This is a test-case study assessing the ability of deep learning methods to generalize to a future climate (end of 21st century) when trained to classify thunderstorms in model output representative of the present-day climate. A convolutional neural network (CNN) was trained to classify strongly-rotating thunderstorms from a current climate created using the Weather Research and Forecasting (WRF) model at high-resolution, then evaluated against thunderstorms from a future climate, and found to perform with skill and comparatively in both climates. Despite training with labels derived from a threshold value of a severe thunderstorm diagnostic (updraft helicity), which was not used as an input attribute, the CNN learned physical characteristics of organized convection and environments that are not captured by the diagnostic heuristic. Physical features were not prescribed but rather learned from the data, such as the importance of dry air at mid-levels for intense thunderstorm development when low-level moisture is present (i.e., convective available potential energy). Explanation techniques also revealed that thunderstorms classified as strongly rotating are associated with learned rotation signatures. Results show that the creation of synthetic data with ground truth is a viable alternative to human-labeled data and that a CNN is able to generalize a target using learned features that would be difficult to encode due to spatial complexity. Most importantly, results from this study show that deep learning is capable of generalizing to future climate extremes and can exhibit out-of-sample robustness with hyperparameter tuning in certain applications.
Poleward ocean heat transport is a key process in the earth system. We detail and review the northward Atlantic Water (AW) flow, Arctic Ocean heat transport, and heat loss to the atmosphere since 1900 in relation to sea ice cover. Our synthesis is largely based on a sea ice-ocean model forced by a reanalysis atmosphere (1900-2018) corroborated by a comprehensive hydrographic database (1950-), AW inflow observations (1996-), and other long-term time series of sea ice extent (1900-), glacier retreat (1984-) and Barents Sea hydrography (1900-). The Arctic Ocean, including the Nordic and Barents Seas, has warmed since the 1970s. This warming is congruent with increased ocean heat transport and sea ice loss and has contributed to the retreat of marine-terminating glaciers on Greenland. Heat loss to the atmosphere is largest in the Nordic Seas (60% of total) with large variability linked to the frequency of Cold Air Outbreaks and cyclones in the region, but there is no long-term statistically significant trend. Heat loss from the Barents Sea (~30%) and Arctic seas farther north (~10%) is overall smaller, but exhibit large positive trends. The AW inflow, total heat loss to the atmosphere, and dense outflow have all increased since 1900. These are consistently related through theoretical scaling, but the AW inflow increase is also wind-driven. The Arctic Ocean CO2 uptake has increased by ~30% over the last century - consistent with Arctic sea ice loss allowing stronger air-sea interaction and is ~8% of the global uptake.
Torrential rainfall and rapid snowmelt in April 2017 caused deadly floods in northwestern Iran. An atmospheric river (AR), propagating across the Middle East and North Africa, was found responsible for this extreme event. The snowmelt was triggered by precipitation and warm advection associated with the AR. Total satellite-based rainfall for April 2017 was moderately below normal, suggesting that a heavy flood can happen during dry years. The AR was fed by moisture from the Mediterranean and Red Seas. Despite its adverse societal consequences, this event was beneficial to the recovery of the desiccating Lake Urmia. The impacts of this AR were not limited to flooding; it also facilitated dust transport to the region. This distinct characteristic of the ARs in the Middle East is attributed to major mineral dust sources located along their pathways. This event was reasonably predicted at 7-day lead time, crucially important for successful early warning systems.
The mainstream media and popular science platforms are rife with misunderstandings about what a “polar vortex” is. The term most aptly describes the stratospheric polar vortex, a single feature dominating the cool-season circulation at ∼15–50 km altitude. Regional upper tropospheric jet stream variations dominate the tropospheric circulation, which is not well-described by the idea of a polar vortex; indeed, there is no single consistent definition of a tropospheric polar vortex in the literature. Stratospheric polar vortex disturbances profoundly influence extreme weather events such as cold air outbreaks (CAO). How the stratospheric polar vortex affects the tropospheric jets, local excursions of which drive CAOs, is not yet fully understood. The most public-facing parts of publications describing research on this topic are sometimes unclear about how the “polar vortex” is defined; greater clarity could help improve communications both within the community and with non-specialist audiences.
