Average Seasonal Cycle Research Articles

12 years of continuous atmospheric O2, CO2 and APO data from Weybourne Atmospheric Observatory in the United Kingdom

Abstract. We present a 12-year time series of continuous atmospheric measurements of O2 and CO2 at the Weybourne Atmospheric Observatory in the United Kingdom. These measurements are combined into the term atmospheric potential oxygen (APO), a tracer that is invariant to terrestrial biosphere fluxes. The CO2, O2 and APO datasets discussed are hourly averages between May 2010 and December 2021. We include details of our measurement system and calibration procedures, and describe the main long-term and seasonal features of the time series. The 2 min repeatability of the measurement system is approximately ±3 per meg for O2 and approximately ±0.005 ppm for CO2. The time series shows average long-term trends of 2.40 ppm yr−1 (2.38 to 2.42) for CO2, −24.0 per meg yr−1 for O2 (−24.3 to −23.8) and −11.4 per meg yr−1 (−11.7 to −11.3) for APO, over the 12-year period. The average seasonal cycle peak-to-peak amplitudes are 16 ppm for CO2, 134 per meg for O2 and 68 per meg for APO. The diurnal cycles of CO2 and O2 vary considerably between seasons. The datasets are publicly available at https://doi.org/10.18160/Z0GF-MCWH (Adcock et al., 2023) and have many current and potential scientific applications in constraining carbon cycle processes, such as investigating air–sea exchange of CO2 and O2 and top-down quantification of fossil fuel CO2.

Empirical upscaling of OzFlux eddy covariance for high-resolution monitoring of terrestrial carbon uptake in Australia

Abstract. We develop high-resolution (1 km) estimates of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) over the Australian continent for the period January 2003 to June 2022 by empirical upscaling of flux tower measurements. We compare our estimates with nine other products that cover the three broad categories that define current methods for estimating the terrestrial carbon cycle and assess if consiliences between datasets can point to the correct dynamics of Australia's carbon cycle. Our results indicate that regional empirical upscaling greatly improves upon the existing global empirical upscaling efforts, outperforms process-based models, and agrees much better with the dynamics of CO2 flux over Australia as estimated by two regional atmospheric inversions. Our nearly 20-year estimates of terrestrial carbon fluxes revealed that Australia is a strong net carbon sink of −0.44 PgC yr−1 (interquartile range, IQR = 0.42 PgC yr−1) on average, with an inter-annual variability of 0.18 PgC yr−1 and an average seasonal amplitude of 0.85 PgC yr−1. Annual mean carbon uptake estimated from other methods ranged considerably, while carbon flux anomalies showed much better agreement between methods. NEE anomalies were predominately driven by cumulative rainfall deficits and surpluses, resulting in larger anomalous responses from GPP than ER. In contrast, we show that the long-term average seasonal cycle is dictated more by the variability in ER than GPP, resulting in peak carbon uptake typically occurring during the cooler, drier austral autumn and winter months. This new estimate of Australia's terrestrial carbon cycle provides a benchmark for assessment against land surface model simulations and a means for monitoring of Australia's terrestrial carbon cycle at an unprecedented high resolution. We call this new estimate of Australia's terrestrial carbon cycle “AusEFlux” (Australian Empirical Fluxes).

THE EFFECT OF HARBOR DEVELOPMENTS ON HIGH-TIDE FLOODING IN MIAMI (FL)

We develop and validate a tidal-inference based methodology that leverages estimates of historical tidal ranges to obtain historical tidal predictions and constituents. Next, the method is applied to a case study in Miami (Florida), where mean tidal range has more than doubled since 1900, from 0.32 m to 0.66 m today. Water level predictions that represent historical and modern water level variations are projected forward in time using different sea level rise (SLR) scenarios, to see how the historical increase in tidal range affects the occurrence of present-day and future high-tide flooding. To this end, we apply the method of Li et al., (2021), which implies to remove tidal oscillations from present-day water levels and replace them with constant tidal prediction, obtained with tidal constituents representing historic conditions. This creates a synthetic time series that approximates what water levels would have been, had tides not evolved. Possible future water levels are estimated for each SLR scenario for (a) a “tide-only” scenario, using tidal oscillation on top of SLR and accounting for average seasonal cycle of mean sea level (MSL); and (b) a “total water level” approach that includes SLR, non-tidal fluctuations and seasonal sea level. While the first approach neglects relevant forcing (e.g., inter-annual to decadal MSL variability, river discharge, storm surge), it enables us to isolate the contribution of tides to HTF and provides useful metrics for quantifying the effects that tides and SLR have on coastal flooding.

