Increased photosynthesis during spring drought in energy-limited ecosystems

Domain: earth_science
Published: 2023-11-29
Editorial outcome: minor_revision

Abstract
Drought is often thought to reduce ecosystem photosynthesis. However, theory suggests there is potential for increased photosynthesis during meteorological drought, especially in energy-limited ecosystems. Here, we examine the response of photosynthesis (gross primary productivity, GPP) to meteorological drought across the water-energy limitation spectrum. We find a consistent increase in eddy covariance GPP during spring drought in energy-limited ecosystems (83% of the energy-limited sites). Half of spring GPP sensitivity to precipitation was predicted solely from the wetness index (R2=0.47, p<0.001), with weaker relationships in summer and fall. Our results suggest GPP increases during spring drought for 55% of vegetated Northern Hemisphere lands (>30°N). We then compare these results to terrestrial biosphere model outputs and remote sensing products. In contrast to trends detected in eddy covariance data, model mean GPP always declined under spring precipitation deficits after controlling for air temperature and light availability. While remote sensing products captured the observed negative spring GPP sensitivity in energy-limited ecosystems, terrestrial biosphere models proved insufficiently sensitive to spring precipitation deficits.

Introduction
Carbon cycling in terrestrial ecosystems can be strongly affected by drought. Drought often limits ecosystem photosynthesis (i.e., gross primary productivity, GPP), reducing the ability of ecosystems to take up atmospheric carbon dioxide (CO2)1–4. With the duration and severity of droughts expected to increase with climate change5, it is essential to understand GPP responses to drought for projecting future changes in the terrestrial carbon cycle6–9. This has led to an extensive body of literature focused on understanding the negative impacts of droughts on various aspects of ecosystem function, from photosynthesis10 to mortality11.

Many environmental factors (e.g., soil parent material, nitrogen availability, carbon dioxide12) can affect GPP, but carbon uptake is primarily controlled by a balance of water vs. energy limitations (e.g., radiation, temperature)1,13–15. The strength and direction of these controls varies across ecosystems, with water availability being the primary driver of interannual variability in GPP at local ecosystem scales16. Plants increase GPP by keeping stomata open and losing water to the atmosphere through transpiration in exchange for carbon12. If soil water becomes limiting or atmospheric demand through vapor pressure deficit (VPD) becomes too high, vegetation can respond by limiting stomatal conductance and reducing carbon assimilation11,17–20. Droughts resulting from low soil moisture often reduce vegetation water availability3. Meteorological droughts, in which there is a precipitation deficit, also often correspond with higher temperatures and increased solar radiation with reduced cloud cover21. In dry climates, ecosystem productivity is water-limited, having a tight positive relationship with precipitation inputs; this relationship is becoming increasingly sensitive to precipitation with rising atmospheric CO2 3,22–24. In energy-limited ecosystems, GPP may increase even as soil moisture declines, due to higher temperatures and more light availability25. This energy-limited response has been shown consistently in tropical rainforests, which are often light-limited26–29, and in high elevation ecosystems30,31. Theory suggests that GPP can increase during negative precipitation anomalies (i.e., meteorological droughts32) in mid- and high-latitude ecosystems as well, a response that has found some support33–37. For example, in regions that are cold or temperate, drought conditions can result in higher solar radiation and air temperatures that can sometimes increase vegetation activity21,30,37, such as during the 2012 drought in the United States38 and the 2018 and 2022 droughts in Europe4,33. Due to the lack of research examining vegetation responses across large scales, there remains much uncertainty regarding the response of energy-limited ecosystems to meteorological drought based on observational data.

Here, we examine how the water-energy limitation spectrum affects the sensitivity of GPP to meteorological drought across the Northern Hemisphere (>30°N)39. We use a newly available compilation of long-term eddy covariance (EC) observations from >60 long-term sites with ≥10 years of data within a given season (spring (61), summer (62), or fall (63)) (Supplemental Fig. S1, Supplemental Table S1), and analyze GPP sensitivities to drought using precipitation, air temperature, and photosynthetically active radiation (PAR) as meteorological descriptors. Sites are categorized as water- or energy-limited based on the wetness index (WI), which describes aridity and is calculated as long-term mean annual precipitation divided by potential evapotranspiration (PET). We then evaluate how well the EC-observed sensitivities are represented in terrestrial biosphere model (TBM) outputs40,41, specifically using the TRENDY ensemble of models42–44, and satellite remote sensing GPP products. We address three central research questions: 1) what is the relationship between aridity and the sensitivity of GPP to meteorological drought for ecosystems >30°N; 2) can we consistently predict when and where GPP will increase or decrease during a meteorological drought in different seasons; and 3) compared to EC observations, how well do gridded estimates from TBMs and satellite remote sensing estimate GPP sensitivity to precipitation? Using EC observations, we find that ecosystem aridity describes nearly half (R2=0.47) of the variability in the sensitivity of GPP to precipitation during spring. Our results demonstrate that during spring meteorological droughts (i.e., precipitation deficits), energy-limited ecosystems routinely increase GPP (83% of the energy-limited EC sites), which is not the case in summer or fall, or for water-limited sites. Satellite remote sensing GPP products capture the increase in GPP during spring droughts that we observe using eddy covariance, but GPP outputs from TBMs are comparatively insensitive to precipitation deficits in energy-limited ecosystems.

Results
Seasonal GPP sensitivity to drought using eddy covariance observations
We compared spring, summer, and fall observations to determine how the sensitivity of GPP to drought changes through the growing season. To calculate sensitivity, seasonal sums of GPP from EC were linearly regressed against precipitation, requiring a ≥10 years of data per season (see Methods). During spring drought, energy-limited ecosystems consistently increase GPP (83% of sites, n=38/46; Fig. 1, Supplemental Fig. S2). This yields a negative GPP sensitivity to precipitation. By contrast, water-limited ecosystems have a positive GPP sensitivity to precipitation during the spring (80%; n=12/15 sites). We find that nearly half of the variability in the sensitivity of spring GPP to precipitation across sites can be attributed to the WI (R2=0.47, p<0.001).

Although the WI accurately describes changes in spring drought sensitivity across the water-energy limitation spectrum, it performs comparatively poorly in summer (R2=0.16, p=0.002; excluding IL-Yat) and fall (R2=0.30, p<0.001) (Supplemental Fig. S3). In summer and fall, unlike in spring, energy-limited ecosystems do not have consistently negative GPP sensitivities to precipitation. Relatively few energy-limited ecosystems increase GPP with summer meteorological drought (28%, n=13/46), while fall is mixed (54%, n=25/46). Water-limited ecosystems, however, nearly always increase GPP when there is more precipitation in summer (94%, n=15/16) and often in fall (71%, n=12/17). When we reduce the number of years needed per site to ≥5 per season, nearly doubling the available sites to 112, we find similar patterns in the GPP sensitivities but with lower R2 in spring and fall and no significant trend in summer (Supplemental Fig. S4). We also find similar patterns when evaluating the strength of correlations (Pearson or Spearman) rather than physical units of g C m−2/mm, suggesting that at the ends of the aridity gradient, the GPP sensitivities have not only a greater magnitude but are also more tightly coupled to meteorological conditions (Supplementals Fig. S5, S6). We find similar patterns when substituting the Palmer Drought Severity Index (PDSI) for precipitation but with lower R2 (Supplementals Fig. S7, S8).

We also compare the sensitivity of GPP to light availability (i.e., photosynthetically active radiation; PAR) and air temperature (see Methods). In spring, there are significant relationships between WI and the sensitivity of GPP to PAR (R2=0.23, p<0.001), and the sensitivity of GPP to air temperature (R2=0.27, p<0.001), but less consistent relationships during summer and fall (Supplemental Fig. S3). Even when controlling for PAR and air temperature in a multiple linear regression in the spring EC observations (see Methods), 61% of energy-limited sites (n=28/46) have a negative GPP sensitivity to precipitation (Supplemental Fig. S9). By contrast, in the partial spring GPP sensitivity to air temperature, 93% (n=43/46) of energy-limited sites have a positive sensitivity. For energy-limited sites, this suggests that warmer springs almost always increase GPP when controlling for precipitation (93%) compared to drier springs when controlling for air temperature (61%). Therefore, our results show that although positive temperature anomalies are the main control on GPP sensitivity during spring for energy-limited sites, meteorological drought is an important and, thus far, overlooked secondary control for ecosystem productivity, particularly as it often co-occurs with warming.

TBMs and remote sensing differ in strength of spring GPP sensitivities observed from EC
We compared the EC sensitivity results to outputs from an ensemble of terrestrial biosphere models (TBMs; TRENDY v6 S2) and remote sensing products of GPP to determine how well spring GPP sensitivity to precipitation is represented (see Methods). We constructed GPP sensitivity relationships for all models and products (similar to Fig. 1; Supplemental Fig. S10) and then calculated the mean (±SE) sensitivities for water-limited and energy-limited sites, respectively. The EC observations have a mean sensitivity for energy-limited sites of −0.22 (±0.05) g C m−2/mm and for water-limited sites of 0.57 (±0.17) g C m−2 / mm (Fig. 2). Neither the TRENDY model outputs nor the remote sensing models produce sensitivities similar to EC observations for both energy- and water-limited ecosystems simultaneously. For energy-limited sites, nearly all TBMs underestimate the strength of negative sensitivity of spring GPP to precipitation observed in the EC data (Fig. 2; Supplemental Fig. S10). The TRENDY model mean sensitivity for energy-limited sites is not significantly different from zero at −0.03 (±0.03) g C m−2/mm (p=0.78, n=46). For water-limited sites, estimates from TBMs spread over a wider range but their mean is not significantly different from the EC observations (0.54 ± 0.11 g C m−2/mm; p=0.87, n=15). By contrast, GPP products driven by remote sensing occupy a different part of the energy-limited vs. water-limited sensitivity space than the TBM outputs. Unlike the TRENDY outputs, remote sensing products produce a reasonable range of sensitivities in energy-limited sites (MODIS-Aqua=−0.33 ± 0.05 to FLUXCOM RS METEO=−0.02 ± 0.01) but underestimate sensitivity in water-limited sites (MODIS-Aqua=0.11 ± 0.07 to FLUXCOM RS=0.19 ± 0.06 g C m−2/mm) when compared with EC observations. Raising or lowering the WI threshold for water vs. energy limitations does not change the overall patterns for TRENDY models and remote sensing products compared to EC (Supplemental Fig. S11), and neither does changing the WI to a larger gridded product (CRUTS 4.05) for the TBMs (Supplemental Fig. S12).

