Responses of leaf traits to climatic gradients: adaptive variation versus compositional shifts

Dynamic global vegetation models (DGVMs) typically rely on plant functional types (PFTs), which are assigned distinct environmental tolerances and replace one another progressively along environmental gradients. Fixed values of traits are assigned to each PFT; modelled trait variation along gradients is thus driven by PFT replacement. But empirical studies have revealed "universal" scaling relationships (quantitative trait variations with climate that are similar within and between species, PFTs and communities); and continuous, adaptive trait variation has been proposed to replace PFTs as the basis for next-generation DGVMs. Here we analyse quantitative leaf-trait variation on long temperature and moisture gradients in China with a view to understanding the relative importance of PFT replacement vs. continuous adaptive variation within PFTs. Leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC) and nitrogen content of dry matter were measured on all species at 80 sites ranging from temperate to tropical climates and from dense forests to deserts. Chlorophyll fluorescence traits and carbon, phosphorus and potassium contents were measured at 47 sites. Generalized linear models were used to relate log-transformed trait values to growing-season temperature and moisture indices, with or without PFT identity as a predictor, and to test for differences in trait responses among PFTs. Continuous trait variation was found to be ubiquitous. Responses to moisture availability were generally similar within and between PFTs, but biophysical traits (LA, SLA and LDMC) of forbs and grasses responded differently from woody plants. SLA and LDMC responses to temperature were dominated by the prevalence of evergreen PFTs with thick, dense leaves at the warm end of the gradient. Nutrient (N, P and K) responses to climate gradients were generally similar within all PFTs. Area-based nutrients generally declined with moisture; Narea and Karea declined with temperature, but Parea increased with temperature. Although the adaptive nature of many of these trait-climate relationships is understood qualitatively, a key challenge for modelling is to predict them quantitatively. Models must take into account that community-level responses to climatic gradients can be influenced by shifts in PFT composition, such as the replacement of deciduous by evergreen trees, which may run either parallel or counter to trait variation within PFTs. The importance of PFT shifts varies among traits, being important for biophysical traits but less so for physiological and chemical traits. Finally, models should take account of the diversity of trait values that is found in all sites and PFTs, representing the "pool" of variation that is locally available for the natural adaptation of ecosystem function to environmental change.

Connections between clouds, radiation, and midlatitude dynamics: a review

We review the effects of dynamical variability on clouds and radiation in observations and models and discuss their implications for cloud feedbacks. Jet shifts produce robust meridional dipoles in upper-level clouds and longwave cloud-radiative effect (CRE), but low-level clouds, which do not simply shift with the jet, dominate the shortwave CRE. Because the effect of jet variability on CRE is relatively small, future poleward jet shifts with global warming are only a second-order contribution to the total CRE changes around the midlatitudes, suggesting a dominant role for thermodynamic effects. This implies that constraining the dynamical response is unlikely to reduce the uncertainty in extratropical cloud feedback. However, we argue that uncertainty in the cloud-radiative response does affect the atmospheric circulation response to global warming, by modulating patterns of diabatic forcing. How cloud feedbacks can affect the dynamical response to global warming is an important topic of future research.

The response of the Southern Hemispheric eddy-driven jet to future changes in shortwave radiation in CMIP5

A strong relationship is found between changes in the meridional gradient of absorbed shortwave radiation (ASR) and Southern Hemispheric jet shifts in 21st century climate simulations of CMIP5 (Coupled Model Intercomparison Project phase 5) coupled models. The relationship is such that models with increases in the meridional ASR gradient around the southern midlatitudes, and therefore increases in midlatitude baroclinicity, tend to produce a larger poleward jet shift. The ASR changes are shown to be dominated by changes in cloud properties, with sea ice declines playing a secondary role. We demonstrate that the ASR changes are the cause, and not the result, of the intermodel differences in jet response by comparing coupled simulations with experiments in which sea surface temperature increases are prescribed. Our results highlight the importance of reducing the uncertainty in cloud feedbacks in order to constrain future circulation changes.

