scholarly journals On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes

2021 ◽  
Author(s):  
Yujie Wang ◽  
Christian Frankenberg
Keyword(s):  
Chlorophyll Fluorescence ◽  
Land Surface ◽  
Climate Modeling ◽  
Global Scale ◽  
Model Complexity ◽  
Transfer Scheme ◽  
Sun Sensor ◽  
Sensor Geometry ◽  
The Impact ◽  
Canopy Model

Abstract. Lack of direct carbon, water, and energy flux observations at global scales makes it difficult to calibrate land surface models (LSMs). The increasing number of remote sensing based products provide an alternative way to verify or constrain land models given its global coverage and satisfactory spatial and temporal resolutions. However, these products and LSMs often differ in their assumptions and model setups, for example, the canopy model complexity. The disagreements hamper the fusion of global scale datasets with LSMs. To evaluate how much the canopy complexity affects predicted canopy fluxes, we simulated and compared the carbon, water, and solar-induced chlorophyll fluorescence (SIF) fluxes using five different canopy complexity setups from a one-layered big-leaf canopy to a multi-layered canopy with leaf angular distributions. We modeled the canopy fluxes using a recently developed Land model by the Climate Modeling Alliance. Our model results suggested that (1) when using the same model inputs, model predicted carbon, water, and SIF fluxes were all higher for simpler canopy setups; (2) when accounting for vertical photosynthetic capacity heterogeneity, differences among canopy complexity levels increased compared to the scenario of a uniform canopy; (3) SIF fluxes modeled with different canopy complexity levels changed with sun-sensor geometry. Given the different modeled canopy fluxes with different canopy complexities, we recommend (1) not misusing parameters inverted with different canopy complexities or assumptions to avoid biases in model outputs, and (2) using complex canopy model with angular distribution and hyperspectral radiation transfer scheme when linking land processes to remotely sensed spectra.

scholarly journals On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes

2022 ◽  
Vol 19 (1) ◽  
pp. 29-45
Author(s):  
Yujie Wang ◽  
Christian Frankenberg
Keyword(s):  
Chlorophyll Fluorescence ◽  
Land Surface ◽  
Climate Modeling ◽  
Global Scale ◽  
Model Complexity ◽  
Transfer Scheme ◽  
Sun Sensor ◽  
Sensor Geometry ◽  
The Impact ◽  
Canopy Model

Abstract. Lack of direct carbon, water, and energy flux observations at global scales makes it difficult to calibrate land surface models (LSMs). The increasing number of remote-sensing-based products provide an alternative way to verify or constrain land models given their global coverage and satisfactory spatial and temporal resolutions. However, these products and LSMs often differ in their assumptions and model setups, for example, the canopy model complexity. The disagreements hamper the fusion of global-scale datasets with LSMs. To evaluate how much the canopy complexity affects predicted canopy fluxes, we simulated and compared the carbon, water, and solar-induced chlorophyll fluorescence (SIF) fluxes using five different canopy complexity setups from a one-layered canopy to a multi-layered canopy with leaf angular distributions. We modeled the canopy fluxes using the recently developed land model by the Climate Modeling Alliance, CliMA Land. Our model results suggested that (1) when using the same model inputs, model-predicted carbon, water, and SIF fluxes were all higher for simpler canopy setups; (2) when accounting for vertical photosynthetic capacity heterogeneity, differences between canopy complexity levels increased compared to the scenario of a uniform canopy; and (3) SIF fluxes modeled with different canopy complexity levels changed with sun-sensor geometry. Given the different modeled canopy fluxes with different canopy complexities, we recommend (1) not misusing parameters inverted with different canopy complexities or assumptions to avoid biases in model outputs and (2) using a complex canopy model with angular distribution and a hyperspectral radiation transfer scheme when linking land processes to remotely sensed spectra.

scholarly journals Global GOSAT, OCO-2 and OCO-3 Solar Induced Chlorophyll Fluorescence Datasets

