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

<p>Land vegetation growth in the northern high latitudes (north of 50˚N) is strongly temperature limited, thus anomalously warm years are expected to result in an increased drawdown of Carbon Dioxide (CO<sub>2</sub>) and vice versa. Piao et al (2017) concluded in an analysis of climate and CO<sub>2</sub> 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<sub>2</sub> 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<sub>2</sub> 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<sub>2</sub> uptake.</p>

scholarly journals Mosaicking Landsat and Sentinel-2 Data to Enhance LandTrendr Time Series Analysis in Northern High Latitude Permafrost Regions

2020 ◽  
Vol 12 (15) ◽  
pp. 2471
Author(s):  
Alexandra Runge ◽  
Guido Grosse
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
High Latitude ◽  
High Latitudes ◽  
Series Analysis ◽  
Data Gaps ◽  
Northern High Latitude ◽  
Study Sites ◽  
Sentinel 2 ◽  
Permafrost Regions

Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9–100 %), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 %, 58.1 %, and 69.7 % for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91–0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92–0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances.

scholarly journals Assessing the feasibility of using a neural network to filter Orbiting Carbon Observatory 2 (OCO-2) retrievals at northern high latitudes

2021 ◽  
Vol 14 (12) ◽  
pp. 7511-7524
Author(s):  
Joseph Mendonca ◽  
Ray Nassar ◽  
Christopher W. O'Dell ◽  
Rigel Kivi ◽  
Isamu Morino ◽  
...  
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
High Latitude ◽  
Total Carbon ◽  
High Latitudes ◽  
Satellite Retrievals ◽  
The World ◽  
Northern High Latitude ◽  
Quality Control Filter

Abstract. Satellite retrievals of XCO2 at northern high latitudes currently have sparser coverage and lower data quality than most other regions of the world. We use a neural network (NN) to filter Orbiting Carbon Observatory 2 (OCO-2) B10 bias-corrected XCO2 retrievals and compare the quality of the filtered data to the quality of the data filtered with the standard B10 quality control filter. To assess the performance of the NN filter, we use Total Carbon Column Observing Network (TCCON) data at selected northern high latitude sites as a truth proxy. We found that the NN filter decreases the overall bias by 0.25 ppm (∼ 50 %), improves the precision by 0.18 ppm (∼ 12 %), and increases the throughput by 16 % at these sites when compared to the standard B10 quality control filter. Most of the increased throughput was due to an increase in throughput during the spring, fall, and winter seasons. There was a decrease in throughput during the summer, but as a result the bias and precision were improved during the summer months. The main drawback of using the NN filter is that it lets through fewer retrievals at the highest-latitude Arctic TCCON sites compared to the B10 quality control filter, but the lower throughput improves the bias and precision.

Cropland carbon uptake delayed by 2019 U.S. Midwest floods

2020 ◽  
Author(s):  
Yi Yin ◽  
Branden Byrne ◽  
Junjie Liu ◽  
Paul Wennberg ◽  
Philipp Köhler ◽  
...  
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Gross Primary Production ◽  
Early Summer ◽  
Satellite Observations ◽  
Mitigation Strategies ◽  
Carbon Uptake ◽  
Vegetation Activity ◽  
The Impact

<p>While large-scale floods directly impact human lives and infrastructures, they also profoundly impact agricultural productivity. New satellite observations of vegetation activity and atmospheric CO<sub>2</sub> offer the opportunity to quantify the effects of such extreme events on cropland carbon sequestration, which are important for mitigation strategies. Widespread flooding during spring and early summer 2019 delayed crop planting across the U.S. Midwest. As a result, satellite observations of solar-induced chlorophyll fluorescence (SIF) from TROPOspheric Monitoring Instrument (TROPOMI) and Orbiting Carbon Observatory (OCO-2) reveal a shift of 16 days in the seasonal cycle of photosynthetic activity relative to 2018, along with a 15% lower peak photosynthesis. We estimate the 2019 anomaly to have led to a reduction of -0.21 PgC in gross primary production (GPP) in June and July, partially compensated in August and September (+0.14 PgC). The extension of the 2019 growing season into late September is likely to have benefited from increased water availability and late-season temperature. Ultimately, this change is predicted to reduce the crop yield over most of the midwest Corn/Soy belt by ~15%. Using an atmospheric transport model, we show that a decline of ~0.1 PgC in the net carbon uptake during June and July is consistent with observed CO<sub>2</sub> enhancements from Atmospheric Carbon and Transport - America (ACT-America) aircraft and OCO-2. This study quantifies the impact of floods on cropland productivity and demonstrates the potential of combining SIF with atmospheric CO<sub>2</sub> observations to monitor regional carbon flux anomalies.</p>

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 Modelling high-latitude electron densities with a coupled thermosphere-ionosphere model

1996 ◽  
Vol 14 (12) ◽  
pp. 1391-1402 ◽  
Author(s):  
J. Schoendorf ◽  
A. D. Aylward ◽  
R. J. Moffett
Keyword(s):  
High Latitude ◽  
Large Scale ◽  
Particle Distribution ◽  
Auroral Zone ◽  
High Latitudes ◽  
Convection Pattern ◽  
Electron Densities ◽  
Particle Precipitation ◽  
Spatial And Temporal Scales ◽  
Ionosphere Model