Modelled and observed changes in aerosols and surface solar radiation over Europe between 1960 and 2009

Substantial changes in anthropogenic aerosols and precursor gas emissions have occurred over recent decades due to the implementation of air pollution control legislation and economic growth. The response of atmospheric aerosols to these changes and the impact on climate are poorly constrained, particularly in studies using detailed aerosol chemistry–climate models. Here we compare the HadGEM3-UKCA (Hadley Centre Global Environment Model-United Kingdom Chemistry and Aerosols) coupled chemistry–climate model for the period 1960–2009 against extensive ground-based observations of sulfate aerosol mass (1978–2009), total suspended particle matter (SPM, 1978–1998), PM10 (1997–2009), aerosol optical depth (AOD, 2000–2009), aerosol size distributions (2008–2009) and surface solar radiation (SSR, 1960–2009) over Europe. The model underestimates observed sulfate aerosol mass (normalised mean bias factor (NMBF) = −0.4), SPM (NMBF = −0.9), PM10 (NMBF = −0.2), aerosol number concentrations (N30 NMBF = −0.85; N50 NMBF = −0.65; and N100 NMBF = −0.96) and AOD (NMBF = −0.01) but slightly overpredicts SSR (NMBF = 0.02). Trends in aerosol over the observational period are well simulated by the model, with observed (simulated) changes in sulfate of −68 % (−78 %), SPM of −42 % (−20 %), PM10 of −9 % (−8 %) and AOD of −11 % (−14 %). Discrepancies in the magnitude of simulated aerosol mass do not affect the ability of the model to reproduce the observed SSR trends. The positive change in observed European SSR (5 %) during 1990–2009 ("brightening") is better reproduced by the model when aerosol radiative effects (ARE) are included (3 %), compared to simulations where ARE are excluded (0.2 %). The simulated top-of-the-atmosphere aerosol radiative forcing over Europe under all-sky conditions increased by > 3.0 W m−2 during the period 1970–2009 in response to changes in anthropogenic emissions and aerosol concentrations.

Inter-plant communication through mycorrhizal networks mediates complex adaptive behaviour in plant communities

Adaptive behaviour of plants, including rapid changes in physiology, gene regulation and defence response, can be altered when linked to neighbouring plants by a mycorrhizal network (MN). Mechanisms underlying the behavioural changes include mycorrhizal fungal colonization by the MN or interplant communication via transfer of nutrients, defence signals or allelochemicals. We focus this review on our new findings in ectomycorrhizal ecosystems, and also review recent advances in arbuscular mycorrhizal systems. We have found that the behavioural changes in ectomycorrhizal plants depend on environmental cues, the identity of the plant neighbour and the characteristics of the MN. The hierarchical integration of this phenomenon with other biological networks at broader scales in forest ecosystems, and the consequences we have observed when it is interrupted, indicate that underground ‘tree talk’ is a foundational process in the complex adaptive nature of forest ecosystems.

Cross-sector surveys assessing perceptions of key stakeholders towards barriers, concerns and facilitators to the appropriate use of adaptive designs in confirmatory trials

Background Appropriately conducted adaptive designs (ADs) offer many potential advantages over conventional trials. They make better use of accruing data, potentially saving time, trial participants, and limited resources compared to conventional, fixed sample size designs. However, one can argue that ADs are not implemented as often as they should be, particularly in publicly funded confirmatory trials. This study explored barriers, concerns, and potential facilitators to the appropriate use of ADs in confirmatory trials among key stakeholders. Methods We conducted three cross-sectional, online parallel surveys between November 2014 and January 2015. The surveys were based upon findings drawn from in-depth interviews of key research stakeholders, predominantly in the UK, and targeted Clinical Trials Units (CTUs), public funders, and private sector organisations. Response rates were as follows: 30(55 %) UK CTUs, 17(68 %) private sector, and 86(41 %) public funders. A Rating Scale Model was used to rank barriers and concerns in order of perceived importance for prioritisation. Results Top-ranked barriers included the lack of bridge funding accessible to UK CTUs to support the design of ADs, limited practical implementation knowledge, preference for traditional mainstream designs, difficulties in marketing ADs to key stakeholders, time constraints to support ADs relative to competing priorities, lack of applied training, and insufficient access to case studies of undertaken ADs to facilitate practical learning and successful implementation. Associated practical complexities and inadequate data management infrastructure to support ADs were reported as more pronounced in the private sector. For funders of public research, the inadequate description of the rationale, scope, and decision-making criteria to guide the planned AD in grant proposals by researchers were all viewed as major obstacles. Conclusions There are still persistent and important perceptions of individual and organisational obstacles hampering the use of ADs in confirmatory trials research. Stakeholder perceptions about barriers are largely consistent across sectors, with a few exceptions that reflect differences in organisations’ funding structures, experiences and characterisation of study interventions. Most barriers appear connected to a lack of practical implementation knowledge and applied training, and limited access to case studies to facilitate practical learning. Keywords: Adaptive designs; flexible designs; barriers; surveys; confirmatory trials; Phase 3; clinical trials; early stopping; interim analyses

