•Environmental Response Function (ERF) maps surface-atmosphere exchanges.•ERF is capable of upscaling tower eddy covariance observations to regional scale.•The regional flux maps de-bias the ...footprint variation in tower observations.•ERF provides unprecedented spatio-temporal attribution and resolution.
Eddy-covariance measurements are widely used to develop and test parameterizations of land-atmosphere interactions in earth system models. However, a fundamental challenge for model-data comparisons lies in the scale mismatch between the eddy-covariance observations with small (10−1–101km2) and temporally varying flux footprint, and the continuous regional-scale (102–104km2) gridded predictions made in simulations. Here, a new approach was developed to project turbulent flux maps at regional scale and hourly temporal resolution using environmental response functions (ERFs). This is based on an approach employed in airborne flux observations, and relates turbulent flux observations to meteorological forcings and surface properties across the flux footprint. In this study, the fluxes of sensible heat, latent heat and CO2 integrated over a 20×20 km2 target domain differed substantially from the tower observations in their expected value (+27%, −9%, and −17%) and spatio-temporal variation (−22%, −21%, and −3%, repsectively) ERF systematic uncertainties are bound within −11%, −1.5% and +16%, respectively, indicating that tower location bias might be even more pronounced for heat and CO2 fluxes than currently detectable. The ERF-projected fluxes showed general agreement with independent observations at a nearby tower location. Lastly, advantages and limitations of ERF compared to other scaling approaches are discussed, and pathways for improving model-data synthesis utilizing the ERF scaling method are pointed out.
Drought is a recurring, complex, and extreme climatic phenomenon characterized by subnormal precipitation for months to years triggering negative impacts on agriculture, energy, tourism, recreation, ...and transportation sectors. Agricultural drought assessment is based on a deficit of soil moisture (SM) during the plant-growing season, whereas meteorological drought corresponds to subnormal precipitation over months to years. However, satellite-derived agricultural and meteorological drought indices (including those comprising root-zone SM) have not been comprehensively compared to evaluate their ability for drought delineation and particularly forecasting across climate regimes, land cover and soil types, and irrigation management (irrigated vs. rainfed) in the contiguous USA (CONUS). Here, we did so from 2015 to 2019 within the CONUS. In most regions except the US Midwest and Southeast, SM-based indices (e.g., Palmer Z, SMAP, SWDI) delineated agricultural drought better than meteorological (e.g., SPI, SPEI) and hybrid (Comprehensive Drought Index, CDI) drought indices. In contrast, the SPI and SPEI showed strong correlation with the aridity index in most part of the CONUS except the Midwest. SM-based and hybrid indices also demonstrated skills for agricultural drought forecasting (represented by end-of-year cumulative GPP), predominantly in the early growing season and particularly in irrigated rather than rainfed croplands. These findings indicate the leading role of SM in controlling ecosystem dryness and confirm “drought memory”, possibly due to SM-memory in land-atmosphere coupling. Proper application of meteorological and agricultural drought indices and their contrasting spatial-temporal controls on plant growth and ecosystem dryness has the potential to improve our understanding of drought evolution and provide early drought forecasting across large regions with diverse climate regimes, land cover types, soil textural classes, and irrigation management.
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•Strong correlation between GPP and soil moisture-based indices.•Hybrid index delineated agricultural and meteorological drought cohesively.•Drought forecasting in irrigated croplands better achieved using SM-based indices.•Rootzone SM controls drought memory and forecast performance of end-season GPP.
