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Yuta Kawai and Hirofumi Tomita

Abstract

Recently, large-eddy simulation (LES) has been increasingly employed in meteorological simulations because it is a promising method for turbulent parameterization. However, it is still difficult to affirm that the numerical accuracy required for a dynamical core is fully understood. In this study, we derived two theoretical criteria for the order of accuracy of the advection term in a typical situation of the atmospheric boundary layer, and demonstrate their validity by numerical experiments. In the targeted grid-spacing of O (10 m), we determined the required order of accuracy as follows: Based on the criterion of the numerical diffusion error, the upwind scheme must have at least seventh-order accuracy. The fourth-order central scheme is barely acceptable with fourth-order explicit diffusion, provided that its coefficient is one or two orders of magnitude smaller than the implicit diffusion coefficient of the third-order upwind scheme. Based on the criterion of numerical dispersion error, at minimum, the seventh or eighth order is required. The dispersion error was indirect for the energy spectra, although we expect it may affect the local turbulence mechanism. We also investigated the effects of temporal discretization for compressible models, and found that relatively lower-order time schemes are available up to the O(10 m) grid spacing if the time step is sufficiently small due to sound wave limitations. The importance of the derived criteria is that the required order of accuracy increases as the grid spacing decreases. This suggests that considerable care should be taken regarding the numerical error problem for future high-resolution LES.

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Christoph Böhm, Jan H. Schween, Mark Reyers, Benedikt Maier, Ulrich Löhnert, and Susanne Crewell

Abstract

In many hyper-arid ecosystems, such as the Atacama Desert, fog is the most important fresh water source. To study biological and geological processes in such water-limited regions, knowledge about the spatio-temporal distribution and variability of fog presence is necessary. In this study, in-situ measurements provided by a network of climate stations equipped, inter alia, with leaf wetness sensors are utilized to create a reference fog data set which enables the validation of satellite-based fog retrieval methods. Further, a new satellite-based fog detection approach is introduced which uses brightness temperatures measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) as input for a neural network. Such a machine learning technique can exploit all spectral information of the satellite data and represent potential non-linear relationships. Compared to a second fog detection approach based on MODIS cloud top height retrievals, the neural network reaches a higher detection skill (Heidke skill score of 0.56 compared to 0.49). A suitable representation of temporal variability on subseasonal time scales is provided with correlations mostly greater than 0.7 between fog occurrence time series derived from the neural network and the reference data for individual climate stations, respectively. Furthermore, a suitable spatial representativity of the neural network approach to expand the application to the whole region is indicated. Three-year averages of fog frequencies reveal similar spatial patterns for the austral winter season for both approaches. However, differences are found for the summer and potential reasons are discussed.

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Emily M. Riley Dellaripa, Aaron Funk, Courtney Schumacher, Hedanqiu Bai, and Thomas Spangehl

Abstract

Comparisons of precipitation between general circulation models (GCMs) and observations are often confounded by a mismatch between model output and instrument measurements, including variable type and temporal and spatial resolution. To mitigate these differences, the radar-simulator Quickbeam within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) simulates reflectivity from model variables at the sub-grid scale. This work adapts Quickbeam to the dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) satellite. The longer wavelength of the DPR is used to evaluate moderate-to-heavy precipitation in GCMs, which is missed when Quickbeam is used as a cloud radar simulator. Latitudinal and land/ocean comparisons are made between COSP output from the Community Atmospheric Model version 5 (CAM5) and DPR data. Additionally, this work improves the COSP sub-grid algorithm by applying a more realistic, non-deterministic approach to assigning GCM grid box convective cloud cover when convective cloud is not provided as a model output. Instead of assuming a static 5% convective cloud coverage, DPR convective precipitation coverage is used as a proxy for convective cloud coverage. For example, DPR observations show that convective rain typically only covers about 1% of a 2° grid box, but that the median convective rain area increases to over 10% in heavy rain cases. In our CAM5 tests, the updated sub-grid algorithm improved the comparison between reflectivity distributions when the convective cloud cover is provided versus the default 5% convective cloud cover assumption.

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Boyin Huang, Chunying Liu, Eric Freeman, Garrett Graham, Tom Smith, and Huai-Min Zhang

Abstract

NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) has recently been updated to v2.1 (January 2016–present). Its accuracy may impact the climate assessment, monitoring and prediction, and environment-related applications. Its performance, together with those of seven other well-known sea surface temperature (SST) products, is assessed by comparison with buoy and Argo observations in the global oceans on daily 0.25°×0.25° resolution from January 2016 to June 2020. These seven SST products are NASA MUR25, GHRSST GMPE, BoM GAMSSA, UKMO OSTIA, NOAA GPB, ESA CCI, and CMC.

