Quantifying the impact of winter warming on the Arctic carbon cycle

The Arctic has undergone extreme changes during the last decades and is warming over twice the global average. There has been increasing interest in understanding how warming and changes in snow and rainfall will affect high-latitude ecosystems. Although observational studies highlight the importance of cold-season carbon fluxes on the annual carbon balance, models, in general, cannot realistically capture these wintertime processes. In this thesis, we developed the LPJ-GUESS ecosystem model to better represent cold season processes. Our aim is to evaluate how changing winter conditions would affect arctic ecosystems and, indirectly, the global carbon and hydrological cycles. In our first study, we introduced a new snow scheme that improved the pan-Arctic model-data correspondence in observed snow depth, snow season length and snow insulation capacity. We used the updated model to examine the relationships between snow conditions and carbon flux changes under different future scenarios. We found that the coldest regions and coldest season are most vulnerable to environmental changes, which corresponds to the areas where we currently have the largest uncertainties. We explored the impact of extreme winter events on ground conditions and carbon fluxes. This study highlighted the still-existing shortcomings of the model in capturing short-term extreme weather phenomena and their impact. We tested a conceptual model to enable the simulation of autumn-time methane emissions at a high-arctic study site. The updated module could simulate both the growing season and autumn-time methane emission peaks, and we proposed further investigation into the possibilities of including physical controls of methane emissions in the model. Our studies improved the model’s performance in simulating wintertime processes across the Arctic. We highlight the importance of further developing snow dynamics and cold season greenhouse exchange processes in ecosystem models. Further improvements are necessary to create more robust future predictions regarding the impact of climate change on arctic ecosystems and their global consequences

Csapadékmezők mintázatának és szélsőségeinek változása Európa déli térségeiben

A klímaváltozás napjaink egyik legfontosabb globális problémája, éppen ezért egy sokat kutatott témakör. Ezen belül is kiemelkedik a csapadékmezők vizsgálata, ugyanis a hőmérséklet emelkedésével nőhet a légkör vízgőztartalma, ez pedig közvetlen hatással van a csapadékos helyzetekre is. Míg a globális hőmérséklet egyértelmű növekvő tendenciát mutat a mérések alapján, addig a csapadék nagyobb időbeli és térbeli változékonysága miatt a változás mértéke és iránya eltérő az egyes régiókban. Még Európán belül sem egységes a csapadékmezők változásának trendje. Modellszimulációk eredményei alapján a kontinens északi régiói csapadékosabbá, míg a déli területek szárazabbá válnak a XXI. század végére (Jacob et al., 2014). Az átlagos csapadékmennyiség változása mellett azonban az egész kontinensen várható a szélsőséges csapadékos helyzetek számának, illetve intenzitásának növekedése, amelyek önmagukban is komoly természeti kockázatot jelentenek, de magukkal vonhatják árvizek, villámárvizek kialakulását is (Rajczak & Schär, 2017). A kontinens déli részén pedig nem csak a csapadékos helyzetek szélsőségek felé tolódása jelenthet a jövőben problémát, hanem a száraz időszakok gyakoriságának és hosszának növekedése is (Jacob et al., 2014). Számos kutatás és tanulmány mutatja, hogy a csapadékmezőkben várhatóan bekövetkező változások, vagyis az extrém csapadékos és extrém száraz helyzetek gyakoriságának növekedése már az utóbbi néhány évtizedben is kimutatható. A kontinens déli területeit érintő várható jövőbeli változások mértékét szem előtt tartva ebben a tanulmányban három, Európa déli területein elhelyezkedő alföldi terület (amelyek közül egyik a magyar Alföld területét is magában foglalja) múltra vonatkozó vizsgálatát mutatjuk be, melyek esetében a klímaváltozás következményei jelentős hatást gyakorolhatnak a gazdaságra, a társadalomra és a környezetre egyaránt

Csapadékszélsőségek változása Európa déli alföldi régióiban az 1951–2019 időszakban

