The Duality of Reforestation Impacts on Surface and Air Temperature

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RESEARCH ARTICLE The Duality of Reforestation Impacts on Surface and 10.1029/2019JG005543 Air Temperature Key Points: Kimberly A. Novick1 and Gabriel G. Katul2 • Evidence is mounting that reforestation cools the land surface 1O'Neill School of Public and Environmental Affairs, Indiana University, Bloomington, Bloomington, IN, USA, 2Nicholas in many places, but its been challenging to understand how School of the Environment, Duke University, Durham, NC, USA reforestation affects air temperature • A novel approach for detecting the fl in uence of land cover on multiple Abstract Evidence is mounting that temperate‐zone reforestation cools surface temperature (Tsurf), fl metrics of air temperature using ux mitigating deleterious effects of climate warming. While T drives many biophysical processes, air tower observations is presented surf • The analysis shows that temperature (Ta) is an equally important target for climate mitigation and adaptation. Whether reductions in reforestation in the southeastern Tsurf translate to reductions in Ta remains complex, fraught by several nonlinear and intertwined processes. United States cools the near‐surface ‐ – In particular, forest canopy structure strongly affects near surface temperature gradients, complicating air temperature by 1 3 °C during ‐ fl daytime but not nighttime cross site comparison. Here the in uence of reforestation on Ta is assessed by targeting temperature metrics that are less sensitive to local canopy effects. Specifically, we consider the aerodynamic temperature (Taero), estimated using a novel procedure that does not rely on the assumptions of Monin‐Obukhov similarity theory, as well as the extrapolated temperature into the surface layer (T ). The approach is tested with Correspondence to: extrap K. A. Novick, flux tower data from a grass field, pine plantation, and mature hardwood stand co‐located in the Duke Forest [email protected] (North Carolina, USA). During growing season daytime periods, Tsurf is 4–6 °C cooler, and Taero and near‐surface Textrap are 2–3 °C cooler, in the forests relative to the grassland. During the dormant season, Citation: daytime differences are smaller but still substantial. At night, differences in Taero are small, and near‐surface Novick, K. A., & Katul, G. G. (2020). Textrap is warmer over forests than grasslands during the growing season (by 0.5 to 1 °C). Finally, the The duality of reforestation impacts on influence of land cover on T at the interface between the surface and mixed layer is small. Overall, surface and air temperature. Journal of extrap Geophysical Research: Biogeosciences, reforestation appears to provide a meaningful opportunity for adaption to warmer daytime Ta in the 124. https://doi.org/10.1029/ southeastern United States, especially during the growing season. 2019JG005543 Plain Language Summary Reforestation—the process of reestablishing trees where they once Received 26 OCT 2019 dominated—has long been viewed as a strategy to remove CO2 from the atmosphere. Recently, attention Accepted 10 JAN 2020 has focused on understanding if reforestation also offers a direct temperature cooling benefit. By using more Accepted article online 13 MAR 2020 water (a cooling process) and increasing the transfer of heat energy away from the surface, forests may offer a meaningful opportunity for local climate mitigation and adaptation. Evidence is mounting that indeed, in the temperature and tropical zones, the surface of forests is cooler than grasslands and croplands. However, due to confounding effects of forest canopies on wind and temperature profiles near the surface, it has previously been hard to assess if forests also cool the air. Here we present a new approach that accounts for canopy effects, allowing for a more direct assessment of the potential for reforestation to cool near‐surface air temperature. Using a case study from the North Carolina Piedmont, we find that while the air cooling effect of forests is not a large as the surface cooling effect, it is still on the order of 2–3°C during summer daytime periods—times when the need for climate adaptation strategies are particularly pressing.

1. Introduction

Reforestation has long been viewed as an instrument for mitigating the pace of climate change, particularly in the temperate and tropical zone where much of the historical forest cover was lost to harvest within the last 200–300 years (Williams, 1989). This view is largely linked to the carbon sequestration potential of these

forests. Observations of the net ecosystem exchange of CO2 from regional and global networks of ﬂux towers, as well as forest inventory data, reveal that forests in the temperate regions are indeed strong carbon sinks (Jung et al., 2011; Pan et al., 2011) and that even maturing temperate forests are capable of assimilating sub-

stantially more CO2 than expected from conventional ecological theory (Novick et al., 2015; Stoy et al., 2008).

©2020. American Geophysical Union. However, the fate of the future forest carbon sink is less certain (Friedlingstein et al., 2014). As atmospheric All Rights Reserved. CO2 continues to rise, forest carbon uptake potential may saturate due to a number of limitations. Some are

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imposed by nutrient and energy availability (Baldocchi & Penuelas, 2019; Oren et al., 2001), while others are

intrinsic to leaf‐level photosynthesis and its saturating behavior with increased CO2. Moreover, expected increases in air temperature, drought, and insect and ﬁre regimes will likely decrease the magnitude, and increase the variability, of forest carbon uptake across much of the world (Frank et al., 2015; McDowell & Allen, 2015; Wear & Coulston, 2015).

