Towards implementing climate services in Peru – The project CLIMANDES

Rosas, Gabriela; Gubler, Stefanie; Oria, Clara; Acuña, Delia; Ávalos, Grinia; Begert, Michael; Castillo, Edgar Moreno; Croci-Maspoli, Mischa; Cubas, Francisco J.; Dapozzo, Marlene; Díaz, A.; Geijtenbeek, D. van; Jacques, Marie‐Agnès; Konzelmann, Thomas; Lavado, Waldo; Matos, Ana Luiza dos Santos; Mauchle, Fabian; Rohrer, Mario; Rossa, Andrea; Scherrer, Simon C.; Valdez, M. Travassos; Valverde, María Cleofé; Виллар, Гнань Аньеро Эрве; Villegas, Esequiel

doi:10.1016/j.cliser.2016.10.001

Cited by 25 publications

(22 citation statements)

References 16 publications

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“…The observational records are a quality controlled and homogenized data set (without gap filling) spanning the period from 1964 to present provided by SENAMHI Peru (Rosas et al , ). For the generation of the gridded data set, a gap filled station data set has been used, containing more stations, than the herein used station data set.…”

Section: Methodsmentioning

confidence: 99%

Summertime precipitation deficits in the southern Peruvian highlands since 1964

Imfeld

Schuler

Marrou

et al. 2019

Intl Journal of Climatology

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Precipitation deficits remain a concern to the rural population in the southern Peruvian highlands and knowledge about their occurrence is lacking because of scarce data availability. For mountainous regions with sparse station networks, reanalyses can provide valuable information; however, known limitations in reproducing precipitation are aggravated due to unresolved topographical effects. In this study, we assess in a first step the representation of precipitation during the rainy season (January-February-March) in seven reanalysis data sets in comparison to a newly generated gridded precipitation data set for Peru. In a second step, we assess summer precipitation deficits in Peru during the second half of the 20th century. In the reanalyses data sets, we find biases strongly influenced by the topography of the models and low correlations for the rainy season. Thus, reanalyses do not solve the problem of data scarcity for this region either. Furthermore, we confirm that El Niño is not a sufficient stratification criterion for precipitation deficits during the rainy season (JFM) in the southern Peruvian highlands. Based on observational records and reanalyses, a considerable fraction of inter-annual variability of precipitation can be explained through upper-tropospheric zonal wind anomalies. Westerly wind anomalies, often related to the warming of the troposphere during an El Niño event, lead to dry conditions, but not all El Niño events produce these westerly wind anomalies. Atmospheric simulations indicate differences between precipitation deficits in central Pacific and eastern Pacific El Niño flavours, which cannot be addressed in observations due to reduced record length: Droughts in the southern Peruvian Andes during eastern Pacific El Niño events seem to be related to a stronger warming in the troposphere above the central Pacific ocean, whereas this is not the case for droughts during central Pacific El Niño events. These results, however, need to be further corroborated by model studies and palaeoclimatological research. K E Y W O R D S drought, ENSO, ERA-20CM, mountain, Peru, rainfall, reanalysis, SPI

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Section: Methodsmentioning

confidence: 99%

Summertime precipitation deficits in the southern Peruvian highlands since 1964

Imfeld

Schuler

Marrou

et al. 2019

Intl Journal of Climatology

View full text Add to dashboard Cite

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“…This was observed in many parts of the world and for various climate variables and indices, such as minimum temperature (López-Moreno et al, 2016), precipitation (Rosas et al, 2016;Vuille et al, 2003), diurnal temperature range (Jaswal et al, 2016;New et al, 2006), and extremes indices (Skansi et al, 2013;You et al, 2013). Certain trend differences may be expected even on short spatial scales due to factors such as topography and feedback processes (You et al, 2010).…”

Section: Introductionmentioning

confidence: 94%

Effects of undetected data quality issues on climatological analyses

et al. 2018

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Abstract. Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are minimum and maximum temperature and precipitation. About 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control approach strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets and hence may affect a high number of climatological studies. It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties.

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“…Therefore, the project CLIMANDES (http://www.senamhi.gob.pe/climandes/), a project within the Global Framework for Climate Services (GFCS), aims at providing user‐tailored climate services in two pilot regions in Peru. One goal of CLIMANDES is to implement a suitable homogenization method at the national meteorological and hydrological service of Peru SENAMHI (Rosas et al ., ). To this end, the recently developed semi‐automatic homogenization procedure HOMER (Mestre et al ., ) was chosen since it is state‐of‐the‐art (developed after the COST Action on Homogenization), freely available, and runs on the open‐source software R (R Development Core Team, ).…”

Section: Introductionmentioning

confidence: 97%

“…Analyses of the homogenized temperature records showed that different approaches (exclusion of stations with quality problems, metadata availability, and different homogenization operators) resulted in differences in the average network trends of 0.06–0.08 °C/decade, while the estimated average network trends range between 0.22 and 0.28 °C/decade for maximum temperature (TX), and between 0.03 and 0.11 °C/decade for minimum temperature (TN) (e.g. Rosas et al ., ). These considerable differences raised questions on the reliability of breakpoint detection and correction in sparse station networks.…”

Section: Introductionmentioning

confidence: 98%

The influence of station density on climate data homogenization

Gubler

Hunziker

Begert

et al. 2017

Intl Journal of Climatology

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ABSTRACT:Relative homogenization methods assume that measurements of nearby stations experience similar climate signals and rely therefore on dense station networks with high-temporal correlations. In developing countries such as Peru, however, networks often suffer from low-station density. The aim of this study is to quantify the influence of network density on homogenization. To this end, the homogenization method HOMER was applied to an artificially thinned Swiss network.Four homogenization experiments, reflecting different homogenization approaches, were examined. Such approaches include diverse levels of interaction of the homogenization operators with HOMER, and different application of metadata. To evaluate the performance of HOMER in the sparse networks, a reference series was built by applying HOMER under the best possible conditions. Applied in completely automatic mode, HOMER decreases the reliability of temperature records. Therefore, automatic use of HOMER is not recommended. If HOMER is applied in interactive mode, the reliability of temperature and precipitation data may be increased in sparse networks. However, breakpoints must be inserted conservatively. Information from metadata should be used only to determine the exact timing of statistically detected breaks. Insertion of additional breakpoints based solely on metadata may lead to harmful corrections due to the high noise in sparse networks.

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Towards implementing climate services in Peru – The project CLIMANDES

Cited by 25 publications

References 16 publications

Summertime precipitation deficits in the southern Peruvian highlands since 1964

Summertime precipitation deficits in the southern Peruvian highlands since 1964

Effects of undetected data quality issues on climatological analyses

The influence of station density on climate data homogenization

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