Kutatásunk célja, hogy a kiválasztott alföldi területek átlagos és extrém csapadékmezőiben bekövetkezett múltbeli változásokat elemezzük az 1951-2019 időszakban. Az Alföld mellett kettő olyan, a kontinens déli részén elhelyezkedő alföldi területet (a Pó-síkságot és a Román-alföldet) választottunk ki, melyekkel az összehasonlítás révén a klímaváltozás gazdaságra, társadalomra és környezetre gyakorolt hatását átfogóan értékelhetjük. A célterületeket objektív módon választottuk ki, nevezetesen az alföldi régióknak a következő két kritériumnak kellett megfelelnie: (i) Az adott síkság egyetlen pontja sem éri el a 200 m tengerszint feletti magasságot, továbbá (ii) a területen belül a szomszédos rácspontok magasságbeli különbsége nem haladhatja meg az 50 m-t. Az extrém csapadékos helyzetek elemzését éves időszakokra végeztük el 17 éghajlati index számításával. A múltra vonatkozó vizsgálataink alapján egyértelmű növekedés mutatható ki a szélsőséges csapadékos helyzetek gyakoriságában és intenzitásában, a száraz időszakok hosszában, valamint a szélsőséges időjárási viszonyok előfordulásában is

State-of-the-art capabilities in LPJ-GUESS

LPJ-GUESS is an advanced DGVM including detailed forest demography and management, croplands, wetlands, specialised arctic processes, emissions of nonCO2 GHGs and a highly flexible land-use change scheme which tracks transitions between different land-uses. It is the vegetation component of the EC-Earth CMIP6 ESM, the RCA-GUESS regional ESM, and also has a European mode operating at tree species level

Snow insulation effects across the Arctic : evaluating a revised snow module in LPJ-GUESS

The effect of future changes in temperature and precipitation patterns on arctic ecosystem functioning is often assessed using state-of-the-art ecosystem models. Many models however lack detailed representation of wintertime processes, as pointed out by recent studies (Wang et al. 2016, Slater and Lawrence 2013). This bias may influence the derived outputs, such as soil temperature, permafrost extent and global carbon budget estimations. In this project, the dynamic vegetation model LPJ-GUESS was applied with different complexity snow schemes, with the aim of assessing whether the developments in snow dynamics enhance the performance of the model in relation to air-soil temperature relationships (snow insulation effect). We hypothesise that refinement of the snow scheme can provide higher agreement between modelled and observational entities. The single site analysis showed that a newly developed Advanced multi-layer, intermediate complexity scheme is best suited to simulate internal snow dynamics, and the derived snow depth and soil temperature outputs are comparable to measured entities. The regional multi-site analysis showed that the Advanced multi-layer scheme can best capture the air-soil temperature variability, but the insulation effect is smaller than observed. The effect of using different snow schemes is evident from the simulated Arctic active layer depth and permafrost extent. Based on these results, the quantification of the snow insulation effect on soil properties and permafrost extent may prompt developments in the model's structural scheme. These updates could help to simulate physical and biogeochemical processes with reduced uncertainty at high latitudes. References: Slater, Andrew G. and David M. Lawrence (2013). “Diagnosing Present and Future Permafrost from Climate Models”. In: Journal of Climate 26.15, pp. 5608–5623. DOI: 10.1175/JCLI-D-12-00341.1. Wang, Wenli et al. (2016). “Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region”. English. In: Cryosphere 10.4, pp. 1721–1737. ISSN: 1994-0416. DOI: 10.5194/tc-10-1721-2016.The effect of future changes in temperature and precipitation patterns on arctic ecosystem functioning is often assessed using state-of-the-art ecosystem models. Many models however lack detailed representation of wintertime processes, as pointed out by recent studies. This bias may influence the derived outputs, such as soil temperature, permafrost extent and global carbon budget estimations. In this project, the dynamic vegetation model LPJ-GUESS was applied with different complexity snow schemes, with the aim of assessing whether the developments in snow dynamics enhance the performance of the model in relation to air-soil temperature relationships (snow insulation effect). We hypothesise that refinement of the snow scheme can provide higher agreement between modelled and observational entities. The single site analysis showed that a newly developed intermediate complexity scheme is best suited to simulate internal snow dynamics, and the derived snow depth and soil temperature outputs are comparable to measured entities. The regional multi-site analysis showed that the new scheme can best capture the air-soil temperature variability, but the insulation effect is smaller than observed. The effect of using different snow schemes is evident from the simulated Arctic active layer depth and permafrost extent. Based on these results, the quantification of the snow insulation effect on soil properties and permafrost extent may prompt developments in the model's structural scheme. These updates could help to simulate physical and biogeochemical processes with reduced uncertainty at high latitudes

