A practical exploration of land cover impacts on surface and air temperature when they are most consequential

Widespread shifts in land cover and land management (LCLM) are being incentivized as tools to mitigate climate change, creating an urgent need for prognostic assessments of how LCLM impacts surface energy balance and temperature. Historically, observational studies have tended to focus on how LCLM impacts surface temperature (T surf), usually at annual timescales. However, understanding the potential for LCLM change to confer climate adaptation benefits, or to produce unintended adverse consequences, requires careful consideration of impacts on both T surf and the near-surface air temperature (T a,local) when they are most consequential for ecosystem and societal well-being (e.g. on hot summer days). Here, long-term data from 130 AmeriFlux towers distributed between 19–71 °N are used to systematically explore LCLM impacts on both T surf and T a,local, with an explicit focus on midday summer periods when adaptive cooling is arguably most needed. We observe profound impacts of LCLM on T surf at midday, frequently amounting to differences of 10 K or more from one site to the next. LCLM impacts on T a,local are smaller but still significant, driving variation of 5–10 K across sites. The magnitude of LCLM impacts on both T surf and T a,local is not well explained by plant functional type, climate regime, or albedo; however, we show that LCLM shifts that enhance ET or increase canopy height are likely to confer a local mid-day cooling benefit for both T surf and T a,local most of the time. At night, LCLM impacts on temperature are much smaller, such that averaging across the diurnal cycle will underestimate the potential for land cover to mediate microclimate when the consequences for plant and human well-being are most stark. Finally, during especially hot periods, land cover impacts on T a,local and T surf are less coordinated, and ecosystems that tend to cool the air during normal conditions may have a diminished capacity to do so when it is very hot. We end with a set of practical recommendations for future work evaluating the biophysical impacts and adaptation potential of LCLM shifts.


Introduction
Land cover and land management (LCLM) exert a powerful influence on surface and air temperature, at local to planetary scales (Diffenbaugh 2009, Bright et al 2017, Williams et al 2021, De Hertog et al 2023. The influence of canopy structure and function on albedo, latent heat flux (or evapotranspiration), and sensible heat exchange can cause annually-averaged changes in surface temperature of 1-2 K or more between co-located ecosystems that experience the same macro-climate but that differ in LCLM (Juang et al 2007, Luyssaert et al 2014, Hemes et al 2018, Zhang et al 2020, Duman et al 2021. At larger scales, plant-driven variability in latent heat, sensible heat, and momentum fluxes can cause changes in cloudiness, precipitation, and circulation patterns that feed back onto surface temperature regimes through non-local effects and 'teleconnections' (Swann et al 2012, Duveiller et al 2018, Lague et al 2019, Winckler et al 2019, Cerasoli et al 2021, De Hertog et al 2023. Now, as scientists and societal leaders grapple with the urgent challenge of stopping climate change, the need to understand the 'biophysical impacts' of LCLM is especially great (Jackson et al 2008). 'Nature-based climate solutions' , which rely on LCLM shifts to enhance ecosystem CO 2 sequestration and storage, are rapidly gaining support in public and private spheres (Nolan et al 2021, Novick et al 2022a, 2022b, Seddon 2022. However, the holistic climate impact of these strategies remains poorly constrained, with biophysical impacts on temperature representing a major source of uncertainty (Hemes et al 2021, Williams et al 2021, Novick et al 2022b. In some cases, LCLM shifts can directly reduce local temperature (Hemes et al 2018, Zhang et al 2020, potentially functioning as a tool for local climate adaptation in addition to global climate mitigation (Seneviratne et al 2018). However, in other situations, a LCLM change designed to enhance carbon uptake can increase temperature (Lee et al 2011, Lombardozzi et al 2018-an adverse consequence at local scales and one that could overwhelm the net cooling from increased carbon uptake at planetary scales (Williams et al 2021). For these reasons, considering biophysical impacts on energy budgets is critical when designing policies that incentivize widespread changes in LCLM (Duveiller et al 2020, Anderegg 2021, Novick et al 2022b.
These biophysical impacts originate from direct, local effects on surface temperature (T surf ), which is arguably the most relevant driver of plant biological function (Luyssaert et al 2014) and it is a first order driver of downstream effects on air temperature. All else being equal, an increase in albedo will tend to decrease T surf (Lee et al 2011, Bright et al 2017. Of course, in the natural world, all else is rarely equal. Ecosystems with more leaf area tend to have lower albedo, but they also tend to evaporate more water, which in isolation promotes local surface cooling (Bonan 2008). Likewise, taller ecosystems tend to be more aerodynamically rough, which increases the efficiency of heat transfer from the surface to the air, thereby reducing T surf (Burakowski et al 2018). The interplay of these three mechanisms has been well-studied, at least for some LCLM shifts. We know, for example, that reforestation tends to promote local surface cooling in the tropics and much of the temperate zone (Li et al 2015, Locatelli et al 2015, Bright et al 2017, Zhang et al 2020 but can lead to local warming in boreal climates where snow-masking albedo effects dominate (Lee et al 2011, Li et al 2015. Likewise, irrigation is widely recognized to have a surface cooling effect driven by increased evapotranspiration (Kueppers et al 2007, Mueller et al 2016, Li et al 2020, but see Gormley-Gallagher et al 2022. However, with notable exceptions (Lobell et al 2008, Teuling et al 2010, Baldocchi and Ma 2013, Seneviratne et al 2018, most data-driven explorations have focused on long-term T surf impacts at the annual timescale. Systematic evaluations of how biophysical impacts differ across seasons, or at night versus during the day, are scarcer. We also lack systematic frameworks for understanding if a long-term biophysical cooling (or warming) effect persists during periods of extreme heat. These are important unknowns, because the consequences of a temperature shift depend very much on when it occurs. For example, a local cooling effect may be most important on hot summer days, when elevated temperatures are especially damaging for ecological and human systems. Moreover, the drivers of longer-term shifts on T surf (e.g. albedo, roughness, and evapotranspiration) can be traced to features of canopy structure that tend to be relatively constant within a season. Thus, LCLM impacts on T surf at daily to weekly timescales could reflect an entirely different set of mechanisms, including stomatal responses to rising atmospheric aridity (Novick et al 2016, Grossiord et al 2020, or changes in incident radiation that frequently co-occur with rising T a (Fu et al 2022).
While T surf is a first-order driver of surface energy budgets and a key regulator of plant function, the near-surface air temperature (T a,local ) is an equally relevant target for climate mitigation and adaptation (Winckler et al 2019, Novick andKatul 2020). This is the temperature experienced by humans, and since air moves, wind-driven advection is one mechanism by which land cover impacts on T a,local can be propagated across the landscape (Cohn et al 2019, Li and Wang 2019, Barnes et al 2023. Overall, land cover effects on T a,local have historically been challenging to evaluate. Part of problem relates to data scarcity, as no technique yet exists to directly measure near-surface T a,local from space, and ground-based measurements are most often constrained to data from meteorological stations, which tend to be situated in grass fields. Land cover impacts on T a,local are also conceptually more complex as they integrate a wider range of boundary-layer processes including shifts in boundary layer height (Helbig et al 2021) and entrainment of air from aloft (Fisch et al 2004).
Finally, whether the target is T surf or T a,local, most investigations of land cover impacts on temperature parse the results on the basis of plant functional type (PFT), for example by comparing evergreen forests to deciduous forests to grasslands to croplands (Lee et al 2011, Bright et al 2017, Duveiller et al 2020, Zhang et al 2020. While the PFT-based classification is convenient, within a given PFT, impacts on T surf or T a,local can vary substantially (Alkama and Cescatti 2016, Duveiller et al 2020, Zhang et al 2020, likely reflecting heterogeneity in climate conditions or canopy structure and function. Approaches that link changes in T surf and T a.local to mappable climate and canopy characteristics would enhance our predictive understanding LCLM impacts on local temperature, within and across PFTs. The objective of this study is to address these knowledge gaps and limitations, by exploring the coupled dynamics of T surf and near-surface T a across a range of timescales, and across broad climatic and land use gradients. Our primary source of data is long-term AmeriFlux tower records, which provide information necessary quantify land-cover driven changes in T surf and T a,local and attribute them to underlying mechanisms. We will meet the objective by pursuing three research questions: [Q1] To what extent, and by what mechanisms, are LCLM impacts on T surf coordinated across seasons and across the diurnal cycle?; [Q2] To what extent does LCLM mediate the within-season dynamics of T surf as macro-climate warms?; and [Q3] To what extent does LCLM impact T a,local and its coupling with T surf over long-and short time-scales? Our approach is designed to provide actionable guidance for understanding biophysical impacts of LCLM shifts. Towards that end, observed temperature shifts will be evaluated for how they relate to relevant climate drivers and ecosystem characteristics that can be easily mapped or estimated from remote sensing. These include albedo, evapotranspiration, and canopy height. We will also approach much of the analysis by evaluating the extent to which land cover impacts on near-surface T a can be predicted by land cover impacts on T surf , since an abundance of T surf data is available from remote sensing (Farella et al 2022).

