ORCID Profile
0000-0003-4734-9085
Current Organisations
Chapman University
,
Hydrosat
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Publisher: Copernicus GmbH
Date: 12-03-2019
DOI: 10.5194/BG-2019-85
Abstract: Abstract. Evaporation (E) and transpiration (T) respond differently to ongoing changes in climate, atmospheric composition, and land use. Our ability to partition evapotranspiration (ET) into E and T is limited at the ecosystem scale, which renders the validation of satellite data and land surface models incomplete. Here, we review current progress in partitioning E and T, and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques provide additional opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully tested. Many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency (EWUE) exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but the conditions under which this assumption holds require further analysis. Another critical assumption for many ET partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been challenged by observational studies. We demonstrate that T frequently exceeds 95 % of ET from some ecosystems, but other ecosystems do not appear to reach this value, which suggests that this assumption is ecosystem-dependent with implications for partitioning. It is important to further improve approaches for partitioning E and T, yet few multi-method comparisons have been undertaken to date. Advances in our understanding of carbon-water coupling at the stomatal, leaf, and canopy level open new perspectives on how to quantify T via its strong coupling with photosynthesis. Photosynthesis can be constrained at the ecosystem and global scales with emerging data sources including solar-induced fluorescence, carbonyl sulfide flux measurements, thermography, and more. Such comparisons would improve our mechanistic understanding of ecosystem water flux and provide the observations necessary to validate remote sensing algorithms and land surface models to understand the changing global water cycle.
Publisher: Springer Science and Business Media LLC
Date: 15-06-2023
DOI: 10.1038/S41559-023-02093-X
Abstract: The temperature sensitivity of ecosystem respiration regulates how the terrestrial carbon sink responds to a warming climate but has been difficult to constrain observationally beyond the plot scale. Here we use observations of atmospheric CO 2 concentrations from a network of towers together with carbon flux estimates from state-of-the-art terrestrial biosphere models to characterize the temperature sensitivity of ecosystem respiration, as represented by the Arrhenius activation energy, over various North American biomes. We infer activation energies of 0.43 eV for North America and 0.38 eV to 0.53 eV for major biomes therein, which are substantially below those reported for plot-scale studies (approximately 0.65 eV). This discrepancy suggests that sparse plot-scale observations do not capture the spatial-scale dependence and biome specificity of the temperature sensitivity. We further show that adjusting the apparent temperature sensitivity in model estimates markedly improves their ability to represent observed atmospheric CO 2 variability. This study provides observationally constrained estimates of the temperature sensitivity of ecosystem respiration directly at the biome scale and reveals that temperature sensitivities at this scale are lower than those based on earlier plot-scale studies. These findings call for additional work to assess the resilience of large-scale carbon sinks to warming.
Publisher: Copernicus GmbH
Date: 09-10-2012
Abstract: Abstract. Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.
Publisher: Copernicus GmbH
Date: 10-2013
DOI: 10.5194/HESS-17-3707-2013
Abstract: Abstract. Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these in idual data sets over the time periods 1989–1995 (7 yr) and 1989–2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the in idual data sets, ET is derived from satellite and/or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13 mm yr−2 in our merged product) followed by a significant decrease in this trend (−0.18 mm yr−2), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493 mm yr−1 (1.35 mm d−1) for both the 1989–1995 and 1989–2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263 mm yr−1 (34 406 km3 yr−1) for a total land area of 130 922 000 km2. Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial.
