2017 CIRA Seminars
Geophysical systems can be characterised as complex, nonlinear and high dimensional. All of these provide major challenges to understanding and modelling. To explore observations and existing knowledge encoded in numerical models to their full extent one can try to combine both sources of information. A systematic tool for doing this is data assimilation. It can be used to provide a description of the full nonlinear evolution of the system including its uncertainty using all information we have.
There is considerable research and management interest in whether and how native faunas are responding to climate change. Evaluating whether empirical data support range-shift hypotheses is complicated by variation in climate, differences in response variables and the extent and resolution of analyses, and mismatches between the resolutions at which climate data typically are available and species respond to environmental heterogeneity.
Lidar remote sensing enables observations of various atmospheric properties and dynamics from the troposphere to the region of the atmosphere considered the edge of space near ~80-100 km. For some aspects of the atmosphere, studying coupling between different altitudes and regions is important for a more in depth understanding. Gravity waves are one aspect integral to understanding atmospheric coupling, as they strongly influence dynamics within the atmosphere via the transport of energy and momentum from the lower atmosphere to the middle and upper atmosphere.
Spatial variations in the solar heating of the climate system drive the atmospheric and oceanic circulation and set patterns of temperature and precipitation. This presentation explores the processes that determine the absorption of solar radiation in the climate system including latitudinal, vertical and seasonal distributions.
Currently, the scientific community is unable to identify how characteristics of clouds will alter as the climate warms in response to emissions of greenhouse gases from human activities, and to what extent changes in cloud characteristics will feed back on surface temperature responses. In particular, models disagree substantially in the magnitude of cloud feedback for the regimes of subtropical marine boundary-layer clouds.
Global warming simulations robustly show that mean precipitation increases at 1-3% per Kelvin, but we do not know what sets these values. Mean precipitation is constrained by radiative cooling, however, and we demonstrate here that radiative cooling profiles exhibit a certain invariance under warming when plotted in temperature coordinates. This invariance can then be leveraged to derive simple analytical equations for precipitation change with warming.
University of Wyoming King Air (UWKA) is a part of NSF-supported Lower Atmosphere Observing Facilities (LAOF). Through multi-year development efforts, UWKA has equipped with integrated observation capabilities for cloud dynamics and microphysics, aerosols, and environment conditions through combining lidar, radar, radiometer and in situ measurements. Approaches were developed to retrieve droplet and ice concentrations in stratiform clouds from combined lidar-radar measurements.
The weather forecasting process has grown more complex in recent years with the growing amount of observational data and model output available to weather forecasters and the trend toward providing more impact-based decision support services. In order to assist forecasters and end-users with the task of managing the firehose of data, I have developed and evaluated machine learning forecast guidance systems for different high-impact weather phenomena.
The extensive cloudiness and resulting high albedo of the Southern Oceans (SO) are predominantly due to the occurrence of widespread marine boundary layer (MBL) clouds. Recent work finds correlations between biogenically enhanced cloud condensation nuclei concentrations and cloud droplet number concentrations derived from passive satellite data.
A key project within the National Oceanic and Atmospheric Administration (NOAA) Global Observing Systems Analysis (GOSA) group is the Sensing Hazards with Operational Unmanned Technology (SHOUT) project. One of the main objectives of SHOUT is to conduct both Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs) to evaluate the impact of real and simulated Unmanned Aircraft Systems (UAS) data on weather forecasts of tropical cyclones and high-impact weather events over the United States.
Pressure perturbations are regions of anomalously low or high pressure in deep convection and play key roles in modulating the magnitude and distribution of vertical velocities within cumulus clouds. A cloud’s vertical momentum budget is primarily regulated by two pressure forces: Effective buoyancy pressure acceleration (EBPA), and dynamic pressure acceleration (DPA).
2016 CIRA Seminars
The importance of precipitating mesoscale convective systems (MCSs) has been quantified from TRMM precipitation radar and microwave imager retrievals. MCSs generate more than 50% of the rainfall in most tropical regions. Typical MCSs have horizontal scales of a few hundred kilometers (km); therefore, a large domain and high resolution are required for realistic simulations of MCSs in cloud-resolving models (CRMs). Almost all traditional global and climate models do not have adequate parameterizations to represent MCSs.
Evidence from models and observations that the stratosphere imparts a significant downward influence on near-surface weather and climate has grown steadily since landmark papers in the early 2000s. It is now clear that the large-scale circulation in the stratosphere can be usefully exploited by seasonal forecasts, and that changes in this circulation are highly relevant both for understanding regional climate change in the past and projecting such changes into the coming century.
