CO-WRITING AN ESSAY WITH CHATGPT: EXPERIENCES AND PERCEPTIONS OF STUDENTS IN HIGHER EDUCATION

This article presents a case study, in which ChatGPT has been integrated into the writing assignment of a course in a Dutch university whereby the students co-wrote an essay with the AI-powered chatbot, ChatGPT 3.5. We examined 1) how the students used it, 2) their experienced challenges and benefits, 3) their view of its responsible use, and 4) their perception of skills required for the effective collaboration with ChatGPT in the co-writing process. Based on the analysis of students’ written self-reflections (N = 43), the results show that the perceived benefits and challenges are associated with different types of uses of ChatGPT. We found that some uses of ChatGPT, namely for content generation, present more challenges to the students than others. We also identified a set of skills that the students perceive they need, to use ChatGPT effectively: critical thinking, creativity, fact checking and evidence gathering, collaborative writing, ethical awareness, and effective instruction of ChatGPT

Changes in surface hydrology, soil moisture and gross primary production in the Amazon during the 2015/2016 El Niño

The 2015/2016 El Niño event caused severe changes in precipitation across the tropics. This impacted surface hydrology, such as river run-off and soil moisture availability, thereby triggering reductions in gross primary production (GPP). Many biosphere models lack the detailed hydrological component required to accurately quantify anomalies in surface hydrology and GPP during droughts in tropical regions. Here, we take the novel approach of coupling the biosphere model SiBCASA with the advanced hydrological model PCR-GLOBWB to attempt such a quantification across the Amazon basin during the drought in 2015/2016. We calculate 30-40% reduced river discharge in the Amazon starting in October 2015, lagging behind the precipitation anomaly by approximately one month and in good agreement with river gauge observations. Soil moisture shows distinctly asymmetrical spatial anomalies with large reductions across the north-eastern part of the basin, which persisted into the following dry season. This added to drought stress in vegetation, already present owing to vapour pressure deficits at the leaf, resulting in a loss of GPP of 0.95 (0.69 to 1.20) PgC between October 2015 and March 2016 compared with the 2007-2014 average. Only 11% (10-12%) of the reduction in GPP was found in the (wetter) north-western part of the basin, whereas the north-eastern and southern regions were affected more strongly, with 56% (54-56%) and 33% (31-33%) of the total, respectively. Uncertainty on this anomaly mostly reflects the unknown rooting depths of vegetation.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.</p

Comment on ‘The central role of forests in the 2021 European floods’

In July 2021, parts of Germany and Belgium were hit by severe floods. In 'The central role of forests in the 2021 European floods', published in Environmental Research Letters (2022 Environ. Res. Lett. 17 064053), Insua-Costa et al reported that 'moisture from North American forests was a more important source [of the rainfall contributing to the event] than evaporation over nearby seas'. This suggests that the event was (partly) caused by anomalous contributions from North America. In this comment, we show that this is a misleading interpretation, as: (1) the relative contribution of North American land was below average for the time of year; and (2) rather, the anomalous moisture that contributed to the floods originated mainly from European land. However, consistent with Insua-Costa et al, we find no enhanced evaporation from Europe prior to the event and we therefore conclude that there is a lack of evidence for the 'central role' of forests in the 2021 European floods

Widespread reduction in sun-induced fluorescence from the Amazon during the 2015/2016 El Nino

The tropical carbon balance dominates year-to-year variations in the CO2 exchange with the atmosphere through photosynthesis, respiration and fires. Because of its high correlation with gross primary productivity (GPP), observations of sun-induced fluorescence (SIF) are of great interest. We developed a new remotely sensed SIF product with improved signal-to-noise in the tropics, and use it here to quantify the impact of the 2015/2016 El Nino Amazon drought. We find that SIF was strongly suppressed over areas with anomalously high temperatures and decreased levels of water in the soil. SIF went below its climatological range starting from the end of the 2015 dry season (October) and returned to normal levels by February 2016 when atmospheric conditions returned to normal, but well before the end of anomalously low precipitation that persisted through June 2016. Impacts were not uniform across the Amazon basin, with the eastern part experiencing much larger (10-15%) SIF reductions than the western part of the basin (2-5%). We estimate the integrated loss of GPP relative to eight previous years to be 0.34-0.48 PgC in the three-month period October-November-December 2015. This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Nino on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'

