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Upper-air data form the backbone of weather analysis and reanalysis products, particularly in the pre-satellite era. However, they are particularly prone to errors and uncertainties, especially data from the early days of aerology. Information that allows us to better characterize the errors of radiosonde data is important. This paper reports on an attempt to collect data from historical upper-air intercomparisons and from historical error assessments reaching back to the 1930s. The digitized numerical data will be made available through Copernicus Climate Change Services; here we publish the full information that includes images, literature, and other metadata that may be relevant and can be used to inform homogenization approaches or reanalysis production. The data collection described in this paper is available on PANGAEA: https://doi.org/10.1594/PANGAEA.925860 (Imfeld et al., 2021).
Noemi Imfeld; Leopold Haimberger; Alexander Sterin; Yuri Brugnara; Stefan Brönnimann. Intercomparisons, error assessments, and technical information on historical upper-air measurements. Earth System Science Data 2021, 13, 2471 -2485.
AMA StyleNoemi Imfeld, Leopold Haimberger, Alexander Sterin, Yuri Brugnara, Stefan Brönnimann. Intercomparisons, error assessments, and technical information on historical upper-air measurements. Earth System Science Data. 2021; 13 (6):2471-2485.
Chicago/Turabian StyleNoemi Imfeld; Leopold Haimberger; Alexander Sterin; Yuri Brugnara; Stefan Brönnimann. 2021. "Intercomparisons, error assessments, and technical information on historical upper-air measurements." Earth System Science Data 13, no. 6: 2471-2485.
We combine atmospheric energy transports from ECMWF's latest reanalysis dataset ERA5 with observation-based TOA fluxes from CERES-EBAF to infer net surface energy fluxes (FSinf) for the period 1985-2018. We present an extensive comparison at scales ranging from global to local using 15 in-situ buoy measurements, parameterized surface fluxes from ERA5, and previous evaluations of FSinf using ERA-Interim. We also combine FSinf with various estimates of the ocean heat content tendency (OHCT) and observation-based oceanic heat transports from RAPID and moorings in Fram Strait and Barents Sea Opening to evaluate the oceanic energy budget in the North Atlantic Ocean basin.
Our results show that the indirectly estimated FSinf has a 1985-2018 ocean mean of 1.7 W m-2 (see J.Mayer et al. (2021); under review), which is in good agreement with the long-term mean OHCT derived from ocean reanalyses as well as independent surface flux estimates presented in recent literature (e.g., von Schuckmann et al. (2020); https://doi.org/10.5194/essd-12-2013-2020), suggesting an only small global ocean mean bias of FSinf. Moreover, our FSinf product is temporally more stable than parameterized surface fluxes from ERA5 and previous FSinf estimates using ERA-Interim, at least from 2000 onwards. The evaluation of the oceanic energy budget in the North Atlantic shows good agreement between FSinf and observation-based divergence of oceanic heat transports and OHCT such that its residual is on the order of <0.2 PW (~7 W m-2). Even on station-scale, FSinf agrees reasonably well with buoy-based surface flux measurements with a bias of 19.7 W m-2 over all 15 buoys (compared to 21.7 W m-2 for parameterized surface fluxes), with largest biases in the Indian Ocean. This assessment demonstrates that our inferred surface flux estimate using ERA5 data outperforms parameterized fluxes from the model on all considered spatial scales (global-regional-local) in terms of bias and temporal stability and thus is well-suited for climate studies and model evaluations.
Johannes Mayer; Michael Mayer; Leopold Haimberger. Comparing inferred surface energy fluxes with observation-based flux estimates over the ocean. 2021, 1 .
AMA StyleJohannes Mayer, Michael Mayer, Leopold Haimberger. Comparing inferred surface energy fluxes with observation-based flux estimates over the ocean. . 2021; ():1.
Chicago/Turabian StyleJohannes Mayer; Michael Mayer; Leopold Haimberger. 2021. "Comparing inferred surface energy fluxes with observation-based flux estimates over the ocean." , no. : 1.
Rapid surface warming in the Arctic region has strong impacts on the Arctic water balance and its individual hydrological components. With the Arctic Ocean being almost entirely surrounded by landmasses and some of the world’s largest rivers draining into it, the link between ocean and surrounding land is remarkably strong. Hence runoff forms one of the key variables in the Arctic freshwater budget and builds the main focus of this study.
Seasonal cycles, as well as annual and seasonal runoff trends are analyzed for the major Arctic watersheds. We first compare river discharge data taken from the reanalysis component from the Global Flood and Awareness System (GloFAS) to available observed river discharge records. GloFAS combines the land surface model from ECMWF’s most recent reanalysis effort ERA5 with a hydrological and channel routing model. Results show that seasonal river discharge peaks are underestimated by GloFAS as well as by direct ERA5 runoff.
Further analysis shows that this discrepancy can be tracked to non-stationary biases in the snow analysis of ERA5, which affect melt and subsequently runoff (Zsoter et al. (2020), https://doi.org/10.21957/p9jrh0xp). It is shown that this bias is substantially improved in ERA5’s downscaled counterpart ERA5-Land. An experimental version of GloFAS that uses ERA5-Land forcing, exhibits improved river discharge values.
Seasonal cycles of ERA5 snow melt show that there is a lag of 1-2 months between the peak in snow melt and observed river discharge, which can be explained by the time it takes for the water to reach the river mouth, but it may also be influenced by water resources management (e.g., Yang et al. (2004), https://doi.org/10.1016/j.jhydrol.2004.03.017 ; Ye et al. (2003), https://doi.org/10.1029/2003WR001991).
In addition, runoff is calculated over the whole pan-arctic region to account for the total freshwater entering the Arctic Ocean from land. Independent mooring-derived estimates of net freshwater flux through the Arctic oceanic gateways show a consistent and strong imprint of the runoff seasonal cycle.
Susanna Winkelbauer; Michael Mayer; Leopold Haimberger. Diagnostic evaluation of runoff into the Arctic Ocean and its impact on freshwater transport through Arctic gateways. 2021, 1 .
AMA StyleSusanna Winkelbauer, Michael Mayer, Leopold Haimberger. Diagnostic evaluation of runoff into the Arctic Ocean and its impact on freshwater transport through Arctic gateways. . 2021; ():1.
Chicago/Turabian StyleSusanna Winkelbauer; Michael Mayer; Leopold Haimberger. 2021. "Diagnostic evaluation of runoff into the Arctic Ocean and its impact on freshwater transport through Arctic gateways." , no. : 1.
The El Niño-Southern Oscillation (ENSO) is linked with energy exchange between the ocean, atmosphere and space. By using the particle dispersion model FLEXPART the atmospheric energy transport originating from the Tropical Pacific is analysed, with special focus on the connection to the Atlantic Ocean during El Niño. The Lagrangian model was filled homogeneously with five million, globally distributed particles, which were then traced forward in time from 1990 until 2016. Due to the domain-filling option used in FLEXPART, the particles represent the atmospheric mass transport. From this 26 year-long Lagrangian Reanalysis Dataset, particles between 5°S-5°N and 170°W-100°W were selected and followed both forward and backward in time. Therefore, the source regions of the energy and moisture in the Tropical Pacific can be detected, but also where they are further transported. Special focus is placed on the connection to the Atlantic Ocean. By analysing the different forms of energy (potential, - internal, - and latent energy), their transport from the Tropical Pacific into the Atlantic Ocean can be quantified. In addition, the differences between El Niño and La Niña are studied, as well as strong and weak El Niño cases.
