Impacts of Climate Extremes on Terrestrial Productivity

Terrestrial biosphere absorbs approximately 28% of anthropogenic CO2 emissions. This terrestrial carbon sink might become saturated in a future climate regime. To explore the issues associated with this topic, an accurate estimate of gross primary production (GPP) of global terrestrial ecosystems is needed. A major uncertainty in modeling global terrestrial GPP is the parameter of light use efficiency (LUE). Most LUE estimates in global models are satellite-based and coarsely measured with emphasis on environmental variables. Others are from eddy covariance towers with much greater spatial and temporal data quality and emphasis on mechanistic processes, but in a limited number of sites. In this study, we conducted a comprehensive global study of tower-based LUE from 237 FLUXNET towers, and scaled up LUEs from in-situ tower level to global biome level. We integrated the tower-based LUE estimates with key environmental and biological variables at 0.5º × 0.5º grid-cell resolutions, using a random forest regression (RFR) approach. Then we developed a RFR-LUE-GPP model using the grid-cell LUE data. In order to calibrate the LUE model, we developed a data-driven RFR-GPP model using random forest regression method only. Our results showed LUE varies largely with latitude. We estimated a global area-weighted average of LUE at 1.23±0.03 gC m-2 MJ-1 APAR, which led to an estimate of global gross primary production (GPP) of 107.5±2.5 Gt C /year from 2001 to 2005. Large uncertainties existed in GPP estimations over sparsely vegetated areas covered by savannas and woody savannas at middle to low latitude (i.e. 20ºS to 40ºS and 5ºN to 40ºN) due to the lack of available data. Model results were improved by incorporating Köppen climate types to represent climate/meteorological information in machine learning modeling. This brought a new understanding to the recognized problem of climate-dependence of spring onset of photosynthesis and the challenges in accurately modeling the biome GPP of evergreen broad leaf forests (EBF). The divergent responses of GPP to temperature and precipitation at mid-high latitudes and at mid-low latitudes echo the necessity of modeling GPP separately by latitudes. We also used a perfect-deficit approach to identify forest canopy photosynthetic capacity (CPC) deficits and analyze how they correlate to climate extremes, based on observational data measured by the eddy covariance method at 27 forest sites over 146 site-years. We found that droughts severely affect the carbon assimilation capacities of evergreen broadleaf forest and deciduous broadleaf forest. The carbon assimilation capacities of Mediterranean forests were highly sensitive to climate extremes, while marine forest climates tended to be insensitive to climate extremes. Our estimates suggest an average global reduction of forest canopy photosynthetic capacity due to unfavorable climate extremes of 6.3 Pg C (~5.2% of global gross primary production) per growing season over 2001-2010, with evergreen broadleaf forests contributing 52% of the total reduction. At biome-scale, terrestrial carbon uptake is controlled mainly by weather variability. Observational data from a global monitoring network indicate that the sensitivity of terrestrial carbon sequestration to mean annual temperature (T) breaks down at a threshold value of 16oC, above which terrestrial CO2 fluxes are controlled by dryness rather than temperature. Here we show that since 1948 warming climate has moved the 16oC T latitudinal belt poleward. Land surface area with T \u3e16oC and now subject to dryness control rather than temperature as the regulator of carbon uptake has increased by 6% and is expected to increase by at least another 8% by 2050

The contribution of surface and submesoscale processes to turbulence in the open ocean surface boundary layer

