke convers land) is either assigned to its default value 0.0014 or alternative values of 0.0007, 0.00055, or 0.0028. For the Tiedtke convection scheme, the entrainment rate coefficients (Tiedtke entrain) are assigned to their default values, multiplied by 3, or divided by 10.

Table 1.
Table 2.

Summary of the configurations applied in 26 simulations during 2010–12 over the Australia domain. Choices of PBL scheme include 1 for modified Holtslag and 2 for the UW-PBL. The max total cloud-cover fraction for radiation is assigned to either its default value 0.75 or 0.90. Choices of explicit moisture scheme (moist sch) are 1 for SUBEX and 2 for Nogherotto–Tompkins. When applying the Nogherotto–Tompkins moisture scheme, choices for the autoconversion parameter (Tompkins autoconv) are 1 for Pincus and Klein (2000), 2 for Khairoutdinov and Kogan (2000), 3 for Kessler (1969), and 4 for Sundqvist (1978). When applying the Nogherotto–Tompkins moisture scheme and Kessler autoconversion, the autoconversion rate coefficient (Tompkins–Kessler autoconv rate) is assigned to its default value 0.001 or alternative values of 0.0001 or 0.002. When applying the Nogherotto–Tompkins moisture scheme and Sundqvist autoconversion, the autoconversion rate coefficient (Tompkins–Sundqvist autoconv rate) is either assigned to its default value 3.332 × 10−4 or 6.664 × 10−4. Choices for the cumulus convection scheme (conv sch) over land and over ocean are 1 for Kuo, 2 for Grell, 4 for MIT–Emanuel, 5 for Tiedtke, and 6 for Kain–Fritsch; when the scheme differs between land and ocean, this is known as a mixed convection scheme. When applying the Grell convection scheme, choices for cumulus closure scheme (Grell closure) are 1 for Arakawa–Schubert and 2 for Fritsch–Chappell. The min and max precipitation efficiency coefficients over the land for the Grell scheme (Grell precip effic–Land) are assigned to their default values, divided by 2, divided by 1.2, or increased by +0.25. The min and max precipitation efficiency coefficients over the ocean for the Grell scheme (Grell precip effic–Ocean) are assigned to their default values, divided by 4, divided by 3, divided by 2, or increased by +0.25. When applying the Grell convection scheme, the coefficients for min and max shear effect on precipitation efficiency (Grell shear effect) are assigned to their default values, decreased by 0.15, or increased by 0.15. Choices for the ocean flux scheme include 1 for BATS1e (using Monin–Obukhov similarity theory) and 2 for Zeng scheme.

Table 2.

Several coefficients are modified in the investigation of optimal model configuration (Giorgi et al. 2012). The Grell convection scheme applies minimum and maximum precipitation efficiency coefficients, which regulate the precipitation efficiency of convective systems, and coefficients for the minimum and maximum shear effect on precipitation efficiency. The Tiedtke scheme includes conversion coefficients from cloud water to rainwater and entrainment rate coefficients for convection. The Nogherotto–Tompkins moisture scheme uses an autoconversion rate coefficient and permits selection of autoconversion parameters among Kessler (1969), Sundqvist (1978), Khairoutdinov and Kogan (2000), and Pincus and Klein (2000), which regulate the development of cloud water and ice particle aggregates into rain and snow droplets. SUBEX specifies a relative humidity threshold, above which clouds begin to develop. By default, RegCM4 assigns 0.75 as the maximum cloud cover fraction for radiation calculations.

c. Model evaluation statistics

Using metrics of mean bias, spatial correlation, and root-mean-square difference (RMSD), the 47 simulations are evaluated in terms of overland air temperature, overland precipitation, overocean precipitation, as well as domainwide cloud cover fraction for each month during 2011/12, and compared against the 0.5° × 0.5° University of Delaware terrestrial temperature and precipitation gridded dataset (Willmott and Matsuura 1995), 0.25° × 0.25° Tropical Rainfall Measuring Mission (TRMM; Kummerow et al. 1998; Huffman et al. 2007, 2010), version 3B43, and 0.5° × 0.5° Pathfinder Atmospheres–Extended (PATMOS-x; Foster and Heidinger 2013; Heidinger et al. 2014) product. TRMM is analyzed for overocean estimates of precipitation, which are absent in the University of Delaware product [see Fekete et al. (2004) and Liu (2015) for an assessment of overland consistency between these products]. The optimal configurations are then used to produce control simulations for 1960–2013 for the Chinese (CTL_CHINA) and Australian (CTL_AUST) monsoon domains.

d. Ensemble experiment design

An ensemble of RegCM4 experiments is developed, aimed at contrasting vegetation feedbacks between tropical and subtropical monsoon regions and examining land–atmosphere feedback mechanisms. For each month during April–September (AMJJAS) for the Chinese domain and November–April (NDJFMA) for the Australian domain, 30 experiments are produced, each 1 month in duration, in which LAI is increased by 0.5 over either the Chinese (ENSINC_CHINA; 20°–40°N, 105°–123°E) or Australian monsoon region (ENSINC_AUST; 20°–10°S, 120°–150°E). Likewise, 30 experiments are produced in which LAI is decreased by 0.5 (ENSDEC_CHINA and ENSDEC_AUST). LAI over the northern Australian monsoon region during February and over the more heavily vegetated portion of the Chinese monsoon region, north of the Yangtze River, during June exhibits a range of 1.0 and 0.5 m2 m−2, respectively, thereby justifying the choice of ±0.5 m2 m−2 anomalies in the ensemble experiments. Within each grid cell, the imposed LAI anomaly of ±0.5 m2 m−2 is distributed among the present PFTs based on each PFT’s fractional cover (larger modifications in LAI are imposed on the most abundant PFTs). Each ensemble member is initialized using a restart file from a different year from the control runs during 1980–2009. The atmospheric responses to enhanced regional LAI (ENSINC minus ENSDEC) are examined and contrasted between the subtropical Chinese monsoon and tropical Australian monsoon, based on ensemble experiments built off a 30-yr period. Statistically significant (p < 0.05) differences between ENSINC and ENSDEC are identified using Student’s t test.