2021 ◽  
Author(s):  
Russell Doughty ◽  
Thomas Kurosu ◽  
Nicholas Parazoo ◽  
Philipp Köhler ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Earth Observation ◽  
Background Information ◽  
Sun Sensor ◽  
Global Target ◽  
Target Sites ◽  
Data Products ◽  
Sensor Geometry ◽  
Indirect Relationship ◽  
Level 2

Abstract. The retrieval of solar induced chlorophyll fluorescence (SIF) from space is a relatively new advance in Earth observation science, having only become feasible within the last decade. Interest in SIF data has grown exponentially, and the retrieval of SIF and the provision of SIF data products has become an important and formal component of spaceborne Earth observation missions. Here, we describe the global Level 2 SIF Lite data products for the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and OCO-3 platforms, which are provided for each platform in daily netCDF files. We also outline the methods used to retrieve SIF and estimate uncertainty, describe all the data fields, and provide users the background information necessary for the proper use and interpretation of the data, such as considerations of retrieval noise, sun-sensor geometry, the indirect relationship between SIF and photosynthesis, and differences among the three platforms and their respective data products. OCO-2 and OCO-3 have the highest spatial resolution spaceborne SIF retrievals to date, and the target and snapshot area mode observation modes of OCO-2 and OCO-3 are unique. These modes provide hundreds to thousands of SIF retrievals at biologically diverse global target sites during a single overpass, and provide an opportunity to better inform our understanding of canopy-scale vegetation SIF emission across biomes.

scholarly journals Impact of an improved WRF urban canopy model on diurnal air temperature simulation over northern Taiwan

2016 ◽  
Vol 16 (3) ◽  
pp. 1809-1822 ◽  
Author(s):  
Chuan-Yao Lin ◽  
Chiung-Jui Su ◽  
Hiroyuki Kusaka ◽  
Yuko Akimoto ◽  
Yang-Fan Sheng ◽  
...  
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Urban Canopy ◽  
Anthropogenic Heat ◽  
Surface Model ◽  
Urban Canopy Model ◽  
Simulation Performance ◽  
The Impact ◽  
Canopy Model ◽  
Northern Taiwan

Abstract. This study evaluates the impact of urbanization over northern Taiwan using the Weather Research and Forecasting (WRF) Model coupled with the Noah land-surface model and a modified urban canopy model (WRF–UCM2D). In the original UCM coupled to WRF (WRF–UCM), when the land use in the model grid is identified as "urban", the urban fraction value is fixed. Similarly, the UCM assumes the distribution of anthropogenic heat (AH) to be constant. This may not only lead to over- or underestimation of urban fraction and AH in urban and non-urban areas, but spatial variation also affects the model-estimated temperature. To overcome the abovementioned limitations and to improve the performance of the original UCM model, WRF–UCM is modified to consider the 2-D urban fraction and AH (WRF–UCM2D).The two models were found to have comparable temperature simulation performance for urban areas, but large differences in simulated results were observed for non-urban areas, especially at nighttime. WRF–UCM2D yielded a higher correlation coefficient (R2) than WRF–UCM (0.72 vs. 0.48, respectively), while bias and RMSE achieved by WRF–UCM2D were both significantly smaller than those attained by WRF–UCM (0.27 and 1.27 vs. 1.12 and 1.89, respectively). In other words, the improved model not only enhanced correlation but also reduced bias and RMSE for the nighttime data of non-urban areas. WRF–UCM2D performed much better than WRF–UCM at non-urban stations with a low urban fraction during nighttime. The improved simulation performance of WRF–UCM2D in non-urban areas is attributed to the energy exchange which enables efficient turbulence mixing at a low urban fraction. The result of this study has a crucial implication for assessing the impacts of urbanization on air quality and regional climate.

scholarly journals Biogeophysical and biogeochemical impacts of land-use change simulated by MIROC-ES2L