Motivated by an observed relationship between marine low cloud cover and surface wind speed, this study investigates how vertical wind shear affects trade-wind cumulus convection, including shallow cumulus and congestus with tops below the freezing level. We ran large-eddy simulations for an idealised case of trade-wind convection using different vertical shears in the zonal wind. Backward shear, whereby surface easterlies become upper westerlies, is effective at limiting vertical cloud development, which leads to a moister, shallower and cloudier trade-wind layer. Without shear or with forward shear, shallow convection tends to deepen more, but clouds tops are still limited under forward shear. A number of mechanisms explain the observed behaviour: First, shear leads to different surface wind speeds and, in turn, surface heat and moisture fluxes due to momentum transport, whereby the weakest surface wind speeds develop under backward shear. Second, a forward shear profile in the subcloud layer enhances moisture aggregation and leads to larger cloud clusters, but only on large domains that generally support cloud organization. Third, any absolute amount of shear across the cloud layer limits updraft speeds by enhancing the downward-oriented pressure perturbation force. Backward shear — the most typical shear found in the winter trades — can thus be argued a key ingredient at setting the typical structure of the trade-wind layer.
Air quality policies based on scientific information have proved to be effective for controlling air pollution and protecting public health. Intensive field studies provide knowledge that combined to data from emission inventories and air quality monitoring allows to understand the causes that trigger air pollution and catalyze the design of effective control measures. We review the case of Mexico City, where past international collaborative studies were fundamental to improve air quality, but a null progress and a possible reversal to high air pollution levels in recent years suggest that a new dedicated field measurement campaign is urgently needed.
We analyzed the scale features of satellite infrared (IR) water vapor (WV) brightness temperature observations of tropical cyclones (TCs). This is to characterize the storm information at dominate scales in all-sky radiance assimilation for TC numerical weather prediction. This paper presents the results from the study of Hurricane Patricia (2015). Our study shows that IR WV brightness temperatures have the ability to observe multiscale structures of TCs, ranging from a size of above 1,000 km that covers the entire storm and its surrounding areas to a scale resolving individual convective clouds embedded in the TC. The atmospheric moisture for TC development is mainly represented by large scales covering the storm and surrounding areas while the storm structures are characterized basically by all scales. The large-scale moisture and small-scale convection demonstrate strong correlation and are closely related to the TC development, suggesting the need for all-sky radiance assimilation at multiple scales.
Important progress has been made in recent years in characterizing surface soil moisture (SSM) at regional scales, through remote sensing estimates and the implementation of new in situ networks. Each of these sources of information has intrinsic features, such as the dynamic range of the SSM and the temporal frequency of acquisition. Another relevant factor is the period of data availability. Improving the knowledge of the limitations and biases of these features is crucial to increase the potential and the consistency of data sources validations. As a case of study we considered an agricultural area in the Argentinean Pampas, characterized by a sub-humid climate with a marked seasonal dynamic. It also holds a synchronized cropping rhythm and is subject to flooding and waterlogging that can last from days to months. The features mentioned above and considering that the region is almost devoid of irrigation, offer a natural laboratory that is distinguished by a wide dynamic range of SSM conditions. In this context, we analyze and expose different sources of SSM data gaps over long periods of time, using information from in situ stations and from the SMOS and SMAP satellite systems, during 2015-2019. We found SMAP data gaps resulting from the filtering of high SSM signals that are not spurious but typical for this flood-prone region. Reports from national institutions and comparison with other data sources allowed us to identify that high soil water content in the same period in which the data gaps occurred. In a different way, the SMOS register has a low-frequency range of data due to radio frequency interference over the study area. This data gap occurs during a long-anomalously wet period and it is relevant to take it into account when analyzing SMOS data for the full period. Our study shows the importance of using multiple sources of information and the relevance of examining the availability of data.
4 Key Points: 5 • The SST contrast increases with warming, primarily because the clear-sky green-6 house effect feedback is stronger in the warm region. 7 • As the climate warms, the integrated cooling rate of the atmosphere increases by 8 moving upward into lower pressures and increasing in strength, giving a more top-9 heavy cooling profile. 10 • The more top-heavy cooling rate profile results in increased cloud ice as the cli-11 mate warms. Abstract 13 Warming experiments with a uniformly insolated, non-rotating climate model with a slab 14 ocean are conducted by increasing the solar irradiance. As the climate warms, the sur-15 face temperature contrast between the warm, rising and cooler, subsiding regions increases, 16 mostly as a result of the stronger greenhouse effect in the warm region. The convective 17 heating rate becomes more top-heavy in warmed climates, producing more cloud ice, prin-18 cipally because the radiative cooling rate moves to lower pressures and increases. To pro-19 duce this more top-heavy convective heating, precipitation shifts from the convective to 20 the stratiform parameterization. The net cloud radiative effect becomes more negative 21 in the warm region as the climate warms. At temperatures above about 310K surface 22 temperature contrast begins to decline, and the climate becomes more sensitive. The re-23 duction in SST contrast above 310K again appears to be initiated by clear-sky radiative 24 processes, although cloud processes in both the rising and subsiding regions contribute. 