Geoscientific Model Development Discussions

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EISSN : 1991-962X
Current Publisher: Copernicus GmbH (10.5194)
Total articles ≅ 1,429
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Geoscientific Model Development Discussions pp 1-25; doi:10.5194/gmd-2021-11-rc2

Abstract:
The newest iteration of the Canadian Earth System Model (CanESM5.0.3) has an Effective Climate Sensitivity (ECS) of 5.65 kelvin, which is a 54 % increase relative to the model's previous version (CanESM2 – 3.67 K), and the highest sensitivity of all current models participating in the sixth phase of the coupled model inter-comparison project (CMIP6). Here, we explore the underlying causes behind CanESM5's increased ECS via comparison of forcing and feedbacks between CanESM2 and CanESM5. We find only modest differences in radiative forcing as a response to CO2 between model versions. Through the use of cloud area fraction output and radiative kernels, we find that more positive shortwave cloud feedbacks – particularly with regards to low clouds across the equatorial pacific, as well as sub/extratropical free troposphere cloud optical depth – are the dominant contributors to CanESM5's increased climate sensitivity. Additional simulations with prescribed sea surface temperatures reveal that the spatial pattern of surface temperature change explains the pattern of change in low cloud fraction, but does not fully explain the increased ECS in CanESM5. The results from CanESM5 are consistent with increased ECS in several other CMIP6 models, which has been primarily attributed to changes in shortwave cloud feedbacks.
Shuqi Lin, , , Dai Yamazaki,
Geoscientific Model Development Discussions pp 1-37; doi:10.5194/gmd-2021-75-rc1

Abstract:
Terrestrial surface water temperature is a key variable affecting water quality and energy balance, and thermodynamics and fluid dynamics are tightly coupled in fluvial and lacustrine systems. Streamflow generally plays a role in the horizontal redistribution of heat, and thermal exchange in lakes predominantly occurs in a vertical direction. However, numerical models simulate the water temperature for uncoupled rivers and lakes, and the linkages between them on a global scale remain unclear. In this study, we proposed an integrated modeling framework: Tightly Coupled framework for Hydrology of Open water Interactions in River–lake network (T-CHOIR). The objective is to simulate terrestrial fluvial and thermodynamics as a continuum of mass and energy in solid and liquid phases redistributed among rivers and lakes. T-CHOIR uses high-resolution geographical information harmonized over fluvial and lacustrine networks. The results have been validated through comparison with in-situ observations and satellite-based data products, and the model sensitivity has been tested with multiple meteorological forcing datasets. It was observed that the coupled mode outperformed the river-only mode in terms of discharge and temperature in downstream of lakes; it was also observed that seasonal and interannual variation in lake water levels and temperature are also more reliable in the coupled mode. The inclusion of lakes in the coupled model resulted in an increase in river temperatures during winter in mid-latitudes and a decrease in temperatures during summer in high latitudes, which reflects the role of lakes as a form of large heat storage. The river–lake coupling framework presented herein provides a basis for further elucidating the role of terrestrial surface water in in Earth’s energy cycle.
Mark Muetzelfeldt, , , , Alison J. Stirling,
Geoscientific Model Development Discussions pp 1-27; doi:10.5194/gmd-2020-388-ac1

Abstract:
A procedure for producing a climatology of tropical wind shear from climate-model output is presented. The procedure is designed to find grid columns in the model where the organization of convection may be present. The climate-model output consists of east–west and north–south wind profiles at 20 equally spaced pressure levels from 1000 hPa to 50 hPa, and the Convective Available Potential Energy (CAPE) as diagnosed by the model’s Convection Parametrization Scheme (CPS). The procedure begins by filtering the wind profiles based on their maximum shear, and on a CAPE threshold of 100 J kg−1. The filtered profiles are normalized using the maximum wind speed at each pressure level, and rotated to align the wind at 850 hPa. From each of the filtered profiles, a sample has been produced with 40 dimensions (20 for each wind direction). The number of dimensions is reduced by using Principal Component Analysis (PCA), where the requirement is that 90 % of the variance must be explained by the principal components. This requires keeping the first seven leading principal components. The samples, as represented by their principal components, can then be clustered using the K-Means Clustering Algorithm (KMCA). 10 clusters are chosen to represent the samples, and the median of each cluster defines a Representative Wind Profile (RWP) – a profile that represents the shear conditions of the wind profiles produced by the climate model. The RWPs are analysed, first in terms of their vertical structure, and then in terms of their geographical and temporal distributions. We find that the RWPs have some features often associated with the organization of convection, such as low-level and mid-level shear. Some of the RWPs can be matched with wind profiles taken from case studies of organization of convection, such as squall lines seen in Tropical Ocean Global Atmosphere, Coupled Atmosphere Ocean Research Experiment (TOGA–COARE). The RWPs’ geographical distributions show that each RWP occurs preferentially in certain regions. Six of the RWPs occur preferentially over land, while three occur preferentially over oceans. The temporal distribution of RWPs shows that they occur preferentially at certain times of the year, with the distributions having mainly one or two modes. Their geographical and temporal distributions are compared with those seen in previous studies of organized convection, and some broad and specific similarities are noted. By performing the analysis on climate-model output, we lay the foundations for the development of the representation of shear-induced organization in a CPS. This would use the same methodology to diagnose where the organization of convection occurs, and modify the CPS in an appropriate manner to represent it.
Guy Munhoven
Geoscientific Model Development Discussions pp 1-22; doi:10.5194/gmd-2020-447-rc2