Katharina Baier; Andreas Stohl; Michael Mayer; Leopold Haimberger. Energy Export from the Tropical Pacific via the Atmosphere – a Lagrangian Perspective. 2021, 1 .
AMA StyleKatharina Baier, Andreas Stohl, Michael Mayer, Leopold Haimberger. Energy Export from the Tropical Pacific via the Atmosphere – a Lagrangian Perspective. . 2021; ():1.
Chicago/Turabian StyleKatharina Baier; Andreas Stohl; Michael Mayer; Leopold Haimberger. 2021. "Energy Export from the Tropical Pacific via the Atmosphere – a Lagrangian Perspective." , no. : 1.
In mountain environments dimensions of climate change are unclear because of limited availability of meteorological stations. However, there is a necessity to assess the scope of local climate change, as the livelihood and food systems of subsistence-based communities are already getting impacted. To provide more clarity about local climate trends in the Pamir Mountains of Tajikistan, this study integrates measured climate data with community observations in the villages of Savnob and Roshorv. Taking a transdisciplinary approach, both knowledge systems were considered as equally pertinent and mutually informed the research process. Statistical trends of temperature and snow cover were retrieved using downscaled ERA5 temperature data and the snow cover product MOD10A1. Local knowledge was gathered through community workshops and structured interviews and analysed using a consensus index. Results showed, that local communities perceived increasing temperatures in autumn and winter and decreasing amounts of snow and rain. Instrumental data records indicated an increase in summer temperatures and a shortening of the snow season in Savnob. As both knowledge systems entail their own strengths and limitations, an integrative assessment can broaden the understanding of local climate trends by (i) reducing existing uncertainties, (ii) providing new information, and (iii) introducing unforeseen perspectives. The presented study represents a time-efficient and global applicable approach for assessing local dimensions of climate change in data-deficient regions.
Isabell Haag; Karim-Aly Kassam; Thomas Senftl; Harald Zandler; Cyrus Samimi. Measurements meet human observations: integrating distinctive ways of knowing in the Pamir Mountains of Tajikistan to assess local climate change. Climatic Change 2021, 165, 1 -22.
AMA StyleIsabell Haag, Karim-Aly Kassam, Thomas Senftl, Harald Zandler, Cyrus Samimi. Measurements meet human observations: integrating distinctive ways of knowing in the Pamir Mountains of Tajikistan to assess local climate change. Climatic Change. 2021; 165 (1-2):1-22.
Chicago/Turabian StyleIsabell Haag; Karim-Aly Kassam; Thomas Senftl; Harald Zandler; Cyrus Samimi. 2021. "Measurements meet human observations: integrating distinctive ways of knowing in the Pamir Mountains of Tajikistan to assess local climate change." Climatic Change 165, no. 1-2: 1-22.
Upper-air data form the backbone of weather analysis and reanalysis products, particularly in the pre-satellite era. However, they are particularly prone to errors and uncertainties, especially data from the early days of aerology. Information that allows to better characterize the errors of radiosonde data is important. This paper reports on an attempt to collect data from historical upper-air intercomparisons and from historical error assessments reaching back to the 1930s. The digitised numerical data will be made available through Copernicus Climate Change Services; here we publish the full information that includes images, literature, and other metadata that may be relevant and can be used to inform homogenization approaches or reanalysis production. The data collection described in this paper is available on PANGAEA https://www.pangaea.de/tok/9e5b464ba828e931103d3794d9ae1d4576f7a03d (Imfeld et al, in review).
Noemi Imfeld; Leopold Haimberger; Alexander Sterin; Yuri Brugnara; Stefan Brönnimann. Intercomparisons, Error Assessments, and Technical Information on Historical Upper-Air Measurements. 2021, 2021, 1 -23.
AMA StyleNoemi Imfeld, Leopold Haimberger, Alexander Sterin, Yuri Brugnara, Stefan Brönnimann. Intercomparisons, Error Assessments, and Technical Information on Historical Upper-Air Measurements. . 2021; 2021 ():1-23.
Chicago/Turabian StyleNoemi Imfeld; Leopold Haimberger; Alexander Sterin; Yuri Brugnara; Stefan Brönnimann. 2021. "Intercomparisons, Error Assessments, and Technical Information on Historical Upper-Air Measurements." 2021, no. : 1-23.
Ozone (O3) is a harmful pollutant when present in the lowermost layer of the atmosphere. Therefore, the European Commission formulated directives to regulate O3 concentrations in near-surface air. However, almost 50% of the 5068 air quality stations in Europe do not monitor O3 concentrations. This study aims to provide a hybrid modeling system that fills these gaps in the hourly surface O3 observations on a site scale with much higher accuracy than existing O3 models. This hybrid model was developed using estimations from multiple linear regression-based eXtreme Gradient Boosting Machines (MLR-XGBM) and O3 reanalysis from European regional air quality models (CAMS-EU). The binary classification of extremely high O3 events and the 1- and 24-hour forecasts of hourly O3 were investigated as secondary aims. In this study thirteen stations in Northern Bavaria, out of which six do not monitor O3, were chosen as test sites. Considering the computational complexity of machine learning algorithms (MLAs), we also applied two recent MLA interpretation methods, namely SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME). With SHAP, we showed an increasing effect of temperature on O3 concentrations which intensifies for temperatures exceeding 17 °C. According to LIME, O3 concentration peaks are mainly governed by meteorological factors under dry and warm conditions on a regional scale, whereas local nitrogen oxide concentrations control base O3 concentrations during cold and wet periods. While recently developed MLAs for the spatial estimation of hourly O3 concentrations had a station-based root-mean-square error (RMSE) above 27 μg/m3, our proposed model significantly reduced the estimation errors by about 66% with an RMSE of 9.49 μg/m3. We also found that logistic regression (LR) and MLR-XGBM performed best in the site-scale classification and 24-hour forecast of O3 concentrations (with a station-averaged accuracy and RMSE of 0.95 and 19.34 μg/m3, respectively).
Seyed Omid Nabavi; Anke C. Nölscher; Cyrus Samimi; Christoph Thomas; Leopold Haimberger; Johannes Lüers; Andreas Held. Site-scale modeling of surface ozone in Northern Bavaria using machine learning algorithms, regional dynamic models, and a hybrid model. Environmental Pollution 2020, 268, 115736 .
AMA StyleSeyed Omid Nabavi, Anke C. Nölscher, Cyrus Samimi, Christoph Thomas, Leopold Haimberger, Johannes Lüers, Andreas Held. Site-scale modeling of surface ozone in Northern Bavaria using machine learning algorithms, regional dynamic models, and a hybrid model. Environmental Pollution. 2020; 268 ():115736.
Chicago/Turabian StyleSeyed Omid Nabavi; Anke C. Nölscher; Cyrus Samimi; Christoph Thomas; Leopold Haimberger; Johannes Lüers; Andreas Held. 2020. "Site-scale modeling of surface ozone in Northern Bavaria using machine learning algorithms, regional dynamic models, and a hybrid model." Environmental Pollution 268, no. : 115736.
