Tasmanian Bushfires

Looks like Colorado.

More than 100 people are missing in catastrophic bushfires that are ravaging the Australian island of Tasmania.

Some 1,000 people were rescued by boat from the east coast after fires cut off roads, but police say they hold ‘grave fears’ for a number of residents who stayed to defend their homes but have not been heard from.

It comes amid the hottest temperatures ever recorded on the island, peaking at 41.8C (107F) on Friday.

As fires continued to burn yesterday, police and rescue experts began the grim search for bodies in the worst-hit towns of Boomer Bay, where 65 properties were lost, and Dunalley, both in the south-east of the island.

More pics at Daily Mail UK: Tasmania-bushfires-Grave-fears-stayed-defend-homes

NASA

Jeff Spross @ Climate Progress has more background and details.
‘Sprawling Heat Wave Of Historical Proportions’ Brings ‘Horrendous’ Wildfires To Australia

Joe Romm @ Climate Progress
Off-The-Charts Heat Wave Brings Australia Its Hottest Average Temperature And New Map Colors For Temps Above 122°F!

Jeff Masters @ Wunderground
Historic heat wave brings Australia its hottest average temperature on record

GREB Decomposition: Hydrology

Conceptually, the hydrology is a very simple model. Water vapor evaporation is driven by surface temperature, wind speed, soil moisture, and surface height via an extended bulk formula. Precipitation is proportional to the water vapor in the atmosphere. I’ve annotated the GREB code below to reference the equations as listed in Dommenget and Floeter (2011).

3.3 Hydrological cycle

The response in viwv_atmos and the latent heat release is central to climate change. We therefore have to simulate the response of the hydrological cycle, which includes the evaporation of water vapor at the surface, the condensation of water in the atmosphere and the associated take-up and releases of latent heat at the surface and in the atmosphere, respectively. The saturation surface air layer specific humidity, q_sat, is given by

q_sat = e^{-z_topo/z_atmos} * 3.75*10^-3 * e^{( 17:08085*(T_surf-273.15)/(T_surf-273.15) )} (6)

which is taken from the textbook from James (1994) and extended to consider changes in surface pressure due to the topographic height, z_topo. The latent heat release to the surface layer associated with evaporation is given by an extended Bulk formula (Peixoto and Oort 1992a, 1992b):

F_latent = L * rho_air * C_w * abs(u) * theta_soil * (q_air – q_sat) (7)

The Bulk formula depends on the difference between q_sat and the actual surface air layer humidity, q_air, the wind speed, abs(u) the constant parameters of the latent heat of evaporation and condensation of water, L, the density of air, q_air, rho_airand the transfer coefficient, C_w. The wind speed, abs(u) is assumed to be the seasonally varying mean winds of the NCEP reanalysis 850 hPa geopotential height winds, abs(u) (see Fig. 1e for the annual mean values) plus a globally constant turbulent part of 3 m/s over oceans and 2 m/s over land. The Bulk formula is extended by a surface wetness fraction, theta_soil, to simulate evaporation over land, where the surface is not always wet. theta_soil is assumed to be a climatological boundary condition in the GREB model, which however varies with the seasonal cycle, see Fig. 1g for the annual mean values.

The atmospheric integrated water vapor, viwv_atmos, is roughly linearly related to the near surface humidity q_air (e.g. Rapti 2005), which is estimated by a linear regression from ECHAM5 simulations scaled by topography:

VIWV_atmos = e^{-z_topo/z_atmos} * 2.6736*10³[kg/m²] * q_air (8)

Note, that the additional scaling by the topography should simulate the effect of nearly exponentially decreasing atmospheric water vapor mixing ratios. Changes per unit time in q_air by evaporation, dq_eva, are given with the help of Eq. 7:

dq_eva = (-F_latent/L) * 1 / 2.6736*10³[kg/m²] (9)

The latent heat release, Q_latent, in the atmosphere due to changes in q_air by condensation or precipitation, dq_precip, is given by

Q_latent = – 2.6736*10³[kg/m²] * dq_precip * L (10)

The condensation or precipitation, dq_precip is assumed to be proportional to q_air

dq_precip = r_precip * q_air (11)

with r_precip = -0.1/24 h, which corresponds to an autoregressive model with a decorrelation (recirculation)

Equation 6 is the Clausius-Clapeyron equation.

The transition from eq 7 to eq 9 was not obvious to me until I found this relationship in the teaching material.

∆q_eva = 1 / r_H2O · ρair · Cw · |u∗ | · v_soil · (q_sat − q_atmos ) (4.13)

Precipitation is given by equation 11 and which sets the precipitation at 10% of the water vapor per day.

