scholarly journals Spatial distributions of <i>X</i><sub>CO<sub>2</sub></sub> seasonal cycle amplitude and phase over northern high-latitude regions

2021 ◽  
Vol 21 (22) ◽  
pp. 16661-16687
Author(s):  
Nicole Jacobs ◽  
William R. Simpson ◽  
Kelly A. Graham ◽  
Christopher Holmes ◽  
Frank Hase ◽  
...  
Keyword(s):  
High Latitude ◽  
Seasonal Cycle ◽  
Atmospheric Transport ◽  
Co2 Flux ◽  
Latitudinal Gradients ◽  
Seasonal Cycles ◽  
Spatial Distributions ◽  
Surface Contact ◽  
Northern High Latitude ◽  
Data Coverage

Abstract. Satellite-based observations of atmospheric carbon dioxide (CO2) provide measurements in remote regions, such as the biologically sensitive but undersampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO2 (XCO2) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high-latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of satellite-observed XCO2 seasonal cycles across a majority of terrestrial northern high-latitude regions. Here, we present an analysis of XCO2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5∘ latitude by 20∘ longitude zones. We quantify the seasonal cycle amplitudes (SCAs) and the annual half drawdown day (HDD). OCO-2 SCAs are in good agreement with ground-based observations at five high-latitude sites, and OCO-2 SCAs show very close agreement with SCAs calculated for model estimates of XCO2 from the Copernicus Atmosphere Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1 and the CarbonTracker2019 model (CT2019B). Model estimates of XCO2 from the GEOS-Chem CO2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 (GC-CT2019) yield SCAs of larger magnitude and spread over a larger range than those from CAMS, CT2019B, or OCO-2; however, GC-CT2019 SCAs still exhibit a very similar spatial distribution across northern high-latitude regions to that from CAMS, CT2019B, and OCO-2. Zones in the Asian boreal forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. In northern high-latitude regions, spanning latitudes from 47 to 72∘ N, longitudinal gradients in both SCA and HDD are at least as pronounced as latitudinal gradients, suggesting a role for global atmospheric transport patterns in defining spatial distributions of XCO2 seasonality across these regions. GEOS-Chem surface contact tracers show that the largest XCO2 SCAs occur in areas with the greatest contact with land surfaces, integrated over 15–30 d. The correlation of XCO2 SCA with these land surface contact tracers is stronger than the correlation of XCO2 SCA with the SCA of CO2 fluxes or the total annual CO2 flux within each 5∘ latitude by 20∘ longitude zone. This indicates that accumulation of terrestrial CO2 flux during atmospheric transport is a major driver of regional variations in XCO2 SCA.

scholarly journals Spatial distributions of <i>X</i><sub><i>CO</i><sub>2</sub></sub> seasonal cycle amplitude and phase over northern high latitude regions

2021 ◽  
Author(s):  
Nicole Jacobs ◽  
William R. Simpson ◽  
Kelly A. Graham ◽  
Christopher Holmes ◽  
Frank Hase ◽  
...  
Keyword(s):  
High Latitude ◽  
Seasonal Cycle ◽  
Atmospheric Carbon Dioxide ◽  
Atmospheric Transport ◽  
Co2 Flux ◽  
Seasonal Cycles ◽  
Longitudinal Gradients ◽  
Northern High Latitude ◽  
Data Coverage ◽  
Carbon Dioxide Co2

Abstract. Satellite-based observations of atmospheric carbon dioxide (CO2) provide measurements in remote regions, such as the biologically sensitive but under sampled northern high latitudes, and are progressing toward true global data coverage. Recent improvements in satellite retrievals of total column-averaged dry air mole fractions of CO2 (XCO2) from the NASA Orbiting Carbon Observatory 2 (OCO-2) have allowed for unprecedented data coverage of northern high latitude regions, while maintaining acceptable accuracy and consistency relative to ground-based observations, and finally providing sufficient data in spring and autumn for analysis of the satellite-observed XCO2 seasonal cycles across a majority of terrestrial northern high latitude regions. Here, we present an analysis of XCO2 seasonal cycles calculated from OCO-2 data for temperate, boreal, and tundra regions, subdivided into 5° latitude by 20° longitude zones. We quantify the seasonal cycle amplitudes (SCA) and the annual half drawdown day (HDD). OCO-2 SCA is in good agreement with ground-based observations at five high latitude sites and OCO-2 SCA show very close agreement with SCA calculated for model estimates of XCO2 from the Copernicus Atmospheric Monitoring Services (CAMS) global inversion-optimized greenhouse gas flux model v19r1. Model estimates of XCO2 from the GEOS-Chem CO2 simulation version 12.7.2 with underlying biospheric fluxes from CarbonTracker2019 yield SCA of larger magnitude and spread over a larger range than those from CAMS and OCO-2; however, GEOS-Chem SCA still exhibit a very similar spatial distribution across northern high latitude regions to that from CAMS and OCO-2. Zones in the Asian Boreal Forest were found to have exceptionally large SCA and early HDD, and both OCO-2 data and model estimates yield a distinct longitudinal gradient of increasing SCA from west to east across the Eurasian continent. Longitudinal gradients in both SCA and HDD are at least as pronounced as meridional gradients (with respect to latitude), suggesting an essential role for global atmospheric transport patterns in defining XCO2 seasonality. GEOS-Chem surface contact tracers show that the largest XCO2 SCA occurs in areas with the greatest contact with land surfaces, integrated over 15–30 days. The correlation of XCO2 SCA with these land contact tracers are stronger than the correlation of XCO2 SCA with the SCA of CO2 fluxes within each 5° latitude by 20° longitude zone. This indicates that accumulation of terrestrial CO2 flux during atmospheric transport is a major driver of regional variations in XCO2 SCA.

