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GFDL Past Events & Seminars - 2020

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Date Speaker Affiliation Title of Presentation
Jan. 8Lunchtime Seminar Series - Johannes QuaasUniversität Leipzig, Institute for Meteorology, Leipzig University, GermanyProgress in quantifying the effective radiative forcing due to aerosol-cloud interactions
The effective radiative forcing due to aerosol-cloud interaction, ERFaci, is composed of the radiative forcing due to aerosol-cloud interactions, RFaci (Twomey effect) that is the immediate response of cloud albedo to an increase in droplet number concentration, Nd. Previous satellite-based quantifications of this effect were hampered by deficiencies in the retrieval of aerosol and also Nd. The talk will firstly discuss progress in this regard, which leads to a stronger estimated RFaci than previous satellite-based approaches. The other component of ERFaci is in the cloud adjustments. These can be split into adjustments of cloud fraction, f, and liquid water path, L. In terms of the latter, statistical relationships between L and Nd show on average negative adjustments of L (a positive forcing component). In turn, the analysis of ship-, volcano- and industry tracks leads to an estimated small overall effect on L; these results are trustworthy since a cause-effect relation is assured. In terms of the f adjustment, the current results point to an increase in cloud fraction at larger Nd. It is unclear which processes lead to this result. The talk will also briefly discuss how cloud-resolving simulations may help to better understand the remaining uncertainties. In the last part, a brief discussion will be presented on initial steps towards an estimate of the response of cirrus to anthropogenic aerosols.
Jan. 9Formal Seminar - Bob KoppRutgers UniversityLinking climate science, economics, and Big Data to estimate climate change impacts and endogenous adaptation
Understanding the likely global economic impacts of climate change is of tremendous practical value to both policymakers and researchers. Yet the economics literature has struggled both to provide empirically founded estimates of the economic damages from climate change and to provide quantitative insight into what climate change will mean at the local level for diverse populations. The Climate Impact Lab (a collaboration among Rutgers, UC-Berkeley, the University of Chicago, and the Rhodium Group) is advancing a method based on combining: (1) probabilistic simple climate model projections of the global mean response to forcing, downscaled and pattern-scaled based on CMIP-class models to translate global mean to local responses, and (2) empirical econometric estimates of the historical response of human systems to weather variability, derived from massive, standardized data sets and incorporating cross-sectional variability to estimate the benefits and costs of climate adaptation. This talk will focus on the example of temperature-related mortality and associated adaptation using sub-national data from 40 countries. Our results demonstrate that the temperature-related mortality impacts fall disproportionately on low-income populations, with high-income counties projected in the median to experience a decline in mortality through 2100, even under RCP 8.5, although the economic benefits of this decline are outweighed by the costs of adaptation. Even moderate emissions reductions result in substantial benefits, with median projected global mortality risk in RCP 4.5 (SSP 3 Socioeconomics) about 85% lower than that under RCP 8.5. Contact: robert.kopp@rutgers.edu
Jan. 15Lunchtime Seminar Series - Yongfei ZhangAssimilation of sea ice observations in MOM6/SIS2 and prospects for improved summer Arctic sea ice predictions
The seasonal prediction of Arctic sea ice, especially in the summertime, is vital to human activities and environment protections. The lack of constraint on sea ice initial conditions is one of the major hurdles for predicting summer Arctic sea ice several months ahead of time. This study exploits data assimilation (DA) to generate a better sea ice reanalysis and study the potential benefits of a more accurate initial condition. The GFDL Sea-Ice Simulator version 2 (SIS2) is coupled with the GFDL Modular Ocean Model version 6 (MOM6) and forced by a single atmosphere from the JRA-55 reanalysis. We link SIS2 and the data assimilation research testbed (DART) to conduct DA experiments. The sea ice concentration (SIC) observations from NSIDC are assimilated every 5 days from 1982 to 2017 through the Ensemble adjustment Kalman filter (EAKF). Before applying DA, we restore the sea surface temperature (SST) to the daily Optimum Interpolation Sea Surface Temperature (OISST), which improves our model background of SIC and also ameliorates an over-shooting problem arisen from SIC DA. We test the influences of different localization cutoffs, observation errors, and DA frequencies on the results. Our best DA experiment increases the September pan-Arctic sea ice extent (SIE) correlation and better reproduces the decreasing trend of pan-Arctic September SIE. Performances of SIC DA at regional scales are also discussed in our study. At the end of the talk, we show that the improved initial conditions of SIC and SIE have prospects for advancing short-lead time predictions of the summer Arctic sea ice.
Jan. 21Informal Seminar - Daniel McCoyUniversity of Leeds, UKEmpirical constraints on midlatitude cloud feedbacks and aerosol-cloud interactions
Constraining how much the Earth's climate will warm in response to greenhouse gas emissions is one of the primary goals of climate science. Our understanding of clouds and their interactions with their environment represent a central uncertainty in constraining climate sensitivity. I will present research seeking empirical constraints on two features of clouds that substantially impact our ability to constrain climate sensitivity: shortwave cloud feedback and aerosol-cloud interactions (aci). I will focus on the midlatitude regime because it has been shown in recent research to contribute strongly to uncertainty in both effective radiative forcing due to aci (ERFaci), and global-mean cloud feedback in global climate models (GCMs).