The ocean surface boundary layer is a critical interface across which momentum, heat, and trace gases are exchanged between the oceans and atmosphere. Surface processes (winds, waves, and buoyancy forcing) are known to contribute significantly to fluxes within this layer. Recently, studies have suggested that submesoscale processes, which occur at small scales (0.1–10 km, hours to days) and therefore are not yet represented in most ocean models, may play critical roles in these turbulent exchanges. While observational support for such phenomena has been demonstrated in the vicinity of strong current systems and littoral regions, relatively few observations exist in the open‐ocean environment to warrant representation in Earth system models. We use novel observations and simulations to quantify the contributions of surface and submesoscale processes to turbulent kinetic energy (TKE) dissipation in the open‐ocean surface boundary layer. Our observations are derived from moorings in the North Atlantic, December 2012 to April 2013, and are complemented by atmospheric reanalysis. We develop a conceptual framework for dissipation rates due to surface and submesoscale processes. Using this framework and comparing with observed dissipation rates, we find that surface processes dominate TKE dissipation. A parameterization for symmetric instability is consistent with this result. We next employ simulations from an ocean front‐resolving model to reestablish that dissipation due to surface processes exceeds that of submesoscale processes by 1–2 orders of magnitude. Together, these results suggest submesoscale processes do not dramatically modify vertical TKE budgets, though such dynamics may be climatically important owing to their ability to remove energy from the ocean

The Past, Present, and Future of Dynamic Performance Research

This article reviews the extensive history of dynamic performance research, with the goal of providing a clear picture of where the field has been, where it is now, and where it needs to go. Past research has established that job performance does indeed change, but the implications of this dynamism and the predictability of performance trends remain unresolved. Theories are available to help explain dynamic performance, and although far from providing an unambiguous understanding of the phenomenon, they offer direction for future theoretical development. Dynamic performance research does suffer from a number of methodological difficulties, but new techniques have emerged that present even more opportunities to advance knowledge in this area. From this review, I propose research questions to bridge the theoretical and methodological gaps of this area. Answering these questions can advance both research involving job performance prediction and our understanding of the effects of human resource interventions

Essays in Environmental Economics and Policy

This dissertation is concerned with the impacts of environmental stressors on people’s health and welfare. In particular, it focuses on air pollution and temperatures anomalies. Air pollution is considered the fifth leading mortality risk factor worldwide (Cohen et al., 2017). Despite impressive improvements in air quality over the last half-century, air pollution remains a global challenge, especially in rapidly urbanizing countries. On an even larger scale, anthropocentric emissions of greenhouse gases have been pushing a shift in the world’s climate that is projected to widen in the coming decades. The ability of economies to cope with changing temperatures is paramount to limiting the damages from climate change. The three chapters of this thesis should be read in the framework set by these challenges. In each chapter, a specific question is brought to the surface, and the road to address it is outlined. Chapter 1 investigates the negative effect of air pollution on physical ability. A large share of the world’s population is employed in manual labor. Yet, our understanding of the productivity cots of air pollution for physically intense work remains limited. The chapter identifies in track and field competitions a natural experiment where cognition plays a minor role. Combining half a million competition results with weather and air quality data, it estimates the change in physical performance induced by variations in air pollution. Chapter 2 considers the methodological tools available to estimate the causal change in air pollution concentrations following a reduction in emissions. It recognizes the challenges to identification posed by fluctuations and trends in atmospheric conditions and proposes a machine learning approach to address them. The chapter then applies this strategy to quantify the reduction in pollution and related health benefits induced by the COVID-19 lockdown of Lombardy, Italy, in spring 2020. This work is a joint effort with Lara Aleluia Reis (RFF-CMCC European Institute on Economics and the Environment), Valentina Bosetti (Bocconi University), and Massimo Tavoni (Politecnico di Milano). Chapter 3 is concerned with the persistence of the effects of temperature anomalies on economic growth. If an adverse temperature shock damages the determinants of economic growth, we can expect losses from climate change - a permanent shift in the mean temperature - to be cumulative over time and, therefore, very costly. Despite the primary importance of this question for modeling the climate-economy interactions, data constraints and data-hungry approaches have led to inconclusive answers. This chapter presents a new and more efficient method to test for the persistence of effects; using three different GDP datasets, evidence emerges that temperature effects are indeed persistent. This chapter has been the output of joint work with Bernardo A. Bastien-Olvera and Frances C. Moore of the University of California at Davis

