Ecosystem effects of CO2 concentration: evidence from past climates

Atmospheric CO2 concentration has varied from minima of 170-200 ppm in glacials to maxima of 280-300 ppm in the recent interglacials. Photosynthesis by C-3 plants is highly sensitive to CO2 concentration variations in this range. Physiological consequences of the CO2 changes should therefore be discernible in palaeodata. Several lines of evidence support this expectation. Reduced terrestrial carbon storage during glacials, indicated by the shift in stable isotope composition of dissolved inorganic carbon in the ocean, cannot be explained by climate or sea-level changes. It is however consistent with predictions of current process-based models that propagate known physiological CO2 effects into net primary production at the ecosystem scale. Restricted forest cover during glacial periods, indicated by pollen assemblages dominated by non-arboreal taxa, cannot be reproduced accurately by palaeoclimate models unless CO2 effects on C-3-C-4 plant competition are also modelled. It follows that methods to reconstruct climate from palaeodata should account for CO2 concentration changes. When they do so, they yield results more consistent with palaeoclimate models. In conclusion, the palaeorecord of the Late Quaternary, interpreted with the help of climate and ecosystem models, provides evidence that CO2 effects at the ecosystem scale are neither trivial nor transient

Projected changes in Australian fire regimes during the 21st century and consequences for ecosystems

Climate projections show Australia becoming significantly warmer during the 21st century, while precipitation decreases over much of the continent. Such changes are generally considered to increase wildfire risk. Nevertheless, using a process-based model of vegetation dynamics and vegetation–fire interactions, we show that while burnt area increases in southern and central Australia, it decreases in northern Australia. Overall the projected increase in fire by the end of the 21st century is small (0.7–1.3% of land area equivalent to 12–24% of current burnt area, depending on the climate scenario). The direct effects of increasing CO2 on vegetation productivity and water-use efficiency influence simulated fire regimes: CO2 effects tend to increase burnt area in arid regions, but increase vegetation density and reduce burnt area in forested regions. Increases in fire promotes a shift to more fire-adapted trees in wooded areas and their encroachment into grasslands, with an overall increase in forested area of 3.9–11.9% of land area by the end of the century. The decrease in fire in northern Australia leads to an increase in tree cover (ca 20%) and an expansion of tropical forest. Thus, although the overall change in burnt area is small it has noticeable consequences for vegetation patterns across the continent

SISAL: bringing added value to Speleothem research

Isotopic records from speleothems are an important source of information about past climates and, given the increase in the number of isotope-enabled climate models, are likely to become an important tool for climate model evaluation. SISAL (Speleothem Isotopes Synthesis and Analysis) have created a global database of isotopic records from speleothems in order to facilitate regional analyses and data-model comparison. The papers in this Special Issue showcase the use of the database for regional analyses. In this paper, we discuss some of the important issues underpinning the use of speleothems and how the existence of this database assists palaeoclimate research. We also highlight some of the lessons learned in the creation of the SISAL database and outline potential research going forward

The Reading Palaeofire Database: an expanded global resource to document changes in fire regimes from sedimentary charcoal records

This research has been supported by the Leverhulme Trust (grant no. RC-2018-023), the European Research Council (grant no. 694481), the German Research Foundation (grant no. FE-1096/6-1), the Swiss Government Excellence Postdoctoral Scholarships (grant no. FIRECO 2016.0310), the National Science Centre of Poland (grant no. 2015/17/B/ST10/01656), the SCIEX Scholarship Fund (grant no. PSPB-013/2010), and the Estonian Research Council (grant no. MOBJD313).Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345.Leverhulme Trust RC-2018-023European Research Council (ERC) European Commission 694481German Research Foundation (DFG) FE-1096/6-1Swiss Government Excellence Postdoctoral Scholarships FIRECO 2016.0310SCIEX Scholarship Fund PSPB-013/2010Estonian Research Council MOBJD31

Underlying causes of Eurasian mid-continental aridity in simulations of mid-Holocene climate

The CMIP5/PMIP3 mid-Holocene simulations show drier conditions in the Eurasian mid-continent and a significant increase in summer temperature; in contrast, paleoenvironmental data (including lake level, vegetation and isotope records, and aeolian deposits) and quantitative climate reconstructions show that the mid-continental extratropics were wetter than today and summers were cooler (Harrison et al., 2015). Eurasian mid-continental aridity and warming has been a persistent feature of model simulations, already present in atmosphere-only simulations (Yu & Harrison, 1996) and appearing more strongly in coupled ocean-atmosphere simulations (e.g. Braconnot et al., 2007b; Wohlfahrt et al., 2008; Harrison et al., 2015) and further exacerbated by vegetation feedback (Wohlfarht et al., 2004). The consistency among multiple lines of paleoenvironmental evidence makes it unlikely that the mismatch reflects misinterpretation of the data. Regional temperature biases in the CMIP5 20th century simulations have been linked to biases in surface energy and water balances, with over- or under-prediction of moisture fluxes and evapotranspiration leading to cold and warm temperature biases respectively (Mueller & Seneviratne, 2014). This suggests that discrepancies in the simulation of mid-Holocene climates might have a similar cause. In this paper, we investigate the processes involved in mid-continental climate changes in the CMIP5/PMIP3 simulations in order to identify the underlying cause of the mismatch with observations

A model analysis of climate and CO2 controls on tree growth and carbon allocation in a semi-arid woodland