The climatological distribution of outgoing longwave radiation (OLR) over Africa is characterized by strong meridional gradients between the very high OLR over northern Africa due to the hot and cloud-free Sahara Desert, very low OLR over central Africa due to the Intertropical Convergence Zone (ITCZ), and moderately high OLR over southern Africa—also due to a region of strong descent within the global Hadley circulation.
This will be an informal seminar where Dr. Purdom will show work he presented at the 7th Asia-Oceania Meteorological Satellite Users’ Conference (AOMSUC) last month and discuss “new’ ways to work with satellite imagery.
Aerosols affect deep convection through their influence on cloud and precipitation microphysics over a wide range of spatiotemporal scales. Despite these important microphysical impacts, studies of aerosol-convection interactions often focus on the sensitivity of a model with a single microphysics representation to perturbations of aerosol, cloud condensation nuclei (CCN) or cloud droplet number concentration (CDNC). However, this approach assumes a reliable representation of microphysical pathways.
Aerosols arguably remain the single greatest uncertainty among anthropogenic perturbations of the climate system. In particular the effects of aerosol-cloud interactions on global and regional radiation budgets and the hydrological cycle remain highly uncertain.
In this presentation, I will critically review some of the achievements made towards quantifying aerosol-cloud interactions in models and observations with a focus on the role of observations in the evaluation of global aerosol-climate models.
Polarimetric upgrades to the U.S. radar network have allowed new insight into the precipitation processes of tropical cyclones. Previous work by the authors compared the horizontal and differential reflectivity observations from two hurricanes to simulated radar observations from the WRF model, and found that the aerosol-aware Thompson microphysical scheme performed the best of several commonly used microphysical parameterizations.
Shallow cumulus clouds predominate in the trade-wind regions, and the response of this widespread regime has been directly linked to the spread in climate model estimates of cloud feedback and climate sensitivity. These findings will be reviewed using CMIP5 climate model simulations. Observations and process-model simulations show that trade-wind regions foster multi-layered cloud structures with complicated relationships to their environment that manifest as different cloud variability near the cloud base versus cloud top as well as inhomogeneous horizontal distributions of cloud.
After the 8th WMO International Workshop on Tropical Cyclones in 2014,the Japanese Meteorological Research Insitute (MRI) at the Japanese Meteorological Administration (JMA) in cooperation with the RSMC Tokyo Typhoon Center sorted out issues in the JMA’s typhoon forecasting and then strengthened research and development activities toward improving the tropical cyclone related services. One of the examples is implementing the Statistical Hurricane Improvement System (SHIPS) with great support of SHIPS developers in the US and at CIRA/CSU.
It is important to know the phase and physical properties of aerosol and cloud particles at the top of mid-level and high tropospheric clouds – anvils and the stratiform cirrus that originate from them – in order to better estimate their effects on the atmospheric energy balance, the moisture budget and regional circulation patterns.
Carbon-cycle feedbacks are one of the most uncertain components of global climate predictions. Over the coming century, atmospheric CO2 will continue to accumulate in the atmosphere at a rate controlled by anthropogenic drivers, natural feedbacks to changing atmospheric composition, and the interaction thereof. In this talk, I will discuss Earth system model results that show the importance of considering both anthropogenic and natural processes in making predictions of long-term carbon cycle evolution.
Between the dawn of the Neolithic revolution and the start industrial revolution the population rose from ~ 4 million to 1 billion using power provided predominantly from natural sources and biomass burning. Since the industrial revolution, world population has risen to over 7 billion, and now 50% are dwelling in urban areas. This has been accompanied by an equivalent rapid increase in the standard of living. This has been possible using the energy provided by fossil fuel combustion and food generated in large part using man-made fertilizers and pesticides.
It is well-known that the U.S. skill in numerical weather prediction (NWP), as measured by NCEP’s Global Forecast System (GFS), lags behind other centers, most notably the ECMWF. In this talk, we will discuss some of the reasons for this discrepancy and provide a potential path forward for the U.S. to match or surpass ECMWF accuracy.
In response to increasing atmospheric greenhouse gas concentrations, most global climate models project that the mid-latitude jet streams will shift poleward over the 21st century. Consequently, the tracks of mid-latitude low-pressure systems and their associated cloud features are also anticipated to shift poleward over this time. As these cloud features move from a lower to a higher latitude, they will move from a latitude of greater incoming solar radiation to one of less incoming solar radiation.