scholarly journals Evaluation of Biases in JRA-25/JCDAS Precipitation and Their Impact on the Global Terrestrial Carbon Balance

2011 ◽  
Vol 24 (15) ◽  
pp. 4109-4125 ◽  
Author(s):  
Makoto Saito ◽  
Akihiko Ito ◽  
Shamil Maksyutov
Keyword(s):  
Carbon Cycle ◽  
Carbon Balance ◽  
Regional Scale ◽  
Precipitation Amount ◽  
Temporal Variations ◽  
Japan Meteorological Agency ◽  
Total Biomass ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Terrestrial Carbon

Abstract This study evaluates a modeled precipitation field and examines how its bias affects the modeling of the regional and global terrestrial carbon cycle. Spatial and temporal variations in precipitation produced by the Japanese 25-yr reanalysis (JRA-25)/Japan Meteorological Agency (JMA) Climate Data Assimilation System (JCDAS) were compared with two large-scale observation datasets. JRA-25/JCDAS captures the major distribution patterns of annual precipitation and the features of the seasonal cycle. Notable problems include over- and undersimulated areas of precipitation amount in South America, Africa, and Southeast Asia in the 30°N–30°S domain and a large discrepancy in the number of rainfall days. The latter problem was corrected by using a stochastic model based on the probability of the occurrence of dry and wet day series; the monthly precipitation amount was then scaled by the comparison data. Overall, the corrected precipitation performed well in reproducing the spatial distribution of and temporal variations in total precipitation. Both the corrected and original precipitation data were used to simulate regional and global terrestrial carbon cycles using the prognostic biosphere model Vegetation Integrative Simulator for Trace Gases (VISIT). Following bias correction, the model results showed differences in zonal mean photosynthesis uptake and respiration release ranging from −2.0 to +3.3 Pg C yr−1, compared with the original data. The difference in the global terrestrial net carbon exchange rate was 0.3 Pg C yr−1, reflecting the compensation of coincident increases or decreases in carbon sequestration and respiration loss. At the regional scale, the ecosystem carbon cycle and canopy structure, including seasonal variations in autotrophic and heterotrophic respiration and total biomass, were strongly influenced by the input precipitation data. The results highlight the need for precise precipitation data when estimating the global terrestrial carbon balance.

scholarly journals The carbon budget of terrestrial ecosystems in East Asia over the last two decades

2012 ◽  
Vol 9 (9) ◽  
pp. 3571-3586 ◽  
Author(s):  
S. L. Piao ◽  
A. Ito ◽  
S. G. Li ◽  
Y. Huang ◽  
P. Ciais ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
East Asia ◽  
Carbon Storage ◽  
Carbon Balance ◽  
Carbon Sink ◽  
Regional Scale ◽  
Terrestrial Ecosystems ◽  
Carbon Accumulation ◽  
Terrestrial Carbon

Abstract. This REgional Carbon Cycle Assessment and Processes regional study provides a synthesis of the carbon balance of terrestrial ecosystems in East Asia, a region comprised of China, Japan, North and South Korea, and Mongolia. We estimate the current terrestrial carbon balance of East Asia and its driving mechanisms during 1990–2009 using three different approaches: inventories combined with satellite greenness measurements, terrestrial ecosystem carbon cycle models and atmospheric inversion models. The magnitudes of East Asia's terrestrial carbon sink from these three approaches are comparable: −0.293±0.033 PgC yr−1 from inventory–remote sensing model–data fusion approach, −0.413±0.141 PgC yr−1 (not considering biofuel emissions) or −0.224±0.141 PgC yr−1 (considering biofuel emissions) for carbon cycle models, and −0.270±0.507 PgC yr−1 for atmospheric inverse models. Here and in the following, the numbers behind ± signs are standard deviations. The ensemble of ecosystem modeling based analyses further suggests that at the regional scale, climate change and rising atmospheric CO2 together resulted in a carbon sink of −0.289±0.135 PgC yr−1, while land-use change and nitrogen deposition had a contribution of −0.013±0.029 PgC yr−1 and −0.107±0.025 PgC yr−1, respectively. Although the magnitude of climate change effects on the carbon balance varies among different models, all models agree that in response to climate change alone, southern China experienced an increase in carbon storage from 1990 to 2009, while northern East Asia including Mongolia and north China showed a decrease in carbon storage. Overall, our results suggest that about 13–27% of East Asia's CO2 emissions from fossil fuel burning have been offset by carbon accumulation in its terrestrial territory over the period from 1990 to 2009. The underlying mechanisms of carbon sink over East Asia still remain largely uncertain, given the diversity and intensity of land management processes, and the regional conjunction of many drivers such as nutrient deposition, climate, atmospheric pollution and CO2 changes, which cannot be considered as independent for their effects on carbon storage.