A seasonal OH minimum region over the Indian Ocean?

As the hydroxyl radical (OH) is the cleansing agent of the atmosphere, reduction in its concentration is a great concern for air quality and transport of trace gases across the latitudes and altitudes. In addition, OH determines the lifetime of most trace gases and non-CO2 greenhouse gases in the atmosphere. Since many pollutants have adverse health effects and are greenhouse gases, the changes in OH concentrations directly or indirectly affect public health and climate. Our analysis with OH data (from Tropospheric Emission Spectrometer and Copernicus Atmosphere Monitoring Service reanalyses) for the past 14 (2005–2018) years finds an OH minimum region over Indian Ocean, in the eastern Bay of Bengal at 6°–14° N and 92°–95° E. The Indian Ocean OH minimum reaches to 15 × 104 molec.cm−3 in April, and a secondary minimum of 18 × 104 molec.cm−3 in September–November. This seasonal minimum found around the Andaman and Nicobar Islands also hosts an active volcano, which releases noticeable amount of SO2 (OH sink) throughout the year. In addition, the biomass burning in spring and thus, the distribution of CO has a profound influence on the OH distribution in this region as Southeast Asia is one of the global hotspots of biomass burning, and the Indian Ocean OH minimum is located near that region. The El Niño and La Niño events also control the tropospheric ozone and CO distribution, and thus the interannual variability of OH minimum there. The WACCM model simulations reproduce the general latitudinal distribution and average seasonal cycle of OH, but not the extreme minimum OH values, although the simulations show the annual minimum in winter (December–January) in both ocean regions, which demands dedicated studies using chemical transport models. Therefore, this study reveals a seasonal OH minimum over Indian Ocean, which is very likely to influence the regional air quality and trace gas transport in the tropics.

Variance of the Equatorial Atmospheric Circulations in the Reanalysis

The structure of the equatorial atmospheric circulation, as defined by the zonal mass streamfunction (ZMS), computed using the new fifth-generation ECMWF reanalysis for the global climate and weather (ERA-5) and the National Centers for Environmental Prediction NCEP–US Department of Energy reanalysis (NCEP-2) reanalysis products, is investigated and compared with Coupled Model Intercomparison Project Phase 6 (CMIP 6) ensemble mean. The equatorial atmospheric circulations majorly involve three components: the Indian Ocean cell (IOC), the Pacific Walker cell (POC) and the Atlantic Ocean cell (AOC). The IOC, POC and AOC average monthly or seasonal cycle peaks around March, June and February, respectively. ERA-5 has a higher IOC intensity from February to August, whereas NCEP-2 has a greater IOC intensity from September to December; NCEP-2 indicates greater POC intensity from January to May, whereas ERA-5 shows higher POC intensity from June to October. For the AOC, ERA-5 specifies greater intensity from March to August and NCEP-2 has a higher intensity from September to December. The equatorial atmospheric circulations cells vary in the reanalysis products, the IOC is weak and wider (weaker and smaller) in the ERA-5 (NCEP-2), the POC is more robust and wider (feebler and teensier) in NCEP-2 (ERA-5) and the AOC is weaker and wider (stronger and smaller) in ERA-5 (NCEP-2). ERA-5 revealed a farther westward POC and AOC compared to NCEP-2. In the CMIP 6 model ensemble mean (MME), the equatorial atmospheric circulations mean state indicated generally weaker cells, with the IOC smaller and the POC greater swinging eastward and westward, respectively, while the AOC is more westward. These changes in equatorial circulation correspond to changes in dynamically related heating in the tropics.