And now, the code …

!+++++++++++++++++++++++++++++++++++++++
subroutine hydro(Tsurf, q, Qlat, Qlat_air, dq_eva, dq_rain)
!+++++++++++++++++++++++++++++++++++++++
!    hydrological model for latent heat and water vapor

  USE mo_numerics,    ONLY: xdim, ydim
  USE mo_physics,     ONLY: rho_air, uclim, vclim, z_topo, swetclim, ityr,   &
&                           ce, cq_latent, cq_rain, z_air, r_qviwv, log_exp

! declare temporary fields
  real, dimension(xdim,ydim)  :: Tsurf, q, Qlat, Qlat_air, qs, dq_eva,        &
&                                dq_rain, abswind

  Qlat=0.; Qlat_air=0.; dq_eva=0.; dq_rain=0.
  if(log_exp  0. ) abswind = sqrt(abswind**2 +2.0**2) ! land
  where(z_topo < 0. ) abswind = sqrt(abswind**2 +3.0**2) ! ocean

! saturated humiditiy (max. air water vapor) 
  qs = 3.75e-3*exp(17.08085*(Tsurf-273.15)/(Tsurf-38.975)); ! eq (6)
  qs = qs*exp(-z_topo/z_air) ! scale qs by topography ! eq (6)
! latent heat flux surface
  Qlat    = (q-qs)*abswind*cq_latent*rho_air*ce*swetclim(:,:,ityr) ! eq (7)

! change in water vapor
  dq_eva  = -Qlat/cq_latent/r_qviwv  ! evaporation ! eq(9) ! cq_latent = 2.257e6
  dq_rain = cq_rain*q                ! rain ! eq(11) !cq_rain   = -0.1/24./3600
! latent heat flux atmos
  Qlat_air = -dq_rain*cq_latent*r_qviwv  ! eq (10)

end subroutine hydro

Wiki states: Again assuming constant relative humidity, the Clausius-Clapeyron equation shows that water vapour increases roughly exponentially with temperature, at approximately 7% for typical temperatures.

The model responds appropriately when I increase surface temperatures by increasing solar insolation – rising 6-7% per degC increase in surface temperatures. Interestingly, the model’s water vapor response curve is slightly less when the surface temp is raised by CO2 forcing. The results are displayed in the chart above.

—————–

Dommenget, D., and J. Floeter 2011: Conceptual Understanding of Climate Change with a Globally Resolved Energy Balance Model. Climate dynamics, 2011, 37, 2143-2165.

Happy New Year: EIA US CO2 Emissions

I’m not making any projections here about future US CO2 emissions. To see where the decrease is occurring, I took the annual emissions and compared 2012 to the peak year of 2007. The annual values for 2012 are estimated from January to September data.

US natural gas emissions climbed 8% while petroleum emissions fell 13% and coal emissions fell 24% for an overall decline of 12.5% in five years.

GREB Decomposition: Long Wave Radiation

Diving once again into Conceptual understanding of climate change with a globally
resolved energy balance model (Dommenget and Floter, 2011), this time to flesh out the emissivity and long wave radiation within the model.

GREB Decomposition: Solar Radiation

I turn to Dommenget and Floter’s 2011Conceptual understanding of climate change with a globally
resolved energy balance model for description of the treatment of solar radiation and albedo within the model.I have reformatted some equations and symbols for readability.

GREB: Multilevel CO2 Doubling Experiment

Now that we’ve got a handle on the input and output files, let’s take the GREB model out for a test drive.

BOE: Rates of Change in Antarctic Ice Core Temperatures

Another line of argument for a catastrophic impact of climate change is in the rate of change of global temperatures and/or ocean acidification. In this post, I’m going to poke a bit at the rates of change in global temperature via ice core data. This step – from globally to particular Antarctic locations – is questionable and I’ll look closer to see if this is justified in a latter post. With that said, what do the ice cores hold ….?

Colorado River Basin Storage: Lake Mead and Lake Powell

Some of my first comments on WUWT were in regards to Lake Powell water levels. Even then I understood that to correctly document water levels on the Colorado River Basin, you had to look, as a minimum, at both Mead and Powell. There was a new river operations approach begun in 2005, formalized in 2007, which has had the net effect of slowly increasing the water levels in Lake Powell, but at the expense of Lake Mead. Indeed, there was little change in the river system as a whole from 2005-2010, with combined water storage for the two reservoirs bouncing around 50%. This winter was the first year in the last eight or so with a sustained improvement in storage – although whether that can be maintained is still in question with forecasts of widespread low water flows.

NCDC DS-9640: CONUS Temperature Anomalies and a couple of ax-grinders

For some reason, two posts at The Blackboard, It’s “Fancy,” Sort of … (Shollenberger) and “To get what he wanted”: Upturned end points. (Lucia), seem to be having difficulty understanding the mechanics of another post at “Open Mind”, In the Classroom (Tamino). But there is nothing unusual or difficult about the methods Tamino used to create the charts which have generated so much smoke and apparent frustration at The Blackboard – resulting in an outbreak of mcintyretude: scorn, derision, insults, and the questioning of motives. Since this is mostly a quick walk through of some code to clear the smoke, I will leave the charts generated to post at the end.

Lines, Sines, and Curve Fitting 14 – more extrapolation revisited

A year ago, I was working through some curve fitting exercises. Girma was advancing his line+sine model as superior to climate modeling partially based on its hight correlation. He had allowed that an equally simple model with a higher correlation than his would be a superior model. I demonstrated 3 variations of an exp+sine model, one for each surface record, each had higher correlation than his. Neither one of us dealt with autocorrelation.

I thought I would make a quick update on that original post. Nothing fancy, just adding the data points for the 2011 annual surface temperature record indexes.