Many studies have failed to show an increase in the radial growth of trees in response to increasing atmospheric CO2 concentration [CO2] despite the expected enhancement of photosynthetic rates and water-use efficiency at high [CO2]. A global light use efficiency model of photosynthesis, coupled with a generic carbon allocation and tree-growth model based on mass balance and tree geometry principles, was used to simulate annual ring-width variations for the gymnosperm Callitris columellaris in the semi-arid Great Western Woodlands, Western Australia, over the past 100 years. Parameter values for the tree-growth model were derived from independent observations except for sapwood specific respiration rate, fine-root turnover time, fine-root specific respiration rate and the ratio of fine-root mass to foliage area (ζ), which were calibrated to the ring-width measurements by Bayesian optimization. This procedure imposed a strong constraint on ζ. Modelled and observed ring-widths showed quantitatively similar, positive responses to total annual photosynthetically active radiation and soil moisture, and similar negative responses to vapour pressure deficit. The model also produced enhanced radial growth in response to increasing [CO2] during recent decades, but the data do not show this. Recalibration in moving 30-year time windows produced temporal shifts in the estimated values of ζ, including an increase by ca 12% since the 1960s, and eliminated the [CO2]-induced increase in radial growth. The potential effect of CO2 on ring-width was thus shown to be small compared to effects of climate variability even in this semi-arid climate. It could be counteracted in the model by a modest allocation shift, as has been observed in field experiments with raised [CO2]

Climate versus carbon dioxide controls on biomass burning: a model analysis of the glacial–interglacial contrast

Climate controls fire regimes through its influence on the amount and types of fuel present and their dryness. CO2 concentration constrains primary production by limiting photosynthetic activity in plants. However, although fuel accumulation depends on biomass production, and hence on CO2 concentration, the quantitative relationship between atmospheric CO2 concentration and biomass burning is not well understood. Here a fire-enabled dynamic global vegetation model (the Land surface Processes and eXchanges model, LPX) is used to attribute glacial–interglacial changes in biomass burning to an increase in CO2, which would be expected to increase primary production and therefore fuel loads even in the absence of climate change, vs. climate change effects. Four general circulation models provided last glacial maximum (LGM) climate anomalies – that is, differences from the pre-industrial (PI) control climate – from the Palaeoclimate Modelling Intercomparison Project Phase~2, allowing the construction of four scenarios for LGM climate. Modelled carbon fluxes from biomass burning were corrected for the model's observed prediction biases in contemporary regional average values for biomes. With LGM climate and low CO2 (185 ppm) effects included, the modelled global flux at the LGM was in the range of 1.0–1.4 Pg C year-1, about a third less than that modelled for PI time. LGM climate with pre-industrial CO2 (280 ppm) yielded unrealistic results, with global biomass burning fluxes similar to or even greater than in the pre-industrial climate. It is inferred that a substantial part of the increase in biomass burning after the LGM must be attributed to the effect of increasing CO2 concentration on primary production and fuel load. Today, by analogy, both rising CO2 and global warming must be considered as risk factors for increasing biomass burning. Both effects need to be included in models to project future fire risks

Evaluation of modern and mid-Holocene seasonal precipitation of the Mediterranean and northern Africa in the CMIP5 simulations

We analyse the spatial expression of seasonal climates of the Mediterranean and northern Africa in pre-industrial (piControl) and mid-Holocene (midHolocene, 6 yr BP) simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Modern observations show four distinct precipitation regimes characterized by differences in the seasonal distribution and total amount of precipitation: an equatorial band characterized by a double peak in rainfall, the monsoon zone characterized by summer rainfall, the desert characterized by low seasonality and total precipitation, and the Mediterranean zone characterized by summer drought. Most models correctly simulate the position of the Mediterranean and the equatorial climates in the piControl simulations, but overestimate the extent of monsoon influence and underestimate the extent of desert. However, most models fail to reproduce the amount of precipitation in each zone. Model biases in the simulated magnitude of precipitation are unrelated to whether the models reproduce the correct spatial patterns of each regime. In the midHolocene, the models simulate a reduction in winter rainfall in the equatorial zone, and a northward expansion of the monsoon with a significant increase in summer and autumn rainfall. Precipitation is slightly increased in the desert, mainly in summer and autumn, with northward expansion of the monsoon. Changes in the Mediterranean are small, although there is an increase in spring precipitation consistent with palaeo-observations of increased growing-season rainfall. Comparison with reconstructions shows most models underestimate the mid-Holocene changes in annual precipitation, except in the equatorial zone. Biases in the piControl have only a limited influence on midHolocene anomalies in ocean–atmosphere models; carbon-cycle models show no relationship between piControl bias and midHolocene anomalies. Biases in the prediction of the midHolocene monsoon expansion are unrelated to how well the models simulate changes in Mediterranean climate

Modelling and prediction of wind damage in forest ecosystems of the Sudety mountains, SW Poland

Windstorms are one of the most important disturbance factors in European forest ecosystems. An understanding of the major drivers causing observed changes in forests is essential to improve prediction models and as a basis for forest management. In the present study, we use machine learning techniques in combination with data sets on tree properties, bioclimatic and geomorphic conditions, to analyse the level of forest damage by windstorms in the Sudety Mountains over the period 2004–2010. We tested four scenarios under five classification model frameworks: logistic regression, random forest, support vector machines, neural networks, and gradient boosted modelling. Gradient boosted modelling and random forest have the best predictive power. Tree volume and age are the most important predictors of windstorm damage; climate and geomorphic variables are less important. Forest damage maps based on forest data from 2020 show lower probabilities of damage compared to the end of 20th and the beginning of 21st century