Wolfgang Knorr

Climate-carbon cycle feedback analysis: Results from the C4MIP model intercomparison

Eleven coupled climate–carbon cycle models used a common protocol to study the coupling between climate change and the carbon cycle. The models were forced by historical emissions and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 anthropogenic emissions of CO2 for the 1850–2100 time period. For each model, two simulations were performed in order to isolate the impact of climate change on the land and ocean carbon cycle, and therefore the climate feedback on the atmospheric CO2 concentration growth rate. There was unanimous agreement among the models that future climate change will reduce the efficiency of the earth system to absorb the anthropogenic carbon perturbation. A larger fraction of anthropogenic CO2 will stay airborne if climate change is accounted for. By the end of the twenty-first century, this additional CO2 varied between 20 and 200 ppm for the two extreme models, the majority of the models lying between 50 and 100 ppm. The higher CO2 levels led to an additional climate warming ranging between 0.1° and 1.5°C. All models simulated a negative sensitivity for both the land and the ocean carbon cycle to future climate. However, there was still a large uncertainty on the magnitude of these sensitivities. Eight models attributed most of the changes to the land, while three attributed it to the ocean. Also, a majority of the models located the reduction of land carbon uptake in the Tropics. However, the attribution of the land sensitivity to changes in net primary productivity versus changes in respiration is still subject to debate; no consensus emerged among the models.

Long-term sensitivity of soil carbon turnover to warming

The sensitivity of soil carbon to warming is a major uncertainty in projections of carbon dioxide concentration and climate. Experimental studies overwhelmingly indicate increased soil organic carbon (SOC) decomposition at higher temperatures, resulting in increased carbon dioxide emissions from soils. However, recent findings have been cited as evidence against increased soil carbon emissions in a warmer world. In soil warming experiments, the initially increased carbon dioxide efflux returns to pre-warming rates within one to three years, and apparent carbon pool turnover times are insensitive to temperature. It has already been suggested that the apparent lack of temperature dependence could be an artefact due to neglecting the extreme heterogeneity of soil carbon, but no explicit model has yet been presented that can reconcile all the above findings. Here we present a simple three-pool model that partitions SOC into components with different intrinsic turnover rates. Using this model, we show that the results of all the soil-warming experiments are compatible with long-term temperature sensitivity of SOC turnover: they can be explained by rapid depletion of labile SOC combined with the negligible response of non-labile SOC on experimental timescales. Furthermore, we present evidence that non-labile SOC is more sensitive to temperature than labile SOC, implying that the long-term positive feedback of soil decomposition in a warming world may be even stronger than predicted by global models.

Uncertainties in global terrestrial biosphere modeling: 1. A comprehensive sensitivity analysis with a new photosynthesis and energy balance scheme

Modeling the terrestrial biospheres carbon exchanges constitutes a key tool for investigation of the global carbon cycle, which has lead to the recent development of numerous terrestrial biosphere models. However, as demonstrated by recent intercomparison studies, results of plant carbon uptake, expressed as net primary productivity (NPP), still diverge to a large degree. Here, we address the question of uncertainty by conducting a series of sensitivity tests with a single, process-based model, the Biosphere Energy-Transfer Hydrology (BETHY) scheme. We calculate NPP globally for a standard model setup and various alternative model setups representing either changes in modeling strategy or approximate uncertainties of the most important model parameters. The results show that estimated uncertainties of many process parameters are still too large for reliable predictions of global NPP. The largest uncertainties come from plant respiration, photosynthesis and soil water storage. The surface radiation balance and day-to-day variations in weather, often not included into terrestrial vegetation models, are also found to contribute significantly to overall uncertainties, while stomatal behavior, the aerodynamic coupling of vegetation and atmosphere, and the choice of the vegetation map turn out to be relatively unimportant. A further comparison with field measurements of NPP suggests that such data are too unreliable for validating biosphere model predictions. We conclude that the inherent uncertainties in process-oriented biosphere modeling are able to explain the discrepancies that have occurred when comparing the results of different models.

Evaluation of terrestrial carbon cycle models through simulations of the seasonal cycle of atmospheric CO2: First results of a model intercomparison study