Exploring the potential of Δ¹⁷O in CO₂ for determining mesophyll conductance

Mesophyll conductance to CO2 from the intercellular air space to the CO2–H2O exchange site has been estimated using δ18O measurements (gm18). However, the gm18 estimates are affected by the uncertainties in the δ18O of leaf water where the CO2–H2O exchange takes place and the degree of equilibration between CO2 and H2O. We show that measurements of Δ17O (i.e. Δ17O = δ17O − 0.528 × δ18O) can provide independent constraints on gm (gmΔ17) and that these gm estimates are less affected by fractionation processes during gas exchange. The gm calculations are applied to combined measurements of δ18O and Δ17O, and gas exchange in two C3 species, sunflower (Helianthus annuus L. cv. ‘sunny’) and ivy (Hedera hibernica L.), and the C4 species maize (Zea mays). The gm18 and gmΔ17 estimates agree within the combined errors (P-value, 0.876). Both approaches are associated with large errors when the isotopic composition in the intercellular air space becomes close to the CO2–H2O exchange site. Although variations in Δ17O are low, it can be measured with much higher precision compared with δ18O. Measuring gmΔ17 has a few advantages compared with gm18: (i) it is less sensitive to uncertainty in the isotopic composition of leaf water at the isotope exchange site and (ii) the relative change in the gm due to an assumed error in the equilibration fraction θeq is lower for gmΔ17 compared with gm18. Thus, using Δ17O can complement and improve the gm estimates in settings where the δ18O of leaf water varies strongly, affecting the δ18O (CO2) difference between the intercellular air space and the CO2–H2O exchange site.</p

Leaf-scale quantification of the effect of photosynthetic gas exchange on δ ¹⁷O of atmospheric CO ₂

Understanding the processes that affect the triple oxygen isotope composition of atmospheric CO2during gas exchange can help constrain the interaction and fluxes between the atmosphere and the biosphere. We conducted leaf cuvette experiments under controlled conditions using three plant species. The experiments were conducted at two different light intensities and using CO2with different δ17O. We directly quantify the effect of photosynthesis on δ17O of atmospheric CO2for the first time. Our results demonstrate the established theory for δ18O is applicable to δ17O.CO2/at leaf level, and we confirm that the following two key factors determine the effect of photosynthetic gas exchange on the δ17O of atmospheric CO2. The relative difference between δ17O of the CO2entering the leaf and the CO2in equilibrium with leaf water and the back-diffusion flux of CO2from the leaf to the atmosphere, which can be quantified by the cm=ca ratio, where ca is the CO2mole fraction in the surrounding air and cm is the one at the site of oxygen isotope exchange between CO2and H2O. At low cm=ca ratios the discrimination is governed mainly by diffusion into the leaf, and at high cm=ca ratios it is governed by back-diffusion of CO2that has equilibrated with the leaf water. Plants with a higher cm=ca ratio modify the 117O of atmospheric CO2more strongly than plants with a lower cm=ca ratio. Based on the leaf cuvette experiments, the global value for discrimination against δ17O of atmospheric CO2during photosynthetic gas exchange is estimated to be-0:57±0:14% using cm=ca values of 0.3 and 0.7 for C4and C3plants, respectively. The main uncertainties in this global estimate arise from variation in cm=ca ratios among plants and growth conditions.</p

