scholarly journals A Nonparametric Approach to the Removal of Documented Inhomogeneities in Climate Time Series

2013 ◽  
Vol 52 (5) ◽  
pp. 1139-1146 ◽  
Author(s):  
Chiara Ambrosino ◽  
Richard E. Chandler
Keyword(s):  
Time Series ◽  
Reanalysis Data ◽  
Climate Data ◽  
Nonparametric Approach ◽  
Satellite Technology ◽  
Abrupt Changes ◽  
Regression Framework ◽  
Climate Time Series ◽  
Climate Analysis

AbstractClimate data often suffer from artificial inhomogeneities, resulting from documented or undocumented events. For a time series to be used with confidence in climate analysis, it should only be characterized by variations intrinsic to the climate system. Many methods (e.g., direct or indirect) have been proposed according to the data characteristics (e.g., location, variable, or data completeness). This paper is focused on the abrupt-changes problem (when the properties of a time series change abruptly), when their timing is known, and suggests that a nonparametric regression framework provides an appealing way to correct for discontinuities in such a way as to recognize and allow for the existence of other structures such as seasonality and long-term smooth trends. The approach is illustrated by using reanalysis data for southern Africa, for which discontinuities are present because of the introduction of satellite technology in 1979.

scholarly journals Distinguishing Trends and Shifts from Memory in Climate Data

2018 ◽  
Vol 31 (23) ◽  
pp. 9519-9543 ◽  
Author(s):  
Claudie Beaulieu ◽  
Rebecca Killick
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Information Criterion ◽  
Steady Increase ◽  
Climate Data ◽  
Surface Warming ◽  
Abrupt Changes ◽  
Short Memory Process ◽  
The 1960S ◽  
And Shifts

The detection of climate change and its attribution to the corresponding underlying processes is challenging because signals such as trends and shifts are superposed on variability arising from the memory within the climate system. Statistical methods used to characterize change in time series must be flexible enough to distinguish these components. Here we propose an approach tailored to distinguish these different modes of change by fitting a series of models and selecting the most suitable one according to an information criterion. The models involve combinations of a constant mean or a trend superposed to a background of white noise with or without autocorrelation to characterize the memory, and are able to detect multiple changepoints in each model configuration. Through a simulation study on synthetic time series, the approach is shown to be effective in distinguishing abrupt changes from trends and memory by identifying the true number and timing of abrupt changes when they are present. Furthermore, the proposed method is better performing than two commonly used approaches for the detection of abrupt changes in climate time series. Using this approach, the so-called hiatus in recent global mean surface warming fails to be detected as a shift in the rate of temperature rise but is instead consistent with steady increase since the 1960s/1970s. Our method also supports the hypothesis that the Pacific decadal oscillation behaves as a short-memory process rather than forced mean shifts as previously suggested. These examples demonstrate the usefulness of the proposed approach for change detection and for avoiding the most pervasive types of mistake in the detection of climate change.

scholarly journals Dynamic wavelet correlation analysis for multivariate climate time series

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Josué M. Polanco-Martínez ◽  
Javier Fernández-Macho ◽  
Martín Medina-Elizalde
Keyword(s):  
Time Series ◽  
Tropical Cyclone ◽  
North Atlantic ◽  
Large Scale ◽  
Southern Oscillation ◽  
Atlantic Multidecadal Oscillation ◽  
Last Millennium ◽  
Climate Data ◽  
The North ◽  
Climate Time Series

AbstractThe wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context.

Towards a long term global snow climate data record from satellite data generated within the Snow Climate Change Initiative

2020 ◽  
Author(s):  
Gabriele Schwaizer ◽  
Lars Keuris ◽  
Thomas Nagler ◽  
Chris Derksen ◽  
Kari Luojus ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Process Models ◽  
Climate Data ◽  
Seasonal Snow ◽  
Snow Water

<p>Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data.</p><p>In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols.</p><p>Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.</p><p>The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes.</p><p>In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.</p>

scholarly journals Surface Temperature Variations in East Africa and Possible Causes