Flux tower data
Long-term eddy covariance flux tower records were acquired from the AmeriFlux network  for 130 sites in the United States that have adopted a CC-By-4.0 data sharing license. Metadata on elevation, International Geosphere-Programme (IGBP) PFT classification, and Köeppen climate zone were also acquired from AmeriFlux. The analysis is constrained to these 130 AmeriFlux sites for a few reasons. First, the majority of AmeriFlux sites have adopted an open CC-By-4.0 data sharing license. Next, our approach requires that tower data be corrected by a gridded temperature product that is relatively insensitive to fine-scale impacts of land cover (see section 2.2.3). NOAA's DAYMET product meets this requirement but is only available for North America. Finally, our approach requires an estimate of canopy height, which frequently must be determined by searching previously published work from individual sites-a labor-intensive step that limits the number of sites that can be included in the analysis. Nonetheless, these 130 sites span a broad range of biomes, including 25 grasslands, 29 croplands or cropland mosaics, 22 deciduous forests, 35 evergreen or mixed forests (which are grouped together since there were only 3 mixed forest sites), 20 shrublands or savannahs (which are grouped together), and 8 wetlands. While 'temperate' (n = 55) or 'warm continental' (n = 24) sites are well represented in the dataset, it also includes 19 sites classified as 'Dry semi-arid or arid' and 18 sites classified as 'boreal,' 'sub-arctic,' or 'polar' (see supplementary information, or SI, section S1 and table S1 for details). However, tropical sites are not represented in this dataset.
Flux tower measurements used in this study include: latent heat flux (LE, W m −2 ), which is an expression of evapotranspiration in units of energy, sensible heat flux (H, W m −2 ), various proxies for temperature (K) described in more detail in the next section, incident and outgoing short-wave radiation (R SW,in and R SW,out , respectively), incident and outgoing long-wave radiation (R LW.in and R LW,out , respectively) and horizontal wind speed (WS, m s −1 ). Analysis of LE and H was limited to mid-day periods (between 10:00-14:00 h local time) when turbulence is well developed. Thus, traditional approaches for filtering flux tower data for periods of insufficient turbulence (e.g. using friction velocity, Pastorello et al 2020) were not applied, since they primarily screen night-time data. Threshold filters were applied to exclude LE or H exceeding ±1000 W m −2 . Information on canopy height was obtained from AmeriFlux or extracted from the published literature. In a few cases, it was estimated based on the IGBP classification and site photographs. Finally, some analyses rely on an estimate of the aerodynamic conductance for heat transfer (hereafter G a ), which was estimated by inverting measured sensible heat fluxes as described in Novick and Katul (2020).