Our assessments indicate that biases and root-mean-square-difference (RMSDs) in reference to all buoys and all Argo floats are low in DOISST. The bias in reference to the independent 10% of buoy SSTs remains low in DOISST, but the RMSD is slightly higher in DOISST than in OSTIA and CMC. The biases in reference to the independent 10% of Argo observations are low in CMC, DOISST, and GMPE; and RMSDs are low in GMPE and CMC. The biases are similar in GAMSSA, OSTIA, GPB, and CCI whether they are compared against all buoys, all Argo, or the 10% of buoy or 10% of Argo observations, while the RMSDs against Argo observations are slightly smaller than those against buoy observations. These features indicate a good performance of DOISST v2.1 among the eight products, which may benefit from ingesting the Argo observations by expanding global and regional spatial coverage of in situ observations for effective bias correction of satellite data.

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Omar V. Müller, Pier Luigi Vidale, Benoît Vannière, Reinhard Schiemann, and Patrick C. McGuire

Abstract

Previous studies showed that high-resolution GCMs overestimate land precipitation when compared against observation-based data. Particularly, high-resolution HadGEM3-GC3.1 shows a significant precipitation increase in mountainous regions, where the scarcity of gauge stations increases the uncertainty of gridded observations and reanalyses. This work evaluates such precipitation uncertainties indirectly through the assessment of river discharge, considering that an increase of ~10% in land precipitation produces ~28% more runoff when the resolution is enhanced from 1° to 0.25°, and ~50% of the global runoff is produced in 27% of global land dominated by mountains. We diagnosed the river flow by routing the runoff generated by HadGEM3-GC3.1 low- and high-resolution simulations. The river flow is evaluated using a set of 344 monitored catchments distributed around the world. We also infer the global discharge by constraining the simulations with observations following a novel approach that implies bias correction in monitored rivers with two methods, and extension of the correction to the river mouth, and along the coast. Our global discharge estimate is 47.4±1.6×103 km 3 yr −1, which is closer to the original high-resolution estimate (50.5 × 103 km 3 yr −1) than to the low-resolution (39.6 × 103 km 3 yr −1). The assessment suggests that high-resolution simulations performbetter in mountainous regions, either because the better-defined orography favours the placement of precipitation in the correct catchment, leading to a more accurate distribution of runoff, or the orographic precipitation increases, reducing the dry runoff bias of coarse resolution simulations. However, high-resolution slightly increases wet biases in catchments dominated by flat terrain. The improvement of model parameterizations and tuning may reduce the remaining errors in high-resolution simulations.

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Yuqing Zhang, Qinglong You, Guangxiong Mao, Changchun Chen, Xin Li, and Jinhua Yu

Abstract

It is essential to assess flash drought risk based on a reliable flash drought intensity (severity) index incorporating comprehensive information of the rapid decline (“flash”) in soil moisture towards drought conditions and soil moisture thresholds belonging to the “drought” category. In this study, we used the Gan River Basin as an example to define a flash drought intensity index that can be calculated for individual time steps (pentads) during a flash drought period over a given grid (or station). The severity of a complete flash drought event is the sum of the intensity values during the flash drought. We explored the spatial and temporal characteristics of flash droughts with different grades based on their respective severities. The results show that decreases in total cloud cover, precipitation, and relative humidity, as well as increases in 500 hPa geopotential height, convective inhibition, temperature, vapour pressure deficit, and wind speed can create favorable conditions for the occurrence of flash droughts. Although flash droughts are relatively frequent in the central and southern parts of the basin, the severity is relatively high in the northern part of the basin due to longer duration. Flash drought severity shows a slightly downward trend due to decreases in frequency, duration, and intensity from 1961 to 2018. Extreme and exceptional flash droughts decrease significantly while moderate and severe flash droughts trend slightly upward. Flash drought severity appears to be more affected by the interaction between duration and intensity as the grade increases from mild to severe. The frequency and duration of flash droughts are higher in July to October. The southern part of the basin is more prone to moderate and severe flash droughts, while the northern parts of the basin are more vulnerable to extreme and exceptional flash droughts due to longer durations and greater severities than other parts. Moderate, severe, extreme, and exceptional flash droughts occurred approximately every 3-6, 5-15, 10-50, and 30-200 year intervals, respectively, based on the copula analysis.

