Minnesota is the U.S. state with the strongest winter warming in the contiguous United States. We performed regional climate projections at 10 km horizontal resolution using the WRF model forced by an ensemble of eight CMIP5 GCMs. The selected GCMs have previously been found to be in relatively good agreement with observations compared to other members of the CMIP5 model ensemble. Our projections suggest ongoing warming in all seasons, especially in winter, as well as shallower snow cover and fewer days with snow cover. On the other hand, we expect significant increases in spring and early summer heavy precipitation events. Our comparisons between different time slices and two different emission scenarios indicate a climate for the state of Minnesota at the end of the 21st century that is significantly different from what has been observed by the end of the 20th century. Winters and summers are expected to be up to 6oC and 4oC warmer, respectively, over northern and central Minnesota and spring precipitation may increase by more than 1 mm d-1 over northern Minnesota. Especially over the central part of the state, winter snow height is suggested to decrease by more than 0.5 meters and the number of days per year with snow height of more than 0.0254 meters (one inch) is expected to decrease by up to 60.
Clouds interact with atmospheric radiation and substantially modify the Earth’s energy budget. Cloud formation processes occur over a vast range of spatial and temporal scales which make their thorough numerical representation challenging. Therefore, the impact of parameter choices for simulations of cloud-radiative effects is assessed in the current study. Numerical experiments were carried out using the ICOsahedral Nonhydrostatic (ICON) model with varying grid spacings between 2.5 and 80 km and with different subgrid-scale parameterization approaches. Simulations have been performed over the North Atlantic with either one-moment or two-moment microphysics and with convection being parameterized or explicitly resolved by grid-scale dynamics. Simulated cloud-radiative effects are compared to products derived from Meteosat measurements. Furthermore, a sophisticated cloud classification algorithm is applied to understand the differences and dependencies of simulated and observed cloud-radiative effects. The cloud classification algorithm developed for the satellite observations is also applied to the simulation output based on synthetic infrared brightness temperatures, a novel approach that is not impacted by changing insolation and guarantees a consistent and fair comparison. It is found that flux biases originate equally from clear-sky and cloudy parts of the radiation field. Simulated cloud amounts and cloud-radiative effects are dominated by marine, shallow clouds, and their behaviour is highly resolution dependent. Bias compensation between shortwave and longwave flux biases, seen in the coarser simulations, is significantly diminished for higher resolutions. Based on the analysis results, it is argued that cloud-microphysical and cloud-radiative properties have to be adjusted to further improve agreement with observed cloud-radiative effects.
Earth’s tropical and subtropical rainbands, such as Intertropical Convergence Zones (ITCZs) and monsoons, are complex systems, governed by both large-scale constraints on the atmospheric general circulation and regional interactions with continents and orography, and coupled to the ocean. Monsoons have historically been considered as regional large-scale sea breeze circulations, driven by land-sea contrast. More recently, a perspective has emerged of a Global Monsoon, a global-scale solstitial mode that dominates the annual variation of tropical and subtropical precipitation. This results from the seasonal variation of the global tropical atmospheric overturning and migration of the associated convergence zone. Regional subsystems are embedded in this global monsoon, localized by surface boundary conditions. Parallel with this, much theoretical progress has been made on the fundamental dynamics of the seasonal Hadley cells and convergence zones via the use of hierarchical modeling approaches, including aquaplanets. Here we review the theoretical progress made, and explore the extent to which these advances can help synthesize theory with observations to better understand differing characteristics of regional monsoons and their responses to certain forcings. After summarizing the dynamical and energetic balances that distinguish an ITCZ from a monsoon, we show that this theoretical framework provides strong support for the migrating convergence zone picture and allows constraints on the circulation to be identified via the momentum and energy budgets. Limitations of current theories are discussed, including the need for a better understanding of the influence of zonal asymmetries and transients on the large-scale tropical circulation.
