Climate-Driven Variability and Trends in Plant Productivity Over Recent Decades Based on Three Global Products

Variability in climate exerts a strong influence on vegetation productivity (gross primary productivity; GPP), and therefore has a large impact on the land carbon sink. However, no direct observations of global GPP exist, and estimates rely on models that are constrained by observations at various spatial and temporal scales. Here, we assess the consistency in GPP from global products which extend for more than three decades; two observation‐based approaches, the upscaling of FLUXNET site observations (FLUXCOM) and a remote sensing derived light use efficiency model (RS‐LUE), and from a suite of terrestrial biosphere models (TRENDYv6). At local scales, we find high correlations in annual GPP among the products, with exceptions in tropical and high northern latitudes. On longer time scales, the products agree on the direction of trends over 58% of the land, with large increases across northern latitudes driven by warming trends. Further, tropical regions exhibit the largest interannual variability in GPP, with both rainforests and savannas contributing substantially. Variability in savanna GPP is likely predominantly driven by water availability, although temperature could play a role via soil moisture‐atmosphere feedbacks. There is, however, no consensus on the magnitude and driver of variability of tropical forests, which suggest uncertainties in process representations and underlying observations remain. These results emphasize the need for more direct long‐term observations of GPP along with an extension of in situ networks in underrepresented regions (e.g., tropical forests). Such capabilities would support efforts to better validate relevant processes in models, to more accurately estimate GPP

Regional but not global temperature variability underestimated by climate models at supradecadal timescales

Knowledge of the characteristics of natural climate variability is vital when assessing the range of plausible future climate trajectories in the next decades to centuries. The reliable detection of climate fluctuations on multidecadal to centennial timescales depends on proxy reconstructions and model simulations, as the instrumental record extends back only a few decades in most parts of the world. Systematic comparisons between model-simulated and proxy-based inferences of natural variability, however, often seem contradictory. Locally, simulated temperature variability is consistently smaller on multidecadal and longer timescales than is indicated by proxy-based reconstructions, implying that climate models or proxy interpretations might have deficiencies. In contrast, at global scales, studies found agreement between simulated and proxy reconstructed temperature variations. Here we review the evidence regarding the scale of natural temperature variability during recent millennia. We identify systematic reconstruction deficiencies that may contribute to differing local and global model–proxy agreement but conclude that they are probably insufficient to resolve such discrepancies. Instead, we argue that regional climate variations persisted for longer timescales than climate models simulating past climate states are able to reproduce. This would imply an underestimation of the regional variability on multidecadal and longer timescales and would bias climate projections and attribution studies. Thus, efforts are needed to improve the simulation of natural variability in climate models accompanied by further refining proxy-based inferences of variability.This study was undertaken by members of CVAS and 2k Network, working groups of the Past Global Changes (PAGES) Global Research association. This is a contribution to the SPACE ERC, STACY and PALMOD projects. The SPACE ERC project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 716092). STACY has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project no. 395588486). This work has also been supported by the German Federal Ministry of Education and Research (BMBF), through the PalMod project (subprojects 01LP1926B (O.B.), 01LP1926D (M.C.) and 01LP1926C (B.E., P.S. and N.W.)) from the Research for Sustainability initiative (FONA). B.E. is supported by the Heinrich Böll Foundation. E.M.-C. was supported by the PARAMOUR project, funded by the Fonds de la Recherche Scientifique–FNRS and the FWO under the Excellence of Science (EOS) programme (grant no. O0100718F, EOS ID no. 30454083). A.H. was supported by a Legacy Grant from the Australian Research Council Centre of Excellence for Australian Biodiversity and Heritage. B.M. was supported by LINKA20102 and the Spanish Ministry of Science and Innovation project CEX2018‐000794‐S. The work originated from discussions at the CVAS working group of PAGES at a workshop at the Internationales Wissenschaftsforum Heidelberg, which was funded by a Hengstberger Prize. We thank N. Beech, C. Brierley, F. Gonzalez-Rouco and M. MacPartland for comments on earlier drafts of the manuscript. This manuscript uses data provided by the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP and PMIP. We thank the research groups for producing and kindly making their model outputs, measurements and palaeoclimate reconstructions available to us. Editorial assistance, in the form of language editing and correction, was provided by XpertScientific Editing and Consulting Services. We acknowledge support by the Open Access Publication Funds of Alfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung.Peer ReviewedPostprint (author's final draft

