General circulation models (GCMs) are valuable tools for understanding how the global ocean–atmosphere–land surface system interacts and are routinely evaluated relative to observational data sets. Conversely, observational data sets can also be used to constrain GCMs in order to identify systematic errors in their simulated climates. One such example is to prescribe sea surface temperatures (SSTs) such that 70 % of the Earth's surface temperature field is observationally constrained (known as an Atmospheric Model Intercomparison Project, AMIP, simulation). Nevertheless, in such simulations, land surface temperatures are typically allowed to vary freely, and therefore any errors that develop over the land may affect the global circulation. In this study therefore, a method for prescribing the land surface temperatures within a GCM (the Australian Community Climate and Earth System Simulator, ACCESS) is presented. Simulations with this prescribed land surface temperature model produce a mean climate state that is comparable to a simulation with freely varying land temperatures; for example, the diurnal cycle of tropical convection is maintained. The model is then developed further to incorporate a selection of “proof of concept” sensitivity experiments where the land surface temperatures are changed globally and regionally. The resulting changes to the global circulation in these sensitivity experiments are found to be consistent with other idealized model experiments described in the wider scientific literature. Finally, a list of other potential applications is described at the end of the study to highlight the usefulness of such a model to the scientific community.
In order to minimize circulation errors in general circulation models (GCMs),
simulations with prescribed sea surface temperatures (SSTs) from past
observations are used
Previous studies that use GCMs with prescribed SSTs have shown the important
role land surface temperatures play in driving the global circulation. For
example,
The aims of this study are to
document the method and code changes that are applied to a GCM in order to prescribe the land surface temperatures; show that simulations with prescribed and freely varying land surface temperatures (with the land temperatures in the prescribed
run being derived from the freely varying simulation in order to avoid
spurious effects) are climatologically comparable; document the results of a series of sensitivity experiments where the land surface temperatures are perturbed; show that the atmospheric responses in those perturbation experiments are physically plausible and agree with the results of other studies in the literature; and overall, provide a “proof of concept” by attaining the aims above and show that GCM simulations with prescribed land surface temperature are realistic and have many potential applications.
It should be noted that the experiments in this paper are designed to be sensitivity tests to identify whether the model atmosphere responds in a physically realistic way to the imposed land surface temperature field. The experiments are not designed to answer specific questions about the processes at work but to highlight the types of experiment that can be run with such a model setup.
The model and methods used in this study are given in
Sect.
The GCM is the atmosphere-only version of the Australian Community Climate
and Earth System Simulator (primarily ACCESS1.0), which is described in more
detail in
Relevant to the experiments used in this study is the surface process
parameterization, which is the Met Office Surface Exchange Scheme
This section gives an overview of the processes that are considered for
calculating the surface temperature (
Schematic diagram of the processes involved with calculating the surface temperature and fluxes in ACCESS. Upper-case lettering refers to the names of individual subroutines within the model. The variables are passed from ATMOS_PHYSICS2 through the explicit calculations, then the implicit calculations, and finally back to ATMOS_PHYSICS2 for use elsewhere. Arrows indicate the transfer of variables through subroutines. Solid lines separate the transfer of variables into and out of the same subroutine where applicable.
A schematic of the model process for updating
Adjustments to the surface sensible and latent heat fluxes are then
calculated implicitly in SF_EVAP depending on the availability of surface
moisture back to freezing if there is sufficient snow that it cannot be melted within a time step (30 min in this case) or by an amount proportional to the energy required to remove all the snow on the tile if it can all be removed within a time step.
The final value of surface temperature that the atmosphere uses in the rest
of the time step (
If there is no melting, then
Given that ACCESS uses a 30 min time step, in order to prescribe the land
surface temperatures, a data set that is available for all surface tiles and
at 30 min intervals is required. Such a data set does not exist in the
observational record and so, therefore, in order to represent both the
diurnal and seasonal cycles, the optimal solution is to take the surface
temperatures from a simulation where they are allowed to vary freely. In this
study, surface temperatures are taken from each time step and tile from a
50-year long simulation that uses prescribed climatological SSTs and sea ice
fractions (denoted as FREE in Table the diurnal and seasonal cycles in surface temperature and the surface heterogeneity over land (i.e. temperatures on individual tiles).
Starting at 00:00 UTC on 1 January, all 50 values for that specific time
produced by the FREE simulation (i.e. one for each year) are averaged
together to produce a representative mean temperature on each land tile and
saved. The process is then repeated on all land tiles for 00:30 UTC on
1 January. The process is repeated for all time steps over the year to
produce a climatological land temperature field that contains a mean diurnal
cycle for each day of the year on each land surface tile. This is illustrated
in Fig.
Examples of how the surface temperature (K) inputs were produced at
individual grid points. Left column: the locations of the example grid
points. Middle column: corresponding surface temperature values for those
points in the left column on 1 and 2 January. Grey lines are the surface
temperatures for each of the 50 years, the black lines represent the
time-step mean (30 min) values from those 50 years on 1 and 2 January, and
the orange lines represent the 3-hourly input–hourly interpolated
temperature field described in Sect.
In Fig.