While this uncertainty about future forest CO2 uptake continues to motivate research, substantial attention is now focused on the potential for forests to mitigate temperature conditions through alterations to the ecosystem energy balance. Theoretical links between land cover and temperature have long been recognized and incorporated into models (Avissar & Werth, 2005; Foley et al., 2003; Pielke et al., 1998; Raupach, 1991). Modeling work has shown that in the tropics, relatively large evapotranspiration causes forests to be cooler than nonforested ecosystems (Costa, 2005). On the other hand, in boreal cli- mates, relatively low forest albedo likely causes forests to be warmer than nonforested ecosystems (Lee et al., 2011; Swann et al., 2010). In the temperate zone, the overall impact of temperate reforestation on surface temperature was, for a long time, not clear (Bonan, 2008; South et al., 2011). Evaporative cooling and emitted longwave radiation from the surface both act to reduce surface temperature, whereas net shortwave radiative load acts to warm the surface. The sensible heat flux, whose efficiency varies with the mean wind and turbulence conditions overlying the surface, plays a dual role and may contri- bute to warming or cooling (Huang et al., 2015). With rapid advancements and proliferation of remote sensing products, observational evidence has emerged to suggest that the combined influence of increased sensible and latent heat in temperate forest ecosystems has an overall surface cooling effect that outweighs albedo‐driving warming by a magnitude of 1–2 °C, annually averaged, across a wide range of temperate ecosystems (Bright et al., 2017; Burakowski et al., 2018; Juang, Katul, et al., 2007; Wickham et al., 2012; Zhang et al., 2020). This work is encouraging, as it suggests a direct and substantial climate mitigation benefit of reforestation in the temperate zone that is mechanistically quite different from the carbon sequestration benefit. However, thus far, observation‐driven studies of land cover effects on microclimate have largely been focused on the response of surface temperature, and not the response of air temperature (e.g. Bright et al., 2017; Juang, Katul, et al., 2007; Luyssaert et al., 2014; Wickham et al., 2012; but see Baldocchi & Ma, 2013). This focus is not surprising for three reasons: (i) Unlike air temperature, surface temperature does not vary with height making it a more logical reference to compare land cover temperature patterns; (ii) likewise, surface temperature, representing the integrated radiometric temperature of all canopy elements, is closely coupled to the temperature experienced by foliage in dense canopies, or by microbes near the soil surface of sparse canopies, and is thus biologically relevant; and (iii) operationally, surface temperature is convenient to estimate from meteorological towers that report patterns of radiation, albedo, and energy fluxes necessary to attribute variations in surface temperature between ecosystems to specific mechanisms (Juang, Katul, et al., 2007; Lee et al., 2011; Luyssaert et al., 2014). However, when considering direct impacts of reforestation and other land cover changes on climate, air tem-

perature (hereafter Ta), as opposed to surface temperature (Tsurf), is an equally important metric. This rele- vance is certainly true for boundary layer dynamics and rainfall initiation (Juang, Katul, et al., 2007, Juang, Porporato, et al., 2007; Manoli et al., 2016; Siqueira et al., 2009) as well as a plethora of associated “hand‐shakes” between the climate system and the land surface (Baldocchi & Ma, 2013; Luyssaert et al., 2014). Ultimately, climate change is driven by long‐term increases in the temperature of the air (or kinetic temperature) due to increases in greenhouse gases and not by surface temperature.

Linking land cover impacts on Tsurf and subsequent changes in Ta is not straightforward. Since H transfers heat from the surface to the atmosphere, ecosystems with relatively cool surfaces may underlie relatively warm air (Baldocchi & Ma, 2013), and vice versa. These heat transfer mechanisms are further mediated by land cover‐driven changes in the height of the planetary boundary layer (Luyssaert et al., 2014), which is generally greater over forests, creating more “room” for heat energy transferred from the surface to the atmosphere. Finally, air temperature gradients near the surface can be steep but also depend on the inﬂuence of rough canopy elements on near‐surface turbulence regimes. Thus, uncertainty can arise from a straightfor-

ward comparison of observed Ta above a forested and nonforested canopy if steps are not taken to control for the inﬂuence of canopy structure on near‐surface temperature gradients.

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The objective of this work is develop and use a “coarse‐grained” model of how land cover change affects air temperature near the surface while ensuring proper matching with surface temperature. The proposed

approach is then applied to the speciﬁc question of how reforestation affects Ta in the southeastern United States, across seasons, and over the diurnal cycle. The study leverages eddy covariance ﬂux tower observations from a co‐located grassland, pine forest, and hardwood forest in central North Carolina (USA), which together represent the three primary phases of secondary succession in the region (grassland to pine forest to mature deciduous forest). The proposed approach relies only on data commonly reported by meteorological towers and thus is transferable to studies on land cover change impacts on temperature in other parts of the world.

2. Approach 2.1. Study Sites The study sites were located in the Blackwood Division of the Duke Forest near Durham, North Carolina (35°58′41″N, 79°05′59″W, 163 m above sea level). The old field grassland, hereafter OF (AmeriFlux site code US‐Dk1), was dominated by the C3 grass Festuca arundinacea Schreb., with minor contributions from forbs and other C3 and C4 grass species (Novick et al., 2004). It was harvested at least once a year to prevent reforestation. The pine forest, hereafter PP (AmeriFlux site code US‐Dk2), was established in 1983 following a clear cut and a burn. Pinus taeda L. (Loblolly pine) seedlings were planted at a 2.0‐m by 2.4‐m spacing with pine density reduced to approximately 1,100 trees/hectare when the site reached maturity. Canopy height increased from 16 m in 2001 to over 20 m in 2008. The hardwood forest, hereafter HW (AmeriFlux site code US‐Dk3), was classified as an uneven‐aged (90–110 year old) oak (Quercus)–hickory (Carya) forest. The stand was dominated by hickories (Carya tomentosa (Poir.) Nutt., C. glabra (P. Mill). Sweet.), yellow poplar (Liriodendron tulipifera L.), sweetgum (Liquidambar styraciflua L.), and oaks (Quercus alba L., Q. michauxii Nutt., Q. phellos L.). The forest was not managed after establishment, and mean canopy height was 25 m. All ecosystems have little topographic variation and lie on Enon silt loam. A clay pan at a depth of ~ 35–50 cm underlies the sites, thereby imposing similar constraints on root‐water access for both ecosystems. Because they are all co‐located to within 1 km, they experience nearly identical macroclimate conditions, characterized by long‐term mean annual temperature and precipitation of 15.5 °C and 1,146 mm, respectively. More details on the study sites are available elsewhere (Novick et al., 2004; Stoy et al., 2008). Much of the analysis is conducted separately for the dormant season (Julian day of year [DOY] <100 or DOY >300) and growing season (150 < DOY < 270). Some analysis focuses exclusively on daytime (9:00–17:00 hr) or nighttime (2100:2400 and 0:600 hr). The analysis relied on data from 2005 to 2008, as this is the period of record for which the towers supported measurements of outgoing long‐wave radiation necessary to infer

