The Permian Basin has become the United States’ largest producer of oil over the past decade. Along with the rise in production, there has been an increase in the rate of low magnitude earthquakes, some of which have been associated with hydrocarbon extraction and wastewater injection. A detailed knowledge of changes to the subsurface can aid in understanding the causes of seismicity, and these changes can be inferred from InSAR surface deformation measurements. In this study, we show that both cm-level cumulative deformation, as well as mm-level coseismic deformation signals, are detectable in West Texas. In a region west of Mentone, TX, we reconstructed the subtle coseismic deformation signal on the order of ~5 mm associated with the recent M4.9 earthquake. Over ~100,000 km2 of the Permian Basin, we created annual cumulative LOS deformation maps, decomposing into vertical and eastward components where overlapping data are available. These maps contain numerous subsidence and uplift features near active production and disposal wells. The most important deformation signatures are linear streaks that extend tens of kilometers near Pecos, TX, where a cluster of increased seismic events was cataloged by TexNet. As validated by independent GPS data, our InSAR processing strategy achieved millimeter-level accuracy. A careful treatment of the InSAR tropospheric noise, which can be as large as 15 cm in West Texas, is required to detect surface deformation signals with such low signal-to-noise ratio. We developed an outlier removal technique based on robust statistics to detect the presence of strong, non-Gaussian noise. We compared the surface deformation solutions of multiple InSAR time series methods, and all of them produced more accurate and consistent deformation trends after removing outlier InSAR measurements. We are exploring a Bayesian generalization of SBAS velocity estimation by including probabilistic data rejection to determine which pixels should be excluded from the model fitting. This technique provides a full posterior distribution of the model parameters along with the best-fit surface velocity.

How and why has Geographic Information Systems (GIS) education been advancing in primary and secondary education, globally? How have GIS tools and approaches been spreading beyond geography and GIScience to earth and environmental science, business, data science, health, and other disciplines in higher education? The adoption and integration of GIS and related geospatial technologies into dozens of academic disciplines has led to worldwide high demand for instruction that is targeted and timely, a combination that is challenging to meet consistently with diverse audiences and in diverse settings. Over the past 40 years, methods and approaches for teaching and learning about geospatial technologies and with geospatial technologies have evolved in tight connection with the advances in the internet and personal computers. Academic degrees, concentrations, minors, certificates, and other programs abound within formal and informal education. During the educationally-disruptive COVID-19 period, online instruction has hastened the adoption of analysis-based and data-rich approaches such as GIS. What are the challenges that remain in the use of GIS in instruction and research, and how can these challenges be addressed? The author, who has been active in promoting and researching GIS in education at all levels for 30 years, will share his perspectives, research results, and offer a pathway forward.

The geospatial Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC; formerly GeoSPEC) pioneers an on-demand science data processing system (SDPS) producing user-customized Level 1 calibrated radiance to Level 3+ data products in anticipation for the 2017-2027 Earth Decadal Survey prioritized spaceborne global imaging spectrometer to advance the study of Surface Biology and Geology (SBG). SBG data volumes (~20 TB/day) of high dimensionality (>224 bands) would be infeasible to download and the breadth of applications of the data across dozens of disciplines presents a need to evolve the traditional NASA SDPS. ImgSPEC streamlines processing data into key SBG observables that have demonstrated algorithms at local-to-regional scales and may vary locally. As such, a traditional, monolithic SDPS could not fully exploit the information in SBG measurements. To remove this barrier to use, ImgSPEC demonstrates an on-demand SDPS prototype that improves imaging spectroscopy data discovery, access, and utility enabling shared knowledge transfer from advanced imaging spectroscopy users to less experienced users such as decision makers and the general public. We test three use cases: 1) standard data processing workflows, 2) customized variants of standard workflows, and 3) algorithm development of new workflows. We create collaborative algorithm development environments that offer services typically restricted to NASA SDPSs such as data product provenance and bulk processing. We leverage existing NASA-funded information technologies such as the hybrid on-premise/ cloud science data system (HySDS), the Multi-mission Algorithm and Analysis Platform (MAAP), ECOSIS – a crowd-sourced spectral database, and ECOSML – a crowd-sourced model database. We demonstrate ImgSPEC on the Terrestrial Ecosystem use case processing through to foliar traits and fractional cover, thus aligning with driving thrusts for the SBG Science and Applications Communities.

