Changes in Andes  snow cover from MODIS data, 2000–2016

Abstract. The Andes span a length of 7000 km and are important for sustaining regional water supplies. Snow variability across this region has not been studied in detail due to sparse and unevenly distributed instrumental climate data. We calculated snow persistence (SP) as the fraction of time with snow cover for each year between 2000 and 2016 from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors (500 m, 8-day maximum snow cover extent). This analysis is conducted between 8 and 36∘ S due to high frequency of cloud (> 30 % of the time) south and north of this range. We ran Mann–Kendall and Theil–Sens analyses to identify areas with significant changes in SP and snowline (the line at lower elevation where SP = 20 %). We evaluated how these trends relate to temperature and precipitation from Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA2) and University of Delaware datasets and climate indices as El Niño–Southern Oscillation (ENSO), Southern Annular Mode (SAM), and Pacific Decadal Oscillation (PDO). Areas north of 29∘ S have limited snow cover, and few trends in snow persistence were detected. A large area (34 370 km2) with persistent snow cover between 29 and 36∘ S experienced a significant loss of snow cover (2–5 fewer days of snow year−1). Snow loss was more pronounced (62 % of the area with significant trends) on the east side of the Andes. We also found a significant increase in the elevation of the snowline at 10–30 m year−1 south of 29–30∘ S. Decreasing SP correlates with decreasing precipitation and increasing temperature, and the magnitudes of these correlations vary with latitude and elevation. ENSO climate indices better predicted SP conditions north of 31∘ S, whereas the SAM better predicted SP south of 31∘ S.

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Changes in Andes Mountains snow cover from MODIS data 2000–2014

10.5194/tc-2017-72 ◽

2017 ◽

Cited By ~ 1

Author(s):

Freddy A. Saavedra ◽

Stephanie K. Kampt ◽

Steven R. Fassnacht ◽

Jason S. Sibold

Keyword(s):

Snow Cover ◽

Significant Loss ◽

Andes Mountains ◽

Climate Indices ◽

Climate Data ◽

Large Area ◽

Temperature And Precipitation ◽

The Andes ◽

Snow Cover Extent ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. The Andes Mountains span a length of 7,000 km and are important for sustaining regional water supplies. Snow variability across this region has not been studied in detail due to sparse and unevenly distributed instrumental climate data. We calculated snow persistence (SP) as the fraction of time with snow cover for each year between 2000–2014 from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors (500 m, 8-day maximum snow cover extent) limited between 8 °S and 36 °S due high frequency of cloud (>30 % of the time) south and north of this range. We ran Mann-Kendall and Theil-Sens analyses to identify significant areas of change in SP and snow line (the line at lower elevation which SP=20%). We evaluated whether these trends in the context of temperature and precipitation (University of Delaware dataset) and climate indices (ENSO, SAM, PDO). North of 29 °S has limited snow cover, and few trends in snow persistence were detected. A large area (70,515 km2) with persistent snow cover between 29–36 °S experienced a significant loss of snow cover (2–5 fewer days of snow year−1). Snow loss was more pronounced (62 %) on the east side of the Andes. We also found a significant increase in the elevation of 10–30 m year−1 south of 29–30 °S. Decreasing SP correlates with decreasing precipitation, increasing temperature, and climate indices and it varies with latitude and elevation. ENSO indices better predicted SP conditions north of 31 °S, and the SAM better predicted SP south of 31 °S.

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Accuracy of Snowmelt Runoff Simulation

Hydrology Research ◽

10.2166/nh.1981.0021 ◽

1981 ◽

Vol 12 (4-5) ◽

pp. 265-274 ◽

Cited By ~ 24

Author(s):

A. Rango ◽

J. Martinec

Keyword(s):

Snow Cover ◽

Input Data ◽

Snowmelt Runoff ◽

Runoff Simulation ◽

Simulation Accuracy ◽

Temperature And Precipitation ◽

Recession Coefficient ◽

Snow Cover Extent ◽

Mountainous Basin

Results of runoff simulations from various basins using a snowmelt runoff model were analyzed in order to predict the accuracy of simulations in future applications of the model. It was found that the model can be applied to nearly any mountainous basin where snowmelt runoff is an important factor if input data on temperature, precipitation, and snow cover are available. The simulation accuracy will depend on the quality of the input data as well as on the density of observations, size of the basin, care in determination of the recession coefficient, and amount of precipitation during snowmelt. Most accurate simulations will result when: 1) temperature and precipitation are recorded at the basin mean elevation; 2) snow cover observations are available once per week; 3) several climatic stations are available for large basins; and 4) a few years of runoff records exist for determination of the recession coefficient. Decreases in simulation accuracy will be expected as these optimum conditions are compromised, however, acceptable simulations will result with the following minimum conditions: 1) temperature and precipitation data are available in the general vicinity of the basin; and 2) snow cover observations are available 2-3 times during the snowmelt season. The availability of satellite observations of snow cover extent has permitted successful application of the model to large basins.

