An inset graph at bottom right illustrates combinations of temperature x-axis and moisture content y-axis in grams per cubic meter of the air mass as it passes over various topographic features on the land surface. Skip to main content.
The Orographic Effect Print The Orographic Effect To take the concept of relative humidity outdoors, let's consider why it rains in some areas and we have deserts in others.
Figure An example of the rate of cooling of an airmass rising from ground level to higher altitudes, and the effect on rate of cooling when reaching the point of saturation with respect to water vapor level of condensation.
Click to expand for a long description. A graph of atmospheric temperature with altitude in meters on the y-axis and temperature in degrees Celsius on the x-axis. One line with two different decreasing slopes separated at m. Orographic Effect Animation. The sequence of frames portrays a westerly wind, blowing onshore from the Pacific Ocean, driven by a large low-pressure system over the northwestern US.
At point 3, the air has sunk into the Central Valley, warming nearly to its original temperature. However, because the airmass lost moisture over the Coast Range, it now has a lower relative humidity.
In the case of an isolated mountain, orographic clouds often have the form of a collar, surrounding the mountain or that of a cap covering the peak Figure 4 , both of which are fairly symmetrical. These clouds give little or no precipitation. In the case of a mountain barrier, observed from the leeward side, cap clouds indicate likely wave activity downstream. Sometimes, the clouds resemble a bank or wall that follows mountain contours.
It is important to remember that their absence does not mean that waves are absent. Under drier conditions, waves may be present without cap clouds. When the wind is strong, the orographic clouds formed near the summit may be observed streaming away from the mountain on the leeward side.
This is a banner cloud and should not be confused with snow blown from the crest or peak. Skip to main content. Toggle navigation. Introduction Foreword to the edition Meteorological definition of a meteor General classification of meteors Hydrometeors Lithometeors Photometeors Electrometeors.
Introduction and principles of cloud classification Definition of a cloud Appearance of clouds Principles of cloud classification Cloud classification summary Cloud abbreviations and symbols. Each point corresponds to the values of a given year in the — period for panel a, and in the — period for panel b.
Solid lines show the linear interpolation of the data. In both cases the correlation coefficients between the mountain and lowland annual precipitation time series equal to 0.
The colours of the points represent the phase of the NAO of the year to which they refer: positive phase red , negative phase blue and neutral phase black. The circles represent points whose NAO phase is not available. As shown in Fig. The variation is mainly related to winter precipitation, while no elevation dependent signal is observed during summers see Fig. S3 in the Supplementary material. This statistically significant change needs to be put into a longer term context, in order to verify whether it is part of a long time trend or not.
In the time series of the OEPI over the Lombardy region, dashed lines refer to the period before in which the low number of stations available makes the series significantly less reliable. The grey band on the time series represents the standard error of the mean for each annual value.
The uncertainty used is the standard error of the mean grey band on the filter. With the aim of extending the above analysis to several decades long time series, we use all available stations in the Lombardy region Central Alps, Italy , regardless of their time of operation.
Since the available stations change with time, a procedure to reduce biases introduced by changing the location of the operating stations at which data are collected is necessary: to this aim, annual precipitation at any given station is normalized by the long term mean precipitation at its elevation see Methods. The average annual mean normalized precipitation has been computed for all mountain and lowland stations, separately see Fig. The Orographic Enhancement of Precipitation Index OEPI is introduced, defined as the ratio of the normalized annual precipitation averaged over mountain stations to the normalized annual precipitation averaged over lowland stations, and then multiplied by the 30 year long ratio between mountainous precipitation and lowland precipitation previously found 1.
The orographic enhancement of precipitation shows consistent behaviour over the two datasets in the overlapping period, increasing for about three decades from s to s and then it decreases until today. In other words, in the s the difference in annual precipitation between high elevation stations and low elevation stations has reached the largest values over the last 70 years. We do not make any statement about the value of the index before as the number of available stations is very low prior to this date Supplementary Fig.
A straightforward definition of the main factors driving the observed change is not possible at present, as many mechanisms could be responsible for this behaviour. Clearly, modifications in the airflow direction and intensity during storms might modify the spatial distribution of precipitation in the region and thus the relationship between precipitation and elevation. Decadal changes in weather type occurrence have been documented 26 , 27 , 28 and cannot be ruled out as the origin of the observed changes in the orographic amplification of precipitation.
However, it is interesting to note that the elevation dependent precipitation change is found both on the Northern and Southern Alpine slopes, as well in Western, Central, and Eastern Alps separately, albeit with more scattered results likely associated to the reduced statistics when subregions are analyzed Supplementary Information, Section S2 and Fig. This suggests that the mechanism responsible for this elevation dependent precipitation change could be independent of dynamical variations.
In this regard, we note that the peak in the orographic enhancement of precipitation occurs at the same time as the maximum air pollution in the region: starting in the s, new regulations led to the decline in anthropogenic aerosol concentration in the GAR, which caused a significant increase of the solar radiation reaching the lowlands 29 , 30 , This brightening has been considered to be at the basis of the peculiar elevation dependent warming observed over the last decades in the Alpine Region: while in the Himalayas and in the Rockies warming has been faster at higher elevations, in the highly urbanized Alpine region warming has been faster in the lowlands, likely because of the increased shortwave radiation reaching the surface as air pollution decreased A varying aerosol concentration has potential impacts on the orographic enhancement of precipitation essentially for two effects: the corresponding change in atmospheric stability and the possible change in the microphysical properties of clouds.