C3S), ECMWF is producing the ERA5 reanalysis, which embodies a detailed record of the global atmosphere, land surface and ocean waves from 1950 onwards once completed. This new reanalysis replaces the ERA‐Interim reanalysis that was started in 2006 (spanning 1979 onwards). ERA5 is based on the Integrated Forecasting System (IFS) Cy41r2 which was operational in 2016. ERA5 thus benefits from a decade of developments in model physics, core dynamics and data assimilation. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA‐Interim, ERA5 has hourly output throughout, and an uncertainty estimate from an ensemble (3‐hourly at half the horizontal resolution). This paper describes the general setup of ERA5, as well as a basic evaluation of characteristics and performance. Focus is on the dataset from 1979 onwards that is currently publicly available. Re‐forecasts from ERA5 analyses show a gain of up to one day in skill with respect to ERA‐Interim. Comparison with radiosonde and PILOT data prior to assimilation shows an improved fit for temperature, wind and humidity in the troposphere, but not the stratosphere. A comparison with independent buoy data shows a much improved fit for ocean wave height. The uncertainty estimate reflects the evolution of the observing systems used in ERA5. The enhanced temporal and spatial resolution allows for a detailed evolution of weather systems. For precipitation, global‐mean correlation with monthly‐mean GPCP data is increased from 67% to 77%. In general low‐frequency variability is found to be well‐represented and from 10 hPa downwards general patterns of anomalies in temperature match those from the ERA‐Interim, MERRA‐2 and JRA‐55 reanalyses. This article is protected by copyright. All rights reserved.
Hans Hersbach; Bill Bell; Paul Berrisford; Shoji Hirahara; Andras Horanyi; Joaquín Muñoz‐Sabater; Julien Nicolas; Carole Peubey; Raluca Radu; Dinand Schepers; Adrian Simmons; Cornel Soci; Saleh Abdalla; Xavier Abellan; Gianpaolo Balsamo; Peter Bechtold; Gionata Biavati; Jean Bidlot; Massimo Bonavita; Giovanna De Chiara; Per Dahlgren; Dick Dee; Michail Diamantakis; Rossana Dragani; Johannes Flemming; Richard Forbes; Manuel Fuentes; Alan Geer; Leo Haimberger; Sean Healy; Robin J. Hogan; Elías Hólm; Marta Janisková; Sarah Keeley; Patrick Laloyaux; Philippe Lopez; Cristina Lupu; Gabor Radnoti; Patricia De Rosnay; Iryna Rozum; Freja Vamborg; Sebastien Villaume; Jean‐Noël Thépaut. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 2020, 146, 1999 -2049.
AMA StyleHans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, Andras Horanyi, Joaquín Muñoz‐Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, Adrian Simmons, Cornel Soci, Saleh Abdalla, Xavier Abellan, Gianpaolo Balsamo, Peter Bechtold, Gionata Biavati, Jean Bidlot, Massimo Bonavita, Giovanna De Chiara, Per Dahlgren, Dick Dee, Michail Diamantakis, Rossana Dragani, Johannes Flemming, Richard Forbes, Manuel Fuentes, Alan Geer, Leo Haimberger, Sean Healy, Robin J. Hogan, Elías Hólm, Marta Janisková, Sarah Keeley, Patrick Laloyaux, Philippe Lopez, Cristina Lupu, Gabor Radnoti, Patricia De Rosnay, Iryna Rozum, Freja Vamborg, Sebastien Villaume, Jean‐Noël Thépaut. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society. 2020; 146 (730):1999-2049.
Chicago/Turabian StyleHans Hersbach; Bill Bell; Paul Berrisford; Shoji Hirahara; Andras Horanyi; Joaquín Muñoz‐Sabater; Julien Nicolas; Carole Peubey; Raluca Radu; Dinand Schepers; Adrian Simmons; Cornel Soci; Saleh Abdalla; Xavier Abellan; Gianpaolo Balsamo; Peter Bechtold; Gionata Biavati; Jean Bidlot; Massimo Bonavita; Giovanna De Chiara; Per Dahlgren; Dick Dee; Michail Diamantakis; Rossana Dragani; Johannes Flemming; Richard Forbes; Manuel Fuentes; Alan Geer; Leo Haimberger; Sean Healy; Robin J. Hogan; Elías Hólm; Marta Janisková; Sarah Keeley; Patrick Laloyaux; Philippe Lopez; Cristina Lupu; Gabor Radnoti; Patricia De Rosnay; Iryna Rozum; Freja Vamborg; Sebastien Villaume; Jean‐Noël Thépaut. 2020. "The ERA5 global reanalysis." Quarterly Journal of the Royal Meteorological Society 146, no. 730: 1999-2049.
This study is part of the Mitigation of Urban Climate and Ozone Risks (MiSKOR) project. MiSKOR aims to use a collection of tools to mitigate the problems of the urban heat island effect and ozone (O3) pollution in and around medium sized cities in northern Bavaria (NB). In this study, we developed modelling tools to estimate (hindcast), classify (O3 >= 120 ug/m3 or O3 < 120 ug/m3), and forecast hourly O3 concentrations at nine unmonitored sites in NB. Three machine learning algorithms (MLAs) including linear- and tree-based eXtreme Gradient Boosting Machines (MLR-XGBM and Tree-XGBM) and logistic regression (LR) are used for O3 modelling. MLAs are trained by using hourly observations of O3 and its chemical and meteorological precursors from seven monitored sites in NB. In addition, the daily average of surface O3 observations along 6-hour back trajectories, produced by HYSPLIT model, is fed into MLAs to provide a rough estimation of O3 transport in a local scale. MLAs are compared with two state of the art regional deterministic models (DMs) namely the ECMWF Copernicus Atmosphere Monitoring Service (CAMS) regional air quality model for Europe (CAMS-EU) and the DLR WRF-POLYPHEMUS air quality system (used only for O3 forecast purpose). Finally, we created a new hybrid model by combining the O3 estimations from the best MLA model and the regional air quality model CAMS-EU.
According to averaged metrics from leave-one-site-out cross-validation (LOOCV), MLR-XGBM outperformed other models in the estimation of O3. This model yielded summertime RMSE and Spearman correlation coefficient (SCC) of 13.6 µg/m3 and 0.91 respectively. Interestingly, the hybrid model significantly improved the accuracy of O3 estimations. It reduced the summertime seasonal RMSE to 11.4 µg/m3 and increased the lowest seasonal SCC to 0.95. MLR-XGBM also yielded the best performance in O3 forecast compared to CAMS-EU and WRF-POLYPHEMUS. With regard to O3 classification LR outperformed other models. We also found that using remotely sensed lower troposphere O3, from IASI/GOME2, improves the classification of high extreme O3 in summertime.
Seyed Omid Nabavi; Anke Nölscher; Leopold Haimberger; Juan Cuesta; Christoph Thomas; Andreas Held; Cyrus Samimi. Site-scale estimation of Ozone in Northern Bavaria using Gradient Boosting Machines, Deterministic Regional Air Quality Models and a Hybrid Model. 2020, 1 .
AMA StyleSeyed Omid Nabavi, Anke Nölscher, Leopold Haimberger, Juan Cuesta, Christoph Thomas, Andreas Held, Cyrus Samimi. Site-scale estimation of Ozone in Northern Bavaria using Gradient Boosting Machines, Deterministic Regional Air Quality Models and a Hybrid Model. . 2020; ():1.