2009 ◽  
Vol 22 (12) ◽  
pp. 3342-3356 ◽  
Author(s):  
John R. Christy ◽  
William B. Norris ◽  
Richard T. McNider
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
East Africa ◽  
Mount Kilimanjaro ◽  
Uncertainty Estimates ◽  
Break Points ◽  
The Difference ◽  
Boundary Layer Dynamics ◽  
Climate Analysis

Abstract Surface temperatures have been observed in East Africa for more than 100 yr, but heretofore have not been subject to a rigorous climate analysis. To pursue this goal monthly averages of maximum (TMax), minimum (TMin), and mean (TMean) temperatures were obtained for Kenya and Tanzania from several sources. After the data were organized into time series for specific sites (60 in Kenya and 58 in Tanzania), the series were adjusted for break points and merged into individual gridcell squares of 1.25°, 2.5°, and 5.0°. Results for the most data-rich 5° cell, which includes Nairobi, Mount Kilimanjaro, and Mount Kenya, indicate that since 1905, and even recently, the trend of TMax is not significantly different from zero. However, TMin results suggest an accelerating temperature rise. Uncertainty estimates indicate that the trend of the difference time series (TMax − TMin) is significantly less than zero for 1946–2004, the period with the highest density of observations. This trend difference continues in the most recent period (1979–2004), in contrast with findings in recent periods for global datasets, which generally have sparse coverage of East Africa. The differences between TMax and TMin trends, especially recently, may reflect a response to complex changes in the boundary layer dynamics; TMax represents the significantly greater daytime vertical connection to the deep atmosphere, whereas TMin often represents only a shallow layer whose temperature is more dependent on the turbulent state than on the temperature aloft. Because the turbulent state in the stable boundary layer is highly dependent on local land use and perhaps locally produced aerosols, the significant human development of the surface may be responsible for the rising TMin while having little impact on TMax in East Africa. This indicates that time series of TMax and TMin should become separate variables in the study of long-term changes.

scholarly journals Homogenization of Radiosonde Temperature Time Series Using Innovation Statistics

2007 ◽  
Vol 20 (7) ◽  
pp. 1377-1403 ◽  
Author(s):  
Leopold Haimberger
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Climate Data ◽  
Climate Change Research ◽  
Temperature Records ◽  
A New Technique ◽  
Climate Analysis ◽  
Data Assimilation System ◽  
Global Mean ◽  
Assimilation System

Abstract Radiosonde temperature records contain valuable information for climate change research from the 1940s onward. Since they are affected by numerous artificial shifts, time series homogenization efforts are required. This paper introduces a new technique that uses time series of temperature differences between the original radiosonde observations (obs) and background forecasts (bg) of an atmospheric climate data assimilation system for homogenization. These obs − bg differences, the “innovations,” are a by-product of the data assimilation process. They have been saved during the 40-yr ECMWF Re-Analysis (ERA-40) and are now available for each assimilated radiosonde record back to 1958. It is demonstrated that inhomogeneities in the obs time series due to changes in instrumentation can be automatically detected and adjusted using daily time series of innovations at 0000 and 1200 UTC. The innovations not only reveal problems of the radiosonde records but also of the data assimilation system. Although ERA-40 used a frozen data assimilation system, the time series of the bg contains some breaks as well, mainly due to changes in the satellite observing system. It has been necessary to adjust the global mean bg temperatures before the radiosonde homogenization. After this step, homogeneity adjustments, which can be added to existing raw radiosonde observations, have been calculated for 1184 radiosonde records. The spatiotemporal consistency of the global radiosonde dataset is improved by these adjustments and spuriously large day–night differences are removed. After homogenization the climatologies of the time series from certain radiosonde types have been adjusted. This step reduces temporally constant biases, which are detrimental for reanalysis purposes. Therefore the adjustments applied should yield an improved radiosonde dataset that is suitable for climate analysis and particularly useful as input for future climate data assimilation efforts. The focus of this paper relies on the lower stratosphere and on the internal consistency of the homogenized radiosonde dataset. Implications for global mean upper-air temperature trends are touched upon only briefly.

scholarly journals Precipitation isotope time series predictions from machine learning applied in Europe

