COMPARISON OF AIR TEMPERATURE TRENDS BASED ON REANALYSIS DATA, MODEL SIMULATIONS DATA AND AEROLOGICAL OBSERVATIONS V.M. Khan, K.G. Rubinshtain, Hydrometeorological Center of Russia ( А.М. Sterin, Russian Institute of Hydrometeorological Information – World Data Center (

Intercomparison of the decadal linear trend [°C (10 yr)  1] in zonal average temperature anomaly during ERA-15 period Jan 1979–Feb 1994 (From: Kistler et al., BAMS, 2001, no.2)

Other sources:  L. Bengtsson, S.Hagemann, and K.Hodges, 2004: Can climate trends be calculated from re-analysis data ? Max- Planck Inst. For Meteorology, Report No. 351, January 2004

Objectives ● Estimate applicability of reanalysis data (NCAR/NCEP and ECMWF) to investigating long-term upper air temperature trends ● Estimate how consistent are trends of U/A temperature based on climate model simulations (RHMC and INM) and U/A data Data used: 1. Monthly temperature of NCAR/NCEP reanalysis ( ) 17- Levels 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10] hPa. 2.5 x Monthly temperature of ECMWF reanalysis 17- Levels 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10] hPa. 2.5 x Agrological data (CARDS) MONADS – monthly mean temperature on standard levels 15 – levels [SURF, 1000, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20] hPa 4. Institute of Numerical Mathematics (INM) and Hydrometeorological Center of Russia (RHMC )- temperature of results of AMIP experiments ( ) 15 levels 1000, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20,10] hPa 2,5 x 2,5

 Мethodology  The analysis of linear correlations between reanalysis NCEP/NCAR and ECMWF for different isobaric levels was performed  The air temperature trends and their statistical characteristics for the monthly values from the CARDS data set and the corresponding interpolated values from reanalysis (NCAR/NCEP and ECMWF) and model (INM - model of Institute of Numerical Mathematics, RHMC - model of Hydrometeorological Center of Russia) data at the grid points closest to the station were calculated.  Vertical profiles of air temperature trends are analyzed for both entire year and for different seasons using reanalysis and aerological data.  A special criterion is applied to evaluate the degree of coincidence by sign between the air temperature trends derived from the two types of data.  Z=(n c -n d )/N,  Vertical sections of the linear trend averaged over the 2.5-degrees zones for the both hemispheres are analyzed.  Estimates of monthly-mean troposphere and stratospheric temperature trends over the past twenty years, from different hydrodynamical models are compared both with each other and with the observed trend analyses using aerological observations.

LOCATION OF SELECTED U/A STATIONS FOR TREND PROFILE ANALYSIS (NCAR/NCEP vs U/A DATA)

Dakar, Senegal VERTICAL PROFILES OF LINEAR TRENDS

Nairobi, Kenya VERTICAL PROFILES OF LINEAR TRENDS

Tahiti, France VERTICAL PROFILES OF LINEAR TRENDS

Yakutat, USA VERTICAL PROFILES OF LINEAR TRENDS

Stavanger, Norway VERTICAL PROFILES OF LINEAR TRENDS

The degree of coincidence by sign (criterion Z) between the air temperature trends derived from the two types of data (NCAR/NCEP and aerological data) for different regions of the world

Trends sections from U/A data (right) and NCAR/NCEP (left),

Trends sections from ECMWF (left) and NCAR/NCEP (right),

Main results: Linear correlation analysis between NCAR/NCEP and ECMWF reanalysis temperature of free atmosphere for period demonstrates good coincidence of data. The results of trend comparisons demonstrate what ECMWF reanalysis of air temperature data represent trend values in the observational data better than NCAR/NCEP reanalysis data. Significant differences of the air temperature trend values are observed near the land surface and in the tropopause layer. A comparative analysis of the trends for the both periods of observation: 1964 through 1998, and 1979 through 1998, - shows that introducing satellite information in the reanalysis data resulted in an increase of the number of stations where the signs of the trend derived from the two sets of data coincide. Analysis of vertical profiles of air temperature trends using the NCAR/NCEP and aerologcal data for different seasons shows that in general, the seasonal variability of vertical distributions of the trend is slim, except in the southeastern part of Asia where winter values of the low troposphere trend differ significantly from those in the other seasons.

Main results: The period for trend comparisons ( ) with the models is not very good! But: Results of the comparison of time temperature trends of two GCM models: RHMC and INM demonstrate reasonable coincidence between RHMC data and aerological observations for the northwestern part of North America, Central America, and northwestern part of Eurasia. The air temperature trends were good simulated by RHMC model in the tropical belt of the world. The INM model demonstrates good coincidence in air temperature trends in the belts from ~55°N to ~80°N and from ~10°N to ~25°S.

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Широтное осреднение трендов для разных уровней

Невязки трендов

Сравнительный анализ месячных данных ре-анализа NCAR/NCEP и аэрологических наблюдений по южному полушарию.

Methodology The analysis of linear correlations between reanalysis NCEP/NCAR and ECMWF for isobaric levels was performed The air temperature trends and their statistical characteristics for the monthly values from the CARDS data set and the corresponding interpolated values from reanalysis and models data at the grid points closest to the station were calculated. Vertical profiles of air temperature trends are analyzed for both entire year and for different seasons. A special criterion is applied to evaluate the degree of coincidence by sign between air temperature trends derived from the two types of data. Z=(nc-nd)/N, Vertical sections of the linear trend averaged over the 2.5-degrees zones for the both hemispheres are analyzed. Estimates of monthly - mean free atmosphere temperature trends over the twenty years, from hydrodynamical models experiments are compared both with each other and with the observed trend analyses using aerological observations.