Tsurf. Static canopy heights of 0.5, 19, and 25 m for the OF, PP, and HW sites, respectively, were assumed for the duration during this period. 2.2. Temperature Metrics: Deﬁnitions and Observations Approaches

2.2.1. Surface Temperature (Tsurf) This study considers four metrics for temperature at or near the surface (see Figure 1). The ﬁrst is the radio-

metric surface temperature (Tsurf) or canopy “skin temperature” (Jin & Dickinson, 2010). Conceptually, it represents the aggregated temperature of solid canopy and soil elements projected to a single location in

the vertical dimension. The Tsurf is highly relevant for biophysical processes occurring within the canopy, including respiration and photosynthesis (Luyssaert et al., 2014). It can be inferred directly at the ecosystem scale from observations of outgoing longwave radiation from a solid surface using the Stefan‐Boltzmann law 1/4 —Tsurf =(σεsRL. out) —where σ is the Stefan‐Boltzmann constant and ϵs is the surface emissivity. Because of its dependency on solid surface properties, ϵs may be empirically related to albedo (Juang, Katul, et al., 2007) though no “causal explanations” are to be implied. Albedo itself can be estimated on a daily time step as the ratio of midday incident to outgoing short‐wave radiation measured from the towers, with midday being the time of day when the incident radiation is most orthogonal to the reﬂecting surface.

Multiple studies have investigated the question of where and why Tsurf varies between forests and grasslands in the study region (Burakowski et al., 2018; Juang, Katul, et al., 2007; Zhang et al., 2020). At the annual time

scale, the Tsurf of forests is substantially cooler than the Tsurf of grasslands, by approximately 1–2 °C in gen- eral, and by approximately 1 °C in the study sites (described later). The cooling effect of forests is enhanced

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Figure 1. Conceptual overview of the key variables and processes. Over forests, the tower observations typically occur in the “roughness sublayer,” where wind and temperature dynamics are heavily inﬂuenced by canopy elements, and the actual Ta gradients (here shown for daytime conditions, yellow dashed line) diverge substantially from those predicted by Monin‐Obukhov Similarity Theory (MOST; orange proﬁles). In contrast, in the grassland, Ta is usually measured in the surface layer, where wind speeds are greater and vertical temperature gradients agree well with MOST predictions. The aerodynamic temperature (Taero) and the radiometric surface temperature (Tsurf) can both be conceptually surrogated to a representative source/sink height in the canopy (i.e., the sum of the roughness height for heat and the zero plane displacement, blue line). Air temperature extrapolated into the surface layer (Textrap) is expected to be well aligned with predictions from MOST.

in the summer when albedo differences between forests and grasslands are smaller but evapotranspiration (and the associated evaporative cooling) is enhanced over forests (Juang, Katul, et al., 2007; Zhang et al., 2020). Given this substantial body of preexisting work, this study will not investigate in detail the

mechanisms driving differences in Tsurf in the study sites here. Instead, this study focuses on the relation between Tsurf and the multiple “metrics” of air temperature. 2.2.2. Air Temperature (Ta) For our purposes, this is the temperature of the air measured at some height on a meteorological tower in

an aspirated housing unit. Differences in elevation at the Ta measurement height were corrected for using the barometric formula to transform raw Ta into potential temperature. As illustrated in Figure 1, the links between Ta, surface temperature and the temperature of the atmospheric surface layer are compli- cated by the presence of a “roughness sublayer”—the very lowest layer of the atmosphere where the ﬂow and temperature statistics are affected by canopy elements. The height of the roughness sublayer remains a subject of inquiry but is usually approximated as 2–5 times the height of the canopy (Raupach & Thom, 1981), in agreement with higher‐order closure modeling studies as well experimental and simulation studies of ﬂow over complex terrain. The lower limit is generally associated with mean momentum exchange, whereas the upper limit is representative of scalar exchanges (Poggi & Katul, 2007; Siqueira & Katul, 2010).

In short stature grasslands, the observation height for Ta usually exceeds the roughness sublayer height and is instead located in the so‐called “surface layer.” The surface layer represents the lower ~10% of atmospheric boundary layer within which gradients of mean air temperature and mean wind speed are represented by their logarithmic shapes corrected for thermal stratiﬁcation using Monin‐Obukhov Similarity Theory (here-

after MOST; Monin & Obukhov, 1954). In contrast, except in the case of very tall towers, the Ta from forested ﬂux towers is observed at a height that almost always falls within the roughness sublayer (as deﬁned above).

More importantly, ﬂow immediately above rough surfaces experiences higher friction velocity (u*), higher turbulent diffusivity, and smaller vertical gradients when compared to surfaces characterized by smaller roughness.

