Detection Of Long-term Trends Research Articles

How well can satellite altimetry and firn models resolve Antarctic firn thickness variations?

Abstract. Elevation changes of the Antarctic Ice Sheet (AIS) related to surface mass balance and firn processes vary strongly in space and time. Their subdecadal natural variability is large and hampers the detection of long-term climate trends. Firn models or satellite altimetry observations are typically used to investigate such firn thickness changes. However, there is a large spread among firn models. Further, they do not fully explain observed firn thickness changes, especially on smaller spatial scales. Reconciled firn thickness variations will facilitate the detection of long-term trends from satellite altimetry; the resolution of the spatial patterns of such trends; and, hence, their attribution to the underlying mechanisms. This study has two objectives. First, we quantify interannual Antarctic firn thickness variations on a 10 km grid scale. Second, we characterise errors in both the altimetry products and firn models. To achieve this, we jointly analyse satellite altimetry and firn modelling results in time and space. We use the timing of firn thickness variations from firn models and the satellite-observed amplitude of these variations to generate a combined product (“adjusted firn thickness variations”) over the AIS for 1992–2017. The combined product characterises spatially resolved variations better than either firn models alone or altimetry alone. It provides a higher resolution and a more precise spatial distribution of the variations compared to model-only solutions and eliminates most of the altimetry errors compared to altimetry-only solutions. Relative uncertainties in basin-mean time series of the adjusted firn thickness variations range from 20 % to 108 %. At the grid cell level, relative uncertainties are higher, with median values per basin in the range of 54 % to 186 %. This is due to the uncertainties in the large and very dry areas of central East Antarctica, especially over large megadune fields, where the low signal-to-noise ratio poses a challenge for both models and altimetry to resolve firn thickness variations. A large part of the variance in the altimetric time series is not explained by the adjusted firn thickness variations. Analysis of the altimetric residuals indicate that they contain firn model errors, such as firn signals not captured by the models, and altimetry errors, such as time-variable radar penetration effects and errors in intermission calibration. This highlights the need for improvements in firn modelling and altimetry analysis.

Evaluating F2-region long-term trends using the International Reference Ionosphere (IRI) model: is this a feasible approximation for experimental trends?

Abstract. The International Reference Ionosphere (IRI) is a widely used empirical ionospheric model based on observations from a worldwide network of ionospheric stations. Therefore, it would be reasonable to expect it to capture long-term changes in key ionospheric parameters, such as foF2 and hmF2 linked to trend forcings like greenhouse gas increasing concentrations and the Earth's magnetic field secular variation. Despite the numerous reported trends in foF2 and hmF2 derived from experimental data and model results, there are inconsistencies that require continuous refinement of trend estimation methods and regular data updates. This ongoing effort is crucial to address the difficulties posed by the weak signal-to-noise ratio characteristic of ionospheric long-term trends. Furthermore, the experimental verification of these trends remains challenging, primarily due to time and spatial coverage limitations of measured data series. Achieving these needs for accurate detection of long-term trends requires extensive global coverage and high resolution of ionospheric measurements together with long enough periods spanning multiple solar cycles to properly filter out variations of shorter terms than the sought trend. Considering these challenges, IRI-modeled foF2 and hmF2 parameters offer a valuable alternative for assessing trends and obtaining a first approximation of a plausible global picture representative of experimental trends. This work presents these global trend patterns, considering the period 1960–2022 using the IRI-Plas 2020 version, which are consistent with other model predictions. While IRI explicitly takes into account the Earth's magnetic field variations, the increase in the concentrations of greenhouse gases appears indirectly through the Ionospheric Global index (IG) which is derived from ionospheric measurements. F2-region trends induced by the first mechanism should be important only around the magnetic equator at the longitudinal range with the strongest displacement, and it should be negligible out of this region. Conversely, trends induced by the greenhouse effect, which are the controversial ones, should be dominant away from the geomagnetic equator and should globally average to negative values in both cases, i.e., foF2 and hmF2. Effectively, these negative global means are verified by trends based on IRI-Plas, even though not for the correct reasons in the hmF2 case. In addition, a verification was performed for more localized foF2 trend values, considering data from nine mid-latitude stations, and a reasonable level of agreement was observed. It is concluded that the IRI model can be a valuable tool for obtaining preliminary approximations of the Earth's magnetic-field-induced long-term changes in foF2 and hmF2, as well as of experimental trends only in the foF2 case. The latter does not hold for hmF2, even if the trends obtained are close to the expected values.

Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time Series

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

Water quality monitoring with purpose: Using a novel framework and leveraging long-term data

Water quality monitoring programs are developed to meet goals including attaining regulatory compliance, evaluating long-term environmental changes, or quantifying the impact of an emergency event. Methods for developing these programs often fail to address multiple aspects of development (hazard identification, parameter selection, monitoring locations/frequency) simultaneously. We develop a framework for monitoring program development that is both versatile and systematic, the Hazard Based Water Quality Monitoring Planning framework, and apply it to the Quabbin watershed in Massachusetts, USA. We use a novel application of dataset deconstruction of long-term water quality datasets and the Seasonal Kendall test for trends to evaluate the effects of sampling frequency on long-term trend detection at several watershed sites. Results showed that when sampling frequency is decreased, ability to detect statistically significant trends often decreases. Absolute error in trend slopes between biweekly (twice monthly) and reduced sampling frequencies was relatively small for specific conductance and turbidity but was high for total coliform, likely due to interannual variation in rainfall and temperature We found that no one sampling reduction method resulted in a consistently lower absolute error compared to the “truth” (biweekly sampling), highlighting the importance of evaluating conditions that may affect water quality at sites in different parts of a watershed. We demonstrate the framework's usefulness, particularly for parameter and sampling frequency selection, using methods that can be readily applied to other watershed systems.

Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

Abstract. Detecting a tropospheric ozone trend from sparsely sampled ozonesonde profiles (typically once per week) is challenging due to the short-lived anomalies in the time series resulting from ozone's high temporal variability. To enhance trend detection, we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess ozone variability across a time series of vertical profiles. Treating the profile time series as a set of individual time series on discrete pressure surfaces, a class of smoothing spline ANOVA (analysis of variance) models is used for the purpose of jointly modeling multiple correlated time series (on separate pressure surfaces) by their associated seasonal and interannual variabilities. This integrated fit method filters out the unstructured variation through a statistical regularization (i.e., a roughness penalty) by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically correlated time series of tropospheric ozone observations from (1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China throughout 1994–2017 and (2) NOAA GML's (Global Monitoring Laboratory) ozonesonde records at Hilo, Hawaii, (1982–2018) and Trinidad Head, California (1998–2018). We illustrate the ability of this technique to detect a consistent trend estimate and its effectiveness in reducing the associated uncertainty in the profile data due to the low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) to determine how many profiles in a month are required for reliable long-term trend detection. When ignoring the vertical correlation, we found that a typical sampling strategy (i.e. four profiles per month) might result in 7 % of sampled trends falling outside the 2σ uncertainty interval derived from the full dataset with an associated 10 % of mean absolute percentage error. Based on a series of sensitivity studies, we determined optimal sampling frequencies for (1) basic trend detection and (2) accurate quantification of the trend. When applying the integrated fit method, we find that a typical sampling frequency of four profiles per month is adequate for basic trend detection; however, accurate quantification of the trend requires 14 profiles per month. Accurate trend quantification can be achieved with only 10 profiles per month if a regular sampling frequency is applied. In contrast, the standard separated fit method, which ignores the vertical correlation between pressure surfaces, requires 8 profiles per month for basic trend detection and 18 profiles per month for accurate trend quantification. While our method improves trend detection from sparse datasets, the key to substantially reducing the uncertainty is to increase the sampling frequency.

Twenty-five years of observations of soil organic carbon in Swiss croplands showing stability overall but with some divergent trends

The temporal evolution of soil organic carbon (SOC) is of major importance given its status as a key parameter in many soil functions. Furthermore, soils constitute an important reservoir of carbon in our environment. In light of climate change, consistent SOC data over extended periods in combination with information on agricultural management are much required, but still scarce. We report SOC changes in the topsoil (0–20 cm) of Swiss cropland measured at well-defined monitoring sites resampled every 5 years from 1990 to 2014 by the Swiss Soil Monitoring Network NABO using consistent sampling protocols and quality assurance. Data on agricultural management practices were retrieved from farmers. Overall, SOC remained stable for the ensemble of monitoring sites, although increasing and decreasing trends were observed for individual sites, ranging from − 11 to + 16% relative change per decade. Changes in the agricultural management of cropland triggered substantial changes in SOC contents for some sites. Moreover, sites with a low ratio of SOC/clay (< 0.1) generally showed more positive trends than sites with higher ratios. We presume that SOC was either at or near steady state, given the consistency of management practices over the last few decades. Finally, our study provides insights into the uncertainties related to (real-world) SOC monitoring and underlines the relevance of short-term SOC variations that could hamper the detection of long-term trends. The minimum detectable change (MDC) by the applied monitoring scheme is estimated at 0.35% per year, in relative terms.