Jan. 22David Lindo-AtichatiCollege of Staten Island, CUNYWhat are Eddy fluxes? Biological and Chemical Feedbacks from (and to) the Ocean
Submesoscale and mesoscale eddies are ubiquitous and highly energetic rotating features of ocean circulation. Their influence on biological and biogeochemical processes stem not only from advective transport but also from the generation of variations in the environment, from the microscale to the mesoscale. A multidisciplinary approach involving sampling, remote sensing, and high-resolution modeling is woven through this presentation in an attempt to: 1) bridge long-standing scientific controversies on the signature of eddies on larval-fish distribution, 2) shed light on the transport and fate of underwater hydrocarbon plumes and surface UV filters, and 3) build a paradigm-shift in marine biophysics; quantifying the relationship of eddy activity at the length scale of biological community aggregations, where the collective behavior and motion of marine animal might also be relevant to the large scale driven motion of eddies.
Jan. 22Chris Bretherton and Oli FuherVulcan Inc., and University of WashingtonThe Vulcan Climate Modeling/GFDL collaboration: First and next steps toward using convection-resolving global SHiELD simulations to train machine learning parameterizations for moist physics in coarser-resolution versions of FV3-GFS
10-10:30 Chris: Vulcan/GFDL project Machine Learning (ML) overview 10:30-11 Oli: Vulcan/GFDL project Domain Specific Language (DSL) overview 11-11:30: DSL discussion 11:30-noon: ML discussion
Jan. 23Formal Seminar - Kyle ArmourUniversity of Washington An update on the pattern effect and its confounding role in estimates of equilibrium climate sensitivity
I'll give an overview of recent work on how radiative feedbacks depend on the spatial pattern of sea-surface temperature (SST) - the so-called 'pattern effect' - and how this dependence confounds our estimates of equilibrium climate sensitivity (ECS) from both instrumental and proxy records. New modeling and observational analyses, such as the use of localized warming patch simulations and the use of satellite observations, provide clarity on the key regions and mechanisms linking radiative feedbacks to SST patterns. New analyses using CMIP5 and CMIP6 models quantify how radiative feedbacks will change as warming patterns evolve in the future, but large uncertainty remains, implying that the historical record currently provides limited information about the upper bound of ECS. It has been suggested that the paleoclimate proxy record may thus provide our strongest constraints on ECS, but the role of SST patterns in the radiative feedbacks estimated from past climate states has not yet been accounted for. I will describe preliminary work to estimate the importance of the pattern effect in the context of the Last Glacial Maximum and the Pliocene. Speakers Email: karmour@uw.edu
Jan. 28Seung Hun BaekColumbia UniversityCharacterizing the Oceanic and Atmospheric Drivers of Spatially Widespread Droughts over the Contiguous United States
Droughts that achieve extreme spatial extent over the contiguous United States (herein pan-CONUS droughts) pose unique challenges because of their potential to strain multiple water resources simultaneously. Understanding the causes of these extreme droughts is critical given the significant financial damages of these droughts: pan-CONUS droughts in 1988 and 2012, for instance, cost an estimated $40 and $30 billion, respectively. The canonical understanding of oceanic influences on North American hydroclimate would suggest that pan-CONUS droughts are forced by a contemporaneous cold tropical Pacific Ocean and warm tropical Atlantic Ocean. However, analyses using mechanism-denial climate model simulations, observations, and paleoclimate reconstructions demonstrate this not to be the case. The contributions of oceanic and atmospheric variability to pan-CONUS droughts are first investigated using three 16-member ensembles atmospheric models forced with observed sea surface temperatures (SST) from 1856 to 2012. The employed SST forcing fields are either (i) global or restricted to the (ii) tropical Pacific or (iii) tropical Atlantic to isolate the impacts of these two ocean regions on pan-CONUS droughts. Model results show that SST forcing of pan-CONUS droughts originates almost entirely from the tropical Pacific because of atmospheric highs from the northern Pacific to eastern North America established by La Niña conditions, with little contribution from the tropical Atlantic. Notably, in all three model configurations, internal atmospheric variability influences pan-CONUS drought occurrence by as much or more than the ocean forcing and can alone cause pan-CONUS droughts by establishing a dominant high centered over the US Montane West. Model results are compared to and reconciled with the observational record. A millennium-length (850 - 1850 C.E.) perspective on the causes of pan-CONUS droughts is also provided using a new paleo reconstruction product that merges climate model information with multiple climate proxies (including tree rings, ice cores, and corals). Composite analyses show robust association between pan-CONUS drought events and cold tropical Pacific conditions, but not with warm Atlantic conditions. Similarly, self-organizing map analyses shows that pan-CONUS drought years are most commonly associated with a global SST patterns displaying strong La Niña and cold Atlantic conditions. These results show that La Niña events in the tropical Pacific are the principal oceanic influence on pan-CONUS droughts, while variability in the Atlantic has not played a significant role; the oceanic drivers over the paleo record are thus consistent with the model-based findings over the observational record.
Jan. 29Lunchtime Seminar Series - John Krasting, Wenhao Dong and Tom JacksonGFDL, UCAR and SAIC respectivelyAn Introduction to the NOAA Model Diagnostics Task Force (MDTF) Analysis Package and Application to GFDL Model Output
Despite decades of research and significant advancements in resolution and complexity, global climate models (GCMs) still suffer from persistent and often common biases that contribute to uncertainty in their projections of weather and climate. The climate and weather forecasting communities have great interest in improving these biases and have sought to better understand the causes and consequences of these long-standing biases. Among these efforts are the development of process-oriented diagnostics (PODs). A POD characterizes a specific physical process or emergent behavior that is found to be closely related to the ability to simulate an observed phenomenon. Applying these designed PODs routinely on model output -- especially in the context of model development -- could lead to improvements aimed at alleviating these model biases. This talk will first describe outcomes of activities by the NOAA Modeling, Analysis, Prediction, and Projections Program (MAPP) Model Diagnostic Task Force (MDTF). The MDTF Diagnostics effort, currently led by the GFDL, builds on prior existing community efforts aimed at developing process-oriented diagnostics. It provides an open-source analysis package that is portable, extensible, and usable to aid the application of PODs to the model development process. Application of this package to three latest GFDL models (i.e. AM4, CM4, and ESM4) will be presented as examples on initial steps towards an evaluation of our model performance and a verification of the capacity and efficiency of the diagnostic package. In the second part, a hands-on training on the use and application of the package will be provided.
