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Advanced topic: Heat transport decomposition

University at Albany (SUNY)

This notebook is part of The Climate Laboratory by Brian E. J. Rose, University at Albany.


1. Spatial patterns of insolation and surface temperature


Let’s take a look at seasonal and spatial pattern of insolation and compare this to the zonal average surface temperatures.

<Figure size 1200x600 with 2 Axes>

This figure reveals something fairly obvious, but still worth thinking about:

Warm temperatures are correlated with high insolation. It’s warm where the sun shines.

More specifically, we can see a few interesting details here:

  • The seasonal cycle is weakest in the tropics and strongest in the high latitudes.

  • The warmest temperatures occur slighly NORTH of the equator

  • The highest insolation occurs at the poles at summer solstice.

The local surface temperature does not correlate perfectly with local insolation for two reasons:

  • the climate system has heat capacity, which buffers some of the seasonal variations

  • the climate system moves energy around in space!


2. Calculating Radiative-Convective Equilibrium as a function of latitude


As a first step to understanding the effects of heat transport by fluid motions in the atmosphere and ocean, we can calculate what the surface temperature would be without any motion.

Let’s calculate a radiative-convective equilibrium state for every latitude band.

Putting realistic insolation into an RCM

This code demonstrates how to create a model with both latitude and vertical dimensions.

climlab Process of type <class 'climlab.process.time_dependent_process.TimeDependentProcess'>. 
State variables and domain shapes: 
  Ts: (40, 1) 
  Tatm: (40, 30) 
The subprocess tree: 
RCM: <class 'climlab.process.time_dependent_process.TimeDependentProcess'>
   Radiation: <class 'climlab.radiation.rrtm.rrtmg.RRTMG'>
      SW: <class 'climlab.radiation.rrtm.rrtmg_sw.RRTMG_SW'>
      LW: <class 'climlab.radiation.rrtm.rrtmg_lw.RRTMG_LW'>
   Insolation: <class 'climlab.radiation.insolation.DailyInsolation'>
   Fixed Relative Humidity: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>
   Convective Adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>

<Figure size 640x480 with 1 Axes>

This new insolation process uses the same code we’ve already been working with to compute realistic distributions of insolation. Here we are using

climlab.radiation.DailyInsolation

but there is also

climlab.radiation.AnnualMeanInsolation

for models in which you prefer to suppress the seasonal cycle and prescribe a time-invariant insolation.

The following code will just integrate the model forward in four steps in order to get snapshots of insolation at the solstices and equinoxes.

Integrating for 81 steps, 81.0 days, or 0.22177064972229385 years.
Total elapsed time is 0.22177064972229385 years.
Integrating for 91 steps, 91.99999999999999 days, or 0.2518876515364325 years.
Total elapsed time is 0.4709203920028956 years.
Integrating for 94 steps, 94.00000000000001 days, or 0.2573634700480941 years.
Total elapsed time is 0.7282838620509897 years.
Integrating for 91 steps, 91.0 days, or 0.24914974228060174 years.
Total elapsed time is 0.9774336043315914 years.
<Figure size 640x480 with 1 Axes>

This just serves to demonstrate that the DailyInsolation process is doing something sensible.

Note that we could also pass different orbital parameters to this subprocess. They default to present-day values, which is what we are using here.

Find the steady seasonal cycle of temperature in radiative-convective equilibrium

Integrating for 1460 steps, 1460.9688 days, or 4.0 years.
Total elapsed time is 4.974781117844542 years.
Integrating for 365 steps, 365.2422 days, or 1.0 years.
Total elapsed time is 5.97411799622278 years.

All climlab Process objects have an attribute called timeave.

