Evaluating Global Land Surface Models in CMIP5: Analysis of Ecosystem Water- and Light-Use Efficiencies and Rainfall Partitioning

2018 ◽  
Vol 31 (8) ◽  
pp. 2995-3008 ◽  
Author(s):  
Longhui Li ◽  
Yingping Wang ◽  
Vivek K. Arora ◽  
Derek Eamus ◽  
Hao Shi ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Simulation ◽  
Gross Primary Production ◽  
Model Development ◽  
Coupled Model ◽  
Functional Constraints ◽  
Model Simulations ◽  
Ensemble Mean ◽  
Dry Conditions ◽  
Use Efficiency

Abstract Water and carbon fluxes simulated by 12 Earth system models (ESMs) that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) over several recent decades were evaluated using three functional constraints that are derived from both model simulations, or four global datasets, and 736 site-year measurements. Three functional constraints are ecosystem water-use efficiency (WUE), light-use efficiency (LUE), and the partitioning of precipitation P into evapotranspiration (ET) and runoff based on the Budyko framework. Although values of these three constraints varied significantly with time scale and should be quite conservative if being averaged over multiple decades, the results showed that both WUE and LUE simulated by the ensemble mean of 12 ESMs were generally lower than the site measurements. Simulations by the ESMs were generally consistent with the broad pattern of energy-controlled ET under wet conditions and soil water-controlled ET under dry conditions, as described by the Budyko framework. However, the value of the parameter in the Budyko framework ω, obtained from fitting the Budyko curve to the ensemble model simulation (1.74), was larger than the best-fit value of ω to the observed data (1.28). Globally, the ensemble mean of multiple models, although performing better than any individual model simulations, still underestimated the observed WUE and LUE, and overestimated the ratio of ET to P, as a result of overestimation in ET and underestimation in gross primary production (GPP). The results suggest that future model development should focus on improving the algorithms of the partitioning of precipitation into ecosystem ET and runoff, and the coupling of water and carbon cycles for different land-use types.

scholarly journals WRF Model Simulations of the Influence of the Athabasca Oil Sands Development on Convective Storms

2018 ◽  
Vol 22 (3) ◽  
pp. 1-25 ◽  
Author(s):  
Daniel Brown ◽  
Gerhard Reuter
Keyword(s):  
Land Surface ◽  
Oil Sands ◽  
Waste Heat ◽  
Model Simulation ◽  
Wrf Model ◽  
Initiation Time ◽  
Athabasca Oil Sands ◽  
Model Simulations ◽  
Surface Disturbance

Abstract The Athabasca oil sands development has created a land surface disturbance of almost 900 km2 in northeastern Alberta. Both through industrial processes and the removal of boreal forest vegetation, this surface disturbance impacts meteorology in the vicinity by releasing waste heat, raising the surface temperature, and lowering the surface humidity. To investigate the effects of the Athabasca oil sands development on thunderstorm intensity, initiation time, and duration, the Weather Research and Forecasting (WRF) Model was employed to simulate the effect of the surface disturbance on atmospheric conditions on 10 case study days. The results suggested the oil sands surface disturbance was not associated with substantial increases in thunderstorm intensity on any of the case study days. On two case study days, however, the WRF Model simulations differed substantially from the observed meteorological conditions and only approached the observations when the oil sands surface disturbance was included in the model simulation. Including the oil sands surface disturbance in the model simulations resulted in thunderstorm initiation about 2 h earlier and increased thunderstorm duration. Data from commercial aircraft showed that the 850–500-mb temperature difference was greater than 30°C (very unstable) only on these 2 days. Such cases are sufficiently rare that they are not expected to affect the overall thunderstorm climatology. Still, in these very unstable cases, the oil sands development appears to have a significant effect on thunderstorm initiation time and duration.

scholarly journals Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe

2017 ◽  
Vol 14 (7) ◽  
pp. 1969-1987 ◽  
Author(s):  
Tea Thum ◽  
Sönke Zaehle ◽  
Philipp Köhler ◽  
Tuula Aalto ◽  
Mika Aurela ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Land Surface ◽  
Large Scale ◽  
Model Simulation ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Coniferous Forest ◽  
Flux Measurement ◽  
Surface Model ◽  
Fluorescence Measurements

Abstract. Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf-level chlorophyll fluorescence measurements (Chl F) to evaluate the performance of the SIF module at a coniferous forest at Hyytiälä, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite-observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. At regional scale the model was able to simulate observed SIF averaged over 5 years with r2 of 0.86. The GOME-2-based SIF was a better proxy for GPP than the remotely sensed fAPAR (fraction of absorbed photosynthetic active radiation by vegetation). The observed SIF captured the seasonality of the photosynthesis at site scale and showed feasibility for use in improving of model seasonality at site and regional scale.