Abstract:
The successful and efficient approach at the basis of SolveSAPHE (Munhoven, 2013), which determines the carbonate system speciation by calculating pH from total alkalinity (AlkT) and dissolved inorganic carbon (C T), and which converges from any physically sensible pair of such data, has been adapted and further developed for work with AlkT & CO2, AlkT & HCO3 − and AlkT & CO3 2−. The mathematical properties of the three modified alkalinity-pH equations are explored. It is shown that the AlkT & CO2 and AlkT & HCO3 − problems have one and only one positive root for any physically sensible pair of data (i.e., such that, resp., [CO2] > 0 and [HCO3 −] > 0). The space of AlkT & CO3 2− pairs is partitioned into regions where there is either no solution, one solution or where there are two. The numerical solution of the modified alkalinity-pH equations is far more demanding than that for the original AlkT-C T pair as they exhibit strong gradients and are not always monotonous. The two main algorithms used from SolveSAPHE v.1 had to be revised in depth to reliably process the three additional data input pairs. The AlkT & CO2 pair is numerically the most challenging. With the Newton-Raphson based solver, it takes about five times as long to solve as the companion AlkT & C T pair, while AlkT & CO2 requires about four times as much time. All in all, it is nevertheless the secant based solver that offers the best performances. It outperforms the Newton-Raphson based one by up to a factor of four, to reach equation residuals that are up to seven orders of magnitude lower. Just like the pH solvers from routines from the v.1 series, SolveSAPHE v.2 includes automatic root bracketing and efficient initialisation schemes for the iterative solvers. For AlkT & CO3 2− pairs of data, it also determines the number of roots and calculates non-overlapping bracketing intervals. An open source reference implementation in Fortran 90 of the new algorithms is made publicly available for usage under the GNU Lesser General Public Licence v.3 or later.
, Risto Makkonen, , , Vaughan T. J. Phillips, Philippe Le Sager,
Geoscientific Model Development Discussions pp 1-43; doi:10.5194/gmd-2021-49-rc1

Abstract:
We have implemented and evaluated a secondary organic aerosol scheme within the chemistry transport model TM5-MP in this work. In earlier versions of TM5-MP the secondary organic aerosol was emitted as Aitken sized particle mass emulating the condensation. In the current scheme we simulate the formation of SOA from oxidation of isoprene and monoterpenes by ozone and hydroxyl radicals which produce semi-volatile organic compounds and extremely low-volatility compounds. Subsequently, SVOC and ELVOC can condense on particles. Furthermore, we have introduced a new particle formation mechanism depending on the concentration of ELVOCs. For evaluation purposes, we have simulated the year 2010 with the old and new scheme, where we see an increase in simulated production of SOA from 39.9 Tg y−1 with the old scheme to 52.5 Tg y−1 with the new scheme. For more detailed analysis, the particle mass and number concentrations and their influence on the simulated aerosol optical depth are compared to observations. Phenomenologically, the new particle formation scheme implemented here is able to reproduce the occurrence of observed particle formation events. However, the concentrations of formed particles are clearly lower as is the subsequent growth to larger sizes. Compared to the old scheme, the new scheme is increasing the number concentrations across the observation stations while still underestimating the observations. The total aerosol mass concentrations in the US show a much better seasonal cycle and removal of a clear overestimation of concentrations. In Europe the mass concentrations are lowered leading to a larger underestimation of observations. Aerosol optical depth is generally slightly increased except in the northern high latitudes. This brings the simulated annual global mean AOD closer to observational estimate. However, as the increase is rather uniform, biases tend to be reduced only in regions where the model underestimates the AOD. Furthermore, the correlation against satellite retrievals and ground-based sun-photometer observations are improved. Although the process based approach to SOA formation causes reduction in model performance in some areas, overall the new scheme improves the simulated aerosol fields.
Geoscientific Model Development Discussions pp 1-27; doi:10.5194/gmd-2021-26-rc2