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Indices for daily temperature and precipitation based on quality controlled and homogenized data in Madagascar

10.5194/egusphere-egu2020-19970 ◽

2020 ◽

Author(s):

Luc Yannick Andréas Randriamarolaza ◽

Enric Aguilar ◽

Oleg Skrynyk

Keyword(s):

Climate Change ◽

Missing Data ◽

Extreme Temperature ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Climate Indices ◽

Low Density ◽

Climate Data ◽

Temperature And Precipitation

Madagascar is an Island in Western Indian Ocean Region. It is mainly exposed to the easterly trade winds and has a rugged topography, which promote different local climates and biodiversity. Climate change inflicts a challenge on Madagascar socio-economic activities. However, Madagascar has low density station and sparse networks on observational weather stations to detect changes in climate. On average, one station covers more than 20 000 km2 and closer neighbor stations are less correlated. Previous studies have demonstrated the changes on Madagascar climate, but this paper contributes and enhances the approach to assess the quality control and homogeneity of Madagascar daily climate data before developing climate indices over 1950 &#8211; 2018 on 28 synoptic stations. Daily climate data of minimum and maximum temperature and precipitation are exploited.Firstly, the quality of daily climate data is controlled by INQC developed and maintained by Center for Climate Change (C3) of Rovira i Virgili University, Spain. It ascertains and improves error detections by using six flag categories. Most errors detected are due to digitalization and measurement.Secondly, daily quality controlled data are homogenized by using CLIMATOL. It uses relative homogenization methods, chooses candidate reference series automatically and infills the missing data in the original data. It has ability to manage low density stations and low inter-station correlations and is tolerable for missing data. Monthly break points are detected by CLIMATOL and used to split daily climate data to be homogenized.Finally, climate indices are calculated by using CLIMIND package which is developed by INDECIS* project. Compared to previous works done, data period is updated to 10 years before and after and 15 new climate indices mostly related to extremes are computed. On temperature, significant increasing and decreasing decade trends of day-to-day and extreme temperature ranges are important in western and eastern areas respectively. On average decade trends of temperature extremes, significant increasing of daily minimum temperature is greater than daily maximum temperature. Many stations indicate significant decreasing in very cold nights than significant increasing in very warm days. Their trends are almost 1 day per decade over 1950 &#8211; 2018. Warming is mainly felt during nighttime and daytime in Oriental and Occidental parts respectively. In contrast, central uplands are warming all the time but tropical nights do not appear yet. On rainfall, no major significant findings are found but intense precipitation might be possible at central uplands due to shortening of longest wet period and occurrence of heavy precipitation. However, no influence detected on total precipitation which is still decreasing over 1950 - 2018. Future works focus on merging of relative homogenization methodologies to ameliorate the results.-------------------*INDECIS is a part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

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A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019

10.5194/essd-2021-279 ◽

2021 ◽

Author(s):

Xiaona Chen ◽

Shunlin Liang ◽

Lian He ◽

Yaping Yang ◽

Cong Yin

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Land Surface ◽

Glass Product ◽

Aggregation Process ◽

Climate Data ◽

Important Indicator ◽

Continental Scale ◽

Snow Cover Extent ◽

Frequency Monitoring

Abstract. Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change due to its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among existing continental scale SCE products hampers its detailed studies. Using the latest Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free SCE covering the NH landmass for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. The development of GLASS SCE contains five steps. First, a decision tree algorithm with multiple threshold tests was applied to distinguish snow cover (NHSCE-D) with other land cover types from daily AVHRR-SR CDR. Second, gridcells with cloud cover and invalid observations were filled by two existing daily SCE products. The gap-filled gridcells were further merged with NHSCE-D to generate combined daily SCE over the NH (NHSCE-Dc). Third, an aggregation process was used to detect the maximum SCE and minimum gaps in each 8-day periods from NHSCE-Dc. Forth, the gaps after aggregation process were further filled by the climatology of snow cover probability to generate the gap-free GLASS SCE. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r = 0.61, p < 0.05) and MOD10C2 during 2001–2019 (r = 0.97, p < 0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies. The GLASS SCE data are available at https://doi.org/10.5281/zenodo.5775238 (Chen et al. 2021).

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Dry-Season Snow Cover Losses in the Andes (18°–40°S) driven by Changes in Large-Scale Climate Modes

Scientific Reports ◽

10.1038/s41598-019-53486-7 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Raul R. Cordero ◽

Valentina Asencio ◽

Sarah Feron ◽

Alessandro Damiani ◽

Pedro J. Llanillo ◽

...

