暖温带典型森林生态系统未来碳平衡的LPJ-GUESS模型模拟与分析

森林作为陆地生态系统的主体，在全球陆地碳循环中起着决定性作用。实测和模型研究均表明北半球的森林是重要的大气CO2汇，在缓解全球碳收支失衡中发挥着关键作用。过去几十年北半球所经历的显著气候变化，已经很大地改变了陆地生态系统的碳平衡状况。随着未来100年气候变化继续增大，对未来气候变化下森林生态系统碳平衡的预测研究就尤为重要。 北京山区森林属于典型的暖温带森林生态系统，前人对本区森林的植被特征、生态系统结构和功能、养分循环以及长期动态变化等都进行了深入的研究。然而长期的人类活动已使本区原生的地带性植被破坏殆尽。因此，对该区域森林生态系统碳平衡的模拟研究可以帮助我们认识其生态系统碳平衡变化特点及未来气候变化对其潜在的影响。 本研究采用LPJ-GUESS植被动态机理模型，利用IPCC于2000年发布的《排放情景特别报告》(SRES)的A2和B2两种情景下不同气候模式对华北地区未来100年温度和降水预测的平均值以及相应大气CO2浓度变化情景进行驱动，模拟北京山区未来100年暖温带森林生态系统的净初级生产力和碳平衡，尽可能真实地反映未来百年的变化趋势。通过比较当前和未来气候情景下北京山区以辽东栎（Quercus liaotungensis）为优势种的落叶阔叶林、以白桦（Betula platyphylla）为主的落叶阔叶林和油松（Pinus tabulaeformis）为主的针阔混交林三种典型暖温带森林生态系统的碳平衡差异，了解未来北京山区这三种暖温带森林生态系统的碳源汇功能，认识气候变化和大气CO2浓度升高对净初级生产力（Net primary productivity, NPP）、净生态系统碳交换（Net ecosystem exchange, NEE）、土壤异养呼吸（Heterotrophic respiration, Rh）和碳储量（Carbon biomass, C biomass）的影响，以及不同生态系统碳平衡对气候变化响应的异质性。 结果表明，未来100年两种气候情景下三种森林生态系统的NPP和Rh均增加，并且A2情景下增加的程度更大。由于三种生态系统树种组成的不同，未来气候情景下各自NPP和Rh增加的比例不同，导致三者NEE的变化也相异：100年后辽东栎林由碳汇转变为弱碳源，白桦林仍保持为碳汇但功能减弱，油松林成为一个更大的碳汇。三种森林生态系统的碳生物量在未来气候情景下均增大，21世纪末与20世纪末相比：辽东栎林在A2情景下碳生物量增加的比例为27.6%，大于B2情景下的19.3%；白桦林和油松林在B2情景下碳生物量增加的比例分别为34.2%和52.2%，大于A2情景下的30.8%和28.4%。各森林类型碳平衡状况不同，原因除气候因素外，主要是由于树种组成的差异所导致。SRES A2和B2两种气候情景相比，相对较低的排放情景（B2）下，生态系统有更高的碳储量。

Regional climate model simulations for Europe at 6 and 0.2 k BP: sensitivity to changes in anthropogenic deforestation