Jan. 30Formal Seminar - Gerard RoeUniversity of WashingtonEnergetic and heat-engine constraints on the spatial patterns of climate and climate change
The climate system operates as a thermodynamic heat engine. A surplus of energy in the tropics and a deficit of energy in the high latitudes must be balanced with a poleward transport of energy by atmospheric and oceanic motions that ultimately do work against frictional dissipation. Sadi Carnot understood as much when formulating the laws of thermodynamics in the early nineteenth century. Atmospheric motions carry approximately eighty percent of the maximum poleward energy transport, and latent heat in the form of water vapor is a crucial component of this transport. Thus, the climatic patterns of temperature, evaporation, precipitation, the isotopic composition of water vapor, and even natural aerosols, are all linked through this transport. Recent research has demonstrated that atmospheric energy transport can be usefully approximated as a linear down-gradient transport of moist enthalpy. This single simple rule for transport explains many features of the mean climate, the predicted climate changes under global warming, and the spread of uncertainty among numerical climate models. Among these features are: polar amplification; the poleward migration of the subtropics, storm tracks, and jet stream under warming; uncertainty in model predictions maximizing in polar regions; hydrologic change as a function of climate state; and the sensitivity of the isotopic composition of precipitation to climate change. Speaker email: gerard@ess.washington.edu
Jan. 31Gerard RoeUniversity of WashingtonCentennial glacier retreat as categorical evidence of regional climate change
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Feb. 5Lunchtime Seminar Series - William KuoUniversity Corporation for Atmospheric Research, Boulder, U.S.A. (UCAR)Impact of Radio Occultation Data on the prediction of Tropical Cyclogenesis
Tropical cyclones are one of the most devastating severe weather systems that are responsible for huge loss of lives and properties every year. Accurate prediction of tropical cyclogenesis by numerical models has been a significant challenge, largely because of the lack of observations over the tropical oceans. The atmospheric limb sounding technique, which makes use of radio signals transmitted by global navigation satellite systems (GNSS), has evolved as a robust global observing system. This technique, known as radio occultation (RO) can provide valuable water vapor and temperature observations for the analysis and prediction of tropical cyclogenesis. Using the WRF modeling and data assimilation system, we show that the assimilation of RO data can substantially improve the skills of the model in predicting the tropical cyclogenesis for ten typhoon cases that took place over the Western Pacific from 2008 to 2010. To gain insight on the impact of GPS RO data assimilation, we perform a detailed analysis of the formation process of Typhoon Nuri (2008), and examine how the assimilation of the GPS RO data enables the model to capture the cyclogenesis. The joint Taiwan-U.S. COSMIC-II mission was launched in June 2019. It is currently going through check-out phase, and will provide 5,000 GPS RO data per day over the tropics when it is fully operational. This will provide a great opportunity for research and operational prediction of tropical cyclogenesis. Host: Leo Donner
Feb. 6Formal Seminar - Kevin ReedStony Brook UniversityDetecting climate change impacts on extreme weather
The next century will see unprecedented changes to the climate system with direct consequences for society. As stated in the National Climate Assessment, "changes in extreme weather events are the primary way that most people experience climate change." In this sense, the characteristics of extreme weather are key indicators of climate change impacts, at both local and regional scales. Understanding potential changes in the location, intensity and structure of such extremes (e.g., tropical cyclones and flooding) is crucial in planning for future adaptation as these events have large economic and social costs. The goal of this work is to better understand climate impacts on extreme weather events in various high-resolution configurations of the Community Atmosphere Model (CAM) run at horizontal grid spacings of approximately 28 km and forced with prescribed sea-surface temperatures and greenhouse gas concentrations for past, present, and future climates. This analysis will include the evaluation of conventional (AMIP-style) decadal simulations typical of climate models, short 7-day ensemble hindcasts of recent devastating events (e.g., Hurricane Florence in 2018), and reduced complexity simulations of idealized states of the climate system. Through this hierarchical modeling approach the impact of climate change on the characteristics (frequency, intensity, rainfall, etc.) of extreme weather, including tropical cyclones, can be quantified. Speaker Email: kevin.a.reed@stonybrook.edu
Feb. 7Informal Seminar - Pavel BerloffImperial College of LondonSome novel approaches for parameterizing mesoscale eddies
This talk focuses on some new approaches for parameterizing oceanic mesoscale eddy effects for use in non-eddy-resolving and eddy-permitting general circulation models. The context is provided in terms of discussing the existing ideas and problems with their realizations. Specific example of eddy-rich eastward jet extensions of western boundary currents and their adjacent recirculation zones is considered in the classical multi-layer quasigeostrophic model of the wind-driven midlatitude circulation. First, the key dynamical mechanism operating in the eddy-resolving model and maintaining the eastward jet is identified as the ''eddy backscatter'', which is based on persistent and positive time-lag correlations between the transient part of the nonlinear eddy forcing and the large-scale flow response. Second, this mechanism has to be ultimately parameterized, and discussing how this can be done is the main part of the talk. We will systematically (but not too technically) discuss 4 different, novel parameterization approaches, which are complimentary to the existing ones: (1) direct stochastic forcing (DSF); (2) implicit stochastic footprints (ISF); (3) data-driven eddy emulations (DEE); and (4) local eddy amplification (LEA). DSF approach explicitly adds statistically constrained stochastic forcing to the coarse model. ISF approach imposes statistically constrained stochastic forcing on an intermediate-complexity eddy-resolving model, obtains its nonlinear response in terms of the coarse-grained footprint, and then imposes local footprints on the coarse model. DEE approach emulates eddies via multi-layer nonlinear regression, then feeds them to the deterministic eddy forcing operator coupled to the large-scale flow fields, and adds the resulting forcing to the coarse model. LEA approach interactively identifies eddies and amplifies them locally and in a simple way --- this is the simplest and also most practical approach for the present state of modelling. Relative strengths and weaknesses of these approaches, as well as some future developments will be also discussed.
