Amplitude Of Seasonal Cycle Research Articles

Drivers for seasonal variability in sea level around the China seas

Globally variable ocean and atmospheric dynamics lead to spatially complex seasonal cycles in sea level. The China Seas, that is the Bohai, Yellow, East China and the South China seas, is a region with strong seasonal amplitudes, and straddles the transition between tropical and temperature zones, monsoonal and westerlies, shelf and deep ocean zones. Here we investigate the drivers for seasonal variability in sea level from tide gauge records, satellite altimetry along with output from the NEMO (Nucleus for European Modeling of the Ocean) model including sea surface height and ocean bottom pressure along with meteorological data in the China Seas. The seasonal cycle accounts for 37% - 94% of monthly sea level variability in 81 tide gauge records, ranging from 18 to 59 cm. We divided the seasonal cycles into four types: 1) an asymmetric sinusoid; 2) a clearly defined peak on a flat background; 3) a relatively flat signal; 4) a symmetric co-sinusoid. Type 1 is found in northern China and Taiwan, Korea, Japan and The Philippines where Inverse Barometer (IB) effects dominates seasonality along with a steric contribution. The seasonal monsoon associated with barotropic response and freshwater exchange play important roles in type 2, (eastern and southern Chinese coasts), type 3 (East Malaysia) and type 4 (Vietnam and Gulf of Thailand). IB corrected seasonal cycle amplitudes are larger in continental shelf areas than the deep ocean, with a maximum in the Gulf of Thailand, and NEMO underestimates the seasonal amplitude along the coast by nearly 50%.

Ongoing Decline in the Atmospheric COS Seasonal Cycle Amplitude over Western Europe: Implications for Surface Fluxes

Atmospheric carbonyl sulfide (COS) was monitored at the GIF site (France) from August 2014 to November 2021. A significant decreasing trend in the seasonal cycle amplitude (SCA) of the COS was observed for the first time in the Northern Hemisphere (−27 ppt over 6 years). The lowest SCA was recorded in 2021 (80 ppt vs. 107 ppt in 2015). The trend in the SCA results revealed a steeper decline in the spring maximum than in that of the autumn minimum (−49 ppt vs. −10 ppt over 6 years, respectively). These negative trends were qualitatively consistent with those in the tropospheric COS put forward by the NDACC network of ground-based FTIR instruments, which were attributed to a slowing in the rate of COS anthropogenic emissions. Simulations using the ORCHIDEE land-surface model showed that a decrease in COS lowers the uptake of this gas by plants. Our observations suggest the existence of a causal relationship between the decline in the SCA and that in the tropospheric COS, implying that the temporal variations in the COS SCA over Western Europe are essentially driven by plant uptake. However, the transport by the LMDz 3-D model of surface fluxes for each component of the COS budget failed to reproduce this feature at GIF, pointing to a likely misrepresentation of the marine and anthropogenic fluxes in the footprint of this station.

Future Amplification of Sea Surface Temperature Seasonality Due To Enhanced Ocean Stratification

AbstractIn many regions the projected future sea surface temperature (SST) response to greenhouse warming is larger in summer than in winter. What causes this amplification of the SST seasonal cycle has remained unclear. To determine robustness of the projected seasonal cycle intensification and ascertain underlying physical mechanisms we analyze a suite of historical and greenhouse warming simulations conducted with 13 coupled general circulation models in the Coupled Model Intercomparison Project Phase 5. In the Representative Concentration Pathway 8.5 scenario, the amplitude of SST seasonal cycle, defined as the difference between climatological maximum and minimum temperature, increases by 30% ± 20% on average by the end of 21st century. Analysis of a simplified mixed layer heat budget demonstrates that the amplification can be attributed to the increasing upper ocean stratification and hence shoaling of the annual‐mean mixed layer. The projected intensification of SST seasonality may have important implications for future changes in marine ecosystems.

Controls on the Boundary Between Thermally and Non‐Thermally Driven pCO2 Regimes in the South Pacific

AbstractRegional and temporal patterns of air–sea carbon exchange are strongly linked to the surface ocean partial pressure of carbon dioxide (pCO2), which varies with sea surface temperature (SST), salinity, dissolved inorganic carbon (DIC), and alkalinity. It is well‐known that temperature controls the pCO2 seasonal cycle in the subtropics, whereas DIC dominates at high latitudes. The balance of mechanisms governing the boundary between these regimes, however, are not well characterized due to lack of year‐round pCO2 data. Here, we use autonomous biogeochemical float measurements from the South Pacific to investigate the processes that control meridional variations in pCO2 seasonality. We find that the transition between pCO2 regimes is linked to the poleward decrease in SST seasonal cycle amplitude, which is closely associated with the northern boundary of deep winter mixed layers. Processes that determine the annual SST range are, therefore, central to the response of oceanic carbon uptake to anthropogenic forcing.