In the sporadic permafrost zone of northwestern Canada, boreal forest carbon dioxide (CO2) fluxes will be altered directly by climate change through changing meteorological forcing and indirectly ...through changes in landscape functioning associated with thaw‐induced collapse‐scar bog (‘wetland’) expansion. However, their combined effect on landscape‐scale net ecosystem CO2 exchange (NEELAND), resulting from changing gross primary productivity (GPP) and ecosystem respiration (ER), remains unknown. Here, we quantify indirect land cover change impacts on NEELAND and direct climate change impacts on modeled temperature‐ and light‐limited NEELAND of a boreal forest–wetland landscape. Using nested eddy covariance flux towers, we find both GPP and ER to be larger at the landscape compared to the wetland level. However, annual NEELAND (−20 g C m−2) and wetland NEE (−24 g C m−2) were similar, suggesting negligible wetland expansion effects on NEELAND. In contrast, we find non‐negligible direct climate change impacts when modeling NEELAND using projected air temperature and incoming shortwave radiation. At the end of the 21st century, modeled GPP mainly increases in spring and fall due to reduced temperature limitation, but becomes more frequently light‐limited in fall. In a warmer climate, ER increases year‐round in the absence of moisture stress resulting in net CO2 uptake increases in the shoulder seasons and decreases during the summer. Annually, landscape net CO2 uptake is projected to decline by 25 ± 14 g C m−2 for a moderate and 103 ± 38 g C m−2 for a high warming scenario, potentially reversing recently observed positive net CO2 uptake trends across the boreal biome. Thus, even without moisture stress, net CO2 uptake of boreal forest–wetland landscapes may decline, and ultimately, these landscapes may turn into net CO2 sources under continued anthropogenic CO2 emissions. We conclude that NEELAND changes are more likely to be driven by direct climate change rather than by indirect land cover change impacts.
Boreal forest–wetland landscapes in the lowlands of northwestern Canada store large organic carbon stocks and act as long‐term CO2 sinks to the atmosphere. Thaw‐induced wetland expansion has negligible effects on net ecosystem CO2 exchange of these landscapes as indicated by nested eddy covariance flux measurements. In contrast, boreal forest–wetland landscapes may no longer act as net CO2 sinks in an exceedingly warmer climate as indicated by combining climate projections with a simple CO2 flux model. These changes in net ecosystem CO2 exchange are five times smaller for a moderate warming scenario (RCP 4.5) compared to the scenario leading to the strongest warming (RCP 8.5). The fate of organic carbon in these landscapes depends therefore largely on the degree of warming during the 21st century.
Surface ozone is damaging to human health and crop yields. When evaluating global air pollution risk, gridded datasets with high accuracy are desired to reflect the local variations in air pollution ...concentrations. Here, a cluster‐enhanced ensemble machine learning method was used to develop a new 0.5‐degree monthly surface ozone data set during 2003–2019 by combining numerous informative variables. The overall accuracy of our data set is 91.5% (90.8% for space and 92.3% for time). Historically, populations in South Asia, North Africa and Middle‐East, and High‐income North America are exposed to the highest ozone concentrations. Globally, the population weighted ozone concentration in the peak season is 47.07 ppbv. Our results highlight that ozone pollution is intensifying in some regions, and implicate air quality management is crucial to secure human health from air pollution.
Plain Language Summary
Surface ozone is one of the most hazardous air pollutants to human health and plants. However, estimation of global surface ozone is still limited. Here, by using state‐of‐the‐art machine learning techniques, we fuse satellite, chemical transport model outputs, atmospheric reanalyses, and emission data with surface observations to construct a full coverage and long‐time period surface ozone data set. We demonstrate that surface population weighted ozone concentration in North America and Europe has decreased from 2003 to 2019, while ozone pollution in East Asia has intensified during 2016–2019. We also show at least 37% of the world's population is exposed to ozone greater than the World Health Organization's interim target one of 50 ppbv (MDA8) in the peak season. Our results could help identify the key regions for improving global air quality and offers an insightful data set for human health assessments and air quality management.
Key Points
A cluster‐enhanced ensemble machine learning method can predict global surface ozone with high accuracy
Populations in South Asia, North Africa and Middle‐East, and High‐income North America are exposed to the highest MDA8 during 2003–2019
At least 37% of world's population is exposed to greater than 50 ppbv MDA8 in peak seasons
The presence of plant leaves has been shown to lower the risks of health problems by reducing atmospheric particulate matter (PM). Leaf PM accumulation capacity will saturate in the absence of ...runoff. Rainfall is an effective way for PM to “wash off” into the soil and renew leaf PM accumulation. However, little is known about how PM wash-off varies with PM size and health problems caused by particulate pollution vary with PM size. This study thus used artificial rainfall with six plant species to find out how size-fractioned PM are washed off during rain processes. Total wash-off masses in fine, coarse and large fractions were 0.6–10.3 μg/cm2, 1.0–18.8 μg/cm2 and 4.5–60.1 μg/cm2 respectively. P. orientalis (cypress) and E. japonicus (evergreen broadleaved shrub) had the largest wash-off masses in each fraction during rainfall. P. cerasifera (deciduous broadleaved shrub) had the largest cumulative wash-off rates in each fraction. Rainfall intensity had more influence on wash-off masses and rates of large particles for six species and for small particles in evergreen species, but limited effect on wash-off proportions. Wash-off proportions decreased in large particles and increased in small particles along with rainfall. The results provide information for PM accumulation renewal of plants used for urban greening.
