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  • View in gallery

    Flowchart of how O3 damage is applied to both photosynthesis and stomatal conductance in the CLM O3. Net photosynthesis (An) is calculated using the Farquhar model, where Ac is RuBisCO-limited photosynthesis, Aj is RuBP-limited photosynthesis, Ap is product-limited photosynthesis, and Rd is dark respiration. Photosynthesis is then modified for O3 damage. Stomatal conductance (gs) is calculated using the Ball–Berry model, where b is the minimum stomatal conductance when An ≤ 0, m is the Ball–Berry slope of the conductance–photosynthesis relationship, hs is the fractional humidity at the leaf surface, and cs is the CO2 concentration at the leaf surface. The stomatal conductance calculation uses net photosynthesis (An; no O3 damage) and is then modified for O3 damage. Transpiration is calculated from the O3-damaged stomatal conductance value based on the vapor pressure deficit (VPD).

  • View in gallery

    (a) Growing season mean hourly O3 concentrations from 2002 to 2009 used in the CLM simulations and (b) growing season cumulative O3 uptake averaged over 20 yr.

  • View in gallery

    Simulated 20-yr average (a) gross primary productivity, (c) transpiration, and (e) water-use efficiency after exposure to O3 and percent change from control in (b) GPP, (d) transpiration, and (f) water-use efficiency due to O3 exposure.

  • View in gallery

    Simulated 20-yr average (a) latent heat flux and (c) sensible heat flux after exposure to O3 and percent change from control in (b) latent heat flux and (d) sensible heat flux due to O3 exposure.

  • View in gallery

    (a) Simulated 20-yr average runoff after exposure to O3 and (b) percent change from control in runoff due to O3 exposure.

  • View in gallery

    (a) Zonal GPP and (b) latent heat flux averaged over 20 yr in CLM simulations with and without simulated O3 exposure compared to FLUXNET data averaged from 1982 through 2004 (presented in Bonan et al. 2011, 2012).

  • View in gallery

    Correlations between simulated GPP and FLUXNET GPP for all sensitivity simulations. Sensitivity simulations are color coded, with each point representing one grid cell averaged over 20 yr and lines representing the linear regression. Correlation values range from r = 0.81 (0 threshold) to r = 0.84 (CLM O3, high and low vulnerabilities, single plant type, and fixed decrease), with the control, 1.6 threshold, and 5 threshold sensitivity simulations falling in between (r = 0.83 for all).

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The Influence of Chronic Ozone Exposure on Global Carbon and Water Cycles

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  • 1 Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, and National Center for Atmospheric Research,* Boulder, Colorado
  • | 2 National Center for Atmospheric Research,* Boulder, Colorado
  • | 3 Department of Biological and Environmental Engineering, Cornell University, Ithaca, New York
  • | 4 Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York
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Abstract

Ozone (O3) is a phytotoxic greenhouse gas that has increased more than threefold at Earth’s surface from preindustrial values. In addition to directly increasing radiative forcing as a greenhouse gas, O3 indirectly impacts climate through altering the plant processes of photosynthesis and transpiration. While global estimates of gross primary productivity (GPP) have incorporated the effects of O3, few studies have explicitly determined the independent effects of O3 on transpiration. In this study, the authors include effects of O3 on photosynthesis and stomatal conductance from a recent literature review to determine the impact on GPP and transpiration and highlight uncertainty in modeling plant responses to O3. Using the Community Land Model, the authors estimate that present-day O3 exposure reduces GPP and transpiration globally by 8%–12% and 2%–2.4%, respectively. The largest reductions were in midlatitudes, with GPP decreasing up to 20% in the eastern United States, Europe, and Southeast Asia and transpiration reductions of up to 15% in the same regions. Larger reductions in GPP compared to transpiration decreased water-use efficiency 5%–10% in the eastern United States, Southeast Asia, Europe, and central Africa; increased surface runoff more than 15% in eastern North America; and altered patterns of energy fluxes in the tropics, high latitudes, and eastern North America. Future climate predictions will be improved if plant responses to O3 are incorporated into models such that stomatal conductance is modified independently of photosynthesis and the effects on transpiration are explicitly considered in surface energy budgets. Improvements will help inform regional decisions for managing changes in hydrology and surface temperatures in response to O3 pollution.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: D. Lombardozzi, National Center for Atmospheric Research, Climate and Global Dynamics, P.O. Box 3000, Boulder, CO 80307. E-mail: danicalombardozzi@gmail.com