The TRENDY model mean GPP spring sensitivity along the aridity gradient is much more tightly controlled by air temperature (R2=0.69) than it is by light availability from PAR (R2=0.40) (Supplemental Fig. S13). Both relationships are stronger in the TBMs than they are for the EC observations (air temperature: R2=0.27, PAR: R2=0.23; Supplemental Fig. S3). The TRENDY model mean has significant relationships within each season for each meteorological variable, except for PAR during summer. This shows more consistent coupling of GPP sensitivity to the meteorological variables in the models than in the EC observations. To test how precipitation, PAR, and air temperature simultaneously affect GPP sensitivity during spring, we performed a multiple linear regression with no interaction (see Methods). Even when accounting for PAR and air temperature, sites in the EC data can have negative spring GPP sensitivities to precipitation, while this is never the case in the TRENDY model ensemble mean (Supplemental Fig. S9). In other words, the models indicate that more precipitation would uniformly correspond to more GPP during spring across the entirety of our aridity gradient, while this is not observed at EC sites.

Spring GPP sensitivity to precipitation across the Northern Hemisphere
Our results allow us to map where the model mean of the TBMs is underestimating the strength of the spring GPP sensitivity to precipitation, using the linear regression (from Fig. 1) derived from the EC data and the WI (see Methods). The sensitivity map based on EC has the same spatial patterns as the WI map from which it is derived, with negative slopes in more humid, energy-limited regions and much higher positive slopes in arid, water-limited regions (Fig. 3a). The sensitivity map predicted from the TRENDY model mean, however, does not show a similar spatial pattern, with many more positive than negative slopes, even in humid regions (Fig. 3b). The standard deviation across the TRENDY models is lowest in humid regions and greatest in arid regions, but there are relatively large differences across models in some regions like north-central Europe (Supplemental Fig. S14). The TRENDY model mean estimates only 36% of the non-barren northern land with a negative spring GPP sensitivity to precipitation, while the EC-WI relationship estimates negative sensitivities at a much larger area of 55%. Among the remote sensing products, patterns of spring sensitivity of GPP to precipitation are not more visually consistent compared to the EC estimate or the TRENDY models (Supplemental Fig. S15).

Subtracting the EC estimate from the TRENDY model mean across space (Fig. 3c), we find that the TRENDY spring precipitation sensitivity of GPP is insufficiently negative and has higher values than EC in many energy-limited regions. These regions include the eastern seaboard of North America; wet mountains, low coastal regions, and the eastern interior of Europe; and a large area of coastal and wet mountainous regions in east Asia. High elevations alone are not necessarily indicative of TRENDY > EC, with TRENDY < EC in large areas of western North America and central Asia that are drier and adjacent to deserts. The TRENDY estimated sensitivity is similar to the EC-derived estimate in many parts of the interior of North America, north-central Asia, and along parts of the Baltic Sea and Mediterranean Sea, in particular regions on the boundary between dry and wet climates, such as the Great Plains of North America. Although there are exceptions in drier parts of northwestern North America and northeastern Asia, higher latitudes often have positive differences.

Comparing these sensitivities based on plant functional types (PFTs) from MODIS land cover, we find that EC sensitivity is far more negative than TRENDY estimates for evergreen needle leaf forests, deciduous broadleaf forests, mixed forests, woody savanna, and wetlands (Fig. 3d). Open shrublands and deciduous needle leaf forests have sensitivities near zero for both EC and TRENDY estimates, perhaps due to cold and/or dry conditions in many of these regions. At the same time, EC sensitivity exceeds TRENDY estimates for grasslands. Grasslands have the highest variability in GPP sensitivity to precipitation across models (mean SD=0.29 g C m−2/mm) and deciduous needle leaf forests have the least (mean SD=0.15 g C m−2 / mm) (Supplemental Fig. S16).

Methods
Eddy covariance observations
To investigate seasonal differences in GPP sensitivities in temperate ecosystems, we used EC data from long-term, non-cropland sites >30° N. We limited our analyses to the Northern Hemisphere because of our focus on seasonality, land extent, and EC data coverage; there are very few long-term EC sites in the Southern Hemisphere. Croplands were excluded because of possible water inputs from irrigation. Because of our need for long time series of EC data across a wide aridity gradient, we included site data from several sources: FLUXNET201570, ONEFlux-Beta (https://ameriflux.lbl.gov/data/download-data-oneflux-beta), ICOS Warm Winter 202071, and ICOS Drought 201872. All data were in FLUXNET format at monthly aggregation and processed by the same ONEFlux codebase70. If a site had data in both FLUXNET2015 and another more recent release, we used the more recent dataset and did not merge or combine across data sources (e.g., we used site US-MMS from ONEFlux-Beta instead of from FLUXNET2015). We developed an initial site list retaining all sites that had ≥5 years of data. The monthly data was aggregated to meteorological seasons (spring: MAM, summer: JJA, fall: SON), taking sums of gap-filled GPP (GPP_NT_VUT_REF). Seasons with >50% of gap-filled GPP data were removed (NEE_VUT_REF_QC<0.5, mean from monthly data), as well as any time period that could be documented as having experienced severe disturbance (e.g., fires). Monthly GPP trends were evaluated for all sites to find unflagged discontinuities or errors in GPP, and anomalous years were further investigated prior to inclusion in the analysis. Because longer time series led to more climatically consistent responses, we further limited our sites based on the number of available years, retaining sites that had ≥10 years of data in spring (n=61 sites), summer (n=62 sites), or fall (n=63 sites) (Supplemental Fig. S1, Supplemental Table S1).

At each site and season, we calculated the sensitivity of GPP to meteorological variables present in the FLUXNET format data: gap-filled precipitation (P_F), air temperature (TA_F), and photosynthetically active radiation (PAR=0.5*SW_IN_F73;). We use incident shortwave radiation (SW_IN_F) to estimate PAR instead of photosynthetic photon flux density (PPFD) because SW_IN_F is available across all sites, while relatively fewer sites have PPFD sensors. In the FLUXNET format data, each meteorological variable uses observations at the flux site when available and is filled with data from ERA-Interim when missing70. Due to inconsistent availability of in situ meteorological data across sites, we did not distinguish between site-collected and filled meteorological data. To mitigate the effect of plant growth increasing GPP over time, sums of GPP were linearly detrended using Sen’s slope if there was a significant trend based on a Mann-Kendall test (p<0.05). Detrending was performed per-season (e.g., there might be an increasing trend in GPP in summer, but not in fall). Meteorological data were not detrended because of the relatively short time series for climatology74, and differences were minimal when testing detrending precipitation for spring (Supplemental Fig. S19). For each season, we calculated the sum of GPP (g C m−2; GPPsum), sum of precipitation (mm; Psum), sum of PAR (MJ m−2; PARsum), and mean air temperature (°C; Tamean).

Seasonal GPP sensitivities to meteorological variables were calculated for each site using slopes from linear regressions. Simple linear regressions were used to estimate sensitivity between GPP and meteorological variables, including:

GPPsum = βP * Psum + β0 (1)

GPPsum = βPAR * PARsum + β0 (2)

GPPsum = βTa * Tamean + β0 (3)

with β0 being the intercept for each equation, and the slopes of βP (GPP sensitivity to precipitation), βPAR (GPP sensitivity to PAR), and βTa (GPP sensitivity to air temperature).

Correspondingly, multivariate linear relationships were used to assess control by different meteorological conditions. The multivariate relationship predicted GPP based on precipitation, air temperature, and PAR with no interaction:

GPPsum = βP * Psum + βTa * Tamean + βPAR * PARsum + β0 (4)

For all equations, error was estimated as the standard error (SE=SD/n) of the slope terms.

We also compared the trend across the aridity gradient using the sensitivity of GPP to the Palmer Drought Severity Index (PDSI) from TerraClimate at 1/24° (~4 km) for each site75. The monthly PDSI value was extracted for each site from the available years for the EC sites that had ≥10 years of data, averaged for each season, and we used the same simple linear regression form:

GPPsum = βPDSI * PDSImean + β0 (5)

with β0 being the intercept and βPDSI being the slope (GPP sensitivity to PDSI). We also tested removing outliers (retained middle 95%) of the sensitivities to determine if anomalous sites had undue influence on the overall trend.

When comparing sensitivities of groups of sites, all statistical comparisons are two-sided student’s t-tests.

Wetness index
For each season and meteorological variable, the GPP sensitivity was regressed against the wetness index (WI). Also known as the aridity index, the WI describes long-term climatological aridity of each site such that greater values are wetter climates. WI was calculated using data for the grid cell containing each site (or nearest grid cell for coastal edges, e.g., IT-Noe) from the TerraClimate climatology product (1981-2010) at 1/24° (~4 km) spatial resolution75. WI was calculated as the long-term mean annual precipitation divided by potential evapotranspiration (PET); therefore, lower values are drier and higher values are wetter. In TerraClimate, PET is calculated as reference evapotranspiration (ET0) using the Penman-Monteith equation. We used the threshold definition of 0.6576, which separates arid and semi-arid from humid ecosystems, to distinguish water-limited (dry; WI<0.65) from energy-limited (wet; WI≥0.65) sites. We also tested WI thresholds of 0.5 and 1.0 to evaluate the consistency of responses based on the threshold value. To test if the TBMs were affected by spatial scale, the WI for TBMs was calculated using precipitation and PET from CRUTS 4.05 (1981-2010) at 0.5° spatial resolution (Supplemental Fig. S12); these changes did not affect the conclusions that the TBMs underestimate the sensitivity for energy-limited sites compared to EC.

