Sentences with phrase «of monthly mean temperatures»

When scientists in the 1960s - 70s compiled data to build their global average temperature series they used state averages of monthly mean temperatures from weather stations around the world.

A similar conclusion was drawn from a similar analysis applied to a (spatially sparse) global network of monthly mean temperatures, where the effect of spatial dependencies for inter-annual and inter-decadal variations could be ruled out (Benestad, 2004).

The first is Bias - Correction Spatial Disaggregation (BCSD)(Wood et al., 2004) following Maurer et al., (2008) with the following modifications: the incorporation of monthly minimum and maximum temperature instead of monthly mean temperature, as suggested by Bürger et al., (2012) and bias correction using detrended quantile mapping with delta method extrapolation following Bürger et al., (2013).

Observed change of monthly mean temperature for Northern Norway since year 1900, relative to the 1901 - 2000 mean.

Observed change of monthly mean temperature for Vardø since year 1840, relative to the 1901 - 2000 mean.

Observed change of monthly mean temperature for Jan Mayen since year 1921, relative to the 1901 - 2000 mean.

Observed change of monthly mean temperature for Svalbard since year 1912, relative to the 1901 - 2000 mean.

Effective May 2, 2011, the Global Historical Climatology Network - Monthly (GHCN - M) version 3 dataset of monthly mean temperature has replaced GHCN - M version 2 as the dataset for operational climate monitoring activities.

Not exact matches

Normalised RMS error in simulation of climatological patterns of monthly precipitation, mean sea level pressure and surface air temperature.

This figure from the McLean et al (2009) research shows that mean monthly global temperature (MSU GTTA) corresponds in general terms with the Southern Oscillation Index (SOI) of seven months earlier.

The monthly mean temperature of the coldest month is below the freezing point.

Data of climate variables (mean precipitation; mean temperature; mean ground frost frequency) at monthly intervals (1961 - 90) were sourced from the IPCC (http://www.ipcc-data.org).

The map is set to chart a course around the world based on a monthly mean temperature of 72ºF (22ºC), which according to Canadian Geographic, is the temperature some medical experts believe to be ideal for the human body.

The mean monthly minimum temperature in winter is 61F and 75F in summer, with maximums of 82F in winter and 91F in summer.

The mean monthly minimum temperature in winter is 61C and 75C in summer, with maximums of 82C in winter and 91C in summer.

There is a difference in monthly mean temperature of 10 °C between the cities of Los Angeles and San Diego.

The warming trends in looking at numerous 100 year temperature plots from northern and high elevation climate stations... i.e. warming trends in annual mean and minimum temperature averages, winter monthly means and minimums and especially winter minimum temperatures and dewpoints... indicate climate warming that is being driven by the accumulation of greenhouse gases in the atmosphere — no visible effects from other things like changes in solar radiation or the levels of cosmic rays.

The monthly mean is obtained as the average of each daily maximum and minimum temperature, so that the warm bias is not reduced by averaging.

This can be done a number of ways, firstly, plotting the observational data and the models used by IPCC with a common baseline of 1980 - 1999 temperatures (as done in the 2007 report)(Note that the model output is for the annual mean, monthly variance would be larger):

Mean temperature, mean monthly precipitation, frequency of hot / cold days / nights, and indices of extreme precipitation are all estimated for each country based on observed and modeled data.

The warmth was most dramatic in September, which saw a mean temperature anomaly of +2.75 C, setting a new monthly record by more than a degree.

Further analysis showed that the absolute monthly maximum / minimum temperature was poorly correlated with that of the previous month, ruling out depeendency in time (this is also true for monthly mean temperature — hence, «seasonal forecasting» is very difficult in this region).

About taking differences (current period figures less prior period figures) of anomalies: the anomalies are the value less the monthly mean (i.e., the mean for the particular month over the years, in this case 32 full years), as is the usual practice with climate data (most notably temperature).

While the changes in both the mean and higher order statistical moments (e.g., variance) of time - series of climate variables affect the frequency of relatively simple extremes (e.g., extreme high daily or monthly temperatures, damaging winds), changes in the frequency of more complex extremes are based on changes in the occurrence of complex atmospheric phenomena (e.g., hurricanes, tornadoes, ice storms).

Normalised RMS error in simulation of climatological patterns of monthly precipitation, mean sea level pressure and surface air temperature.

Further to my last post on the climate at Heathrow a couple of hours ago, I have now analysed the weather there and at Oxford since 1958 using annual rather than monthly data on sun, rain, CO2, and mean maximum temperature.

