In the Antarctic ozone hole, ozone mixing ratios have been decreasing to extremely low values of 0.01–0.1 ppm in nearly all spring seasons since the late 1980s, corresponding to 95–99% local chemical ...loss. In contrast, Arctic ozone loss has been much more limited and mixing ratios have never before fallen below 0.5 ppm. In Arctic spring 2020, however, ozonesonde measurements in the most depleted parts of the polar vortex show a highly depleted layer, with ozone loss averaged over sondes peaking at 93% at 18 km. Typical minimum mixing ratios of 0.2 ppm were observed, with individual profiles showing values as low as 0.13 ppm (96% loss). The reason for the unprecedented chemical loss was an unusually strong, long‐lasting, and cold polar vortex, showing that for individual winters the effect of the slow decline of ozone‐depleting substances on ozone depletion may be counteracted by low temperatures.
Plain Language Summary
The severe stratospheric chemical ozone loss in the Antarctic ozone hole and its impact on human health and climate have generated widespread public, political, and scientific interest. In contrast, Arctic stratospheric ozone reduction has been much more limited because of higher temperatures and higher transport variability in the Northern Hemisphere (lower temperatures lead to more chemical loss, and more transport can increase ozone values). In the Arctic spring 2020, however, observations of balloon sondes and satellites show that locally, absolute values of ozone (measured in mixing ratios, i.e., molecules of ozone per molecules of air) are significantly lower than in any previous year and are comparable to typical local values in the Antarctic ozone hole, albeit over a much narrower vertical layer. Locally, the chemical loss of ozone peaked at 93% in the Arctic spring of 2020, compared to values of 95–99% in the Antarctic in most winters since the late 1980s. The reason for the unprecedented loss was unusually cold and stable conditions in the Arctic stratosphere.
Key Points
Local minimum ozone mixing ratios of 0.1–0.2 ppm observed by sondes in Arctic spring 2020 are significantly lower than in any previous year
Local ozone loss (93%) and low mixing ratios are comparable to typical values in the Antarctic ozone hole (95–99%, 0.01–0.1 ppm)
The reason for the unprecedented chemical loss was an unusually strong, long‐lasting, and record cold polar vortex
Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne ...dry-air CO2 column (XCO2) retrievals. Several recent studies have shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger seasonal amplitudes and a more negative annual net CO2 balance than those inferred from the in situ data. The cause of this elevated European uptake of CO2 is still unclear, but some recent studies have suggested that this is a genuine scientific phenomenon. Here, we put forward an alternative hypothesis and show that realistic levels of bias in GOSAT data can result in an erroneous estimate of elevated uptake over Europe. We use a global flux inversion system to examine the relationship between measurement biases and estimates of CO2 uptake from Europe. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate conventional surface mole fraction observations and XCO2 retrievals from the surface-based Total Carbon Column Observing Network (TCCON). We use the same EnKF system to assimilate two independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.40 ± 0.19 GtC a-1 compared to a value of 0.58 ± 0.14 GtC a-1 for our control inversion that uses only in situ data. To reconcile these two estimates, we perform a series of numerical experiments that assimilate observations with added biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that for our global flux inversions, a large portion (60–90 %) of the elevated European uptake inferred from GOSAT data in 2010 is due to retrievals outside the immediate European region, while the remainder can largely be explained by a sub-ppm retrieval bias over Europe. We use a data assimilation approach to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. The inferred biases represent an estimate of systematic differences between GOSAT XCO2 retrievals and the inversion system at regional or sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.20 GtC a-1. Our results highlight the sensitivity of CO2 flux estimates to regional observation biases, which have not been fully characterized by the current observation network. Without further dedicated measurements we cannot prove or disprove that European ecosystems are taking up a larger-than-expected amount of CO2. More robust inversion systems are also needed to infer consistent fluxes from multiple observation types.