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Julia A. Shates, Claire Pettersen, Tristan S. L’Ecuyer, Steve J. Cooper, Mark S. Kulie, and Norman B. Wood

Abstract

The prevailing snowfall regimes at two Scandinavian sites, Haukeliseter, Norway and Kiruna, Sweden, are documented using ground-based in-situ and remote sensing methods. Micro Rain Radar (MRR) profiles indicate three distinct snowfall regimes occur at both sites: shallow, deep, and intermittent snowfall. The shallow snowfall regime produces the lowest mean snowfall rates and radar echo tops are confined below 1.5 km above ground level (AGL). Shallow snowfall occurs under areas of large scale subsidence with a moist boundary layer and dry air aloft. The atmospheric ridge coinciding with shallow snowfall is highly anomalous over Haukeliseter, but is more common in Kiruna where shallow snowfall was frequently observed. The shallow snowfall particle size distributions (PSDs) are broad with lower particle concentrations than other regimes, especially small particles. Deep snowfall events exhibit MRR profiles that extend above 2 km AGL, and tend to be associated with weak low pressure and high relative humidity throughout the troposphere. The PSDs in deep events are narrower with high concentrations of small particles. Increasing MRR reflectivity towards the surface suggests aggregation as a possible growth process during deep snowfall events. The heaviest mean snowfall rates are associated with intermittent events that are characterized by deep MRR profiles, but have variations in intensity and height. The intermittent regime is associated with anomalous, deep low pressure along the coast of Norway, and enhanced relative humidity at lower levels. The PSDs reveal high concentrations of small and large particles. The analysis reveals that there are unique characteristics of shallow, deep, and intermittent snowfall regimes that are common between the sites.

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Qianrong Ma, Jie Zhang, Yujun Ma, Asaminew Teshome Game, Zhiheng Chen, Yi Chang, and Meichen Liu

Abstract

The variability of extreme precipitation in eastern Central Asia (ECA) during summer (June–August) and its corresponding mechanisms were investigated from a multi-scale synergy perspective. Extreme precipitation in ECA displayed a quasi-monopole increasing pattern with abrupt change since 2000/2001, which was likely dominated by increased high latitude North Atlantic SST anomalies as shown by diagnosed and numerical experiment results. Increased SST via adjusting the quasi-stationary wave train which related to the negative North Atlantic Oscillation and the East Atlantic/Western Russia pattern guided cyclonic anomaly in CA, deepened the Balkhash Lake trough and enhanced the moisture convergence in ECA. These anomalies also exhibited interdecadal enhancement after 2000. On the synoptic-scale, two synoptic transient wave trains correlated with extreme precipitation in ECA by amplifying the amplitude of the quasi-stationary waves and guiding transient eddies in ECA. The induced transient eddies and deepened Balkhash Lake trough strengthened positive meridional vorticity advection and local positive vorticity, which promoted ascending motions, and guided the southerly warm moisture in ECA especially after 2000. Meanwhile, additional meso-scale vortices were stimulated and strengthened near the Tianshan Mountain in front of the wave trough, which, together with the enhanced meridional circulation, further increased extreme precipitation in ECA.

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Matthew Henry and Geoffrey K. Vallis

Abstract

Observations of warm past climates and projections of future climate change show that the Arctic warms more than the global mean, particularly during winter months. Previous work has attributed this reduced Arctic land seasonality to the effects of sea ice or clouds. In this paper, we show that the reduced Arctic land seasonality is a robust consequence of the relatively small surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission, without recourse to other processes or feedbacks. We use a General Circulation Model (GCM) with no clouds or sea ice and a simple representation of land. In the annual mean, the equator-to-pole surface temperature gradient falls with increasing CO2, but this is only a near-surface phenomenon and is not caused by the change in total meridional heat transport, which is virtually unaltered. The high-latitude land has about twice as much warming in winter than in summer, whereas high-latitude ocean has very little seasonality in warming. A surface energy balance model shows how the combination of the smaller surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission gives rise to the reduced seasonality of the land surface. The increase in evaporation over land also leads to winter amplification of warming over land, although amplification still occurs without it. While changes in clouds, sea ice, and ocean heat transport undoubtedly play a role in high-latitude warming, these results show that enhanced land surface temperature warming in winter can happen in their absence for robust reasons.

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Sanjib Sharma, Michael Gomez, Klaus Keller, Robert Nicholas, and Alfonso Mejia

Abstract

Flood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and implementation of flood risk management strategies. Here we develop an integrated modeling framework to assess projected changes in regional riverine flood inundation risks. The framework samples climate model outputs to force a hydrologic model and generate streamflow projections. Together with a statistical and hydraulic model, we use the projected streamflow to map the uncertainty of flood inundation projections for extreme flood events. We implement the framework for rivers across the state of Pennsylvania, United States. Our projections suggest that flood hazards and exposure across Pennsylvania are overall increasing with future climate change. Specific regions, including the main stem Susquehanna River, lower portion of the Allegheny basin and central portion of Delaware River basin, demonstrate higher flood inundation risks. In our analysis, the climate uncertainty dominates the overall uncertainty surrounding the flood inundation projection chain. The combined hydrologic and hydraulic uncertainties can account for as much as 37% of the total uncertainty. We discuss how this framework can provide regional and dynamic flood-risk assessments and help to inform the design of risk-management strategies.

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