Temperature metrics
We consider multiple temperature metrics that together provide a holistic perspective of LCLM impacts on surface and air temperature (see summary in table 1).

Land surface temperature (T surf )
The T surf , which is akin to the 'skin temperature,' represents the projected temperature of all elements of the ecosystem that are unobscured when viewed from aloft. In dense forests, it mostly represents the temperature of sunlit canopy leaves. In sparser ecosystems, it includes both vegetation and background soil. T surf is sensitive to variation in radiation and features of canopy structure and function that impact albedo, evapotranspiration, and G a . While it can be estimated from spaceborne retrievals of thermal infrared imagery (Farella et al 2022), here it was inferred radiometrically from measurements of outgoing long-wave radiation using the Stefan-Boltzmann law: . The emissivity (ε s ) was prescribed from an empirical relationship with albedo (α) (ε s = 0.99 − 0.16 α, Juang et al 2007), where α is determined from the measured R SW,out /R SW,in at midday.

Local near-surface air temperature (T a,local )
A consistent definition of near-surface air temperature that can be applied across ecosystems of varying heights has been elusive (Bonan 2008, Körner and Hiltbrunner 2018, Winckler et al 2019. A good starting point is the air temperature measured ∼2 m above the ground (the so-called 'screen height'). Most long-running meteorological stations measure and report air temperature in this way. Since these stations are usually situated in short grass fields over which the roughness sublayer is very thin, weather station air temperature measurements are made in the surface layer where temperature varies predictably with elevation according to similarity theory (Campbell and Norman 2000).
Outside of meteorological station networks, one of the best sources of data on T a,local comes from flux tower networks, which provide infrastructure to enable T a measurements above the canopies of even very tall forests. However, forested flux towers often reside entirely in the roughness sublayer, where similarity theory does not apply, and canopy structure exerts a strong influence on near-surface temperature profiles (Novick and Katul 2020). Consequently, comparing T a measured on a forest flux tower to T a measured on a grassland or cropland flux tower may primarily reflect the tower measurement heights, as opposed to biophysical impacts. To minimize this bias, we adopt the approach described by Novick and Katul (2020) to extrapolate air temperature to a reference elevation in the surface layer that scales with canopy height, and where canopy effects should be minimal. The approach carries some uncertainties, including in the estimation of the G A (see discussion in SI section S2), which were reduced by filtering T a,extrap whenever it exceeded logical thresholds (e.g. |T a,extrap | > 60 • C) or when the difference between T a,extrap and the tower-measured air temperature (T a,tower ) exceeded 15 • C. Importantly, all analyses were also repeated using the T a,tower as the proxy for T a,local (as opposed to T a,extrap , see results in the SI). Overall, conclusions do not fundamentally change whether T a,extrap or T a,tower is used to represent T a,local , though LCLM effects on T a,local are less obvious when using T a,tower .

Macro-scale near-surface air temperature (T a,macro )
To understand how land cover affects local temperature at scales over which macro-climate can vary, it is necessary to have a reference against which T surf and T a,local can be corrected and thus become intercomparable. Here, the 'reference' is information on the macro-scale maximum and minimum near-surface T a (hereafter T a,macro ) provided by the 1 km gridded Daymet product (Thornton et al 2022). Daymet interpolates and extrapolates weather station data using statistical techniques that minimize the influence of heterogeneous land cover (see Barnes et al 2023 for more detail). Estimates of daily minimum and maximum Daymet T a were extracted for all pixels containing the flux towers used in this study, for the entire period of each flux tower record.
The locally measured maximum and minimum T surf and T a,local were corrected by the Daymet T a,macro products according to: ∆T a,local,min = T a,local,min − T a,macro,min .
These metrics function as direct indicators of the LCLM impacts on local surface and air temperature. When evaluating the results, the difference in the ∆T surf from one site to the next is more informative than the absolute value of these metrics. For example, prior work suggests (and this study will confirm) that ∆T surf,max is almost always positive (Mildrexler et al 2011, Guo et al 2022, meaning that during mid-day periods, the surface is usually warmer than the air. The question then becomes how much warmer is ∆T surf,max in one site compared to another.