The Space Physics Data Facility (SPDF https://spdf.gsfc.nasa.gov), as the non-solar NASA Heliophysics active final archive, works with current operating missions and the heliophysics community to ingest, preserve and serve a wide range of past and current public science-quality data from the mesosphere into the furthest reach of deep-space exploration. SPDF facilitates scientific analysis of multi-instrument and multi-mission datasets to enhance the science return of the many missions. SPDF develops and maintains the Common Data Format (CDF) and the associated ISTP/SPDF metadata guidelines. SPDF services include CDAWeb, which supports both survey and burst mode data with graphics, listings and data superset/subset functions. All public data held by SPDF are also available for direct file download by HTTPS or FTPS links from the SPDF home page (https://spdf.gsfc.nasa.gov). SPDF is currently receiving and serving from missions including Parker Solar Probe, MMS, Van Allen Probes, THEMIS/ARTEMIS, GOLD, ACE, Cluster, IBEX, Voyager, Geotail, Wind and many others, and >120 Ground-Based investigations. SPDF recently added support for ARASE/ERG and MAVEN as supplementary access at the requests of those missions, and is expecting Solar Orbiter and ICON data. SPDF also operates the multi-mission orbit displays and query services of SSCWeb and the Java-based 4D Orbit Viewer, as well as the Heliophysics Data Portal (HDP) discipline-wide data inventory and access service, and OMNIWeb and COHOWeb for near-Earth and deep-space solar wind plasma, magnetic field, and energetic particle database, respectively.

The Adler Planetarium’s Teen Programs engages hundreds of young people in authentic STEM experiences each year. Several of our programs focus on artificial light at night (ALAN), a critical environmental, health, ecological and astronomical issue. Our work in ALAN Community Advocacy and Education began in 2015 through Youth Organization for Lights Out (YOLO), a bilingual program in English and Spanish based in Chicago’s predominantly Mexican and Mexican-American Little Village neighborhood. YOLO program participants use tools to collect and analyze light pollution data, attend field trips to local and state dark sky sites, facilitate telescope viewing at the Adler and in their community, and develop prototype solutions and action plans to increase awareness of light pollution’s local effects in Chicago. Programs focused on Instrumentation and Research grew out of the Adler’s educational high-altitude ballooning program, Far Horizons. In 2018, Far Horizons astronomers and engineers began developing Mission NITELite (Night Imaging of Terrestrial Environments), a high altitude balloon-based light pollution mapping mission along with undergraduate interns. To complement NITELite, Far Horizons designed GONet (Ground Observing Network), a low-cost all-sky imaging system to measure sky quality at night as part of its Stratonauts teen program. In 2019, high school students helped design, test, and build 50 GONet units as a potential new standard for worldwide ALAN monitoring. In 2020, Adler teen interns are working with the Cook County Forest Preserves to quantify regional sky quality with GONets in support of an application for an Urban Night Sky Place designation from the International Dark-Sky Association. Reflecting the collaborative nature of science, teens in the Instrumentation and Research programs partner closely with peers in the Community Advocacy and Education programs, learning from one another’s perspective while undertaking joint projects.