Abstract

Ozone (O3) is a phytotoxic greenhouse gas that has increased more than threefold at Earth’s surface from preindustrial values. In addition to directly increasing radiative forcing as a greenhouse gas, O3 indirectly impacts climate through altering the plant processes of photosynthesis and transpiration. While global estimates of gross primary productivity (GPP) have incorporated the effects of O3, few studies have explicitly determined the independent effects of O3 on transpiration. In this study, the authors include effects of O3 on photosynthesis and stomatal conductance from a recent literature review to determine the impact on GPP and transpiration and highlight uncertainty in modeling plant responses to O3. Using the Community Land Model, the authors estimate that present-day O3 exposure reduces GPP and transpiration globally by 8%–12% and 2%–2.4%, respectively. The largest reductions were in midlatitudes, with GPP decreasing up to 20% in the eastern United States, Europe, and Southeast Asia and transpiration reductions of up to 15% in the same regions. Larger reductions in GPP compared to transpiration decreased water-use efficiency 5%–10% in the eastern United States, Southeast Asia, Europe, and central Africa; increased surface runoff more than 15% in eastern North America; and altered patterns of energy fluxes in the tropics, high latitudes, and eastern North America. Future climate predictions will be improved if plant responses to O3 are incorporated into models such that stomatal conductance is modified independently of photosynthesis and the effects on transpiration are explicitly considered in surface energy budgets. Improvements will help inform regional decisions for managing changes in hydrology and surface temperatures in response to O3 pollution.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: D. Lombardozzi, National Center for Atmospheric Research, Climate and Global Dynamics, P.O. Box 3000, Boulder, CO 80307. E-mail: danicalombardozzi@gmail.com

1. Introduction

The world’s vegetation regulates climate in part by controlling carbon and water fluxes between the biosphere and the atmosphere (Bonan 2008), with terrestrial ecosystems sequestering an estimated 2.6 petagrams (Pg) of carbon annually (Le Quéré et al. 2013). Carbon and water fluxes through plants are linked by the common control of stomatal conductance, which regulates the rate of carbon dioxide (CO2) entering and water exiting the leaf (Jones 1998). Land models that incorporate physiology often couple carbon and water fluxes by calculating stomatal conductance, the primary plant control over water loss (transpiration), directly from rates of photosynthesis (carbon gain; Collatz et al. 1991). While this method of predicting carbon and water fluxes is accurate under optimal environmental conditions, stomatal conductance and transpiration are not accurately predicted under the oxidative stress caused by chronic ozone (O3) exposure (Lombardozzi et al. 2012a,b) because O3 can cause photosynthesis and stomatal conductance to become decoupled (Mikkelsen 1995; Tjoelker et al. 1995; Maurer et al. 1997; Soldatini et al. 1998; Novak et al. 2005; Paoletti and Grulke 2005; Calatayud et al. 2007; Francini et al. 2007; Lombardozzi et al. 2012b, 2013).

Ozone is a powerful oxidant that damages the physiology of most plants (Lombardozzi et al. 2013), with the potential to alter global climate (Sitch et al. 2007) and hydrology (McLaughlin et al. 2007; Felzer et al. 2009). Photosynthesis consistently decreases in response to chronic O3 exposure because it oxidizes cellular membranes, enzymes, and chlorophyll (Heagle et al. 1996; Ojanperä et al. 1998; Farage and Long 1999; Bortier et al. 2000; Sharma et al. 2003; Fiscus et al. 2005; Herbinger et al. 2007). Conductance, on average, also decreases in response to O3, though with a smaller magnitude than photosynthesis (Lombardozzi et al. 2013). Ozone oxidizes components of the stomata, causing changes in guard cell turgor pressure and disrupting signaling pathways, both of which lead to increasing internal leaf CO2 concentration (Freer-Smith and Dobson 1989; Maier-Maercker and Koch 1991; Hassan et al. 1994; Torsethaugen et al. 1999; Manes et al. 2001; Calatayud et al. 2007; Herbinger et al. 2007; Mills et al. 2009). With chronic exposure, O3 can also cause stomata to respond sluggishly to environmental cues, increasing stomatal conductance and rates of transpiration (McLaughlin et al. 2007; Paoletti and Grulke 2010). The difference in measured rates of change in photosynthetic and stomatal responses to O3 suggests that models that assume equivalent rates of change overpredict stomatal closure and underestimate transpiration losses in response to O3 (Lombardozzi et al. 2012a).

Accurate predictions of stomatal responses, which regulate transpiration, are critical to understanding changes in energy and water fluxes and are therefore important in predicting regional drought and flooding potential, as well as changes in surface temperatures (e.g., Sellers et al. 1996). For example, simulations predicting the influence of elevated CO2 suggest that increases in runoff occur because of a suppression of transpiration when stomata close (Gedney et al. 2006; Betts et al. 2007). Despite the importance of O3 in altering stomatal conductance, few studies document the resulting hydrologic and surface energy effects, and those that do are limited by an inability to account for the decoupling of photosynthesis and transpiration (e.g., Felzer et al. 2009). While transpiration, runoff, and surface energy partitioning are expected to change in response to chronic O3 exposure, the magnitude of these responses is not documented.

The primary objective of this work is to predict global changes in transpiration, gross primary productivity (GPP), and runoff in response to chronic O3 exposure. We use the parameterization created by Lombardozzi et al. (2012a) that independently alters photosynthesis and stomatal conductance in the Community Land Model, version 4.5 (CLM4.5SP) and expand it to include responses of multiple plant types to O3 as described in a recent literature review (Lombardozzi et al. 2013) and mean hourly ozone data from 2002 to 2009. Parameterizing the CLM4.5SP in this manner is the first time a global-scale model uses hourly O3 values that can allow for variation in diurnal responses to chronic O3 exposure resulting in more accurate estimations of O3 uptake, and it is novel in its method of modifying conductance independently of photosynthesis using a mechanistic approach that more realistically mimics physiological responses to chronic O3 exposure. We additionally test the sensitivity of the O3 response functions and the O3 threshold values that are used to modify plant responses to O3.