The impact of submesoscales on the stratification dynamics in the Southern Ocean

Submesoscale dynamics O(1-10 km, hours to days) are considered to strongly affect the stratification of the upper ocean. In the Southern Ocean, studies of submesoscale dynamics are biased to regions preconditioned for strong frontal activity and topographical influence. This dissertation considers the role of submesoscales on the evolution of mixed layer depth and upper ocean stratification in the open-ocean Subantarctic Ocean. First, we present autonomous ocean glider measurements from spring to late-summer to show that transient increases in stratification within the mixed layer during spring result in rapid mixed layer shoaling events. A realistically-forced simulation using a one-dimensional mixed layer model fails to explain these observed stratification events. We show that during this time, baroclinic mixed layer instabilities periodically induce a restratification flux of over 1000 W. m2, suggesting that the unexplained restratification is likely a result of submesoscale flows. Second, we study four separate years of seasonal-length (mid-winter to latesummer) glider experiments to define how submesoscale flows may induce interannual variations in the onset of spring/summer mixed layer restratification. Sustained temporal increases of stratification above the winter mixed layer, which defines the onset of seasonal restratification, can differ by up to 28 days between the four years studied. To explain this discrepancy, equivalent heat fluxes of baroclinic mixed layer instabilities (restratification) and Ekman buoyancy flux (restratification or mixing) are parameterized into a one-dimensional mixed layer model. Simulations including the parameterizations reveal a seasonal evolution of mixed layer stratification which is significantly more comparable to the glider observations than model simulations using heat and freshwater fluxes alone. Furthermore, the parameterization dramatically improves the sub-seasonal variability of mixed layer stratification, particularly during the onset of seasonal restratification when the mixed layer remains deep despite a positive surface heat flux. Following this, we characterize the full seasonal cycle of submesoscale flows using a realistically-forced 1/36 NEMO simulation of the Atlantic Southern Ocean. We show that deep winter mixed layers enhance the upper ocean available potential energy, which through the release of baroclinic mixed layer instabilities drive increased vertical buoyancy flux and potential to kinetic energy. These processes are associated with strong vertical velocities within the mixed layer characterized by large instantaneous upwelling and downwelling fluxes at the location of fronts. The insights from the glider observations propose that baroclinic mixed layer instabilities lead to increased near surface restratification in winter to spring, but are regulated by the synoptic-scale increases in Ekman buoyancy flux, which can keep the mixed layer deep for up to a month after surface warming. We propose the balance between restratification by baroclinic mixed layer instabilities and strong Ekman buoyancy flux driven by the passing of Southern Ocean storms is key in setting the large inter-annual variations of seasonal mixed layer restratification in the Subantarctic Ocean. Finally, we constrain the ability of gliders to represent regional submesoscale dynamics to provide context to current observations and inform future field work operations. Virtual gliders simulated within the 1/36 simulation show that horizontal buoyancy gradients in the Subantarctic are largely isotropic. We show that increasing the number of gliders sampling simultaneously over one month from one to a swarm of six results in improving the representation of the total distribution of horizontal buoyancy gradients across the Subantarctic from 10% to 42%. Similarly, by having a single glider sampling for six consecutive months, the distribution of horizontal buoyancy gradients observed increases to 47% of the total distribution. The insights presented in this dissertation enhance our understanding of submesoscale flows in the open-ocean Southern Ocean. These results are likely to have direct implications for physical and biological processes related to the ocean’s role on climate

Hybrid modeling of aboveground biomass carbon using disturbance history over large areas of boreal forest in eastern Canada

