Observation-based Product Research Articles

Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations

Abstract. Soil moisture is a key variable in monitoring climate and an important component of the hydrological, carbon, and energy cycles. Satellite products ameliorate the sparsity of field measurements but are inherently limited to observing the near-surface layer, while water available in the unobserved root-zone controls critical processes like plant water uptake and evapotranspiration. A variety of approaches exist for modelling root-zone soil moisture (RZSM), including approximating it from surface layer observations. While the number of available RZSM datasets is growing, they usually do not contain estimates of their uncertainty. In this paper we derive a long-term RZSM dataset (2002–2020) from the Copernicus Climate Change Service (C3S) surface soil moisture (SSM) COMBINED product via the exponential filter (EF) method. We identify the optimal value of the method's model parameter T, which controls the level of smoothing and delaying applied to the surface observations, by maximizing the correlation of RZSM estimates with field measurements from the International Soil Moisture Network (ISMN). Optimized T-parameter values were calculated for four soil depth layers (0–10, 10–40, 40–100, and 100–200 cm) and used to calculate a global RZSM dataset. The quality of this dataset is then globally evaluated against RZSM estimates of the ERA5-Land reanalysis. Results of the product comparison show satisfactory skill in all four layers, with the median Pearson correlation ranging from 0.54 in the topmost to 0.28 in the deepest soil layer. Temporally dynamic product uncertainties for each of the RZSM product layers are estimated by applying standard uncertainty propagation to SSM input data and by estimating structural uncertainties in the EF method from ISMN ground reference measurements taken at the surface and at varying depths. Uncertainty estimates were found to exhibit both realistic absolute magnitudes and temporal variations. The product described here is, to the best of our knowledge, the first global, long-term, uncertainty-characterized, and purely observation-based product for RZSM estimates up to 2 m depth.

Time-varying changes and uncertainties in the CMIP6 ocean carbon sink from global to local scale

Abstract. As a major sink for anthropogenic carbon, the oceans slow the increase in carbon dioxide in the atmosphere and regulate climate change. Future changes in the ocean carbon sink, and its uncertainty at a global and regional scale, are key to understanding the future evolution of the climate. Here we report on the changes and uncertainties in the historical and future ocean carbon sink using output from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble and compare to an observation-based product. We show that future changes in the ocean carbon sink are concentrated in highly active regions – 70 % of the total sink occurs in less than 40 % of the global ocean. High pattern correlations between the historical uptake and projected future changes in the carbon sink indicate that future uptake will largely continue to occur in historically important regions. We conduct a detailed breakdown of the sources of uncertainty in the future carbon sink by region. Consistent with CMIP5 models, scenario uncertainty dominates at the global scale, followed by model uncertainty and then internal variability. We demonstrate how the importance of internal variability increases moving to smaller spatial scales and go on to show how the breakdown between scenario, model, and internal variability changes between different ocean regions, governed by different processes. Using the CanESM5 large ensemble we show that internal variability changes with time based on the scenario, breaking the widely employed assumption of stationarity. As with the mean sink, we show that uncertainty in the future ocean carbon sink is also concentrated in the known regions of historical uptake. Patterns in the signal-to-noise ratio have implications for observational detectability and time of emergence, which we show to vary both in space and with scenario. We show that the largest variations in emergence time across scenarios occur in regions where the ocean sink is less sensitive to forcing – outside of the highly active regions. In agreement with CMIP5 studies, our results suggest that for a better chance of early detection of changes in the ocean carbon sink and to efficiently reduce uncertainty in future carbon uptake, highly active regions, including the northwestern Atlantic and the Southern Ocean, should receive additional focus for modeling and observational efforts.

Combining Argo and Satellite Data Using Model-Derived Covariances: Blue Maps

Blue Maps aims to exploit the versatility of an ensemble data assimilation system to deliver gridded estimates of ocean temperature, salinity, and sea-level with the accuracy of an observation-based product. Weekly maps of ocean properties are produced on a 1/10°, near-global grid by combining Argo profiles and satellite observations using ensemble optimal interpolation (EnOI). EnOI is traditionally applied to ocean models for ocean forecasting or reanalysis, and usually uses an ensemble comprised of anomalies for only one spatiotemporal scale (e.g., mesoscale). Here, we implement EnOI using an ensemble that includes anomalies for multiple space- and time-scales: mesoscale, intraseasonal, seasonal, and interannual. The system produces high-quality analyses that produce mis-fits to observations that compare well to other observation-based products and ocean reanalyses. The accuracy of Blue Maps analyses is assessed by comparing background fields and analyses to observations, before and after each analysis is calculated. Blue Maps produces analyses of sea-level with accuracy of about 4 cm; and analyses of upper-ocean (deep) temperature and salinity with accuracy of about 0.45 (0.15) degrees and 0.1 (0.015) practical salinity units, respectively. We show that the system benefits from a diversity of ensemble members with multiple scales, with different types of ensemble members weighted accordingly in different dynamical regions.