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Akihiko Ito ◽  
Tomohiro Hajima
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Surface ◽  
Carbon Stock ◽  
Climate Change Impacts ◽  
Global Scale ◽  
Model Complexity ◽  
Earth System ◽  
Land Use Impacts ◽  
Soil Carbon Stocks

Abstract Land-use change is one of the focal processes in Earth system models because it has strong impacts on terrestrial biogeophysical and biogeochemical conditions. However, modeling land-use impacts is still challenging because of model complexity and uncertainty. This study examined the results of simulations of land-use change impacts by the Model for Interdisciplinary Research on Climate, Earth System version 2 for long-term simulations (MIROC-ES2L) conducted under the Land-Use Model Intercomparison Project protocol. In a historical experiment, the model reproduced biogeophysical impacts such as decreasing trends in land-surface net radiation and evapotranspiration by about 1970. Among biogeochemical impacts, the model captured the global decrease of vegetation and soil carbon stocks caused by extensive deforestation. By releasing ecosystem carbon stock to the atmosphere, land-use change shortened the mean residence time of terrestrial carbon and accelerated its turnover rate, especially in low latitudes. Future projections based on Shared Socioeconomic Pathways indicated substantial alteration of land conditions caused primarily by climatic change and secondarily by land-use change. Sensitivity experiments conducted by exchanging land-use data between different future projection baseline experiments showed that, at the global scale, the anticipated extent of land-use conversion would likely play a modest role in the future terrestrial radiation, water, and carbon budgets. Regional investigations revealed that future land use would exert a considerable influence on runoff and vegetation carbon stock. Further model refinement is required to improve its capability to analyze its complicated terrestrial linkages or nexus (e.g., food, bioenergy, and carbon sequestration) to climate-change impacts.

scholarly journals Parametrization of a lake water dynamics model MLake in the ISBA-CTRIP land surface system (SURFEX v8.1)

2021 ◽  
Vol 14 (3) ◽  
pp. 1309-1344
Author(s):  
Thibault Guinaldo ◽  
Simon Munier ◽  
Patrick Le Moigne ◽  
Aaron Boone ◽  
Bertrand Decharme ◽  
...  
Keyword(s):  
Land Surface ◽  
Lake Level ◽  
Climate Models ◽  
Hydrological Modeling ◽  
Lake Victoria ◽  
Global Scale ◽  
Water Dynamics ◽  
Lake Outlet ◽  
The Impact ◽  
River Routing

Abstract. Lakes are of fundamental importance in the Earth system as they support essential environmental and economic services, such as freshwater supply. Streamflow variability and temporal evolution are impacted by the presence of lakes in the river network; therefore, any change in the lake state can induce a modification of the regional hydrological regime. Despite the importance of the impact of lakes on hydrological fluxes and the water balance, a representation of the mass budget is generally not included in climate models and global-scale hydrological modeling platforms. The goal of this study is to introduce a new lake mass module, MLake (Mass-Lake model), into the river-routing model CTRIP to resolve the specific mass balance of open-water bodies. Based on the inherent CTRIP parameters, the development of the non-calibrated MLake model was introduced to examine the influence of such hydrological buffer areas on global-scale river-routing performance. In the current study, an offline evaluation was performed for four river networks using a set of state-of-the-art quality atmospheric forcings and a combination of in situ and satellite measurements for river discharge and lake level observations. The results reveal a general improvement in CTRIP-simulated discharge and its variability, while also generating realistic lake level variations. MLake produces more realistic streamflows both in terms of daily and seasonal correlation. Excluding the specific case of Lake Victoria having low performances, the mean skill score of Kling–Gupta efficiency (KGE) is 0.41 while the normalized information contribution (NIC) shows a mean improvement of 0.56 (ranging from 0.15 to 0.94). Streamflow results are spatially scale-dependent, with better scores associated with larger lakes and increased sensitivity to the width of the lake outlet. Regarding lake level variations, results indicate a good agreement between observations and simulations with a mean correlation of 0.56 (ranging from 0.07 to 0.92) which is linked to the capability of the model to retrieve seasonal variations. Discrepancies in the results are mainly explained by the anthropization of the selected lakes, which introduces high-frequency variations in both streamflows and lake levels that degraded the scores. Anthropization effects are prevalent in most of the lakes studied, but they are predominant for Lake Victoria and are the main cause for relatively low statistical scores for the Nile River However, results on the Angara and the Neva rivers also depend on the inherent gap of ISBA-CTRIP process representation, which relies on further development such as the partitioned energy budget between the snow and the canopy over a boreal zone. The study is a first step towards a global coupled land system that will help to qualitatively assess the evolution of future global water resources, leading to improvements in flood risk and drought forecasting.

