Search

Future Arctic sea ice loss has a known impact on Arctic amplification (AA) and mean atmospheric circulation. Furthermore, several studies have shown it leads to a decreased variance in temperature over North America. In this study, we analyze results from two fully coupled Community Earth System Model (CESM) Whole Atmosphere Community Climate Model (WACCM4) simulations with sea ice nudged to either the ensemble mean of WACCM historical runs averaged over the 1980–99 period for the control (CTL) or projected RCP8.5 values over the 2080–99 period for the experiment (EXP). Dominant large-scale meteorological patterns (LSMPs) are then identified using self-organizing maps applied to winter daily 500-hPa geopotential height anomalies ( Z 500 ′ ) over North America. We investigate how sea ice loss (EXP − CTL) impacts the frequency of these LSMPs and, through composite analysis, the sensible weather associated with them. We find differences in LSMP frequency but no change in residency time, indicating there is no stagnation of the flow with sea ice loss. Sea ice loss also acts to de-amplify and/or shift the Z 500 ′ that characterize these LSMPs and their associated anomalies in potential temperature at 850 hPa. Impacts on precipitation anomalies are more localized and consistent with changes in anomalous sea level pressure. With this LSMP framework we provide new mechanistic insights, demonstrating a role for thermodynamic, dynamic, and diabatic processes in sea ice impacts on atmospheric variability. Understanding these processes from a synoptic perspective is critical as some LSMPs play an outsized role in producing the mean response to Arctic sea ice loss. Significance Statement: The goal of this study is to understand how future Arctic sea ice loss might impact daily weather patterns over North America. We use a global climate model to produce one set of simulations where sea ice is similar to present conditions and another that represents conditions at the end of the twenty-first century. Daily patterns in large-scale circulation at roughly 5.5 km in altitude are then identified using a machine learning method. We find that sea ice loss tends to de-amplify these patterns and their associated impacts on temperature nearer the surface. Our methodology allows us to probe more deeply into the mechanisms responsible for these changes, which provides a new way to understand how sea ice loss can impact the daily weather we experience. [ABSTRACT FROM AUTHOR]

Stations are an important source of meteorological data, but often suffer from missing values and short observation periods. Gap filling is widely used to generate serially complete datasets (SCDs), which are subsequently used to produce gridded meteorological estimates. However, the value of SCDs in spatial interpolation is scarcely studied. Based on our recent efforts to develop a SCD over North America (SCDNA), we explore the extent to which gap filling improves gridded precipitation and temperature estimates. We address two specific questions: 1) Can SCDNA improve the statistical accuracy of gridded estimates in North America? 2) Can SCDNA improve estimates of trends on gridded data? In addressing these questions, we also evaluate the extent to which results depend on the spatial density of the station network and the spatial interpolation methods used. Results show that the improvement in statistical interpolation due to gap filling is more obvious for precipitation, followed by minimum temperature and maximum temperature. The improvement is larger when the station network is sparse and when simpler interpolation methods are used. SCDs can also notably reduce the uncertainties in spatial interpolation. Our evaluation across North America from 1979 to 2018 demonstrates that SCDs improve the accuracy of interpolated estimates for most stations and days. SCDNA-based interpolation also obtains better trend estimation than observation-based interpolation. This occurs because stations used for interpolation could change during a specific period, causing changepoints in interpolated temperature estimates and affect the long-term trends of observation-based interpolation, which can be avoided using SCDNA. Overall, SCDs improve the performance of gridded precipitation and temperature estimates. [ABSTRACT FROM AUTHOR]

Current generation general circulation models (GCMs) simulate synoptic-scale climate state variables such as geopotential heights, specific humidity, and integrated vapor transport (IVT) more reliably than mesoscale precipitation. Statistical downscaling methods that condition precipitation on GCM-based, synoptic-scale climate features have shown promise in the reproduction of local precipitation. However, current approaches to climate-state-informed downscaling impose some limitations on the skill of precipitation reproduction, including hard clustering of climate modes into a discrete set of states, utilization of numerical clustering methodologies poorly suited to nonnormal data, and a tendency to focus on relationships to a limited set of large-scale climate modes. This study presents a methodology based on emerging machine learning techniques to develop global approximators of regional precipitation and discharge extremes given a suite of synoptic-scale climate state variables. Archetypal analysis is first used to define regional modes of winter and summer extreme precipitation and discharge across the eastern contiguous United States. A 2D convolution neural network (NN) is then used to predict the co-occurrence of the archetypes using 300- and 700-hPa geopotential heights, 300- and 700-hPa specific humidity, and IVT. Results suggest that 300-hPa geopotential height, 700-hPa specific humidity, and IVT yield the most reliable predictions, although with some important differences by season and region. Finally, we demonstrate that the trained activations of NN convolutional layers can be used to infer the causal pathways between synoptic-scale climate features and regional extremes. [ABSTRACT FROM AUTHOR]