Keyword(s):

Snow Cover ◽

El Niño ◽

Large Scale ◽

El Nino ◽

Southern Oscillation ◽

Average Rate ◽

Dry Season ◽

Positive Trend ◽

Entire Study ◽

Snow Cover Extent

AbstractThe Andean snowpack is the primary source of water for many communities in South America. We have used Landsat imagery over the period 1986–2018 in order to assess the changes in the snow cover extent across a north-south transect of approximately 2,500 km (18°–40°S). Despite the significant interannual variability, here we show that the dry-season snow cover extent declined across the entire study area at an average rate of about −12% per decade. We also show that this decreasing trend is mainly driven by changes in the El Niño Southern Oscillation (ENSO), especially at latitudes lower than 34°S. At higher latitudes (34°–40°S), where the El Niño signal is weaker, snow cover losses appear to be also influenced by the poleward migration of the westerly winds associated with the positive trend in the Southern Annular Mode (SAM).

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Coincident recruitment patterns of Southern Hemisphere fishes

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2015-0069 ◽

2016 ◽

Vol 73 (2) ◽

pp. 270-278 ◽

Cited By ~ 5

Author(s):

Claudio Castillo-Jordán ◽

Neil L. Klaer ◽

Geoffrey N. Tuck ◽

Stewart D. Frusher ◽

Luis A. Cubillos ◽

...

Keyword(s):

South Africa ◽

New Zealand ◽

Southern Hemisphere ◽

Southern Oscillation ◽

Global Climate ◽

Southern Annular Mode ◽

Fish Stock ◽

Climate Indices ◽

Dynamic Factor ◽

Recruitment Patterns

Three dominant recruitment patterns were identified across 30 stocks from Australia, New Zealand, Chile, South Africa, and the Falkland Islands using data from 1980 to 2010. Cluster and dynamic factor analysis provided similar groupings. Stocks exhibited a detectable degree of synchrony among species, in particular the hakes and lings from Australia, New Zealand, Chile, and South Africa. We tested three climate indices, the Interdecadal Pacific Oscillation (IPO), Southern Annular Mode (SAM), and Southern Oscillation Index (SOI), to explore their relationship with fish stock recruitment patterns. The time series of IPO and SOI showed the strongest correlation with New Zealand hoki (blue grenadier, Macruronus novaezelandiae) and Australian jackass morwong (Nemadactylus macropterus) (r = 0.50 and r = –0.50), and SAM was positively related to Australian Macquarie Island Patagonian toothfish (Dissostichus eleginoides) (r = 0.49). Potential linkages in recruitment patterns at sub-basin, basin, and multibasin scales and regional and global climate indices do account for some of the variation, playing an important role for several key Southern Hemisphere species.

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A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring

Earth System Science Data Discussions ◽

10.5194/essdd-7-669-2014 ◽

2014 ◽

Vol 7 (2) ◽

pp. 669-691 ◽

Cited By ~ 2

Author(s):

T. W. Estilow ◽

A. H. Young ◽

D. A. Robinson

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Environmental Data ◽

Climatic Data ◽

Climate Data ◽

Snow Cover Extent ◽

Data Record ◽

Climate Data Record ◽

Climate Studies

Abstract. This paper describes the long-term, satellite-based visible snow cover extent NOAA climate data record (CDR) currently available for climate studies, monitoring, and model validation. This environmental data product is developed from weekly Northern Hemisphere snow cover extent data that have been digitized from snow cover maps onto a Cartesian grid draped over a polar stereographic projection. The data has a spatial resolution of 190.5 km at 60 ° latitude, are updated monthly, and span from 4 October 1966 to present. The data comprise the longest satellite-based CDR of any environmental variable. Access to the data are provided in netCDF format and are archived by the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA) under the satellite climate data record program (doi:10.7289/V5N014G9). The basic characteristics, history, and evolution of the dataset are presented herein. In general, the CDR provides similar spatial and temporal variability as its widely used predecessor product. Key refinements to the new CDR improve the product's grid accuracy and documentation, and bring metadata into compliance with current standards for climate data records.

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Climate variability and breeding parameters of a transhemispheric migratory seabird over seven decades

Marine Ecology Progress Series ◽

10.3354/meps13328 ◽

2020 ◽

Vol 642 ◽

pp. 191-205 ◽

Cited By ~ 1

Author(s):

CA Price ◽

K Hartmann ◽

TJ Emery ◽

EJ Woehler ◽

CR McMahon ◽

...