Terrestrial biosphere models TRENDY v6 outputs
We compared EC sensitivities to estimates of GPP sensitivity from TBM outputs using the Trends in Net Land-Atmosphere Exchange project (TRENDY) v642–44. TRENDY is an ensemble of models that estimates ecosystem carbon dynamics from initial forcings. We used outputs from simulation scenario S2, which allows for changes in climate and CO2 concentration through time, but with stable preindustrial land cover (Supplemental Table S2). Outputs are produced at monthly time steps from January 1901 to December 2016. Model outputs initially had different spatial resolutions and were re-gridded to 0.5° prior to analysis77. Since all models had the same input meteorological data from CRU NECP v8, for consistency we used precipitation (pr), air temperature (tas), and incident solar downwelling shortwave radiation (rsds, converted to PAR) extracted from the model data provided for the CABLE model at 0.5° spatial resolution44. The model SDGVM was excluded because we observed an unrealistic drop in GPP after 200778. The TRENDY model outputs were analyzed for the time period overlapping with the eddy covariance data availability (1992-2016; 1992 is the earliest full year of data from US-Ha1, the Harvard Forest EMS tower site79). We did not match individual modeled years with the available years from the observed EC data; we focused on the overall trend in GPP sensitivities across the WI rather than the precise sensitivity at each EC site. GPP sensitivity to precipitation, air temperature, and PAR were calculated per model and season, similar to the EC analysis. To ensure geographic compatibility between the EC data and the models, the model data were extracted for the grid cell which contained each EC flux tower location. We evaluated sensitivities for each model, and averaged GPP across models to develop a TRENDY model mean GPP sensitivity for each flux tower site. The WI for each site was the same as from the EC extracted values (i.e., WI was not regridded to 0.5°).

Remote sensing GPP products
The EC and TBM results were compared with gridded GPP products derived from satellite remote sensing and meteorological upscaling. Specifically, we used MODIS GPP59, GOSIF58, and FLUXCOM57,80 with time series data extracted for each site location. For each year and season, all GPP products were summed (g C m−2 season−1). MODIS GPP was extracted at 500 m spatial resolution from AppEEARS from the gap-filled Version 6.0 product at 8-day temporal resolution, for both the Terra (MOD17, 2000-) and Aqua (MYD17, 2002-) satellite platforms. GOSIF (2000-) is derived from OCO-2 SIF soundings that have been spatially and temporally scaled to MODIS surface reflectance time series, from which GPP is calculated. GOSIF GPP was extracted at 0.05° spatial resolution and at monthly temporal resolution. FLUXCOM GPP was estimated from two related products: from remote sensing only (RS V006, 2001-) at 0.083° spatial resolution, and with both remote sensing and meteorological forcing from ERA5 (RS METEO ERA5, 1992-) at 0.5° spatial resolution. Both products were at monthly temporal resolution. All slopes estimated from these products were calculated similarly to the gridded TBMs, with estimated precipitation, air temperature, and PAR data coming from the CABLE model inputs for consistency. Except for FLUXCOM RS METEO ERA5, each time series started near the beginning of the MODIS satellite era (2000-2002) and for this analysis were cut in 2016 at the end of the CABLE meteorological data. We also regridded the remote sensing products (excluding MODIS-Aqua) to 0.5° and estimated spring GPP sensitivity to precipitation, again using the CABLE precipitation inputs similar to the TBMs.