This study aims to evaluate the direct effects of anthropogenic deforestation on simulated climate at two contrasting periods in the Holocene, ~6 and ~0.2 k BP in Europe. We apply We apply the Rossby Centre regional climate model RCA3, a regional climate model with 50 km spatial resolution, for both time periods, considering three alternative descriptions of the past vegetation: (i) potential natural vegetation (V) simulated by the dynamic vegetation model LPJ-GUESS, (ii) potential vegetation with anthropogenic land use (deforestation) from the HYDE3.1 (History Database of the Global Environment) scenario (V + H3.1), and (iii) potential vegetation with anthropogenic land use from the KK10 scenario (V + KK10). The climate model results show that the simulated effects of deforestation depend on both local/regional climate and vegetation characteristics. At ~6 k BP the extent of simulated deforestation in Europe is generally small, but there are areas where deforestation is large enough to produce significant differences in summer temperatures of 0.5–1 °C. At ~0.2 k BP, extensive deforestation, particularly according to the KK10 model, leads to significant temperature differences in large parts of Europe in both winter and summer. In winter, deforestation leads to lower temperatures because of the differences in albedo between forested and unforested areas, particularly in the snow-covered regions. In summer, deforestation leads to higher temperatures in central and eastern Europe because evapotranspiration from unforested areas is lower than from forests. Summer evaporation is already limited in the southernmost parts of Europe under potential vegetation conditions and, therefore, cannot become much lower. Accordingly, the albedo effect dominates in southern Europe also in summer, which implies that deforestation causes a decrease in temperatures. Differences in summer temperature due to deforestation range from −1 °C in south-western Europe to +1 °C in eastern Europe. The choice of anthropogenic land-cover scenario has a significant influence on the simulated climate, but uncertainties in palaeoclimate proxy data for the two time periods do not allow for a definitive discrimination among climate model results.

Last glacial vegetation of northern Eurasia

In order to investigate the potential role of vegetation changes in megafaunal extinctions during the later part of the last glacial stage and early Holocene (42–10 ka BP), the palaeovegetation of northern Eurasia and Alaska was simulated using the LPJ-GUESS dynamic vegetation model. Palaeoclimatic driving data were derived from simulations made for 22 time slices using the Hadley Centre Unified Model. Modelled annual net primary productivity (aNPP) of a series of plant functional types (PFTs) is mapped for selected time slices and summarised for major geographical regions for all time slices. Strong canonical correlations are demonstrated between model outputs and pollen data compiled for the same period and region. Simulated aNPP values, especially for tree PFTs and for a mesophilous herb PFT, provide evidence of the structure and productivity of last glacial vegetation. The mesophilous herb PFT aNPP is higher in many areas during the glacial than at present or during the early Holocene. Glacial stage vegetation, whilst open and largely treeless in much of Europe, thus had a higher capacity to support large vertebrate herbivore populations than did early Holocene vegetation. A marked and rapid decrease in aNPP of mesophilous herbs began shortly after the Last Glacial Maximum, especially in western Eurasia. This is likely implicated in extinction of several large herbivorous mammals during the latter part of the glacial stage and the transition to the Holocene.

Transient Earth system responses to cumulative carbon dioxide emissions: linearities, uncertainties, and probabilities in an observation-constrained model ensemble

Information on the relationship between cumulative fossil CO2 emissions and multiple climate targets is essential to design emission mitigation and climate adaptation strategies. In this study, the transient response of a climate or environmental variable per trillion tonnes of CO2 emissions, termed TRE, is quantified for a set of impact-relevant climate variables and from a large set of multi-forcing scenarios extended to year 2300 towards stabilization. An  ∼ 1000-member ensemble of the Bern3D-LPJ carbon–climate model is applied and model outcomes are constrained by 26 physical and biogeochemical observational data sets in a Bayesian, Monte Carlo-type framework. Uncertainties in TRE estimates include both scenario uncertainty and model response uncertainty. Cumulative fossil emissions of 1000 Gt C result in a global mean surface air temperature change of 1.9 °C (68 % confidence interval (c.i.): 1.3 to 2.7 °C), a decrease in surface ocean pH of 0.19 (0.18 to 0.22), and a steric sea level rise of 20 cm (13 to 27 cm until 2300). Linearity between cumulative emissions and transient response is high for pH and reasonably high for surface air and sea surface temperatures, but less pronounced for changes in Atlantic meridional overturning, Southern Ocean and tropical surface water saturation with respect to biogenic structures of calcium carbonate, and carbon stocks in soils. The constrained model ensemble is also applied to determine the response to a pulse-like emission and in idealized CO2-only simulations. The transient climate response is constrained, primarily by long-term ocean heat observations, to 1.7 °C (68 % c.i.: 1.3 to 2.2 °C) and the equilibrium climate sensitivity to 2.9 °C (2.0 to 4.2 °C). This is consistent with results by CMIP5 models but inconsistent with recent studies that relied on short-term air temperature data affected by natural climate variability.

