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Brune, Sebastian; Buschow, Sebastian; Friederichs, Petra
doi: 10.1002/qj.3751pmid: N/A
High‐resolution simulations (grid spacing 2.5 km) are performed with ICON‐LEM to characterize convective organization in the Tropics during August 2016 over a large domain ranging from northeastern South America, along the tropical Atlantic to Africa (8,000×3,000 km). The degree of organization is measured by a refined version of the wavelet‐based organization index (WOI), which is able to characterize the scale, the intensity and anisotropy of convection based on rain rates alone. Exploiting the localization of wavelets both in space and time, we define a localized version of the convective organization index (LWOI). We compare convection observed in satellite‐derived rain rates with the corresponding processes simulated by ICON‐LEM. Model and observations indicate three regions with different kinds of convective organization. Continental convection over West Africa has a predominantly meridional orientation and is more organized than over South America, because it acts on larger scales and is more intense. Convection over the tropical Atlantic is zonally oriented along the ITCZ and less intense. ICON and observations agree on the number and intensity of the African easterly waves during the simulation period. The waves are associated with strong vorticity anomalies and are clearly visible in a spatiotemporal wavelet analysis. The central speed and the wavelength of the waves is simulated well. Both the scale and intensity components of LWOI in ICON are significantly correlated with environmental variables. The scale of precipitation is related to wind shear, CAPE and its tendency, while the intensity strongly correlates with column‐integrated humidity, upper‐level divergence and maximum vertical wind speed. This demonstrates that the LWOI components capture important characteristics of convective precipitation.
Semakin, A. N.; Rastigejev, Y.
doi: 10.1002/qj.3752pmid: N/A
Substantial numerical difficulties associated with the computational modeling of multiscale global atmospheric chemical transport impose severe limitations on the spatial resolution of nonadaptive fixed grids. The crude spatial discretization introduces a large amount of numerical diffusion into the system, which, in combination with strong flow stretching, causes large numerical errors. To resolve this issue, we have developed an optimized wavelet‐based adaptive mesh refinement (OWAMR) method. The OWAMR is a three‐dimensional adaptive method that introduces a fine grid dynamically only in the regions where small spatial structures occur. The algorithm uses a new two‐parameter adaptation criterion that significantly (by factors between 1.5 and 2.7) reduces the number of grid points compared with the more conventional one‐parameter grid adaptation used by wavelet‐based adaptive techniques and high‐order upwind schemes, which enable one to increase the accuracy of approximation of the advection operator substantially. It has been shown that the method simulates the dynamics of a pollution plume that travels on a global scale, producing less than 3% error. To achieve such accuracy, conventional three‐dimensional nonadaptive techniques would require five orders of magnitude more computational resources. Therefore, the method provides a realistic opportunity to model accurately a variety of the most demanding multiscale problems in the area of atmospheric chemical transport, which are difficult or impossible to simulate on existing computational facilities with conventional fixed‐grid techniques.
Gilbert, E.; Orr, A.; King, J. C.; Renfrew, I. A.; Lachlan‐Cope, T.; Field, P. F.; Boutle, I. A.
doi: 10.1002/qj.3753pmid: N/A
Surface melting on Antarctic Peninsula ice shelves can influence ice shelf mass balance, and consequently sea level rise. We show that summertime cloud phase on the Larsen C ice shelf on the Antarctic Peninsula strongly influences the amount of radiation received at the surface and can determine whether or not melting occurs. While previous work has separately evaluated cloud phase and the surface energy balance (SEB) during summertime over Larsen C, no previous studies have examined this relationship quantitatively. Furthermore, regional climate models frequently produce surface radiation biases related to cloud ice and liquid water content. This study uses a high‐resolution regional configuration of the UK Met Office Unified Model (MetUM) to assess the influence of cloud ice and liquid properties on the SEB, and consequently melting, over the Larsen C ice shelf. Results from a case‐study show that simulations producing a vertical cloud phase structure more comparable to aircraft observations exhibit smaller surface radiative biases. A configuration of the MetUM adapted to improve the simulation of cloud phase reproduces the observed surface melt most closely. During a five‐week simulation of summertime conditions, model melt biases are reduced to <2 W·m−2: a four‐fold improvement on a previous study that used default MetUM settings. This demonstrates the importance of cloud phase in determining summertime melt rates on Larsen C.
