Abstract

Climate variability and weather phenomena can cause extremes and pose significant risk to society and ecosystems, making continued advances in our physical understanding of such events of utmost importance for regional and global security. Advances in machine learning (ML) have been leveraged for applications in climate variability and weather, empowering scientists to approach questions using big data in new ways. Growing interest across the scientific community in these areas has motivated coordination between the physical and computer science disciplines to further advance the state of the science and tackle pressing challenges. During a recently held workshop that had participants across academia, private industry, and research laboratories, it became clear that a comprehensive review of recent and emerging ML applications for climate variability and weather phenomena that can cause extremes was needed. This article aims to fulfill this need by discussing recent advances, challenges, and research priorities in the following topics: sources of predictability for modes of climate variability, feature detection, extreme weather and climate prediction and precursors, observation–model integration, downscaling, and bias correction. This article provides a review for domain scientists seeking to incorporate ML into their research. It also provides a review for those with some ML experience seeking to broaden their knowledge of ML applications for climate variability and weather.

Abstract

The correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multimodel Ensemble, and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and subtropics than over the extratropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with time scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean–atmosphere models.

Abstract

The Diabatic Influences on Mesoscale Structures in Extratropical Storms (DIAMET) project aims to improve forecasts of high-impact weather in extratropical cyclones through field measurements, high-resolution numerical modeling, and improved design of ensemble forecasting and data assimilation systems. This article introduces DIAMET and presents some of the first results. Four field campaigns were conducted by the project, one of which, in late 2011, coincided with an exceptionally stormy period marked by an unusually strong, zonal North Atlantic jet stream and a succession of severe windstorms in northwest Europe. As a result, December 2011 had the highest monthly North Atlantic Oscillation index (2.52) of any December in the last 60 years. Detailed observations of several of these storms were gathered using the U.K.’s BAe 146 research aircraft and extensive ground-based measurements. As an example of the results obtained during the campaign, observations are presented of Extratropical Cyclone Friedhelm on 8 December 2011, when surface winds with gusts exceeding 30 m s^–1 crossed central Scotland, leading to widespread disruption to transportation and electricity supply. Friedhelm deepened 44 hPa in 24 h and developed a pronounced bent-back front wrapping around the storm center. The strongest winds at 850 hPa and the surface occurred in the southern quadrant of the storm, and detailed measurements showed these to be most intense in clear air between bands of showers. High-resolution ensemble forecasts from the Met Office showed similar features, with the strongest winds aligned in linear swaths between the bands, suggesting that there is potential for improved skill in forecasts of damaging winds.

Abstract

Advanced Quantitative Precipitation Information (AQPI) is a synergistic project that combines observations and models to improve monitoring and forecasts of precipitation, streamflow, and coastal flooding in the San Francisco Bay area. As an experimental system, AQPI leverages more than a decade of research, innovation, and implementation of a statewide, state-of-the-art network of observations, and development of the next generation of weather and coastal forecast models. AQPI was developed as a prototype in response to requests from the water management community for improved information on precipitation, riverine, and coastal conditions to inform their decision making processes. Observation of precipitation in the complex Bay Area landscape of California’s coastal mountain ranges is known to be a challenging problem. But, with new advanced radar network techniques, AQPI is helping fill an important observational gap for this highly populated and vulnerable metropolitan area. The prototype AQPI system consists of improved weather radar data for precipitation estimation; additional surface measurements of precipitation, streamflow and soil moisture; and a suite of integrated forecast modeling systems to improve situational awareness about current and future water conditions from sky to sea. Together these tools will help improve emergency preparedness and public response to prevent loss of life and destruction of property during extreme storms accompanied by heavy precipitation and high coastal water levels - especially high-moisture laden atmospheric rivers. The Bay Area AQPI system could potentially be replicated in other urban regions in California, the United States, and world-wide.

Abstract

The largest uncertainty in the historical radiative forcing of climate is caused by changes in aerosol particles due to anthropogenic activity. Sophisticated aerosol microphysics processes have been included in many climate models in an effort to reduce the uncertainty. However, the models are very challenging to evaluate and constrain because they require extensive in situ measurements of the particle size distribution, number concentration, and chemical composition that are not available from global satellite observations. The Global Aerosol Synthesis and Science Project (GASSP) aims to improve the robustness of global aerosol models by combining new methodologies for quantifying model uncertainty, to create an extensive global dataset of aerosol in situ microphysical and chemical measurements, and to develop new ways to assess the uncertainty associated with comparing sparse point measurements with low-resolution models. GASSP has assembled over 45,000 hours of measurements from ships and aircraft as well as data from over 350 ground stations. The measurements have been harmonized into a standardized format that is easily used by modelers and nonspecialist users. Available measurements are extensive, but they are biased to polluted regions of the Northern Hemisphere, leaving large pristine regions and many continental areas poorly sampled. The aerosol radiative forcing uncertainty can be reduced using a rigorous model–data synthesis approach. Nevertheless, our research highlights significant remaining challenges because of the difficulty of constraining many interwoven model uncertainties simultaneously. Although the physical realism of global aerosol models still needs to be improved, the uncertainty in aerosol radiative forcing will be reduced most effectively by systematically and rigorously constraining the models using extensive syntheses of measurements.

