Journal of Advances in Modeling Earth Systems最新文献

Sea ice thickness (SIT) estimates derived from CryoSat-2 radar freeboard measurements are assimilated into the Met Office's Forecast Ocean Assimilation Model. We test the sensitivity of winter simulations to the snow depth, radar freeboard product and assumed radar penetration through the snowpack in the freeboard-to-thickness conversion. We find that modifying the snow depth has the biggest impact on the modeled SIT, changing it by up to 0.88 m (48%), compared to 0.65 m (33%) when modifying the assumed radar penetration through the snowpack and 0.55 m (30%) when modifying the freeboard product. We find a doubling in the thermodynamic volume change over the winter season when assimilating SIT data, with the largest changes seen in the congelation ice growth. Next, we determine that the method used to calculate the observation uncertainties of the assimilated data products can change the mean daily model SIT by up to 36%. Compared to measurements collected at upward-looking sonar moorings and during the Operation IceBridge campaign, we find an improvement in the SIT simulations' variability representation when assuming partial radar penetration through the snowpack and when improving the method used to calculate the CryoSat-2 observation uncertainties. This paper highlights a concern for future SIT data assimilation and forecasting, with the chosen parameterization of the freeboard-to-thickness conversion having a substantial impact on model results.

The anelastic theory of effective buoyancy has been generalized to include effects of momentum flux convergence, and has suggested that the dynamics—mediated by the nonlocal perturbation pressure—tends to average over forcing details, yielding vertical acceleration robust to small-scale variations of the flow. Here we aim to substantiate this theoretical assertion through examining a large-eddy simulation (LES) with a 100-m horizontal grid spacing. Specifically, instances of convection in the LES are identified. For these, the buoyancy and dynamic contributions to the vertical momentum tendency are separately diagnosed, and their sensitivity resulting from averaging over sub-cloud-scale features quantified. In the absence of a background shear or vorticity, both buoyancy and vertical momentum flux convergence are the leading effect in the vertical acceleration while the influence of the horizontal momentum flux convergence on the vertical motion appears to be substantially weaker. For deep-convective cases, these contributions at the cloud scale ($��\sim �8�$ km) exhibit a robustness, as measured in a root-mean-square sense, to horizontally smoothing out turbulent features of scales $��\lesssim �3�$ km. As expected, such scales depend on the size of the convective element of interest, while dynamic contributions tend to be more susceptible to horizontal smoothing than does the buoyancy contribution. We thus argue that including the anelastic nonlocal dynamics can help capture the evolution of convective-cloud-scale flows without fully resolving the finer-scale turbulent features embedded in the flow. Results here lend support to simplifying the subgrid-scale representation of moist convection for global climate models and storm-resolving simulations.

In this study, a new open-source package for cloud and precipitation modeling is introduced. Based on Mie theory and existing ice crystal data sets, the scheme generates optical properties for user-defined gas bands, particle size distribution, and crystal habits, ensuring continuity across wide spectral bands and from small particles (clouds) to large particles (precipitation). Compared with existing schemes in GFDL's AM4-MG2, it reduces shortwave reflection of liquid clouds at the top of the atmosphere (TOA) by 1.50 Wm⁻² and increases that of ice clouds by 1.62 Wm⁻², based on offline radiative calculations. Using the new scheme, we find that cloud radiative effects are sensitive to microphysics variables such as particle size and habit, which affect the effective radius. Systematic flux biases may arise if the effective radius is not fully predicted in microphysics due to predefined size and habit distributions. We show that assuming spherical ice crystals underestimates ice-cloud radiative effects by 3.20 Wm⁻² in the longwave TOA and 2.76 Wm⁻² in the shortwave TOA. These biases can be addressed by improving the effective radius approximation with a volume-to-radius ratio derived from in-situ measurements. Combining these findings, we propose that climate models use a set of optics parameterizations for each hydrometeor type while adequently accounting for radiation effects caused by size and habit distributions. Uncertainties due to this simplification are evaluated. This study offers a consistent and physically based representation of radiative processes of clouds and precipitation in weather and climate simulations.

Despite the increasing use of large-scale Land Surface Models (LSMs) in predicting hydrological responses in extreme conditions, there's a critical gap in understanding the uncertainties in these predictions. This study addresses this gap through a detailed diagnostic evaluation of the uncertainties arising from meteorological forcing selection and model parametrization in hydrological simulations of the Community Land Model version 5 (CLM5). CLM5 is configured at a spatial scale of about 12-km to simulate runoff processes for 464 headwater watersheds, selected from the Catchment Attributes for Large-Sample Studies (CAMELS) data set to be representative of physiographic and climatic gradients across the conterminous United States. For each watershed, CLM5 is driven by five commonly used gridded forcing data sets in combination with a large ensemble (>1,200) of key CLM5 hydrologic parameters. Our results suggest that uncertainty in CLM5 runoff simulations resulting from both forcing and parametric sources is markedly higher in arid regions, for example, Great Plains and Midwest regions. Uncertainty in low flow is dominated by parametric uncertainty, while the selection of meteorological forcing contributes more dominantly to high flow and seasonal flows during fall and spring. Our analysis also demonstrates that the selection of forcing data sets and the metrics used to calibrate CLM5 significantly impact the model's predictive accuracy in extreme event severity for both floods and droughts. Overall, the results from this study highlight the need to understand and account for forcing and parametric uncertainties in CLM5 simulations, particularly for hazard and risk assessments addressing hydrologic extremes.