3. Results

a. Determination of optimal model configuration

In search of the optimal model configuration per domain, RegCM4’s performance is evaluated against the University of Delaware, TRMM, and PATMOS-x products in a series of 21 runs across the Chinese domain and 26 runs across the Australian domain (Figs. 27). For each simulation, 288 performance metrics (4 variables × 3 metrics × 24 individual months) are computed per domain, consisting of mean bias, spatial correlation, and RMSD for each of 24 calendar months during 2011/12 for overland air temperature (vs University of Delaware), overland precipitation (vs University of Delaware), overocean precipitation (vs TRMM), and across-domain cloud-cover fraction (vs PATMOS-x). For both domains, variations in model configuration have the greatest (least) impact on cloud-cover fraction (air temperature), according to the across-run standard deviation in observation–model spatial correlations (Figs. 2 and 3).

Fig. 2.
Fig. 2.

Seasonal cycle of mean bias, spatial correlation, and RMSD in RegCM4-simulated (a)–(c) overland air temperature (°C), (d)–(f) overland precipitation (mm day−1), (g)–(i) overocean precipitation (mm day−1), and (j)–(l) cloud-cover fraction (over land and ocean) for the China domain during 2011/12, compared to the University of Delaware, TRMM, and PATMOS-x products. Results are shown from 21 simulations of 2010–12, with the first year discarded as spinup. The red lines indicate results from the China19 simulation, whose simulation was considered of highest quality and whose configuration was used for the extended 1960–2013 control simulation.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

Fig. 3.
Fig. 3.

As in Fig. 2, but for the Australian domain, with the red line representing the Aust16 simulation.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

Fig. 4.
Fig. 4.

Mean air temperature (°C) from (a),(d),(g),(j) RegCM4 and (b),(e),(h),(k) the University of Delaware observations, along with the (c),(f),(i),(l) mean bias, for (a)–(c) DJF, (d)–(f) MAM, (g)–(i) JJA, and (j)–(l) SON for 1960–2013 across the China domain. The RegCM4 control simulation is produced based on the configuration used in the China19 run.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

Fig. 5.
Fig. 5.

As in Fig. 4, but for the Australian domain, and the RegCM4 control simulation is produced based on the configuration used in the Aust16 run.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

Fig. 6.
Fig. 6.

Mean precipitation (mm day−1) from (a),(d),(g),(j) RegCM4 and (b),(e),(h),(k) the University of Delaware observations, along with the (c),(f),(i),(l) mean bias, for (a)–(c) DJF, (d)–(f) MAM, (g)–(i) JJA, and (j)–(l) SON for 1960–2013 across the China domain. The RegCM4 control simulation is produced based on the configuration used in the China19 run.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

Fig. 7.
Fig. 7.

As in Fig. 6, but for the Australian domain, and the RegCM4 control simulation is produced based on the configuration used in the Aust16 run.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

For the Chinese domain, the worst overall model performance is noted in China8, China3, and China6 and best performance is noted in China18, China14, and China19. Given that China18 and China14 produce sizable annual wet biases on land, while minimal annual overland precipitation biases occur in China19, the optimal model configuration for the Chinese domain is determined to be China19. China19 applies the mixed convection scheme with Tiedtke scheme over land and Grell scheme over ocean, Fritsch–Chappell closure scheme for Grell, SUBEX, Holtslag PBL scheme, increased maximum cloud-cover fraction for radiation, increased relative humidity threshold over land in SUBEX, and reduced entrainment rate coefficients and conversion coefficient from cloud water over land in the Tiedtke scheme (Table 1).

For the Australian domain, Aust5, Aust3, and Aust9 exhibit the worst overall performance, while the best performance is noted in Aust1, Aust21, Aust20, and Aust16. All of these high-performing configurations display minimal annual overland precipitation bias. It is determined that Aust16 contains the optimal model configuration, given its superior performance regarding overland temperature and precipitation. Aust16 applies the Grell convection scheme over land and ocean, Fritsch–Chappell closure scheme for Grell, Nogherotto–Tompkins explicit moisture scheme, Holtslag PBL scheme, Zeng ocean flux scheme, increased maximum cloud-cover fraction for radiation, and Kessler’s autoconversion parameters for the Nogherotto–Tompkins scheme (Table 2). The appendix summarizes the parameterization performance. All subsequent analyses will focus on China19, Aust16, or control runs applying these optimal model configurations.