Weakened amplitude and delayed phase of the future temperature seasonal cycle over China during the twenty‐first century

AbstractThe surface air temperature seasonal cycle in China has clearly changed in response to global warming, but its future projection has yet to be studied. Here, we investigate the performance and projection of the temperature seasonal cycle in China in 32 global climate models participating in the Coupled Model Intercomparison Project 6 (CMIP6). The characteristics of the temperature seasonal cycle, as represented by the amplitude and phase, are measured by the Fourier transform. The results show that the CMIP6 models can reasonably capture the observed typical temperature seasonal cycle feature with clear north–south gradients both for amplitude and phase in China. In the twenty‐first century, the models robustly project a delay in the phase across the whole country and a reduction in the amplitude in most of China relative to the reference period. Those changes are similar in the two shared socioeconomic pathway (SSP) scenarios, with overall larger magnitudes in the SSP5‐8.5 scenario than in the SSP2‐4.5 scenario. Projected amplitude changes are mainly linked to surface energy flux changes, especially latent heat flux changes. In response to future global warming, larger increases in the latent heat flux and thus stronger surface evaporative cooling lead to weaker surface warming in summer than in winter, contributing to a decrease in the amplitude of the temperature seasonal cycle in most regions of China.

Spatio-temporal variability of XCO2 over Indian region inferred from Orbiting Carbon Observatory (OCO-2) satellite and Chemistry Transport Model

Investigation of spatio-temporal variability of column-averaged dry-air mole fraction of CO2 (XCO2) over the Indian region using remote sensing satellite measurements is of interest due to the sparseness of ground-based observations. In this study, we utilized OCO-2 satellite retrievals in conjunction with an atmospheric chemistry-transport model (ACTM) simulations for a set of known (bottom-up) and optimized (top-down) flux for the period September 2014 to December 2018. Results showed the highest XCO2 during the pre-monsoon season, due to prevailing seasonal-high temperatures and drier soil conditions that resulted in increased respiration and suppressed photosynthesis. In contrast, a reduction in XCO2 during the monsoon season is found as precipitation increased soil moisture and moderated the air temperature, driving vegetation growth by which photosynthesis exceeded respiration by the land biosphere. Model - observation differences of XCO2 have shown the overestimation (underestimation) during monsoon (pre-monsoon) especially over the central India region, which might be due to underestimation of the modeled peak-to-trough biospheric fluxes. Analyses of the latitudinal distribution of XCO2 averaged over Indian landmass shows a clear increasing trend and seasonality. An enormous increase in XCO2 of about ~12 ppm during 2015–2018 is estimated from OCO-2, which is in good agreement with model-simulated XCO2 (optimized flux case) and consistent with the global growth rate from surface observations. The time series and seasonal cycle of XCO2 have also been examined using model simulations over different parts of the Indian region and agreed well with those from OCO-2. Over the northern regions, especially over the Indo Gangetic Plain, the peak-to-trough seasonal cycle amplitudes of 2–3 ppm are twice than those in the southern and oceanic regions,~1–1.5 ppm. An annual total CO2 flux of −397 ± 99 TgC/yr is estimated by the inversion and that is consistent with the XCO2 measurements.

Seasonal and annual variations of CO2 and CH4 at Shadnagar, a semi-urban site

Carbon dioxide (CO2) and methane (CH4) are the most important greenhouse gases (GHGs) due to their significant role in anthropogenic global climate change. The spatio-temporal variations of their concentration are characterized by the terrestrial biosphere, seasonal weather patterns and anthropogenic emissions. Hence, to understand the variability in regional surface GHG fluxes, high precision GHGs measurements were initiated by the National Remote Sensing Center (NRSC) of India. We report continuous CO2 and CH4measurements during 2014 to 2017 for the first time from Shadnagar, a suburban site in India. Annual mean CO2 and CH4 concentrations are 399.56 ± 5.46 ppm and 1.929 ± 0.09 ppm, respectively, for 2017. After the strong El Niño of 2015–2016, an abnormal rise in CO2 growth rate of 5.5 ppm year−1 was observed in 2017 at the study site, compared to 3.03 ppm year−1 at Mauna Loa. Thus, the repercussion of the El Niño effect diminishes the net uptake by the terrestrial biosphere accompanied by increased soil respiration. Seasonal tracer to tracer correlation between CO2 and CH4 was also analyzed to characterize the possible source-sink relationship between the species. We compared CO2 and CH4 concentrations to simulations from an atmospheric chemistry transport model (ACTM). The seasonal phases of CH4 were well captured by the ACTM, whereas the seasonal cycle amplitude of CO2 was underestimated by about 30%.