A study of adaptive thermal comfort in a well-controlled climate chamber

This paper aims to critically examine the application of Predicted Mean Vote (PMV) in an air-conditioned environment in the hot-humid climate region. Experimental studies have been conducted in a climate chamber in Chongqing, China, from 2008 to 2010. A total of 440 thermal responses from participants were obtained. Data analysis reveals that the PMV overestimates occupants' mean thermal sensation in the warm environment (PMV > 0) with a mean bias of 0.296 in accordance with the ASHRAE thermal sensation scales. The Bland–Altman method has been applied to assess the agreement of the PMV and Actual Mean Vote (AMV) and reveals a lack of agreement between them. It is identified that habituation due to the past thermal experience of a long-term living in a specific region could stimulate psychological adaptation. The psychological adaptation can neutralize occupants’ actual thermal sensation by moderating the thermal sensibility of the skin. A thermal sensation empirical model and a PMV-revised index are introduced for air-conditioned indoor environments in hot-humid regions. As a result of habituation, the upper limit effective thermal comfort temperature SET* can be increased by 1.6 °C in a warm season based on the existing international standard. As a result, a great potential for energy saving from the air-conditioning system in summer could be achieved.

Coordinated weather balloon solar radiation measurements during a solar eclipse

Solar eclipses provide a rapidly changing solar radiation environment. These changes can be studied using simple photodiode sensors, if the radiation reaching the sensors is unaffected by cloud.Transporting the sensors aloft using standard meteorological instrument packages modified to carry extra sensors, provides one promising but hitherto unexploited possibility for making solar eclipse radiation measurements. For the 20th March 2015 solar eclipse, a coordinated campaign of balloon-carried solar radiation measurements was undertaken from Reading (51.44N, 0.94W), Lerwick (60.15N, 1.13W) and Reykjavik (64.13N, 21.90W), straddling the path of the eclipse.The balloons reached sufficient altitude at the eclipse time for eclipse-induced variations in solar radiation and solar limb darkening to be measured above cloud. Because the sensor platforms were free to swing, techniques have been evaluated to correct the measurements for their changing orientation. In the swing-averaged technique, the mean value across a set of swings was used to approximate the radiation falling on a horizontal surface; in the swing-maximum technique, the direct beam was estimated by assuming the sensing surface becomes normal to the solar beam direction at a maximum swing. Both approaches, essentially independent,give values that agree with theoretical expectations for the eclipse-induced radiation changes.

The EarthCARE satellite: the next step forward in global measurements of clouds, aerosols, precipitation, and radiation

The collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint European Space Agency (ESA)–Japan Aerospace Exploration Agency (JAXA) Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite mission, scheduled for launch in 2018, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Aqua. Specifically, EarthCARE’s cloud profiling radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle, and raindrop fall speeds. EarthCARE’s 355-nm high-spectral-resolution lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The multispectral imager will provide a context for, and the ability to construct, the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross section. The consistency of the retrievals will be assessed to within a target of ±10 W m–2 on the (10 km)2 scale by comparing the multiview broadband radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains.

Adaptive evolution toward larger size in mammals

The notion that large body size confers some intrinsic advantage to biological species has been debated for centuries. Using a phylogenetic statistical approach that allows the rate of body size evolution to vary across a phylogeny, we find a long-term directional bias toward increasing size in the mammals. This pattern holds separately in 10 of 11 orders for which sufficient data are available and arises from a tendency for accelerated rates of evolution to produce increases, but not decreases, in size. On a branch-by-branch basis, increases in body size have been more than twice as likely as decreases, yielding what amounts to millions and millions of years of rapid and repeated increases in size away from the small ancestral mammal. These results are the first evidence, to our knowledge, from extant species that are compatible with Cope’s rule: the pattern of body size increase through time observed in the mammalian fossil record. We show that this pattern is unlikely to be explained by several nonadaptive mechanisms for increasing size and most likely represents repeated responses to new selective circumstances. By demonstrating that it is possible to uncover ancient evolutionary trends from a combination of a phylogeny and appropriate statistical models, we illustrate how data from extant species can complement paleontological accounts of evolutionary history, opening up new avenues of investigation for both.

