The rainy season in the Southern Peruvian Andes: A climatological analysis based on the new Climandes index

The rainy season is of high importance for livelihoods in the Southern Peruvian Andes (SPA), especially for agriculture, which is mainly rain fed and one of the main income sources in the region. Therefore, knowledge and predictions of the rainy season such as its onset and ending are crucial for planning purposes. However, such information is currently not readily available for the local population. Moreover, an evaluation of existing rainy season indices shows that they are not optimally suited for the SPA and may not be directly applicable in a forecasting context. Therefore, we develop a new index, named Climandes index, which is tailored to the SPA and designed to be of use for operational monitoring and forecasting purposes. Using this index, we analyse the climatology and trends of the rainy season in the SPA. We find that the rainy season starts roughly between September and January with durations between 3 and 8 months. Both onset and duration show a pronounced northeast-southwest gradient, regions closer to the Amazon Basin have a considerably longer rainy season. The inter-annual variability of the onset is very high, that is, 2–5 months depending on the station, while the end of the rainy season shows a much lower variability (i.e., 1.5–3 months). The spatial patterns of total precipitation amount and dry spells within the rainy season are only weakly related to its timing. Trends in rainy season characteristics since 1965 are mostly weak and not significant, but generally indicate a tendency towards a shortening of the rainy season in the whole study area due to a later onset and an increase in precipitation sums during the rainy season in the northwestern study area

A combined view on precipitation and temperature climatology and trends in the southern Andes of Peru

In the southern Peruvian Andes, communities are highly dependent on climatic conditions due to the mainly rain-fed agriculture and the importance of glaciers and snow melt as a freshwater resource. Longer-term trends and year-to-year variability of precipitation or temperature severely affect living conditions. This study evaluates seasonal precipitation and temperature climatologies and trends in the period 1965/66–2017/18 for the southern Peruvian Andes using quality-controlled and homogenized station data and new observational gridded data. In this region, precipitation exhibits a strong annual cycle with very dry winter months and most of the precipitation falling from spring to autumn. Spatially, a northeast–southwest gradient in austral spring is observed, related to an earlier start of the rainy season in the northeastern partof the study area. Seasonal variations of maximum temperature are weak withan annual maximum in austral spring, which is related to reduced cloud coverin austral spring compared to summer. On the contrary, minimum tempera-tures show larger seasonal variations, possibly enhanced through changes inlongwave incoming radiation following the precipitation cycle. Precipitationtrends since 1965 exhibit low spatial consistency except for austral summer,when in most of the study area increasing precipitation is observed, and in aus-tral spring, when stations in the central-western region of the study area regis-ter decreasing precipitation. All seasonal and annual trends in maximum temperature are larger than trends in minimum temperature. Maximum temperature exhibits strong trends in austral winter and spring, whereas minimum temperature trends are strongest in austral winter. We hypothesize, that these trends are related to precipitation changes, as decreasing (increasing) precipita-tion in spring (summer) may enhance maximum (minimum) temperature trends through changes in cloud cover. El Niño Southern Oscillation (ENSO), however, has modifying effects onto precipitation and temperature, and thereby leads to larger trends in maximum temperatures

Greenland surface mass-balance observations from the ice-sheet ablation area and local glaciers

Glacier surface mass-balance measurements on Greenland started more than a century ago, but no compilation exists of the observations from the ablation area of the ice sheet and local glaciers. Such data could be used in the evaluation of modelled surface mass balance, or to document changes in glacier melt independently from model output. Here, we present a comprehensive database of Greenland glacier surface mass-balance observations from the ablation area of the ice sheet and local glaciers. The database spans the 123 a from 1892 to 2015, contains a total of similar to 3000 measurements from 46 sites, and is openly accessible through the PROMICE web portal (http://www.promice.dk). For each measurement we provide X, Y and Z coordinates, starting and ending dates as well as quality flags. We give sources for each entry and for all metadata. Two thirds of the data were collected from grey literature and unpublished archive documents. Roughly 60% of the measurements were performed by the Geological Survey of Denmark and Greenland (GEUS, previously GGU). The data cover all regions of Greenland except for the southernmost part of the east coast, but also emphasize the importance of long-term time series of which there are only two exceeding 20 a. We use the data to analyse uncertainties in point measurements of surface mass balance, as well as to estimate surface mass-balance profiles for most regions of Greenland