Analysis of the ENSO Cycle in the NCEP Coupled Forecast Model

2007 ◽  
Vol 20 (7) ◽  
pp. 1265-1284 ◽  
Author(s):  
Qin Zhang ◽  
Arun Kumar ◽  
Yan Xue ◽  
Wanqiu Wang ◽  
Fei-Fei Jin
Keyword(s):  
Model Simulation ◽  
Southern Oscillation ◽  
Coupled Model ◽  
Coupled System ◽  
Forecast Model ◽  
Enso Cycle ◽  
Positive Contribution ◽  
Test Bed ◽  
Model Simulations ◽  
Thermocline Feedback

Abstract Simulations from the National Centers for Environmental Prediction (NCEP) coupled model are analyzed to document and understand the behavior of the evolution of the El Niño–Southern Oscillation (ENSO) cycle. The analysis is of importance for two reasons: 1) the coupled model used in this study is also used operationally to provide model-based forecast guidance on a seasonal time scale, and therefore, an understanding of the ENSO mechanism in this particular coupled system could also lead to an understanding of possible biases in SST predictions; and 2) multiple theories for ENSO evolution have been proposed, and coupled model simulations are a useful test bed for understanding the relative importance of different ENSO mechanisms. The analyses of coupled model simulations show that during the ENSO evolution the net surface heat flux acts as a damping mechanism for the mixed-layer temperature anomalies, and positive contribution from the advection terms to the ENSO evolution is dominated by the linear advective processes. The subsurface temperature–SST feedback, referred to as thermocline feedback in some theoretical literature, is found to be the primary positive feedback, whereas the advective feedback by anomalous zonal currents and the thermocline feedback are the primary sources responsible for the ENSO phase transition in the model simulation. The basic mechanisms for the model-simulated ENSO cycle are thus, to a large extent, consistent with those highlighted in the recharge oscillator. The atmospheric anticyclone (cyclone) over the western equatorial northern Pacific accompanied by a warm (cold) phase of the ENSO, as well as the oceanic Rossby waves outside of 15°S–15°N and the equatorial higher-order baroclinic modes, all appear to play minor roles in the model ENSO cycles.

scholarly journals Global cropland monthly Gross Primary Production in the year 2000

2014 ◽  
Vol 11 (2) ◽  
pp. 3465-3488
Author(s):  
T. Chen ◽  
G. R. van der Werf ◽  
N. Gobron ◽  
E. J. Moors ◽  
A. J. Dolman
Keyword(s):  
Primary Production ◽  
Survey Data ◽  
Land Surface ◽  
Gross Primary Production ◽  
Co2 Exchange ◽  
Light Use Efficiency ◽  
Natural Ecosystems ◽  
Use Efficiency ◽  
Crop Types ◽  
Year 2000

Abstract. Croplands cover about 12% of the ice-free terrestrial land surface. Compared with natural ecosystems, croplands have distinct characteristics due to anthropogenic influences. Their global gross primary production (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr−1. We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite observations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (ϵ*GPP) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These ϵ*GPP values were calculated based on flux tower CO2 exchange measurements and a literature survey of field studies, and ranged from 1.20 g CMJ−1 to 2.96 g CMJ−1. Global cropland GPP was estimated to be 11.05 Pg C yr−1 in the year 2000. Maize contributed most to this (1.55 Pg C yr−1), and the continent of Asia contributed most with 38.9% of global cropland GPP. In the continental United States, annual cropland GPP (1.28 Pg C yr−1) was close to values reported previously (1.24 Pg C yr−1) constrained by harvest records, but our estimates of ϵ*GPP values were much higher. Our results are sensitive to satellite information and survey data on crop type and extent, but provide a consistent and data-driven approach to generate a look-up table of ϵ*GPP for the 26 crop types for potential use in other vegetation models.