Most machine learning applications in Earth system modeling currently rely on gradient-based supervised learning. This imposes stringent constraints on the nature of the data used for training (typically, residual time tendencies are needed), and it complicates learning about the interactions between machine-learned parameterizations and other components of an Earth system model. Approaching learning about process-based parameterizations as an inverse problem resolves many of these issues, since it allows parameterizations to be trained with partial observations or statistics that directly relate to quantities of interest in long-term climate projections. Here we demonstrate the effectiveness of Kalman inversion methods in treating learning about parameterizations as an inverse problem. We consider two different algorithms: unscented and ensemble Kalman inversion. Both methods involve highly parallelizable forward model evaluations, converge exponentially fast, and do not require gradient computations. In addition, unscented Kalman inversion provides a measure of parameter uncertainty. We illustrate how training parameterizations can be posed as a regularized inverse problem and solved by ensemble Kalman methods through the calibration of an eddy-diffusivity mass-flux scheme for subgrid-scale turbulence and convection, using data generated by large-eddy simulations. We find the algorithms amenable to batching strategies, robust to noise and model failures, and efficient in the calibration of hybrid parameterizations that can include empirical closures and neural networks.
Modelling of the land surface water-, energy-, and carbon balance provides insight into the behaviour of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large-scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyse the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS-1D was simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in-depth analysis of the soil SHPs and derived soil characteristics was performed to analyse why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time-integrated behaviour of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby et al. (1984) for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter-comparison studies to avoid artefacts originating from the choice of PTF rather from different model structures.
Several bills moving through Congress are likely to provide significant funding for expanding research and results in climate change solutions (CCS). This is also a priority of the Biden-Harris Administration. The National Science Foundation (NSF) will be expected to distribute and manage much of this funding through its grant processes. Effective solutions require both a continuation and expansion of research on climate change–to understand and thus plan for potential impacts locally to globally and to continually assess solutions against a changing climate–and rapid adoption and implementation of this science with society at all levels. NSF asked AGU to convene its community to help provide guidance and recommendations for enabling significant and impactful CCS outcomes by 1 June. AGU was asked in particular to address the following: 1. Identify the biggest, more important interdisciplinary/convergent challenges in climate change that can be addressed in the next 2 to 3 years 2. Create 2-year and 3-year roadmaps to address the identified challenges. Indicate partnerships required to deliver on the promise. 3. Provide ideas on the creation of an aggressive outreach/communications plan to inform the public and decision makers on the critical importance of geoscience. 4. Identify information, training, and other resources needed to embed a culture of innovation, entrepreneurialism, and translational research in the geosciences. Given the short time frame for this report, AGU reached out to key leaders, including Council members, members of several committees, journal editors, early career scientists, and also included additional stakeholders from sectors relevant to CCS, including community leaders, planners and architects, business leaders, NGO representatives, and others. Participants were provided a form to submit ideas, and also invited to two workshops. The first was aimed at ideation around broad efforts and activities needed for impactful CCS; the second was aimed at in depth development of several broad efforts at scale. Overall, about 125 people participated; 78 responded to the survey, 82 attended the first workshop, and 28 attended the more-focused second workshop (see contributor list). This report provides a high-level summary of these inputs and recommendations, focusing on guiding principles and several ideas that received broader support at the workshops and post-workshop review. These guiding principles and ideas cover a range of activities and were viewed as having high importance for realizing impactful CCS at the scale of funding anticipated. These cover the major areas of the charge, including research and solutions, education, communication, and training. The participants and full list of ideas and suggestions are provided as an appendix. Many contributed directly to this report; the listed authors are the steering committee.
The weakening of the Madden-Julian Oscillation (MJO) as it propagates over the Maritime Continent (MC) is often referred to as the MC barrier. Here, we use 3-hourly precipitable water vapor (PWV) data obtained from the Sumatran GPS Array and the ERA5 reanalysis to investigate the role played by the column moisture over the MC. Over Sumatra and the whole MC, we find a stronger dependence of precipitation on PWV over the ocean as compared to both inland and coastal regions. The MJO modulates the PWV over the ocean and over the MC by roughly the same amount, and the weaker precipitation variations over the MC between the MJO phases may be interpreted in terms of its weaker dependence on PWV over the MC. This different precipitation dependence on column moisture between the MC and the ocean may contribute to the MC barrier effect.