scholarly journals Spatial-Temporal Changes of Soil Respiration across China and the Response to Land Cover and Climate Change

2018 ◽  
Vol 10 (12) ◽  
pp. 4604 ◽  
Author(s):  
Jiqun Wen ◽  
Xiaowei Chuai ◽  
Shanchi Li ◽  
Song Song ◽  
Jiasheng Li ◽  
...  
Keyword(s):  
Soil Respiration ◽  
Environmental Changes ◽  
Global Climate ◽  
Regional Scale ◽  
Management Strategies ◽  
Terrestrial Ecosystems ◽  
Temporal Variations ◽  
Control Measures ◽  
Terrestrial Carbon ◽  
Geostatistical Model

Soil respiration (Rs) plays an important role in the carbon budget of terrestrial ecosystems. Quantifying the spatial and temporal variations in Rs in China at the regional scale helps improve our understanding of the variations in terrestrial carbon budgets that occur in response to global climate and environmental changes and potential future control measures. In this study, we used a regional-scale geostatistical model that incorporates gridded meteorological and pedologic data to evaluate the spatial Rs variations in China from 2000 to 2013. We analysed the relationship between Rs and environmental factors, and suggest management strategies that may help to keep the terrestrial carbon balance. The simulated results demonstrate that the mean annual Rs value over these 14 years was 422 g/m2/year, and the corresponding total amount was 4.01 Pg C/year. The Rs estimation displayed a clear spatial pattern and a slightly increasing trend. Further analysis also indicated that high Rs values may occur in areas that show a greater degree of synchronicity in the timing of their optimal temperature and moisture conditions. Moreover, cultivated vegetation exhibits higher Rs values than native vegetation. Finally, we suggest that specific conservation efforts should be focused on ecologically sensitive areas where the Rs values increase significantly.

scholarly journals The carbon budget of terrestrial ecosystems in East Asia over the last two decades

2012 ◽  
Vol 9 (3) ◽  
pp. 4025-4066 ◽  
Author(s):  
S. Piao ◽  
A. Ito ◽  
S. Li ◽  
Y. Huang ◽  
P. Ciais ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
East Asia ◽  
Carbon Storage ◽  
Carbon Balance ◽  
Southern China ◽  
Carbon Sink ◽  
Regional Scale ◽  
Terrestrial Ecosystems ◽  
Carbon Accumulation

Abstract. This REgional Carbon Cycle Assessment and Processes regional study provides a synthesis of the carbon balance of terrestrial ecosystems in East Asia, a region comprised of China, Japan, North- and South-Korea, and Mongolia. We estimate the current terrestrial carbon balance of East Asia and its driving mechanisms during 1990–2009 using three different approaches: inventories combined with satellite greenness measurements, terrestrial ecosystem carbon cycle models and atmospheric inversion models. The magnitudes of East Asia's natural carbon sink from these three approaches are comparable: −0.264 ± 0.033 Pg C yr−1 from inventory-remote sensing model-data fusion approach, −0.393 ± 0.141 Pg C yr−1 (not considering biofuel emissions) or −0.204 ± 0.141 Pg C yr−1 (considering biofuel emissions) for carbon cycle models, and −0.270 ± 0.507 Pg C yr−1 for atmospheric inverse models. The ensemble of ecosystem modeling based analyses further suggests that at the regional scale, climate change and rising atmospheric CO2 together resulted in a carbon sink of −0.289 ± 0.135 Pg C yr−1, while land use change and nitrogen deposition had a contribution of −0.013 ± 0.029 Pg C yr−1 and −0.107 ± 0.025 Pg C yr−1, respectively. Although the magnitude of climate change effects on the carbon balance varies among different models, all models agree that in response to climate change alone, southern China experienced an increase in carbon storage from 1990 to 2009, while northern East Asia including Mongolia and north China showed a decrease in carbon storage. Overall, our results suggest that about 13–26% of East Asia's CO2 emissions from fossil fuel burning have been offset by carbon accumulation in its terrestrial ecosystems over the period from 1990 to 2009. The underlying mechanisms of carbon sink over East Asia still remain largely uncertain, given the diversity and intensity of land management processes, and the regional conjunction of many drivers such as nutrient deposition, climate, atmospheric pollution and CO2 changes, which cannot be considered as independent for their effects on carbon storage.