(c) The global mean (80 ° N to 80 ° S) radiative signature of upper - tropospheric moistening is given by monthly time series of combinations of satellite brightness temperature anomalies (°C), relative to the period 1982 to 2004, with the dashed line showing the linear trend of the key brightness temperature in °C per decade.

During the drought years of 2012 — 2015, mean monthly water temperatures in the freshwater regions of the Delta from April to July were on average higher than between 1995 and 2011 (two - factor ANOVA, Bonferroni corrected P < 0.01), demonstrating the effects of drought on surface water temperatures (Fig. 1B).

Screen shot of the «Monthly mean maximum temperature» page for St Helens Aerodrome at the Bureau's website

The four climate variables were the first two axes of two principal components analyses (PCA), one based on the 12 monthly mean temperatures and one on the 12 monthly precipitations, respectively (Fig.

A comparison of the long term and short term mean for monthly precipitation and temperature from the eight NOAA State of Washington Division 5 Weather Stations (Cascade Mountains) illustrates three important climate changes in the North Cascades for the 1984 - 1994 period.

• On the climatic scale, the model whose results for temperature are closest to reality (PCM ‐ 20C3M) has an efficiency of 0.05, virtually equivalent to an elementary prediction based on the historical mean; its predictive capacity against other indicators (e.g. maximum and minimum monthly temperature) is worse.

High values of the Hurst exponent of H = 0.66 ± 0.05 for deseasonalized monthly mean surface temperatures in the sample period 1850 - 2015 suggest persistence and long term memory in the temperature time series.

An appreciable number of nonurban stations in the United States and Canada have been identified with statistically significant (at the 90 % level) decreasing trends in the monthly mean diurnal temperature range between 1941 - 80.

The source of the monthly mean station temperatures for the GISS analysis is the Global Historical Climatology Network (GHCN) of Peterson and Vose [1997] and updates, available electronically, from the National Climatic Data Center (NCDC).

Monthly averages of global mean surface temperature (GMST) include natural variability, and they are influenced by the differing heat capacities of the oceans and land masses.

Our study suggests that these patterns may also exist in deseasonalized monthly means of the measured temperature record in the post industrial era, a period that is normally associated with global warming and climate change.

Running twelve - month averages of global - mean and European - mean surface air temperature anomalies relative to 1981 - 2010, based on monthly values from January 1979 to March 2018.

Running twelve - month averages of global - mean and European - mean surface air temperature anomalies relative to 1981 - 2010, based on monthly values from January 1979 to April 2018.

Running twelve - month averages of global - mean and European - mean surface air temperature anomalies relative to 1981 - 2010, based on monthly values from January 1979 to February 2018.

The warm anomalies in June lasted throughout the entire month (increases in monthly mean temperature of up to 6 to 7 °C), but July was only slightly warmer than on average (+1 to +3 °C), and the highest anomalies were reached between 1st and 13th August (+7 °C)(Fink et al., 2004).

Instead of changes in monthly values of Temp and precip (and cloud cover) changes in ANNUAL mean temperature were used to force LPJ.

The record - warm September means 11 of the past 12 consecutive months dating back to October 2015 have set new monthly high - temperature records.

Similarly, the evaporative demand, expressed as mean day - time vapor pressure deficit (VPD) each month and the number of frost days (< − 2 °C) were calculated from monthly mean temperature extremes using the 3 - PG function tools (3PGpjs version 2.7, 3 - PG version 1, September 2010).

The lower two panels compare the reconstructions using the TRW chronology (d) and MXD chronology (e) with the mean of May to August monthly temperature from Bottenviken over the period 1860 to 2006.

The March 2010 global mean temperature was affected by about 2/100 of a degree Celsius, well below the margin of error (about 15/100 of a degree for monthly global means).

Anomalies simply take the average of the observed temperatures (daily, monthly, annual, max, min, or what have you), and convert them to a scale with a different zero point — a zero defined as the mean observed temperature over some accepted calibration period.

In regards the gridded network» stations, I have been informed that the Climate Research Unit's (CRU) monthly mean surface temperature dataset has been constructed principally from data available on the two websites identified in my letter of 12 March 2007.

We calculated three metrics of thermal history: (1) the mean of the annual maximum DHW from 1985 — 2003 (2) the proportion of years from 1985 to 2003 in which the maximum DHW exceeded 4 °C · week, and (3) a year - to - year temperature variability metric from [16], [46], which is the standard deviation of the maximum monthly SST from 1985 — 2000 scaled such that the mean for the world's coral reefs is 1 °C.

Obtain high - resolution climatologies of maximum, minimum, and mean temperature and precipitation in British Columbia, on a monthly and annual basis at 30 arc second (~ 1 km) resolution (developed using PRISM).