This is a dictionary of time-averaged diagnostics, which are automatically calculated during the most recent call to integrate_years() or integrate_days().

dict_keys(['Ts', 'Tatm', 'OLR', 'OLRclr', 'OLRcld', 'TdotLW', 'TdotLW_clr', 'LW_sfc', 'LW_sfc_clr', 'LW_flux_up', 'LW_flux_down', 'LW_flux_net', 'LW_flux_up_clr', 'LW_flux_down_clr', 'LW_flux_net_clr', 'ASR', 'ASRclr', 'ASRcld', 'TdotSW', 'TdotSW_clr', 'SW_sfc', 'SW_sfc_clr', 'SW_flux_up', 'SW_flux_down', 'SW_flux_net', 'SW_flux_up_clr', 'SW_flux_down_clr', 'SW_flux_net_clr', 'insolation', 'coszen', 'irradiance_factor', 'q'])

Here we use the timeave['insolation'] to plot the annual mean insolation.

(We know it is the annual average because the last call to model.integrate_years was for exactly 1 year)

<Figure size 640x480 with 1 Axes>

Compare annual average temperature in RCE to the zonal-, annual mean observations.

<Figure size 640x480 with 1 Axes>

Our modeled RCE state is far too warm in the tropics, and too cold in the mid- to high latitudes.

Vertical structure of temperature: comparing RCE to observations

<Figure size 1400x600 with 4 Axes>

Again, this plot reveals temperatures that are too warm in the tropics, too cold at the poles throughout the troposphere.

Note however that the vertical temperature gradients are largely dictated by the convective adjustment in our model. We have parameterized this gradient, and so we can change it by changing our parameter for the adjustment.

We have (as yet) no parameterization for the horizontal redistribution of energy in the climate system.

TOA energy budget in RCE equilibrium

Because there is no horizontal energy transport in this model, the TOA radiation budget should be closed (net flux is zero) at all latitudes.

Let’s check this by plotting time-averaged shortwave and longwave radiation:

<Figure size 640x480 with 1 Axes>

Indeed, the budget is (very nearly) closed everywhere. Each latitude is in energy balance, independent of every other column.


3. Observed and modeled TOA radiation budget


We are going to look at the (time average) TOA budget as a function of latitude to see how it differs from the RCE state we just plotted.

Ideally we would look at actual satellite observations of SW and LW fluxes. Instead, here we will use the NCEP Reanalysis for convenience.

But bear in mind that the radiative fluxes in the reanalysis are a model-generated product, they are not really observations.

TOA budget from NCEP Reanalysis

<Figure size 640x480 with 1 Axes>

We find that ASR does NOT balance OLR in most locations.

Across the tropics the absorbed solar radiation exceeds the longwave emission to space. The tropics have a net gain of energy by radiation.

The opposite is true in mid- to high latitudes: the Earth is losing energy by net radiation to space at these latitudes.

TOA budget from the control CESM simulation

Load data from the fully coupled CESM control simulation that we’ve used before.

Attempting to open the dataset  http://thredds.atmos.albany.edu:8080/thredds/dodsC/CESMA/cpl_1850_f19/concatenated/cpl_1850_f19.cam.h0.nc
Attempting to open the dataset  http://thredds.atmos.albany.edu:8080/thredds/dodsC/CESMA/cpl_CO2ramp_f19/concatenated/cpl_CO2ramp_f19.cam.h0.nc
Attempting to open the dataset  http://thredds.atmos.albany.edu:8080/thredds/dodsC/CESMA/som_1850_f19/concatenated/som_1850_f19.cam.h0.nc
Attempting to open the dataset  http://thredds.atmos.albany.edu:8080/thredds/dodsC/CESMA/som_1850_2xCO2/concatenated/som_1850_2xCO2.cam.h0.nc

Now we can make the same plot of ASR and OLR that we made for the observations above.

<Figure size 640x480 with 1 Axes>

Essentially the same story as the reanalysis data: there is a surplus of energy across the tropics and a net energy deficit in mid- to high latitudes.

There are two locations where ASR = OLR, near about 35º in both hemispheres.


4. The energy budget for a zonal band


The basic idea

Through most of the previous notes we have been thinking about global averages.