Previous work has established that warming is associated with an increase in dry static stability, a weakening of the tropical circulation, and a decrease in the convective mass flux. Using a set of idealized simulations with specified surface warming and super-parameterized convection, we find support for these previous conclusions. We use an energy and mass balance framework to develop a simple diagnostic that links the fractional area covered by the region of upward motion to the strength of the mean circulation. We demonstrate that the diagnostic works well for our idealized simulations, and use it to understand how changes in tropical ascent area and the strength of the mean circulation relate to changes in heating in the ascending and descending regions. We show that the decrease in the strength of the mean circulation can be explained by the relatively slow rate at which atmospheric radiative cooling intensifies with warming. In our simulations, decreases in tropical ascent area are balanced by increases in non-radiative heating in convective regions. Consistent with previous work, we find a warming-induced decrease in the mean convective mass flux. However, when we condition by the sign of the mean vertical motion, the warming-induced changes in the convective mass flux are non-monontonic and opposite between the ascending and descending regions.
Mesoscale convective systems (MCSs) have been identified as an important source of precipitation in the Tibetan Plateau (TP) region. However, the characteristics and structure of MCS-induced precipitation are not well understood. Infrared satellite imagery has been used for MCS tracking, but cirrus clouds or cold surfaces can cause misclassifications of MCS in mountain regions. We therefore combine brightness temperatures from IR imagery with satellite precipitation data from GPM and track MCSs over the TP, at the boundary of the TP (TPB) and in the surrounding lower-elevation plains (LE) between 2000 and 2019. We show that MCSs are less frequent over the TP than earlier studies have suggested and most MCSs over land occur over the Indo-Gangetic Plain (LE) and the south of the Himalayas (TPB). In the LE and TPB, MCSs have produced 10 % to 55 % of the total summer precipitation (10 % to 70 % of summer extreme precipitation), whereas MCSs over the TP account for only 1 % to 10 \% to the total summer precipitation (1 % to 30 % of the total summer extreme precipitation). Our results also show that MCSs that produce the largest amounts of convective precipitation are characterized by longevity and large extents rather than by high intensities. These are mainly located south of the TP, whereas smaller-scale convection makes a greater contribution to total and total extreme precipitation over the TP. These results highlight the importance of convective scale modeling to improve our understanding of precipitation dynamics over the TP.
A striking feature of the Earth system is that the Northern and Southern Hemispheres reflect identical amounts of sunlight. This hemispheric albedo symmetry comprises two asymmetries: The Northern Hemisphere is more reflective in clear skies, whereas the Southern Hemisphere is cloudier. The most-cited explanation is that the clear-sky asymmetry is primarily due to the relatively-bright continents being disproportionately located in the Northern Hemisphere. However, it is the atmosphere, not the surface, that contributes most to the clear-sky asymmetry. Here we show that the continent-based component of the clear-sky surface asymmetry is largely offset by greater reflection from the Southern Hemisphere poles, allowing the clear-sky asymmetry to be dominated by aerosol. Climate model simulations suggest that aerosol emissions since the pre-industrial era have driven a large increase in the clear-sky asymmetry that would reverse in future low-emission scenarios. High-emission scenarios also show a decrease in asymmetry, but instead driven by declines in Northern Hemisphere ice and snow cover. Strong clear-sky hemispheric albedo asymmetry is therefore a transient, rather than fixed, feature of Earth’s climate. If all-sky symmetry is maintained despite changes in the clear-sky asymmetry, compensating cloud changes would have uncertain but important implications for Earth’s energy balance and hydrological cycle.
