Uneven consequences of global climate mitigation pathways on regional water quality in the 21st century.

Future socioeconomic climate pathways have regional water-quality consequences whose severity and equity have not yet been fully understood across geographic and economic spectra. We use a process-based, terrestrial-freshwater ecosystem model to project 21st-century river nitrogen loads under these pathways. We find that fertilizer usage is the primary determinant of future river nitrogen loads, changing precipitation and warming have limited impacts, and CO2 fertilization-induced vegetation growth enhancement leads to modest load reductions. Fertilizer applications to produce bioenergy in climate mitigation scenarios cause larger load increases than in the highest emission scenario. Loads generally increase in low-income regions, yet remain stable or decrease in high-income regions where agricultural advances, low food and feed production and waste, and/or well-enforced air pollution policies balance biofuel-associated fertilizer burdens. Consideration of biofuel production options with low fertilizer demand and rapid transfer of agricultural advances from high- to low-income regions may help avoid inequitable water-quality outcomes from climate mitigation.

Abrupt loss and uncertain recovery from fires of Amazon forests under low climate mitigation scenarios.

Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO2 and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree-grass competition. Enhanced productivity due to CO2 fertilization promotes increases in forest biomass that, under low emission scenarios, last until the end of the century. Under high emissions, positive trends reverse after 2060, when simulated fires prompt forest loss that results in a 40% decline in tropical forest biomass by 2100. Projected fires occur under dry conditions associated with El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation, a response observed under current climate conditions, but exacerbated by an overall decline in precipitation. Following the initial disturbance, grassland dominance promotes recurrent fires and tree competitive exclusion, which prevents forest recovery. EC-Earth3-Veg, an ESM with a dynamic vegetation model of similar complexity, projected comparable wildfire forest loss under high emissions but faster postfire recovery rates. Our results reveal the importance of complex nonlinear responses to assessing climate change impacts and the urgent need to research postfire recovery and its representation in ESMs.

Ni/(R2O3,CaO) Nanocomposites Produced by the Exsolution of R1.5Ca0.5NiO4 Nickelates (R = Nd, Sm, Eu): Rare Earth Effect on the Catalytic Performance in the Dry Reforming and Partial Oxidation of Methane.

In order to clarify the role of R2O3 in the metal-oxide catalysts derived from complex oxide precursors, a series of R1.5Ca0.5NiO4 (R = Nd, Sm, Eu) complex oxides was obtained. A significant systematic increase in the orthorhombic distortion of the R1.5Ca0.5NiO4 structure (K2NiF4 type, Cmce) from Nd to Eu correlates with a corresponding decrease in their ionic radii. A reduction of R1.5Ca0.5NiO4 in the Ar/H2 gas mixture at 800 °C causes a formation of dense agglomerates of CaO and R2O3 coated with spherical 25-30 nm particles of Ni metal. The size of metal particles and oxide agglomerates is similar in all Ni/(R2O3,CaO) composites in the study. Their morphology is rather similar to the products of redox exsolution obtained by the partial reduction of complex oxides. All obtained composites demonstrated a significant catalytic activity in the dry reforming (DRM) and partial oxidation (POM) of methane at 700-800 °C. A systematic decrease in the DRM catalytic activity of composites from Nd to Eu could be attributed to the basicity reduction of R2O3 components of the composite catalysts. The maximum CH4 conversion in POM reaction was observed for Ni/(Sm2O3,CaO), while the maximum selectivity was demonstrated by Nd2O3-based composite. The possible reasons for the observed difference are discussed.

Global implication of mesoproterozoic (~ 1.4 Ga) magmatism within the Sette-Daban Range (Southeast Siberia).

Mesoproterozoic period included several global tectonic events like break-up of Nuna and formation of Rodinia. However, although Siberia is a significant piece of both supercontinents, Mesoproterozoic time is marked by quiescence of magmatic and tectonic activity in it. We report here a mafic dyke (named Gornostakh dyke) in the southeastern Siberian Craton dated at 1419 ± 32 Ma by LA-ICPMS U-Pb geochronology of apatite. The dyke has tholeiitic compositions with high MgO and alkaline content, low-Ti, and arc-like trace element pattern. Due to the absence of subduction tectonics in the study area, geochemical data could be attributed to a significant contribution from metasomatically enriched subcontinental lithospheric mantle previously modified by subduction processes. That kind of composition is common for low-Ti dykes of intraplate flood basalt provinces similar to, for example, Permian-Triassic Siberian large igneous province (LIP). Paleogeographic reconstructions suggest that Siberia was connected to Laurentia and Baltica and their reconfiguration interrupts a prolonged tectonic quiescence in the Siberian Craton from ca. 1.88 Ga reflecting a transition from Nuna to Rodinia configuration. The mafic magmatism on 1419 Ma on the southeastern margin of the Siberian Craton together with coeval extensional tectonics observed in the structure of the Sette-Daban ridge proposes a hypothetical LIP which may be a direct consequence of the beginning of this transition.

