Half of Earth's land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decisionmaking processes work in specific contexts.

There is much confidence in the global temperature change and its attribution to human activities. Global climate models have attained unprecedented complexity in representing the climate system and its response to external forcings. However, climate prediction remains a serious challenge and carries large uncertainty, particularly when the scale of interest becomes small. With the increasing interest in regional impact studies for decision-making, one of the urgent tasks is to make a systematic, quantitative evaluation of the expected skill of climate models over a range of spatiotemporal scales. The first part of this dissertation was devoted to this task, with focus on the predictive skill in the linear trend of surface air temperature. By evaluating the hindcasts for the last 120 year period in the form of deterministic and probabilistic predictions, it was found that the hindcasts can reproduce broad-scale changes in the surface air temperature, showing reliable skill at spatial scales larger than or equal to a few thousand kilometers (30° x 30°) and at temporal scales of 30 years or longer. However, their skill remains limited at smaller spatiotemporal scales, where we saw no significant improvement over climatology or a random guess. Over longer temporal scales, the feedbacks from the carbon cycle to atmospheric CO₂ concentration become important. Therefore the rest of the dissertation attempts to find key processes in the climate-carbon cycle feedback using one of the leading land-climate models, the National Center for Atmospheric Research Community Land Model. Evaluation of site-level simulations using field observations from the Amazon forest revealed that the current formulation for drought-related mortality, which lacks the ecophysiological link between short- and long-term drought stress, prevent the model from simulating realistic forest response. Global simulations showed that such dynamics of vegetation strongly influences the control of the nitrogen cycle on vegetation productivity, which then alters the sensitivity of the terrestrial biosphere to surface air temperature. This implies that if the state of the terrestrial biosphere is inconsistent with the simulated climate, either biased or prescribed, then its feedback to anthropogenic forcing could be also inconsistent.