Le feu joue un rôle important dans la succession de la forêt boréale du nord-est de l’Amérique et le temps depuis le dernier feu (TDF) devrait être utile pour prédire la distribution spatiale du carbone. Les deux premiers objectifs de cette thèse sont: (1) la spatialisation du TDF pour une vaste région de forêt boréale de l'est du Canada (217,000 km2) et (2) la prédiction du carbone de la biomasse aérienne (CBA) à l’aide du TDF à une échelle liée aux perturbations par le feu. Un modèle non paramétrique a d’abord été développé pour prédire le TDF à partir d’historiques de feu, des données d'inventaire et climatiques à une échelle de 2 km2. Cette échelle correspond à la superficie minimale d’un feu pour être inclus dans la base de données canadienne des grands feux. Nous avons trouvé un ajustement substantiel à l’échelle de la région d’étude et à celle de paysages régionaux, mais la précision est restée faible à l’échelle de cellules individuelles de 2 km2. Une modélisation hiérarchique a ensuite été développée pour spatialiser le CBA des placettes d’inventaire à la même échelle de 2 km2. Les proportions des classes de densité du couvert étaient les variables les plus importantes pour prédire le CBA. Le CBA co-variait également avec la vitesse de récupération du couvert au travers de laquelle le TDF intervient indirectement. Finalement, nous avons comparé des estimations de CBA obtenues par télédétection satellitaire avec celles obtenues précédemment. Les résultats indiquent que les proportions des classes de densité du couvert et des types de dépôts ainsi que le TDF pourraient servir comme variables auxiliaires pour augmenter substantiellement la précision des estimés de CBA par télédétection. Les résultats de cette étude ont montré: 1) l'importance d’allonger la profondeur temporelle des historiques de feu pour donner une meilleure perspective des changements actuels du régime de feu; 2) l'importance d'intégrer l’information sur la reprise du couvert après feu aux courbes de rendement de CBA dans les modèles de bilan de carbone; et 3) l'importance de l'historique des feux et de la récupération de la végétation pour améliorer la précision de la cartographie de la biomasse à partir de la télédétection.Fire is as a main succession driver in northeastern American boreal forests and time since last fire (TSLF) is seen as a useful covariate to infer the spatial variation of carbon. The first two objectives of this thesis are: (1) to elaborate a TSLF map over an extensive region in boreal forests of eastern Canada (217,000 km2) and (2) to predict aboveground carbon biomass (ABC) as a function of TSLF at a scale related to fire disturbances. A non-parametric model was first developed to predict TSLF using historical records of fire, forest inventory data and climate data at a 2-km2 scale. Two kilometer square is the minimum size for fires to be considered important enough and included in the Canadian large fire database. Overall, we found a substantial agreement at the scale of both the study area and landscape units, but the accuracy remained fairly low at the scale of individual 2-km2 cells. A hierarchical modeling approach is then presented for scaling-up ABC from inventory plots to the same 2 km2 scale. The proportions of cover density classes were the most important variables to predict ABC. ABC was also related to the speed of post-fire canopy recovery through which TSLF acts indirectly upon ABC. Finally, we compared remote sensing based aboveground biomass estimates with our inventory based estimates to provide insights on improving their accuracy. The results indicated again that abundances of canopy cover density classes of surficial deposits, and TSLF may serve as ancillary variables for improving substantially the accuracy of remotely sensed biomass estimates. The study results have shown: 1) the importance of lengthening the historical records of fire records to provide a better perspective of the actual changes of fire regime; 2) the importance of incorporating post-fire canopy recovery information together with ABC yield curves in carbon budget models at a spatial scale related to fire disturbances; 3) the importance of adding disturbance history and vegetation recovery trends with remote sensing reflectance data to improve accuracy for biomass mapping

Exploring transition processes in Germany with environmental data : Empirical essays on international trade and the environment, regulation by information and spatial environmental data

This cumulative dissertation is a collection of publications and working papers exploring recent transition processes in Germany with environmental data. The individual chapters deal with the topics of international trade and the environment, regulation by information and spatial environmental data. A short description and summary of each topic can be found in the General Introduction of this dissertation

Forecasting shifts in habitat suitability across the distribution range of a temperate small pelagic fish under different scenarios of climate change