We’ve been working with an energy budget that looks something like this:

Column sketch

When we start thinking about regional climates, we need to modify our budget to account for the additional heating or cooling due to transport in and out of the column:

Column sketch 2

Conceptually, the additional energy source is the difference between what’s coming in and what’s going out:

h=Hin−Houth = \mathcal{H}_{in} - \mathcal{H}_{out}

where hh is a dynamic heating rate in W m−2^{-2}.

A more careful budget

Let’s now consider a thin band of the climate system, of width δϕ\delta \phi , and write down a careful energy budget for it.

Zonal energy budget sketch

Let H(ϕ)\mathcal{H}(\phi) be the total rate of northward energy transport across the latitude line ϕ\phi, measured in Watts (usually PW).

Let T(ϕ,t)T(\phi,t) be the zonal average surface temperature (“zonal average” = average around latitude circle).

We can write the energy budget as

∂E∂t=energy in−energy out\frac{\partial E}{\partial t} = \text{energy in} - \text{energy out}

where EE is the total energy content of the column, which is useful to write as

E=∫bottomtopρ e dzE = \int_{bottom}^{top} \rho ~ e ~ dz

and ee is the local enthalpy of the fluid, in units of J kg−1^{-1}. The integral energy content EE thus has units of J m−2^{-2}.

We have written the time tendency as a partial derivative now because EE varies in both space and time.

Now there are two energy sources and two energy sinks to think about: Radiation and dynamics (horizontal transport)

∂E∂t=RTOA−(transport out−transport in) / area of band\frac{\partial E}{\partial t} = R_{TOA} - (\text{transport out} - \text{transport in})~/ ~\text{area of band}

where we define the net incoming radiation at the top of atmosphere as

RTOA=ASR−OLR=(1−α)Q−OLRR_{TOA} = \text{ASR} - \text{OLR} = (1-\alpha) Q - \text{OLR}

The surface area of the latitude band is

A=Circumference × north-south widthA = \text{Circumference} ~\times ~ \text{north-south width}
A=2πacos⁡ϕ × aδϕA = 2 \pi a \cos \phi ~ \times ~ a \delta \phi
A=2πa2cos⁡⁡ϕ δϕA = 2 \pi a^2 \cos⁡\phi ~ \delta\phi

We will denote the energy transport in and out of the band respectively as H(ϕ),H(ϕ+δϕ)\mathcal{H}(\phi), \mathcal{H}(\phi + \delta\phi)

Then the budget can be written

∂E∂t=ASR−OLR−12πa2cos⁡⁡ϕ δϕ(H(ϕ+δϕ)−H(ϕ))\frac{\partial E}{\partial t} = \text{ASR} - \text{OLR} - \frac{1}{2 \pi a^2 \cos⁡\phi ~ \delta\phi} \Big( \mathcal{H}(\phi + \delta\phi) - \mathcal{H}(\phi) \Big)

For thin bands where δϕ\delta\phi is very small, we can write

1δϕ(H(ϕ+δϕ)−H(ϕ))=∂H∂ϕ\frac{1}{\delta\phi} \Big( \mathcal{H}(\phi + \delta\phi) - \mathcal{H}(\phi) \Big) = \frac{\partial \mathcal{H}}{\partial \phi}

So the local budget at any latitude ϕ\phi is

∂E∂t=ASR−OLR−12πa2cos⁡⁡ϕ∂H∂ϕ\frac{\partial E}{\partial t} = \text{ASR} - \text{OLR} - \frac{1}{2 \pi a^2 \cos⁡\phi } \frac{\partial \mathcal{H}}{\partial \phi}

The dynamical heating rate in W m−2^{-2} is thus

h=−12πa2cos⁡⁡ϕ∂H∂ϕh = - \frac{1}{2 \pi a^2 \cos⁡\phi } \frac{\partial \mathcal{H}}{\partial \phi}

which is the convergence of energy transport into this latitude band: the difference between what’s coming in and what’s going out.