2. Methods

a. Model description

This study simulated global biosphere responses to O3 using the CLM4.5SP, originally described by Lawrence et al. (2011) with technical details updated by Oleson et al. (2013). The CLM4.5SP uses coupled Farquhar photosynthesis and Ball–Berry stomatal conductance models (Bonan et al. 2011) to simulate plant physiology. The CLM4.5SP uses the original CLM4SP with updated physiology, described by Bonan et al. (2011, 2012), which corrected the two-stream radiation transfer to sunlit and shaded leaves; updated the photosynthesis code to account for colimitation of RuBisCO-, light-, and export-limited photosynthesis; used an exponential canopy scaling gradient based on profiles of leaf nitrogen; and used photosynthetic capacity (Vcmax) from a comprehensive leaf trait database named TRY (Kattge et al. 2009). The CLM4.5SP was run in offline mode forced with Climate Research Unit (CRU)–National Centers for Environmental Prediction (NCEP) climate forcing data (downloaded from http://dods.ipsl.jussieu.fr/igcmg/IGCM/BC/OOL/OL/CRU-NCEP/), which combines the CRU time series 3.1 (TS3.1) 0.5° × 0.5° monthly climatology with the NCEP–National Center for Atmospheric Research (NCAR) reanalysis 2.5° × 2.5° 6-hourly climatology to generate a historical atmospheric dataset that includes observed precipitation, temperature, downward solar radiation, surface wind speed, specific humidity, and air pressure from 1901 through 2010. The simulations did not include the influences of land use change, nitrogen deposition, or changing CO2 concentrations. Average hourly O3 values used in the simulations were generated from the Community Atmosphere Model (CAM) simulations (see Lamarque et al. 2012) for present-day (an average of 2002 through 2009) emissions scenario and included the impact of biomass burning. Simulations were run for a total of 25 yr, with the first 5 yr being discarded in the analyses to allow for stabilization of accumulated O3 damage.

b. O3 effects

The updated CLM4.5SP was modified to incorporate the effects of O3 on photosynthesis and stomatal conductance using methods similar to those described by Lombardozzi et al. (2012a), in which photosynthesis and stomatal conductance were directly modified based on responses to the cumulative uptake of O3 (CUO) calculated in the model (Fig. 1). Because of its dependence on stomatal conductance, CUO only accumulates during the growing season (defined as leaf area index >0.5; Lombardozzi et al. 2012a) so that O3 concentration values [O3] are inconsequential during other times of year. The method used here modified photosynthesis directly after it was calculated in the model, rather than modifying Vcmax as in Lombardozzi et al. (2012a),
eq1
where FpO3 is the O3 damage factor multiplied by photosynthesis and ap and bp are slope and intercept photosynthetic constants defined in Table 1. Similar to methods used by Lombardozzi et al. (2012a), stomatal conductance was initially calculated based on optimal (non–O3 affected) photosynthesis rates using the Ball–Berry formulation in CLM4.5SP (see Bonan et al. 2011). After optimal rates of stomatal conductance were calculated, they were directly modified for O3 damage,
eq2
where FcO3 is the O3 damage factor multiplied by stomatal conductance and ac and bc are slope and intercept constants for stomatal conductance defined in Table 1. Modifying stomatal conductance in this manner separates the conductance O3 response from the photosynthetic O3 response and does not alter the photosynthesis calculations in the model. The photosynthetic and stomatal O3 damage equations were applied regardless of the value of the slope so that, in cases where the slope was 0, the accrued O3 damage was equal to the intercept.
Fig. 1.
Fig. 1.

Flowchart of how O3 damage is applied to both photosynthesis and stomatal conductance in the CLM O3. Net photosynthesis (An) is calculated using the Farquhar model, where Ac is RuBisCO-limited photosynthesis, Aj is RuBP-limited photosynthesis, Ap is product-limited photosynthesis, and Rd is dark respiration. Photosynthesis is then modified for O3 damage. Stomatal conductance (gs) is calculated using the Ball–Berry model, where b is the minimum stomatal conductance when An ≤ 0, m is the Ball–Berry slope of the conductance–photosynthesis relationship, hs is the fractional humidity at the leaf surface, and cs is the CO2 concentration at the leaf surface. The stomatal conductance calculation uses net photosynthesis (An; no O3 damage) and is then modified for O3 damage. Transpiration is calculated from the O3-damaged stomatal conductance value based on the vapor pressure deficit (VPD).

Citation: Journal of Climate 28, 1; 10.1175/JCLI-D-14-00223.1

Table 1.

Values used to parameterize plant functional types in CLM. Slopes (per mmol m−2) and intercepts (unitless) are based on values presented in Lombardozzi et al. (2013).

Table 1.