Feb. 10Informal Seminar - Rebecca BeadlingU of ArizonaSimulation of large-scale circulation and properties in the North Atlantic and Southern Ocean in coupled climate models
The global oceans act as a mediator of Earth's climate due to their role in the storage of heat and carbon. Presently, the oceans account for the storage of approximately 93% of the anthropogenic heat on our planet and ~27% of the anthropogenic CO2. Two regions in particular, the Southern and North Atlantic Ocean (SO,NA), act as gateways for the exchange of CO2 and heat between the atmosphere and the interior ocean, due to the unique deep and intermediate water formation processes that occur here. Large uncertainty exists with respect to understanding how the ocean circulation patterns and properties are projected to change in these regions throughout the 21st century. One avenue of reducing projection uncertainty is through improved representation of ocean circulation and properties in these regions in historical simulations relative to the observational record and through the interpretation of projected trends with knowledge of mean state biases. In the subtropical NA, a key region through which properties from the tropics are advected to the subpolar latitudes, the volume transports of the major flow regimes are reasonably represented in many CMIP5 models relative that observed by the RAPID array at 26oN. As the climate warms, the NA subtropical gyre is weakened in response to a reduced wind stress curl, which acts as a source of significant additional weakening to the northward western boundary current flow. In the SO, despite its dominant role in the oceanic uptake of anthropogenic carbon and heat relative to other basins, the large-scale circulation and properties have been poorly represented in climate models, resulting in low confidence ascribed to 21st century projections of the state of the SO. A comprehensive assessment performed across ensembles of models contributed to the past three CMIP generations (CMIP3 - CMIP6) show improved representation of key observable-metrics in this region including surface wind stress and wind stress curl, strength of the ACC, and density gradients in the region of the ACC. However, some persistent biases have carried over into CMIP6 including an upper ocean that remains too fresh and too warm, significant warm biases at depth in several simulations, and a poor representation of Antarctic sea ice extent. These biases in observable metrics need to be considered when interpreting projected trends or biogeochemical properties in this region.
Feb. 12Lunchtime Seminar Series -Prof Jan ZikaUniversity of New South Wales, Sydney, AustraliaChanges in ocean water masses reveal the distribution of excess heat in the climate system
Over 90% of the excess heat trapped in the earth system is contained in the ocean and the consequent thermal expansion was the largest contributor to sea level rise in recent decades. Since 2006 ocean warming and hence sea level rise has been spatially heterogenous, with some regions such as the Southern Ocean showing intense warming and others such as the sub-polar North Atlantic showing intense cooling. This heterogeneity may be due to spatial variability in the rate at which heat added to the ocean at the sea surface propagates into the ocean interior or to changes in circulation which redistribute the existing heat reservoirs within the ocean. However, the importance of these two mechanisms at a regional scale is unclear. Here we show that the spatial variability in warming and sea level rise is dominated by changes in ocean circulation. In some regions the redistribution term is 10 times larger than the excess heat component which is distributed much more homogenously across the oceans. In the North Atlantic, substantial excess heat uptake is balanced by cooling due to redistribution associated with a slowdown in the Atlantic Meridional Overturning Circulation. Both circulation change and heat uptake drive intense warming in the Southern Ocean with an anomalous poleward heat transport of 118 ±50 PW the largest effect. Our results suggest near term projections of sea level change will hinge on understanding and predicting changes in ocean circulation. Host: Steve Griffies
Feb. 13Formal Seminar - Ron KwokNASA JPLChanges in Arctic Ocean sea-ice thickness, volume, and multiyear ice coverage: A record from multiple sources
In parallel to the widely reported decline in Arctic ice extent, there have also been dramatic losses in sea ice thickness and volume, and in multiyear sea ice coverage. Instead of a relatively a consistent satellite record from passive microwave radiometers, assessments of large-scale decadal changes in thickness, volume, and multiyear sea ice coverage are dependent on observations from multiple sources: submarine and airborne surveys, and satellite altimetry and scatterometry. The submarine ice draft record spans the period between 1958 and 2000, the satellite altimeter records of thickness estimates between 2003 and 2018, and the scatterometer records of multiyear sea ice coverage between 1999 and present. Even though there is sometimes sparse sampling (in space and time), lack of consistency in measurement approaches and continuity in individual records, these datasets broadly depict an ice cover that has thinned everywhere and a multiyear sea ice cover that is rapidly thinning. The recently launched ICESat-2, equipped with a photon counting altimeter, adds to the record of thickness and volume estimates. I will discuss the multi-decadal record trends and highlight the potential contribution ¬ ¬of this unique ICESat-2 instrument - with examples from recent results - to various aspects of cryospheric science and oceanography of the ice-covered oceans. Speakers email: ronald.kwok@jpl.nasa.gov
Feb. 18Informal Seminar - Rei ChemkeColumbia UniversityRecent atmospheric circulation trends: two major flaws in reanalyses and in climate models