Influence of spatial coherence of temperature anomalies on the supposed breakdown of the warmer spring – larger carbon uptake mechanism in northern high latitudes

2020 ◽  
Author(s):  
Peter Joyce ◽  
Manuel Gloor ◽  
Roel Brienen ◽  
Wolfgang Buermann
Keyword(s):  
High Latitude ◽  
Large Scale ◽  
Atmospheric Transport ◽  
Carbon Uptake ◽  
High Latitudes ◽  
Spring Temperature ◽  
Uptake Mechanism ◽  
Temperature Anomalies ◽  
Northern High Latitude ◽  
Significant Change

&lt;p&gt;Land vegetation growth in the northern high latitudes (north of 50&amp;#730;N) is strongly temperature limited, thus anomalously warm years are expected to result in an increased drawdown of Carbon Dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) and vice versa. Piao et al (2017) concluded in an analysis of climate and CO&lt;sub&gt;2&lt;/sub&gt; data from Point Barrow, Alaska that there was a weakening response of northern high latitude spring carbon uptake to temperature anomalies over the last 40 years. They proposed that this is due to a weakening control of temperature on productivity. We have analysed northern high latitude climate and remote sensing vegetation indices, as well as atmospheric CO&lt;sub&gt;2&lt;/sub&gt; data at Point Barrow, with atmospheric transport analyses of the footprint seen at Barrow. Our results show no large-scale significant change in the spring NDVI-temperature relationship inside the footprint of Barrow, and across the high northern latitudes as a whole. This casts doubt on the assertion that the changing relationship between CO&lt;sub&gt;2&lt;/sub&gt; uptake and temperature is driven by a change in vegetation response to temperature. We thus tested several alternative mechanisms that could explain the apparent weakening, including a change in interannual variability of atmospheric transport (i.e. the footprint seen by Barrow) and the spatial agreement of temperature anomalies. We find that the heterogeneity of temperature anomalies increased over time, whereas there is no significant change in interannual variation in the footprint seen by Barrow. These results offer an additional explanation for the apparent decrease in spring temperature sensitivity of northern high latitude CO&lt;sub&gt;2&lt;/sub&gt; uptake.&lt;/p&gt;

scholarly journals A segmentation algorithm for characterizing Rise and Fall segments in seasonal cycles: an application to XCO<sub>2</sub> to estimate benchmarks and assess model bias

2018 ◽  
Author(s):  
Leonardo Calle ◽  
Benjamin Poulter ◽  
Prabir K. Patra
Keyword(s):  
Time Series ◽  
Seasonal Cycle ◽  
Terrestrial Ecosystem ◽  
Segmentation Algorithm ◽  
Latitudinal Gradients ◽  
Seasonal Cycles ◽  
Inversion Point ◽  
Model Biases ◽  
Segmentation Analysis ◽  
Signal Characteristics

Abstract. There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as non-stationary changes in signal characteristics – amplitude, period, and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each Rise (increasing) and Fall (decreasing) segment, in the seasonal cycle is visually discernable in a graph of the time series. The Rise and Fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment’s signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the Rise and Fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions on the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10˚ degrees of latitude for the satellite data, with similar gradients for Rise and Fall segments. This translates to a south-north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in-situ CO2 data (~ 19 ppm). The latitudinal gradients in period of the satellite-derived seasonal cycles were of opposing sign and magnitude (-9 days/10˚ latitude for Fall segments, and 10 days/10˚ latitude for Rise segments), and suggests that a specific latitude (~ 2˚ N) exists which defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of Rise segments are less (greater) than the periods of Fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and period between Rise and Fall segments introduces a novel pattern in seasonal cycle analyses, but while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period, and phase, independently for Rise and Fall segments. We offer an extended discussion on how such information on model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

scholarly journals Role of emission sources and atmospheric sink on the seasonal cycle of CH<sub>4</sub> and δ<sup>13</sup>-CH<sub>4</sub>: analysis based on the atmospheric chemistry transport model TM5