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•Wash-off masses of large particles increased by rainfall intensity.•Wash-off rates of small particles increased by intensity for evergreen plants.•Large fractions were washed off preferentially in early rainfall.•Wash-off dynamics of three size fractions differed among plant species.
Size distribution of PM wash-off dynamics during rainfall processes differed among plant species, rainfall intensities and intervals.
A better understanding of ecosystem water-use efficiency (WUE) will help us improve ecosystem management for mitigation as well as adaption to global hydrological change. Here, long-term flux tower ...observations of productivity and evapotranspiration allow us to detect a consistent latitudinal trend in WUE, rising from the subtropics to the northern high-latitudes. The trend peaks at approximately 51°N, and then declines toward higher latitudes. These ground-based observations are consistent with global-scale estimates of WUE. Global analysis of WUE reveals existence of strong regional variations that correspond to global climate patterns. The latitudinal trends of global WUE for Earth's major plant functional types reveal two peaks in the Northern Hemisphere not detected by ground-based measurements. One peak is located at 20° ~ 30°N and the other extends a little farther north than 51°N. Finally, long-term spatiotemporal trend analysis using satellite-based remote sensing data reveals that land-cover and land-use change in recent years has led to a decline in global WUE. Our study provides a new framework for global research on the interactions between carbon and water cycles as well as responses to natural and human impacts.
Background An acceleration of model-data synthesis activities has leveraged many terrestrial carbon datasets, but utilization of soil respiration (RS) data has not kept pace. Scope We identify three ...major challenges in interpreting RS data, and opportunities to utilize it more extensively and creatively: (1) When RS is compared to ecosystem respiration (RECO) measured from EC towers, it is not uncommon to find RS > RECO. We argue this is most likely due to difficulties in calculating RECO, which provides an opportunity to utilize RS for EC quality control. (2) RS integrates belowground heterotrophic and autotrophic activity, but many models include only an explicit heterotrophic output. Opportunities exist to use the total RS flux for data assimilation and model benchmarking methods rather than less-certain partitioned fluxes. (3) RS is generally measured at a very different resolution than that needed for comparison to EC or ecosystem- to global-scale models. Downscaling EC fluxes to match the scale of RS, and improvement of RS upscaling techniques will improve resolution challenges. Conclusions RS data can bring a range of benefits to model development, particularly with larger databases and improved data sharing protocols to make RS data more robust and broadly available to the research community.
The climate sensitivity of plant seasonal life cycles, or phenology, may impart significant carbon cycle feedbacks on climatic change. Analysis of interannual ecosystem carbon exchange provides one ...way to assess this climate sensitivity. Multiyear eddy covariance carbon dioxide flux observations from five different ecosystems (deciduous forest, northern hardwood mixed forest, old‐growth forest, shrub wetland, and mixed wetland‐forest) in the Upper Great Lakes, United States, located within 400 km of each other and exhibiting coherent interannual variability, were used to parameterize a simple ecosystem model. The model, when properly constrained with an interannual sensitive cost function, was able to explain a significant proportion of the interannual variation of carbon fluxes in all ecosystems except the old‐growth forest. The results reveal that spring or autumn climate thresholds impact annual carbon uptake, though the magnitude and strength varied by site. When the model was forced to maintain the same climate‐phenology relationship across the five sites, most of the interannual variability could still be explained except at the old‐growth forest and the forest farthest in distance from the others. These results suggest that at least for this region, coarse spatial resolution carbon‐climate models could likely specify general climate‐phenological relationships at grid scales on order of 100 km without appreciably sacrificing ability to model interannual carbon cycling.
The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, ...called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively.
Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000–2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.
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