25 The response of clear-sky outgoing longwave to surface warming begins to accelerate in 26 the region of rising motion and decline in the region of subsidence, driving the SST con-27 trast to smaller values. One-dimensional simulations are used to isolate the most rele-28 vant physics. 29 Plain Language Summary 30 A global model of a non-rotating Earth with an ocean that stores heat but does 31 not transport it is run to equilibrium with different values of globally uniform solar heat-32 ing. Despite the complete uniformity of the system, it still develops regions of warm sea 33 surface temperature where rain and rising motion occur, and regions with downward, 34 subsiding air motion where rainfall does not occur. These contrasts look very similar to 35 what is observed in the present-day tropics. As the climate is warmed from current tem-36 peratures toward warmer temperatures, the warm regions warm faster, mostly because 37 the rising regions contain more water vapor. The clouds rise to higher altitudes in the 38 warmer climates, and produce more cloud ice. These changes are shown to arise from 39 well-understood physical processes that are expected to operate in nature. 40
Ionospheric conductance is a crucial factor in regulating the closure of magnetospheric field-aligned currents through the ionosphere as Hall and Pedersen currents. Despite its importance in predictive investigations of the magnetosphere - ionosphere coupling, the estimation of ionospheric conductance in the auroral region is precarious in most global first-principles based models. This impreciseness in estimating the auroral conductance impedes both our understanding and predictive capabilities of the magnetosphere-ionosphere system during extreme space weather events. In this article, we address this concern, with the development of an advanced Conductance Model for Extreme Events (CMEE) that estimates the auroral conductance from field aligned current values. CMEE has been developed using nonlinear regression over a year’s worth of one-minute resolution output from assimilative maps, specifically including times of extreme driving of the solar wind-magnetosphere-ionosphere system. The model also includes provisions to enhance the conductance in the aurora using additional adjustments to refine the auroral oval. CMEE has been incorporated within the Ridley Ionosphere Model (RIM) of the Space Weather Modeling Framework (SWMF) for usage in space weather simulations. This paper compares performance of CMEE against the existing conductance model in RIM, through a validation process for six space weather events. The performance analysis indicates overall improvement in the ionospheric feedback to ground-based space weather forecasts. Specifically, the model is able to improve the prediction of ionospheric currents which impact the simulated dB/dt and ΔB, resulting in substantial improvements in dB/dt predictive skill.
Satellite data reveal widespread changes in Earth’s vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.
This article is composed of one integrated commentary about the state of ICON principles (Goldman et al., 2021) in natural hazards and a discussion on the opportunities and challenges of adopting them. Natural hazards pose risks to society, infrastructure, and the environment. Hazard interactions and their cascading phenomena in space and time can further intensify the impacts. Natural hazards’ risks are expected to increase in the future due to environmental, demographic, and socioeconomic changes. It is important to quantify and effectively communicate risks to inform the design and implementation of risk mitigation and adaptation strategies. Multihazard multisector risk management poses several nontrivial challenges, including: i) integrated risk assessment, ii) Earth system data-model fusion, iii) uncertainty quantification and communication, and iv) crossing traditional disciplinary boundaries. Here, we review these challenges, highlight current research and operational endeavors, and underscore diverse research opportunities. We emphasize the need for integrated approaches, coordinated processes, open science, and networked efforts (ICON) for multihazard multisector risk management.
Understanding past changes in precipitation extremes could help us predict their dynamics under future conditions. We present a novel approach for analyzing trends in extremes and attributing them to changes in the local precipitation regime. The approach relies on the separation between intensity distribution and occurrence frequency of storms. We examine the relevant case of the eastern Italian Alps, where significant trends in annual maximum precipitation over the past decades were observed. The model is able to reproduce observed trends at all durations between 15 minutes and 24 hours, and allows to quantify trends in extreme return levels. Despite the significant increase in storms occurrence and typical intensity, the observed trends can be only explained considering changes in the tail heaviness of the intensity distribution, that is the proportion between heavy and mild events. Our results suggest these are caused by an increased proportion of summer convective storms.
Covid- 19 dominantly impacted the Indian agricultural sector. During the period of COVID-19 the southwest monsoon covered a major part of the country, thus resulting in an increase of 9 percent coverage in rainfall than the usual average period. Due to the good amount of rainfall the area under cultivation during the kharif season stood above 4.8% than the previous year. During, the initial lockdown period the agriculture has not been much affected and an increase in migration resulted an increase in people employed in agriculture. Through regression analysis the relationship between the yield and rainfall has been determined. The R2 values have been calculated and the spatial relationship between them has been established. Regions with higher R2 values have been found to be more dominantly affected by Covid-19, though in certain areas strong R2 has shown a weaker spatial relationship owing to certain other factors and policies taken by the Government. Therefore, regression analysis can be used as a suitable method to study the relationship of rainfall and agricultural yield during Covid-19. Keywords: Agriculture, Regression Analysis, Spatial relationship, Rainfall, Covid-19.