Exploring how the temperature metrics are coordinated and coupled
Addressing Q1 requires an analysis of LCLM impacts on maximum and minimum T surf across seasons and over the diurnal cycle. For this, the ∆T surf,max and ∆T surf,min were determined separately for the winter (December, January, and February) and summer (June, July, August). They were then compared to each other for how they relate to ecosystem characteristics including daytime mean LE (measured at midday between 10:00-14:00), canopy height, and aerodynamic conductance.
Next, to understand how LCLM affects the within-season dynamics of maximum T surf (e.g. Q2), the sensitivity of summer T surf,max to T a,macro,max (hereafter dT surf,max /dT a,macro,max ) was calculated in each site as the slope of the relationship between these two variables, using all available hourly data collected from midday periods. The sensitivity of LE, H, and incident solar radiation (R in , sum of short and longwave radiation) to maximum macro-scale air temperature (e.g. the dLE/dT a,macro,max , dH/dT a,macro,max , and dR in /dT a,macro,max ) were also calculated for mid-day summer periods to explore how well they predict dT surf,max /dT a,macro,max across sites.
The remaining analyses focus on the extent to which surface and air temperature dynamics are coupled over a range of timescales (e.g. Q3). First, the min and maximum ∆T a,local were compared to min and max ∆T surf , to understand how well seasonally-averaged LCLM impacts on T a,local can be predicted given measured impacts on T surf . Next, the sensitivity (or slope) of T a,local,max to T surf,max (e.g. dT a,local,max / dT surf,max ) was also calculated for each site at midday. The dT a,local /dT surf is a particularly useful metric for examining the extent to which local surface and air temperature are coordinated within a site, and how cross-site variability in land cover affects this coupling. Following from the analysis presented in SI section S2, beginning with the surface energy budget equation, and focusing on periods during which leaf area, height and albedo are relatively stationary, the maximum dT a,local /dT surf can be expressed as: Term 1 Term 2 Term 3 where ρ is air density, c p is the specific heat capacity of dry air, σ is the Stefan-Boltzmann constant, and all other terms have been previously defined. This expression suggests that the dT a,local /dT surf should be positively related to the sensitivity of LE to surface temperature (Term 1), negatively related to the sensitivity of R in to surface temperature (Term 2), and influenced by the reference surface temperature (here surrogated to mean summer T surf , Term 3). All three terms are mediated by the G a .
The prediction that the dT a,local /dT surf should be positively related to dLE/dT surf is somewhat counterintuitive, given the fact that an increase in LE tends to cause a decrease in local temperature. As explained in more detail SI section 3, if an increase in LE occurs despite an increase in T surf (e.g. both dLE and dT surf are positive), then in the absence of changes in R in , a decrease in sensible heat flux is required to sustain energy balance. In this scenario where T surf is increasing, a decrease in H can only be accomplished by an even larger increase in T a,local .  (b) show ∆T surf,max in summer, and winter, respectively, while (c) presents their relation. Panels (d) and (e) show ∆T surf,min in summer and winter, respectively, with (f) presenting their relation. GRA = grasslands, CRO = croplands, DBF = deciduous broadleaf forests, EF = evergreen forests, SAV = savannahs & shrublands, and WET = wetlands. Panel (g) shows results similar to (a) but organized by Köeppen climate zone for the six zones containing at least five sites. Cfa = humid subtropical, Dfa = hot summer continental, Dfb = warm summer continental, Csa = Mediterranean hot summer, Dfc = Subarctic/boreal, and Bsh = hot semi-arid. Panel (h) is similar to (g), but shows data from specific functional types in specific climate zones (focusing on the five unique combinations for which data from more than five sites was available).

The coordination of land cover effects on T surf across seasons and the diurnal cycle (e.g. Q1)
Land cover impacts on T surf,max are profound. As expected, the ∆T surf,max is almost always positive (averaging 6.2 K in summer and 5 K in winter). In the context of the goals of this study, the more important result is that the range in ∆T surf,max varies by more than an order of magnitude across sites (from <0 to more than 20 K), and moreover, the range approaches or exceeds 10 K in most PFTs (figures 1(a) and (b)). In general, the summer ∆T surf,max is highest in grasslands and savannahs ( figure 1(a), where it frequently approaches or exceeds 15-20 K), and lowest in croplands, deciduous forests, and wetlands. However, the standard deviation of summer ∆T surf,max ranges from 1.7-6.1 K within each PFT, which is comparable to the standard deviation across all sites (≈5 K). The variability across sites remains high when the data are parsed by Köeppen climate zone (figure 1(g)) or even restricted to a single PFT within a single climate zone ( figure 1(h)). During the dormant season, the magnitude and variability of ∆T surf,max are somewhat reduced (compare figures 1(a) and (b)), The winter and summer ∆T surf,max are reasonably well correlated (R 2 = 0.70), but the slope defining the linear relationship between these two variables is 1.27, driven by the fact that ∆T surf,max tends to be higher in summer versus winter in many grasslands and savannahs ( figure 1(c)).
Land cover impacts on minimum temperatures are smaller in magnitude and less variable within and across PFTs (figures 1(d) and (e)), and the winter and summer ∆T surf,min are strongly correlated across sites (R 2 = 0.78, slope = 0.94, figure 1(f)). Finally, the cross-site relationship between ∆T surf,min and ∆T surf,max was very weak in both summer and winter (R 2 < 0.1) and the scatterplots for these variables again reveal the tendency for ∆T surf,max to greatly exceed ∆T surf,min (see SI figure S1).
Next, the ∆T surf,max and ∆T surf,min were explored for how they vary in relation to LE, canopy height, and albedo. During the summer, ∆T surf,max decreases as mean mid-day LE increases (figure 2(a)), which is expected from first principles related to evaporative cooling. After controlling for LE, sites with higher albedo tend to have higher ∆T surf,max , particularly when mean summer daytime LE is <250 W m −2 (figure 2(a)). All else being equal, brighter sites should be relatively cool, and thus this result is somewhat surprising. It can be explained by observing that, over this same range of LE, sites with canopy height >2 m tend to be associated with surfaces that are much cooler than the surfaces of shorter ecosystems ( figure 2(b)). Canopy height is directly proportional to aerodynamic conductance (R 2 = 0.57, and see SI figure S2(a)), enhancing sensible heat flux and surface cooling in taller sites. Canopy height is also related to albedo (figure S2(b)), with taller sites having lower albedo. Nonetheless, the G A impacts appear to dominate. During mid-day summer periods, after controlling for evaporative effects, increases in G a linked to increased canopy height overwhelm warming effects of lower albedo in taller (and mostly forested) sites. During winter, the relationship between ∆T surf,max and mean daytime LE is not as clear (figures 2(c) and (d)). However, wintertime ∆T surf,max still tends to be higher in brighter ecosystems (figure 2(c)), but lower in taller ecosystems (figure 2(d)), and canopy height is still positively related to winter G a (R 2 = 0.6, SI figure S2(c)). Thus, the same conclusion holds-increases in G a in taller and more aerodynamically rough stands overwhelm albedo-driven increases in ∆T surf,max , even in winter.
In contrast, minimum temperatures are not strongly influenced by cross-site differences in LE, canopy height, or albedo (figures 2(e) and (f)). There is, however, a tendency for taller forested ecosystems to have relatively high minimum surface temperatures in both summer and winter, especially when daytime LE is relatively high (figures 2(f) and (h)).

