By the end of 2016 surveillance and reconnaissance satellites will have been monitoring Arctic-wide sea ice conditions for decades. Situated at the boundary between atmosphere and ocean, Arctic sea ice retreat has been one of the most conspicuous indication of climate change, especially in the two most recent decades. The 2001 annual minimum extent of Arctic sea ice marks the last year above the 1981 -- 2012 long-term average extent. Ever since then only lower than average Arctic sea ice has been observed at the end of each summer's melt season. For more than a century climate scientists have postulated that the darkening of the Arctic due to retreating sea ice and therefore more exposed open ocean would be the consequence of global warming. In the first decade of the 2000s the human influence on that warming in the Arctic was indeed detected in observations and attributed to increasing atmospheric greenhouse-gas concentrations. In this study we direct our attention to a potential offsetting effect from other anthropogenic (OANT) forcing agents, mainly aerosols, that has potentially out masked a fraction of greenhouse-gas induced warming by a combined cooling effect. We acknowledge that multiple sources of uncertainty exist in our method, in particular in the observed records of Arctic sea ice and corresponding simulations from climate models. No formal detection and attribution (DA) analysis has yet been carried out to try to detect the combined cooling effect from aerosols in observations of Arctic sea ice extent. We use three publicly available observational data sets of Arctic sea ice and climate simulations from eight models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). In our detection and attribution study observations are regressed on model-derived climate response pattern, or fingerprints, under all known historical (ALL), greenhouse-gas only (GHG) and known natural-only (NAT) forcing factors using an optimal fingerprinting method. We estimate regression coefficients (scaling factors) for each forcing group that scale the fingerprints to best match the observed record. From the scaled ALL, GHG and NAT fingerprints we calculate the relative contribution of the observed sea ice decline attributable to OANT forcing agent. Based on our DA results we show that the simulated climate response patterns to changes in GHG, OANT and NAT forcing are detected in the observed records of September Arctic sea ice extent for the 1953 to 2012 period. / Graduate

Terrestrial evapotranspiration (ET) is a significant component of the energy and water balances at the land surface. However, direct, continuous measurements of ET are spatially limited and only available since the 1990s. Due to this lack of observations, detecting and attributing long-term regional trends in ET remains difficult. This dissertation aims to alleviate the data limitation and detect long-term trends by developing a method to infer ET from data collected at common weather stations, which are spatially and temporally abundant. The methodology used to infer ET from historical meteorological data is based on an emergent relation between the land surface and atmospheric boundary layer. We refer to this methodology as the Evapotranspiration from Relative Humidity at Equilibrium method, or the “ETRHEQ method”. In the first section of this dissertation, we develop the ETRHEQ method for use at common weather stations and demonstrate the utility of the method at twenty eddy covariance sites spanning a wide range of climate and plant functional types. Next, we apply the ETRHEQ method at historical weather stations across the continental U.S. and show that ET estimates obtained via the ETRHEQ method compare well with watershed scale ET, as well as ET estimates from land surface models. From 1961 to 1997, we find negligible or increasing trends in summertime ET over the central U.S. and the west coast and negative trends in the eastern and western U.S. From 1998 to 2014, we find a sharp decline in summertime ET across the entire U.S. We show that this decline is consistent with decreasing transpiration associated with declines in humidity. Lastly, we assess the sensitivity of ET to perturbations in soil moisture and humidity anticipated with climate change. We demonstrate that the response of ET to changing humidity and soil moisture is strongly dependent on the biological and hydrological state of the surface, particularly the degree of water stress and vegetation fraction. In total, this dissertation demonstrates the utility of the ETRHEQ method as a means to estimate ET from weather station data and highlights the critical role of vegetation in modulating ET variability.

In this thesis, using the principles of confirmatory factor analysis (CFA) and the cause-effect concept associated with structural equation modelling (SEM), a new flexible statistical framework for evaluation of climate model simulations against observational data is suggested. The design of the framework also makes it possible to investigate the magnitude of the influence of different forcings on the temperature as well as to investigate a general causal latent structure of temperature data. In terms of the questions of interest, the framework suggested here can be viewed as a natural extension of the statistical approach of 'optimal fingerprinting', employed in many Detection and Attribution (D&amp;A) studies. Its flexibility means that it can be applied under different circumstances concerning such aspects as the availability of simulated data, the number of forcings in question, the climate-relevant properties of these forcings, and the properties of the climate model under study, in particular, those concerning the reconstructions of forcings and their implementation. It should also be added that although the framework involves the near-surface temperature as a climate variable of interest and focuses on the time period covering approximately the last millennium prior to the industrialisation period, the statistical models, included in the framework, can in principle be generalised to any period in the geological past as soon as simulations and proxy data on any continuous climate variable are available.  Within the confines of this thesis, performance of some CFA- and SEM-models is evaluated in pseudo-proxy experiments, in which the true unobservable temperature series is replaced by temperature data from a selected climate model simulation. The results indicated that depending on the climate model and the region under consideration, the underlying latent structure of temperature data can be of varying complexity, thereby rendering our statistical framework, serving as a basis for a wide range of CFA- and SEM-models, a powerful and flexible tool. Thanks to these properties, its application ultimately may contribute to an increased confidence in the conclusions about the ability of the climate model in question to simulate observed climate changes. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript. Paper 3: Manuscript.</p>