Abstract:
Atmospheric chemical forecasts highly rely on various model parameters, which are often insufficiently known, as emission rates and deposition velocities. However, a reliable estimation of resulting uncertainties by an ensemble of forecasts is impaired by the high-dimensionality of the system. This study presents a novel approach to efficiently perturb atmospheric-chemical model parameters according to their leading coupled uncertainties. The algorithm is based on the idea that the forecast model acts as a dynamical system inducing multi-variational correlations of model uncertainties. The specific algorithm presented in this study is designed for parameters which depend on local environmental conditions and consists of three major steps: (1) an efficient assessment of various sources of model uncertainties spanned by independent sensitivities, (2) an efficient extraction of leading coupled uncertainties using eigenmode decomposition, and (3) an efficient generation of perturbations for high-dimensional parameter fields by the Karhunen-Loéve expansion. Due to their perceived simulation challenge the method has been applied to biogenic emissions of five trace gases, considering state-dependent sensitivities to local atmospheric and terrestrial conditions. Rapidly decreasing eigenvalues state high spatial- and cross-correlations of regional biogenic emissions, which are represented by a low number of dominating components. Consequently, leading uncertainties can be covered by low number of perturbations enabling ensemble sizes of the order of 10 members. This demonstrates the suitability of the algorithm for efficient ensemble generation for high-dimensional atmospheric chemical parameters.
, Risto Makkonen, , , Vaughan T. J. Phillips, Philippe Le Sager,
Geoscientific Model Development Discussions pp 1-43; doi:10.5194/gmd-2021-49-cc1

Abstract:
We have implemented and evaluated a secondary organic aerosol scheme within the chemistry transport model TM5-MP in this work. In earlier versions of TM5-MP the secondary organic aerosol was emitted as Aitken sized particle mass emulating the condensation. In the current scheme we simulate the formation of SOA from oxidation of isoprene and monoterpenes by ozone and hydroxyl radicals which produce semi-volatile organic compounds and extremely low-volatility compounds. Subsequently, SVOC and ELVOC can condense on particles. Furthermore, we have introduced a new particle formation mechanism depending on the concentration of ELVOCs. For evaluation purposes, we have simulated the year 2010 with the old and new scheme, where we see an increase in simulated production of SOA from 39.9 Tg y−1 with the old scheme to 52.5 Tg y−1 with the new scheme. For more detailed analysis, the particle mass and number concentrations and their influence on the simulated aerosol optical depth are compared to observations. Phenomenologically, the new particle formation scheme implemented here is able to reproduce the occurrence of observed particle formation events. However, the concentrations of formed particles are clearly lower as is the subsequent growth to larger sizes. Compared to the old scheme, the new scheme is increasing the number concentrations across the observation stations while still underestimating the observations. The total aerosol mass concentrations in the US show a much better seasonal cycle and removal of a clear overestimation of concentrations. In Europe the mass concentrations are lowered leading to a larger underestimation of observations. Aerosol optical depth is generally slightly increased except in the northern high latitudes. This brings the simulated annual global mean AOD closer to observational estimate. However, as the increase is rather uniform, biases tend to be reduced only in regions where the model underestimates the AOD. Furthermore, the correlation against satellite retrievals and ground-based sun-photometer observations are improved. Although the process based approach to SOA formation causes reduction in model performance in some areas, overall the new scheme improves the simulated aerosol fields.
Paolo Pelucchi, ,
Geoscientific Model Development Discussions pp 1-26; doi:10.5194/gmd-2020-384-ac1

Abstract:
In this study, we implement a vertical grid refinement scheme in the radiation routine of the global aerosol-climate model ECHAM-HAM, aiming to improve the representation of stratocumulus clouds and address the underestimation of their cloud cover. The scheme is based on a reconstruction of the temperature inversion as a physical constraint for the cloud top. On the refined grid, the boundary layer and the free troposphere are separated and the cloud's layer is made thinner. The cloud cover is re-calculated either by conserving the cloud volume (SC-VOLUME) or by using the Sundqvist cloud cover routine on the new grid representation (SC-SUND). In global climate simulations, we find that the SC-VOLUME approach is inadequate, as in most cases there is a mismatch between the layer of the inversion and of the stratocumulus cloud, which prevents its application and is itself likely caused by too-low vertical resolution. With the SC-SUND approach, the possibility for new clouds to be formed on the refined grid results in a large increase in mean total cloud cover in stratocumulus regions. In both cases, however, the changes exerted in the radiation routine are too weak to produce a significant improvement of the simulated stratocumulus cloud cover. The grid refinement scheme could be used more effectively for this purpose if implemented directly in the model's cloud microphysics and cloud cover routines.
Mingshuai Zhang, Chun Zhao, Yuhan Yang, Qiuyan Du, Yonglin Shen, Shengfu Lin,
Geoscientific Model Development Discussions pp 1-46; doi:10.5194/gmd-2021-29-rc3