b. Analysis of model biases under optimal configuration

Domainwide biases in overland temperature, overland precipitation, overocean precipitation, and cloud cover are examined for 2011/12 in the two runs with optimal model configuration, China19 and Aust16, and for 1960–2013 in the extended control runs, CTL_CHINA and CTL_AUST. The Chinese domain is characterized by a cold bias in late spring–early summer in western China and Indochina, wet bias in winter–spring, and dry bias in mid- to late summer (Figs. 2, 4, and 6). The Australian domain displays a summertime cold bias across the coastal regions and Western Australia, wintertime warm bias across the northern portions of Western Australia and Northwest Territory, and late summertime dry bias (Figs. 3, 5, and 7). Air temperature is more accurately simulated across the Australian domain than the Chinese domain (Figs. 2a–c and 3a–c). The temperature bias is substantially lower in the Australian domain than the Chinese domain, with an annual average RMSD between the model and observations of 2.2°C for the Australian domain and 3.4°C for the Chinese domain. The two domains perform comparably in terms of overland precipitation (Figs. 2d–f and 3d–f), with minimal annual precipitation biases. The monsoon terminates too early in both regions, with substantial overland dry biases of −0.99 mm day−1 in September across the Chinese domain and −1.99 mm day−1 in March across the Australian domain. While the Chinese domain (2.6 mm day−1) exhibits a slightly lower annually averaged RMSD for overland precipitation than the Australian domain (3.0 mm day−1), the annually averaged spatial correlation between model and observations is higher for the Australian domain (0.62) than the Chinese domain (0.53). Substantial annual dry biases in overocean precipitation are identified (Figs. 2g–i and 3g–i), consisting of −0.50 and −1.04 mm day−1 in the Chinese and Australian domains, respectively. The annually averaged RMSD in overocean precipitation is lower in the Australian domain (3.9 mm day−1) than the Chinese domain (5.0 mm day−1). Cloud cover is better simulated across the Chinese domain (Figs. 2j–l and 3j–l). Both regions exhibit comparable annual mean biases in cloud-cover fraction of +0.03 in the Chinese domain and −0.02 in the Australian domain. However, the annually averaged spatial correlation is higher in the Chinese domain (0.56) than the Australian domain (0.41), and the annually averaged RMSD is lower in the Chinese domain (0.17) than Australian domain (0.21).

Compared to the full ensemble set, the optimized simulations are characterized by reductions in RMSD across the domains, compared to observations, that are substantial for overland precipitation (−14% for China and −12% for Australia), moderate for air temperature (−7% for China and −5% for Australia) and cloud cover (−9% for China and −2% for Australia), and modest for overocean precipitation (−4% for China and −1% for Australia). Overall, the Chinese simulations benefited more from optimization than the Australian simulations in terms of RMSD. The resulting climatic biases in the optimized RegCM4 simulations are notably smaller than those simulated by CCSM3.5 (Notaro et al. 2011a).

c. Evaluation of ensemble size

Ensemble sizes of 30 members of positive LAI anomalies and 30 members of negative LAI anomalies are sufficient to achieve stable local climatic responses to modified LAI. Here, the select variables for examination are surface albedo, 2-m air temperature, and diurnal temperature range (DTR) during the pre-, mid-, and postmonsoon periods, consisting of April, June, and August for the Chinese monsoon region and November, January, and March for the Australian monsoon region. For the three time periods and two regions, Fig. 8 shows the 10th, 50th, and 90th percentiles of 10 000 random selections of differences (ENSINC − ENSDEC) among ensemble members, as a function of the number of ensemble members ranging from 1 to 30. In general, the number of ensemble members necessary for consistent statistically significant (p < 0.05) differences (rigidly determined for the 10th percentile) in surface albedo is 3, in air temperature the number varies from 5 (for June across China) to 29 (for February across Australia), and in DTR the number varies from 7 (for November across Australia) to 30 (for May across China). The Chinese monsoon region typically does not display a significant local DTR response during June–September (Fig. 8), when substantial reductions in maximum temperature are nearly of similar magnitude to minimum temperature reductions. The largest responses in surface albedo and DTR occur during the premonsoon period and in 2-m air temperature occur in the postmonsoon period, with substantially larger impacts noted for Australia than China.

Fig. 8.
Fig. 8.

Local difference (ENSINC − ENSDEC) in (a),(d),(g) surface albedo (fraction LAI−1), (b),(e),(h) 2-m air temperature (°C LAI−1), and (c),(f),(i) 2-m DTR (°C LAI−1) in (a)–(c) April, (d)–(f) June, and (g)–(i) August across the Chinese monsoon region (blue lines) and (a)–(c) November, (d)–(f) January, and (g)–(i) March across the Australian monsoon region (red lines) between ENSINC and ENSDEC. Results are shown as a function of the number of ensemble members on the x axis, ranging from 1 to 30. White dots indicate the differences between ENSINC and ENSDEC are statistically significant (p < 0.05). Among 10 000 random iterations, results are shown for the 10th, 50th, and 90th percentiles, with a thicker lines used for the 50th percentile.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