The dynamics of T surf as summer T a,macro rises (e.g. Q2)
In summer, the change in T surf as the macro-scale air temperature rises (e.g. dT surf,max /dT a,macro,max) is highly variable across sites (figure 3). In a plurality of sites (∼52%), the T surf,max rises noticeably more slowly than T a,macro (dT surf,max /dT a,macro,max <0.9 K/K). In 27% of sites, dT surf,max /dT a,macro,max is closer to one (e.g. 0.9 <dT surf,max /dT a,macro,max <1 K/K). In the remaining sites (∼21%), the surface temperature rises faster than the macro-scale air temperature (dT surf,max /dT a,macro,max >1.1). The dT surf,max /dT a,macro,max is usually less than 1 in croplands, deciduous forests, and wetlands (figure 3(d)). In grasslands, evergreen forests, and savannahs/shrublands, the dT surf,max /dT a,macro,max is both higher and more variable across sites. However, because dT surf /dT a,macro tends to be <1 more often than not, in most sites, the ∆T surf,max is reduced as T a,macro,max shifts from 298 K to 306 K (figure 3(e)).
The dT surf,max /dT a,macro,max is only weakly correlated to the seasonally-averaged impacts of land cover on T surf across sites (∆T surf,max ), with R 2 = 0.06 and p = 0.01 ( figure 4(a)). A stronger, negative relationship is observed between the dT surf,max /dT a,macro,max and the sensitivity of LE to rising air temperature (dLE/dT a,macro,max ), with R 2 = 0.18 and p < 0.0001 ( figure 4(b)). In other words, the dT surf,max /dT a,macro,max is negatively associated with a higher rate of change in LE as macro-scale air temperature rises. When controlling for LE effects, dT surf,max /dT a,macro,max is larger at sites where R in increases more rapidly as the macro-climate warms ( figure 4(b)). Thus, dT surf,max /dT a,macro,max tends to be higher where the increase in incident radiation as a function of T a,macro (dR in /dT a,macro,max ) is particularly large (compare lines in panel (b)).