Abstract:
Biogenic volatile organic compounds (BVOCs) simulated by current air quality and climate models still have large uncertainties, which can influence atmosphere chemistry and secondary pollutant formation over East China. These uncertainties are generally resulted from two sources. One is from different biogenic emission schemes coupled in model, representing for different treatments of physical and chemistry progresses during the emissions of BVOCs. The other is from the biased distribution of vegetation types over a specific region. In this study, the version of WRF-Chem updated by the University of Science and Technology of China (USTC version of WRF-Chem) from the public WRF-Chem(v3.6) is used. The modeling results over East China with different versions (v1.0, v2.0, v3.0) of Model of Emissions of Gases and Aerosols from Nature (MEGAN) in WRF-Chem are examined and documented. Sensitivity experiments with these three versions of MEGAN and two vegetation datasets are conducted to investigate the difference of three MEGAN versions in modeling biogenic VOCs and its dependence on the vegetation distributions. The experiments are also conducted for spring (April) and summer (July) to examine the seasonality of the modeling results. The results indicate that MEGANv3.0 simulates the largest amount of biogenic isoprene emissions over East China. The different performance among MEGAN versions is primarily due to their different treatments of applying emission factors and vegetation types. In particular, the results highlight the importance of considering sub-grid vegetation fraction in estimating BVOCs emissions. Among all activity factors, temperature-dependent factor dominates the seasonal change of activity factor in all three versions of MEGAN, while the different response to the leaf area index (LAI) change determines the difference among the three versions in seasonal variation of BVOC emissions. The simulated surface ozone concentration due to BVOCs can be significantly different among the experiments with three versions of MEGAN, which is mainly due to their impacts on surface VOCs and NOx concentrations. This study suggests that there is still large uncertain range in modeling BVOCs and their impacts on photochemistry and ozone production. More accurate vegetation distribution and measurements of biogenic emission flux and species concentration are needed to evaluate the model performance and reduce the uncertainties.
Yoshihiro Nakayama, Dimitris Menemenlis, Ou Wang, Hong Zhang, Ian Fenty, An T. Nguyen
Geoscientific Model Development Discussions pp 1-27; doi:10.5194/gmd-2020-421-rc2

Abstract:
The Antarctic coastal ocean is impacting sea level rise, deep-ocean circulation, marine ecosystems, and global carbon cycle. To better describe and understand these processes and their variability, it is necessary to combine the sparse available observations with best-possible numerical descriptions of ocean circulation. In particular, high ice-shelf melting rates in the Amundsen Sea have attracted many observational campaigns and we now have some limited oceanographic data that capture seasonal and interannual variability during the past decade. One method to combine observations with numerical models that can maximize the information extracted from the sparse observations is the adjoint method, as developed and implemented for global ocean state estimation by the Estimating the Circulation and Climate of the Ocean (ECCO) project. Here, for the first time, we apply the adjoint-model estimation method to a regional configuration of the Amundsen and Bellingshausen Seas, Antarctica, including explicit representation of sub-ice shelf cavities. We utilize observations available during 2010–2014, including ship-based and seal-tagged CTD measurements, moorings, and satellite sea-ice concentration estimates. After 20 iterations of the adjoint-method minimization algorithm, the cost function, here defined as a sum of weighted model-data difference, is reduced by 65 % relative to the baseline simulation by adjusting initial conditions, atmospheric forcing, and vertical diffusivity. The sea-ice and ocean components of the cost function are reduced by 59 % and 70 %, respectively. Major improvements include better representations of (1) Winter Water (WW) characteristics and (2) intrusions of modified Circumpolar Deep Water (mCDW) towards the Pine Island Glacier. Sensitivity experiments show that ~40 % and ~10 % of improvements in sea ice and ocean state, respectively, can be attributed to the adjustment of air temperature and wind. This study is a preliminary demonstration of adjoint-method optimization with explicit representation of ice-shelf cavity circulation. Despite the 65 % cost reduction, substantial model-data discrepancies remain, in particular with annual and interannual variability observed by moorings in front of the Pine Island Ice Shelf. We list a series of possible causes for these residuals, including limitations of the model, the optimization methodology, and observational sampling. In particular, we hypothesize that that residuals could be further reduced if the model could more accurately represent sea ice concentration and coastal polynyas.
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