The Impact of Snow Model Complexity at Three CLPX Sites

2008 ◽  
Vol 9 (6) ◽  
pp. 1464-1481 ◽  
Author(s):  
Xia Feng ◽  
Alok Sahoo ◽  
Kristi Arsenault ◽  
Paul Houser ◽  
Yan Luo ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Model Complexity ◽  
Snow Albedo ◽  
Community Land Model ◽  
Snow Model ◽  
The Impact

Abstract Many studies have developed snow process understanding by exploring the impact of snow model complexity on simulation performance. This paper revisits this topic using several recently developed land surface models, including the Simplified Simple Biosphere Model (SSiB); Noah; Variable Infiltration Capacity (VIC); Community Land Model, version 3 (CLM3); Snow Thermal Model (SNTHERM); and new field measurements from the Cold Land Processes Field Experiment (CLPX). Offline snow cover simulations using these five snow models with different physical complexity are performed for the Rabbit Ears Buffalo Pass (RB), Fraser Experimental Forest headquarters (FHQ), and Fraser Alpine (FA) sites between 20 September 2002 and 1 October 2003. These models simulate the snow accumulation and snowpack ablation with varying skill when forced with the same meteorological observations, initial conditions, and similar soil and vegetation parameters. All five models capture the basic features of snow cover dynamics but show remarkable discrepancy in depicting snow accumulation and ablation, which could result from uncertain model physics and/or biased forcing. The simulated snow depth in SSiB during the snow accumulation period is consistent with the more complicated CLM3 and SNTHERM; however, early runoff is noted, owing to neglected water retention within the snowpack. Noah is consistent with SSiB in simulating snow accumulation and ablation at RB and FA, but at FHQ, Noah underestimates snow depth and snow water equivalent (SWE) as a result of a higher net shortwave radiation at the surface, resulting from the use of a small predefined maximum snow albedo. VIC and SNTHERM are in good agreement with each other, and they realistically reproduce snow density and net radiation. CLM3 is consistent with VIC and SNTHERM during snow accumulation, but it shows early snow disappearance at FHQ and FA. It is also noted that VIC, CLM3, and SNTHERM are unable to capture the observed runoff timing, even though the water storage and refreezing effects are included in their physics. A set of sensitivity experiments suggest that Noah’s snow simulation is improved with a higher maximum albedo and that VIC exhibits little improvement with a larger fresh snow albedo. There are remarkable differences in the vegetation impact on snow simulation for each snow model. In the presence of forest cover, SSiB shows a substantial increase in snow depth and SWE, Noah and VIC show a slight change though VIC experiences a later onset of snowmelt, and CLM3 has a reduction in its snow depth. Finally, we observe that a refined precipitation dataset significantly improves snow simulation, emphasizing the importance of accurate meteorological forcing for land surface modeling.

scholarly journals Estimating water discharge from large radar altimetry datasets

2013 ◽  
Vol 17 (3) ◽  
pp. 923-933 ◽  
Author(s):  
A. C. V. Getirana ◽  
C. Peters-Lidard
Keyword(s):  
Land Surface ◽  
Amazon Basin ◽  
Water Discharge ◽  
Land Surface Model ◽  
Global Scale ◽  
Surface Model ◽  
Rating Curve ◽  
Radar Altimetry ◽  
Discharge Data ◽  
The Impact

