Evaluation of multiple reanalyses in reproducing the spatio‐temporal variability of temperature and precipitation indices over southern South America

Author(s):  
Rocio Balmaceda‐Huarte ◽  
Matias Ezequiel Olmo ◽  
Maria Laura Bettolli ◽  
Maria Mercedes Poggi
2021 ◽  
Vol 56 (2) ◽  
pp. 220-233
Author(s):  
María Eugenia Fernández ◽  
Jorge Osvaldo Gentili ◽  
Ana Casado ◽  
Alicia María Campo

The objective of this work is to analyze the spatio-temporal distribution of Global Horizontal Irradiation (GHI) on a regional scale and its relationship with frequent synoptic situations in the south of the Pampeana region (Argentina). It was verified that the latitudinal pattern of distribution of the GHI is modified in the region by cloud cover, which is in turn determined by the seasonal dynamics of action centers and the passage of fronts in summer and winter. The South America Monsoon System (SAMS) defines differential situations of cloudiness and rainfall in the region, which affect GHI. GHI increased successively between the decades 1981–2010, a factor associated with the variability of rainfall that characterizes the region.


Author(s):  
Bárbara VENTO ◽  
Gabriela G. PUEBLA ◽  
Diego PINZÓN ◽  
Mercedes PRÁMPARO

It is widely recognized that fossil leaves are good proxies for paleoclimate estimates, and leaf physiognomy analysis is a traditional technique used to make climate estimates. There are only a few paleoclimate reconstructions for the southern part of South America based on this technique. Here we report climate parameters using fossil leaves from the Río Turbio (Eocene-Oligocene) and Río Guillermo (Oligocene-early Miocene?) formations in southern South America, Cuenca Austral, Argentina. We used univariate (leaf margin and leaf foliar area analysis) and multivariate methods (CLAMP, DiLP) on two datasets from South America, in the Southern Hemisphere. Lower and upper members of the Río Turbio Formation show a mixed paleoflora represented by paratropical as well as cool-temperate taxa such as Nothofagus, with a similar percentage of untoothed fossil leaves. Climate estimates indicate warm and humid conditions for both Río Turbio Formation members. The Río Guillermo Formation is represented by mostly cool-temperate elements, mainly Nothofagus, and most with toothed margins. The paleoclimate analysis indicates a decrease in temperature and precipitation when comparing the two studied formations. Today, temperate forests in southern Argentina have a plant composition and climate more similar to the estimates made for the Río Guillermo Formation.


2007 ◽  
Vol 43 (11) ◽  
Author(s):  
Shiraj Khan ◽  
Gabriel Kuhn ◽  
Auroop R. Ganguly ◽  
David J. Erickson ◽  
George Ostrouchov

2019 ◽  
Vol 23 (8) ◽  
pp. 3189-3217 ◽  
Author(s):  
Todd A. N. Redpath ◽  
Pascal Sirguey ◽  
Nicolas J. Cullen

Abstract. A 16-year series of daily snow-covered area (SCA) for 2000–2016 is derived from MODIS imagery to produce a regional-scale snow cover climatology for New Zealand's largest catchment, the Clutha Catchment. Filling a geographic gap in observations of seasonal snow, this record provides a basis for understanding spatio-temporal variability in seasonal snow cover and, combined with climatic data, provides insight into controls on variability. Seasonal snow cover metrics including daily SCA, mean snow cover duration (SCD), annual SCD anomaly and daily snowline elevation (SLE) were derived and assessed for temporal trends. Modes of spatial variability were characterised, whilst also preserving temporal signals by applying raster principal component analysis (rPCA) to maps of annual SCD anomaly. Sensitivity of SCD to temperature and precipitation variability was assessed in a semi-distributed way for mountain ranges across the catchment. The influence of anomalous winter air flow, as characterised by HYSPLIT back-trajectories, on SCD variability was also assessed. On average, SCA peaks in late June, at around 30 % of the catchment area, with 10 % of the catchment area sustaining snow cover for > 120 d yr−1. A persistent mid-winter reduction in SCA, prior to a second peak in August, is attributed to the prevalence of winter blocking highs in the New Zealand region. In contrast to other regions globally, no significant decrease in SCD was observed, but substantial spatial and temporal variability was present. rPCA identified six distinct modes of spatial variability, characterising 77 % of the observed variability in SCD. This analysis of SCD anomalies revealed strong spatio-temporal variability beyond that associated with topographic controls, which can result in snow cover conditions being out of phase across the catchment. Furthermore, it is demonstrated that the sensitivity of SCD to temperature and precipitation variability varies significantly across the catchment. While two large-scale climate modes, the SOI and SAM, fail to explain observed variability, specific spatial modes of SCD are favoured by anomalous airflow from the NE, E and SE. These findings illustrate the complexity of atmospheric controls on SCD within the catchment and support the need to incorporate atmospheric processes that govern variability of the energy balance, as well as the re-distribution of snow by wind in order to improve the modelling of future changes in seasonal snow.


2019 ◽  
Author(s):  
Todd A. N. Redpath ◽  
Pascal Sirguey ◽  
Nicolas J. Cullen

Abstract. A 16-year series of daily snow covered area (SCA) for 2000–2016 is derived from MODIS imagery to produce a regional scale snow cover climatology for New Zealand's largest catchment, the Clutha Catchment. Filling a geographic gap in observations of seasonal snow, this record provides a basis for understanding spatio-temporal variability in seasonal snow cover, and combined with climatic data, provides insight into controls on variability. Metrics including daily SCA, mean snow cover duration (SCD), annual SCD anomaly and daily snowline elevation (SLE) were derived and assessed for temporal trends. Raster principal components analysis (rPCA) was applied to maps of annual SCD anomaly to characterise modes of spatial variability whilst preserving temporal signals. Semi-distributed analysis between SCD and temperature and precipitation anomalies allowed sensitivity of SCD to climatic forcings to be assessed spatially. The influence of anomalous winter air flow, as characterised by HYSPLIT back-trajectories, on SCD variability was also assessed. On average, SCA peaks in late June, at around 30 % of the catchment area, with 10 % of the catchment area sustaining snow cover for > 120 days per year. A reduction in SCA through mid-winter, prior to a second peak in August and persistent throughout the time series is attributed to the prevalence of winter blocking highs in the New Zealand region. In contrast to other regions globally, no significant decrease in SCD was observed. rPCA identified six distinct modes of spatial variability, characterising 77 % of the observed variability in SCD. rPCA and semi-distributed analysis of SCD anomalies reveal strong spatio-temporal variability beyond that associated with topographic controls, which can result in snow cover conditions being out of phase across the catchment. Furthermore, it is demonstrated that the sensitivity of SCD to temperature and precipitation variability varies significantly across the catchment. While two large scale climate modes, the SOI and SAM, fail to explain observed variability, specific spatial modes of SCD are favoured by anomalous airflow from the NE, E and SE. These findings illustrate the complexity of atmospheric controls on SCD within the catchment and support the need to incorporate atmospheric processes that govern variability of the energy balance, as well as the re-distribution of snow by wind in order to improve the modelling of future changes in seasonal snow.


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