Long-term trend detection and spatiotemporal analysis of groundwater levels using GIS techniques in Lower Bhavani River basin, Tamil Nadu, India

Groundwater resources are used in various parts of the world to meet out drinking water supply, irrigational practices and industrial applications. These valuable resources are naturally replenished by rainfall infiltration. Due to population growth and industrialization, groundwater resources are often overexploited in different parts of the world particularly in the hard rock areas. It leads to rapid declination in the groundwater level. Therefore, groundwater fluctuation with respect to space and time governs attention throughout the world for the purpose of sustainable management of water resources. In the present study, long-term trend detection and spatiotemporal variation of groundwater levels were analyzed using Geographical Information System (GIS) and performing statistical tests for the Lower Bhavani River basin, Tamil Nadu, India. For this purpose, 32 years long-term groundwater-level data (1984–2015) of 57 observation wells spread over the study area were collected from the government departments. Seasonal variation of groundwater levels was plotted spatially for pre-monsoon (March to May), post-monsoon (January and February), southwest (SW) monsoon (June to September) and northeast (NE) monsoon (October to December) seasons using GIS. The trend variation of groundwater levels was predicted by performing statistical tests such as Mann–Kendall test and Sen’s slope estimator. The present study indicates that the average annual groundwater level has lowered beyond 15 m (below ground level) during all the monsoon seasons in the year 2003 and 2004, which highlights less rainfall infiltration and overexploitation of groundwater. This leads the hard rock aquifer into stress. The study also shows that the groundwater fluctuation is very high in the southeastern and northeastern parts of the basin, and it is moderate in the northern and northwestern parts of the basin. However, the fluctuation is comparatively less in the central part of the basin because of replenishment of groundwater by the Bhavani River. The trend analysis highlights that declining water table is mostly found during SW monsoon season (summer season), which is observed more than 50% area of the basin. The places such as Emmampoondi, Kumbapanai, Kandisalai, Alukuli, Perikoduveri, P.Mettupalayam, Pudupalayam, Sathyamangalam, Nallagoundanpudur, Kullampalayam and Baguthampalayam are mostly affected by the declining trend in the groundwater level. Therefore, this study recommends for the implementation of large-scale rainwater harvesting system in the Lower Bhavani River basin to augment groundwater resources.

Inter-comparison of satellite-retrieved and Global Land Data Assimilation System-simulated soil moisture datasets for global drought analysis

The multi-satellite-retrieved (ESA CCI SM) and the Global Land Data Assimilation System-Noah-simulated (GLDAS-Noah) surface soil moisture (SM) datasets are compared for global drought analysis over a multi-decadal time period (1991–2015). Global drought events and their duration, frequency and severity are assessed on a grid basis with soil moisture anomaly percentage index (SMAPI). The results show that the ESA CCI SM and the GLDAS-Noah based SMAPI values are significantly (p < 0.05) correlated over most (83%) of the study region, of seasonally dependent. Both datasets show similar global patterns in drought duration, drought frequency and drought severity. The droughts present generally longer duration, higher frequency and more severity in arid and semi-arid regions than humid and sub-humid regions. The ESA CCI SM droughts are relatively higher in frequency and more intense in severity than the GLDAS-Noah SM droughts in many regions of the globe, while the two datasets show considerable differences in drought duration over arid, semi-arid and highly vegetated regions. For long-term trend detection, both datasets show high consistency in spatial pattern of SMAPI, with major significant drying trends in arid and semi-arid regions. Part (~20%) trends are confirmed by the Global Precipitation Climatology Centre (GPCC) precipitation dataset using the Standard Precipitation Index (SPI). The two SM datasets exhibit large disparity in trending drought duration, drought frequency and drought severity. Despite that, both show major significant increasing trends in arid and semi-arid regions. Both soil moisture datasets are capable of identifying extreme drought events reported in southern China, North America, Europe and southern Africa. The ESA CCI SM dataset is more effective in determining the severity and spatial pattern of drought excluding densely vegetated regions, while the GLDAS-Noah dataset is more powerful in detecting drought occurrence, even over densely vegetated regions. Overall, the ESA CCI SM and GLDAS-Noah SM show potential in global drought analysis, yet cautions should be paid to arid and semi-arid regions where drying trends are prevalent and large discrepancy presents between two datasets.