Allometric constraints and competition enable the simulation of size structure and carbon fluxes in a dynamic vegetation model of tropical forests (LM3PPA-TV).

Tropical forests are a key determinant of the functioning of the Earth system, but remain a major source of uncertainty in carbon cycle models and climate change projections. In this study, we present an updated land model (LM3PPA-TV) to improve the representation of tropical forest structure and dynamics in Earth system models (ESMs). The development and parameterization of LM3PPA-TV drew on extensive datasets on tropical tree traits and long-term field censuses from Barro Colorado Island (BCI), Panama. The model defines a new plant functional type (PFT) based on the characteristics of shade-tolerant, tropical tree species, implements a new growth allocation scheme based on realistic tree allometries, incorporates hydraulic constraints on biomass accumulation, and features a new compartment for tree branches and branch fall dynamics. Simulation experiments reproduced observed diurnal and seasonal patterns in stand-level carbon and water fluxes, as well as mean canopy and understory tree growth rates, tree size distributions, and stand-level biomass on BCI. Simulations at multiple sites captured considerable variation in biomass and size structure across the tropical forest biome, including observed responses to precipitation and temperature. Model experiments suggested a major role of water limitation in controlling geographic variation forest biomass and structure. However, the failure to simulate tropical forests under extreme conditions and the systematic underestimation of forest biomass in Paleotropical locations highlighted the need to incorporate variation in hydraulic traits and multiple PFTs that capture the distinct floristic composition across tropical domains. The continued pressure on tropical forests from global change demands models which are able to simulate alternative successional pathways and their pace to recovery. LM3PPA-TV provides a tool to investigate geographic variation in tropical forests and a benchmark to continue improving the representation of tropical forests dynamics and their carbon storage potential in ESMs.

Publisher Correction: Prominence of the tropics in the recent rise of global nitrogen pollution.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Urban heat island: Aerodynamics or imperviousness?

More than half of the world's population now live in cities, which are known to be heat islands. While daytime urban heat islands (UHIs) are traditionally thought to be the consequence of less evaporative cooling in cities, recent work sparks new debate, showing that geographic variations of daytime UHI intensity were largely explained by variations in the efficiency with which urban and rural areas convect heat from the land surface to the lower atmosphere. Here, we reconcile this debate by demonstrating that the difference between the recent finding and the traditional paradigm can be explained by the difference in the attribution methods. Using a new attribution method, we find that spatial variations of daytime UHI intensity are more controlled by variations in the capacity of urban and rural areas to evaporate water, suggesting that strategies enhancing the evaporation capability such as green infrastructure are effective ways to mitigate urban heat.

Prominence of the tropics in the recent rise of global nitrogen pollution.

Nitrogen (N) pollution is shaped by multiple processes, the combined effects of which remain uncertain, particularly in the tropics. We use a global land biosphere model to analyze historical terrestrial-freshwater N budgets, considering the effects of anthropogenic N inputs, atmospheric CO2, land use, and climate. We estimate that globally, land currently sequesters 11 (10-13)% of annual N inputs. Some river basins, however, sequester >50% of their N inputs, buffering coastal waters against eutrophication and society against greenhouse gas-induced warming. Other basins, releasing >25% more than they receive, are mostly located in the tropics, where recent deforestation, agricultural intensification, and/or exports of land N storage can create large N pollution sources. The tropics produce 56 ± 6% of global land N pollution despite covering only 34% of global land area and receiving far lower amounts of fertilizers than the extratropics. Tropical land use should thus be thoroughly considered in managing global N pollution.

Potential for western US seasonal snowpack prediction.

Western US snowpack-snow that accumulates on the ground in the mountains-plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of the century and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 months in advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

The impact of anthropogenic land use and land cover change on regional climate extremes.

Land surface processes modulate the severity of heat waves, droughts, and other extreme events. However, models show contrasting effects of land surface changes on extreme temperatures. Here, we use an earth system model from the Geophysical Fluid Dynamics Laboratory to investigate regional impacts of land use and land cover change on combined extremes of temperature and humidity, namely aridity and moist enthalpy, quantities central to human physiological experience of near-surface climate. The model's near-surface temperature response to deforestation is consistent with recent observations, and conversion of mid-latitude natural forests to cropland and pastures is accompanied by an increase in the occurrence of hot-dry summers from once-in-a-decade to every 2-3 years. In the tropics, long time-scale oceanic variability precludes determination of how much of a small, but significant, increase in moist enthalpy throughout the year stems from the model's novel representation of historical patterns of wood harvesting, shifting cultivation, and regrowth of secondary vegetation and how much is forced by internal variability within the tropical oceans.

Confronting terrestrial biosphere models with forest inventory data.