Exploring the potential of Δ17O in CO2 for determining mesophyll conductance

Mesophyll conductance to CO2 from the intercellular air space to the CO2–H2O exchange site has been estimated using δ18O measurements (gm18). However, the gm18 estimates are affected by the uncertainties in the δ18O of leaf water where the CO2–H2O exchange takes place and the degree of equilibration between CO2 and H2O. We show that measurements of Δ17O (i.e. Δ17O = δ17O − 0.528 × δ18O) can provide independent constraints on gm (gmΔ17) and that these gm estimates are less affected by fractionation processes during gas exchange. The gm calculations are applied to combined measurements of δ18O and Δ17O, and gas exchange in two C3 species, sunflower (Helianthus annuus L. cv. ‘sunny’) and ivy (Hedera hibernica L.), and the C4 species maize (Zea mays). The gm18 and gmΔ17 estimates agree within the combined errors (P-value, 0.876). Both approaches are associated with large errors when the isotopic composition in the intercellular air space becomes close to the CO2–H2O exchange site. Although variations in Δ17O are low, it can be measured with much higher precision compared with δ18O. Measuring gmΔ17 has a few advantages compared with gm18: (i) it is less sensitive to uncertainty in the isotopic composition of leaf water at the isotope exchange site and (ii) the relative change in the gm due to an assumed error in the equilibration fraction θeq is lower for gmΔ17 compared with gm18. Thus, using Δ17O can complement and improve the gm estimates in settings where the δ18O of leaf water varies strongly, affecting the δ18O (CO2) difference between the intercellular air space and the CO2–H2O exchange site

Moisture origins of the Amazon carbon source region

The southeastern Amazon has recently been shown to be a net carbon source, which is partly caused by drying conditions. Drying depends on a number of factors, one of which is the land cover at the locations where the moisture has originated as evaporation. Here we assess for the first time the origins of the moisture that precipitates in the Amazon carbon source region, using output from a Lagrangian atmospheric moisture tracking model. We relate vegetation productivity in the Amazon carbon source region to precipitation patterns and derive land-cover data at the moisture origins of these areas, allowing us to estimate how the carbon cycle and hydrological cycle are linked in this critical part of the Amazon. We find that, annually, 13% of the precipitation in the Amazon carbon source region has evaporated from that same area, which is half of its land-derived moisture. We further find a moisture-recycling-mediated increase in gross primary productivity of roughly 41 Mg carbon km−2 yr−1 within the Amazon carbon source region if it is fully forested compared to any other land cover. Our results indicate that the parts of the Amazon forest that are already a net carbon source, still help sustain their own biomass production. Although the most degraded parts of the Amazon depend mostly on oceanic input of moisture, further degradation of this region would amplify carbon losses to the atmosphere

Guidance note on best statistical practices for TOAR analyses

The aim of this guidance note is to provide recommendations on best statistical practices and to ensure consistent communication of statistical analysis and associated uncertainty across TOAR publications. The scope includes approaches for reporting trends, a discussion of strengths and weaknesses of commonly used techniques, and calibrated language for the communication of uncertainty. The focus of this guidance note is placed on trend analysis, which is expected to be the main statistical topic of interest across many TOAR-II focus working groups, but some of the recommendations and principles provided below are also valid for other applications. Recommendations are highlighted and numbered from R1 to R9

Interannual variations in the "( 17O) signature of atmospheric CO 2 at two mid-latitude sites suggest a close link to stratosphere-troposphere exchange

▵ (17O) measurements of atmospheric CO2 have the potential to be a tracer for gross primary production and stratosphere-troposphere mixing. A positive ▵ (17O) originates from intrusions of stratospheric CO2, whereas values close to -0.21 result from the equilibration of CO2 and water, which predominantly happens inside plants. The stratospheric source of CO2 with high ▵ (17O) is, however, not well defined in the current models. More, and long-term, atmospheric measurements are needed to improve this. We present records of the ?(17O) of atmospheric CO2 obtained with laser absorption spectroscopy from Lutjewad in the Netherlands (53 24' N, 6 21' E) and Mace Head in Ireland (53 20' N, 9 54' W) that cover the period 2017-2022. The records are compared with a 3-D model simulation, and we study potential model improvements. Both records show significant interannual variability of up to 0.3 . The total range covered by smoothed monthly averages from the Lutjewad record is -0.34 to -0.12 , which is significantly higher than the range of -0.20 to -0.17 for the model simulation. The 100 hPa 60-90 N monthly-mean temperature anomaly was used as a proxy to scale stratospheric downwelling in the model. This strongly improves the correlation coefficient of the simulated and observed year-to-year ?(17O) variations over the period 2019-2021 from 0.40 to 0.82. As the ?(17O) of atmospheric CO2 seems to be dominated by stratospheric influx, its use as a tracer for stratosphere-troposphere exchange should be further investigated.</p