scholarly journals Carbon–nitrogen interactions in idealized simulations with JSBACH (version 3.10)

2017 ◽  
Vol 10 (5) ◽  
pp. 2009-2030 ◽  
Author(s):  
Daniel S. Goll ◽  
Alexander J. Winkler ◽  
Thomas Raddatz ◽  
Ning Dong ◽  
Ian Colin Prentice ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Nitrogen Loss ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Carbon Decomposition ◽  
Model Average ◽  
Nitrogen Inputs ◽  
Surface Scheme

Abstract. Recent advances in the representation of soil carbon decomposition and carbon–nitrogen interactions implemented previously into separate versions of the land surface scheme JSBACH are here combined in a single version, which is set to be used in the upcoming 6th phase of coupled model intercomparison project (CMIP6).Here we demonstrate that the new version of JSBACH is able to reproduce the spatial variability in the reactive nitrogen-loss pathways as derived from a compilation of δ15N data (R = 0. 76, root mean square error (RMSE)  = 0. 2, Taylor score  = 0. 83). The inclusion of carbon–nitrogen interactions leads to a moderate reduction (−10 %) of the carbon-concentration feedback (βL) and has a negligible effect on the sensitivity of the land carbon cycle to warming (γL) compared to the same version of the model without carbon–nitrogen interactions in idealized simulations (1 % increase in atmospheric carbon dioxide per year). In line with evidence from elevated carbon dioxide manipulation experiments, pronounced nitrogen scarcity is alleviated by (1) the accumulation of nitrogen due to enhanced nitrogen inputs by biological nitrogen fixation and reduced losses by leaching and volatilization. Warming stimulated turnover of organic nitrogen further counteracts scarcity.The strengths of the land carbon feedbacks of the recent version of JSBACH, with βL = 0. 61 Pg ppm−1 and γL = −27. 5 Pg °C−1, are 34 and 53 % less than the averages of CMIP5 models, although the CMIP5 version of JSBACH simulated βL and γL, which are 59 and 42 % higher than multi-model average. These changes are primarily due to the new decomposition model, indicating the importance of soil organic matter decomposition for land carbon feedbacks.

scholarly journals Carbon-nitrogen interactions in idealized simulations with JSBACH (version 3.10)

2017 ◽  
Author(s):  
Daniel S. Goll ◽  
Alexander J. Winkler ◽  
Thomas Raddatz ◽  
Ning Dong ◽  
Ian Colin Prentice ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Soil Carbon ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Nitrogen Loss ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Carbon Decomposition ◽  
Model Average ◽  
Nitrogen Inputs

Abstract. Recent advances in the representation of soil carbon decomposition (Goll et al., 2015) and carbon-nitrogen interactions (Parida, 2011; Goll et al., 2012) implemented previously into separate versions of the land surface scheme JSBACH are here combined in a single version which is set to be used in the upcoming 6th phase of coupled model intercomparison project (CMIP6) (Eyring et al., 2016). Here we demonstrate that the new version of JSBACH is able to reproduce the spatial variability in the reactive nitrogen loss pathways as derived from a compilation of δ15N data (r=.63, RMSE=.26, Taylor score=.81). The inclusion of carbon-nitrogen interactions leads to a moderate reduction (−10 %) of the carbon-concentration feedback (βL) and has a negligible effect on the sensitivity of the land carbon cycle to warming (γL) compared to the same version of the model without carbon-nitrogen interactions in idealized simulations (1 % increase in atmospheric carbon dioxide per yr). In line with evidence from elevated carbon dioxide manipulation experiments (Shi et al., 2015; Liang et al., 2016), pronounced nitrogen scarcity is alleviated by (1) the accumulation of nitrogen due to enhanced nitrogen inputs by biological nitrogen fixation and reduced losses by leaching and volatilization as well as the (2) enhanced turnover of organic nitrogen. The strengths of the land carbon feedbacks of the recent version of JSBACH, with βL=0.61 Pg ppm−1 and γL=−27.5 Pg °C−1, are 34 % and 53 % less than the averages of CMIP5 models (Arora et al., 2013), although the CMIP5 version of JSBACH simulated βL and γL which are 59 % and 42 % higher than multi-model average. These changes are primarily due to the new decomposition model, stressing the importance of getting the basics right (here: the decomposition of soil carbon) before increasing the complexity of the model (here: carbon-nitrogen interactions).

scholarly journals Evaluating terrestrial CO<sub>2</sub> flux diagnoses and uncertainties from a simple land surface model and its residuals

2014 ◽  
Vol 11 (2) ◽  
pp. 217-235 ◽  
Author(s):  
T. W. Hilton ◽  
K. J. Davis ◽  
K. Keller
Keyword(s):  
Carbon Dioxide ◽  
Parameter Estimation ◽  
North American ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Ecosystem Respiration ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Considerable Uncertainty