Publisher: Wiley
Date: 21-07-2014
DOI: 10.1111/GCB.12652
Abstract: Determining the spatial and temporal distribution of terrestrial gross primary production (GPP) is a critical step in closing the Earth's carbon budget. Dynamical global vegetation models (DGVMs) provide mechanistic insight into GPP variability but erge in predicting the response to climate in poorly investigated regions. Recent advances in the remote sensing of solar-induced chlorophyll fluorescence (SIF) opens up a new possibility to provide direct global observational constraints for GPP. Here, we apply an optimal estimation approach to infer the global distribution of GPP from an ensemble of eight DGVMs constrained by global measurements of SIF from the Greenhouse Gases Observing SATellite (GOSAT). These estimates are compared to flux tower data in N. America, Europe, and tropical S. America, with careful consideration of scale differences between models, GOSAT, and flux towers. Assimilation of GOSAT SIF with DGVMs causes a redistribution of global productivity from northern latitudes to the tropics of 7-8 Pg C yr(-1) from 2010 to 2012, with reduced GPP in northern forests (~3.6 Pg C yr(-1) ) and enhanced GPP in tropical forests (~3.7 Pg C yr(-1) ). This leads to improvements in the structure of the seasonal cycle, including earlier dry season GPP loss and enhanced peak-to-trough GPP in tropical forests within the Amazon Basin and reduced growing season length in northern croplands and deciduous forests. Uncertainty in predicted GPP (estimated from the spread of DGVMs) is reduced by 40-70% during peak productivity suggesting the assimilation of GOSAT SIF with models is well-suited for benchmarking. We conclude that satellite fluorescence augurs a new opportunity to quantify the GPP response to climate drivers and the potential to constrain predictions of carbon cycle evolution.
Publisher: Wiley
Date: 07-10-2009
Publisher: Elsevier BV
Date: 12-2013
Publisher: American Geophysical Union (AGU)
Date: 12-2019
DOI: 10.1029/2018MS001583
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-4801
Abstract: & & Tropical forest degradation through selective logging, fragmentation, and understory fires substantially changes forest structure and composition.& In the Amazon, degradation is as widespread as deforestation however, studies addressing the effects of forest degradation on tropical ecosystem functions are scarce. Here, we integrate small-footprint airborne lidar over the Brazilian Amazon (& 250,000 ha), collected between 2016& #8211 , with recent ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) land surface temperature and evapotranspiration products (70-m resolution, data acquired in 2018& #8211 ) to investigate the role of forest structure, forest fragmentation, and disturbance history on dry-season land surface temperature and evapotranspiration.& During the dry season, degraded forests, especially those affected by multiple degradation events, are significantly warmer (up to 9.3& #176 C) and show reduced evapotranspiration (10% less than intact forests). Likewise, forest near the edges (& 350m) experience the greatest warming (up to 6.5& #176 C) and the greatest reduction (9%) in evapotranspiration. We also used the airborne lidar dataset to initialize the Ecosystem Demography Model (ED-2.2) to investigate the impact of degradation on the gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H) under a broader range of climate conditions, including severe droughts. Consistent with ECOSTRESS, the simulations during the dry season in typical years showed that severely degraded forests experienced water-stress with declines in ET (34% reduction), GPP (35% reduction), and increases of H (43% increases) and daily mean ground temperatures (up to 6.5& #176 C) relative to intact forests.& In the model, the simulated changes are mostly driven by increased below-ground water stress, which can be attributed to the shallower rooting profile of degraded forests. However, relative to intact forest, the impact of degradation on energy, water, and carbon cycles markedly diminishes under extreme droughts such as 2015& #8211 , when all forests experience severe stress. Our results indicate the potentially important role of tropical forest degradation changing the carbon, water, and energy cycles in the Amazon, and consequently a much broader influence of land use activities on functioning of tropical ecosystems.& &
Publisher: Proceedings of the National Academy of Sciences
Date: 25-04-2016
Abstract: Carbon uptake by terrestrial ecosystems mitigates the impact of anthropogenic fossil fuel emissions on atmospheric CO 2 concentrations, but the strength of this carbon sink is highly sensitive to large-scale extreme climate events. In 2012, the United States experienced the most severe drought since the Dust Bowl period, along with the warmest spring on record. Here, we quantify the impact of this climate anomaly on the carbon cycle. Our results show that warming-induced earlier vegetation activity increased spring carbon uptake, and thus compensated for reduced carbon uptake during the summer drought in 2012. This compensation, however, came at the cost of soil moisture depletion from increased spring evapotranspiration that likely enhanced summer heating through land-atmosphere coupling.