Analysis of atmospheric greenhouse gases in north Xinjiang

Flask air samples were collected weekly at the Akedala (AKD) regional atmosphere station in north Xinjiang, China, and atmospheric carbon dioxide (CO2) and methane (CH4) mole fractions were measured using cavity-ring down spectroscopy. AKD station is under a typical temperate continental semiarid and arid monsoon climate with westerly air flow. This paper presents the long-term trends and seasonal variations in atmospheric CO2 and CH4 concentrations for the first time and discusses the possible influencing mechanisms. The CO2 and CH4 background mixing ratios were 412.1 ± 0.36 ppm and 1968.9 ± 0.95 ppb, respectively, in 2018. The growth rates were 2.62 ppm y−1 and 9.67 ppb y−1 for CO2 and CH4, respectively, during 2010–2018, which were higher than the surface measurements at Waliguan Baseline Observatory at similar latitudes. The averaged CO2 seasonal cycle showed a minimum in summer and a maximum in winter, and the annual amplitude was high at 18.7 ppm. The CH4 seasonal cycle showed a rapid recovery in summer during July and August. Back-trajectory analysis showed that the atmospheric information at AKD station can provide some implications for uptake from Kazakhstan and Russia, as well as information about natural and anthropogenic emissions in northern Xinjiang. This study revealed that the long-term upward trend of the CO2 concentration in AKD might be attributed to emissions from energy consumption of heavy industry. The comprehensive effects of livestock, geological micro seepage and oil and gas production, and vertical transport also enhanced the atmospheric CH4 concentrations. The results of this study may improve our basic understanding of observed atmospheric CO2 and CH4 in China and Central Asia and the underlying fluxes to be used in atmospheric inversion models.

Evaluating the performance of CMIP6 Earth system models in simulating global vegetation structure and distribution

Evaluation of vegetation structure and distribution simulations in Earth system models (ESMs) is the basis for understanding historical reconstruction and future projection of changes in terrestrial ecosystems, carbon cycle, and climate based on these ESMs. Such assessments can also provide important information of models’ merits and shortcomings or systematic biases, and so clues for model development. Vegetation structure and distribution in ESMs are primarily characterized by three variables: leaf area index (LAI), tree height, and fractional coverage of plant functional type (PFT). However, for the ongoing Coupled Model Intercomparison Project Phase 6 (CMIP6), only temporal variabilities of global-averaged LAI time series were evaluated, others remain largely uninvestigated. This study systematically investigates the spatial and/or temporal variability of the three critical variables from 27 ESMs in CMIP6 using satellite observations. Our results show that all models and the multi-model ensemble mean (MME) can generally reproduce the observed LAI spatial pattern but all of them overestimate the global mean LAI mainly due to overestimation of LAI in non-forested vegetated areas. Most CMIP6 models fail to capture the temporal variability in the annual LAI because of large biases in both the simulated trend magnitude and temporal pattern of interannual variability. The average LAI seasonal cycles in different latitude zones are roughly reproduced by the models, but 1–2 months delays in the LAI peak appear in the Arctic-boreal zone. Additionally, CMIP6 models overall overestimate tree height, and largely overestimate the global grass area but underestimate tree and shrub areas, especially in the middle and high latitudes. It should be kept in mind that such biases may have further impacts on the simulations of the related carbon and land–atmosphere interaction variables (e.g., ecosystem production, carbon storage, transpiration, and temperature) for global change research. Hence, bias-correction should be made to improve reliability of vegetation structure and distribution when future projections and historical reconstructions. They also underscore the urgent need in development and parameterization of dynamic vegetation within Earth system models, such as phenology, allocation, and morphology schemes.

A mass-weighted isentropic coordinate for mapping chemical tracers and computing atmospheric inventories

Abstract. We introduce a transformed isentropic coordinate Mθe, defined as the dry air mass under a given equivalent potential temperature surface (θe) within a hemisphere. Like θe, the coordinate Mθe follows the synoptic distortions of the atmosphere but, unlike θe, has a nearly fixed relationship with latitude and altitude over the seasonal cycle. Calculation of Mθe is straightforward from meteorological fields. Using observations from the recent HIAPER Pole-to-Pole Observations (HIPPO) and Atmospheric Tomography Mission (ATom) airborne campaigns, we map the CO2 seasonal cycle as a function of pressure and Mθe, where Mθe is thereby effectively used as an alternative to latitude. We show that the CO2 seasonal cycles are more constant as a function of pressure using Mθe as the horizontal coordinate compared to latitude. Furthermore, short-term variability in CO2 relative to the mean seasonal cycle is also smaller when the data are organized by Mθe and pressure than when organized by latitude and pressure. We also present a method using Mθe to compute mass-weighted averages of CO2 on a hemispheric scale. Using this method with the same airborne data and applying corrections for limited coverage, we resolve the average CO2 seasonal cycle in the Northern Hemisphere (mass-weighted tropospheric climatological average for 2009–2018), yielding an amplitude of 7.8 ± 0.14 ppm and a downward zero-crossing on Julian day 173 ± 6.1 (i.e., late June). Mθe may be similarly useful for mapping the distribution and computing inventories of any long-lived chemical tracer.

