KrigR – A tool for downloading and statistically downscaling climate reanalysis data

Advances in climate science have rendered obsolete the gridded observation data widely used in downstream applications. Novel climate reanalysis products outperform legacy data products in accuracy, temporal resolution, and provision of uncertainty metrics. Consequently, there is an urgent need to develop a workflow through which to integrate these improved data into biological analyses. The ERA5 product family (ERA5 and ERA5-Land) are the latest and most advanced global reanalysis products created by the European Center for Medium-range Weather Forecasting (ECMWF). These data products offer up to 83 essential climate variables (ECVs) at hourly intervals for the time-period of 1981 to today with preliminary back-extensions being available for 1950-1981. Spatial resolutions range from 30x30km (ERA5) to 11x11km (ERA5-Land) and can be statistically downscaled to study-requirements at finer spatial resolutions. Kriging is one such method to interpolate data to finer resolutions and has the advantages that one can leverage additional covariate information and obtain the uncertainty associated with the downscaling. The KrigR R-package enables users to (1) download ERA5(-Land) climate reanalysis data for a user-specified region, and time-period, (2) aggregate these climate products to desired temporal resolutions and metrics, (3) acquire topographical co-variates, and (4) statistically downscale spatial data to a user-specified resolution using co-variate data via kriging. KrigR can execute all these tasks in a single function call, thus enabling the user to obtain any of 83 (ERA5) / 50 (ERA5-Land) climate variables at high spatial and temporal resolution with a single R-command. Additionally, KrigR contains functionality for computation of bioclimatic variables and aggregate metrics from the variables offered by ERA5(-Land). This R-package provides an easy-to-implement workflow for implementation of state-of-the-art climate data while avoiding issues of storage limitations at high temporal and spatial resolutions by providing data according to user-needs rather than in global data sets. Consequently, KrigR provides a toolbox to obtain a wide range of tailored climate data at unprecedented combinations of high temporal and spatial resolutions thus enabling the use of world-leading climate data through the R-interface and beyond.

How well are we able to close the water budget at the global scale?

The water budget equation describes the exchange of water between the land, ocean, and atmosphere. Being able to adequately close the water budget gives confidence in our ability to model and/or observe the spatio-temporal variations in the water cycle and its components. Due to advances in observation techniques, satellite sensors, and modelling, a number of data products are available that represent the components of water budget in both space and time. Despite these advances, closure of the water budget at the global scale has been elusive. In this study, we attempt to close the global water budget using precipitation, evapotranspiration, and runoff data at the catchment scale. The large number of recent state-of-the-art datasets provides a new evaluation of well-used datasets. These estimates are compared to terrestrial water storage (TWS) changes as measured by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. We investigated 189 river basins covering more than 90 % of the continental land area. TWS changes derived from the water balance equation were compared against GRACE data using two metrics: the Nash–Sutcliffe efficiency (NSE) and the cyclostationary NSE. These metrics were used to assess the performance of more than 1600 combinations of the various datasets considered. We found a positive NSE and cyclostationary NSE in 99 % and 62 % of the basins examined respectively. This means that TWS changes reconstructed from the water balance equation were more accurate than the long-term (NSE) and monthly (cyclostationary NSE) mean of GRACE time series in the corresponding basins. By analysing different combinations of the datasets that make up the water balance, we identified data products that performed well in certain regions based on, for example, climatic zone. We identified that some of the good results were obtained due to the cancellation of errors in poor estimates of water budget components. Therefore, we used coefficients of variation to determine the relative quality of a data product, which helped us to identify bad combinations giving us good results. In general, water budget components from ERA5-Land and the Catchment Land Surface Model (CLSM) performed better than other products for most climatic zones. Conversely, the latest version of CLSM, v2.2, performed poorly for evapotranspiration in snow-dominated catchments compared, for example, with its predecessor and other datasets available. Thus, the nature of the catchment dynamics and balance between components affects the optimum combination of datasets. For regional studies, the combination of datasets that provides the most realistic TWS for a basin will depend on its climatic conditions and factors that cannot be determined a priori. We believe that the results of this study provide a road map for studying the water budget at catchment scale.