Attribution of Soil Surface Temperature Sensitivity to Hydro-climatic Drivers

Greenhouse gas emissions caused by human economic activity are altering the global hydrologic cycle and the energy exchanges at the land surface. In large portions of the western US there is evidence of reduced summertime precipitation and increased air temperatures and longwave irradiation. At local scales, these changes can translate into more frequent and intense extreme land surface temperature events during the summer, with potential impacts on wildfire activity, forest health, soil biochemical cycles, and thermal comfort for human populations. However, because increases in radiation and sensible heat (air temperature) inputs to the land surface are confounded with changes in water availability, which alter the way the surface energy balance is reapportioned, it is difficult to disentangle the specific contributions of these factors to the observed dynamics of land surface temperatures. This thesis contributes insight into this problem using a combination of analytical and numerical model applications in a plot and for the city of Missoula, MT. In the first chapter of this thesis we used analytical method on a surface energy balance equation to identify and assess the attribution of surface temperature sensitivities to key hydro-climatic drivers in a plot of soil with and without vegetation canopy cover. The second chapter uses an ecohydrological model to investigate the effect of perturbations in water input regimes (additions to soil moisture) on surface temperatures for different land covers in a semi-arid urban area (Missoula, MT)

PREDICTING CLIMATE-INDUCED IMPACTS ON SEASONAL STREAM TEMPERATURES IN THE CROWN OF THE CONTINENT ECOSYSTEM

Changes in seasonal climate patterns are altering thermal distributions of freshwater ecosystems worldwide. The Crown of the Continent Ecosystem is one of the most biologically diverse ecosystems in North America, spanning northwestern Montana, USA, Alberta and British Columbia, Canada. The fluvial landscape consists of pristine freshwater habitats that provide strongholds for many aquatic species. My dissertation work provides the first broad scale analysis of seasonal climate effects on spatiotemporal patterns of stream temperature in the Crown of the Continent, and a multi-scalar analysis of potential impacts to bull trout (Salvelinius confluentus) populations, the most stenothermic cold-water fish in the northern Rocky Mountains. Seasonal stream temperature models were developed to predict monthly temperatures under current and future climate scenarios. Future climate simulations forecast increasing stream temperatures during spring, summer, and fall, with the largest absolute increases predicted for July, August, and September and the largest increases relative to historic temperatures predicted for April and November. Results portend a temporal shift in seasonal stream temperatures, including an earlier onset and extended duration of warm summer stream temperatures. Stream temperature warming was most pronounced in high-elevation montane and alpine streams, where glacial-fed streams were predicted to experience the largest magnitude (\u3e50%) of change due to the loss of alpine glaciers. Thermal riverscapes were used to assess spatiotemporal shifts in habitat distributions of bull trout. Models predicted thermal preferences for juvenile bull trout within tributary habitats during the summer months \u3c 12°C, while preferred temperatures for sub-adult and adult bull trout within river habitats were \u3c 15°C. Future stream temperature warming is likely to result in a contraction of thermally optimal habitats, suggesting a shift in the distributional range of bull trout further north in latitudes and higher in elevation. Thermal sensitivities during the summer months are likely to be highest in the southern periphery of their distributional range, while model simulations under extreme climate scenarios predict headwater tributaries within the Oldman, Flathead, and South Fork Flathead basins to provide cold-water refugia into the future. My dissertation work provides a decision support framework for predicting climate-induced stream temperature impacts on freshwater riverscapes and sensitive aquatic species to prioritize climate adaptation strategies in the Crown of the Continent

Decomposing uncertainties in the future terrestrial carbon budget associated with emission scenarios, climate projections, and ecosystem simulations using the ISI-MIP results