The weakening of the Hadley cell and of the midlatitude eddy heat fluxes are two of the most robust responses of the atmospheric circulation to increasing concentrations of greenhouse gases. These changes have important global climatic impacts, as the large-scale circulation acts to transfer heat and moisture from the tropics to polar regions. Here, we examine Hadley cell and eddy heat flux trends in recent decades: contrasting model simulations with reanalyses, we uncover two important flaws -- one in the reanalyses and other in the model simulations -- that have, to date, gone largely unnoticed. First, we find that while climate models simulate a weakening of the Hadley cell over the past four decades, most atmospheric reanalyses indicate a considerable strengthening. Interestingly, that discrepancy does not stem from biases in climate models, but appears to be related to artifacts in the representation of latent heating in the reanalyses. This suggests that when dealing with the divergent part of the large-scale circulation, reanalyses may be fundamentally unreliable for the calculation of trends, even for trends spanning several decades. Second, we examine recent trends in eddy heat fluxes at midlatitudes, which are directly linked to the equator-to-pole temperature gradient. In the Northern Hemisphere models and reanalyses are in good agreement, and show a robust weakening that has emerged from the internal variability around the year 2000, and we attribute it to increasing greenhouse gases. In the Southern Hemisphere, however, models disagree on the trends while reanalyses indicate a robust strengthening. In this case, the flaw is found to be with the climate models, which are unable to simulate the observed multidecadal cooling of the Southern Ocean at high-latitudes, and the accompanying increase in sea ice. While the biases in modeled Antarctic sea ice trends have been widely reported, our results demonstrates that such biases have important implications well beyond the high Southern latitudes, as they impact the equator-to-pole temperature and, as a consequence, the midlatitude atmospheric circulation.
Feb. 25Informal Seminar - Youngji Joh (postdoc candidate)Georgia Institute of TechnologyCouplings in the Pacific in a changing climate: Theories, Observations, and Models
Pacific climate and weather extremes including heatwaves, drought, and hydrological hazard, which drive significant impact on the U.S. community and thus have been paid great attention, are dynamically linked to not only local air-sea interactions, but also large-scale climate variability (e.g., Pacific decadal variability and El Niño Southern Oscillation). This study aims at improving the theories of climate coupling within the North Pacific and across to the central tropical Pacific with investigating their response to anthropogenic forcing. Using multiple observational reanalyses and global climate model ensembles, we first show that winter ocean temperature extremes over the Northeast Pacific significantly resemble the representations of the North Pacific decadal variability (e.g., North Pacific Gyre Oscillation, NPGO and Pacific Decadal Oscillation, PDO). We find that the multi-year warm anomalies in the Northeast Pacific are associated with the consecutive occurrences of NPGO-like and PDO-like ocean signatures via ENSO atmospheric teleconnections. The results suggest that the increasing coupling between NPGO and PDO leads to the prolonged North Pacific marine heatwaves, and those warm events are becoming stronger in amplitude with a larger area under anthropogenic forcing. Combining satellite data with several observation reanalysis products, we next offer observational evidence revealing that a preferred decadal timescale (~10yrs) in the North Pacific western boundary current system, the Kuroshio Extension (KE) region, may arise from an interaction with the central tropical Pacific (CP) (e.g., CP-ENSO). The results show that the KE decadal dynamic state can drive a persistent downstream wind stress curl that projects on atmospheric forcing of the CP-ENSO, which in turn excites westward oceanic Rossby waves in the central North Pacific that reach the western boundary back. Consistent with this hypothesis, the cross-correlation function between the KE and CP-ENSO indices exhibits a significant sinusoidal shape corresponding to a preferred spectral power at 10yrs. Using high-resolution coupled climate models, we finally show that the decadal KE dynamics are not independent of the central tropics and their coupling is becoming stronger under anthropogenic forcing. The results suggest that a higher amplitude quasi-decadal KE/CP-ENSO sequence under warmer climate may allow a stronger basis for decadal predictions of Pacific climate variability, further for societally relevant biogeochemical quantities (e.g., salinity, oxygen, and chlorophyll-A) and fisheries.
Feb. 26Lunchtime Seminar Series - Noemi VergopolanCivil and Environmental Engineering Department at Princeton University (postdoc Field-scale land surface modeling and remote sensing for hydrologic predictions at the decision-making scales
Accurate and detailed information on soil moisture is critical for estimating agricultural water demands, forecasting extreme drought and flood events, monitoring wildfires and landslides, understanding the spatiotemporal distribution of species, and providing initial conditions for climate models and numerical weather prediction. One of the challenges in monitoring and predicting soil moisture dynamics is the gap in spatial scales between observations, models, and applications. While in-situ observations are sparse and expensive, microwave satellite retrievals can cover the entire globe but are only available at coarse scales (36 km). A promising path forward to overcome this scale gap is to combine physical models and observations. However, most of the land surface models used for this purpose still operate at coarser scales (5-25 km) than what is required at the stakeholder's level decision-making (1-100 m). Also, these models often do not account for human activities that influence soil moisture dynamics, such as irrigation, groundwater pumping, reservoir operation, etc. To address this gap, I am continuing the development of HydroBlocks, a field-scale land surface model that takes advantage of big data, machine learning, and high-performance computing to model the land surface processes at an effective 30-m spatial resolution. HydroBlocks solves the field-scale spatial heterogeneity of the landscape through interacting Hydrologic Response Units (HRUs), also known as tiles or mosaics. In this presentation, I will demonstrate how HydroBlocks' HRUs can be leveraged to improve soil moisture predictions at relevant scales for water resources decision-making. More specifically, (i) I will show how this framework can be used for HRU-based assimilation and downscaling of coarse-scale microwave satellite soil moisture to 30-m spatial resolution. For this purpose, radiative transfer modeling and Bayesian merging are used to combine field-scale estimated and satellite observed brightness temperature, to subsequently retrieve updated soil moisture estimates. Also, (ii) I will present a water management module I developed for HydroBlocks to account for the influence of human activities on the simulation of soil moisture dynamics. This module quantifies at fine-spatial scales the impact of surface and groundwater withdraws to meet agricultural, livestock, domestic, and industrial water demands. In specific, I will show results that demonstrate the effects of irrigation from groundwater pumping on the land surface fluxes at agricultural regions in Nebraska. This work paves the way towards hydrologically consistent field-scale soil moisture estimates. It highlights the value of land surface modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications.
