scholarly journals Characterizing Large-Scale Meteorological Patterns and Associated Temperature and Precipitation Extremes over the Northwestern United States Using Self-Organizing Maps

2017 ◽  
Vol 30 (8) ◽  
pp. 2829-2847 ◽  
Author(s):  
Paul C. Loikith ◽  
Benjamin R. Lintner ◽  
Alex Sweeney
Keyword(s):  
United States ◽  
Large Scale ◽  
Climate Model ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Local Scale ◽  
Precipitation Extremes ◽  
Self Organizing Maps ◽  
Temperature And Precipitation ◽  
Self Organizing

The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns with local-scale temperature and precipitation extremes. SOMs are used to construct a set of 12 characteristic LSMPs (nodes) based on daily reanalysis circulation fields spanning the range of observed synoptic-scale variability for the summer and winter seasons for the period 1979–2013. Composites of surface variables are constructed for subsets of days assigned to each node to explore relationships between temperature, precipitation, and the node patterns. The SOMs approach also captures interannual variability in daily weather regime frequency related to El Niño–Southern Oscillation. Temperature and precipitation extremes in high-resolution gridded observations and in situ station data show robust relationships with particular nodes in many cases, supporting the approach as a way to identify LSMPs associated with local extremes. Assigning days from the extreme warm summer of 2015 and wet winter of 2016 to nodes illustrates how SOMs may be used to assess future changes in extremes. These results point to the applicability of SOMs to climate model evaluation and assessment of future projections of local-scale extremes without requiring simulations to reliably resolve extremes at high spatial scales.

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 Spatial-Scale Dependence of Climate Model Performance in the CMIP3 Ensemble

2011 ◽  
Vol 24 (11) ◽  
pp. 2680-2692 ◽  
Author(s):  
David Masson ◽  
Reto Knutti
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Model ◽  
Spatial Scales ◽  
Global Climate Models ◽  
Small Scale ◽  
Climate Projections ◽  
Model Errors ◽  
Climate Signal ◽  
Temperature And Precipitation

Abstract About 20 global climate models have been run for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to predict climate change due to anthropogenic activities. Evaluating these models is an important step to establish confidence in climate projections. Model evaluation, however, is often performed on a gridpoint basis despite the fact that models are known to often be unreliable at such small spatial scales. In this study, the annual mean values of surface air temperature and precipitation are analyzed. Using a spatial smoothing technique with a variable-scale parameter it is shown that the intermodel spread, as well as model errors from observations, is reduced as the characteristic smoothing scale increases. At the same time, the ability to reproduce small-scale features is reduced and the simulated patterns become fuzzy. Depending on the variable of interest, the location, and the way that data are aggregated, different optimal smoothing scales from the gridpoint size to about 2000 km are found to give good agreement with present-day observation yet retain most regional features of the climate signal. Higher model resolution surprisingly does not imply much better agreement with temperature observations, in particular with stronger smoothing, and resolving smaller scales therefore does not necessarily seem to improve the simulation of large-scale climate features. Similarities in mean temperature and precipitation fields for a pair of models in the ensemble persist locally for about a century into the future, providing some justification for subtracting control errors in the models. Large-scale to global errors, however, are not well preserved over time, consistent with a poor constraint of the present-day climate on the simulated global temperature and precipitation response.

scholarly journals Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adeoluwa Akande ◽  
Ana Cristina Costa ◽  
Jorge Mateu ◽  
Roberto Henriques
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Northern Region ◽  
Extreme Weather Events ◽  
Self Organizing Map ◽  
Spatial And Temporal Patterns ◽  
Self Organizing Maps ◽  
The North ◽  
Precipitation Regimes ◽  
Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

scholarly journals Changes in Temperature and Precipitation Extremes in Superparameterized CAM in Response to Warmer SSTs

2017 ◽  
Vol 30 (24) ◽  
pp. 9827-9845 ◽  
Author(s):  
Xin Zhou ◽  
Marat F. Khairoutdinov
Keyword(s):  
Large Scale ◽  
Rain Rate ◽  
Precipitable Water ◽  
Heavy Precipitation ◽  
Global Scale ◽  
Precipitation Extremes ◽  
Temperature And Precipitation ◽  
Hourly Data ◽  
Cloud Resolving Model ◽  
Cold Extremes

