Global and Regional Comparison of Daily 2-m and 1000-hPa Maximum and Minimum Temperatures in Three Global Reanalyses

A. J. Pitman; S. E. Perkins

doi:10.1175/2009jcli2799.1

Global and Regional Comparison of Daily 2-m and 1000-hPa Maximum and Minimum Temperatures in Three Global Reanalyses

Journal of Climate ◽

10.1175/2009jcli2799.1 ◽

2009 ◽

Vol 22 (17) ◽

pp. 4667-4681 ◽

Cited By ~ 24

Author(s):

A. J. Pitman ◽

S. E. Perkins

Keyword(s):

Probability Distribution ◽

Probability Density Functions ◽

Distribution Functions ◽

Maximum Temperature ◽

Daily Maximum ◽

Density Functions ◽

Regional Comparison ◽

Probability Distribution Functions ◽

Air Temperatures ◽

The Mean

Abstract A comparison of three global reanalyses is conducted based on probability density functions of daily maximum and minimum temperature at 2-m and 1000-hPa levels. The three reanalyses compare very favorably in both maximum and minimum temperatures at 1000 hPa, in both the mean and the 99.7th and 0.3rd percentiles of both quantities in most regions. At 2 m, there are large and widespread differences in the mean and 99.7th percentiles in maximum temperature between the three reanalyses over land commonly exceeding ±5°C and regionally exceeding ±10°C. The 2-m minimum temperatures compare unfavorably between the three reanalyses over virtually all continental surfaces with differences exceeding ±10°C over widespread areas. It is concluded that the three reanalyses are generally interchangeable in 1000-hPa temperatures. The three reanalyses of 2-m temperatures are very different owing to the methods used to diagnose these quantities. At this time, the probability distribution functions of the 2-m temperatures from the three reanalyses are sufficiently different that either the 2-m air temperatures should not be used or all three products should be used independently in any application and the differences highlighted.

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Growth and Photosynthesis of Magnolia grandiflora `St. Mary' in Response to Constant and Increased Container Volume

Journal of the American Society for Horticultural Science ◽

10.21273/jashs.116.3.439 ◽

1991 ◽

Vol 116 (3) ◽

pp. 439-445 ◽

Cited By ~ 6

Author(s):

Chris A. Martini ◽

Dewayne L. Ingram ◽

Terril A. Nell

Keyword(s):

Carbon Assimilation ◽

Growing Season ◽

Limiting Factors ◽

Maximum Temperature ◽

Daily Maximum ◽

Magnolia Grandiflora ◽

Air Temperatures ◽

Growing Medium ◽

Early Fall ◽

The Mean

Growth of Magnolia grandiflora Hort. `St. Mary' (southern magnolia) trees in containers spaced 120 cm on center was studied for 2 years. During the 1st year, trees were grown in container volumes of 10, 27, or 57 liter. At the start of the second growing season, trees were transplanted according to six container shifting treatments [10-liter containers (LC) both years, 10 to 27LC, 10 to 57LC, 27LC both years, 27 to 57LC, or 57LC both years]. The mean maximum temperature at the center location was 4.8 and 6.3C lower for the 57LC than for the 27 and 10LC, respectively. Height and caliper, measured at the end of 2 years, were” greatest for magnolias grown continuously in 27 or 57LC. Caliper was greater for trees shifted from 10LC to the larger containers compared with trees grown in 10LC both years. Trees grown in 10LC both years tended to have fewer roots growing in tbe outer 4 cm of the growing medium at the eastern, southern, and western exposures. During June and August of the 2nd year, high air and growth medium temperatures may have been limiting factors to carbon assimilation. Maintenance of adequate carbon assimilation fluxes and tree growth, when container walls are exposed to solar radiation, may require increasing the container volume. This procedure may be more important when daily maximum air temperatures are lower during late spring or early fall than in midsummer, because low solar angles insolate part of the container surface.

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Assessment of Reanalysis Daily Extreme Temperatures with China’s Homogenized Historical Dataset during 1979–2001 Using Probability Density Functions

Journal of Climate ◽

10.1175/2010jcli3581.1 ◽

2010 ◽

Vol 23 (24) ◽

pp. 6605-6623 ◽

Cited By ~ 31

Author(s):

Jiafu Mao ◽

Xiaoying Shi ◽

Lijuan Ma ◽

Dale P. Kaiser ◽

Qingxiang Li ◽

...

Keyword(s):