2021 ◽  
Author(s):  
Vilma Kangasaho ◽  
Aki Tsuruta ◽  
Leif Backman ◽  
Pyry Mäkinen ◽  
Sander Houweling ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Seasonal Cycle ◽  
Oil And Gas ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Seasonal Cycles ◽  
Enteric Fermentation ◽  
Niwot Ridge ◽  
Straight Lines ◽  
Spatially Varying

Abstract. This study investigates the contribution of different CH4 sources to the seasonal cycle of 𝛿13C during years 2000–2012 using the TM5 atmospheric transport model. The seasonal cycles of anthropogenic emissions from two versions of the EDGAR inventories, v4.3.2 and v5.0 are examined. Those includes emissions from Enteric Fermentation and Manure Management (EFMM), rice cultivation and residential sources. Those from wetlands obtained from LPX-Bern v1.4 are also examined in addition to other sources such as fires and ocean sources. We use spatially varying isotopic source signatures for EFMM, coal, oil and gas, wetlands, fires and geological emission and for other sources a global uniform value. We analysed the results as zonal means for 30° latitudinal bands. Seasonal cycles of 𝛿13C are found to be an inverse of CH4 cycles in general, with a peak-to-peak amplitude of 0.07–0.26 ‰. However, due to emissions, the phase ellipses do not form straight lines, and the anti-correlations between CH4 and 𝛿13C are weaker (−0.35 to −0.91) in north of 30° S. We found that wetland emissions are the dominant driver in the 𝛿13C seasonal cycle in the Northern Hemisphere and Tropics, such that the timing of 𝛿13C seasonal minimum is shifted by ∼90 days in 60° N–90° N from the end of the year to the beginning of the year when seasonality of wetland emissions is removed. The results also showed that in the Southern Hemisphere Tropics, emissions from fires contribute to the enrichment of 𝛿13C in July–October. In addition, we also compared the results against observations from the South Pole, Antarctica, Alert, Nunavut, Canada and Niwot Ridge, Colorado, USA. In light of this research, comparison to the observation showed that the seasonal cycle of EFMM emissions in EDGAR v5.0 inventory is more realistic than in v4.3.2. In addition, the comparison at Alert showed that modelled 𝛿13C amplitude was approximately half of the observations, mainly because the model could not reproduce the strong depletion in autumn. This indicates that CH4 emission magnitude and seasonal cycle of wetlands may need to be revised. Results from Niwot Ridge indicate that in addition to biogenic emissions, the proportion of biogenic to fossil based emissions may need to be revised.

scholarly journals A segmentation algorithm for characterizing rise and fall segments in seasonal cycles: an application to XCO<sub>2</sub> to estimate benchmarks and assess model bias

2019 ◽  
Vol 12 (5) ◽  
pp. 2611-2629 ◽  
Author(s):  
Leonardo Calle ◽  
Benjamin Poulter ◽  
Prabir K. Patra
Keyword(s):  
Time Series ◽  
Seasonal Cycle ◽  
Terrestrial Ecosystem ◽  
Segmentation Algorithm ◽  
Latitudinal Gradients ◽  
Seasonal Cycles ◽  
Inversion Point ◽  
Model Biases ◽  
Segmentation Analysis ◽  
Signal Characteristics

Abstract. There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.

scholarly journals Carbon and nutrient mixed layer dynamics in the Norwegian Sea

2008 ◽  
Vol 5 (5) ◽  
pp. 1395-1410 ◽  
Author(s):  
H. S. Findlay ◽  
T. Tyrrell ◽  
R. G. J. Bellerby ◽  
A. Merico ◽  
I. Skjelvan
Keyword(s):  
High Latitude ◽  
Co2 Flux ◽  
Ecosystem Model ◽  
Seasonal Cycles ◽  
Surface Layers ◽  
Carbonate System ◽  
Saturation State ◽  
Norwegian Sea ◽  
Calcite Saturation ◽  
Time Of Year

Abstract. A coupled carbon-ecosystem model is compared to recent data from Ocean Weather Station M (66° N, 02° E) and used as a tool to investigate nutrient and carbon processes within the Norwegian Sea. Nitrate is consumed by phytoplankton in the surface layers over the summer; however the data show that silicate does not become rapidly limiting for diatoms, in contrast to the model prediction and in contrast to data from other temperate locations. The model estimates atmosphere-ocean CO2 flux to be 37 g C m−2 yr−1. The seasonal cycle of the carbonate system at OWS M resembles the cycles suggested by data from other high-latitude ocean locations. The seasonal cycles of calcite saturation state and [CO32-] are similar in the model and in data at OWS M: values range from ~3 and ~120 μmol kg−1 respectively in winter, to ~4 and ~170 μmol kg−1 respectively in summer. The model and data provide further evidence (supporting previous modelling work) that the summer is a time of high saturation state within the annual cycle at high-latitude locations. This is also the time of year that coccolithophore blooms occur at high latitudes.

scholarly journals Does atmospheric CO<sub>2</sub> seasonality play an important role in governing the air-sea flux of CO<sub>2</sub>?