Modelling global Gross Primary Production using the correlation between key leaf traits

Sophisticated ecosystem models make it possible to evaluate the potential future changes of the carbon sequestration capacity of the terrestrial biosphere, as a response to the rapid environmental and climatic changes. Accuracy of model estimates is however strongly dependent on the parametrisation of driving parameters. A previous study of Wang et al. (2012) suggests, that the knowledge of the relationship between key leaf traits may be used to constrain modelled global terrestrial GPP ranges. Access to extensive leaf trait databases (such as GLOPNET and TRY) open possibilities to develop a more mechanistic rather than empirical based methods of representing vegetation in ecosystem models. Prentice et al. (2015) suggests that a stochastic parametrisation approach – like the one applied here – should be considered as a future improvement in ecosystem model development. This thesis discusses the effect of varying key leaf attribute values on derived GPP estimates. Leaf parameters – specifically leaf longevity, leaf nitrogen content and leaf mass per area – are varied within their potential ranges, either individually one-at-a-time or leaf longevity and leaf N traits simultaneously. The methods are applied for LPJ-GUESS DGVM and a simple idealised model (LEIA), that accounts for GPP’s dependency on leaf traits. According to the results, adjusting leaf lifespan values for evergreen and summergreen groups, as well as leaf N yielded a substantial reduction in global annual GPP variance, along with a decrease in mean global estimates. Findings suggest that using the correlation between leaf attributes may significantly improve LPJ-GUESS’s performance.Sophisticated ecosystem models make it possible to evaluate how the carbon uptake capacity of the terrestrial biosphere will change, as a response to the rapid environmental and climatic changes. The accuracy of model derived estimates is however strongly dependent on the values of parameters describing the included processes. A previous study of Wang et al. (2012) suggests, that the knowledge of the relationship between specific leaf traits may be used to constrain modelled global terrestrial gross primary production (GPP, defines the amount of carbon taken up by the vegetation through the process of photosynthesis) ranges. Having access to extensive leaf trait databases open possibilities to develop the representation of vegetation properties in ecosystem models. This thesis discusses the effect of varying key leaf attribute values – specifically leaf longevity, leaf mass per area and leaf nitrogen content - on derived GPP estimates. The described methods are applied for LPJ-GUESS dynamic global vegetation model and a simple idealised model (LEIA), that accounts for GPP’s dependency on leaf traits. Findings suggest that using the correlation between leaf attributes may decrease the uncertainty in model derived estimates and thus significantly improve models’ performance

A biographical sketch of albert szent-györgyi La vitamina C y algo más. Un premio Nobel poco conocido en Chile

© 2015 Sociedad Medica de Santiago. All rights reserved. Albert Szent-Györgyi was a Hungarian biochemist and physiologist. He identified the structure and function of vitamin C, naming it as ascorbic acid. His research on cellular respiration and oxidation provided the basis for Krebs’ citric acid cycle. He was awarded the Nobel Prize in 1937. With his collaborators, he discovered the biochemical basis of muscle contractility, isolating the basic proteins, giving them the name myosin and actin. Later on, he worked on the theory of carcinogenesis, linked to electron movements. He was one of the first researchers to describe the connection between free radicals and cancer. He lived a long, very complete life, defending always his opinion and freedom