The seasonal cycle accounts for a dominant mode of total column CO2 (XCO2) annual variability and is connected to CO2 uptake and release; it thus represents an important quantity to test the accuracy ...of the measurements from space. We quantitatively evaluate the XCO2 seasonal cycle of the Greenhouse Gases Observing Satellite (GOSAT) observations from the Atmospheric CO2 Observations from Space (ACOS) retrieval system and compare average regional seasonal cycle features to those directly measured by the Total Carbon Column Observing Network (TCCON). We analyse the mean seasonal cycle amplitude, dates of maximum and minimum XCO2, as well as the regional growth rates in XCO2 through the fitted trend over several years. We find that GOSAT/ACOS captures the seasonal cycle amplitude within 1.0 ppm accuracy compared to TCCON, except in Europe, where the difference exceeds 1.0 ppm at two sites, and the amplitude captured by GOSAT/ACOS is generally shallower compared to TCCON. This bias over Europe is not as large for the other GOSAT retrieval algorithms (NIES v02.21, RemoTeC v2.35, UoL v5.1, and NIES PPDF-S v.02.11), although they have significant biases at other sites. We find that the ACOS bias correction partially explains the shallow amplitude over Europe. The impact of the co-location method and aerosol changes in the ACOS algorithm were also tested and found to be few tenths of a ppm and mostly non-systematic. We find generally good agreement in the date of minimum XCO2 between ACOS and TCCON, but ACOS generally infers a date of maximum XCO2 2–3 weeks later than TCCON. We further analyse the latitudinal dependence of the seasonal cycle amplitude throughout the Northern Hemisphere and compare the dependence to that predicted by current optimized models that assimilate in situ measurements of CO2. In the zonal averages, models are consistent with the GOSAT amplitude to within 1.4 ppm, depending on the model and latitude. We also show that the seasonal cycle of XCO2 depends on longitude especially at the mid-latitudes: the amplitude of GOSAT XCO2 doubles from western USA to East Asia at 45–50° N, which is only partially shown by the models. In general, we find that model-to-model differences can be larger than GOSAT-to-model differences. These results suggest that GOSAT/ACOS retrievals of the XCO2 seasonal cycle may be sufficiently accurate to evaluate land surface models in regions with significant discrepancies between the models.
Fluid injection into geological formations for energy
resource development frequently induces (micro)seismicity. Moderate- to
large-magnitude induced earthquakes may cause injuries and/or economic ...loss,
with the consequence of jeopardizing the operation and future development of
these geo-energy projects. To achieve an improved understanding of the
mechanisms of induced seismicity, develop forecasting tools and manage the
associated risks, it is necessary to carefully examine seismic data from
reported cases of induced seismicity and the parameters controlling them.
However, these data are challenging to gather together and are
time-consuming to collate as they come from different disciplines and
sources. Here, we present a publicly available, multi-physical database of
injection-induced seismicity (Kivi et al., 2022a;
https://doi.org/10.20350/digitalCSIC/14813), sourced from an extensive
review of published documents. Currently, it contains 158 datasets of
induced seismicity caused by various subsurface energy-related applications
worldwide. Each dataset covers a wide range of variables, delineating
general site information, host rock properties, in situ geologic and
tectonic conditions, fault characteristics, conducted field operations, and
recorded seismic activities. We publish the database in flat-file formats
(i.e., .xls and .csv tables) to facilitate its dissemination and utilization
by geoscientists while keeping it directly readable by computer codes for
convenient data manipulation. The multi-disciplinary content of this
database adds unique value to databases focusing only on seismicity data. In
particular, the collected data aim at facilitating the understanding of the
spatiotemporal occurrence of induced earthquakes, the diagnosis of
potential triggering mechanisms, and the development of scaling relations of
maximum possible earthquake magnitudes and operational parameters. The
database will boost research in seismic hazard forecasting and mitigation,
paving the way for increasing contributions of geo-energy resources to
meeting net-zero carbon emissions.
TROPOMI (the TROPOspheric Monitoring Instrument), on board the Sentinel-5 Precursor (S5P) satellite, has been monitoring the Earth's atmosphere since October 2017 with an unprecedented horizontal ...resolution (initially 7 km.sup.2 x3.5 km.sup.2, upgraded to 5.5 km.sup.2 x3.5 km.sup.2 in August 2019). Monitoring air quality is one of the main objectives of TROPOMI; it obtains measurements of important pollutants such as nitrogen dioxide, carbon monoxide, and formaldehyde (HCHO). In this paper we assess the quality of the latest HCHO TROPOMI products versions 1.1.(5-7), using ground-based solar-absorption FTIR (Fourier-transform infrared) measurements of HCHO from 25 stations around the world, including high-, mid-, and low-latitude sites. Most of these stations are part of the Network for the Detection of Atmospheric Composition Change (NDACC), and they provide a wide range of observation conditions, from very clean remote sites to those with high HCHO levels from anthropogenic or biogenic emissions. The ground-based HCHO retrieval settings have been optimized and harmonized at all the stations, ensuring a consistent validation among the sites.