Subdaily temperature and precipitation extremes in response to warmer SSTs are investigated on a global scale using the superparameterized (SP) Community Atmosphere Model (CAM), in which a cloud-resolving model is embedded in each CAM grid column to simulate convection explicitly. Two 10-yr simulations have been performed using present climatological sea surface temperature (SST) and perturbed SST climatology derived from the representative concentration pathway 8.5 (RCP8.5) scenario. Compared with the conventional CAM, SP-CAM simulates colder temperatures and more realistic intensity distribution of precipitation, especially for heavy precipitation. The temperature and precipitation extremes have been defined by the 99th percentile of the 3-hourly data. For temperature, the changes in the warm and cold extremes are generally consistent between CAM and SP-CAM, with larger changes in warm extremes at low latitudes and larger changes in cold extremes at mid-to-high latitudes. For precipitation, CAM predicts a uniform increase of frequency of precipitation extremes regardless of the rain rate, while SP-CAM predicts a monotonic increase of frequency with increasing rain rate and larger change of intensity for heavier precipitation. The changes in 3-hourly and daily temperature extremes are found to be similar; however, the 3-hourly precipitation extremes have a significantly larger change than daily extremes. The Clausius–Clapeyron scaling is found to be a relatively good predictor of zonally averaged changes in precipitation extremes over midlatitudes but not as good over the tropics and subtropics. The changes in precipitable water and large-scale vertical velocity are equally important to explain the changes in precipitation extremes.

scholarly journals Variability of Cloudiness over Mountain Terrain in the Western United States

2017 ◽  
Vol 18 (5) ◽  
pp. 1227-1245 ◽  
Author(s):  
Edwin Sumargo ◽  
Daniel R. Cayan
Keyword(s):  
United States ◽  
Large Scale ◽  
Spatial Scales ◽  
High Elevation ◽  
Empirical Orthogonal Functions ◽  
Western United States ◽  
Spatial And Temporal Variability ◽  
Regional Patterns ◽  
Cloud Albedo

Abstract This study investigates the spatial and temporal variability of cloudiness across mountain zones in the western United States. Daily average cloud albedo is derived from a 19-yr series (1996–2014) of half-hourly Geostationary Operational Environmental Satellite (GOES) images. During springtime when incident radiation is active in driving snowmelt–runoff processes, the magnitude of daily cloud variations can exceed 50% of long-term averages. Even when aggregated over 3-month periods, cloud albedo varies by ±10% of long-term averages in many locations. Rotated empirical orthogonal functions (REOFs) of daily cloud albedo anomalies over high-elevation regions of the western conterminous United States identify distinct regional patterns, wherein the first five REOFs account for ~67% of the total variance. REOF1 is centered over Northern California and Oregon and is pronounced between November and March. REOF2 is centered over the interior northwest and is accentuated between March and July. Each of the REOF/rotated principal components (RPC) modes associates with anomalous large-scale atmospheric circulation patterns and one or more large-scale teleconnection indices (Arctic Oscillation, Niño-3.4, and Pacific–North American), which helps to explain why anomalous cloudiness patterns take on regional spatial scales and contain substantial variability over seasonal time scales.

scholarly journals The Interannual Variability of the Haines Index over North America

2013 ◽  
Vol 52 (11) ◽  
pp. 2396-2409 ◽  
Author(s):  
Lejiang Yu ◽  
Shiyuan Zhong ◽  
Xindi Bian ◽  
Warren E. Heilman ◽  
Joseph J. Charney
Keyword(s):  
United States ◽  
Pacific Ocean ◽  
North America ◽  
Interannual Variability ◽  
Large Scale ◽  
Southern Oscillation ◽  
Southeastern United States ◽  
Lower Atmosphere ◽  
The Arctic ◽  
Fire Weather Index

