We welcome abstracts on Atmospheric Rivers and extremes related to them in the climate system.  Atmospheric rivers (ARs) are narrow and elongated regions of horizontal water vapor transport that play important roles in the global water cycle and weather, water, and climate extremes. Increasing evidence shows that ARs have footprints and impacts in many regions across the world, and that they may undergo notable changes in future climate. The range of processes and the hazards/benefits associated with ARs represent a key challenge to both the science and applications communities. Meeting this challenge in the context of climate variations and change calls for better communication, understanding, and collaboration across a variety of disciplines. To highlight and enhance cross-boundary recognition of ARs as a particular type of extreme events, this session encourages contributions from multiple disciplines and those focusing on different geographic/socioeconomic locations. Expected topics include atmospheric, oceanic, hydrologic, ecologic, and other aspects of ARs under the current and future climates, the interactions between ARs and other weather/climate phenomena, and emerging applications of the AR science in the management of extreme events.

We invite abstracts focusing on climate impacts on high latitude systems in northern and southern polar regions at both regional (e.g., Bering Sea, Beaufort Sea, Barents Sea, Labrador Sea) and broader spatial scales (e.g., Arctic Ocean and the Southern Ocean). We seek papers that evaluate climate impacts at time scales ranging from seasonal, decadal, to multi-decadal. Topics of particular interest include: (a) Seasonal time scales, for example, estimates of summer ice conditions in the Arctic (or Antarctic) and links to preceding winter and early spring atmosphere and ocean conditions; (b) The impacts of climate change on high latitude and Arctic or Southern Ocean storms and their impacts and feedbacks on the upper ocean; (c) The role of inertial gravity waves, mesoscale and sub-mesoscale eddies and related processes on mixed layer depths, vertical mixing, and on the ice edge and related ice conditions; (d) Estimates of climate and climate change on longer time scales, up to the next several decades, e.g. following IPCC scenarios; and (e) Climate impacts on high latitude ecosystems.

In this session we will examine the role of the stratosphere in climate variability, change, and prediction from the tropics to the extratropics.  Increasingly, stratospheric processes have been found to increase predictive skill of tropospheric climate on S2S and longer timescales.  Additionally, we will consider the role of the stratosphere on climate variability from seasonal to decadal timescales as well as the stratosphere's role in long term climate change.  We are also interested in studies that examine how stratospheric processes might be better simulated to enhance predictability.

Cloud cover in the midlatitudes plays an important role in the Earth’s radiative balance. The heating associated with the clouds might also impact atmospheric circulation. On the other hand, it has long been accepted that organized atmospheric circulation systems such as fronts, anticyclones, and cold air outbreaks all can generate clouds. However, a robust characterization of the role of clouds in climate variability, especially over the oceans, has only recently been made possible due to an expanding observations network. At this time, there is an ever-expanding set of literature on the behavior and trends of midlatitude clouds and their representation in numerical weather and climate models. Therefore, this session seeks to bring together and report on recent research on the physics of midlatitude clouds and the relationship between the clouds and climate variability. The session seeks to span multiple temporal and spatial scales, with an interest in understanding the relative roles of: (i) boundary layer processes, (ii) synoptic circulation, and (iii) microphysics. The session also invites studies that focus on how cloud radiative forcing can impact local and remote atmospheric dynamics.

Land-atmosphere (L-A) interactions are a key component of the global climate system. Water, energy, and carbon transfer between the land surface and planetary boundary layer (PBL) have important impacts on weather and climate variability, predictability, and extremes such as drought. This session focuses on land-atmosphere interactions and characterization of water, energy, and carbon cycle fluxes, and subsequent feedbacks and coupling between the surface and PBL. In particular, the impacts of soil moisture and evapotranspiration on PBL, cloud, and precipitation development remain a challenge to quantify across a range of scales. We invite observation, satellite, and model-based studies of land-atmosphere interactions, particularly at the process-level, and their applications in weather and climate modeling and predictability.  In particular, we emphasize studies that utilize satellite observations and remote sensing for L-A studies.

This session welcomes submissions addressing the past and projected evolution of stratospheric ozone and its interplay with climate. Topics covered include estimates of trends and variability in stratospheric ozone, the effects of increasing greenhouse gases on ozone recovery, the climate effects of HFCs, how changes in stratospheric ozone translate to changes in atmospheric circulation and surface climate,  stratospheric aerosol–ozone–climate interactions associated with volcanic eruptions or geoengineering schemes, and changes associated with controls imposed by the Montreal Protocol and its subsequent amendments. Modeling and observational studies on local to global spatial scales and seasonal to centennial time scales are welcome.

