1,286 research outputs found
Emergence of anthropogenic signals in the ocean carbon cycle
No full textThe attribution of anthropogenically forced trends in the climate system requires an understanding of when and how such signals emerge from natural variability. We applied time-of-emergence diagnostics to a large ensemble of an Earth system model, which provides both a conceptual framework for interpreting the detectability of anthropogenic impacts in the ocean carbon cycle and observational sampling strategies required to achieve detection. We found emergence timescales that ranged from less than a decade to more than a century, a consequence of the time lag between the chemical and radiative impacts of rising atmospheric CO2 on the ocean. Processes sensitive to carbonate chemical changes emerge rapidly, such as the impacts of acidification on the calcium carbonate pump (10 years for the globally integrated signal and 9–18 years for regionally integrated signals) and the invasion flux of anthropogenic CO2 into the ocean (14 years globally and 13–26 years regionally). Processes sensitive to the ocean’s physical state, such as the soft-tissue pump, which depends on nutrients supplied through circulation, emerge decades later (23 years globally and 27–85 years regionally)
Theoretical frameworks for the learning of geometrical reasoning
No full textWith the growth in interest in geometrical ideas it is important to be clear about the nature of geometrical reasoning and how it develops. This paper provides an overview of three theoretical frameworks for the learning of geometrical reasoning: the van Hiele model of thinking in geometry, Fischbein’s theory of figural concepts, and Duval’s cognitive model of geometrical reasoning. Each of these frameworks provides theoretical resources to support research into the development of geometrical reasoning in students and related aspects of visualisation and construction. This overview concludes that much research about the deep process of the development and the learning of visualisation and reasoning is still needed
Time of Emergence and Large Ensemble Intercomparison for Ocean Biogeochemical Trends
No full textAnthropogenically forced changes in ocean biogeochemistry are underway and critical for the ocean carbon sink and marine habitat. Detecting such changes in ocean biogeochemistry will require quantification of the magnitude of the change (anthropogenic signal) and the natural variability inherent to the climate system (noise). Here we use Large Ensemble (LE) experiments from four Earth system models (ESMs) with multiple emissions scenarios to estimate Time of Emergence (ToE) and partition projection uncertainty for anthropogenic signals in five biogeochemically important upper-ocean variables. We find ToEs are robust across ESMs for sea surface temperature and the invasion of anthropogenic carbon; emergence time scales are 20-30 yr. For the biological carbon pump, and sea surface chlorophyll and salinity, emergence time scales are longer (50+ yr), less robust across the ESMs, and more sensitive to the forcing scenario considered. We find internal variability uncertainty, and model differences in the internal variability uncertainty, can be consequential sources of uncertainty for projecting regional changes in ocean biogeochemistry over the coming decades. In combining structural, scenario, and internal variability uncertainty, this study represents the most comprehensive characterization of biogeochemical emergence time scales and uncertainty to date. Our findings delineate critical spatial and duration requirements for marine observing systems to robustly detect anthropogenic change
Quantifying Errors in Observationally Based Estimates of Ocean Carbon Sink Variability
No full textReducing uncertainty in the global carbon budget requires better quantification of ocean CO2 uptake and its temporal variability. Several methodologies for reconstructing air-sea CO2 exchange from pCO(2) observations indicate larger decadal variability than estimated using ocean models. We develop a new application of multiple Large Ensemble Earth system models to assess these reconstructions' ability to estimate spatiotemporal variability. With our Large Ensemble Testbed, pCO(2) fields from 25 ensemble members each of four independent Earth system models are subsampled as the observations and the reconstruction is performed as it would be with real-world observations. The power of a testbed is that the perfect reconstruction is known for each of the original model fields; thus, reconstruction skill can be comprehensively assessed. We find that a neural-network approach can skillfully reconstruct air-sea CO2 fluxes when it is trained with sufficient data. Flux bias is low for the global mean and Northern Hemisphere, but can be regionally high in the Southern Hemisphere. The phase and amplitude of the seasonal cycle are accurately reconstructed outside of the tropics, but longer-term variations are reconstructed with only moderate skill. For Southern Ocean decadal variability, insufficient sampling leads to a 31% (15%:58%, interquartile range) overestimation of amplitude, and phasing is only moderately correlated with known truth (r = 0.54 [0.46:0.63]). Globally, the amplitude of decadal variability is overestimated by 21% (3%:34%). Machine learning, when supplied with sufficient data, can skillfully reconstruct ocean properties. However, data sparsity remains a fundamental limitation to quantification of decadal variability in the ocean carbon sink