Long-term record of top-of-atmosphere albedo over land generated from AVHRR data

Top-of-atmosphere (TOA) albedo is a fundamental component of Earth's energy budget. To date, long-term global land TOA albedo products with spatial resolution higher than 20-km are not available. In this study, we propose a novel algorithm to retrieve TOA albedo from multispectral imager observations acquired by Advanced Very High Resolution Radiometer (AVHRR), which provides the longest continuous record of global satellite observations since 1981. Direct estimation models were established first to derive instantaneous TOA broadband albedo under various atmospheric and surface conditions, including cloudy-sky, clear-sky (snow-free) and snow-cover conditions. To perform long-term series analysis, the instantaneous TOA albedo were then converted to daily/monthly mean values based on the diurnal curves from multi-year Clouds and the Earth's Radiant Energy System (CERES) 3-hourly flux dataset. Calibration differences between sequential AVHRR sensors were further mitigated by invariant targets normalization. The retrieved TOA albedo at 0.05° × 0.05° was validated against two TOA albedo datasets, CM SAF (Climate Monitoring Satellite Application Facility) flux data and CERES flux data, at spatial resolutions of 0.05° × 0.05°, 20 km × 20 km and 1° × 1°. The instantaneous TOA albedo had an overall Root-Mean-Square-Error (RMSE) of 0.047 when compared with 20-km CERES fluxes, whereas the 1° by 1° monthly mean TOA albedo showed closer agreements with both CM SAF and CERES, with RMSE ranging from 0.029 to 0.040 and from 0.022 to 0.031, respectively. Moreover, our product was found to be highly consistent with both CERES and CM SAF at long-term trend detection. The extensive validation indicated the robustness of our algorithm and subsequently, comparable data quality with existing datasets. With global coverage and long time series (1981–2017), our product is expected to provide valuable information for climate change studies.

Detection of climate change-driven trends in phytoplankton phenology.

The timing of the annual phytoplankton spring bloom is likely to be altered in response to climate change. Quantifying that response has, however, been limited by the typically coarse temporal resolution (monthly) of global climate models. Here, we use higher resolution model output (maximum 5days) to investigate how phytoplankton bloom timing changes in response to projected 21st century climate change, and how the temporal resolution of data influences the detection of long-term trends. We find that bloom timing generally shifts later at mid-latitudes and earlier at high and low latitudes by ~5days per decade to 2100. The spatial patterns of bloom timing are similar in both low (monthly) and high (5day) resolution data, although initiation dates are later at low resolution. The magnitude of the trends in bloom timing from 2006 to 2100 is very similar at high and low resolution, with the result that the number of years of data needed to detect a trend in phytoplankton phenology is relatively insensitive to data temporal resolution. We also investigate the influence of spatial scales on bloom timing and find that trends are generally more rapidly detectable after spatial averaging of data. Our results suggest that, if pinpointing the start date of the spring bloom is the priority, the highest possible temporal resolution data should be used. However, if the priority is detecting long-term trends in bloom timing, data at a temporal resolution of 20days are likely to be sufficient. Furthermore, our results suggest that data sources which allow for spatial averaging will promote more rapid trend detection.

The Role of Paleoecology in Restoration and Resource Management—The Past As a Guide to Future Decision-Making: Review and Example from the Greater Everglades Ecosystem, U.S.A

Resource managers around the world are challenged to develop feasible plans for sustainable conservation and/or restoration of the lands, waters, and wildlife they administer – a challenge made greater by anticipated climate change and associated effects over the next century. Increasingly, paleoecologic and geologic archives are being used to extend the period of record of observed data and provide information on centennial to millennial scale responses to long-term drivers of ecosystem change. The development of paleoecology from an emerging field investigating past environments to a highly relevant applied science is reviewed and general examples of the application of paleoecologic research to resource management questions in diverse habitats and regions are provided. Specific examples of the application of paleoecologic research to the restoration of the Greater Everglades Ecosystem of south Florida (U.S.A) are presented. Conducting valuable scientific research that would benefit resource management decisions, however, is not enough. Scientists and resource managers need to be engaged in collaborative discussions from the beginning of the research process in order to ensure that management questions are being addressed and that the science reaches the people who will benefit from the information. Paleoecology and related disciplines provide an understanding of how ecosystems and individual species function and change over time in response to both natural and anthropogenic drivers. Information on pre-anthropogenic baseline conditions is provided by paleoecologic research, but it is the detection of long-term trends and cycles that allow resource managers to set realistic goals and targets by moving away from the fixed-point baseline concept to one of dynamic landscapes that anticipates and incorporates an expectation of change into decision-making.