2012 ◽  
Vol 9 (6) ◽  
pp. 2311-2323 ◽  
Author(s):  
P. R. Halloran
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Seasonal Cycle ◽  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Seasonal Cycles ◽  
Earth System ◽  
Model Simulations ◽  
Anthropogenic Co2 ◽  
The Impact

Abstract. The amplitude, phase, and form of the seasonal cycle of atmospheric CO2 concentrations varies on many time and space scales (Peters et al., 2007). Intra-annual CO2 variation is primarily driven by seasonal uptake and release of CO2 by the terrestrial biosphere (Machta et al., 1977; Buchwitz et al., 2007), with a small (Cadule et al., 2010; Heimann et al., 1998), but potentially changing (Gorgues et al., 2010) contribution from the ocean. Variability in the magnitude, spatial distribution, and seasonal drivers of terrestrial net primary productivity (NPP) will be induced by, amongst other factors, anthropogenic CO2 release (Keeling et al., 1996), land-use change (Zimov et al., 1999) and planetary orbital variability, and will lead to changes in CO2atm seasonality. Despite CO2atm seasonality being a dynamic and prominent feature of the Earth System, its potential to drive changes in the air-sea flux of CO2 has not previously (to the best of my knowledge) been explored. It is important that we investigate the impact of CO2atm seasonality change, and the potential for carbon-cycle feedbacks to operate through the modification of the CO2atm seasonal cycle, because the decision had been made to prescribe CO2atm concentrations (rather than emissions) within model simulations for the fifth IPCC climate assessment (Taylor et al., 2009). In this study I undertake ocean-model simulations within which different magnitude CO2atm seasonal cycles are prescribed. These simulations allow me to examine the effect of a change in CO2atm seasonal cycle magnitude on the air-sea CO2 flux. I then use an off-line model to isolate the drivers of the identified air-sea CO2 flux change, and propose mechanisms by which this change may come about. Three mechanisms are identified by which co-variability of the seasonal cycles in atmospheric CO2 concentration, and seasonality in sea-ice extent, wind-speed and ocean temperature, could potentially lead to changes in the air-sea flux of CO2 at mid-to-high latitudes. The sea-ice driven mechanism responds to an increase in CO2atm seasonality by pumping CO2 into the ocean, the wind-speed and solubility-driven mechanisms, by releasing CO2 from the ocean (in a relative sense). The relative importance of the mechanisms will be determined by, amongst other variables, the seasonal extent of sea-ice. To capture the described feedbacks within earth system models, CO2atm concentrations must be allowed to evolve freely, forced only by anthropogenic emissions rather than prescribed CO2atm concentrations; however, time-integrated ocean simulations imply that the cumulative net air-sea flux could be at most equivalent to a few ppm CO2atm. The findings presented here suggest that, at least under pre-industrial conditions, the prescription of CO2atm concentrations rather than emissions within simulations will have little impact on the marine anthropogenic CO2 sink.

Climatic thresholds shape northern high-latitude fire regimes and imply vulnerability to future climate change

Ecography ◽  
2016 ◽  
Vol 40 (5) ◽  
pp. 606-617 ◽  
Author(s):  
Adam M. Young ◽  
Philip E. Higuera ◽  
Paul A. Duffy ◽  
Feng Sheng Hu
Keyword(s):  
Climate Change ◽  
High Latitude ◽  
Fire Regimes ◽  
Future Climate ◽  
Future Climate Change ◽  
Northern High Latitude

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

2019 ◽  
Author(s):  
David D. Parrish ◽  
Richard G. Derwent ◽  
Simon O'Doherty ◽  
Peter G. Simmonds
Keyword(s):  
Time Series ◽  
Seasonal Cycle ◽  
Linear Trend ◽  
Power Series Expansion ◽  
Mathematical Representation ◽  
Seasonal Cycles ◽  
Data Sets ◽  
Significant Information ◽  
Long Term Changes

Abstract. We present an approach to derive 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 twelve 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 non-linear aspects of the long-term trends. Additional implications, advantages and limitations of this approach are discussed.

Sign in / Sign up

Export Citation Format

Share Document