NEXT PHASE OF CLOUD RESEARCH

Global HX Picture

Some Principles to Follow Remember that it is a Complicated Problem of a Complex System with Large Uncertainties  Dynamics means we need to work out Time Representations of Questions instead of Static Comparisons Use Multiple Analysis Approaches Emphasize Observations but use models to pose & investigate hypotheses

Dynamics -- Weather Some Tasks Finish linkage of dynamics (including fair weather & storms) to cloud property distributions Complete quantification of cloud-weather feedbacks [esp. severe storms] Some Questions What are the proper differential relationships? What are the relevant quantities? [horizontal divergence (ie, vertical motion) but also RH tendency] What are scale dependencies? How is scale-dependent coupling to be represented? How do we understand fair-foul weather distribution [ultimately a feedback question]?

Dynamics -- Environment Some Tasks Investigate land surface -- ABL feedbacks that are altered by cloud processes  hydrology & land biosphere Cloud effects on ocean-atmosphere coupling  ocean biosphere Some Questions How does Cloudy ABL evolve in response to surface fluxes & large-scale atmosphere changes? How would ABL differ without cloud processes? Do cloud processes change scale of coupling to surface?

Dynamics -- Climate Some Tasks Connect general circulation and global cloudiness Determine cloud-dependent causes/alterations of natural variability Some Questions How do we relate general circulation intensity to global cloud property distributions? Can we relate transient and equilibrium feedbacks? The Big Question: What is the climate sensitivity with and without cloud processes?

Some Tests of Success Matching scale-dependent variability distributions Matching evolution of weather conditions (Eulerian, Lagrangian), specifically storm formation & evolution & motion Weather-to-decadal scale-dependent energy and water exchange rates – Distribution of contributions to budgets by different Weather & Climate States

Equations X (t + Δt) = G [P(t]] + ε Where X are observables, P are System State Parameters and ε are Measurement Errors X (t + Δt) = G [X(t)] Where X can include past values Linearize about some System State X(t 0 ) X(t 0 + Δt) – X(t 0 ) = H [X(t 0 )] ΔX(t 0 ) Where H = ∂X(t 0 + Δt) /∂X(t 0 ) So ∂X /∂t = [∂X(t 0 + Δt) /∂X(t 0 )] ΔX(t 0 ) ΔX(t 0 ) = (∂X /∂t) [∂X(t 0 + Δt) /∂X(t 0 )] Where ΔX(t 0 ) is climate anomaly (e.g., ENSO) relative to base state, ∂X /∂t are tendencies going into-out-of the anomalous state and H is the “sensitivity” matrix

What’s Next?