The coupling between surface and air temperature (Q3)
Land cover impacts on T a,local are smaller than the impacts on T surf , but still significant. During daytime, the ∆T a,local,max averages near zero across sites in both summer and winter (figures 5(a) and (b)). However, the variability, which is a key indicator of the influence of land cover effects, is quite large, with ∆T a,local,max as low as −5 K and more than +5 K in some sites. In contrast, at night, the ∆T a,local,min tends to be positive in most sites (figures 5(c) and (d)), indicating the local temperature at these sites is elevated above the macro-scale temperature more often than not. This observation holds true when using tower-measured air Figure 4. In summer, dT surf,max /dTa,macro,max is not well predicted by the seasonally-averaged impacts of land over on T surf (here referenced to a Ta,macro,max of 25 • C, panel (a). The dT surf,max /dTa,macro,max is negatively related to the change in LE as macro-scale air temperature rises (dLE/dTa,macro,max, panel (b)) and tends to be higher where the increase in incident radiation as a function of Ta,macro (dR in /dTa,macro,max) is especially large (compare lines in panel (b)). Panel (b) shows data as binned averages, similar to figures 2. temperature instead of extrapolated air temperature as the proxy for T a,local , although the tower-measured temperature is generally lower and less variable than the extrapolated T a (see SI figure S3).
Across seasons, the land cover impacts on air temperature are correlated with the land cover impacts on surface temperature (figure 6). However, the magnitude of the impacts on T a,local tends to be noticeably smaller when compared to LCLM impacts on T surf , at least during the daytime (figures 6(a) and (b)). Finally, during summer, the ∆T a,local,max tended to decrease as both canopy height and mean summer LE increased, which is consistent with the way these variables impact T surf , though the correlations were not particularly strong (R 2 < 0.2, relationships not shown).
Next, we turn our attention to the dynamic coupling between surface and air temperature in summertime. The sensitivity of local-to macro-scale air temperature (e.g. dT a,local,max /dT a,macro,max ) is reasonably well coupled to the sensitivity of surface temperature to macro-scale air temperature (e.g. dT surf,max /dT a,macro,max , R 2 = 0.68, figure 7). In the majority of croplands and many deciduous forests, the dT a,local,max /dT a,macro,max is greater than dT surf,max /dT a,macro,max (e.g. points falling above the dotted 1:1 line in figure 7) which means the local air temperature warms more quickly than the surface as the macro-climate warms. Nonetheless, in the vast majority of sites (including most croplands), the dT a,local /dT a,macro is still less than one, meaning the local air temperature to warms less quickly than the macro-scale air temperature.  The sensitivity of local air temperature (y-axis) and surface temperature (x-axis) to macro-scale air temperature. Sites above the dashed 1:1 line represent scenarios where local air temperature rises more quickly than surface temperature as the macro-climate warms. Conversely, sites below the 1:1 line experience more rapid warming of the surface. The shaded areas represent regions of the strongest decoupling between the within-season dynamic of local air temperature and surface temperature.
Another perspective on the within-season coupling of surface and air temperature is gained by evaluating the sensitivity of T a,local,max to T surf,max (e.g. dT a,local,max /dT surf,max ). Across all sites, it is less than 1.0, though it tends to be relatively high in many forests, croplands, and wetlands ( figure 8(a)). This is a somewhat counterintuitive result, since those sites also tend to be associated with the most pronounced surface and air cooling when averaged over the course of the entire summer (e.g. see figure 1). The framework presented in the SI section S3, and discussed in section 2.3, is useful for understanding this result. The framework predicts, and the results confirm, that that dT a,local,max /dT surf,max should be positively related to the sensitivity of LE to increasing surface temperature (e.g. dLE/dT surf,max, figure 8(c)). Moreover, the dLE/dT surf,max tends to be greater in forests, croplands, and wetlands ( figure 8(b))-the same ecosystems with relatively high dT a,local,max /dT surf,max . The framework also predicts that incident radiation and the reference T surf may be important drivers of dT a,local,max /dT surf,max . However, in general, these terms were small and uncorrelated with dT a,local,max /dT surf,maxf (see SI figures 5(b) and (c)), suggesting that evaporation dynamics are the primary driver of the coupling between local surface and air temperature as the macro-climate warms.
Ultimately, however, the influence of land cover on T a,local,max (e.g. ∆T a,local,max ) reflects not only the influence of dLE/dT surf,max , but also the sensitivity of T surf,max to rising macro-scale air temperature (e.g. ∆T a,local,max ∝ dLE dT surf,max · dT surf,max dTa,macro ). Since the latter term is usually much less than 1 ( figure 8(a)), the ∆T a,local,max is usually more negative when the macro-scale air temperature is elevated. The ∆T a,local,max is enhanced when it is especially hot in only 20% of sites (e.g. sites above the 1:1 line in figure 8(d)).

Discussion
This study was organized around three research questions concerning how land cover impacts on surface and air temperature evolve over sub-annual and sub-daily timescales, and during periods of excessive heat. We first asked: 'To what extent, and by what mechanisms, are LCLM impacts on T surf coordinated across seasons and the diurnal cycle?' We find that LCLM impacts on maximum T surf are very large in both summer and winter (figures 1(a) and (b)), but they are not well-coupled to impacts on minimum temperature, which tend to be smaller and less variable (figures 1(d) and (e)). The mechanistic underpinnings of this result are not well explained on the basis of PFT or climate classification, though a substantial amount of cross-site variability can be traced to variability in mid-day latent heat flux and canopy height (figure 2). We next asked: 'To what extent does land cover mediate the within-season dynamics of T surf as macro-climate warms?' focusing specifically on mid-day periods. As macroclimate warms, the rate at which T surf increased varied substantially across sites (figure 4(a)) in ways that were not well explained by seasonally-averaged impacts on T surf,max or by PFT ( figure 3(b)). However, in sites where latent heat flux increased as macro-climate warmed, the surface tended to warm more slowly ( figure 4(b)), such that local surface cooling benefits tend to be enhanced during hot summer days in more mesic sites.
Third, we asked: 'To what extent does land cover impact T a,local and its coupling with T surf over long-and short time-scales?' At seasonal timescales, we find that land cover impacts on T a,local are smaller than, but well coupled with, the impacts on T surf ( figure 6). However, within a season as macro-scale air temperature rises, the dynamics of T a,local are not always coupled with the dynamics of T surf ( figure 8). Consequently, ecosystems that tend to promote relatively cool T a,local most of the time may not necessarily sustain this cooling benefit during periods of extreme heat. In the rest of this discussion, these results are explored in more detail, concluding with an assessment of their practical implications.

Land cover impacts on midday T surf are profound
Land cover impacts on midday T surf are stark in both summer and winter, with the summer ∆T surf,max varying from as low as −2 K to more than 20 K across sites. Relatively few observational studies have explored specifically how land cover affects the maximum summer T surf , despite the fact that midday summer periods are arguably the time when the capacity of land cover to mediate rising temperatures is most important. The limited set of studies that do explore biophysical impacts on summertime T surf,max tend to confirm the possibility of land cover impacts that range from 5-10 K (Zhang et al 2020, Guo et al 2022 or more (Mildrexler et al 2011).
Beyond representing a key first-order driver of downstream biophysical impacts on air temperature, T surf is also a particularly important driver of plant function (Luyssaert et al 2014) that is well surrogated with the temperature of sunlit canopy leaves in closed canopies. Thus, while it is common to relate cross-site variability in processes like carbon uptake and water use to variability in air temperature (Körner and Hiltbrunner 2018), our results suggest this should be done cautiously, and motivate the use of surface temperature data instead. Moreover, averaging biophysical impacts on T surf over the diurnal cycle will tend to obscure the profound impacts of land cover on surface temperatures at midday, when plants are most active and most sensitive to limitations from drought and heat stress.

