MPO 624 Intake Survey
Name, program, year: Steven Simon, MPO, Year 1
Advisor and research topic (if applicable):
Advisor: Dr. Brian Soden
Tentative Research Topic: Employing coupled climate model data (CMIP5) and remotely
sensed satellite data (i.e., brightness temperatures, upper tropospheric relative humidity,
cloud radiative forcing, etc.) to unravel the ties amongst a globally warming Earth,
projected trends in the general atmospheric circulation, and the climate sensitivity
uncertainties associated with simulated and observed cloud feedbacks.
Career goals/ hopes:
1. Expand upon my academic background in meteorology and climate dynamics in the
process of completing my PhD in Meteorology and Physical Oceanography at RSMAS.
2. Obtain a better understanding of the analysis approaches and methodologies that I can
employ within my original research as a professional atmospheric scientist/climatologist.
3. Conduct original and publishable research which aims to fill the gaps in the scientific
community’s current understanding of how the Earth’s general circulation and regional
hydroclimates respond to anthropogenic forcings on multiple spatiotemporal scales.
4. Effectively communicate my research findings to a multitude of audiences (peers,
general public, government agencies, etc.).
Hopes for this course (please be as specific and detailed as you like, perhaps from the
outline above):
My main hope for this course is that it provides a platform for me to learn about and
employ a litany of quantitative analysis methods commonly used in the development of
computational algorithms directed towards tackling a variety of posed research questions.
This includes not only an exposure to various computational languages and data
visualization software (Python, MATLAB, GRADS, etc.), but also a survey of rigorous
statistical and probabilistic methods that I can readily apply in analyses which parse
through multiple sources of variability within climatological datasets, uncover dominant
spatial patterns within climatological time series, and highlight significant trends within a
variety of climatological variables.
Computer system you will work on (Windows, Mac, Linux; RAM if you know):
Windows and Linux via a MobaXterm client.
Describe your computer experience in narrative form. What are your thoughts,
philosophy, worries, hopes about working with computers. Don't try to impress, just
show me where we are all at here at the beginning. Read the course goal again.
My computer experience initially consists of a thorough background in utilizing
Microsoft Office applications (Word, Excel, and Powerpoint) in order to compose written
documents, perform simple calculations with imported datasets and visualize the results
from these datasets in graphical formats, and construct presentations for talks. In the
completion of my bachelor’s and master’s degrees, I obtained experience in operating
within a Unix environment, in order to import, archive, and manipulate an assortment of
datasets. To conduct analyses on these datasets, I made use of MATLAB, NCL, and
FORTRAN to conduct large calculations and produce plots which visualize the generated
analyses in various space and time constraints. For my master’s thesis, I utilized NCAR’s
Yellowstone Supercomputer to host a suite of AMIP style GCM simulations with
NCAR’s CESM 1.0.5/CAM 5.1.1. Overall, I see computers as devices which allow us to
evaluate our seemingly abstract research questions and hypotheses in terms of both
tangible qualitative and quantitative analyses that can be readily shared with a variety of
audiences (the scientific community and beyond). My current worries associated with
working with computers stem from a relatively limited exposure to computing languages
beyond MATLAB, NCL, and FORTRAN and their syntax. Also, I have some concerns
when it comes to debugging and troubleshooting code within scenarios without precedent
and adequate documentation. My hope is to improve my fluency in both of the
aforementioned areas within this class and in my professional career.
Have you used the command line in a terminal?
Yes.
Favorite commands: cd, ls, more, less, cp, rm, scp.
Have you edited code? If so, in what editor?
Yes. Gedit, Emacs, and Vi.
Favorite or proudest few lines of code (any language):
NCL: Calculation of Vertically-Integrated Zonal and Meridional Moisture Fluxes for the
Lower Troposphere (1000-700mb) from AMIP Style Climate Model Simulations of
North American Hydroclimate during the Little Ice Age (LIA)
liadpConform= conform(liauq(time|:,lon|:,lat|:,lev|20:29),dp,3)
liauqSum1 = liauq(time|:,lon|:,lat|:,lev|0)
liauqSum1 = dim_sum(liauq(time|:,lon|:,lat|:,lev|20:29)*liadpConform)
liaintvertuq=liauqSum1(time|:,lat|:,lon|:)
liaintvertuq=liauqSum1(time|:,lat|:,lon|:)*(liaps/g)
liaintvertuq@long_name = "integral: SUM[u*q], surface to 700mb"
liaintvertuq@_FillValue = -999.0
delete(liadpConform)
liadpConform= conform(liavq(time|:,lon|:,lat|:,lev|20:29),dp,3)
liavqSum1 = liavq(time|:,lon|:,lat|:,lev|0)
liavqSum1 = dim_sum(liavq(time|:,lon|:,lat|:,lev|20:29)*liadpConform)
liaintvertvq=liavqSum1(time|:,lat|:,lon|:)
liaintvertvq=liavqSum1(time|:,lat|:,lon|:)*(liaps/g)
liaintvertvq@long_name = "integral: SUM[v*q], surface to 700mb"
liaintvertvq@_FillValue = -999.0
delete(liadpConform)
Nicest figure you have made from data (paste, explain):
Climate model-simulated Medieval Climate Anomaly (MCA)-Little Ice Age (LIA)
precipitation rate anomalies (mm/day) overlaid by anomalies in 1000-700mb vertically
integrated moisture flux (kg/ms) over Western North America. These anomalies were
annually-averaged and were forced by proxy-reconstructed SST anomalies imposed in
the North Pacific, Tropical Pacific, and North Atlantic basins for both the MCA and LIA
periods. This figure highlights a dipole in precipitation in the regional Western North
American hydroclimate during the MCA and LIA periods. This precipitation anomaly
dipole resembles spatiotemporal variations in precipitation associated with ENSO-like,
PDO-like, and AMO-like forcing in the Tropical Pacific, North Pacific, and North
Atlantic basins, respectively.