Evaluating the Sensitivity of Forest Structural Diversity Characterization to LiDAR Point Density

Recent expansion in data sharing has created unprecedented opportunities to explore structure-function linkages in ecosystems across spatial and temporal scales. However, characteristics of the same data product, such as resolution, can change over time or spatial locations, as protocols are adapted to new technology or conditions, which may impact the data’s potential utility and accuracy for addressing end user scientific questions. The National Ecological Observatory Network (NEON) provides data products for users from 81 sites and over a planned 30-year time frame, including discrete return Light Detection and Range (LiDAR) from an airborne observatory platform. LiDAR is a well-established and increasingly available remote sensing technology for measuring three-dimensional (3D) characteristics of ecosystem and landscape structure, including forest structural diversity. The LiDAR product that NEON provides can vary in point density from 2 – 25+ points/m2 depending on instrument and acquisition date. We used NEON LiDAR from five forested sites to (1) identify the minimum point density at which structural diversity metrics can be robustly estimated across forested sites from different ecoclimatic zones in the USA and (2) to test the effects of variable point density on the estimation of a suite of structural diversity metrics and multivariate structural complexity types within and across forested sites. Twelve out of sixteen structural diversity metrics were sensitive to LiDAR point density in at least one of the five NEON forested sites. The minimum point density to reliably estimate the metrics ranged from 2.0 to 7.5 pt/m2, but our results indicate that point densities above 7-8 pt/m2 should provide robust measurements of structural diversity in forests for temporal or spatial comparisons. The delineation of multivariate structural complexity types from a suite of 16 structural diversity metrics was robust within sites and across forest types for a LiDAR point density of 4 pt/m2 and above. This study shows that different metrics of structural diversity can vary in their sensitivity to the resolution of LiDAR data and users of these open-source data products should consider the point density of their data and use caution in metric selection when making spatial or temporal comparisons from these datasets.

Analyse und Kommunikation der beobachteten Klimatrends in Deutschland

<p>Eine der Aufgaben des Deutschen Wetterdienstes ist die Klimaüberwachung für Deutschland. Dazu verwendet der DWD die Daten der Wetterstationen in seinem Messnetz in Kombination mit den historischen Klimadaten, die auch durch Vorgängerorganisationen des DWD erhoben wurden. Für den Zeitraum seit 1881 sind somit flächendeckende und systematisch erhobene Messungen verfügbar, die für eine Beschreibung des Klimawandels in Deutschland genutzt werden. Für den Zeitraum 1881 bis 2020 beträgt der lineare Trend der Temperatur +1,6°C. Das zurückliegende Jahrzehnt lag dabei allerdings deutlich oberhalb der Trendlinie, wodurch das aktuelle Jahrzehnt (2011-2020) bereits um 2 °C wärmer war als der Beginn der Zeitreihe (1881-1910).</p> <p>Für eine verlässliche Kommunikation des Klimazustands und eine einordnende Beschreibung von Klimaänderungen ist es zu einen essentiell, kontinuierlich die Qualität der zugrundeliegenden Daten zu analysieren und gegebenenfalls zu korrigieren, zum anderen die Darstellungs- und Kommunikationsformen an die Öffentlichkeit weiterzuentwickeln.</p> <p>Um die Homogenität der meteorologischen Zeitreihen zu gewährleisten, betreibt der DWD mehrere Klimareferenzstationen, an denen Parallelmessungen von historischen und operationellen Messinstrumenten durchgeführt werden. Mithilfe dieser Messungen werden die Vergleichbarkeit der Messungen untersucht, Messunsicherheiten abgeschätzt und gegebenenfalls Methoden zur Homogenisierung der Messreihen entwickelt.</p> <p>Zurzeit werden klimatologische Indizes sowohl innerhalb des DWD als auch mit nationalen und internationalen Partnern standardisiert. Für einige der gebräuchlichsten Indizes (z.B. Heiße Tage und Tropische Nächte) existieren unterschiedliche Definitionen, die parallel verwendet werden. Um widersprüchliche Aussagen zu vermeiden, müssen einheitliche Definitionen verwendet werden oder es sollte ausdrücklich auf die jeweils verwendete Definition hingewiesen werden.</p> <p>Für die Kommunikation des beobachteten Klimawandels werden unterschiedliche grafische Aufbereitungen der Daten für verschiedene Medien und Plattformen eingesetzt. In diesem Vortrag wird auch ein Überblick über aktuelle Kommunikationskanäle (z. B. Deutscher Klimaatlas, DWD-Klima-Twitterkanal) sowie die Zugangsmöglichkeiten zu den zugrundeliegenden Klimadaten des DWD gegeben.</p> <p> </p> <p><strong>Literatur und weiterführende Links:</strong></p> <ul> <li>Zeitreihen der Klimaänderung in Deutschland: https://www.dwd.de/zeitreihen</li> <li>Informationsportal 'Beobachteter Klimawandel in Deutschland':<br />https://www.dwd.de/klima-deutschland</li> <li>Deutscher Klimaatlas: https://www.deutscher-klimaatlas.de</li> <li>Twitterkanal 'DWD Klima und Umwelt': https://twitter.com/DWD_klima</li> <li>Offener Zugang zu den Klimadaten des DWD: https://opendata.dwd.de/climate_environment/CDC/  https://cdc.dwd.de/portal</li> <li>Kaspar, F., Müller-Westermeier, G., Penda, E., Mächel, H., Zimmermann, K., Kaiser-Weiss, A., Deutschländer, T.: Monitoring of climate change in Germany – data, products and services of Germany's National Climate Data Centre, Adv. Sci. Res., 10, 99–106, https://doi.org/10.5194/asr-10-99-2013, 2013.</li> </ul>

