Seasonal, synoptic, and intraseasonal variability of the West African monsoon

2012 Fall.Includes bibliographical references.The simulation of the West African monsoon is examined in two coupled general circulation models (CGCMs). The first model is the standard Community Climate System Model (CCSM) which uses traditional parameterizations to represent convective processes. The second model is the superparameterized-CCSM (SP-CCSM), in which convective parameterizations have been replaced by embedding a two-dimensional cloud resolving model into each gridbox. Superparameterization is intended to improve simulation of the complex multiscale interactions that occur between the large-scale environment and clouds. Key features of West African climate are analyzed in both models including: the mean annual cycle of the monsoon, African easterly wave (AEW) activity and dynamics, and the intraseasonal modulation of precipitation. Adding superparameterization improves the position and intensity of the summer maximum in precipitation which is shifted from over the Gulf of Guinea in CCSM (not realistic), to over the continent in SP-CCSM which is in keeping with the observations. AEWs and their relationship with convection are also improved in the SP-CCSM: In the standard model, little to no easterly wave activity occurs over West Africa, and the relationship with convection is tenuous at best. SP-CCSM on the other hand produces strong AEWs over the region that exhibit similar horizontal and vertical structures to observations. AEWs in SP-CCSM are strongly coupled to convection, more so than is supported by observations. An examination of the energetics of the simulated AEWs suggests that convection drives the generation and propagation the waves in SP- CCSM. Consistent with observations, intraseasonal variations in West African precipitation in SP-CCSM appear to be linked to variations in convection in the Indo-Pacific region corresponding with the MJO and the Indian monsoon. Because of these physically-realistic relationships, SP-CCSM has potential to deepen our understanding of the teleconnections between the MJO and West Africa, helping to improve seasonal rainfall forecasts

LSTM-Based Data Integration to Improve Snow Water Equivalent Prediction and Diagnose Error Sources

Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Re-cently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow-and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values dmax were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with sta-ble seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies

Climate models show Colorado drying sooner and with greater certainty east of the Continental Divide

Many studies have examined the aridity of the Colorado River Basin and the possible impacts of climate change which could further strain already over-allocated water resources in the region. Fewer studies have examined the multiple Colorado Rocky Mountain headwater regions specifically. This is especially true of areas East of the Continental Divide, despite water originating there being critical to cities and agriculture in Eastern Colorado and further downstream. This paper explores and compares drying trends in the Eastern and Western Colorado Rocky Mountains using single-model initial-condition large ensembles from ten global climate models. The use of multiple models allows us to identify signals that are consistent across different physics parameterizations, model grids, and other model intricacies. The large ensembles also allow us to quantify the time of emergence of these climate change signals-that is, when did (or when will) the long term change due to anthropogenic greenhouse gasses exceed the internal variability of the climate system. Consistent with previous studies, we find evidence of drying on both sides of the Continental Divide. That drying is more pronounced, occurs sooner, and is more consistent across global climate models in the East, however, highlighting the region’s importance despite generally receiving less attention than the West

Anticipating how rain-on-snow events will change through the 21st century: lessons from the 1997 new year’s flood event

The California-Nevada 1997 New Year’s flood was an atmospheric river (AR)-driven rain-on-snow (RoS) event and remains the costliest in their history. The joint occurrence of saturated soils, rainfall, and snowmelt generated inundation throughout northern California-Nevada. Although AR RoS events are projected to occur more frequently with climate change, the warming sensitivity of their flood drivers across scales remains understudied. We leverage the regionally refined mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) to recreate the 1997 New Year’s flood with horizontal grid spacings of 3.5 km across California, with forecast lead times of up to 4 days, and across six warming levels ranging from pre-industrial conditions to +3.5∘C. We describe the sensitivity of the flood drivers to warming including AR duration and intensity, precipitation phase, intensity and efficiency, snowpack mass and energy changes, and runoff efficiency. Our findings indicate current levels of climate change negligibly influence the flood drivers. At warming levels ≥1.7∘C, AR hazard potential increases, snowpack nonlinearly decreases, antecedent soil moisture decreases (except where the snowline retreats), and runoff decreases (except in the southern Sierra Nevada where antecedent snowpack persists). Storm total precipitation increases, but at rates below warming-induced increases in saturation-specific humidity. Warming intensifies short-duration, high-intensity rainfall, particularly where snowfall-to-rainfall transitions occur. This study highlights the nonlinear tradeoffs in 21st-century RoS flood hazards with warming and provides water management and infrastructure investment adaptation considerations