Extreme precipitation events from the western disturbances have significant impacts on the water management and ecosystem services in the Upper Indus basin, a part of the Hindukush Himalayan region. Further, there are changes in the duration, intensity, spatial extent and frequency of extreme events due to climate change. In this domain, a few studies have assessed the impact of climate change in the study area with limited data. There will be high uncertainty in the outcomes obtained from the investigation of extreme events with limited data. Therefore, a comprehensive analysis of extreme precipitation events from western disturbances considering high resolution with long-term data is required in the study area. Accordingly, In the present study, Precipitation based ETCCDI Indices are calculated for every year, and non-parametric Mann-Kendall test is applied Sen slope is calculated to detect the changes in the monthly precipitation during the winter season for the period 1901-2019. The findings of the present study reveal the northwest region has an increasing trend in RX1day extreme precipitation of 1.85 mm per decade in December, and the rate has amplified due to the effect of western disturbances in January and February. Also, the pattern of RX5day extreme precipitation is consistent in January and February. Overall the wetness in increasing over the north-west part of the study area In recent decades, the northwest region of the upper Indus basin had faced more extreme events with severe impacts due to western disturbances, and outcomes from the study can improve the understanding of extreme events.

Data maturity describes the degree of the formalisation/standardisation of a data object with respect to FAIRness and quality of the (meta-) data. Therefore, a high (meta-) data maturity increases the reusability of data. Moreover, it is an important topic in data management, which is reflected by a growing number of tools and theories trying to measure it, e.g. the FAIR testing tools assessed by RDA(1) or the NOAA maturity matrix(2). If the results of stewardship tasks cannot be shown directly in the metadata, reusers of data cannot easily recognise which data is easy to reuse. For example, the DataCite Metadata Schema does not provide an explicit property to link/store information on data maturity (e.g. FAIRness or quality of data/metadata). The AtMoDat project (3, Atmospheric Model Data) aims to improve the reusability of published atmospheric model data by scientists, the public sector, companies, and other stakeholders. These data are valuable because they form the basis to understand and predict natural events, including the atmospheric circulation and ultimately the atmospheric and planetary energy budget. As most atmospheric data has been published with DataCite DOIs, it is of high importance that the maturity of the datasets can be easily found in the DOI’s Metadata. Published data from other fields of research would also benefit from easily findable maturity information. Therefore, we developed a Maturity Indicator concept and propose to introduce it as a new property in the DataCite Metadata Schema. This indicator is generic and independent of any scientific discipline and data stewardship tool. Hence, it can be used in a variety of research fields. 1 https://doi.org/10.15497/RDA00034 2 Peng et al., 2015: https://doi.org/10.2481/dsj.14-049 3 www.atmodat.de

A large number of General Circulation Models (GCMs) are currently available for modelling the atmospheric conditions over the Earth. However, there is a large variability in the future climate predicted by the available set of climate models. Hence, the climate data introduces the most amount of uncertainty in the climate change impact assessment. Regional-scale climate change impact studies based on these models may produce a wide range of possible impacts that becomes unusable for policymakers. A robust GCM selection procedure is introduced in the current study to bring the uncertainty to a realistic range. The proposed approach takes into account the process representation in the climate models by checking teleconnections in data along with their ability to predict the regional climate in spatial and temporal scale. The interdependence between the climate models are also accounted for in the proposed approach to avoid underestimation of uncertainty. The procedure is validated in the Bharathapuzha River Basin, Kerala, India. The study considers 22 GCMs that participated in the Coupled Model Inter-comparison Project-5 and 6 Regional Climate Models (RCMs) that are recommended for the Indian subcontinent. The climate models BNU-ESM, CMCC-CM, GFDL-ESM2G, GFDL-ESM2M and MPI-ESM-MR are found to be performing well for the prediction of both precipitation and temperature. The proposed climate model selection procedure can bring down the band width of uncertainty from 376 mm to 162 mm in monthly rainfall prediction with a containing ratio of 44%. The downscaling of the climate predictions can further increase the containing ratio by removing the systematic error. The bandwidth of uncertainty has reduced from 10.82 K to 3.83 K in the prediction of minimum temperature and from 8.35 K to 4.52 K for maximum temperature. The proposed GCM selection procedure provides more confidence in the predicted future climate since regionally significant correlations between climate variables are preserved in the selected models. The model selection procedure is validated for the period 2006-2018 with the observed climatic variables, and the selected models are found to be performing well.