Multi-Source Hydrological Data Products to Monitor High Asian River Basins and Regional Water Security

This project explored the integrated use of satellite, ground observations and hydrological distributed models to support water resources assessment and monitoring in High Mountain Asia (HMA). Hydrological data products were generated taking advantage of the synergies of European and Chinese data assets and space-borne observation systems. Energy-budget-based glacier mass balance and hydrological models driven by satellite observations were developed. These models can be applied to describe glacier-melt contribution to river flow. Satellite hydrological data products were used for forcing, calibration, validation and data assimilation in distributed river basin models. A pilot study was carried out on the Red River basin. Multiple hydrological data products were generated using the data collected by Chinese satellites. A new Evapo-Transpiration (ET) dataset from 2000 to 2018 was generated, including plant transpiration, soil evaporation, rainfall interception loss, snow/ice sublimation and open water evaporation. Higher resolution data were used to characterize glaciers and their response to environmental forcing. These studies focused on the Parlung Zangbo Basin, where glacier facies were mapped with GaoFeng (GF), Sentinal-2/Multi-Spectral Imager (S2/MSI) and Landsat8/Operational Land Imager (L8/OLI) data. The geodetic mass balance was estimated between 2000 and 2017 with Zi-Yuan (ZY)-3 Stereo Images and the SRTM DEM. Surface velocity was studied with Landsat5/Thematic Mapper (L5/TM), L8/OLI and S2/MSI data over the period 2013–2019. An updated method was developed to improve the retrieval of glacier albedo by correcting glacier reflectance for anisotropy, and a new dataset on glacier albedo was generated for the period 2001–2020. A detailed glacier energy and mass balance model was developed with the support of field experiments at the Parlung No. 4 Glacier and the 24 K Glacier, both in the Tibetan Plateau. Besides meteorological measurements, the field experiments included glaciological and hydrological measurements. The energy balance model was formulated in terms of enthalpy for easier treatment of water phase transitions. The model was applied to assess the spatial variability in glacier melt. In the Parlung No. 4 Glacier, the accumulated glacier melt was between 1.5 and 2.5 m w.e. in the accumulation zone and between 4.5 and 6.0 m w.e. in the ablation zone, reaching 6.5 m w.e. at the terminus. The seasonality in the glacier mass balance was observed by combining intensive field campaigns with continuous automatic observations. The linkage of the glacier and snowpack mass balance with water resources in a river basin was analyzed in the Chiese (Italy) and Heihe (China) basins by developing and applying integrated hydrological models using satellite retrievals in multiple ways. The model FEST-WEB was calibrated using retrievals of Land Surface Temperature (LST) to map soil hydrological properties. A watershed model was developed by coupling ecohydrological and socioeconomic systems. Integrated modeling is supported by an updated and parallelized data assimilation system. The latter exploits retrievals of brightness temperature (Advanced Microwave Scanning Radiometer, AMSR), LST (Moderate Resolution Imaging Spectroradiometer, MODIS), precipitation (Tropical Rainfall Measuring Mission (TRMM) and FengYun (FY)-2D) and in-situ measurements. In the case study on the Red River Basin, a new algorithm has been applied to disaggregate the SMOS (Soil Moisture and Ocean Salinity) soil moisture retrievals by making use of the correlation between evaporative fraction and soil moisture.

Evaluation of altimetry satellite data products and sea level trends in the Indonesian maritime continent

This study examines the accuracy of the sea surface height anomaly (SSHA) altimetry data products of Copernicus, Colorado University (CU), and X-TRACK-Centre for Topographic studies of the Ocean and Hydrosphere (X-TRACk-CTOH). The SSHA derived from altimetry accuracy was tested by comparison with tide gauge (TG) observations. Taking measurements along the IMC coast demonstrates the excellent agreement between the SSHA derived from altimetry and the TG observations, with an average root mean square deviation (RMSD) as low as 10 cm and a strong correlation. The study’s findings revealed that the Copernicus data products could be used to monitor sea-level variability and trends in the IMC accurately. The 25-year time series data from SSHA demonstrated that the sea-level trend in the IMC is higher than the global trend.