Abstract. The objective of this study is to evaluate the potential of large altimetry datasets as a complementary gauging network capable of providing water discharge in ungauged regions. A rating curve-based methodology is adopted to derive water discharge from altimetric data provided by the Envisat satellite at 475 virtual stations (VS) within the Amazon basin. From a global-scale perspective, the stage–discharge relations at VS are built based on radar altimetry and outputs from a modeling system composed of a land surface model and a global river routing scheme. In order to quantify the impact of model uncertainties on rating-curve based discharges, a second experiment is performed using outputs from a simulation where daily observed discharges at 135 gauging stations are introduced in the modeling system. Discharge estimates at 90 VS are evaluated against observations during the curve fitting calibration (2002–2005) and evaluation (2006–2008) periods, resulting in mean normalized RMS errors as high as 39 and 15% for experiments without and with direct insertion of data, respectively. Without direct insertion, uncertainty of discharge estimates can be mostly attributed to forcing errors at smaller scales, generating a positive correlation between performance and drainage area. Mean relative streamflow volume errors (RE) of altimetry-based discharges varied from 15 to 84% for large and small drainage areas, respectively. Rating curves produced a mean RE of 51% versus 68% from model outputs. Inserting discharge data into the modeling system decreases the mean RE from 51 to 18%, and mean NRMSE from 24 to 9%. These results demonstrate the feasibility of applying the proposed methodology to the continental or global scales.

scholarly journals TROPOMI reveals dry-season increase of solar-induced chlorophyll fluorescence in the Amazon forest

2019 ◽  
Vol 116 (44) ◽  
pp. 22393-22398 ◽  
Author(s):  
Russell Doughty ◽  
Philipp Köhler ◽  
Christian Frankenberg ◽  
Troy S. Magney ◽  
Xiangming Xiao ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Forest Canopy ◽  
Dry Season ◽  
Amazon Forest ◽  
Wet Season ◽  
Carbon Cycles ◽  
Sun Sensor ◽  
Sensor Geometry ◽  
Earth Observation Satellites

Photosynthesis of the Amazon rainforest plays an important role in the regional and global carbon cycles, but, despite considerable in situ and space-based observations, it has been intensely debated whether there is a dry-season increase in greenness and photosynthesis of the moist tropical Amazonian forests. Solar-induced chlorophyll fluorescence (SIF), which is emitted by chlorophyll, has a strong positive linear relationship with photosynthesis at the canopy scale. Recent advancements have allowed us to observe SIF globally with Earth observation satellites. Here we show that forest SIF did not decrease in the early dry season and increased substantially in the late dry season and early part of wet season, using SIF data from the Tropospheric Monitoring Instrument (TROPOMI), which has unprecedented spatial resolution and near-daily global coverage. Using in situ CO2 eddy flux data, we also show that cloud cover rarely affects photosynthesis at TROPOMI’s midday overpass, a time when the forest canopy is most often light-saturated. The observed dry-season increases of forest SIF are not strongly affected by sun-sensor geometry, which was attributed as creating a pseudo dry-season green-up in the surface reflectance data. Our results provide strong evidence that greenness, SIF, and photosynthesis of the tropical Amazonian forest increase during the dry season.

Using different solutions for radiative transfer of solar-induced fluorescence in a land surface model

2021 ◽  
Author(s):  
Tea Thum ◽  
Javier Pacheco-Labrador ◽  
Troy Magney ◽  
Mirco Migliavacca ◽  
Tristan Quaife ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Radiative Transfer ◽  
Land Surface ◽  
Land Surface Model ◽  
Global Scale ◽  
Photochemical Quenching ◽  
Surface Model ◽  
Simple Fraction ◽  
Non Photochemical Quenching ◽  
The Relationship