These simulations used plant functional type (PFT)-specific O3 response equations presented in Lombardozzi et al. (2013) based on a literature review rather than the single response equation used in Lombardozzi et al. (2012a). The values used to modify photosynthetic and conductance responses in these simulations are listed in Table 1 and are adapted from Tables 2 and 3 in the dataset compiled by Lombardozzi et al. (2013). Ozone modifications to all broadleaf tree and shrub PFTs used the temperate deciduous tree response equation, needleleaf tree and shrub PFTs used the temperate evergreen tree response equation, and grasses (C3 and C4) and crop PFTs used the crop response equation. A critical O3 flux threshold of 0.8 nmol O3 m−2 s−1 was used (Lombardozzi et al. 2012a) to account for the ability of plants to detoxify O3, so O3 only accumulated as CUO when O3 fluxes were larger than this threshold value. When the O3 flux was less than 0.8 nmol O3 m−2 s−1, CUO did not increase (no additional O3 damage); when CUO equaled zero, there was no O3 damage to photosynthesis and stomatal conductance.

c. Sensitivity simulations

In addition to mean responses to O3, four simulations were run to test the sensitivity of the model to different photosynthetic and stomatal O3 response functions. The first two O3 response sensitivity simulations, a high and a low O3 vulnerability simulation, were parameterized with PFT-specific photosynthetic and stomatal responses using plants that were either vulnerable or resistant to O3 to determine the range of vegetation responses. The high and low vulnerability response equations used for each PFT were presented in Lombardozzi et al. (2013), and slope and intercept values are summarized in Table 1. The other two O3 response sensitivity simulations were based on results found by Lombardozzi et al. (2013), where plant types responded similarly to one another and correlations with CUO were typically weak or not significant. One sensitivity, the “fixed decrease” simulation, did not use a relationship to describe damage to photosynthesis or stomatal conductance as CUO increased but instead used a standard fixed decrease applied to photosynthesis and stomatal conductance in each plant type at all CUO values (see values in Table 1) to test the importance of including correlations with CUO. Another sensitivity, the “single plant type response” simulation tested the importance of including different plant type responses. Lombardozzi et al. (2013) found that accounting for different plant types did not change the responses of photosynthesis or stomatal conductance to CUO. To determine the influence of including the O3 responses of different plant types in the model, the same O3 response equation [average response of all plant types considered together from Lombardozzi et al. (2013)] was used to modify photosynthesis and stomatal conductance in all plant functional types (see Table 1 for values).

Threshold values are often used to account for the ability of plants to detoxify O3, though incorporating these into global models can be problematic because plants, even within the same species, have varying capacities to detoxify O3. Additionally, thresholds based on CUO values do not directly account for internal defenses that can vary widely by plant type. Therefore, additional sensitivity analyses were run to test the importance of the O3 threshold value used in the CLM O3. Three simulations were run using varying threshold values to test the sensitivity of modeled plant detoxification capacity. Values of 0, 1.6, and 5 nmol O3 m−2 s−1 were tested and compared to the 0.8 nmol O3 m−2 s−1 threshold used in the standard CLM O3 simulation and other sensitivity simulations.

3. Results

Average annual mean surface [O3] during the growing season were highest in the midlatitudes of the Northern Hemisphere, including the continental United States, southern Europe, western and central Asia, and northeastern Africa (Fig. 2a). Average cumulative O3 uptake, which accumulated over the growing season, was highest in tropical and midlatitudes, including southeast North America, Southeast Asia, and parts of South America and Africa (Fig. 2b). Cumulative O3 uptake was not always correlated with [O3] because of its dependence on stomatal conductance values. The 20-yr mean cumulative O3 uptake ranged approximately from 0 to 100 mmol m−2 globally (Fig. 2b). Regions with low cumulative O3 uptake were correlated with either low O3 concentrations, such as in tropical regions or regions with low productivity like northern Africa.

Fig. 2.
Fig. 2.

(a) Growing season mean hourly O3 concentrations from 2002 to 2009 used in the CLM simulations and (b) growing season cumulative O3 uptake averaged over 20 yr.

Citation: Journal of Climate 28, 1; 10.1175/JCLI-D-14-00223.1

GPP was reduced globally by 10.8% in response to O3 (Figs. 3a,b) and global transpiration was reduced by 2.2% (Figs. 3c,d). The largest percent reductions in GPP (20%–25%) and transpiration (15%–20%) typically occurred in midlatitudes in the Northern Hemisphere, as well as in South America and Africa, where O3 uptake was high or where GPP or transpiration values were small before O3 exposure. While GPP was reduced by 10% or more in most regions globally, transpiration reductions of this magnitude were limited to middle and high latitudes and were smaller (<5%) in large areas of the tropics and subtropics. The larger decrease in GPP compared to transpiration caused average global water-use efficiency (WUE; carbon gained per water lost) to decrease by ~3%, with regional decreases of 5%–10% in the eastern United States, Southeast Asia, Europe, and central Africa (Figs. 3e,f).

Fig. 3.
Fig. 3.

Simulated 20-yr average (a) gross primary productivity, (c) transpiration, and (e) water-use efficiency after exposure to O3 and percent change from control in (b) GPP, (d) transpiration, and (f) water-use efficiency due to O3 exposure.