Верификация данных ре-анализов для тропической зоны Индийского океана. Часть 2. Характеристики осредненного сезонного цикла и межгодовой изменчивости

The aim of the study is to evaluate the performance of ORAS5/SODA3/GLORYS re-analyses using the RAMA in the tropical Indian Ocean. To assess the reproducibility of the seasonal cycle and characteristics of interannual variability, we used the data on the potential temperature, salinity, and zonal component of the current vector obtained south of the equator for the period 2010–2014. It is shown that at 55°E south of the equator, the GLORYS re-analysis better reproduces the five-year averaged seasonal cycle and interannual variability than the SODA3 and ORAS5 re-analyses.

West African Summer Monsoon Precipitation Variability as Represented by Reanalysis Datasets

Focusing on West Africa, a region riddled with in situ data scarcity, we evaluate the summer monsoon monthly rainfall characteristics of five global reanalysis datasets: ERA5, ERA-Interim, JRA-55, MERRA2, and NCEP-R2. Their performance in reproducing the West African monsoon (WAM) climatology, interannual variability, and long-term trends for the main monsoon months are compared to gauge-only and satellite products. We further examine their ability to reproduce teleconnections between sea surface temperatures and monsoon rainfall. All reanalyses are able to represent the average rainfall patterns and seasonal cycle; however, regional biases can be marked. ERA5, ERA-Interim, and NCEP-R2 underestimate rainfall over areas of peak rainfall, with ERA5 showing the strongest underestimation, particularly over the Guinea Highlands. The meridional northward extent of the monsoon rainband is well captured by JRA-55 and MERRA2 but is too narrow in ERA-Interim, for which rainfall stays close to the Guinea Coast. Differences in rainband displacement become particularly evident when comparing strong El Niño Southern Oscillation (ENSO) years, where all reanalyses except ERA-Interim reproduce wetter Sahelian conditions for La Niña, while overestimating dry conditions at the coast except for NCEP-R2. Precipitation trends are not coherent across reanalyses and magnitudes are generally overestimated compared to observations, with only JRA-55 and NCEP-R2 displaying the expected positive trend in the Sahel. ERA5 generally outperforms ERA-Interim, highlighting clear improvements over its predecessor. Ultimately, we find the strengths of reanalyses to strongly vary across the region.

Particle Fluxes at the Australian Southern Ocean Time Series (SOTS) Achieve Organic Carbon Sequestration at Rates Close to the Global Median, Are Dominated by Biogenic Carbonates, and Show No Temporal Trends Over 20-Years

Particle fluxes at the Southern Ocean time series (SOTS) site in the Subantarctic zone (SAZ) south of Australia (~47°S, 142°E, 4600m water depth) were collected from 1997 – 2017 using moored sediment traps at nominal depths of 1000m, 2000m and 3800m. Annually integrated mass fluxes showed moderate variability of 14 ± 6 g m-2 yr-1 at 1000m, 20 ± 6 g m-2 yr-1 at 2000m and 21 ± 4 g m-2 yr-1 at 3800m. Particulate organic carbon (POC) fluxes were similar to the global median, indicating that the Subantarctic Southern Ocean exports considerable amounts of carbon to the deep sea despite its high-nutrient, low chlorophyll characteristics. The interannual flux variations were larger than those of net primary productivity as estimated from satellite observations. Particle compositions were dominated by carbonate minerals (> 60% at all depths), opal (~10% at all depths), and particulate organic matter (~17% at 1000m, decreasing to ~10% at 3800m), with seasonal and interannual variability much smaller than for their flux magnitudes. The carbonate counter-pump effect reduced carbon sequestration by ~8±2%. The average seasonal cycle at 1000m had a two-peak structure, with a larger early spring peak (October/November) and a smaller late summer (January/February) peak. At the two deeper traps, these peaks became less distinct with a greater proportion of the fluxes arriving in autumn. Singular value decomposition (SVD) shows that this temperate seasonal structure accounts for ~80% of the total variance (SVD Mode 1), but also that its influence varies significantly relative to Modes 2 and 3 which describe changes in seasonal timings. This occurrence of significant interannual variability in seasonality yet relatively constant annual fluxes, is likely to be useful in selecting appropriate models for the simulation of environmental-ecological coupling and its role in controlling the biological carbon pump. No temporal trends were detected in the mass or component fluxes, or in the time series of the SVD Modes. The SOTS observations provide an important baseline for future changes expected to result from warming, stratification, and acidification in this globally significant region.