Pervasive alterations to snow-dominated ecosystem functions under climate change
No full text© 2022 National Academy of Sciences. All rights reserved.Climate change projections consistently demonstrate that warming temperatures and dwindling seasonal snowpack will elicit cascading effects on ecosystem function and water resource availability. Despite this consensus, little is known about potential changes in the variability of ecohydrological conditions, which is also required to inform climate change adaptation and mitigation strategies. Considering potential changes in ecohydrological variability is critical to evaluating the emergence of trends, assessing the likelihood of extreme events such as floods and droughts, and identifying when tipping points may be reached that fundamentally alter ecohydrological function. Using a single-model Large Ensemble with sophisticated terrestrial ecosystem representation, we characterize projected changes in the mean state and variability of ecohydrological processes in historically snow-dominated regions of the Northern Hemisphere. Widespread snowpack reductions, earlier snowmelt timing, longer growing seasons, drier soils, and increased fire risk are projected for this century under a high-emissions scenario. In addition to these changes in the mean state, increased variability in winter snowmelt will increase growing-season water deficits and increase the stochasticity of runoff. Thus, with warming, declining snowpack loses its dependable buffering capacity so that runoff quantity and timing more closely reflect the episodic characteristics of precipitation. This results in a declining predictability of annual runoff from maximum snow water equivalent, which has critical implications for ecosystem stress and water resource management. Our results suggest that there is a strong likelihood of pervasive alterations to ecohydrological function that may be expected with climate change.11Nsciescopu
Ubiquity of human-induced changes in climate variability
No full text© 2021 Keith B. Rodgers et al.While climate change mitigation targets necessarily concern maximum mean state changes, understanding impacts and developing adaptation strategies will be largely contingent on how climate variability responds to increasing anthropogenic perturbations. Thus far Earth system modeling efforts have primarily focused on projected mean state changes and the sensitivity of specific modes of climate variability, such as the El Niño-Southern Oscillation. However, our knowledge of forced changes in the overall spectrum of climate variability and higher-order statistics is relatively limited. Here we present a new 100-member large ensemble of climate change projections conducted with the Community Earth System Model version 2 over 1850-2100 to examine the sensitivity of internal climate fluctuations to greenhouse warming. Our unprecedented simulations reveal that changes in variability, considered broadly in terms of probability distribution, amplitude, frequency, phasing, and patterns, are ubiquitous and span a wide range of physical and ecosystem variables across many spatial and temporal scales. Greenhouse warming in the model alters variance spectra of Earth system variables that are characterized by non-Gaussian probability distributions, such as rainfall, primary production, or fire occurrence. Our modeling results have important implications for climate adaptation efforts, resource management, seasonal predictions, and assessing potential stressors for terrestrial and marine ecosystems.11Nsciescopu
The shaping of student knowledge: learning with dynamic geometry software
No full textThe focus of this paper is a software genre usually referred to as ‘dynamic geometry’ because of the ability of the user to dynamically manipulate geometrical figures created with the software tool. Using data from a longitudinal study of 12-13 students’ use of dynamic geometry software, the focus of the analysis is on the interpretations the students make of geometrical objects and relationships when using this form of software. The analysis suggests that the students’ mathematical reasoning is shaped by their interactions with the software in that their ability to explain geometrical facts and relationships evolves from imprecise, ‘everyday’ expressions, through reasoning that is overtly mediated by the software environment, to mathematical explanations of the geometric situation that transcend the particular tool being used. Such findings suggest that curriculum initiatives that encourage the use of dynamic geometry software are appropriate but that the incorporation of such software into classroom practices is unlikely to be straightforward