Chicago/Turabian StyleSeyed Omid Nabavi; Anke Nölscher; Leopold Haimberger; Juan Cuesta; Christoph Thomas; Andreas Held; Cyrus Samimi. 2020. "Site-scale estimation of Ozone in Northern Bavaria using Gradient Boosting Machines, Deterministic Regional Air Quality Models and a Hybrid Model." , no. : 1.
Central European forests face challenges with climate changing much faster than they can adapt. Extremely hot and dry summers like in 2018 deprive forests of soil moisture, leaving them with low ground water levels. While individuals with deep and well-established root systems survive, young individuals and shallow-rooted species perish.
In southern Germany, die-off of single trees or small groups got noticeable recently. Such effects of harsher conditions rarely occur over large areas, but more in a spotted, irregular manner. This makes the phenomenon difficult to detect and to estimate its extent. The share of trees lately deteriorated may be larger than expected and represent a considerable portion of forests. Therefore, we see the great need for monitoring. Remote sensing data is suitable to examine inaccessible areas at a large scale. To quantify mortality of individual trees among a majority of vital ones, sensor platforms and respective data have to fulfill certain criteria regarding spatial, temporal and spectral resolution. Dead trees can be distinguished from others due to discoloration and defoliation. This change in appearance affects the spectral response, even in pixels larger than the tree’s extent.
This study aims at recommending a suitable spatial scale for space-borne multispectral imagery products to achieve this task. We evaluate commercial and free remote sensing data products and their ability to estimate fractional cover of dead vegetation. Satellite data employed in this study comes from Landsat 8 (30 m), Sentinel-2 (10 m), RapidEye (6.5 m) and PlanetScope (3 m). Classification performance is tested against high-resolution multispectral aerial imagery (17 cm) acquired with a Micasense RedEdge-M camera.
High-resolution Micasense images are capable of detecting single dead trees, even after downgrading the resolution from 17 cm to 3 m. For all data products tested, fraction of dead trees per pixel did not differ significantly among land cover types (dead vegetation, vital vegetation, pavement, open soil). This indicates that individual dead trees may not be detectable in vital forest stands. The finding even seems to be valid for a resolution of 3 m (PlanetScope), which is identical to the downgraded Micasense data. In the near future the detection of this phenomenon might profit from technical developments towards even higher spatial detail of space-borne sensors. Alternatively, high resolution images from aerial campaigns, manned or unmanned, could bridge this gap when flight time and spatial coverage are increased significantly and facilitating policies are in place.
Johannes Heisig; Cyrus Samimi. Detecting drought effects on tree mortality in forests of Franconia (Germany). 2020, 1 .
AMA StyleJohannes Heisig, Cyrus Samimi. Detecting drought effects on tree mortality in forests of Franconia (Germany). . 2020; ():1.
Chicago/Turabian StyleJohannes Heisig; Cyrus Samimi. 2020. "Detecting drought effects on tree mortality in forests of Franconia (Germany)." , no. : 1.
Environmental change is a trigger of land use change and possibly for migration in the eastern Hindu Kush mountains. Vegetation along the river valleys has undergone alterations by the impact of geomorphological processes and flood dynamics, but little research has been carried out to detect and map these changes. This study aims to close research gaps by detecting change within Landsat time series for the eastern Hindu Kush region.
The study area is approximately 25000 km² large and located in the highlands of northern Pakistan and eastern Afghanistan. It is part of upper Indus basin and is prone to natural hazards such as floods, glacial lake outbursts and landslides.
The opening of the United States Geological Survey (USGS) Landsat data archive in 2008 led to the development of several satellite image-based time series methods for change detection. Among them, Breaks For Additive Seasonal and Trend (BFAST) was developed in 2010 to detect changes in both trend and seasonal components of the time series. The BFAST tool iteratively decomposes the time series into trend, seasonal and remainder components. The changes in the trend component denote abrupt and gradual changes while changes in seasonal component represent phenological changes.
In this study we use Landsat data in time series analysis to detect change by using BFAST. All available Surface reflectance derived data is accessed from the Landsat data archive of USGS (World Reference System-2, Path 151 and Row 35) for the years 1988 to 2019. Data is acquired from the corresponding scenes of Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI). It is processed to Landsat Level-2 Surface Reflectance Product by USGS and therefore has already undergone geo-referencing, atmospheric correction and detection of clouds and shadow. Data have spatial and temporal resolutions of 30 m and 16 days respectively.
The BFAST approach was first tested on locations with a known history of change (e.g. floods) and then scaled up to the whole study area. The magnitude and timing of the change was detected and mapped for the study area. We expect that the findings of the research will benefit future local and regional risk studies.
Saeed Akhtar Khan; Oliver Sass; Cyrus Samimi. Detecting change in Landsat time series with BFAST in the eastern Hindu Kush region. 2020, 1 .
AMA StyleSaeed Akhtar Khan, Oliver Sass, Cyrus Samimi. Detecting change in Landsat time series with BFAST in the eastern Hindu Kush region. . 2020; ():1.
Chicago/Turabian StyleSaeed Akhtar Khan; Oliver Sass; Cyrus Samimi. 2020. "Detecting change in Landsat time series with BFAST in the eastern Hindu Kush region." , no. : 1.
Gridded datasets are of paramount importance to globally derive precipitation quantities for a multitude of scientific and practical applications. However, as most studies do not consider the impacts of temporal and spatial variations of included measurements in the utilized datasets, we conducted a quantitative assessment of the ability of several state of the art gridded precipitation products (CRU, GPCC Full Data Product, GPCC Monitoring Product, ERA-interim, ERA5, MERRA-2, MERRA-2 bias corrected, PERSIANN-CDR) to reproduce monthly precipitation values at climate stations in the Pamir mountains during two 15 year periods (1980–1994, 1998–2012) that are characterized by considerable differences in incorporated observation data. Results regarding the GPCC products illustrated a substantial and significant performance decrease with up to four times higher errors during periods with low observation inputs (1998–2012 with 2 stations on average per 124,000 km2) compared to periods with high quantities of regionally incorporated station data (1980–1994 with 14 stations on average per 124,000 km2). If independent stations were considered, the coefficient of efficiency indicated that only three of the gridded datasets (MERRA–2 bias corrected, GPCC, GPCC MP) performed better than the long term station mean for characterizing surface precipitation. Error patterns and magnitudes show that in complex terrain, evaluation of temporal and spatial variations of included observations is a prerequisite for using gridded precipitation products for scientific applications and to avoid overly optimistic performance assessments.
Harald Zandler; Isabell Haag; Cyrus Samimi. Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions. Scientific Reports 2019, 9, 1 -15.
AMA StyleHarald Zandler, Isabell Haag, Cyrus Samimi. Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions. Scientific Reports. 2019; 9 (1):1-15.
Chicago/Turabian StyleHarald Zandler; Isabell Haag; Cyrus Samimi. 2019. "Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions." Scientific Reports 9, no. 1: 1-15.