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

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.

Evaluation of CMIP6 GCMs for simulations of temperature over Thailand and nearby areas in the early 21st century

This study evaluates the performance of 13 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating the temperature over Thailand during 2000–2014, for land-only, sea-only, and both land and sea. Both observation and reanalysis datasets are employed to compare with the GCMs, evaluated by five performance metrics including mean annual temperature, mean bias errors, mean seasonal cycle amplitude, correlation coefficient, and root mean square error. GCMs are ranked by relative error of all performance metrics. Results show that the temperatures from most GCM simulations are below the mean reference data (i.e., average of ground-based and reanalysis datasets), with north to south gradient in the range from 19 °C to 33 °C. In addition, all the GCM biases range from -0.07 °C to 2.78 °C and show severity of the temperature changes in spatial pattern ranging from -5 °C to 15 °C. The correlations of most GCMs range from 0.70 to 0.95, while the magnitudes of error are less than 2 °C. Study cases point out that the 13-MODEL ENSEMBLE, CESM2, and CNRM-CM6-1 perform better than the other models in simulating the temperature over Thailand for land-only and sea-only, and both land and sea cases, respectively, while MIROC6 performs the worst for all study cases in this study area. From the designed methodology, CNRM-CM6-1 has the best performance and is the most appropriate choice to simulate the temperature for the overall Thailand area.

The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter-annual scales.

High-quality atmospheric CO2 measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO2 . In this study, we present the first 6years (2014-2019) of continuous, high-precision measurements of atmospheric CO2 at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO2 regional signal ( ) that has a marked seasonal cycle with an amplitude of about 4ppm. At both seasonal and inter-annual scales, we find differences in phase between and the local eddy covariance net ecosystem exchange (EC-NEE), which is interpreted as an indicator of a decoupling between local and non-local drivers of . In addition, we present how the 2015-2016 El Niño-induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter-annual variability of together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO2 exchange. We use both non-optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO2 from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data-driven non-optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.

An Improved Parameterization of Wind‐Driven Turbulent Vertical Mixing Based on an Eddy‐Resolving Climate Model

AbstractTurbulent vertical mixing in the stratified ocean interior has a huge impact on global ocean circulations and the climate system. Although parameterizations of vertical mixing furnished by internal tides have been built into state‐of‐the‐art coupled global climate models (CGCMs), efforts in parameterizing wind‐driven vertical mixing in CGCMs are still limited. In this study, we apply a modified finescale parameterization (MFP) to an eddy‐resolving Community Earth System Model (CESM) to represent the wind's contribution to vertical mixing in the stratified ocean interior. The spatial pattern of the MFP‐parameterized wind‐driven vertical mixing in the thermocline agrees with the observation derived from the finestructure measurements of Argo floats, reproducing the enhanced values in the Kuroshio, Gulf Stream extensions, and the Southern Ocean where the winds inject great amount of energy into the internal wave field. The MFP also captures the observed seasonal variation of wind‐driven vertical mixing in the thermocline of these regions that exhibits enhancement and weakening in winter and summer, respectively. Application of the MFP to a non‐eddy‐resolving CESM fails to reproduce the observed wind‐driven vertical mixing. Specifically, the magnitude of parameterized wind‐driven vertical mixing in the thermocline of Kuroshio, Gulf Stream extensions, and the Southern Ocean is systemically smaller than those in the observation and eddy‐resolving CESM; so is the case for the amplitude of seasonal cycle. The results highlight the benefit of eddy‐resolving CESM compared to its standard‐resolution counterpart in parameterizing the wind‐driven vertical mixing and provide insight into developing parameterizations for wind‐driven vertical mixing in eddy‐resolving CGCMs.