Feb. 27Formal Seminar - Amy ButlerNOAA/ESRLtratospheric polar vortex influence on sub-seasonal predictive skill of near-surface temperature
Stratospheric variability is an important potential source of predictive skill of surface weather on sub-seasonal to seasonal (S2S) timescales. In particular, variations of the stratospheric polar vortex can perturb the large-scale extratropical circulation for weeks to months. In this seminar I will show the influence of initializing forecasts during weak and strong Northern Hemisphere polar vortex events, and during the springtime breakdown of the vortex (the so-called "final warming"), on weeks 3-4 predictive skill of near-surface temperature. In general, skill increases and root mean square error decreases across the Northern Hemisphere for forecasts initialized during weak and strong vortex events and early final warming events relative to control forecasts; but in some regions, skill decreases. I will also briefly discuss the most recent disruption of the Southern Hemisphere polar vortex and how it influenced the forecast for the anomalously hot and dry austral spring in 2019. These results come in part from international community efforts within WCRP/SPARC to analyze the S2S project database of historical forecasts in order to better understand how stratospheric information contributes to surface predictive skill. Speaker email: Amy.Butler@noaa.gov
Feb. 28Informal Seminar - MadhuLatha Akkisetti-via Google.MeetsKorean Institute of Atmospheric Prediction SystemsSurface and Boundary-Layer Interactions in Continental Convection: Improving convective-scale simulations through better representation of turbulence and land-surface heterogeneity
Convective storms and especially organized deep convective systems are a crucial source of precipitation during what would otherwise be a hot, dry summer over continental regions. In some areas, particularly agricultural regions like the central US, convective systems are the principal source of rain during the warm season. However these systems also pose hazards to life and property as severe convective storms bring flooding, frequent cloud to ground lightning, high winds, large hail, and tornadoes to these regions. Despite their importance, continental convection has proven to be a significant challenge for global weather and climate models to represent realistically. The initiation, growth, and organization of convective storms are strongly governed by mesoscale and small-scale processes not resolved by large-scale global models, and typically the study and prediction of such storms is done by regional high-resolution models; however these regional models are only useful for simulations of at most a few days, before boundary errors and mean-state drift contaminate the interior solution. Further, both the pre-storm environment as well as the evolution of convective storms are strongly coupled to the planetary boundary layer and to the land surface, which are also difficult for many models to represent realistically. Both modeling and observational studies support that the differential heating of the atmosphere by a heterogeneous land surface can induce a secondary circulation that influences the turbulent transport in the planetary boundary layer (PBL) and development of clouds (Taylor et al., 2007). Convective initiation is affected by the distribution of soil moisture which partitions the surface available energy into latent heat and sensible heat fluxes and in turn affect the boundary layer evolution (Betts et al., 1996). Many different processes (Figure 1) over a wide range of space and time scales govern the interactions of the PBL and clouds with a heterogeneous land surface. A variable resolution global model with two-way global to regional interaction, (Madhulatha et al., 2018, Harris et al., 2019) can be a powerful tool for both examining the relevant interactions across temporal and spatial scales as well exploiting these processes to enable skillful prediction of continental convection across these scales. Join Hangouts Meet meet.google.com/tns-tqhm-fos
Mar. 4Lunchtime Seminar Series - Pragallva BarpandaUniversity of Chicago (postdoc candidate)What controls the hemispheric asymmetry in the seasonality of extratropical storm track intensity? - New insights from the moist static energy budget.
Extratropical storm tracks are collective paths of synoptic scale (~1000 km) cyclones that exist in the midlatitudes of both hemispheres. The storm track seasonality is controlled by solar insolation gradient, yet they exhibit distinct hemispheric seasonality. In the Northern Hemisphere (NH) the zonal-mean storm track weakens by ~2.5 PW and shifts poleward by 10 degrees from winter to summer. In contrast, the Southern Hemisphere (SH) storm track shows much weaker seasonality ( dc) produce surface heat fluxes that are out of phase with atmospheric shortwave absorption resulting in small storm track seasonality and 2) small mixed layer depths (d < dc) produce surface heat fluxes that are in phase with atmospheric shortwave absorption resulting in large storm track seasonality. The aquaplanet simulations confirm the above hypotheses thus establishing that surface heat fluxes (dominated by surface turbulent fluxes) play a causal role in damping the seasonality of SH stormtrack.