AbstractThe Haines index (HI) is a fire-weather index that is widely used as an indicator of the potential for dry, low-static-stability air in the lower atmosphere to contribute to erratic fire behavior or large fire growth. This study examines the interannual variability of HI over North America and its relationship to indicators of large-scale circulation anomalies. The results show that the first three HI empirical orthogonal function modes are related respectively to El Niño–Southern Oscillation (ENSO), the Arctic Oscillation (AO), and the interdecadal sea surface temperature variation over the tropical Pacific Ocean. During the negative ENSO phase, an anomalous ridge (trough) is evident over the western (eastern) United States, with warm/dry weather and more days with high HI values in the western and southeastern United States. During the negative phase of the AO, an anomalous trough is found over the western United States, with wet/cool weather and fewer days with high HI, while an anomalous ridge occurs over the southern United States–northern Mexico, with an increase in the number of days with high HI. After the early 1990s, the subtropical high over the eastern Pacific Ocean and the Bermuda high were strengthened by a wave train that was excited over the tropical western Pacific Ocean and resulted in warm/dry conditions over the southwestern United States and western Mexico and wet weather in the southeastern United States. The above conditions are reversed during the positive phase of ENSO and AO and before the early 1990s.

scholarly journals The advective Brewer-Dobson circulation in the ERA5 reanalysis: variability and trends

2020 ◽  
Author(s):  
Mohamadou Diallo ◽  
Manfred Ern ◽  
Felix Ploeger
Keyword(s):  
Gravity Wave ◽  
Large Scale ◽  
Climate Model ◽  
Southern Oscillation ◽  
Natural Variability ◽  
Radiation Budget ◽  
Residual Circulation ◽  
Quasi Biennial Oscillation ◽  
Acceleration Rate ◽  
Good Agreement

Abstract. The stratospheric Brewer-Dobson circulation (BDC) is an important element of climate as it determines the transport and distributions of key radiatively active atmospheric trace gases, which affect the Earth’s radiation budget and surface climate. Here, we evaluate the inter-annual variability and trends of the BDC in the ERA5 reanalysis and inter-compare with the ERA-Interim reanalysis for the 1979–2018 period. We also assess the modulation of the circulation by the Quasi-Biennial Oscillation (QBO) and the El Niño-Southern Oscillation (ENSO), and the forcings of the circulation by the planetary and gravity wave drag. A comparison of ERA5 and ERA-Interim reanalyses shows a very good agreement in the morphology of the BDC and in its structural modulations by the natural variability related to QBO and ENSO. Despite the good agreement in the spatial structure, there are substantial differences in the strength of the BDC and of the natural variability impacts on the BDC between the two reanalyses, particularly in the upper troposphere and lower stratosphere (UTLS), and in the upper stratosphere. Throughout most regions of the stratosphere, the variability and trends of the advective BDC are stronger in the ERA5 reanalysis due to stronger planetary and gravity wave forcings, except in the UTLS below 20 km where the tropical upwelling is about 40 % weaker due to a weaker gravity wave forcings at the equatorial flank of the subtropical jet. In the extra-tropics, the large-scale downwelling is stronger in ERA5 than in ERA-Interim linked to significant differences in planetary and gravity wave forcings. Analysis of the BDC trend shows a global acceleration of the annual mean residual circulation with an acceleration rate of about 1.5 % per decade at 70 hPa due to the long-term intensification in gravity and planetary wave breaking, consistent with observed and future climate model predicted BDC changes.

scholarly journals Statistical downscaling of a climate simulation of the last glacial cycle: temperature and precipitation over Northern Europe

2014 ◽  
Vol 10 (4) ◽  
pp. 1489-1500 ◽  
Author(s):  
N. Korhonen ◽  
A. Venäläinen ◽  
H. Seppä ◽  
H. Järvinen
Keyword(s):  
Large Scale ◽  
Climate Model ◽  
Ice Sheet ◽  
Additive Models ◽  
Northern Europe ◽  
Glacial Cycle ◽  
Explanatory Variables ◽  
Last Glacial ◽  
Temperature And Precipitation ◽  
The Last Glacial

Abstract. Earth system models of intermediate complexity (EMICs) have proven to be able to simulate the large-scale features of glacial–interglacial climate evolution. For many climatic applications the spatial resolution of the EMICs' output is, however, too coarse, and downscaling methods are needed. In this study we introduce a way to use generalized additive models (GAMs) for downscaling the large-scale output of an EMIC in very different climatological conditions ranging from glacial periods to current relatively warm climates. GAMs are regression models in which a combination of explanatory variables is related to the response through a sum of spline functions. We calibrated the GAMs using observations of the recent past climate and the results of short time-slice simulations of glacial climate performed by the relatively high-resolution general circulation model CCSM (Community Climate System Model) and the regional climate model RCA3 (Rossby Centre regional Atmospheric climate model). As explanatory variables we used the output of a simulation by the CLIMBER-2 (CLIMate and BiosphERe model 2) EMIC of the last glacial cycle, coupled with the SICOPOLIS (SImulation COde for POLythermal Ice Sheets) ice sheet model, i.e. the large-scale temperature and precipitation data of CLIMBER-2, and the elevation, distance to ice sheet, slope direction and slope angle from SICOPOLIS. The fitted GAMs were able to explain more than 96% of the temperature response with a correlation of >0.98 and more than 59% of the precipitation response with a correlation of >0.72. The first comparison with two pollen-based reconstructions of temperature for Northern Europe showed that CLIMBER-2 data downscaled by GAMs corresponded better with the reconstructions than did the bilinearly interpolated CLIMBER-2 surface temperature.