Abstract. Global terrestrial atmosphere–ecosystem carbon dioxide fluxes are well constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency land surface model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.

scholarly journals Climate Extremes over the Arabian Peninsula Using RegCM4 for Present Conditions Forced by Several CMIP5 Models

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 675 ◽  
Author(s):  
Almazroui
Keyword(s):  
Land Surface ◽  
Arabian Peninsula ◽  
Climate Model ◽  
Regional Climate ◽  
Coupled Model ◽  
Climate Extremes ◽  
Maximum Temperature ◽  
Cmip5 Models ◽  
Study Region ◽  
Temperature And Precipitation

This paper investigates the temperature and precipitation extremes over the Arabian Peninsula using data from the regional climate model RegCM4 forced by three Coupled Model Intercomparison Project Phase 5 (CMIP5) models and ERA–Interim reanalysis data. Indices of extremes are calculated using daily temperature and precipitation data at 27 meteorological stations located across Saudi Arabia in line with the suggested procedure from the Expert Team on Climate Change Detection and Indices (ETCCDI) for the present climate (1986–2005) using 1981–2000 as the reference period. The results show that RegCM4 accurately captures the main features of temperature extremes found in surface observations. The results also show that RegCM4 with the CLM land–surface scheme performs better in the simulation of precipitation and minimum temperature, while the BATS scheme is better than CLM in simulating maximum temperature. Among the three CMIP5 models, the two best performing models are found to accurately reproduce the observations in calculating the extreme indices, while the other is not so successful. The reason for the good performance by these two models is that they successfully capture the circulation patterns and the humidity fields, which in turn influence the temperature and precipitation patterns that determine the extremes over the study region.

scholarly journals Response of microbial decomposition to spin-up explains CMIP5 soil carbon range until 2100

2014 ◽  
Vol 7 (6) ◽  
pp. 2683-2692 ◽  
Author(s):  
J.-F. Exbrayat ◽  
A. J. Pitman ◽  
G. Abramowitz
Keyword(s):  
Soil Carbon ◽  
Carbon Storage ◽  
Land Surface ◽  
Coupled Model ◽  
Surface Fluxes ◽  
Cmip5 Models ◽  
Historical Period ◽  
Microbial Decomposition ◽  
Transient Simulations ◽  
Business As Usual

Abstract. Soil carbon storage simulated by the Coupled Model Intercomparison Project (CMIP5) models varies 6-fold for the present day. Here, we confirm earlier work showing that this range already exists at the beginning of the CMIP5 historical simulations. We additionally show that this range is largely determined by the response of microbial decomposition during each model's spin-up procedure from initialization to equilibration. The 6-fold range in soil carbon, once established prior to the beginning of the historical period (and prior to the beginning of a CMIP5 simulation), is then maintained through the present and to 2100 almost unchanged even under a strong business-as-usual emissions scenario. We therefore highlight that a commonly ignored part of CMIP5 analyses – the land surface state achieved through the spin-up procedure – can be important for determining future carbon storage and land surface fluxes. We identify the need to better constrain the outcome of the spin-up procedure as an important step in reducing uncertainty in both projected soil carbon and land surface fluxes in CMIP5 transient simulations.

scholarly journals Intermodel Variability and Mechanism Attribution of Central and Southeastern U.S. Anomalous Cooling in the Twentieth Century as Simulated by CMIP5 Models

2013 ◽  
Vol 26 (17) ◽  
pp. 6215-6237 ◽  
Author(s):  
Zaitao Pan ◽  
Xiaodong Liu ◽  
Sanjiv Kumar ◽  
Zhiqiu Gao ◽  
James Kinter
Keyword(s):  
United States ◽  
Twentieth Century ◽  
Land Surface ◽  
Regional Scale ◽  
Coupled Model ◽  
The United States ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Central United States ◽  
Intercomparison Project

Abstract Some parts of the United States, especially the southeastern and central portion, cooled by up to 2°C during the twentieth century, while the global mean temperature rose by 0.6°C (0.76°C from 1901 to 2006). Studies have suggested that the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) may be responsible for this cooling, termed the “warming hole” (WH), while other works reported that regional-scale processes such as the low-level jet and evapotranspiration contribute to the abnormity. In phase 3 of the Coupled Model Intercomparison Project (CMIP3), only a few of the 53 simulations could reproduce the cooling. This study analyzes newly available simulations in experiments from phase 5 of the Coupled Model Intercomparison Project (CMIP5) from 28 models, totaling 175 ensemble members. It was found that 1) only 19 out of 100 all-forcing historical ensemble members simulated negative temperature trend (cooling) over the southeast United States, with 99 members underpredicting the cooling rate in the region; 2) the missing of cooling in the models is likely due to the poor performance in simulating the spatial pattern of the cooling rather than the temporal variation, as indicated by a larger temporal correlation coefficient than spatial one between the observation and simulations; 3) the simulations with greenhouse gas (GHG) forcing only produced strong warming in the central United States that may have compensated the cooling; and 4) the all-forcing historical experiment compared with the natural-forcing-only experiment showed a well-defined WH in the central United States, suggesting that land surface processes, among others, could have contributed to the cooling in the twentieth century.

