TECHNICAL RESPONSE◥

CLIMATE CHANGE

Response to Comment on“Satellites reveal contrastingresponses of regional climate to thewidespread greening of Earth”Giovanni Forzieri1*, Ramdane Alkama1, Diego G. Miralles2, Alessandro Cescatti1

Li et al. contest the idea that vegetation greening has contributed to boreal warming andargue that the sensitivity of temperature to leaf area index (LAI) is instead likely driven by theclimate impact on vegetation.We provide additional evidence that the LAI-climate interplayis indeed largely driven by the vegetation impact on temperature and not vice versa, thuscorroborating our original conclusions.

On the basis of statistical analysis of satellitedata, we argued that the observed vegeta-tion greening has contributed to thewarm-ing of cold and humid boreal zones througha reduction of surface albedo (1). In their

Comment, Li et al. (2) contend that “this positivesensitivity of temperature to the greening can bederived from the positive response of vegetationto boreal warming.” They base this claim on twomajor points: (i) The statistical analysis is un-

suitable to isolate the climate response tovegetation greening, and (ii) the importanceof radiative processes associated with LAI-driven changes in boreal albedo is limited. Inaddition, Li et al. underpin a climate impactof greening dominated by evapotranspirationcooling in the boreal regions as suggested bymodel simulations (3), implicitly assuming thatland models correctly represent the vegetation-climate interplay.

Li et al. support the first claim (i) by applyingthe statistical method used in (1) to decoupledsimulations from five global terrestrial eco-system models and conclude that “over theboreal regions, dTLAI

decoupled [i.e., @Ta /@LAIdecoupled]is positive and at the same magnitude as dTLAI

[i.e., @TS/@LAI]…, indicating that the positivedTLAI regressedwith satellite data… can be derivedfrom the positive response of vegetation to bo-real warming.” Even thoughwe acknowledge therelevance of feedback loops in plant-climate in-teractions, we argue that this modeling experi-ment, being based on decoupled model runs,does not support the conclusions of Li et al. be-cause it cannot exclude the direct impact ofchanges in LAI on surface temperature, and be-cause it does not quantify the relative magnitudeof the bidirectional effects and therefore can-not support any conclusion on which of the twodominates.We stress that our methodology isolates the

interactions between LAI and TS and factorsout the signal of covarying drivers such as pre-cipitation and incoming radiation. Nonetheless,we recognize that the statistical approach cannotfully disentangle the short-term temperaturecontrol on boreal vegetation and separate it fromthe feedback. However, our results support theconclusion that at the interannual time scale, the

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Forzieri et al., Science 360, eaap9664 (2018) 15 June 2018 1 of 3

1European Commission, Joint Research Centre, Directorate forSustainable Resources, Ispra, Italy. 2Laboratory of Hydrologyand Water Management, Department of Forest and WaterManagement, Ghent University, B-9000 Ghent, Belgium.*Corresponding author. Email: giovanni.forzieri@ec.europa.eu

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Fig. 1. Sensitivity of temperature to variations in LAI and interannualvariations in LAI across climatological gradients. (A to C) For borealregions and based on satellite GIMMS3g observations, values of sensitivity ofsurface temperature to LAI (@TS/@LAI) are illustrated against the climato-logical median temperature (Ta, y axis) and precipitation (A), snow waterequivalent (B), and evapotranspiration (C) (P, SWE, and ET; x axis). (D toF) Same as (A) to (C) but for multiproduct satellite observations (GIMMS3g,GLASS, GLOBMAP, LTDR, and MODIS). (G to I) Same as (A) to (C) but forTRENDY (CLM4.5, JULES, OCN, ORCHIDEE, and VISIT) model-derived

sensitivity of air temperature to LAI (@Ta/@LAI). (J to L) Same as (A) to (C)but for the differences in interannual variations of LAI, expressed as thecoefficient of variation (CVLAI), retrieved from each combination of satelliteproducts and TRENDYmodels (25-member ensemble). Black dots show binswith average value statistically different from zero (P < 0.05). Statisticalsignificance in (A) to (C) is calculated for all the observed values falling into thegiven bin and reflects the robustness of the observed signal. Statisticalsignificance in (D) to (L) is calculated across themembers of the ensemble andreflects the robustness of their average signal.