scholarly journals Using Self-Organizing Maps to Identify Coherent CONUS Precipitation Regions

2019 ◽  
Vol 32 (22) ◽  
pp. 7747-7761 ◽  
Author(s):  
Leif M. Swenson ◽  
Richard Grotjahn
Keyword(s):  
Extreme Events ◽  
Annual Cycle ◽  
Large Scale ◽  
Robust Statistics ◽  
Grid Point ◽  
Future Application ◽  
Self Organizing Maps ◽  
Annual Cycles ◽  
Extreme Precipitation Events ◽  
Self Organizing

Abstract Extreme precipitation events have major societal impacts. These events are rare and can have small spatial scale, making statistical analysis difficult; both factors are mitigated by combining events over a region. A methodology is presented to objectively define “coherent” regions wherein data points have matching annual cycles. Regions are found by training self-organizing maps (SOMs) on the annual cycle of precipitation for each grid point across the contiguous United States (CONUS). Using the annual cycle for our intended application minimizes problems caused by consecutive dry periods and localized extreme events. Multiple criteria are applied to identify useful numbers of regions for our future application. Criteria assess these properties for each region: having many more events than experienced by a single grid point, good connectedness and compactness, and robustness to changing the number of regions. Our methodology is applicable across datasets and is tested here on both reanalysis and gridded observational data. Precipitation regions obtained align with large-scale geographical features and are readily interpretable. Useful numbers of regions balance two conflicting preferences: larger regions contain more events and thereby have more robust statistics, but more compact regions allow weather patterns associated with extreme events to be aggregated with confidence. For 6-h precipitation, 12–15 regions over the CONUS optimize our metrics. The regions obtained are compared against two existing region archetypes. For example, a popular set of regions, based on nine groups of states, has less coherent regions than defining the same number of regions with our SOM methodology.

scholarly journals The Role of Synoptic Cyclones for the Formation of Arctic Summer Circulation Patterns as Clustered by Self-Organizing Maps

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 474 ◽  
Author(s):  
Min-Hee Lee ◽  
Joo-Hong Kim
Keyword(s):  
Arctic Ocean ◽  
Large Scale ◽  
The Arctic ◽  
Cyclone Activity ◽  
Summer Circulation ◽  
Self Organizing Maps ◽  
Weather Patterns ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
Self Organizing

Contribution of extra-tropical synoptic cyclones to the formation of mean summer atmospheric circulation patterns in the Arctic domain (≥60° N) was investigated by clustering dominant Arctic circulation patterns based on daily mean sea-level pressure using self-organizing maps (SOMs). Three SOM patterns were identified; one pattern had prevalent low-pressure anomalies in the Arctic Circle (SOM1), while two exhibited opposite dipoles with primary high-pressure anomalies covering the Arctic Ocean (SOM2 and SOM3). The time series of their occurrence frequencies demonstrated the largest inter-annual variation in SOM1, a slight decreasing trend in SOM2, and the abrupt upswing after 2007 in SOM3. Analyses of synoptic cyclone activity using the cyclone track data confirmed the vital contribution of synoptic cyclones to the formation of large-scale patterns. Arctic cyclone activity was enhanced in the SOM1, which was consistent with the meridional temperature gradient increases over the land–Arctic ocean boundaries co-located with major cyclone pathways. The composite daily synoptic evolution of each SOM revealed that all three SOMs persisted for less than five days on average. These evolutionary short-term weather patterns have substantial variability at inter-annual and longer timescales. Therefore, the synoptic-scale activity is central to forming the seasonal-mean climate of the Arctic.

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