d. Climatic responses to modified LAI in monsoon regions

The most distinct climatic impacts of LAI anomalies are simulated during China’s pre-to-midmonsoon period and Australia’s premonsoon period, with less pronounced impacts during the late-to-postmonsoon period (Fig. 9). The Australian monsoon region displays a greater climatic sensitivity to LAI anomalies than that of the Chinese monsoon region. Based on a comparison of ENSINC and ENSDEC, mean LAI is increased by +0.93 m2 m−2 during AMJJAS in the Chinese monsoon region and by +0.88 m2 m−2 during NDJFMA in the Australian monsoon region. The resulting reduction in surface albedo peaks in the premonsoon period by −0.026 LAI−1 in April for China and −0.037 LAI−1 in November for Australia. During the premonsoon period, mean LAI is typically low, so an imposed LAI reduction exposes a significant amount of bare ground in ENSDEC and triggers a substantial albedo change (between vegetation and soil). The increase in wind stress and reduction in 10-m wind speed, which averages −0.19 m s−1 LAI−1 during AMJJAS across the Chinese monsoon region and −0.31 m s−1 LAI−1 during NDJFMA across the Australian monsoon region, likewise peaks during the premonsoon period. A combined increase in transpiration and canopy evaporation and decrease in ground evaporation produce enhanced total ET, by +0.29 mm day−1 LAI−1 for the Chinese monsoon region and +0.56 mm day−1 LAI−1 for the Australian monsoon region during the monsoon evolution, most notably during the early monsoon season. Vegetation-induced reductions in surface albedo, increases in roughness, and enhanced ET trigger radiation, momentum, and moisture feedbacks, respectively, of varying intensity.

Fig. 9.
Fig. 9.

Local responses in (a) LAI (m2 m−2), (b) surface albedo (fraction LAI−1), (c) wind stress (N m−2 LAI−1), (d) 10-m wind speed (m s−1 LAI−1), (e) ET (mm day−1 LAI−1), (f) SHF (W m−2 LAI−1), (g) LHF (W m−2 LAI−1), (h) 2-m mean air temperature (°C LAI−1), (i) 2-m max air temperature (°C LAI−1), (j) 2-m min air temperature (°C LAI−1), (k) DTR (°C LAI−1), (l) ground temperature (°C LAI−1), (m) 2-m specific humidity (g kg−1 LAI−1), (n) PBL height (m LAI−1), (o) vertical motion at sigma level 0.83 (hPa s−1 LAI−1), and (p) precipitation (mm day−1 LAI−1) across the Chinese monsoon region in April–September (blue) and Australian monsoon region in November–April (red) to an LAI increase of 1 m2 m−2, based on ENSINC minus ENSDEC. Green and yellow dots identify statistically significant differences at p < 0.1 and p < 0.05, respectively.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

Greater LAI, based on ENSINC minus ENSDEC, supports a reduced Bowen ratio. The SHF declines by −2.23 and −4.20 W m−2 LAI−1 and LHF increases substantially more by +7.19 and +16.04 W m−2 LAI−1 during AMJJAS for the Chinese monsoon region and NDJFMA for the Australian monsoon region, respectively. The reduction in SHF, because of a greater decline in ground SHF than increase in vegetation SHF, peaks during China’s midmonsoon period and Australia’s pre-to-early-monsoon period, while the greatest increase in LHF occurs during China’s early monsoon period and Australia’s pre-to-early-monsoon period. Averaged across the 6-month period, in response to enhanced ET, 2-m mean air temperature is reduced by −0.76° and −1.00°C LAI−1 for the Chinese and Australian monsoon regions, respectively; this consists of modest reductions in maximum air temperature of −0.54° and −0.48°C LAI−1 and more substantial reductions in minimum air temperature of −0.86° and −1.25°C LAI−1. During the fringe months of the monsoon seasons, the DTR is increased in ENSINC compared to ENSDEC, most notably during the postmonsoon period with +0.42°C LAI−1 in September across the Chinese monsoon region and +0.97°C LAI−1 in April across the Australian monsoon region. Enhanced ET supports higher 2-m specific humidity, particularly during China’s midmonsoon period and Australia’s premonsoon period, on the order of +0.32 and +0.64 g kg−1 LAI−1 across the Chinese and Australian monsoon regions, respectively. Low-level cooling generates a more stable atmosphere, with reduced PBL height particularly during China’s midmonsoon period (−87 m during June–July) and Australia’s early monsoon period (−167 m during December) and anomalous subsidence during China’s premonsoon period (+4.33 × 10−5 hPa s−1 LAI−1 at model sigma level 0.83 in May) and Australia’s early monsoon period (+10.54 × 10−5 hPa s−1 LAI−1 at model sigma level 0.83 in December]. Precipitation is enhanced slightly across the Chinese monsoon region during AMJJAS by +0.11 mm day−1 LAI−1 and more substantially across the Australian monsoon region during NDJFMA by +0.48 mm day−1 LAI−1; the increase in November precipitation by +0.29 mm day−1 LAI−1 across the Australian monsoon region is the only statistically significant (p < 0.05) precipitation change (Fig. 9).

The general expectation with vegetation feedbacks, based on past studies, is that an increase in LAI should support a moisture feedback, with greater ET leading to enhanced moisture recycling and rainfall, low-level cooling, and atmospheric stabilization; a radiation feedback, with lower surface albedo supporting low-level warming, ascending motion, and greater precipitation; and a momentum feedback, with greater roughness leading to low-level convergence, ascending motion, and greater rainfall. RegCM4 suggests that the moisture feedback dominates over the other two mechanisms across both monsoon regions, especially given that the simulated low-level cooling and anomalous subsidence are counter to expectations from the radiation and momentum feedbacks.