Initial test experiments with the time step data resulted in two problems.
The time step (30 min) data set is too large to be read into the current ACCESS framework as one single input field. Surface air temperatures (1.5 m above the surface) over the Antarctic were lower by
To combat the first problem, surface temperatures are read into the model
every 3 h and interpolated hourly between those points (orange line overlaid
in Fig.
In order to prevent the negative temperature anomalies from developing over
Antarctica in the prescribed runs relative to the FREE simulation, the
surface temperatures on permanent land ice tiles were allowed to vary freely.
The impact of this exception is small and discussed in
Sect.
In order to prescribe the land surface and sea ice temperature, Eq. (1) in
SF_IMPL (Fig.
The full list of experiments considered in this study is outlined in
Table
A list of the experiments run with ACCESS. The SST and sea ice fractional cover are climatological mean values representative of 1961–1990.
The following experiments are designed to either create the data necessary to
prescribe the land surface temperatures or use those data. These first three
experiments represent a suite of control simulations.
FREE. This simulation uses prescribed, climatological soil moisture, deep soil temperatures, SSTs, and sea ice fractions (monthly mean, 1961–1990
values), but allows the land temperatures to vary freely. The surface
temperatures from each surface type are used in each of the subsequent
experiments below. This is denoted as the “free running” (FREE)
simulation. CON1. Control run number 1, which is the same as FREE, except the surface land temperatures are prescribed using the data set described in Sect. CON2. Control run number 2, which is identical to CON1, except different initial conditions are used for the atmosphere.
Perturbation experiments are described in the following list where the
surface state is changed by either increasing ( ALL10K. Identical to CON1 except all land surface temperatures are increased by 10 K. This simulation
is used to illustrate how the global circulation responds to an artificial
enhancement of the land–sea thermal contrast. AMA10K. The same as CON1 except the land temperatures within the box 285–310 MC10K. The same as CON1 except the land temperatures within the box 100–160 AUS10K. Identical to CON1 except surface temperatures are increased by 10 K over Australia. This is to identify the impact of land
surface heating on the Australian monsoon and the Southern Hemisphere (SH) extratropical circulation. AM10K. Identical to CON1 except surface temperatures over the North American continent are increased by 10 K. This simulation is run to
identify the impact of heating a large Northern Hemisphere (NH) continent on the extratropical circulation. AMm10K. Identical to CON1 except surface temperatures over the North American continent are decreased by 10 K. This simulation is run to
identify the impact of cooling a large NH continent on the extratropical circulation.
The differences in
Increasing the prescribed surface temperatures on all land points (ALL10K)
acts to significantly increase
In both the AMA10K and MC10K experiments, the largest increases in
In the AUS10K simulation,
Differences in annual mean surface air temperature at 1.5 m (K) for
The increases in
Interestingly, in the experiments with higher land surface temperatures
(ALL10K, AMA10K, MC10K, AUS10K, and AM10K), the
The differences in the annual mean precipitation between CON1 and FREE are
generally within
For ALL10K relative to CON1 there are statistically significant changes to
the precipitation over all land areas (Fig.
Differences in annual mean precipitation (%) for
In both of the tropical experiments (AMA10K and MC10K), precipitation
increases by
Increasing Australian land surface temperatures causes precipitation to
increase in the north and east of the continent but to decrease over the
eastern Indian Ocean (Fig.
For AM10K, increased precipitation coincides with the surface heating except
in the centre of the continent (this also occurs in ALL10K – compare
Fig.
When prescribing the surface temperatures it is important to maintain the
diurnal cycle, particularly in regards to the impact of the daily heating and
cooling of the land surface on tropical convection. Accepting that ACCESS
West Africa, 0 northern Australia, 135 the Maritime Continent (Borneo), 112.5 northern South America (central Amazonia), 300
In West Africa (Fig.
Diurnal cycle of convective precipitation in the tropics
(mm 3 h
Convective rainfall in northern Australia peaks at 11:00 LT in FREE, CON1,
and CON2; however, as over West Africa, the prescribed simulations have
higher precipitation in the afternoon (around 17:00 LT). Despite the higher
rainfall around 17:00 LT, the diurnal cycle still occurs in the prescribed
simulations. Interestingly, the secondary peak in rainfall (around 02:00 LT)
associated with the modelled diurnal cycle of the heat low circulation
For the Maritime Continent (Fig.
Finally, peak convective rainfall occurs at 13:30 LT in all simulations for
the Amazonian point (Fig.
The differences in mean sea level pressure (MSLP) between FREE and CON1
(Fig.
The largest differences in MSLP occur in the ALL10K experiment, with
reductions of 0.5 to 2.0 hPa over most global land surfaces, the Atlantic
Ocean, the Arctic, and the Southern Ocean between 180 and 30
There are also significant changes in global MSLP in both the AMA10K and
MC10K simulations. The MSLP decreases over the Amazon by more than 4 hPa in
AMA10K, with reductions of more than 0.5 hPa over much of the Atlantic Ocean
(Fig.
Differences in annual mean, mean sea level pressure (hPa) for
In the AUS10K experiment (Fig.