In this paper we present the validation results of the operational vertical ozone profiles retrieved from the nadir observations by the Ozone Monitoring Instrument (OMI) aboard the NASA Earth ...Observing System (EOS) Aura platform. The operational ozone profile retrieval algorithm was developed at the Royal Netherlands Meteorological Institute and the OMI mission data has been processed and made publicly available. Advantages of these nadir sounded ozone profiles are the excellent spatial resolution at nadir and daily global coverage while the vertical resolution is limited to 6–7 km. Comparisons with well‐validated ozone profile recordings by the Microwave Limb Sounder (MLS) and the Tropospheric Emission Spectrometer (TES), both aboard the NASA EOS‐Aura platform, provide an excellent opportunity for validation because of the large amount of collocations with OMI due to the instruments significant geographical overlap. In addition, comparisons with collocated ozone profiles from the Stratospheric Aerosol and Gas Experiment (SAGE‐II), the Halogen Occultation Experiment (HALOE), the Global Ozone Monitoring by the Occultation of Stars (GOMOS) and the Optical Spectrograph and Infrared Imager System (OSIRIS) satellite instruments and balloon‐borne electrochemical concentration cell (ECC) ozonesondes are presented. OMI stratospheric ozone profiles are found to agree within 20% with global correlative data except for both the polar regions during local spring. For ozone in the troposphere OMI shows a systematic positive bias versus the correlative data sets of order 60% in the tropics and 30% at midlatitude regions. The largest source of error in the tropospheric ozone profile is the fit to spectral stray light in the operational algorithm.
Key Points
Operational OMI ozone profiles
Validation against satellite and sondes
Good stratosphere poor troposphere
The comparison of simultaneous humidity measurements by the Vaisala RS92 radiosonde and by the Cryogenic Frostpoint Hygrometer (CFH) launched at Alajuela, Costa Rica, during July 2005 reveals a large ...solar radiation dry bias of the Vaisala RS92 humidity sensor and a minor temperature-dependent calibration error. For soundings launched at solar zenith angles between 10' and 30', the average dry bias is on the order of 9% at the surface and increases to 50% at 15 km. A simple pressure- and temperature-dependent correction based on the comparison with the CFH can reduce this error to less than 7% at all altitudes up to 15.2 km, which is 700 m below the tropical tropopause. The correction does not depend on relative humidity, but is able to reproduce the relative humidity distribution observed by the CFH. PUBLICATION ABSTRACT
Inland waters, such as lakes, reservoirs and rivers, are important sources of climate forcing trace gases. A key parameter that regulates the gas exchange between water and the atmosphere is the gas ...transfer velocity, which itself is controlled by near‐surface turbulence in the water. While in lakes and reservoirs, near‐surface turbulence is mainly driven by atmospheric forcing, in shallow rivers and streams it is generated by bottom friction of gravity‐forced flow. Large rivers represent a transition between these two cases. Near‐surface turbulence has rarely been measured in rivers and the drivers of turbulence have not been quantified. We analyzed continuous measurements of flow velocity and quantified turbulence as the rate of dissipation of turbulent kinetic energy over the ice‐free season in a large regulated river in Northern Finland. Measured dissipation rates agreed with predictions from bulk parameters, including mean flow velocity, wind speed, surface heat flux, and with a one‐dimensional numerical turbulence model. Values ranged from ∼10−10m2s−3 to 10−5m2s−3. Atmospheric forcing or gravity was the dominant driver of near‐surface turbulence for similar fraction of the time. Large variability in near‐surface dissipation rate occurred at diel time scales, when the flow velocity was strongly affected by downstream dam operation. By combining scaling relations for boundary‐layer turbulence at the river bed and at the air‐water interface, we derived a simple model for estimating the relative contributions of wind speed and bottom friction of river flow as a function of depth.