Probability Density ◽

Skill Score ◽

Probability Density Functions ◽

Department Of Energy ◽

Maximum Temperature ◽

Daily Maximum ◽

Cold Bias ◽

Density Functions ◽

Extreme Temperatures ◽

Skill Scores

Abstract Using a recently homogenized observational daily maximum (TMAX) and minimum temperature (TMIN) dataset for China, the extreme temperatures from the 40-yr ECMWF Re-Analysis (ERA-40), the Japanese 25-year Reanalysis (JRA-25), the NCEP/Department of Energy Global Reanalysis 2 (NCEP-2), and the ECMWF’s ERA-Interim (ERAIn) reanalyses for summer (June–August) and winter (December–February) are assessed by probability density functions for the periods 1979–2001 and 1990–2001. For 1979–2001, no single reanalysis appears to be consistently accurate across eight areas examined over China. The ERA-40 and JRA-25 reanalyses show similar representations and close skill scores over most of the regions of China for both seasons. NCEP-2 generally has lower skill scores, especially over regions with complex topography. The regional and seasonal differences identified are commonly associated with different geographical locations and the methods used to diagnose these quantities. All the selected reanalysis products exhibit better performance for winter compared to summer over most regions of China. The TMAX values from the reanalysis tend to be systematically underestimated, while TMIN is systematically closer to observed values than TMAX. Comparisons of the reanalyses to reproduce the 99.7 percentiles for TMAX and 0.3 percentiles for TMIN show that most reanalyses tend to underestimate the 99.7 percentiles in maximum temperature both in summer and winter. For the 0.3 percentiles in TMIN, NCEP-2 is relatively inaccurate with a −12°C cold bias over the Qinghai–Tibetan Plateau in winter. ERA-40 and JRA-25 generally overestimate the extreme TMIN, and the extreme percentage differences of ERA-40 and JRA-25 are quite similar over all of the regions. The results are generally similar for 1990–2001, but in contrast to the other three reanalysis products the newly released ERAIn is very reasonable, especially for wintertime TMIN, with a skill score greater than 0.83 for each region of China. This demonstrates the great potential of this product for use in future impact assessments on continental scales where those impacts are based on extreme temperatures.

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Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions

Journal of Climate ◽

10.1175/jcli4253.1 ◽

2007 ◽

Vol 20 (17) ◽

pp. 4356-4376 ◽

Cited By ~ 381

Author(s):

S. E. Perkins ◽

A. J. Pitman ◽

N. J. Holbrook ◽

J. McAneney

Keyword(s):

Probability Density ◽

Minimum Temperature ◽

Climate Models ◽

Quantitative Measure ◽

Probability Density Functions ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Density Functions ◽

Coupled Climate Models

Abstract The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated. The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, and maximum temperature. The evaluation is based on probability density functions and a simple quantitative measure of how well each climate model can capture the observed probability density functions for each variable and each region is introduced. Across all three variables, the coupled climate models perform better than expected. Precipitation is simulated reasonably by most and very well by a small number of models, although the problem with excessive drizzle is apparent in most models. Averaged over Australia, 3 of the 14 climate models capture more than 80% of the observed probability density functions for precipitation. Minimum temperature is simulated well, with 10 of the 13 climate models capturing more than 80% of the observed probability density functions. Maximum temperature is also reasonably simulated with 6 of 10 climate models capturing more than 80% of the observed probability density functions. An overall ranking of the climate models, for each of precipitation, maximum, and minimum temperatures, and averaged over these three variables, is presented. Those climate models that are skillful over Australia are identified, providing guidance on those climate models that should be used in impacts assessments where those impacts are based on precipitation or temperature. These results have no bearing on how well these models work elsewhere, but the methodology is potentially useful in assessing which of the many climate models should be used by impacts groups.

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On the conditional expected value of contributions from a renewal process

Journal of Applied Probability ◽

10.2307/3212132 ◽

1966 ◽

Vol 3 (2) ◽

pp. 464-480 ◽

Cited By ~ 1

Author(s):

M. F. James

Keyword(s):

Probability Distribution ◽

Cross Sections ◽

Renewal Process ◽

Mean Value ◽

Distribution Functions ◽

Total Contribution ◽

Probability Distribution Functions ◽

General Terms ◽

The Mean ◽

Neutron Cross Sections

The problem discussed in this paper arose from the study of the effects of unresolved resonances on neutron cross-sections, but it is considered here in more general terms.Events in a modified renewal process occur at successive intervals x1, x2, …, and the ith event has associated with it a parameter Γi. The random variables xi and Γi are all independent, and their probability distribution functions are known. Each event contributes to two quantities F(u) and G(v) measured at u and v respectively. The value of the total contribution of all events to G(v) is assumed to be known from observation: this paper gives formulae for the mean value of F(u) conditional on this known value of G(v).

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Peak Wind Power Statistics

Journal of Energy Resources Technology ◽

10.1115/1.2905754 ◽

1990 ◽

Vol 112 (3) ◽

pp. 170-173 ◽

Cited By ~ 1

Author(s):

B. Alibe

Keyword(s):

Wind Speed ◽

Probability Distribution ◽

Gaussian Process ◽

Wind Power ◽

Wind Velocity ◽

Distribution Functions ◽

Stationary Gaussian Process ◽

Probability Distribution Functions ◽

Mean Wind Speed ◽

The Mean

Probability distribution functions, mean upcrossing rates and other descriptors are developed for the power that can be potentially extracted from the wind. Wind power is proportional to the cube of the wind velocity. The wind velocity is modeled as a stationary Gaussian process. The distribution of the extreme power is developed from mean upcrossing rates and the assumption that crossings of high thresholds follow a Poisson probability law. The results obtained are valid for any amount of the mean wind speed.

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On the conditional expected value of contributions from a renewal process

Journal of Applied Probability ◽

10.1017/s0021900200114263 ◽

1966 ◽

Vol 3 (02) ◽

pp. 464-480

Author(s):

M. F. James

Keyword(s):

Probability Distribution ◽

Cross Sections ◽

Renewal Process ◽

Mean Value ◽

Distribution Functions ◽

Total Contribution ◽

Probability Distribution Functions ◽

General Terms ◽

The Mean ◽

Neutron Cross Sections

The problem discussed in this paper arose from the study of the effects of unresolved resonances on neutron cross-sections, but it is considered here in more general terms. Events in a modified renewal process occur at successive intervals x 1, x 2, …, and the ith event has associated with it a parameter Γ i . The random variables xi and Γ i are all independent, and their probability distribution functions are known. Each event contributes to two quantities F(u) and G(v) measured at u and v respectively. The value of the total contribution of all events to G(v) is assumed to be known from observation: this paper gives formulae for the mean value of F(u) conditional on this known value of G(v).

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