Understanding these impacts requires that we move beyond 'plant functional types' and consider non-radiative processes
In general, the surfaces of deciduous forests tend to be ∼2 K cooler than the surfaces of croplands, wetlands, and evergreen forests, which in turn are 5-7 K cooler than the surfaces of grasslands, savannahs, and shrublands ( figure 1(a)). That forests are relatively cool is consistent with prior work demonstrating that the combination of increased evaporation and sensible heat fluxes tend to suppress forest T surf (Juang et al 2007, Jackson et al 2008, Bright et al 2017, Burakowski et al 2018, Zhang et al 2020. Croplands tend to have cooler surfaces at midday than grasslands, particularly in summer, which is noteworthy since the biophysical impacts of reforestation and deforestation are often evaluated by comparing forests to just one of these two ecosystem types (Trail et al 2013, Devaraju et al 2018, Breil et al 2020, Zhang et al 2020. Our results suggest that cropland to forest and grassland to forest conversions should be evaluated separately. However, while some general patterns emerge across biome types, land cover impacts on land surface temperature are nonetheless quite large within PFTs ( figure 1(a)), within Köeppen climate zones (figure 1(g)), and even with within specific PFTs in specific climate zones ( figure 1(h)). Thus, we also explored the extent to which ∆T surf,max can be explained by ecosystem characteristics (including albedo, mid-day LE and canopy height) that are first order drivers of ecosystem energy balance, and which moreover can be mapped across the landscape (Schaaf et al 2002, Simard et al 2011, Fisher et al 2020. In summer, mean daytime LE was the strongest driver of cross-site variability in ∆T surf,max , and sites with high daytime LE had relatively cool surfaces at midday (figure 2). Albedo effects were not immediately apparent, as they were obscured by increases in aerodynamic conductance in taller (but darker) canopies (figure 2). Taken together, these results suggest that land cover transitions that increase LE or increase canopy height are more likely to result in local surface cooling.
Our study is limited to ecosystems in the United States, which are diverse with respect to PFT and climate classification (see section 2.1 and the SI), but are overrepresented with respect to temperate sites, and do not include tropical sites or ecosystems managed with agricultural or forestry practices more common in other countries. Thus, future work could focus more strongly on the relative importance of albedo-versus non-radiative forcings on ∆T surf,max across an expanded range of PFTs and climate zones. A systematic effort to collect and redistribute canopy height information from the global set of flux tower sites would enable a more globally representative analysis.
Finally, the relative importance of albedo-versus non-radiative forcings (e.g. LE and H) can shift at coarser scales. Energy consumed by LE and H can be redistributed and re-emitted in the boundary layer Jackson 2014, Novick et al 2022b), such that the global effects of LCLM change on albedo may be more important than their effects on LE and H (e.g. Bala et al 2007, Devaraju et al 2015, Williams et al 2021. However, it is also important to keep in mind that increased LE can lead to increased cloud formation which increases planetary albedo (Cerasoli et al 2021) and that changes in H and LE can affect the rate at which radiation is emitted back to space (Zeng et al 2017).

On hot summer days, the dynamics of evapotranspiration control the dynamics of land cover impacts on T surf
In summer, as the macro-scale air temperature rises, the rate at which T surf changes (e.g. dT surf,max / dT a,macro,max ) is very diverse across sites and within PFTs ( figure 3). Moreover, dT surf,max /dT a,macro,max is only weakly correlated with the mean growing season ∆T surf,max (figure 4(a)), which implies that the mechanisms that determine the seasonally averaged impacts of land cover on ∆T surf,max do not necessarily determine T surf dynamics within a season. Here, the strongest driver of dT surf,max /dT a,macro,max across all sites was the concurrent change in LE as the macro-climate warmed (e.g. dLE/dT a,macro,max ). When it is high, dT surf,max /dT a,macro,max is relatively small (figure 4(b)). A smaller but noticeable influence of variation in incident solar radiation was also detected, with relatively higher dT surf,max /dT a,macro,max in sites where dR in /dT a,macro was relatively high.
Ultimately, because dT surf /dT a,macro tends to be negative more often than not, in most sites, the ∆T surf,max actually declines as T a,macro shifts from 298 to 306 K. This means that the potential for vegetation to confer a local surface cooling benefit is usually enhanced when it is especially hot, and especially in sites where water supply is sufficiently abundant to permit LE to increase as macro-scale air temperature rises.

At seasonal timescales, land cover impacts on T a,local are smaller than, and coupled with, land cover impacts on T surf
During daytime, the magnitude of ∆T a.local,max is usually much lower than the ∆T surf,max (figures 6(a) and (b)), and the land cover impacts on the daytime T a,local were almost never greater than the land cover impacts on T surf (e.g. only one site fell above the 1:1 line in figures 6(a) or (b)). Thus, most of the time, a change in land use or land cover that has a cooling effect on the surface is unlikely to produce a warming effect in the air. Moreover, especially in forests, wetlands and croplands (for which ∆T a.local,max is closer to the ∆T surf,max ), the readily measurable ∆T surf,max could function as a very conservative upper bound for LCLM impacts on T a,local . Overall, these results are broadly consistent with prior observational work showing that deforestation impacts surface temperature more than air temperature (Alkama andCescatti 2016, Winckler et al 2019) and that the slope of the relationship between gridded air and surface temperature products is usually less than one (Mildrexler et al 2011).