Changes in climate can be favorable as well as detrimental for natural and anthropogenic systems. Temperatures in Central Asia have risen significantly within the last decades whereas mean precipitation remains almost unchanged. However, climatic trends can vary greatly between different subregions, across altitudinal levels, and within seasons. Investigating in the seasonally and spatially differentiated trend characteristics amplifies the knowledge of regional climate change and fosters the understanding of potential impacts on social, ecological, and natural systems. Considering the known limitations of available climate data in this region, this study combines both high-resolution and long-term records to achieve the best possible results. Temperature and precipitation data were analyzed using Climatic Research Unit (CRU) TS 4.01 and NASA’s Tropical Rainfall Measuring Mission (TRMM) 3B43. To study long-term trends and low-frequency variations, we performed a linear trend analysis and compiled anomaly time series and regional grid-based trend maps. The results show a strong increase in temperature, almost uniform across the topographically complex study site, with particular maxima in winter and spring. Precipitation depicts minor positive trends, except for spring when precipitation is decreasing. Expected differences in the development of temperature and precipitation between mountain areas and plains could not be detected.
Isabell Haag; Philip D. Jones; Cyrus Samimi. Central Asia’s Changing Climate: How Temperature and Precipitation Have Changed across Time, Space, and Altitude. Climate 2019, 7, 123 .
AMA StyleIsabell Haag, Philip D. Jones, Cyrus Samimi. Central Asia’s Changing Climate: How Temperature and Precipitation Have Changed across Time, Space, and Altitude. Climate. 2019; 7 (10):123.
Chicago/Turabian StyleIsabell Haag; Philip D. Jones; Cyrus Samimi. 2019. "Central Asia’s Changing Climate: How Temperature and Precipitation Have Changed across Time, Space, and Altitude." Climate 7, no. 10: 123.
Aims Vegetation‐plot records provide information on the presence and cover or abundance of plants co‐occurring in the same community. Vegetation‐plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. Results sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community‐weighted means and variances of traits using gap‐filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of community richness and community‐weighted means of key traits. Conclusions The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale.
Helge Bruelheide; Jürgen Dengler; Borja Jiménez‐Alfaro; Oliver Purschke; Stephan M. Hennekens; Milan Chytrý; Valério D. Pillar; Florian Jansen; Jens Kattge; Brody Sandel; Isabelle Aubin; Idoia Biurrun; Richard Field; Sylvia Haider; Ute Jandt; Jonathan Lenoir; Robert K. Peet; Gwendolyn Peyre; Francesco Maria Sabatini; Marco Schmidt; Franziska Schrodt; Marten Winter; Svetlana Aćić; Emiliano Agrillo; Miguel Alvarez; Didem Ambarlı; Pierangela Angelini; Iva Apostolova; Mohammed A. S. Arfin Khan; Elise Arnst; Fabio Attorre; Christopher Baraloto; Michael Beckmann; Christian Berg; Yves Bergeron; Erwin Bergmeier; Anne D. Bjorkman; Viktoria Bondareva; Peter Borchardt; Zoltán Botta‐Dukát; Brad Boyle; Amy Breen; Henry Brisse; ChaeHo Byun; Marcelo R. Cabido; Laura Casella; Luis Cayuela; Tomáš Černý; Victor Chepinoga; János Csiky; Michael Curran; Renata Ćušterevska; Zora Dajić Stevanović; Els De Bie; Patrice De Ruffray; Michele De Sanctis; Panayotis Dimopoulos; Stefan Dressler; Rasmus Ejrnæs; Mohamed Abd El‐Rouf Mousa El‐Sheikh; Brian Enquist; Jörg Ewald; Jaime Fagúndez; Manfred Finckh; Xavier Font; Estelle Forey; Georgios Fotiadis; Itziar García‐Mijangos; André Luis De Gasper; Valentin Golub; Alvaro G. Gutierrez; Mohamed Hatim; Tianhua He; Pedro Higuchi; Dana Holubová; Norbert Hölzel; Jürgen Homeier; Adrian Indreica; Deniz Işık Gürsoy; Steven Jansen; John Janssen; Birgit Jedrzejek; Martin Jiroušek; Norbert Jürgens; Zygmunt Kącki; Ali Kavgacı; Elizabeth Kearsley; Michael Kessler; Ilona Knollová; Vitaliy Kolomiychuk; Andrey Korolyuk; Maria Kozhevnikova; Łukasz Kozub; Daniel Krstonošić; Hjalmar Kühl; Ingolf Kühn; Anna Kuzemko; Filip Küzmič; Flavia Landucci; Michael T. Lee; Aurora Levesley; Ching‐Feng Li; Hongyan Liu; Gabriela Lopez‐Gonzalez; Tatiana Lysenko; Armin Macanović; Parastoo Mahdavi; Peter Manning; Corrado Marcenò; Vasiliy Martynenko; Maurizio Mencuccini; Vanessa Minden; Jesper Erenskjold Moeslund; Marco Moretti; Jonas V. Müller; Jérôme Munzinger; Ülo Niinemets; Marcin Nobis; Jalil Noroozi; Arkadiusz Nowak; Viktor Onyshchenko; Gerhard E. Overbeck; Wim A. Ozinga; Anibal Pauchard; Hristo Pedashenko; Josep Peñuelas; Aaron Pérez‐Haase; Tomáš Peterka; Petr Petřík; Oliver L. Phillips; Vadim Prokhorov; Valerijus Rašomavičius; Rasmus Revermann; John Rodwell; Eszter Ruprecht; Solvita Rūsiņa; Cyrus Samimi; Joop H.J. Schaminée; Ute Schmiedel; Jozef Šibík; Urban Šilc; Željko Škvorc; Anita Smyth; Tenekwetche Sop; Desislava Sopotlieva; Ben Sparrow; Zvjezdana Stančić; Jens‐Christian Svenning; Grzegorz Swacha; Zhiyao Tang; Ioannis Tsiripidis; Pavel Dan Turtureanu; Emin Uğurlu; Domas Uogintas; Milan Valachovič; Kim André Vanselow; Yulia Vashenyak; Kiril Vassilev; Eduardo Vélez‐Martin; Roberto Venanzoni; Alexander Vibrans; Cyrille Violle; Risto Virtanen; Henrik Von Wehrden; Viktoria Wagner; Donald A. Walker; Desalegn Wana; Evan Weiher; Karsten Wesche; Timothy Whitfeld; Wolfgang Willner; Susan Wiser; Thomas Wohlgemuth; Sergey Yamalov; Georg Zizka; Andrei Zverev. sPlot – A new tool for global vegetation analyses. Journal of Vegetation Science 2019, 30, 161 -186.