Of immediate widespread concern is the accelerating transition from Holocene-like weather patterns to unknown, and likely unstable, Anthropocene patterns. A fell example is irreversible Arctic phase change. It is not clear if existing AOGCMs are adequate to model anticipated global impacts in detail; however, the GISS ModelE AOGCM can be used to locally compare and extend the PIOMAS Arctic ocean historical ice-volume dataset into the near future. Arctic Amplification (AA) mechanisms are poorly understood; to enable timely results, a simple linear, Arctic TOA grid-boundary energy-input is used to enforce AA, avoiding the perils of arbitrary modification of relatively well-studied parameterizations (e.g., restriction of cloud-top height to induce local warming). Only PIOMAS springtime/max and fall/min Arctic ice-volume decadal, linear trends were enforced. This temporally-broad grid-boundary modification produces a surprisingly detailed consonance with 10 out of 12 temporal profiles falling within 1-sigma of PIOMAS temporal data for the entire history modeled (2003 to 2021). The data are then integrated to 2050. The result is a zero-ice-volume, summer/fall half-year, beginning ca. 2035 (onset 1-sigma of ± ~5 years), with mean annual Arctic temperatures increasingly trending above freezing. Persistent, Arctic phase change follows this half-year transition about 20 years later. Also present in later stages, the 500 hPa height minimum is no longer nearly-coincident with the pole, suggesting jet stream disruption and its consequences. Hypothesized large clathrate-methane releases likely associated with Arctic temperature and phase change are also examined. A basic assumption is that the Arctic ice (i.e., temperature) must be preserved at all costs. This work establishes a reasonably detailed timeline for the Arctic phase change based on well-studied AOGCM physics, slightly tuned to decades of PIOMAS data. This result also points to the Arctic as a key, near-term site for localized, nondestructive intervention to mitigate Arctic phase change (e.g., Stjern ), thereby slowing the Holocene -> Anthropocene growing-season disruption. Although such an intervention cannot itself accomplish the requirements of the IPCC SP-15 , nor Planetary Boundaries theory, delaying the Arctic phase change will likely extend the time-window for accomplishing those critical tasks and ultimately to at least slow the rate of increase of climate emergencies.
Satellite observations of tropical maritime convection indicate an afternoon maximum in anvil cloud fraction that cannot be explained by the diurnal cycle of deep convection peaking at night. We use idealized cloud-resolving model simulations of single anvil cloud evolution pathways, initialized at different times of the day, to show that tropical anvil clouds formed during the day are more widespread and longer lasting than those formed at night. This diurnal difference is caused by shortwave radiative heating, which lofts and spreads anvil clouds via a mesoscale circulation that is largely absent at night, when a different, longwave-driven circulation dominates. The nighttime circulation entrains dry environmental air that erodes cloud top and shortens anvil lifetime. Increased ice nucleation in more turbulent nighttime conditions supported by the longwave cloud top cooling and cloud base heating dipole cannot overcompensate for the effect of diurnal shortwave radiative heating. Radiative-convective equilibrium simulations with a realistic diurnal cycle of insolation confirm the crucial role of shortwave heating in lofting and sustaining anvil clouds. The shortwave-driven mesoscale ascent leads to daytime anvils with larger ice crystal size, number concentration, and water content at cloud top than their nighttime counterparts.
Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. To leverage these new ‘data-driven’ technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from ‘process-based’, mechanistic modelling. This paper revisits those fundamentals and addresses 10 questions often posed by earth and environmental scientists with the aid of a real-world modelling experiment. The overarching objective is to contribute to a future of AI-assisted earth and environmental sciences where DL models can (1) embrace the typically ignored knowledge base available, (2) function credibly in ‘true’ out-of-sample prediction, and (3) handle non-stationarity in earth and environmental systems. Comparing and contrasting earth and environmental problems with prominent AI applications, such as playing chess and trading in stock markets, provides critical insights for better directing future research in this field.