Results of an intercomparison among terrestrial biogeochemical models (TBMs) are reported, in which one diagnostic and five prognostic models have been run with the same long-term climate forcing. Monthly fields of net ecosystem production (NEP), which is the difference between net primary production (NPP) and heterotrophic respiration RH, at 0.5° resolution have been generated for the terrestrial biosphere. The monthly estimates of NEP in conjunction with seasonal CO2 flux fields generated by the seasonal Hamburg Model of the Oceanic Carbon Cycle (HAMOCC3) and fossil fuel source fields were subsequently coupled to the three-dimensional atmospheric tracer transport model TM2 forced by observed winds. The resulting simulated seasonal signal of the atmospheric CO2 concentration extracted at the grid cells corresponding to the locations of 27 background monitoring stations of the National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory network is compared with measurements from these sites. The Simple Diagnostic Biosphere Model (SDBM1), which is tuned to the atmospheric CO2 concentration at five monitoring stations in the northern hemisphere, successfully reproduced the seasonal signal of CO2 at the other monitoring stations. The SDBM1 simulations confirm that the north-south gradient in the amplitude of the atmospheric CO2 signal results from the greater northern hemisphere land area and the more pronounced seasonality of radiation and temperature in higher latitudes. In southern latitudes, ocean-atmosphere gas exchange plays an important role in determining the seasonal signal of CO2. Most of the five prognostic models (i.e., models driven by climatic inputs) included in the intercomparison predict in the northern hemisphere a reasonably accurate seasonal cycle in terms of amplitude and, to some extent, also with respect to phase. In the tropics, however, the prognostic models generally tend to overpredict the net seasonal exchanges and stronger seasonal cycles than indicated by the diagnostic model and by observations. The differences from the observed seasonal signal of CO2 may be caused by shortcomings in the phenology algorithms of the prognostic models or by not properly considering the effects of land use and vegetation fires on CO2 fluxes between the atmosphere and terrestrial biosphere.

Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle

[1] This paper demonstrates a new method of assimilating atmospheric concentration data into terrestrial biosphere models. Using a combination of adjoint and tangent linear models of both the underlying biosphere model and the atmospheric transport model, we directly infer optimal model parameters and their uncertainties. We also compute biospheric fluxes and their uncertainties arising from these parameters. We demonstrate the method using the Simple Diagnostic Biosphere Model (SDBM) and data on the seasonal cycle of CO2 from 41 observing sites. In the model, the light-use efficiency for several biomes is well-constrained by concentration observations. Optimal values generally increase with latitude as required to match the seasonal cycle. Modeled Q10 values are poorly constrained unless local flux measurements are also used. Values also increase with latitude but are less than the commonly assumed value of 2. INDEX TERMS: 0315 Atmospheric Composition and Structure: Biosphere/atmosphere interactions; 1640 Global Change: Remote sensing; 1615 Global Change: Biogeochemical processes (4805); 3210 Mathematical Geophysics: Modeling; KEYWORDS: carbon cycle data assimilation, inverse modeling, adjoint, terrestrial biosphere, uncertainty analysis, atmospheric carbon dioxide Citation: Kaminski, T., W. Knorr, P. J. Rayner, and M. Heimann, Assimilating atmospheric data into a terrestrial biosphere model: A case study of the seasonal cycle, Global Biogeochem. Cycles, 16(4), 1066, doi:10.1029/2001GB001463, 2002.

Climate and interannual variability of the atmosphere‐biosphere 13CO2 flux

We present a bottom-up approach to simulate the terrestrial isotopic carbon variations using the Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM). LPJ is extended to include isotopic fractionation of C-13 at the leaf level during assimilation and includes a full isotopic terrestrial carbon cycle. The model thus allows a quantitative analysis of the net biosphere exchange of CO2 and (CO2)-C-13 with the atmosphere as a function of changes in climate, atmospheric CO2, and the isotope ratio of CO2. LPJ simulates a global mean isotopic fractionation of 17.7% at the leaf level with interannual variations of ca. 0.3%. Interannual variability in the net (CO2)-C-13 flux between atmosphere and terrestrial biosphere is of the order of 15 PgC% yr(-1). It is reduced to 4 PgC% yr(-1) if the leaf-level fractionation factor is held constant at the long term mean. Taking climate driven variable fractionation effects into account in double deconvolution studies we estimate that this could imply shifts of up to 0.8 PgC yr(-1) in the inferred partitioning between terrestrial and oceanic carbon sinks.

Modeling the dynamics of terrestrial carbon storage since the Last Glacial Maximum

A dynamic global vegetation model (DGVM) was used to simulate global terrestrial carbon storage and stable carbon isotope composition changes for the last 21000 years. A paleoclimate scenario was provided by interpolation of coupled AGCM/mixed-layer ocean model experiments; [CO2](atm) data were obtained from the Byrd and Taylor Dome ice core records. According to the model results, terrestrial carbon storage at the Last Glacial Maximum (LGM, 21 ka) was 821 Pg C less than today. The modeled isotopic composition (delta(13)C) of total terrestrial carbon at LGM was enriched by 1.5parts per thousand compared to present. During the deglaciation (17-9 ka), vegetation expanded rapidly into formerly glaciated areas and carbon storage correspondingly increased. Increasing NPP sustained a continuing increase in terrestrial carbon storage through the Holocene. These results do not support the published hypothesis that terrestrial CO2 outgassing drove the ca. 20 ppm increase in [CO2](atm) after 8 ka. They are consistent with an alternative explanation based on the oceanic CaCO3 compensation response to the extraction of carbon from the atmosphere-ocean system during the deglaciation.