Seasonal trends in the Southeast Florida current and shelf CO2 fluxes

An anthropogenically driven increase in atmospheric CO2 concentrations is known to drive an increase in the globally integrated ocean carbon sink. However, there is significant regional and seasonal variability, and significant changes in seasonal cycle amplitudes have been predicted. Three decades of sea surface observations collected from the southeast Florida Current and shelf waters reveal behavior over seasonal and interannual timescales for a region important to global carbon storage and transport. The strong temperature dependence of fCO2 in the region dominates the seasonal cycle of air-sea flux, although biophysical processes are also important. The possibility of wet and dry seasonal asymmetric trends through time were evaluated, but no statistically significant trends were found. The results indicate that uptake in the region has not changed over the last three decades after accounting for sampling bias and demonstrate the importance of robust seasonally unbiased data sets in conducting climate studies. Even in this highly sampled region, data distribution biases were found, mainly in the early 1990s. Seasonally biased data are thus not only a problem for polar regions but can also affect flux calculations in sub-tropical regions. Although not statistically significant to date, different responses in different seasons with time may lead to seasonal amplification and a decoupling of the annual mean and the seasonal means in the future.

COS-derived GPP relationships with temperature and light help explain high-latitude atmospheric CO2 seasonal cycle amplification

In the Arctic and Boreal region (ABR) where warming is especially pronounced, the increase of gross primary production (GPP) has been suggested as an important driver for the increase of the atmospheric CO2 seasonal cycle amplitude (SCA). However, the role of GPP relative to changes in ecosystem respiration (ER) remains unclear, largely due to our inability to quantify these gross fluxes on regional scales. Here, we use atmospheric carbonyl sulfide (COS) measurements to provide observation-based estimates of GPP over the North American ABR. Our annual GPP estimate is 3.6 (2.4 to 5.5) PgC · y-1 between 2009 and 2013, the uncertainty of which is smaller than the range of GPP estimated from terrestrial ecosystem models (1.5 to 9.8 PgC · y-1). Our COS-derived monthly GPP shows significant correlations in space and time with satellite-based GPP proxies, solar-induced chlorophyll fluorescence, and near-infrared reflectance of vegetation. Furthermore, the derived monthly GPP displays two different linear relationships with soil temperature in spring versus autumn, whereas the relationship between monthly ER and soil temperature is best described by a single quadratic relationship throughout the year. In spring to midsummer, when GPP is most strongly correlated with soil temperature, our results suggest the warming-induced increases of GPP likely exceeded the increases of ER over the past four decades. In autumn, however, increases of ER were likely greater than GPP due to light limitations on GPP, thereby enhancing autumn net carbon emissions. Both effects have likely contributed to the atmospheric CO2 SCA amplification observed in the ABR.

Lower Stratospheric Water Vapor Variations Diagnosed from Satellite Observations, Reanalysis Data, and a Chemistry—Climate Model

Stratospheric water vapor variations, which may play an important role in surface climate, have drawn extensive studies. Here, the variation in stratospheric water vapor is investigated using data from observations of the Microwave Limb Sounder on the Aura satellite (MLS), ERA(全称? )-Interim reanalysis (ERAI), and simulations bythe Whole Atmosphere Community Climate Model (WACCM). We find that the differences of annual-mean stratospheric water vapor among these datasets may be partlycaused by the differences in vertical transports. Using budget analysis, we find that the upward transport of water vapor at 100 hPa is mainly located over the Pacific warm pool region and South America in the equatorial tropics in boreal winter and over thesoutheast of the South Asian high and south of North America in boreal summer. It is found that temperature averaged over regions withupward transportis a better indicator of interannual variability of tropical mean stratospheric water vapor than the tropical mean temperature. It seems that the distributions of the seasonal cycle amplitude of lower stratospheric water vapor in the tropics can also be impacted by the vertical transport. The radiative effects of the interannual changes in water vapor in the lowermost stratosphere are underestimated by approximately 29% in both ERAI and WACCM compared to MLS, although the interannual variations of water vapor in the lowermost stratosphere are dramatically overestimated in ERAI and WACCM.The results here indicate that the radiative effect of long-term changes in water vapor in the lowermost stratosphere may be underestimated in both ERAI and WACCM simulations.

Unusual characteristics of the carbon cycle during the 2015-2016 El Niño.