Maps of difference in sensitivity between EC, TBMs, and remote sensing
To map the spatial differences between spring GPP sensitivity to precipitation inferred from the EC-WI relationship and from the TRENDY model mean, we calculated WI at 0.5° spatial resolution, spatially aggregating the precipitation and PET from TerraClimate. Next, we used the regression of spring GPP sensitivity to precipitation from EC and the WI (from Fig. 1) to estimate GPP sensitivity to precipitation across the WI grid at 0.5° spatial resolution. The WI-estimated GPP sensitivity was then subtracted from the TRENDY v6 GPP sensitivity to precipitation (difference = TRENDY model mean – estimate from EC observations). We used a 0.5° aggregated land cover class map from MODIS (MCD12 Collection 5)81 to mask grid cells that were water, barren/sparsely vegetated, or permanent snow/ice. GPP sensitivities within land cover classes were estimated from extents within MODIS land cover, using the middle 95% of the sensitivities from EC and the TRENDY model mean to mitigate the effect of extreme values on the slopes. We calculated the standard deviation of the estimated spring GPP sensitivity to precipitation across TRENDY models and compared these differences across IGBP classes.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors investigate the response of GPP to meteorological drought at eddy covariance sites across different seasons. The authors show that energy-limited sites often increase their GPP during spring droughts but not other seasons, with implications for carbon cycling. This is an interesting finding across many EC sites and showing the seasonal aspect of the GPP response is intriguing. As such, the paper is well-suited for publication in Nat Comms and of wide interest.\nI do have a few comments about the paper that I hope the authors will consider. If I’ve understood the methods correctly (L378), the wetness index from EC sites (terraclimate) was used in the TRENDY tower-scale results (Figure 2). At the site scale, there could be quite a large discrepancy between the tower WI and the WI in the forcing dataset that the models “experienced”. To confirm this is not a major factor in the obs-model differences, I would suggest that the authors repeat Figure 2 using WI calculated from the model forcings.\nI’m also finding the model analysis rather superficial. The authors mainly concentrate on multi-model means and only gloss over the inter-model differences. In several instances, the authors do not even show the model spread (both panels in Figure 3). There is also very little discussion on the potential reasons behind model discrepancies, aside from a few fairly superficial statements. I therefore don’t find the model analysis very insightful as the main result of large model differences is hardly surprising. Can you at least provide any explanation for the outlier models (e.g. LPX) or provide more insight based on past literature (one specific suggestion below)?\nSpecific comments:\nL72: clarify that these are long-term mean precip and PET\nL84: Would be good to have a couple of sentences here describing how you calculated the GPP-P sensitivity given the journal format of including Methods at the end.\nFigure 3: Would be useful to show the model spread spatially to identify areas where models agree most strongly. Similarly it would be useful to get an idea of model spread for the individual biomes\nFigure 3: would be useful to have this map repeated for the remote sensing products in the supplementary. Do they show a more coherent spatial pattern compared to EC?\nL245: You could check from the data if the energy-limited sites that do not show increased productivity with spring drought are indeed particularly cold/cloudy sites.\nL259: There are many papers for individual TRENDY models evaluating simulated GPP and ET against eddy covariance observations during water-stressed conditions. They generally point to land surface/ecosystem models responding too strongly to precipitation deficits (i.e. ET/GPP declining with rainfall, leading to a positive sensitivity) which may be one important factor explaining why the simulated sensitivities in energy-limited sites are generally more positive than observed at EC sites. This may imply a wrong sensitivity to precip declines rather than the models not capturing the co-benefits of precip decline (i.e. higher temp and/or radiation). This should be discussed as one possible explanation, making reference to past studies.\nL325: Should this be ERA-Interim?\nL328: what about cases where increasing GPP was driven by met conditions rather than plant growth (particularly at mature sites)? In this case you have detrended the GPP but not the corresponding met data. Can this introduce biases in the analysis?\nL336: GPPsum is not defined in the text? For clarity, would it be better to use different “GPPsum” names for the P, PAR and Ta equations? E.g. Psum_P, Psum_PAR and Psum_Ta\nL350: Do you mean the grid cell containing the site?\nL370: please briefly state why SDGVM was excluded rather than just including the reference.\nL372: Did you match the years to the EC sites or use 1992-2016 for all TRENDY site pixels? Are your results sensitive to this assumption if you didn’t match the years?", "reviewer_2": "Miller et al. perform an analysis of spring GPP sensitivity to precipitation based on eddy-covariance (EC) data from >60 sites with >10 years observation distributed over Europe and North America and encompassing a variety of non-crop biomes. Precipitation sensitivity is defined as the slope of the linear relationship between spring GPP and spring precipitation. Miller et al. show that half of sensitivity variance across space can be predicted from a climate wetness index (WI = P/PET), with arid sites exhibiting a positive sensitivity, while wet sites exhibit a negative one. Miller et al. then compare EC sensitivity to that of a vegetation model ensemble and remote sensing and GPP products and show that models tend to overestimate the precipitation sensitivity of wet sites while remote sensing products underestimate that of arid sites. Finally, the authors use the fitted linear relationship between EC sensitivity and WI to extrapolate the sensitivity over the Northern Hemisphere and spatially characterize the mismatch between EC and model sensitivity.\nWhile I found overall the paper to read well and to be interesting as it quantifies a perhaps under-studied phenomenon and provides useful benchmarking of vegetation models and remote sensing products, I have several concerns.\nMy first concern is regarding the definition of (meteorological) drought in the paper. While this is a key concept in the paper and is rightly so argued to matter for C cycling under climate change, drought is nowhere clearly defined in the paper. This is a (minor) issue in itself as meteorological drought can have several definitions and should thus be made clear from the beginning. A more in depth read of the paper suggests that drought is only indirectly defined through GPP sensitivity to precipitations. Thus, when the authors argue that GPP increases during spring drought in energy-limited sites, what they mean is that GPP is negatively correlated to precipitation at these sites. In my opinion this is not quite equivalent. Although this result does suggest that spring precipitation deficit enhances GPP, a more thorough definition of meteorological drought taking into account temperature and atmospheric water demand (e.g., based on meteorological drought indices such as SPEI or PDSI) might have yielded different results. This is even more relevant in the context of climate change, as globally drier conditions may arise essentially as a result of increased temperature and VPD, whereas changes in precipitation pattern will be more heterogeneous.\nAs shown by increasingly positive sensitivity of spring GPP to temperature and radiation across the climate wetness gradient, and as discussed by the authors, the paper main result is most likely driven by the positive effect of temperature and/or radiation at sites where water is not limiting. Taking into account the effect of temperature and radiation leads to much lower sensitivity to precipitation, in absolute value. Hence a more adequate conclusion would be that GPP at energy-limited sites is positively related to temperature, which is a well-known result.\nComparison of EC vs model and remote sensing estimate of GPP sensitivity to precipitation is valuable. However, as is, I don’t think that the spatial analysis of model-EC differences (i.e., Figure 3) adds much to the conclusion from Figure 2 that models tend to overestimate GPP sensitivity to precipitation. That is because vegetation models account for much more complexity than the EC extrapolation, which is based only on WI, does. Hence much of the spatial differences may be due to factors ignored by EC extrapolation. For example, from Figure 1, it seems that broadleaf forests tend to have a more negative sensitivity to precipitation than needleleaf, which may reflect contrasting species physiology or forest structure. Ignoring these differences may lead to overestimating the model error over needleleaf forests while underestimating it in the case of broadleaf. Soil factors (water capacity, rooting depth) are likely to be important factors as well of variations in GPP sensitivity to precipitation and driving model-EC differences.", "reviewer_3": "Summary:\nThis study integrates GPP datasets from in-situ EC flux towers, remote sensing, and DGVM simulations to investigate the sensitivity of spring GPP responses to meteorological drought. The flux tower data analysis shows that the 'gross' sensitivity of spring (MAM) GPP to inter-annual variability in precipitation, calculated with simple linear regression, is highly correlated with site aridity (wetness index). In moist, more energy-limited sites, the GPP~precipitation sensitivity can be negative, which is mostly driven by associated increases in temperature during such meteorological drought. Comparison with TRENDY model simulations suggests the models overall underestimate the magnitude of such negative precipitation sensitivity. Meanwhile, the remote sensing data set can capture the negative sensitivity but cannot capture the positive sensitivity in water-limited ecosystems. Finally, the study analyzes spatial patterns of model-data differences in spring GPP sensitivity to precipitation by empirically scaling site-level sensitivity to the whole northern hemisphere.\nComments:\nThe paper is well written. The analysis is comprehensive and careful given so many different data sets are involved. The figures are nice and easy to read.\nMy main concern is that the lack of unwrapping of 'drought' in the title and main text is misleading and confusing. For example, Fig. 1&2 both focus on the idea that models cannot capture the 'negative' GPP sensitivity to precipitation. However, as the authors recognized in the study, 'meteorological drought' is often accompanied by higher radiation and temperature. In fact, Fig. S7 nicely shows the partial effect of GPP to water, radiation, and temperature. It is interesting to see that the average partial sensitivity of GPP to precipitation in energy-limited systems is not negative but around 0 (Fig. S7 panel) while TRENDY model is positive. This indicates that the models are too sensitive to moisture and underestimate sensitivity to temperature (e.g. phenology). I am worried that the current framing of the manuscript can result in an erroneous interpretation that 'moisture-dependence' is wrong in the models, which is not the case based on Fig. S7. Overall, I suggest using the 'partial sensitivities' instead of gross sensitivities in the main text.\nSome minor comments:\nLine 29. this is inaccurate, drought does not increase GPP. Associated temperature and radiation increase (mostly temperature based on the analysis) drives GPP increase.\nLine. 43-44. To me, energy-limited denotes radiation-limited (e.g. the classic paper by Nemani et al. 2003) in contrast to temperature and moisture limited. Temperature is surely one kind of energy limitation. It is ok for such a generalization but more clear definition would be better here.\nLine 91, Fig.1 Why not put 4 rows in this figure? First row is gross sensitivity to precipitation, then followed by partial sensitivity to precipitation, radiation, and temperature. As I expressed above, this unwrapping is important.\nLine 106-107, 'Few energy-limited', there is still a quarter of sites, which is more than 'few'.\nLine 157, I am curious why some models have closer sensitivity to EC? e.g. JSBACH. Does JSBACH have better moisture dependence and phenology? Include such a figure for partial sensitivity would be helpful as well.\nLine 330-331. Why meteorological data was not detrended?"}, "author_response": "MAJOR REVISIONS\nReviewer #1 (Remarks to the Author):\nThe authors investigate the response of GPP to meteorological drought at eddy\ncovariance sites across different seasons. The authors show that energy-limited\nsites often increase their GPP during spring droughts but not other seasons, with\nimplications for carbon cycling. This is an interesting finding across many EC\nsites and showing the seasonal aspect of the GPP response is intriguing. As\nsuch, the paper is well-suited for publication in Nat Comms and of wide interest.