Southern Ocean anthropogenic carbon sink constrained by sea surface salinity

The ocean attenuates global warming by taking up about one quarter of global anthropogenic carbon emissions. Around 40% of this carbon sink is located in the Southern Ocean. However, Earth system models struggle to reproduce the Southern Ocean circulation and carbon fluxes. We identify a tight relationship across two multimodel ensembles between present-day sea surface salinity in the subtropical-polar frontal zone and the anthropogenic carbon sink in the Southern Ocean. Observations and model results constrain the cumulative Southern Ocean sink over 1850-2100 to 158 ± 6 petagrams of carbon under the low-emissions scenario Shared Socioeconomic Pathway 1-2.6 (SSP1-2.6) and to 279 ± 14 petagrams of carbon under the high-emissions scenario SSP5-8.5. The constrained anthropogenic carbon sink is 14 to 18% larger and 46 to 54% less uncertain than estimated by the unconstrained estimates. The identified constraint demonstrates the importance of the freshwater cycle for the Southern Ocean circulation and carbon cycle.

Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates

Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P, 14 % to 26 % for ET, 28 % to 82 % for R, 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R, 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.

Multi-Frequency Analysis of Simulated versus Observed Variability in Tropospheric Temperature

Studies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets, the method for separating signal and noise, the frequency range considered, and the statistical model used to represent observed natural variability. Of particular interest is the amplitude of the interdecadal noise against which an anthropogenic tropospheric warming signal must be detected. We find that on time scales of 5–20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models participating in the Coupled Model Intercomparison Project. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.

Understanding the Distribution of Multimodel Ensembles

When analyzing multimodel climate ensembles it is often assumed that the ensemble is either truth centered or that models and observations are drawn from the same distribution. Here we analyze CMIP5 ensembles focusing on three measures that separate the two interpretations: the error of the ensemble mean relative to the error of individual models, the decay of the ensemble mean error for increasing ensemble size, and the correlations of the model errors. The measures are analyzed using a simple statistical model that includes the two interpretations as different limits and for which analytical results for the three measures can be obtained in high dimensions. We find that the simple statistical model describes the behavior of the three measures in the CMIP5 ensembles remarkably well. Except for the large-scale means we find that the indistinguishable interpretation is a better assumption than the truth centered interpretation. Furthermore, the indistinguishable interpretation becomes an increasingly better assumption when the errors are based on smaller temporal and spatial scales. Building on this, we present a simple conceptual mechanism for the indistinguishable interpretation based on the assumption that the climate models are calibrated on large-scale features such as, e.g., annual means or global averages.

Assessment of the Sea Surface Temperature Predictability Based on Multimodel Hindcasts

Based on historical forecasts of four individual forecasting systems, we conducted multimodel ensembles (MME) to predict the sea surface temperature anomaly (SSTA) variability and assessed these methods from a deterministic and probabilistic point of view. To investigate the advantages and drawbacks of different deterministic MME methods, we used simple averaged MME with equal weighs (SCM) and the stepwise pattern projection method (SPPM). We measured the probabilistic forecast accuracy by Brier skill score (BSS) combined with its two components: reliability (Brel) and resolution (Bres). The results indicated that SCM showed a high predictability in the tropical Pacific Ocean, with a correlation exceeding 0.8 with a 6-month lead time. In general, the SCM outperformed the SPPM in the tropics, while the SPPM tend to show some positive effect on the correction when at long lead times. Corrections occurred for the spring predictability barrier of ENSO, in particular for improvements when the correlation was low or the RMSE was large using the SCM method. These qualitative results are not susceptible to the selection of the hindcast periods, it is as a rule rather by chance of these individual systems. Performance of our probabilistic MME was better than the Climate Forecast System version2 (CFSv2) forecasts in forecasting COLD, NEUTRAL, and WARM SSTA categories for most regions, mainly due to the contribution of Brel, indicating more adequate ensemble construction strategies of the MME system superior to the CFSv2.