The 2015-2016 El Niño was one of the strongest on record, but its influence on the carbon balance is less clear. Using Northern Hemisphere atmospheric CO2 observations, we found both detrended atmospheric CO2 growth rate (CGR) and CO2 seasonal-cycle amplitude (SCA) of 2015-2016 were much higher than that of other El Niño events. The simultaneous high CGR and SCA were unusual, because our analysis of long-term CO2 observations at Mauna Loa revealed a significantly negative correlation between CGR and SCA. Atmospheric inversions and terrestrial ecosystem models indicate strong northern land carbon uptake during spring but substantially reduced carbon uptake (or high emissions) during early autumn, which amplified SCA but also resulted in a small anomaly in annual carbon uptake of northern ecosystems in 2015-2016. This negative ecosystem carbon uptake anomaly in early autumn was primarily due to soil water deficits and more litter decomposition caused by enhanced spring productivity. Our study demonstrates a decoupling between seasonality and annual carbon cycle balance in northern ecosystems over 2015-2016, which is unprecedented in the past five decades of El Niño events.

Thermodynamic simulation of the seasonal cycle of temperature, pressure and ice caps on Mars

We present a thermodynamic model to simulate the atmospheric temperature and pressure, the regolith temperature and the polar CO 2 ice caps vertical thickness and horizontal extent of Mars. This is done using the temperatures of the atmosphere and regolith for each Martian day of the year, determined from the thermodynamic equation. Assuming a CO 2 atmosphere, we calculate its emission spectrum using the spectral calculator E-Trans with the HITRAN database (E-Trans/HITRAN), resulting in only a band at 15 mm and the rest transparent, through which the radiation emitted by the regolith and ice caps (considered as black bodies) goes toward space. We also include the solar radiation extinction and longwave emission of atmospheric dust. The equation calculates the energy balance between the absorbed solar radiation and the outgoing longwave radiation, incorporating also the latent heat released by CO 2 condensation, the sensible heat flux from the surface to the atmosphere, the latent heat flux due to the CO 2 ice sublimation and the heat exchange between the surface regolith layer and its lower layers. The atmospheric planetary scale horizontal turbulent heat transport is parameterized with an exchange coefficient, which is an order of magnitude smaller than that employed in the terrestrial troposphere. Considering a long-time average, the regolith vertical temperature profile is explicitly found using the thermal inertia including conductivity; its temperature regulation is achieved through the thermal conduction from the surface during the warm season. This stored energy goes back to the surface during the cold season. Our model simulates the seasonal variation of the polar ice caps and consequently of the surface atmospheric pressure through the CO 2 mass balance between them. Finally, it is explicitly shown that the amplitude of the global seasonal pressure cycle is modulated by a local thermal-orographic effect, which increases (decreases) this amplitude in the low (high) regions, which agrees with the observations of the Viking Landers 1 and 2, and the Mars Climate Data Base. Our prediction for Hellas Planitia gives an amplification factor of up to 2.15, coinciding with other authors.

A decade of GOSAT Proxy satellite CH<sub>4</sub> observations

Abstract. This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).

Spatially resolved evaluation of Earth system models with satellite column-averaged CO<sub>2</sub>

Abstract. Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) showed large uncertainties in simulating atmospheric CO2 concentrations. We utilize the Earth System Model Evaluation Tool (ESMValTool) to evaluate emission-driven CMIP5 and CMIP6 simulations with satellite data of column-average CO2 mole fractions (XCO2). XCO2 time series show a large spread among the model ensembles both in CMIP5 and CMIP6. Compared to the satellite observations, the models have a bias of +25 to −20 ppmv in CMIP5 and +20 to −15 ppmv in CMIP6, with the multi-model mean biases at +10 and +2 ppmv, respectively. The derived mean atmospheric XCO2 growth rate (GR) of 2.0 ppmv yr−1 is overestimated by 0.4 ppmv yr−1 in CMIP5 and 0.3 ppmv yr−1 in CMIP6 for the multi-model mean, with a good reproduction of the interannual variability. All models capture the expected increase of the seasonal cycle amplitude (SCA) with increasing latitude, but most models underestimate the SCA. Any SCA derived from data with missing values can only be considered an “effective” SCA, as the missing values could occur at the peaks or troughs. The satellite data are a combined data product covering the period 2003–2014 based on the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY)/Envisat (2003–2012) and Thermal And Near infrared Sensor for carbon Observation Fourier transform spectrometer/Greenhouse Gases Observing Satellite (TANSO-FTS/GOSAT) (2009–2014) instruments. While the combined satellite product shows a strong negative trend of decreasing effective SCA with increasing XCO2 in the northern midlatitudes, both CMIP ensembles instead show a non-significant positive trend in the multi-model mean. The negative trend is reproduced by the models when sampling them as the observations, attributing it to sampling characteristics. Applying a mask of the mean data coverage of each satellite to the models, the effective SCA is higher for the SCIAMACHY/Envisat mask than when using the TANSO-FTS/GOSAT mask. This induces an artificial negative trend when using observational sampling over the full period, as SCIAMACHY/Envisat covers the early period until 2012, with TANSO-FTS/GOSAT measurements starting in 2009. Overall, the CMIP6 ensemble shows better agreement with the satellite data than the CMIP5 ensemble in all considered quantities (XCO2, GR, SCA and trend in SCA). This study shows that the availability of column-integral CO2 from satellite provides a promising new way to evaluate the performance of Earth system models on a global scale, complementing existing studies that are based on in situ measurements from single ground-based stations.