Land-atmosphere (L-A) interactions are a key component of the global climate system. Water, energy, and carbon transfer between the land surface and planetary boundary layer (PBL) have important impacts on weather and climate variability, predictability, and extremes such as drought. This session focuses on land-atmosphere interactions and characterization of water, energy, and carbon cycle fluxes, and subsequent feedbacks and coupling between the surface and PBL. In particular, the impacts of soil moisture and evapotranspiration on PBL, cloud, and precipitation development remain a challenge to quantify across a range of scales. We invite observation, satellite, and model-based studies of land-atmosphere interactions, particularly at the process-level, and their applications in weather and climate modeling and predictability.  In particular, we emphasize studies that utilize satellite observations and remote sensing for L-A studies.

Land-atmosphere (L-A) interactions are a key component of the global climate system. Water, energy, and carbon transfer between the land surface and planetary boundary layer (PBL) have important impacts on weather and climate variability, predictability, and extremes such as drought. This session focuses on land-atmosphere interactions and characterization of water, energy, and carbon cycle fluxes, and subsequent feedbacks and coupling between the surface and PBL. In particular, the impacts of soil moisture and evapotranspiration on PBL, cloud, and precipitation development remain a challenge to quantify across a range of scales. We invite observation, satellite, and model-based studies of land-atmosphere interactions, particularly at the process-level, and their applications in weather and climate modeling and predictability.  In particular, we emphasize studies that utilize satellite observations and remote sensing for L-A studies.

Many challenges exist with respect to the collection, storage, transfer, and analysis of "Big Data" across the Earth sciences. New Earth observing sensors and models generate richer and bigger datasets, and many methods are being used or developed to overcome the difficulty of working with them. This session discusses ongoing efforts to identify, assemble, and make large multivariate Earth datasets widely accessible for comparative AI research. Speakers will address real-world Big Data challenges they have encountered in their workflows and/or the solutions they have used to overcome them. Solutions could include data mining or machine learning algorithms, high-performance parallel computing systems or platforms, advanced data storage and transfer capabilities, and more.

Land-atmosphere (L-A) interactions are a key component of the global climate system. Water, energy, and carbon transfer between the land surface and planetary boundary layer (PBL) have important impacts on weather and climate variability, predictability, and extremes such as drought. This session focuses on land-atmosphere interactions and characterization of water, energy, and carbon cycle fluxes, and subsequent feedbacks and coupling between the surface and PBL. In particular, the impacts of soil moisture and evapotranspiration on PBL, cloud, and precipitation development remain a challenge to quantify across a range of scales. We invite observation, satellite, and model-based studies of land-atmosphere interactions, particularly at the process-level, and their applications in weather and climate modeling and predictability.  In particular, we emphasize studies that utilize satellite observations and remote sensing for L-A studies.

Land-atmosphere (L-A) interactions are a key component of the global climate system. Water, energy, and carbon transfer between the land surface and planetary boundary layer (PBL) have important impacts on weather and climate variability, predictability, and extremes such as drought. This session focuses on land-atmosphere interactions and characterization of water, energy, and carbon cycle fluxes, and subsequent feedbacks and coupling between the surface and PBL. In particular, the impacts of soil moisture and evapotranspiration on PBL, cloud, and precipitation development remain a challenge to quantify across a range of scales. We invite observation, satellite, and model-based studies of land-atmosphere interactions, particularly at the process-level, and their applications in weather and climate modeling and predictability.  In particular, we emphasize studies that utilize satellite observations and remote sensing for L-A studies.

Extreme weather and climate events in the mid-latitudes are associated with severe impacts on society and the environment, often having lasting socioeconomic impacts.  Quantification of risks associated with these events remains challenging, particularly under anthropogenically forced non-stationarity in the climate system.  Part of this challenge stems from an incomplete understanding of the physical mechanisms associated with extreme events in the current climate and uncertainty in the fidelity with which climate models resolve key driving mechanisms and processes.  Furthermore, representing and projecting change in extremes using climate models, specifically at local to regional scales, is often challenged by model resolution limitations. This session explores advances in how we analyze extreme events in the mid-latitudes (e.g. polar-air outbreaks, droughts, floods, storms, heat waves) in present and future climates.  Efforts towards improved understanding of the driving physics and dynamics behind mid-latitude extremes, novel approaches to evaluating climate model skill at simulating these features, new approaches to address model resolution limitations, and cutting-edge methodology to assess projections of future change are of particular interest. Interdisciplinary work addressing the applications of scientific research on extremes to stakeholder decision-making processes is encouraged.