Along the first line, radiative effects of aerosols can induce a surface cooling and an accompanied warming at the levels in which solar radiation is absorbed by opaque aerosols such as black carbon. The net effect on air column stability is however not simply determined and it should be assessed performing modeling studies. Along the second line, the indirect effect of aerosols on precipitation is still unclear: the change of the number concentration, chemical properties, and size distribution of aerosols affect cloud microphysics by influencing the properties of cloud condensation nuclei and of ice nuclei.
At present it is thus unclear what process would dominate in determining the response of precipitation to varying aerosol loads. Our results seem to indicate that the increased pollution might have increased the orographic enhancement of precipitation, but it is not clear whether this should be related to a suppression of precipitation in the heavily polluted lowlands or an increased precipitation at higher elevations.
This study demonstrates that the orographic enhancement of precipitation dependence on anthropogenic forcing is an important research line, and more observational data and modeling work are necessary in order to quantify the effects of both aerosols and global warming on it, which will allow us to make more reliable predictions of precipitation and water storage in the mountain regions in the coming decades. It will also be important to assess whether the distribution of precipitations and extreme conditions such as heavy rains, consecutive wet days, consecutive dry days present long term variability that systematically depends on elevation.
In this work two different precipitation datasets have been used. The first one is a high density monthly dataset over Italy and the Alpine region for the period — To this aim, many different datasets have been included. Starting in early s, many mechanical rain gauges have been dismissed and replaced with automatic instruments. In most cases, this has been done without maintaining the old and the new instruments operative at the same time, to calibrate the new instruments, and for this reasons the time series cannot be merged to generate longer time series.
Moreover, precipitation data have been collected at the national level for several decades and the operations have been regionalized at the end of the 20 th century.
For these reasons, the period — has been chosen to guarantee a degree of homogeneity of data availability. Careful quality checks and data filling have been performed At the end of this procedure, the dataset provides monthly precipitation at every station for the 30 year long period with no gaps. The number of available stations in the area considered in this paper is A second dataset is comprised of historical time series of monthly precipitation for stations in the Lombardy region.
This dataset has been created at the Department of Environmental Science and Policy of University of Milan by analyzing and digitizing a large amount of historical data archives monographic studies, bulletins, reports, etc. In a second step available time series have been homogenized to remove non-climatic changes in the data: in the last decade, the scientific community has become aware of the fact that the real climate signal in original series of meteorological data is generally hidden behind non-climatic noise caused by station relocation, changes in instruments and instrument screens, changes in observation times, observers, and observing regulations, algorithms for the calculation of means and so on.
So, at present, the statement that time series of meteorological data cannot be used for climate research without a clear knowledge about the state of the data in terms of homogeneity has a very large consent Different stations have data available for different time periods and some data between the starting and end times of each station are missing.
So a preliminary treatment of the dataset has been necessary. For each station, a climatological monthly precipitation was computed. We then considered, for each station, only years in which at least 9 months of data are present and we replaced the missing ones by the corresponding climatological value. We then computed the annual and the seasonal precipitation at each station by summing over the considered months. At the end of the procedure we obtained a dataset of precipitation with a variable number of stations every year.
In the Supplementary Fig. In this work, the focus has been on the period from onward in order to have a relatively stable statistics. Each of those time series has been normalized, either on its own mean value of annual precipitation and on an estimate of the mean annual precipitation appropriate for its elevation, as described below.
A model of the elevation dependent annual mean precipitation has been constructed: using the high density database for the — period, we have divided the data in 10 classes based on station altitude, where the class limits are chosen so that each class has the same number of stations The annual mean precipitation for each station has been computed and then the mean value of the annual mean precipitation over the stations in the same altitude class has been computed, together with its standard deviation, and its standard error.
The results of this procedure are shown in Fig. A linear interpolation between nearby classes has been performed to define the annual mean precipitation at each elevation between the central value of each class. This model has then been used to normalize the precipitation data from the Lombardy region.
To this aim, the model annual mean precipitation corresponding to the elevation of each Lombardy station has been calculated.
The annual precipitation of each station in the Lombardy region has then been divided by these model values. We refer to this dataset as the Lombardy region normalized dataset. To verify that results are not sensitive to the details of the normalization procedure, we also used as normalizing factor the mean precipitation of the individual Lombardy station. The results are similar and all the figures presented in the paper refer to the former normalization method. This allows to form two classes with nearly equal number of stations see Supplementary Fig.
We varied the precise value of the elevation threshold used to define mountain and lowland stations to verify that the results do not show a strong sensitivity. A time series has been constructed for each of the two classes, taking for each year the average of the normalized annual precipitation over all the available station in each class. The ratio between the resulting dimensional time series is the Orographic Enhancement of Precipitation Index, as shown in Fig.
Annual and seasonal indices have been obtained by averaging the monthly index over the appropriate months. Positive and negative NAO phases have been defined as values of the index whose distance from the mean is larger than the standard deviation. The correlation between detrended variables has been calculated through the Pearson correlation coefficient which measures their linear dependence.
The datasets generated and analysed during the current study are available from the authors on reasonable request. Viviroli, D. Mountains of the world, water towers for humanity: Typology, mapping, and global significance. Water resources research 43 Beniston, M. Regional behavior of minimum temperatures in switzerland for the period — Giorgi, F.
Elevation dependency of the surface climate change signal: a model study. Fyfe, J. Enhanced climate change and its detection over the rocky mountains. Gao, X.
0コメント