Seasonal cycles in baseline mixing ratios of a large number of trace gases at the Mace Head, Ireland atmospheric research station

Abstract A flexible approach for quantifying average long-term changes and seasonal cycles has been applied to the baseline mixing ratios of thirty-two trace gases monitored at the Atmospheric Research Station located at Mace Head, Ireland on the Atlantic Ocean coastline of Europe. Almost all these trace gases exhibit minima in their baseline seasonal cycles during a two-month period from mid-July to the end of September and maxima during a six-month period between November and May. Relative seasonal cycle amplitudes are largest for those trace gases with the shortest atmospheric lifetimes and smallest for the longest. Robust relationships were found between the timings of the seasonal maxima and minima and their relative amplitudes with the atmospheric lifetimes of the trace gases. These relationships provide a basis for understanding the reasons that the seasonal cycles of a few species (that is, H2, CCl4 and the longest-lived gases) deviate significantly; they also have excellent potential as tools to assess global chemistry-transport model performance.

Flexible approach for quantifying average long-term changes and seasonal cycles of tropospheric trace species

Abstract. We present an approach for deriving a systematic mathematical representation of the statistically significant features of the average long-term changes and seasonal cycle of concentrations of trace tropospheric species. The results for two illustrative data sets (time series of baseline concentrations of ozone and N2O at Mace Head, Ireland) indicate that a limited set of seven or eight parameter values provides this mathematical representation for both example species. This method utilizes a power series expansion to extract more information regarding the long-term changes than can be provided by oft-employed linear trend analyses. In contrast, the quantification of average seasonal cycles utilizes a Fourier series analysis that provides less detailed seasonal cycles than are sometimes represented as 12 monthly means; including that many parameters in the seasonal cycle representation is not usually statistically justified, and thereby adds unnecessary “noise” to the representation and prevents a clear analysis of the statistical uncertainty of the results. The approach presented here is intended to maximize the statistically significant information extracted from analyses of time series of concentrations of tropospheric species, regarding their mean long-term changes and seasonal cycles, including nonlinear aspects of the long-term trends. Additional implications, advantages and limitations of this approach are discussed.

The Spatiotemporal Variability of pH in Waters of the Black Sea

Based on archival data of the Institute of Natural and Technical Systems for the period from 1956 to 2010, the large-scale structure, seasonal variability, and long-term trends of pH variations in the upper 150-m layer of the deep-water part of the Black Sea were analyzed. The spatial climatic inhomogeneities of pH in the surface layer of the open part of the Black Sea are about 0.06 pH units; the magnitude of the average seasonal cycle is 0.05 pH units. In the deep-water part of the sea, low values of pH are observed in the vicinity of the centers of cyclonic gyres, where more acidic waters move upward. A long-term increase in the acidity of waters of the surface layer is observed. Thetrend of pH in this layer is generally conditioned by the increase in the CO2 content in the lower troposphere and absorption of part of the excess CO2 by sea water. The increase in the acidity manifests itself in the decrease in pH (at a level of –0.06 pH units per 50 years) in waters of the surface layer. This is close to the estimations for other regions of the World Ocean. However, the intermediate waters are characterized by a negative pH trend, the absolute value of which exceeds more than fivefold the pH trend on the surface. A likely reason for the intense decrease in pH here is the intensification long-term vertical circulation in the intermediate layer of the Black Sea waters, resulting in the ascension of more acidic waters with a typical velocity of ~1 m per year. This can lead to more intense acidification of surface waters in the sea over the next 10 years.

Evaluation and Analysis of the Seasonal Cycle and Variability of the Trend from GOSAT Methane Retrievals

Methane ( CH 4 ) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane ( XCH 4 ). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43 ∘ S and 53.13 ∘ N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4 . These differences are linked to the regional CH 4 sources and sinks, and call for further research.