\nI do have a few comments about the paper that I hope the authors will consider. If\nI’ve understood the methods correctly (L378), the wetness index from EC sites\n(terraclimate) was used in the TRENDY tower-scale results (Figure 2). At the site\nscale, there could be quite a large discrepancy between the tower WI and the WI\nin the forcing dataset that the models “experienced”. To confirm this is not a\nmajor factor in the obs-model differences, I would suggest that the authors repeat\nFigure 2 using WI calculated from the model forcings.\nThank you for this comment. We used the wetness index (WI) derived from\nTerraClimate PET and precipitation to estimate site aridity, and we agree there is\npotentially a difference between the tower scale WI and model scale WI because of\nspatial scaling factors. However, we decided to use TerraClimate WI to have a\nconsistent index across all models and remote sensing variables to have a fair\ncomparison.\nThe reviewer is correct though that the difference in forcing data used could potentially\naffect the results. In order to test whether this is a major factor in the observation-model\ndifference, we have followed the reviewer’s suggestion and reproduced Figure 2 using\nWI calculated from the model forcings (CRU TS). Specifically, we used data from CRU\nTS 4.05 for the TBMs at the same time range as the TerraClimate normals (1981-2010)\n(Supplemental Figure S12). CRU TS 4.05 is at a 0.5 degree spatial resolution (same as\nregridded TBMs used here), is from a similar base source to the original forcings that\nwere used for the TRENDY models (CRU NECP v8), and is available at monthly time\nranges. Although using CRU to calculate WI resulted in some of the individual points for\nthe TBMs shifting by a small amount, the overall conclusions of Figure 2 remain\nunchanged (i.e., energy-limited sites in TBMs underestimate the negative sensitivity\nshown in EC). This shows that any discrepancy between tower WI and model WI does\nnot influence the results that we report. We have added this new figure as\nSupplementary Figure S12 to clarify this point to the readers.\nAbove: Figure 2. All points are using the TerraClimate WI to determine energy- vs-\nwater limited sites (split at threshold of WI = 0.65).\nSupplemental Figure S12: Changing TBM WI input from TerraClimate to CRU TS 4.05\nshifts some TBMs a small amount but does not change the overall conclusions from\nFigure 2: the TBMs underestimate the negative sensitivity from energy-limited sites that\nis shown in the EC data.\nFigure 2 is energy-limited vs. water-limited sites, and so the wetness index itself is not\ndirectly on the plot, but rather the grouping of which EC sites are considered energy-\nlimited (x) or water-limited (y). Within this plot, some sites shift their location depending\non which side of the WI range they are on, thus affecting the distribution of sites within\nthe TBMs (the sites that are considered energy or water limited), but this does not\nchange the overall structure of Figure 2.\nIn addition, Supplemental Figure S11 (new numbering) shows what Figure 2 would look\nlike if we changed the WI threshold for energy vs water limited, and it shows the same\ngeneral patterns even if we lower the 0.65 WI threshold to 0.5 or raise the threshold to\n1.0.\nSupplemental Figure S11: Even when changing WI thresholds, EC observations of\nspring GPP sensitivities to precipitation occupy a different region of sensitivity space\nthan models and satellite products. Same as Figure 2 but changing the WI threshold to\n(a) 0.50 and (b) 1.00. (The version shown in Figure 2 in main text is listed at 0.65.) All\naxes are scaled such that 1 SE is the same size in both directions within each plot.\nI’m also finding the model analysis rather superficial. The authors mainly\nconcentrate on multi-model means and only gloss over the inter-model\ndifferences. In several instances, the authors do not even show the model spread\n(both panels in Figure 3). There is also very little discussion on the potential\nreasons behind model discrepancies, aside from a few fairly superficial\nstatements. I therefore don’t find the model analysis very insightful as the main\nresult of large model differences is hardly surprising. Can you at least provide\nany explanation for the outlier models (e.g. LPX) or provide more insight based\non past literature (one specific suggestion below)?\nThank you for this recommendation, other reviewers had concerns about this as well.\nWe updated the paper to incorporate this suggestion, in particular we expanded the\ndiscussion and interpretation of the between-model differences.\nWe now added a new text (L279-286, line numbers refer to clean version of\nmanuscript): “A possible explanation is that TBMs may reduce GPP too strongly under\nprecipitation deficits, which might suggest an excessive GPP sensitivity to precipitation\nreduction rather than due to co-occurring increases in temperature and solar radiation;\nfor example, in their meta-analysis, Piao et al. (2013) found that all 10 of the studied\nTBMs had a positive GPP sensitivity to variations in precipitation in the northern\nhemisphere and that TBMs were more sensitive to precipitation than temperature. In\naddition, the regression of net biome productivity, which is influenced by GPP, to\nprecipitation was overestimated in 9 of 10 of their studied models compared to the\nobserved residual land sink from Friedlingstein et al. (2010).”\nFollowing this, we also added a new paragraph in the Discussion on model specific\ndetails explaining differing sensitivities, new L287-305:\n“We find a wide array of GPP-precipitation sensitivities across models, with several\noutliers in how individual models respond to spring drought (Figure 2; Supplemental\nFigure S8). Under conditions of water stress, LPX downregulates photosynthesis to\nmatch water demand and supply since it has a supply and demand driven approach46\n(Pachalis et al., 2020). Therefore, photosynthesis and water availability have a positive\ncorrelation by design in this model, which may be contributing to the positive correlation\nbetween spring GPP and precipitation we observe in Figure 2 for both energy- and\nwater-limited ecosystems. LPJ, LPJ-GUESS, and ORCHIDEE modify the maximum rate\nof carboxylation (Vcmax) to environmental conditions for photosynthetic carbon gain35,46\n(Fisher et al., 2014; Pachalis et al., 2020), and this likely influences the higher sensitivity\nof GPP to changes in precipitation in these models in water-limited regions.\nORCHIDEE-MICT modifies Vcmax as well46 (Paschalis et al., 2020), but it does not\nhave the high sensitivities for water-limited sites that are present in ORCHIDEE; this\nmay be due to the higher water holding capacity of soils in ORCHIDEE-MICT47\n(Guimberteau et al., 2018), such that water-limited sites are more buffered against\nprecipitation deficits. JSBACH in particular does well in comparison to the EC data; this\nmay be due to its many sub-models for individual plant functional types (PFTs) and\ndifferent stomatal conductance calculations under stressed and unstressed soil water\nconditions; however, specific attribution is beyond the scope of this analysis and we\nrefer to Reick et al.48 (2021) for more details on this model. Other models that adjust for\nstomatal conductance (e.g., CABLE, DLEM) also do well for water-limited sites46\n(Paschalis et al., 2020), but do not have the sensitivity for energy-limited sites seen in\nJSBACH.\nWe have also added more supplemental figures (Figures S13, S15) to support this\nfurther based on specific comments for Figure 3. Figure S14 shows the standard\ndeviation of the GPP~precipitation sensitivity across models, with humid regions having\nthe lowest SD and arid regions having the highest. Figure S16 shows the standard\ndeviation of each IGBP vegetation class, with grassland (GRA) having the greatest\nvariation in estimated sensitivity across models (see below comment on Figure 3).\nSupplemental Figure S14: Standard deviation in GPP~precipitation sensitivity across all\nincluded TRENDY models during the spring. Models are most consistent (lowest SD) in\nhumid regions and least consistent (highest SD) in arid regions with some exceptions:\nnorth-central Europe has relatively high SD despite being humid, and western China\nhas relatively low SD despite being arid.\nSupplemental Figure S16: Standard deviation across all included TRENDY models by\nbiome. Grasslands have the greatest variation in estimates across models.\nSpecific comments:\nL72: clarify that these are long-term mean precip and PET\nChanged to “…and is calculated as long-term mean annual precipitation divided by\npotential evapotranspiration (PET).”\nL84: Would be good to have a couple of sentences here describing how you\ncalculated the GPP-P sensitivity given the journal format of including Methods at\nthe end.\nAdded new sentence: “To calculate sensitivity, seasonal sums of GPP from EC were\nlinearly regressed against precipitation, requiring a minimum of 10 years of data per\nseason (see Methods).”\nFigure 3: Would be useful to show the model spread spatially to identify areas\nwhere models agree most strongly. Similarly it would be useful to get an idea of\nmodel spread for the individual biomes\nWe have added new figures to the supplemental material (S14, S16; see earlier\ncomments for figures) and new text to the Results section “Spring GPP sensitivity to\nprecipitation across the Northern Hemisphere” (~new Lines 196-198). The new figures\nshow model SD spatially and SD within biomes: “The standard deviation across the\nTRENDY models is lowest in humid regions and greatest in arid regions, but there are\nlarge relatively large differences across models in some regions like north-central\nEurope (Supplemental Figure S14).”\nThe new supplemental figure showing difference in SD across biomes and new text at\nlines 222-224: “[Grassland] GRA has the highest variability in GPP sensitivity to\nprecipitation across models (mean SD = 0.29 g C m-2 / mm) and deciduous needleleaf\nforest (DNF) has the least (mean SD = 0.15 g C m-2 / mm) (Supplemental Figure S16).”\nFigure 3: would be useful to have this map repeated for the remote sensing\nproducts in the supplementary. Do they show a more coherent spatial pattern\ncompared to EC?\nWe have regridded the remote sensing data to a 0.5 degree spatial resolution and\nadded this new Supplemental Figure S15 (below) to the supplemental section, along\nwith some text:\nL201: “Among the remote sensing products, there are not more visually consistent\npatterns of spring sensitivity of GPP to precipitation compared to the EC estimate or the\nTRENDY models (Supplemental Figure S15).”\nSupplemental Figure S15: Maps of sensitivity of GPP in remote sensing products to\nspring precipitation. Note that MODIS MOD17 is MODIS-Terra.\nL245: You could check from the data if the energy-limited sites that do not show\nincreased productivity with spring drought are indeed particularly cold/cloudy\nsites.\nThank you for this helpful suggestion. We have followed your recommendation and now\ndiscuss both spring and summer in this section; we have included new supplemental\nfigures S17 and S18 (see below).\nL256-262: “Energy-limited ecosystems that increase GPP with precipitation during the\nspring have significantly lower PAR (p = 0.018) and air temperature (p = 0.021) than\nsites that decrease GPP with precipitation (i.e., increase GPP with meteorological\ndrought; Supplemental Figure S17). In summer, energy-limited ecosystems that\nincrease GPP with meteorological drought have lower PAR (p < 0.001) but are not\nnecessarily colder (p = 0.22) than ecosystems that do not (Supplemental Figure S18).”\nSupplemental Figure S17: Energy-limited sites with positive GPP~precip sensitivities in\nthe spring tend to have significantly lower PAR and air temperature than energy-limited\nsites with negative sensitivities in the spring. Comparing energy-limited site GPP~precip\nsensitivities, for sites with positive (>0) or negative (<0) sensitivities for (a) the sum of\nmedian spring PAR (p = 0.018) and (b) median spring air temperature (p = 0.021). Bars\nacross the top indicate p-values, with both plots having significant differences at alpha =\n0.05.\nSupplemental Figure S18: Energy-limited sites that maintain negative GPP~precip\nsensitivities during the summer receive less solar radiation ( (a) lower PAR, p < 0.001),\nbut (b) are not necessarily colder (p = 0.22). Points are individual EC sites and bars\nacross the top indicate p-values.\nL259: There are many papers for individual TRENDY models evaluating simulated\nGPP and ET against eddy covariance observations during water-stressed\nconditions. They generally point to land surface/ecosystem models responding\ntoo strongly to precipitation deficits (i.e. ET/GPP declining with rainfall, leading to\na positive sensitivity) which may be one important factor explaining why the\nsimulated sensitivities in energy-limited sites are generally more positive than\nobserved at EC sites. This may imply a wrong sensitivity to precip declines rather\nthan the models not capturing the co-benefits of precip decline (i.e. higher temp\nand/or radiation). This should be discussed as one possible explanation, making\nreference to past studies.\nThank you for this comment. We agree and have added additional discussion to place\nthe results within the context of previous analyses. Please also see our response to this\npoint raised in the reviewer’s last main comment above.\nL325: Should this be ERA-Interim?