Citation: Journal of Climate 28, 1; 10.1175/JCLI-D-14-00223.1

Chronic O3 exposure caused a 2.3% global decrease in latent heat flux (Figs. 4a,b) and a 2.3% global increase in sensible heat flux (Figs. 4c,d). The largest changes in both energy fluxes were in middle and high latitudes, and each was largely unaffected in arid regions. Spatial patterns of change were different for sensible and latent heat flux; the changes in sensible heat flux were larger than latent heat flux in the midlatitudes of North and South America, Europe, and the tropics. The largest O3-caused decreases in both latent and sensible heat fluxes were in eastern North America, Europe, and the high latitudes of the Northern Hemisphere.

Fig. 4.
Fig. 4.

Simulated 20-yr average (a) latent heat flux and (c) sensible heat flux after exposure to O3 and percent change from control in (b) latent heat flux and (d) sensible heat flux due to O3 exposure.

Citation: Journal of Climate 28, 1; 10.1175/JCLI-D-14-00223.1

Average global runoff increased by 5.4% in response to O3 exposure (Figs. 5a,b) and changes were minimal in arid regions that have little or no natural runoff. Changes were also small in the Amazon and in Australia, where O3 had little effect on transpiration. Runoff increased more than 10% in most of central Africa, most of Europe, and eastern North America and more than 30% in many Arctic regions, where total runoff is small (<100 mm yr−1).

Fig. 5.
Fig. 5.

(a) Simulated 20-yr average runoff after exposure to O3 and (b) percent change from control in runoff due to O3 exposure.

Citation: Journal of Climate 28, 1; 10.1175/JCLI-D-14-00223.1

Including O3 in the CLM4.5SP resulted in a global GPP prediction of 130 Pg C yr−1, which falls within the range of observation-based values, 119–175 Pg C yr−1 (Table 2). The inclusion of O3 resulted in similar predicted zonal patterns of GPP (r = 0.84) to simulations without O3 (r = 0.83) when each was compared to globally extrapolated Flux Network (FLUXNET) model tree ensemble (MTE) data (Jung et al. 2011; Fig. 6a), though GPP is still too large in CLM O3 simulations. When including O3, the CLM4.5SP also overpredicts latent heat flux compared to FLUXNET MTE data (Jung et al. 2011) in most latitudes. Predictions of latent heat flux were only slightly improved by O3 in tropical latitudes and were not impacted in other latitudes (Fig. 6b).

Table 2.

Global values from CLM simulations and observations [GPP, evapotranspiration (ET), and runoff values].

Table 2.
Fig. 6.
Fig. 6.

(a) Zonal GPP and (b) latent heat flux averaged over 20 yr in CLM simulations with and without simulated O3 exposure compared to FLUXNET data averaged from 1982 through 2004 (presented in Bonan et al. 2011, 2012).

Citation: Journal of Climate 28, 1; 10.1175/JCLI-D-14-00223.1

Sensitivity simulations

Low and high plant vulnerability simulations performed similarly to CLM O3 simulations, with the same correlations for all when comparing simulated GPP with FLUXNET MTE data (Jung et al. 2011; r = 0.84 for all; Fig. 7). Simulations run with low and high plant vulnerability to chronic O3 resulted in a smaller magnitude reduction of GPP (7.9% and 8.2%, respectively) but a larger reduction in transpiration (2.3% and 2.4%, respectively) compared to the CLM O3 simulation (Table 2). Spatial patterns of decrease in GPP and transpiration in high vulnerability simulations were similar to the mean responses to O3, though GPP decreases were typically smaller in magnitude in many regions and there were larger decreases in transpiration in a few localized regions (U.S. Midwest and isolated parts of northern Africa and Asia). Low vulnerability simulations had larger GPP and transpiration reductions than CLM O3 simulations in a few regions (U.S. Midwest and central Asia; transpiration only: northern Europe and northern Africa), but GPP reductions were smaller in many other regions (western North America, Amazonia, Africa, and most boreal regions).

Fig. 7.
Fig. 7.

Correlations between simulated GPP and FLUXNET GPP for all sensitivity simulations. Sensitivity simulations are color coded, with each point representing one grid cell averaged over 20 yr and lines representing the linear regression. Correlation values range from r = 0.81 (0 threshold) to r = 0.84 (CLM O3, high and low vulnerabilities, single plant type, and fixed decrease), with the control, 1.6 threshold, and 5 threshold sensitivity simulations falling in between (r = 0.83 for all).

Citation: Journal of Climate 28, 1; 10.1175/JCLI-D-14-00223.1

Simulations using a single, average plant type response caused larger reductions in global GPP (11.2%) compared to CLM O3, though overall decreases in transpiration were of similar magnitude (2.2%; Table 2). Correlations between GPP and FLUXNET MTE data (Jung et al. 2011; r = 0.84; Fig. 7) and spatial patterns of decrease were similar to the CLM O3 simulation, though transpiration decreases were ~5% larger in the tropics and ~5% smaller in the central and southeastern United States, Europe, and Asia.

Gross primary productivity decreased more in simulations that used a fixed decrease for each plant type compared to CLM O3 simulations (11.4%; Table 2), and correlations to FLUXNET MTE data (Jung et al. 2011) were similar (r = 0.84; Fig. 7) compared to CLM O3 simulations. Spatial transpiration patterns were similar, though the overall magnitude of decrease was smaller (2.0%) in simulations that used a fixed decrease compared to CLM O3 simulations (Table 2). The differences in transpiration between the simulations were largest in northern latitudes, where not including regressions resulted in a 5% smaller decrease.