Calculating heat transport from the steady-state energy budget

Notice that if the above budget is in equilibrium then ∂E/∂t=0\partial E/ \partial t = 0 and the budget says that divergence of heat transport balances the net radiative heating at every latitude.

If we can assume that the budget is balanced, i.e. assume that the system is at equilibrium and there is negligible heat storage, then we can use the budget to infer H\mathcal{H} from a measured (or modeled) TOA radiation imbalance.

Setting ∂E/∂t=0\partial E/ \partial t = 0 and rearranging:

∂H∂ϕ=2π a2cos⁡⁡ϕ RTOA\frac{\partial \mathcal{H}}{\partial \phi} = 2 \pi ~a^2 \cos⁡\phi ~ R_{TOA}

Now integrate from the South Pole (ϕ=−π/2\phi = -\pi/2):

∫−π/2ϕ∂H∂ϕ′dϕ′=2π a2∫−π/2ϕcos⁡⁡ϕ′ RTOAdϕ′\int_{-\pi/2}^{\phi} \frac{\partial \mathcal{H}}{\partial \phi^\prime} d\phi^\prime = 2 \pi ~a^2 \int_{-\pi/2}^{\phi} \cos⁡\phi^\prime ~ R_{TOA} d\phi^\prime
H(ϕ)−H(−π/2)=2π a2∫−π/2ϕcos⁡⁡ϕ′ RTOAdϕ′\mathcal{H}(\phi) - \mathcal{H}(-\pi/2) = 2 \pi ~a^2 \int_{-\pi/2}^{\phi} \cos⁡\phi^\prime ~ R_{TOA} d\phi^\prime

Our boundary condition is that the transport must go to zero at the pole. We therefore have a formula for calculating the heat transport at any latitude, by integrating the imbalance from the South Pole:

H(ϕ)=2π a2∫−π/2ϕcos⁡⁡ϕ′ RTOAdϕ′\mathcal{H}(\phi) = 2 \pi ~a^2 \int_{-\pi/2}^{\phi} \cos⁡\phi^\prime ~ R_{TOA} d\phi^\prime

What about the boundary condition at the other pole? We must have H(π/2)=0\mathcal{H}(\pi/2) = 0 as well, because a non-zero transport at the pole is not physically meaningful.

Notice that if we apply the above formula and integrate all the way to the other pole, we then have

H(π/2)=2π a2∫−π/2π/2cos⁡⁡ϕ′ RTOAdϕ′\mathcal{H}(\pi/2) = 2 \pi ~a^2 \int_{-\pi/2}^{\pi/2} \cos⁡\phi^\prime ~ R_{TOA} d\phi^\prime

This is an integral of the radiation imbalance weighted by cosine of latitude. In other words, this is proportional to the area-weighted global average energy imbalance.

We started by assuming that this imbalance is zero.

If the global budget is balanced, then the physical boundary condition of no-flux at the poles is satisfied.


5. Observed and modeled poleward heat transport


Here we will code up a function that performs the above integration.

Let’s now use this to calculate the total northward heat transport from our control simulation with the CESM:

<Figure size 640x480 with 1 Axes>

The total heat transport is very nearly symmetric about the equator, with poleward transport of about 5 to 6 PW in both hemispheres.

The transport peaks in magnitude near 35º latitude, the same latitude where we found that ASR = OLR. This is no coincidence!

Equatorward of 35º (across the tropics) there is net heating by radiation and net cooling by dynamics. The opposite is true poleward of 35º.

What about the “observations”, i.e. the reanalysis data?

We can try to do the same calculation.

<Figure size 640x480 with 1 Axes>

Our integral does NOT go to zero at the North Pole!. This means that the global energy budget is NOT balanced in the reanalysis data.

Let’s look at the global imbalance:

The net downward TOA radiation flux in NCEP renalysis data is -12.0 W/m2.

Evidently there is a substantial net flux out to space in this dataset.

Before we can compute heat transport from this data, we need to balance the global data.