<p>Chlorophyll fluorescence (ChlF) takes place in green leaves of the plants during photosynthesis. It has therefore been proposed that ChlF can be used to track the photosynthetic activity of plants and the current possibility to observe sun-induced chlorophyll fluorescence (SIF) via remote sensing provides an unprecedented tool to monitor terrestrial photosynthesis at global scale. However, the relationship between photosynthesis and ChlF is not linear at all scales and is partly controlled by the non-photochemical quenching - which dissipates excess energy as heat. The relationship between the photochemical and fluorescence yields changes when the photochemical quenching is dominating at low irradiance conditions or at high stress conditions. Interpretation of observed SIF is complicated by its dependence on incoming absorbed radiation, observation geometry and radiative transfer of SIF photons within the canopy. To fully exploit remotely sensed SIF to estimate photosynthesis at ecosystem and global scales, it is important to account for these aspects through modelling that include ecosystem processes.</p><p>In this work we have implemented a ChlF model into a state-of-the-art land surface model QUantifying Interactions between terrestrial Nutrient CYcles and the climate system (QUINCY) simulating the terrestrial energy, water and biogeochemical cycles of carbon, nitrogen and phosphorus. The simulation of radiative transfer is highly influential for the simulated SIF signal, but the complex solutions of radiative transfer are computationally too heavy, making them impractical approaches at global scale. Therefore, we have investigated different radiative transfer techniques for the SIF signal of varying complexity at site scale in Niwot Ridge, U.S. <!-- we have now one clean growing season of data in a beech forests if you want to compare a deciduous and a evergreen, and of course a coule of years of grasslands (even if you don't want to put it in the abstract) -->The most complex solution is based on the mSCOPE and Fluspects model, that explicitly calculates signal transfer. The intermediate solution is based on a two-stream flux approach and the most simple is using a simple fraction for the escape ratio of SIF. Our aim is to assess which solution is most suitable for simulating the SIF signal at different scales and also test different formulations for modelling of non-photochemical quenching.</p>

scholarly journals Impact of an improved WRF-urban canopy model on diurnal air temperature simulation over northern Taiwan

2015 ◽  
Vol 15 (20) ◽  
pp. 28483-28516
Author(s):  
C.-Y. Lin ◽  
C.-J. Su ◽  
H. Kusaka ◽  
Y. Akimoto ◽  
Y. F. Sheng ◽  
...  
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Urban Canopy ◽  
Anthropogenic Heat ◽  
Surface Model ◽  
Urban Canopy Model ◽  
Simulation Performance ◽  
The Impact ◽  
Canopy Model ◽  
Northern Taiwan

Abstract. This study evaluated the impact of urbanization over northern Taiwan using the Weather Research and Forecasting (WRF) model coupled with the Noah land-surface model and a modified Urban Canopy Model (WRF-UCM2D). In the original UCM coupled in WRF (WRF-UCM), when the land use in the model grid net is identified as "urban", the urban fraction value is fixed. Similarly, the UCM assumes the distribution of anthropogenic heat (AH) to be constant. Such not only may lead to over- or underestimation, the temperature difference between urban and non-urban areas has also been neglected. To overcome the above-mentioned limitations and to improve the performance of the original UCM model, WRF-UCM is modified to consider the 2-D urban fraction and AH (WRF-UCM2D). The two models were found to have comparable simulation performance for urban areas but large differences in simulated results were observed for non-urban, especially at nighttime. WRF-UCM2D yielded a higher R2 than WRF-UCM (0.72 vs. 0.48, respectively), while bias and RMSE achieved by WRF-UCM2D were both significantly smaller than those attained by WRF-UCM (0.27 and 1.27 vs. 1.12 and 1.89, respectively). In other words, the improved model not only enhanced correlation but also reduced bias and RMSE for the nighttime data of non-urban areas. WRF-UCM2D performed much better than WRF-UCM at non-urban stations with low urban fraction during nighttime. The improved simulation performance of WRF-UCM2D at non-urban area is attributed to the energy exchange which enables efficient turbulence mixing at low urban fraction. The achievement of this study has a crucial implication for assessing the impacts of urbanization on air quality and regional climate.

Sign in / Sign up

Export Citation Format

Share Document