Climate change often leads to shifts in the distribution of small pelagic fish, likely by changing the match-mismatch dynamics between these sensitive species within their environmental optima. Using present-day habitat suitability, we projected how different scenarios of climate change (IPCC Representative Concentration Pathways 2.6, 4.5 and 8.5) may alter the large scale distribution of European sardine Sardina pilchardus (a model species) by 2050 and 2100. We evaluated the variability of species-specific environmental optima allowing a comparison between present-day and future scenarios. Regardless of the scenario, sea surface temperature and salinity and the interaction between current velocity and distance to the nearest coast were the main descriptors responsible for the main effects on sardine's distribution. Present-day and future potential “hotspots” for sardine were neritic zones ( 20 (PSU), on average. Most variability in projected shifts among climatic scenarios was in habitats with moderate to low suitability. By the end of this century, habitat suitability was projected to increase in the Canary Islands, Iberian Peninsula, central North Sea, northern Mediterranean, and eastern Black Sea and to decrease in the Atlantic African coast, southwest Mediterranean, English Channel, northern North Sea and Western U.K. A gradual poleward-eastward shift in sardine distribution was also projected among scenarios. This shift was most pronounced in 2100 under RCP 8.5. In that scenario, sardines had a 9.6% range expansion which included waters along the entire coast of Norway up and into the White Sea. As habitat suitability is mediated by the synergic effects of climate variability and change on species fitness, it is critical to apply models with robust underlying species-habitat data that integrate knowledge on the full range of processes shaping species productivity and distribution.Preprin

Assessment of the effects of climate change on littoral ecosystems

RESUMEN: El objetivo de esta tesis es evaluar los efectos del cambio climático en la distribución de macroalgas en Europa. Para ello, en primer lugar, se desarrolló una base de datos de variables ambientales con sentido ecológico, tanto para el periodo histórico como en escenarios de cambio climático (OCLE, http://ocle.ihcantabria.com). Los datos se recopilaron de fuentes con series temporales homogéneas y largas (1985-2015 y 2015-2099) para 16 variables relevantes en la distribución de macroalgas. Posteriormente se seleccionaron cinco especies representativas en Europa: Saccorhiza polyschides, Gelidium spinosum, Sargassum muticum, Pelvetia canaliculata y Cystoseira baccata. Su riesgo frente al cambio climático se evaluó mediante modelos de distribución de especies. Para reducir su incertidumbre en las proyecciones temporales, se desarrolló una metodología para seleccionar los algoritmos más transferibles en el tiempo, que se aplicó a los escenarios RCP 4.5 y 8.5 para el medio (2040-2069) y el largo plazo (2070-2099). Los resultados contribuyen a la mejora del conocimiento de la relación entre la ecología de las macroalgas y los factores ambientales, por un lado, y provee de herramientas a gestores e investigadores por el otro, para la construcción de modelos robustos con una metodología objetiva, reproducible, eficiente y aplicable a nivel mundial.ABSTRACT: The objective of this thesis is to assess the effects of climate change on macroalgae distribution in Europe. First, an ecologically-driven database of present and future drivers for marine life in Europe, the Open access database on Climate change effects on Littoral and oceanic Ecosystems (OCLE), was developed (http://ocle.ihcantabria.com). Data were gathered for homogeneous and long time series (1985-2015 and 2015-2099) for 16 variables relevant in seaweeds distribution. the Five species were selected as representative of European macroalgae: Saccorhiza polyschides, Gelidium spinosum, Sargassum muticum, Pelvetia canaliculata and Cystoseira baccata. To assess the risk due to climate change, species distribution models were selected as an appropriate tool. To reduce uncertainty in temporal extrapolation, a step-wise methodology to select the most transferable algorithms in time was developed. It was applied to RCPs 4.5 and 8.5 for the mid-term (2040-2069) and the long term (2070-2099). Results help to fill the gap in knowledge between seaweeds ecology and environmental drivers on the one-hand and between science and managers on the other, by paying particular attention to building robust models with objective, reproducible, globally applicable and efficient methodology.Esta tesis no habría sido posible sin el apoyo económico de la Fundación Instituto de Hidráulica Ambiental de la Universidad de Cantabria y el Ministerio de Economía y Competitividad (BES‐2016‐076434)