Detection of long term trends in underwater noise levels

The risk of adverse impact to marine ecosystems from underwater noise pollution is increasingly recognised by scientists, policymakers, and wider society. Deep water measurements from the Northeast Pacific indicate that ocean noise has increased substantially over recent decades. Policymakers are now considering establishing noise monitoring programs to determine noise levels and trends in their waters. However, the ability of noise monitoring to detect statistically significant trends is a function of the temporal extent, variance, and autocorrelation of the time series. This has implications for the feasibility of evaluating quantitative policy targets within prescribed time frames and hence should inform the formulation of such targets. The present work demonstrates that methods developed in other environmental science disciplines (e.g., atmospheric temperature measurement) to design long-term monitoring networks and assess their statistical power can be applied to noise monitoring programmes. Example datasets are used to show the application of these methods both to assess the significance of long-term trends in ambient noise, and the required monitoring period to detect a given magnitude of trend (e.g., 3 dB per decade). The implications for the design of noise monitoring networks and target setting for policy purposes are then discussed.

Variable climatic conditions dominate recent phytoplankton dynamics in Chesapeake Bay.

Variable climatic conditions strongly influence phytoplankton dynamics in estuaries globally. Our study area is Chesapeake Bay, a highly productive ecosystem providing natural resources, transportation, and recreation for nearly 16 million people inhabiting a 165,000-km2 watershed. Since World War II, nutrient over-enrichment has led to multiple ecosystem impairments caused by increased phytoplankton biomass as chlorophyll-a (chl-a). Doubled nitrogen (N) loadings from 1945–1980 led to increased chl-a, reduced water clarity, and low dissolved oxygen (DO), while decreased N loadings from 1981–2012 suggest modest improvement. The recent 30+ years are characterized by high inter-annual variability of chl-a, coinciding with irregular dry and wet periods, complicating the detection of long-term trends. Here, we synthesize time-series data for historical and recent N loadings (TN, NO2 + NO3), chl-a, floral composition, and net primary productivity (NPP) to distinguish secular changes caused by nutrient over-enrichment from spatio-temporal variability imposed by climatic conditions. Wet years showed higher chl-a, higher diatom abundance, and increased NPP, while dry years showed lower chl-a, lower diatom abundance, and decreased NPP. Our findings support a conceptual model wherein variable climatic conditions dominate recent phytoplankton dynamics against a backdrop of nutrient over-enrichment, emphasizing the need to separate these effects to gauge progress toward improving water quality in estuaries.

The SPARC Data Initiative: comparisons of CFC-11, CFC-12, HF and SF&amp;lt;sub&amp;gt;6&amp;lt;/sub&amp;gt; climatologies from international satellite limb sounders

Abstract. A quality assessment of the CFC-11 (CCl3F), CFC-12 (CCl2F2), HF, and SF6 products from limb-viewing satellite instruments is provided by means of a detailed intercomparison. The climatologies in the form of monthly zonal mean time series are obtained from HALOE, MIPAS, ACE-FTS, and HIRDLS within the time period 1991–2010. The intercomparisons focus on the mean biases of the monthly and annual zonal mean fields and aim to identify their vertical, latitudinal and temporal structure. The CFC evaluations (based on MIPAS, ACE-FTS and HIRDLS) reveal that the uncertainty in our knowledge of the atmospheric CFC-11 and CFC-12 mean state, as given by satellite data sets, is smallest in the tropics and mid-latitudes at altitudes below 50 and 20 hPa, respectively, with a 1σ multi-instrument spread of up to ±5 %. For HF, the situation is reversed. The two available data sets (HALOE and ACE-FTS) agree well above 100 hPa, with a spread in this region of ±5 to ±10 %, while at altitudes below 100 hPa the HF annual mean state is less well known, with a spread ±30 % and larger. The atmospheric SF6 annual mean states derived from two satellite data sets (MIPAS and ACE-FTS) show only very small differences with a spread of less than ±5 % and often below ±2.5 %. While the overall agreement among the climatological data sets is very good for large parts of the upper troposphere and lower stratosphere (CFCs, SF6) or middle stratosphere (HF), individual discrepancies have been identified. Pronounced deviations between the instrument climatologies exist for particular atmospheric regions which differ from gas to gas. Notable features are differently shaped isopleths in the subtropics, deviations in the vertical gradients in the lower stratosphere and in the meridional gradients in the upper troposphere, and inconsistencies in the seasonal cycle. Additionally, long-term drifts between the instruments have been identified for the CFC-11 and CFC-12 time series. The evaluations as a whole provide guidance on what data sets are the most reliable for applications such as studies of atmospheric transport and variability, model–measurement comparisons and detection of long-term trends. The data sets will be publicly available from the SPARC Data Centre and through PANGAEA (doi:10.1594/PANGAEA.849223).