We examined the changes to global net primary production (NPP), vegetation biomass carbon (VegC), and soil organic carbon (SOC) estimated by six global vegetation models (GVMs) obtained from the Inter-Sectoral Impact Model Intercomparison Project. Simulation results were obtained using five global climate models (GCMs) forced with four representative concentration pathway (RCP) scenarios. To clarify which component (i.e., emission scenarios, climate projections, or global vegetation models) contributes the most to uncertainties in projected global terrestrial C cycling by 2100, analysis of variance (ANOVA) and wavelet clustering were applied to 70 projected simulation sets. At the end of the simulation period, changes from the year 2000 in all three variables varied considerably from net negative to positive values. ANOVA revealed that the main sources of uncertainty are different among variables and depend on the projection period. We determined that in the global VegC and SOC projections, GVMs are the main influence on uncertainties (60 % and 90 %, respectively) rather than climate-driving scenarios (RCPs and GCMs). Moreover, the divergence of changes in vegetation carbon residence times is dominated by GVM uncertainty, particularly in the latter half of the 21st century. In addition, we found that the contribution of each uncertainty source is spatiotemporally heterogeneous and it differs among the GVM variables. The dominant uncertainty source for changes in NPP and VegC varies along the climatic gradient. The contribution of GVM to the uncertainty decreases as the climate division becomes cooler (from ca. 80 % in the equatorial division to 40 % in the snow division). Our results suggest that to assess climate change impacts on global ecosystem C cycling among each RCP scenario, the long-term C dynamics within the ecosystems (i.e., vegetation turnover and soil decomposition) are more critical factors than photosynthetic processes. The different trends in the contribution of uncertainty sources in each variable among climate divisions indicate that improvement of GVMs based on climate division or biome type will be effective. On the other hand, in dry regions, GCMs are the dominant uncertainty source in climate impact assessments of vegetation and soil C dynamics

The value of considering demographic contributions to connectivity - a review

Connectivity is a central concept in ecology, wildlife management, and conservation science. Understanding the role of connectivity in determining species persistence is increasingly important in the face of escalating anthropogenic impacts on climate and habitat. These connectivity augmenting processes can severely impact species distributions and community and ecosystem functioning. One general definition of connectivity is that it is an emergent process arising from a set of spatial interdependencies between individuals or populations, and increasingly realistic representations of connectivity are being sought. Generally, connectivity consists of a structural component, relating to the distribution of suitable and unsuitable habitat, and a functional component, relating to movement behavior, yet the interaction of both components often better describes ecological processes. Additionally, although implied by ‘movement', demographic measures such as the occurrence or abundance of organisms are regularly overlooked when quantifying connectivity. Integrating such demographic contributions based on the knowledge of species distribution patterns is critical to understanding the dynamics of spatially structured populations. Demographically-informed connectivity draws from fundamental concepts in metapopulation ecology while maintaining important conceptual developments from landscape ecology, and the methodological development of spatially-explicit hierarchical statistical models that have the potential to overcome modeling and data challenges. Together, this offers a promising framework for developing ecologically realistic connectivity metrics. This review synthesizes existing approaches for quantifying connectivity and advocates for demographically-informed connectivity as a general framework for addressing current problems across ecological fields reliant on connectivity-driven processes such as population ecology, conservation biology and landscape ecology. Using supporting simulations to highlight the consequences of commonly made assumptions that overlook important demographic contributions, we show that even small amounts of demographic information can greatly improve model performance. Ultimately, we argue demographic measures are central to extending the concept of connectivity and resolves long-standing challenges associated with accurately quantifying the influence of connectivity on fundamental ecological processes.Publisher PDFPeer reviewe

The Value of Considering Demographic Contributions to Connectivity: A Review

Connectivity is a central concept in ecology, wildlife management, and conservation science. Understanding the role of connectivity in determining species persistence is increasingly important in the face of escalating anthropogenic impacts on climate and habitat. These connectivity augmenting processes can severely impact species distributions and community and ecosystem functioning. One general definition of connectivity is that it is an emergent process arising from a set of spatial interdependencies between individuals or populations, and increasingly realistic representations of connectivity are being sought. Generally, connectivity consists of a structural component, relating to the distribution of suitable and unsuitable habitat, and a functional component, relating to movement behavior, yet the interaction of both components often better describes ecological processes. Additionally, although implied by \u27movement\u27, demographic measures such as the occurrence or abundance of organisms are regularly overlooked when quantifying connectivity. Integrating such demographic contributions based on the knowledge of species distribution patterns is critical to understanding the dynamics of spatially structured populations. Demographically-informed connectivity draws from fundamental concepts in metapopulation ecology while maintaining important conceptual developments from landscape ecology, and the methodological development of spatially-explicit hierarchical statistical models that have the potential to overcome modeling and data challenges. Together, this offers a promising framework for developing ecologically realistic connectivity metrics. This review synthesizes existing approaches for quantifying connectivity and advocates for demographically-informed connectivity as a general framework for addressing current problems across ecological fields reliant on connectivity-driven processes such as population ecology, conservation biology and landscape ecology. Using supporting simulations to highlight the consequences of commonly made assumptions that overlook important demographic contributions, we show that even small amounts of demographic information can greatly improve model performance. Ultimately, we argue demographic measures are central to extending the concept of connectivity and resolves long-standing challenges associated with accurately quantifying the influence of connectivity on fundamental ecological processes