Chapter 1 : Overview. Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II

Jay, A.; Reidmiller, D. R.; Avery, Christopher W.; Barrie, Daniel; DeAngelo, Benjamin; Dave, Apurva C.; Dzaugis, Matthew P.; Kolian, Michael; Lewis, Kristin; Reeves, Katie; Winner, Darrell A.

doi:10.7930/nca4.2018.ch1

Cited by 43 publications

(30 citation statements)

References 30 publications

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“…Precipitation characteristics are of great importance to the central United States, a significant grain growing region in North America and the breadbasket of the world (USDA, 2019). While rainfall with extreme intensity can induce flooding and cause significant economic losses and impact livelihoods (Conant, 2018;Kloesel, 2018), the timing and frequency of nonextreme rainfall events during the warm season can significantly impact vegetation productivity, which is intricately linked to agriculture and livestock in the region (Angel et al, 2018;Conant, 2018;Feng et al, 2013;Jay, 2018;Knapp et al, 2008). Therefore, it is critical to better understand the rainfall characteristics in the central United States and their long-term changes, especially given their significant impacts on human-ecosystem interactions that have large downstream consequences (Conant, 2018;Knapp et al, 2008;Weltzin et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Observed Warm‐Season Characteristics of MCS and Non‐MCS Rainfall and Their Recent Changes in the Central United States

Leung

Feng

2020

Geophysical Research Letters

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Warm‐season rainfall characteristics in the central United States are investigated as they play important roles in ecohydrology and agricultural productivity. Using rainfall observations, we compare the April–August mesoscale convective systems (MCS) and non‐MCS rainfall characteristics and examine their linear trends between 1997 and 2018. MCS rainfall is found to be approximately seven times more intense than non‐MCS rainfall but it occurs less frequently in time and space. MCS rainfall peaks in nocturnal hours, with synchronized timing of rainfall intensity, area, and occurrence, while non‐MCS rainfall peaks in late‐afternoon hours, mostly attributed to the timing of peak rainfall area. MCS rainfall has increased in the last 22 years due to an increase in frequency and a longer duration per MCS. In contrast, non‐MCS rainfall has decreased mainly due to a reduction in rainfall area, leading to fewer total wet days and increased dry intervals between events.

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Section: Introductionmentioning

confidence: 99%

Observed Warm‐Season Characteristics of MCS and Non‐MCS Rainfall and Their Recent Changes in the Central United States

Leung

Feng

2020

Geophysical Research Letters

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“…In the Western USA, changes in climate have introduced a suite of impacts that are projected to increase in frequency and severity in the future, including drought, forest fire, and flooding (Gordon & Ojima 2015;Gonzalez et al 2018;Reidmiller et al 2018). These impacts pose a challenge to public land management agencies, who manage an average of 48.3% of the land in each of the 12 western states (Vincent et al 2017), and the people whose livelihoods are dependent upon federal lands (Gordon & Ojima 2015;Kemp et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In 2017, President Trump removed the threat of climate change from his National Security Strategy and withdrew from the Paris Agreement in 2017 (Shear 2017;Trump 2017), and the DOI Strategic Plan for 2018-2022 contains no mentions of climate change (Department of Interior (DOI), 2018a). Despite a lack of active climate change-related policy from the current administration, the reality of climate change will require action in the future (Reidmiller et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Planning for change? Assessing the integration of climate change and land-based livelihoods in Colorado BLM planning documents

2020

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Many western communities are surrounded by public lands that support land-based and local economies. Bureau of Land Management (BLM) decision-making affects the vulnerability of those land-based livelihoods, especially in the context of climate change. We analyzed Colorado BLM planning documents to evaluate how they are considering climate change, sensitive resources, impacts, and land-based livelihoods in their planning processes using both quantitative word counts and qualitative coding. Documents published in recent years (2011-2015) include more mentions of climate change than older documents (1985-1997). However, the review showed that while climate change is discussed within the National Environmental Policy Act (NEPA) planning documents, the final Resource Management Plans contain few mentions of climate change. Further, there is minimal consideration of how climate change may impact land-based livelihoods. These results prompt questions about the planning process, how climate change considerations are integrated into the final documents, and how that impacts on-theground management. The review suggests a need for increased consideration of climate change throughout the BLM's planning process so that landscapes can be managed with more attention and awareness to climate change and the associated impacts to resources and dependent communities. Keywords Land-based livelihoods. Climate change. BLM. Gray literature. Planning. NEPA 1 Animal unit month (AUM) is defined as the forage needed for one "animal unit" for 1 month. An "animal unit" is considered a 1000-lb cow and her suckling calf. AUM is the unit used to calculate federal grazing fees by the BLM.

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“…Climate prediction is a cornerstone in numerous scientific investigations and decision-making processes (e.g., Stocker et al, 2013;Jay et al, 2018). In the long term (decades to centuries), different possible climate outcomes pose very different hazards, risks, and societal challenges, such as build-ing and maintaining infrastructure (e.g., Moss et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

et al. 2020

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Abstract. We investigate techniques for using deep neural networks to produce surrogate models for short-term climate forecasts. A convolutional neural network is trained on 97 years of monthly precipitation output from the 1pctCO2 run (the CO2 concentration increases by 1 % per year) simulated by the second-generation Canadian Earth System Model (CanESM2). The neural network clearly outperforms a persistence forecast and does not show substantially degraded performance even when the forecast length is extended to 120 months. The model is prone to underpredicting precipitation in areas characterized by intense precipitation events. Scheduled sampling (forcing the model to gradually use its own past predictions rather than ground truth) is essential for avoiding amplification of early forecasting errors. However, the use of scheduled sampling also necessitates preforecasting (generating forecasts prior to the first forecast date) to obtain adequate performance for the first few prediction time steps. We document the training procedures and hyperparameter optimization process for researchers who wish to extend the use of neural networks in developing surrogate models.

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Chapter 1 : Overview. Impacts, Risks, and Adaptation in the United States: The Fourth National Climate Assessment, Volume II

Cited by 43 publications

References 30 publications

Observed Warm‐Season Characteristics of MCS and Non‐MCS Rainfall and Their Recent Changes in the Central United States

Observed Warm‐Season Characteristics of MCS and Non‐MCS Rainfall and Their Recent Changes in the Central United States

Planning for change? Assessing the integration of climate change and land-based livelihoods in Colorado BLM planning documents

Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

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