Changing the O3 uptake threshold had a large impact on both GPP and transpiration. The highest threshold that was tested (5 nmol m−2 s−1) had little impact on transpiration or GPP (changes in both were <1%), while eliminating the threshold (0 nmol m−2 s−1) substantially decreased GPP (31%) and transpiration (2.4%). GPP was highly sensitive to changes in threshold, with a range in global GPP of 46 Pg C yr−1 across the different threshold values. Only the 0.8 nmol m−2 s−1 threshold in the CLM O3 simulation improved correlations with FLUXNET MTE GPP (Jung et al. 2011; r = 0.84) compared to the control simulation (r = 0.83), while the other threshold values either did not change the correlation (1.6, 5 nmol m−2 s−1; r = 0.83) or reduced it (no threshold; r = 0.81; Fig. 7). Transpiration was also sensitive to the threshold value used, though less so, with a range from 68.45 to 70.07 × 103 km3 H2O yr−1.

4. Discussion

Changes in transpiration are important to regional climate dynamics through altering atmospheric water vapor concentrations and surface energy fluxes (Sellers et al. 1996), but few studies have quantified how O3-induced changes in transpiration will impact climate. This study predicts transpiration to decrease after chronic O3 exposure (Fig. 3d) up to 25% in some regions. However, the simulated decrease in total global transpiration (2.2%) is smaller compared to the 11%–16% reduction in conductance estimated by Lombardozzi et al. (2013) and Wittig et al. (2007) from quantitative literature analyses. These differences are perhaps not surprising because plants measured in experiments as were compiled by Lombardozzi et al. (2013) and Wittig et al. (2007) are often grown in controlled environmental conditions to highlight O3 responses, and transpiration measurements rely on vapor pressure deficit in addition to stomatal conductance. Further, transpiration decreases predicted here are inconsistent with increases in ecosystem-level transpiration rates measured in southeastern U.S. forests under chronic O3 exposure that are attributed to sluggishness of stomatal responses (McLaughlin et al. 2007; Sun et al. 2012). Stomatal sluggishness was not explicitly considered in the CLM4.5SP simulations but can be included in future simulations using these methods when data become more widely available. The responses in southeastern U.S. forests were estimated in response to natural O3 variation and may therefore also include interactions with other environmental variables not considered in this study. Ultimately, the decreases in transpiration predicted by the CLM O3 are more consistent with the conductance decreases in the compiled datasets, though future simulations can focus on comparison to regional responses such as southeastern forests and possibly include diurnal and sluggish responses to O3 once more data are available.

Changing transpiration has significant climate feedbacks because it alters atmospheric water vapor, which affects regional and global temperatures. These simulations demonstrate that, by decreasing transpiration, O3 changes surface energy budgets by decreasing latent heat flux and increasing sensible heat flux (Fig. 4), though these changes lead to only slight improvements in zonal averages of latent heat flux (Fig. 6b). Although the changes in the energy fluxes described here do not incorporate feedbacks from changing water vapor or atmospheric CO2 because this study used prescribed atmospheric data, the differences do suggest that O3, through changing transpiration, alters surface energy fluxes and may contribute to changes in regional and global surface temperatures.

Changes in transpiration will also modify regional hydrology by changing precipitation patterns, soil moisture and runoff (e.g., Gedney et al. 2006). For example, Felzer et al. (2009) found that O3-induced stomatal closure doubled the increase in runoff compared to elevated CO2 in temperate forests of the United States, and Bernacchi et al. (2011) found that O3 increased soil water content in soybean fields. Similarly, the current study found that chronic O3 exposure elicited increases in runoff by decreasing transpiration (Fig. 5) in subtropical, temperate, and boreal regions. Though overall global changes in runoff were not large, the regional changes in runoff, such as the 10%–30% increases in the southeastern United States, may influence flood potential in these regions. It should be noted that large changes in other regions, such as the Arctic, have low total runoff (10–100 mm yr−1), so large percent changes may not be consequential in these regions.

Reductions in simulated GPP were larger and more widespread than reductions in transpiration (Fig. 3) and resulted in a 10.8% globally averaged reduction in simulated GPP for the CLM O3 simulation (Fig. 3b). The overall reduction in GPP predicted in this study was smaller than the average 21% decrease after chronic O3 exposure documented in several plant functional types (Lombardozzi et al. 2013), likely because the plants were measured under artificially high [O3], whereas the CLM O3 simulation determined the impact under present-day ambient [O3]. In a meta-analysis of tree species, Wittig et al. (2007) estimated an 11% reduction in photosynthesis since the industrial revolution, similar to the 10.8% global GPP decrease estimated by CLM O3. The 10.8% decrease in response to O3 (average from 2002 to 2009) was smaller than the 14%–23% range of decrease in GPP between 1901 and 2100 simulated by the Met Office Surface Exchange Scheme (MOSES) model that used vegetation from the Top-Down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) vegetation dynamics model (MOSES-TRIFFID) (Sitch et al. 2007), likely because of differences in the time period sampled. Irrespective of these differences, the decrease in GPP predicted by CLM O3 suggests that, similar to MOSES-TRIFFID, the effect of O3 will indirectly increase CO2 concentrations through decreasing the land carbon sink.

