Perspectives and Challenges in High-Resolution Whole-Atmosphere Modeling

Changes in mass contained by mountain glaciers and ice caps can modify the Earth’s hydrological cycle on multiple scales. On a global scale, the mass loss from glaciers contributes to sea-level rise. On regional and local scales, glacier meltwater is an important contributor to and modulator of river flow. In light of strongly accelerated worldwide glacier retreat, the associated glacier mass losses raise concerns over the sustainability of water supplies in many parts of the world. Here, we review recent attempts to quantify glacier mass changes and their effect on river runoff on regional and global scales. We find that glacier runoff is defined ambiguously in the literature, hampering direct comparison of findings on the importance of glacier contribution to runoff. Despite consensus on the hydrological implications to be expected from projected future warming, there is a pressing need for quantifying the associated regional-scale changes in glacier runoff and responses in different climate regimes.

Abstract. Gravity waves play a significant role in driving the semiannual oscillation (SAO) of the zonal wind in the tropics. However, detailed knowledge of this forcing is missing, and direct estimates from global observations of gravity waves are sparse. For the period 2002–2018, we investigate the SAO in four different reanalyses: ERA-Interim, JRA-55, ERA-5, and MERRA-2. Comparison with the SPARC zonal wind climatology and quasi-geostrophic winds derived from Microwave Limb Sounder (MLS) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) satellite observations show that the reanalyses reproduce some basic features of the SAO. However, there are also large differences, depending on the model setup. Particularly, MERRA-2 seems to benefit from dedicated tuning of the gravity wave drag parameterization and assimilation of MLS observations. To study the interaction of gravity waves with the background wind, absolute values of gravity wave momentum fluxes and a proxy for absolute gravity wave drag derived from SABER satellite observations are compared with different wind data sets: the SPARC wind climatology; data sets combining ERA-Interim at low altitudes and MLS or SABER quasi-geostrophic winds at high altitudes; and data sets that combine ERA-Interim, SABER quasi-geostrophic winds, and direct wind observations by the TIMED Doppler Interferometer (TIDI). In the lower and middle mesosphere the SABER absolute gravity wave drag proxy correlates well with positive vertical gradients of the background wind, indicating that gravity waves contribute mainly to the driving of the SAO eastward wind phases and their downward propagation with time. At altitudes 75–85 km, the SABER absolute gravity wave drag proxy correlates better with absolute values of the background wind, suggesting a more direct forcing of the SAO winds by gravity wave amplitude saturation. Above about 80 km SABER gravity wave drag is mainly governed by tides rather than by the SAO. The reanalyses reproduce some basic features of the SAO gravity wave driving: all reanalyses show stronger gravity wave driving of the SAO eastward phase in the stratopause region. For the higher-top models ERA-5 and MERRA-2, this is also the case in the lower mesosphere. However, all reanalyses are limited by model-inherent damping in the upper model levels, leading to unrealistic features near the model top. Our analysis of the SABER and reanalysis gravity wave drag suggests that the magnitude of SAO gravity wave forcing is often too weak in the free-running general circulation models; therefore, a more realistic representation is needed.

Characteristic properties of gravity waves from convection over the continental United States are derived from idealized high-resolution numerical simulations. In a unique modeling approach, waves are forced by a realistic thermodynamic source based on observed precipitation data. The square of the precipitation rate and the gravity wave momentum fluxes both show lognormal occurrence distributions, with long tails of extreme events. Convectively generated waves can give forces in the lower stratosphere that at times rival orographic wave forcing. Throughout the stratosphere, zonal forces due to convective wave drag are much stronger than accounted for by current gravity wave drag parameterizations, so their contribution to the summer branch of the stratospheric Brewer–Dobson circulation is in fact much larger than models predict. A comparison of these forces to previous estimates of the total drag implies that convectively generated gravity waves are a primary source of summer-hemisphere stratospheric wave drag. Furthermore, intermittency and strength of the zonal forces due to convective gravity wave drag in the lower stratosphere resemble analysis increments, suggesting that a more realistic representation of these waves may help alleviate model biases on synoptic scales. The properties of radar precipitation and gravity waves seen in this study lead to a proposed change for future parameterization methods that would give more realistic drag forces in the stratosphere without compromising mesospheric gravity wave drag.