Satori 2017

The Satori is a student literary publication that expresses the artistic spirit of the students of Winona State University. Student poetry, prose, and graphic art are published in the Satori every spring since 1970. The Satori 2017 editors are: Editor-in-Chief: Sajda Omar Poetry Editor: Karl Hanson Art/Design Editor: Danielle Eberhard Prose Editor: Cassie Douglas Poetry Committee: Kelly Johnson and Lydia Papenfuss Art/Design Committee: Aurie Brighton and Xinyue Wang Prose Committee: Katie McCoy, Madison Wilke, Megan Back, Alayna Godfrey, Madelyn Hall, and Sam Stormoen Faculty Advisor: Dr. Gary Eddy, Professor of Englishhttps://openriver.winona.edu/satori/1013/thumbnail.jp

Satori 2018

The Satori is a student literary publication that expresses the artistic spirit of the students of Winona State University. Student poetry, prose, and graphic art are published in the Satori every spring since 1970. The Satori 2018 editors are Sajda Omar (Editor-in-Chief), Kylie Hoff, Keyanna Hultman, Audrey Sitte, Elyse Hoffmann. Art Director and Designer by Elyse Hoffmann. The 2018 Faculty advisor is Dr. Elizabeth Oness, Professor of English.https://openriver.winona.edu/satori/1012/thumbnail.jp

Great Plains Drought in Simulations of the Twentieth Century

Abstract Coupled global circulation models (CGCMs) have been widely used to explore potential future climate change. Before these climate projections can be trusted, the ability of the models to simulate present-day climate must be assessed. This study evaluates the ability of three CGCMs that participated in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change to simulate long-term drought over the Great Plains region with the same frequency and intensity as was observed during the twentieth century. The three models evaluated in this study are the Geophysical Fluid Dynamics Laboratory Coupled Model, version 2.0 (CM2.0); the National Centers for Atmospheric Research Community Climate System Model, version 3 (CCSM3); and third climate configuration of the Met Office Unified Model (HadCM3). The models are shown to capture the broad features of the climatology of the Great Plains, with maximum precipitation occurring in early summer, as observed. However, all of the models overestimate annual precipitation rates. Also, in CCSM3, precipitation and evapotranspiration experience unrealistic decreases between the months of June and August. Long-term droughts are found in each simulation of the twentieth century that are comparable in duration, severity, and spatial extent as has been observed. However, the processes found to be associated with simulated long-term droughts vary among the models. In both CM2.0 and HadCM3, low-frequency variations in Great Plains precipitation are found to correspond with low-frequency variations in tropical Pacific SSTs. In CCSM3, on the other hand, there appears to be no significant correlation between tropical Pacific SST variability and Great Plains precipitation. Strong land–atmosphere coupling in CCSM3 may explain the persistence of long-term droughts in this model.</jats:p

Quantifying and Diagnosing Sources of Uncertainty in Midcentury Changes in North American Snowpack from NARCCAP

Abstract The NARCCAP RCM–GCM ensemble is used to explore the uncertainty in midcentury projections of snow over North America that arise when multiple RCMs are used to downscale multiple GCMs. Various snow metrics are examined, including snow water equivalent (SWE), snow cover extent (SCE), snow cover duration (SCD), and the timing of the snow season. Simulated biases in baseline snow characteristics are found to be sensitive to the choice of RCM and less influenced by the driving GCM. By midcentury, domain-averaged SCE and SWE are projected to decrease in all months of the year. However, using multiple RCMs to downscale multiple GCMs inflates the uncertainty in future projections of both SCE and SWE, with projections of SWE being more uncertain. Spatially, the RCMs show winter SWE decreasing over most of North America, except north of the Arctic rim, where SWE is projected to increase. SCD is also projected to decrease with both a later start and earlier termination of the snow season. For all metrics considered, the magnitude of the climate change signal varies across the RCMs. The ensemble spread is large over the western United States, where the RCMs disagree on the sign of the change in SWE in some high-elevation regions. Future projections of snow (both magnitude and spatial patterns) are more similar between simulations performed with the same RCM than the simulations driven by the same GCM. This implies that climate change uncertainty is not sufficiently explored in experiments performed with a single RCM driven by multiple GCMs.</jats:p