scholarly journals Improving the ISBA<sub>CC</sub> land surface model simulation of water and carbon fluxes and stocks over the Amazon forest

2015 ◽  
Vol 8 (6) ◽  
pp. 1709-1727 ◽  
Author(s):  
E. Joetzjer ◽  
C. Delire ◽  
H. Douville ◽  
P. Ciais ◽  
B. Decharme ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Simulation ◽  
Meta Analysis ◽  
Land Surface Model ◽  
Carbon Fluxes ◽  
Surface Model ◽  
Amazon Forest ◽  
Autotrophic Respiration ◽  
Soil Water Stress ◽  
Use Efficiency

Abstract. We evaluate the ISBACC (Interaction Soil Biosphere Atmosphere Carbon Cycle) land surface model (LSM) over the Amazon forest, and propose a revised parameterization of photosynthesis, including new soil water stress and autotrophic respiration (RA) functions. The revised version allows the model to better capture the energy, water and carbon fluxes when compared to five Amazonian flux towers. The performance of ISBACC is slightly site dependent although similar to the widely evaluated LSM ORCHIDEE (Organizing Carbon and Hydrology In Dynamic Ecosystems – version 1187), which is based on different assumptions. Changes made to the autotrophic respiration functions, including a vertical profile of leaf respiration, lead to yearly simulated carbon use efficiency (CUE) and carbon stocks which is consistent with an ecophysiological meta-analysis conducted on three Amazonian sites. Despite these major improvements, ISBACC struggles to capture the apparent seasonality of the carbon fluxes derived from the flux tower estimations. However, there is still no consensus on the seasonality of carbon fluxes over the Amazon, stressing a need for more observations as well as a better understanding of the main drivers of autotrophic respiration.

scholarly journals Improving the ISBA<sub>CC</sub> land surface model simulation of water and carbon fluxes and stocks over the Amazon forest

2015 ◽  
Vol 8 (2) ◽  
pp. 1293-1336
Author(s):  
E. Joetzjer ◽  
C. Delire ◽  
H. Douville ◽  
P. Ciais ◽  
B. Decharme ◽  
...  
Keyword(s):  
Land Surface ◽  
Model Simulation ◽  
Meta Analysis ◽  
Land Surface Model ◽  
Carbon Fluxes ◽  
Surface Model ◽  
Amazon Forest ◽  
Autotrophic Respiration ◽  
Soil Water Stress ◽  
Use Efficiency

Abstract. We evaluate the ISBACC land surface model over the Amazon forest, and propose a revised parameterization of photosynthesis, including new soil water stress and autotrophic respiration functions. The revised version allows the model to better capture the energy, water and carbon fluxes when compared to five Amazonian fluxtowers. The performance of ISBACC is slightly site-dependent but similar to the widely evaluated land surface model ORCHIDEE, based on different assumptions. Changes made to the autotrophic respiration functions, including a vertical profile of leaf respiration, leads to simulate yearly carbon use efficiency and carbon stocks consistent with an ecophysiological meta analysis conducted on three Amazonian sites. Despite these major improvements, ISBACC struggles to capture the apparent seasonality of the carbon fluxes derived from the fluxtower estimations. However, there is still no consensus on the seasonality of carbon fluxes over the Amazon, stressing a need for more observations as well as a better understanding of the main drivers of autotrophic respiration.

scholarly journals The ARM Data-Oriented Metrics and Diagnostics Package for Climate Models: A New Tool for Evaluating Climate Models with Field Data

2020 ◽  
Vol 101 (10) ◽  
pp. E1619-E1627
Author(s):  
C. Zhang ◽  
S. Xie ◽  
C. Tao ◽  
S. Tang ◽  
T. Emmenegger ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Climate Modeling ◽  
Model Development ◽  
Coupled Model ◽  
Department Of Energy ◽  
Climate Model Evaluation ◽  
User Facility

AbstractThe U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud, and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Intercomparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.

scholarly journals Coupled Climate Model Simulation of Tropical–Extratropical Cloud Bands over Southern Africa

