Indian Ocean Dynamic Sea Level, Variability And Projections In CMIP6 Models

The regional sea level variability and its projection amidst the global sea level rise is one of the major concerns for coastal communities. The dynamic sea level plays a major role in the observed spatial deviations in regional sea level rise from the global mean. The present study evaluates 27 climate model simulations from the sixth phase of the coupled model intercomparison project (CMIP6) for their representation of the historical mean states, variability and future projections for the Indian Ocean. Most models reproduce the observed mean state of the dynamic sea level realistically, however consistent positive bias is evident across the latitudinal range of the Indian Ocean. The strongest sea level bias is seen along the Antarctic Circumpolar Current (ACC) regime owing to the stronger than observed south Indian Ocean westerlies and its equatorward bias. Further, this equatorward shift of the wind field resulted in stronger positive windstress curl across the southeasterly trade winds in the southern tropical basin and easterly wind bias along the equatorial waveguide. These anomalous easterly equatorial winds cause upwelling in the eastern part of the basin and keeps the thermocline shallower in the model than observed, resulted in enhanced variability for the dipole zonal mode or Indian Ocean dipole in the tropics. In the north Indian Ocean, the summer monsoon winds are weak in the model causing weaker upwelling and positive sea level bias along the western Arabian Sea. The high-resolution models compare better in simulating the sea level variability, particularly in the eddy dominated regions like the ACC regime in interannual timescale. However, these improved variabilities do not necessarily produce a better mean state likely due to the enhanced mixing driven by parametrizations set in these high-resolution models. Finally, the overall pattern of the projected dynamic sea level rise is found to be similar for the mid (SSP2-4.5) and high-end (SSP5-8.5) scenarios, except that the magnitude is higher under the high emission situation. Notably, the projected dynamic sea level change is found to be milder when only the best performing models are used compared to the full ensemble.

Impact of Model Resolution on the Simulation of Precipitation Extremes over China

Climate models tend to overestimate light precipitation and underestimate heavy precipitation due to low model resolution. This work investigated the impact of model resolution on simulating the precipitation extremes over China during 1995–2014, based on five models from Coupled Model Intercomparison Project 6 (CMIP6), each having low- and high-resolution versions. Six extreme indices were employed: simple daily intensity index (SDII), wet days (WD), total precipitation (PRCPTOT), extreme precipitation amount (R95p), heavy precipitation days (R20mm), and consecutive dry days (CDD). Models with high resolution demonstrated better performance in reproducing the pattern of climatological precipitation extremes over China, especially in the western Sichuan Basin along the eastern side of the Tibetan Plateau (D1), South China (D2), and the Yangtze-Yellow River basins (D3). Decreased biases of precipitation exist in all high-resolution models over D1, with the largest decease in root mean square error (RMSE) being 48.4% in CNRM-CM6. The improvement could be attributed to fewer weak precipitation events (0 mm/day–10 mm/day) in high-resolution models in comparison with their counterparts with low resolutions. In addition, high-resolution models also show smaller biases over D2, which is associated with better capturing of the distribution of daily precipitation frequency and improvement of the simulation of the vertical distribution of moisture content.

Overview of spatial verification methods and their application to ensemble forecasting

State-of-the-art high-resolution NWP models simulate mesoscale systems with a high degree of detail, with large amplitudes and high gradients of fields of weather variables. Higher resolution leads to the spatial and temporal error growth and to a well-known double penalty problem. To solve this problem, the spatial verification methods have been developed over the last two decades, which ignore moderate errors (especially in the position), but can still evaluate the useful skill of a high-resolution model. The paper refers to the updated classification of spatial verification methods, briefly describes the main methods, and gives an overview of the international projects for intercomparison of the methods. Special attention is given to the application of the spatial approach to ensemble forecasting. Popular software packages are considered. The Russian translation is proposed for the relevant English terms. Keywords: high-resolution models, verification, double penalty, spatial methods, ensemble forecasting, object-based methods

Computational methods to study enveloped viral entry

The protein–membrane interactions that mediate viral infection occur via loosely ordered, transient assemblies, creating challenges for high-resolution structure determination. Computational methods and in particular molecular dynamics simulation have thus become important adjuncts for integrating experimental data, developing mechanistic models, and suggesting testable hypotheses regarding viral function. However, the large molecular scales of virus–host interaction also create challenges for detailed molecular simulation. For this reason, continuum membrane models have played a large historical role, although they have become less favored for high-resolution models of protein assemblies and lipid organization. Here, we review recent progress in the field, with an emphasis on the insight that has been gained using a mixture of coarse-grained and atomic-resolution molecular dynamics simulations. Based on successes and challenges to date, we suggest a multiresolution strategy that should yield the best mixture of computational efficiency and physical fidelity. This strategy may facilitate further simulations of viral entry by a broader range of viruses, helping illuminate the diversity of viral entry strategies and the essential common elements that can be targeted for antiviral therapies.

Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

Traditionally, topographic surveying in earth sciences requires high financial investments, elaborate logistics, complicated training of staff and extensive data processing. Recently, off-the-shelf drones with optical sensors already reduced the costs for obtaining a high-resolution dataset of an Earth surface considerably. Nevertheless, costs and complexity associated with topographic surveying are still high. In 2020, Apple Inc. released the iPad Pro 2020 and the iPhone 12 Pro with novel build-in LiDAR sensors. Here we investigate the basic technical capabilities of the LiDAR sensors and we test the application at a coastal cliff in Denmark. The results are compared to state-of-the-art Structure from Motion Multi-View Stereo (SfM MVS) point clouds. The LiDAR sensors create accurate high-resolution models of small objects with a side length > 10 cm with an absolute accuracy of ± 1 cm. 3D models with the dimensions of up to 130 × 15 × 10 m of a coastal cliff with an absolute accuracy of ± 10 cm are compiled. Overall, the versatility in handling outweighs the range limitations, making the Apple LiDAR devices cost-effective alternatives to established techniques in remote sensing with possible fields of application for a wide range of geo-scientific areas and teaching.

Resolution Dependence of Regional Hydro-Climatic Projection: A Case-Study for the Johor River Basin, Malaysia

High resolution models from the High-Resolution Model Intercomparison Project (HighResMIP), part of CMIP6, have the capacity to allow a better representation of the climate system in tropical regions, but how different model resolutions affect hydrological outputs remains unclear. This research aims to evaluate projections of hydro-climatic change of the Johor River Basin (JRB) in southern Peninsular Malaysia between 1985 to 2015 and 2021 to 2050, focusing on uncertainty quantification of hydrological outputs from low (>1°), medium (0.5° to 1°) and high (≤0.5°) horizontal resolution models. These projections show future increases in annual precipitation of 0.4 to 3.1%, minimum and maximum temperature increases of 0.8 to 0.9 °C and 0.9 to 1.1 °C, respectively. These projected climate changes lead to increases in annual mean streamflow of 0.9% to 7.0% and surface runoff of 7.0% to 20.6% in the JRB. These annual mean changes are consistent with those during the wet period (November to December), e.g., streamflow increases of 4.9% to 10.8% and surface runoff of 28.8 to 39.9% in December. Disagreement in the direction of change is found during the dry seasons, (February to March and May to September), where high resolution models project a decrease in future monthly precipitation and streamflow, whilst increases are projected by the medium- and low-resolution models.

Global Land High-Resolution Cloud Climatology Based on an Improved MOD09 Cloud Mask

Clouds play an important role in the energy and moisture cycle of the earth–atmosphere system, which affects many important processes in nature and human societies. However, there are very few fine-grained and high-precision global cloud climatology data available for high-resolution models. In this paper, we produced a fine-grained (1 km resolution) global land cloud climatology (GLHCC) report based on MOD09 cloud masks from 2001 to 2016, with a temporal resolution of 10 days. The two improvements (short-wave infrared and Band 2/6 ratio threshold method) on the original MOD09 cloud mask have reduced the snow, ice, and bright areas mistakenly classified as clouds. The preliminary cloud products undergo the removal of orbital artifacts by Variational Stationary Noise Remover (VSNR) and the removal of abnormal albedo areas to generate the final cloud climatology data. The new product was directly validated by ground-based cloud observations collected from 3777 global weather stations. PATMOS-X from the Advanced Very High Resolution Radiometer (AVHRR) and MOD/MYD35 served as comparison products for consistency check of GLHCC. The assessment results show that GLHCC demonstrated a strong correlation with ground station observations, MOD/MYD35, and PATMOS-X. When the ground observations were taken as the truth value, GLHCC and MOD/MYD35 displayed higher accuracy than PATMOS-X. In most selected interested areas where the three behave differently, GLHCC matched the facts better than MOD/MYD35 and PATMOS-X. The GLHCC can well represent the cloud distribution over the past 16 years and will play an important role in the fine-grained demands of many aspects of nature and human society.

Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

Wildfire smoke is one of the most significant concerns of human and environmental health, associated with its substantial impacts on air quality, weather, and climate. However, biomass burning emissions and smoke remain among the largest sources of uncertainties in air quality forecasts. In this study, we evaluate the smoke emissions and plume forecasts from 12 state-of-the-art air quality forecasting systems during the Williams Flats fire in Washington State, US, August 2019, which was intensively observed during the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Model forecasts with lead times within 1 d are intercompared under the same framework based on observations from multiple platforms to reveal their performance regarding fire emissions, aerosol optical depth (AOD), surface PM2.5, plume injection, and surface PM2.5 to AOD ratio. The comparison of smoke organic carbon (OC) emissions suggests a large range of daily totals among the models, with a factor of 20 to 50. Limited representations of the diurnal patterns and day-to-day variations of emissions highlight the need to incorporate new methodologies to predict the temporal evolution and reduce uncertainty of smoke emission estimates. The evaluation of smoke AOD (sAOD) forecasts suggests overall underpredictions in both the magnitude and smoke plume area for nearly all models, although the high-resolution models have a better representation of the fine-scale structures of smoke plumes. The models driven by fire radiative power (FRP)-based fire emissions or assimilating satellite AOD data generally outperform the others. Additionally, limitations of the persistence assumption used when predicting smoke emissions are revealed by substantial underpredictions of sAOD on 8 August 2019, mainly over the transported smoke plumes, owing to the underestimated emissions on 7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecasts show both positive and negative overall biases for these models, with most members presenting more considerable diurnal variations of sPM2.5. Overpredictions of sPM2.5 are found for the models driven by FRP-based emissions during nighttime, suggesting the necessity to improve vertical emission allocation within and above the planetary boundary layer (PBL). Smoke injection heights are further evaluated using the NASA Langley Research Center's Differential Absorption High Spectral Resolution Lidar (DIAL-HSRL) data collected during the flight observations. As the fire became stronger over 3–8 August, the plume height became deeper, with a day-to-day range of about 2–9 km a.g.l. However, narrower ranges are found for all models, with a tendency of overpredicting the plume heights for the shallower injection transects and underpredicting for the days showing deeper injections. The misrepresented plume injection heights lead to inaccurate vertical plume allocations along the transects corresponding to transported smoke that is 1 d old. Discrepancies in model performance for surface PM2.5 and AOD are further suggested by the evaluation of their ratio, which cannot be compensated for by solely adjusting the smoke emissions but are more attributable to model representations of plume injections, besides other possible factors including the evolution of PBL depths and aerosol optical property assumptions. By consolidating multiple forecast systems, these results provide strategic insight on pathways to improve smoke forecasts.

Tropical North Atlantic response to ENSO: sensitivity to model spatial resolution

In this study the ENSO teleconnection with the Tropical North Atlantic (TNA) sea surface temperatures (SSTs) in boreal spring is analyzed in ocean-atmosphere coupled global circulation models. To assess the role played by horizontal resolution of models on this teleconnection, we used a multi-model dataset which is the first to combine models with both low and high resolution. The TNA response to ENSO projects onto the most significant SST mode of the tropical Atlantic at interannual timescales, the Atlantic meridional mode (AMM). Its evolution is primary driven by the wind-evaporation-SST (WES) feedback, which in turn is based on the development of an initial SST gradient. This study examines and quantifies the relative contribution of a dynamic-related (upwelling) and a thermodynamic-related (evaporation) process in triggering this gradient in the case of the ENSO-TNA teleconnection. While no major contribution is found with the evaporation, a consistent contribution from the coastal upwelling off north-west Africa is identified. This contribution is enhanced in high resolution models and highlights the close link between the upwelling in winter and the development of the AMM in spring. It is further shown that high resolution models present a thinner and more realistic ocean mixed layer within the upwelling area, which enhances the effect of surface winds on upwelling and SSTs. As a consequence high resolution models are more sensitive than low resolution models to surface wind errors, thereby do not ensuring an improved reliability nor predictability of the TNA SST response to ENSO.