Observing climate change trends in ocean biogeochemistry: when and where.

Understanding the influence of anthropogenic forcing on the marine biosphere is a high priority. Climate change‐driven trends need to be accurately assessed and detected in a timely manner. As part of the effort towards detection of long‐term trends, a network of ocean observatories and time series stations provide high quality data for a number of key parameters, such as pH, oxygen concentration or primary production (PP). Here, we use an ensemble of global coupled climate models to assess the temporal and spatial scales over which observations of eight biogeochemically relevant variables must be made to robustly detect a long‐term trend. We find that, as a global average, continuous time series are required for between 14 (pH) and 32 (PP) years to distinguish a climate change trend from natural variability. Regional differences are extensive, with low latitudes and the Arctic generally needing shorter time series (<~30 years) to detect trends than other areas. In addition, we quantify the ‘footprint’ of existing and planned time series stations, that is the area over which a station is representative of a broader region. Footprints are generally largest for pH and sea surface temperature, but nevertheless the existing network of observatories only represents 9–15% of the global ocean surface. Our results present a quantitative framework for assessing the adequacy of current and future ocean observing networks for detection and monitoring of climate change‐driven responses in the marine ecosystem.

Changes in radial growth of earlywood in Quercus crispula between 1970 and 2004 reflect climate change

Earlywood width in Quercus crispula increased from 1970 to 2004 without changes of vessel anatomy and ring growth. The increase in diameter of a tree stem is an important indicator of forest productivity. Xylem traits, such as the number and cross-sectional area of earlywood vessels, are also critical parameters of forest growth because of the physiological and structural contribution of xylem to the growth of the tree stem. Forest productivity appears to be affected by climate change and, indeed, trees might be expected to acclimate to gradual long-term climate change. The aim of this study was to identify long-term changes in increases in stem diameter and in earlywood vessels by examining tree rings of Quercus crispula. Focusing on 20 mature specimens of Q. crispula, we examined annual ring growth from 1970 to 2004 and measured earlywood traits, namely, the width, cross-sectional area (henceforth referred to as area) and number of earlywood vessels, by digital image analysis. We developed a hierarchical Bayesian model for detection of long-term trends in these traits. We found that earlywood width, as well as the total number and area of earlywood vessels, increased during the 35 years under analysis. One possible cause of these changes might be the long-term elevation of temperatures in early spring, which determine the timing of the onset of cambial reactivation from winter dormancy. In contrast to the long-term changes, short-term, yearly changes in earlywood traits fluctuated to a smaller extent than yearly changes in tree ring width. Therefore, the observed long-term changes in earlywood appear to represent acclimation to long-term climate change.

On the temporal consistency of chlorophyll products derived from three ocean-colour sensors

Satellite ocean-colour sensors have life spans lasting typically five-to-ten years. Detection of long-term trends in chlorophyll-a concentration (Chl-a) using satellite ocean colour thus requires the combination of different ocean-colour missions with sufficient overlap to allow for cross-calibration. A further requirement is that the different sensors perform at a sufficient standard to capture seasonal and inter-annual fluctuations in ocean colour. For over eight years, the SeaWiFS, MODIS-Aqua and MERIS ocean-colour sensors operated in parallel. In this paper, we evaluate the temporal consistency in the monthly Chl-a time-series and in monthly inter-annual variations in Chl-a among these three sensors over the 2002–2010 time period. By subsampling the monthly Chl-a data from the three sensors consistently, we found that the Chl-a time-series and Chl-a anomalies among sensors were significantly correlated for >90% of the global ocean. These correlations were also relatively insensitive to the choice of three Chl-a algorithms and two atmospheric-correction algorithms. Furthermore, on the subsampled time-series, correlations between Chl-a and time, and correlations between Chl-a and physical variables (sea-surface temperature and sea-surface height) were not significantly different for >92% of the global ocean. The correlations in Chl-a and physical variables observed for all three sensors also reflect previous theories on coupling between physical processes and phytoplankton biomass. The results support the combining of Chl-a data from SeaWiFS, MODIS-Aqua and MERIS sensors, for use in long-term Chl-a trend analysis, and highlight the importance of accounting for differences in spatial sampling among sensors when combining ocean-colour observations.