The climatic responses to LAI anomalies across the Chinese and Australian monsoon regions are clearly heterogeneous, reflecting the background biome distribution. The linear responses to elevated LAI, computed as (ENSINC − ENSDEC)/2, are examined for the full monsoon months of June for China and January for Australia in Figs. 10 and 11. The heterogeneity in the response fields is greatest in the early-to-midmonsoon period (viz., June for China and January–February for Australia), based on the ratio of the responses (of the variables shown in Figs. 10 and 11) to the north and south of the Yangtze River for China and the ratio of the responses between the shrublands and grasslands of northern Australia. Figures 10 and 11 therefore focus on June for China and January for Australia. For both regions, higher LAI supports increases in DTR, wind stress, ET, and 2-m specific humidity and decreases in surface albedo and 10-m wind speed. The most pronounced, statistically significant responses are often restricted to the croplands to the north of China’s Yangtze River [consistent with Zheng et al. (2002) and Chen et al. (2004)] and to the Australian shrublands, farther inland than the grasslands bordering the Arafura Sea. The Chinese croplands and Australian shrublands are characterized by relatively low–moderate mean LAI, so imposed LAI reductions in ENSDEC generate a conversion to substantial bare ground area and thus a pronounced climatic impact. The spatial distribution of the linear response in rainfall is not shown in Figs. 10 and 11, as it is rather noisy and much less significant than the area-average result.

Fig. 10.
Fig. 10.

Linear response in (a) surface albedo (fraction), (b) DTR (°C), (c) wind stress (N m−2), (d) 10-m wind speed (m s−1), (e) ET (mm day−1), and (f) 2-m specific humidity (g kg−1) to an increase in LAI across the Chinese monsoon region during June, based on (ENSINC − ENSDEC)/2. Only statistically significant differences (p < 0.05) are shown.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

Fig. 11.
Fig. 11.

As in Fig. 10, but for the northern Australian monsoon region during January.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

The linear response in low-level (10 m) wind to elevated LAI is examined during the monsoon evolution. While China’s low-level wind response is minimal and insignificant, northern Australia displays an anomalous reduction in onshore flow from the Arafura Sea during the monsoon fringe months of November–December and April, reflecting anomalous low-level divergence (Fig. 12). In Australia, greater ET generates low-level cooling, atmospheric stabilization, and anomalous subsidence (as seen in Fig. 12) according to the moisture mechanism, dominating over the roughness mechanism, which should favor anomalous low-level convergence and ascent but is absent in the monthly mean (as absent in Fig. 12).

Fig. 12.
Fig. 12.

Linear response in 10-m wind vectors (m s−1) during (a) November, (b) December, (c) January, (d) February, (e) March, and (f) April to an increase in LAI across the northern Australian monsoon region, based on (ENSINC − ENSDEC)/2. Only statistically significant differences (p < 0.05) are shown. The area of modified LAI is identified with a red box.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

The seasonal cycle of the vertical profile of the linear responses in specific humidity, air temperature, vertical motion, and cloud-cover fraction is investigated for the Chinese and Australian monsoon regions (Fig. 13). The impacts of enhanced LAI on air temperature are deeper and more significant for China and on specific humidity, vertical motion, and cloud-cover fraction are greater for Australia. These impacts generally are most notable during the pre- and early monsoon season for Australia and the midmonsoon season for China. Greater LAI leads to increased specific humidity in the lower PBL during Australia’s premonsoon and China’s midmonsoon, cooling in the PBL during Australia’s premonsoon and China’s midmonsoon, anomalous lower-to-midtropospheric subsidence during both Australia’s and China’s pre- to early monsoon, and increased low-level cloud cover and decreased midlevel cloud cover (more stabilized atmosphere, with less deep convection) during Australia’s midmonsoon and China’s mid- to late monsoon.

Fig. 13.
Fig. 13.

Vertical profile (model sigma level on y axis) of the linear response in (a),(b) specific humidity (g kg−1), (c),(d) air temperature (°C), (e),(f) vertical motion (105 hPa s−1), and (g),(h) cloud-cover fraction to an increase in LAI across the (a),(c),(e),(g) Chinese monsoon region during April–September or (b),(d),(f),(h) Australian monsoon region during November–April, based on (ENSINC − ENSDEC)/2. Statistically significant differences (p < 0.05) are dotted.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

e. Exploring vegetation feedback mechanisms through across-ensemble-member spread

A deeper understanding of vegetation feedbacks on the monsoon regions is attained through an analysis of the across-ensemble-member spread, beyond simply computing the mean differences between ENSINC and ENSDEC. Scatterplots are produced of the differences in both local ET and precipitation in ENSINC compared to ENSDEC during AMJJAS for the Chinese monsoon and NDJFMA for the Australian monsoon, with one dot for each of 30 ensemble-member pairs (Fig. 14). While an increase in LAI (ENSINC − ENSDEC) produces greater ET in every month and both regions, across all ensemble members, the sign of the precipitation response is less consistent among ensemble members. The correlations between the responses in ET and precipitation are generally largest during the monsoons’ fringe months, when ocean–atmosphere interactions impose a reduced control on rainfall patterns and permit more pronounced land–atmosphere feedbacks. The mean correlations between ET and precipitation responses among the six months are substantially greater for the Australian monsoon (0.66, ranging from 0.22 in February to 0.85 in March) than the Chinese monsoon (0.13, ranging from −0.11 in June to 0.49 in September), with statistically significant (p < 0.05) correlations found in five months for Australia and only one month for China. These results suggest a dominant moisture feedback from vegetation across the Australian monsoon region, active during the entire monsoon’s evolution, as opposed to the Chinese monsoon region, where a weak moisture feedback is active only in the fringe months of May and September. Likewise, the mean correlations between ET and PBL height responses among the six months are much greater for the Australian monsoon (−0.79) than the Chinese monsoon (−0.38) as enhanced LAI produces greater ET, low-level cooling, a more stable atmosphere, and shallower PBL (not shown). Further scatterplots between precipitation responses and the responses in either 10-m wind speed or surface albedo suggest a potential contribution of the roughness mechanism for China in May (correlation of −0.60) and no significant contribution of the radiation/albedo mechanism in either region (not shown).