Observation of the gravity waves from GPS/MET radio occultation data

Changes in daily extreme temperatures have been identified in many studies conducted at local, regional or global scales. For Romanian territory, only little research on this issue was done. In this article, the extra‐Carpathians regions of Romania located southward and eastward from the Carpathians Chain were considered. This study is focused on analyzing daily extreme temperature trends at a regional scale (eastern, southern and southeastern regions of Romania) across 50 years (1961–2010). Data sets of daily minimum and maximum temperature recorded in 14 weather stations were analyzed. The main goal was to find changes in extreme daily temperatures using a set of 20 indices adopted from the core indices developed by ETCCDMI with appropriate modifications to suit to the analyzed territory. The main results suggest that regional temperature trends at the scale of extra‐Carpathians areas of Romania are similar to those calculated for global and European continental scales; the climate has become warmer during the last decades. It has been identified that both extreme daily maximum and minimum temperatures have increased in the analyzed areas. For all the indices related to hot temperature most trends are significantly positive. The strongest increase was detected for hot related extremes such as summer days and tropical nights as well as for maximum values of maximum and minimum daily temperatures. For indices related to cold there are different sign slopes, but negative slopes prevail, especially for number of days under a defined threshold. This is also an evidence of the important warming in the area. Generally, it was found that the daily maximum temperature is getting more extreme, whereas the minimum is getting less extreme. Copyright © 2012 Royal Meteorological Society

<p>The increasing risk of dry extremes and droughts and their further projected exacerbation due to climate change urges the development of reliable risk assessments and mitigation pathways on a regional and global scale. This foremost requires accurate and unambiguous model predictions of dry extremes, as this underpins the effectiveness of the proposed strategies. At present, however, the confidence in regional drought projections is defined as ‘medium to low' by the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report (AR6), and reducing this uncertainty remains one of the main goals in coming years. <br>In this study, the bias in future projected changes in annual meteorological drought duration (hereafter, longest annual drought, LAD) is assessed in the ensemble of CMIP5 and CMIP6 models. The analyses show that it is the present-day inter-model spread in LAD climatology that largely determines the inter-model uncertainty in future predicted LAD changes. Hereby, both CMIP5 and CMIP6 model ensembles indicate a robust “dry-model-gets-drier” relationship in future LAD projections on a global and regional scale. Correcting for this bias using emerging constraint principles and past observational LAD information, we find that nearly half of the world's land area with projected increases in drought duration is underestimating the predicted model ensemble mean change, imposing higher-than-expected risks to the societies and ecosystems. Analysis of physical mechanisms that could underlie this emergent “present-future relationship” points to differences in the responses of “dry models” and “wet models” to CO2 forcing. Dry and wet models show differences in climate states, which support the role of land–atmosphere feedbacks and convective scheme sensitivity to atmospheric moisture in the spread of future LAD change projections. <br>In conclusion, the study reveals world regions where climate change may cause stronger drought duration aggravation than expected, and emphasizes the importance of reducing systematic model errors, which are presently largely owed to rainfall biases. Correcting these biases will increase the confidence of future dry extremes predictions, a prerequisite for the effective drought risk reduction in the near future with direct benefits for human and natural systems.</p>

Land and Earth system modeling is moving towards more explicit biophysical representations, requiring increasing variety of datasets for initialization and benchmarking. However, researchers often have difficulties in identifying and integrating non-standardized datasets from various sources. We aim towards a standardized database and one-stop distribution method of global datasets. Here, we present the GriddingMachine as (1) a database of global-scale datasets commonly used to parameterize or benchmark the models, from plant traits to vegetation indices and geophysical information and (2) a cross-platform open source software to download and request a subset of datasets with only a few lines of code. The GriddingMachine datasets can be accessed either manually through traditional HTTP, or automatically using modern programming languages including Julia, Matlab, Octave, Python, and R. The GriddingMachine collections can be used for any land and Earth modeling framework and ecological research at the regional and global scales, and the number of datasets will continue to grow to meet the increasing needs of research communities.