LPJ-LMfire simulations for Europe for the Last Glacial Maximum and preindustrial control, link to netCDF files

This data sets contains LPJ-LMfire dynamic global vegetation model output covering Europe and the Mediterranean for the Last Glacial Maximum (LGM; 21 ka) and for a preindustrial control simulation (20th century detrended climate). The netCDF data files are time averages of the final 30 years of the model simulation. Each netCDF file contains four or five variables: fractional cover of 9 plant functional types (PFTs; cover), total fractional coverage of trees (treecover), population density of hunter-gatherers (foragerPD; only for the "people" simulations), fraction of the gridcell burned on 30-year average (burnedf), and vegetation net primary productivity (NPP). The model spatial resolution is 0.5-degrees For the LGM simulations, LPJ-LMfire was driven by the PMIP3 suite of eight GCMs for which LGM climate simulations were available. Also provided in this archive is the result of an LPJ-LMfire run that was forced by the average climate of all GCMs (the "GCM-mean" files), and the average of each of the individual LPJ-LMfire runs over the eight LGM scenarios individually (the "LPJ-mean" files). The model simulations are provided that include the influence of human presence on the landscape (the "people" files), and in a "world without humans" scenario (the "natural" files). Finally this archive contains the preindustrial reference simulation with and without human influence ("PI_reference_people" and "PI_reference_nat", respectively). There are therefore 22 netCDF files in this archive: 8 each of LGM simulations with and without people (total 16) and the "GCM mean" simulation (2 files) and the "LPJ mean" aggregate (2 files), and finally the two preindustrial "control" simulations ("PI"), with and without humans (2 files). In addition to the LPJ-LMfire model output (netCDF files), this archive also contains a table of arboreal pollen percent calculated from pollen samples dated to the LGM at sites throughout (lgmAP.txt), and a table containing the location of archaeological sites dated to the LGM (LGM_archaeological_site_locations.txt).

Modelling fire and the terrestrial carbon balance

Four CO2 concentration inversions and the Global Fire Emissions Database (GFED) versions 2.1 and 3 are used to provide benchmarks for climate-driven modeling of the global land-atmosphere CO2 flux and the contribution of wildfire to this flux. The Land surface Processes and exchanges (LPX) model is introduced. LPX is based on the Lund-Potsdam-Jena Spread and Intensity of FIRE (LPJ-SPITFIRE) model with amended fire probability calculations. LPX omits human ignition sources yet simulates many aspects of global fire adequately. It captures the major features of observed geographic pattern in burnt area and its seasonal timing and the unimodal relationship of burnt area to precipitation. It simulates features of geographic variation in the sign of the interannual correlations of burnt area with antecedent dryness and precipitation. It simulates well the interannual variability of the global total land-atmosphere CO2 flux. There are differences among the global burnt area time series from GFED2.1, GFED3 and LPX, but some features are common to all. GFED3 fire CO2 fluxes account for only about 1/3 of the variation in total CO2 flux during 1997–2005. This relationship appears to be dominated by the strong climatic dependence of deforestation fires. The relationship of LPX-modeled fire CO2 fluxes to total CO2 fluxes is weak. Observed and modeled total CO2 fluxes track the El Niño–Southern Oscillation (ENSO) closely; GFED3 burnt area and global fire CO2 flux track the ENSO much less so. The GFED3 fire CO2 flux-ENSO connection is most prominent for the El Niño of 1997–1998, which produced exceptional burning conditions in several regions, especially equatorial Asia. The sign of the observed relationship between ENSO and fire varies regionally, and LPX captures the broad features of this variation. These complexities underscore the need for process-based modeling to assess the consequences of global change for fire and its implications for the carbon cycle.