Optimal model complexity for terrestrial carbon cycle prediction using data assimilation

2020 ◽  
Author(s):  
Caroline A. Famiglietti ◽  
T. Luke Smallman ◽  
Sophie Flack-Prain ◽  
Rong Ge ◽  
Victoria Meyer ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Carbon Balance ◽  
Prediction Accuracy ◽  
Structural Complexity ◽  
Model Complexity ◽  
Cycle Model ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle

<p>The future role of the terrestrial biosphere in the global carbon cycle is highly uncertain. Modeling and predicting the terrestrial net carbon balance is difficult due to the numerous processes driving variability of gross fluxes. Many approaches to reducing this model uncertainty have focused on model structure, namely by adding additional processes (<em>e.g., </em>nutrient dynamics or vegetation demography) and thus increasing complexity. While these developments seek to achieve greater structural realism by mirroring the complexity of the natural world, they often rely, by necessity, on poorly-determined or over-generalized parameters. Furthermore, increased structural complexity may increase the risk that parameters with compensating errors are found during model development, thereby reducing model accuracy in prediction. It is not clear whether or to what extent carbon cycle predictability scales with structural complexity, or whether an intermediate, optimum level of complexity exists that may balance the costs of a low (more biased) or high (more variant) complexity model. Here, we explore and define the relationship between carbon cycle model complexity and prediction accuracy. To do so, we leverage the CARbon Data MOdel fraMework (CARDAMOM), a Bayesian data assimilation system that retrieves terrestrial carbon cycle variables (including pools, fluxes, and static parameters) by combining multiple observations with a relatively simple ecosystem carbon balance model. CARDAMOM includes several ecological and dynamical constraints that can prevent ecologically unrealistic parameter combinations and reduce compensating errors between parameters (also known as equifinality). Furthermore, it is a flexible framework to which process representations, parameters, and constraints can easily be added and removed. We used CARDAMOM to develop a suite of model versions spanning a broad range of structural complexity, including the number of carbon pools and the allocation of carbon to the canopy. We assessed a model’s complexity based on its inherent dimensionality, determined via a principal component analysis that reduces the parameter space to its principal components. We tested and compared the training and forecast accuracies of net ecosystem exchange predictions using 14 increasingly complex versions of CARDAMOM, each with 48 different experimental designs (<em>i.e., </em>combinations of data constraints and error assumptions) at 5 globally-distributed eddy covariance sites representing a range of biomes and vegetation types across a total of 70 site-years. We also compared the model performance values against a range of machine learning approaches, which are assumed to represent the limit of infinite model complexity due to their large number of underlying parameters. In this presentation, we use this population to demonstrate and explain patterns in the mapping of model complexity and other assimilation choices to prediction accuracy, offering theoretical and empirical insights into the optimal structure of a carbon cycle model.</p>

scholarly journals Areal Precipitation Estimation Using Satellite Derived Rainfall Data over an Irrigation Area

2021 ◽  
Vol 9 (2) ◽  
pp. 386-394
Author(s):  
Mehmet Ali Akgül ◽  
Hakan Aksu
Keyword(s):  
Spatial Interpolation ◽  
Spatial Scales ◽  
Precipitation Amount ◽  
Monthly Precipitation ◽  
Left Bank ◽  
Precipitation Data ◽  
Irrigation Area ◽  
Thiessen Polygon ◽  
Precipitation Estimation ◽  
Areal Precipitation

The average precipitation on the irrigation field can be estimated from the Meteorology Observation Stations by using spatial interpolation methods such as Thiessen polygon and isohyetal curves. However, the fact that precipitation doesn't occur homogenous in spatial scales, spatial interpolation methodologies need a large number of meteorology stations for more accurate results. In recent years, remote sensing methods have diversified to estimate precipitation. In this study, performance of the satellite-based precipitation data was assessed to determine areal precipitation over an irrigation area. This study was conducted over left bank irrigation area located in the Çukurova Plain of Turkey. Relationship between CHIRPS satellite based on monthly precipitation data and 4 meteorology stations’ data were analyzed. Determination coefficients (R2) of the stations were found between 0.64 and 0.77, for point based comparison, R2 was calculated as 0.84 with Thiessen polygon method. It is concluded that the precipitation amount in the irrigated area can be estimated as accurately as classical methods such as Thiessen polygon with satellite-based precipitation data.

scholarly journals Multilayer Soil Moisture Mapping at a Regional Scale from Multisource Data via a Machine Learning Method