Scale-dependent measurement and analysis of ground surface temperature variability in alpine terrain

Measurements of environmental variables are often used to validate and calibrate physically-based models. Depending on their application, the models are used at different scales, ranging from few meters to tens of kilometers. Environmental variables can vary strongly within the grid cells of these models. Validating a model with a single measurement is therefore delicate and susceptible to induce bias in further model applications. To address the question of uncertainty associated with scale in permafrost models, we present data of 390 spatially-distributed ground surface temperature measurements recorded in terrain of high topographic variability in the Swiss Alps. We illustrate a way to program, deploy and refind a large number of measurement devices efficiently, and present a strategy to reduce data loss reported in earlier studies. Data after the first year of deployment is presented. The measurements represent the variability of ground surface temperatures at two different scales ranging from few meters to some kilometers. On the coarser scale, the depen- dence of mean annual ground surface temperature on elevation, slope, aspect and ground cover type is modelled with a multiple linear regression model. Sampled mean annual ground surface temperatures vary from −4 ◦C to 5 ◦C within an area of approximately 16 km2 subject to elevational differences of approximately 1000 m. The measurements also indicate that mean annual ground surface temperatures vary up to 6 ◦C (i.e., from −2 ◦C to 4 ◦C) even within an elevational band of 300 m. Furthermore, fine-scale variations can be high (up to 2.5◦C) at distances of less than 14m in homogeneous terrain. The effect of this high variability of an environmental variable on model validation and applications in alpine regions is discussed

Sensitivities and uncertainties of modeled ground temperatures in mountain environments

Model evaluation is often performed at few locations due to the lack of spatially distributed data. Since the quantification of model sensitivities and uncertainties can be performed independently from ground truth measurements, these analyses are suitable to test the influence of environmental variability on model evaluation. In this study, the sensitivities and uncertainties of a physically based mountain permafrost model are quantified within an artificial topography. The setting consists of different elevations and exposures combined with six ground types characterized by porosity and hydraulic properties. The analyses are performed for a combination of all factors, that allows for quantification of the variability of model sensitivities and uncertainties within a whole modeling domain. <br><br> We found that model sensitivities and uncertainties vary strongly depending on different input factors such as topography or different soil types. The analysis shows that model evaluation performed at single locations may not be representative for the whole modeling domain. For example, the sensitivity of modeled mean annual ground temperature to ground albedo ranges between 0.5 and 4 °C depending on elevation, aspect and the ground type. South-exposed inclined locations are more sensitive to changes in ground albedo than north-exposed slopes since they receive more solar radiation. The sensitivity to ground albedo increases with decreasing elevation due to shorter duration of the snow cover. The sensitivity in the hydraulic properties changes considerably for different ground types: rock or clay, for instance, are not sensitive to uncertainties in the hydraulic properties, while for gravel or peat, accurate estimates of the hydraulic properties significantly improve modeled ground temperatures. The discretization of ground, snow and time have an impact on modeled mean annual ground temperature (MAGT) that cannot be neglected (more than 1 °C for several discretization parameters). We show that the temporal resolution should be at least 1 h to ensure errors less than 0.2 °C in modeled MAGT, and the uppermost ground layer should at most be 20 mm thick. <br><br> Within the topographic setting, the total parametric output uncertainties expressed as the length of the 95% uncertainty interval of the Monte Carlo simulations range from 0.5 to 1.5 °C for clay and silt, and ranges from 0.5 to around 2.4 °C for peat, sand, gravel and rock. These uncertainties are comparable to the variability of ground surface temperatures measured within 10 m × 10 m grids in Switzerland. The increased uncertainties for sand, peat and gravel are largely due to their sensitivity to the hydraulic conductivity

Uncertainties of parameterized surface downward clear-sky shortwave and all-sky longwave radiation