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land-climate interplay is largely affected by theLAI impact on temperature. Here, we extend ouranalyses to provide further evidence. Figure 1 un-equivocally shows that the observed temperaturevariations associated with boreal greening de-pend in sign and magnitude on the backgroundclimate, with a pattern that is fully consistentwith the expected biophysical impact of vegeta-tion on temperature. As such, @TS/@LAI is posi-tive in cold-humid boreal regions with extendedsnow cover, where the greening can trigger largereductions in albedo but only limited increasesin evapotranspiration. On the contrary, in borealareas with limited snow cover and water avail-ability, the increase in transpiration associatedwith the greening ultimately yields a negative@TS/@LAI (Fig. 1, A to C). We note that despitethe spread observed over multiple satellite LAIproducts (4), the multiproduct average signalof @TS/@LAI shows largely consistent patterns(Fig. 1, D to F). If the sensitivity of temperatureto boreal greening was mainly driven by thepositive response of vegetation to warming, assuggested by Li et al., we should expect a positivevalue of @TS/@LAI over the whole boreal climatedomain, consistent with the results of themodel-ing experiment (Fig. 1, G to I). On the contrary,the clear contrast between observations and de-coupled simulations suggests that the interplaybetween climate and LAI at the interannual scaleis largely modulated by the vegetation impactson climate as suggested in (1), and not vice versa.Furthermore, relative to observations, models

substantially underestimate the interannual var-iability of LAI in cold-wet zones, as highlighted

by the large difference (up to 8%; P < 0.05) in thecoefficient of variation (Fig. 1, J to L). Similardiscrepancies are also found for growing-seasonLAI (not shown). Such model limitations arelikely to hamper their capacity to represent theinterplay between biophysical processes and veg-etation changes (5, 6). As acknowledged also byLi et al., we stress that the largemodel uncertaintymay hinder the assessment of the net impact ofvegetation greening on boreal climate.On the second point (ii), Li et al. claim that

there is a limited impact of LAI on radiativebiophysical processes (e.g., albedo) in the borealzone: “the observed boreal greening occurs dur-ing the growing season when the snow-albedofeedback is minimal (3)…, the climate impactof growing season greening seems to be domi-nated by evapotranspiration cooling even in theboreal regions.” This conclusion is based on therather uncertain predictions of land surfacemodels on the relationship between vegetationand albedo off-season (7, 8). On the other hand,remote sensing observations report on the abun-dance and spreading of evergreen species in theboreal zone (9), whose variation in LAI may sub-stantially affect albedo and temperature alsoduring the dormancy season via the snow-albedofeedback (10). The relevance of off-season radia-tive biophysical processes is suggested by thepositive values of winter/spring @TS/@LAI in thewhole boreal climate domain (Fig. 2, A and B).In warm-dry regions, the observed sensitivitybecomes negative during the growing season,thanks to the stronger control that LAI exerts ontranspiration in less humid ecosystems. These

seasonal dynamics cannot be the simple effectof temperature-driven greening, as claimed byLi et al., because in that case we would expect apositive sensitivity throughout the year acrossthe whole boreal region. On the contrary, theemerging seasonal patterns confirm that LAIvariation can indeed influence in space and timethe short-termnet signal of the vegetation-climateinteractions.Ultimately, the net annual LAI impact on

climate depends on the relative magnitude andduration of the periods dominated by radiativeand nonradiative biophysical processes (11, 12),as clearly proved by the consistency between theclimate patterns of @TS/@LAI in Fig. 1A and thelength of the season with negative sensitivity(LNSS) in Fig. 2, C and D. Unfortunately, modelsshow severe limitations in predicting the season-ality of vegetation and the length of the growingseason (LGS), here expressed as the number ofdays when LAI is larger than 30% of its amplitude(13) and derived after linear interpolation ofmonthly LAI values (Fig. 2E). In particular,modelssystematically overestimate LGS (16 ± 2 days and44 ± 3 days in cold-wet and warm-dry zones,respectively; multimodel average DLGS ± SE).We highlight that the threshold-based approachused to derive LGS is not sensitive tomissing LAIdata during winter (14). The emerging modelbias in the estimate of LGS is consistent withprevious studies (15) and may be one of thecauses of the overestimation of LAI-drivenevaporative cooling, with potential influenceson the derived net climate impact of borealgreening.