Estimating ocean biogeochemistry (BGC) parameters in Earth System Models is challenging due to multiple error sources and interlinked parameter sensitivities. Reducing the temperature and salinity bias in the ocean physical component of the Norwegian Earth System Model (NorESM) diminishes the BGC state bias at intermediate depth but leads to a greater bias increase near the surface. This suggests that BGC parameters are tuned to compensate for the physical ocean model biases. We successfully apply the iterative ensemble smoother data assimilation technique to estimate BGC parameters in NorESM with reduced bias in its physical ocean component. We estimate BGC parameters based on the monthly climatological error of nitrate, phosphate, and oxygen in a coupled reanalysis of NorESM that assimilates observed monthly climatology of temperature and salinity. First, we compare the performance of globally uniform and spatially varying parameter estimations. Both approaches reduce BGC bias obtained with default parameters, even for variables not assimilated in the parameter estimation (e.g., $��{\text{CO}}_2�$ fluxes and primary production). While spatial parameter estimation performs locally best, it also increases biases in areas with few observations, and overall performs poorer than global parameter estimation. A second iteration further reduces the bias in the near-surface BGC with global parameter estimation. Finally, we assess the performance of global estimated parameters in a 30-year coupled reanalysis produced by assimilating time-varying temperature and salinity observations. This reanalysis reduces error by 10%–20% for phosphate, nitrate, oxygen, and dissolved inorganic carbon compared to a reanalysis done with default parameters.

This study develops a surrogate-based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and land surface. Perturbed physics ensembles of the model during the 2018 summer are used to train a neural network surrogate model to predict lake surface temperature (LST) and near-surface air temperature (T2m). Average physics uncertainties are determined to be 1.5$��°�$C for LST and T2m over land, and 1.9$��°�$C for T2m over lake, but these have significant spatiotemporal variations. We find that atmospheric physics parameterizations alone are the dominant sources of uncertainty (45%–53%), while lake and land parameterizations account for 33% and 38% of the uncertainty of LST and T2m over land respectively. Interactions of atmosphere physics parameterizations with those of the land and lake contribute to an additional 13%–17% of the total variance. LST and T2m over the lake are more uncertain in the deeper northern lakes, particularly during the rapid warming phase that occurs in late spring/early summer. The LST uncertainty increases with sensitivity to the lake model's surface wind stress scheme. T2m over land is more uncertain over forested areas in the north, where it is most sensitive to the land surface model, than the more agricultural land in the south, where it is most sensitive to the atmospheric planetary boundary and surface layer scheme. Uncertainty also increases in the southwest during multiday temperature declines with higher sensitivity to the land surface model.

Dynamical modeling is widely utilized for Antarctic sea ice prediction. However, the relative impact of initializing different model components remains unclear. We compare three sets of hindcasts of the Norwegian Climate Prediction Model (NorCPM), which are initialized by ocean, ocean/sea-ice, or atmosphere data and referred to as the OCN, OCNICE, and ATM hindcasts hereafter. The seasonal cycle of sea ice extent (SIE) in the ATM reanalysis shows a slightly better agreement with observations than the OCN and OCNICE reanalyzes. The trends of sea ice concentration (SIC) in the OCN and OCNICE reanalyzes compare well to observations, but the ATM reanalysis is poor over the western Antarctic. The OCNICE reanalysis yields the most accurate estimation of sea ice variability, while the OCN and ATM reanalyzes are comparable. Evaluation of the hindcasts reveals the predictive skill varies with region and season. Austral winter SIE of the western Antarctic can be skillfully predicted 12 months ahead, while the predictive skill in the eastern Antarctic is low. Austral winter SIE predictability can be largely attributed to high sea surface temperature predictability, thanks to skillful initialization of ocean heat content. The ATM hindcast from July or October performs best due to the effective initialization of sea-ice thickness, which enhances prediction skills until early austral summer via its long memory. Meanwhile, the stratosphere-troposphere coupling contributes to the prediction of springtime. The comparable skill between the OCN and OCNICE hindcasts implies limited benefits from SIC data on prediction when using ocean data.