Similarly, increasing and decreasing North American land surface temperatures
has a large impact on the NH mid-latitude circulation. An increase in North
American land surface temperature decreases the MSLP locally by
0.5–2.0 hPa, but there is also lower MSLP over western Europe
(Fig.
Over most of the globe, the differences in
Time series of
To investigate this hypothesis, the snow mass at 277.5
The lower values of
The differences in
Previous work by
An increase in precipitation over almost all tropical land surfaces can be
seen in the ALL10K experiment (Fig.
The climatological mean (averaged over all years of simulation)
pressure vertical velocity at 500 hPa (
The mean pressure vertical velocity at 500 hPa (
In both the AMA10K and MC10K experiments, there is evidence of alternating
The differences in the zonal mean deviation of the 300 hPa streamfunction
(contours) for AMA10K and MC10K relative to CON1 are plotted in
Fig.
Differences in the deviation of the zonal mean streamfunction at
300 hPa between AMA10K and CON1 for
The importance of the location of the surface temperature perturbation
relative to the background flow, rather than simply the areal extent of the
heating source, is more obvious when the seasonal (DJF and JJA) averages are
considered. In DJF (Fig.
In JJA, the Amazonian heating source lies entirely south of the band of
background easterly flow at 300 hPa, and there is little wave activity
apparent in the streamfunction field in the NH as a result
(Fig.
Overall, the circulation responses to both of these tropical heating sources
are broadly consistent with the results of
Previous work has shown that Australian rainfall has changed regionally over
the last 60 years
Precipitation increases are primarily in the north and east
(Fig.
The change in convective rainfall over Australia also appears to be driving
changes in the SH mid-latitude circulation. MSLP increases by
The difference in the 850 hPa zonal flow (m s
Such an impact on the SAM was not discussed in
Increasing (AM10K) and decreasing (AMm10K) the North American continental
surface temperatures induce local decreases and increases in MSLP,
respectively (Fig.
The largest changes in precipitation occur in JJA (boreal summer, not shown)
where the increased surface temperature (Fig.
The JJA-mean differences between AM10K and CON1 simulations for
Locally, the increased surface temperatures and induced convection act to
decrease the surface MSLP in AM10K (relative to CON1), which can also be seen
as a negative 850 hPa geopotential height (Zg
Interestingly, the differences in circulation in Fig.
The aims of this paper are to present a method of prescribing land surface
temperatures in a GCM and show that the resulting simulated climate state is
comparable with a simulation that uses freely evolving land temperatures.
Furthermore, the study has shown that the atmospheric responses to land
surface temperature perturbations broadly agree with physical processes noted
in previous studies using idealized GCM simulations. The main conclusions
from this study therefore are the following. It is possible to prescribe land surface temperatures in ACCESS
(excluding Antarctica) and produce a simulated atmospheric state similar to
that of a freely varying land temperature simulation. The diurnal cycle in tropical convection is maintained in the prescribed
simulations. Increasing all land surface temperatures by 10 K generally increases
(decreases) precipitation over the land (ocean). Regional increases in tropical surface temperatures may cause the formation
of stationary Rossby waves that are dependent on the location of the heat
source and the background state atmospheric zonal flow. Increasing the surface temperatures over the Australian continent
causes
an increase in monsoon rainfall and also acts to shift the SH mid-latitude
westerlies poleward. Increasing and decreasing the land surface temperatures over North
America act to either strengthen (increasing land temperatures) or weaken
(reducing land temperatures) the North Pacific summertime high-pressure cell.
The experiments in this study showcase some specific examples of the
potential applications for simulations with prescribed land surface
temperatures. Further experiments/applications that could be developed
include the following.
Develop prescribed land surface temperature simulations that are compatible with the Community Atmosphere Biosphere
Land Exchange Remove the soil temperature and soil moisture constraints. This will allow the soil moisture to respond freely to
the imposed surface temperature field, which could have an impact on the modelled climate. For example, the circulation
response in the ALL10K experiment may not be as strong once the local moisture supply for land-based convection has been evaporated away. The adjusted radiative forcing has previously been calculated in simulations with prescribed SSTs that allow the
atmosphere and land surface to respond freely to changes in CO AMIP simulations with perturbed SSTs (e.g. uniform increase in global SST by 4 K – AMIP4K) and greenhouse gases
(quadrupled CO Three-hourly surface temperature data are available from other CMIP5 models (apart from just ACCESS). Therefore,
given the method described in this paper, those other models' surface
temperature fields could be applied to ACCESS in order to identify whether
the circulation biases in individual CMIP5 models are driven by errors in
their surface temperatures (i.e. if circulation errors are surface
temperature driven, then they should occur when applied to ACCESS). Instead of holding the surface temperature to a fixed value, the approach can be altered by adding a flux correction
term to the surface temperature tendency equation
While this list is not exhaustive, it presents some logical steps forward for further testing and development.
The model source code for ACCESS is not publicly
available; however, more information can be found through the ACCESS-wiki at
This project was funded by the ARC Centre of Excellence for Climate System Science (CE110001028). The ACCESS simulations were undertaken with the assistance of the resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government.Edited by: J. Kala