The 10.8% decrease caused globally summed GPP (131 Pg C yr−1) to fall toward the low end of the observed GPP range, whereas GPP in the control CLM4.5SP simulation (146 Pg C yr−1) falls within the middle of the observed range (Table 2). Global estimates of GPP from observations are variable, ranging from 119 Pg C yr−1 (Jung et al. 2011; Table 2) to 175 Pg C yr−1 (Welp et al. 2011), with estimates made from scaling FLUXNET MTE data using eddy covariance towers on the low end of this range [119 ± 6 Pg C (Jung et al. 2011) and 123 ± 8 Pg C (Beer et al. 2010)] and estimates using stable isotopes of CO2 measured by the Scripps Institution of Oceanography global flask network on the high end of this range (150–175 Pg C; Welp et al. 2011). Given the spread in observational GPP estimates and the uncertainties associated with simulating plant O3 damage, it is difficult to evaluate the accuracy of the CLM O3 simulation on a global scale.

Exposure to O3 is expected to decrease WUE because O3 decreases carbon gain more than water loss (Lombardozzi et al. 2013). The global reduction in carbon gain is nearly 5 times larger than the reduction in water loss in CLM O3 simulations, and the differential changes in carbon gain and water loss with O3 exposure result in a ~3% decrease in global WUE. Keenan et al. (2013) found that WUE increased in temperate and boreal forests of the Northern Hemisphere over the past two decades, and Holmes (2014) proposed that this might be due in part to decreasing O3 concentrations over the same time period. Our results show that present-day O3 concentrations do decrease WUE in the Northern Hemisphere, 5%–10% in temperate regions, compared to simulations with no O3, supporting the Holmes (2014) theory that decreasing O3 concentrations can improve WUE in these regions, as well as other regions. Additionally, Keenan et al. (2013) found that the increase in WUE over the past two decades is larger than predicted by terrestrial biosphere models, none of which incorporate differential stomatal and photosynthetic responses to O3. Incorporating independent responses of photosynthesis and stomatal conductance to O3, as done here, will likely improve model representation of changes in WUE, though there is still considerable uncertainty in the parameterization of O3 responses.

It is important to note that the O3 damage parameterizations (based on Lombardozzi et al. 2013) have large uncertainties, including the lack of an observed relationship between cumulative O3 uptake and GPP or stomatal conductance for many plant functional types. When the slope of the relationship with cumulative O3 uptake was zero, the CLM O3 applied a constant decrease (i.e., the intercept) to the plant functional type if O3 uptake accumulated above the minimum threshold value. Given the lack of observed relationship with O3 uptake (Lombardozzi et al. 2013), it is possible that using other O3 indices based on accumulated concentration (see Mauzerall and Wang 2001), such as accumulated O3 over a threshold of 40 ppb (AOT40) or sum of growing season hourly O3 concentrations above 60 ppb (SUM06), can also be used to simulate O3 damage to plants. However, Lombardozzi et al. (2013) found that there was no general correlation between O3 concentration and O3 damage across many plant species. To put bounds on the uncertainty in the O3 parameterization, several sensitivity analyses were run and they are described below.

a. Sensitivity simulations

Vulnerability to O3 can play a large role in the magnitude of plant physiological response (Bassin et al. 2007; Coleman et al. 1995; Lombardozzi et al. 2013), but the relative vulnerability of vegetation on regional or global scales is not well documented. Assessing ecosystem sensitivity to O3 relies on species-level factors like detoxification and physical defense, community-level interactions with O3 like competition and diversity, and ecosystem-level factors like nutrient availability (Bassin et al. 2007). Many of these factors are not studied thoroughly enough to extrapolate to regional and global vegetation sensitivity. To understand the range of possible responses caused by plant vulnerability, we ran sensitivity analyses to simulate global high and low vulnerability responses to O3 to determine the possible magnitude of change in GPP and transpiration. Global predictions with plants that were both vulnerable and resistant to O3 resulted in decreases in GPP relative to simulations that did not include O3. Though the high and low vulnerability simulations were expected to bracket the CLM O3 responses, GPP decreases were smaller in both vulnerability simulations than those in CLM O3. In high vulnerability simulations, the reduction in stomatal conductance acted as a defense by minimizing O3 uptake, making the decreases in GPP smaller than in CLM O3, demonstrating the importance of stomatal conductance in simulating O3 damage. As expected, the low vulnerability simulations also decreased GPP less than the CLM O3 response. A few isolated regions demonstrated larger decreases because higher stomatal conductances allowed for larger O3 uptake. Transpiration decreases in low vulnerability simulations were also larger than the decreases in CLM O3, possibly a result of higher O3 uptake, especially at low O3 uptake values (Table 2), causing more damage through time. These simulations are not realistic depictions of global responses to O3 because of the limited information available on the species-level or even plant functional type vulnerability to chronic O3 exposure, but they do provide a range of possible responses and illustrate the importance of stomatal conductance in estimating O3 damage.