To do this requires making assumptions about the spatial distribution of the imbalance.

The simplest assumption we can make is that the imbalance is uniform across the Earth.

The net downward TOA radiation flux after balancing the data is -1.66e-06 W/m2.
<Figure size 640x480 with 1 Axes>

We now get a physically sensible result (zero at both poles).

The heat transport is poleward everywhere, and very nearly anti-symmetric across the equator. The shape is very similar to what we found from the CESM simulation, with peaks near 35º.

However the magnitude of the peaks is substantially smaller. Does this indicate a shortcoming of the CESM simulation?

Probably not!

It turns out that our result here is very sensitive to the details of how we balance the radiation data.

As an exercise, you might try applying different corrections other than the globally uniform correction we used above. E.g. try weighting the tropics or the mid-latitudes more strongly.

An example of a recently published observational estimate of meridional heat transport

Fasullo and Trenberth 2008b, Figure 7

The ERBE period zonal mean annual cycle of the meridional energy transport in PW by (a) the atmosphere and ocean as inferred from ERBE RTR_T, NRA δ\deltaA_E/δ\deltat, and GODAS δ\deltaO_E/δ\deltat; (b) the atmosphere based on NRA; and (c) by the ocean as implied by ERBE + NRA FSF_S and GODAS δ\deltaO_E/δ\deltat. Stippling and hatching in (a)–(c) represent regions and times of year in which the standard deviation of the monthly mean values among estimates, some of which include the CERES period (see text), exceeds 0.5 and 1.0 PW, respectively. (d) The median annual mean transport by latitude for the total (gray), atmosphere (red), and ocean (blue) accompanied with the associated ±2σ\pm2\sigma range (shaded).

This is a reproduction of Figure 7 from Fasullo and Trenberth (2008), “The Annual Cycle of the Energy Budget. Part II: Meridional Structures and Poleward Transports”, J. Climate 21, doi:10.1175/2007JCLI1936.1

This figure shows the breakdown of the heat transport by season as well as the partition between the atmosphere and ocean.

Focussing just on the total, annual transport in panel (d) (black curve), we see that is quite consistent with what we computed from the CESM simulation.


6. Energy budgets for the atmosphere and ocean


The total transport (which we have been inferring from the TOA radiation imbalance) includes contributions from both the atmosphere and the ocean:

H=Ha+Ho\mathcal{H} = \mathcal{H}_{a} + \mathcal{H}_{o}

We have used the TOA imbalance to infer the total transport because TOA radiation is the only significant energy source / sink to the climate system as a whole.

However, if we want to study (or model) the individual contributions from the atmosphere and ocean, we need to consider the energy budgets for each individual domain.

We will therefore need to broaden our discussion to include the net surface heat flux, i.e. the total flux of energy between the surface and the atmosphere.

Surface fluxes

Let’s denote the net upward energy flux at the surface as FSF_S.

There are four principal contributions to FSF_S:

  1. Shortwave radiation

  2. Longwave radiation

  3. Sensible heat flux

  4. Evaporation or latent heat flux

Sensible and latent heat fluxes involve turbulent exchanges in the planetary boundary layer. We will look at these in more detail later.

<Figure size 640x480 with 2 Axes>

Discuss... Large net fluxes over ocean, not over land.

Energy budget for the ocean

Using exactly the same reasoning we used for the whole climate system, we can write a budget for the OCEAN ONLY:

∂Eo∂t=−FS−12πa2cos⁡⁡ϕ∂Ho∂ϕ\frac{\partial E_o}{\partial t} = -F_S - \frac{1}{2 \pi a^2 \cos⁡\phi } \frac{\partial \mathcal{H_o}}{\partial \phi}

In principle it is possible to calculate Ho\mathcal{H}_o from this budget, analagously to how we calculated the total H\mathcal{H}.