Extending Limited In Situ Mountain Weather Observations to the Baseline Climate: A True Verification Case Study

The availability of in situ atmospheric observations decreases with elevation and topographic complexity. Data sets based on numerical atmospheric modeling, such as reanalysis data sets, represent an alternative source of information, but they often suffer from inaccuracies, e.g., due to insufficient spatial resolution. sDoG (statistical Downscaling for Glacierized mountain environments) is a reanalysis data postprocessing tool designed to extend short-term weather station data from high mountain sites to the baseline climate. In this study, sDoG is applied to ERA-Interim predictors to produce a retrospective forecast of daily air temperature at the Vernagtbach climate monitoring site (2640 MSL) in the Central European Alps. First, sDoG is trained and cross-validated using observations from 2002 to 2012 (cross-validation period). Then, the sDoG retrospective forecast and its cross-validation-based uncertainty estimates are evaluated for the period 1979–2001 (hereafter referred to as the true evaluation period). We demonstrate the ability of sDoG to model air temperature in the true evaluation period for different temporal scales: day-to-day variations, year-to-year and season-to-season variations, and the 23-year mean seasonal cycle. sDoG adds significant value over a selection of reference data sets available for the site at different spatial resolutions, including state-of-the-art global and regional reanalysis data sets, output by a regional climate model, and an observation-based gridded product. However, we identify limitations of sDoG in modeling summer air temperature variations particularly evident in the first part of the true evaluation period. This is most probably related to changes of the microclimate around the Vernagtbach climate monitoring site that violate the stationarity assumption underlying sDoG. When comparing the performance of the considered reference data sets, we cannot demonstrate added value of the higher resolution data sets over the data sets with lower spatial resolution. For example, the global reanalyses ERA5 (31 km resolution) and ERA-Interim (80 km resolution) both clearly outperform the higher resolution data sets ERA5-Land (9 km resolution), UERRA HARMONIE (11 km resolution), and UERRA MESCAN-SURFEX (5.5 km resolution). Performance differences among ERA5 and ERA-Interim, by contrast, are comparably small. Our study highlights the importance of station-scale uncertainty assessments of atmospheric numerical model output and downscaling products for high mountain areas both for data users and model developers.

Impact of Nonzero Intercept Gas Transfer Velocity Parameterizations on Global and Regional Ocean–Atmosphere CO2 Fluxes

Carbon dioxide (CO2) fluxes between the ocean and atmosphere (FCO2) are commonly computed from differences between their partial pressures of CO2 (ΔpCO2) and the gas transfer velocity (k). Commonly used wind-based parameterizations for k imply a zero intercept, although in situ field data below 4 m s−1 are scarce. Considering a global average wind speed over the ocean of 6.6 m s−1, a nonzero intercept might have a significant impact on global FCO2. Here, we present a database of 245 in situ measurements of k obtained with the floating chamber technique (Sniffle), 190 of which have wind speeds lower than 4 m s−1. A quadratic parameterization with wind speed and a nonzero intercept resulted in the best fit for k. We further tested FCO2 calculated with a different parameterization with a complementary pCO2 observation-based product. Furthermore, we ran a simulation in a well-tested ocean model of intermediate complexity to test the implications of different gas transfer velocity parameterizations for the natural carbon cycle. The global ocean observation-based analysis suggests that ignoring a nonzero intercept results in an ocean-sink increase of 0.73 Gt C yr−1. This corresponds to a 28% higher uptake of CO2 compared with the flux calculated from a parameterization with a nonzero intercept. The differences in FCO2 were higher in the case of low wind conditions and large ΔpCO2 between the ocean and atmosphere. Such conditions occur frequently in the Tropics.

Can CMIP5 Earth System Models Reproduce the Interannual Variability of Air–Sea CO2 Fluxes over the Tropical Pacific Ocean?