Fig. 14.
Fig. 14.

Scatterplots of the difference (ENSINC − ENSDEC) in monthly ET (x axis; mm day−1) vs monthly precipitation (y axis; mm day−1) in ENSINC compared to ENSDEC for the (a)–(f) Chinese monsoon region during April–September and the (g)–(l) Australian monsoon region during November–April. Each red dot represents one of 30 ensemble members. Correlation coefficients are provided in each panel label (statistically significant correlations, at p < 0.05, are in boldface italics). Blue shading indicates increases in both precipitation and ET.

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

The ratio of the mean response in precipitation to the mean response in ET is most pronounced during the mid-to-late-monsoon season, when large-scale ascending motion supports the conversion of transpired moisture into clouds and rainfall. This ratio is, on average, over twice as strong for the Australian monsoon than the China monsoon, especially during the midmonsoon season (e.g., 0.60 for China in July vs 1.50 for Australia in February, based on Figs. 14d,j). For example, the ratio of 1.50 for Australia in February indicates that the response in precipitation exceeds the response in ET. This is further evidence of the critical contribution of moisture recycling across the Australian monsoon region, with enhanced ET driving much of the rainfall response.

For the evolution of both the Chinese monsoon season across AMJJAS and Australian monsoon season during NDJFMA, scatterplots are produced of the differences in both local lower-to-midtropospheric (model sigma level 0.825) vertical motion and precipitation in ENSINC compared to ENSDEC (Fig. 15). The mean correlations between vertical motion and precipitation responses among the six months are more strongly negative for the Chinese monsoon (−0.53, ranging from −0.78 in May to −0.35 in April) than the Australian monsoon (+0.16, varying in sign by month, ranging from −0.50 in February to +0.63 in November), with statistically significant (p < 0.05) correlations found in five months for China and four months for Australia. This provides evidence of a roughness feedback active during the entire evolution of the Chinese monsoon, most notably during the premonsoon month of May, which is largely absent over Australia. Nonetheless, given the clear response of subsidence among the ensemble members, it is apparent that the moisture feedback, by which greater LAI supports enhanced ET and precipitation despite anomalous subsidence, dominates over the roughness feedback, by which greater LAI favors increased ascent and precipitation.

Fig. 15.
Fig. 15.

Scatterplots of the difference (ENSINC − ENSDEC) in monthly vertical motion at sigma level 0.825 (x axis; 105 hPa s−1) vs monthly precipitation (y axis; mm day−1) in ENSINC compared to ENSDEC for the (a)–(f) Chinese monsoon region during April–September and the (g)–(l) Australian monsoon region during November–April. Each red dot represents one of 30 ensemble members. Correlation coefficients are provided in each panel label (statistically significant correlations, at p < 0.05, are in boldface italics).

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

f. Temporal redistribution of rainfall

The impact of LAI anomalies on the temporal distribution of rainfall across the monsoon regions is examined by computing the percent difference in the frequency of 6-hourly precipitation totals for bins of 0, 0.1–2, 2.1–4 mm, and so on, due to greater LAI, according to (ENSINC − ENSDEC)/(ENSDEC × 100) (Fig. 16). The rainfall redistribution is minor for the Chinese monsoon region, including a 5% reduction in the frequency of dry periods when LAI is increased. However, the probability density function (PDF) of subdaily rainfall across the Australian monsoon region is notably sensitive to LAI anomalies during NDJFMA. A comparison between ENSINC and ENSDEC indicates that enhanced LAI in northern Australia supports a 12% reduction in the frequency of dry periods (notable signal in afternoon large-scale precipitation), a greater frequency of drizzle and light–moderate rainfall periods with ≤34 mm in 6 h (notable signal in evening convective precipitation), and a modest decline in the rare occurrence of extremely wet periods with ≥38 mm in 6 h (notable signal in afternoon convective precipitation). Positive LAI anomalies reduce the frequency of periods with minimal ET and extremely low specific humidity, thereby reducing the occurrence of nonprecipitating periods.

Fig. 16.
Fig. 16.

Percent difference in the frequency of 6-hourly precipitation totals for bins of 0, 0.1–2, 2.1–4, …, 50.1–52 mm, across the (a) Chinese monsoon region during April–September and the (b) northern Australian monsoon region during November–April, due to an increase in regional LAI. Percent difference is computed as (ENSINC − ENSDEC)/(ENSDEC × 100).

Citation: Journal of Climate 30, 5; 10.1175/JCLI-D-16-0669.1

4. Summary and discussion

The hypothesis by Notaro et al. (2011a), that subtropical versus tropical monsoon regions exhibit unique responses to vegetation feedbacks, is investigated. That study concluded that a reduction in LAI led to an earlier subtropical Chinese monsoon in contrast to a delayed, weaker tropical Australian monsoon. The study applied the coarse-resolution fully coupled CCSM3.5, which unfortunately exhibited substantial biases in regional climate and LAI across the monsoon regions, with excessive forest cover across China resulting from the absence of land use in the model. These biases obfuscate the reliability of the findings on land–atmosphere interactions (Xu et al. 2014) and resulting hypothesis.