Model-Based Matching of Line Drawings by Linear Combinations of Prototypes

We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (also called prototypes) of an object class. The models consist of a linear combination ofsprototypes. The flow fields giving pixelwise correspondences between a base prototype and each of the other prototypes must be given. A novel image of an object of the same class is matched to a model by minimizing an error between the novel image and the current guess for the closest modelsimage. Currently, the algorithm applies to line drawings of objects. An extension to real grey level images is discussed.

A fast radiative transfer model for the assimilation of water vapor radiances from the Kalpana very high resolution radiometer

A fast radiative transfer model (RTM) to compute emitted infrared radiances for a very high resolution radiometer (VHRR), onboard the operational Indian geostationary satellite Kalpana has been developed and verified. This work is a step towards the assimilation of Kalpana water vapor (WV) radiances into numerical weather prediction models. The fast RTM uses a regression‐based approach to parameterize channel‐specific convolved level to space transmittances. A comparison between the fast RTM and the line‐by‐line RTM demonstrated that the fast RTM can simulate line‐by‐line radiances for the Kalpana WV channel to an accuracy better than the instrument noise, while offering more rapid radiance calculations. A comparison of clear sky radiances of the Kalpana WV channel with the ECMWF model first guess radiances is also presented, aiming to demonstrate the fast RTM performance with the real observations. In order to assimilate the radiances from Kalpana, a simple scheme for bias correction has been suggested.

The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model

A process-based fire regime model (SPITFIRE) has been developed, coupled with ecosystem dynamics in the LPJ Dynamic Global Vegetation Model, and used to explore fire regimes and the current impact of fire on the terrestrial carbon cycle and associated emissions of trace atmospheric constituents. The model estimates an average release of 2.24 Pg C yr−1 as CO2 from biomass burning during the 1980s and 1990s. Comparison with observed active fire counts shows that the model reproduces where fire occurs and can mimic broad geographic patterns in the peak fire season, although the predicted peak is 1–2 months late in some regions. Modelled fire season length is generally overestimated by about one month, but shows a realistic pattern of differences among biomes. Comparisons with remotely sensed burnt-area products indicate that the model reproduces broad geographic patterns of annual fractional burnt area over most regions, including the boreal forest, although interannual variability in the boreal zone is underestimated.

A reconstruction of radiocarbon production and total solar irradiance from the Holocene ¹⁴C and CO₂ records: implications of data and model uncertainties

Radiocarbon production, solar activity, total solar irradiance (TSI) and solar-induced climate change are reconstructed for the Holocene (10 to 0 kyr BP), and TSI is predicted for the next centuries. The IntCal09/SHCal04 radiocarbon and ice core CO2 records, reconstructions of the geomagnetic dipole, and instrumental data of solar activity are applied in the Bern3D-LPJ, a fully featured Earth system model of intermediate complexity including a 3-D dynamic ocean, ocean sediments, and a dynamic vegetation model, and in formulations linking radiocarbon production, the solar modulation potential, and TSI. Uncertainties are assessed using Monte Carlo simulations and bounding scenarios. Transient climate simulations span the past 21 thousand years, thereby considering the time lags and uncertainties associated with the last glacial termination. Our carbon-cycle-based modern estimate of radiocarbon production of 1.7 atoms cm−2 s−1 is lower than previously reported for the cosmogenic nuclide production model by Masarik and Beer (2009) and is more in-line with Kovaltsov et al. (2012). In contrast to earlier studies, periods of high solar activity were quite common not only in recent millennia, but throughout the Holocene. Notable deviations compared to earlier reconstructions are also found on decadal to centennial timescales. We show that earlier Holocene reconstructions, not accounting for the interhemispheric gradients in radiocarbon, are biased low. Solar activity is during 28% of the time higher than the modern average (650 MeV), but the absolute values remain weakly constrained due to uncertainties in the normalisation of the solar modulation to instrumental data. A recently published solar activity–TSI relationship yields small changes in Holocene TSI of the order of 1 W m−2 with a Maunder Minimum irradiance reduction of 0.85 ± 0.16 W m−2. Related solar-induced variations in global mean surface air temperature are simulated to be within 0.1 K. Autoregressive modelling suggests a declining trend of solar activity in the 21st century towards average Holocene conditions.