AMA StyleHelge Bruelheide, Jürgen Dengler, Borja Jiménez‐Alfaro, Oliver Purschke, Stephan M. Hennekens, Milan Chytrý, Valério D. Pillar, Florian Jansen, Jens Kattge, Brody Sandel, Isabelle Aubin, Idoia Biurrun, Richard Field, Sylvia Haider, Ute Jandt, Jonathan Lenoir, Robert K. Peet, Gwendolyn Peyre, Francesco Maria Sabatini, Marco Schmidt, Franziska Schrodt, Marten Winter, Svetlana Aćić, Emiliano Agrillo, Miguel Alvarez, Didem Ambarlı, Pierangela Angelini, Iva Apostolova, Mohammed A. S. Arfin Khan, Elise Arnst, Fabio Attorre, Christopher Baraloto, Michael Beckmann, Christian Berg, Yves Bergeron, Erwin Bergmeier, Anne D. Bjorkman, Viktoria Bondareva, Peter Borchardt, Zoltán Botta‐Dukát, Brad Boyle, Amy Breen, Henry Brisse, ChaeHo Byun, Marcelo R. Cabido, Laura Casella, Luis Cayuela, Tomáš Černý, Victor Chepinoga, János Csiky, Michael Curran, Renata Ćušterevska, Zora Dajić Stevanović, Els De Bie, Patrice De Ruffray, Michele De Sanctis, Panayotis Dimopoulos, Stefan Dressler, Rasmus Ejrnæs, Mohamed Abd El‐Rouf Mousa El‐Sheikh, Brian Enquist, Jörg Ewald, Jaime Fagúndez, Manfred Finckh, Xavier Font, Estelle Forey, Georgios Fotiadis, Itziar García‐Mijangos, André Luis De Gasper, Valentin Golub, Alvaro G. Gutierrez, Mohamed Hatim, Tianhua He, Pedro Higuchi, Dana Holubová, Norbert Hölzel, Jürgen Homeier, Adrian Indreica, Deniz Işık Gürsoy, Steven Jansen, John Janssen, Birgit Jedrzejek, Martin Jiroušek, Norbert Jürgens, Zygmunt Kącki, Ali Kavgacı, Elizabeth Kearsley, Michael Kessler, Ilona Knollová, Vitaliy Kolomiychuk, Andrey Korolyuk, Maria Kozhevnikova, Łukasz Kozub, Daniel Krstonošić, Hjalmar Kühl, Ingolf Kühn, Anna Kuzemko, Filip Küzmič, Flavia Landucci, Michael T. Lee, Aurora Levesley, Ching‐Feng Li, Hongyan Liu, Gabriela Lopez‐Gonzalez, Tatiana Lysenko, Armin Macanović, Parastoo Mahdavi, Peter Manning, Corrado Marcenò, Vasiliy Martynenko, Maurizio Mencuccini, Vanessa Minden, Jesper Erenskjold Moeslund, Marco Moretti, Jonas V. Müller, Jérôme Munzinger, Ülo Niinemets, Marcin Nobis, Jalil Noroozi, Arkadiusz Nowak, Viktor Onyshchenko, Gerhard E. Overbeck, Wim A. Ozinga, Anibal Pauchard, Hristo Pedashenko, Josep Peñuelas, Aaron Pérez‐Haase, Tomáš Peterka, Petr Petřík, Oliver L. Phillips, Vadim Prokhorov, Valerijus Rašomavičius, Rasmus Revermann, John Rodwell, Eszter Ruprecht, Solvita Rūsiņa, Cyrus Samimi, Joop H.J. Schaminée, Ute Schmiedel, Jozef Šibík, Urban Šilc, Željko Škvorc, Anita Smyth, Tenekwetche Sop, Desislava Sopotlieva, Ben Sparrow, Zvjezdana Stančić, Jens‐Christian Svenning, Grzegorz Swacha, Zhiyao Tang, Ioannis Tsiripidis, Pavel Dan Turtureanu, Emin Uğurlu, Domas Uogintas, Milan Valachovič, Kim André Vanselow, Yulia Vashenyak, Kiril Vassilev, Eduardo Vélez‐Martin, Roberto Venanzoni, Alexander Vibrans, Cyrille Violle, Risto Virtanen, Henrik Von Wehrden, Viktoria Wagner, Donald A. Walker, Desalegn Wana, Evan Weiher, Karsten Wesche, Timothy Whitfeld, Wolfgang Willner, Susan Wiser, Thomas Wohlgemuth, Sergey Yamalov, Georg Zizka, Andrei Zverev. sPlot – A new tool for global vegetation analyses. Journal of Vegetation Science. 2019; 30 (2):161-186.
Chicago/Turabian StyleHelge Bruelheide; Jürgen Dengler; Borja Jiménez‐Alfaro; Oliver Purschke; Stephan M. Hennekens; Milan Chytrý; Valério D. Pillar; Florian Jansen; Jens Kattge; Brody Sandel; Isabelle Aubin; Idoia Biurrun; Richard Field; Sylvia Haider; Ute Jandt; Jonathan Lenoir; Robert K. Peet; Gwendolyn Peyre; Francesco Maria Sabatini; Marco Schmidt; Franziska Schrodt; Marten Winter; Svetlana Aćić; Emiliano Agrillo; Miguel Alvarez; Didem Ambarlı; Pierangela Angelini; Iva Apostolova; Mohammed A. S. Arfin Khan; Elise Arnst; Fabio Attorre; Christopher Baraloto; Michael Beckmann; Christian Berg; Yves Bergeron; Erwin Bergmeier; Anne D. Bjorkman; Viktoria Bondareva; Peter Borchardt; Zoltán Botta‐Dukát; Brad Boyle; Amy Breen; Henry Brisse; ChaeHo Byun; Marcelo R. Cabido; Laura Casella; Luis Cayuela; Tomáš Černý; Victor Chepinoga; János Csiky; Michael Curran; Renata Ćušterevska; Zora Dajić Stevanović; Els De Bie; Patrice De Ruffray; Michele De Sanctis; Panayotis Dimopoulos; Stefan Dressler; Rasmus Ejrnæs; Mohamed Abd El‐Rouf Mousa El‐Sheikh; Brian Enquist; Jörg Ewald; Jaime Fagúndez; Manfred Finckh; Xavier Font; Estelle Forey; Georgios Fotiadis; Itziar García‐Mijangos; André Luis De Gasper; Valentin Golub; Alvaro G. Gutierrez; Mohamed Hatim; Tianhua He; Pedro Higuchi; Dana Holubová; Norbert Hölzel; Jürgen Homeier; Adrian Indreica; Deniz Işık Gürsoy; Steven Jansen; John Janssen; Birgit Jedrzejek; Martin Jiroušek; Norbert Jürgens; Zygmunt Kącki; Ali Kavgacı; Elizabeth Kearsley; Michael Kessler; Ilona Knollová; Vitaliy Kolomiychuk; Andrey Korolyuk; Maria Kozhevnikova; Łukasz Kozub; Daniel Krstonošić; Hjalmar Kühl; Ingolf Kühn; Anna Kuzemko; Filip Küzmič; Flavia Landucci; Michael T. Lee; Aurora Levesley; Ching‐Feng Li; Hongyan Liu; Gabriela Lopez‐Gonzalez; Tatiana Lysenko; Armin Macanović; Parastoo Mahdavi; Peter Manning; Corrado Marcenò; Vasiliy Martynenko; Maurizio Mencuccini; Vanessa Minden; Jesper Erenskjold Moeslund; Marco Moretti; Jonas V. Müller; Jérôme Munzinger; Ülo Niinemets; Marcin Nobis; Jalil Noroozi; Arkadiusz Nowak; Viktor Onyshchenko; Gerhard E. Overbeck; Wim A. Ozinga; Anibal Pauchard; Hristo Pedashenko; Josep Peñuelas; Aaron Pérez‐Haase; Tomáš Peterka; Petr Petřík; Oliver L. Phillips; Vadim Prokhorov; Valerijus Rašomavičius; Rasmus Revermann; John Rodwell; Eszter Ruprecht; Solvita Rūsiņa; Cyrus Samimi; Joop H.J. Schaminée; Ute Schmiedel; Jozef Šibík; Urban Šilc; Željko Škvorc; Anita Smyth; Tenekwetche Sop; Desislava Sopotlieva; Ben Sparrow; Zvjezdana Stančić; Jens‐Christian Svenning; Grzegorz Swacha; Zhiyao Tang; Ioannis Tsiripidis; Pavel Dan Turtureanu; Emin Uğurlu; Domas Uogintas; Milan Valachovič; Kim André Vanselow; Yulia Vashenyak; Kiril Vassilev; Eduardo Vélez‐Martin; Roberto Venanzoni; Alexander Vibrans; Cyrille Violle; Risto Virtanen; Henrik Von Wehrden; Viktoria Wagner; Donald A. Walker; Desalegn Wana; Evan Weiher; Karsten Wesche; Timothy Whitfeld; Wolfgang Willner; Susan Wiser; Thomas Wohlgemuth; Sergey Yamalov; Georg Zizka; Andrei Zverev. 2019. "sPlot – A new tool for global vegetation analyses." Journal of Vegetation Science 30, no. 2: 161-186.