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2.2.3. Aerodynamic Air Temperature (Taero) This variable represents the air temperature at the “at the apparent source/sink of heat within the canopy” (Chehbouni et al., 2001), which is an idealized plane often speciﬁed as the combined height of a zero‐plane

displacement (zd) for momentum or heat and a roughness length for heat (zo,h). Both variables are key MOST parameters. The magnitude of Taero cannot be measured directly, but it is constrained to be within the bounds of Ta measured near the surface and Tsurf (Jin & Dickinson, 2010, and see Figure 1). In bulk aerodynamic representation, it is a key driver of sensible heat ﬂux (H) toward (or away from) the surface, given as H ¼ ðÞ′ − ′ ; ga;h z Taero Ta z (1) cp

where cp is the speciﬁc heat capacity for dry air at constant pressure and ga,h(z′) is the aerodynamic conduc- ′ tance at height z = z − zd. The ga,h(z′) can be expressed as

ðÞ′ 2 ðÞ¼′ hi Uzhik ; ga;h z (2) z′ z′ ln − ΨmðÞζ ln − ΨhðÞζ zo;m zo;h

where U is the mean wind speed at z′, k=0.4 is the von Karman constant, ρ is the mean air density, and zo,m is the roughness length for momentum. The Ψm(ζ) and Ψh(ζ) are stability correction functions that depend ′ on the atmospheric stability parameter ζ = z /L, which is a measure of buoyancy production (or destruction) to mechanical production of turbulent kinetic energy (TKE), and L is the Obukhov length. Their form is presented in Appendix A for unstable (i.e., when buoyancy is a source of TKE, ζ < 0) and stably stratiﬁed (i.e., when buoyancy is a sink of TKE, ζ > 0) atmosphere.

The zd can be reasonably estimated as 0.6–0.7 times the height of the canopy for dense canopies when treated as the centroid of the drag force within the canopy (Jackson, 1981; Thom, 1971). The zo,m is commonly approximated as 0.1 times the height of the canopy (Campbell & Norman, 1998); alternatively, it can be inferred with reasonable conﬁdence from the diabatic proﬁle equation for wind speed: u* z′ UzðÞ¼′ ln − ΨmðÞζ ; (3) 0:4 zo;m

where u* is friction velocity. Finally, by combining equations (2) and (3), the form of the diabatic proﬁle

equation for temperature can be obtained that depends on the friction velocity, but not zo,m or Ψm(ζ).

′ ðÞ¼′ − H z − Ψ ðÞζ : Tz Taero * ln h (4) 0:4ρcpu zo;h

* Eddy covariance ﬂux towers provide U, u ,H, Ta, and Ψh(ζ) (as well as Ψm(ζ)). When they are measured above the roughness sublayer, the Taero can be inferred from equation (4) provided zd and zo.h are known. −4 −1 However, the zo,h is a dynamic variable that varies by orders of magnitude (from 10 to 10 times canopy height), depending on heat and wind regimes (Sun & Mahrt, 1995). Thus, equation (4) is not

closed—both Taero and zo,h remain unknown. Moreover, in forests where fluxes are often measured in the roughness sublayer, the gradient of the temperature profile will differ from that predicted by equation (4); see Figure 1). Sun and Mahrt (1995) discuss a range of strategies for contending with this problem. The majority of these approaches, which include developing relations between momentum heights for roughness and heat (Brutsaert, 1982) or redefining the roughness height for heat as the “radiometric roughness height” (Kohsiek et al., 1993), rely on the assumption that all relevant fluxes and state variables (including mean air temperature and wind speed) are measured above the roughness sublayer. A fourth approach, which is commonly used in prognostic models, requires the introduction of a leaf boundary conductance term to

mechanistically transform Tsurf into Taero (Lhomme et al., 1988); however, this comes at the cost of new uncertainty driven by the introduction of additional model parameters, including representative leaf dimension and representing the entire ﬂow ﬁeld within the canopy.

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To overcome this challenge in a way that is applicable to the hundreds of datasets in the AmeriFlux, FluxNet, and National Ecological Observatory Network (NEON) repositories, an alternative approach is proposed

here that estimates Taero for the unique conditions when sensible heat flux is near zero (defined here as |H| < 30 W/m2, roughly commensurate with the accuracy of the eddy covariance flux measurements

of sensible heat). In this case, the Taero and the Ta must be equivalent, though the Ta and Tsurf likely are not. Thus, by assuming Taero = Ta when H is small, it is possible to explore how the ratio

Taero ΧT ¼ K=K (5) Tsurf

varies as a function of time of day and season. The diurnal patterns of ΧT can then be used to estimate 2 the Taero for periods when |H| > 30 W/m , separately for dormant and growing seasons. This approach assumes that the ΧT does not depend on the magnitude of H, and we apply two checks on the reason- ableness of this assumption. First, in the OF (where measurements are typically made above the rough-

ness sublayer), we also estimated Taero by inverting equation (4), assuming a range of zo,h between 0.001 and 0.2 times canopy height (hereafter h), and compared the resulting inferred ΧT to that determined by equation (5). Second, as described in more detail in section 2.2.4, we explore the extent to which varia-

tion in the roughness lengths for heat (which depends on our estimates of ΧT) and momentum agree with theoretical predictions, focusing speciﬁcally on their variation with wind regime and leaf area

index (LAI). When calculating the ΧT, Ta and Tsurf are converted to degrees Kelvin to avoid dividing by a number that is close to zero.

As a ﬁnal step, Taero was reconstructed in all three sites for all time periods for which Tsurf data were available. Procedural uncertainty is incorporated by adopting an iterative, probabilistic approach. Speciﬁcally,

normal distributions of the ΧT for each hour of the day were created using the empirical mean and standard error of the mean describing ΧT values in each bin. These distributions were created separately for the dormant season and the growing season. Next, for each half‐hourly measurement period, n = 20 estimates of

Taero were generated by drawing ΧT randomly from these distributions. In the OF, a second estimate of continuous Taero was also constructed using the mean ΧT derived from MOST (hereafter Taero,MOST) considering a wide range of possible zo,h. Ultimately, these reconstructions were constrained by ensuring that Taero must fall in the range bounded by the observed Tsurf and Ta for each half‐hourly measurement period. 2.2.4. Air Temperature in the Surface Layer (Textrap,0–10 and Textrap,150) Finally, the analysis is extended vertically to estimate direct land‐cover impacts on mean air temperature both near to the surface, and also at the transition between the surface layer and the mixed layer, where air temperature does not vary appreciably with additional gain in elevation (see Figure 1). Having developed

a strategy for determining Taero (see previous section), only an estimate of zo,h is required to use equation (4) to extrapolate Ta into the surface layer. The zo,h and zo,m will vary across sites as a function of canopy structure and external conditions (Brutsaert, 1982). Within a site, when leaf area and height are

stationary, the zo,h also varies substantially as a function of wind and temperature regimes. * * Thus, the zo,h was estimated as a function of u by inverting equation (4) within 15 discrete u bins encom- * passing the range of u observed at each site, and assuming zd is 0.6h. The analysis was performed separately for the growing season and dormant season, and the bin edges were selected as the 10th, 20th, …, 100th per- centiles of the observed range of u* in each site season. This analysis was limited to daytime conditions excluding extremely unstable (ζ < − 2) or very stable (ζ > 1) conditions and excluding conditions of near zero H.