The Alfred Wegener Institute Climate Model (AWI-CM) participates for the first time in the Coupled Model Intercomparison Project (CMIP), CMIP6. The sea ice-ocean component, FESOM, runs on an unstructured mesh with horizontal resolutions ranging from 8 to 80 km. FESOM is coupled to the Max-Planck-Institute atmospheric model ECHAM 6.3 at a horizontal resolution of about 100 km. Using objective performance indices, it is shown that AWI-CM performs better than the average of CMIP5 models. AWI-CM shows an equilibrium climate sensitivity of 3.2°C, which is similar to the CMIP5 average, and a transient climate response of 2.1°C which is slightly higher than the CMIP5 average. The negative trend of Arctic sea ice extent in September over the past 30 years is 20-30% weaker in our simulations compared to observations. With the strongest emission scenario, the AMOC decreases by 25% until the end of the century which is less than the CMIP5 average of 40%. Patterns and even magnitude of simulated temperature and precipitation changes at the end of this century compared to present-day climate under the strong emission scenario SSP585 are similar to the multi-model CMIP5 mean. The simulations show a 11°C warming north of the Barents Sea and around 2 to 3°C over most parts of the ocean as well as a wetting of the Arctic, subpolar, tropical and Southern Ocean. Furthermore, in the northern mid-latitudes in boreal summer and autumn as well as in the southern mid-latitudes a more zonal atmospheric flow is projected throughout the year.
The Antarctic stratospheric sudden warming (SSW) occurred on August 30, 2019, and was a vortex displacement minor warming event. We investigated variations in gravity waves (GWs) before and after this rare Antarctic SSW event using two satellite measurements (AIRS and CIPS) and reanalysis data (GEOS-5 FP). The observations showed that the GW activities decreased after the SSW onset, with a weakening of zonal wind. The decrease in GW activity coincided with a reversal of the zonal wind around September 8 in GEOS-5 FP. The temporal variation of GWs was similar to that of Arctic GWs during vertex displacement minor SSWs. The decline in GW activities was probably caused by wind filtering and polar night jet breaking. However, the GW activities over the Andes and the Antarctic peninsula decreased at the onset, although the westly wind was 40–60 ms-1. This decrease could have been caused by wave saturation.
This study focuses on the projections and time of emergence (TOE) for temperature extremes over Australian regions in the phase 6 of Coupled Model Intercomparison Project (CMIP6) models. The model outputs are based on the Shared Socioeconomic Pathways (SSPs) from the Tier 1 experiments (i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) in the Scenario Model Intercomparison Project (ScenarioMIP), which is compared with the Representative Concentration Pathways (RCPs) in CMIP5 (i.e., RCP2.6, RCP4.5 and RCP8.5). Furthermore, two large ensembles (LEs) in CMIP6 are used to investigate the effects of internal variability on the projected changes and TOE. As shown in the temporal evolution and spatial distribution, the strongest warming levels are projected under the highest future scenario and the changes for some extremes follow a “warm-get-warmer” pattern over Australia. Over subregions, tropical Australia usually shows the highest warming. Compared to the RCPs in CMIP5, the multi-model medians in SSPs are higher for some indices and commonly exhibit wider spreads, likely related to the different forcings and higher climate sensitivity in a subset of the CMIP6 models. Based on a signal-to-noise framework, we confirm that the emergence patterns differ greatly for different extreme indices and the large uncertainty in TOE can result from the inter-model ranges of both signal and noise, for which internal variability contributes to the determination of the signal. We further demonstrate that the internally-generated variations influence the noise. Our findings can provide useful information for mitigation strategies and adaptation planning over Australia.
Water isotopes measured in Antarctic ice cores enable reconstruction at the first order of the past temperature variations. However, the seasonality of the precipitation and episodic events, including synoptic-scale disturbances, influence the isotopic signals recorded in ice cores. In this study, we adopted an isotope-enabled atmospheric general circulation model from 1981 to 2010 to investigate variations in climatic factors in δ18O of precipitation (δ18Op) at Dome Fuji, East Antarctica. The Southern Annular Mode (SAM), the primary mode of atmospheric circulation in the southern mid-high latitudes, significantly contributes to the isotope signals. Positive δ18Op anomalies, especially in the austral winter, are linked to the negative polarity of the SAM, which weakens westerly winds and increases the southward inflow of water vapor flux. Daily variations in temperature and δ18Op in Dome Fuji are significantly small in the austral summer, and their contribution to the annual signals is limited. The isotope signals driven by the SAM are a locational feature of Dome Fuji, related to the asymmetric component of the large-scale atmospheric pattern.