This study aims to explore the spatial estimation of fine particulate matter (PM2.5) using 10-km merged dark target and deep blue (DB_DT) Aerosol Optical Depth (AOD) and 1-km Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD over Tehran. The ability of four Machine Learning Algorithms (MLAs) to predict PM2.5 concentrations is also investigated. Results show that the association of satellite AOD with surface PM significantly increases after considering the contribution of relative humidity in PM mass concentration and normalization of AOD to Planetary boundary layer height (PBLH). The examination of derived aerosol layer height (ALH) from 159 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) profiles shows that PBLH could successfully represent the top of aerosol-laden layers. Surprisingly, the highest correlation was found between normalized 10-km DB_DT AOD and corrected PM2.5 measurements. Consequently, random forest (RF) fed by this AOD product has yielded the best performance (R2 = 0.68, RMSE = 17.52 and MRE = 27.46%). Importance analysis of variables reveals that DB_DT and meteorological fields are of highest and least importance among selected variables, respectively. The RF performance is less satisfactory during summer which is assumed to be caused by the omission of unknown features representing the formation of secondary aerosols. The inferior accuracy of estimation in the north and east of Tehran is also linked to lacking features which could feed the transportation of PM2.5 from west to the east of the study area into MLAs.
Seyed Omid Nabavi; Leopold Haimberger; Esmail Abbasi. Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms. Atmospheric Pollution Research 2018, 10, 889 -903.
AMA StyleSeyed Omid Nabavi, Leopold Haimberger, Esmail Abbasi. Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms. Atmospheric Pollution Research. 2018; 10 (3):889-903.
Chicago/Turabian StyleSeyed Omid Nabavi; Leopold Haimberger; Esmail Abbasi. 2018. "Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms." Atmospheric Pollution Research 10, no. 3: 889-903.
Because of the lack of ground-based observations in large parts of West Asia, Aerosol Optical Depth (AOD) is mainly monitored by using remote sensing techniques. AOD can also be predicted by short term forecasts with commonly called Deterministic weather prediction models (DMs). The skill of DMs in reproducing remotely sensed observations when averaged over monthly time scales over West Asia is rather limited due to significant uncertainties in inputs and complexity of dust, which is the dominant type of aerosols in the region. Machine Learning Algorithms (MLAs), which require much less computational expenses than DMs, can be used. Using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) AOD as the representative of response variable, MLAs, especially Multivariate Adaptive Regression Splines (MARS) and Support Vector Machines (SVM), outperformed DMs on monthly time scale. MLAs have yielded lower prediction error (RMSE) and higher correlation with observations than DMs. In addition, findings disclosed that DMs, especially MACC, have failed to simulate observed AOD values over western Iran where the Zagros Mountains prevent advection of fine dust particles to the east of the study area. Prediction errors of MLAs and DMs along with major DB AOD peaks, over Iraq, can be traced back to the rough resolution of variable datasets, omission of some unknown influential predictors representing the life cycle of dust and/or other aerosols, and scarcity of extreme cases. It also remains to be tested in how far the results presented can be generalized to other regions and time scales.
Seyed Omid Nabavi; Leopold Haimberger; Reyhaneh Abbasi; Cyrus Samimi. Prediction of aerosol optical depth in West Asia using deterministic models and machine learning algorithms. Aeolian Research 2018, 35, 69 -84.
AMA StyleSeyed Omid Nabavi, Leopold Haimberger, Reyhaneh Abbasi, Cyrus Samimi. Prediction of aerosol optical depth in West Asia using deterministic models and machine learning algorithms. Aeolian Research. 2018; 35 ():69-84.
Chicago/Turabian StyleSeyed Omid Nabavi; Leopold Haimberger; Reyhaneh Abbasi; Cyrus Samimi. 2018. "Prediction of aerosol optical depth in West Asia using deterministic models and machine learning algorithms." Aeolian Research 35, no. : 69-84.
M.J. Mayr; K.A. Vanselow; Cyrus Samimi. Fire regimes at the arid fringe: A 16-year remote sensing perspective (2000–2016) on the controls of fire activity in Namibia from spatial predictive models. Ecological Indicators 2018, 91, 324 -337.
AMA StyleM.J. Mayr, K.A. Vanselow, Cyrus Samimi. Fire regimes at the arid fringe: A 16-year remote sensing perspective (2000–2016) on the controls of fire activity in Namibia from spatial predictive models. Ecological Indicators. 2018; 91 ():324-337.
Chicago/Turabian StyleM.J. Mayr; K.A. Vanselow; Cyrus Samimi. 2018. "Fire regimes at the arid fringe: A 16-year remote sensing perspective (2000–2016) on the controls of fire activity in Namibia from spatial predictive models." Ecological Indicators 91, no. : 324-337.
Patrick Laloyaux; Eric De Boisseson; Magdalena Balmaseda; Jean-Raymond Bidlot; Stefan Broennimann; Roberto Buizza; Per Dalhgren; Dick Dee; Leopold Haimberger; Hans Hersbach; Yuki Kosaka; Matthew Martin; Paul Poli; Nick Rayner; Elke Rustemeier; Dinand Schepers. CERA-20C: A Coupled Reanalysis of the Twentieth Century. Journal of Advances in Modeling Earth Systems 2018, 10, 1172 -1195.
AMA StylePatrick Laloyaux, Eric De Boisseson, Magdalena Balmaseda, Jean-Raymond Bidlot, Stefan Broennimann, Roberto Buizza, Per Dalhgren, Dick Dee, Leopold Haimberger, Hans Hersbach, Yuki Kosaka, Matthew Martin, Paul Poli, Nick Rayner, Elke Rustemeier, Dinand Schepers. CERA-20C: A Coupled Reanalysis of the Twentieth Century. Journal of Advances in Modeling Earth Systems. 2018; 10 (5):1172-1195.
Chicago/Turabian StylePatrick Laloyaux; Eric De Boisseson; Magdalena Balmaseda; Jean-Raymond Bidlot; Stefan Broennimann; Roberto Buizza; Per Dalhgren; Dick Dee; Leopold Haimberger; Hans Hersbach; Yuki Kosaka; Matthew Martin; Paul Poli; Nick Rayner; Elke Rustemeier; Dinand Schepers. 2018. "CERA-20C: A Coupled Reanalysis of the Twentieth Century." Journal of Advances in Modeling Earth Systems 10, no. 5: 1172-1195.