Next, while zo,m is not strictly necessary to extrapolate mean air temperature into the surface layer, under- * standing its relation with u and zo,h provides additional constraints about the plausibility of the approach and about conditions when the momentum and heat roughness lengths are dissimilar. Existing theory sug- * gests that zo,h/zo,m should increase as a function of the roughness Reynolds number Re* = u zo,m/ν (where ν is the kinematic viscosity of air, Brutsaert, 1982; Kohsiek et al., 1993). It is also expected to decrease as LAI increases. Increasing LAI restricts heat exchange to the upper canopy (Brutsaert, 1982; Verhoef et al., 1997; * Yang & Friedl, 2003), effectively reducing the zo,h. Thus, variation in estimated zo,m as a function of u was explored by inverting equation (3) and using a binning approach analogous to that described for the zo.h.

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Finally, continuous estimates of zo,h were developed by binning the data * according to u and assigning the half hourly mean zo,h as the mean * inferred zo,h within each u bin.

With zo.h estimated, the final step is to extrapolate the air temperature into the surface layer using equation (4). We consider two reference locations: near the surface (taken as the integrated mean temperature over the first ′ ′ z =10 m, hereafter Textrap,0–10) and at z =150 m, where the mean gradient dT/dz is sufficiently small and air temperatures are representative of those at the boundary between the surface and mixed layer (hereafter ′ Textrap,150). The choice of z =150 m ensures that the Coriolis effects remain small and do not appreciably modify MOST.

This approach effectively tunes zo,h so that the extrapolated temperature profile passes through the measured Ta at zm. This is an imperfect solution in the forested sites where Ta is measured in the roughness sublayer, and where MOST does not strictly apply. The influence of the canopy elements on the shape of the extrapolated profile is expected to diminish as z increases into the surface layer. However, near the surface, forcing the

proﬁles through measured Ta introduces some positive bias in the forests, where canopy elements reduce the near‐surface dT/dz, elevating the Ta at the top of the tower over that which would be expected from MOST theory.

Uncertainty in zo,h and Taero is incorporated into extrapolations through an iterative, parametric approach. Speciﬁcally, for each “ith” half‐hourly

measurement period, n = 20 proﬁles were produced. Each time, Taero was speciﬁed using the parametric approach described in the previous

section. Likewise, each half‐hour, the zo,h was selected from a normal distribution with a mean and standard deviation determined from the distri- * bution of zo,h from the u bin associated with the ith measurement period. 2.3. Flux Tower Observations and Other Relevant Data * Figure 2. Difference in (a) Tsurf and (b) Ta between the old grass field (OF) Eddy covariance u and H fluxes were measured above the canopies using and either the pine plantation (PP) or hardwood forest (HW). Data are triaxial sonic anemometery (CSAT3, Campbell Scientific, Logan, UT, shown as the difference between grassland and forest temperature, such that USA). Eddy covariance data were collected at 10 Hz, and fluxes were pro- a positive value indicates the grassland temperature is warmer. The error bars indicate the standard error of the mean, which is often very small. cessed in real time as described elsewhere (Novick et al., 2004; Stoy et al., 2008, following a 2‐D coordinate rotation). The measurement heights for the eddy covariance instrumentation and all ancillary meteorological instruments used in this study were 2.7, 21, and 39 m in OF, PP, and HW, respectively. Fluxes were screened to remove measurements collected when the footprint was not representative of the study ecosystem as described elsewhere (Novick et al., 2015). Data were then screened for periods of insufficient turbulence and gapfilled using the online ReddyProc gapfilling tool (Wutzler et al., 2018). The incoming and outgoing shortwave and long‐wave radiation were measured using a four‐component radiometer (CRN4 or CRN1, Kipp & Zonen, Delft, The Netherlands) from 2005 to 2008, which comprises the study period for this investigation. Mean air temperature was measured at the same height using an HMP‐45C Temperature/relative humidity probe (Vaisala, Finland). Friction velocity (u*) was calculated from the high‐frequency wind velocity data.

3. Results 3.1. Surface and Air Temperature The surface temperature is substantially cooler in the forests compared to the grasslands. The difference in

Tsurf is greatest during midday periods (Figure 2), especially during the growing season, when it often exceeds 5 °C (see Juang, Katul, et al., 2007, and Zhang et al., 2020, for a thorough discussion of the mechan-

ism driving Tsurf dynamics). In contrast, the differences in potential air temperature measured on the tower

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are much smaller (compare Figure 2a to Figure 2b), rarely exceeding 1 °C on average, and nearly negligible when comparing OF to PP over the entire diurnal cycle.

3.2. Relations Between Aerodynamic Temperature and Surface Temperature

When |H| is small and Taero is assumed equivalent to Ta, the diurnal and seasonal patterns of the ratio XT = Taero/Tsurf agree reasonably well across sites and seasons (Figure 3). During the growing season, the ratio of Taero to Tsurf is typically ΧT= 0.985–1.0 (K/K) in all three sites, with slightly lower ratios during midday, and in the OF and HW sites. In the dormant

season, ΧT is sometimes estimated to be exceed 1 during nighttime. The uncertainty in XT is of similar magnitude during the dormant and growing season.