As many environmental models rely on simulating the energy balance at the Earth’s surface based on parameterized radiative fluxes, knowledge of the inherent model uncertainties is important. In this study we evaluate one parameterization of clear-sky direct, diffuse and global shortwave downward radiation (SDR) and diverse parameterizations of clear-sky and all-sky longwave downward radiation (LDR). In a first step, SDR is estimated based on measured input variables and estimated atmospheric parameters for hourly time steps during the years 1996 to 2008. Model behaviour is validated using the high quality measurements of six Alpine Surface Radiation Budget (ASRB) stations in Switzerland covering different elevations, and measurements of the Swiss Alpine Climate Radiation Monitoring network (SACRaM) in Payerne. In a next step, twelve clear-sky LDR parameterizations are calibrated using the ASRB measurements. One of the best performing parameterizations is elected to estimate all-sky LDR, where cloud transmissivity is estimated using measured and modeled global SDR during daytime. In a last step, the performance of several interpolation methods is evaluated to determine the cloud transmissivity in the night. We show that clear-sky direct, diffuse and global SDR is adequately represented by the model when using measurements of the atmospheric parameters precipitable water and aerosol content at Payerne. If the atmospheric parameters are estimated and used as a fix value, the relative mean bias deviance (MBD) and the relative root mean squared deviance (RMSD) of the clear-sky global SDR scatter between between −2 and 5 %, and 7 and 13 % within the six locations. The small errors in clear-sky global SDR can be attributed to compensating effects of modeled direct and diffuse SDR since an overestimation of aerosol content in the atmosphere results in underestimating the direct, but overestimating the diffuse SDR. Calibration of LDR parameterizations to local conditions reduces MBD and RMSD strongly compared to using the published values of the parameters, resulting in relative MBD and RMSD of less than 5 % respectively 10 % for the best parameterizations. The best results to estimate cloud transmissivity during nighttime were obtained by linearly interpolating the average of the cloud transmissivity of the four hours of the preceeding afternoon and the following morning. Model uncertainty can be caused by different errors such as code implementation, errors in input data and in estimated parameters, etc. The influence of the latter (errors in input data and model parameter uncertainty) on model outputs is determined using Monte Carlo. Model uncertainty is provided as the relative standard deviation σrel of the simulated frequency distributions of the model outputs. An optimistic estimate of the relative uncertainty σrel resulted in 10 % for the clear-sky direct, 30 % for diffuse, 3 % for global SDR, and 3 % for the fitted all-sky LDR

Inferring snowpack ripening and melt-out from distributed measurements of near-surface ground temperatures

Seasonal snow cover and its melt regime are heterogeneous both in time and space. Describing and modelling this variability is important because it affects diverse phenomena such as runoff, ground temperatures or slope movements. This study presents the derivation of melting characteristics based on spatial clusters of ground surface temperature (GST) measurements. Results are based on data from Switzerland where ground surface temperatures were measured with miniature loggers (iButtons) at 40 locations referred to as footprints. At each footprint, up to ten iButtons have been distributed randomly over an area of 10 m × 10 m, placed a few cm below the ground surface. Footprints span elevations of 2100–3300 m a.s.l. and slope angles of 0–55◦, as well as diverse slope expositions and types of surface cover and ground material. Based on two years of temperature data, the basal ripening date and the melt-out date are determined for each iButton, aggregated to the footprint level and further analysed. The melt-out date could be derived for nearly all iButtons; the ripening date could be extracted for only approximately half of them because its detection based on GST requires ground freezing below the snowpack. The variability within a footprint is often considerable and one to three weeks difference between melting or ripening of the points in one footprint is not uncommon. The correlation of mean annual ground surface temperatures, ripening date and melt-out date is moderate, suggesting that these metrics are useful for model evaluation

Estimating velocity from noisy GPS data for investigating the temporal variability of slope movements

Detecting and monitoring of moving and potentially hazardous slopes requires reliable estimations of velocities. Separating any movement signal from measurement noise is crucial for understanding the temporal variability of slope movements and detecting changes in the movement regime, which may be important indicators of the process. Thus, methods capable of estimating velocity and its changes reliably are required. In this paper we develop and test a method for deriving velocities based on noisy GPS (Global Positioning System) data, suitable for various movement patterns and variable signal-to-noise-ratios (SNR). We tested this method on synthetic data, designed to mimic the characteristics of diverse processes, but where we have full knowledge of the underlying velocity patterns, before applying it to explore data collected