Global model simulations typically base vegetation responses to O3 on a single or a few types of plants because of a lack of available data for multiple plant types. Similarly, our simulation used the best available published data and included O3 responses for crops, temperate deciduous trees, and temperate evergreen trees. It is often assumed that plant types respond differently to O3 exposure because of different inherent sensitivities (e.g., stomatal conductance, antioxidants). However, the results from Lombardozzi et al. (2013) suggest that modeling vegetation as a single or few plant types might be reasonable because plant types respond similarly to chronic O3 exposure. To test this, we simulated O3 responses using an average single plant type rather than separating O3 responses by plant type. Simulations using a single plant type response resulted in larger GPP (11.2%) decreases compared to CLM O3 simulations (Table 2), caused by the difference in O3 response equations (see Table 1). Though transpiration decreased by approximately the same magnitude as the simulation using multiple plant types, spatial patterns changed in several regions. The larger decreases in GPP and the spatial changes in transpiration decreases in the single plant type simulation highlight the importance of including the responses of multiple plant types to O3 in all simulations and emphasize the necessity for using comprehensive datasets to parameterize O3 responses.

The correlations of photosynthesis and conductance with CUO described in Lombardozzi et al. (2013) had high variance, so a sensitivity simulation was run to determine the importance of including the relationships with CUO in the model by including responses of photosynthesis and stomatal conductance as a fixed decrease for each plant type, regardless of CUO. Simulations with a fixed decrease resulted in larger decreases in GPP and smaller decreases in transpiration (Table 2). This result is not surprising because crop is the only plant functional type in which GPP decreases with O3 uptake (Table 1). The similarity in spatial patterns of decrease demonstrates that sensitivity to using correlations compared to fixed decreases is low.

Using an O3 uptake threshold is one way of accounting for the ability of plants to detoxify O3 within the leaf. However, many plants have varying detoxifying capacities, so implementing a single threshold value can be problematic. We found that O3 simulations were highly sensitive to O3 uptake thresholds. Decreases in GPP and transpiration range from <1%, at a high threshold of 5 nmol O3 m−2 s−1, to 31% (GPP) and 2.4% (transpiration) when the threshold was eliminated. A few studies have found that correlations between O3 uptake and reductions in biomass deteriorate at higher threshold values (Karlsson et al. 2004; Uddling et al. 2004), and we similarly find that correlations between observed and predicted GPP are strongest in simulations using a lower threshold value (Fig. 7). Given the large sensitivity to O3 threshold values, the largest improvements to simulated O3 responses will likely come from improving the O3 threshold values as data become more widely available in the literature.

b. Conclusions

Efforts to model O3 responses have considerable associated uncertainties because of the large variability in plant responses to O3 (Lombardozzi et al. 2013). Our simulations attempt to improve modeled O3 responses by using the most comprehensive data available and to understand the magnitude of uncertainty by testing several possible sensitivities. Future model efforts will be improved with better empirical data of antioxidant defense capacities for different plant types and how those relate to critical O3 uptake thresholds. Though our simulations were not sensitive to plant vulnerability, additional knowledge about the proportion of plants that are resistant to O3 may improve the accuracy of future simulations, given that plants currently survive under high [O3] in many regions (e.g., central Asia) and are therefore likely either to be resistant to or adapted to high [O3] (Heagle et al. 1991). Models can also be improved with additional observational data, particularly for tropical trees and C4 grasses. For example, the simulated GPP decreases in tropical forest and subtropical grassland ecosystems have large uncertainties because of the lack of available data, so results in these regions must be interpreted with caution. It is also important for models to include interactions with drought stress, nitrogen deposition, and increasing CO2 concentrations, as these factors can all potentially reduce the impact of O3. Regardless, including responses to O3 does decrease zonal predictions of GPP compared to unmodified simulations in most latitudes (Fig. 6a) and should therefore be considered an important predictor of GPP in future simulations.

Through changing the physiological processes of photosynthesis and transpiration, O3 indirectly affects climate by increasing atmospheric CO2 concentrations (Sitch et al. 2007), changing surface energy budgets, and decreasing water vapor concentrations. The results presented here suggest that current [O3] have acted to suppress GPP by 10.8% and transpiration by 2.2%, with the strongest effects in temperate and subtropical ecosystems. The spatial extent and magnitude of the decreases in both GPP and transpiration are sensitive to responses of O3 uptake threshold values, and simulations can be improved by including plant type–specific data. Additionally, by altering transpiration, O3 has increased runoff and changed surface energy budgets. Simulations that modify transpiration indirectly through changing photosynthesis (e.g., Sitch et al. 2007) will therefore underestimate the indirect impact of O3 on radiative forcing by overestimating the decreases in transpiration and latent heat flux. Future projections of climate will benefit by incorporating the effects of chronic O3 exposure on photosynthesis and transpiration independently to accurately capture changes in regional and global hydrology and surface energy budgets.

Acknowledgments

We wish to thank Natalie Mahowald and Christine Goodale for helpful comments and insights in preparing this manuscript and Keith Oleson for providing scientific support. An NSF DDIG 1010892 awarded to Danica Lombardozzi through the DEB-Ecosystem Science cluster and National Science Foundation Grants EF-1048481 and 1048827 provided funding for this work.

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