scholarly journals Effect of ENSO Phase on Large-Scale Snow Water Equivalent Distribution in a GCM

2009 ◽  
Vol 22 (23) ◽  
pp. 6153-6167 ◽  
Author(s):  
Debbie Clifford ◽  
Robert Gurney ◽  
Keith Haines
Keyword(s):  
Large Scale ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Skewed Distribution ◽  
Enso Events ◽  
Long Run ◽  
Snow Water ◽  
The Mean ◽  
June July ◽  
The Impact

Abstract Understanding links between the El Niño–Southern Oscillation (ENSO) and snow would be useful for seasonal forecasting, as well as for understanding natural variability and interpreting climate change predictions. Here, a 545-yr run of the third climate configuration of the Met Office Unified Model (HadCM3), with prescribed external forcings and fixed greenhouse gas concentrations, is used to explore the impact of ENSO on snow water equivalent (SWE) anomalies. In North America, positive ENSO events reduce the mean SWE and skew the distribution toward lower values, and vice versa during negative ENSO events. This is associated with a dipole SWE anomaly structure, with anomalies of opposite sign centered in western Canada and the central United States. In Eurasia, warm episodes lead to a more positively skewed distribution and the mean SWE is raised. Again, the opposite effect is seen during cold episodes. In Eurasia the largest anomalies are concentrated in the Himalayas. These correlations with February SWE distribution are seen to exist from the previous June–July–August (JJA) ENSO index onward, and are weakly detected in 50-yr subsections of the control run, but only a shifted North American response can be detected in the analysis of the 40-yr ECMWF Re-Analysis (ERA-40). The ENSO signal in SWE from the long run could still contribute to regional predictions, although it would only be a weak indicator.

scholarly journals Influence of snow water equivalent on droughts and their prediction in the USA

2018 ◽  
Author(s):  
Daniel Abel ◽  
Felix Pollinger ◽  
Heiko Paeth
Keyword(s):  
Sierra Nevada ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Original Data ◽  
The United States ◽  
Early Summer ◽  
Drought Severity ◽  
Southern Us ◽  
Snow Water

Abstract. Droughts can result in enormous impacts for environment, societies, and economy. In arid or semiarid regions with bordering high mountains, snow is the major source of water supply due to its role as natural water storage. The goal of this study is to examine the influence of snow water equivalent (SWE) on droughts in the United States and find large-scale climatic predictors for SWE and drought. For this, a Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition, is performed with snow data from the ERA–Interim reanalysis and the self-calibrating Palmer Drought Severity Index (sc–PDSI) as drought index. Furthermore, the relationship of resulting principal components and original data with atmospheric patterns is investigated. The leading mode shows the spatial connection between SWE and drought via downstream water/moisture transport. Especially the Rocky Mountains in Colorado (CR) play a key role for the central and western South, but the Sierra Nevada and even the Appalachian Mountains are relevant, too. The temperature and precipitation based sc–PDSI is able to capture this link because increased soil moisture results in higher evapotranspiration with lower sensible heat and vice versa. A time shifted MCA indicates a prediction skill for drought conditions in spring and early summer for the downstream regions of CR on the basis of SWE in March. Furthermore, the phase of the El Niño–Southern Oscillation is a good predictor for drought in the southern US and SWE around Colorado. The influence of the North Atlantic Oscillation and Pacific North American Pattern is not that clear.

Retreat of Pine Island Glacier: The impact of El Nino Southern Oscillation events 

2021 ◽  
Author(s):  
Bradley Reed ◽  
Mattias Green ◽  
Hilmar Gudmundsson ◽  
Adrian Jenkins
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Thermocline Depth ◽  
Ice Shelf ◽  
Amundsen Sea ◽  
Enso Events ◽  
Deep Water Layer ◽  
Grounding Line ◽  
Ocean Conditions ◽  
The Impact