Observational Constraints on the Response of High‐Latitude Northern Forests to Warming

AbstractSince the 1960s, carbon cycling in the high‐latitude northern forest (HLNF) has experienced dramatic changes: Most of the forest is greening and net carbon uptake from the atmosphere has increased. During the same time period, the CO2 seasonal cycle amplitude (SCA) has increased by ~50% or more. Disentangling complex processes that drive these changes has been challenging. In this study, we substitute spatial sensitivity to temperature for time to quantify the impact of temperature increase on gross primary production (GPP), total ecosystem respiration (TER), the fraction of Photosynthetic Active Radiation (fPAR), and the resulted contribution of these changes in amplifying the CO2 SCA over the HLNF since 1960s. We use the spatial heterogeneity of GPP inferred from solar‐induced chlorophyll Fluorescence in combination with net ecosystem exchange (NEE) inferred from column CO2 observations made between 2015 and 2017 from NASA's Orbiting Carbon Observatory‐2. We find that three quarters of the spatial variations in GPP can be explained by the spatial variation in the growing season mean temperature (GSMT). The long term hindcast captures both the magnitude and spatial variability of the trends in observed fPAR. We estimate that between 1960 and 2010, the increase in GSMT enhanced both GPP and the SCA of NEE by ~20%. The calculated enhancement of NEE due to increase in GSMT contributes 56–72% of the trend in the CO2 SCA at high latitudes, much larger than simulations by most biogeochemical models.

Evaluating two soil carbon models within the global land surface model JSBACH using surface and spaceborne observations of atmospheric CO<sub>2</sub>

Abstract. The trajectories of soil carbon in our changing climate are of the utmost importance as soil is a substantial carbon reservoir with a large potential to impact the atmospheric carbon dioxide (CO2) burden. Atmospheric CO2 observations integrate all processes affecting carbon exchange between the surface and the atmosphere and therefore are suitable for carbon cycle model evaluation. In this study, we present a framework for how to use atmospheric CO2 observations to evaluate two distinct soil carbon models (CBALANCE, CBA, and Yasso, YAS) that are implemented in a global land surface model (JSBACH). We transported the biospheric carbon fluxes obtained by JSBACH using the atmospheric transport model TM5 to obtain atmospheric CO2. We then compared these results with surface observations from Global Atmosphere Watch stations, as well as with column XCO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The seasonal cycles of atmospheric CO2 estimated by the two different soil models differed. The estimates from the CBALANCE soil model were more in line with the surface observations at low latitudes (0–45∘ N) with only a 1 % bias in the seasonal cycle amplitude, whereas Yasso underestimated the seasonal cycle amplitude in this region by 32 %. Yasso, on the other hand, gave more realistic seasonal cycle amplitudes of CO2 at northern boreal sites (north of 45∘ N) with an underestimation of 15 % compared to a 30 % overestimation by CBALANCE. Generally, the estimates from CBALANCE were more successful in capturing the seasonal patterns and seasonal cycle amplitudes of atmospheric CO2 even though it overestimated soil carbon stocks by 225 % (compared to an underestimation of 36 % by Yasso), and its estimations of the global distribution of soil carbon stocks were unrealistic. The reasons for these differences in the results are related to the different environmental drivers and their functional dependencies on the two soil carbon models. In the tropics, heterotrophic respiration in the Yasso model increased earlier in the season since it is driven by precipitation instead of soil moisture, as in CBALANCE. In temperate and boreal regions, the role of temperature is more dominant. There, heterotrophic respiration from the Yasso model had a larger seasonal amplitude, which is driven by air temperature, compared to CBALANCE, which is driven by soil temperature. The results underline the importance of using sub-annual data in the development of soil carbon models when they are used at shorter than annual timescales.