Abstract. A few of the difficulties in accurately modelling high-latitude electron densities with a large-scale numerical model of the thermosphere and ionosphere are addressed by comparing electron densities calculated with the Coupled Thermosphere-Ionosphere Model (CTIM) to EISCAT data. Two types of simulations are presented. The first set of simulations consists of four diurnally reproducible model runs for a Kp index of 4o which differ only in the placement of the energetic-particle distribution and convection pattern input at high latitudes. These simulations predict varying amounts of agreement with the EISCAT data and illustrate that for a given Kp there is no unique solution at high-latitudes. Small changes in the high-latitude inputs cause dramatic changes in the high-latitude modelled densities. The second type of simulation consists of inputting statistical convection and particle precipitation patterns which shrink or grow as a function of Kp throughout a 3-day period 21–23 February 1990. Comparisons with the EISCAT data for the 3 days indicate that equatorward of the particle precipitation the model accurately simulates the data, while in the auroral zone there is more variability in the data than the model. Changing the high-latitude forcing as a function of Kp allows the CTIM to model the average behavior of the electron densities; however at auroral latitudes model spatial and temporal scales are too large to simulate the detailed variation seen in individual nights of data.

scholarly journals High-Latitude Contribution to Global Variability of Air–Sea Sensible Heat Flux

2012 ◽  
Vol 25 (10) ◽  
pp. 3515-3531 ◽  
Author(s):  
Xiangzhou Song ◽  
Lisan Yu
Keyword(s):  
Heat Flux ◽  
Air Temperature ◽  
High Latitude ◽  
Large Scale ◽  
Decadal Variability ◽  
Sensible Heat Flux ◽  
The Arctic ◽  
High Latitudes ◽  
Sensible Heat ◽  
Arctic Oscillation Index

Abstract The study examined global variability of air–sea sensible heat flux (SHF) from 1980 to 2009 and the large-scale atmospheric and ocean circulations that gave rise to this variability. The contribution of high-latitude wintertime SHF was identified, and the relative importance of the effect of the sea–air temperature difference versus the effect of wind on decadal SHF variability was analyzed using an empirical orthogonal function (EOF) approach. The study showed that global SHF anomalies are strongly modulated by SHF at high latitudes (poleward of 45°) during winter seasons. Decadal variability of global wintertime SHF can be reasonably represented by the sum of two leading EOF modes, namely, the boreal wintertime SHF in the northern oceans and the austral wintertime SHF in the southern oceans. The study also showed that global wintertime SHF is modulated by the prominent modes of the large-scale atmospheric circulation at high latitudes. The increase of global SHF in the 1990s is attributable to the strengthening of the Southern Hemisphere annular mode index, while the decrease of global SHF after 2000 is due primarily to the downward trend of the Arctic Oscillation index. This study identified the important effects of wind direction and speed on SHF variability. Changes in winds modify the sea–air temperature gradient by advecting cold and dry air from continents and by imposing changes in wind-driven oceanic processes that affect sea surface temperature (SST). The pattern of air temperature anomalies dominates over the pattern of SST anomalies and dictates the pattern of decadal SHF variability.

scholarly journals How robust is the apparent break‐down of northern high‐latitude temperature control on spring carbon uptake?

2021 ◽  
Author(s):  
Peter Joyce ◽  
Roel Brienen ◽  
Wolfgang Buermann ◽  
Chris Wilson ◽  
Martyn P. Chipperfield ◽  
...  
Keyword(s):  
Temperature Control ◽  
High Latitude ◽  
Carbon Uptake ◽  
Northern High Latitude

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.

scholarly journals Assessing the Feasibility of Using a Neural Network to Filter OCO-2 Retrievals at Northern High Latitudes

2021 ◽  
Author(s):  
Joseph Mendonca ◽  
Ray Nassar ◽  
Christopher O'Dell ◽  
Rigel Kivi ◽  
Isamu Morino ◽  
...  
Keyword(s):  
Neural Network ◽  
Quality Control ◽  
High Latitude ◽  
Total Carbon ◽  
High Latitudes ◽  
Satellite Retrievals ◽  
The World ◽  
Northern High Latitude ◽  
Quality Control Filter

Abstract. Satellite retrievals of XCO2 at northern high latitudes currently have sparser coverage and lower data quality than most other regions of the world. We use a neural network (NN) to filter OCO-2 B10 bias-corrected XCO2 retrievals and compare the quality of the filtered data to the quality of the data filtered with the standard B10 quality control filter. To assess the performance of the NN filter, we use Total Carbon Column Observing Network (TCCON) data at selected northern high latitude sites as a truth proxy. We found that the NN filter decreases the overall bias by 0.25 ppm (~50 %), improves the precision by 0.18 ppm (~12 %), and increases the throughput by 16 % at these sites when compared to the standard B10 quality control filter. Most of the increased throughput was due to an increase in throughput during the spring, fall, and winter seasons. There was a decrease in throughput during the summer, but as a result the bias and precision were improved during the summer months. The main drawback of using the NN filter is that it lets through fewer retrievals at the highest latitude Arctic TCCON sites compared to the B10 quality control filter, but the lower throughput improves the bias and precision.

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