In the OF, the empirical ΧT (estimated from equation (5)) falls within the range of uncertainty of the ΧT determined by inverting equation (4) most of the time (Figure 4). This is true for both the dormant and growing seasons. During the dormant season, agreement is somewhat poorer during the night, though the confidence intervals nonetheless frequently overlap, and the diurnal patterns are similar. Figure 3. The ratio of aerodynamic to radiometric surface temperature (K/ K) inferred from periods when sensible heat flux was small (|H| < 30 W/m2). During daytime, the continuous Taero (constructed from the distributions – The error bars show one standard error of the mean. of XT and Tsurf) is typically 0.5 2 °C higher than the measured Ta at the top of the tower, but 1–2 °C cooler than the radiometric Tsurf (Figure 5). The largest differences between Taero, Ta, and Tsurf were observed in the OF; the three temperature metrics were more similar in HW and difficult to distinguish in PP (Figure 5).

In the OF, the Taero derived from the empirical approach (red line in Figure 5a) and from MOST (Taero, MOST; blue line in Figure 5a) agree well.

3.3. The Inferred zo,h and zo,m In all sites, the momentum roughness length inferred from equation (3) increased as a function of u* (dashed lines in Figures 6a–6c). In the OF and HW, it was lower in the dormant versus the growing sea-

son (compare gray to black dashed lines in Figures 6a and 6c). Seasonal differences in zo,m were less pronounced in the evergreen PP. In the forests, the zo,m was usually constrained to within 0.05–0.15 times the height of the canopy (h), which is close to the expected value of 0.1h derived from a large corpus of

experiments over many surfaces. In the old grass ﬁeld, the zo,m was inferred to be substantially higher than 0.lh for high u*; however, the overall h in the OF is small (~0.5 m) so that small errors can translate

to large differences in the ratio of zo,m to h even if variability in zo,m across seasons may be small. The zo,h inferred from equation (4) was almost always lower than the zo,m in the forests (compare solid to dashed lines in Figures 6b and 6c), with greatest differences between the two roughness lengths observed under * * high u . In the grass ﬁeld, zo,h was greater than the inferred zo,m when u was low and lower than zo,m when u* was high (Figure 6a).

The ratio of zo,m to zo,h increased with increasing roughness Reynolds number in the OF and was higher in the dormant season versus the growing season. In the forests, the sensitivity of zo,m/zo,h to Reynolds number was less pronounced but was lower in the growing versus the dormant season, especially in the HW forest.

The zo,m/zo,h was lower in PP when compared to HW and OF. During the growing season when roughness Reynolds number was especially high (relative to the range experienced in each site), the zo,m/zo,h of the forests was lower than that of the grassland.

3.4. Surface Layer Air‐Temperature Extrapolations

During growing season daytime periods, the extrapolated near‐surface air temperature (Textrap,0–10) is substantially warmer (by >2 °C) over the grassland when compared to the forests (Figure 7a). However, at night,

the Textrap,0–10 is cooler in the grassland (by ~1 °C; Figure 7b). In other words, during the growing season,

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forests reduce the Textrap,1–10 during daytime periods but keep it somewhat elevated at night, lowering the overall diurnal temperature range.

During the dormant season, Textrap,0–10 is also warmer over forests compared to grasslands (by 1–2 °C; Figure 7c) during the day, but land cover effects are difﬁcult to distinguish at night (Figure 7d). Land cover affects

temperature extrapolated to 150 m (Textrap,150) similarly, with one exception: during growing season daytime periods, while the Textrap,0–10 above the HW forest is substantially cooler than the grassland near the surface,

the Textrap,150 is indistinguishable above the two ecosystems (Figure 7a).

3.5. Summary of Temperature Differences Within and Across Sites and Seasons Overall, the forests have a cooling effect that is greatest during the daytime, and during the growing season (Figure 8). Of all the temperature metrics, forests affect surface temperature the most—reducing it by more than 5 °C during midday growing season periods. The aerodynamic temperature is also substantially lower over forests as compared to grasslands, by up to 3–4 °C during midday growing season periods (Figures 8a and 8d). During daytime periods, the extrapolated air tem-

perature near the surface (Textrap,1–10) is also cooler over the forests by several degrees. However, the Textrap,1–10 tends to be warmer over forests at night, at least during the growing season. This reduces the sur- Figure 4. The empirically derived XT (K/K) agrees reasonably well with that inferred in the old grass ﬁeld using Monin‐Obukhov Similarity face air cooling effect of forests over the full diurnal cycle to between Theory (MOST). The red line is the same as that shown in Figure 3. The gray ~0.5 and 1 °C. lines show the range of XT predicted from MOST by inverting equation (4) The effects of land cover on extrapolated air temperature near the top of and using a range of assumed zo.h/h varying from 0.001 to 0.2. the surface layer (Textrap,150) are similar to the effects of land cover on Textrap,1–10 during the dormant season; however, during the growing season, land cover has an overall lower effect on Textrap,150 during the daytime, and at night the Textrap,150 is inferred to be higher over forests than grasslands. Thus, over the full diurnal cycle, forests have a negligible effect (in the case of the pine site) or

Figure 5. Diurnal patterns of Tsurf, Ta, and Taero at the three sites and across two seasons. In the (a and d) old grass ﬁeld (OF), where observations of Ta are typically made in the surface layer, also shown is the Taero inferred from estimate of XT determined by inverting equation (4) assuming a range of zo,h between 0.001 and 0.2 times canopy height.