Being one of the major consequences of anthropogenic climate change, sea level rise forms a threat for many coastal areas and their inhabitants. Because all processes that cause sea-level changes have a spatially-varying fingerprint, local sea-level changes deviate substantially from the global mean. As a consequence, there is no single location on the earth that is subject to the global-mean sea-level change. To understand and forecast future changes in both global and regional sea level, a thorough understanding of its major underlying processes and their regional fingerprints is necessary. Nowadays, remote sensing from satellite altimetry provides an accurate estimate of changes in sea level on global and regional scales (Leuliette et al., 2004; Nerem et al., 2010; Ablain et al., 2017). The emergence of satellite gravimetry, in the form of the GRACE mission (Tapley et al., 2004), and the global coverage of in-situ subsurface temperature and salinity observations by the Argo programme (Roemmich et al., 2009; Roemmich and Gilson, 2009) has resulted in an extensive increase of our understanding of sea-level changes over the past decade, and the reliability of the estimates of the individual processes behind sea-level changes has reached the level where we can almost fully explain the observed sea-level changes from these contributors (Rietbroek et al., 2016; Leuliette and Miller, 2009; Dieng et al., 2015; Leuliette, 2015; Kleinherenbrink et al., 2016). However, before this period the spatially-varying signals have been sampled only sparsely by in-situ observations, mainly by means of tide gauges, which limits our current understanding of sea-level changes on global and regional scales. This thesis aims to find an answer to the question whether the sum of the underlying processes that cause sea-level changes can explain the observations, not only on a global scale, which has been assessed a multitude of times (Moore et al., 2011; Church et al., 2011; Gregory et al., 2013; Jevrejeva et al., 2016b), but also on scales of individual ocean basins and coastal regions. The assessment of this so-called sea-level budget has been done for two regional cases, and for the global ocean and individual basins. Furthermore, the effect of ocean bottom deformation on the difference between relative and geocentric observations has been quantified. Finally, we have applied an alternative approach to time-series analysis, in which the various contributors of sealevel variability are co-estimated with a time-varying trend using a Kalman filter and smoother approach, on tide gauge observations.

Global Scale Atmospheric Mixing

Remote sensing of precipitation is critical for regional, continental, and global weather, water, and climate research. This study develops a machine learning mechanism to link between point-wise rain gauge measurements, ground-based and spaceborne radar reflectivity observations. Two neural network models are designed to construct a hybrid rainfall system, where the ground radar is used to bridge the scale gaps between rain gauge and satellite. The first model is trained for ground radar using rain gauge data as target labels, whereas the second one is for spaceborne radar using ground radar estimates as training labels. Data from TRMM Precipitation Radar (PR) and GPM Dual-frequency Precipitation Radar (DPR) are utilized to illustrate the application of this hybrid rainfall system. Validation using independent ground-based observations as well as the standard PR and DPR products demonstrates the promising performance and generality of this innovative machine learning algorithm.

Among the largest uncertainties in quantifying the radiative impacts of clouds are those that arise from the inherent difficulty in precisely specifying the vertical distribution of cloud optical properties using passive satellite measurements. Motivated by the need to address this problem, CloudSat was launched in April 2006 carrying into orbit the first millimeter wavelength cloud radar to be flown in space. Retrieved profiles of liquid and ice cloud microphysical properties from this Cloud Profiling Radar form the basis of the CloudSat's fluxes and heating rates algorithm, 2B‐FLXHR, a standard product that provides high vertical resolution profiles of radiative fluxes and atmospheric heating rates on the global scale. This paper describes the physical basis of the 2B‐FLXHR algorithm and documents the first year of 2B‐FLXHR data in the context of assessing the radiative impact of clouds on global and regional scales. The analysis confirms that cloud contributions to atmospheric radiative heating are small on the global scale because of a cancelation of the much larger regional heating from high clouds in the tropics and cooling from low clouds at higher latitudes. Preliminary efforts to assess the accuracy of the 2B‐FLXHR product using coincident CERES data demonstrate that outgoing longwave fluxes are better represented than those in the shortwave but both exhibit good agreement with CERES on scales longer than 5 days and larger than 5°. Colocated CALIPSO observations of clouds that are undetected by CloudSat further indicate that while thin cirrus can introduce modest uncertainty in the products, low clouds that are obscured by ground clutter represent a far more important source of error in the current 2B‐FLXHR product that must be addressed in subsequent versions of the algorithm.