Detection of long-term trends in monthly hydro-climatic series of Colombia through Empirical Mode Decomposition

We test for the existence of long-term trends in 25- to 50-year long series of monthly rainfall, average river discharges, and minimum air temperatures in Colombia. The Empirical Mode Decomposition method is used as a mathematical filter to decompose a given time series into a finite number of intrinsic mode functions, assuming the coexistence of different frequency oscillatory modes in the series, and that the residual captures the likely existing long-term trends. The Mann-Kendall test for autocorrelated data is used to assess the statistical significance of the identified trends, and the Sen test is used to quantify their magnitudes. Results show that 62 % of river discharge series exhibit significant decreasing trends between 0.01-1.92 m 3 s −1 per year, which are highly consistent downstream albeit with different ratios between the trend magnitudes and mean discharges. Most minimum temperature series (87 %) exhibit increasing trends (0.01-0.08 °Cyr −1). Results for precipitation series are inconclusive owing to the mixing between increasing trends (41 %, between 0.1-7.0 mm yr −1) and decreasing trends (44 %, between 0.1-7.4 mm yr −1), with no clear-cut geographical pattern, except for the increasing trend identified along the Pacific region, consistent with the increasing trend identified in the strength of the Choco low-level wind jet off the Pacific coast of Colombia, an important moisture source of continental precipitation. Our results contribute to discerning between signals of climate change and climate variability in tropical South America.

Is the Last Years Abrupt Warming in the National Observatory of Athens Records a Climate Change Manifestation?

Instrumental data time series show an average global warming of approximately 0.90C over the last century. Eastern Mediterranean air temperature follows the northern hemisphere (NH) secular trend till 1970s, while the NH warming of the last 30 years is not noticeable in the eastern Mediterranean till 1990s. National Observatory of Athens (NOA) meteorological time series start from the last decades of the 19th Century, therefore are at first suitable for detection of long term trends in the region. In this study we investigate whether the abrupt increase of the NOA air temperature time series, which appears during the last few years, is the finger-print of the broader scale climatic change or it is a discontinuity in the record of urban effect or of station’s problems origin. It is shown that NOA air temperature records display a statistically significant discontinuity attributable to change of the station thermometers on 1995 and therefore NOA records must be treated with caution for long term air temperature trends detection.

A novel method for tracking western Lake Erie Microcystis blooms, 2002–2011

After a period of improvement from the late 1970s through the mid 1990s, western Lake Erie has returned to eutrophic conditions and harmful algal blooms now dominated by the cyanobacterium Microcystis aeruginosa. The detection of long-term trends in Microcystis blooms would benefit from a convenient method for quantifying Microcystis using archived plankton tows. From 2002 to 2011, summer Microcystis blooms in western Lake Erie were quantified using plankton tows (N=649). A flotation separation method was devised to quantify Microcystis biovolume in the tows, and the method was tested against whole water cell counts. Floating Microcystis biovolume (mL) in preserved tows was highly correlated with total Microcystis cells (R2=0.84) and biomass (R2=0.95) in whole water samples. We found that Microcystis annual biovolume was highly variable among years; the 2011 bloom was 2.4 times greater than the second largest bloom (2008) and 29.0 times greater than the smallest bloom (2002). Advantages of the method include use of archived samples, high sampling volume, and low effort and expense. Limitations include specificity for cyanobacterial blooms dominated by large Microcystis colonies and the need for site-specific validation. This study indicates that the flotation method can be used to rapidly assess past and present Microcystis in western Lake Erie and that there was high variability in the timing, duration, and intensity of the annual Microcystis blooms over a 10-year period. The data made possible by this method will aid further investigations into the underlying causal factors of blooms.