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Figure 6. (a–c) The ratio of inferred momentum roughness length (zo,m, dashed lines) and inferred roughness length for heat (zo,h, solid lines) as a function of u* for the dormant (gray) and growing season (black). (d) The ratio of zo,m to * zo,h as a function of the roughness Reynolds number (= u zo,m/υ) for growing season (closed symbols) and dormant season (open symbols). In (d), theoretical curves for three different ecosystems published by Brutsaert (1982) are shown for reference (sourced from Figure 4.24 in the Brutsaert study). That the old grass ﬁeld (OF) is more similar to the theoretical curve for corn versus grass is not altogether surprising, given the mesic conditions of the study sites and the overall high productivity of the grassland.

minor warming effect (in the case of the hardwood site) on the extrapolated air temperature near the top of

the surface layer. Finally, differences in measured Ta are relatively small across sites, regardless of season or time of day and reﬂecting the inﬂuence of canopy elements in the roughness sublayer, which are accounted for in the extrapolated air temperatures.

Figure 7. Surface layer air temperature extrapolations across sites, time of day, and season. The thick line shows the mean extrapolated temperature (averaging across all half‐hourly periods except those for which conditions were extremely stable, i.e., ζ > 1 or very unstable, i.e., ζ < − 5). Each half‐hourly estimate of Taero and zo,h incorporated uncertainty in the speciﬁcation of each, as described in the methods. The thin lines show one standard error of the mean.

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Figure 8. A summary of the difference in the temperature metrics between the grassland and the forests over the diurnal cycle and between seasons. A positive ΔT indicates that the grassland is warmer. The asterisks on the right‐hand side of each panel show the mean value of each metric averaged over the entire course of the day.

4. Discussion 4.1. The Effects of Reforestation on Air Temperature There is great interest in whether reforestation and other managed land cover changes favorably impact microclimate in ways that combat rising air temperature (Bright et al., 2017; Davin et al., 2014; Seneviratne et al., 2018). To date, the majority of work on the topic has focused on land cover impacts on surface temperature, which can be inferred from outgoing long‐wave radiation observed by ﬂux towers or

satellites provided the surface emissivity is known. The analysis of Tsurf here conﬁrms prior work showing that across many temperate ecosystems, reforestation tends to have a “skin” cooling effect (Bright et al., 2017; Burakowski et al., 2018; Juang, Katul, et al., 2007; Zhang et al., 2020). The effect is most pronounced during daytime periods in the growing season—the time when plants and many animal species are most physiologically active. Ultimately, assessing the climate mitigation and adaptation potential of reforestation requires an understanding of how land cover change affects not only surface temperature but also air temperature near the surface (where most physiological functions occurs) and at the interface of the surface and mixed layer (an important point of connection between land surface and climate models). To this point, extending land cover effects on surface temperature to effects in the atmosphere has been difﬁcult and is fraught by nonli- nearities. For this reason, seemingly paradoxical results of warmer surfaces underlying cooler air and vice versa have been reported (Baldocchi & Ma, 2013). Such was the case in two of the study sites here: when

averaged over the entire data record, the Ta recorded on the hardwood forest flux tower is 0.2 °C warmer than the recorded grass tower Ta, despite the fact that the hardwood forest Tsurf is ~1 °C cooler than the grass field at the annual time step. This paradox extends to regional‐scale assessments. While most surface temperature studies suggest that forests have a cooling effect in the temperate zone (Bright et al., 2017; Juang, Katul, et al., 2007), some prior work focused on measured or modeled air temperature comes to the opposite conclusion (Bonan, 1997; Trail et al., 2013). Much of this difficulty can be linked to the shape of the air temperature profiles in the roughness sublayer (see Figure 1). Given what we know about near‐surface air temperature gradients in the roughness sublayer, comparing air temperature measured in the roughness sublayer above a forest to air temperature measured in the surface layer above a grassland is an “apples to oranges” comparison, heavily influenced by canopy

structure on near‐surface mean dTa/dz gradients. It is quite possible that conclusions reached about land cover effects on near‐surface Ta depend largely on the choice of measurement height.

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Here to allow an “apples‐to‐apples” comparison of how reforestation affects the air temperature, a different

comparison strategy is required. First, an approach to estimate the aerodynamic temperature Taero that does not rely on MOST, and thus is robust to whether tower observations occur within the roughness sublayer, is

presented. Cross‐site comparisons of Taero lead to the conclusion that forests are cooler, especially during midday periods, though the magnitude of the cooling effect is not as large when using Taero versus Tsurf as the reference (Figure 8). Nonetheless, to the extent that Taero (a conceptual computing quantity) is linked to the actual air temperature within the canopy, these results suggest that the radiometric cooling effects of forests extend to within the canopy air layer that supports the majority of terrestrial physiological processes. A strategy for extrapolating flux tower observations to estimate mean gradients of temperature across the surface layer is also proposed. This strategy conceptually “flattens” the canopies and replaces them with a rough surface characterized by two roughness heights—one for momentum absorption and one for heat transfer. This “flattening” removes complexities introduced by canopy effects on gradients within the canopy and in the roughness sublayer. The results show that in the first 10 m of the surface layer, air temperature extrapolated in this way is cooler by several degrees C above the forests (relative to the grassland) during growing season daytime periods, though it is warmer at night. This study may be the first instance where such a conclusion has been reached, given the uniqueness of the study site and the data analysis conducted. Closer to the top of the surface layer, land cover has a smaller influence on extrapolated air temperature, and at least in the case of the hardwood forest, the upper reaches of the surface layer are actually slightly warmer relative to the grassland during the growing season. Or in other words, our conclusions about reforestation affects on air temperature are quite different whether we consider the lower or upper boundary of the surface layer. The latter may be especially relevant for climate modeling work.