The NOAA Physical Sciences Laboratory produces the Global Ensemble Forecasting System (GEFS) which comprises 11 ensemble members (1 control and 10 perturbation runs) for over a 36-year period (December 1984 to present), with forecasts initialized every day for the next 16 days (first 8-day forecasts obtained from a high-resolution grid and the next 8-day forecasts from a low-resolution grid). The system provides 36 variables related to a wide range of hydrometeorological processes. In this study, we assess the predictability of precipitation within the context of statistical downscaling using a minimum set of predictor variables (precipitation and temperature). We use feedforward backpropagation neural networks with a suite of training algorithms to determine which variables (features) are of most relevance at different forecast lead times. The outcome of this study will significantly benefit short-term flood forecasting using GEFS data.

Conventionally, radar-based nowcasting methods have been conducted by shifting or extrapolating the most recent estimated precipitation field along the estimated motion field to lead times up to 3 hr. However, such methods assume that these fields do not evolve during the lead-time period. A more recent nowcasting approach is the short-term ensemble prediction system known as STEPS. STEPS aims to filter small spatial scales of rain as they have an expected short life-time compared to large spatial scales. Besides, it perturbs the precipitation field during extrapolation so that the prediction uncertainties related to the evolution of precipitation is considered, resulting in an ensemble nowcast. In this work, the configuration, implementation, and performance of STEPS are studied for its radar-based ensemble nowcasting application in Germany. Attention is given to the spatial localization (i.e., the adjustment of parameters that control the spatio-temporal evolution of precipitation in large domains). For such purpose, the capability of STEPS is analyzed using multiple rain events collected by the German radar network. Preliminary analysis regarding the spatial scale filtering of STEPS (without perturbation) shows an improvement against conventional extrapolation of 20 to 30 % on the critical success index starting at lead times of 30 min at a rainfall threshold of 0.1 mm hr-1. In terms of the spatial structure, STEPS (without perturbation) provides a consistent nowcast for lead times up to 3 hr. Although small spatial structures are filtered, the spatial structure at scales of 32 km or larger is maintained. The skill of a probabilistic nowcast (STEPS with perturbation) was also analyzed. It is shown that for the probability of exceeding the 5.0 mm hr-1 threshold, the predictability of the nowcast is compromised for lead times on the order of 1 hr. However, the capability of identifying rain and non-rain areas is still valuable: the hit rates are still larger than the false alarm rates. Our preliminary results highlight aspects needed in the configuration of STEPS such as the evolution model and the perturbation model. In this manner, this study can serve as a basis for an improved nowcasting system in Germany and as a reference for forecasters to understand the characteristics of the examined nowcast system.

Managing river temperature in highly urbanized stream systems is critical for maintaining aquatic ecosystems and associated beneficial uses. Elevated river temperatures arise from warm surface inflows from impervious areas, channelization, the absence of riparian forests, and the lack of groundwater-surface water interactions. In the current work, we utilize a mechanistic river temperature model, i-Tree Cool River, to evaluate the cooling impacts of alternative ecological restoration scenarios: a) shading effects of tree planting in riparian areas and b) alternative streambed materials. The model was calibrated and validated on a 4.2 km reach of the Compton Creek in the Los Angeles (LA) River watershed, California, for low and high flow periods. The Arroyo Chub and Stickleback were considered the target species for analyzing thermal habitat suitability. River temperature simulations showed that like the ambient air temperature. The thermal response of the river in high flow periods was a function of upstream river temperature , where in low flow periods river water temperature was most affected by the tested restoration scenarios. Tree planting in the riparian corridor decreased the median thermal metrics: Max Weekly Max, Max Weekly Average, and Min Weekly Min Temperatures by an average of 3 ℃ (13%) to 20.4 ℃, 19.7 ℃, and 17.8 ℃, respectively. Using limecrete as an alternative bed material to the current concrete bottom decreased the median thermal metrics by an average of 0.9 ℃ (4%) to 22.7 ℃, 22 ℃, and 19 ℃, respectively. Combining the two scenarios decreased the river temperature metrics by an average of 4 ℃ (18%) to 18.2 ℃. Besides riparian vegetation, altering bed material is an impactful option in case of groundwater contamination and if channelized urban corridors lack the substrate to support vegetation. The use of ecological restoration scenarios resulted in summertime temperatures were within the documented spawning temperature thresholds and therefore temperature would not be a limiting factor in the potential reintroduction of the Arroyo Chub and Stickleback to Compton Creek. This tributary could be considered as a potential refuge and improved fish habitat in the LA basin during low flow periods.