El Nino events are characterized by anomalously warm tropical Pacific surface waters and concurrent ocean heat discharge, a precursor of subsequent cold La Nina conditions. Here we show that El Nino 2015/16 departed from this norm: despite extreme peak surface temperatures, Tropical Pacific (30N-30S) upper ocean heat content (OHC) increased by 9.6±1.7 ZJ (1ZJ=1021J), in stark contrast to the previous strong El Nino in 1997/98 (-11.5±2.9 ZJ). Unprecedented reduction of Indonesian Throughflow volume and heat transport played a key role in the anomalous 2015/16 event. We argue that this anomaly is linked with the previously documented intensified warming and associated rising sea levels in the Indian Ocean during the last decade. Additionally, increased absorption of solar radiation acted to dampen Pacific OHC discharge. These results explain the weak and short-lived La Nina conditions in 2016/17 and indicate the need for realistic representation of Indo-Pacific energy transfers for skilful seasonal-to-decadal predictions.
Michael Mayer; Magdalena Alonso Balmaseda; Leopold Haimberger. Unprecedented 2015/2016 Indo‐Pacific Heat Transfer Speeds Up Tropical Pacific Heat Recharge. Geophysical Research Letters 2018, 45, 3274 -3284.
AMA StyleMichael Mayer, Magdalena Alonso Balmaseda, Leopold Haimberger. Unprecedented 2015/2016 Indo‐Pacific Heat Transfer Speeds Up Tropical Pacific Heat Recharge. Geophysical Research Letters. 2018; 45 (7):3274-3284.
Chicago/Turabian StyleMichael Mayer; Magdalena Alonso Balmaseda; Leopold Haimberger. 2018. "Unprecedented 2015/2016 Indo‐Pacific Heat Transfer Speeds Up Tropical Pacific Heat Recharge." Geophysical Research Letters 45, no. 7: 3274-3284.
Indigenous and rural societies who have contributed least to anthropogenic climate change are facing its harshest consequences. One of the greatest challenges of climate change is lack of predictability, especially at the local scale. An estimated 70-80% of the world’s food is produced by small-holders with less than two hectares of land (FAO 2014; Lowder et al. 2016). These small-scale farmers and herders face an ever-shifting ‘new normal’ climate, increasing inconsistency in the seasonality of temperature and precipitation, and higher frequency of what were once considered extreme weather events (Jolly et al. 2002; Thornton et al. 2014). Climate variability is disrupting food systems and generating a debilitating anxiety (Carroll et al. 2009; Kassam 2009a,b; Coyle and Susteren 2011; UN Human Rights Council 2016). Anticipatory capacity – the ability to envision possible futures and develop a plan of action to deal with uncertainties – is needed urgently (Tschakert and Dietrich 2010). Communities and researchers must create innovative systems to recognize and respond to climate trends and prepare for a greater range of possible scenarios (Reid et al. 2014; Cuerrier et al. 2015). To build anticipatory capacity for climate change, communities need systems that are effective at the scale of the village and valley (Berkes and Jolly 2001; Downing and Cuerrier 2011). While climate scientists have increased model capabilities to make more accurate projections of global climate conditions, the uncertainties of global climate modeling together with those of downscaling methods means that these models are not always reliable at regional and local scales (Salick and Ross 2009). Synergy between indigenous ecological knowledge and climate science has already benefitted many local communities, as well as international understanding of climate change drivers and impacts (Jolly et al. 2002; Nickels et al. 2005; Nyong et al. 2007; Kassam 2009a; Alexander et al. 2011; Boillat and Berkes 2013; Rapinski et al. 2017;). Similarly, ground-truthing climate models with indigenous ecological knowledge can be used to refine downscaling methods and to inform planning and policies at local, regional, and national levels. Projections of climate models are least accurate within mountainous regions, where weather stations are scarce and rugged topographies dramatically alter climate patterns (Hall 2014). In addition, significant environmental degradation in many mountain regions, such as reduction of vegetation cover due to overgrazing or hydrological transformations resulting from road and dam construction, are obscuring the entangled effects of climate change. Nevertheless, food producers in these remote regions require the ability to anticipate patterns of temperature, precipitation, and runoff from glaciers and snowfields. Many indigenous and rural societies have developed unique systems to recognize and respond to climatic trends and variability. Over the course of multiple generations living in particular landscapes, indigenous people have accumulated knowledge of the relative timing of celestial, meteorological, and ecological phenomena. Understanding these relationships has enabled these communities to anticipate weather and other seasonal processes, and thereby coordinate their livelihood activities (Acharya 2011; Turner and Singh 2011). However, indigenous knowledge systems have suffered centuries of disruption and destruction as a result of colonialism, violent conflicts, and loss of land. Global climate change introduces unprecedented rates and magnitudes of change, exacerbating existing inequities (Turner and Clifton 2009). Although inadequately investigated, evidence suggests that climate change has impacted the physical, mental, and emotional health of indigenous peoples (Cunsolo Willox et al. 2012, 2014). In this brief communication we suggest a new approach for applied participatory action research to build anticipatory capacity for climate change. Specifically, we describe the development of ecological calendars that integrate indigenous knowledge and scientific data, and therefore require input from both communities of inquirers and communities of practice. We provide a case study of our ongoing work in the Pamir Mountains of Afghanistan, China, Kyrgyzstan, and Tajikistan, where we are in the midst of transdisciplinary research with indigenous agropastoralists. Developing anticipatory capacity for anthropogenic climate change requires a re-conceptualization of the notion of time. In industrialized societies, time is thought of as a metronomic progression represented by the familiar Gregorian calendar. However, we know that our experience of time is context-specific and therefore unique because it is embedded in socio-cultural meaning. Furthermore the timing of ecological processes and events are not consistent from year to year, and as a result of climate change, they increasingly vary, taking place at different dates on the Gregorian calendar. At our study sites in the Pamir Mountains of Central Asia, ice break-up, thawing, ploughing, sowing, and harvesting begin between 15 and 40 days earlier than a decade ago. In order to make sense of this variability, it may help to think of time as relational. For instance, our research from Lake Oneida in the Northeast United States indicates that historically, the blossoming of a certain flower indicates that the ground has thawed, ploughing can begin, burial services can be performed for those who have died during the winter, a particular fish is running in the river, and traps can be set for small animals that have emerged from hibernation. Knowledge of temporal relations allows communities to synchronize their livelihood activities within their ecological context. Re-conceiving time as relational may help us anticipate climatic variation, enabling us to coordinate our activities with variable biophysical phenomena even as...
Karim-Aly S. Kassam; Morgan L. Ruelle; Cyrus Samimi; Antonio Trabucco; Jianchu Xu. Anticipating Climatic Variability: The Potential of Ecological Calendars. Human Ecology 2018, 46, 249 -257.
AMA StyleKarim-Aly S. Kassam, Morgan L. Ruelle, Cyrus Samimi, Antonio Trabucco, Jianchu Xu. Anticipating Climatic Variability: The Potential of Ecological Calendars. Human Ecology. 2018; 46 (2):249-257.
Chicago/Turabian StyleKarim-Aly S. Kassam; Morgan L. Ruelle; Cyrus Samimi; Antonio Trabucco; Jianchu Xu. 2018. "Anticipating Climatic Variability: The Potential of Ecological Calendars." Human Ecology 46, no. 2: 249-257.