Plain Language Summary
Inland water bodies such as lakes, reservoirs and rivers are an important source of climate forcing trace gases to the atmosphere. Gas exchange between water and the atmosphere is regulated by the gas transfer velocity and the concentration difference between the water surface and the atmosphere. The gas transfer velocity depends on near‐surface turbulence, but robust formulations have not been developed for river systems. Their surface area is sufficiently large for meteorological forcing to cause turbulence, as in lakes and reservoirs, but turbulence generated from bed and internal friction of gravity‐driven flows is also expected to contribute. Here we quantify near‐surface turbulence using data from continuous air and water side measurements conducted over the ice‐free season in a large subarctic regulated river in Finland. We find that turbulence, quantified as the dissipation rate of turbulent kinetic energy, is well described using equations for predicting turbulence from meteorological data for sufficiently high wind speeds whereas the contribution from bottom shear dominated at higher flow velocities. A one‐dimensional river model successfully captured these processes. We provide a fundamental model for estimating the relative contributions of atmospheric forcing and bottom friction as a function of depth.
Key Points
Wind and river flow make comparable contributions to near‐surface turbulence in a regulated river
Dissipation rates predicted from wind speed and flow velocity are in good agreement with observations
Diel variability in dissipation rates occurs in response to flow regulation and atmospheric forcing
Nitric acid trihydrate (NAT) particles in the polar stratosphere have been shown to be responsible for vertical redistribution of reactive nitrogen (NOy). Recent observations by Cloud–Aerosol Lidar ...with Orthogonal Polarization (CALIOP) aboard the CALIPSO satellite have been explained in terms of heterogeneous nucleation of NAT on foreign nuclei, revealing this to be an important formation pathway for the NAT particles. In state of the art global- or regional-scale models, heterogeneous NAT nucleation is currently simulated in a very coarse manner using a constant, saturation-independent nucleation rate. Here we present first simulations for the Arctic winter 2009/2010 applying a new saturation-dependent parametrisation of heterogeneous NAT nucleation rates within the Chemical Lagrangian Model of the Stratosphere (CLaMS). The simulation shows good agreement of chemical trace species with in situ and remote sensing observations. The simulated polar stratospheric cloud (PSC) optical properties agree much better with CALIOP observations than those simulated with a constant nucleation rate model. A comparison of the simulated particle size distributions with observations made using the Forward Scattering Spectrometer Probe (FSSP) aboard the high altitude research aircraft Geophysica, shows that the model reproduces the observed size distribution, except for the very largest particles above 15 μm diameter. The vertical NOy redistribution caused by the sedimentation of the NAT particles, in particular the denitrification and nitrification signals observed by the ACE-FTS satellite instrument and the in situ SIOUX instrument aboard the Geophysica, are reproduced by the improved model, and a small improvement with respect to the constant nucleation rate model is found.
We present 5 years of GOSAT XCH4 retrieved using the “proxy” approach. The Proxy XCH4 data are validated against ground-based TCCON observations and are found to be of high quality with a small bias ...of 4.8 ppb (∼0.27 %) and a single-sounding precision of 13.4 ppb (∼ 0.74 %). The station-to-station bias (a measure of the relative accuracy) is found to be 4.2 ppb. For the first time theXCH4/XCO2 ratio component of the Proxy retrieval is validated (bias of 0.014 ppbppm-1 (∼0.30 %), single-sounding precision of 0.033 ppbppm-1 (∼0.72 %)).The uncertainty relating to the model XCO2 component of the Proxy XCH4 is assessed through the use of an ensemble ofXCO2 models. While each individual XCO2 model is found to agree well with the TCCON validation data (r=0.94–0.97), it is not possible to select one model as the best from our comparisons. The median XCO2 value of the ensemble has a smaller scatter against TCCON (a standard deviation of 0.92 ppm) than any of the individual models whilst maintaining a small bias (0.15 ppm). This model medianXCO2 is used to calculate the Proxy XCH4 with the maximum deviation of the ensemble from the median used as an estimate of the uncertainty.We compare this uncertainty to the a posteriori retrieval error (which is assumed to reduce with sqrt(N)) and find typically that the model XCO2 uncertainty becomes significant during summer months when the a posteriori error is at its lowest due to the increase in signal related to increased summertime reflected sunlight.We assess the significance of these model and retrieval uncertainties on flux inversion by comparing the GOSAT XCH4 against modelled XCH4 from TM5-4DVAR constrained by NOAA surface observations (MACC reanalysis scenario S1-NOAA). We find that for the majority of regions the differences are much larger than the estimated uncertainties. Our findings show that useful information will be provided to the inversions for the majority of regions in addition to that already provided by the assimilated surface measurements.
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