In response to these concerns, the present study applies a version of RegCM4 coupled to CLM with land use that applies an interannually varying reconstructed LAI dataset as boundary conditions. RegCM4 is run at 40-km grid spacing for China and Australia. The RCM matches the observed dominance of cropland, grassland, and evergreen forest in southern China and of grassland and deciduous shrubland in northern Australia. Improvements beyond the CCSM-based study of Notaro et al. (2011a) include reductions in biases in regional climate, LAI, and forest cover; an accurate representation of biome distribution; higher spatial resolution; and the incorporation of land use. To determine optimal model configurations per domain, 21 simulations of the Chinese domain and 26 simulations of the Australian domain are produced, differing in terms of PBL scheme, explicit moisture scheme, convection scheme, cumulus closure scheme, and numerous coefficients. The 47 simulations are evaluated against observations in terms of overland air temperature, overland precipitation, overocean precipitation, and domainwide cloud-cover fraction. The optimal configurations are then used to produce control simulations for 1960–2013. In general, air temperature is better simulated in the Australian domain, and cloud cover is better simulated in the China domain. The Chinese domain is characterized by a cold bias in late spring–early summer, wet bias in winter–spring, and dry bias in mid- to late summer, and the Australian domain is characterized by a summertime cold bias, wintertime warm bias, and late summertime dry bias. The present study aims to better represent vegetation–atmosphere interactions across the monsoon regions by applying a high-resolution RCM and minimizing its biases in simulated LAI, precipitation, and air temperature for each domain. An alternative approach, requiring further investigation, is to compare simulated feedbacks between the two regions using identical model configurations and parameterizations, although such an approach will yield much greater climatic biases.

Simulated climatic biases are compared with past RegCM studies across China and Australia, which sometimes reach inconsistent conclusions regarding recommended model configurations. Using RegCM3–BATS, Song et al. (2008) produced a year-round wet bias of approximately +0.4 mm day−1 across Australia, including excessive summer monsoonal rainfall. The present study applies RegCM4–CLM3.5, and in the optimal configuration simulation of Aust16, minimal annual precipitation bias occurs, and, in contrast, insufficient summer monsoonal rainfall is generated. The China simulation by Liu et al. (2014), applying RegCM4–CLM with the MIT–Emanuel convection scheme, generates a similar spatial pattern of precipitation biases to the present study’s optimal configuration run China19, while the substantial annual wet bias that characterizes the run from Liu et al. (2014) is eliminated in China19. Gao et al. (2016) also recommended the use of the MIT–Emanuel scheme in RegCM4–CLM, arguing that it outperforms the Grell, Tiedtke, and mixed convection schemes in terms of temperature and precipitation biases. They argued against the use of the Tiedtke scheme because of a generated summertime warm bias, while the present study concludes that the Tiedtke scheme over land performs the best and actually produces a cold bias. The spatial correlation between observed and simulated winter precipitation patterns across China is substantially higher in the China19 simulation compared to all of the runs produced by Gao et al. (2016). Here, the benefits of exploring different PBL schemes, explicit moisture schemes, and parameterization coefficients are demonstrated, beyond the common approach of many studies to only vary the choice of convective parameterization. We hope this more rigorous parameterization sensitivity testing can serve as guidance for future work over these two regions.

A RegCM4 ensemble is developed to contrast vegetation feedbacks between tropical and subtropical monsoon regions. For each month during AMJJAS for the Chinese domain and NDJFMA for the Australian domain, 30 experiments are produced, each 1 month in duration, in which LAI is increased by 0.5 over either the Chinese or Australian monsoon region only and 30 experiments are produced in which LAI was decreased by 0.5. In general for both monsoon regions, greater LAI supports reductions in surface albedo, air temperature within the PBL, low-level wind speed, PBL height, lower-to-midtropospheric ascending motion, and midlevel clouds and increases in DTR, wind stress, ET, specific humidity within the PBL, low-level clouds, and the probability of precipitation. In response to greater LAI, mean rainfall is enhanced in Australia’s pre-to-midmonsoon season but does not significantly change over the Chinese monsoon region. Likewise, significant anomalous subsidence is generated during the entire pre- and full monsoon season across northern Australia yet restricted to the early monsoon month of June for southern China. Modified LAI leads to dramatic changes in the temporal distribution and intensity of rainfall events for the Australian monsoon only, with greater LAI favoring fewer dry days, more light–moderate rainfall days, and fewer heavy rainfall days. The monsoon regions’ responses in surface albedo, DTR, wind stress, ET, and low-level wind speed and specific humidity exhibit clear spatial heterogeneity based on biome distribution, with amplified impacts across China’s croplands and Australia’s shrublands.