4.2. The Approach for Estimating Taero and zo,h: Uncertainties and Opportunities Even when all measurements are made above the roughness sublayer, using ﬂux data combined with

MOST to describe near‐surface temperature proﬁles is made difﬁcult by the fact that both Taero and zo,h are unknown in equation (4). The existing suite of solutions typically involve either (a) using surface temperature instead of Taero, which may overestimate the true Taero as we have shown here (see Cleugh et al., 2007, for a discussion of biases associated with this approach), or (b) prescribing zo,h by making a priori assumptions about its relation to zo,m that are based on a limited number of studies from relatively few biomes (Brutsaert, 1982; Sun & Mahrt, 1995). Here a new approach is presented that

relies on the assumption that, when H is sufficiently small, the Taero and Ta are equilibrated. We then explored how the ratio of the inferred Taero and Tsurf (e.g., XT) vary across seasons and time of day, and used the results to reconstruct half‐hourly Taero time series. This approach has its own issues as it may be sensitive to processes that further decouple Taero and Tsurf when H is large. However, it is encouraging that in the OF where MOST often applies, the Taero derived in this way is similar to Taero inferred by inverting equation (4) while considering a range of potential zo,h (Figure 4). This approach would benefit from further testing in other sites where fluxes are monitored well above the surface and emitted longwave radiation measurements as well as aspirated air temperature measurements are available.

Having determined Taero using the approach proposed here, we completed our analysis by inverting equation (4) to determine zo.h. This approach is, to say the least, imperfect as it applies MOST to data collected in the roughness sublayer and essentially tunes zo,h so that the modeled mean temperature is forced through measured Ta. The consequences of this approach are discussed in the methods, but likely result in an overestimation of Ta at higher distances above the surface, which in turn would reduce the apparent cooling effect of forests. Nonetheless, conﬁdence in the approach is gained by

exploring how zo,h relates to zo,m (where the latter is inferred by inverting equation (3)). Most of the time, the zo,h is less than the zo,m,(Figures 6a–6c) conﬁrming results from prior work (Brutsaert, 1982; Stewart et al., 1994; Verhoef et al., 1997) and the theoretical expectation that zo,m is impacted by form and viscous drag, whereas heat exchange at the interface is primarily molecular leading to lower zo,h. An exception is observed in the grassland during periods of low u* (Figure 6a); however, it should be

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noted that due to the low canopy height of the grassland, the actual zo,h and zo,m are themselves quite small (i.e., <50 cm), and small uncertainties that may increase or decrease zo,h and zo,m by even 10– 20 cm can result in large differences in the ratio of the roughness lengths to canopy height, and to each other.

Moreover, in OF and HW, the zo,h/zo,m is also lower during the growing season than the dormant season (Figure 6d), consistent with expectations that the ratio tends to be reduced as LAI increases (Brutsaert, 1982; Verhoef et al., 1997; Yang & Friedl, 2003). Also, except for cases of very high LAI (e.g., the PP

and HW in the growing season), the ratio of zo,h to zo,m tends to increase as a function of Reynolds roughness number (Figure 6d) as predicted by theoretical considerations over rough surfaces (Brutsaert, 1982). 4.3. Summary and Implications Making the connection between land cover, surface temperature, and air temperature is becoming necessary for obtaining a complete picture of the climate mitigation and adaptation potential of managed land cover changes, including reforestation but also conservation agriculture practices (Georgescu et al., 2011; Kaye & Quemada, 2017). This energy balance perspective on the climate mitigation and adaptation potential of reforestation is especially relevant right now. Intensive reforestation has been proposed as a strategy to

remove large amounts of CO2 from the atmosphere (Canadell & Raupach, 2008), yet the sustainability of the terrestrial carbon sink is in doubt due to limitations imposed by energetics (Baldocchi & Penuelas, 2019), nutrients (Oren et al., 2001), and increasing occurrence of drought, insect outbreaks, and forest fires (Frank et al., 2015). The primary conclusion of the analysis here—which was focused specifically on reforestation—is that the widely documented cooling effect of temperate forests on surface temperature extends to the aerodynamic temperature, and the near‐surface layer temperature once canopy effects on dT/dz gradients are accounted for. Overall, reforestation cools the air to a lesser extent than it cools the surface, but the effects are not minor. They amount 0.5 to 1 °C at the annual timescale and are substantially greater (e.g., cooling of 2–3 °C) during daytime growing season periods, when plants are most active physiologically, and when wind speeds are greatest. Finally, the cooling effect of forests diminishes near the transition region between the surface layer and mixed layer, implying that land cover effects on forest temperature are perhaps less significant at heights where land surface and climate models typically interact.

Appendix A: Atmospheric Stability Correction Functions The ζ represents the ratio of convective to mechanical production of TKE. It is determined from

′ − ′ ζ ¼ z ¼ k·z ·g·H ; *3 (A1) L ρ·cp·T·u

where T is the air virtual potential temperature in K, and all other parameters were previously defined. When ζ > 1, the flow may be characterized as strongly stable where much of the mechanical production of TKE is destroyed by overcoming buoyancy forces instead of viscous forces as expected from a conventional turbulent energy cascade. In those conditions, turbulence is not “fully developed,” and meandering or non- turbulent phenomenon dominate the transport of momentum or heat. These conditions lead to a breakdown in the constant flux assumption. When −ζ > 2, the flow may be characterized as dynamically convective where the constant flux assumption becomes questionable due to asymmetric transport of sweeps and ejec- tions (e.g., Li et al., 2018). For the atmospheric surface layer where the turbulent fluxes of momentum and heat are presumed constant with z′, the diabatic correction factors are (Campbell & Norman 1998) "# 1 1 þ ðÞ1−16ζ 2 Ψ ¼ 2ln ; Ψ ¼ 0:6Ψ when ζ < 0ðÞ unstable (A2) h 2 m h

and

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Ψh ¼ Ψm ¼ −6lnðÞ 1 þ ζ : when ζ > 0ðÞ stable (A3)

For the roughness sublayer, deviations from these forms have been reported (Kaimal and Finnigan, 1994), though no universal functions have been proposed yet (theoretically or empirically except for some idealized cases such as uniform leaf area density).

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