Subduction zone earthquakes show varying energy release patterns and frequency content, based on their tectonic settings and hypocentral depths. Resolving these features from the nonlinear and non-stationary seismograms is a challenge. Our work in the Japan Trench follows studies by Huang et al. (1998, 2001) and Zhang et al. (2003), who demonstrated the use of empirical mode decomposition to separate records into multiple timescales, or intrinsic mode functions (IMFs). Zhang et al. observed that IMFs 2-5 represented the source rupture process for the 1994 Northridge earthquake. Chauhan (master’s thesis, 2019) used time-frequency distributions, short-time Fourier and continuous wavelet transforms, of IMFs of strong-motion data for a pair of interplate-intraslab earthquakes to identify the dominant, short duration, low-frequency energy release for the intraslab event. He found a high correlation between the original signal and a linear combination of IMFs 3 and 4, possibly representing the source. Chatterjee et al. (AGU, 2018) observed an association between time-frequency-energy distributions of certain IMFs and moment rate functions (MRFs) from teleseismic waveform models, for five earthquakes. Chatterjee et al. (AGU, 2019) and Mache et al. (AGU, 2019) used Hilbert spectral analysis (Huang et al., 1998) of IMFs selected based on their frequency and energy and observed better match between the two. This new function, which they regard as the Energy Rate Function (ERF), can reproduce the MRF’s essential elements, i.e., its duration and shape, but Mache (master’s thesis, 2020) observed that results depended on the selection of stations. As the next step, Mache and Rajendran (JpGU-AGU, 2020) based the selection criteria on the slip distribution, strike, and JMA intensity distribution maps (JMA 1996) and applied the method to 7 earthquakes from various tectonic settings of the Japan Trench. Here we present an overview of the various methods for analyzing KiK-net strong-motion data for selected earthquakes to extract information on their time-frequency-energy distributions. The ERF generated through this analysis is a physically compatible expression of the MRF and, therefore, more useful in predicting the shaking effects of earthquakes.

The Chilean central Andes are known for its variety of cryospheric landforms, which have included almost every kind of glacier since their first exploration back in the XIX century. However, there has been a severe reduction of the glacierized area since the 1950s, driven by climate change and enhanced due to the megadrought, which has endured for over a decade in the region. Such decline in glacier volume combined with temperature increasing and precipitation reduction can lead to different types of instabilities. In mountainous regions of high public affluence, glacial instabilities are considered as potential hazards leading to the loss of lives and infrastructure. Here we analyze the Rio Volcan basin (-32.82/-70.00), located 40 km east of Santiago city in the international border with Argentina. The region is known for its closeness to the capital, which favors outdoor activities and hydroelectric power development. Elevation ranges from 3380 to over 6000 m a.s.l. at the San José Volcanic Complex, allowing conditions for coexistence of mountain glaciers, valley glaciers, rock glaciers and glaciarets. According to the public Chilean Glacier Inventory, there are more than 140 mapped cryoforms occupying an area of 57 km2 . Beside snow avalanches, there are multiple factor that provide ideal conditions for cryospheric hazards involving glaciers. Some of those factors are pointed out on the following: The presence of an active volcanic complex sets up the triggering agent for lahars and mixed snow/ice avalanche occurrence. There are three moraine-dammed glacial lakes with a cumulated area of up to 24 hectares in front of the El Morado glacier and two innominates. The lakes are still enlarging along with the glacier shrinkage, conforming three potential glofs in the region. Several debris-free glaciers have a very steep front, steeper than 30 degrees, favoring the occurrence of ice falls and ice avalanches. There is a reported surge event in the Nieves Negras glacier, located at the south face of the San Jose; volcano. The latter would have happened in the late 1940s according to literature. In addition, at least four glaciers showed abnormal advance rates in the early 1990s of up to 100 m/yr, along with the surge-like behavior of the Loma Larga glacier. Providing further knowledge of this complex region is key in order to enhance understanding and hazard management on a day to day basis.