Natural disasters are creating a major collapse in human existence. Among the many, drought is becoming more frequent and intense. Therefore, mitigation/adaptation measures have to be set to reduce the impact. This can be achieved via the application of Nature-based solutions (NBSs). This concept is now gaining more attention than ever but with fewer applications. The primary goal of this review paper is to analyze the different NBSs targeted for drought impact mitigation. The study constitutes the application of NBS at a global, continental and regional scale. Extensive literature was made to assess; NBS type, location, start and ending period of implementation, status, and level of effectiveness, recommendations set by researchers, and insight for future applications. The comprehensive review revealed that there are only a few scientific publications, hence, grey and non-scientific literature need to be included. Only four papers included a quantitative assessment for evaluating the effectiveness of NBS targeting drought. However, the continental and regional performance of NBS is not mentioned. Therefore, a common effectiveness evaluation framework shall be created to give policymakers a clear view of the different NBS’s. Furthermore, a more collaborative approach, including different stakeholder groups, is recommended, with specific attention to the local communities. In Flanders, most projects are in the pilot project stage. Moreover, the few successfully implemented projects are only very local and have a long realization time which the earlier limits to acquire visible impact at a larger scale. Finally, the loss of wetlands at a global scale and in Flanders (70% are lost), increases the vulnerability of catchments to drought. Therefore, future research should increase the evidence base and implementation of NBS, such as wetlands, in Flanders.

Understanding the nature of large scale variations in the internal thermal field of Earth often require integrated use of both experimental and estimated values of terrestrial heat flow. In the present work we report progress obtained in blending such data sets for obtaining statistically meaningful representation of the geothermal field on regional and global scales. An experimental geothermal data set consisting of over 40000 heat flow values was employed for this purpose. The estimated values were derived on the basis of the well-known empirical relations between geologic age and heat flow. The age values were determined from tectonic maps of continental and oceanic regions, a task which required manipulation of geologic information on local and regional scales using GIS techniques. The results obtained have allowed comparative analysis of global maps of terrestrial heat flow derived from experimental data and theoretical values on a global scale.

Although our knowledge of climate change impacts on energy systems has increased substantially over the past few decades, there remains a lack of comprehensive overview of impacts across spatial scales. Here, we analyse results of 220 studies projecting climate impacts on energy systems globally and at the regional scale. Globally, a potential increase in cooling demand and decrease in heating demand can be anticipated, in contrast to slight decreases in hydropower and thermal energy capacity. Impacts at the regional scale are more mixed and relatively uncertain across regions, but strongest impacts are reported for South Asia and Latin America. Our assessment shows that climate impacts on energy systems at regional and global scales are uncertain due partly to the wide range of methods and non-harmonized datasets used. For a comprehensive assessment of climate impacts on energy, we propose a consistent multi-model assessment framework to support regional-to-global-scale energy planning.

Acoustic and gravity waves propagating in planetary atmospheres have been studied intensively as markers of specific phenomena such as tectonic events or explosions or as contributors to atmosphere dynamics. To get a better understanding of the physics behind these dynamic processes, both acoustic and gravity waves propagation should be modelled in a 3-D attenuating and windy atmosphere extending from the ground to the upper thermosphere. Thus, in order to provide an efficient numerical tool at the regional or global scale, we introduce a finite difference in the time domain (FDTD) approach that relies on the linearized compressible Navier–Stokes equations with a background flow (wind). One significant benefit of such a method is its versatility because it handles both acoustic and gravity waves in the same simulation, which enables one to observe interactions between them. Simulations can be performed for 2-D or 3-D realistic cases such as tsunamis in a full MSISE-00 atmosphere or gravity-wave generation by atmospheric explosions. We validate the computations by comparing them to analytical solutions based on dispersion relations in specific benchmark cases: an atmospheric explosion, and a ground displacement forcing.