Saffin, Leo ; Hatfield, Sam; Düben, Peter; Palmer, Tim
doi: 10.1002/qj.3754pmid: N/A
Reducing numerical precision can save computational costs which can then be reinvested for more useful purposes. This study considers the effects of reducing precision in the parametrizations of an intermediate complexity atmospheric model (SPEEDY). We find that the difference between double‐precision and reduced‐precision parametrization tendencies is proportional to the expected machine rounding error if individual timesteps are considered. However, if reduced precision is used in simulations that are compared to double‐precision simulations, a range of precision is found where differences are approximately the same for all simulations. Here, rounding errors are small enough to not directly perturb the model dynamics, but can perturb conditional statements in the parametrizations (such as convection active/inactive) leading to a similar error growth for all runs. For lower precision, simulations are perturbed significantly. Precision cannot be constrained without some quantification of the uncertainty. The inherent uncertainty in numerical weather and climate models is often explicitly considered in simulations by stochastic schemes that will randomly perturb the parametrizations. A commonly used scheme is stochastic perturbation of parametrization tendencies (SPPT). A strong test on whether a precision is acceptable is whether a low‐precision ensemble produces the same probability distribution as a double‐precision ensemble where the only difference between ensemble members is the model uncertainty (i.e., the random seed in SPPT). Tests with SPEEDY suggest a precision as low as 3.5 decimal places (equivalent to half precision) could be acceptable, which is surprisingly close to the lowest precision that produces similar error growth in the experiments without SPPT mentioned above. Minor changes to model code to express variables as anomalies rather than absolute values reduce rounding errors and low‐precision biases, allowing even lower precision to be used. These results provide a pathway for implementing reduced‐precision parametrizations in more complex weather and climate models.
doi: 10.1002/qj.3755pmid: N/A
The depth of the jet streams seen in Jupiter's outer weather layer has long been debated, with alternative suggestions of confinement to the weather layer and extensions deep into the planet being considered. Interpretation of measurements from NASA's Juno probe have suggested that weather‐layer jets do extend deep into the planet, down to depths of 𝒪 (3,000 km). However, this relies on the assumption that the jet profile does not change its spatial structure with depth, which may not be the case. In this work, we consider a simple 1.5‐layer shallow‐water model of Jupiter‐like jet streams, with prescribed deep jets in the lower layer, and look at the parameters affecting the strength of the coupling between the layers. We find the value of the Rossby deformation scale, L D, to be particularly important, not just in setting the magnitude of variations in layer depth, but also in dictating the effectiveness of radiative damping. We also find the radiative damping timescales, the energy injection rate, and the spacing of deep jets to be important. We combine these findings into our best‐guess simulations of the real Jupiter and find that low latitudes are relatively uncoupled between the layers, with high latitudes being more tightly coupled. These effects can be tied to the smallness of Jupiter's L D and the effectiveness of radiative damping as a coupling mechanism. These simulations do, however, produce equatorial subrotation and eddy‐momentum fluxes unlike those on the real planet. It may be, therefore, that spatially varying forcing and very long radiative damping timescales are required for this model to be more Jupiter‐like.
doi: 10.1002/qj.3756pmid: N/A
Accurate forecasting of air quality demands better estimates of aerosol emissions. The accuracy of conventional bottom‐up approaches to estimate aerosol emissions depends on the degree to which various influencing parameters are estimated. The availability of satellite observations not only enhances the capability of determining various influencing parameters, but also provides alternate ways of assessing aerosol sources. The present study employs a Lagrangian approach to the Advection Diffusion Equation (ADE) to estimate the transported aerosols and hence the Aerosol Source Strength (ASS) using satellite‐measured Aerosol Optical Depth (AOD) and reanalysis wind data. This top‐down approach is based on the advection and diffusion of atmospheric aerosols considering wind circulation and atmospheric conditions rather than using indicative parameters. ASS was computed every 3 hr at a 0.25°×0.25° grid across California during July 2018. For the computation, AOD retrievals were obtained from the Geostationary Operational Environmental Satellite (GOES)‐16 with observations every 15 min. The data were resampled to the grid every 3 hr, and backward trajectories were run at every gridpoint to ascertain the initial aerosol concentration for the ADE. The final aerosol concentrations obtained from the ADE model were then compared with the observed AOD to obtain the ASS during that time period. The results are indicative of higher ASS around wildfire locations. The ASS values also show good correlation (R2=0.886) with Fire Radiative Power (FRP) obtained from Terra MODIS fire product. The method was further applied to investigate the spatial correlation of ASS with power plant density, which reveals a steady increase in ASS with power plant density (R2=0.82).