Forzieri et al., Science 360, eaap9664 (2018) 15 June 2018 2 of 3

Fig. 2. Seasonal patterns of the sensitivityof surface temperature to LAI and length of theseason with negative sensitivity (LNSS).(A) Satellite-derived monthly sensitivity of surfacetemperature to LAI for cold-wet boreal regions(T < 280 K and P > 800 mm, in blue); lines andshaded areas denote the median values andconfidence bounds (±SE). Note that monthlysensitivity of surface temperature to LAI is computedby using annual LAI values and monthly-scaleclimate drivers in order to minimize the potentialbiases of satellite retrieval of LAI in snow coverconditions. (B) Same as (A) but for warm-dry borealregions (T > 280 K and P < 800 mm, in red).(C and D) Spatial (C) and climate (D) pattern ofsatellite-derived LNSS (number of days withnegative sensitivity of surface temperature toLAI). (E) Interannual differences in length of thegrowing season (DLGS) were estimated fromobservations and models; circles and bars reflect theensemble median and the five-member ensembledata range, respectively.The GIMMS3g product isused as the reference observational dataset forconsistency with (1).

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Our findings in (1), further corroborated by theresults reported here, suggest that the impactsof LAI variations on climate are modulatingthe overall signal of the interannual vegetation-climate interplay. They also highlight the impor-tance of realistically representing the temporalvariation of vegetation in land surfacemodels forsimulating present and future changes in theenergy and water cycle (5, 6). We agree that atlonger time scales, like those investigated in (3),the strong trend in temperature and covaryingchanges in atmospheric CO2 and N depositionmay have driven the northern greening. On theother hand, our analysis suggests that at shortertemporal scales, LAI variations affect the inter-annual spatial and seasonal variability of surfacetemperature. We agree with Li et al. that futurestudies based on the integration of modelsand observations are required to reconcile thesefindings and derive conclusive statements about

the climate effects of the widespread greening ofEarth.

REFERENCES AND NOTES

1. G. Forzieri, R. Alkama, D. G. Miralles, A. Cescatti, Science 356,1180–1184 (2017).

2. Y. Li, Z. Zeng, L. Huang, X. Lian, S. Piao, Science 360,eaap7950 (2018).

3. Z. Zeng et al., Nat. Clim. Change 7, 432–436 (2017).4. C. Jiang et al., Glob. Change Biol. 23, 4133–4146 (2017).5. Y. Zhang et al., Sci. Rep. 6, 19124 (2016).6. G. Forzieri et al., J. Adv. Model. Earth Syst. 10.1002/

2018MS001284 (2018).7. A. J. Pitman et al., Geophys. Res. Lett. 36, L14814 (2009).8. J. P. Boisier et al., J. Geophys. Res. Atmos. 117, D12116 (2012).9. Y. He et al., J. Clim. 30, 5021–5039 (2017).10. F. S. Chapin III et al., Glob. Change Biol. 6 (S1), 211–223

(2000).11. G. B. Bonan, Science 320, 1444–1449 (2008).12. R. M. Bright et al., Nat. Clim. Change 7, 296–302 (2017).13. A. Verger, I. Filella, F. Baret, J. Peñuelas, Remote Sens. Environ.

178, 1–14 (2016).14. Z. Zhu et al., Remote Sens. 5, 927–948 (2013).15. G. Murray-Tortarolo et al., Remote Sens. 5, 4819–4838 (2013).

ACKNOWLEDGMENTS

Supported by the Seventh Framework Program (FP7) LUC4C project(grant 603542). D.G.M. acknowledges support from the EuropeanResearch Council under grant agreement 715254 (DRY-2-DRY). SatelliteLAI products are provided by the Global Inventory Modeling andMapping Studies (GIMMS3g, http://sites.bu.edu/cliveg/datacodes/),Global Land Surface Satellite (GLASS, http://glcf.umd.edu/data/lai/),Global Mapping (GLOBMAP, www.globalmapping.org/), NOAA LongTerm Data Record (LTDR, https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00898), and Moderate Resolution ImagingSpectroradiometer (MODIS, https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mcd15a3h_v006). Satellite-derived evapotranspiration and snow water equivalent data arederived from the Global Land Evaporation Amsterdam Model Version3a (GLEAM v3a, www.gleam.eu/) and from the Finnish MeteorologicalInstitute (GLOBSNOW, www.globsnow.info/index.php?page=SWE),respectively. Availability and locations of additional satellite data maybe found in the supplementary materials of (1). Offline simulationsfrom five global terrestrial ecosystem models (i.e., CLM4.5, JULES,OCN, ORCHIDEE, and VISIT) were obtained from the TRENDY project(http://dgvm.ceh.ac.uk/node/9).

28 September 2017; accepted 20 April 201810.1126/science.aap9664

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widespread greening of Earth''Response to Comment on ''Satellites reveal contrasting responses of regional climate to the

Giovanni Forzieri, Ramdane Alkama, Diego G. Miralles and Alessandro Cescatti

DOI: 10.1126/science.aap9664 (6394), eaap9664.360Science 

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