The use of machine learning techniques is already widespread in the climate community. Examples include the application of deep learning to characterize climate model outputs, high-resolution climate downscaling, or the use of neural networks for the modeling of climate time series. Submissions are sought for broad contributions that AI can make to climate studies and particularly topics relevant to the work of the National Climate Assessment Committee.

As shown on the NIDIS drought portal (https://www.drought.gov/drought/data-maps-tools/current-conditions) current operational drought monitoring and early warning in the U.S. relies on a combination of the U.S. Drought Monitor (USDM; Svoboda et al., 2002) and the Weekly Palmer Drought Index (PDSI) (Palmer, 1965; Heim 2002; 2005).  In contrast to the USDM, which relies on a manual, convergence-of-evidence approach, the PDSI is an objective approach that has been attractive for monitoring, early warning, and climate projection (e.g., Abatzoglou et al., 2017; Dai, 2011; Cook et al., 2015)

 

While the water balance approach encapsulated in PDSI has the advantage that it may be estimated with limited input data, issues with the approach, particularly the calculation of potential evapotranspiration (PET) have been widely reported (e.g., Sheffield et al., 2012).    Moreover, the atmospheric-centric formulation of PDSI ignores the feedbacks from groundwater, soils, and vegetation on the drought state.  For monitoring and forecasting seasonal to interannual variability of drought, it is critical to capture the effects of soil moisture and vapor pressure deficit on increased surface resistances that reduce ET primarily though stomatal closure (Milly 2016; Novick et al. 2016).   For longer-term climate projections of drought, recent work further suggests that ignoring the stomatal response due to increasing CO₂ in PDSI leads to an overestimation of future projected drought area, while other metrics that include actual ET (e.g., P-E) lead to dramatically reduced projections of future drought area (Swann et al., 2016).

 

There have been attempts to develop so-called “objective blends” that can mimic the USDM (e.g., Xia et al., 2014), but these efforts do not accurately reflect the response of vegetation to drought stress or the stomatal responses because the land surface models used in the North American Land Data Assimilation System (NLDAS) do not include prognostic vegetation states or represent VPD feedbacks and their controls on stomatal conductance.   Moreover, from a monitoring perspective, these models often do not reflect rapidly developing droughts like thermal remote sensing-based indices such as the Evaporative Stress Index (ESI; Otkin et al., 2013).  They also do not take advantage of other remotely sensed and in situ observations such as Soil Moisture from ground-based networks and SMAP and terrestrial water storage from GRACE.

 

With this context, the proposed theme for the Third NOAA MAPP Drought Task Force is Drought Monitoring, Early Warning, and Projection in the 21^st Century—Beyond PDSI.   We seek an objective drought index that reflects the state of drought science and includes modern observational systems and models.  Ultimately, a well-informed and objective declaration of the state of drought must integrate various measures.  We invite submissions related to all aspects of drought monitoring, early warning and projection.

Communicating climate change information to the public and to policy makers can be a challenge.  Although climate scientists thoroughly understand the processes, conveying the information to non-scientists may leave them confused.  Broadcast meteorologists specialize in presenting complex issues to the public in a concise and easy to understand manner. Although we are not the foremost experts in the science, perhaps we can provide examples of best practices for explaining climate science to the public and to politicians.  We also welcome abstracts focusing on communicating climate science to young people.

New research across multiple areas of climate science is emphasizing the potential hazards, and opportunities for prediction, associated with exceptionally warm sea surface temperatures. While such extremes are an anticipated climate change outcome, the science surrounding our ability to describe, explain and predict the climate hazards associated with these anomalous ocean conditions is still rapidly evolving. We welcome talks looking at observed or modeled analyses of oceanic temperature extremes or marine heat waves and the associated climatic hazards affecting marine ecosystems (fisheries) or terrestrial environments (droughts floods hurricanes). Also of interest would be presentations looking at relevant modes of coupled ocean-atmosphere climate variability, such as the El Nino-Southern Oscillation, studies treating the predictability of such extremes on seasonal or decadal time scales, and analyses discussing the design and implementation of early warning and information systems capable of coping with future extreme SST states. As the earth warms, more extreme sea surface temperatures appear likely. In many ways, our current modeling and monitoring systems appear well positioned to help us cope with these extremes, mainstreaming climate adaptation through enhanced climate services. This session will seek to address multiple aspects of such systems in one integrative set of presentations.