2020 ◽  
Vol 33 (19) ◽  
pp. 8579-8602
Author(s):  
Rachel James ◽  
Neil C. G. Hart ◽  
Callum Munday ◽  
Chris J. C. Reason ◽  
Richard Washington
Keyword(s):  
Southern Africa ◽  
Climate Models ◽  
Climate Model ◽  
Model Simulation ◽  
Regional Climate ◽  
Model Development ◽  
Longwave Radiation ◽  
Congo Basin ◽  
Image Processing Algorithm ◽  
Dry Conditions

AbstractThere are increasing efforts to use climate model output for adaptation planning, but meanwhile there is often limited understanding of how models represent regional climate. Here we analyze the simulation in global coupled climate models of a key rainfall-generating mechanism over southern Africa: tropical temperate troughs (TTTs). An image-processing algorithm is applied to outgoing longwave radiation data from satellites and models to create TTT event sets. All models investigated produce TTTs with similar circulation features to observed. However, there are large differences among models in the number, intensity, and preferred longitude of events. Five groups of models are identified. The first group generates too few TTTs, and relatively dry conditions over southern Africa compared to other models. A second group generates more TTTs and wet biases. The contrast between these two groups suggests that the number of TTTs could explain intermodel variations in climatological rainfall. However, there is a third group of models that simulate up to 92% more TTTs than observed, but do not have large rainfall biases, as each TTT event is relatively weak. Finally, there are a further two groups that concentrate TTTs over the subcontinent or the ocean, respectively. These distinctions between models are associated with the amount of convective activity in the Congo Basin, the magnitude of moisture fluxes into southern Africa, and the degree of zonal asymmetry in upper-level westerly flow. Model development focused on tropical convection and the representation of orography is needed for improved simulation of TTTs, and therefore southern African rainfall.

scholarly journals Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe

2016 ◽  
Author(s):  
Tea Thum ◽  
Sönke Zaehle ◽  
Philipp Köhler ◽  
Tuula Aalto ◽  
Mika Aurela ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Land Surface ◽  
Large Scale ◽  
Model Simulation ◽  
Gross Primary Production ◽  
Coniferous Forest ◽  
Land Surface Model ◽  
Flux Measurement ◽  
Surface Model ◽  
Fluorescence Measurements

Abstract. Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf level chlorophyll fluorescence measurements (ChlF) to evaluate the performance of the SIF module at a coniferous forest at Hyytiälä, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. The GOME-2 based SIF was a better proxy for GPP than the remotely sensed fAPAR (fraction of absorbed photosynthetic active radiation by vegetation), even though high SIF values occurred during early spring at the northern latitudes, although these are not likely to be associated with photosynthesis.

scholarly journals Global cropland monthly gross primary production in the year 2000

2014 ◽  
Vol 11 (14) ◽  
pp. 3871-3880 ◽  
Author(s):  
T. Chen ◽  
G. R. van der Werf ◽  
N. Gobron ◽  
E. J. Moors ◽  
A. J. Dolman
Keyword(s):  
Primary Production ◽  
Survey Data ◽  
Land Surface ◽  
Gross Primary Production ◽  
Co2 Exchange ◽  
Light Use Efficiency ◽  
Natural Ecosystems ◽  
Use Efficiency ◽  
Crop Types ◽  
Year 2000

Abstract. Croplands cover about 12% of the ice-free terrestrial land surface. Compared with natural ecosystems, croplands have distinct characteristics due to anthropogenic influences. Their global gross primary production (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr−1. We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite observations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (&amp;varepsilon;*GPP) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These &amp;varepsilon;*GPP values were calculated based on flux tower CO2 exchange measurements and a literature survey of field studies, and ranged from 1.20 to 2.96 g C MJ−1. Global cropland GPP was estimated to be 11.05 Pg C yr−1 in the year 2000. Maize contributed most to this (1.55 Pg C yr−1), and the continent of Asia contributed most with 38.9% of global cropland GPP. In the continental United States, annual cropland GPP (1.28 Pg C yr−1) was close to values reported previously (1.24 Pg C yr−1) constrained by harvest records, but our estimates of &amp;varepsilon;*GPP values were considerably higher. Our results are sensitive to satellite information and survey data on crop type and extent, but provide a consistent and data-driven approach to generate a look-up table of &amp;varepsilon;*GPP for the 26 crop types for potential use in other vegetation models.

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