Assuming that

  • surface fluxes are well-known

  • the ocean heat storage is negligible (a big assumption!)

we can write

Ho(ϕ)=2π a2∫−π/2ϕ−cos⁡⁡ϕ′ FSdϕ′\mathcal{H}_o(\phi) = 2 \pi ~a^2 \int_{-\pi/2}^{\phi} - \cos⁡\phi^\prime ~ F_S d\phi^\prime

where the minus sign account for the fact that we defined FSF_S as positive up (out of the ocean).

Energy budget for the atmosphere

The net energy source to the atmosphere is the sum of the TOA flux and the surface flux. Thus we can write

∂Ea∂t=RTOA+FS−12πa2cos⁡⁡ϕ∂Ha∂ϕ\frac{\partial E_a}{\partial t} = R_{TOA} + F_S - \frac{1}{2 \pi a^2 \cos⁡\phi } \frac{\partial \mathcal{H_a}}{\partial \phi}

and we can similarly integrate to get the transport:

Ha(ϕ)=2π a2∫−π/2ϕcos⁡⁡ϕ′ (RTOA+FS)dϕ′\mathcal{H}_a(\phi) = 2 \pi ~a^2 \int_{-\pi/2}^{\phi} \cos⁡\phi^\prime ~ \big( R_{TOA} + F_S \big) d\phi^\prime

Note that these formulas ensure that H=Ha+Ho\mathcal{H} = \mathcal{H}_a + \mathcal{H}_o.

Atmospheric water budget and latent heat transport

Water vapor contributes to the atmopsheric energy transport because energy consumed through evaporation is converted back to sensible heat wherever the vapor subsequently condenses.

If the evaporation and the condensation occur at different latitudes then there is a net transport of energy due to the movement of water vapor.

We can use the same kind of budget reasoning to compute this latent heat transport. But this time we will make a budget for water vapor only.

The only sources and sinks of water vapor to the atmosphere are surface evaporation and precipitation:

Lv∂Q∂t=Lv(Evap−Precip)−12πa2cos⁡⁡ϕ∂HLH∂ϕL_v \frac{\partial Q}{\partial t} = L_v \big( Evap - Precip \big) - \frac{1}{2 \pi a^2 \cos⁡\phi } \frac{\partial \mathcal{H}_{LH}}{\partial \phi}

Here we are using

  • QQ is the depth-integrated water vapor (the “precipitable water”) in kg m−2^{-2}

  • Evap and Precip are in kg m−2^{-2} s−1^{-1} (equivalent to mm/s)

  • Lv=2.5×106L_v = 2.5 \times 10^6 J kg−1^{-1} is the latent heat of vaporization

  • HLH\mathcal{H}_{LH} is the northward latent heat transport

All terms in the above equation thus have units of W m−2^{-2}.

Using the now-familiar equilibrium reasoning, we can use this water balance to compute the latent heat transport from the net surface evaporation minus precipitation:

HLH(ϕ)=2π a2∫−π/2ϕcos⁡⁡ϕ′ Lv (Evap−Precip)dϕ′\mathcal{H}_{LH}(\phi) = 2 \pi ~a^2 \int_{-\pi/2}^{\phi} \cos⁡\phi^\prime ~ L_v ~\big( Evap - Precip \big) d\phi^\prime

From this we can then infer all the energy transport associated with the motion of dry air as a residual:

HDry=Ha−HLH\mathcal{H}_{Dry} = \mathcal{H}_a - \mathcal{H}_{LH}

7. Calculating the partitioning of poleward energy transport into different components


This function implements the above formulas to calculate the following quantities from CESM simulation output:

  • Total heat transport, H\mathcal{H}

  • Ocean heat transport, Ho\mathcal{H}_o

  • Atmospheric heat transport, Ha\mathcal{H}_a

  • Atmospheric latent heat transport, HLH\mathcal{H}_{LH}

  • Atmospheric dry heat transport, HDry\mathcal{H}_{Dry}

<Figure size 1600x600 with 2 Axes>

Discuss the shape of these curves, before and after the global warming.