Efforts to test and improve terrestrial biosphere models (TBMs) using a variety of data sources have become increasingly common. Yet, geographically extensive forest inventories have been under-exploited in previous model-data fusion efforts. Inventory observations of forest growth, mortality, and biomass integrate processes across a range of timescales, including slow timescale processes such as species turnover, that are likely to have important effects on ecosystem responses to environmental variation. However, the large number (thousands) of inventory plots precludes detailed measurements at each location, so that uncertainty in climate, soil properties, and other environmental drivers may be large. Errors in driver variables, if ignored, introduce bias into model-data fusion. We estimated errors in climate and soil drivers at U.S. Forest Inventory and Analysis (FIA) plots, and we explored the effects of these errors on model-data fusion with the Geophysical Fluid Dynamics Laboratory LM3V dynamic global vegetation model. When driver errors were ignored or assumed small at FIA plots, responses of biomass production in LM3V to precipitation and soil available water capacity appeared steeper than the corresponding responses estimated from FIA data. These differences became nonsignificant if driver errors at FIA plots were assumed to be large. Ignoring driver errors when optimizing LM3V parameter values yielded estimates for fine-root allocation that were larger than biometric estimates, which is consistent with the expected direction of bias. To explore whether complications posed by driver errors could be circumvented by relying on intensive study sites where driver errors are small, we performed a power analysis. To accurately quantify the response of biomass production to spatial variation in mean annual precipitation within the eastern United States would require at least 40 intensive study sites, which is larger than the number of sites typically available for individual biomes in existing plot networks. Driver errors may be accommodated by several existing model-data fusion approaches, including hierarchical Bayesian methods and ensemble filtering methods; however, these methods are computationally expensive. We propose a new approach, in which the TBM functional response is fit directly to the driver-error-corrected functional response estimated from data, rather than to the raw observations.

Historical warming reduced due to enhanced land carbon uptake.

Previous studies have demonstrated the importance of enhanced vegetation growth under future elevated atmospheric CO2 for 21st century climate warming. Surprisingly no study has completed an analogous assessment for the historical period, during which emissions of greenhouse gases increased rapidly and land-use changes (LUC) dramatically altered terrestrial carbon sources and sinks. Using the Geophysical Fluid Dynamics Laboratory comprehensive Earth System Model ESM2G and a reconstruction of the LUC, we estimate that enhanced vegetation growth has lowered the historical atmospheric CO2 concentration by 85 ppm, avoiding an additional 0.31 ± 0.06 °C warming. We demonstrate that without enhanced vegetation growth the total residual terrestrial carbon flux (i.e., the net land flux minus LUC flux) would be a source of 65-82 Gt of carbon (GtC) to atmosphere instead of the historical residual carbon sink of 186-192 GtC, a carbon saving of 251-274 GtC.

The influence of large-scale wind power on global climate.

Large-scale use of wind power can alter local and global climate by extracting kinetic energy and altering turbulent transport in the atmospheric boundary layer. We report climate-model simulations that address the possible climatic impacts of wind power at regional to global scales by using two general circulation models and several parameterizations of the interaction of wind turbines with the boundary layer. We find that very large amounts of wind power can produce nonnegligible climatic change at continental scales. Although large-scale effects are observed, wind power has a negligible effect on global-mean surface temperature, and it would deliver enormous global benefits by reducing emissions of CO(2) and air pollutants. Our results may enable a comparison between the climate impacts due to wind power and the reduction in climatic impacts achieved by the substitution of wind for fossil fuels.

Regions of strong coupling between soil moisture and precipitation.

Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.

Mapping of 99 new microsatellite-derived loci in rye (Secale cereale L.) including 39 expressed sequence tags.

The genetic map of rye contains predominantly restriction fragment length polymorphism (RFLP) markers but also a limited number of microsatellite markers, which are known to be more reliable and easier to apply. We report here the saturation of the genomic map of rye with additional microsatellite-derived markers that we obtained from the rye expressed sequence tag (EST) databases and the Gatersleben collection of wheat microsatellite markers (WMS). A total of 99 loci (39 EST and 60 WMS) were mapped into the RFLP frameworks of four rye mapping populations consisting of 139, 64, 58 and 60 RFLPs, respectively. For another ten WMS loci, which amplified PCR products not polymorphic in any of the mapping populations, chromosome and chromosome arm locations were determined using wheat-rye addition lines. Chromosomes 1R, 2R, 3R, 4R, 5R, 6R and 7R were enriched with 9, 19, 9, 13, 27, 16 and 16 microsatellite loci, respectively. The microsatellite loci mapped were evenly distributed along the chromosomes, which is important for the further application of these markers for gene mapping or diversity studies in rye. Forty-four of the WMS loci mapped in rye were found to be homologous to those mapped in bread wheat ( Triticum aestivum L.).