Adaptive climate change governance for urban resilience

Climate change poses new challenges to cities and new flexible forms of governance are required that are able to take into account the uncertainty and abruptness of changes. The purpose of this paper is to discuss adaptive climate change governance for urban resilience. This paper identifies and reviews three traditions of literature on the idea of transitions and transformations, and assesses to what extent the transitions encompass elements of adaptive governance. This paper uses the open source Urban Transitions Project database to assess how urban experiments take into account principles of adaptive governance. The results show that: the experiments give no explicit information of ecological knowledge; the leadership of cities is primarily from local authorities; and evidence of partnerships and anticipatory or planned adaptation is limited or absent. The analysis shows that neither technological, political nor ecological solutions alone are sufficient to further our understanding of the analytical aspects of transition thinking in urban climate governance. In conclusion, the paper argues that the future research agenda for urban climate governance needs to explore further the links between the three traditions in order to better identify contradictions, complementarities or compatibilities, and what this means in practice for creating and assessing urban experiments.

Climate variability and socio-environmental changes in the northern Aegean (NE Mediterranean) during the last 1500 years

We provide new evidence on sea surface temperature (SST) variations and paleoceanographic/paleoenvironmental changes over the past 1500 years for the north Aegean Sea (NE Mediterranean). The reconstructions are based on multiproxy analyses, obtained from the high resolution (decadal to multidecadal) marine record M2 retrieved from the Athos basin. Reconstructed SSTs show an increase from ca. 850 to 950 AD and from ca. 1100 to 1300 AD. A cooling phase of almost 1.5 �C is observed from ca. 1600 AD to 1700 AD. This seems to have been the starting point of a continuous SST warming trend until the end of the reconstructed period, interrupted by two prominent cooling events at 1832 ± 15 AD and 1995 ± 1 AD. Application of an adaptive Kernel smoothing suggests that the current warming in the reconstructed SSTs of the north Aegean might be unprecedented in the context of the past 1500 years. Internal variability in atmospheric/oceanic circulations systems as well as external forcing as solar radiation and volcanic activity could have affected temperature variations in the north Aegean Sea over the past 1500 years. The marked temperature drop of approximately ~2 �C at 1832 ± 15 yr AD could be related to the 1809 АD ‘unknown’ and the 1815 AD Tambora volcanic eruptions. Paleoenvironmental proxy-indices of the M2 record show enhanced riverine/continental inputs in the northern Aegean after ca. 1450 AD. The paleoclimatic evidence derived from the M2 record is combined with a socio-environmental study of the history of the north Aegean region. We show that the cultivation of temperature-sensitive crops, i.e. walnut, vine and olive, co-occurred with stable and warmer temperatures, while its end coincided with a significant episode of cooler temperatures. Periods of agricultural growth in Macedonia coincide with periods of warmer and more stable SSTs, but further exploration is required in order to identify the causal links behind the observed phenomena. The Black Death likely caused major changes in agricultural activity in the north Aegean region, as reflected in the pollen data from land sites of Macedonia and the M2 proxy-reconstructions. Finally, we conclude that the early modern peaks in mountain vegetation in the Rhodope and Macedonia highlands, visible also in the M2 record, were very likely climate-driven.

A fast adaptive tunable RBF network for nonstationary systems

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption.

A constrained recursive least squares algorithm for adaptive combination of multiple models

In this paper, we develop a novel constrained recursive least squares algorithm for adaptively combining a set of given multiple models. With data available in an online fashion, the linear combination coefficients of submodels are adapted via the proposed algorithm.We propose to minimize the mean square error with a forgetting factor, and apply the sum to one constraint to the combination parameters. Moreover an l1-norm constraint to the combination parameters is also applied with the aim to achieve sparsity of multiple models so that only a subset of models may be selected into the final model. Then a weighted l2-norm is applied as an approximation to the l1-norm term. As such at each time step, a closed solution of the model combination parameters is available. The contribution of this paper is to derive the proposed constrained recursive least squares algorithm that is computational efficient by exploiting matrix theory. The effectiveness of the approach has been demonstrated using both simulated and real time series examples.