Imbalances among geography, ecology, economy, society and institutions are compromising the future sustainability of cities. Rapid demographic growth, economic expansion, and the increasing environmental footprint of cities have triggered dynamics that institutions are unable to manage effectively. There are now more than 37 megacities – cities with populations of more than 10 million inhabitants – and more than half of urban populations are concentrated in cities with at least one million inhabitants. Furthermore, most cities are located in areas highly vulnerable to disasters, with cities in less-developed regions both at higher risk of exposure to disaster and more vulnerable to disaster-related economic losses and mortality. There is a pressing need for new tools and integrated approaches that strengthen city governance to reduce disaster risk and protect human, economic, and environmental assets. Earth observation data has emerged as an important resource for monitoring environmental hazards, quantifying risk and providing complex visualizations on the interconnectedness of populations, key infrastructure, and climate-related processes. For example, new research and tools are using remote sensing imagery to delineate human settlements as well as the locations of critical infrastructure, e.g., roads, highways, bridges. Thus, Earth observations are helping city decision-makers to address disaster risk and develop long-term plans in a consistent and systematic way. This session will explore how Earth observations are being applied at the city scale, particularly when they are integrated with information about the cities’ economic, physical and social systems. Specifically, the session will examine how remote sensing and geospatial data, and Earth system modeling, can enhance local organizations' planning and operations. This session is seeking submissions that consider the following: 1)      Methods for advancing the application of Earth observation data, models and tools for the benefit of cities at short-term (e.g., air quality, water quality, disaster response) and longer-term scales (e.g., infrastructure planning, transportation policy); 2)      Research combining data, models, and methods derived from both social science and physical science to highlight the interlinkages between these systems and improve understanding of vulnerability and exposure within cities; and 3)      Examples of programmatic and technical approaches that build predictive capacity within city institutions and local networks by harnessing Earth observations.

As shown on the NIDIS drought portal (https://www.drought.gov/drought/data-maps-tools/current-conditions) current operational drought monitoring and early warning in the U.S. relies on a combination of the U.S. Drought Monitor (USDM; Svoboda et al., 2002) and the Weekly Palmer Drought Index (PDSI) (Palmer, 1965; Heim 2002; 2005).  In contrast to the USDM, which relies on a manual, convergence-of-evidence approach, the PDSI is an objective approach that has been attractive for monitoring, early warning, and climate projection (e.g., Abatzoglou et al., 2017; Dai, 2011; Cook et al., 2015)

 

While the water balance approach encapsulated in PDSI has the advantage that it may be estimated with limited input data, issues with the approach, particularly the calculation of potential evapotranspiration (PET) have been widely reported (e.g., Sheffield et al., 2012).    Moreover, the atmospheric-centric formulation of PDSI ignores the feedbacks from groundwater, soils, and vegetation on the drought state.  For monitoring and forecasting seasonal to interannual variability of drought, it is critical to capture the effects of soil moisture and vapor pressure deficit on increased surface resistances that reduce ET primarily though stomatal closure (Milly 2016; Novick et al. 2016).   For longer-term climate projections of drought, recent work further suggests that ignoring the stomatal response due to increasing CO₂ in PDSI leads to an overestimation of future projected drought area, while other metrics that include actual ET (e.g., P-E) lead to dramatically reduced projections of future drought area (Swann et al., 2016).

 

There have been attempts to develop so-called “objective blends” that can mimic the USDM (e.g., Xia et al., 2014), but these efforts do not accurately reflect the response of vegetation to drought stress or the stomatal responses because the land surface models used in the North American Land Data Assimilation System (NLDAS) do not include prognostic vegetation states or represent VPD feedbacks and their controls on stomatal conductance.   Moreover, from a monitoring perspective, these models often do not reflect rapidly developing droughts like thermal remote sensing-based indices such as the Evaporative Stress Index (ESI; Otkin et al., 2013).  They also do not take advantage of other remotely sensed and in situ observations such as Soil Moisture from ground-based networks and SMAP and terrestrial water storage from GRACE.

 

With this context, the proposed theme for the Third NOAA MAPP Drought Task Force is Drought Monitoring, Early Warning, and Projection in the 21^st Century—Beyond PDSI.   We seek an objective drought index that reflects the state of drought science and includes modern observational systems and models.  Ultimately, a well-informed and objective declaration of the state of drought must integrate various measures.  We invite submissions related to all aspects of drought monitoring, early warning and projection.