Abstract Interannual variability of air–sea CO2 exchange is an important metric that represents the interaction between the carbon cycle and climate change. Although previous studies report a large bias in the CO2 flux interannual variability in many Earth system models (ESMs), the reason for this bias remains unclear. In this study, the performance of ESMs in phase 5 of the Coupled Model Intercomparison Project (CMIP5) is assessed in the context of the variability of air–sea CO2 flux over the tropical Pacific related to El Niño–Southern Oscillation (ENSO) using an emission-driven historical experiment. Using empirical orthogonal function (EOF) analysis, the first principal component of air–sea CO2 flux shows a significant relationship with the Niño-3.4 index in both the observation-based product and models. In the observation-based product, the spatial pattern of EOF1 shows negative anomalies in the central Pacific, which is, however, in contrast to those in several ESMs, and even opposite in sign to those in HadGEM2-ES and MPI-ESM-LR. The unrealistic response of the air–sea CO2 flux to ENSO mainly originates from the biases in the anomalous surface-water CO2 partial pressure (). A linear Taylor expansion by decomposing the anomalous into contributions from salinity, sea surface temperature, dissolved inorganic carbon (DIC), and alkalinity is applied to diagnose the biases. The results show that decreased during El Niño results from reduced upwelling of high-concentration DIC from deeper layers that overwhelms the increasing caused by warmer sea surface temperature. Overly weak reduction of vertical motion during El Niño and weak vertical gradients of climatological DIC concentration are the main reasons for biases in the negative surface DIC anomalies and eventually the anomalies. This study highlights the importance of both physical ocean responses to El Niño and climatological distributions of carbon-related tracers in the simulation of the interannual variability of air–sea CO2 fluxes.

Strengthening seasonal marine CO2 variations due to increasing atmospheric CO2

The increase of atmospheric CO2 (ref. 1 ) has been predicted to impact the seasonal cycle of inorganic carbon in the global ocean2,3, yet the observational evidence to verify this prediction has been missing. Here, using an observation-based product of the oceanic partial pressure of CO2 (pCO2) covering the past 34 years, we find that the winter-to-summer difference of the pCO2 has increased on average by 2.2 ± 0.4 μatm per decade from 1982 to 2015 poleward of 10° latitude. This is largely in agreement with the trend expected from thermodynamic considerations. Most of the increase stems from the seasonality of the drivers acting on an increasing oceanic pCO2 caused by the uptake of anthropogenic CO2 from the atmosphere. In the high latitudes, the concurrent ocean-acidification-induced changes in the buffer capacity of the ocean enhance this effect. This strengthening of the seasonal winter-to-summer difference pushes the global ocean towards critical thresholds earlier, inducing stress to ocean ecosystems and fisheries 4 . Our study provides observational evidence for this strengthening seasonal difference in the oceanic carbon cycle on a global scale, illustrating the inevitable consequences of anthropogenic CO2 emissions. Uptake of anthropogenic CO2 changes the surface ocean inorganic carbon system. Analysis of observations shows an increase in the seasonal oceanic carbon cycle, amplifying the ocean acidification signal with implications for marine biota.

The Intermediate Complexity Atmospheric Research Model (ICAR)

Abstract With limited computational resources, there is a need for computationally frugal models. This is particularly the case for atmospheric sciences, which have long relied on either simplistic analytical solutions or computationally expensive numerical models. The simpler solutions are inadequate for many problems, while the cost of numerical models makes their use impossible for many problems, most notably high-resolution climate downscaling applications spanning large areas, long time periods, and many global climate projections. Here the Intermediate Complexity Atmospheric Research model (ICAR) is presented to provide a new step along the modeling complexity continuum. ICAR leverages an analytical solution for high-resolution perturbations to wind velocities, in conjunction with numerical physics schemes, that is, advection and cloud microphysics, to simulate the atmosphere. The focus of the initial development of ICAR is for predictions of precipitation, and eventually temperature, humidity, and radiation at the land surface. Comparisons between ICAR and the Weather Research and Forecasting (WRF) Model for simulations over an idealized mountain are presented, as well as among ICAR, WRF, and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) observation-based product for a year-long simulation over the Colorado Rockies. In the ideal simulations, ICAR matches WRF precipitation predictions across a range of environmental conditions with a coefficient of determination r2 of 0.92. In the Colorado Rockies, ICAR, WRF, and PRISM show very good agreement, with differences between ICAR and WRF comparable to the differences between WRF and PRISM in the cool season. For these simulations, WRF required 140–800 times more computational resources than ICAR.

Carbon cycle. The dominant role of semi-arid ecosystems in the trend and variability of the land CO₂ sink.

The growth rate of atmospheric carbon dioxide (CO2) concentrations since industrialization is characterized by large interannual variability, mostly resulting from variability in CO2 uptake by terrestrial ecosystems (typically termed carbon sink). However, the contributions of regional ecosystems to that variability are not well known. Using an ensemble of ecosystem and land-surface models and an empirical observation-based product of global gross primary production, we show that the mean sink, trend, and interannual variability in CO2 uptake by terrestrial ecosystems are dominated by distinct biogeographic regions. Whereas the mean sink is dominated by highly productive lands (mainly tropical forests), the trend and interannual variability of the sink are dominated by semi-arid ecosystems whose carbon balance is strongly associated with circulation-driven variations in both precipitation and temperature.