8. Mechanisms of heat transport


Energy is transported across latitude lines whenever there is an exchange of fluids with different energy content: e.g. warm fluid moving northward while colder fluid moves southward.

Thus energy transport always involves correlations between northward component of velocity vv and energy ee

The transport is an integral of these correlations, around a latitude circle and over the depth of the fluid:

H=∫02π∫bottomtopρ v e dz acos⁡ϕ dλ\mathcal{H} = \int_0^{2\pi} \int_{\text{bottom}}^{\text{top}} \rho ~ v ~ e ~ dz ~ a \cos\phi ~ d\lambda

The total transport (which we have been inferring from the TOA radiation imbalance) includes contributions from both the atmosphere and the ocean:

H=Ha+Ho\mathcal{H} = \mathcal{H}_{a} + \mathcal{H}_{o}

We can apply the above definition to both fluids (with appropriate values for bottom and top in the depth integral).

The appropriate measure of energy content is different for the atmosphere and ocean.

For the ocean, we usually use the enthalpy for an incompressible fluid:

eo≈cw Te_o \approx c_w ~ T

where cw≈4.2×103c_w \approx 4.2 \times 10^{3} J kg−1^{-1} K−1^{-1} is the specific heat for seawater.

For the atmosphere, it’s a bit more complicated. We need to account for both the compressibility of air, and for its water vapor content. This is because of the latent energy associated with evaporation and condensation of vapor.

It is convenient to define the moist static energy for the atmosphere:

MSE=cp T+g Z+Lv qMSE = c_p ~T + g~ Z + L_v ~q

whose terms are respectively the internal energy, the potential energy, and the latent heat of water vapor (see texts on atmopsheric thermodynamics for details).

We will assume that MSEMSE is a good approximation to the total energy content of the atmosphere, so

ea≈MSEe_a \approx MSE

Note that in both cases we have neglected the kinetic energy from this budget.

The kinetic energy per unit mass is ek=∣v⃗∣2/2e_k = |\vec{v}|^2/2, where v⃗=(u,v,w)\vec{v} = (u,v,w) is the velocity vector.

In practice it is a very small component of the total energy content of the fluid and is usually neglected in analyses of poleward energy transport.

As we have seen, we can further divide the atmospheric transport into transports due to the movement of dry air (the tranport of dry static energy) and transport associated with evaporation and condensation of water vapor (the latent heat transport)

Mechanisms of energy transport in the ocean

Assuming the ocean extends from z=−Hz=-H to z=0z=0 we can then write

Ho≈acos⁡ϕ∫02π∫−H0cw ρ v T dz dλ\mathcal{H}_o \approx a \cos\phi \int_0^{2\pi} \int_{-H}^{0} c_w ~\rho ~ v ~ T ~ dz ~ d\lambda

setting v T=0v ~ T = 0 at all land locations around the latitude circle.

The northward transport Ho\mathcal{H}_o is positive if there is a net northward flow of warm water and southward flow of cold water.

This can occur due to horizontal differences in currents and temperatures.

The classic example is flow in the subtropical gyres and western boundary currents. In the subtropical North Atlantic, there is rapid northward flow of warm water in the Gulf Stream. This is compensated by a slow southward flow of cooler water across the interior of the basin.

Because the water masses are at different temperatures, equal and opposite north-south exchanges of mass result in net northward transport of energy.

Energy transport can also result from vertical structure of the currents.

There is a large-scale overturning circulation in the Atlantic that involves near-surface northward flow of warmer water, compensated by deeper southward flow of colder water.

Again, equal exchange of water but net transport of energy.


Credits

This notebook is part of The Climate Laboratory, an open-source textbook developed and maintained by Brian E. J. Rose, University at Albany.

It is licensed for free and open consumption under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Development of these notes and the climlab software is partially supported by the National Science Foundation under award AGS-1455071 to Brian Rose. Any opinions, findings, conclusions or recommendations expressed here are mine and do not necessarily reflect the views of the National Science Foundation.