Climate statistics

Hack, J. J., Boville, B. A., Kiehl, J. T., Rasch, P. J. and Williamson, D. L. (1994). Climate statistics from the National Center for Atmospheric Research community climate model (CCM2), /. Geophys. Res. 99, 20785-20813. 

As previously mentioned, for the climatic temporal variability at the different timescales to be assimilated into the models, the time series should comprise a period of time at least equal to the period to be simulated. Fortunately, hindcast techniques provide long time series, over 40 years, of climatic statistic descriptors (i.e., HIPOCAS UE project). 

Nature of climate. Consider seasonal and daily temperature variations, dust, fog, tornados, hurricanes, earthquakes. Define duration of conditions for design. Determine from U.S. Weather Bureau yearly statistics for above, as well as rainfall. Establish if conditions for earthquakes, hurricanes prevail. For stormy conditions, structural design for 100 miles per hour winds usually sufficient. For hurricanes, winds of 125 miles per hour may be design basis. 

Electronic computers programmed with sophisticated statistical routines (e.g. variance spectral analysis) facilitate the search for climatic rhythms. The motivation behind this effort is obvious Isolation of real periodicities in climate would be a powerful tool in climate forecasting. However, climatologists have identified only a few statistically significant cycles that are useful for climate forecasting over decades. 

Cycles established as statistically real are the familiar annual and diurnal radiation/temperature cycles, a quasibiennial (about every 2 years) fluctuation in various climatic elements, and the interannual variability of June rainfall in northern India. The first merely means that winters are cooler than summers and nights are cooler than days. Examples of the second cycle include Midwestern rainfall, a lengthy temperature record from central England, and winds over the western Paciflc and eastern Indian Ocean. According to Campbell et al (19), the third cycle may be a response to the monthly solar-lunar tide and its influence on the monsoon circulation. 

How well do GCMs simulate the spatial variability of climatic change Today s GCMs utilize data grids that partition the atmosphere into cells, each covering an area about the size of Colorado. A mean state of the atmosphere (temperature, humidity, cloud cover, for example) is computed for each cell. Consequently, any ou ut statistics (the prediction) has a lower spatial resolution (more genei ized, less detailed) than the real atmosphere is likely to manifest. 

Contain a stochastic climate generator capable of simulating daily precipitation and other weather parameters that are similar in amount and statistical variability to historical weather records for the site. 

The obtained results allow us to advance with the basic assumption the north sector, subject to anthropogenic influence, it showed a carbon stock 23% lower than the south sector, which had less accessibility and a better state of conservation (Table 4). These differences were statistically significant (H = 11.20, p < 0.001) only for the AGB stratum, but not for the other strata studied nor for the total carbon stock. Under similar conditions of climate, soil, geomorphology, altitude, and latitude, the human influence could explain these differences, as the AGB stratum is the easiest to appropriate by humans [10,17,19, 21]. The AGB make the largest contribution in both sectors to the carbon stock (53, 55%), followed by SOC (28-31%) and finally BGB (8-10%) depending on the sector analyzed (Figure 3). 

The bias-correction is necessary to correct both the absolute magnitude and the seasonal cycle to that of the observations. This approach assumes that the same model biases persist in the future climate and thus GCMs more accurately simulate relative change than absolute values. It provides a correction of monthly mean climate only and does not correct biases in higher order statistics including the simulation of extreme events and persistence. 

The statistical estimation of heavy metal concentrations in the Spruce Forest ecosystems of the Boreal climatic zone is the subject of wide variation, with coefficient of variation from 36 to 330%. However, we can note the clear trend in biogeochemical peculiarities and relevant exposure to heavy metal uptakes by dominant plant species. 

It is veiy well known that in tropical climate there are two main seasons, rainy season and dry season. Under this conditions, the acceleration caused by chlorides should be higher in the dry season (winter period) and lower in the rainy season. As an example, on Table VII presents statistical parameters calculated for corrosion rate of steel at Viriato coastal stations for periods of six months corresponding to the wet season (may to October) and dry season (november to april). All steel samples were exposed for a six months period corresponding, starting on may or on november. Data correspond to the period may/1987 to November/1991. 

Another area of global-scale dimensions that is commanding increased attention is the potential impact of atmospheric trace gases and aerosol particles on climate, the subject of this chapter. Climate is the longterm statistical characterization of parameters describing what we commonly term weather, such as surface temperature. For example, the mean surface temperature with its associated variability over some time period, typically taken as 30 years, is one measure of climate. Thus, climate is distinguished from short-term, e.g., day-to-day, variations, which are typically referred to as weather. ... 

Concerning the correlation of 8I5N with climate, there have been observations on specific areas, where an inverse relationship with altitude was documented (67). In the last decade, a substantial body of evidence on 8ISN change over time has being collected, and its relationship with climatic conditions is becoming clear (62). More specifically, the dataset considered by Schwarcz and coworkers is used to explore the correlation with precipitation, which appears to be inverse and statistically strong (68). 

We also reviewed the method for estimating paleo-moist-enthalpy. To estimate paleoenthalpy from plant fossils, Forest et al. (1999) quantified a relationship between leaf physiognomy and enthalpy from present-day plants and their local climate. Using Canonical Correlation Analysis, mean annual moist enthalpy can be estimated with an uncertainty of 5.5 kJ/kg. The contribution to the uncertainty in altitude is 560 m and is comparable to using temperature alone. Other statistical techniques that improve the ability to estimate enthalpy could replace the current method. 

Oort A (1983) Global atmospheric circulation statistics, 1958-1973. NOAA Prof Paper 14 Peixoto JP, Oort AH (1992) The Physics of Climate. American Institute of Physics... 

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