The rapid acceleration of Greenland Ice Sheet mass loss over, particularly the last two decades, is well documented.However, limits in early remote sensing restricted the details with which we could examine local changes on an ice-sheet-wide scale, particularly in areas of slow motion, along shear margins and complex coastal terrain. We explore the localcharacter of rapid contemporary change marine-terminating glaciers using satellite-derived ice sheet surface velocities,glacier terminus advance/retreat history, and surface elevation-change data from the 1980s to the present. Widespread glacierterminus retreat is a strong and more consistent climate response indicator than velocity change, but local changes in velocityare critical indicators of rapid ice sheet reconfiguration. Ice thickness changes related to changing ice dynamics often providethe first evidence of processes that initiate outlet glacier retreats and mass loss, such as the development of sub-ice shelfcavities and subglacial hydrology changes. Reconfiguration is observed locally as narrowing zones of fast-flow, ice flowrerouting, shear margin migration, and likely glacier outlet abandonment. These patterns are apparent in all ice sheet sectorsand observable from space-borne instruments. The rapid reconfiguration now well underway in Greenland has wide-rangingimplications, including expected changes in subglacial hydrology, ice discharge, freshwater flux to the ocean, and transport ofnutrients and sediment. Lacking detailed observations of earlier deglaciations and current limits on ice-sheet modelcapabilities, the expanding details of these combined observational records may provide a valuable analog for studying pastice sheet dynamics and projecting future ice loss.

Urban expansion is a global trend primarily determined by overpopulation, particularly in developing countries like India. The pattern and boundary of urban expansion could be observed and modelled on a Spatio-temporal dimension in the Asan watershed, situated in western Doon Valley, Uttarakhand, India. The specific objectives of the present study are: 1) Monitoring Land Use Land Cover (LULC) change detection and its impact on the hydrological regime of the watershed, 2) Generation of urban growth pattern map to identify the urban expansion along with the directions and distance from the watershed centroid. To achieve the objectives, the four LULC maps of different years are generated through Landsat and LISS-IV dataset from 1980 to 2016. The eight classified classes of LULC includes: build-up (urban and rural), cropland, fallow land, plantation, evergreen needle leaf forest, deciduous broadleaf forest, mixed forest, and water bodies. The LULC change analysis concludes that the built-up area continuously expanding at an alarming rate, nearly four folds in the last 36 years. The major areal portion of cropland and plantation are converted into build-up at a faster rate after 1995 whereas the areal extent of water bodies are also reduced. Based on monitoring of urban growth, the wind rose tool revealed that the southwest direction is the dominant direction of the urban expansion till 1980 and 1980-1995 which contribute 48% and 47.61% respectively of the total urban area of the watershed. However, in the period 1995-2008 and 2008-2016, the eastern direction is predominant, which contribute 36.44% and 35.27% respectively from the total urban areal expansion. This reflects a situation where the eastern direction is expected to dominate in the next years. Consequences of urban expansion create pressure on the into the watershed which leads to degradation of natural resources, forest environment and hydrological elements of the watershed.