Keyword(s):

Climate Variability ◽

Environmental Conditions ◽

Large Scale ◽

Southern Oscillation ◽

Southern Annular Mode ◽

Trophic Levels ◽

Climate Indices ◽

Ecological Processes ◽

Spatial And Temporal Scales ◽

Breeding Parameters

Climate variability affects physical oceanographic systems and environmental conditions at multiple spatial and temporal scales. These changes can influence biological and ecological processes, from primary productivity to higher trophic levels. Short-tailed shearwaters Ardenna tenuirostris are transhemispheric migratory procellariiform seabirds that forage on secondary consumers such as fish (myctophids) and zooplankton (euphausiids). In this study, we investigated the breeding parameters of the short-tailed shearwater from a colony of 100 to 200 breeding pairs at Fisher Island, Tasmania, Australia, for the period 1950 to 2012, with the aim to quantify the relationship between breeding parameters with large-scale climate indices in the Northern (i.e. Northern Pacific Index and Pacific Decadal Oscillation) and Southern Hemispheres (i.e. El Niño-Southern Oscillation and Southern Annular Mode). Through the use of generalised linear models, we found that breeding participation among short-tailed shearwaters was affected by climate variability with a 12-mo temporal lag. Furthermore, breeding success decreased in years of increased rainfall at the colony. These findings demonstrate that both large-scale climate indices and local environmental conditions could explain some of the variability among breeding parameters of the short-tailed shearwater.

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The Dynamics of Australian Monsoon Bursts

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-15-0071.1 ◽

2015 ◽

Vol 73 (1) ◽

pp. 55-69 ◽

Cited By ~ 15

Author(s):

Gareth J. Berry ◽

Michael J. Reeder

Keyword(s):

Southern Oscillation ◽

Convective Available Potential Energy ◽

Southern Annular Mode ◽

Climate Indices ◽

Time Interval ◽

Wet Season ◽

Large Variability ◽

Australian Monsoon ◽

Sequence Of Events ◽

Australian Continent

Abstract The wet season of the Australian monsoon is characterized by subseasonal periods of excessively wet or dry conditions, commonly known as monsoon bursts and breaks. This study is concerned with the synoptic evolution prior to monsoon bursts, which are defined here by abrupt transitions of the area-averaged rainfall over the tropical parts of the Australian continent. There is large variability in the number of monsoon bursts from year to year and in the time interval between consecutive monsoon bursts. Reanalysis data are used to construct a lag composite of the sequence of events prior to a monsoon burst. It is determined that a burst in the Australian monsoon is preceded by the development of a well-defined extratropical wave packet in the Indian Ocean, which propagates toward the Australian continent in the few days leading up to the onset of heavy rainfall in the tropics. As in previous studies on the monsoon onset, the extratropical disturbances propagate equatorward over the Australian continent. These extratropical systems are accompanied by lower-tropospheric airmass boundaries, which also propagate into low latitudes. Ahead of these boundaries, relatively warm moist air is advected from the surrounding oceans, locally increasing the convective available potential energy. Commonly employed climate indices show that monsoon bursts are more likely to occur when the active phase of the Madden–Julian oscillation is in the vicinity of Australia. Neither El Niño–Southern Oscillation nor the southern annular mode has a significant impact on the occurrence of monsoon bursts.

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Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty

The Cryosphere ◽

10.5194/tc-5-219-2011 ◽

2011 ◽

Vol 5 (1) ◽

pp. 219-229 ◽

Cited By ~ 281

Author(s):

R. D. Brown ◽

D. A. Robinson

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Climate Data ◽

The Past ◽

The North ◽

Central Russia ◽

Air Temperatures ◽

Snow Cover Extent ◽

The North Atlantic Oscillation ◽

Regional Character

Abstract. An update is provided of Northern Hemisphere (NH) spring (March, April) snow cover extent (SCE) over the 1922–2010 period incorporating the new climate data record (CDR) version of the NOAA weekly SCE dataset, with annual 95% confidence intervals estimated from regression analysis and intercomparison of multiple datasets. The uncertainty analysis indicates a 95% confidence interval in NH spring SCE of ±5–10% over the pre-satellite period and ±3–5% over the satellite era. The multi-dataset analysis shows larger uncertainties monitoring spring SCE over Eurasia (EUR) than North America (NA) due to the more complex regional character of the snow cover variability and larger between-dataset variability over northern Europe and north-central Russia. Trend analysis of the updated SCE series provides evidence that NH spring snow cover extent has undergone significant reductions over the past ~90 yr and that the rate of decrease has accelerated over the past 40 yr. The rate of decrease in March and April NH SCE over the 1970–2010 period is ~0.8 million km2 per decade corresponding to a 7% and 11% decrease in NH March and April SCE respectively from pre-1970 values. In March, most of the change is being driven by Eurasia (NA trends are not significant) but both continents exhibit significant SCE reductions in April. The observed trends in SCE are being mainly driven by warmer air temperatures, with NH mid-latitude air temperatures explaining ~50% of the variance in NH spring snow cover over the 89-yr period analyzed. However, there is also evidence that changes in atmospheric circulation around 1980 involving the North Atlantic Oscillation and Scandinavian pattern have contributed to reductions in March SCE over Eurasia.

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