Mar. 5Formal Seminar - Yoshimitsu ChikamotoUtah State (yoshi.chikamoto@usu.edu)Earth system predictability on inter-annual-to-decadal timescales
Skillful hydroclimate forecasts on longer timescales are crucial for decision-makers and resource managers to mitigate climate-driven natural disasters, such as multi-year drought, agricultural losses, and an increase in wildfire dangers. Decadal climate prediction based on the Earth system model with proper initialization can provide skillful hydroclimate predictions in the western US. However, the most decadal climate predictions still suffer from large model biases and initialization shocks, which can severely contaminate model forecasts and may substantially reduce predictive skills, particularly for non-Gaussian distributed variables, such as rainfall, streamflow, groundwater, and wildfire. In this presentation, I will show that our drift-free decadal climate prediction system has demonstrated the multi-year predictive skill of soil water anomalies in the western US. This system consists of an ocean data assimilation approach using the coarse resolution of a fully coupled Community Earth system model (CESM1.0) and significantly reduces the model climate drift during prediction by adjusting the model biases in the mean state and the climate response to the radiative forcing during the assimilation process. The detailed approach in our drift-free prediction system and its application on the Earth system predictability will be discussed. Host: Xiaosong Yang
Mar. 5Wei Zhang - Postdoc CandidateVisiting Scientist Program (Univ of Maimi)Understanding the Signal-to-noise Paradox in Climate Predictions
Increasing evidence has been documented in recent years for the existence of the signal-to-noise paradox, where in the ensemble-based climate prediction, model ensemble mean forecast generally shows higher correlations with observations than with individual ensemble members. This seems to lead to a paradox referred to as the signal-to-noise paradox that the model makes better predictions for the reality than predicting itself. The signal-to-noise paradox highlights a potentially serious problem with climate model predictions as previous seasonal-to-decadal model predictions may be underestimated due to the existence of the paradox. Here we introduce a simple Markov model framework to represent the ensemble forecasts and aim to explain why the paradox exists. With the Markov model framework, one can easily reproduce the signal-to-noise paradox, the existence of which is dependent on the relative amplitude of the persistence and noise variance in models and observations. The North Atlantic Oscillation indices based on uninitialized historical simulations of 40 CMIP5 models have been analyzed, suggesting that the signal‐to‐noise paradox is common in currently available coupled models, and the paradox is not due to problems with initialization processes used in the seasonal‐to‐decadal predictions in previous studies and is instead a general model problem. We also identify the widespread existence of the signal-to-noise paradox in SST and SLP fields in CMIP5 models and the results suggest that the regions with the signal-to-noise paradox are very likely to underestimate the predictability. Increased ocean or atmospheric model resolution may have the potential to eliminate the signal-to-noise issue.
Apr. 1Virtual Lunchtime Seminar - Chris CollimoreCUNYThe Effect of High Aerosol Concentrations on Tropical Cyclone Formation
Prior studies have shown that high levels of aerosols in the environment of convective clouds can cause the convection to become more vigorous through a five step process. Tropical cyclones (TCs) start as clusters of convective clouds and vigorous convection is important for the development of a cluster into a TC. This study tests the hypothesis that high aerosol content in the vicinity of a tropical convective cloud cluster increases the chance that the cluster will develop into a TC by invigorating its convection. To test this hypothesis, this study centers on 63 clusters that developed into TCs (developers) and 98 clusters that dissipated before becoming a TC (nondevelopers). Using aerosol observations from the MODIS satellite instrument, it was established that the average aerosol content surrounding developers was significantly higher than that surrounding nondevelopers. Furthermore, other satellite measurements (from MODIS and AIRS) provide evidence that each of the five steps associated with convective invigoration by aerosols took place in the developers, suggesting that the large aerosol content surrounding developers invigorated their convection. Altogether, the data suggest convective cloud clusters embedded in regions with elevated aerosol levels may have a greater likelihood of developing into TCs because the aerosols may invigorate their convection. Join Hangouts Meet meet.google.com/jji-dqkp-cht Meeting ID meet.google.com/jji-dqkp-cht Phone Numbers (‪US‬) ‪+1 405-561-1457‬ PIN: ‪685 812 451#‬
Apr. 30Formal Seminar - Gabriele PfisterNCAR (Boulder, CO)FRAPPÉ - Air Quality Research as a Key to Addressing Societal Needs
High ozone pollution during summertime has been an issue for the Colorado Front Range for many years. Urban and industrial emissions with the addition of a rapidly expanding oil and natural gas sector create a highly reactive chemical mix, which is complicated by complex terrain driven meteorology and elevated ozone background levels. To characterize the main contributions to local ozone pollution, two large air quality studies involving four aircraft and extensive ground-based measurements were conducted in the area in the summer of 2014: the NCAR/NSF/State of Colorado Front Range Air Pollution and Photochemistry Éxperiment (FRAPPÉ) and the 4th deployment of the NASA DISCOVER-AQ. I will give an overview of the campaigns, and present a summary of the results which are focused around Front Range dynamics, ozone production and source attribution. The main findings suggest major contributions from the transportation sector as well as from the oil and gas extraction sector, with minor contributions from energy generation and industry. The meteorological conditions were also found to be critical in creating situations conducive to high ozone in the area. This is a VIRTUAL SEMINAR. Google.Hangout address is as follows: Meeting ID meet.google.com/zak-biuh-ffw Join by phone ‪+1 617-675-4444‬ PIN: ‪601 965 633 2741‬#
May. 13Lunchtime Seminar Series - Nicole ShibleyYale UniversityInferring Mixing from Acoustic Observations of Double-Diffusive Staircases in the Arctic Ocean
Double-diffusive convection is a small-scale convective mixing process that may occur in the ocean where temperature and salinity both increase with depth. It is identifiable by its distinct staircase structure, consisting of thick mixed layers separated by high-gradient interfaces in temperature and salinity. In the Arctic Ocean, these staircases are widely present in the interior basin and are responsible for transporting heat upwards to the overlying sea ice cover. However, they are largely absent around basin boundaries, likely due to the effect of intermittent turbulence. Recent acoustic observations of the Arctic Ocean provide a high-resolution method of visualizing an individual staircase evolve in both space and time. In this talk, I will show how these acoustic observations may be used to infer mixing levels associated with double diffusion and to understand the persistence of double diffusion in a setting of weak background turbulence.