Model results of ocean physical field of the reference state and the targets in NetCDF format

In a feasibility study, the potential of proxy data for the temperature and salinity during the Last Glacial Maximum (LGM, about 19 000 to 23 000 years before present) in constraining the strength of the Atlantic meridional overturning circulation (AMOC) with a general ocean circulation model was explored. The proxy data were simulated by drawing data from four different model simulations at the ocean sediment core locations of the Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) project, and perturbing these data with realistic noise estimates. The results suggest that our method has the potential to provide estimates of the past strength of the AMOC even from sparse data, but in general, paleo-sea-surface temperature data without additional prior knowledge about the ocean state during the LGM is not adequate to constrain the model. On the one hand, additional data in the deep-ocean and salinity data are shown to be highly important in estimating the LGM circulation. On the other hand, increasing the amount of surface data alone does not appear to be enough for better estimates. Finally, better initial guesses to start the state estimation procedure would greatly improve the performance of the method. Indeed, with a sufficiently good first guess, just the sea-surface temperature data from the MARGO project promise to be sufficient for reliable estimates of the strength of the AMOC.

Holocene carbon cycle dynamics, links to model files

We are investigating the late Holocene rise in CO2 by performing four experiments with the climate-carbon-cycle model CLIMBER2-LPJ. Apart from the deep sea sediments, important carbon cycle processes considered are carbon uptake or release by the vegetation, carbon uptake by peatlands, and CO 2 release due to shallow water sedimentation of CaCO3. Ice core data of atmospheric CO2 between 8 ka BP and preindustrial climate can only be reproduced if CO2 outgassing due to shallow water sedimentation of CaCO3 is considered. In this case the model displays an increase of nearly 20 ppmv CO2 between 8 ka BP and present day. Model configurations that do not contain this forcing show a slight decrease in atmospheric CO2. We can therefore explain the late Holocene rise in CO2 by invoking natural forcing factors only, and anthropogenic forcing is not required to understand preindustrial CO2 dynamics.

Ground water model calibration using pilot points and regularization

Use of nonlinear parameter estimation techniques is now commonplace in ground water model calibration. However, there is still ample room for further development of these techniques in order to enable them to extract more information from calibration datasets, to more thoroughly explore the uncertainty associated with model predictions, and to make them easier to implement in various modeling contexts. This paper describes the use of pilot points as a methodology for spatial hydraulic property characterization. When used in conjunction with nonlinear parameter estimation software that incorporates advanced regularization functionality (such as PEST), use of pilot points can add a great deal of flexibility to the calibration process at the same time as it makes this process easier to implement. Pilot points can be used either as a substitute for zones of piecewise parameter uniformity, or in conjunction with such zones. In either case, they allow the disposition of areas of high and low hydraulic property value to be inferred through the calibration process, without the need for the modeler to guess the geometry of such areas prior to estimating the parameters that pertain to them. Pilot points and regularization can also be used as an adjunct to geostatistically based stochastic parameterization methods. Using the techniques described herein, a series of hydraulic property fields can be generated, all of which recognize the stochastic characterization of an area at the same time that they satisfy the constraints imposed on hydraulic property values by the need to ensure that model outputs match field measurements. Model predictions can then be made using all of these fields as a mechanism for exploring predictive uncertainty.

A Bayesian Model for Learning Using Flashcards

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015