Fram Strait sea ice export affected by thinning: comparing high-resolution simulations and observations

Variability and trends of Fram Strait sea ice area and volume exports are examined for the period of 1990–2010. Simulations from a high-resolution version of the MPIOM model (STORM project) reproduce area and volume export well when compared with NSIDC and ICESat satellite data and in-situ ice thickness observations. The fluxes derived from ice thickness and drift satellite products vary considerably, indicating a high uncertainty in these estimates which we mostly assign to the drift observations. The model captures the observed average seasonal cycles and interannual variability of ice export. The simulated mean annual sea ice area export is 860 × 103 km2 a− 1 (1990–2010), and the correlation with the NSIDC-based area fluxes is r = 0.67. The simulated mean annual volume export is 3.3 × 103 km3 a− 1 (1990–2010), close to the ICESat/ULS values, with a correlation of r = 0.58. The simulated monthly area export has a significant positive trend of + 10% per decade, explained by wind forcing. The major contribution to the robust trend in area export between June and September. Fram Strait ice volume export variability is mainly controlled by ice drift with a dominant role of the Transpolar Drift and, to a lesser extent thickness variability. The area export increase reflects increasing ice-drift speed, but is balanced with a reduced thickness over time when it comes to volume export, giving no significant trend in volume export. The spatial variability of ice drift indicates that the export influences a large area upstream in the Trans-Polar Drift stream, and that high volume export events lead to a thinner thickness there. The central Arctic is well connected drift-wise to the Fram Strait via the Transpolar Drift while for thickness, the region north of Greenland is dominated and controlled by the Fram Strait ice export.

Sub-seasonal prediction skill for the stratospheric meridional mass circulation variability in CFSv2

This study evaluates the prediction skill for the stratospheric mass circulation variability in winter (November–March) in the Climate Forecast System, version 2 (CFSv2), from 2011 to 2018. Three stratospheric mass circulation indices measuring meridional mass transport into the polar stratosphere by the total flow (ST60N), wavenumber-1 (ST60N_W1), and wavenumber-2 waves (ST60N_W2) are considered. The variability of ST60N is mainly contributed by ST60N_W1 and ST60N_W2, and these indices are good indicators of the timing and locations of the continental-scale cold-air outbreaks (CAOs) at mid-latitudes. Thus, their potentially useful prediction skill can be utilized to make sub-seasonal forecasts of CAOs in a dynamical and statistical hybrid paradigm. Systematic forecast bias is found in both the 7-year averaged winter mean and seasonal cycle, which is tied to the overestimation of damping in amplitude and westward tilting variations of total waves and difficulties in forecasting the exact contributions from different spatial scales of waves. The intraseasonal variations of stratospheric mass circulation indices, with the systematic forecast bias corrected, can be modestly predicted at a forecast lead time of about 20 days, in terms of both the anomaly value and timing of negative and positive peak events. The sub-seasonal predictability of the ST60N value mainly comes from the ST60N_W1, while the predictability of the timing of positive and negative peaks comes from the ST60N_W2. The 20-day prediction limit of the stratospheric mass circulation indices is mainly due to the 2-week limit of the CFSv2 model in predicting the variability of anomalous wave tilt angle, whereas the prediction limit of the wave amplitude anomaly can exceed 50 days.

Atmospheric CO2 and δ13C Measurements from 2012 to 2014 at the Environmental Research Station Schneefernerhaus, Germany: Technical Corrections, Temporal Variations and Trajectory Clustering

ABSTRACT This study presents continuous atmospheric CO2 and δ13C measurements by wavelength-scanned cavity ring down spectroscopy (Picarro G1101-i) at the high-mountain station Schneefernerhaus, Germany. δ13C values were post-corrected for methane and water spectral interferences using accompanying measurements of CH4 and H2O, and CO2 in dried air, respectively. The best precision of ±0.2‰ for δ13C and of ±4 ppb for CO2 was obtained with an integration time of about 1 hour for δ13C and 2 hours for CO2. The seasonality of CO2 and δ13C was studied by fitting background curves for a complete 2-year period. Peak-to-peak amplitudes of the averaged seasonal cycle were 15.5 ± 0.15 ppm for CO2 and 1.97 ± 0.53‰ for δ13C, respectively. Based on the HYSPLIT Model, air masses were classified into five clusters, with westerly and northeasterly flows being the most and the least frequent, respectively. In the wintertime, northwest and northeast clusters had a higher median level for ΔCO2 and a lower median level for Δδ13C (the difference between observed and background concentrations), likely caused by anthropogenic emissions. In the summertime, air masses from the northwest had the lowest ΔCO2 and the highest Δδ13C. Potential source contribution functions (PSCFs) were used to identify the potential source and sink areas. In winter, source areas for high CO2 mixing ratios (> 75th percentile) were mainly located in northwestern Europe. In summer, areas with high δ13C ratios (> 75th percentile), indicating a carbon sink, were observed in the air from Eastern and Central Poland.