\nYes, this should be ERA-Interim, thank you for catching this (L378). This is what is used\nin the ONEFlux processing from Pastorello et al. (2020).\nL328: what about cases where increasing GPP was driven by met conditions\nrather than plant growth (particularly at mature sites)? In this case you have\ndetrended the GPP but not the corresponding met data. Can this introduce biases\nin the analysis?\nThank you for this thoughtful comment. We did not detrend the met data because of the\nrelatively short time compared to climatological time scales (~60 years, Wu et al., 2007,\nhttps://doi.org/10.1073/pnas.0701020104) that are usually needed for detrending,\nand have added a new sentence clarifying this in the Methods (L383): “Meteorological data\nwere not detrended because of the relatively short time series for climatology (Wu et al.,\n2007), and differences were minimal when testing detrending precipitation for spring\n(Supplemental Figure S19).”\nHowever, to test whether there is any potential impact we performed the same analysis\nas the GPP detrending and detrended the precipitation data with a Mann-Kendall test\nand Sen’s slope.\nDetrending precipitation during spring changes precipitation values and thus GPP\nsensitivities to precipitation for 4 sites: AT-Neu, BE-Vie, FI-Hyy, NL-Loo, but the\nresulting trend for a similar plot to Figure 1 for these sites is essentially the same as\nFigure 1 (y = -1.26 * log10(x) – 0.08; R2 = 0.47, p < 0.001), so it doesn’t change\nanything in a meaningful way.\nNew regression for GPP~precip during spring: y = -1.24 * log10(x) – 0.08\nR2 = 0.48, p < 0.001\nSupplemental Figure S19: Detrending precipitation for the GPP~precipitation slopes in\nspring produces nearly an identical regression to Figure 1. The only sites that change\ntheir slopes are AT-Neu, BE-Vie, FI-Hyy, and NL-Loo.\nL336: GPPsum is not defined in the text? For clarity, would it be better to use\ndifferent “GPPsum” names for the P, PAR and Ta equations? E.g. Psum_P,\nPsum_PAR and Psum_Ta\nIt is the same GPPsum for each equation for each flux site. We now added the definition\nto the text, and also corrected air temperature in the equation (Ta needed to be\nsum\nTa ).\nmean\nL350: Do you mean the grid cell containing the site?\nYes, in nearly all cases, and we have revised the text to reflect this. There were some\ncoastal sites (e.g., IT-Noe) that were just outside the gridding for TerraClimate and so\nwe used the nearest grid cell in those few cases.\nNew L416: “WI was calculated using the grid cell containing each site (or nearest grid\ncell for coastal edges e.g., IT-Noe)…”\nL370: please briefly state why SDGVM was excluded rather than just including the\nreference.\nThank you, this has been clarified by our co-author who processed the data. We now\nstate (L441): “The model SDGVM was excluded because we observed an unrealistic\ndrop in GPP after 2007.”\nL372: Did you match the years to the EC sites or use 1992-2016 for all TRENDY\nsite pixels? Are your results sensitive to this assumption if you didn’t match the\nyears?\nWe did not match years because some of the sites required going beyond 2016 to\nacquire 10 years of data, and TRENDY v6 ends in 2016. Results may be sensitive to\nthe assumption that we did not match years, but we do not intend to predict the precise\nsensitivity at each individual site, we are more interested in the overall trend across the\nwetness index.\nWe added a new statement to make that clear for the readers (L445): “We did not\nmatch individual modeled years with the available years from the observed EC data; we\nfocused on the overall trend in GPP sensitivities across the WI rather than the precise\nsensitivity at each EC site.”\nReviewer #2 (Remarks to the Author):\nMiller et al. perform an analysis of spring GPP sensitivity to precipitation based\non eddy-covariance (EC) data from >60 sites with >10 years observation\ndistributed over Europe and North America and encompassing a variety of non-\ncrop biomes. Precipitation sensitivity is defined as the slope of the linear\nrelationship between spring GPP and spring precipitation. Miller et al. show that\nhalf of sensitivity variance across space can be predicted from a climate wetness\nindex (WI = P/PET), with arid sites exhibiting a positive sensitivity, while wet sites\nexhibit a negative one. Miller et al. then compare EC sensitivity to that of a\nvegetation model ensemble and remote sensing and GPP products and show that\nmodels tend to overestimate the precipitation sensitivity of wet sites while remote\nsensing products underestimate that of arid sites. Finally, the authors use the\nfitted linear relationship between EC sensitivity and WI to extrapolate the\nsensitivity over the Northern Hemisphere and spatially characterize the mismatch\nbetween EC and model sensitivity.\nWhile I found overall the paper to read well and to be interesting as it quantifies a\nperhaps under-studied phenomenon and provides useful benchmarking of\nvegetation models and remote sensing products, I have several concerns.\nMy first concern is regarding the definition of (meteorological) drought in the\npaper. While this is a key concept in the paper and is rightly so argued to matter\nfor C cycling under climate change, drought is nowhere clearly defined in the\npaper. This is a (minor) issue in itself as meteorological drought can have several\ndefinitions and should thus be made clear from the beginning.\nThank you for bringing this important omission to our attention. We have revised the\nmanuscript to include a clear definition, on L56:\n“Theory suggests that GPP can increase during negative precipitation anomalies (i.e.,\nmeteorological droughts [(Wilhite and Glantz, 1985)]) in mid- and high-latitude\necosystems as well, a response that has found some support.”\nA more in depth read of the paper suggests that drought is only indirectly defined\nthrough GPP sensitivity to precipitations. Thus, when the authors argue that GPP\nincreases during spring drought in energy-limited sites, what they mean is that\nGPP is negatively correlated to precipitation at these sites. In my opinion this is\nnot quite equivalent. Although this result does suggest that spring precipitation\ndeficit enhances GPP, a more thorough definition of meteorological drought\ntaking into account temperature and atmospheric water demand (e.g., based on\nmeteorological drought indices such as SPEI or PDSI) might have yielded\ndifferent results. This is even more relevant in the context of climate change, as\nglobally drier conditions may arise essentially as a result of increased\ntemperature and VPD, whereas changes in precipitation pattern will be more\nheterogeneous.\nThank you for this comment for improvement, we agree with the reviewer and have\nfollowed their suggestion to assess whether using a drought index influences the\nresults. We thus downloaded the monthly PDSI product from TerraClimate for each of\nthe sites and made an additional analysis (supplemental figures S7 & S8) substituting it\nfor precipitation (following Figure 1). We used the mean PDSI for the spring (three\nmonths). Positive PDSI are wetter conditions, negative PDSI are drier (same direction\nas precipitation totals, see figures below). The trends we observe with GPP and\nprecipitation hold when using PDSI, albeit with a weaker relationship.\nSupplemental Figure S7: GPP sensitivity to TerraClimate PDSI during the spring.\nFollowing Figure 1, (a) sensitivity of each site along aridity gradient and (b) density of\nsite sensitivities. 14/15 = 93% of the water-limited sites having positive GPP~PDSI\nrelationships, and 33/46 = 72% of the energy-limited sites having negative GPP~PDSI\nrelationships.\nIf we remove outliers by only taking the middle 95% of the sensitivities, we get a similar\nrelationship:\nSupplemental Figure S8: GPP sensitivity to TerraClimate PDSI during the spring,\nremoving outliers by retaining only the middle 95% of site sensitivities. Following Figure\n1, (a) sensitivity of each site along aridity gradient and (b) density of site sensitivities.\n12/13 = 92% of the water-limited sites having positive GPP~PDSI relationships, and\n31/44 = 70% of the energy-limited sites having negative GPP~PDSI relationships.\nThis analysis, using the 61 sites with 10+ years of data for spring, suggests that results\nwe report with GPP and precipitation hold when using PDSI, albeit with a weaker\nrelationship.\nAs shown by increasingly positive sensitivity of spring GPP to temperature and\nradiation across the climate wetness gradient, and as discussed by the authors,\nthe paper main result is most likely driven by the positive effect of temperature\nand/or radiation at sites where water is not limiting. Taking into account the effect\nof temperature and radiation leads to much lower sensitivity to precipitation, in\nabsolute value. Hence a more adequate conclusion would be that GPP at energy-\nlimited sites is positively related to temperature, which is a well-known result.\nThank you and this interpretation is correct: higher temperatures and higher solar\nradiation co-occur with rainfall deficits (i.e., meteorological droughts). This is true as it is\na well-known result that higher temperatures and PAR during spring lead to higher\nGPP, but we are approaching it from a meteorological drought perspective because the\ndrought conditions themselves have been indicative of outlying increases in GPP in\nprevious studies, without providing a clear mechanism behind them. Or in other words,\ndrought can have beneficial effect on GPP depending on the overall limiting factors,\nwhich has not been shown in a systematic way across biomes yet. This is why we have\nset up our results this way.\nWe address this through the new PDSI plots (Supplemental Figure S7 and S8; Main\ntext Lines 118-120) and new boxplots comparing spring and summer temperature and\nPAR for energy-limited sites (Supplemental Figure S17 and S18; Main text Lines 256-\n262).\nComparison of EC vs model and remote sensing estimate of GPP sensitivity to\nprecipitation is valuable. However, as is, I don’t think that the spatial analysis of\nmodel-EC differences (i.e., Figure 3) adds much to the conclusion from Figure 2\nthat models tend to overestimate GPP sensitivity to precipitation. That is because\nvegetation models account for much more complexity than the EC extrapolation,\nwhich is based only on WI, does. Hence much of the spatial differences may be\ndue to factors ignored by EC extrapolation. For example, from Figure 1, it seems\nthat broadleaf forests tend to have a more negative sensitivity to precipitation\nthan needleleaf, which may reflect contrasting species physiology or forest\nstructure. Ignoring these differences may lead to overestimating the model error\nover needleleaf forests while underestimating it in the case of broadleaf. Soil\nfactors (water capacity, rooting depth) are likely to be important factors as well of\nvariations in GPP sensitivity to precipitation and driving model-EC differences.\nWe agree that there are many local factors (e.g., edaphic factors such as the mentioned\nwater capacity and rooting depth) that we are not accounting for in our analysis with the\nEC extrapolation, and the TBMs allow for much more complexity in the sensitivity than\nthe EC-WI regression. We feel however that our analysis still holds important value in\nthat it allows for a direct comparison to remote sensing and TBM-derived values; this\ncan be used as a point of comparison through which the influence of other local factors\ncan be further examined in future work. There are likely differences in plant function,\nstructure, and diversity of species that we do not address, but the scope of these\ndifferences is beyond our current analysis and we feel would be best reserved for a\nfuture study. We address this will a new paragraph in the Discussion, L317:\n“We upscaled estimates of spring GPP sensitivity to precipitation from EC and the WI to\nprovide a first-order approximation of spatial patterns across the Northern Hemisphere\n(Figure 3). These estimates can be directly compared with results from TBMs and\nremote sensing and can be used as a basis for future comparisons (e.g., Wolf et al.,\n2016; Bastos et al., 202030,35). However, we acknowledge that, beyond aridity, there are\nmany local environmental factors that vary across different regions and are incorporated\nby TBMs and remote sensing observations—this includes edaphic or microclimatic\nfactors (e.g., soil texture, water availability, plant structure and diversity) (Fisher et al.,\n2014; Stocker et al., 2019; Piao et al., 2013; Lian et al., 2021)37,48,53,60. Within individual\nTBMs, the diversity of these responses will depend on how variables are parameterized\n(Fisher et al., 2014)37. Our results can be used as a reference to evaluate what would\nbe expected from a climatological perspective (i.e., water vs. energy limitation) alone,\nand there remains unexplained variability in our regression (Figure 1).”\nWe have added new supplemental figures comparing the SD of sensitivities spatially\n(Supplemental Figure S14) and across biome types in TRENDY models (Supplemental\nFigure S16), and we also have maps for remote sensing data products (Supplemental\nFigure S15).