Assignments

Assignments#

Cycle 1: Tropical Thermodynamics & Static Stability#

Instructions#

  • Teams: Work in pairs.

  • Topic Selection: By Day 1 of Cycle 1, choose one of the three state-of-the-art topics below. Sign up on the shared spreadsheet.

  • Timeline:

    • Day 1: Topic sign-up & kickoff

    • Day 3: Submit 1-page project proposal

    • Day 7: Interim check-in (5 min stand-up)

    • Day 14: Final deliverables due

  • Starter Code: Use the provided notebook template to download and plot IGRA2 and ERA5 data.

  • Collaboration: Track your work in Git (one repo per team).

Topic Options#

  1. MJO Preconditioning of Low-Level θₑ

    • Objective: Quantify boundary-layer θₑ anomalies leading active/inactive MJO phases.

    • Key Tasks:

      1. Download IGRA2 soundings (Darwin & Gan Island) for four MJO events.

      2. Compute θ and θₑ profiles, derive daily anomalies vs. 1981–2010 climatology.

      3. Composite low-level θₑ anomalies against TRMM/IMERG precipitation.

    MJO1

    MJO3

    MJO4

  2. Decadal Trends in Tropical Static Stability

    • Objective: Detect trends in lapse rate (850–500 hPa) and CAPE over the eastern tropical Pacific (1960–2020).

    • Key Tasks:

      1. Extract lapse rate from IGRA2 and ERA5.

      2. Perform linear regression + Mann–Kendall tests.

      3. Map spatial patterns and relate to ENSO indices.

Data & Tools#

Deliverables#

  • Jupyter Notebook (.ipynb):

    • Data download & parsing

    • Computation of thermodynamic variables (θ, θₑ, lapse rate, CAPE, TIL metrics)

    • Plots: T–p profiles, θ/θₑ vertical profiles, anomaly composites, trend maps, etc.

    • Inline markdown explaining each step

  • Presentation (5 min + 2 min Q&A in class Week 3):

    1. Introduction

    2. Data & Methods (brief)

    3. Results & Interpretation

    4. Conclusions & Future Work

    5. References (consistent style)

    • Slide deck (max 5 slides) summarizing your question, methods, and key findings

Be Prepared To#

  • Defend your methodological choices (data selection, QC flags, analysis window).

  • Discuss limitations and possible extensions (e.g., alternative datasets, other basins).

  • Propose follow-up analyses based on your results.

1. MJO Preconditioning of Low-Level θₑ#

Scientific importance:

  • The Madden–Julian Oscillation (MJO) is the dominant mode of tropical intra-seasonal variability, driving bursts of deep convection that modulate global weather (e.g., monsoons, midlatitude teleconnections).

  • Low-level equivalent potential temperature (θₑ) in the boundary layer reflects the reservoir of moist static energy available to fuel convection. Quantifying how θₑ anomalies lead or lag convective bursts is key to improving MJO prediction.

State-of-the-art aspects:

  • Recent studies employ high-resolution reanalyses and satellite composites to pinpoint the preconditioning signals preceding active MJO phases (e.g., timing and vertical structure of θₑ buildup) and link them to moisture–convection coupling.

  • Advances in machine-learning MJO forecasts increasingly use θₑ composites as input features, but fundamental understanding of the physical lead-time remains an open research frontier.

References:

  • Wheeler, M. C., & Hendon, H. H. (2004). An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Monthly Weather Review, 132(8), 1917–1932. :contentReference[oaicite:0]{index=0}

  • Benedict, J. J., & Randall, D. A. (2007). Observed characteristics of the MJO relative to maximum rainfall. Journal of the Atmospheric Sciences, 64(12), 2332–2354. :contentReference[oaicite:1]{index=1}

3. Climatology of the Tropopause Inversion Layer (TIL)#

Scientific importance:

  • The Tropopause Inversion Layer—an increase in temperature just below the cold-point tropopause—modulates stratosphere–troposphere exchange and the vertical distribution of water vapor, a powerful greenhouse gas.

  • Understanding TIL variability is crucial for accurately representing upper-tropospheric humidity in climate models, which strongly affects radiative forcing and stratospheric water vapor sources.

State-of-the-art aspects:

  • Recent satellite (e.g., MLS, SABER) and high-resolution reanalysis products enable global mapping of TIL strength and height with unprecedented vertical resolution.

  • Current research focuses on linking TIL variability to deep-convection overshoots, gravity-wave breaking, and lower-stratospheric humidity trends under climate change.

References:

  • Birner, T., Sankey, D., & Shepherd, T. G. (2006). The tropopause inversion layer in models and analyses. Geophysical Research Letters, 33(14), L14804. doi:10.1029/2006GL026549 :contentReference[oaicite:4]{index=4}

  • Pan, L. L., & Munchak, L. A. (2011). Relationship of cloud top to the tropopause and jet structure from CALIPSO data. Journal of Geophysical Research, 116, D12201. doi:10.1029/2010JD015462 :contentReference[oaicite:5]{index=5}

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