<p>The Amundsen Sea sector in West Antarctica is undergoing dramatic changes, with thinning ice shelves and accelerating, retreating glaciers. One of the largest and fastest flowing ice streams in the region is Pine Island Glacier (PIG). In recent decades it has retreated over 30 km, experienced a 75% increase in velocity and thinned by more than 100m. However, these changes have not been constant, there have been alternating periods of acceleration and stabilisation since the start of the observational era in the 1970s. This has been attributed to variable ocean conditions, where interannual and decadal changes in the Circumpolar Deep Water layer have been linked to large-scale climate variability. The initial ungrounding and subsequent retreat of PIG from a submarine ridge is believed to have been caused by extreme changes in ocean conditions linked to El Niño Southern Oscillation (ENSO) events during the 1940s and 1970s. However, the exact role that these events have played over the last century is not fully understood.</p><p>In this study the ice flow model Úa is used to assess how the retreat of PIG has been impacted by ENSO events. During these events, variations in thermocline depth affect the amount of heat available for basal melting beneath the ice shelf. To represent these changing ocean conditions a melt rate parameterisation based on a 1D plume model is used, which depends on ice shelf geometry, grounding line depth and ambient ocean properties. Results will show if a gradually warming ocean is enough to initiate grounding line retreat or if brief, large changes in temperature are required. Further investigations will determine whether cooler years contributed to a slow down of the ice stream. This work will help us understand and model the response of other glaciers to extreme changes in ocean conditions caused by ENSO events in a warming future.</p>

scholarly journals Variability Common to First Leaf Dates and Snowpack in the Western Conterminous United States

2013 ◽  
Vol 17 (26) ◽  
pp. 1-18 ◽  
Author(s):  
Gregory J. McCabe ◽  
Julio L. Betancourt ◽  
Gregory T. Pederson ◽  
Mark D. Schwartz
Keyword(s):  
United States ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Late Winter ◽  
Modes Of Variability ◽  
Temperature And Precipitation ◽  
The Pacific ◽  
Snow Water ◽  
Value Decomposition

Abstract Singular value decomposition is used to identify the common variability in first leaf dates (FLDs) and 1 April snow water equivalent (SWE) for the western United States during the period 1900–2012. Results indicate two modes of joint variability that explain 57% of the variability in FLD and 69% of the variability in SWE. The first mode of joint variability is related to widespread late winter–spring warming or cooling across the entire west. The second mode can be described as a north–south dipole in temperature for FLD, as well as in cool season temperature and precipitation for SWE, that is closely correlated to the El Niño–Southern Oscillation. Additionally, both modes of variability indicate a relation with the Pacific–North American atmospheric pattern. These results indicate that there is a substantial amount of common variance in FLD and SWE that is related to large-scale modes of climate variability.

scholarly journals The Andes climate and weather

2009 ◽  
Vol 22 ◽  
pp. 3-11 ◽  
Author(s):  
R. D. Garreaud
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Temperature And Precipitation ◽  
The Andes ◽  
Oscillation Phenomenon ◽  
Instrumental Observations ◽  
Precipitation Regimes ◽  
The Mean ◽  
The Impact ◽  
Andes Cordillera

Abstract. This paper documents the main features of the weather, climate and climate variability over Andes cordillera in South America on the basis of instrumental observations. We first provide a basic physical understanding of the mean annual cycle of the atmospheric circulation and precipitation and over the Andes and adjacent lowlands. In particular, the diversity of precipitation, temperature and wind patterns is interpreted in terms of the long meridional extent of the Andes and the disruption of the large-scale circulation by this formidable topographic barrier. We also document the impact of the El Niño Southern Oscillation phenomenon on the temperature and precipitation regimes along the Andes.

scholarly journals Interactive effects of tree size, crown exposure and logging on drought-induced mortality

2018 ◽  
Vol 373 (1760) ◽  
pp. 20180189 ◽  
Author(s):  
Alexander Shenkin ◽  
Benjamin Bolker ◽  
Marielos Peña-Claros ◽  
Juan Carlos Licona ◽  
Nataly Ascarrunz ◽  
...  
Keyword(s):  
El Niño ◽  
Large Scale ◽  
El Nino ◽  
Southern Oscillation ◽  
Tree Height ◽  
Stem Diameter ◽  
Interactive Effects ◽  
Enso Events ◽  
Large Trees ◽  
The Impact

Large trees in the tropics are reportedly more vulnerable to droughts than their smaller neighbours. This pattern is of interest due to what it portends for forest structure, timber production, carbon sequestration and multiple other values given that intensified El Niño Southern Oscillation (ENSO) events are expected to increase the frequency and intensity of droughts in the Amazon region. What remains unclear is what characteristics of large trees render them especially vulnerable to drought-induced mortality and how this vulnerability changes with forest degradation. Using a large-scale, long-term silvicultural experiment in a transitional Amazonian forest in Bolivia, we disentangle the effects of stem diameter, tree height, crown exposure and logging-induced degradation on risks of drought-induced mortality during the 2004/2005 ENSO event. Overall, tree mortality increased in response to drought in both logged and unlogged plots. Tree height was a much stronger predictor of mortality than stem diameter. In unlogged plots, tree height but not crown exposure was positively associated with drought-induced mortality, whereas in logged plots, neither tree height nor crown exposure was associated with drought-induced mortality. Our results suggest that, at the scale of a site, hydraulic factors related to tree height, not air humidity, are a cause of elevated drought-induced mortality of large trees in unlogged plots. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