\nReviewer #3 (Remarks to the Author):\nSummary:\nThis study integrates GPP datasets from in-situ EC flux towers, remote sensing,\nand DGVM simulations to investigate the sensitivity of spring GPP responses to\nmeteorological drought. The flux tower data analysis shows that the 'gross'\nsensitivity of spring (MAM) GPP to inter-annual variability in precipitation,\ncalculated with simple linear regression, is highly correlated with site aridity\n(wetness index). In moist, more energy-limited sites, the GPP~precipitation\nsensitivity can be negative, which is mostly driven by associated increases in\ntemperature during such meteorological drought. Comparison with TRENDY\nmodel simulations suggests the models overall underestimate the magnitude of\nsuch negative precipitation sensitivity. Meanwhile, the remote sensing data set\ncan capture the negative sensitivity but cannot capture the positive sensitivity in\nwater-limited ecosystems. Finally, the study analyzes spatial patterns of model-\ndata differences in spring GPP sensitivity to precipitation by empirically scaling\nsite-level sensitivity to the whole northern hemisphere.\nComments:\nThe paper is well written. The analysis is comprehensive and careful given so\nmany different data sets are involved. The figures are nice and easy to read.\nMy main concern is that the lack of unwrapping of 'drought' in the title and main\ntext is misleading and confusing. For example, Fig. 1&2 both focus on the idea\nthat models cannot capture the 'negative' GPP sensitivity to precipitation.\nHowever, as the authors recognized in the study, 'meteorological drought' is\noften accompanied by higher radiation and temperature. In fact, Fig. S7 nicely\nshows the partial effect of GPP to water, radiation, and temperature. It is\ninteresting to see that the average partial sensitivity of GPP to precipitation in\nenergy-limited systems is not negative but around 0 (Fig. S7 panel) while\nTRENDY model is positive. This indicates that the models are too sensitive to\nmoisture and underestimate sensitivity to temperature (e.g. phenology). I am\nworried that the current framing of the manuscript can result in an erroneous\ninterpretation that 'moisture-dependence' is wrong in the models, which is not the\ncase based on Fig. S7. Overall, I suggest using the 'partial sensitivities' instead of\ngross sensitivities in the main text.\nThank you for this comment, and we acknowledge the significance of both gross\nsensitivities and partial sensitivities in this study. While our paper primarily emphasizes\nthe sensitivity of precipitation in relation to meteorological drought and GPP, we also\naddress the influences of temperature and PAR to encompass partial sensitivities.\nFurthermore, we have incorporated a PDSI regression (Supplemental Figure S19) to\nprovide a more technically defined assessment of drought.\nWe find that the gross (univariate) sensitivity moisture dependence in the TBMs for GPP\n~ precipitation in the spring is not sufficiently negative for energy-limited ecosystems\ncompared to eddy covariance (and remote sensing; Fig. 2). With the partial\n(multivariate) sensitivity, the TBMs always have positive GPP~precip sensitivities for\nenergy-limited sites, while energy-limited EC sites are centered around zero\n(Supplemental Figure old S7, S9 in new numbering). We agree that the TBM GPP is too\nsensitive to moisture in the partial sensitivities, but it is not sensitive enough in the gross\n(univariate) sensitivities. Based on Figure old S7 (new S9), the moisture dependence is\ntoo positive in models, when it should be more neutral; based on Figure 2, the moisture\ndependence is not negative enough in models; this is the same direction (shifted more\npositive) in both cases.\nWe also agree that the models are not sensitive enough to temperature compared to\nEC, but this is not the primary focus of our paper and many studies have examined\nmodel sensitivities to temperature (e.g., Buermann, W., Forkel, M., O’Sullivan, M. et\nal. Widespread seasonal compensation effects of spring warming on northern plant\nproductivity. Nature 562, 110–114 (2018). https://doi.org/10.1038/s41586-018-0555-7).\nIn the revised manuscript, we further emphasize the importance of the partial\nsensitivities in the Results and Discussion (e.g., Lines 121-134, 248-268), and discuss\nhow our use of gross sensitivity to precipitation should be interpreted in the relative\ncontext of partial sensitivities, including other environmental factors (e.g., VPD, energy\nbalance).\nSome minor comments:\nLine 29. this is inaccurate, drought does not increase GPP. Associated\ntemperature and radiation increase (mostly temperature based on the analysis)\ndrives GPP increase.\nThank you, we have rephrased this to: “GPP increases during spring drought, primarily\ndue to the associated increases in temperature and radiation during periods of\nanomalously low precipitation, …”\nLine. 43-44. To me, energy-limited denotes radiation-limited (e.g. the classic paper\nby Nemani et al. 2003) in contrast to temperature and moisture limited. \nTemperature is surely one kind of energy limitation. It is ok for such a\ngeneralization but more clear definition would be better here.\nThank you for this suggestion. There are various factors to include in the limitations of\nenergy, and Nemani et al. (2003) is one perspective we have cited here, as well as\nChurkina and Running (1998) and the more recent Donohue et al. (2013) and Sippel et\nal. (2018). We have added “(e.g., radiation, temperature)” after energy limitations in the\nsentence to be more explicit. Both radiation and temperature are included in the Budyko\nframework for aridity due to PET scaling, which is how we are deriving our definitions\nhere (see Sippel et al. 2018 https://doi.org/10.1007/s40641-018-0103-4 for more\ndetails).\nLine 91, Fig.1 Why not put 4 rows in this figure? First row is gross sensitivity to\nprecipitation, then followed by partial sensitivity to precipitation, radiation, and\ntemperature. As I expressed above, this unwrapping is important.\nThank you for this suggestion. To the left\nis a mock-up of what Figure 1 might look\nlike if we included the partial\nsensitivities. While this does show more\nof the details regarding the differences\nbetween precipitation, temperature, and\nPAR in how they combined to affect\nspring GPP, with respect we feel that it\nwould take the focus off the main\nmessage of the paper, which is the\ngross precipitation response (in the top\npanel).\nWe agree that unwrapping these\ndifferent sensitivities is an important\npoint to characterize the results, and this\nis why we have included details related\nto Figure old S7 (new S9) in the\nsupplemental material immediately after\npresenting this result. The temperature\nsensitivity itself would not be a new\nresult in the previous literature, but the\nconsistency of the precipitation response\nacross EC sites (our main takeaway) is\nnew because we are able to show it with\nobservational data.\nThe partial sensitivities could also be\nargued to be incomplete because they\ndo not account for all differences\nbetween ecosystems, or compensating\neffects, and there are other factors that\ndiffer between sites that would not be\naccounted for by the partial relationships\nof precipitation, temperature, and PAR\n(e.g., VPD, edaphic factors). A complete\nanalysis of this form would require a\ndetailed modeling or machine learning\napproach which we feel is beyond the\nscope of the present study.\nLine 106-107, 'Few energy-limited', there is still a quarter of sites, which is more\nthan 'few'.\nChanged to “relatively few”\nLine 157, I am curious why some models have closer sensitivity to EC? e.g.\nJSBACH. Does JSBACH have better moisture dependence and phenology?\nInclude such a figure for partial sensitivity would be helpful as well.\nWe now added discussion of TRENDY models in the Discussion, and including this\nsentence on JSBACH, L299: “JSBACH in particular does well in comparison to the EC\ndata; this may be due extensive parameterization for individual plant functional types\n(PFTs) and different stomatal conductance calculations under stressed and unstressed\nsoil water conditions; however, the specific attribution is beyond the scope of this\nanalysis and we refer to Reick et al.48 (2021) for more details on this model.”\nLine 330-331. Why meteorological data was not detrended?\nThank you for this comment, and Reviewer 2 had a similar comment and we include our\nresponse below.\nWe did not detrend the met data because of the relatively short time compared to\nclimatological time scales (~60 years, Wu et al., 2007,\nhttps://doi.org/10.1073/pnas.0701020104) that are usually needed for detrending, and\nhave added a new sentence clarifying this in the Methods (L383): “Meteorological data\nwere not detrended because of the relatively short time series for climatology (Wu et al.,\n2007), and differences were minimal when testing detrending precipitation for spring\n(Supplemental Figure S19).”\nHowever, to test whether there is any potential impact we performed the same analysis\nas the GPP detrending and detrended the precipitation data with a Mann-Kendall test\nand Sen’s slope.\nDetrending precipitation during spring changes precipitation values and thus GPP\nsensitivities to precipitation for 4 sites: AT-Neu, BE-Vie, FI-Hyy, NL-Loo, but the\nresulting trend for a similar plot to Figure 1 for these sites is essentially the same as\nFigure 1 (y = -1.26 * log10(x) – 0.08; R2 = 0.47, p < 0.001), so it doesn’t change\nanything in a meaningful way.\nNew regression for GPP~precip during spring: y = -1.24 * log10(x) – 0.08\nR2 = 0.48, p < 0.001\nSupplemental Figure S19: Detrending precipitation for the GPP~precipitation slopes in\nspring produces nearly an identical regression to Figure 1. The only sites that change\ntheir slopes are AT-Neu, BE-Vie, FI-Hyy, and NL-Loo."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I have no further comments on this manuscript and recommend it for publication", "reviewer_2": "Overall, the authors have addressed all comments in a satisfactory manner The manuscript clarity has improved and the discussion and conclusions are robust to the suggested changes in the analysis. I still only have a couple minor comments. Thus, I recommend this manuscript for publication in Nature Communications with minor revisions.\nI agree that spatial comparison of EC and model GPP sensitivity to precipitation holds value despite its limitations. The added paragraph makes a good job at highlighting these limitations. However, please consider the following slightly different approach, which may be more satisfying. Instead of calculating a map of model vs EC deviations based on the difference between interpolated EC sensitivity and model sensitivity, why not try to first regress deviations against WI and then interpolate based on this regression? I think this would provide the advantage of more accurately represent the expected effect of WI on model-EC deviations across biomes.\nThe result mentioned L131-134 that spring GPP still decreases with precipitation, even when accounting for temperature and radiation is interesting but I think that discussion of potential mechanisms is missing in the Discussion. This could pinpoint to what process is lacking in TBMs that lead to TBMs not reproducing this negative sensitivity to precipitation as mentioned L184.", "reviewer_3": "The revision improves the manuscript, especially with added information on drought definition and model differences. Yet, as I expressed in my comments before, I still think focusing mainly on 'gross' sensitivity undermines the comprehensive analysis and can be misleading.\nFor instance, the current abstract ends with \"our results show that GPP in TBMs may not be sufficiently sensitive to spring droughts\". \"not sufficiently sensitive to spring droughts\" is very vague. Spring drought is a syndrome incorporating various changes in climate forcing (temperature, precipitation, etc.). So, is the model not sensitive enough to water or temperature? I think the authors have the answer in the partial sensitivity analysis, which should be highlighted more.\nIn addition, the result section (Line 132 - 134) says \"Therefore, our results show that although positive temperature anomalies are the main control on GPP sensitivity during spring for energy-limited sites, meteorological drought is an important and thus far underestimated secondary control for ecosystem productivity, particularly as it often co-occurs with warming.\" Please show evidence that meteorological drought is a far underestimated secondary control.\nFinally, I suggest using \"meteorological drought\" instead of drought in the title. Again, I am just worried that the manuscript leads to a misinterpretation that erroneous moisture dependence in TBMs led to biases in spring drought"}, "author_response": "REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nI have no further comments on this manuscript and recommend it for publication\nThank you again for your earlier comments on our manuscript and for your\nrecommendation for publication.\nReviewer #2 (Remarks to the Author):\nOverall, the authors have addressed all comments in a satisfactory manner The\nmanuscript clarity has improved and the discussion and conclusions are robust\nto the suggested changes in the analysis. I still only have a couple minor\ncomments. Thus, I recommend this manuscript for publication in Nature\nCommunications with minor revisions.