The unequal spatial distribution of ambient nitrogen dioxide (NO2), an air pollutant related to traffic, leads to higher exposure for minority and low socioeconomic status communities. We exploit the unprecedented drop in urban activity during the COVID-19 pandemic and use high-resolution, remotely-sensed NO2 observations to investigate disparities in NO2 levels across different demographic subgroups in the United States. We show that prior to the pandemic, satellite-observed NO2 levels in the least white census tracts of the United States were nearly triple NO2 levels in the most white tracts. During the pandemic, the largest lockdown-related NO2 reductions occurred in urban neighborhoods that have 2.0 times more non-white residents and 2.1 times more Hispanic residents than neighborhoods with the smallest reductions. NO2 reductions were likely driven by the greater density of highways and interstates in these racially and ethnically diverse areas. Although the largest reductions occurred in marginalized areas, the effect of lockdowns on racial, ethnic, and socioeconomic NO2 disparities was mixed and, for many cities, non-significant. For example, the least white tracts still experienced ~1.5 times higher NO2 levels during the lockdowns than the most white tracts experienced prior to the pandemic. Future policies aimed at eliminating pollution disparities will need to look beyond reducing emissions from only passenger traffic and also consider other collocated sources of emissions such as heavy-duty trucks, power plants, and industrial facilities.
We study how the vertical distribution of relative humidity (RH) affects climate sensitivity, even if it remains unchanged with warming. Using a radiative-convective equilibrium model, we show that the climate sensitivity depends on the shape of a fixed vertical distribution of humidity, tending to be higher for atmospheres with higher humidity. We interpret these effects in terms of the effective emission height of water vapor. Differences in the vertical distribution of RH are shown to explain a large part of the 0 to 30% differences in clear-sky sensitivity seen in climate and storm-resolving models. The results imply that convective aggregation reduces climate sensitivity, even when the degree of aggregation does not change with warming. Combining our findings with relative humidity trends in reanalysis data shows a tendency toward Earth becoming more sensitive to forcing over time. These trends and their height variation merit further study.
Beamforming determines the quality of received signal by an antenna array using Signal-to-Noise-Interference Ratio (SINR) in cellular base stations. This paper will help in the installation of current heterogeneous wireless networks. Here, adaptive BF is implemented on the Machine Learning (ML) platform. The applicable ML methods to estimate the SINR of Multiple-Input-Multiple-Output (MIMO-mm-Wave) 5G wireless network are explored. The significant BF features are used in predicting the SINR. The cross-validation experiment is performed to assess the robustness of the best predictive method. The performance analysis parameters’ result shows the maximum value of accuracy, in value having the acceptable error on the data set.
Deep convective system maximum areal extent is driven by the stratiform anvil area since system convective area fractions are much less than unity when systems reach peak size. It is important to understand the processes that drive system size given the impact large systems have on rainfall and since anvils may strongly impact high cloud feedbacks. Using satellite diabatic heating and convective-stratiform information mapped to convective systems, composite analyses suggest that system maximum sizes occur at the temporal mid-point of system lifecycles with both maximum size and duration correlating with peak heating above the melting level. However, variations in system growth rates exist, with the overall smooth composites emerging as the average of highly variable system trajectories. Thus, this study focuses on understanding convective system growth rates on short (30-minute) timescales via development of a simple analytical source - sink model that predicts system area changes. Growth occurs when detrained convective mass (inferred from the vertical gradient of diabatic heating and temperature lapse rates) and/or generation of convective area exceeds a sink term whose magnitude is proportional to the current cloud shield size. The model works well for systems over land and ocean, and for systems characterized by varying degrees of convective organization and duration (1.5 - 35 hr, with correlations often >0.8 across lifetime bins). The model may serve as a useful foundation for improved understanding of processes driving changes in tropics-wide convective system cloud shields, and further supports conceptual development and evaluation of prognostic climate model stratiform anvil area parameterizations.
The quasi-biennial oscillation (QBO) of tropical stratospheric winds was disrupted during the 2019/20 Northern Hemisphere winter. We show that this latest disruption to the regular QBO cycling was similar in many respects to that seen in 2016, but initiated by horizontal momentum transport from the Southern Hemisphere. The predictable signal associated with the QBO’s quasi-regular phase progression is lost during disruptions and the oscillation reemerges after a few months significantly shifted in phase from what would be expected if it had progressed uninterrupted. We infer from an increased wave-momentum flux into equatorial latitudes seen in climate model projections that disruptions to the QBO are likely to become more common in future. Consequently it is possible that in future the QBO could be a less reliable source of predictability on lead times extending out to several years than it currently is.