2019 ◽  
Vol 11 (3) ◽  
pp. 284 ◽  
Author(s):  
Linglin Zeng ◽  
Shun Hu ◽  
Daxiang Xiang ◽  
Xiang Zhang ◽  
Deren Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Temporal Variations ◽  
Training Data ◽  
Estimation Accuracy ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Deep Soil

Soil moisture mapping at a regional scale is commonplace since these data are required in many applications, such as hydrological and agricultural analyses. The use of remotely sensed data for the estimation of deep soil moisture at a regional scale has received far less emphasis. The objective of this study was to map the 500-m, 8-day average and daily soil moisture at different soil depths in Oklahoma from remotely sensed and ground-measured data using the random forest (RF) method, which is one of the machine-learning approaches. In order to investigate the estimation accuracy of the RF method at both a spatial and a temporal scale, two independent soil moisture estimation experiments were conducted using data from 2010 to 2014: a year-to-year experiment (with a root mean square error (RMSE) ranging from 0.038 to 0.050 m3/m3) and a station-to-station experiment (with an RMSE ranging from 0.044 to 0.057 m3/m3). Then, the data requirements, importance factors, and spatial and temporal variations in estimation accuracy were discussed based on the results using the training data selected by iterated random sampling. The highly accurate estimations of both the surface and the deep soil moisture for the study area reveal the potential of RF methods when mapping soil moisture at a regional scale, especially when considering the high heterogeneity of land-cover types and topography in the study area.

scholarly journals Error Correction of Multi-Source Weighted-Ensemble Precipitation (MSWEP) over the Lancang-Mekong River Basin

2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Xiongpeng Tang ◽  
Jianyun Zhang ◽  
Guoqing Wang ◽  
Gebdang Biangbalbe Ruben ◽  
Zhenxin Bao ◽  
...  
Keyword(s):  
Error Correction ◽  
River Basin ◽  
Regional Scale ◽  
Mekong River ◽  
Precipitation Data ◽  
Climate Data ◽  
Hydrological Simulation ◽  
Mekong River Basin ◽  
Precipitation Estimation ◽  
Simulation Results

The demand for accurate long-term precipitation data is increasing, especially in the Lancang-Mekong River Basin (LMRB), where ground-based data are mostly unavailable and inaccessible in a timely manner. Remote sensing and reanalysis quantitative precipitation products provide unprecedented observations to support water-related research, but these products are inevitably subject to errors. In this study, we propose a novel error correction framework that combines products from various institutions. The NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), the Multi-Source Weighted-Ensemble Precipitation Version 1.0 (MSWEP), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Records (PERSIANN) were used. Ground-based precipitation data from 1998 to 2007 were used to select precipitation products for correction, and the remaining 1979–1997 and 2008–2014 observe data were used for validation. The resulting precipitation products MSWEP-QM derived from quantile mapping (QM) and MSWEP-LS derived from linear scaling (LS) are evaluated by statistical indicators and hydrological simulation across the LMRB. Results show that the MSWEP-QM and MSWEP-LS can better capture major annual precipitation centers, have excellent simulation results, and reduce the mean BIAS and mean absolute BIAS at most gauges across the LMRB. The two corrected products presented in this study constitute improved climatological precipitation data sources, both time and space, outperforming the five raw gridded precipitation products. Among the two corrected products, in terms of mean BIAS, MSWEP-LS was slightly better than MSWEP-QM at grid-scale, point scale, and regional scale, and it also had better simulation results at all stations except Strung Treng. During the validation period, the average absolute value BIAS of MSWEP-LS and MSWEP-QM decreased by 3.51% and 3.4%, respectively. Therefore, we recommend that MSWEP-LS be used for water-related scientific research in the LMRB.

scholarly journals Century‐scale patterns and trends of global pyrogenic carbon emissions and fire influences on terrestrial carbon balance

2015 ◽  
Vol 29 (9) ◽  
pp. 1549-1566 ◽  
Author(s):  
Jia Yang ◽  
Hanqin Tian ◽  
Bo Tao ◽  
Wei Ren ◽  
Chaoqun Lu ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Carbon Balance ◽  
Terrestrial Carbon ◽  
Pyrogenic Carbon

scholarly journals Potential strong contribution of future anthropogenic land-use and land-cover change to the terrestrial carbon cycle

2018 ◽  
Vol 13 (6) ◽  
pp. 064023 ◽  
Author(s):  
Benjamin Quesada ◽  
Almut Arneth ◽  
Eddy Robertson ◽  
Nathalie de Noblet-Ducoudré
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Carbon Cycle ◽  
Land Cover Change ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Anthropogenic Land Use
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