Publisher: American Geophysical Union (AGU)
Date: 20-01-2011
DOI: 10.1029/2010JD014545
Publisher: Elsevier BV
Date: 02-2014
Publisher: IOP Publishing
Date: 04-2008
Publisher: American Geophysical Union (AGU)
Date: 04-2017
DOI: 10.1002/2016WR020175
Publisher: IOP Publishing
Date: 02-2018
Publisher: Copernicus GmbH
Date: 10-2019
Abstract: Abstract. Evaporation (E) and transpiration (T) respond differently to ongoing changes in climate, atmospheric composition, and land use. It is difficult to partition ecosystem-scale evapotranspiration (ET) measurements into E and T, which makes it difficult to validate satellite data and land surface models. Here, we review current progress in partitioning E and T and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques create new opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully tested. For ex le, many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but more analysis is necessary to determine the conditions for which this assumption holds. Another critical assumption for many partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been challenged by observational studies. We demonstrate that T can exceed 95 % of ET from certain ecosystems, but other ecosystems do not appear to reach this value, which suggests that this assumption is ecosystem-dependent with implications for partitioning. It is important to further improve approaches for partitioning E and T, yet few multi-method comparisons have been undertaken to date. Advances in our understanding of carbon–water coupling at the stomatal, leaf, and canopy level open new perspectives on how to quantify T via its strong coupling with photosynthesis. Photosynthesis can be constrained at the ecosystem and global scales with emerging data sources including solar-induced fluorescence, carbonyl sulfide flux measurements, thermography, and more. Such comparisons would improve our mechanistic understanding of ecosystem water fluxes and provide the observations necessary to validate remote sensing algorithms and land surface models to understand the changing global water cycle.
Publisher: Copernicus GmbH
Date: 23-02-2016
Abstract: Abstract. The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project aims to advance the development of land evaporation estimates on global and regional scales. Its main objective is the derivation, validation, and intercomparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. Three commonly used process-based evaporation methodologies are evaluated: the Penman–Monteith algorithm behind the official Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets, and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss, and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. In a companion article (Part 1), Michel et al. (2016) inspect the performance of these three models at local scales using measurements from eddy-covariance towers and include in the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results indicate that the Priestley and Taylor products (PT-JPL and GLEAM) perform best overall for most ecosystems and climate regimes. While all three evaporation products adequately represent the expected average geographical patterns and seasonality, there is a tendency in PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared to surface water balances from 837 globally distributed catchments and to separate evaporation estimates from ERA-Interim and the model tree ensemble (MTE). Nonetheless, all products show large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into its different components. This observed inter-product variability, even when common forcing is used, suggests that caution is necessary in applying a single data set for large-scale studies in isolation. A general finding that different models perform better under different conditions highlights the potential for considering biome- or climate-specific composites of models. Nevertheless, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, is proposed as the best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation.
Publisher: Copernicus GmbH
Date: 12-02-2016
Abstract: Abstract. Although plant photosynthetic capacity as determined by the maximum carboxylation rate (i.e., Vc, max25) and the maximum electron transport rate (i.e., Jmax25) at a reference temperature (generally 25 °C) is known to vary considerably in space and time in response to environmental conditions, it is typically parameterized in Earth system models (ESMs) with tabulated values associated with plant functional types. In this study, we have developed a mechanistic model of leaf utilization of nitrogen for assimilation (LUNA) to predict photosynthetic capacity at the global scale under different environmental conditions. We adopt an optimality hypothesis to nitrogen allocation among light capture, electron transport, carboxylation and respiration. The LUNA model is able to reasonably capture the measured spatial and temporal patterns of photosynthetic capacity as it explains ∼ 55 % of the global variation in observed values of Vc, max25 and ∼ 65 % of the variation in the observed values of Jmax25. Model simulations with LUNA under current and future climate conditions demonstrate that modeled values of Vc, max25 are most affected in high-latitude regions under future climates. ESMs that relate the values of Vc, max25 or Jmax25 to plant functional types only are likely to substantially overestimate future global photosynthesis.