Within the summer season, land cover impacts on air temperature are not always coupled with impacts on surface temperature
The change in near-surface air temperature as surface temperature rises during summer (dT a,local,max /dT surf,max ) is usually less than one ( figure 8(a)). However, it tends to be relatively high in croplands, forests and wetlands, which are the ecosystems associated with the most surface-and near-surface cooling. The mathematical framework in SI section S3 provides a foundation for understanding this result. In particular, it reveals that in sites where changes in LE are coordinated with changes in T surf (e.g. dLE /dT surf,max ), as they are in many croplands, forests, and wetlands (figure 8), the air temperature must rise more rapidly than the surface temperature to achieve a decrease in H that is necessary to close the energy budget. Overall, these results imply that some sites with relatively cool T a,local during normal summer conditions (e.g. T a,macro = 298 K) may have relatively warm T a,local when it is especially hot (e.g. sites above the 1:1 line in figure 7(e)), and vice versa.

A note on local versus non-local effects, and communication between observations and models
The local effects of LCLM on both surface and air temperature have important ramifications for plant biophysical function and for the potential for LCLM shifts to confer an adaptive air cooling benefit that can extend beyond ecosystem boundaries. However, a complete understanding of biophysical impacts of LCLM shifts must also consider non-local changes in circulation patterns, humidity, and cloudiness that can feed back onto both T surf and T a,local (Swann et al 2012, Pongratz et al 2021. Understanding these non-local impacts has largely been the purview of modeling studies (Bonan 2008), which tend to report substantial non-local feedbacks (Lague et al 2019, De Hertog et al 2023 that can sometimes counteract local impacts , Williams et al 2021. However, results are sometimes contradictory from one model to the next (De Hertog et al 2023), and models can also struggle to reproduce the local impacts with a precision necessary to be benchmarked against observations (Burakowski et al 2018). The results presented here cannot directly bridge this gap between local and non-local effects, though they may provide new perspectives against which modeled LCLM change can be evaluated. For example, future work could explore the extent to which models capture the correlation between wintertime and summertime impacts on max and min T surf (e.g. figure 1), or within-season coupling of T a,local and T surf as macro-scale air temperature rises (figures 7 and 8). Model-data comparisons like these may be facilitated by the vertical T a,local extrapolation approach used here, which is thoroughly presented along with the necessary code in Novick and Katul (2020). Since the extrapolation can be applied throughout the surface layer, it can allow temperature impacts to be evaluated across models and observations at a wide range of heights. Additional observations of air temperature profiles made throughout the boundary layer would also benefit future attempts to bridge the models with observations (Helbig et al 2021).

Conclusions and practical recommendations
We conclude by offering the following practical recommendations for evaluating the biophysical impacts of managed land cover transitions on both surface and air temperature: [1] Biophysical impacts on surface temperature and air temperature are large and should be a key consideration in policy design. The profound impacts of LCLM on surface temperature (e.g. 10 K or more) will have important ramifications for ecophysiological function that ultimately determines carbon uptake. The smaller but still sizeable impacts on air temperature are notable for their potential to be advected across the landscape and impact nearby ecosystems and communities. Finally, the large, within-site differences in surface and air temperature reported here should demotivate the use of air temperature as a factor explaining spatial variability in many ecosystem processes (e.g. photosynthesis), for which surface temperature is likely the more relevant driver.
[2] Albedo changes alone are not sufficient to understand the local impacts of managed land use and land cover transitions. Indeed, across the sites investigated here, the warming effect of reduced albedo was almost completely obscured by non-radiative processes linked to latent and sensible heat flux. While this result may be surprising given the historic emphasis on ecosystem albedo management in the literature, it is consistent with other works reporting that non-radiative forcings can easily overwhelm albedo impacts at local scales, at least in temperate sites (Bright et al 2017, Burakowski et al 2018, Zhang et al 2020.
[3] PFT is not a sufficient basis on which to evaluate the biophysical impacts of land cover exchange. While our results generally confirm prior results that forests, croplands and wetlands have relatively cool surfaces, we also report extremely high variability within PFTs, and even within PFTs parsed to specific climate zones. Thus, heuristics like 'forests cool the surface' are not universally true, and rich opportunities exist to expand on the results presented here through activites that link cross-site biophysical impacts to mappable ecosystem characteristics like average evaporation and canopy height.
[4] Land cover changes that increase evapotranspiration and/or that substantially increase canopy height are likely to confer a surface and air cooling benefit. This explains the tendency for forests, croplands, and wetlands to have relatively cool surfaces and to underlie relatively cool atmospheres, but also explains some of the cross-site variability in land cover impacts on surface and air temperature within these functional groups.
[5] Especially when considering the adaptation potential of LCLM shifts, it is important to focus on time periods when the negative consequences of rising temperature are most severe. LCLM impacts on both T surf and T a,local were far more pronounced during the day than during the night, and were more apparent in summer than winter. Thus, studies that average these impacts over the course of an entire year, or even an entire day, will underestimate the potential for land cover to mediate micro-climate when the consequences for plant and human well-being are most stark (e.g. summer days). Finally, we note that during periods of extreme heat, the dynamics of surface and near-surface air temperature become decoupled. As a consequence, the biophysical impacts of LCLM observed during 'normal' environmental conditions may not always predict impacts during more extreme conditions. Some ecosystems that tend to cool the air during normal conditions may have a diminished capacity to do so when it is very hot.