May. 14Virtual Formal Seminar - Sonia SeneviranteETH, Zurich SwitzerlandClimate extremes at 1.5°C vs 2°C global warming: The role of regional climate sensitivity
In this presentation, I will highlight new findings regarding the role of regional climate sensitivity vs that global climate sensitivity for changes in climate extremes (Seneviratne and Hauser, in press). Projections show substantial differences in extremes at 1.5°C vs 2°C global warming, as well as associated irreversible impacts when global warming reaches 2°C or higher (IPCC, 2018; Hoegh-Guldberg et al., in press; Seneviratne et al. 2016, 2018a; Wartenburger et al. 2017). While multi-model mean projections scale well with projected global warming, CMIP5 and CMIP6 models show substantial intermodel spread in these projections, which are mostly related to the representation of regional processes (Seneviratne and Hauser, in press). Changes in land processes and land use play an important role in these projections, both through land-atmosphere feedbacks as well as through changes in land forcing, for instance associated with re-/afforestation, the expansion of biofuels, and/or agricultural management (Vogel et al. 2017, Hirsch et al. 2018, Seneviratne et al. 2018b). This highlights regional climate sensitivity and in particular land processes as key factors to assess and better constrain to inform climate mitigation and adaptation in the coming decade.
May. 20Virtual Lunchtime Seminar Series - Yuan Yu XiePrinceton/GFDLSevere Impacts of wildfires on fine particulate air quality in present and future climate
Large wildfires are increasing around the world in recent decades, with severe impacts on natural and human systems. In this talk, I will discuss how wildfires affect fine particulate (PM2.5) air quality means and extremes in present and future climate. Using observations and model simulations (ESM4.1) with nudged meteorology during 1988-2018, we show large year-to-year variability in western U.S. PM2.5 pollution caused by both local and remote fires. Widespread wildfires, combined with stagnation, caused PM2.5 pollution in 2017 and 2018 to exceed 2 standard deviations over long-term averages. ESM4.1 with a fire emission inventory constrained by satellite-derived aerosol optical depth captures the observed surface PM2.5 means and extremes above the 35 μg/m3 U.S. air quality standard. However, black carbon and organic aerosol emissions from the widely used Global Fire Emission Database (GFED4) must be increased by a factor of 5 for ESM4.1 to match observations. On days when observed PM2.5 reached 70-150 μg/m3, wildfire emissions can explain ~90% of total PM2.5, with smoke transported from Canada contributing ~25% in northern states, according to our model sensitivity simulations. GFED4 is the default fire emission inventory used in ESM4.1 for CMIP6 historical simulations. Our results suggest fire emission uncertainties may explain ESM4.1 underestimation of aerosol optical depth in fire-prone regions. It is thus necessary to carefully evaluate the emission factors for primary aerosols used in the interactive fire emission model currently under development at GFDL. The impact of wildfire outbreaks in a warming climate on PM2.5 pollution has often been overlooked in current air quality projections. The current generation of chemistry-climate models typically do not include interactive emissions of primary aerosols from wildfires. Here we leverage projections of wildfires coupled to climate and vegetation state from three CMIP6 Earth system models, combined with a multilinear regression model developed from historical observations, to predict changes in PM2.5 pollution associated with increasing fires in a warming climate. The CMIP6 models show a two- to four-fold increase in wildfire emissions of carbon under the SSP370 and SSP585 warming scenarios in the late 21st century (2080-2100) relative to present day (1990-2010), particularly over the northern mid-latitude semi-arid regions, such as western North America and Mediterranean Europe. Our statistical model projects a two- to three-fold increase in mean PM2.5 levels over the western U.S. in the late 21st century. In contrast, the increase is only 20% based on the CMIP6 chemistry-climate models that do not account for the impacts from increasing wildfire emissions. Notably, we show that the 2017-like air pollution extremes due to wildfires over the western U.S. are likely to become a new normal by the late 21 century, with substantial implications for regional air quality.
Jun. 24Virtual Lunchtime Seminar Series - Pierre GentineColumbia University- Dept of Earth and Environmental EngineeringHybrid modeling: best of both worlds ?
In recent years, we have witnessed an explosion in the applications of machine learning, especially for environmental problems.Yet for broader use, those algorithms may need to respect exactly some physical constraints such as the conservation of mass and energy. In addition, environmental applications (e.g. drought, heat waves) are typically focusing on extremes and on out-of-sample generalization rather than on interpolation. This can be a problem for typical algorithms, which interpolate well but have difficulties extrapolating. I will here show how a hybridization of machine learning algorithms, imposing physical knowledge within them, can help with those different issues and offer a promising avenue for climate applications and process understanding. BIO Pierre Gentine is an associate professor in Earth and Environmental Engineering at Columbia. He studies the terrestrial water and carbon cycles and their changes with climate change. Pierre Gentine is recipient of the NSF, NASA and DOE early career awards, as well as the American Geophysical Union Global Environmental Changes Early Carrer and American Meteorological Society Meisinger award.