A comprehensive global three‐dimensional model of δ18O in atmospheric CO2: 1. Validation of surface processes

[1]xa0We have built the first comprehensive global three-dimensional model of δ18O in atmospheric CO2. The constructed model goes beyond all other approaches made until now, by simulating the diurnal variations and transport of CO2, δ18O of water, and δ18O of CO2. The CO18O fluxes are thereby dependent on the atmospheric CO18O composition. We have validated the model surface processes, showing that it compares well to other estimates and measurements of NPP, NEE, and stomata-internal CO2 mixing ratio (ci), except for high northern latitudes. Here, the model is considerably lower in NPP and higher in ci than other model estimates. However, estimates derived indirectly from observations tend to support our model findings. The water isotopes of rain are reproduced very well at all latitudes. The soil bucket model used in the model integrates incoming rain in one single value. The bucket approach overattenuates the isotopic variations of rain, and hence our isotopic source signature of respiration shows almost no seasonal cycle and is thus isotopically too depleted during summer.

A comprehensive global three‐dimensional model of δ18O in atmospheric CO2: 2. Mapping the atmospheric signal

[1]xa0We have modeled the distribution of δ18O in atmospheric CO2 with a new comprehensive global three-dimensional model. We have focused in this study on the seasonal cycle and the meridional gradient in the atmosphere. The model has been compared with a data set of δ18O-CO2, which merges measurements made by different laboratories, with allowance for recently elucidated calibration biases. The model compares well with the seasonal cycle of CO2, but advances the measured δ18O-CO2 seasonal cycle by two months. The calculated seasonal amplitude is typically 2/3 of the measured value, but the sensitivity to uncertainties in the input parameter set is such that a range of amplitudes over a factor of 3 is accommodated. Unlike the case for the amplitude, the sensitivity analyses demonstrate that the modeled phase of the seasonal cycle and the north-south gradient are practically unaffected by uncertainty in the parameter set. The north-south gradient comes, on the one hand, from the disequilibrium of the δ18O-CO2 isofluxes at every grid point and, on the other hand, from rectification gradients, a covariance of the varying δ18O-CO2 source with the atmospheric transport. The model exhibits a very strong rectification gradient that can lead to a misinterpretation of the measurements compared to the model. We therefore restrict comparison to the latitudinal means of only ocean grid cells with measurements from stations sampling the marine boundary layer. Assimilation and respiration are the determining factors of the seasonal cycle and the north-south gradient of δ18O-CO2. In a number of sensitivity studies we have explored the range of possible processes affecting the simulated seasonal cycle and hemispheric gradient. None of these processes contributed significantly to improve the model-observation mismatch. The contribution of assimilation and respiration to the total signal does change significantly in the sensitivity studies, but, because of feedback processes, they change in such a way that the overall response of the model is only marginally altered. In particular, prescribing δ18O-H2O soil values to monthly means of rain does not significantly change the modeled signal, either in the seasonal cycle or in the meridional gradient. This highlights the need to accurately model assimilation and respiration in order to understand δ18O in atmospheric CO2.

Uncertainties in global terrestrial biosphere modeling, Part II: Global constraints for a process‐based vegetation model

The terrestrial biosphere is one of several key components of the global carbon cycle. Because the mechanisms by which climate determines terrestrial biosphere carbon fluxes are not well understood, significant uncertainties concerning model results exist even for the current state of the system, with important consequences for our ability to predict changes under future climate change scenarios. We assess how far this uncertainty can be reduced by constraining a global mechanistic model of vegetation activity, either with global satellite-derived vegetation index data or with measurements of the seasonal CO2 cycle in the atmosphere. We first show how constraining the model with satellite data from the National Oceanic and Atmospheric Administration advanced very high resolution radiometer reduces the sensitivity to estimated uncertainties in model parameters, and thus the estimated error range of net primary productivity. Regionally, the satellite data deliver the largest constraint for vegetation activity in boreal and arctic as well as in tropical water-limited environments. In a second analysis through an atmospheric tracer transport model, we check the consistency of those results with the measured seasonal cycle of CO2 at various remote monitoring sites. While before including the satellite data into model calculations, some simulations within the error range lead to a CO2 seasonal cycle outside the observations, there is a good agreement with the additional constraint. The conclusion is that the constraint delivered by the satellite data is at least as significant as that delivered by atmospheric CO2 measurements. We also show that the CO2 data mainly reflect the activity of northern vegetation, in particular conifers and C3 grasses. This suggests that satellite measurements provide the most useful global data currently available for checking and improving terrestrial vegetation models and that consistency with CO2 measurements is a necessary but not a sufficient requirement for their realism.