Abstract

The Convective Transport of Active Species in the Tropics (CONTRAST) experiment was conducted from Guam (13.5°N, 144.8°E) during January–February 2014. Using the NSF/NCAR Gulfstream V research aircraft, the experiment investigated the photochemical environment over the tropical western Pacific (TWP) warm pool, a region of massive deep convection and the major pathway for air to enter the stratosphere during Northern Hemisphere (NH) winter. The new observations provide a wealth of information for quantifying the influence of convection on the vertical distributions of active species. The airborne in situ measurements up to 15-km altitude fill a significant gap by characterizing the abundance and altitude variation of a wide suite of trace gases. These measurements, together with observations of dynamical and microphysical parameters, provide significant new data for constraining and evaluating global chemistry–climate models. Measurements include precursor and product gas species of reactive halogen compounds that impact ozone in the upper troposphere/lower stratosphere. High-accuracy, in situ measurements of ozone obtained during CONTRAST quantify ozone concentration profiles in the upper troposphere, where previous observations from balloonborne ozonesondes were often near or below the limit of detection. CONTRAST was one of the three coordinated experiments to observe the TWP during January–February 2014. Together, CONTRAST, Airborne Tropical Tropopause Experiment (ATTREX), and Coordinated Airborne Studies in the Tropics (CAST), using complementary capabilities of the three aircraft platforms as well as ground-based instrumentation, provide a comprehensive quantification of the regional distribution and vertical structure of natural and pollutant trace gases in the TWP during NH winter, from the oceanic boundary to the lower stratosphere.

Abstract

Remote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100 × 100 km². In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparison of satellite and in situ measurements. This paper synthesizes present knowledge of the magnitude and the processes that contribute to the formation and evolution of vertical and horizontal variability in near-surface salinity. Rainfall, freshwater plumes, and evaporation can generate vertical gradients of salinity, and in some cases these gradients can be large enough to affect validation of satellite measurements. Similarly, mesoscale to submesoscale processes can lead to horizontal variability that can also affect comparisons of satellite data to in situ data. Comparisons between satellite and in situ salinity measurements must take into account both vertical stratification and horizontal variability.

A comprehensive and cohesive aerosol measurement record with consistent, well-understood uncertainties is a prerequisite to understanding aerosol impacts on long-term climate and environmental variability. Objectives to attaining such an understanding include improving upon the current state-of-the-art sensor calibration and developing systematic validation methods for remotely sensed microphysical properties. While advances in active and passive remote sensors will lead to needed improvements in retrieval accuracies and capabilities, ongoing validation is essential so that the changing sensor characteristics do not mask atmospheric trends. Surface-based radiometer, chemical, and lidar networks have critical roles within an integrated observing system, yet they currently undersample key geographic regions, have limitations in certain measurement capabilities, and lack stable funding. In situ aircraft observations of size-resolved aerosol chemical composition are necessary to provide important linkages between active and passive remote sensing. A planned, systematic approach toward a global aerosol observing network, involving multiple sponsoring agencies and surface-based, suborbital, and spaceborne sensors, is required to prioritize trade-offs regarding capabilities and costs. This strategy is a key ingredient of the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON) framework. A set of recommendations is presented.

Abstract

The authors present results for simulated climate and climate change from a newly developed high-resolution global climate model [Geophysical Fluid Dynamics Laboratory Climate Model version 2.5 (GFDL CM2.5)]. The GFDL CM2.5 has an atmospheric resolution of approximately 50 km in the horizontal, with 32 vertical levels. The horizontal resolution in the ocean ranges from 28 km in the tropics to 8 km at high latitudes, with 50 vertical levels. This resolution allows the explicit simulation of some mesoscale eddies in the ocean, particularly at lower latitudes.

Analyses are presented based on the output of a 280-yr control simulation; also presented are results based on a 140-yr simulation in which atmospheric CO₂ increases at 1% yr⁻¹ until doubling after 70 yr.

Results are compared to GFDL CM2.1, which has somewhat similar physics but a coarser resolution. The simulated climate in CM2.5 shows marked improvement over many regions, especially the tropics, including a reduction in the double ITCZ and an improved simulation of ENSO. Regional precipitation features are much improved. The Indian monsoon and Amazonian rainfall are also substantially more realistic in CM2.5.

The response of CM2.5 to a doubling of atmospheric CO₂ has many features in common with CM2.1, with some notable differences. For example, rainfall changes over the Mediterranean appear to be tightly linked to topography in CM2.5, in contrast to CM2.1 where the response is more spatially homogeneous. In addition, in CM2.5 the near-surface ocean warms substantially in the high latitudes of the Southern Ocean, in contrast to simulations using CM2.1.

The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate.

This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds.

Observing system simulation experiments, conducted using two different general circulation models, have shown 1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and 2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substantially improve tropical cyclone forecasts. Similarly, to improve water vapor flux divergence calculations, a direct measure of the ageostrophic wind is needed since the present level of uncertainty cannot be reduced with better temperature and moisture soundings alone.