2021 ◽  
Vol 118 (26) ◽  
pp. e2024107118
Author(s):  
Daniel B. Nelson ◽  
David Basler ◽  
Ansgar Kahmen
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Climate Data ◽  
Modeling Framework ◽  
History Of ◽  
Oxygen Isotopic ◽  
Input Variables ◽  
Hydrogen And Oxygen Isotope ◽  
High Level

Hydrogen and oxygen isotope values of precipitation are critically important quantities for applications in Earth, environmental, and biological sciences. However, direct measurements are not available at every location and time, and existing precipitation isotope models are often not sufficiently accurate for examining features such as long-term trends or interannual variability. This can limit applications that seek to use these values to identify the source history of water or to understand the hydrological or meteorological processes that determine these values. We developed a framework using machine learning to calculate isotope time series at monthly resolution using available climate and location data in order to improve precipitation isotope model predictions. Predictions from this model are currently available for any location in Europe for the past 70 y (1950–2019), which is the period for which all climate data used as predictor variables are available. This approach facilitates simple, user-friendly predictions of precipitation isotope time series that can be generated on demand and are accurate enough to be used for exploration of interannual and long-term variability in both hydrogen and oxygen isotopic systems. These predictions provide important isotope input variables for ecological and hydrological applications, as well as powerful targets for paleoclimate proxy calibration, and they can serve as resources for probing historic patterns in the isotopic composition of precipitation with a high level of meteorological accuracy. Predictions from our modeling framework, Piso.AI, are available at https://isotope.bot.unibas.ch/PisoAI/.

scholarly journals How long do satellites need to overlap? Evaluation of climate data stability from overlapping satellite records

2017 ◽  
Author(s):  
Elizabeth C. Weatherhead ◽  
Jerald Harder ◽  
Eduardo A. Araujo-Pradere ◽  
Jason M. English ◽  
Lawrence E. Flynn ◽  
...  
Keyword(s):  
Climate Data ◽  
Long Term Stability ◽  
Earth Observations ◽  
Satellite Technology ◽  
Irradiance Data ◽  
Earth’S Climate ◽  
Added Uncertainty ◽  
The Stability

Abstract. Sensors on satellites provide unprecedented understanding of the Earth’s climate system by measuring incoming solar radiation, as well as both passive and active observations of the entire Earth with outstanding spatial and temporal coverage that would be currently impossible without satellite technology. A common challenge with satellite observations is to quantify their ability to provide well-calibrated, long-term, stable records of the parameters they measure. Ground-based intercomparisons offer some insight, while reference observations and internal calibrations give further assistance for understanding long-term stability. A valuable tool for evaluating and developing long-term records from satellites is the examination of data from overlapping satellites. Prior papers have used overlap periods to identify the offset between data from two satellites and estimate the added uncertainty to long-term records. This paper addresses the length of overlap needed to identify an offset or a drift in the offsets of data between two sensors. The results are presented for the general case of sensor overlap by using the case of overlap of the SORCE SIM and SOLSTICE solar irradiance data as an example. To achieve a 1 % uncertainty in estimating the offset for these two instruments’ measurement of the Mg II core (280 nm) requires approximately 5 months of overlap. For relative drift to be identified within 0.1 % yr−1 uncertainty, the overlap for these two satellites would need to be 2.6 years. Additional overlap of satellite measurements is needed if, as is the case for solar monitoring, unexpected jumps may occur because these jumps add to the uncertainty of both offsets and drifts; the additional length of time needed to account for a single jump in the overlap data may be as large as 50 % of the original overlap period in order to achieve the same desired confidence in the stability of the merged dataset. Extension of the results presented here are directly applicable to satellite Earth observations. Approaches for Earth observations may be challenged by the complexity of those observations but may also benefit from ancillary observations taken from ground-based and in situ sources. Difficult choices need to be made when monitoring approaches are considered; we outline some attempts at optimizing networks based on economic principles. The careful evaluation of monitoring overlap is important to the appropriate application of observational resources and to the usefulness of current and future observations.

scholarly journals Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1842 ◽  
Author(s):  
Christof Lorenz ◽  
Carsten Montzka ◽  
Thomas Jagdhuber ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
High Resolution ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Brightness Temperatures ◽  
Climate Analysis

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

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