De Luca, P.; Messori, G.; Pons, F. M. E.; Faranda, D.
doi: 10.1002/qj.3757pmid: N/A
We propose a novel approach to the study of compound extremes, grounded in dynamical systems theory. Specifically, we present the co‐recurrence ratio (α), which elucidates the dependence structure between variables by quantifying their joint recurrences. This approach is applied to daily climate extremes, derived from the ERA‐Interim reanalysis over the 1979–2018 period. The analysis focuses on concurrent (i.e., same‐day) wet (total precipitation) and windy (10 m wind gusts) extremes in Europe and concurrent cold (2 m temperature) extremes in Eastern North America and wet extremes in Europe. Results for wet and windy extremes in Europe, which we use as a test‐bed for our methodology, show that α peaks during boreal winter. High α values correspond to wet and windy extremes in northwestern Europe, and to large‐scale conditions resembling the positive phase of the North Atlantic Oscillation (NAO). This confirms earlier findings which link the positive NAO to a heightened frequency of extratropical cyclones impacting northwestern Europe. For the Eastern North America–Europe case, α extremes once again reflect concurrent climate extremes – in this case cold extremes over North America and wet extremes over Europe. Our analysis provides detailed spatial information on regional hotspots for these compound extreme occurrences, and encapsulates information on their spatial footprint which is typically not included in a conventional co‐occurrence analysis. We conclude that α successfully characterises compound extremes by reflecting the evolution of the associated meteorological maps. This approach is entirely general, and may be applied to different types of compound extremes and geographical regions.
Fielding, Mark D.; Schäfer, Sophia A. K.; Hogan, Robin J.; Forbes, Richard M.
doi: 10.1002/qj.3758pmid: N/A
To represent the effects of unresolved cloud processes in numerical weather prediction and climate models, parametrizations of the subgrid properties of clouds are required. In this paper, we describe a method for specifying the “cloud‐edge length” within a model grid‐box, which is an important parameter for approximating the subgrid mixing of air at cloud boundaries. We begin by proposing three conceptual models that predict the cloud‐edge length using the grid‐box cloud fraction and a length‐scale to be derived empirically. The conceptual models are then evaluated using a wide range of observations and cloud‐resolving models. Based on the finding that the “effective cloud spacing” approach fits both these data best, we parametrize the effective cloud spacing as a function of pressure and model resolution. An application of this parametrization to the cloud erosion scheme in the ECMWF forecast model is then demonstrated. The effective cloud spacing approach is compared to the “effective cloud scale” approach and is shown to increase cloud fraction in stratocumulus regions, while decreasing cloud fraction in cumulus regions. These cloud changes have the overall effect of decreasing the error of the modelled top‐of‐atmosphere net short‐wave irradiance when compared to CERES observations by around 3%. Additionally, the cloud‐edge length is an important parameter for approximating subgrid radiative transfer and it is hoped that this parametrization will be useful to quantify the effect of representing 3D cloud radiative transfer in global models.
Li, Ying; Stechmann, Samuel N.
doi: 10.1002/qj.3759pmid: N/A
For tropical rainfall, there are several potential sources of predictability, including synoptic‐scale convectively coupled equatorial waves (CCEWs) and intraseasonal oscillations such as the Madden–Julian Oscillation (MJO). In prior work, predictability of these waves and rainfall has mainly been explored using forecast model data. Here, the goal is to estimate the intrinsic predictability using, instead, mainly observational data. To accomplish this, Tropical Rainfall Measuring Mission (TRMM) data are decomposed into different wave types using spectral/Fourier filtering. The predictability of MJO rainfall is estimated to be 22–31 days, depending on longitude, as measured by the lead time when the pattern correlation skill drops below 0.5. The predictability of rainfall associated with convectively coupled equatorial Rossby waves, Kelvin waves, and a background spectrum or nonwave component is estimated to be 8–12, 2–3, and 0–3 days, respectively. Combining all wave types, the overall predictability of tropical rainfall is estimated to be 3–6 days over the Indian and Pacific Ocean regions and on equatorial synoptic and planetary length‐scales. For comparison, outgoing longwave radiation (OLR) was more predictable than rainfall by 5–10 days over these regions. Wave‐removal tests were also conducted to estimate the contribution of each wave type to the overall predictability of rainfall. In summary, no single wave type dominates the predictability of tropical rainfall; each of the types (MJO, CCEWs, and nonwave component) has an appreciable contribution, due to the variance contribution, length of decorrelation time, or a combination of these factors.
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