Quantifying the Role of Seasonality in the Marine Carbon Cycle Feedback: An ESM2M Case Study
No full textObservations and climate models indicate that changes in the seasonal amplitude of sea surface carbon dioxide partial pressure (A-pCO(2)) are underway and driven primarily by anthropogenic carbon (C-ant) accumulation in the ocean. This occurs because pCO(2) is more responsive to seasonal changes in physics (including warming) and biology in an ocean that contains more C-ant. A-pCO(2) changes have the potential to alter annual ocean carbon uptake and contribute to the overall marine carbon cycle feedback. Using the GFDL ESM2M Large Ensemble and a novel analysis framework, we quantify the influence of C-ant accumulation on pCO(2) seasonal cycles and sea-air CO2 fluxes. Specifically, we reconstruct alternative evolutions of the contemporary ocean state in which the sensitivity of pCO(2) to seasonal thermal and biophysical variation is fixed at preindustrial levels, however the background, mean-state pCO(2) fully responds to anthropogenic forcing. We find near-global A-pCO(2) increases of >100% by 2100, under RCP8.5 forcing, with rising C-ant accounting for similar to 100% of thermal and similar to 50% of nonthermal pCO(2) component amplitude changes. The other similar to 50% of nonthermal pCO(2) component changes are attributed to modeled changes in ocean physics and biology caused by climate change. C-ant-induced A-pCO(2) changes cause an 8.1 +/- 0.4% (ensemble mean +/- 1 sigma) increase in ocean carbon uptake by 2100. The is because greater wintertime wind speeds enhance the impact of wintertime pCO(2) changes, which work to increase the ocean carbon sink. Thus, the seasonal ocean carbon cycle feedback works in opposition to the larger, mean-state feedback that reduces ocean carbon uptake by similar to 60%.11Nsciescopu
Trophic level decoupling drives future changes in phytoplankton bloom phenology
No full text© 2022, The Author(s), under exclusive licence to Springer Nature Limited.Climate change can drive shifts in the seasonality of marine productivity, with consequences for the marine food web. However, these alterations in phytoplankton bloom phenology (initiation and peak timing), and the underlying drivers, are not well understood. Here, using a 30-member Large Ensemble of climate change projections, we show earlier bloom initiation in most ocean regions, yet changes in bloom peak timing vary widely by region. Shifts in both initiation and peak timing are induced by a subtle decoupling between altered phytoplankton growth and zooplankton predation, with increased zooplankton predation (top-down control) playing an important role in altered bloom peak timing over much of the global ocean. Only in limited regions is light limitation a primary control for bloom initiation changes. In the extratropics, climate-change-induced phenological shifts will exceed background natural variability by the end of the twenty-first century, which may impact energy flow in the marine food webs.11Nsciessciscopu
The CESM2 Single-Forcing Large Ensemble and Comparison to CESM1: Implications for Experimental Design
No full textSingle-forcing large ensembles are a relatively new tool for quantifying the contributions of different an-thropogenic and natural forcings to the historical and future projected evolution of the climate system. This study introdu-ces a new single-forcing large ensemble with the Community Earth System Model, version 2 (CESM2), which can be used to separate the influences of greenhouse gases, anthropogenic aerosols, biomass burning aerosols, and all remaining forc-ings on the evolution of the Earth system from 1850 to 2050. Here, the forced responses of global near-surface temperature and associated drivers are examined in CESM2 and compared with those in a single-forcing large ensemble with CESM2's predecessor, CESM1. The experimental design, the imposed forcing, and the model physics all differ between the CESM1 and CESM2 ensembles. In CESM1, an "all-but-one" approach was used whereby everything except the forcing of interest is time evolving, while in CESM2 an "only" approach is used, whereby only the forcing of interest is time evolving. This ex-perimental design choice is shown to matter considerably for anthropogenic aerosol-forced change in CESM2, due to state dependence of cryospheric albedo feedbacks and nonlinearity in the Atlantic meridional overturning circulation (AMOC) response to forcing. This impact of experimental design is, however, strongly dependent on the model physics and/or the imposed forcing, as the same sensitivity to experimental design is not found in CESM1, which appears to be an inherently less nonlinear model in both its AMOC behavior and cryospheric feedbacks.11Nsciescopu
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