The tropical Americas, including the Caribbean, Gulf of Mexico, Central America, and portions of the Pacific, Atlantic, and North and South America, are uniquely positioned to serve as the pulse of climate change and climate variability. In fact, the Central American region is the most prominent tropical “HotSpot” identified by the Regional Climate Change Index, but global climate model biases are large in this region. Climate extremes such as droughts, floods, heatwaves, and tropical cyclones can lead to large socioeconomic impacts in the IAS region. The region also plays a vital role in weather, climate, and extreme events on adjacent continents, including North America. Therefore, it is of foremost importance to expand our observational record to understand climate extremes in and around the tropical Americas region. The lack of observations, both spatially and temporally, in the tropical Americas is a major hindrance to resolving climate model biases, as we are unable to reliably constrain and evaluate simulations with the observed climate extremes. There is a strong need for a more comprehensive, end-to-end approach for climate extreme assessments at a regional scale including better integration of paleoclimatic evidence, atmospheric and oceanic observations, physical understanding, model evaluation, and projections.

This session seeks contributions to increase our understanding of climate extremes in this region in the past, present, and future using observations, paleoclimate proxies, and model simulations. Paralleling the theme of the annual meeting, we especially encourage contributions with an Interdisciplinary approach to paleoclimate, oceanography, and atmospheric science activities to address climate extremes in the Tropical Americas, and from International partners in the region.

Land use and land cover change (LULC) is shown to have dramatic impacts on local and regional climate. Physical changes to land surfaces manifest in the form of deforestation/reforestation, wildfires and controlled burns, agricultural practices, mining activities, expansion of impervious surfaces, and greenbelting in urban areas. Through redistribution of water, changes to the surface energy budget, modification of surface roughness, alteration of lower tropospheric biogeochemistry, and other anthropogenic effects, rapid changes in climate regimes occur. A primary means of quantifying resultant anomalies is through the utilization of high-resolution, numerical prediction models such as WRF and its variants (ARW, FIRE, etc.). This session serves as a platform to disseminate research that investigates the impact of LULC on climate across multiple spatio-temporal scales via high resolution, numerical prediction models. Research on best practices and emerging methods in application and implementation of LULC are also welcome, as no uniform methodology exists in the current literature.

As shown on the NIDIS drought portal (https://www.drought.gov/drought/data-maps-tools/current-conditions) current operational drought monitoring and early warning in the U.S. relies on a combination of the U.S. Drought Monitor (USDM; Svoboda et al., 2002) and the Weekly Palmer Drought Index (PDSI) (Palmer, 1965; Heim 2002; 2005).  In contrast to the USDM, which relies on a manual, convergence-of-evidence approach, the PDSI is an objective approach that has been attractive for monitoring, early warning, and climate projection (e.g., Abatzoglou et al., 2017; Dai, 2011; Cook et al., 2015)

 

While the water balance approach encapsulated in PDSI has the advantage that it may be estimated with limited input data, issues with the approach, particularly the calculation of potential evapotranspiration (PET) have been widely reported (e.g., Sheffield et al., 2012).    Moreover, the atmospheric-centric formulation of PDSI ignores the feedbacks from groundwater, soils, and vegetation on the drought state.  For monitoring and forecasting seasonal to interannual variability of drought, it is critical to capture the effects of soil moisture and vapor pressure deficit on increased surface resistances that reduce ET primarily though stomatal closure (Milly 2016; Novick et al. 2016).   For longer-term climate projections of drought, recent work further suggests that ignoring the stomatal response due to increasing CO₂ in PDSI leads to an overestimation of future projected drought area, while other metrics that include actual ET (e.g., P-E) lead to dramatically reduced projections of future drought area (Swann et al., 2016).

 

There have been attempts to develop so-called “objective blends” that can mimic the USDM (e.g., Xia et al., 2014), but these efforts do not accurately reflect the response of vegetation to drought stress or the stomatal responses because the land surface models used in the North American Land Data Assimilation System (NLDAS) do not include prognostic vegetation states or represent VPD feedbacks and their controls on stomatal conductance.   Moreover, from a monitoring perspective, these models often do not reflect rapidly developing droughts like thermal remote sensing-based indices such as the Evaporative Stress Index (ESI; Otkin et al., 2013).  They also do not take advantage of other remotely sensed and in situ observations such as Soil Moisture from ground-based networks and SMAP and terrestrial water storage from GRACE.

 

With this context, the proposed theme for the Third NOAA MAPP Drought Task Force is Drought Monitoring, Early Warning, and Projection in the 21^st Century—Beyond PDSI.   We seek an objective drought index that reflects the state of drought science and includes modern observational systems and models.  Ultimately, a well-informed and objective declaration of the state of drought must integrate various measures.  We invite submissions related to all aspects of drought monitoring, early warning and projection.