Publisher: American Geophysical Union (AGU)
Date: 04-2020
DOI: 10.1029/2019WR026058
Abstract: The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level‐3 (L3) latent heat flux ( LE ) data products. These data are generated from the Level‐2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear‐sky ET product (L3_ET_PT‐JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear‐sky instantaneous/time of overpass: r 2 = 0.88 overall bias = 8% normalized root‐mean‐square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS s les the diurnal cycle), though temperate sites are overrepresented. The 70‐m‐high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1‐km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data.
Publisher: American Geophysical Union (AGU)
Date: 11-2017
DOI: 10.1002/2017GB005733
Publisher: Elsevier BV
Date: 12-2015
Publisher: Elsevier BV
Date: 12-2013
Publisher: Springer Science and Business Media LLC
Date: 23-08-2023
Publisher: American Geophysical Union (AGU)
Date: 03-2011
DOI: 10.1029/2010GL046230
Publisher: Copernicus GmbH
Date: 23-02-2016
Abstract: Abstract. The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R2 = 0.67), the agreement of the satellite-based ET estimates is only marginally lower (R2 = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85 towers (model inputs res led to a common grid to facilitate global estimates) confirmed the original findings.
Publisher: Copernicus GmbH
Date: 15-01-2016
DOI: 10.5194/GMD-9-1-2016
Abstract: Abstract. Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next-generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. In this study, we add a capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This model development incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station – a Long Term Ecological Research site within the US Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.
Publisher: Wiley
Date: 21-10-2020
DOI: 10.1111/NPH.16866
Abstract: Atmospheric carbon dioxide concentration ([CO 2 ]) is increasing, which increases leaf‐scale photosynthesis and intrinsic water‐use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO 2 ] increase and thus climate change. However, ecosystem CO 2 responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO 2 ]‐driven terrestrial carbon sink can appear contradictory. Here we synthesize theory and broad, multidisciplinary evidence for the effects of increasing [CO 2 ] (iCO 2 ) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre‐industrial times. Established theory, supported by experiments, indicates that iCO 2 is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO 2 responses are high in comparison to experiments and predictions from theory. Plant mortality and soil carbon iCO 2 responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO 2 , albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.
Publisher: Elsevier BV
Date: 06-2014
Publisher: American Geophysical Union (AGU)
Date: 20-05-2021
DOI: 10.1029/2020AV000310
Abstract: Large uncertainties in North American terrestrial carbon fluxes hinder regional climate projections. Terrestrial biosphere models (TBMs), the essential tools for understanding continental‐scale carbon cycle, erge on whether temperate forests or croplands dominate carbon uptake in North America. Evidence from novel photosynthetic proxies, such as those based on chlorophyll fluorescence, has cast doubt on the “weak cropland, strong forest” carbon uptake patterns simulated by most TBMs. However, no systematic evaluation of TBMs has yet been attempted to pin down space‐time patterns that are most consistent with regional CO 2 observational constraints. Here, we leverage atmospheric CO 2 observations and satellite‐observed photosynthetic proxies to understand emergent space‐time patterns in North American carbon fluxes from a large suite of TBMs and data‐driven models. To do so, we evaluate how well the atmospheric signals resulting from carbon flux estimates reproduce the space‐time variability in atmospheric CO 2 , as is observed by a network of continuous‐monitoring towers over North America. Models with gross or net carbon fluxes that are consistent with the observed CO 2 variability share a salient feature of growing‐season carbon uptake in Midwest US croplands. Conversely, the remaining models place most growing‐season uptake in boreal or temperate forests. Differences in model explanatory power depend mainly on the simulated annual cycles of cropland uptake—especially, the timing of peak uptake—rather than the distribution of annual mean fluxes across biomes. Our results suggest that improved model representation of cropland phenology is crucial to robust, policy‐relevant estimation of North American carbon exchange.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Joshua Fisher.