\nI agree that spatial comparison of EC and model GPP sensitivity to precipitation\nholds value despite its limitations. The added paragraph makes a good job at\nhighlighting these limitations. However, please consider the following slightly\ndifferent approach, which may be more satisfying. Instead of calculating a map of\nmodel vs EC deviations based on the difference between interpolated EC\nsensitivity and model sensitivity, why not try to first regress deviations against\nWI and then interpolate based on this regression? I think this would provide the\nadvantage of more accurately represent the expected effect of WI on model-EC\ndeviations across biomes.\nThank you for your suggestion. Although we understand the motivation for your\nsuggestion, we feel that there are some important aspects of the suggested analysis\nthat undermine its utility with respect to identifying the expected effect of WI on model-\nEC deviations across biomes.\nIn our current approach, we generate a map of expected spring GPP~precipitation\nsensitivity from the linear regression of this sensitivity to the WI, derived from EC\nobservations, by extrapolating across a grid of WI values from TerraClimate (Figure 3a).\nWe then subtract this from the TRENDY model mean GPP~precipitation sensitivity\n(Figure 3b) to yield a difference map (Figure 3c). Importantly, this method retains the\nspatial variability that is present in the TRENDY model gridded data at 0.5 degree\nspatial resolution - essential information that would be lost in the suggested method.\nUsing the method suggested, we would first calculate the difference between TRENDY\nmodel mean and EC-estimated GPP~precipitation sensitivities for each EC site. While\nthis would give a difference of the estimates for each site, the resulting value would\nincorporate error from both TRENDY and from EC sensitivities. In our current approach,\nwe do not make inference from the sensitivities for each individual site, but instead our\nresults are driven by the trend across the aridity gradient (i.e., WI). With the suggested\napproach, we expect to have more error because of the very different spatial scales\nbetween the EC sites themselves (hundreds of meters) and the TRENDY estimates (0.5\ndegree, or roughly 25 km). While a comparison in the overall trends across the WI can\nbe made, a comparison of the GPP~precipitation sensitivity from TRENDY and from EC\nis not appropriate for each individual site because they are measuring very different\nareas. A different sensitivity should perhaps thus be expected, and neither the model\nnor the EC data may be incorrect despite their differences. Extrapolating such data\nspatially would thus be quite questionable.\nWe have however implemented the approach suggested by the reviewer to assess how\nthe analysis adds to the interpretation of the results. If we subtract the EC GPP~precip\nslopes from the TRENDY model mean GPP~precip slopes as suggested, and do a\nlinear regression with the log10 of the wetness index (WI), this yields the equation:\ny = difference in slopes, TRENDY - EC\nx = WI\ny = 0.35*log10(x) + 0.15, R2 = 0.058, p = 0.061\nApplying this to the TerraClimate grid of WI, following the reviewer’s suggestion, yields\nthe following map (with the same ranges as our original approach presented in Figure\n3c):\nTop is the new map based on the reviewer’s suggestion, and bottom is the difference\nmap (original version of Figure 3c). The color scales are the same in both figures.\nAs we hypothesized, much of the spatial information is lost and the resulting map is\nprimarily dominated by spatial changes in WI. We would prefer not to include the new\nsuggested map because it does not adequately describe spatial differences between\nthe EC-estimated slopes and the TRENDY model mean slopes.\nThe result mentioned L131-134 that spring GPP still decreases with precipitation,\neven when accounting for temperature and radiation is interesting but I think that\ndiscussion of potential mechanisms is missing in the Discussion. This could\npinpoint to what process is lacking in TBMs that lead to TBMs not reproducing\nthis negative sensitivity to precipitation as mentioned L184.\nThank you for this comment. In the Discussion section, starting at Line 270 (in the\nupdated clean draft), we emphasize how the sensitivities observed in TBMs differ from\nthe EC data, and why this might be in the case. In the following paragraph, starting at\nLine 288, we discuss several possible reasons why different models are not reproducing\nthe negative sensitivity to precipitation we observe (mentioned in Line 184, now Lines\n182-187). For example, we mention that LPX has a positive correlation between\nphotosynthesis and water availability by design in this model. But this is not necessarily\nthe case for other models because of differences in design, and we mention a few\ncases to try to attribute the responses we observe. Each model likely has a subtly\ndifferent reason as to why it does not exactly emulate the processes observed by the\nEC, as this form of attribution for each model is beyond the scope of our analysis since\nthis is an observational comparison study rather than a model development study.\nParagraph at Line 270:\nTBMs are a key tool in predicting the impacts of climatic change on the carbon cycle\n37,38,41. We found that TBMs from the widely used TRENDY ensemble consistently\nunderestimated the strength of spring GPP sensitivity to precipitation deficits in energy-\nlimited ecosystems observed by EC. Despite having much larger grid cells (0.5°) than\nthe flux tower footprints 46, the TBMs had an accurate span of positive GPP sensitivities\nfor water-limited sites, but not negative sensitivities for energy-limited sites. This\nsuggests that GPP-precipitation relationships in many TBMs may be discounting the\npotential co-benefits of precipitation reductions on spring GPP, and solely focus on\nprecipitation as increasing GPP, which is well-represented for water-limited ecosystems.\nAs a consequence, TBMs may also underestimate the risks of spring drought\ncontributing to later loss of productivity during the growing season, such as through\nstructural overshoot 45,47. A possible explanation is that TBMs may reduce GPP too\nstrongly under precipitation deficits, which might suggest an excessive GPP sensitivity\nto precipitation reduction rather than due to co-occurring increases in temperature and\nsolar radiation; for example, in their meta-analysis, Piao et al.48 found that all 10 of the\nstudied TBMs had a positive GPP sensitivity to variations in precipitation in the northern\nhemisphere and that TBMs were more sensitive to precipitation than temperature. In\naddition, the regression of net biome productivity, which is influenced by GPP, to\nprecipitation was overestimated in 9 of 10 of their48 studied models compared to the\nobserved residual land sink from Friedlingstein et al.49.\nParagraph at Line 288:\nWe find a wide array of GPP-precipitation sensitivities across models, with several\noutliers in how individual models respond to spring drought (Figure 2; Supplemental\nFigure S8). Under conditions of water stress, LPX downregulates photosynthesis to\nmatch water demand and supply since it has a supply and demand driven approach50.\nTherefore, photosynthesis and water availability have a positive correlation by design in\nthis model, which may be contributing to the positive correlation between spring GPP\nand precipitation we observe in Figure 2 for both energy- and water-limited\necosystems. LPJ, LPJ-GUESS, and ORCHIDEE modify the maximum rate of\ncarboxylation (Vcmax) to environmental conditions for photosynthetic carbon gain37,50,\nand this likely influences the higher sensitivity of GPP to changes in precipitation in\nthese models in water-limited regions. ORCHIDEE-MICT modifies Vcmax as well50, but\nit does not have the high sensitivities for water-limited sites that are present in\nORCHIDEE; this may be due to the higher water holding capacity of soils in\nORCHIDEE-MICT51, such that water-limited sites are more buffered against\nprecipitation deficits. JSBACH in particular does well in comparison to the EC data; this\nmay be due to extensive parameterization for individual PFTs and different stomatal\nconductance calculations under stressed and unstressed soil water conditions;\nhowever, specific attribution is beyond the scope of this analysis and we refer to Reick\net al.52 for more details on this model. Other models that adjust for stomatal\nconductance (e.g., CABLE, DLEM) also do well for water-limited sites50, but do not have\nthe sensitivity for energy-limited sites seen in JSBACH.\nLines 182-187:\nEven when accounting for PAR and air temperature, sites in the EC data can have\nnegative spring GPP sensitivities to precipitation, while this is never the case in the\nTRENDY model ensemble mean (Supplemental Figure S9). In other words, the models\nindicate that more precipitation would uniformly correspond to more GPP during spring\nacross the entirety of our aridity gradient, while this is not observed at EC sites.\nReviewer #3 (Remarks to the Author):\nThe revision improves the manuscript, especially with added information on\ndrought definition and model differences. Yet, as I expressed in my comments\nbefore, I still think focusing mainly on 'gross' sensitivity undermines the\ncomprehensive analysis and can be misleading.\nFor instance, the current abstract ends with \"our results show that GPP in TBMs\nmay not be sufficiently sensitive to spring droughts\". \"not sufficiently sensitive to\nspring droughts\" is very vague. Spring drought is a syndrome incorporating\nvarious changes in climate forcing (temperature, precipitation, etc.). So, is the\nmodel not sensitive enough to water or temperature?\nI think the authors have the answer in the partial sensitivity analysis, which should be highlighted\nmore.\nWe have edited the Abstract to clarify that we are specifically looking at precipitation\ndeficits. The associated sentence now reads (L27-30): “While remote sensing products\nrepresent the negative spring GPP sensitivity for energy-limited ecosystems well, our\nresults show that GPP in TBMs may not be sufficiently sensitive to precipitation deficits\nduring spring.”\nWe discuss the partial sensitivities in the Results and Discussion and do not highlight\nthem to the full extent in the Abstract, largely because they are supportive of the main\ntakeaways and the brevity of the Abstract necessitates that they be left out. The models\nare not sensitive enough to precipitation and they are quite sensitive to temperature.\nIn addition, the result section (Line 132 - 134) says \"Therefore, our results show\nthat although positive temperature anomalies are the main control on GPP\nsensitivity during spring for energy-limited sites, meteorological drought is an\nimportant and thus far underestimated secondary control for ecosystem\nproductivity, particularly as it often co-occurs with warming.\" Please show\nevidence that meteorological drought is a far underestimated secondary control.\nWe feel there has been a misunderstanding here, related to phrasing in this sentence,\nand we have inserted commas to better emphasize the intended meaning. The\nsentence intended to convey that “meteorological drought is an important and, thus far,\nunderestimated secondary control” (meaning: to date, it is underestimated) instead of\nmeteorological drought being far underestimated as a secondary control. While it is\nunderestimated, we agree that it is not “far” (i.e., greatly) underestimated as a\nsecondary control.\nOur evidence for meteorological drought as underestimated secondary control is\nhighlighted in the sentences prior to the one stated in the comment:\n“Even when controlling for PAR and air temperature in a multiple linear regression in the\nspring EC observations (see Methods), 61% of energy-limited sites (n = 28/46) have a\nnegative GPP sensitivity to precipitation (Supplemental Figure S9). By contrast, in the\npartial spring GPP sensitivity to air temperature, 93% (n = 43/46) of energy-limited sites\nhave a positive sensitivity. For energy-limited sites, this suggests that warmer springs\nalmost always increase GPP when controlling for precipitation (93%) compared to drier\nsprings when controlling for air temperature (61%).”\nFinally, I suggest using \"meteorological drought\" instead of drought in the title.\nAgain, I am just worried that the manuscript leads to a misinterpretation that\nerroneous moisture dependence in TBMs led to biases in spring drought\nWe appreciate this suggestion and understand the Reviewer’s desire for clarity in the\ntitle of our manuscript. However, with respect, we wish to keep the title as it currently is\nand not insert “meteorological” in the title. We have three main reasons for this: (1)\nthere are many different definitions of drought in various disciplines (e.g., ecosystem\nscience, agriculture, hydrology, meteorology), and we wish to keep the paper as widely\napplicable to many audiences and not artificially restrict to those only interested in\nmeteorological drought, which itself can also have variable definitions in the literature;\n(2) we clearly state that we are discussing “meteorological drought” in both of the first\ntwo sentences of the abstract, and so the form of drought we are investigating should\nbe apparent to those who read beyond the title; and (3), our opinion is that inserting\n“meteorological” into the title would make it many syllables longer and more unwieldy\nthan it currently is."}]

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