SURATLANT: a 1993–2017 surface sampling in the central part of the North Atlantic subpolar gyre

Abstract. This paper presents the SURATLANT data set (SURveillance ATLANTique). It consists of individual data of temperature, salinity, parameters of the carbonate system, nutrients, and water stable isotopes (δ18O and δD) collected mostly from ships of opportunity since 1993 along transects between Iceland and Newfoundland (https://doi.org/10.17882/54517). We discuss how the data are validated and qualified, their accuracy, and the overall characteristics of the data set. The data are used to reconstruct seasonal cycles and interannual anomalies, in particular of sea surface salinity (SSS); inorganic nutrients; dissolved inorganic carbon (DIC); and its isotopic composition δ13CDIC, total alkalinity (At), and water isotope concentrations. Derived parameters such as fCO2 and pH are also estimated. The relation between salinity and At is estimated from these data to investigate the possibility to replace missing At when estimating other parameters of the carbonate system. When examining the average seasonal cycle in the deep ocean, in both these data with other climatologies, we find a period of small seasonal change between January and late April. On the Newfoundland shelf and continental slope, changes related with spring stratification and blooms occur earlier. The data were collected in a period of multi-decennial variability associated with the Atlantic multi-decadal variability with warming between 1994 and 2004–2007, and with the recent cooling having peaked in 2014–2016. We also observe strong salinification in 2004–2009 and fresher waters in 1994–1995 as well as since 2010 south of 54° N and in 2016–2017 north of 54° N. Indication of multi-decadal variability is also suggested by other variables, such as phosphate or DIC, but cannot be well resolved seasonally with the discrete sampling and in the presence of interannual variability. As a whole, over the 24 years, the ocean fCO2 trend (+1.9 µatm yr−1) is close to the atmospheric trend and associated with an increase in DIC (+0.77 µmol kg−1 yr−1). The data also revealed a canonical pH decrease of −0.0021 yr−1. There is also a decrease in δ13CDIC between 2005 and 2017 (in winter, −0.014 ‰ yr−1, but larger in summer, −0.042 ‰ yr−1), suggesting a significant anthropogenic carbon signal at play together with other processes (mixing, biological activity).

Productivity and Temperature as Drivers of Seasonal and Spatial Variations of Dissolved Methane in the Southern Bight of the North Sea

Dissolved CH4 concentrations in the Belgian coastal zone (North Sea) ranged between 670 nmol l−1 nearshore and 4 nmol l−1 offshore. Spatial variations of CH4 were related to sediment organic matter (OM) content and gassy sediments. In nearshore stations with fine sand or muddy sediments, the CH4 seasonal cycle followed water temperature, suggesting methanogenesis control by temperature in these OM-rich sediments. In offshore stations with permeable sediments, the CH4 seasonal cycle showed a yearly peak following the chlorophyll-a spring peak, suggesting that in these OM-poor sediments, methanogenesis depended on freshly produced OM delivery. This does not exclude the possibility that some CH4 might originate from dimethylsulfide (DMS) or dimethylsulfoniopropionate (DMSP) or methylphosphonate transformations in the most offshore stations. Yet, the average seasonal CH4 cycle was unrelated to those of DMS(P), very abundant during the Phaeocystis bloom. The annual average CH4 emission was 126 mmol m−2 y−1 in the most nearshore stations (~4 km from the coast) and 28 mmol m−2 y−1 in the most offshore stations (~23 km from the coast), 1260–280 times higher than the open ocean average value (0.1 mmol m−2 y−1). The strong control of CH4 by sediment OM content and by temperature suggests that marine coastal CH4 emissions, in particular in shallow areas, should respond to future eutrophication and warming of climate. This is supported by the comparison of CH4 concentrations at five stations obtained in March 1990 and 2016, showing a decreasing trend consistent with alleviation of eutrophication in the area.