scholarly journals Simulating shrubs and their energy and carbon dioxide fluxes in Canada's Low Arctic with the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC)

2020 ◽  
Author(s):  
Gesa Meyer ◽  
Elyn R. Humphreys ◽  
Joe R. Melton ◽  
Alex J. Cannon ◽  
Peter M. Lafleur
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Biogeochemical Cycles ◽  
Dwarf Shrub ◽  
Land Surface Scheme ◽  
Canadian Land Surface Scheme ◽  
Shrub Tundra ◽  
Flux Measurements ◽  
Surface Scheme ◽  
Process Based Models

Abstract. The Arctic is warming more rapidly than other regions of the world leading to ecosystem change including shifts in vegetation communities, permafrost degradation and alteration of tundra surface-atmosphere energy and carbon (C) fluxes, among others. However, year-round C and energy flux measurements at high-latitude sites remain rare. This poses a challenge for evaluating the impacts of climate change on Arctic tundra ecosystems and for developing and evaluating process-based models, which may be used to predict regional and global energy and C feedbacks to the climate system. Our study used 14 years of seasonal eddy covariance (EC) measurements of carbon dioxide (CO2), water and energy fluxes and winter soil chamber CO2 flux measurements at a dwarf-shrub tundra site underlain by continuous permafrost in Canada's Southern Arctic ecozone to evaluate the incorporation of shrub plant functional types (PFTs) in the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC), the land surface component of the Canadian Earth System Model. In addition to new PFTs, a modification of the efficiency with which water evaporates from the ground surface was applied. This modification addressed a high ground evaporation bias that reduced model performance when soils became very dry, limited heat flow into the ground and reduced plant productivity through water stress effects. Compared to the grass and tree PFTs previously used by CLASSIC to represent the vegetation in Arctic permafrost-affected regions, simulations with the new shrub PFTs better capture the physical and biogeochemical impact of shrubs on the magnitude and seasonality of energy and CO2 fluxes at the dwarf-shrub tundra evaluation site. The revised model, however, tends to overestimate gross primary productivity, particularly in spring, and overestimated late winter CO2 emissions. On average, annual net ecosystem CO2 exchange was positive for all simulations, suggesting this site was a net CO2 source of 18 ± 4 g C m−2 year−1 using shrub PFTs, 15 ± 6 g C m−2 year−1 using grass PFTs, and 25 ± 5 g C m−2 year−1 using tree PFTs. These results highlight the importance of using appropriate PFTs in process-based models to simulate current and future Arctic surface-atmosphere interactions.

Evaluation of snow albedo feedback simulated by CMIP6 coupled climate models

2020 ◽  
Author(s):  
jiangling hu ◽  
duoying ji
Keyword(s):  
Climate Change ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Simulation Research ◽  
Albedo Feedback ◽  
Snow Albedo ◽  
Autumn And Winter

&lt;p&gt;As the land surface warms, a subsequent reduction in snow and ice cover reveals a less reflective surface that absorbs more solar radiation, which further enhances the initial warming. This positive feedback climate mechanism is the snow albedo feedback (SAF), which will exacerbate climate warming and is the second largest contributor to Arctic amplification. Snow albedo feedback will increase the sensitivity of climate change in the northern hemisphere, which affects the accuracy of climate models in simulation research of climate change, and further affects the credibility of future climate prediction results.&lt;/p&gt;&lt;p&gt;Using the latest generation of climate models from CMIP6 (Coupled Model Intercomparison Project Version 6), we analyze seasonal cycle snow albedo feedback in Northern Hemisphere extratropics. We find that the strongest SAF strength is in spring (mean: -1.34 %K&lt;sup&gt;-1&lt;/sup&gt;), second strongest is autumn (mean: -1.01 %K&lt;sup&gt;-1&lt;/sup&gt;), the weakest is in summer (mean: -0.18 %K&lt;sup&gt;-1&lt;/sup&gt;). Except summer, the SAF strength is approximately 0.15% K&lt;sup&gt;-1&lt;/sup&gt; larger than CMIP5 models in the other three seasons. The spread of spring SAF strength (range: -1.09 to -1.37% K&lt;sup&gt;-1&lt;/sup&gt;) is larger than CMIP5 models. Oppositely, the spread of summer SAF strength (range: 0.20 to -0.56% K&lt;sup&gt;-1&lt;/sup&gt;) is smaller than CMIP5 models. When compared with CMIP5 models, the spread of autumn and winter SAF strength have not changed much.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document