The present study is largely consistent with the findings of Notaro et al. (2011a) for the Australian monsoon region, with both studies emphasizing a moisture–hydrologic feedback from Australian vegetation. Note that the ensembles of Notaro et al. (2011a) imposed a reduction in annual total vegetation cover fraction (and thus LAI) over the monsoon regions, rather than single-month increases or decreases in LAI as in the present study’s ensembles, but for the purpose of comparison here, discussion will focus on the impacts of enhanced LAI between the two studies. Both studies indicate that an increase in LAI across northern Australia results in enhanced ET and LHF (although of a much greater magnitude in the present study), exceeding the change in SHF; lower air temperatures in spring–summer; and greater springtime precipitation. The studies disagree in terms of the dynamic response to elevated LAI, with anomalous ascent and greater low-level onshore flow from the Arafura Sea produced by CCSM3.5 versus anomalous descent and reduced low-level onshore flow produced by RegCM4. The roughness feedback appears to contribute more substantially in CCSM3.5 than RegCM4. For example, for a given increase in LAI, the increase in springtime–summertime surface wind stress is roughly 17 (4) times greater in CCSM3.5 than RegCM4 for the Australian (Chinese) monsoon region, perhaps resulting from excessive woody vegetation cover in CCSM3.5.

The atmospheric responses to modified LAI over the Chinese monsoon region are largely inconsistent between the present study and that of Notaro et al. (2011a). Greater LAI supports modest increases in both SHF and LHF in CCSM3.5 versus a modest decrease in SHF and much greater increase in LHF in RegCM4. The RCM generates roughly 10 times the increase in ET than that of the GCM. While both models produce low-level cooling in response to elevated LAI, a statistically significant reduction in springtime precipitation is produced over China in CCSM3.5 with no significant changes in RegCM4. CCSM3.5 suggests that enhanced LAI delays the Chinese monsoon, with anomalous descent in May and ascent in September, while RegCM4 consistently produces anomalous subsidence throughout spring–summer. The spatial heterogeneity of the atmospheric responses in RegCM4 (e.g., greatest impacts across the croplands to the north of the Yangtze River) is much more pronounced than those found in the coarse CCSM3.5, as the RCM better represents the complex spatial distribution of observed biomes. It is suggested that the atmospheric responses to modified LAI are inconsistent between the present study and that of Notaro et al. (2011a) because of CCSM3.5’s biases in simulated vegetation. The fully coupled CCSM3.5 with dynamic vegetation simulates an extremely excessive annual mean LAI across both monsoon regions, close to 6 m2 m−2, far above observed and RegCM4 as applied here, and, because of the lack of land use, simulates an excessive 87% forest cover across China (lacking croplands to the north of the Yangtze River as observed and in RegCM4). The excessive tree LAI in CCSM3.5 supports an exaggerated roughness feedback to imposed vegetation changes. Likewise, because of the excessive mean LAI in CCSM3.5, the imposed reduction in vegetation cover transitions the monsoon regions from heavily vegetated to moderately vegetated, as opposed to RegCM4, which produces moderate vegetation cover in ENSINC and the control runs and sparse vegetation in ENSDEC. As a consequence, a greater change in surface albedo is induced in RegCM4 than CCSM3.5, although it is ultimately dominated by the moisture feedback. The present study, through a more accurate representation of vegetation type and abundance, partly challenges the hypothesis from the CCSM-based study of Notaro et al. (2011a) that vegetation feedbacks in tropical and subtropical monsoon regions produce contrasting climatic responses (e.g., reduced vegetation cover led to a delayed, weakened Australian monsoon vs an earlier Chinese monsoon in CCSM3.5), although the two studies consistently agree that the Australian monsoon is clearly more sensitive to land surface perturbations than the Chinese monsoon. The current investigation demonstrates the need to accurately represent land surface characteristics and associated feedbacks in modeling studies of the global monsoon systems.

Acknowledgments

The authors acknowledge funding from the National Science Foundation Climate and Large Scale Dynamics program (1343904) and computational resources from the National Center for Atmospheric Research. The reconstructed LAI dataset was provided by Reto Stoeckli. Suggestions by three anonymous reviewers were greatly appreciated.

APPENDIX

Evaluation of Parameterizations

Several conclusions are made regarding the performance of select parameterizations across the Chinese and Australian monsoon regions, which may guide future modeling efforts. The Tiedtke convection scheme best represents overland precipitation across China but generates a large annual wet bias across Australia. The Grell scheme consistently outperforms other options in terms of overocean precipitation. Large overocean dry biases are produced by the MIT–Emanuel and Kain–Fristch schemes in the Chinese domain, while an excessive annual overocean wet bias results from the Tiedtke scheme in the Australian domain. Within the Grell convection scheme, the Fritsch–Chappel closure scheme results in a reduced ocean dry bias compared to the Arakawa–Schubert scheme. For the Chinese domain, decreasing the overland conversion coefficients from cloud water to rainwater in the Tiedtke scheme reduces the annual wet bias. A lower entrainment rate coefficient for convection in the Tiedtke scheme helps reduce the RMSD in overland precipitation and the negative cloud cover bias in the Chinese domain. While the Nogherotto–Tompkins moisture scheme exaggerates the cold bias and supports a large overland wet bias in the Chinese domain, it produces a smaller overland and overocean precipitation bias than SUBEX in the Australian domain. Raising the SUBEX relative humidity threshold for cloud formation over land in the Chinese domain reduces cold biases. Use of the Sundquist autoconversion parameters for the Nogherotto–Tompkins moisture scheme causes pronounced negative cloud cover biases in the Australian domain. The UW-PBL scheme exaggerates the cold bias (O’Brien et al. 2012) in both domains. The Zeng ocean flux scheme generates a lower RMSD in overocean precipitation than does BATS across the Australian domain. Increasing the maximum cloud-cover fraction partly addresses the simulated cloud-cover deficiency.

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