A figure you admire for graphical reasons (paste, explain):
Graphically, this figure compares the response of the zonally-averaged meridional mass
streamfunction during composited 20th century El Nino vs. La Nina events (shaded), as
compared to climatology, and that due to a projected 21st Century Global Warming Trend
(shaded), as compared to climatology (Lu et al., 2008). Although simple in construction,
this figure shows clear and coherent shifts in the Hadley Cell circulation due to a globally
warming Earth that differ from those due to interannual ENSO-forced variations.
A figure you admire for content reasons (paste, explain):
I admire this figure for content purposes because it reveals how projected fractional
decreases in convective mass flux (M) are forced by projected fractional increases in
global temperature (T) and related to the differing fractional increases in boundary layer
specific humidity (q) and precipitation (p) (Held and Soden, 2006).
A figure you are puzzled about or intrigued by (paste, explain why its puzzling):
(a) Multi-model ensemble mean P-E in the IPCC’s A2 scenario. Shading indicates the
difference between the first and the last 20 years of the 21st century and the black line
denotes the 0-isopleths averaged from 2001 to 2020. The zonal mean averaged over
2001–2020 (black) and 2081–2100 (red) is shown; units are mm/day. (b) Number count
out of the total 15 models that simulate a moistening (i.e., Δ(P-E) > 0) at each grid point.
Lu et al., 2007.
I find this figure to be puzzling and intriguing because it shows that the largest ensemble
mean changes in the projected 21st century mean P-E do not necessarily overlap with a
strong consensus amongst the P-E spread generated by the ensemble component models.
Favorite ADA-related Web site or software not mentioned in course materials so far:
Wolfram Alpha
Initial brainstorming thoughts on a possible topic or ingredients for your term project.
You might as well choose your research or something related to your other coursework,
so you can double-count the effort and do better work instead of just more work. Any or
all of the following thought-provokers may be your springboard. Again, don’t worry
about impressing or grading or anything else, just help me (and us all, we will look these
over on screen in class) understand where you’re at and your interests.
Your application: what behavior of what system might you be trying to
characterize, or compare to what other system or model or forecast?
A study on the changes, trends, and variations in the zonal mean meridional circulation
(i.e. the Hadley cell circulation) and/or the Walker circulation during the reanalysis and
satellite era of the 20th century and a comparison with some of the projected changes and
trends within the 21st century, as shown in CMIP5 datasets.
Your data: what dataset(s) might contain the information you would like to
explore or address? Feel free to use this as a springboard for a conversation with your
advisor.
CMIP5 simulations for the 21st century, 20th-21st century NCEP/NCAR Reanalysis data
set, 20th -21st century ECMWF ERA Interim dataset, and NOAA HIRS dataset.
Your analysis: what kinds of questions do you imagine you will ask about
the system or the datasets? What would constitute an answer or an addressing of the
issue? What kinds of figures would you like to create?
1. a. How have the global zonally-averaged mean meridional circulation and/or the
Walker circulation changed over the course of the 20th century and in 21st century
projections, with regards to circulation strength, its associated hydrological cycling, and
dominant spatiotemporal variations?
b. How do the reanalysis datasets, CMIP5 datasets, and satellite observations compare
in their depiction in the major trends and variations?
c. What are the implications of these trends and variations with regards to the
underlying physical mechanisms?
2. An answer/set of answers to the posed questions would hopefully manifest as the
uncovering of a trend or major variation in the datasets that either persists from the 20th
century into the projected 21st century or undergoes significant deviations from its prior
behaviors in the 20th century.
3. The figures that I would like to create are two-fold in structure:
a. Zonally-averaged time series highlighting long term trends and variations within the
mean meridional circulation and/or Walker circulation and the pertinent uncertainties
associated with them.
b. Spatial maps that highlight the mean circulation anomalies and trends and lay out the
dominant modes of variability.