scholarly journals Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

2019 ◽  
Vol 13 (11) ◽  
pp. 3045-3059 ◽  
Author(s):  
Nick Rutter ◽  
Melody J. Sandells ◽  
Chris Derksen ◽  
Joshua King ◽  
Peter Toose ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Layer Thickness ◽  
Snow Water Equivalent ◽  
Estimation Accuracy ◽  
Snow Layer ◽  
Data Set ◽  
Snow Water ◽  
Retrieval Errors ◽  
Snow Microstructure ◽  
The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

Impact of strong El Niño events on river discharge in South America

2021 ◽  
Author(s):  
Markus Deppner ◽  
Bedartha Goswami
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
South America ◽  
River Discharge ◽  
Amazon Basin ◽  
Missing Values ◽  
Southern Oscillation ◽  
Enso Events ◽  
Streamflow Data ◽  
The Impact

&lt;p&gt;The impact of the El Ni&amp;#241;o Southern Oscillation (ENSO) on rivers are well known, but most existing studies involving streamflow data are severely limited by data coverage. Time series of gauging stations fade in and out over time, which makes hydrological large scale and long time analysis or studies of rarely occurring extreme events challenging. Here, we use a machine learning approach to infer missing streamflow data based on temporal correlations of stations with missing values to others with data. By using 346 stations, from the &amp;#8220;Global Streamflow Indices and Metadata archive&amp;#8221; (GSIM), that initially cover the 40 year timespan in conjunction with Gaussian processes we were able to extend our data by estimating missing data for an additional 646 stations, allowing us to include a total of 992 stations. We then investigate the impact of the 6 strongest El Ni&amp;#241;o (EN) events on rivers in South America between 1960 and 2000. Our analysis shows a strong correlation between ENSO events and extreme river dynamics in the southeast of Brazil, Carribean South America and parts of the Amazon basin. Furthermore we see a peak in the number of stations showing maximum river discharge all over Brazil during the EN of 1982/83 which has been linked to severe floods in the east of Brazil, parts of Uruguay and Paraguay. However EN events in other years with similar intensity did not evoke floods with such magnitude and therefore the additional drivers of the 1982/83&amp;#160; floods need further investigation. By using machine learning methods to infer data for gauging stations with missing data we were able to extend our data by almost three-fold, revealing a possible heavier and spatially larger impact of the 1982/83 EN on South America's hydrology than indicated in literature.&lt;/p&gt;

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

&lt;p&gt;Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake &amp;#214;veruman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km&lt;sup&gt;2&lt;/sup&gt; grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.&lt;/p&gt;

scholarly journals Northern winter stratospheric temperature and ozone responses to ENSO inferred from an ensemble of Chemistry Climate Models

2009 ◽  
Vol 9 (22) ◽  
pp. 8935-8948 ◽  
Author(s):  
C. Cagnazzo ◽  
E. Manzini ◽  
N. Calvo ◽  
A. Douglass ◽  
H. Akiyoshi ◽  
...  
Keyword(s):  
Climate Models ◽  
Southern Oscillation ◽  
Polar Vortex ◽  
Atmospheric Modeling ◽  
Residual Circulation ◽  
Model Simulations ◽  
Enso Events ◽  
Stratospheric Temperature ◽  
The Mean ◽  
Column Ozone

Abstract. The connection between the El Niño Southern Oscillation (ENSO) and the Northern polar stratosphere has been established from observations and atmospheric modeling. Here a systematic inter-comparison of the sensitivity of the modeled stratosphere to ENSO in Chemistry Climate Models (CCMs) is reported. This work uses results from a number of the CCMs included in the 2006 ozone assessment. In the lower stratosphere, the mean of all model simulations reports a warming of the polar vortex during strong ENSO events in February–March, consistent with but smaller than the estimate from satellite observations and ERA40 reanalysis. The anomalous warming is associated with an anomalous dynamical increase of column ozone north of 70° N that is accompanied by coherent column ozone decrease in the Tropics, in agreement with that deduced from the NIWA column ozone database, implying an increased residual circulation in the mean of all model simulations during ENSO. The spread in the model responses is partly due to the large internal stratospheric variability and it is shown that it crucially depends on the representation of the tropospheric ENSO teleconnection in the models.

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