The Environment-Vulnerability-Decision-Technology (EVDT) integrated modeling framework considers the interactions between the environment, societal impact, human decision-making, and technology design to support decision making. EVDT has been expanded to include a public health model in the Vida Decision Support System, which will help local leaders understand the relationships between societal factors relating to COVID-19. Key to the development of Vida are collaborative design and mutual learning with international and interdisciplinary teams. Collaborations with researchers and government officials (including public health, economics, environmental, and demographic data collection officials) in Angola, Brazil, Chile, Indonesia, Mexico and the United States provide in-depth understanding of local contexts. Lessons learned from these collaborations include the value of dialogues with teams from the same region but different topic areas (such as a space agency compared to a public health agency), allowing for time to learn the best way to combine diverse data types and find the tools each collaborator prefers, and encouraging the use of the preferred language of collaborators. During Vida’s development, each collaborator has worked to create their own version of Vida using local data sources, the US team has provided prototype analyses and models, and collaborators have shared individual insights among the whole network. These partnerships have yielded promising initial results to support decision making, with prototype tools incorporating local data on COVID cases, the environment, and socio-economic factors from Rio De Janeiro and Chile being evaluated. This collaborative design process will develop insights for decision-making, create a network of international collaborators that can exchange technical methods beyond the pandemic, and emphasize the principles of inclusive innovation and decoloniality by submitting to the preferences of local leaders in each country.

To understand the influence of the Sun on Earth's system, long and accurate measurements of solar irradiance are a prerequisite. The available direct measurements of solar irradiance since 1978 are clearly not sufficient for this purpose. This stimulated development of models used to reconstruct past solar irradiance variations from alternative observations. The main driver of the irradiance variations on time scales of days to millennia is the evolution of the solar surface magnetic field in form of dark sunspots and bright faculae and network. Therefore, models require input data describing the contributions of these various magnetic regions on the Sun at earlier times. Unfortunately, records that can be used to describe the facular and network contributions are barely longer than the direct irradiance measurements. Thus, irradiance reconstructions to earlier periods have to rely on sunspot data alone. Data that have hardly been used for solar irradiance reconstructions until now are full-disc solar observations in the Ca II K line. Such data exist since 1892 from various observatories and include all the needed information describing faculae and the network. However, they are plagued by a bunch of various problems and artefacts, and recovering the non-linear response of the photographic material to the radiation is non-trivial since the required information is usually missing, too. We have developed a method to process Ca II K observations from various sources and demonstrated the higher accuracy achieved by our method compared to other techniques presented in the literature. Here we use the carefully reduced Ca II K observations from multiple archives to reconstruct solar irradiance variations.

In this study, we assess pan-Arctic and regional seasonal sea ice forecast skill in versions 1 and 2 of the Canadian Seasonal to Inter-annual Prediction System (CanSIPSv1 and CanSIPSv2) dynamical seasonal prediction systems. Each version applies a multi-model ensemble approach using two coupled general circulation models. CanSIPSv2 features a new model formulation (where one of the underlying models, CanCM3, was replaced with GEM-NEMO) and improved sea ice initialization. We show that the modifications made in the development of CanSIPSv2 substantially enhance forecast skill. For example, the lead time for skillful forecasts of detrended pan-Arctic September sea ice area increases from three months in CanSIPSv1 to seven months in CanSIPSv2. We also show that forecasts of detrended winter sea ice area are improved, with CanSIPSv2 producing skillful forecasts for all considered lead times (up to 11 months) for December, January, and February. We find that improvements in pan-Arctic forecast skill are due primarily to improved initialization methods.Further, a potential predictability experiment is conducted for one of the two CANSIPSv2 models, CanCM4, in order to establish – in conjunction with similar studies – the potential to further increase forecast skill with improved models, observations and initialization methods.